fuzzy-rules-generator/density_fuzzy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.06250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Density\n",
       "0  20    0.0   0.0  1.06250\n",
       "1  25    0.0   0.0  1.05979\n",
       "2  35    0.0   0.0  1.05404"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.04158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.08438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Density\n",
       "0  30   0.00   0.0  1.05696\n",
       "1  55   0.00   0.0  1.04158\n",
       "2  25   0.05   0.0  1.08438"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "density_train = pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
    "density_test = pd.read_csv(\"data/density_test.csv\", sep=\";\", decimal=\",\")\n",
    "\n",
    "display(density_train.head(3))\n",
    "display(density_test.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
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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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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phDCqpLQkRu8ZTaWClejt11t1nOeS4iaP1C9biHeDfZm+/gwX7zxSHUcI49kaDg+uQMhCsLVXnSZvlG0Kdd7XLy1x76LqNEIYzdwjc7nx8AaTGkzC3sZ0r28pbvLQsFaV8C7gzJBlUaSlS/O1sEBXIyFyHrwSCkVNs7naaJqPB1cviOijX2pCCAtzKPYQ/zn1H/rV6EfZAmVVx3khKW7ykLODLTO6BHDsehzf7LqkOo4QhpXyWP+L3ScQ6vdVnSbvOeaHkAVw7YC+wBPCgiSmJhK6O5TqRavzdpW3Vcd5KSlu8litUh582KgsX2w6x5mYBNVxhDCcTWPhUSyEzAcbW9Vp1ChVX1/YbQ2H22dUpxHCYGYdmsW9pHuEB4djawbXtxQ3CgxqUZ7ShfMxeGkUKWnSPSUswKXtcHCRvmumkGk3VxvdK6HgUQoiPob0VNVphMi1yJuRLD27lIE1B1LSraTqOFkixY0Cjna2zOpanXOxD5m77YLqOELkTlI8rPwUSjfSD6q1dvbO+sHUt47B7i9UpxEiVx6mPCRsbxiBXoG8UekN1XGyTIobRaoVd+fTV8oxb9sFjl2PUx1HiJzbMAqexEGneWAjP1IAKFFLfxv8jmn6IkcIM/X5wc95mPKQCcETsNGYz/VtPkktUN+m5ahczJUhy6JISpW7K4QZOrcBjvwIrSfrV/0W/9N4OBSppF+CIi1ZdRohsm3HtR2suLCCYXWG4Z3fW3WcbJHiRiF7Wxtmda3O1XuJfLHpnOo4QmRP4n1Y1Q/Kt4Qapn/3RJ6zc9AvPXH3nL4FRwgzEpcUx7jIcTQs3pDO5TqrjpNtUtwoVsHTlcEtK/DNrkv8deW+6jhCZN2fw/QtEh3mgEajOo1p8vKDJsP1Y2+u/6U6jRBZNvnAZFLSUxgXNA6NGV7fUtyYgA8alqGGTwGGLo8iMSVNdRwhXu7USji+HNp+Dm7FVKcxbcGDoFh1ffdU6hPVaYR4qY1XNvLn5T8ZGTiSoi5FVcfJESluTICtjYYZXQKISUhi2p8yN4YwcY/uwJpBULkD+HVRncb02drpu6fiomHLRNVphHihe0/uEb4vnOYlm9OudDvVcXJMihsTUaZIfka0rsQPkVfZe+Gu6jhCPJtOB2sG6v/d7gvpjsqqIhWh2RjYNx+u7FGdRohn0ul0TIicAEBovVCz7I76mxQ3JqRnfV/qlynEZ78d42GSTP4lTNDx5XBmDbT/AvIXUZ3GvNT7BErW0y9RkSyL5wrTs+bSGrZe20pY/TAKORdSHSdXpLgxITY2Gqa/7k9cYgrha06rjiNEZgk3Yd1QfVdUlU6q05gfG1v90hSP7+hXDxfChMQ+jmXKgSm0Ld2W5qWaq46Ta1LcmBifgi6Etq/C0r+use3MbdVxhNDT6WBVf7BzhjbTVacxXwXLQIsJ8NdiuLhVdRohAH131NjIsTjZOjEqcJTqOAYhxY0JeqOOD40rFGH478eIS0xRHUcIOPJfuLAJOs4Bl4Kq05i32r2hdGP9khVJ8arTCMEf5/9gz409jAsah7uju+o4BiHFjQnSaDRMe82fpNR0xq06qTqOsHZx0bB+FNR4Cyq0Up3G/NnY6JeqSEqA9SNVpxFW7sajG0w/OJ3O5TrTqEQj1XEMRoobE+Xl7sT4TlWJOHqT9SduqY4jrJVWCyv7gnMBaDVFdRrLUcAHWk+Boz/B2T9VpxFWSqvTErYnDHdHd4bVGaY6jkFJcWPCQqoXp1VVT0avOMHdR7I2jVDg4LdweSd0mgtObqrTWJYab0H5VvqxTIkyO7nIe7+c+YUDMQeYEDyB/A75VccxKCluTJhGo2FSZz90QOiKE+h0OtWRhDW5dxE2j4U6H0CZJqrTWB6NRj+GKT1FfxeaEHnoasJVZh+azRsV36BesXqq4xicFDcmrnB+R8JDqrH+ZAwrj95UHUdYC226fj6W/J7QYrzqNJbL1QvazYQTv8PJFarTCCuRrk1n9O7RFHEpwqBag1THMQopbsxAW79idAzwJmzlCWITklTHEdYgch5cOwAhC8Ahn+o0lq3aa1C5I6wZDI9k+gdhfP859R+O3TlGeHA4LvYuquMYhRQ3ZmJCp6o42tsy/Pdj0j0ljOv2GdgaDvX7Qqn6qtNYPo1GP+OzxgZWD9TPKSSEkVx4cIGvjnxFzyo9qelZU3Uco1Fe3MybNw9fX1+cnJwIDAzkwIEDL9x/9uzZVKxYEWdnZ3x8fBg0aBBJSZbfmlHAxYFpr/mx/ewdlv11TXUcYanSUyHiY/DwhVfGqE5jPfIVhg6z4exaOLZUdRphoVK1qYzeMxofVx/61eynOo5RKS1uli5dyuDBgxk7diyHDx8mICCAVq1acfv2s5tmf/75Z0aMGMHYsWM5ffo0ixcvZunSpYwaZRkzKr7MK5U86Vq7BBNWn+La/UTVcYQl2v0F3DoGnReAvZPqNNalcgfw7wbrhkH8DdVphAX69vi3nL1/lkkNJuFo66g6jlEpLW5mzZrFBx98wLvvvkuVKlVYuHAhLi4ufPfdd8/cf+/evQQHB/Pmm2/i6+tLy5Yt6d69+0tbeyzJmPZVKODiwLDfjqHVSvO1MKBbx2DHNGg4GIrXUp3GOrWZBg4usKqfdE8Jgzp97zTfRH1Db7/eVCtcTXUco1NW3KSkpHDo0CGaN//fAl02NjY0b96cyMjIZz4nKCiIQ4cOZRQzly5dYt26dbRt2/a550lOTiYhISHTw5y5Otkz/XV/Ii/d47/7rqqOIyxFWjKs+BiKVIZGljWZl1lx9oCOX8HFLXBoieo0wkKkpKcwavcoyhYoy8f+H6uOkyeUFTd3794lPT0dT0/PTNs9PT2JiYl55nPefPNNJkyYQIMGDbC3t6ds2bI0adLkhd1SU6ZMwd3dPePh4+Nj0NehQnC5wvSsX4opf57m8t3HquMIS7BjGtw9p++OsnNQnca6lW8BNXvCxlB4cEV1GmEBFkQt4ErCFSY1mIS9rb3qOHlC+YDi7Ni+fTuTJ09m/vz5HD58mD/++IO1a9cyceLE5z5n5MiRxMfHZzyuXbOMwbgj2lTC082JocujSJfuKZEb1//Sj7VpMhy8/FSnEQAtJ4FzQYjoq18CQ4gciroTxXcnvqNPQB8qFqyoOk6eUVbcFC5cGFtbW2JjYzNtj42NxcvL65nPGTNmDG+//Tbvv/8+fn5+dO7cmcmTJzNlyhS0z/kB4OjoiJubW6aHJXBxsGNmlwAORz/g212XVMcR5ir1ib47qlh1CLbMybzMkpMbhMyDq7vhwDeq0wgz9STtCaG7Q6lSsArvVXtPdZw8pay4cXBwoFatWmzZsiVjm1arZcuWLdSv/+y5NRITE7GxyRzZ1tYWwCrnfqntW5APGpZh5sZznIt9qDqOMEdbJupX/e68EGztVKcR/1S6EdT9CDaPg7sXVKcRZmjO4TncfHSTSQ0mYWdjXde30m6pwYMHs2jRIn744QdOnz5Nnz59ePz4Me+++y4APXv2ZOTIkRn7d+jQgQULFvDrr79y+fJlNm3axJgxY+jQoUNGkWNtBreoQMlCLgxZFkVqujRfi2y4sgf2zYdmYVDEepqrzUrzseBWTD/3kDZddRphRg7GHOSn0z/Rv2Z/yhQoozpOnlNaynXr1o07d+4QFhZGTEwM1atXZ/369RmDjKOjozO11ISGhqLRaAgNDeXGjRsUKVKEDh06MGnSJFUvQTkne1tmdgng1QV7mb/tIgOal1cdSZiD5Ef6taNK1oN6fVSnEc/jkA9CFsL3rWHvHGggXYfi5RJTExmzZww1itbgrcpvqY6jhEZnZf05CQkJuLu7Ex8fbzHjbwBmbjzLgu0XiegbTLXi7qrjCFO3ZjBE/QJ99kBB6/urzuxsHAP7F8KHO8Cziuo0wsRNjJzI6kur+b3D7/i4mf8dwjlhVndLiefr90p5ynu6MmRZFMlp0nwtXuDiVvhrMbSYIIWNuWg6Wv+9WvGRfokMIZ5j7429LDu3jMG1BlttYQNS3FgMBzsbZnUN4NLdR3y5+bzqOMJUJcXDyk+hTBOo3Vt1GpFV9k76Qd+xJ2HXTNVphIlKSEkgbG8Y9YrVo2vFrqrjKCXFjQWpXMyNgc0rsHDHRQ5HP1AdR5ii9SMh+SF0nAs2cvmbFe8a0Ggo7Pwcbh5VnUaYoGkHpvE49TETgiZgo7Hu69u6X70F+qhRGfxKFGDosiiepEj3lPiHs3/C0Z+g9RQoYL3N1Wat4VAoWlk/N1Fasuo0woRsi97GqourGFZnGMXyF1MdRzkpbiyMna0NM7sEcCPuCZ9vOKs6jjAVifdhVX+o0Bqq91CdRuSUnQN0/hruXYBtk1WnESYiLimO8ZHjaVyiMSHlQlTHMQlS3FigckXz81mriny35zL7Lt1THUeYgnVDIT0FOnwJGo3qNCI3PKtC05H6W8OvHVCdRpiASfsnkapNZWz9sWjk+gakuLFY7wWXpm7pgnz2WxSPktNUxxEqnVwBJ36HdjPB9dlLmwgzEzQAvGvqu6dSElWnEQqtv7Ke9VfWMzpwNEVciqiOYzKkuLFQNjYaZrwewL1HKUxed1p1HKHKo9v6OW2qdIJqr6lOIwzF1k5/91TCDdgyQXUaocjdJ3eZtG8SLUq1oE3pNqrjmBQpbixYyUIujGpbmZ/3R7Pj3B3VcURe0+lg9UDQ2EC7WdIdZWkKl9cvnbF/AVzepTqNyGM6nY7xkeOx0dgQWi9UuqP+RYobC9cjsCQNyxdm+G/HiH8ik39ZlWNL4exa/TibfIVVpxHGENgHSgXDyk/0t/gLq7H60mq2X9tOWP0wCjoVVB3H5EhxY+E0Gg3TXvPncXIa41efVB1H5JX4G7BuGPh3g8rtVacRxmJjA53mweN7sDFUdRqRR2IexzB1/1Tal2lPs5LNVMcxSVLcWAHvAs6EdajCH4dvsPFkjOo4wth0OljVDxxcoM001WmEsRUsDS0nwqElcGGz6jTCyHQ6HWP3jsXZzpkRdUeojmOypLixEq/XKkHzykUZteI49x+nqI4jjOnQEri4RT8LsbOH6jQiL9R+D8o0hZX94Emc6jTCiH47/xt7b+5lfPB43B1lkeTnkeLGSmg0Gia/6keaVseYlSdUxxHG8uCKvnuiZi8o31x1GpFXNBroNBdSHsF6+WveUl1/eJ3PD37Oa+Vfo0HxBqrjmDQpbqxIUVcnJnaqxtpjt1gddVN1HGFoWi1E9AXngtBqkuo0Iq+5l4DWUyHqFzizVnUaYWBanZYxe8bg4ejBZ3U+Ux3H5ElxY2U6BHjTzr8YY1ae4PbDJNVxhCEd+Aau7oaQeeDoqjqNUKH6m1ChDaweoB9kLCzGz6d/5q/Yv5gYPJF89vlUxzF5UtxYoYmdqmFno2Hk78fR6XSq4whDuHsBNo+Duh9B6Uaq0whVNBr9rf/aNFg7WHUaYSCX4y8z+/Bs3qz0JnWL1VUdxyxIcWOFCuZzYMqr/mw5c5vfDl1XHUfkljYdIj4GN29oPk51GqGaq6d+qY1TEfplN4RZS9emE7onFK98XgysNVB1HLMhxY2ValHFk9dqlmDC6lPcjHuiOo7Ijb1z4MYhCFmgv/1biGqvQZUQWDsEHsaqTiNyYcnJJZy4e4Lw4HCc7ZxVxzEbUtxYsbAOVcjnaMew345J95S5ij0F2yZDUD8oGag6jTAl7WaBjR2s7q+f+0iYnfMPzjPv6Dx6Ve1F9aLVVccxK1LcWDF3Z3umv+7P7gt3+XF/tOo4IrvSU2HFR1CwDDQZpTqNMDX5CunH35xbD0d/Vp1GZFOqNpXRu0dT0rUkfav3VR3H7EhxY+UaVSjCm4ElmbLuNFfvPVYdR2THrpkQe1K/OrS9k+o0whRVagcB3fVz38TL+DpzsujYIs49OMekhpNwtHVUHcfsSHEjGNW2MoXyO/DZ8mNotdJ8bRZuHoWdn0OjoeBdQ3UaYcpaTwWH/LDyU+meMhMn751k0bFFfOD/AVULVVUdxyxJcSPI72jH568HcODKfb7bc1l1HPEyacmw4mMoWgUaDlWdRpg65wLQ6Su4tA3++k51GvESKekphO4OpbxHeT70+1B1HLMlxY0AoF6ZQrwXXJrpG85y4fYj1XHEi2ybDPcu6Luj7BxUpxHmoFxzqPUObBwD9+UPGFM27+g8riRcIbxBOPa29qrjmC0pbkSGYa0rUsLDmSHLo0hL16qOI57l2gH9rd9NR4GnNFeLbGgZrh9kHPGJfqkOYXKO3j7KkpNL6Fu9LxU8KqiOY9akuBEZnOxtmdklgOPX4/h65yXVccS/pSTqu6O8a0JQf9VphLlxdIVO8yF6L+xfoDqN+JcnaU8I3RNKtULVeKfqO6rjmD0pbkQmNUp68HHjsszefI5TNxNUxxH/tGUCJNzQd0fZ2qlOI8xR6YYQ2Ef/WbpzTnUa8Q9fHv6SmMcxhDcIx85Gru/ckuJGPGVA8/KULZKfIcujSEmT5muTcHmX/q/tZmOhcHnVaYQ5axYGbsX1S3akp6lOI4ADtw7w0+mfGFBzAKXdS6uOYxGkuBFPcbSzZUaXAM7HPuSrredVxxHJD2HlJ1CqAQR+rDqNMHcOLvrWv5tHYO+XqtNYvcepjwnbG0Ztz9r0qNxDdRyLIcWNeKZqxd3p36w887dfJOpanOo41m1jKDy+B53mgo1cssIAfOrqx21tmwIxJ1SnsWoz/prB/aT7TAiegI1Grm9DkXdSPFefJmWpUsyNIcujSEpNVx3HOl3YDIeWQKtwKCjN1cKAmo6CQuX03VNpKarTWKXdN3bz27nfGFp7KD6uPqrjWBQpbsRz2dvaMLNrANH3E5m58azqONbnSRys7AdlX4Fa76pOIyyNnaO+e+r2af1s1yJPxSfHM3bvWIK8g+hSoYvqOBZHihvxQhU8XRnSogLf7r7MwSv3VcexLutHQMpj6PgVaDSq0whL5F0dGn2mX6fsxmHVaazKtAPTeJL6hPFB49HI9W1wUtyIl3q/YRlqlvRg6PIoElPk7oo8cWYtRP0CbaaCewnVaYQlazgEvKpBRB9ITVKdxipsid7C6kurGV53OF75vFTHsUhS3IiXsrXRMKNLALEJSUz984zqOJbv8T1YPQAqttWv6CyEMdnaQ8hCuH8Jtk1SncbiPUh6wITICTTxaULHsh1Vx7FYUtyILCldOB8j21TmP5FX2XPhruo4lm3tYNCmQfvZ0h0l8oZnFf0A471fQfR+1Wkslk6nY+K+iaTr0hlbf6x0RxmRFDciy96uV4qgsoUY9tsxEpJSVcexTCd+h1MR0G4WuHqqTiOsSVB/KFFbf/dUymPVaSzS+ivr2XR1E6H1QinsXFh1HIsmxY3IMhsbDdNf9yf+SSrha06pjmN5HsbC2iFQtTNUe1V1GmFtbGz13VMJt2DzeNVpLM6dxDtM2j+JVr6taO3bWnUciyfFjciWEh4ujGlfmWV/XWfL6VjVcSyHTger+4ONHbSdqTqNsFaFy0HzcXDga7i0Q3Uai6HT6RgfOR47jR2jA0erjmMVpLgR2da1tg9NKxZhxB/HefBYJv8yiKM/w7n10GEO5CukOo2wZnU/BN+GsPJTSJLFcw1h5cWV7Li+g7H1x+Lh5KE6jlWQ4kZkm0ajYepr/qSkaRm76qTqOOYv/rp+TpuA7lCpreo0wtrZ2OiX+nhyHzZKK0NuxTyOYdqBaXQs25GmJZuqjmM1pLgROeLp5sSETlVZFXWTdcdvqY5jvnQ6/V/IDvmh9VTVaYTQ8/CFluFw+D9wfpPqNGZLp9MxZs8YXOxdGF53uOo4VkWKG5FjHQO8aV3Vi9CIE9x5mKw6jnn66zu4tA06fQXOBVSnEeJ/ar0DZZvBqn7w5IHqNGZp2dll7Lu1j4lBE3FzcFMdx6pIcSNyTKPREN65Ghpg9Irj6HQ61ZHMy/3LsHGMft2ocs1VpxEiM41Gv/RHSiL8Ka0O2XUt4RozD82kS4UuBBUPUh3H6khxI3KlcH5HJnWuxsZTsUQcvaE6jvnQaiHiE/3g4ZYTVacR4tnci0Pb6XBsKZxerTqN2dDqtITuCaWgU0GG1B6iOo5VkuJG5FrrasUIqe5N2MqTxMTL2jRZsn8BRO+FkAXg6Ko6jRDP598NKraD1QPhscxOnhU/nvqRw7cPMzF4Ivns86mOY5WkuBEGMb5jNZztbRn++zHpnnqZO+dgywQI7AO+DVSnEeLFNBroMBt0WlgzSD8IXjzXpfhLfHn4S96q/BZ1vOqojmO1pLgRBuHuYs+01/zZce4Ovx68pjqO6UpP009v714CmoWpTiNE1uQvCu1nwelV+iVCxDOladMI3R2Kd35vBtQcoDqOVZPiRhhM00pFeaOOD+FrTnHtfqLqOKZp75dw84h+mnsHF9VphMi6qp2h2mv6JUISZPqHZ/n+xPecvHeS8AbhONk5qY5j1aS4EQY1ul1lCrg48NlvUWi10nydScwJ2DYFggeAjzRXCzPUdgbYOcLqAdI99S9n759lftR83q36LgFFAlTHsXpS3AiDcnWy5/PX/dl36T4/RF5RHcd0pKXou6MKl4cmI1WnESJnXApChy/h/AY48qPqNCYjNT2V0btH4+vmyyfVP1EdRyDFjTCCoHKF6VW/FNPWn+HSnUeq45iGnZ/D7dP6u6PsHFWnESLnKraB6j1g/UiIi1adxiR8fexrLsZdZFKDSTjYOqiOI5DiRhjJ8DaV8HJzYujyKNKtvXvqxmHYNRMafQbe1VWnESL3Wk8BJ3f90iFareo0Sp24e4Jvj3/Lh/4fUqVQFdVxxP+T4kYYhYuDHTO7BnD0WhyLdl1SHUed1CRY8TF4VYOGMpmXsBBO7volQy7vgL8Wq06jTHJ6MqN3j6aCRwXe939fdRzxD1LcCKOpVaogHzQsw6yN5zgb81B1HDW2TYIHl6Hz12BrrzqNEIZT9hWo3Rs2hcG9i6rTKDH3yFyuPbzG5AaTsbeR69uUSHEjjGpQiwqUKuTC4GVHSU23subr6H2w9ytoOhqKVladRgjDazFBPwfOyr6gTVedJk8duX2EH07+wKc1PqWcRznVccS/SHEjjMrJ3pZZXatzJuYh87ZdUB0n76Q8hog+UKIOBPVTnUYI43DMD53m6wv5ffNVp8kziamJjN49Gv8i/vSq0kt1HPEMUtwIo/Mr4U7fpuWYu/UCJ27Eq46TNzaP0090FrIAbGxVpxHCeHyDod4nsGUi3DmrOk2emH14NncS7xAeHI6tXN8mSYobkSc+bVqOCp6uDF52lOQ0C2++vrQDDnwDzcdBYWmuFlag2RgoUFI/eD49TXUao9p3ax+/nPmFgbUG4uvuqzqOeA4pbkSecLCzYVa3AC7ffcwXm86rjmM8SQn68Qe+DaHuh6rTCJE37J2h80K4dRT2fKE6jdE8SnlE2J4w6njVoXul7qrjiBeQ4kbkmUpebgxqUYFvdl7k0NUHquMYx8bR8OQBdJoHNnJ5CStSojY0GATbp0HMcdVpjOLzvz4nPjmeicETsdHI9W3KlH935s2bh6+vL05OTgQGBnLgwIEX7h8XF0ffvn0pVqwYjo6OVKhQgXXr1uVRWpFbHzYsg3+JAgxdHsWTFAvrnjq3EQ7/B1pNAo9SqtMIkfcaD4fCFfTdU2kpqtMY1M7rO/nj/B98VucziucvrjqOeIkcFzdbtmyhffv2lC1blrJly9K+fXs2b96crWMsXbqUwYMHM3bsWA4fPkxAQACtWrXi9u3bz9w/JSWFFi1acOXKFX777TfOnj3LokWLKF5cPmjmws7WhpldA7gZ94Rp68+ojmM4ifdhVT8o1xxqyt0TwkrZOeq7p+6cgR3TVKcxmPjkeMbtHUdw8WBeK/+a6jgiC3JU3MyfP5/WrVvj6urKgAEDGDBgAG5ubrRt25Z58+Zl+TizZs3igw8+4N1336VKlSosXLgQFxcXvvvuu2fu/91333H//n0iIiIIDg7G19eXxo0bExAgK7Cak7JF8jOsdSWW7L3C3ot3VccxjD+HQ9oT6PgVaDSq0wihTjF/fQvO7i/g+iHVaQxiyoEpJKUnMb7+eDRyfZsFjU6X/XXrS5QowYgRI/j0008zbZ83bx6TJ0/mxo0bLz1GSkoKLi4u/Pbbb4SEhGRs79WrF3FxcaxcufKp57Rt25aCBQvi4uLCypUrKVKkCG+++SbDhw/H1vbZt+MlJyeTnJyc8f8JCQn4+PgQHx+Pm5tbFl+xMDStVkf3Rfu4EfeE9QMbkd/RTnWknDu1Cpa9rZ+FOOAN1WmEUC89FRa30M/39NFO/YBjM7X56mYGbR/E5AaT6VC2g+o4Ioty9BslLi6O1q1bP7W9ZcuWDB8+PEvHuHv3Lunp6Xh6emba7unpyZkzz+6uuHTpElu3bqVHjx6sW7eOCxcu8Mknn5CamsrYsWOf+ZwpU6Ywfvz4LGUSecfGRsOMLgG0mr2TSWtPM+VVP9WRcubxXVgzCCq1B/9uqtPkiE6nIy3Nsm/fNWe2trbYmNvgdFt7CFkIXzeCreH6cWhm6N6Te0zcN5FXfF6hfZn2quOIbMhRcdOxY0dWrFjBZ599lmn7ypUrad/eeB8ArVZL0aJF+eabb7C1taVWrVrcuHGDzz///LnFzciRIxk8eHDG///dciPU8ynowuh2lRm94gStqnrSpGJR1ZGyR6eDNQNBp4X2X5hld1RaWhp37twhBw24Ig+5uLjg7u5uXl0iRSvBK6H6tacqtYNSQaoTZYtOpyN8XzhanZYx9ceY13svclbcVKlShUmTJrF9+3bq168PwL59+9izZw9Dhgxhzpw5Gfv279//mccoXLgwtra2xMbGZtoeGxuLl5fXM59TrFgx7O3tM3VBVa5cmZiYGFJSUnBwcHjqOY6Ojjg6Omb7NYq88Wbdkqw/EcPw34+xcWBj3F3MaPG547/B6dXQ5Qf9+jpmRqfTERcXh42NDR4eHvLD2wTpdDpSUlJISEgAoECBAmoDZVf9vnBmrX4pko/36JdrMBPrLq9jc/RmZjaeSWHnwqrjiGzK0Zib0qVLZ+3gGg2XLl167tcDAwOpW7cuX331FaBvmSlZsiSffvopI0aMeGr/UaNG8fPPP3Pp0qWMZtovv/ySadOmcfPmzSxlSkhIwN3dXcbcmJBb8U9o+cVOWlT2ZFa36qrjZE3CLZhfD8o1g9efPQDe1KWnpxMbG4uHhwfOzuY7JsIaPHr0iISEBLy8vMyvi+reRVjYAKq/Ce1mqk6TJbcTbxOyMoQG3g2Y3ni66jgiB3LUcnP58mWDnHzw4MH06tWL2rVrU7duXWbPns3jx4959913AejZsyfFixdnypQpAPTp04e5c+cyYMAA+vXrx/nz55k8efJzW4eEeSjm7sy4DlUZsjyKVtW8aFX12S13JkOng9UD9Le9tp2hOk2OabX6VdqfNxhfmI6/W6XT09PNr7gpVBaaj4c/P9OPTSvbVHWiF9LpdIzbOw5HW0dGBY5SHUfkkNJbVLp168adO3cICwsjJiaG6tWrs379+oxBxtHR0ZkuZB8fHzZs2MCgQYPw9/enePHiDBgwIMuDmIXperVmcdafjGH0iuPULuVBofwm3JV45Ec4vwG6LwWXgqrT5Jp0R5k+s/8e1XkfzqyGlZ/CJ3vByV11oudacWEFu27sYu4rcyngVEB1HJFDWe6WGjx4MBMnTiRfvnyZBug+y6xZswwSzhikW8p03XmYTMsvdlCvTCHm96hpmj/Q46JhfhBU6QQhWZ/TyRSlpqZy584dihQpgr29GY11skIW8b36+9qp2km/PIkJuvnoJq+uepUWpVowMXii6jgiF7LccnPkyBFSU1Mz/v08JvkLSZiFIq6OhIf40ffnw6yKukmn6iY287RWq//L08kdWk9WnUYI81KgpP66WdUPKnWAik9PJ6KSVqclbE8Yrg6uDKszTHUckUtZLm62bdv2zH8LYUjt/Ivx54lihK08Sf0yhSjq5qQ60v/8tRgu74C3I0y6Wd3SNWnShOrVqzN79mzVUUR21Xhbf4fh6v7gs8+kunWXnl3K/pj9fNPiG1wdXFXHEblkZiPThDWY2Kka9rY2jPjjuOnMv3Lvon6+jtq9TX5ApBAmS6OBDnMgLRnWffby/fNIdEI0Xxz6gm4Vu1Hfu77qOMIAclTcPH78mDFjxhAUFES5cuUoU6ZMpocQueGRz4Gpr/qx9cxtlh+6rjoOaNNhZV/9XDYtJqhOI4R5cysGbT+HE7/ByQjVaUjXphO6J5RCToUYXOvF40mF+cjR3VLvv/8+O3bs4O2336ZYsWIyzkYYXPMqnrxeqwQTVp8iqGwhSni4qAuzbz5E74N315nVJGTW4MGDBwwYMIDVq1eTnJxM48aNmTNnDuXLl0en01G0aFEWLFjA66+/DkD16tWJjY3l1q1bAOzevZtmzZrx4MEDXFwUfsasjV8XOL0K1g6GUsGQv4iyKP899V+O3j7K962/x8VePgOWIkfFzZ9//snatWsJDg42dB4hMoR1qMLeC3cZ/vsx/vteIDY2CoroO2dhy0T9TKtmNn18TjxJSefinUd5ft6yRfLj7JD9+Xbeeecdzp8/z6pVq3Bzc2P48OG0bduWU6dOYW9vT6NGjdi+fTuvv/46Dx484PTp0zg7O3PmzBkqVarEjh07qFOnjhQ2eU2jgXZfwPxA/RIm3X5UsnzJxbiLfHXkK96u8ja1PGvl+fmF8eSouPHw8KBgQdMZCCYsk5uTPdNe9+ftxQf4af9V3q7vm7cB0tNgxcfgUUq/Ro4VuHjnEe2/2p3n513TrwHVimdvkPbfRc2ePXsICtIXnj/99BM+Pj5ERETQpUsXmjRpwtdffw3Azp07qVGjBl5eXmzfvp1KlSqxfft2GjdubPDXI7IgfxFoPxuWvQ3HlkFA3i48m6ZNY/Tu0RR3LU6/Gv3y9NzC+HJU3EycOJGwsDB++OEH+YtHGFXD8kV4q15JJq87Q8PyRfAtnC/vTr7nC7h1FHpvBnvrWJ6gbJH8rOnXQMl5s+v06dPY2dkRGBiYsa1QoUJUrFiR06dPA9C4cWMGDBjAnTt32LFjB02aNMkobnr37s3evXsZNkxu+1WmSkd9F9Wfn0HphuDmnWenXnx8Mafvn+bHNj/iZGdCd2UKg8hycVOjRo1MY2suXLiAp6cnvr6+T00qdfjwYcMlFFZvZJvK7Dx3l89+i+LXD+tjmxfdUzHHYfs0aDAISlhPc7Wzg222W1BMmZ+fHwULFmTHjh3s2LGDSZMm4eXlxbRp0zh48CCpqakZrT5CkTbT4fIu/fw3PX7Lk+6pM/fPsDBqIb2r9caviJ/RzyfyXpaLm5CQECPGEOL58jna8fnr/ryxaB/f7b7MB42MfEdeWoq+O6pIRWgsS3uYqsqVK5OWlsb+/fszCpR79+5x9uxZqlSpAugnFW3YsCErV67k5MmTNGjQABcXF5KTk/n666+pXbs2+fLlYWugeJpLQej4FfzcBQ7/B2r1MurpUtNTGb17NGUKlOHjgI+Nei6hTpaLm7FjxxozhxAvFFimEL2DS/P5xrM0rVSEckWNOMnWjmlw5wx8sE2/OKYwSeXLl6dTp0588MEHfP3117i6ujJixAiKFy9Op06dMvZr0qQJQ4YMoXbt2uTPr+/+atSoET/99BOffWY6c61YtQot9RP8bRgFZZrox7kZyYKoBVyKu8Qv7X/BwdbBaOcRauVonptr165x/fr/5h85cOAAAwcO5JtvvjFYMCH+bWirivh4ODN4WRRp6VrjnOT6Idj9BTQeAcX8jXMOYTDff/89tWrVon379tSvXx+dTse6desydZU3btyY9PR0mjRpkrGtSZMmT20TirWaDM4e+jmltMa5vo/fOc7iE4v5OOBjKhWsZJRzCNOQ5YUz/6lhw4Z8+OGHvP3228TExFChQgWqVavG+fPn6devH2FhYcbIahCycKZ5O3otjlfn72FQ8wr0a1besAdPfQJfNwKHfPpBxLY5Gm9vNixiMUYrYTXfq0vb4T+d9ONwAj8y6KGT0pLouqYrLnYu/Nj2R+xsLPv6tnY5ark5ceIEdevWBWDZsmX4+fmxd+9efvrpJ5YsWWLIfEJkUt2nAH2alGXO1vOcvBlv2INvDYcHVyFkocUXNkKYpDJNoM4HsGmsfskTA/rqyFfceHiDSQ0mSWFjBXJU3KSmpuLoqB+LsHnzZjp27AhApUqVMmb+FMJY+jcrT9ki+RmyLIqUNAM1X1/dC5Hz9PPZFJXmaiGUaTEeXL30g/q16QY55KHYQ/z31H/pV6MfZQuUNcgxhWnLUXFTtWpVFi5cyK5du9i0aROtW+uXrr958yaFChUyaEAh/s3RzpaZXQO4cPsRc7acz/0Bkx9BRB/wCdTPRCyEUMchH3ReCNcPQuTcXB8uMTWR0N2hVC9anbervG2AgMIc5Ki4mTZtGl9//TVNmjShe/fuBAQEALBq1aqM7iohjKmqtzsDmpVn/vYLHIl+kLuDbR4Lj25DyHywyf4SAEIIAytZD4I+1XcV3z6dq0PNOjSLe0n3CA8Ox1aub6uR7Y5HnU5HmTJliI6OJi0tDQ8Pj4yvffjhhzJjscgzfZqUZfPpWIYsj2Jd/4Y42efgB9fFbXDwW2g7AwpJc7UQJqNpKJzbqO+een8z2GZ/IHXkzUiWnl3KqMBRlHQraYSQwlRlu+VGp9NRrlw5YmJiMhU2AL6+vhQtWtRg4YR4ETtbG2Z2DeD6gyfM2HA2+wdIioeVn0LpRlC7t+EDCiFyzt4JOi/Qzxa+a1a2n/4w5SFhe8MI9AqkW8W8XbdKqJft4sbGxoby5ctz7949Y+QRIlvKFXXls5YVWbznMgcu38/ekzeM0hc4neaBTY56aIUQxlS8FjQcDDunw62obD11+sHpPEx5yITgCdho5Pq2Njn6jk+dOpXPPvuMEydOGDqPENn2XoPS1CrpwdDlUTxOTsvak86uhyM/QuvJUECaq4UwWY2GQZHK+u6ptOQsPWXHtR1EXIhgWJ1heOfPu8U4henIUXHTs2dPDhw4QEBAAM7OzhQsWDDTQ4i8ZGujYUaXAO48TGbKn1kYfJh4H1b3h/L/P+W7EMJ02Tno7566ex62T33p7nFJcYyLHEfD4g3pXK5zHgQUpihHMxnNnj3bwDGEyB3fwvkY1bYSY1aepFVVLxqWL/L8ndd9pv8LsMOcPFmBWAiRS17VoMkI2DYJKrYFnzrP3XXygcmkpKcwLmgcGrm+rVaOiptevYy7aqsQOdEjsBTrT8Yw7LdjbBjUCDenZ9xdcTICTvwGr34LbsXyPKMQIoeCB8LZdRDxMXy0CxyevjN345WN/Hn5T6Y2nEpRF7m5xZrleJTVxYsXCQ0NpXv37ty+fRuAP//8k5MnTxosnBDZYWOjYfrrATxMSmPC6lNP7/DoDqwdDJU7gN/reR9QmK309HS0RlrMUWSRrZ1+aZT467B14lNfvvvkLuH7wmlesjltS7dVEFCYkhwVNzt27MDPz4/9+/fzxx9/8OjRIwCioqIYO3asQQMKkR3FCzgT1r4Kvx26zuZTsf/7gk4HawYCGmj3hXRHmbn169fToEEDChQoQKFChWjfvj0XL+rXIgoKCmL48OGZ9r9z5w729vbs3LkTgOTkZIYOHUrx4sXJly8fgYGBbN++PWP/JUuWUKBAAVatWkWVKlVwdHQkOjqagwcP0qJFCwoXLoy7uzuNGzfm8OHDmc515swZGjRogJOTE1WqVGHz5s1oNBoiIiIy9rl27Rpdu3alQIECFCxYkE6dOnHlyhWjvFcWpUgFeGUM7FsAV3ZnbNbpdEyMnIhGoyG0Xqh0R4mcdUuNGDGC8PBwBg8ejKura8b2V155hblzcz9dthC50aV2CdafjGHEH8fZVMoDj3wOcGwZnFkDXf8L+V8wHsfapSTC3XN5f97CFZ7ZzfA8jx8/ZvDgwfj7+/Po0SPCwsLo3LkzR48epUePHkyfPp2pU6dm/JJbunQp3t7eNGzYEIBPP/2UU6dO8euvv+Lt7c2KFSto3bo1x48fp3x5/WrziYmJTJs2jW+//ZZChQpRtGhRLl26RK9evfjqq6/Q6XTMnDmTtm3bcv78eVxdXUlPTyckJISSJUuyf/9+Hj58yJAhQzJlT01NpVWrVtSvX59du3ZhZ2dHeHg4rVu35tixYzg4OBjoTbVQ9frAmbUQ8Qn02QuO+VlzaQ1br23liyZfUMhZlgASoNHpdLrsPil//vwcP36c0qVL4+rqSlRUFGXKlOHKlStUqlSJpKQkY2Q1iISEBNzd3YmPj8fNzU11HGEktxOSaPHFThpVKMJX7Txhfj0o3wpeW6Q6mslITU3lzp07FClSBHv7/x+fdPMofNM478N8uAO8q+f46Xfv3qVIkSIcP34cT09PvL292bp1a0YxExQURKNGjZg6dSrR0dEZs6x7e//vNuHmzZtTt25dJk+ezJIlS3j33Xc5evRoxvIyz6LVailQoAA///wz7du3Z/369XTo0IFr167h5eUF6BcXbtGiBStWrCAkJIQff/yR8PBwTp8+nVF8paSkUKBAASIiImjZsuVT53nm98qa3b8EC4Ih4A1im46g88rONPJpxNSGL7+bSliHHLXcFChQgFu3blG6dOlM248cOULx4sUNEkyI3Cjq5sSETlUZ8OsRxsSNoaidM7SdrjqW6StcQV9oqDhvNpw/f56wsDD279/P3bt3M8bDREdHU61aNVq2bMlPP/1Ew4YNuXz5MpGRkXz99dcAHD9+nPT0dCpUyHzO5OTkTAv/Ojg44O/vn2mf2NhYQkND2b59O7dv3yY9PZ3ExESio6MBOHv2LD4+PhmFDfDUentRUVFcuHAhU6s3QFJSUkbXmniJgmWgxQR064YyNv06TnZOjKw7UnUqYUJyVNy88cYbDB8+nOXLl6PRaNBqtezZs4ehQ4fSs2dPQ2cUIkc6BnjzcO9iisbuIr7zz7g7e7z8SdbOwSVXLSh5pUOHDpQqVYpFixbh7e2NVqulWrVqpKSkANCjRw/69+/PV199xc8//4yfnx9+fn4APHr0CFtbWw4dOoStbeb1yPLnz5/xb2dn56fGbvTq1Yt79+7x5ZdfUqpUKRwdHalfv37GebPi0aNH1KpVi59++umprxUpIl2mWVa7N7+f/pk9cWeY13A67o7uqhMJE5Kj4mby5Mn07dsXHx8f0tPTqVKlCunp6bz55puEhoYaOqMQOaKJi6bHg4VEaJqx5qgni/x1MtDQAty7d4+zZ8+yaNGijG6n3bt3Z9qnU6dOfPjhh6xfv56ff/450x9dNWrUID09ndu3b2c8P6v27NnD/PnzadtWfzfOtWvXuHv3bsbXK1asyLVr14iNjcXT0xOAgwcPZjpGzZo1Wbp0KUWLFpWu8Vy4kXiLz20f82pCMo2OrYYybVRHEiYkR3dLOTg4sGjRIi5evMiaNWv48ccfOXPmDP/973+f+ktICCW0WljZF41LQfJ3ms7m07H8cfiG6lTCADw8PChUqBDffPMNFy5cYOvWrQwePDjTPvny5SMkJIQxY8Zw+vRpunfvnvG1ChUq0KNHD3r27Mkff/zB5cuXOXDgAFOmTGHt2rUvPHf58uX573//y+nTp9m/fz89evTA2dk54+stWrSgbNmy9OrVi2PHjrFnz56MP/j+Lqx79OhB4cKF6dSpE7t27eLy5cts376d/v37c/36dUO9TRZNq9MyZs8Y3J0K8Fnd4XD0JzizTnUsYUJytZpYyZIladOmDV26dMm4w0AIk3BwEVzZBZ3m0rx6OTrXKM641Se5Ff9EdTKRSzY2Nvz6668cOnSIatWqMWjQID7//POn9uvRowdRUVE0bNiQkiUzrx/2/fff07NnT4YMGULFihUJCQnh4MGDT+33b4sXL+bBgwfUrFmTt99+m/79+1O06P8mi7O1tSUiIoJHjx5Rp04d3n//fUaPHg2Ak5MTAC4uLuzcuZOSJUvy6quvUrlyZXr37k1SUpK05GTRL2d+4WDMQSYGTyR/rfegQmtYPUC/tIoQ5PBuKdBf5F988QXnz58H9H/RDBw4kPfff9+gAQ1N7payAvcu6u+kqPEWtJsBQHxiKi1n76CCpyv/ea+udE8hd+DklT179tCgQQMuXLhA2bJlc3QM+V79z5X4K3RZ3YXO5TszKnCUfuPDGJgXCGVfgS7fqw0oTEKOWm7CwsIYMGAAHTp0YPny5SxfvpwOHTowaNAgwsLCDJ1RiKzTputXD3b1ghbjMza7u9gz7TV/dp2/yy8HrikMKCzdihUr2LRpE1euXGHz5s18+OGHBAcH57iwEf+Trk0ndE8oRV2KMrDmwP99wdUL2s2Ek3/AiT+U5ROmI0cDihcsWMCiRYsy9WN37NgRf39/+vXrx4QJEwwWUIhsiZwL1w/Ce+vBIV+mLzWpWJTudX0IX3uKBuUKU7JQ1ieNEyKrHj58yPDhw4mOjqZw4cI0b96cmTNnqo5lEX449QPH7hzjhzY/4GL/r+u32mtwehWsHQK+DSC/rC1lzXLUcpOamkrt2rWf2l6rVi3S0tJyHUqIHLl9GraGQ9CnULLeM3cZ3a4KBfM58NlvUWi1OeqRFeKFevbsyblz50hKSuL69essWbIk0/w5ImcuPLjA3CNz6VW1FzWK1nh6B40G2s0CjY1+/E3ORlwIC5Gj4ubtt99mwYIFT23/5ptv6NGjR65DCZFt6an67iiP0tD0+dMR5He0Y/rr/uy/fJ8le6/kXT4hRI6lalMZtXsUPq4+fFrj0+fvmK8wdPhSv3p41K95F1CYnCx3S/3zVkuNRsO3337Lxo0bqVdP/xfy/v37iY6Olkn8hBq7ZkHMcXh/E9g7vXDXoLKFeSfIl2nrz9C4YhHKFsn/wv2FEGp9e/xbzj04x09tf8LR1vHFO1duD/7d4M/hULoRuMus+dYoy3dLNW3aNGsH1GjYunVrrkIZk9wtZYFuRcGiV6DBIHgla5NIPklJp+2cXRRwsWf5R/Wxs83VrAhmSe7AMR/W/L06de8UPdb2oLdf7xe32vzTkwcwvz4UrQxv/aHvshJWJce3gpsrKW4sTFoyfNMENLbwwVawy/qKyoeu3qfLwkiGtqrIJ03KGS+jibLmX5jmxlq/VynpKXRb0w07Gzt+bvsz9rbZeO3nN8NPr0H72VD7XaNlFKbJ+v5cFZZl+1S4ex46L8xWYQNQq1RBPmxUli82neNMTIKRAgohcmr+0flcSbhCeHB49gobgPLNoWYv2DAaHlwxSj5hunJ0K3hSUhJfffUV27Zt4/bt2xkr8v7t8OHDBgknxAtdOwh7ZkPT0eBVLUeHGNSiPFvPxDJkWRQrPgnGwU7qfSFMQdSdKL4/+T2fVv+UigUr5uwgrSbBxW0Q0Rd6rQYbub6tRY6+071792b69OmUKlWK9u3b06lTp0wPIYwuJREiPgbvGhA8MMeHcbSzZWaX6pyNecjcbRcMl08YTZMmTRg4cOBzv67RaIiIiMjy8bZv345GoyEuLi7X2YRhPEl7QujuUKoWqsq71XLRpeToCiHz4OpuOPC14QIKk5ejlps1a9awbt06goODDZ1HiKzZOhHir8Mbv4Btjj7GGfxKuNO3aTnmbrtAi8qe+JVwN1BIocKtW7fw8PBQHUPkwpzDc7j1+BZfvvIldja5u74p3QjqfgSbx0G55lBY1kG0BjlquSlevDiurq6GziJE1lzZDfsWwCtjoEgFgxzy01fKUcnLlcHLjpKUmm6QYwo1vLy8cHR8ye3CwmQdjDnIj6d/pF+NfpRxL2OYgzYfB27FIaKPfokWYfFyVNzMnDmT4cOHc/XqVUPnEeLFkh9BxCdQsj7U62Oww9rb2jCra3Wu3kvki03nDHZcYRxarZZhw4ZRsGBBvLy8GDduXMbX/t0ttXfvXqpXr46TkxO1a9cmIiICjUbD0aNHMx3z0KFD1K5dGxcXF4KCgjh79mzevBiR4XHqY8bsGUPNojV5q/JbhjuwgwuELIAbh2DvHMMdV5isHLX31a5dm6SkJMqUKYOLi8tTtybevy/Lzgsj2TQGHt+BnhFgY2vQQ1f0cmVQiwpM33CGllU9qVWqoEGPbw6epD3hcvzlPD9vaffSONs5Z3n/H374gcGDB7N//34iIyN55513CA4OpkWLFpn2S0hIoEOHDrRt25aff/6Zq1evPne8zujRo5k5cyZFihTh448/5r333mPPnj25eVkim2b+NZP7SfdZ1GIRtga+vikZCEH9YNtkKN8KPKsY9vjCpOSouOnevTs3btxg8uTJeHp6opEJkkReuLAF/vpOv/pvQQM1V//Lh43KsPFUDEOWRbFuQENcHHLZ329mLsdfptuabnl+3qXtl1KlUNZ/2fj7+zN27FgAypcvz9y5c9myZctTxc3PP/+MRqNh0aJFODk5UaVKFW7cuMEHH3zw1DEnTZpE48aNARgxYgTt2rUjKSkJJ6cXz3gtDGPPjT0sP7ec0MBQfNx8jHOSJqPg3AZY8ZF+Xqzs3l4uzEaOfnLv3buXyMhIAgICDJ1HiGd7Eger+kGZJlC7t9FOY2ujYWaXANrO2cX09WcZ17Gq0c5likq7l2Zp+6VKzpsd/v7+mf6/WLFi3L59+6n9zp49i7+/f6YCpW7dui89ZrFixQC4ffs2JUuWzFY2kX0JKQmE7Q2jXrF6dK3Y1XgnsnfSz4m1qBnsnAFNRxrvXEKpHBU3lSpV4smTJ4bOIsTzrR8JyQ+h41yjT6Vepkh+hreuxPjVp2hZxZOgcoWNej5T4mznnK0WFFX+3RWu0Wiemm8rN8f8uzU6t8cUWTPtwDQSUxOZEDTB+D0B3jWg0VDYNQMqttb/v7A4ORpQPHXqVIYMGcL27du5d+8eCQkJmR5CGNSZdRD1M7SeAgWM1Fz9L73q+1KvTEE+++0YD5NS8+ScwvAqVqzI8ePHSU5Ozth28OBBhYnEv22L3saqi6sYVmcYxfIXy5uTNhwKRavAij76JVyExclRcdO6dWsiIyNp1qwZRYsWxcPDAw8PDwoUKCDzSwjDSrwPqwdAhdZQvUeendbGRsPnrwcQl5jCpLWn8+y8wrDefPNNtFotH374IadPn2bDhg3MmDEDQMYKmoAHSQ8YHzmexiUaE1IuJO9ObOeg7566d0E/wFhYnBx1S23bts3QOYR4trVDID0FOnyZ5yv7+hR0IbR9FUb+cZxWVb1oWqlonp5f5J6bmxurV6+mT58+VK9eHT8/P8LCwnjzzTdloLAJmLR/EqnaVMbWH5v3xaZnVWg6Sj8haKV24PPssVjCPMmq4MJ0nfgDfnsXXlsMfq8riaDT6Xjn+4OcvpXAxkGNKOCSvcU5TZm1rjT9008/8e677xIfH4+zc9ZvP1fJEr9X6y+v57OdnzG90XTalG6jJkR6GnzXCp48gI936+fDERYhx6uI7dq1i7feeougoCBu3LgBwH//+192795tsHDCij2M1bfaVOkE1V5TFkOj0TDtNX+SUtMZt+qkshwi5/7zn/+we/duLl++TEREBMOHD6dr165mU9hYortP7hK+P5wWpVrQ2re1uiC2dvruqYQbsGW8uhzC4HJU3Pz++++0atUKZ2dnDh8+nDFYLz4+nsmTpf9S5JJOB2sG6ifpazcrz7uj/s3L3YlxHasScfQm60/cUppFZF9MTAxvvfUWlStXZtCgQXTp0oVvvvlGdSyrpdPpGB85HluNLaH1QtWPfSpcHpqNhf0L4fJOtVmEweSouAkPD2fhwoUsWrQoUxNpcHAwhw8fNlg4YaWifoWz66D9bMhnGrdhd65RnJZVPBm94gR3H8ndFeZk2LBhXLlyhaSkJC5fvswXX3yBi4t0P6iy6uIqtl/bTlj9MAo6mcgs4IEfQ6kGsLKvfsoJYfZyVNycPXuWRo0aPbXd3d2duLi43GYS1iz+Bvw5HPzfgMrtVafJoNFomNTZDx0QuuIEVjZUTQiDiHkcw7QD0+hQpgPNSjZTHed/bGyg01x4fA82hqpOIwwgR8WNl5cXFy5ceGr77t27KVPGONPiCyug08GqT/WD+tpMVZ3mKUVcHQkPqcb6kzGsirqpOo7BSKFm+izhe6TT6Ri7dyzOds4MrztcdZynFSwNrcLh0BI4v1l1GpFLOboV/IMPPmDAgAF89913aDQabt68SWRkJEOHDmXMmDGGziisxaHv4eJW6PE7OJvmfElt/YrRMcCbMREnqFemEJ5u5ns7sa2tLRqNhocPH+Lq6qp+7IN4ik6nIz09nYSEBDQaDXZ25rvW2fJzy9l7cy8Lmi/A3dFddZxnq/UunF6tX+rlk70m+3NIvFyObgXX6XRMnjyZKVOmkJiYCICjoyNDhw5l4sSJBg9pSHIruIm6fxkWBOtv+e44R3WaF4pLTKHFFzup5u3Gd+/UMeuiIDk5mfv371tEy4Alc3BwoECBAmZb3Fx7eI3XVr1G29JtGRc0TnWcF4u/DvODoGIbePVr1WlEDuVqnpuUlBQuXLjAo0ePqFKlCvnz5zdkNqOQ4sYEabXwQweIj4Y+e8HRVXWil9pyOpbeP/zFtNf86FbHvBdW1Gq1pKenq44hnsPGxgYbGxuzLaK1Oi3vbXiPmMcx/N7xd/LZ51Md6eWO/gwRfaDbTyY19k9kXbb+DHjvvfeytN93332XrRDz5s3j888/JyYmhoCAAL766qvnrtz7T7/++ivdu3enU6dOREREZOucwoQc+Bqu7oZeq82isAFoVtmTLrVKMHHNaYLLFaaEh/neffP3L08hjOGn0z9xKPYQi1suNo/CBiCgu757as1AKFkf8hVSnUhkU7Z+oi1ZsoRt27YRFxfHgwcPnvvIjqVLlzJ48GDGjh3L4cOHCQgIoFWrVty+ffuFz7ty5QpDhw6lYcOG2TqfMDF3z8PmcfpbMUs/fQeeKRvToQpuTnYM++0YWq106wjxb5fjL/Pl4S/pUbkHdYuZ0fIGGo1+KgptGqwdpL/ZQZiVbHVL9e3bl19++YVSpUrx7rvv8tZbb1GwYO7mKQgMDKROnTrMnTsX0DeR+/j40K9fP0aMGPHM56Snp9OoUSPee+89du3aRVxc3HNbbpKTkzOtCJyQkICPj490S5mC9DT4vrV+cUwznfp89/m7vLV4P+M7VqVXkK/qOEKYjDRtGr3+7EV8SjzLOyzH2c4MZ4Q2gSVgRM5kq+Vm3rx53Lp1i2HDhrF69Wp8fHzo2rUrGzZsyNGAxJSUFA4dOkTz5s3/F8jGhubNmxMZGfnc502YMIGiRYvSu3fvl55jypQpuLu7Zzx8fHyynVMYyd45cOMQhCwwy8IGoEH5wrxdrxRT/zzD5buPVccRwmQsObmEE/dOEB4cbp6FDUC1V6FqZ1g3FB7GqE4jsiHbHe2Ojo50796dTZs2cerUKapWrconn3yCr68vjx49ytax7t69S3p6Op6enpm2e3p6EhPz7A/S7t27Wbx4MYsWLcrSOUaOHEl8fHzG49q1a9nKKIwk9iRsnwJB/aBkoOo0uTKiTSWKujkydHkU6dI9JQTnHpxj3tF59Krai+pFq6uOkzttZ4KNPaweIN1TZiRXowj/HsH/91wMxvbw4UPefvttFi1aROHCWZuW39HRETc3t0wPoVh6Kqz4GAqWhSajVKfJtXyOdszoEsDh6Acs3n1JdRwhlEpNTyV0dyi+br70rd5XdZzcy1cIOnwJ59br76ISZiHbxU1ycjK//PILLVq0oEKFChw/fpy5c+cSHR2d7VvBCxcujK2tLbGxsZm2x8bG4uXl9dT+Fy9e5MqVK3To0AE7Ozvs7Oz4z3/+w6pVq7Czs+PixYvZfTlChZ0z9C03nReAvflOgvdPdXwL8n6D0szYcI5zsbI2jbBe3xz/hnMPzhHeIBxHW0fVcQyjUlsIeBPWj9DPgyNMXraKm08++YRixYoxdepU2rdvz7Vr11i+fDlt27bN0a2kDg4O1KpViy1btmRs02q1bNmyhfr16z+1f6VKlTh+/DhHjx7NeHTs2JGmTZty9OhRGU9jDm4egZ2fQ6PPwLuG6jQGNaRlRUoWcmHIsihS07Wq4wiR507eO8miY4v40P9DqhaqqjqOYbWeAg759YtrSveUycvW3VI2NjaULFmSGjVqvHBCqT/++CPLAZYuXUqvXr34+uuvqVu3LrNnz2bZsmWcOXMGT09PevbsSfHixZkyZcozn//OO++88G6pf5NJ/BRKTYJvmoCtPXywVf9fCxN1LY5XF+xlQLPy9G9WXnUcIfJMcnoy3VZ3w8HWgZ/a/YS9jeVd31zYAj++Cu1mQp33VacRL5CtSfx69uxp8Fkyu3Xrxp07dwgLCyMmJobq1auzfv36jEHG0dHRMsGYpdg+Ge5fhA+3W2RhAxDgU4BPmpRlzpbzvFKpKNWKm+gaOkIY2Lyj84h+GM2v7X+1zMIGoFwz/fpTG8Og7CtQUBaKNlW5Wn7BHEnLjSLR+/Vz2rwyBhoOVp3GqFLStHSatwedTsfKT4NxtLNVHUkIozp6+yg9/+xJ/5r9ed/Pwls0kh/q18FzKw7vrAX549skyXdFGF9Kon6dluK1IKi/6jRG52Bnw8wuAVy884gvN59XHUcIo0pMTWT07tH4FfHjnarvqI5jfI6uEDIfovfC/gWq04jnkOJGGN+W8ZBwQz9Zn615rmqcXVW83RjQrDwLd1zkcHT2liQRwpx8efhLYhNjCQ8Ox87GOq5vfBtAvU9g83i4c051GvEMUtwI47q8E/YvhObjoLB1DbD9uHFZ/Iq7M3RZFE9SZNVtYXkO3DrAz2d+ZmDNgZR2L606Tt5qFgYFfCDiY/1SMsKkSHEjjCf5IUT0hVINoO5HqtPkOTtbG2Z2rc6NuCd8vuGs6jhCGNSjlEeM2TOG2p61ebPym6rj5D17ZwhZqJ/eYs9s1WnEv0hxI4xnw2hIvAch86x20F25ovn5rFVFvt97mX2X7qmOI4TBzPhrBg+SHzAxeCI2Guu8vvGpA8EDYPtUiDmhOo34Byv9RAqjO78ZDv8ArcLBw1d1GqXeDS5NnVIF+ey3KB4nS/O1MH+7ru/i9/O/M7T2UEq4llAdR60mI/Vd7is+hrQU1WnE/5PiRhjekwew6lP9PBC13lWdRjlbGw2fd/Hn7sMUJq87rTqOELkSnxzPuL3jCPIOokuFLqrjqGfnCJ0Xwp3T+tnXhUmQ4kYY3p8j9Ld/d5wLBp700VyVKpSPUe0q89P+aHacu6M6jhA5NvXAVJ6kPWF80HiDT+pqtooFQKNhsGsm3DikOo1AihthaKfXwLFfoc00cC+uOo1JeSuwJA3LF2b4b8eIf5KqOo4Q2bYlegtrLq1hROAIvPI9vbixVWs4GLz8YEUf/VIzQikpboThPL4LawZCxbYQ8IbqNCZHo9Ew7TV/HienMWH1KdVxhMiW+0n3mRA5gSY+TehQpoPqOKbH1l7fPfXgMmwLV53G6klxIwxDp4O1g0GbDu1nS3fUc3gXcCasQxV+P3ydTadiVccRIkt0Oh3h+8LR6rSMrT9WuqOep2hlaDoa9s6F6H2q01g1KW6EYZz4HU6t1K+W6+qpOo1Je71WCZpVKsrIP45z/7HcXSFM3/or69l0dROj642msHNh1XFMW1A/KFFHf/dUymPVaayWFDci9x7GwNohUPVVqPaq6jQmT6PRMOVVP9K0WsaslLkxhGm7k3iH8H3htPZtTWvf1qrjmD4bW3331MMY2DxOdRqrJcWNyB2dDlYPAFsHfauNyJKibk5M6FSNtcdusTrqpuo4QjyTTqdjfOR47G3sGR04WnUc81GoLLQYDwe+gUvbVaexSlLciNw5+jOcWw8dvgSXgqrTmJUO/sVo51eMMStPcPuh3F0hTE/EhQh2XN/B2PpjKeBUQHUc81LnA/BtCCs/haQE1WmsjhQ3Iufir8P6ERDwJlRqqzqN2dFoNEwMqYadjYZRfxxHp9OpjiREhluPbjH94HQ6lu1I05JNVccxPzY20GmeflLTDaNUp7E6UtyInNHpYGVfcMgPraeoTmO2CuZzYHJnPzafvs3vh2+ojiMEoO+OCtsbRj77fAyvO1x1HPPlUQpaTYIj/4VzG1WnsSpS3Iic+Wuxvi+501xwLqA6jVlrWdWLV2sWZ/yqk9yMe6I6jhAsO7uMfbf2MSFoAm4ObqrjmLeavaBcc1jVDxLvq05jNaS4Edl3/xJsDNOvG1Wumeo0FmFsh6rkc7Rj+O/HpHtKKHUt4RozD82ka4WuBBUPUh3H/Gk00PErSHsCf0orWF6R4kZkj1YLEX0hX2FoOVF1Govh7mzPtNf92XX+Lj/tj1YdR1ipdG06oXtCKehUkCG1h6iOYzncvKHN53B8GZxapTqNVZDiRmTP/gUQvRdC5oOjq+o0FqVxhSK8GViSyetOE30vUXUcYYV+PP0jR24fYWLwRFzsXVTHsSz+XaFSe1gzSL9UjTAqKW5E1t05B5vHQ71PwLeB6jQWaVTbyhTM58DQ5VFotdI9JfLOpbhLzDk8hx6Ve1DHq47qOJZHo4H2XwA6/Rp80v1sVFLciKxJT4OIj6GADzQLU53GYuV3tGNGlwAOXLnPd3suq44jrESaNo3Ru0fjnd+bATUHqI5jufIXhXaz4PRqOP6b6jQWTYobkTV7ZsPNIxCyEOydVaexaPXKFOLdYF8+33CWC7cfqY4jrMD3J77n1P1TTGowCSc7J9VxLFvVEKj2OqwbCgm3VKexWFLciJeLOQHbp0LwAPCR5uq8MKxVJYoXcGbI8ijS0rWq4wgLdvb+WeZHzee9au/hX8RfdRzr0PZzsHOE1f2le8pIpLgRL5aWol/dtnB5aDJSdRqr4exgy4yuARy/HsfXOy+pjiMsVGp6KqN3j6a0e2n6BPRRHcd6uBSEDnPg/EY48qPqNBZJihvxYjs/hzun9avc2jmqTmNVapb04KPGZZm9+Rynb8naNMLwFh5byMW4i0wKnoSDrYPqONalYmuo/hasHwlxMv2DoUlxI57vxiHYNRMaDYNiAarTWKWBzctTpnB+Bi+LIiVNuqeE4Zy4e4LFxxfzYcCHVC5UWXUc69R6Mji565ey0cr1bUhS3IhnS02CFX3Ayw8aDladxmo52tkys2sA52MfMnfredVxhIVITk9m9O7RVCxYkff93lcdx3o5ueuXsLm8U7+kjTAYKW7Es20LhweX9d1Rtvaq01i1asXd6fdKeeZtv0jUtTjVcYQFmHtkLtceXmNS8CTsbeT6VqpsU6jzPmwKg3sXVaexGFLciKdF74O9c6HpaCgqzdWm4JOmZalSzI0hy6NISk1XHUeYscOxh/nh5A/0q9GPch7lVMcRAM3H6+fAifgEtHJ9G4IUNyKzlMf6u6NK1IGgfqrTiP9nb2vDzK4BRN9LZNamc6rjCDOVmJpI6J5QAooE0LNKT9VxxN8c80PIAri2H/bNV53GIkhxIzLbPA4exui7o2xsVacR/1DB05UhLSuwaNclDl65rzqOMENfHPqCO4l3CG8Qjq1c36alVBDU7wtbJsLtM6rTmD0pbsT/XNoOB76BFuOhUFnVacQzvN+wDDVLejB0eRSJKWmq4wgzsu/WPn49+ysDaw2klFsp1XHEs7wSCh6l9EvdpMv1nRtS3Ai9pARY+Sn4NoQ6H6hOI57D1kbDjC4BxCYkMfVP+etOZM2jlEeE7QmjrlddulfqrjqOeB57Z/0SN7eiYPcXqtOYNSluhN6GUfDkAXSaBzbysTBlpQvnY0TrSvwn8ip7LtxVHUeYgc//+pz45HgmBE/ARiPXt0krUQsaDIYd0+DWMdVpzJZ8ygWc2whH/gutJumbRIXJ61nfl/plCjHst2MkJKWqjiNM2M7rO/nj/B98VucziucvrjqOyIrGw6FIRYjoo18CR2SbFDfWLvE+rOoH5ZpDzV6q04gssrHRMP11f+KfpBK+5pTqOMJExSfHM27vOIKLB/Na+ddUxxFZZeegv6njzll9C47INilurN2fwyHtCXT8CjQa1WlENvgUdCG0XWWW/XWdrWdiVccRJmjy/skkpScxvv54NHJ9mxcvP30Lzu5ZcP2Q6jRmR4oba3ZqFRxfBm0+Bzdv1WlEDnSr40OTikUY/vtx4hKl+Vr8z6arm1h3eR0j647EM5+n6jgiJxoMgmLV9XdPpT5RncasSHFjrR7dgTWDoFJ78O+qOo3IIY1Gw7TX/ElOTWfsqpOq4wgTce/JPSZGTuQVn1doX6a96jgip2zt9N1TD67C1nDVacyKFDfWSKeDtYMAHbT/QrqjzJynmxMTOlVj5dGbrDt+S3UcoZhOpyN8n/4X4Zj6Y6Q7ytwVqQjNxkDkPLi6V3UasyHFjTU6/hucXg3tZunXMxFmr1N1b1pV9SQ04gR3HyWrjiMUWnt5LZujNxNaL5TCzoVVxxGGUO8T8AnU3z2V/Eh1GrMgxY21SbgF64ZAtdehaojqNMJANBoNkzr7ATDqj+PodDrFiYQKtxNvM3n/ZNqUbkNL35aq4whDsbGFkPnw6DZsHqs6jVmQ4saa6HSwuj/YOUHbz1WnEQZWOL8jkztXY+OpWCKO3lAdR+QxnU7H2L1jcbR1ZHTgaNVxhKEVKgstJsDBb+HiNtVpTJ4UN9bkyH/h/EboMAdcCqpOI4ygdbVidKruzdiVJ4mJT1IdR+ShFRdWsPvGbsbVH4e7o7vqOMIYaveG0o31S+UkxatOY9KkuLEWcdGwfhRUfwsqtladRhjR+I5VcbK3Zfjvx6R7ykrcfHST6QenE1IuhMY+jVXHEcZiY6NfIicpXv/zXDyXFDfWQKuFlX3ByR1aT1adRhhZARcHpr3mz45zd1h68JrqOMLItDotYXvCcHVwZVidYarjCGMr4KP/OX70Rzi7XnUakyXFjTX4azFc3gmd5uoLHGHxmlYqSrfaPkxcc4pr9xNVxxFG9OuZX9kfs58JQRNwdXBVHUfkhRpvQ/mW+jGUifdVpzFJUtxYunsXYVMY1HkfyjZVnUbkodD2lSng4sBnv0Wh1Ur3lCW6mnCV2Ydn061iN+p711cdR+QVjUY/djItGdZ9pjqNSZLixpJp0yHiE/1cNs3Hq04j8pirkz2fv+7Pvkv3+U/kFdVxhIGla9MJ3R1KIadCDK41WHUckdfcikHbGXDiNzgZoTqNyZHixpJFzoNr+yFkATjmV51GKBBUrjC96pdi6vozXLojk39Zkv+e+i9Rd6IIbxCOi72L6jhCBb/XoXIHWDtYPweOyCDFjaW6fUa/Fkn9vlAqSHUaodDwNpXwcnNi6PIo0qV7yiJcjLvIV0e+4u0qb1PLs5bqOEIVjQbafQFo9GsFyt2RGaS4sUTpafpVZD1KwSuhqtMIxVwc7JjRJYAj1+JYtOuS6jgil1K1qYzePZrirsXpV6Of6jhCtfxF9GsEnlkDx5apTmMypLixRLu/gFtRELIQ7J1VpxEmoLZvQT5oWIZZG89xNuah6jgiFxYfX8zp+6eZFDwJJzsn1XGEKajSEfy66gcXJ9xUncYkSHFjaW4dgx1TocFgKCHN1eJ/BreoQMlCLgxZfpTUdK3qOCIHztw/w9dRX9O7Wm/8ivipjiNMSdvp+j9mV/WT7imkuLEsacn6VWOLVILGw1WnESbGyd6WWV0DOH3rIfO2XVAdR2RTSnoKo3aPokyBMvQJ6KM6jjA1zh7Q8Su4sBkO/6A6jXJS3FiSHdPgzlnovBDsHFSnESbIv0QB+jYpy9ytFzhxQ9amMScLoxZyOf4ykxtMxt7WXnUcYYoqtNRP8LdhNDy4qjqNUlLcWIrrf+nH2jQeDl7SXC2e79NXylPB05XBy46SnJauOo7IgmN3jrH4xGI+9v+YigUrqo4jTFmryfpWnJV99UvvWCkpbixB6hNY8TEUqw4NBqlOI0ycg50Ns7oFcPnuY2ZvPq86jniJpLQkRu8eTZWCVejt11t1HGHqnNz0i2te2QUHF6lOo4wUN5Zga7h+1e/OC8HWTnUaYQYqebkxsHkFvt5xkUNXH6iOI15gzpE53Hx0k0kNJmFnI9e3yIIyjaHuh7BpLNy1zvF1UtyYu6t79TMRNxsDRaS5WmTdR43K4F+iAEOXR/EkRbqnTNFfMX/x46kf6V+zP2UKlFEdR5iT5uP0SzRE9NEvxWNlTKK4mTdvHr6+vjg5OREYGMiBAweeu++iRYto2LAhHh4eeHh40Lx58xfub9GSH+k/uD6BUO8T1WmEmbGztWFm1wBuxj1h+oYzquOIf0lMTSR0TyjVi1bnrcpvqY4jzI1DPv3SO9cPwt6vVKfJc8qLm6VLlzJ48GDGjh3L4cOHCQgIoFWrVty+/ex1MrZv30737t3Ztm0bkZGR+Pj40LJlS27cuJHHyU3ApjD9eiIh88HGVnUaYYbKFsnPsNaV+H7PFSIv3lMdR/zDrEOzuJ90n/DgcGzl+hY5UbIeBH0K2ybB7dOq0+QpjU6ndrafwMBA6tSpw9y5cwHQarX4+PjQr18/RowY8dLnp6en4+Hhwdy5c+nZs+dL909ISMDd3Z34+Hjc3NxynV+Zi1vhv531q8LW/UB1GmHGtFodbyzax824J6wf2Ij8jjKuQ7W9N/fy0aaPGBU4iu6VuquOI8xZahJ83QjsneD9LWAl0wgobblJSUnh0KFDNG/ePGObjY0NzZs3JzIyMkvHSExMJDU1lYIFCz7z68nJySQkJGR6mL2keFj5KZRuDLXl7gmROzY2Gma8HsD9xylMWmtdf92ZoocpDwnbE0ZgsUC6VeymOo4wd/ZO+ptNYk7Arlmq0+QZpcXN3bt3SU9Px9PTM9N2T09PYmJisnSM4cOH4+3tnalA+qcpU6bg7u6e8fDx8cl1buXWj4KkBP3tfjbKexaFBShZyIVRbSvzy4Fotp99dpewyBvTDkzjUeojJgZNxEYj17cwgOI1oeEQ2Dkdbh5VnSZPmPWVM3XqVH799VdWrFiBk9OzF5AbOXIk8fHxGY9r167lcUoDO7sejv4IradAAQso1ITJ6BFYkoblCzPi9+PEJ6aqjmOVtl/bzsqLKxleZzjF8hdTHUdYkkafQdHK+ptQ0pJVpzE6pcVN4cKFsbW1JTY2NtP22NhYvLy8XvjcGTNmMHXqVDZu3Ii/v/9z93N0dMTNzS3Tw2wl3ofV/aF8S6ghd08Iw9JoNEx7zZ/HKWmMX31SdRyrE5cUx7i942hYvCEh5UJUxxGWxs4BQhbC3fOwfYrqNEantLhxcHCgVq1abNmyJWObVqtly5Yt1K9f/7nPmz59OhMnTmT9+vXUrl07L6KahnVD9RV3hzmg0ahOIyyQdwFnxnaoyh9HbrDhZNa6hoVhTN4/mVRtKuOCxqGR61sYg1c1aDIC9nwJ1w6qTmNUyrulBg8ezKJFi/jhhx84ffo0ffr04fHjx7z77rsA9OzZk5EjR2bsP23aNMaMGcN3332Hr68vMTExxMTE8OjRI1UvIW+cXAEnftffHeUmzdXCeF6rWZzmlT0ZveI49x5ZfvO1KdhwZQN/XvmTUYGjKOpSVHUcYcmCB4J3DYj4GFISVacxGuXFTbdu3ZgxYwZhYWFUr16do0ePsn79+oxBxtHR0dy6dStj/wULFpCSksLrr79OsWLFMh4zZsxQ9RKM79FtWDMYKncEv9dVpxEWTqPRMPnVaqRpdYxZeQLFs0VYvLtP7hK+L5wWpVrQtnRb1XGEpbO103dPxV+HrRNVpzEa5fPc5DWzm+dGp4Olb0H0Pui7H/IVVp1IWIk1x27y6c9HmNO9Bh0DvFXHsUg6nY4B2wYQdSeKFZ1WUNDp2VNaCGFwkfNgwyh4Zy34NlCdxuCUt9yIlzi2FM6sgfZfSGEj8lR7f2/a+xdjTMQJbickqY5jkdZcWsO2a9sYU2+MFDYibwX2gZJBEPEJJD9UncbgpLgxZfE3YN0w8OsKVTqqTiOs0MRO1bC3tWHkH8ele8rAYh7HMGX/FNqVaUfzUs+ep0sIo7GxgZB58PgubByjOo3BSXFjqnQ6WNUPHFyg7XTVaYSV8sjnwJRX/dhy5jbLD11XHcdi6HQ6xu0dh7OdMyPrjnz5E4QwhoJloOUEOPQ9XNisOo1BSXFjqg7/ABe3QMevwNlDdRphxVpU8eS1miWYuPoUN+KeqI5jEX47/xt7bu5hXNA43B3dVccR1qx2byjTFFb2gydxqtMYjBQ3pujBVdgwGmq8DeVbqE4jBGEdqpDfyY7hvx1Dq5Xuqdy4/vA6Mw7O4LXyr9GwREPVcYS102ig01xIeQTrLacVUYobU6PVwsq++taaVpNVpxECAHdne6a95s/uC3f5af9V1XHMllanZcyeMbg7ujO09lDVcYTQcy8BradC1M9wZp3qNAYhxY2pOfANXNmlXxTTyQxuVRdWo1GFIvQILMnkdWe4eu+x6jhm6Zczv/BX7F9MDJ5Ifof8quMI8T/V34QKrWH1AHh8T3WaXJPixpTcvQCbx0HdD6FMY9VphHjKqLaVKezqwNDlUaRL91S2XIm/wuxDs+leqTuBxQJVxxEiM40GOnwJ2lRYN0R1mlyT4sZUaNP1q7W6FYPm41SnEeKZ8jnaMeP1AP66+oDv91xWHcdspGvTCd0TSlGXogysOVB1HCGezdVLv8TP38v9mDEpbkzF3q/g+kEIWQAO+VSnEeK5AssU4r3g0kzfcJYLty1v8i9j+OHUDxy7c4zwBuG42LuojiPE81V7DaqEwNoh8DBWdZock+LGFMSegm2TIOhTKFlPdRohXuqzVhUp4eHMkGVRpKVrVccxaecfnGfukbm8U/UdahStoTqOEC+m0UC7WWBjB2sG6udcM0NS3KiWnqpfndWjNDQNVZ1GiCxxsrdlZpcAjt+IZ+GOi6rjmKxUbSqjd4+mpGtJ+tboqzqOEFmTrxC0nw1n10HUL6rT5IgUN6rtmgkxJ6DzQrB3Up1GiCyrUdKDPk3K8uWW85y6maA6jkn69ti3nHtwjkkNJuFo66g6jhBZV7k9+L8Bf47QryBuZqS4UenmUdj5OTQcAsVrqk4jRLb1b1aeskXyM3jZUVLSpHvqn07dO8U3x77hfb/3qVq4quo4QmRfm6n6MaCr+pld95QUN6qkJcOKj6FoZWj0meo0QuSIo50tM7sGcOH2I+ZsOa86jslISU9h9O7RlPMox0f+H6mOI0TOOHvolwC6uFW//pQZkeJGle1T4N4FCFkIdg6q0wiRY1W93enfrDwLdlzk6LU41XFMwvyj87mScIVJDSZhb2uvOo4QOVe+OdR6BzaEwn3zmf5BihsVrh2EPV9CkxHgVU11GiFyrU+TslT1dmPIsqMkpaarjqNU1J0ovj/5PX2r96WCRwXVcYTIvZbh+kHGKz/VLxFkBqS4yWspifq7o7xrQPBA1WmEMAh7Wxtmdgng2oMnzNhwVnUcZZ6kPSF0dyhVC1XlnarvqI4jhGE4ukKn+XB1Nxz4WnWaLJHiJq9tmaAfeR6yEGztVKcRwmDKe7oytGUFFu+5zIHL91XHUWLO4TncenyL8Abh2NnI9S0sSOmGEPixfomgu6Y/vk6Km7x0eRfsXwDNwqCINFcLy9O7QRlqlfRg6PIoHienqY6Tpw7GHOTH0z/Sv0Z/yriXUR1HCMNrNhbciuuXCko37etbipu8kvwQVn4CJYMgsI/qNEIYha2NhhldArjzMJmpf55RHSfPPE59zJg9Y6jlWYu3qrylOo4QxuHgop+T7cYh2DtHdZoXkuImr2wco19GPmQe2MjbLiyXb+F8jGxbif/uu8qu83dUx8kTM/+ayf2k+0wMnoiNRq5vYcF86kJQf/0dv7EnVad5LrkK88KFzfo5AlpOgILSXC0s31uBpQgqW4hhvx0jISlVdRyj2nNjD8vPLWdo7aH4uPqojiOE8TUdBQXL6udqSzfN61uKG2N7Egcr+0GZplC7t+o0QuQJGxsN01/352FSGhNXn1Idx2gSUhII2xtG/WL16VKhi+o4QuQNO0fovABun4KdM1SneSYpboxt/QhIeQSd5upXWxXCSpTwcCGsfRWWH7rOltOxquMYxbQD00hMTWRC8AQ0cn0La+JdAxoO1S8hdPOI6jRPkeLGmM6s1a+o2noquJdQnUaIPNeldgmaVizCiD+O8+Bxiuo4BrU1eiurLq5ieN3heOXzUh1HiLzXaCh4VoUVfSA1SXWaTKS4MZbH92D1AKjQGqq/qTqNEEpoNBqmvuZPSpqWsFWmO/gwux4kPWB85HialGhCp7KdVMcRQg1be+j8Ndy/CNsnq06TiRQ3xrJuCGjToMOX0h0lrJqnmxMTOlVlddRN1h67pTqOQUzaP4l0XTpjg8ZKd5Swbp5V9AOM934F0ftVp8kgxY0xnPgdTq6AtjPAVZqrhegY4E2bal6ERhznzsNk1XFyZf3l9Wy4soHQwFAKOxdWHUcI9YL6Q/Fa+sn9UhJVpwGkuDG8h7GwdghUCYFqr6lOI4RJ0Gg0hIdUw0ajYdSK4+h0OtWRcuTuk7uE7w+nZamWtC7dWnUcIUyDja1+SaGEm7BlvOo0gBQ3hqXTwZqBYGMH7WZJd5QQ/1AovyOTOldj06lYVhy5oTpOtul0OsbvHY+txpbQeqGq4whhWgqXg+ZjYf9CuLxTdRopbgwq6hc4uw7az9YvDy+EyKR1tWJ0rlGcsatOciv+ieo42bLq4iq2X99OWP0wPJw8VMcRwvTU/QhKNYCIvvolhxSS4sZQ4q/DnyPA/w2o3F51GiFM1rgOVXFxsGX47+bTPRXzOIapB6bSoUwHmpVspjqOEKbJxka/xNCT+7BhtNooSs9uKXQ6WNUPHPJBm6mq0whh0txd7Jn6mj87z93hlwPXVMd5KZ1OR9ieMFzsXRhed7jqOEKYNg9faBkOh3+A85uVxZDixhAOfQ8Xt0LHr8BZmquFeJmmFYvyRh0fwtee4tp907i74nmWn1tO5K1IxgeNx93RXXUcIUxfrXegbDNY9Sk8eaAkghQ3uXX/MmwI1X8zyzdXnUYIszG6XWU8XBwYujwKrdY0u6euPbzGjL9m8HqF12lQvIHqOEKYB41G/8d+SqJ+uIYCUtzkhlYLK/vqBw+3DFedRgiz4upkz+dd/Nl/+T5L9l5RHecpWp2WMXvGUNCpIENrD1UdRwjz4l4c2kyDY7/C6TV5fnopbnJj/0K4ugc6zQdHV9VphDA7QWUL806QL9PWn+HinUeq42Ty0+mfOBR7iInBE8lnn091HCHMT8AbULGdfoqUx3fz9NRS3OTU3fP6yYoCP4bSDVWnEcJsDW9dCe8CzgxdHkVaulZ1HAAux1/my8Nf0qNyD+p41VEdRwjzpNFAh9mgTYe1g/U33+QRKW5yIj0NVnwMbsWh2VjVaYQwa84Otszo4k/UtTi+2XVJdRzStGmE7g7FK58XA2oOUB1HCPOWvyi0nwWnVuqXJsojUtzkxN45cPMwdF4IDi6q0whh9mqVKsgHjcowe9N5zsQkKM2y5OQSTtw7QXhwOM52zkqzCGERqnaGqq/qlyZ6GJMnp5TiJrtiT8K2yfqFwnzqqk4jhMUY1LwCpQq5MGRZFKmKuqfOPTjHvKPzeKfqO1QvWl1JBiEsUruZYOsAqwfkSfeUFDfZkZai744qVE6/xLsQwmCc7G2Z1bU6Z2IeMnfrhTw/f2p6KqN3j8bXzZe+1fvm+fmFsGguBaHjHDi3Ho7+ZPTTSXGTHbtmwO1T0HkB2DmqTiOExfEr4c6nTcsxd9sFjl+Pz9Nzf3P8Gy48uEB4g3AcbB3y9NxCWIWKbaB6D1g/EuKMOzu5FDdZdfMI7JwBDYeCdw3VaYSwWJ++Uo5KXq4MWX6UpNT0PDnnybsnWXRsER/4f0DVQlXz5JxCWKXWU/RTp6z61KjdU1LcZEVqkr47yrMqNJLJvIQwJntbG2Z2DeDK3US+2HzO6OdLTk9m9O7RVPCowAf+Hxj9fEJYNSd3/ezFl7bDX4uNdhopbrJi+2S4fwk6fw229qrTCGHxKnm5MbBFeb7ZeYlDV+8b9Vzzjswj+mE0kxpMwt5Grm8hjK5cM6j9Hmwco//dagRS3LxM9H7YM0c/gNiziuo0QliNDxuWobpPAYYsiyIxJc0o5zh6+yhLTi6hb/W+lPcob5RzCCGeocVEyFcEIvrqJ/kzMCluXiTlMUR8DCVq62/9FkLkGTtbG2Z2CSAmIYnp688a/PiJqYmM3j0avyJ+vFP1HYMfXwjxAo75IWQBREfCvgUGP7wUNy+yeTwk3IKQhWBjqzqNEFanTJH8DGtViSV7r7D3omHXpvny8JfcTrzNpOBJ2Mr1LUTe8w2Gep/Alglwx7B/wEhx8zyXd8KBr6H5WChcTnUaIazWO0G+BJYuyGfLj/EwKdUgx9x/az8/n/mZATUH4Ovua5BjCiFyoNkYKFBSf9NOuuG6n6W4eZakBH0/YKkGUPcj1WmEsGo2NhpmdAkgLjGFSWtP5/p4j1IeEbYnjNqetXmz8psGSCiEyDF7Z/1SRreOwp7ZBjusFDfPsjEUntyHkHlgI2+REKr5FHRhdLsq/HrwGtvO3s7VsWb8NYO45DgmBk/ERiPXtxDKlagNwQNh+1SIOW6QQ8qV/W/nN8HhH6BlOHj4qk4jhPh/3ev60KhCEUb8foz4xJx1T+26vovfz//O0DpDKeFawsAJhRA51mQEFK4AK/rolzrKJSlu/unJA1jVD8o2g1rvqE4jhPgHjUbDtNf8SExJZ9zqk9l+fnxyPOP2jiPYO5jXy79uhIRCiByzc9R3T905DTun5/pwUtz805/DISVRP3uiRqM6jRDiX4q5OzO+Y1VWHLnB+hMx2Xru1ANTeZL2hHFB49DI9S2E6SnmD42Hw65ZcONQrg4lxc3fTq+GY0uhzTRwL646jRDiOTrXKE6LKp6MXnGce4+Ss/ScLVe3sObSGkYEjsArn5eREwohcqzBIPDy03dPpSbl+DBS3AA8vgurB0LFdhDwhuo0QogX0Gg0TO7sh1anY/SKE+hesvje/aT7TNg3gaY+TelQpkMepRRC5Iitvb576sEV2Bae48NIcaPTwZpBoNNCh9nSHSWEGSji6kh4iB/rT8awKurmc/fT6XSE7wtHq9MSVj9MuqOEMAdFK8Mro2HvXLgamaNDSHFz4nc4vQraz4L8RVWnEUJkUTv/YnQI8CZs5UliE57dfP3n5T/ZdHUTofVCKexcOI8TCiFyrP6n4FMXIvrol0LKJusubh7GwNohUPVVqNpZdRohRDZN6FgVBzsbRvx+7KnuqduJt5m0fxKtfVvTyreVooRCiByxsdWvPfUwBjaNzf7TjRDJPOh0sKo/2DpAu5mq0wghcsAjnwNTX/Vj29k7LP/resZ2nU7H+Mjx2NvYMzpwtMKEQogcK1QWWkyAg4vg0vZsPdUkipt58+bh6+uLk5MTgYGBHDhw4IX7L1++nEqVKuHk5ISfnx/r1q3L/kmPLYPzG6DjHHApmMPkQgjVmlX2pEutEkxYc4rrDxIBiLgQwc7rOxkXNI4CTgXUBhRC5Fyd98G3Iaz8VL80UhYpL26WLl3K4MGDGTt2LIcPHyYgIIBWrVpx+/azp1jfu3cv3bt3p3fv3hw5coSQkBBCQkI4ceJE9k68aSxU7wEV2xjgVQghVBrToQpuTnYM++0Y1xNuMO3gNDqV7UQTnyaqowkhcsPGBjrNgydxsGFUlp+m0b3sPkojCwwMpE6dOsydOxcArVaLj48P/fr1Y8SIEU/t361bNx4/fsyaNWsyttWrV4/q1auzcOHCl54vISEBd3d34idVwG3wAXByN9yLEUIos/v8Xd5aHEnVWktJJoYVnVbg6uCqOpYQwhAO/QCr+8O4+CztbmfkOC+UkpLCoUOHGDlyZMY2GxsbmjdvTmTks2//ioyMZPDgwZm2tWrVioiIiGfun5ycTHLy/yb6io/XvzFBLrbY/qdRLl+BEMKUuPimcfmejpQbvai+f6PqOEIIg8nHV7bVaJCQgKur60undVBa3Ny9e5f09HQ8PT0zbff09OTMmTPPfE5MTMwz94+JefZU7FOmTGH8+PFPbT856HQOUwshTN/Trb5CCPPWDmC6O/Hx8bi5ub1wX6XFTV4YOXJkppYerVbL/fv3KVSokMVM6JWQkICPjw/Xrl176TfcUsl7IO8ByHvwN3kf5D0Ay30PXF1f3t2stLgpXLgwtra2xMbGZtoeGxuLl9ez13/x8vLK1v6Ojo44Ojpm2lagQIGchzZhbm5uFvUBzgl5D+Q9AHkP/ibvg7wHYJ3vgdK7pRwcHKhVqxZbtmzJ2KbVatmyZQv169d/5nPq16+faX+ATZs2PXd/IYQQQlgX5d1SgwcPplevXtSuXZu6desye/ZsHj9+zLvvvgtAz549KV68OFOmTAFgwIABNG7cmJkzZ9KuXTt+/fVX/vrrL7755huVL0MIIYQQJkJ5cdOtWzfu3LlDWFgYMTExVK9enfXr12cMGo6OjsbG5n8NTEFBQfz888+EhoYyatQoypcvT0REBNWqVVP1EpRzdHRk7NixT3W/WRN5D+Q9AHkP/ibvg7wHYN3vgfJ5boQQQgghDEn5DMVCCCGEEIYkxY0QQgghLIoUN0IIIYSwKFLcCCGEEMKiSHGj0M6dO+nQoQPe3t5oNJrnro/1T9u3b6dmzZo4OjpSrlw5lixZkunr48aNQ6PRZHpUqlQp0z5JSUn07duXQoUKkT9/fl577bWnJkbMS8Z4H3x9fZ96HzQaDX379s3Yp0mTJk99/eOPPzbwq8ua7L4Ht27d4s0336RChQrY2NgwcODAZ+63fPlyKlWqhJOTE35+fqxbty7T13U6HWFhYRQrVgxnZ2eaN2/O+fPnDfSqsscY78GiRYto2LAhHh4eeHh40Lx5cw4cOJBpn3feeeepz0Hr1q0N+MqyzhjvwZIlS556fU5OTpn2MaXPARjnfXjW9a7RaGjXrl3GPub8Wfjjjz9o0aIFRYoUwc3Njfr167Nhw4an9ps3bx6+vr44OTkRGBj41PVgar8fckqKG4UeP35MQEAA8+bNy9L+ly9fpl27djRt2pSjR48ycOBA3n///ac+wFWrVuXWrVsZj927d2f6+qBBg1i9ejXLly9nx44d3Lx5k1dffdVgryu7jPE+HDx4MNN7sGnTJgC6dOmS6VgffPBBpv2mT59uuBeWDdl9D5KTkylSpAihoaEEBAQ8c5+9e/fSvXt3evfuzZEjRwgJCSEkJIQTJ05k7DN9+nTmzJnDwoUL2b9/P/ny5aNVq1YkJSUZ5HVlhzHeg+3bt9O9e3e2bdtGZGQkPj4+tGzZkhs3bmTar3Xr1pk+B7/88kuuX09OGOM9AP0Mtf98fVevXs30dVP6HIBx3oc//vgj03tw4sQJbG1tn/qZYK6fhZ07d9KiRQvWrVvHoUOHaNq0KR06dODIkSMZ+yxdupTBgwczduxYDh8+TEBAAK1ateL27dsZ+5ja74cc0wmTAOhWrFjxwn2GDRumq1q1aqZt3bp107Vq1Srj/8eOHasLCAh47jHi4uJ09vb2uuXLl2dsO336tA7QRUZG5ii7IRnqffi3AQMG6MqWLavTarUZ2xo3bqwbMGBAbuIaRVbeg3963uvo2rWrrl27dpm2BQYG6j766COdTqfTabVanZeXl+7zzz/P+HpcXJzO0dFR98svv+Qou6EY6j34t7S0NJ2rq6vuhx9+yNjWq1cvXadOnbIf0sgM9R58//33Ond39+c+z5Q/Bzqd8T4LX3zxhc7V1VX36NGjjG2W8ln4W5UqVXTjx4/P+P+6devq+vbtm/H/6enpOm9vb92UKVN0Op3p/37IDmm5MSORkZE0b94807ZWrVoRGRmZadv58+fx9vamTJky9OjRg+jo6IyvHTp0iNTU1EzHqVSpEiVLlnzqOKYqq+/D31JSUvjxxx957733nlos9aeffqJw4cJUq1aNkSNHkpiYaLTcee1l79Ply5eJiYnJtI+7uzuBgYFm81nIrsTERFJTUylYsGCm7du3b6do0aJUrFiRPn36cO/ePUUJjePRo0eUKlUKHx8fOnXqxMmTJzO+Zo2fA4DFixfzxhtvkC9fvkzbLeWzoNVqefjwYcZnPSUlhUOHDmX6PtvY2NC8efOM77Ml/H74m/IZikXWxcTEZMzc/DdPT08SEhJ48uQJzs7OBAYGsmTJEipWrMitW7cYP348DRs25MSJE7i6uhITE4ODg8NTi4d6enoSExOTh68m57LyPvxTREQEcXFxvPPOO5m2v/nmm5QqVQpvb2+OHTvG8OHDOXv2LH/88YexX0KeeN779Pf3+e//vmgfSzN8+HC8vb0z/fBu3bo1r776KqVLl+bixYuMGjWKNm3aEBkZia2trcK0hlGxYkW+++47/P39iY+PZ8aMGQQFBXHy5ElKlChhlZ+DAwcOcOLECRYvXpxpuyV9FmbMmMGjR4/o2rUrAHfv3iU9Pf2Z3+czZ84AWMTvh79JcWNh2rRpk/Fvf39/AgMDKVWqFMuWLaN3794Kk6mzePFi2rRpg7e3d6btH374Yca//fz8KFasGM2aNePixYuULVs2r2MKI5s6dSq//vor27dvzzSg9o033sj4t5+fH/7+/pQtW5bt27fTrFkzFVENqn79+pkWFg4KCqJy5cp8/fXXTJw4UWEydRYvXoyfnx9169bNtN1SPgs///wz48ePZ+XKlRQtWlR1HCWkW8qMeHl5PTVqPTY2Fjc3t6daK/5WoEABKlSowIULFzKOkZKSQlxc3FPH8fLyMkpuQ8vO+3D16lU2b97M+++//9LjBgYGAmS8V+buee/T39/nv//7on0sxYwZM5g6dSobN27E39//hfv+X3v3HtLU+8cB/L2vbprtD79ll2VoXlKiAk2p7GaYXYgiK9KuZmUWJRE0qaDIDEq6/RNBEDWjMrMIFghqtxWYlaWh3axsOQLDii5atkw/3z/8deikldVvU9f7Bftj5/nsnOf57PHss+ecYWBgIHx8fFxmHnxLq9UiPDxcdU4A/o55ALTcqJuTk9OuL3tdcS7k5OQgOTkZubm5qhVKHx8fuLm5/fSc0NU/H75gcdOFREVF4cKFC6pt586dU30r+1Z9fT2qqqpgMBgAABEREdBqtar9VFZWwmaz/XA/ncmv5MFkMqF3796qn3t+z+3btwFAyVVX97M8BQQEoG/fvqqYd+/e4fr1611mLrTHzp07sW3bNuTn5yMyMvKn8c+ePcOrV69cZh58q6mpCRUVFcr4/pZ58MWpU6dgt9uxcOHCn8Z2tblw4sQJLFmyBCdOnGh1ztPpdIiIiFC9z83Nzbhw4YLyPrvC54Oio+9o/pvV1dVJWVmZlJWVCQDZu3evlJWVSXV1tYiIbNiwQRYtWqTEP3nyRLy8vCQtLU3u378v+/fvFzc3N8nPz1di1q1bJxaLRaxWqxQVFUlsbKz4+PhIbW2tErNy5Urx8/OTixcvys2bNyUqKkqioqKcN/BvOCIPIi2/BPDz85P169e3Oubjx48lIyNDbt68KVarVcxmswQGBsq4ceMcO9jv+NUciIgSHxERIfPnz5eysjK5e/eu0l5UVCTu7u6ye/duuX//vmzZskW0Wq1UVFQoMZmZmeLt7S1ms1nKy8tlxowZEhAQIA0NDc4Z+FcckYPMzEzR6XRy+vRpqampUR51dXXKMY1GoxQXF4vVapXz58/LsGHDZODAgfLx40fnDf5/HJGDrVu3SkFBgVRVVcmtW7dk7ty54unp2SpPnWUeiDgmD1+MGTNGEhIS2jxmV54Lx48fF3d3d9m/f79qrr9580aJycnJEQ8PD8nKypJ79+5JSkqKeHt7y/Pnz5WYzvb58LtY3HSgS5cuCYBWj8WLF4tIy88So6OjW70mLCxMdDqdBAYGislkUrUnJCSIwWAQnU4nvr6+kpCQII8fP1bFNDQ0yKpVq+Tff/8VLy8vmTlzptTU1DhwpD/miDyIiBQUFAgAqaysbNVms9lk3Lhx0qNHD/Hw8JDg4GBJS0uTt2/fOmCEP/c7OWgr3t/fXxWTm5srISEhotPpZPDgwZKXl6dqb25uls2bN0ufPn3Ew8NDJkyY0Ga+nMEROfD3928zZsuWLSIi8uHDB5k0aZL06tVLtFqt+Pv7y/Lly1Une2dyRA7Wrl0rfn5+otPppE+fPjJ16lQpLS1V7aMzzQMRx/09PHjwQABIYWFhq2N29bkQHR39w/gv9u3bp8yH4cOHy7Vr11Ttne3z4XdpRER+d9WHiIiIqLPhPTdERETkUljcEBERkUthcUNEREQuhcUNERERuRQWN0RERORSWNwQERGRS2FxQ0RERC6FxQ0RERG5FBY3RNQpjB8/HmvXrnXKsdLT0xEWFuaUYxGR87G4IaK/jtFoVP1zwKSkJMTFxXVch4jo/8q9oztARORser0eer2+o7tBRA7ClRsicrr3798jMTERer0eBoMBe/bsUbXb7XYYjUb4+vqie/fuGDFiBCwWi9KelZUFb29vFBQUYNCgQdDr9ZgyZQpqamqUGIvFguHDh6N79+7w9vbG6NGjUV1dDUB9WSo9PR1HjhyB2WyGRqOBRqOBxWJBTEwMUlNTVf168eIFdDqdatWHiDofFjdE5HRpaWm4fPkyzGYzCgsLYbFYUFpaqrSnpqaiuLgYOTk5KC8vx5w5czBlyhQ8evRIifnw4QN2796No0eP4sqVK7DZbDAajQCAz58/Iy4uDtHR0SgvL0dxcTFSUlKg0Wha9cVoNCI+Pl4pjmpqajBq1CgkJycjOzsbdrtdiT127Bh8fX0RExPjwOwQ0Z/iZSkicqr6+nocOnQIx44dw4QJEwAAR44cQf/+/QEANpsNJpMJNpsN/fr1A9BSgOTn58NkMmH79u0AgMbGRhw4cABBQUEAWgqijIwMAMC7d+/w9u1bTJs2TWkfNGhQm/3R6/Xo1q0b7HY7+vbtq2yfNWsWUlNTYTabER8fD6BlxSgpKanNIomIOg8WN0TkVFVVVfj06RNGjBihbOvRowdCQ0MBABUVFWhqakJISIjqdXa7HT179lSee3l5KYULABgMBtTW1ir7S0pKwuTJkzFx4kTExsYiPj4eBoOh3f309PTEokWLcPjwYcTHx6O0tBR37tzB2bNnf2vcROQ8LG6IqFOpr6+Hm5sbbt26BTc3N1Xb1zcBa7VaVZtGo4GIKM9NJhPWrFmD/Px8nDx5Eps2bcK5c+cwcuTIdvclOTkZYWFhePbsGUwmE2JiYuDv7/+bIyMiZ+E9N0TkVEFBQdBqtbh+/bqy7fXr13j48CEAIDw8HE1NTaitrUVwcLDq8fVlo/YIDw/Hxo0bcfXqVQwZMgTZ2dltxul0OjQ1NbXaPnToUERGRuLgwYPIzs7G0qVLf+n4RNQxWNwQkVPp9XosW7YMaWlpuHjxIu7cuYOkpCT880/L6SgkJAQLFixAYmIizpw5A6vVihs3bmDHjh3Iy8tr1zGsVis2btyI4uJiVFdXo7CwEI8ePfrufTcDBgxAeXk5Kisr8fLlSzQ2NiptycnJyMzMhIhg5syZf54AInI4FjdE5HS7du3C2LFjMX36dMTGxmLMmDGIiIhQ2k0mExITE7Fu3TqEhoYiLi4OJSUl8PPza9f+vby88ODBA8yePRshISFISUnB6tWrsWLFijbjly9fjtDQUERGRqJXr14oKipS2ubNmwd3d3fMmzcPnp6efzZwInIKjXx9kZqIiFSePn2KoKAglJSUYNiwYR3dHSJqBxY3RERtaGxsxKtXr2A0GmG1WlWrOUTUufGyFBFRG4qKimAwGFBSUoIDBw50dHeI6Bdw5YaIiIhcClduiIiIyKWwuCEiIiKXwuKGiIiIXAqLGyIiInIpLG6IiIjIpbC4ISIiIpfC4oaIiIhcCosbIiIicin/ASVN1ahJxkm2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from skfuzzy import control as ctrl\n",
    "import skfuzzy as fuzz\n",
    "\n",
    "# temp = ctrl.Antecedent(np.arange(20, 70, 1), \"temp\")\n",
    "# al = ctrl.Antecedent(np.arange(0, 0.3, 0.001), \"al\")\n",
    "# ti = ctrl.Antecedent(np.arange(0, 0.3, 0.001), \"ti\")\n",
    "# density = ctrl.Consequent(np.arange(1, 1.3, 0.00001), \"density\")\n",
    "\n",
    "temp = ctrl.Antecedent(density_train[\"T\"].sort_values().unique(), \"temp\")\n",
    "al = ctrl.Antecedent(density_train[\"Al2O3\"].sort_values().unique(), \"al\")\n",
    "ti = ctrl.Antecedent(density_train[\"TiO2\"].sort_values().unique(), \"ti\")\n",
    "density = ctrl.Consequent(np.arange(1.03, 1.22, 0.0001), \"density\")\n",
    "\n",
    "temp.automf(3, variable_type=\"quant\")\n",
    "temp.view()\n",
    "al.automf(3, variable_type=\"quant\")\n",
    "al.view()\n",
    "ti.automf(3, variable_type=\"quant\")\n",
    "ti.view()\n",
    "density.automf(3, variable_type=\"quant\")\n",
    "# density[\"low\"] = fuzz.trimf(density.universe, [1.03, 1.06, 1.09])\n",
    "# density[\"average\"] = fuzz.trimf(density.universe, [1.06, 1.14, 1.18])\n",
    "# density[\"high\"] = fuzz.trimf(density.universe, [1.09, 1.18, 1.22])\n",
    "density.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule11 = ctrl.Rule(\n",
    "    temp[\"low\"] & al[\"low\"] & ti[\"low\"],\n",
    "    density[\"low\"],\n",
    ")\n",
    "rule12 = ctrl.Rule(\n",
    "    temp[\"average\"] & al[\"low\"] & ti[\"low\"],\n",
    "    density[\"low\"],\n",
    ")\n",
    "rule13 = ctrl.Rule(\n",
    "    temp[\"high\"] & al[\"low\"] & ti[\"low\"],\n",
    "    density[\"low\"],\n",
    ")\n",
    "\n",
    "rule21 = ctrl.Rule(\n",
    "    temp[\"low\"] & al[\"average\"] & ti[\"low\"],\n",
    "    density[\"average\"],\n",
    ")\n",
    "rule22 = ctrl.Rule(\n",
    "    temp[\"average\"] & al[\"average\"] & ti[\"low\"],\n",
    "    density[\"low\"],\n",
    ")\n",
    "rule23 = ctrl.Rule(\n",
    "    temp[\"high\"] & al[\"average\"] & ti[\"low\"],\n",
    "    density[\"low\"],\n",
    ")\n",
    "\n",
    "rule31 = ctrl.Rule(\n",
    "    temp[\"low\"] & al[\"high\"] & ti[\"low\"],\n",
    "    density[\"high\"],\n",
    ")\n",
    "rule32 = ctrl.Rule(\n",
    "    temp[\"low\"] & al[\"high\"] & ti[\"low\"],\n",
    "    density[\"high\"],\n",
    ")\n",
    "rule33 = ctrl.Rule(\n",
    "    temp[\"high\"] & al[\"high\"] & ti[\"low\"],\n",
    "    density[\"average\"],\n",
    ")\n",
    "\n",
    "rule41 = ctrl.Rule(\n",
    "    temp[\"low\"] & al[\"low\"] & ti[\"average\"],\n",
    "    density[\"average\"],\n",
    ")\n",
    "rule42 = ctrl.Rule(\n",
    "    temp[\"average\"] & al[\"low\"] & ti[\"average\"],\n",
    "    density[\"average\"],\n",
    ")\n",
    "rule43 = ctrl.Rule(\n",
    "    temp[\"high\"] & al[\"low\"] & ti[\"average\"],\n",
    "    density[\"low\"],\n",
    ")\n",
    "\n",
    "rule51 = ctrl.Rule(\n",
    "    temp[\"low\"] & al[\"low\"] & ti[\"high\"],\n",
    "    density[\"high\"],\n",
    ")\n",
    "rule52 = ctrl.Rule(\n",
    "    temp[\"average\"] & al[\"low\"] & ti[\"high\"],\n",
    "    density[\"high\"],\n",
    ")\n",
    "rule53 = ctrl.Rule(\n",
    "    temp[\"high\"] & al[\"low\"] & ti[\"high\"],\n",
    "    density[\"average\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[IF (temp[low] AND al[low]) AND ti[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND al[low]) AND ti[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[high] AND al[low]) AND ti[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND al[average]) AND ti[low] THEN density[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND al[average]) AND ti[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[high] AND al[average]) AND ti[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[high] AND al[high]) AND ti[low] THEN density[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND al[low]) AND ti[average] THEN density[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND al[low]) AND ti[average] THEN density[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[high] AND al[low]) AND ti[average] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND al[low]) AND ti[high] THEN density[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND al[low]) AND ti[high] THEN density[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[high] AND al[low]) AND ti[high] THEN density[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzzy_rules = [\n",
    "    rule11,\n",
    "    rule12,\n",
    "    rule13,\n",
    "    rule21,\n",
    "    rule22,\n",
    "    rule23,\n",
    "    rule31,\n",
    "    rule32,\n",
    "    rule33,\n",
    "    rule41,\n",
    "    rule42,\n",
    "    rule43,\n",
    "    rule51,\n",
    "    rule52,\n",
    "    rule53,\n",
    "]\n",
    "\n",
    "density_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
    "\n",
    "sim = ctrl.ControlSystemSimulation(density_cntrl)\n",
    "\n",
    "fuzzy_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: temp                    = 20\n",
      "  - low                             : 1.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "Antecedent: al                      = 0.3\n",
      "  - low                             : 0.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 1.0\n",
      "Antecedent: ti                      = 0.0\n",
      "  - low                             : 1.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF (temp[low] AND al[low]) AND ti[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 1.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                       (temp[low] AND al[low]) AND ti[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF (temp[average] AND al[low]) AND ti[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                   (temp[average] AND al[low]) AND ti[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF (temp[high] AND al[low]) AND ti[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[high]                                             : 0.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                      (temp[high] AND al[low]) AND ti[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF (temp[low] AND al[average]) AND ti[low] THEN density[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 1.0\n",
      "  - al[average]                                            : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                   (temp[low] AND al[average]) AND ti[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          density[average] : 0.0\n",
      "\n",
      "RULE #4:\n",
      "  IF (temp[average] AND al[average]) AND ti[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - al[average]                                            : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "               (temp[average] AND al[average]) AND ti[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #5:\n",
      "  IF (temp[high] AND al[average]) AND ti[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[high]                                             : 0.0\n",
      "  - al[average]                                            : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                  (temp[high] AND al[average]) AND ti[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #6:\n",
      "  IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 1.0\n",
      "  - al[high]                                               : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                      (temp[low] AND al[high]) AND ti[low] = 1.0\n",
      "  Activation (THEN-clause):\n",
      "                                             density[high] : 1.0\n",
      "\n",
      "RULE #7:\n",
      "  IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 1.0\n",
      "  - al[high]                                               : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                      (temp[low] AND al[high]) AND ti[low] = 1.0\n",
      "  Activation (THEN-clause):\n",
      "                                             density[high] : 1.0\n",
      "\n",
      "RULE #8:\n",
      "  IF (temp[high] AND al[high]) AND ti[low] THEN density[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[high]                                             : 0.0\n",
      "  - al[high]                                               : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "                     (temp[high] AND al[high]) AND ti[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          density[average] : 0.0\n",
      "\n",
      "RULE #9:\n",
      "  IF (temp[low] AND al[low]) AND ti[average] THEN density[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 1.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[average]                                            : 0.0\n",
      "                   (temp[low] AND al[low]) AND ti[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          density[average] : 0.0\n",
      "\n",
      "RULE #10:\n",
      "  IF (temp[average] AND al[low]) AND ti[average] THEN density[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[average]                                            : 0.0\n",
      "               (temp[average] AND al[low]) AND ti[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          density[average] : 0.0\n",
      "\n",
      "RULE #11:\n",
      "  IF (temp[high] AND al[low]) AND ti[average] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[high]                                             : 0.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[average]                                            : 0.0\n",
      "                  (temp[high] AND al[low]) AND ti[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #12:\n",
      "  IF (temp[low] AND al[low]) AND ti[high] THEN density[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 1.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[high]                                               : 0.0\n",
      "                      (temp[low] AND al[low]) AND ti[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             density[high] : 0.0\n",
      "\n",
      "RULE #13:\n",
      "  IF (temp[average] AND al[low]) AND ti[high] THEN density[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[high]                                               : 0.0\n",
      "                  (temp[average] AND al[low]) AND ti[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             density[high] : 0.0\n",
      "\n",
      "RULE #14:\n",
      "  IF (temp[high] AND al[low]) AND ti[high] THEN density[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[high]                                             : 0.0\n",
      "  - al[low]                                                : 0.0\n",
      "  - ti[high]                                               : 0.0\n",
      "                     (temp[high] AND al[low]) AND ti[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          density[average] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: density                  = 1.1882499824468897\n",
      "  low:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  average:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  high:\n",
      "    Accumulate using accumulation_max : 1.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(1.1882499824468897)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.input[\"temp\"] = 20\n",
    "sim.input[\"al\"] = 0.3\n",
    "sim.input[\"ti\"] = 0.0\n",
    "sim.compute()\n",
    "sim.print_state()\n",
    "display(sim.output[\"density\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fuzzy_pred(row):\n",
    "    sim.input[\"temp\"] = row[\"T\"]\n",
    "    sim.input[\"al\"] = row[\"Al2O3\"]\n",
    "    sim.input[\"ti\"] = row[\"TiO2\"]\n",
    "    sim.compute()\n",
    "    return sim.output[\"density\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'density'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m result_train \u001b[38;5;241m=\u001b[39m density_train\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m----> 3\u001b[0m result_train[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDensityPred\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mresult_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfuzzy_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m result_train\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m15\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m  10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[0;32m  10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[0;32m  10363\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  10364\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  10372\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m  10373\u001b[0m )\n\u001b[1;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[0;32m    914\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[1;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m   1062\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 1063\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1064\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1065\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n",
      "File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m   1079\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[0;32m   1080\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[1;32m-> 1081\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1082\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[0;32m   1083\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[0;32m   1084\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[0;32m   1085\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "Cell \u001b[1;32mIn[12], line 6\u001b[0m, in \u001b[0;36mfuzzy_pred\u001b[1;34m(row)\u001b[0m\n\u001b[0;32m      4\u001b[0m sim\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mti\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTiO2\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m      5\u001b[0m sim\u001b[38;5;241m.\u001b[39mcompute()\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdensity\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'density'"
     ]
    }
   ],
   "source": [
    "result_train = density_train.copy()\n",
    "\n",
    "result_train[\"DensityPred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
    "\n",
    "result_train.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "      <th>DensityPred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.05696</td>\n",
       "      <td>1.060000</td>\n",
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       "    <tr>\n",
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       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.04158</td>\n",
       "      <td>1.060000</td>\n",
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       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>0.05</td>\n",
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       "      <td>1.08438</td>\n",
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       "      <td>30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.08112</td>\n",
       "      <td>1.097343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.07781</td>\n",
       "      <td>1.097343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.07446</td>\n",
       "      <td>1.088627</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.06053</td>\n",
       "      <td>1.060000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.17459</td>\n",
       "      <td>1.159583</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.14812</td>\n",
       "      <td>1.126222</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.07424</td>\n",
       "      <td>1.096363</td>\n",
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       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.07075</td>\n",
       "      <td>1.096363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.06721</td>\n",
       "      <td>1.097343</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.22417</td>\n",
       "      <td>1.163333</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.21310</td>\n",
       "      <td>1.161428</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.20265</td>\n",
       "      <td>1.162778</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.18265</td>\n",
       "      <td>1.141253</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.17261</td>\n",
       "      <td>1.126667</td>\n",
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      ],
      "text/plain": [
       "     T  Al2O3  TiO2  Density  DensityPred\n",
       "0   30   0.00  0.00  1.05696     1.060000\n",
       "1   55   0.00  0.00  1.04158     1.060000\n",
       "2   25   0.05  0.00  1.08438     1.096363\n",
       "3   30   0.05  0.00  1.08112     1.097343\n",
       "4   35   0.05  0.00  1.07781     1.097343\n",
       "5   40   0.05  0.00  1.07446     1.088627\n",
       "6   60   0.05  0.00  1.06053     1.060000\n",
       "7   35   0.30  0.00  1.17459     1.159583\n",
       "8   65   0.30  0.00  1.14812     1.126222\n",
       "9   45   0.00  0.05  1.07424     1.096363\n",
       "10  50   0.00  0.05  1.07075     1.096363\n",
       "11  55   0.00  0.05  1.06721     1.097343\n",
       "12  20   0.00  0.30  1.22417     1.163333\n",
       "13  30   0.00  0.30  1.21310     1.161428\n",
       "14  40   0.00  0.30  1.20265     1.162778\n",
       "15  60   0.00  0.30  1.18265     1.141253\n",
       "16  70   0.00  0.30  1.17261     1.126667"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test = density_test.copy()\n",
    "\n",
    "result_test[\"DensityPred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
    "\n",
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_train': 0.024131296333542294,\n",
       " 'RMSE_test': 0.03056470202709202,\n",
       " 'RMAE_test': 0.16058558998263095,\n",
       " 'R2_test': 0.7532040538162098}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "rmetrics = {}\n",
    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_train[\"Density\"], result_train[\"DensityPred\"])\n",
    ")\n",
    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
    ")\n",
    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
    ")\n",
    "rmetrics[\"R2_test\"] = metrics.r2_score(\n",
    "    result_test[\"Density\"], result_test[\"DensityPred\"]\n",
    ")\n",
    "\n",
    "rmetrics"
   ]
  }
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