2-Feature-Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable.\n",
    "\n",
    "Feature selection is often straightforward when working with real-valued input and output data, such as using the Pearson’s correlation coefficient, but can be challenging when working with numerical input data and a categorical target variable.\n",
    "\n",
    "The two most commonly used feature selection methods for numerical input data when the target variable is categorical (e.g. classification predictive modeling) are:\n",
    "\n",
    "- ANOVA-f Statistic.\n",
    "- Mutual Information Statistics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA f-test (Attack Model)\n",
    "\n",
    "ANOVA is an acronym for “analysis of variance” and is a parametric statistical hypothesis test for determining whether the means from two or more samples of data (often three or more) come from the same distribution or not.\n",
    "\n",
    "An F-statistic, or F-test, is a class of statistical tests that calculate the ratio between variances values, such as the variance from two different samples or the explained and unexplained variance by a statistical test, like ANOVA. The ANOVA method is a type of F-statistic referred to here as an ANOVA f-test.\n",
    "\n",
    "Importantly, ANOVA is used when one variable is numeric and one is categorical, such as numerical input variables and a classification target variable in a classification task.\n",
    "\n",
    "The results of this test can be used for feature selection where those features that are independent of the target variable can be removed from the dataset.\n",
    "\n",
    "\"When the outcome is numeric, and […] the predictor has more than two levels, the traditional ANOVA F-statistic can be calculated.\" — Page 242, Feature Engineering and Selection, 2019.\n",
    "\n",
    "**The scikit-learn machine library provides an implementation of the ANOVA f-test in the *f_classif()* function. This function can be used in a feature selection strategy, such as selecting the top k most relevant features (largest values) via the SelectKBest class.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./df.csv')\n",
    "\n",
    "X = df.iloc[:, :-2]\n",
    "y = df.iloc[:,-2].apply(lambda y: 0 if y > 10 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "import matplotlib.pyplot as plt\n",
    "    \n",
    "# feature selection\n",
    "def select_features(X_train, y_train, X_test):\n",
    "    # configure to select all features\n",
    "    fs = SelectKBest(score_func=f_classif, k='all')\n",
    "    # learn relationship from training data\n",
    "    fs.fit(X_train, y_train)\n",
    "    # transform train input data\n",
    "    X_train_fs = fs.transform(X_train)\n",
    "    # transform test input data\n",
    "    X_test_fs = fs.transform(X_test)\n",
    "    return X_train_fs, X_test_fs, fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 1: posicao_relativa 2.542500\n",
      "Feature 2: distancia 71.455804\n",
      "Feature 3: altitude_blue 20.888212\n",
      "Feature 4: altitude_red 6.468142\n",
      "Feature 5: velocidade_blue 2.769417\n",
      "Feature 6: velocidade_red 0.116010\n",
      "Feature 7: blue_rwr 2.869926\n",
      "Feature 8: red_rwr 1138.845306\n",
      "Feature 9: missil_blue_a 67.539712\n",
      "Feature 10: missil_blue_b 32.956473\n",
      "Feature 11: missil_blue_c 197.674793\n",
      "Feature 12: missil_est_red_a 0.186071\n",
      "Feature 13: missil_est_red_b 0.576515\n",
      "Feature 14: missil_est_red_c 0.106524\n",
      "Feature 15: missil_red_a 11.304604\n",
      "Feature 16: missil_red_b 7.390415\n",
      "Feature 17: missil_red_c 36.375822\n",
      "Feature 18: missil_est_blue_a 1.367845\n",
      "Feature 19: missil_est_blue_b 2.039896\n",
      "Feature 20: missil_est_blue_c 6.706839\n",
      "Feature 21: shotphi_blue_s 368.467076\n",
      "Feature 22: shotphi_blue_m 0.206590\n",
      "Feature 23: shotphi_blue_l 338.600122\n",
      "Feature 24: shotphi_est_red_s 0.164204\n",
      "Feature 25: shotphi_est_red_m 0.001296\n",
      "Feature 26: shotphi_est_red_l 0.132043\n",
      "Feature 27: shotphi_red_s 134.147122\n",
      "Feature 28: shotphi_red_m 3.244233\n",
      "Feature 29: shotphi_red_l 176.625744\n",
      "Feature 30: shotphi_est_blue_s 53.390120\n",
      "Feature 31: shotphi_est_blue_m 2.554926\n",
      "Feature 32: shotphi_est_blue_l 31.347943\n",
      "Feature 33: threshold_before_blue 0.985878\n",
      "Feature 34: threshold_after_blue 1.105407\n",
      "Feature 35: threshold_before_red 12.045052\n",
      "Feature 36: threshold_after_red 8.697677\n"
     ]
    }
   ],
   "source": [
    "dic = {}\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
    "# feature selection\n",
    "X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n",
    "# what are scores for the features\n",
    "for i in range(len(fs.scores_)):\n",
    "    print('Feature %d: %s %f' % (i+1,df.columns[i], fs.scores_[i]))\n",
    "    dic[df.columns[i]] = fs.scores_[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>red_rwr</td>\n",
       "      <td>1138.845306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>shotphi_blue_s</td>\n",
       "      <td>368.467076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>shotphi_blue_l</td>\n",
       "      <td>338.600122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>missil_blue_c</td>\n",
       "      <td>197.674793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>shotphi_red_l</td>\n",
       "      <td>176.625744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>shotphi_red_s</td>\n",
       "      <td>134.147122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>distancia</td>\n",
       "      <td>71.455804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>missil_blue_a</td>\n",
       "      <td>67.539712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>shotphi_est_blue_s</td>\n",
       "      <td>53.390120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>missil_red_c</td>\n",
       "      <td>36.375822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>missil_blue_b</td>\n",
       "      <td>32.956473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>shotphi_est_blue_l</td>\n",
       "      <td>31.347943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>altitude_blue</td>\n",
       "      <td>20.888212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>threshold_before_red</td>\n",
       "      <td>12.045052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>missil_red_a</td>\n",
       "      <td>11.304604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>threshold_after_red</td>\n",
       "      <td>8.697677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>missil_red_b</td>\n",
       "      <td>7.390415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>missil_est_blue_c</td>\n",
       "      <td>6.706839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>altitude_red</td>\n",
       "      <td>6.468142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>shotphi_red_m</td>\n",
       "      <td>3.244233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>blue_rwr</td>\n",
       "      <td>2.869926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>velocidade_blue</td>\n",
       "      <td>2.769417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>shotphi_est_blue_m</td>\n",
       "      <td>2.554926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>posicao_relativa</td>\n",
       "      <td>2.542500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>missil_est_blue_b</td>\n",
       "      <td>2.039896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>missil_est_blue_a</td>\n",
       "      <td>1.367845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>threshold_after_blue</td>\n",
       "      <td>1.105407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>threshold_before_blue</td>\n",
       "      <td>0.985878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>missil_est_red_b</td>\n",
       "      <td>0.576515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>shotphi_blue_m</td>\n",
       "      <td>0.206590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>missil_est_red_a</td>\n",
       "      <td>0.186071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>shotphi_est_red_s</td>\n",
       "      <td>0.164204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>shotphi_est_red_l</td>\n",
       "      <td>0.132043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>velocidade_red</td>\n",
       "      <td>0.116010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>missil_est_red_c</td>\n",
       "      <td>0.106524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>shotphi_est_red_m</td>\n",
       "      <td>0.001296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature        score\n",
       "7                 red_rwr  1138.845306\n",
       "20         shotphi_blue_s   368.467076\n",
       "22         shotphi_blue_l   338.600122\n",
       "10          missil_blue_c   197.674793\n",
       "28          shotphi_red_l   176.625744\n",
       "26          shotphi_red_s   134.147122\n",
       "1               distancia    71.455804\n",
       "8           missil_blue_a    67.539712\n",
       "29     shotphi_est_blue_s    53.390120\n",
       "16           missil_red_c    36.375822\n",
       "9           missil_blue_b    32.956473\n",
       "31     shotphi_est_blue_l    31.347943\n",
       "2           altitude_blue    20.888212\n",
       "34   threshold_before_red    12.045052\n",
       "14           missil_red_a    11.304604\n",
       "35    threshold_after_red     8.697677\n",
       "15           missil_red_b     7.390415\n",
       "19      missil_est_blue_c     6.706839\n",
       "3            altitude_red     6.468142\n",
       "27          shotphi_red_m     3.244233\n",
       "6                blue_rwr     2.869926\n",
       "4         velocidade_blue     2.769417\n",
       "30     shotphi_est_blue_m     2.554926\n",
       "0        posicao_relativa     2.542500\n",
       "18      missil_est_blue_b     2.039896\n",
       "17      missil_est_blue_a     1.367845\n",
       "33   threshold_after_blue     1.105407\n",
       "32  threshold_before_blue     0.985878\n",
       "12       missil_est_red_b     0.576515\n",
       "21         shotphi_blue_m     0.206590\n",
       "11       missil_est_red_a     0.186071\n",
       "23      shotphi_est_red_s     0.164204\n",
       "25      shotphi_est_red_l     0.132043\n",
       "5          velocidade_red     0.116010\n",
       "13       missil_est_red_c     0.106524\n",
       "24      shotphi_est_red_m     0.001296"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "scores = pd.DataFrame(dic.items(),columns = ['feature', 'score'])    \n",
    "scores = scores.sort_values('score',ascending=False)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jgO2Bh3ox7VnAcZI2Kbm8WdIbSn5HSNpI0qbA+3qxz4iIiIiIfjHkZkKo9mZMlbQaeAk4HpjZQd0zgemSFgMrgU+U8p8CMyW9HziplD0E3Aa8EZhi+4XqxVZrz/bNkt4O3FViPgsca3uBpCuAhcBjwO0dR4mIiIiIGBxkZ7VPVyTNAK633dFgZlCp1WpubW3t7zQiIiIiYoiTNN92rbF8yC3HioiIiIiIgW0oLsfqdbYn9aSdpFHA7HZuHWT7yZ7mI+mbwH4Nxefbnt7TmBERERERfSXLsYahsduO9HdP27+/0xgU9pt8fX+nEBERETFoZTlWREREREQMCEN6ECJpuaQtm6g/XtK+3ag3SdIFHdy7UdLIJtLsTl5nSjq565oREREREQNf9oT8tfFUr8e9s6cBbB/WnXqq3sUr26t72ldERERExGA0ZGZCJG0s6QZJiyQtlTSx3DpJ0gJJSySNLXW3kHStpMWS7pY0TtJoYArwBUkLJR0gaYakiyTdLulhSe+t63JrSTdJWibpa3V5dDj7Imm0pF9K+hawANhG0hclzSu5nFVX9zRJD0n6ObBTF8/+OUkPlBg/6qDOZEmtklqfevbFrn/QiIiIiIh1ZCjNhBwK/Nb2ewAkbQ6cS3UK+h6STgBOBv4BOAu4z/YHJL0buMR2i6SLgGdtn1difAoYDbwLGAP8QtIOpb8WYHdgFfCQpP+y/atu5LkT8EnbJ0g6GNgR2BsQcJ2kA4HngI+U+K+hGrDM7yTmKcB2tld1tBTM9jRgGlQb07uRZ0RERETEOjFkZkKAJcAESedKOsD206X86vI5n2pAAbA/cCmA7VuAUWXQ0p4rba+2vQx4BBhbymfbftr2C8ADwLbdzPMx23eX64PLv/uoBhpjqQYlBwDX2F5pewVwXRcxFwOXSzoWeLmbeURERERE9IshMwix/TCwJ9Vg5GxJZ5Rbq8rnK7w686P2QnQUuoPvq+rK6mN35bm6awFn224p/3aw/d0u8mnPe4BvUj3/fElDaYYrIiIiIoaYITMIkbQ1sNL2ZcB5wB6dVJ8DHFPajadasrUCeAbYtKHuUZJGSBoDbA881ItpzwKOk7RJyeXNkt5Q8jtC0kaSNgXe11EASSOAbWz/AvhnYCSwSS/mGBERERHRq4bS/zHfFZgqaTXwEnA8MLODumcC0yUtBlYCnyjlPwVmSno/cFIpewi4DXgjMMX2C9WLrdae7ZslvR24q8R8FjjW9gJJVwALgceA2zsJsx5wWVlOJuDrtp/qrN9Nttohh/BFRERERL/JiemdkDQDuN52R4OZQalWq7m1tbW/04iIiIiIIS4npkdERERExIAwlJZj9Trbk3rSTtIoYHY7tw6y/WRP85H0TWC/huLzbU9vJs6KJ5Yx67vdOlOxXxzyqRv7O4WIiIiIWIcyCOkBSWdS7d/YDJhj++f198tAo0XSB4CHbT/QG/3aPrH0/+X2+o2IiIiIGAwyCFkLts/oosoHgOupzhHpy34jIiIiIgas7AnpJkmnSXpI0s+pTj1H0gxJR5brcyQ9IGmxpPMk7QscTvXGroWSxkj6tKR5khZJukrS6+rifEPSnZIeaYtZ7v2zpCWlzTnt9HtGiblU0jT11qu7IiIiIiLWkcyEdIOkPYGPALtT/WYLqE5gb7u/BXAEMNa2JY20/ZSk66h7u5akp2x/p1x/FfgU8F8lzJuoTnIfS3VC+kxJf081m/JO2ytLP40usP3lEvNS4L1UrxpufIbJwGSAN2yx4dr8HBERERERayUzId1zAHCN7ZXlUMPrGu6vAF4ALpb0QaqzR9qzi6TbJS2hOixx57p719peXfaPvLGUTQCm214JYPtP7cT8O0n3lJjvboj5F7an2a7Zrm2+6Wu7fuKIiIiIiHUkg5Du6/BAFdsvA3sDV1HNXNzUQdUZwGdt7wqcBdRPSayqu1bdZ4f9StoQ+BZwZIn5nYaYEREREREDTgYh3TMHOELSRpI2Bd5Xf1PSJsDmtm8E/hFoKbeeATatq7op8DtJ61PNhHTlZuC4ur0jjcux2gYcT5QcjiQiIiIiYoDLnpBusL1A0hXAQuAx4PaGKpsCPykzEwK+UMp/BHxH0ueoBgj/CtxTYizhrwco7fV7k6QWoFXSi8CNwKl195+S9J0Sazkwr+dPGRERERHRN2R3uNonhqharebW1tb+TiMiIiIihjhJ823XGsuzHCsiIiIiIvpUBiEREREREdGnsidkGHrqiWVcM/3Q/k5jDUd8sqOXikVERETEUJKZkIiIiIiI6FPDchAi6XBJp/Sg3Z3lc7SkpZ3UmyTpgg7uPdtsvxERERERQ8mwXI5l+zrWPPW8O+32XQfpREREREQMK0NuJqTMUjwo6WJJSyVdLmmCpLmSlknau36mQtJRpd4iSXNK2c6S7pW0UNJiSTuW8mZmMbaRdJOkhyT9Wzt5jpd0fd33CyRNKtd7SrpN0nxJsyS9qZPn/bSkeSX/q9oONoyIiIiIGKiG3CCk2AE4HxgHjAU+CuwPnEzdYX/FGcAhtncDDi9lU4DzbbcANeDXPchhb6pT0VuAoySt8X7k9pTT1P8LONL2nsD3gH/vpMnVtvcq+f8S+FQHcSdLapXUuuLZF5t4jIiIiIiI3jVUl2M9ansJgKT7gdm2LWkJMLqh7lxghqQrgatL2V3AaZLeQvVH/rIe5PAz20+WHK6mGgR154TAnYBdgJ9JAlgP+F0n9XeR9FVgJLAJMKu9SranAdMAdhi9eU6ojIiIiIh+M1QHIavqrlfXfV9NwzPbniLpncB7gIWSWmz/QNI9pWyWpH+wfUuTOTT+od/4/WX+eiZqw/Ip4H7b+3SznxnAB2wvKsu5xjeXZkRERERE3xqqy7G6TdIY2/fYPgN4gmovx/bAI7a/QbWBfVwPQv8fSVtI2gj4ANWMS73HgHdI2kDS5sBBpfwhYCtJ+5T81pe0cyf9bAr8rizjOqYHeUZERERE9KmhOhPSjKll47mA2cAi4BTgWEkvAb8HvtyDuHcAl1LtT/mB7b9aimX7V2UJ2GJgGXBfKX9R0pHAN8rg5DXAfwL3d9DPvwL3UA1qllANSjo1cssdczBgRERERPQb2dkeMNzUajW3tnZne0pERERERM9Jmm97jRc0DfvlWBERERER0beyHGstSDoEOLeh+FHbR6yDvr4J7NdQfL7t6c3GevLJZVw645DeSawJH5vU7ou7IiIiImKYySBkLdieRQevxF0HfZ3YF/1ERERERKxr/bIcS9JySVs2UX+8pH27Ue8vJ6G3c+9GSSObSLOrfrbuok67zyjpTEkn90YeERERERGD0WDZEzIe6HIQ0hnbh9l+qleygUlAp4OQiIiIiIho3zofhEjaWNINkhZJWippYrl1kqQFkpZIGlvqbiHpWkmLJd0taZyk0cAU4AuSFko6QNIMSRdJul3Sw5LeW9fl1pJukrRM0tfq8uh09kXSsZLuLX18W9J65d+MkvcSSV8or8+tAZeXuht18vhfLDHvlbRDO33eKqlWrreUtLxcrydpqqR55bf4TCd5v0nSnJLLUkkHdJJPRERERES/64uZkEOB39rezfYuQNsBFU/Y3gO4EGhbnnQWcJ/tccCpwCW2lwMXAV+33WL79lJ3NPAuqlPNL5LUduJ4CzAR2BWYKGmbrhKU9PbSZj/bLcArVAf/tQBvtr2L7V2B6bZnAq3AMSWf5zsJvcL23sAFVGd9dNengKdt7wXsBXxa0nYd1P0oMKvkvRuwsINnnCypVVLrM8+82EQqERERERG9qy8GIUuACZLOlXSA7adL+dXlcz7VgAJgf6oD/rB9CzCqHNjXnittr7a9DHgEGFvKZ9t+2vYLwAPAtt3I8SBgT2CepIXl+/Yl7vaS/kvSocCKbj3xq35Y97lPE+0OBj5ecrkHGAXs2EHdecAnJZ0J7Gr7mfYq2Z5mu2a7tummr20ilYiIiIiI3rXO345l+2FJewKHAWdLurncWlU+X6nLQ+2F6Ch0B99X1ZXVx+6MgO/b/pc1bki7AYcAJwIfBo7rRrz2cmzvOV7m1YHghnXlAk4qb9/qvAN7jqQDqWaELpU01fYlTeQYEREREdGn+mJPyNbAStuXAecBe3RSfQ7VMigkjadasrUCeAbYtKHuUZJGSBpDNWvx0FqkORs4UtIbSt9bSNq27CEZYfsq4F/rcm8vn/ZMrPu8q537y6lmYACOrCufBRwvaf2Sz9skbdxeB5K2Bf5o+zvAd+n8942IiIiI6Hd9cU7IrsBUSauBl4DjgZkd1D0TmC5pMbAS+EQp/ykwU9L7gZNK2UPAbcAbgSm2X5Dam0jpmu0HJJ0O3CxpRMnzROD5kk/bYK1tpmQG1T6U54F9OtkXsoGke6gGe0e3c/884EpJHwNuqSu/mGqJ2gJVD/U48IEO+hhPtQH+JeBZ4OOdPy2MGrVjDg6MiIiIiH4ju6PVTgOXpBnA9WWTeDSpVqu5tbW1v9OIiIiIiCFO0nzbtcbywXJOSEREREREDBF9sRyr19me1JN2kkZR7f9odJDtJ3sY8xqg8fW5X+rOpvIm+9mV8uawOqtsv7PZWI8/uYxvX3pI7yQGfOZjWdoVEREREd03KAchPVUGGi29HPOI3ozXST9L6OXcIyIiIiL6Q5Zj1ZF0uKRTetDuzvI5WtLSdZDXX05Wj4iIiIgY7IbVTEhXbF8HXNeDdvs220bSa2y/3Gy7iIiIiIjBbtjMhJRZigclXSxpqaTLJU2QNFfSMkl7S5ok6YJS/6hSb5GkOaVsZ0n3SlooabGkHUv5s93MYZKkH0v6KdXrgDeW9D1J8yTdV15BjKSNJP2o9HEFsFEXcQ+VtKDk2t6el4iIiIiIAWO4zYTsABwFTAbmAR8F9gcOB04Frq2rewZwiO3fSBpZyqYA59u+XNJrgfV6kMM+wDjbf5L0H8Atto8rfdwr6efAZ6gOeBwnaRywoKNgkrYCvgMcaPtRSVt0UG9yeW62GLVhe1UiIiIiIvrEsJkJKR61vcT2auB+YLarg1KWUB0OWG8uMEPSp3l1sHEXcKqkLwHbdnJIYWd+ZvtP5fpg4BRJC4FbgQ2BtwIHApcB2F4MLO4k3t8Cc2w/Wur/qb1KtqfZrtmubbLpa3uQdkRERERE7xhug5BVdder676vpmFWyPYU4HRgG2ChpFG2f0A1a/I8MEvSu3uQw3N11wI+ZLul/Hur7V+2pdDNeGqibkREREREvxtug5BukzTG9j22zwCeALaRtD3wiO1vUG1gH7eW3cwCTpKk0ufupXwOcEwp26WLfu4C3iVpu1K/3eVYEREREREDRQYhHZsqaUl55e4cYBEwEVhalk+NBS5Zyz6+AqwPLC79fKWUXwhsImkx8M/AvR0FsP041V6PqyUtAq5Yy5wiIiIiItYpVVsiYjip1WpubW3t7zQiIiIiYoiTNN/2GufdZSYkIiIiIiL61HB7RW+fkHQIcG5D8aO2j1jLuPcAGzQUf8z2kmbi/OFPy/i/PzxkbVIB4P87etZax4iIiIiI4SeDkHXA9iyqTee9HfedvR0zIiIiIqKvDcvlWJIOl3RKD9rdWT5Hl43kHdX7y8nr7dzr1unqTeR0q6Q11tlFRERERAxUw3ImxPZ1VK/YbbbdvusgnYiIiIiIYWXIzYSUWYoHJV0saamkyyVNkDRX0jJJe9fPVEg6qtRbJGlOKdtZ0r2SFkpaLGnHUt7MLMY2km6S9JCkf2snz/GSrq/7foGkSeV6T0m3SZovaZakN3XR17GS7izPsXcTOUZERERE9LkhNwgpdgDOpzrkbyzwUWB/4GTg1Ia6ZwCH2N6N6jR0gCnA+bZbgBrw6x7ksDfVgYMtwFHdXTIlaX3gv4Ajbe8JfA/49y6abVxmaU4o9duLO1lSq6TW5555sZuPEBERERHR+4bqcqxH294YJel+YLZtS1oCjG6oOxeYIelK4OpSdhdwmqS3AFfbXtaDHH5m+8mSw9VUg6DuHM6xE7AL8LNykPp6wO+6aPNDANtzJG0maaTtp+or2J4GTAPYZvvNczhMRERERPSboToIWVV3vbru+2oantn2FEnvBN4DLJTUYvsH5XW47wFmSfoH27c0mUPjH/qN31/mr2eiNiyfAu63vU8v9hURERERMWAM1eVY3SZpjO17bJ8BPEG1l2N74BHb36DawD6uB6H/j6QtJG0EfIBqxqXeY8A7JG0gaXPgoFL+ELCVpH1KfutL2rmLviaWuvsDT9t+ugf5RkRERET0iaE6E9KMqWXjuYDZwCLgFKrN3i8Bvwe+3IO4dwCXUu1P+YHtv1qKZftXZQnYYmAZcF8pf1HSkcA3yuDkNcB/Avd30tefy+uDNwOO60GuERERERF9RnZW7gw3tVrNra3d2Z4SEREREdFzkubbXuMFTcN+OVZERERERPStLMdaC5IOAc5tKH7U9hHroK9vAvs1FJ9ve3qzsX7z52Wc/uND1zqnrx5101rHiIiIiIjhJ4OQtWB7FjCrj/o6sS/6iYiIiIhY1/p9OZak5ZK2bKL+eEn7dqPeX05Fb+fejZJGNpFmV/1s3UWddp9R0pmSTu6NPEq80ZKW9la8iIiIiIh1od8HIT0wHuhyENIZ24c1Hua3FiYBnQ5CIiIiIiLiVX06CJG0saQbJC2StFTSxHLrJEkLJC2RNLbU3ULStZIWS7pb0jhJo4EpwBckLZR0gKQZki6SdLukhyW9t67LrSXdJGmZpK/V5dHp7IukYyXdW/r4tqT1yr8ZJe8lkr5QXqVbAy4vdTfq5PG/WGLeK2mHdvq8VVKtXG8paXm5Xk/SVEnzym/xmW792BERERERA1Rfz4QcCvzW9m62dwHadjY/YXsP4EKgbXnSWcB9tscBpwKX2F4OXAR83XaL7dtL3dHAu6hOOL9IUtvp4y1UB/ntCkyUtE1XCUp6e2mzn+0W4BXgmBLrzbZ3sb0rMN32TKAVOKbk83wnoVfY3hu4gOrcj+76FNUBhHsBewGflrRdE+0BkDRZUquk1pUrXmy2eUREREREr+nrQcgSYIKkcyUdUHey99Xlcz7VgAJgf6rD/rB9CzCqHN7Xnittr7a9DHgEGFvKZ9t+2vYLwAPAtt3I8SBgT2CepIXl+/Yl7vaS/kvSocCKbj3xq35Y97lPE+0OBj5ecrkHGAXs2GTf2J5mu2a79rrNXtts84iIiIiIXtOnb8ey/bCkPYHDgLMl3VxurSqfr9TlpPZCdBS6g++r6srqY3dGwPdt/8saN6TdgEOAE4EP09zp5O7gus3LvDoo3LCuXMBJ5U1cERERERGDXl/vCdkaWGn7MuA8YI9Oqs+hWgaFpPFUS7ZWAM8AmzbUPUrSCEljqGYtHlqLNGcDR0p6Q+l7C0nblj0kI2xfBfxrXe7t5dOeiXWfd7VzfznVDAzAkXXls4DjJa1f8nmbpI2beJ6IiIiIiAGlr88J2RWYKmk18BJwPDCzg7pnAtMlLQZWAp8o5T8FZkp6P3BSKXsIuA14IzDF9gtSexMpXbP9gKTTgZsljSh5ngg8X/JpG7i1zZTMoNqH8jywTyf7QjaQdA/VwO/odu6fB1wp6WPALXXlF1MtUVug6qEeBz7Qo4eLiIiIiBgAZHe0wmlwkDQDuL5sEo9uqNVqbm1t7e80IiIiImKIkzTfdq2xfDCeExIREREREYNYXy/H6nW2J/WknaRRVPs/Gh1k+8kexrwGaHx97pd6e1O5pF0pbw6rs8r2O7vTfvlTy/jkNYd2u7/pR9zUdaWIiIiIiG4a9IOQnioDjZZejnlEb8brpJ8l9HLuERERERF9ZVgtx2o7KV3SSEkn1JVvLWlmuW6RdFgPYp8p6eSua67RbkY5eb2xfLyk65uNFxEREREx0A2rQUidkcBfBiG2f2u7bSDQQnWOSURERERErANDdhAi6VpJ8yXdL2lyw+1zgDGSFkqaKmm0pKWSXgt8GZhY7k1snOEo9UaX69MkPSTp58BOdXXGSLqp9H+7pLF0bkKp97Ck97bzLJ3lcKyke0u+35a0XnO/VERERERE3xrKe0KOs/0nSRsB8yRdVXfvFGAX2y0AbX/Q235R0hlAzfZny70z2wteTn7/CLA71e+4AJhfbk+jOq9kmaR3At8C3t1JrqOBdwFjgF9I2qE7Dyjp7VSHH+5n+yVJ36I64PGSdupOBiYDbLzVho23IyIiIiL6zFAehHxOUttG8W2AHXs5/gHANbZXAki6rnxuAuwL/LjuwMQNuoh1pe3VwDJJjwBdzZy0OYjqlPV5pa+NgD+2V9H2NKrBEVvusPngPhwmIiIiIga1ITkIkTQemEB1gvlKSbcCPf3f/y/z18vW6uO098f8COCptlmWbmqM0/i9oxwEfN/2vxARERERMUgM1T0hmwN/LgOQscDfNtx/Bti0g7aN95YDewBI2oNXzwGZAxwhaSNJmwLvA7C9AnhU0lGljSTt1kW+R0kaIWkMsD3wUMP9jnKYDRwp6Q3l3haStu2ir4iIiIiIfjVUByE3Aa+RtBj4CnB3/c1yRsjcssF7akPbXwDvaNuYDlwFbCFpIXA88HCJsQC4AlhY6txeF+MY4FOSFgH3A+/vIt+HgNuA/6baS/JCw/2OcngAOB24uTzrz4A3ddFXRERERES/kp3tAcNNrVZza2trf6cREREREUOcpPm2a43lQ3UmJCIiIiIiBqghuTF9IJJ0GnBUQ/GPbf97X+ey7KnH+PuffLrde//9/u/0cTYRERERMdxkENJHymCjzwccEREREREDTY+WY0kaKemEcj1e0vW9mxZImiHpyCbqj5a0tIN7t0paYy1a3f1nm8xtK0n3SLpP0gHNtF2XJE2SdEF/5xERERER0Zme7gkZCZzQTANJ6/Wwr4HoIOBB27vbvr3L2qzd8w+x3y4iIiIihrmeDkLOAcaUV8ZOBTaRNFPSg5IuVzm+W9JySWdIuoPqLIyDJd0laYGkH5fTxZF0jqQHJC2WdF5dPwdKulPSI22zIuXcjanl9bpLymt0/0o5u+NHJd4VVCeJd0rS/y15zZa0VSkbI+kmSfMl3S5prKQW4GvAYeU1vhtJOrrkslTSuXUxn5X0ZUn3APtIOlbSvaXdtzsbXHS3raRPSnpY0m3Afl09Z0REREREf+vpIOQU4H/LqeBfBHYH/hF4B9Vhe/V/DL9ge3/g51RnWkywvQfQCvyTpC2AI4CdbY8DvlrX9k3A/sB7qQY+AB8EWoDdqE5Fnyqp8WyM44GVJd6/A3t28TwbAwtKXrcB/1bKpwEn2d4TOBn4lu2FwBnAFeX5Xw+cC7y75LWXpA/UxV1q+53Ak8BEYL/S7hWq80Q6y6nTtuW5z6L6vf8P1e/fLkmTJbVKan1xReMxJBERERERfae3Nqbfa/vXAGV2ZDRwR7l3Rfn8W6o/kueWiZLXAncBK4AXgIsl3QDU7y+51vZq4AFJbyxl+wM/tP0K8IcyA7AXsLiu3YHANwBsLy4H+XVmdV2elwFXl1mafYEfl3wBNmin7V7ArbYfL89/een/WqrBwlWl3kFUg6F5Jd5GwB87yak7bd/Z0PcVwNvaC2Z7GtWgis132CqHw0REREREv+mtQciquutXGuI+Vz4F/Mz20Y2NJe1N9Yf2R4DPUs0qNMZVw2dX1uYPbVPNEj1VZh4601k+L5TBUlu979v+l27m0GXbMuOSAUVEREREDCo9XY71DLBpk23uBvaTtAOApNdJeluZcdjc9o1US7pauogzB5goab2yd+NA4N526hxT+tkFGNdFzBFA25u4PgrcYXsF8Kiko0ocSdqtnbb3AO+StGXZp3E01ZKuRrOBIyW9ocTbQtK2XeTVVdt7gPGSRklanzXPIYmIiIiIGHB6NBNi+0lJc8srcZ8H/tCNNo9LmgT8UFLbsqbTqQY0P5G0IdX/8f9CF6GuAfYBFlHNAvyz7d9LGl1X50JgelmGtZA1BymNngN2ljQfeJpq/wVUA5kLJZ0OrA/8qPRb/1y/k/QvwC9K/jfa/kk7z/9AiXOzpBHAS8CJwGNd5NZhW9t3SzqTalnb74AFQN6kFREREREDmuys5hluarWaW1tb+zuNiIiIiBjiJM23vcZ5fT1djhUREREREdEjvbUxfVAoZ240vuHqY7aX9Ec+0D85LXvqtxx27b+uUX7jB76yrrqMiIiIiPiLYTUIKWduDCgDMaeIiIiIiHUpy7HqSDpc0ik9aHdn+RxdNuv3dl63SlpjLV1ERERExGA0rGZCumL7OuC6HrTbt9k2kl5j++Vm20VEREREDHbDZiakzFI8KOliSUslXS5pQnnV8DJJe0uaJOmCUv+oUm+RpDmlbGdJ90paKGmxpB1L+bPdzGGSpB9L+inV63Y3lvQ9SfMk3Sfp/aXeRpJ+VPq4guqE9M7iXiipVdL9ks5am98pIiIiImJdG24zITtQHeg3GZhHdTDh/sDhwKnAtXV1zwAOsf0bSSNL2RTgfNuXS3otPTuTYx9gnO0/SfoP4Bbbx5U+7pX0c+AzwErb4ySNozr/ozOnlXjrAbMljbO9uL6CpMnludlwq816kHZERERERO8YNjMhxaO2l9heDdwPzHZ1UMoSYHRD3bnADEmf5tXBxl3AqZK+BGxr+/ke5PAz238q1wcDp0haCNwKbAi8leoU+MsAymBi8Zph/sqHJS0A7gN2Bt7RWMH2NNs127XXbrZxD9KOiIiIiOgdw20QsqruenXd99U0zArZnkJ1ovs2wEJJo2z/gGrW5HlglqR39yCH5+quBXzIdkv591bbv2xLoTvBJG0HnAwcZHsccAPVYCYiIiIiYkAaboOQbpM0xvY9ts8AngC2kbQ98Ijtb1BtYB+3lt3MAk6SpNLn7qV8DnBMKduli342oxrYPC3pjcDfr2VOERERERHrVAYhHZsqaUl55e4cYBEwEVhalk+NBS5Zyz6+AqwPLC79tJ0WeCGwiaTFwD8D93YUwPYiqmVY9wPfo1pGFhERERExYKnaEhHDSa1Wc2tra3+nERERERFDnKT5ttc47y4zIRERERER0aeG2yt6+4SkQ4BzG4oftX3EWsa9B9igofhjtpc0E2fZU7/nPddM/auyG4744tqkFhERERHRbRmErAO2Z1FtOu/tuO/s7ZgREREREX1tnSzHkjRS0gnleryk69dBHzMkHdlE/dFl83d7926VtMZatW7EPKCcUr5Q0j6SDms2Rm9q9jeJiIiIiOgP62pPyEjghGYalNO+B5tjgPNstwA7AU0NQiR1ORM1SH+XiIiIiIgOratByDnAmPIq26lUr5udKelBSZfXnYuxXNIZku4AjpJ0sKS7JC2Q9GNJm5R650h6QNJiSefV9XOgpDslPdI2A6DKVElLyyt2JzYmJ2kjST8q8a4ANursYSRdKKm1zHqcVcr+AfgwcIakHwJfBiaWWZGJkjaW9D1J8yTdJ+n9pd2k8mw/BW7uoL/xkn4h6QfAEknrlWeaV3L+TN2zXlB+mxuAN3TvP09ERERERP9ZV3tCTgF2sd0iaTzwE2Bn4LdU51jsB9xR6r5ge39JWwJXAxNsPyfpS8A/SboAOAIYa9uSRtb18yZgf6ozO64DZgIfBFqA3YAtgXmS5jTkdzyw0vY4SeOABV08z2m2/1RmJWZLGmf7Ykn7A9fbnilpElCz/VkASf8B3GL7uJLzvZJ+XuLtA4yz/adO+ty7/IaPSpoMPG17L0kbAHMl3QzsTjUDsyvwRuABqrNC1lBiTAbYcKuRXTxuRERERMS601ev6L3X9q9trwYWAqPr7l1RPv8WeAfVH9gLgU8A2wIrgBeAiyV9EFhZ1/Za26ttP0D1RzhUg5If2n7F9h+A24C9GvI5ELgMwPZiYHEX+X9Y0gKqQwF3Lnl25WDglPIstwIbAm8t937WxQAEqt/s0bpYHy+x7gFGATuW52h71t8Ct3QUzPY02zXbtddutnE30o+IiIiIWDf66u1Yq+quX2no97nyKao/zo9ubCxpb+Ag4CPAZ4F3txNXDZ9d6dYpjZK2A04G9rL9Z0kzqAYUXTYFPmT7oYZ47+TVZ+5MfR0BJ5W3btXHOoxuPkdERERExECxrmZCngE2bbLN3cB+knYAkPQ6SW8r+0I2t30j8I9US606M4dqb8Z6kraimi24t506x5R+dgHGdRJvM6oBwdOS3gj8fQf1Gp95FnBS3f6X3bvIuzOzgOMlrV9ivU3SxuU5PlKe9U3A361FHxERERERfWKdzITYflLS3PJK3OeBP3SjzeNlX8UPy74HgNOp/rj/iaQNqWYEvtBFqGuo9lwsopol+Gfbv5c0uq7OhcB0SYuploc1DlLq81ok6T7gfuARqj0t7fkFry6/Ohv4CvCfwOIyEFkOvLeL3DtyMdUStgUl1uPAB6ie9d3AEuBhqqVnEREREREDmuys5hluarWaW1tb+zuNiIiIiBjiJM23vcZ5fH21MT0iIiIiIgLou43pg4Kke4ANGoo/ZnvJOupvV+DShuJVtt+5Lvprs+ypx3nP1d/6y/cbPtjUuZIREREREWslg5A66/qP/3b6W0LXG+0jIiIiIoaULMcqJB0u6ZQetLuzfI4uG/F7O69bJa2xjq7u/rO93WdERERExLqUmZDC9nVUp643227fZttIeo3tl5ttFxERERExFAyLmZAyS/GgpIslLZV0uaQJ5TXCyyTtLWmSpAtK/aNKvUWS5pSynSXdK2mhpMWSdizl3ZqJKPF/LOmnwM2SNpb0PUnzJN0n6f2l3kaSflT6uALYqBux/6+kBZJml7NRIiIiIiIGrGExCCl2AM6nOphwLPBRYH+q09BPbah7BnCI7d2Aw0vZFOB82y1ADfh1D3LYB/iE7XcDpwG32N6L6pDBqeUAwuOBlbbHAf8O7NlFzI2BBbb3oDon5N/aqyRpsqRWSa0vPp0VXBERERHRf4bTIORR20tsr6Y6eHC2q0NSllAdBFhvLjBD0qeB9UrZXcCpkr4EbGv7+R7k8DPbfyrXB/Pq4Ya3AhsCb6U64f0yANuLgcVdxFwNXFGuL6MaWK3B9jTbNdu1126+SQ9Sj4iIiIjoHcNpELKq7np13ffVNOyNsT2F6rT2bYCFkkbZ/gHVrMjzwCxJ7+5BDs/VXQv4kO2W8u+ttn/ZlkIPYv8l/bVoGxERERGxzg2nQUi3SRpj+x7bZwBPANtI2h54xPY3qDawj1vLbmYBJ0lS6XP3Uj4HOKaU7dKNfkYAR5brjwJ3rGVeERERERHrVN6O1b6pZeO5gNnAIuAU4FhJLwG/B768ln18BfhPYHEZiCwH3gtcCEyXtBhYCNzbRZzngJ0lzQeeBiauZV4REREREeuUqm0RMZzUajW3trb2dxoRERERMcRJmm97jTPvshwrIiIiIiL6VJZj9TJJhwDnNhQ/avuItYx7D7BBQ/HHbC9pNtb//PkJ3nvV9/7y/foPHbc2qUVERERENCWDkF5mexbVpvPejvvO3o4ZEREREdEfshwrIiIiIiL61KAZhEg6XNIpPWh3Z/kcLWlpL+bTeMp64/0O+5N0q6Q1NuhERERERAwHg2YQYvs62+f0oN2+6yIfoNNBSEREREREtG9ADELKrMGDki6WtFTS5ZImSJoraZmkvSVNknRBqX9UqbdI0pxStrOkeyUtlLS4nPOBpGe7mcN6kqZKmlfaf6aUv0nSnBJ3qaQDJJ0DbFTKLu8k7Gskfb/Emynpde30+2zd9ZGSZpTrrSRdVfKZJ2m/TnLfRNJ0SUtKXx9qp85kSa2SWl9c0a2fJCIiIiJinRgQg5BiB+B8qhPCx1Kd/r0/cDJrzjqcARxiezfg8FI2BTjfdgtQA37dZP+fAp62vRewF/BpSduVPGaVuLsBC22fAjxvu8X2MZ3E3AmYZnscsAI4oYl8zge+XvL5EHBxJ3X/teS+a+nrlsYKtqfZrtmuvXazTZpIIyIiIiKidw2kt2M92va6WUn3A7NtW9ISYHRD3bnADElXAleXsruA0yS9Bbja9rIm+z8YGCfpyPJ9c2BHYB7wPUnrA9faXthEzF/ZnluuLwM+B5zXzbYTgHdUh6kDsJmkTW0/00Hdj7R9sf3nJnKMiIiIiOhTA2kQsqruenXd99U05Gl7iqR3Au8BFkpqsf2DcpbGe4BZkv7B9hozAp0QcFJ5xe5f35AOLHEvlTTV9iXdjNl4HH17x9PXl21Ydz0C2Mf2893oRx3EjoiIiIgYcAbScqxukzTG9j22zwCeALaRtD3wiO1vANdRLetqxizg+DLjgaS3SdpY0rbAH21/B/gusEep/1Jb3U68VdI+5fpo4I526vxB0tsljQDqDzS8Gfhs2xdJLZ3001j39V3kFRERERHRbwbSTEgzppaN5wJmA4uAU4BjJb0E/B74cpMxL6Za9rVA1Rqox4EPAOOBL5a4zwIfL/WnAYslLehkX8gvgU9I+jawDLiwnTqnANcDvwKWAm0bNj4HfFPSYqr/TnOo9r2056ul7lLgFeAsXl2mtoYdXr9lTkmPiIiIiH4jO6t4hptarebW1tb+TiMiIiIihjhJ822vcT7eoFyOFRERERERg9dgXY7VY5IOAc5tKH7U9hHt1e9GvFFUS8IaHWT7yZ7E7KSvTwKfbyiea/vEZuL8z5//xHtnvnq8yfVHdvaW4YiIiIiI3jXsBiHl7VdrvAFrLeI9CbT0Vrwu+poOTO+LviIiIiIi1pUsxwIkLZe0paSRkk6oK99a0sxy3SLpsB7EPlPSyb2Zbxf95Tj0iIiIiBjQMgj5ayOpO9Xc9m9ttx1e2AI0PQjpDZKG3YxVRERERAxdw24QIulaSfMl3S9pcsPtc4AxkhZKmipptKSlkl5L9crfieXexMYZjlJvdLk+TdJDkn4O7FRXZ4ykm0r/t0sa20meMyT9P0m/AM7tqK2k7STdJWmepK90Em+ypFZJrS+uWNGDXy4iIiIioncMx//DfpztP0naCJgn6aq6e6cAu9huAWgbVNh+UdIZQM32Z8u9M9sLLmlP4CPA7lS/7wJgfrk9DZhie1k58f1bwLs7yfVtwATbr0ia3UHb84ELbV8iqcMN6ranlf4ZOWb7vJc5IiIiIvrNcByEfE5S25uwtgF27OX4BwDX2F4JIOm68rkJsC/w4+osRAA26CLWj8sApLO2+wEfKteXsuabvyIiIiIiBpRhNQiRNB6YAOxje6WkW4ENexjuZf56OVt9nPZmGkYAT7XNsnTTc91sm5mNiIiIiBg0htuekM2BP5cByFjgbxvuPwNs2kHbxnvLgT0AJO0BbFfK5wBHSNpI0qbA+wBsrwAelXRUaSNJu3Un6S7azqVa/gWQAz8iIiIiYsAbVjMhwE3AFEmLgYeAu+tv2n5S0lxJS4H/Br5Zd/sXwCmSFgJnA1cBHy/f5wEPlxgLJF0BLAQeA26vi3EMcKGk04H1gR8Bi7qZe0dtPw/8QNLnS05d2uH1W+SAwoiIiIjoN7Kzkme4qdVqbm1t7e80IiIiImKIkzTfdq2xfLgtx4qIiIiIiH6WQUg/K2eKLGz4d9q67PN//vxn3jtz5rrsIiIiIiKiQ8NtT8iAY/vfgX/v7zwiIiIiIvrKkJoJkbRc0pZN1B8vad9u1Jsk6YIO7t0oaWQTaXYnr786jT0iIiIiYigZ7jMh44FngTt7GsD2Yd2pp+qUQdle3dO+IiIiIiKGgkE7EyJpY0k3SFokaamkieXWSZIWSFpSzgJB0haSrpW0WNLdksZJGg1MAb5Q9mEcIGmGpIsk3S7pYUnvretya0k3SVom6Wt1eXQ4+yJptKRfSvoWsADYRtIXJc0ruZxVV/c0SQ9J+jmwUxfPfqukr0uaU+LvJenqkttXO2gzWVKrpNYXV6zo+geOiIiIiFhHBu0gBDgU+K3t3WzvQnUGCMATtvcALgTaljSdBdxnexxwKnCJ7eXARcDXbbfYbjvPYzTwLuA9wEWS2k5CbwEmArsCEyVt0808dyr97V6udwT2LvH2lHSgpD2pDhzcHfggsFc34r5o+8DyDD8BTgR2ASZJGtVY2fY02zXbtddutlk3U4+IiIiI6H2DeRCyBJgg6VxJB9h+upRfXT7nUw0oAPYHLgWwfQswStLmHcS90vZq28uAR4CxpXy27adtvwA8AGzbzTwfs912KOLB5d99VDMjY6kGJQcA19heWU5Hv64bcdvqLAHut/0726tKzt0dIEVERERE9LlBuyfE9sNlBuEw4GxJN5dbq8rnK7z6fGovREehO/i+qq6sPnZXnqu7FnC27W/XV5D0j53k05G2fFY35La6idwiIiIiIvrcoJ0JkbQ1sNL2ZcB5wB6dVJ8DHFPajadasrUCeAbYtKHuUZJGSBoDbA881ItpzwKOk7RJyeXNkt5Q8jtC0kaSNgXe14t9RkREREQMKIP5/5jvCkyVtBp4CTge6OgEvjOB6ZIWAyuBT5TynwIzJb0fOKmUPQTcBrwRmGL7herFVmvP9s2S3g7cVWI+Cxxre4GkK4CFwGPA7R1HWXs7vP71XH/kkeuyi4iIiIiIDsludhXQ0CVpBnC97SF9nHitVnNra2t/pxERERERQ5yk+bZrjeWDdjlWREREREQMToN5OVavsz2pJ+3KK3Fnt3PrINtP9jQfSd8E9msoPt/29J7GBPifPz/N4TOv57oj39t15YiIiIiIXpZBSC8oA42WdRD3xN6OGRERERHR37Icq0nlFPSl7ZTfKmmN9W4REREREfHXMggZBCQ1NWMlab11lUtERERExNrKIKRnXiPp+5IWS5op6XX1NyU9W3d9ZHnrFpK2knSVpHnlX+N+j/oYZ0qaVg5hvETSjZLGlXv3STqjXH9F0j9IGi/pF5J+QHWKekRERETEgJRBSM/sBEyzPQ5YAZzQzXbnA1+3vRfwIeDiLurvCbzf9kepDjQ8QNJmwMu8umF9f149V2Rv4DTb72gMJGmypFZJrS+ueLqb6UZERERE9L4MQnrmV7bnluvLqAYC3TEBuEDSQuA6YLNyQnpHrrP9fLm+HTiw9HUDsEmZgRltu+1U93ttP9peINvTbNds11672ebdTDciIiIiovfl7Vg903jCY2ffN6y7HgHsUzew6MpzddfzgBrwCPAzYEvg08D8DupHRERERAxImQnpmbdK2qdcHw3c0XD/D5LeLmkEcERd+c3AZ9u+SGrpboe2XwR+BXwYuJtqZuRkXl2KFRERERExKGQQ0jO/BD4haTGwBXBhw/1TgOuBW4Df1ZV/DqiVDe0PAFOa7Pd24A+2V5brt9CDQcgOr988BxVGRERERL+R3biSKIa6Wq3m1tbW/k4jIiIiIoY4SfNtr3GWXmZCIiIiIiKiT2UQ0s8kfVLSwoZ/31yXff7Pn5/hAzNnr8suIiIiIiI6lLdj9TPb04Hp/Z1HRERERERfGRIzIfUnlDfZ7suSJrRTPl7S9U3GulXSGuvdutFuuaQt2yk/U9LJzcaLiIiIiBjohvVMiO0z+juHiIiIiIjhZkDOhEg6V9IJdd/PlPT/SfqipHnlFbdntdNOkqZKWippiaSJdff+uZQtknROKZsh6chyfaikByXdAXywrt3eku6UdF/53KmUbyTpRyWXK4CN6tocLOkuSQsk/VjSJl088hcl3Vv+7dDOc/1llkXSlpKWl+v1yvO2/Saf6c7vGxERERHRnwbkIAT4ETCx7vuHgceBHYG9gRZgT0kHNrT7YLm3GzABmCrpTZL+HvgA8E7buwFfq28kaUPgO8D7gAOAv6m7/SBwoO3dgTOA/yjlxwMrbY8D/h3Ys8TaEjgdmGB7D6AV+KcunneF7b2BC4D/7KJuvU8BT9veC9gL+LSk7dqrKGmypFZJrS+ueKqJLiIiIiIieteAXI5l+z5Jb5C0NbAV8GdgHHAwcF+ptgnVoGROXdP9gR/afoXq1PLbqP44fxcwvRzyh+0/NXQ5FnjU9jIASZcBk8u9zYHvS9oRMLB+KT8Q+EaJt7gcXAjwt8A7gLmSAF4L3NXFI/+w7vPrXdStdzAwrm02p+S6I/BoY0Xb04BpACPH7JTDYSIiIiKi3wzIQUgxEziSalbiR8Bo4Gzb3+6kjTop7+oP747ufwX4he0jJI0Gbu2ijYCf2T66i/466ru9mC/z6qzVhg19nWR7VhN9RURERET0q4G6HAuqgcdHqAYiM4FZwHFt+yskvVnSGxrazAEmlr0SW1HNVtwL3Fzavq603aKh3YPAdpLGlO/1A4jNgd+U60kNfR1T4u1CNVMDcDewX9veDkmvk/S2Lp51Yt1ne7MmyynLvah+jzazgOMlrV/6epukjbvoKyIiIiKiXw3YmRDb90vaFPiN7d8Bv5P0duCusszpWeBY4I91za4B9gEWUc0o/LPt3wM3SWoBWiW9CNwInFrX1wuSJgM3SHoCuAPYpdz+GtVyrH8Cbqnr60JgelmGtZBqsIPtxyVNAn4oaYNS93Tg4U4edwNJ91ANCtubQTkPuFLSxxpyuJhqhmiBqh/lcaq9L53a4fWbcu2RB3VVLSIiIiJinZCd7QHDTa1Wc2tra3+nERERERFDnKT5ttc4S28gL8eKiIiIiIghaMAuxxpqJF0DNL4+90v9san8f//8XF93GRERERHxFxmE9BHbR/R3DhERERERA0GfLMeStLwc4tfd+uMl7duNepMkXdDBvRsljWwiza762bqLOu0+Yznt/eTeyCMiIiIiYigYqHtCxgNdDkI6Y/sw20/1SjbVq3k7HYRERERERET39PogRNLGkm6QtEjSUkltZ2CcJGmBpCWSxpa6W0i6VtJiSXdLGlcOBJwCfEHSQkkHSJoh6SJJt0t6WNJ767rcWtJNkpZJ+lpdHp3Ovkg6VtK9pY9vl7NF1it9LS15fqGcRl4DLi91N+rk8b9YYt7bdk5IQ5+3SqqV6y0lLS/X60maKmle+S0+00ne4yXdJunK8lucI+mY0ueSurNOIiIiIiIGpHUxE3Io8Fvbu9neBbiplD9hew+q8zXaliedBdxnexzVuR2X2F4OXAR83XaL7dtL3dHAu4D3ABdJajs5vIXqkL9dqQ4q3KarBMt5IxOB/Wy3AK9QHTzYArzZ9i62dwWm254JtALHlHye7yT0Ctt7AxcA/9lVHnU+BTxtey9gL+DTkho3sdfbDfg81TN/DHhb6fdi4KT2GkiaLKlVUuuqFU81kVpERERERO9aF4OQJcAESedKOsD206X86vI5n2pAAbA/cCmA7VuAUZI27yDulbZX214GPAKMLeWzbT9t+wXgAWDbbuR4ENUJ5PMkLSzfty9xt5f0X5IOBVZ064lf9cO6z32aaHcw8PGSyz3AKGDHTurPs/0726uA/6U6ER6q3350ew1sT7Nds13bYLORTaQWEREREdG7ev3tWLYflrQncBhwtqS2P5BXlc9X6vpVeyE6Ct3B91V1ZfWxOyPg+7b/ZY0b0m7AIcCJwIeB47oRr70c23uOl3l14LdhXbmAk5p4XW/9M6+u+76avPEsIiIiIga4dbEnZGtgpe3LgPOAPTqpPodqGRSSxlMt2VoBPANs2lD3KEkjyp6H7YGH1iLN2cCRkt5Q+t5C0rZlD8kI21cB/1qXe3v5tGdi3edd7dxfTjUDA3BkXfks4HhJ65d83iZp4yaeJyIiIiJi0FgX/9d8V2CqpNXAS8DxwMwO6p4JTJe0GFgJfKKU/xSYKen9vLrH4SHgNuCNwBTbL0jtTaR0zfYDkk4HbpY0ouR5IvB8yadtcNY2UzKDah/K88A+newL2UDSPVSDu6PbuX8ecKWkjwG31JVfTLWMaoGqh3oc+ECPHi4iIiIiYoCT3dHqp4FD0gzg+rJJPNZSrVZza2trf6cREREREUOcpPm2a43lA/WckIiIiIiIGKIGxSZm25N60k7SKKr9H40Osv1kD2NeAzS+PvdLTWwq724/u1LeHFZnle13rm3sR/7c2VuGIyIiIiLWrUExCOmpMtBo6eWYR/RmvE76WUIv5x4RERERMRAM6eVYkqZI+nh/59FG0rNd3B8p6YS671tLyj6YiIiIiBhShvQgxPZFti/pq/5UWZvfdCTwl0GI7d/aPrLj6hERERERg8+AHYRIGi3pQUnfl7RY0kxJr5N0kKT7JC2R9D1JG5T650h6oNQ9r5SdKenkcr2DpJ9LWiRpgaQxkjaRNLt8X1JeCdzW/z9JWlr+/WMXef5S0reABcA2kr4oaV7J5ax22nTU7znAGEkLJU0tsZeWNvdI2rkuxq2S9pS0t6Q7y29yp6Sd1vrHj4iIiIhYhwbsIKTYCZhmexywAvgnqjM7JtrelWpPy/GStgCOAHYudb/aTqzLgW/a3g3YF/gd8AJwhO09gL8D/m+ZzdgT+CTwTuBvgU9L2r2LPC+xvXu53hHYm2pPx56SDmyo326/wCnA/9pusf3FhjY/ojrBHUlvAra2PR94EDiw9H0G8B/tJShpsqRWSa2rVvy5k0eJiIiIiFi3Bvog5Fe255bry4CDgEdtP1zKvg8cSDVAeQG4WNIHqQ4+/AtJmwJvtn0NgO0XbK8EBPxHOSzx58CbqQ5D3B+4xvZztp8FrgYO6CTPx2zfXa4PLv/uo5oZGUs1KPmrlDrotzNXAkeV6w8DPy7XmwM/LjMmXwd2bqcttqfZrtmubbDZ67voKiIiIiJi3Rnob8fq1kmKtl+WtDfVIOUjwGeBd9dV6eho9WOArYA9bb8kaTmwYSf1O/JcQ19n2/52J/U76rdDtn8j6UlJ44CJwGfKra8Av7B9hKTRwK1N5h4RERER0acG+kzIWyXtU66Pppo1GC1ph1L2MeA2SZsAm9u+EfhHGl5ta3sF8GtJHwCQtIGk11HNIvyxDAT+Dti2NJkDfKDsQdmYaqnX7d3MeRZwXMkJSW+W9IaGOh31+wywaSexfwT8c3nWJXWxflOuJ3Uzx4iIiIiIfjPQByG/BD5Rli1tQbXc6JNUy4+WAKuBi6j+cL++1LsN+EI7sT4GfK7UuRP4G6p9IjVJrVSzEw8C2F5AtffkXuAe4GLb93UnYds3Az8A7io5zmTNgUVH/T4JzC2b4ae2E34m1UzPlXVlXwPOljQXWK87OUZERERE9CfZ3Vrx1OfK0qLrbe/S37kMNbVaza2trf2dRkREREQMcZLm2641lg/0mZCIiIiIiBhiBuzGdNvLgQEzCyJpFDC7nVsHlWVUg8avnnqxv1OIiIiIiGFswA5CBpoy0Gjp7zwiIiIiIga7QbEcS9Lhkk7pQbs7y+dfTh7vpXxO7eJ+h/2Vk87XWBe3Frn85VT4iIiIiIjBYFAMQmxfZ/ucHrTbd13kA3Q6CImIiIiIiI71+yCkzBo8KOni8mrayyVNkDRX0jJJe0uaJOmCUv+oUm+RpDmlbGdJ90paKGmxpB1L+bPdzGE9SVMlzSvtP1PK3yRpTom7VNIBks4BNipll3cS9jWSvl/izSznkjT2+2zd9ZGSZpTrrSRdVfKZJ2m/Lh5hN0m3lN/r09155oiIiIiI/tLvg5BiB+B8YBwwFvgosD9wMmvOOpwBHGJ7N+DwUjYFON92C1ADft1k/58Cnra9F7AX8GlJ25U8ZpW4uwELbZ8CPG+7xfYxncTcCZhmexywAjihiXzOB75e8vkQcHEX9ccB7wH2Ac6QtHVjBUmTJbVKan1+xZ+aSCUiIiIioncNlEHIo7aX2F4N3A/MdnWAyRJgdEPducCM8n/82w7nuws4VdKXgG1tP99k/wcDH5e0kOpwwlHAjsA84JOSzgR2tf1MEzF/ZXtuub6MalDVXROAC0o+1wGbSersJPWf2H7e9hPAL4C9GyvYnma7Zru20WZbNJFKRERERETvGihvx1pVd7267vtqGnK0PUXSO6n+z/9CSS22fyDpnlI2S9I/2L6lif4FnGR71ho3pANL3EslTbV9STdjNp4C2d6pkPVlG9ZdjwD2aWIw1Z2+IiIiIiIGhIEyE9JtksbYvsf2GcATwDaStgcesf0NqpmDcU2GnQUcL2n90sfbJG0saVvgj7a/A3wX2KPUf6mtbifeKmmfcn00cEc7df4g6e2SRgBH1JXfDHy27Yukli76er+kDctZJuOpZnAiIiIiIgakQTcIAaZKWlJegTsHWARMBJaW5Utjge7OVrS5GHgAWFDifptqBmY81WzLfVR7M84v9acBi7vYmP5L4BOSFgNbABe2U+cU4HrgFuB3deWfA2plU/sDVHteOnMvcANwN/AV27/ton5ERERERL9RtfUihpNarebW1tb+TiMiIiIihjhJ822vcUbeYJwJiYiIiIiIQWygbEzvE5IOAc5tKH7U9hHt1e9GvFHA7HZuHWT7yZ7E7KSvTwKfbyiea/vEZmP94amXeiepiIiIiIgeyHKsYWibHXbzr/5nUX+nERERERFD3KBejiXpcEmn9KDdneVzdNlw3lv5NB6g2Hi/w/4k3Sppjf8QERERERHDxaAYhNi+zvY5PWi377rIhzVPcY+IiIiIiG7q90FImTV4UNLFkpZKulzSBElzJS2TtLekSZIuKPWPKvUWSZpTynaWdK+kheW1tjuW8me7mcN6kqZKmlfaf6aUv0nSnBJ3qaQDJJ0DbFTKOntF72skfb/Emynpde30+2zd9ZGSZpTrrSRdVfKZJ2m/TnLfW9Kdku4rnzt155kjIiIiIvpLvw9Cih2ozuAYR3XOx0eB/YGTWXPW4QzgENu7AYeXsinA+bZbgBrw6yb7/xTwtO29gL2AT0varuQxq8TdDVho+xTgedstto/pJOZOwDTb44AVwAlN5HM+8PWSz4eozjHpyIPAgbZ3p/pt/qO9SpImS2qV1Prcil7dMx8RERER0ZSB8nasR20vAZB0PzDbtiUtAUY31J0LzJB0JXB1KbsLOE3SW4CrbS9rsv+DgXGSjizfNwd2pDp5/HvldPRrbS9sIuavbM8t15dRHUB4XjfbTgDeIant+2aSNrX9TDt1Nwe+X2Z/DLR7krvtaVSHLLLNDrvlbQQRERER0W8GykzIqrrr1XXfV9MwULI9BTgd2IbqNPNRtn9ANSvyPDBL0rub7F/ASWV2o8X2drZvtj0HOBD4DXCppI83EbPxD/32/vCvL9uw7noEsE9dPm/uYAAC8BXgF7Z3Ad7XECciIiIiYsAZKIOQbpM0xvY9ts8AngC2kbQ98IjtbwDXUS3rasYs4Pgy44Gkt0naWNK2wB9tfwf4LrBHqf9SW91OvFXSPuX6aOCOdur8QdLbJY0A6s8quRn4bNsXSS2d9LM51SAJYFIXOUVERERE9LtBNwgBpkpaUl6BOwdYBEwElkpaSLWn5JImY14MPAAsKHG/TTUDM55qtuU+qr0Z55f604DFXWxM/yXwCUmLgS2AC9upcwpwPXAL8Lu68s8BtbKp/QGqPS8d+RpwtqS5wHqdPmVERERExACQwwqHoVqt5tbW1v5OIyIiIiKGuEF9WGFERERERAwdA+XtWH1C0iHAuQ3Fj9o+or363Yg3Cpjdzq2DbPfqe3AlfRL4fEPxXNsnNhvrT39+uXeSioiIiIjogSzHGoa2H9PiR/53YX+nERERERFD3DpbjiVppKQTyvV4Sdevbcx2+phRd4ZHd+qPLhvM27t3q6Q1fohuxDxA0v3lpPR9JB3WbIwO4nb4m0laLmnL3ugnIiIiImKg6I09ISNp7jRwJA3GtzgdA5xXTk/fCWhqECJpWC19i4iIiIjoSG8MQs4BxpTX404FNpE0U9KDki5XOfa7/F/9MyTdARwl6WBJd0laIOnHkjYp9c6R9EB5PW39CeMHSrpT0iNtsyKqTJW0tLy2d2JjcpI2kvSjEu8KYKPOHkbShZJay6zHWaXsH4APA2dI+iHwZWBimRWZWM4U+Z6keZLuk/T+0m5SebafUp390ZHNJF1Tnvuicm5IfU5/NbMj6WRJZ5brMZJukjRf0u2Sxnb2fBERERER/a03/u/8KcAutlskjQd+AuwM/BaYC+zHqwf1vWB7/7LE6Gpggu3nJH0J+CdJF1Ad2jfWtiWNrOvnTcD+VOeAXAfMBD4ItAC7AVsC8yTNacjveGCl7XGSxgELunie02z/qczWzJY0zvbFkvYHrrc9U9IkoGb7swCS/gO4xfZxJed7Jf28xNsHGGf7T530uTfwDuAx4KbyXDO7yLPNNGCK7WWS3gl8C1jjxHhJk4HJAFtu+ZZuho6IiIiI6H3r4hW999r+te3VwEJgdN29K8rn31L90T23zKB8AtgWWAG8AFws6YPAyrq219pebfsB4I2lbH/gh7Zfsf0H4DZgr4Z8DgQuA7C9GFjcRf4flrQAuI9qMPWObjzzwcAp5VluBTYE3lru/ayLAQhUv9kjtl8Bflieq0tl9mhf4Mel729TDdbWYHua7Zrt2qabjepO+IiIiIiIdWJd7FNYVXf9SkMfz5VPUf1xfnRjY0l7AwcBHwE+y6v/V78+rho+u9KtV4BJ2g44GdjL9p8lzaAaUHTZFPiQ7Yca4r2TV5+5mfwav7/MXw8Y23IaATxV9qlERERERAwKvTET8gywaZNt7gb2k7QDgKTXSXpb+T/7m9u+EfhHqqVWnZlDtTdjPUlbUc163NtOnWNKP7sA4zqJtxnVoOFpSW8E/r6Deo3PPAs4qW7/y+5d5N1ob0nblb0gE3l1+VqbPwBvkDRK0gbAewFsrwAelXRU6VeSdmuy74iIiIiIPrXWMyG2n5Q0t2ycfp7qD+au2jxe9lX8sPxRDXA61R/3P5G0IdXswhe6CHUN1Z6LRVSzB/9s+/eSRtfVuRCYLmkx1fKwxkFKfV6LJN0H3A88QrWnpT2/4NXlV2cDXwH+E1hcBiLLKQOFbrqLaoP/rlSDpmsa8npJ0peBe4BHgQfrbh8DXCjpdGB94EdUv0dERERExICUwwqHoVqt5tbW1v5OIyIiIiKGOK2rwwojIiIiIiKaMWwP0JN0D7BBQ/HHbC9ZR/3tClzaULzK9jvXRX+defrPL/d1lxERERERfzFsByF9/cd/Gdy09GWfEREREREDUafLsSSNlHRCuR4v6freTkDSjLYT0LtZ/69OD2+4d6ukNdac1d1/tsnctpJ0TzkF/YBm2jbRR7s5Nfu7REREREQMFl3tCRkJnNBMwHLS+FBxEPCg7d1t396dBkPs+SMiIiIiel1Xg5BzgDHlVbRTgU0kzZT0oKTL687FWC7pDEl3AEdJOljSXZIWSPpxOf8DSedIekDSYknn1fVzoKQ7JT3S9n//y5kXUyUtlbRE0sTG5CRtJOlHJd4VwEZdPbCk/1vyml3OFkHSGEk3SZov6XZJYyW1AF8DDpO0sPR1dMllqaRz62I+K+nLZZ/JPpKOlXRvafftrgYm7eXUcH+5pC3LdU3SreV6Y0nfkzSvzNa8v6vnj4iIiIjob10NQk4B/recyP1FYHeqQwTfAWwP7FdX9wXb+wM/pzrzY4LtPYBW4J8kbQEcAexsexzw1bq2bwL2pzpb45xS9kGqPRS7AROAqZLe1JDf8cDKEu/fgT27eJ6NgQUlr9uAfyvl04CTbO9JdWL6t2wvBM4ArijP/3rgXKoT3FuAvSR9oC7u0rLP5EmqAwf3K+1eoRyW2GRO3XEacIvtvYC/o/qNNm6voqTJklolta5Y8WQTXURERERE9K5mN6bfa/vXAGV2ZDSvnu59Rfn8W6pBytwyUfJaqsP4VgAvABdLugGo319yre3VwAOqTiqHalDyQ9uvAH+QdBuwF7C4rt2BwDcAbC8uBxJ2ZnVdnpcBV5dZmn2BH5d8Yc23ZlH6vtX24+X5Ly/9X0s10Liq1DuIajA0r8TbCPhjMzl18Qz1DgYOl3Ry+b4h8Fbgl40VbU+jGmyx45iWHA4TEREREf2m2UHIqrrrVxraP1c+BfzM9tGNjSXtTfVH+keAz1LNKjTGVcNnV9bmD2pTzQY9VWYtOtNZPi+UwVJbve/b/pe1yKnRy7w6a7VhQ04fsv1QD/uKiIiIiOhzXS3HegbYtMmYdwP7SdoBQNLrJL2tzDhsbvtGqiVdLV3EmQNMlLRe2SdxIHBvO3WOKf3sAozrIuYIoO2NUx8F7rC9AnhU0lEljiTt1k7be4B3Sdqy7PE4mmr5VKPZwJGS3lDibSFp22ZyaqfOcl5davahuvJZwEl1e3N276SfiIiIiIgBodOZENtPSppbXon7PPCHrgLaflzSJOCHktqWNZ1ONaD5iaQNqf4P/he6CHUNsA+wiGp24J9t/17S6Lo6FwLTyzKshaw5SGn0HLCzpPnA01R7N6AayFwo6XRgfeBHpd/65/qdpH8BflHyv9H2T9p5/gdKnJsljQBeAk4EHmsyp3pnAd+VdCrVYKjNV4D/BBaXgchyqn01EREREREDluxsDxhuarWaW1tb+zuNiIiIiBjiJM23vcY5fl0tx4qIiIiIiOhVzW5MHxTKeR2Nb7j6mO0l/ZEPDMycIiIiIiL6w5AchJTzOgaUgZhTRERERER/GLTLsSQdLumUHrS7s3yOLhvueyufU3srVkPcM+vOAWnv/q2S1lhnFxERERExUA3aQYjt62yf03XNNdrtuy7yAbo9CCmvAR60v31ERERExNoYkH8Il1mKByVdLGmppMslTSivC14maW9JkyRdUOofVeotkjSnlO0s6V5JCyUtlrRjKX+2mzmsJ2mqpHml/WdK+ZskzSlxl0o6QNI5wEal7PJOnumXkr4FLAC2kfTFuvhn1dU9TdJDkn4O7NSNdI+VdGfJZ+/uPF9ERERERH8ZyHtCdgCOAiYD86gO8tsfOJxq1uHaurpnAIfY/o2kkaVsCnC+7cslvRZYr8n+PwU8bXuvct7JXEk3Ax8EZtn+93Jo4ets3y7ps904dX0n4JO2T5B0MLAjsDfVuSPXSTqQ6tyQjwC7U/33WQDM7yLuxrb3Le2/B+zSWEHSZKrfkre+9a3def6IiIiIiHViIA9CHm17c5Sk+4HZti1pCTC6oe5cYIakK4GrS9ldwGmS3gJcbXtZk/0fDIyT1Haa+eZUg4Z5wPckrQ9ca3thEzEfs313XfyDgfvK901K/E2Ba2yvBJB0XTfi/hDA9hxJm0kaafup+gq2pwHToDonpImcIyIiIiJ61YBcjlWsqrteXfd9NQ2DJ9tTqE5l3wZYKGmU7R9QzZo8D8yS9O4m+xdwku2W8m872zfbngMcCPwGuFTSx5uI+VxD/LPr4u9g+7ttj9Rkro31M8iIiIiIiAFrIA9Cuk3SGNv32D4DeIJqv8X2wCO2vwFcB4xrMuws4Pgy44Gkt0naWNK2wB9tfwf4LrBHqf9SW90m4h8naZMS/82S3gDMAY6QtJGkTYH3dSPWxBJjf6olZE83kUdERERERJ8ayMuxmjG1bDwXMBtYBJxCtWH7JeD3wJebjHkx1bKvBZIEPA58ABgPfLHEfRZomwmZBiyWtMD2MV0Ft32zpLcDd1XheRY41vYCSVcAC4HHgNu7keufy6uHNwOO6+4DRkRERET0B9lZuTPc1Go1t7a29ncaERERETHESZpve40z7YbEcqyIiIiIiBg8hspyrB6TdAhwbkPxo7aP6GG8UVRLwhodZPvJnsQscb8J7NdQfL7t6T2NGRERERHRH4b9IMT2LKpN4r0V70mgpbfi1cU9sbdjRkRERET0h0G5HEvScklbNlF/vKR9u1HvL6ewt3PvxrqDEBvvjZa0tIN7t0paYx1cRERERMRwNVxmQsZTvX3qzp4GsH1Yr2UTERERETGMDfiZkHI2xw2SFklaKmliuXWSpAWSlkgaW+puIelaSYsl3S1pnKTRwBTgC5IWSjpA0gxJF0m6XdLDkt5b1+XWkm6StEzS1+ry6Gr25TWSvl/6ninpde08y7N110dKmlGut5J0laR55V/j3o/6GGeWfm4uOX1Q0tfK73BTk2eVRERERET0uQE/CAEOBX5rezfbuwA3lfInbO8BXAicXMrOAu6zPQ44FbjE9nLgIuDr5WTytnM3RgPvAt4DXCRpw1LeQnX4367AREnbdDPPnYBppe8VwAlNPOP5Jb+9gA9RnVHSmTEl7/cDlwG/sL0r1enw72mvgaTJkloltT7++ONNpBYRERER0bsGwyBkCTBB0rmSDqg7Dfzq8jmfakABsD9wKYDtW4BRkjbvIO6VtlfbXgY8Aowt5bNtP237BeABYNtu5vkr23PL9WUll+6aAFwgaSHV6e6bldPSO/Lftl+i+m3W49WB2RJe/S3+iu1ptmu2a1tttVUTqUVERERE9K4BvyfE9sOS9gQOA86WdHO5tap8vsKrz6H2QnQUuoPvq+rK6mN3mWo3+q0v27DuegSwj+3nu9nXKgDbqyW95FdPnFzNIPhvGhERERHD24CfCZG0NbDS9mXAecAenVSfAxxT2o2nWrK1AngGaJxZOErSCEljgO2Bh9Yy1bdK2qdcHw3c0U6dP0h6u6QRQP05JDcDn237IqllLXOJiIiIiBiwBvwghGpvxr1lqdJpwFc7qXsmUJO0GDgH+EQp/ylwRNvG9FL2EHAb8N/AlLL8am38EvhE6XsLqr0qjU4BrgduAX5XV/65trwlPUC1kT4iIiIiYkjSqyt5ho/yVqrrbc/s71z6Q61Wc2tra3+nERERERFDnKT5ttc4M28wzIRERERERMQQMiw3Mdue1JN2kkYBs9u5dZDtJ9cqqTX7+iTw+YbiubZP7M1+IiIiIiL62rAchPRUGWi09FFf04HpfdFXRERERERfynKsiIiIiIjoU4N2ECLpcEmn9KDdneVztKSlvZjPqb0VqyHumZJO7rpmRERERMTgMGgHIbavs31OD9rtuy7yAbo9CFFl0P72ERERERFrY0D+IVxmKR6UdLGkpZIulzRB0lxJyyTtLWmSpAtK/aNKvUWS5pSynSXdW84GWSxpx1L+bDdzWE/SVEnzSvvPlPI3SZpT4i6VdICkc4CNStnlnTzTLyV9C1gAbCPpi3Xxz6qre5qkhyT9HNipizw/XWIsknSVpNd1UG+ypFZJrY8//nh3foKIiIiIiHViQA5Cih2A84FxwFjgo8D+wMmsOetwBnCI7d2Aw0vZFOB82y1ADfh1k/1/Cnja9l7AXsCnJW1X8phV4u4GLLR9CvC87Rbbx3QScyfgEtu7l+sdgb2pNrvvKelASXsCHwF2Bz5Y+u7M1bb3Ks/+y5L3GmxPs12zXdtqq6268/wREREREevEQH471qO2lwBIuh+YbduSlgCjG+rOBWZIuhK4upTdBZwm6S1Uf6gva7L/g4Fxko4s3zenGjTMA74naX3gWtsLm4j5mO276+IfDNxXvm9S4m8KXGN7JYCk67qIuYukrwIjS4xZTeQTEREREdHnBvJMyKq669V131fTMHiyPQU4HdgGWChplO0fUM2KPA/MkvTuJvsXcFKZ3WixvZ3tm23PAQ4EfgNcKunjTcR8riH+2XXxd7D93bZHaiLmDOCztncFzgI2bKJtRERERESfG8iDkG6TNMb2PbbPAJ6g2m+xPfCI7W8A11Et62rGLOD4MuOBpLdJ2ljStsAfbX8H+C6wR6n/UlvdJuIfJ2mTEv/Nkt4AzAGOkLSRpE2B93URZ1Pgd6XvzpaCRUREREQMCAN5OVYzppaN56I60XwRcApwrKSXgN8DX24y5sVUy74WSBLwOPABYDzwxRL3WaBtJmQasFjSgi72hQBg+2ZJbwfuqsLzLHCs7QWSrgAWAo8Bt3cR6l+Be0rdJVSDkoiIiIiIAUt2Myt/Yiio1WpubW3t7zQiIiIiYoiTNN92rbF8SCzHioiIiIiIwWOoLMfqMUmHAOc2FD9q+4gexhtFtSSs0UG2n+xJzBL3m8B+DcXn257e05gREREREf1h2A9CbM+iF19rWwYaLb0Vry7uib0dMyIiIiKiPwyo5ViSlkvason64yXt2416fzldvZ17N0oa2USaXfWzdW/Eaog7XtL1vR03IiIiIqI/DKhBSA+MB7ochHTG9mG2n+qVbGAS0O1BiKT1eqnfiIiIiIhBo98GIeXMjRskLZK0VNLEcuskSQskLZE0ttTdQtK1khZLulvSOEmjgSnAFyQtlHSApBmSLpJ0u6SHJb23rsutJd0kaZmkr9Xl0ensi6RjJd1b+vi2pPXKvxkl7yWSvlBOVq8Bl5e6G3UQb7mkMyTdARwl6WBJd5Vn/nHduSGHSnqw1PtgF7/lu0qfCyXdV84XaawzWVKrpNbHH3+8s3AREREREetUf86EHAr81vZutncBbirlT9jeA7gQOLmUnQXcZ3sccCpwie3lwEXA18uJ423naYwG3gW8B7hIUtsJ4i3ARGBXYKKkbbpKsJzjMRHYz3YL8ArVgYAtwJtt71JOKp9ueybQChxT8nm+k9Av2N4f+DnVSe8TyjO3Av9Ucv4O1UGFBwB/00WqJwMnlhwPoDol/q/Ynma7Zru21VZbdfXoERERERHrTH8OQpYAEySdK+kA20+X8qvL53yqAQXA/sClALZvAUZJ2ryDuFfaXm17GfAIMLaUz7b9tO0XgAeAbbuR40HAnsA8SQvL9+1L3O0l/ZekQ4EV3XriV11RPv8WeAcwt8T/RMlrLNUbupa5Osjlsi7izQX+n6TPASNtv9xkPhERERERfabf3o5l+2FJewKHAWdLurncWlU+X+HV/NReiI5Cd/B9VV1ZfezOCPi+7X9Z44a0G3AIcCLwYeC4bsRr81xd/J/ZProhdgsdP98abJ8j6Qaq3/JuSRNsP9hEPhERERERfaY/94RsDay0fRlwHrBHJ9XnUC2DQtJ4qiVbK4BngMb9D0dJGiFpDNWsxUNrkeZs4EhJbyh9byFp27KHZITtq4B/rcu9vXw6czewn6QdSvzXSXob8CCwXXkGgKM7ClDajbG9xPa5VEu6xnZWPyIiIiKiP/XnOSG7AlMlrQZeAo4HZnZQ90xguqTFwEqqZUsAPwVmSno/cFIpewi4DXgjMMX2C1J7Eylds/2ApNOBmyWNKHmeSLXnYnopA2ibKZlBtQ/leWCfLvaFYPtxSZOAH0raoBSfXmaJJgM3SHoCuAPYpZNQ/yjp76hmeB4A/rvZZ42IiIiI6CuqthwMDZJmANeXTeLRgVqt5tbW1v5OIyIiIiKGOEnzbdcaywf7OSERERERETHI9OdyrF5ne1JP2kkaRbX/o9FBtp/sYcxrgO0air9ke1ZP4pWYnwQ+31A81/aJPY0ZEREREdHXhtQgpKfKQKOll2Me0ZvxSszpwPTejhsRERER0ZcG5HKsrk4xb6f+eEn7dqPeJEkXdHDvRkkjm0izq3627o1YDXHHS7q+i37bfb6IiIiIiIFiQA5CemA80OUgpDO2D7P9VK9kA5OAbg9CJK3XS/1GRERERAx4/T4IkbSxpBskLZK0VNLEcuskSQskLZE0ttTdQtK1khZLulvSOEmjgSnAFyQtlHSApBmSLpJ0u6SHJb23rsutJd0kaZmkr9Xl0ensi6RjJd1b+vi2pPXKvxkl7yWSviDpSKAGXF7qbtRBvOWSzpB0B9XZJgdLuqs8848lbVLqHSrpwVLvg2vxU0dEREREDAj9PggBDgV+a3s327sAN5XyJ2zvAVwInFzKzgLusz0OOBW4xPZy4CLg67ZbbN9e6o4G3gW8h+rsjg1LeQswkeqckomStukqQUlvL232s91CdR7HMSXWm23vYntXYHp5PXArcEzJp7OzQl6wvT/wc+B0YEJ55lbgn0rO3wHeBxwA/E1XuXbyDJMltUpqffzxx3saJiIiIiJirQ2EQcgSYIKkcyUdYPvpUn51+ZxPNaAA2B+4FMD2LcAoSZt3EPdK26ttLwMe4dVTxGfbftr2C1QH+23bjRwPAvYE5klaWL5vX+JuL+m/JB0KrOjWE7/qivL5t8A7gLkl/idKXmOBR20vc3Wgy2VNxv8L29Ns12zXttpqq56GiYiIiIhYa/3+dqxyOviewGHA2ZJuLrdWlc9XeDXP9o4+7+i0xcbytu+r6srqY3dGwPdt/8saN6TdgEOoTlL/MHBcN+K1ea4u/s9sH90Qu4WOny8iIiIiYlDq95mQ8haplbYvA84D9uik+hyqZVBIGk+1ZGsF8AywaUPdoySNkDSGatbiobVIczZwpKQ3lL63kLRt2UMywvZVwL/W5d5ePp25G9hP0g4l/uskvQ14ENiuPAPA0R0FiIiIiIgYLPp9JoRqb8ZUSauBl4DjgZkd1D0TmC5pMbCSatkSwE+BmZLeD5xUyh4CbgPeCEyx/YLU3kRK12w/IOl04GZJI0qeJwLPl3zaBnNtMyUzqPahPA/s08W+EGw/LmkS8ENJG5Ti08ss0WTgBklPAHcAu/ToISIiIiIiBghVWw2GFkkzgOvLJvFoUKvV3Nra2t9pRERERMQQJ2m+7Vpjeb8vx4qIiIiIiOFlICzH6nW2J/WknaRRVPs/Gh1k+8kexrwG2K6h+Eu2Z/UkXon5SeDzDcVz7f9/e3ceLVdVpnH49xqmhERsJoUAJswiQ4ALHWUwSlZAEQEbDDQog2ulwYh0oyIqTYMsBcXV7YxGIKBEBiPYEVsSBjUaGXIzkAQJYkPQIEqQFmQ0kLf/ODtSVO7MTd2q3PdZK6vq7HP2V/vcbxHy3b1PbU/ua8yIiIiIiEZZJ4uQviqFxph+jnl0f8YrMacCU/s7bkREREREI6zTy7EkPd3Hfp+RNL6D9nGSbuplrJ9JWmMd3NrQl/FFRERERDRaZkI6YPu8gR5DLUlDbL800OOIiIiIiOgPLTUTUnZV/1DN8fmSPirp45LmSlok6YIO+knSJZKWSFosaWLNubNL2z2SLi5tV0o6prw/TNJSSb8E3lvTb39Jv5K0oLzuUtqHSrq2jOU6YGhNnwmS7pA0X9L3JQ3v4l6XSTqvfO6xnfXtbHwREREREc2qpYoQ4FpgYs3x+4AVwE7A/lTPc+wr6eC6fu8t5/YCxlPtS7KVpHcCRwH/aHsv4Au1nSRtBHwbOAI4CHhDzemlwMG29wbOAz5X2k+n2nxxT+CzwL4l1ubAucB42/sA7cBZ3dzv87YPBG7tqG8343sFSZMktUtqX7FiRTcfGxERERGx9rTUcizbCyRtWXZZ3wL4P2BPYAKwoFw2nKoomV3T9UDgmrKk6U+Sfg7sB7wNmGr72RL/ibqP3BV4yPYDAJKuBiaVc5sAV0naCTCwfmk/GPhKibeobKwIMBbYDZhTNk3cALijm1u+rpu+XY3vFWxPAaZAtU9IN58bEREREbHWtFQRUkwHjqH6rf+1wCjgItvf6qJPZ1uli6qA6Epn5y8Efmr7aEmjgJ9100fALbaP7+bzaj3TVV9JY7oYX0REREREU2q15VhQFR7HURUi04GZwKk1z0iMlLRlXZ/ZwERJQyRtQTVbcTcwq/QdVvpuWtdvKTBa0g7luLYI2AR4pLw/ue6zTijxdqeaqQG4EzhA0o7l3DBJO/fwnjvr29X4IiIiIiKaUssVIbbvBUYAj9h+1PYs4HvAHZIWUxUmI+q63QgsAu4BbgfOtv1H2zcDM4B2SQuBj9V91vNUy5t+XB78frjm9BeAiyTNAYbUtF8KDC/LsM6mKnawvYKqWLmmnLuTajlVT+65w77djC8iIiIioinJzmqewaatrc3t7e0DPYyIiIiIWMdJmmd7jT3zWm4mJCIiIiIiWlsrPpi+TpF0IzC6rvkTtmcOxHgiIiIiIta2FCEDzPbRAz2GiIiIiIhGaunlWJLeI+mcPvT7VXkdJWlJP47nU/0Vqy7u+ZI+1v2VERERERHNr6WLENszbF/ch35vXRvjAXpchKjS0j//iIiIiIi+aNp/BJdZiqWSLpO0RNI0SeMlzZH0gKT9JZ0s6Wvl+mPLdfdIml3a3izpbkkLJS0qu5sj6ekejmGIpEskzS39/6W0byVpdom7RNJBki4Ghpa2aV3c032SvgHMB7aV9PGa+BfUXPtpSfdLuhXYpZtx7ijp1nLv82v2DYmIiIiIaDrN/kzIjsCxVHthzAX+GTgQeA/VrMMPa649DzjU9iOSXlfaTgO+bHuapA145X4ePfFB4Enb+0naEJgjaRbwXmCm7c9KGgIMs/0LSR+2PaabmLsAp9j+kKQJwE7A/lS7os+QdDDVTunHAXtT5Wg+MK+LmNOAi23fKGkjOiguJU2i+jmy3Xbb9fT+IyIiIiL6XbMXIQ/ZXgwg6V7gNtsumxKOqrt2DnClpOuBG0rbHcCnJW0D3GD7gV5+/gRgT0nHlONNqIqGucAVktYHfmh7YS9iPmz7zpr4E4AF5Xh4iT8CuNH2swCSZnQWTNIIYKTtG+HvGyyuwfYUYApU+4T0YrwREREREf2qaZdjFS/UvF9Vc7yKugLK9mnAucC2wEJJm9n+HtWsyXPATEnv6OXnCzjD9pjyZ7TtWbZnAwcDjwDflfSBXsR8pi7+RTXxd7R9+epb6sUYIyIiIiJaRrMXIT0maQfbd9k+D3ic6nmL7YEHbX8FmAHs2cuwM4HTy4wHknaWtLGkNwKP2f42cDmwT7l+5eprexH/VEnDS/yRkrYEZgNHSxpaZjqO6CyA7aeA5ZKOKjE2lDSsd7cZEREREdE4zb4cqzcuKQ+eC7gNuAc4BzhR0krgj8BnehnzMqplX/MlCVgBHAWMAz5e4j4NrJ4JmQIskjTf9gndBbc9S9KbgDuq8DwNnGh7vqTrgIXAw8Avugn1fuBbkj4DrKR6jubBnt9mRERERETjyM7jAYNNW1ub29vbB3oYEREREbGOkzTPdlt9+zqzHCsiIiIiIlrDurQcq88kHQp8vq75IdtH9zHeZlRLwuodYvvPfYlZ4n4dOKCu+cu2p/Y1ZkREREREo6UIAWzPpHpIvL/i/RkY01/xauJO7u+YERERERGNNmDLsSQtk7R5L64fJ+mtPbju77uod3Duf2o2MnxVyuds3R+x6uKOk3RTf8eNiIiIiGgWrfRMyDig2yKkK7bfZfsv/TIaOBnocRFSdlaPiIiIiBj0GrIcS9LGwPXANsAQ4MJy6gxJRwDrA8faXippU+AKYHvgWWAS8BRwGvCSpBOBM4APAs8DbwZeD5xle/UMwtaSbgZ2oNp5/OwyjmVAm+3HOxnnicBHgA2Au4APlVOXA21UGwheAfy+HE+T9BzwFtvPdRBvWbl+AvA1SU8AFwAbAv8LnGL7aUmHAV+i2t9kfjc/y/OB0cBWwM7AWcBY4J1UmyceYXtlVzEiIiIiIgZSo2ZCDgP+YHsv27sDN5f2x23vA1wKfKy0XQAssL0n8CngO7aXAd8E/qvsLL5634xRwNuAw4FvStqotI8BJgJ7ABMlbdvdAMt+HROBA2yPAV4CTiixRtre3fYewFTb04F24IQynjUKkBrP2z4QuJVqR/fx5Z7bgbPKmL9NtSHhQcAbuhsrVXF1OHAkcDXw0zK250p7R/c3SVK7pPYVK1b04CMiIiIiItaORhUhi4Hxkj4v6SDbT5b2G8rrPKqCAuBA4LsAtm8HNpO0SSdxr7e9yvYDVJvz7Vrab7P9pO3ngV8Db+zBGA8B9gXmSlpYjrcvcbeX9NUyY/FUj+74ZdeV17HAbsCcEv+kMq5dqb6J6wFXm7Zc3YOYPymzHYupZpZWF3WLefnn+Aq2p9hus922xRZb9PIWIiIiIiL6T0OWY9n+jaR9gXcBF0maVU69UF5fqhmLOgrRWehOjl+oaauN3RUBV9n+5BonpL2AQ4HJwPuAU3sQb7VnauLfYvv4uthj6Pz+OvMCgO1Vklb65R0nV5FvPIuIiIiIJteQmZDyLVLP2r4a+CKwTxeXz6ZaBoWkcVRLtp4C/gqMqLv2WEmvkbQD1azF/a9imLcBx0jasnz2ppLeWL7B6zW2fwD8e83YOxpPV+4EDpC0Y4k/TNLOwFJgdLkHgOM7CxARERERsS5o1G/N9wAukbQKWAmcDkzv5NrzgamSFlE9mH5Saf8RMF3SkVQPpkNVdPyc6sH002w/L3U0kdI927+WdC4wS9JryjgnUz1nMbW0AayeKbmS6jmUTh9Mr4u/QtLJwDWSNizN55ZZoknAjyU9DvwS2L1PNxERERER0QL08kqe1iLpSuCm8pB49EJbW5vb29sHehgRERERsY6TNM92W317nh8YhObNm/e0pFezdC0G1uZUX+ccrSn5a23JX2tL/lpb8teaOvyCqJadCekrSZtRPf9R7xDbf+5jzBup9u6o9QnbM/sSr8Q8BTizrnmO7cl9jVkTu72jijRaQ/LX2pK/1pb8tbbkr7Ulf+uWQTcTUgqNMf0c8+j+jFdiTgWm9nfciIiIiIiB1qh9QiIiIiIiIoAUIYPVlIEeQLwqyV9rS/5aW/LX2pK/1pb8rUMG3TMhERERERExsDITEhERERERDZUiZBCRdJik+yX9VtI5Az2eWJOkbSX9VNJ9ku6VdGZp31TSLZIeKK//UNPnkyWn90s6dOBGH6tJGiJpgaSbynHy1yIkvU7SdElLy3+Hb0n+Woekfyt/dy6RdI2kjZK/5iXpCkmPSVpS09brfEnaV9Licu4r6uvO1dFQKUIGCUlDgK8D7wR2A46XtNvAjio68CLwUdtvAsYCk0uezgFus70T1VdMnwNQzh0HvBk4DPhGyXUMrDOB+2qOk7/W8WXgZtu7AntR5TH5awGSRgIfAdps7w4MocpP8te8rqT62dfqS74uBSYBO5U/9TGjCaUIGTz2B35r+0HbfwOuBY4c4DFFHduP2p5f3v+V6h9AI6lydVW57CrgqPL+SOBa2y/Yfgj4LVWuY4BI2gY4HLispjn5awGSXgscDFwOYPtvtv9C8tdK1gOGSloPGAb8geSvadmeDTxR19yrfEnaCnit7TtcPej8nZo+0cRShAweI4Hf1xwvL23RpCSNAvYG7gJeb/tRqAoVYMtyWfLafL4EnA2sqmlL/lrD9sAKYGpZTneZpI1J/lqC7UeALwK/Ax4FnrQ9i+Sv1fQ2XyPL+/r2aHIpQgaPjtZH5qvRmpSk4cAPgH+1/VRXl3bQlrwOEEnvBh6zPa+nXTpoS/4GznrAPsCltvcGnqEsBelE8tdEyrMDRwKjga2BjSWd2FWXDtqSv+bVWb6SxxaVImTwWA5sW3O8DdU0dTQZSetTFSDTbN9Qmv9Uppwpr4+V9uS1uRwAvEfSMqolj++QdDXJX6tYDiy3fVc5nk5VlCR/rWE88JDtFbZXAjcAbyX5azW9zdfy8r6+PZpcipDBYy6wk6TRkjagerhrxgCPKeqUb/S4HLjP9n/WnJoBnFTenwT8d037cZI2lDSa6oG8uxs13ngl25+0vY3tUVT/jd1u+0SSv5Zg+4/A7yXtUpoOAX5N8tcqfgeMlTSs/F16CNVzdclfa+lVvsqSrb9KGlvy/oGaPtHE1hvoAURj2H5R0oeBmVTfGHKF7XsHeFixpgOA9wOLJS0sbZ8CLgaul/RBqv/RHgtg+15J11P9Q+lFYLLtlxo+6uhO8tc6zgCmlV/WPAicQvULu+Svydm+S9J0YD5VPhZQ7bA9nOSvKUm6BhgHbC5pOfAf9O3vy9OpvmlrKPCT8ieaXHZMj4iIiIiIhspyrIiIiIiIaKgUIRERERER0VApQiIiIiIioqFShEREREREREOlCImIiIiIiIZKERIREREREQ2VIiQiIuJVkJQ9tyIieilFSEREDDqSNpb0Y0n3SFoiaaKk/ST9qrTdLWmEpI0kTZW0WNICSW8v/U+W9H1JPwJmlXhXSJpbrjtygG8xIqKp5bc3ERExGB0G/MH24QCSNqHaYXui7bmSXgs8B5wJYHsPSbtSFRw7lxhvAfa0/YSkzwG32z5V0uuAuyXdavuZBt9XRERLyExIREQMRouB8ZI+L+kgYDvgUdtzAWw/ZftF4EDgu6VtKfAwsLoIucX2E+X9BOAcSQuBnwEblZgREdGBzIRERMSgY/s3kvYF3gVcBMwC3MGl6iJM7SyHgH+yfX//jTIiYt2VmZCIiBh0JG0NPGv7auCLwFhga0n7lfMjygPns4ETStvOVLMbHRUaM4EzJKlcu/fav4uIiNaVmZCIiBiM9gAukbQKWAmcTjWb8VVJQ6meBxkPfAP4pqTFwIvAybZfKLVGrQuBLwGLSiGyDHh3A+4jIqIlye5o9jkiIiIiImLtyHKsiIiIiIhoqBQhERERERHRUClCIiIiIiKioVKEREREREREQ6UIiYiIiIiIhkoREhERERERDZUiJCIiIiIiGipFSERERERENNT/A6GFgvFR3HWAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# plot the scores\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x=scores['score'],y=[i for i in scores['feature']],orient='horizontal' );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we can see that some features stand out as perhaps being more relevant than others, with much larger test statistic values.\n",
    "\n",
    "A bar chart of the feature importance scores for each input feature is created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual Information (Attack Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mutual information from the field of information theory is the application of information gain (typically used in the construction of decision trees) to feature selection.\n",
    "\n",
    "Mutual information is calculated between two variables and measures the reduction in uncertainty for one variable given a known value of the other variable.\n",
    "\n",
    "Mutual information is straightforward when considering the distribution of two discrete (categorical or ordinal) variables, such as categorical input and categorical output data. Nevertheless, it can be adapted for use with numerical input and categorical output.\n",
    "\n",
    "For technical details on how this can be achieved, see the 2014 paper titled “Mutual Information between Discrete and Continuous Data Sets.” (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3929353/)\n",
    "\n",
    "**The scikit-learn machine learning library provides an implementation of mutual information for feature selection with numeric input and categorical output variables via the *mutual_info_classif()* function. Like *f_classif()*, it can be used in the *SelectKBest* feature selection strategy (and other strategies).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "import matplotlib.pyplot as plt\n",
    "     \n",
    "# feature selection\n",
    "def select_features(X_train, y_train, X_test):\n",
    "    # configure to select all features\n",
    "    fs = SelectKBest(score_func=mutual_info_classif, k='all')\n",
    "    # learn relationship from training data\n",
    "    fs.fit(X_train, y_train)\n",
    "    # transform train input data\n",
    "    X_train_fs = fs.transform(X_train)\n",
    "    # transform test input data\n",
    "    X_test_fs = fs.transform(X_test)\n",
    "    return X_train_fs, X_test_fs, fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 1: posicao_relativa 0.002514\n",
      "Feature 2: distancia 0.008984\n",
      "Feature 3: altitude_blue 0.000000\n",
      "Feature 4: altitude_red 0.002267\n",
      "Feature 5: velocidade_blue 0.000805\n",
      "Feature 6: velocidade_red 0.007925\n",
      "Feature 7: blue_rwr 0.000000\n",
      "Feature 8: red_rwr 0.091155\n",
      "Feature 9: missil_blue_a 0.008599\n",
      "Feature 10: missil_blue_b 0.005573\n",
      "Feature 11: missil_blue_c 0.014806\n",
      "Feature 12: missil_est_red_a 0.000000\n",
      "Feature 13: missil_est_red_b 0.000580\n",
      "Feature 14: missil_est_red_c 0.001868\n",
      "Feature 15: missil_red_a 0.003992\n",
      "Feature 16: missil_red_b 0.000000\n",
      "Feature 17: missil_red_c 0.006930\n",
      "Feature 18: missil_est_blue_a 0.000346\n",
      "Feature 19: missil_est_blue_b 0.001804\n",
      "Feature 20: missil_est_blue_c 0.005146\n",
      "Feature 21: shotphi_blue_s 0.030129\n",
      "Feature 22: shotphi_blue_m 0.003748\n",
      "Feature 23: shotphi_blue_l 0.028166\n",
      "Feature 24: shotphi_est_red_s 0.000000\n",
      "Feature 25: shotphi_est_red_m 0.001115\n",
      "Feature 26: shotphi_est_red_l 0.000000\n",
      "Feature 27: shotphi_red_s 0.006705\n",
      "Feature 28: shotphi_red_m 0.000000\n",
      "Feature 29: shotphi_red_l 0.016007\n",
      "Feature 30: shotphi_est_blue_s 0.003193\n",
      "Feature 31: shotphi_est_blue_m 0.001776\n",
      "Feature 32: shotphi_est_blue_l 0.009256\n",
      "Feature 33: threshold_before_blue 0.004818\n",
      "Feature 34: threshold_after_blue 0.000000\n",
      "Feature 35: threshold_before_red 0.000932\n",
      "Feature 36: threshold_after_red 0.006041\n"
     ]
    }
   ],
   "source": [
    "dic = {}\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
    "# feature selection\n",
    "X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n",
    "# what are scores for the features\n",
    "for i in range(len(fs.scores_)):\n",
    "    print('Feature %d: %s %f' % (i+1,df.columns[i], fs.scores_[i]))\n",
    "    dic[df.columns[i]] = fs.scores_[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>red_rwr</td>\n",
       "      <td>0.091155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>shotphi_blue_s</td>\n",
       "      <td>0.030129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>shotphi_blue_l</td>\n",
       "      <td>0.028166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>shotphi_red_l</td>\n",
       "      <td>0.016007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>missil_blue_c</td>\n",
       "      <td>0.014806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>shotphi_est_blue_l</td>\n",
       "      <td>0.009256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>distancia</td>\n",
       "      <td>0.008984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>missil_blue_a</td>\n",
       "      <td>0.008599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>velocidade_red</td>\n",
       "      <td>0.007925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>missil_red_c</td>\n",
       "      <td>0.006930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>shotphi_red_s</td>\n",
       "      <td>0.006705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>threshold_after_red</td>\n",
       "      <td>0.006041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>missil_blue_b</td>\n",
       "      <td>0.005573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>missil_est_blue_c</td>\n",
       "      <td>0.005146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>threshold_before_blue</td>\n",
       "      <td>0.004818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>missil_red_a</td>\n",
       "      <td>0.003992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>shotphi_blue_m</td>\n",
       "      <td>0.003748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>shotphi_est_blue_s</td>\n",
       "      <td>0.003193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>posicao_relativa</td>\n",
       "      <td>0.002514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>altitude_red</td>\n",
       "      <td>0.002267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>missil_est_red_c</td>\n",
       "      <td>0.001868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>missil_est_blue_b</td>\n",
       "      <td>0.001804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>shotphi_est_blue_m</td>\n",
       "      <td>0.001776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>shotphi_est_red_m</td>\n",
       "      <td>0.001115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>threshold_before_red</td>\n",
       "      <td>0.000932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>velocidade_blue</td>\n",
       "      <td>0.000805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>missil_est_red_b</td>\n",
       "      <td>0.000580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>missil_est_blue_a</td>\n",
       "      <td>0.000346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>blue_rwr</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>missil_red_b</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>missil_est_red_a</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>shotphi_est_red_l</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>altitude_blue</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>threshold_after_blue</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>shotphi_est_red_s</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>shotphi_red_m</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature     score\n",
       "7                 red_rwr  0.091155\n",
       "20         shotphi_blue_s  0.030129\n",
       "22         shotphi_blue_l  0.028166\n",
       "28          shotphi_red_l  0.016007\n",
       "10          missil_blue_c  0.014806\n",
       "31     shotphi_est_blue_l  0.009256\n",
       "1               distancia  0.008984\n",
       "8           missil_blue_a  0.008599\n",
       "5          velocidade_red  0.007925\n",
       "16           missil_red_c  0.006930\n",
       "26          shotphi_red_s  0.006705\n",
       "35    threshold_after_red  0.006041\n",
       "9           missil_blue_b  0.005573\n",
       "19      missil_est_blue_c  0.005146\n",
       "32  threshold_before_blue  0.004818\n",
       "14           missil_red_a  0.003992\n",
       "21         shotphi_blue_m  0.003748\n",
       "29     shotphi_est_blue_s  0.003193\n",
       "0        posicao_relativa  0.002514\n",
       "3            altitude_red  0.002267\n",
       "13       missil_est_red_c  0.001868\n",
       "18      missil_est_blue_b  0.001804\n",
       "30     shotphi_est_blue_m  0.001776\n",
       "24      shotphi_est_red_m  0.001115\n",
       "34   threshold_before_red  0.000932\n",
       "4         velocidade_blue  0.000805\n",
       "12       missil_est_red_b  0.000580\n",
       "17      missil_est_blue_a  0.000346\n",
       "6                blue_rwr  0.000000\n",
       "15           missil_red_b  0.000000\n",
       "11       missil_est_red_a  0.000000\n",
       "25      shotphi_est_red_l  0.000000\n",
       "2           altitude_blue  0.000000\n",
       "33   threshold_after_blue  0.000000\n",
       "23      shotphi_est_red_s  0.000000\n",
       "27          shotphi_red_m  0.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "scores = pd.DataFrame(dic.items(),columns = ['feature', 'score'])    \n",
    "scores = scores.sort_values('score',ascending=False)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# plot the scores\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x= scores['score'],y=[i for i in scores['feature']],orient='horizontal' );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA f-test (Defense Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./df.csv')\n",
    "\n",
    "X = df.iloc[:, :-2]\n",
    "y = df.iloc[:,-1].apply(lambda y: 0 if y > 10 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "import matplotlib.pyplot as plt\n",
    "    \n",
    "# feature selection\n",
    "def select_features(X_train, y_train, X_test):\n",
    "    # configure to select all features\n",
    "    fs = SelectKBest(score_func=f_classif, k='all')\n",
    "    # learn relationship from training data\n",
    "    fs.fit(X_train, y_train)\n",
    "    # transform train input data\n",
    "    X_train_fs = fs.transform(X_train)\n",
    "    # transform test input data\n",
    "    X_test_fs = fs.transform(X_test)\n",
    "    return X_train_fs, X_test_fs, fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 1: posicao_relativa 4.781743\n",
      "Feature 2: distancia 58.204092\n",
      "Feature 3: altitude_blue 5.119203\n",
      "Feature 4: altitude_red 18.434176\n",
      "Feature 5: velocidade_blue 0.197726\n",
      "Feature 6: velocidade_red 4.245877\n",
      "Feature 7: blue_rwr 1126.324176\n",
      "Feature 8: red_rwr 1.337776\n",
      "Feature 9: missil_blue_a 9.101433\n",
      "Feature 10: missil_blue_b 2.319630\n",
      "Feature 11: missil_blue_c 20.508999\n",
      "Feature 12: missil_est_red_a 6.643082\n",
      "Feature 13: missil_est_red_b 0.613911\n",
      "Feature 14: missil_est_red_c 11.185366\n",
      "Feature 15: missil_red_a 84.298674\n",
      "Feature 16: missil_red_b 27.040965\n",
      "Feature 17: missil_red_c 206.765738\n",
      "Feature 18: missil_est_blue_a 0.003756\n",
      "Feature 19: missil_est_blue_b 0.764794\n",
      "Feature 20: missil_est_blue_c 0.661946\n",
      "Feature 21: shotphi_blue_s 107.713306\n",
      "Feature 22: shotphi_blue_m 2.583141\n",
      "Feature 23: shotphi_blue_l 140.379647\n",
      "Feature 24: shotphi_est_red_s 21.090438\n",
      "Feature 25: shotphi_est_red_m 1.789302\n",
      "Feature 26: shotphi_est_red_l 10.158883\n",
      "Feature 27: shotphi_red_s 388.798365\n",
      "Feature 28: shotphi_red_m 0.065695\n",
      "Feature 29: shotphi_red_l 369.129038\n",
      "Feature 30: shotphi_est_blue_s 0.744137\n",
      "Feature 31: shotphi_est_blue_m 0.917332\n",
      "Feature 32: shotphi_est_blue_l 0.009293\n",
      "Feature 33: threshold_before_blue 5.606629\n",
      "Feature 34: threshold_after_blue 9.257640\n",
      "Feature 35: threshold_before_red 13.434579\n",
      "Feature 36: threshold_after_red 10.456798\n"
     ]
    }
   ],
   "source": [
    "dic = {}\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
    "# feature selection\n",
    "X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n",
    "# what are scores for the features\n",
    "for i in range(len(fs.scores_)):\n",
    "    print('Feature %d: %s %f' % (i+1,df.columns[i], fs.scores_[i]))\n",
    "    dic[df.columns[i]] = fs.scores_[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "    .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>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>blue_rwr</td>\n",
       "      <td>1126.324176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>shotphi_red_s</td>\n",
       "      <td>388.798365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>shotphi_red_l</td>\n",
       "      <td>369.129038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>missil_red_c</td>\n",
       "      <td>206.765738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>shotphi_blue_l</td>\n",
       "      <td>140.379647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>shotphi_blue_s</td>\n",
       "      <td>107.713306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>missil_red_a</td>\n",
       "      <td>84.298674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>distancia</td>\n",
       "      <td>58.204092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>missil_red_b</td>\n",
       "      <td>27.040965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>shotphi_est_red_s</td>\n",
       "      <td>21.090438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>missil_blue_c</td>\n",
       "      <td>20.508999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>altitude_red</td>\n",
       "      <td>18.434176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>threshold_before_red</td>\n",
       "      <td>13.434579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>missil_est_red_c</td>\n",
       "      <td>11.185366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>threshold_after_red</td>\n",
       "      <td>10.456798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>shotphi_est_red_l</td>\n",
       "      <td>10.158883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>threshold_after_blue</td>\n",
       "      <td>9.257640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>missil_blue_a</td>\n",
       "      <td>9.101433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>missil_est_red_a</td>\n",
       "      <td>6.643082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>threshold_before_blue</td>\n",
       "      <td>5.606629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>altitude_blue</td>\n",
       "      <td>5.119203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>posicao_relativa</td>\n",
       "      <td>4.781743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>velocidade_red</td>\n",
       "      <td>4.245877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>shotphi_blue_m</td>\n",
       "      <td>2.583141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>missil_blue_b</td>\n",
       "      <td>2.319630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>shotphi_est_red_m</td>\n",
       "      <td>1.789302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>red_rwr</td>\n",
       "      <td>1.337776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>shotphi_est_blue_m</td>\n",
       "      <td>0.917332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>missil_est_blue_b</td>\n",
       "      <td>0.764794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>shotphi_est_blue_s</td>\n",
       "      <td>0.744137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>missil_est_blue_c</td>\n",
       "      <td>0.661946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>missil_est_red_b</td>\n",
       "      <td>0.613911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>velocidade_blue</td>\n",
       "      <td>0.197726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>shotphi_red_m</td>\n",
       "      <td>0.065695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>shotphi_est_blue_l</td>\n",
       "      <td>0.009293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>missil_est_blue_a</td>\n",
       "      <td>0.003756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature        score\n",
       "6                blue_rwr  1126.324176\n",
       "26          shotphi_red_s   388.798365\n",
       "28          shotphi_red_l   369.129038\n",
       "16           missil_red_c   206.765738\n",
       "22         shotphi_blue_l   140.379647\n",
       "20         shotphi_blue_s   107.713306\n",
       "14           missil_red_a    84.298674\n",
       "1               distancia    58.204092\n",
       "15           missil_red_b    27.040965\n",
       "23      shotphi_est_red_s    21.090438\n",
       "10          missil_blue_c    20.508999\n",
       "3            altitude_red    18.434176\n",
       "34   threshold_before_red    13.434579\n",
       "13       missil_est_red_c    11.185366\n",
       "35    threshold_after_red    10.456798\n",
       "25      shotphi_est_red_l    10.158883\n",
       "33   threshold_after_blue     9.257640\n",
       "8           missil_blue_a     9.101433\n",
       "11       missil_est_red_a     6.643082\n",
       "32  threshold_before_blue     5.606629\n",
       "2           altitude_blue     5.119203\n",
       "0        posicao_relativa     4.781743\n",
       "5          velocidade_red     4.245877\n",
       "21         shotphi_blue_m     2.583141\n",
       "9           missil_blue_b     2.319630\n",
       "24      shotphi_est_red_m     1.789302\n",
       "7                 red_rwr     1.337776\n",
       "30     shotphi_est_blue_m     0.917332\n",
       "18      missil_est_blue_b     0.764794\n",
       "29     shotphi_est_blue_s     0.744137\n",
       "19      missil_est_blue_c     0.661946\n",
       "12       missil_est_red_b     0.613911\n",
       "4         velocidade_blue     0.197726\n",
       "27          shotphi_red_m     0.065695\n",
       "31     shotphi_est_blue_l     0.009293\n",
       "17      missil_est_blue_a     0.003756"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "scores = pd.DataFrame(dic.items(),columns = ['feature', 'score'])    \n",
    "scores = scores.sort_values('score',ascending=False)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# plot the scores\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x=scores['score'],y=[i for i in scores['feature']],orient='horizontal' );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual Information (Defense Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "import matplotlib.pyplot as plt\n",
    "     \n",
    "# feature selection\n",
    "def select_features(X_train, y_train, X_test):\n",
    "    # configure to select all features\n",
    "    fs = SelectKBest(score_func=mutual_info_classif, k='all')\n",
    "    # learn relationship from training data\n",
    "    fs.fit(X_train, y_train)\n",
    "    # transform train input data\n",
    "    X_train_fs = fs.transform(X_train)\n",
    "    # transform test input data\n",
    "    X_test_fs = fs.transform(X_test)\n",
    "    return X_train_fs, X_test_fs, fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 1: posicao_relativa 0.002283\n",
      "Feature 2: distancia 0.010045\n",
      "Feature 3: altitude_blue 0.000000\n",
      "Feature 4: altitude_red 0.000000\n",
      "Feature 5: velocidade_blue 0.002633\n",
      "Feature 6: velocidade_red 0.000000\n",
      "Feature 7: blue_rwr 0.091484\n",
      "Feature 8: red_rwr 0.003061\n",
      "Feature 9: missil_blue_a 0.000000\n",
      "Feature 10: missil_blue_b 0.001307\n",
      "Feature 11: missil_blue_c 0.000000\n",
      "Feature 12: missil_est_red_a 0.000000\n",
      "Feature 13: missil_est_red_b 0.000000\n",
      "Feature 14: missil_est_red_c 0.000000\n",
      "Feature 15: missil_red_a 0.003729\n",
      "Feature 16: missil_red_b 0.003393\n",
      "Feature 17: missil_red_c 0.008137\n",
      "Feature 18: missil_est_blue_a 0.000000\n",
      "Feature 19: missil_est_blue_b 0.000550\n",
      "Feature 20: missil_est_blue_c 0.001906\n",
      "Feature 21: shotphi_blue_s 0.005359\n",
      "Feature 22: shotphi_blue_m 0.006207\n",
      "Feature 23: shotphi_blue_l 0.011071\n",
      "Feature 24: shotphi_est_red_s 0.006990\n",
      "Feature 25: shotphi_est_red_m 0.005591\n",
      "Feature 26: shotphi_est_red_l 0.000000\n",
      "Feature 27: shotphi_red_s 0.036867\n",
      "Feature 28: shotphi_red_m 0.000631\n",
      "Feature 29: shotphi_red_l 0.027372\n",
      "Feature 30: shotphi_est_blue_s 0.006385\n",
      "Feature 31: shotphi_est_blue_m 0.000000\n",
      "Feature 32: shotphi_est_blue_l 0.000000\n",
      "Feature 33: threshold_before_blue 0.000000\n",
      "Feature 34: threshold_after_blue 0.000665\n",
      "Feature 35: threshold_before_red 0.000117\n",
      "Feature 36: threshold_after_red 0.004484\n"
     ]
    }
   ],
   "source": [
    "dic = {}\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
    "# feature selection\n",
    "X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n",
    "# what are scores for the features\n",
    "for i in range(len(fs.scores_)):\n",
    "    print('Feature %d: %s %f' % (i+1,df.columns[i], fs.scores_[i]))\n",
    "    dic[df.columns[i]] = fs.scores_[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>blue_rwr</td>\n",
       "      <td>0.091484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>shotphi_red_s</td>\n",
       "      <td>0.036867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>shotphi_red_l</td>\n",
       "      <td>0.027372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>shotphi_blue_l</td>\n",
       "      <td>0.011071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>distancia</td>\n",
       "      <td>0.010045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>missil_red_c</td>\n",
       "      <td>0.008137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>shotphi_est_red_s</td>\n",
       "      <td>0.006990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>shotphi_est_blue_s</td>\n",
       "      <td>0.006385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>shotphi_blue_m</td>\n",
       "      <td>0.006207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>shotphi_est_red_m</td>\n",
       "      <td>0.005591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>shotphi_blue_s</td>\n",
       "      <td>0.005359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>threshold_after_red</td>\n",
       "      <td>0.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>missil_red_a</td>\n",
       "      <td>0.003729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>missil_red_b</td>\n",
       "      <td>0.003393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>red_rwr</td>\n",
       "      <td>0.003061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>velocidade_blue</td>\n",
       "      <td>0.002633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>posicao_relativa</td>\n",
       "      <td>0.002283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>missil_est_blue_c</td>\n",
       "      <td>0.001906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>missil_blue_b</td>\n",
       "      <td>0.001307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>threshold_after_blue</td>\n",
       "      <td>0.000665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>shotphi_red_m</td>\n",
       "      <td>0.000631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>missil_est_blue_b</td>\n",
       "      <td>0.000550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>threshold_before_red</td>\n",
       "      <td>0.000117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>altitude_red</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>threshold_before_blue</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>shotphi_est_blue_l</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>shotphi_est_blue_m</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>altitude_blue</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>missil_est_blue_a</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>missil_est_red_c</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>shotphi_est_red_l</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>missil_blue_a</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>missil_blue_c</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>missil_est_red_a</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>missil_est_red_b</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>velocidade_red</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature     score\n",
       "6                blue_rwr  0.091484\n",
       "26          shotphi_red_s  0.036867\n",
       "28          shotphi_red_l  0.027372\n",
       "22         shotphi_blue_l  0.011071\n",
       "1               distancia  0.010045\n",
       "16           missil_red_c  0.008137\n",
       "23      shotphi_est_red_s  0.006990\n",
       "29     shotphi_est_blue_s  0.006385\n",
       "21         shotphi_blue_m  0.006207\n",
       "24      shotphi_est_red_m  0.005591\n",
       "20         shotphi_blue_s  0.005359\n",
       "35    threshold_after_red  0.004484\n",
       "14           missil_red_a  0.003729\n",
       "15           missil_red_b  0.003393\n",
       "7                 red_rwr  0.003061\n",
       "4         velocidade_blue  0.002633\n",
       "0        posicao_relativa  0.002283\n",
       "19      missil_est_blue_c  0.001906\n",
       "9           missil_blue_b  0.001307\n",
       "33   threshold_after_blue  0.000665\n",
       "27          shotphi_red_m  0.000631\n",
       "18      missil_est_blue_b  0.000550\n",
       "34   threshold_before_red  0.000117\n",
       "3            altitude_red  0.000000\n",
       "32  threshold_before_blue  0.000000\n",
       "31     shotphi_est_blue_l  0.000000\n",
       "30     shotphi_est_blue_m  0.000000\n",
       "2           altitude_blue  0.000000\n",
       "17      missil_est_blue_a  0.000000\n",
       "13       missil_est_red_c  0.000000\n",
       "25      shotphi_est_red_l  0.000000\n",
       "8           missil_blue_a  0.000000\n",
       "10          missil_blue_c  0.000000\n",
       "11       missil_est_red_a  0.000000\n",
       "12       missil_est_red_b  0.000000\n",
       "5          velocidade_red  0.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "scores = pd.DataFrame(dic.items(),columns = ['feature', 'score'])    \n",
    "scores = scores.sort_values('score',ascending=False)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# plot the scores\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x= scores['score'],y=[i for i in scores['feature']],orient='horizontal' );"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}