tracevec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tracevec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Learning word embedding models (_Word2Vec and Doc2Vec_) based on the electrical consumption of various home appliances.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project consists of five connecting parts, which are:\n",
    "1. [**Training word embedding models**](#1_training_word_embedding_models) (_using [Gensim](https://radimrehurek.com/gensim/) topic modelling library_)\n",
    "2. [**Clustering**](#2_clustering) (_Doc2Vec vectors into clusters_)\n",
    "3. [**Classification**](#3_classification) (_of the electrical device type using Doc2Vec vectors_)\n",
    "4. [**Prediction**](#4_prediction) (_of the next electricity consumption category using Word2Vec vectors_)\n",
    "5. [**RNN Forecasting**](#5_rnn_forecasting) (_the next electricity consumption category using RNN with GRU_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first train the `Doc2Vec` model, where each vector represents one device, and then the `Word2Vec` model, where each vector represents one power consumption category. Trained vectors are analyzed by **clustering**, **classification**, and **predicting the next consumption**, which is performed in two different ways. The first way is to guess the next word using the `Word2Vec` model, and the second is done using the `RNN` with `GRU`. With the project, we wanted to test whether the learned vectors capture the original different patterns of electricity consumption."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data sets required to run the code are included in the [tracevec](https://github.com/mpinta/tracevec) repository. If you are running code without the included data sets, it is only necessary to clone the [tracebase](https://github.com/areinhardt/tracebase) repository, which represents projects main data set, into the `datasets` directory. All the other modified data sets (_consumptions, samples, forecast-train and forecast-test_) are gradually created by the notebook code itself. The tracebase data set is not our property and is used only as a depencency (_submodule_) - we appreciate the work done by the authors. You can find our `Word2Vec` and `Doc2Vec` models in the `models` directory (_skip the model part training if you don’t want to create new ones_)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import glob\n",
    "import torch\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import multiprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from torch import nn\n",
    "from matplotlib import rcParams\n",
    "from collections import Counter\n",
    "from sklearn import preprocessing\n",
    "from matplotlib_venn import venn3\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans\n",
    "from torch.autograd import Variable\n",
    "from gensim.models import Doc2Vec, Word2Vec\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from gensim.models.doc2vec import TaggedDocument\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style='whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 11.7, 8.27"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dirs(path):\n",
    "    \"\"\"\n",
    "    The function creates new directories.\n",
    "    \n",
    "    Args:\n",
    "        path [str]: Path of directories to create.\n",
    "        \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    if not os.path.exists(path):\n",
    "        os.makedirs(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_to_string_array(path):\n",
    "    \"\"\"\n",
    "    The function reads file into a string array.\n",
    "    \n",
    "    Args:\n",
    "        path [str]: Path to read file from.\n",
    "        \n",
    "    Returns:\n",
    "        [list]: File data in a string format.\n",
    "    \"\"\"\n",
    "    \n",
    "    with open(path, 'r') as f:\n",
    "        return f.read().split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_to_int_array(path):\n",
    "    \"\"\"\n",
    "    The function reads file into an integer array.\n",
    "    \n",
    "    Args:\n",
    "        path [str]: Path to read file from.\n",
    "        \n",
    "    Returns:\n",
    "        [numpy.ndarray]: File data in an integer format.\n",
    "    \"\"\"\n",
    "    \n",
    "    with open(path, 'r') as f:\n",
    "        return np.array(list(map(int, [r for r in csv.reader(f, delimiter=' ')][0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1_training_word_embedding_models'></a>\n",
    "\n",
    "## 1. Training word embedding models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vectorization, more precisely the construction of `Doc2Vec` and `Word2Vec` models, is the first task we will perform.**\n",
    "\n",
    "The original `tracebase` data set consists of several device directories and associated files, but they are not in the correct date sequence. Because a lot of measurements are taken a few days apart, power consumption data needs to be properly processed and broken down into smaller separate devices, as these are then used to train the models. `tracebase` contains several different subsets - will use its `complete` data set. This part of the notebook presents the transformer and the associated functions that are used to pre-process and change the power consumption of devices to the appropriate format. Finally, learning the models and reviewing the obtained vectors follows.\n",
    "\n",
    "> [*The Tracebase data set is a collection of power consumption traces which can be used in energy analytics research.*](https://github.com/areinhardt/tracebase)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Preparing power consumption data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s prepare the paths to the `tracebase` data set, `consumptions` directory, `Doc2Vec` and `Word2Vec` models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracebase_path = 'datasets/tracebase/complete/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumptions_path = 'datasets/consumptions/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2v_model_path = 'models/d2v_model.bin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model_path = 'models/w2v_model.bin'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to determine the **mapping ranges** that will be used to digitize each consumption value into a whole number. These numbers will help us monitor different patterns of power consumption rather than actual power consumption values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.34482759 0.37037037 0.4        0.43478261 0.47619048 0.52631579\n",
      " 0.58823529 0.66666667 0.76923077 0.90909091 1.1        1.3\n",
      " 1.5        1.7        1.9        2.1        2.3        2.5\n",
      " 2.7        2.9       ]\n"
     ]
    }
   ],
   "source": [
    "maximum = 3\n",
    "minimum = 1.1\n",
    "steps = 0.2\n",
    "\n",
    "intervals = np.arange(minimum, maximum, steps)\n",
    "mapping = np.sort(np.concatenate([intervals, 1 / intervals]))\n",
    "\n",
    "print(mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following functions must be defined for the operation of the transformer class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_to_consumptions(dirname, filename, array, dir_path):\n",
    "    \"\"\"\n",
    "    The function writes transformed consumption of a device into a file.\n",
    "    \n",
    "    Args:\n",
    "        dirname  [str]:           Device directory name.\n",
    "        filename [str]:           Device file name.\n",
    "        array    [numpy.ndarray]: Consumption data to write.\n",
    "        dir_path [str]:           Path to write consumption to.\n",
    "        \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    create_dirs(dir_path + dirname)\n",
    "    \n",
    "    with open(dir_path + dirname + os.sep + filename + '.csv', 'w') as f:\n",
    "        np.savetxt(f, array, fmt='%d', newline=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_unique_filenames(path, dirname):\n",
    "    \"\"\"\n",
    "    The function finds unique file names in the directory.\n",
    "    \n",
    "    Args:\n",
    "        path    [str]: Path to search from.\n",
    "        dirname [str]: Device directory name.\n",
    "        \n",
    "    Returns:\n",
    "        [list]: Unique file names in the directory.\n",
    "    \"\"\"\n",
    "    \n",
    "    filenames = []\n",
    "    \n",
    "    for i in glob.glob(os.path.join(path + dirname, '*.csv')):\n",
    "        filename = i.rsplit(os.sep, 1)[1]\n",
    "        cut = -2 if filename.rsplit('_')[-1] == 'CRT-TV.csv' else -1  \n",
    "        filenames.append('_'.join(filename.rsplit('_')[:cut])) \n",
    "        \n",
    "    return sorted(list(set(filenames)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_and_digitize_series(series, unique_dates, mapping):\n",
    "    \"\"\"\n",
    "    The function splits and digitizes series data based on device unique dates.\n",
    "    \n",
    "    Args:\n",
    "        series       [pandas.Series]: Device consumption data to split.\n",
    "        unique_dates [list]:          Device unique dates.\n",
    "        mapping      [numpy.ndarray]: Mapping ranges.\n",
    "        \n",
    "    Returns:\n",
    "        [list]: Splitted series data.\n",
    "    \"\"\"\n",
    "    \n",
    "    current = []\n",
    "    splitted = []\n",
    "    \n",
    "    for i, v in series.items():\n",
    "        if i.strftime('%Y-%d-%m') in unique_dates:\n",
    "            current.append(v)\n",
    "        elif len(current) > 0:\n",
    "            splitted.append(np.digitize(np.array(current), mapping))\n",
    "            current = []\n",
    "    \n",
    "    splitted.append(np.digitize(np.array(current), mapping))\n",
    "    \n",
    "    return splitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropout_last(array, drop_perc):\n",
    "    \"\"\"\n",
    "    The function drops given percent of last consumption data.\n",
    "    \n",
    "    Args:\n",
    "        array     [numpy.ndarray]: Consumption data to drop from.\n",
    "        drop_perc [float]:         Percent of last consumption data to drop.\n",
    "    \n",
    "    Returns:\n",
    "        [numpy.ndarray]: Consumption data on which dropping was performed.\n",
    "    \"\"\"\n",
    "    \n",
    "    return array[:-int(round(len(array) * drop_perc))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **transformer class** converts power consumption data into the appropriate format for training the `Doc2Vec` and `Word2Vec` models.\n",
    "1. Depending on the number of devices, the class opens the device folder and obtains its unique file names. \n",
    "2. It then goes through them and retrieves the paths of all the files in the folder, that contain the current name.\n",
    "3. Then it reads and combines the data of these files, removes one column, and changes the format of the date time column. \n",
    "4. This is followed by sorting the data according to the date time column, setting up a new index, grouping the data frame by index and summing the duplicated rows. \n",
    "5. Next, unique dates are obtained, the data is resampled and divided by its average value. \n",
    "6. In the end, several separate devices are created from the data frame according to previously obtained unique dates. In doing so, their consumption is digitized according to the mapping ranges. \n",
    "7. Depending on which model is being trained, the mapped consumption is returned either as a `TaggedDocument` or as a `list`, with some percentage of last consumption dropped from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Transformer(object):\n",
    "    \"\"\"\n",
    "    A class used to transform power consumption data.\n",
    "    \n",
    "    Attributes:\n",
    "        path      [str]:           Data set path.\n",
    "        mapping   [numpy.ndarray]: Mapping ranges.\n",
    "        n_devices [int]:           Number of devices to read.\n",
    "        agg_time  [str]:           Time-series resampling conversion value.\n",
    "        drop_perc [float]:         Percent of last consumption data to drop.\n",
    "        doc       [bool]:          Indicating if Doc2Vec is being trained.\n",
    "        names     [list]:          Data frame column names.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, path, mapping, n_devices, agg_time='5T', drop_perc=0.3, doc=False):\n",
    "        self.path = path\n",
    "        self.mapping = mapping\n",
    "        self.n_devices = n_devices\n",
    "        self.agg_time = agg_time\n",
    "        self.drop_perc = drop_perc\n",
    "        self.doc = doc\n",
    "        self.names = ['datetime', '1s_average', '8s_average']\n",
    "\n",
    "    def __iter__(self):\n",
    "        for dirname in os.listdir(self.path)[:self.n_devices]:\n",
    "            tag = -1\n",
    "            unique_filenames = find_unique_filenames(self.path, dirname)\n",
    "\n",
    "            for filename in unique_filenames:\n",
    "                files = [i for i in glob.glob(os.path.join(self.path + dirname, '*.csv')) \n",
    "                         if filename in i.rsplit(os.sep, 1)[1]]\n",
    "\n",
    "                df = pd.concat([pd.read_csv(i, sep=';', names=self.names) for i in files], ignore_index=True)\n",
    "                df.drop(['8s_average'], axis=1, inplace=True)\n",
    "                df['datetime'] = pd.to_datetime(df['datetime'].astype(str), format='%d/%m/%Y %H:%M:%S')\n",
    "                df.sort_values(by='datetime', ascending=True, inplace=True)\n",
    "                df.set_index('datetime', inplace=True)\n",
    "                df = df.groupby(level=0).sum()\n",
    "\n",
    "                unique_dates = [i.strftime('%Y-%d-%m') for i in df.index.map(pd.Timestamp.date).unique()]\n",
    "                \n",
    "                df_r = df.resample(self.agg_time)['1s_average'].sum()\n",
    "                df_r = df_r / df_r.mean()\n",
    "                \n",
    "                for i in split_and_digitize_series(df_r, unique_dates, self.mapping):\n",
    "                    if self.doc:\n",
    "                        tag += 1\n",
    "                        write_to_consumptions(dirname.lower(), str(tag) + '_' + dirname.lower(), \n",
    "                                              i, consumptions_path)\n",
    "                        yield TaggedDocument(np.char.mod('%d', i).tolist(), [str(tag) + '_' + dirname.lower()])\n",
    "                    else:\n",
    "                        yield np.char.mod('%d', dropout_last(i, self.drop_perc)).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Training Doc2Vec model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define **parameters**, which are same for training both the `Doc2Vec` and `Word2Vec` models. We will train  the models using `Gensim` library.\n",
    "\n",
    "> [*Gensim is an open-source library for unsupervised topic modeling and natural language processing.*](https://radimrehurek.com/gensim/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 5\n",
    "vector_size = 50\n",
    "\n",
    "n_epochs = 200\n",
    "n_devices = 43\n",
    "n_workers = multiprocessing.cpu_count() - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to train the model, we need to create new `Transformer` and `Doc2Vec` objects.\n",
    "\n",
    "> [*Doc2Vec is a model that represents each document as a vector.*](https://radimrehurek.com/gensim/models/doc2vec.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2v_transformer = Transformer(tracebase_path, mapping, n_devices=n_devices, doc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2v_model = Doc2Vec(vector_size=vector_size, window=window_size, workers=n_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that, a **vocabulary** needs to be built and then the model can be **trained** and **saved**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2v_model.build_vocab(d2v_transformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2v_model.train(d2v_transformer, total_examples=d2v_model.corpus_count, epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2v_model.save(d2v_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load newly created `Doc2Vec` model and print some of its **properties** below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2v_model = Doc2Vec.load(d2v_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are vocabulary length, vocabulary words, a sample of vector names and an actual example of a `0_toaster` device vector. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "print(len(list(d2v_model.wv.vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '20', '2', '4', '5', '7', '3', '8', '6', '18', '19', '9', '17', '15', '16', '14', '11', '13', '12', '10', '1']\n"
     ]
    }
   ],
   "source": [
    "print(list(d2v_model.wv.vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0_toaster', '1_toaster', '2_toaster', '3_toaster', '4_toaster']\n"
     ]
    }
   ],
   "source": [
    "print(d2v_model.docvecs.index2entity[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.25131676 -0.5422203   0.6973609  -0.02512717  0.56432164  0.11338314\n",
      " -0.22850733 -0.638559    0.13102405 -0.28289267 -0.32152718 -0.32209548\n",
      "  0.5892098  -0.10315282  0.16415334  0.5954432   0.45891964 -0.69004554\n",
      " -0.9453215   0.37720335 -0.08026756  0.3616484   0.41242233 -0.08861385\n",
      "  0.41390908  0.338499    0.7038394   0.6721943   0.19987959 -0.6132844\n",
      "  1.4496475  -0.20318168 -0.36810943 -0.82601875 -1.0772575  -0.28324795\n",
      "  0.44871673  0.42344505  0.7580702  -0.70324457 -0.82988983  0.5306042\n",
      " -0.02509752  1.4340112   1.3418086  -0.5437436   0.03444461 -0.9632389\n",
      "  0.61627346  1.5514088 ]\n"
     ]
    }
   ],
   "source": [
    "print(d2v_model['0_toaster'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because vector data is easiest to present in a **data frame**, we need to define the function for converting the `Doc2Vec` model into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d2v_model_to_dataframe(model):\n",
    "    \"\"\"\n",
    "    The function creates a data frame from Doc2Vec model.\n",
    "    \n",
    "    Args:\n",
    "        model [gensim.Doc2Vec]: Doc2Vec model.\n",
    "        \n",
    "    Returns:\n",
    "        [panda.DataFrame]: Data frame with Doc2Vec vector data.\n",
    "    \"\"\"\n",
    "    \n",
    "    return pd.DataFrame(model.docvecs[i] for i in model.docvecs.index2entity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_d2v = d2v_model_to_dataframe(d2v_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.282893</td>\n",
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       "      <td>-0.829890</td>\n",
       "      <td>0.530604</td>\n",
       "      <td>-0.025098</td>\n",
       "      <td>1.434011</td>\n",
       "      <td>1.341809</td>\n",
       "      <td>-0.543744</td>\n",
       "      <td>0.034445</td>\n",
       "      <td>-0.963239</td>\n",
       "      <td>0.616273</td>\n",
       "      <td>1.551409</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.283454</td>\n",
       "      <td>-0.945170</td>\n",
       "      <td>0.905071</td>\n",
       "      <td>0.197265</td>\n",
       "      <td>0.783477</td>\n",
       "      <td>0.062736</td>\n",
       "      <td>-0.456336</td>\n",
       "      <td>-1.057102</td>\n",
       "      <td>0.146308</td>\n",
       "      <td>-0.528121</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.573930</td>\n",
       "      <td>0.854392</td>\n",
       "      <td>-0.144063</td>\n",
       "      <td>2.363049</td>\n",
       "      <td>2.326744</td>\n",
       "      <td>-0.808261</td>\n",
       "      <td>-0.214464</td>\n",
       "      <td>-1.494301</td>\n",
       "      <td>0.937580</td>\n",
       "      <td>2.623432</td>\n",
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       "      <td>-0.087975</td>\n",
       "      <td>-0.275250</td>\n",
       "      <td>0.387133</td>\n",
       "      <td>0.388291</td>\n",
       "      <td>0.270675</td>\n",
       "      <td>0.090464</td>\n",
       "      <td>-0.185362</td>\n",
       "      <td>-0.455264</td>\n",
       "      <td>-0.164989</td>\n",
       "      <td>-0.221152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.703456</td>\n",
       "      <td>0.444184</td>\n",
       "      <td>-0.222669</td>\n",
       "      <td>0.800059</td>\n",
       "      <td>1.069233</td>\n",
       "      <td>-0.210168</td>\n",
       "      <td>-0.163844</td>\n",
       "      <td>-0.450154</td>\n",
       "      <td>0.585877</td>\n",
       "      <td>1.033662</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.125783</td>\n",
       "      <td>-1.024553</td>\n",
       "      <td>1.119424</td>\n",
       "      <td>0.506711</td>\n",
       "      <td>0.935346</td>\n",
       "      <td>0.167841</td>\n",
       "      <td>-0.369862</td>\n",
       "      <td>-1.260723</td>\n",
       "      <td>0.149401</td>\n",
       "      <td>-0.640517</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.783431</td>\n",
       "      <td>1.170877</td>\n",
       "      <td>-0.181855</td>\n",
       "      <td>2.615109</td>\n",
       "      <td>2.654473</td>\n",
       "      <td>-0.749771</td>\n",
       "      <td>-0.281561</td>\n",
       "      <td>-1.741567</td>\n",
       "      <td>1.130616</td>\n",
       "      <td>3.163152</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.402792</td>\n",
       "      <td>-0.012620</td>\n",
       "      <td>0.410766</td>\n",
       "      <td>0.705638</td>\n",
       "      <td>0.221510</td>\n",
       "      <td>0.226542</td>\n",
       "      <td>-0.070957</td>\n",
       "      <td>-0.516422</td>\n",
       "      <td>-0.399924</td>\n",
       "      <td>-0.203983</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.632816</td>\n",
       "      <td>0.577174</td>\n",
       "      <td>-0.355534</td>\n",
       "      <td>0.528383</td>\n",
       "      <td>1.132488</td>\n",
       "      <td>0.076279</td>\n",
       "      <td>-0.233031</td>\n",
       "      <td>-0.387620</td>\n",
       "      <td>0.745580</td>\n",
       "      <td>0.854994</td>\n",
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       "         0         1         2         3         4         5         6   \\\n",
       "0  0.251317 -0.542220  0.697361 -0.025127  0.564322  0.113383 -0.228507   \n",
       "1  0.283454 -0.945170  0.905071  0.197265  0.783477  0.062736 -0.456336   \n",
       "2 -0.087975 -0.275250  0.387133  0.388291  0.270675  0.090464 -0.185362   \n",
       "3  0.125783 -1.024553  1.119424  0.506711  0.935346  0.167841 -0.369862   \n",
       "4 -0.402792 -0.012620  0.410766  0.705638  0.221510  0.226542 -0.070957   \n",
       "\n",
       "         7         8         9   ...        40        41        42        43  \\\n",
       "0 -0.638559  0.131024 -0.282893  ... -0.829890  0.530604 -0.025098  1.434011   \n",
       "1 -1.057102  0.146308 -0.528121  ... -1.573930  0.854392 -0.144063  2.363049   \n",
       "2 -0.455264 -0.164989 -0.221152  ... -0.703456  0.444184 -0.222669  0.800059   \n",
       "3 -1.260723  0.149401 -0.640517  ... -1.783431  1.170877 -0.181855  2.615109   \n",
       "4 -0.516422 -0.399924 -0.203983  ... -0.632816  0.577174 -0.355534  0.528383   \n",
       "\n",
       "         44        45        46        47        48        49  \n",
       "0  1.341809 -0.543744  0.034445 -0.963239  0.616273  1.551409  \n",
       "1  2.326744 -0.808261 -0.214464 -1.494301  0.937580  2.623432  \n",
       "2  1.069233 -0.210168 -0.163844 -0.450154  0.585877  1.033662  \n",
       "3  2.654473 -0.749771 -0.281561 -1.741567  1.130616  3.163152  \n",
       "4  1.132488  0.076279 -0.233031 -0.387620  0.745580  0.854994  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_d2v.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Training Word2Vec model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before training the model, we need to recreate all the necessary objects, which are `Transformer` and `Word2Vec` objects.\n",
    "\n",
    "> [*Word2Vec is a model that embeds words in a lower-dimensional vector space using a shallow neural network.*](https://radimrehurek.com/gensim/models/word2vec.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_transformer = Transformer(tracebase_path, mapping, n_devices=n_devices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model = Word2Vec(size=vector_size, window=window_size, workers=n_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that, we need to rebuild the **vocabulary**, start **training** the model and **save** it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model.build_vocab(w2v_transformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model.train(w2v_transformer, total_examples=w2v_model.corpus_count, epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model.save(w2v_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we load newly created `Word2Vec` model and print some of its **properties**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model = Word2Vec.load(w2v_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are vocabulary length, vocabulary words and an actual example of a `0` consumption vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "print(len(list(w2v_model.wv.vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '20', '2', '4', '5', '7', '3', '8', '6', '18', '19', '9', '17', '15', '16', '14', '11', '13', '12', '10', '1']\n"
     ]
    }
   ],
   "source": [
    "print(list(w2v_model.wv.vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.16483591 -2.627785    0.02058786  0.03126768  0.73000026  0.13671306\n",
      "  0.28634915  0.8207739  -0.04503812  0.71593773 -0.9494604   0.30443463\n",
      "  1.0695629  -2.3345072  -0.02407067  0.1034523  -0.7267006  -0.7716735\n",
      " -0.9812443   0.19766578  2.1132755   0.0659349   0.11627307 -1.9110117\n",
      " -0.06560331 -0.47028217 -0.2065368  -1.8414251   0.32779074 -0.45751464\n",
      "  0.16330029 -0.14546116 -0.49462837 -0.09179021  0.30638048 -0.90382946\n",
      " -0.6957215   1.9362437  -0.34744295  0.12307446 -0.5325359   0.6307974\n",
      " -0.78562146 -0.0028429  -1.084195    1.9951036  -0.2936932  -1.2518326\n",
      "  0.02299066  2.0401921 ]\n"
     ]
    }
   ],
   "source": [
    "print(w2v_model.wv['0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with `Doc2Vec`, we need to define a function to convert the `Word2Vec` model into a **data frame**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "def w2v_model_to_dataframe(model):\n",
    "    \"\"\"\n",
    "    The function creates a data frame from Word2Vec model.\n",
    "    \n",
    "    Args:\n",
    "        model [gensim.Word2Vec]: Word2Vec model.\n",
    "        \n",
    "    Returns:\n",
    "        [panda.DataFrame]: Data frame with Word2Vec vector data.\n",
    "    \"\"\"\n",
    "    \n",
    "    return pd.DataFrame(model.wv[i] for i in model.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w2v = w2v_model_to_dataframe(w2v_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.164836</td>\n",
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       "      <td>-0.532536</td>\n",
       "      <td>0.630797</td>\n",
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       "      <td>-0.293693</td>\n",
       "      <td>-1.251833</td>\n",
       "      <td>0.022991</td>\n",
       "      <td>2.040192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.951559</td>\n",
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       "      <td>-1.097207</td>\n",
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       "      <td>0.395586</td>\n",
       "      <td>0.739340</td>\n",
       "      <td>-2.855621</td>\n",
       "      <td>4.122703</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.626497</td>\n",
       "      <td>0.891378</td>\n",
       "      <td>3.644045</td>\n",
       "      <td>-1.865407</td>\n",
       "      <td>0.852793</td>\n",
       "      <td>0.486151</td>\n",
       "      <td>-2.308506</td>\n",
       "      <td>0.562205</td>\n",
       "      <td>1.079576</td>\n",
       "      <td>-1.113847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222462</td>\n",
       "      <td>0.747169</td>\n",
       "      <td>1.519686</td>\n",
       "      <td>1.345147</td>\n",
       "      <td>0.531670</td>\n",
       "      <td>-0.466570</td>\n",
       "      <td>-0.536966</td>\n",
       "      <td>0.900969</td>\n",
       "      <td>-0.010163</td>\n",
       "      <td>-1.451500</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200071</td>\n",
       "      <td>2.852473</td>\n",
       "      <td>1.723763</td>\n",
       "      <td>0.807578</td>\n",
       "      <td>0.202305</td>\n",
       "      <td>1.909688</td>\n",
       "      <td>-3.145110</td>\n",
       "      <td>-1.580944</td>\n",
       "      <td>-0.229414</td>\n",
       "      <td>-0.431983</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160816</td>\n",
       "      <td>-0.175354</td>\n",
       "      <td>1.068746</td>\n",
       "      <td>1.708935</td>\n",
       "      <td>1.902866</td>\n",
       "      <td>-0.788524</td>\n",
       "      <td>1.019361</td>\n",
       "      <td>1.109324</td>\n",
       "      <td>-0.776418</td>\n",
       "      <td>0.063942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.612565</td>\n",
       "      <td>2.596014</td>\n",
       "      <td>1.302939</td>\n",
       "      <td>0.606638</td>\n",
       "      <td>0.120380</td>\n",
       "      <td>0.489317</td>\n",
       "      <td>-1.848691</td>\n",
       "      <td>-1.041271</td>\n",
       "      <td>-0.545641</td>\n",
       "      <td>-1.409320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043275</td>\n",
       "      <td>0.272504</td>\n",
       "      <td>1.935189</td>\n",
       "      <td>1.098960</td>\n",
       "      <td>0.784432</td>\n",
       "      <td>-1.261794</td>\n",
       "      <td>1.411864</td>\n",
       "      <td>0.749969</td>\n",
       "      <td>-0.874281</td>\n",
       "      <td>-0.000428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -0.164836 -2.627785  0.020588  0.031268  0.730000  0.136713  0.286349   \n",
       "1  1.951559  2.659264 -1.505808  2.511912 -1.767032 -0.288314 -3.670078   \n",
       "2 -0.626497  0.891378  3.644045 -1.865407  0.852793  0.486151 -2.308506   \n",
       "3  0.200071  2.852473  1.723763  0.807578  0.202305  1.909688 -3.145110   \n",
       "4  0.612565  2.596014  1.302939  0.606638  0.120380  0.489317 -1.848691   \n",
       "\n",
       "         7         8         9   ...        40        41        42        43  \\\n",
       "0  0.820774 -0.045038  0.715938  ... -0.532536  0.630797 -0.785621 -0.002843   \n",
       "1 -1.097207 -0.959779 -0.255822  ...  1.507688 -0.712253  1.335687 -4.005666   \n",
       "2  0.562205  1.079576 -1.113847  ...  0.222462  0.747169  1.519686  1.345147   \n",
       "3 -1.580944 -0.229414 -0.431983  ...  0.160816 -0.175354  1.068746  1.708935   \n",
       "4 -1.041271 -0.545641 -1.409320  ...  0.043275  0.272504  1.935189  1.098960   \n",
       "\n",
       "         44        45        46        47        48        49  \n",
       "0 -1.084195  1.995104 -0.293693 -1.251833  0.022991  2.040192  \n",
       "1 -2.751504 -1.317367  0.395586  0.739340 -2.855621  4.122703  \n",
       "2  0.531670 -0.466570 -0.536966  0.900969 -0.010163 -1.451500  \n",
       "3  1.902866 -0.788524  1.019361  1.109324 -0.776418  0.063942  \n",
       "4  0.784432 -1.261794  1.411864  0.749969 -0.874281 -0.000428  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w2v.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2_clustering'></a>\n",
    "\n",
    "## 2. Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this part of the notebook, we will group the previously trained `Doc2Vec` vectors into multiple clusters.**\n",
    "\n",
    "We expect the vectors to be clustered according to similar patterns of their consumption. The consumption patterns of each of the clusters will be analyzed using descriptive statistics and plotted on different graphs. We will also check which devices have been classified into which clusters and whether they are also similar in practice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Pre-processing Doc2Vec vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the `Doc2Vec` data frame is already created, we can use it to form a `StandardScaler` object and **standardize** the vectors.\n",
    "\n",
    "> [*Standard scaler is used to standardize features by removing the mean and scaling to unit variance.*](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(df_d2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled = pd.DataFrame(scaler.transform(df_d2v), columns=df_d2v.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.231845</td>\n",
       "      <td>-0.021342</td>\n",
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       "      <td>0.016550</td>\n",
       "      <td>0.131640</td>\n",
       "      <td>-0.035676</td>\n",
       "      <td>0.168808</td>\n",
       "      <td>0.145285</td>\n",
       "      <td>-0.172823</td>\n",
       "      <td>0.200427</td>\n",
       "      <td>-0.362992</td>\n",
       "      <td>0.409825</td>\n",
       "      <td>0.276924</td>\n",
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       "      <td>1.282384</td>\n",
       "      <td>-0.194107</td>\n",
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       "      <td>-0.009167</td>\n",
       "      <td>-0.571924</td>\n",
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       "      <td>-1.037043</td>\n",
       "      <td>0.773391</td>\n",
       "      <td>0.002335</td>\n",
       "      <td>1.537512</td>\n",
       "      <td>1.395583</td>\n",
       "      <td>-0.602224</td>\n",
       "      <td>0.096363</td>\n",
       "      <td>-1.224497</td>\n",
       "      <td>0.880511</td>\n",
       "      <td>1.789909</td>\n",
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       "      <td>0.001198</td>\n",
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       "4 -0.126938 -0.398246  0.001198  ...  0.085464  0.249085 -0.159416 -0.036058   \n",
       "\n",
       "         44        45        46        47        48        49  \n",
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       "1  1.137099 -0.648769  0.155678 -1.059545  0.713717  1.406443  \n",
       "2  0.145285 -0.172823  0.200427 -0.362992  0.409825  0.276924  \n",
       "3  1.395583 -0.602224  0.096363 -1.224497  0.880511  1.789909  \n",
       "4  0.195175  0.055124  0.139265 -0.321276  0.547818  0.149982  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next task is the **normalization** of transformed vectors, which is performed using `preprocessing.normalize` function. \n",
    "\n",
    "> [*Normalization is the process of scaling individual samples to have unit norm.*](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_norm = pd.DataFrame(preprocessing.normalize(df_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.087227</td>\n",
       "      <td>0.158809</td>\n",
       "      <td>0.061153</td>\n",
       "      <td>-0.006133</td>\n",
       "      <td>-0.130812</td>\n",
       "      <td>-0.002156</td>\n",
       "      <td>-0.080557</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.158064</td>\n",
       "      <td>0.093757</td>\n",
       "      <td>0.007125</td>\n",
       "      <td>0.255785</td>\n",
       "      <td>0.215856</td>\n",
       "      <td>-0.123156</td>\n",
       "      <td>0.029552</td>\n",
       "      <td>-0.201134</td>\n",
       "      <td>0.135485</td>\n",
       "      <td>0.266986</td>\n",
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       "      <td>-0.156849</td>\n",
       "      <td>0.202898</td>\n",
       "      <td>0.185301</td>\n",
       "      <td>0.078138</td>\n",
       "      <td>-0.033607</td>\n",
       "      <td>-0.122991</td>\n",
       "      <td>-0.011322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008779</td>\n",
       "      <td>0.069833</td>\n",
       "      <td>-0.018926</td>\n",
       "      <td>0.089550</td>\n",
       "      <td>0.077072</td>\n",
       "      <td>-0.091680</td>\n",
       "      <td>0.106324</td>\n",
       "      <td>-0.192562</td>\n",
       "      <td>0.217407</td>\n",
       "      <td>0.146905</td>\n",
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       "      <td>0.061192</td>\n",
       "      <td>-0.070710</td>\n",
       "      <td>0.207828</td>\n",
       "      <td>-0.031458</td>\n",
       "      <td>0.157375</td>\n",
       "      <td>0.068892</td>\n",
       "      <td>0.004053</td>\n",
       "      <td>-0.145988</td>\n",
       "      <td>-0.001486</td>\n",
       "      <td>-0.092688</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.168067</td>\n",
       "      <td>0.125339</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.249175</td>\n",
       "      <td>0.226174</td>\n",
       "      <td>-0.097599</td>\n",
       "      <td>0.015617</td>\n",
       "      <td>-0.198447</td>\n",
       "      <td>0.142699</td>\n",
       "      <td>0.290080</td>\n",
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       "      <td>0.145633</td>\n",
       "      <td>0.319320</td>\n",
       "      <td>-0.010839</td>\n",
       "      <td>0.156329</td>\n",
       "      <td>0.222582</td>\n",
       "      <td>0.102912</td>\n",
       "      <td>-0.058548</td>\n",
       "      <td>-0.183684</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039419</td>\n",
       "      <td>0.114886</td>\n",
       "      <td>-0.073528</td>\n",
       "      <td>-0.016631</td>\n",
       "      <td>0.090021</td>\n",
       "      <td>0.025425</td>\n",
       "      <td>0.064233</td>\n",
       "      <td>-0.148183</td>\n",
       "      <td>0.252671</td>\n",
       "      <td>0.069177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  0.150756 -0.023606  0.282300 -0.197174  0.194833  0.112729  0.035994   \n",
       "1  0.100187 -0.071626  0.209555 -0.087227  0.158809  0.061153 -0.006133   \n",
       "2  0.092282  0.063958  0.356827 -0.156849  0.202898  0.185301  0.078138   \n",
       "3  0.061192 -0.070710  0.207828 -0.031458  0.157375  0.068892  0.004053   \n",
       "4 -0.058082  0.145633  0.319320 -0.010839  0.156329  0.222582  0.102912   \n",
       "\n",
       "         7         8         9   ...        40        41        42        43  \\\n",
       "0 -0.077000 -0.006727 -0.031052  ... -0.032385  0.063061  0.044978  0.196153   \n",
       "1 -0.130812 -0.002156 -0.080557  ... -0.158064  0.093757  0.007125  0.255785   \n",
       "2 -0.033607 -0.122991 -0.011322  ...  0.008779  0.069833 -0.018926  0.089550   \n",
       "3 -0.145988 -0.001486 -0.092688  ... -0.168067  0.125339  0.000378  0.249175   \n",
       "4 -0.058548 -0.183684  0.000553  ...  0.039419  0.114886 -0.073528 -0.016631   \n",
       "\n",
       "         44        45        46        47        48        49  \n",
       "0  0.109247 -0.132901  0.113932 -0.213865  0.132239  0.195521  \n",
       "1  0.215856 -0.123156  0.029552 -0.201134  0.135485  0.266986  \n",
       "2  0.077072 -0.091680  0.106324 -0.192562  0.217407  0.146905  \n",
       "3  0.226174 -0.097599  0.015617 -0.198447  0.142699  0.290080  \n",
       "4  0.090021  0.025425  0.064233 -0.148183  0.252671  0.069177  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our vectors have many dimensions, more precisely they have as many as `50` columns. Since such vectors are almost impossible to visualize on graphs, they must first be **reduced to fewer components**. To do this, we will use the `t-SNE` (t-distributed Stochastic Neighbor Embedding) transformation and transform columns to `2` components.\n",
    "\n",
    "> [*t-SNE converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data.*](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne_array = TSNE(n_iter=5000,\n",
    "                  perplexity=30,\n",
    "                  random_state=102,\n",
    "                  learning_rate=300,\n",
    "                  n_components=n_components).fit_transform(df_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tr = pd.DataFrame(tsne_array, columns=[str(i) for i in range(n_components)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the shape of newly transformed data. It is seen that the number of rows is equal to `480` and the number of columns to only `2`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(480, 2)\n"
     ]
    }
   ],
   "source": [
    "print(df_tr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot transformed data on a `relplot` graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='0', y='1', alpha=.8, height=8, data=df_tr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 K-Means clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before performing the vector clustering, we need to find the appropriate **number of clusters**. We will do this using the `dendrogram` graph.\n",
    "\n",
    "> [*The dendrogram illustrates how each cluster is composed by drawing a U-shaped link between a non-singleton cluster and its children.*](https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.dendrogram.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "linked = linkage(df_tr, method='complete', metric='cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 10))\n",
    "\n",
    "dendrogram(p=5, \n",
    "           Z=linked,\n",
    "           leaf_rotation=90,\n",
    "           leaf_font_size=8,\n",
    "           orientation='top',\n",
    "           truncate_mode='level',\n",
    "           labels=d2v_model.docvecs.index2entity)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another method of finding the appropriate number of clusters is to calculate the `inertia` for several numbers of clusters and display results on the `lineplot` graph. We can do this using the `intertia_` attribute of the `KMeans` object.\n",
    "\n",
    "> [*Inertia is a sum of squared distances of samples to their closest cluster center.*](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_inertia(df, n):\n",
    "    \"\"\"\n",
    "    The function calculates interia of a certain number of clusters.\n",
    "    \n",
    "    Args:\n",
    "        df [pandas.DataFrame]: Data frame to calculate inertia from.\n",
    "        n  [int]:              Number of clusters.\n",
    "        \n",
    "    Returns:\n",
    "        [dict]: Inertia values for each of a certain number of clusters.\n",
    "    \"\"\"\n",
    "    \n",
    "    return {i:KMeans(n_clusters=i).fit(df).inertia_ for i in range(1, n + 1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inertia_clusters = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = calculate_inertia(df_tr, n_inertia_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 10))\n",
    "\n",
    "sns.lineplot(x=list(inertia.keys()), y=list(inertia.values()))\n",
    "\n",
    "plt.xlim([1, n_inertia_clusters])\n",
    "\n",
    "plt.ylabel('Inertia')\n",
    "plt.xlabel('Number of clusters')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the `dendrogram` and `inertia` graphs we can conclude, that the most suitable number of clusters is equal to `3`. We can now perform `K-Means` clustering of the `Doc2Vec` vectors into `3` clusters.\n",
    "\n",
    "> [*The K-Means algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares.*](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_clusters = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(df_tr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at the number of vectors assigned to each of the clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 193, 2: 154, 0: 133})\n"
     ]
    }
   ],
   "source": [
    "print(Counter(kmeans.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, previously `t-SNE` transformed data are color marked and plotted on a `relplot` graph. The black dots represent the centers our `3` clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 622.6x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='0', y='1', hue=kmeans.labels_, alpha=.8, height=8, data=df_tr, palette='tab10')\n",
    "\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=150, alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Cluster consumption analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a data frame that stores data about the vectors and the clusters, that were assigned to them during `K-Means` clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tc = pd.DataFrame({'tag': d2v_model.docvecs.index2entity, 'cluster': kmeans.labels_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to analyze device consumption patterns, we must first read them. Therefore, we **read the mapped consumptions** that were prior saved during the training of the `Doc2Vec` model, using `write_to_consumptions` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_consumptions(tags, path):\n",
    "    \"\"\"\n",
    "    The function reads devices consumption data.\n",
    "    \n",
    "    Args:\n",
    "        tags [list]: Doc2Vec model vectors names.\n",
    "        path [str]:  Path to read consumptions from.\n",
    "        \n",
    "    Returns:\n",
    "        [dict]: Devices consumption data.\n",
    "    \"\"\"\n",
    "    \n",
    "    return {i:pd.read_csv(path + i.rsplit('_')[1] + os.sep + i + '.csv', \n",
    "                          header=None, delim_whitespace=True).T for i in tags}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumptions = read_consumptions(d2v_model.docvecs.index2entity, consumptions_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to **merge** the consumption of all devices per cluster together into one consumption column per cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_by_clusters(df, consumptions, n_clusters):\n",
    "    \"\"\"\n",
    "    The function merges consumption data by clusters.\n",
    "    \n",
    "    Args:\n",
    "        df           [pandas.DataFrame]: Data frame of vectors names and their clusters.\n",
    "        consumptions [dict]:             Devices consumption data.\n",
    "        n_clusters   [int]:              Number of clusters.\n",
    "        \n",
    "    Returns:\n",
    "        [dict]: Consumption data for each of the clusters.\n",
    "    \"\"\"\n",
    "    \n",
    "    return {i:pd.concat([consumptions[j] for j in get_cluster_tags(df, i)]) for i in range(0, n_clusters)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cluster_tags(df, cluster_n):\n",
    "    \"\"\"\n",
    "    The function obtains device names of a particular cluster.\n",
    "    \n",
    "    Args:\n",
    "        df        [pandas.DataFrame]: Data frame of vector names and their clusters.\n",
    "        cluster_n [int]:              Cluster number.\n",
    "        \n",
    "    Returns:\n",
    "        [pandas.Series]: Device names of a particual cluster.\n",
    "    \"\"\"\n",
    "    \n",
    "    return df.loc[df['cluster'] == cluster_n].tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumptions_c = merge_by_clusters(df_tc, consumptions, n_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define and run a function that evaluates each cluster consumption data with **descriptive statistics**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_consumptions(consumptions_c, agg_functions, n_clusters):\n",
    "    \"\"\"\n",
    "    The function describes each cluster consumption data with descriptive statistics. \n",
    "    \n",
    "    Args:\n",
    "        consumptions_c [dict]: Consumption data for each of the clusters.\n",
    "        agg_functions  [list]: Additional functions for statistical analysis.\n",
    "        n_clusters     [int]:  Number of clusters.\n",
    "        \n",
    "    Returns:\n",
    "        [pandas.DataFrame]: Descriptive statistics data of each cluster.\n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.concat([pd.concat([consumptions_c[i].describe() for i in range(0, n_clusters)], axis=1), \n",
    "                    pd.concat([consumptions_c[i].agg(agg_functions) for i in range(0, n_clusters)], axis=1), \n",
    "                    pd.concat([consumptions_c[i].mode() for i in range(0, n_clusters)], axis=1)]).T\n",
    "    df.rename(columns = {0: 'mode'}, inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    df.drop('index', axis=1, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_functions = ['sum', 'median', 'nunique']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>median</th>\n",
       "      <th>nunique</th>\n",
       "      <th>mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>189297.0</td>\n",
       "      <td>8.819432</td>\n",
       "      <td>6.890723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1669492.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>174060.0</td>\n",
       "      <td>2.160445</td>\n",
       "      <td>6.054003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>376047.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161689.0</td>\n",
       "      <td>4.800110</td>\n",
       "      <td>7.998856</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>776125.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count      mean       std  min  25%   50%   75%   max        sum  \\\n",
       "0  189297.0  8.819432  6.890723  0.0  0.0  10.0  14.0  20.0  1669492.0   \n",
       "1  174060.0  2.160445  6.054003  0.0  0.0   0.0   0.0  20.0   376047.0   \n",
       "2  161689.0  4.800110  7.998856  0.0  0.0   0.0   8.0  20.0   776125.0   \n",
       "\n",
       "   median  nunique  mode  \n",
       "0    10.0     21.0   0.0  \n",
       "1     0.0     21.0   0.0  \n",
       "2     0.0     21.0   0.0  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe_consumptions(consumptions_c, agg_functions, n_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that results are not accurate due to the frequent occurrence of values `0` and `20`. Therefore, we define a new function that **removes** a given value from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_from_consumptions(consumptions_c, value):\n",
    "    \"\"\"\n",
    "    The function removes particular value from consumption data.\n",
    "    \n",
    "    Args:\n",
    "        consumptions_c [dict]: Consumption data for each of the clusters.\n",
    "        value          [int]:  Particular consumption value to remove.\n",
    "        \n",
    "    Returns:\n",
    "        [pandas.Series]: Consumption data for each of the clusters without particular value.\n",
    "    \"\"\"\n",
    "    \n",
    "    return {i:consumptions_c[i][consumptions_c[i][0] != value] for i in consumptions_c}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumptions_c_r = remove_from_consumptions(remove_from_consumptions(consumptions_c, 0), 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>median</th>\n",
       "      <th>nunique</th>\n",
       "      <th>mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114407.0</td>\n",
       "      <td>11.505170</td>\n",
       "      <td>3.625526</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1316272.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4670.0</td>\n",
       "      <td>12.768094</td>\n",
       "      <td>4.677060</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>59627.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21706.0</td>\n",
       "      <td>6.888648</td>\n",
       "      <td>4.840377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>149525.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count       mean       std  min  25%   50%   75%   max        sum  \\\n",
       "0  114407.0  11.505170  3.625526  1.0  9.0  11.0  14.0  19.0  1316272.0   \n",
       "1    4670.0  12.768094  4.677060  1.0  9.0  13.0  17.0  19.0    59627.0   \n",
       "2   21706.0   6.888648  4.840377  1.0  3.0   6.0  11.0  19.0   149525.0   \n",
       "\n",
       "   median  nunique  mode  \n",
       "0    11.0     19.0  10.0  \n",
       "1    13.0     19.0  19.0  \n",
       "2     6.0     19.0   1.0  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe_consumptions(consumptions_c_r, agg_functions, n_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Cluster consumption values analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s look at the **number of occurrences** of the consumption values per cluster. From this we will be able to find out whether the clusters were formed according to the consumption patterns of the devices. For this purpose, we define a new function that obtains the specified data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_consumption_freq(consumptions_c, n_clusters):\n",
    "    \"\"\"\n",
    "    The function obtains the number of occurrences of each consumption value per cluster.\n",
    "    \n",
    "    Args:\n",
    "        consumptions_c [dict]: Consumption data for each of the clusters.\n",
    "        n_clusters     [int]:  Number of clusters.\n",
    "        \n",
    "    Returns:\n",
    "        [pandas.DataFrame]: Occurrence data of each consumption value per cluster.\n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.concat([consumptions_c[i].value_counts() for i in range(0, n_clusters)], axis=1)\n",
    "    df.index.names = ['Consumption value']\n",
    "    df.reset_index(inplace=True)\n",
    "    df.set_index('Consumption value', inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f = get_consumption_freq(consumptions_c_r, n_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qxKGWgyLihSwWCzfffDMLFy5k/vz5JCUl8fjjj7fa9VV8i4i0Y6fb3/unkqKD6R4bQkWtgzV7SpvlnCLiW278/EY+2v5Rs/+9qSIjIznjjDM8twcOHMj+/ftP9dM5aSq+RUTasUOdTpqn+AY4O7Vxt0tNPRER7+ZyuXjrrbcYN25cq13Tr9WuJCIiXmdjTilw+p1ODnd2Wiz/XpbNt9sKuO+CXs12XhHxDa9e8Oop/b2qqqpJjz8ZDz/8MMHBwVxzzTWn9PxToZFvEZF2Kq+8lrzyOsIC/EiOCWm28za2HNycW06+Wg6KiJeaM2cOu3fv5h//+AdWa+uVxCq+RUTaqcb53n0TI7A2w2LLRoH+NkYcbDmo3S5FxBs9+eSTZGRk8Nxzz2G321v12iq+RUTaqQ3N1N/7aBpbDqrft4h4m6ysLF544QXy8/O56qqrmDx5MrfffnurXV9zvkVE2qmN+0qB5ut0cjj3ostNLM0qwOF04WfTWI+IeIeePXuybds2066vV0MRkXbIMIxDO1smRjb7+bvEBNO9QwjltQ7W7i1t9vOLiLRVKr5FRNqh3LJaCivriQjyJyk6qEWuMbZxt0u1HBQR8VDxLSLSDm08bL63xdJ8iy0P17jb5bea9y3SLhmGYXaEZtHcn4eKbxGRdsizs2Uz9vf+qTO6RRPob2XT/nLyK9RyUKQ98ff3p6amxuwYzaKhoQE/v+ZbJqniW0SkHWrJTieNAv1tjOjubjmorici7UtcXBw5OTlUV1e36RFwl8tFXl4eERHN91qpbiciIu2MYRiHdTqJbNFrnZ0WxzfbCvg2s4BfDk1q0WuJiPcIDw8HYP/+/TQ0NDTLOevr61u9JzdASEgIHTp0aLbzqfgWEWln9pXUUFLdQEyInU4RgS16rcZ+30sz1XJQpL0JDw/3FOHNYfXq1QwYMKDZzmcWvQqKiLQzjYst+7XgYstGXWNC6Haw5eA6tRwUEVHxLSLS3mzY19jfu+Xmex9ubGpjy0HN+xYRUfEtItLObMwpBVp+vnejxqkn32aq37eIiIpvEZF2xDAMz8h3S7YZPNyZ3WMI8LOSkaOWgyIiKr5FRNqR3UXVVNQ6iA0LID48oFWuGehvY0SKu+XgkszCVrmmiIi3UvEtItKOePp7J7b8YsvDnZ2qreZFREDFt4hIu3Kov3frTDlp1LjV/NKsQhxOV6teW0TEm6j4FhFpRzydTlq5+E7uEEJyTDBlNQ2sP/gLgIhIe6TiW0SknXC5DDIOTjvp20qLLQ/XOPqtloMi0p6p+BYRaSd2FlZRVe8kISKQuLCW3dnyaMamqd+3iIiKbxGRdsLT39uEUW+AEQdbDm7MKaOgos6UDCIiZlPxLSLSTpg137tRoL+NM7s3thzU6LeItE8qvkVE2omNjZvrtNLOlkdzaLdLFd8i0j61SvE9Z84cxo0bR1paGpmZmZ77x40bxwUXXMDkyZOZPHkyS5cu9RzLzs5mypQpTJgwgSlTprBr167TPiYi0l45nC427S8HzJt2Aoe3HCzA6TJMyyEiYpZWKb7Hjx/P3LlzSUxM/Nmxp59+mnnz5jFv3jxGjx7tuX/mzJlMnTqVhQsXMnXqVGbMmHHax0RE2qsdBVXUNDjpHBVEdIjdtBzdOoTQNSaY0uoG1u0tNS2HiIhZWqX4Hjp0KAkJCU1+fFFREZs3b2bixIkATJw4kc2bN1NcXHzKx0RE2rMNB3trmzXf+3CNu10u1m6XItIOmT7ne9q0aUyaNIlZs2ZRXu5+SzQ3N5f4+HhsNhsANpuNuLg4cnNzT/mYiEh7tvFgf+9+iZHmBuGwft+a9y0i7ZCfmRefO3cuCQkJ1NfX8+ijjzJ79mwef/xxMyN5ZGRkmB3hCKtXrzY7whG8LQ94XyblOT5vywPel6k58/ywrQiAwOo8Vq8uNTVPgMPA3+ruvvL1shVEBNpMzdOcvC2T8hyft+UB78ukPM3P1OK7cSqK3W5n6tSp3HbbbZ778/LycDqd2Gw2nE4n+fn5JCQkYBjGKR07WX379iUgIKBZP99TtXr1aoYMGWJ2DA9vywPel0l5js/b8oD3ZWrOPA1OF7s/XAjA5WcPIyLY39Q8ACM2rWBJZgGlQZ0YN7iz6Xmag7dlUp7j87Y84H2ZlOf46urqTmmw1rRpJ9XV1VRUVABgGAaffvop6enpAMTExJCens6CBQsAWLBgAenp6URHR5/yMRGR9iozr4J6h4vkmOBTKrxbQuO8b+12KSLtTauMfD/yyCN88cUXFBYWcuONNxIZGckLL7zAnXfeidPpxOVykZKSwsyZMz3PmTVrFg888ADPP/884eHhzJkz57SPiYi0R97Q3/unzk6LZfYCWHKw5aDNajE7kohIq2iV4nv69OlMnz79Z/d/9NFHx3xOSkoK7733XrMeExFpjzYcXGzZ38T+3j/VrUMIXaKD2VNczfp9pQzuEmV2JBGRVmF6txMREWlZh0a+vaf4tlgsh3a71NQTEWlHVHyLiPiwOoeTrQfKsVigT6dws+McobH4Vr9vEWlPVHyLiPiwbQcqaHAadO8QQligdyy2bDSiewfsflY25JRRVFlndhwRkVah4ltExIdtODjlpL8XLbZsFGS3cUa3aAzDvfBSRKQ9UPEtIuLDPPO9vWix5eE8u11q3reItBMqvkVEfJin04kXLbY8XOO87yWZ7paDIiK+TsW3iIiPqm1wkplXgdUCvb1ssWWj7h1CSIoOoqS6gQ37Ss2OIyLS4lR8i4j4qM255ThdBj3jwgi2t8q2DifNYrFwdqqmnohI+6HiW0TER3ljf++j8fT7zlTxLSK+T8W3iIiPOtTpxLuL7xEpMdhtVjbsK1XLQRHxeSq+RUR81MacUsB7O500Crb7cUZ3d8vBpVmFZscREWlRKr5FRHxQVZ2D7fmV+FktpCd452LLw41NbdxqXrtdiohvU/EtIuKDNueW4zIgNT6MQH+b2XFOqLHf95KsQlxqOSgiPkzFt4iID2or870bpcSG0DkqiOKqek9vchERX6TiW0TEB2XktI1OJ40sFsuhrieaeiIiPkzFt4iID2rcsKZ/YqSpOU6G+n2LSHug4ltExMdU1Daws7AKu81KasdQs+M02cge7paD6/eVUlxVb3YcEZEWoeJbRMTHbNpfjmFAr4QwAvy8f7Flo2C7H8O7NbYc1Oi3iPgmFd8iIj7Gs7Oll/f3PppD875VfIuIb1LxLSLiYxq7hbSVTieHayy+l2QWqOWgiPgkFd8iIj5m48HFlv3a0GLLRimxoSRGBlFUVc9GtRwUER+k4ltExIeUVTewq6iaAD8rPePbzmLLRke2HNTUExHxPSq+RUR8SMZ+92hx707h+Nva5kt8426X32aq37eI+J62+cosIiJHtaENL7ZsNDLF3XJw3d5SStRyUER8jIpvEREfsjGnFGjbxXdIgB/DukVhGLBELQdFxMeo+BYR8SGNI9/9O0eaG+Q0Ne52uVjzvkXEx6j4FhHxEcVV9ewrqSHI30ZKbIjZcU5L46LLxWo5KCI+RsW3iIiPaGzN16dTOH5tdLFlox5xh1oONi4iFRHxBW371VlERDw8/b3b4OY6P2WxWBirloMi4oNUfIuI+IhD873bfvENcHZqY/GtloMi4jtUfIuI+IjGaSdtcWfLoxnZowP+Ngvr9pZSWq2WgyLiG1R8i4j4gPyKWnLLagmx2+jeoW0vtmwUGuDHsORoXAYsySo0O46ISLNQ8S0i4gMyDo56902MwGq1mJym+Rzaal5TT0TEN6j4FhHxAb4237tR41bzS9RyUER8hIpvEREfsLFxW/k2vrnOT/WMC6VTRCCFlfVs2l9udhwRkdOm4ltEpI0zDIMNB6ed9G/D28ofjbvloHv0W1NPRMQXqPgWEWnj8srrKKioIyzQj64xwWbHaXaeed+Z6vctIm2fim8RkTZuw8HNdfp3jsBi8Z3Flo3OOthycO2eErUcFJE2T8W3iEgb52v9vX8qNMCPoV3dLQeXquWgiLRxKr5FRNo4X+10criztdW8iPgIP7MDiIjIqTMM47CRb18uvuP4y2dbWexFLQfLqhtYu7eENXtKycqr4MyYOoaYHUpEvJ6KbxGRNiyntIbiqnqigv3pHBVkdpwWkxofSkJEILlltWzObf2Wgy6XwY6CSlbvLmHNHnfBvT2/8ojH7Izx5/oLWz2aiLQxKr5FRNqww/t7++Jiy0YWi4Wz02J5a8Vevt2Wz4gWHuQvr21g3Z5ST6G9bk8J5bWOIx5jt1np1zmCQUmR/OeHXWQVNVBW3UBEsH/LhhORNk3Ft4hIG+ar/b2PZmxq3MHiu4ARwwOa7bwul8HOwirW7Clh7Z4SVu8uISu/EuMns1sSIgIZ3CWKQV0iGdw1ij6dwgnwswHuRa/Ls4v5bnshF/dPaLZsIuJ7VHyLiLRhh0a+fb/4PqtHDH5WC2v2lFA5MO6Uz1NZ52D93lLW7C5h9Z4S1u4ppaym4YjH+Nss9OkUweAuUQzuGsngLlF0ijz2tJ6xabEszy5mSWaBim8ROS4V3yIibZRhGEf0+PZ1YYH+DE2O4sedxazPq2NsE55jGAbZhVWsaZxCsruEzLwKfrpmMy4swFNoD+kaRZ9OEQT625qcbUzPWB77fBtLsgowDMOnpwCJyOlR8S0i0kbtKa6mvNZBh9AAOoYHmh2nVZydFsePO4tZe+Dom+1U1TlYv6+UtXvcI9tr95ZSXHXkY/2sFvomhh8stqMY3CWSxMig0yqYeyeEExlgJbeslqz8SlLjw075XCLi21ql+J4zZw4LFy4kJyeH+fPnk5qaSklJCffddx979uzBbrfTtWtXZs+eTXR0NADjxo3DbrcTEOCe1zdt2jRGjx4NQHZ2Ng888AClpaVERkYyZ84ckpOTT3hMRMSXHN7fu72MtJ6dFstfP9vK2gN1uFwGe0uqD45ou0e2tx6owPmTYe0OoQEMPjhPe3CXKPolRhBkb/qodlNYrRYGdLSzeHctSzILVHyLyDG1SvE9fvx4rrvuOq6++mrPfRaLhZtvvpkzzjgDcBfojz/+OH/+8589j3n66adJTU392flmzpzJ1KlTmTx5MvPmzWPGjBn897//PeExERFf0h76e/9UWnwYHcMDOVBey+BHvqS0+si52rbDR7UPfiRFn96odlMNjA9g8e5aFmcWcPPo7i1+PRFpm1ql+B46dOjP7ouMjPQU3gADBw7krbfeOuG5ioqK2Lx5M6+++ioAEydO5OGHH6a4uBjDMI55rHFEXUTEV7Sn+d6NLBYLE/rE858fdlNa3UB0iJ3BXSIZdLDQHpAUQbDdnBmVAzoGYLHA8uxiauqdzT66LiK+wSvmfLtcLt566y3GjRt3xP3Tpk3DMAyGDBnCvffeS3h4OLm5ucTHx2OzuV/UbDYbcXFx5ObmYhjGMY+p+BYRX+JyGWTkuDebaU8j3wD3XdCLRFsZ5585kK4xwV4z5SYiwErfThEH2w4WcXbaqXdkERHf5RXF98MPP0xwcDDXXHON5765c+eSkJBAfX09jz76KLNnz+bxxx9vtUwZGRmtdq2mWL16tdkRjuBtecD7MinP8XlbHvC+TMfLk1PhoLLOQXSQlb1Zm9hrcp7WNiQhkKLdWynabXaSI/UMc7AReP+7TYRVtsZ35fi86XsGytMU3pZJeZqf6cX3nDlz2L17Ny+88AJWq9Vzf0KCu0+q3W5n6tSp3HbbbZ778/LycDqd2Gw2nE4n+fn5JCQkYBjGMY+drL59+3oWe5pt9erVDBkyxOwYHt6WB7wvk/Icn7flAe/LdKI8e9fmAIUM6RbbKrnb2tfHDKtXr+aqsf35YOsPbCm1mJ7P275GynNi3pZJeY6vrq7ulAZrrSd+SMt58sknycjI4LnnnsNut3vur66upqKiAnD3aP30009JT08HICYmhvT0dBYsWADAggULSE9PJzo6+rjHRER8iafTSTubcuLtBnWJJDTAjx0FVewrqTY7joh4oVYZ+X7kkUf44osvKCws5MYbbyQyMpJ//OMfvPDCCyQnJ3PVVVcB0LlzZ5577jmKioq48847cTqduFwuUlJSmDlzpud8s2bN4oEHHuD5558nPDycOXPmNOmYiIiv2JhTCrSPnS3bEn+blZEpMXyxOY8lmYVMPaOL2ZFExMu0SvE9ffp0pk+f/rP7t23bdtTHJyUl8dFHHx3zfCkpKbz33nsnfUxExBc4XQab9rfPxZZtwdi02IPFd4GKbxH5GVOnnYiIyMnbWVBJdb2TxMggYkK9Y22KHDKmZywAy7YX0uB0mZxGRLyNim8RkTbm8J0txfskRQfTPTaEijoH6/aWmh1HRLyMim8RkTbGs7Olim+v1Tj6vSSzwOQkIuJtVHyLiLQxnp0tEyNNzSHHNjZVxbeIHJ2KbxGRNsThdGmxZRtwRvdo7H5WNuSUUVxVb3YcEfEiKr5FRNqQrPxK6hwuusYEExHsb3YcOYZgux/Dk6MxDFiapdFvETlExbeISBuy8eBiS416e78xqR0AWKypJyJyGBXfIiJtyIaDm+uo04n3G5saB8DSrEIMwzA5jYh4CxXfIiJtyKGR70hzg8gJpcaH0jE8kIKKOrbkVpgdR0S8hIpvEZE2ot7h8hRxfRPDTU4jJ2KxWBjdU1NPRORIKr5FRNqIzLwK6p0uuncIISxQiy3bgrFpajkoIkdS8S0i0kY07mypzXXajlE9OmC1wKrdxVTVOcyOIyJeQMW3iEgbsfHgYkt1Omk7IoPtDEiKpMFp8MOOIrPjiIgXUPEtItJGNI589+8caW4QOSmerebV71tEUPEtItIm1DY42XagAosF+nTSYsu2ZIy2mheRw6j4FhFpA7YeqMDhMugRG0pIgJ/ZceQkDOgcQUSQP7uKqtldVGV2HBExmYpvEZE2YOO+UkCLLdsiP5uVUT3cLQc1+i0iKr5FRNoAz3xvLbZsk7TVvIg0UvEtItIGbMxpbDMYaW4QOSWN875/2FFEvcNlchoRMZOKbxERL1dT7yQzrwKb1ULvBC22bIsSIoJIjQ+lqt7J6t0lZscREROp+BYR8XKbc8twGdAzLpQgu83sOHKKGlsOauqJSPum4ltExMsd6u+t+d5tmbaaFxFQ8S0i4vU27tN8b18wLDmaQH8rm3PLya+oNTuOiJhExbeIiJfbkKNOJ74g0N/Gmd1jAFiaWWhyGhExi4pvEREvVlnnYEdBJf42C70SwsyOI6dJW82LiIpvEREvtimnDMOAtI5hBPhpsWVb19hycGlWIS6XYXIaETGDim8RES/m6e+dGGluEGkWKbEhJEYGUVxVT8b+MrPjiIgJVHyLiHgxdTrxLRaLxTP6ra4nIu2Tim8RES92aORbxbevGKut5kXaNRXfIiJeqqymgezCKux+VlLjtdjSV4zs0QGb1cKaPaWU1zaYHUdEWpmKbxERL7Xp4Kh3ekI4dj+9XPuK8EB/BneJxOky+H57kdlxRKSV6dVcRMRLqb+37xqbqq3mRdorFd8iIl7q0M6WKr59zeGLLg1DLQdF2hMV3yIiXmpDTimgTie+qG+nCKJD7OSU1rCzsMrsOCLSilR8i4h4oYo6F3uLawj0t9IjNtTsONLMrFYLo3se7HqyTVNPRNoTFd8iIl5oR4m7C0afThH42fRS7Yu01bxI+6RXdBERL9RYfKu/t+8afbDf9487i6htcJqcRkRai4pvEREv1Fh8a76374oLC6R3Qji1DS5W7io2O46ItBIV3yIiXmhHsYrv9kBbzYu0Pyq+RUS8TGFlHYU1LkLsNrp10GJLXzZGW82LtDsqvkVEvMzGg5vr9EmMwGa1mJxGWtLQrtEE221k5lWSW1ZjdhwRaQUqvkVEvEzj5jra2dL32f2sjEyJAWBpZqHJaUSkNaj4FhHxMhu0s2W7oq3mRdqXJhffX331FQ6HoyWziIgIsNGzs2WkqTmkdTQuuvxueyEOp8vkNCLS0ppcfD/11FOMGjWK2bNns379+pbMJCLSbmXklJFXXkewv4Wu0cFmx5FW0DUmhK4xwZTVNLD+4LseIuK7mlx8f/zxx7z22msEBARw5513MmHCBJ5//nn27dvXkvlERNqN2gYn97yzDoAxXYKwarFluzFWLQdF2o2TmvPdq1cv7r//fhYvXszMmTP5/PPPOe+887j66qv5+OOPcbn0dpmIyKl69JMtZOVXkhIbwrX91WKwPdFW8yLth9/JPmHPnj18/PHHfPzxx1gsFu666y4SEhKYO3cuX3zxBc8++2xL5BQR8Wlfbs7j9R9342+z8NRVg6g7sN3sSNKKRqTE4G+zsH5vKaXV9UQG282OJCItpMkj33PnzuXKK6/kl7/8JYWFhTz22GMsXLiQ2267jUsvvZTXXnuNZcuWHfW5c+bMYdy4caSlpZGZmem5Pzs7mylTpjBhwgSmTJnCrl27WvSYiIg3yiuv5b733Wtp7r+gF33VYrDdCQnwY2jXaFyGe+GliPiuJhffS5Ys4cYbb2Tp0qXMmjWLgQMHHnE8KCiIZ5555qjPHT9+PHPnziUxMfGI+2fOnMnUqVNZuHAhU6dOZcaMGS16TETE27hcBn94dz0l1Q2M7tmBm87qZnYkMYm2mhdpH5pcfD/99NOce+652O2H3gpraGigvr7ec3vUqFFHfe7QoUNJSEg44r6ioiI2b97MxIkTAZg4cSKbN2+muLi4RY6JiHijf32XzXfbC4kOsfPELwdokWU7dvhW84ZhmJxGRFpKk+d833TTTfzxj388YsR706ZNPPHEE7z++usnfeHc3Fzi4+Ox2WwA2Gw24uLiyM3NxTCMZj8WHR19UvkyMjJO+nNqSatXrzY7whG8LQ94XyblOT5vywOtn2lnSQNzvioC4JaBwezN2sReE/OciPKc2OlkMgyDyEAreeV1fPTNcrpE+JuapyUoz4l5WyblaX5NLr63bdvGgAEDjrivf//+bN26tdlDeYO+ffsSEBBgdgzA/Q9tyJAhZsfw8LY84H2ZlOf4vC0PtH6m6noHf3zmOxwGXDeiK7dM6mtqnhNRnhNrjkzjdq7jgzU5FPrFcdmQ7qbnaU7Kc2Lelkl5jq+uru6UBmubPO0kPDycwsIjF4EUFhYSFBR00hcFSEhIIC8vD6fTCYDT6SQ/P5+EhIQWOSYi4k0eXrCZnQVVpMaH8qeL0s2OI15CW82L+L4mF9/nn38+f/jDH8jMzKSmpoZt27Zx//33c+GFF57ShWNiYkhPT2fBggUALFiwgPT0dKKjo1vkmIiIt/hsYy5vrdiL3c/K078aRKC/zexI4iVG94zFYoEV2cVU1zvMjiMiLaDJxfc999xDSkoKv/zlLxk8eDBTpkyhW7du3HvvvSd87iOPPMKYMWM4cOAAN954IxdffDEAs2bN4o033mDChAm88cYbPPTQQ57ntMQxERGz5ZbV8MAHGwH404W96NUx3ORE4k2iQ+z0S4yg3uli+U41CxDxRU2e8x0QEMDMmTOZMWMGJSUlREVFYbE0bVX+9OnTmT59+s/uT0lJ4b333jvqc1rimIiImZwug3veWUdZTQPnpMVy/chksyOJFxqbGsuGfWUszizgnF5xZscRkWZ2UjtcVlRUkJ2dTVVV1RH3jxgxollDiYj4oheX7ODHncV0CA3gb78c0OQBDGlfxqTG8szX27XVvIiPanLx/cEHHzB79myCg4MJDAz03G+xWPjqq69aJJyIiK9Yv7eUv3/h3uH38V/2p0Ood3RTEu8zMCmSsAA/dhZUsbe4mqToYLMjiUgzanLx/eSTT/LUU08xduzYlswjIuJzKusc3P32Whwug5vO6sbZaZpKIMfmb7NyVo8OfL7pAEuyCrj6jK5mRxKRZtTkBZdOp/OYO1iKiMixzfp4E7uKqklPCOf+C9PMjiNtgLaaF/FdTS6+f/Ob3/DPf/4Tl8vVknlERHzK/PX7eX/1PgL8rDx91UAC/NRWUE6scav5ZduLaHDq566IL2nytJPXXnuNwsJCXnnlFSIjI4849u233zZzLBGRtm9fSTV/+tDdVvD/JvamZ3yYyYmkregcFUxKbAg7CqpYu6eU4d20X4WIr2hy8f23v/2tJXOIiPgUh9PFPe+so6LWwXm947n6jC5mR5I2ZkxqLDsKqlicma/iW8SHNLn4Hj58eEvmEBHxKc9/u4OVu0qICwtgzhX91VZQTtrY1FheXbaLJZmF/HGC2WlEpLk0ec53fX09Tz75JOPHj2fIkCEAfPfdd7zxxhstFk5EpC1avbuEp77KwmKBJ6cMJDrEbnYkaYPO6BaD3c/KxpwyCivrzI4jIs2kycX3n//8ZzIzM3n88cc9Izg9e/bkrbfearFwIiJtTXltA3e/vRany+C3o7tzVo8OZkeSNirIbuOMg9NNvssqNDmNiDSXJhffixYt4oknnmDQoEFYre6nxcfHk5eX12LhRETamhkfZbCvpIa+ieH84Xy1FZTTM1YtB0V8TpOLb39/f5xO5xH3FRcX/6zziYhIe/Xh2n18tG4/Qf42nr5qEHa/Jr/EihyVp993ViEul2FyGhFpDk3+yXDBBRdw//33s3fvXgDy8/OZPXs2F198cYuFExFpK/YUVfN/H20CYNYlvekeG2pyIvEFPeNC6RgeSGFlHZtzy82OIyLNoMnF9z333ENiYiKXXHIJ5eXlTJgwgbi4OG6//faWzCci4vUanC7ufmctlXUOLurXkSuHJpkdSXyExWI5NPUkS1NPRHxBk1sN2u12HnzwQR588EGKi4uJiopS6ywREeCZr7JYu6eUhIhA/nKZ2gpK8xqTGss7q/ayeFsBvzu7h9lxROQ0Nbn4bpxu0qiqqsrz96QkjfKISPu0IruYZ7/Z7mkrGBHsb3Yk8TGjenTAanG3sKyscxAa0OQf3SLihZr8P/i8887DYrFgGIcWfDSO7mzZsqX5k4mIeLmy6gZ+//ZaXAbccU4PzuweY3Yk8UERwf4MTIpkzZ5SfthRxHm9482OJCKnocnF99atW4+4XVBQwLPPPsvQoUObPZSIiLczDIM/fbSR/WW1DEyK5O5ze5odSXzYmNRY1uwpZXFmvopvkTbulPtgxcbG8uCDD/L3v/+9OfOIiLQJ763exycbcgmx23jqqoH429RWUFrOoX7f2mxHpK07rZ8WO3fupKamprmyiIi0CdmFVcz62N1WcPbkvnSNCTE5kfi6/p0jiQz2Z09xNbsKq078BBHxWk2edjJ16tQjVvDX1NSwfft2tRoUkXal3uHi7rfXUl3v5JIBnbh8cKLZkaQdsFktnNWjA59syGVxZgHJHfQLn0hb1eTi+5e//OURt4OCgujVqxfJycnNnUlExGs9uSiTDfvKSIwM4pHL+qqtoLSasamxfLIhlyWZBVw/MtnsOCJyippcfF922WUtmUNExOt9v6OQFxbvwGqBp64aSHig2gpK6xnT0z3v+4edRdQ5nAT42UxOJCKnosnF91NPPdWkx919992nHEZExFuVVNVz7zvrMQy4+9yeDE2ONjuStDMdIwJJiw9jW14Fq3eVMLJHB7MjicgpaHLxvXv3br744gv69u1LYmIi+/fvZ+PGjZx//vkEBAS0ZEYREVMZhsEDH2zgQHktQ7tGccc52mVQzDE2LZZteRUszipQ8S3SRjW5+DYMgyeeeIIJEyZ47vviiy/4/PPP+ctf/tIi4UREvMFbK/aycFMeYQF+PDllIH5qKygmGdMzlpeW7GTxtgL+34XpZscRkVPQ5J8gS5Ys4dxzzz3ivvHjx7N48eJmDyUi4i2251cye4G7reAjl/UlKTrY5ETSng1NjiLQ38rWAxXkl9eaHUdETkGTi++uXbsyd+7cI+5788036dKlS7OHEhHxBnUOJ3e9tZbaBheXD05k8kC1FRRzBfrbGNE9BoAlWdpwR6QtavK0k0ceeYQ77riDV155hfj4ePLy8vDz8+OZZ55pyXwiIqZ5fOE2NueW0yU6mNmT+5odRwRwbzX/zbYCFmcW8Ishnc2OIyInqcnFd+/evVm4cCHr168nPz+f2NhYBg4ciL+/Wm2JiO9ZklnAy0uzsVktPHXVQEIDmvxyKdKiGrea/y6rAKfLwGZVr/m2rqSqnp2FVVQ3uMyOIq3glH+aDBs2jOrqahoaGggO1hxIEfEdRZV1/OG99QDce14qg7pEmZxI5JBuHULoHBXEvpIaMnLKGJAUaXYkaaI6h5Pt+ZVsO1DB1saP3HLyK+oA6BdnZ/SZJoeUFtfk4nvbtm3cdttt2O128vLyuOiii1i5ciUffvgh//jHP1owoohI6zEMg/ve30BBRR1ndIvm1rEpZkcSOYLFYmFMaixvLt/D4swCFd9eyDAM9pXUsO1ABdvyKtiSW862AxXsLKzC6TJ+9vhguw2Hy2Bjfj0/7ChiREqMCamltTS5+J41axZ33XUXl156KcOGDQPco9/Tp09vsXAiIq3t9R9389XWfMID3W0F9Za+eKOxB4vvJZkF3DW+p9lx2rWymgZ3kX2g3DOanXmggoo6x88ea7VA99gQenUMo1fHcNI6hpHeMZzOUUE8+812/v5lJk9/laXi28c1ufjevn07kydPBty/dQMEBwdTV1fXMslERFrZtgMVPPrJFgD+ekV/OkUGmZxI5OhGpsTgZ7Wwdm8pZTUNRARp/VVLa3C62FlQxdaDRfa2g1NG9pcdveVjh1C7p8BuLLZ7xocS6G876uNvOCuZF7/N4oedRazILmZ4N+2i66uaXHwnJiaSkZFBv379PPdt2LBBrQZFxCfUOw3uemstdQ4XU4YmcVG/BLMjiRxTWKA/g7tEsWJXMd9vL+RC/XttNoZhcKC89ogCe+uBCnYUVNLg/PmUkQA/K6nx7gI7rWMY6QnugrtD6Mnt/h0e6M/EniG8s7mSp77KZO7Nmvztq5pcfN99993ccsstXHXVVTQ0NPDiiy/y9ttv8/DDD7dkPhGRVvH6hgq25VXTvUMIMyb1NjuOyAmNTYtlxa5ilmQVqPg+RVV1DrblVbA11z1tZMvBgruspuFnj7VYoGtMMGnxYfRKCD84mh1G15iQZpuedlHPYD7dUcuy7UWs2lXM0GSNfvuiJhff55xzDi+//DLvvfcew4YNIycnh2eeeYa+fdX7VkTatm+25vPp9mr8bRaeumoQIWorKG3AmJ6x/G3hNhZvK8AwDM+UUDmxFdnF3P1ZAbnvLTzq8chgf89UkcYR7dT4sBZ/bQi1W7nxrGSe/no7T32Vxeu/PqNFryfmaNK/IqfTyYQJE/j000+ZNWtWC0cSEWk9BRV1/PF9d1vBaeen0a9zhMmJRJqmT6dwYkLs7C+rZUdBJT3iwsyO1Gb85bMt5FY6sduspMSFkn6wwG4c0Y4LCzDtl5mbRnXj38t2sTSrkDV7ShisVqc+p0nFt81mw2azUVdXh91ub+lMIiKt5u9fZlJYWU+/ODu/Gd3d7DgiTWa1WhjdswMfrdvP4sxCFd9NtHp3MWv3lBLqb+HH6ed53QZakcF2rh/Zlee+2cHTX2Xx2o3DzY4kzcza1Aded911/P73v2fFihXs2bOHvXv3ej5ERNqiXYVVvLtqLzarhd8ODseqtoLSxow5uNvl4swCk5O0HS8vyQbg/JRgryu8G908qjvBdhvfbitg3d5Ss+NIMzvhv7qCggJiY2M9Cyu///57DOPQal+LxcKWLVtaLqGISAt5clEmTpfBlKFJdAr7+QIrEW83uqe7+F6+s4jaBucx29iJ267CKhZuPoDdZuWiHt67O3dUiJ3rRiTzwuIdPPNVFv+6YZjZkaQZnXDke8KECQBs3bqVrVu3Mm7cOM/ft27dqsJbRNqkLbnlfLx+P3ablbvO1SYl0jbFhgXQp1M4dQ4XK7KLzY7j9f69LBvDgEsGdiIqyLt/UfnN6G4E+dv4ams+G/eVmR1HmtEJi+/DR7kBVq5c2WJhRERayxNfbMMw4Oozu5CozXSkDdPUk6Ypra7nvVX7ALh5dDeT05xYTGgA143oCsBTX2WZnEaa0wmL75+u9v1pMS4i0tas3l3Coi35BNtt/O7sHmbHETktYw8W30tUfB/X3OV7qGlwMiY1ll4dw82O0yQ3j+5OoL+VRVvyyMjR6LevOOGcb6fTyY8//ugpun96G2DEiBEtl1BEpBkZhsHfFm4F4KazuhEbdnK70Il4m8Fdogix28jKr2R/aQ2d9E7Oz9Q5nLz2/S7APZ2jrYgNC+CaM7ryynfZPPN1Fi9eO9TsSNIMTlh8x8TE8Kc//clzOzIy8ojbFouFr776qmXSiYg0s2Xbi/hxZzERQf78ZoxaC0rbZ/ezMiKlA4u25LEks4CrhncxO5LXmbduPwUVdfTqGMaoHh3MjnNSfju2O6//uJuFm/LYkltOekLbGLWXYzth8f3111+3Rg4RkRZ3+Kj3rWNTiAjyNzmRSPMYmxbrLr6zVHz/lGEY/Gupu73gzaO7t7mdQOPCApl6RhdeXbaLZ77O4vmrh5gdSU6T6Q0u9+3bx+233+65XVFRQWVlJStWrGDcuHHY7XYCAtxvC0+bNo3Ro0cDkJ2dzQMPPEBpaSmRkZHMmTOH5OTkEx4TkfZr4aY81u8rIzYsgOtHdjU7jkizGXuw5eDSrEIcThd+tiZv4+HzlmQVsi2vgvjwAC4Z0MnsOKfk1rEpzF2+h083HmDbgQrSOmpDpbbM9P+dnTt3Zt68eZ6P8ePHM3HiRM/xp59+2nOssfAGmDlzJlOnTmXhwoVMnTqVGTNmNOmYiLRPTpfBE19sA+DOcT0Itps+9iDSbLrEBJMcE0xFrYP1+0rNjuNVXlm6E4DrRyZj9zO97Dkl8eGB/GpYEgDPfK3OJ22dV/0rrK+vZ/78+VxxxRXHfVxRURGbN2/2FOkTJ05k8+bNFBcXH/eYiLRf89blkJVfSeeoIK4aprflxfeM9bQcLDQ5iffYvL+cpVmFBNttXD28bb/bdevZKdhtVj7ZmEtWXoXZceQ0eFXx/fXXXxMfH0+fPn08902bNo1JkyYxa9YsysvLAcjNzSU+Ph6bzd0g32azERcXR25u7nGPiUj7VO9w8eSiTAB+f25qmx39Ejke9fv+uVe+c496Xzk0iYjgtr3GIyEiiCnDkjAMeObr7WbHkdPgVe+7/u9//zti1Hvu3LkkJCRQX1/Po48+yuzZs3n88cdbJUtGRkarXKepVq9ebXaEI3hbHvC+TMpzfK2Z5/Md1ewtrqFzmI0uxgFWr84zPVNTKM/xeVseMDdTgMOFnxU27C3l2+9XEhZg9bqvUWvmKa5x8vHaAqzAsIjKo17b274+cPxMZ8U4ecsC89fvZ3zHejqHt3wZ521fI2/Lcyq8pvjOy8tj5cqVPPbYY577EhISALDb7UydOpXbbrvNc39eXh5OpxObzYbT6SQ/P5+EhAQMwzjmsZPRt29fz0JPs61evZohQ7xndbO35QHvy6Q8x9eaeWrqndz2+TcATJ88gOF9j/5a0J6/Rk2hPCfmDZmGb/iR73cUUR6SSJgj1/Q8h2vtr8+cz7fiMAq4qF9HLh778+t6w/frp5qS6crCjby5fA/f5gfw5DkDTc/TmrwtT11d3SkN1nrNe68ffvghY8eOJSoqCoDq6moqKtxzmgzD4NNPPyU9PR1w9x5PT09nwYIFACxYsID09HSio6OPe0xE2p///LCL/Io6+iVGMKFPR7PjiLSoMdrtEoCqOgdzf9wNuNsL+pLfnZ2Cn9XCvHU57CyoNDuOnAKvKr4Pn3JSVFTEtddey6RJk5g4cSLZ2dnMnDnTc3zWrFm88cYbTJgwgTfeeIOHHnqoScdEpP0or23gn9/uAOCPE9LaXH9fkZN1+Fbzh+9E3d68u2ov5bUOhnSNYnCXKLPjNKvOUcH8YkhnXAY8980Os+PIKfCaaScLFy484nZSUhIfffTRMR+fkpLCe++9d9LHRKT9eGXJTspqGjijWzSje7atXe1ETkWvjmHEhQWQX1HH7jIH7XEzcqfL4N/L3Jvq/MbHRr0b3X5OD95bvY+P1uVw1/gedI0JMTuSnASvGfkWEWlOhZV1vPKd+wewRr2lvbBYLIw+uOHOurx6k9OYY+GmA+wtrqFrTDDn9Y43O06LSIoO5vJBiThdBs+q80mbo+JbRHzSP7/dQXW9k3G94hiarDUf0n6MTTtYfB+oMzlJ6zMMg5eWuNsL/npUN2xW3/2l+45xPbBZLXywNoe9xdVmx5GToOJbRHzO/tIaXj+42Gra+WkmpxFpXaN7dMDPaiEjv54N7Wy3y9W7S1i3t5SIIH9+MaSz2XFaVNeYEC4d6B79fu4bjX63JSq+RcTnPP1VFvUOF5MGdKJ3p3Cz44i0qqgQOzeelYwBTP8oA6er/Sy8fPngVvLXntmVYLvXLGtrMXeM64HVAu+v3se+Eo1+txUqvkXEp+wsqOS91fuwWS3cc25Ps+OImOLuc1OJDrKyYV8Zby7fbXacVrGrsIovNudht1m5bmTb3kq+qbp1CGHywEQcLoPnv1Xnk7ZCxbeI+JQnF2XhdBn8ckhnuseGmh1HxBShAX7cNND9rs9jC7dRUOH787//9V02hgGTB3YiLizQ7Dit5o5xPbBY4L1Ve8kprTE7jjSBim8R8Rmb95czf/1+7DYrd43XqLe0b2cmBjA2NZaKWgd//nSL2XFaVElVPe+t3gv43qY6J5ISG8qk/p1ocBq8oNHvNkHFt4j4jCe+2AbANWd2pVNkkMlpRMxlsViYPbkPdj8rH67N4YcdRWZHajFzl++mtsHF2NRY0jqGmR2n1d15cPT7nZV7yS3T6Le3U/EtIj5h1a5ivtqaT7Ddxu/OSTE7johX6BoTwu1n9wDg/+ZlUO9wmZyo+dU5nLz2vXteu69uqnMiPePDuKhfAvVOl0a/2wAV3yLS5hmGwWML3aPeN4/qRofQAJMTiXiPW8Z2p1uHELbnV/LKdzvNjtPs5q3dT2FlHb06hnFWjxiz45jmrnHuqXZvrdxLXnmtyWnkeFR8i0ibtzSrkBXZxUQE+XPzmPY58iVyLIH+NmZP7gO423D6Uks6wzA8v1D8ZnT3dr2TbVrHMC7q15F6h4sXFmv025up+BaRNs0wDP52cNT7trNTCA/0NzmRiPcZ3TOWif0TqG1wMevjzWbHaTaLMwvIzKskPjyASQM6mR3HdHceHP1+c/ke8is0+u2tVHyLSJu2cNMBNuaUERsWwPUjks2OI+K1/m9ib0ID/Fi0JY8vN+eZHadZvLI0G4AbRnbD7qeSJj0hnAl94qlzuHhpse9NMfIV+pcqIm2W02Xw+BeZANw1rgdBdpvJiUS8V3x4IPeclwrArI83UV3vMDnR6dm0v4zvthcSbLcxdXgXs+N4jcY2q28s390u+ru3RSq+RaTN+nBtDtvzK0mKDmLKMP3wFTmR60d0JT0hnJzSGp79ervZcU7Lvw6Oel85NImIYE03a9SnUwTnpsdT2+DilaUa/fZGKr5FpE2qczh58kv3qPc956bqLWeRJvCzWXnk0r4AvLx0J9vzK0xOdGoOlNXy8fr9WC3w61HdzI7jde4+OPr93x92U1Sp0W9vo59WItImvbPSvZVyz7hQJg9MNDuOSJsxpGsUvxqeRIPTYPpHGRiGYXakk/ba97twuAwu7JtAUnSw2XG8Tr/OEYzrFUdNg5OXD75DIN5DxbeItDnV9Q6e/sr9lvkfzk/DZm2/7cVETsV9E3oRHWLnx53FzFu33+w4J6WyzsHc5e5NdW4erVHvYzk0+r2L4qp6k9PI4VR8i0ib85/vd1NYWUf/zhFM6BNvdhyRNicqxM4DF/YC4JFPNlNW02ByoqZ7d+VeKmodDO0axaAuUWbH8VoDkiI5Oy2W6non//LBzZXaMhXfItKmlNU0eDaQ+OOEtHa9qYbI6fjF4M4M7RpFYWU9T3yxzew4TeJwuvj3Mvc0it9oQ60Taux88p/vd1NardFvb6HiW0TalJeX7KSspoEzu0czqkcHs+OItFlWq4WHL+2LzWrh9R93s2FfqdmRTmjhpjz2ldSQHBPMuel61+tEBneJYnTPDlTWOfj3d5r77S1UfItIm1FQUecZ9frjhF4a9RY5TekJ4dx0VjKGAdM/ysDp8t7Fl4Zh8NLB1nm/HtVNaz2a6Pfnuke/X122i7LqtjO9yJep+BaRNuP5b7dTXe/k3PQ4hnTVXE+R5nD3ual0DA9kw74y3lyxx+w4x7Rqdwnr95YSGezPL4YkmR2nzRjSNZqzesRQUefg1e81+u0NVHyLSJuQU1rD3B/dhcEfzk8zOY2I7wgN8GPmpN4APPb5Vq/dFfHlJe5R72vP7KrdbE/S3ePdO5v++7tsyms1+m02Fd8i0iY8vSiLeqeLSwZ0Ij0h3Ow4Ij7lgr4dGZsaS0Wtg798usXsOD+TXVjFl1vysNusXDuiq9lx2pzh3aIZ0T2G8loHry3bZXacdk/Ft4h4vR0Flby3ei82q4V7zks1O46Iz7FYLMye3Ae7n5UP1ubww44isyMd4V/f7cQw4NJBnYgLCzQ7TpvU2PnkX99lU6HRb1Op+BYRr/f3LzNxGXDl0M506xBidhwRn9Q1JoTbz+4BwP/Ny6De4TI5kVtJVT3vr94HwM2j1V7wVI1IiWF4t2jKahr47w+7zY7Trqn4FhGvlpFTxicbcrH7WT0jNyLSMm4Z251uHULYnl/JK16yMcsbP+6mtsHF2WmxpMaHmR2nTWvc9fLlpTuprHOYnKb9UvEtIl6tcfOP687sSkJEkMlpRHxboL+Nhy7pA8DTX2Wxr6Ta1Dy1DU7+c3CU9jca9T5tI1NiGNo1itLqBl7X6LdpVHyLiNdauauYb7YVEGK3cdvZKWbHEWkXxqTGcnH/BGobXDw0f7OpWeaty6Gwso70hHBGpsSYmsUXWCwW7j730Oh3lUa/TaHiW0S8kmEY/O1z96j3r0d3JyY0wOREIu3HjIm9CQ3w48vNeSzanGdKBsMweGXpwa3kR3fTplrNZFSPDgzqEklxVT1v/KjRbzOo+BYRr7Q4s4AVu4qJDPbn5tHdzI4j0q7Ehwd6OgvN/HgTNfXOVs/wbWYBWfmVdAwPZGL/Tq1+fV9lsVg8c79fWrLTlO9te6fiW0S8jstl8LeF7lHv28amEB7ob3Iikfbn+hFdSU8IJ6e0hme+zmr1679ycCv5G85Kxu6ncqU5jU2NZUBSJEVV9cxdrtHv1qZ/zSLidT7fdIBN+8uJCwvguhHJZscRaZf8bFYeubQv4J4fvD2/otWuvWl/Gcu2FxFit/Gr4V1a7brthXv0291W8oXFO6lt0Oh3a1LxLSJexeF0eTqc3DW+p7aRFjHRkK5R/Gp4Eg1Og+kfZWAYRqtct3Gu95XDkogI0jtfLeGctDj6JUZQWFnHm8v3mB2nXVHxLSJe5cO1OewoqKJLdDBXDk0yO45Iu3ffhF5Eh9j5cWcx89btb/Hr5ZbVMH/9fqwWuOksrfdoKRaLxbN3wguLd2j0uxWp+BYRr1HncPKPRe65pfec11PzPEW8QFSInQcu7AXAI59soaymZbcmf+37XThcBhf2SyApOrhFr9XenZseR59O4eRX1PHOyr1mx2k39JNNRLzGW8v3kFNaQ2p8KJcMSDQ7jogc9IvBnRnaNYrCyjrPtLCWUFnn8EyB0KY6Le/w0e9/fruDOodGv1uDim8R8QrV9Q6e/WY7AH84Pw2bVT19RbyF1Wrh4Uv7YrNaeP3H3WzcV9Yi13ln5V4qah0MS45iYFJki1xDjnReejy9OoZxoLyWdzX63SpUfIuIV3h12S4KK+sZkBTJ+b3jzY4jIj+RnhDOTWclYxjw4Ecbcbqad/Glw+ni3981bqqjUe/WYrUe6vv9vEa/W4WKbxExXVl1Ay8u3gHAfRPStJOdiJe6+9xUOoYHsmFfGW+uaN4OGZ9vOkBOaQ3dOoRwbrp+AW9NE/p0JC0+jNyyWt5fvc/sOD5PxbeImO6lpTsor3UwMiWGs3p0MDuOiBxDaIAfMyf1BuCxz7dSUFHXLOc1DIOXl7g31blpVDesmnbWqqxWC3ce7Pv9/Dc7qHe4TE7k21R8i4ipCirq+Pd3uwCYNiHN3DAickIX9O3I2NRYKmod/OXTLc1yzpW7Sli/r4yoYH9+Mbhzs5xTTs5FfRPoGRdKTmkNH6zR6HdLUvEtIqZ67pvt1DQ4OTc9nsFdosyOIyInYLFYmD25D3Y/Kx+szeHHnUWnfc6XD24lf+2ZXbWxlknco9/uud/PfrOdBqdGv1uKim8RMc2+kmrmLt+NxQJ/OD/V7Dgi0kRdY0K4/Wz3NIXpH2Wc1jSFnQWVLNqSh93PyrUjkpspoZyKi/slkBIbwr6SGj5cm2N2HJ+l4ltETPPUoiwanAaXDOhEekK42XFE5CTcMrY73TqEsD2/kn8d7FJyKv71XTaGAZcNTCQ2LKAZE8rJslkt3DnOPfr93Dfbm72jjbip+BYRU2zPr+R/a/bhZ7Vwz7ka9RZpawL9bTx0SR8Anv4qi30l1Sd9juKqek93jZtHayt5bzCxfwLdOoSwu6iaJXtqzY7jk7yi+B43bhwXXHABkydPZvLkySxduhSA7OxspkyZwoQJE5gyZQq7du3yPOdUj4mId3jyy0xcBlw5LInkDiFmxxGRUzAmNZaL+ydQ0+DkofmbT/r5b/y4mzqHi3PSYukZH9YCCeVk+dms3HGOe0rR+5srqapzmJzI93hF8Q3w9NNPM2/ePObNm8fo0aMBmDlzJlOnTmXhwoVMnTqVGTNmeB5/qsdExHw7Sxr4ZGMudj8rdx18i1NE2qYZE3sTGuDHl5vzWLQ5r8nPq21w8t8fdgHaVMfbTB7YidT4UA5UOfnj++sxDE0/aU5eU3z/VFFREZs3b2bixIkATJw4kc2bN1NcXHzKx0TEO7yZUQHA9SO60jEi0OQ0InI64sMDuec899SxmR9voqa+aTskfrQ2h8LKenonhDMiJaYlI8pJ8rNZ+ec1Qwj2s/DpxgM89812syP5FK8pvqdNm8akSZOYNWsW5eXl5ObmEh8fj83mbjlks9mIi4sjNzf3lI+JiPmW7yxi7YF6Quw2bjvYLUFE2rbrR3QlPSGcnNIanv0m64SPd7kMXmncSn5MN+1q64VSYkP5/ZkRWCzwxJeZfLWl6e9qyPH5mR0AYO7cuSQkJFBfX8+jjz7K7NmzueGGG0zNlJGRYer1f2r16tVmRziCt+UB78ukPD/nMgymf+1+F+riHoFkb93IqfdIaH7e8DU6nPIcn7flAe/L1Jp5rk3340+58OK3O0i1l9E5/OclRmOe1bl1bM+vJDrISifHAVavNqew87bvF3hXpiEJgfyqTyhvZlRyx9zV/HV8zFG/r63Jm74+p8oriu+EhAQA7HY7U6dO5bbbbuP//b//R15eHk6nE5vNhtPpJD8/n4SEBAzDOKVjJ6Nv374EBHhHy6PVq1czZMgQs2N4eFse8L5MynN076zcQ1ZxHtGBVmZMGUVogFe8BAHe8zVqpDzH5215wPsytXaeIcDGyg28tWIvb2cZvPmbwUeMaB+e54mXfwTglrNTOWNYSqtlPJy3fb/A+zKtXr2aR68eQ9mba/lkYy5Pra7hw9vPIiLI37Q83vT1qaurO6XBWtOnnVRXV1NR4Z7/aRgGn376Kenp6cTExJCens6CBQsAWLBgAenp6URHR5/yMRExT2l1PXM+3wbAdQPCvKrwFpHmcd+EXkQF+/PDziLmrdt/1Mdk5JTx/Y4iQuw2rhrepZUTysmyWCz87Zf96dUxjJ2FVfz+7bXq/32aTC++i4qKuPbaa5k0aRITJ04kOzubmTNnAjBr1izeeOMNJkyYwBtvvMFDDz3ked6pHhMRczzxRSbFVfWc0S2aUUlaZCnii6JC7Py/C9MBeOSTLZTVNPzsMa8c3Ep+yrAupo2gyskJtvvx8nVDiQr255ttBTzxxTazI7Vppg89JSUl8dFHHx31WEpKCu+9916zHhOR1peRU8bc5buxWS3MntyXypxMsyOJSAv5xZDOvLtqL6t2l/DEF9uYPbmv51huWQ0LNuRis1q48axk80LKSUuKDua5qYO59t8reP7bHfTuFM7E/p3MjtUmmT7yLSK+zeUymDEvA5cB149IJq2jNtIQ8WVWq4WHL+2LzWrh9R93s3FfmefYa8t24XAZXNi3I0nRwSamlFMxskcHHrzI/c7GH9/bwOb95SYnaptUfItIi/pgbQ5r9pTSITSA35+nDXVE2oP0hHBuOisZw4DpH23E6TKobnDx5vI9gDbVactuPCuZKwZ3pqbByW/+u4riqnqzI7U5Kr5FpMWU1TTw18+2APD/LuxFeKDmd4q0F3efm0rH8EDW7yvjzRV7+Cq7hoo6B8OToxmQFGl2PDlFFouFRy/ry4CkSHJKa7h97hoanC6zY7UpKr5FpMU8+WUmhZX1DO0axeWDE82OIyKtKDTAj5mTegPw2OdbmZ9ZBcBvxmjUu60L9Lfx4jVDiA0L4IedRTz6yRazI7UpKr5FpEVsyS3nvz/swmqB2ZP7agc7kXbogr4dGZsaS0Wtg6IaF907hDC+V5zZsaQZdIwI5IVrBuNvs/Da97t4d9VesyO1GSq+RaTZGYbBzHmbcBlw7Zld6d0p3OxIImICi8XC7Ml9sPu5y42bRnXDatUv4r5iSNdoHj7YzWb6hxms3VNicqK2QcW3iDS7eev2s2JXMTEhdu49P83sOCJioq4xIfz9ygFckBLML4d2NjuONLOrhnfh2jO7Uu90cesbq8kvrzU7ktdT8S0izaqitoFHP3XP/7v/gl7aRENEmNi/E78ZHE6An83sKNICZkzqzfBu0eSV13HLG6upczjNjuTVVHyLSLN6alEWBRV1DEyK5BdDNMolIuLr/G1Wnr96MJ0iAlm7p5QZH23CMLQF/bGo+BaRZpOZV8Gr3+/CYoGHJ/fV3E4RkXaiQ2gAL103lEB/K++s2svrP+42O5LXUvEtIs2icZGl02UwdXgX+nWOMDuSiIi0or6JEcy5oj8As+dv5sedRSYn8k4qvkWkWSzYkMsPO4uIDPZnmhZZioi0S5MHJnLLmO44XAa/m7uGfSXVZkfyOiq+ReS0VdU5PJss3DehF1EhdpMTiYiIWe67oBdjUmMprqrnltdXU1OvBZiHU/EtIqft6a+zOFBeS//OEUwZlmR2HBERMZHNauGZqwaRHBPMpv3l3Pe/DVqAeRgV3yJyWrbnV/Lv77KxHNzJ0qZFliIi7V5EsD8vXTeUELuN+ev38+KSnWZH8hoqvkXklBmGwUPzN9HgNJgyNImBSZFmRxIRES+RGh/Gk1MGAjDn8618uy3f3EBeQsW3iJyyzzMOsDSrkPBAP/44QYssRUTkSOf36cjvz+2JYcBdb61lV2GV2ZFMp+JbRE5Jdb2DhxdsBuCPE9KICQ0wOZGIiHiju8b15Pze8ZTXOvjNf1dRWecwO5KpVHyLyCl5/psd7C+rpU+ncKae0dXsOCIi4qWsVgt/nzKQnnGhZOVXcs8763C52u8CTBXfInLSsgureOng4hktshQRkRMJDfDj5euGEh7ox5eb83jqqyyzI5lGxbeInJTGRZb1The/GNKZIV2jzI4kIiJtQHKHEJ6ZOhirBZ76KovPMw6YHckUKr5F5KR8uTmPb7cVEBbox/0X9DI7joiItCFjU2M9Pzv+8O46MvMqTE7U+lR8i0iT1TY4mX1wkeW956USG6ZFliIicnJ+O6Y7lwzoRFW9k9/8dxWl1fVmR2pVKr5FpMn++e0O9pXU0KtjGNeeqUWWIiJy8iwWC3Ou6E+fTuHsLqrmzrfW4nC6zI7ValR8i0iT7Cmq5p+LdwDuRZZ+Nr18iIjIqQmy23jpuqHEhNhZmlXIYwu3mR2p1einp4g0yewFm6h3uLh0YCeGd4s2O46IiLRxiZFBPH/1YPysFl5aspOP1uaYHalVqPgWkRP6emsei7bkExrgx58uSjc7joiI+Igzuscwc1JvAO7/3wY27iszOVHLU/EtIsdV2+DkofnuRZa/P7cnceGBJicSERFfcs2ZXblqWBJ1Dhe3vL6Kwso6syO1KBXfInJcLy/Zye6ialLjQ7l+ZLLZcURExMdYLBYemtyHwV0i2V9Wy+/eWEO9w3cXYKr4FpFj2ldSzXPfbgdg1iV98NciSxERaQEBfjZeuGYI8eEBrNhVzOwFm8yO1GL0k1REjunhBZupbXAxsX8CI1M6mB1HRER8WFx4IC9eOxS7n5U3ftzDWyv2mB2pRaj4FpGjWpxZwMJNeQTbbTx4sRZZiohIyxuYFMmfL+sHwIx5GazaVWxyouan4ltEfqbO4eShj91v+d01vicJEUEmJxIRkfbiF0M6c+NZyTQ4DW59Yw25ZTVmR2pWKr5F5Gf+9V02Owur6B4bwk1ndTM7joiItDMPXpTOyJQYCivruPX11dQ2OM2O1GxUfIvIEfaX1vDMV+5Flg9d0ge7n14mRESkdfnZrDw7dTCdo4JYv6+MP324EcMwzI7VLPzMDiAi3uXRT7ZQ0+Dkwr4dGd0z1uw4IiLSTkWH2Hn5uqFc/vz3fLAmhxBHGF1Saz3HDy/FD6/LjcOO/LReP/I5x37cEc85xrn9OLXReBXfIuKxbHshn2zMJcjfxvSJvc2OIyIi7Vx6QjiP/3IAt7+5htc3VPD6hq/MjuQRG2zjhYtPfpBKxbeIAFDvcDHz4CLLO8b1IDFSiyxFRMR8F/dPIK+8N899tRWrn7/nfsthj7FYjnyO5bCjhx/7ycOw/PSJR3vOMc6dEHpqZbSK72N4LeM1bhlyi9kxRFrNa99nsz2/kuSYYG4erUWWIiLiPW4a1Y0BQcUMGTLE7CgedXV1ZGRknPTztJLqGLqGdwXA4XKYnESk5eWV1/LUoiwAZl7ShwA/m8mJREREfJOK72M4p8s5VDVUceWCK3l327s+s8JW5Gj+/OkWquqdnNc7nnPS4syOIyIi4rNUfB/HF7u+IKski3e3vasRcPFZP+4sYt66/QT4WZmhRZYiIiItSnO+j+OynpcR5BdEalQq/jZ/Pt7xMfHB8ZyRcIbZ0USaRYPTxcx57kWWvzu7B0nRwSYnEhER8W0qvk/ggm4XAJBdls1D3z9Eg6uBDy75gB5RPUxOJnL6/vvDbrblVZAUHcQtY7ubHUdERMTnqfhuoqSwJH7d79cU1hTSI6oHFfUVlNSW0CW8i9nRRE5JfkUt//gyE4CZE/sQ6K9FliIiIi1NxXcT+Vn9+N3A32EYBoZh8PAPD7N432IeG/MYY5PGmh1P5KT99bOtVNQ5GNcrjnN7x5sdR0REpF3QgsuTZLFYcLgcOA0nBgbJEckAOF2ntsWoiBlW7SrmgzU52P2szJykRZYiIiKtxfSR75KSEu677z727NmD3W6na9euzJ49m+joaMaNG4fdbicgIACAadOmMXr0aACys7N54IEHKC0tJTIykjlz5pCcnHzCY83B3+bP42MfZ2/FXrqEd2FL0RbuX3o/s0fOZmDcwGa7jkhLcDhd/N/BRZa3julO15gQkxOJiIi0H6aPfFssFm6++WYWLlzI/PnzSUpK4vHHH/ccf/rpp5k3bx7z5s3zFN4AM2fOZOrUqSxcuJCpU6cyY8aMJh1rztyN871fzXiV7LJsFuxc0OzXEWluc5fvYUtuOYmRQdx2thYOi4iItCbTi+/IyEjOOONQ676BAweyf//+4z6nqKiIzZs3M3HiRAAmTpzI5s2bKS4uPu6xlvLoqEe5Z8g9TBs6DcMweGH9C+RW5rbY9UROVWFlHU98sQ2A/5vYmyC7FllK27OlaAsP7XiIDQUbzI4iInLSTC++D+dyuXjrrbcYN26c575p06YxadIkZs2aRXl5OQC5ubnEx8djs7kLB5vNRlxcHLm5ucc91lL8bf7c1PcmAv0CmbdjHs+te44bPr+BBmdDi11T5FQ89vlWymsdjEmNZUIfLbKUtsPpcjJ/x3zqnHV0DutMmaOMu76+i6qGKrOjiYicFNPnfB/u4YcfJjg4mGuuuQaAuXPnkpCQQH19PY8++iizZ88+YkpKS8rIyDil50U4IhgQNoAzIs5gw7oNHKg7QJR/FAHWgNPKs3r16tN6fnPztjzgfZm8Lc9bX/zAu6uK8bPAL7sbrFmzxtQ83vb1Ae/LpDyHvLj3RX4o+4E1WWu4sMOFdAroxJioMWzdsNW0TEej79nxKc+JeVsm5WkBhpf461//atx4441GXV3dUY9v3brVOOeccwzDMIzCwkJjyJAhhsPhMAzDMBwOhzFkyBCjqKjouMeaora21li1apVRW1t7yp+Ly+UyDMMwKusrjYv+d5Ex8YOJxt7yvad8vlWrVp3yc1uCt+UxDO/L5G15lq9caVz89BKj6/0LjL9+tsXsOF739TEM78ukPIaxt3yvsWTvEsMwDOP7nO+N8e+ONxZmLzQMwzBWrlxpGIZhlNWVGc+tfc5wOB2tnu+n9D07PuU5MW/LpDzHd6o1o1dMO3nyySfJyMjgueeew263A1BdXU1FRQUAhmHw6aefkp6eDkBMTAzp6eksWOBe4LhgwQLS09OJjo4+7rHWYrFYACiuLcZus2O32YkNjsUwDFyGq9Vy+LK8qjxmLJvBtMXTPPdV1FeYmMi7LdpZQ0ZOOQkRgdw5TossxfvtKtvF5I8m88DSByitLWVEpxF8evmnnJ98PuB+nTUMgzu/upN/rv8nz69/3uTEIiJNY/q0k6ysLF544QWSk5O56qqrAOjcuTMPPPAAd955J06nE5fLRUpKCjNnzvQ8b9asWTzwwAM8//zzhIeHM2fOnCYda01JYUm8dfFblNSWEGAL4O2tb/Pt3m95ZNQjdAjqYEqmtqrWUcurGa+ypXgLT53zFMH+wXy842PAXXRvqtzEne/fyR+H/ZHLel5mclrvUlJVz5sZ7l9Mpl/cm2C76f/tRY7KZbiYv2M+47qMo2t4V4Z2HEp0YDROw72Pgt1mP+LxFouFuwbfxd9W/o0rel5hRmQRkZNm+k/hnj17sm3btqMe++ijj475vJSUFN57772TPtbaAv0CSQhNoM5ZxysbXyGvOo81eWs8ozdybPO2z2NZzjJmjZxFoF8gb297m+LaYnaU7qBHVA9mnzWb1KhUQvxDWFO+hoqGCgpqCgD3D3GrxSve2DFNvcPF2j0lvLRkJ5X1Bmf1iOGifh3NjiVyTI/8+AjvZb7HjaU3cu/Qe3l23LP42/yP+5wh8UN48+I3sVqsrM1fS72znjMSzjjuc0REzGR68d1eBNgCePPiN1m4ayHnJ59PRX0Fr29+nV/3+zUBttNbjOkrVh1Yxbd7v+WqXlfROawz72x7h42FG5mYMpExncdw75B7CbeHkxiWCMAlKZd4nntNwjVcNvAyRiaOxOly8psvf8OIhBHc0PcG/K3H/+HtS3YVVrE0q4DFmYX8sKOQqnr3iKGfBR66pI9nSpSIt8ipzCGjMIMJyRO4IvUKluYsJT3GPcXwRIV3I6vFyqaiTfx64a8JtAXyzqR3SApLasnYIiKnTMV3K4oLjuPa3tcC8Jflf2H+zvnkVObw6KhHTU5mjr3le1m8bzGD4wfTO6Y3b297m4W7FpIYlsivev2Ka9Kvoay+jPRo9w/iyT0mH/NcFouFsUljAfg+53tWHljJ7vLdXNXrKvztvlt8V9Q28P2OIpZkFrA0q5A9xdVHHO8ZF8qY1Fh6BZbTIy7MpJQiR5dXlcfkj9z/r/t36E+fmD58evmnp/QLc3p0OqMTRxMfEk9CSEJzRxURaTYqvk0yNX0qmSWZ/KbfbwDIKsmiR2QPnx6ZrKivYHnucqIDoxkcP5j3st7j1YxXub739fSO6c3E7hPpFNqJQXGDALio+0WndJ2RiSN56byXMDAIs4fx1e6vWJ2/mjsG3kGwf3BzfkqtzukyyMgpY0lmAUuyClizpxSny/AcjwjyZ1TPDoztGcuonh3oFBkE+EhrJvEJLsPFxzs+ZlTiKOJD4hmXNA6r1Yqf1f3j6FTfqbJarDx+9uP4W/0xDIMPsz5kYveJTR49FxFpLSq+TdK3Q1/em/QeFouFrcVbmfrJVMZ0HsPjYx/3/BBq61yGiy1FW6h2VDOs4zA+y/6Mh398mPO6nsfg+MGck3QOeVV5DOs4DICzk87m7KSzm+XaIzqNAKDB2cBfVvyFvOo8ekT24PKelzfL+VvTgbJalmQVsCSzgGXbCympPrR5k81qYWjXKMakxjImNZZ+iRHYrL77C5y0fX9f9Xf+s/k/XNbjMmafNZs/j/5zs73mNRbuj618jDe2vMG6gnU8NPKhZjm3iEhz8Y0qr41qHOXOr84nwBZATGAMfla/Nr1YsLCmkF1luxjacSg/7P+BWxfdSu+Y3rwz8R1GdhrJ4LjBDI4bDMCguEGeUe6W4m/z56lxT/Hetve4tMelNDgbeH7981zf+3oiAyNb9NqnqrbByfLsYpYeHN3OzKs84njnqCB3sd0zlpE9YggP1MieeLecyhxWHljJpT0u5Zdpv2TRnkUMTxgO0CKDDRNTJvJZ9meM7zK+2c8tInK6VHx7gTGdx/C/S/5HVGAUhmFw35L7SApL4ncDf+f1iwXrnfVklWTRp0Mf9lbs5aIPLiIqIIpvp3zLkPghJIcn0zemLy7DReewzvznwv806/U37ivjte93UVFaRv+yLBIigkiIDHT/GRFIoL+NPjF96DOyDwAvZbzEKxtfYdWBVbx+0evNmuVUGYZBZl6lZyrJ8uxi6h2H+sEH222MTIlhdE/36HZyTLBPT08S31JaW8pl8y6j3llP35i+9IjqwSeXfYLNamuxa/aJ6cNnV3xGkF8QxbXFbC/Z7in2RUTMpuLbS3QK7QTA5qLNfLn7S4L8gvhF6i9IDE00OdmRHC4H+XX5GIaBw+XgnHfPobKhkiVTltA5tDPdI7rTKbQTZXVlRAVGMf+y+S2W5b1Ve3nwowxPofrFzsyfPSY6xE5CRODBjyCCQtLpFtKfcztez56iarKr15AalUJCaOsu0Cquque77YUHF0oWkFded8TxvonhjOkZy+iesQzpGoXdr22+EyLtU2O/7kFxg+gS3oVLUi6hor6CUHsoQIsW3o2C/IIoqini6k+vpri2mP9c8B9PFxURETOp+PYyvWN686/z/0VpXSmJoYlsKtzEf/f/l9iesXQJ78JbW98iuyybq3pdRfeI7vw7499sL9nOTX1vokdUD55b9xxbi7dyx8A7SItO44lVT7CpaBN/GPoH+sT04ZEfH2FDwQamnzmd/rH9efC7B1mTt4Y/j/4zg+IGce+397LqwCr+fvbfGdpxKLcuupXluct54dwXOCPhDG798laWH1hOSnoKqVGppEWnUVJbQl51HhEBEXw4+cMWnzJT73Dx8ILNvP7jbgCmDE0i1FGKX3gsuWW15JbVsL+0lrzyWoqr6imuqmfT/vLDzvArNqyqY5btY0JSnsBiMUis/hNdwrrQKdI9Yt4xItDz9/jwQPxtp/c5NThdrNldwtKsQpZkFbAxpwzj0DpJYsMCGN2zA2NTYzmrRwc6hKr9pLRdL65/kefXP8+4pHE8Ne4p/nTGn0yZShcdGM2Q+CHsLN2pjc1ExGuo+PZCQzsOBdy7Ot635D72VOxhSsUUuoR34es9X/Nj7o+c3flsukd054f9P/Bj7o9M7D6RHlE9WJe/jh9zf+RXab8CYFvxNlYeWElZbRkAe8r3sKV4C5X17nnEBdUF7KvcR3WDu0VdZX0lJXUl1DvrAXC5XDhcDpwud79of5s/YbYwcipySI1K5Z/n/vOIPuUt/QM2v6KW372xhlW7S7DbrMye3Ierhndh9erVDBly5KiW02VQVFnH/rJacktr2F9Wy4GyGs/tnIoGyqtTwOJg6147Wy17sfqX4KqPP+I8FgvEhgaQEBlEp4hDU1oap7d0igwkNjQAv58U6LuLqg5OJSnkhx1FVNY5PMfsNivDukUx5uBUkl4dwzSVRNq0/ZX7+Xbvt0xNn8oVqVfwafannNv1XAzDMG0Ni8ViYeaImTgNJ0F+QazJW0N6TDpBfkGm5BERARXfXi3AFsAfhv6BxZsWezaMuCrtKs5JOoduEd0AuKHPDUxKmUSPqB4A3DbgNn7V61ekRacB8Pshv+fm+ps9t//fGf+PGkcNXcK6ADD7rNk0OBvoEOweFfrb2L/hNJyE+bt7Qj87/lksWDxvE//z3H+6C90uQzwZW8uaPSXc9sZq8srr6BgeyD+vGcygLlHHfLzNaiEuPJC48EAGJkUe9TEO5yXsKyujqAJe3vQPfij4mEHBNxFUN9JTrOdX1Hk+1u89zrXCAkiICCQ2LID1uwo5UHXgiMf0iAtldM8OjEmN5cxuMQTZW/6td5HWUN1QzS/n/5Ly+nJ6x/RmYNxA5l06zysWjjduSb9g5wKmfzedcV3G8fjYx70im4i0Tyq+vZjFYmFcl3FEFESQFO4uvsd3PXL1/lmJZx1xe3D84CNu947pfcTtxqK9UceQI7cbjwiIOOJ24w8us725fA8zP86gwWkwPDma564eTGzY6Rf+fjYrydFRdI0y6Jzrj7UQ/jhuHH1i+lBcW0x0YDQNThd55bUHp7S4R81zy2rZX1rDgfJa9pfWUlhZ5zneKCLIn1E9OjC6ZwdGp8aSGKnRNvEdjfO606LT6BXdi6t6XcXeir2e1xRvK27To9MJ9guma3hXs6OISDun4lu8Wp3DyayPN/HWCveQ8w0jk3nw4vTTnoP9UxaLhelnTufGvjeSGJrIvop9XPHxFUzuMZn7h91P56hgOkcde4OeOoeTvLI6cstqyKuooypvN1eee6Z6bovPen3z6zy+6nGGxA/h1QmvcsfAO7x66lRKZArzLp1HbHAsTpeTTUWb6NOhj9mxRKQd8q6hCZHDHCirZcqLP/LWir0E+Fn5+5UDmHVJn2YvvA/X2F1mTf4a6px1lNaWYrPacLgcx31egJ+NLjHBnNE9hksGdCI1xq7CW3xObmUur2x8BcMwuKznZaRFpXFFzysAvLrwbhQbHEuDq4Hff/N7rv3sWtbmrzU7koi0Qxr5Fq+0IruY381dQ2FlHYmRQbx47RD6Jkac+InN5JKUS0iLSiMmKAaA2T/MptZZy/3D7vfcJ9KeOFwOrv70agpqCkiNSmVM5zGeXXrbEn+rPwmhCQT5BZ3wl2oRkZag4lu8imEY/PeH3Ty8YDMOl8FZPWJ45leDiQ5p/bnnjYtU86vz+XzX5zhdTm4bcJuKb2lXthRtwd/qj5/Vjxv73sjGgo30jOwJtI3R7qO5b9h93NDnBjqFdiKvKo9g/2DC7GFmxxKRdkLFt3iN2gYnf/pwIx+syQHgt2O6c9+EtJ+18GttccFx/O+S/7GpcBPdIrqRVZLFU2ue4oHhD9A5rLOp2URa0orcFdy26DY6hXbi/sT7uSb9Giy922bBfTg/qx+dQjuxtXgrty+6nZ7RPXl23LMtstW9iMhP6ZVGvMK+kmpufWM1GTnlBPnbmPOL/lwyoJPZsTySwpI87R6fXP0kS3OW0im0E386408mJxNpfvXOeuw2O3079KVLeBcGxQ3CZrG12ZHuYwn1D6XB1UCto5YaR41Gv0WkVWjBpZju++2FXPLsMjJyyukSHcwHvxvpVYX3T80+azZT0qZw56A7AXhm7TNsK95mciqR5rGzdCdXzr+SD7M+JNg/mDcueoMZI2Zgt3pH29Hm1DmsM69d8Bovn/cyYfYw9lfuNzuSiLQDKr7FNIZh8MrSnVzzr+UUV9UzJjWWj+84i/SEcLOjHVeHoA5MP3M6YfYwvtnzDS9teIlff/Frzy6h0jT1zno2FW1iV9kuAJwuJ6W1pfo6miyjKIMdZTt4a+tbOF1OQvxDzI7UorpHdsff5s9rGa9x8YcX833O92ZHEhEfp2knYorqegf3/28j89e7R5puPyeFe89La3Pt+YZ1HMavev2K7hHdCfYPZlPhJmocNVhoW59Ha6hx1PD21rfZXb6bSQGTyKvK46oFV9E5tDOfXfEZ+6v2c9EHF5EYmsjnV3zOvop9XP7x5SSHJ/PupHc5UHWAW7+8lc5hnXl2/LMUVBcw4/sZJIQkMGPEDIpri3lu7XPEBcdxy4BbKKsr4/3M94kOjOaynpdR1VDFkn1LiLBHMDJxJLWOWrYWbyXUP9SzQ2x7Vees498b/831fa7nkpRLcLqcTEie4NnZtj0ory/H4XKwvXQ7IxNHmh1HRHyYim9pdXuKqvnt66vYeqCCELuNJ64cwAV9E8yOdUpC7aGeed8NzgamL5vO9tLt/DH5jwxhiMnpvEODswF/mz+V9ZU8t+456px1DOkxhHhLPOnR6cQFxwHuke+IgAjC7e53PuqcddQ4aqhz1gHuLcx3lO3AhQuAivoKvsv5zrNra2ldKe9mvktyeDK3DLiFotoi/rHmHySHJ3NZz8vIr87nviX3kRyezPzL5pNblcu1n13ruW0YBjtLd9I9srsJXyVzzVg2g0+zPyWnModHRj3CZT0vMztSq7tj0B2M7jyaQXGDaHA1UOOo8fxbFBFpTiq+pVUtzizgrrfWUlbTQLcOIbx07RB6xvvOIqfzup5HdUM1PYN74nQ5eXT5o1yScgkD4waaHc0U72e+z/Prnuepc56iX2w/pg2dRpewLthz7XQO68y7k971PDY5IpnvrvrOc7tbRDeWT12Ow3D3Yk4MS+SDSz7wbFseHxLPc+Ofw25zz0WODojmwTMe9EyTCLeHc2PfG4kMiAQg0BbIBckXeFpF+ln86B/bn47B7u3Ql5ct58V5L3Jzv5u5a/BdLfuF8RL51fnudwr630JWaRZT06eaHck0VouVQXGDKKsr4w/f/oEGVwMvn/+y59+XiEhzUfEtrcIwDJ7/dgePf7ENw4Bz0+P4+5SBhAf6mx2t2fjb/PndwN9xU9+b2LR+E1/u+ZL3Mt/jh/0/sOCyBYD7B7yvdYz4qXpnPSW1JcSHxLOvYh8FNQV8kv0J/WL7cVWvqwBYfWD1Cc9jtVgJ9g/23A6wBdAzqqfndoh/CGM6j/HcjgyM9Jwf3HPz7x1yr+d2QmgCfxv7N8/tpPAk5l4013O7sKEQq8XqaR+ZW5lLfEi8p9j3JS7DxZOrn+Sdbe/wxkVvkBqVyvuT3vfJz/Vk1TnryC7PxulyklOZ43lnRUSkuaj4lhZXWefgj++t57OMAwD8/tye3DWuJ9Y2Nr+7qQL9AgEYFj+M3/T7DckRydisNj7I+oC5W+Zyz5B7GJU4yuSULWNT4SZ+/+3v6RrelVfOf4Ub+txAv9h+jEsaZ3a0E5oYO5HrR1xPl/Au1Dnr+PUXvybCHsE/zvkH8SHxZsdrVlaLlZLaEuqd9Wwu2kxqVKoK74PiguN4fvzzhNvDSQhNoKqhyucXnYpI69KrrbSonQWVXPbcMj7LOEBYgB+vXDeU35+b6rOF9+FigmK4a/BdXJJyCQDzd8wnsyST0rpSADJLMj3zmdsyh8vBN3u+wTAMOod1pqq+iqKaIsrqyogMjGR8l/FtZrQ/OSIZq8XK3vK91Dncc86jg6JxuByU1JaYHe+0fb//e+asmINhGDx45oO8fuHrXNrjUrNjeZ206DQSQhNYnbeaiz64iEW7F5kdSUR8iEa+pcV8tSWP37+9joo6Bz3iQnnp2iF0jw01O5ZpXjjvBT7P/pwLki+gwdnA7xb9DofLwesXvk5SeJLZ8U6JYRhc/9n1bCjcwEvnvcSITiN4/aLX6RbRrU2PpPaI6sH8y+aTV52Hv9WfN7e8yXPrnuPBMx7kou4XmR3vlJTVlXHPN/dQ7ajmjIQzODvpbPrF9jM7llfbULCB4tpiPs3+lHO7nmt2HBHxESq+pdm5XAZPf53FPxZlAXBBn448fuUAQgPa9z+3AFsAk3tMBiCnMofowGjqnfUkhiVS46jh3xn/ZkraFDoEdTA56fE5XU4W7lpIbHAswzoO45wu51BWX4ZhGACkRKaYnLB5BPsHe+b7rs5bTXl9OQF+AYC700pb2Q2xuLaYivoKuoZ3ZfqZ09lXsY/RiaPNjtUm3NDnBuKD4zk/+Xzg0M6fIiKno31XQ9LsymsbuPeddSzako/FAtPOT+N3Z6e0mWkHrSUxNJF3Jr5DUW0RVouVD7I+4IX1L7AidwX/ufA/Zsc7rg+3f8hDPzxE75jevH3x21zX+zpu6HMDflbffTl5fOzjrMpbxdD4oVTWVzL5o8mM7DSSP53xpyMWhXqbHaU7+O2XvyXEP4S3L36bSSmTzI7UplgsFi7qfhEuw8Wza59lyb4lvHbBa179PRcR79d23xcWr7M9v4JLn13Goi35RAT58+oNw7j9nB4qvI/BYrF4RrkHxg1kXNI4rutzHQD/zvg39y2+j52lO82MCLinlny1+yvuW3IfLsPFxd0vpn9sf65MvRKX4cJus/t04Q3u79WwjsOwWCyszV9LaV0pu8p3EegXSIOzAYfLYXbEIzS+C5EQkkC4PZzIgEiqHdo59FRVNVTxafanbCvZxqq8VWbHEZE2zrd/Ykqr+Twjlz+8u56qeie9Oobx4rVD6BqjDgFN1SemD0+NewpwT+uYu2Uu+dX5XNbzMrpHdie3MpeOIR1b9RcZwzAwMGhwNfDnFX8mvzqfCV0nML7r+CNa9LU3ozuP5sPJH+J0ObFarLyY8SKLdi9i1ohZXjGHury+nOnfTeecpHO4rOdlvHjei0QFRuFv9Z22nq0tzB7Gs+OfJacih9GdR+N0OdvV7p8i0rxUfMtpcboM/v7lNp77ZgcAkwZ0Ys4V/Qi265/WqbJZbcy9aC6fZX/GmQlnUlFfweUfX06PyB68cN4LrdL2bOWBlfx91d+5tve1XNT9Iu4Zcg8V9RWM7qy5wgBdw7sC7k4vC7MXsqNsB7XOWgAaXA2mFrrLcpbxzd5v2Fi4kQu7XejZQVROT/eI7nSP6E5hTSG//+b3/CL1F+oUIyKnRBWSnLKy6gbuenstizMLsFrg/12Yzs2ju2maSTPoGNKRG/veCEBWSRY2qw1/mz8h/iEU1RTx/f7vuaDbBc1a5BmGQXFtMTFBMewp30NGUQbvZr7LRd0vYmL3ic12HV/iZ/Xj7Ylvs3jfYoZ1HEZhTSG/+uRXXJt+Ldf0vqbVOr4YhsEn2Z8wLmkcF3a7kJzKHCYkT/D0nJfmsyxnGesL1lNcW8xF3S7SAkwROWkqvuWUbMkt55bXV7OnuJqoYH+enTqYs3p4d5eOtmpw/GC+uOILimuLAZi7ZS4vb3yZlQdWMvus2c1yjYLqAqYtnkZ+dT4fX/Yxl/S4hHpXPZNTJjfL+X1ZoF8gE5InAPB59uccqDrAj7k/cm3va3EZrlYpwJ9d9ywvbXiJi7tfzF9H/5Wb+93c4tdsryb3mExlQyUTkidgt9kxDEMDDiJyUrTgUk7asr01XP789+wprqZPp3Dm3zlKhXcLC/YP9mx7nhqVSreIblze83IA/rnun7y4/kXK6spO+rxr89fS4GwgKjDK3ZKuoYKdpTvxt/rzq16/UleHk3RN72t4bvxzPDD8ASwWC39b+TfuW3wf+dX5LXK96gb3IsqLu11MTGCMWgi2kqvTr6ZDUAeW5Szjmk+voby+3OxIItKGaORbfqa2wUleeS0Hymo58JM/95fVsn6vu8i7fHAif76sH4H+WnjUmi7odgHnJ5+P1WKlrK6Mf2f8m1pnLWcnnU1EQESTt8N+5MdHeGfbO/zfmf/HlWlX8sTZT9AppBOh9va7EVJzGNN5DODe1ObD7R9S46jhpn43ERcc16yjpPN3zOdvK//GKxNeITUqlc+v+FzTTFqR0+XkH2v+wdbirby77V292yAiTabiux0xDIPyWgd55bXkltWSV+b+011Y13CgvI4DZTWUVDcc9zw2C/zfxN5cPzJZb7eapHEqQ7g9nOfGP8fKvJWkRadxoOoAkz+azEXdL2LGmTN+9v3JKMygsKaQs5POZljHYSzYuYAGl/v7nRqV2uqfhy+LCIjgg0s+YHnucnpF92JvxV7u/fZe7h1yLyM6jTjt8684sIKSuhI+z/6c1KhUFd6tzGa18Y9z/sEnOz/hpr43mR1HRNoQFd8+wukyKKqs40BjYX1YgX346HV1vfOE5/KzWogPD6RjRCAdwwOJDw8kISKQ+Aj3nxX7dzBuZLdW+KzkRCwWC8MThjM8YTgAy3OXU+OoobK+EovFQk5lDqV1pQCsy1/HtZ9dS4egDpyZcCbndT2PMxPOJCIgwsTPwLd1Cu3EZT0vA+A/m/7D1uKtfLzj41MuvvdW7OWTnZ9wS/9b+NMZf2JEwggu7HZhc0aWk5AYmshv+/8Wp8vJnJVzWJK9hDf7vklEQASLdi8iJiiGfh36+XwffJF2w+WE0t1QmAWFmRA3EDj56Zl6RWgDGpwGe4urjxylLqvjQHmNu6guqyW/og6HyzjhuYLtNk9R3fhnQkRjgR1EfEQAHUICsFqPPaK9uii7OT89aUaTe0xmYNxArAeXc7y84WU+3P4hT6Q+wfjY8QyJH0L/2P44XA4C/QJVeLei+4bdR6fQTkzq7t5l8uEfHqZjSEeu73N9kzpm1DnruOGzG8ivyScxNJFJKZO4qPtFLR1bmmB13mre3vo2Bgah/qE4XA6mLZ6Gy3Cx8pqV2AwbUxZMoUNQB5465yn8bf58s+cbEkITSItK0zuIIt6mthyKssDZAF3OdN+XtQje/hU46w897sx7IPbkdw5W8X0M9767jrI6cBkGLsPAMMAwDrsNuAz3VI7G4z+/fdifHHyuq3HzksZzHbxtHLrtMgw47HZNgxPIO2Hm6BD7oVHqg396iuyDH2EBfnqh93GNPagBogOjCbQF8n3p95xrOZdXJ7yq779J7Da7Z3rCzrKdvJv5LnarnQu6XUBSWNIxn9fgaqCopoiOIR35/ZDfs2j3IsYmjW2t2NIEwxOGs/AXC1m0ahE2q43y+nLO7Xou1Q3VBNgCKKwpZEvxFiIDIvG3+VNeX85d39xFkF8Qy6cup85Zx/WfXU+X8C7MGT0Hp+FkRe4KOod1pkt4F7M/PRHfVVcJe350F9qFmQdHtLOg8oD7eKfB8Ntv3H8P6+guvMMToUNPiOnpLsxrTv6yKr6PYUV2CQXVJ56i0RqsFo6YBvLzkesg4sIDtPBRfuauwXdxy4BbWLNmDYAKby/RPaI7L5//MnvK95AUlsS24m38c/0/+eOwP5IYmuh5XGFNIfd+ey+ldaW8ffHbTEqZxMTuE/V99EJxwXGkhaQB7rUYj4993HMsIiCC9ye97+mKUtNQw5jOY7BarJ7pYZuKNlFeX47FYiGvKo9bFt1CbFAsX1/5NcW1xdz77b30iu7FA8MfoM5Zx6bCTSSFJREbHGvK5yvSZtRXQdH2Q1NFGmrg/IfdxypyYe4VP3+OXyDE9ID43ofui0uH/5cDAYc1Jairg4yMk46k4vsYnrhyABabH1aLBQvuosVqAav1J7ctFizH+NNqcT/OQuNt97FDx92P4Se3LViwWPFce9PGdQwfOtTUr4e0XQG2AOxWbQTibc5MOJMzE9xvZz655kmW5SwjITie+3tdC5V5BJVtJ7g6koq6MqoaqthfuZ8eUT1UeLdB/lZ/0qLTPLfjQ+J5bvxzntudQjrx+oWve3ZJrXfWM7zjcM+0sL0Ve1mdt9rTWjK7LJvrP7+e7hHdmXfpPPZW7OXPy/9M/w79uW3gbVQ3VLO7fDflDnex3+BqoN5Zj91qx99m3u6rIsdiGAZOw4mf1Y9dZbvYVLSJbhHd6B3Tm/k75vPR9o+YlDKJJJL4MOtD3s98n8k9JnNl2pV8v/97fsj5nhGdRjIycSQ78zPYvexvpJQX0qVoF1UVOTgsFkJcLnfR6xcE5z4EVitEJUO3Me5CO6YndEiFDj0gIgmsPxnQtNqOLLxPg4rvYzijWzQBAQFmxwDAph+2Im2XYUBNCVTmHfzId//ZbSwk9AdgdkMIL1bXc9vCOfDZX7k6IZ6LK6vovaSSp/38CBk3g+ioHu7zrXrV/TZpcAwER0NIh4N/P/hnRCLYT9xqUrxHoF8gA+MGem53i+jGvyb8y3M7JSKFl857CfeERXC4HPSN6UvXCPcUs11lu/gu5zsanO7ORZuKNnHTwpvoG9qXc844h+W5y7lt0W2M7DSSF897ke9zvueub+7irE5n8dS4p1h5YCV/+u5PDO84nEdHPcq6/HX8ZcVfGBw3mPuH38+mwk08s+4Z+nXox+0Db2db8Tb+u/m/pEWlcV2f69hZtpMPMj8gJTKFy3pext6KvXy5+0u6hHXh3K7nejaeqqysZAhDKKguYE3+GuKC4xgUN4jCmkI2FW4iJiiGvh36UlJbwraSbUQFRJEWnUZZXRnZZdmEB4TTPaI7FfUV7KvYR6g9lKSwJKobqjlQfYBgv2A6hnSkxlFDSW0JgX6BRAdGU++sp6K+ArvNTpg9jAZXA3WOOupdh83dlRZTWV9JTmUOwf7BJIUlsfLASr7c/SVD44dyfvL5vL75dZ5a8xTX97meOwfdyRe7v+CZtc9wU9+b6B3Tm4KaAlYcWEGv6F50MeLZl7eWDYUbGOWwQMaXrC1dx2t+tQTaAhiZOJKFOYt5vnwNt5SUcUd5Ga9HRfFcZBi/scVxV8cxzDXKeO/jy5iafjVXpl3J1+fcw7KcZZzTpT+jEkexpWgLO3d9Tnp0Ot0ju1NSW0Kds47IgMhm6yql4ltE5FTUVx9ZTFfmQeeh0GmQ+/iSv7kL5cp8cB2lfecFf/UU33GWAP4v7wBYbHwfHc+GQD+yAgK4kFCSqoogtNOh5+3+Hja+e+xclzwDg69z//2bP0P2koPF+WEfjQV7wgAIjWumL4i0lFB7qLtDjrMBqovpG5XGWxPfch/c8yN9infzVNfLCHQ0wOLHcJRvp4cthMTyHCjaAUCQXxCB1SWw8hXqq3Ooc9bhqMiF7CVUl27jQNUBSmuKAHeP+s1Fm4kJjAHc05+W5SzzLOQ+UHWAj3d8zOjE0VzX5zr2lu/lP5v/w+jE0VzW8zKyy7J5cvWTjEocxbldzyWzJJP/W/Z/9A/tzzVcw5biLUxbPI3RiaN5/tzn2Vy0mTu+vsNze2PhRm7/6nbP7fUF64+4vTZ/7RG3V+WtOuL2ygMrj7j9Y+6PR9z+Yf8P3P7V7fQN7cuIYSNYlrOMu7+5m9GJo3nynCdZeWAls76fxfCE4cwcMZMNBRv4x5p/MDB2IHcNvoutxVv5z6b/0DumN9f2vpbssmzmbZ9HSmQKk1ImkVOZw5J9S0gKS2JU4igKawrZULCB+OB4+nToQ3l9OXsr9hIZEEliaCJ1zjoq6isI9nN3zXAZLvc74GYMvLlc4Kh1fwQetiC/IBPqK93/Bp31NDTUUO+oJgQLu6oOsCI4iM5RPRjZaSSLvpnOK3nLODsokVvDevFh2WYeK9/I1MAu/L+g7mR17c9bW9/C6XJy/vqPCSjbTJ1RR+m6N2Dl/0g3qplgs9Jz+auQcgXndT2PXtG96PbR3XTMf5Qkm4XRfn7E7s0Bh5OzAuwEBQYwMCwZgM7hXRgblkKPHmdCr19i2fsFYZtfJ7z/tdD3Bg6seoId+7+kor4CgHUF63g3810SQhMYlTiKhbsW8q+Mf3HXoLvoHtmd1ze/zssbX+aOgXdwy4BbeHnDy7yf+T6/7vdrJief2i7QKr5FRBq5nFBVeNgodZ57nl/iEPfx5S+6Pyrz4eAL9xHGTT9UfDvqoTzH/ffACAiNP/gR5/6zY79Dzxv9Bxh1DwRHMwwXf87+nOr91UTdMMU9cm64Dj32jFug+9lQXQTVhQf/LHbnri6CsMMK9bxNsOeHY3++v/g39D043/Gj22HnN+7R9MZRdE+xHoN//WGLQrcvgvL9h2U7+GfjyvS0CyHy4OM3z4OS3Yc95rDHYUC/X0B0d/dj1851z8084nwHP3fDBUNuhFh3P/rY7I+g4H9g8we/APefNvvBD3/oOeFQhr0r3e8+ND7mp48PSwD7wXZhDbVgsbqPN2fxU3HA/X2qq3D/26mrcC/2qqtwFzUDp0KEexdbPv8T5K6HunL3scbHOg6u7Lr5K/cvegALHyQ6ZxXjDrvUCODDxhvVxYxKGsWKq1fAy+Nh7R8YA6y0WDDYC2sXcobFwkKbFf+R7raYg+IH8ZYjhuDl78Lyj+hrD+K5oCAiM5fDjjGk+dl5eOBtxHUcCEC3rG+4NySNTlW18M1fSDRquSF6EMmEwMb3iY3qzCUplxBUFQR7VxBTkMV5UX3pZdhh4/tEVexmdFh3+tTVQ1UR4fZwhnccTmqd+3xhNbkM8I+mW0kOfDLt/7d373FR1fnjx18MMMAgMNwvqUkUipiKoKLijbzLLff7/VqmW5treVnNNveraTcv/Mr67tqW1y3rt223XR+6KlTftdS8bZqLlJqUrpKXQJCbAsIAM/P948wNBJy05pC9n4/HPJgz533OvOcMfHifz/mcc9AZSumh8aWzVg+At7s33dx9CS86Cm9lojXWEOnmTuC5z2F9Ch7UExTgb7uBmNsnS/E1m4kszYMXYzD4eGHwd6fpzG44OYwrg3/Nuepz3BFwB+x4irLy4xxuOI2u/AxcvEBRUzm5lZ9TU/wF07qM5tvL37Lx+EaGB8aRftXAyeoz/L9Tf2F4UC9SjB4cq/iKx758mWFRKawZvY4jJUeYu2uuMm3ZGZi7ay7DOg/jYf3D7Dm/h3m75zGiywheTX2V/f/OZeHn2QwN7csL8Y9wsDSf5d+8zaCAO3mqy3jy/EP4w9H1JEYk8ltTAF+Wf8WGqqP0cffnUa/OnDCU8Xb9BeLR8kDmXzhlrGHLqS3Efr2DeytK+dbcwD88TEQb6hhTU813Hu4c8PGh88Q/4kU0X5R+waZtv6RndTkPXKlhaydfng4N5t7qGpaVVZDfyZflocFkxGQwOGowdV/n8pXOSNfys3Aph9t0PtwZGIC+8ihU7Sep1xb+u/9/Ex8cD/+aQVrFaSYCvmblyM5Qy8P699hFH6uclG40YXZzIzQgmtCQWOWkx5BY+obcRd/gu8BX2VlMj0knPcZ+BZJHg2fxaN9ZtunpvaaTHpNOkHcQAKO7jibKN4reoUpnSGxgLOO7jbfd+0LrriVMF0aQjxJ/qe4SRbVFNBhv/MiJFN9CiFuXyQT1VZbCtMz+M7QH3D5YifniXfhsLb2rLkDu5eaFLihFsbX4brwKFUpPIu7a5sV0p3CI6GNfbsAMSJiqzPf0aT9Pyz8NAE/cSY9JJ68qT3nBzQ3cHMYedk6yF17XM2YFDJxpKdBbPGrLQN/NHnvlgrKzYN1haMFn4Av2ic/Wwumdbb9v0B32wjfvz+3HRvWzF99fbVEK+7bE3GMrvgNKPoPjh9uOfaCbPYc9L7S/3imbIHaM8vyvD9hjNa0U9lnrINpSGvzvYijKt8z3JLbyEvwLe7E8+W3oOlCJ3fRQ+ztCXZPtxffFo3B2/7Uxbhrw8mt+qbNuKeAfpbzu5QfaTpbnnTh77jy3B3azx/bMhIi70TTW4d14VTnxrLEO76Y6ohrrwDKMxV/rTy8j0NgINBLcWMuwWvtqIoCs8f8DUX2V1E/t4lfFX9rmxwBP2KZeI27GbrJTssnLy4MPHiP+4jH+YJv/JncDa62TCb+hb1RfZdjN+hS4uJkE4G1b/KckAZsA7lkHKFebyanxVLYbkAzscNhsg4E9GZ/adoyH1jdw8OJ52/wRV+FQhWVHy2xmUGBPtmdtx8vdC976DxLKCnhN64m/qRga8olzd2eFjzdhTSVQe4luAd2YmzCXLgdfhyPTidR6MtmvE3eW7YW8Dwn00jIiwJ94nwgAdB464kwauhZ8CAf0aHz9CAoOIODUTvqUb2WPtwZC9LYjDfUfP80VXRN1p3bAgXep0flwLjyUO8vPwsG/UjnpFY6WHSXEJwRO/JOyq+fZFx6KZ20hlO6kWOdDTngotbVXeaC+inN1Rbxd8DYjm9y5t+ocp3U+rA4KZWStiTE11Xzt48vyED0jv/2QB0PnUFZXxnatmSv6cB7w74nevQkNZRj8oyBkMLE0MimiK/0ilPtNDI77T96uKSIqMgDiA0h19yTVYcc4NrIfsT73KNs7bRW6poZrd56tPwMcdvpn7ufIF8dI7D+Am6H31qP31tum7w69m7tD7Z0hE+6Y0OwyrjP7zGRmn5m26cf6PcYve/4SP63fDecgxbcQ4qfD2GTv8bUV1JbpiN4Ql6bEHfkLfPIc1FVcW0wDDHjEXnwbaqDkGLbT0HQhLXqoe9uX6zMFuk9Q5nnr2+8Z7QjDOYKilYcz7nvXXpRfrWhRrJfR6BVkj41JVXqLrWeQu2kAy083N2XcuVXPDOXoQWtxbpYTnqz63K8Uoa3F4aacCGVxqVsGAQlZSiHa7KEcFrcV3qAU+LiB0WCfb2xQjk4YG5Ri1cpNoxTdpkbl0dBiyJCpyf685Dic+6dt8pp/xfWX7c8Do5XtaimMlULZzz7t77DNRi2FxlrLPH97Qe3pc+3v3OilLd/VpkyTx+2dHK6GMmRem7HXeORT5e+tqc5WpCuPq8qQhGD7d0HKb5We/Wti65TXfB1y6JKs/F1pPMHdw/LT0z7tE2iP7fcg1F5qJdbj2sJs3PPKVS2s81rGWnfwAKZtBcx8cfQYfe+OR9NkQNdkUH4/mgzoQnsQbT0SMnYFgVcrSHaYH9lkINM63Smc6E5hPNL7ESj5DgJ70qOpgacssQQY6NtUz6vGBoibCig7C39r0EOVsqM7rLaaPbX2I2n3NMLR+3MxRfQCYLiuC/uqCtG4B0HkbQz20JKDJ97+3hDiR//wRP4y/i9KMajrQZ+aIlY31RDkFQADutLT1MCKuiLCfULA/zbu9NaxIGkBndHCvcncXlfCjO/2Eq2/E+66l6iKr/nPk5voHtgdaqFXSC+WDl6qHAkI60uKqYkjuOFuOSExHnD8LQwesYRgnBQ9zNlI5fe/5UmQKvD19MXXUzmvxmAw3NA6pPgWQqinqQFqS68tpK3TUf0g6VdKbN6fIaed4qHvVHvx7aZRlgdlyIcuxDLOOUTpZe4y0L5cz0zo0p+jZ0roPege5R91W/zClcetSOurPPStX1e6Li/PPjH4N86vN/Eh52Pv/g+nQy9HDIHEROeCU5c4n8MDm5SfJpNSfFsL+iaD8txxp2r8SmXnxBJzsvAcsb0SlYLZqxP4OOyw3LvO+Rw6O/m5fmzuHuDu13znpDXxWc6vc+L/XD/GasAM52O7pTgfaznSZNT6X38nOSa1/fmO2tkRusaje5SfJpNlR7AejA18cewr+iYOxM3Tx3axBc8Ht6N3WFQHdHOYDgD6WieSZxECON4JIBJwHJl8u7c/D8Y/aJu+k2jm3ZZsm44LjuOZQc8AkJeXR4RvBJPummSbL3dsvXmyBYX4OTObLYVFveUfgMH+3NPH3it5pRi+y7P0/DQ0jzcaICgGelka5++OwOev2XqJbDHW5aKHQrAytpTjm2HrzFZTA5S7jFmLb29/wM0+Jtl60qC1qL7NoWDpmQF33qPMv96l1SwFdWNx3vVjxc+HRgMaL2XYSVvC4ppNVlfnwW39fuTExC1FowGNN3gqV9Ewas/bzz8Qt6xbtvguLCxk0aJFVFVVodfrWblyJd26dXN+BSe2gcZE80OfbsrJTLcPUmKqS5SCxPHQqOMhUt8Q+0lVVyuUC7w7rstNY4/19rcXOoYa5cLvllht7XdQEWg5QQnw1IF/pEPsRYfEW9xi3lNnPwRsqFZOkgL7uhyXcez1qr8Cl8+3Egs+V05DZbA93/rLlhOq2uDlZz/0XX8ZKr91eOsW+Xr72w8P1lVBZTu3svfyh+AYANwbqpWTqswmMBsdTuyyPKxXdgDL9/ava2OsJ3f5RdrHdFaehdO7ro2xPgK72Xtby/6tjFk1m4gs+g4uf6ScwGeNDe2ujAEGuHgc8v7/tbmaLD8je8OgOUrshTw4sMo+z/awLNt5gL1n7+w/leEWpubr7VlbDQc0yudK/6MSW5ADf53a9vbtkQb3vWPJ4TD8bVr7sdbi+8p38OW7bcf6R2E7JtkpDDpFOFwuL7h5D3VoD4f3SIdnyp077OjlRG+dEEIIoYJbtvh+9tlnmTJlCpmZmWzbto1nnnmGt956y/kV7HgK6lq5pXvsOHvxXXQE3r+/7XXEjoMpf1Wenz8E793nXOy3+5rF3g2wy7nY9te73/nYswfajO0JUOwY+8/vsd7vEXvuM6djO1UchX883U7seJjyvvL8uzx4f0r7sdbiu+QryJ3ffqyt+D4Ju7MBiAI42UqstfiuOgeHX2t7vfVV9uK7pkQplNvieN3Ruirld60F2+l+lp0VQBkTaf3pYenhc/cCD63y09/hqhn+tyljna1XirDFeimvOfYARvaFzDX2+dYYD29l3bpgOKNc0ow774EF37T92Ry537LNlRBCiJ+RW/K/WXl5OSdOnODNN98EIC0tjeXLl1NRUUFQUNB1lraIy4CmK9f2dlrO7gaUk0hix9vn0aJXNLyXPdY7QOmhbDXWDPrb7bGePsrJLGYTmIwYGhocbvjjppysYovVKYf8HTmekOMXaX+u9VXu3mQPbL6M9Ux7UE7wCevZauzVujp0juNCvfwg3OGyaS05fjYvv+aXWGuZh+PJV17+9t7q1gTaTyRr8vSH25LsRxPcNEoPqfUIQ4TDd+EXrhSSzY5YODwc31PfRTnpp2WM9RHm0DMbfCcMXQBuGooulhB1W2f7lSrcNM1P+AnvCeNfstzu1L3Fet2bF7639YP/ess+r9ln1Ci9xFZdk+HhfzjEKp/vxDcn6Xl3P8vQDYu7xsAzlco6rqdzItz/3vXjrNssoZ0edQDKnVuXEEIIcYtxM5tbHvf/6Tt+/DgLFy7kgw8+sL02YcIEXnrpJeLj49td1mAwcPz48R87RSGEEEIIcQvo1avX97or+i3Z8/1D+L4b8seUl5dHorNn9btAR8sHOl5Okk/7Olo+0PFyknza19HygY6Xk+TTvo6WD3S8nCSf9t1oh60Tx5t/eiIjIykpKcFoNAJgNBopLS0lMjLyOksKIYQQQgjx47kli+/g4GDi4uLIzc0FIDc3l7i4OOfHewshhBBCCPEjuGWHnTz33HMsWrSItWvX4u/vz8qVK9VOSQghhBBC/MzdssV3TEwMmzZtUjsNIYQQQgghbG7JYSdCCCGEEEJ0RFJ8CyGEEEII4SJSfAshhBBCCOEiUnwLIYQQQgjhIlJ8CyGEEEII4SJSfAshhBBCCOEiUnwLIYQQQgjhIlJ8CyGEEEII4SJSfAshhBBCCOEiUnwLIYQQQgjhIlJ8CyGEEEII4SJSfAshhBBCCOEiHmon0NGYzWYAGhoaVM6kOYPBoHYKzXS0fKDj5ST5tK+j5QMdLyfJp30dLR/oeDlJPu3raPlAx8tJ8mmbtVa01o7OcjN/3yVucdXV1Zw8eVLtNIQQQgghxE9AbGwsfn5+TsdL8d2CyWSitrYWT09P3Nzc1E5HCCGEEEJ0QGazmcbGRnx9fdFonB/JLcW3EEIIIYQQLiInXAohhBBCCOEiUnwLIYQQQgjhIlJ8CyGEEEII4SJSfAshhBBCCOEiUnwLIYQQQgjhIlJ8CyGEEEII4SJSfAshhBBCCOEiUnxbrFy5ktTUVLp3794h7nBZWVnJjBkzGDt2LOnp6fzmN7+hoqJC7bSYPXs2GRkZZGVlMWXKFAoKCtROCYDVq1d3iO8uNTWVcePGkZmZSWZmJvv27VM1H4PBwLPPPsuYMWNIT0/n6aefVjWfCxcu2LZNZmYmqampDBgwQNWcdu/eTVZWFpmZmaSnp7Njxw5V8/n000+59957SU9PZ+rUqZw/f96l799WW1hYWMjkyZMZO3YskydP5ttvv1U9J7Xa7dbeV802u63toFZ7fb3vRY32uq2c1Gqz28pHrTa7tXzUbK/b2j5qttdt5XRDbbZZmM1ms/nw4cPmoqIi88iRI83ffPON2umYKysrzQcPHrRNv/DCC+Ynn3xSxYwUV65csT3/+OOPzVlZWSpmozh+/Lh5+vTp5hEjRqj+3XWU3x+r5cuXm7Ozs80mk8lsNpvNly5dUjmj5lasWGFeunSpau9vMpnMSUlJtu+soKDA3LdvX7PRaFQln6qqKvOAAQPMZ86cMZvNZvPWrVvNDz/8sEtzaKstnDZtmnnr1q22vKZNm6Z6Tmq12629r5ptdlvbQa32ur3vRa32uq2c1Gqz28pHrTbbmb8lV7bXreWjdnvdWk432mZLz7dFUlISkZGRaqdho9frGThwoG26b9++FBUVqZiRws/Pz/a8pqYGNzc3FbOBhoYGli1bxrPPPqt6Lh1NbW0tW7du5bHHHrNtm5CQEJWzsmtoaCAnJ4df/OIXquah0Wiorq4GoLq6mrCwsO91m+Af0tmzZwkJCSE6OhqA4cOHs3//fpce9WqtLSwvL+fEiROkpaUBkJaWxokTJ1yWV1vts1rtdmvvq2ab3dZ2UKu9bisfNdvrjvY/vrV81Gyzr7d9XN1et5WPmu11azndaJvt8aNlKX4wJpOJ9957j9TUVLVTAWDJkiUcOHAAs9nM66+/rmouf/zjH8nIyKBLly6q5uFowYIFmM1mEhMT+e1vf4u/v78qeZw/fx69Xs/q1as5dOgQvr6+PPbYYyQlJamST0u7du0iPDyc+Ph41XJwc3Pj5ZdfZvbs2eh0Ompra9mwYYNq+URHR1NWVsbRo0fp3bs3OTk5ABQXFxMUFKRaXsXFxYSHh+Pu7g6Au7s7YWFhqufVUXWkNlva6+uTNvv6pL1u3Y222dLz/ROwfPlydDodU6dOVTsVALKzs/n00095/PHHefHFF1XLIz8/n2PHjjFlyhTVcmjpnXfeYfv27WzevBmz2cyyZctUy6WpqYnz58/Ts2dPtmzZwoIFC5g7dy41NTWq5eRo8+bNqvd6NzU1sWHDBtauXcvu3btZt24djz/+OLW1tark4+fnx6pVq3j++eeZNGkS5eXl+Pv74+Eh/SQ/JR2pzZb2un3SZjtH2uvW3WibLcV3B7dy5UrOnj3Lyy+/rNqh8LZkZWVx6NAhKisrVXn/w4cPc+bMGe655x5SU1O5ePEi06dPZ//+/arkA9gOSWm1WqZMmcKRI0dUyyUqKgoPDw/bUIE+ffoQGBhIYWGhajlZlZSUcPjwYdLT01XNo6CggNLSUhITEwFITEzEx8eH06dPq5bT4MGDee+999iyZQtTp06lvr5e9Z7CyMhISkpKMBqNABiNRkpLSzvUYfyOoqO22dJet07a7OuT9rp9N9Jmd5yWQVxj1apVHD9+nDVr1qDVatVOh9raWoqLi23Tu3btIiAgAL1er0o+jzzyCPv372fXrl3s2rWLiIgINm7cSEpKiir5XL161TYWzWw28+GHHxIXF6dKLgBBQUEMHDiQAwcOAMrVKsrLy7n99ttVy8nq73//O8OHDycwMFDVPCIiIrh48SJnzpwB4PTp05SVldG1a1fVcrp06RKgDF34wx/+wH333YdOp1MtH4Dg4GDi4uLIzc0FIDc3l7i4OBly0kJHarOlvb4+abOdI+11+26kzXYzm81mVyTX0a1YsYIdO3ZQVlZGYGAger2eDz74QLV8Tp06RVpaGt26dcPb2xuAzp07s2bNGtVyKisrY/bs2dTV1aHRaAgICGDhwoWqjgFzlJqayvr164mNjVXl/c+fP8/cuXMxGo2YTCZiYmJ46qmnCAsLUyUfa06LFy+mqqoKDw8P5s+fz/Dhw1XLx2rs2LEsWbKEYcOGqZ0K27dv57XXXrOd4DRv3jxGjRqlWj5LlizhyJEjNDY2MmTIEBYvXoyXl5fL3r+ttvD06dMsWrSIK1eu4O/vz8qVK7njjjtUzUmtdru193355ZdVa7Nby+fPf/6zau21M9+Lq9vr1nJav369am12W9tIrTa7ve9Mjfa6rXzUbK/byulG2mwpvoUQQgghhHARGXYihBBCCCGEi0jxLYQQQgghhItI8S2EEEIIIYSLSPEthBBCCCGEi0jxLYQQQgghhItI8S2EEOJ7W79+PUuWLFE7jWamTZvGpk2b1E5DCCHaJfcsFkKIH0BOTg5vvvkmhYWF+Pr60qNHD2bOnElSUpLaqd20Q4cO8bvf/Y69e/faXps5c6aKGQkhxE+XFN9CCHGT3nzzTf70pz+xdOlSUlJS8PT0ZN++fezcufOWKL6FEEL8cGTYiRBC3ITq6mpeeeUVnnnmGcaMGYNOp8PT05PU1FQWLlwIQENDA9nZ2aSkpJCSkkJ2djYNDQ2A0qs8bNgw3njjDQYNGkRKSgqbN2+2rX/Pnj1MmDCBhIQEhg4dysaNGwHYsmUL999/f7NcunfvztmzZwFYtGgRzz33HL/+9a9JSEjgvvvu49KlS2RnZ9O/f3/GjRvHiRMnbMumpqayYcMGJkyYQP/+/XnyyScxGAxcvXqVGTNmUFpaSkJCAgkJCZSUlPDqq6+yYMEC2/I7d+5k4sSJJCUlMW3aNE6fPt1s3Rs3biQ9PZ3ExETmz5+PwWC4Zls2NDSQlJTEyZMnba9VVFTQu3dvysvLuXz5Mo8++ijJycn079+fRx99lIsXL7b6vbTM78KFC3Tv3p2mpibb97Z48WJSUlIYOnQoq1atwmg0tvdVCyHED0KKbyGEuAn5+fkYDAZGjx7dZsy6dev48ssv2bZtG9u3b+fYsWOsXbvWNr+srIzq6mr27t1LdnY2y5Yt4/Lly4Byu/lly5aRn59Pbm4uycnJTuf20UcfMX/+fA4ePIhWq2Xy5MnEx8dz8OBBxo4dy/PPP98sPicnh40bN/Lxxx9TWFjI2rVr0el0vPbaa4SFhZGfn09+fj7h4eHNlissLOSJJ55g8eLFfPbZZwwbNoyZM2fadjCsubz++uvs3LmTb775hi1btlyTr1arZfTo0c1uRf7RRx/Rv39/goODMZlMTJo0id27d7N79268vLxYtmyZ09vD0cKFC/Hw8GDHjh1s3bqVAwcOyHhxIYRLSPEthBA3oaqqisDAQDw82h7Fl5OTw5w5cwgODiYoKIg5c+awfft223wPDw/mzJmDp6cnw4cPR6fTUVhYaJv373//m5qaGgICAoiPj3c6t9GjR9OrVy+8vLwYPXo0Xl5eZGVl4e7uzoQJEygoKGgW/8ADDxAZGYler2fWrFnNiuD2fPjhhwwfPpwhQ4bg6enJ9OnTqa+vJz8/3xYzbdo0wsPD0ev1jBw58pr3tkpPTyc3N7fZtktPTwcgMDCQsWPH4uPjQ6dOnZg1axaHDx92entYlZWVsXfvXhYvXoxOpyM4OJiHHnrI6c8rhBA3Q8Z8CyHETdDr9VRWVtLU1NRmAV5aWkpUVJRtOioqitLS0mbrcFzWx8eHq1evAvDKK6+wbt06fv/739O9e3eeeOIJEhISnMotODjY9tzb25uQkJBm09b3sIqMjGwzx/a0/HwajYbIyEhKSkpsr4WGhjb7fG2tOzk5GYPBwJdffklISAhff/01o0aNAqCuro7nn3+effv22Y4M1NbWYjQacXd3dypXgKKiIpqamkhJSbG9ZjKZmn1+IYT4sUjxLYQQNyEhIQEvLy8++eQTxo0b12pMWFgYRUVF3HXXXQAUFxcTFhbm1Pp79+7NunXraGxs5J133mH+/Pns2bMHHx8f6uvrbXGXLl266c9SXFxse15UVGTL0c3Nrd3lwsLCmo3TNpvNFBcXXzM8xRkajYZx48aRm5tLSEgII0aMoFOnTgC88cYbFBYW8re//Y3Q0FAKCgrIysrCbDZfs56W26esrMz2PCIiAq1Wy8GDB9s9YiGEED8GGXYihBA3wc/Pj3nz5rFs2TI++eQT6urqaGxsZM+ePbz44osATJw4kXXr1lFRUUFFRQVr1qyxDaVoT0NDA9u3b6e6uhpPT098fX1tPbw9evTg1KlTFBQUYDAYePXVV2/6s7z77rtcvHiRqqoq28mXoPSgV1VVUV1d3epy48ePZ8+ePXz22Wc0NjbyxhtvoNVqne6hbyk9PZ2PPvqInJwc0tLSbK/X1tbi5eWFv78/VVVVrF69us11xMXFcfjwYYqKiqiurmbDhg22eWFhYQwZMoQXXniBmpoaTCYT586d4/PPP7+hfIUQ4vuQ4lsIIW7Sr371KxYtWsTatWsZNGgQI0aM4J133rENl5g9eza9evUiIyODjIwM4uPjmT17tlPr3rZtG6mpqfTr14/333/fVtBHR0czZ84cHnroIcaMGUNiYuJNf460tDQefvhhRo0aRZcuXZg1axYAMTExTJw4kVGjRpGUlNRsOAnAHXfcwUsvvcTy5ctJTk5m9+7drF+/Hq1We0N59OnTxzY0ZdiwYbbXH3zwQQwGA8nJyUyePJmhQ4e2uY4hQ4YwYcIEMjIymDRpEiNHjmw2/8UXX6SxsdF2dZd58+b9IEcPhBDietzMrR2vE0II8bOSmprKihUrGDx4sNqpCCHELU16voUQQgghhHARKb6FEEIIIYRwERl2IoQQQgghhItIz7cQQgghhBAuIsW3EEIIIYQQLiLFtxBCCCGEEC4ixbcQQgghhBAuIsW3EEIIIYQQLiLFtxBCCCGEEC7yfxkViL1yOapMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots()\n",
    "\n",
    "sns.lineplot(data=df_f, linewidth=2, palette='tab10')\n",
    "\n",
    "ax.set_xlim(df_f.index[0], df_f.index[-1])\n",
    "ax.set_xticks(range(df_f.index[0], df_f.index[-1] + 1))\n",
    "\n",
    "ax.set(ylabel='Frequency')\n",
    "ax.legend(title='Cluster')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we also want to display the data in a **percentage** format, we define a function that will change the occurence data into percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_freq_perc(df):\n",
    "    \"\"\"\n",
    "    The function changes occurence data of each consumption value to percentages.\n",
    "    \n",
    "    Args:\n",
    "        df [pandas.DataFrame]: Occurrence data of each consumption value per cluster.\n",
    "        \n",
    "    Returns:\n",
    "        [pandas.DataFrame]: Occurrence data of each consumption value per cluster in percentages.\n",
    "    \"\"\"\n",
    "    \n",
    "    for i in df:\n",
    "        df[i] = df[i] / df[i].sum()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fp = get_freq_perc(df_f.copy(deep=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots()\n",
    "\n",
    "sns.lineplot(data=df_fp, linewidth=2, palette='tab10')\n",
    "\n",
    "ax.set_xlim(df_fp.index[0], df_fp.index[-1])\n",
    "ax.set_xticks(range(df_fp.index[0], df_fp.index[-1] + 1))\n",
    "\n",
    "ax.set(ylabel='Frequency percentage')\n",
    "ax.legend(title='Cluster')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our next analysis is to **count** the occurrence of consumption `0` and all other consumption values per cluster and display the results on a `bar` graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_zero_consumption_freq(df):\n",
    "    \"\"\"\n",
    "    The function counts zero and all other consumption values per cluster.\n",
    "    \n",
    "    Args:\n",
    "        df [pandas.DataFrame]: Occurrence data of each consumption value per cluster.\n",
    "        \n",
    "    Returns:\n",
    "        [pandas.DataFrame]: Occurrence data of zero and all other consumption values per cluster.\n",
    "    \"\"\"\n",
    "    \n",
    "    df_z = pd.concat([df.iloc[0].to_frame(), df.iloc[1:,].sum()], axis=1)\n",
    "    df_z.columns = ['Zero consumption', 'Other consumptions']\n",
    "    df_z.index.names = ['Cluster']\n",
    "    return df_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_z = get_zero_consumption_freq(get_consumption_freq(consumptions_c, n_clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zero consumption</th>\n",
       "      <th>Other consumptions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57229</td>\n",
       "      <td>132068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153569</td>\n",
       "      <td>20491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108653</td>\n",
       "      <td>53036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Zero consumption  Other consumptions\n",
       "Cluster                                      \n",
       "0                   57229              132068\n",
       "1                  153569               20491\n",
       "2                  108653               53036"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_z.plot(kind='barh')\n",
    "plt.xlabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we also change obtained data into percentages and plot it once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zp = get_freq_perc(df_z.copy(deep=True).T)\n",
    "df_zp = df_zp.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zero consumption</th>\n",
       "      <th>Other consumptions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.302324</td>\n",
       "      <td>0.697676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.882276</td>\n",
       "      <td>0.117724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.671988</td>\n",
       "      <td>0.328012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Zero consumption  Other consumptions\n",
       "Cluster                                      \n",
       "0                0.302324            0.697676\n",
       "1                0.882276            0.117724\n",
       "2                0.671988            0.328012"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_zp.plot(kind='barh')\n",
    "plt.xlabel('Frequency percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Cluster device types analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also interesting to check which **types of devices** were most often classified into which cluster. For this purpose, we define the function below and plot data on the `Venn` graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unique_cluster_tags(df, n_clusters):\n",
    "    \"\"\"\n",
    "    The function obtains number of each device type occurence per cluster.\n",
    "    \n",
    "    Args:\n",
    "        df         [pandas.DataFrame]: Data frame of vector names and their clusters.\n",
    "        n_clusters [int]:              Number of clusters.\n",
    "        \n",
    "    Returns:\n",
    "        [dict]: Number of each device type occurence per cluster.\n",
    "    \"\"\"\n",
    "    \n",
    "    return {i:Counter([j.rsplit('_')[1] for j in get_cluster_tags(df, i)]) for i in range(0, n_clusters)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = get_unique_cluster_tags(df_tc, n_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "venn3([tags[i].keys() for i in range(0, n_clusters)], ['Cluster ' + str(i) for i in range(0, n_clusters)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below changes previously obtained dictionary into a **data frame**. We will use the data frame to plot the data on `bar` graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tags_freq(tags):\n",
    "    \"\"\"\n",
    "    The function creates a data frame from number of each device type occurence per cluster dictionary.\n",
    "    \n",
    "    Args:\n",
    "        tags [dict]: Number of each device type occurence per cluster.\n",
    "        \n",
    "    Returns:\n",
    "        [pandas.DataFrame]: Number of each device type occurence per cluster.\n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.concat([pd.DataFrame.from_dict(tags[i], orient='index') for i in range(0, n_clusters)], axis=1)\n",
    "    df.columns = [str(i) for i in range(0, n_clusters)]\n",
    "    df.fillna(0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t = get_tags_freq(tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_t.plot.bar(figsize=(30, 10), width=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we change obtained data into percentages and plot them once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tp = get_freq_perc(df_t.copy(deep=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tp.plot.bar(figsize=(30, 10), width=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the devices are mostly arranged in clusters as follows:\n",
    "\n",
    "**Cluster** `0`:\n",
    "- printer\n",
    "- digitaltvreceiver\n",
    "- solarthermalsystem\n",
    "- multimediacenter\n",
    "- beantocupcoffeemaker\n",
    "- usbhub\n",
    "- router\n",
    "- usbharddrive\n",
    "- pc-desktop\n",
    "- refrigerator\n",
    "- ethernetswitch\n",
    "- alarmclock\n",
    "- waterboiler\n",
    "- remotedesktop\n",
    "- charger-smartphone\n",
    "- pc-laptop\n",
    "\n",
    "**Cluster** `1`:\n",
    "- amplifier\n",
    "- microwaveoven\n",
    "- dishwasher\n",
    "- washingmachine\n",
    "- toaster\n",
    "- charger-psp\n",
    "- vacuumcleaner\n",
    "- freezer\n",
    "- cookingstove\n",
    "- waterkettle\n",
    "- breadcutter\n",
    "- waterfountain\n",
    "- iron\n",
    "- subwoofer\n",
    "\n",
    "\n",
    "**Cluster** `2`:\n",
    "- monitor-tft\n",
    "- amplifier\n",
    "- coffeemaker\n",
    "- tv-lcd\n",
    "- usbhub\n",
    "- monitor-crt\n",
    "- lamp\n",
    "- tv-crt\n",
    "- dvdplayer\n",
    "- laundrydryer\n",
    "- freezer\n",
    "- subwoofer\n",
    "- playstation3\n",
    "- projector\n",
    "- cdplayer\n",
    "- xmaslights\n",
    "- videoprojector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3_classification'></a>\n",
    "\n",
    "## 3. Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The classification will be performed by predicting the type of device, according to a random pattern of its consumption.**\n",
    "\n",
    "For this purpose, we will use the `Doc2Vec` model, as we are looking for the most similar vector in the model - in our case, vector represents the most similar device. First, we will create a new data set, convert each random consumption into a new vector and find its most similar device. The results will be evaluated using `top-1` and `top-5` accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Preparing samples data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define **new data set path**, which name is `samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_path = 'datasets/samples/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below is used to **create** a new `samples` data set. In general, consumption data is first read and then, to obtain a random sample, some percentage of first and last values is removed from it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_to_samples(consumptions_path, samples_path, drop_perc=0.2):\n",
    "    \"\"\"\n",
    "    The function reads, prepares samples and writes them into a file.\n",
    "    \n",
    "    Args:\n",
    "        consumptions_path [str]:   Path to read consumptions from.\n",
    "        samples_path      [str]:   Path to write samples to.\n",
    "        drop_perc         [float]: Percent of last consumption data to drop.\n",
    "    \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    for dirname in os.listdir(consumptions_path):\n",
    "            create_dirs(samples_path + dirname)\n",
    "            \n",
    "            for filename in os.listdir(consumptions_path + dirname):\n",
    "                with open(consumptions_path + dirname + os.sep + filename, 'r') as f:\n",
    "                    data = list(filter(str.strip, [r for r in csv.reader(f, delimiter=' ')][0]))\n",
    "                \n",
    "                with open(samples_path + dirname + os.sep + filename, 'w') as f:\n",
    "                    f.write(' '.join(dropout_first_and_last(data, drop_perc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropout_first_and_last(array, drop_perc):\n",
    "    \"\"\"\n",
    "    The function drops given percent of first and last consumption data.\n",
    "    \n",
    "    Args:\n",
    "        array     [numpy.ndarray]: Consumption data to drop from.\n",
    "        drop_perc [float]:         Percent of first and last consumption data to drop.\n",
    "        \n",
    "    Returns:\n",
    "        [numpy.ndarray]: Consumption data on which dropping was performed.\n",
    "    \"\"\"\n",
    "    \n",
    "    return array[int(round(len(array) * drop_perc)):-int(round(len(array) * drop_perc))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_to_samples(consumptions_path, samples_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Getting similarities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Similarities** will be obtained via the function defined below, which uses the `most_similar` function of the `Doc2Vec` model to find the most similar random consumption devices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_similarities(model, path, n=50):\n",
    "    \"\"\"\n",
    "    The function obtains most similar random consumption devices.\n",
    "    \n",
    "    Args:\n",
    "        model [gensim.Doc2Vec]: Doc2Vec model.\n",
    "        path  [str]:            Path to read samples from.\n",
    "        n     [int]:            Number of similarities to obtain.\n",
    "    \n",
    "    Returns:\n",
    "        [dict]: Most similar random consumption devices.\n",
    "    \"\"\"\n",
    "    \n",
    "    similarities = {}\n",
    "    \n",
    "    for i in os.listdir(path):\n",
    "        for j in os.listdir(path + i):\n",
    "            vector = model.infer_vector(file_to_string_array(path + i + os.sep + j))\n",
    "            similar = model.docvecs.most_similar(positive=[vector], topn=n)\n",
    "            similarities[j[:-4]] = [k[0].rsplit('_')[1] for k in similar]\n",
    "            \n",
    "    return similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarities = get_similarities(d2v_model, samples_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The obtained similarity results will be evaluated with a new function that acquires **`top-n` accuracy**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_n_accuracy(similarities, n):\n",
    "    \"\"\"\n",
    "    The function obtains accuracy of similarities.\n",
    "    \n",
    "    Args:\n",
    "        similarities [dict]: Most similar random consumption devices.\n",
    "        n            [int]:  Type of accuracy to obtain.\n",
    "    \n",
    "    Returns:\n",
    "        [list]: Bools that indicate if device classification is correct.\n",
    "    \"\"\"\n",
    "    \n",
    "    return [True if i.rsplit('_')[1] in similarities[i][:n] else False for i in similarities]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_1_accuracy = get_top_n_accuracy(similarities, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_accuracy = get_top_n_accuracy(similarities, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Printing similarity results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we **review** the acquired similarities and **print** the `top-1` and `top-5` accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_n_similarities(similarities, n):\n",
    "    \"\"\"\n",
    "    The function prints a certain number of most similar random consumption devices.\n",
    "    \n",
    "    Args:\n",
    "        similarities [dict]: Most similar random consumption devices.\n",
    "        n            [int]:  Number of device similarities to print.\n",
    "    \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    [print(i + '\\n' + str(similarities[i][:n]) + '\\n') for i in similarities]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_accuracy(accuracy):\n",
    "    \"\"\"\n",
    "    The function calculates and prints classification accuracy.\n",
    "    \n",
    "    Args:\n",
    "        accuracy [list]: Bools that indicate if device classification is correct.\n",
    "    \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"{0:20} {1}\".format('True predictions:', accuracy.count(True)))\n",
    "    print(\"{0:20} {1}\".format('False predictions:', accuracy.count(False)))\n",
    "    print(\"{0:20} {1}\".format('Accuracy:', round(accuracy.count(True) / len(accuracy), 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we print **first** similarities and **`top-1` accuracy**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_waterboiler\n",
      "['waterboiler']\n",
      "\n",
      "2_subwoofer\n",
      "['subwoofer']\n",
      "\n",
      "3_subwoofer\n",
      "['waterkettle']\n",
      "\n",
      "0_subwoofer\n",
      "['amplifier']\n",
      "\n",
      "1_subwoofer\n",
      "['microwaveoven']\n",
      "\n",
      "2_digitaltvreceiver\n",
      "['router']\n",
      "\n",
      "3_digitaltvreceiver\n",
      "['ethernetswitch']\n",
      "\n",
      "0_digitaltvreceiver\n",
      "['multimediacenter']\n",
      "\n",
      "1_digitaltvreceiver\n",
      "['digitaltvreceiver']\n",
      "\n",
      "4_digitaltvreceiver\n",
      "['digitaltvreceiver']\n",
      "\n",
      "2_dvdplayer\n",
      "['dvdplayer']\n",
      "\n",
      "0_dvdplayer\n",
      "['dvdplayer']\n",
      "\n",
      "1_dvdplayer\n",
      "['waterkettle']\n",
      "\n",
      "23_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "3_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "5_pc-desktop\n",
      "['monitor-tft']\n",
      "\n",
      "9_pc-desktop\n",
      "['lamp']\n",
      "\n",
      "1_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "8_pc-desktop\n",
      "['monitor-tft']\n",
      "\n",
      "13_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "7_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "20_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "18_pc-desktop\n",
      "['videoprojector']\n",
      "\n",
      "0_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "22_pc-desktop\n",
      "['monitor-tft']\n",
      "\n",
      "11_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "17_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "10_pc-desktop\n",
      "['amplifier']\n",
      "\n",
      "14_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "2_pc-desktop\n",
      "['ethernetswitch']\n",
      "\n",
      "12_pc-desktop\n",
      "['dishwasher']\n",
      "\n",
      "6_pc-desktop\n",
      "['monitor-tft']\n",
      "\n",
      "25_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "21_pc-desktop\n",
      "['pc-laptop']\n",
      "\n",
      "19_pc-desktop\n",
      "['lamp']\n",
      "\n",
      "16_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "24_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "15_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "4_pc-desktop\n",
      "['pc-desktop']\n",
      "\n",
      "3_videoprojector\n",
      "['videoprojector']\n",
      "\n",
      "2_videoprojector\n",
      "['videoprojector']\n",
      "\n",
      "4_videoprojector\n",
      "['videoprojector']\n",
      "\n",
      "1_videoprojector\n",
      "['laundrydryer']\n",
      "\n",
      "0_videoprojector\n",
      "['videoprojector']\n",
      "\n",
      "6_videoprojector\n",
      "['tv-crt']\n",
      "\n",
      "5_videoprojector\n",
      "['videoprojector']\n",
      "\n",
      "11_lamp\n",
      "['waterkettle']\n",
      "\n",
      "26_lamp\n",
      "['lamp']\n",
      "\n",
      "4_lamp\n",
      "['lamp']\n",
      "\n",
      "31_lamp\n",
      "['waterkettle']\n",
      "\n",
      "9_lamp\n",
      "['videoprojector']\n",
      "\n",
      "10_lamp\n",
      "['pc-laptop']\n",
      "\n",
      "5_lamp\n",
      "['lamp']\n",
      "\n",
      "19_lamp\n",
      "['waterkettle']\n",
      "\n",
      "7_lamp\n",
      "['lamp']\n",
      "\n",
      "1_lamp\n",
      "['lamp']\n",
      "\n",
      "6_lamp\n",
      "['lamp']\n",
      "\n",
      "13_lamp\n",
      "['lamp']\n",
      "\n",
      "21_lamp\n",
      "['waterkettle']\n",
      "\n",
      "14_lamp\n",
      "['lamp']\n",
      "\n",
      "16_lamp\n",
      "['lamp']\n",
      "\n",
      "23_lamp\n",
      "['lamp']\n",
      "\n",
      "3_lamp\n",
      "['monitor-crt']\n",
      "\n",
      "24_lamp\n",
      "['waterkettle']\n",
      "\n",
      "27_lamp\n",
      "['waterkettle']\n",
      "\n",
      "17_lamp\n",
      "['monitor-crt']\n",
      "\n",
      "25_lamp\n",
      "['waterkettle']\n",
      "\n",
      "2_lamp\n",
      "['pc-laptop']\n",
      "\n",
      "29_lamp\n",
      "['ethernetswitch']\n",
      "\n",
      "22_lamp\n",
      "['waterkettle']\n",
      "\n",
      "20_lamp\n",
      "['waterkettle']\n",
      "\n",
      "15_lamp\n",
      "['lamp']\n",
      "\n",
      "0_lamp\n",
      "['playstation3']\n",
      "\n",
      "30_lamp\n",
      "['monitor-tft']\n",
      "\n",
      "32_lamp\n",
      "['lamp']\n",
      "\n",
      "28_lamp\n",
      "['waterkettle']\n",
      "\n",
      "12_lamp\n",
      "['lamp']\n",
      "\n",
      "18_lamp\n",
      "['lamp']\n",
      "\n",
      "8_lamp\n",
      "['waterkettle']\n",
      "\n",
      "5_toaster\n",
      "['waterkettle']\n",
      "\n",
      "1_toaster\n",
      "['waterkettle']\n",
      "\n",
      "0_toaster\n",
      "['waterkettle']\n",
      "\n",
      "4_toaster\n",
      "['microwaveoven']\n",
      "\n",
      "3_toaster\n",
      "['waterkettle']\n",
      "\n",
      "2_toaster\n",
      "['waterkettle']\n",
      "\n",
      "6_microwaveoven\n",
      "['multimediacenter']\n",
      "\n",
      "24_microwaveoven\n",
      "['monitor-crt']\n",
      "\n",
      "4_microwaveoven\n",
      "['multimediacenter']\n",
      "\n",
      "16_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "26_microwaveoven\n",
      "['microwaveoven']\n",
      "\n",
      "14_microwaveoven\n",
      "['coffeemaker']\n",
      "\n",
      "21_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "17_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "18_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "19_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "23_microwaveoven\n",
      "['microwaveoven']\n",
      "\n",
      "7_microwaveoven\n",
      "['microwaveoven']\n",
      "\n",
      "9_microwaveoven\n",
      "['microwaveoven']\n",
      "\n",
      "1_microwaveoven\n",
      "['refrigerator']\n",
      "\n",
      "0_microwaveoven\n",
      "['microwaveoven']\n",
      "\n",
      "13_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "22_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "25_microwaveoven\n",
      "['microwaveoven']\n",
      "\n",
      "10_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "15_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "20_microwaveoven\n",
      "['waterkettle']\n",
      "\n",
      "8_microwaveoven\n",
      "['coffeemaker']\n",
      "\n",
      "5_microwaveoven\n",
      "['multimediacenter']\n",
      "\n",
      "12_microwaveoven\n",
      "['microwaveoven']\n",
      "\n",
      "2_microwaveoven\n",
      "['refrigerator']\n",
      "\n",
      "11_microwaveoven\n",
      "['dishwasher']\n",
      "\n",
      "3_microwaveoven\n",
      "['multimediacenter']\n",
      "\n",
      "7_pc-laptop\n",
      "['pc-laptop']\n",
      "\n",
      "4_pc-laptop\n",
      "['monitor-tft']\n",
      "\n",
      "10_pc-laptop\n",
      "['waterkettle']\n",
      "\n",
      "0_pc-laptop\n",
      "['pc-laptop']\n",
      "\n",
      "1_pc-laptop\n",
      "['dishwasher']\n",
      "\n",
      "9_pc-laptop\n",
      "['waterkettle']\n",
      "\n",
      "6_pc-laptop\n",
      "['pc-laptop']\n",
      "\n",
      "12_pc-laptop\n",
      "['pc-desktop']\n",
      "\n",
      "3_pc-laptop\n",
      "['pc-laptop']\n",
      "\n",
      "11_pc-laptop\n",
      "['pc-laptop']\n",
      "\n",
      "8_pc-laptop\n",
      "['waterkettle']\n",
      "\n",
      "5_pc-laptop\n",
      "['pc-laptop']\n",
      "\n",
      "2_pc-laptop\n",
      "['digitaltvreceiver']\n",
      "\n",
      "1_usbhub\n",
      "['lamp']\n",
      "\n",
      "0_usbhub\n",
      "['usbhub']\n",
      "\n",
      "39_washingmachine\n",
      "['waterkettle']\n",
      "\n",
      "0_washingmachine\n",
      "['washingmachine']\n",
      "\n",
      "24_washingmachine\n",
      "['waterkettle']\n",
      "\n",
      "7_washingmachine\n",
      "['monitor-crt']\n",
      "\n",
      "22_washingmachine\n",
      "['waterkettle']\n",
      "\n",
      "14_washingmachine\n",
      "['lamp']\n",
      "\n",
      "17_washingmachine\n",
      "['monitor-crt']\n",
      "\n",
      "16_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "19_washingmachine\n",
      "['waterkettle']\n",
      "\n",
      "33_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "6_washingmachine\n",
      "['monitor-tft']\n",
      "\n",
      "29_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "27_washingmachine\n",
      "['monitor-tft']\n",
      "\n",
      "36_washingmachine\n",
      "['lamp']\n",
      "\n",
      "1_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "23_washingmachine\n",
      "['lamp']\n",
      "\n",
      "37_washingmachine\n",
      "['lamp']\n",
      "\n",
      "28_washingmachine\n",
      "['waterkettle']\n",
      "\n",
      "9_washingmachine\n",
      "['lamp']\n",
      "\n",
      "20_washingmachine\n",
      "['amplifier']\n",
      "\n",
      "38_washingmachine\n",
      "['lamp']\n",
      "\n",
      "32_washingmachine\n",
      "['amplifier']\n",
      "\n",
      "15_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "8_washingmachine\n",
      "['waterkettle']\n",
      "\n",
      "31_washingmachine\n",
      "['lamp']\n",
      "\n",
      "4_washingmachine\n",
      "['washingmachine']\n",
      "\n",
      "35_washingmachine\n",
      "['lamp']\n",
      "\n",
      "34_washingmachine\n",
      "['monitor-crt']\n",
      "\n",
      "26_washingmachine\n",
      "['monitor-crt']\n",
      "\n",
      "13_washingmachine\n",
      "['waterkettle']\n",
      "\n",
      "3_washingmachine\n",
      "['amplifier']\n",
      "\n",
      "12_washingmachine\n",
      "['washingmachine']\n",
      "\n",
      "25_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "5_washingmachine\n",
      "['lamp']\n",
      "\n",
      "18_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "2_washingmachine\n",
      "['microwaveoven']\n",
      "\n",
      "40_washingmachine\n",
      "['playstation3']\n",
      "\n",
      "10_washingmachine\n",
      "['dvdplayer']\n",
      "\n",
      "21_washingmachine\n",
      "['pc-laptop']\n",
      "\n",
      "30_washingmachine\n",
      "['lamp']\n",
      "\n",
      "11_washingmachine\n",
      "['solarthermalsystem']\n",
      "\n",
      "2_projector\n",
      "['projector']\n",
      "\n",
      "0_projector\n",
      "['waterkettle']\n",
      "\n",
      "5_projector\n",
      "['waterkettle']\n",
      "\n",
      "4_projector\n",
      "['coffeemaker']\n",
      "\n",
      "1_projector\n",
      "['projector']\n",
      "\n",
      "3_projector\n",
      "['projector']\n",
      "\n",
      "1_beantocupcoffeemaker\n",
      "['beantocupcoffeemaker']\n",
      "\n",
      "0_beantocupcoffeemaker\n",
      "['beantocupcoffeemaker']\n",
      "\n",
      "21_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "16_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "6_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "30_waterkettle\n",
      "['lamp']\n",
      "\n",
      "15_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "1_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "31_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "12_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "5_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "19_waterkettle\n",
      "['microwaveoven']\n",
      "\n",
      "0_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "24_waterkettle\n",
      "['lamp']\n",
      "\n",
      "3_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "2_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "13_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "11_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "28_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "14_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "18_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "32_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "20_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "7_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "26_waterkettle\n",
      "['microwaveoven']\n",
      "\n",
      "10_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "23_waterkettle\n",
      "['dishwasher']\n",
      "\n",
      "22_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "27_waterkettle\n",
      "['washingmachine']\n",
      "\n",
      "25_waterkettle\n",
      "['lamp']\n",
      "\n",
      "8_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "29_waterkettle\n",
      "['lamp']\n",
      "\n",
      "9_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "4_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "17_waterkettle\n",
      "['waterkettle']\n",
      "\n",
      "0_alarmclock\n",
      "['alarmclock']\n",
      "\n",
      "7_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "4_usbharddrive\n",
      "['multimediacenter']\n",
      "\n",
      "1_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "3_usbharddrive\n",
      "['microwaveoven']\n",
      "\n",
      "5_usbharddrive\n",
      "['multimediacenter']\n",
      "\n",
      "0_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "13_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "9_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "2_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "8_usbharddrive\n",
      "['router']\n",
      "\n",
      "12_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "6_usbharddrive\n",
      "['multimediacenter']\n",
      "\n",
      "10_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "11_usbharddrive\n",
      "['usbharddrive']\n",
      "\n",
      "3_router\n",
      "['digitaltvreceiver']\n",
      "\n",
      "8_router\n",
      "['router']\n",
      "\n",
      "6_router\n",
      "['router']\n",
      "\n",
      "4_router\n",
      "['digitaltvreceiver']\n",
      "\n",
      "5_router\n",
      "['ethernetswitch']\n",
      "\n",
      "1_router\n",
      "['router']\n",
      "\n",
      "2_router\n",
      "['multimediacenter']\n",
      "\n",
      "7_router\n",
      "['router']\n",
      "\n",
      "0_router\n",
      "['router']\n",
      "\n",
      "2_solarthermalsystem\n",
      "['solarthermalsystem']\n",
      "\n",
      "1_solarthermalsystem\n",
      "['refrigerator']\n",
      "\n",
      "0_solarthermalsystem\n",
      "['solarthermalsystem']\n",
      "\n",
      "0_monitor-crt\n",
      "['lamp']\n",
      "\n",
      "4_monitor-crt\n",
      "['lamp']\n",
      "\n",
      "7_monitor-crt\n",
      "['monitor-crt']\n",
      "\n",
      "3_monitor-crt\n",
      "['lamp']\n",
      "\n",
      "6_monitor-crt\n",
      "['lamp']\n",
      "\n",
      "1_monitor-crt\n",
      "['monitor-tft']\n",
      "\n",
      "2_monitor-crt\n",
      "['monitor-crt']\n",
      "\n",
      "5_monitor-crt\n",
      "['lamp']\n",
      "\n",
      "1_tv-crt\n",
      "['lamp']\n",
      "\n",
      "4_tv-crt\n",
      "['tv-crt']\n",
      "\n",
      "2_tv-crt\n",
      "['waterkettle']\n",
      "\n",
      "0_tv-crt\n",
      "['microwaveoven']\n",
      "\n",
      "3_tv-crt\n",
      "['monitor-tft']\n",
      "\n",
      "5_tv-crt\n",
      "['washingmachine']\n",
      "\n",
      "0_vacuumcleaner\n",
      "['waterkettle']\n",
      "\n",
      "0_waterfountain\n",
      "['refrigerator']\n",
      "\n",
      "1_waterfountain\n",
      "['waterfountain']\n",
      "\n",
      "2_waterfountain\n",
      "['waterfountain']\n",
      "\n",
      "0_remotedesktop\n",
      "['remotedesktop']\n",
      "\n",
      "6_printer\n",
      "['printer']\n",
      "\n",
      "2_printer\n",
      "['printer']\n",
      "\n",
      "0_printer\n",
      "['printer']\n",
      "\n",
      "1_printer\n",
      "['printer']\n",
      "\n",
      "4_printer\n",
      "['printer']\n",
      "\n",
      "3_printer\n",
      "['printer']\n",
      "\n",
      "5_printer\n",
      "['printer']\n",
      "\n",
      "5_ethernetswitch\n",
      "['ethernetswitch']\n",
      "\n",
      "3_ethernetswitch\n",
      "['pc-desktop']\n",
      "\n",
      "1_ethernetswitch\n",
      "['pc-desktop']\n",
      "\n",
      "2_ethernetswitch\n",
      "['pc-desktop']\n",
      "\n",
      "4_ethernetswitch\n",
      "['pc-desktop']\n",
      "\n",
      "0_ethernetswitch\n",
      "['pc-desktop']\n",
      "\n",
      "0_iron\n",
      "['waterkettle']\n",
      "\n",
      "0_cdplayer\n",
      "['cdplayer']\n",
      "\n",
      "8_refrigerator\n",
      "['monitor-tft']\n",
      "\n",
      "6_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "4_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "0_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "10_refrigerator\n",
      "['monitor-tft']\n",
      "\n",
      "11_refrigerator\n",
      "['waterfountain']\n",
      "\n",
      "15_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "1_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "7_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "5_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "9_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "13_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "18_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "3_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "14_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "2_refrigerator\n",
      "['subwoofer']\n",
      "\n",
      "17_refrigerator\n",
      "['dvdplayer']\n",
      "\n",
      "12_refrigerator\n",
      "['laundrydryer']\n",
      "\n",
      "16_refrigerator\n",
      "['refrigerator']\n",
      "\n",
      "17_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "5_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "19_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "13_monitor-tft\n",
      "['pc-desktop']\n",
      "\n",
      "34_monitor-tft\n",
      "['lamp']\n",
      "\n",
      "46_monitor-tft\n",
      "['lamp']\n",
      "\n",
      "50_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "8_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "12_monitor-tft\n",
      "['waterkettle']\n",
      "\n",
      "41_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "16_monitor-tft\n",
      "['pc-laptop']\n",
      "\n",
      "2_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "27_monitor-tft\n",
      "['lamp']\n",
      "\n",
      "23_monitor-tft\n",
      "['lamp']\n",
      "\n",
      "11_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "43_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "31_monitor-tft\n",
      "['pc-laptop']\n",
      "\n",
      "18_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "25_monitor-tft\n",
      "['monitor-crt']\n",
      "\n",
      "39_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "42_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "36_monitor-tft\n",
      "['waterkettle']\n",
      "\n",
      "32_monitor-tft\n",
      "['amplifier']\n",
      "\n",
      "29_monitor-tft\n",
      "['dishwasher']\n",
      "\n",
      "47_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "24_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "38_monitor-tft\n",
      "['waterkettle']\n",
      "\n",
      "40_monitor-tft\n",
      "['waterkettle']\n",
      "\n",
      "45_monitor-tft\n",
      "['amplifier']\n",
      "\n",
      "35_monitor-tft\n",
      "['waterkettle']\n",
      "\n",
      "48_monitor-tft\n",
      "['tv-crt']\n",
      "\n",
      "9_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "14_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "26_monitor-tft\n",
      "['lamp']\n",
      "\n",
      "0_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "10_monitor-tft\n",
      "['amplifier']\n",
      "\n",
      "49_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "1_monitor-tft\n",
      "['pc-desktop']\n",
      "\n",
      "28_monitor-tft\n",
      "['waterkettle']\n",
      "\n",
      "33_monitor-tft\n",
      "['amplifier']\n",
      "\n",
      "15_monitor-tft\n",
      "['refrigerator']\n",
      "\n",
      "37_monitor-tft\n",
      "['lamp']\n",
      "\n",
      "6_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "3_monitor-tft\n",
      "['projector']\n",
      "\n",
      "44_monitor-tft\n",
      "['lamp']\n",
      "\n",
      "30_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "4_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "7_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "20_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "21_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "22_monitor-tft\n",
      "['monitor-tft']\n",
      "\n",
      "0_freezer\n",
      "['amplifier']\n",
      "\n",
      "1_freezer\n",
      "['waterfountain']\n",
      "\n",
      "0_charger-psp\n",
      "['waterkettle']\n",
      "\n",
      "0_cookingstove\n",
      "['waterkettle']\n",
      "\n",
      "1_cookingstove\n",
      "['microwaveoven']\n",
      "\n",
      "3_cookingstove\n",
      "['pc-desktop']\n",
      "\n",
      "2_cookingstove\n",
      "['microwaveoven']\n",
      "\n",
      "0_amplifier\n",
      "['amplifier']\n",
      "\n",
      "14_amplifier\n",
      "['lamp']\n",
      "\n",
      "4_amplifier\n",
      "['monitor-crt']\n",
      "\n",
      "6_amplifier\n",
      "['amplifier']\n",
      "\n",
      "16_amplifier\n",
      "['waterkettle']\n",
      "\n",
      "11_amplifier\n",
      "['lamp']\n",
      "\n",
      "2_amplifier\n",
      "['amplifier']\n",
      "\n",
      "9_amplifier\n",
      "['amplifier']\n",
      "\n",
      "8_amplifier\n",
      "['lamp']\n",
      "\n",
      "7_amplifier\n",
      "['amplifier']\n",
      "\n",
      "3_amplifier\n",
      "['monitor-crt']\n",
      "\n",
      "5_amplifier\n",
      "['monitor-tft']\n",
      "\n",
      "12_amplifier\n",
      "['lamp']\n",
      "\n",
      "13_amplifier\n",
      "['pc-laptop']\n",
      "\n",
      "1_amplifier\n",
      "['amplifier']\n",
      "\n",
      "10_amplifier\n",
      "['lamp']\n",
      "\n",
      "15_amplifier\n",
      "['amplifier']\n",
      "\n",
      "22_tv-lcd\n",
      "['tv-lcd']\n",
      "\n",
      "13_tv-lcd\n",
      "['lamp']\n",
      "\n",
      "0_tv-lcd\n",
      "['lamp']\n",
      "\n",
      "10_tv-lcd\n",
      "['microwaveoven']\n",
      "\n",
      "21_tv-lcd\n",
      "['tv-lcd']\n",
      "\n",
      "7_tv-lcd\n",
      "['waterkettle']\n",
      "\n",
      "6_tv-lcd\n",
      "['monitor-tft']\n",
      "\n",
      "12_tv-lcd\n",
      "['pc-laptop']\n",
      "\n",
      "18_tv-lcd\n",
      "['tv-lcd']\n",
      "\n",
      "3_tv-lcd\n",
      "['monitor-tft']\n",
      "\n",
      "15_tv-lcd\n",
      "['microwaveoven']\n",
      "\n",
      "14_tv-lcd\n",
      "['projector']\n",
      "\n",
      "4_tv-lcd\n",
      "['waterkettle']\n",
      "\n",
      "2_tv-lcd\n",
      "['microwaveoven']\n",
      "\n",
      "8_tv-lcd\n",
      "['lamp']\n",
      "\n",
      "24_tv-lcd\n",
      "['tv-lcd']\n",
      "\n",
      "5_tv-lcd\n",
      "['waterkettle']\n",
      "\n",
      "11_tv-lcd\n",
      "['projector']\n",
      "\n",
      "25_tv-lcd\n",
      "['tv-lcd']\n",
      "\n",
      "26_tv-lcd\n",
      "['waterkettle']\n",
      "\n",
      "16_tv-lcd\n",
      "['tv-lcd']\n",
      "\n",
      "9_tv-lcd\n",
      "['washingmachine']\n",
      "\n",
      "1_tv-lcd\n",
      "['monitor-crt']\n",
      "\n",
      "19_tv-lcd\n",
      "['monitor-crt']\n",
      "\n",
      "23_tv-lcd\n",
      "['tv-lcd']\n",
      "\n",
      "20_tv-lcd\n",
      "['waterkettle']\n",
      "\n",
      "17_tv-lcd\n",
      "['waterkettle']\n",
      "\n",
      "1_multimediacenter\n",
      "['multimediacenter']\n",
      "\n",
      "0_multimediacenter\n",
      "['microwaveoven']\n",
      "\n",
      "4_multimediacenter\n",
      "['multimediacenter']\n",
      "\n",
      "5_multimediacenter\n",
      "['multimediacenter']\n",
      "\n",
      "2_multimediacenter\n",
      "['multimediacenter']\n",
      "\n",
      "3_multimediacenter\n",
      "['multimediacenter']\n",
      "\n",
      "10_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "14_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "20_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "1_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "6_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "3_coffeemaker\n",
      "['dishwasher']\n",
      "\n",
      "17_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "0_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "4_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "16_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "2_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "22_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "19_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "11_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "15_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "18_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "5_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "12_coffeemaker\n",
      "['dishwasher']\n",
      "\n",
      "7_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "13_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "9_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "8_coffeemaker\n",
      "['coffeemaker']\n",
      "\n",
      "21_coffeemaker\n",
      "['waterkettle']\n",
      "\n",
      "2_laundrydryer\n",
      "['laundrydryer']\n",
      "\n",
      "0_laundrydryer\n",
      "['laundrydryer']\n",
      "\n",
      "1_laundrydryer\n",
      "['laundrydryer']\n",
      "\n",
      "5_laundrydryer\n",
      "['pc-laptop']\n",
      "\n",
      "7_laundrydryer\n",
      "['microwaveoven']\n",
      "\n",
      "6_laundrydryer\n",
      "['waterkettle']\n",
      "\n",
      "3_laundrydryer\n",
      "['waterkettle']\n",
      "\n",
      "4_laundrydryer\n",
      "['waterkettle']\n",
      "\n",
      "2_xmaslights\n",
      "['amplifier']\n",
      "\n",
      "0_xmaslights\n",
      "['waterkettle']\n",
      "\n",
      "1_xmaslights\n",
      "['monitor-tft']\n",
      "\n",
      "0_breadcutter\n",
      "['waterkettle']\n",
      "\n",
      "4_playstation3\n",
      "['playstation3']\n",
      "\n",
      "0_playstation3\n",
      "['playstation3']\n",
      "\n",
      "1_playstation3\n",
      "['playstation3']\n",
      "\n",
      "2_playstation3\n",
      "['playstation3']\n",
      "\n",
      "3_playstation3\n",
      "['playstation3']\n",
      "\n",
      "5_playstation3\n",
      "['monitor-tft']\n",
      "\n",
      "4_dishwasher\n",
      "['waterkettle']\n",
      "\n",
      "14_dishwasher\n",
      "['monitor-crt']\n",
      "\n",
      "17_dishwasher\n",
      "['videoprojector']\n",
      "\n",
      "35_dishwasher\n",
      "['amplifier']\n",
      "\n",
      "27_dishwasher\n",
      "['waterkettle']\n",
      "\n",
      "3_dishwasher\n",
      "['monitor-tft']\n",
      "\n",
      "19_dishwasher\n",
      "['washingmachine']\n",
      "\n",
      "10_dishwasher\n",
      "['coffeemaker']\n",
      "\n",
      "26_dishwasher\n",
      "['microwaveoven']\n",
      "\n",
      "37_dishwasher\n",
      "['amplifier']\n",
      "\n",
      "18_dishwasher\n",
      "['projector']\n",
      "\n",
      "25_dishwasher\n",
      "['playstation3']\n",
      "\n",
      "21_dishwasher\n",
      "['waterkettle']\n",
      "\n",
      "28_dishwasher\n",
      "['microwaveoven']\n",
      "\n",
      "39_dishwasher\n",
      "['dishwasher']\n",
      "\n",
      "32_dishwasher\n",
      "['waterkettle']\n",
      "\n",
      "6_dishwasher\n",
      "['lamp']\n",
      "\n",
      "20_dishwasher\n",
      "['amplifier']\n",
      "\n",
      "1_dishwasher\n",
      "['waterkettle']\n",
      "\n",
      "11_dishwasher\n",
      "['dishwasher']\n",
      "\n",
      "8_dishwasher\n",
      "['microwaveoven']\n",
      "\n",
      "22_dishwasher\n",
      "['videoprojector']\n",
      "\n",
      "13_dishwasher\n",
      "['cdplayer']\n",
      "\n",
      "0_dishwasher\n",
      "['amplifier']\n",
      "\n",
      "38_dishwasher\n",
      "['monitor-tft']\n",
      "\n",
      "16_dishwasher\n",
      "['playstation3']\n",
      "\n",
      "24_dishwasher\n",
      "['waterkettle']\n",
      "\n",
      "15_dishwasher\n",
      "['dishwasher']\n",
      "\n",
      "30_dishwasher\n",
      "['playstation3']\n",
      "\n",
      "36_dishwasher\n",
      "['microwaveoven']\n",
      "\n",
      "34_dishwasher\n",
      "['monitor-tft']\n",
      "\n",
      "2_dishwasher\n",
      "['amplifier']\n",
      "\n",
      "7_dishwasher\n",
      "['laundrydryer']\n",
      "\n",
      "9_dishwasher\n",
      "['amplifier']\n",
      "\n",
      "12_dishwasher\n",
      "['printer']\n",
      "\n",
      "5_dishwasher\n",
      "['washingmachine']\n",
      "\n",
      "29_dishwasher\n",
      "['microwaveoven']\n",
      "\n",
      "33_dishwasher\n",
      "['dishwasher']\n",
      "\n",
      "31_dishwasher\n",
      "['lamp']\n",
      "\n",
      "23_dishwasher\n",
      "['waterkettle']\n",
      "\n",
      "1_charger-smartphone\n",
      "['tv-lcd']\n",
      "\n",
      "0_charger-smartphone\n",
      "['charger-smartphone']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_n_similarities(similarities, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True predictions:    201\n",
      "False predictions:   279\n",
      "Accuracy:            0.419\n"
     ]
    }
   ],
   "source": [
    "print_accuracy(top_1_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we print first **five** similarities and **`top-5` accuracy**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_waterboiler\n",
      "['waterboiler', 'laundrydryer', 'multimediacenter', 'lamp', 'monitor-tft']\n",
      "\n",
      "2_subwoofer\n",
      "['subwoofer', 'monitor-tft', 'monitor-tft', 'amplifier', 'laundrydryer']\n",
      "\n",
      "3_subwoofer\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "0_subwoofer\n",
      "['amplifier', 'subwoofer', 'monitor-tft', 'laundrydryer', 'laundrydryer']\n",
      "\n",
      "1_subwoofer\n",
      "['microwaveoven', 'waterkettle', 'monitor-crt', 'toaster', 'waterkettle']\n",
      "\n",
      "2_digitaltvreceiver\n",
      "['router', 'digitaltvreceiver', 'router', 'digitaltvreceiver', 'digitaltvreceiver']\n",
      "\n",
      "3_digitaltvreceiver\n",
      "['ethernetswitch', 'ethernetswitch', 'pc-desktop', 'router', 'digitaltvreceiver']\n",
      "\n",
      "0_digitaltvreceiver\n",
      "['multimediacenter', 'digitaltvreceiver', 'router', 'tv-lcd', 'printer']\n",
      "\n",
      "1_digitaltvreceiver\n",
      "['digitaltvreceiver', 'router', 'digitaltvreceiver', 'digitaltvreceiver', 'router']\n",
      "\n",
      "4_digitaltvreceiver\n",
      "['digitaltvreceiver', 'router', 'digitaltvreceiver', 'router', 'digitaltvreceiver']\n",
      "\n",
      "2_dvdplayer\n",
      "['dvdplayer', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'ethernetswitch']\n",
      "\n",
      "0_dvdplayer\n",
      "['dvdplayer', 'monitor-crt', 'monitor-crt', 'monitor-tft', 'amplifier']\n",
      "\n",
      "1_dvdplayer\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "23_pc-desktop\n",
      "['pc-desktop', 'monitor-tft', 'monitor-tft', 'pc-desktop', 'usbharddrive']\n",
      "\n",
      "3_pc-desktop\n",
      "['pc-desktop', 'ethernetswitch', 'pc-desktop', 'xmaslights', 'pc-desktop']\n",
      "\n",
      "5_pc-desktop\n",
      "['monitor-tft', 'pc-desktop', 'monitor-tft', 'usbharddrive', 'usbharddrive']\n",
      "\n",
      "9_pc-desktop\n",
      "['lamp', 'amplifier', 'monitor-tft', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "1_pc-desktop\n",
      "['pc-desktop', 'router', 'ethernetswitch', 'monitor-tft', 'tv-lcd']\n",
      "\n",
      "8_pc-desktop\n",
      "['monitor-tft', 'waterkettle', 'amplifier', 'washingmachine', 'monitor-tft']\n",
      "\n",
      "13_pc-desktop\n",
      "['pc-desktop', 'amplifier', 'pc-desktop', 'pc-laptop', 'monitor-tft']\n",
      "\n",
      "7_pc-desktop\n",
      "['pc-desktop', 'monitor-tft', 'printer', 'microwaveoven', 'printer']\n",
      "\n",
      "20_pc-desktop\n",
      "['pc-desktop', 'laundrydryer', 'monitor-tft', 'pc-desktop', 'dishwasher']\n",
      "\n",
      "18_pc-desktop\n",
      "['videoprojector', 'videoprojector', 'monitor-crt', 'coffeemaker', 'videoprojector']\n",
      "\n",
      "0_pc-desktop\n",
      "['pc-desktop', 'monitor-tft', 'monitor-tft', 'washingmachine', 'monitor-tft']\n",
      "\n",
      "22_pc-desktop\n",
      "['monitor-tft', 'waterkettle', 'tv-crt', 'monitor-tft', 'lamp']\n",
      "\n",
      "11_pc-desktop\n",
      "['pc-desktop', 'microwaveoven', 'ethernetswitch', 'ethernetswitch', 'pc-desktop']\n",
      "\n",
      "17_pc-desktop\n",
      "['pc-desktop', 'monitor-tft', 'usbharddrive', 'laundrydryer', 'multimediacenter']\n",
      "\n",
      "10_pc-desktop\n",
      "['amplifier', 'monitor-tft', 'lamp', 'waterkettle', 'amplifier']\n",
      "\n",
      "14_pc-desktop\n",
      "['pc-desktop', 'ethernetswitch', 'ethernetswitch', 'pc-desktop', 'pc-desktop']\n",
      "\n",
      "2_pc-desktop\n",
      "['ethernetswitch', 'pc-desktop', 'pc-laptop', 'pc-laptop', 'tv-lcd']\n",
      "\n",
      "12_pc-desktop\n",
      "['dishwasher', 'pc-desktop', 'dishwasher', 'pc-laptop', 'pc-desktop']\n",
      "\n",
      "6_pc-desktop\n",
      "['monitor-tft', 'xmaslights', 'pc-desktop', 'amplifier', 'pc-desktop']\n",
      "\n",
      "25_pc-desktop\n",
      "['pc-desktop', 'refrigerator', 'remotedesktop', 'refrigerator', 'dvdplayer']\n",
      "\n",
      "21_pc-desktop\n",
      "['pc-laptop', 'lamp', 'washingmachine', 'monitor-crt', 'monitor-tft']\n",
      "\n",
      "19_pc-desktop\n",
      "['lamp', 'ethernetswitch', 'tv-lcd', 'monitor-crt', 'pc-desktop']\n",
      "\n",
      "16_pc-desktop\n",
      "['pc-desktop', 'projector', 'waterkettle', 'microwaveoven', 'coffeemaker']\n",
      "\n",
      "24_pc-desktop\n",
      "['pc-desktop', 'pc-desktop', 'amplifier', 'amplifier', 'dishwasher']\n",
      "\n",
      "15_pc-desktop\n",
      "['pc-desktop', 'ethernetswitch', 'pc-desktop', 'ethernetswitch', 'pc-desktop']\n",
      "\n",
      "4_pc-desktop\n",
      "['pc-desktop', 'pc-desktop', 'dishwasher', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "3_videoprojector\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'monitor-tft', 'refrigerator']\n",
      "\n",
      "2_videoprojector\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'videoprojector', 'washingmachine']\n",
      "\n",
      "4_videoprojector\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'monitor-tft', 'videoprojector']\n",
      "\n",
      "1_videoprojector\n",
      "['laundrydryer', 'waterboiler', 'cdplayer', 'dishwasher', 'pc-desktop']\n",
      "\n",
      "0_videoprojector\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'videoprojector', 'washingmachine']\n",
      "\n",
      "6_videoprojector\n",
      "['tv-crt', 'microwaveoven', 'lamp', 'washingmachine', 'lamp']\n",
      "\n",
      "5_videoprojector\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'videoprojector', 'videoprojector']\n",
      "\n",
      "11_lamp\n",
      "['waterkettle', 'lamp', 'monitor-tft', 'tv-crt', 'lamp']\n",
      "\n",
      "26_lamp\n",
      "['lamp', 'lamp', 'monitor-tft', 'waterkettle', 'tv-crt']\n",
      "\n",
      "4_lamp\n",
      "['lamp', 'monitor-tft', 'pc-desktop', 'printer', 'pc-desktop']\n",
      "\n",
      "31_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "9_lamp\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'lamp', 'monitor-tft']\n",
      "\n",
      "10_lamp\n",
      "['pc-laptop', 'lamp', 'monitor-crt', 'monitor-tft', 'laundrydryer']\n",
      "\n",
      "5_lamp\n",
      "['lamp', 'amplifier', 'lamp', 'lamp', 'lamp']\n",
      "\n",
      "19_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "7_lamp\n",
      "['lamp', 'monitor-tft', 'lamp', 'tv-crt', 'washingmachine']\n",
      "\n",
      "1_lamp\n",
      "['lamp', 'lamp', 'monitor-tft', 'microwaveoven', 'waterkettle']\n",
      "\n",
      "6_lamp\n",
      "['lamp', 'microwaveoven', 'waterkettle', 'lamp', 'washingmachine']\n",
      "\n",
      "13_lamp\n",
      "['lamp', 'lamp', 'monitor-tft', 'washingmachine', 'microwaveoven']\n",
      "\n",
      "21_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "14_lamp\n",
      "['lamp', 'lamp', 'lamp', 'usbhub', 'microwaveoven']\n",
      "\n",
      "16_lamp\n",
      "['lamp', 'lamp', 'lamp', 'usbhub', 'microwaveoven']\n",
      "\n",
      "23_lamp\n",
      "['lamp', 'microwaveoven', 'waterkettle', 'washingmachine', 'washingmachine']\n",
      "\n",
      "3_lamp\n",
      "['monitor-crt', 'amplifier', 'waterkettle', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "24_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "27_lamp\n",
      "['waterkettle', 'monitor-tft', 'pc-laptop', 'monitor-crt', 'tv-crt']\n",
      "\n",
      "17_lamp\n",
      "['monitor-crt', 'pc-laptop', 'tv-lcd', 'monitor-tft', 'pc-desktop']\n",
      "\n",
      "25_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "2_lamp\n",
      "['pc-laptop', 'waterkettle', 'monitor-crt', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "29_lamp\n",
      "['ethernetswitch', 'monitor-crt', 'lamp', 'monitor-tft', 'tv-lcd']\n",
      "\n",
      "22_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "20_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "15_lamp\n",
      "['lamp', 'lamp', 'lamp', 'usbhub', 'monitor-tft']\n",
      "\n",
      "0_lamp\n",
      "['playstation3', 'dishwasher', 'dishwasher', 'dishwasher', 'washingmachine']\n",
      "\n",
      "30_lamp\n",
      "['monitor-tft', 'amplifier', 'washingmachine', 'washingmachine', 'waterkettle']\n",
      "\n",
      "32_lamp\n",
      "['lamp', 'waterkettle', 'microwaveoven', 'lamp', 'monitor-tft']\n",
      "\n",
      "28_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "12_lamp\n",
      "['lamp', 'tv-crt', 'tv-lcd', 'waterkettle', 'tv-lcd']\n",
      "\n",
      "18_lamp\n",
      "['lamp', 'lamp', 'waterkettle', 'monitor-tft', 'tv-crt']\n",
      "\n",
      "8_lamp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "5_toaster\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "1_toaster\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "0_toaster\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "4_toaster\n",
      "['microwaveoven', 'waterkettle', 'toaster', 'waterkettle', 'waterkettle']\n",
      "\n",
      "3_toaster\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "2_toaster\n",
      "['waterkettle', 'toaster', 'microwaveoven', 'waterkettle', 'waterkettle']\n",
      "\n",
      "6_microwaveoven\n",
      "['multimediacenter', 'usbharddrive', 'multimediacenter', 'usbharddrive', 'multimediacenter']\n",
      "\n",
      "24_microwaveoven\n",
      "['monitor-crt', 'monitor-tft', 'washingmachine', 'monitor-tft', 'monitor-crt']\n",
      "\n",
      "4_microwaveoven\n",
      "['multimediacenter', 'multimediacenter', 'microwaveoven', 'multimediacenter', 'usbharddrive']\n",
      "\n",
      "16_microwaveoven\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "26_microwaveoven\n",
      "['microwaveoven', 'multimediacenter', 'tv-crt', 'washingmachine', 'washingmachine']\n",
      "\n",
      "14_microwaveoven\n",
      "['coffeemaker', 'microwaveoven', 'projector', 'dishwasher', 'dishwasher']\n",
      "\n",
      "21_microwaveoven\n",
      "['waterkettle', 'toaster', 'microwaveoven', 'monitor-crt', 'waterkettle']\n",
      "\n",
      "17_microwaveoven\n",
      "['waterkettle', 'toaster', 'waterkettle', 'microwaveoven', 'waterkettle']\n",
      "\n",
      "18_microwaveoven\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "19_microwaveoven\n",
      "['waterkettle', 'toaster', 'monitor-crt', 'microwaveoven', 'waterkettle']\n",
      "\n",
      "23_microwaveoven\n",
      "['microwaveoven', 'dishwasher', 'dishwasher', 'playstation3', 'waterkettle']\n",
      "\n",
      "7_microwaveoven\n",
      "['microwaveoven', 'multimediacenter', 'pc-desktop', 'charger-smartphone', 'pc-laptop']\n",
      "\n",
      "9_microwaveoven\n",
      "['microwaveoven', 'microwaveoven', 'playstation3', 'playstation3', 'playstation3']\n",
      "\n",
      "1_microwaveoven\n",
      "['refrigerator', 'multimediacenter', 'multimediacenter', 'multimediacenter', 'microwaveoven']\n",
      "\n",
      "0_microwaveoven\n",
      "['microwaveoven', 'pc-desktop', 'lamp', 'lamp', 'monitor-tft']\n",
      "\n",
      "13_microwaveoven\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'toaster']\n",
      "\n",
      "22_microwaveoven\n",
      "['waterkettle', 'waterkettle', 'toaster', 'monitor-crt', 'waterkettle']\n",
      "\n",
      "25_microwaveoven\n",
      "['microwaveoven', 'monitor-tft', 'lamp', 'amplifier', 'monitor-crt']\n",
      "\n",
      "10_microwaveoven\n",
      "['waterkettle', 'toaster', 'waterkettle', 'waterkettle', 'microwaveoven']\n",
      "\n",
      "15_microwaveoven\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "20_microwaveoven\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'toaster', 'waterkettle']\n",
      "\n",
      "8_microwaveoven\n",
      "['coffeemaker', 'microwaveoven', 'playstation3', 'dishwasher', 'playstation3']\n",
      "\n",
      "5_microwaveoven\n",
      "['multimediacenter', 'multimediacenter', 'usbharddrive', 'multimediacenter', 'microwaveoven']\n",
      "\n",
      "12_microwaveoven\n",
      "['microwaveoven', 'dishwasher', 'washingmachine', 'lamp', 'monitor-tft']\n",
      "\n",
      "2_microwaveoven\n",
      "['refrigerator', 'multimediacenter', 'multimediacenter', 'microwaveoven', 'usbharddrive']\n",
      "\n",
      "11_microwaveoven\n",
      "['dishwasher', 'dishwasher', 'dishwasher', 'dishwasher', 'washingmachine']\n",
      "\n",
      "3_microwaveoven\n",
      "['multimediacenter', 'multimediacenter', 'usbharddrive', 'multimediacenter', 'usbharddrive']\n",
      "\n",
      "7_pc-laptop\n",
      "['pc-laptop', 'monitor-crt', 'monitor-crt', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "4_pc-laptop\n",
      "['monitor-tft', 'pc-desktop', 'usbharddrive', 'pc-laptop', 'usbharddrive']\n",
      "\n",
      "10_pc-laptop\n",
      "['waterkettle', 'monitor-tft', 'amplifier', 'washingmachine', 'lamp']\n",
      "\n",
      "0_pc-laptop\n",
      "['pc-laptop', 'waterkettle', 'monitor-tft', 'monitor-crt', 'monitor-tft']\n",
      "\n",
      "1_pc-laptop\n",
      "['dishwasher', 'pc-laptop', 'pc-desktop', 'dishwasher', 'pc-desktop']\n",
      "\n",
      "9_pc-laptop\n",
      "['waterkettle', 'monitor-tft', 'monitor-tft', 'lamp', 'washingmachine']\n",
      "\n",
      "6_pc-laptop\n",
      "['pc-laptop', 'pc-laptop', 'usbharddrive', 'washingmachine', 'pc-laptop']\n",
      "\n",
      "12_pc-laptop\n",
      "['pc-desktop', 'router', 'router', 'monitor-tft', 'charger-smartphone']\n",
      "\n",
      "3_pc-laptop\n",
      "['pc-laptop', 'usbharddrive', 'pc-laptop', 'pc-desktop', 'digitaltvreceiver']\n",
      "\n",
      "11_pc-laptop\n",
      "['pc-laptop', 'digitaltvreceiver', 'router', 'pc-desktop', 'ethernetswitch']\n",
      "\n",
      "8_pc-laptop\n",
      "['waterkettle', 'monitor-tft', 'monitor-tft', 'tv-crt', 'microwaveoven']\n",
      "\n",
      "5_pc-laptop\n",
      "['pc-laptop', 'multimediacenter', 'tv-lcd', 'microwaveoven', 'dishwasher']\n",
      "\n",
      "2_pc-laptop\n",
      "['digitaltvreceiver', 'pc-laptop', 'ethernetswitch', 'tv-lcd', 'tv-lcd']\n",
      "\n",
      "1_usbhub\n",
      "['lamp', 'lamp', 'lamp', 'usbhub', 'usbhub']\n",
      "\n",
      "0_usbhub\n",
      "['usbhub', 'monitor-tft', 'monitor-tft', 'pc-desktop', 'refrigerator']\n",
      "\n",
      "39_washingmachine\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "0_washingmachine\n",
      "['washingmachine', 'playstation3', 'dishwasher', 'dishwasher', 'dishwasher']\n",
      "\n",
      "24_washingmachine\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "7_washingmachine\n",
      "['monitor-crt', 'microwaveoven', 'lamp', 'pc-desktop', 'monitor-tft']\n",
      "\n",
      "22_washingmachine\n",
      "['waterkettle', 'lamp', 'monitor-tft', 'microwaveoven', 'amplifier']\n",
      "\n",
      "14_washingmachine\n",
      "['lamp', 'lamp', 'microwaveoven', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "17_washingmachine\n",
      "['monitor-crt', 'amplifier', 'monitor-tft', 'amplifier', 'monitor-tft']\n",
      "\n",
      "16_washingmachine\n",
      "['microwaveoven', 'monitor-tft', 'lamp', 'monitor-tft', 'microwaveoven']\n",
      "\n",
      "19_washingmachine\n",
      "['waterkettle', 'amplifier', 'waterkettle', 'lamp', 'microwaveoven']\n",
      "\n",
      "33_washingmachine\n",
      "['microwaveoven', 'monitor-crt', 'lamp', 'toaster', 'waterkettle']\n",
      "\n",
      "6_washingmachine\n",
      "['monitor-tft', 'monitor-crt', 'lamp', 'toaster', 'washingmachine']\n",
      "\n",
      "29_washingmachine\n",
      "['microwaveoven', 'waterkettle', 'tv-crt', 'lamp', 'waterkettle']\n",
      "\n",
      "27_washingmachine\n",
      "['monitor-tft', 'lamp', 'toaster', 'monitor-crt', 'washingmachine']\n",
      "\n",
      "36_washingmachine\n",
      "['lamp', 'lamp', 'microwaveoven', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "1_washingmachine\n",
      "['microwaveoven', 'lamp', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "23_washingmachine\n",
      "['lamp', 'waterkettle', 'microwaveoven', 'lamp', 'monitor-tft']\n",
      "\n",
      "37_washingmachine\n",
      "['lamp', 'microwaveoven', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "28_washingmachine\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "9_washingmachine\n",
      "['lamp', 'lamp', 'dvdplayer', 'monitor-crt', 'monitor-tft']\n",
      "\n",
      "20_washingmachine\n",
      "['amplifier', 'waterkettle', 'washingmachine', 'amplifier', 'monitor-tft']\n",
      "\n",
      "38_washingmachine\n",
      "['lamp', 'lamp', 'waterkettle', 'monitor-tft', 'tv-crt']\n",
      "\n",
      "32_washingmachine\n",
      "['amplifier', 'solarthermalsystem', 'amplifier', 'waterkettle', 'pc-desktop']\n",
      "\n",
      "15_washingmachine\n",
      "['microwaveoven', 'lamp', 'tv-crt', 'tv-lcd', 'lamp']\n",
      "\n",
      "8_washingmachine\n",
      "['waterkettle', 'monitor-tft', 'microwaveoven', 'waterkettle', 'lamp']\n",
      "\n",
      "31_washingmachine\n",
      "['lamp', 'microwaveoven', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "4_washingmachine\n",
      "['washingmachine', 'digitaltvreceiver', 'router', 'amplifier', 'digitaltvreceiver']\n",
      "\n",
      "35_washingmachine\n",
      "['lamp', 'microwaveoven', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "34_washingmachine\n",
      "['monitor-crt', 'tv-lcd', 'laundrydryer', 'lamp', 'subwoofer']\n",
      "\n",
      "26_washingmachine\n",
      "['monitor-crt', 'washingmachine', 'tv-lcd', 'lamp', 'waterkettle']\n",
      "\n",
      "13_washingmachine\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "3_washingmachine\n",
      "['amplifier', 'amplifier', 'washingmachine', 'lamp', 'lamp']\n",
      "\n",
      "12_washingmachine\n",
      "['washingmachine', 'pc-desktop', 'subwoofer', 'lamp', 'amplifier']\n",
      "\n",
      "25_washingmachine\n",
      "['microwaveoven', 'monitor-crt', 'monitor-crt', 'monitor-tft', 'microwaveoven']\n",
      "\n",
      "5_washingmachine\n",
      "['lamp', 'lamp', 'microwaveoven', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "18_washingmachine\n",
      "['microwaveoven', 'lamp', 'tv-crt', 'tv-lcd', 'washingmachine']\n",
      "\n",
      "2_washingmachine\n",
      "['microwaveoven', 'lamp', 'toaster', 'waterkettle', 'monitor-crt']\n",
      "\n",
      "40_washingmachine\n",
      "['playstation3', 'dishwasher', 'washingmachine', 'dishwasher', 'dishwasher']\n",
      "\n",
      "10_washingmachine\n",
      "['dvdplayer', 'lamp', 'monitor-crt', 'xmaslights', 'tv-lcd']\n",
      "\n",
      "21_washingmachine\n",
      "['pc-laptop', 'washingmachine', 'monitor-tft', 'monitor-crt', 'monitor-crt']\n",
      "\n",
      "30_washingmachine\n",
      "['lamp', 'monitor-tft', 'microwaveoven', 'lamp', 'lamp']\n",
      "\n",
      "11_washingmachine\n",
      "['solarthermalsystem', 'pc-desktop', 'washingmachine', 'waterkettle', 'amplifier']\n",
      "\n",
      "2_projector\n",
      "['projector', 'playstation3', 'dishwasher', 'microwaveoven', 'refrigerator']\n",
      "\n",
      "0_projector\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "5_projector\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "4_projector\n",
      "['coffeemaker', 'projector', 'projector', 'microwaveoven', 'dishwasher']\n",
      "\n",
      "1_projector\n",
      "['projector', 'projector', 'monitor-tft', 'coffeemaker', 'printer']\n",
      "\n",
      "3_projector\n",
      "['projector', 'videoprojector', 'videoprojector', 'monitor-tft', 'lamp']\n",
      "\n",
      "1_beantocupcoffeemaker\n",
      "['beantocupcoffeemaker', 'beantocupcoffeemaker', 'usbharddrive', 'monitor-tft', 'ethernetswitch']\n",
      "\n",
      "0_beantocupcoffeemaker\n",
      "['beantocupcoffeemaker', 'beantocupcoffeemaker', 'solarthermalsystem', 'ethernetswitch', 'tv-lcd']\n",
      "\n",
      "21_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "16_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "6_waterkettle\n",
      "['waterkettle', 'waterkettle', 'toaster', 'waterkettle', 'waterkettle']\n",
      "\n",
      "30_waterkettle\n",
      "['lamp', 'waterkettle', 'monitor-tft', 'washingmachine', 'tv-crt']\n",
      "\n",
      "15_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'toaster', 'waterkettle']\n",
      "\n",
      "1_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "31_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'toaster', 'waterkettle']\n",
      "\n",
      "12_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "5_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "19_waterkettle\n",
      "['microwaveoven', 'lamp', 'toaster', 'waterkettle', 'microwaveoven']\n",
      "\n",
      "0_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "24_waterkettle\n",
      "['lamp', 'microwaveoven', 'washingmachine', 'monitor-tft', 'washingmachine']\n",
      "\n",
      "3_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "2_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "13_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'toaster']\n",
      "\n",
      "11_waterkettle\n",
      "['waterkettle', 'toaster', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "28_waterkettle\n",
      "['waterkettle', 'waterkettle', 'toaster', 'waterkettle', 'monitor-crt']\n",
      "\n",
      "14_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "18_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "32_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "20_waterkettle\n",
      "['waterkettle', 'waterkettle', 'toaster', 'waterkettle', 'waterkettle']\n",
      "\n",
      "7_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "26_waterkettle\n",
      "['microwaveoven', 'waterkettle', 'toaster', 'waterkettle', 'waterkettle']\n",
      "\n",
      "10_waterkettle\n",
      "['waterkettle', 'toaster', 'microwaveoven', 'waterkettle', 'waterkettle']\n",
      "\n",
      "23_waterkettle\n",
      "['dishwasher', 'playstation3', 'dishwasher', 'dishwasher', 'dishwasher']\n",
      "\n",
      "22_waterkettle\n",
      "['waterkettle', 'waterkettle', 'microwaveoven', 'monitor-crt', 'waterkettle']\n",
      "\n",
      "27_waterkettle\n",
      "['washingmachine', 'lamp', 'washingmachine', 'monitor-tft', 'microwaveoven']\n",
      "\n",
      "25_waterkettle\n",
      "['lamp', 'microwaveoven', 'washingmachine', 'amplifier', 'tv-crt']\n",
      "\n",
      "8_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "29_waterkettle\n",
      "['lamp', 'microwaveoven', 'washingmachine', 'monitor-tft', 'tv-crt']\n",
      "\n",
      "9_waterkettle\n",
      "['waterkettle', 'microwaveoven', 'toaster', 'waterkettle', 'waterkettle']\n",
      "\n",
      "4_waterkettle\n",
      "['waterkettle', 'waterkettle', 'toaster', 'waterkettle', 'monitor-crt']\n",
      "\n",
      "17_waterkettle\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "0_alarmclock\n",
      "['alarmclock', 'refrigerator', 'refrigerator', 'refrigerator', 'playstation3']\n",
      "\n",
      "7_usbharddrive\n",
      "['usbharddrive', 'router', 'router', 'multimediacenter', 'microwaveoven']\n",
      "\n",
      "4_usbharddrive\n",
      "['multimediacenter', 'multimediacenter', 'multimediacenter', 'usbharddrive', 'microwaveoven']\n",
      "\n",
      "1_usbharddrive\n",
      "['usbharddrive', 'beantocupcoffeemaker', 'usbharddrive', 'ethernetswitch', 'monitor-tft']\n",
      "\n",
      "3_usbharddrive\n",
      "['microwaveoven', 'monitor-tft', 'usbharddrive', 'lamp', 'pc-desktop']\n",
      "\n",
      "5_usbharddrive\n",
      "['multimediacenter', 'usbharddrive', 'usbharddrive', 'multimediacenter', 'usbharddrive']\n",
      "\n",
      "0_usbharddrive\n",
      "['usbharddrive', 'usbharddrive', 'usbharddrive', 'beantocupcoffeemaker', 'monitor-tft']\n",
      "\n",
      "13_usbharddrive\n",
      "['usbharddrive', 'monitor-tft', 'multimediacenter', 'monitor-tft', 'refrigerator']\n",
      "\n",
      "9_usbharddrive\n",
      "['usbharddrive', 'usbharddrive', 'usbharddrive', 'usbharddrive', 'monitor-tft']\n",
      "\n",
      "2_usbharddrive\n",
      "['usbharddrive', 'usbharddrive', 'usbharddrive', 'monitor-tft', 'multimediacenter']\n",
      "\n",
      "8_usbharddrive\n",
      "['router', 'usbharddrive', 'tv-lcd', 'pc-laptop', 'tv-lcd']\n",
      "\n",
      "12_usbharddrive\n",
      "['usbharddrive', 'usbharddrive', 'usbharddrive', 'usbharddrive', 'monitor-tft']\n",
      "\n",
      "6_usbharddrive\n",
      "['multimediacenter', 'multimediacenter', 'microwaveoven', 'usbharddrive', 'multimediacenter']\n",
      "\n",
      "10_usbharddrive\n",
      "['usbharddrive', 'monitor-tft', 'router', 'usbharddrive', 'usbharddrive']\n",
      "\n",
      "11_usbharddrive\n",
      "['usbharddrive', 'usbharddrive', 'usbharddrive', 'usbharddrive', 'monitor-tft']\n",
      "\n",
      "3_router\n",
      "['digitaltvreceiver', 'router', 'digitaltvreceiver', 'router', 'digitaltvreceiver']\n",
      "\n",
      "8_router\n",
      "['router', 'router', 'dishwasher', 'monitor-tft', 'usbharddrive']\n",
      "\n",
      "6_router\n",
      "['router', 'digitaltvreceiver', 'digitaltvreceiver', 'router', 'digitaltvreceiver']\n",
      "\n",
      "4_router\n",
      "['digitaltvreceiver', 'router', 'router', 'digitaltvreceiver', 'digitaltvreceiver']\n",
      "\n",
      "5_router\n",
      "['ethernetswitch', 'ethernetswitch', 'pc-desktop', 'router', 'digitaltvreceiver']\n",
      "\n",
      "1_router\n",
      "['router', 'dishwasher', 'ethernetswitch', 'dvdplayer', 'pc-desktop']\n",
      "\n",
      "2_router\n",
      "['multimediacenter', 'router', 'digitaltvreceiver', 'tv-lcd', 'ethernetswitch']\n",
      "\n",
      "7_router\n",
      "['router', 'monitor-tft', 'router', 'monitor-tft', 'microwaveoven']\n",
      "\n",
      "0_router\n",
      "['router', 'pc-desktop', 'ethernetswitch', 'monitor-tft', 'pc-desktop']\n",
      "\n",
      "2_solarthermalsystem\n",
      "['solarthermalsystem', 'tv-lcd', 'pc-laptop', 'digitaltvreceiver', 'monitor-tft']\n",
      "\n",
      "1_solarthermalsystem\n",
      "['refrigerator', 'usbharddrive', 'solarthermalsystem', 'pc-laptop', 'pc-laptop']\n",
      "\n",
      "0_solarthermalsystem\n",
      "['solarthermalsystem', 'amplifier', 'waterkettle', 'amplifier', 'laundrydryer']\n",
      "\n",
      "0_monitor-crt\n",
      "['lamp', 'lamp', 'monitor-tft', 'washingmachine', 'lamp']\n",
      "\n",
      "4_monitor-crt\n",
      "['lamp', 'lamp', 'amplifier', 'lamp', 'monitor-tft']\n",
      "\n",
      "7_monitor-crt\n",
      "['monitor-crt', 'amplifier', 'monitor-tft', 'tv-lcd', 'waterkettle']\n",
      "\n",
      "3_monitor-crt\n",
      "['lamp', 'tv-lcd', 'tv-lcd', 'waterkettle', 'tv-crt']\n",
      "\n",
      "6_monitor-crt\n",
      "['lamp', 'tv-crt', 'lamp', 'monitor-crt', 'lamp']\n",
      "\n",
      "1_monitor-crt\n",
      "['monitor-tft', 'monitor-crt', 'monitor-tft', 'monitor-crt', 'usbharddrive']\n",
      "\n",
      "2_monitor-crt\n",
      "['monitor-crt', 'monitor-tft', 'monitor-crt', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "5_monitor-crt\n",
      "['lamp', 'lamp', 'monitor-tft', 'microwaveoven', 'monitor-tft']\n",
      "\n",
      "1_tv-crt\n",
      "['lamp', 'lamp', 'monitor-tft', 'tv-crt', 'washingmachine']\n",
      "\n",
      "4_tv-crt\n",
      "['tv-crt', 'microwaveoven', 'lamp', 'tv-lcd', 'amplifier']\n",
      "\n",
      "2_tv-crt\n",
      "['waterkettle', 'amplifier', 'lamp', 'washingmachine', 'monitor-tft']\n",
      "\n",
      "0_tv-crt\n",
      "['microwaveoven', 'lamp', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "3_tv-crt\n",
      "['monitor-tft', 'lamp', 'tv-crt', 'usbhub', 'monitor-tft']\n",
      "\n",
      "5_tv-crt\n",
      "['washingmachine', 'washingmachine', 'washingmachine', 'pc-desktop', 'washingmachine']\n",
      "\n",
      "0_vacuumcleaner\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'toaster']\n",
      "\n",
      "0_waterfountain\n",
      "['refrigerator', 'waterfountain', 'refrigerator', 'monitor-crt', 'monitor-tft']\n",
      "\n",
      "1_waterfountain\n",
      "['waterfountain', 'refrigerator', 'usbhub', 'coffeemaker', 'printer']\n",
      "\n",
      "2_waterfountain\n",
      "['waterfountain', 'waterfountain', 'monitor-tft', 'coffeemaker', 'monitor-tft']\n",
      "\n",
      "0_remotedesktop\n",
      "['remotedesktop', 'printer', 'digitaltvreceiver', 'projector', 'monitor-tft']\n",
      "\n",
      "6_printer\n",
      "['printer', 'remotedesktop', 'digitaltvreceiver', 'pc-laptop', 'multimediacenter']\n",
      "\n",
      "2_printer\n",
      "['printer', 'printer', 'printer', 'printer', 'monitor-tft']\n",
      "\n",
      "0_printer\n",
      "['printer', 'printer', 'printer', 'printer', 'laundrydryer']\n",
      "\n",
      "1_printer\n",
      "['printer', 'printer', 'printer', 'monitor-tft', 'tv-lcd']\n",
      "\n",
      "4_printer\n",
      "['printer', 'printer', 'printer', 'monitor-tft', 'printer']\n",
      "\n",
      "3_printer\n",
      "['printer', 'printer', 'printer', 'monitor-tft', 'printer']\n",
      "\n",
      "5_printer\n",
      "['printer', 'printer', 'multimediacenter', 'router', 'pc-laptop']\n",
      "\n",
      "5_ethernetswitch\n",
      "['ethernetswitch', 'pc-laptop', 'microwaveoven', 'lamp', 'multimediacenter']\n",
      "\n",
      "3_ethernetswitch\n",
      "['pc-desktop', 'pc-desktop', 'ethernetswitch', 'ethernetswitch', 'pc-desktop']\n",
      "\n",
      "1_ethernetswitch\n",
      "['pc-desktop', 'ethernetswitch', 'tv-lcd', 'solarthermalsystem', 'pc-laptop']\n",
      "\n",
      "2_ethernetswitch\n",
      "['pc-desktop', 'ethernetswitch', 'pc-desktop', 'pc-desktop', 'xmaslights']\n",
      "\n",
      "4_ethernetswitch\n",
      "['pc-desktop', 'ethernetswitch', 'pc-desktop', 'ethernetswitch', 'pc-desktop']\n",
      "\n",
      "0_ethernetswitch\n",
      "['pc-desktop', 'ethernetswitch', 'router', 'monitor-tft', 'pc-laptop']\n",
      "\n",
      "0_iron\n",
      "['waterkettle', 'waterkettle', 'toaster', 'waterkettle', 'monitor-crt']\n",
      "\n",
      "0_cdplayer\n",
      "['cdplayer', 'microwaveoven', 'laundrydryer', 'dishwasher', 'printer']\n",
      "\n",
      "8_refrigerator\n",
      "['monitor-tft', 'monitor-tft', 'monitor-tft', 'usbharddrive', 'videoprojector']\n",
      "\n",
      "6_refrigerator\n",
      "['refrigerator', 'refrigerator', 'usbharddrive', 'usbharddrive', 'refrigerator']\n",
      "\n",
      "4_refrigerator\n",
      "['refrigerator', 'monitor-tft', 'pc-desktop', 'refrigerator', 'microwaveoven']\n",
      "\n",
      "0_refrigerator\n",
      "['refrigerator', 'router', 'dishwasher', 'usbharddrive', 'monitor-tft']\n",
      "\n",
      "10_refrigerator\n",
      "['monitor-tft', 'monitor-tft', 'multimediacenter', 'refrigerator', 'pc-desktop']\n",
      "\n",
      "11_refrigerator\n",
      "['waterfountain', 'refrigerator', 'pc-laptop', 'washingmachine', 'monitor-crt']\n",
      "\n",
      "15_refrigerator\n",
      "['refrigerator', 'refrigerator', 'refrigerator', 'alarmclock', 'laundrydryer']\n",
      "\n",
      "1_refrigerator\n",
      "['refrigerator', 'waterfountain', 'lamp', 'monitor-tft', 'tv-lcd']\n",
      "\n",
      "7_refrigerator\n",
      "['refrigerator', 'refrigerator', 'refrigerator', 'usbharddrive', 'usbharddrive']\n",
      "\n",
      "5_refrigerator\n",
      "['refrigerator', 'refrigerator', 'refrigerator', 'usbharddrive', 'monitor-tft']\n",
      "\n",
      "9_refrigerator\n",
      "['refrigerator', 'dishwasher', 'monitor-tft', 'router', 'dishwasher']\n",
      "\n",
      "13_refrigerator\n",
      "['refrigerator', 'washingmachine', 'pc-desktop', 'refrigerator', 'pc-desktop']\n",
      "\n",
      "18_refrigerator\n",
      "['refrigerator', 'refrigerator', 'refrigerator', 'pc-desktop', 'alarmclock']\n",
      "\n",
      "3_refrigerator\n",
      "['refrigerator', 'tv-lcd', 'laundrydryer', 'printer', 'monitor-tft']\n",
      "\n",
      "14_refrigerator\n",
      "['refrigerator', 'multimediacenter', 'multimediacenter', 'multimediacenter', 'tv-lcd']\n",
      "\n",
      "2_refrigerator\n",
      "['subwoofer', 'laundrydryer', 'printer', 'refrigerator', 'waterfountain']\n",
      "\n",
      "17_refrigerator\n",
      "['dvdplayer', 'coffeemaker', 'refrigerator', 'coffeemaker', 'monitor-tft']\n",
      "\n",
      "12_refrigerator\n",
      "['laundrydryer', 'refrigerator', 'printer', 'printer', 'printer']\n",
      "\n",
      "16_refrigerator\n",
      "['refrigerator', 'refrigerator', 'refrigerator', 'alarmclock', 'laundrydryer']\n",
      "\n",
      "17_monitor-tft\n",
      "['monitor-tft', 'refrigerator', 'pc-desktop', 'pc-desktop', 'tv-lcd']\n",
      "\n",
      "5_monitor-tft\n",
      "['monitor-tft', 'usbhub', 'monitor-tft', 'monitor-tft', 'pc-desktop']\n",
      "\n",
      "19_monitor-tft\n",
      "['monitor-tft', 'waterkettle', 'microwaveoven', 'tv-crt', 'amplifier']\n",
      "\n",
      "13_monitor-tft\n",
      "['pc-desktop', 'projector', 'monitor-tft', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "34_monitor-tft\n",
      "['lamp', 'monitor-tft', 'amplifier', 'monitor-crt', 'monitor-tft']\n",
      "\n",
      "46_monitor-tft\n",
      "['lamp', 'washingmachine', 'toaster', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "50_monitor-tft\n",
      "['monitor-tft', 'waterfountain', 'monitor-tft', 'coffeemaker', 'dishwasher']\n",
      "\n",
      "8_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'monitor-tft', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "12_monitor-tft\n",
      "['waterkettle', 'monitor-tft', 'washingmachine', 'monitor-tft', 'solarthermalsystem']\n",
      "\n",
      "41_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'monitor-tft', 'pc-desktop', 'monitor-tft']\n",
      "\n",
      "16_monitor-tft\n",
      "['pc-laptop', 'monitor-tft', 'amplifier', 'monitor-tft', 'laundrydryer']\n",
      "\n",
      "2_monitor-tft\n",
      "['monitor-tft', 'microwaveoven', 'projector', 'projector', 'lamp']\n",
      "\n",
      "27_monitor-tft\n",
      "['lamp', 'lamp', 'amplifier', 'lamp', 'waterkettle']\n",
      "\n",
      "23_monitor-tft\n",
      "['lamp', 'lamp', 'waterkettle', 'washingmachine', 'monitor-tft']\n",
      "\n",
      "11_monitor-tft\n",
      "['monitor-tft', 'waterkettle', 'refrigerator', 'amplifier', 'monitor-tft']\n",
      "\n",
      "43_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'monitor-tft', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "31_monitor-tft\n",
      "['pc-laptop', 'monitor-tft', 'pc-laptop', 'lamp', 'waterkettle']\n",
      "\n",
      "18_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'monitor-tft', 'lamp', 'monitor-tft']\n",
      "\n",
      "25_monitor-tft\n",
      "['monitor-crt', 'monitor-tft', 'monitor-crt', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "39_monitor-tft\n",
      "['monitor-tft', 'washingmachine', 'monitor-tft', 'pc-desktop', 'monitor-tft']\n",
      "\n",
      "42_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'monitor-tft', 'pc-desktop', 'usbharddrive']\n",
      "\n",
      "36_monitor-tft\n",
      "['waterkettle', 'microwaveoven', 'lamp', 'waterkettle', 'washingmachine']\n",
      "\n",
      "32_monitor-tft\n",
      "['amplifier', 'monitor-tft', 'monitor-tft', 'monitor-crt', 'amplifier']\n",
      "\n",
      "29_monitor-tft\n",
      "['dishwasher', 'lamp', 'pc-desktop', 'pc-desktop', 'monitor-tft']\n",
      "\n",
      "47_monitor-tft\n",
      "['monitor-tft', 'pc-laptop', 'waterkettle', 'amplifier', 'monitor-tft']\n",
      "\n",
      "24_monitor-tft\n",
      "['monitor-tft', 'monitor-crt', 'monitor-tft', 'monitor-crt', 'usbharddrive']\n",
      "\n",
      "38_monitor-tft\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "40_monitor-tft\n",
      "['waterkettle', 'lamp', 'lamp', 'monitor-tft', 'amplifier']\n",
      "\n",
      "45_monitor-tft\n",
      "['amplifier', 'lamp', 'lamp', 'monitor-tft', 'amplifier']\n",
      "\n",
      "35_monitor-tft\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'toaster', 'monitor-crt']\n",
      "\n",
      "48_monitor-tft\n",
      "['tv-crt', 'microwaveoven', 'tv-lcd', 'lamp', 'tv-lcd']\n",
      "\n",
      "9_monitor-tft\n",
      "['monitor-tft', 'tv-lcd', 'amplifier', 'amplifier', 'monitor-tft']\n",
      "\n",
      "14_monitor-tft\n",
      "['monitor-tft', 'waterkettle', 'amplifier', 'monitor-crt', 'lamp']\n",
      "\n",
      "26_monitor-tft\n",
      "['lamp', 'tv-lcd', 'tv-crt', 'tv-lcd', 'waterkettle']\n",
      "\n",
      "0_monitor-tft\n",
      "['monitor-tft', 'pc-desktop', 'usbharddrive', 'refrigerator', 'refrigerator']\n",
      "\n",
      "10_monitor-tft\n",
      "['amplifier', 'monitor-crt', 'monitor-tft', 'ethernetswitch', 'monitor-tft']\n",
      "\n",
      "49_monitor-tft\n",
      "['monitor-tft', 'dishwasher', 'pc-desktop', 'amplifier', 'amplifier']\n",
      "\n",
      "1_monitor-tft\n",
      "['pc-desktop', 'monitor-tft', 'pc-desktop', 'ethernetswitch', 'refrigerator']\n",
      "\n",
      "28_monitor-tft\n",
      "['waterkettle', 'monitor-crt', 'monitor-tft', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "33_monitor-tft\n",
      "['amplifier', 'lamp', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "15_monitor-tft\n",
      "['refrigerator', 'monitor-tft', 'freezer', 'lamp', 'lamp']\n",
      "\n",
      "37_monitor-tft\n",
      "['lamp', 'lamp', 'waterkettle', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "6_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'router', 'router', 'amplifier']\n",
      "\n",
      "3_monitor-tft\n",
      "['projector', 'monitor-tft', 'projector', 'microwaveoven', 'remotedesktop']\n",
      "\n",
      "44_monitor-tft\n",
      "['lamp', 'amplifier', 'lamp', 'microwaveoven', 'monitor-tft']\n",
      "\n",
      "30_monitor-tft\n",
      "['monitor-tft', 'pc-desktop', 'monitor-tft', 'pc-laptop', 'pc-laptop']\n",
      "\n",
      "4_monitor-tft\n",
      "['monitor-tft', 'lamp', 'lamp', 'lamp', 'monitor-tft']\n",
      "\n",
      "7_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'waterboiler', 'printer', 'lamp']\n",
      "\n",
      "20_monitor-tft\n",
      "['monitor-tft', 'monitor-crt', 'monitor-tft', 'monitor-crt', 'pc-desktop']\n",
      "\n",
      "21_monitor-tft\n",
      "['monitor-tft', 'xmaslights', 'amplifier', 'tv-lcd', 'monitor-tft']\n",
      "\n",
      "22_monitor-tft\n",
      "['monitor-tft', 'monitor-tft', 'monitor-tft', 'projector', 'dishwasher']\n",
      "\n",
      "0_freezer\n",
      "['amplifier', 'monitor-tft', 'lamp', 'amplifier', 'pc-desktop']\n",
      "\n",
      "1_freezer\n",
      "['waterfountain', 'monitor-crt', 'monitor-tft', 'xmaslights', 'lamp']\n",
      "\n",
      "0_charger-psp\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "0_cookingstove\n",
      "['waterkettle', 'monitor-tft', 'monitor-tft', 'lamp', 'tv-crt']\n",
      "\n",
      "1_cookingstove\n",
      "['microwaveoven', 'waterkettle', 'lamp', 'toaster', 'waterkettle']\n",
      "\n",
      "3_cookingstove\n",
      "['pc-desktop', 'monitor-tft', 'monitor-crt', 'projector', 'monitor-tft']\n",
      "\n",
      "2_cookingstove\n",
      "['microwaveoven', 'lamp', 'waterkettle', 'toaster', 'washingmachine']\n",
      "\n",
      "0_amplifier\n",
      "['amplifier', 'amplifier', 'pc-desktop', 'solarthermalsystem', 'amplifier']\n",
      "\n",
      "14_amplifier\n",
      "['lamp', 'microwaveoven', 'lamp', 'waterkettle', 'washingmachine']\n",
      "\n",
      "4_amplifier\n",
      "['monitor-crt', 'microwaveoven', 'waterkettle', 'lamp', 'microwaveoven']\n",
      "\n",
      "6_amplifier\n",
      "['amplifier', 'waterkettle', 'monitor-tft', 'monitor-tft', 'lamp']\n",
      "\n",
      "16_amplifier\n",
      "['waterkettle', 'toaster', 'microwaveoven', 'monitor-crt', 'waterkettle']\n",
      "\n",
      "11_amplifier\n",
      "['lamp', 'microwaveoven', 'lamp', 'lamp', 'amplifier']\n",
      "\n",
      "2_amplifier\n",
      "['amplifier', 'pc-desktop', 'amplifier', 'solarthermalsystem', 'monitor-tft']\n",
      "\n",
      "9_amplifier\n",
      "['amplifier', 'amplifier', 'monitor-crt', 'washingmachine', 'monitor-tft']\n",
      "\n",
      "8_amplifier\n",
      "['lamp', 'monitor-tft', 'waterkettle', 'lamp', 'monitor-crt']\n",
      "\n",
      "7_amplifier\n",
      "['amplifier', 'waterkettle', 'solarthermalsystem', 'pc-laptop', 'amplifier']\n",
      "\n",
      "3_amplifier\n",
      "['monitor-crt', 'amplifier', 'monitor-tft', 'monitor-tft', 'laundrydryer']\n",
      "\n",
      "5_amplifier\n",
      "['monitor-tft', 'waterkettle', 'lamp', 'amplifier', 'monitor-tft']\n",
      "\n",
      "12_amplifier\n",
      "['lamp', 'amplifier', 'lamp', 'washingmachine', 'monitor-tft']\n",
      "\n",
      "13_amplifier\n",
      "['pc-laptop', 'monitor-tft', 'lamp', 'waterkettle', 'monitor-tft']\n",
      "\n",
      "1_amplifier\n",
      "['amplifier', 'digitaltvreceiver', 'digitaltvreceiver', 'digitaltvreceiver', 'lamp']\n",
      "\n",
      "10_amplifier\n",
      "['lamp', 'monitor-tft', 'toaster', 'washingmachine', 'microwaveoven']\n",
      "\n",
      "15_amplifier\n",
      "['amplifier', 'monitor-tft', 'monitor-tft', 'waterkettle', 'monitor-crt']\n",
      "\n",
      "22_tv-lcd\n",
      "['tv-lcd', 'ethernetswitch', 'ethernetswitch', 'router', 'tv-lcd']\n",
      "\n",
      "13_tv-lcd\n",
      "['lamp', 'lamp', 'amplifier', 'monitor-tft', 'waterkettle']\n",
      "\n",
      "0_tv-lcd\n",
      "['lamp', 'lamp', 'monitor-tft', 'microwaveoven', 'waterkettle']\n",
      "\n",
      "10_tv-lcd\n",
      "['microwaveoven', 'washingmachine', 'lamp', 'waterkettle', 'amplifier']\n",
      "\n",
      "21_tv-lcd\n",
      "['tv-lcd', 'tv-lcd', 'ethernetswitch', 'ethernetswitch', 'tv-lcd']\n",
      "\n",
      "7_tv-lcd\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "6_tv-lcd\n",
      "['monitor-tft', 'waterkettle', 'monitor-tft', 'monitor-crt', 'lamp']\n",
      "\n",
      "12_tv-lcd\n",
      "['pc-laptop', 'monitor-crt', 'monitor-tft', 'pc-laptop', 'monitor-tft']\n",
      "\n",
      "18_tv-lcd\n",
      "['tv-lcd', 'waterkettle', 'lamp', 'lamp', 'refrigerator']\n",
      "\n",
      "3_tv-lcd\n",
      "['monitor-tft', 'amplifier', 'monitor-crt', 'pc-desktop', 'monitor-tft']\n",
      "\n",
      "15_tv-lcd\n",
      "['microwaveoven', 'monitor-crt', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "14_tv-lcd\n",
      "['projector', 'pc-laptop', 'videoprojector', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "4_tv-lcd\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "2_tv-lcd\n",
      "['microwaveoven', 'lamp', 'lamp', 'toaster', 'monitor-tft']\n",
      "\n",
      "8_tv-lcd\n",
      "['lamp', 'washingmachine', 'toaster', 'monitor-tft', 'lamp']\n",
      "\n",
      "24_tv-lcd\n",
      "['tv-lcd', 'ethernetswitch', 'ethernetswitch', 'tv-lcd', 'tv-lcd']\n",
      "\n",
      "5_tv-lcd\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "11_tv-lcd\n",
      "['projector', 'monitor-tft', 'pc-desktop', 'ethernetswitch', 'monitor-tft']\n",
      "\n",
      "25_tv-lcd\n",
      "['tv-lcd', 'router', 'digitaltvreceiver', 'ethernetswitch', 'ethernetswitch']\n",
      "\n",
      "26_tv-lcd\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "16_tv-lcd\n",
      "['tv-lcd', 'usbharddrive', 'monitor-tft', 'waterkettle', 'refrigerator']\n",
      "\n",
      "9_tv-lcd\n",
      "['washingmachine', 'lamp', 'monitor-tft', 'lamp', 'amplifier']\n",
      "\n",
      "1_tv-lcd\n",
      "['monitor-crt', 'amplifier', 'monitor-tft', 'tv-lcd', 'pc-desktop']\n",
      "\n",
      "19_tv-lcd\n",
      "['monitor-crt', 'amplifier', 'monitor-tft', 'waterkettle', 'pc-laptop']\n",
      "\n",
      "23_tv-lcd\n",
      "['tv-lcd', 'digitaltvreceiver', 'pc-laptop', 'pc-laptop', 'pc-desktop']\n",
      "\n",
      "20_tv-lcd\n",
      "['waterkettle', 'lamp', 'waterkettle', 'lamp', 'waterkettle']\n",
      "\n",
      "17_tv-lcd\n",
      "['waterkettle', 'waterkettle', 'lamp', 'lamp', 'lamp']\n",
      "\n",
      "1_multimediacenter\n",
      "['multimediacenter', 'multimediacenter', 'usbharddrive', 'multimediacenter', 'refrigerator']\n",
      "\n",
      "0_multimediacenter\n",
      "['microwaveoven', 'multimediacenter', 'projector', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "4_multimediacenter\n",
      "['multimediacenter', 'router', 'digitaltvreceiver', 'tv-lcd', 'ethernetswitch']\n",
      "\n",
      "5_multimediacenter\n",
      "['multimediacenter', 'pc-laptop', 'microwaveoven', 'pc-desktop', 'cdplayer']\n",
      "\n",
      "2_multimediacenter\n",
      "['multimediacenter', 'multimediacenter', 'multimediacenter', 'usbharddrive', 'refrigerator']\n",
      "\n",
      "3_multimediacenter\n",
      "['multimediacenter', 'multimediacenter', 'multimediacenter', 'monitor-tft', 'usbharddrive']\n",
      "\n",
      "10_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'microwaveoven', 'coffeemaker']\n",
      "\n",
      "14_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'multimediacenter', 'amplifier']\n",
      "\n",
      "20_coffeemaker\n",
      "['coffeemaker', 'dishwasher', 'coffeemaker', 'microwaveoven', 'playstation3']\n",
      "\n",
      "1_coffeemaker\n",
      "['coffeemaker', 'dishwasher', 'dishwasher', 'projector', 'microwaveoven']\n",
      "\n",
      "6_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker']\n",
      "\n",
      "3_coffeemaker\n",
      "['dishwasher', 'dishwasher', 'coffeemaker', 'dishwasher', 'dishwasher']\n",
      "\n",
      "17_coffeemaker\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'monitor-crt', 'toaster']\n",
      "\n",
      "0_coffeemaker\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "4_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker']\n",
      "\n",
      "16_coffeemaker\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "2_coffeemaker\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'toaster', 'monitor-crt']\n",
      "\n",
      "22_coffeemaker\n",
      "['waterkettle', 'toaster', 'microwaveoven', 'waterkettle', 'waterkettle']\n",
      "\n",
      "19_coffeemaker\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "11_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker']\n",
      "\n",
      "15_coffeemaker\n",
      "['waterkettle', 'microwaveoven', 'toaster', 'waterkettle', 'waterkettle']\n",
      "\n",
      "18_coffeemaker\n",
      "['waterkettle', 'toaster', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "5_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker']\n",
      "\n",
      "12_coffeemaker\n",
      "['dishwasher', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker']\n",
      "\n",
      "7_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker']\n",
      "\n",
      "13_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'dvdplayer']\n",
      "\n",
      "9_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'dishwasher']\n",
      "\n",
      "8_coffeemaker\n",
      "['coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker', 'coffeemaker']\n",
      "\n",
      "21_coffeemaker\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "2_laundrydryer\n",
      "['laundrydryer', 'pc-desktop', 'projector', 'projector', 'monitor-tft']\n",
      "\n",
      "0_laundrydryer\n",
      "['laundrydryer', 'usbharddrive', 'usbharddrive', 'xmaslights', 'amplifier']\n",
      "\n",
      "1_laundrydryer\n",
      "['laundrydryer', 'pc-desktop', 'ethernetswitch', 'pc-desktop', 'amplifier']\n",
      "\n",
      "5_laundrydryer\n",
      "['pc-laptop', 'lamp', 'laundrydryer', 'washingmachine', 'monitor-tft']\n",
      "\n",
      "7_laundrydryer\n",
      "['microwaveoven', 'lamp', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "6_laundrydryer\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "3_laundrydryer\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "4_laundrydryer\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "2_xmaslights\n",
      "['amplifier', 'amplifier', 'washingmachine', 'monitor-tft', 'xmaslights']\n",
      "\n",
      "0_xmaslights\n",
      "['waterkettle', 'monitor-crt', 'pc-laptop', 'waterkettle', 'waterkettle']\n",
      "\n",
      "1_xmaslights\n",
      "['monitor-tft', 'monitor-crt', 'lamp', 'pc-desktop', 'pc-laptop']\n",
      "\n",
      "0_breadcutter\n",
      "['waterkettle', 'microwaveoven', 'washingmachine', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "4_playstation3\n",
      "['playstation3', 'playstation3', 'playstation3', 'playstation3', 'microwaveoven']\n",
      "\n",
      "0_playstation3\n",
      "['playstation3', 'dishwasher', 'dishwasher', 'dishwasher', 'dishwasher']\n",
      "\n",
      "1_playstation3\n",
      "['playstation3', 'playstation3', 'playstation3', 'playstation3', 'microwaveoven']\n",
      "\n",
      "2_playstation3\n",
      "['playstation3', 'playstation3', 'playstation3', 'playstation3', 'microwaveoven']\n",
      "\n",
      "3_playstation3\n",
      "['playstation3', 'playstation3', 'playstation3', 'playstation3', 'microwaveoven']\n",
      "\n",
      "5_playstation3\n",
      "['monitor-tft', 'microwaveoven', 'monitor-crt', 'washingmachine', 'toaster']\n",
      "\n",
      "4_dishwasher\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "14_dishwasher\n",
      "['monitor-crt', 'playstation3', 'washingmachine', 'monitor-tft', 'dishwasher']\n",
      "\n",
      "17_dishwasher\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'videoprojector', 'dishwasher']\n",
      "\n",
      "35_dishwasher\n",
      "['amplifier', 'monitor-tft', 'dishwasher', 'monitor-tft', 'amplifier']\n",
      "\n",
      "27_dishwasher\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "3_dishwasher\n",
      "['monitor-tft', 'monitor-tft', 'monitor-crt', 'tv-lcd', 'playstation3']\n",
      "\n",
      "19_dishwasher\n",
      "['washingmachine', 'videoprojector', 'videoprojector', 'dishwasher', 'coffeemaker']\n",
      "\n",
      "10_dishwasher\n",
      "['coffeemaker', 'projector', 'dishwasher', 'microwaveoven', 'dishwasher']\n",
      "\n",
      "26_dishwasher\n",
      "['microwaveoven', 'waterkettle', 'toaster', 'waterkettle', 'lamp']\n",
      "\n",
      "37_dishwasher\n",
      "['amplifier', 'amplifier', 'washingmachine', 'solarthermalsystem', 'washingmachine']\n",
      "\n",
      "18_dishwasher\n",
      "['projector', 'monitor-tft', 'dishwasher', 'pc-desktop', 'coffeemaker']\n",
      "\n",
      "25_dishwasher\n",
      "['playstation3', 'dishwasher', 'dishwasher', 'dishwasher', 'washingmachine']\n",
      "\n",
      "21_dishwasher\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "28_dishwasher\n",
      "['microwaveoven', 'lamp', 'toaster', 'monitor-crt', 'waterkettle']\n",
      "\n",
      "39_dishwasher\n",
      "['dishwasher', 'router', 'dishwasher', 'router', 'dishwasher']\n",
      "\n",
      "32_dishwasher\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "6_dishwasher\n",
      "['lamp', 'microwaveoven', 'lamp', 'monitor-tft', 'tv-lcd']\n",
      "\n",
      "20_dishwasher\n",
      "['amplifier', 'amplifier', 'solarthermalsystem', 'waterkettle', 'pc-desktop']\n",
      "\n",
      "1_dishwasher\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "11_dishwasher\n",
      "['dishwasher', 'microwaveoven', 'coffeemaker', 'coffeemaker', 'videoprojector']\n",
      "\n",
      "8_dishwasher\n",
      "['microwaveoven', 'lamp', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "22_dishwasher\n",
      "['videoprojector', 'videoprojector', 'videoprojector', 'videoprojector', 'washingmachine']\n",
      "\n",
      "13_dishwasher\n",
      "['cdplayer', 'laundrydryer', 'dishwasher', 'tv-lcd', 'waterboiler']\n",
      "\n",
      "0_dishwasher\n",
      "['amplifier', 'solarthermalsystem', 'amplifier', 'pc-desktop', 'pc-laptop']\n",
      "\n",
      "38_dishwasher\n",
      "['monitor-tft', 'waterkettle', 'microwaveoven', 'tv-crt', 'amplifier']\n",
      "\n",
      "16_dishwasher\n",
      "['playstation3', 'dishwasher', 'monitor-tft', 'dishwasher', 'dishwasher']\n",
      "\n",
      "24_dishwasher\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "15_dishwasher\n",
      "['dishwasher', 'pc-desktop', 'laundrydryer', 'monitor-tft', 'cdplayer']\n",
      "\n",
      "30_dishwasher\n",
      "['playstation3', 'dishwasher', 'dishwasher', 'dishwasher', 'washingmachine']\n",
      "\n",
      "36_dishwasher\n",
      "['microwaveoven', 'monitor-crt', 'waterkettle', 'toaster', 'waterkettle']\n",
      "\n",
      "34_dishwasher\n",
      "['monitor-tft', 'microwaveoven', 'lamp', 'subwoofer', 'washingmachine']\n",
      "\n",
      "2_dishwasher\n",
      "['amplifier', 'solarthermalsystem', 'amplifier', 'pc-desktop', 'pc-laptop']\n",
      "\n",
      "7_dishwasher\n",
      "['laundrydryer', 'playstation3', 'monitor-crt', 'tv-lcd', 'subwoofer']\n",
      "\n",
      "9_dishwasher\n",
      "['amplifier', 'solarthermalsystem', 'amplifier', 'waterkettle', 'pc-desktop']\n",
      "\n",
      "12_dishwasher\n",
      "['printer', 'pc-desktop', 'printer', 'microwaveoven', 'dishwasher']\n",
      "\n",
      "5_dishwasher\n",
      "['washingmachine', 'pc-laptop', 'pc-laptop', 'lamp', 'monitor-tft']\n",
      "\n",
      "29_dishwasher\n",
      "['microwaveoven', 'lamp', 'monitor-crt', 'monitor-tft', 'toaster']\n",
      "\n",
      "33_dishwasher\n",
      "['dishwasher', 'router', 'router', 'pc-desktop', 'tv-lcd']\n",
      "\n",
      "31_dishwasher\n",
      "['lamp', 'microwaveoven', 'lamp', 'monitor-tft', 'monitor-tft']\n",
      "\n",
      "23_dishwasher\n",
      "['waterkettle', 'waterkettle', 'waterkettle', 'waterkettle', 'waterkettle']\n",
      "\n",
      "1_charger-smartphone\n",
      "['tv-lcd', 'charger-smartphone', 'router', 'dvdplayer', 'ethernetswitch']\n",
      "\n",
      "0_charger-smartphone\n",
      "['charger-smartphone', 'microwaveoven', 'multimediacenter', 'pc-desktop', 'lamp']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_n_similarities(similarities, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True predictions:    310\n",
      "False predictions:   170\n",
      "Accuracy:            0.646\n"
     ]
    }
   ],
   "source": [
    "print_accuracy(top_5_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4_prediction'></a>\n",
    "\n",
    "## 4. Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We will use the `Word2Vec` model to predict one category of the next device consumption.**\n",
    "\n",
    "Before that, we will create two new data sets - the first will contain training consumption and the second testing consumption data. Then, `Word2Vec` model will try to predict the next word or in our case the next consumption vector. Prediction will be divided into devices, that are either on/off and all other devices. The results will be evaluated using `MSE` error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Preparing forecast data sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define **new data set paths**, which names are `forecast-train` and `forecast-test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_train_path = 'datasets/forecast-train/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_test_path = 'datasets/forecast-test/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below is used to **create** two new data sets - `forecast-train` and `forecast-test`. Consumption data is read, divided into training and testing data and written to files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_to_forecast(consumptions_path, forecast_train_path, forecast_test_path, drop_perc=0.3):\n",
    "    \"\"\"\n",
    "    The function reads, prepares forecast train in test data and writes them into a file.\n",
    "    \n",
    "    Args:\n",
    "        consumptions_path   [str]:   Path to read consumptions from.\n",
    "        forecast_train_path [str]:   Path to write forecast train data to.\n",
    "        forecast_test_path  [str]:   Path to write forecast test data to.\n",
    "        drop_perc           [float]: Percent of first consumption data to drop.\n",
    "        \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    for dirname in os.listdir(consumptions_path):\n",
    "            create_dirs(forecast_train_path + dirname)\n",
    "            create_dirs(forecast_test_path + dirname)\n",
    "            \n",
    "            for filename in os.listdir(consumptions_path + dirname):\n",
    "                with open(consumptions_path + dirname + os.sep + filename, 'r') as f:\n",
    "                    data = list(filter(str.strip, [r for r in csv.reader(f, delimiter=' ')][0]))\n",
    "                \n",
    "                with open(forecast_train_path + dirname + os.sep + filename, 'w') as f:\n",
    "                    f.write(' '.join(dropout_last(data, drop_perc)))\n",
    "                \n",
    "                with open(forecast_test_path + dirname + os.sep + filename, 'w') as f:\n",
    "                    f.write(' '.join(dropout_first(data, drop_perc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropout_first(array, drop_perc):\n",
    "    \"\"\"\n",
    "    The function drops given percent of first consumption data.\n",
    "    \n",
    "    Args:\n",
    "        array     [numpy.ndarray]: Consumption data to drop from.\n",
    "        drop_perc [float]:         Percent of first consumption data to drop.\n",
    "        \n",
    "    Returns:\n",
    "        [numpy.ndarray]: Consumption data on which dropping was performed.\n",
    "    \"\"\"\n",
    "    \n",
    "    return array[len(array) - int(round(len(array) * drop_perc)):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_to_forecast(consumptions_path, forecast_train_path, forecast_test_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Getting predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define which types of devices are **on/off** devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "of_devices = ['amplifier',\n",
    "              'microwaveoven',\n",
    "              'dishwasher',\n",
    "              'washingmachine',\n",
    "              'toaster',\n",
    "              'charger-psp',\n",
    "              'vacuumcleaner',\n",
    "              'freezer',\n",
    "              'cookingstove',\n",
    "              'waterkettle',\n",
    "              'breadcutter',\n",
    "              'waterfountain',\n",
    "              'iron',\n",
    "              'subwoofer',\n",
    "              'dvdplayer',\n",
    "              'videoprojector',\n",
    "              'projector',\n",
    "              'tv-lcd',\n",
    "              'laundrydryer',\n",
    "              'lamp',\n",
    "              'monitor-tft']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predictions** will be obtained from the function below, which uses the `predict_output_word` function of the `Word2Vec` model to predict the next consumption category. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_predictions(model, path, n=5, of=False):\n",
    "    \"\"\"\n",
    "    The function obtains predictions of the devices next consumption category.\n",
    "    \n",
    "    Args:\n",
    "        model [gensim.Word2Vec]: Word2Vec model.\n",
    "        path  [str]:             Path to read forecast train data from.\n",
    "        n     [int]:             Number of predictions to obtain.\n",
    "        of    [bool]:            Bool indicating if consumptions of on/off devices are being predicted.\n",
    "    \n",
    "    Returns:\n",
    "        [dict]: Predictions of the devices next consumption category.\n",
    "    \"\"\"\n",
    "    \n",
    "    predictions = {}\n",
    "    \n",
    "    for i in os.listdir(path):\n",
    "        if (of and i in of_devices) or (not of and i not in of_devices):\n",
    "            for j in os.listdir(path + i):\n",
    "                prediction = model.predict_output_word(file_to_string_array(path + i + os.sep + j), n)\n",
    "                predictions[j[:-4]] = [int(k[0]) for k in prediction]\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = get_predictions(w2v_model, forecast_train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "of_predictions = get_predictions(w2v_model, forecast_train_path, of=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The obtained predictions will be evaluated with a new function that acquires **`MSE` errors**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mse(predictions, path):\n",
    "    \"\"\"\n",
    "    The function obtains MSE errors of next consumption category predictions. \n",
    "    \n",
    "    Args:\n",
    "        predictions [dict]: Predictions of the devices next consumption category.\n",
    "        path        [str]:  Path to read forecast test data from.\n",
    "    \n",
    "    Returns:\n",
    "        [dict]: MSE errors of next consumption category predictions.\n",
    "    \"\"\"\n",
    "    \n",
    "    mse = {}\n",
    "    \n",
    "    for i in predictions:\n",
    "        actual = file_to_string_array(path + i.rsplit('_')[1] + os.sep + i + '.csv')\n",
    "        mse[i] = mean_squared_error([int(actual[0])], [predictions[i][0]])\n",
    "    \n",
    "    return mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = get_mse(predictions, forecast_test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "of_mse = get_mse(of_predictions, forecast_test_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Printing prediction results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The defined function **prints** next consumption category predictions `MSE` errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_mse(mse):\n",
    "    \"\"\"\n",
    "    The function prints MSE errors.\n",
    "    \n",
    "    Args:\n",
    "        mse [dict]: MSE errors.\n",
    "    \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    [print(\"{0:25} {1}\".format(i, mse[i])) for i in mse]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we print `MSE` errors for **other** devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_waterboiler             196.0\n",
      "2_digitaltvreceiver       4.0\n",
      "3_digitaltvreceiver       0.0\n",
      "0_digitaltvreceiver       1.0\n",
      "1_digitaltvreceiver       25.0\n",
      "4_digitaltvreceiver       16.0\n",
      "23_pc-desktop             0.0\n",
      "3_pc-desktop              0.0\n",
      "5_pc-desktop              0.0\n",
      "9_pc-desktop              400.0\n",
      "1_pc-desktop              36.0\n",
      "8_pc-desktop              0.0\n",
      "13_pc-desktop             0.0\n",
      "7_pc-desktop              400.0\n",
      "20_pc-desktop             0.0\n",
      "18_pc-desktop             0.0\n",
      "0_pc-desktop              256.0\n",
      "22_pc-desktop             400.0\n",
      "11_pc-desktop             0.0\n",
      "17_pc-desktop             4.0\n",
      "10_pc-desktop             0.0\n",
      "14_pc-desktop             0.0\n",
      "2_pc-desktop              121.0\n",
      "12_pc-desktop             0.0\n",
      "6_pc-desktop              0.0\n",
      "25_pc-desktop             121.0\n",
      "21_pc-desktop             400.0\n",
      "19_pc-desktop             0.0\n",
      "16_pc-desktop             4.0\n",
      "24_pc-desktop             0.0\n",
      "15_pc-desktop             0.0\n",
      "4_pc-desktop              0.0\n",
      "7_pc-laptop               0.0\n",
      "4_pc-laptop               4.0\n",
      "10_pc-laptop              400.0\n",
      "0_pc-laptop               400.0\n",
      "1_pc-laptop               0.0\n",
      "9_pc-laptop               0.0\n",
      "6_pc-laptop               4.0\n",
      "12_pc-laptop              121.0\n",
      "3_pc-laptop               25.0\n",
      "11_pc-laptop              1.0\n",
      "8_pc-laptop               0.0\n",
      "5_pc-laptop               169.0\n",
      "2_pc-laptop               0.0\n",
      "1_usbhub                  1.0\n",
      "0_usbhub                  4.0\n",
      "1_beantocupcoffeemaker    0.0\n",
      "0_beantocupcoffeemaker    1.0\n",
      "0_alarmclock              0.0\n",
      "7_usbharddrive            0.0\n",
      "4_usbharddrive            0.0\n",
      "1_usbharddrive            81.0\n",
      "3_usbharddrive            144.0\n",
      "5_usbharddrive            1.0\n",
      "0_usbharddrive            81.0\n",
      "13_usbharddrive           121.0\n",
      "9_usbharddrive            1.0\n",
      "2_usbharddrive            64.0\n",
      "8_usbharddrive            64.0\n",
      "12_usbharddrive           1.0\n",
      "6_usbharddrive            1.0\n",
      "10_usbharddrive           4.0\n",
      "11_usbharddrive           1.0\n",
      "3_router                  25.0\n",
      "8_router                  1.0\n",
      "6_router                  16.0\n",
      "4_router                  4.0\n",
      "5_router                  0.0\n",
      "1_router                  100.0\n",
      "2_router                  1.0\n",
      "7_router                  1.0\n",
      "0_router                  49.0\n",
      "2_solarthermalsystem      0.0\n",
      "1_solarthermalsystem      0.0\n",
      "0_solarthermalsystem      0.0\n",
      "0_monitor-crt             400.0\n",
      "4_monitor-crt             400.0\n",
      "7_monitor-crt             196.0\n",
      "3_monitor-crt             400.0\n",
      "6_monitor-crt             0.0\n",
      "1_monitor-crt             256.0\n",
      "2_monitor-crt             0.0\n",
      "5_monitor-crt             400.0\n",
      "1_tv-crt                  0.0\n",
      "4_tv-crt                  0.0\n",
      "2_tv-crt                  0.0\n",
      "0_tv-crt                  0.0\n",
      "3_tv-crt                  400.0\n",
      "5_tv-crt                  0.0\n",
      "0_remotedesktop           9.0\n",
      "6_printer                 9.0\n",
      "2_printer                 9.0\n",
      "0_printer                 0.0\n",
      "1_printer                 225.0\n",
      "4_printer                 1.0\n",
      "3_printer                 0.0\n",
      "5_printer                 121.0\n",
      "5_ethernetswitch          1.0\n",
      "3_ethernetswitch          0.0\n",
      "1_ethernetswitch          121.0\n",
      "2_ethernetswitch          0.0\n",
      "4_ethernetswitch          1.0\n",
      "0_ethernetswitch          36.0\n",
      "0_cdplayer                36.0\n",
      "8_refrigerator            0.0\n",
      "6_refrigerator            0.0\n",
      "4_refrigerator            0.0\n",
      "0_refrigerator            0.0\n",
      "10_refrigerator           0.0\n",
      "11_refrigerator           400.0\n",
      "15_refrigerator           9.0\n",
      "1_refrigerator            400.0\n",
      "7_refrigerator            1.0\n",
      "5_refrigerator            1.0\n",
      "9_refrigerator            169.0\n",
      "13_refrigerator           64.0\n",
      "18_refrigerator           4.0\n",
      "3_refrigerator            0.0\n",
      "14_refrigerator           144.0\n",
      "2_refrigerator            324.0\n",
      "17_refrigerator           324.0\n",
      "12_refrigerator           0.0\n",
      "16_refrigerator           196.0\n",
      "1_multimediacenter        1.0\n",
      "0_multimediacenter        1.0\n",
      "4_multimediacenter        0.0\n",
      "5_multimediacenter        25.0\n",
      "2_multimediacenter        1.0\n",
      "3_multimediacenter        1.0\n",
      "10_coffeemaker            361.0\n",
      "14_coffeemaker            0.0\n",
      "20_coffeemaker            9.0\n",
      "1_coffeemaker             0.0\n",
      "6_coffeemaker             121.0\n",
      "3_coffeemaker             0.0\n",
      "17_coffeemaker            0.0\n",
      "0_coffeemaker             0.0\n",
      "4_coffeemaker             361.0\n",
      "16_coffeemaker            0.0\n",
      "2_coffeemaker             0.0\n",
      "22_coffeemaker            0.0\n",
      "19_coffeemaker            0.0\n",
      "11_coffeemaker            9.0\n",
      "15_coffeemaker            0.0\n",
      "18_coffeemaker            0.0\n",
      "5_coffeemaker             361.0\n",
      "12_coffeemaker            0.0\n",
      "7_coffeemaker             1.0\n",
      "13_coffeemaker            0.0\n",
      "9_coffeemaker             1.0\n",
      "8_coffeemaker             1.0\n",
      "21_coffeemaker            0.0\n",
      "2_xmaslights              400.0\n",
      "0_xmaslights              400.0\n",
      "1_xmaslights              400.0\n",
      "4_playstation3            1.0\n",
      "0_playstation3            0.0\n",
      "1_playstation3            1.0\n",
      "2_playstation3            0.0\n",
      "3_playstation3            1.0\n",
      "5_playstation3            0.0\n",
      "1_charger-smartphone      4.0\n",
      "0_charger-smartphone      0.0\n"
     ]
    }
   ],
   "source": [
    "print_mse(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11793.0\n"
     ]
    }
   ],
   "source": [
    "print(sum(mse.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we print `MSE` errors for **on/off** devices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2_subwoofer               0.0\n",
      "3_subwoofer               0.0\n",
      "0_subwoofer               0.0\n",
      "1_subwoofer               0.0\n",
      "2_dvdplayer               324.0\n",
      "0_dvdplayer               0.0\n",
      "1_dvdplayer               0.0\n",
      "3_videoprojector          0.0\n",
      "2_videoprojector          0.0\n",
      "4_videoprojector          0.0\n",
      "1_videoprojector          0.0\n",
      "0_videoprojector          9.0\n",
      "6_videoprojector          0.0\n",
      "5_videoprojector          1.0\n",
      "11_lamp                   400.0\n",
      "26_lamp                   0.0\n",
      "4_lamp                    0.0\n",
      "31_lamp                   0.0\n",
      "9_lamp                    400.0\n",
      "10_lamp                   0.0\n",
      "5_lamp                    400.0\n",
      "19_lamp                   0.0\n",
      "7_lamp                    0.0\n",
      "1_lamp                    400.0\n",
      "6_lamp                    400.0\n",
      "13_lamp                   0.0\n",
      "21_lamp                   0.0\n",
      "14_lamp                   0.0\n",
      "16_lamp                   0.0\n",
      "23_lamp                   0.0\n",
      "3_lamp                    400.0\n",
      "24_lamp                   0.0\n",
      "27_lamp                   0.0\n",
      "17_lamp                   0.0\n",
      "25_lamp                   0.0\n",
      "2_lamp                    0.0\n",
      "29_lamp                   400.0\n",
      "22_lamp                   0.0\n",
      "20_lamp                   0.0\n",
      "15_lamp                   1.0\n",
      "0_lamp                    0.0\n",
      "30_lamp                   289.0\n",
      "32_lamp                   0.0\n",
      "28_lamp                   0.0\n",
      "12_lamp                   0.0\n",
      "18_lamp                   0.0\n",
      "8_lamp                    0.0\n",
      "5_toaster                 0.0\n",
      "1_toaster                 0.0\n",
      "0_toaster                 0.0\n",
      "4_toaster                 0.0\n",
      "3_toaster                 0.0\n",
      "2_toaster                 0.0\n",
      "6_microwaveoven           0.0\n",
      "24_microwaveoven          0.0\n",
      "4_microwaveoven           0.0\n",
      "16_microwaveoven          0.0\n",
      "26_microwaveoven          0.0\n",
      "14_microwaveoven          4.0\n",
      "21_microwaveoven          0.0\n",
      "17_microwaveoven          0.0\n",
      "18_microwaveoven          0.0\n",
      "19_microwaveoven          0.0\n",
      "23_microwaveoven          0.0\n",
      "7_microwaveoven           121.0\n",
      "9_microwaveoven           1.0\n",
      "1_microwaveoven           16.0\n",
      "0_microwaveoven           9.0\n",
      "13_microwaveoven          0.0\n",
      "22_microwaveoven          0.0\n",
      "25_microwaveoven          0.0\n",
      "10_microwaveoven          0.0\n",
      "15_microwaveoven          0.0\n",
      "20_microwaveoven          0.0\n",
      "8_microwaveoven           9.0\n",
      "5_microwaveoven           0.0\n",
      "12_microwaveoven          400.0\n",
      "2_microwaveoven           0.0\n",
      "11_microwaveoven          0.0\n",
      "3_microwaveoven           0.0\n",
      "39_washingmachine         0.0\n",
      "0_washingmachine          0.0\n",
      "24_washingmachine         0.0\n",
      "7_washingmachine          0.0\n",
      "22_washingmachine         0.0\n",
      "14_washingmachine         0.0\n",
      "17_washingmachine         0.0\n",
      "16_washingmachine         0.0\n",
      "19_washingmachine         0.0\n",
      "33_washingmachine         0.0\n",
      "6_washingmachine          0.0\n",
      "29_washingmachine         0.0\n",
      "27_washingmachine         0.0\n",
      "36_washingmachine         400.0\n",
      "1_washingmachine          0.0\n",
      "23_washingmachine         0.0\n",
      "37_washingmachine         0.0\n",
      "28_washingmachine         0.0\n",
      "9_washingmachine          0.0\n",
      "20_washingmachine         0.0\n",
      "38_washingmachine         400.0\n",
      "32_washingmachine         0.0\n",
      "15_washingmachine         0.0\n",
      "8_washingmachine          0.0\n",
      "31_washingmachine         0.0\n",
      "4_washingmachine          0.0\n",
      "35_washingmachine         0.0\n",
      "34_washingmachine         0.0\n",
      "26_washingmachine         0.0\n",
      "13_washingmachine         0.0\n",
      "3_washingmachine          0.0\n",
      "12_washingmachine         0.0\n",
      "25_washingmachine         0.0\n",
      "5_washingmachine          400.0\n",
      "18_washingmachine         0.0\n",
      "2_washingmachine          0.0\n",
      "40_washingmachine         0.0\n",
      "10_washingmachine         0.0\n",
      "21_washingmachine         0.0\n",
      "30_washingmachine         0.0\n",
      "11_washingmachine         0.0\n",
      "2_projector               0.0\n",
      "0_projector               0.0\n",
      "5_projector               0.0\n",
      "4_projector               9.0\n",
      "1_projector               0.0\n",
      "3_projector               0.0\n",
      "21_waterkettle            0.0\n",
      "16_waterkettle            0.0\n",
      "6_waterkettle             0.0\n",
      "30_waterkettle            0.0\n",
      "15_waterkettle            0.0\n",
      "1_waterkettle             0.0\n",
      "31_waterkettle            0.0\n",
      "12_waterkettle            0.0\n",
      "5_waterkettle             0.0\n",
      "19_waterkettle            0.0\n",
      "0_waterkettle             0.0\n",
      "24_waterkettle            0.0\n",
      "3_waterkettle             0.0\n",
      "2_waterkettle             0.0\n",
      "13_waterkettle            0.0\n",
      "11_waterkettle            0.0\n",
      "28_waterkettle            0.0\n",
      "14_waterkettle            0.0\n",
      "18_waterkettle            0.0\n",
      "32_waterkettle            0.0\n",
      "20_waterkettle            0.0\n",
      "7_waterkettle             0.0\n",
      "26_waterkettle            0.0\n",
      "10_waterkettle            0.0\n",
      "23_waterkettle            0.0\n",
      "22_waterkettle            0.0\n",
      "27_waterkettle            0.0\n",
      "25_waterkettle            0.0\n",
      "8_waterkettle             0.0\n",
      "29_waterkettle            0.0\n",
      "9_waterkettle             0.0\n",
      "4_waterkettle             0.0\n",
      "17_waterkettle            0.0\n",
      "0_vacuumcleaner           0.0\n",
      "0_waterfountain           0.0\n",
      "1_waterfountain           0.0\n",
      "2_waterfountain           400.0\n",
      "0_iron                    0.0\n",
      "17_monitor-tft            0.0\n",
      "5_monitor-tft             196.0\n",
      "19_monitor-tft            0.0\n",
      "13_monitor-tft            0.0\n",
      "34_monitor-tft            0.0\n",
      "46_monitor-tft            0.0\n",
      "50_monitor-tft            0.0\n",
      "8_monitor-tft             0.0\n",
      "12_monitor-tft            0.0\n",
      "41_monitor-tft            0.0\n",
      "16_monitor-tft            0.0\n",
      "2_monitor-tft             4.0\n",
      "27_monitor-tft            400.0\n",
      "23_monitor-tft            400.0\n",
      "11_monitor-tft            400.0\n",
      "43_monitor-tft            400.0\n",
      "31_monitor-tft            400.0\n",
      "18_monitor-tft            400.0\n",
      "25_monitor-tft            0.0\n",
      "39_monitor-tft            0.0\n",
      "42_monitor-tft            289.0\n",
      "36_monitor-tft            0.0\n",
      "32_monitor-tft            0.0\n",
      "29_monitor-tft            0.0\n",
      "47_monitor-tft            0.0\n",
      "24_monitor-tft            256.0\n",
      "38_monitor-tft            0.0\n",
      "40_monitor-tft            400.0\n",
      "45_monitor-tft            400.0\n",
      "35_monitor-tft            0.0\n",
      "48_monitor-tft            0.0\n",
      "9_monitor-tft             0.0\n",
      "14_monitor-tft            0.0\n",
      "26_monitor-tft            400.0\n",
      "0_monitor-tft             0.0\n",
      "10_monitor-tft            400.0\n",
      "49_monitor-tft            0.0\n",
      "1_monitor-tft             0.0\n",
      "28_monitor-tft            0.0\n",
      "33_monitor-tft            400.0\n",
      "15_monitor-tft            0.0\n",
      "37_monitor-tft            400.0\n",
      "6_monitor-tft             169.0\n",
      "3_monitor-tft             16.0\n",
      "44_monitor-tft            0.0\n",
      "30_monitor-tft            324.0\n",
      "4_monitor-tft             144.0\n",
      "7_monitor-tft             4.0\n",
      "20_monitor-tft            0.0\n",
      "21_monitor-tft            0.0\n",
      "22_monitor-tft            1.0\n",
      "0_freezer                 0.0\n",
      "1_freezer                 0.0\n",
      "0_charger-psp             0.0\n",
      "0_cookingstove            0.0\n",
      "1_cookingstove            0.0\n",
      "3_cookingstove            0.0\n",
      "2_cookingstove            0.0\n",
      "0_amplifier               0.0\n",
      "14_amplifier              0.0\n",
      "4_amplifier               0.0\n",
      "6_amplifier               0.0\n",
      "16_amplifier              0.0\n",
      "11_amplifier              400.0\n",
      "2_amplifier               0.0\n",
      "9_amplifier               0.0\n",
      "8_amplifier               400.0\n",
      "7_amplifier               400.0\n",
      "3_amplifier               0.0\n",
      "5_amplifier               0.0\n",
      "12_amplifier              0.0\n",
      "13_amplifier              0.0\n",
      "1_amplifier               0.0\n",
      "10_amplifier              0.0\n",
      "15_amplifier              400.0\n",
      "22_tv-lcd                 4.0\n",
      "13_tv-lcd                 0.0\n",
      "0_tv-lcd                  0.0\n",
      "10_tv-lcd                 0.0\n",
      "21_tv-lcd                 9.0\n",
      "7_tv-lcd                  0.0\n",
      "6_tv-lcd                  0.0\n",
      "12_tv-lcd                 0.0\n",
      "18_tv-lcd                 0.0\n",
      "3_tv-lcd                  0.0\n",
      "15_tv-lcd                 0.0\n",
      "14_tv-lcd                 0.0\n",
      "4_tv-lcd                  0.0\n",
      "2_tv-lcd                  0.0\n",
      "8_tv-lcd                  0.0\n",
      "24_tv-lcd                 81.0\n",
      "5_tv-lcd                  0.0\n",
      "11_tv-lcd                 0.0\n",
      "25_tv-lcd                 0.0\n",
      "26_tv-lcd                 0.0\n",
      "16_tv-lcd                 0.0\n",
      "9_tv-lcd                  0.0\n",
      "1_tv-lcd                  0.0\n",
      "19_tv-lcd                 0.0\n",
      "23_tv-lcd                 1.0\n",
      "20_tv-lcd                 0.0\n",
      "17_tv-lcd                 0.0\n",
      "2_laundrydryer            0.0\n",
      "0_laundrydryer            0.0\n",
      "1_laundrydryer            0.0\n",
      "5_laundrydryer            0.0\n",
      "7_laundrydryer            400.0\n",
      "6_laundrydryer            0.0\n",
      "3_laundrydryer            0.0\n",
      "4_laundrydryer            0.0\n",
      "0_breadcutter             0.0\n",
      "4_dishwasher              0.0\n",
      "14_dishwasher             16.0\n",
      "17_dishwasher             0.0\n",
      "35_dishwasher             0.0\n",
      "27_dishwasher             0.0\n",
      "3_dishwasher              0.0\n",
      "19_dishwasher             0.0\n",
      "10_dishwasher             0.0\n",
      "26_dishwasher             0.0\n",
      "37_dishwasher             0.0\n",
      "18_dishwasher             400.0\n",
      "25_dishwasher             0.0\n",
      "21_dishwasher             0.0\n",
      "28_dishwasher             0.0\n",
      "39_dishwasher             0.0\n",
      "32_dishwasher             0.0\n",
      "6_dishwasher              0.0\n",
      "20_dishwasher             400.0\n",
      "1_dishwasher              0.0\n",
      "11_dishwasher             0.0\n",
      "8_dishwasher              0.0\n",
      "22_dishwasher             0.0\n",
      "13_dishwasher             0.0\n",
      "0_dishwasher              0.0\n",
      "38_dishwasher             0.0\n",
      "16_dishwasher             0.0\n",
      "24_dishwasher             0.0\n",
      "15_dishwasher             0.0\n",
      "30_dishwasher             0.0\n",
      "36_dishwasher             0.0\n",
      "34_dishwasher             0.0\n",
      "2_dishwasher              0.0\n",
      "7_dishwasher              0.0\n",
      "9_dishwasher              0.0\n",
      "12_dishwasher             0.0\n",
      "5_dishwasher              0.0\n",
      "29_dishwasher             0.0\n",
      "33_dishwasher             0.0\n",
      "31_dishwasher             400.0\n",
      "23_dishwasher             0.0\n"
     ]
    }
   ],
   "source": [
    "print_mse(of_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15107.0\n"
     ]
    }
   ],
   "source": [
    "print(sum(of_mse.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purpose of comparing the results with new, we **merge** `MSE` error variables and **sort** them alphabetically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse.update(of_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_mse = dict(sorted(mse.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_alarmclock              0.0\n",
      "0_amplifier               0.0\n",
      "0_beantocupcoffeemaker    1.0\n",
      "0_breadcutter             0.0\n",
      "0_cdplayer                36.0\n",
      "0_charger-psp             0.0\n",
      "0_charger-smartphone      0.0\n",
      "0_coffeemaker             0.0\n",
      "0_cookingstove            0.0\n",
      "0_digitaltvreceiver       1.0\n",
      "0_dishwasher              0.0\n",
      "0_dvdplayer               0.0\n",
      "0_ethernetswitch          36.0\n",
      "0_freezer                 0.0\n",
      "0_iron                    0.0\n",
      "0_lamp                    0.0\n",
      "0_laundrydryer            0.0\n",
      "0_microwaveoven           9.0\n",
      "0_monitor-crt             400.0\n",
      "0_monitor-tft             0.0\n",
      "0_multimediacenter        1.0\n",
      "0_pc-desktop              256.0\n",
      "0_pc-laptop               400.0\n",
      "0_playstation3            0.0\n",
      "0_printer                 0.0\n",
      "0_projector               0.0\n",
      "0_refrigerator            0.0\n",
      "0_remotedesktop           9.0\n",
      "0_router                  49.0\n",
      "0_solarthermalsystem      0.0\n",
      "0_subwoofer               0.0\n",
      "0_toaster                 0.0\n",
      "0_tv-crt                  0.0\n",
      "0_tv-lcd                  0.0\n",
      "0_usbharddrive            81.0\n",
      "0_usbhub                  4.0\n",
      "0_vacuumcleaner           0.0\n",
      "0_videoprojector          9.0\n",
      "0_washingmachine          0.0\n",
      "0_waterboiler             196.0\n",
      "0_waterfountain           0.0\n",
      "0_waterkettle             0.0\n",
      "0_xmaslights              400.0\n",
      "10_amplifier              0.0\n",
      "10_coffeemaker            361.0\n",
      "10_dishwasher             0.0\n",
      "10_lamp                   0.0\n",
      "10_microwaveoven          0.0\n",
      "10_monitor-tft            400.0\n",
      "10_pc-desktop             0.0\n",
      "10_pc-laptop              400.0\n",
      "10_refrigerator           0.0\n",
      "10_tv-lcd                 0.0\n",
      "10_usbharddrive           4.0\n",
      "10_washingmachine         0.0\n",
      "10_waterkettle            0.0\n",
      "11_amplifier              400.0\n",
      "11_coffeemaker            9.0\n",
      "11_dishwasher             0.0\n",
      "11_lamp                   400.0\n",
      "11_microwaveoven          0.0\n",
      "11_monitor-tft            400.0\n",
      "11_pc-desktop             0.0\n",
      "11_pc-laptop              1.0\n",
      "11_refrigerator           400.0\n",
      "11_tv-lcd                 0.0\n",
      "11_usbharddrive           1.0\n",
      "11_washingmachine         0.0\n",
      "11_waterkettle            0.0\n",
      "12_amplifier              0.0\n",
      "12_coffeemaker            0.0\n",
      "12_dishwasher             0.0\n",
      "12_lamp                   0.0\n",
      "12_microwaveoven          400.0\n",
      "12_monitor-tft            0.0\n",
      "12_pc-desktop             0.0\n",
      "12_pc-laptop              121.0\n",
      "12_refrigerator           0.0\n",
      "12_tv-lcd                 0.0\n",
      "12_usbharddrive           1.0\n",
      "12_washingmachine         0.0\n",
      "12_waterkettle            0.0\n",
      "13_amplifier              0.0\n",
      "13_coffeemaker            0.0\n",
      "13_dishwasher             0.0\n",
      "13_lamp                   0.0\n",
      "13_microwaveoven          0.0\n",
      "13_monitor-tft            0.0\n",
      "13_pc-desktop             0.0\n",
      "13_refrigerator           64.0\n",
      "13_tv-lcd                 0.0\n",
      "13_usbharddrive           121.0\n",
      "13_washingmachine         0.0\n",
      "13_waterkettle            0.0\n",
      "14_amplifier              0.0\n",
      "14_coffeemaker            0.0\n",
      "14_dishwasher             16.0\n",
      "14_lamp                   0.0\n",
      "14_microwaveoven          4.0\n",
      "14_monitor-tft            0.0\n",
      "14_pc-desktop             0.0\n",
      "14_refrigerator           144.0\n",
      "14_tv-lcd                 0.0\n",
      "14_washingmachine         0.0\n",
      "14_waterkettle            0.0\n",
      "15_amplifier              400.0\n",
      "15_coffeemaker            0.0\n",
      "15_dishwasher             0.0\n",
      "15_lamp                   1.0\n",
      "15_microwaveoven          0.0\n",
      "15_monitor-tft            0.0\n",
      "15_pc-desktop             0.0\n",
      "15_refrigerator           9.0\n",
      "15_tv-lcd                 0.0\n",
      "15_washingmachine         0.0\n",
      "15_waterkettle            0.0\n",
      "16_amplifier              0.0\n",
      "16_coffeemaker            0.0\n",
      "16_dishwasher             0.0\n",
      "16_lamp                   0.0\n",
      "16_microwaveoven          0.0\n",
      "16_monitor-tft            0.0\n",
      "16_pc-desktop             4.0\n",
      "16_refrigerator           196.0\n",
      "16_tv-lcd                 0.0\n",
      "16_washingmachine         0.0\n",
      "16_waterkettle            0.0\n",
      "17_coffeemaker            0.0\n",
      "17_dishwasher             0.0\n",
      "17_lamp                   0.0\n",
      "17_microwaveoven          0.0\n",
      "17_monitor-tft            0.0\n",
      "17_pc-desktop             4.0\n",
      "17_refrigerator           324.0\n",
      "17_tv-lcd                 0.0\n",
      "17_washingmachine         0.0\n",
      "17_waterkettle            0.0\n",
      "18_coffeemaker            0.0\n",
      "18_dishwasher             400.0\n",
      "18_lamp                   0.0\n",
      "18_microwaveoven          0.0\n",
      "18_monitor-tft            400.0\n",
      "18_pc-desktop             0.0\n",
      "18_refrigerator           4.0\n",
      "18_tv-lcd                 0.0\n",
      "18_washingmachine         0.0\n",
      "18_waterkettle            0.0\n",
      "19_coffeemaker            0.0\n",
      "19_dishwasher             0.0\n",
      "19_lamp                   0.0\n",
      "19_microwaveoven          0.0\n",
      "19_monitor-tft            0.0\n",
      "19_pc-desktop             0.0\n",
      "19_tv-lcd                 0.0\n",
      "19_washingmachine         0.0\n",
      "19_waterkettle            0.0\n",
      "1_amplifier               0.0\n",
      "1_beantocupcoffeemaker    0.0\n",
      "1_charger-smartphone      4.0\n",
      "1_coffeemaker             0.0\n",
      "1_cookingstove            0.0\n",
      "1_digitaltvreceiver       25.0\n",
      "1_dishwasher              0.0\n",
      "1_dvdplayer               0.0\n",
      "1_ethernetswitch          121.0\n",
      "1_freezer                 0.0\n",
      "1_lamp                    400.0\n",
      "1_laundrydryer            0.0\n",
      "1_microwaveoven           16.0\n",
      "1_monitor-crt             256.0\n",
      "1_monitor-tft             0.0\n",
      "1_multimediacenter        1.0\n",
      "1_pc-desktop              36.0\n",
      "1_pc-laptop               0.0\n",
      "1_playstation3            1.0\n",
      "1_printer                 225.0\n",
      "1_projector               0.0\n",
      "1_refrigerator            400.0\n",
      "1_router                  100.0\n",
      "1_solarthermalsystem      0.0\n",
      "1_subwoofer               0.0\n",
      "1_toaster                 0.0\n",
      "1_tv-crt                  0.0\n",
      "1_tv-lcd                  0.0\n",
      "1_usbharddrive            81.0\n",
      "1_usbhub                  1.0\n",
      "1_videoprojector          0.0\n",
      "1_washingmachine          0.0\n",
      "1_waterfountain           0.0\n",
      "1_waterkettle             0.0\n",
      "1_xmaslights              400.0\n",
      "20_coffeemaker            9.0\n",
      "20_dishwasher             400.0\n",
      "20_lamp                   0.0\n",
      "20_microwaveoven          0.0\n",
      "20_monitor-tft            0.0\n",
      "20_pc-desktop             0.0\n",
      "20_tv-lcd                 0.0\n",
      "20_washingmachine         0.0\n",
      "20_waterkettle            0.0\n",
      "21_coffeemaker            0.0\n",
      "21_dishwasher             0.0\n",
      "21_lamp                   0.0\n",
      "21_microwaveoven          0.0\n",
      "21_monitor-tft            0.0\n",
      "21_pc-desktop             400.0\n",
      "21_tv-lcd                 9.0\n",
      "21_washingmachine         0.0\n",
      "21_waterkettle            0.0\n",
      "22_coffeemaker            0.0\n",
      "22_dishwasher             0.0\n",
      "22_lamp                   0.0\n",
      "22_microwaveoven          0.0\n",
      "22_monitor-tft            1.0\n",
      "22_pc-desktop             400.0\n",
      "22_tv-lcd                 4.0\n",
      "22_washingmachine         0.0\n",
      "22_waterkettle            0.0\n",
      "23_dishwasher             0.0\n",
      "23_lamp                   0.0\n",
      "23_microwaveoven          0.0\n",
      "23_monitor-tft            400.0\n",
      "23_pc-desktop             0.0\n",
      "23_tv-lcd                 1.0\n",
      "23_washingmachine         0.0\n",
      "23_waterkettle            0.0\n",
      "24_dishwasher             0.0\n",
      "24_lamp                   0.0\n",
      "24_microwaveoven          0.0\n",
      "24_monitor-tft            256.0\n",
      "24_pc-desktop             0.0\n",
      "24_tv-lcd                 81.0\n",
      "24_washingmachine         0.0\n",
      "24_waterkettle            0.0\n",
      "25_dishwasher             0.0\n",
      "25_lamp                   0.0\n",
      "25_microwaveoven          0.0\n",
      "25_monitor-tft            0.0\n",
      "25_pc-desktop             121.0\n",
      "25_tv-lcd                 0.0\n",
      "25_washingmachine         0.0\n",
      "25_waterkettle            0.0\n",
      "26_dishwasher             0.0\n",
      "26_lamp                   0.0\n",
      "26_microwaveoven          0.0\n",
      "26_monitor-tft            400.0\n",
      "26_tv-lcd                 0.0\n",
      "26_washingmachine         0.0\n",
      "26_waterkettle            0.0\n",
      "27_dishwasher             0.0\n",
      "27_lamp                   0.0\n",
      "27_monitor-tft            400.0\n",
      "27_washingmachine         0.0\n",
      "27_waterkettle            0.0\n",
      "28_dishwasher             0.0\n",
      "28_lamp                   0.0\n",
      "28_monitor-tft            0.0\n",
      "28_washingmachine         0.0\n",
      "28_waterkettle            0.0\n",
      "29_dishwasher             0.0\n",
      "29_lamp                   400.0\n",
      "29_monitor-tft            0.0\n",
      "29_washingmachine         0.0\n",
      "29_waterkettle            0.0\n",
      "2_amplifier               0.0\n",
      "2_coffeemaker             0.0\n",
      "2_cookingstove            0.0\n",
      "2_digitaltvreceiver       4.0\n",
      "2_dishwasher              0.0\n",
      "2_dvdplayer               324.0\n",
      "2_ethernetswitch          0.0\n",
      "2_lamp                    0.0\n",
      "2_laundrydryer            0.0\n",
      "2_microwaveoven           0.0\n",
      "2_monitor-crt             0.0\n",
      "2_monitor-tft             4.0\n",
      "2_multimediacenter        1.0\n",
      "2_pc-desktop              121.0\n",
      "2_pc-laptop               0.0\n",
      "2_playstation3            0.0\n",
      "2_printer                 9.0\n",
      "2_projector               0.0\n",
      "2_refrigerator            324.0\n",
      "2_router                  1.0\n",
      "2_solarthermalsystem      0.0\n",
      "2_subwoofer               0.0\n",
      "2_toaster                 0.0\n",
      "2_tv-crt                  0.0\n",
      "2_tv-lcd                  0.0\n",
      "2_usbharddrive            64.0\n",
      "2_videoprojector          0.0\n",
      "2_washingmachine          0.0\n",
      "2_waterfountain           400.0\n",
      "2_waterkettle             0.0\n",
      "2_xmaslights              400.0\n",
      "30_dishwasher             0.0\n",
      "30_lamp                   289.0\n",
      "30_monitor-tft            324.0\n",
      "30_washingmachine         0.0\n",
      "30_waterkettle            0.0\n",
      "31_dishwasher             400.0\n",
      "31_lamp                   0.0\n",
      "31_monitor-tft            400.0\n",
      "31_washingmachine         0.0\n",
      "31_waterkettle            0.0\n",
      "32_dishwasher             0.0\n",
      "32_lamp                   0.0\n",
      "32_monitor-tft            0.0\n",
      "32_washingmachine         0.0\n",
      "32_waterkettle            0.0\n",
      "33_dishwasher             0.0\n",
      "33_monitor-tft            400.0\n",
      "33_washingmachine         0.0\n",
      "34_dishwasher             0.0\n",
      "34_monitor-tft            0.0\n",
      "34_washingmachine         0.0\n",
      "35_dishwasher             0.0\n",
      "35_monitor-tft            0.0\n",
      "35_washingmachine         0.0\n",
      "36_dishwasher             0.0\n",
      "36_monitor-tft            0.0\n",
      "36_washingmachine         400.0\n",
      "37_dishwasher             0.0\n",
      "37_monitor-tft            400.0\n",
      "37_washingmachine         0.0\n",
      "38_dishwasher             0.0\n",
      "38_monitor-tft            0.0\n",
      "38_washingmachine         400.0\n",
      "39_dishwasher             0.0\n",
      "39_monitor-tft            0.0\n",
      "39_washingmachine         0.0\n",
      "3_amplifier               0.0\n",
      "3_coffeemaker             0.0\n",
      "3_cookingstove            0.0\n",
      "3_digitaltvreceiver       0.0\n",
      "3_dishwasher              0.0\n",
      "3_ethernetswitch          0.0\n",
      "3_lamp                    400.0\n",
      "3_laundrydryer            0.0\n",
      "3_microwaveoven           0.0\n",
      "3_monitor-crt             400.0\n",
      "3_monitor-tft             16.0\n",
      "3_multimediacenter        1.0\n",
      "3_pc-desktop              0.0\n",
      "3_pc-laptop               25.0\n",
      "3_playstation3            1.0\n",
      "3_printer                 0.0\n",
      "3_projector               0.0\n",
      "3_refrigerator            0.0\n",
      "3_router                  25.0\n",
      "3_subwoofer               0.0\n",
      "3_toaster                 0.0\n",
      "3_tv-crt                  400.0\n",
      "3_tv-lcd                  0.0\n",
      "3_usbharddrive            144.0\n",
      "3_videoprojector          0.0\n",
      "3_washingmachine          0.0\n",
      "3_waterkettle             0.0\n",
      "40_monitor-tft            400.0\n",
      "40_washingmachine         0.0\n",
      "41_monitor-tft            0.0\n",
      "42_monitor-tft            289.0\n",
      "43_monitor-tft            400.0\n",
      "44_monitor-tft            0.0\n",
      "45_monitor-tft            400.0\n",
      "46_monitor-tft            0.0\n",
      "47_monitor-tft            0.0\n",
      "48_monitor-tft            0.0\n",
      "49_monitor-tft            0.0\n",
      "4_amplifier               0.0\n",
      "4_coffeemaker             361.0\n",
      "4_digitaltvreceiver       16.0\n",
      "4_dishwasher              0.0\n",
      "4_ethernetswitch          1.0\n",
      "4_lamp                    0.0\n",
      "4_laundrydryer            0.0\n",
      "4_microwaveoven           0.0\n",
      "4_monitor-crt             400.0\n",
      "4_monitor-tft             144.0\n",
      "4_multimediacenter        0.0\n",
      "4_pc-desktop              0.0\n",
      "4_pc-laptop               4.0\n",
      "4_playstation3            1.0\n",
      "4_printer                 1.0\n",
      "4_projector               9.0\n",
      "4_refrigerator            0.0\n",
      "4_router                  4.0\n",
      "4_toaster                 0.0\n",
      "4_tv-crt                  0.0\n",
      "4_tv-lcd                  0.0\n",
      "4_usbharddrive            0.0\n",
      "4_videoprojector          0.0\n",
      "4_washingmachine          0.0\n",
      "4_waterkettle             0.0\n",
      "50_monitor-tft            0.0\n",
      "5_amplifier               0.0\n",
      "5_coffeemaker             361.0\n",
      "5_dishwasher              0.0\n",
      "5_ethernetswitch          1.0\n",
      "5_lamp                    400.0\n",
      "5_laundrydryer            0.0\n",
      "5_microwaveoven           0.0\n",
      "5_monitor-crt             400.0\n",
      "5_monitor-tft             196.0\n",
      "5_multimediacenter        25.0\n",
      "5_pc-desktop              0.0\n",
      "5_pc-laptop               169.0\n",
      "5_playstation3            0.0\n",
      "5_printer                 121.0\n",
      "5_projector               0.0\n",
      "5_refrigerator            1.0\n",
      "5_router                  0.0\n",
      "5_toaster                 0.0\n",
      "5_tv-crt                  0.0\n",
      "5_tv-lcd                  0.0\n",
      "5_usbharddrive            1.0\n",
      "5_videoprojector          1.0\n",
      "5_washingmachine          400.0\n",
      "5_waterkettle             0.0\n",
      "6_amplifier               0.0\n",
      "6_coffeemaker             121.0\n",
      "6_dishwasher              0.0\n",
      "6_lamp                    400.0\n",
      "6_laundrydryer            0.0\n",
      "6_microwaveoven           0.0\n",
      "6_monitor-crt             0.0\n",
      "6_monitor-tft             169.0\n",
      "6_pc-desktop              0.0\n",
      "6_pc-laptop               4.0\n",
      "6_printer                 9.0\n",
      "6_refrigerator            0.0\n",
      "6_router                  16.0\n",
      "6_tv-lcd                  0.0\n",
      "6_usbharddrive            1.0\n",
      "6_videoprojector          0.0\n",
      "6_washingmachine          0.0\n",
      "6_waterkettle             0.0\n",
      "7_amplifier               400.0\n",
      "7_coffeemaker             1.0\n",
      "7_dishwasher              0.0\n",
      "7_lamp                    0.0\n",
      "7_laundrydryer            400.0\n",
      "7_microwaveoven           121.0\n",
      "7_monitor-crt             196.0\n",
      "7_monitor-tft             4.0\n",
      "7_pc-desktop              400.0\n",
      "7_pc-laptop               0.0\n",
      "7_refrigerator            1.0\n",
      "7_router                  1.0\n",
      "7_tv-lcd                  0.0\n",
      "7_usbharddrive            0.0\n",
      "7_washingmachine          0.0\n",
      "7_waterkettle             0.0\n",
      "8_amplifier               400.0\n",
      "8_coffeemaker             1.0\n",
      "8_dishwasher              0.0\n",
      "8_lamp                    0.0\n",
      "8_microwaveoven           9.0\n",
      "8_monitor-tft             0.0\n",
      "8_pc-desktop              0.0\n",
      "8_pc-laptop               0.0\n",
      "8_refrigerator            0.0\n",
      "8_router                  1.0\n",
      "8_tv-lcd                  0.0\n",
      "8_usbharddrive            64.0\n",
      "8_washingmachine          0.0\n",
      "8_waterkettle             0.0\n",
      "9_amplifier               0.0\n",
      "9_coffeemaker             1.0\n",
      "9_dishwasher              0.0\n",
      "9_lamp                    400.0\n",
      "9_microwaveoven           1.0\n",
      "9_monitor-tft             0.0\n",
      "9_pc-desktop              400.0\n",
      "9_pc-laptop               0.0\n",
      "9_refrigerator            169.0\n",
      "9_tv-lcd                  0.0\n",
      "9_usbharddrive            1.0\n",
      "9_washingmachine          0.0\n",
      "9_waterkettle             0.0\n"
     ]
    }
   ],
   "source": [
    "print_mse(pre_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5_rnn_forecasting'></a>\n",
    "\n",
    "## 5. RNN Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The next consumption prediction will be once again performed using the `RNN` neural network, as we will work with time-series data.**\n",
    "\n",
    "This time, we will define a `GRU` neural network and train it to predict the next devices consumption. The results will be evaluated with using `MSE` error.\n",
    "\n",
    "> [*GRU applies a multi-layer gated recurrent unit RNN to an input sequence.*](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Preparing training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **sequence length** number represents the size of the data sequences that will be used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_length = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also define a `MinMaxScaler` object, so we can **scale** data between numbers `-1` and `1`.\n",
    "\n",
    "> [*Scaler transforms each feature individually such that it is in the given range on the training set, e.g. between zero and one.*](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = MinMaxScaler(feature_range=(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will divide the data into **sequences** using the function below. The function prepares several sequences of  consecutive consumptions `x` and their target consumptions `y`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sliding_windows(data, seq_length):\n",
    "    \"\"\"\n",
    "    The function divides data into sequences and their target consumptions.\n",
    "    \n",
    "    Args:\n",
    "        data       [numpy.ndarray]: Data to divide into sequences.\n",
    "        seq_length [int]:           One sequence length.\n",
    "    \n",
    "    Returns:\n",
    "        [numpy.ndarray, numpy.ndarray]: Sequences and their target consumptions.\n",
    "    \"\"\"\n",
    "    \n",
    "    x = []\n",
    "    y = []\n",
    "\n",
    "    for i in range(len(data)-seq_length-1):\n",
    "        _x = data[i:(i+seq_length)]\n",
    "        _y = data[i+seq_length]\n",
    "        x.append(_x)\n",
    "        y.append(_y)\n",
    "        \n",
    "    return np.array(x), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function is used to **load and prepare training data**. The data is read first, then scaled and divided into sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_data(path, sc, seq_length):\n",
    "    \"\"\"\n",
    "    The function loads and prepares training data.\n",
    "    \n",
    "    Args:\n",
    "        path       [str]:                  Path to read training data from.\n",
    "        sc         [sklearn.MinMaxScaler]: Min-max scaler.\n",
    "        seq_length [int]:                  One sequence length.\n",
    "    \n",
    "    Returns:\n",
    "        [numpy.ndarray, numpy.ndarray]: Training x and y data.\n",
    "    \"\"\"\n",
    "    \n",
    "    x = None\n",
    "    y = None\n",
    "    \n",
    "    for dirname in os.listdir(path):\n",
    "        for filename in os.listdir(path + dirname):\n",
    "            data = file_to_int_array(path + dirname + os.sep + filename)\n",
    "            scaled = sc.fit_transform(data.reshape(-1, 1))\n",
    "            _x, _y = sliding_windows(scaled, seq_length)\n",
    "\n",
    "            if x is None:\n",
    "                x, y = _x, _y\n",
    "            else:\n",
    "                x = np.concatenate((x, _x), axis=0)\n",
    "                y = np.concatenate((y, _y), axis=0)\n",
    "                \n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = load_train_data(forecast_train_path, sc, seq_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prepared data is also converted into `torch.Variable` objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = Variable(torch.Tensor(x_train))\n",
    "y_train = Variable(torch.Tensor(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **GRU class** represents a neural network model that we will train to predict the next device consumption with.\n",
    "\n",
    "1. The class accepts the parameters input size, hidden size, output size, and number of layers.\n",
    "2. The GRU neural network is defined with the `nn.GRU` object.\n",
    "3. Fully connected layer is defined with `nn.Linear` object.\n",
    "4. In the forward function, we define how the model will work from input to output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GRU(nn.Module):\n",
    "    \"\"\"\n",
    "    A class used to represent GRU neural network model.\n",
    "    \n",
    "    Attributes:\n",
    "        input_size  [int]: Size of input layer.\n",
    "        hidden_size [int]: Size of hidden layer.\n",
    "        output_size [int]: Size of output layer.\n",
    "        n_layers    [int]: Number of layers.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, input_size, hidden_size, output_size, n_layers):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.hidden_size = hidden_size\n",
    "        self.n_layers = n_layers\n",
    "        \n",
    "        self.gru = nn.GRU(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        h = Variable(torch.zeros(self.n_layers, x.size(0), self.hidden_size))\n",
    "        \n",
    "        _, h_out = self.gru(x, h)\n",
    "        \n",
    "        h_out = h_out.view(-1, self.hidden_size)\n",
    "        \n",
    "        return self.fc(h_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we set neural network **parameters** and **define** the `GRU` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 1\n",
    "hidden_size = 25\n",
    "output_size = 1\n",
    "n_layers = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "gru = GRU(input_size, hidden_size, output_size, n_layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before training, we need to determine the number of **epochs** and **learning rate**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_epochs = 100\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also necessary to define a **criterion function** and an **optimization algorithm**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(gru.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code is used to **train** the `GRU` model. We insert the training data over and over again, calculate the loss, and adjust the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, loss: 0.81603\n",
      "Epoch: 10, loss: 0.21242\n",
      "Epoch: 20, loss: 0.15824\n",
      "Epoch: 30, loss: 0.14002\n",
      "Epoch: 40, loss: 0.12855\n",
      "Epoch: 50, loss: 0.12484\n",
      "Epoch: 60, loss: 0.12282\n",
      "Epoch: 70, loss: 0.12187\n",
      "Epoch: 80, loss: 0.12147\n",
      "Epoch: 90, loss: 0.12123\n",
      "Epoch: 99, loss: 0.12104\n"
     ]
    }
   ],
   "source": [
    "for i in range(n_epochs):\n",
    "    outputs = gru(x_train)\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    loss = criterion(outputs, y_train)\n",
    "\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if i % 10 == 0:\n",
    "        print('Epoch: %d, loss: %1.5f' % (i, loss.item()))\n",
    "\n",
    "print('Epoch: %d, loss: %1.5f' % (i, loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Model testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before testing, we prepare `MSE` dictionary and evaluate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "gru_mse = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRU(\n",
      "  (gru): GRU(1, 25, batch_first=True)\n",
      "  (fc): Linear(in_features=25, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(gru.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With below function, **testing data will we loaded and prepared**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_test_data(path, sc, seq_length):\n",
    "    \"\"\"\n",
    "    The function loads and prepares testing data.\n",
    "    \n",
    "    Args:\n",
    "        path       [str]:                  Path to read testing data from.\n",
    "        sc         [sklearn.MinMaxScaler]: Min-max scaler.\n",
    "        seq_length [int]:                  One sequence length.\n",
    "    \n",
    "    Returns:\n",
    "        [numpy.ndarray, numpy.ndarray]: Training x and y data.\n",
    "    \"\"\"\n",
    "    \n",
    "    data = file_to_int_array(path)     \n",
    "    scaled = sc.transform(data.reshape(-1, 1))\n",
    "    return sliding_windows(scaled, seq_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code is used to **test** the predictions. First, we load test data, convert it into `torch.Variable` objects and then predict the next consumption. After that we inverse the data and calculate the `MSE` error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dirname in os.listdir(forecast_test_path):\n",
    "    for filename in os.listdir(forecast_test_path + dirname):\n",
    "        x_test, y_test = load_test_data(forecast_test_path + dirname + os.sep + filename, sc, seq_length)\n",
    "\n",
    "        x_test = Variable(torch.Tensor(x_test))\n",
    "        y_test = Variable(torch.Tensor(y_test))\n",
    "\n",
    "        pred = gru(x_test)\n",
    "\n",
    "        y_pred = sc.inverse_transform(pred.data.numpy())\n",
    "        y_true = sc.inverse_transform(y_test.data.numpy())\n",
    "\n",
    "        gru_mse[filename[:-4]] = mean_squared_error(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the `0_charger-smartphone` device predictions on the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_charger-smartphone\n"
     ]
    }
   ],
   "source": [
    "print(filename[:-4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DKQnzxgahjA1BrqwpdEhERGSSpTW1O3fuRGNjI8477zzTr+3s7LQyFMoiHA5Pe6x3OIVUKjHjc5SdNNqLwBtPQ6lbhIkPfBa9B7sAzLzOlB9zXetUdBydR1M4v7oF7/zyGSRbzrcosvLDz7U9uM724VrbJ99rbWlS+9RTT+Ezn/nMrF7b2toKj8djZTg0g3A4jNAMJ8XfPzaMyjfemPE5mpmu6xh74xcYfONJ1P2v9ahsXTX1XKZ1JutZsdZxuQsDrx9D8/mXIX58L+bx925G/Fzbg+tsH661faxY60QikXUT1LKfN/f29mLXrl1Yu3atVbckG6mqBllmTa1RWjKO/m3/gpFd29H8hY2nJLRUegI+GdFYCr4lH0Ds4JvQdb3QIRERkUmWJbVPP/00LrvsMtTUsBatFKX71LKm1ohk/1Ec/4/bAEFAy5//A9z1CwodEs1Rhc+NaDwFuWY+BMmFVORooUMiIiKTTGUxmzZtwqpVq9DT04P169fjyiuvnHru6aefnnXpARWeorCllxHRvbvQ9fg3EfyDT2He2pshciJYWQj4XBifSEEQhBO7tW8VOiQiIjLJVE1te3s72tvbZ3xux44dlgREhaFoTGpzGev8Hwz+/PuYf+0d8HKIQlkJ+FyIxlIAAN+SD2B8969Q/aE1BY6KiIjMYBZDALhTm8vIqz/D4M7H0fT5u5jQlqGAV8ZEPAVN0+Fb/AHEjr4DXU0VOiwiIjKBWQwBSNfUShy+MKOhl36MkVe2ofmGjXA3LCx0OJQHkiTC45YQTyqQ/JVw1TQhfnxfocMiIiITmNQSgPSYXBd3ak+h6zoGdv4A42//D5q/sBGuYGOhQ6I8CvjcGJ+YLEE4H7GDbxY4IiIiMoNZDAFIJ7WyzI/DJF1TEfnvf0P8UCear98IubK20CFRnlX4XIjG00mtf8kFPCxGRFRiLB2+QKVLUVlTO0lXFfRt+xeo40No+vxdED2+QodENgj4XBg/cVjMs/AcJPuPQItHIXoDBY6MiIiMYBZDAFhTO0lLJdD7o/ugJ+OYf+0dTGgdJOD9fQcEUXbD27IcscMc301EVCqY1BIA1tQCgJaIoef/3QPR40fjZ/4Wootjm52kwv/7pBYA+9USEZUYZ2cxNMXpLb10XUPfMw9BDjai4VP/B4LEyhynObn8AGBSS0RUapybxdApFE1zdPnB8Is/ghobR8Mnb4Qg8I+FE51cfgAA7sbFUOPjUEb6CxgVEREZxb+9CQCgKLpjyw8m9oUx+vrzaPz0BgiSq9DhUIGcPFUMAARBhG/x+Zhgay8iopLgzCyGpnFqS6/UYBf6tj+Cxk9vgFxZU+hwqIAqTis/AFiCQERUSkxlMR0dHWhra8Py5cuxd+/eqccTiQTuvPNOXH755Vi7di2+8Y1vWB4o5ZeiapBEZyW1WjKGnic7ULvqOngXLC90OFRgp+/UAif61R7aDV3XChQVEREZZeo0zOrVq3HDDTfg85///CmP/+M//iM8Hg927NgBQRAQiUQsDZLyL71T65yaWl3X0b/tUXhblqHy4ssLHQ4VgZl2auXqBkjeCiR7D8Mzf0mBIiMiIiNMJbUrV66c9lg0GsXWrVvxq1/9CoKQTorq6+utiY5so6jOqqkd+d0zUEb60XTDxqnPLTnbTDu1wGQJwptMaomIitycs5ijR48iGAzikUcewac//Wl84QtfwKuvvmpFbGQjRdEgOSSpnTjwJkZe3obGP/lbiLK70OFQkQicNCb3ZKyrJSIqDXNuxqkoCo4ePYpzzz0Xt912G95880381V/9FZ5//nlUVFQYvk9nJyf32CUcDk97rK9/EJXyGMJyeZeOiBPDqPzd9xG94GpE9h0GcDhv7zXTOlN+WLHW8aSG0fH49HulFASPvIPwKy8D7F/Mz7VNuM724VrbJ99rPefv0M3NzZBlGWvWrAEAXHDBBaipqcHBgwdx/vnnG75Pa2srPB5OcMq3cDiMUCg07fGfv70LZy9tRuiilgJEZQ8tlUDX9+9A5WV/irM+tCav75Vpncl6Vq21pum476mf4MKLLoYknlqScvzd7Ti3wQffkg/M+X1KGT/X9uA624drbR8r1jqRSGTdBJ3zz5tra2txySWX4KWXXgIAHDx4EAMDA1i0aNFcb002UtTyHr6g6zoiP90Md/0CVH3wykKHQ0VIFAX4vC5MZChBYL9aIqLiZiqp3bRpE1atWoWenh6sX78eV16ZTg6+9a1v4bHHHsPatWvxta99Dffddx+qqqryEjDlh6LqZd2nduS3W5HsO4L6K/+aB8Moo8yHxS5A7ODuAkRERERGmSo/aG9vR3t7+7THFy5ciB/84AeWBUX2U1QNcpkeFBt97TmMvvYcmm/YBNHFEhfKbKa2XgDgbTkbqaFuqBNjkPyVBYiMiIhyKc8shkxLJ7Xlt4M5/vavMfTrJ9G07puQq+oKHQ4VuQqfC9GJ6UmtILngW7gCsUPsgkBEVKyY1BIAQFX1stupje57FQPP/180fe4bcNU2FTocKgEBnwvjM9TUAidaex1iCQIRUbEqryyGZi1VZuUHsUO70b/9UTR+9na4551R6HCoRAS8M9fUAoCrfiFSQz02R0REREaVTxZDc6Io5ZPUxo/vQ+/TD6Dx0/8fvC1nFzocKiEV/sxJrRSohhodsTkiIiIyqjyyGJozVSuPmtpk3xH0PvkPaFjzZfgWtRY6HCoxmbofAOmkVptgUktEVKyY1BIAQFFKv6VXarAb3U9sRN3/+nMEzl5Z6HCoBAW8M3c/AADJXwU1Ng5dU22OioiIjCjtLIYsk1I1yGLpfhyU0QF0/+fdqPnYZ1Fx3scKHQ6VqGw7tYIoQfQGoMXGbY6KiIiMKN0shiylqlrJ7tSq0RF0/+e3UBW6AlUXX17ocKiEZepTO0nyV0GNDtsXEBERGVaaWQxZrlSHL+hqCj0/ug+B5R9C8MNXFzocKnHZdmoBQAoEeViMiKhIlV4WQ3lRqsMXBp77D0i+StT84bpCh0JlIOdOLTsgEBEVLSa1BABQSnD4wujrzyN2uBPzPvV/IAilFTsVp9w7tdVQ2QGBiKgoMRMg6LoORdUglVBSGz/2LgZf+E80fvZ2iB5/ocOhMhHwuRDNMFEMACR/NWtqiYiKlGzm4o6ODuzYsQPHjx/Htm3bsGzZMgBAW1sb3G43PB4PAGDDhg342Md4Ar1UaJoOQRAgiaVRfqCMDaL3x/+EeWv+Bu665kKHQ2XE65agKBpSigbXDAcnpUAQiePvFSAyIiLKxVRSu3r1atxwww34/Oc/P+25f/7nf55Kcqm0KJoOuUQSWl1Jofepf0TVxVfAf3ao0OFQmREEAQGfCxPxFKorPNOeZ00tEVHxMpXUrlzJhvblSFFKo52XruuI/Ow7kCtrEfzIZwodDpWpwInDYkxqiYhKi6mkNpsNGzZA13WEQiF87WtfQ1VVlanXd3Z2WhUK5RAOh0/5OhpXAV2b9nixcR8Jw3vkLYz+wZ/h2GuvFTqcnIp9PcuJlWstaEm8+tpu9NS7pz0nTgyjYqjP0b+3Tv6124nrbB+utX3yvdaWJLVbtmxBU1MTkskk7rnnHtx99924//77Td2jtbV1qiaX8iccDiMUOvXH9gMjMXh/Pjjt8WISO7IHfb/+HZr/7O/hqplf6HBymmmdKT+sXut5u36DhYuX4uLl86Y9pyXjOPyb7+Liiy+GIJRGyY6V+Lm2B9fZPlxr+1ix1olEIusmqCU/c25qagIAuN1urFu3Dq+VwC4a/V6xt/NSRiPoe/oBNKy9uSQSWiptAX/mtl6i2wsIIvRkzOaoiIgolzlnMhMTExgbGwOQrnn86U9/ihUrVsw5MLJPMU8T05Qken90H6o/tAb+pRcVOhxyAA5gICIqTabKDzZt2oTnnnsOkUgE69evRzAYxObNm3HzzTdDVVVomoalS5fizjvvzFe8lAfF3KN2+KWnIFfPQ/UffKrQoZBDBLwGBjBER+CqbbIxKiIiysVUUtve3o729vZpj2/dutWqeKgAFEWDq0iT2on3X0f95esdWb9IhWFoqhh3aomIik5xZjJkK0XVIMvFlzSqsXGkBo/D03xWoUMhB6nIUlMLcKoYEVGxYlJLUFQdklh8H4X4kbfhXbAcguQqdCjkILnLD4JQJ7hTS0RUbIovkyHbKerMI0ELLXa4E75F5xc6DHKYAA+KERGVpOLLZMh2xdr9IHaoE97FTGrJXhWsqSUiKknFl8mQ7RRFgyQVV02tMj4MdTQCz/wlhQ6FHMbYTu2wfQEREZEhTGoJilZ8wxfiR96G94xzIYhSoUMhh6nwuRCNs6aWiKjUFFcmQwVRjC29Yoc64WPpARVAwOfC+EQKuq7P+Hy6+8GozVEREVEuxZXJUEGkhy8UV/lB/PBueBe1FjoMciC3S4IgAElFm/F50ReAloxDVzPv5hIRkf2Y1BIUtbjKD5TRCNR4FO55ZxQ6FHKobAMYBEGE5K/kbi0RUZEpnkyGCiY9fKF4PgqxQ53wnXEeBKF4YiJnyd0BIcgOCERERYZZAxVdS6/Y4U74FrP0gAon96jcKh4WIyIqMqYymY6ODrS1tWH58uXYu3fvtOcfeeSRjM9R8VKLKKnVdR3xQ6ynpcLK3dYryLZeRERFxlQms3r1amzZsgUtLS3Tnnv77bfxxhtvoLm52bLgyB4pVYNcJAfFlOFe6JoKV930zxiRXSq8OZJaPwcwEBEVG1NJ7cqVK9HU1DTt8WQyibvvvht33nknBKE4kiMyTlGK56BY7NBu+Bafz88RFVTAz6liRESlxpJM5uGHH8ZVV12FhQsXWnE7spmqFU/5QexwJ0sPqOAMjcplTS0RUVGR53qD119/Hbt378aGDRvmdJ/Ozs65hkIGhcPhU74+dnwYFT4J4fBYgSI6QddRve91dNV9ANppMZai09eZ8sfqtR4aGMNEQs34Z0Lu74e36wiOOvD3mJ9re3Cd7cO1tk++13rOSe2uXbtw4MABrF69GgDQ09ODL37xi7j33nvx0Y9+1PB9Wltb4fF45hoO5RAOhxEKhU557NUjb6GpLoBQaGmBokpL9h9Fjy+Aiz62uqBxWGGmdab8yMda9ycPYd/RYYRCF874fKK7Bv3HXsFyh/0e83NtD66zfbjW9rFirROJRNZN0DkntTfeeCNuvPHGqa/b2tqwefNmLFu2bK63Jpuoql4UfWrT9bQsPaDCqzBUUztsX0BERJSTqUxm06ZNWLVqFXp6erB+/XpceeWV+YqLbFQsfWpjhzvhXXx+ocMgQsBrpE/tKHR95lG6RERkP1M7te3t7Whvb896zc6dO+cUENkvVQRJra5riB95G/VXfKmgcRABJ/rUxjMntYLkguj2QotFIfkrbYyMiIgyKfz2HBWcquoF71Ob7D0EyV8FubK2oHEQASe6H0xkTmoBdkAgIio2TGqpKMoPYoc74VvE0gMqDrkmigGTAxiG7QmIiIhyYlJLSClawQ+KxQ+xnpaKR8DnwkQ8BV3XM16THpXLnVoiomLBpJagqhpksXAfBV1VEDv6DnyLzitYDEQnkyURLllEPKlmvIZTxYiIiguTWoKi6pDlwtXUJnoOwFXdAMlfVbAYiE4X8LkwnqWulkktEVFxYVJLBa+pjbH0gIpQwOdCNEsHBNbUEhEVFya1VPCkNn54N3yLOHSBikuFL1ev2iC7HxARFREmtWQ6qR1/97dIDfVY8t66kkL8+F74zjjXkvsRWSWQM6mthhodtTEiIiLKhkktnUhqjdfUjr7yLHr+3z1QY+Nzfu/48b1w1y2A6A3M+V5EVsrV1oujcomIiguTWoKi6KZ2arVUElJlLfp+fD90VZnTe6dH47L0gIpPhdeF8Vgy4/McvkBEVFyY1BIUzVz5ga4kUPe/1gOSjIHn/2NO7x0/tBs+HhKjIhTwuxCNZf5Hm+D2AZoGLZWwMSoiIsqESS1BMTl8QVdSEN1eNF79VcQOd2Lk1f+e1ftqyTgSPQfhXXDOrF5PlE+5DooJggDJX8W2XkRERcJUUtvR0YG2tjYsX74ce/funXr8pptuwlVXXYWrr74a69atwzvvvGN5oJQ/iqpBEo3X1OpKEoLshugNYP6f/h2GX/wRJg68Yfp948fehWf+Eohur+nXEuVbwJs9qQUmp4oN2xMQERFlZSqpXb16NbZs2YKWlpZTHu/o6MBPfvITbN26FX/xF3+Br3/965YGSfmlqDpcpnZqkxBkFwDAVTMf8z79NfQ98zCSkWOG76ElYxh74xfwcooYFan0QbHMNbUABzAQERUTU0ntypUr0dTUNO3xysrKqf8eHx+HIBRuOhWZZ7all66kIMjuqa99Z5yH2j+6Hj0/vBfqxFj216oKRsM/w9F/vRkQBFSv/OSs4ybKp3RLr+wHIZnUEhEVD9mqG91xxx146aWXoOs6vvvd75p+fWdnp1WhUA7hcPiUr1OKhjffeB2ikRIEXUdQSeL1N3cDp/zjJQhf9SK8//1vYnzl5wBRmvY6V8878O37FTRfELHzr4FaPR/H3n1/7r+gInX6OlP+5GOtuweT6B8cyXpv72gMmNiDuFZj+fsXK36u7cF1tg/X2j75XmvLktp77rkHALB161bcd999+M53vmPq9a2trfB4PFaFQxmEw2GEQqGpr1VNh/jEMXzwgysNvV5LJXD45y6EVk6/Xr/oQvQ+2YHa/tdQ/4n/PbVjHzu0G4M7fwBdB2qvvhn+JRdY84spYqevM+VPvta6ZyCKp1/5TdZ7j6jdSA33ot4hv9f8XNuD62wfrrV9rFjrRCKRdRPU8u4HV199NV5++WUMDQ1ZfWvKg1mVHrjcMz4niBLmXf1VxI+9h9FXf4pEzwF0P3E3+n+6GdV/8Cm0/MU/OCKhpfJQ4XMhOpGjptbP8gMiomIx553aaDSK0dHRqVrbnTt3orq6GsFgcK63JhsoigbJVFKbPKWe9nSix4f5f/p36Pq/fwcACH70T1B10cchSK45x0pkJ5/XhVhCgabpGUtzxABbehERFQtTSe2mTZvw3HPPIRKJYP369QgGg/j+97+PW265BbFYDKIoorq6Gps3b+ZhsRJhfqc2e1ILAK7gPLT8xX0QvX6Ibt9cQyQqCEkU4PPImEgoqPDN/I8yORDkVDEioiJhKqltb29He3v7tMd/+MMfWhYQ2UtRNbhkEz1qU79v55WNXFU3l7CIikLgxACGTEmtyPIDIqKiwYliDqeo+izKD3igj5whkGOqmOSvhBaPQtdUG6MiIqKZMKl1ONVk+YGmJCFmOChGVG5yDWAQRAmiNwB1YtTGqIiIaCZMah0uNauaWh76ImcwPiqXJQhERIXGpNbhFEWDLJmoqT1tmhhROavwG0lqWVdLRFQMmNQ6nKrplnc/ICoX6fIDA6Ny2QGBiKjgmNQ6XEoxWX6QSjCpJceo8GavqQUmBzAM2xMQERFlxKTW4dItvUxOFGNSSw6Rq/sBwJpaIqJiwaTW4VRVh5RhWtJMNCWZcUwuUbkxVlNbBTXK7gdERIXGpNbhFFWDbGqnNgmR3Q/IIdLdD3LV1AZZfkBEVASY1Dqc6ZZeKR4UI+cI+FyIxnPs1PqrofGgGBFRwTGpdTizwxd0lUktOUfA58L4RI6DYhXVUFhTS0RUcKaS2o6ODrS1tWH58uXYu3cvAGBoaAh/+Zd/iSuuuAJr167F3/zN32BwcDAvwZL1FNVkn1ru1JKDVPjcuWtq/dXQoiPQdd2mqIiIaCamktrVq1djy5YtaGlpmXpMEAR86Utfwo4dO7Bt2zYsXLgQ999/v+WBUn6kFPapJcok4JNzlh+ILg8gydATEzZFRUREMzGV1K5cuRJNTU2nPBYMBnHJJZdMfX3hhReiq6vLmugo71TN7JjcFLsfkGP4PDISKQ2qqmW9jgMYiIgKz9KaWk3T8MQTT6Ctrc3K21IeKYq57geakoTInVpyCEEQEPDKGOeoXCKioidbebONGzfC7/fj+uuvN/3azs5OK0OhLMLh8NR/Hzo8hmhCPeWxbCoG+tF76DCUKNt65WJ0TWnu8rnWsqjh5VffQF1l5m+XgaSOgbdeQ6qv/EsQ+Lm2B9fZPlxr++R7rS1Lajs6OnD48GFs3rwZomh+A7i1tRUej8eqcCiDcDiMUCg09fX7Q+8hkVQRCp1r6PVd7zyDM845F77F5+crxLJw+jpT/uR7rev+5wUsWboMZy+syXhNf++r8DTWoarMf8/5ubYH19k+XGv7WLHWiUQi6yaoJUntgw8+iM7OTvzbv/0b3G7+aLqUqKrJg2KpJAQX//FBzmFsVC6nihERFZqppHbTpk147rnnEIlEsH79egSDQTz00EPYvHkzFi9ejOuuuw4AsGDBAjz66KN5CZispagavG7jHwNdSUKQWHpAzpFOanNPFUv1H7UpIiIimomppLa9vR3t7e3THn/vvfcsC4jslVI0VPhM9Kll9wNymIDXhfFYjgEMgWrED/NcABFRIXGimMOpmrnyA3Y/IKep8BsYwMDuB0REBcek1uHMtvTi8AVymoDPQEsvP5NaIqJCY1LrcIqqQTLRrYJjcslpKrxGDooFoUaH7QmIiIhmxKTW4RRVg0s2U1ObZE0tOUrA58q5Uyt6A9BSSehK9uuIiCh/mNQ6nGKipZeuKoAgQBClPEdFVDyM1NQKggDJX8VRuUREBcSk1uEUVYNkNKlVkhBktvMiZwkYKD8AeFiMiKjQmNQ6nKJqcBlOalOspyXHCfhkRONGk9rh/AdEREQzYlLrcIqiGS4/0JQE23mR4xiZKAZMHhbjTi0RUaEwqXU4RdUhScYOirHzATlRwOfC+ISRpLaKSS0RUQExqXU4RTW+U8vyA3Iij0uCpgPJlJr1OikQ5EExIqICYlLrcOmWXiYOirGdFzmMIAio8Lly1tVyAAMRUWExqXW49PAFg+UHnCZGDhXwyRyVS0RU5EwltR0dHWhra8Py5cuxd+/enI9T8VNU3fCY3HRNLVt6kfMYGcDApJaIqLBMJbWrV6/Gli1b0NLSYuhxKn5mWnppKndqyZkqfLkHMHBULhFRYclmLl65cqWpx6n4mToolkpCdHnyHBFR8THS1kvyV0GNjUHXNQgCK7uIiOxmKqnNp87OzkKH4BjhcHjqv2PxBN5+uxNH/blH37qP7oM8MoKjJ72eMgtznWyT77WeGB/GnveiCGi9Wa+rltx4/XcvQXf78xpPIfFzbQ+us3241vbJ91oXTVLb2toKj4e7gPkWDocRCoWmvha29uHiiy5AdUXutR/RepHyAfUnvZ5mdvo6U/7Ysda7u99GwOdCKLQs63VHd9Wi9azFcDcszGs8hcLPtT24zvbhWtvHirVOJBJZN0H5MzKHM93SizW15EAV/tw1tQDraomIColJrcOpqgbJRE0tux+QExnpfgCcmCo2MWpDREREdDpTSe2mTZuwatUq9PT0YP369bjyyiuzPk7Fz9RBMTUJQWaJCDlPhTf3QTGAO7VERIVkqqa2vb0d7e3thh+n4qZqOgAYHr6gpZJwBYJ5jIioOBnpfgBwqhgRUSGx/MDBzJQeAKypJecK+GSD5QdMaomICoVJrYOZKT0AJpNa1tSS8xg/KMakloioUJjUOlhKmU1Sy51acp6A14VoPHdSK/oqocbGbIiIiIhOx6TWwdI7tcbqaYHJ7gdMasl5Aj4Z0VgKuq5nvU50+6AnYzZFRUREJ2NS62CqqkM22KMW4E4tOZdLliCKIhIpNet1oscHjUktEVFBMKl1MLM1tZqSguhiUkvOVGGgA4Lo9kFLxm2KiIiITsak1sFSZssPuFNLDmZkAIPg9kJPcKeWiKgQmNQ6mKrq7H5AZJCRnVpBdkPXVOiqYlNUREQ0iUmtg82upRd3asmZjAxgEAQBosfPuloiogJgUutgplt6pTgml5wr4M1dfgAAotsLjSUIRES2Y1LrYKrG4QtERlX4jY3KFTxs60VEVAimktqOjg60tbVh+fLl2Lt379TjBw8exLXXXosrrrgC1157LQ4dOmR1nJQHiqKbPCiWgsDuB+RQRsoPgMkOCExqiYjsZiqpXb16NbZs2YKWlpZTHr/zzjuxbt067NixA+vWrcM3v/lNS4Ok/FBUDZLBnVpd16CrCgSJO7XkTMbLD3wsPyAiKgBTSe3KlSvR1NR0ymMDAwPYs2cP1qxZAwBYs2YN9uzZg8HBQeuipLxQVA0ug8MXdCUFQXZBEIzv7BKVE6M7tYLby161REQFMOea2u7ubjQ2NkKSJACAJEmYN28euru75xwc5ZeZ7gcckUtOV2GgTy2QnirGmloiIvvJhQ5gUmdnZ6FDcIxwOAwA2HcgipHhxNTX2QjxMVTpMHQtpXGt7GPHWh/tiqMvMpbzvXzDY9CSe5FQqvMeUyHwc20PrrN9uNb2yfdazzmpbWpqQm9vL1RVhSRJUFUVfX1908oUcmltbYXHw3ZR+RYOhxEKhQAAA8phRLVBhEIX5XxdarAb3W9WTL2Wsjt5nSm/7Fprb80AXju0J+d7DY6+C8HlQU0Z/v7zc20PrrN9uNb2sWKtE4lE1k3QOZcf1NXVYcWKFdi+fTsAYPv27VixYgVqa2vnemvKM1PlB2znRQ7ndUtIJNWc14kedj8gIioEU0ntpk2bsGrVKvT09GD9+vW48sorAQB33XUXHn/8cVxxxRV4/PHH8a1vfSsvwZK1FEWDZLCll6akILKmlhzM65ERT+Yefyu4fdDZ/YCIyHamyg/a29vR3t4+7fGlS5fiySeftCwosoei6iZ2ahM8KEaO5nVLhpJa9qklIioMThRzMFMtvdj9gBzO65YRN1J+wKSWiKggmNQ6mLma2hSTWnK09E6tCl3Xs14neNinloioEJjUOlh6opixmlpdSXJELjmaJImQRAEpRct6nciaWiKigmBS62CKqsNlqvsBk1pytsnd2mxYfkBEVBhMah3MTPmBlmJLLyKPO3cHBLb0IiIqDCa1DpYuPzC4U6sm2dKLHM/nkRBPZE9qBbePNbVERAXApNbBFMXEQTF2PyA6sVObq/zACz0Zz3mgjIiIrMWk1sHSLb1MHBRjUksOZ6RXrSBKEGQX9BR3a4mI7MSk1sFUVYckmmjpxe4H5HCmetUmmNQSEdmJSa2DpVQNstHhC9ypJYLXLSGRyJ3UCm4vD4sREdmMSa2DKapmuKUXux8QpXdqYwZH5epMaomIbGVZUvvCCy/gmmuuwdq1a3H99dfj6NGjVt2a8kRRTAxfUJMQZU+eIyIqbl5P7ppagG29iIgKwZKkdmRkBLfddhseeOABbNu2DZ/97Gdx1113WXFryiNV09n9gMgEr1tGwnBNLZNaIiI7WZLUHj58GPX19ViyZAkA4LLLLsOLL76IwcFBK25PeZIy09KLY3KJ4HVLiOXoUwuka2p19qolIrKVJUntkiVLEIlE8NZbbwEAtm3bBgDo7u624vaUJ+mJYmZaerGmlpzN6zGxU8vyAyIiW8lW3KSyshIPPvgg7r33XiQSCaxatQpVVVWQZeO37+zstCIUMiAcDgMAxsaj2Lf3PYxHcu/AVo4Mo+/9A1AjiXyHVzYm15nyz6617u0ex/GBFMLhZNbrfMOj0GL7kNDrbInLTvxc24PrbB+utX3yvdaWJLUAcOmll+LSSy8FAEQiEXzve9/DwoULDb++tbUVHg8PIuVbOBxGKBQCALh+sRPnn38eFs2vyvm6Y689jkWtH4CncXGeIywPJ68z5Zedaz0uHMNIsifn+w1F90PXVdSW2WeAn2t7cJ3tw7W2jxVrnUgksm6CWtb9oL+/HwCgaRoeeOABXHfddfD7/VbdnvIgXX5gpqUXa2rJ2dITxQz0qfV4obGmlojIVpbt1D700EN47bXXkEql8JGPfAQbNmyw6taUJ6qJpFZXUhB5UIwcLj1RzGCfWnY/ICKylWVJ7T333GPVrcgm5g+KMaklZzPcp5YHxYiIbMeJYg6WUkz0qWX3A6ITO7XsfkBEVIyY1DqYqhkrP9B1HbqS4k4tOZ7HLSFupE+th31qiYjsxqTWwRRFgywb+AhoCiCIEEQp/0ERFTGfx8ROLWtqiYhsxaTWwRRVgyzmrqnVU5wmRgSc2Kll+QERUVFiUutQqqZDByAaSGo1JQWR9bRE8LgkpBQVqqZnvU5gUktEZDsmtQ412c5LEAzs1LLzAREAQBAEeN0SEjk6IIgetvQiIrIbk1qHYjsvotnxuGUkcpQgCLIbuqZCV3MfKiMiImswqXUoRTXRzovTxIimeN0SYjl2agVBgOhhCQIRkZ2Y1DqUmRG5usqklmiS18BOLcC6WiIiuzGpdSjD7bzA7gdEJ/O6JcQTRjogeKEn2KuWiMguTGodStE0yKKx335NSUKQ2P2ACAC8Hjln+QHAtl5ERHZjUutQ6Z1aowfFUhC5U0sEAIa6HwBgTS0Rkc1kq270y1/+Eg8//DB0XYemabj55ptx+eWXW3V7spipg2JKgjW1RCd43TJiBsoPWFNLRGQvS5JaXddx6623YsuWLVi2bBneffddfO5zn8PHP/5xiAZ/xE32UlQNErsfEJnm9cjGdmrd7FVLRGQnyzJOURQxNjYGABgbG8O8efOY0BYxRdXgMtz9IMWklugEr+FRuV7u1BIR2UjQdT37vEeDfvvb3+IrX/kK/H4/otEoHnvsMVx00UU5X5dIJNDZ2WlFCGTCwd44frV7DH/+8Yac13oO/BZiKobY8jYbIiMqbjvfGoEoCPjD86uyXufd+wIguRBf+hF7AiMicojW1lZ4PJ5pj1tSfqAoCh577DF8+9vfRigUQjgcxle/+lU8++yzCAQCcwqQrBUOhxEKhSC814c3j76PUCiU8zWD0fcBALUGrqW0yXWm/LN7rQ+N7MNoNIlQ6Lys1w3FD0NLRFFXRp8Dfq7twXW2D9faPlasda6NUEvqA9555x309fVNBRsKheDz+bB//34rbk95YGr4gpKEKLOlFxEwWX5gpKbWCz3JPrVERHaxJKmdP38+enp6cODAAQDA/v37EYlEcMYZZ1hxe8oDRdHgMjp8QeFBMaJJHrdsrKaWLb2IiGxlSflBQ0MD7rrrLtxyyy0QhHTv03vvvRfBYNCK21MeKKoGSTTYp5bdD4im+DyywZ1aHzR2PyAiso1lfWqvuuoqXHXVVVbdjvJMUXXjY3K5U0s0xWOw+4Hg9kHnTi0RkW3Yc8uhTLX0UlIQOFGMCMCJmtqE0YlirKklIrILk1qHMjV8gTu1RFO8HoM1tW4vtMSEDRERERHApNax0t0PjNXUakoSArsfEAEwsVPLMblERLZiUutQiqKbbOnFHsJEwORBMQM1tR4/yw+IiGzEpNahTPWpZfcDoiket4yEiT61Fg1tJCKiHJjUOpRqcvgCD4oRpXndEmJJNWeyKogSBEmGnkrYFBkRkbMxqXWolKpBlg32qWVNLdEUWRIhCgIUVct5LQcwEBHZh0mtQymqDlk00dKL5QdEU7zsVUtEVHSY1DqUqmqGhy9oShIik1qiKV63hJjRDggJHhYjIrIDk1qHSvGgGNGseT0yEkZ71XKnlojIFkxqHUpRjPWp1TUV0FRAsmyiMlHJS5cf5N6pFdw+DmAgIrKJJZnKsWPH8OUvf3nq67GxMYyPj+OVV16x4vaUB6pmrE/t5IhcQTB2qIzICTxuGfGEgZ1ajw86e9USEdnCkqR2wYIFeOaZZ6a+vueee6Cqub/hU+Gkd2qNJLXsfEB0uvQAhuKfKqbrOkZf/SmqVn6S/zAlorJneflBMpnEtm3b8JnPfMbqW5OFjNbUsvMB0XQeg90PCt3SSx2NYOC5f4c6NliwGIiI7GJ5Urtz5040NjbivPPOs/rWZCFV1Y3V1CoJJrVEp/G5ZcQNdD9I19TmJ6lVVA2P/+ydrNckeg4AAFIDx/MSAxFRMbH89M9TTz01q13azs5Oq0OhDMLhMAaGhnDwoAI50ZX1WmmsD/6UinA4bFN05YNrZh+713p0ZAj79o+iVo5kvc7TNwAxPoaDeYivfySF/3q+F0uC4/C6Zt6f8O57CT4A+9/4HRKDKUvel59re3Cd7cO1tk++19rSpLa3txe7du3CfffdZ/q1ra2t8Hg8VoZDMwiHwwiFQvjxKy9hxTnLcMHZDVmvjx/fi4ED1VgWCtkUYXmYXGfKv0Ks9e7utxHwuRAKLct63agwgET3fjTkIb7fdXYD6EVjy9k4a2Fwxmu69/039DPORZVfQr0FMfBzbQ+us3241vaxYq0TiUTWTVBLyw+efvppXHbZZaipqbHytpQHipmaWhfLD4hOlj4oVtia2q7+cQDA8RP/fzpd15HsOYCK8z6G1CDLD4io/Fme1PKAWGlIJ7VGamo5eIHodB63se4H+aypPd4fRVXAPZXcnk4dG4Su6/At+QBSA9nLjIiIyoGl5Qc7duyw8naUR4pisE9tii29iE7ndUvG+tS6fdDztVMbGcfKFY3oikRnfD7RcwCe+WdCrm6AGh2BlkpAdLHEi4jKFyeKOZSiGSw/ULlTS3Q6r5k+tXnaqe3qH8cHz23MWH6Q6N4Pz/wzIYgS5JpGpAa78xIHEVGxYFLrUIqiQZZz//ZrqSQEmbs7RCfzuiUkDNXUevNSUzsRT2E8puD8pfXo6h+HruvTrkn2HIC76UwAgKu2mW29iKjsMal1KOMHxZIQWX5AdAqvW0LMUJ9af16S2q5IFM31AVRXeCCKIkbGk9OuSZcfLAUAuOtamNQSUdljUutQiuHhC+x+QHQ6r0c2uFPrg56MW/7+Xf3jaGmoAAA0NwSmlSAoY0PQ1RTk6nTLPlddCw+LEVHZY1LrUGZ2allTS3Qqr9HuB7IbuqpA13InwGYc74+iuSEAAGhpqJjWASHRsx+e+UshCOl/uLrqmpFkUktEZY5JrUMZTmpTHJNLdDqvW0LMwE6tIAgQ3V7LD4t19Y+juT7zTm2y+wA8J+ppgRM7tYPHZ6y9JSIqF0xqHUpRNEhGyg/UFJNaotN43TISBnZqgXSvWqvbeh0/qfygpaFiWluvRM9+uOf/PqmVvAGILg/UsUFL4yAiKiZMah1K0XS4DPepZVJLdLL0QTFjJQVWTxXTdT19UCxr+UG6R+3J0ru1LEEgovLFpNaBVE2HrusQxdw7tZqShMiDYkSncLskKIoKTcv943yre9WORpMQAFQF0n8um+oC6B6YmIpFGR+GnkpCDjae8jq29SKicsek1oFUVYMkilOHSLJJHxRjSy+ik4miALdLQiJlZKqYtb1qJ0sPJv/8ej0yqvwuRIbT75Hs2Q/P/CXT/ny76lqQZFJLRGWMSa0DKaoGl5w7oQXY/YAoE69bRtxQr1pryw+6+senSg8mNTdUTB0WS3QfOKWedpKrrpltvYiorDGpdaB0j1pjv/VMaolm5vVIiBvqVeuHbmH5wfH+6NQhsUnNJ9XVJnoOwNO0dNrr3OxVS0RlzrKkNpFI4M4778Tll1+OtWvX4hvf+IZVtyaLKaoGyWhSy4NiRDMy2qs2XX5g3QCG4ye185rU0hDA8RMdEGY6JAYAcnAe1PEhaKmEZbEQERUT2aob/eM//iM8Hg927NgBQRAQiUSsujVZzGiPWuDERDEmtUTTeNwS4gY6IFjd/SBT+cHre/uhRkegJyYg18yf9jpBlCDXNCI12A1P42LL4iEiKhaWJLXRaBRbt27Fr371q6nDCfX19VbcmvJAUTVD7bwAdj8gysRndKqYhX1qNU1HdySK5obTd2rT5QeJnnQ9baZDoJNtvZjUElE5siSpPXr0KILBIB555BG8/PLLCAQCuOWWW7By5UrD9+js7LQiFDLgzbc6kUolEA6Hc15bFR3D2+/uhXaUO+9mGVlfskYh1joWG8Pb7+yFNn4063We3n6I0UEctCDG4agCjwvY0/nmKY+rmo7+oQnsf/XXkIQAujO8lzcpYmD3LsQnPLOOgZ9re3Cd7cO1tk++19qSpFZRFBw9ehTnnnsubrvtNrz55pv4q7/6Kzz//POoqKjIfQMAra2t8Hhm/42WjAmHwzjnnBWoeO01hEKhnNcffknA+RdeDLmqzoboykc4HDa0vjR3hVrrX74TRsvCeQiFFma9bkweQezw25hnQYxv7O3D4mZlxl9v48+HUaGMo+HCj6HivJnfa0weQezQ7lnHws+1PbjO9uFa28eKtU4kElk3QS05KNbc3AxZlrFmzRoAwAUXXICamhocPHjQituTxVKKBllm9wOiuTDa/UDw+KAlJix5z+P90Wn1tJOaGyqg9B6Ee/70zgeTXHUtHMBARGXLkqS2trYWl1xyCV566SUAwMGDBzEwMIBFixZZcXuymKJqkA1MEwNOdD9gTS3RNF63jISh7gfW1dR2nRi8MJMzagQgGYWrdvohsUmuumYkB7qg67knoRERlRrLuh9861vfwte//nV0dHRAlmXcd999qKqqsur2ZCFV1Q3t1Oq6fqL7ASeKEZ3O65YQM9L9wO2zrKXX8f5xXHB2w4zPLfEMYdjdCEHI/Gdb8lVCkF1Qx4cgV9ZaEhMRUbGwLKlduHAhfvCDH1h1O8qjlNGWXqoCSFLWvySJnMrrkTE+kcx5nZUtvbqylB80av3Yq+XuOuM+UYLApJaIyg2zFQcy2qdWU5IQWU9LNKP0Tq2x8gPNgoliKUVD/3AMjbUzJ7WBiS7sm6jOeR8XJ4sRUZliUutAqqpBlnLX1HKaGFFm6YliBg6KWVRT2zsYRUPQB1em0qGBQ3g/HsREPJX1Pum6Wh4WI6Lyw6TWgRRFN7RTq6tMaoky8XokJAwktenyg/icD2dlKz1QY2NQJ0bhqmlG94lxuZm4apu5U0tEZYlJrQOlVGMtvdj5gCgzr1tGzMhEMVGCIMnQldz1t9kcz9L5INlzEJ7GJWiZV4mu/hxJbV0LUoPcqSWi8sOk1oFUVYMsGkhqlSQEiZ0PiGbidRvbqQUAwe2dc13t8f7xaeNxJ02Ox21uCOB4ZDzrfVzBeVDHhqDNMckmIio2TGodSDG6U6ukuFNLlEG6pjb3Ti0w2at2bgMYuvqjaMlQfpDo3g9P05lorq/A8f7sSa0gyZCD86AMds8pHiKiYsOk1oFSBg+KaUqC3Q+IMvC4JcQNdD8AJjsgzK1Xba6dWs/8pWhpqEBXjqQW+P0QBiKicmJZn1oqHapq8KAYux8QZeTzGOt+AMy9V20soWA8lkJ9tW/ac2o8CnV8GK66ZjT7FBzvj0LXdQhC5n+4clwuEZUj7tQ6kNE+tbrCpJYoE69bMpzUCm7vnJLarv5xNNX5Ic4w3jrZcwDuxkUQRAlVATcEAKPR7PWybia1RFSGmNQ6kKIYTWpZU0uUicctGy8/8Pjn1Ku2KxLNWXoAAIIgnChByNUBgW29iKj8MKl1IEXTjQ1f4E4tUUYuWYQgpCd95TLXqWJdWdp5TR4Sm9TcEMh5WMxV24LkYNece+cSERUTy2pq29ra4Ha74fF4AAAbNmzAxz72MatuTxZSFA1+T+7f+nRSy5ZeRJl4TnRAcOX4x5/o9kJLzv6g2PH+cXzgrPoZn0v2HEDNR/5k6uvmhgp05WjrJfkrIUgy1PFhyJU1s46LiKiYWHpQ7J//+Z+xbNkyK29JeWC8pRd3aomy8bolxBMqKv3ZrxPcczso1tUfxR9/ePG0x7XEBJSxQbjqW6Yea6mvwItv5a6XddU2IzV4nEktEZUNlh84UErVIM9w4OR0WioJUfbYEBFRaTLaq1b0+GZdU6vrOo5lKD9I9ByEe176kNik5oZAzppaAHCzrpaIyoylO7UbNmyArusIhUL42te+hqqqKitvTxZRVd3wTq3oq7QhIqLS5PVIxpJatw+pxOwSyMlOBlWB6T81SfTsh2f+mac8li4/iELT9Bm7JUxiWy8iKjeCbtFJge7ubjQ1NSGZTOKee+5BNBrF/fffn/N1iUQCnZ2dVoRABj3920EsafTgwjNnnk40ybfnOWj+GiQWf9CmyIhKy3/8vA9/dH41Fjdm/4mGq+ttuPv2InrhNabf40h/AjteG8ZfXtE47Tn/m89AqVuM5IILTnn8n57uwhcvn4dgIPO+hatvLzxHXsf4ymtNx0REVEitra1TZ7hOZtlObVNTEwDA7XZj3bp1+Ou//mtTr88UIFkrHA6juroGS5fOR+jiBVmv7e95BZ7mpai6KGRTdOUjHA4jFOK62aGQa73ttd9i0ZIzEVoxPeE8WbRSw2j0KM6ZRZxDrxzB2Ys8M/4aj+76Pub9QRs8jYtPeXzxy3HUzV+CC5fNy3jf5MB89Bz4NZabiImfa3twne3DtbaPFWudayPUkpraiYkJjI2NAUjXf/30pz/FihUrrLg15YGiaXBx+ALRnHndMmIGetXOpaa2KzLzeFwtGYcy0g93/fR/nDY3BNAVydGrNtgIdWwQmpJ9UAMRUamwZKd2YGAAN998M1RVhaZpWLp0Ke68804rbk15oCjsU0tkBa9HQsJQTa1/1i29uvqjuPQDTdMeV4b7IFc3QJCmfxtvrq/I2atWkGTIwQYogz1wzztjVrERERUTS5LahQsXYuvWrVbcimygqBokAzu16e4HTGqJMkl3P8g9Klf0zH5M7vH+mXdqU8O9kIMzlxe0NASwe38k573TQxiOM6klorLAll4OpKgmyg84JpcoI69bMlR+ILhnV36gaXp6RG799EOdynAvXMGZa3mbG3Lv1AIcl0tE5YVJrQOZG77AiWJEmXg9MhJGdmpnOSZ3YCSOCp8Mv3f6n8PUcB/kDEnt/LoAIsOxnCN800kt23oRUXlgUutA6fIDIzW1KdbUEmXhdUuGyg8Elwe6qkDXcl97sq4MpQdAuqbWlaH8wCWLqK/2oXcw+2Exd90C7tQSUdlgUutAiqpDNlR+kGBSS5SFx+BEMUEQILi9pndrj0dmniQGAMpI5ppawNhkscmdWovalRMRFRSTWgcyXFObYvcDomx8bgnxhLHdV9HtNV1Xe7x/fMZ6Wl3Xs5YfAECLgbpayV8FiCLU6IipuIiIihGTWgdSFIPlByrLD4iyMbpTC5yoqzWZ1Hb1R2fuURsbgyBKkLyZpwJOjsvNheNyiahcMKl1IEUzVn6gpZIQ2f2AKCOfRzKZ1JrrVdvVP3P5QWqoN+suLQA01wfQZaQDQi2TWiIqD0xqHUhRNLgMdz9gUkuUidE+tUB6qpiZmlpF1dA/HMP8Ov/050YyHxKbZKT8AADcdc1IDfKwGBGVPia1DqSoGiQx+2+9rqmArgOiZFNURKXH45YQN9CnFjDfq7Z3cAJ11V645Ol/BpUsgxcm1Qd9GIsmc/bRddU1IxnhTi0RlT4mtQ6kGuhTO7lLKwi5a2+JnMrnMblTayKpzTRJDDjRo7Y6e/mBKApoqg+gO0ddrauuhTu1RFQWmNQ6UErVIec4KKanOE2MKBePWzI0fAEwP4AhUz0tkL1H7cmMTBZz1TRCHR2ArqQMx0ZEVIyY1DqQkZZeupKEIHGaGFE2XreMmMGDYoLba3KnNoqWGdp5AUDKQPkBkK6rzXVYTJBckKrqkBruNRwbEVExsjypfeSRR7B8+XLs3bvX6luTBTRdh6bpEMXsO7WakmLnA6IcPC4JqZQKTcs9vEA0WVPb1T+Ophl2anVNhTIaMZjUBgwdFnPVzIcyxKSWiEqbpUnt22+/jTfeeAPNzc1W3pYspGmALIk5a2XZ+YAoN1EU4HJJSKZylyCY7X6QqfxAHRuE5KuCaODPZ1O9wV61NfORGuo2HBsRUTGyLKlNJpO4++67ceedd/JwURFTNR0u2cDgBSa1RIZ43ZKhEgQzfWrjCQWj0STqg75pz6UnieXepQWMlR8AgFzTiBR3aomoxMlW3ejhhx/GVVddhYULF87q9Z2dnVaFQlmoGqDrGsLhcNbr5IHD8MYSOa+jzLh29inkWgu6ildfexO1Fdm/nbp6uuDu68JRA7H2DadQ6RfxxuuvTXvOfewtyJqMHgP30XUdiaSCF3+7Cz535j0M10AUnmPv4bCBe/JzbQ+us3241vbJ91pbktS+/vrr2L17NzZs2DDre7S2tsLj8VgRDmXxwouvwOtxIxQKZb1u4n0BIwN7sDzHdTSzcDicc43JGoVe6+qdO3HW2edgSXN11usm9osYGdmPcwzEGn63FwsbtRl/XYPj+4DgOag1+Gue94tfYOHi5VjUVJXxmmRfA3qP/Cbnn/dCr7VTcJ3tw7W2jxVrnUgksm6CWlJ+sGvXLhw4cACrV69GW1sbenp68MUvfhEvvviiFbcnC6kGR+Smyw/Y/YAoF69bNtTWS/T4DdfURobjM5YeAOnBC64cI3JPVh/0ITKS/X3lmkYow/3poStERCXKkp3aG2+8ETfeeOPU121tbdi8eTOWLVtmxe3JQumDYkZqalPsU0tkgNcjIW64ptZYUjswEkNd0Dvjc6nhPlSaSWqrfYgMZ6/lFV0eiL5KqGODkKsbDN+biKiYsE+twxjdqdWUhKHT1URO53XLiCVy73AKHq/hg2KR4RgaMu7UGhu8MKk+6ENkOHcy7appRGqox/B9iYiKTV6S2p07d3KXtkgZLj9IsfsBkRHp8gNjO7V6YsLQPSPDMdRVT09qNSUJNTYKqbLWcHz1QS8GcpQfAIBcM59JLRGVNO7UOoxquPyASS2REenyAwM1tSdaeul67kENkZHYjDW1ykg/5Kp6CKJkOL66ah/6De3UMqklotLGpNZhjB8USzGpJTLA4zZWUytIMgRRgq4kc16b6aCY2dIDAGgI+gzt1LpqGjlVjIhKGpNah1E1HbJssPsBD4oR5eRzy4Z2agFAMDBVbCKegq7rCHinn+NNDfVCNnFIDADqgrkPigGAXNPEnVoiKmlMah1G1QBZNHJQjC29iIzwuCXEE7l3agFAdHuh5+iA0D+cLj2YaTKjMtJreqc2nRzriMZSWa+bPChmpDyCiKgYMal1GM3ETi27HxDl5jWxU2ukrdfAcBz1MxwSA9LlB2Z3agVBQF117l61kq8SgihCmxg1dX8iomLBpNZheFCMyFo+g31qgRMDGAzs1GbrUStXm9upBSZ71RrogBCcj9Qw62qJqDQxqXUYVdMhsaUXkWU8Zmpq3V7oiez1rQMZOh8AszsoBkz2qs1dV8tetURUypjUOoyq6XCx+wGRZbymampzlx9EhmMzlh+o8Sh0TYHorzIdY53BXrWumvlQmNQSUYliUusw6fIDdj8gsorXY7KmNscAhshwhh61w+lDYjMdIMulweBUsfQABpYfEFFpYlLrMOnyA9bUElnFa7BPLXCipVeOUbmRkcw9auVqc4fEJtUZrKlND2DontV7EBEVGpNah9E0GCw/YFJLZITXLSOeMLpT6zVYfjD9oFhquA/yLOppgRM1tYbLD7hTS0SlaXp371m66aabcOzYMYiiCL/fj2984xtYsWKFVbcni6iaDo+Bll4aW3oRGeJ1y0gY7X7g9kGNDmd8fmrwgm96j2hluBeu2qZZxWj0oJhUWQMtMQEtGYPonvmwGhFRsbIsqe3o6EBlZSUA4Oc//zm+/vWv4+mnn7bq9mQRVdMhiQbKD1KsqSUywuuREDNRU5sazPzj/chwDHXVMw9eSA33wXfmhbOKMeCVoes6JuIp+L2Zh6oIggg5OA+poV54GhfP6r2IiArFsvKDyYQWAMbHx2d1mIHyT9VgfEyuxIliRLmY2alN19RmLgOIDMfRkLGdVy9cJgcvTL2vIKA+6EO/wbpaliAQUSmybKcWAO644w689NJL0HUd3/3ud628NVnEVEsv7tQS5eSSReg6kFI0uHL8g1F0+6BnOSgWGZl58IKu61BG+iEHG2YdZ321DwPDcSyan70lmMzDYkRUogQ9D4O+t27dimeffRbf+c53cl6bSCTQ2dlpdQiUwXOvD8PvEfHRc7P/xRZ87j4Mr/4qwN1aopzuffI4vvKpJvjc2ZNaeeAwvO//D8Yv+cKMz//yrRHoOtB2QfUpjwvxcVT95rsYafvKrGPc+rtBLKz3IHRWIOt1nsOvQhrvx8R5n5j1exER5VNrays8Hs+0xy3dqZ109dVX45vf/CaGhoZQU1Nj6DWZAiRr/Sz8Cyw64wyEQkszXqPrOg7+TMXFH/wQBIENMmYjHA4jFAoVOgxHKIa1rng2gnNWtGacBDYp0R1E/5HfYHmGeF96/3UsO6MGodDiUx6PH3sXA3tbcNYcfp3v9L8DURAQCp2T9bqJagEjr2zDihneqxjW2gm4zvbhWtvHirXOtRFqScYSjUbR3f37H1ft3LkT1dXVCAaDVtyeLJQuP8he76yrKQiSzISWyCCjvWqFHBPFBjL0qJ1LO69J9UZ71dbO56hcIipJluzUxmIx3HLLLYjFYhBFEdXV1di8eTMPixUhIwfF2PmAyByPwV616ZrazIllf8ZpYn2zPiQ2qT7ow2/e6sp5nVzdAGVsELqqQJDy8sM8IqK8sOQ7Vn19PX74wx9acSvKs3RLrxxJLQcvEJni88iGdmpFjxdaIttObYbBC0O98C5YNqcY0wMYcveqFSQX5IpaKCP9s+6LS0RUCPz5ssOomp57p1ZJQpB5QIzIKI9bQtxAr1rB5YWuKtC16ddOxFNQtQyDF0Z6LSg/8BoqPwAAV00jSxCIqOQwqXUY1cCYXO7UEpljuKZWECC4PNBmaOuVHo878+AFK8oPAj7X1ACGXNJtvZjUElFpYVLrMJqmQ8p1UCyVhCCzEwWRUV6DNbVA5rrayEgc9TP1qFUVKONDkKvq5xSjIAioM3pYrGY+FCa1RFRimNQ6jKrpkHPs1GosPyAyxehOLZC5rjaS6ZDYaARyIGjJoa2GoA+R4dx1ta6a+UhxqhgRlRgmtQ5jrPwgBZHdD4gMSx8UM7pT65+xrdfAifKD0ynDfZBr5lZ6MKku6EVkJPdOrVwzH6lh7tQSUWlhUuswqpHyA9bUEpnicRvrfgAAgmfmXrX9wzHUzdijthdytTVJreFetcFGKEO90HXNkvclIrIDk1qHMdSnluUHRKZ43ZKJmlov9BnKDwZG4mjI2KN2bp0PJtUHjSW1oscH0eODOjZkyfsSEdmBSa3DGKmpTSe1PChGZJTXYJ9aIH1QbKad2shIDHUz9agdnns7r0n1QR8GDPSqBViCQESlh0mtwxhKalMsPyAyw+uWkDBcU+vLeFAs806tReUHQR/6DfeqnQ+Fh8WIqIQwqXUYTQPkHDW1msIxuURmeN0SYgkzNbWn7pZmH7zQB9mqpLbaiwEDB8UAwBVkr1oiKi1Mah3GWPlBCiJraokM87plUzu1p/epTQ9e8E4bvKAlY9ASMUgVQUviDPhcUDWjAxg4VYyISsvcGx8CGBoawq233oojR47A7XZj0aJFuPvuu1FbW2vF7clCxpLaBMsPiEzwmuh+ILq9SEWHT3ksPXhhptKDfsjVDTNOGZsNQRCmOiCcMT/7P1w5gIGISo0lO7WCIOBLX/oSduzYgW3btmHhwoW4//77rbg1WUzVYPCgGJNaIqO8Hslwn1phhoNiA8Mx1M3Qo9bKQ2KT6oNeRAwcFuMABiIqNZYktcFgEJdccsnU1xdeeCG6urqsuDVZTNX03C29eFCMyBRTO7Ue/7SDYhkPiY1Yd0hskuG2Xv4q6JoKNTZu6fsTEeWL5TW1mqbhiSeeQFtbm9W3JguoGiCLuYYvpJjUEplguk/taQfFIiPxDIMX+qzfqa32YcBAUisIAksQiKikWFJTe7KNGzfC7/fj+uuvN/W6zs5Oq0OhGWiajrfeegNSlsTW39cDRQ8gqYVtjKz8hMNcP7sUeq0TKQ3ReNJQHNLQMfgH+9F10rUHjvSj3juOcHjglGsDh/Yi2dyK/Rb++qIj4+gaTCEcjua8NgA3Bl/7LVLdI1OPFXqtnYLrbB+utX3yvdaWJrUdHR04fPgwNm/eDFE0twnc2toKj4cN//NJ03Ro/3kMH1wZynrwpOfgL1Bx9jJUnBOyMbryEg6HEQpx/exQDGutajrUH/0EF110McQcPwlRo2fhWOczWFAjwX/mhQCAf9+5E3+w8nwsaa4+5dpjrz2OhtCH4Zl/pmWx6v5edP36gKE1GxjZA9HjR82Ja4thrZ2A62wfrrV9rFjrRCKRdRPUsvKDBx98EJ2dnXj00UfhdvNH18VI1TSIInKepNaVJESWHxAZJokCZFlCMpW7BEEKVGPepzeg75mHkeg9BCB9UOz07ge6rp8oP7C2prau2ouI4V61jUgNsvyAiEqDJUntvn37sHnzZvT19eG6667Dpz71KXz5y1+24tZkIUXVs5YdTGL3AyLzfCY6IPjOOBf1l38RPT+8F2P9vVA0HRWnDV7QYmMQRAmSN2BpnA0GD4oBgKu2CQpH5RJRibCk/ODss8/Ge++9Z8WtKI8UVUOObl4ATnQ/cLEUhMgMz1QHBGN/dirO+yiUkT70/+heNFddPu0nKKnhPsjV1h4SA04dwOD3Zu9VywEMRFRKOFHMQRRFM7hTm4IgcaIYkRlet/Gd2knVH74GiepF+BPp59DVU1uCKcO9cNVYW3oATA5g8GLAQK9aubIO2sQYtFTC8jiIiKzGpNZBTJUfuFh+QGSGz0Sv2kmCIODYkk/BJcuI/Ow70HV96jklD+28JtUHfeg30tZLlCBXN0AZ7stLHEREVmJS6yBGyw80HhQjMs3jlpAw2Kv2ZJGxFA4uW4dE934M/+bpqcdTw72Qq63fqQWAOoO9agGWIBBR6WBS6yCKquVsNwTwoBjRbHjdMmImd2qB9DSxmrog5l/7dYy9tgPjnb8GkN6pdeVpp9bUYbGaJia1RFQSmNQ6SHqnlkktUT54TXQ/OFlkJIb6ai/kylrMv/YORJ7/d8SOvJ3eqc1TUlsX9CFioKYWAFw1jZwqRkQlgUmtg5jrfsCklsgMr1tGPGF+p/bkHrXueWeg8eqvou/H/wRlNJK/mloTvWrlmvlIDfXmJQ4iIisxqXUQRcl9UGzyBLYgSnaERFQ2ZtP9AEiXH5w8eMG35AOobfsCXNXz8lbbXm+q/GA+e9USUUmwdEwuFTdFy71Tqysp7tISzYLXIyNhsqZ2Ip5CSp0+eKHyA3+EitZVVoZ3ivqgiYNiwXlQRiLQNfMJOxGRnbhT6yBG+tSynpZodrxuCTGT5QcDI3E0BL0zjq7O509LKnwuKCcGMOQiym6IgWooo5G8xUNEZAUmtQ5i5KCYpiSY1BLNgsctIWGy/CAyHENdtS/3hRYzM4ABSB8WYwcEIip2TGodRFF1iAbKD9ijlsi89PAFc0ntwMip9bR2qqs2UVcbnA+Fh8WIqMgxqXUQIzu1eorlB0SzMZs+tf3D8YIltWYOi6U7IHCnloiKm2VJbUdHB9ra2rB8+XLs3bvXqtuShQwltRyRSzQrXo/58oOBEz1qC6HeZK9aJrVEVOwsS2pXr16NLVu2oKWlxapbksVUA31q0wfFXNkvIqJpvG4ZcdM7tYUrPzDX1quJ5QdEVPQsa+m1cuVKq25FeZJSDfSpZfcDolnxuCXTwxcGCpnUVnvxu06jSe2JnVpdn3pMVxUoYwNQRvrT/xvuhzI2AKkiCHfjYnjmLYZc0whBYJUbEdmjaPrUdnZ2FjqEsnfw4DgkEQiHwxmvcfW8C/f4BLqyXEPGZFtnslYxrHX/SArDo1FTsfQOjOPYofcw2G3/sJO+oSSOdQ8ajrcaAgJvbsV7rzwOKTYCIRGF7glA9VVB8wWheaugeasgdndDeu9NSGO9EFNxqJUNUCvnQalshFo5D2rlPID/cM6pGD7TTsG1tk++17poktrW1lZ4PJ5Ch1HWjoy9j6G9hxEKhTJeM7Z7HLFUP87Jcg3lFg6Hs64zWadY1rpvaAL/9dKLhmOJJRRoT3bjox/+4Ix9avNtbCKJH/zyecPxjvu/jIN738VZF34QcnUD5Mo6CFL2v0LU2DiSfYfT/+s9hOSRl5CMHINv8QcQ/Mhn4G0524pfStkpls+0E3Ct7WPFWicSiayboEWT1FL+KaoGScpVfpBi+QHRLHjdsqnyg8hw+pBYIRJaID2AIaWmBzD4vbnr6CtWfBjJCTd8i1oNv4fkq4Bv0XnwLTpv6jEtlcDYmzvR9+P74aprRvDST8O7qLVg60BE5YNJrYMoqm7soBi7HxCZ5vNIpvrURgpYTwukBzA0BNMDGIwktVYRXR5Ur/wEqi76OMY7f43Ifz8G0VeFmo98Br6zLmZyS0SzZlkF/6ZNm7Bq1Sr09PRg/fr1uPLKK626NVlEUTVIOf7CYPcDotmRJRGarkNRNUPXF3LwwiQzAxisJkguVF7QhgX/+2FUf+hKDL6wBce/97cYf+c30DVzrdGIiAALd2rb29vR3t5u1e0oD1RVg5TjPAq7HxDNjiAI8LrTu7UVvtz7BZGROOoK1KN2Un3Qh4GRwiS1kwRRQsW5H0FgxaWY2Pcqhl96CkO/egLVl1yFitZVEF08a0FExrD8wEFSRocvuP02RURUXrxuGYmkggpf7p92RIZjWNpSbUNUmdUHfegfNjaAId8EQUBg2QfhP3sl4oc7MfLKdgy+8J+ovKAN1Ss/AbmqvtAhElGRY1LrIIqiQcxRfqClknAFgvYERFRmvG4JMYOHxSLDMXzovPl5jii7+mov9h8fKWgMpxMEAb7F58O3+HykBrsx8up/49h3/j/4Fp+Pqg9+Et6FK1h3S0QzYlLrIKqmw5Wz/CAFQWJNLdFspKeKGasHjQzH0FDgmtr6oA+/e7t4x9+6aptQf/lfoPayz2HsrV8i8uy3Ibi8qP7gJxE476MQWSpFRCdhUusgKUWDVzZQfsDuB0Sz4vVISBhNakfiqKsufFI7UKCDYmaIHh+qP/hJVK38Y8T2v4GRXc9i8JePw924BIAATH1bE07dxRUE6JoGaCp0VZn6HzTllK9Fjw+uuha461rgqm+Bq24B3HXNED0sxSIqJUxqHURRNUiuXEltggfFiGbJ65YNlR/EEgpSioZKf2F/KlJX7UNkpDhqao0QBBH+sy6G/6yLkRzogjLUA0AHTkzv1aGfMsoXug6IIgRJnvofRPmUrwVRghqPIjVwHKmB45jY/zpSL29HarALoicAV3062XU3LoFvcSvkYCPLH4iKFJNaB1FVPXf3gxS7HxDNltGd2kIPXphU6XchpWiIJRT4PKX114G7rhnuumZL7iVXN8DTuPiUx3RdgzIaQSqSTnbjR97G0P/8FwRRhHfx+fAtaoVv8fmQq+osiYGI5q60vovRnChGuh+onChGNFvpmtrcO7XF0KMWSB/Kqq/2IjIcw8LGykKHU1QEQYSreh5c1fOApRcBAHRdR2qwC/FDuzHx/qsY+MX3IXkD8C46H77FrfAuOAdSZW3B/7FC5FRMah0kpWrIkdNCSyUhsqaWaFY8bsnQqNxCTxM7WX3Qx6TWIEEQ0qUIdS2oCv0xdF1Dsu8I4oc709PRdnwXuqpM1ea669L1ua76BXDVNEIQc/yojIjmpGiS2v7tj0JWSqe2qxT90dAgquJAz3/9T8ZrUv1HIMhsdk40Gz6D3Q+KYfDCpGIYwFCqBEGEp3ExPI2LUf2hNQAAdWIMqYHjSA4cQypyHPGjP0cycgzq+BDk4DzI1Q0QhOzDOQIjI+h5f4cdvwTH41rbJ+daiyLqP/FXkCuCs36PoklqA62r4LZsaC/NZE/3HpzdIGD5xSsyXlO18hNwNy6yMSqi8uF1S4gZKD+IDMdwZoEHL0yqq/aW1GGxYif5KyH5z4F34TmnPK6lEkgNdkMdHUgfaMui7/33UXnWWfkMk07gWtsn11oLogTJVzGn9yiapNa/+Hx4PNwhzKcDwgSagzICZ68sdChEZcnjljE0ljtBLIbBC5Magr6iG8BQjkSXJ30Y7bQDaTNJjQoInB3Ke0zEtbaTHWtt2d7owYMHce211+KKK67Atddei0OHDll1a7KIommQJB5gIMoXn0cyVH4wMBJHfYF71E6qO1FTS0RU6ixLau+8806sW7cOO3bswLp16/DNb37TqluTRRQld/cDIpo9j1s2dFCsv4gOijUwqSWiMmFJUjswMIA9e/ZgzZp0ofyaNWuwZ88eDA4OWnF7ski6pVehoyAqX163lLOlVyyhIJVSCz54YVKpDWAgIsrEkpra7u5uNDY2QjrR2V+SJMybNw/d3d2ora01dI/vPtMJAxscNAeDo3FIItv2EOWL1yNj75EhPPCf4YzXxJMq6oK+oullOjmA4Z+2hJErpIGBQfzqvcy/NrIG19k+XGv75FprSRTx52vORXXF7M9XFc1BsSrXBJJC9hOhNDdXrqxGdUBCOMw/wHbgOtunWNY6qWj4w/MC0PRoxmuqXcCKi/xFEzMAXPvRGozFMsc8qXq+B0Du62huuM724VrbJ9daS6KAd/bshmsOZ38sSWqbmprQ29sLVVUhSRJUVUVfXx+ampoM3+NPP/khdj+wQTgcRijEk575xnW2T7Gt9YcvKXQE5hldvWJb63LFdbYP19o+Vqx1IpFAZ2dnxuctqbCsq6vDihUrsH37dgDA9u3bsWLFCsOlB0REREREc2FZ+cFdd92F22+/Hd/+9rdRVVWFjo4Oq25NRERERJSVZUnt0qVL8eSTT1p1OyIiIiIiw9jgiYiIiIhKHpNaIiIiIip5TGqJiIiIqOQxqSUiIiKiksekloiIiIhKHpNaIiIiIip5TGqJiIiIqOQxqSUiIiKiksekloiIiIhKHpNaIiIiIip5TGqJiIiIqOQxqSUiIiKikjfnpPaZZ57B2rVrce655+Lxxx+3IiYiIiIiIlPmnNSuWLECDz74INasWWNFPEREREREpslzvcGyZcsAAKI4u/xY13UAQDKZnGsoZFAikSh0CI7AdbYP19o+XGt7cJ3tw7W2z1zXejJXnMwdTyfomZ4x6fbbb0drayuuv/56U68bGxvD3r17rQiBiIiIiMrcsmXLUFlZOe3xnDu111xzDbq6umZ87je/+Q0kSZpTYIFAAMuWLYPL5YIgCHO6FxERERGVJ13XkUqlEAgEZnw+Z1L79NNPWx7UyURRnDHbJiIiIiI6mdfrzfgcW3oRERERUcmbc03t9u3bcd9992F0dBQulws+nw///u//jrPOOsuqGImIiIiIsrLsoBgRERERUaGw/ICIiIiISh6TWiIiIiIqeUxqiYiIiKjkMaklIiIiopI35zG5c3Hw4EHcfvvtGB4eRjAYREdHBxYvXlzIkMpGR0cHduzYgePHj2Pbtm1T44y55tYaGhrCrbfeiiNHjsDtdmPRokW4++67UVtby7XOg5tuugnHjh2DKIrw+/34xje+gRUrVnCt8+SRRx7Bv/zLv0x9D+E6W6+trQ1utxsejwcAsGHDBnzsYx/jWudBIpHA3//93+O3v/0tPB4PLrzwQmzcuJFrbbFjx47hy1/+8tTXY2NjGB8fxyuvvJL/tdYL6Atf+IK+detWXdd1fevWrfoXvvCFQoZTVnbt2qV3dXXpf/RHf6S/9957U49zza01NDSk/+53v5v6+h/+4R/0v/u7v9N1nWudD6Ojo1P//fzzz+tXX321rutc63zo7OzUv/jFL+p/+Id/OPU9hOtsvdO/R0/iWltv48aN+j333KNrmqbruq739/frus61zrdNmzbp3/rWt3Rdz/9aFyypjUQieigU0hVF0XVd1xVF0UOhkD4wMFCokMrSyd8wueb597Of/Uz/sz/7M661DZ5++mn9mmuu4VrnQSKR0P/0T/9UP3LkyNT3EK5zfsyU1HKtrTc+Pq6HQiF9fHz8lMe51vmVSCT0Sy65RO/s7LRlrQtWftDd3Y3GxkZIkgQAkCQJ8+bNQ3d3N2prawsVVlnjmueXpml44okn0NbWxrXOozvuuAMvvfQSdF3Hd7/7Xa51Hjz88MO46qqrsHDhwqnHuM75s2HDBui6jlAohK997Wtc6zw4evQogsEgHnnkEbz88ssIBAK45ZZb4PV6udZ5tHPnTjQ2NuK8885DZ2dn3teaB8WILLJx40b4/X5cf/31hQ6lrN1zzz144YUX8NWvfhX33XdfocMpO6+//jp2796NdevWFToUR9iyZQt+8pOf4KmnnoKu67j77rsLHVJZUhQFR48exbnnnosf//jH2LBhA26++WZMTEwUOrSy9tRTT+Ezn/mMbe9XsKS2qakJvb29UFUVAKCqKvr6+tDU1FSokMoe1zx/Ojo6cPjwYTz00EMQRZFrbYOrr74aL7/8MubPn8+1ttCuXbtw4MABrF69Gm1tbejp6cEXv/hFHDlyhOucB5Pr53a7sW7dOrz22mv8/pEHzc3NkGUZa9asAQBccMEFqKmpgdfr5VrnSW9vL3bt2oW1a9cCsCcHKVhSW1dXhxUrVmD79u0AgO3bt2PFihXc7s8jrnl+PPjgg+js7MSjjz4Kt9sNgGudD9FoFN3d3VNf79y5E9XV1Vxri91444148cUXsXPnTuzcuRPz58/H9773PXzyk5/kOltsYmICY2NjAABd1/HTn/4UK1as4Gc6D2pra3HJJZfgpZdeApDuBDQwMIDFixdzrfPk6aefxmWXXYaamhoA9vy9KOi6rlt2N5P279+P22+/HaOjo6iqqkJHRwfOPPPMQoVTVjZt2oTnnnsOkUgENTU1CAaDePbZZ7nmFtu3bx/WrFmDxYsXw+v1AgAWLFiARx99lGttsUgkgptuugmxWAyiKKK6uhq33XYbzjvvPK51HrW1tWHz5s1YtmwZ19liR48exc033wxVVaFpGpYuXYr29nbMmzePa50HR48exde//nUMDw9DlmV85StfwWWXXca1zpMrrrgCd9xxB1atWjX1WL7XuqBJLRERERGRFXhQjIiIiIhKHpNaIiIiIip5TGqJiIiIqOQxqSUiIiKiksekloiIiIhKHpNaIiIiIip5TGqJiIiIqOQxqSUiIiKikvf/AwqbyFVi/9LAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_true, linewidth=1)\n",
    "plt.plot(y_pred, linewidth=1)\n",
    "\n",
    "plt.axis([0, len(y_pred), 0, 20])\n",
    "plt.yticks(np.arange(-1, 22, 1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we **sort** the `MSE` values alphabetically and **print** them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "gru_mse = dict(sorted(gru_mse.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_alarmclock              6.036890029907227\n",
      "0_amplifier               6.459870338439941\n",
      "0_beantocupcoffeemaker    2.410295009613037\n",
      "0_breadcutter             9.96654224395752\n",
      "0_cdplayer                6.813695907592773\n",
      "0_charger-psp             0.08824290335178375\n",
      "0_charger-smartphone      12.550207138061523\n",
      "0_coffeemaker             0.08824289590120316\n",
      "0_cookingstove            4.653611660003662\n",
      "0_digitaltvreceiver       1.7190954685211182\n",
      "0_dishwasher              0.08824289590120316\n",
      "0_dvdplayer               11.759687423706055\n",
      "0_ethernetswitch          2.640399932861328\n",
      "0_freezer                 55.34463119506836\n",
      "0_iron                    3.7361083030700684\n",
      "0_lamp                    14.540789604187012\n",
      "0_laundrydryer            0.08824289590120316\n",
      "0_microwaveoven           1.3969545364379883\n",
      "0_monitor-crt             5.4900712966918945\n",
      "0_monitor-tft             9.039022445678711\n",
      "0_multimediacenter        5.764163017272949\n",
      "0_pc-desktop              3.5742545127868652\n",
      "0_pc-laptop               6.385065078735352\n",
      "0_playstation3            0.4530649781227112\n",
      "0_printer                 9.312712669372559\n",
      "0_projector               0.08824289590120316\n",
      "0_refrigerator            59.790916442871094\n",
      "0_remotedesktop           2.9058985710144043\n",
      "0_router                  3.3551244735717773\n",
      "0_solarthermalsystem      7.08721399307251\n",
      "0_subwoofer               14.433603286743164\n",
      "0_toaster                 7.678434371948242\n",
      "0_tv-crt                  17.290010452270508\n",
      "0_tv-lcd                  38.58525848388672\n",
      "0_usbharddrive            10.2172269821167\n",
      "0_usbhub                  4.027666091918945\n",
      "0_vacuumcleaner           10.18982219696045\n",
      "0_videoprojector          3.94706392288208\n",
      "0_washingmachine          0.08824289590120316\n",
      "0_waterboiler             18.062108993530273\n",
      "0_waterfountain           90.2194595336914\n",
      "0_waterkettle             0.08824289590120316\n",
      "0_xmaslights              17.577295303344727\n",
      "10_amplifier              7.666777610778809\n",
      "10_coffeemaker            7.649886131286621\n",
      "10_dishwasher             8.219724655151367\n",
      "10_lamp                   9.063535690307617\n",
      "10_microwaveoven          7.305012226104736\n",
      "10_monitor-tft            12.955492973327637\n",
      "10_pc-desktop             12.13330078125\n",
      "10_pc-laptop              4.36509370803833\n",
      "10_refrigerator           63.362571716308594\n",
      "10_tv-lcd                 2.5614774227142334\n",
      "10_usbharddrive           7.640468120574951\n",
      "10_washingmachine         9.438919067382812\n",
      "10_waterkettle            3.7910358905792236\n",
      "11_amplifier              5.342926979064941\n",
      "11_coffeemaker            12.59536075592041\n",
      "11_dishwasher             12.8587646484375\n",
      "11_lamp                   2.4114432334899902\n",
      "11_microwaveoven          6.6090006828308105\n",
      "11_monitor-tft            7.585511684417725\n",
      "11_pc-desktop             0.3961584270000458\n",
      "11_pc-laptop              1.3041328191757202\n",
      "11_refrigerator           58.97685623168945\n",
      "11_tv-lcd                 13.633659362792969\n",
      "11_usbharddrive           6.971139430999756\n",
      "11_washingmachine         0.08824289590120316\n",
      "11_waterkettle            10.63017463684082\n",
      "12_amplifier              5.015042304992676\n",
      "12_coffeemaker            4.072742462158203\n",
      "12_dishwasher             8.086204528808594\n",
      "12_lamp                   3.3223304748535156\n",
      "12_microwaveoven          3.4772109985351562\n",
      "12_monitor-tft            14.401552200317383\n",
      "12_pc-desktop             7.1110076904296875\n",
      "12_pc-laptop              2.7554051876068115\n",
      "12_refrigerator           58.58603286743164\n",
      "12_tv-lcd                 21.000669479370117\n",
      "12_usbharddrive           3.6787290573120117\n",
      "12_washingmachine         0.08824290335178375\n",
      "12_waterkettle            2.3836638927459717\n",
      "13_amplifier              4.287698745727539\n",
      "13_coffeemaker            29.820524215698242\n",
      "13_dishwasher             5.696531772613525\n",
      "13_lamp                   2.7325494289398193\n",
      "13_microwaveoven          3.7910356521606445\n",
      "13_monitor-tft            0.08824289590120316\n",
      "13_pc-desktop             5.039784908294678\n",
      "13_refrigerator           33.200626373291016\n",
      "13_tv-lcd                 7.359235763549805\n",
      "13_usbharddrive           15.539637565612793\n",
      "13_washingmachine         11.569869995117188\n",
      "13_waterkettle            2.394193172454834\n",
      "14_amplifier              0.08824289590120316\n",
      "14_coffeemaker            13.700474739074707\n",
      "14_dishwasher             0.08886682242155075\n",
      "14_lamp                   2.3827390670776367\n",
      "14_microwaveoven          3.5866761207580566\n",
      "14_monitor-tft            12.228260040283203\n",
      "14_pc-desktop             0.17970189452171326\n",
      "14_refrigerator           4.532607078552246\n",
      "14_tv-lcd                 8.11920166015625\n",
      "14_washingmachine         0.08824289590120316\n",
      "14_waterkettle            1.4404144287109375\n",
      "15_amplifier              6.289540767669678\n",
      "15_coffeemaker            1.994734764099121\n",
      "15_dishwasher             0.08824289590120316\n",
      "15_lamp                   0.5703189969062805\n",
      "15_microwaveoven          0.08824289590120316\n",
      "15_monitor-tft            10.349932670593262\n",
      "15_pc-desktop             0.2794298529624939\n",
      "15_refrigerator           40.13202667236328\n",
      "15_tv-lcd                 11.023555755615234\n",
      "15_washingmachine         0.08824289590120316\n",
      "15_waterkettle            0.08824289590120316\n",
      "16_amplifier              5.672630310058594\n",
      "16_coffeemaker            0.08824289590120316\n",
      "16_dishwasher             0.08824289590120316\n",
      "16_lamp                   4.170513153076172\n",
      "16_microwaveoven          7.678434371948242\n",
      "16_monitor-tft            14.921270370483398\n",
      "16_pc-desktop             11.494681358337402\n",
      "16_refrigerator           40.282684326171875\n",
      "16_tv-lcd                 6.110199928283691\n",
      "16_washingmachine         5.427247047424316\n",
      "16_waterkettle            0.08824289590120316\n",
      "17_coffeemaker            0.08824289590120316\n",
      "17_dishwasher             0.08824289590120316\n",
      "17_lamp                   2.078315019607544\n",
      "17_microwaveoven          3.1518070697784424\n",
      "17_monitor-tft            8.513174057006836\n",
      "17_pc-desktop             1.8302149772644043\n",
      "17_refrigerator           49.294307708740234\n",
      "17_tv-lcd                 9.961915969848633\n",
      "17_washingmachine         0.08824288100004196\n",
      "17_waterkettle            0.08824289590120316\n",
      "18_coffeemaker            3.6694517135620117\n",
      "18_dishwasher             3.6411209106445312\n",
      "18_lamp                   2.5614774227142334\n",
      "18_microwaveoven          17.685216903686523\n",
      "18_monitor-tft            13.078571319580078\n",
      "18_pc-desktop             10.392830848693848\n",
      "18_refrigerator           38.15916061401367\n",
      "18_tv-lcd                 7.239141941070557\n",
      "18_washingmachine         0.08824289590120316\n",
      "18_waterkettle            0.08824289590120316\n",
      "19_coffeemaker            8.086276054382324\n",
      "19_dishwasher             0.08824288100004196\n",
      "19_lamp                   8.086275100708008\n",
      "19_microwaveoven          5.393380165100098\n",
      "19_monitor-tft            13.416986465454102\n",
      "19_pc-desktop             9.637445449829102\n",
      "19_tv-lcd                 9.957524299621582\n",
      "19_washingmachine         7.141481876373291\n",
      "19_waterkettle            14.521782875061035\n",
      "1_amplifier               4.905848503112793\n",
      "1_beantocupcoffeemaker    11.068185806274414\n",
      "1_charger-smartphone      1.7700058221817017\n",
      "1_coffeemaker             1.989855170249939\n",
      "1_cookingstove            12.585097312927246\n",
      "1_digitaltvreceiver       4.194206237792969\n",
      "1_dishwasher              0.08824289590120316\n",
      "1_dvdplayer               14.046656608581543\n",
      "1_ethernetswitch          7.232330799102783\n",
      "1_freezer                 52.9609260559082\n",
      "1_lamp                    12.254220008850098\n",
      "1_laundrydryer            0.08824289590120316\n",
      "1_microwaveoven           7.108457088470459\n",
      "1_monitor-crt             4.496903896331787\n",
      "1_monitor-tft             17.785186767578125\n",
      "1_multimediacenter        5.093756675720215\n",
      "1_pc-desktop              2.8156471252441406\n",
      "1_pc-laptop               12.599859237670898\n",
      "1_playstation3            9.385818481445312\n",
      "1_printer                 7.449737071990967\n",
      "1_projector               0.08824289590120316\n",
      "1_refrigerator            62.99221420288086\n",
      "1_router                  6.2624735832214355\n",
      "1_solarthermalsystem      9.499198913574219\n",
      "1_subwoofer               11.575838088989258\n",
      "1_toaster                 1.8788472414016724\n",
      "1_tv-crt                  6.7205119132995605\n",
      "1_tv-lcd                  3.632418632507324\n",
      "1_usbharddrive            13.043333053588867\n",
      "1_usbhub                  7.208154201507568\n",
      "1_videoprojector          10.955077171325684\n",
      "1_washingmachine          0.08824289590120316\n",
      "1_waterfountain           86.42257690429688\n",
      "1_waterkettle             1.8788472414016724\n",
      "1_xmaslights              18.347496032714844\n",
      "20_coffeemaker            4.315316200256348\n",
      "20_dishwasher             0.19200772047042847\n",
      "20_lamp                   14.083486557006836\n",
      "20_microwaveoven          5.042768478393555\n",
      "20_monitor-tft            17.584474563598633\n",
      "20_pc-desktop             7.551897048950195\n",
      "20_tv-lcd                 10.695429801940918\n",
      "20_washingmachine         5.154709339141846\n",
      "20_waterkettle            3.5718038082122803\n",
      "21_coffeemaker            3.602220058441162\n",
      "21_dishwasher             0.08824289590120316\n",
      "21_lamp                   8.473509788513184\n",
      "21_microwaveoven          4.764900207519531\n",
      "21_monitor-tft            8.648775100708008\n",
      "21_pc-desktop             9.843306541442871\n",
      "21_tv-lcd                 4.070879936218262\n",
      "21_washingmachine         0.08824289590120316\n",
      "21_waterkettle            0.08824289590120316\n",
      "22_coffeemaker            0.08824288100004196\n",
      "22_dishwasher             6.357892036437988\n",
      "22_lamp                   8.086276054382324\n",
      "22_microwaveoven          5.0427680015563965\n",
      "22_monitor-tft            3.592672824859619\n",
      "22_pc-desktop             9.822153091430664\n",
      "22_tv-lcd                 3.0835025310516357\n",
      "22_washingmachine         0.08824289590120316\n",
      "22_waterkettle            11.59427547454834\n",
      "23_dishwasher             0.08824289590120316\n",
      "23_lamp                   2.5136642456054688\n",
      "23_microwaveoven          6.580352306365967\n",
      "23_monitor-tft            5.4900712966918945\n",
      "23_pc-desktop             9.489424705505371\n",
      "23_tv-lcd                 0.539068341255188\n",
      "23_washingmachine         0.08824289590120316\n",
      "23_waterkettle            22.816953659057617\n",
      "24_dishwasher             11.974724769592285\n",
      "24_lamp                   9.992058753967285\n",
      "24_microwaveoven          4.9362969398498535\n",
      "24_monitor-tft            4.496903896331787\n",
      "24_pc-desktop             6.417484283447266\n",
      "24_tv-lcd                 5.374664306640625\n",
      "24_washingmachine         13.686603546142578\n",
      "24_waterkettle            14.2584810256958\n",
      "25_dishwasher             0.08824289590120316\n",
      "25_lamp                   14.730607032775879\n",
      "25_microwaveoven          10.173662185668945\n",
      "25_monitor-tft            5.405139446258545\n",
      "25_pc-desktop             3.9718644618988037\n",
      "25_tv-lcd                 2.549205780029297\n",
      "25_washingmachine         5.922059059143066\n",
      "25_waterkettle            11.993609428405762\n",
      "26_dishwasher             3.507478952407837\n",
      "26_lamp                   6.82776403427124\n",
      "26_microwaveoven          3.602220058441162\n",
      "26_monitor-tft            2.0626399517059326\n",
      "26_tv-lcd                 11.710607528686523\n",
      "26_washingmachine         4.117821216583252\n",
      "26_waterkettle            7.0211334228515625\n",
      "27_dishwasher             6.821240425109863\n",
      "27_lamp                   0.08824288100004196\n",
      "27_monitor-tft            6.35333251953125\n",
      "27_washingmachine         0.08824289590120316\n",
      "27_waterkettle            4.365145206451416\n",
      "28_dishwasher             0.08824289590120316\n",
      "28_lamp                   12.737509727478027\n",
      "28_monitor-tft            4.635469436645508\n",
      "28_washingmachine         12.261799812316895\n",
      "28_waterkettle            10.51584529876709\n",
      "29_dishwasher             12.738526344299316\n",
      "29_lamp                   6.993500232696533\n",
      "29_monitor-tft            10.98969554901123\n",
      "29_washingmachine         6.383781433105469\n",
      "29_waterkettle            12.546531677246094\n",
      "2_amplifier               5.501838684082031\n",
      "2_coffeemaker             0.16760116815567017\n",
      "2_cookingstove            7.827304840087891\n",
      "2_digitaltvreceiver       3.149794101715088\n",
      "2_dishwasher              6.0542168617248535\n",
      "2_dvdplayer               3.498516082763672\n",
      "2_ethernetswitch          7.205277442932129\n",
      "2_lamp                    8.167734146118164\n",
      "2_laundrydryer            0.08824289590120316\n",
      "2_microwaveoven           0.1607685685157776\n",
      "2_monitor-crt             5.405139446258545\n",
      "2_monitor-tft             14.5830717086792\n",
      "2_multimediacenter        1.9481831789016724\n",
      "2_pc-desktop              7.140614032745361\n",
      "2_pc-laptop               0.36276882886886597\n",
      "2_playstation3            5.454632759094238\n",
      "2_printer                 2.92161226272583\n",
      "2_projector               0.08824289590120316\n",
      "2_refrigerator            54.9648323059082\n",
      "2_router                  1.496508240699768\n",
      "2_solarthermalsystem      6.959651947021484\n",
      "2_subwoofer               11.215195655822754\n",
      "2_toaster                 3.7910358905792236\n",
      "2_tv-crt                  7.507652759552002\n",
      "2_tv-lcd                  14.168182373046875\n",
      "2_usbharddrive            13.390033721923828\n",
      "2_videoprojector          19.604787826538086\n",
      "2_washingmachine          0.08824289590120316\n",
      "2_waterfountain           82.4351806640625\n",
      "2_waterkettle             3.6022207736968994\n",
      "2_xmaslights              8.26191520690918\n",
      "30_dishwasher             4.503072261810303\n",
      "30_lamp                   0.08995535969734192\n",
      "30_monitor-tft            8.235506057739258\n",
      "30_washingmachine         0.08824289590120316\n",
      "30_waterkettle            8.935396194458008\n",
      "31_dishwasher             0.09745859354734421\n",
      "31_lamp                   14.161150932312012\n",
      "31_monitor-tft            10.07643985748291\n",
      "31_washingmachine         0.08824289590120316\n",
      "31_waterkettle            3.454287052154541\n",
      "32_dishwasher             8.054910659790039\n",
      "32_lamp                   5.637330055236816\n",
      "32_monitor-tft            4.636972427368164\n",
      "32_washingmachine         0.08824289590120316\n",
      "32_waterkettle            16.084306716918945\n",
      "33_dishwasher             5.3776373863220215\n",
      "33_monitor-tft            9.104328155517578\n",
      "33_washingmachine         13.686604499816895\n",
      "34_dishwasher             5.803868770599365\n",
      "34_monitor-tft            8.514314651489258\n",
      "34_washingmachine         0.08824289590120316\n",
      "35_dishwasher             4.301609516143799\n",
      "35_monitor-tft            13.029158592224121\n",
      "35_washingmachine         0.08824289590120316\n",
      "36_dishwasher             0.08824289590120316\n",
      "36_monitor-tft            11.054466247558594\n",
      "36_washingmachine         0.12395885586738586\n",
      "37_dishwasher             7.011822700500488\n",
      "37_monitor-tft            5.711717128753662\n",
      "37_washingmachine         0.08824289590120316\n",
      "38_dishwasher             7.719437599182129\n",
      "38_monitor-tft            23.796016693115234\n",
      "38_washingmachine         2.5017130374908447\n",
      "39_dishwasher             5.063595294952393\n",
      "39_monitor-tft            5.157504558563232\n",
      "39_washingmachine         11.836706161499023\n",
      "3_amplifier               10.054082870483398\n",
      "3_coffeemaker             3.600562572479248\n",
      "3_cookingstove            3.0800955295562744\n",
      "3_digitaltvreceiver       1.0609852075576782\n",
      "3_dishwasher              0.08824289590120316\n",
      "3_ethernetswitch          0.2996118664741516\n",
      "3_lamp                    10.607047080993652\n",
      "3_laundrydryer            23.86711883544922\n",
      "3_microwaveoven           1.7738659381866455\n",
      "3_monitor-crt             2.0626399517059326\n",
      "3_monitor-tft             10.112570762634277\n",
      "3_multimediacenter        6.530745983123779\n",
      "3_pc-desktop              7.206445693969727\n",
      "3_pc-laptop               7.666601181030273\n",
      "3_playstation3            7.911937713623047\n",
      "3_printer                 10.432235717773438\n",
      "3_projector               2.5665395259857178\n",
      "3_refrigerator            44.310325622558594\n",
      "3_router                  4.18659782409668\n",
      "3_subwoofer               12.150520324707031\n",
      "3_toaster                 0.6944881677627563\n",
      "3_tv-crt                  0.10477805137634277\n",
      "3_tv-lcd                  10.590164184570312\n",
      "3_usbharddrive            5.509106636047363\n",
      "3_videoprojector          6.8615241050720215\n",
      "3_washingmachine          11.973250389099121\n",
      "3_waterkettle             0.12467896938323975\n",
      "40_monitor-tft            4.120267868041992\n",
      "40_washingmachine         0.08824289590120316\n",
      "41_monitor-tft            6.900881767272949\n",
      "42_monitor-tft            4.743739604949951\n",
      "43_monitor-tft            6.588089466094971\n",
      "44_monitor-tft            0.17402617633342743\n",
      "45_monitor-tft            4.764242649078369\n",
      "46_monitor-tft            12.098455429077148\n",
      "47_monitor-tft            4.796432971954346\n",
      "48_monitor-tft            10.194497108459473\n",
      "49_monitor-tft            7.878111839294434\n",
      "4_amplifier               12.685626029968262\n",
      "4_coffeemaker             10.231354713439941\n",
      "4_digitaltvreceiver       4.055685043334961\n",
      "4_dishwasher              0.08824289590120316\n",
      "4_ethernetswitch          0.3928474485874176\n",
      "4_lamp                    5.998588562011719\n",
      "4_laundrydryer            10.64716625213623\n",
      "4_microwaveoven           0.4458734393119812\n",
      "4_monitor-crt             6.35333251953125\n",
      "4_monitor-tft             4.679576873779297\n",
      "4_multimediacenter        1.607960820198059\n",
      "4_pc-desktop              5.141708850860596\n",
      "4_pc-laptop               8.511622428894043\n",
      "4_playstation3            2.479295492172241\n",
      "4_printer                 6.4820356369018555\n",
      "4_projector               6.032443046569824\n",
      "4_refrigerator            38.55497360229492\n",
      "4_router                  3.2076499462127686\n",
      "4_toaster                 6.617738246917725\n",
      "4_tv-crt                  6.4220194816589355\n",
      "4_tv-lcd                  11.130970001220703\n",
      "4_usbharddrive            4.137225151062012\n",
      "4_videoprojector          6.471659183502197\n",
      "4_washingmachine          11.640726089477539\n",
      "4_waterkettle             3.6911818981170654\n",
      "50_monitor-tft            9.116874694824219\n",
      "5_amplifier               13.165793418884277\n",
      "5_coffeemaker             7.317461013793945\n",
      "5_dishwasher              12.933828353881836\n",
      "5_ethernetswitch          1.6969407796859741\n",
      "5_lamp                    10.426444053649902\n",
      "5_laundrydryer            5.090484142303467\n",
      "5_microwaveoven           0.658929705619812\n",
      "5_monitor-crt             7.674072265625\n",
      "5_monitor-tft             4.781679630279541\n",
      "5_multimediacenter        2.193535804748535\n",
      "5_pc-desktop              5.022897720336914\n",
      "5_pc-laptop               2.5582876205444336\n",
      "5_playstation3            8.31515121459961\n",
      "5_printer                 8.276153564453125\n",
      "5_projector               9.745420455932617\n",
      "5_refrigerator            8.85129165649414\n",
      "5_router                  0.9336217045783997\n",
      "5_toaster                 0.08824289590120316\n",
      "5_tv-crt                  0.08824288100004196\n",
      "5_tv-lcd                  18.702091217041016\n",
      "5_usbharddrive            3.815965175628662\n",
      "5_videoprojector          6.6404194831848145\n",
      "5_washingmachine          0.09784255921840668\n",
      "5_waterkettle             0.08824285864830017\n",
      "6_amplifier               6.989353179931641\n",
      "6_coffeemaker             1.1738368272781372\n",
      "6_dishwasher              0.08824289590120316\n",
      "6_lamp                    7.206446170806885\n",
      "6_laundrydryer            10.965373992919922\n",
      "6_microwaveoven           0.26776206493377686\n",
      "6_monitor-crt             5.823185443878174\n",
      "6_monitor-tft             13.003498077392578\n",
      "6_pc-desktop              0.20223820209503174\n",
      "6_pc-laptop               5.250915050506592\n",
      "6_printer                 4.0656962394714355\n",
      "6_refrigerator            9.919214248657227\n",
      "6_router                  3.9461987018585205\n",
      "6_tv-lcd                  4.041927814483643\n",
      "6_usbharddrive            6.568648815155029\n",
      "6_videoprojector          4.715786457061768\n",
      "6_washingmachine          0.08824289590120316\n",
      "6_waterkettle             2.394193172454834\n",
      "7_amplifier               0.19059467315673828\n",
      "7_coffeemaker             7.484562873840332\n",
      "7_dishwasher              0.08824289590120316\n",
      "7_lamp                    5.17862606048584\n",
      "7_laundrydryer            17.191329956054688\n",
      "7_microwaveoven           52.844112396240234\n",
      "7_monitor-crt             5.719590663909912\n",
      "7_monitor-tft             10.72809886932373\n",
      "7_pc-desktop              7.794618129730225\n",
      "7_pc-laptop               9.904457092285156\n",
      "7_refrigerator            11.720276832580566\n",
      "7_router                  0.3916691839694977\n",
      "7_tv-lcd                  10.181877136230469\n",
      "7_usbharddrive            2.0310616493225098\n",
      "7_washingmachine          0.08824289590120316\n",
      "7_waterkettle             0.08824289590120316\n",
      "8_amplifier               3.193169116973877\n",
      "8_coffeemaker             10.097638130187988\n",
      "8_dishwasher              0.08824289590120316\n",
      "8_lamp                    0.08824289590120316\n",
      "8_microwaveoven           7.801903247833252\n",
      "8_monitor-tft             21.667095184326172\n",
      "8_pc-desktop              14.437339782714844\n",
      "8_pc-laptop               9.752644538879395\n",
      "8_refrigerator            46.959590911865234\n",
      "8_router                  0.13896417617797852\n",
      "8_tv-lcd                  13.57873821258545\n",
      "8_usbharddrive            5.623088359832764\n",
      "8_washingmachine          10.393381118774414\n",
      "8_waterkettle             3.57814359664917\n",
      "9_amplifier               5.733398914337158\n",
      "9_coffeemaker             17.822399139404297\n",
      "9_dishwasher              0.08824289590120316\n",
      "9_lamp                    12.272433280944824\n",
      "9_microwaveoven           9.300613403320312\n",
      "9_monitor-tft             9.353412628173828\n",
      "9_pc-desktop              4.682226657867432\n",
      "9_pc-laptop               4.279904842376709\n",
      "9_refrigerator            10.372310638427734\n",
      "9_tv-lcd                  9.601292610168457\n",
      "9_usbharddrive            5.335859298706055\n",
      "9_washingmachine          0.08824289590120316\n",
      "9_waterkettle             14.26565933227539\n"
     ]
    }
   ],
   "source": [
    "print_mse(gru_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Comparing MSE values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function prints **both** `MSE` dictionaries together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_both_mses(gru_mse, pre_mse):\n",
    "    \"\"\"\n",
    "    The function prints both MSE errors.\n",
    "    \n",
    "    Args:\n",
    "        gru_mse [dict]: GRU MSE errors.\n",
    "        pre_mse [dict]: Predifction MSE errors.\n",
    "    \n",
    "    Returns:\n",
    "        Function does not return anything.\n",
    "    \"\"\"\n",
    "    \n",
    "    [print(\"{0:25} {1:25} {2:25}\".format(i, gru_mse[i], pre_mse[i])) for i in gru_mse]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_alarmclock                      6.036890029907227                       0.0\n",
      "0_amplifier                       6.459870338439941                       0.0\n",
      "0_beantocupcoffeemaker            2.410295009613037                       1.0\n",
      "0_breadcutter                      9.96654224395752                       0.0\n",
      "0_cdplayer                        6.813695907592773                      36.0\n",
      "0_charger-psp                   0.08824290335178375                       0.0\n",
      "0_charger-smartphone             12.550207138061523                       0.0\n",
      "0_coffeemaker                   0.08824289590120316                       0.0\n",
      "0_cookingstove                    4.653611660003662                       0.0\n",
      "0_digitaltvreceiver              1.7190954685211182                       1.0\n",
      "0_dishwasher                    0.08824289590120316                       0.0\n",
      "0_dvdplayer                      11.759687423706055                       0.0\n",
      "0_ethernetswitch                  2.640399932861328                      36.0\n",
      "0_freezer                         55.34463119506836                       0.0\n",
      "0_iron                           3.7361083030700684                       0.0\n",
      "0_lamp                           14.540789604187012                       0.0\n",
      "0_laundrydryer                  0.08824289590120316                       0.0\n",
      "0_microwaveoven                  1.3969545364379883                       9.0\n",
      "0_monitor-crt                    5.4900712966918945                     400.0\n",
      "0_monitor-tft                     9.039022445678711                       0.0\n",
      "0_multimediacenter                5.764163017272949                       1.0\n",
      "0_pc-desktop                     3.5742545127868652                     256.0\n",
      "0_pc-laptop                       6.385065078735352                     400.0\n",
      "0_playstation3                   0.4530649781227112                       0.0\n",
      "0_printer                         9.312712669372559                       0.0\n",
      "0_projector                     0.08824289590120316                       0.0\n",
      "0_refrigerator                   59.790916442871094                       0.0\n",
      "0_remotedesktop                  2.9058985710144043                       9.0\n",
      "0_router                         3.3551244735717773                      49.0\n",
      "0_solarthermalsystem               7.08721399307251                       0.0\n",
      "0_subwoofer                      14.433603286743164                       0.0\n",
      "0_toaster                         7.678434371948242                       0.0\n",
      "0_tv-crt                         17.290010452270508                       0.0\n",
      "0_tv-lcd                          38.58525848388672                       0.0\n",
      "0_usbharddrive                     10.2172269821167                      81.0\n",
      "0_usbhub                          4.027666091918945                       4.0\n",
      "0_vacuumcleaner                   10.18982219696045                       0.0\n",
      "0_videoprojector                   3.94706392288208                       9.0\n",
      "0_washingmachine                0.08824289590120316                       0.0\n",
      "0_waterboiler                    18.062108993530273                     196.0\n",
      "0_waterfountain                    90.2194595336914                       0.0\n",
      "0_waterkettle                   0.08824289590120316                       0.0\n",
      "0_xmaslights                     17.577295303344727                     400.0\n",
      "10_amplifier                      7.666777610778809                       0.0\n",
      "10_coffeemaker                    7.649886131286621                     361.0\n",
      "10_dishwasher                     8.219724655151367                       0.0\n",
      "10_lamp                           9.063535690307617                       0.0\n",
      "10_microwaveoven                  7.305012226104736                       0.0\n",
      "10_monitor-tft                   12.955492973327637                     400.0\n",
      "10_pc-desktop                        12.13330078125                       0.0\n",
      "10_pc-laptop                       4.36509370803833                     400.0\n",
      "10_refrigerator                  63.362571716308594                       0.0\n",
      "10_tv-lcd                        2.5614774227142334                       0.0\n",
      "10_usbharddrive                   7.640468120574951                       4.0\n",
      "10_washingmachine                 9.438919067382812                       0.0\n",
      "10_waterkettle                   3.7910358905792236                       0.0\n",
      "11_amplifier                      5.342926979064941                     400.0\n",
      "11_coffeemaker                    12.59536075592041                       9.0\n",
      "11_dishwasher                      12.8587646484375                       0.0\n",
      "11_lamp                          2.4114432334899902                     400.0\n",
      "11_microwaveoven                 6.6090006828308105                       0.0\n",
      "11_monitor-tft                    7.585511684417725                     400.0\n",
      "11_pc-desktop                    0.3961584270000458                       0.0\n",
      "11_pc-laptop                     1.3041328191757202                       1.0\n",
      "11_refrigerator                   58.97685623168945                     400.0\n",
      "11_tv-lcd                        13.633659362792969                       0.0\n",
      "11_usbharddrive                   6.971139430999756                       1.0\n",
      "11_washingmachine               0.08824289590120316                       0.0\n",
      "11_waterkettle                    10.63017463684082                       0.0\n",
      "12_amplifier                      5.015042304992676                       0.0\n",
      "12_coffeemaker                    4.072742462158203                       0.0\n",
      "12_dishwasher                     8.086204528808594                       0.0\n",
      "12_lamp                          3.3223304748535156                       0.0\n",
      "12_microwaveoven                 3.4772109985351562                     400.0\n",
      "12_monitor-tft                   14.401552200317383                       0.0\n",
      "12_pc-desktop                    7.1110076904296875                       0.0\n",
      "12_pc-laptop                     2.7554051876068115                     121.0\n",
      "12_refrigerator                   58.58603286743164                       0.0\n",
      "12_tv-lcd                        21.000669479370117                       0.0\n",
      "12_usbharddrive                  3.6787290573120117                       1.0\n",
      "12_washingmachine               0.08824290335178375                       0.0\n",
      "12_waterkettle                   2.3836638927459717                       0.0\n",
      "13_amplifier                      4.287698745727539                       0.0\n",
      "13_coffeemaker                   29.820524215698242                       0.0\n",
      "13_dishwasher                     5.696531772613525                       0.0\n",
      "13_lamp                          2.7325494289398193                       0.0\n",
      "13_microwaveoven                 3.7910356521606445                       0.0\n",
      "13_monitor-tft                  0.08824289590120316                       0.0\n",
      "13_pc-desktop                     5.039784908294678                       0.0\n",
      "13_refrigerator                  33.200626373291016                      64.0\n",
      "13_tv-lcd                         7.359235763549805                       0.0\n",
      "13_usbharddrive                  15.539637565612793                     121.0\n",
      "13_washingmachine                11.569869995117188                       0.0\n",
      "13_waterkettle                    2.394193172454834                       0.0\n",
      "14_amplifier                    0.08824289590120316                       0.0\n",
      "14_coffeemaker                   13.700474739074707                       0.0\n",
      "14_dishwasher                   0.08886682242155075                      16.0\n",
      "14_lamp                          2.3827390670776367                       0.0\n",
      "14_microwaveoven                 3.5866761207580566                       4.0\n",
      "14_monitor-tft                   12.228260040283203                       0.0\n",
      "14_pc-desktop                   0.17970189452171326                       0.0\n",
      "14_refrigerator                   4.532607078552246                     144.0\n",
      "14_tv-lcd                          8.11920166015625                       0.0\n",
      "14_washingmachine               0.08824289590120316                       0.0\n",
      "14_waterkettle                   1.4404144287109375                       0.0\n",
      "15_amplifier                      6.289540767669678                     400.0\n",
      "15_coffeemaker                    1.994734764099121                       0.0\n",
      "15_dishwasher                   0.08824289590120316                       0.0\n",
      "15_lamp                          0.5703189969062805                       1.0\n",
      "15_microwaveoven                0.08824289590120316                       0.0\n",
      "15_monitor-tft                   10.349932670593262                       0.0\n",
      "15_pc-desktop                    0.2794298529624939                       0.0\n",
      "15_refrigerator                   40.13202667236328                       9.0\n",
      "15_tv-lcd                        11.023555755615234                       0.0\n",
      "15_washingmachine               0.08824289590120316                       0.0\n",
      "15_waterkettle                  0.08824289590120316                       0.0\n",
      "16_amplifier                      5.672630310058594                       0.0\n",
      "16_coffeemaker                  0.08824289590120316                       0.0\n",
      "16_dishwasher                   0.08824289590120316                       0.0\n",
      "16_lamp                           4.170513153076172                       0.0\n",
      "16_microwaveoven                  7.678434371948242                       0.0\n",
      "16_monitor-tft                   14.921270370483398                       0.0\n",
      "16_pc-desktop                    11.494681358337402                       4.0\n",
      "16_refrigerator                  40.282684326171875                     196.0\n",
      "16_tv-lcd                         6.110199928283691                       0.0\n",
      "16_washingmachine                 5.427247047424316                       0.0\n",
      "16_waterkettle                  0.08824289590120316                       0.0\n",
      "17_coffeemaker                  0.08824289590120316                       0.0\n",
      "17_dishwasher                   0.08824289590120316                       0.0\n",
      "17_lamp                           2.078315019607544                       0.0\n",
      "17_microwaveoven                 3.1518070697784424                       0.0\n",
      "17_monitor-tft                    8.513174057006836                       0.0\n",
      "17_pc-desktop                    1.8302149772644043                       4.0\n",
      "17_refrigerator                  49.294307708740234                     324.0\n",
      "17_tv-lcd                         9.961915969848633                       0.0\n",
      "17_washingmachine               0.08824288100004196                       0.0\n",
      "17_waterkettle                  0.08824289590120316                       0.0\n",
      "18_coffeemaker                   3.6694517135620117                       0.0\n",
      "18_dishwasher                    3.6411209106445312                     400.0\n",
      "18_lamp                          2.5614774227142334                       0.0\n",
      "18_microwaveoven                 17.685216903686523                       0.0\n",
      "18_monitor-tft                   13.078571319580078                     400.0\n",
      "18_pc-desktop                    10.392830848693848                       0.0\n",
      "18_refrigerator                   38.15916061401367                       4.0\n",
      "18_tv-lcd                         7.239141941070557                       0.0\n",
      "18_washingmachine               0.08824289590120316                       0.0\n",
      "18_waterkettle                  0.08824289590120316                       0.0\n",
      "19_coffeemaker                    8.086276054382324                       0.0\n",
      "19_dishwasher                   0.08824288100004196                       0.0\n",
      "19_lamp                           8.086275100708008                       0.0\n",
      "19_microwaveoven                  5.393380165100098                       0.0\n",
      "19_monitor-tft                   13.416986465454102                       0.0\n",
      "19_pc-desktop                     9.637445449829102                       0.0\n",
      "19_tv-lcd                         9.957524299621582                       0.0\n",
      "19_washingmachine                 7.141481876373291                       0.0\n",
      "19_waterkettle                   14.521782875061035                       0.0\n",
      "1_amplifier                       4.905848503112793                       0.0\n",
      "1_beantocupcoffeemaker           11.068185806274414                       0.0\n",
      "1_charger-smartphone             1.7700058221817017                       4.0\n",
      "1_coffeemaker                     1.989855170249939                       0.0\n",
      "1_cookingstove                   12.585097312927246                       0.0\n",
      "1_digitaltvreceiver               4.194206237792969                      25.0\n",
      "1_dishwasher                    0.08824289590120316                       0.0\n",
      "1_dvdplayer                      14.046656608581543                       0.0\n",
      "1_ethernetswitch                  7.232330799102783                     121.0\n",
      "1_freezer                          52.9609260559082                       0.0\n",
      "1_lamp                           12.254220008850098                     400.0\n",
      "1_laundrydryer                  0.08824289590120316                       0.0\n",
      "1_microwaveoven                   7.108457088470459                      16.0\n",
      "1_monitor-crt                     4.496903896331787                     256.0\n",
      "1_monitor-tft                    17.785186767578125                       0.0\n",
      "1_multimediacenter                5.093756675720215                       1.0\n",
      "1_pc-desktop                     2.8156471252441406                      36.0\n",
      "1_pc-laptop                      12.599859237670898                       0.0\n",
      "1_playstation3                    9.385818481445312                       1.0\n",
      "1_printer                         7.449737071990967                     225.0\n",
      "1_projector                     0.08824289590120316                       0.0\n",
      "1_refrigerator                    62.99221420288086                     400.0\n",
      "1_router                         6.2624735832214355                     100.0\n",
      "1_solarthermalsystem              9.499198913574219                       0.0\n",
      "1_subwoofer                      11.575838088989258                       0.0\n",
      "1_toaster                        1.8788472414016724                       0.0\n",
      "1_tv-crt                         6.7205119132995605                       0.0\n",
      "1_tv-lcd                          3.632418632507324                       0.0\n",
      "1_usbharddrive                   13.043333053588867                      81.0\n",
      "1_usbhub                          7.208154201507568                       1.0\n",
      "1_videoprojector                 10.955077171325684                       0.0\n",
      "1_washingmachine                0.08824289590120316                       0.0\n",
      "1_waterfountain                   86.42257690429688                       0.0\n",
      "1_waterkettle                    1.8788472414016724                       0.0\n",
      "1_xmaslights                     18.347496032714844                     400.0\n",
      "20_coffeemaker                    4.315316200256348                       9.0\n",
      "20_dishwasher                   0.19200772047042847                     400.0\n",
      "20_lamp                          14.083486557006836                       0.0\n",
      "20_microwaveoven                  5.042768478393555                       0.0\n",
      "20_monitor-tft                   17.584474563598633                       0.0\n",
      "20_pc-desktop                     7.551897048950195                       0.0\n",
      "20_tv-lcd                        10.695429801940918                       0.0\n",
      "20_washingmachine                 5.154709339141846                       0.0\n",
      "20_waterkettle                   3.5718038082122803                       0.0\n",
      "21_coffeemaker                    3.602220058441162                       0.0\n",
      "21_dishwasher                   0.08824289590120316                       0.0\n",
      "21_lamp                           8.473509788513184                       0.0\n",
      "21_microwaveoven                  4.764900207519531                       0.0\n",
      "21_monitor-tft                    8.648775100708008                       0.0\n",
      "21_pc-desktop                     9.843306541442871                     400.0\n",
      "21_tv-lcd                         4.070879936218262                       9.0\n",
      "21_washingmachine               0.08824289590120316                       0.0\n",
      "21_waterkettle                  0.08824289590120316                       0.0\n",
      "22_coffeemaker                  0.08824288100004196                       0.0\n",
      "22_dishwasher                     6.357892036437988                       0.0\n",
      "22_lamp                           8.086276054382324                       0.0\n",
      "22_microwaveoven                 5.0427680015563965                       0.0\n",
      "22_monitor-tft                    3.592672824859619                       1.0\n",
      "22_pc-desktop                     9.822153091430664                     400.0\n",
      "22_tv-lcd                        3.0835025310516357                       4.0\n",
      "22_washingmachine               0.08824289590120316                       0.0\n",
      "22_waterkettle                    11.59427547454834                       0.0\n",
      "23_dishwasher                   0.08824289590120316                       0.0\n",
      "23_lamp                          2.5136642456054688                       0.0\n",
      "23_microwaveoven                  6.580352306365967                       0.0\n",
      "23_monitor-tft                   5.4900712966918945                     400.0\n",
      "23_pc-desktop                     9.489424705505371                       0.0\n",
      "23_tv-lcd                         0.539068341255188                       1.0\n",
      "23_washingmachine               0.08824289590120316                       0.0\n",
      "23_waterkettle                   22.816953659057617                       0.0\n",
      "24_dishwasher                    11.974724769592285                       0.0\n",
      "24_lamp                           9.992058753967285                       0.0\n",
      "24_microwaveoven                 4.9362969398498535                       0.0\n",
      "24_monitor-tft                    4.496903896331787                     256.0\n",
      "24_pc-desktop                     6.417484283447266                       0.0\n",
      "24_tv-lcd                         5.374664306640625                      81.0\n",
      "24_washingmachine                13.686603546142578                       0.0\n",
      "24_waterkettle                     14.2584810256958                       0.0\n",
      "25_dishwasher                   0.08824289590120316                       0.0\n",
      "25_lamp                          14.730607032775879                       0.0\n",
      "25_microwaveoven                 10.173662185668945                       0.0\n",
      "25_monitor-tft                    5.405139446258545                       0.0\n",
      "25_pc-desktop                    3.9718644618988037                     121.0\n",
      "25_tv-lcd                         2.549205780029297                       0.0\n",
      "25_washingmachine                 5.922059059143066                       0.0\n",
      "25_waterkettle                   11.993609428405762                       0.0\n",
      "26_dishwasher                     3.507478952407837                       0.0\n",
      "26_lamp                            6.82776403427124                       0.0\n",
      "26_microwaveoven                  3.602220058441162                       0.0\n",
      "26_monitor-tft                   2.0626399517059326                     400.0\n",
      "26_tv-lcd                        11.710607528686523                       0.0\n",
      "26_washingmachine                 4.117821216583252                       0.0\n",
      "26_waterkettle                   7.0211334228515625                       0.0\n",
      "27_dishwasher                     6.821240425109863                       0.0\n",
      "27_lamp                         0.08824288100004196                       0.0\n",
      "27_monitor-tft                     6.35333251953125                     400.0\n",
      "27_washingmachine               0.08824289590120316                       0.0\n",
      "27_waterkettle                    4.365145206451416                       0.0\n",
      "28_dishwasher                   0.08824289590120316                       0.0\n",
      "28_lamp                          12.737509727478027                       0.0\n",
      "28_monitor-tft                    4.635469436645508                       0.0\n",
      "28_washingmachine                12.261799812316895                       0.0\n",
      "28_waterkettle                    10.51584529876709                       0.0\n",
      "29_dishwasher                    12.738526344299316                       0.0\n",
      "29_lamp                           6.993500232696533                     400.0\n",
      "29_monitor-tft                    10.98969554901123                       0.0\n",
      "29_washingmachine                 6.383781433105469                       0.0\n",
      "29_waterkettle                   12.546531677246094                       0.0\n",
      "2_amplifier                       5.501838684082031                       0.0\n",
      "2_coffeemaker                   0.16760116815567017                       0.0\n",
      "2_cookingstove                    7.827304840087891                       0.0\n",
      "2_digitaltvreceiver               3.149794101715088                       4.0\n",
      "2_dishwasher                     6.0542168617248535                       0.0\n",
      "2_dvdplayer                       3.498516082763672                     324.0\n",
      "2_ethernetswitch                  7.205277442932129                       0.0\n",
      "2_lamp                            8.167734146118164                       0.0\n",
      "2_laundrydryer                  0.08824289590120316                       0.0\n",
      "2_microwaveoven                  0.1607685685157776                       0.0\n",
      "2_monitor-crt                     5.405139446258545                       0.0\n",
      "2_monitor-tft                      14.5830717086792                       4.0\n",
      "2_multimediacenter               1.9481831789016724                       1.0\n",
      "2_pc-desktop                      7.140614032745361                     121.0\n",
      "2_pc-laptop                     0.36276882886886597                       0.0\n",
      "2_playstation3                    5.454632759094238                       0.0\n",
      "2_printer                          2.92161226272583                       9.0\n",
      "2_projector                     0.08824289590120316                       0.0\n",
      "2_refrigerator                     54.9648323059082                     324.0\n",
      "2_router                          1.496508240699768                       1.0\n",
      "2_solarthermalsystem              6.959651947021484                       0.0\n",
      "2_subwoofer                      11.215195655822754                       0.0\n",
      "2_toaster                        3.7910358905792236                       0.0\n",
      "2_tv-crt                          7.507652759552002                       0.0\n",
      "2_tv-lcd                         14.168182373046875                       0.0\n",
      "2_usbharddrive                   13.390033721923828                      64.0\n",
      "2_videoprojector                 19.604787826538086                       0.0\n",
      "2_washingmachine                0.08824289590120316                       0.0\n",
      "2_waterfountain                    82.4351806640625                     400.0\n",
      "2_waterkettle                    3.6022207736968994                       0.0\n",
      "2_xmaslights                       8.26191520690918                     400.0\n",
      "30_dishwasher                     4.503072261810303                       0.0\n",
      "30_lamp                         0.08995535969734192                     289.0\n",
      "30_monitor-tft                    8.235506057739258                     324.0\n",
      "30_washingmachine               0.08824289590120316                       0.0\n",
      "30_waterkettle                    8.935396194458008                       0.0\n",
      "31_dishwasher                   0.09745859354734421                     400.0\n",
      "31_lamp                          14.161150932312012                       0.0\n",
      "31_monitor-tft                    10.07643985748291                     400.0\n",
      "31_washingmachine               0.08824289590120316                       0.0\n",
      "31_waterkettle                    3.454287052154541                       0.0\n",
      "32_dishwasher                     8.054910659790039                       0.0\n",
      "32_lamp                           5.637330055236816                       0.0\n",
      "32_monitor-tft                    4.636972427368164                       0.0\n",
      "32_washingmachine               0.08824289590120316                       0.0\n",
      "32_waterkettle                   16.084306716918945                       0.0\n",
      "33_dishwasher                    5.3776373863220215                       0.0\n",
      "33_monitor-tft                    9.104328155517578                     400.0\n",
      "33_washingmachine                13.686604499816895                       0.0\n",
      "34_dishwasher                     5.803868770599365                       0.0\n",
      "34_monitor-tft                    8.514314651489258                       0.0\n",
      "34_washingmachine               0.08824289590120316                       0.0\n",
      "35_dishwasher                     4.301609516143799                       0.0\n",
      "35_monitor-tft                   13.029158592224121                       0.0\n",
      "35_washingmachine               0.08824289590120316                       0.0\n",
      "36_dishwasher                   0.08824289590120316                       0.0\n",
      "36_monitor-tft                   11.054466247558594                       0.0\n",
      "36_washingmachine               0.12395885586738586                     400.0\n",
      "37_dishwasher                     7.011822700500488                       0.0\n",
      "37_monitor-tft                    5.711717128753662                     400.0\n",
      "37_washingmachine               0.08824289590120316                       0.0\n",
      "38_dishwasher                     7.719437599182129                       0.0\n",
      "38_monitor-tft                   23.796016693115234                       0.0\n",
      "38_washingmachine                2.5017130374908447                     400.0\n",
      "39_dishwasher                     5.063595294952393                       0.0\n",
      "39_monitor-tft                    5.157504558563232                       0.0\n",
      "39_washingmachine                11.836706161499023                       0.0\n",
      "3_amplifier                      10.054082870483398                       0.0\n",
      "3_coffeemaker                     3.600562572479248                       0.0\n",
      "3_cookingstove                   3.0800955295562744                       0.0\n",
      "3_digitaltvreceiver              1.0609852075576782                       0.0\n",
      "3_dishwasher                    0.08824289590120316                       0.0\n",
      "3_ethernetswitch                 0.2996118664741516                       0.0\n",
      "3_lamp                           10.607047080993652                     400.0\n",
      "3_laundrydryer                    23.86711883544922                       0.0\n",
      "3_microwaveoven                  1.7738659381866455                       0.0\n",
      "3_monitor-crt                    2.0626399517059326                     400.0\n",
      "3_monitor-tft                    10.112570762634277                      16.0\n",
      "3_multimediacenter                6.530745983123779                       1.0\n",
      "3_pc-desktop                      7.206445693969727                       0.0\n",
      "3_pc-laptop                       7.666601181030273                      25.0\n",
      "3_playstation3                    7.911937713623047                       1.0\n",
      "3_printer                        10.432235717773438                       0.0\n",
      "3_projector                      2.5665395259857178                       0.0\n",
      "3_refrigerator                   44.310325622558594                       0.0\n",
      "3_router                           4.18659782409668                      25.0\n",
      "3_subwoofer                      12.150520324707031                       0.0\n",
      "3_toaster                        0.6944881677627563                       0.0\n",
      "3_tv-crt                        0.10477805137634277                     400.0\n",
      "3_tv-lcd                         10.590164184570312                       0.0\n",
      "3_usbharddrive                    5.509106636047363                     144.0\n",
      "3_videoprojector                 6.8615241050720215                       0.0\n",
      "3_washingmachine                 11.973250389099121                       0.0\n",
      "3_waterkettle                   0.12467896938323975                       0.0\n",
      "40_monitor-tft                    4.120267868041992                     400.0\n",
      "40_washingmachine               0.08824289590120316                       0.0\n",
      "41_monitor-tft                    6.900881767272949                       0.0\n",
      "42_monitor-tft                    4.743739604949951                     289.0\n",
      "43_monitor-tft                    6.588089466094971                     400.0\n",
      "44_monitor-tft                  0.17402617633342743                       0.0\n",
      "45_monitor-tft                    4.764242649078369                     400.0\n",
      "46_monitor-tft                   12.098455429077148                       0.0\n",
      "47_monitor-tft                    4.796432971954346                       0.0\n",
      "48_monitor-tft                   10.194497108459473                       0.0\n",
      "49_monitor-tft                    7.878111839294434                       0.0\n",
      "4_amplifier                      12.685626029968262                       0.0\n",
      "4_coffeemaker                    10.231354713439941                     361.0\n",
      "4_digitaltvreceiver               4.055685043334961                      16.0\n",
      "4_dishwasher                    0.08824289590120316                       0.0\n",
      "4_ethernetswitch                 0.3928474485874176                       1.0\n",
      "4_lamp                            5.998588562011719                       0.0\n",
      "4_laundrydryer                    10.64716625213623                       0.0\n",
      "4_microwaveoven                  0.4458734393119812                       0.0\n",
      "4_monitor-crt                      6.35333251953125                     400.0\n",
      "4_monitor-tft                     4.679576873779297                     144.0\n",
      "4_multimediacenter                1.607960820198059                       0.0\n",
      "4_pc-desktop                      5.141708850860596                       0.0\n",
      "4_pc-laptop                       8.511622428894043                       4.0\n",
      "4_playstation3                    2.479295492172241                       1.0\n",
      "4_printer                        6.4820356369018555                       1.0\n",
      "4_projector                       6.032443046569824                       9.0\n",
      "4_refrigerator                    38.55497360229492                       0.0\n",
      "4_router                         3.2076499462127686                       4.0\n",
      "4_toaster                         6.617738246917725                       0.0\n",
      "4_tv-crt                         6.4220194816589355                       0.0\n",
      "4_tv-lcd                         11.130970001220703                       0.0\n",
      "4_usbharddrive                    4.137225151062012                       0.0\n",
      "4_videoprojector                  6.471659183502197                       0.0\n",
      "4_washingmachine                 11.640726089477539                       0.0\n",
      "4_waterkettle                    3.6911818981170654                       0.0\n",
      "50_monitor-tft                    9.116874694824219                       0.0\n",
      "5_amplifier                      13.165793418884277                       0.0\n",
      "5_coffeemaker                     7.317461013793945                     361.0\n",
      "5_dishwasher                     12.933828353881836                       0.0\n",
      "5_ethernetswitch                 1.6969407796859741                       1.0\n",
      "5_lamp                           10.426444053649902                     400.0\n",
      "5_laundrydryer                    5.090484142303467                       0.0\n",
      "5_microwaveoven                   0.658929705619812                       0.0\n",
      "5_monitor-crt                        7.674072265625                     400.0\n",
      "5_monitor-tft                     4.781679630279541                     196.0\n",
      "5_multimediacenter                2.193535804748535                      25.0\n",
      "5_pc-desktop                      5.022897720336914                       0.0\n",
      "5_pc-laptop                      2.5582876205444336                     169.0\n",
      "5_playstation3                     8.31515121459961                       0.0\n",
      "5_printer                         8.276153564453125                     121.0\n",
      "5_projector                       9.745420455932617                       0.0\n",
      "5_refrigerator                     8.85129165649414                       1.0\n",
      "5_router                         0.9336217045783997                       0.0\n",
      "5_toaster                       0.08824289590120316                       0.0\n",
      "5_tv-crt                        0.08824288100004196                       0.0\n",
      "5_tv-lcd                         18.702091217041016                       0.0\n",
      "5_usbharddrive                    3.815965175628662                       1.0\n",
      "5_videoprojector                 6.6404194831848145                       1.0\n",
      "5_washingmachine                0.09784255921840668                     400.0\n",
      "5_waterkettle                   0.08824285864830017                       0.0\n",
      "6_amplifier                       6.989353179931641                       0.0\n",
      "6_coffeemaker                    1.1738368272781372                     121.0\n",
      "6_dishwasher                    0.08824289590120316                       0.0\n",
      "6_lamp                            7.206446170806885                     400.0\n",
      "6_laundrydryer                   10.965373992919922                       0.0\n",
      "6_microwaveoven                 0.26776206493377686                       0.0\n",
      "6_monitor-crt                     5.823185443878174                       0.0\n",
      "6_monitor-tft                    13.003498077392578                     169.0\n",
      "6_pc-desktop                    0.20223820209503174                       0.0\n",
      "6_pc-laptop                       5.250915050506592                       4.0\n",
      "6_printer                        4.0656962394714355                       9.0\n",
      "6_refrigerator                    9.919214248657227                       0.0\n",
      "6_router                         3.9461987018585205                      16.0\n",
      "6_tv-lcd                          4.041927814483643                       0.0\n",
      "6_usbharddrive                    6.568648815155029                       1.0\n",
      "6_videoprojector                  4.715786457061768                       0.0\n",
      "6_washingmachine                0.08824289590120316                       0.0\n",
      "6_waterkettle                     2.394193172454834                       0.0\n",
      "7_amplifier                     0.19059467315673828                     400.0\n",
      "7_coffeemaker                     7.484562873840332                       1.0\n",
      "7_dishwasher                    0.08824289590120316                       0.0\n",
      "7_lamp                             5.17862606048584                       0.0\n",
      "7_laundrydryer                   17.191329956054688                     400.0\n",
      "7_microwaveoven                  52.844112396240234                     121.0\n",
      "7_monitor-crt                     5.719590663909912                     196.0\n",
      "7_monitor-tft                     10.72809886932373                       4.0\n",
      "7_pc-desktop                      7.794618129730225                     400.0\n",
      "7_pc-laptop                       9.904457092285156                       0.0\n",
      "7_refrigerator                   11.720276832580566                       1.0\n",
      "7_router                         0.3916691839694977                       1.0\n",
      "7_tv-lcd                         10.181877136230469                       0.0\n",
      "7_usbharddrive                   2.0310616493225098                       0.0\n",
      "7_washingmachine                0.08824289590120316                       0.0\n",
      "7_waterkettle                   0.08824289590120316                       0.0\n",
      "8_amplifier                       3.193169116973877                     400.0\n",
      "8_coffeemaker                    10.097638130187988                       1.0\n",
      "8_dishwasher                    0.08824289590120316                       0.0\n",
      "8_lamp                          0.08824289590120316                       0.0\n",
      "8_microwaveoven                   7.801903247833252                       9.0\n",
      "8_monitor-tft                    21.667095184326172                       0.0\n",
      "8_pc-desktop                     14.437339782714844                       0.0\n",
      "8_pc-laptop                       9.752644538879395                       0.0\n",
      "8_refrigerator                   46.959590911865234                       0.0\n",
      "8_router                        0.13896417617797852                       1.0\n",
      "8_tv-lcd                          13.57873821258545                       0.0\n",
      "8_usbharddrive                    5.623088359832764                      64.0\n",
      "8_washingmachine                 10.393381118774414                       0.0\n",
      "8_waterkettle                      3.57814359664917                       0.0\n",
      "9_amplifier                       5.733398914337158                       0.0\n",
      "9_coffeemaker                    17.822399139404297                       1.0\n",
      "9_dishwasher                    0.08824289590120316                       0.0\n",
      "9_lamp                           12.272433280944824                     400.0\n",
      "9_microwaveoven                   9.300613403320312                       1.0\n",
      "9_monitor-tft                     9.353412628173828                       0.0\n",
      "9_pc-desktop                      4.682226657867432                     400.0\n",
      "9_pc-laptop                       4.279904842376709                       0.0\n",
      "9_refrigerator                   10.372310638427734                     169.0\n",
      "9_tv-lcd                          9.601292610168457                       0.0\n",
      "9_usbharddrive                    5.335859298706055                       1.0\n",
      "9_washingmachine                0.08824289590120316                       0.0\n",
      "9_waterkettle                     14.26565933227539                       0.0\n"
     ]
    }
   ],
   "source": [
    "print_both_mses(gru_mse, pre_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we calculate and print **sum of all `MSE` values** for both `Word2Vec` and `RNN` neural network predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_mse(mse):\n",
    "    \"\"\"\n",
    "    The function sums MSE errors.\n",
    "    \n",
    "    Args:\n",
    "        mse [dict]: MSE errors.\n",
    "    \n",
    "    Returns:\n",
    "        [float]: The sum of MSE errors. \n",
    "    \"\"\"\n",
    "    \n",
    "    return round(sum(mse.values()), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4009.1\n"
     ]
    }
   ],
   "source": [
    "print(sum_mse(gru_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26900.0\n"
     ]
    }
   ],
   "source": [
    "print(sum_mse(pre_mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we count and print **number of zero `MSE` values** for both `Word2Vec` and `RNN` neural network predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_zero_mse(mse):\n",
    "    \"\"\"\n",
    "    The function counts number of zero MSE errors.\n",
    "    \n",
    "    Args:\n",
    "        mse [dict]: MSE errors.\n",
    "    \n",
    "    Returns:\n",
    "        [float]: Number of zero MSE errors.\n",
    "    \"\"\"\n",
    "    \n",
    "    return sum(i == 0 for i in mse.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(count_zero_mse(gru_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "324\n"
     ]
    }
   ],
   "source": [
    "print(count_zero_mse(pre_mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Acknowledgements\n",
    "_Contains information from the tracebase data set, which is made available at  [http://www.tracebase.org](http://www.tracebase.org/)  under the Open Database License (ODbL)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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