{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Klasyfikacja za pomocą naiwnej metody bayesowskiej (rozkłady ciągłe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Skład grupy:\n",
    "- Nowak Ania,\n",
    "- Łaźna Patrycja,\n",
    "- Bregier Damian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pandas==1.2.4\n",
    "#!pip install numpy==1.20.3\n",
    "#!pip install sklearn==0.0\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import typing\n",
    "import os, pickle\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Podstawowe informacje o zbiorze danych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W projekcie wykorzystany został GTZAN Dataset poruszający problem wieloklasowej klasyfikacji danych na przykładzie gatunków muzycznych. Zbiór ten składa się z 10 gatunków obejmujacych: blues, muzykę klasyczną, country, disco, hip-hop, jazz, pop, reggae oraz rock. Każdy ze wspomnianych gatunków jest reprezentowany przez 100 plików audio o długości 30 sekund, a same próbki były zbierane w latach 2000-2001 ze zdyfersyfikowanych źródeł obejmujących: stacje radiowe, prywatne płyty CD oraz nagrania własne.\n",
    "\n",
    "Zbiór danych jest niezwykle bogaty i rozbudowany, ponieważ do każdego utworu zostało przypisanych 60 unikalnych parametrów. Parametry te obejmują takie dane jak: długość utworu, etykietę z nazwą gatunku, tempo, harmoniczność, variancję czy częstotliwość melodyczną (MFCC).\n",
    "\n",
    "Dokładne dane na temat tego zbioru danych można znaleźć pod adresem: https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Wczytywanie i normalizacja danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Słownik zawierający 10 gatunków muzycznych, które zostały sparowane z\n",
    "# odpowiadającymi im wartościami numerycznymi\n",
    "genre_dict = {\n",
    "    \"blues\" : 1,\n",
    "    \"classical\" : 2,\n",
    "    \"country\" : 3,\n",
    "    \"disco\" : 4,\n",
    "    \"hiphop\" : 5,\n",
    "    \"jazz\" : 6,\n",
    "    \"metal\" : 7,\n",
    "    \"pop\" : 8,\n",
    "    \"reggae\" : 9,\n",
    "    \"rock\" : 10\n",
    "}\n",
    "# nazwa pliku w którym umieszczane są parametry po wstępnym przetworzeniu\n",
    "filename = 'music_genre.csv'\n",
    "model_path = 'model.model'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>genre</th>\n",
       "      <th>chroma_stft_mean</th>\n",
       "      <th>chroma_stft_var</th>\n",
       "      <th>rms_mean</th>\n",
       "      <th>rms_var</th>\n",
       "      <th>spectral_centroid_mean</th>\n",
       "      <th>spectral_centroid_var</th>\n",
       "      <th>spectral_bandwidth_mean</th>\n",
       "      <th>spectral_bandwidth_var</th>\n",
       "      <th>rolloff_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc16_var</th>\n",
       "      <th>mfcc17_mean</th>\n",
       "      <th>mfcc17_var</th>\n",
       "      <th>mfcc18_mean</th>\n",
       "      <th>mfcc18_var</th>\n",
       "      <th>mfcc19_mean</th>\n",
       "      <th>mfcc19_var</th>\n",
       "      <th>mfcc20_mean</th>\n",
       "      <th>mfcc20_var</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.350088</td>\n",
       "      <td>0.088757</td>\n",
       "      <td>0.130228</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>1784.165850</td>\n",
       "      <td>1.297741e+05</td>\n",
       "      <td>2002.449060</td>\n",
       "      <td>85882.761315</td>\n",
       "      <td>3805.839606</td>\n",
       "      <td>...</td>\n",
       "      <td>52.420910</td>\n",
       "      <td>-1.690215</td>\n",
       "      <td>36.524071</td>\n",
       "      <td>-0.408979</td>\n",
       "      <td>41.597103</td>\n",
       "      <td>-2.303523</td>\n",
       "      <td>55.062923</td>\n",
       "      <td>1.221291</td>\n",
       "      <td>46.936035</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.340914</td>\n",
       "      <td>0.094980</td>\n",
       "      <td>0.095948</td>\n",
       "      <td>0.002373</td>\n",
       "      <td>1530.176679</td>\n",
       "      <td>3.758501e+05</td>\n",
       "      <td>2039.036516</td>\n",
       "      <td>213843.755497</td>\n",
       "      <td>3550.522098</td>\n",
       "      <td>...</td>\n",
       "      <td>55.356403</td>\n",
       "      <td>-0.731125</td>\n",
       "      <td>60.314529</td>\n",
       "      <td>0.295073</td>\n",
       "      <td>48.120598</td>\n",
       "      <td>-0.283518</td>\n",
       "      <td>51.106190</td>\n",
       "      <td>0.531217</td>\n",
       "      <td>45.786282</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.363637</td>\n",
       "      <td>0.085275</td>\n",
       "      <td>0.175570</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>1552.811865</td>\n",
       "      <td>1.564676e+05</td>\n",
       "      <td>1747.702312</td>\n",
       "      <td>76254.192257</td>\n",
       "      <td>3042.260232</td>\n",
       "      <td>...</td>\n",
       "      <td>40.598766</td>\n",
       "      <td>-7.729093</td>\n",
       "      <td>47.639427</td>\n",
       "      <td>-1.816407</td>\n",
       "      <td>52.382141</td>\n",
       "      <td>-3.439720</td>\n",
       "      <td>46.639660</td>\n",
       "      <td>-2.231258</td>\n",
       "      <td>30.573025</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.404785</td>\n",
       "      <td>0.093999</td>\n",
       "      <td>0.141093</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>1070.106615</td>\n",
       "      <td>1.843559e+05</td>\n",
       "      <td>1596.412872</td>\n",
       "      <td>166441.494769</td>\n",
       "      <td>2184.745799</td>\n",
       "      <td>...</td>\n",
       "      <td>44.427753</td>\n",
       "      <td>-3.319597</td>\n",
       "      <td>50.206673</td>\n",
       "      <td>0.636965</td>\n",
       "      <td>37.319130</td>\n",
       "      <td>-0.619121</td>\n",
       "      <td>37.259739</td>\n",
       "      <td>-3.407448</td>\n",
       "      <td>31.949339</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.308526</td>\n",
       "      <td>0.087841</td>\n",
       "      <td>0.091529</td>\n",
       "      <td>0.002303</td>\n",
       "      <td>1835.004266</td>\n",
       "      <td>3.433999e+05</td>\n",
       "      <td>1748.172116</td>\n",
       "      <td>88445.209036</td>\n",
       "      <td>3579.757627</td>\n",
       "      <td>...</td>\n",
       "      <td>86.099236</td>\n",
       "      <td>-5.454034</td>\n",
       "      <td>75.269707</td>\n",
       "      <td>-0.916874</td>\n",
       "      <td>53.613918</td>\n",
       "      <td>-4.404827</td>\n",
       "      <td>62.910812</td>\n",
       "      <td>-11.703234</td>\n",
       "      <td>55.195160</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.302456</td>\n",
       "      <td>0.087532</td>\n",
       "      <td>0.103494</td>\n",
       "      <td>0.003981</td>\n",
       "      <td>1831.993940</td>\n",
       "      <td>1.030482e+06</td>\n",
       "      <td>1729.653287</td>\n",
       "      <td>201910.508633</td>\n",
       "      <td>3481.517592</td>\n",
       "      <td>...</td>\n",
       "      <td>72.549225</td>\n",
       "      <td>-1.838263</td>\n",
       "      <td>68.702026</td>\n",
       "      <td>-2.783800</td>\n",
       "      <td>42.447453</td>\n",
       "      <td>-3.047909</td>\n",
       "      <td>39.808784</td>\n",
       "      <td>-8.109991</td>\n",
       "      <td>46.311005</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0.291328</td>\n",
       "      <td>0.093981</td>\n",
       "      <td>0.141874</td>\n",
       "      <td>0.008803</td>\n",
       "      <td>1459.366472</td>\n",
       "      <td>4.378594e+05</td>\n",
       "      <td>1389.009131</td>\n",
       "      <td>185023.239545</td>\n",
       "      <td>2795.610963</td>\n",
       "      <td>...</td>\n",
       "      <td>83.248245</td>\n",
       "      <td>-10.913176</td>\n",
       "      <td>56.902153</td>\n",
       "      <td>-6.971336</td>\n",
       "      <td>38.231800</td>\n",
       "      <td>-3.436505</td>\n",
       "      <td>48.235741</td>\n",
       "      <td>-6.483466</td>\n",
       "      <td>70.170364</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.307955</td>\n",
       "      <td>0.092903</td>\n",
       "      <td>0.131822</td>\n",
       "      <td>0.005531</td>\n",
       "      <td>1451.667066</td>\n",
       "      <td>4.495682e+05</td>\n",
       "      <td>1577.270941</td>\n",
       "      <td>168211.938804</td>\n",
       "      <td>2954.836760</td>\n",
       "      <td>...</td>\n",
       "      <td>70.438438</td>\n",
       "      <td>-10.568935</td>\n",
       "      <td>52.090893</td>\n",
       "      <td>-10.784515</td>\n",
       "      <td>60.461330</td>\n",
       "      <td>-4.690678</td>\n",
       "      <td>65.547516</td>\n",
       "      <td>-8.630722</td>\n",
       "      <td>56.401436</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.408879</td>\n",
       "      <td>0.086512</td>\n",
       "      <td>0.142416</td>\n",
       "      <td>0.001507</td>\n",
       "      <td>1719.368948</td>\n",
       "      <td>1.632828e+05</td>\n",
       "      <td>2031.740381</td>\n",
       "      <td>105542.718193</td>\n",
       "      <td>3782.316288</td>\n",
       "      <td>...</td>\n",
       "      <td>50.563751</td>\n",
       "      <td>-7.041824</td>\n",
       "      <td>28.894934</td>\n",
       "      <td>2.695248</td>\n",
       "      <td>36.889568</td>\n",
       "      <td>3.412305</td>\n",
       "      <td>33.698597</td>\n",
       "      <td>-2.715692</td>\n",
       "      <td>36.418430</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.273950</td>\n",
       "      <td>0.092316</td>\n",
       "      <td>0.081314</td>\n",
       "      <td>0.004347</td>\n",
       "      <td>1817.150863</td>\n",
       "      <td>2.982361e+05</td>\n",
       "      <td>1973.773306</td>\n",
       "      <td>114070.112591</td>\n",
       "      <td>3943.490565</td>\n",
       "      <td>...</td>\n",
       "      <td>59.314602</td>\n",
       "      <td>-1.916804</td>\n",
       "      <td>58.418438</td>\n",
       "      <td>-2.292661</td>\n",
       "      <td>83.205231</td>\n",
       "      <td>2.881967</td>\n",
       "      <td>77.082222</td>\n",
       "      <td>-4.235203</td>\n",
       "      <td>91.468811</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre  chroma_stft_mean  chroma_stft_var  rms_mean   rms_var  \\\n",
       "0      1          0.350088         0.088757  0.130228  0.002827   \n",
       "1      1          0.340914         0.094980  0.095948  0.002373   \n",
       "2      1          0.363637         0.085275  0.175570  0.002746   \n",
       "3      1          0.404785         0.093999  0.141093  0.006346   \n",
       "4      1          0.308526         0.087841  0.091529  0.002303   \n",
       "5      1          0.302456         0.087532  0.103494  0.003981   \n",
       "6      1          0.291328         0.093981  0.141874  0.008803   \n",
       "7      1          0.307955         0.092903  0.131822  0.005531   \n",
       "8      1          0.408879         0.086512  0.142416  0.001507   \n",
       "9      1          0.273950         0.092316  0.081314  0.004347   \n",
       "\n",
       "   spectral_centroid_mean  spectral_centroid_var  spectral_bandwidth_mean  \\\n",
       "0             1784.165850           1.297741e+05              2002.449060   \n",
       "1             1530.176679           3.758501e+05              2039.036516   \n",
       "2             1552.811865           1.564676e+05              1747.702312   \n",
       "3             1070.106615           1.843559e+05              1596.412872   \n",
       "4             1835.004266           3.433999e+05              1748.172116   \n",
       "5             1831.993940           1.030482e+06              1729.653287   \n",
       "6             1459.366472           4.378594e+05              1389.009131   \n",
       "7             1451.667066           4.495682e+05              1577.270941   \n",
       "8             1719.368948           1.632828e+05              2031.740381   \n",
       "9             1817.150863           2.982361e+05              1973.773306   \n",
       "\n",
       "   spectral_bandwidth_var  rolloff_mean  ...  mfcc16_var  mfcc17_mean  \\\n",
       "0            85882.761315   3805.839606  ...   52.420910    -1.690215   \n",
       "1           213843.755497   3550.522098  ...   55.356403    -0.731125   \n",
       "2            76254.192257   3042.260232  ...   40.598766    -7.729093   \n",
       "3           166441.494769   2184.745799  ...   44.427753    -3.319597   \n",
       "4            88445.209036   3579.757627  ...   86.099236    -5.454034   \n",
       "5           201910.508633   3481.517592  ...   72.549225    -1.838263   \n",
       "6           185023.239545   2795.610963  ...   83.248245   -10.913176   \n",
       "7           168211.938804   2954.836760  ...   70.438438   -10.568935   \n",
       "8           105542.718193   3782.316288  ...   50.563751    -7.041824   \n",
       "9           114070.112591   3943.490565  ...   59.314602    -1.916804   \n",
       "\n",
       "   mfcc17_var  mfcc18_mean  mfcc18_var  mfcc19_mean  mfcc19_var  mfcc20_mean  \\\n",
       "0   36.524071    -0.408979   41.597103    -2.303523   55.062923     1.221291   \n",
       "1   60.314529     0.295073   48.120598    -0.283518   51.106190     0.531217   \n",
       "2   47.639427    -1.816407   52.382141    -3.439720   46.639660    -2.231258   \n",
       "3   50.206673     0.636965   37.319130    -0.619121   37.259739    -3.407448   \n",
       "4   75.269707    -0.916874   53.613918    -4.404827   62.910812   -11.703234   \n",
       "5   68.702026    -2.783800   42.447453    -3.047909   39.808784    -8.109991   \n",
       "6   56.902153    -6.971336   38.231800    -3.436505   48.235741    -6.483466   \n",
       "7   52.090893   -10.784515   60.461330    -4.690678   65.547516    -8.630722   \n",
       "8   28.894934     2.695248   36.889568     3.412305   33.698597    -2.715692   \n",
       "9   58.418438    -2.292661   83.205231     2.881967   77.082222    -4.235203   \n",
       "\n",
       "   mfcc20_var  label  \n",
       "0   46.936035  blues  \n",
       "1   45.786282  blues  \n",
       "2   30.573025  blues  \n",
       "3   31.949339  blues  \n",
       "4   55.195160  blues  \n",
       "5   46.311005  blues  \n",
       "6   70.170364  blues  \n",
       "7   56.401436  blues  \n",
       "8   36.418430  blues  \n",
       "9   91.468811  blues  \n",
       "\n",
       "[10 rows x 59 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Index(['genre', 'chroma_stft_mean', 'chroma_stft_var', 'rms_mean', 'rms_var',\n",
       "       'spectral_centroid_mean', 'spectral_centroid_var',\n",
       "       'spectral_bandwidth_mean', 'spectral_bandwidth_var', 'rolloff_mean',\n",
       "       'rolloff_var', 'zero_crossing_rate_mean', 'zero_crossing_rate_var',\n",
       "       'harmony_mean', 'harmony_var', 'perceptr_mean', 'perceptr_var', 'tempo',\n",
       "       'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean',\n",
       "       'mfcc3_var', 'mfcc4_mean', 'mfcc4_var', 'mfcc5_mean', 'mfcc5_var',\n",
       "       'mfcc6_mean', 'mfcc6_var', 'mfcc7_mean', 'mfcc7_var', 'mfcc8_mean',\n",
       "       'mfcc8_var', 'mfcc9_mean', 'mfcc9_var', 'mfcc10_mean', 'mfcc10_var',\n",
       "       'mfcc11_mean', 'mfcc11_var', 'mfcc12_mean', 'mfcc12_var', 'mfcc13_mean',\n",
       "       'mfcc13_var', 'mfcc14_mean', 'mfcc14_var', 'mfcc15_mean', 'mfcc15_var',\n",
       "       'mfcc16_mean', 'mfcc16_var', 'mfcc17_mean', 'mfcc17_var', 'mfcc18_mean',\n",
       "       'mfcc18_var', 'mfcc19_mean', 'mfcc19_var', 'mfcc20_mean', 'mfcc20_var',\n",
       "       'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# skrypt ten realizuje dwie podstawowe funkcje\n",
    "# 1) sprawdza czy plik music_genre.csv istnieje i jeżeli tak to wczytuje go\n",
    "# 2) w przeciwnym przypadku dokonuje preprocessingu danych w ramach którego\n",
    "#    gatunki zamieniane są na wartości licznowe, a wartości takie jak nazwa \n",
    "#    pliku, etykieta czy długość są usuwane\n",
    " \n",
    "if os.path.isfile(filename):\n",
    "    print(\"Loading prepared data...\")\n",
    "    data = pd.read_csv(filename)\n",
    "else:\n",
    "    print(\"Preparing data...\")\n",
    "    data = pd.read_csv('music_genre_raw.csv')\n",
    "    column = data[\"label\"].apply(lambda x: genre_dict[x])\n",
    "    data.insert(0, 'genre', column, 'int')\n",
    "    data = data.drop(columns=['filename', 'length'])\n",
    "    data.to_csv(filename, index=False)\n",
    "display(data.head(10))\n",
    "\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Podział danych na zbiory: uczący i testowy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>chroma_stft_mean</th>\n",
       "      <th>chroma_stft_var</th>\n",
       "      <th>rms_mean</th>\n",
       "      <th>rms_var</th>\n",
       "      <th>spectral_centroid_mean</th>\n",
       "      <th>spectral_centroid_var</th>\n",
       "      <th>spectral_bandwidth_mean</th>\n",
       "      <th>spectral_bandwidth_var</th>\n",
       "      <th>rolloff_mean</th>\n",
       "      <th>rolloff_var</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc16_var</th>\n",
       "      <th>mfcc17_mean</th>\n",
       "      <th>mfcc17_var</th>\n",
       "      <th>mfcc18_mean</th>\n",
       "      <th>mfcc18_var</th>\n",
       "      <th>mfcc19_mean</th>\n",
       "      <th>mfcc19_var</th>\n",
       "      <th>mfcc20_mean</th>\n",
       "      <th>mfcc20_var</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>0.516547</td>\n",
       "      <td>0.072241</td>\n",
       "      <td>0.267380</td>\n",
       "      <td>0.001175</td>\n",
       "      <td>3338.581900</td>\n",
       "      <td>172002.893292</td>\n",
       "      <td>2697.128636</td>\n",
       "      <td>45771.294278</td>\n",
       "      <td>6670.863091</td>\n",
       "      <td>3.556853e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>37.339474</td>\n",
       "      <td>-8.121326</td>\n",
       "      <td>33.968277</td>\n",
       "      <td>4.910113</td>\n",
       "      <td>42.063385</td>\n",
       "      <td>-2.474697</td>\n",
       "      <td>35.162354</td>\n",
       "      <td>3.192656</td>\n",
       "      <td>36.478157</td>\n",
       "      <td>metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.344511</td>\n",
       "      <td>0.085002</td>\n",
       "      <td>0.046747</td>\n",
       "      <td>0.001542</td>\n",
       "      <td>1503.869486</td>\n",
       "      <td>554576.511533</td>\n",
       "      <td>1754.216082</td>\n",
       "      <td>283554.933422</td>\n",
       "      <td>2799.283099</td>\n",
       "      <td>2.685679e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>50.311016</td>\n",
       "      <td>-1.503434</td>\n",
       "      <td>41.141155</td>\n",
       "      <td>0.221949</td>\n",
       "      <td>55.707256</td>\n",
       "      <td>-1.991485</td>\n",
       "      <td>50.006485</td>\n",
       "      <td>-3.353825</td>\n",
       "      <td>49.906403</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>0.368345</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>0.111073</td>\n",
       "      <td>0.004402</td>\n",
       "      <td>2446.919077</td>\n",
       "      <td>490397.099115</td>\n",
       "      <td>2449.159840</td>\n",
       "      <td>215375.540632</td>\n",
       "      <td>4958.057490</td>\n",
       "      <td>2.650020e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>78.892769</td>\n",
       "      <td>-1.054999</td>\n",
       "      <td>79.877068</td>\n",
       "      <td>4.496278</td>\n",
       "      <td>112.834435</td>\n",
       "      <td>-0.978958</td>\n",
       "      <td>75.059898</td>\n",
       "      <td>-5.256925</td>\n",
       "      <td>120.275269</td>\n",
       "      <td>disco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.360042</td>\n",
       "      <td>0.083953</td>\n",
       "      <td>0.116724</td>\n",
       "      <td>0.000789</td>\n",
       "      <td>2148.410463</td>\n",
       "      <td>253618.158995</td>\n",
       "      <td>2107.165355</td>\n",
       "      <td>72155.551685</td>\n",
       "      <td>4479.264304</td>\n",
       "      <td>9.787046e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>37.060532</td>\n",
       "      <td>-13.479134</td>\n",
       "      <td>50.848667</td>\n",
       "      <td>3.308529</td>\n",
       "      <td>47.726006</td>\n",
       "      <td>-3.704957</td>\n",
       "      <td>56.781952</td>\n",
       "      <td>1.085497</td>\n",
       "      <td>54.243389</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>0.425788</td>\n",
       "      <td>0.091852</td>\n",
       "      <td>0.139799</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>1803.774378</td>\n",
       "      <td>659241.158049</td>\n",
       "      <td>1973.418903</td>\n",
       "      <td>201432.199120</td>\n",
       "      <td>3777.969679</td>\n",
       "      <td>2.632339e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>64.068756</td>\n",
       "      <td>-2.219202</td>\n",
       "      <td>99.249870</td>\n",
       "      <td>5.304260</td>\n",
       "      <td>64.088127</td>\n",
       "      <td>-6.597187</td>\n",
       "      <td>62.661850</td>\n",
       "      <td>-2.923168</td>\n",
       "      <td>67.490440</td>\n",
       "      <td>reggae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>0.495959</td>\n",
       "      <td>0.072854</td>\n",
       "      <td>0.117362</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>2657.912854</td>\n",
       "      <td>189139.438926</td>\n",
       "      <td>2345.662472</td>\n",
       "      <td>32730.579626</td>\n",
       "      <td>5358.261979</td>\n",
       "      <td>5.918222e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>27.937113</td>\n",
       "      <td>-10.676390</td>\n",
       "      <td>26.519361</td>\n",
       "      <td>3.875155</td>\n",
       "      <td>25.613684</td>\n",
       "      <td>-4.943561</td>\n",
       "      <td>24.334734</td>\n",
       "      <td>3.255899</td>\n",
       "      <td>25.199259</td>\n",
       "      <td>metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>0.395137</td>\n",
       "      <td>0.093939</td>\n",
       "      <td>0.114246</td>\n",
       "      <td>0.004025</td>\n",
       "      <td>1716.249594</td>\n",
       "      <td>920189.339374</td>\n",
       "      <td>2062.885827</td>\n",
       "      <td>358557.016423</td>\n",
       "      <td>3790.901258</td>\n",
       "      <td>4.734865e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>66.090370</td>\n",
       "      <td>-4.590122</td>\n",
       "      <td>72.595345</td>\n",
       "      <td>4.261040</td>\n",
       "      <td>63.185764</td>\n",
       "      <td>-2.127876</td>\n",
       "      <td>50.693245</td>\n",
       "      <td>-3.665569</td>\n",
       "      <td>89.750290</td>\n",
       "      <td>reggae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>0.249535</td>\n",
       "      <td>0.087563</td>\n",
       "      <td>0.060560</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>1465.857446</td>\n",
       "      <td>143302.098295</td>\n",
       "      <td>1738.858902</td>\n",
       "      <td>58868.399307</td>\n",
       "      <td>2822.406728</td>\n",
       "      <td>7.392007e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>109.811813</td>\n",
       "      <td>-0.027696</td>\n",
       "      <td>113.660950</td>\n",
       "      <td>2.098475</td>\n",
       "      <td>160.025497</td>\n",
       "      <td>1.109709</td>\n",
       "      <td>136.810165</td>\n",
       "      <td>2.935807</td>\n",
       "      <td>95.914490</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>0.353474</td>\n",
       "      <td>0.087755</td>\n",
       "      <td>0.052264</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>1993.352766</td>\n",
       "      <td>64753.479332</td>\n",
       "      <td>2127.165109</td>\n",
       "      <td>36027.039069</td>\n",
       "      <td>4248.194549</td>\n",
       "      <td>3.987029e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>57.230133</td>\n",
       "      <td>-1.110214</td>\n",
       "      <td>48.080849</td>\n",
       "      <td>-0.784249</td>\n",
       "      <td>57.033504</td>\n",
       "      <td>-2.984207</td>\n",
       "      <td>55.737625</td>\n",
       "      <td>0.350456</td>\n",
       "      <td>64.126846</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>0.416089</td>\n",
       "      <td>0.087772</td>\n",
       "      <td>0.142935</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>3009.958707</td>\n",
       "      <td>435134.775688</td>\n",
       "      <td>2778.049758</td>\n",
       "      <td>135548.871316</td>\n",
       "      <td>6131.200719</td>\n",
       "      <td>1.788624e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>42.315434</td>\n",
       "      <td>-3.953057</td>\n",
       "      <td>48.761936</td>\n",
       "      <td>-3.092345</td>\n",
       "      <td>49.514446</td>\n",
       "      <td>-2.731183</td>\n",
       "      <td>58.219994</td>\n",
       "      <td>-0.909785</td>\n",
       "      <td>63.111858</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chroma_stft_mean  chroma_stft_var  rms_mean   rms_var  \\\n",
       "687          0.516547         0.072241  0.267380  0.001175   \n",
       "500          0.344511         0.085002  0.046747  0.001542   \n",
       "332          0.368345         0.090390  0.111073  0.004402   \n",
       "979          0.360042         0.083953  0.116724  0.000789   \n",
       "817          0.425788         0.091852  0.139799  0.003601   \n",
       "620          0.495959         0.072854  0.117362  0.000867   \n",
       "814          0.395137         0.093939  0.114246  0.004025   \n",
       "516          0.249535         0.087563  0.060560  0.001276   \n",
       "518          0.353474         0.087755  0.052264  0.000316   \n",
       "940          0.416089         0.087772  0.142935  0.003150   \n",
       "\n",
       "     spectral_centroid_mean  spectral_centroid_var  spectral_bandwidth_mean  \\\n",
       "687             3338.581900          172002.893292              2697.128636   \n",
       "500             1503.869486          554576.511533              1754.216082   \n",
       "332             2446.919077          490397.099115              2449.159840   \n",
       "979             2148.410463          253618.158995              2107.165355   \n",
       "817             1803.774378          659241.158049              1973.418903   \n",
       "620             2657.912854          189139.438926              2345.662472   \n",
       "814             1716.249594          920189.339374              2062.885827   \n",
       "516             1465.857446          143302.098295              1738.858902   \n",
       "518             1993.352766           64753.479332              2127.165109   \n",
       "940             3009.958707          435134.775688              2778.049758   \n",
       "\n",
       "     spectral_bandwidth_var  rolloff_mean   rolloff_var  ...  mfcc16_var  \\\n",
       "687            45771.294278   6670.863091  3.556853e+05  ...   37.339474   \n",
       "500           283554.933422   2799.283099  2.685679e+06  ...   50.311016   \n",
       "332           215375.540632   4958.057490  2.650020e+06  ...   78.892769   \n",
       "979            72155.551685   4479.264304  9.787046e+05  ...   37.060532   \n",
       "817           201432.199120   3777.969679  2.632339e+06  ...   64.068756   \n",
       "620            32730.579626   5358.261979  5.918222e+05  ...   27.937113   \n",
       "814           358557.016423   3790.901258  4.734865e+06  ...   66.090370   \n",
       "516            58868.399307   2822.406728  7.392007e+05  ...  109.811813   \n",
       "518            36027.039069   4248.194549  3.987029e+05  ...   57.230133   \n",
       "940           135548.871316   6131.200719  1.788624e+06  ...   42.315434   \n",
       "\n",
       "     mfcc17_mean  mfcc17_var  mfcc18_mean  mfcc18_var  mfcc19_mean  \\\n",
       "687    -8.121326   33.968277     4.910113   42.063385    -2.474697   \n",
       "500    -1.503434   41.141155     0.221949   55.707256    -1.991485   \n",
       "332    -1.054999   79.877068     4.496278  112.834435    -0.978958   \n",
       "979   -13.479134   50.848667     3.308529   47.726006    -3.704957   \n",
       "817    -2.219202   99.249870     5.304260   64.088127    -6.597187   \n",
       "620   -10.676390   26.519361     3.875155   25.613684    -4.943561   \n",
       "814    -4.590122   72.595345     4.261040   63.185764    -2.127876   \n",
       "516    -0.027696  113.660950     2.098475  160.025497     1.109709   \n",
       "518    -1.110214   48.080849    -0.784249   57.033504    -2.984207   \n",
       "940    -3.953057   48.761936    -3.092345   49.514446    -2.731183   \n",
       "\n",
       "     mfcc19_var  mfcc20_mean  mfcc20_var   label  \n",
       "687   35.162354     3.192656   36.478157   metal  \n",
       "500   50.006485    -3.353825   49.906403    jazz  \n",
       "332   75.059898    -5.256925  120.275269   disco  \n",
       "979   56.781952     1.085497   54.243389    rock  \n",
       "817   62.661850    -2.923168   67.490440  reggae  \n",
       "620   24.334734     3.255899   25.199259   metal  \n",
       "814   50.693245    -3.665569   89.750290  reggae  \n",
       "516  136.810165     2.935807   95.914490    jazz  \n",
       "518   55.737625     0.350456   64.126846    jazz  \n",
       "940   58.219994    -0.909785   63.111858    rock  \n",
       "\n",
       "[10 rows x 58 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Podział ten jest dokonywany w proporcji 80:20, gdzie 80% danych trafia do zbioru uczącego, a 20%\n",
    "# do zbioru testowego, podejście to jest standardową praktyką w dziedzinie uczenia maszynwego\n",
    "\n",
    "# wartość X reprezentuje 57 parametrów opisujących poszczególne utwory\n",
    "X = data.drop([\"genre\"], axis=1)\n",
    "# wartość Y zawiera kolumnę gatunków wyrażonych przy pomocy wartości liczbowych od 1 do 10\n",
    "Y = data[\"genre\"]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.20, random_state = False)\n",
    "display(X_train.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ilość krotek dla poszczególnych gatunków z podziałem na zbiory: uczący i testowy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blues\ttest: 15\ttrain: 85\tall: 100\n",
      "classical\ttest: 11\ttrain: 89\tall: 100\n",
      "country\ttest: 27\ttrain: 73\tall: 100\n",
      "disco\ttest: 22\ttrain: 78\tall: 100\n",
      "hiphop\ttest: 23\ttrain: 77\tall: 100\n",
      "jazz\ttest: 18\ttrain: 82\tall: 100\n",
      "metal\ttest: 20\ttrain: 80\tall: 100\n",
      "pop\ttest: 24\ttrain: 76\tall: 100\n",
      "reggae\ttest: 15\ttrain: 85\tall: 100\n",
      "rock\ttest: 25\ttrain: 75\tall: 100\n"
     ]
    }
   ],
   "source": [
    "# skrypt odpowiadający za przeiterowanie po słowniku i zliczenie liczebności poszczególnych gatunków\n",
    "# w ramach podziału na zbiory: uczący i testowy\n",
    "\n",
    "for key in genre_dict.keys():\n",
    "    count = len(data[data[\"genre\"]==genre_dict[key]])\n",
    "    count_train = len(X_train[Y_train==genre_dict[key]])\n",
    "    count_test = len(X_test[Y_test==genre_dict[key]])\n",
    "    print(f\"{key}\\ttest: {count_test}\\ttrain: {count_train}\\tall: {count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Wizualizacja danych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot dla tempa gatunków"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jedną z najciekawszych i najbardziej intuicyjnych wartości mierzalnych dla poszczególnych utworów jest tempo. Parametr ten został przedstawiony przy pomocy wykresu pudełkowego w odniesieniu do wspomnianych wcześniej 10 gatunków muzycznych.\n",
    "\n",
    "Ze zgromadzonych danych jednoznacznie wynika, że najwyższą medianę dla tempa mają utwory z gatunku Reggee, zaś na drugim i trzecim miejscu znajdują się odpowiednio muzyka klasyczna oraz blues. Podczas gdy najniższe wartości mają gatunki hip-hop oraz pop. \n",
    "\n",
    "Z kolei największe rozbieżności pomiędzy wartościami zauważalne są w przypadku muzyki klasycznej, country i metalu, chociaż najwięcej obserwacji odstających pojawia się w przypadku hiphopu oraz popu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(16, 9));\n",
    "\n",
    "sns.boxplot(x = \"label\", y = \"tempo\", data = data[[\"label\", \"tempo\"]], palette = 'pastel');\n",
    "\n",
    "plt.title('Zależność pomiędzy tempem a gatunkiem', fontsize = 25)\n",
    "plt.xticks(fontsize = 14)\n",
    "plt.yticks(fontsize = 10);\n",
    "plt.xlabel(\"Genre\", fontsize = 15)\n",
    "plt.ylabel(\"Tempo\", fontsize = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot dla średnich melowych współczynników cepstralnych sygnału dla poszczególnych gatunków"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesujące wyniki pojawiły się także na wykresie pokazującym zależność pomiędzy MFCC mean, czyli średnimi wartościami dla melowych współczynników cepstralnych sygnału a gatunkami muzycznimi. \n",
    "\n",
    "Najwyższe wartości MFCC_mean dotyczą metalu oraz bluesa, podczas gdy najniższe wartości uzyskiwane są w przypadu popu i muzyki klasycznej. Z kolei najwięcej obserwacji odstających pojawia się w przypadku reggae."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zQkNDHcvCwsKMunXrGj4+Pka/fv0Sbe+/v49xcXHGkCFDHOeQvXv3Jvv6t3/+9u7da1SpUsWoWLGiMWHCBOPGjRuOZbt373acr958880kX9/Hx8fo1KmTcfbsWcMw4n/nEup4+OGHjYoVKxqzZ8827Ha7YRiGcfHiRaNNmzaGj4+P0bVr1ySPFQDroVsyAJdhGIbeeustRUREyMPDQxMnTky2e+P27dsVGxurggUL6v333080gZOnp6eGDBnimKzkwIEDqb72+PHjZRiG3nzzTXXr1i3R5DVPPPGExowZIyl+fOClS5dS3FZYWJheffVVxcbGqlevXmrfvn2S6/Xv31+tWrVy/Ozt7a233npLUnz3xtvHT86YMUPXrl2Tj4+PJk2apAcffNCxrGTJkpoxY4YKFiyoU6dOKSgoyLEs4XYs7du3TzQZV8mSJTV48GA1aNBAxYsXdzx+9uxZLVq0SJL0ySefqHbt2onqGzt2rPLkyaPTp087WkidNWbMGJUpU8bx80svveQ4zo8++qiGDBniqDFXrlzq3bu3JOnkyZOpdhHdt2+ffvzxR+XKlUuzZ89ONOY5R44cGjhwoJo3b66bN2/q888/dyxbs2aNDh06pLx582ry5MmJbidTr149ffDBB3e1jzNnztSiRYuUM2dOffHFFypcuLAkyc3NTW3atJH0vxba261YsUJxcXGqU6fOPU2UlTNnTkd34/92Y7Xb7VqzZo3y5MmjgIAAp7b3zjvvpNjdOmGca4Lb3x9nJytLSnptJyUXLlzQwYMHJUkjRoxIdLxtNpvatGmjmjVrSpLT3XA3bdqkHTt2yNPTU9OnT1fJkiUdy/z8/JK9n/fmzZvl7u6uypUrq1+/fokmWMqXL58GDx4sKb6nR0rjqdPbZ599lqiltVy5curRo4ek+BbS5BiGoaFDh2rZsmV68MEH9c033+jhhx926jWnTJmimJgYde3aVQMGDEg0ZrtSpUqaPHmy3N3dtWrVKh06dOiO52fLlk3jx4939JJxc3NznEPi4uL0zDPP6MUXX3R0gc+fP7/jnud79uxxqkYAro9wC8BlfPHFF46usG+//bb8/f2TXbdRo0bauXOn1q5dm+TtaW7evOmYiCapbqW3O3HihPbu3StJyXZvrFevnvLnz68bN27ojz/+SHZbFy5cUO/evXX16lU1btxYgwYNSnbdpMY+li9f3vHv28fQbtiwQZLUqVOnJGeMzps3ryNE3z5mt3Tp0pKkYcOG6Y8//lBsbKxjWcOGDTV9+nT16dPH8djGjRtlGIaKFSt2x0y3UvwXB999952Cg4NVp06dZPftv/Llyyc/P79Ej+XKlcsRYJLqEnh76EhtoqBff/1VklSzZk1HoPyvZ555RlJ8EEkYu5dwXBs1aqS8efPe8ZyWLVs6fcuhn376SePHj5fNZtMnn3xyRzfMdu3ayWazKTQ0VIcPH060LGFs8b3cfidBwljd/4bbbdu26cKFC2rSpInTs42XKVNG1atXT/Y/Hx+fROvf/uXS7Z+xu3X7jNzJdftOqwIFCmjr1q0KCQm5Yz+k+C8DEsbu3rhxw6ltJvzONWnS5I4hCJIc4+P/q0uXLgoJCdGCBQuS3O7tXchTO4+ll0KFCiXZ7TjhHsqRkZHJPvfDDz/UokWLlC9fPn3zzTdJHt+kxMTEaNOmTZKSPwdXrFhRDz/8sAzDcMzH8N/lRYoUSfTY7V/cJXU+SzjHMBEZkHUw5haAS9i2bZujdeOpp55yfKOempw5c2rfvn3at2+fY7zeoUOHdPDgQcdFtvH/k4kkJ6EVR4qf5CY5N2/elKQ7gkmCGzduqF+/fjpx4oQqVaqksWPHJpoo57+SCmG3X8wmBLCoqCjH/SyrVKmS7PYSLkhvb+F566231K9fP4WEhKhHjx7y9PTUY489ptq1a6t+/fqJWlIlOSZ4uX3c7n/dHsCdldx4wISwlVQr3e1fWjj7Hu7ateuOscoJEt6/6OhoRUREqFixYo5jVaFChSSfkzC2eefOnSm+/o4dOzRkyBAZhqEBAwboqaeeumOdEiVKqFatWtq6datWrFjhGEMZGhqqsLAw5cuXL02TPdWtW1e5c+fW3r17dezYMccXGwmzJLds2dLpbd3tfW5v/yLi0qVLTo9T/a/bg2FqPSTSKmfOnDp9+rRCQkJ0/PhxhYeHKywsTHv37nWMH759FueUJHz+UmqlrFKlSrKfoxw5cig0NFQHDhxwnMcOHDiQ6FyT2u9Aeknuy6GEc1NyXzr8+uuvji8DoqOjHRPFOePo0aOO9T/88MNkv4Q5deqUpKTPwUmdY27fTv78+e9Y7gr37QaQvvitBmC68+fPa9CgQbLb7SpbtqxGjRrl1PM2btyoiRMn3tGlrFChQnrqqae0adMmp2Y8vb0lIqUud0mtnyAuLk5vvfWWQkJCVLBgQX3xxRep3hc2tXs8JlzMRkdHOx5LaTbYhGXXrl2TYRiy2WyqW7eulixZoi+//FIbNmxQdHS0Nm7cqI0bN2rMmDF69NFH9dFHHzkm9rl8+bIkpfs9bVO7729KXwI4I+E9uXDhQrIzQN/u6tWrKlasmKN1PKX9TapF93bHjh3Tyy+/rJs3b6p169ZJTuiUoH379tq6datWrVqlAQMGyGazOVptW7Vqlab7OGfPnl0NGzbUqlWr9PPPP6tPnz6KjY3Vr7/+qgIFCjjuh5sRypYt6/j3wYMHVaJEiVSfExcXp/3796tixYqO979UqVJyd3eX3W7XwYMHVatWLadef+/evapQoYLTYeXw4cP69NNPtXHjxkQB1svLSzVq1NDZs2cdXfqdkRDEU/ocJRf4V6xYoc8///yO2bxLlCihZ5991jFMILPc671nb9y4oSJFiqhYsWKOL3uWLFni1PZuP6fu2rXrrtZPkNHnGADWQLgFYKq4uDgNGjRI586dU65cuTR58mSnbuexdetW9e3bV3FxcapWrZpatWolHx8flS9f3jFu0tluswkXpPny5XPcsuduffrpp1qzZo1jrOV/u8elxe0XxSl1n0sI8p6enrLZbI7HH3nkEY0fP16xsbEKCQnRtm3btGXLFu3YsUN///23evTooTVr1sjT09NxgXh7oLaChLpffPFFxzhFZyR0XU/puKbUNfXSpUvq3bu3Ll26JH9//1S/mGnWrJlGjBihkydP6u+//1a1atX0448/SlKyY7PvRvPmzROF2y1btujy5cvq0qVLonHk6e2RRx5xzDa8efNmp1qgQ0JC1LFjR+XNm1dz585V5cqVlT9/flWvXl1//fWXNm/erK5du6a6nYiICLVt21a5cuXSZ5995rj9UnIuXLigrl276sKFCypWrJief/55VapUSeXKlVOJEiVks9k0aNCguwq3CZ+/lD5HSf1OLV++XEOGDJEUf75q0qSJKlSooPLlyytv3ryKjY2953CbXEtvRnVvLlasmL755hu5ubnp6aef1r59+zR9+nS99tprqT739i8FduzYcc8t/wDA11gATDVlyhRt3bpVUnx3NGfHaH355ZeKi4vT448/rgULFqhr166qWbOmI9jGxMQ43a0xodXp8uXLOnfuXLLrbd++XWFhYXeEnfnz52vu3LnJjrVMKy8vL0e3z5RaNRKWJXQ1ttvtOnbsmP766y9J8S0yNWrU0CuvvKL58+dr/vz5stlsOnfunGNyqITn3t5V+78mT56snj17avny5WndtXST8B6mVPelS5f0999/69SpU44L/4TnJYy5/i/DMBQWFpbkspiYGL3yyis6evSoihcvrmnTpqXa8pojRw7H2Nhff/1Vf/31ly5fvqxHHnlEjzzySMo76YQ6derI29tbe/bs0fHjx7V69WpJuut7596LhNdYvny5U63n8+fPlxTfonZ7t/CE7tMbN250ajK4BQsWyDAMxcbGJppILDlLly7VhQsXlC9fPi1dulT9+vVTvXr1VLJkSceXQgnDAJyVcN5KaQKqpJbNmDFDktSmTRvNmjVLHTp0UPXq1R29Bc6cOXNXdUhyfImRXLfgs2fP3vU2nfHoo4+qZMmSKl68uAYMGCApfv+S+926XcmSJR11JzVZVILQ0FDt37/fcl++Acg8hFsAptm8ebOmT58uSerYsaNjwh9nnDhxQlL8GLekWqS+//57x5jb1CamKV++vGN84rx585Jc5++//1aXLl3UokUL/fPPP47HN2zY4GitS26sZXpIaAn79ttvk7xovXLliqN7a8LkTAcPHlTTpk3VvXv3JEO7v7+/o4UkoWtm3bp15ebmppMnTyY5cdaNGze0ZMkSBQcHZ9oYQGckHJ8//vgj2TD62WefqXPnzgoMDHTsb8I9b3/77bckA8369euTPHaGYWjIkCH6+++/lTt3bk2fPj3RTMspSWih/fXXX7Vu3TpJaZtI6nYJXZMl6YcfftC6detUtGhRPfroo+my/ZT06dNHBQsWVFRUlN577z3HGOekrF27Vj/88IPjebd/KfDcc8/Jx8dHdrtd7777rqOrfFJCQ0M1Z84cSfHnkNtnEU9OwrmjWLFiSY71PnTokON3PGHce2pu/xxdvHjxjuVhYWHavn17srUkd9/YJUuWOP793/NYQhD/7+9hwtjSK1euJPklQ8LkaxkpMDBQ1apVU2xsrIYMGZLqJGNeXl6OGaq/+eabJNcJDw9X586d1bp16zsmTQOABIRbAKaIiIjQW2+9pbi4OFWvXl3vvffeXT0/YebOH3/8MVGYuXnzpubNm6eRI0c6HnNmxtPXX39dUvytXL788stEAXL79u2O5dWqVXOMXdyzZ48GDhwou92uDh06qG/fvne1D3fjpZdeUu7cuXXgwAG9/vrriS5aw8PD1adPH50/f16FCxdW9+7dJcUH/4SQ8MYbbyRqBYqJidGECRMUFRUlT09P1ahRQ1J8C0rC7YnefvvtRBPgXLlyRW+//bYiIiJUvHjxTGkNdFaNGjVUp04d3bp1Sy+99FKisdMxMTH6/PPPtXjxYkmJb0FUv359Va9eXdeuXVPfvn0VHh7ueN727duT/VxOnDhRP/74ozw8PDRt2jSnexxIkq+vrypWrKiTJ09q6dKl8vDwSHRLqLRq3ry5JGnWrFm6evWqmjdvnqibekbx9vbWiBEj5OHhofXr16tLly76/fffE4WvqKgoff755xowYIAMw1Dt2rXvmDwuW7ZsGjVqlLy9vfXvv/+qQ4cO+vnnnxMFzZs3b2rBggXq0aOHYmJi5OPjozfeeMOpOhPOHfv27dMvv/zieNwwDG3atEm9evVyhDFnu/A2aNBAlStXVlRUlF555RWdPn3asezAgQN6+eWXk5ycKqGWhQsXJvpyJSoqSlOmTNHMmTMdj/33PJbQlffkyZOJHvfz85OHh4cMw9Do0aMdz4uNjdXXX3+dKWN43dzcNHLkSHl4eDi6J6fmtddek7u7u3744QeNGTMmUevsgQMH1Lt3b8XGxqp48eLp+vsCIGthzC0AUyxevNgR0CIjIx2BLDX16tVT37599corr2jLli06d+6cWrVqpTJlyih79uw6duyYrl27pgceeEBly5bVvn37nOra17JlSx09elRTpkzRuHHjNGPGDJUpU0YXL150XDyWLVs20T1SBw8erGvXrsnd3V3nzp1Tz549dfPmzSRbexLqvlclS5bU5MmT9frrr+u3335TvXr19NBDD8lut+vQoUOKi4tTsWLFNHXq1EStURMmTFDHjh31559/qnHjxipRooRy5cqlEydO6OrVq3J3d9dHH32U6DlDhw7V6dOn9eeff6pjx44qXbq0PD09deTIEd24cUP58uXT5MmTE83s7ArGjh2rPn36KCQkRJ06dVKJEiWUN29ehYeHOyaO6t69uzp27Oh4jpubmz777DP16tVLe/bsUbNmzeTj46Pr16/r6NGjKlGihAoXLpyoa+WuXbscF+sFCxbU119/rRkzZigmJibJ1uwPPvhAlSpVSvRYu3btNGbMGF27dk3NmjVLcibXe/Xkk08qT548jn3OzC8hGjRooNmzZ6t///76999/1atXL+XJk0clSpTQrVu3dOTIEUdwfPrppzVq1Kgke174+vpq/vz56tu3r44eParXX39dnp6eKlmypNzc3HT48GFHy3Dt2rU1fvx4pydBe/bZZ7VgwQIdO3ZM/fv3V/HixZU/f36dPn1aFy5ckIeHh2rWrKk///zT6e7J7u7umjBhgrp27aodO3aocePGqlChgm7duqVDhw4pT548KlOmjI4ePZpofwcOHKiXX35Zhw4dUqNGjRzd5I8dO6abN286ukofP378jvNYpUqVdODAAc2aNUubNm1SkyZN9PLLLytv3rzq2bOnpk+frh9++EG///67SpQooZMnT+ry5cvq1KlTsj0V0lOFChXUp08fTZ06VTNmzFDjxo1T7Hr/6KOPasSIERo2bJi++uorfffddypfvryio6N17NgxGYahBx98ULNnz07TxGsAsjbCLQBT3B4CUhon+V8J3YerVKmiFStWaNq0adq5c6eOHz+u7Nmzq1SpUqpfv766deumDRs26N1339WGDRs0ZMiQVFuvXnnlFQUEBCgoKEjbt2/Xvn375OHhoUqVKqlJkybq3r17oolOEloW7Ha7fvvtN6fqTouAgAD9+OOPmjt3rjZu3KgjR47Iw8NDjzzyiJ566il17NhRefLkSfSchx56SMuXL9fs2bP1xx9/OMabFipUSE2aNNELL7xwx21wvLy8NHfuXC1btkzff/+9Dhw4oFOnTqlw4cJq0KCBXnrppWRvF2Km/Pnza/78+Vq2bJl++OEH7d+/39Hts1atWurevbsaNWp0x/OKFSumhQsX6ptvvtFPP/2kI0eOyMvLS+3bt9cbb7yhN998M9H6t7conTp1ynF7kuQkNbNr69at9emnn8put6dbl+QE2bNnV+PGjbVs2TKVLl063ceAp6ZWrVpas2aNlixZoo0bNyosLEwHDx6Uu7u7ihUrpkcffVTt27d39BZITsWKFbV69WotX75cv/32m/bt26fDhw/LZrOpYMGC8vX1VZs2bVS/fv27qs/Ly8sxg/j69et14sQJnT9/XkWKFFH9+vXVvXt3eXp6qnHjxtq3b59OnTqlYsWKpbrd0qVLa8WKFZo+fbp+++03hYWFycvLS61atVL//v01fPhwHT16NNGsvg0aNNCSJUv0+eefa8+ePTp8+LBy5colHx8fNW3aVF26dNHs2bM1bdo0rV+/PlEr9+DBg3X9+nVt2bJFhw8fTtSDZeDAgXrooYf07bffau/evTpy5IgqVqzo6Nab2vkqvfTp00e//PKLDh486Jg9OSXt27dXtWrV9PXXX2vLli06ePCgbDabypcvr/r16+vFF190uvs/gPuTzXClQVMAAKSTEydO6Pnnn1f37t3Vp08fs8tJZP/+/WrdurUKFiyojRs3ZuhMxnAN7du3165duzRu3Di61QJABmHMLQAgSxozZoyuX7+uF1980exS7pAw/rd9+/YE2yxgyZIlatasWaKx/reLiIhwzJb83y7qAID0Q7dkAECWlHB7o1u3bjkm2MmMiZWSs2fPHuXNm1cbNmzQt99+q+zZs6tz586m1YP0U6VKFR09elTh4eHy8/PT008/7fisnThxQm+++aZiY2P1+OOPq3z58iZXCwBZF92SAQBZ0tNPP62DBw+qcOHCypUrl2bPnq0SJUqYVk+zZs109OhRx88DBw7M0Bm2kblGjRrluI1NgQIFVLRoUUVFRen48eOKi4vTQw89pNmzZ6tIkSImVwoAWRfdkgEAWdKYMWNUuXJlXblyRV5eXvLy8jK1npo1a8rT01NFihTRoEGDCLZZzHvvvaevvvpKjRs3lqenpw4ePKgrV66oatWqjsmUCLYAkLFouQUAAAAAWB4ttwAAAAAAy8uSE0pduhStuDgapAEAAAAgK3Fzsyl//txJLsuS4TYuziDcAgAAAMB9hG7JAAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACwvm9kFWFVo6E6FhOxI83aio6MkSblze6VpO35+1eXr65/megAAAADAikwPt7GxsRoyZIhOnjwpNzc3jRgxQtmyZdOQIUNks9lUoUIFDRs2TG5uWbOROSoqUlLawy0AAAAA3M9shmEYZhawdu1arVq1SpMmTdLmzZv13XffKTY2Vi+88IJq1aqloUOHqk6dOmrSpInT27xwIUpxcabultOCgmZLkgIDe5pcCQAAAAC4Njc3mwoUSLph0PTm0LJly8putysuLk5RUVHKli2bdu/erZo1a0qS6tatqy1btphcJQAAAABkLZGRkQoKmuXoTWp1pndL9vT01MmTJ9W8eXNdunRJ06dP119//SWbzSZJyp07tyIj7+5gJ5fkXZGHh7skqWBBb5MrAQAAAHA/2bDhZ4WHH9f27Zv13HPPmV1Ompkebr/66isFBARo0KBBOn36tLp3767Y2FjH8ujoaOXJk+eutmmlbsmxsXZJ0rlzWePbEgAAAACuLzIyUtu2bZNhGNq2bZtq1HhSXl6u3+Dm0t2S8+TJI2/v+IOYN29e3bp1S5UqVdK2bdskSZs2bVKNGjXMLBEAAAAAspTg4PVKmH7JMAwFB28wt6B0YHq47dGjh3bv3q3OnTure/fuGjhwoIYOHaopU6aoQ4cOio2NVbNmzcwuEwAAAACyjF27QmW3x/citdvt+vffEJMrSjvTuyXnzp1bkyZNuuPxefPmmVANAAAAAGR9Var4KiRkh+x2u9zd3VW1qp/ZJaWZ6S23AAAAAIDMFRDQwDGJr81mU0BAfXMLSgeEWwAAAAC4z3h7e8vX1182m01+ftUtMZlUakzvlgwAAAAAyHwBAQ10/vzZLNFqKxFuAQAAAOC+5O3trcDAXmaXkW7olgwAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8rgVEAAAgAsIDd2pkJAdad5OdHSUJCl3bq80b8vPr7p8ff3TvB0AyAyEWwAAgCwkKipSUvqEWwCwEsItAACAC/D19U+XVtKgoNmSpMDAnmneFgBYCWNuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAA4HIiIyMVFDRLUVGRZpcCiyDcAgAAAHA5wcHrFR5+XMHBG8wuBRZBuAUAAADgUiIjIxUaulOGYSgkZAett3AK4RYAAACASwkOXi/DMCRJhmHQegunEG4BAAAAuJRdu0Jlt9slSXa7Xf/+G2JyRbCCbGYXAABAWoWG7lRIyI40byc6OkqSlDu3V5q35edXXb6+/mneDgDcj6pU8VVIyA7Z7Xa5u7uralU/s0uCBdByCwDA/4uKimRcFwC4gICABrLZbJIkm82mgID65hYES6DlFgBgeb6+/unSShoUNFuSFBjYM83bAgDcO29vb/n6+mvnzu3y86suLy9vs0uCBRBuAQAAALicgIAGOn/+LK22cBrhFgAAAIDL8fb2VmBgL7PLgIUw5hYAAAAAYHmEWwAAAACA5RFuAQAAAACWx5hbAAAAALAQ7u+eNMItAAAAANyHEu7tnh7h1hUQbgEAAADAQri/e9IYcwsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAFxOZGSkgoJmOe7FCqSG+9wC97HQ0J0KCdmR5u1ER0dJSp8bgPv5VU+X+7YBAABrCw5er/Dw4woO3qCnnmpldjmwAFpuAaRZVFQk36oCAIB0ExkZqdDQnTIMQyEhO7jOgFNouQXuY76+/unSShoUNFuSFBjYM83bAgArWrPmJ0VEnDa7DEly1JFwbnYFhQsXVdOmLcwuAxYSHLxehmFIkgzDoPUWTiHcAgAApFFExGmdPXVaRfIUNLsUeWfzlCS5Rd0yuZJ4Z66eM7sEWNCuXaGy2+2SJLvdrn//DSHcIlWEWwAAgHRQJE9Bvfj4c2aX4XLmbF2sOLOLgOVUqeKrkJAdstvtcnd3V9WqfmaXBAtgzC0AAAAAlxIQ0EA2m02SZLPZFBBQ39yCYAmEWwAAAAAuxdvbW76+/rLZbPLzqy4vL2+zS4IF0C0ZAAAAgMsJCGig8+fP0moLpxFuAQBAqrgvNoDM5u3trcDAXmaXAQsh3AIAgEyTcK/K9Ai3AADcjnALAABSxX2xAQCujgmlAAAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWl83sAgAgqwsN3amQkB1p2kZ0dJQkKXdurzTX4+dXXb6+/mneDgAAgCsh3AKABURFRUpKn3ALAACQFRFuASCD+fr6p7mlNChotiQpMLBnepQEAACQ5TDmFgAAAABgeYRbAAAAAIDluUS35BkzZui3335TbGysOnXqpJo1a2rIkCGy2WyqUKGChg0bJjc3cjgAAAAAIGmmJ8Zt27Zp586d+vbbbxUUFKQzZ85ozJgxGjBggBYsWCDDMLRu3TqzywQAAAAAuDDTW26Dg4Pl4+OjV155RVFRUXr77be1aNEi1axZU5JUt25dbd68WU2aNDG5UgAAAACpSY9b4EncBg93z/Rwe+nSJZ06dUrTp0/XiRMn1K9fPxmGIZvNJknKnTu3IiMj72qbBQpY51YZHh7ukqSCBb1NrgS4d3yOMx7HOHNwnDNeVj3GHh7usuuW2WW4LA8P9yz3niN53t45Hb/raZEQbvPly5vmbXl75+QzmISsdk42Pdzmy5dP5cqVU/bs2VWuXDnlyJFDZ86ccSyPjo5Wnjx57mqbFy5EKS7OSO9SM0RsrF2SdO7c3QV4wJXwOc54HOPMwXHOeFn1GMfG2s0f6+XCYmPtWe49R/LKln1EZcs+kubtJNwGr2PHHmnelpT1zjvpwYrnZDc3W7KNmaafhx999FH9/vvvMgxDERERun79up544glt27ZNkrRp0ybVqFHD5CoBAAAAAK7M9JbbBg0a6K+//tKzzz4rwzA0dOhQlShRQh988IHGjx+vcuXKqVmzZmaXCQAAAABwYaaHW0l6++2373hs3rx5JlQCAAAAALAi07slAwAAAACQVoRbAAAAAIDlEW4BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlucStgDLTmjU/KSLitNllOCTUEhQ02+RK4hUuXFRNm7YwuwwAAAAAuCv3XbiNiDitk6dPK3f+wmaXIkmyZc8tSbp8I87kSqToSxFmlwAAAAAA9+S+C7eSlDt/Yfk2DjS7DJcTujbI7BIAAAAA4J7cl+EWAAAgPUVFRSn66lXN2brY7FJczumrZ5VbecwuA8B9gAmlAAAAAACWR8stAABAGnl5eSmPcurFx58zuxSXM2frYsV5cckJIOPRcgsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLy2Z2AQAAAFnBmavnNGfrYrPLUNTNaEmSV47cJlcS78zVcyrkVdTsMgDcBwi3AAAAaVS4cHx4izO5DkmKjL4mSfIskNfkSuIV8irqOD4AkJEItwAAAGnUtGkLs0twCAqaLUkKDOxpciUAkLkYcwsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPCaXgskJDdyokZEeatxMdHSVJyp3bK83b8vOrLl9f/zRvBwAAAED6Itwiy4uKipSUPuEWAAAAgGsi3MJl+fr6p0srKbdEAAAAALI+xtwCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyP2ZIBAKZZs+YnRUScNrsMh4RaEmZZN1vhwkXVtGkLs8sAAMASCLcAANNERJzWmdNn9EC+omaXIknKmd1bkhRz3WZyJdLFy64T+gEAsALCLQDAVA/kK6qn6/cxuwyX88OGGZIMs8sAAMAyGHMLAAAAALA8wi0AAAAAwPIItwAAAAAAy2PMLQAAAABkAu4SkLq03CmAcAsAAAAAmSAi4rROR5xW3sKFzS5FkuSRO7ck6ZriTK4k3pWIiDQ9n3ALAAAAAJkkb+HCqtOtq9lluKTfv5mXpucTbgELoktL6tLSpQUAAADWQ7gFLCgi4rROnAlXzgc9zS5FkmTkskmSzt+6YHIl8W6cv2Z2CQAAAMhkhFvAonI+6KnSbSubXYZLOrZ8t9klAAAAIJPdd+E2KipK0ZGRCl0bZHYpLif6UoSyeXubXQYAAAAA3DXucwsAAAAAsLz7ruXWy8tLt7J5yrdxoNmluJzQtUHyysn3HQAAAACshyQDAAAAALC8+67lFgCA+wm3Dksdtw4DgKyBcAsAQBYWEXFaZ06Fq0CeXGaXIknK+f9XHrFR580t5P9duHrd7BIAAOmEcAsAQBZXIE8uta79sNlluKSVW/aZXQIAIJ0w5hYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5WUzuwAAAAAA5luz5idFRJw2uwyHhFqCgmabXMn/FC5cVE2btjC7DCSDcAsAAABAERGndfZsuIoU8TK7FEmSt3d8J1M3t0smVxLvzJkos0tAKgi3AAAAACRJRYp4qVcvf7PLcEmzZu1UXFzathEVFaWr0ZH6/Zt56VNUFnM5IkJxub3v+fmMuQUAAAAAWB4ttwAAAACQCby8vOTm5ak63bqaXYpL+v2befJMQ/sr4RYAkuFKE2swqQYAAEDKCLcAkIyIiNM6ezJcRXLnNrsUedtskiS3yxdNriTemehos0sAAABIhHALACkokju3evlWNrsMlzMrdLfSOKcGAABAumJCKQAAAACA5d1zuG3VqpVOn3aNsWgAAAAAgPtbit2Sp06dmuyyI0eOaM6cOcqbN68k6dVXX73nIi5cuKB27dppzpw5ypYtm4YMGSKbzaYKFSpo2LBhcnOjgRkAAAAAkLwUw+2SJUsUERGhwoULK3v27ImW2e12rVu3TtmyZZPNZrvncBsbG6uhQ4cqZ86ckqQxY8ZowIABqlWrloYOHap169apSZMm97RtAAAAAMD9IcUm0VWrVql58+bKkSOHxo4dqzVr1jj+y5kzp77++mutWbNGv/zyyz0X8Mknn6hjx44qVKiQJGn37t2qWbOmJKlu3brasmXLPW8bAAAAAHB/SLHl1tvbW+PHj9ePP/6ovn376vnnn1f//v3l7u6eLi++bNkyPfDAA6pTp45mzpwpSTIMQ7b/v+VF7ty5FRkZedfbLVDAK9llHh7u0g3m+EyOh4e7Chb0NruMdOXhEf95zUr75eHhLt0yuwrXlh6fZQ8Pd9nTqZ6sKL2Occx1zsnJSa9jHJtO9WRVWe1vX1b8u4fM4eHhLjt/+FKU1vOFh4e7FMvfvZSk5Rg7dSugli1bqkaNGnr33XfVrl07ffLJJ44AmhZLly6VzWbTH3/8ob1792rw4MG6ePF/93CMjo5Wnjx57nq7Fy5EKS7OSHJZbCy/sSmJjbXr3Lm7/0LBlSW851lpv/gcpy49PsuxsXamlE9Beh1jKe1/T7Kq9DvGSElW+9uXFf/uIXPExtrFVDcpS+v5gnNy6lI7xm5utmQbM52+z23hwoU1e/ZsffPNN+ratatu3rx595X+x/z58x3/DgwM1PDhwzV27Fht27ZNtWrV0qZNm/T444+n+XUAAK4pKipKkZFR+mHDDLNLcTkXLp+Wtz35nkgAACCxu/5uplu3blq4cKH69evnmCk5PQ0ePFhTpkxRhw4dFBsbq2bNmqX7awAAAAAAshanW24l6caNGzp06JBiY2NVu3ZtHTp0yLGsevXqaSokKCjI8e958+alaVsAAGvw8vJSdndvPV2/j9mluJwfNsxQ9lxJD7EBAAB3cjrcrl27Vu+8846ioqIckz4ZRvwfXZvNpr1792ZYkQAAAAAApMTpcDt16lTVqFFDr7/+ury9mX0PAAAgPYWG7lRIyI40byci4rQkKShodpq35edXXb6+/mneDgBkBqfD7dGjRzVu3Dg99NBDGVkPAAAA0sDLi0YIAPcnp8NtuXLlFBERQbgFAADIAL6+/rSSAkAaOB1u+/btq+HDh6tXr14qXbq0smfPnmh5WieUAgAAAADgXjkdbvv37y9JGjZs2B3LmFAKyFxRUVG6EXVNx5bvNrsUl3Tj/DVFeeUwuwzAJURFRSnq6jWt3LLP7FJc0oWr1+SlKLPLAACkA6fD7bp16zKyDgAAAAAA7pnT4bZ48eLJLjtz5ky6FJNZoi9FKHRtUOorZoKY6/HfFmfP5WVyJfHHJV/RomaXASd4eXnpRs6bKt22stmluKRjy3fLK5v5v1OAK/Dy8lIO3VDr2g+bXYpLWrllnzy8OF8AQFbgdLgNDw/XJ598ogMHDshut0uSDMNQTEyMLl68qD179mRYkempcGHXCm8RV6IlSfny5zG5Eilf0aIud3wAAAAAwBlOh9vhw4fr5MmTatWqlWbMmKGXXnpJx44d0+rVq/XRRx9lZI3pqmnTFmaXkEjCPegCA3uaXAkAAAAAWJfT4Xbnzp2aOXOmatSoofXr16tevXqqVq2aypUrp3Xr1um5557LyDoBAAAAAEiW0+H21q1bjnG3ZcuW1b59+1StWjW1atVK3377bYYVCAAAAKSX0NCdCgnZkebtREfHz5uSO3faxmz7+VXn/sZAOnFzdsXSpUsrJCREUny43bVrlyTp+vXrunbtWsZUBwAAALigqKhIRUVFml0GgNs43XLbuXNnDRkyRHFxcWrWrJnatm2rXLly6e+//5afn19G1ggAAACkC19f/3RpKWXeFMD1OB1uO3XqpAceeEAPPPCAKlSooFGjRikoKEgPPvigPvjgg4ysEQAAAEAGi4qKUnR0lGbN2ml2KS7p9Oko5c7tYXYZSIHT4VaSmjVr5vj3M888o2eeeSbdCwIAAAAA4G7dVbj966+/NGPGDB0+fFhBQUFatmyZSpYsqTZt2mRQeQAAAAAyg5eXl/LkiVWvXkxwlZRZs3YqLi5tE4ghYzk9odTGjRvVq1cvFS1aVOfPn1dcXJxsNpvee+89LV26NCNrBAAAAAAgRU6H26lTp+rtt9/WiBEj5O7uLkl69dVXNXjwYM2ZMyfDCgQAAAAAIDVOh9tDhw6pbt26dzzeoEEDhYeHp2tRAAAAAADcDafH3ObPn1/h4eEqWbJkosd37dqlBx98MN0LAwCzxc8aGa1ZobvNLsXlnI6OVu5s2c0uAwAAwMHpltvnn39eH374oTZu3ChJOn78uJYsWaIRI0aobdu2GVYgAAAAAACpcbrltk+fPoqMjNRrr72mmJgY9ezZU9myZdMLL7ygl19+OSNrBABTeHl5Kc+tGPXyrWx2KS5nVuhuxXkxYyQAAHAdTodbm82mt956S6+88orCwsLk4eGhMmXKKGfOnBlZHwAAAAAAqbqr+9xGRkbq2LFjio2NVWxsrPbs2eNYVr169XQvDgAAAAAAZzgdbr///nsNGzZMMTExMgwj0TKbzaa9e/eme3EAAAAAADjD6XA7ceJEtW7dWj169KArMgAAAADApTgdbq9cuaKePXuqTJkyGVgOAAAA0iIyMlLff79Qbdt2kJeXt9nlAECmcfpWQI0aNVJwcHBG1gIAAIA0Cg5er/Dw4woO3mB2KQCQqZxuuX377bfVqlUr/fLLLypVqpTc3BLn4hEjRqR7cQAAAHBeZGSkQkN3yjAMhYTsUEBAfVpvAdw3nA63o0ePVnR0tK5fv67jx48nWmaz2dK9MAAAANyd4OD1jok/DcNQcPAGPfVUK5OrAoDM4XS43bBhg7744gvVqVMnI+sBAADAPdq1K1R2u12SZLfb9e+/IYRbAPcNp8fc5s+fX8WKFcvIWgAAAJAGVar4yt3dXZLk7u6uqlX9TK4IADKP0y23r7/+ukaPHq3hw4erZMmSGVkTLG7Nmp8UEXHa7DIcEmoJCpptciX/U7hwUTVt2sLsMgAAWUxAQAOFhu6UFD9sLCCgvrkFAUAmcjrczpgxQ+Hh4WratKkkOb4VTLBr1670rQyWFRFxWhGnjquId3azS5EkebnHSZJskWdMriTemcgYs0sAAGRR3t7e8vX1186d2+XnV53JpADcV5wOt717987IOpDFFPHOrhdqFTe7DJc0d9tJGWYXAQDIsgICGuj8+bO02gK47zgdbtu2bevUej179tSYMWNUqFChey4KAAAA98bb21uBgb3MLgMAMp3TE0o5a8eOHbp582Z6bxYAAAAAgGSle7gFAAAAACCzEW4BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlpXu4tdls6b1JAAAAAABS5PR9bp3l7u6e3psEAAAAkAnOnInSrFk7zS5DkhQVFSNJ8vLKbnIl8c6ciVKhQvnTvJ0rERH6/Zt56VBR2t2IipIk5fTyMrmSeFciIuRZuOg9P/+ew+3MmTPVsWNH5cmTJ9Hjf/311z0XAwAAAMAchf8/VMTFmVzI/4uMPC1J8vRMe6BMD4UK5Xcco3uV1uent8joaEnSA155Ulkzc3gWLpqmY5RiuD116lSyy7744gtVr15dxYoVkyTH/wFkjhvnr+nY8t1mlyFJunUtVpKUzdPD5Eri3Th/TSpSwOwyAACwlKZNW5hdQiJBQbMlSYGBPU2uJP1wjDNWiuG2YcOGyY6hNQxDgYGBMgxDNptNe/fuzZACAdzJ1b71i7ge/83qg3lcJFAWKeByxwgw04Wr17Vyyz6zy5AkXbsZ/2WYZw7X+DLswtXrKuIavfEAAGmUYridOHGihg8frooVK+rll19WtmzxqxuGoV69emn06NEqXLhwphQK4H/41g+As1zti54b0fFfhuUt8KDJlcQr4uV6xwgAcG9SDLdPPfWUHn30Ub377rv6+OOP9emnn6pChQqS4mdFrlq1qkqWLJkphQIAsqaLl0/rhw0zzC5DknT9RqQkKVdOb5MriT8uRXIVSfN2+DIMAHC/SHVCqYIFC+rLL7/U/Pnz1aVLF7300kvq1atXZtQGAMji/tdiZphaR4JLV+PDbd785vdTLZKrCC2KAADcBadnS+7SpYtq166twYMHa926dbLb7RlZFwDgPkCrIgAASC9ud7Ny2bJl9e233yogIEAFCxZ0jMEFAAAAAMBMdxVut2zZoj/++EOvvvqq1q1bpzlz5mjr1q0ZVRsAAAAAAE5xOtx+//336t27tw4fPux47MqVK+rVq5dWr16dIcUBAAAAAOAMp/sVz5w5U8OGDdNzzz3neOzTTz9VjRo19Pnnn6t58+YZUiAAAAAAAKlxuuX25MmTevzxx+94/IknntDx48fTtSgAAAAAAO6G0+G2VKlS2rhx4x2Pb968WUWLcqsCAAAAAIB5nO6W3LNnT73//vvas2ePqlatKknatWuXVq5cqaFDh2ZYgQAAAAAApMbpcNumTRtlz55d33zzjVavXi0PDw+VK1dOEyZMUOPGjTOyRgAAAAAAUnRXN6pt0aKFGjdurOzZs0uSTp06pWLFimVIYQAAAAAAOMvpMbfnzp1T586dNXXqVMdj7du3V2BgoC5evJghxQEAAAAA4Aynw+3IkSNls9nUrl07x2Pz5s1TXFycPv744wwpDgAAAAAAZzjdLfmPP/7Q/PnzVaZMGcdj5cuX1wcffKAePXpkQGkAAAAAADjH6ZZbm82m69ev3/G43W5XbGxsuhYFAAAAAMDdcLrlNiAgQKNHj9b48eMdk0idPn1aH3/8sZ588skMKxDWExUVpejIm5q77aTZpbikM5E3ldsWZXYZAAAAQJbidLh999139cILL6hRo0Z64IEHJEkXL15UpUqVNG7cuAwrEAAAAACA1KQYbvfs2aOKFSvK3d1dBQoU0PLly7VlyxYdPHhQ2bJlU/ny5VW7dm3ZbLbMqhcW4OXlJW8jSi/UKm52KS5p7raTMry8zC4DAAAAyFJSDLddu3bVTz/9pCJFiqhbt26aOnWq6tSpozp16mRWfQAAAAAApCrFcOvh4aHFixerVq1a+vPPP/Xnn38qb968Sa772GOPZUiBAAAAAACkJsVw27NnT02YMEHTpk2TzWbTq6++muR6NptNe/fuzZACAQAAAABITYrhtnfv3uratasiIyNVr149LV++3DGZFAAAAAAAriLV2ZI9PT3l6empb775RhUqVFC2bE5PsAwAAAAAQKZwOqn6+/tryZIlOnjwoGJiYu5YPmLEiHQtDAAAAEiwZs1Piog4bXYZDgm1BAXNNrmSeIULF1XTpi3MLgMwldPhdsiQIVqzZo0eeeQR5ciRI9EybgUEAACAjBQRcVqnT590mSFyCdfDN29eN7kS6eLFi2aXALgEp8Ptxo0bNX78eDVp0iQj6wEAAACS9MADD6h58+Zml+FyVq9ebXYJgEtwc3ZFLy8vlS1bNiNrAQAAAADgnjgdbnv37q1PP/1Up06dysh6AAAAAAC4a053S65cubImTZqkRo0ayc3N7Y5xtrt27Ur34gAAAAAAcIbT4fbdd99VmTJl1Lp1a3l6emZkTQDgMs5ER2tW6G6zy1DU/89S75U9u8mVxDsTHa1C+VxjUhcAAADpLsJteHi4Vq5cqTJlymRgOQDgOgoXLipJijO5DkmK/P9bTni6SKAslO8Bx/EBAABwBU6H2ypVqujYsWPpHm5jY2P17rvv6uTJk4qJiVG/fv300EMPaciQIbLZbKpQoYKGDRsmNzenhwcDQLpwpfsFJtxHMTCwp8mVAAAAuCanw22XLl303nvv6bnnnlOpUqWULVvip7Zq1eqeCli5cqXy5cunsWPH6tKlS2rbtq0efvhhDRgwQLVq1dLQoUO1bt06bkEEAAAAAEiW0+F20KBBkqQvvvjijmU2m+2ew+1TTz2lZs2aOX52d3fX7t27VbNmTUlS3bp1tXnzZsItAAAAACBZTofbffv2ZUgBuXPnliRFRUWpf//+GjBggD755BPHbMy5c+dWZGRkhrw2AAAAACBrcDrcZqTTp0/rlVdeUefOndWqVSuNHTvWsSw6Olp58uS5q+0VKOCV3iVmGA8Pd0lSwYLeJleSfjw83HXL7CJcnIeHe5Z7z6Ws9Tl2NRzjzMFxzngcY9wrDw933bxpdhWuK6tdW0icLzJDVjvGpofb8+fP68UXX9TQoUP1xBNPSJIqVaqkbdu2qVatWtq0aZMef/zxu9rmhQtRioszMqLcdBcba5cknTuXdVqnY2PtsqW+2n0tNtae5d5zKWt9jl0NxzhzcJwzHscY9yrhs4OkZbVrC4nzRWaw4jF2c7Ml25hp+hTE06dP19WrV/X5558rMDBQgYGBGjBggKZMmaIOHTooNjY20ZhcAAAAAAD+y/SW2/fff1/vv//+HY/PmzfPhGoAAAAAAFZkesstAAAAAABpRbgFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5WUzuwCrCg3dqZCQHWneTkTEaUlSUNDsNG3Hz6+6fH3901wPAACAK4qKilJk5BWtXr3a7FJczsWLF+XtndfsMgDTEW5N5uXlbXYJAAAAAGB5hNt75OvrT0tpCs5ExmjutpNmlyFJirpplyR55XA3uZJ4ZyJjVJjvNAAAuCteXl7y8HBX8+bNzS7F5axevVo5cuQyuwzAdIRbpLvChYtKkgyT60gQdS2+63du7yImVxKvsPf/jhEAAACA9EG4Rbpr2rSF2SUkkjCeOTCwp8mVAAAAAMgozJYMAAAAALA8wi0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8ZksG7mOhoTsVErIjzduJiIi/3VLCzNRp4edXnXtIAwAA4K4RbgGkmZeXt9klAAAA4D5HuAXuY76+/rSSAgAAIEtgzC0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAy8tmdgEAAACAMy5evKjVq1ebXYYk6fr165KkXLlymVxJ/HEpWrS42WUApiPcAgAAwOUVLlzU7BISuXz5siQpX74HzC1EUtGixV3u+ABmINwCAADA5TVt2sLsEhIJCpotSQoM7GlyJQASEG4BAJYXGrpTISE70rydiIjTkv530ZoWfn7V5evrn+btAAAA5xBuAQD4f15e3maXAAAA7hHhFgBgeb6+/rSSAgBwn+NWQAAAAAAAyyPcAgAAAAAsj3ALAAAAALA8xtwCAAAASDfMYA+zEG4BAAAAuBxmsMfdItwCAAAASDfMYA+zEG4BAECq6GYIILNFRkbq++8Xqm3bDrTiwilMKAUAADKNl5c3F6kAnBIcvF7h4ccVHLzB7FJgEbTcAgCAVNHNEEBmioyMVGjoThmGoZCQHQoIqM8XY0gVLbcAAAAAXEpw8HoZhiFJMgyD1ls4hXALAAAAwKXs2hUqu90uSbLb7fr33xCTK4IVEG4BAAAAuJQqVXzl7u4uSXJ3d1fVqn4mVwQrINwCAAAAcCkBAQ1ks9kkSTabTQEB9c0tCJZAuAUAAADgUry9veXr6y+bzSY/v+pMJgWnMFsyAAAAAJcTENBA58+fpdUWTiPcAgAAAHA53t7eCgzsZXYZsBC6JQMAAAAALI9wCwAAAACwPMItAAAAAMDyGHMLAAAAABYSGrpTISE70rydiIjTkqSgoNlp3pafX3X5+vqneTtpQcstAAAAAJcTGRmpoKBZioqKNLuULMvLyztL3WaJllsAAAAALic4eL3Cw48rOHiDnnqqldnluBRfX3/TW0ldES23AAAAAFxKZGSkQkN3yjAMhYTsoPUWTiHcAgAAAHApwcHrZRiGJMkwDAUHbzC3IFgC4RYAAACAS9m1K1R2u12SZLfb9e+/ISZXBCsg3AIAAABwKVWq+Mrd3V2S5O7urqpV/UyuCFZAuAUAAADgUgICGshms0mSbDabAgLqm1sQLIFwCwAAAMCleHt7y9fXXzabTX5+1bPU7WqQcbgVEAAAAACXExDQQOfPn6XVFk4j3AIAAABwOd7e3goM7GV2GbAQuiUDAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAADA5URGRiooaJaioiLNLgUWQbgFAAAA4HKCg9crPPy4goM3mF0KLIJwCwAAAMClREZGKjR0pwzDUEjIDlpv4RTCLQAAAACXEhy8XoZhSJIMw6D1Fk4h3AIAAABwKbt2hcput0uS7Ha7/v03xOSKYAWEWwAAAAAupUoVX7m7u0uS3N3dVbWqn8kVwQpcMtzGxcVp6NCh6tChgwIDA3Xs2DGzSwIAAACQSQICGshms0mSbDabAgLqm1sQLMElw+3atWsVExOjhQsXatCgQfr444/NLgkAAABAJvH29pavr79sNpv8/KrLy8vb7JJgAdnMLiApf//9t+rUqSNJqlatmnbt2mVyRQBw70JDdyokZEeathERcVqSFBQ0O831+PlVl6+vf5q3AwBARgoIaKDz58/SagunuWS4jYqKkpeXl+Nnd3d33bp1S9myOVdugQJeqa+E+4aHR/x4jYIF+cYP5vD2zun4HN6rvHnzSlKat5NQD78PAJA2XF9kvIIFvfXGGwPNLgMW4pLh1svLS9HR0Y6f4+LinA62knThQpTi4oyMKA0WFBsbP9PeuXPcHw3mKFv2EZUt+4jZZSTC7wMApA3XF4A53NxsyTZmuuSY2+rVq2vTpk2SpH/++Uc+Pj4mVwQAAAAAcGUu2XLbpEkTbd68WR07dpRhGBo9erTZJQEAAAAAXJhLhls3Nzd99NFHZpcBAADSWWRkpL7/fqHatu3A7KcAgHTlkt2SAQBA1hQcvF7h4ccVHLzB7FIAAFkM4RYAAGSKyMhIhYbulGEYCgnZoagoJuIBAKQfwi0AAMgUwcHrZRjxdzMwDIPWWwBAuiLcAgCATLFrV6js9vjbp9jtdv37b4jJFQEAshLCLQAAyBRVqvjK3d1dkuTu7q6qVf1MrggAkJUQbgEAQKYICGggm80mSbLZbAoIqG9uQQCALIVwCwAAMoW3t7d8ff1ls9nk51edWwEBANKVS97nFgAAZE0BAQ10/vxZWm0BAOmOcAuXFRq6UyEhO9K8nYiI05KkoKDZad6Wn191+fr6p3k7AHC/8vb2VmBgL7PLAABkQYRbZHl0ewMAAACyPsItXJavrz+tpAAAAACcwoRSAAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8myGYRhmF5HeLlyIUlxcltstAAAApFFo6E6FhOxI83YiIk5LkgoXLpqm7fj5VZevr3+a6wHuF25uNhUo4JXksmyZXAsAAABgeV5e3maXAOA/aLkFAAAAAFhCSi23jLkFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAAAYHmEWwAAAACA5RFuAQAAAACWR7gFAAAAAFge4RYAAAC4S5GRkQoKmqWoqEizSwHw/wi3AAAAwF0KDl6v8PDjCg7eYHYpAP4f4RYAAAC4C5GRkQoN3SnDMBQSsoPWW8BFEG4BAACAuxAcvF6GYUiSDMOg9RZwEYRbAAAA4C7s2hUqu90uSbLb7fr33xCTKwIgEW4BAACAu1Kliq/c3d0lSe7u7qpa1c/kigBIJofbyMhI9e3bV127dlWHDh20c+dOSdI///yj5557Th07dtTUqVPNLBEAAABIJCCggWw2myTJZrMpIKC+uQUBkGRyuJ07d64ef/xxzZs3T2PGjNFHH30kSRo2bJg+++wzffvttwoJCdHu3bvNLBMAAABw8Pb2lq+vv2w2m/z8qsvLy9vskgBIymbmi/fo0UPZs2eXFD9eIUeOHIqKilJMTIxKlSolSQoICNAff/yhypUrm1kqAAAA4BAQ0EDnz5+l1RZwIZkWbhcvXqyvv/460WOjR4+Wr6+vzp07p7feekvvvvuuoqKi5OXl5Vgnd+7cCg8Pz6wyAQAAgFR5e3srMLCX2WUAuE2mhdvnnntOzz333B2P79+/X2+88Ybefvtt1axZU1FRUYqOjnYsj46OVp48ee7qtQoU8Ep9JQAAAABAlmFqt+RDhw7p9ddf18SJE/Xwww9Lkry8vOTh4aHjx4+rZMmSCg4O1quvvnpX271wIUpxcUZGlAwAAAAAMImbmy3ZxkybkXAHahP069dP+/fvV/HixSXFB9svvvhC//zzj0aPHi273a6AgAANHDjwrrZLuAUAAACArMdlw21GIdwCAAAAQNaTUrg19VZAAAAAAACkB8ItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMvLZnYBGcHNzWZ2CQAAAACAdJZS1rMZhmFkYi0AAAAAAKQ7uiUDAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcJsOTpw4oYoVK+rYsWN3LFu2bJnq1q1rQlXWltIxTS/btm1TxYoVdevWrTRth/fYORcuXNBPP/1kdhkuacKECQoMDOSzdI/S6xw8ZcoUderUKb3Luy9kxjkb9+5uzr/p9bcRQNYQGBioCRMmmF2G07KZXQBgFn9/fwUHBytbNn4NMsO4ceMUGxurFi1amF2Ky2rRooXq169vdhlZCsc0cxQtWlTBwcF64IEHzC4FSeD8C+B+wVU97lvZs2dXwYIFzS7jvmEYhtkluLycOXMqZ86cZpeRpXBMM4e7uzvnUxfG+RfA/YJuyelozZo1qlevnqpXr66RI0fe0aUnqa4+Q4YM0Ztvvun4ee3atWrZsqX8/PzUtm1bbdq0ybFs//796tKli6pVq6Ynn3xSH3/8cZboNhQeHq4+ffrI399fdevW1fTp0+9YJywsTL169ZK/v7+qVq2qTp066eDBg47lkyZNUp06dVS1alV16NBBO3fuTHXZf9+PlOrYuXOnOnfuLD8/P1WrVk09e/ZURERERh2SDJfcvp45c0avv/66atasqVq1aumjjz7SzZs3JSXdvfP2ripDhgzRyJEj9cYbb6hatWpq1qyZli1bJim+u+fy5cu1atUqNWzYUJJUsWJFTZw4UY8//rh69Oih5s2b68svv0y0/eeff15z587N0GNhhkOHDqlTp07y8/PTCy+8oMuXL0u68xin9Lnes2ePunbtKj8/PzVq1EhLlixxLAsLC1PPnj1VvXp1BQQEaMqUKYqLi8u0/TPLb7/9piZNmsjX11d9+vTRpUuXEh3Tbdu26cknn9T8+fNVq1YtPfHEE5o6dWqibdy6dUsjR47Uo48+qieeeEKzZs1yLIuLi9OsWbPUuHFj+fr6qmvXrtq3b59jecWKFbVo0SI1adJE/v7+euONNxQVFZU5O2+y27slp3S+HjJkiCpWrHjHf3/++acCAwOTXHby5EmT984cCcd03bp1atiwofz9/fXxxx9r//79ateunapVq6a+ffvq2rVrkqSFCxeqUaNG8vf3V6dOnRQaGiop6fNvan9T73cJx37lypWqW7euatSooY8++kixsbGS4q8JOnXqpGrVqqlhw4aaP3++47lDhgzRhx9+qH79+snX11fPPPOMtm/fbtauZLqEYzdt2jQ99thjeuedd1K8to2Li9O4ceNUq1Yt1apVS59//rmaNGmibdu2SZJu3Lih9957T48++qjq1KmjxYsXq1KlSjpx4oSk1D/LBw8eVLdu3eTr66smTZpozpw598WXPUm9D+vXr1fbtm3l6+ur5s2ba/Xq1Y717Xa7Jk+erDp16qh69erq16+fzp49e8d2T548qYCAAH3yySeZuTt3hXCbjhYvXqzx48dr+vTpWrt2raZMmXJXz9+3b5/eeustvfTSS1q1apWef/55vfrqq9q7d68k6a233lK5cuW0atUqTZw4UStWrEh0QWtFMTEx6tmzp7Jly6aFCxdq1KhRmjVrllatWuVYxzAMvfzyyypWrJhWrFih7777TnFxcfr0008lSb/++qvmz5+vcePG6aefflKlSpXUv39/xcXFpbjMmTpWrlypqKgo9enTR7Vr19YPP/yg2bNn68SJE/riiy8y9Vill+T2dfny5erevbuuXbumb775RpMmTdKmTZv08ccfO73t7777To888oiWLVumgIAADR8+XJcvX9aLL76o5s2bq1mzZok+s+vWrdOCBQv03nvvqWXLlolOtCdPntS///6r5s2bp+v+my0mJka9e/dWiRIltGzZMjVu3FiLFy++Y72UPrsXL15Ujx49VK5cOS1fvlwDBw7U8OHDtX37dl28eFGdO3dWoUKFtHjxYg0fPlzz58/XnDlzTNjbzLVs2TJ99tlnCgoK0p49ezRz5sw71rl8+bKWLl2qOXPmaMSIEZo7d64WLFjgWJ4QCJYvX64+ffpo7Nix2r9/vyRp2rRpmjNnjt555x0tX75cJUqUUK9evRIF2MmTJ+vdd9/VN998o4MHD+r999/P4L12Lamdr9977z0FBwc7/mvYsKGqVasmf39/TZkyxfH4pk2bVKVKFTVr1kzFixc3ea/M9eWXX+rzzz/X8OHDNXfuXPXv319vvfWWvvzyS/31119aunSpfvvtN02aNMnx2axbt666d++us2fP3nH+Te09wv9MmzZN48eP17Rp07R27VpNnDhRYWFh6t69ux577DEtX75cr732msaOHZvo79fixYtVvnx5LV++XLVq1VLv3r11/vx5E/ck823fvl1Lly5V9+7dU7y2nTFjhr7//nuNGzdOc+fO1YYNGxQeHu7YzsiRI/X3339r1qxZmjBhgmbNmiW73S4p9fPNjRs31KtXL1WrVk0rV67U+++/r6+//lrz5s3L/ANikoT3oVq1anrttdf0zDPPaMWKFerQoYPefPPNRF+CLVq0SCNHjtTixYt18+ZNDR48ONG2Ll26pF69eqlu3bp3LHMpBtIsPDzc8PHxMdatW+d4bNmyZUbNmjWNpUuXGnXq1DEMwzC2bt1q+Pj4GLGxsY71Bg8ebAwaNMgwDMN48803jREjRiTa9pAhQ4x33nnHMAzDqF69uvHZZ58Zt27dMgzDMP79918jPDw8Q/cto61fv97w8/Mzrl696nhsxYoVxty5cw0fHx/j6NGjRnR0tDFz5kwjKirKsc63335r1K9f3zAMw5g7d67xxBNPGMePHzcMwzAiIyONLVu2GLGxsSkuu/39SK6OX3/91Th79qwxa9YsIy4uzrFs3LhxRpcuXQzDMBK9x1aQ3L4uX77c8PX1NS5duuR4fOPGjcYjjzxiXL16Ncn97Nq1qzF+/HjDMOI/y23btnUsi4yMNHx8fIw///zTsTzhs24YhuHj42MEBQU5fj569Kjh4+PjeK++/PJLo2vXrum34y5i/fr1RrVq1RJ9nl977TWja9euiY5xSp/defPmGfXr13ecCwzDMIKCgoxt27YZX3/9tVGnTh0jJibGsWzBggVGrVq1MmkPM1/COXjDhg2Ox0aNGmX06NEjyXPw7t27HetNmjTJaN26tWEYhjF58mTjySefNOx2u2N5jRo1jBUrVhhxcXFGzZo1jfnz5zuWxcTEGPXq1TPmzZtnGEb8Z3ru3LmO5X/88Yfx8MMPJ/qdyqoS3oO9e/emeL6+3YIFC4yaNWsap06dumPZ2LFjjSZNmhiRkZEZWrcrS+pzXbNmTWPSpEmOn/v27WsMHz7c6NSpU6LPnmHEn5+nTp1qGEbi829qf1OTula53yQc+zVr1jgeW7JkiVGzZk1j9OjRxrPPPpto/bFjxxrt2rUzDCP+WLdq1cqxzG63Gw0bNrzj/cmqEo7db7/9ZhhG6te2AQEBxnfffedYFhYWZvj4+Bhbt241oqKijMqVKxu///67Y/mmTZsMHx8fIzw8PNXP8qJFixK9F4YRf33eqFGj9N1pF/Tf9+GVV14xXn/99UTrDBgwwHjttdeMuLg44/HHHzcWLVrkWHbs2DHjs88+M+x2u9G1a1djzJgxRocOHYzXXnst0bWHK2LMbTqqWrWq49+VKlXS5cuXdfHiRaefHxYWpgMHDmjp0qWOx2JjY+Xr6ytJeuONNzRy5EgtXLhQdevWVcuWLVWlSpX02wETHDp0SKVKlZK3t7fjsdatW+vEiRMaM2aMJMnT01OdO3fWihUrtGvXLh0+fFh79uxRvnz5JEmtWrXSsmXL1KRJE1WtWlUNGzbUs88+q2zZsqW4zJk6ErRt21ZfffWV9u7dq0OHDmn//v2O98VqktvXmTNnqlSpUo7jKknVq1eX3W7X0aNHndp2yZIlHf/28vKSpBS7zt/eIlO6dGlVrVpVq1evVu/evfXTTz/p+eefd3KvrOPQoUMqWbKkcufO7XisSpUq+v333xOtl9Jn99ChQ3r44Yfl7u7uWL9r166SpB9//FGVKlWSh4eHY5m/v78uXbqkixcvZukJf27//Hl7ezu61N8uR44cqlSpkuPnKlWqJGrhLV68uNzc/tepKWE7Fy5c0OXLl+Xn5+dY5uHhoSpVqigsLMzxmL+/f6Jtx8XF6ciRI4kez8py5cqV4vk6QWhoqMaMGaMpU6aoaNGiiZatXbtWQUFB+u677xznkftZiRIlHP/OkSOHihUr5vg5Z86ciomJUVhYmMaPH69JkyY5lsXExKhIkSJ3bC+1v6n4n//+Pl++fFl79uxJdB5IWO/2rsm3P8/NzU2VKlXS4cOHM75gF5Lw9z2la9uLFy/q7Nmzia6fy5Urp7x580qSDh8+rNjY2ETLbz+2qX2WDx8+rEOHDiV6TlxcnGJiYhQTE6Ps2bNnyL67ktvfh/9eU/n7+2vRokWO64PKlSs7lpUqVUpvvPGG4+f58+crNjZWXbt2TXTt4YoIt+no9gsi4//7899+gWmz2e54zu0X/na7XT179lS7du0SrZPwy9elSxc1aNBA69at04YNG/Tyyy+rX79+eu2119J1PzLT7ccnOdHR0Xr22WeVN29eNW7cWE8//bQOHz7suCAtUKCAli1bpj/++EMbN27UwoULNX/+fC1dulSFCxdOdpmzdURERKh9+/Z65JFHFBAQoOeff14bNmzQ33//nbadN0ly+5ojR447Hkvo+hMXF5fq5ze5bRspjG3572s+/fTTWrVqlZo3b64DBw6oWbNmyT7Xyv57TJKasTulz3VKn9ek3seEbvhZfdztf//gJvXZ++86cXFxic7dt//7dkkdVyn+dyTh9+S/20843sltMyu6efNmiudrKb5r2+uvv64XXnhB9erVS/T8Y8eOaciQIXrvvff0yCOPZHb5Lum/54ekPk92u12DBw9WQEBAosc9PT3vWDe1v6n4n6R+n5MSFxeX6Dzw3/fMbrcn+Tc0K0s4Z6Z0bZtwnP57rk74Oanlt/87tc/yrVu3VLNmTX344Yd31He/3Ckj4X1I7trAbrc7dS3u4+Ojfv36qX///o5rYld1//zFzQQHDhxw/Ds0NFQFCxZM1DqW8OG5fXxWwoB4SSpbtqzCw8NVunRpx38rVqzQr7/+qps3b2rkyJGy2WwKDAzU7Nmz9eqrr1r+vqFlypRReHj4HWPWbp/k5c8//9SZM2cUFBSkXr16qXbt2jp16pTjBLdhwwYtXLhQderU0fvvv6+ff/5Z0dHR+vvvv1Nc5kwdQ4YM0a+//qrcuXPryy+/VPfu3VWjRg2Fh4dbdkKC5Pb1yy+/1PHjxx2TG0nSP//8I3d3d5UqVUoeHh6Kjo527LdhGIk+v6lx5g97ixYttHfvXi1ZskS1a9dW/vz5nd8xi6hQoYKOHz+uK1euOB7bs2fPHeul9NktXbq09u/fn+hi65133tGkSZNUvnx57dmzxzHxiRQ/+Um+fPmydKuts65du6bjx487fv73339VsWLFVJ/n7e2tggULKiQkxPFYbGysdu/erbJlyzoeSxhHJkm7du2Sh4eHypUrl07Vu77UztdxcXF68803VaJECfXv3z/Rc69fv67XXntNDRo0yJK9NjJS2bJldebMmUTXD3PmzNGff/4pKfH5N7X3CP9z+4Rxu3bt0oMPPqhq1aolOg9I8efY5M4Ddrtd+/btc+o8kxWldG2bJ08eFSpUSLt373asHx4erqtXr0qS49rj9uW7du1y/Du1z3LZsmV19OhRFS9e3PHae/fu1ZdffnlffekoSeXLl0/2c+vt7a0HHngg0bXI0aNHVbt2bcc1YUBAgBo3bqyGDRvqww8/dOnzxf31zmawkSNH6p9//tGWLVs0efJkvfjii4mWV6hQQTlz5tTUqVMVHh6uuXPnJvog9ejRQz///LO++uorHTt2TN9++62mT5+uUqVKKUeOHNqxY4dGjBihsLAw7d+/X5s2bUrUhcCKAgICVKRIEb3//vsKCwvTxo0bFRQUlOhiMF++fLp+/bp+/fVXnThxQosXL9b8+fMVExMjSY7JA37++WedOHFCq1atUkxMjB5++OEUlzlTR926dZUvXz6dPXtWmzdvVnh4uGbOnKk1a9Y4Xt9qktvXd999V2XKlNHbb7+tffv2adu2bRo5cqRatGih/Pnzq2rVqoqKitKXX36p8PBwffrpp4kCWmo8PT116tSpFGeZLlSokB577DHNnTtXLVu2TI/ddTm1a9dWsWLF9O677+rQoUNasmSJfvnllzvWS+mz27p1a0VHR2v06NE6cuSIfvjhB/3www+qU6eOnn76acXFxWno0KEKCwvTunXrNGXKFHXs2PG++2OenPfff18HDhzQL7/8oqCgIHXp0sWp57344ouaOnWq1q1bp7CwMA0dOlQ3b97U008/7Vhn6tSp2rZtm0JCQjRq1Ci1bt060ZecWd3DDz+c4vl68uTJ2rdvn4YPH65Lly7p3LlzOnfunKKjozV06FDFxsZq0KBBOn/+vGPZjRs3TN4r1/fCCy8oKChIy5cv1/HjxzV16lQtXbrU8bf09vNvan9T8T+jR4/Wv//+qz/++EOTJ09W586d1aVLFx04cEDjx4/XkSNH9P3332vBggWOoSGSHBMgHT58WKNHj9a1a9ey7N+01KR0bSvF33Vh6tSp2rx5s/bt26d33nlHUvwXMrlz51a7du00ZswY/fPPP/rnn380atQox/LUPsutW7dWTEyM43pn8+bN+uijjxzdnu8nPXr00K+//qqvvvpKR48e1VdffaVff/3V8fevW7dumjJlijZv3qywsDB99NFHqlSp0h3DFQYPHqw9e/bc0QPSldwfbfKZpGvXrnrllVcUExOj5557Tj169ND333/vWO7l5aURI0ZowoQJWrx4sRo3bqxu3brpzJkzkqRq1app3Lhxmjp1qsaNG6fixYtr9OjRql+/viRpwoQJ+uijjxzfaDdo0EAffPBBZu9munJ3d9fnn3+ujz76SG3btlWBAgX0yiuvqHHjxvrss88kxY8JePXVVzVixAjdvHlTPj4+GjZsmN555x2dOnVKDRs21IABA/Tpp5/q7NmzKlWqlD777DOVK1dO5cqVS3bZuXPnUq2jRYsWstvt+uuvvzRgwABJ8WOr33nnHU2YMMGSF10p7auvr69GjBihDh06yNPTU61atdKgQYMkxY+JHTx4sGbNmqXp06erXbt2d/XH+plnntEvv/yi1q1ba+vWrcmu17JlS/3zzz9q1KhRmvfVFXl4eGjmzJl6//331a5dOz388MPq3LnzHa23KX2uJWnmzJkaNWqUFi5cqGLFimn06NGqXr26JGnWrFkaOXKk2rRpowceeEDdunVT3759M31fXVX9+vXVpUsX5cqVSwMHDlSbNm2cel6PHj0UFRWlYcOGKTIyUtWqVVNQUJAefPBBxzpt27bVO++8oytXrujpp5/Wu+++m0F74ZoKFiyY4vl65cqVOn/+vFq0aJHoea+++qpWrlwpSXd0VR4zZswdXRqRWIsWLXThwgVNnTpVZ8+eVbly5TRt2jRH18H/nn9Teo/wPy1btlTfvn1lt9vVsWNH9evXT25ubpoxY4Y+/fRTzZkzR8WKFdPgwYP13HPPOZ5Xv359bd++XZMnT1alSpX01Vdf3ZeBSkr92vbFF1/U2bNn9frrr8vd3V29evXSzp07Hb0dBw8erGHDhumFF16Ql5eXunbtqvHjx8vDwyPV68NixYpp1qxZGjNmjNq2bas8efKobdu2GjhwoIlHxBxVq1bVuHHjNHnyZI0bN05ly5bVxIkT9eSTT0qSXnrpJV29elWDBg1SbGysAgICNHTo0Du2U7JkSb344osaN26cGjdu7JJj9W2GK7crA7jvTJ06VYcOHdLEiRPNLgVZzLZt29StWzft3r07Q8ZbVaxYUXPnzlXt2rXTfduu7tixY2ratKnWr1+faMIjwIpOnDihRo0aac2aNSpduvRdPXfIkCG6deuWxo0bl0HVZS0Jt/1KGDZz8eJFPfHEE1q3bp1KlCihtWvX6oknnnBMwhgaGqrOnTsnCsDA7Wi5BeAS9u/fr7179yooKEgTJkwwuxwAToqIiNDvv/8uDw8PxnUDuCsJkyW+9dZbstlsmjRpkqpWreqYJXzq1Kn67bff1KdPH0VHR2vs2LFq2LAhwRbJYgAWAJewZ88eDR8+XK1atbovW74Aq/rqq680YcIE9enTRzlz5jS7HAAWMnToULm7u6tjx456/vnnFRcXp2nTpjmWjxs3TidPnlSbNm30wgsvqESJEo5xt0BS6JYMAAAAALA8Wm4BAAAAAJZHuAUAAAAAWB7hFgAAAABgecyWDACACeLi4rRw4UJ9//33Onz4sG7evKnSpUurZcuWeuGFF5QjRw6zSwQAwFKYUAoAgEx269Yt9enTR3v27NErr7yiJ554Qjly5NDOnTs1ceJElSxZUnPnzpXNZjO7VAAALIOWWwAAMtmcOXO0bds2LV26VBUrVnQ8XqJECfn5+al58+bauHGj6tevb16RAABYDGNuAQDIRIZhaMGCBWrTpk2iYJugVKlS+umnn1SvXj1J0vbt29WxY0f5+vqqUaNG+uyzz3Tz5k3H+hUrVtSSJUvUpUsX+fr66qmnntLChQsdy4cMGaIBAwYoMDBQjz76qBYsWCBJWrRokZo1ayZfX1+1atVKy5cvz+A9BwAgYxFuAQDIRCdOnNDp06f1+OOPJ7tO6dKlZbPZtHfvXvXs2VNNmjTRqlWrNHLkSK1fv17Dhw9PtP64cePUpUsXLV++XDVq1NDw4cN18uRJx/LVq1erSZMmWrRokZo0aaIFCxZowoQJGjhwoH744Qf16tVLo0aNIuACACyNbskAAGSi8+fPS5Ly58+f6PHWrVsrPDzc8XOrVq107do11atXTz179pQUH3o//PBDde7cWQMHDlShQoUkSe3bt1eLFi0kSW+//bYWL16s0NBQFS9eXJJUsGBBdevWzbHt6dOn69VXX9VTTz0lKb61+NSpU5o+fbratm2bQXsOAEDGItwCAJCJ8uXLJ0m6cuVKosenT5+u2NhYSdLgwYMVExOjvXv36tixY/L393eslzAPZFhYmCPclilTxrE8T548kuTYlhQ/ljfBxYsXFRERoU8++UTjxo1zPH7r1i3Z7XbFxMQoe/bs6bCnAABkLsItAACZqFSpUnrwwQe1fft2R2urJBUrVszx75w5c0qSPDw81KZNG7300kt3bKdgwYKOfycVRm+/GULC9hK2KUkffPCBatasecfzsmXj0gAAYE2MuQUAIBO5u7urS5cuWrZsmcLCwu5YHhMTo4sXL0qSHnroIYWFhal06dKO/y5evKhPPvlE0dHR9/T63t7eKly4sE6cOJFou1u2bNHs2bPl5salAQDAmvgLBgBAJuvdu7eeeOIJderUSXPnztXBgwcVHh6uVatWqX379jp8+LAeffRRvfTSSwoNDdWYMWMUFhamP//8U4MHD1ZkZGSiltu71a9fP3311VdauHChjh8/rlWrVunjjz9O0zYBADAbfY8AAMhk2bJl0+eff64VK1Zo2bJlmj59uq5du6ZixYopICBAU6ZMcYyjnTFjhiZNmqQFCxbI29tbDRo00Ntvv52m1+/UqZNiYmI0e/ZsjRgxQoULF9bLL7+s3r17p8PeAQBgDptx+6AcAAAAAAAsiG7JAAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADL+z+k/XMez133CwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(16, 9));\n",
    "sns.boxplot(x = \"label\", y = \"mfcc4_mean\", data = data[[\"label\", \"mfcc4_mean\"]], palette = 'pastel');\n",
    "\n",
    "plt.title('Zależność między MFCC a gatunkiem', fontsize = 25)\n",
    "plt.xticks(fontsize = 14)\n",
    "plt.yticks(fontsize = 10);\n",
    "plt.xlabel(\"Genre\", fontsize = 15)\n",
    "plt.ylabel(\"mfcc4_mean4\", fontsize = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Korelacja między cechami średnimi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W procesie badania zależności pomiędzy dostępnymi cechami wykorzystana została mapa ciepła, która jednak pokazała, że w wielu przypadkach korelacje nie zachodzą, co jest szczególnie widoczne w przypadku średniej częstotliwości melodycznej cepstrum2 (mfcc2_mean), a jeżeli takowe korelacje zachodza to mają stosunkowo niewielkie wartości.Występowanie zależności widać w górnej oraz środkowej częsci mapy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_cols = [col for col in data.columns if 'mean' in col]\n",
    "mean_correlation = data[mean_cols].corr()\n",
    "\n",
    "\n",
    "mask = np.triu(np.ones_like(mean_correlation, dtype=bool))\n",
    "f, ax = plt.subplots(figsize=(16, 11))\n",
    "cmap = sns.diverging_palette(150, 275, as_cmap=True, s = 90, l = 45, n = 5)\n",
    "\n",
    "sns.heatmap(mean_correlation, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5)\n",
    "\n",
    "plt.title('Correlation Heatmap (means)', fontsize = 20)\n",
    "plt.xticks(fontsize = 10)\n",
    "plt.yticks(fontsize = 10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Korelacja między cechami wariancji"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Odwrotna sytuacja ma miejsce w przypadku mapy ciepła dla cech wariancji, w przypadku ktorych korelacja nie zachodzi wyłącznie dla dwóch parametrów czyli harmony i perceptr w środkowej cześci wykresu. Z kolei stosunkow wysokie wartości korelacji można zaobserwować dla parametrów \"skrajnych\", czyli pierwszych i ostatnich na liście parametrów.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_cols = [col for col in data.columns if 'var' in col]\n",
    "var_correlation = data[var_cols].corr()\n",
    "\n",
    "\n",
    "mask = np.triu(np.ones_like(var_correlation, dtype=bool))\n",
    "f, ax = plt.subplots(figsize=(16, 11))\n",
    "cmap = sns.diverging_palette(240, 10, as_cmap=True, s = 90, l = 45, n = 5)\n",
    "\n",
    "sns.heatmap(var_correlation, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5)\n",
    "\n",
    "plt.title('Correlation Heatmap (vars)', fontsize = 20)\n",
    "plt.xticks(fontsize = 10)\n",
    "plt.yticks(fontsize = 10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cechy średnie dają lepsze rezultaty niż cechy wariancji ze względu na mniejszą korelację pomiędzy poszczególnymi parametrami.(https://datascience.stackexchange.com/questions/9087/correlation-and-naive-bayes). \n",
    "\n",
    "W toku przeprowadzanych testów okazało sie, że dokładność stworzonego i wytrenowanego modelu zależy od rodzaju cech. W przypadku cech wariancji dokładność jest niższa niż w przypadku cech średnich. Z kolei najwyższą dokładność udało się uzyskać poprzez wykorzystanie kombinacji 8 różnych kolumn.\n",
    "\n",
    "- dla var_cols accuracy = 0.3875,\n",
    "- dla mean_cols accuracy = 0.4375,\n",
    "- dla ['mfcc4_mean', 'mfcc12_mean', 'mfcc9_var', 'mfcc1_mean', 'rms_mean', 'chroma_stft_mean', 'mfcc6_var', 'mfcc9_mean'] accuracy = 0.56125 \n",
    "\n",
    "Równocześnie uzyskane wyniki mogłyby mieć zdecydowanie wyższą dokładność jednak ograniczeniem okazał się specyfika samego datasetu, który posiada niewielkie zróżnicowanie wartości cech i niewielką korelację pomiędzy poszczególnymi cechami!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wykres punktowy przedstawiający zależność pomiędzy chroma_stft_mean (wysokością dźwięku) a mfcc12_mean (melowym współczynnikiem cepstralnym )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wykres ten pokazuje, że chociaż pojawia się pewna pula obserwacji odstających, to jednak wraz ze wzrostem wartości mfcc12_mean rosną wartości chroma_stft_mean. Tym samym zależność pomiędzy tymi dwiema wartościami, w ogólności, ma charakter liniowy, co potwierdza wynik uzyskany na heatmapie, gdzie korelacja wynosiła 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-bcf947f4e0ac>:3: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_subplot()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "chart = fig.add_subplot()\n",
    "ax = fig.add_subplot()\n",
    "colors = ['red', 'green', 'blue', 'brown','purple', 'gray', 'pink', 'black', 'yellow', 'orange']\n",
    "for genre in genre_dict:\n",
    "    genre_data = data[data[\"genre\"]==genre_dict[genre]]\n",
    "    ax.scatter(genre_data['chroma_stft_mean'],genre_data['mfcc12_mean'], c=colors[genre_dict[genre]-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Wykorzystanie algorytmu Bayesa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NaiveBayesContinues:\n",
    "    def __init__(self, X, Y):\n",
    "        self.classes = Y.unique()\n",
    "        self.priors = [] # prawdopodobieństwo każdej z klas\n",
    "        self.stds = [] #lista odchyleń standardowych każdej z cech dla każdej z klas\n",
    "        self.means = [] #lista średnich dla każdej z cech dla każdej z klas\n",
    "        for c in self.classes:\n",
    "            x_with_c_class = X[c == Y]\n",
    "            self.priors.append(len(x_with_c_class) / len(X))\n",
    "            self.means.append(x_with_c_class.mean(axis=0))\n",
    "            self.stds.append(x_with_c_class.std(axis=0))\n",
    "\n",
    "            \n",
    "    def predict(self, X, display_results=False):\n",
    "        y_preds = []\n",
    "        for x in X:\n",
    "            posteriors = []\n",
    "            for i, c in enumerate(self.classes):\n",
    "                prior = self.priors[i] # prawdopodobieństwo dla rozpatrywanej klasy\n",
    "                mean = self.means[i] # średnia cech dla rozpatrywanej klasy\n",
    "                std = self.stds[i] # odchylenie standardowe cech dla rozpatrywanej klasy\n",
    "                \n",
    "                posterior = 1 #P(X1|Yi)*P(X2|Yi)*P(X3|Yi)...\n",
    "                for j, feature in (enumerate(x)):\n",
    "                    P_X_yi = np.exp((-(feature - mean[j]) ** 2) / (2 * std[j] ** 2)) / np.sqrt(2 * np.pi * std[j] ** 2) #P(Xj|Yi)\n",
    "                    posterior *= P_X_yi\n",
    "                \n",
    "                posterior = (posterior * prior) #P(Yi)P(X1|Yi)*P(X2|Yi)*P(X3|Yi)...\n",
    "                posteriors.append(posterior)\n",
    "                \n",
    "            if(display_results):\n",
    "                print(\"posteriors\")\n",
    "                print(posteriors)\n",
    "                print(np.argmax(posteriors))\n",
    "                \n",
    "            y_pred = self.classes[np.argmax(posteriors)] # Wzięcie klasy z największym prawdopodobieństem\n",
    "            y_preds.append(y_pred)\n",
    "        return y_preds"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
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WX8SmpTf2X2V7feWaihj91U+Ift2Mp6rVkKP6dacLeWW2v2oiXv1CP1oNmxr6UR0OiXwHmvAX5+nnqEfG7nduVjHl694p4VesFfn/JK+ExPc//+0LQgso9oMwiuwDCRQVgdjDTzwY3DwAiwoPO6sQsDrnBvk6MQR8aAjszG9Ml8TF3XhEhLQM1WDRu1/hQud7eGbCjSgdNAV/bt2LxdXlilDvN7EeH5z+Oz5r2Ywlk29Wjv/91lO2tvq4fgGftB7HN0K0ShmLg+QQbbLD18N9Vk3E8i370Na6F6+tmIaBlWFUTW3ABx2bjdl9sRKyacdqjK+sxD0NJ038oueP4YMTqt+EbqoT98ImJZ5yE6nGuO6dEn6ZLoj8f5EjSol7Op10vR8Tin3vmbJEEiCBrBKg2M8q3oAUrts/C/Eg/gdJeMYNkSQIbe3pLSdEv/4Az80Yq4jTsshg3D3nRXxwTjvoH2fw7nMzMXqwcBgNIzykFjMW/gXvuUxGpZsDJTJzsTTFN19le/0bZzTgrw2P477qWxRGpaGBqJ4yB6t2nsC3WlZi4azcMHuCwXiqo3NtD1oXqisCVhMe4yWgYjrcmETppkBOCb8KDbrsZC6uuUCtCBUIbIr9AhkINoMESMAtAbPY5yyQW27FdZweflAR+6Hchz/MLe0ubJtZoghPV066WWtcrB1WJ9OsVZmTgs32+tO2XvS2VsUMShX71pdS3V6/7OdN+DxZrZJz7s+ebvGP2ZrkZyB+r/5wLE42GIW1n2I/x+PRffJlTB5QiQmNCZZDc9yeTKr7dH0NhL1heqnQM6nZ5tzudtQPL8cND23FWW1mxeYobvIhgeip3ahbs0b7/wx+M+E/DEdCVcgNxIiHl8SO+eNOHO/xYUfZZA8JmF8IS/pOxKtfelh8ARalz6gLe3s3M8BZ6cLFN/BgH+Gcqtuz9+DLj/bjmN+ddGV7/YwyC9tTjx7VHZqn4HWTS0TsOq6sO2x/srRVj5YkTLRWH/ZR2FOZr4g6FH4Cb3dJHePHjAlQ7GeM0H0B3QeexejKCox4ck8gBKmYOSukH6UeBaFiXCOOU/C7vzAL/EjdnjMyohY148Y7/h8+sKKgrskCRxvc5lki8RTSfSpr0CUHz7GNp7JWjVPBp1+pVVYXlJlZcQ8W4r98eHI/AqdCC2CfyV7/9jocShb+MsU26/e4XjfU4WP5XCkcqnXGXz5M/6w7RocnNeFzXz0DYy81xkrcSb1X/OsFgaIV+5++MEy1tdMdbVz+Lbu1BjXjZ2HhXz7E2RRmD3Uh2vveBhy/6sXQ5b+MQhP7gsjV95cpId+Y/TH/1wdbQAJ5IyDZsBtmAb4SP+mRE3blSjx2PcJLesWkeVYsXvrtWhbUTxvvDkTIU5O9/mzvozrp4VCtYv/0pqnKeMZHR7IZoivNeKq/6ni9osNHs/paV0xmd+VhuHFGtqHATQkIFKnY78FnO1bFzALu/pFJ+PcbMwfLDLMB3XxgDZbMngwxc6i8eYqXg9BA/KrJxQyKkmykHGIp+fkAZRQsRLEP9KB1yVBljEY1cGogwe+em0kg0ATk8IPK/Xp0Y3J750AQic2Qem5XnpRPzMn0V5u/wuX3l2NYFkxekjbD8wOybK8v2itWZYaXQ8SYX9F2BbjehU82/x7DtElINzbs4sVKXOvCcsCPVlN6+FhdX5nDj3o+qEVXYJGKffM4n9syVRLw1XB8K7b8CMWF6RzLtgcHVw1Xyh9ZJyXTMDfBl98KU+wDMGY4qqFNMPmSLxtNAiSQHgHdLEIXDtYIJ+mV6o+z9FXkuBCOOWh+9Hwzlmg+NRVD5+DVzzy2d8lBH+KqkO3Js/jyIqL3PDfjDlQqAj+CH0+fh1+P6qtoh2T2+sqY9xEJz7S8CXGdKPwN1lj7QQ6Vm4/RoNgH0Lrkh4bYd5dSPTaDIR4mTg5RwtZvvHBYqppucbzJx3B7W2fBin0A+ixHYLNmejuULI0EAkVANrsQ9+hiEvtiILsPLFNmhRnC0IPLursNi/urkXKyZgvf04Ueq5mZ5Oy8qM3BLOfaGTRNKUNplb8tB0yTruVhVDzdYspJ4MFIFnURFPtSqCrxUHAbtsz6Fmq/ZKqmFxfl/nS+j8JgufxJFLLYVxzDlKgQYcxtdrhRuuwrDyMBEvAPAev9uRhnCU9veUiZJaY5Y+bXbfTrY2hrPaEmDcu8OFMJl5oXqOY6VVPwuhQxSp+wKh3d4BBwogcHl1ejNFJrn9nYVFNhfynm1bhcjEzRi/3o+e2YrIlCIcqdTXJiQ2JN2GL3MPFtGKxYNx0/FbTYF7b7WpKSPg/t8qUNoyN87iQBEkhIwEgspNk8292fE54coB3nFSEZwdzmKwHqVZC6EnNqlsPDRk/8yZVFwLntM1BRNRGr96tZeP1Mxir2uSrv7WgWvdg30owrDrdJ7PUN9maHnUQvCXoYrNIshOoympLHD4Ut9gF9bEv61mLDF3kExapJgARySkDcm8R9Wf9PZ7+c4mdlKRAQOkEI/Q2af8P5lmcxfkAFSgdNwYYABfRIhsTqVO/GKTlZmdwfI1D0Yl+eASq5aQ7evhSDk/CT7LCT6CVBOsZXmewSdjp+R6GLfUjh99ykkI/vIbeQAAn4j0D8ZIybGOX+6ydbHAgC187gnRXTMLBSfTmNjJyCx9bvQ2eRJZWKfv1Xk5WFNQxpIMY6j50obrEfPYOmSWXG7I/bZSNTgo3yMJTY+ZaY+/IxQZ1VKnixL42vW1+MPP4WWTUJkIAnBGLhJ/WZfYp9T8CyEBLIGoE4sc8sup6yLm6xL838ioeCnggkGWHDPEeZ1a+xDdVpOIiFqlPPHnj9At5veBz3Vd+ivIiIRF6TFm1B5z+0ll2/gNbnHjBmAqrumYPNnyVrtff7PRX7pj5FMOi+JH0SjDauRt36fY7ZiPVIS736zWf6be8vAZZYgAQud76HBiknSFlkMO6cMger3v0iLuKH/IAVx5kyFN/1E4SMB64qoJWkgpYsxqMHV0KxiZey1oaHiGzHoxUHUX05vvtsB15bPRsT7/qJkihI3HNFeTMWbcEhT2cxKfYL8LJkk0jAkYB8LxL3hqLIeu1IxNudRS32v39/mTGrL5JZLGpJ7sSkhzRTZoxCA/HLP5+0DQ+li8xUYx13n3wZD97SV7HXW/iXnWhr3YuXnhiptFOYGf3t9CE8c2d/9B45C89u2Yf3dzyP+4f2VWz+XpU8+b29TOxL80zsX+lE47R+Sp+Xb9mH1+b/TO1vxXS8fs6u7pjzbWl5BHP3JB43w5FavHQxx5YdTG4LCoHrF/DOkrtUIR2pxaN/3ILm1v14o+FxjK5UkwGW3bEUe2VTxcsdeElJIPgMfqPFR1fubcpExkDcPedFTYh34eCqnxsiXT9GmBw8uuyPWPX2V0D0DN5asxp1Cx4xJR8UoYmfmDsWFeUDMeLhJVi/ZR/aWnfgpfm/0mKKqwkKH97+lUcjYRH76Uy4eNQSFkMCJOCSwLU2PN1bDXFKse+SWQqHFbXYN9nrh2dhp/wQtIF4fs9y46EpQl091mQv9MVDzzAPSsE5V0+GUj2zyTzTda3diPOrPGRFzH4h7K+dwVtzRhgvLG4jCdl0La1N3oh9NXRY2W1z8Kb2smKsipSHYWtrL/GQIxjYdUJ30hXcuJRvR4jbAkFAxNoWL8xCpIt42x9/Z+pW98k/xexhHbLJnm6KJRgUNrMfyrG/pfta1dQGfPBV4oRJ0cNrMVi0RfsfHjsPm0+Y2yQa2H14rRp1RDkugrF/9uCNXPKXUurni77pWuAXEihIAtLKoPjdJnu2F2QfCrhRRSz2zbM/Ygb+z60foq11v+X/Dry0dDbGD67UHlwis91KvGnz4DLGWbpo9SVsY1/CD9oy+ag6HLpqOciSC2BC4ynlAFnIih+HrTC2FOXlVy/EfvToWgwrj2Da1tis3sHl5YZIsOuTOEcXEgpfWZBYOiiH88oo/F60Ey8M72O0Sxcxnv21xFi2dINfScCRgBGTW9wHtPuD9QTZ/HDa9ovW3ep3+aXBYtoY/Xo7Jg+oQL9JDThuvUdZSpOX5JXVzW8sB0hf5ReM0lANNmRqkijdf5XfJ8W+RJsfSaBACfB3m9WBKV6xb7HXdxJtis3rw0vw7Pot6HCYzTJGqrsZT/VW01y7zdyozISFqvHb3TYPYak8+W336r4FxrK6kibb1uTFaBW6j/wJ9/+kEhU/necu6lDsVNtPmYt9zRzntnkx04LuNjzVX2WnhCOzCZlpmOaUh5Es0pEsOjIS+wDOH//A8iJofTHM4Hv7F/jW4aXFdgC4kQQEASPTpprN2970DZBfkp0c1vUVRkMoH1NXEZWVg6qJ2HAi8Yy+PiDy7y6p7e21Y6gfEnvB7zs/w8yZFA36MPAvCfiHAH+3WR2rohX7Ysa3Qlpmts+Amx57+UHnVux/+sp/ombKypjolaqW22qdyT7/iSpAjyUR+oB98g6pmpQ/Ziz2xYrF1LH4xcpYdmF5taKfbTIskZW4lzHDnmzc0hmLlEEU0AlOL63cFzMr8SsLu0vt9Cu1xu/BcSVRmuBI5rAuUtfr90fhK1S3YgKEf4y8AmfXFn2b/LtLKvYByKt5bo7X67H9G1DR4Ndrlu32/33Hbgxtf3uZbAzo7zYTJF6eW7Ri/9PGGuMB6cnSsTQq8oPOrdiXTo/7KLc12Ux23MnShnO7F+K+8bOw4t3/lram/zFjsR9XdRe2zSzRxiUCW1ODBKsccUVpG7wei0T1FMp2u5sytwXnYRt/ncVe4sU493mwCZ3/04Vv7f5fjK04yiuE8WWKLSJWvbrCpl8/I57c4xj5Si5H/t25Ee+Xdj4m3Y+HpB7BzFR5u8nRTwRf8NI5PysrfEeTZ0DVx4F/g/N79vNYyj85Tz5T7HuCMVEhRSr2zfb6Xme4lR90mYt9+WEeKSgnU8/FvmyOkCDB2fftdbEZx2ENOJ7E9MXbsUj0M+J2EsgTgWg7/tA/FsHCtXhwYxt/pQOLq2PmNankC5F/d27EvuxbI/qQkTN9NkVDtnx3Bk1Rgy7k6TJitSSQdwLZ/N3mvXP5b0Bxin1pdlg8WPo/uQdehnmWH3QZi32prcln43J7QXkt9uUoPDfOth+TVFc5PB2L3OJlbSSQnIDlAZnx/cZUYw/emfNvxox7KmGE5d9doMS+iQ+/kAAJeEbAci/zekXOs3b6tKCiFPuyDbwQ+8nsvlMeW+mizfTha2rr6OQz2XFtvdKJ1+ZPUOJZl906EQt3x6LexB2b4gZvxb4bW3x5lSMMNzONsui4re6wbU4Et93OyvK9Hv2p/b8zapvbPvC4gBGwROrK9H4j0zm9SdjtR1Bz12hD8Ds59srnyr87N2JfftFXHvLCKTjdf9L9V1np8NiMJ91m8TwSIAEHAvzdOsDJfFdRin05vr7X9vrKkEgXraPDnGX8Ln/egTZLVBa5rYns9Y+s/wXufGwXzlrKE3H4RQSNkSsPoEtLWOHl6oCnYl82R0hkYpDGKodsHpBRNJ5sLd/rTuJcxrdevfzukoDs3FoytAHHXZ7ndJgSkadPBcKTGnHsgtmcxy4crrWs1MS+8A/4V+OFovT2pdibyVKrdP+l2LeODL+TQIES4O82qwNThGI/u/b66mjF6hCJaT5OOoRqYinlwVQexoCntdBzllk725lsLVSl3YuAcHrrq9u1G/bwEaw+nLRBrg7wWuzr2fMSzQSms8ohR/fJyA7YFREeRAK5J6DmqlCdFkv61mKDTbhauVXd7WtxX+0S/C1R7PvudjwzvBylInmfthAo1yEmSJI5vKYk9o17k9qHO+ozW4EDk2rJw83PJOAPAhaxLzJvJwoj7I8OFVYri0/sS+HnhLhOZBue6TC1LvmhMlPlxs41en57LLulElFjK8RzOHrq5Vh2yQRL0UfWDVcyZr4a94BXXzh0E6VL22eo7dHFf6YdVMLl/QCJhHnKxUsz+/Zl9qB1yXIFv7cAACAASURBVFBj9s+tSY4Rk1/wy8Q0IOUO8QQSyBWBBJMFdtVf60RjbW/0vrcRn9s6t2tliZwfu8wmf3LirtIkJoWy2BchOxO/aJvb7iZfiF23zNtiky3qzH6G0X3MhfMbCZBANgho1gf6pKe9DshGxcVRZtGJ/e/fX2YIRnFRuVmSTudSEHGq3T5oop0vY3R5hXr8oCnYcPSftqHvVnSYW3K+eYGSffaehpM29t49+PKjQzj7D/HWcEZ5wIv2ZGTKYq4ens7sQ0uwpZm1rPhQrqwHR9bdbUThcRYP8nmA8dJ183y8nYlpgLlYfiOBwiJgynwbwZj6AzZJ2jRhXTURzyv3GEsXus/gnSV3Kb8z20mKa514YUwsi/TtDtP7ZrEfRmmk1vZlW2TP1eP5lyZql6WZyb9axH6m0X2SV8gjSIAEMiRgvWdQ7GcI1HJ64MV+9Otm/GnNGtStWYMlsydj+EBNVGuiMjxkIh5d9kdl/7NvnLARzRZiLr/KAt7W/EYuR2SQFMvmoSF4eONhXDjdgZeeGKk8IOfu/FCxuxdCXcx6bT76d5w5sgMNj45VnOfuqT+Ab2xn6GIV6CsEYok/fgUgdlyqn7wV+yIT6G787hYttvegKVi780O8/+ZGzJt8s+kFzfXyXvQMmiaVKee6dSxMlQGPJ4GCIXD9Alrrf6k44yv3i1sn4tHlL2LTO9LvaNAUrNsfi+kePbUbdctmY+JdP4mJbv3eOKkRx3u03kXPYNts1dFfmcTQjomMnIUF4v76x52xY8X8wtd/NVYrhZ/QjEfHKferOx9/ETuEU/o7m7FSuYeppjvhsfOw+bPkmXndsabYd8eJR6VM4HIn3m14HOMHVyrPlbLIYNz58Epsao/9plIukycoBOR7hrjHUOx7e2EEXuyf08xX9AdUZEQtasaNR8240cZDUd838g8HvAvBKdmNVug2+E5jd/kYXpv/K/VlJDQQIx5eiTdPfKeecf0CPtq4CPdV36LeYG6twaQ5f3R9gzlSP0Q9796meCdepzYl2ee52Bf1XT6GN1bMxGj9Zqr09UX810uPK30QY1Xmth+SyVa2VnCSIOJuEsg5gejFTry7cRF+PW48BlaGIQRJzfhZWPiXfei0rG4JP5iyW2u0e6K4L6r/xe/P9LDV7GntjhX30v7hJ0wrZ/KDWy/ncud7aJAmXFShtATrd56wWYXIBJvF4Zcz+5nA5LkagejZ7XjwlkrcNe8v6PjyO1w+fQxvPBd7ub5JBMlIMvFGmIkJyPcM8ZxXgpuQZ2JgKe4JvNhPkYenh+si252TrqdVxwoTqwZD1MQ4uv2+CCHZ8WXms2hZEfuxlps+yRFHJjSeMu1L9EV3zvUyAlGiurg9CYHrXfjso+PKKpT+cs2/6qy2E4ckVAt2t/zg1sV+Lhsr7k0yV/3el8s2sK4gEVBXi348vyVuQlCYounX2qiGk0HqdE77IltDCJ6pRDLMaUN9WhnFfhYHTjefETbmXkXASbW5V/ctMNvgauI/qWmRi4o8E/vabP6dU+Zg41Gbl5ArzXiqv2beI0UIcW5izAeg30O7FIdn5+O5N5sElEhKoWosarliPBj1ByT/Jhb92RyTbJadb7EvhywW15eXvkrZ5MayNQJiNXvzKvzm/nGx1alx4zFj4YvYcVRb8c4hLFmI3mxNwimFhO7Vbyn22TzCcthU31Ylh8kWv1nXK/i+7XFuG06xn1XeXdg2s0QRN33na+E0s1pffOEHV6kOdaHHdikzEmK2u+/NS/GhBzckT8S+5DysiL7Rjfjc0g0l4pBmI3zPOjtnZMsJ4qsezi9UowhMmyO4KYcElFWuTOOn57C9rCozAvkW+6YkXRT7mQ1mjs++3LEe991SoZif/XzOSsWnRDdh1ScGhI/H38zBorLayu/b6wyflviVetlHJH8Te1kFkIPCrWLfywSBOWh+wVdBsZ/lIdLjU7t2KvW4PadfqVVeNsJ/OIDuU5sweUClQxi81Cr3QuzLokDcyH9qWSbtPrAMwzSh329SA45fdddGPUzgDQ9tpR2lO2TZO0pbTcpWmNvsNZwlp0tA/l3nw4xH5BjRhaH4S+GQ7kjm9jz9fl89sxH7LXkgLh/bhN9Vh2LjGqnFhlxZzYiQtb+6CaWhgfhlo3XCiWLfi6vEiGCoPe/tcgd5UU+xlkGxn/WRF+Hu1PjwlXUeZbNKpc1XOvHS7JGKM3LZrROxouViKmc7HuuF2IfhRBvBoBkvmG7w3SdfxuQBavQkIfQPWZwLEzZON/upmogNJzxYwkhYEXe4ISBeeAc7xlp3UwqP8ROBfIt9zhL66WrR2trdjsVDQyi7Yyn2XrJvv57ZWX+RKwi77mvtWNxffQnJx4utPSn/beVqXHbHjGI/u3zV0q9o6eYDltjJE7GPHhxcNVyZramaulIJE9jWuhevrfiVGi0pNBC/WNmSwuy8nnwrAvv8A7kYcNYhE1Dsp6umY+c5eSs/B45A9AzeWrNaCWNsDZd705Qlyva6Fz/Mif+MEIX39pHCLNuYBwaOv887pAdUEEK+Yuj0hGGi9cAXuuCftt27Cax0EIp267ki3CZ7TKeeoJ+jmxzr4+qFX2HQmaXSP4r9VGhlcKwyIzGgIlDhpLwR+wJqDz7bsQq/mfAf2k0zglvHzMKC9VvQ8VVqM/NX31+GweVhjHhyTwovCBkMLE91JhDtROOY3mCeA2dMgdgrpbuPhTiOhfIUD/GczcQaK4aq83PJ0AYcDwTk4HbC6lTdJ0FgBWtizLyaaOk5coRD6ag6HHJpZhrcUUy/Z0JP6EK/VAvmkH5pPNNKgGLfSiSL37sPPIvRlRUIiu2yd2LfG+j6C1XV1EbXtv3e1MxSEhHQo1gw9GEiQtyeFQLRdvxBM60QAoLmFVmh7GmhVrFfmmA1RjYRy+kLZFxvtYzU5WGEx9VT6MfxSWWD7PcQRmmoBhs+S+V8HpuMAMV+MkIe79ft0Me6jBXvcfWeFvfp+hpExtZhv1tbek9rtxTW3a5kIR4xdys6/2HZx695IyAcxJnnIG/4i7his3jgNVj4l4KYGLhP89ESjrAPb7cPt1MoYv/0pqnKSvRNjzbxmZPp5SUlIeXLeaYw7c+n2Lfnwq0kQAKZEtDCqjJecqYgeX46BMxmAUPw/LF0SuE5OSXQfQaftO7HMQfzTTkMphCGrlfKRdktO/DSmjWq/0iyv+v3JTQF1aMGjVzSoiQKzCmjIFZ2rQ1P945FWsqZuV8QWSboE8V+AjDcTAIkkBkBfQbObcbjzGrj2SRgJnC6aVLMBrg87FnIYXMt/JZrAp821kjjGkFSB93Lx/Da/AlqwActrKN4SUj6P1SNFW1X4rqnmuPaBIC41o76cb9O6FgcVxA3GAT0Z4U+JnpeIOMAfsiYAMV+xghZAAmQgB0BEUpNmE9s+MJuL7eRQHYJWB05mUU3u7xzUrqWs0MXhb3vdc69crljLcbrpkFuBL5xjAgFHe/7pfqFVeKBjafQY+mwMEMaVTEdOy35ASyH8asNAWuo3Nufz1UCBZvGBHQTxX5AB5bdIoH8EtCyR49uwPHr+W0Jay9OAtbwm32f3KNkES9OGsHotUiWpoe5LK2aiOeP/jNhx3RTG+XFYNAULN/yMc7q/lzdZ3Bw3QNGWb1+VI+PE5ak7dBCaP/4iSY0t+5Hm+X/a4tGID67brJCuV8QsMbYZ9hN768Lin3vmbJEEiCBi2/gwT4VGNXAGRpeDHkiIIUCVQRfgugueWodq02VgJ4sUcy+h2owd7e9A68oVo/MJsa97I55+JvtoT14Z86/GeY8i1oSvzjgyjHUT7jROFZfWbD+ZYjhVAdVPd4UY1/kI+JjIz2QDmdR7DvA4S4SIIH0CIgZOPFAXn3Y4QGaXtE8iwRcEriAbdNKDYHWq998vF0IkcNctp6HSQSunUHTtH7qWIZq8Put8WY0xtHXOtFY21s5VpgROs3+yzPKE5ps3wiUYq1mJlaRr3+P1B82msEPbgmYf6clN/N36pZcKsdR7KdCi8eSAAm4INCFt2b1QuntdTiUWk40F2X7+5DLne+hYfYEDKxUHQTDQ2ox45kt6GB24awMrMmZkzOGWWGc/UJj8exLI7WYuyuxKBdtubT7/xjmOT+d3+JouiU7cdOnI/sjaVuDJScGV0dsKWW8kWI/Y4QsgARIwESguxlP9e4Lpo43UcHpLQ+hMlKL3zd9jLP/04UzR3agfvJN2mxl4rji5lL4LRUCVidd2gKnQq8wjtXj2Qsb/dX7/56kUZqvkGLqU41FLfHRdOQCWpf80Fj5YeI/mUzuPlt9a+icmx32FPvZ4cpSSaBoCVxtXgCmOzcPvxJarnww5jZbxIcwT5hSZpgn0FbVzC3jb5d345E+fQ1BF66jmUXGTHNYgO5k23vkLGw86mKZUBrvpOYgciInEWazgyaHORxaoyrleaFHQQolf0EzTuSHlAhQ7KeEiweTAAk4E+hB68IQSm9fir1u7aOvX8BHm1fhN/ePU8xbyiKDUTNuPGYsfBE7jn7nXJ1P9hrmJKGBmLvHLPjlh13F0y1xIf180sXCbGb0DJomaS9Twlnz3iacLcyWslUWArqTrXCw3fyZndC/gG2zx+DOJ3YZYxo9uhaDNeGYzBwkeupljO9Tob4IjqqnyaGFf66+Hlk3xHgZLwnPws5Luaq5uOqh2C+u8WZvSSC7BLrb8FT/vkhmK6s34nLHetx3SwWEwP/5nJWoWzYb91XfYtz8heNbeGyiaBp6KYX/V442YZ1dlhPKuAoBWPjdLagWUkwU1HC4akz0fDOeqg4pkXTe/DLBKVrW1Z89HbPLlx1pk5kRnn6l1rjPMPFfAsZZ33whtrLJl/Gs0qbYzypeFk4CxUVAPGwrQtXx5io2GPQl+uqZjdhvSURz+dgm/K46lj5dOOZt8HE4NpFwR7zUlA6agg0nzLOUFPs2F4eHm5RrUjcTKA9jRYeHhbMo7wlo8ezFjH5CoS/Ca3a+jNHlFRi14ZTRBtn+29HhVgrjmSwxl1E4P3hPQDK7EhM7DNXsPWK9RIp9nQT/kgAJZEzg4PJylNw0B28nW4rtbsfioWLmbin2JjhWWcbXl9nLwygZ1oDjGbew8AqQxWhoNhM/eT5C2mqTHh7RUQR6XjkLTImAHmIzNAS//cu+uMRVciKr1+b/TJmZ/+1uySxOssNPbBInRfdJkpgrpbbz4JQJmBzoGao5ZX6pnECxnwotHksCJJCYgJbK/kYXgtVkpz50Ol79wr7YI/Uxe04h1qZtv2h/oG+39uCdeZoDKZ0EszSKmh+JNrtPu/0sYc64WEmESysx+kua7V+bcKrntkxVQm+WVEzH69aQttcv4J2F1cpLgmunX61f0fMf4LkH/10pu2LodKyziQx0vm09Zs1Zib3O0UEzJhWUAmQTu1L6TWR1WCn2s4qXhZNA8RCIHhbOcRFXgtx0ky8Po89Du2Cx5FHAmWZ+ysMI2sy3cCgcpgmbkXWH6ZybpZ/L1X0LjNjrtiIwS/WyWPcEdJFuK+oTiH97h84eHFx1pzreItSttkKwfePTqildeQR3Pv4iPrC+CDg1VTEt6o8HNp7Et6c24d4+FSi5eSk+lC3yrrVjcX/V9NAxG69TPcW0T1qFEWN+Rz3vf9kcfor9bNJl2SRQRASEgHcrpKxiv3R0Iz63YSXbs4sHQqBMeTTb5NLyCMbwQWcz+h5uspjyBG+FyENWeSrq4PIfGA6zbgW/cj+4bt/g8y2vYN6MO1CpvShERk7Bo8v/gve+SD3Cl1hhDD22S0nQpUf86RV+wpSRWfchKKmYgtc5s28/KNJWnZcy1jThkchk5yPFfna4slQSKC4C2ixNsnB3OhTFYXWAFvYulDihVGDFvmGbPBAPN53ijL5+YWTxr/An0UVkopWkLFbPov1KQJmxH2A4duumhbr417t1btNU5foK1ISE3rks/JWjIZWObsDxBC9tWai6KIuk2C/KYWenScBbAvoszYSmFKa0us/gk9b9OPaVvBZubtf37XWG+YUQak7+AJc/78D2jatRt2ZN6v/X78PZJA+b6MVOfLRnM/7ksvxnt5xIIOI12+RQDRa1BM0HwTx+hfRNNTMLq4KsYjp22tmNFVKD2ZbCIHD9Aj5pPY5vxP1BMtUxZ2PuwluzeinXVmLH4MLoTkG0InoGjbW9jZdvhj7N/qhQ7GefMWsggcATEEmjSvpOxKuJYmKnScBIRqUsxSfwB7h2BtvmjjG9FOgzuK7/CufYNimqh9Te6NcxxzzX5QmTI1semj2xTRSQ79vXomZak605k9QcfkyXgEVg0JQnXZDFe57u+2E14ZFfAnhdJb8+TAnNqqZjZyr+E8mL5xE2BCj2baBwEwmQQAoENBHleZQTLbqPLrBt42Hr5jAJHPj0c53/Coe9Jhy/Gt/n09t/bzjQOpehzhgbx4QG4u5Fe+JWC05vmoq+VdPxqk1GUPFiQ/OS+DHwcsul7TOM2UTPr1cvG8qyCpKAbgpmDRSgr2yW9K1NGFmsIDuUp0bJkzhuEzDmqamBqZZiPzBDyY6QQH4I6LM0Xi/FXtr5WGy23mYmHNBD9WnRNSRzIEXUWUJZivJKQ9VY1GI/g2+lJ4R5hfYS0W/MHKx/9wt806MeJUx63lg4whCObkS6mkRssH388JbNSsZQa3Zda5v4PUMCkqOuEGYbEoR8zbAWnh5EAlL0mF9tNpsr6vb6vX5Uh0NJzAGDiCalPsmTOHTMTQldJgdT7GdCj+eSAAlAedCFavC8lxlupQyXpaEazN1tfrgK7OrLQAT3NBzGt6YHbBe2zSxByc3zTdEylFm5UA02fJZ80PTsvmKmvt+kBttZf1zrxAtj+iiC395kJ1ZP9+G1GK87JDusQkzbShv+GLXsfDqybrjxkhapP5ydSlhq8AhE2/F0bzW0pjkLs8iV8a/KNWWd8Q8ehMx7pJtCiXtreFITPjfduzMvnyXYE6DYt+fCrSRAAq4IXEDTlDJ4Gk1BNs0J1eD3W+2i1XThI+GMa+dYe/ENPNinAqbIQNFONI7pjThbW7s+Si8aZbfNwZvx7xnGWXK4QKfY2vJxhqlPnOiPYDW1p8E2ax8u7sYjA9REZr36zcc+G/OtrNXNgv1LINqOP2hx9GWxr2T61l7k+bKebHjViRjlHqgEKHC3ypqsVO5PToBiPzkjHkECJJCIgCasvUuIopvmhFEaqcXcXQ5KO0GbdLvsURtOxY64vBuP9OnrKk7/py8MM2Z+k5kmtS75oXHsb3fzwRUDXtif5Nn9lCJIFXa32LosE9Cvm9v+cEBZTbx8bBN+V63O9gsTQU9XN7Pcl3wULycR5Kx+bkeAYj+3vFkbCRQUgS+3P4Z1Hek3SbWNr8Hqw/9MvxDpTMNOvmoiVtuko5cOtf+oR1yx2OvriXCSxsCW7EmTmeYgegZNk8pUsW+pz75x3FowBKTVG9pZF8yoFH5Drl9A63MPY/hANUdIxdDpeOKRO5R7AK+jZMMnzJ3UFTVhmrmiw5tnRrJauV8lQLHPK4EEipSAbpeevlOoFlv69jocShwq3zVdvT29R87CxqPpFag7C5fevhR7u2JVK865LjLw6i8FyjJzgqy+RqnaaoE41m3mYONcfsg7Ad2pUowfzS/yPhyF34DrPfj2H9ZmxsxSBiw9kCCvhvWc4vwuIhaN76O+JP109h4wzUVurwOK/dzyZm0kUBAEZDvTpLPdiVqsid3b6g5n/JDT21N2xzxstglLCVzAttljcOcTu3A2UXsA6Nktrcm39FBvJUMbcNzhfP2lQAjAZP2SHc36PbSLDy8HrgW5S6ziDFez6pbcvJS2+wU5SAXSqIvNSrSs0vII5Eg8hoD1OkBBgXTbu2ZIs/pVExme1Duwrkui2HeNigeSQEAIXOnA4mpV5Ciz0rbJn5L39WrzgpRCWSYqMXpefZAKof9moqRc19qUSBg/e7oF0oS9uUgprKLVDlt3kE3moOt+trcHrQtjtrpzm2mvbx4Mf3zrbn/WyKNwe4OX4aT80X+20h2Bc1umGr45xr1FROOqFdG4Ipi2NXXfInc1B+OomK1+BBP+fDLjyaFgUMltLyj2c8ubtZFAfgnIkW6kaDCpmzFo4eYs5jIpd0578XAU+gD0pDUmp1tLZadfqdXs5+PDgOpiX7zcyJE0LEXg+/eXGQ91J4dbY0avPIyfzt4VlzzLWi6/FyoBkdFYDcVJU6xCHaP8tyt6eC0Gl4dxzwY1MpjIqv3MhBtRGhqIX1K8Og+QeOaIiG1KqM1GHNdylTifxL1eE6DY95ooyyOBgiWgRboJ1eDJN1/HYi2MnLgJpxwfWptFzyj7of7iERpin2iqdT/atP+vzf+Z8rBIKMAlx9pSGx+Cg6vUePiir2MbpSg91rGS7PCNGTzrMXq7y8MQLyl/46SelZC/vksrXf2f3JN45chfvWJrPSbw5Z7luP8nlcp9qOzWGkya8yLe++I7j2sJXnFiVURJTmibGDF4/S3UHlHsF+rIsF0k4DGBS80LUFkuklCJZdRYIhghgJOZt1ibotirh6qRvvmKFGJTWmEQbUn43yG0nR5uU5xrZ2svm+f0+lE9PrZ2yPgem+m1ja5x+Rgap/VT2thv4krsTUHon29bp4mFCAbNeAH7rR5q1y/g/YbH8ejKFq4UGOORmw+ymYFTvoTctIa1kEBACBg5LSKY0OQwyRKQ7hZyNyj2C3l02DYS8JRADz55c68hJGVnVCGSUxE5IhttyU1z8Pal9BpozPY4iXvLvpLwLOy0q08PtymOD1VjUUu8/fz37XXq7JJyTJIsutfOYNvMiPrSMWgK1u78EG2te/Ha6tkYXVmhLN3/YuVOHEvoPBDPRIk0VDUdG098h9NNk5SyrdE7vj+wTGljr35LsS+9YETxFXOLawJ62Fdr5mXXBfBAEiABiYCI1qZG3xnx5B7juSMdwI85JECxn0PYrIoEColA9Ou/YrIWCk2IfdchOK+1KyZA1og3qfRNtqFPOJNvFfvDGnDcLrV6tBMvDFfNdMKTEtiESpFXqqYmOEbuwPUufLJjFX4z4T+0l4QIbh0zCwvWb0HHVykqcc3EaOYu9SXkyLohiti3mk7pEYN637/VMeKQ3Ex+9pJAbLUpk2vbyxaxLBLwKwF9QqffpAYcZ5bqvA8jxX7eh4ANIIF8EbhgOE4Jwe02BKfqrBbBtO0X89Xw+Hp7uvDt/3Shx+5lQD/azTH6sR7+FTP2oR/V4ZBom+RboIt/tarYWDAqjIfwUy1K8scwj0+qBfF4EiheAnIoZfo0FcZ1QLFfGOPAVpBAXgjos8mK2HcZglOJZV81HTvP5aXJvqs0ev4YPjjxd6XduqlOnEmS5BicijmV72D4ocG6wy5jp/thtNjGQiOg/37okFtQI0OxX1DDwcaQQG4JmDLGloeRMAKN3qzoMdQPKUefB7cyiZTOxPXfWGx+qwmP8RJQMR07rY67rsvngV4RMGYm722iSZVXUFlOURBQzBRDNZi7O4XoBUVBJr+dpNjPL3/WTgL5JaDZ3+t288lEvP5ykHpc/vx2syBql1hbQ4jqKyxlP2/C5wXRWDaCBEiABEggKAQo9oMykuwHCaRFILUQnEKUlrg090mrOQE+SX9RKqmYgtdNk14xe/3KusMBJsCukQAJkAAJ5IMAxX4+qLNOEiggAlebF6hhJrXoNwltxqOdaBzTG2U0bUhr9ISpjlhB6XVDnTnOf3cznurdV9lnnfFPqyKeRAIkQAIkQAISAYp9CQY/kkBREpCcQ4UYTRSCM3rqZYzvU5Hcrr8oISbvtB7r3yr2jfjuFdPxOp2ek4PkESRAAiRAAikRoNhPCRcPJoEgEoiZkShReUQ8e5tunn6lVjHh2fCFzU5uSk5Aj/UfqsaKtiuAiOW/+fcYpq2ouA19mrwiHkECJEACJEACMQIU+zEW/EQCRUtAdxBVHHVD1Xj+pBWF9kIwOkFiK+vh/G5LIHr+Azw34w5UKgI/gh9Pn4dfj1JNeGivb4uMG0mABEiABDIkQLGfIUCeTgJBIKA7jypi3yYEp55td1RD3FtAELqfmz702CT9uvgGHlSyGEewqO2fuWkHayEBEiABEigqAhT7RTXc7CwJJCAghYUUgt8agvPS9hkoDdVg9WEK0gQEHTdfal6gmutUTcHrX8YO/bTxbtU5mismMSj8RAIkQAIk4CkBin1PcbIwEvAvgYPLy42oPL3CT+DtLr0vXdg2swSlo+px6H/1bfzrnsAFbJtWqrCVw5ZGT/xJcXgurZpuCcXpvmQeSQIkQAIkQALJCFDsJyPE/SRQJAQShuDUovXcVncYPUXCwutuHqkfojo3f6a+LZ1veRbjB1SgdNAUbDjK1RKvebM8EiABEiCBGAGK/RgLfiKB4iZgCcFZ8XSLIu4v7XwMpSKCTAdFadoXyLUzeGfFNAysDCsz/JGRU/DY+n3oNFZP0i6ZJ5IACZAACZCAIwGKfUc83EkCxUTAHIJTjQevZdi9fSn2UpgW08XAvpIACZAACQSEAMV+QAaS3SABLwjEheD8aK+S3fVnT7eAWt8LwiyDBEiABEiABHJLgGI/t7xZGwkUNAFrCM4hI0ajIlSNRS1XCrrdbBwJkAAJkAAJkIA9AYp9ey7cSgLFScASglOJu3/bPOy9VJw42GsSIAESIAES8DsBin2/jyDbTwIeE5BDcAqxf+PsPTTh8ZgxiyMBEiABEiCBXBGg2M8VadZDAj4hYA7BGcFvd9OExydDx2aSAAmQAAmQQBwBiv04JNxAAkVOQA7BWTUdO88VOQ92nwRIgARIgAR8TIBi38eDx6aTQHYIxEJw9ntoF77JTiUslQRIgARIgARIIAcEKPZzAJlVkIDfCOghOKdtvei3prO9JEACJEACJEACEgGKfQkGP5IACagEohc70dZ6CGf/cUYFEAAAAiJJREFUQSIkQAIkQAIkQAJ+JkCx7+fRY9tJgARIgARIgARIgARIwIEAxb4DHO4iARIgARIgARIgARIgAT8ToNj38+ix7SRAAiRAAiRAAiRAAiTgQIBi3wEOd5EACZAACZAACZAACZCAnwlQ7Pt59Nh2EiABEiABEiABEiABEnAgQLHvAIe7SIAESIAESIAESIAESMDPBCj2/Tx6bDsJkAAJkAAJkAAJkAAJOBCg2HeAw10kQAIkQAIkQAIkQAIk4GcCFPt+Hj22nQRIgARIgARIgARIgAQcCFDsO8DhLhIgARIgARIgARIgARLwMwGKfT+PHttOAiRAAiRAAiRAAiRAAg4EKPYd4HAXCZAACZAACZAACZAACfiZAMW+n0ePbScBEiABEiABEiABEiABBwIU+w5wuIsESIAESIAESIAESIAE/EyAYt/Po8e2kwAJkAAJkAAJkAAJkIADAYp9BzjcRQIkQAIkQAIkQAIkQAJ+JkCx7+fRY9tJgARIgARIgARIgARIwIEAxb4DHO4iARIgARIgARIgARIgAT8ToNj38+ix7SRAAiRAAiRAAiRAAiTgQIBi3wEOd5EACZAACZAACZAACZCAnwlQ7Pt59Nh2EiABEiABEiABEiABEnAgQLHvAIe7SIAESIAESIAESIAESMDPBCj2/Tx6bDsJkAAJkAAJkAAJkAAJOBCg2HeAw10kQAIkQAIkQAIkQAIk4GcC/x+MdHctmiZchgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wzór bayesa\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "\n",
    "Gausowski Naiwny Bayes\n",
    "Stosowany w przypadku pracy na danych o charakterze ciągłym.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W procesie trenowania i testowania modelu wykorzystany został skrypt losujący kolumny i zapisujący uzyskiwane wartości accuracy w celu znalezienia najbardziej efektywnej kombinacji cech. W ten sposób wybranych zostało 8 cech, w tym sześć cech należących do kategorii średnich i dwie do wariancji."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_np = X_train[['mfcc4_mean', 'mfcc12_mean', 'mfcc9_var', 'mfcc1_mean', 'rms_mean', 'chroma_stft_mean', 'mfcc6_var', 'mfcc9_mean']].to_numpy()\n",
    "X_test_np = X_test[['mfcc4_mean', 'mfcc12_mean', 'mfcc9_var', 'mfcc1_mean', 'rms_mean', 'chroma_stft_mean', 'mfcc6_var', 'mfcc9_mean']].to_numpy()\n",
    "\n",
    "model = NaiveBayesContinues(X_train_np, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_predicted = model.predict(X_train_np[:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ewaluacja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zbiór trenujący"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Train data) Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[27,  3, 11,  3,  1,  8, 18,  0, 10,  4],\n",
       "       [ 1, 75,  2,  0,  0, 10,  0,  0,  1,  0],\n",
       "       [10,  1, 29,  9,  1,  4,  2,  8,  6,  3],\n",
       "       [ 2,  0,  2, 39,  1,  1, 12, 12,  5,  4],\n",
       "       [ 0,  0,  0,  8, 36,  0, 11, 13,  8,  1],\n",
       "       [ 8, 20,  2,  0,  0, 45,  1,  0,  0,  6],\n",
       "       [ 1,  0,  0,  3,  2,  0, 71,  0,  0,  3],\n",
       "       [ 1,  1,  2,  2,  5,  0,  0, 63,  2,  0],\n",
       "       [ 1,  0,  8,  8,  6,  3,  3,  5, 48,  3],\n",
       "       [ 4,  0, 10, 15,  0,  6, 14,  5,  5, 16]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Train data) Accuracy:\n",
      "0.56125\n"
     ]
    }
   ],
   "source": [
    "Y_train_predicted = model.predict(X_train_np)\n",
    "cm = confusion_matrix(Y_train, Y_train_predicted)\n",
    "ac = accuracy_score(Y_train, Y_train_predicted)\n",
    "print(\"(Train data) Confusion matrix:\")\n",
    "display(cm)\n",
    "print(\"(Train data) Accuracy:\")\n",
    "print(ac)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zbiór testowy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 5,  0,  2,  0,  0,  2,  4,  0,  1,  1],\n",
       "       [ 0,  8,  0,  0,  0,  3,  0,  0,  0,  0],\n",
       "       [ 7,  0,  6,  6,  0,  4,  1,  0,  1,  2],\n",
       "       [ 0,  0,  2,  7,  1,  0,  2,  4,  5,  1],\n",
       "       [ 0,  0,  0,  2, 10,  0,  6,  1,  4,  0],\n",
       "       [ 0,  3,  0,  1,  0, 14,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  1,  0, 17,  0,  2,  0],\n",
       "       [ 1,  0,  0,  2,  1,  0,  0, 18,  1,  1],\n",
       "       [ 0,  1,  0,  0,  3,  1,  0,  4,  6,  0],\n",
       "       [ 5,  0,  3,  5,  1,  1,  5,  1,  0,  4]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:\n",
      "0.475\n"
     ]
    }
   ],
   "source": [
    "Y_test_predicted = model.predict(X_test_np)\n",
    "cm = confusion_matrix(Y_test, Y_test_predicted)\n",
    "ac = accuracy_score(Y_test, Y_test_predicted)\n",
    "print(\"Confusion matrix:\")\n",
    "display(cm)\n",
    "print(\"Accuracy:\")\n",
    "print(ac)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przykładowe porównania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y: 10\tPredicted: 10\n",
      "Y: 9\tPredicted: 8\n",
      "Y: 3\tPredicted: 1\n",
      "Y: 6\tPredicted: 6\n",
      "Y: 7\tPredicted: 7\n",
      "Y: 10\tPredicted: 7\n",
      "Y: 1\tPredicted: 1\n",
      "Y: 3\tPredicted: 6\n",
      "Y: 4\tPredicted: 4\n",
      "Y: 8\tPredicted: 10\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(f\"Y: {Y_test.to_numpy()[i]}\\tPredicted: {Y_test_predicted[i]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayes z wykorzystaniem gotowej biblioteki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle, os\n",
    "import typing\n",
    "\n",
    "class Bayes:\n",
    "    def __init__(self):\n",
    "        self.classifier = GaussianNB()\n",
    "\n",
    "\n",
    "    def train(self, X: pd.DataFrame, Y: pd.Series) -> None:\n",
    "        self.classifier.fit(X, Y)\n",
    "\n",
    "\n",
    "    def predict(self, X: pd.DataFrame) -> np.ndarray:\n",
    "        predictions = self.classifier.predict(X)\n",
    "        return predictions\n",
    "\n",
    "\n",
    "    def eval(self, Y: pd.Series, Y_pred: np.ndarray) -> typing.Tuple[np.ndarray, np.float64]:\n",
    "        cm = confusion_matrix(Y, Y_pred)\n",
    "        ac = accuracy_score(Y, Y_pred)\n",
    "        return (cm, ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:\n",
      "0.56125\n",
      "Test:\n",
      "0.475\n"
     ]
    }
   ],
   "source": [
    "bayes = Bayes()\n",
    "bayes.train(X_train_np, Y_train)\n",
    "\n",
    "Y_predicted = bayes.predict(X_train_np)\n",
    "eval_result = bayes.eval(Y_train, Y_predicted)\n",
    "print(\"Train:\")\n",
    "print(eval_result[1])\n",
    "\n",
    "Y_predicted = bayes.predict(X_test_np)\n",
    "eval_result = bayes.eval(Y_test, Y_predicted)\n",
    "print(\"Test:\")\n",
    "print(eval_result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# skrypt losujacy kolumny ze zbioru i sprawdzajacy accuracy na zbiorze trenujacym\n",
    "\n",
    "for i in range(100):\n",
    "    X = data.drop([\"genre\", \"label\", \"tempo\"], axis=1)\n",
    "    X_rand = X.sample(n=10, axis='columns')\n",
    "    Y = data[\"genre\"] \n",
    "    \n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X_rand, Y, test_size = 0.20, random_state = False)\n",
    "    \n",
    "    model = GaussianNB()\n",
    "    model.fit(X_train, Y_train)\n",
    "    Y_train_predicted = model.predict(X_train)\n",
    "    ac = accuracy_score(Y_train, Y_train_predicted)\n",
    "    filename = 'accuracy.txt'\n",
    "\n",
    "    if os.path.exists(filename):\n",
    "        append_write = 'a'\n",
    "    else:\n",
    "        append_write = 'w'\n",
    "\n",
    "    acc_random = open(filename, append_write)\n",
    "    acc_random.write(str(ac) + \" \" + str(list(X_rand.columns)) + '\\n')\n",
    "    acc_random.close()\n",
    "\n",
    "#!sort -k1,1nr -k2,2 accuracy.txt"
   ]
  }
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