Boston Housing regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from matplotlib import pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression, Lasso, ElasticNet\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, AdaBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, median_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = datasets.load_boston(return_X_y=True)\n",
    "\n",
    "names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO',\n",
    "        'B', 'LSTAT']\n",
    "\n",
    "df = pd.DataFrame(X, columns = names)\n",
    "df['MEDV'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       float64\n",
       "ZN         float64\n",
       "INDUS      float64\n",
       "CHAS       float64\n",
       "NOX        float64\n",
       "RM         float64\n",
       "AGE        float64\n",
       "DIS        float64\n",
       "RAD        float64\n",
       "TAX        float64\n",
       "PTRATIO    float64\n",
       "B          float64\n",
       "LSTAT      float64\n",
       "MEDV       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  \n",
       "3     18.7  394.63   2.94  33.4  \n",
       "4     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "      <td>506.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.6135</td>\n",
       "      <td>11.3636</td>\n",
       "      <td>11.1368</td>\n",
       "      <td>0.0692</td>\n",
       "      <td>0.5547</td>\n",
       "      <td>6.2846</td>\n",
       "      <td>68.5749</td>\n",
       "      <td>3.7950</td>\n",
       "      <td>9.5494</td>\n",
       "      <td>408.2372</td>\n",
       "      <td>18.4555</td>\n",
       "      <td>356.6740</td>\n",
       "      <td>12.6531</td>\n",
       "      <td>22.5328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.6015</td>\n",
       "      <td>23.3225</td>\n",
       "      <td>6.8604</td>\n",
       "      <td>0.2540</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>0.7026</td>\n",
       "      <td>28.1489</td>\n",
       "      <td>2.1057</td>\n",
       "      <td>8.7073</td>\n",
       "      <td>168.5371</td>\n",
       "      <td>2.1649</td>\n",
       "      <td>91.2949</td>\n",
       "      <td>7.1411</td>\n",
       "      <td>9.1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3850</td>\n",
       "      <td>3.5610</td>\n",
       "      <td>2.9000</td>\n",
       "      <td>1.1296</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>187.0000</td>\n",
       "      <td>12.6000</td>\n",
       "      <td>0.3200</td>\n",
       "      <td>1.7300</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0820</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.1900</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4490</td>\n",
       "      <td>5.8855</td>\n",
       "      <td>45.0250</td>\n",
       "      <td>2.1002</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>279.0000</td>\n",
       "      <td>17.4000</td>\n",
       "      <td>375.3775</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>17.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9.6900</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5380</td>\n",
       "      <td>6.2085</td>\n",
       "      <td>77.5000</td>\n",
       "      <td>3.2074</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>330.0000</td>\n",
       "      <td>19.0500</td>\n",
       "      <td>391.4400</td>\n",
       "      <td>11.3600</td>\n",
       "      <td>21.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.6771</td>\n",
       "      <td>12.5000</td>\n",
       "      <td>18.1000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6240</td>\n",
       "      <td>6.6235</td>\n",
       "      <td>94.0750</td>\n",
       "      <td>5.1884</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>666.0000</td>\n",
       "      <td>20.2000</td>\n",
       "      <td>396.2250</td>\n",
       "      <td>16.9550</td>\n",
       "      <td>25.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.9762</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>27.7400</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>8.7800</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>12.1265</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>711.0000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>396.9000</td>\n",
       "      <td>37.9700</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "count  506.0000  506.0000  506.0000  506.0000  506.0000  506.0000  506.0000   \n",
       "mean     3.6135   11.3636   11.1368    0.0692    0.5547    6.2846   68.5749   \n",
       "std      8.6015   23.3225    6.8604    0.2540    0.1159    0.7026   28.1489   \n",
       "min      0.0063    0.0000    0.4600    0.0000    0.3850    3.5610    2.9000   \n",
       "25%      0.0820    0.0000    5.1900    0.0000    0.4490    5.8855   45.0250   \n",
       "50%      0.2565    0.0000    9.6900    0.0000    0.5380    6.2085   77.5000   \n",
       "75%      3.6771   12.5000   18.1000    0.0000    0.6240    6.6235   94.0750   \n",
       "max     88.9762  100.0000   27.7400    1.0000    0.8710    8.7800  100.0000   \n",
       "\n",
       "            DIS       RAD       TAX   PTRATIO         B     LSTAT      MEDV  \n",
       "count  506.0000  506.0000  506.0000  506.0000  506.0000  506.0000  506.0000  \n",
       "mean     3.7950    9.5494  408.2372   18.4555  356.6740   12.6531   22.5328  \n",
       "std      2.1057    8.7073  168.5371    2.1649   91.2949    7.1411    9.1971  \n",
       "min      1.1296    1.0000  187.0000   12.6000    0.3200    1.7300    5.0000  \n",
       "25%      2.1002    4.0000  279.0000   17.4000  375.3775    6.9500   17.0250  \n",
       "50%      3.2074    5.0000  330.0000   19.0500  391.4400   11.3600   21.2000  \n",
       "75%      5.1884   24.0000  666.0000   20.2000  396.2250   16.9550   25.0000  \n",
       "max     12.1265   24.0000  711.0000   22.0000  396.9000   37.9700   50.0000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 4)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      3\n",
       "CHAS       0\n",
       "NOX        3\n",
       "RM         0\n",
       "AGE        2\n",
       "DIS        3\n",
       "RAD        1\n",
       "TAX        2\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      1\n",
       "MEDV       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Shows the amount of correlation coefficients above or below .7 and -.7 for each variable\n",
    "#This is useful for focusing attention to specific areas in the following correlation coefficient matrix\n",
    "\n",
    "big_pos = df.corr(method = 'pearson') >= .7\n",
    "big_neg = df.corr(method = 'pearson') <= -.7\n",
    "big_pos = big_pos.sum() - 1                 #remove one because every variable has itself in the matrix for a correlation coeffiction of 1\n",
    "big_neg = big_neg.sum()\n",
    "big_corrs = big_pos + big_neg\n",
    "big_corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.2005</td>\n",
       "      <td>0.4066</td>\n",
       "      <td>-0.0559</td>\n",
       "      <td>0.4210</td>\n",
       "      <td>-0.2192</td>\n",
       "      <td>0.3527</td>\n",
       "      <td>-0.3797</td>\n",
       "      <td>0.6255</td>\n",
       "      <td>0.5828</td>\n",
       "      <td>0.2899</td>\n",
       "      <td>-0.3851</td>\n",
       "      <td>0.4556</td>\n",
       "      <td>-0.3883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.2005</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.5338</td>\n",
       "      <td>-0.0427</td>\n",
       "      <td>-0.5166</td>\n",
       "      <td>0.3120</td>\n",
       "      <td>-0.5695</td>\n",
       "      <td>0.6644</td>\n",
       "      <td>-0.3119</td>\n",
       "      <td>-0.3146</td>\n",
       "      <td>-0.3917</td>\n",
       "      <td>0.1755</td>\n",
       "      <td>-0.4130</td>\n",
       "      <td>0.3604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.4066</td>\n",
       "      <td>-0.5338</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0629</td>\n",
       "      <td>0.7637</td>\n",
       "      <td>-0.3917</td>\n",
       "      <td>0.6448</td>\n",
       "      <td>-0.7080</td>\n",
       "      <td>0.5951</td>\n",
       "      <td>0.7208</td>\n",
       "      <td>0.3832</td>\n",
       "      <td>-0.3570</td>\n",
       "      <td>0.6038</td>\n",
       "      <td>-0.4837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.0559</td>\n",
       "      <td>-0.0427</td>\n",
       "      <td>0.0629</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0912</td>\n",
       "      <td>0.0913</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>-0.0992</td>\n",
       "      <td>-0.0074</td>\n",
       "      <td>-0.0356</td>\n",
       "      <td>-0.1215</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>-0.0539</td>\n",
       "      <td>0.1753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.4210</td>\n",
       "      <td>-0.5166</td>\n",
       "      <td>0.7637</td>\n",
       "      <td>0.0912</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.3022</td>\n",
       "      <td>0.7315</td>\n",
       "      <td>-0.7692</td>\n",
       "      <td>0.6114</td>\n",
       "      <td>0.6680</td>\n",
       "      <td>0.1889</td>\n",
       "      <td>-0.3801</td>\n",
       "      <td>0.5909</td>\n",
       "      <td>-0.4273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.2192</td>\n",
       "      <td>0.3120</td>\n",
       "      <td>-0.3917</td>\n",
       "      <td>0.0913</td>\n",
       "      <td>-0.3022</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.2403</td>\n",
       "      <td>0.2052</td>\n",
       "      <td>-0.2098</td>\n",
       "      <td>-0.2920</td>\n",
       "      <td>-0.3555</td>\n",
       "      <td>0.1281</td>\n",
       "      <td>-0.6138</td>\n",
       "      <td>0.6954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.3527</td>\n",
       "      <td>-0.5695</td>\n",
       "      <td>0.6448</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>0.7315</td>\n",
       "      <td>-0.2403</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.7479</td>\n",
       "      <td>0.4560</td>\n",
       "      <td>0.5065</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>-0.2735</td>\n",
       "      <td>0.6023</td>\n",
       "      <td>-0.3770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.3797</td>\n",
       "      <td>0.6644</td>\n",
       "      <td>-0.7080</td>\n",
       "      <td>-0.0992</td>\n",
       "      <td>-0.7692</td>\n",
       "      <td>0.2052</td>\n",
       "      <td>-0.7479</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.4946</td>\n",
       "      <td>-0.5344</td>\n",
       "      <td>-0.2325</td>\n",
       "      <td>0.2915</td>\n",
       "      <td>-0.4970</td>\n",
       "      <td>0.2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.6255</td>\n",
       "      <td>-0.3119</td>\n",
       "      <td>0.5951</td>\n",
       "      <td>-0.0074</td>\n",
       "      <td>0.6114</td>\n",
       "      <td>-0.2098</td>\n",
       "      <td>0.4560</td>\n",
       "      <td>-0.4946</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9102</td>\n",
       "      <td>0.4647</td>\n",
       "      <td>-0.4444</td>\n",
       "      <td>0.4887</td>\n",
       "      <td>-0.3816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.5828</td>\n",
       "      <td>-0.3146</td>\n",
       "      <td>0.7208</td>\n",
       "      <td>-0.0356</td>\n",
       "      <td>0.6680</td>\n",
       "      <td>-0.2920</td>\n",
       "      <td>0.5065</td>\n",
       "      <td>-0.5344</td>\n",
       "      <td>0.9102</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.4609</td>\n",
       "      <td>-0.4418</td>\n",
       "      <td>0.5440</td>\n",
       "      <td>-0.4685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.2899</td>\n",
       "      <td>-0.3917</td>\n",
       "      <td>0.3832</td>\n",
       "      <td>-0.1215</td>\n",
       "      <td>0.1889</td>\n",
       "      <td>-0.3555</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>-0.2325</td>\n",
       "      <td>0.4647</td>\n",
       "      <td>0.4609</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.1774</td>\n",
       "      <td>0.3740</td>\n",
       "      <td>-0.5078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.3851</td>\n",
       "      <td>0.1755</td>\n",
       "      <td>-0.3570</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>-0.3801</td>\n",
       "      <td>0.1281</td>\n",
       "      <td>-0.2735</td>\n",
       "      <td>0.2915</td>\n",
       "      <td>-0.4444</td>\n",
       "      <td>-0.4418</td>\n",
       "      <td>-0.1774</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.3661</td>\n",
       "      <td>0.3335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.4556</td>\n",
       "      <td>-0.4130</td>\n",
       "      <td>0.6038</td>\n",
       "      <td>-0.0539</td>\n",
       "      <td>0.5909</td>\n",
       "      <td>-0.6138</td>\n",
       "      <td>0.6023</td>\n",
       "      <td>-0.4970</td>\n",
       "      <td>0.4887</td>\n",
       "      <td>0.5440</td>\n",
       "      <td>0.3740</td>\n",
       "      <td>-0.3661</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.7377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDV</th>\n",
       "      <td>-0.3883</td>\n",
       "      <td>0.3604</td>\n",
       "      <td>-0.4837</td>\n",
       "      <td>0.1753</td>\n",
       "      <td>-0.4273</td>\n",
       "      <td>0.6954</td>\n",
       "      <td>-0.3770</td>\n",
       "      <td>0.2499</td>\n",
       "      <td>-0.3816</td>\n",
       "      <td>-0.4685</td>\n",
       "      <td>-0.5078</td>\n",
       "      <td>0.3335</td>\n",
       "      <td>-0.7377</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CRIM      ZN   INDUS    CHAS     NOX      RM     AGE     DIS  \\\n",
       "CRIM     1.0000 -0.2005  0.4066 -0.0559  0.4210 -0.2192  0.3527 -0.3797   \n",
       "ZN      -0.2005  1.0000 -0.5338 -0.0427 -0.5166  0.3120 -0.5695  0.6644   \n",
       "INDUS    0.4066 -0.5338  1.0000  0.0629  0.7637 -0.3917  0.6448 -0.7080   \n",
       "CHAS    -0.0559 -0.0427  0.0629  1.0000  0.0912  0.0913  0.0865 -0.0992   \n",
       "NOX      0.4210 -0.5166  0.7637  0.0912  1.0000 -0.3022  0.7315 -0.7692   \n",
       "RM      -0.2192  0.3120 -0.3917  0.0913 -0.3022  1.0000 -0.2403  0.2052   \n",
       "AGE      0.3527 -0.5695  0.6448  0.0865  0.7315 -0.2403  1.0000 -0.7479   \n",
       "DIS     -0.3797  0.6644 -0.7080 -0.0992 -0.7692  0.2052 -0.7479  1.0000   \n",
       "RAD      0.6255 -0.3119  0.5951 -0.0074  0.6114 -0.2098  0.4560 -0.4946   \n",
       "TAX      0.5828 -0.3146  0.7208 -0.0356  0.6680 -0.2920  0.5065 -0.5344   \n",
       "PTRATIO  0.2899 -0.3917  0.3832 -0.1215  0.1889 -0.3555  0.2615 -0.2325   \n",
       "B       -0.3851  0.1755 -0.3570  0.0488 -0.3801  0.1281 -0.2735  0.2915   \n",
       "LSTAT    0.4556 -0.4130  0.6038 -0.0539  0.5909 -0.6138  0.6023 -0.4970   \n",
       "MEDV    -0.3883  0.3604 -0.4837  0.1753 -0.4273  0.6954 -0.3770  0.2499   \n",
       "\n",
       "            RAD     TAX  PTRATIO       B   LSTAT    MEDV  \n",
       "CRIM     0.6255  0.5828   0.2899 -0.3851  0.4556 -0.3883  \n",
       "ZN      -0.3119 -0.3146  -0.3917  0.1755 -0.4130  0.3604  \n",
       "INDUS    0.5951  0.7208   0.3832 -0.3570  0.6038 -0.4837  \n",
       "CHAS    -0.0074 -0.0356  -0.1215  0.0488 -0.0539  0.1753  \n",
       "NOX      0.6114  0.6680   0.1889 -0.3801  0.5909 -0.4273  \n",
       "RM      -0.2098 -0.2920  -0.3555  0.1281 -0.6138  0.6954  \n",
       "AGE      0.4560  0.5065   0.2615 -0.2735  0.6023 -0.3770  \n",
       "DIS     -0.4946 -0.5344  -0.2325  0.2915 -0.4970  0.2499  \n",
       "RAD      1.0000  0.9102   0.4647 -0.4444  0.4887 -0.3816  \n",
       "TAX      0.9102  1.0000   0.4609 -0.4418  0.5440 -0.4685  \n",
       "PTRATIO  0.4647  0.4609   1.0000 -0.1774  0.3740 -0.5078  \n",
       "B       -0.4444 -0.4418  -0.1774  1.0000 -0.3661  0.3335  \n",
       "LSTAT    0.4887  0.5440   0.3740 -0.3661  1.0000 -0.7377  \n",
       "MEDV    -0.3816 -0.4685  -0.5078  0.3335 -0.7377  1.0000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method = 'pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize = (16,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvvs6Xk+fPh23WxflCAYOt4MdtTu4dsK1/faNM8cxK38Wa8rXBFyOY83HAChIKeiyfXiqJ/vAYdthZuTNCPhxNeGJ48iRLmEdQMfCw/YjR0iaE/xy9ZHCQOwE8OSR9tHdNsjKysKXvvOtt94KuIzBtPAy6D2nqV/9IyGnaWu7y+ORjvdco+gUeJqwp6kCUrv+wJNaALZy0AaeJoLZU7eHNlcbU3OmDqj/7PzZ7G/YT01rTUDlKG8uBzjBI12cUoxJTByyaY90LOE4dgzrsK66YM33pHF3VujQjkgjmIZ0Pb1nP/Crf1hnPvDS0u7yxEgneFZmR0IKPE0M47CDvQFS8rpuTx0Gbge01hojl0YTALbVbAPoNX90d2ble1JtratYF1A5jjV5PdJJXS9YrWYreUl5HG06GtDjxQoi8n0R+cRoOQaDUgpnZSWW/K71j0xJSZhSU3WMdAQSTEN6JXCu9/UCYNVA+ouIGZiBJ7dpxGF3uEiMM5MS71l166t0qNGEJc3eSTulW1E7n4e6Uf/AayKXPfV7SLQkUpRa1H9nYHLOZBItiaytWBtQOcpbyrGIhZzEnBPahiUP6/BYawaOiMQDQytlawDupiaU3Y6lB2egJT8PZ2X/Vfs04UXQDOm+sh+IyKXAYjyG86veXZ4Gvgp8DDyjlIrI+sQ+j3RSnMX7PjwN6eN1RWKDWPu8A6apN0Pae9vRFtk/8JH+f490+Y1md/1uxmaMxSQD+6mzmqxMz50eeI908zHyk/Mxm05Ma1aQXNARQ60ZFN8AAloaNBT4PM6WvLwT2qx5+TjCKLQjVuefwX7uoK6C6yP7wVKl1OlKqWFKqau92xqVUpcqpU5VSj0bTLmCSavDRVKcmWSvR7qpzdXPHqEnISGBmpqamPmSKKWoqakhISHBaFHCjyav9+OE0A6vR9oWuT/wka7nWm+Hzp66PYzNOKHyeZ/Mzp/N7rrdNLQ1BEyOY03HKEgu6LHN55F2K70eYaCIiBU4Uyn1Xh99wnJNldMrS48e6by8sAntiPT501/8mXd1QZYA09ruIiHOTLJ3sWFLGC42LC4upqysjHCaXIJNQkICxd1WSWvoZEh380j73kewRzoa9Fzrrf/U2mupsdf0mz+6O7MLZqNQrKtYxzkjzul/hwFQ3lzOzPyeC8IMSx6Gw+2g1l7bY+iHpkeuB/osCaqUWgQsApg9e3bYWIPHPdI9hXbke8qHh0FRlmiYP/1lsPOuNqQDTKvDRZLVTLI3tCMcc0lbrVZGjRpltBiacMAX2pHcbVK3xEFSTkR7pLWexzZ76/cCMC5j3KD2m5IzBYvJwoaqDQExpF1uFxUtFSdk7PDh23606ag2pAfOBGCGiNwOTBaR7yilHutvp3DguEf6xNAOS14uuFy4amt79FiHEj1/Dhyd4DiAKKVo9S427PBIt4dfaIdG00FTBSRlg9l6YlvqsIj2SGtim911uwEYmzm40I54czylWaVsrNzYf+cBUNVahUu5eg3t8G3XcdIDRyl1X6eqslsjxYgGj0falJSEOSX5hDZfCrxwipPW9I82pANIm9ONUpAYZyYpTmft0EQATZUnhnX4SC0Am87aoYlM9tTvIS0ujdzEwXv2puVOY1vNNhxux5Dl8GXk6DVGOmVYl36awaGUOt1oGQaDo7Kyx4WGQEdKvHCJk9YMDG1IBxCf9znRaibeYsJsEl2QRRPeNFWcuNDQR2o+2HQqJk1ksrd+L2MzxiIig953eu507C47u2p3DVmO3oqx+Ei1ppJsTdYe6RjBWVXVa9iGz8DWKfAiC21IB5BWh8eQToozIyIkx5lpDsOsHRpNB00VkNKzp4yUAk+eabfWYU3ksb9hP6PS/YvxnJ7rSU+8sWro4R0+A7k3Q1pEGJY8rKNoiya6cVZW9e6Rzs4GEe2RjjC0IR1AWr1hHAlWT1hHcrwlbPNIazQo5Q3t6M0jXQDKDc3VoZVLoxkije2N1LXVMTJtpF/7FyQXkJeYFzBDOtWaSkpcSp/H0x7p6Ecp1adHWiwWzNnZHQsSNZGBNqQDSGu7Jw+orxhLkvZIa8KZtkZw2nuPkfZtb9Kxm5rI4lDjIQBGpI3wa38RYXre9IAZ0gW93fXxoqsbxgbupiZUa2uvHmnwZO5wVmpDOpIYkCEtIq+KyCUiAywPFaP4vM+JXo90SrxFLzY0mKuvvpp///vfuN262MEJ9FbV0EdHURYdr2cEWnf952DjQQBK0kr8HmN67nSONB2hunVod2TKm8t7DevwMSx5GHVtddid9iEdK1KJFRujr2IsPiy5uTiqdGhHJDFQpX0C+AqwW0QeFpGJQZQpYvHFSCd6M3YkxVn0YkOD+eY3v8kLL7zAuHHjuP/++9mxY4fRIoUPHcVYepnUtUfaULTu+s+hxkMIQnGq/8VspuVOA4YeJz0QQ9qX0SOGvdIxYWP0VR7chyU3V4d2RBgDMqSVUu8qpb4KzAQOAMtE5FMRudlbqlODp6ohHPdIJ8dbdGiHwSxYsIDnn3+e9evXU1JSwnnnncepp57KX//6VxyOoae2img6irH0Mql3VDfUHmkj6Et3gR5TUYjIIyLysYg82m17oYi85523F3Rre0NEfha8TxJ6DtoOMix5GPHmeL/HKM0uxWKysKlqk99jtDhaqG+r7zX1nQ+foR2rcdKxYmN0GNJ9eKSteXm4ampRTu2EixQGfBtFRLKBm4BvAJ8Dj+JR+mVBkSwC6Zy1AyA53qwXG4YBNTU1PPvsszz99NOcdNJJ3Hnnnaxfv57zzjvPaNGMxbeIsHtVQx/WBEjI0B5pA+lNd4Hx3fuKyEwgWSk1H4gTkTmdmu8HfgSc73327TMdSAjqhzCAgw0H/Y6P9hFvjmdS1qQheaTLW/rOIe1De6Rjw8ZwVnnm3J7Kg/uw5OaC242zpjZUYmmGyEBjpP8JfAwkAZcppS5XSr2klPoO0PtS5BijI490p9COJu2RNpQvfOELzJ8/n5aWFt58803eeOMNrr32Wh577DGampqMFs9YmqtATJCU1Xuf1AJd3dAg+tJdep675wHvel+/C5zSqW0asFIp1QTYRCTVu/27wJ+C9BEMQSnFQdtBvzN2dGZ67nS2Vm/1uzBLeVPfOaR95CflI0jMGtKxYmM4q6qQxERMySdWNfTh81br8I7IwTLAfk8rpd7qvEFE4pVSbUqp2UGQKyKxd4uRTtEeacP5xje+wcUXX9xlW1tbG/Hx8axdu9avYg1RQ3Olpzy4ydx7n5T847HUmpDSl+4C23vYJQPY633dAEzu1GZWSqlObZkiUgRUAvWBk9p46tvqsbXbGJE6NI80eOKkF29fzO663ZRmlw56//5ySPuwmq3kJubGbGgHMWJjOKurseTk9Pm701GUpaqSrl9hTbgy0NCOnuLnVgZSkGigpVuMdFKchZZ2F2636ms3TRD50Y9+dMK2efPmGSBJGNJc3Xt8tI/UAh0jbRB+6G49kOZ9nUZXA7nzrTFf2/fx3D7vFRFZKCJrRWRtVYR4yHwZOwLlkQb8jpM+1nwMk5jITeq/THlBSkznko4JG8NZXd1nfDR08kjrFHgRQ58eaREpAIqARBE5ieMLXNLw3ILRdKLV4cJqFqxmz/VJcrzHoG5xuEiJH6jzXxMIysvLOXLkCK2trXz++ef4nHGNjY20tLQYLF2Y0FQJyTl990nJ98RIKwWx7L0PIUPQ3ZXAbcDLwALg2U5tm0RkHrAJSFNKNYrISG+fLCBbRJYppT7sPKBSahGwCGD27NkR4RE4ZBtaDunODEseRk5iDhurNnLdxOv67e9wOCgrK8Nu96Sxm8UsppROYc/OPf3u++1h38bhdrB9e083GyKPhIQEiouLsVp7XysYazaGs6qK+DFj+uxjycnp6KuJDPqz7i7AE/xfDPyu03Yb8MMgyRSxtLa7OqoagidrB0BLm1Mb0iHm7bff5tlnn6WsrIzvf//7HdtTU1N56KGHDJQsjGiugqJZffdJLQBXO7TW9R1LrQkY/uquUmq9iNhF5GNgo1JqjYg85o0z/RXwHJAIPODtfwGAiJwFLOhuREcqBxsPYhYzxSn+p77zISJMy5k2YI90WVkZqamplJSUICIcaDiAGzej00f3u295czm19lomZk2M+JAzpRQ1NTWUlZUxalSfZdpjysZwVleTfPLJffYRqxVzVpY2pCOIPq07pdTfgL+JyNVKqVdDJFPE0tru6sjYAZDsrXDY1OaknxvomgBz4403cuONN/Lqq69y9dVXGy1OeNJc3Xt5cB8duaQrtCEdIoaiu0qpO7u9/473uQw4p5d9PgA+8EvYMORQ4yEKUwqxmgOTNW163nTeO/wedfY6MhMy++xrt9s7jGgAh9tBgmVgSVGsJitKKVzKhUUi2/EiImRnZ9NfOFAs2RjutjbcDQ19ZuzwYcnN7UiVpwl/+gvt+JpSajFQIiLf796ulPpdD7vFLC0OV0d8NHTySLfrzB2hZvHixXzta1/jwIED/O53J6ppZ09fTOJohXZb/6EdHdUNyyFvUvDl0mjdHSIHG4ee+q4z03I8hVk2VW3izOFn9tvfZ0QrpXC4HaSaUvvZw4PV5DH8HW4HFlNkG9LAgLzqsWRjuKq9qe9y+plz8Sw41B7pyKG/xYa+HC0pQGoPD00nWttdJMYdnwCTvd5pXd0w9DQ3NwPQ1NSEzWY74RHzNHsn6f4WG6Z4DWmduSNkaN31H6UUBxsPMjJ16AsNfUzOmYxZzIPOJ+10O1FKEWeKG1B/nwfd4RpaoaiUFE+2uAMHDiAivnSJANxxxx08++yzANx0002MGjWK6dOnM378eG644QaOHDlywjg+nn32We644w4Adu7cyVlnncWMGTOYNGkSCxcu9FfcmLExnF5D2jwQQ1pXN4wo+gvteNL7/L+hESeyaXU4SbQevzZJ8nqkm3UKvJBz2223AfDAAw8YLEmY0uQzpPu5zegL/dC5pEOG1l3/qbHX0OJsCahHOtGSyPjM8WyqHlzmDl/u6YGGmHT2SAeKvLw8Hn30UW677Tbi4k406H/961/zxS9+EaUUv//97zn77LPZsmVLj307893vfpe77rqLK664AoDNmzf7JV8s2Rg+w7i/rB2+Ps7qapTLhZj7SE+qCQsGWpDlVyKSJiJWEVkuItUi8rVgCxdpeGKkj1+bpMT7PNI6tMMo7r33XhobG3E4HJx77rnk5OSwePFio8UynuYBGtLxqWBNOl5OXBMytO4OnkCmvuvMtNxpbK7ajMs98Lm8w5A2DcyQNosZEQmoIZ2bm8u5557L3/72tz77iQh33XUXBQUF/Oc//+l33GPHjlFcfHwx59SpU4ckZyzYGM6O0I4BGNJ5ueBy4aqrC7ZYmgAw0DzS5yulGoFLgTI85WnvCZpUEUpLt6wdPqNah3YYxzvvvENaWhpLly6luLiYXbt28etf/9posYzHZ0in9DOpixxPgacJKVp3B8+hRk/qu0CGdoAnn3SLs4W9DXv77+yl3dUODNyQFhGsJmtADWmA+++/n9/+9re4XP1fBMycOZMdO3b02++uu+7inHPO4aKLLuKRRx6hvr5+qGJGvY3hrKoGESzZ/S/a1tUNI4uBrmjwzQQXA0uUUrWRnp4nGNgdPWftaNaLDQ3D4fD8KL311lt8+ctfJitLZ54APFUNoX+PNOiiLAahdXfwHGw8iEUsDEvpu5LgYOlcmGV85vgB7fOHz//ArtpdJFkHng7Z7vTkn+4t08fErIncN/e+AY8HMGrUKObOncsLL7zQb9/jxS97xve7f/PNN3PBBRfw3//+l9dff50nn3ySjRs3+qpu+kPU2xjOqirMWVmIpX+z63hRlkqYpBd5hzsD9Ui/KSI7gNnAchHJBezBEysyaWnvmrUjyVeQRXukDeOyyy5j4sSJrF27lnPPPZeqqioSEgaWjiqqaa6GuFSwJvbfV3ukDUHr7uA5ZDtEcWpxwLNeDE8dTmZ85qAWHLqVe9D5oEUEN+7BitcvP/zhD/nlL3+J29332J9//jmTvIZbYmIi7e3tHW21tbXkdFooV1hYyC233MLrr7+OxWJhy5YtQxEx6m0MX3nwgWDtKBOuPdKRwIBmG6XU/SLyS6BRKeUSkWbgiuCKFnm0OlwkdvJIW80m4iwmmvRiQ8N4+OGHue+++0hLS8NsNpOcnMzrr7/e5z4i8gieCX1957y8IlIILAYSgJ8opd4VkVuBr3u7/EEp1b/bJxwYSFVDH6kFsGd5cOXRnIA/uhvrBDr1nQ8RYVruwAuzANw85WbiTHGDkqeypZKqliomZU/CJAP1c/XPxIkTKS0tZenSpcydO/eEdqUUjz32GMeOHePCCy8E4Mwzz2Tx4sXccssttLa28vLLL/OrX/0KgP/+97+ce+65WK1WysvLqampoaioyG/5YsHGGIwhbdahHRHFYC7bJ+HJ9dh5n+cCLE9E40l/13WFbUq8hRa92NBQtm/fzoEDB3A6j1/Q3HDDDT32FZGZQLJSar6IPCEic5RSn3mb7wd+hKfM8lLgXWCZUuopEbECq4DIMKSbqwYW1gEej3S7DdqbIS65//6agDEY3Y11lFIcth1mbsGJhmIgmJY7jQ/LPqSxvZG0uLR+ZXG4HCRbB/d98cVTO91O4swDS5s3UP7nf/6Hk046qcu2e+65h//7v/+jpaWFU045hffff78jY4cv28cf/vAHlFLccMMNnHHGGYAnfv/OO+/suEPy61//moKCgqGKGNU2hrOqivjR/Ve4BDDFxWFOT8ehi7JEBAMypEXk78AYYAPgswoVUaTkQ8XhcuN0K5KsXQ3ppDizTn9nINdffz179+5lxowZmL1phESkL2NkHh4DGe/zKYDPkJ4G3KmUUiJiE5FUpdQBb5uT49+N8Ke5GrL6LN17nM5FWbLHBE8mTRf80N2YprKlklZna8AzdviYluspzLK5ajOnFZ3WZ1+XcuFW7gHnkPbROQWev4Z0U1MTACUlJV3CLaZPn94ltMOXT7o3ioqKWLp0aY9tv/vd73osFuQv0W5jKKU8HuncAd4FRBdliSQG6pGeDZSq/lYidGOQt8hvAn4AHAPWKKXuHcyxjMZXvbC7Rzo5zqKzdhjI2rVr2bZt22BiFTMA39L8BmBypzZzp+9AA5AJ+Cpk3A78q7dBRWQhsBBgxIjA33oeNM2VMHzOwPp2LhOuDemQ4YfuxjSHbJ6MHcEI7QCYmjMVQdhUtalfQ9qXsWOwxnAwcklHCH7ZGJGCu6EBHI4Bh3aALsoSSQw0CGsLMKj7Np1vkQNxItL5V9t3i/x877OPXyulzoo0Ixo8GTugB0M63qxLhBvIlClTKC8f1EK5esB33zbN+95H539kR5uInIxntfkvextUKbVIKTVbKTU7dwAJ+YOK2wUtNf1XNfTR2SOtCRl+6G5ME6wc0j6SrcmMzRzLxur+Fxx2GNKD9UgHqLphBDJoGyOSGEwxFh+WvDycldqQjgQG6pHOAbaJyBqgzbdRKXV5H/sM6ha5d/v3ROQG4H+VUhG1uqnDI23tbkhbaNIeacOorq6mtLSUuXPndknN9MYbb/S2y0rgNuBlYAHwbKe2TSIyD0+MdJpSqlFEioDfApcrpSLjiqmlFpR7EDHSuky4EfihuzHNocZDWE1WCpKCZ49Nz53O2wfexq3cfS4GbHN5fiYHWtXQh0lMWEyWWPRID9rG8DowHsHj4FirlLor6FL6ic+QHkh5cB8d1Q2V0nelwpyBGtIP+jF2BoO7Rf4vPPFQ2cA7IjK7u2ESdrfHO9HaiyGdFGemsrGtp100IeDBBx8cVH+l1HoRsYvIx8BGpdQaEXlMKfUd4Fd4dDQR8NVv/gmQD/zTO9ldpJRqDZT8QcGXyi41f2D9k7LAZNUe6RAzWN2NdQ42HmR46nDMpuCVVJ6WM41Xdr3Cvvp9jM0c22MfpRTt7nbizHF+Zd6IM8d1GOKRzCCjNB704xAHgXOUUnYReV5Epiql/KtVHmQcFZ5Fg9ZBLMi05OaCw4Grvh5LZmawRNMEgIGmv/tQREYC47zxzElAf7NVPYO4Re6tagRQJSK78BgnR7vJsQhYBDB79uywiqVqdXi8zknxXU9pcrxFLzY0kDPPPJODBw+ye/duFixYQEtLS78VvjrH83vff8f7XAac063ttkDLHHR8BnHqAItWiEDaMGg8EjyZNCfgj+7GModsh4IWH+1j7jBPRpDV5at7NKQTEhKoqamhzdzm92LBOFMcTY6mIclpNEopampqBpz33B8bQynV+co+rBd7Oys8olryB+i8wBPaAZ6iLNqQDm8GmrXjVjye4Cw8K2uLgD8D5/ax22BvkfueE4FxQEQFBzV7U9wl68WGYcVTTz3FokWLqK2tZe/evRw5coTbb7+d5csjKnIosNiOeZ5TB3ELPGMk1B0MjjyaHtG6O3Dcys1h22FOK+x7EeBQKUopoiiliDXH1vDVSV89ob24uJjDZYc5WH2QZGsyLfEtgz6Grd2Grd2Gq9wV0bf0ExISKC4uHlBfP20M377TgByl1LZu28PmDrajvBxzZiamQVR+tOT5qhtWwYQJwRJNEwAGGtrxbWAusBpAKbVbRPpcqeTHLfK7RORCPAsgH1ZKRVSQmC9GOimuJ4902F4oRz2PP/44a9as4eSTTwZg3LhxVMZ6bk5fue+UgXtHyBwJu5cFRx5Nj2jdHTgVzRW0udqC7pEGOGXYKbxz8B1cbtcJYSRWqxVrtpXvffQ9Hpz3IKeMP2XQ4/93/3+556N7ePXyVwdcjjwKGLSNASAiWcAfgWu6t4XTHWxneQWWQebZtuiiLBHDQAO42pRSHbVCvQnT+1VMpdSdSqn5Sqk7vO87bpErpc5RSs1TSr3j3fa/3vcnK6VeHvxHMZYWb/hGUjePdGqChXanuyOrhya0xMfHdxQYAHA6nRHt5QkItmOQmAWWgXtHyCzxLDZsH7yHTeMfWncHzkFbcDN2dGZuwVxs7Ta2127vsX1n3U4AJmT550X0XQwcbjzsn4CRyaBtDG+fxcA93cI8wg5HRQXWQYR1QCdDWl88hz0DNaQ/FJEfAokich7wD+DN4IkVefi8zknxXQ3prGTPD2Ftc/sJ+2iCz5lnnslDDz1Ea2sry5Yt40tf+hKXXXaZ0WIZi6184PHRPjJKPM/1hwIujqZntO4OnEONHr0MiSHtjZNedWxVj+276nZhEhNjMvzLuT48dThw/OIgRvDHxvgSMAf4pYh84A0XDUuc5eVYCgZnSJsSEjClpWmPdAQwUEP6fjwxy5vxxD2/Rdf8zzFPq9cjndwttEMb0sby8MMPk5uby9SpU3nyySe5+OKL+dnPfma0WMZiOzbwjB0+Mr0GSn1M/bgbij+6KyKPiMjHIvJot+2FIvKeiHwqIgu8224VkVXex1eC+FGCzv6G/SRaEslLGmBu9CGQk5jDhMwJfFz2cY/tO2t3MiJ1BImWRL/GT41LJSshq+PiIEYYtI2hlFqilMr11p44Sym1MgRyDhq33Y6rrm5QGTt86KIskcFAs3a4ReRfwL+UUvq/2gO+xYbd099pQ9pYTCYTV155JVdeeSWGF0IJF2zlkDdpcPtklnie9YLDkDFY3e1cBEtEnhCROUopX+5+XxGsTcBSPLn9lymlnhIRK7AKeCE4nyT47K3fy6j0UX6lm/OHc0eeyxMbnqC6tZqcxOO5gZVSbKzayCmFg4+N7szItJHsb9g/VDEjhmi2MZwVnjUplnw/DOm8XB3aEQH0OeuIhwdFpBrYAewUkSoR+UloxIscWtqdJFrNmExdYxi1IW0MSikefPBBcnJymDhxIhMmTCA3N5ef/vSnRotmLG63J9Z5MBk7wFO8xZoEdQeCIpbmOEPQ3Z6KYPmYBqxUSjUBNhFJVUod8LaFdeqwgbCvYR9j0kNXvn7BiAUoFO8deq/L9sO2w9TYa5iZN3NI44/NGMue+j2DzcUcccSCjeEo9xjS1mHaIx2t9Hf5/j3gNGCOUipbKZUFnAycJiJhW0XICFraXSTHn5j2MitJG9JG8Pvf/54VK1bw2WefUVNTQ21tLatXr2bFihU88sgjRotnHC3VoFyDj5EW8Xila/f221UzNIaguxmALx+/r9CVj56KYPm4HU9BrBMQkYUislZE1laF6Q96U3sTFS0VjM4YHbJjjs0Yy4jUEbxz8J0u29dWrAUIiCHd2N5IVWt4nvMA8j2i3MbwJ4e0D0uuxyMd7RdUkU5/hvQNwJeVUh33mJRS+4Cveds0XlraXSTGnWhIpydaMZtEG9Ih5rnnnmPJkiWMGjWqY9vo0aNZvHgxzz33nIGSGYyvqMpgDWnwhINU9pypQBM4hqC79QyiCBZ0lFm+GPhlTwMqpRYppWYrpWaHa2jUvoZ9AIxOD50hLSJcOvpSVh9b3SWW+f3D7zMseZjfCw19jMscB8Duut1DGicCiHobw3HMY0gPNmsHgHVYIcrhwFVdHWixNAGkP0PaqpQ64T/ojWGyBkekyKSl3XnCQkMAk0nITLJSow3pkOJwOMjJyTlhe25uLg5HRKUoDyy+rBsZwwe/b+4kz2LDtsiuuhbuDEF3V3K8gMUCPHHPPjaJyDwRSeZ4Eawi4LfAjUqpiA3t2FvvuUsyVON1sHxx/BexiIUlO5YA0NjeyKdHPuXcEecOOU3huAyPIb2nfs+Q5Qxzot7GcJQdxpyTgykpadD7xg33FLRpP1wWaLE0AaQ/Q7ov609bhp1oaXedkEPaR3ZyPFW2thBLFNt0zr87mLaop96bmzbdD0Pat0Cxemfg5NGcgL+6q5RaD/iKYLl9RbC8zb8Cfo4ndvoh77afAPnAP73pw/xLM2Ew+xv2E2eKoyilKKTHzU3K5ZLRl/DSzpfY17CPF7a/QLu7ncvHXD7ksTMSMshNzGVX3a4ASBrWRL2N0X64jLgBVnjsjnW4J6e4oyymcopHHP1l7ZguIo09bBcgIQjyRCzNbc4Tqhr6KMpM5Gh9a4glim02btxIWlraCduVUtjtdgMkChPqD0FcKiRm9t+3Oz5DumIbFM0KrFyaDoaiu0qpO7u97yiCBZzTre22IQsbBuxt2EtJegkW00AL9QaOO2feyYdlH/K1t75Gi6OF80aex6TsQWbE6YVxmePYWRv1F61Rb2M4Dh8mcaZ/MfPWokIQof2wNqTDmT5nHqVUzy5WzQm0tLvISem5UlxxZiJrD9SGWKLYxuWK2DvVwaXhsCesw59bz5mjID4djqyDmdcHXjYNoHV3sOys3cmsfGMu7HKTcnn6/Kd5dP2jZCZkct/c+wI29tScqTy1+SlaHC0kWQcfFhAJRLuNoRwOHMeOkT7cP4+0KT4eS34+Dh3aEdaE/hI+Smlud5Ic3/PpLM5MpNHupNHuIC0hKsK+NJFK/SHIGOHfviYTDJ8Dh3qu6KbRhJo6ex0VLRWUZpcaJsOErAn8acGfAj7utNxpuJWbrTVbmVMwJ+Dja4KP49gxcLuxFvsRSuclrriYdh3aEdaEJnt9DNDY6iQtoTdD2uNNOFTTEkqRNJoTqT/sX3y0j+GnQNV2aK0LnEwajZ9sr/VkkZmYNdFgSQLPtJxpAGys2miwJBp/aT/kMYDj/PRIA1iHD9ce6TBHG9IBwO1W2OwO0hJ79jaPz08FYGe5LZRiaTRdaa2Htgb/Mnb4GHmq53nfhwERSaMZCttroteQzkjIYGTaSDZVbTJaFI2ftB/yVIK1Dvd/zrUOL8ZZUYE7ltf2hDnakA4Aze1O3IpewzZG5SSTYDWx7VhPayo0mhBR4y2mkjWENGHDT4akbNj+RmBk0miGwI7aHRSlFJEen260KEHhpLyTWFexDpdbx81HIu179mJKTvarGIuP+JISz1j7Y6dkfKShDekA0Gh3ApCW2HNoh9kkTBqWxueH9O1wjYHUeIs75IzzfwyzBSZdDjveguaawMil0fjJ9trtTMoKTJaMcOS0wtNobG9kc/Vmo0XR+EHbnj3EjR0zpLzi8ePGdYylCU+0IR0AGls9RRLSewntAJg/NocNh+upb4mK1JiaSKR6N4jZk31jKJzyTXDa4ePfBkYujcYPGtoaONh40NCFhsFmXuE8TGLikyOfGC2Kxg/a9u4lfuzYIY0RN3IkWCy07daGdLiiDekA0OA1pPvKyHHWxDzcCt7bURkqsTSartTshswSsAyxIE3uBJh1E6x6HD78NdgqAiGdRjMoPq/8HPCEP0Qr6fHpTMuZxvuH3zdaFM0gcdbV4aqpIX7M0AxpiYsjflQJbbujvlx8xKIN6QDg80j3ttgQYEZxBiXZSbyw+lCoxNJoulK9G3LGB2asC38BpVfA+z+D346H306EV2+FfR+AUoE5hkbTB+sr1mM1WZmaO9VoUYLKRaMuYlfdrlgozhJV+Azf+HFDM6QB4saO1aEdYYw2pAOAL0Y6tZf0dwAmk/C1U0ay9mAdGw/Xh0gyjcaLww7Vu45XJxwq1kS45jm47SO48GEYeRrsXQ7PXQEvXKu91Jqgs75yPZOzJxNv7rkQVrRw8aiLsZgs/GvPv4wWRTMI7Nu2ARA/YcKQx0oYPx7H4cO4bDrzVziiDekAUNPUBkB2L5UNfVw7ZziZSVZ+8472LGhCTOU2cDuhcEZgxx023RMz/cW/wF3b4Pyfw/4PYdGZcEznv9UEhxZHC1trtnJSfvSGdfjISMjg/JHn8+ruV6mz6wXrkYJ902Ysw4Zhzcsb8liJ06cD0LpRp0IMR7QhHQAqbW0kxZlJ6aWyoY/UBCvfOmssH++uZtU+nfFAE0KObfA8D5sevGNYE+DUO+Abyz2LGp+5CHb+J3jH08Qsq46twul2clrhaUaLEhIWTluI3Wln0aZFRouiGSCtmzaRODUwYUcJ06aBCK0bNgRkPE1g0YZ0AKiytZGbOrDbi9fPG0lhegIPvL4Vh8sdZMk0Gi9HN0BCBmSMDP6xCqbArcs9afaWfBlW/EHHTWsCykdlH5FiTWFm3kyjRQkJYzLGcPX4q3l++/N8Vv6Z0eJo+sFZXY2jrIzE6dMCMp45JYX4ceO0IR2maEM6AFTa7OQN0JBOsJr56RVT2Flh44kP9gZZMo3Gy8FPYfhcGEI+00GRWgA3/8ezIHHZj+H1b4OjNTTH1kQ1LreLj8o+4tTCU7Gae1/gHW38v9n/j5FpI/nOe9/h06OfGi2Opg+aP/X8f5LmnhywMRNPOonWDRtQDkfAxtQEBm1IB4DBeKQBFpTmc/n0Qn7/7i4+2V0dRMk0GqChzJP6bvRZoT1uXBJ88a9w5v2w4Xn48+lwaFVoZdBEHauPraaqtYrzS843WpSQkmxN5qnznyI/KZ/blt3GHcvv4N2D79LsaDZaNE03mj75BHNmJgmTA5fjPGX+6bibmmhZtz5gY2oCgzakh4hSivIGO3mpCYPa7xdfmMrYvBTuWLKePZVNQZJOo8GTkg5Cb0gDmExw9g/ghtfB2Q7PXAAv3whVu0Ing8vheWiign/t+RdpcWmcPfxso0UJOQXJBbx46Yt8a/q32Fy9mbs+uIvTXzyd25fdzpt736TF0WK0iDGPcjho/vgTkk87DTEFzsRKnjcPsVppej96coorpWhasYJDt93GrpNPYdcp8zj8rW/TvGq10aINCm1ID5Gqpjaa212Mykke1H7J8RaevmEOFpNw4zNrKG+wB0lCTcyz9TVIHwF5BlaAG30WfOtTOPM+2PMuPD4XFl/tWYzocgb+eIdWw7++Bb8rhf/L8Tx+V+rJdb39TY9Rr4k4jjUdY9mhZVw25jLizEMsLBShJFoS+eaMb/Lul97lmQue4WuTvsaBxgP88JMfcsGrF/CXzX/RBrWBNH30Ea66OtIuuTig45qSk0k+/XQa33orKsI7XE3NHL3nXg5//Ru07dhJ6gUXkLLgXFo3b+LQTTdx9L77IibdX99pJjT9sr/Kc1ttsIY0wIjsJJ69eS7XPrmSG59Zw8u3z+uzzLhGM2gaj8Le9+H074UuPro34lPh7B/C3IXw2dOw7llYch0kZsKES2DiJTDiFEjK8m98twt2/Bs+/QOUfQbx6TD2HMidBCio2gl734PNL0NiFky7BmZ8BQqmGX9uNANi0WZP1oobS280WBLjsZqszCmYw5yCOdw16y7WVazjr1v+yu/X/57ntz/PHSfdwRVjrsBsMhstakxR9+JLmHNySJk/P+BjZ1zzJcrefx/b8uWkXXhhwMcPFfbt2znyvbtoP3yYnO9+h+xvfANTnOfC2G23U7NoEdVPLqJl7TqKHvkdidMCs2gzWGhDeohsPtIAwISCVL/2n1KUzpPXz+bmZ9dw9ROf8vtrZzClKD2QImpimRWPep5n3mCsHJ1JzoGz7of5d8Out2Hb67D9Ddiw2NOeMx4KpkL2WMgaA1mjIb0IUvKhJ6PAVu4ZY80iqNnjKYN+0a89RnJ8Ste+LqfHmN74Aqz9K6z+M+RPgelf9hjWKUPP+aoJDusr1vPqrlf56qSvMixlmNHihBUmMXUY1RsqN/Cbtb/hgU8fYPH2xdw9625OK4qNNIFG07JuHc0ff0zu97+PWAJvXqWccQZxI0dS9YfHSD33XMQaWY43pRR1S5ZQ+fAvMWdkMPJvz5I0Z06XPqaEBHK/+11SzjiDI9+/mwNf+Sp53/8+WTfdGNBQmUAiKohpqUTkEWA2sF4pdWen7YXAYiAB+IlS6l0RSQVeALKAJ5VSz/U19uzZs9XatWuDJvtAWfjcWrYda+ST+84Z0jif7qnmO0s+p6a5nVkjM5k3OpsZwzOYMSKDnH4KvWj8R0TWKaVmd9sWNL2FEOru4c88MckzvgJX/DH4xxsKzjY4vAYOr/Z4kyu3Q8NhUJ1SRIoZUodBWiEkZoDTDvWHoO6Ap33YDDjtTk+mkIF44VrrYMs/YcMLcGStZ/zRZ3o84xMugbTwNdZ60lvv9qicc/fW7+WWt28hyZLEK5e/QrJ18HcAYwmlFO8cfIffr/s9ZU1lnFZ4Gt+f/X3GZ443WrReddePcXrU9Z4Ihe666uvZf821qPZ2xrz1b0xJSUE5ju299yj71rfJ+vot5N9zT1COEQzay45Q8Ytf0LR8OclnzKfw4YexZPV999HV0MCxH/0Y27JlJJ95BsP+7/8CUuDGX3rT3aB5pEVkJpCslJovIk+IyByllC8B5v3Aj4BNwFLgXeBWYAnwEvC+iLyolArrQMbKRjsf7KziKyePGPJYp47N4b27z+Lvqw6wbFsFf/pgD27vNc6E/FQumJzP+ZMLmFyYhnS7Da2UotHuxGZ3EG8xk2A1kWg1YzGH59VbOBMVeutywo6l8OadHk/u+f9nqDgDwhIPo+Z7Hj6cbR4juXY/NB7xhKk0HvW8bqr07DNshsfbPuESyJs4uGMmZsKcr3seVTth4xLY9gb8+27Po3gOlMyH4Sd7KkKm5Id1CEhU6G43qlureWPvGyzatIgEcwKPn/u4NqIHgIhwQckFnD38bF7c8SJPbnqSL735Jc4dcS7njzyfWfmzyE3KNVpMv+lH10OKcrtpWb2a8v/7Gc5jxxjxt2eDZkQDpJ5zDhlfvo7avzyDq76e3O98B2tBQdCONxTcbW20fr6BhjfeoHHpUjCZyLvnHrJuvmlA3mVzejpFf3jU48X+xcPsPe980q+6kvRLLiFh+vSOcBCjCWZoxzw8kzXe51MAn6JPA+5USikRsXk9I/OAbyulXCKyEZgAbB7MAVvbXfzfvz317T2OdoVSntfK95rj7+l4rzptP/6ezvt5jdrO42w90oAI3HhqiV8nqDvpSVbuOGccd5wzjpZ2J5vLGvj8cD3v7ajkj+/v4Q/v7SEvNZ4RWUnEWUw0tzmpbmqnqqmNdueJxV2sZiHBaibRaiYxzkyCxUxCnJlEq4nkOAtJ8RaSrGZEwOlWuLwPp9uN3eHG7nDR6nDR2u4CIN5iIs5iIt5i9j6bSLCaibeYcCuFw6Vod7lpd3r2tTtctDndmE3Spa/v2WI2EQqzZERWEredOWag3UOut4BncdymFz0eWKWOP6O6bXOfuA11/LW9wVMOvLXOE7Jw3fMegzESscRD7gTPI9jkToAFD8K5D0DVDs+FyM7/eOKt3d7FkJZEyBzpOZ/xqRCXAuY4EJP3Id6HCZDAGN2TLoMxA77bZYjuLtmxhN11u3F77x64lds7T6qOZzfuLu9PaPft431vd9o50nSEI01HUChOKzqNB055QId0DJI4cxw3TL6BK8ZewdObn+aNvW+w7OAyALISsshPyicrIYskaxIWkwWryYrFZMEkJqSf2bm/9jtOuoPMhKDNPX3p+oBQDgflP/+558fc7UYpN7i9c6nvvbcN5UZ1b3MrXA0NtO/bh6uuDsuwYQx/+mmSZga/SFDBj36EOSWFmmf+SsOr/yRu1CisRUWY01IRqxWJixvYXTl/cLtQLje4XJ7z4HKj3C5wec+Ty427uRnHsWM4jh0DhwNJSiL9qqvIuf02rMMG9x0WEbK+8hVSTj+d6iefpOGfr1H/4ktgsRA3ciSW7GzMGRlIXBxiNoPFjJjMnoxRfkzB1oICcm6/fVD7BNOQzgB8FUcagMmd2szqeExJA5Dp7d/YbVsXRGQhsBBgxIgTvcBOt5t3tpbjO3sinleeZ+n0vlN7D22CZ4jO7339fPsBjMlL4Xdnj/VroWF/JMVZOHl0NiePzub2M8dQ09TGu9srWLWvlqP1rbQ73WQkxTEmL4Xc1HhyU+JJS7DS5nJjb/cYwD5D2O5w0dLue++mtd1JeaOd1nYXze0eI8FiMmEyeZ8FEuM8BnhKvIWclHgEaHe5aXO4aXW4qGtp9xjMThdtDo+xbDWbsJo9zwlWj2c8MykOt1LYHS5qm9tpcxzfx+kOTWXHqUXpgzGkMwiw3kL/ukvDYU82CaSTUWbqapSdsK2HfnHJMPFSGHceTLgYYqhgRUAQgbxJnscZ90B7CxxdDxVboe4g1B/0XKw0VUL7PnC1c/xqvduFTiDIKx2MIZ1BiOdcgA2VG1h9bDUiggmTd+6UDmPMM4d6njsbaF3avX1826xmK1NzpnLVuKs4s/hMJmSF4GIqikmPT+fu2XfzvZnfY3P1ZrZUb2F3/W6qW6upaa3haPNRXG4XTrcTp9uJS7n6HM/rauqTr0/9Opk9T4eBIIPedR0YgO4qhe2dZWAyeWwCEY/xZRLEN7f62ky+994273tTSjIp55xN8imnkLpgAabExGB93q6fzWwm7+67ybjmGhr/+1/smzbhOFaO48gRVHu75xGMsF2lPJ5ks9nzbDKB2eQxXH3PJhOmxEQSp0wh7YILSJx5Eklz5mJOGZqdFDdiBIU//zn5P/ghzStWYN+6lbZ9e3HV1dO2d68nk4nTiXK7US4nHbf0B0n8uHFhZUjXA2ne12ne9z46f0t9bb7+9h76A6CUWgQsAk/MU/f21AQra3903hDFDk+yU+K5ds4Irp0z9DASTZ/UE2C9hf51l6lf9Dw04UVcEpSc7nmEP/WEeM4F+OUZvxyCyJpQYjaZmZE3gxl5M4wWZajU07uuA/3rrsTFMf7TFcGTMATEDR9Ozq23Gi1GSDGnJJN2wfmkXRA+BZmCGUS7EjjX+3oB0Lmk2SYRmSciyUCaUqrR119EzMAMYGcQZdNoekPrrSZS0bqriRX60nWNJqQEzZBWSq0H7CLyMeBWSq0Rkce8zb8Cfo4ntukh77anga8CHwPPKKXagiWbRtMbWm81kYrWXU2s0JOuGy2TJnYJavq7YCIiVcBB79scoNpAcXpCyzQwjJZppFIqpMvXu+luuGH0/yOcCOdzofW2K+H8vxookf4ZBiq/1t0TifT/faAI9/PQo+5GrCHdGRFZG4i8lIFEyzQwwlGmWEb/P46jz0XkEA3/q0j/DJEuv5Hoc+chUs+DTjSs0Wg0Go1Go9H4gTakNRqNRqPRaDQaP4gWQ3qR0QL0gJZpYISjTLGM/n8cR5+LyCEa/leR/hkiXX4j0efOQ0Seh6iIkdZoNBqNRqPRaEJNtHikNRqNRqPRaDSakKINaY1Go9FoNBqNxg8i2pAWkZtEZKeIfCAiv/Jus4jI30XkExG53yC5HhGRj0XkUSOO75WhREQqvOfmHe+2e7zn5XkRsYZQlkIRWS8idhGx9CaLiHxVRD4VkaUiktb3qJpAEw56G2q0bkY+kai3InKyV58+FpFHvNsMmZ+Hgoh8X0Q+8b6OOPnDgUjU30DT0zwcSUS0Ie3l10qps5RS93rfXw5sV0qdDpwuIgWhFEZEZgLJSqn5QJyIzAnl8buxzHtuzheRXOBs73nZBFwZQjlq8ZRzXQXQkyzeifd24Azg78BtIZQv5gkzvQ0lWjcjmAjW24PAOV6580RkPsbNz34hIvHAdO9rI39fIpYI1t9A02UejjSiwZD+noh8JCLnet/Pw1MGF+B9INSK2fn47wKnhPj4nTnbe6V7FzAX+MC7PaRyKaXsSqm6Tpt6kmU8sFkp5Qy1fBogvPQ2ZGjdjHgiUm+VUuVKKbv3rROYhkHz8xD4BvA372vDfl8inIjU30DTwzwcUUS6If0vPBPQ1cBvRMQMZACN3vYGIDPEMhl9fB/H8BgAZwMLgNlhIhf0fI562qYJHRno8w9aNyONDCL4fyMi0/CURa4ngj6H9y7NmUqp97ybMogg+cOIDPR5i3giIhbFG57xYrfN5Uqp67yvq0RkF5CPZ0LyxTCmAXtCIuRxuh+/PsTHB0Ap1Qa0AYjIUjxf1iKj5fJSz4my1BMG5y2GqUeff9C6GWnUE6H/GxHJAv4IXAPMInzm54FwPfBCp/f1RJb84UI9Eaq/muNEhEfaexvsrG6P63yLfkQkERgHVAEr8cTagMcb+1mIxe18/AUYFPMjIqmd3p6G54LiTO97w+Ty8lkPsuwCpnjvKhgtXywSFnobBmjdjCwiUm+9C6oWA/copcrpWe/CmQnAN0Xkv8BkPHc8I0n+cCEi9VfTlYgwpPvgLhFZiSc262GllAN4E8+P3ifASqXUsVAKpJRaD9hF5GPArZRaE8rjd2K+iKwTkU+Bo0qp1cBH3vMyA09YTEgQEauIvItnYcrbwKjusnj/d08BHwM3Ak+GSj5NWOltSNG6GdlEsN5+Cc/6nV+KyAfAGAyan/1BKXWfUuoCpdSFwFal1P8SQfKHCxGsvwGl+zwsIicbLdNg0JUNNRqNRqPRaDQaP4h0j7RGo9FoNBqNRmMI2pDWaDSaKKG34g7eggfveYuALPBuu1VEVnkfXzFGYo1Go4lstCGt0Wg0UUA/xR3uB34EnO99Bk/BplOA+cDdIRVWo9FoooSISH/XEzk5OaqkpMRoMTQRzrp166qVUrmhPKbWXc1Q6UVveyru4MtaNA24UymlRMQmIqlKqQPeNifg6u+YWm81gUDPuZpIpTfdjVhDuqSkhLVr1xothibCEZGDoT6m1l3NUOlFbzOAvd7XDXjSkvkwq+Mry32FH2ze97fTS5YFEVkILAQYMWKE1lvNkNFzriZS6U13dWiHRqPRRAf19F7cobPHuaPNm2bqYuCXPQ2olFqklJqtlJqdmxtSJ6JGo9FEBNqQNgClFI72fu+kajQxh6O9DZ2S02/6Ku6wSUTmiUgykKaUahSRIuC3wI1KKT0hRSDK6Ua59fclGnG5XLhc+msZCURsaEekYm928NafNnFsbwMjJmex4KZSElPjjBZLozGcjcveYvlf/szE087gojvuRkSMFimiUEqtFxFfcYeNSqk1IvKYUuo7wK+A54BE4AHvLj8B8oF/es/1RUqpViNk1wyelg2V1L26G1OKldyvT8WSk2i0SJoAUV9fzzPPPAPA17/+ddLT0w2WSNMX2pAOMSte2U3F/kamnFnE9k+P8cYfNnDV92cSl2jcv8LhcFBWVobdbjdMhmCTkJBAcXExVqvVaFE0PdBub+XjJX/DZDax/ZMPKJ1/NiUzZvk9XrTo9GD1Vil1Z7f33/E+lwHndGu7bajyRct57o1wnTecdXZqX9mNNS8RZ20bda/vIffrU40WSxMgPvnkExobGwH44IMPuOKKKwyWqG+ibR4Y7PdeG9IhpLm+jV2rK5h6VjGnXzOOkmk5vPX4Jv79p01c+p3pWOPMhshVVlZGamoqJSUlUekFVEpRU1NDWVkZo0aNMlocTQ/s/3wtbc3NfPF/fsZ//vQ7Nix7a0iGdDTodCTobTSc594I5/Pf9PERcCuyb5hM66YqGt7aT/uxZuKGJRstmmaIuN1utmzZwrRp07BYLGzatImLLrqIuLjwvXMdTfOAP997HSMdQnauLsftVkw9uwiAkZOzWXBzKUf31POfJzZhb3YYIpfdbic7OzvivwC9ISJkZ2dHzdVyNHJ42xasCYkMnzyVCaeczoGN62lvbfF7vGjQ6UjQ22g4z70Rruff3e6ieW0FSdNzsWTEkzQzD0zQurHSaNE0AeDo0aPY7XbGjRvHlClTcDqd7Nu3z2ix+iSa5gF/vvfakA4hh7bWkF2cQnpuUse2cXPyOef6SRzZVc/zP1nF+8/vYMtHR6iv8N+I8Ido+AL0RbR/vkinbNtmiiZMwmQ2M27uqbgcDg5u3jCkMaPhfx4JnyESZPSXcPxsbbvrUO0ukmblAWBOiSN+TAat22oMlkwTCA4fPgx4UvaNGDGCuLg4du/ebbBU/ROO3xV/Gexn0aEdIaLd7uTY3gamnzv8hLZJpw4jpziFdf85wJ7PKtj28VEARk3P4ZwbJpGQHF7xeRpNIHG0t1F7pIxxJ58GwLDxE7BY4yjbtoVxc081WDqNJrxo3VKDKclC/KjjC9ASxmbQ8J8DuGztmPXi9YimoqKClJQUUlNTARg5ciSHDh0yWCpNX2iPdIgo39uA26UYPjGrx/bcEalceNtUvvHIGXz1p6dw8uWjObi1hjf/sAGnI/pT4IgId999vErxb37zGx588MGO94sWLWLixIlMnDiRuXPn8sknnwDwu9/9jq9//esd/Z5//nkuueSSkMmtGTp1R4+glJuc4SMAMFusDBs/kbLtWw2WbGiYzWZmzJjBlClTuOyyy6ivrwfgwIEDiAg//vGPO/pWV1djtVq54447DJI28nnttdcQEXbs2NGxbc2aNZx11lmMGzeOmTNncskll7B582YAHnzwQYqKipgxY0bHw/c/CleUy03r9hoSJmUj5uM/3/GjMwBo21dvjGCagFFRUUFeXl7H++HDh1NVVUVrq06o0xe++Xby5MlMnz6d3/3ud7jdbsCzYPPSSy8FPOf30ksvZfr06ZSWlnLxxRcP+djakA4RlYc8RcTyRqX12U9EyMhLYvbFJVzwjSlUHrSx+vXwjo8KBPHx8fzzn/+kurr6hLalS5fy5JNP8sknn7Bjxw7+/Oc/85WvfIXy8nK++93vsm7dOlasWEF9fT0/+tGPeOyxxwz4BBp/qT3iuZWZVXT8bk3xpClUHtxHW0uzUWINmcTERDZs2MCWLVvIysri8ccf72gbPXo0S5cu7Xj/j3/8g8mTJ/c0jGaALFmyhNNPP50XX3wR8PxgXnPNNTz00EPs3r2b9evX84Mf/IC9e/d27HPXXXexYcOGjkdGRoZB0g8Mx9FmlN1FwoTMLtuthSmI1UT7IVsve2oiAbfbTVVV1QmGNHgW9Gl6xzffbt26lWXLlvHWW2/xv//7vyf0+8lPfsJ5553Hxo0b2bZtGw8//PCQj60N6RBRdchGem4i8YNIczd6Ri6lpw1j03tlNFSFNmY61FgsFhYuXMgjjzxyQtsvf/lLfv3rX5OTkwPAzJkzufHGG3n88cexWCz86U9/4tvf/jb33nsvt9xyC6NHjw61+JohUHPkMCImMocVdWwbNnY8KEXlgei4iJw3bx5HjhzpeJ+YmMikSZM6yha/9NJLXHPNNUaJF/E0NTWxYsUK/vKXv3QY0n/84x+58cYbOfXU4+FBp59+OldeeaVBUg6dtgMNAMSXdM0rLGbBOiyZ9iNNRoilCRC1tbU4nU7y8/M7thUWFiIiOrxjEOTl5bFo0SL++Mc/nlDg69ixYxQXF3e8nzZt2pCPF1Yx0iIyBViEp5ztHuAWFSVlzqoO2sjvxxvdE3MvH82uzyr4bOkBFtxcGgTJuvLxy7uoPhzYyThneArzrxnfb79vf/vbTJs2jXvvvbfL9q1btzJrVtdUaLNnz+Zvf/sbAKeeeiqTJk3i3XffZfv27YETXBMSassOk1FQgKVTzs68UWMAqNy/l+GlQ8uP+/6zi6g8GFiDPG/kaM6+aeGA+rpcLpYvX94lBAnguuuu48UXX6SgoACz2UxhYSFHjx4NqJyhpP7NvbQfDewdhLjCZDIuG9Nvv3/9619ceOGFjB8/nqysLNavX8/WrVu58cYb+9zvkUceYfHixQBkZmby/vvvB0TuYNG2vxFzdgLmtBPjoK1FKbSsq0S5FWKKnoVfsURtbS0A2dnZHdvi4+PJz8/vciEezvznP/+hvLw8oGMWFBRw0UUXDWqf0aNH43a7qazsms3m29/+Ntdeey1//OMfWbBgATfffDOFhYVDki/cPNI7lVKnKqXme9/PNlSaAGFvcmCrtZM7InXQ+yanx1N6eiG711bQ3NAWBOnCh7S0NG644Qb+8Ic/9NtXKdWxsrapqYm1a9ficDioqqoKtpiaANNQVUl6XkGXbckZmaRkZVOxf28ve4U/ra2tzJgxg+zsbGpraznvvPO6tF944YUsW7aMJUuWcO211xokZXSwZMkSrrvuOsBzgbJkyZIT+px88slMmjSJO+88XrOmc2hHuBvRSinaDzYQP7Jnh0xcUSqq3YWzWsfSRiq+GP3MzK6hO0VFRRw9erQj5lczMHryw15wwQXs27ePW2+9lR07dnDSSScN2W4IK4+0UqpzIuU24LBRsgSSmqMeD29OcYpf+087u5hN75ex5aMjnHxZcMMWBuI5Dibf+973mDlzJjfffHPHttLSUtatW8c55xwvzLZ+/XpKSz0e+gceeICvfe1r5Ofnc9ddd/GPf/wjKLKJyCN4Lu7Wd64gJyIJwOPAKGCrr5qcZmA01dWQO7LkhO15o8ZQGQBDeqCe40Dji9lraGjg0ksv5fHHH+e73/1uR3tcXByzZs3it7/9LVu3buXNN980RM5AMRDPcTCoqanhvffeY8uWLYgILpcLEeHGG29k/fr1HVXhVq9ezSuvvNIlNj2ScFa14m52dsnW0RmrtxiLo6IZa15Sj3004U19fT1ms5nk5K6FdYqKili3bh21tbUdIY7hymA9x8Fi3759mM1m8vLyTrhTnZWVxVe+8hW+8pWvcOmll/LRRx9x9dVX+32scPNIIyKXi8gWIA+IisSYvpzQGfn+TW7puUmUTM1h60dHcDmj+4o0KyuLa665hr/85S8d2+69917uu+8+amo86rBhwwaeffZZvvWtb7F582b+/e9/c99997Fw4UIOHjzIsmXLAi6XiMwEkr13S+JEZE6n5u8CLyilztFG9OBwOZ0019eRkpV9QlteyRhqj5ThaI/sOzHp6en84Q9/4De/+Q0OR9eiS3fffTe//OUvu9zK1QyOV155hRtuuIGDBw9y4MABDh8+zKhRozj//PN59tln+fTTTzv6trRE7loTX3x0XEnPHmlLbiIAzkrtkY5U6uvrycjIwGTqapoVFXnWj0RKeIfRVFVVcfvtt3PHHXeckBP6vffe65gHbDYbe/fuZcSIEUM6XtgZ0kqpN5RSU4AjwKWd20RkoYisFZG1kXQLv76iBbPFRGpWgt9jTD2ziFabg32fR87n9pe77767S/aOyy+/nFtuuYVTTz2ViRMncuutt7J48WIKCgr45je/ySOPPEJCQgImk4k//elP3HnnnbS3twdarHnAu97X7wKndGo7C7hcRD4QkcsDfeBoprm+DpQiJfNEQzJn+EiUclN3NPJ/PE466SSmT5/esRDOx+TJk/uN49X0zZIlS7jqqqu6bLv66qt54YUXeOmll/jBD37A2LFjOfXUU3nllVe6pBh85JFHuqS/O3DgQIilHzjt+xsxpVix5CT22G6KM2POjMdRGbkXC7FOXV1dj5ljcnNzsVqt2pDuA18o3eTJk1mwYAHnn38+DzzwwAn91q1bx+zZs5k2bRrz5s3jG9/4BnPmzOlhxIETVqEdIhKvlPK5nxqBLpfWSqlFeBYjMnv27IhZhFhf0UJ6XuKQFoAMn5RFWm4iWz46wrg5+f3vEGE0NR1f4Jifn3+C5+ib3/wm3/zmN0/Yz5dP2sfs2bPZtm1bMETMAHxxBg1A51xlY4DfAz8EPhCRt5RSzs47i8hCYCEw5KvfaKKp1nOXoSePdHaxJ+1TTdkh8koiLxNLZ50GuoRubNmy5YT+N910EzfddFOwxYo6PvjggxO2dQ6h+fDDD3vc78EHH+ySqz7caTvQQHxJWp9V16x5STi1IR2x1NfX97jwzWQyUVhYqA3pPnC5eq+3cdZZZ3HWWWcBcM8993DPPfcE9Njh5pG+UEQ+FJEPgXzgHaMFCgR1FS1k+hnW4UNMwuT5hRzdXU+NTnFkBPWA755qmve9jwbgQ6VUM55sMydc6SilFimlZiulZufm5gZZ1Mihqa53QzpzWCFiMlFTFhVLJTQav3E2tOGqayOupOf4aB+W3CQcVa0od8T4mfpERB4RkY9F5NFu2x8QkZXex7lGyRdI2traaG1tPWGhoY+ioiLKy8txOp09tmuMI6wMaaXU60qpM72PbyilIj4g2OVy01ht9zs+ujOTTh2G2WJi60f6qtQAVgK+CXsBsKpT26fANBExAyVA9MffBAifRzq1B0PabLGSWVBITZnOn6qJbdo78kf3nULVmpcETjeuOnsoxAoq/axLeU4pNQ+4CDjx/n0E0tDg+R+np/d8sVRUVITL5TohnZvGeMLKkI5GGr3egUAY0okpcYydlceO1eW02wN7VRol6bp7ZaifTym1HrCLyMeAWym1RkR8JRR/CfwcWAE8rZQKeIB2tNJUW4PJbCExtWcDIbt4BDVH/PNIR4NOR8JniAQZ/SVcPlvb/kYkzox1WN+Znyx5nvhpR1VULDjsdV2KUmq/92UbEB7/pCHiCwVLSen5fxzuCw7D5bsSCAb7WbQhHWTqvSuoA2FIA0w5swiH3cWuNRUBGQ8gISGBmpqaqPoidEYpRU1NDQkJ/i/29I5zp1JqvlLqDu/773ifjymlzldKnaKU+kvfo2g601RbQ0pWNmLqeSrKLh5OfflRnN2yXfRHNOh0oPQ2mETDee6NcDr/7QcaiBuZipj7XmdjyfYY0q6aqDCkM/CslQJP+FxPMQ8PAk/2tHOkJSfoz5BOT08nOTk5LA3paJoH/Pneh9Viw2ik0esZSM/teaX1YMkflUbO8BS2fHiEyfML+1x4MlCKi4spKyuL6mImCQkJXcqCasIDnyHdG1nFI1BuN/XHjpAzomTA40aLToe73kbLee6NcDj/7hYHjooW0qb2v7bClGJF4kw4ayM/tIO+16UgIlcB2UqpF3raOdKSE9hsNgBSU3su3CYiFBUVhaUhHW3zwGC/99qQDjK2WjsWq4mEFGv/nQeAiDDljCI+eH4n5fsaGTam78UnA8FqtTJq1KgASKfRDI6muhpyS3ov5JFV6JnMao+WDcqQ1jodGvR5Dj5tBxtB9Z4/ujMigiUrEWdNVBjSK4HbgJfxrEt51tcgItOAbwOXGCJZEGhqasJisRAfH99rn6KiInbt2oXdbg+LOyU+Yn0e0KEdQcZWayc1OyEgnmMf4+bkY00ws+l9nc1AE7kopbDV1pCaldVrn6xhnrjA2iNloRJLowkr2g80glmIH9Gzp7I75qwEnLWRH9rRz7qUX+PJjvS2iLxumJABpKmpiZSUlD5tBV+c9LFjx0IllmYAaI90kLHV2IdUiKUn4hIsTD2ziPXvHGLWhU1+lx7XaIykraUZZ1tbj8VYfFgTEkjNyaX2qDakNbFJ24FG4opSEKt5QP0t2QnYd9Wi3GpItQvCAaXUnd3e+9alXGCMRMHDZ0j3hS/H9JEjR2LaAxxuaI90kGmqs5MSYEMa4KTzRxKfaGHV63v776zRhCF9FWPpTFZhsTakNTGJu91Fe5mNuFEDD+GzZCeAU+Gy6eRBkYTNZus1PtpHUlISWVlZYRknHctoQzqIONpdtNocAfdIAyQkW5l5wUgObq7hyM66gI+v0QSbARvSRcXUHimLihXhGs1gaD9sA5cifjCGdFZUZe6IGQbikQbCdsFhLKMN6SDS5F05nZodnEUB084uJiUrnk9e2R01law0scNxQzqnz35ZhcNxtNk7+ms0sULbvgaQ/guxdMbi/b2JkgWHMYHD4cButw/YkG5sbOzI8qExHm1IBxGbdyILhkcawBJnZt5VY6g+3MSOVeVBOYZGEyw6DOnM3hcbQqfMHXrBoSbGaN/fgLUwBVPCwJczmTPiwUS0pMCLCZqbm4Hec0h3xrfg8PBhnWwgXAiaIS0ir4rIJSISs8a6LcgeaYBxs/PJH5XGqtf34mhzBe040cTVV1/Nv//9b9zuiK9AH9E01dWQkJqGJS6uz35ZRb4UeLH1w6H1NLZRTjdth2yD8kYDiNmEOSMhrAxprct9018O6c4UFhYSHx/P7t27gy2WZoAE08h9AvgKsFtEHhaRiUE8Vlhiq7UjJiE5vW9DYSiICKd9cRwtDe18/s7BoB0nmvjmN7/JCy+8wLhx4wCKYlE3wwFbbQ2p/XijAZIzMolLTIq5BYed9fT+++9nx44dRoukCSHtZTZwuokfPfhaAZasBFxhZEjrObdv+qtq2Bmz2cyYMWPYvXu3XjcSJgTNkFZKvauU+iowEzgALBORT0XkZhEJTHWSMMdWayc5Iw6TObhO+WFj0hk7K4/P3zlEU134TJ7hyoIFC3j++edZv349QDsxqJvhQFNN31UNfYhIx4LDWKKznpaUlHDeeedx6qmn8te//hUgsvOaafqlbb+nOnZciX+GdDh5pPWc2zeDMaQBxo8fT1NTk84nHSYE1cITkWzgJuAbwOfAo3gM62XBPG640FTbFrT46O7Mu2oMbqVY/fq+kBwv0qmpqeHZZ58FyCEGdTMcaKobmCENsZsCz6enTz/9NCeddBJ33nmnzxgZb7RsmuDStqcOa0ES5uTB25nmzATczQ7cYRTup+fc3vEZ0snJyQPqP3bsWAB27twZNJk0AyeYMdL/BD4GkoDLlFKXK6Ve8iZUj4kKIrYae1DjozuTlpPI1DOK2bmmgkad9qhPvvCFLzB//nxaWloA9sSibhqNy+mgpaF+UIZ0U20N7a0tQZYsfOisp2+++SZvvPEG1157LY899hjoheJRjbvNRduBRuLH9x/61BMWrwPHFSZ3KPWc2zc2m43k5GTM5oEV3UlJSaGkpITNmzfr8I4wIJiT8dNKqVKl1C+UUscARCQeQCk1O4jHDQvcLjdN9W2kZobGkAaYvmA4AmxaHnueu8HwjW98g23btvGDH/wAwAGxpZvhQHOdJ/f5gA3pjgWHsZM/tbOeDhs2DIC2tjZf83bDBNMEnbZ99eBSJIzP9Gt/nyEdLuEdes7tm4HmkO7MtGnTqK2t1Tmlw4BgGtI/62HbyiAeL6xobmhHuVXIPNLgSbM3bk4+W1ccxd7sCNlxI40f/ehHPW2OGd0MB2ze1Hep/eSQ9pFVOBwgpsI7etLTefPmGSCJJtTYd9UhcaZBZ+zwYQ4zQ1rPuX3jjyFdWlqKxWJh48aNQZJKM1AGnpxygIhIAVAEJIrISRxfFJOGJ8yjr31PBh4BXMBapdRdgZYvVHSkvgtRjLSPk84fwc7V5Wz5sIzZF48K6bHDnfLyco4cOUJrayuff/6575ZYkoicRT+6qQksA61q6COjoAAxmWJiwWEvekpjY6Pv1rgmyrHvqiN+dAZi8c/XZUqyIHFmwzN36Dl3YNhsNvLy8ga1T0JCApMmTWLTpk0sWLCA+Pj4IEmn6Y+AG9LABXgWGBYDv+u03Qb8sJ99DwLnKKXsIvK8iExVSm0OgoxBp6MYSwg90gDZRSmMnJLNpvfLmL5gBNa4gcVcxQJvv/02zz77LGVlZXz/+9/3bS4Gvk//uomIPALMBtYrpe7s1ibABuAxpdTTARU8ChmsIW22WMnIHxYTuaR70VNSU1N56KGHDJRMEwoclS24auyknl7k9xgiEhaZO4Y658YCbreb5ubmQXukAebMmcPmzZvZvHkzs2fHfISMYQTckFZK/Q34m4hcrZR6dZD7di7P58TjmY5IfB7plBDGSPuYecFIXvvteravOMa0s4tDfvxw5cYbb+TGG2/k1Vdf5eqrrwZARHYppS7vb18RmQkkK6Xmi8gTIjJHKfVZpy6XA5XBkTz6aKqrwWy1kpDSfwECH7GSAq8nPdXEDq2bqkAgcfLAwp56w5yVgNPghedDmXNjhdbWVtxut1+G9PDhwykoKGDNmjXMmjULjz9HE2qCEdrxNaXUYqBERL7fvV0p9bsedus+xjQgRym1rdv2hcBCgBEjRgRI4uBgq7WTkGLFGh96j/CwsekUjE5nw7JDTD6jEHOQ81hHCosXL+ZrX/saBw4c4He/61DDfJ+e9qOb84B3va/fBU4BOhvSXwZeCrDIUUtTrSf13WAm/qzCYg5sWIfb5cI0wNXtkUgvetpBZy+1Jvpo2VxN3Mg0zGlDK+RlyUqgbXcdSinDDKwhzrkxwWBzSHdGRJg7dy5vvPEGBw8epKSkJMDSaQZCMCwsXyLEFCC1h0efiEgW8Efg693blFKLlFKzlVKzc3NzAydxEGiqtYc8PtqHiDDrwpHYau3s+azCEBnCkebmZsAzcdlsNl9ZVhMD080MoNH7ugHoWE4vIhcAH+K5i9IjIrJQRNaKyNqqqip/P0LU0FRbQ0rmwMI6fGQVFuNyOmmoim6d7klPOz800YujsgVnRQtJU4fmjQawZMajHG7cTcYtPB/inBsTDKY8eE9MmTKFhIQE1qxZE0ixNIMgGKEdT3qf/3ew+4qIBVgM3NMtzCPisNXYyRw2sOTqwWDklGyyi5JZ8+8DjJmVh8UavR68gXLbbbcB8MADD3Rse/DBB48NUFfr8SyYxftc36ntG8D1wHW97ayUWgQsApg9e3bMJ/5sqq0hf8y4Qe3TkQLvSBmZBYXBECss6ElPB0pvcfwiUohnbk0AfqKUeldELsKzuLtaKXV6AETXDJGWzys9YR1Thm5Im7MTAU/mDnPq0Lzb/jLEOTcmGIpHGiAuLo6ZM2eycuVK6uvrycjICKB0moEQzIIsvxKRNBGxishyEakWka/1s9uXgDnAL0XkAxGJyFxPSilsdaGratgTYhJO+9I4GqtaWfefg4bJEY7ce++9NDY24nA4AMYPUDdXAud6Xy8AVnVqGwf8C7gb+J6ITAywyFGFUqojtGMwZBb6cklHf5w0dNXTc889l5ycHBYvXtxr/85x/ECciMzp1Hw/8CPgfO8zeHR4epDE1wwS5VI0r60gYUIW5vShZ2CwZHrGCIeiLH7OuTHBUA1pgLlz5wJor7RBBDN49nylVCNwKVCGp6TtPX3toJRaopTKVUqd5X1EZJ5Je7MDZ5vLUEMaYPjELMafnM+6/xxg7/rj6+DcLjfVZU3UHG2KyapI77zzDmlpaSxduhSgnYHp5nrALiIfA26l1BoReczbNkMpdSHwW+D3Sqkdwf0EkU2rrRGno5207MF53RJTUklKz4iJBYfQVU+Li4vZtWsXv/71r/vapac4fh/TgJVKqSbAJiKpSqk6pVRb90E0xmDfUYvb1k7ynIKAjGf2LnR31hhvSPsz58YKTU1NWK3WIaWvy8jIYNKkSaxfv75z0SZNiAhG+jsfVu/zxcASpVRtrKwo7Uh9Z7AhDXDWVybSUNnKfxdtYdjYdFBQddiGs90NQMHoNC5cOJXkjNjJQen1ivDWW28B1A5UN7unvPOWt+38/tmACRnF+FLfpWYPfp1DVmFxzHikO+vpl7/8ZbKy+i0XnQHs9b5uACZ3ajOr41fNvhj/fgOuI2mBd6TTtOII5rQ4Eib6Vxa8O6Y4M6ZUK84w8Ej7O+fGAjabze/46M7MmzePbdu2sXHjxg4PtSY0BNMj/aaI7MATr7dcRHIB47/RIaCp1nNFGOoc0j1hjTdz5fdPYs6lo3C7FAiUnlbIgptLmX/teGqONLP08Y04HRGbaXDQXHbZZUycOJG1a9eCxzsXM7oZDthqPIstU7IHF9oBsWVId9bTc889l6qqKhIS+pxT6uk9jr/zF7x7W69E0gLvSKbtUCNt+xpImV+EmANnYFqyEg0vygJ6zu0Lf6oa9kRxcTFFRUWsXr0at9sdAMk0AyVohrRS6n48txpnK6UcQDNwRbCOF04YVdWwNyxWM3MvHcUX75vNF/7fLOZfO54JJxcw7exizv/6ZKoPN7FxefQXuvDx8MMPs3LlSt+krogh3QwHbDWDKw/emayiYuy2RloaGwItVtjRWU+tVivJycm8/vrrfe3SVxz/JhGZJyLJQJo37E4TJtiWH8KUZCF57rCAjmvJjDe8KAvoObcvAmVIiwinnHIKNTU17NmzJwCSaQZKMEM7ACbhySfd+TjPBfmYhmOrsWOJNxOfHOzTO3RKpuVQMi2H9f89yJQzi4lPDH+ZA8H27ds5cOAAQDbwRe/mqNfNcKCpthqT2UySH6vLszotOExKSw+wZOGHT0+dzuOZFW+44YYe+yql1ouIL45/oy+O3xuC9Cs8+p0IPAAgIrOBh4EpIvIucKlSynirK8aw76rDvrOO9ItKMAW47oA5KwHXxiqUy40YXE9Az7k9Y7PZGDt2bEDGKi0t5Z133mHVqlWMHz8+IGNq+idoVpOI/B0Yg6dssu+2oiIGvji2WjupmfERU2VoziUl/GNTNTtXHWPa2cONFifoXH/99ezdu5cZM2YAJOHJFBMTuhkO2KqrSM7MwmQavNFwPAXeYYonTu6nd2TTWU/N3gI0ItKrIQ29x/ErpcqAc7q1rcXjudYYhHK4qV+6F3N2Aimn+V8SvDcsWQmgwFXfhsWbDs8I9JzbM+3t7bS3twfEIw1gNpuZO3cuy5cvp6Kigvz8/ICMq+mbYLofZwOlnRa4xAy2WntYxEcPlLyRaeSNTGXLh0eYelZxxFwA+MvatWvZtm0bIsITTzxxuPuiQU1wsdXW+BXWAZCWk0dcYhKV+/cFWKrwo7OeaqKThv/ux1nZSs7NkxFL4D3GFm94obPWbqghrefcnhlqMZaemDVrFh9++CGrV6/m8st1JfZQEMx7PVuAwOTxiTBsNcZVNfSX0tMLqStvofpwk9GiBJ0pU6ZQXh7R9X4imqbaalIHmfrOh5hM5I8eS/ne3QGWKvzQehrdtGysomnFUZLnDSNhQmAydXTH3MmQNhKtyz0TiBzS3UlKSmL69Ols3LixY3xNcAmmIZ0DbBORt0XkDd8jiMcLCxxtLuzNjojySAOMPikXMQl7OuWbjlaqq6spLS3lggsuABgbK7oZDiilsNXUkOKnIQ1QMGYcVQf343QYV/o4FHTW08svv7zjoYl87LvrqH15J3ElaWRcPDpoxzGnxYNZDC/K4u+cKyKPiMjHIvJot+23iMh+Eem9QlEEEAyPNHhS4SmleP/99wM6rqZnghna8WAQxw5bwimH9GBITImjeEIGe9dVcsoVo6P6dvKDDz7Y8fqdd94px1NIRRMC7E02nO1tfod2gMeQdrucVB86QMEgy4xHEp31VBM9NK+roO6fu7HmJpJzQyliDZ4/S0yCJTPBcI+0P3Nu50qdIvKEiMxRSn3mbX4D+IgItzOC4ZEGyMnJYe7cuaxatYpZs2ZRWFgY0PE1XQmaIa2U+lBERgLjlFLvikgSENglyWGIrS4yDWmA0Sfl8eELO6k71kJWYbLR4gSNM888k4MHD7J7926AJuAzYkA3w4GGygoA0vP8XwRTMMazGr187+6oNqQ76+mCBQtoaWnB5YqdfO9GotyK1q3VtKyrpP1IE8rpxpIeT9yoNBInZhE/JmPQMc3OejsNb+2ndVM18aPTyb6+FFMIsiSZwyAFnp9zbk+VOj8DUEpVi0hgrU8DsNlsmEwmkpKSAj72mWeeydatW3n11VdZuHDhkConavommFk7bsVTESsLT/aOIuDPHM9zGpX4PNIpEWhIj5ziKZBxcGtNVBvSTz31FIsWLaK2tta3KSZ0MxxoqPTESQ7FkE7NySUxNY3yvbvwFE6NTjrr6d69ezly5Ai33347y5cvN1q0qMZZa6f2xR20H7JhzownYXwmYjXhrLXTsraC5pXHkAQziZOySZySQ8L4DMTas02onG7a9jXQsqGSlo2eQkRpF5SQekZxQAuv9IUlK4HWzdUhOVZv+DnnZtB7pc5+iYSqnL4c0sG4A5yYmMgXvvAFnnvuOV577TW+9KUvdWT/0QSWYF4OfxuYC6wGUErtFpG8IB4vLLDV2jGZJCJLbqdmJZBVmMyhrTWcdF54TjyB4PHHH2fNmjWcfPLJQOzoZjhQXzF0Q1pEGDZuAsd27QiUWGFJdz0dN24clZXRv4bBSNoP26h6ZgsoReaXxpN0Uh5iOm7kKIcb+546WrfU0LqthpbPK5E4E9bCFCzZiR15oF1N7Thr7TiONYNLIfFmkucUkHpWMZaM0DpZLFkJuFucuO1OTAnG1Anwc86tp/dKnf2ilFoELAKYPXt2WGYPC1R58N4YNWoUF154If/5z3947bXXuPLKK7FYYqNWRCgJ5hltU0q1+660vEVZwlKZA4mtxk5yZjwmU2TGGI8ozWLTB2W0253EGTTpBpv4+Hji4uI63seKboYDDZXlJKalE5c4tFuZRRMns2/9ZzTX15GckRkg6cKL7nrqdDqjeu2C0bQdbKT6mS2Ykq3kfn1Kj+nixGryeKInZaNcHm9z69YaHOXN2HfVoRxuQGFOicOcFkfKaYXEl6STMC4zqLHQfdE5c0dcoTHREH7OuSuB24CX8eQ7fzZY8hlFU1MTmZnBnb9OPvlknE4ny5Yto7m5mWuuuYbERONSIUYjwbSUPhSRHwKJInIe8C3gzSAeLyyw1dhJi7CMHZ0ZMSWbDe8e5siuekZN839BWDhz5pln8tBDD9Ha2goeT8c/iAHdDAcaKsrJyBt6VszhpVMBKNu+lQnzTh/yeOFIZz1dtmwZf/rTn7jsssuMFisqcVS3UvO3rZhT48i9dSrm9P7vKIrZRMK4TBLGhfeFnCXT83vkqrODQYa0P3NuX5U6ReRS4H5gjIi8qpS6OugfIgg0NTWFJOzktNNOIzk5mTfeeIO//OUvXH/99aSnR39l2FARzEvk+4EqYDOeq8q3gB8F8XhhQUNVC2m5kXu1VzgmA0u8mUNbaowWJWg8/PDD5ObmMnXqVPCkaYwJ3QwHGirLSc8fuiGdN2oM1vgEyrZvDoBU4UlnPX3yySe5+OKL+dnPfma0WFGHq9lBzbNbQSDn5skDMqIjCUsY5JL2d85VSt2plJqvlLrD+95XqXOpUup0pdSwSDWinU4nLS0tAc/Y0RszZszg+uuvx2az8cwzz1BdbWzcfDQRNENaKeUG/gV8Syn1RaXUU9Fe5bDd7qTV5iA9gg1ps9VE8YRMDm2LXkPaZDJx5ZVX8qc//QlgXyzoZjjgcjpprK4iPQAeabPFQuGESZRt2xIAycKTznr6yiuvcOutt+rQjgCj3IraJTtw1tvJvmGyodX/goUpyYokmA01pPWceyK+1HfBjJHuzqhRo7jxxhtxOBz89a9/paYmen/nQ0nADWnx8KCIVAM7gJ0iUiUiPwn0scKNhqpWANJzA5/KJpSMKM2isdpOfWWL0aIEFKUUDz74IDk5OUycOJEJEyYATI8F3QwHbDXVKLd7SAsNO1M8aQrVhw/S0tgQkPHChZ70NDc3l5/+9KdGixZ1NL57kLY99WReOY74kWn97xChWLIScBlgSOs5t3caGjzzVqhDLAoLC7n55ptRSrF48WKam5tDevxoJBge6e8BpwFzlFLZSqks4GTgNBG5q68dRaRQRHxxURG30q2h0mdIR7ZXY3ipp1zt4W21/fSMLH7/+9+zYsUKPvvsM2pqanypmLYzAN2EPqtsPSAiK70PnUKvF2qPHgYgq7A4IOONmDINgMNbNwVkvHChJz1dvXo1K1as4JFHHjFavKjBvqsO2/uHSZqdT/LswFzchSuWzAScBlQ3HOqcG800NjYCoTekAXJzc/nyl7+MzWbjH//4B263O+QyRBPBMKRvAL6slNrv26CU2gd8zdvWF7V48kquCoJcQaexOjoM6Yy8JNJyEjgUZYb0c889x5IlSxg1alTnze0MQDc7V9kC4kRkTuehlVLzgIuABwIsdtRQU+Y1pIuHB2S8grHjSUhJZd/6z/rvHEH0pKejR49m8eLFPPfccwZKFj0469uofXEH1vwkMi4fY7Q4QcecnYCztg3lDm00xVDm3GjH55FOSzPmTsjw4cO55JJLOHDgAB999JEhMkQLwTCkrUqpE6LYlVJVgLWvHZVSdqVUXRBkCgkNlS0kplqJC0G1qmAzojSbIzvrcDmj50rV4XCQk3NiJpKB6CY9V9ny7e+7aGxDp9HrlZqyQySlZ5CYEpiYQJPJTMn0mezfsA4VRR6V3vQ0NzcXh8NhgETRhXJ54qKVS5H11UmY4qK/SIU1Nwmcbk/mjhAyxDk3qmloaCAhIcHQioMzZsxg2rRpfPjhhxw8eNAwOSKdYBjS7X629YuILBSRtSKytqqqaihDBYWGqtaIj4/2Mbw0C0ebi/K90RN/2jmPaQ/0p5sZQKP3dQPQU86rB4Ene9o53HU3FNQeOUx2cWBTPY0+aTatjQ1U7NsT0HGNpC897UeHNQPA9nEZ7QcbybxqrMfAjAEs+Z7P6SgP7bqXIc65UU1DQ4PhKehEhEsuuYSMjAz++c9/YrcbW0o+UgmGIT1dRBp7eNiAqUMZWCm1SCk1Wyk1Ozc3N0DiBg6PIR3ZYR0+iidkYjJJVIV3bNy4kbS0tC4P4KQB6mY9fVTZEpGrgGyl1As97RzuuhtslFLUlB0mqygwYR0+Rk6fCSLs+zx6wjt60tO0tDRSU1PZvDl60/2FAkd5M43LDpI4NYfE6bHzPbT6DOnK0C4sG+KcG9WEgyENnmI5X/jCF2hsbOStt94yWpyIJOCGtFLKrJRK6+GRqpSK2ls57XYnTXVtZORHh4cjLtFCwZj0qEqD53K5aGxs7PIAPh+gbq7EE78PnipbHXH8IjIN+Lb3oekBW0017a0tZAcoPtpHUlo6w8ZNYO+6NQEd10h60tPGxkZsNpsO7RgCyuWm9uWdmBIsZFwxJqZSCZriLZgz4nFUhNYjPcQ5N6oJF0MaPPHSZ555Jps2bWLLluhNKRosjKlZ2gsiYhWRd4HpwNsicrLRMg2U2mOeK/2swmSDJQkcw0uzqD7cREtjTN+BAzxVtgBflS23r8qWt/nXQD4enX3dMCHDmIr9ntCL/FFjAz72uDnzqNy/l/qK8oCPrYkeGt87jONoM5lXjcWcEnshMtb8JJwhDu3Q9ExbWxt2uz1sDGmA+fPnU1xczNKlSzsWQmoGRlgZ0koph1JqgVIqUyl1rlJqtdEyDZQ6nyE9LHoM6RG+NHjboye8Yyj0UWXrAqXUVKXUWUqpK4yVMjyp2LsHMZnILRnVf+dBMv4UT4nwXas+CfjYmuig/UiTJ9XdjFwSp5y4+C0WsOQn4ahqQbn0emijMSqHdF+YzWa+8IUv4HK5eO2113RKvEEQVoZ0JFN7tBmzxRTR5cG7kzs8lcRUK4e2Rk94h8YYyvfuImf4SKxxgV+hnp6XT8GYcdqQ1vSIcnpDOlKsMZHqrjes+cngUjhrWo0WJebxVRTMysoyWJKuZGVlcdFFF3HgwAFWrlxptDgRgzakA0TtsWYyCpIwmaIn7k5MQvHELA5vrw15/lFN9KCUomLfHvJHjwvaMcbPm0/Fvj06vENzAo3vHsRZ0ULm1eMwJcVuWK61MAXweOc1xhKuhjTASSedxKRJk1i+fDnHjh0zWpyIQBvSAaL2aHNUhXX4GDklm1abg4oDjf131mh6oPbIYexNNoaNmxC0Y4w/+TQAdn6qCwtojtN2qBHbh2Ukzc4ncUL4GS2hxJqXhFhNOMpsRosS89TW1pKUlERiYvjdwRYRLrvsMpKTk3nppZdoadFx9f2hDekAYG920FTXRnZR9BnSJdNyMFmEPesqjRZFE6Ec3LwRgJFTpwftGOl5+RRNLGXrh8tRSt890YC73UXdy7swp8eTceloo8UxHDEL1sIU2su0R9poampqyM7ONlqMXklKSuLaa6/FZrPx6quv6njpftCGdACoPOjx1uaVGFPqM5jEJ1oYUZrN3vWVOrxD4xeHt24kPS+f9LyCoB5nytnnU3fsCEd2bA3qcTSRQePbB3BWt5L5xXGYEiK/2mwgiCtOwXG0SS84NJhwN6QBiouLueSSS9i7dy/Lly83WpywRhvSAaDygOdWWd6IwJQ+DjfGzsqjqa5Nh3doBo3DbufAps8pmT4z6MeacMrpxCUmsuX9ZUE/lia8advXQNOnR0k+ZRgJY3sqQhqbxA1PRTncOCpCW5hFc5y2tjaamprCMj66OzNnzmT27NmsWLFC55fuA21IB4DKg41k5CcRH6ULWUZNy8FsMbFrtV7IpRkc+z7/DGdbGxPmzQ/6sawJCUw89Ux2rvyElkadBzVWcbe5qH1lF+asBNIvCny6xUgmbqTnrmnbPv39MIrKSk+YZF5ensGSDIwLL7yQ4cOH8/rrr1NRUWG0OGGJNqSHiFKKiv2N5I2MTm80eKocjj4pl51rKnC0u4wWRxNBbH7vHVIysyiaNDkkx5t58RU4He2sf0vXxYlVGt7ah6vOTtYXx2OKNxstTlhhyUzAnJ1A2956o0WJWcrLPQ6pgoLghroFCovFwjXXXENCQgIvvviiXnzYA9qQHiL1FS20NLZTOC7DaFGCypQzimhvdbJnrb4i1QyMin17OLjpc2ZceBkmU2gMmuzi4Yw/+TQ+/++bNNfXheSYmvChdUctzavLSZlfTPyo8Cl2EU4kjMmgbV+DjpM2iGPHjpGYmBhWxVj6IzU1lWuuuYaGhga9+LAHtCE9RMp2eH6siydGdxzesLHpZA5LZuPysgEtOqw6bGP92wfZ/EGZLjEegyi3m/f++iQJqWlMP++ikB77tGuvx+V08u7Tj+sMHjGEq9lB3Su7sBYkkX7+SKPFCVvix2ag2ly06zR4hlBeXk5BQQEikVVzYvjw4R2LD9966y09t3ZCG9JD5PD2WlKzE0jLCb98kIFERJh1wQhqjjSxb2NVr/1cLjfvP7+Dl3/+GStf28tHL+7i7z9eya7PdHx1LLHi5cUc3bWds67/OgnJKSE9dlZhEadfez17PlvFu089jqO9LaTH14QepRT1/9yNu9VJ5jUTEIv+aeuNhLEZYBZadcXakNPe3k55eTmFhYVGi+IXs2bN4rTTTmPt2rW8/fbb2pj2onMCDQFnu4uyHXWMm5sfcVeX/jBuTj5r/3OQVf/ax8gp2VisXW/Xt7c6+e+izRzeXseM80Yw64KRtDS288ELO1j2zDZcDsWkU4cZJL0mVGz7+H1Wv/Yy0869kNIzzjFEhlmXXkWLrZHPXn+FvevXMOP8S5hx/iUkpITWqNeEhqYVR2ndWkP6xaOIK9T/474wJVlJGJtB66Yq0i8qiYnfrnDh8OHDuN1uSkpKjBbFbxYsWIDT6WTVqlW43W4uvPBCTKbYvnCN7U8/RA5urcHR5mLszMhYfTtUTGYT868dR31FC6te29elranOzj9/s54jO+s554aJnHb1WBJSrGQVJnP5nTMYPimL9xfv4PCOWoOk14SCIzu3886fH2V46VTOueV2w36kRYQzvnIT1zzwC3JHlLDipb/z1B0388mLf8fepAtSRBNtBxpoeGs/CaXZpMwvMlqciCBxWi6u+jbadUrTkHLgwAFEhBEjRhgtit+ICBdeeCGnnHIKa9as4dVXX8XpdBotlqFoQ3oI7FxVTmKqlaLxGUaLEjJGlGYz9axiNr53mE9f3YOt1s7uzyr4x8Nraaxp5ZI7pjHp1K63rSxWMxcunEJmQRJvL9pCfaVe9RuNNFZV8vpvfkZqTi6X3f1DzBbjb3gNL53K1T/8Kdf/8g+UTJ/F6tde4unvfp1V/3yJNr36POJxVLVQ8/ftWDLjybpmvPauDpDEqTlIooWmT48aLUpMsWvXLoqLi4mPjzdalCEhIlxwwQUsWLCArVu38sILL9DWFrshdNqQ9pOGqhb2b6qm9LRCTObYOo2nXzOO0vmFfL7sEM/98FPe+ctWElOsXH3PLEaU9lytKS7BwsXfnAYC/358E/ZmR4il1gSTlsYG/vnwg7idTq667wESU8IrHWReyWguu+t+bvjVYxRPmuLxUH/7Zj56/q801epY0UjE2dBG9TNbAEX2TZN19cJBYIozkzK3gNYt1Ti0YyMkVFdXU1FRweTJoUkFGmxEhNNPP50rrriC/fv388wzz1BbG5t3nGPLAgwgq9/Yj9lsYupZxUaLEnJMJuHsr07kSz+YzRnXjefS70znmh/OIbuo79jE9NxELr59Ko01rbz1p004dU7qqKChspx//N//0FBRzhX3/IiswvD9TuSOHMWV9/yYrz70CCOnz2Ttm6/x1B1f592/PEFTXWz+CEQijsoWqv60EXeLk5ybp2DNTTJapIgjZX4REm+m/s29etFYCPjss88wmUyUlpYaLUpAOemkk/jqV79KQ0MDixYtYuvWrTGnT2FlSIvIIyLysYg8arQsfbF/YxW7P6vgpPNHkJwR2bdohkLeyDSmnlXMyMnZA/bKF47L5LybJ3NsXwNLH99IW0vkeKZ7008RKRSR90TkUxFZYJR8oUS53dSUHWbFy8/z3L3fxVZdxZX3/oThpVONFm1AFIwZx2Xfu49bHl3ElLMWsHn5f/nLd77BB889ReWBfRH7QzAYHRWRVBF5U0RWiMgNxkg8OJRSNK+roPLxDSiXm9yF04grDq+7H5GCOSWO9AtKaNtdj+2Dw0aL0yPRMufW19ezfv16pkyZQlpamtHiBJyxY8eycOFCMjIy+Mc//sHTTz/NypUr2bt3L9XV1djt9oidUwdC2NwLE5GZQLJSar6IPCEic5RSnxktV3f2fV7Fsme3kTcylVkX6lyl/jB2Vh4uxyTe+/sOXv7FWs64bjwjSrPCOr6xH/28H/gRsAlYCrxrlJyBQrndNNXX0lhVRWN1JY1VldiqK2msrqKhsoLGygqcDk9+8LFzTuHMr32djILIy8iSkV/AeQvvYM7lV7PylRdY/9abrPv362QOK2LsnFMYO+cUho2dgETAqnQ/dPRWYAnwEvC+iLyolArLpO/K5ca+qw7b+4dpP2QjblQ6WdeOx5KRYLRoEU3yKcNoO9BI49sHcbc4STtvJKa48KgGGS1zrt1u55VXXgHgnHOMyWIUCrKysrj11lv5/PPPWb16NW+//XaXdqvVSkpKCtnZ2RQUFDBs2DAKCgrIzMyM+KwfYWNIA/M4/mV4FzgFGJQh7XYrGqtaAY/nQilAgfL8QSnPdrwXRl36eK+WlFt5mjv1tbc4qCtv4eDmasr3NZI7IpWLvzUNS5hMOJHIhFOGkZqdyHvPbWfpYxvJHJbMiNIssotSSMmIx5poxmw2YbaYEBNDMrLNVhOpWUP+we1LP6cBdyqllIjYRCRVKTWoagdtLS20NNR59BHVSU99r736CaDUcX3t3OZ9fVzflXd/Om1Xnb4TCntLM7bqamw1VTTV1mCrqaapthpbTQ1uV9eV2AkpqaTl5JFVWMyok2aTVVjEqBmzSc3OGdyZDEMyCoZx0R13c+YN32DPmpXsWr2Cdf/+F5+98SpJ6RmMmX0yI6fOICUzm7ikJEQEMZkQMSECBOkiMDElbTAp+walo97+31ZKuURkIzAB2DxYGV2Nbbjb3R265dNHn7p1ft+1rYftbs8Gd7sbt60dZ30bjqNNtB9sxN3ixJweR8ZVY0meU4CYwvfCO1IQEbKuGU99gpmmj4/Qsq6ChNJs4opSMGcmYEowI3Fmz7nurOfe1339B8yZ8cjQ1g8Fdc5VSlFbW9t1Lu3l2Z82u93OsWPHWL9+PTabjS996UtkZGQMRsSIw2w2M3v2bGbPnk1jYyM1NTU0NjbS1NSEzWbDZrNRXV3Nvn37OqojxsXFkZOTQ2JiIomJiSQlJXU8x8fHEx8fT1xcHPHx8VitVsCjtz6boPPrQH2Gwf6fwsmQzgD2el83AIOOyHfYnTz/wKpAytSF7KIUTv/SOKacWYRZJ/wfMoXjMrjuJ3PZtaaCnavK2fLhEVzOwJceLRidxtX3zh7qMBn0rp9mdfy+VQOQCQxqUt+1+hPe+fMfhiqj35itVlKzckjNzqFwQikpWdmk5eSRnptHWm4eqTm5xCVEd9EhgKS0dKYtuJBpCy7E3tzE/g3r2PPZKnas+IjNy9/uf4AAc8bXbmHOZV8YaPcMBqejGUBjt22Dpu7V3dh3Bqkcu4AlJ5GESdkkTs4mYXymLrYSYMRsIvOqcSTNzKfp06O0bqmhZW3FkMctuHcOlqE5MDII4pzrcrl47LHHhiLfgBg5ciRf+tKXGD58eNCPFU6kpaX1GsbicDioqqri2LFjlJeXU1dXR2trK7W1tbS2tmK320Ms7XEKCgq4/fbbB7VPOBnS9YDvrKd533dBRBYCC4Ee8zBa4swsuNkTyC8mEDxX0R1XLl22cfyKWnxXNd7XCJjwtglxCWbS85KITwyn0xUdWKxmSk8rpPS0QlxON011dprr23C0uXE5PY/+SpL3F3qVmGINhKj19K6fnVdN+qW7xZOmcNEdd3fo3HHd9F1t+66+vduQTl4hb3+vMovIcT3uNE7n/XxjxiclkZqdQ2JaeliH1hhBQnIKk047k0mnnYnT4aD2yGFaGuppt7d67ly5XV08UsEgf9SYwXSvZ3A66utv76E/0L/eAqScUUzSjDw63JPi003w6OPx1x3bpas+A133iTNhTo3DnBKHWLXhHAriR6YRPzINpRSuxnbcje247U5Um+v4HbJOdw/6U3tT8pDn3XqCOOeaTCauuuqqLp7N7s99tfXXp7OnVdMVq9VKYWFhrxUe3W43ra2ttLW10dbWRnt7O21tbTgcnjVV3e8IBHIO9uf/FU6W4UrgNuBlYAHwbPcOSqlFwCKA2bNnn3DmzBYTE04uCK6UmqBhtphIz00iPTxX4Peln5tEZB6eeL00pdQJVQ76093MgkIyCyKzbGwsYLFaySsZbbQY/TEoHRWRlcC5IvIyMAPY2X3A/vQWIGFMRuA+gcZwRARLejykG76QPqhzrslkYvr06UEQWzNUTCYTycnJJCcnGy3KgAibS32l1HrALiIfA26l1BqjZdJofPSknyLiuy/4K+DneOL4HjJKRk1s44eOPg18FfgYeEYpFbsVFTRhh55zNZFCOHmkUUrdabQMGk1vdNdPpdR3vM9lQPQux9ZEDIPRUa8X79LQSafRDA4952oiAYnU3H4iUgUcNFqOIJEDVBstRJgRrHMyUimVG4RxeyXKdbczsazHwf7sWm+PE8t61hPhfj5iVXfD/f8SaKLx8/aouxFrSEczIrJWKTXkNBPRhD4nkUcs/89i+bOHGn2uu6LPR3gSa/+XWPq8YRMjrdFoNBqNRqPRRBLakNZoNBqNRqPRaPxAG9LhySKjBQhD9DmJPGL5fxbLnz3U6HPdFX0+wpNY+7/EzOfVMdIajUaj0Wg0Go0faI+0RqPRaDQajUbjB9qQDgNE5BER+VhEHu22/UER2SgiH4jI942SL5SISKGIrBcRu4hYemh7T0Q+FZEFRsmo6RsRKRGRCq/evmO0PKGgJ70VkXtE5BMReV5EAlKnXtOV3ubOaGeg+iYiX/XOl0tFJK3vUTXBIhb0NJbnQG1IG4yIzASSlVLzgTgRmdOty91KqbOUUr8zQDwjqAXOBVb10HY/8CPgfO+zJnxZ5tXb840WJER00VsRyQXOVkqdjqeM8ZXGiRadDGDujGb61Tev4XI7cAbwdzzltjUhJob0NGbnQG1IG888PGVO8T6f0q39lyLyrojMCKlUBqGUsiul6nppngasVEo1ATYRSQ2haJrBcbbXA3OX0YKEgh70di7wgfd1T99rzdDpb+6MWgaob+OBzUopJzF2fsKMmNDTWJ4DtSFtPBlAo/d1A5DZqe0PSqlZwDeBx0IsVzhiVsdXx3Y/V5rw4RieH/GzgQUiMs1geYwgg96/15rAkIE+xz4yOPFc9LRNE3oyiM3/QwYx8rm1IW089YAvdi3N+x4ApVSt93l3yKUKT1ydXnc5V5rwQSnVppRq9nrClgJTjJbJAOrp5XutCRj16HPso54Tz0VP2zShp57Y/D/UEyOfWxvSxrMST1wRwAI6xQb7FoeISA5gOXHXmGOTiMwTkWQgTSnV2O8empDTLeTmNGCvUbIYyGfAmd7XXb7XmoDR69wZg/Skb7uAKSJiRp8fI4lVPY2ZOVAb0gajlFoP2EXkY8CtlFojIr4wjl+LyArgTTwL7aIeEbGKyLvAdOBtETm50/n4FfBzPPFWDxklo6Zf5ovIOhH5FDiqlFpttEDBprveAqOAj0TkE2AG8C/jpItOepo7jZYpVAxE35RSDuAp4GPgRuBJg8SNaWJFT2N5DtQFWTQajUaj0Wg0Gj/QHmmNRqPRaDQajcYPtCGt0Wg0Go1Go9H4gTakNRqNRqPRaDQaP9CGtEaj0Wg0Go1G4wdBTakmIo8As4H1Sqk7O22/BfgxsEIp9TXvtlTgBSALeFIp9VxfY+fk5KiSkpJgia6JEdatW1etlMoN5TG17mqGitZbTaSidVcTqfSmu0EzpDvXlxeRJ0RkjlLqM2/zG8BHwIOddrkVWAK8BLwvIi8qpdp7G7+kpIS1a9cGSXpNrCAiB0N9TK27mqGi9VYTqWjd1UQqveluMEM7eq0vr5SqBpw99VdKuYCNwIQgyqbRaDQajUaj0QyJYBrSGQyuzvpg+0c0jY2b2bHzx1RU/sdoUTQav3mvppF7dx7m3RpdZFKjCSS7Kmz8+F9b2HKkwWhRNJrg4XLAR7+G7W8aLYnfBDNGup7B1Vn39bf31l9EFgILAUaMGBEQIY2guXkv/7+9+46vurofP/5635vcjHuzFyQhEPYmICKgKALuvUfds1at1S5r/bV2uWq12vqtWlvRoqit1j0pooiIhLD3CpAFISHj3qybe8/vj3tvCJBxb3Jncp6ax733M9/35vDJ+57PGauKrsTpbKK09DWcYx5n4MBLQh2Wpvnkk8pabtiwGwFeKaviubGDuTCrT3//1bSgaG51cONLKymtaeSTjRV89dNTiTMZQx2Wpvnfyhdh8e9dz3+8DRKyQhtPDwQykV4O3A68iWue9flebD9XRN7ENZ3k1qM3UEq9ALwAMHXq1IidknHbtt9iMMQyY/rnbNx4H9t3PEJm5lkYjfEhi8lut1NSUkJTU1PIYgik2NhYcnNziY6ODnUofUKTw8n920qYYInjPwXDuHrdLh7YXsLs1ASSowPah7lDfbX8hnu57aufeyB58zv9YG05pTWN3HXqcP76xQ7eX1fG5VMHBTHKvi9Sym64XwN6bc1rEJMEzbWw8b8w/fuhjshnAfuLp5QqEhHP/PJrlVLfichflFJ3i8i5wP3AMBF5Syl1CfAirlE77gZeUEo1Byq2ULJat1F96GuGDf0psbHZDB/+MwpXXUZZ+X8YlHtdyOIqKSkhISGBIUOGICIhiyMQlFJUVVVRUlJCfn5+qMPpE96oqKaixc5fx+aRFB3FoyNzOa1wG/8qq+LuwcGvUeiL5TcSym1f/NwDydvf6ScbK8hOiuW+00bydlEJn23crxNpP4uEshsJ14BeaayBinUw50EoegX2fhORiXRAx5FWSt2jlJqllLrL/fpu9+MHSqmTlFID3Uk0Sqk6pdS5SqmZSqn5gYwrlMrK30TERHb25QAkJU3BYhlLRcW7IY2rqamJtLS0sL2g9IaIkJaWFvY1D5Hk9fJqxlliOTHZAsCEhHhOTrHwj5KDtDqDf7OoL5bfSCi3ffFzDyRvfqdNdgdfbz/IvLFZGAzCqaMzWb7zII4Q/LvqyyKh7EbCNaBXyla7HnOmwqDpsPfb0MbTQ3pCliBSSlFZuYjU1BMxmVLblg/IOpe6ujU0Nu4NYXSE9QWlt/ryewu24sZmVtc3cFFmyhGf6/U56VS02FlWYw1JXH3xdxwJ7ykSYgwn3X1eRXsO0Wh3cOqoTACmDknB1uJg+4H6YITXr0RC2Y2EGHusdJXrMXsyDJwI1v1gqwptTD2gE+kgstm20dS0j4z0uUcsz8w8C4CDVUtCEFX4MBqNFBQUMH78eM477zxqamqOWD9p0iSuuuqqI5bdcMMN5OfnM2nSJEaOHMl1111HaWlpEKPuf94/UAPABUd1LJybmkiC0cDb+w+FIKrQqqqqoqCggIKCAgYMGEBOTk7b6/379xMdHc3zzz/ftn19fT3Dhg1j+/btgKu95oQJE1ixYkWo3kLEan/duOyyyygtLe30d9HS0uLTdeall15q29dkMjFhwgQKCgq4//77mT9/PnfddVfbfi+88AKjR49m9OjRTJs2ja+//rpH76dor+vfz5TBrn9fkwe5HlfvrelsFy1CecripEmTmDJlCt98802oQwquA5sgeTDEJUO6e8Tjqu0hDakndCIdRJ5EOf2oRDouLo+42Dyqq5eFIKrwERcXx5o1a9iwYQOpqak8++yzbes2b96M0+nkq6++wmazHbHfH//4R9auXcvWrVuZPHkyp556Ki0tnc7lo/XSV4fqGWOOZVCs6YjlsUYDZ2Uk8VFlDfZ+dhs6LS2NNWvWsGbNGr7//e9z7733tr1+6623mD59OgsXLmzbPiEhgUceeYQ777wTgCeeeIKZM2dywgknhOotRKz21w2TycQbb7zR6e/CZDL5dJ258cYb2/bNzs7miy++YM2aNTz66KNHxPDBBx/w/PPP8/XXX7Nlyxaee+45rr76aioqKnx+P0V7axieaSEpztW5bHBaPElx0azXw+D1OZ6yuHbtWh555BF+8YtfhDqk4KraCWnDXc/TR7geK48ZZyLs6UQ6iGpqviM+fjgxMZnHrEtJncmhQ9/idB49T03/NGPGjCNqll977TWuvfZaTj/9dN57770O9xER7r33XgYMGMDHH+vxuQOh0eHku1obJ6ckdLj+zPQk6h1OVtSGpnlHOFq4cCF/+tOfKCkpOaJMX3755RgMBh5//HGee+45HnnkkRBG2TfMmjWLHTt2eL19T64zHXnsscf44x//SHp6OgBTpkzh+uuvPyJJ94ZSitV7DzElL7ltmYgwMsvC9v26aUdfVldXR0pKPxo+VCmo3gVpw1yvk/PAGAMHt4U2rh4I/jhV/ZRSTmprV5GZeXaH61NTT6Ss7HXq6zeQlFQQ3OCOsm3b76i3bvbrMRMsYxg58v95ta3D4eB///sfN998c9uyN954g88//5ytW7fy17/+9ZgmHu1NmTKFLVu2cMEFF/Q6bu1IK2ttNDsVs1I7TqRPTknAJMKiqjpO6iTZDrT/t72EDdZGvx5zvCWO343I9Xm/ffv2UVFRwbRp07j88st54403uO+++9rW//nPf2bMmDG88MILpKamdnGk8Peb9zeyqcy/E/OMzU7k1+eN82rb1tZWPv74Y84880yvtu/tdaa9jRs3ctxxxx2xbOrUqbz88ste7e9RXNXAoQY7k/OOTKhGZCXw4bpylFJ9u81sqHx8P1Ss9+8xB0yAsx7tcpPGxkYKCgpoamqivLycxYsX+zeGcGY7CM11kOpOpA1GSM2HQ8UhDasndI10kFht22htrSc56fgO1ycnuS7CtXWrgxlWWPFcVNLS0qiurua0004DYOXKlWRkZDB48GDmzp1LUVERhw513g5XKZ+aFQwSkaUi8nT7hSKSLSKLReQbEZnnXpYgIu+LyDIRuc697HQR+VpElovIH3x9z5Hm60P1RAnMSDJ3uN4SZWRGsoX/6ZkOAXj99de5/HLXCD1XXnnlEc07AD755BMGDhzIhg0bQhFen+C5bkydOpW8vLwjEuOutu/tdaY7PUl615XUADApN/mI5SMzLdQ22qms75OjwvZbnqYdW7Zs4ZNPPuG6667z9e9X5Kre6Xr01EgDJOVCbUlo4ukFXSMdJLU1hQAkJ0/tcH1MTBaxMdnU1q6GQTcGM7RjeFtz7G+ei0ptbS3nnnsuzz77LD/84Q9ZuHAhW7ZsYciQIYDrFthbb73FLbfc0uFxVq9ezdy5cztc115RURGAQSk1S0T+JiLHK6VWulffDzwIrAM+ABYBtwILgTeAL0TkdeALpdRnACLyhYhkKKUqe/4phLdVdQ2Ms8Rhjup8lrXT0hN5cHspxY3NDImLCWJ0Lj2pOQ6UhQsXsn//fl599VUAysrK2L59OyNGjKCsrIxnnnmG7777jlNPPZWbb76ZiRMnhjjinvO25tjfPNcNX7fv7XWmvbFjx7Jq1SrmzJnTtqyoqIixY8f69F42lNZiijIwIstyxPKRWa67O9v2W8lMjPXpmJoXuqk5DoYZM2Zw8OBBKisrycw8tvlnn1PlTqRThx5elpR7eEi8CKJrpIOktraIGFMWsbGd/5FPTCqgrm5N8IIKU0lJSTzzzDM88cQTNDc38+9//5t169ZRXFxMcXEx77777jE1e+CqAXrmmWcoLy/36vbu8uXLATxVp4uA6e1WTwSWK6WsQL2IJAAzgEVKKQewFhillLIDiIgRqGh3vD7HoRRr6huYkthxbbTH3NREgH5fK71161ZsNhulpaVtZfcXv/gFr7/+OgD33nsvDzzwALm5uTz55JPceeedAamNEpGnOrnr8mv3nZTlItL9N88+pqfXmY787Gc/4+c//zlVVa6hu9asWcP8+fP5wQ9+4FNMG0rrGDMggWjjkX+ah2a4EuvdB/tG34MuymRHdwLPEpEtIvJ1u+2iRORf7ruB9wc7/kDYsmULDoeDtLS0UIcSHHXuvglJ7XKipEHQUAUtDaGJqYd0Ih0kVusWLAlju7zVl5Q4maamUpqbDwQxsvA0efJkJk2axJtvvklOTg45OTlt604++WQ2bdpEeXk5AD/96U/bhr9buXIlX3zxBSaTqbNDt3EPe+Vwv6wF2jdMNKrDWY1nXTKHE+W27UXkNlxT2ld1NiOniNwmIoUiUlhZGZkV1ttsTdgcTqYkdj2VfX58DPlxJr6o7t+doxYuXMhFF110xLJLLrmEhQsX8vnnn7N37962ZgjnnXceKSkpvPLKK36NQUSmAGal1CzAJCLt25a9opSaAZwF/NqvJ44QvlxnunL++edz0003MXPmTEaPHs2tt97KggULGDhwoNexKKXYUFbL+JykY9ZlJsQQE2VgT1VkJRidiKfzMum5E3i6+xHgW2DSUcc4H9islDoJOElEBgQ45oDwNDMqKCjgiiuu4OWXX8Zo7PxuX59SVwrmDIhqd9cyadDhdRFEN+0IAqfTjq1hF2lps7vcLjHJda2oq1tHRsa8IEQWXqzWI2tb3n//fQCuvfbaI5Ybjca2P27z58/v8fmSk5MBPFetRKCm3WpHu+eedTXu503tt1dKvSAi/wDeEpHJSqlj7k0ppV4AXgCYOnVqRDaCW13n+iPeXSINcGpqIgvLq2hyOIk19q/v6w899FCn6yZOnMimTZsA2trmevgySoQPZuC62wKH77qsBFBK7XYvbwYiskzCsdeN9jr6XfTkOgNQXFx8xPobbriBG264oe31HXfcwR133OFl1MfaV91IfVNrh4m0wSDkpcazp7pPJNJmOimTuO4E3qOUUiJSLyIJSqlDcMzEJDOAf7uffwEcD7wf6MD9zeFwdL9RX1VXBonZRy7z1E7X7js8HF4E6F9/4UKkobEYpexYLKO63M5iHg0IVj+PmKF1bMaMGeBKiAHm4ar58FgnIjNExAwkKqXqgOXAXHczjgJgq4jEALibe9gA/w4XEUZW1dlIjjIy1It2z6emJtDoVKyotXW7rRZQyXRwF+UoDwHPd7C8T9xJiRQbylzjRI/PPjaRBtd40nv7Ro20kc7LZEd3AjuS3MUxtEhQWwqJOUcu8yTSNfuCH08v6EQ6CGxW1wDjZvPILreLijITHz/E70PPaR2bMmUKgFNElgJOpdR3IvIX9+rHgT/gqjF52L3sReB7wFLgn+5mHDeKyBJ3+71dSqktQX0TQVRU18DkxHivRiKYmWIhxiAs7uftpMNADYe/LB591wURuQhIU0q91tHOSqkXlFJTlVJTMzIyAhlnv7ehtJYogzBygKXD9XmpZvZWN/SFUR0cdF4mO7oT2JGaLo4B6C+BYa+ug0Q6wd1Cx7o/+PH0gk6kg8Bq24aIEbN5aLfbWixjsNbrRDqI9imlZiml7gJQSt3tfixRSs1RSs3wjMqhlKpTSp2rlJqplJrvXvacUmq2UuokpVRohjsJgkaHk622JgoSum/WAWA2GpmeZGFxtU6kQ2w54OlIeMRdFxGZCNzp/tFCbENZHSOzEojpZEScwWnxNNodfWEIPBudlEk6vhPYkfbl+lQONw1po78EhrEWGzTVHNu0IyoG4lJ0Iq0dy2bdRlxcPgZD97fEEyxjaWzaS2tr8Dtq9YGajk715fcWDFttTTiBcZY4r/eZk5bA9oZm9jUFZ7r2vvg77u17UkoVAU2d3HX5I5AFfCoi74Yqxv6mo89LKcXG0lrG5yR2sIdLXprrS2wfaCfdQOdl8pg7gSIyVUQWAeNFZJGIxOJqDz3efSdwuVKq+x6hHYiEshsJMfqsrsz1eHSNNIBlANRXBDeeXtKdDYPAattKQsJ4r7a1WEYDUG/dQkpyx5O3BEJsbCxVVVWkpaX1uZmzlFJUVVURG6vHX+2pTTZX0++xPiTSp6Ym8mvK+KKqjuty0gMVGtA3y6+/yq1S6p6jXnvuupzRqwPTNz/3QOrsd1pR10SVrYUJHXQ09Bic6k6kqxo4fkhkz4LZRZksAeYcta4QV8310b7Xmxgioez22b9dbUPfdZRIZ4I1skYu04l0gDkcDTQ27mPAgIu92j4hwTWAv7V+U1AT6dzcXEpKSuirbcliY2PJzQ2fiToizWZrI3EGA4Pjuh9W0GNEfAy5sdEsrg58It1Xy2+4l9u++rkHUke/0zV7awA6HLHDIyfF9SW29FCf7c8cVJFSdsP9GtAjbTXS2ceuSxgAe5cHN55e0ol0gNlsOwCFxdJ1R0MPkymT6OhU6q2bAhvYUaKjo8nPzw/qObXIscnaxGhzLEYfam5EhDmpiby1/xAtTicmQ+BakunyGxr6c/ePwj2HiIkyMK6TETsAYqKMZCbEUFoT8U07woIuuyFU666RTuggkfbUSCsFYXqn4Gi6jXSAWa3bALB0M2KHh4hgsYzG6h7pQ9NCTSnFZlsjYy2+3148NTUBm8PJSj0MnqZ1qnDPISblJmOK6vpPcnZyHKU1ukZai3B1pRCfDtEd/E2xDIDWJmiqDX5cPaQT6QCz2bZhMMQSF5fn9T5m8whsth0o5QxgZJrmnf0trVTbHYzxoX20x6yUBKIEFvfzWQ41rTONLQ42ltZy3JDuh0LOSYmjrKYpCFFpWgB1NBmLhyXL9RhB7aR1Ih1gVts2zObhuObw8I7FPAKns5GmpsiaJlPrmzZZ3R0Nzb4n0pYoI9OSLHyhx5PWtA6tLamh1amYOtiLRNpdI+109sGRHLT+o768i0Q60/VojZyRO3QiHWBW61avm3V4mN3tqW227YEISdN80pZI96BpB8Cc1AQ22Zoobw7OMHiaFkm+2laJ0SBM9WIkjpzkOFpanRy0RfxY0lp/Vl9xePKVo7VNyqJrpDXAbj9ES8uBtsTYW+Z41xzzVp1Ia2Fgs62JnJhokqN71jd5TpprbNwvdPMOTTvGF1srmTo4haS46G63zU523RXSzTu0iOWwg63S1Ra6I2010pEzKYtXibSIvCUi54iITrx9cLij4Sif9ouOTiQmZgA227ZAhNVnXXLJJXz44Yc4nbptuT9tsjb2qH20xxhzLANjovnsYOR0HglXnjKu9Q37qhvYXF7HqaMzvdo+Jzm8hsDT11zNZ9YDgOq8Rjo2GYwxETUpi7eJ8d+Aq4HtIvKoiIwOYEx9htWdCPtaIw2eDoe6RtoXd9xxB6+99hojRozg/vvvZ8uWLaEOKeK1OJ1sb2hirLnnEwKICOdlJLO4qp4ae6sfo+t/PGUc16xu+loc4d4qKkEEzp040KvtPWNJl4XJyB36mqv5zNP2OaGTMi/i6nDY12qklVKLlFLfA6YAxcDnIvKNiNwoIt3fj+qnbLZtREUlEmPK8nlfVyK9U4/c4YN58+bx6quvUlRUxJAhQzjttNOYOXMmL730Ena7PdThRaQdDc20Kt9mNOzIRVkptCjFR5W6Vro3PGUc2Iy+Fkc0u8PJvwtLmDE0jdyUeK/2SYyNwhITFTZD4OlrruYzT01zZzXSEHGzG3rdVENE0oAbgFuA1cDTuBLrzwMSWR/g6mg4qkfTj7pG7miisXFfACLru6qqqpg/fz4vvvgikydP5p577qGoqIjTTjst1KFFJE9Hw9407QAoSIgjP87Efw8c8kdY/VpVVRVAGvpaHNFeX7mP0ppGbpnl/aQgIkJOchwlYdK0A/Q1V/NRfbnrsbMaaXDVSNvCe8bJ9rzqPSQibwOjgX8B5yml3J8Eb4hIYaCCi2RKKWy2bWRlnd+j/c1mV4dDm2078fGD/Rlan3XxxRezZcsWrr32Wt5//30GDnT9Q73iiiuYOnVqiKOLTJusTZhEGBYX06vjiAgXZqbw5z372d9sJytGV572hKeM46oE0dfiCLXjQD2PfrSZ6UNTOXWUd+2jPbKTY8OmaYe+5mo+q68AMYA5vfNtLBlQ8l3wYuolb7vhv6iU+qj9AhGJUUo1K6X0v5YONDdX0Npa7/PQdx5m83DAlUhnZMzzZ2h91i233MLZZ599xLLm5mZiYmIoLNQ5Rk9stjUyyhxLlKH3U7VenJXCU3v2886BQ9w+yLfkIRAqmu3cu2Uvda0OHh81iHG9rHUPBk8ZF5EKTxKtr8WR5ZMN5Tzw3w3EmaL40+UFPt+xzEmJo2hvTWCC85G+5mo+qy931Tgbuphbw5IFtoPgaAVjz0aLCiZvm3b8voNly/0ZSF9jtbmm+DZbfBuxwyMqKoGYmIG6w6EPHnzwwWOWzZgxIwSR9B2uETt63tGwvRHmWAoS4nmzotovx+sNpRR3b97DtzVWdjc2c+P63TQ4wr8/QkdlHH0tjggVtU3c9koh319QxIDEWN68fXrbKBy+yEmOp7bRjrU59B139TVX81lXY0h7mDMABQ1VQQmpt7pM9UVkAJADxInIZMDz1TkR8K53RD9laxv6bkSPj2Exj9BjSXuhoqKC0tJSGhsbWb16NUq5Zv2qq6ujoaEhxNFFroMtrexvae3RjIaduWxACr/cXspGa2NIa4CX1VhZesjKwyNyGGmO5dI1O/l3RTXX53RxuzGEji7jQLyITEFfiyPCqj3V3PxyIY0tDn5+5mhumZVPtLFno8lmJ7u+2JbVNDIyK8GfYXpNX3O1HquvgOS8rrdpmyZ8PyT4PlhDsHVXZ34Grg6GucCT7ZbXAw8EKKY+wWrbRowpi+jo5B4fw2wewaGaFSjl8GmK8f7m008/Zf78+ZSUlHDfffe1LU9ISODhhx/ubvdBIrIUKFJK3eNZKCLZwAIgFviVUmqRiCQArwGpwPNKqVdE5Dzgl4AC/qOU+pNf31wIbbF5ZjT0X8J7UVYKD+0o482Kan4zPMdvx/XVq2VVJEcZuXpgGjEGYaIljpdKD4ZtIt1BGc8F/oS+Foe9TWV1XPPid2QlxvD2HTMZmmHp1fFyUw6PJR2qRLqX11ytP6svh0HTut7Gk0jbImPkji4TaaXUy8DLInKJUuqtIMXUJ9is23o0fnR7ZvNInM5mGhv3Eh/vfc/u/ub666/n+uuv56233uKSSy7xer+ioiIAg1Jqloj8TUSOV0qtdK++H3gQWAd8ACwCbgUWAm8AX4jI68Ba4ETACSwRkReVUn1ijLfDI3b4p2kHQGp0FKelJfL2/kP8v6HZfml77Subw8FHB2u5emAase5awSsGpvLL7aXsaGhieLz/3q+/HF3GRWSbUurUUMeldc3a3MqtrxSSFBfNm7fPIDOx92XLM7thKIfA6+k1V+vnWltczTW6GrEDXJ0NIWKGwOuuacc1SqkFwBARue/o9UqpJzvYrd9TyoGtYTu5Kdf26jhmS/uRO3Qi3ZkFCxZwzTXXUFxczJNPHlsk29eYtLd8+XKAOvfLRcB0wJNITwTuUUopEal310bPAO5USjlEZC0wSim13nM8EXHgSqj7hE3WJjJMUWSY/DvCxmUDUvjoYC1LDtUzzz19eDAtO2Sl2ak4Kz2pbdmZ6Un8cnspn1TWctfg8EukOyjjWe2vyfpaHJ6e/GwbZbWN/Of7M/2SRANkJrg6/4ZyCLyeXnO1fs4zyYqlm+Ya5siaJry7ph1m92Pv7kX1M42Ne3E6W3pfIx3vGrnDat1GRsbp/gitT7LZbABYrVaf9qupqQFwuF/WAuParTYqT8M/17oUIJnDibdnGQAichawQylV39G5ROQ24DaAvLxu2oeFiU22Rsb0YkbDzsxNSyQ12si/K6pDkkh/UV1PnMHAtCRz27KcWBMTLHEsqqrjrsHh1yavgzJuAEJzX1/zytaKeuZ/s5vvnZDHcYNTut/BS0aDkJ0cF9Ia6Z5ec7V+rr6bWQ09YiwQbQZrZIwl3V3Tjufdj78JTjh9g9XqGrHDYu7ZiB0eUVFmYmMHtU01rnXs9ttvB+DXv/61T/slJycDeBqfJwI17VY72j33rKtxP29qv72IDAV+Bpzb2bmUUi8ALwBMnTpVdbZduGh1KrbamrgxAG2GTQYDF2am8Gp5FbX2VpKigzu80ZfV9cxMtrQ16/A4McXCS6UHaXI4j1kXakeX8YceeqhcX5fD218Wbycu2shPTu/d34GO5KbEUXIodJ36enrN1fq5tslYuhm1A9yzG0ZGjbRXfy1E5HERSRSRaBH5n4gcFJFrAh1cpHIlvtI2FnRvWCyjsOlE2is/+9nPqKurw263M3fuXNLT01mwYEGn27uHafJUic4Dvm23ep2IzBARM5ColKrDNczYXHH1/CwAtrqbfMwHblZK2fz+pkJkV2MzzU7l146G7V0+IJVmp+K9ypqAHL8zh+yt7GpsZnqy+Zh1M5MtNDsVRXXhO+qAp4wDoq/F4WvHASsfri/nuplDSI43+f34rkQ69JOy+HrN1fo5b2ukwdX8oy8l0sDp7kTiXKAEGAn8tLudROQpEVkqIk8ftTxbRBaLyDciMs+97AYR2SoiS0TkcR/fR1ixWbcRFzcYo7H3SYjFPJKGhl04nc1+iKxv++yzz0hMTOSDDz4gNzeXbdu28cc//rHT7adMmQLgdI/a4VRKfScif3Gvfhz4A662055u6C8C3wOWAv9USjUDdwH5wD/dZbdPNGb3dDQM1BB1kxLiGBEfw78rgjtl+Lr6Rvf5jx0x7oQkMwIsrwnf29WeMg4k4cO1WAuuF5fuIibKwC0nBeZykJsST2V9M012R/cbB5Cv11ytn6svB0MUxKd1v60lI2KmCff2nqqnt9HZwEKlVHV3szG5xzg1+zAiAsAflVIv+vQOwpDVthVLL9tHe5gtI92dF3eTYBntl2P2VXa7HYCPPvqIq666itTUVG9226eUmuV5oZS62/1YAsxpv2G7L5Ptlz0CPNK7yMPPJmsjUQLD43s3NXhnRITLB6Tyh13l7G5oJj9A5znaunpXbfOEhGO/ICRFRzHGHEthXfjeWPCUcVyJtFfXYi246prsvLumjAsm5ZBmCUy5bhsCr6aRYb0cTq83enjN1fqr+gr3rIZe1OFasqD468DH5Afe1ki/LyJbgKnA/0QkA1c70a7M4HCC7BkRwWMisFwpZQU8IyIA/EhEvhKRuV7GFXYcjmYaGoox93Bq8KN52lnb3O2utc6dd955jB49msLCQubOnUtlZSWxseE3AkMk2GhtYkR8LDHeXPB66JKsFAT49/7gzXS4tr6BvFgTKZ20yy5IjGdNXQOH+5mGF08Zx9UR3NtrsRZE764updHu4HvTA9epODfFdUcl1M079DVX84nVi1kNPSxZ0HjINWRemPPqr6RS6n5cifFUpZQdsAEXdLNbMp2McEDHIyK8gyvBvgR4QjqYgUREbhORQhEprKwMzyr/hoadgBNLD6cGP1p8fD4i0brDoRceffRRli9fTmFhIdHR0ZjNZt59991QhxWRNtsaA9Y+2iM71sTJKQn8u+IQziAlruvqG5nYQW20R0FCPIdaHextCs+Lt6eMA5t8uBZrQaKU4tUVexmfk8jE3OSAncdTIx3KDoegr7maj+rKweJlIm12jyUdAc07fOkuPwbXeNLt93mli+1rONyRq9sREdy3zQEqRWQbkAWUtT9gJIx84Bmxw9yLqcHbMxiiMccPbTuu1rXNmzdTXFxMa2tr27LrrrsuhBFFnkP2Vsqa7QFPpME1pvRdm/eyotbGjOTA3qI+ZG9lb1ML12Z33j5vcqKrpm91XQOD44LT3MRXmzdvBkgVkfYFu6trsRYkRXtr2FJRz8MXTQjoebISQz+WtIe+5mpeqyuF/JO927b9NOFJoZsF1xteJdIi8i9gGLCGw0mwouuL93LgduBNXCMizG+3bp2IzMDVRjpRKVUnIp7HOGAEEP5fQzpgtW3BYDARH+e/TiZmyyhqawr9dry+6tprr2Xnzp0UFBRgNLpuaIiIvqj7yNPRcGwAxpA+2lkZSZi3GXizojrgifT6Ljoaeow2xxFrENbUN3Bhlv/G/vUXTxnHNbb/8e7F3V2LtSB5bcVezCYj5xdkB/Q8nrGkQ51I62uu5rWmWmiug6Rc77a3uCdl6UM10lOBscqHhoNKqSIRaXKPiLDWMyKCuzPX47gu/HGAZyDKe0XkTFzNTR5137aMOFbrVszmERgM/hsb12Ieyf7979HaWk9UlJ6DoTOFhYVs2rQJ3fmqdzZZXU1uAzViR3tmo5Gz0pP4pLKWJ0YpjAH83a3toqOhR7RBGGeJY02YDoHnKeMGg2Gvp2OsFh5qG+x8sK6MS47LxRIT+LHRQz2WNOhrruaD2lLXo7e1y5bImd3Q255EGwAvG7YcppS6Ryk1Syl1l/t124gISqk5SqkZSqnP3Mt+4359glLqTV/PFS6s1i1YzP4dXcPT3lq3k+7a+PHjqaioCHUYEW+TrZG06CgyTMGZKOW09EQOtTpYVRvY0TK662joUZAQzzprI44w7HDYkzLexTCkN4nIbhHRA//6wX9Xl9Dc6uTqacGZuTQcxpLu6TXXx6FxE0TkfRFZ5mnOJCKzRWSPe8hRfTcmEtSWuB6TBnm3fQRNE+7tX8p0YJOIfAe0DWislDo/IFFFqJaWg7S0VGLx8zB1ZvfIHVbrVpKTjvPrsfuSgwcPMnbsWKZNm0ZMzOH2re+9914Io4o8G62NjLPEBq2WaXZKAlECn1fVMS2AzTu662joMSkxnn+UHmRHQzOjgtC8xReeMg6MEJG2gt3ZtbibYUjfA74CHgpw2H2eUorXV+5jYm4S43OSgnLO9mNJx0Yf0zc/KHp4zY3Ht6FxbwUWAm8AX4jI6+5t/6WUetC/70gLmNp9rkdvm3ZEx0JMUkRME+5tIv1QIIPoK9qmBvfTiB0esbHZGI2WiJrhsKFhN6WlCxGDiUG51xITkxXwcz700EMBP0df1+pUbLM1cUMApgbvTFJ0FCckWfi8qo5fDgtM21JvOhp6eJLtdfUNYZdIe8r47Nmzy4E/ebFLR8OQrgRQSh0UkdANQtyHrN7n6mT4yMWB7WTYXjiMJd3Da66ZTsokrpG77lFKKRHxDI07A7hTKeUQkbWA5w/sVSJyCvB/SqmFPX0PWpDUlbomY7H4kAtEyDThXiXSSqkvRWQwMEIptUhE4oHQfAUOY4cTaf/WSIsIFvMIrNbISKRragpZs/YmnE474KCs7A2Om/IGZvPQgJ73lFNOYc+ePWzfvp158+bR0NCAwxHamb8izY7GJpqcKijto9s7LS2Rh3aWsbexmbwAjJbhTUdDjxHxscQZDKytb+CyAeE1wYSnjAPivi53dy1OBna6n9cC43w5n4jcBtwGkJcXnCYLkWihu5PheZMC28mwvfZjSYcqke7hNdfIkUPjti+THQ2Nm8yxQ+kWAqMBE7BIRBYppY6outRlN8zUlkBCNhh8SB0tWWA9ELiY/MSrNtIicivwH+B596IcXOM+a+1YrVswmTIwmbyY/tJHFstorNbNYTtRhIfdfoj1G+7GZMpg5ozFTJv2IQDrN/wApzOwY/P+/e9/59JLL+X2228HoLS0lAsvvDCg5+xr1tZ5n3D602nprpEyF1XVdbNlz3jT0dDDKMKEhLi26cTDiaeMA4Pdi7q7FtfQ+TCk3VJKvaCUmqqUmpqRkeFTrP1FXZOd99eVcX5BTlA6GXqEw1jSPbzmOvBhaFw6KMNKKatSyq6UsuFqnnTMeLO67IaZ2hLfh7GzZICtjyTSwJ3Aibi/FSqltgOZgQoqUlltW/xeG+2RkDCO1tY6mpr2BeT4/rJ7919paTnIhPHPEBs7EIt5BGPGPIrNtp19JS8H9NzPPvssy5YtIzHRdc0dMWIEBw6E/z/CcLK2vgGz0cCwIE3Z7TEsPpb8OFNAE2lvOhp6TEyIY319+HU49JRxwAleXYuXA56ZYucB3wY0wH7oje/20WR38r0TglvrmZUYS7RR2FsdukS6h9dcG52XyXUiMkNEzLiHxsVdht2TtBUAW0UkEcC97Hig2D/vSAuY2hLv20d79KUaaaBZKdVWneielCW8/sKEmNPZis223e/toz0SEl1t7+rq1gfk+P7Q1FRGSemrZA+8lISEw3frMtLnkpo6iz17XsDhCNxFPyYmBpPJ1Pa6tbVVD8vko7X1DUywxAV0GLrOzE1L5JsaK40Op9+P7W1HQ49JCfE0Op1sbwiv2bePLuPdXYuVUkWAZxhSp2cYUve+5wILcCUpbwU28r7J7nDy0rLdnJCfGrROhh5Gg5CXGk/xwcCOdtOVHl5zG+ikTOIaGvcPuNpOP+xe9iLwPWAp8E+lVDNwuXvwg2XAu0qpIyZv08KM0wl1ZZDoY420OcM19rQ9/O4OtudtIv2liDwAxInIacC/gfcDF1bkaWjYidPZgsUyJiDHt5hHImKivn5DQI7vDyWlr6GUgyFD7jxmXf6Qu7Dbqykr+3fAzn/KKafw8MMP09jYyOeff85ll13GeeedF7Dz9TV2p2KjtZFJicFt1uExJzWRJqdieY3Vr8f1dDT0pbnKRPe24da8w1PGAfH2WtzFMKQfKKVOUkoNVEpdEujY+6KP1pdTVtvErbMC2/+jM/npFnaHMJHu6TXXx6Fx65RS5yqlZiql5ruXvaiUmqaUmq6UerrTE2nhoa4UnHZIGeLbfm2zG4Z3rbS3ifT9uGYaXI9rtsKPcA1Ro7l5aooTEyYG5PgGgwmLZRR19eFZI+1wNFNW9gbp6XOIizv29k1y8lQSEwsoLVsYsHbejz76KBkZGUyYMIHnn3+es88+m9///vcBOVdftK3B1dGwIMjtoz1mJFuINQiLq/3bvMOXjoYew+NjiDcaWBtmE7N4yjjQiL4Wh5TTqXj+y10MTTczZ3RoWjoOzTBTXNWAwxmaG8T6mqt5pXqX6zHVxy+cEZJIeztqh1NE3gHeObpnrOZSV78eo9FCfPyQgJ0jMWE8+w98gFIq7JosHDjwEXZ7NYNyO58aNif7CjZv+QW1tatITp7q9xgMBgMXXnghF154Ibpzie88HfKC3dHQI85oYGayhcVV9R10Heo5XzoaehhFGG8Jvw6HnjJ+22237VVKXRrqePqzjzdUsKm8jicvn4TBEJrrcX66mZZWJ2U1jQxKDf6/W33N1bzS40TaXabCvMNhlzXS4vKQiBwEtuBq5F8pIr8KTniRo75uHQkJ4xDxtpLfdwmJE2htraexcU/AztFTFRX/JS42j5SUmZ1uk5l5DkajhbIy/05cqZTioYceIj09ndGjRzNq1CgyMjL47W9/69fz9HVr6xpIjDIwJM7U/cYBMictkV2NzRQ3Nne/sZfW1Tf61NHQY1JCHBusjbSGqLavvaPLODBeX4v9y5c7ZQ6n4snPtzIi08IFBT62+/Sj/HQzQNCbd+hrruaT6l1gjPG9jbTFPaF2fbn/Y/Kj7rK+H+EareN4pVSaUioVOAE4UUTuDXRwkcLpbKHeuoXExMA06/BITBgPEHbtpJubK6k+tJysAed1WVMeFWUmM/MsDlR+isPhv0Tpz3/+M8uWLWPlypVUVVVRXV3NihUrWLZsGU899VR3uw/q5VS1k0VkvYgU++0Nhcia+gYmWOIxhPBux9xUV+////lx9I619Q0+dTT0mBhGHQ6PLuPAGvS12C8cTsUjH29m7K8+5dQnlrBka/e1X28W7mNnpY0fnz4SY4hqowGGuhPp4qrgJtK9vOZq/U31Llf7aIOPFY2WLDBEH55ePEx1966uA65SSu32LFBK7QKuca/TAKttG0q1kJgQ2FmtzOYRiJjCrp30gQMfAk6ysrrvZJKVdS4Oh5Wq6iV+O/8rr7zCwoULyc/Pb1s2dOhQFixYwCuvvNLpfkVFRQAGpdQswCQix7db7Zmq9nQOt0H1TFV7MnCLiJiAHbhm5grvf+ndsDkcbLA2cnySOaRx5MfHkB9n8lsi3ZOOhh7h1OGwozKur8X+8ZfF23n+y13MGZ2JyWjg5pcL+WRDRafbV1mbeeyTLUzLT+WMcQOCGOmxMhJiMJuM7KoMbiLd02uu1k9V7/a9WQe4Eu/E7IhPpKOVUgePXuhuJx0dmJAiT72no2FiYBNpg8FEYsI4amuLAnoeX1Xs/wCLZQwWc/cNW1OSpxMdncr+/R/47fx2u5309GOntM7IyMBut3e63/Lly+HwjFmeqWo9JgLLlVJWoP1UtYuUUg5gLTBKKVXvnhQgoq2ua8ChCHkiDa7RO/w1DN66HnQ09PB0OFxXH/oOh52VcX0t7p191Q38dfEOLizI5tnvTeGtH8xkQk4S976xhk1lx36ZU0rx2w82YW1q5fcXjg95XxURIT/DHPSmHT295mr9kFKuGumeJNIASYMiPpHuaiq6wE5TF0Fqa1cTHZ1KbOyggJ8rKXkqdXXrcThCf7sZoLFxL3V1q72qjQYwGKLIzDybgwcX09rqn4t/+3FMfVlXU1MDh2fS8kw96+HtVLVeEZHbRKRQRAorK8Ovv+53tTYEmBqioe/am5Pmv2HwVte5ytikHjTtMIowwRLX1lkxlLoqx+hrcY89+8UOjAbh/rNcw5ZaYqJ44drjSIqL5tZXCqmoPfI6+2bhPt5dU8Zdc4YzMishFCEfIxRD4PX0mqv1Q3Wl0NoIaT1MpJMHQU14T0TXXSI9SUTqOvipBwJb/RpBampXkpx0XFBqJ5KTj0cpO3V16wJ+Lm94apazMs/1ep+srHNxOps4ePB/folh7dq1JCYmHvOTkJDA+vWdN4NJTk4GMLpf9miqWm9jDPfpalfW2hhljiXJxw55gTDTj8PgralvYFhcTI/f18SEODaGQYfDo8s4MFlfi3unscXB+2vLuKAgmwFJsW3LMxNj+ft1U6lpaOHaf6ygtMZ1V+Od1aU88N8NnDQ8nbvn+HFYmV4amm5m36EGGlsc3W/sJz295mr90P5NrsfMsT3bPykX6svA0eq/mPysy78uSiljV+s1aG4+QGPjXnJzrgnK+ZKTpgCu5D0lZVpQztmV/Qc+JClpCnFx3vfGTU46jpiYAew/8CEDBpzf6xgcjp79AZkxYwYcToznAfPbrV4nIjOAdbinqhURz1S1b+KeqraHIYcVh1KsrLVxcZbXFewBFWc0MMNPw+CtrmtgVkrPaw4nJcTzd+dBtjc0Mcbie622vxxdxkVktVLK/2NI9iOfbarA1uLgosnHjns/ITeJF68/nlteXsmcJ5aQnRzH7oM2jh+SwnPXHhfSDoZHGzMwAaVg2/56Jg1KDso5e3rN1fqhAxtdj71JpJXTNXJHcuDv+vdE4MZq6ydqalYCrpriYIiOTsFsHkFtTWFQztcVm20nVusWsjLP8Wk/EQNZmedQVfUldnttgKLr3pQpUwCcvZmqVkQGicgiXMORLRKRIUF9E36wxdaE1eFkWhi0j/aY64dh8MqbW9jf0kpBL5qreDochkPzDs2/3lldSnZSLCfkp3a4fsawND6+52SuOH4QwzMt/Ob8cbx263QsMaG/a9Pe6AGuuoAtFf6dyEjT/GL/JkjMhbjknu2f5P6iWxu+zTvC64oQgWpqV2IwxAVsavCOJCdNpWL/+yjlQCR0Nw0OHPgIEDIyz/R536ysc9m77x9UVn5OdnZI55XY5x61AzhyqlpgTvsNlVJ1wLlHLduHqzY7Yi2trgdcMwuGi7mpiTxIKf+rquPm3J41hVntnpWwNzM1DmvrcNjIlQN7fBgtzFTWN/PV9oPcdvLQLidTyUuL57cXjA9iZL7LS40n3mRkc3l9qEPRtGMd2ARZPayNBkjKcz2GcYdDXSPdSzU1K0lKmozBELyO8ykp03E4rCFvJ73/wIckJ00lNsb3IaASEiYQF5vH/gP+G71D65kvD9UzPD6GnNjw6STkGQZvcVXPk4M1dQ1ECYzrRZMMowgTw6TDoeY/H6wrw+FUXDQ5dJOp+IvBIIwakKBrpLXw09oMlVt73qwDDtdIHyr2S0iBoBPpXmhuPoDVuoXUlBODet7U1BMBA1VVXwb1vO1Zrduw2baTmeVbsw4PESEr6xwOHfqGlpZjRljUgqTJ4eTbGisn96IdcaC4hsGrp6mHw+B9V2tjnCWOOGPvLnMTE+LZaG3EHgYzHGr+8d/VpYzLTgybkTd6a/SARDaX1/s0O6OmBVz5OnDaIbcX3TlM8a4ZEat2+i8uP9OJdC9UVy8FIC3t5KCeNzo6hcTESVRVfxXU87Z34MDHgJCZ4XuzDo+srPNQysGBA5/6LzDNJ4V1Nhqditmp4ZdQzElLpLGHw+A1OpwU1TX4pbnK1CQzTU4VFuNJa72344CVdSW1faI22mPswARqG+2U14bHsKiaBsC+Fa7H3F4OjJA2HKq29z6eANGJdC9UVX2FyZQR1PbRHmlpp1BXt46Wlqqgn1sp5WrWkTyNmJieD+VmNo/EbB6hm3eE0JLqeqLENeRcuOnNMHir6xpoUcov78tzjK8P9X5cay303l1TikHg/EnZoQ7Fb8bnJAGwdl9NaAPRtPb2rYDkwZCQ1bvjpI+Aqh2uyV3CkE6ke8jpbKWq+mvSUk8OyexWrlpwRVVV8Gul6+vX09Cwk6ws78eO7oiIkJV5DjU1K2lq7nxKXi1wPj1Yy/QkC5ao8Bvp8ohh8Hy0vMaKgF9GIkk3RTHWHMvXNbozV6RzOhVvF5Vy4vB0MhNju98hQozLTsIUZaBo76FQh6JpLkq5EulBfhimN204NNWCLTybgepEuodqar6jtbWG9PQ53W8cAIkJE4iJGcCByo+Dfu6y8rcwGGIY4OVshl1xJeOKA/s/6n1gmk+22ZrY3tDM2RlJoQ6lU3PTEtnZ2MzuBt+GwVteY2WsJZZkP00wc1JKAitrbTQ7ez9tuRY6y3dVUVrTyGVTw3M82p4yRRmYmJPEqj06kdbCRMV6sO6HobN7f6w094QCYdq8QyfSPbT/wIcYjfGkpZ0SkvMfHov5K+z24PXWdjia2b//PTIyziAqqvftauPj80lMmEhZ+Zu6o0yQfVzpGsP7rDBOpM9KT0KAt/d7nyDUtzpYUWvzawfKk1IsNDkVhbXBnYpZ869/F+4jMTaK08f28lZzGJoyOIUNpXU0t+rJUrQwsP0z1+Pw03p/rHR3Il25pffHCgCdSPeA09lKZeWnpKfPxWgM3WxnmVnnoJSdysrPgnbOgwc/p7W1juyB/hv7OSfnamy27dTUhn6Smf7kvcpDTE6IZ2BM+Ax7d7ScWBMnJlv49/5qr79ofVldj10pTk/33xeEGckWTCJ8XqWHGItUB+qb+GhDBRcU5BAbHX5NmXprSl4KLQ4n60tCN8mVprXZ8gFkT+59+2iA5DyITYLytb0/VgDoRLoHqquXYrcfIivz7JDGkZgwkbi4wZSV/zto5ywpfY3Y2BxSUmb47ZhZWecQFZVAaelrfjum1rX19Q1stDZx2YDwmBa8K5cNSKW4sYWVXtYGf1ZVS3KUkeMT/TdTY0KUkVkpCXxUWavvnESof35dTKvDyc0n5Yc6lICYMTQNg8CX2ypDHYrW3x3YAmWrYcJl/jmeCAycBGVr/HM8P9OJdA+UlL6KyZRBWtqpIY1DRMjJuZra2kLqrYG/5VFXt56amhUMyr0eEf8VHaMxngEDLuLAgU9o1mNKB8Vr5dXEGISLs8I/kT43I4l4o4FXyrofoabZ6eTzg3XMTUskqosZ63rinIwk9ja1sNHa6NfjaoG3r7qB+d/s5pyJ2QxJ998XrHCSFB/NlLwUlmzVibQWYqvmgxj9l0iDq3b7wCZobfHfMf1EJ9I+amwsoapqCdnZlwd1NsPOZA+8FIMhhpKSVwJ+rn37XsJotJCdfbnfj52bcy1K2dm3959+P7Z2JFurg7f3H+Ks9CS/dcYLJHOUkasGpPLOgUOUN3d9EV1UVcehVgeXBOALwunpSRiA9yv1rfNI0mR3cN+bazCI8IuzRoc6nICaPSqD9aW1VNb71jlX0/zGWulKpCdeDpZM/x13YAE4WuDARv8d0090Iu2jPXufRySKnOyrQh0KANHRyQwccDHl5f+lsbE0YOex2XZSsf99crKv8Esnw6OZzUPJyjyHktJ/0dJS7ffja4ctKK+ittXBrbk9HwM82G4blIFTwYslXd+xWFheTZYpilMCMMFMuimKU1MTeb28Ss9yGObqmuxsLKvl3TWlXPrcN6wsPsRjl0wkOzl0fVqC4dTRrsTls016OFEtRJY8DI5mmPVj/x4393jX457l/j2uH+hE2geNjaWUlf2b7OzLiY0dGOpw2gwZ8gMAdhf/JWDn2LnrTxiN8QwefHvAzjFkyJ04HI3s2fNcwM7R3zU5nDy/r5KZyRaO88MYy8EyOC6GC7NS+GdJJaVNHddKb7Y2sqiqjmuy0zAGaGz363PS2N/SyqcHda10uDlQ18RD721k+sP/Y+JDn3HOM19zz+trOFjfwvPXHsd5fWgCls6MHZjIyCwLb60qCXUoWn+08wso/CeccMfhkTb8JXmQaxi8nf/z73H9QCfSPti+4/eIGBky+PuhDuUIsbHZ5OZeQ3n5W9TWrvb78auqv6ay8lPy8m7BZErz+/E9LJaRZA+8jH0lL2O1bgvYefqz5/YdoKzZzn1DIm/4r18MHYgT+PWO0g47/D1RXIHZaOCWANa0z01LJDc2mr/uPaA7HYaRTzaUM/uJJby6Yg+T85J54OzR/N/3pvDB3Sfx9c9P5YxxA0IdYlCICJdMyaVobw3b9+sJhLQgKl8L/74eMkbDnF8G5hzD5kDxMrCHVz8VnUh7af+Bj6ms/Iz8/B8SGxt+NRtD839ITEwWmzb/HIfDf4WstbWeLZt/QXz8MAbn3ea343Zm+PCfERWVwObNP8fp1O38/Gl3QzNP7znAORlJnOTHMZaDZVCsifsGD+CDylpePqrj4QcHaviwspa78jJJCWC7b6MI9w0ZwJr6Bj7StdIhp5Ti/5bs4PsLihg1IIFF953C3645jttOHsbZEwYyPieJKGP/+jN36XG5xEUb+duSnaEOResPlIL1/4F/ngWmBLj6TTAF6G7nqLOgtRG2htcEbv3rCtNDVus2Nm/+OYmJk8gbdFOow+lQVFQCY8c8RkPDbjZuvBelej8ov1IONmy8l+aW/Ywd8yhGY4wfIu1adHQKo0f/gbr6dWzb/ntd6+cnjQ4n399UTIxB+O3wnFCH02N3Dc5kTmoCv9hWwlPFFZQ3t/Cfimru2ryHgoR47szzY+eWTlyelcrI+Fh+tb2UGntrwM+ndayl1cnP/rOOxz/ZyvmTsll463QGp0VOc6VASbPEcM30PN5ZU8qmMj3uedhztML2RfDZg/DaFfDS2fDalfDhT2DlP1xtghuqXQlrOFEKdn8F/7oI3roZMkfDLYsgZXDgzpl/MiQNgqJ/Be4cPRD+XfZDrL5+E6vX3IDRGM+E8c+GxUgdnUlNPZGRI37Jtu2/Y8OGexg79gmMxtgeHcvpbGHjpp9QVfUFo0b9jqSkKX6OtnOZGWcwOO929ux9HqMhjuHD7/frcHv9TZPDya0bi1lX38hL4/PJiQ3fCVi6YxThH+PzuWfLXh7bXcFju12dqiYnxPPKxHxMhsCXkyiD8PSYPM4r2sbdm/fyz/H5RPt5qD2ta5X1zfzg1VWsLD7EPXNH8KN5I5AAtYuPRD+YPZz/ri7jx/9ey9t3zCTO1PcmoIl4DdWuRHnVS1BXCkYTpI+C2ESoLYE9y6C53Reh6HhIzHGNhGFOh/h0MGe4nluyXOMsJ+e5xlwOBEcrHNzmmvq7ZCVs+wRq97liOONhmHY7GAOcUhqMcNwNsPh3sHcF5J0Q2PN5KaDvWkSeAqYCRUqpe9otzwYWALHAr5RSi0QkAXgNSAWeV0oFfjy3LjgcTZSUvMKu3U8RHZ3K5IJ/hVUHw84MGnQDTmVnx45Hsa7czqiRvyYlZYZPf2Tq6zexecsD1NevZ/iwn5Obc3UAI+7YsGE/xeFsYO++f2C1bmXUqN8QHz8kEKcaJCJL6WEZFZEo4CUgH/hAKfVoIILsqc3WRn60ZS9r6xt5fGQuZ4bxdODeijMaeGHcEL4/yMbqugbyYk2cmur/caO7Mjkxnt+NyOUX20q4ccNunhmTR2qYDiXoy3U4RCH6ZNGm/Tz4zgZqGlv4y1WT+0UnQl+lmE08fukEbn65kNv+Vchfr55CUlz4VAL1NjcI9+tul2pL4dv/g8KXwG5ztfs96zEYPg+i240qo5QroT6wCap2uParKwXbQdeEJ7ZKaDxqhKuEgTDoBNcIF7lTXcl1tI8j1bQ2Q9VOOLgVDm6Hyq2uBLpyq2s0DoCoOBh2Kpz6AIy7GKJ7VmHXI9PvgO/+Du/fA7d8DjGhb6YYsCu/iEwBzEqpWSLyNxE5Xim10r36fuBBYB3wAbAIuBVYCLwBfCEiryulgjbytlKK5pb9WOs3UVX9Ffv3f4TdXkV6+lzGjH4Ykyk9WKH02uC8W7GYR7Fl64OsXnMtCZZxZGScTnLy8cTHD8VkSj8isXY4Gmho2E1t3VoqD3xK9aGviY5OZcL4/yMz84yQvAcRYeSIX2Mxj2Lb9j/w7YrTSU+fR2bGGSQmTiIuLq/XtdRFRUUAht6UUeBcYLNS6loR+UBEBiilQjb2lFKKkmY7hbU23j9QwycHa0mMMvLyhHzO8OOU2eFgSqKZKX6cvdBXN+akI8D/217KzG83c212GmelJzHGEkdcmLTL7cF1OOwopSivbeLrHQd5c+U+CvccYlRWAv+4YSrjsvtWmfanOaOzeOySiTzw9npOe/JLbjopn9PGZpGfZsYQ2jso8fQyNyDMrrtdcrS6EuG938Dm92HXEkBgwqVw4o8ga2zH+4m4RqpIHgR08nfY0QoNVa4Eu3QV7P0W9n0Hm95xH8PoqsVOyoG4VIiKcf2IwTUms8Pu+mltAtsBqN/vStBp14wkOQ/SR7qaVQycBAMmuEbPCHTtc2dMZrjoOVhwiasZzJmPQN4MV211iATyk5jB4QvzImA64PnHMhG4RymlRKTe/Y1zBnCnUsohImuBUcB6X07Y2mpj46Z7UcoJyoFCgXKi2j/H4W5360QpJ0rZsbccosVejVJ2AAyGGNLSZjMo9zpSUqb3/pMIgbS0k5l+wqeUV/yX8rJ/s2v3nzn8j0MwGs2IGHE4Gmn/fSU2dhBD8+8lN/daoqND+0fKNXPjVaSnz2Pv3r9Tsf89Kis/da+LwhSdRrQpFYMhBoNEYzCYEEMUIO4fEIR48zBGDL//mOMvX74cwHPvrKdldAbgmaP9C+B44H1f3+tX1fW8WFKJE3AqhcJVIeFE4VS0LYfDz12PoNzbVNtbOdDSit29XXp0FHfkZXJnXmbY1pZGuhty0pmWZOaPuyv4694D/GXvAQQYEBNNUpSRBKORGINgEDAgSLvHw6W0c1cOTOXsjOTehOjTdVgp5fNQD08v2s66kpojyq3rUbmfux/V4bJNu+Vd7ddkd1JW00hDi6vPR15qPA+dN5bvTR9MdJh8WQlnl08dxMisBB75aDOPfryFRz/eginKQE5yHOYYI2ZTFKYo1+coIq4y6Smb7V73xCMXTyQjocN+NWZ6nxv0/rrrsMMb17jbHqsjH5Xz2GXgWn7M9h0tU6AcruYb9RWu5+BKSmf9GCZfAylDfAq3Q8YoSMhy/eRMgWm3upbX74fSQigtcjW/qC2Fmj2u2ubWZlfMUSYwRLualESZXAl39hRXrXb6CFfynDYcTPG9j9Pfhp0KV70O79wB88+BmERX2+m4FFdCbTC6vkQYjHR/lT1KyhA4y7cbHIH865oMeLoN1wLj2q0zqsO9yGqBFPf2dUctO4KI3AbcBpCXl9fhSZubKkAEwQBidF8MjCAGRIwYxOR6jrjXG0lMmEi0KY2YmCwSLGNISBiL0RiGhcdHRmMcuTlXk5tzNS0tVdTXb6KhYRct9mocDhvK2YrRGE9UVAJx8UOwmEcRH58fdm0NY2IyGDHiAYYP/zlW6xbq6zfS2LiX5paD2O2HcDqbUU47DmcTqtWO+88y7r/WREcnd3jcmpoaAE+vzJ6W0Y6WHaO7stvgcFLebD+caEFb8mVo+8Pmeh0tYMCAp1LJs81oSyxZpmhyY01MSYxnrDkuqM0d+quxljhempBPVUsry2qsbLc1sbephfpWB3WtDlqUQjnBidP9pUi1JY3dsTqcvQ0vGd+uw0ck0t5ccw81tLC/vglp9wUBaV9uPeXZtUBwVYgJBgwG2vZzn++I/WKiDJw8IoO81DhOGJrGqKyEUNemRpyCQcm8cfsM9lU3sGzHQXYdtLV9ObE1t2Jtbj1cHo/4UqN61b/N0fmkRUaOvGYG5LrrTdmlrsz9TUHaPRo6WOZejoCnH0b7ZZ0dY8BESMyGlHzImw6pQwPXhrm9hCwYfY7rp68aeTr8aD1s+RD2rYD6ctcXl1Y7OB2uLy/OHgy60IMvDoFMpGuARPfzRPdrj/bvzrPOs31TB9sDoJR6AXgBYOrUqcf8K42KMjNt2nu9DLtvMpnSSEubRVrarFCH0mMiRhISxpGQMK77jb2QnJwMros69LyMepZ5ttvR0bm6K7tnZiT1ifbL/VmaKYrzM5NDHcbRavDtOnyE7sotwEPn++ffoxZYg1LjuXJaJwllcDnofW7gWebZ7pjrbrdl1xgN31/as3eghQdTPEy8zPUTQoG8N7YcmOt+Pg/4tt26dSIyQ0TMQKJSqs6zvYgYgQJgawBj0zRmzJgBhy/GPS2j7cv5qRy+Ralp4cDX67CmBZqN3ucG+rqrhY2AJdJKqSKgyT0iglMp9Z2IeOawfhz4A672UQ+7l70IfA9YCvxTKaVn49ACasqUKQDOXpbR94HxIvI1sFwpVR7M96BpXenBdVjTAq2B3ucG+rqrhQ2J1AkvRKQS2BPqOI6SDhwMdRAB1tfe42ClVODmlO6AH8puX/sd+EK/d5dILLed6c+/06701c+lL5Vdf4mE33UkxAiBjbPDshuxiXQ4EpFCpdTUUMcRSP3hPYa7/vw70O+97733vvq+ekt/Lv1HJPyuIyFGCE2cevwgTdM0TdM0TesBnUhrmqZpmqZpWg/oRNq/Xgh1AEHQH95juOvPvwP93vuevvq+ekt/Lv1HJPyuIyFGCEGcuo20pmmapmmapvWArpHWNE3TNE3TtB7QibSmaZqmaZqm9YBOpP1ERJ4SkaUi8nSoY/EnETlBRL5xv7en3Mt+KiJfi8irIhId6hj7k75aztoTkSEisl9ElojIZ+5lx5Q5Efmeu2x+ICKJXR81vIlItogUiUiTiES5l3n1nkVkjogsF5EvRCQ3lO/DG96+1/5GX2v7p3C6pndSBmvd1+IlIpLqXhbSa2+4/Y3QibQfiMgUwKyUmgWYROT4UMfkR3uAOe73likis4BTlVInAeuAC0MZXH/Sx8vZ0T5XSs1WSp0uIhkcVebcF8rvAycD/wJuD2Gs/lCNa8rjbwF8fM//DzgduB/4RZDj7olu32voQgspfa3tZ8Lwmn50GZwArHdfi2crparD6NobNn8jdCLtHzNwTWmK+3F6CGPxK6VUhVKqyf2yFZgILHG/7lPvNQL02XLWgVPdtSL3AtM4tsyNxHWBb6UPfBZKqSal1KF2i7x6zyISDzQqpeqVUiuAsUEMu0e8fK/9jr7W9kthdU3voAw6gDHua/GjIiKEz7U3bP5G6ETaP5KBOvfzWiAldKEEhohMxDX1Zg19/L2GsWT6x2dfjusieCowD5jKse87uYNlfUky3r3nlHbLAIzBCc+vkunbv0uf6Gttv5JMGP6OPWVQKbUJGIGrVjcFOI/wiDms/kboRNo/agBP+5tE9+s+w90u6q/AzfTx9xrmaugHn71SqlkpZXPXJHwA7ODY913TwbK+pAbv3vOhdssAnMEIzs9q6Nu/S6/pa22/U0OY/Y6PKoMopaqVa5zkd4DxhEHM4fY3QifS/rEcV5s/cH07+jaEsfiVuzPQAuCnSqkKYCVwint1n3qvEaDPlrP2RCSh3csTcV0kjy5z24DxImKkb34WHf07O+Y9K6UagDgRsYjINGBTSKLtHX1NQV9r+6mwuqYfXQZFxOy+3oDrWryTMLj2htvfCJ1I+4FSqghoEpGlgFMp9V2oY/Kjy4DjgcdEZAkwDPhKRL4GCnB9S9WCoI+Xs/ZmicgqEfkGKHO3/T2izCml7MDfgaXA9cDzIYvWD0QkWkQWAZOAT4F8vH/PfwA+Bx4DHg1y6D7z5r2GLrqQ0tfafiYMr+lHl8GJwEp3fIOA/4TJtTes/kbomQ01TdM0TdM0rQd0jbSmaZqmaZqm9YBOpDVN0zRN0zStB3QirWmapmmapmk9EBXIg7unmJwKFCml7mm3PBtXz9BY4FdKqUUiYgAeByYD1Uqpy7o6dnp6uhoyZEjAYtf6h1WrVh1USmW0XxbIcgu67Gq911G51TRN04IvYIl0+6kvReRvInK8Umqle/X9wIO4pnL8ANesM5cCm5VSP/Hm+EOGDKGwsDAQoWv9iIjsOep1QMst6LKr9d7R5VbTNE0LjUA27ehq6suJwHKllBWod48JeC4wVkSWiMitAYxL07qiy62maZqmaV4JZCKdTOfTMxrV4XH3POuygK24Bs7+nohkBTC2sNDY2khxbTFOFYmTkfVZyQSg3IrIbSJSKCKFlZWVAQk83Dlqamg9dCjUYWiapmma3wQyka6h8+kZHe2ee9bVAl+6p3xcDgw/+oB9KRnZVLWJM986k/PeOY9L37+UfXX7Qh2S5lKDn8stgFLqBaXUVKXU1IyM/te0tWHlSrafMpsds0/F9l2o5xzQNE3TNP8IZGfD5cDtwJu4auvmt1u3TkRm4GprmqiUqnPPUDMRV+3eRODZow+olHoBeAFg6tSpETuTTLOjmR998SNMRhM/O/5nPL/ueb6/6Pu8ds5rJMUkBeScdrudkpISmpqaAnL8cBcbG0tubi7R0dHdber3ctvfKYeD8v/3K6LS01FOJ/t/9zvy330XMXT/PV6XW6/LraZpmhYCAUuklVJFIuKZ+nKtUuo7EfmLUupuXKMcvALEAb927/IP4GUR+RHwqVKqJFCxhdrb29+m3FbOi6e/yAkDT2BC+gRu/ORGHl/5OH846Q8BOWdJSQkJCQkMGTIEEQnIOcKVUoqqqipKSkrIz8/vbltdbv3M+uWXtBQXk/PUkzibmin/xS9oXL2a+OOO63ZfXW69K7eapmlaaAR0+Lv2Q4e5X9/tfiwB5hy1rh64OJDxhIv/bv8v49LGMW3ANAAKMgu4cfyN/H3937lg2AVMGzjN7+dsamrql8kIgIiQlpaGt82BdLn1r7qPP8GYnEzCaaehmpup+M1vqPv4E68SaV1uvS+3mqZpWvDpCVmCbG/dXjZXb+as/LOOSA5un3Q7A8wD+HPRnzncn82/+mMy4tGf33soKbsd65dfYpk9G4mKwmA2E3/ccdi+Xe71Mfrz764/v3dN07RIoBPpIPti3xcAnD749COWxxhjuGPSHaw/uJ7F+xaHIjRN87uGVUU46+qwzD1ckW+eMZ2WHTuxHzgQwsg0TdM0rfd0Ih1khfsLyUvIY6Bl4DHrzh92PkMSh/DX1X/F4XR0sHdks1gsxyzbunUrs2fPpqCggDFjxnDbbbfx6aefUlBQQEFBARaLhVGjRlFQUMB1110HwH//+19EhC1btgBwwgknUFBQQF5eHhkZGW37FhcXB/PtaR1oWFUIIpinHx6OO37qVACa1q0LVVg+ERGuvfbattetra1kZGRw7rnnAjB//vwjyl1BQQGbNm2iuLiYuLg4Jk+ezJgxY5g2bRovv/wyAEuWLGHGjBlHnKe1tZWsrCzKy8uD9+Y0TdO0XgloG2ntSE7lpGh/EXPz5na4PsoQxZ0Fd/LTr37Kp8WfcvbQs4McYfD98Ic/5N577+WCCy4AYP369UyYMIEzzjgDgNmzZ/PEE08w1Z18ASxcuJCTTjqJ119/nYceeogVK1YAroSmsLCQv/71r8F/I1qHGtetI2b4MIwJCW3LYkaPBqORxo0bSZg3L4TRecdsNrNhwwYaGxuJi4vj888/Jycn54htrrjiimPKXXFxMcOGDWP16tUA7Nq1i4svvhin08n1119PSUkJxcXFeKaLX7RoEePHj2fgwGO/ZGuapmnhSddIB9HOmp3UtdRxXFbnnaxOH3I6w5OH87e1f6PV2RrE6EKjvLyc3NzcttcTJkzocnur1cqyZcv4xz/+weuvvx7o8LReUErRtHYdsRMnHrHcEBtLzLBhNG3cGKLIfHfWWWfx4YcfAq4vcldddZXPxxg6dChPPvkkzzzzDAaDgcsuu4w33nijbf3rr7/eo+NqmqZpoaNrpINoS7WrKcL49PGdbmMQA3cW3Mm9S+7lo90fcf6w8/0ex2PfPdYWi7+MTh3Nz6f93Of97r33XubMmcPMmTM5/fTTufHGG0lOTu50+3feeYczzzyTkSNHkpqaSlFREVOmTOlF5Fqg2PfuxVFTQ9zEScesix03DuuXX6KU8rpDXcXDD9O82b/lNmbMaAY88EC321155ZX89re/5dxzz2XdunXcdNNNLF26tG39G2+8wddff932evnyjjtTTpkypa1J0lVXXcVtt93Gz3/+c5qbm/noo4946qmnevmONE3TtGDSNdJBtLV6KyaDicGJg7vcbk7eHEanjuZva/6G3WkPUnShceONN7J582Yuu+wylixZwvTp02lubu50+4ULF3LllVcCruRm4cKFwQpV81Gjuw10XEEHifSY0Tiqq3FUVwc7rB6ZOHEixcXFLFy4kLPPPrbJ1RVXXMGaNWvafuLi4jo8TvsReY4//nisVitbt27l448/Zvr06aSkpHS4n6ZpmhaedI10EG05tIURKSOIMnT9sXtqpe9efDfv73yfi0f4d5jintQcB1J2djY33XQTN910E+PHj2fDhg0c18EYw1VVVSxevJgNGzYgIjgcDkSExx9/XA8TFoYa16xF4uOJGX7srOmmocMAaN6xk6i0NK+O503NcSCdf/75/OQnP2HJkiVUVVX16BirV69mzJgxba+vvPJKXn/9dTZv3qybdWiapkUgXSMdJEoptlVvY1TqKK+2PyX3FManjef5tc9jd/TdWulPPvkEu931/ioqKqiqqjqmI5fHf/7zH6677jr27NlDcXEx+/btIz8//4hb6lr4aFy3jrhx4xCj8Zh1McNdiXTLrp3BDqvHbrrpJn71q191246/M8XFxfzkJz/h7rvvblt21VVXsWDBAhYvXsz55/u/GZemaZoWWDqRDpLKxkoONR9iZMpIr7YXEe6cfCdltjLe3v52gKMLjoaGBnJzc9t+nnzyST777DPGjx/PpEmTOOOMM/jjH//IgAEDOtx/4cKFXHTRRUcsu+SSS3jttdeCEb7mA2dzM01btnTYrAMgKisLg9lM847ISaRzc3O55557Olz3xhtvHDH83TfffAPAzp0724a/u/zyy7n77ru58cYb2/YbO3Ys8fHxzJkzB7PZHJT3oWmapvmPbtoRJMW1xQAMTRrq9T4nZp/IlMwp/G3t3zhv2HnER8cHKLrgcDqdHS5/8sknO91nyZIlHT73+OEPf9j2/IYbbuCGG27oaXiaHzVt2gR2+zEjdniICKZhw2iOgBppq9V6zLLZs2cze/ZsoOty19jY2O3x165d25vwNE3TtBDSNdJBsqd+D0C3HQ3bExHum3ofVU1VvLTxpUCFpml+55lspaMROzxihg6lJYJqpDVN0zTtaDqRDpK9dXsxGUwMMHfcbKEzkzImcfrg03l548scaNBTKmuRoXHtOqIGDiQ6K7PTbUzDhtJaWYmjvj6IkWmapmma/+hEOkj21O1hUMIgDOL7R/6jKT/C7rTz7JpnexVD+6G3+pv+/N5DoXHtWuI6adbhYRrsujvTsmdvl9v1599df37vmqZpkUAn0kGyp26PT8062huUOIirRl/FOzveYduhbT06RmxsLFVVVf3yD7NSiqqqKmJjY0MdSr/QWlWFvbSUuEmdN+uAw4m0fV/nibQut7rcapqmhTPd2TAIHE4H++r3cUruKT0+xu0Tb+edHe/w5KoneW7ecz7vn5ubS0lJCZWVlT2OIZLFxsYeMRW5FjiNa93toyd1UyM9aBDQdY20Lre63GqapoUznUgHQUVDBXannbzEvB4fIykmidsn3s4ThU/wTdk3zMye6dP+0dHR5Ofn9/j8muatxrVrwWgkduzYLrczxMcTlZFBy97OE2ldbjVN07Rwppt2BMGeOt9H7OjIVaOvIseSw58K/4TD6fBHaJrmd43r1hI7ahSGTqbJbi96cB4te/cEISpN0zRN8z+dSAdBmbUMgFxL727Rmowm7plyD9sObeP9Xe/7IzRN8yvlcNC0bj2x3TTr8DANysPeTWdDTdM0TQtXOpEOgnJbOQYxkBGf0etjnTnkTMamjeXv6/6ua6W1sNO8YydOm434ggKvtjcNzqO1shJnQ0NgA9M0TdO0ANCJdBBU2CrIjM8kytD7Jukiwk3jb2Jv/V6W7FvS6+Npmj81rl4NQNzkyV5tb8pz9Rto2bcvYDFpmqZpWqAENJEWkadEZKmIPH3U8mwRWSwi34jIvKPWvScivw9kXMFWbitnoHmg3443N28uOZYcXt70st+OqR2my23PNa5ZgzE1lWj3iBzdic5zjyXdRYdDTdM0TQtXAUukRWQKYFZKzQJMInJ8u9X3Aw8Cp7sfPftMAvrcoKnl1nKfZzTsSpQhimvHXsvqA6tZW7nWb8fVdLntrcbVq4mbPBkR8Wp7U54r4bbrRFrTNE2LQF4l0iLyloicI+LTtHwzgEXu54uA6e3WTQSWK6WsQL2IJLiX/xD4Px/OEfacysn+hv1+rZEGuGj4RZijzby59U2/HrevueSSS/jwww9xOp3e7qLLbQ+1HjpEy549xBV0PRFLe8bERIzJyd3ObqhpmqZp4cjbxPhvwNXAdhF5VERGe7FPMlDnfl4LpLRbZ1SHpyqrBVLcxzwA1HgZU0SobqrG7rT7PZGOj47n7Pyz+az4M+pb6v167L7kjjvu4LXXXmPEiBHcf//9bNmypbtdkglAuRWR20SkUEQK++rkIo2r1wB43dHQwzUEnk6kNU3TtMjjVSKtlFqklPoeMAUoBj53txO9UUSiO9mtBkh0P0/kyESj/XATnnX3AUe0ST1aJCYj5dZyAL8n0gCXjLiEJkcTH+/+2O/H7ivmzZvHq6++SlFREUOGDOG0005j5syZvPTSS9jt9o52qcHP5RZAKfWCUmqqUmpqRkbvR28JR41r1kBUFLHjx/u0nylvMC179FjSmqZpWuTxuqmGiKQBNwC3AKtxJQ9TgM872WU5MNf9fB7wbbt160RkhoiYgUSlVB0wGJgPPA5cJSLHzKcdiclIuc2VSPuzjbTH2LSxjEoZxdvb3/b7sfuSqqoq5s+fz4svvsjkyZO55557KCoq4rTTTutoc7+X2/6iYeVKYseO9WoilvZMgwfTWlGBs6kpQJFpmqZpWmB420b6bWApEA+cp5Q6Xyn1hlLqbsDS0T5KqSKgSUSWAk6l1Hci8hf36seBP+Bqg/qwe/szlFJnAj8DFiqlvuzNGwsXnkR6oMX/NdIiwnnDzmNj1Ub21evhwzpy8cUXM2vWLBoaGnj//fd57733uOKKK/jLX/6C1Wo9ZntdbnvGYbXRuH495unTu9/4KKYhQ0Ap3bxD0zRNizjeDmz8olLqo/YLRCRGKdWslJra2U5KqXuOen23+7EEmNPJPkuAJV7GFfYqbBXER8WTEJ3Q/cY9cNrg03ii8Ak+K/6MmyfcHJBzRLJbbrmFs88++4hlzc3NxMTEUFhY2OHoErrc+q5xVSG0tmKefoLP+5oGu4fA27OH2JEj/R2apmmapgWMt007Ohofd7k/A+mrPGNIezscmK+yLdlMTJ/Ip8WfBuT4ke7BBx88ZtmMGTNCEEnfZvt2BRId7fVELO2ZhrgT6eJiP0elaZqmaYHVZY20iAwAcoA4EZkMeLLBRFzNPLRulNvKGWDxf/vo9k4fcjpPFD7Bvrp9DEr0biKMvq6iooLS0lIaGxtZvXo1nsE26urqaNDTUfudbcW3xE2e7HP7aABjQgLGtDTd4VDTNE2LON017TgDVwfDXODJdsvrgQcCFFOfUmGrYGza2ICeY97geTxR+ARf7PuC68ZdF9BzRYpPP/2U+fPnU1JSwn333de2PCEhgYcffjiEkfU9rYcO0bx5C+l339XjY5iGDNE10pqmaVrE6TKRVkq9DLwsIpcopd4KUkx9RlNrE9VN1QEZ+q69HEsOQ5OG8nXp1zqRdrv++uu5/vrreeutt7jkkktCHU6f1vDdSlCqRx0NPUyDB2Nd+pUfo9I0TdO0wOuuacc1SqkFwBARue/o9UqpJzvYTXOrsFUAgRlD+mizcmbx2pbXaLA3EB+tW90sWLCAa665huLiYp588thi2r6WWuudhhXfIvHxxE2Y0ONjmIYMwfH22zisVoyWDgcC0jRN07Sw011nQ7P70QIkdPCjdSGQY0gf7aTck7A77XxX8V3AzxUJbDYbAFarlfr6+mN+NP+xfbuC+KnHIdGdzc3UvbYOh7qdtKZpmhZBumva8bz78TfBCadvCWaN9JTMKcRHxbO0ZCmzB80O+PnC3e233w7Ar3/96xBH0rfZ9++nZdcuknvZfMY0eAjgGrkjbtw4P0SmaZqmaYHn7YQsj4tIoohEi8j/ROSgiFwT6OAiXbmtHEHIis8K+LlMRhMnDDyBr0u/bhuhQoOf/exn1NXVYbfbmTt3Lunp6SxYsCDUYfUZDStWAGCe0fP20QCmwXmArpHWNE3TIou340if7p4O+VygBBgJ/DRgUfURFbYKMuIyiDb2/Ja3L07KOYkyWxnFdcVBOV8k+Oyzz0hMTOSDDz4gNzeXbdu28cc//jHUYfUZtm9XYEhKImb06F4dxxAbS1T2QFp27fZTZJqmaZoWeN4m0p5M8Gxc0yBXByiePiUYY0i3NzN7JgDflH0TtHOGO7vdDsBHH33EVVddRWpqaogj6juUUti+XY552jTE4O2lpHMxI0bQvH27HyLTNE3TtODw9q/f+yKyBZgK/E9EMoCmwIXVN1TYKoLSPtojNyGXwYmDWVa6LGjnDHfnnXceo0ePprCwkLlz51JZWUlsbGyow+oT7Pv20VpWTnwvm3V4xI4cSfOuXSj3lx9N0zRNC3deJdJKqfuBGcBUpZQdsAEXBDKwSKeUctVIxwevRhpctdKF+wtpdjQH9bzh6tFHH2X58uUUFhYSHR2N2Wzm3XffDXVYfYLt228BejV+dHsxI0eB3U7zbt28Q9M0TYsM3c1s2N4YXONJt9/nFT/H02ccaj5Es6OZgZbg1UiDq530wi0LKdpfxIzsGUE9d7javHkzxcXFtLa2ti277jo9cU1vNXy7gqiMDEz5+X45XsyokQA0b91G7MiRfjmmpmmapgWSV4m0iPwLGAasARzuxQqdSHfKM4Z0MJt2AEzNmkq0IZpvyr7RiTRw7bXXsnPnTgoKCjAajQCIiE6ke0kphW3FCswnzkRE/HLMmPx8iI6medtWXP2aNU3TNC28eVsjPRUYq/S4al6rsAZvDOn24qPjmZI5hWVly/gxPw7qucNRYWEhmzZt8luyp7k0b9+Oo6oK8wn+adYBINHRxAwdStPWrX47pqZpmqYFkredDTcAwW3sG+FCVSMNMDNnJtsPbedAw4GgnzvcjB8/noqKilCH0ec0fOseP3r6CX49buzo0TRt3KTHQtc0TdMigrc10unAJhH5DmjrxaaUOj8gUfUBZbYy4qLiSIpJCvq5T8w+kadWPcU3Zd9w4fALg37+cHLw4EHGjh3LtGnTiImJaVv+3nvvhTCqyGf79lui8/KIzsnx63HjJhdQ++672EtKMA0a5Ndja5qmaZq/eZtIPxTIIPqiClsFA8wDQtKkYGTKSNLj0vmmVCfSDz30UKhD6HNUaysN331H4lln+f3YcZMnA9C4erVOpDVN07Sw51UirZT6UkQGAyOUUotEJB4wBja0yFZuLQ9Jsw5wdaabmT2Tr0q+wuF0YDT031/VKaecwp49e9i+fTvz5s2joaEBh8PR/Y5ap5o2b8ZptRLv52YdADHDh2Mwm2lYvZqk8/UNL03TNC28edVGWkRuBf4DPO9elAO8E6CY+oRyW+gSaXCNJ13TXMPm6s0hiyEc/P3vf+fSSy/l9ttvB6C0tJQLL7wwtEFFONty9/jRJ/g/kRajkbhJk2gsWu33Y2uapmmav3nb2fBO4ESgDkAptR3IDFRQka7Z0UxVUxUDzKHrnzkjewaC9PtZDp999lmWLVtGYmIiACNGjODAAd0Jszdsy78hZsQIotLTA3L8+BNOoHnrVuz696RpmqaFOW8T6WalVIvnhXtSlm671YvIUyKyVESePmp5togsFpFvRGSee9mtIvKt++dqX95EuNlv2w+EZsQOj9TYVMakjWFZWf9OpGNiYjCZTG2vW1tbu2233l/LrTecDQ00Fq7CfNJJATuHZfYpANiWLg3YOTRN0zTNH7xNpL8UkQeAOBE5Dfg38H5XO4jIFMCslJoFmETk+Har7wceBE53PwJ8rpSaDsyCyB4AOZRD37U3e9Bs1hxY05bY90ennHIKDz/8MI2NjXz++edcdtllnHfeeZ1u35/LrTcaCgtRdjvmk04M2DliRo4kasAArEuWBOwcmqZpmuYP3ibS9wOVwHrgduAjDicSnZkBLHI/XwS0n7lhIrBcKWUF6kUkQSlV7F7XyuHZEyNSuCTS5+Sfg0Lx8e6PQxpHKD366KNkZGQwYcIEnn/+ec4++2x+//vfd7VLvy233rB+/TUSE0P8cccF7BwiQsLcuVi//ApHbW3AzqNpmqZpveXtqB1OEXkHeEcpVenlsZOBne7ntcC4duuM7WZJrAVSgHr36+8T4R0ZPYl0ljkrpHHkJeYxIX0CH+7+kBvG3xDSWELFYDBw4YUXcuGFF5KRkeHNLskEoNyKyG3AbQB5eXnehh92bMu+If744zHExgb0PEkXXcShV1+l7qOPSLnqqoCeS9M0TdN6qssaaXF5SEQOAluArSJSKSK/8uLYNUCi+3mi+7VH+5q7tnUicgJwNvBYJ/HcJiKFIlJYWeltPh98pfWlZMZlYjKaut84wM4Zeg5bqrew4eCGUIcSVEopHnroIdLT0xk9ejSjRo0iIyOD3/72t93tWoOfy607nheUUlOVUlO9TOjDjr28nJadOzGfGLhmHR6x48YSM2oUh15biHI6A34+TdM0TeuJ7pp2/AjXaB3HK6XSlFKpwAnAiSJybzf7Lgfmup/PA75tt26diMwQETOQqJSqE5Ec4E/A9UqpDm+RR0oysq9+H7kJuaEOA4ALhl2AOdrMgs0LQh1KUP35z39m2bJlrFy5kqqqKqqrq1mxYgXLli3jqaee6mpXv5fbvsL69dcAWALYPtpDREi75Waat2+n/vNF3e+gaZqmaSHQXSJ9HXCVUmq3Z4FSahdwjXtdp5RSRUCTiCwFnEqp70TkL+7VjwN/wNUG9WH3sl8BWcDbIrJEROJ8fjdhYl/9PgYlhMesbBaThYuGX8Snuz+lwlYR6nCC5pVXXmHhwoXk5+e3LRs6dCgLFizglVde6XS//lxuu2Nd/AXR2dmYhg8PyvkSzz4b05AhVD79NM6Wlu530DRN07Qg666NdLRS6uDRC5VSlSIS3d3BlVL3HPX6bvdjCTDnqHW3dx9u+GtsbaSysTJsEmmAa8Zew+tbX+e5tc/x0MyHQh1OUNjtdtI7GOc4IyMDu93e5b79sdx2x2mzYVu2jOQrrgjatPdiNJL1wC/Yd9vtVD33PBk/vDso59U0TdM0b3VXI91VNZCuIupASX0JQFgl0jmWHK4YdQX/3fFfdtbs7H6HPqD92NG+rNM6Zl22DNXSQsLcud1v7EeWk08m8fzzOPjCCzSsWhXUc2uapmlad7pLpCeJSF0HP/XAhGAEGGn21e8DwiuRBrh94u2Yo838dvlvcaq+33lr7dq1JCYmHvOTkJDA+vXrQx1exLH+bzGGpCTipwZu2LvODPh//4/onGxKfvQjPduhpmmaFla6TKSVUkalVGIHPwlKqW6bdvRH4ZpIp8Sm8LPjf0bRgSIWblkY6nACzuFwUFdXd8xPfX19t007tCMpux3rkiUkzJ6NRHk1YqZfGRMSyH3mLzitNkrvvQ+lf3+apmlamPB2QhbNS/vq95EQnUBSTFKoQznGBcMu4KSck/jzqj+z7dC2UIejRQjrsmU4amtJOOP0kMUQO2okA3/3OxpXrWL/438MWRyapmma1p5OpP2spL6E3ITcoHXI8oWI8LsTf4fFZOHHS36MzW4LdUhaBKh7732MyclYTjoppHEknXsOqddfz6F//Yva994LaSyapmmaBjqR9rudtTsZmjw01GF0Kj0uncdPfpy99Xv5zTe/4fBEfZp2LIfVSv3//kfi2WchYdBJM/MnPyb++OMp/9Wvadq8OdThaJqmaf2cTqT9yNpipcJWwbCkYaEOpUvHDzieuwru4uPij/nHhn+EOhwtjNV/vgjV3EziueeFOhQAJDqanKeexJiURMndP8RhtYY6JE3TNK0f04m0H+2q3QXAsOTwTqQBbp5wM2cNOYuni57mk+JPQh2OFqZq332X6Nxc4iYXhDqUNlHp6eQ89RT2sjL2P/JIqMPRNE3T+jGdSPuRZ4zmSEikDWLgdyf9jsmZk/nl0l+yvGx5qEPSwkzz7t00fPstyZdcHHZt/uOnTCbtlluofett6hcvDnU4mqZpWj+lE2k/2lGzgxhjDLmW3FCH4pUYYwzPnPoMg5MGc+f/7uTzPZ+HOiQtjNS8/gZERZF86aWhDqVDGXfdSczo0ZT/v1/RWl0d6nA0TdO0fkgn0n607dA2hiYNxWgwhjoUryXHJvPSGS8xJnUM9y25j8e+e4zG1sZQh6WFmLOpiZp33iHhtHlEZWSEOpwOiclE9mOP4airo+K3vwt1OJqmaVo/pBNpP3EqJxsPbmR8+vhQh+KzpJgkXjrzJa4efTULNi/g/HfO55Pdn+gRPfqxuo8+xllbS8qVV4U6lC7FjhpJxp13Uv/JJ9R9/HGow9E0TdP6GZ1I+8neur3U2+uZkB6ZM6ebjCZ+ccIvmH/mfJJjkvnpVz/lts9vY2/d3lCHpgWZUorql1/GNHwY8dOOD3U43Uq75WZiJ0yg4je/pfXgwVCHo2mapvUjOpH2k/UH1wMwLn1ciCPpneOyjuP1c17nlyf8kg0HN3Dxexfz/s73Qx2WFkS2pUtp3rqVtJtvCbtOhh2RqCiyH3kYZ0MDFb/RY6NrmqZpwaMTaT9Zf3A9cVFxYT+GtDeMBiNXjr6Sdy98l4kZE3ng6wd4pugZnaD0E1Uv/J2oAQNIOufsUIfitZjhw8m454fUf76Iug8+7PFxlFK0VlWh7HY/Rqdpmqb1VTqR9pNvy79lStaUiOpo2J3M+EyeP+15LhlxCX9f/3eeKHxCJ9N9XEPRahoKC0m78YawmMnQF6k33EDcpElU/P73tOzb59O+TpuNg3/7G9tPmsX2E09i24yZVD7zF51Qa5qmaV3SibQfVNgq2F27m5kDZ4Y6FL+LNkTz6xm/5urRV/PKpld4eMXDOJUz1GFpAaCU4sCTf8KYnh62Q951RYxGsh9/DAH23Xqb1+2l6xd/wc5zz6Py6WeImzCBrAd+gfnEEzn4f/9HyT0/0sm0pmma1imdSPvBVyVfATAje0aIIwkMEeH+afdzw7gbeH3r6zzw9QPYnTq56GusS5bQWLiKjDt/gMFsDnU4PWIaPJjc/3sWe0UFxZdfQUNhYYfbqZYWrF8vY+/Nt1Dygx9gtJgZ/OoCBj33N1Kvu47cp/9M1oMPYl28mPJf/VrfidE0TdM6FBXqAPqCj3Z/RH5SPsOTh4c6lIAREe477j4STYk8s/oZDjYe5JGTHiEjPjzHGNZ842xsZP8jj2IaMiQia6Pbiz/uOAb/61+U/PCH7LnmWmLHjiVm7BgMJhMOqxX73n00b9uGs6EBY1oamT/9KanXXnNMU5bUa76H49AhDj77LLETxpN69dUhekeapmlauNKJdC/tq9/Hqv2ruHvy3RExwkFviAi3TryV9Lh0Hl7xMBe9dxG3TriVy0ZeRnx0fKjD03qh8s9PY9+7l7yXX0aio0MdTq/FTRjPsI8+5NAbb1C/aBHWL78EeyuG+HiiBw0i6cILMJ94IuYTT8QQG9vpcdLv/AFNGzey/+FHiB09hvgpk4P4LjRN07RwJ5F6y3Lq1KmqsJPbtsH00DcP8d7O9/j44o/JMmeFOpyg2VWzi8dXPs6ysmXER8VzxpAzOCv/LKYOmEq0IXISMRFZpZSaGsxzhkvZ9aj79DNK77mHlKuvYsCvfhXqcMKOo66O3ZddhtNqY8hrr2IaPDjUIYWk3GqapmnHCmgbaRF5SkSWisjTRy3PFpHFIvKNiMxzL0sQkfdFZJmIXBfIuPxlXeU63tnxDpeOvLRfJdEAQ5OH8txpz/Gvs/7F6UNO55PiT7jt89uY/cZsHlj6AIv2LKLB3hDqMHukr5fb9uqXLKHsZz8jbtIkMu+/P9ThhCVjYiKD/vYcOJ3svfkW7KWloQ5J0zRNCxMBS6RFZApgVkrNAkwi0n6KtPuBB4HT3Y8AtwILgZOBW0QkrMfe2lK9hR998SOy4rO4a/JdoQ4nZAoyC/jdib/jyyu+5OlTn2b2oNl8VfoV9y65l1PeOIUfLv4hb259k921uyOiw1ZfL7cejpoaDjzxBCU/uJOYYcPIfe5vGCJsuLtgihmaz6AXnsdRW8vuSy+j7tPPIqI8a5qmaYEVyDbSM4BF7ueLgOnASvfricA9SiklIvUikuDe/k6llENE1gKjgPW+nLDV2crqA6tRStH2n+r4EVzDfTmVs21b1/8dL1NK4cTJgYYDrD6wmqUlS0mNTeWvc/9KoinRDx9XZIuLimNO3hzm5M2h1dnKqv2rWLx3MYv3LeaLfV8AkBGXwcjUkeQn5jPQPJDEmEQSohOINkZjEEPbj1H8Oxa3JdrCmLQx3m4e9HIL0FpZSfOu3aAUrkLn+lFOdewydfj5scs4vL3T6V7uWqaam2gpLaV502ZsK1agmppIuuRisn7xAEZLZI7SEUxxEyYw5M03KL3nR5Tecw/Rg/OwnDQL07ChGJOSMFosYIwCcfUnwPOD57nv5zQNGkT0wIF+fy+apmmafwQykU4Gdrqf1wLt5842qsPVObVAinv7uqOW+aShtYGbPr2pJ7H6ZKB5INeOvZabx99McmxywM8XaaIMUZww8AROGHgC90+7n731e/mu4jtW7V/FrppdFO0vorG1MWjxTMqYxIKzF3i7eTJBLrcA1q++ovyXD3a/YW8ZDJjy8ki++GKSr7iC2FEjA3/OPiQmP5/8t9+i7sMPqX33PWr/+1+cDYFrwpT505+SdnPgr2mapmlazwQyka4BPFW1ie7XHo52zz3rPNs3dbA9ACJyG3AbQF5e3jEnjI+K5x+n/6Nt9AxBEBEMYkDc1UEiguc/gxhclUWe/+Sox3bPPdulx6WTYEro6WfSCHR4jQAABVVJREFU74gIgxMHMzhxMJeNvAwAp3JS31Lf9tPqbMWhHCgUDqfD7xO+mE0+1bbW4OdyC92XXfOsWeTNn99WcykGw1E1mu5aTs/ydrWc3m0LEm0iOiuzT4zKEUoSFUXSBReQdMEFKKeT1sqDOK31OK1WlMPRwZ0C3HcJfGfKG+Tf4DVN0zS/CmQivRy4HXgTmAfMb7dunYjMANYBiUqpOhFZDswVkTeBAmDr0QdUSr0AvACukQ+OXh9liGLawGl+fhuavxnEQFJMEkkxSaEOpSN+L7fQfdmNzswkOjPTj29DCwYxGIjOyoQs/bvTNE3rjwLW2VApVQQ0ichSwKmU+k5E/uJe/TjwB1xtUB92L3sR+B6wFPinUqo5ULFpWmd0udU0TdM0zVsBnZBFKXXPUa/vdj+WAHOOWlcHnBvIeDTNG7rcapqmaZrmjYidkEVE6unkNnoIpQMHQx1EO+EWD4RfTIOVUkGd51xEKoE9wTznUcLtdwA6Jm+0jyfo5VbTNE07ViQn0oXhNrNXuMUUbvFAeMbU34Tj70DH1L1wi0fTNE0L8MyGmqZpmqZpmtZX6URa0zRN0zRN03ogkhPpF0IdQAfCLaZwiwfCM6b+Jhx/Bzqm7oVbPJqmaf1exLaR1jRN0zRN07RQiuQaaU3TNE3TNE0LmYhLpEXkVhH51v1ztXtZlIj8S0S+FpH7QxTXUyKyVESeDsX53TGcICLfuON4yr3sp+7P5VURCcnc0CJyn4h8HS7x9GdhXE5rRWSJ+yc1BDENEZH97vN/5l4W0rIqIme2+0zKReTCUH9OmqZp2pEiLpEGPldKTQdmAT92Lzsf2KyUOgk4SUQGBDMgEZkCmJVSswCTiBwfzPO3sweY444jU0RmAae6P5d1wIXBDkhEYoBJ7ucZoY6nPwvjcjoBWK+Umu3+qQ5RXJ+7z396OJRVpdQnns8E2ItrRs1w+Jw0TdM0t4hLpJVSxe6nrYDD/XwGrj8yAF8AwU4Q2p9/ETA9yOcHQClVoZRqcr9sBSYCS0Ic1y3Ay+7n08Ignv4sXMupAxjjrqF+VEQkFHEBp7pjuJcwKqsiMhTYr5SyEh6fk6ZpmuYWcYl0O98H3nE/Twbq3M9rgZQgxxLq8x9BRCbimgWthhDG5b4dfopSarF7UXIo49HC6/P3lFOl1CZgBHCyO6bzQhBOOTASOBWYB0wlfD6ri4H/up+H+nPSNE3T2gnbRFpEBrRrC+j5ed297gTgbOAx9+Y1QKL7eaL7dTCF+vxt3O0m/wrcTOjjuhZ4rd3rUMfT39UQJp//UeUUpVS1cg0h9A4wPtjxKKWalVI2pVQr8AGwgzD5rHAlzO9B6D8nTdM07Uhhm0i7b//OPurnShHJAf4EXK+U8jTtWA7MdT8/FVgZ5HDbn38e8G2Qzw+4Ol0CC4CfKqUqcH0Op4QwrlHAHSLyCTAOVy1fKOPp78KynIqIWUSM7tUnAjtDEFNCu5cn4kqkQ15W3f09WpRSVeHwOWmapmlHCttEugu/ArKAt9211HHA+8B498gQy5VS5cEMSClVBDSJyFLAqZT6Lpjnb+cyXO3DHxORJcAw4Cv351LA4aYwQaGU+rlS6gyl1JnARqXUb0IZT38XxuV0IrDSHdcg4D8hiGmWiKwSkW+AMqXUCsKjrF4AvOt+PoLQf06apmlaO3pCFk3TNE3TNE3rgUiskdY0TdM0TdO0kNOJtKZpmqZpmqb1gE6kNU3TNE3TNK0HdCKtaZqmaZqmaT2gE2lN0zRN0zRN6wGdSGuapmmapmlaD+hEWtM0TdM0TdN6QCfSmqZpmqZpmtYD/x+lcMSANAqPdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'density', subplots = True, layout = (4,4), legend = True, fontsize = 8, figsize = (12,8), sharex = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box', subplots = True, layout = (4,4), sharex = False, sharey = False, fontsize = 8, figsize = (12,8), legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,8))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(df.corr(method = 'pearson'), vmin = -1, vmax = 1, interpolation = 'none', cmap = 'YlGnBu')\n",
    "fig.colorbar(cax)\n",
    "ticks = np.arange(0,14)\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(df.columns)\n",
    "ax.set_yticklabels(df.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = np.array(df.values)\n",
    "X = values[:, 0:13]\n",
    "y = values[:, 13]\n",
    "test_size = .3\n",
    "seeds = list(range(0,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('Lasso', Lasso()))\n",
    "models.append(('EN', ElasticNet()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('DT', DecisionTreeRegressor()))\n",
    "models.append(('SVR', SVR()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results = []\n",
    "\n",
    "for seed in seeds:\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = seed)\n",
    "    kf = KFold(n_splits = 10, random_state = seed, shuffle = True)\n",
    "    results = []\n",
    "    names = []\n",
    "    \n",
    "    for name, model, in models:\n",
    "        cv_results = cross_val_score(model, X_train, y_train, cv = kf, scoring = 'neg_mean_squared_error')\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "    \n",
    "    all_results.append(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Organize the results into separate lists for each model\n",
    "\n",
    "lr_results = []\n",
    "lasso_results = []\n",
    "en_results = []\n",
    "knn_results = []\n",
    "dt_results = []\n",
    "svr_results = []\n",
    "\n",
    "master_results = [lr_results, lasso_results, en_results, knn_results, dt_results, svr_results]\n",
    "\n",
    "for results in all_results:\n",
    "    for idx, result in enumerate(results):\n",
    "        if idx == 0:\n",
    "            lr_results.append(result)\n",
    "        if idx == 1:\n",
    "            lasso_results.append(result)\n",
    "        if idx == 2:\n",
    "            en_results.append(result)\n",
    "        if idx == 3:\n",
    "            knn_results.append(result)\n",
    "        if idx == 4:\n",
    "            dt_results.append(result)\n",
    "        if idx == 5:\n",
    "            svr_results.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -23.8796 (11.5514)\n",
      "Lasso: -28.3373 (11.5358)\n",
      "EN: -27.5496 (11.2421)\n",
      "KNN: -43.3322 (16.511)\n",
      "DT: -23.9242 (15.3986)\n",
      "SVR: -66.6963 (27.5296)\n"
     ]
    }
   ],
   "source": [
    "#Collapses the results for each model into a single array so summary statistics can be performed\n",
    "\n",
    "for idx, results in enumerate(master_results):\n",
    "    results = np.stack(results)\n",
    "    mean = results.mean()\n",
    "    std = results.std()\n",
    "    name = names[idx]\n",
    "    msg = \"{}: {} ({})\"\n",
    "    print(msg.format(name, round(mean, ndigits = 4), round(std, ndigits = 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates an array of the results suitable for plotting into a box and whisker plot\n",
    "\n",
    "plot_results = np.array([])\n",
    "for idx, results in enumerate(master_results):\n",
    "    results = np.stack(results)\n",
    "    plot_results = np.append(plot_results, results)\n",
    "plot_results = np.split(plot_results, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates a dataframe of the results suitable for descriptive statistics\n",
    "\n",
    "df_results = pd.DataFrame()\n",
    "\n",
    "for idx, col in enumerate(plot_results):\n",
    "    df_results[idx] = col\n",
    "\n",
    "df_results.columns = names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>LR</th>\n",
       "      <th>Lasso</th>\n",
       "      <th>EN</th>\n",
       "      <th>KNN</th>\n",
       "      <th>DT</th>\n",
       "      <th>SVR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-23.8796</td>\n",
       "      <td>-28.3373</td>\n",
       "      <td>-27.5496</td>\n",
       "      <td>-43.3322</td>\n",
       "      <td>-23.9242</td>\n",
       "      <td>-66.6963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.6096</td>\n",
       "      <td>11.5939</td>\n",
       "      <td>11.2988</td>\n",
       "      <td>16.5941</td>\n",
       "      <td>15.4762</td>\n",
       "      <td>27.6683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-87.0795</td>\n",
       "      <td>-79.9738</td>\n",
       "      <td>-78.2031</td>\n",
       "      <td>-115.2018</td>\n",
       "      <td>-87.2654</td>\n",
       "      <td>-163.8563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-28.1619</td>\n",
       "      <td>-33.5744</td>\n",
       "      <td>-32.7891</td>\n",
       "      <td>-47.9024</td>\n",
       "      <td>-29.5494</td>\n",
       "      <td>-83.1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-20.4244</td>\n",
       "      <td>-27.6383</td>\n",
       "      <td>-26.2482</td>\n",
       "      <td>-41.2085</td>\n",
       "      <td>-18.1993</td>\n",
       "      <td>-64.8080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-16.9852</td>\n",
       "      <td>-19.9715</td>\n",
       "      <td>-19.5374</td>\n",
       "      <td>-32.8911</td>\n",
       "      <td>-13.9053</td>\n",
       "      <td>-45.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-8.8458</td>\n",
       "      <td>-10.5034</td>\n",
       "      <td>-10.4142</td>\n",
       "      <td>-20.0630</td>\n",
       "      <td>-6.0706</td>\n",
       "      <td>-21.0511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             LR     Lasso        EN       KNN        DT       SVR\n",
       "count  100.0000  100.0000  100.0000  100.0000  100.0000  100.0000\n",
       "mean   -23.8796  -28.3373  -27.5496  -43.3322  -23.9242  -66.6963\n",
       "std     11.6096   11.5939   11.2988   16.5941   15.4762   27.6683\n",
       "min    -87.0795  -79.9738  -78.2031 -115.2018  -87.2654 -163.8563\n",
       "25%    -28.1619  -33.5744  -32.7891  -47.9024  -29.5494  -83.1015\n",
       "50%    -20.4244  -27.6383  -26.2482  -41.2085  -18.1993  -64.8080\n",
       "75%    -16.9852  -19.9715  -19.5374  -32.8911  -13.9053  -45.0090\n",
       "max     -8.8458  -10.5034  -10.4142  -20.0630   -6.0706  -21.0511"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,5))\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(plot_results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines = []\n",
    "\n",
    "pipelines.append(('ScaledLR', Pipeline([('Scaler', StandardScaler()), ('LR', LinearRegression())])))\n",
    "pipelines.append(('ScaledLasso', Pipeline([('Scaler', StandardScaler()), ('Lasso', Lasso())])))\n",
    "pipelines.append(('ScaledEN', Pipeline([('Scaler', StandardScaler()), ('EN', ElasticNet())])))\n",
    "pipelines.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()), ('KNN', KNeighborsRegressor())])))\n",
    "pipelines.append(('ScaledDT', Pipeline([('Scaler', StandardScaler()), ('DT', DecisionTreeRegressor())])))\n",
    "pipelines.append(('ScaledSVR', Pipeline([('Scaler', StandardScaler()), ('SVR', SVR())])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results = []\n",
    "\n",
    "for seed in seeds:\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = seed)\n",
    "    kf = KFold(n_splits = 10, random_state = seed, shuffle = True)\n",
    "    results = []\n",
    "    names = []\n",
    "    \n",
    "    for name, model, in pipelines:\n",
    "        cv_results = cross_val_score(model, X_train, y_train, cv = kf, scoring = 'neg_mean_squared_error')\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "    \n",
    "    all_results.append(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Organize the results into separate lists for each model\n",
    "\n",
    "lr_results = []\n",
    "lasso_results = []\n",
    "en_results = []\n",
    "knn_results = []\n",
    "dt_results = []\n",
    "svr_results = []\n",
    "\n",
    "master_results = [lr_results, lasso_results, en_results, knn_results, dt_results, svr_results]\n",
    "\n",
    "for results in all_results:\n",
    "    for idx, result in enumerate(results):\n",
    "        if idx == 0:\n",
    "            lr_results.append(result)\n",
    "        if idx == 1:\n",
    "            lasso_results.append(result)\n",
    "        if idx == 2:\n",
    "            en_results.append(result)\n",
    "        if idx == 3:\n",
    "            knn_results.append(result)\n",
    "        if idx == 4:\n",
    "            dt_results.append(result)\n",
    "        if idx == 5:\n",
    "            svr_results.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScaledLR: -23.8796 (11.5514)\n",
      "ScaledLasso: -28.8733 (13.1673)\n",
      "ScaledEN: -30.3165 (14.6206)\n",
      "ScaledKNN: -23.2159 (14.4643)\n",
      "ScaledDT: -25.2896 (14.8993)\n",
      "ScaledSVR: -32.4358 (17.4395)\n"
     ]
    }
   ],
   "source": [
    "#Collapses the results for each model into a single array so summary statistics can be performed\n",
    "\n",
    "for idx, results in enumerate(master_results):\n",
    "    results = np.stack(results)\n",
    "    mean = results.mean()\n",
    "    std = results.std()\n",
    "    name = names[idx]\n",
    "    msg = \"{}: {} ({})\"\n",
    "    print(msg.format(name, round(mean, ndigits = 4), round(std, ndigits = 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates an array of the results suitable for plotting into a box and whisker plot\n",
    "\n",
    "plot_results = np.array([])\n",
    "for idx, results in enumerate(master_results):\n",
    "    results = np.stack(results)\n",
    "    plot_results = np.append(plot_results, results)\n",
    "plot_results = np.split(plot_results, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates a dataframe of the results suitable for descriptive statistics\n",
    "\n",
    "df_results = pd.DataFrame()\n",
    "\n",
    "for idx, col in enumerate(plot_results):\n",
    "    df_results[idx] = col\n",
    "\n",
    "df_results.columns = names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>ScaledLR</th>\n",
       "      <th>ScaledLasso</th>\n",
       "      <th>ScaledEN</th>\n",
       "      <th>ScaledKNN</th>\n",
       "      <th>ScaledDT</th>\n",
       "      <th>ScaledSVR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-23.8796</td>\n",
       "      <td>-28.8733</td>\n",
       "      <td>-30.3165</td>\n",
       "      <td>-23.2159</td>\n",
       "      <td>-25.2896</td>\n",
       "      <td>-32.4358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.6096</td>\n",
       "      <td>13.2336</td>\n",
       "      <td>14.6942</td>\n",
       "      <td>14.5372</td>\n",
       "      <td>14.9744</td>\n",
       "      <td>17.5274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-87.0795</td>\n",
       "      <td>-92.7119</td>\n",
       "      <td>-102.1213</td>\n",
       "      <td>-88.8508</td>\n",
       "      <td>-89.5074</td>\n",
       "      <td>-114.6016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-28.1619</td>\n",
       "      <td>-34.5724</td>\n",
       "      <td>-36.8072</td>\n",
       "      <td>-28.1611</td>\n",
       "      <td>-34.1896</td>\n",
       "      <td>-44.6473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-20.4244</td>\n",
       "      <td>-26.4251</td>\n",
       "      <td>-28.5038</td>\n",
       "      <td>-20.3167</td>\n",
       "      <td>-19.4396</td>\n",
       "      <td>-29.8259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-16.9852</td>\n",
       "      <td>-19.4842</td>\n",
       "      <td>-19.5862</td>\n",
       "      <td>-13.2665</td>\n",
       "      <td>-13.8324</td>\n",
       "      <td>-18.9571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-8.8458</td>\n",
       "      <td>-11.5454</td>\n",
       "      <td>-11.8197</td>\n",
       "      <td>-6.7060</td>\n",
       "      <td>-7.7151</td>\n",
       "      <td>-8.1381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ScaledLR  ScaledLasso  ScaledEN  ScaledKNN  ScaledDT  ScaledSVR\n",
       "count  100.0000     100.0000  100.0000   100.0000  100.0000   100.0000\n",
       "mean   -23.8796     -28.8733  -30.3165   -23.2159  -25.2896   -32.4358\n",
       "std     11.6096      13.2336   14.6942    14.5372   14.9744    17.5274\n",
       "min    -87.0795     -92.7119 -102.1213   -88.8508  -89.5074  -114.6016\n",
       "25%    -28.1619     -34.5724  -36.8072   -28.1611  -34.1896   -44.6473\n",
       "50%    -20.4244     -26.4251  -28.5038   -20.3167  -19.4396   -29.8259\n",
       "75%    -16.9852     -19.4842  -19.5862   -13.2665  -13.8324   -18.9571\n",
       "max     -8.8458     -11.5454  -11.8197    -6.7060   -7.7151    -8.1381"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,5))\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(plot_results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results = []\n",
    "\n",
    "for seed in seeds:\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = seed)\n",
    "    kf = KFold(n_splits = 10, random_state = seed, shuffle = True)\n",
    "    results = []\n",
    "    names = []\n",
    "    \n",
    "    for name, model, in models:\n",
    "        cv_results = cross_val_score(model, X_train, y_train, cv = kf, scoring = 'neg_mean_squared_error')\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "    \n",
    "    all_results.append(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_results = []\n",
    "best_params = []\n",
    "\n",
    "for seed in seeds:\n",
    "\n",
    "    k_values = list(range(1,15))\n",
    "    param_grid = dict(n_neighbors = k_values)\n",
    "    model = KNeighborsRegressor()\n",
    "    kf = KFold(n_splits = 10, random_state = seed, shuffle = True)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = seed)\n",
    "    scaler = StandardScaler().fit(X_train)\n",
    "    rescaledX = scaler.transform(X_train)\n",
    "    grid = GridSearchCV(estimator = model, param_grid = param_grid, scoring = 'neg_mean_squared_error', cv = kf)\n",
    "    grid_result = grid.fit(rescaledX, y_train)\n",
    "    best_results.append(grid_result.best_score_)\n",
    "    best_params.append(grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>params</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>-20.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>-23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>-21.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'n_neighbors': 2}</td>\n",
       "      <td>-20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>-20.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'n_neighbors': 11}</td>\n",
       "      <td>-20.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'n_neighbors': 2}</td>\n",
       "      <td>-16.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>-20.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'n_neighbors': 2}</td>\n",
       "      <td>-22.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'n_neighbors': 2}</td>\n",
       "      <td>-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                params scores\n",
       "0   {'n_neighbors': 3} -20.01\n",
       "1   {'n_neighbors': 3}  -23.7\n",
       "2   {'n_neighbors': 3} -21.03\n",
       "3   {'n_neighbors': 2}  -20.6\n",
       "4   {'n_neighbors': 3} -20.65\n",
       "5  {'n_neighbors': 11} -20.51\n",
       "6   {'n_neighbors': 2} -16.09\n",
       "7   {'n_neighbors': 3} -20.95\n",
       "8   {'n_neighbors': 2} -22.25\n",
       "9   {'n_neighbors': 2}    -24"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_best = pd.DataFrame(data = [best_params, best_results])\n",
    "df_best = df_best.transpose()\n",
    "df_best.columns = ['params', 'scores']\n",
    "df_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.239597953216364\n",
      "15.800928362573096\n",
      "22.937807017543854\n",
      "25.08812865497076\n",
      "22.04344298245614\n",
      "26.970175438596495\n",
      "37.96676169590643\n",
      "25.252156432748535\n",
      "20.22347953216374\n",
      "15.239904970760234\n",
      "\n",
      "\n",
      "KNN Average MSE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "23.87623830409356"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsRegressor(n_neighbors = 3)\n",
    "results = []\n",
    "\n",
    "for seed in seeds:\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = seed)\n",
    "    scaler_train = StandardScaler().fit(X_train)\n",
    "    rescaledX_train = scaler.transform(X_train)\n",
    "    scaler_test = StandardScaler().fit(X_test)\n",
    "    rescaledX_test = scaler.transform(X_test)\n",
    "    kf = KFold(n_splits = 10, random_state = seed, shuffle = True)\n",
    "    model.fit(rescaledX_train, y_train)\n",
    "    predictions = model.predict(rescaledX_test)\n",
    "    mse = mean_squared_error(y_test, predictions)\n",
    "    print(mse)\n",
    "    results.append(mse)\n",
    "    \n",
    "results = np.array(results)\n",
    "print('\\n')\n",
    "print(\"KNN Average MSE\")\n",
    "results.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.195965766883177\n",
      "19.831323672063142\n",
      "23.038812866283532\n",
      "22.431936070737535\n",
      "30.05399330712417\n",
      "30.697037704088583\n",
      "28.32786925256163\n",
      "25.76787949083666\n",
      "23.716180926283545\n",
      "19.295749073363375\n",
      "\n",
      "\n",
      "LR Average MSE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "25.035674813022535"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "results = []\n",
    "\n",
    "for seed in seeds:\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = seed)\n",
    "    scaler_train = StandardScaler().fit(X_train)\n",
    "    rescaledX_train = scaler.transform(X_train)\n",
    "    scaler_test = StandardScaler().fit(X_test)\n",
    "    rescaledX_test = scaler.transform(X_test)\n",
    "    kf = KFold(n_splits = 10, random_state = seed, shuffle = True)\n",
    "    model.fit(rescaledX_train, y_train)\n",
    "    predictions = model.predict(rescaledX_test)\n",
    "    mse = mean_squared_error(y_test, predictions)\n",
    "    print(mse)\n",
    "    results.append(mse)\n",
    "results = np.array(results)\n",
    "print('\\n')\n",
    "print('LR Average MSE')\n",
    "results.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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