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bug20.ok
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TiMBL 6.8 (c) CLST/ILK/CLIPS 1998 - 2022.
Tilburg Memory Based Learner
Centre for Language and Speech Technology, Radboud University
Induction of Linguistic Knowledge Research Group, Tilburg University
CLiPS Computational Linguistics Group, University of Antwerp
Tue Aug 2 12:02:03 2022
Starting Cross validation test on files:
./test_cv/dummy_data.dat_1
./test_cv/dummy_data.dat_2
./test_cv/dummy_data.dat_3
Reading weights from test_cv/test_weights
Examine datafile './test_cv/dummy_data.dat_1' gave the following results:
Number of Features: 3
InputFormat : C4.5
DB Entropy : 1.9056391
Number of Classes : 4
Feats Vals InfoGain GainRatio
1 2 0.73806465 0.73806465
2 3 0.82214495 0.82214495
3 3 0.61502776 0.61502776
Starting to test, Testfile: ./test_cv/dummy_data.dat_1
Writing output in: ./test_cv/dummy_data.dat_1.cv
Algorithm : CV
Global metric : Value Difference, Prestored matrix
Deviant Feature Metrics:(none)
Size of value-matrix[1] = 48 Bytes
Size of value-matrix[2] = 48 Bytes
Size of value-matrix[3] = 48 Bytes
Total Size of value-matrices 144 Bytes
Weighting : User Defined (test_cv/test_weights)
Feature 1 : 0.738064654828046
Feature 2 : 0.822144954051390
Feature 3 : 0.615027758167667
Tested: 1 @ Tue Aug 2 12:02:03 2022
Tested: 2 @ Tue Aug 2 12:02:03 2022
Tested: 3 @ Tue Aug 2 12:02:03 2022
Tested: 4 @ Tue Aug 2 12:02:03 2022
Ready: 4 @ Tue Aug 2 12:02:03 2022
Seconds taken: 0.0002 (26490.07 p/s)
overall accuracy: 0.000000 (0/4)
There were 2 ties of which 0 (0.00%) were correctly resolved
Reading weights from test_cv/test_weights
Examine datafile './test_cv/dummy_data.dat_2' gave the following results:
Number of Features: 3
InputFormat : C4.5
DB Entropy : 2.25000000
Number of Classes : 5
Feats Vals InfoGain GainRatio
1 2 0.73806465 0.73806465
2 3 0.82214495 0.82214495
3 3 0.61502776 0.61502776
Starting to test, Testfile: ./test_cv/dummy_data.dat_2
Writing output in: ./test_cv/dummy_data.dat_2.cv
Algorithm : CV
Global metric : Value Difference, Prestored matrix
Deviant Feature Metrics:(none)
Size of value-matrix[1] = 48 Bytes
Size of value-matrix[2] = 48 Bytes
Size of value-matrix[3] = 48 Bytes
Total Size of value-matrices 144 Bytes
Weighting : User Defined (test_cv/test_weights)
Feature 1 : 0.738064654828046
Feature 2 : 0.822144954051390
Feature 3 : 0.615027758167667
Tested: 1 @ Tue Aug 2 12:02:03 2022
Tested: 2 @ Tue Aug 2 12:02:03 2022
Tested: 3 @ Tue Aug 2 12:02:03 2022
Tested: 4 @ Tue Aug 2 12:02:03 2022
Ready: 4 @ Tue Aug 2 12:02:03 2022
Seconds taken: 0.0001 (48192.77 p/s)
overall accuracy: 0.000000 (0/4), of which 1 exact matches
There was 1 tie of which 0 (0.00%) was correctly resolved
Reading weights from test_cv/test_weights
Examine datafile './test_cv/dummy_data.dat_3' gave the following results:
Number of Features: 3
InputFormat : C4.5
DB Entropy : 2.25000000
Number of Classes : 5
Feats Vals InfoGain GainRatio
1 2 0.73806465 0.73806465
2 3 0.82214495 0.82214495
3 3 0.61502776 0.61502776
Starting to test, Testfile: ./test_cv/dummy_data.dat_3
Writing output in: ./test_cv/dummy_data.dat_3.cv
Algorithm : CV
Global metric : Value Difference, Prestored matrix
Deviant Feature Metrics:(none)
Size of value-matrix[1] = 48 Bytes
Size of value-matrix[2] = 48 Bytes
Size of value-matrix[3] = 48 Bytes
Total Size of value-matrices 144 Bytes
Weighting : User Defined (test_cv/test_weights)
Feature 1 : 0.738064654828046
Feature 2 : 0.822144954051390
Feature 3 : 0.615027758167667
Tested: 1 @ Tue Aug 2 12:02:03 2022
Tested: 2 @ Tue Aug 2 12:02:03 2022
Tested: 3 @ Tue Aug 2 12:02:03 2022
Tested: 4 @ Tue Aug 2 12:02:03 2022
Ready: 4 @ Tue Aug 2 12:02:03 2022
Seconds taken: 0.0001 (48780.49 p/s)
overall accuracy: 0.000000 (0/4), of which 2 exact matches
There were 4 ties of which 0 (0.00%) were correctly resolved
TiMBL 6.8 (c) CLST/ILK/CLIPS 1998 - 2022.
Tilburg Memory Based Learner
Centre for Language and Speech Technology, Radboud University
Induction of Linguistic Knowledge Research Group, Tilburg University
CLiPS Computational Linguistics Group, University of Antwerp
Tue Aug 2 12:02:03 2022
Starting Cross validation test on files:
./test_cv/dummy_data.dat_1
./test_cv/dummy_data.dat_2
./test_cv/dummy_data.dat_3
Reading Probability Arrays from test_cv/bug20.probs
Examine datafile './test_cv/dummy_data.dat_1' gave the following results:
Number of Features: 3
InputFormat : C4.5
DB Entropy : 1.9056391
Number of Classes : 4
Feats Vals InfoGain GainRatio
1 2 0.70443400 0.73806465
2 3 1.1556391 0.82214495
3 3 0.79879494 0.61502776
Starting to test, Testfile: ./test_cv/dummy_data.dat_1
Writing output in: ./test_cv/dummy_data.dat_1.cv
Algorithm : CV
Global metric : Value Difference, Prestored matrix
Deviant Feature Metrics:(none)
Size of value-matrix[1] = 48 Bytes
Size of value-matrix[2] = 48 Bytes
Size of value-matrix[3] = 48 Bytes
Total Size of value-matrices 144 Bytes
Weighting : GainRatio
Feature 1 : 0.738064654828046
Feature 2 : 0.822144954051390
Feature 3 : 0.615027758167667
Tested: 1 @ Tue Aug 2 12:02:03 2022
Tested: 2 @ Tue Aug 2 12:02:03 2022
Tested: 3 @ Tue Aug 2 12:02:03 2022
Tested: 4 @ Tue Aug 2 12:02:03 2022
Ready: 4 @ Tue Aug 2 12:02:03 2022
Seconds taken: 0.0003 (11661.81 p/s)
overall accuracy: 0.000000 (0/4), of which 1 exact matches
There were 3 ties of which 0 (0.00%) were correctly resolved
Reading Probability Arrays from test_cv/bug20.probs
Examine datafile './test_cv/dummy_data.dat_2' gave the following results:
Number of Features: 3
InputFormat : C4.5
DB Entropy : 2.25000000
Number of Classes : 5
Feats Vals InfoGain GainRatio
1 2 0.75000000 0.75000000
2 3 1.04879494 0.80751388
3 3 1.29879494 1.00000000
Starting to test, Testfile: ./test_cv/dummy_data.dat_2
Writing output in: ./test_cv/dummy_data.dat_2.cv
Algorithm : CV
Global metric : Value Difference, Prestored matrix
Deviant Feature Metrics:(none)
Size of value-matrix[1] = 48 Bytes
Size of value-matrix[2] = 48 Bytes
Size of value-matrix[3] = 48 Bytes
Total Size of value-matrices 144 Bytes
Weighting : GainRatio
Feature 1 : 0.750000000000000
Feature 2 : 0.807513879083833
Feature 3 : 1.000000000000000
Tested: 1 @ Tue Aug 2 12:02:03 2022
Tested: 2 @ Tue Aug 2 12:02:03 2022
Tested: 3 @ Tue Aug 2 12:02:03 2022
Tested: 4 @ Tue Aug 2 12:02:03 2022
Ready: 4 @ Tue Aug 2 12:02:03 2022
Seconds taken: 0.0002 (22346.37 p/s)
overall accuracy: 0.250000 (1/4), of which 2 exact matches
Reading Probability Arrays from test_cv/bug20.probs
Examine datafile './test_cv/dummy_data.dat_3' gave the following results:
Number of Features: 3
InputFormat : C4.5
DB Entropy : 2.25000000
Number of Classes : 5
Feats Vals InfoGain GainRatio
1 2 0.95443400 1.00000000
2 3 0.75000000 0.50000000
3 3 1.31127812 0.83987478
Starting to test, Testfile: ./test_cv/dummy_data.dat_3
Writing output in: ./test_cv/dummy_data.dat_3.cv
Algorithm : CV
Global metric : Value Difference, Prestored matrix
Deviant Feature Metrics:(none)
Size of value-matrix[1] = 48 Bytes
Size of value-matrix[2] = 48 Bytes
Size of value-matrix[3] = 48 Bytes
Total Size of value-matrices 144 Bytes
Weighting : GainRatio
Feature 1 : 1.000000000000000
Feature 2 : 0.500000000000000
Feature 3 : 0.839874782024115
Tested: 1 @ Tue Aug 2 12:02:03 2022
Tested: 2 @ Tue Aug 2 12:02:03 2022
Tested: 3 @ Tue Aug 2 12:02:03 2022
Tested: 4 @ Tue Aug 2 12:02:03 2022
Ready: 4 @ Tue Aug 2 12:02:03 2022
Seconds taken: 0.0002 (21621.62 p/s)
overall accuracy: 0.000000 (0/4), of which 2 exact matches
There were 2 ties of which 0 (0.00%) were correctly resolved
1,4c1,4
< small,compact,none,screw,nut { screw 1.00000, nut 2.00000, scissors 1.00000 } 0.50000000000000
< small,other,2,key,nut { screw 1.00000, key 1.00000, nut 2.00000, scissors 1.00000 } 0.40375693954192
< large,long,none,pen,scissors { key 1.00000, pen 1.00000, scissors 2.00000 } 0.0000000000000
< small,compact,1,nut,nut { screw 1.00000, key 1.00000, nut 2.00000 } 0.0000000000000
---
> small,compact,none,screw,nut { screw 1.00000, key 1.00000, nut 2.00000 } 0.61502775816767
> small,other,2,key,scissors { screw 1.00000, key 1.00000, scissors 2.00000 } 0.55354849112103
> large,long,none,pen,key { screw 1.00000, key 2.00000, pen 1.00000, scissors 1.00000 } 0.55354849112103
> small,compact,1,nut,key { key 2.00000, pen 1.00000, nut 2.00000 } 0.0000000000000