{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вариантс2. [Набор данных для анализа и прогнозирования сердечного приступа](https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/heart_2022_no_nans.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 246022 entries, 0 to 246021\n",
      "Data columns (total 40 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   State                      246022 non-null  object \n",
      " 1   Sex                        246022 non-null  object \n",
      " 2   GeneralHealth              246022 non-null  object \n",
      " 3   PhysicalHealthDays         246022 non-null  float64\n",
      " 4   MentalHealthDays           246022 non-null  float64\n",
      " 5   LastCheckupTime            246022 non-null  object \n",
      " 6   PhysicalActivities         246022 non-null  object \n",
      " 7   SleepHours                 246022 non-null  float64\n",
      " 8   RemovedTeeth               246022 non-null  object \n",
      " 9   HadHeartAttack             246022 non-null  object \n",
      " 10  HadAngina                  246022 non-null  object \n",
      " 11  HadStroke                  246022 non-null  object \n",
      " 12  HadAsthma                  246022 non-null  object \n",
      " 13  HadSkinCancer              246022 non-null  object \n",
      " 14  HadCOPD                    246022 non-null  object \n",
      " 15  HadDepressiveDisorder      246022 non-null  object \n",
      " 16  HadKidneyDisease           246022 non-null  object \n",
      " 17  HadArthritis               246022 non-null  object \n",
      " 18  HadDiabetes                246022 non-null  object \n",
      " 19  DeafOrHardOfHearing        246022 non-null  object \n",
      " 20  BlindOrVisionDifficulty    246022 non-null  object \n",
      " 21  DifficultyConcentrating    246022 non-null  object \n",
      " 22  DifficultyWalking          246022 non-null  object \n",
      " 23  DifficultyDressingBathing  246022 non-null  object \n",
      " 24  DifficultyErrands          246022 non-null  object \n",
      " 25  SmokerStatus               246022 non-null  object \n",
      " 26  ECigaretteUsage            246022 non-null  object \n",
      " 27  ChestScan                  246022 non-null  object \n",
      " 28  RaceEthnicityCategory      246022 non-null  object \n",
      " 29  AgeCategory                246022 non-null  object \n",
      " 30  HeightInMeters             246022 non-null  float64\n",
      " 31  WeightInKilograms          246022 non-null  float64\n",
      " 32  BMI                        246022 non-null  float64\n",
      " 33  AlcoholDrinkers            246022 non-null  object \n",
      " 34  HIVTesting                 246022 non-null  object \n",
      " 35  FluVaxLast12               246022 non-null  object \n",
      " 36  PneumoVaxEver              246022 non-null  object \n",
      " 37  TetanusLast10Tdap          246022 non-null  object \n",
      " 38  HighRiskLastYear           246022 non-null  object \n",
      " 39  CovidPos                   246022 non-null  object \n",
      "dtypes: float64(6), object(34)\n",
      "memory usage: 75.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PhysicalHealthDays</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>4.119026</td>\n",
       "      <td>8.405844</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MentalHealthDays</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>4.167140</td>\n",
       "      <td>8.102687</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SleepHours</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>7.021331</td>\n",
       "      <td>1.440681</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeightInMeters</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>1.705150</td>\n",
       "      <td>0.106654</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeightInKilograms</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>83.615179</td>\n",
       "      <td>21.323156</td>\n",
       "      <td>28.12</td>\n",
       "      <td>68.04</td>\n",
       "      <td>81.65</td>\n",
       "      <td>95.25</td>\n",
       "      <td>292.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>28.668136</td>\n",
       "      <td>6.513973</td>\n",
       "      <td>12.02</td>\n",
       "      <td>24.27</td>\n",
       "      <td>27.46</td>\n",
       "      <td>31.89</td>\n",
       "      <td>97.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std    min    25%    50%  \\\n",
       "PhysicalHealthDays  246022.0   4.119026   8.405844   0.00   0.00   0.00   \n",
       "MentalHealthDays    246022.0   4.167140   8.102687   0.00   0.00   0.00   \n",
       "SleepHours          246022.0   7.021331   1.440681   1.00   6.00   7.00   \n",
       "HeightInMeters      246022.0   1.705150   0.106654   0.91   1.63   1.70   \n",
       "WeightInKilograms   246022.0  83.615179  21.323156  28.12  68.04  81.65   \n",
       "BMI                 246022.0  28.668136   6.513973  12.02  24.27  27.46   \n",
       "\n",
       "                      75%     max  \n",
       "PhysicalHealthDays   3.00   30.00  \n",
       "MentalHealthDays     4.00   30.00  \n",
       "SleepHours           8.00   24.00  \n",
       "HeightInMeters       1.78    2.41  \n",
       "WeightInKilograms   95.25  292.57  \n",
       "BMI                 31.89   97.65  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., 15.,  4., 25.,  5., 30., 27.,  3.,  2.,  1., 10., 20., 21.,\n",
       "        6.,  7.,  8., 14.,  9., 12., 18., 29., 28., 17., 11., 16., 13.,\n",
       "       26., 22., 24., 19., 23.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned = df.drop(columns=list(filter(lambda x: not pd.api.types.is_numeric_dtype(df[x]), df.columns)))\n",
    "df_cleaned['MentalHealthDays'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим лингвистические переменные\n",
    "\n",
    "Входные:\n",
    "- SleepHours - количество часов сна\n",
    "- BMI - индекс массы тела\n",
    "\n",
    "Выходные:\n",
    "- HadStroke - наличие сердечного приступа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skfuzzy import control as ctrl\n",
    "\n",
    "sleep = ctrl.Antecedent(np.arange(df[\"SleepHours\"].min(), df[\"SleepHours\"].max() + 1, 1), \"SleepHours\")\n",
    "bmi = ctrl.Antecedent(np.arange(df[\"BMI\"].min(), df[\"BMI\"].max() + 0.1, 0.1), \"BMI\")\n",
    "had_stroke = ctrl.Consequent(np.arange(0, 1 + 0.01, 0.01), \"HadStroke\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Произведем настройку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nspotapov/ulstu/AIM-PIbd-31-Potapov-N-S/lab_7/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2EAql4YiNulBdbCBkLVI+Z2ByzCAaUGGvSC4j5PYAggAxP/7/iImIstLB74FnR2T+da9fA3TpH/flp5xySvSfDQYDOnbsiL59+0YfKy0tBQDs378fW7ZswdChQyEIQvT7w4cPh8fjwZ49e3D48GEEAgEMGTIk+v0OHTrghBNOiH799ddfIxwO4/jjj49Zh9/vR8eOHaNfm83mmLUBQHV1Ne6++25UVlZi//79CIfDqKurw65du1r9Gb/66iusXbs2JgMUDofh8/lQV1eHvLzs+Juj20BISsM+QtGT5yMyWBoDlIbpZI7ZkLweiHZ7zH9kREQ5qeR4JSjR4nUTYDKZYr4WBCHmscj/HqvVrOzxeGAwGLBhwwYYDLH/x9/eqD/UZrM1+VswefJk/Pzzz3j88cfRtWtXWCwWDB06FIFA68M5Ho8Hc+bMwa9//esm37Nm0XFOug2Ewk16hNTZWTo6Og8AlkIAQub2ErK78EX1Fwk/L+x2Q+TEGBG1B+a8hDIzueDEE0/E//3f/0GW5WiQsnbtWjgcDjidTnTo0AEmkwmfffYZjjvuOADA4cOH8f3332PECCU7duqppyIcDmP//v0444wzEnr9tWvX4qmnnsIFF1wAANi9ezcOHjwYc43JZEI4HI55bMCAAdi6dSt69szuUwt02ywdPXRVlhtKYypkhILHZIREEbAWZm53aYcTe9x7IDfuf4qD5PHCwP4gIqKsdMMNN2D37t246aab8N133+HNN9/E7NmzMWPGDIiiCLvdjmuvvRa33XYbPvjgA2zevBlXX301xEZHRx1//PGYMGECJk2ahNdffx07d+7E+vXrMX/+fCxfvrzV1+/Vqxf+9re/YcuWLfjss88wYcIE2Gy2mGu6deuG1atXo6qqCocPK//nf9asWfjrX/+KOXPm4JtvvsGWLVuwbNmyaG9UttBtIBTtEZIb0o4qjc/H9AgBGd9U0Rf24WD9wbYvbkRyuzkxRkSUpSoqKrBixQqsX78e/fr1w29/+1tce+21MQHFn//8Z5xxxhkYM2YMRo0ahdNPPx0DBw6Muc8LL7yASZMm4Q9/+ANOOOEEXHLJJfj888+jWaSWPP/88zh8+DAGDBiAiRMn4ve//z06d+4cc80jjzyCVatWweVy4dRTTwUAjB49Gm+//Tbee+89nHbaafjlL3+JRx99FF27pnd4KFGCnGj6IMfV1taisLAQs15bjzn/7zQg5Afu7wxc+gzQ74qU7n3ua+dibI+xmHbqtKMPPjsSKDsFuPgvqS08DtsOb8Ov3/o1Xjr/JQwoHRD383ZPmwY5EMBxzz6bxtUREaknGAziwIED6NSpU5N+G8pdWryvus0IRUtjUkNNU4V9hLwBb2xpDFAmxzJUGquwK4emJtowLbk9MNiZESIiIv3RbSB0tDTWEAiJqf0qZFmGN9RMIJTB0lieKQ8ltpKER+glj4e7ShMRkS7pNhCSoxmhhq2+U2yWrg/VQ5KlZnqEijM2NQYkd/hq2ONmIERERLqk20AomhGK7NGQYmmsycnzERksjQHJBUKSx8sDV4mISJd0GwhFj9hQKSPkCSrHVDRfGjuS0r0T4bQrI/SJkDweHq9BRES6pNtAKBQ+tkdIpYxQc6Ux/5GjTdlp5nQ4cch3KLqetsiBAGS/n+PzRESkS7oNhMJNMkKpBUItZoQi5435MpMVipxCH29WKOxVAibRnt/GlURERO2PfgMhlcfnvYGWMkJFyucMNUw7Hcppw/EGQpLbDQAwMCNEREQ6pNtAqMk+QmnrESpWPmdohL6jtSNsRlvcDdOSR1k3e4SIiEiPdBsIRUtjKvYImUUzTIZjdsKMlsYykxESBCGhybGwWwmEODVGRJQZI0eOxM0339zi9wVBwBtvvBH3/SorKyEIAmpqalJemx7x9HmVpsaanDwfES2N1aR0/0S4HK64d5eWvA0ZIe4jRESUFfbt24fi4mKtl6Eb+s0INekRSu1X0eTk+QizXQmyMripotPujL801tAjxKkxIqLsUFZWBovFovUydEO/gZDK+wg1e/I8AAiCJpsq7vPsQyjys7Ui7PEAJhMEszkDKyMiIgCQJAm33347OnTogLKyMtx7773R7x1bGvvkk0/Qv39/WK1WDBo0CG+88QYEQcCmTZti7rlhwwYMGjQIeXl5GDZsGLZu3ZqZHybH6TcQathQGnLDP6gwPt9sRgjI6HljgBIIheQQqrxVbV4ruT0w5OdDEIQMrIyIiADgpZdeQn5+Pj777DP86U9/wn333YdVq1Y1ua62thZjxoxB3759sXHjRsydOxd33HFHs/e866678Mgjj+CLL76A0WjENddck+4fo13QbY+QpHaPUKCFjBDQcN5YTUr3T0RkhH63e3f0n1sieT0sixFRu1EfqsfOIzsz/rq/KPwFbEZb3NefcsopmD17NgCgV69eePLJJ7F69Wqce+65Mde9/PLLEAQBixcvhtVqRZ8+fbB3715MnTq1yT0feOABjBgxAgBw55134sILL4TP54PVak3hJ2v/dBsIhVTeR8gT9KBzXufmv5nh0li5vRwGwYDd7t0YiqGtXht288BVImo/dh7ZiXFvj8v467560avo07FP3NefcsopMV+Xl5dj//79Ta7bunUrTjnllJhgZvDgwW3es7y8HACwf/9+HHfccXGvS490Gwg1zQilPj7fckaoCKj9KaX7J8IkmlCWXxbX5Jjk8cLAQIiI2olfFP4Cr170qiavmwiTKXarFUEQIEUOAU9S43tG2h1Svace6DYQSsc+QvnmlnqEioHqb1O6f6KcjvgOX5U8HmaEiKjdsBltCWVmst0JJ5yA//3f/4Xf749Okn3++ecar6p90XGzdCQj1BAtq1AaazEjlOHSGIC4N1UMe9wQuZkiEVFWuvLKKyFJEq6//nps2bIF7777Lh5++GEA4JCLSvQbCKVhfL71qbHM7SMENGyq6N4DOfJztoClMSKi7FVQUIB//etf2LRpE/r374+77roLs2bNAgA2QatEt6WxaI+QCqWxYDgIf9jf+tRYsA4IBQBjZvbrcTlc8AQ9qPHXoNja8g6lktsN0c6pMSKiTKmsrGzyWON9g479P7DDhg3DV199Ff166dKlMJlM0SbokSNHNnlO//792/w/wqTQbSDU9IiN5AMhb7CFk+cjoueN1QD2FibLVOa0Hx2hbzUQYo8QEVFW++tf/4ru3bujoqICX331Fe644w5cfvnlsNniH9enljEQUmF8PnryfIvN0kXK5/rDGQuEXA4XAGCPew9O6XRKs9fIsoywx8MDV4mIslhVVRVmzZqFqqoqlJeX47LLLsMDDzyg9bLaDf0GQvIxgVAKPUKRjFC+sZWpMSCjmyrazXYUW4pbbZiWfT4gHGZGiIgoi91+++24/fbbtV5Gu6XbZulQWL0eoTYzQo1LYxnkdLR++Go4cuBqPgMhIiLSJ90GQpKKU2Nt9gg1Lo1lkNPhbHVTRcmjrJulMSIi0ivdBkJNe4SS/1W0GQiZbIDRmtHSGND2XkKSpyEjxNIYERHplOaB0MKFC9GtWzdYrVYMGTIE69evb/X6xx57DCeccAJsNhtcLhduueUW+Hy+hF835ogNwQCksDGVJ+iBAKH1A/c02lRxf91++ELN/34kj1LS46GrRESkV5oGQq+++ipmzJiB2bNnY+PGjejXrx9Gjx7d7MFzgHIK75133onZs2djy5YteP755/Hqq6/ij3/8Y8KvHT10VZZSP16j4eT5Vnf5tBVnvjTWMEL/k6f5c87C7oZAKL+F3iYiIqJ2TtNAaMGCBZg6dSqmTJmCPn36YNGiRcjLy8OSJUuavf6TTz7B8OHDceWVV6Jbt24477zzMH78+DazSM2J6RFKcVdpT9DTcqN0hK1Ik9IYgBbLY5GMEHeWJiIivdIsEAoEAtiwYQNGjRp1dDGiiFGjRmHdunXNPmfYsGHYsGFDNPD54YcfsGLFClxwwQUtvo7f70dtbW3MB9AoIySFUz5nrNWT5yOsRRnPCHXK6wSLwdJyIOT1QLBaIRxzCjIREemLIAgxu1sfq7KyEoIgoKamJu573nvvvejfv3/Ka0s3zfYROnjwIMLhMEpLS2MeLy0txXfffdfsc6688kocPHgQp59+OmRZRigUwm9/+9tWS2Pz58/HnDlzmjwemZ5XMkKpH7iaZ8pr/SJbMXBoR0qvkyhREFFhr2gxEAq7eeAqERG1bdiwYdi3bx8KCwu1XorqNG+WTkRlZSXmzZuHp556Chs3bsTrr7+O5cuXY+7cuS0+Z+bMmThy5Ej0Y/duJSiIOWss1R6heDJCGpTGgIbDV1sYoZc8Xhi4hxARUU4LBoNpfw2z2YyysrJ2eeK9ZoFQSUkJDAYDqqurYx6vrq5GWVlZs8+55557MHHiRFx33XXo27cvLr30UsybNw/z58+HJEnNPsdisaCgoCDmAzhmfF6F0liLJ89HaFAaA1ofoZc8bk6MERFl0LPPPosuXbo0+Zs1duxYXHPNNQCAN998EwMGDIDVakX37t0xZ84chEKh6LWCIODpp5/GxRdfjPz8fNx///3o2bMnHn744Zh7btq0CYIgYPv27XGt7eDBg7j00kuRl5eHXr164a233op+r7nS2OLFi+FyuZCXl4dLL70UCxYsQFFRUZP7/u1vf0O3bt1QWFiIK664Au6GzXyzhWaBkNlsxsCBA7F69eroY5IkYfXq1Rg6dGizz6mrq4Moxi7ZYFCCmGRO2ZUkWQmEVGiWbjsjVKyMz2f4NGCnw4m97r2Q5KaBYtjjgWjnxBgRUaZcdtll+Pnnn/Hhhx9GHzt06BBWrlyJCRMm4KOPPsKkSZMwffp0fPvtt3jmmWfw4osvNjlb7N5778Wll16Kr7/+Gtdeey2uueYavPDCCzHXvPDCCzjzzDPRs2fPuNY2Z84cXH755fjPf/6DCy64ABMmTMChQ4eavXbt2rX47W9/i+nTp2PTpk0499xzmz3/bMeOHXjjjTfw9ttv4+2338aaNWvw4IMPxrWeTNH0rLEZM2Zg8uTJGDRoEAYPHozHHnsMXq8XU6ZMAQBMmjQJFRUVmD9/PgBgzJgxWLBgAU499VQMGTIE27dvxz333IMxY8ZEA6JEhCQZZjVKY4E4MkK2IiAcAIL1gLmNfiIVuRwuBKQA9tftR1l+bKZNcns4MUZE7YpUXw//Dz9k/HUt3btDjOM0+OLiYvzqV7/Cyy+/jHPOOQcA8Nprr6GkpARnnXUWzjvvPNx5552YPHkyAKB79+6YO3cubr/9dsyePTt6nyuvvDL6txIArr76asyaNQvr16/H4MGDEQwG8fLLLzfJErXm6quvxvjx4wEA8+bNw1/+8hesX78e559/fpNrn3jiCfzqV7/CrbfeCgA4/vjj8cknn+Dtt9+OuU6SJLz44otwNFQfJk6ciNWrV2fVobGaBkLjxo3DgQMHoqfq9u/fHytXrow2UO/atSsmA3T33XdDEATcfffd2Lt3Lzp16oQxY8Yk/QuVZFm1Zmm7OY6pMUApj2UwEHI6lL2Edrt3Nw2EPB4YO3XK2FqIiNLN/8MP+PH//Sbjr9vt/16D7aST4rp2woQJmDp1Kp566ilYLBYsXboUV1xxBURRxFdffYW1a9fG/F0Lh8Pw+Xyoq6tDXp7y92PQoEEx9+zSpQsuvPBCLFmyBIMHD8a//vUv+P1+XHbZZXH/DKecckr0n/Pz81FQUNDivn5bt27FpZdeGvPY4MGDmwRC3bp1iwZBAFBeXt7iPbWi+enz06ZNw7Rp05r9XmVlZczXRqMRs2fPjomKUxGKlMYyMT4fOYHeVwMUVqT0eomosFdAgIA97j04rey0mO+FPW4er0FE7Yqle3d0+7/XNHndeI0ZMwayLGP58uU47bTT8NFHH+HRRx8FAHg8HsyZMwe//vWvmzzParVG/zm/mY1wr7vuOkycOBGPPvooXnjhBYwbNy4aOMXDdMxWKoIgtNh/q+U91aZ5IKSlsAo9QpIsxdcsHT14tSbp10qGxWBB57zOzTZMSx4vD1wlonZFtNnizsxoxWq14te//jWWLl2K7du344QTTsCAAQMAAAMGDMDWrVvj7utp7IILLkB+fj6efvpprFy5Ev/+97/VXnrUCSecgM8//zzmsWO/zhUMhFLsEaoP1UOGHN/UGKDZ5Nged9MResnthsjxeSKijJswYQIuuugifPPNN7jqqquij8+aNQsXXXQRjjvuOPzmN7+Jlss2b96M+++/v9V7GgwGXH311Zg5cyZ69erV4uCRGm666SaceeaZWLBgAcaMGYMPPvgA77zzTk6O1+fUPkJqUzJCqfUIRU6ejzsjlOGDV4HmR+hlSYLk9XJDRSIiDZx99tno0KEDtm7diiuvvDL6+OjRo/H222/jvffew2mnnYZf/vKXePTRR9G1a9e47nvttdciEAjENFKnw/Dhw7Fo0SIsWLAA/fr1w8qVK3HLLbfElO9yBTNCKfYIeYLKeV1t9ggZTIDZrsmmik6HEx/s/iDmMamuTlkWe4SIiDJOFEX89FPzB2KPHj0ao0ePbvG5rW0Xs3fvXphMJkyaNCmh9TR3z8Z7Bo0cObLJNVOnTsXUqVNjvm5c0rv33ntx7733xjzn5ptvxs0335zQ2tJN34FQdGos+V+DNxBnRgjQdFPFI/4jqA3UosCsbCgpNWxoxQ0ViYhyn9/vx4EDB3Dvvffisssua3J8VTo8/PDDOPfcc5Gfn4933nkHL730Ep566qm0v67a9F0aC8uALKVUGotmhNoanweObqqYYZFT6Bv3CUVOnmePEBFR7nvllVfQtWtX1NTU4E9/+lPM95YuXQq73d7sx0kpNJavX78e5557Lvr27YtFixbhL3/5C6677rpUf5SMY0Yo1YxQQ49Qm6UxQNPzxgBlL6E+HfsAAMJuJRDi1BgRUe67+uqrcfXVVzf7vYsvvhhDhgxp9nvHjrcn4u9//3vSz80m+g6EJKmhRyj5xFgkI9Tm6fNAQyCU+dJYgbkADpMjNiPkbcgIsUeIiKhdczgcMZsaUix9l8YkqJIRshqsMIlxRNXWIk1KY4IgwOlwxkyOsUeIiNqDZM6ZpOylxfup60AoJEkp7yPkCXjiywYBmpXGgKZ7CYUjPUIJ7DpKRJQtIudLBgIBjVdCagqHwwDQ5ID1dNJ1aUySkPLO0t5QHMdrRNiKNSmNAcoI/eaDm6NfS24PxPx8CBn8l42ISC2iKCIvLw+1tbUAALPZnJOb+dFRsiyjtrYWZrOZgVCmhCI9QimOz8c1Og8cLY1JEpDhAMTlcKGqrgrBcBAmgwmS18OyGBHltMLCQgCIBkOU+wRBQMeOHTMa1Oo6EJLkyBEb5qTvEdfJ8xG2ImVcP+AGrIVJv2YyXA4XJFnCT96f0LWgK8JuD0R7nAEcEVEWEgQBRUVFKCgoiJZUKLcZjcaMZ/Z0HQip1Swdd0YocgJ9fU3GAyGnwwlAGaHvWtAVkscDg50ZISLKfaIoZrSUQu2Lrv/NiZbGUjxiI+4eocjBqxpMjpXllcEoGqMN05LHzdF5IiLSPV0HQqo0SyeUESpSPmvQMG0QDaiwV0RH6MMeDw9cJSIi3dN1IKTW+HxCU2OAZiP0jfcSkjxeHrhKRES6p+tASIoesZF8IJRQRshSCEDQpDQGAE67E3s8DaUxtxsie4SIiEjndB0IhcJyyj1CCQVCoghYCzTbSyiyqaIsywh7OTVGRESk60BIyQgl3yMUCAcQkALxj88DDZsq1iT1eqlyOVyoD9XjZ9/PkNweGLiPEBER6ZyuA6GQJKfUIxQ5eT7ujBCg2XljQKMR+sM7Ift8EPPZI0RERPqm60AoLMkp7SMUOXk+7mZpQLMT6AGlRwgAfqreAYAnzxMRETEQksKAkNyvIZIRSiwQ0q40lmfKQ4mtBNUHdgIADByfJyIinWMglEpGKKBkhHKlNAYoWaGDB3YBYEaIiIiIgZAspdwjlHiztDalMUBpmK45tA8AOD5PRES6p9tASBSAsKxOj1BCGSFbEVB/JKnXU4PL4YL7cDUAcHyeiIh0T7eBkEEUG/UIJZ8REgURVoM1/idZiwD/EeV1NeB0OBFy1wIAx+eJiEj3dBsIiWLjHqHkm6XzTfkQBCH+J0WO2fBpkxVyOVzI8wMwiBCsCQRwRERE7ZBuAyGjKDT0CCW/oWJCJ89HaHjwKqBkhGwBQMqzJhbAERERtUO6DYREIZIRklIqjSXUHwQopTFAsxH6jtaOKAgaEbSZNHl9IiKibJJcKqQdMIpi6s3SiZw8HxEtjWmTERIEAZ2kfNRbNHl5IiKirKLjjJCAcDj1IzbyzQlmhKKlsZqkXlMNHcJWeM2SZq9PRESULXQbCBlEoVFGKLlAKKkeIbNdKcVpuJdQYdCEI8agZq9PRESULfQdCIUlZUPFFHqEEg6EBEEpj2m4u3R+QMBhow8hKaTZGoiIiLKBrgMhKRIIpDA1lmfKS/yJtiJNS2NWvwSvWUZ1XbVmayAiIsoGug6E5HDDpoYp9AglnBEClMkxDQMhU30IdRZgt3u3ZmsgIiLKBjoPhFLLCCU1Pg9oXhoTvPXwWUQGQkREpHv6DYQEAVLkmAsh8V+DJEvJZ4Q0Lo3J9fUw2wsYCBERke7pNhASRUGZGAOSygjVBesAIPHxeaChNKbN1JgsSZD9ftgdHbDHvUeTNRAREWUL3QZCSkYoEggl3iMUOXk+uYyQdqUx2e8HABQUdGIgREREuqfbQEgUBSCFHiFv0Asg2UCoSLPSmOTzAQCKCkqx270bsixrsg4iIqJsoNtAyCg27hFKPiOUdLN00AuEAok/N0VyQyDUsagcnqAHR/xHMr4GIiKibKHbQMggNM4IJR4IeQNKRiipQChy8KoG5TGpXgmEOhVXAOAIPRER6Zt+AyFRgCwlv49QahmhIuWzBuUx2VcPAOjcwQWAgRAREembvgMhuSEjlERpLNIjlHRpDNBkckzyKc3SdnsHFFmKsMfDhmkiItIv3QZCogggurN0cs3SNqMNxmQ2Y9SwNBbJCAk2G1wOFzNCRESka7oNhJSMUGqlsaSyQUCj0pgWGSGlR0i0WuF0OBkIERGRruk6EBJSaZZOdldpADDZAKNVox4hJRASrFY47QyEiIhI3/QbCAmpj88nnREClPKYhlNjosUCl8OF/XX74Q/7M74OIiKibKDfQEgUIMgp9AgFUsgIAQ2bKmpRGqsHTCYIJhNcDmVybK97b8bXQURElA30GwgJAuSwRj1CgDI5pklpzA/RYgGAaCDE8hgREemVfgMhUQDk1I7YyMnSmK8egs0KAOiU1wlm0cwReiIi0i3dBkKiKACR0piQ+K8h5UBIo9KY7PNDtNoAAKIgcnKMiIh0TbeBkFEUIEjJ9wh5gh7Yzan0CGlTGpN8PohWS/Rr7iVERER6pttASBQFIIUjNnK1NCb76iE0ZIQAwOlwYo+bpTEiItIn3QZCBkEApOR6hGRZVjJCakyNyXLy90iC1KhZGlAyQnvceyDJUkbXQURElA30GwjF9AgllhEKSAGEpFDqU2PhABCsT/4eSZB99RBsRzNCLocLASmA/XX7M7oOIiKibKDbQEiM2UcosUDIE1BOnk8pI6TReWNSvQ+i1Rr92ml3AuAIPRER6ZNuA6HYZunEAqHIyfMpN0sDGZ8ck30+CI0CoQpHBQQI7BMiIiJd0m0gJArJ7yztCSoZoTxTXvILiB68WpP8PZKgTI0dDYQsBgs653VmRoiIiHRJt4GQMYUeoWhGKAdLY7LfF91QMSLSME1ERKQ3ug2ERCH58flIIJTyhopAxktjUr0PoiU2EHI6nNxdmoiIdEnzQGjhwoXo1q0brFYrhgwZgvXr17d6fU1NDW688UaUl5fDYrHg+OOPx4oVKxJ+XYMoQERyO0tHSmMpZYQMJsBs16Q01lxGiKUxIiLSo6QDodWrV+Oiiy5Cjx490KNHD1x00UV4//33E7rHq6++ihkzZmD27NnYuHEj+vXrh9GjR2P//uZHuQOBAM4991z8+OOPeO2117B161YsXrwYFRUVCa9fFKFkhAQDIAgJPdcb8MIoGGExWNq+uDXWIk2apcVGGyoCSiBU46+BO+DO6FqIiIi0llQg9NRTT+H888+Hw+HA9OnTMX36dBQUFOCCCy7AwoUL477PggULMHXqVEyZMgV9+vTBokWLkJeXhyVLljR7/ZIlS3Do0CG88cYbGD58OLp164YRI0agX79+Cf8MRlGAKIeTPl4j35wPIcEAqglbcebH530+CNbYAC4yQs8+ISIi0pukAqF58+bh0UcfxSuvvILf//73+P3vf4+XX34Zjz76KObNmxfXPQKBADZs2IBRo0YdXYwoYtSoUVi3bl2zz3nrrbcwdOhQ3HjjjSgtLcXJJ5+MefPmIRwOt/g6fr8ftbW1MR+AUhoTZCnp4zVSKotF2IoyWhqTg0EgFGo2IwRwLyEiItKfpAKhmpoanH/++U0eP++883DkyJG47nHw4EGEw2GUlpbGPF5aWoqqqqpmn/PDDz/gtddeQzgcxooVK3DPPffgkUcewf3339/i68yfPx+FhYXRD5dL+aOvBEKh5DNCqTRKR1gLM1oak/x+AIB4TI9QoaUQDpODgRAREelOUoHQxRdfjH/+859NHn/zzTdx0UUXpbyolkiShM6dO+PZZ5/FwIEDMW7cONx1111YtGhRi8+ZOXMmjhw5Ev3YvVv5Yx/NCCXYKA2omRHKbGlMrleO8xCOmRoTBAFOh5OBEBER6U7i6RAAffr0wQMPPIDKykoMHToUAPDpp59i7dq1+MMf/oC//OUv0Wt///vfN3uPkpISGAwGVFdXxzxeXV2NsrKyZp9TXl4Ok8kEg+FoOevEE09EVVUVAoEAzGZzk+dYLBZYLE2bmsVUeoQCntQ2U4zIcGlM8vkANM0IARyhJyIifUoqEHr++edRXFyMb7/9Ft9++2308aKiIjz//PPRrwVBaDEQMpvNGDhwIFavXo1LLrkEgJLxWb16NaZNm9bsc4YPH46XX34ZkiRBFJVMzvfff4/y8vJmg6DWGKDsLC2LBiTa8uwNeVFsKU7wWc3I8NSY3BAIHZsRApQ+oXd/fDdjayEiIsoGSQVCO3fuVOXFZ8yYgcmTJ2PQoEEYPHgwHnvsMXi9XkyZMgUAMGnSJFRUVGD+/PkAgN/97nd48sknMX36dNx0003Ytm0b5s2b12Kw1RpRFGAUpKQyQt6ANzpplRJbMeA7AshywiP8yWgtI+RyuLDPuw/BcBAmgyntayEiIsoGSQVCahk3bhwOHDiAWbNmoaqqCv3798fKlSujDdS7du2KZn4AwOVy4d1338Utt9yCU045BRUVFZg+fTruuOOOhF/bKAoQkVyPkCfoUW9qTA4DfjdgLUj9fm2IZoSszZfGJFnCPu8+HFdwXNrXQkRElA3iDoRmzJiBuXPnIj8/HzNmzGj12gULFsS9gGnTprVYCqusrGzy2NChQ/Hpp5/Gff+WGEQBRoQhi8bES2NBL/LNakyNFSmf6w9nJBCKZoSaCYQaj9AzECIiIr2IOxD68ssvEQwGo//ckpQ3GcwQQ1ZkhBr6jHw1ALqmfr82SJGpsWYCobK8MhgFIyfHiIhIV+IOhD788MNm/zlXGQQBRkiQhcSqg2EpjPpQvXqlMSBjk2NydB8hW5PvGUQDuti7MBAiIiJd0fzQVa2IDRkhOcGMkDekwsnzEY1LYxkQzQg1s50AoJTHeMwGERHpSVLN0l6vFw8++CBWr16N/fv3Q5KkmO//8MMPqiwunaI9QkJiR2x4A2oGQoUAhIxtqij7fBCs1hbLl06HExv3b8zIWoiIiLJBUoHQddddhzVr1mDixIkoLy/Pmb6gxgyiAAMkyAmeNeYJegCoFAiJBqVJOkOlMcnnb7ZROsLlcOGtHW9BluWcfE+JiIgSlVQg9M4772D58uUYPny42uvJGEO0NJZgRiioZIRU6REClIbpDJXGZF99s43SEU6HE/Whevzs+xkltpKMrImIiEhLSfUIFRcXo0OHDmqvJaMMgrKhYtKBkFmlQMhalLHSmFTvazMjBIB9QkREpBtJBUJz587FrFmzUFdXp/Z6MsYgJJcRUrU0BjScN5ahjJDfB6GZibGIyG7ZnBwjIiK9iLs0duqpp8b0jWzfvh2lpaXo1q0bTKbYIxk2bsz+hluDAck1SzdkhPKMKhy6CiilsbpD6tyrDVK9D2ILE2MAkGfKQ0drR2aEiIhIN+IOhCIHo7YXYjQjlFiblCfgQZ4xD4YEm6xbZC0CDmVmyk7JCLVcGgOU8hgzQkREpBdxRwGzZ89O5zoyziiKMEKClGAg5A161WuUBjJaGlMyQq0HQk6Hk4EQERHpRlI9Qrt378aePUfLJ+vXr8fNN9+MZ599VrWFpZsoQhmfT/BX4Al61DlnLMJWDNQfUe9+rZB89cwIERERNZJUIHTllVdGj9moqqrCqFGjsH79etx111247777VF1guhhFEQaEISXRI5RvVDEQshYB/iOAFFbvni2QfX6I1pabpQElEPrZ9zPqgrnbCE9ERBSvpAKhzZs3Y/DgwQCAv//97+jbty8++eQTLF26FC+++KKa60ubSEZISvSIDbVOno+InDfmS39WSPL5IFhbbpYGlNIYAOzxsGGaiIjav6QCoWAwCEvD9NH777+Piy++GADQu3dv7Nu3T73VpZEoCDAgnHiztFonz0dETqDPQJ+QXF8fV0YI4Ag9ERHpQ1KB0EknnYRFixbho48+wqpVq3D++ecDAH766Sd07NhR1QWmi1IakyEl+CvwBr3q7SEEHD14NQObKkp+P8Q2eoQ6WjvCZrRxhJ6IiHQhqUDooYcewjPPPIORI0di/Pjx6NevHwDgrbfeipbMsp0oAAYh8R4h9TNCRcrnDGWEhDamxgRBQIW9ghkhIiLShYTPGpNlGd27d8euXbsQCoVQXFwc/d7111+PvDyVNhpMMyUjJCWeEQqonBGKlsZq1LtnC+LJCAFKeYwZISIi0oOEM0KyLKNnz56oqqqKCYIAoFu3bujcubNqi0snpVk6yYyQWueMAYDZDgiGtJfGZFlWMkKtnDUWwRF6IiLSi4QDIVEU0atXL/z888/pWE/GGBoyQuEEfgWyLKu/oaIgZGRTRdnvB4BWD12NcDqc+MnzE8IZGOknIiLSUlI9Qg8++CBuu+02bN68We31ZIxBEGBMcENFX9iHsBxGnknl8p+tOO2lMdnnA4C4M0IhOYSquqq0romIiEhrCfcIAcCkSZNQV1eHfv36wWw2w3bMieaHDmXmENFUGAzKWWPhBEpjkQNXVc0IAcrkWJpLY1JDIBRPRqjxCH2FvSKt6yIiItJSUoHQY489pvIyMs8gCAk3S0cCIVWbpYGMZISk+noA8WWEuuR3gSiISsN0eVqXRUREpKmkAqHJkyervY6MM4jKhoqBBDJCnqAHQBoyQrYioDa9G1FGe4RsrW+oCAAmgwlleWVsmCYionYvqR4hANixYwfuvvtujB8/Hvv37wcAvPPOO/jmm29UW1w6KYFQghmhQBpLY2lulo5mhCytH7ERwckxIiLSg6QCoTVr1qBv37747LPP8Prrr8PjUTIlX331FWbPnq3qAtNF2VBRQhiJZ4RUPWsMUEpj6R6fj/QIxZERApTJMe4lRERE7V1SgdCdd96J+++/H6tWrYLZbI4+fvbZZ+PTTz9VbXHpJDT0CCUyPp+2ZmlbUfp7hBJolgaOBkKyLKdzWURERJpKKhD6+uuvcemllzZ5vHPnzjh48GDKi8oUIyRICWaETKIJZoO57YsTYS0Cgl4gFFD3vo0kMj4PKKUxd9CNI/4jaVsTERGR1pIKhIqKipo9Zf7LL79ERUXujFsnkxFSPRsEHD1mI43lMckX/4aKAE+hJyIifUgqELriiitwxx13oKqqCoIgQJIkrF27FrfeeismTZqk9hrTxoBwYj1CAY/6mykCjQ5erVH/3g1kXz1gNEIwmeK63ulwAmAgRERE7VtSgdC8efPQu3dvuFwueDwe9OnTB2eeeSaGDRuGu+++W+01po3SLC3EfX3aMkLWIuVzGifHpHofxDgnxgCgwFyAQksh9njYME1ERO1XUvsImc1mLF68GPfccw82b94Mj8eDU089Fb169VJ7fWllgIRQAhkhb1Dlk+cjMlAak/0+CHFOjEW47ByhJyKi9i2pQCjiuOOOg8ul9JIIQvyZlWxhgISwHH9STPWT5yMyUBqT6n1x9wdFcISeiIjau6Q3VHz++edx8sknw2q1wmq14uSTT8Zzzz2n5trSTukRSqxZOi0ZIZMNMFjSWxrz1UOwxl8aA7ipIhERtX9JZYRmzZqFBQsW4KabbsLQoUMBAOvWrcMtt9yCXbt24b777lN1kWkhSRAhI5RgRqhrQdf0rCfNmyrKPj9Ea4KlMYcL++v2wx/2w2JILIgiIiLKBUkFQk8//TQWL16M8ePHRx+7+OKLccopp+Cmm27KjUBIDgNAdozPA2nfVDGZjJDT4YQMGXs9e9G9sHuaVkZERKSdpEpjwWAQgwYNavL4wIEDEQqFUl5URkhKIBRK4FfgCXjSUxoD0n7eWLIZIQDsEyIionYrqUBo4sSJePrpp5s8/uyzz2LChAkpLyojIhmhBEpjaesRAtJeGpN89RBtiTVLd87rDLNoZp8QERG1W3GXxmbMmBH9Z0EQ8Nxzz+G9997DL3/5SwDAZ599hl27duXOhoqSkrmKd3w+JIXgC/vSGAgVAYd2pufeAOR6H4SCwoSeIwoiKhwVzAgREVG7FXcg9OWXX8Z8PXDgQADAjh07AAAlJSUoKSnBN998o+Ly0kiKZITiG/uPHriajvF5QMkI1W9Mz70BSH5/whkhAHDancwIERFRuxV3IPThhx+mcx2ZJ0kAgGCcGaFIIJTWHqF0To3V10NIsEcIUPqEPt33aRpWREREpL2k9xHKeXJiGSFP0AMA6Z8ak+W03F7y+SAmODUGKIHQXs9eSLKUhlURERFpK6nxeZ/PhyeeeAIffvgh9u/fD0mK/SO5cWP6SjyqaegRSjQjlL5AqBgI+4FgPWBW/2BX2eeDkODO0oAyQu8P+3Gg7gBK80tVXxcREZGWkgqErr32Wrz33nv4zW9+g8GDB+fk8RqJ9gh5AkpGKK2lMUApj6UhEFIyQsmVxgDlFHoGQkRE1N4kFQi9/fbbWLFiBYYPH672ejJHbsgIxTk+n/5m6SLlc/1hoKCL6reXfL6EN1QEgAp7BQBgj2cPBqHp3lFERES5LKkeoYqKCjgcDrXXklkNvTjxHrHhCXogQIDNmHhWJS6RE+jTsLu0HAoBwWBSGSGr0YrOeZ05OUZERO1SUoHQI488gjvuuAP//e9/1V5P5kiJZ4TyTHkQhTT1lzcujalM8vkBIKmMEMAReiIiar+SKo0NGjQIPp8P3bt3R15eHkwmU8z3Dx06pMri0ipyxEYCgVDa+oOA2NKYymS/DwAg2pLLZrkcLuw8kr7NHomIiLSSVCA0fvx47N27F/PmzUNpaWmONksnlhHyBD3pmxgDAIMJMNvTUhqT6hsCoSSmxgAlEPr3nn+ruSQiIqKskFQg9Mknn2DdunXo16+f2uvJnIZ9hIIJ7Cyd1kAISNumirKvHgCSGp8HlBH6w/7D8AQ86WsWJyIi0kBSDS+9e/dGfX292mvJrARLY2k9eT7CVpSW0likRyiVjBAA9gkREVG7k1Qg9OCDD+IPf/gDKisr8fPPP6O2tjbmIydEjtiQEsgIpTsbYitOz9RYNCOUfI8QoIzQExERtSdJlcbOP/98AMA555wT87gsyxAEAeFwOPWVpVvk9PkEeoQ65XVK54oAa2F6psaiPULJTY0VWYqQb8pnRoiIiNqdpAKhdnEAa0OPUCCbeoRsRcD+LarfNjI1JiQ5NSYIAlwOFwMhIiJqd5IKhEaMGKH2OjJPijRLx58RyjOpf/RFjDSVxlKdGgOU8tgeN0tjRETUviS9O+BHH32Eq666CsOGDcPevXsBAH/729/w8ccfq7a4tEp0aiyQu1NjUqRHyJJcaQxQJseYESIiovYmqUDo//7v/zB69GjYbDZs3LgRfr8ylXTkyBHMmzdP1QWmjRxplm77VyDLMryhNG+oCDRMjdVEj/9Qi+zzQ7BYIIjJ74rttDtR5a1CUAqquDIiIiJtJfWX8f7778eiRYuwePHimF2lhw8fjo0bN6q2uLSKbKgYRyBUH6qHJEsZ6BEqVjJVfreqt5V89SmVxQClNBaWw6jyVKm0KiIiIu0lFQht3boVZ555ZpPHCwsLUVNTk+qaMkOKNEu3fWnaT56PSNN5Y7LPn/RmihHcS4iIiNqjpAKhsrIybN++vcnjH3/8Mbp3757yojIiesSGoc1LPUEPAGSgNBY5gV7dTRXVyAiV5ZfBKBgZCBERUbuSVCA0depUTJ8+HZ999hkEQcBPP/2EpUuX4tZbb8Xvfvc7tdeYHg09QgGp7UujGaFMjM8Dqk+OyfW+lDNCRtGIcns5N1UkIqJ2Janx+TvvvBOSJOGcc85BXV0dzjzzTFgsFtx666246aab1F5jekR7hLIoIxQpjamdEfL7Us4IAeBeQkRE1O4kFQgJgoC77roLt912G7Zv3w6Px4M+ffrAbs+hAzmlBDJCgQxlhKyFAAT1e4TqfUlvptiY0+7EpgObUl8QERFRlkgoELrmmmvium7JkiUJLWLhwoX485//jKqqKvTr1w9PPPEEBg8e3Obzli1bhvHjx2Ps2LF44403EnpNSCHIEOI6YiNjGSHRAFgLVC+NST4fxBT2EIpwOVx4+4e3o0epEBER5bqEAqEXX3wRXbt2xamnngpZpb1uXn31VcyYMQOLFi3CkCFD8Nhjj2H06NHYunUrOnfu3OLzfvzxR9x6660444wzknthOQxJEBGW2k4JeYNemEUzTAZTm9emzFqkemlM9vkgFhSkfB+Xw4W6UB0O+Q6ho62jCisjIiLSVkKB0O9+9zu88sor2LlzJ6ZMmYKrrroKHTp0SGkBCxYswNSpUzFlyhQAwKJFi7B8+XIsWbIEd955Z7PPCYfDmDBhAubMmYOPPvqo1ZF9v98f3fARAGpra5V/kMOQBSPCUtsBXUZOno+wFateGpN8PhhbCSrj5XQ4ASgj9AyEiIioPUhoamzhwoXYt28fbr/9dvzrX/+Cy+XC5ZdfjnfffTepDFEgEMCGDRswatSoowsSRYwaNQrr1q1r8Xn33XcfOnfujGuvvbbN15g/fz4KCwujHy6Xsh8OpDBkQYwrEPIEPekvi0XYitKSERKSPHm+Me4lRERE7U3C4/MWiwXjx4/HqlWr8O233+Kkk07CDTfcgG7dusHj8SR0r4MHDyIcDqO0tDTm8dLSUlRVNb+D8ccff4znn38eixcvjus1Zs6ciSNHjkQ/du9u+CMuhSELBoTjCOAycvJ8hLUoPT1C1tSbpfNMeehg7cAReiIiajeSmhqLEEURgiBAlmWEw2G11tQit9uNiRMnYvHixSgpKYnrORaLBZbmGoXlhkAo6zJCxcDhnareUvLVQ7ClPj4P8BR6IiJqXxIOhPx+P15//XUsWbIEH3/8MS666CI8+eSTOP/88yEmeKhnSUkJDAYDqqurYx6vrq5GWVlZk+t37NiBH3/8EWPGjIk+JjU0OxuNRmzduhU9evSI78WlUNylsYycPB8ROXhVRbLPD9GiTiDEU+iJiKg9SSgQuuGGG7Bs2TK4XC5cc801eOWVV+LOzDTHbDZj4MCBWL16NS655BIASmCzevVqTJs2rcn1vXv3xtdffx3z2N133w23243HH3/8aP9PPCQJsmCEJKPNcXBP0IPS/NIWv6+qdJXGVMwIrd+3XpV7ERERaS2hQGjRokU47rjj0L17d6xZswZr1qxp9rrXX3897nvOmDEDkydPxqBBgzB48GA89thj8Hq90SmySZMmoaKiAvPnz4fVasXJJ58c8/yioiIAaPJ4m2SlWRoAwpIMo6HlQMgb9CLfmMHSmP+Iciis2Pau122RZRlyfT0ElTJCLocLB+oPoD5UD5sx9b4jIiIiLSUUCE2aNEn1jfTGjRuHAwcOYNasWaiqqkL//v2xcuXKaAP1rl27Ei65xUUKQRaVHz8kyTC2EnN4gh7kmzM4NQYAviNAXmpbEwCAHAwCsqxqRggA9rj3oFdxL1XuSUREpJWEN1RMh2nTpjVbCgOAysrKVp+b9JokKZoRktqYHMv41BigjNCrEQjV1wMABBWmxgDlmA2AgRAREbUPaUi15AgpBAhKHNhWw7Q36M3s1Big2qaKks8HABBV2EcIAEpsJbAZbWyYJiKidkG/gZAsQW7owWktEAqGg/CH/ZmdGgNUa5iWGwIhtTJCgiCgwl7BQIiIiNoFHQdCIUBoOxDyBjN08nxEJCOk0u7S0YyQSj1CgDJCz00ViYioPdBvINRoKqu1QCh68nymmqXNdiVAU6k0Fs0IqTQ1BnBTRSIiaj90HQjJkR6hVpqlM54REgRVN1WU6tXPCLkcLuzx7EFYSv9u4kREROmk30BIPpoRCoXjyAhlqlkaUMpjqpXGIlNjKpbG7E6EpBCq66rbvpiIiCiL6TcQalQaa218PpIRymggZC1SsTTmBwCIKgZCjfcSIiIiymX6DoQamqVD2dQsDahbGmvICKkZCFXYKyAKIifHiIgo5+k4EAoBDTtLS200SwsQMnuchK1Y3fF5gwEwmVS5HwCYDCaU5ZUxECIiopyn30CocY9QaxmhhpPn1T5apFUqlsYknw+ixaL6+jlCT0RE7YF+AyFJghDHPkIZPWcsQsXSmOzzQbCpn81yOVzMCBERUc7TcSAU3z5CGT1nLELVqTElI6Q2p8PJQIiIiHKefgMhOXQ0EGplaswT9GR2YgxQSmNBLxAOpnwruT49GSGnwwl3wI0j/iOq35uIiChTdBwIhSEY2j50VZuMUJHyWYXymOTzqToxFsEReiIiag/0GwjFe8RGwIM8U16mVqVQ8bwx2edTdTPFiEggxPIYERHlMl0HQoKYpRkha5HyWYXJsXRlhArMBSgwFzAQIiKinKbfQEiWIMTZLJ3xHiFVS2P1EFQ8Z6yxyJljREREuUq/gZAUOpoRaqNZ2m7WYGoMUKk05oeo4snzjXGEnoiIcp2OA6FwdGfpcCuHrmpSGjPZAINFpdJYejNCDISIiCiX6TgQCh2dGmshIyTJkjalMUC1Yzbkeh9Ea3qOB3E6nKj2ViMQDqTl/kREROmm30Aojh6h+lA9ZMiZzwgBDbtLp14ak/w+iFb1N1QElIyQDBl7PXvTcn8iIqJ0028gJLW9j5An4AEAbTJCKp03Jtf7IKQpI8QReiIiynW6DoTENjJC3qAXADLfLA2oVhqTPB6I9vQEcp1snWASTQyEiIgoZ+k3EJJDbWeEgkpGKM+Y4Q0VAVVKY3I4DKmuDgZ7egI5g2hAhb2Cu0sTEVHO0nEgFIbYxoaKmmaEVCiNSV5l/WKaAiGgYS8hBkJERJSj9BsINe4RamFqLBoIadIsnXppTPIoGS3R7lBhQc3jKfRERJTLdB0IQTDAKAoItVUay/RZY4AqpbGwW1m/IU09QsDR3aXlVjalJCIiylb6DoREEaIoQGqlNGY1WGESTRleHJTSWNgPBOuTvoXkbcgIOdKXEXI5XPCH/ThQfyBtr0FERJQu+g2EZGVn6VYzQgGPNqPzQKNjNmqSvoXkdgNIf48QwBF6IiLKTfoNhCQJEAwwCK1nhDRplAYaHbyafHks7ImUxtL3M1TYKwCADdNERJSTdBwIhQDRCIOh9R4hzTJC1iLlcwqTY5LbA4gihLz09ThZjVZ0tnVmRoiIiHKSfgMhOQyIDRmhFhp9NQ2EVDiBXvJ6INrtEARBpUU1j5NjRESUq/QbCEkNgZAoINTC6fOaHbgKNCqN1SR9i3Aad5VuzOlwsjRGREQ5Sb+BkKyMzxtEodV9hDTZQwgADCbAlJ9yacyQxj2EIiIj9ERERLlGv4EQZKVHqI3xec0yQkDDpooplMY8nrROjEW4HC4c8h2KbkBJRESUK3QcCOFoaayV8XnNMkJAw6aKNUk/PexxZ6Q0xhF6IiLKVToPhBoyQq2VxrQanweUjFAqpTGPNyOlMafDCYAj9ERElHv0HQgJIgxCy83Smk6NAYC1MLXSmNudkdJYsaUY+aZ8ZoSIiCjn6DsQaiUjFAgHEJSCOV0akzweiI70r18QBLgcLgZCRESUc3QeCEV6hKQm34ocuKp5s3QKpbGwx5PWXaUbc9q5lxAREeUenQdCSkYo3DQOgjegTEBpWxorUmFqLP09QkDDCD17hIiIKMfoOxCK7CPUTEbIG1ICoawojbXQzN0aORCA7PdnpEcIUBqm93n3ISgFM/J6REREatB3ICQqzdLNZYQ8gSwpjclhoGEtiQh7lUDOkIEeIUAJhMJyGFWeqoy8HhERkRp0HggZW84INWwOqOn4fOTg1STKY1LDyfOZyghF9xLysE+IiIhyh74DoegRG02/lR3N0kXK5yQmxyS3GwAy1iNUnl8Oo2BknxAREeUUfQdCbWSEDIIBVoNVg4U1iJxAn8TkWLghI2TIwM7SAGAUjSi3l3NyjIiIcopR6wVoSjTAIALhZo7YiGymKAiCBgtroEZpzJGZjBCgjNAzI0RERLlE5xkhA4yi0HwgFNB4V2lA2VkaSK40luEeIQDcVJGIiHKOvgMhwQBRaD4Q0vzkeQAQDUowlExpzO0GTCYIZrP662pBJBCSkxj3JyIi0oK+AyHRCKOh+dPnPUGNT56PSHJTReXAVXtGS3tOhxN1oToc8h3K2GsSERGlQueBkJIRau6ssbpgHfLNGmeEgKTPG5M8mTlwtbHICP0eD/uEiIgoN+g8EDK23COULRmhJM8bC2fowNXGnA4nALBPiIiIcoa+AyFBhNhCIOQNerMjEEq2NOb2wJCf2fXnm/LRwdqBgRAREeUMfQdCbWSENG+WBpSMUJJTY5kcnY9wOjhCT0REuUPngVDk0NVmMkKBLMkI2YqSKo0pJ89nPpDjKfRERJRLdB4IGZXx+WaapbMmI5RkaSzs8cCQoeM1GuNeQkRElEv0HQgJyoaKoWMOGwtLYdSF6rQ9cDXCVgz4aoFmjgFpjZIRyvz6nXYnDtQfQH2oPuOvTURElCh9B0KiAaLYdHy+LlQHAMgz5Wmxqli2IgAy4D+S0NMktzvjU2PA0RH6ve69GX9tIiKiROk+EDKKTTdU9Aa9AJAdPUJJnDcmyzLCXmVDxUyLBEIsjxERUS7QdyAkNGSEsjkQipxAn8DkmOzzAaEQRA16hEpsJbAarAyEiIgoJ+g7EGoYnz82I+QJKgeWZkWztK1I+ZzA5NjRA1czv35BEJQReu4uTUREOUDngZABBqGZjFAgizJCSZTGwm4lENKiNAYoewkxI0RERLkgKwKhhQsXolu3brBarRgyZAjWr1/f4rWLFy/GGWecgeLiYhQXF2PUqFGtXt8qwQCDKLacEcqGs8YsDkAwJFQak7wNGSENNlQEuJcQERHlDs0DoVdffRUzZszA7NmzsXHjRvTr1w+jR4/G/v37m72+srIS48ePx4cffoh169bB5XLhvPPOw969iU4pCYAowiCiydRYpEco35gFgZAgJLypouR2A4Am4/OAMkK/17MXYSmsyesTERHFS/NAaMGCBZg6dSqmTJmCPn36YNGiRcjLy8OSJUuavX7p0qW44YYb0L9/f/Tu3RvPPfccJEnC6tWrE3thwQAALWaEbEYbDKIhqZ9JdQluqhj2aFsaczlcCEpB7K9rPpglIiLKFpoGQoFAABs2bMCoUaOij4miiFGjRmHdunVx3aOurg7BYBAdOnRo9vt+vx+1tbUxH8oLRQIhNDliI2t2lY5I8LwxqaFHSMzX5mfgCD0REeUKTQOhgwcPIhwOo7S0NObx0tJSVFVVxXWPO+64A126dIkJphqbP38+CgsLox8ul/JHGqIRgJIROjYQyppzxiISLY15PRBsNggmU9qW1Jou9i4QIDAQIiKirKd5aSwVDz74IJYtW4Z//vOfsFqtzV4zc+ZMHDlyJPqxe3fDH+dIaUxomhHyhrzZlRGyFiWUEQprdOBqhNlgRll+GUfoiYgo6xm1fPGSkhIYDAZUV1fHPF5dXY2ysrJWn/vwww/jwQcfxPvvv49TTjmlxessFgssFkvTb0RKYwaxabN01mWEioEDW+O+XHJrc+BqYzx8lYiIcoGmGSGz2YyBAwfGNDpHGp+HDh3a4vP+9Kc/Ye7cuVi5ciUGDRqU3ItHAiGh+Q0VsyojlGhpTKMDVxvjXkJERJQLNC+NzZgxA4sXL8ZLL72ELVu24He/+x28Xi+mTJkCAJg0aRJmzpwZvf6hhx7CPffcgyVLlqBbt26oqqpCVVUVPA2TUnFrCISMogBZRsymit6gNztOno9IeGrMDYMGB642xr2EiIgoF2haGgOAcePG4cCBA5g1axaqqqrQv39/rFy5MtpAvWvXLoji0Xjt6aefRiAQwG9+85uY+8yePRv33ntv/C/c0CMkigIAICzLEKH8c/ZlhIqBgAcIBwFD2w3QkscLMV/7jFBtoBZH/EdQaCnUdC1EREQt0TwQAoBp06Zh2rRpzX6vsrIy5usff/xRnRdtND4PKA3TpoZtg7zBbOsRKlI+19cA9k5tXi653TCWlKR1SW1x2ZXpvD3uPQyEiIgoa2leGtOMeHRDRSB2cizrMkKR88bi7BOSvB6IGpfGnA4nAO4lRERE2U2/gRCONksDSmkMAGRZhjeQZePztmLlc5wj9GG3R7NdpSMKLYUoMBdwhJ6IiLKafgOhaEaoIRAKK4FQQAogJIeyLBAqUj7H2TCtTI1pOz4PcHKMiIiyHwMhMTYj5Ako02dZ1SOUQGlMliRIXq+mGypGcC8hIiLKdroPhIyRQKihRyhy8nxWjc+bbIDBEldpTKqrA2QZBof2GSGO0BMRUbbTbyB07Ph8QyDkCSoZoawqjQmCUh6LozQmNeynpPWGioASCFV5qxAIB7ReChERUbP0Gwg1HLraYkYom0pjgFIei6M0JrndALIjEHLanZAhY69nr9ZLISIiapaOA6GGjJBwTEYokIUZIUCZHIujNBZuyAhpPTUGKBkhACyPERFR1tJ9IGQ0KIFQ6JjSWFb1CAGJl8ayoEeoc15nmEQTG6aJiChr6TcQEpQfPZIRipxA7w16YRSMMItmzZbWLFtxfKWxLOoRMogGVNgrGAgREVHW0m8gdEyPUCh8NCOUb86H0BAgZQ1rUXylMbcbEASIeXlpX1I8nA4nS2NERJS19BsICbH7CEUyQnXBuuxrlAYSKI15IebnQxCz4611OVzcXZqIiLJWdvy11EJDRsjQzPh81jVKAwmUxtxZURaLcNqVjJAsy21fTERElGH6DYQaeoQigVCo0fh8VmaErEVAyAcE61u9LOzxwKDxgauNuRwu+MI+HKg/oPVSiIiImtBvIHTM+LzU6IiN7MwIFSmf2+gTktweiPnZFQgBHKEnIqLspPtA6Nhm6azNCEVOoG+jPCZ5PFkxOh9R4agAAE6OERFRVtJxIBTbIxTNCDVMjWWdyMGrbTRMS15PVhy4GmEz2tDJ1omBEBERZSX9BkLHTI017hHKN2ZPIBEVZ2ks7PbAYM+ejBDAU+iJiCh76TcQEmObpSUpRzJC8ZTGsmhqDGjYS4gj9ERElIV0HAi1nBHKyh4hoxkw5bdZGgt73BCzaGoM4KaKRESUvfQbCEVKY40OXQ1LYdSH6rMzEAIaNlWsafUSyePNigNXG3M5XDjkOwRv0Kv1UoiIiGLoNxCKNEsbjgZC3pDyhzorx+eBNjdVlINByPX1ELOsR8hpdwLgCD0REWUf/QZCx2aEZBnegBIIZW1GyFrUamlM8irrz6apMeDoXkJsmCYiomxj1HoBmjmmRygsSfA07Nqclc3SQJulsXDDyfOGLNpHCAA6WDsgz5jHQIiIiLIOM0LRQAjRHpaszQjZilotjUkNgVC2TY0JgsCGaSIiykr6DYTEY5ulJXiCSiCRtT1CbZXG3G4AyKojNiK4lxAREWUj3QdCoihAEJSMUNYHQnGXxhgIERERxUPHgdDR9iijKCAsSdFm6Txjnlaral1kaqzhOJBjSZ5Is3T2BUJOuxP7vPsQlIJaL4WIiChKv4GQcPRHFwVBGZ8PepFnzIOhIVuUdaxFgBQCAp5mvy153IDRCMFqzey64uByuBCWw6jyVmm9FCIioij9BkLHZoTkLN5VOqKN88bCHg8M+fkQGvqesglH6ImIKBvpOBBqlBFqKI1l7TljEbZi5XMLDdOS2wMxy0bnI8rsZTAIBk6OERFRVtFxIHQ0I2QQhej4fFZnhNo4eDUbD1yNMIkmlOWXMRAiIqKsot9ASDjaB2RsnBHK1okxoFFGqKbZb4c97qw7Z6wxTo4REVG20XEgdGyztDI+n90ZoULlc0ulMY83azNCAAMhIiLKPvoNhFoYn88zZenoPKDsfWQpbLk05nZnbY8QAGV3ac8eyC2M/xMREWWafgOhRqUxURQQluXszwgBgK2w5dKY15N1B6425nK44A16cdjf8u7YREREmaTfQEiM7REKNewjlNU9QoDSJ9RCaSx8uAaGgsIMLyh+HKEnIqJsw0AISkZIagiE7OYszwhZi5otjYU9XoT27YOl+y8yvqR4Oe1OAAyEiIgoezAQgpIRCoal7B+fB1o8byywYzsAwNyzZ2bXkwC72Y5iSzFH6ImIKGvoNxBq3CMkCAhKfoTlcM6WxvzbtgGCAEuPHhosKn6cHCMiomyi30Co8dSYQYAvXAcA2Z8RaqE05t+2HabjXBCz8JyxxpwOJzNCRESUNXQcCB3NCBkEAQFZCYSyPyNU1GxpzL99Oyw9e2V8OYliIERERNmEgRCUIzb8oYaMULY3S9uKAd8RQJJiHvZv2wZLr+ztD4pwOVzYX78fvpBP66UQERHpOBBq9KMbxEYZIWOWZ4SsRQBkwH8k+lC4thah/ftzIiMUGaFnVoiIiLKBfgOhYw5dDUj1AJDdp88DSmkMiGmY9m9XJsYsvbI/EOIIPRERZRMdB0KxpbGgrARCWd8s3czBq/7vtwEGA8y/6KbJkhLRKa8TLAYL9niYESIiIu3pOBBqnBESEZTrYBJNMBvMGi4qDtYi5XOjyTH/9u0wd+0K0ZzlawcgCiKcdiczQkRElBX0Gwg1On3eIABBqT77s0FA86WxbdtyoiwW4XQwECIiouyg30Do2NIY6rN/dB4ALAXKZpCNS2Pbt8OSxTtKH8vlcLFZmoiIsgIDISiBUEiuz/7ReQAQBMBaGC2NhQ4dQvjnn3NidD7C6XBir2cvwlJY66UQEZHO6TcQEpoGQjmREQIaNlVUSmP+bbkzMRbhcrgQlILYX7df66UQEZHO6TcQOqZZOpQrpTGg4byxGgCAf/s2wGSC+bjjtF1TApwOZYSek2NERKQ1HQdCjY/YAMK5lBFqdN6Yf/t2WH7xCwgmk6ZLSoTT7oQAgQ3TRESkOf0GQo2nxkQRYcGXG1NjQMx5Y/5t23KqURoAzAYzSvNLGQgREZHmdBwINe4RAiTkUiCklMZkWUZg2/acapSO4F5CRESUDfQbCB3TIyTBl3OlsdCBAwgfOZJTjdIRHKEnIqJsoONA6JiMkJAj4/NAdGosEDljLMdKY4ASCDEjREREWtNxIHQ0IyQKEmQhmDsZIVsxEPDA//1WCBYLTC6X1itKmMvhQm2gFkf8R7ReChER6Zh+AyFBiP6jBD+AHDhwNaLhvDH/d9/C3KM7BIOh9euzEEfoiYgoG+g3EGqUEZJE5eT53MkIFQHIvaM1GnM5lCwWy2NERKQlHQdCR7MoYfgA5FIgVAxZBvw//DcnG6UBoNBSCIfZwYZpIiLSlI4DoUYZISgZoVwqjYXqREh19TmbEQKUEXoGQkREpKWsCIQWLlyIbt26wWq1YsiQIVi/fn2r1//jH/9A7969YbVa0bdvX6xYsSLxFxVyOSNUBH+tspN0rmaEAE6OERGR9jQPhF599VXMmDEDs2fPxsaNG9GvXz+MHj0a+/c3fyDnJ598gvHjx+Paa6/Fl19+iUsuuQSXXHIJNm/enNgLNyqNheSGjFCujM+b8uCvtUKwmGDq0kXr1SSNgRAREWlN80BowYIFmDp1KqZMmYI+ffpg0aJFyMvLw5IlS5q9/vHHH8f555+P2267DSeeeCLmzp2LAQMG4Mknn0zshRtNjYWFekAWYDPaUvlRMkcQ4PfYYCkrhCBq/hYmzelwospbhUA4oPVSiIhIp4xtX5I+gUAAGzZswMyZM6OPiaKIUaNGYd26dc0+Z926dZgxY0bMY6NHj8Ybb7zR7PV+vx9+vz/69ZEjyr41nw08FfkNY+cTGj4+nX9S8j9MhpkkGY5uP+LgnR20XkrSCi0mhMo7YtCz/SC0fTkREencRxM+g8PhgCCo91dD00Do4MGDCIfDKC0tjXm8tLQU3333XbPPqaqqavb6qqqqZq+fP38+5syZ0+Tx87b/kOSqs8h2AO9rvYhUNV8CJSIiOlbhDYXYv38/OnXqpNo9NQ2EMmHmzJkxGaSamhp07doVu3btQmFhoYYro9raWrhcLuzevRsFBQVaL0f3+H5kD74X2YPvRfaIvBdms1nV+2oaCJWUlMBgMKC6ujrm8erqapSVlTX7nLKysoSut1gssFgsTR4vLCzkv9RZoqCggO9FFuH7kT34XmQPvhfZQ82yGKBxs7TZbMbAgQOxevXq6GOSJGH16tUYOnRos88ZOnRozPUAsGrVqhavJyIiImqJ5qWxGTNmYPLkyRg0aBAGDx6Mxx57DF6vF1OmTAEATJo0CRUVFZg/fz4AYPr06RgxYgQeeeQRXHjhhVi2bBm++OILPPvss1r+GERERJSDNA+Exo0bhwMHDmDWrFmoqqpC//79sXLlymhD9K5duyA2GhEfNmwYXn75Zdx999344x//iF69euGNN97AySefHNfrWSwWzJ49u9lyGWUW34vswvcje/C9yB58L7JHut4LQZZlWdU7EhEREeWI3N2Nj4iIiChFDISIiIhItxgIERERkW4xECIiIiLdapeB0MKFC9GtWzdYrVYMGTIE69evb/X6f/zjH+jduzesViv69u2LFStWZGil7V8i78XixYtxxhlnoLi4GMXFxRg1alSb7x0lJtH/NiKWLVsGQRBwySWXpHeBOpLoe1FTU4Mbb7wR5eXlsFgsOP744/m/VSpJ9L147LHHcMIJJ8Bms8HlcuGWW26Bz+fL0Grbr3//+98YM2YMunTpAkEQWjxDtLHKykoMGDAAFosFPXv2xIsvvpj4C8vtzLJly2Sz2SwvWbJE/uabb+SpU6fKRUVFcnV1dbPXr127VjYYDPKf/vQn+dtvv5Xvvvtu2WQyyV9//XWGV97+JPpeXHnllfLChQvlL7/8Ut6yZYt89dVXy4WFhfKePXsyvPL2KdH3I2Lnzp1yRUWFfMYZZ8hjx47NzGLbuUTfC7/fLw8aNEi+4IIL5I8//ljeuXOnXFlZKW/atCnDK29/En0vli5dKlssFnnp0qXyzp075XfffVcuLy+Xb7nllgyvvP1ZsWKFfNddd8mvv/66DED+5z//2er1P/zwg5yXlyfPmDFD/vbbb+UnnnhCNhgM8sqVKxN63XYXCA0ePFi+8cYbo1+Hw2G5S5cu8vz585u9/vLLL5cvvPDCmMeGDBki/8///E9a16kHib4XxwqFQrLD4ZBfeumldC1RV5J5P0KhkDxs2DD5ueeekydPnsxASCWJvhdPP/203L17dzkQCGRqibqR6Htx4403ymeffXbMYzNmzJCHDx+e1nXqTTyB0O233y6fdNJJMY+NGzdOHj16dEKv1a5KY4FAABs2bMCoUaOij4miiFGjRmHdunXNPmfdunUx1wPA6NGjW7ye4pPMe3Gsuro6BINBdOjQIV3L1I1k34/77rsPnTt3xrXXXpuJZepCMu/FW2+9haFDh+LGG29EaWkpTj75ZMybNw/hcDhTy26Xknkvhg0bhg0bNkTLZz/88ANWrFiBCy64ICNrpqPU+vut+c7Sajp48CDC4XB0V+qI0tJSfPfdd80+p6qqqtnrq6qq0rZOPUjmvTjWHXfcgS5dujT5F50Sl8z78fHHH+P555/Hpk2bMrBC/Ujmvfjhhx/wwQcfYMKECVixYgW2b9+OG264AcFgELNnz87EstulZN6LK6+8EgcPHsTpp58OWZYRCoXw29/+Fn/84x8zsWRqpKW/37W1taivr4fNZovrPu0qI0Ttx4MPPohly5bhn//8J6xWq9bL0R23242JEydi8eLFKCkp0Xo5uidJEjp37oxnn30WAwcOxLhx43DXXXdh0aJFWi9NdyorKzFv3jw89dRT2LhxI15//XUsX74cc+fO1XpplKR2lREqKSmBwWBAdXV1zOPV1dUoKytr9jllZWUJXU/xSea9iHj44Yfx4IMP4v3338cpp5ySzmXqRqLvx44dO/Djjz9izJgx0cckSQIAGI1GbN26FT169EjvotupZP7bKC8vh8lkgsFgiD524oknoqqqCoFAAGazOa1rbq+SeS/uueceTJw4Eddddx0AoG/fvvB6vbj++utx1113xZyNSenV0t/vgoKCuLNBQDvLCJnNZgwcOBCrV6+OPiZJElavXo2hQ4c2+5yhQ4fGXA8Aq1atavF6ik8y7wUA/OlPf8LcuXOxcuVKDBo0KBNL1YVE34/evXvj66+/xqZNm6IfF198Mc466yxs2rQJLpcrk8tvV5L5b2P48OHYvn17NBgFgO+//x7l5eUMglKQzHtRV1fXJNiJBKgyj+7MKNX+fifWx539li1bJlssFvnFF1+Uv/32W/n666+Xi4qK5KqqKlmWZXnixInynXfeGb1+7dq1stFolB9++GF5y5Yt8uzZszk+r5JE34sHH3xQNpvN8muvvSbv27cv+uF2u7X6EdqVRN+PY3FqTD2Jvhe7du2SHQ6HPG3aNHnr1q3y22+/LXfu3Fm+//77tfoR2o1E34vZs2fLDodDfuWVV+QffvhBfu+99+QePXrIl19+uVY/QrvhdrvlL7/8Uv7yyy9lAPKCBQvkL7/8Uv7vf/8ry7Is33nnnfLEiROj10fG52+77TZ5y5Yt8sKFCzk+H/HEE0/Ixx13nGw2m+XBgwfLn376afR7I0aMkCdPnhxz/d///nf5+OOPl81ms3zSSSfJy5cvz/CK269E3ouuXbvKAJp8zJ49O/MLb6cS/W+jMQZC6kr0vfjkk0/kIUOGyBaLRe7evbv8wAMPyKFQKMOrbp8SeS+CwaB87733yj169JCtVqvscrnkG264QT58+HDmF97OfPjhh83+DYj8/idPniyPGDGiyXP69+8vm81muXv37vILL7yQ8OsKssxcHhEREelTu+oRIiIiIkoEAyEiIiLSLQZCREREpFsMhIiIiEi3GAgRERGRbjEQIiIiIt1iIERERES6xUCIiIiIdIuBEBFlvZEjR+Lmm2/W7PWvvvpqXHLJJZq9PhGlDwMhIlJFS8FCZWUlBEFATU2Naq+1c+dOXHnllejSpQusViucTifGjh2L7777DgDw448/QhAEbNq0SbXXJKL2yaj1AoiIEhEMBnHuuefihBNOwOuvv47y8nLs2bMH77zzTsLBViAQ4OntRDrHjBARZczPP/+M8ePHo6KiAnl5eejbty9eeeWVmGu8Xi8mTZoEu92O8vJyPPLIIzHf/+abb7Bjxw489dRT+OUvf4muXbti+PDhuP/++/HLX/4SAPCLX/wCAHDqqadCEASMHDkSwNGs1QMPPIAuXbrghBNOAAB8/fXXOPvss2Gz2dCxY0dcf/318Hg8Lf4cn3/+OTp16oSHHnoIAFBTU4PrrrsOnTp1QkFBAc4++2x89dVXqvzOiCi9GAgRUcb4fD4MHDgQy5cvx+bNm3H99ddj4sSJWL9+ffSa2267DWvWrMGbb76J9957D5WVldi4cWP0+506dYIoinjttdcQDoebfZ3I/d5//33s27cPr7/+evR7q1evxtatW7Fq1Sq8/fbb8Hq9GD16NIqLi/H555/jH//4B95//31Mmzat2Xt/8MEHOPfcc/HAAw/gjjvuAABcdtll2L9/P9555x1s2LABAwYMwDnnnINDhw6l/DsjojRL+Lx6IqJmTJ48WTYYDHJ+fn7Mh9VqlQHIhw8fbvZ5F154ofyHP/xBlmVZdrvdstlslv/+979Hv//zzz/LNptNnj59evSxJ598Us7Ly5MdDod81llnyffdd5+8Y8eO6Pd37twpA5C//PLLJmssLS2V/X5/9LFnn31WLi4ulj0eT/Sx5cuXy6IoylVVVdHnjR07Vn799ddlu90uL1u2LHrtRx99JBcUFMg+ny/mtXr06CE/88wz8f3yiEgzzAgRkWrOOussbNq0Kebjueeei34/HA5j7ty56Nu3Lzp06AC73Y53330Xu3btAgDs2LEDgUAAQ4YMiT6nQ4cO0RJWxI033oiqqiosXboUQ4cOxT/+8Q+cdNJJWLVqVZtr7Nu3b0xf0JYtW9CvXz/k5+dHHxs+fDgkScLWrVujj3322We47LLL8Le//Q3jxo2LPv7VV1/B4/GgY8eOsNvt0Y+dO3dix44dCfz2iEgLbJYmItXk5+ejZ8+eMY/t2bMn+s9//vOf8fjjj+Oxxx5D3759kZ+fj5tvvhmBQCDh13I4HBgzZgzGjBmD+++/H6NHj8b999+Pc889t801JqNHjx7o2LEjlixZggsvvBAmkwkA4PF4UF5ejsrKyibPKSoqSuq1iChzmBEiooxZu3Ytxo4di6uuugr9+vVD9+7d8f3330e/36NHD5hMJnz22WfRxw4fPhxzTXMEQUDv3r3h9XoBIJrxaamHqLETTzwRX331VfS5kXWKohiTiSopKcEHH3yA7du34/LLL0cwGAQADBgwAFVVVTAajejZs2fMR0lJSRy/FSLSEgMhIsqYXr16YdWqVfjkk0+wZcsW/M///A+qq6uj37fb7bj22mtx22234YMPPsDmzZtx9dVXQxSP/k/Vpk2bMHbsWLz22mv49ttvsX37djz//PNYsmQJxo4dCwDo3LkzbDYbVq5cierqahw5cqTFNU2YMAFWqxWTJ0/G5s2b8eGHH+Kmm27CxIkTUVpaGnNt586d8cEHH+C7777D+PHjEQqFMGrUKAwdOhSXXHIJ3nvvPfz444/45JNPcNddd+GLL75Q+TdIRGpjIEREGXP33XdjwIABGD16NEaOHImysrImmzD++c9/xhlnnIExY8Zg1KhROP300zFw4MDo951OJ7p164Y5c+ZgyJAhGDBgAB5//HHMmTMHd911FwDAaDTiL3/5C5555hl06dIlGiA1Jy8vD++++y4OHTqE0047Db/5zW9wzjnn4Mknn2z2+rKyMnzwwQf4+uuvMWHCBEiShBUrVuDMM8/ElClTcPzxx+OKK67Af//73yaBFBFlH0GWZVnrRRARERFpgRkhIiIi0i0GQkRERKRbDISIiIhItxgIERERkW4xECIiIiLdYiBEREREusVAiIiIiHSLgRARERHpFgMhIiIi0i0GQkRERKRbDISIiIhIt/4/tctf81YkumUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import skfuzzy as fuzz\n",
    "\n",
    "sleep[\"low\"] = fuzz.zmf(sleep.universe, 0, 6)\n",
    "sleep[\"normal\"] = fuzz.trapmf(sleep.universe, [4, 6, 10, 12])\n",
    "sleep[\"much\"] = fuzz.smf(sleep.universe, 10, 14)\n",
    "sleep.view()\n",
    "\n",
    "bmi[\"low\"] = fuzz.zmf(bmi.universe, 12, 18.5)\n",
    "bmi[\"normal\"] = fuzz.trapmf(bmi.universe, [16, 18.5, 25, 30])\n",
    "bmi[\"much\"] = fuzz.smf(bmi.universe, 25, 40)\n",
    "bmi.view()\n",
    "\n",
    "# Определение функций принадлежности для had_stroke\n",
    "had_stroke[\"low\"] = fuzz.zmf(had_stroke.universe, 0, 0.01)  # Низкая вероятность (менее 1%)\n",
    "had_stroke[\"moderate\"] = fuzz.trapmf(had_stroke.universe, [0.005, 0.01, 0.05, 0.1])  # Умеренная вероятность (1% - 5%)\n",
    "had_stroke[\"high\"] = fuzz.trapmf(had_stroke.universe, [0.01, 0.05, 0.1, 0.2])  # Высокая вероятность (5% - 10%)\n",
    "had_stroke[\"very_high\"] = fuzz.smf(had_stroke.universe, 0.1, 0.15)  # Очень высокая вероятность (более 10%)\n",
    "\n",
    "# Визуализация функций принадлежности\n",
    "had_stroke.view()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформируем базу нечетких правил\n",
    "\n",
    "Объяснение правил:\n",
    "\n",
    "Правило 1: Если часы сна низкие (low) и ИМТ низкий (low), то вероятность сердечного приступа низкая (low).\n",
    "\n",
    "Правило 2: Если часы сна низкие (low) и ИМТ нормальный (normal), то вероятность сердечного приступа умеренная (moderate).\n",
    "\n",
    "Правило 3: Если часы сна низкие (low) и ИМТ высокий (much), то вероятность сердечного приступа высокая (high).\n",
    "\n",
    "Правило 4: Если часы сна нормальные (normal) и ИМТ низкий (low), то вероятность сердечного приступа низкая (low).\n",
    "\n",
    "Правило 5: Если часы сна нормальные (normal) и ИМТ нормальный (normal), то вероятность сердечного приступа низкая (low).\n",
    "\n",
    "Правило 6: Если часы сна нормальные (normal) и ИМТ высокий (much), то вероятность сердечного приступа умеренная (moderate).\n",
    "\n",
    "Правило 7: Если часы сна высокие (much) и ИМТ низкий (low), то вероятность сердечного приступа низкая (low).\n",
    "\n",
    "Правило 8: Если часы сна высокие (much) и ИМТ нормальный (normal), то вероятность сердечного приступа низкая (low).\n",
    "\n",
    "Правило 9: Если часы сна высокие (much) и ИМТ высокий (much), то вероятность сердечного приступа умеренная (moderate).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rules = [\n",
    "    ctrl.Rule(sleep['low'] & bmi['low'], had_stroke['low']),\n",
    "    ctrl.Rule(sleep['low'] & bmi['normal'], had_stroke['moderate']),\n",
    "    ctrl.Rule(sleep['low'] & bmi['much'], had_stroke['high']),\n",
    "    ctrl.Rule(sleep['normal'] & bmi['low'], had_stroke['low']),\n",
    "    ctrl.Rule(sleep['normal'] & bmi['normal'], had_stroke['low']),\n",
    "    ctrl.Rule(sleep['normal'] & bmi['much'], had_stroke['moderate']),\n",
    "    ctrl.Rule(sleep['much'] & bmi['low'], had_stroke['low']),\n",
    "    ctrl.Rule(sleep['much'] & bmi['normal'], had_stroke['low']),\n",
    "    ctrl.Rule(sleep['much'] & bmi['much'], had_stroke['moderate']),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим нечеткую систему и добавим созданные нечеткие правила в ее базу знаний"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nspotapov/ulstu/AIM-PIbd-31-Potapov-N-S/lab_7/.venv/lib/python3.12/site-packages/skfuzzy/control/controlsystem.py:135: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Создание системы управления и добавление правил\n",
    "stroke_ctrl = ctrl.ControlSystem(rules)\n",
    "\n",
    "# Создание симуляции\n",
    "stroke_simulation = ctrl.ControlSystemSimulation(stroke_ctrl)\n",
    "\n",
    "# Теперь можно визуализировать правила\n",
    "stroke_ctrl.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: SleepHours              = 5\n",
      "  - low                             : 0.05555555555555555\n",
      "  - normal                          : 0.5\n",
      "  - much                            : 0.0\n",
      "Antecedent: BMI                     = 22\n",
      "  - low                             : 0.0\n",
      "  - normal                          : 1.0\n",
      "  - much                            : 0.0\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF SleepHours[low] AND BMI[low] THEN HadStroke[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[low]                                        : 0.05555555555555555\n",
      "  - BMI[low]                                               : 0.0\n",
      "                              SleepHours[low] AND BMI[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            HadStroke[low] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF SleepHours[low] AND BMI[normal] THEN HadStroke[moderate]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[low]                                        : 0.05555555555555555\n",
      "  - BMI[normal]                                            : 1.0\n",
      "                           SleepHours[low] AND BMI[normal] = 0.05555555555555555\n",
      "  Activation (THEN-clause):\n",
      "                                       HadStroke[moderate] : 0.05555555555555555\n",
      "\n",
      "RULE #2:\n",
      "  IF SleepHours[low] AND BMI[much] THEN HadStroke[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[low]                                        : 0.05555555555555555\n",
      "  - BMI[much]                                              : 0.0\n",
      "                             SleepHours[low] AND BMI[much] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           HadStroke[high] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF SleepHours[normal] AND BMI[low] THEN HadStroke[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[normal]                                     : 0.5\n",
      "  - BMI[low]                                               : 0.0\n",
      "                           SleepHours[normal] AND BMI[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            HadStroke[low] : 0.0\n",
      "\n",
      "RULE #4:\n",
      "  IF SleepHours[normal] AND BMI[normal] THEN HadStroke[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[normal]                                     : 0.5\n",
      "  - BMI[normal]                                            : 1.0\n",
      "                        SleepHours[normal] AND BMI[normal] = 0.5\n",
      "  Activation (THEN-clause):\n",
      "                                            HadStroke[low] : 0.5\n",
      "\n",
      "RULE #5:\n",
      "  IF SleepHours[normal] AND BMI[much] THEN HadStroke[moderate]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[normal]                                     : 0.5\n",
      "  - BMI[much]                                              : 0.0\n",
      "                          SleepHours[normal] AND BMI[much] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                       HadStroke[moderate] : 0.0\n",
      "\n",
      "RULE #6:\n",
      "  IF SleepHours[much] AND BMI[low] THEN HadStroke[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[much]                                       : 0.0\n",
      "  - BMI[low]                                               : 0.0\n",
      "                             SleepHours[much] AND BMI[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            HadStroke[low] : 0.0\n",
      "\n",
      "RULE #7:\n",
      "  IF SleepHours[much] AND BMI[normal] THEN HadStroke[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[much]                                       : 0.0\n",
      "  - BMI[normal]                                            : 1.0\n",
      "                          SleepHours[much] AND BMI[normal] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            HadStroke[low] : 0.0\n",
      "\n",
      "RULE #8:\n",
      "  IF SleepHours[much] AND BMI[much] THEN HadStroke[moderate]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - SleepHours[much]                                       : 0.0\n",
      "  - BMI[much]                                              : 0.0\n",
      "                            SleepHours[much] AND BMI[much] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                       HadStroke[moderate] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: HadStroke                = 0.032127197249789186\n",
      "  low:\n",
      "    Accumulate using accumulation_max : 0.5\n",
      "  moderate:\n",
      "    Accumulate using accumulation_max : 0.05555555555555555\n",
      "  high:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  very_high:\n",
      "    Accumulate using accumulation_max : None\n",
      "\n",
      "0.032127197249789186\n"
     ]
    }
   ],
   "source": [
    "stroke_simulation.input['SleepHours'] = 5  # Пример входного значения\n",
    "stroke_simulation.input['BMI'] = 22        # Пример входного значения\n",
    "stroke_simulation.compute()\n",
    "stroke_simulation.print_state()\n",
    "stroke_p = stroke_simulation.output['HadStroke']\n",
    "print(stroke_p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация функции принадлежности для выходной переменной had_stroke\n",
    "\n",
    "Функция получена в процессе аккумуляции и используется для дефаззификации значения выходной переменной had_stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "had_stroke.view(sim=stroke_simulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравним результаты работы системы с реальными данными"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fuzzy_pred(row):\n",
    "    stroke_simulation.input[\"SleepHours\"] = row[\"SleepHours\"]\n",
    "    stroke_simulation.input[\"BMI\"] = row[\"BMI\"]\n",
    "    stroke_simulation.compute()\n",
    "    return stroke_simulation.output[\"HadStroke\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SleepHours</th>\n",
       "      <th>BMI</th>\n",
       "      <th>HadStroke</th>\n",
       "      <th>HadStrokePredicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231476</th>\n",
       "      <td>8.0</td>\n",
       "      <td>23.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236378</th>\n",
       "      <td>8.0</td>\n",
       "      <td>30.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109247</th>\n",
       "      <td>5.0</td>\n",
       "      <td>33.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0.057174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69198</th>\n",
       "      <td>8.0</td>\n",
       "      <td>24.39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133337</th>\n",
       "      <td>7.0</td>\n",
       "      <td>24.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93935</th>\n",
       "      <td>7.0</td>\n",
       "      <td>24.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218410</th>\n",
       "      <td>9.0</td>\n",
       "      <td>31.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100042</th>\n",
       "      <td>6.0</td>\n",
       "      <td>30.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58400</th>\n",
       "      <td>8.0</td>\n",
       "      <td>31.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35904</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2460 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SleepHours    BMI  HadStroke  HadStrokePredicted\n",
       "231476         8.0  23.03          0            0.003333\n",
       "236378         8.0  30.68          0            0.047222\n",
       "109247         5.0  33.82          0            0.057174\n",
       "69198          8.0  24.39          0            0.003333\n",
       "133337         7.0  24.84          0            0.003333\n",
       "...            ...    ...        ...                 ...\n",
       "93935          7.0  24.03          0            0.003333\n",
       "218410         9.0  31.32          0            0.046591\n",
       "100042         6.0  30.41          0            0.047472\n",
       "58400          8.0  31.01          0            0.046903\n",
       "35904          6.0  20.94          0            0.003333\n",
       "\n",
       "[2460 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.copy()\n",
    "result = result.sample(frac=0.01)\n",
    "result[\"HadStrokePredicted\"] = result.apply(fuzzy_pred, axis=1)\n",
    "result[\"HadStroke\"] = result.apply(lambda x: 1 if x[\"HadStroke\"] == 'Yes' else 0, axis=1)\n",
    "result.loc[:, [\"SleepHours\", \"BMI\", \"HadStroke\", \"HadStrokePredicted\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим эти результаты с помощью метрик для задачи регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 0.1888734235934381,\n",
       " 'RMAE': 0.251645328415333,\n",
       " 'R2': -0.0013951099998465999}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "rmetrics = {}\n",
    "rmetrics[\"RMSE\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result[\"HadStroke\"], result[\"HadStrokePredicted\"])\n",
    ")\n",
    "rmetrics[\"RMAE\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result[\"HadStroke\"], result[\"HadStrokePredicted\"])\n",
    ")\n",
    "rmetrics[\"R2\"] = metrics.r2_score(\n",
    "    result[\"HadStroke\"], result[\"HadStrokePredicted\"]\n",
    ")\n",
    "\n",
    "rmetrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод\n",
    "\n",
    "Значение RMSE=0.1889 может считаться относительно небольшим, но его интерпретация зависит от масштаба данных. Если целевая переменная (например, вероятность сердечного приступа) находится в диапазоне от 0 до 1, то ошибка в 0.1889 может быть значительной.\n",
    "\n",
    "Значение RMAE=0.2516 также указывает на значительные ошибки в предсказаниях. Это подтверждает, что модель плохо справляется с задачей.\n",
    "\n",
    "Отрицательное значение R² (-0.0014) указывает на то, что модель работает хуже, чем простое предсказание среднего значения целевой переменной. Это явный признак того, что модель не подходит для данных.\n",
    "\n",
    "Заключение\n",
    "\n",
    "Исходя из оценок, модель неэффективна для предсказания сердечного приступа на основе выбранных признаков. Необходимо пересмотреть подход к построению модели, улучшить данные и, возможно, использовать более подходящие методы машинного обучения."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
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 },
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