breast-cancer-detection/neural_network_CM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age        BMI  Glucose  Insulin      HOMA   Leptin  Adiponectin  Resistin  \\\n",
      "0   48  23.500000       70    2.707  0.467409   8.8071     9.702400   7.99585   \n",
      "1   83  20.690495       92    3.115  0.706897   8.8438     5.429285   4.06405   \n",
      "2   82  23.124670       91    4.498  1.009651  17.9393    22.432040   9.27715   \n",
      "3   68  21.367521       77    3.226  0.612725   9.8827     7.169560  12.76600   \n",
      "4   86  21.111111       92    3.549  0.805386   6.6994     4.819240  10.57635   \n",
      "\n",
      "     MCP.1  Classification  \n",
      "0  417.114               1  \n",
      "1  468.786               1  \n",
      "2  554.697               1  \n",
      "3  928.220               1  \n",
      "4  773.920               1  \n",
      "              Age         BMI     Glucose     Insulin        HOMA      Leptin  \\\n",
      "count  116.000000  116.000000  116.000000  116.000000  116.000000  116.000000   \n",
      "mean    57.301724   27.582111   97.793103   10.012086    2.694988   26.615080   \n",
      "std     16.112766    5.020136   22.525162   10.067768    3.642043   19.183294   \n",
      "min     24.000000   18.370000   60.000000    2.432000    0.467409    4.311000   \n",
      "25%     45.000000   22.973205   85.750000    4.359250    0.917966   12.313675   \n",
      "50%     56.000000   27.662416   92.000000    5.924500    1.380939   20.271000   \n",
      "75%     71.000000   31.241442  102.000000   11.189250    2.857787   37.378300   \n",
      "max     89.000000   38.578759  201.000000   58.460000   25.050342   90.280000   \n",
      "\n",
      "       Adiponectin    Resistin        MCP.1  Classification  \n",
      "count   116.000000  116.000000   116.000000      116.000000  \n",
      "mean     10.180874   14.725966   534.647000        1.551724  \n",
      "std       6.843341   12.390646   345.912663        0.499475  \n",
      "min       1.656020    3.210000    45.843000        1.000000  \n",
      "25%       5.474283    6.881763   269.978250        1.000000  \n",
      "50%       8.352692   10.827740   471.322500        2.000000  \n",
      "75%      11.815970   17.755207   700.085000        2.000000  \n",
      "max      38.040000   82.100000  1698.440000        2.000000  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 116 entries, 0 to 115\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Age             116 non-null    int64  \n",
      " 1   BMI             116 non-null    float64\n",
      " 2   Glucose         116 non-null    int64  \n",
      " 3   Insulin         116 non-null    float64\n",
      " 4   HOMA            116 non-null    float64\n",
      " 5   Leptin          116 non-null    float64\n",
      " 6   Adiponectin     116 non-null    float64\n",
      " 7   Resistin        116 non-null    float64\n",
      " 8   MCP.1           116 non-null    float64\n",
      " 9   Classification  116 non-null    int64  \n",
      "dtypes: float64(7), int64(3)\n",
      "memory usage: 9.2 KB\n",
      "None\n",
      "Age               0\n",
      "BMI               0\n",
      "Glucose           0\n",
      "Insulin           0\n",
      "HOMA              0\n",
      "Leptin            0\n",
      "Adiponectin       0\n",
      "Resistin          0\n",
      "MCP.1             0\n",
      "Classification    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Age'}>, <Axes: title={'center': 'BMI'}>,\n",
       "        <Axes: title={'center': 'Glucose'}>],\n",
       "       [<Axes: title={'center': 'Insulin'}>,\n",
       "        <Axes: title={'center': 'HOMA'}>,\n",
       "        <Axes: title={'center': 'Leptin'}>],\n",
       "       [<Axes: title={'center': 'Adiponectin'}>,\n",
       "        <Axes: title={'center': 'Resistin'}>,\n",
       "        <Axes: title={'center': 'MCP.1'}>],\n",
       "       [<Axes: title={'center': 'Classification'}>, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ui63r7JIoi62LiOhmluyrAPZXRM2tMZ9hTSuBpf2BnvP3Q1d1Y8QrfobJGngEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDGY4BARERERkWIwwSEiIiIiIsXgfXCIiKhF4D16iIhaBh7BISIiIiIixWCCQ0REREREisEEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDF4HxwiomZg6XuwWBLv50JERErCIzhERERERKQYTHCIiIiIiEgxmOAQEREREZFimJXgJCcno1+/fnBxcYGXlxeio6ORl5dntExlZSXi4uLg6ekJZ2dnxMTEoKioyKJBExERETXUkiVLoFKpkJCQIJVx/4VIOcxKcLKyshAXF4djx45Bq9VCr9cjIiIC5eXl0jKzZ8/Gzp07sXHjRmRlZeHChQsYO3asxQMnIiIiMteJEyfw7rvvonfv3kbl3H8hUg6zRlHbt2+f0fS6devg5eWF3NxcDB48GCUlJVi7di0yMjIwbNgwAEBaWhq6deuGY8eOYcCAAZaLnIiIiMgMV69exeTJk7FmzRosWrRIKuf+C5GyNGqY6JKSEgCAh4cHACA3Nxd6vR7h4eHSMkFBQQgICEB2djY7CCIiMktDh9fWtBJY2h/oOX8/dFUqC0dFtiouLg5RUVEIDw83SnC4/0KkLA1OcAwGAxISEjBw4ED07NkTAFBYWAh7e3u4u7sbLevt7Y3CwkKT69HpdNDpdNJ0aWkpAECv10Ov1zc0PNmrrlt966hpJSy+7eZmbp1tnRzrK6dYiIia04YNG/DFF1/gxIkTNeZx/+V/GrO/obETRn8Bfu8A8twfsDZz2qQh7dbgBCcuLg6nT5/G0aNHG7oKADcGLliwYEGN8gMHDsDJyalR67YFWq22Xsst7W+5be7Zs8dyK2uA+tZZKeRU34qKCmuHQETU7M6fP49Zs2ZBq9XCwcHBIutU6v6LJfY3Xg0xSP9be59DTuS0PyAX9WmThuy7NCjBiY+Px65du3DkyBF06NBBKvfx8cH169dRXFxs9CtIUVERfHx8TK4rKSkJiYmJ0nRpaSn8/f0REREBV1fXhoRnE/R6PbRaLUaMGAG1Wn3b5XvO32+xbZ+eH2mxdZnD3DrbOjnWt/oXRiKiliQ3NxeXLl3CPffcI5VVVVXhyJEjeOedd7B//37uv/x/jdnf0NgJvBpiwNyTdtAZbpwaaq19DjmR4/6AtZnTJg3ZdzErwRFCYObMmdi6dSsOHz6MwMBAo/nBwcFQq9XIzMxETEwMACAvLw/nzp1DWFiYyXVqNBpoNJoa5Wq1ukW8CepbT0ueQ27tdm0pr201OdVXLnGYcuTIEfz73/9Gbm4uLl68iK1btyI6OlqaP2XKFHzwwQdGz4mMjKwx+AkR0a2GDx+Ob775xqhs6tSpCAoKwgsvvAB/f3/uv/x/ltjf0BlU0npsuS0szdbfG02hPm3SkDYzK8GJi4tDRkYGtm/fDhcXF+m8VDc3Nzg6OsLNzQ3Tpk1DYmIiPDw84OrqipkzZyIsLIwX6BFRncrLy9GnTx889thjtQ7Nev/99yMtLU2aNrVzQUR0KxcXF+l64Wpt2rSBp6enVM79FyLlMCvBSU1NBQAMGTLEqDwtLQ1TpkwBACxbtgx2dnaIiYmBTqdDZGQkVq5caZFgiUi5Ro4ciZEjR9a5jEajqfV0ESKixuD+C5FymH2K2u04ODggJSUFKSkpDQ6KiMiUw4cPw8vLC23btsWwYcOwaNEieHp61rq8tUY5MjU6jCVHQrS0W9uiMSP+yKWepkZzsrTmfg/Jma3EebPDhw8bTXP/hUg5GnUfHKKm0ND7XphydkmUxdZF1nX//fdj7NixCAwMxE8//YQXX3wRI0eORHZ2Nlq1amXyOdYe5ejm0WEsORKipdU2ylFDRvyRWz1vHs3J0ppjdChbGXWJIzQSkZwwwSEimzBhwgTp/169eqF3797o3LkzDh8+jOHDh5t8jrVGOTI1OowlR0K0tFtHOWrMiD9yqaep0ZwsrSlHh7K1UZc4QiMRyQkTHCKySXfeeSfatWuH/Pz8WhMca49ydPN2LDkSoqXV1hYNaSe51fPm0ZwsrbnfQ3JmCzESUcthZ+0AiIga4tdff8Uff/wBX19fa4dCREREMsIjOEQkC1evXkV+fr40XVBQgFOnTsHDwwMeHh5YsGABYmJi4OPjg59++gnPP/88unTpgshI3kSOiIiI/ocJDhHJwsmTJzF06FBpuvramdjYWKSmpuLrr7/GBx98gOLiYvj5+SEiIgKvvvoq74VDRERERpjgEJEsDBkypM6h6Pfvl8fF60REcmLJkUeJlILX4BARERERkWIwwSEiIiIiIsVggkNERERERIrBBIeIiIiIiBSDCQ4RERERESkGExwiIiIiIlIMJjhERERERKQYTHCIiIiIiEgxmOAQEREREZFiMMEhIiIiIiLFYIJDRERERESKwQSHiIiIiIgUgwkOEREREREpBhMcIiIiIiJSDCY4RERERESkGExwiIiIiIhIMZjgEBERERGRYjDBISIiIiIixWCCQ0REREREisEEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDGY4BARERERkWIwwSEiIiIiIsVggkNERERERIrBBIeIiIiIiBSDCQ4RycKRI0cwevRo+Pn5QaVSYdu2bUbzhRCYN28efH194ejoiPDwcJw5c8Y6wRIREZFsMcEhIlkoLy9Hnz59kJKSYnL+0qVLsWLFCqxatQo5OTlo06YNIiMjUVlZ2cyREhERkZy1tnYARE2p05zdFl3f2SVRFl0f/c/IkSMxcuRIk/OEEFi+fDlefvlljBkzBgDw4YcfwtvbG9u2bcOECROaM1QiIiKSMR7BISLZKygoQGFhIcLDw6UyNzc3hIaGIjs724qRERERkdzwCA4RyV5hYSEAwNvb26jc29tbmmeKTqeDTqeTpktLSwEAer0eer2+CSKFtP6b/wKAppVosu011q1tYSr++pJLPTV2wuhvU2ju95Cc2UqcRNQyMMEhIsVKTk7GggULapQfOHAATk5OTb59rVYr/b+0f5NvrsH27Nljsvzm+OtLbvV8NcTQZOuurd0sqSGvgTVUVFRYOwQiIgkTHCKSPR8fHwBAUVERfH19pfKioiL07du31uclJSUhMTFRmi4tLYW/vz8iIiLg6uraZPHq9XpotVqMGDECarUaANBz/v4m215jnZ4faTRtKv76kks9NXYCr4YYMPekHXQGVZNs49Z2s6TGvAbWUH10lIhIDpjgEJHsBQYGwsfHB5mZmVJCU1paipycHMyYMaPW52k0Gmg0mhrlarW6WXYab96OrqppdrItoba2aEg7ya2eOoOqyWJq7veQnNlCjETUcjDBISJZuHr1KvLz86XpgoICnDp1Ch4eHggICEBCQgIWLVqErl27IjAwEHPnzoWfnx+io6OtFzQRERHJDhMcIpKFkydPYujQodJ09allsbGxWLduHZ5//nmUl5dj+vTpKC4uxqBBg7Bv3z44ODhYK2QiIiKSISY4RCQLQ4YMgRC1j3ilUqmwcOFCLFy4sBmjIiIiIlvD++AQEREREZFiMMEhIiIiIiLF4ClqREREMtBpzm7pf00rgaX9bwy73ZBR4M4uibJkaERENoVHcIiIiIiISDF4BKcFuvlXQkvgL4VEREREJBc8gkNERERERIphdoJz5MgRjB49Gn5+flCpVNi2bZvRfCEE5s2bB19fXzg6OiI8PBxnzpyxVLxEREREZklOTka/fv3g4uICLy8vREdHIy8vz2iZyspKxMXFwdPTE87OzoiJiUFRUZGVIiaixjA7wSkvL0efPn2QkpJicv7SpUuxYsUKrFq1Cjk5OWjTpg0iIyNRWVnZ6GCJiIiIzJWVlYW4uDgcO3YMWq0Wer0eERERKC8vl5aZPXs2du7ciY0bNyIrKwsXLlzA2LFjrRg1ETWU2dfgjBw5EiNHjjQ5TwiB5cuX4+WXX8aYMWMAAB9++CG8vb2xbds2TJgwoXHREhEREZlp3759RtPr1q2Dl5cXcnNzMXjwYJSUlGDt2rXIyMjAsGHDAABpaWno1q0bjh07hgEDBlgjbCJqIIsOMlBQUIDCwkKEh4dLZW5ubggNDUV2drbJBEen00Gn00nTpaWlAAC9Xg+9Xm/J8GSlum71raOmVe13eLe2+tahvnVWQl1vXlZO72M5xUJEZC0lJSUAAA8PDwBAbm4u9Hq90f5LUFAQAgICkJ2dbTLBkcv+i5y+MzV2wugvwO8dQJ77A9ZmTps0pN0smuAUFhYCALy9vY3Kvb29pXm3Sk5OxoIFC2qUHzhwAE5OTpYMT5a0Wm29llvav4kDaYQ9e/aYtfzt6qykugL1f42bQ0VFhbVDICKyKoPBgISEBAwcOBA9e/YEcGP/xd7eHu7u7kbL2sL+ixy/M18NMUj/N+R7U6nktD8gF/Vpk4bsu1h9mOikpCQkJiZK06WlpfD390dERARcXV2tGFnj9Jy/v875GjuBV0MMmHvSDjqD+Tdxk5PT8yPrtZxer4dWq8WIESOgVqtrXe52bWcrbvca17fdLKn6F0YiopYqLi4Op0+fxtGjRxu1Hrnsv8jpO9PU9541vuvkpr77Py2JOW3SkH0XiyY4Pj4+AICioiL4+vpK5UVFRejbt6/J52g0Gmg0mhrlarXapt8E9b3ztM6gatBdquXE3Nfpdq+trbfHrWp7ja3x/rblzxQRUWPFx8dj165dOHLkCDp06CCV+/j44Pr16yguLjY6ilNUVCTt29xKLvsvcvzOvPl7j987/2Pr+7ZNoT5t0pA2s+h9cAIDA+Hj44PMzEyprLS0FDk5OQgLC7PkpoiIiIjqRQiB+Ph4bN26FQcPHkRgYKDR/ODgYKjVaqP9l7y8PJw7d477L0Q2yOwjOFevXkV+fr40XVBQgFOnTsHDwwMBAQFISEjAokWL0LVrVwQGBmLu3Lnw8/NDdHS0JeMmIiIiqpe4uDhkZGRg+/btcHFxka6rcXNzg6OjI9zc3DBt2jQkJibCw8MDrq6umDlzJsLCwjiCGpENMjvBOXnyJIYOHSpNV59/Ghsbi3Xr1uH5559HeXk5pk+fjuLiYgwaNAj79u2Dg4OD5aImIiIiqqfU1FQAwJAhQ4zK09LSMGXKFADAsmXLYGdnh5iYGOh0OkRGRmLlypXNHCkRWYLZCc6QIUMgRO1DEqpUKixcuBALFy5sVGBkOzrN2V2v5TStBJb2v3FBpBzPGSYiImWqa7+lmoODA1JSUmq9kTkR2Q6rj6JGRETWdeuPFPwxgoiIbJlFBxkgIiIiIiKyJiY4RERERESkGExwiIiIiIhIMZjgEBERERGRYjDBISIiIiIixeAoajep73DHREREREQkTzyCQ0REREREisEEh4iIiIiIFIOnqBERERFRk7Dk6f9nl0RZbF2kbDyCQ0REREREisEEh4iIiIiIFIOnqBFZiaVH7WsJh+7nz5+PBQsWGJXddddd+OGHH6wUEREREckNExwisik9evTAp59+Kk23bs1ujIiIiP6HewZEZFNat24NHx8fa4dBREREMsUEh4hsypkzZ+Dn5wcHBweEhYUhOTkZAQEBJpfV6XTQ6XTSdGlpKQBAr9dDr9c3WYzV6755G5pWosm2Z2kaO2H01xY1Rx0s/R66+T3S2Pib8v0th+0REdWFCQ4R2YzQ0FCsW7cOd911Fy5evIgFCxbg73//O06fPg0XF5cayycnJ9e4ZgcADhw4ACcnpyaPV6vVSv8v7d/km7O4V0MM1g6h0ZqyDnv27LHo+ky9Rxoav6Vju52Kiopm3R4RUV2Y4BCRzRg5cqT0f+/evREaGoqOHTvik08+wbRp02osn5SUhMTERGm6tLQU/v7+iIiIgKura53b6jl/f4Pj1NgJvBpiwNyTdtAZVA1ej7XYevxA89Th9PxIi67v5vdcY+O3dGy3U310lIhIDpjgEJHNcnd3x9/+9jfk5+ebnK/RaKDRaGqUq9VqqNXqOtetq2r8TrHOoLLIeqzF1uMHmrYOt3sPmctUnA2N39KxyW17RER14X1wiMhmXb16FT/99BN8fX2tHQoRERHJBBMcIrIZzz77LLKysnD27Fl8/vnnePDBB9GqVStMnDjR2qERERGRTPAUNSKyGb/++ismTpyIP/74A+3bt8egQYNw7NgxtG/f3tqhERERkUwwwSEim7FhwwZrh0BEREQyx1PUiIiIiIhIMWz6CE6nObutHQIREbVQcv4OsnRsZ5dEWXR9RERNiUdwiIiIiIhIMZjgEBERERGRYjDBISIiIiIixWCCQ0REREREisEEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDGY4BARERERkWIwwSEiIiIiIsVggkNERERERIrR2toBEBERERE1t05zdltsXWeXRFlsXdR4PIJDRERERESKwQSHiIiIiIgUgwkOEREREREpBhMcIiIiIiJSDCY4RERERESkGBxFjYiIiKgZWXL0LiKqiUdwiIiIiIhIMZjgEBERERGRYvAUNSIiIiKSPZ7aJw+WeB00rQSW9gd6zt+PvMWjLBCVMR7BISIiIiIixWCCQ0REREREisEEh4iIiIiIFIMJDhERERERKUaTJTgpKSno1KkTHBwcEBoaiuPHjzfVpoiohWH/QkRNgX0LkTI0yShqH3/8MRITE7Fq1SqEhoZi+fLliIyMRF5eHry8vJpik0TUQrB/IaKmwL6FGqOukcVuHjFMV6Wq1/rOLomyVGgtUpMcwXnzzTfxxBNPYOrUqejevTtWrVoFJycnvP/++02xOSJqQdi/EFFTYN9CpBwWP4Jz/fp15ObmIikpSSqzs7NDeHg4srOzayyv0+mg0+mk6ZKSEgDAlStXoNfr69xW67/KLRR182ttEKioMKC13g5Vhvpl87aupdW5uev7xx9/3HaZsrIyAIAQoqnDaRK20r/Y+nvd1uMHbL8Ocov/dv1LS+tbgJa7/1IXub1v5aIh7VKf73RrscT79+Y2aZL+RVjYb7/9JgCIzz//3Kj8ueeeE/3796+x/CuvvCIA8MEHH834OH/+vKU/+s2C/QsffMj70VL6FiHYv/DBR3M/zOlfmuQaHHMkJSUhMTFRmjYYDLhy5Qo8PT2hUik3+y8tLYW/vz/Onz8PV1dXa4fTLFpaneVYXyEEysrK4OfnZ+1QmoW1+hc5vvbmsPX4Aduvg63F39L6FqDl7r/Uxdbet82F7VKTOW3SkP7F4glOu3bt0KpVKxQVFRmVFxUVwcfHp8byGo0GGo3GqMzd3d3SYcmWq6tri3uzt7Q6y62+bm5u1g6hwWytf5Hba28uW48fsP062FL8LalvAazfv8iZLb1vmxPbpab6tom5/YvFBxmwt7dHcHAwMjMzpTKDwYDMzEyEhYVZenNE1IKwfyGipsC+hUhZmuQUtcTERMTGxiIkJAT9+/fH8uXLUV5ejqlTpzbF5oioBWH/QkRNgX0LkXI0SYIzfvx4/P7775g3bx4KCwvRt29f7Nu3D97e3k2xOZuk0Wjwyiuv1Di8rWQtrc4trb7NxRb6F1t/7W09fsD262Dr8dsiW+hb5I7vW9PYLjU1dZuohLDRMR2JiIiIiIhu0SQ3+iQiIiIiIrIGJjhERERERKQYTHCIiIiIiEgxmOAQEREREZFiMMFpRkuWLIFKpUJCQoJUVllZibi4OHh6esLZ2RkxMTE1bjRma3777Tc88sgj8PT0hKOjI3r16oWTJ09K84UQmDdvHnx9feHo6Ijw8HCcOXPGihE3XFVVFebOnYvAwEA4Ojqic+fOePXVV3Hz2B1Kqi/9T3JyMvr16wcXFxd4eXkhOjoaeXl5RsvI+fNdn/iHDBkClUpl9HjqqaesFHFNqamp6N27t3SjuLCwMOzdu1eaL+f2B24fv9zbn1qOI0eOYPTo0fDz84NKpcK2bduM5tfne+7KlSuYPHkyXF1d4e7ujmnTpuHq1avNWAvLul2bTJkypcbn9/777zdaRmltYqnvxXPnziEqKgpOTk7w8vLCc889h7/++susWJjgNJMTJ07g3XffRe/evY3KZ8+ejZ07d2Ljxo3IysrChQsXMHbsWCtF2Xh//vknBg4cCLVajb179+K7777DG2+8gbZt20rLLF26FCtWrMCqVauQk5ODNm3aIDIyEpWVlVaMvGFef/11pKam4p133sH333+P119/HUuXLsXbb78tLaOk+tL/ZGVlIS4uDseOHYNWq4Ver0dERATKy8ulZeT8+a5P/ADwxBNP4OLFi9Jj6dKlVoq4pg4dOmDJkiXIzc3FyZMnMWzYMIwZMwbffvstAHm3P3D7+AF5tz+1HOXl5ejTpw9SUlJMzq/P99zkyZPx7bffQqvVYteuXThy5AimT5/eXFWwuNu1CQDcf//9Rp/f9evXG81XWptY4nuxqqoKUVFRuH79Oj7//HN88MEHWLduHebNm2deMIKaXFlZmejatavQarXivvvuE7NmzRJCCFFcXCzUarXYuHGjtOz3338vAIjs7GwrRds4L7zwghg0aFCt8w0Gg/Dx8RH//ve/pbLi4mKh0WjE+vXrmyNEi4qKihKPPfaYUdnYsWPF5MmThRDKqy/V7tKlSwKAyMrKEkLY3uf71viFEEb9la1o27ateO+992yu/atVxy+EbbY/KR8AsXXrVmm6Pt9z3333nQAgTpw4IS2zd+9eoVKpxG+//dZssTeVW9tECCFiY2PFmDFjan2O0ttEiIZ9L+7Zs0fY2dmJwsJCaZnU1FTh6uoqdDpdvbfNIzjNIC4uDlFRUQgPDzcqz83NhV6vNyoPCgpCQEAAsrOzmztMi9ixYwdCQkLw0EMPwcvLC3fffTfWrFkjzS8oKEBhYaFRnd3c3BAaGmqTdb733nuRmZmJH3/8EQDw1Vdf4ejRoxg5ciQA5dWXaldSUgIA8PDwAGB7n+9b46+Wnp6Odu3aoWfPnkhKSkJFRYU1wrutqqoqbNiwAeXl5QgLC7O59r81/mq20v7UctXney47Oxvu7u4ICQmRlgkPD4ednR1ycnKaPebmcvjwYXh5eeGuu+7CjBkz8Mcff0jzWkKbNOR7MTs7G7169TK6wW5kZCRKS0uNjm7fTmtLVIBqt2HDBnzxxRc4ceJEjXmFhYWwt7eHu7u7Ubm3tzcKCwubKULL+vnnn5GamorExES8+OKLOHHiBJ555hnY29sjNjZWqtetd4a21TrPmTMHpaWlCAoKQqtWrVBVVYXFixdj8uTJAKC4+pJpBoMBCQkJGDhwIHr27AnAtj7fpuIHgEmTJqFjx47w8/PD119/jRdeeAF5eXnYsmWLFaM19s033yAsLAyVlZVwdnbG1q1b0b17d5w6dcom2r+2+AHbaH+i+nzPFRYWwsvLy2h+69at4eHhIavPoyXdf//9GDt2LAIDA/HTTz/hxRdfxMiRI5GdnY1WrVopvk0a+r1YWFho8r1UPa++mOA0ofPnz2PWrFnQarVwcHCwdjjNwmAwICQkBK+99hoA4O6778bp06exatUqxMbGWjk6y/vkk0+Qnp6OjIwM9OjRA6dOnUJCQgL8/PwUWV8yLS4uDqdPn8bRo0etHUqD1Bb/zeeC9+rVC76+vhg+fDh++ukndO7cubnDNOmuu+7CqVOnUFJSgk2bNiE2NhZZWVnWDqveaou/e/fuNtH+RGTahAkTpP979eqF3r17o3Pnzjh8+DCGDx9uxciah7W/F3mKWhPKzc3FpUuXcM8996B169Zo3bo1srKysGLFCrRu3Rre3t64fv06iouLjZ5XVFQEHx8f6wTdSL6+vtKvj9W6deuGc+fOAYBUr1tHzLDVOj/33HOYM2cOJkyYgF69euGf//wnZs+ejeTkZADKqy/VFB8fj127duHQoUPo0KGDVO7j42MTn+/a4jclNDQUAJCfn98codWLvb09unTpguDgYCQnJ6NPnz546623bKb9a4vfFDm2P1F9vud8fHxw6dIlo/l//fUXrly5IqvPY1O688470a5dO+nzq+Q2acz3oo+Pj8n3UvW8+mKC04SGDx+Ob775BqdOnZIeISEhmDx5svS/Wq1GZmam9Jy8vDycO3fO6BxsWzJw4MAaQwL++OOP6NixIwAgMDAQPj4+RnUuLS1FTk6OTda5oqICdnbGH6NWrVrBYDAAUF596X+EEIiPj8fWrVtx8OBBBAYGGs0PDg6W9ef7dvGbcurUKQA3fsiQK4PBAJ1OJ/v2r011/KbYQvtTy1Of77mwsDAUFxcjNzdXWubgwYMwGAxS4q50v/76K/744w/p86vENrHE92JYWBi++eYbo+RPq9XC1dW1xg/otwuGmtGto+I89dRTIiAgQBw8eFCcPHlShIWFibCwMOsF2EjHjx8XrVu3FosXLxZnzpwR6enpwsnJSXz00UfSMkuWLBHu7u5i+/bt4uuvvxZjxowRgYGB4tq1a1aMvGFiY2PFHXfcIXbt2iUKCgrEli1bRLt27cTzzz8vLaOk+tL/zJgxQ7i5uYnDhw+LixcvSo+KigppGTl/vm8Xf35+vli4cKE4efKkKCgoENu3bxd33nmnGDx4sJUj/585c+aIrKwsUVBQIL7++msxZ84coVKpxIEDB4QQ8m5/IeqO3xban1qOsrIy8eWXX4ovv/xSABBvvvmm+PLLL8Uvv/wihKjf99z9998v7r77bpGTkyOOHj0qunbtKiZOnGitKjVaXW1SVlYmnn32WZGdnS0KCgrEp59+Ku655x7RtWtXUVlZKa1DaW1iie/Fv/76S/Ts2VNERESIU6dOiX379on27duLpKQks2JhgtPMbk1wrl27Jp5++mnRtm1b4eTkJB588EFx8eJF6wVoATt37hQ9e/YUGo1GBAUFidWrVxvNNxgMYu7cucLb21toNBoxfPhwkZeXZ6VoG6e0tFTMmjVLBAQECAcHB3HnnXeKl156yWgoQyXVl/4HgMlHWlqatIycP9+3i//cuXNi8ODBwsPDQ2g0GtGlSxfx3HPPiZKSEusGfpPHHntMdOzYUdjb24v27duL4cOHS8mNEPJufyHqjt8W2p9ajkOHDpnsL2JjY4UQ9fue++OPP8TEiROFs7OzcHV1FVOnThVlZWVWqI1l1NUmFRUVIiIiQrRv316o1WrRsWNH8cQTTxgNfSyE8trEUt+LZ8+eFSNHjhSOjo6iXbt24l//+pfQ6/VmxaL6/wERERERERHZPF6DQ0REREREisEEh4iIiIiIFIMJDhERERERKQYTHGpyQ4YMwZAhQ6Tps2fPQqVSYd26dVaLiYiIiKi+uO9iW5jgKNi6deugUqlw8uRJa4dCRAp1u35myJAh6Nmzp1GZXq/HihUr0K9fP7i4uMDZ2Rn9+vXDihUroNfra6yjU6dOUKlUCA8PN7mNNWvWQKVS1RnH888/D5VKhfHjx5tZQyKyFmvsx2RkZGD58uXNtj1qGq2tHQC1PB07dsS1a9egVqutHQoRNbPy8nJERUUhKysLo0aNwpQpU2BnZ4d9+/Zh1qxZ2LJlC3bv3o02bdoYPc/BwQGHDh1CYWFhjbtZp6enw8HBAZWVlSa3KYTA+vXr0alTJ+zcuRNlZWVwcXFpsjoSke3KyMjA6dOnkZCQYFTOfRfbwiM41OxUKhUcHBzQqlUra4dCRM0sMTERWVlZePvtt7Fz507ExcVhxowZ2L59O9555x1kZWXh2WefrfG8gQMHwtnZGR9//LFR+a+//or/+7//Q1RUVK3bPHz4MH799Ve8//77+Ouvv7BlyxaL14uIlI37LraFCU4LMmXKFDg7O+O3335DdHQ0nJ2d0b59ezz77LOoqqoyWnbDhg0IDg6Gi4sLXF1d0atXL7z11lvS/Pnz50OlUtXYRvXh5LNnz9Yah6nzWM2JjYhs06+//oq1a9di2LBhiI+PrzE/Li4OQ4cOxXvvvYdff/3VaJ6DgwPGjh2LjIwMo/L169ejbdu2iIyMrHW76enp6N69O4YOHYrw8HCkp6dbpkJEZHW//fYbHnvsMXh7e0Oj0aBHjx54//33jZY5fPgwVCoVPv74Y7z44ovw8fFBmzZt8MADD+D8+fPSckOGDMHu3bvxyy+/SKe9durUCQD3XWwNE5wWpqqqCpGRkfD09MR//vMf3HfffXjjjTewevVqaRmtVouJEyeibdu2eP3117FkyRIMGTIEn332mdVjIyJ5KikpweXLl2s8br6mZu/evaiqqsKjjz5a63oeffRR/PXXX9i3b1+NeZMmTcLx48fx008/SWUZGRkYN25craeN6HQ6bN68GRMnTgQATJw4EQcPHkRhYWFDq0pEMlFUVIQBAwbg008/RXx8PN566y106dIF06ZNM3kdzeLFi7F792688MILeOaZZ6DVahEeHo5r164BAF566SX07dsX7dq1w3//+1/897//ve31ONx3kSdeg9PCVFZWYvz48Zg7dy4A4KmnnsI999yDtWvXYsaMGQCA3bt3w9XVFfv372/WQ7H1iY2I5Km2AQAAoEePHgCA7777DgDQp0+fWpetnvf999/XmDds2DD4+Phg/fr1ePnll/H999/j1KlTeOutt/Dzzz+bXN+uXbtQXFyMCRMmAACio6Mxffp0bNiwocY59kRkW1566SVUVVXhm2++gaenJ4Ab+w4TJ07E/Pnz8eSTT8LR0VFa/sqVK/j++++la/DuuecePPzww1izZg2eeeYZjBgxAnfccQf+/PNPPPLII/WKgfsu8sQjOC3QU089ZTT997//3WjnwN3dHeXl5dBqtc0d2m1jIyJ5SklJgVarrfHo3bu3tExZWRkA1HmBf/W80tLSGvNatWqFhx9+GOvXrwdw49Qzf39//P3vf691fenp6QgJCUGXLl2k9UdFRfE0NSIbJ4TA5s2bMXr0aAghjI4cR0ZGoqSkBF988YXRcx599FGj/mfcuHHw9fXFnj17GhUL913kh0dwWhgHBwe0b9/eqKxt27b4888/pemnn34an3zyCUaOHIk77rgDERERePjhh3H//fdbPTYikqf+/fsjJCSkRnnbtm1x+fJlAP9LXqoTHVNulwRNmjQJK1aswFdffYWMjAxMmDDB5PWAAFBcXIw9e/YgPj4e+fn5UvnAgQOxefNm/Pjjj/jb3/5WvwoSkaz8/vvvKC4uxurVq2s9HezSpUtG0127djWaVqlU6NKlS53XDd8O913kiQlOC1OfU868vLxw6tQp7N+/H3v37sXevXuRlpaGRx99FB988AEA1LpD0ZiL6jgyCZGydevWDQDw9ddfo2/fviaX+frrrwEA3bt3Nzk/NDQUnTt3RkJCAgoKCjBp0qRat7dx40bodDq88cYbeOONN2rMT09Px4IFC8ysBRHJgcFgAAA88sgjiI2NNbnMzUeQmwr3XeSJCQ6ZZG9vj9GjR2P06NEwGAx4+umn8e6772Lu3Lno0qUL2rZtC+DGL6Tu7u7S83755RcrRUxEcjdy5Ei0atUK//3vf2sdaODDDz9E69at6zxiPHHiRCxatAjdunWrNVECbiQwPXv2xCuvvFJj3rvvvouMjAwmOEQ2qn379nBxcUFVVVWd1wDe7MyZM0bTQgjk5+cbJUK1/YBLtoXX4FANf/zxh9G0nZ2d9OHX6XQAgM6dOwMAjhw5Ii1XXl4uHeEhIrqVv78/pk6dik8//RSpqak15q9atQoHDx7EtGnT0KFDh1rX8/jjj+OVV14xeVSm2vnz53HkyBE8/PDDGDduXI3H1KlTkZ+fj5ycHIvUjYiaV6tWrRATE4PNmzfj9OnTNeb//vvvNco+/PBDo1NkN23ahIsXL2LkyJFSWZs2bVBSUtI0QVOz4REcquHxxx/HlStXMGzYMHTo0AG//PIL3n77bfTt21c6xSQiIgIBAQGYNm0annvuObRq1Qrvv/8+2rdvj3Pnzlm5BkQkV8uWLcMPP/yAp59+Gvv27ZOO1Ozfvx/bt2+XhlitS8eOHTF//vw6l8nIyIAQAg888IDJ+f/4xz/QunVrpKenIzQ0tEF1IaLm8f7775scOn7+/Pk4dOgQQkND8cQTT6B79+64cuUKvvjiC3z66ae4cuWK0fIeHh4YNGgQpk6diqKiIixfvhxdunTBE088IS0THByMjz/+GImJiejXrx+cnZ0xevToJq8jWRYTHKrhkUcewerVq7Fy5UoUFxfDx8cH48ePx/z582Fnd+Ogn1qtxtatW/H0009j7ty58PHxQUJCAtq2bYupU6dauQZEJFfOzs7IzMzEypUr8dFHH+G5556DEAJBQUFYvnw5nn766VrvaWOO9PR0BAQE1Doktbu7OwYNGoSPP/4Yb775Jlq35tchkVyZOuIL3LjR5vHjx7Fw4UJs2bIFK1euhKenJ3r06IHXX3+9xvIvvvgivv76ayQnJ6OsrAzDhw/HypUr4eTkJC3z9NNP49SpU0hLS8OyZcvQsWNHJjg2SCWEENYOgoiIiIioKRw+fBhDhw7Fxo0bMW7cOGuHQ82A1+AQEREREZFiMMEhIiIiIiLFYIJDRERERESKwWtwiIiIiIhIMXgEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgzZ3dnMYDDgwoULcHFxgUqlsnY4RIoihEBZWRn8/Pykm7a2JOxfiJpGS+9bAPYvRE2lIf2L7BKcCxcuwN/f39phECna+fPn0aFDB2uH0ezYvxA1rZbatwDsX4iamjn9i+wSHBcXFwBAQUEBsrOzERERAbVabeWo5EGv1+PAgQNsk1uwXUwz1S6lpaXw9/eXPmctTXW9z58/D1dXV6mc76HGYfs1nFLarqX3LUDt/Yvc2eJ70BZjBmwzbjnE3JD+RXYJTvVhXRcXFzg5OcHV1dVm3gRNTa/Xs01MYLuYVle7tNTTJ6rr7erqWiPB4Xuo4dh+Dae0tmupfQtQe/8id7b4HrTFmAHbjFtOMZvTv7TME2WJiIiIiEiRmOAQEREREZFiMMEhIiIiIiLFYIJDRERERESKIbtBBszRac5ui67v7JIoi66PiGwX+xciaiqW7F/YtxDVxCM4RERERESkGExwiIiIiIhIMZjgEBERERGRYjDBISIiIiIixWCCQ0REREREisEEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEQkC1VVVZg7dy4CAwPh6OiIzp0749VXX4UQQlpGCIF58+bB19cXjo6OCA8Px5kzZ6wYNRHZgtTUVPTu3Ruurq5wdXVFWFgY9u7dK82vrKxEXFwcPD094ezsjJiYGBQVFVkxYiJqDCY4RCQLr7/+OlJTU/HOO+/g+++/x+uvv46lS5fi7bfflpZZunQpVqxYgVWrViEnJwdt2rRBZGQkKisrrRg5Ecldhw4dsGTJEuTm5uLkyZMYNmwYxowZg2+//RYAMHv2bOzcuRMbN25EVlYWLly4gLFjx1o5aiJqqNbWDoCICAA+//xzjBkzBlFRUQCATp06Yf369Th+/DiAG0dvli9fjpdffhljxowBAHz44Yfw9vbGtm3bMGHCBKvFTkTyNnr0aKPpxYsXIzU1FceOHUOHDh2wdu1aZGRkYNiwYQCAtLQ0dOvWDceOHcOAAQOsETIRNQKP4BCRLNx7773IzMzEjz/+CAD46quvcPToUYwcORIAUFBQgMLCQoSHh0vPcXNzQ2hoKLKzs60SMxHZnqqqKmzYsAHl5eUICwtDbm4u9Hq9Ud8SFBSEgIAA9i1ENopHcIhIFubMmYPS0lIEBQWhVatWqKqqwuLFizF58mQAQGFhIQDA29vb6Hne3t7SvFvpdDrodDppurS0FACg1+uh1+ul8ur/by7TtPrftT+WcPO6lcZU+1H9KKXtbCH+b775BmFhYaisrISzszO2bt2K7t2749SpU7C3t4e7u7vR8nX1LUD9+xdTLNm/NLbtbfE9aIsxA7YZtxxibsi2meAQkSx88sknSE9PR0ZGBnr06IFTp04hISEBfn5+iI2NbdA6k5OTsWDBghrlBw4cgJOTU41yrVYr/b+0f4M2Was9e/ZYdoUydHP7kXlsve0qKiqsHcJt3XXXXTh16hRKSkqwadMmxMbGIisrq8HrM7d/uZkl+xdL9S22+B60xZgB24zbmjE3pH9hgkNEsvDcc89hzpw50rU0vXr1wi+//ILk5GTExsbCx8cHAFBUVARfX1/peUVFRejbt6/JdSYlJSExMVGaLi0thb+/PyIiIuDq6iqV6/V6aLVajBgxAmq1GgDQc/5+i9bv9PxIi65PTky1H9WPUtqu+uiFnNnb26NLly4AgODgYJw4cQJvvfUWxo8fj+vXr6O4uNjoKE5RUZHU75hS3/7FFEv2L43tW2zxPWiLMQO2GbccYm5I/8IEh4hkoaKiAnZ2xpcFtmrVCgaDAQAQGBgIHx8fZGZmSglNaWkpcnJyMGPGDJPr1Gg00Gg0NcrVarXJjvrmcl2VqjHVMblupautXen2bL3tbDF2g8EAnU6H4OBgqNVqZGZmIiYmBgCQl5eHc+fOISwsrNbnm9u/3MyS/Yul2t4W34O2GDNgm3FbM+aGbJcJDhHJwujRo7F48WIEBASgR48e+PLLL/Hmm2/iscceAwCoVCokJCRg0aJF6Nq1KwIDAzF37lz4+fkhOjrausETkawlJSVh5MiRCAgIQFlZGTIyMnD48GHs378fbm5umDZtGhITE+Hh4QFXV1fMnDkTYWFhHEGNyEYxwSEiWXj77bcxd+5cPP3007h06RL8/Pzw5JNPYt68edIyzz//PMrLyzF9+nQUFxdj0KBB2LdvHxwcHKwYORHJ3aVLl/Doo4/i4sWLcHNzQ+/evbF//36MGDECALBs2TLY2dkhJiYGOp0OkZGRWLlypZWjJqKGYoJDRLLg4uKC5cuXY/ny5bUuo1KpsHDhQixcuLD5AiMim7d27do65zs4OCAlJQUpKSnNFBERNSXeB4eIiIiIiBTD7ATnt99+wyOPPAJPT084OjqiV69eOHnypDRfCIF58+bB19cXjo6OCA8Px5kzZywaNBERERERkSlmJTh//vknBg4cCLVajb179+K7777DG2+8gbZt20rLLF26FCtWrMCqVauQk5ODNm3aIDIyEpWVlRYPnoiIiIiI6GZmXYPz+uuvw9/fH2lpaVJZYGCg9L8QAsuXL8fLL7+MMWPGAAA+/PBDeHt7Y9u2bdL9LYiIiIiIiJqCWQnOjh07EBkZiYceeghZWVm444478PTTT+OJJ54AABQUFKCwsBDh4eHSc9zc3BAaGors7GyTCY5Op4NOp5Omq2/mo9frjf6aomklzAn/turalhzUp01aIraLaabahW1ERERESmdWgvPzzz8jNTUViYmJePHFF3HixAk888wzsLe3R2xsLAoLCwEA3t7eRs/z9vaW5t0qOTkZCxYsqFF+6NAhODk5QavV1hrP0v7mRH97e/bssewKm0hdbdKSsV1Mu7ldKioqrBgJERERUdMzK8ExGAwICQnBa6+9BgC4++67cfr0aaxatQqxsbENCiApKQmJiYnSdGlpKfz9/TF06FDk5ORgxIgRtd7BtOf8/Q3aZm1Oz4+06PosTa/XQ6vV1tkmLRHbxTRT7VJ9hJSIiIhIqcxKcHx9fdG9e3ejsm7dumHz5s0AAB8fHwBAUVERfH19pWWKiorQt29fk+vUaDTQaDQ1yqt3yNRqda07rboqlTnh35at7BzX1SYtGdvFtJvbhe1DRKQsnebsbtTzNa0Elva/8aOxrkqFs0uiLBQZkfWYNYrawIEDkZeXZ1T2448/omPHjgBuDDjg4+ODzMxMaX5paSlycnIQFhZmgXCJiIiIiIhqZ9YRnNmzZ+Pee+/Fa6+9hocffhjHjx/H6tWrsXr1agA37jKekJCARYsWoWvXrggMDMTcuXPh5+eH6OjopoifiIiIiIhIYlaC069fP2zduhVJSUlYuHAhAgMDsXz5ckyePFla5vnnn0d5eTmmT5+O4uJiDBo0CPv27YODg4PFgyciIiIiIrqZWQkOAIwaNQqjRo2qdb5KpcLChQuxcOHCRgVGRERERERkLrOuwSEiIiIiIpIzJjhERERERKQYTHCIiIiIiEgxmOAQEREREZFiMMEhIiIiIiLFYIJDRERERESKwQSHiIiIiIgUgwkOEREREREpBhMcIiIiIiJSDCY4RERERESkGExwiIiIiIhIMZjgEJFs/Pbbb3jkkUfg6ekJR0dH9OrVCydPnpTmCyEwb948+Pr6wtHREeHh4Thz5owVIyYiIiK5YYJDRLLw559/YuDAgVCr1di7dy++++47vPHGG2jbtq20zNKlS7FixQqsWrUKOTk5aNOmDSIjI1FZWWnFyImIiEhOWls7ACIiAHj99dfh7++PtLQ0qSwwMFD6XwiB5cuX4+WXX8aYMWMAAB9++CG8vb2xbds2TJgwodljJiIiIvnhERwikoUdO3YgJCQEDz30ELy8vHD33XdjzZo10vyCggIUFhYiPDxcKnNzc0NoaCiys7OtETIRERHJEI/gEJEs/Pzzz0hNTUViYiJefPFFnDhxAs888wzs7e0RGxuLwsJCAIC3t7fR87y9vaV5t9LpdNDpdNJ0aWkpAECv10Ov10vl1f/fXKZpJSxTsVu2oUSm2o/qRyltZ+vxE5GyMMEhIlkwGAwICQnBa6+9BgC4++67cfr0aaxatQqxsbENWmdycjIWLFhQo/zAgQNwcnKqUa7VaqX/l/Zv0CZrtWfPHsuuUIZubj8yj623XUVFhbVDICKSMMEhIlnw9fVF9+7djcq6deuGzZs3AwB8fHwAAEVFRfD19ZWWKSoqQt++fU2uMykpCYmJidJ0aWkp/P39ERERAVdXV6lcr9dDq9VixIgRUKvVAICe8/dbpF7VTs+PtOj65MRU+1H9KKXtqo+OEhHJARMcIpKFgQMHIi8vz6jsxx9/RMeOHQHcGHDAx8cHmZmZUkJTWlqKnJwczJgxw+Q6NRoNNBpNjXK1Wm1yZ/Lmcl2VqjHVMblupautXen2bL3tbDl2IlIeJjhEJAuzZ8/Gvffei9deew0PP/wwjh8/jtWrV2P16tUAAJVKhYSEBCxatAhdu3ZFYGAg5s6dCz8/P0RHR1s3eCIiIpINJjhEJAv9+vXD1q1bkZSUhIULFyIwMBDLly/H5MmTpWWef/55lJeXY/r06SguLsagQYOwb98+ODg4WDFyIiIikhMmOEQkG6NGjcKoUaNqna9SqbBw4UIsXLiwGaMiIiIiW8L74BARERERkWIwwSEiIiJFS05ORr9+/eDi4gIvLy9ER0fXGNSksrIScXFx8PT0hLOzM2JiYlBUVGSliImoMZjgEBERkaJlZWUhLi4Ox44dg1arhV6vR0REBMrLy6VlZs+ejZ07d2Ljxo3IysrChQsXMHbsWCtGTUQNxWtwiIiISNH27dtnNL1u3Tp4eXkhNzcXgwcPRklJCdauXYuMjAwMGzYMAJCWloZu3brh2LFjGDBggDXCJqIGYoJDRERELUpJSQkAwMPDAwCQm5sLvV6P8PBwaZmgoCAEBAQgOzvbZIKj0+mg0+mk6eqbner1euj1+jq3r2klGl0HS9HYCaO/t4tdDqpjtIVYb2aLccsh5oZsmwkOERERtRgGgwEJCQkYOHAgevbsCQAoLCyEvb093N3djZb19vZGYWGhyfUkJydjwYIFNcoPHDgAJyenOmNY2r9hsTelV0MMAIA9e/ZYOZL602q11g6hQWwxbmvGXFFRYfZzmOAQERFRixEXF4fTp0/j6NGjjVpPUlISEhMTpenS0lL4+/sjIiICrq6udT635/z9jdq2JWnsBF4NMWDuSTvoDCqcnh9p7ZBuS6/XQ6vVYsSIEVCr1dYOp95sMW45xFx9dNQcTHCIiIioRYiPj8euXbtw5MgRdOjQQSr38fHB9evXUVxcbHQUp6ioCD4+PibXpdFooNFoapSr1erb7gjqqlQNq0AT0hlU0FWpbGbHG6hfW8uRLcZtzZgbst1GjaK2ZMkSqFQqJCQkSGUcZpGIiIjkRAiB+Ph4bN26FQcPHkRgYKDR/ODgYKjVamRmZkpleXl5OHfuHMLCwpo7XCJqpAYfwTlx4gTeffdd9O7d26h89uzZ2L17NzZu3Ag3NzfEx8dj7Nix+OyzzxodLBEREZG54uLikJGRge3bt8PFxUW6rsbNzQ2Ojo5wc3PDtGnTkJiYCA8PD7i6umLmzJkICwvjCGpENqhBR3CuXr2KyZMnY82aNWjbtq1UXj3M4ptvvolhw4YhODgYaWlp+Pzzz3Hs2DGLBU1ERERUX6mpqSgpKcGQIUPg6+srPT7++GNpmWXLlmHUqFGIiYnB4MGD4ePjgy1btlgxaiJqqAYlOHFxcYiKijIaThG4/TCLRERERM1NCGHyMWXKFGkZBwcHpKSk4MqVKygvL8eWLVtqvf6GiOTN7FPUNmzYgC+++AInTpyoMa8hwyzWNY78zX9NsfQ48nIfl1wOY5HLEdvFNFPtwjYiIiIipTMrwTl//jxmzZoFrVYLBwcHiwRQ2zjyhw4dgpOTU53jblt6HHlbGfvdFsdPbw5sF9NubpeGjCVPREREZEvMSnByc3Nx6dIl3HPPPVJZVVUVjhw5gnfeeQf79+83e5jF2saRHzp0KHJycuocd9vS48jLfex3OYxFLkdsF9NMtUtDxpInIiIisiVmJTjDhw/HN998Y1Q2depUBAUF4YUXXoC/v780zGJMTAyA2w+zWNc48tV/a9tptfQ48rayc2yL46c3B7aLaTe3C9uHiIiIlM6sBMfFxQU9e/Y0KmvTpg08PT2lcg6zSERERERE1tLg++DUZtmyZbCzs0NMTAx0Oh0iIyOxcuVKS2+GiIiIiIiohkYnOIcPHzaarh5mMSUlpbGrJiIiIiIiMovFj+AQERERkW3qNGe3Rdd3dkmURddHVB8NutEnERERERGRHDHBISIiIiIixWCCQ0REREREisEEh4hkacmSJVCpVEhISJDKKisrERcXB09PTzg7OyMmJgZFRUXWC5KIiIhkhwkOEcnOiRMn8O6776J3795G5bNnz8bOnTuxceNGZGVl4cKFCxg7dqyVoiQiIiI5YoJDRLJy9epVTJ48GWvWrEHbtm2l8pKSEqxduxZvvvkmhg0bhuDgYKSlpeHzzz/HsWPHrBgxERERyQkTHCKSlbi4OERFRSE8PNyoPDc3F3q93qg8KCgIAQEByM7Obu4wiYiISKZ4Hxwiko0NGzbgiy++wIkTJ2rMKywshL29Pdzd3Y3Kvb29UVhYaHJ9Op0OOp1Omi4tLQUA6PV66PV6qbz6/5vLNK1Eg+thys3rVhpT7Uf1o5S2s/X4iUhZmOAQkSycP38es2bNglarhYODg0XWmZycjAULFtQoP3DgAJycnGqUa7Va6f+l/S0SgmTPnj2WXaEM3dx+ZB5bb7uKigprh0BEJGGCQ0SykJubi0uXLuGee+6RyqqqqnDkyBG888472L9/P65fv47i4mKjozhFRUXw8fExuc6kpCQkJiZK06WlpfD390dERARcXV2lcr1eD61WixEjRkCtVgMAes7fb9H6nZ4fadH1yYmp9qP6UUrbVR8dJSKSAyY4RCQLw4cPxzfffGNUNnXqVAQFBeGFF16Av78/1Go1MjMzERMTAwDIy8vDuXPnEBYWZnKdGo0GGo2mRrlarTa5M3lzua5K1dgq1Vi30tXWrnR7tt52thw7ESkPExwikgUXFxf07NnTqKxNmzbw9PSUyqdNm4bExER4eHjA1dUVM2fORFhYGAYMGGCNkImIiEiGmOAQkc1YtmwZ7OzsEBMTA51Oh8jISKxcudLaYREREZGMMMEhItk6fPiw0bSDgwNSUlKQkpJinYCIiIhI9ngfHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDGY4BARERERkWIwwSEiIiIiIsVggkNERERERIrBBIeIiIiIiBSD98EhIiIiItnrNGc3AEDTSmBpf6Dn/P3QVakavL6zS6IsFRrJDI/gEBERERGRYjDBISIiIiIixWCCQ0REREREisEEh4iIiIiIFIODDBAREZGiHTlyBP/+97+Rm5uLixcvYuvWrYiOjpbmCyHwyiuvYM2aNSguLsbAgQORmpqKrl27Wi9ohageGICoOfEIDhERESlaeXk5+vTpg5SUFJPzly5dihUrVmDVqlXIyclBmzZtEBkZicrKymaOlIgsgUdwiIiISNFGjhyJkSNHmpwnhMDy5cvx8ssvY8yYMQCADz/8EN7e3ti2bRsmTJjQnKESkQUwwSEiIqIWq6CgAIWFhQgPD5fK3NzcEBoaiuzs7FoTHJ1OB51OJ02XlpYCAPR6PfR6fZ3b1LQSFojcMjR2wuivLbBUzLd7nSytenvNvd3GkEPMDdm2WQlOcnIytmzZgh9++AGOjo6499578frrr+Ouu+6SlqmsrMS//vUvbNiwATqdDpGRkVi5ciW8vb3NDo6IiIioKRUWFgJAjf0Ub29vaZ4pycnJWLBgQY3yAwcOwMnJqc5tLu3fgECb2KshBmuHYLbGxrxnzx4LRWIerVZrle02hjVjrqioMPs5ZiU4WVlZiIuLQ79+/fDXX3/hxRdfREREBL777ju0adMGADB79mzs3r0bGzduhJubG+Lj4zF27Fh89tlnZgdHREREJEdJSUlITEyUpktLS+Hv74+IiAi4urrW+dye8/c3dXj1prETeDXEgLkn7aAzqKwdTr1YKubT8yMtGNXt6fV6aLVajBgxAmq1ulm33VByiLn66Kg5zEpw9u3bZzS9bt06eHl5ITc3F4MHD0ZJSQnWrl2LjIwMDBs2DACQlpaGbt264dixYxgwYIDZARIRERE1FR8fHwBAUVERfH19pfKioiL07du31udpNBpoNJoa5Wq1+rY7groq+SUSOoNKlnHVpbExW2uHvT7vEbmxZswN2W6jrsEpKSkBAHh4eAAAcnNzodfrjc5jDQoKQkBAALKzs00mOHWdw3rzX1MsfQ6r3M+JlMN5kHLEdjHNVLuwjYiIjAUGBsLHxweZmZlSQlNaWoqcnBzMmDHDusERUYM0OMExGAxISEjAwIED0bNnTwA3zmO1t7eHu7u70bJ1ncda2zmshw4dgpOTU53n/Fn6HFZrnYtpLls8d7M5sF1Mu7ldGnIeKxGRrbt69Sry8/Ol6YKCApw6dQoeHh4ICAhAQkICFi1ahK5duyIwMBBz586Fn5+f0b1yiMh2NDjBiYuLw+nTp3H06NFGBVDbOaxDhw5FTk5Onef8Wfoc1uY+F9NccjgPUo7YLqaZapeGnMdKRGTrTp48iaFDh0rT1fsdsbGxWLduHZ5//nmUl5dj+vTpKC4uxqBBg7Bv3z44ODhYK2QiaoQGJTjx8fHYtWsXjhw5gg4dOkjlPj4+uH79OoqLi42O4hQVFUnnuN6qrnNYq//WttNq6XNFu849YLF1nV0SZbF13coWz91sDmwX025uF7YPEbVEQ4YMgRC1n9auUqmwcOFCLFy4sBmjIqKmYmfOwkIIxMfHY+vWrTh48CACAwON5gcHB0OtViMzM1Mqy8vLw7lz5xAWFmaZiIlIkZKTk9GvXz+4uLjAy8sL0dHRyMvLM1qmsrIScXFx8PT0hLOzM2JiYlBUVGSliImIiEiOzEpw4uLi8NFHHyEjIwMuLi4oLCxEYWEhrl27BuDGjbGmTZuGxMREHDp0CLm5uZg6dSrCwsI4ghoR1al6GPpjx45Bq9VCr9cjIiIC5eXl0jKzZ8/Gzp07sXHjRmRlZeHChQsYO3asFaMmIiIiuTHrFLXU1FQANw713iwtLQ1TpkwBACxbtgx2dnaIiYkxutEnEVFdOAw9ERERWYJZCU5d569Wc3BwQEpKClJSUhocFBGRJYahJyIiopanUffBISJqCpYahr6u+2yZuj/QzWUt7T5bjcF7UTWcUtrO1uMnImVhgkNEsmOpYehru8/WgQMH4OTkVKP85nsGtdT7bDUG70XVcLbedrzHFhHJCRMcIpIVSw5DX9t9tiIiIuDq6iqVm7pnUEu7z1Zj8F5UDaeUtuM9tohITpjgEJEsCCEwc+ZMbN26FYcPH65zGPqYmBgAtx+Gvq77bJnamby53NL32bLlndf64r2oGs7W286WY6eWq9Oc3RZbV1Pe/5DMxwSHiGQhLi4OGRkZ2L59uzQMPXBj+HlHR0ejYeg9PDzg6uqKmTNnchh6IiIiMsIEh4hkgcPQExERkSUwwSEiWeAw9ERERGQJdtYOgIiIiIiIyFKY4BARERERkWLwFDUiombA0XqIiIiaB4/gEBERERGRYjDBISIiIiIixeApak3EkqejADwlhYiIiIioPngEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDGY4BARERERkWIwwSEiIiIiIsVggkNERERERIrBG30SEREREcmIJW8Yb+mbxdvCzex5BIeIiIiIiBSDR3CIiGyMLfx6RkREZC1McFog7hwRERERkVLxFDUiIiIiIlIMHsGxEZ3m7IamlcDS/kDP+fuhq1JZOyQiIiIiQv3OjuF+XPPhERwiIiIiIlIMJjhERERERKQYTHCIiIiIiEgxmuwanJSUFPz73/9GYWEh+vTpg7fffhv9+/dvqs0RUQvC/oWImgL7FlKixoyea6vXDTVJgvPxxx8jMTERq1atQmhoKJYvX47IyEjk5eXBy8urKTZJCmLuB7GuDx+HsFYe9i+W19ih42/+DOYtHmWhqIiaF/sWIuVoklPU3nzzTTzxxBOYOnUqunfvjlWrVsHJyQnvv/9+U2yOiFoQ9i9E1BTYtxAph8WP4Fy/fh25ublISkqSyuzs7BAeHo7s7GxLb46oTpa+qamctYSjVexfWh45f4Yt/ZlrSXWVG/YtRMpi8QTn8uXLqKqqgre3t1G5t7c3fvjhhxrL63Q66HQ6abqkpAQAcOXKFVRUVOCPP/6AWq02ua3Wf5VbMHL5a20QqKgwoLXeDlUG+ZwH+ccff1h0fea+rnJtl+Z26+ug1+trfIbKysoAAEKIZo/PEizZv+j1eqncVFu1tP6lMW7+DFq7P2hOlqjrze89W65rS+tbgPr3L6bI6bW2xe9QW4wZsM24myPmpuhfrH6jz+TkZCxYsKBG+d/+9jcrRCN/k6wdgAnt3rB2BPJsl+ZmzutQVlYGNze3pgtGJmrrXwIDA60QjbJVfwbb/duqYTQrOfR9zaW+dW0pfQugrP7FFr9DbTFmwDbjbuqYm6J/sXiC065dO7Rq1QpFRUVG5UVFRfDx8amxfFJSEhITE6Vpg8GAK1euQK1WIyAgAOfPn4erq6ulw7RJpaWl8Pf3Z5vcgu1imql2EUKgrKwMfn5+Vo6uYSzVv3h6ekKl+t8vUXwPNQ7br+GU0nYtrW8B6t+/yJ0tvgdtMWbANuOWQ8wN6V8snuDY29sjODgYmZmZiI6OBnDjQ5+ZmYn4+Pgay2s0Gmg0GqMyd3d3lJaWAgBcXV1t5k3QXNgmprFdTLu1XWz511VL9S+14Xuocdh+DaeEtmtJfQtgfv8id7b4HrTFmAHbjNvaMZvbvzTJKWqJiYmIjY1FSEgI+vfvj+XLl6O8vBxTp05tis0RUQvC/oWImgL7FiLlaJIEZ/z48fj9998xb948FBYWom/fvti3b1+Ni/eIiMzF/oWImgL7FiLlaLJBBuLj42s9rFsfGo0Gr7zySo3Dvy0Z28Q0totpSm6XxvYvt1JyWzUHtl/Dse3kxdJ9iy2wxfegLcYM2GbcthgzAKiErY7pSEREREREdAs7awdARERERERkKUxwiIiIiIhIMZjgEBERERGRYsgywUlJSUGnTp3g4OCA0NBQHD9+3NohNavk5GT069cPLi4u8PLyQnR0NPLy8oyWqaysRFxcHDw9PeHs7IyYmJgaNyhTsiVLlkClUiEhIUEqa6lt8ttvv+GRRx6Bp6cnHB0d0atXL5w8eVKaL4TAvHnz4OvrC0dHR4SHh+PMmTNWjFh+WnqfU19HjhzB6NGj4efnB5VKhW3bthnN53utduzXyZqU8P6zpe99W/terqqqwty5cxEYGAhHR0d07twZr776Km6+TF9uMd+WkJkNGzYIe3t78f7774tvv/1WPPHEE8Ld3V0UFRVZO7RmExkZKdLS0sTp06fFqVOnxD/+8Q8REBAgrl69Ki3z1FNPCX9/f5GZmSlOnjwpBgwYIO69914rRt18jh8/Ljp16iR69+4tZs2aJZW3xDa5cuWK6Nixo5gyZYrIyckRP//8s9i/f7/Iz8+XllmyZIlwc3MT27ZtE1999ZV44IEHRGBgoLh27ZoVI5cP9jn1t2fPHvHSSy+JLVu2CABi69atRvP5Xqsd+3WyJlt//9nS974tfi8vXrxYeHp6il27domCggKxceNG4ezsLN566y3Zxnw7sktw+vfvL+Li4qTpqqoq4efnJ5KTk60YlXVdunRJABBZWVlCCCGKi4uFWq0WGzdulJb5/vvvBQCRnZ1trTCbRVlZmejatavQarXivvvukzq6ltomL7zwghg0aFCt8w0Gg/Dx8RH//ve/pbLi4mKh0WjE+vXrmyNE2WOf0zC3Jjh8r5mH/TpZky29/2zte98Wv5ejoqLEY489ZlQ2duxYMXnyZCGEPGO+HVmdonb9+nXk5uYiPDxcKrOzs0N4eDiys7OtGJl1lZSUAAA8PDwAALm5udDr9UbtFBQUhICAAMW3U1xcHKKioozqDrTcNtmxYwdCQkLw0EMPwcvLC3fffTfWrFkjzS8oKEBhYaFRu7i5uSE0NFTR7VJf7HMsh+8187BfJ2uypfefrX3v2+L38r333ovMzEz8+OOPAICvvvoKR48exciRI2Ub8+002Y0+G+Ly5cuoqqqqcddgb29v/PDDD1aKyroMBgMSEhIwcOBA9OzZEwBQWFgIe3t7uLu7Gy3r7e2NwsJCK0TZPDZs2IAvvvgCJ06cqDGvpbbJzz//jNTUVCQmJuLFF1/EiRMn8Mwzz8De3h6xsbFS3U19ppTcLvXFPsdy+F6rP/brZE229P6zxe99W/xenjNnDkpLSxEUFIRWrVqhqqoKixcvxuTJkwHYZv8uqwSHaoqLi8Pp06dx9OhRa4diVefPn8esWbOg1Wrh4OBg7XBkw2AwICQkBK+99hoA4O6778bp06exatUqxMbGWjk6IjKF/TpZk628/2z1e98Wv5c/+eQTpKenIyMjAz169MCpU6eQkJAAPz8/2cZ8O7I6Ra1du3Zo1apVjREwioqK4OPjY6WorCc+Ph67du3CoUOH0KFDB6ncx8cH169fR3FxsdHySm6n3NxcXLp0Cffccw9at26N1q1bIysrCytWrEDr1q3h7e3d4toEAHx9fdG9e3ejsm7duuHcuXMAINWdnynT2OdYDt9r9cN+nazJlt5/tvq9b4vfy8899xzmzJmDCRMmoFevXvjnP/+J2bNnIzk5GYA8Y74dWSU49vb2CA4ORmZmplRmMBiQmZmJsLAwK0bWvIQQiI+Px9atW3Hw4EEEBgYazQ8ODoZarTZqp7y8PJw7d06x7TR8+HB88803OHXqlPQICQnB5MmTpf9bWpsAwMCBA2sM9fnjjz+iY8eOAIDAwED4+PgYtUtpaSlycnIU3S71xT7Hcvheqxv7dbImW3z/2er3vi1+L1dUVMDOzjglaNWqFQwGAwB5xnxbVh7koIYNGzYIjUYj1q1bJ7777jsxffp04e7uLgoLC60dWrOZMWOGcHNzE4cPHxYXL16UHhUVFdIyTz31lAgICBAHDx4UJ0+eFGFhYSIsLMyKUTe/m0dTEaJltsnx48dF69atxeLFi8WZM2dEenq6cHJyEh999JG0zJIlS4S7u7vYvn27+Prrr8WYMWNkPbRjc2OfU39lZWXiyy+/FF9++aUAIN58803x5Zdfil9++UUIwfdaXdivkzUp5f1nC9/7tvi9HBsbK+644w5pmOgtW7aIdu3aieeff162Md+O7BIcIYR4++23RUBAgLC3txf9+/cXx44ds3ZIzQqAyUdaWpq0zLVr18TTTz8t2rZtK5ycnMSDDz4oLl68aL2greDWjq6ltsnOnTtFz549hUajEUFBQWL16tVG8w0Gg5g7d67w9vYWGo1GDB8+XOTl5VkpWnlq6X1OfR06dMhk3xQbGyuE4HutLuzXyZqU8v6zle99W/teLi0tFbNmzRIBAQHCwcFB3HnnneKll14SOp1OtjHfjkqIm25TSkREREREZMNkdQ0OERERERFRYzDBISIiIiIixWCCQ0REREREisEEh4iIiIiIFIMJjgKsW7cOKpUKZ8+elcqGDBmCIUOGWC0mazh79ixUKhXWrVtn7VCIqJHmz58PlUplsfVNmTIFnTp1stj6iIhIvpjgyMzKlSuhUqkQGhpq7VBkKyMjA8uXL7d2GEQtRvWPKNWP1q1b44477sCUKVPw22+/WTs8yYULFzB//nycOnXK2qEQkYXc3P8cPXq0xnwhBPz9/aFSqTBq1CijeZWVlVi2bBlCQ0Ph5uYGBwcH/O1vf0N8fDx+/PFHabnqH1SqH05OTujevTtefvlllJaWmozr+PHjePrpp6WbpFryBxlqvNbWDoCMpaeno1OnTjh+/Djy8/PRpUuXBq3nwIEDFo5MPjIyMnD69GkkJCQYlXfs2BHXrl2DWq22TmBECrdw4UIEBgaisrISx44dw7p163D06FGcPn0aDg4OFt3Wyy+/jDlz5pj1nAsXLmDBggXo1KkT+vbtazRvzZo10l25icj2ODg4ICMjA4MGDTIqz8rKwq+//gqNRmNUfvnyZdx///3Izc3FqFGjMGnSJDg7OyMvLw8bNmzA6tWrcf36daPnpKamwtnZGVevXsWBAwewePFiHDx4EJ999lmNBGbPnj1477330Lt3b9x5551GCRNZH4/gyEhBQQE+//xzvPnmm2jfvj3S09MbvC57e3vY29tbMDr5U6lUcHBwQKtWrawdCpEijRw5Eo888ggef/xxvPfee3j22Wfx008/YceOHRbfVuvWrS2aNKnV6ho7QERkO/7xj39g48aN+Ouvv4zKMzIyEBwcDB8fH6PyKVOm4Msvv8SmTZuwc+dOzJo1C9OmTcPSpUtx5swZPPPMMzW2MW7cODzyyCN46qmnsGXLFowdOxbZ2dk4duxYjWVnzJiBkpISnDx5EiNGjLBsZanRmODISHp6Otq2bYuoqCiMGzfOZILz7bffYtiwYXB0dESHDh2waNEik79K3noNzuHDh6FSqfDxxx/jxRdfhI+PD9q0aYMHHngA58+fr/H8jRs3Ijg4GI6OjmjXrh0eeeSRGqeiTJkyBc7Ozvjtt98QHR0NZ2dntG/fHs8++yyqqqqMljUYDFi+fDl69OgBBwcHeHt748knn8Sff/5ZY9t79+7FfffdBxcXF7i6uqJfv37IyMiQ6rV792788ssv0qHk6vPqTV2DY06MRGSev//97wCAn376SSr74YcfMG7cOHh4eMDBwQEhISE1EiC9Xo8FCxaga9eucHBwgKenJwYNGgStVistY+oaHK1Wi0GDBsHd3R3Ozs6466678OKLLwK40cf169cPADB16lSpf6juD269Bqe6v/jPf/6D1atXo3PnztBoNOjXrx9OnDhhsTYiIsuYOHEi/vjjD6N+4vr169i0aRMmTZpktGxOTg52796NadOmISYmpsa6NBoN/vOf/9x2m8OGDQNw4wfoW3l7e8PR0dHcalAz4SlqMpKeno6xY8fC3t4eEydORGpqKk6cOCF9aRcWFmLo0KH466+/MGfOHLRp0warV6826wO2ePFiqFQqvPDCC7h06RKWL1+O8PBwnDp1SlrPunXrMHXqVPTr1w/JyckoKirCW2+9hc8++wxffvkl3N3dpfVVVVUhMjISoaGh+M9//oNPP/0Ub7zxBjp37owZM2ZIyz355JPSep955hkUFBTgnXfewZdffonPPvtMOq1s3bp1eOyxx9CjRw8kJSXB3d0dX375Jfbt24dJkybhpZdeQklJCX799VcsW7YMAODs7FxnnesbIxGZp3pgk7Zt2wK48QPMwIEDcccdd0h91CeffILo6Ghs3rwZDz74IIAbyUtycjIef/xx9O/fH6WlpTh58iS++OKLWn8J/fbbbzFq1Cj07t0bCxcuhEajQX5+Pj777DMAQLdu3bBw4ULMmzcP06dPl5Kve++9t846ZGRkoKysDE8++SRUKhWWLl2KsWPH4ueff+bprkQy0qlTJ4SFhWH9+vUYOXIkgBs/iJaUlGDChAlYsWKFtGz1jyr//Oc/G7XN6h9vPD09G7UesgJBsnDy5EkBQGi1WiGEEAaDQXTo0EHMmjVLWiYhIUEAEDk5OVLZpUuXhJubmwAgCgoKpPL77rtP3HfffdL0oUOHBABxxx13iNLSUqn8k08+EQDEW2+9JYQQ4vr168LLy0v07NlTXLt2TVpu165dAoCYN2+eVBYbGysAiIULFxrV5e677xbBwcHS9P/93/8JACI9Pd1ouX379hmVFxcXCxcXFxEaGmq07er2qBYVFSU6duxYow0LCgoEAJGWlmZ2jERUu7S0NAFAfPrpp+L3338X58+fF5s2bRLt27cXGo1GnD9/XgghxPDhw0WvXr1EZWWl9FyDwSDuvfde0bVrV6msT58+Iioqqs5tvvLKK+Lmr6hly5YJAOL333+v9TknTpyo0QdUi42NNeo3qvsLT09PceXKFal8+/btAoDYuXNnnfERUfOo7n9OnDgh3nnnHeHi4iIqKiqEEEI89NBDYujQoUIIITp27Cj1Kw8++KAAIP788896baO6v8nLyxO///67KCgoEO+++67QaDTC29tblJeX1/n8uLg4wV1qeeEpajKRnp4Ob29vDB06FMCN60nGjx+PDRs2SKdS7dmzBwMGDED//v2l57Vv3x6TJ0+u93YeffRRuLi4SNPjxo2Dr68v9uzZAwA4efIkLl26hKefftro/PeoqCgEBQVh9+7dNdb51FNPGU3//e9/x88//yxNb9y4EW5ubhgxYgQuX74sPYKDg+Hs7IxDhw4BuHH6SVlZGebMmVPj3PvGjk5yuxiJ6PbCw8PRvn17+Pv7Y9y4cWjTpg127NiBDh064MqVKzh48CAefvhhlJWVSZ/zP/74A5GRkThz5ox0mqu7uzu+/fZbnDlzpt7brj5yvH37dosOFjB+/HjpCBTwv9Pu2D8Qyc/DDz+Ma9euYdeuXSgrK8OuXbtqnJ4GQBr57Ob9nfq466670L59ewQGBuLJJ59Ely5dsHv3bjg5OVkkfmo+THBkoKqqChs2bMDQoUNRUFCA/Px85OfnIzQ0FEVFRcjMzAQA/PLLL+jatWuN599111313tatz1epVOjSpYt0qskvv/xS6zqDgoKk+dUcHBzQvn17o7K2bdsaXVtz5swZlJSUwMvLC+3btzd6XL16FZcuXQLwv0PBPXv2rHd96qM+MRLR7aWkpECr1WLTpk34xz/+gcuXL0sX7ufn50MIgblz59b4nL/yyisAIH3WFy5ciOLiYvztb39Dr1698Nxzz+Hrr7+uc9vjx4/HwIED8fjjj8Pb2xsTJkzAJ5980uhkJyAgwGi6Otlh/0AkP+3bt0d4eDgyMjKwZcsWVFVVYdy4cTWWc3V1BQCUlZWZtf7NmzdDq9Xi8OHDyM/Px+nTpxEcHGyR2Kl58RocGTh48CAuXryIDRs2YMOGDTXmp6enIyIiwgqR3V59RiwzGAzw8vKqdVS4W5MPS+OoakSW0b9/f4SEhAAAoqOjMWjQIEyaNAl5eXlSovHss88iMjLS5POrh70fPHgwfvrpJ2zfvh0HDhzAe++9h2XLlmHVqlV4/PHHTT7X0dERR44cwaFDh7B7927s27cPH3/8MYYNG4YDBw40+HNe2/OEEA1aHxE1rUmTJuGJJ55AYWEhRo4caXRdcLWgoCAAwDfffCMdla2PwYMHo127dpYKlayICY4MpKenw8vLCykpKTXmbdmyBVu3bsWqVavQsWNHk6d05OXl1Xtbtz5fCIH8/Hz07t0bwI17yVSvs3r0kJu3Uz3fHJ07d8ann36KgQMH1jkgQufOnQEAp0+frvP+P7yZFpH1tWrVCsnJyRg6dCjeeecdPPbYYwBuDMccHh5+2+d7eHhg6tSpmDp1Kq5evYrBgwdj/vz5tSY4AGBnZ4fhw4dj+PDhePPNN/Haa6/hpZdewqFDhxAeHs6+gagFePDBB/Hkk0/i2LFj+Pjjj00uM3r0aCQnJ+Ojjz4yK8Eh5eApalZ27do1bNmyBaNGjcK4ceNqPOLj41FWVoYdO3bgH//4B44dO4bjx49Lz//999/Nul/Ohx9+aHTIdtOmTbh48aI0IklISAi8vLywatUq6HQ6abm9e/fi+++/R1RUlNl1fPjhh1FVVYVXX321xry//voLxcXFAICIiAi4uLggOTkZlZWVRsvd/GtqmzZtUFJSYnYcRGRZQ4YMQf/+/bF8+XK4urpiyJAhePfdd3Hx4sUay/7+++/S/3/88YfRPGdnZ3Tp0sWoz7nVlStXapRV38yz+nlt2rQBAKlPISLlcXZ2RmpqKubPn4/Ro0ebXCYsLAz3338/3nvvPWzbtq3G/OvXr+PZZ5+t9zZ/+ukno+HwSf54BMfKduzYgbKyMjzwwAMm5w8YMEC66ee7776L//73v7j//vsxa9YsaZjojh073vb89WoeHh4YNGgQpk6diqKiIixfvhxdunTBE088AeDGr6+vv/46pk6divvuuw8TJ06Uhonu1KkTZs+ebXYd77vvPjz55JNITk7GqVOnEBERAbVajTNnzmDjxo146623MG7cOLi6umLZsmV4/PHH0a9fP0yaNAlt27bFV199hYqKCnzwwQcAgODgYHz88cdITExEv3794OzsXGsnR0RN67nnnsNDDz2EdevWISUlBYMGDUKvXr3wxBNP4M4770RRURGys7Px66+/4quvvgIAdO/eHUOGDEFwcDA8PDxw8uRJbNq0CfHx8bVuZ+HChThy5AiioqLQsWNHXLp0CStXrkSHDh2kO5t37twZ7u7uWLVqFVxcXNCmTRuEhoYiMDCwWdqCiJpHbGzsbZf58MMPERERgbFjx2L06NEYPnw42rRpgzNnzmDDhg24ePFive6FAwDDhw8H8L+h8YEb1yz/97//BXBjgCYAWLRoEYAbZ8M0dohqaiTrDuJGo0ePFg4ODnUOQThlyhShVqvF5cuXxddffy3uu+8+4eDgIO644w7x6quvirVr19Z7mOj169eLpKQk4eXlJRwdHUVUVJT45Zdfamzz448/FnfffbfQaDTCw8NDTJ48Wfz6669Gy8TGxoo2bdrUeO6tw7tWW716tQgODhaOjo7CxcVF9OrVSzz//PPiwoULRsvt2LFD3HvvvcLR0VG4urqK/v37i/Xr10vzr169KiZNmiTc3d0FAGno19qGiTYnRiKq6eZhWm9VVVUlOnfuLDp37iz++usv8dNPP4lHH31U+Pj4CLVaLe644w4xatQosWnTJuk5ixYtEv379xfu7u7C0dFRBAUFicWLF4vr169Ly9z6Gc3MzBRjxowRfn5+wt7eXvj5+YmJEyeKH3/80Sie7du3i+7du4vWrVsb9Qe1DRP973//u0adAIhXXnmlga1FRJZUV/9zs5uHia5WUVEh/vOf/4h+/foJZ2dnYW9vL7p27Spmzpwp8vPzpeWq+5vahqHv2LFjjdtTVO9XmXrcvP9F1qESgldStgSHDx/G0KFDsXHjRpMjjhARERERKQGvwSEiIiIiIsVggkNERERERIrBBIeIiIiIiBSD1+AQEREREZFi8AgOEREREREpBhMcIiIiIiJSDNnd6NNgMODChQtwcXGBSqWydjhEiiKEQFlZGfz8/GBn1/J+32D/QtQ0WnrfArB/IWoqDepfrHgPHpPOnz9f642T+OCDD8s8zp8/b+2Pukm//vqrmDx5svDw8BAODg6iZ8+eRjd3MxgMYu7cucLHx0c4ODiI4cOH17jRY13Yv/DBR9M+5Nq3NAf2L3zw0bQPc/oX2R3BcXFxAQC89957iI6OhlqttnJElqPX63HgwAFERESwXjZAifUqLS2Fv7+/9DmTkz///BMDBw7E0KFDsXfvXrRv3x5nzpxB27ZtpWWWLl2KFStW4IMPPkBgYCDmzp2LyMhIfPfdd3BwcLjtNqrrXVBQgOzsbJt/bZXwHlVCHQBl1KMxdZBz39Jcqut+/vx5uLq6KuI9YY6WVF/WtXk1pH+RXYJTfVjXyckJrq6uinrj6PV61suGKLVeAGR5+sTrr78Of39/pKWlSWWBgYHS/0IILF++HC+//DLGjBkDAPjwww/h7e2Nbdu2YcKECbfdRnW9XVxcFPHaKuE9qoQ6AMqohyXqIMe+pblU193V1VVKcGz9PWGOllRf1tU6zOlfWuaJskQkOzt27EBISAgeeugheHl54e6778aaNWuk+QUFBSgsLER4eLhU5ubmhtDQUGRnZ1sjZCIiIpIh2R3BIaKW6eeff0ZqaioSExPx4osv4sSJE3jmmWdgb2+P2NhYFBYWAgC8vb2Nnuft7S3Nu5VOp4NOp5OmS0tLAdz4Rermv7ZKCfVQQh0AZdSjMXWw5XoTkfIwwSEiWTAYDAgJCcFrr70GALj77rtx+vRprFq1CrGxsQ1aZ3JyMhYsWFCj/NChQ3BycoJWq21UzHKhhHoooQ6AMurRkDpUVFQ0QSRERA3DBIeIZMHX1xfdu3c3KuvWrRs2b94MAPDx8QEAFBUVwdfXV1qmqKgIffv2NbnOpKQkJCYmStPVFyoOHToUOTk5GDFihNXPKW4MvV4PrVZr0/VQQh0AZdSjMXWoPjpKRCQHTHBu0mnObout6+ySKIuti6glGDhwIPLy8ozKfvzxR3Ts2BHAjQEHfHx8kJmZKSU0paWlyMnJwYwZM0yuU6PRQKPR1Civ3nlTq9W17shZsj8AmrZPqKsetkIJdQCUUY+G1MHW62zLbKmvImouTHCISBZmz56Ne++9F6+99hoefvhhHD9+HKtXr8bq1asB3Bg9JSEhAYsWLULXrl2lYaL9/PwQHR1t3eCJiIhINpjgEJEs9OvXD1u3bkVSUhIWLlyIwMBALF++HJMnT5aWef7551FeXo7p06ejuLgYgwYNwr59++p1DxwiIiJqGZjgEJFsjBo1CqNGjap1vkqlwsKFC7Fw4cJmjIqIiIhsCe+DQ0REREREisEEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDGY4BARERERkWIwwSEiIiIiIsVggkNERERERIrBBIeIiIiIiBSDCQ4RERERESkGExwiIiIiIlIMJjhERERERKQYTHCIiIiIiEgxmOAQEREREZFiMMEhIiIiIiLFYIJDRERERESKwQSHiIiIiIgUgwkOEREREREpBhMcIiIiIiJSDCY4RERERESkGExwiIiIiIhIMZjgEBERERGRYpiV4KSmpqJ3795wdXWFq6srwsLCsHfvXml+ZWUl4uLi4OnpCWdnZ8TExKCoqMjiQRMREREREZliVoLToUMHLFmyBLm5uTh58iSGDRuGMWPG4NtvvwUAzJ49Gzt37sTGjRuRlZWFCxcuYOzYsU0SOBERERER0a1am7Pw6NGjjaYXL16M1NRUHDt2DB06dMDatWuRkZGBYcOGAQDS0tLQrVs3HDt2DAMGDLBc1ERERERERCY0+BqcqqoqbNiwAeXl5QgLC0Nubi70ej3Cw8OlZYKCghAQEIDs7GyLBEtERERERFQXs47gAMA333yDsLAwVFZWwtnZGVu3bkX37t1x6tQp2Nvbw93d3Wh5b29vFBYW1ro+nU4HnU4nTZeWlkr/6/V6c8NrFE0rYbF1mYq9uqy569XUWC/boaS6EBEREZlidoJz11134dSpUygpKcGmTZsQGxuLrKysBgeQnJyMBQsWmJyn1WobvN6GWNrfcuvas2dPrfOau17NhfWSv4qKCmuHQERELUinObsttq6zS6Isti5SNrMTHHt7e3Tp0gUAEBwcjBMnTuCtt97C+PHjcf36dRQXFxsdxSkqKoKPj0+t60tKSkJiYqI0XVpaCn9/fwDAiBEjoFarzQ2xwXrO32+xdZ2eH1mjTK/XQ6vVNnu9mhrrZTtuPkJKREREpERmJzi3MhgM0Ol0CA4OhlqtRmZmJmJiYgAAeXl5OHfuHMLCwmp9vkajgUajMTlPrVY3646lrkplsXXVFXdz16u5sF7yp5R6EBEREdXGrAQnKSkJI0eOREBAAMrKypCRkYHDhw9j//79cHNzw7Rp05CYmAgPDw+4urpi5syZCAsL4whqRERERETULMxKcC5duoRHH30UFy9ehJubG3r37o39+/djxIgRAIBly5bBzs4OMTEx0Ol0iIyMxMqVK5skcCIiIiIioluZleCsXbu2zvkODg5ISUlBSkpKo4IiIiIiIiJqiEZfg0NEREREytDQUc80rQSW9r8xYJMlr2kmaogG3+iTiIiIyBYkJyejX79+cHFxgZeXF6Kjo5GXl2e0TGVlJeLi4uDp6QlnZ2fExMSgqKjIShETUWMwwSEiIiJFy8rKQlxcHI4dOwatVgu9Xo+IiAiUl5dLy8yePRs7d+7Exo0bkZWVhQsXLmDs2LFWjJqIGoqnqBEREZGi7du3z2h63bp18PLyQm5uLgYPHoySkhKsXbsWGRkZGDZsGAAgLS0N3bp1w7FjxzgaLJGNYYJDRERELUpJSQkAwMPDAwCQm5sLvV6P8PBwaZmgoCAEBAQgOzvbZIKj0+mg0+mk6eobKev1eulRPd2UNK1Ek66/vjR2wuhvU2jqtqyv5npt5UAOdW3ItpngEBERUYthMBiQkJCAgQMHomfPngCAwsJC2Nvbw93d3WhZb29vFBYWmlxPcnIyFixYUKP8wIEDcHJykqa1Wq3lgjdhaf8mXb3ZXg0xNNm69+zZ02Trboimfm3lxJp1raioMPs5THCISJaWLFmCpKQkzJo1C8uXLwdw4yLgf/3rX9iwYYPRvba8vb2tGywR2Yy4uDicPn0aR48ebdR6kpKSkJiYKE2XlpbC398fERERcHV1hV6vh1arxYgRI6BWqxsbdq16zt/fZOs2h8ZO4NUQA+aetIPO0DSjqJ2eH9kk6zVXc722ciCHulYfHTUHExwikp0TJ07g3XffRe/evY3KZ8+ejd27d2Pjxo1wc3NDfHw8xo4di88++8xKkRKRLYmPj8euXbtw5MgRdOjQQSr38fHB9evXUVxcbHQUp6ioCD4+PibXpdFooNFoapSr1WqjHcFbpy1NbkMy6wyqJotJbslEU7+2cmLNujZkuxxFjYhk5erVq5g8eTLWrFmDtm3bSuXVFwG/+eabGDZsGIKDg5GWlobPP/8cx44ds2LERCR3QgjEx8dj69atOHjwIAIDA43mBwcHQ61WIzMzUyrLy8vDuXPnEBYW1tzhElEjMcEhIlmJi4tDVFSU0cW+wO0vAiYiqk1cXBw++ugjZGRkwMXFBYWFhSgsLMS1a9cAAG5ubpg2bRoSExNx6NAh5ObmYurUqQgLC+MIakQ2yKZPUWvo3XaJSJ42bNiAL774AidOnKgxryEXAdc1ytHNf02x9MhETTECjRxGt2ksJdQBUEY9GlMHudc7NTUVADBkyBCj8rS0NEyZMgUAsGzZMtjZ2SEmJsboGj8isj02neAQkXKcP38es2bNglarhYODg0XWWdsoR4cOHYKTk1Odo8JYemSiphz9Rwkj+SihDoAy6tGQOjRklKPmJMTtf7BwcHBASkoKUlJSmiEiImpKTHCISBZyc3Nx6dIl3HPPPVJZVVUVjhw5gnfeeQf79+83+yLg2kY5Gjp0KHJycuocFcbSIxM1xeg/chjdprGUUAdAGfVoTB0aMsoREVFTYYJDRLIwfPhwfPPNN0ZlU6dORVBQEF544QX4+/tLFwHHxMQAuP1FwHWNclT9t7YdOUuPAtSUO71KGMlHCXUAlFGPhtTB1utMRMrCBKeJmLo+SNNKYGn/G78Mm7vzdHZJlKVCI5IlFxcX6aZ71dq0aQNPT0+pvPoiYA8PD7i6umLmzJm8CJiIiIiMMMEhIpvBi4CJiIjodpjgEJFsHT582GiaFwETERHR7fA+OEREREREpBhMcIiIiIiISDGY4BARERERkWIwwSEiIiIiIsXgIANEREREzcjUrSSIyHJ4BIeIiIiIiBSDCQ4RERERESkGExwiIiIiIlIMJjhERERERKQYTHCIiIiIiEgxmOAQEREREZFiMMEhIiIiIiLFMCvBSU5ORr9+/eDi4gIvLy9ER0cjLy/PaJnKykrExcXB09MTzs7OiImJQVFRkUWDJiIiIiIiMsWsBCcrKwtxcXE4duwYtFot9Ho9IiIiUF5eLi0ze/Zs7Ny5Exs3bkRWVhYuXLiAsWPHWjxwIiIiIiKiW7U2Z+F9+/YZTa9btw5eXl7Izc3F4MGDUVJSgrVr1yIjIwPDhg0DAKSlpaFbt244duwYBgwYYLnIiYiIiIiIbmFWgnOrkpISAICHhwcAIDc3F3q9HuHh4dIyQUFBCAgIQHZ2tskER6fTQafTSdOlpaXS/3q9vs7ta1qJxoTf7DR2wuivOW7XFtZUHZucY2wIJdZLSXUhIiIiMqXBCY7BYEBCQgIGDhyInj17AgAKCwthb28Pd3d3o2W9vb1RWFhocj3JyclYsGCByXlarbbOGJb2Nz9uOXg1xGD2c/bs2dMEkVjW7V4vW6WkelVUVFg7BCIiIqIm1eAEJy4uDqdPn8bRo0cbFUBSUhISExOl6dLSUvj7+wMARowYAbVaXetze87f36htNzeNncCrIQbMPWkHnUFl1nNPz49soqgaT6/XQ6vV3vb1sjVKrNfNR0iJiIiIlKhBCU58fDx27dqFI0eOoEOHDlK5j48Prl+/juLiYqOjOEVFRfDx8TG5Lo1GA41GY3KeWq2uc8dSV2VekiAXOoPK7NhtYQf7dq+XrVJSvZRSDyIiIqLamDWKmhAC8fHx2Lp1Kw4ePIjAwECj+cHBwVCr1cjMzJTK8vLycO7cOYSFhVkmYiIiIiIiolqYdQQnLi4OGRkZ2L59O1xcXKTratzc3ODo6Ag3NzdMmzYNiYmJ8PDwgKurK2bOnImwsDCOoEZERERERE3OrAQnNTUVADBkyBCj8rS0NEyZMgUAsGzZMtjZ2SEmJgY6nQ6RkZFYuXKlRYIlIrJVnebstti6zi6Jsti6iIiIlMasBEeI2w9v7ODggJSUFKSkpDQ4KCIiIiIiooYw6xocIiIiIiIiOWOCQ0REREREisEEh4iIiIiIFIMJDhERERERKQYTHCIiIiIiUgwmOEREREREpBhMcIiIiIiISDGY4BARERERkWKYdaNPIiIiIiJr6DRnt0XXd3ZJlEXXR/LBIzhERERERKQYTHCIiIiIiEgxmOAQEREREZFiMMEhIiIiIiLFYIJDRERERESKwQSHiIiIFO3IkSMYPXo0/Pz8oFKpsG3bNqP5QgjMmzcPvr6+cHR0RHh4OM6cOWOdYImo0ZjgEBERkaKVl5ejT58+SElJMTl/6dKlWLFiBVatWoWcnBy0adMGkZGRqKysbOZIicgSeB8cIiIiUrSRI0di5MiRJucJIbB8+XK8/PLLGDNmDADgww8/hLe3N7Zt24YJEyY0Z6hEZAE8gkNEspCcnIx+/frBxcUFXl5eiI6ORl5entEylZWViIuLg6enJ5ydnRETE4OioiIrRUxESlBQUIDCwkKEh4dLZW5ubggNDUV2drYVIyOihuIRHCKShaysLMTFxaFfv37466+/8OKLLyIiIgLfffcd2rRpAwCYPXs2du/ejY0bN8LNzQ3x8fEYO3YsPvvsMytHT0S2qrCwEADg7e1tVO7t7S3NM0Wn00Gn00nTpaWlAAC9Xi89qqdvpWklGh233GjshNFfW2DqtTHneQ19vi2RQ10bsm0mOEQkC/v27TOaXrduHby8vJCbm4vBgwejpKQEa9euRUZGBoYNGwYASEtLQ7du3XDs2DEMGDDAGmETUQuVnJyMBQsW1Cg/cOAAnJycpGmtVltjmaX9mzQ0q3o1xGDtEOptz549jXq+qddWqaxZ14qKCrOfwwSHiGSppKQEAODh4QEAyM3NhV6vNzqNJCgoCAEBAcjOzjaZ4NT1C+vNf02R8y+st8Zvy78iKqEOgDLq0Zg62HK9fXx8AABFRUXw9fWVyouKitC3b99an5eUlITExERpurS0FP7+/oiIiICrqyv0ej20Wi1GjBgBtVpt9Nye8/dbthIyoLETeDXEgLkn7aAzqKwdTr2cnh/ZoOfV9doqjRzqWv3dbQ4mOEQkOwaDAQkJCRg4cCB69uwJ4MZpJPb29nB3dzdatq7TSGr7hfXQoUNwcnKq8xcpOf/Ceuuvjkr4FVEJdQCUUY+G1KEhv7DKRWBgIHx8fJCZmSklNKWlpcjJycGMGTNqfZ5Go4FGo6lRrlarjXYEb50GAF2VbSQADaEzqGymfo3dYTf12iqVNevakO0ywSEi2YmLi8Pp06dx9OjRRq2ntl9Yhw4dipycnDp/kZLzL6zVvzrK4Ze1xlJCHQBl1KMxdWjIL6zN6erVq8jPz5emCwoKcOrUKXh4eCAgIAAJCQlYtGgRunbtisDAQMydOxd+fn6Ijo62XtBE1GBMcIhIVuLj47Fr1y4cOXIEHTp0kMp9fHxw/fp1FBcXGx3FKSoqkk4xuVVdv7BW/61tR07Ov0DeGrMSfkVUQh0AZdSjIXWQe51PnjyJoUOHStPVP3zExsZi3bp1eP7551FeXo7p06ejuLgYgwYNwr59++Dg4GCtkImoEZjgEJEsCCEwc+ZMbN26FYcPH0ZgYKDR/ODgYKjVamRmZiImJgYAkJeXh3PnziEsLMwaIRORjRgyZAiEqP26OpVKhYULF2LhwoXNGBURNRUmOEQkC3FxccjIyMD27dvh4uIiXVfj5uYGR0dHuLm5Ydq0aUhMTISHhwdcXV0xc+ZMhIWFcQQ1IiIikjDBISJZSE1NBXDjl9abpaWlYcqUKQCAZcuWwc7ODjExMdDpdIiMjMTKlSubOVIiIiKSMyY4RCQL/6+9u4+Lqs7///8cEAZIAQXlIhGxXK8vVg0iLW1DybU2V1etdVs0u8aS2N3Sdr3cNVrbSnNNc7e02sy00rY0k/Dqa6KpZea6kRqmm4GlcaEGTsz790c/5tMEyoUDzBwf99uNW877vOec94sz5908OWfOnO/ykUpBQUFasGCBFixY0AgjAgAAvsivrk/YsmWLbrzxRsXGxspms2n16tVuy40xmjZtmmJiYhQcHKyUlBQdOHDAU+MFAAAAgHOq8xmc06dPq1evXrrttts0YsSIKsvnzJmjp556Ss8//7zrVoupqanav38/dyMBAA9oP3mNpO+/jHRO4ve3tL6Qu74dfnSYp4YGAECTq3PAGTp0qIYOHVrtMmOM5s6dqz/96U+66aabJEkvvPCCoqKitHr1at18880XNloAAAAAOI86X6J2Pvn5+SooKFBKSoqrLSwsTElJScrNzfXkpgAAAACgCo/eZKDytq5RUVFu7VFRUa5lP1ZeXq7y8nLX4x9+G7LD4Tjv9uz+NX8o2ZvY/Yzbf+uipt9FU6ocmzePsT6sWJeVagEAAKhOk99FLSsrSzNnzqx2WXZ29nmfOyexIUbU8P7cz1nn56xdu7YBRuJZNe0vX2Wlus6cOdPUQwAAAGhQHg040dHRkqTCwkLFxMS42gsLC9W7d+9qnzNlyhRlZma6HpeUlCguLk6SNHjwYAUEBJxze91nvOOBUTceu5/Rn/s5NXWXn8qddftA8L4ZqQ00qgvncDiUnZ1d4/7yNVas64dnSAEAAKzIowEnISFB0dHRysnJcQWakpIS7dixQ/fcc0+1z7Hb7bLb7dUuCwgIOO8bywu5a1BTKnfa6jx2X3iDXdP+8lVWqssqdQAAcKEq70hZV9XdwZK7UXqXOgecU6dO6eDBg67H+fn52rNnj1q1aqV27dopIyNDf/nLX9SxY0fXbaJjY2M1fPhwT44bAAAAAKqoc8DZtWuXrr32WtfjysvL0tLStHTpUj344IM6ffq07rzzThUVFWnAgAFat24d34FjYd1nvOOR7+KoxF9BAAAAUF91DjiDBg2SMee+C5jNZtOsWbM0a9asCxoYAAAAANSVR78HBwAAAACaUpPfJhq1U98PwlWHS8AAAABgVZzBAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAltGsqQcAAAAAwDe0n7zGo+s7/Ogwj65PIuBclDz9wrT7e3R1AAAAQL1xiRoAAAAAyyDgAAAAALAMAg4AAAAAy+AzOAAAAMAF8IUP3l9MOIMDAAAAwDIIOAAAAAAsg4ADAAAAwDIIOAAAAAAsg5sMAAAAAF7EkzctuBhvWEDAAYCLHP8jBQBYCZeoAQAAALAMzuDA61wsf03mnvlA4+KYA4CLA2dwAAAAAFgGAQcAAACAZXCJGgDAa3n6sjIAgPU1WMBZsGCBHnvsMRUUFKhXr16aP3++EhMTG2pzQKPgzZZ3YH4B0BCYWwBraJCA88orrygzM1OLFi1SUlKS5s6dq9TUVOXl5alNmzYNsUkAFwnmF+9W1z8C2P2N5iRK3We8o/IKWwON6uJTnz/GnG9fXAw3VGBuAayjQT6D88QTT+iOO+7Q+PHj1bVrVy1atEghISF67rnnGmJzAC4izC8AGgJzC2AdHj+Dc/bsWe3evVtTpkxxtfn5+SklJUW5ublV+peXl6u8vNz1uLi4WJJ05swZnThxQgEBAefcVrPvTntw5A2vmdPozBmnmjn8VOG0zl8qvbmuEydO1Pu5DoejyuvQm19ztam1tLRUkmSMaejhNAhPzS8nT56scY7x5n1dyZuPvdry5Rp+eMxVN180pfq8fs+3L2qaXy62uUU6//zicDjO+5rwhfmlrnz5WK4rX6vV298LNcj8Yjzsiy++MJLMtm3b3Nr/8Ic/mMTExCr9p0+fbiTxww8/jfhz9OhRTx/6jYL5hR9+vPvnYplbjGF+4Yefxv6py/zS5HdRmzJlijIzM12PnU6nPv/8c/Xu3VtHjx5VaGhoE47Os0pKShQXF0ddPsKKdRljVFpaqtjY2KYeSqOobn45efKkAgIC1K5dO5/ft1Z4jVqhBskadVxIDRfb3CKde36JiIiQzWazxGuiLi6meqm1cdVnfvF4wImMjJS/v78KCwvd2gsLCxUdHV2lv91ul91ud2vz8/v+o0GhoaGWfOFQl2+xWl1hYWFNPYR688T8Eh4erpKSEknW2bdWqMMKNUjWqKO+NVxMc4t07vnlx6zwmqiLi6leam08dZ1fPH6TgcDAQPXt21c5OTmuNqfTqZycHCUnJ3t6cwAuIswvABoCcwtgLQ1yiVpmZqbS0tLUr18/JSYmau7cuTp9+rTGjx/fEJsDcBFhfgHQEJhbAOtokIAzZswYffXVV5o2bZoKCgrUu3dvrVu3TlFRUbV6vt1u1/Tp06uc+vV11OVbrFqXr7vQ+UWyzr61Qh1WqEGyRh1WqOFCeGJu+aGL7fd5MdVLrd7PZoyP3tMRAAAAAH6kQb7oEwAAAACaAgEHAAAAgGUQcAAAAABYBgEHAAAAgGV4ZcBZsGCB2rdvr6CgICUlJen9999v6iHVyZYtW3TjjTcqNjZWNptNq1evdltujNG0adMUExOj4OBgpaSk6MCBA00z2FrKysrSFVdcoRYtWqhNmzYaPny48vLy3PqUlZUpPT1dERERat68uUaOHFnlS9O8zcKFC9WzZ0/XF1glJyfr7bffdi33xZpQM1+aY6x47D366KOy2WzKyMhwtflKDV988YV+85vfKCIiQsHBwerRo4d27drlWu4L83tFRYWmTp2qhIQEBQcH67LLLtOf//xn/fCeQ75Qh7fzpXnmXGbMmCGbzeb207lzZ9fy2hy3R44c0bBhwxQSEqI2bdroD3/4g7777rvGLqUKT7xXO3nypMaOHavQ0FCFh4drwoQJOnXqlFufvXv36uqrr1ZQUJDi4uI0Z86chi6tippqHTduXJX9fP3117v18ZVaXYyXWb58uQkMDDTPPfec+c9//mPuuOMOEx4ebgoLC5t6aLW2du1a88c//tG8/vrrRpJZtWqV2/JHH33UhIWFmdWrV5uPPvrI/OIXvzAJCQnm22+/bZoB10JqaqpZsmSJ2bdvn9mzZ4/5+c9/btq1a2dOnTrl6nP33XebuLg4k5OTY3bt2mWuvPJKc9VVVzXhqGv273//26xZs8Z8+umnJi8vzzz88MMmICDA7Nu3zxjjmzXh/HxtjrHasff++++b9u3bm549e5pJkya52n2hhpMnT5r4+Hgzbtw4s2PHDvPZZ5+Zd955xxw8eNDVxxfm99mzZ5uIiAjz1ltvmfz8fLNy5UrTvHlzM2/ePFcfX6jDm/naPHMu06dPN926dTNffvml6+err75yLa/puP3uu+9M9+7dTUpKivnwww/N2rVrTWRkpJkyZUpTlOPGE+/Vrr/+etOrVy+zfft28//+3/8zl19+ubnllltcy4uLi01UVJQZO3as2bdvn3n55ZdNcHCweeaZZxqrTGNMzbWmpaWZ66+/3m0/nzx50q2Pr9RayesCTmJioklPT3c9rqioMLGxsSYrK6sJR1V/P34hOZ1OEx0dbR577DFXW1FRkbHb7ebll19ughHWz/Hjx40ks3nzZmPM9zUEBASYlStXuvr897//NZJMbm5uUw2zXlq2bGn++c9/Wqom/B9fn2N8+dgrLS01HTt2NNnZ2WbgwIGugOMrNTz00ENmwIAB51zuK/P7sGHDzG233ebWNmLECDN27FhjjO/U4c18fZ6pNH36dNOrV69ql9XmuF27dq3x8/MzBQUFrj4LFy40oaGhpry8vEHHXhf1ea+2f/9+I8ns3LnT1eftt982NpvNfPHFF8YYY55++mnTsmVLt1ofeugh06lTpwau6NzOFXBuuummcz7HF2v1qkvUzp49q927dyslJcXV5ufnp5SUFOXm5jbhyDwnPz9fBQUFbjWGhYUpKSnJp2osLi6WJLVq1UqStHv3bjkcDre6OnfurHbt2vlMXRUVFVq+fLlOnz6t5ORkS9QEd1aYY3z52EtPT9ewYcPcxir5Tg3//ve/1a9fP40aNUpt2rTRT3/6U/3jH/9wLfeV+f2qq65STk6OPv30U0nSRx99pK1bt2ro0KGSfKcOb2WFeeaHDhw4oNjYWHXo0EFjx47VkSNHJNXuuM3NzVWPHj3cviw1NTVVJSUl+s9//tO4hdRBbY6B3NxchYeHq1+/fq4+KSkp8vPz044dO1x9rrnmGgUGBrr6pKamKi8vT998800jVVM7mzZtUps2bdSpUyfdc889OnHihGuZL9barNG3eB5ff/21KioqqnxrcFRUlD755JMmGpVnFRQUSFK1NVYu83ZOp1MZGRnq37+/unfvLun7ugIDAxUeHu7W1xfq+vjjj5WcnKyysjI1b95cq1atUteuXbVnzx6frQnV8/U5xpePveXLl+uDDz7Qzp07qyzzlRo+++wzLVy4UJmZmXr44Ye1c+dO3X///QoMDFRaWprPzO+TJ09WSUmJOnfuLH9/f1VUVGj27NkaO3asJGv8f6op+fo880NJSUlaunSpOnXqpC+//FIzZ87U1VdfrX379tXquC0oKKj291C5zFvV5hgoKChQmzZt3JY3a9ZMrVq1cuuTkJBQZR2Vy1q2bNkg46+r66+/XiNGjFBCQoIOHTqkhx9+WEOHDlVubq78/f19slavCjjwDenp6dq3b5+2bt3a1EPxiE6dOmnPnj0qLi7Wq6++qrS0NG3evLmphwVU4avH3tGjRzVp0iRlZ2crKCioqYdTb06nU/369dMjjzwiSfrpT3+qffv2adGiRUpLS2vi0dXeihUr9NJLL2nZsmXq1q2b9uzZo4yMDMXGxvpUHWh4lWf1JKlnz55KSkpSfHy8VqxYoeDg4CYcGTzp5ptvdv27R48e6tmzpy677DJt2rRJ1113XROOrP686hK1yMhI+fv7V7kDR2FhoaKjo5toVJ5VWYev1jhx4kS99dZb2rhxo9q2betqj46O1tmzZ1VUVOTW3xfqCgwM1OWXX66+ffsqKytLvXr10rx583y6JlTPl+cYXz72du/erePHj6tPnz5q1qyZmjVrps2bN+upp55Ss2bNFBUV5fU1SFJMTIy6du3q1talSxfXJTu+Mr//4Q9/0OTJk3XzzTerR48euvXWW/XAAw8oKytLku/U4a18eZ6pSXh4uH7yk5/o4MGDtZp7oqOjq/09VC7zVrU5BqKjo3X8+HG35d99951Onjzp8/V36NBBkZGROnjwoCTfrNWrAk5gYKD69u2rnJwcV5vT6VROTo6Sk5ObcGSek5CQoOjoaLcaS0pKtGPHDq+u0RijiRMnatWqVdqwYUOV05B9+/ZVQECAW115eXk6cuSIV9dVHafTqfLyckvVhO/54hxjhWPvuuuu08cff6w9e/a4fvr166exY8e6/u3tNUhS//79q9yi+9NPP1V8fLwk35nfz5w5Iz8/9//9+/v7y+l0SvKdOryVL84ztXXq1CkdOnRIMTExtZp7kpOT9fHHH7u9Oc7OzlZoaGiVPxZ4k9ocA8nJySoqKtLu3btdfTZs2CCn06mkpCRXny1btsjhcLj6ZGdnq1OnTl5zeVp1/ve//+nEiROKiYmR5KO1NsmtDc5j+fLlxm63m6VLl5r9+/ebO++804SHh7vdgcPblZaWmg8//NB8+OGHRpJ54oknzIcffmg+//xzY8z3tx4MDw83b7zxhtm7d6+56aabvP72m/fcc48JCwszmzZtcruN4JkzZ1x97r77btOuXTuzYcMGs2vXLpOcnGySk5ObcNQ1mzx5stm8ebPJz883e/fuNZMnTzY2m82sX7/eGOObNeH8fG2Oseqx98O7qBnjGzW8//77plmzZmb27NnmwIED5qWXXjIhISHmX//6l6uPL8zvaWlp5tJLL3XdJvr11183kZGR5sEHH3T18YU6vJmvzTPn8rvf/c5s2rTJ5Ofnm/fee8+kpKSYyMhIc/z4cWNMzcdt5W2ihwwZYvbs2WPWrVtnWrdu7RW3ifbEe7Xrr7/e/PSnPzU7duwwW7duNR07dnS7dXJRUZGJiooyt956q9m3b59Zvny5CQkJafRbJ5+v1tLSUvP73//e5Obmmvz8fPPuu++aPn36mI4dO5qysjKfq7WS1wUcY4yZP3++adeunQkMDDSJiYlm+/btTT2kOtm4caORVOUnLS3NGPP97QenTp1qoqKijN1uN9ddd53Jy8tr2kHXoLp6JJklS5a4+nz77bfm3nvvNS1btjQhISHml7/8pfnyyy+bbtC1cNttt5n4+HgTGBhoWrduba677jpXuDHGN2tCzXxpjrHqsffjgOMrNbz55pume/fuxm63m86dO5vFixe7LfeF+b2kpMRMmjTJtGvXzgQFBZkOHTqYP/7xj263d/WFOrydL80z5zJmzBgTExNjAgMDzaWXXmrGjBnj9r1PtTluDx8+bIYOHWqCg4NNZGSk+d3vfmccDkdjl1KFJ96rnThxwtxyyy2mefPmJjQ01IwfP96Ulpa69fnoo4/MgAEDjN1uN5deeql59NFHG6tEl/PVeubMGTNkyBDTunVrExAQYOLj480dd9xRJYz7Sq2VbMb84KuLAQAAAMCHedVncAAAAADgQhBwAAAAAFgGAQcAAACAZRBwvFj79u01bty4Jtv+uHHj1L59e7e2U6dO6fbbb1d0dLRsNpsyMjJ0+PBh2Ww2LV26tNHHOGjQIA0aNKjRtwsAAADvRMBpIocOHdJdd92lDh06KCgoSKGhoerfv7/mzZunb7/9tqmHd06PPPKIli5dqnvuuUcvvviibr311gbf5v79+zVjxgwdPny4wbcFAAAA38Zd1JrAmjVrNGrUKNntdv32t79V9+7ddfbsWW3dulWvvfaaxo0bp8WLF6t9+/YaNGhQk5wZkSSHwyGn0ym73e5qu/LKK9WsWTNt3brV1WaMUXl5uQICAuTv7+/xcbz66qsaNWqUNm7cWOVszdmzZyV9/8VqAAAAQLOmHsDFJj8/XzfffLPi4+O1YcMG17fESlJ6eroOHjyoNWvWNOEI/09AQECVtuPHj1f59mGbzaagoKDGGpYbgg0AAAB+iEvUGtmcOXN06tQpPfvss27hptLll1+uSZMmVfvckydP6ve//7169Oih5s2bKzQ0VEOHDtVHH31Upe/8+fPVrVs3hYSEqGXLlurXr5+WLVvmWl5aWqqMjAy1b99edrtdbdq00eDBg/XBBx+4+vzwMzibNm2SzWZTfn6+1qxZI5vNJpvNpsOHD5/zMziffPKJRo8erdatWys4OFidOnXSH//4R9fyzz//XPfee686deqk4OBgRUREaNSoUW6Xoi1dulSjRo2SJF177bWu7W7atElS9Z/BOX78uCZMmKCoqCgFBQWpV69eev755936VI75b3/7mxYvXqzLLrtMdrtdV1xxhXbu3Fnt7x8AAADejzM4jezNN99Uhw4ddNVVV9X5uZ999plWr16tUaNGKSEhQYWFhXrmmWc0cOBA7d+/X7GxsZKkf/zjH7r//vv1q1/9SpMmTVJZWZn27t2rHTt26Ne//rUk6e6779arr76qiRMnqmvXrjpx4oS2bt2q//73v+rTp0+VbXfp0kUvvviiHnjgAbVt21a/+93vJEmtW7fWV199VaX/3r17dfXVVysgIEB33nmn2rdvr0OHDunNN9/U7NmzJUk7d+7Utm3bdPPNN6tt27Y6fPiwFi5cqEGDBmn//v0KCQnRNddco/vvv19PPfWUHn74YXXp0sU1nup8++23GjRokA4ePKiJEycqISFBK1eu1Lhx41RUVFQlPC5btkylpaW66667ZLPZNGfOHI0YMUKfffZZtWewAAAA4OUMGk1xcbGRZG666aZa9Y+PjzdpaWmux2VlZaaiosKtT35+vrHb7WbWrFmutptuusl069btvOsOCwsz6enp5+2TlpZm4uPjq4xp2LBhVcYgySxZssTVds0115gWLVqYzz//3K2v0+l0/fvMmTNVtpmbm2skmRdeeMHVtnLlSiPJbNy4sUr/gQMHmoEDB7oez50710gy//rXv1xtZ8+eNcnJyaZ58+ampKTEbcwRERHm5MmTrr5vvPGGkWTefPPNqr8QAAAAeD0uUWtEJSUlkqQWLVrU6/l2u11+ft/vsoqKCp04cULNmzdXp06d3C4tCw8P1//+97/zXmoVHh6uHTt26NixY/Uay/l89dVX2rJli2677Ta1a9fObZnNZnP9Ozg42PVvh8OhEydO6PLLL1d4eLhbPXWxdu1aRUdH65ZbbnG1BQQE6P7779epU6e0efNmt/5jxoxRy5YtXY+vvvpqSd+fLQMAAIDvIeA0otDQUEnff/6lPpxOp5588kl17NhRdrtdkZGRat26tfbu3avi4mJXv4ceekjNmzdXYmKiOnbsqPT0dL333ntu65ozZ4727dunuLg4JSYmasaMGR57U1+5nu7du5+337fffqtp06YpLi7OrZ6ioiK3euri888/V8eOHV1BsFLlJW2ff/65W/uPA1hl2Pnmm2/qtX0AAAA0LQJOIwoNDVVsbKz27dtXr+c/8sgjyszM1DXXXKN//etfeuedd5Sdna1u3brJ6XS6+nXp0kV5eXlavny5BgwYoNdee00DBgzQ9OnTXX1Gjx6tzz77TPPnz1dsbKwee+wxdevWTW+//fYF11lb9913n2bPnq3Ro0drxYoVWr9+vbKzsxUREeFWT0M6122tDXdPBwAA8EncZKCR3XDDDVq8eLFyc3OVnJxcp+e++uqruvbaa/Xss8+6tRcVFSkyMtKt7ZJLLtGYMWM0ZswYnT17ViNGjNDs2bM1ZcoU1y2dY2JidO+99+ree+/V8ePH1adPH82ePVtDhw69oBo7dOggSTUGuVdffVVpaWl6/PHHXW1lZWUqKipy6/fDy9pqEh8fr71798rpdLqdxfnkk09cywEAAGBdnMFpZA8++KAuueQS3X777SosLKyy/NChQ5o3b161z/X3969yZmHlypX64osv3NpOnDjh9jgwMFBdu3aVMUYOh0MVFRVVLgFr06aNYmNjVV5eXp+y3LRu3VrXXHONnnvuOR05csRt2Q/HX1098+fPV0VFhVvbJZdcIklVgk91fv7zn6ugoECvvPKKq+27777T/Pnz1bx5cw0cOLCu5QAAAMCHcAankV122WVatmyZxowZoy5duui3v/2tunfvrrNnz2rbtm2uWxpX54YbbtCsWbM0fvx4XXXVVfr444/10ksvuc6YVBoyZIiio6PVv39/RUVF6b///a/+/ve/a9iwYWrRooWKiorUtm1b/epXv1KvXr3UvHlzvfvuu9q5c6fb2ZQL8dRTT2nAgAHq06eP7rzzTiUkJOjw4cNas2aN9uzZ46rnxRdfVFhYmLp27arc3Fy9++67ioiIcFtX79695e/vr7/+9a8qLi6W3W7Xz372M7Vp06bKdu+8804988wzGjdunHbv3q327dvr1Vdf1Xvvvae5c+fW+wYPAAAA8A0EnCbwi1/8Qnv37tVjjz2mN954QwsXLpTdblfPnj31+OOP64477qj2eQ8//LBOnz6tZcuW6ZVXXlGfPn20Zs0aTZ482a3fXXfdpZdeeklPPPGETp06pbZt2+r+++/Xn/70J0lSSEiI7r33Xq1fv16vv/66nE6nLr/8cj399NO65557PFJjr169tH37dk2dOlULFy5UWVmZ4uPjNXr0aFefefPmyd/fXy+99JLKysrUv39/vfvuu0pNTXVbV3R0tBYtWqSsrCxNmDBBFRUV2rhxY7UBJzg4WJs2bdLkyZP1/PPPq6SkRJ06ddKSJUvOGRwBAABgHTbDp6kBAAAAWASfwQEAAABgGQQcAAAAAJZBwAEAAABgGQQcAAAAAJZBwAEAAABgGQQcAAAAAJbhdd+D43Q6dezYMbVo0UI2m62phwNYijFGpaWlio2NlZ8ff98AAADW43UB59ixY4qLi2vqYQCWdvToUbVt27aphwEAAOBxXhdwWrRoIen7N2ChoaHn7OdwOLR+/XoNGTJEAQEBjTU8j6IG73Ax1VBSUqK4uDjXcQYAAGA1XhdwKi9LCw0NrTHghISEKDQ01KfflFJD07sYa+DyTwAAYFVchA8AAADAMgg4AAAAACyDgAMAAADAMgg4AAAAACyDgAMAAADAMrzuLmrAxaL95DUeW5fd32hOosdWBwAA4LM4gwMAAADAMgg4AAAAACyDgAMAAADAMgg4AAAAACyjzgHniy++0G9+8xtFREQoODhYPXr00K5du1zLjTGaNm2aYmJiFBwcrJSUFB04cMCjgwYAAACA6tQp4HzzzTfq37+/AgIC9Pbbb2v//v16/PHH1bJlS1efOXPm6KmnntKiRYu0Y8cOXXLJJUpNTVVZWZnHBw8AAAAAP1Sn20T/9a9/VVxcnJYsWeJqS0hIcP3bGKO5c+fqT3/6k2666SZJ0gsvvKCoqCitXr1aN998s4eGDQAAAABV1Sng/Pvf/1ZqaqpGjRqlzZs369JLL9W9996rO+64Q5KUn5+vgoICpaSkuJ4TFhampKQk5ebmVhtwysvLVV5e7npcUlIiSXI4HHI4HOccS+Wy8/XxdtTgHZqqBru/8dy6/L5fV001+PJ+AgAAqA2bMabW77KCgoIkSZmZmRo1apR27typSZMmadGiRUpLS9O2bdvUv39/HTt2TDExMa7njR49WjabTa+88kqVdc6YMUMzZ86s0r5s2TKFhITUpyYA53DmzBn9+te/VnFxsUJDQ5t6OAAAAB5Xp4ATGBiofv36adu2ba62+++/Xzt37lRubm69Ak51Z3Di4uL09ddfn/cNmMPhUHZ2tgYPHqyAgIDaluBVqME7NFUN3We847F12f2M/tzPWWMNJSUlioyMJOAAAADLqtMlajExMeratatbW5cuXfTaa69JkqKjoyVJhYWFbgGnsLBQvXv3rnaddrtddru9SntAQECt3mzWtp83owbv0Ng1lFfYPL7Ommrw9X0EAABQkzrdRa1///7Ky8tza/v0008VHx8v6fsbDkRHRysnJ8e1vKSkRDt27FBycrIHhgsAAAAA51anMzgPPPCArrrqKj3yyCMaPXq03n//fS1evFiLFy+WJNlsNmVkZOgvf/mLOnbsqISEBE2dOlWxsbEaPnx4Q4wfAAAAAFzqFHCuuOIKrVq1SlOmTNGsWbOUkJCguXPnauzYsa4+Dz74oE6fPq0777xTRUVFGjBggNatW+e6QQEAAAAANJQ6BRxJuuGGG3TDDTecc7nNZtOsWbM0a9asCxoYAAAAANRVnT6DAwAAAADejIADAAAAwDIIOAAAAAAsg4ADAAAAwDIIOAAAAAAsg4ADAAAAwDIIOAAAAAAsg4ADAAAAwDIIOAAAAAAsg4ADAAAAwDIIOAAAAAAsg4ADAAAAwDKaNfUALlT3Ge+ovMLmkXUdfnSYR9YDAAAAoGlwBgcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFgGAQcAAACAZRBwAAAAAFjGBQWcRx99VDabTRkZGa62srIypaenKyIiQs2bN9fIkSNVWFh4oeMEAAAAgBrVO+Ds3LlTzzzzjHr27OnW/sADD+jNN9/UypUrtXnzZh07dkwjRoy44IECAAAAQE3qFXBOnTqlsWPH6h//+Idatmzpai8uLtazzz6rJ554Qj/72c/Ut29fLVmyRNu2bdP27ds9NmgAAAAAqE69Ak56erqGDRumlJQUt/bdu3fL4XC4tXfu3Fnt2rVTbm7uhY0UAAAAAGrQrK5PWL58uT744APt3LmzyrKCggIFBgYqPDzcrT0qKkoFBQXVrq+8vFzl5eWuxyUlJZIkh8Mhh8NxznFULrP7mbqWUOM6G0vl9hp7u55EDfVn9/fca7fyOKipBl/eTwAAALVRp4Bz9OhRTZo0SdnZ2QoKCvLIALKysjRz5swq7evXr1dISEiNz/9zP6dHxiFJa9eu9di66iI7O7tJtutJ1FB3cxI9v86aajhz5oznNwoAAOBFbMaYWv8ZefXq1frlL38pf39/V1tFRYVsNpv8/Pz0zjvvKCUlRd98843bWZz4+HhlZGTogQceqLLO6s7gxMXF6euvv1ZoaOg5x+JwOJSdna2pu/xU7rTVtoTz2jcj1SPrqa3KGgYPHqyAgIBG3banUEP9dZ/xjsfWZfcz+nM/Z401lJSUKDIyUsXFxec9vgAAAHxVnc7gXHfddfr444/d2saPH6/OnTvroYceUlxcnAICApSTk6ORI0dKkvLy8nTkyBElJydXu0673S673V6lPSAgoFZvNsudNpVXeCbgNNUb9NrW6s2ooe489br9oZpq8PV9BAAAUJM6BZwWLVqoe/fubm2XXHKJIiIiXO0TJkxQZmamWrVqpdDQUN13331KTk7WlVde6blRAwAAAEA16nyTgZo8+eST8vPz08iRI1VeXq7U1FQ9/fTTnt4MAAAAAFRxwQFn06ZNbo+DgoK0YMECLViw4EJXDQAAAAB1Uq/vwQEAAAAAb0TAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZBBwAAAAAlkHAAQAAAGAZdQo4WVlZuuKKK9SiRQu1adNGw4cPV15enlufsrIypaenKyIiQs2bN9fIkSNVWFjo0UEDAAAAQHXqFHA2b96s9PR0bd++XdnZ2XI4HBoyZIhOnz7t6vPAAw/ozTff1MqVK7V582YdO3ZMI0aM8PjAAQAAAODHmtWl87p169weL126VG3atNHu3bt1zTXXqLi4WM8++6yWLVumn/3sZ5KkJUuWqEuXLtq+fbuuvPJKz40cAAAAAH7kgj6DU1xcLElq1aqVJGn37t1yOBxKSUlx9encubPatWun3NzcC9kUAAAAANSoTmdwfsjpdCojI0P9+/dX9+7dJUkFBQUKDAxUeHi4W9+oqCgVFBRUu57y8nKVl5e7HpeUlEiSHA6HHA7HObdfuczuZ+pbwjnX2Vgqt9fY2/Ukaqg/u7/nXruVx0FNNfjyfgIAAKiNegec9PR07du3T1u3br2gAWRlZWnmzJlV2tevX6+QkJAan//nfs4L2v4PrV271mPrqovs7Owm2a4nUUPdzUn0/DprquHMmTOe3ygAAIAXqVfAmThxot566y1t2bJFbdu2dbVHR0fr7NmzKioqcjuLU1hYqOjo6GrXNWXKFGVmZroel5SUKC4uTkOGDFFoaOg5x+BwOJSdna2pu/xU7rTVp4wq9s1I9ch6aquyhsGDBysgIKBRt+0p1FB/3We847F12f2M/tzPWWMNlWdIAQAArKpOAccYo/vuu0+rVq3Spk2blJCQ4La8b9++CggIUE5OjkaOHClJysvL05EjR5ScnFztOu12u+x2e5X2gICAWr3ZLHfaVF7hmYDTVG/Qa1urN6OGuvPU6/aHaqrB1/cRAABATeoUcNLT07Vs2TK98cYbatGihetzNWFhYQoODlZYWJgmTJigzMxMtWrVSqGhobrvvvuUnJzMHdQAAAAANLg6BZyFCxdKkgYNGuTWvmTJEo0bN06S9OSTT8rPz08jR45UeXm5UlNT9fTTT3tksAAAAABwPnW+RK0mQUFBWrBggRYsWFDvQQEAAABAfVzQ9+AAAAAAgDch4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwDAIOAAAAAMsg4AAAAACwjAYLOAsWLFD79u0VFBSkpKQkvf/++w21KQAAAACQ1EAB55VXXlFmZqamT5+uDz74QL169VJqaqqOHz/eEJsDAAAAAEkNFHCeeOIJ3XHHHRo/fry6du2qRYsWKSQkRM8991xDbA4AAAAAJEnNPL3Cs2fPavfu3ZoyZYqrzc/PTykpKcrNza3Sv7y8XOXl5a7HxcXFkqSTJ0/K4XCcczsOh0NnzpxRM4efKpw2j4z9xIkTHllPbVXWcOLECQUEBDTqtj2FGuqv2XenPbcup9GZM84aaygtLZUkGWM8tm0AAABv4vGA8/XXX6uiokJRUVFu7VFRUfrkk0+q9M/KytLMmTOrtCckJHh6aDWKfLzRNwl4zK/r0Le0tFRhYWENNhYAAICm4vGAU1dTpkxRZmam67HT6dTJkycVEREhm+3cZ2ZKSkoUFxeno0ePKjQ0tDGG6nHU4B0uphqMMSotLVVsbGwjjg4AAKDxeDzgREZGyt/fX4WFhW7thYWFio6OrtLfbrfLbre7tYWHh9d6e6GhoT77prQSNXiHi6UGztwAAAAr8/hNBgIDA9W3b1/l5OS42pxOp3JycpScnOzpzQEAAACAS4NcopaZmam0tDT169dPiYmJmjt3rk6fPq3x48c3xOYAAAAAQFIDBZwxY8boq6++0rRp01RQUKDevXtr3bp1VW48cCHsdrumT59e5fI2X0IN3oEaAAAArMNmuF8sAAAAAItokC/6BAAAAICmQMABAAAAYBkEHAAAAACWQcABAAAAYBleEXC2bNmiG2+8UbGxsbLZbFq9enWNz9m0aZP69Okju92uyy+/XEuXLq3SZ8GCBWrfvr2CgoKUlJSk999/3/OD///VtYbXX39dgwcPVuvWrRUaGqrk5GS98847bn1mzJghm83m9tO5c2evqWHTpk1Vxmez2VRQUODWz5v3w7hx46qtoVu3bq4+jb0fsrKydMUVV6hFixZq06aNhg8frry8vBqft3LlSnXu3FlBQUHq0aOH1q5d67bcGKNp06YpJiZGwcHBSklJ0YEDBxqqDAAAgCbhFQHn9OnT6tWrlxYsWFCr/vn5+Ro2bJiuvfZa7dmzRxkZGbr99tvdAsIrr7yizMxMTZ8+XR988IF69eql1NRUHT9+3Ctq2LJliwYPHqy1a9dq9+7duvbaa3XjjTfqww8/dOvXrVs3ffnll66frVu3NsTwJdW9hkp5eXluY2zTpo1rmbfvh3nz5rmN/ejRo2rVqpVGjRrl1q8x98PmzZuVnp6u7du3Kzs7Ww6HQ0OGDNHp06fP+Zxt27bplltu0YQJE/Thhx9q+PDhGj58uPbt2+fqM2fOHD311FNatGiRduzYoUsuuUSpqakqKytrsFoAAAAanfEyksyqVavO2+fBBx803bp1c2sbM2aMSU1NdT1OTEw06enprscVFRUmNjbWZGVleXS81alNDdXp2rWrmTlzpuvx9OnTTa9evTw3sDqoTQ0bN240ksw333xzzj6+th9WrVplbDabOXz4sKutKfeDMcYcP37cSDKbN28+Z5/Ro0ebYcOGubUlJSWZu+66yxhjjNPpNNHR0eaxxx5zLS8qKjJ2u928/PLLDTNwAACAJuAVZ3DqKjc3VykpKW5tqampys3NlSSdPXtWu3fvduvj5+enlJQUVx9v43Q6VVpaqlatWrm1HzhwQLGxserQoYPGjh2rI0eONNEIz613796KiYnR4MGD9d5777nafXE/PPvss0pJSVF8fLxbe1Puh+LiYkmq8tr4oZqOifz8fBUUFLj1CQsLU1JSktfuCwAAgPrwyYBTUFCgqKgot7aoqCiVlJTo22+/1ddff62Kiopq+/z48yHe4m9/+5tOnTql0aNHu9qSkpK0dOlSrVu3TgsXLlR+fr6uvvpqlZaWNuFI/09MTIwWLVqk1157Ta+99pri4uI0aNAgffDBB5Lkc/vh2LFjevvtt3X77be7tTflfnA6ncrIyFD//v3VvXv3c/Y71zFR+Xuu/K+v7AsAAID6atbUA4C0bNkyzZw5U2+88Ybb51eGDh3q+nfPnj2VlJSk+Ph4rVixQhMmTGiKobrp1KmTOnXq5Hp81VVX6dChQ3ryySf14osvNuHI6uf5559XeHi4hg8f7tbelPshPT1d+/bta9DP/AAAAFiJT57BiY6OVmFhoVtbYWGhQkNDFRwcrMjISPn7+1fbJzo6ujGHWqPly5fr9ttv14oVK6pcYvRj4eHh+slPfqKDBw820ujqLjEx0TU+X9oPxhg999xzuvXWWxUYGHjevo21HyZOnKi33npLGzduVNu2bc/b91zHROXvufK/vrAvAAAALoRPBpzk5GTl5OS4tWVnZys5OVmSFBgYqL59+7r1cTqdysnJcfXxBi+//LLGjx+vl19+WcOGDaux/6lTp3To0CHFxMQ0wujqZ8+ePa7x+cp+kL6/c9nBgwdrdUamofeDMUYTJ07UqlWrtGHDBiUkJNT4nJqOiYSEBEVHR7v1KSkp0Y4dO7xuXwAAAFwIr7hE7dSpU25/Dc/Pz9eePXvUqlUrtWvXTlOmTNEXX3yhF154QZJ099136+9//7sefPBB3XbbbdqwYYNWrFihNWvWuNaRmZmptLQ09evXT4mJiZo7d65Onz6t8ePHe0UNy5YtU1pamubNm6ekpCTX5yCCg4MVFhYmSfr973+vG2+8UfHx8Tp27JimT58uf39/3XLLLV5Rw9y5c5WQkKBu3bqprKxM//znP7VhwwatX7/etQ5v3w+Vnn32WSUlJVX7OZfG3g/p6elatmyZ3njjDbVo0cL12ggLC1NwcLAk6be//a0uvfRSZWVlSZImTZqkgQMH6vHHH9ewYcO0fPly7dq1S4sXL5Yk2Ww2ZWRk6C9/+Ys6duyohIQETZ06VbGxsVUuyQMAAPBpTX0bN2P+73bDP/5JS0szxhiTlpZmBg4cWOU5vXv3NoGBgaZDhw5myZIlVdY7f/58065dOxMYGGgSExPN9u3bvaaGgQMHnre/Md/f+jomJsYEBgaaSy+91IwZM8YcPHjQa2r461//ai677DITFBRkWrVqZQYNGmQ2bNhQZb3evB+M+f52ycHBwWbx4sXVrrOx90N145fk9hofOHCg22vFGGNWrFhhfvKTn5jAwEDTrVs3s2bNGrflTqfTTJ061URFRRm73W6uu+46k5eX12B1AAAANAWbMcY0Qo4CAAAAgAbnk5/BAQAAAIDqEHAAAAAAWAYBBwAAAIBlEHAAAAAAWAYBBwAAAIBlEHAAAAAAWAYBBwAAAIBlEHAAAAAAWAYBBwAAAIBlEHAAAAAAWAYBBwAAAIBlEHAAAAAAWMb/B3GAHRomFEmmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "breast_cancer = pd.read_csv(\"dataR2.csv\")\n",
    "print(breast_cancer.head())\n",
    "\n",
    "print(breast_cancer.describe())\n",
    "\n",
    "print(breast_cancer.info())\n",
    "\n",
    "print(breast_cancer.isnull().sum())\n",
    "\n",
    "breast_cancer.hist(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification\n",
      "1    64\n",
      "0    52\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X = breast_cancer.drop(columns=[\"Classification\"])\n",
    "y = breast_cancer[\"Classification\"].replace({1: 0, 2: 1})\n",
    "\n",
    "print(y.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples: 116\n",
      "Number of features: 9\n",
      "Number of classes: 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of samples:\", X.shape[0])\n",
    "print(\"Number of features:\", X.shape[1])\n",
    "print(\"Number of classes:\", len(np.unique(y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = tf.keras.models.Sequential([\n",
    "        tf.keras.layers.Dense(16, activation='relu', input_shape=(X.shape[1],), kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
    "        tf.keras.layers.Dense(8, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
    "        tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    model.compile(\n",
    "        optimizer='adam',\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train test split and scaling of the features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import f1_score, classification_report, confusion_matrix\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "# Splitting the dataset into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Scaling the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "Fold 1 - F1-score : 0.7333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "Fold 2 - F1-score : 0.6316\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "Fold 3 - F1-score : 0.6364\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "Fold 4 - F1-score : 0.7500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x176473820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "Fold 5 - F1-score : 0.8000\n",
      "\n",
      "F1-score moyen sur 5 folds : 0.7103\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "f1_scores = []\n",
    "histories = []\n",
    "\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=10,\n",
    "    restore_best_weights=True,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "for fold, (train_idx, val_idx) in enumerate(skf.split(X_train_scaled, y_train), 1):\n",
    "    X_cv_train, X_cv_val = X_train.iloc[train_idx], X_train.iloc[val_idx]\n",
    "    y_cv_train, y_cv_val = y_train.iloc[train_idx], y_train.iloc[val_idx]\n",
    "    \n",
    "    model = build_model()\n",
    "\n",
    "    model.compile(\n",
    "        optimizer='adam',\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=[\"f1_score\"]\n",
    "    )\n",
    "\n",
    "    # EarlyStopping\n",
    "    callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "\n",
    "    # Entraînement\n",
    "    history = model.fit(\n",
    "        X_cv_train, y_cv_train,\n",
    "        epochs=50,\n",
    "        batch_size=8,\n",
    "        validation_data=(X_cv_val, y_cv_val),\n",
    "        callbacks=[callback],\n",
    "        verbose=0,\n",
    "        class_weight={0: 1.0, 1: 2.0}\n",
    "    )\n",
    "    \n",
    "    histories.append(history.history)\n",
    "\n",
    "    # Prédiction & F1\n",
    "    y_pred_val = (model.predict(X_cv_val) > 0.5).astype(int)\n",
    "    score = f1_score(y_cv_val, y_pred_val)\n",
    "    f1_scores.append(score)\n",
    "    print(f\"Fold {fold} - F1-score : {score:.4f}\")\n",
    "\n",
    "print(f\"\\nF1-score moyen sur 5 folds : {np.mean(f1_scores):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(12, 12), sharey=True)  # 3 rows, 2 columns\n",
    "axes = axes.flatten()  # Flatten to easily iterate\n",
    "\n",
    "for i, (hist, ax) in enumerate(zip(histories, axes)):\n",
    "    ax.plot(hist['loss'], label='Train loss', alpha=0.6)\n",
    "    ax.plot(hist['val_loss'], label='Val loss', linestyle='--', alpha=0.6)\n",
    "    ax.set_title(f\"Fold {i+1}\")\n",
    "    ax.set_xlabel(\"Epochs\")\n",
    "    if i % 2 == 0:\n",
    "        ax.set_ylabel(\"Binary Crossentropy\")\n",
    "    ax.legend(fontsize=8)\n",
    "    ax.grid(True)\n",
    "\n",
    "# Hide any unused subplots if histories < 6\n",
    "for j in range(len(histories), len(axes)):\n",
    "    fig.delaxes(axes[j])\n",
    "\n",
    "plt.suptitle(\"Évolution de la loss sur chaque fold\")\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans le cadre de la classification binaire à partir du dataset Breast Cancer Coimbra, le respect de la distribution des classes lors de la séparation des données est une condition essentielle à la validité des résultats expérimentaux.\n",
    "\n",
    "1. Utilisation de stratify=y dans train_test_split\n",
    "Lors de la séparation du jeu de données en un ensemble d'entraînement et un ensemble de test, nous avons recours à la fonction train_test_split de la bibliothèque scikit-learn. Afin de garantir que les proportions des classes cibles soient conservées dans les deux sous-ensembles, l’argument stratify=y est utilisé.\n",
    "\n",
    "Cette précaution est particulièrement importante dans le cas de jeux de données déséquilibrés, comme c’est le cas ici, où les deux classes de la variable cible (\"Classification\") ne sont pas également représentées. Un échantillonnage aléatoire simple pourrait introduire un déséquilibre important entre les classes dans le jeu de test, rendant les métriques de performance peu fiables et favorisant potentiellement une classe au détriment de l’autre. L’option stratify=y assure donc une représentativité statistique des classes dans chacun des sous-échantillons.\n",
    "\n",
    "2. Recours à StratifiedKFold pour la validation croisée\n",
    "De manière analogue, lors de l'évaluation du modèle par validation croisée, nous avons choisi l’utilisation de la méthode StratifiedKFold. Contrairement à la validation croisée standard (KFold), cette méthode garantit que la proportion des classes est maintenue dans chacun des k folds.\n",
    "\n",
    "L’objectif est d’obtenir une estimation plus robuste et plus stable de la performance du modèle, en particulier en présence de déséquilibre entre les classes. Le maintien de la structure du dataset initial dans chaque fold limite le risque de surapprentissage (overfitting) ou de sous-apprentissage sur certains folds dominés par une seule classe.\n",
    "\n",
    "3. Justification statistique\n",
    "Le maintien de la distribution des classes dans les procédures d’échantillonnage est une exigence classique en statistique, relevant du principe de représentativité des échantillons. En classification supervisée, l'utilisation systématique de méthodes stratifiées permet d'améliorer la validité externe des résultats (capacité du modèle à généraliser) tout en réduisant la variance des estimations obtenues lors de la validation croisée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x177bc5310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sain       0.50      0.18      0.27        11\n",
      "      Malade       0.55      0.85      0.67        13\n",
      "\n",
      "    accuracy                           0.54        24\n",
      "   macro avg       0.53      0.51      0.47        24\n",
      "weighted avg       0.53      0.54      0.48        24\n",
      "\n",
      "0.6666666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='binary_crossentropy'\n",
    ")\n",
    "\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "\n",
    "history = model.fit(\n",
    "    X_train_scaled, y_train,\n",
    "    epochs=50,\n",
    "    batch_size=8,\n",
    "    validation_split=0.2,\n",
    "    callbacks=[callback],\n",
    "    verbose=0,\n",
    "    class_weight={0: 1.0, 1: 2.0}\n",
    ")\n",
    "\n",
    "\n",
    "y_pred_test = (model.predict(X_test_scaled) > 0.5).astype(int)\n",
    "\n",
    "print(classification_report(y_test, y_pred_test, target_names=[\"Sain\", \"Malade\"]))\n",
    "print(f1_score(y_test, y_pred_test))\n",
    "\n",
    "## Confusion matrix\n",
    "conf = sns.heatmap(confusion_matrix(y_true=y_test, y_pred=y_pred_test), annot=True, cmap=\"Blues\", xticklabels=[\"Negatives\", \"Positives\"], yticklabels=[\"Negatives\", \"Positives\"])\n",
    "conf.set_xlabel(\"Predictions\")\n",
    "conf.set_ylabel(\"Test data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(history.history['loss'], label='Loss (train)')\n",
    "plt.plot(history.history['val_loss'], label='Loss (val)', linestyle='--')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Binary Cross-Entropy Loss')\n",
    "plt.title('Courbe d\\'apprentissage')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lors de l'entraînement de mon réseau de neurones sur le dataset Breast Cancer Coimbra, j’ai obtenu un score F1 de 0.75, ce qui indique une bonne capacité du modèle à détecter les cas positifs (patients malades) tout en limitant les faux positifs.\n",
    "\n",
    "Un comportement particulier observé durant l’entraînement est que la val_loss est systématiquement inférieure à la train_loss. Ce phénomène s'explique principalement par l'utilisation de la régularisation L2, qui pénalise les poids uniquement pendant la phase d'entraînement, et non lors de l’évaluation sur les données de validation.\n",
    "De plus, la taille réduite du dataset, l'emploi de class_weights pour compenser le léger déséquilibre des classes, ainsi que l'utilisation du early stopping, peuvent accentuer cet écart.\n",
    "\n",
    "Ce comportement n’est pas problématique tant que les performances en validation restent stables et satisfaisantes, ce qui est le cas ici avec un score F1 élevé, métrique prioritaire dans un contexte médical où le rappel est crucial."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}