boston-house-classification-evaluation-validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9e589d11-548b-452d-b8be-41de2d70f5b6",
    "_uuid": "924af894-0746-4a57-b024-445605e99145"
   },
   "source": [
    "## Boston House - Price Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:01.973353Z",
     "start_time": "2020-10-10T10:02:55.706886Z"
    },
    "_cell_guid": "22a885fe-9add-4171-92ae-dff23e1d7022",
    "_uuid": "edf8a963-2223-44a5-b8fa-0e216152f294"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:03.923922Z",
     "start_time": "2020-10-10T10:03:03.908493Z"
    },
    "_cell_guid": "aeb2062a-c9b3-43a9-aa40-2e3481fdeb89",
    "_uuid": "70b4d723-2cc2-4027-a1a5-0e93dbdc0fbc"
   },
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"C:\\\\Users\\\\Arslan Mushtaq\\\\Desktop\\\\data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:04.903352Z",
     "start_time": "2020-10-10T10:03:04.791241Z"
    },
    "_cell_guid": "b44d2235-7caf-493c-8bc8-624a504092d7",
    "_uuid": "fb65617f-2993-42e2-a3dd-78af8acff233"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<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</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</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</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</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</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</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</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</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</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</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  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:05.784589Z",
     "start_time": "2020-10-10T10:03:05.747570Z"
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    "_uuid": "bada6263-27f4-4a12-9672-73fe62388ac3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 511 entries, 0 to 510\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     511 non-null    float64\n",
      " 1   ZN       511 non-null    float64\n",
      " 2   INDUS    511 non-null    float64\n",
      " 3   CHAS     511 non-null    int64  \n",
      " 4   NOX      511 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      511 non-null    float64\n",
      " 7   DIS      511 non-null    float64\n",
      " 8   RAD      511 non-null    int64  \n",
      " 9   TAX      511 non-null    int64  \n",
      " 10  PTRATIO  511 non-null    float64\n",
      " 11  B        511 non-null    float64\n",
      " 12  LSTAT    511 non-null    float64\n",
      " 13  MEDV     511 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 56.0 KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:06.594089Z",
     "start_time": "2020-10-10T10:03:06.576407Z"
    },
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    "_uuid": "96abbc41-5f42-4f43-82de-8d14f6fa397f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    476\n",
       "1     35\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:07.557246Z",
     "start_time": "2020-10-10T10:03:07.400005Z"
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    {
     "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>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.584139</td>\n",
       "      <td>11.252446</td>\n",
       "      <td>11.151096</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>0.554757</td>\n",
       "      <td>6.287589</td>\n",
       "      <td>68.616243</td>\n",
       "      <td>3.783876</td>\n",
       "      <td>9.485323</td>\n",
       "      <td>407.440313</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>356.600900</td>\n",
       "      <td>12.879550</td>\n",
       "      <td>22.682192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.564433</td>\n",
       "      <td>23.234838</td>\n",
       "      <td>6.828175</td>\n",
       "      <td>0.252838</td>\n",
       "      <td>0.115310</td>\n",
       "      <td>0.703802</td>\n",
       "      <td>28.099130</td>\n",
       "      <td>2.098631</td>\n",
       "      <td>8.688469</td>\n",
       "      <td>167.903532</td>\n",
       "      <td>2.200348</td>\n",
       "      <td>90.882679</td>\n",
       "      <td>7.797416</td>\n",
       "      <td>9.484262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082325</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.050000</td>\n",
       "      <td>2.100350</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.500000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>374.710000</td>\n",
       "      <td>7.065000</td>\n",
       "      <td>17.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.261690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.209000</td>\n",
       "      <td>77.300000</td>\n",
       "      <td>3.152300</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>391.340000</td>\n",
       "      <td>11.450000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.621175</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.629750</td>\n",
       "      <td>94.050000</td>\n",
       "      <td>5.118000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.210000</td>\n",
       "      <td>17.105000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  511.000000  511.000000  511.000000  511.000000  511.000000  506.000000   \n",
       "mean     3.584139   11.252446   11.151096    0.068493    0.554757    6.287589   \n",
       "std      8.564433   23.234838    6.828175    0.252838    0.115310    0.703802   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082325    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.261690    0.000000    9.690000    0.000000    0.538000    6.209000   \n",
       "75%      3.621175   12.500000   18.100000    0.000000    0.624000    6.629750   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  511.000000  511.000000  511.000000  511.000000  511.000000  511.000000   \n",
       "mean    68.616243    3.783876    9.485323  407.440313   18.500000  356.600900   \n",
       "std     28.099130    2.098631    8.688469  167.903532    2.200348   90.882679   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.050000    2.100350    4.000000  279.500000   17.400000  374.710000   \n",
       "50%     77.300000    3.152300    5.000000  330.000000   19.100000  391.340000   \n",
       "75%     94.050000    5.118000   24.000000  666.000000   20.200000  396.210000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   23.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  511.000000  511.000000  \n",
       "mean    12.879550   22.682192  \n",
       "std      7.797416    9.484262  \n",
       "min      1.730000    5.000000  \n",
       "25%      7.065000   17.050000  \n",
       "50%     11.450000   21.200000  \n",
       "75%     17.105000   25.000000  \n",
       "max     76.000000   67.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:08.790657Z",
     "start_time": "2020-10-10T10:03:08.058584Z"
    },
    "_cell_guid": "3f797e24-9db3-4c30-91c3-be8a5c67058b",
    "_uuid": "19aedf73-8d81-4046-945b-d9436e766ad8"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:19.000479Z",
     "start_time": "2020-10-10T10:03:08.956441Z"
    },
    "_cell_guid": "bb785641-a7d9-419c-ab43-ddf66cfbaa69",
    "_uuid": "ea56e846-0016-4770-97f6-1a4b452eae88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'AGE'}>,\n",
       "        <AxesSubplot:title={'center':'B'}>,\n",
       "        <AxesSubplot:title={'center':'CHAS'}>,\n",
       "        <AxesSubplot:title={'center':'CRIM'}>],\n",
       "       [<AxesSubplot:title={'center':'DIS'}>,\n",
       "        <AxesSubplot:title={'center':'INDUS'}>,\n",
       "        <AxesSubplot:title={'center':'LSTAT'}>,\n",
       "        <AxesSubplot:title={'center':'MEDV'}>],\n",
       "       [<AxesSubplot:title={'center':'NOX'}>,\n",
       "        <AxesSubplot:title={'center':'PTRATIO'}>,\n",
       "        <AxesSubplot:title={'center':'RAD'}>,\n",
       "        <AxesSubplot:title={'center':'RM'}>],\n",
       "       [<AxesSubplot:title={'center':'TAX'}>,\n",
       "        <AxesSubplot:title={'center':'ZN'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # For plotting histogram\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2de0d8fe-9657-4349-97cb-92d968b1c0ba",
    "_uuid": "1ce74725-38de-4f03-9b00-5930dcafd347"
   },
   "source": [
    "## Train-Test Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:21.306485Z",
     "start_time": "2020-10-10T10:03:21.271488Z"
    },
    "_cell_guid": "5f56eab5-90b0-4181-ab18-e02130e3491a",
    "_uuid": "ce25f1ea-5a97-4121-b047-a99876d02143"
   },
   "outputs": [],
   "source": [
    "# For learning purpose\n",
    "import numpy as np\n",
    "def split_train_test(data, test_ratio):\n",
    "    np.random.seed(42)\n",
    "    shuffled = np.random.permutation(len(data))\n",
    "    print(shuffled)\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled[:test_set_size]\n",
    "    train_indices = shuffled[test_set_size:] \n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2d9535d1-b6dd-49c3-9e3d-366e45147727",
    "_uuid": "9152fab9-85be-4230-8e32-e64e6af6c416"
   },
   "source": [
    "2nd Method for Training And Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:25.493627Z",
     "start_time": "2020-10-10T10:03:25.485443Z"
    },
    "_cell_guid": "8ca222bc-59c6-4b82-900e-66b68bac5eed",
    "_uuid": "fe8a24e1-0a86-4771-9ce6-77f7038601fa"
   },
   "outputs": [],
   "source": [
    "# train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:26.365188Z",
     "start_time": "2020-10-10T10:03:26.355798Z"
    },
    "_cell_guid": "252aff92-d66a-49cb-9f5f-9f6b026d20f0",
    "_uuid": "01a2714a-a786-4105-98bf-45c399c80c6e"
   },
   "outputs": [],
   "source": [
    "# print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:29.408586Z",
     "start_time": "2020-10-10T10:03:27.412929Z"
    },
    "_cell_guid": "2ff6e68b-a9e9-4ab0-8e5b-b77f27e88d1f",
    "_uuid": "0ca1e334-3f43-448d-a252-7e0c211257dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in train set: 408\n",
      "Rows in test set: 103\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set  = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:34.722143Z",
     "start_time": "2020-10-10T10:03:34.700101Z"
    },
    "_cell_guid": "8f6d3ed3-0d4d-4e13-bbe5-e7a929de7dbd",
    "_uuid": "5e163904-79b9-4f96-b1d8-98dad820cd56"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing['CHAS']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:37.813322Z",
     "start_time": "2020-10-10T10:03:37.795166Z"
    },
    "_cell_guid": "35cd3048-70b8-4df0-b0b7-a57aea40da2d",
    "_uuid": "4adbc0a7-9393-4512-8b40-2c7d4b92c5ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    96\n",
       "1     7\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:40.995213Z",
     "start_time": "2020-10-10T10:03:40.981817Z"
    },
    "_cell_guid": "93d9ec28-f481-4057-b20c-e11290f50f39",
    "_uuid": "b7451d23-e7cb-4606-ba14-9b6aa5eb8223"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    380\n",
       "1     28\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:43.570702Z",
     "start_time": "2020-10-10T10:03:43.550483Z"
    },
    "_cell_guid": "4898295f-cb7b-43d2-945d-c817cd909859",
    "_uuid": "e9e8f96c-de50-4980-af3b-a5c5d9f04954"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.571428571428571"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " 95/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:46.566753Z",
     "start_time": "2020-10-10T10:03:46.540518Z"
    },
    "_cell_guid": "2fb3c9ae-abe4-4258-a807-92a5a84ffc03",
    "_uuid": "ae8b38e6-1c1a-4714-9a78-8379f96887cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.428571428571429"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "376/28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:49.113686Z",
     "start_time": "2020-10-10T10:03:49.097399Z"
    },
    "_cell_guid": "dfe57ef1-5591-4519-9097-3a846c07f54c",
    "_uuid": "2bc75863-6faf-4831-af29-f5975989f711"
   },
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "33633175-b48a-4dc4-8ac6-dbee480e29cb",
    "_uuid": "2ff0737e-7f7a-4371-a7a9-0a1410d68f02"
   },
   "source": [
    "## Looking for Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:52.162018Z",
     "start_time": "2020-10-10T10:03:52.136526Z"
    },
    "_cell_guid": "3c1cbb7b-f270-445c-b048-260d19b66ac2",
    "_uuid": "3c85942f-2eb3-4e3c-860e-dc7317df3c3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.681481\n",
       "B          0.350283\n",
       "ZN         0.341767\n",
       "DIS        0.250670\n",
       "CHAS       0.191563\n",
       "AGE       -0.366230\n",
       "RAD       -0.381955\n",
       "CRIM      -0.395849\n",
       "NOX       -0.425104\n",
       "TAX       -0.467171\n",
       "PTRATIO   -0.475870\n",
       "INDUS     -0.481414\n",
       "LSTAT     -0.720350\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:03:58.935071Z",
     "start_time": "2020-10-10T10:03:55.240530Z"
    },
    "_cell_guid": "be916936-2e4f-41d9-99f7-5b47c9d6ec42",
    "_uuid": "7ce4435e-8737-4218-8ff7-e9224b286831"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='MEDV', ylabel='MEDV'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='MEDV'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='MEDV'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='MEDV'>],\n",
       "       [<AxesSubplot:xlabel='MEDV', ylabel='RM'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='RM'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='RM'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='RM'>],\n",
       "       [<AxesSubplot:xlabel='MEDV', ylabel='ZN'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='ZN'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='ZN'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='ZN'>],\n",
       "       [<AxesSubplot:xlabel='MEDV', ylabel='LSTAT'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='LSTAT'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='LSTAT'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='LSTAT'>]], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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asBCu1HTSRRVJMqXqXEvj+i0QrBpPbWhY8Gv6ozl+fWGaRq+Dj+1qFXrjggWzoleMJElW4BnDMF6a+VUAuO4Rm5n5+dbXfEOSpGOSJB2LxWIrMk6BQCC4Xy5OZsnPbCsPLVF5yP1ydTpHpqSSLqoMxPKrPRyBYFU4N56louqMJovElqDBV/DosdLLrS8D37rp5wxwvVPBB6RvfYFhGH9uGMY+wzD2NTQsfIUpEAgEq8HGZi8Oq0LQZaUr7L7j8y5MZPnV+SkSK3AT7wq7iGbLxHJlOkOuZT+fQLBcTGfL/Or8FP3R3IJfu6XVh1WRaAs4icyYBgkEC2Gl9xE3ArskSfpjYCsQAXYA3wXeDfyXFR6PQCAQLAstfid/75m7dzDlyiq/vjCFYZhax5/Z17GsYxpPl2n0mU2CE+kyDV7RMCh4MPn1+Sni+SqXJnP88TMu7Jb5Sy9uaPKyoUnU/AsWz4pmng3D+OeGYbzXMIz3AecNw/iXQFmSpNcAzTCMIys5HoFAILiZdLHKf3ljkP/yxiDpYnXZz6fpBoVKDU03Vkin1uCdawmODCaBBXiFCwSriK4bDMYLs8x0fDN64S6bzIsXovzHVwY4O5ZZrSEKHjFWrYPFMIwnZv4v5OkEAsGKkyxUuBYr0BV20+A1t26vRvOkiuYNuj+aZ98yNhUahsEPToyjyBIeu2VJ9KbvxVC8QGpmUTCcKLKzY/XcBtcqNU1HkaVFm+cIlp5XrkQ5PZrBbpX52uFuXDYLH9jewkiyiMdm4VtHRshXavzo1Bh9jW5cttVvzhU83IgWU4FA8MhxfDjFP/3eGf7T7wb47rFR9Bnt496IG7ddwW1X6IncuU55KdB0g3ylht2iYLfKKPLyB2vZUo2SqlFSNXLl2rKf70Hj3HiGP325n28dGUHV9NUejmCGbMm8ViuqTkU1/y5WRaavwUOjz0570Mk71xK8NZDg//uzCxiG2FURLC8ieBYIBI8cl6dyGIZBrlyr21WDqRu7tdXPeKrE//yLi3z/+Fg9sF5qLIrMB3e0sLnFx/u2NS/LOW5lV2eADY0eNjV72XGTU9tKMZYq8mev9PPNt4YoVpcmeDcMg0zxhj36/XBkMMm58QxHB5OkCstftiO4N1emc6SLVTx2hfdsaSLonq2vKEkSH97ZSqPXjttuYTBeZCJdmvNYhcrs73ulpvGtd0b4Dy/3c02ozwgWgNjbEAgEjxx7u4IkChUkJD69rw15Juv76pUY33pnmCvRPDIwGDcl5tqCTk6NptnR5ufwuqUzVllphzeXTaFhpmHQaZ1/g9VS8fZAgrcHElgVmd2dAba3Be77mC9dinJmLEOL38Fn93fcV7lFVdOp1HRsio5IXq4NXr4UpVjVkCWJLS1z24g7rApffbybv3h9ELfNwk9OT/J7j3fjuOkaH4jl+dnpSawWic/t7yRfrvF3R0foj+bZ0OTl4mSO3gfAbVGwNhDBs0AgeCTQdKNeGrGx2UumpJIqVnHeVB9ZqNbwOq24bQq5cg0JeGsgTnfEjaoZHBtO3TN41nWDyWyZsNs26+a9FihUNRpnFDaKN2XgVopyTWciVcRhU9CXqCpiOFEEYDJTplLT7+sz39URoFTV8Dos+F0r0cD5YHEtlufSVI5trX46wysjddgacNIfzdMScNQXuTdzdizDG/1x/C4rz2xoJF1SqdS0ug09QKmqcXo0jabr6KrEdLbMQCyPLIFuGKiaztbWuQNzgWAuFhw8S5LUbBjG1HIMRiAQCJYaXTf48elxhhNFHl8XYX93iLFUkZ+dmSCRr3J0MMF7tjSzvzvE0xsasFsUukIuYrkKE+kSFkXG57SSLFTvmPm6mV+dn+LSVI6gy8qXH+tekVrm+bKjzU+qUEWSYGvrypdtXJnOEc1XscgS0Wx5SY755PoIR4dSrG/y3PdiZUurj2i2TFfIveYWPvNhOlvm3HiGdY2eu2qLLwbDMPjF2UlUzWAsVeQbT90uwzicKNAfzbOtzU+Tb3EyiBPpEhcns2xo8tIRcvHB7S2kilUCt9hhpgpVqprOX7w+yDuDCWwWmUM9YdpDLlr8dlwzf79qTedv3xkmnqtQ1Q32dAboa/BgVSQGYwWe3tjIp/e2P5B/b8HqsZjM8ylJks4B3wa+bxhGemmHJBAIBEtHUdUYipvZyUuTWfZ3h3BaFQZieZL5KuPpIm67FZ/TyuYWH5mSypsDCUpVjXVNHlr8Tt6zpYkm79yZr1u57liWLqmomo4ir52b8mSmzLnxLJIEG5t9tAWcK3r+Sk3HY7cgSZApq/d+wTxY3+Rl/RJp9r58Kcq1WIGBWIGOsIuQ+8HyL//5mUkyJZWLk1n+5Jl187pe54skSfhdNuK5ym2BLJiL1J+enkDVDIYTRX7/iZ5FnednZyYoVDQuT+fq7yF8i5HJiZEUv7sco6RqjKeLFCo1wMJYukST38FIssTl6RybW3z15li7VaE34ORDO1oBWNfo5e+/y7Okn5Hg0WExwXMbpqHJ54D/WZKktzED6R8bhjF3lb5AIBCsEm6bwtZWH0OJArs7TWm2oMvGoZ4wV6M5yjPd+x67OR2Wqhq6YSBLsK8ryPNbmxfkQvbspkaOD6foa7j/TOhSM5UtoxsGGGaWcqWD52882Us0WybktvGBbS0reu75cP0asFlk7JYHr5/eY7eQKak4beYCZan59N52pjJlWue4biQJnDYLakmtf46LwW23UKhodz1GvSHQgE0tPrwOC06rhUN9IWI5s9HTPVOO5XdaeXpjA6PJIod6w7OOc6/AeSpTxqJIwoVQcBsLvsINw9CAXwG/kiTJBrwfM5D+vyRJetEwjC8u8RgFAoFg0UiSxPNbZ6tZyLLE1x7v5uhQirJaY0dbgPYZu+qP72njrYE45ZpZExy+Jfs4miySKlbZ3OLDqtweYLUHXbQH16b19dYWHydH0sgS8ypBWWrylRo72gMoskShquG6jyDrOoVKjSvTOTpCrnkFOYZh3LGp8JmNjXSFXYTcpnLDg8ZHdrUykizSGnAui061w6rQfQcJR0mS+Oz+DibSpfuyfv/E7nZGU0Xag2aArukGFyezeOyW+rkP9YapqDphj40tLT5ylRq9ETeSJDGcKGBVZFoDTgzDQNMNrLLM7o7ggkpJLk1leeHsFLIk8al97Su+0BSsbe5rdjAMoypJ0gXgIrAX2LwkoxIIBIIlJJor88qlGEG3jec2NSLLEpmSyunRNAAhtx3NMDg9mibssbOx2YtuwLHhFNFchd2dQZ7e0EAiX+H7J8YwDEjkq7xrU+PqvrEFMpgozGxxw1CiwKbm5Q2gKzWNo4MpnDaFPZ0BkjMGLZpukCmpdXOa++HnZyYZT5dwWBX+8MkeLHMsaK7zen+Mv3pjmJaAg//+A5ux37IzoMgS6xofXNtmh1VZVdvp681396NU4rTNfg/vDCZ451oSgM/u76A14CTiMbWdr0znqNZ09nYF64uF67Xek5kSPzw5zliqhM9uwWFT+MKBzro9PZiGOEeGkiiSxP7u0KxMdHJGqlA3DNLFqgieBbNYVPAsSVIHZrb584Abs2zjI4ZhXFrCsQkEAsG8yJRUzo1n6Ai6ZqkAaLrBUKLAieEU4+kS4+kSm5rNRqSb1R4qNY0fnhzn6FASqyxzeF0YTTcYjhcoVTWsisTTGxrQDKMeGNSWSf95OVE1ncmMueVdrS2/CcjRwRRHh8zAJ+CycrAnhDpT99zXsDQNbdf/DrphWji/eClKyG3jY7vasN1SevHC2Smmc2UShQoXJ7Ps6hQOi0uFYRh89+gouXKNFn+Gzx3oXJLj3qzfXazW6I/mCbqsvDmQoD+ao1DRuBrN8/uP9+C0Kfzy3BRHhhLUagYeh4VCpYahG9itCtotUf3psXQ9MHfbLWxru9FEu6czOPPdl5d9kSl48FiM2sabmHXP3wP+0DCM40s+KoFAIFgAvzo/xXiqxPHhFN94qrdea/zixWmODCYZSxVpD7po9NnrTWCdYRfv29ZMoVJje5uf8xNZNN3AZZPxOqzohkHHjOrG5pmbZ6PXwYd3tpDIV9nZEVitt7toDMPcXpeAleiTctrMv4MkmRrTLpvlthKa++WD21s4P5mhO+zm+HCKUlVjvFpiOlum45byAb/TQqpQxeOw0OS7PetdVjXOT2Ro9Dpue63g7hiG2RAKzDIiuV8O9YZxWBW8DgvHhlJMZsoE3VYafXYuTmbxO62omk5N1ylVzWbC8+NZZMksodrTGaQj6CRbrtXdCa9zs423yzZ7F8JhVXhuc9OSvQ/Bw8ViMs//AnjNEP6XAoFgjVCqakxny7QEHLMapdIzygP5itlt//kDnbNqWTffVPf7pUNdbGryMhDL47AoNPnt9dKMZzaa5RnZssqJ4TSKLLGjPbBSb2/JqNQ0zoymkSSoqO3Lfr49nQECLisum0KLf3m2vf0uK4f7TO3tYlVjOFEg7LHPWd+6pdWPrpvujsoc5R0vX4pyaSqHLJk18X6n0HqeL7Is8dFdrfRH82y5g2ZyfzTPQCzPzvYAzf751R9bFZn93SHANDGq1nSOD6d4cl2EP3q6l3xFoy3gxGO38PKlKLFcmaqms67Bw/Nbm2kPOvnp6QlqM7tQXz7UVVfv2Nziw2VTkCVJLJYEC2IxDYOvSpL0VUmS/iGwaebXF4F/ZxjGN5d0dAKBQHAPCpVa3Uq5yevAbrmRQXpuUyNvDSSYGkszkizyn18dYF9XiCfWR25TDPA5rGxr93N6LEO+UsBqkfiHz62f9Zzz41nGZzr9L01l6+odDwqvXomTKFQAidf74+ydCUqWC0mSVtRBcV2jh3/w7Po7Pv7UhgYcFoVGn71uFrOUjCaLvDkQpyPkqgf0N5MqVDk9lqYz5Hoo3ezu1iyrajq/ODuJphtMZcp89XD3go//ge0t/MdX+hlLlvjJ6Qme3dTEzo4Ax4aSnJ9I89KlGC1+B5tbfBzui9AacPDNt4YZTRYpqdqssozrLLUetuDRYDFlG18F/jHw3wAnAAnYA/xvkiQZhmH89ZKOUCAQCO6CfpN6whsDcQIuK+/d2owkSegGHO4LM5woUNN1rsUKtPidvDmQ4FN72xlNFjk2nKQn4mFXRwCnVcFmkanWdHyO27OOnWEXx4eTSJK0ZhU17kZLwIFtZnHRPEfZwnKTK6u8cjmGx27hqQ0NK24g43NYefeWO2/FP72hgZpu0NfgXlTW+fX+OFOZMhPpMltb/Le5FP76whQT6TJnxjL84ZO99bKWpeLoUJKjQ0k2t/h418a11cyqSBJuu4VsScXnXJxWQXvQxYYmL6PJEpWazpmxNC9dmibssTGdLeO0WihVNZ7b3MS2Nj+VmobDqtAVduOyKXxwR8ttmtGrxYWJLJens+xoD6zoAlOwNCzmCv57wMcNwxi66XcvSZL0SeDvABE8CwSCFcPrsPKRXa18880hQm4bFydzPLG+gWS+wp+9MkBZ1eiLuKnqBrpuoGpaXQbrlctR4vkqQ/EiG5u8eB1Wvniwk1RRpXsO++G2gJOvP9mLLEm3NaM9CHzxQBcBpxVZgg9sb13x878zmOSN/jhWRaYj5FxzyhbvDCXpj+YZTRXpjrhn1cTOh/agk6mMqWPtst8eGF+vxbcq8rIsHE6OpKioOqdG0jy1fukXJ8lClcF4nnUN3gXbl8uyxOf2dzCVLdNxHwvPD+9soz3oIp4vM5IocmEyy2iyiMtuYUd7gC0tvnqZh92i8IWDncRyFVr9Ts5NZMCArjvI7a0Uum7wmwvT6IZBLFcRwfMDyGKCZ98tgTMAhmEMSZIkWlIFAsF9YRgGV6N5bIp8R03ZW4nnK1gsMtmyyv7uEG6bwn+9FGUwXkDCNC65PJ1jLFNGQmJri49fnJnkyFAKh0Vma6uvHgwHXLY5HdSus9aMTxbCSLLI946NI0uwoz1AR2hlg4horsxgvIAsSRSrGoPxAqqms77Rsyy6xAtlMF7g7Hgaj91CsVJbcPD85PoGtrX68Tgsc2qAv29b88zuh2NZFl9bW/0cHUqyqdm3LMH594+Pka/UODeeXVTZhdtuua9AMV+pMZ4qsa87hCzB//6ry+TKNSRgfYOHp9Y3cPAWIxSfw4rPYeVv3x7mZ2cmsVlk/qePb1uSnSPDMHjlSozxVImn1jfMUvoBuBbLoxvGbYtEWZZo8NqZzpYXbWMuWF0WEzzfzUVQOAwKBIL74sxYhpcuRQH42O42eu4SQE9ny0xlyvzpS1epaQa7OwN8dFcbAE1+B40+O5IB79rUwLmJLACqrvPrC9P86NQ41ZpOd8jF81ubVkR9YrX5yzcGOTmaAuCv3xrmv/vglhU9f3fIzY52P4oskS/XePGi+Xd+bnPjijRg6rrBQCxP0G2b01DFKku4bBZcNguLFfIL3sXS225RZjWpLhTDMMiWangcljmD48fXRTjcF162hYiBUR/HavDDE2PE81V8Tit/8EQPe7uCnB3PkC2pBN02fE4r/+mVATrDLj68o3WWbvN4uoRuGGRLVU4Op+rBc7FaQ5akRS2Kk4Uqp0bSALw9mJgVPPdH8/z09AQA79mi31Zv/el97STy1SXROhesPIsJnjdLknRmjt9LQO99jkcgEDziVLUbYYuqzR3CJPIV3hlMcnIkRbGqUahoKLKELElcmMgSzZUplFXsM05jXWEPXzvczU/PTLC+0UPAZSXgtBLNVShUa/z1W8PYrQqf3tfxUJsh6IZOoWJm6mDlA6ADPSEcNgWP3TLLSGMlNKcBfnc1xqmRNFZF4iuHu2+ra9/S6iOerxJ0Wdek0sZvL0Y5N56hNeDgM/s65gySlzOD/4k97VyLFVjfuDplBlXNvGiqNR3DMHhmYyOvXY1zeTqHbsAPT45RqGjECxXcdguHesNkSypWReZzBzqI5cpMZMpcms7RNZYm6LLxo5PjSBJ8el/HbVngiXSJkWSRLa2+OXsgvA4rYY+NRL5K9y2Nhzdf09U55jGrIs9bcUSw9lhU8LzkoxAIBIIZds/oJ1sV+Y436R+fmmA4UWAgVmBHu58tLT7CHhuxXIU/+dvjWGQJv9NK94xlb75So6/Bw4d3ttI7U8tqSlTBWwNJzk9kKKoaiiTxhzfpRD9sXIsWuO450R8trPj5LYrMnhmFEsMwKKmNqJrOrvvQzD46lOCX56bY0xnigzta7vrcYsXUH1Y1w9T8vSV22dsVYmOzD4dFvqtT4WoxkiwCMJEuo2oGNsvKbpdEPPZ5WaAvFx/Z2crlqRzrZsp8fE4rn9zbzqtXYhSrGqOJIrkZB02/w8qFGe32ZKFK2GNjZ3uQ1kAFVdM5NZqm0Wuvm+xMpEs4LAo/PzuJRZF4fksTPzxp7k4NxQtzmr7YLDJfONBJecb452Y2t3ip1DQ03WDnAyhrKbg7iwmendedBCVJshuGUbn+gCRJh4DhpRqcQCB49LDcpOt6JxRZIuCy0dcocag3zIGeELlyjd//L0dI5CvohilbZrco7OkKEnLb+Ms3BkkXVU4Mp/j6k708NiMlZrMojKVLNCgysiyxBkpvlw2rRUaZeX826+oGh5Ik3VfQfJ1vvjlMNFfh4mSOpzdE8MyRIbzOUxsiuOwKDR77HbfLbw2C1hJPro9wdCjJhibvomqmz41nOD2WZmurf0k++5WmwXv7363V7yRRqKDrsL7JgyxLJPJVJEmirJoOgWOpIrphYBgGe7tCvHY1RjRbIZE3bbftVpnNLT5ODKeYzpYBGIjm63OBfJeaLosi45ljoSVJ0gMnZSmYP4uZJb6FKU0H8NZN/wb4s1t+FggeOY4OJXnnWoL1TV7eu8Ruao8KhmGQq9Tw2Cxz3rg+truNgVie3oi73tynyBJehxWnzYLbbtaWarpBPFcxrbYTRd4aiGORZba1+Tk001i0uzPIpmYfl6ay5o3U8nBmnQHev7WZ167GAfjgtrtnaedLqaohy6za5xbx2InmKvid1nsGlF6Hdc1JuC2EDU1eNjQtXqHktatxyqpGPBdjZ7t/TTRp3i8XJjM0+8xSq31dQYJuG2GPjalMmZ6wmxOjKdx2hXy5hkWROTueJlVUafGbspQf233Dxr0r4ubESArDMJ0KzZ0Kg7FkkTf74xxed7t291qhWtNRNfO/H5wYRzcMPr67bc1I8z1sLCZ4lu7w77l+FggeOU6PplE1gwsTWZ7Z2PBQB2PLxQvnprg8laO3wV1vALwZv9Na3/6/jlWR+UfPrec3F6Zo8TvwOKxMpMsMJQpMZ0qE3FZkScJulTk+lGR3Z6D+t3HalEciS/TylRj2ma3+31yM8uE5PtuFMBgv8NPTE1gUic/t76xbn68k//g9Gzg9mmZ9o6euYX03qjUdiyzdNZv4sNIVdnF5Kkdn2PlQBM4AHSEXl6Zy2BSJ3V3B+s5Bk9dBTTd4dlMTT29opFit8eLFaQbjRSIeG5uavezuDM5acLUFnHzjqT6GEwV+dmYSWYJr8SJ9DR5OjaXXbPCcr9T41jvDFKsanSEXmZIKmE2LInheHhazb2fc4d9z/SwQPHLsaA9gkSU2t/geucC5rGq8fCnKm/1xdH3x08FQwqzHHYoX79rZP5Yq8stzUwzGzefbrQpWi4JFUdjQ5CFdrPLOtQT//Y/O4bJZ2NDkpdXvJFVU+bOXBzgxklr0GB9EtrZ5KdcMyjWDnW33ryw6miyi6Wb98GRm4WJLmZJKcsYdcrH4nVae2tDAeLrEby5Mk5+peZ2Lc+MZ/uyVfv7mnWEqNe2+zvsg8v5tzfz+Ez18dOf9LZrWEltb/bQFnRSrOkcGE4A5D/3VW0P8x1cGODqU4D++0s+fvtRPyG3DZpHrzX0vXpxmdKaOPFtW+fX5Kc5NZOgIuWj02XHYFJ5c34BFltjRFrivcebKKr+5MM3x4aWfc2K5CoWKhmGAYYDPacXrsNC3So2djwKLyTy3S5L07zCzzNf/zczPD883UiBYJAd6QhzoWV7b47XKieEUp0bTAIQ8NjY1Ly5Ae3JdA6fG0mxt9d01Q/bC2SnylRr90Rx//13r6J/Rh04WqhwZNJU4EgWz/nEoXuDffn43iXyFb75ltmb0T+dvy2A/zFyeLOCecbU7N5m97+PtbA8wlSljt8qsW+CNeipT5rvHRtENgw/taLkvw5SxVLFejqIbxh3LpfqjeQwDEvkqiXz1Nov2hx1Jktakisj9oOsGY8kSiixxdTrPs5uaiGYrpItm9vXtgSRvDyaoaQYht42//651TGXKfPvICADHh1N0hFy8diXOlekcYGagv3iwa0nH+frVOJemzOO3Bhy0+Jfu2usIOtnU7CVdUnlqQ4OQv1sBFhM8/7Ob/n3slsdu/VkgEDxC+GZuzJLEnNJO86W3wY3TptARclKo1HBalTm32f1OK/lKDZ/TiiRJ7O0KkiurNPnsXJnK0+C14bGbyhrXa5xDbhtbWn2Mp0rs7X50AmeA925v4s0BM8h83xLU4/tdVj6zv2NBrymrpqxgPG/WogNEc5X7Cp49dgsWWaKmG3cNDvd0BkkXqzR4HcKc4iFBliUO9oa4OJljb5f5fW7x2/E5rZRVjX1dQU6PpalIOj6HGfIE3VZCbhupYrWuI3/9urFZ5CW3Tb/5+FZFWrD5zr2wKDLv3740PQyC+bHgv6BhGH+1HAMRCAQPPtva/ARcZuNWo/fewYmuG+iGUZcFOz2aZiRZ4NJUjitTORKFKn0NHnZ1BHhifYQmnwMZeK0/Tixf4cl1ESo1nRa/g9FkkZKqoRug6fDuLU1cjeb45J52WgLO+g1LkqRZmUlNN3i932ykemp9w7LcONcKTotCVdORAPcc9tHLTX80z8/PTOKwynxybzvdYRdlVWN3hxn05MoqPzszQdht5z1bmpAkiclMiePDKbrD7tuMJq4TcNn40qEucuUaHaE7Z/Q6wy6+9njPkr0fXTcYT5cIuW2417BKx0Ko1vQ1bT0/kS7hcVhmLc4P90U43HejHvnUWIZ0scpQvMBYsohVkanpBiPJIj8/M8ljfWG+dKgLVdPrspSPrwvTEXLid5qOhH/1xiD/9fgY7SEXn97XzuG+yH1JWD7WF6Yt6MTnWJsa4oKFseBvuyRJP7nb44ZhfGTxwxEIBA8687W9zVdq/N2REUpVjQ/tbCVVrPLNN4cIuKycHEmTr9RIFapousFkpsQvzpoNPJ0hF6/1x8lXanw37Oafv38TA7E858azXI3m6m5he7sCfHx3+z3HcWU6x4mZOkSP3cLja7QpaCn4X35xkfyM1vH/9PNLvPCPV1Z5YjhRQDcMilWNCxNZfnZmkmpNY3u7n21tAb59ZIQXL0aRJWj02tnVGeS3F6PEcxX6o3n6Gjx3XNwE3ba7uvstB69ciXJ6NIPbrvDVw91rvsdhPF3i3HiGTc1eusK3O3f+6vwUFyaybGn1rUmloCODSd7oj2OzyHzpUNesIPTsWIaToym2tvo4PpziN+enGE4WcdlkNB3aAi6G4gXKM6oUH9vdhiLf+HtJklT/TKazZf7dy1fJlWpcieYolFV+eyHKh3e28lhfmNFkkWxZXZAN+s3H13WDS1M5PHbLbZbeggeDxSyVHwNGgW8D7yAUNgQCwSKYTJfIlc3mrrcG4vz09ATnJ7LYFJmA24phGES8dhxWhalsmUxRBQlSxSoT6TK6bnCxmuPnZybRdIOr0znGUyXcdgvNPgdnxjJYFeWe9X9Blw1ZktANY1XUIlaSVLEuy08yv/AGv8WQKamcGk3TFnCyqyNANGe6vx25FufIYBKAv3xjiP/jM7vQdYNS1dTmvU7YbSOeq+B1WLEqa+t2E8+bzY6Fika5qiNLEgOxPE1ex5yB/HCiwNvXEnSH3RycKSNaSX5xZnKmRyDPnzzTx8XJHGfHTd3nbW1+rszU5F6Zyq3J4DmRN6/fak0nV1brwbNhGHzn6CjFao3To2lyZZVY3jRDyZYMLIrEtbjZD3FpMkuqUKGq6bx7c1P9O58pqkxmS1hkmZ+cGqdYMQ1OJAmKVQ2HVebKdI6WgJ1/+9urOK0WPrC9hSfWL3yxfWQoyVsDZnPjZ/d3PHK19w8Diwmem4H3AJ8HvgD8HPi2YRjnl3JgK0H3v/j5ag9BIHggSRaq/PjUOBZZ4qO72xZc31yqarQFnXSEXBQqNZr9DpIzWeaCVkORYU9ngJ0dAfIVnVOjKaazFZq9pqavJIEigSLfMDO4Gs1TVs2saqFa49hQil+dn6Yz5OLJ9RH23cF4pdnv4MuPmVu4D3sdrHFTrkNbobzHby9MM5Iscmokze8/0c3nZ5za/iJXxmaRMAxzAQOwsdnL0aEkbrulHlC8d2sz29v8RDx2jg+n+LNX+ol47PzLj2zD41jdUolnNjZwZDBJe9CF32XlF2cnuTyVw26V+f3He27b5n/tapxYrsJEuszWNv+KG7JIEoymivTNOG++fDlKtaYTzVbY1ubnYG+YM2NpdqxRR7zrUnFBt62+w1Wo1PjF2UmGE3lGkkUMYGe7H4si0+p3kCyoWC0SNc3AbVe4Gs1zLZbn0mSOV6/E+OSedh5fF+HbR81dMDDoj+bxO63kyjU6Q06e2tCAy25hX3eQN64mmEibRirxXOUOI51Nqapht8j1vo2adkNB6OZ/P4qoms6PT02QLFR479bmOXdE1iKLqXnWgF8Cv5QkyY4ZRL8iSdK/NAzjT5d6gALBw0hZ1XijP45FkXliXWTeW39rhctTuXo3+0A0z+7OIGVV43eXozht5k3mrYEEiizxxLrILKvjEyMpfnc5Sq5cY09nkOc2tdAfyxN0WcmUVCyyjNdhIVvWOD6cZjhRJFOu0uq30xV08fZQCossYbUqtPsdDMUL6IaOIkvohhkgNHrtuGwKZ8aKhN02Toyk7hg8Aw99xvk61eoNGbdaVV2SY0ZzZXO3wDX3Z3i9zMKiSFhkGU03kCX40qEuUgWVYlXjH717/cyzJbojHqyyRFnVZ35D3fnxO0dH6Y/mGYwXeOdague2NC14vBPpEj6ndUkC12ypRjRbwabIGIZBYUYmT60Zddvnm2kLOonlKkQ8NpyrYAGv6wZumwV95uf2oJNrsQJtQXOh0uC10+J3rlm1Br/Teltj3K/PT3F2PEOyoBJy25BlCYsi80dP9TKVLXNsMEk0XyFfNvshyqpGsaIRz1cxDLMUpC3goFjRkCQIe+wMJ0xt52xZ5Yl1EQ70hvE5LLx2Jc54poTfaaGmGbQGb19sT2ZKeOwWvA4rhUqNv3hjkMF4gb1dQT63v5NToynOjqeJ58usb/LSEpjfgt0wDN6+liRbVnl8XaR+/b52NcaV6TwHe0J37AlYy0xlynW5wDNjmYc3eAbTlhv4IGbg3A38O+CHSzcsgeDh5tRomjNjGcDcln7QJr2+Bjenx9IoklTXTP3LN4Z45XLUdPfKlhlPmWUBIbeNrrCbkUSRqqbxb355mWShik2RkSWJY8MpLk5kmM6VCblt9DS40XTIldR6NlmtGWDA6wNxKjUD3QCLJDGcKmIgEXJbCbotRNw2DMBlt5ApqdgsMpmyyjMPsKvcUlK4SQI5fX/yygBcnMzyy3NTKLLEZ/d3zJm5f8+WJnoibpp8DqK5Mj89PYHTZuGz+zv4b9+7cdZzXTaFRN4s6/DOZJV/fWGKi5M5/E4rLX67WdNuUWjyL3yX4M3+OO8MJnFYFb7yWNd9N/kdG0qSKalkSir7u0O8Z0sTJ0fTdASdcwbn79rYyM72AF6HZVEL5lJVoz+apyPkvONi5W44bAoh943A/cM7WkmXbpQ/vHBukoqqM5ws8CfPrFvw8VeaTEnl1atxprJlWv12vC4rEhJOq8JossT5iQxDCdOa2+ew4nFYKKtmkCxJkCxWiefL/PzsFE6bTMBpw2FT+NTeDkaSBQbjBWRJYipT4v/zowGmcmUwIOKx0eJ38vrVOP1RU2P+uc2NDMYLvHYlxmS2zK6OAB67wsnhNIVqDa/dQr5S462BBCPJIiPJIhGPg7PjmXnJZY4ki7x9zSz1yJZUtrT66A67ODZk9mu8M5h84O4jAI0+03I9VaiyuWXxijsrzWIaBr8JbAN+AfxLwzDOLfmoBIKHnOuZTkmCgGt1O6/LqoZuGAuST2r0Ofijp3pnaTDHc+ZWZjJfpcXvYDxVQpLMLfnvHRslV67x9rUEY6kiyXyVRq8NTdcZiBVIlVQSeZWCVaOsajisFjY0eYjnK/gcViyKNNPsYwbOAJWahkWR8TktuO0WDvaEefNaAgzzMy2rOnu7QoTdVpr9DmK5Cg1eez3L0RESjTr3SyJfJV2sosgSyUJ1zuDZqshsbjH1vk+OpFA1A7WkMpEu3WY1PZkpU6yataaJQpWg20Zspq44W1Z595ZmLk/laA062XCLrnQ0VyZbqtHX4L6jNnh8xpClrGrkK7X7Dp4VWeLIYJKOkBO3TUGSJdoCTiJ3cXW7n12On56eYDxdwmVT+PqTvQsOwD+6q5Wz41m2NJufuyxLs8YTdJm21sFFBOarwViqSGvAQUXV0AyDjqCbZKFCyG3n6FCSbLlWL5fQDXMXI+i2mXMeEk0+O26bwmC8QHvAgaoZaLpBvqyyuzPA94+PIUkSp8fSqLpBTdMxkCipOv2xPP3RHA1eB09vbOToUBIJibF0iRPDKY4OJulr9NDsdRDNGeztCmKZuT6uTOfwO231+XE+eB1WLLJEvlLj+HCKsVSJ7W0+uiMuhuJFNjQ9mIYodovClw51YRjGA+V6uZiZ40tAAfhHwD+86c1KgGEYxv3bVgkEDzkbmrz4D1pRZOmuN9rlJpor871jY2i6wcd3ty0ooLx1ovvAjlZcdoVNzT6eXN/AhiYvsiRRqtbqJSq2mSBL1XWkGVUMGQPrzE28UFGZzlZw2mp47QrPbIzQFXIzkiyRyFUoVTWqmo5VMW+GHruF3gY3/+3zG/nzV6/hsprZnb4GDwGXjfFUCYui8NPTEyiyxOG+cN1M48M778+Y40FE4oYN7FIUDRiGwdnxDDaLjH0e8mbb2vyMJIu47RY673CtWWa23aWZkT63qZFjwyl6wm5eujRNrqJxZTrPpakcOzoCgFmD/3dHRtF0g4M9oTvaKD+xLoIsQYPHviT17ZpusLcrgCJLFKoab11LcHkqh8Oq8HuPd9+XtNlcVDSz4ELV9BnnzYUFG2/0m+OL5cpzKtF8Yk8bU5kyzYvI6q8GvREPnSEXmm7Ua4c3NvsoVmu0B5zYFBmHTSZbVMmXazT6POzpCHB6PMNwokCL38U7Qykqqkal5mVbmx/dMHBYFX5zMcpUtoJFltjdae4W5MsqbruFza0+jg8lqc1IFY6ni7xvWzPtQSeXp7McHUyi6gb90Ry9ERff2NNHxGPj//faIJemsrQGHPgcNj6+u7U+546lihwfTtEb8bC9fXYGeSpT5p3BBDs7/PhdNn54YgxV01E1g4/taqOq6fNWernbeVaTBylwhsXVPK9dAUiB4AFiLTSnTabLVGvmDXk0VbyvbOzermDdpABuvL8XzibpibhJFqo8v7mRi1NZarpMvlrjpUtRJtIl3DYLTX47hqGjGaDpOoPxAhcms4BEo9esZewIOSnXzPrmbKlGT4ObPV0h9naF6AhOoesGuzoCfHB7K6quE9lu59fnp5jOltF0o16nDdQl2x4ljDv8e7FcnMxikSUMw+DKdI7ehrtnvyIeO/u7Q7hsypyB5bpGD4NxL3arTMRjXj+tAScfmWkefHvQXPgosoTDeuNWVFK1uuHK3ey5Q24bH9rRurA3eRe2t/tJFKq0B0194Os1z5WaNmfN8/3ywe0tnJ/I0BNxz+ojmC/DCXPXZSRRmjPTZ7coD0zNKZj19J/d34lhGJwaTVPTDfZ0BlFkie8fH2MkWWQwnmdYL1CtmQH2x3a3c2hdhJcvRTk5kqKimiopVkVmZ7uPn52ewqrIlKsaTT47umHwz967gX/9y8v0NHgwDIMvHOikVNE4PpLC67Dw7s2NXI3meftagvUNHg73hfntxWkyRY0fnBgnUVD50I4W9Jm6+LKqk8jn+a/Hx1jX5OUjO1t56VKURL7KYLzA+ibPrO/H765E602KbUEnmm6QK6s8sT6CJEkLkkh8+VKU+Mx51jXeWfpRcHdWtNVXkqSDwP8J6MBRwzD+iSRJ/wz4KDAMfM0wjKXpYhEIBPdkY7OXoUSBmmawfZ71ckeHklyZzrG/O3TbtvtcrG/y0h/N0xV2c2Y8Y26LVmp47VayZRXNgJphEMtVCTjNzHGlZlBBo6JqyBLE8gY9EQ8f291GpaZTqmooMnSF3Ty32Wwa++rhbq5M54h47PzVW0NoulGXkrJaZEIus7bcNWMOMt/3+7Ci3/sp92Rdo4cfnRrHqsj0Ntw76Hr9apwfnhzDokh89bFuusNujJvcKLe1+WkPOrFblPpNvaxqDMTytPpNy+TWmYa2DTdZv7cFnDy7qZFkscrBnjs3hi41W1v9bG29cR29e3MTJ0dTtAddy6KkEXLbeHJ9w6Jf/+T6CKfHTGm6By3TdzckSWL3LXXDH9zRQn80T8TdzneOj3FpMsdTGxpo8jvoCLvY3OLjL18fZCheoFLTeHJ9hP/j11eJ5cq0Bl2sb/DQONNAeS1WoH86R7JQZX2Tj+1tfj6xp21GxQPOjGU5PpxiOltme5uPyUyZsmouoIpVjcFYnmKlRsht471bm4l4bZwcSSNJEtdieXTdoMFjJ5GvEnBaZ0k1grnonEiX8TosdcdVh1W57XnzocFrJ56v4nda17QZzlpnpXV+hoFnDcMoS5L0t5IkPQ28yzCMJyRJ+ufAx4DvrfCYBIJHFodV4aO72ub9/GpN5+VLUWRJ4rWrcWyKzFiqxI4O/5xydemiWWP6R0/1UtF0/pvvniBXqaHpUFZraIaCyyrT4LFRqGqMp4vkK2ZYF3Ba0XUdkEA3sCoSU5kynz/Qic0i0+RzoMgSZ8bSlFWd3Z0BOkIuLkxk61nIWK7CxmYv77qpYfBmJ7JHmaW4bSqKzOPrIsiSRLV270zrhckMk5kysVyFQmUARZZY3+jlI7ta6zbJtzbCvXBukqF4EYdV4Q+e6GZzi69uA38zO2dKOFaToNvGs5sWrgCyUmxr8z+QTWWLwWFV6u/1/dta0HWz/OHYUJLDM2oVn9zbznS2jMOq8DdvDRPNlSlUalydzpHIVeo68//qZxeJ5szdK7/Tws/PTvHU+gg/Oj1BVdM4M5amWtPQdJ3JTBmbxWw+zJbNpmVNhxOjaVTNoMFjY3dnALui8Na1BPu7g/zNO8OUqxrPbW5kQ5P3tlr2Zzc1sqXVR9Bl44WzkxwdStIddmNZRNPp81ua2dEeIOS2zVkzf13uc6lLjh42VjR4Ngxj6qYfVWAr8MrMz78FvogIngWCNctgPM/V6TzJQoVNzV6iMzee6WyZT+6dXUNZqmp868gIU+kyZ8bSqLpOPFdB182SAc0Ah2y6bjX6HFRrGq9dLaMbIEtgt8q0BrxM58pUanq9ycvrsBD22ClUanz32CinR9P1usfH+sJsaPIwlfVTVnX2dAVW5XN6EFiKzPOWFh/XYnnsFmVemee9nUGuxfKUVA2bRSaer6AbBpPpUj14Hk+XcNuUehB9XbJO1XTeupbgxHAaRZb40qGuR0ZiUHB/uGwKkgSGwawm0daAk9aAk9evxhhLF3FaTQk6j8NCqaLRGXJiGBJlVaNU1ZAliZpuzkU2i0xF1RhKzLgYagZlVSeaLdMWdPGFg50UKjVSpSrDiRLFisaRwQSZUo0Xzk3R5HVQ0w0UWapnkAfjBYYSRbpCrlmLQUmSaPGbpUtFVWP9TK9GSdXwLiD73B/Nkymp7Gj3z5m1nkiX+MGJMQA+tbfjgal9Xw1WRWFekqQdQAOQ5sYcngECczz3G8A3ADo7O1dmgAKBYE5GkiV6GlwMRLO8etVspnn/9pY5t//OjmU4M5ZmNFFkKFHEMAx0w8BuldF1gya/HRkJRYKRRIFsWaXJZyOWrWBRZA51B7iWMINpr93CVKYCUprfXYnxiT3tHB1KMpIoMpkp47Fb6mOwKPKazv49TDR47fze4z3zfv7+nhARr52RZJGJdIliVaPBa68HCseHU7x6JYZFlvjiTHD8/m3NM/qvLq5M5wGzUa9Yrd0WPE+kS7x4KUrEbeP5rc0PnH76cnN6NM2p0TRbW3131T1/2GgPuvjs/g7Kql5fpGXLKr88O4UkmcF1k89Bg8dOX4OHn56ewCLL+Jw2eiIurkxnieclGjx2dnUE+dDOFvLlGr0RNxOZMtmShs0iUajWqNRkgm6Nrz/Vy28uTPOTU+OUVA23TSaeq1AzDKJZA9dM2UUyX6U95ODceI5r8TxdIRcD0Tx9jZ5ZpT/Xa9SfWt/AGwNx/A7rgmqdpzKmTGRZ1ZjOlvnALXrZYC5c1ZnGy/F0kYjHxlvXEugGHO4LL6pM5GFlxYNnSZJCwJ8CnwH2AtfTVT7MYHoWhmH8OfDnAPv27Xu0rXgEglVmT2eAsVSRmm4GMBZZYke7n3dtmq2jnK/UePNanFJVp1CpYWAGtddjGU0zSORVHBaZ8XQZzTBwWMzyDb/bTovfQUWX2NLqw6KYGUqvRcZlVeiPmgFUxGPH57Syrc3HezY3s6czsLIfhoDxdJE/fbEfl93CP3n3hns6/kmSRG+D546NhalChWShgs0iky2ZphcBl42nNph1vg1eOxZZIuCy1h3mbub4cIp4rkI8ZzrmrUU5wlShisdhWZVA5I2BOBVV543+BHu7gg9V3fO9uJ65vc6FiSwDsTwWWWJrm5+OoAuX3cJzM7XzNovMRLrEt95JkCxUkCSJoqphVSQavQ5CLp1DfREmMmVKVY3L0zlsiozTZh7DZbNw5FqScxNZdN3gqGbQHHCSyFfZ2upjb3eQtwcSOGxmWcfGZi8DsTzxvCn9eGEiw4GeMBOpIv/Dj8+jSPDhna1EPHbsFplLUzmSxSpfPNg1r/cvSWYZnam2UmH7HN+PLS0+RmaaSje3+Dg/ka3rSHvsCnu7Hp0F171Y6YZBC/A3wD81DGNKkqSjwJ8A/wZ4N/D2So7nQed+7cWH/vUHl2gkgkeFsMfMNBoY/Ob8NN0RN09taKhnQOL5Cj8+NYGh6yTyFWK5MplSjbDbis9hJeyxc2I4SbGqowM1Xa+rElQ1HbfDglqsUdMMtrX56Y/mKVZrHOgO4LbbqNR0Pr3PXG9va/NTqNS4Mp3D57Q+UoHAWuFHJye4FjdNIl66PM1Hds6/fj5XVvnRyXGqmsFHdrbS4LWjyDLJgorDKuO03R5cumyW2xZqN9MTcTMQy+NzWFdVAvJOvHI5ysmRNBGPjS8c7FrxzHhH0MXRoSQ72gOP/PclX6lxZiyNbsAzmxp479Z1yJK5wHvfthaGEgVOjqTRdKOuSJQv1/jx6Qn2d4fY3OJnY7OXfd0hfnZ6gha/kza/g02tPp7Z2MB/fKWf02Pp+msbfXYavQ78Tgt//EwfsiSRKqhMpMsYhlnr73NYCLlNhY+fn5nk5EiaCxMZLk5mTOOoSo2DPWGShSoeu8JvL05TrGp8fHfbPa/3Jp+Dvd0hyjWdRq+dWL5yW/DsnqkDv87NvQX+OfoMHmVWOvP8aWA/8G9mvrj/b+BVSZJeB0aA/2uFxyMQPJREs2WODCXpCrkXreVpGAbnJ7IoslQ3ubjO1x7r4V0bmwi5bbPMVa5M5UgXq4wkixQqNfKVGhZFYqYHhbJaMzV8JR3JMA00ricrNzZ7qOkSNotOxGsn4LSaBhyShCQpPLe5iSfWR5jMlHjtSozL0zkGYnkavQ5+e3GazS3eZQ8IpjJlxlJFNrX4lkVNYSVZik9qa6uP16/GsSgSm5oXJvE/GC8Qy1UAuDKdMy2hJVPBA5hXA+KtbGvzs67Rg1WR12TJxtiM62Y8X6Wkait+DeXLKh67hUJFiFpVVJ3uiJv+aJ5fnJmiweOge6akY29XkJ3tfq5MZTk7lmFjk4fBeIHJTAnJkHj1apyBWIHhRJGr0TyP9YUZTxV5z9Zm3ru5mf/82rV6g2Fvg4eOkIM/fKKXFy/F0HSDly/FOLwuTKWmYRgwnCxSqek4LDLT2TIeh4WhRNE0RBlJU6jUTAUai8JUpsxTGyKcGctQqtS4OJFlc7OXJ+ahwvL8liZsigRIbG299/e1J+Lm8wc6MTBuy9wvhkKlxsXJLG1B55Ic726oms5rV2MYBjy5vmHJlUVWumHw28C3b/n1W8D/upLjEAgedl66FGUyU+bqdJ7uiAvvHEoY9+L0WIaXL0UBU1f3Zlk6WZbqtYPXiebKfOfoCEeGknjtFh5fF2EsVcJjt+CwKkQ8NlJFlfWNHq5M5zEwCLvt7OkKUKxoPL2hgeMjadz2Mh6bwq8vTBPPV8hWajhtCusaPZwcSfHK5RgnR1JsavGRKlSJeOy0eB31wFnXDeL5CkG3bUm3xis1je+fGKNaMzWoP72vY8mOvVLcbJKyFKHlY31h0kUVt01h3T00nm8l4LRyfiKLqut1ucHHesNYZdM1crElF2tZJeDJ9RHevpagO+xelcVXvqJhVWTyFe2Bc3Rbag70hDg/kaHF78DrsJArz9YHtygy//S9m0z1jWieH58cI3FRJVNW2djkZSJtLoT8Tgsum8LzW1t4emMDf3tkhIuTWSJuG32Nbna0B/jqY90YwCtX4pRVjd9enObbR0YoqzVsFplqzSDgtJIr11jX6OZwX4Rz4xl+dmaSak2nyefgUG8YiyKj6QZeh4VStcZEpkw0V+VDO26vX54LqyKzpzNIyG2bt074nZoGMyWV4USBnoi7fn/JFFUkmTmVlwB+eW6KkWQRqyLx9Sd7l/W7em48w+nRDGA6zi51ycmDnToRCARzEnLbmMyYuqALaSq5E8Y8koAnhtOcHc9QrGjUNPPGfKA7RL5a44PbWmjy23nxYpTX++MEXFZUzZiRmssScts4N5Hlwztb+NHJcdIlFYsisa3Vj99l5RtP9RFy2zg/kSFZqDCdLTOWKrGuwcOOdj/P3CRF9/Ozk/RH8zT5HHzh4PI0GT+ozRcycN0aZinCpuNDKQZnyjY6w242Ns/fsTFdUutSYteNTRxWhSfWL15KsD+a55fnJgl77HxyT/ua07HtCrtX1YTk/dubOT+RZVPz8u/SrHUavHb+yXs28NZAAkU2+ytuxarIBFw2dnUEGIwXKFQ12oOmss/7trVweizNRxpb6Y2YlvC/PDfFS5eiOKwyiiLTG/FQqGhkSiqNPgef3tfOT05O8NvoNJOZEnarTKPXgT6jAvLk+ghbW81a5I1NXs6MpclXzAB7a5ufyRmjFMMwDW1a/NddFM05fiRR5KdnJvA5LHxqb8dtBig/OzPBtViB1oCDz+6/v7nx+8fHyMz0JXz1cDeD8QI/OTWBJMGn9rbTGljezPK9CLlN+3Pz30tfwiWCZ4HgIeTdm5vY3OIj7LEtOoBo8trZ3RGg0eeYV1DUHXERdtuI56tmF3mhSvNMVsdqkZnKVLg6naei6kQ8dpw2hRa/g4FYgXi+gs9pxWm10B50YbVU6Gtws7crSMRjr6sqHOwN8+ZAgt4GD1OZEslSle8dG6PZ56yXp0xlyuiGwekZVYGl0v+1WxQ+uaed0VTxtjKWBwWnTSJfNUN/r+P+A0uLInF1OodFkXFaF3a8vgYPFyayqJq+ZJ/nxcksqmYwlSkznS2vyYbB1aQ96Jqz0fJRxarI9WbUez3vg9tbyJZU8pUaXWE3zX4Hzf5mitUbGetCpYbbrlDTDdY1ekgWqlgVqR7cNnodbG/387Mz40iSRFnV65nl929rrpdeVGs6V6J5DvVGsFkUGjw2trX62dcVIltW2dHmZ2ubn5+cGuf1/jh/9nI/I4kiIbeNak0nnq/SH83jcVjoCDrrWebJjBl8T2cr973zoN5kFW8e05x3MUx9/bmC5/dta+bCZJb2oPO2rHOxWuOVyzFsiswzGxsW5aB5M11hd72ZssErgmeBQDAPTCvr+d0kX74UZTBe4PF1kXqQPBDL85NTEwB8dNf87Iw3Nfv4f752gP/lhQtMZSqkilVOjKRwWBUeXx/m+HCGzrALt8PC+7c1s6sjwG8vTNPb4GE6U8HntBDNl3HbLTRLEod6w+xoDwDw6pUY/dE8B3tDfPFgJ//h5X4qNR2rAU0+O6OpYj14fnZzIz84MYYsm+UrbrulXkd7v5g3zAdX+9TntJCvmvWuftf9NwBVajqNM2Y1C7U7d9stfO7A7OxXvlLjjf44PoeVQ72hBd/ct7X5GUuVCHtsD/TfSbD2cNstfPVwN6qm1/s8Xr4U5dRomgavHVmSSBYqdARdJItVQm4rj68LE3bbZ5UxbGvz1Rv3WnwOClX9NrfWNwbinBpJA/CZA+28cGaKf/PLSxzuC/NHT/chSRIRj529XSFeuRxD1QyODCb5B8+uYzBewG238Hp/jLKqs67Rw4d3mnP4uzc3cno0w+YW333vPHx8dxtXo/n6uHe2B0jMKIVcXwxrulFvwrz+Ge6/g0TiyZE0l6dyALQEHLOcOxfLcgTN1xHBs0DwCJMrq5waTWMYBi9ditLX4MaiyGRKNxqKbv73rYwmi7x6NUZrwFl38ZvKVOiP5uuSTooskSvVWNfoYSCW5xO72+hr9PDNt4a5NJmlM+SiUtOwW+3saAuwryvIC+emGEoU2dTsQzcMjg+bcknvXEvymf0dtAddtAWcjCQLhN12dt8kU9fX4OG5zU387nIMYE02jq0WJfVGwUmpev82KaZ6gBWLLBHx3L9hyTvXElyYyALQ4r/RwDVfeiJu/t4zffc9DoFgLqyKPKuPYiBmymaeHEnR6HWgajpuu4LP6aY/WuBgT5jgLVrkR4ZSeOxWNjX7qGo6Ea+Fw+vCNPrMxZ5hGJwfz3B8OIWBgdumMBgvUKnpHBtO8flSrb7w3djsZXu7nxMjKba0eult8PBHT3soqxr/+XfXgNnz97pGL60B0248Xaze5ua5EBp9jvqYwZTCUzWdYlWnXNMYT5f42ekJ3HYLn93fMcucZs7jee1IEsgzC4O1jgieBYJHGLfNQlvAySuXozhtCt87PsbnD3Syvc1fb6C5k51vTdP59y9dZTRVojfiZkebn5+fmWQwXqCmGzT7HciyBAYki1UO9Ub40I4WJEniV+enyJVVksUqEa+dzx7opCfiwmO38hdvDPLSxSght411DR42t3hp8Ts4O25KfJUqNdqCTgaieSyKggFcmszVu7d/dyXG6dE0YbeN9pAL3z20h++FqukcH07htCprwgL6fijftMVcVmt3eeb86G1ws7XVh8tuWZIsz/WbuUWW8N7n300gWCr6o3mmMmV2dQZmNXoe6g1zbCjJuoYGfnx6glJVY3+PmVkNe+wEXDaG4gVGU0V2tAXwu6yEZq7x3gYPhYqKw2rh6FCKAz1hwFRiieYqVGsaVotZ/gaQKVZZ3+jh3750halMmU3NPt6zuZF0QaVc1RhJlIjlKjTMWIq/b1szg/E8FkXmyGCSPZ0BLIrMT09PMJEu47YrfP2JXnOOXgKuTOfq/Q/nxjLkKzVqukGmpDKZKbGu8e6lf+ubvHx5ppHxQZDFE7OTQPCIUqjUePVKjAavja2tPuL5KiOJIjVNx6rIPH2PWsBEoYquG+i6QbGq4XNacdkVwh4bsVyVba1+PA4LlyazvHQxxrVYgSfWRdjdFaQz5OLiZJbHesO8f3sL09ky//7FfgxMtQzDMEjNbH1KkoSmG2SKKj88Oc7l6Tz/9Pn1JLoC/PikWVpSqd3Iop4ZNbVZT4+lSRSqXJjI8JXD3XfsAL8XRweTvDOYBMDjsNC3QFWJtcRNsfOsLPRiOT6c4vxMprjZZ7/nDfJe7O0K0ux3zLLnFghWk0xR5WdnJjAMSBQqfHTXDS3zbW1+trX5KVU1rsyYN4XcNj67v4N8ucabA3HeGkjgsCpMZsp8Zl8HOzsCNHjtOK0Kr/XHGYjm6bypxG4qU2YwnqdUrRH2uEmXVXwOC3mnlbPjGfLlGkVVw67IvKDpHBtOkp+RgLtefxzLmWVzAZeNtwYSAMgS7OsO1edKVTOWtPG5xe/EZjHVQDpCLiyKxFiqhNdxd+UcXTd49WqMYlXjqQ0NS6pCky5WyZVry9L7IIJngeAR5fhwiouTWSRJqk90zX4HRVXDN49mjbDbxsHeMG2pIu/d2oxVkXnfthYK5RoXJjMEPVYkJBRFrjti2Swyk5kynzvQSWfIhVWReftagu8cHWEsVcJhVdjU7GVXRwCPw4I8UytXrGokiyrRXAXrdI4fnZwgVVQxgLaAg4qqMZos0hFysbnFx9GhJM0zW4qqZlAo1zg5YpanPL4usiAJO/tNjXD2NabesFBuzjHJS3DnvPnzWApVF4C2Ve7SFwhuRlEkLLKEqhl3vMadNoVnNjZyLZbnQE8Iu0Xh26dHiOUqXJrKsavDP+u7cr2Z7sM7WshVaritCufGMxwdSpIuVlFrBk6bhee3NLG11c///uvLSJiW4mVVQzPgwmSWTFmtK4Ls7wnRGnAynS3zv75wCZdNIeCyosjmea+P/YPbW7gwmaUn4p6zpM0wDKayZYIuGw6rgqYbXJjI4nNa7qoU0+C18wdP9GAY1FU+fv+Jnvrjum7MmeUeiOU5OVPf7Zr5HJeCdLHK37w9jKoZHOoN8Vjf4lV85kIEzwLBI0hN0zkxkuTIYJJ1TR62tvrrUk2FSm1eWdofnBhnNFXkUG+YLTPNHefGM5weyzCdKxNyO3j3lkY+tbed6UyZ316MoshSPSDOlWtcmcryveNjKJJEpabTGXIxnStTUTXyFY13riX5Hz+8hQ/uaKGmm+UTDR472bJZxycBQ4kiNkUmmqvwlcNdXJjMYFVktrb6zE53q8JUtsyJmbppr8OyIM3PPZ1BvA4rTqvy4CsVLLHQ864OcxvbblGEsoXgocRjt/CZ/R3EcpVZTX23srcryN6uYP1nWZawKDK7Ovy8Z0sT6+d4bVnV+eGJcbIllULVNEIZjBeIeG0MxYu8M5jAbpH5+O5WBhNFzo2lsVtkzk5kiecrqJrB/u4g79vewsGZcpFfX5gmlqugajrv3tLEuzc3UdP1+q5Q2GPnybsYqrx8OcqrV2IEXTb+3jPreGcwwbGhFJIEnz/QSZPvzo241xU0MkUVm0XGaVOoaTo/ODHOZKbMs5sabzPtCrptWGSJmm4saa1zrlxD1QxShSrfPjLK1Wiez+zrWDJtaRE8P8IIe+9Hl2SximFIbGvz0xl28YHtLbw1kCDisc3p/JQpqpwdz9AVdtERcvHK5SjfPzGGw6rgtpmGKADRmZq7dEkl4LJSqupEsxV2dgRoC7p4ayDBtXie//LmEMOxAm8PxqnpZhb760/2cGI4xaWpHOOpIqpu0B12c2U6xwd3tPLHT/dxcTKHLJs2uS9dmsY+kxkB0z72lUtR3ug3t0lbAnYkZK5MZ5lIldEx2Njsxe9cWDmAJEl3vWk+SNxs2le7/35BJEmaMygQCB4mGr0OGr0LU2/56K42+qN5eiLuutTmrUxmSvWa5utZ4C0tPq7FCwTdNhwWhZ+emcDrsFJWNVoCTpxWhXOTOQwgWajyeF+43qw9nCggA5tbfVRrGp/Y0z7r3K9eifGXbwwS8dj57z6wieAc+se/OT/Nq1fjKDK8e0sTFyayHB1KEvHY6nPt3bg4meVX56ewWWS+cKCTdEnlpUtRs4HcZ78teI547HzlcDeVmrbgz/hudIRcPL4uwosXp3HbFRL5KlOZ8oKbkO+ECJ4FgkeQiNtOX6OplXy4L4zfaeV925rv+PwXzk0ymSlzciTFHz7Vy3S2TLPfQTxfYVPLjeDpiXURRpMFWv1OdAN+cnqcXLnG7x3u5kBvGN0wMAwYSxa5PG1q8gLs6w7y7s1NDCeKuO1m+UaDw0qDx459Zktza6uPLa0+pjIlvnt0jIl0Eb/TSsRj58n1Eba3+/nxyQm6wi6yJZWdbUF+c3GaaLZCuaaxocnLuzY2Lpls3YPIzYnnB9XoRSB4EPA7rbMy0XPRFnTSHnSSKam8b1szYbed7xwdocnnIFmoYrcp2K0KQ/ECmZKK22Yh5LazryvIa1djNHodjKbL6LrB2fEMvzg7SbWm8f7tLWxt9d+mcPHK5SjFqsZIssj5iWxdVzpdrPLipSgXJ7MMxk2lJJdNIZqtoBsGrQEnDot816zzdSbSJQzDtD+P5yuMJktIEqSKVcJ3WESYDYJL3yR4oCdEi9/Br85P4Xdal9S4RQTPAsEaZrksdGVZ4iM756ffDGCzyBiGQVHVqWk6j6+LIEkS7UEnh2+qJQu5bXgdVqoeg8tTGcZSZRRZ4uRomgO9Yba2+hhLFVnf5KHF7+DMeIYmn4OvPd6N32nlmY0NtPgdJApVZEmiK+zi7FiGs2Tq+qEvXY7yzmCCSk1jU7OPVotCb4MHu0Xh8fURkKA94GRbu5+qrmOb6VjvbfDMynroukE0VyHkXryRzING0CmTLJkp54hn7dpYCwQrxVLNsfF8BadVuack283YLQqf3tcx63c2i9lcWFY1HBaZ7a1+ZCQ6w9AScPL8libCHvuMM6GBTZGRJDNoPTOWQTcMdnUGONATJlmoMpIssr7Rg9tu4Yn1DfRH80S8drbcZEz0Rn+CI4NJhuIF2oMuXHYLm5t9PN4Xxm6VUTWDTc1eFFmiWK2Rr9TumCXe2xUkU1Lx2C30RDzUdHPHz6rI91xMLAcdIRdff7J3yY8rgmeBYI3y2wvTnJvIsKczOC8XrIWg6wYvXoqSKlZ5dlPjPWvNPrC9hW++OUQ8X+H7J8b5zL4Ont/SNKciwsYmL2/mExzsDROJFlA1nQ0z5ivrm7z1bX7DMIjlK1hlmR+fGqdQ1fjIztZ6PXKqUOWHp8ZJFasEXTesVvun8xiGQbWm43Mo7OsO1Lcm2wJOPnPTzWhPZ5A9nXNP2L84N8nV6TwNXjtfPNj5SNgVtwXdJEumEUFXRJRbCB5dKjWN7x0bI12s8v7tLfelonN6NM1Ll6LYLDJfOti1IAOiM2NpMiWV/d0hHFaFj+5qRZElrsXygMSzm5t4emMDIM3aNfvGU71cixXoDLmQJIm+Rg+NPjtWRcZptaDpBt89NkqpqnFpMssHdrRwuC/Ms5tub8gLe2z4nWZfR4PXTlfYbCYcSRWJ56t0hV08sT5CoVLjr98eplTVeGJ9ZE7Dk4DLxif2tNd/3tTso8nrwGaRb1tYlFWNX1+YRtcN3rOlaUELj9XmwRmpQPAIYRgG5yYyGAacm8gsefA8mipybjwDmFJs79/ectfnO6wKDpuC02Yhnq/wzbeGKFY1HusLc6g3TKpQ5dWrMQIuG0+tj7C3K4hFkSlUaqRLKq1zuL1JkmmicnkqR6poNgBejebqjWcvnJskniujaQbv2tjIpmYzU7KhyctEpsRoooTPYePiZI7H1y3885masaqN5ytouoFFefiD54lMqf7vkURxFUciEKwu05kKsVwFMHXi7yd4ns6ac0m1ppMsVucdPI+lirx4MQqYevLPbjIDyE/vbef8RBa7VabnDjW6brtl1k7a+kYPn93fQbZU47G+MIZh1GuUx9Ml/uL1QewWhS8c6MRulVFkqa46dKg3TGfIhdumMJ4u86vzU4wkivy7F6/S4HXgsVs4N27aapeq2qz3PB9uNYq5zqWpHAMzEn9nxzMc6g3P+5irzQMdPN9vw5tAsFaRJIk9nUHOTWTY3bH4rS51RrP5VsIeOy6bQrGqzVsl4V0bGzk2nCTotHFyNA3AWMoMxt4ZTHAtVgAK9Ebc9WO67ZZZ2YSJdImTI2k6Q676xN8ZctHkc1Cs1uq2roVKjZ+fmWQ0VWJnxw0lEICP7GqlK+zi+HAKVTNo8jn47rFRKqrGB7a3EJ5nx/Zzm5s4OZJiQ5MXywKk6x5k2gMOEgXzZtUVEvbVgkeXZr+D9qCTZKHKtjbfvV9wFw72hCnXdHwOC133mE813eAXZyeJ5yvs7gwiSxK6YcxSgZBl6bbGunshSdKsEjqAT+xpYzBWIJarcG4iw+nRNPF8GZfNnJc/v78Tl13Bqsj1emCbRcFrtzCVLWFVJEaSRZ5YZ5bTtfgdHOgJEc9XeGyRge7N96RmnwOrIqEbD55E5QMdPAsEDzNPbWhYdMbZMAx+fGqCwXiBAz2huhrGdTTdYE9XkNaAc96TVlnVyJdrNHkd7O0KMjnTbAimVevFyRx2q3zHrMvLl6P8369eQ8Ksi+uOuEwJOJvCFw52znruVLZMVTNvRrpuzKpJdtksPNYX4UBPmGxJZTxd5DcXzOzNuYnsPc1drtMTcd8xq/OwYnDjc9R5NBYMAsFc2CzybfXGi8Xvss67h2QqW6Z/Jts6nCjw2f0d5MoqTT47Z8bSdIXdS+aw1+J30uJ3ki5WOTuewWWzEM1V8Np1ZEnip2cmiOUqrGv08OGZ8TttCl8+3EWyWOX4cIqOoItNLd56QuTWe8nNlFWNF85NomoG79vWfJvk6QtnJ7k0lWNnh59nNzXR7Hfw+zPa0LeWbFyL5anpBusbPWuypE4Ez4JFI6Tu1i6Vml63Sr00lbttwvvesVFy5RoRj40vP9YNmDXGsXyF3oh7zkzsmwMJkoUq8XySP366ry6ED2ZtcUfQhct254aZY4NJ4vkKhUqN7rD7rqYaXSEX29v8DCUKvHtz05zPkQBZkmgLuHDbFao1nd5HLBheKNWb9OnUpdCqEwgECyLisRH22EgVVNY3emn2O2j2O/jrt4aI56v4nFb+4CZzkbmo1DSqNR3vPF1TAy4bXz3czY9PjVPTDXwOK2GPjfG0uXPYH82j6UZdLs9uUfjyoS403cyI30mloqbpXIsXiHjshNw2Lk/lGIqb5WDnxjOzMuGGYXB52uy3uDyV59lN5rzust1+vxiI5fnJKdM99t2bmxachV8JRPAsEDyEOKwKuzsDXJ3Os797dtmHYRh1ibjqzP9LVY1vHRmhWtPZ3OKbU7auK+wiWajS4nfM6bTX4L17ucTWNj+nx9K4wm6e2dRwV4ULiyLzP354K9WaPut5qUKVH58aR5kxIJjKlFnX6OHrT/SiG8YjU36xWLrCTi5Nm1mv7gZhaiIQrDTXA9Oabswqqbthm333RW2hUuNv3xmmUNEWFFh2hFz88dN9yJJUd/o7OZLi2FCKTS3e29wGWwJO/uSZdbx8eZojg0nUms7BW0o1XrwU5cJEFptF5vce76Y1YFp067pBxy2GUpIkcaA7xPmJLHu6Ancd682fQVXT5vX+VhoRPAsEDyD5So3LUzk6Q647Bq3PbGyc0+pUkiQ+saeNq9N5Ns6oYKi6Xp+witXaHY+3rzuEy6rMabN6Lz6wvYXOkItCpcbuziCabnB+IoPHbqH3lmadWK6CTbm9BOTy9I3mwulcmSavg5FkEVmWkJfCMu8hp9lnx24xP6eWeWi2CgSCpUeSJKy3NCh/dFcbl6dyrG+6e+NiIl+lUDEDytFU8Z7Bc03TOT+Rxe+03mYQsrszyO47qBEB+JwWhhJFDAOOj6RuC56vNw+qmo5aM3BYZT6ys5UGr31OJ7/D6yIcvkvZx3U2Nnm5Op2nVK2xoy1wz+evBiJ4FjyyPMhlJz8/M8FEuozdKvONJ3sXnHFt8jlmCd77HFY+sL2F8VTpjtJuYFrVXovlMWBWd/pgvMCbA3G6Qm6eWD97cnyjP85QosBjvWG2tZkTfX80z09OjTOcLNIWcPLFQ1312uuLk1l+eW4KRZb43P4OGm8aZ2+Dm1OjaWQJPtTdwkiyxI55ZF6yZZVjQ0mafA62tq69LcCVoqZDZcZmsKYJmxSBYK3Q4LXfc/cOoD3oZGurj1Sxyr7uuefqVy5HOT2aZm9XCM0wODFs2mt/4UDnrPn0Zq7F8lyLFdjR4a9rOEuSxJYWH+cnMvgcFkaTxVkN5u/a1Ih/OGWWdUjwzbeGqdZ0ntoQYW9XiLevJeiP5jnYE1qQE+lwolivCz89lmbfHJJ4d2MoXqA/mmd7u39exi6LQQTPAsEDyHWXVMNYOqe4DU1eWvwOvn98jKqm89FdbbdNPFemc/z8zCQA79vWXFfHeHMgTjRbIZqtsKPDX28UyZVVjgwmZ56ToLfBw7nxDN8/PsZb1xLYFHOLT7/J9jWeN+WjNN0gWazOmuwbvQ7+6ClT8H4hTSSvXI7NSCJlaPY55q3I8bDx6xlZLIBfnJvif/jwtlUcjUAgWCiyLPH81ju7wZ6fyPDNN4cpVGvE8xUOzTR1G8aN+8atqJrOz85MoukGE5kSX5npgwF4fqvZ+PfWtQTfPzHGZ/Z11Gug/U4r75rRjR5NFus9FbFclbKq8dZAAoDX++N3DZ4H4wVeODdJaEYjWjduDHQ+luA3U9N0fnp6gppuMJYq8rXH714/vlhE8CwQPIB8cEcLFyeydIXdc0rRLZT+aJ50sYoiS/WyiMtTuduC54p6oxatclPDWVfITTRbocFrx31TA4jLZqHBayeWq9AdNrcMq5qOVZHwOy34HDaeXN8wK5uxtytIvlzDYVNY33j7hHs9aC6rGnaLfM8g+uJklnPjaao1nQavA/sc24mPCu0BB7FcFYCu0IMlDSUQCO5Ntabjd1kpVGt4HVae3dhEs8+B32mleQ69fTAbr102hVy5hmeOhm9VN+d6w5jddGwYBm8NJEgWqzzeF+ZAT4hUscpjvWHsFpkWv4PJTLk+9992XE1Hwgz4K6rOZKbMVKZMb4OH921rpqRq7GwPzHrNYLxAPF9he5t/ztIQWZJwzryX5TRdEcGzQPAA4nNYb6s/OzOWZiJd5mBP6I6i9HMRzZb56Wmzs3ljk5egy0pV0+v10DeztdVHVdOoaQbpQpWXL0d5vC/CE+sj7Ojw47ZZZjWeKLLE5w90UqjW6tnone0BNN3gQG+YrrCLdbfUO7tslnuatrx9LcFbAwnaAk4+tbf9jjXYNU3n1+ensVsUZMyxzHVzeFQ40BPm1GgWCTjQs7CtUIFAsPbZ0R7gCwcNSlWNx3rDWC1y3bX1TiiyxGf3dzCZKdMVvpHIqNZ03hiIY+gG+7uD+J22WXXT4+kS78zsLCqSdNu8/el9HbPm/psZT5f44YkxFFnmUG+IkWSRkMtGk9/cFdzccrv29vWGccMwa7/namyXZYnPHehkIl2ic54eBmD2Eb3ZHyfots3pnHgrj+5dRCB4iEgWqnWnqpJa4+O72+/xipuQQJLMrILLrvC1HXfe5pJlib1dIc6MpXlzZkvOa7ewrzs05wQJ5sR8/THDMEgXq+zuCNyXMsbVmXq48XSpnmG507lDbivxfJXNrb66jfejymCsUHdS7I8Jh0GB4GFDkaV5BX+34nVYb5tHz4ylOTWSBuC5zY23NSf6nFZsFplqTScyR732zXP/rQzHC6iagappyJLEnzyzjlJVo6zqd5QxlSSQkDAwuFvPusduYcMCaqwB3uyPc34iC5jmLfcyDxPBs0DwEOC0KtitMhVVJ+BcWIDY6HXw8d1tpIoq21rn57Tlc1jrAfdCBP1/ezHKufEMjT47n9/fuSjVDoCDPSFevxqnJ+K+q9apJEl8Zn8H8XyVpnk04zzsHF4XNhc9Ejy5/t5d7wKB4NHl+twuSXPP8z6Hla881kWhot2xJORObGn1MZgoYFVk1jd5SBaqfPvICKqm84HtLXMGvwGXjU/saSOeryx54/f192eRJbyOe4fGIngWCB4CnDaFLx3qIpmvLmir6jpdYTddC3Bb7Y64+cKBTjTDoMU//9rZ8ZSZ7YxmK1Q1nUrZ3BYMuW0cWoDd64Ym77wzC3aL8sBZvy4X65t87OoMIEnQE7m7JJZAIHi0Wd/k5XMHLCiSdEeVjlsz1pOZEseGUvRE3HV1pbkIuGx88WBX/eeRZLZeTz2eLt1xfu8Iue6ZFV4MB3vDtAaceOwWAq57J6BE8CxYNe5XKk4wG5/DesctsuXgTpPpdeL5CuOpEhubvfXGjqc2NHBsKEVfoxuHVeGlS1EuT5muUx0hlwhyl5vr6iyG0MQWCASzyZRUhuIFehrc9XvJQpIjAC9dihLNVhiI5eltcM/pIDgXfQ0eNrd4KanaXeVSl5OFBOUieBYIBEtOpabxnaOjVGs6A7E8n9hj1mD3NnhmGaKEZ2qQbRZ5XltlgvujUK3N6DsbdYMDgUAgAPj+8TEyJZXgiHXREm9ht41otoLXYcW2gL4WqyLzvm13bxRfS4i7lUAgWHIMg7p2893MOA72hukIufA4LCuaNX9UafI52NHuR4I5G3wEAsGjS02/bhG+ePeA57c0s7XVT8Rjv6+m8LWOCJ4FAsGS47AqfGx3G6PJIlvvUvcG1AX3BcvP9jY/mmEgSxJb59kcKhAIHg0+truN/un8gtwAb0WWpWWpSV5riOBZIBAsC8vV2CFYPLIsrVo9oUAgWNs0eh11a27B3ZEMY6nMfZefSCRidHd3r/YwBA8ZqqZjkWUW4PY8J0NDQ4jrU7AWWUvXZk0zkCRmmekIHl2W8tpUNR1FlpDvdzIXCGY4fvy4YRjGbfUnD1Tmubu7m2PHjq32MAQPET8/M8mV6RxBl5WvPNa9aN1hgH379onrU7AmWSvX5tmxDL+9OI1Flvj8wU4iHlF3/aizVNfmq1diHB9O4bIpfPVw95zWzQLBQpEk6cRcv394q7kFgnkQz1cASJdU1JlmCYFAsDxc/77VdNNpUiBYKq5fW8WqRqFSW+XRCB52HqjMs0Cw1Dy3uZHjwynWNXruaAkqEAiWhv09IcqqhstuoVeYtAiWkCfXN2BVErQGHITFjoZgmRHBs+CRpj3ooj0omtoEgpXAY7fw/u0Pjpar4MGhwWvnwztbV3sYgkcEETwLBIIl537dI4f+9QeXaCQCgUAgECwtIngWPHKUVQ2bIt9Xc6BgeRHB98NLpaahSNJDbaAgWH103aCq6aJxULAsiOBZ8EhxbCjJa1fjNHjtfG5/B8lClVOjaXobPKxrFDWYgoebmqbz9rUkkgSHesMrLhd3LZbnZ2cmcVhlPnegU7hKCubNdLbM6dE06xo99Dbcfa5WNZ2/OzpKPFfhqQ0N7O0S2uaCpUUs/QWPFAOxPACxXIVsucavzk9xZizDL85OUq0JtQ3Bw83psQxHh5IcGUxybjyz4ucfjBdQNZ18ucZUprzi5xc8uLxwdpLzE1l+fmaSmnb3uTpTUonnTPWNazNz/s0YhoF6j2MIBHdDZJ4Fa55UoUq+UrvNra6salyeytHid9Dou7crkq4b7O0KUlYTtAWcBF1WJjNljg+naPY5EFUcgocdn8NCLFdGAryOlZ/+Qy4bFyeyuB0WIm4bsVyFmq7T4hcW7YK743VYSRSqlGs61ZrOVKZMslhlY7P3NqWksNvGtjY/E+kS+7tDsx4rqxrfOTpKuqjy3m1NbGoWNvWChSOCZ8GaJlWo8jdvD1PTDZ5cH2HfTRPhby5M0x/NY1Uk/uCJXpy2O9e2pYtVvntsFFUz+NjuNtoC5s066LKxocmDx2GlphsItTrBw8xUtszl6RwSErFc5Z7b30tNslhla5sfgHMTWU6MpDAMeP/2ZhHECO7Kh3a28JdvDJIp1vgXPzhDNFeh2efgvVubb1NwkSSJ92xpmvM4sVyFZMHUGL86nRfXnWBRiLINwZomV65R000L+VRRnfVYbcbURDdAu4fN/GiyRKGiUa3pDMYK9d8/1hemK+zmYE9INJYIHnpGk0UssowiS4wkiyt+/u1tfkJuGx0hF16Hhetf21RBvfsLBY88douCTVFQZImJdBnDMEgUqvX7w3xp8TvobXDjd1rZ1RFYnsEKHnpE5lmwpukIOTncFyZdUnmsLzzrsfdsaebMWJq2gBOP/e6Xcl+jm/MTDqqazuYWb/3329r8bJvJhAkEDzvv39bCcKKILEk8v3XuzNxy0uhz8NXD3QBoujGzONbZ3RlY8bEIHjzeu62Z06Np2oIORhIlWgNOntvcuKBjWBSZj+5qW6YRCh4VVj14liTJBXwPcAMZ4DOGYVRWd1SC5aCm6ZybyOJ3WumJuOf1GkmSONgbnvMxj93C4b7IvI7jsln43IHOeY9VIHgYCbisfGRXK7IkrbrShSJLPLWhYVXHIHiwaAs46yV3+UqNy1NZ8pUaLtuqhzKCR4y1cMW9D3jHMIx/JUnSfz/z849XeUyCJUDXDX55fopotsyzm5oYThY4NpQC4PMHOmn237vJb7kZihc4OpRkXaOH3Z1CzkjwcPPOYJL/+9VrAPzJu9YJCS/BknJ2LMOlqSy7O4NLJv15LZbnV+enSBSq7OsK8d6tTUiSxM/PTDCRLmO3ynzjyV6hGy5YUdbC1TaAmXUGCACJ1RuKYCmJ5ipcnsqRKqocH0lyc2mawcLq1JaLVy5HGUuV+N2VGJWattrDEQiWlaF4gXylRr5SYyh+u4SXQLBYNN3gxUvTjKVKvHwpumTHfeVyjBPDKc6OZTg3niGeN5v9rtfLGwZr5G4ieJRYC5nnq8BjkiSdB6LAP7/5QUmSvgF8A6CzU2y7P0gE3VYiHhuJQpV1DV42tXjx2C34nZY1I02lG/D2tQRbWnxY5bWwlhQIlo8n1oX5zYVpJBkO9YXu/QKB4Cai2TL90Twbmr1EPPZZjymyRLPPwWSmTEtg6XYVWwMOAi7zPmIYBuOpIidGUhzuCzOVrdAZcmEVWWfBCrMWguevAj81DON/kyTpnwJfAr55/UHDMP4c+HOAffv2iQXmA4TdovDFg12oul7X4VztbeJCpYbDqtSd1U6OJFE1nUtTWYpqDY9dOJ4JHl6mc1W2tJrSXPGcSltgdccjeHAwDIMfnBynVNW4NJXj95/oue05n9jdxkS2TEdg6ZIjz29pRtUMIh4bqqbz41MTOG0K2ZLKp/d1LNl5BIKFsBaWaxKQnPl3HBDSBw8RsizdJmB/PyTyFU6MpMiVb0hbFas1ToykiGbv7lh2fDjJn796jb95e7juJihLMpmiiqYbIvMseOhxWmVOj6Y5M5bGdRdd9OVC0w3OjmXmdH0TrG0kScIyk3SwKnM7Sv3s7CQ/PDHOi/dRtqHpBmX1RgmdLEuEPTZsFgVZkhiI5fjthWlevxpjNFnkF2cnOTOWXvT5BILFsBYyz98CviNJ0pcBFfjsKo9HsELkyiqTmTJdYde8AmxdN/je8TEz8zGZ4wsHzTKeF85OMZIsYrPIfP3JnjseazBu6tomC1UyJZUGrx2HVUaSwW5V1kwdtkCwXLzRH+fCVBYJeOtagvVN3nu+Zik5Mpjk7WtmW8un97XTHnTd4xWCtcSn93YwmCjQ23C7WpKmG3Xt8KFE4bbH50OlpvF3R0ZJFas8t6mJ7e1mLu1QTxifw8rx4RSvXImRKalMZSv8/MwEJVXn8lSOrpAbv2t+O4e6bnAtnifoshG+pfxEIJgPqx48G4aRBt672uMQrCy6bvCdo6PkyjU6Qy4+ubf9nq8xAH2mS0S/yRTlukGKYRjczSvlYE+IsqrR4ncQ8dgAUDWDsNuOBMx4rggEDy2jqSKVqnmhj66CSYpx0xf0Hr5GgjWI32Vllysw52OKLPH4ugiXJrPsWWR5XrJQrbv/DcTy9eBZliW2tfkZjBcIuWxUVI2Ix0Z32M3FqRwum4LdOv+dw99djXFqJI1VkfjK4e5Vl20UPHisevAseLA5Mpjk4mSWvV3BBZmNaIZBsWpuzRWqtXm9RpElPrmnncF4gc03Waq+f1sz5yeydIZcOKwKmZLKK5ejeOwWntnYWK9v7gi5+NKhrlnH/L3He/jNhSl2dgRw3cNoRSB40Hl6QwO/uxJHBp5cNz+N9KWkt9HNq1di+F1WmnyrL1UpWFr2d4fY3734RtQmr4PNLV6iucqc/THv3tyEy6pwajRN0G1jR3uALa1+Qh7bHR1iDcPglcsxRlNFnlgXobfBQ6Fi3nNUzaCi6iAuRcECEdGCYNFousEb/XEA3hyILyh4tioyH97ZykA0z472uV+n6QaxXIWwx1bvpm7yOW676XodVg7dZKRybCjJtRkL7q6wi3WNd96a3t7ur2c3BIKHHbfdyt4ZPXPnKiwWz49nsVsVyqrOUKLAhhUuGxHMpqbpxPPVWXPsaiLLEu/b1nLb73XdIJqrEHRb2dUZ4Mx4hkxJ5c1rCT51j13LdFHl1GgaMJM9vQ0ent7QgMum0Oh10OAVZRuChSOCZ8GiUWSJnoibwXiB3sjCBfF7Iu67Og3+7MwE12IFmnyOen3zfGj2OzgzlsFmkQm7xcQoEFyn1e+kK+xGkqBlFUyKuiNuzo1ncdrkNWGS9Kjz41MTjCSLtAWcfGb/2lWu+M3FaS5MZAm6rHxmfwdeh4VcuTava9jrsNDosxPNVur3G6/DyrObVt6eXvDwIIJnwX3x0V2tlFRtWexRp2fUM2K5CrpuIMtzd3jfytZWP61+JzaLjFuUYggEdTrDLr72eDeSxKrUefY1ePijp3tRZGlNZDofdaZz5hwbzd1dqWi1ieYqAKSKKhISXzrURa5cm1fW2KLIfH5/J+Xa8tynBI8mYvYS3BeSJC3bhPTc5ia6Iy7es6UJWZbIFFXOjWfq9Wp3I+i2zStwHojl+c7REY4PJ+/5XIHgQadYrfHK5SivXI7NkgNbKXTdYChRYCqztoO1R4Xnt1yfY5tXeyiAWXL3naMjt0kZPr0+gs0is7XVh9Om4LAqCyq3kOXlu08JHk3E1SS4jUxRpabrsyR8SlWNgVie9qCTgMu2IuPoa/DQ12CWgxiGwfeOm+ocCy3juBuvXomRLqpMpMtsbfXfselEIHgYODee5fx4FiRoCzhX3LTo2HCq3ifx2f0dtC6BmcZqzE0PC+savXftCVlJyqrGa1fNa+O1q3F6G26UAo6ny1RrOhcms+xoD9yx5KdU1UiXqjT7HEiSuVOp6waXp3P4ndYlud4EAhDB87ISzZb59YVp/E4r79/WjOUB2KaczJT43rExdMPgQzta6hPrT89MMJ4q4bIpfP3J3rqCxUphGFCZMTap1pYuY9YedJEuZmjyObBb1v7fRyC4H3LlKi9enEaS4Mn1K6+2Ubnpu6tqS6MN+bMzE4yt4twkWBpsilyvTW4Pzg5yqzPXimHc+bqp1DT+5u1h8pUauzsDPLOxETD1zI8MJpEk+OLBrgemQdAwDH5zYZrpbJlnNjbSERKa6GsJETwvEsMwmMqW8Tutd9wOOjGSJparEMtVGEkWZ62k1yqJfBVNNwVYo7lKPXi+Hriqmo5uGCjMvkFpusFbAwlUXefxPnOLbSmRZYmP725jIJZnc4vv3i+YJ+/e3MjeriA+h6WeqRAIHlbG02U8DgsSMJYqrfj5d7QFODOWIeiy0blEwcC95ibBg4EsS3x2XwfZco3gLWYnj/WGsSkyXofljkFkqaqRnynpi83USMONBZth3AjCwTTpevtakpDbtqw7MGVVI1kws+Hz7dsB8z2cn8gCpkqICJ7XFiJ4XiSv98c5NpTCZVP46uHuObf7eyJuLk/lcNsVGh8QTdONzV6ms2VUTWd3x40JZXdHgIFYnj2dwTkbfS5OZjk6ZNYNu20WDvQsXuvzTrQGnEu+7SZJEiG32OoVPBr0RNyUazoy0HcXpZvl4uRoimpNZzpb5lq8UC/Luh8+sL2Fc+MZeiLuezYh6rrBcLJI0GUVJR5rEIsizzkf2ywyj/WF53jFDQIuG09tiDCWKvHYjHTpaLLIukYPDouC32Wl7ab7xxv9cS5O5gBTeWY5SjpUTedv3xkhW1LZ1ubnPVvmr/ARcNkIe2wk8lV65nB0FKwuInheJNdXtsWZ1e5cwfPGZi9dYRcWWXogSjbA1F9+bvONL/jrV+McHUqSLan4XVZ2dwQwDKOepS1Uavzg5DhTmRLjqRKVms6Odv+C1DEEAsHK4LQqvHtzIxIS1lUoU/I7rdQ0HUWW8DqW5vZTrNbIlFRy5RrRXJlXLscIu228a2PjbXPQ6/1xjg+nsFlkvvJYF17hLLcgfnclxrnxDHs6g/cMZlcKXTf42dlJxlJFntnQSGvAyUuXogRcVi5O5pAk+PS+DoIuK788N4XTpvDEugg+p/m3V2TpjqU+um4gSSx6V7JS08mWVGB2Nnw+2CwyXzzYRbWm47SJXpy1hgieF8mT6xuwKAla/Q4injvXUD2IDWjT2TI+h5WyqnF0KMlYqki6pNKJi//n9UE6Qi4+uL2F31yY5sp0Dosi47YplFWNck3j3790leFEka881oVFkdF1g5puLHkph0AgWBibmr28cG4SWZJWxaCkUNF4+1oCt93C55eo6ffFi1GShSoDsTxdYRfjKXMhv7HZS3tw9lZ3tmwGMtWaTknVbgueJ1Ilvnd8lL5GDx/a0bok43tYMAyDkyMpDMPcQVip4DmRr3B5Kkdfo6dukJUpqbw1kCDssbGuwUP/dA7dgLevxTk1mkaWJHwOC0G3HbWmMxTPM6AbXJw0yyCafQ4e6w0Tdtv59YUpvn1khPdubZ5VEjiZKfGDE+PYFJnP7OvA71r4Qstjt/CuTY0MJwqL2o1VZEkEzmsUETwvkgavnY/sfPgm1zf747wzmMRtV/j8gU68DguVmk7QaSXktqFqOrlyjV+cneS3F6dRNQOH1dxSC7lt/PL8NDZF4s3+OJ/Y04bDqvDdY6MkC1Xes6WJra3CzU8gWC3+7ugI3z8xjgR0hlx8+bHuZT/nzdm7I4MJLIpMpaZzdixDi//+t8pDbhvJQpWA00p3yM1QvIjHbplz+/+pDQ3YLQqNXjuN3ttL6f7zqwNcmsrx5kCCLS2+B6JPZaWQJIntbX7OjWfZfgc32f5onqvTOba3+29buCyWn5yeIF1UOT2W4Y+f7kWSzPvLpakcxWqNiyEnl6ZyZMsqrX4nFyZz/3/2/js4svxK7Hy/9970PgEkvEd577q6urvasQ2btuk5NMOhmeE4mZ1d6c1Iq3jxNBtvY/RCCm1IK+1oNJrhaBx9s2mbbLLZ3pX3DlXwLpHeZ173/rhANlAwhaoCCijU7xPR0ahEmgvgmnN/v/M7Bwn49IEWuup8vHJ5kneuJarVoxRZIuyxI0kSLruMOpUz3xfLzwqer0bzVDSDimYwmCiw03Nr1649bSH2tIVu99cgrDEieL6LlTWdYkVfcu5eXyzPWKrI7rbQgjWQp4vm58s6JdVgc4OfVy9PMqkZ7GgJYpjgcSi0hj283hsDDJ7c1sCXH+ziG2/0cXIoRb6s0Rh0EXTbGU2XiOcqgHViFcGzIKye86MZylP1nc+Nplf884YSBZ4/OYLLrvDZ+9p4ZkcTvZM5Ai4bh7qXZ+TygzubGE0VifiduOwK3fU+XHYZp23uiF3AZV8073R6Kt9qsCRG/K73xNYG3relft40Bt0w+emZMXTDZDRd4muHu5blM6dTHm0zUiumr3nDySK6YZIuquxpC3FpIkuNx06houN32tjaFODsiDXa7LLLfHp/K7ppVtcg2WSJ/niesmrwgZ2za11vbfLTG83isCl0i5xj4ToieL5LlVSrLE+2pHF4Yx33dS4+JZQpqfzw5Cj98Tw/OTPGP3tiY3UKDKwOU16HjcMbIshSjIaAi4jfybVJubqavawZ/O6jPdXX+F0KvdEcj0+VBNrTHmY0VcKuSPz+Yz1IkkRjwMWGeh+xXJl97Xe2puxS6IY568IrCOvZ9uYgPzs7ARLsvAM3sr3RHKpuouoaI6ki25oD/OmzO3Da5GXLN1ZkaVYlgqD71t/3nzy+gVdbJumMeGkIrL2awFcncxwbSLKpwT/vaGaurHE1mqO9xkN4hRZCL5T/K0vW7356FuBWZUoqfZN5Omu9BD12PranmWP9SdprPdXPPtRdQ2vYzfGBJJcnrEV/E5kSz+xo5MJohoF4ntF0mbevJXhkUx1DiSJ72oO8cG6cbEnjoQ11HOyqYTBRpKvOml0oqrNL4NX6nHz5oeW5AZhPRTNI5CtE/E5RXvEuJILn6+iGldelyBJ72kJrtnzZ9AIZgOFk4YbBsyJJlFSN0VSRWp+Dly9FaQlZTQXKmsGrlydx2mW+eKiDZ/e0VF93X2cNH93dzJmRNMlChVcuT/LopggAr16OMZwsMpQo8On72tnTFmJ7cwCbLGGYVrcoRZb48K6mNft7/NnZMa5M5Ah57PzWA51ikaOwrsXzZXTDChImcze3gOlWbG8O0B/P43EodNZ6OTuS5nvHhnE7ZL52uJug244Ja6ZVt9Ou8NT2tdFtbz6vXJokXVQZTRXZ1hSYs47k+ZMjRDNlvE6F33m4+46edyVJ4ultDZwYSnHoNqotPXd8hES+QsBt52uHuzg7kuav3+zDaVf4Vx/YQlPQjcuu0FbjoSXk5i9fu0bIY8fvslGs6PzuI908d3KUimZgVyT2d9Swv8PquzDzmnmwq6Y6uizLEhvrby1F59pkjsFEARNo8LvY1ry0UqrfPTbMRKZEd8Q765or3B1E8Hydk0Opapcjp01Z8oFwp9X7nezrCDORKfFA93vNDi6NZ7k4buWkzczXm16gU9FM7DaJX12Iki6q9ER8bG6cquWsGqQLKoEZI0KyLPGFQx38j9f7yBRVjg8kOdRdg0OROTGUIpmvcGwwiYnEU9sarNQOw+Q/v3SFd/sSdNV5cdmVJddmfu3KJKOpIoc3RmaVFVop0ykl6aKKahg4ZTH6LKxfJwaTqLpVx/3EUGrFP68+4OIrM0bv3rkW5/xYBkmCfUNJeqN5NN3gY3tbli1HdjGJfIXXe2PU+53zpo1cmcjyywtRGoNOPrq7Zc2NCDaHXKSLKhG/E7syd9u0qb+tZpiYJtzJMQvTNPnhqVEKFZ1kocIX7u+4pfeZboKiTf3/2ECKkmpQUg2++e4gLruNTQ1+PrSriZFUke8eGyaaLaHIMh21Hq7F8vyTx3r4xYUJdMMkVagQ8jioD7jY3xFmPFPiwR7rmhnyOG4r7z9TUvnRqTGuTeYoazqbGwNzSuLNxzDMavWNiYxoVX83EsHzdWZ2mVut6hDHBhKcHk6zqzW0YPF2SZKqI8DTTNPk5+fG0Q2T8XSJj+5RGIgX2NoUIOi2017j5V8+s5kr0Sx/8+YAyUKFaKbEbz7QweWJLE6bTLJQodbnmNP4ZXODnyP9CbojXpw2BdM02dYU4OJ4Bpfd+j2lCtZK9mShwni6hG6YTGRKS/49xnNljvYnAWvh4qcPtN3U7+1WPLmtgeMDSXoivnlzJAVhPan3u6zFe0DDKnRaaw65qfE6cCgy5YpBsWLlXw/EC0sKnsfTJV48P07I47ilrq2v98a4Gs1xNZqjq847K3UN4PRwmpKq0x8rEM+Vb7o+f1nTuTKRo/EGVZhu1fu3N3Kgs4ag2z7vqPJHdjdzcSxDd8R3x2fRTBN00wrepxtt3Ypn97RwZSLLhqmR4Ke21XNlIovHqVTP0b3RHKZpEs2WyJZUFFlCMwzsikw0UyJVVIllK8So8NbVOB/Y2QTAgc4wNlm+qWt7NFvi2mSeTQ3+WYtQ82WNH54c5dxoGrsio8gSkgT2JfzeZVni6e0NXBzPsLs1dBO/HWGtuCeC54lMiViuzOYG/w1Ptjtagjhs1oFwfQH/YsUq3VbjdbBjgdXGy+HN3jiaYfLW1dhNdT6SJIk6n5OJTIkar4M/+9lFJrNl2mo8bKz30V7j4altDWxrCvJAdw2D8TyZksZbvXF+84EO/vrNPi6MZXnrqvXv6QC6UNHwOhV+42BbdXW8JEl86cEOzo2kSRUqBNwODnRa2xryWF87bDIP9dQuuRGCz2Uj5LGTKqh3ZBQKoCXkviMj3IKwFuxsCfHTM+MA7FqFCgB7O8K8dTVO2GvnkU0R8qqOqhtsX+IM34nBJLFchViuwkiqSEetlzPDad7pi7Oh3ldtybyQkNvG+NT50TfPoultzQFGU0Uagq5bap70i3MT9EZzOGwyXzvctezrKKbP8Qup8Tp4cMOdb7sOVkD4yX2tVhfYxvn/nmVNJ56r0BBwLTiqH/E7Z7XQ3twY4D98ZjeKLHFiKMXJwRSbG/2cG81QUQ1qvQ6SRZXOsJf2iIf7OmtoDLpw2GQqmlF9r7evxfnrN/oIeRx8fG8zZ0cydEe8vG/L4o1Lnjs+QqGic340w1dnLIK8NJFlMlumvcZDd8TL9uYgNV7Hkm+4tjYFlrVb7rT+WJ6/ePUqYa+Trx3uWvJ+nCpUGE4W6Yn4RHm8JVi3wfO1yRwjqSJddV6+f3ykOho7swHIQmbWPzUMk19djJIsVGa1tK3zOWkMrkzXQIdN5mRfgvtvIW+sJ+KhpGoc6q7huRPWz31xLENjwMW50Qz3d9US9Ng52FXLy5cmiWbLHB1IcF9XmGMDScbSRbwOG8PJIk9vq2d/Zw0/OT3GcLKI0y7zOw93V/MT7bLMhfEsFc3ggztD1QuFIkt8an8bn9p/cyPHTpvCFw91kC9rovuXIKyAly5OoE6NCv7qwjhfPNR5Rz//F+fGuTSRRZElHtmU5TM3ObvUHfFxeSKHz2WrBkVH+hNkSxonBlMc6q5dNGAtayZuu9XAW9WNOd9vr/Gwtz1MY9B5S42tKtp0yoE57+jr+dEMb/TG6Ki1BjLW6lqQW9UQcM0ZzZ9mmibfOjJEPFdhY8PN1dGe/lvsaw+ztdHPN48M8uL5KH6Xjeawm1q/k9FUkbDPGtgKuh381oOd5MsaTpvMP7wzyHePDVHRDFIFlZ+eGcc04fyYNfJbu8gNiTz1N7o+2G8Le3DaZRw2B49vqV+WsovL4R+PDHJ1Mo8Uy/NAdw2HN743Q90Xy/POtTjdEd+sutO6Yf1tChWd8+HMTR2XF8YyJPMV9nWE76lF9+syeM6VNX50agzDNOmbzGNMTSVNV424GcPJIr84N85EpkTE56S1xoMiS2RLKiOpApsbA/OOYNyOiqazudGPapjkyhqqZmBTJH54ahTDhI/sapo3uDw/muY/vngFExhJFXl8c4R3+hI8tjlCSTVoCbnJlTUGEnk21vvZ3hxg7PwEBVXn5UuTRHxOYtkykgSXxjP0xfI0nx6jxuvAaVMwpvLopk3mypSnViiPJIvL0nTBrsgicBaEFTKUyM/4unjHP3/m+cM0rRxkzTDmrbk8n+murdPT5NOPTa+vcN5gOr5vMseFsQweh0JR1Qld9/3nT47w+pUYPpeNf/3BrTd9LnpqewOnh9K0hN3zlgM9NpgkV9Y4N5rhgZ7au67DYSJfoS+WY0PEf9NNQ1TdJJG31pjcbLe9aS+en+D0UIor0RymaXX4bQm7eflilIpmcMHMcGLQqkbic9rwOW386sIEL1+KksxXqOgGrTVu9rWH+O+v9aFqBv/+5xf52sPdbKifff0aiOdJF1U+treZgbjV0l3TjWogH/E7+Z2HuzHNO5Pi+WZvjAvjWQ50hNk9Y9aoL2Zt5/bmAHZFpi3k4fxoBrsis6Fh9qzva1cmiecqjKVL7GgJVGeXDdOs3kzeTJw0kSnxwllrJitf0W+q/fjdbl0Gz4okYVMkKppJrc/JoZ5aopky+zpCN/1eNT4H0WyJsmbgtCt8aFcTPodSXc3bG83x2fvaMQyTn5wZYzxd4vEt9XTXeW+qrWc0WyKRr7Cx3k97rZdrk3mCbjvfeKMPzTDpqPVweTzLUKJIoaLxu4/0zHp9Ml/hBydHGE4WCbjtFCs6qmGyvTlIrc/Jx/Y0c3IoxZ+/cpUar4PRVImdrSH64wV6ozkmsiVU3eD+7tqpEfYC45kyimxNE25vDtJR65l1kuiq87Kl0U++oq/JMnRLkchXODuSpqvOO6vclSCsR7mpagNAtW3wnfTsnmakqZJm9X4nf/vWAIZp8qFdTUu++b5+dGu67NhSKnaEPA7awh48TmVWID/tajRHPF8hWahUF5rdjESuwkAij4lJV93c2sBbGv28PjXV73XcfZff7x0bJlfWODuS4bce7Lyp1zpsMk9ta6A3mmNv261dL65Es+TKGhXN4MGeGlTd5NxoGsO0BnbcdoXNM1JGDMMkWajQO5lDNUy2NgV4oLuWXFnH77KRK2kcG0hR5x/lo7ub2Ti1D05kSjx3YgTTtCpORbMlXrtSoDXsnrUWZ3qf03SD3skctd7ZKSe3aiRV5Gdnxgh5HHx0dzOyBO/0JQB4py9eDZ6jmRLPn7S2M11UeXRThE/f18r+zjDNQfecG5y2sIdY1srld81Y42NXZJ7d00JfLH9TKal2RUaWJAzz3usgfPcdvUvgdlgF+cfTJTY1+HHY5FseFfU5bXx4VzOvXp7EME3euRbnia31gMnliSznRjNsqPfTVuOmN5oD4FcXJnhBN6qNAW40upAuqnzr3SE0w2SsrcRHdjVb5YjSBb7xxgCaYdIedjORKZEqVrgWzfGjUyO01XgoVgw66zyUVQO33cau1iAFVefTB1p582oCVTe4Gs3x/RPDPH9ilIlMiT1tYTY2+DBNq2mAXZFxKDKf2NvK7rYQiixxbTLHG70xVN3k4Y111ZPKTHZFri7EuFv99MwYk9kyp4ZS/O6jPffcCUC4t8yKF+eLHldYyOPgS1PVDc6OpKuzgvFcBaYGrUZTVurYQiObpmnOGpQwDGtEM+xx3PD4PdRdS6GiEfHPn17w0MY6iqpOjdd5S3We37oWJ5opE82U2dUSmvMz3NdZw9620C2lhCyHS2MZXroU5aENdey6hYVq5tQeZN7ivrO9OXhbjbIOdNTw/7zcS63XQdDjYEujnxNDKWyyRFejj3/70e1smcojTuYrfOfYEJfGszhkCZ/XTnPAxVi6RFnV2d8R5vK4VaZOlqRqFZpUoUK6qFoj2WMZJjIlAm4bbrttwcoYr1ye5PRwGpss8VsPdc6qWHUrzgynyJY0siWtmn7aU+/jnWtxBhM63zoyyCf2tWLy3mE8/Tdx2hapbiVBSTMIe+xzFpS21Xhoq/FwcSzNf/n1FXoiPj53sH3RAcAar4PP3NdKMq9Wq3bdK9Zl8AxWTvJyrXbOlTX8LhtDySINARfnRrN8cEcTfZMF6vwOzo2m2dUapDHoIpop47BJFCpWY4DRVInNjYsfSKpuoE3lxxVVnf54nnf6ElyNZhlMFFBkiVcux1B1g3TBKrUUcNn49tFhdjQHOD5o5+sPd3GwK8xQokB7rYdTQ2k+sa+Ff3hnkGJZ4z++eIV8WcNtV5AleN+WeuyyhMep8OS2eq5G8/TF8nRHvIQ8DrojProjPtIFdV132poexXLYZNZYVSpBWHbFqe6CALmyvsgzl8/0DF1DwDkrt3RLo5/JXBlNN9nbHgLg+GCSVy5NYpsqkXn9Yqdrkzl+emaMoMfBp/e34rIr/PzcOBfGMuTKGvs7wjy8KbJg8NJe61m08cXjm+vZWO8n7HUsedFUPFfG57LhtCl01HgYT5eo8zvnPW8WKhoXxjK0hDwrsmbm8kSW4wNJtjQF5m2i8h9evEy6qPL2tQTf+Mp9N51z/Yl9rVybzN9yTeTbdaAjzIM9deTKGjZZ4vRQimimhMdhw+tUSBZUJtJF+uIFTg+nuDyRozeaI11SsasK49kSiXGViUyJ+zpr+Hef3EVf3LrGbm3yc3Uyx49OjYIJw4kCp4ZSeBw2HtkUYVNDYMGFrdPHlWaY1Xbft2Njg58rEzn8LhuNUzd5H93dTEnVGUkWGU1ZRRCagm4+uqeZVEFlV+uNb0oujmVx25WptBdz3r//P747zMXxDGdHMhzsqpmTznK9pqB7zeR730nrNni+GUf7E1ydzHGwq3beqTbTtKYZJzIlkoUKAbeNzjqvFXRO5tndGsKuyHzuYDu6YRLPlfnJmTG8DhsdtYunAqSLKmGPg2d2NDKZLbO/I8w33uzn3GiaiXQJmyJjmia6YXB5Iksib021vnw5hmGaKJLE/d01yLLMgz11nBvNkC/ryLJEU9DNpgY/JwaSKJKEx27D51bY2x7mr9/oZyRVZFO9j73tVu1LgG+80Y/boXB/dy25ksaR/gR1PgefO9i+aqMlK+nDu5rojeZoDbvX5c8nCDMFXAqTeetCH/LcmdP/Ly9MWKN/NpmvPtRVDUptilztTjotMVV3XTNMMkV1TvB8cTyLqpvEsmXG0yU667xMTDW/OD+Vy6zIMs/suLVGJxfHs7x4foLGgItP7Gu54TnhtSuTHO1PEnTb+eKhDh7cUMeO1iAeuzLva392ZpzBRGHFqnG8cmmSXFljPFNiZ0twziK36Zxwp01esMV2PF+m1jt/17vlHJS6FbIssaXJz0sXogRcNgaSBVJFlYpqIEnw/WND9CcKlFQdE+sGrb3Gg4TVAMdpU4j4ZUxMiqrOxNQ1d1o0U8Y04eJ4hiN9CdIllUJFxzANPryracESfI9trifgslN/3Q3ireqJ+PjDxzfMGR3e1x4mnrM6OMayZbxO25KrWWVKKsWKxnCqOJU+Nf+NU0vYxcXxDD6X7a5MLbpT7snfjDY10uuyKxQrerUpyquXJ+ms9aAZZjWXqaIZ7O8Iky2rbG0KcHkixzvXEmyq9/P0tkaODSYpqTq6YVbTHSYyZT57X1s1GX8yW+aVy5PU+hw8timCJEmkCyo/OTPKaKpEY9DF5w62V6daaqfKKJW9Djx2hSsTWfpjecqaFRQ7FAlMk/s7a9BNqy6mIkvEc+VqHtNHdlsrmZ/a1kBzyE17nYfBeIGHNtTy64sxJjJFMiWNGo+Dim7gtMsUylZx+zqfk2MDyWr95liuQkHVCazD4NJlV1a07KAgrCUzG2vcqRSl0vSonG6iGQZgnaMMw+Tdfiu17P6uWhw2mUM9tWiGScA9/8DD5gY/b1+LU+930TxVYvLxLfW8fClKXyzP5YkcexZZfzGeLvLtI0N01nn56Dxd3c6PZtANk5FUkUS+csOyY6Mpa9FluqhSqGg4bI5Fp+ynQ69bTXu4kZawm0vjWZqD7nmD3//9Q9t48+okB7vmNogB+MGJEQYTBdprPHxyf+uKbOPtujiWJeC2M54pYVdkGoMubJKM32VjMm+lXOTLGiG3ncF4gaagi7DXQUPAydcf6eG5EyNcHM/gd9nwzJhdmMyW6YvlUHUDr0Mh5LW67wZcdh7qqeOv3uhD1Uw+vLtpTsDqc1qj0yOpYnWh4cWxLHvaQ7ecMjozcJ7IlJCADfU+NtT7+Id3Bnnh3Dght53tLQHOj2a5r7Nm1kLC650YTCFJEm1hz6ILdL/yUBc7W4LU+500hdzVG4a11jBota3L4Lk/lmc4WWRna5Cge/aJLFfW+Md3BilUdD60q5HuOh91fqvKRHPIxbeODDGeKfHopgh728N899gQfbE87TUe6gMu8mWNoUSFkVSBkZTE61OBd1HV8ThsvH5lktF0iW8fG+Lje1rY1Rrk798ZoFDRcScUNtb7iPid/MO7g7zbF8dtV1BkiaKqV6t21PrsjKZKBF0K0WyJaLaMZpj4nAoRlx3DBKdN4vhQit96oLN6ETnSnyA+tZo5W9QIexy47Ar7O8Ls7wgzkiryt2/2E8+VSRQqNExdgHY0B9nZEqSiG7x8aZKr0Rxbm/y0hNy80Runo9Zz2zlcgiCsvnTpvVSNVEFb5JnL58ltDZwcTNESds9a/3F+NM1/e+VqtRveI5siJHIVrk7mCLjt7Gu3Sl+NpIoc7U/QXecjli8T9jhQdYNkwaoX3FHr5fHNDYylS1Q0Y9Fp87987RpnRjK8cTXO5sbAnDzNXa1BJnNlGgOuJY0gHt4Y4c3eGK1hDyGPgwtjGV69PEl7jYdndjTOGd17Zkcj50cztIbdK1LW6wM7GjnUXTvnujetMejiE/sWLkM2PQM5vka73uXKGiVVZzxd5JFN9RzeUMfzJ0coqhq1PievXI7RXitRVg1GU0VMNF69HKOjzloo93dvDzCZLVHvd1HndRHLlemL5XnuxAiY1s1HRTP45IFWWsIeLo1neN+WejY0+OmdtCrVDCUK8472vtEb45fnJ/C7bMRzFer8TlLFypzgeSRV5PkTI6RLKoe6a3l8c72VWlK0Ui9M05oNd9pl9rWHuRbL88OTo0iSNVDWGHDxyuUoqYJKQ8DFqaEUk7kKlyeyOBSZtlrPvBXAWkIuTgxaa5XqF1nUaFdkHpjqwBjLlfnO0WFMTD61r/WmmwYtRjdMjg8msckSe9pCd13ZxnUXPBcqGj88NYpumIyli3O61I0kC1ydzOG0yVybzLOh3s9v3NdGtqRhmiZ/82Y/lydyvH0tTk/Ex+lhK+cpV9b4+J4Wfnl+Aq/TxpH+JA9P1U9UdYNfX4ritimMpktcm7TuXr+t6rx2xU2qoBLLlTm8sY6wx4Gmm1Q0g7YaD7myxv1dNdWd/dJ4lm+8McBoqsi5QgXdMClMjdx4HQqqZl008hUdCfjbt/ppr/Xw0IY6mkNuLoxlcTsUwt65Nw3fOzZM72QOmyzxUE+EP3y8B+eME7jLrvDR3c3VUXSAjtq5aSyCINyd8pX3RjxTpTuT8xxwWQ1Rrnd80MpJBash1CObIlwYz1DRDGLZMsPJIhvqfbx0MUosawU5HTUeLk9kpgLP936WsNdOyOOgWNGrgwnz8busNBCbIuObJyd5Y4N/3sXRC2kJza6+cGIwRaGic3E8ywM9tXOqdfictln1dZebJEm31Nxl2r72MK9cis7bunwteP1KDM0wqQ+42NtuLcj80lTVj//wi0tcGssAJr/9cDffPTbMYKIAklVZRtMN3uiN0Rxy43PaqA842NTg50++d4ZYrkyhojGRLWGTZXa2BmgKuhhJFnm3L8nT2xqJ+J1cGs/imbpmvn01zpH+BB11XrrrvPzk9BhXJ3NsbvQTmLp5UWSJ7xwdYlODvzoq/ONTo7zRG6NQ0dE0k031PivPGqtroV2Rq5U1/C472akKOaZpLWZUpkaPXfYyHTUehqZ6T2SKGv/v588ymSvzzI5G/pcnN/Hm1RiXxnMc7Kphf0eYrx52YZflJefzD8QLJKeqz5wZSfPEMgbPJwaT1cFHt0NhywKNddaqdRc8y5KEIkvoxvylU0ZSRVIFlYqu0xq2TrJ2RabG68A0TVrDHk4MpdB0g4F4HkWSCHvsbKz343XZ2FDv4+RQijevxvA5bRzeWIvTpvDSxSimCfs6QlQ0nWS+wmCySLasYZNl7u+u5UsPdOKyK+TLGs0hJx21Hh7dFCHksaPqVmvRly5G8TltjKWLaIbJ9CCKBFR0A0mSMHivAH9B1RhPW6MEu1pDtNd4cNmVOaMa09OEmxr8uO0Kn7u/fVbgPJOYnhEEYaX1TM3CmabJpgbrwrmtKUB/LE/Aba+enyM+B7FsmaDbTjxfYShRxOtUyJY0pl6G32Xnyw92UlL1RcvL/f5j3Wxt8tNR66VlmbqYpgsqHqeCXZHZ2uQnmi3RGvasSA3nvlieYwNJNtb7Fp2iv1VnR9I47QpnR9I80LP2AuiAywpZHIo8K+WirOmcGEwymSvjdSi83hunp95Lulhha6OfsmbSO5mnqOo4FIlHN9WzszVo1UKu9xHPl+mJ+Kj1O3ApCom8Wq10ocgSZ0bTDMTzXJ7I8uevFjgzkuYf3x2kpOoc6KzBubOJOp+DRN5OrdfB7z7aQ76s8dwJq3zsSKrItuYAuZIVHCNBslAhmiuRnlEyUpKY9XO57QpddV4yJRUJSBTKfP/4CJpusKctyDM7mihWdK5N5rgWy/Odo8OousH5UatPw6mhNGAFqvs7wjc9g7ypwcdfvmblkF+ZyC2pydxSzYzPHHdhSuiyBM+SJP2nxb5vmuY/W47PWYrp8nDj6RIb5lkRLEtSdaoufN0duiRJfGJfC0hwtD+JXYYan5PuiJdHNlq5yvvbw1yL5TgxmOJ/vtXP4Q0R/vgDWzg/muHSRJYP7GxkS2OAI31xBhNFwh4HBVXjC/e347Ir6IbJn/7oPP3xPH6nldd3/myGyWyZfe0hYrkydptMwG0nmVeRpkZXZMkKav0uGxVNx6EYIEk0Bt08NKMd60IXDr/Lzsf3tjCeKbGjOSjabwqCsKoe2lCHbSpl7dGpkWmHTZ66+Zernd2e3tbIztYQtV4Hf/naNbxOm5XqVpk9cj7foMH1HDaFp7ff2mLC+bzZG+OdvgQ1Xgefv7+dve1hdreG5iz0Wi6/vhglXVQZThbY2hS46bz1oUSBc6NptjQG6Jxncfz0wIlNWZsDKA/01NIUchNw2arXuvhUaoHPaaPG66Ap6GZbc4BCWcVuUzg1kqEt7CFTrKBNNVY5N5rhh6dH2dMW5tndTXzqQCtNARdnRtL86kKUWp+Dwz11jKWK+Fw2ajzOaik7VTP5xfkJ0kUVE6t2+sHOMOdG0iiyjG6YDMQLbG60Uh8vjmeJ+J1cGM3wq4tRFEnikY0R0sUKdkUh6HHw7J5mMiWNHc0B8hUdmyzhsFmDenZFZkPEx68uTvDyxUmKqo7XqfDJ/a3VKhfdER+T2RKnhlL0xwvs6wjTVedlc6OfyxPZWy4P6HPa2NMWolDRcdqXN8Dd2RLEPbWwdr5CDWvdco08/x5wFvg2MIo1ULpqFlsR/NCGOvwuG0G3fd7yKjZF5jMH2nhySz0vnJvAaZP5wI6m6km5u95HY9BFWbXqOE83NxlLlwi47JweTvOZA20c6q5lPF3izEiKrjov3zk6hG7AMzsaGE4WGEsVuFzW+Q8/v0SmrFLndfBGb4xUvozbodActHKvNMNANwAJumq9lHWDGp8TwwSXTUZG4uJ4hnq/84Yn7Ok6joIg3Jsk3kt2WI2xnmJF561rMTwOG/d31dASdqPp75XMOjmUIpGvkMhXGEwU2FDv41oszxtXJ9lY7+fZPS2oukmdzzGrGcZqGUoWAKvOdK6kEfY6VixwBmgKukgXVSJ+56zFn0v1s7Nj5Ms6Vyfz/MFjPXPyTD+5v5W+WH7NBjOSJM3ZtpcuRolmrRHcRzZFeHZ3M1uaApwcStEXLzCeLtEXy1GoGNhka5Q6li8TzZR4/cok6UKFj+5poS3sqc5cX43m6az18kdPbaKiG4Q8Dva0BzkznCKWK1PnsdMcchN02/jfnt7Em1cTRLNlxtJF7Iq1eN8wfLTVeGgMuNjRGqze+PTH8jy8qY5D3XXUeB20hNyYpsnF8SwjKasMXbas0TuYZSJT4o+e3MTrvTHiuQpFVUfVDUJu95zBv4jfxX/5wv5Zj31wZxMfmCf3/mZ+31Z5wtyydBC+/r1vJkVqrVmu4LkJ+DTwWUADvgV81zTN1DK9/7KxKzL7O2bnnP3o1CgvX4qyqyVIWTeQJYnGoKtaEL13IkdTyMVrV2JousFXHugk5HYwEC/w2OYIPqeNkMdOqqBWpxoBsiWV86NZfnhylLF0CadNxm6TqPM76Y3mCLhsXJnMYZMkotkKuVKFkmrgsMl8en8r79/eyKnhFGdH0sTzFaLZEhG/i42tPi6MZ4lnK1yL5fj+sWGagu5Zi18S+QoVzViRWqKCINz9livEm5iqerCUXNt3+uLVqWRVNzjanwSsqkA7WoL0RHycGkoRcNtpmjp3/f07A5wfzRBwx/m/PrubP3pq0zJt+e0Lexy8eD5KV52nmue6kt6/vZH9nWHCHsctBURBt518WSfgslVfX1L16uBQ0G2ftz70WhZ02/G77BzqruU3DrZXm99srPfRU+fj8ngOw5RQZECScNltpAoVAi472bKG16UwnCwAtbNmLvJljcGENYL8naNDHOlLVBdSpssyH9rVxO883I3LrnA5msOuSORK1oLGoWSRy2/1871jw9gUmd9/rIeDXTW8fDmK32XjaH+SQkXnA9sb+bc/Osfl8SzttVYVjEc21pHIV6wU06LKf325l0PdtQwlCgTddhoCTj68a27Fj3RBJV/R5uT8X7+fFCoavzg3QdBj59GNkRve7EX8TpKFCrpx+/Wr15NlCZ5N04wDfw78uSRJrcBvAOclSfpj0zT/djk+43bohskLZ8eJ58u8b0s9rTNy3QoVjR+dGiVdVLkazXN/dw0uu0JTCGxTO9Urlye5MJ7h2mQOhyIzmSvze4/2cGooxfHBFKeHU2xu8PPs7mZqfE7G0laZo1ND6amVsCUciowJ2CSrRNOOFj9DiSJlzUA3DTQDDAN0wyrp1BvN8UdPbWZ0KvfZ41AIeqwR9XRR49P7W/nHdwYZTZd4pz/BM1MLFQDG0yW+dWQIwzR5envDnCkb3TD56ZkxYrkyT25tEKPRN0nTDUZSRSJ+Z7UcoSDcDWYWSFuO5YLnRzP8/Nw4siTx2fvabnizPl0FQmK6sYQJSBSmUjDKmo4iSxiGWW0cNT1dbhgma+36nchXqo0zMkV1zmjgcpNladEyYzfysb0tjCSL1QDr5+fGOT+aYUuj/67tFvvE1gY2Nvip8zmqeeanh1P88vwExwdTPNhdQ+9knlqfg3xZp63Gzc/PT6BpBk0hFz11fh7aUEe6oGJi8oEdjSQLFd7ui6NqJlejOXJlDVU3ME2w22Q6az08sjHy3ox0nY9fnY+Sr1h50meHU4ylC1MzE/DDU6Pc31XLM9sbee7EKJlSmZKq859fusw7fUlkCUqaTr3fRY3Pye892sP/8ePzaIaBYcKhnlr8bnt1ZDxV1ChWdH56ZozeaJbmkNWBWJFlHtlUN2eAEKyyu2PpIs+dGOGtq3F8LhtNQdcNF+r9t1eu8tLFKLVeB//Hx3YsWIVG0w1+dnacVKHCk9saZs3sT2RKTGRKbG7047Stj5TRZb3yS5K0D/gc8BTwM+DYEl/3JeC3sAqAfsE0zZHl3K6xdJHLE1nAWuE9M3h22xXaajykR9J0RTzU+hw4FJnDPRGe3NrAcKLAf3v1GudG08RyZYJuB+PpEkOJAi9fnuTMsNViVjfg/u5aUoUK3zk6jG6YZIoVDNNa5BfxO6n3O3nh3ATZkkZT0EWdz8l4pkShYiBLJnZFwWGTcCgyZ0cyfPWv38UwrTs/v9PG9uYAr16JkSxYo9SyJCFJ0BpyVS80AKlipdr2NplXud5YulhtJX5iKCWC55v0s7Pj1syB21qkJBZYCveqxFRpTMM0SRYqNwye97aHqfM5eflSlGP9CS5P5GgKumgNWa8bSRaRJYmyZkydb+18/mA7v74UZXOjH8O0ZgpDHjuHN9StenmrPe0h0pcmaQ17FiwPt5Yc7U9ybCDJzpYgj2+pr14HrkRzfGCVt+1WKfLsVA7TNOmP5bkSzZEuVrgUNWgOufE7bXxsbwvvXIvjUiR8XhfNQQ+/cbANVTf5qzf6KFZ0Ytky12I5rkRzRHx2GgJu2ms8xPMVtjT6+fCuJuy22QsWTUyaQi5CMTu5ks6LFyaozCiZWKho/Oi0NUhX73PgUCQSuQonh9IkCyqKBO/f3sRT2xqqP8u/+dBWTg6l6Kzz8OrlGG67zIZ6L9mSxp62EJcnspwfzXB2NM21yTyKbKVBxKcaDV3v+ZPW4sVjA9Zsz3TH4evFcmV+dnYct12ZaiJmxU7xqRraCwXPI6n34oqTgymadlrBc66s8e0jQ2iGyXCyyAfv0pu06y3XgsE/BT4EXAC+Cfwr0zSXVERUkqQW4FHTNJ9Yjm2ZT53PSchjJ11U6YnMzpeSJIn//YNb6E8UaPC7UGSJKxM5NMPAabPTUeslX7ZqMNtkmeaQi6Kq82cvXKSk6gRc9mp6hM9pTcfEc2VCHmvhgmaY6EYB3TDpjxeYzJbRTJOJTIl8WaNYMawOgiY4bVYtRt2AXEnlWiyP12nDBPa0hSiqBumiSiJf4Zxq0FPvY09biKagmwdnrIzeVO8n2mHd2c7snjTz91HjdZAqqGxYYnci4T2pwlQt7ZKKqhso8vq4kxaEm3WgM0yupOJyKEvOiWwNu6cuxBoVzSDid9EXL9Ac9nBfZw3ZkkbAbadzqkzmtuYA26ZGd1+YunEF6Kz1zrnx13SDC2NZwl77rEGSlbKlMXBXldg6NZxCN0xOD6d5bHOEQ901nBxKs3MdNYp64ew450YzxPNluut8lDWNgVgBn8vG1myZ//Xpzfhddi6OZ9jU4OeFs+Psbgvx1tU4umFS0XQqmoFhmMRzFTQDbLJMsaJjmiZnRtIE3A56o3m+eMhB2GOljTzYU8dgIk/veG6qvJyBTVaI+J101nqxyRKqbuBy2AiYJkcHkiQLKl6nQr3fyWcPts3al6bLJr50cYK3r8UZShR4Zkcjv/lAJ2DdNPjcCrmSRr6isbctzNYm/4JVUlIFayBtQ8RLxO9kQ71v3lK0Z0bSxLJlwKru8sl9bTx3coTNDX66F4kXIn4nAbedXEmja0acZZgm02N7qr7Gpo5uw3KNPP8boA/YPfXf/zk1IiABpmmauxZ57fsBRZKkXwHngf/FNM0lzyieHUmTLWns7wgvuPLYZVf4rQc6qejGvKuxZVmmu87aKZ47MUx/rIDTLvPbh7tx2GSe3dPMK5cnqWg6dkXhwngGTGgNu3hkU4T3b29EwhrVfuuadQDaZDg7msZhU9jdFmQkVaJvOMW1yTySBDVex1QQa6cwtbrWpsgUVau7kcOmYJjWVKBNkeiN5tjSGKAp5CZX1nDZFWRJYk9bmMc2R2YtkJRlad66qjN/H196oANVn7+cn7C4p7Y1cnwwSXfEuyKNDgThbvHW1Tj//heXcNhl2sIeeuapcHQ9SZJ4eGOE44PJqXxVGxsbrNeFvQ4+sLMRmyzPO6MT8Tu5MGZV5Zgvx/i1KzFODqWQJYkvHmpfllbJ68me1hDHB5Nsbwla1aM6auad4r+b9cXzuOwKO1tC7GkLcWEsg2FaqRcb633YFZk/eHwDb/TG+M+/usJrV2Ls7wjTUeshXVTZ3hTk3Fga1TBxyBK5ipWykauoDKdUcmWdg101NARc2BWJH58eoy+Wp8br4L7OGkaTRfTphf42A6ddojHg4uP7WuiPFRhLFzkxmCBdVHEoEj0RL196qJtcSePPX7nK5kb/nLb1b/TGqGg6Z0bcPLIpQqGiMZmt8KVDnRzrT1pl9ZIFntq2fcGZ0Gd2NPJOXxybLBHxu3ho4/wxQnedl7PDaZx2meaQm61NAQ4toWyhx2Hjyw92Es+XuTyeY9BeoH2qwdpH9zQzli6yuzV0s3/ONWu5gueu23htA+AwTfMJSZL+HfAs8P2lvHAwXuDF8xMAaIZRbVpyvb5YnlcvT5IpquxsDfLYdTvmTKpm3SLpumlNReYr9MXyXBnPUut34LSZNPidJAsqHbU+djYHOT+W4dVLk1aNSaeNWp+T3mieC2NZXHaZB3u62Nse4pVLExgApnUHtqHey3jaxnimRNBtp1DRGE2VKFR0Njb46KjxcmIwSb6iky1pPLSxjo/vbeGv3+jjWizPxnov93WG2btIO9qFSJKEwybSDW5FY9C1bqaeBOF2/Pj0KEVVp6jqvHBujD+s33jD1ySnpr/nmxW7OJbhH98dJOix8zsPd8+plby/I0xbjRuPwzZvF7Xp9DXDNNFXqAX23ezBDXU8OKO06Xr08IYIJ4eSPLo5wr72MNubA/z0zBhu++zZkbKmV9cA+J02GoMuVN3k2T3NfEpqpTea4ztHh+iP57kay5HMV3DYrOY8zUE3n9jfQsjjqHb1TRVU2sJuDnTVYGK1bM+WNCJeq0JKPFdhc6MfRYbvHM0Tz1fwOBS2NAX54I5G/vtr1xhJFolly+xo9lOoGDQFXfz6YpTJbAlVN7k0nuWnZ0b5q9f78ToUPn9/BxG/C1UvEvTYMUwT5bqlwEOJAi+cHSfottMSdjOUsOpOXxrPzHvj1FHr5fce66nmV98MRZb49cUoo6kSxweTfP2RbsqqwbGBJB6HcscG6yYyJfpiebY2Bgh6ViadarkWDA7M97gkSTJWDvS835+SBl6Z+vol4MB17/F14OsA7e3ts15ot1k5v6a5cJHt0WSRf//zi/TFCnTWetAMk0cWWWH6/h2NnB1JV5uNvNEb4/hgkpF0kdxUULuzNcQTW+tpCrh5tz/Bz8+OMZ4ps6s1SEvYanf9jTf6yBRV4nmD7x8fwibLZIrvZbIEXDZcdhtlNU+yUCFbqhD2OLEpEuWKRqGs0xPxMZoqki9r+F12ciWVJ7c28K8/tI2/e9v6lY5nyov8aoUb0XSD4WSR+oBY/CcIN+vpbQ28dDGKwybzzLYb10+eXmDosMl87mD7nAodv7owwcXxLBLw8MYI93XOvbgvtmDu4Y11BFw2an2O21pYt1LyZY0LYxlawx5RCWmF7GwNsrP1vTSUhoDLmsUtabx4foKP7W0B4MHuOk4PpSlUNB7dVMfW5iCDiQIvnZ/g1EiawNSsiMehYOgQctmoGBBwO3A7FBK5CudGMiTzZQJuBz0RLx6HQqqoUr/LydXJPBXNwOeycXIoSbas8fS2Roqqla4UcNkIe534XXYyRZVEvsy7fXH8bhv/5B/SOGwy25oDvNuXIFfWcdokFOAnp8dIFSqMpw2+8WYfv/9oDz6XnW3NAasBy3XOjqTJlTVyZY3OOm81gF1srdP175Mra7xyaRKvU1k0foL3mp/YFAlZkjg+lGQoYS2c7In4ZlUFWwm6YfK948OUVYPeaI4vHupYkc9ZrpznAPCHQAvwQ+BF4J8A/xtwCvj7RV7+JvA7U1/vwUr/qDJN8y+AvwA4cODArKGEpqCbT+5rJV/RSOYr/O3bA9zXGZ6VN3RmJEU8Zy2gy5U1tjUHqn/4syNpopkSQ8kidkXmQzubCHrss5qONARcxHIVQKI17OZfvn8zTQEXTN2VJfMVIgEX6ZJGa9jNR3e3YFckhlMFKrqB1yGTLWn0xwrVvB9FAkWRuRLNcmUyT1E1kAFNK+F02DAMk/F0ib97ewBZgqJq0BNReOtqgsc211Pnc7CnLcRousj9K9jq9V7wkzNjXJu0Opp95cHOFa3RKgjrzZGBJG67giTB0aEkPTfIe74wnuHkUBK7IvPopsic4Lkl7MHntOG0y9TeQuUKl13h/jXaWhqsfNzBRAG7IvHbU2XOhJUlSVaTMR1mnd/fvGa1yD4zkkY3ofFqnJ+cHmMkVUQ1rLbZTSE3umES8tqqAXIsV+HNqzGi2RKjU3WZnTaZ8bQfv8vOR3Y30xPx8ssLUd69Fuf4YJKJTImSarCpwcdQokjvZA6nXeELB9toq/HwV2/08+rlGGVNp5zRp1KSbGQKFXxOG0GXDZdDAVnCJVsjuJIkEfY4GMuU+cP9bXN+bk03sCkymxv9XJ3MEXTbaa9xc27UGpt22hRM0yRT1PC5bHNGmVXd4KdnxsiUNHwOhf64FQC3ht1sqPfz1tU4Z0fS7G4LzWo5/4EdTfRGrYXADptMW9jN6aE0dptEvX/hNKqKZvDCuXGKFesm43Yq10w3WJJXcEHxcg21/S2QBN4Cfhv411j5zh8zTfPkYi80TfOkJElFSZJeBmLAf7yZD26r8VDRDP7LmV4A3uiNzwqeO+t8bG0OUFJ1fv/RHtqnEuSvTeZ4/uQI71xLoOoGB7tq6Kj1zAqcAfwuGwc6wiQLFXY0B/G77PzVG/2UNZ1n97Tw+JZ6fP0JPrijiRqfg795q598WUWaqhUtAY0BF1cmckiSFTiH3HYyBZVkvoymGygSGCbYbDIBt3WnKwGTuTI+pw1JslIFWkJuFFlCkiQe37Jw6omwdNOtUXMlDdUwcIrFf4KwdKbJ9PVJWkKWhEORcNkVHMr8Oc3v395I0GMn4LIvujhJuDXxXJlLE1k2RHzUB+6NkW+Pw8an9rcxmi6yrWn24s7pUnDj6SJXo1kGEgV03cSmWB3+Qm472aJGZ53PakomWY9LWCOc041RarwOxtIl/C47ZU0nV9Y4O5JmMlemqOr4XfZqqtFrV2J4HTZU3eDHp8cwkXDaZFTdRNNNnHaFHS0hbIrE7z/Ww2iqSLJQodbr5OVLk4A1w3Itlifgss+JWQBeODvGhbEsu9uCvG9LA3/4+AYkSeLdvkR14WBvNMtktsKFsQwtYTefOTA7AB9MFLg2mQegNJUiZVesgB3gSH8C3TA50p+YFTy77Ao7ZixA3VDv52sPu7HJ0qI3i9diOa5OV+sYTs3J+14qRZb41P5WBuIFNjWs3DlkuYLnbtM0dwJIkvSXwBjQbppmaSkvNk3zX9zOh9sViZawm5Fkkc7a2VMRmxut+ssXxjPkyvqM18jkyho2RcI0JfIVnY7audMYdT4nLWE3PqeNBzfUMZwskCtrJPIV/u+XrvDQxjo+vLMZWZZ47cokFc1AQmJDxE+mpPLxPc188+gwLWE3qYLKpkYfk9kKo8kiJU23uuxE3MiyjCJJ7G4PMZYqE82WcDsUUgWVHc1BvvJQFy0h95wcQOH2PLWtgZODKXrqfeum/qQgLGRmh8Hl2Nv/xdNb8DhsBFx2Pn1g7ujX9Xa1huiPFXA7lHmnja0BhpvPtbxbPLOjkfNjGVrD7lUZdX7+pFUu7fRwmt97tOeOf/5qaQy65qTJHN4QIeh2cLCrhr7JPIlChXRJQ9N1tjeHeHZvM1ejeew22QqEs2XcdoWHNtTyiX2tmKbVMVNGoink4v6uWnrqvWydGrxrDbvpj1md+VpCLvZ11PB//fIysVwZMNnbHrYGbXSDWq+d3a1BhpJFZAlawm6+drjL6jkRdGMYJmCtw4rnKpwdTRPxu9jREpzT1MacquQxnChM9bZoqJZ07KrzcnwwiYRVreb4QAqA0VSRimbMykluCLjwu2zkyzqPb4kQcNtx2RUCUzHI5kY/50czbF5ClZ351ifM+RsFXLjsChXNoP02y+cu1mV6uSxX8FwtJmyapi5J0vBSA+dbNZQokC6qbG0KWHcaU+kb8wWXxwZTZIoq0cwEmxp82BSZthoPX36wk5cuRgl5HDy9rWHWyuyKZvDto0NcGs9Q43Xw2OZ6NtT7KFZ0WkIZrkSzxLIVhpJDdNd5yZV0vntsGMOE9rCb1rCLnS2NbG7y85ev9zGeLuOyK6iqCaaJiXWHZJdlvvRgN5/c30qupHFsIMm50QypghvDhIDbxr62MBvqfSJwXgFNQXe1HqUgrHcOGcpT1aJcy3D2dzkU/tenNy/5+R21Xv7g8Q3I0tzOZ2BV7zg5lAKgxuOgfZ4BjcUMJ61F5LU+Jx/c0YhtgbUwq8XrtM2bx32nWINFZrUB2L3MYZPZ3xHGNE003eDsaIaP7W2hNeSpLjJ7oLuOyWyZ//MnFxiMF1A1g4puEvG7ePlSlFxZo1DRUGTrWl3vd/I3b/VT63OyqcHPYLxArqIxmatwfDBJIq9iVySCbic9ES/v9CXpqPXwOw930xJ28z9e66Os6uiGyfR61/F0iT9/pZdcWeN3DndT2+jk/FiGWLbMK5eiRPzOWQH09HGVL+v4XQbZklqNHSJ+J7/7SHf1eQ9vquP4QAq3XebPX7mKy66wtz3EpgY/QbedrzzUNVW2d+6N3vu3N/LElvplO8ZCHgdfPdyJYYDbsfYHspYreN4tSVJm6msJcE/9e7pU3bIWwpzMlvne8WFM0yrS/8gmK4F9oeAy4neSKarUeO2zRjQ2NwbYvECNzliuzHi6xLlRK3iWJZk9bSGyJZX2Wg+Humr5yZkxXHbrrvTXl6Ik8iol1aA56CJf1nmnL0HQbadYsbpmeZ0K8XwFp8NqZWuYJi1T7bydNgWnT2F7i7VooSVktduezJY5P5bh7GiGj+xuElOZgiDcMn1GaoW6DCVXTdPk6mQep01ecrOlxUaVpxtPyJKEy3HzF+UTgylSBZVUQWUsXRINoK5T53NwbiRDZ93c+r73omimxPeOj6DI8Kn9bfO2mA977PicilUtQpHZ3hS0Ui+LqlWvvKTSHytYg15Ftbr/1fkcyLI0VbpWxibLfHBnI6OpEg9vrGMsXeLwhjqagi62TqWTfO3hLs4Op2kNe3A7FAbjBd7pi3NhLItmGPzpT85zX2cN25oC/CIxgWZYeck7W4KzjqtDXbV4HbbqaPFMM29ap+uU//DUKLph8tKFCXqjWbY1BfjyQ10osrRoH4PrA+eypiMhVUewTdO8qUZGQ4niTZ1LVtNyVdu4o7cJM+/KtCX0a/3QziYmMiVqfY4l/yHrp/q5WzWYNRoDTl66GOUHJ0dpCbmo9Tr4wI4mfnZ2jL96vZ+iqltJ+HaFvskck7kKRVVH1TQaAy4mc2UciszDG+uI5ysEXHZ2tQVJ5CuzysW0hNz89sPd1X+fHUlzbCCJYZqMpkqLBs/RTIlfXogS9th5envjup36FIQb6fyTn9zW6/v/7EPLtCVri9chkS5bJ8+A8/ZP20f6k/z92wPYFIl/9sTGeZsuLKRQ1vi/f91LWdP5vUc3EPE7OdhVQ63Pid9lu6VqGRvqfVybzBN024gssjjpXtUfL9AYdDGUKN50YLMeXZ3MU1KtdM7+eH7e4NmmyBzqqSPstYLhJ7bWI8sS79/RSCxfweuwYWLSWetlS6OP4wNJHDaFjlo3AzEXW5v8NAXdDCeLxPIl9neEeWprA8+fHGU4VWBLY6CaMhFw2aulBL/xZh9vX41T43VYedYa+J32auyjGjpnhjN4nXlyZW1Wh8snttazrTlAjdcxp3LGUKLAiaEUmxp81fVhu1uDXBrPkCxYxRVCnptfrDeSKvLc8WEkSeIzB9qsroqnRvE5bXzmvrYbVrM6NZTipYtRAD65r/WmZ53utLuyNtd0nd1UocKe9tANn6/qVmc+n8u2YF5rsaJzfixNU9BNc8iNTZHpjvio9TpQdZNdbSFeODtGMm/lK7WGPexoCfLj06PkKxoBl52eiJeWsKdawWMkWeAHpyrsaQ3SE/Gxod7Hga4aAi47jUEXPTcYRS5UNF6+FOXVy5NEAs4b1hY+NpCs9pDf1hy4qQuZIAjrnzQj61lahpvrU0MpxjNWht6l8ewNzzmjqSI/OT2G12nDrkjVVsHPnxzhtx/uRpIkNiyh0cpCtjYF2FDvQ5EkUTlnHgc6ajg+mGRHc/CeD5wBtjT6uTyRRZYX3+8e3xKhoutcGMvwi/PjfGxvC01BN//i6c2UVB27LFkVtCay1PtdyBJ8610rkBzPlNjXHmYoWSCRU0nkVGq8DoZTRWK5Cs+fGuHtvjifO9heDYCtRiopkgUVJIl/+fRm3A6Fq9EcuYrOgc4a3umLE/LY8TptnBtJz6rfLUkSzaH50xFfPD9BuqjSN5lnQ8RKY+2o9fKBHU2UVYNEvsKBjjAjqSLj6RLbmwNLys8fjBdQdSs3ezhpdVPOlazeFf2xPNuaF+9iOX0TA1BUl9wnb9XclcEzcFO1An94apSRZBG/y8ZXH+qa96T6i/PjXJvMY5Mlvnq4C6/TxjM7GjkznGJDvZ9arwOHTWFbU4CNDX7ev6ORdEFlR0uQQkVnV2uA33t0g3X36nHwpz8+h02RcdpkHtoQoajqlDWDLY2Bau/6G+mPFRhNlfC5bATd9qmWnwvrqPVyaSKLz2lb8WR5QRDuPm21XlKjWYBlmbq/rzPMhfEMdkVme/ONs/MujGWqNWc31HuxKRKGAd2R5bvRn6/WrWA52FUzqzLCvS7sdfBbD3be8Hkehw23XcFttzGaKjGaKlWv4zMDS6/TNpVXbnURTuQrOGwyLodCZ62X3mgOn9OGppvohtWEzVqEpzOZLVWD54DLzoZ6H6ZpNQba2Wrd7GycsTjvi/d3UlKvEfLYb2qUttbnIF1UCV+Xxrq1KUCyUEEzTHa3hvi7twfQDZOxdJEP72q+4ftubwlwbTKHIktsbvRT63PwrSNDSJLV8vtGwfO+jjCGaeWir2SVjOVy1wbPi7l+OqpYse5iSqqOYZrILHzHPbPaUkvITcuMu7entzVgkyW6pkaMz46kUXWD7U0BdraGaA27qxek//S5vfztWwOEPQ4+d7ANJCv3aeaBdmUiy5tX43TWeXl0nnba7bUeuiNeCqrGnrbQDUfZtzUH6KzzYFdkcQERBGGOB3pqOTdmBc8PdN1+p7k97WFqfU6cNnlJpc82Nfi5OJ7F61B435YG9rSFKKsmG+6Ci6Vwb9vcGODqVE+AphmVO96+FufSeJb9HWF2tAT53MF2SqpOe42H/niBkNsqvbijJUh3xItDkdEMk2RBpSnoQjMMarxOOmfM2nidNr52uItcWZuTvmSaJi+en+BKNMeXH+ygvdZ7U5WiPrSzibF0iYjfOStOUmSp2qU5W6rWgMA0zSWl+FQ0g3RJRddNEvkKjQE3u9tC6IY5NSK9OLsi88AS2oCvFesueD4znOali1GaQy4+sa8VRZb4wM5Gzo1k6JmaopjPU9saeOlCFN2wysHMdH40ww9PjZAuqLSE3XzxUAcOm8w71xKMp0tWvcdMmVxZq+YKNYc8/PEHts56H7tirQb/wYkRLoxlMEyTjfX+qbzn8JxyLj6njd9+uLtayPzbR4YwTJOP722ZVRlkJtElTxCEhZweSlabNZ0aSS7Le97M4p62Gg9/8FhP9ULsXUIJK0FYC7rqvLP2XbAakbx1NQ5YlWJ2tARpmHETaZgmPzs7TnfEy6Hu2ur12abAh3YtnobpcdjmvZ6PZ0o8d2KERL5CbzTHv//07htuu6obnBhMEcuVqPU62dMeWjTg9rvsfHxvC6eHU/z4zBgvno/yx89spiW88LE+kixSVg1Kqs7fvTXAnvYwH9zZyFCyyM6WhUedK5rBcyeGiecrPLO98a4pirDuhifPj6UxTJPhZJF0UaVY0au1DKcrWyzkWizPtVieX5ybmPX4ry9FGU4W6YvnUXWTbEnDZVNoCLhoCbtpq3Gzpy20pCT7t67GefXKJCcGU5waSnN8MElJ1fnGG338/Nz4vK+RJImrsRzpokq2pHFlqpC4IAjCzbg48d654+xU+sadJnJthbvN6eEU/+XXvfzkzBjmjME1K1/YCih76uemHr3RG2MiU+Ktq3EKlcXTLpcq7HFUZ8idNnlO0YSypvOLc+P88vwEqm597+1rcV44O8bfvDnAz86O8+ZUwH89TTd482qMt67GaQ65iecrJHIVJjIlfj3VoGUhmxr8tNd4rNxlyUrRAnh8c/2iaaQTGSsNpqwanBvNLPi8tWbd3fbvbguRLEzSEnITctt561q8+kdsCLjYNiMvbyJT4uVLUer9Lg5116DIEsWKzomhJHabzNPbGnDaZGq8dlpCbup8Dg5119IadiNJEp+9r23OKtcb6aj1EnDZMcwCDptMY8CJqhuousn50QyPborMm5zfXeflxGAKwzBva0GNIAj3rqDLRqpopbHVum+9/a0g3EtODaepaAZXJnJkN2nVRiEAH9/bQlHV5x0lbqvxEM9VaAi4cC0xtcI0TdJFqzbzfBWzXHaFf/2BLbzbn2B7c3DOCPLp4XQ1CK2bqgFtk+UZLavBLs8/bnpmJM071xKAVTbyYGcNv74YRZKkak3sX1+KEstWeGxzZFaqltuh8KFdTcRyZY4PJtnaFCDiu3EqV0PARVPQRTxfWdK6ibVi3QXP03ULp9X6rAuEIktzytC8fS1eTf7f2hTg0wdaefHcBKNpuBrNcbHGw2iqyFiqRHPISteYuTMrsnRTgTNYCzY2N/r51YVxjg4kifhcdNZ6GEoW2VjvW3BVa8jj4GuHu27qswRBEGba1hJiIGnNrG1tXXwBjyAIlp0tQV69PElHrQf/dalGkiQtmC75+OZ69rWF8blsS67+8vNz41wYy87bMntae62X9gUq29R6HUiSVVmndirmub+rhoDbxlPbGvA6bdW60tebmUbldSpsqPfzX7+wHxMTj8PGSKrIqaE0AO/0JfjI7tkLCS+NZylUdLrqvOxpC1WbzSzGYZP5jYPtN3zeWrPugufrbWkMUOt1YlekOWkVrWEP1ybz+F02Qh6rmPjDGyP84OQIsgRNQRdvX4sjSRKpgsrNVj7SDZMLYxmCbvusvMCg284n9rVxX6eVHH83FAQXBOHu90BPHe/0JZCQeLD77lmcIwiraU9baE4b7KVaSgA502CiAFhlHXXDvOl+Dd0RH1881IEsvTdgKMsS229Q7QKs1Av3fmsAbzoumdntz2oYYyNX1midJw22KejCrkgosm3eqmIlVefyRJamoPuur8O+7oNnYME/0v6OMBsiPtwOpdoRp73Ww9cOdyFJVsL+o5sinBxKsbUpsGiuXq6s8eL5cWyyzFPbGnDZFd6+FufdvgSSBJ8/2D5nNboImgVBuJM2RHzU+53IkkTXMpaHEwRheTy6qZ7jg0k2N/pvudFZnc/Ju30Jrk7muL+r5qYW4S0Wl3gcNr70YAcl1Zh31r0+4OJrh7vRTXNOAQSAF86O0xfL47DJ/PbDXTdVJWStWffBc280y0SmzJ620Lwru+e7K5z5vK1NgQWnOGY6PZyiP2bdMbbXeNjdFqIylaxvmqAaNy7VIgiCsJL6Yvlq7mN/LM/BLjH6LAhryeZG/031sZhPoaLxRm8MgNd7Y8tawcJpUxYNemeOVF9vegGjMaNLNFgj0scHk9R4HbPSbteydR08pwoVfnx6DNOERL5Szc/JlFQG4wU667zz3h3diKob2GRp1kh0S8iNIkvVdA+AB3tq8dgVAm77rHrRgiAIq6HO56CiGUjSwjNygiDc3Vw2hfqAk2imPG96xXKpaAaXJ7LUB5xz6lHP5/07Gjk7nKatxjNrfddrV2KcHbFyqcMex6xyf7fDMEx001yRvhfrMniuaAZv9MaqzVHAKuky7btHh0kXVep8Dn7zgc6beu/zo1Z7zhqvg33tYd66Gqcl7ObJLfV8+kArIbejeufltCncL/IK17ySquO0yaKElrDuDcTzjCQLIMFAIr/amyMIS2aaJhXdWLNT/b88P8G1WI4He+roqLWCw9VqVibLEp890Dar98RKeOniBBfGstgVia881HXDuu0Bl31WG/Fp02mzsiRhu0GqyrGBBNFMmQd6ahf92YoVnW8eGSRT1PjAzkY2NdzeaP711mXwfGYkzcmhFAA7W4M0BlyzpkGm0ynKmjHfyxd1JZpF100G4wUKZY2ianB+NEN/LE9ZM3iwp/a2AuaypvP8yVEyRZVndjTSukhRcuH2vXZlkqP9STpqPXx8b4sIoIV17aWLUYqaNV/6qwuTfPnB7lXeIkG4MU03+PbRYSYyJR7ZFGF/R3i1N2mWQkXjzNTI6XMnhvE57YQ8dj5/f3s12J/IlPjx6TG8DoWP7W1ZsLLWcrEp8ooGzvBeDKUZJtptpKbuaw/hssm01XgWbAAHEM2UePWylY6iGiYf3b1w2/BotkSqYHVK7I3mlj14XndNUsBaESpJIEnWApkdLcFZd4Af29PCgc4wj26OMJEpzXrt0f4Ez58cmfN4PFfmu8eGKZQ1rsVyjKSKGKb1GWGPvTrKPRAv3Na2jySLjCSLZEtadRpDWDlXpppGDMQLt3QzJQh3k1ndU8U6DOEukS6q1Wtyb3R1mvssxm1X6KyzBrqmg+VUQSVdeK/N9bnRNJmiyli6dNtxwlrxxNYG7uus4SO7m2+6bC9YWQLfOzbMv/nBWV67EiMzoy14Il8hmp0dh3mcNpx2K5arucGNQXPITXfEi12RGEkV+eX5CYxlPOety5Hn7oiPz9/fjoQ0b15fY9CFLME3jwyhGyZPb29ge3OQVKHCa1esu5qyZsyqsXikP8FQooBumNT7XQTcdpx2hd99tAdFlnj5UpSRVPG2e7M3Bd3UeB1kiuqy3ykJc93fXcM71xJsbFi4xrYgrBcBt4PpuRX/TZbQEoTVUuN1sL05wEiqyIHOmtXenDkkSeLje1vRDZOxdJFXL8doCrpmxR8b6/1cGMvitis37HZ8t/A5bRzeODcNY6kujGU4MZhkPF3CLkuMpUpsabT+zt89Ooxhmnx4VxMbp2Ihn9PGFw91kC6oN8zltisyz+5p4YWzY1wYy3JmJM3GBh8dC9THvlnrMngGbpi8niqq6FN3IfFcBbBWifpdNrIljfrrgu6WkIcLY1l8LhsP9tQynCqyvyNcLSXz2Ob6Zdlut0Phtx7sxDDMJRdVF27d9ubgkupfCsJ6cLCzhuMDSUDi/q61F4QIwnwkSeLp7Y2rvRk3pMgSrWEPn79/btOPthoPv/9oj7iuz1Drc1DrdZL2q2xq9LNvKh0nma9UZ8ni+QobZ7wm4LLP6vB4I9Oxm9uhzGmUdzvWbfB8IxsiPvZ3hClUdA50Wn8wp03hi4c6SBVUGgKzg+edrUHaazw47fIdGaEUB5ggCMvttx7qJOC2o0jwsX2tq705gnBPEdf12VrDHr5yuBPDZFZgu6XRz2S2jGaYt9ycZtpKxW73bPAsyxKPbIrMedxlV2gMzv8LvtlOQYIgCGuJ06bcla1wBUFYn+Zb1GhTZB7fsjyz+bAysdu6XDC4VLph8tqVSX55foKSqt/4BbegWNFJFSor8t6CIAg3o6IZ/OT0KD89M1ptWCAIgnAnXJ3M8ZPTYwzeoQWT2ZI6axHicrpnR54BLk9kOdqfBMDjUOatP3g70gWVv393gLJq8NS2Bna0iNxa4e7Q+Sc/We1NEFbALy9M8LdvDYAEdkXhqW0Nq71JgiDcA0zT5GdnxlB1k5FUga8/0rOinzecLPD94yMAfHxvy6Jtx2/FPT3yHPLYq61qV6IeYjxfpqxaoztj6dINni0IgrCyKrqBCZgmVPSVmW0TBEG4niRJBKfirPAK158GmMiU0Q0T3TAZzyx//LXuRp7PjqT59cUoLWE3z+5pqVbDmE9T0M0XD7Wj6iaNweVpBzlTZ62XXa1BMiWV+zrXVlF3QRDuPQfaw/z87BgSEvvaxTlJEIQ759P7W5nIlGgKrnypvu3NAcbTJUxMdi5h1r8vluenZ8YIeex8cl/rDRcXrrvg+dxoGs0wGYgXSBfVG5YmWaybze2SZYkntoppUUEQ1oaRVJFNDQEARlPFO3IREwRBAKsgw3LVWV7KZ31oV9OSn39hLENFM4hmyoynS3TWLb6d6y5tY1drCIdNpjviJXQLHW8EQRDWq40NfoJuq3VwT8S32psjCIKwJuxoDuJ2KDSHXDSFbpyJsO5Gnrc2BdjaFJjz+DvX4hwdSLKtOcDjy9TQRBAE4W4iAZI09X9EzVlBENaefFnjuRMjlFSdj+xupiGw/Gm112uv9fB7jy59EeO6G3leyMmhFBXN4ORgaln7mwuCINwtLk9kSRVUkgWV3snsam+OIAjCHP3xPJPZMtmSxoWxzGpvzrzumeB5R0sQWZLY3hwQXX4EQbgn9dT78DoVfE4bXXUibUMQhLWnvcZDyGPHZVfY1OBf7c2Z17pL21jIQxvqeLCnFkkSgbMgCPemOp9zxeurCoIg3A6/y85XHupa7c1Y1D0z8gwsKXDWdIOrkzmyK9SVRhAEYTWNpIqMpYurvRmCIKxR0WzpjnUBvFvdMyPPS/XCuXGuTOTwOhW+8lAXduWeur8QBGEd641m+dGpMQCe3dNMt6i4IQjCDBOZEt98dwjDNHlsc4S9oh78vNZM8CxJ0h8BnzRN8/BqbkemqAFQrBhouskN6mQLgrAO3W578v4/+9AybcnySk+d3wCyJW2RZwqCcC/KlTUM0yqqkBHniAWtieBZkiQnsGe1twPgqW0NnBhM0lnnxe0QkbMgCOvHrtYghYqGhLV4WhAEYabuOi8PbagjX9a4v6tmtTdnzVoTwTPwNeBvgD9d7Q2J+J08vb1xtTdDEARh2dkVmYc3RlZ7MwRBWKMkSeKgCJpvaNWDZ0mS7MBjpmn+V0mS5gTPkiR9Hfg6QHt7+53ePEG4K91u2oEgCIIgCPNb9eAZ+E3gHxb6pmmafwH8BcCBAwfuue4mFc3glxcmKGs6T25twO8SLccFYa1bqznThYrGi+cnkCSJp7c14BKLOgRhxSTzFV66GMXvsvHE1gYU0WNi3VgLpSQ2A78vSdILwHZJkv7pam/QWnJ5Isul8Sz9sQKnhtKrvTmCINzFzo5kuDaZ52o0x7nRtdm5SxDWi6MDSQYTBc6NZuiP51d7c4RltOojz6Zp/vH015IkvW6a5n9eze1ZaxoCLhw2GU03aQqtfH93QRDWr6agC0WWkKa+FgRh5bSE3JwbTeO0KdT5nKu9OcIyWvXgeabVLlO3FkX8Tr7yUCe6YYqUDUEQbktbjYevHu5CArzONXX6F4R1Z1tzgJawG6dNFilS64w4e94FPA7xZxIEYXn4RNAsCHdM0C0GvdYjcRYVBEFYY9bqgkNBEARBBM/r2tmRNFcnc+zvCNMa9qz25giCsMrKms4rlyaRJYlHNkVw2NbCmnFBuDOS+Qqv98aI+J0c6q5d7c0R7mLizLlOlVSdX16Y4NpknpcuRld7cwRBWAPODKc5N5rhzEiac6Oieo9wb3m9N0ZvNMdbV+OMp0urvTnCXUyMPK9TDkUm5LaTLKhile9dSjQ6EZZbrc+JNFVqttYrzgvCvaXO56Q3msNlV/C7RPgj3Dqx96xTsizxGwfbiecrNAZESarVIIJfYa3pqvPym4c6kCSJGq9jtTdHEO6oB3pq6azz4HfZRbUZ4bZIprkyTfskSWoGfgxsA3ymaWqSJP1H4ABw3DTNfz71vDmPLaSurs7s7Oxcke0VhNvV39+P2D+FtUjsm8JaJfZNYS07duyYaZrmnBTnlbz1SgBPAM8BSJK0DyuIfliSpP9HkqT7AP36x0zTPLLQG3Z2dnL06NEV3GShUNE4NZSmPuCkJ+Jb7c25qxw4cEDsn2tUNFOiN5pjY4OfiP/eS1cQ+6awVt3KvjmWLtI3mWdrU4CwmEERVpAkScfne3zFFgyaplkyTTM546FDwItTX/8SeGCBx4RV9OuLk7x9Lc6PTo2SKlRWe3MEYVk8d2KEd/oSPH9yZLU3RRCE26AbJt8/bh3PPz49utqbI9yj7mS1jRCQmfo6PfXv+R6bRZKkr0uSdFSSpKOTk5MrvpH3OptirSaSJQlZllZ5awRhedgU61RnV0SBIUG4m0mAberaJI5nYbXcyYz5NBCY+joApLDSNq5/bBbTNP8C+AuAAwcOrEyCtlD1+OZ6moNuIn4nAdEOXFgnPrW/lf5Ynq6Id7U3RRCE2yDLEp850MZgosCGepFaKKyOO3nb9hZWDjTAk8DbCzwmrCKHTWZna5DGoKjQIawfQbed3W0hcUMoCOtA2Otgd1tIVMwQVs2K7XmSJNmBnwG7gZ8D/xooSZL0GnDSNM13p5435zFhbRmMF5Bl5nQpzJc1hpNF2ms8uB3KKm3d0umGybXJHLU+Z7VMV1nT6Y8VaAq5RGB1j8mWVEZTJTrrPDht1v6bLqqMp0t01XmXtfteRTPoj+dpCLgIut/bz+K5Mol8hZ6I746mSYn238LdYvoc3Rxy4XfZMQyTa7EcQbeDsMfO0YEkDkVmb3sISRKphsKdsWLBs2maKtZo8kzvzPO8RcvTCavrwliGF86OA/CxvS101b037f2do0MkCyqNQRefO9i+Wpu4ZL++GOXMSBqHTebLD3biddr4yekxBuIFfE4bXz3chSLyvO8JhmHyrSNDZEsabTUePrW/FVU3+Oa7gxQqOt0RL8/uaVm2z3vh3DhXozk8DoWvHu7CrshkSir/+O4gqm6ypz3E45vrl+3zBGG9+PGpMQYTBfwuG199qIs3r8Y50p9AkSVCHjs/PTOGLEl8/ZFuHhPHkHCHiGx7YVH5sjbv16Zpkq/ocx5fy/IVaztV3aCiGVOPWT9DUdXRDZFSf68wTJPi1N++MLVf6IZJeXq/KOvL+nnTx0hJNar7WVk1UHXr68Iyf54grBfT5+1iRUc3TXLl947XWK6MaVpfp4vqam6mcI8RCUPCona3hahoBrIssa0pUH1ckiQ+uruZyxNZtjUHFnmHtePxLfUEXEkag65qbdBntjdyejhFd8S3rNP0wtpmU2Q+sruZq5M5drYGAXDZFT60q4mBeJ7draFl/byntzVwajhFe40Xl91KEYn4nTy1rYHJXJn7OmuW9fMEYb14ZkcjZ4bT9ER82BWZhzfW4bTL1HoddNR4CbrtOG0KT29rXO1NFe4hIngWFmVXZB7cUDfv99pqPLTVeOb93loUcNl5fMvsab2I38kTWxtWaYuE1dRZ56Wzbnb1jZ6Ib0WaA9X6nLxvy9z9bEdLcNk/SxDWk3q/iye2vreA3eu0zUpx+trh7tXYLOEeJ4ba7nHpokpJvb0p40JFI1O6O6fMTNMkniuj6cZqb4qwAlKFCmXt1vfvTEmtpnUsl1ShctvHnCDcaxa6VmVKKqlChUReNPUS7hwx8nwPOz+a4Rfnx3HYZL5wsIOg5+arTcRzZb55ZAhVN/jwriY21PtXYEtXzovnJzg3mqE+4OTzB9vFau115NhAglcvx/A5bXzxUMdNV4Tpjeb48elR7IrMZ+9ro853+229Tw2leOliFI9D4QuHOvCJUluCcEMLXat6ozmePznCudEMPREvj26q5/DG+WdKBWE5iZHne9hIqohpWguXJnPlW3qPaLZMRTMwTRhJlZZ5C1feSKoIQDRTri4WE9aH4aT1t82VNVLFmx+VGp06PiqaQTRza8fH9ab3t0JFJ5ETI2WCsBSjC1yrRlNFKppBvqyRL2uMpAqruJXCvUQMe9zD7usMkymq+F22WSXobsaGeh9bm/wUVZ297aHl3cA74NFNEY72J+mp91UXcgnrwwPdtVQ0g4jfSWPg5pv+7GkPkchXcNllNjYsTx70wa4aChWdGq+d1rB7Wd5TENa7A51hMiUVn3P2tWrv1DHqtivUB5w8tMD6HEFYbiJ4voeFPA4+ub/1tt7Drsg8s6NpmbbozuuO+OhegQViwuqrD7j49IG2W359wGXnY3uXr9YzQJ3Pyadu85gThHtNyOPgE/vmHjf+FThGBWEpRNqGIAiCIAiCICyRGHle4zIlle8eHaaiG3xsTwuNQReabvDciRHG0yWe2Npw19RZFoTblS6qfPfYMKpu8PG9LQTddr57bJh0UeUDOxrFLIIgCJwcSvHq5UkrNcqE4VSRxzZH2LXM9duFe5cYeV7jBuMF0kWVYkXnSjQLQKJQYThZRDNMzo2mV3kLBeHOGYwXyEwfDxM5xtMlJqcWrV4cz6725gmCsAacHUmjGyaXxrNcjmbRDZNzo5nV3ixhHRHB8xrXWeelzufA77KxudHPZLbMWKpEa9iN0y6v6zvpfFnjzHCadOHurCEt3LrRVJFzo+k5LdM76zzUzjgemkIumkMu3A6FHc2r23BkIJ7n0ngW0xRt3gVhOZVUnbMj6SXXct7TFsJhk9nREmR7c2DqWvne+WE4WeDCWAbDEMeqcGtE2sYa53Pa+M0HOgHrBPI/Xu+johlsqPfxB49tWN2NW2E/ODlCNFPG77LxtcNdogbzPSKRr/Cdo8MYpslktsxjM7qJ+V12vjR1PEz77H3td3gL5xpKFPj+8REAcuU69neIdtuCsFx+emaMgXgBl13ha4e7cNgWH/fb0RJcsHtnNFPiu8eGMU1I5isLdtAVhMWIkee7iG6YaLp1p1y5B2oST/+MFdH9756i6QaGeXft5zP3UVEvXBCWlzp1fM08N9yqim71JQAoi2uLcIvEyPMKU3WDX5ybIF/WeHJbAx6Hwgtnx9ENk/fvaJzTYUw3TF48P06qoPLE1gYifqur2VtX41ydzLGnLchEpkyqWOHNqzEe7Fn8rnkwXuC13klawx4e3RRZsZ9zJXxkdzMXx7L01HvFqPNdZjRV5OVLkzQEnLxvSz1Xojne7UuwqcHPwa6FR2XPjaY5MZiis85Dg9/Fvo7wgsfEQq//H6/10RBw8UdPbcRhuzO1u3siPp7YWk+xorOvI3xHPlMQ7hXPbG/izEiajlrPnHr8JwaT/M83+2mr8fDPn9yEIlvXir5Ynjevxuio8aDqJmPpEo9sqqM17OH92xtJF1U2N/r53rFhFFni/dsbF+xCmsxXePH8BB6nwvu3N2JXxLjjvU4EzyusP5bn8oS1kOnEYJJan5O+WB6AM8NpHuipnfX8wUSBC2PW848NJHlmRyOFisbb1+KAFVzLEmSKGu9cS7C7NYR3kRa/b1+LE82UiWbK7GwJUuN1rMSPuSLqfE4Ob7z9lsjCnfdOX5yJTImJTIntzUFeuxIjU1SZzJbZ3RbEuUBQ+9qVGMWKTiwHH9vTgiRJ9MfyM46JxKJ1xb93fISRVJGRVJFjg0ke6L5zU7Lref2BIKymoMe+YNvt7x0bZjRdYjRd4uxImt1tIQDe6I0xmS3TF8tjkyWcNoV3riVo3e+pVqh6+1qcwYTVlfDieIa97fPf+J4YSla7g25qyLOpwb/MP6FwtxG3Tyus3m8tZpIlifYaD81BFw6bjE2W5u0wVudz4HUqSBK013gAcNkUGqY6pHXUemivtTosNQRcuG/QFa+91nqP6UVWgnAntNdY+2jQbSfksdMxtS83h1w4Fhm1mX5ee42nOttQ53dWj4m2qe8vZGdLAAnwuWz0iLJ1grDuTQfCIY+9er0D6Jy6TraG3dXZqo7a2eePlpAbmyzhsMk0hxbu+NkW9iBLEi67QoP/5ruVCuuPdDetDD9w4IB59OjR1d6Mm1bWdDTdrI4Ql1Qd02TBKaKKZlDRjVkpHbphkq9oBFx2wKr/7HXYqlNUi8mUVDx2BZuYalpRBw4c4G7cP1dKtqTisivYFRnTNMmUNHzOxffZ6ef5nTbkGc+b75hYyES6iM9lw+u0L8vPsR5cv292/slPbuv9+v/sQ7e7SYIALM95cyxdJOiy47nu/DB9nTRNk6Kq43fNPScUKzqSxJx0kOvlyxo2RVpw1kxYnyRJOmaa5oHrH7+jQ5GSJD0D/MnUPzcDvw/8DXBi6rFPmKaZuJPbdCc4bQozj+kbHaQOmzxnNbEiS9XAGZj19Y3czHMFYbnMvFBJkkTQfeP9cKHnzXdMLKQhuPAIkiAI60/TAsf8e9c+Cf8Cg0cLDWJdb7H0SOHec0f3BtM0XwBeAJAk6R3gl8AZ0zQfu5PbIcxmmibv9iXIVzQe6K5b8smkP5bn4niWHS0BWsOLT6cLwkqLZkucGEzRUethS+PSum6WNZ03r8ZxKjKHumtnjXYLgnDnDSUKnBvNsLXJT8dU6sV8DMPknb4EJVXngZ7aGw5KCcJyWpVbKUmSuoEJ0zRzkiRtlSTpNeAN4F+Zd1MeyTpxLZbnzavWgkRZkmbV1V2IaZr8+PQoqm4ylCjwO490r/RmCsKifnk+ykSmxIWxDB013iXdBJ4YTHFyMAVAyOMQre4FYZX99MwYhYrO1ckcf/j4wr0Mrk7mqgvp7Yq84IJCQVgJq5UE+wnguamvNwKPAGHgI9c/UZKkr0uSdFSSpKOTk5N3cBPvHX6nDXlqcdZSptbBml4PTD034BbTWcLqm953vQ4bNmVpI8jT07qSJPZjQVgL3ruuLH4t8rvsN33dEoTlslpXi49gBdBM5zhLkvQDYC/ww5lPNE3zL4C/AGvB4B3dyntEfcDFFw61U6zoN6xmMNNnDrQxmirSMk/VEEG4096/vYGtTX7qA64l12Hd1hwg6LFjlyXqA2IVvSCsto/vbWE0VVy0+gVAY9DF5+9vp6Te3HVLEJbDHQ+eJUlqBCqmacYlSfICJdM0deAh4Myd3h7BUue7+XrKLrtCtygHJqwRNkW+pf2x5QYXaUEQ7pybua4s1jBJEFbSaqRtPAs8P/X1RuCIJEmvAm3Ad1dhe9as3miOU0MpdGP5BtyHEgWODSQpqfqyvacg3Am6YXJ6OEVvNLus7xvPlTk2kCBdUJf1fQVBWHmmaXJ+NMNPz4xyZjiFWDYl3Al3fOTZNM3/NuPrk8C+O70Nd4OhRIEfnRoFoFDR53QivBXposr3j49gmCbRTIkP7Fy4U5sgrDVH+hO8NbWw9ZP7lFkNEW6VaZp87/gw+bLOudEMX3qg87bfUxCEO+f8WIZ/fHeQKxM5eiI+Pnsf1S6DgrBSRNeMu4DJ8t9Ji3tz4W4zc0BpOY+J6fcVA1aCcPcxTaoXNHNFrpaCMJdYXr5GtdV4+NCuJvJljZ0twWV5z6Dbzsf3tjCRLS3bewrCnXKwqwaXXcbjsC1a//VmSJLEJ/a10hfLs6lB5O8Lwt1me3OA3zjYztXJHJ21HnFtE+4IETyvYZsa/Mv+nu21nmWZ7haEO02RJfa2h5f9fSN+p1h4JAh3KUmS2NESZIcImoU7SKRtCIIgCIIgCMISieBZEARBEARBEJZIBM+CIAiCIAiCsEQieBYEQRAEQRCEJRLBsyAIgiAIgiAskQie7xDDMMmVtdt+H90wyS/D+8ynohmi86BwR+TKGsYyds4EUHWDYmX+/bes6ZQ1sW8Lwno1fW28mWttsaKj6cYKb5mwHolSdXfIcydGGEwU2NkS5MltDbf0Hppu8M0jQ0xmyxzqrl2WroPTUoUK3zwyREUz+PCuJrojouatsDJ+fSnKycEULWE3n97fiiRJt/2emZLKP74zSEk1+ODORjbOKPM4li7yvWPDSJLEp/a30hBw3fbnCYKwdqjT18ZMiYpu4rTJ7G0P8djm+gVfc3YkzS8vTOBz2vjC/R24Hcod3GLhbidGnu8ATTcYTBQA6Ivlb/l98mWdyWz5tt9nPmPpEsWKjm6YDExtqyCshL5Ja98dSRYpa8sz6hPNlChUdAzTZCA+e/8dThZRdZOKZjCcFPu2IKw3uZJGLFtGN0x6o1kA+m9wjeyP5zFNyJY0YrnyndhMYR0RI893gE2ReWhDHZfGM+zruPUmD0GPnb3tIYYShWUddQbojnjpjngpVnT2tIaW9b0FYaYHN9RypC/Bhno/LvvyjPZ01HrZUO8jV9bY0x6a9b2tTQH6Y3kkSWJLY2BZPk8QhLUj7HWwpz3EcLJIT72PfFnj/u7Fr5EHOmpIF1VqvQ6aQ+47tKXCeiGC5zvkYFcNB7tqbvt9FpuGuh1Om8Kze1pW5L0FYaYtjYFlD2LtisxHdjfP+z2f08anD7Qt6+cJgrC2PH6T18bGoIsv3N+xQlsjrHcibUMQBEEQBEEQlkiMPK9Bb/bGOD2SZldrkAd76qqPXxjL8OrlSVrDHj6woxFZvv2FVjP9+lKUS+NZ7usMs7/j9kfJBWGlaLrBj06PMpkt89S2RrrqvLO+3xfL8+L5cSJ+Jx/Z1YxNkTEMkx+fGWMsVeR9W+pnLSoUBOHu9NLFCS5P5Li/q4a97YunRWZKKs+fGEE3TD66p4Uar4OBeJ5fnJugxuvgo3uasStiTFG4MbGXrEHHBpIUKzrHB5KzHj85lKJQ0bk8kSVdVJf1M1Xd4ORgaupzU8v63oKw3CayZfpjBfJlndPDqTnfPz2cIl/W6Y8VmJhaZBvPV7gazVGo6JwcmvsaQRDuLmVN59RQ2rpuDaZu+Pyr0RyxXIVkQeXSuLWw8PRwmlxZYzBRYDxdWuEtFtaLRUeeJUn60mLfN03zfy7v5ggA25oDHB9MUudzUVJ1VN1gLF1iQ72XiUyJlpAbl13h8kSWxqCLgMt+259pV2Q2N/q5PJFlW7NYVCWsPk036Ivlqfe7CHrsDMYLKIpES8hNxOekPuAknquwpTGAbphcm8xR53MS9jrY0hhgIF6g1ucg4nMCEPbYaQ65GE+X2dok9nFBuNs5bQobG3z0RnNsbbrxTFJHjYeSqmNTJJw2idFUkS2NfvpieWq8DuoDzupzS6rOYKJAc8iNzykm6YXZbrRH3LfA4x8FWoCbCp4lSeoE3gEuABXTNJ+WJOlfAs8CA8CXTdNc3iHVu9ATWxsYShSI5Sp899gwxYpOrqzRUevhn71vI7Is8YMTI/TF8vicNr56uAtlGVI4PriziWe2L386iCDcihfPT3BxPIvLrvBAdw2/vjQJwMf3ttBZ5+UL93dgGCayLPGLc+OcG83gsMl85aFONjf62Vjvm7Uv2xSZz97XXn2NIAh3vw/val7yMX01lsdpk4lly/z07Dheh41P7W/lnzy+Yc7rf3RqlOFkEb/LxtcOdy1LPXph/Vg0eDZN859Ofy1Ze84XgD8G3gb+v7f4mS+apvnFqfesBx43TfOwJEl/DHwM+M4tvu+6YZom+alOadmShjrVASlf1qoH+HQHpaKqoxkGirw8Jb9EUCGsFdP7eFnTyZS0OY/De/trvmI9puoGqmaCY+F9WezjgrC+LPWYzpU1JEmirBs4pq6ruRnX1ZmmO/lO9z+wKeK8IbznhnMRkiTZgC8D/wIraP6UaZqXbuMzH5ck6TXg+8Al4OWpx3+JFZzf88GzJEl8dHczlyeybG0KUFJ1+mJ5ds2ov/z+7Y2cHk7RVefFaROdkYT158mtDRwfTNJW46GrzosiSyiyxLZ5Ui7et7mBY+4ETUE3Qc/tpzEJgrD+PNBdiyxJPNBTi6ab2BWJzQssHP7gzibOjKTpifiwiUWEwnVulPP8h8A/B34FPGOaZv9tft4YsAkoA88DfiA69b00ELrN91832mo8tNV4qv++vl12xO/kia231uZbEO4GYa9j1j7+0Ia6BZ8b9Nh53xZxPAiCsDCXXeHRTZElPbc+4OKJgGuFt0i4W91o5Pk/YwW3h4GHZuT8SIBpmuaum/kw0zTLWIEzkiT9GMhg5U4DBIDU9a+RJOnrwNcB2tvbb+bjBEEQBEEQBGFZ3Sh4/gPgBcCc53ufvdkPkyTJb5pmduqfD2EF558H/n/Ak1hpIbOYpvkXwF8AHDhwYL7tEARBEARBEIQ74kaJPP8F+CtAM01zYOZ/wG/cwuc9LEnSMUmS3gRGTNN8B3hVkqTXgT3AD27hPQVBEARBEAThjrjRyPNp4B+BtyVJ+iPTNL8743s3vfTUNM2fAj+97rF/B/y7m30v4faUNZ2j/Uk8DgVZksiWNA50hnHZFQzD5NhgEt0wOdARXnCxxEiqaNWFbgrQMJUbdnUyx1CigN9lI1vS2NcRXpY61IKwmKFEgd7JHDuag0T8Vq1W0zQ5MZRiMF7A61TY3Rai3m/tp+mCyomhJK1hDxvqZ68nSOTLfOvIEI1BFx/b0yJKVAnCKopmSpwby7Cx3kdr2DPn+0f6EhzpT7CjOYBqmKSKKh01HvZ3hKvHbqGicbQ/Sa3Pwfbm4J3+EYR16EbBs2ma5n+XJOkV4O8lSfoQ8IemaRaYP5VDuEu825fgaH+SdFHFNE1CHgeqYfD45nrOj2V4/UoMALsizduq2zRNnj85Qlk1uBrN8dsPd1OoaPz41BhFVeNqNMe25iDJQoWP72290z+ecA/RDWtfVHWTwXiB33qwE4Crk3levhjlSL9VhWMkWeTLD3UB8OKFCYYSBU4Npfnaw12zmiD81Rv9HOlLANAW9nCgU7SqF4TV8qPTY2SKKudHM/zBYz2zbmZHU0W+8WY/sVyZF86O017rIZYrs789jM9lY0ujVZnn1cuTXBizMkYjfmf1JloQbtWS6q+YpnkZeACYAE5IknT/im6VsOLcdqu8nV2RsE+NLHumHnM7lBnPm//+SpIkXLbZz1dkCYdNRpYknNPvtcDrBWG5SFir6OG9/Rqs/VKWJGyKjE2R8Dje2xer+79NwnZdjdeAy3qeLEv4nWLWRBBW0/Sx6rIrc2aBnDYZx9T1y2WXcSoyiiwhSdKsc8H0+UGRJZyKKO0q3L4bRTbVPdU0TQ34E0mSXsBK5VhavRdhTdrfESbkseNx2JAkyJW06vR1T8THJ/e1opsmXXXeBd/jM/e1MZQo0FFrTaU5bQq/cV8b45kSYY+dVFFlw3Ul9gRhucmyxGfua2MkWZy1v7aE3Hz6vjYe31KPXZFmlXt8ensD3REvDQFX9cI67asPddFVZ31v8xJa/gqCsHI+vreF/nie1rB7zvdqfU7+5TObOTOSZmujn3xFp6Lp1Hids0q9PrwxQlPQTdhrF3XghWVxo+D5317/gGmaL0uStB/43ZXZJOFOkCSJDfUzAoPr0sDaa+fmll3P57Sx9bqGFWGvg7DXAUBjcO7JThBWQsBlJ9A096LYEnLTEpq7H9oVec6+O82myDy1rXHZt1EQhJvndigLHqsAzSE3zfMc4zMpssTmRnEjLCyfRdM2TNP8wQKPJ03T/LMV2SJhllShwtmRNIWKduMnA+miytmR9KwWxjMVKzpnR9Ik85UbvlcsV+bsSJpUocxPT49yaih1M5suCCvuzHCKH50epbDA/l7RDM6OpBlPlzg/mmE0VQQgmi1xdiRNRTMWff8bHU+CINw5ubLG2ZE0mZJKIm9dG0uqPu9zr0Wz/LdXrnJiMDnr8YF4nssTWUxTLNsSbp1ISF3DDMPk20eHyJd1mkMuPnvfjZvEfOfoENmSRsTv5IuHOuZ8/8enRxlOFnE7FH77cNeClTRKqs63jgxR0QwuTWRJ5ivYZIn/z0e3s3GBdqaCcCcNJfL82QsX0XSTC6MZ/l/PbJnznF9emODSeJaRVJF6vxOnTeET+1qqCwyHEgU+sLNpwc+YPp7q/E5+c57jSRCEO+e5EyPEsmW8Thu6YVJSdS5PZPnEvtmL0uO5Mv/qubMMJwv88OQI/+nze+mJ+BmI5/n+8REAHtscYW97eDV+DGEdEMHzGmYCqm7dHZdvMEIGVgWMim4s+vzpxzXdWLRcim6YaFOfPX1nb5hQUm+8HYJwJ5RUA8OY/nr+0aeyZj1e0QxM08QwTcqajmbc+LgyTbN6/N1ohFoQhJVXnjrOy6pevX7Nd2xqhok6dS3UDJPy1HVr5nPFMS3cDhE8r2GKLPHsnmb6Ynm2LZLzNU2SJD6+t4XeaK5aoud6H9rZxNnRNJ213mqVjfl4nTY+uqeZ4WSBj+1p4lcXJ2kNu9nRcuPtEIQ7YWODn68d7uLqZI6P722Z9zlPbm3g1FCaZ7Y3kCyohL0ONtT7+chuibFUid1tC9d8lSSJj+1tpjeaE/mSgrAGfHRPM5fGs2ys91PRDAYSeXa2zD2GGwIu/sXTm/jJ6TEO9dRWc6Y31Pt4fEs9ZVVnf4cYdRZunQie17jWsGfewvALaQq6aVpkoV7Y6+DhjUsrlNJV561WL/jtiAgehLXnyW0NPEnDgt/3u+wc3lg35/GeiI+eJVSCudHxJAjCnVPvd82q0bzYwvYHN0R4cMPsa50kSexpC63U5gn3kCXVeRZujm6YjKWLC04LpQvqkhbsrQUlVWc8XRKLK4Rbki6qxHPlGz4vV9aYzN74eXeSaZqMp0sLpoQIgrD2lDXrmmUYN75mJfMV0gV1WT8/mimRFwuM1z0x8rwCfnx6lGuTeeoDTr5w/+xFRiOpIt89OoyJyUd3N8+qPTtNN0wS+Qo1XgeKfHOtgZP5Ci67MqvRya0qazp/9/YA2ZLG3vYQj22uv+33FO4dE5kS3z4yhG6afHBnE5sWWGiaKlT4+3cGqWgGT2ytZ1draMmfYRgm8XyFsMeOJEm3fNzM59eXopwaShNw2/nSAx2LpjkJgrD6DMPkm+8OkchX2Nrk55kdCy8G7ovlef7kCBISn9zfQmvYQyJfweNQcNmV6nU47LEvuLD+ekf6E7x+JYbLrvCbD3TM6lwqrC/iL7sCohlrBC2WraAb5qwLeSxbxpgaxY1my/MGz8+fHGEgbjUfuX4V8WJODaV46WIUl13hi4fa8bveq3s7mS0hIVHndy75/YoVnWzJuoOeyJRIFSr4XfYFAxNNN8iVNUIex5I/Q1i/YrlydWFeNFNeMHhO5CvVWZrpY2epfnxmjKvRHC0hN7IsMRDP0xR08/n751amMQyTTEkl4LIjLyG4nh51Nk2ToqpXg+eyplPWDAIu0WxBENaSim6QLFizuhMzziWZkopDkdEME5ss4bIr9E3m0HUTWYZYrsJEpsSrl2PYZInP39/O670xrk3maQm7+cyBtiV9/kSmBFgztpmiKoLndUz8ZVfAk9saODmUZHNDYE6gubUpwESmhGGaC+ZeTdeiHUuXbupzx9LW60qqTjKvVoPnly9F+cYb/QB89XAXj2xaWs5zyOPg4Y11DCeLqLrBX7/RT1PQxWfva5vTJtUwTL55ZIjJbJk9bSEe3yJGqe91mxv8jKdLlDWDfR2hBZ/XWetlb3uIbEnjYHfNTX3G2IxjRTcMTg+nODaQpDvi5VB37aznTgfaXXVePrbAAsOZVN2gP55na2OgGijnyxr/8M4gubLG+7bUs1vkTwrCmuGyK7xvSz3XJvPVBYFnR9L88sIEhbKGTZHxOm20hF1cmciRzFd4ZFOEbU0Bfn5unHSxwsXxLNmyil2RsclyNW3x+mvefB7sqUM3TOp8zhs2bhHubiJ4XgEzF9pdz2GTeXr74t3LntjawNmRNDvmWUW8mINdtRRVnZDbMauV6dXJHMWpvM3eaG7JwTPAgc4aDnTCX73eB1hBSkU3cNpmp4WUNL2aszqULNzUdgvrk02ReWLrwov5psmydMspQe/bUs/JoRRbmwJkSioXxrK0hp0MJQpzguehRGHW/28kV9bZ0jj7BjiRr1QbpgwlCyJ4FoQ1ZldraFbq13CyiGlaM71Btx27InNpLIfDJhPxu3h0Uz0Om8yh7lrOjaZpDLhw2RQ2NfgpqjrbmgNLCpwBarwOnt1z4xtz4e4ngudVpunGnHyqrU0BeiJe5BkHrKYbmIaJKUk4bPPnX9V4HXx879w0j6e2NjKWskaxn9p242BmPo9squNIf5JNDb45gTOAx2HjwZ5arsXy3N91c6OHgjDNqsXMgqlBpmmiGyYmVovtnoiP7oiv+vyKajCaLvLghrkVNh7dFOHMSJptTQEMw7xh6sb082eWwmoJudnZEiSRr3BQ7OeCsKYYU+eGmeePA51hkoUKXXUeVN3E67TRVevh+FCKjfX+6vqgiN/JP3nfRl48P45NlnlyWwMu++JrhyqqjqLIsz5P1Q2xPuIeIILnVaIbJt89NsRYusQjmyLsm9Hp6EenRvnO0SHCHgf//MmN5Ms63zk6xNt9cer9Lv7kA1tualS6vdbDv/nwttva3g31fjbUL16u7v7uWu6/brRPEJYqV9b41pEhCmWND+9unjN7o+kG3z46xMuXJqnxOnh6ewPXJvOUVJ1n97TgtMmcH88gSxLOeW4wd7QEqQ84+d6xEd68GudT+1uJLLIGYEdLcM5xJssST97iDaggCCsnnivznWPD6IbJJ/e10hh0oRsmL12IMj51nd3fEWYoUeD5kyO47ApbmmZf03xO27wDUNfTdINvvNnPy5eidNZ6+adPbKQh4OLdvgRv9MZoq/Hwib0tS1pbIdydxO3RKskUVUZTJUwTLo9nZ33v2ECCsmYQzZbpjea4NJFlJFUgX9bIlzXe7Uus0lYLwsoZSxXJFFU0w+RqNDfn+8mCylCiSLqoEsuVOdKXJFvSUHWT3skc12J5yqpBsaIzEJ8/NWMgXqCk6pRUnYF4fqV/JEEQ7pDBRIFiRaeiGfTFrGM7W1IZmVoXcXnCus72TuZQdZNsSWMkWbylz8qUNC5PZFF1k/FMiWuT1uddmvqMoUSBfEWUq1vPxMjzKgl57GxtCnBhPMO1yTx//UYfn9zfSsBl58mtDcRyFep8DlIFlQtjGUIeO80hD/UBJ09srUfTDZ47MUI0W+apbQ0LVjIQhLtFe62HthoPuZLKztYgl8azPHdihKFEgf0dYT61v5VdrUES+TI2RSZfVhlLa2xrDrCjOYhDkemN5pAliU0N8zdA2dTgr15EN4mugYKwbmxs8HNxPItmmGydGlHuj+Xpj+eRJYkP7rTWGu1oDjIYL+B2KHTVefn1xShnR9Ls7wjPm+41068uTHB+NMO+9hAPdNeSK2tsqPdVP+9AR5g3emN01npnVbsS1p87GjxLknQ/8B8BAzhimuYfSZKUBk5MPeUTpmneE8Oq0v+fvf+Ojiw973PRZ4fKGVUo5NTonOPkwAkkh0EkxSTJokRRgbLO9bGcji1fL69zr33l6+tzbNnH0pEtB8myGEQxDMOQnOEMJ89093TO3ehuZKBQqJyrdrp/VKEaaKDR6Jy+Zy2sVajae+PbhR1++/3e9/dKEi9sbsfnVNk/nCJT0hhJFNnaHeSJNa08saaVmm7yJ6+fx++y0RVy8W+/uKO5fixbYaLx1HxiMivEs+Cex6EqfH7XpSnTd4YSxLJlEoUqk5ky4+kSH9vSwce2dPD6mThHxjOEPA6eXNPaTL/40iN9V9o8AAGXbZH3ukAguPfxOlR+5aGFFpXHp3L0h+vpXx0N94tWn4MvP9YP1Gsojk5ksCw4MpFZVjybpsWxiWxzu3/76UF+8TIr2Q0d/mYrcMH9ze1O2xgFnrUs6wkgKknSFuC4ZVkfavw8EMJ5PmvavHgdKiG3jf7LcjztqsymTj+qLC0oWgKIeO30tLixq/I1u3IIBPcCm7sCtAdcRLwOuoIu+sKXzo/1HT7cdoWw105vy8rb1wsEggeHrV0BbIrE6qgX3xKey5Iksa07iCpLbL9KcyZZltjWE0CVJbZ2i3vug85tjTxblhWb96sGGMAGSZLeBt4F/ql1D/aBzlU0fnxsGlmS+MTWDjxLnKTjqRI/PxOn1edgbZuXd4YSdIfcPLchyu88tWrBsjXd5MfHp8lXdVo8NrxOFY9D5Xy8wDtDs3SH3LT5HewfTuK0KTiXcL8QCO4GNKN+LOfKGh/Z1E6b3wnUO2H+8OgUZ2J5BqNefmFbB1Gfs7neG2fjDCeK/OKOLta1+zg2keFvDoyzocPPI6vCtPudrI56GUuVmM5WWB2tp2m8eW6Wi7MFHh0Ms75dRIAEggeZbT1BtvUEmUyX+Yd/cxQJ2NUXYjhRxONQeWwwwjProwv6EpimxSunYhwcTeOyqTyxJtJ01nl2fRvPrl9ZwXCxqvPS8WkMs95hNeCqp3HM5qv89GQMr0PhE1s6r+ieJbi7uSM5z5IkbQVaLcs6JUnSGiAN/CfgF4Af3Ikx3QinpnLNhiZnZ/ILnDPmODSWJlWskSrWGE+VKNUM0qV6nlXIs7Aj31iqyHCiiGFa7L+YZFWrl70Xk3gcKumSRrqU5eDopaKovReT9IZF9E1w9zGWKjWLaY6MZ/how+P8xFSWoXieC7MFLMvi2Lib5zfWxXOxqnN4LAPAvuEk69p9vH8hSalm8P6FJHv6W8hXtOYU6gcjKVZHvZRqOodG0/X1LqaEeBYIBAC8fCrGZLpMVTPIlDTKmkHAZUNCYk9/aIGP82yhyunpPCencngdKoq8eJmVcG4m3yxIPDWV49HBuhPVsYkMiXyVRB5GkkWRcnmPctsfeSRJagH+GPgtAMuyUo1o84vA5iWW/6okSQckSTowOzt7W8e6UvrC9fQJh02mJ7S0iB1s9SJJdS/mLY0pn6jfgc+5+PmlPeDC61AbaRv1ZQej3mZ0rc3vZGdfCJdNIeCyXbE4SiC407T7nficKrIksWpeWlJ/2EPAZcdjVwm67Qy0XvrMZVPoauQnrm60r5879le1elBkCZ/T1oxiDzaWcdkUuhrNgeaWFwgEgp29IeyKjMdhY02bl7DHTshtYzDqWSSKQ247Ya+dsMdOi8e+5DIroafFjcMmY1dl+uYFtwYi9WuY16HSHnAuswXB3Yx0O7MkJElSqUeW/1+WZe2XJMkDVCzLMiRJ+v9Qz3/+6yutv3v3buvAgQO3a7jXhGaYAE1zdNO0ePPcLOlSjSfWRDgyliFb1vjwxjaCbjsVzcCuyIynS+wfTrGq1dtsJwp1WKwa0wAAmBFJREFUH2jdNLErMlXdbJq1p4s1/uhn5zg+mWVNm4fP7ujmkcEIpmnx+tk4uYrGM+uiBN32xYNsYFkWbw0lSOSrPLW2dVmv2zuNaVq8cS5OtqzxobXRRVH6u4ndu3dztx6ft4sTk1m+vn+MnpCL33piFXZVbh7LlzfXqekmlmWBRPOzoZk8h8czbGj3sTrqZe9winSxxtNrW/E4VByqjCRJHB5L8439Y8gy7O5t4fE1EToCLizLap4vJyaznJquV8Zf7lGeLWm8fjaO16HyzProFZuy3Cnmn6NPr2sl4r2xc/TyY7P/D166oe2N/OtP3ND6AsEc13Ld/N6hCb6+f4ydvSH+4GPrryhqLcvi7aEEs/kqT6wJcyZWYDJd4sk1EbqCbmqN+/Xc9WQ+Fc3gZydnmMlXsCkSmzoDOG0Kp6ZzbO8JLhsp1g2T187EqWgGz66P4rIpmBaLUjOquoEqyzd03anpJq+dnkEzLZ5bH10yXVRw40iSdNCyrN2Xv3+7v+0vAHuAf9M4YP8p8CeSJBWAYeB/v83juWlc3lFoPF3iyHgGgHSpnvMJ9anrD62LNsXwi4cnOTWdIzSaZmOHv9ntSJElFLn+en6Xo0Njad4amiVdrDGTqxDxOtneG2I6W2lOY++3p5ZtAT6drTSnt/deTPIL2zpvwjdwaxhLlTg6fmm/PnqV1uaCO8s3949xIV7gQrzAwwNhdvaFFhzL81kq1++Hx6Y4F8uzfzjJ3312DUea6RspPr6lo7nc1/aNcXG2wEyugkNRQJL4/K5uJEnCaVMwTYvXTscxLYtMqbZIPB8YTTW9YPsjnrsuUj112Tn6ya137zkqENwu/ts7w6RLNaYzFX55TzcDrUsL2ZlclYON8+cnxzXSpfr99/B4lp4WD84lrkdzHJ/Mcn42zxtnZ3HaZCbTZRw2BVmSSBVry4rnoXiBU1M5AILuDE+vbV1yuaW69F4rZ2I5zjR6RES8dh4bXN5mT3Bzud0Fg98AvnHZ2ztv5xhuFyGPHadNoaIZrG3zcmwii2aYdARcC5abyVUYb+SFfuODMR4fjLCu4T9rmhZvn09Qrhk8tTaC267SH/bgdarkKhpeh0qqWGPvhSTbeoI4bDJVzaQz6FpqSE0CLhtuu0KmrDGaLPLyiRi6adLicTTzspaiVNN561wCt13hiYalzzf2j/HW0Cy7ekP0hj3kKxqKLDHY6mVbT/DGvkSgxXvpe9QMkx8cnWJbd2CB88J0tsyBkTQDEc9VnUdm81X2XkzSHXKxozfEwdE0Pzw6xe6+EJ+8ix8i7gXGkiXKuklZM4j6nHQGF09JJgtV3r+YxG1TKGkGHQHXghmXbKl+o9NNC82oR6wvzhYXOWqsbvUwmizidai47Qqdl01/yrJEe8DBVKZCR8DFicksPz8TJ+Sx8cKmDtoDznohY0Uj6LJR0YwFx847Qwl+dnqGp9ZEeG5DG8OJYrO19+qol3SxxnsXkrT6HIvadE9myhwaTbOq1cOmzsCi31dCsHGOjiZLKLLEZKaMppscm8yysePK3T5rusnbQ7NYFjy5NnJTbtICwZ2mUNV5+9wsHofKbL6CTVXIlOtNSI6MZzgwkmrO1G7rCbGxw0+mVCOer/LJrR0cGE0zkiiSKFRp9zsxLYvxVAmwyJV1OoIuHl8dIeCy0e53UqwalGoG2bLGmViej21uZzRVIl/ReO98gkcHw0tGvVt9DmyKhG5aFCo6Pzw6xZ7+FqayZaYyZR4eCDdnes/N5DkTyy+6n62UNr8TVZYwLRbpCsGtR8T5bxF+p43feKyfimYQ8tjZ2RtCNywC7oXG6dt6gpyJ5alodRGcL+sMtnpQFZmheKEZfXLbFZ5a28pg1Muf/8YexlMlxtNlzscLHB7P0B508pXHBpp/bzk8DpUvP9bPT09MM5wo8YOjU4S9dkJuO90hFz1XsP76YCTN6en6U3V7wIllWfz1gXGShSpD8QLbugPkKzoBl43RZIn+iKdZYXyj32OppvO1fWMYpkU8V+G3n7zkUPLa6Tiz+SoXZgusavXgtl/5sH7jbJyJxvc2EPHw3965SLJQ40wsx9PrWoWx/Q3wyqkYXQEnPofC3312DS1LpBq8PZRgOFHk9HSOvrCboZn6/6Glccx+dHM7NkWmxWOnI+gk4nWgGRbj6RKlmt783/7u04M8v7GdUON8Ci/xtz63s5t0ScNlk/njn5/n8HgGj13FbVN5eFULEa+D2XyVA6NpshWNwVZvc+bnv71zkVLN4EK8wFNrW/npiRgVzWA8VWJ1dDXvnE9wPl7g3EyevrC7mX8N8PPTMyQKNS7MFhhs9fLa6RmS836fP5N0JTwNz9o/ef08hmnx6qkZKlr9hj6aKPJ3nvUuefM+OZVtzkAF3TZ297csWkYguNfYdzHJmViePf0hOgJO2vwO3jqXoDfs5vUzcT4YSZEt1wi47GTLOgGXitdZT/OK5SrMZCsUqzoHR9PYVRlZkohly5SqBiXNYGOnH1mCFzZ30NPi5jef6CdVrDKSLNHqc7CpK4BNkRlLldg3nKI75F6ySD/idfCVxwfIljW+dWAcy4JYtkyhagBQ0Uw+v6sb07T46YkYhmkxk60sctxaCW1+J195YgDDtG74Piu4doRHyi3EZVeaQtbjUBcJZ4BPbunk0cEIq6Ne3HaFkMfWzIMKuS+9Dnvt6IbJu+cTnJspsLU7yJqoD1mSUGSJkNu+4O9dDadNoT9Sn6r2OlScNgW7KuNf4iQ0TItSTSfc2LYiSwTdNlo8DvyNgseAU8VjV3HZFNx2tbHNm3N4uewKYa+juW9h78J9jDR+9zlt2JXl/+Zc7qjHoeC0KbQ3RE/IbcclonQ3RNhrR5IkBqO+JYUz0BTJQXf9f+W2K7jtl773xwYjfPWpVfzG4/247SodAScum7LofytJde/WsNdB2OvAMC3eO5/gnaEEeiOfUVVkWn0OnLb6eWFTpMaxZMfntOF32fA5VVx2Bb/ThiLVnT4Aoo3jIuy1o8pS85ibG//cueCwyYtyDVs89X0PuGzYFJnwZb+vlPkFRWGvvfmAEPY6rpjrGfY4kCSQpKUfKASCe5G5Y9mhKuzpb8FpUwl5bHjsSvNeY1oW5ZqBXZWJeB34nTZcdpWuoIug247DpuB1KPid9fuTy67ic6mostQ4xy+dL51BN19+bICdvSF6WzxEvA4GWusPrDZFwu+6coDG41Bp9TnwNq4L7QEXHofS2I/6dUOWpSvez64Fr0MVwvkOcVsLBm+Uu7lg8EYoVHVShSpQv2nPj0xlSjVquknU7+TIeIbXz8QB+NC6Vnb0hojnKthVedkCweWIZSu47Aqlmo7HoeK/LPJa002++cEYyUKNp9ZG6A65ccz7e7O5KqdjWVZHvThtKppuopkmLR77shHg66GqG8RzVToCTtR5IsQ0LaayZcIeRzNyeCUsy2IyUybktuNxqNR0kyPjada2+a77O5zjQS8Y1A2T6WyFqN9xxXQBy7KYylYIOFWyFZ2gy7ZsoctK/7dHxzP8vHFuPLW2dUEqCEC5ZjCRLuG0K3QHXUiSRK6iNWsRwh47Lx2PMZ4qsb0nyMMDLZyYqqdpeJ02NMMklq3Q5nc2c7WnMmV8TnXRbIVhWkxny0S8deF++e/XQkUzmM1X6Qy6MC2L2FW+X6inxliwoMhQFAwK7lZWet2cu1d57ArT2UrzwbhU09l7Iclb5+JYSDy9rpVn17dRrOpkyhqdASepYo3hRJE2v6N5nc9XdGLZCj89We/R8JtPDCzwmgeI5yvYZLkpdKezZdz2lQnWcs0gWazSGXBR1ozmWOYefOfuZ+0B5zU9VAtuL3dLwaBgCbwOtfmUejnzBd38ZeYs7qL+G7O6mYtsXelikKtoJAs1AC7OFtnVt3AauNXvoNUfXWrVm45DVZZMKZFlie4rWARejiQtXNauyjw0cOU8b8HKURX5iik/c0iS1LSh864gRWal/9v5lo/+JewfXXaFNZcV+vidtubDYk03GzmQcDFR5Jn10QXHhW2JfbtSbYFy2Zgv//1acNouHfMK0lW/XxARZ8H9yXxbt/nngduusq7Dz7HJLJYFrd76ch6H2nwwn5uhmo/PaWMsVcJlqy8zmS4vEs+X/34tucUuu0K33b1oLHNc6X4muDcQ4vkeYnXUyxf39AA0BcitJuyxs6UrwHS2XuwgENyNrGr18kt7ejAt67qEql2VeWRVmKF4nt19Ik9YILiX6Aq6+JWHeqlq5jU1DNvY6WcsWUKWpWahvkCwEoR4vse4XaJ5DkmSeH7jytqRCgR3kqu5zFyNRwfDy7rNCASCu5e265iF9TttzYCUQHAtiEQbgUAgEAgEAoFghYjI83VyPp7n52fitAdcfGJLB4oscXQ8w96LSVZHvficNg6NpdncGeCJNXVP5H//6jmOjmd4YXM7v7Snt7mt2XyVHx2bwqEqdIec/OREDMuCT2/voqIbnI3leWighZ29oSsNZwGlms73j0xRrhl8clvHorwtgeBWcWwiw4+OTZMp1nhsdYRPbu1YUNwJ8H/+9Cw/OTnNpk4//+6L2/mD7x7n0FiaZ9a18s8/uQmoW1MdHs+wpSvA46uF+b9AILj5aIbJD45MkS7V+MjGNk5M5RhPlXh6XSvr2/3XvL3RZJFXTs4Q8dXTHf/n3lFShRqf3tHF8xuWn8E9NpHh/QtJBlu9C2Z74/kKPzo6jcuu8JntXUsWTld1gx8cmSJb1nhhc3szda1Q1Xnx8CS6YfIL2zrv6nqIRKHKD49OYVdlPrO9667vmCgiz9fJkfEsxWrdBzZZrDtlHBxNU6oZHJvIsvdiknLN4MBoCsuyyFc03r+QpFQzeO10fMG2Tk/nyJQ0ZnIVXj0dZyRRYiRRZP9wkiNjGco1o+n3vBKGE0Vi2QrZssbp6fxN3W+BYDkOjqYZTRYZTZUYmskz23CRmaOqG7x2pu5ZfGQ8y9HxDPuHk1Q1gzfOzjZt5g6Mpuvnz0iae8kRSCAQ3DtMZcqMpUrkKzp7L6Y4G8tTqhnNzqbXytGJLIWqzkiixFtDCS7OFonnq7x7PkFNN5dd91BDPxyfzFKuGc33T03lyJY1YtlKsyvq5Uymy0yky+QrOicms833L8QLzOarpEsaZ2N3txaY00HxXPWK+3k3IcTzdbK+ve6x3BFw0tJwxNjY6UeSYDDqZWujW9mGDj+SJOFz2hqvYc/Awgjy6qgXuyrjc6o8PNBCxGcn4nOwuSvQbAW6oWPlT8E9LW58ThW7Kt91bYcF9zcbOvy0eh3NJidzHsdzOFSFXf0hJEliVcTNhg4/69r8gMSOnmAzSr2x09/Ynu+KnsYCgUBwI7T5nUQaXu7beoJ0h1xIEqy/hvvtfNa3+1BkiYjPwe6+EG1+Jx67yrbuQNPi8krM6YNVrZ4FPRLm64OelqXrOjoCLlo89f2Y3z68L+zG61Bx2GRWtd7dWmCw1YvDNrefd78LifB5vgFM00KWpSu+t9Tnum6iLnESzf0fJEnCNOuvl9vO1bAsC8vimtcTXD8Pus/zHJcfv0tRqxnY500/Xv773HbE8XtzED7PgruVu+G6ebX79o1sC1Z+H77S3zZNq9H8aPntLLX+fG1xt3M3jlX4PN8CljrI57+31OdLCWdYeLBcvt71nMiSJHEXHX+CB4iVHK+XC+XLf1/pdgT3JkK8C+4mrnbfvl3butLyK93OUsvdTUL0atxLYxVpGwKBQCAQCAQCwQoR4lkgEAgEAoFAIFghQjwLBAKBQCAQCAQrRIhngUAgEAgEAoFghQjxLBAIBAKBQCAQrBAhngUCgUAgEAgEghVyV4hnSZL+SJKktyVJ+g93eiwCgUAgEAgEAsGVuOM+z5Ik7QS8lmU9KUnSn0qStMeyrA/u9LgEAoFAcHcifKIFAsGd5I6LZ+AR4GeN168CjwLLiueKZvDdQ5PM5itYloXDpuJzqqSKNTTD5KVjU6SKGv1hNz0tLl4+OUPNsHCoEs9vaGMyUyZTrCEBsXwV3bRQJAuf0062XKNmgE2W8DoVcmUd4xqaMEqAKoF2Cxs3SsDc5hUJVkW8/Omv7UQ3LH7vrw6SKWt8bkcne4fTjKVKPLUmws7eEP/h50NUNZP+iAunTWUmV6U/7Oaff3IjGzvr7cT/+PUhvnNggvaAi99+YoBT0zl6w27a/E72D6dY2+blhc0djCSKvHR8mqDbxud2duO0KZycyvLz03G6Qi4eHQzz4uFJTk7mKNV0+iIePrSulZNTOfxOG5/f1c0bZ+OcmynwyKowDw20YJgWLx6eZCpT5tkNUTY1xjSfl0/GOBvL89BAC16Hyutn4vS0uPnUts6mQfx4qsQPj03hc6h8flcPLrtCrqLxnYMT1HSTz+zoos3vBGDXv3iFZEnDpkgM/eHHV/T9HxxN8d75JKtavXx8S/siY/f9wyn2Xkw2v6uliOcrvHh4ElWW+dyubgIu26Jlfn5mhhOTOXb2hnhiTWTBZwdGUrx3IcnqqJePb+nAMC2+d3iSWLbMcxva0AyT//LWRQzT4suP9fPwqvCC9V86NsW/f/Uc6ZKGZVmUazqSJNMZdPL02igeh0KxajCcKLK9N8i2riB/+f4I5+J5ChWdbFlrnhchl8Lm7hCZYo2RZJFC1cCiPq1lrugbvXEkQJLAJkPVWPj+HHZFwq7W9/Ejmzp442yc87NFnKrM2jYvdkVmMOrlraEE+bLOx7a0848+uo5CRecHR6dwqjIW8PrZOIMRD+s6/Mzmqjy5tpXtPcHm33n11AynpnMMRNxcmC3y5rlZUoUqW7qD/MMPr+G1M7MAfG5nN3uHk/zgyBQ+p8qvPdLPlu76MT+eKvHvfnaOXFnj1x7toz/sWXS+3UluVLzeaYT4vvuwLIsfHp3ipWPThL0OvvJ4P2vafBwZS/MvXzrFxca5WtNN8lUdC3CpMg6bgm5aYFmUagYmYJclZBlAolQzFtzDPXYFhypRrBlYFngcCrv7WxhLlhhPldANE1mWsEyL6hIXMAka7bMtKpqFxcJ7sk0GywL9Mg3Q4bcTz9eW1BPNbUoSdlmiWNXRrfr9HVhWgyiNi99A2EWb38W5eJ5cWQMk2vwONncGUGSJ4USR8/E8Fd1CkWBrd4D/9dk1nJjKEXDZ2HsxyZHxNPmKTlU3cdkUuoIuLswW0Q2TjqCTLz/aj9OmkK/qhNw2jk9mOTudoy3g4he3d/F/vnKW6UwFt1NhU4cfl10hXazvs8eh0BNyMxQvsKM3xJcf7eOb+8c5NJ5iNFFCMyx6Wlw8tjrCE6vD/Oc3h6nqBv/gI2vZ0hUEQDNMvndokni+wkc3tbNmXivy+bx8MsbB0RRHx7NYwNqolw2d/mWvnXOaJuS289mdXVe9xt4N4jkIXGy8zgKb5n8oSdJXga8C9Pb2AhDLVpjJVUgVqySLNVZFPJyayrKq1ctrp+Nkyhqlmk4sV2EiXaLWOPKqusX7F5M4FImqblKo6pgW6CYYQLJYo9FNE820yJb0a775W9xa4Tz3N+YwLBhPlzg6niVVqJAo1sCyePXMLMlCFYB9wynyVZ1S1cC0LEaSZbwOlapmMJOr8u75JBs7A1Q0g7fPJagZJpOZEj87HaPN7+LibJGJdBnDtDg9neeZ9VFOT+eo6SbxXJVYtkJ/xMPJyRy6aTGaLOG2KSQKNSYyJTTdwqbKvHU2gdepMqtVGUkWOT2dB+D4ZJaHBlpIl2qMpUoAnJzKLRLPmmFyaioHwInJLB6Him5aDCeKZMsaIY8dgLOxPFXNpKrVmEiXWNPmYzRRIlPSABiaKTTFc7LxnmZYvHMuzhNro1f9/o9PZNFNi3MzeZ5dH8V1WXe8YxOZBd+VQ118Ep6fKVCsGoDBSKLItnniC+o3kmMTWSwLjk1mFonnYxNZDNPibKw+hlxFY7zx3Z2YzFKs6cTz9f//gdH0AvFsWVZd0BVrFGsGumE2jnuDWK7KqaksnSE3sWwZzbAYS5bIlXVGkiXSxRplbeHNqFA1OD6RwWlTmsIZbp9whvo5YVkLhfPc+3NUDQvDMpjJVnl7aJbRZImabqDpBhfiRXrDbt46O0umrKEZJscmMowlS8wWqpRrBtmSxrmZ+rE1FC9Qqpm0B5ycmMw2xbNpWhyfzALw9lACWZKYTJewLDgfL/DWuQT5ig7AuZk8R8YypIo1ChWd45PZpng+MZllKlMG4L0LCco1Y9H5JhBcL3fy4edKDx4VzeToRIZMWaOim5ycyrGmzcdbQwkS+SrFqk65BjW9LlgtwDAMypoBSJiW1bwuaYaFKtXvj5ffjks1g4oG5txnVYNjE1lqukFFb1wLzSvfxK3GWK3L3ptDu8KFL5arLRrL/PXLmokiQWXemFcSuDMskCyL0WSZmmGRK2kNzWMxk6vitheQJYnpbIVKQ9EbFlyYLfLzM3HCXgfvDiWYyJTIlurfvWVB0dQZTRapGfUdiuernInlcNtVwl4H75xPki3ViBdq2FSFbx0cI12qUdZ1jLLJmViekNtORTco1Qx6Qi7eGkoQ9Tk4MpZmR0+AyUyJqUyFRKGKIkuMJEzWtfv58fEY6VINgHfOJZriOVGoMtm4Ls4dH5czpxMmUmWms2UcqsL52QJhr2PZa+ephqaZyVVWdI29G8RzFvA3XvuBzPwPLcv6M+DPAHbv3m0BdASddAScqLJExOfAqSo8tjpCslDloxuj/PDYNJYFnQEX/a1ufnRsGk23sNskHl8dYTJdJl2sEnLZmc5XkI36k5jfZSdTqlI16lEqn1MlW9IWPUEuhwSo8pVPoJvBXDTNov7U2Rd2s60niGlZfOODCbJljY9t6eDd87OMJcs8NhhmR2+IoxNZKprBYMSDw6Ywk63QHnDyZEOYOW0KH1rXyt8cmKAj4OSjm9o5NZ2nt8VNu9/BvuEUa9p8OFSFTZ0BRpIlQm4bHcG6EN3SHWC2UKUr6GJHX4jRVIneFjfFqkF30MWz66Mcn8rid9roD3vY1Onn3EyebQ3BEHLb6Y+4mcpU2NK1OOpsU2Q2dwU4G8uxtTuIx6GQKtboDrkWRG7Xd/i4MFvA41DpDrkB6I+4afHYqekma9u9zWVbvXZmCzXsirQi4QywtSfI+xeSDLZ6FglngO09wUbk2bekcAZY0+bj1HQOVZYYaF18kkqSxLaeICcns2zvDi76fFtPkPcvJFgd9eG0KdgUufndbe0OohkmR8Yy6KbFwwMti7b93PooR8czKCUNyzIp1QwkSaIz4GRzdxCPXSHssTOcKDIQcbOlO8hYsohumuQrGtmyhm7Wj0WfQ2Vbb4hUsUapppOv1AW0Qv2h9HYwF3m2K9DQplze6NWuythViY6gi2fWRYFZzs3kcakKa9t82FSJnX0h3jgTJ1fR2dkToi/sJuy1MzSTJ+yx0RF08PMzswy2eljX7mM2X2Nr96VjVZYltvcEOTmV5em1US7MFuht8ZAsVlnX5uVD66O8ejqOBKxr95Eta0ymS3idtgXb2dwVoDvkIlfWeHJ1K71h96LzTXDnEJHrm4/TJrOzN8RkukLEZ2dzV10WfGhdK+8MzVKoGjhtMjXNINs4yd02GbutHkSRsChU6hFpmyKjyoAkUajoiyPPNoli40Hf41DZ2RtsiM8immagKDKWaVJZ4gI2FyWWGpFnk4WRZ7sClrk4iNYVcBDLVZfUE1JjX5Ak7IpEoaqjmSuPPEuSxKpWD1Gfk6puki1rYNW10tqoD0mWUBWZSi1PWTdRZFgT9fLhjVGOT+Z4el0r711MkilrFCoaFa0eee5uqUeKDcOk3e9kU2cAh60+k/vU2ggnJrNUdYNWn4PP7OhiInWGqmbWI89dAZw2iXRRwzTB7VDY3BXg3EyBnX1BtnYHOR3LkyjUMEyLmm42ZrkdPD4YYTJTpqqZPLW2tbmvrV4H3SEX8Xy1eXxczpxOqOoG6cb3sKbNQ0fAuey1c1Onn9FruMZKlnWLw6RXG0A95/l3Lcv6XUmS/m/gLyzL2r/Usrt377YOHDhwewcoEKyQ3bt3I45Pwd3I5cfmvZ52Ibh3ufzBQVw3BXczkiQdtCxr96L377R4Bmi4bOwEjliW9b9eablIJGL19/fftnEJBNfCyMgI4vgU3I2IY1NwtyKOTcHdzMGDBy3LshY5090NaRtYlvX7K1muv7//qk+o46kS75xP0BV0LQj3CwS3mjsZQZnLY57OVnhqbStdQdcdGYfg7mTu2CxWdV45FUNC4qOb2pdMOxIIbici8iy4m5Ek6dBS798VPs83k/cvJIllKxwcTZMu1u70cASC20KiUOPwWIZYtsL7F5J3ejiCu5STUzlGEiWGE0VOTefu9HAEAoHgnuS+E889LfUCsRaPHa/zrgisCwS3HL9LJeiuF032Ns4BgeByOoP1QmubItEpCg8FAoHgurjv1OWjg2E2dvpx2+sOBALBg4BDVfjSI32UasaSntECAUB3yM1vPTmAhCRSNgQCgeA6ue/EMyDEg+CBxKbIBFzigVGwPG77fXnZFwgAYSMouD2IO61AIBAIBAKBQLBChHheIZZlMZosNrv2CQT3OtmSxsXZAuYy3bQE9x9TmXrnLYFAIBBcH2L+boXsG07x/oUkqizx/MY2ukMufM6rp4dUdQPNsPA6xFctuL1kyxoeu4K6RO5/qabztf2jVDWTbT0Bnl3fxlSmjM+prui4FtybnI/n+dq+MQB+7ZE+VrV6r7KGQCAQCC7ngVB0umFSM8wbyvXLlTUARpJFvvXBOK1+B7/+aP+yojhX0fj6vjEqmsFHN7WzoWPpdpICwc3mnaEEH4ykiPgc/MqenkUCuqKZVBs95HNlnb0Xk7x/IYldlfn1R/tui4DWDBPdsETh2m3k3fMJXjkRAwnWtfmEeBYILkPkTAtWwn0vniuawTf2j5EpaTyzPsr2nuB1befx1REUWUKRwTChqpkUKvqy4nk2X6VcM4B68xYhngW3i7FUCYBEvkpJM/BfJp5bPHae39DGdLbMQwMtvHu+7g1d003yFf2Wi+diVefr+8Yo1nTxYHkbSZc0qkb9oUn44AsEAsH1cd+L50xJI1NqRI0TxesWzx6HynMb2tjVF+Ld80kiXjvtgeV9UvvDHjZ0+MiVdXb1ha7r7woE18Pjq8O8ez5JX9iN/wpCeEt3gC3dAQCeWB0BIOy103kbuhPG81UKVR2A0WRRiOfbxOYuPz0hN5IEm7sCd3o4AoFAcE9y34vnqM/Bpk4/8XyVPQMtN7y9oNvOJ7Z2rGhZRZZ4YfPKlhUIbiZ9YQ99Yc+Klw+4bSs+rm8GPSEXa9t8ZMsaO8WD5W3jkVUR0kUNWZLY1S++d4FAILge7nvxLMsSH9nUfqeHIRAI5qEq8m0V64I6XofKF3b33OlhCAQCwT2NsKoTCAQCgUAgEAhWyH0rnks1nVJNv9PDEAjuCaq6Qa6i3elhCG4D4tooEAgEN8Z9mbYxmSnz3YMTAHxuV/dtKYASCO5VClWdr+8bpVg1eH5DW7OIUHD/MZEu8b1DkwB8fnc3HQFxbRQIBIJr5b6MPE9nyuimhW5aopOWQHAVUoUaxWrDUjFdusOjEdxKpjKV5rVxKlO508MRCASCe5L7MvK8sdPPZKYumjd2iCiaQLAc3SEXm7sCpIs19vTfuCON4O5lc5efqUwZSYKNwh5QIBAIrov7Ujy77Sqf3t614D3LssiUNPwuG4osAfU8T8O0bqjzoEBwJ5g7nn1Odcn221fDNC0KNR2fQ0WWJT68se0WjFJwt+G2qzy5JoIkITo7CgQCwXVyW1WjJEmbgT8DDOA88JvAvwN2A4csy/r9W/W3f3IixtlYnu6Qiy/s7iFdrPHND8ap6Saf3NbBoGhTK7iHeO10nOOTWdr8Tn7loR4kSbqm9X9wdIrhRJF17T4+vkVYxj0oHBxN8W9fOYckwT/56Hq2XmfTKIFAIHiQud0h17OWZT0GIEnSnwMPAV7Lsp6UJOlPJUnaY1nWB9ez4ZpukirWiPocyPJiITHRyOWczJQxTYtYrkJFq+d5jqVKN008l2o6TlVZcgwCwc1iLjd5Jlehqps4bSuPIlqWxWiyvv5IsnhLxleuGdhVuTnLM4dpWswWqoTcduzqfVlycVdzYjJHtXHdOzGVuyniuaIZKLKE7TpmQASC+43+P3jphtYf+defuEkjEdxKbqt4tixrvhdWFXgO+Fnj91eBR4FrFs+WZfHXB8ZJ5KuLImnJQpVEocYTqyMcnciyocOPLEsMtnoZjHqp1Ax23KToy5vnZjk0mqY75OLzu7qvORooEKyUJ9e08sFIitVR7zUJZwBJknhqbYSTU7kl29VPpEvUdJNV1/lAeXQ8w+tn4wRdNn75od4F43vlVIzT03nCXjtferhPPGTeZrZ2+/n6vlEkSWJL143nPI8mi3z/yBQ2ReaX9/QQ8thvwigFAoHg7ua2J/tKkvQp4F8BQ8A0kGt8lAU2LbH8V4GvAvT29i65Tc2wSBaqAMSylyrIC1Wdb+wfQzMstnQF+JWHLq1vV2U+ta1z0bYqmkG2rBH1Oa4ofl8/G+dcLM9DAy3s6L3U4vbibAGAiXT5mqOBVyNTqvGdQ5OYpsUv7uwi4nXctG0L7j1WR72sjl7/bMmO3hAht50fHpvixGSWT2/vwmVXGE+V+HbD5vG5DVG2dgeXXH82X+XFw5PIssTnd3YTcNuanw0nilgWpEsa6VJtgR3aTK5+nqaKNWqGiVMWebe3k70XUszkKoDEB8Mptlzh/7tSRpIlDNPCMA2msmUhngUCwQPBbZ9nsyzrB5ZlbQYmAB2YC3/4gcwSy/+ZZVm7Lcva3drauuQ27arM8xva6Au7eXZ9lPFUifcvJEkXa+imBUCpMVW5HFXd4K/2jvL1fWO8NZRYcpmabnJkLEOpZnBwNL3gs0cHw7R47Dw80HJThTPAxUSRXFmjUNU5Hy/c1G0LHjyyZY0/fn2Id4YSHBpLM5yop2+UapfOk/mvL+d8vEChqpMra1xMLDwed/eHiHjtbOjw0+ZzLvjsmXVR+iP18/RGzpF8ReP9C8lmOpZgZQzF85RqBqWazrl4/oa3t7UrQHvASX/ELepGBALBA8PtLhh0WJZVbfyaAyzqqRvfAp4H/uJ6t725K8DmrgDlmsF/ffsiumnR2+Lmhc3txLIVdvWFrrqNcs0gX6l33qpHZxZjV2XWtHkZmikssnpa3+5nffutsX8ajHg5Op7BMC3W3EDEUSAAyJY0fA4bslzBtCx6WurR4bVtXvKVCDXdXPacWdPm5eRUFkWWWBVZeDx2h9z82qP9S67XG3bTG3bf8Ph/eiLGRLrMgRGJ33lq1U1/WL1f6Wlx13OTJegN3fj/IeSxL5jREwgEggeB25228YIkSf+g8XqIejrGH0mS9DZwxLKs/Tf6BySJeh6laaHI0lUFbaZUnz6O+pwE3XaeWBNhIl3i0VWRK67zya2dmKZ11XxNy7I4Hy/gc9poDziXXfZqBNw2vvL4wA1tQ/BgUdEMEoUqHQHXosK9nhYXz6yPsrUnwIfWtuJz1tMuJEli9zyv5+FEEVWW6GlZKLQiXge//eSqW78TV0BV6vsjcqavjQ+ti7J/OImExBNrl57JEwgEAsHy3O6Cwe8D37/s7ZtqT+e0KXxhVzdT2Qrr233LLhvLVvjWgXEM0+JjW9pZ3+5nT3/LihpFrOSmvfdiir0Xk8iSxN96uJdWn8hTFtweTNPim/vHSJc01rR5+eTWhfn99aLB5cXTyaksr5ycAeAzO7oYiHhu2XivlRc2dXB2Jk9n0CmiztdAqlCjUDWRqAcOBAKBQHDt3JfdQaJ+J1H/1SO9qWINo5ETnSwsfSOp6gYHRtLUdIOpbIWgy84Lm9sXRfKWoqzVU0BMy2ra4gkEtwPdtMiU6+Y2iXz1KksvzfxjtlTTb8q45pMu1jg6kaG3xd109jBNi1dOxUgUajy3Ibqg2HA+LruypFOIYHlGk0USjeLqsZTIFxcIBILr4b4Uz5czk6twfCLL6qiX/nnRs3XtPmbyFaqayc7epfM7911McXA0zfl4nojXQdBtZ1Onf8F2EoUqEhC+zAHjscEINkUm4LItmvYWCG4ldlXmwxvbuDBbvG4rxm3dQWq6hapIbLhKLn+qWOPwWJreFjdtASfFqn5F4TvHK6diTGUqHJvI8jtPrsJlV5jKljk9XS9k+2Akzae2Lb8NwbVhWfWAgEQ9rUwgEAgE184DIZ5/cnyadEnj9HSO3/vQYLOdsSJLPLMuuuy6cy1sQ247NkXG51QXpF+MJIq8eGQSgM9s71ogqp02hSfXiLxCwZ1hU2eATZ2B615fVWQeHQyvaNmfNYTwgdE09sb59cSayLIpUHPpFjblUjOViNeB32UjX9EYCN89aSL3C+lyDc2oi+ZM6ebPJggEAsGDwD0tnnXD5JVTMxQqOs9vbKPlCh6jHodKuqThsisL0i00w+T0dI6w10FXcOkI1+6+EMOJAhXNYHdfiGfWR5viGyBZrDEXwEkWawvEs0BwP1Gq6ZybKdAVdBHPV3jj7CzdIRe/sLUTr8MGVLAsC900UWW56b1+JT62uYMPRpKcjeV542yc5ze04bQpfPnRPmqGidt+T1+e7kqCbhuWZSE1XgsEAoHg2rmn+6mOJIucjeWZzJQ5dJnn8nw+saUDr0PBtCwmM+Xm+2+cneW103G+fWCCbElbcl1JkpjN13DaFE7H8guEM0DIbSNTqqHKElu66lG+fEXjR8em+PmZmWZOtUBwr/PSsWlePxPnbw6Oc2QsQ003OTqe4S/eG8bjUPjk1g5+7+lBIl4H2bJ21Q6FdlUmXdLIlnVOTuWa52a2rPHdQ5N8++CEqBW4yQzN5CnXDEo1g/MzN+7zLBAIBA8i97R4bvU5cdkVJIllvWNTpRqFqkGxanBoLNN83zBNACwsjGXy/7Z0BVDmieP57B9OEXTb0U2LcqOpxIHRNEMzBY6OZ5tdBwWCe525B0HTtNjQ6UOVJUo1g0xJ4/BYppHzL5EpaQRcNg6PXfmBdo6+lvpMjc+pEvbWZ46OT2aZzVcZT5W4OFu8ZfvzIDKdvTQbMJld2steIBAIBMtzT8+LBlw2vvJ4P7ph4XFc2hXTtPjJiRjj6RJPr21lIOKpR4jLGoOtHsZTJc7E8qxt8xF022n1Oa6Y8gHw1NrWK9p6dYfcTGcrtHjsuB31HM5oIyfapkjLblcguJs4E8sxkSqzuz9E0L34uP3Ylg5OTmXpC3voCrrY2dvCexcS7LuYIui24W2cgz6nSr6ir8jxZkt3gP6IG4eqYFfrz/KrIl6OT2Sxq/IV06muhdfPxjkzneehgRC7+q5uQ3k/87HN7XwwUn+o+fjm9js8GoFgMf1/8NKdHoJAcFXuafEM4FAVHJftRbasca4xJXlkPMOGDj+//mg/mmniUBX+5PXz1HSTkUSR33nqxho9PLEmwqZOP16nWu/cBWxo93N6Oke+oou0DcE9Qbas8dMTMSyr/vpzu7oXLRNw2XhscGHzoMcGI2zs8OO2q03x+6VH+siWteZD5Hwm0iXeOpegPeDgmXVRJElqNmiZozfs5nefHkSRpRVZQi6Hbpgcacw2HRrNPPDiuajpRLw2JKC4TPt1gUAgEFyZezpt40r4G9ZwsiQ1W2jLsoRDrUeGfc662vZcrrobjCaLTKRX7oEa8tibwhlgMlNmPFUmU9KaUR6B4G7GocpN8et1Ln1e1HSTs7E82fLC+oCg295cF+ouGm1+J5K0WPjuu5hiJlfh6HiW2WUKCu2qfMPCGeqOIevbfUgSbOxc3m7vQcAmyegmaCao8n15+RcIBIJbzj0feV4KRZb4/K7uelX5Ejfwz+/qZjJdXtJ7+dRUjpdPxoDr76oW9trxOVUKVZ2+ZXKxBYK7BadN4Vcf6mO2UGEgsnSh34+PTzOcKOK2K/zWEwOLimdXQn/EzViqRNBtI+C6PW4PH9vSwQub25e8FjxoDEa9tPsdSJJ0V3WMFAgEgnuJ+1I8zyFJEtmyxssnYzhUmRc2t+NQFdx2lTVtS7funt9JrVhd6INqmha5Sr0Yarkbsduu8uXH+qnqZjMPVCC42wm4bQSWsS8rNs6Nqm6imxbqdXTF3tXXQmfAxVtDCX58fJoXNnU0vdTnY1kW2bKGz2m7KRFoIZzrSLKE32VHAkTgWSAQCK6P+07ZZcsah0bTdIVcrG3zcWwiw2S6boE1NFMg5LHz0xMxgi4bv7CtE7sqY5gWlmWhKjJOm4IqS2zo8LOxw09VN/juoUnSpRqKVHcXWNvm4xNbO5Ydh02RF6RyNMdX0vhgJEVH0HlDDSwEgtvNRze1c2wiQ3/YQ7lm8N/fGSbosfHFXT3XFIW+MFtgKlMCJM7Ecuy4rLtnTTf5w5dOMRQv8OiqMH/n2dVN8Zsp1fjh0SkkSWJbd4BYrsrW7gBtKyhOFMDRsTTvDs2CBE+vifDQwMqa4AgEAoHgEvedeH7t9AyjyRJHJzK0B5z0hNwcHsugKhIdAScfjKTIlTVyZY2JdImg2863DoxTrhl0h5ycms7jd9rIljVkWSKWrhDL1ps/XEwUGWz1MpZaeT705fz87AwjiRLHJ7N0BV1LuhoIBHcjEa+DZ9e3AfAffz7EO0MJADoDLh4bjHB0IkPIbWN1dOlZHYAPRlK8djrOZKbMlu7Akm4aM7kKw4kihmlxajqHZljY1bp4PhPLkyjUMC2Lr+3L0uZ3Mpku8RuPD9yCPb7/+JsDY+gWYME3D4zxlSdvrGBaIBDcXG7UbWTkX3/iJo1EsBzXLJ4lSeq1LGvsVgzmZjDXlcymyNhkmf6Ih995chWyXHfmWNPmY2imgM+p0hFwMRSvNw04E8sxnCgwnangc6k4bfVIWnvAiWGaHJ3IsL7dj2FaPLkmstwQlsXnqE+L21UZmyKmkgVX5vR0jlNTObb1BJYVpLcby7IINR76FFki4LLxzvlZjo5nAfhbD9uuGAkemingd9lw2xV+aXfPIju7i7MFhuIFNnb6uDhb4oXN7QuKEVdFPBwaSyMjoQYkTIsFbh2Fqs7rZ2aQJYnnGh0LBZdo8zsZTtb9ndsDIlovEAgE18P1RJ5fBHbe5HFcE5ZlsfdiikypxuNrIvjn3Tyf3xBlIOIh6nM0cynn51QOtnr5X55ZjSzV8yBXR72cmc4zna0QdNvIlnWiPiflmsFsvsJMrsrxybrt3CunZvjM9i5ylaW7Ea6EZ9ZH6Y94kCT4q71jSBJ8bmd3o8GEQFDHsix+dqreoXK2UL1rxPNYssQPj03htit89alVRH0OtnQHeeNsHABJqqddnJzKkitpJEs1dve1NIXanv4Qbw0l6Gtx03qZlV1VN/jRsWkM06KnxcMffGzjor8f9Tv5208NAlDSDGLZhYW/Lx2b4sXDk1hW3Yrti7t7btVXcW9iXXpgFyaaAoFAcH1cj3i+4+HSiXSZvReTQN2C7qObLpn9q4rMuvZLQkMzTL53eJLZfJWPbGxjTZtvQQGS267yxT09fGp7J6enc6yOeplMlwm5bfzle6OMp0vEshVcNhm1sV6jMeF1och1wb5/OEWp4bM6kiwK8SxYgCRJtPkdTGUqtN9F+bxnZ/LUdJOabvLkGndT1D+xOkKLx07QZefAaKreYXMiS1+LmwMjaf7hR9bic9pY0+a7YrGuIkk4bTLFqoHHfuVLk9w4D70OddFDharIWMwVCF6Sh2djeV49PUPU5+AXd3Rdl1PI/UC+eunBP1++/iCAQCAQPMhcj3jukiTp/7rSh5Zl/d0bGM+K8Dtt2FWZmm4SvkoHv0Sh2iwYPDGVXXTjrmgGpmXhtqvs6A2xvSfIbL6KZpr8s++eoKwZ9IXdfGJLO2GvE5ddZmt38Ib3YU3Uy6mpbD363Xp3RBUFdxef3dlNqlgjchc9WG3q9DOSKOJzqnSHLkV8VeXSefHehQSKJIFlMRTPE/U5ee10nE9v7yRf1fHa1aYAno+qyPzSnl5i2Qr9keuzePzk1g4CThuGafLkvK6gJyaz1HSTiXSZZLH2wBYYrmnzcTpWQALWtgnfa4FAILgerkc8l4GDN3sg10LAbeNLj/RRrOp0XqV9b6vXQXfIRTxfZfNl7hbJQpW/PjCOblh8altnI51CIup3ki1prO/wEctWeHx1hE/vWNxx7UYIeeyiyEmwLDZFvutEXmfQddWunC9sbufYRJbnNkR56fg0TpuC06bw8skZTk/n6G1xL9nBEOpdDG/E/9mhKjy/sW3R+1u6A8RyFdr8zqs+cN/PPLehjXfPJ5EkmsWfAoFAILg2rkc8Jy3L+h83fSTXyEpvsqoi84Ur5D1OZytUNRPTsnjn/Cweh9rMwwy4bfzm4wOkSjXWLJNvmq9oWLAg7/pG0A2TRKFG2Gtf0upOILjb0U2Lh1e14FAVulvczOarrI56+e/vDAMwni5hmtaS0ec5yjWDoXgeuyqzNupbdtmVsLbNx9orpIs8SLT5XWzvCSJJEFmifbpAIBAIrs71qLPaUm9KkvSEJEl/coPjua2safOyqtVDplRjeLbIX38wtqAxStTvZG3Ux39/Z5h/+aNTi1p2T2XK/Pm7I/z5OyOMJS99phsmB0ZSnJrKLVi+ohn84OgU3zk4wcVEgXiusmhMPzg6xTf2j/G9Q5M3eW8FgptHvqIxniphWQvLzt4ZSvA/3x/lr/aOUdUNIl4HGzr82BSZJ9ZEcKgyVc3g9bNxLKvur/7G2Tjf2D/WPL8qmsGfvnmBP3zpNP/5zYu836hvWA7NMKlo9RqCdLHGexcSzDTOr4pm8PbQLCcmszf5W7j32Hsxwc/PzvLamVkOj179exUIBALBYq5ZPFuW9cjca0mSdkiS9H9IkjQC/EvgzE0c2y3HoSr8wtZOdNPixFSOk1NZjoxnmjdhgAOjKV49PcOJySxf27vQoW8mV8EwLUzLIjZPCO8fTvH2UIKXT8YYTRab7w/NFLgQL3BsIsMf//w8X98/xoXZwmXbrDa3Lbg7OT2d48/fHeatc7N3eih3hHLN4D+9eYE/ef08r56eWfDZVLZeX5AraxSrxoLPNnUG6Aq5sKsKxyayxHIVEoUah8cyxLIV9l5MUajqfH3vGAdHU+imSbGqU9UXbudy0sUa//XtYf7srYuMJov88NgU+y6m+M6hCUzT4u2hBAdG0vzs1AzTjfE9qHxj3yiGaWGYFn/5/uidHo5AIBDck1yzeJYkaa0kSf+7JElngP8IjAGSZVnPWJb1H6+y7sOSJL0nSdI7kiT9UeO9/63x+9ckSbo5uQ9LcD5e4NsHJxZFn6q6SdBtp83vIF3U2D+c4uWTsebn7X5n02d2fn61ZVkE3Db6wm7WtHnZ0nUpn3q+m8f81+2B+rZqhoXPoWJZ9Y5p8/nwxih9YTcf3iTyEe9W9l1MkilpHBxNU64tL+zuR2YLVQ6NZhhOFHnvQmLBZ0+uidDT4uaRVWFalsgtnrOV8zlVFEnizXNx4vkKpmXRE3JxZjpHtqLR7neyps3LZ7Z38tjg8r7qU9kyFc3AMC1Gk6XmOadIEpIErobXsyxJ2B/wVCjnPNtOl+PmfBfxfGXRdUwgEAjuZ64n5/kM8DbwScuyzgNIkvT3V7juKPCsZVmVhlh+GnjGsqwnJEn6J8BngL+5jjFdlZ+fmaFYNZhMl9nY4W/mULrsCo8PRjgTyzHWmIY+Np4lV9F5ek0rvWEPf/iZLSQKVbb3BJvbe3sowcHRNG67wq8/2o/LrpCvaCiyxJ7+FjwOFY9joSNBq8/Bbz0xQEUzODyeAQu2dAUXjHN11HfXePoKlmZ11McHIym6Q65mM537kbkUJo9j4WUi4Ko/NOYr2iLHho6Ai89foRgQYGdviNVRL05V4e2hWcZTZSJeB89taGN7T5B4roJdlVnV6uXzu7qXLJgcT5V449wsnQEnz66PMtjqZSBSoKIZbO0OsKM3yPl4gb5wvQD4scEwrT4HQbftgbeE/OiGdv7LO8Mgwcc3ddzw9k5P5/jpiRiKLPFLe3ruugJXgUAguBVcj3j+LPDLwOuSJP0U+CYr9H62LCs271cN2AS80fj9VeBXuQXi+fB4mouzRRRZYktXYFHx0RNrIjyxJsJYssTJqSzHJjIk8lX2DSfpDbubPwCGYXJkIsv7FxIoskypZlCq6cTzFV48PIUiwxd397C5K7DUUJrOA8+si97s3VxEqaYzm6/SFXQ9sL62t4In1kTY3R/CocoNP+H7j4l0ie8emkQCPrure0Ebbd0w+eLuHqq6ybaepY/z+RybyDCaLNERcJKv6mztCuB32mgPODk2kcVpU+hrRKSjfidfbbh5XKlgdu/FJIl8lUS+ytbuIK0+B5/Z0bVgmR29oeZrWZZY1+5D0w1GEgXa/K4FjZMeJEIee3MmLeC+cdeRZKEecTZMi9QDbAEoEAgeLK5ZPFuW9SLwoiRJHuDTwN8DopIk/SnwPcuyXrnaNiRJ2gq0AhlgruVIFghe63iuxplYjj965RxV3WB1tD4NfCV6w27aA05mC1WShRpdIReWZTUF0vl4nr96f5TRVImBiAePQ+aptRHCXgdnZ/KYloVpwLmZPOdnC6yKeO9YC1zDtPjG/nFyZY3VUS+/sO3K+y24du73ts+xbD2fv/663BTPY8kS3z08AcCnt3fhXqaZCdSj16+djmOYFt8/MsWmTj+T6TJfeqSPTZ0BOgMu7Kq8ILp9NZeZrpCL984n6Aq5VmxrlyxU+Rc/OkWiUOXhgTD/j2dWL0ipelB489wsZa1+yX3j7Cy/8fjytoNXY1dfiEJVx2GThZuJQCB4YLienOe/ALAsq2hZ1tcty/oFoBs4DPyTFazfAvwx8FvUBfPcvK+fupi+fPmvSpJ0QJKkA7Oz116glSzUGjd4Cb/TjuMqoseuyvzqw31s7PTz/oUk//zFE/y3d4YZT5U4E8sjSRKlmoFlwbPro+zqawFga3eQVa0e1rR5ORvLs+9iiu8erhcs3QmGZvIMzxawLEvkIwqumU2dAQajXgajXjZ2XIouJ4tVLAssC1LF6lW3Y1dlfE4VCQi46gLZNe8cDHnsi9JCAHIVjSPjmSWP3aPjGQ6PZ/jpiRinp3OLPl+KkWSRbFlDNyzGUyX0G2kTeg+TKV3qKpi9CR0GnTaZ3hY3vS3uB/JhRCAQPJhcT9rG1svfsCwrDfxZ4+eKSJKkAn8F/CPLsmKSJH0A/C/AvwGeB/Yuse3mdnfv3n3NSnRnb4hsWaNY1fnElo4VTbMrssREukyhojMULxBw2zgynmFbd5DpTIXOoIvHVodp9Tp4/Uwcj0NhV18Ln9rWybmZPEMzeUBClesFSzdKqaYTz1XpDq0s/WI0WeQnJ2JY1MXLUk0jBILlcNkVPrXEbMWmzgCpYg3L4oqpSfOxKfWH0WSxiseuEstVGIh4Fi03lixyMVFke0+QgMvGX70/SrlmEHDb+O0nF0ZHz8cLaIaJZsBPT0yRKdd4qD+8ZCpGrqLhsimsjvrY3RdiPFXm87u7cKj398zBldja6+dULA/AtpvQKfXQWJq3ztWLRj+7s4u+8OL/rUAgENxvXI94dkuStIMr5DlblnVomXW/AOwB/k1DxP5T4C1Jkt6h7trx769jPMvisit8fMu1F8Y8PNCCYZqYWEhIrI566A27+Z2nVlHVDL5zaILvHpokV9FY3173sdVNi//79fPkqzof2dDG53Z2c2oqR8BtwzShpOmsa/NdU56saVp8c/842bLGqlYPn97eddV19Ea0O+J1sKe/hY7A8l0YBYKVYldlnttw5Ycxy7I4H6+3f17V6kU3LWyK1CycDc1z4JhrlDKZKfPvXx1iMlNmVauHhwZaePX0DA5V4em1i502vvJYP1OZCrIEB0czHB7LcmFzkd+8rGPnobE0b56dxedU+dIjffydZ9fcnC/hHiaVvxTJT65g5uBq6MaleIZmXPss23CiiGaYrIl679v6AYFAcP9xPeK5C/i3LC2eLeDZK61oWdY3gG9c9vb7wP/vOsZx3dS9Y80lrbTm2NwVYHNXgJ+djHFsIsuhsQxjyRL7R1LUNJNUqUZFMyhWdXJlDbsqM5MqUaoZKJKEblm8fjbOdw5OYAHr2nwE3XbOTOdY2+ZnQ8fVRXSuopGvaOQrddeD+VOuyzHY6uXDG9uo6sZNiS4JBEsxm69yNpaj3e9idZsXgOOTWV48PMmZWJ6+FhchjwO/y8Znd3ShmxZRnwNVkXntzAyvnYqzrt3L2jYfhYazR1Uzee9CvXlHulTjoVUti/7u5u4gX/+dhzk0luZfvXQa3TI5MZElV9EWdPocT9WbruQrOulSTTxEAscnM83Xx25C05jd/S2oioRDVVgd9V7TusOJIi8erjeDem5DlK3iWiUQCO4Rrkc8n7cs64oC+W4nU6rxtX1j1HSTD29su+rU84VEkaph8ld7RzEMi3SpRk+Lmw6/EwsLr0NlS3eATZ0BVrd6ORPLk6vofGxTO985NIlpQVkzyFd1LKt+Qx9OlKgZJtt7gpyN5dl7Mclgq5eNnX5ePTWDx6Gyqy/Itw9OoBkWa9t8SBLs6A2ueD9XMqUuEFwvpmnxjf1j7B9OYlcUfuvJAR5fHUEzTDIlDcO0iOWqWEjYFJm/fH8URZboj7j5xR3dvHZ6hmMTWX54dIqeFhdbu4Js6PDx6GDd9SZVrLE6aqfFfclaTjNMTk3lCHvtdIfcnJsp4HWqTKbL+F0q3z88ya892k9NN5nJVdjRG6JcM2j1OWgXLhAAFCqXOqjmb0IthCJLzbqPa6Wmm0u+FggEgrud6xHP9zTJYq15oZ7OVq4qMh9dFebHx6dxKDJ5XcemyCTyVco1nVxFx67KzObr058Om8Lfe35tc92n17aybzhJZ9DJZ3d28ZPjMbJljahmYDQKlvZeTJIq1kgVU+QqGpOZegc0uyKhGRa6YXJoNMVAq/eBzdMU3J1UdRPNsLApVvMc2N4TIlvSeGsoQTxXbohXO/FG58x4rsrB0RSZksZMrt4cJVmoUtENfvvJVficNnb2hugNuylUdN44G6d1yskLm9v54dEpvntoAlmW+Bef2kwiX23OrARc9ma60ncPTTCdrdAZdPLRTe1MpMuUNeOqziAPArV5qRUV7c4K1rVtXspaFK0RSBAIBIJ7heu5myxw1Gh0BdwMTFqWFb8po7qFDIQ9bOsJkK/oPNR/9YhJf9iDbph0BJ0EqwZbegLEslX2j6TIV3RCbhudQSdf3zfKgZE0H9nUzgub24G6UH9sMIJlWRTKOoWKRjxfoT/sZntP3Yd2IOIhVazRFXQxGPHwxtk4s/kqmzr8bO8NcnY6j2nVp8hPTmV5ck3rLf1+BIKVIMsSX36sj1avHYdN5ok19dxkRZZ4dkMbLrvKa6dnuDBbYCZX4SOb2rkQL7K5y8+Pjk0zGPGSyFUZiucxLAi6VP7ivRECThuPrQ7zyKow3z8ySbqkkS5plDWdHxydIl3S8DpULiaKPL2ulZ+dmuEzO7po8ztZ17BKSxbrEdVEocZfHxinXDM4Pe3ii3t67tj3dbfgkCWqDQHttt14jrFlWZybKeBQZfqXKARdDkmShGgWCAT3JNfVJEWSpEnLsk5KkhSgnrNsAC2SJP2jRl7zXYssSzy7fuXuE98/OsmxySyz+SobOnx85bEB/ss7w/idKqZRz5tu97v4yYlpSjWD7xwc5+m1rVyYLXByKsPR8SyaaTKeLvHO+QQ1w8JlV8iVNUIeO0+tbWV3fwinqiDLEh0BF60+B6djeX7/+TXs6Any1x+Moxkm/aKSXXAXEfU5+Y3LivTmaA84GE4UuTBbIFvW6Gvx8vnd9c6D3aEsI4kiiWIVw7KQTIvD41kCrhJV3WQqW+aTWzsZbPUynCjidaiMJIqsjnopV7Ps6Q+xqzfEwbEUsiQxmizxxOrWZvfAj21u5+RUjnXtPl45We/LVDNuTpQ1W9aIZeuOIXPNRu4lNGte5Pk6Cvwu5/B4hjfP1i1EP7ezu9lMSiAQCO5nrkc8P2lZ1t9uvP4KcM6yrM9IktQO/ITFBYH3JKZpcWIqSyxbIeCyUa7Vc5Z/cHSaX3+kD9O0OD6ZJei28f7FBIWqjmZYuB0qlmXx9lCCqUyFqUwZr0Pl+EQWSZLw2GWcqoKqXIr6zJ9O3tYT4Oh4lnXt9Sha0G3nt59chWlZV20eIRDcaTKlGv/jvREm0mXCXjv5ihO7qiDNO3Q/u6OLY5MZfnhkAtOCqmHS0+IiVdQIumzYFZmqbjDY6sXvVJnNV7ApCj6Hjb//kbU8NrjYgWM+q1q9DEQ8nJspsLu/Bd2w2NTpX3adlaAZJt/cP0apZqzY+eZuQ1VkaKStqfKNX08W5C0bxg1vTyAQCO4Frkc8z68y+TCNdtoN3+abMqi7gYNjad4ZSpCraEQ8Dtx2mVzZ4OhEmguzeda3+7Esi6HZAol8FZdNZnXUTYvbzltDCfrDbk5P5+gIuojnKoS9dpw2mYDLzt9/fi0+p21B98I5nl3fxlNrWhf4OSuyhLKyDugCwS0hWahydCJDf9jDqtYruyq8PZTg6HiWYk1nc6efrz41iKpIPDTQ0jzeZVliLFmkalg4VJlPbe3k+U3tbO70cX62iGZYbOsOcnA0zbmZQj367FRZ3erF3jgvxlMlxlNlPA6V5zdEafU5FozjyHiGNxoR0c/u7FpgkXe9GKZFtSEWi9V7Uyh67Cr5av0SPte05kbY3RdCliScNpnVUdFhUCAQPBhcz9UzI0nSJ4FJ4HHqnQLnGqDcU15Q6WKNqm4u20J7LFmiP+KhxevBHpF4ayhBm9/BT07E2NUXwrLqkWOXTSHgsqEqMpph8smtHezsC3F6KsdEpsyb52axKTLbuoNkKxrfOzzBWLLM0+taF+T9VTSDiXSJrqB7yaYPAsGd4OWTM8zkKpyYzPHVp1ZdsT15i8dOV8jFVKbM7oGWZoOen56Y5kwsz8YOP5s6/Xzv8FTzfPnc7h48DhWPw7bAuWEuNUJVJHJljUJV593zSXb1hXi/UWgL4F+iRbe2wH/45qRsOG0Kn9jawWiyeM9aQBrzOitejy/z5aiKTLvficMmZsUEAsGDw/WI598F/i+gHfh7lmXFGu8/B7x0swZ2q4nnKnzzg3EM01rSsm5Xb6gZ5QIIuGw8uSbC4fEMqWKNSCO/8iMb2yhrJr0tbgYibuK5Ktt6gkiSRJvficsm8+a5WToCDibTJpIkcXA0zampHGvavBwYSeF1qAxEPCiyxPcOTxLL1iPVv/5o/+38SgSCK+Jx1MWyQ5WXbcP82GCYvrAbv8vW9FzWDZPT03lKNZ2v7R3F5VAYSZRIFat0hZx87/AkXofKmjYvn9x6qathq8/B44NhnKqMqsiossSqVg+SJNERcHJ4LE1f2LPA23mOnb1BJKk+3psZER1s9TK4TOT9bqcj4GS2WACgK3jj9n3HJ7K8enoGSYIv7O6hK3hPxU8EAoHgurhm8WxZ1jnghSXef1mSpA03ZVS3gUy57kULNCNYFc3g9TNxJAmeWR9lW0+QtW0+RlNFfA6Vl45PE3DZ2NDh55GBFh5aFcahKoylSkS8dnxOG6ujPk5MZpnOVrArEu+eT3Iunkc3LB5fHWZzZ4B9w0kcqkyqUKOsGfzw6BRbugI8v7GNfKXeCCU/z49VILjTfGxzB8OJIu1+5xVz76u6weGxDH6nrdlREOrRye09Qd4emkWRZYZmCuimSdTv5KH+lnrerONSE6C3zsWZTFeYzpZ4/ewsg61eHhsM88ltnbgaEe+JdJmgy44M2JTFYl5VZPaswE3nQcNa8PrGU8HmrleWtdBDWiAQCO5nbrbx6T/gFrTYvhWsbvWyu7/eRGHuJnt8MsuZWB6AqN/Jzt4QLrvC+nY/Pz0Ro1Cp+zz3hz08vCqCy67w8skYp6ZyuO0Kv/xQD6+cnOGd8wks0yJX1ciW6l3P8ppOqlDj7aEE5+IF1rV5WR318ebZOG6bQr6qMZYs4VAVHKrCs+ujzbHGshVqutmsZC/VdDIljY6A86a0tC1WdfZeTBLy2NnZG7rh7QnuP+yq3CxivRLvXUjy5tlZFEki4B6gK+ji4myB6WyFPQMtnI/nODiaxjAtNnT46Ay6+fiWdt4+l0A3TB7qD/FvXz7Lz07PkCtr1HQDp13l5FSOL+zuaRbWJgpVso2unjXDxLLgek+D2XyVw2NpBiIe1rTd/zm7sWyl+XoqU7rh7e3qD6GZ9dz1tW1Xj8hXNIP3Lybx2FX29IdES26BQHBPcrPF8z1zJZRlqemZ/N75BBcSRXpDLuTGxTx6WQFSe8DJ6ekcg1EvH9/a0cxHzpY1TMvi3Eyev9o7Sq6kcS6WJ1Wskq/qeB021LDMQ/0tFKo6HofKjp4gLR4bPzgySaGq43Wq/O2nV/OtA+MUqjqGaZIp1Qh57PWOiHvHyFU0Pruzi81dAb62d4xCVWd7b5Bn1kW5Ud45n+DUVA6ANr9TTL0KrovJdJnT0zkkCWZzFXxOlR8enca0LGLZMvtHMjhtCqWaQcht57eeHOA/vXGBAyMpPA6Vk1NZhhMlsmUNp00GJAzDZH1vgIcHGg+4jTSBUk1nIl0i6LZzajp33R01XzkVI56rcno6z++2uK+Yy32/kJ0XHU43Iv03gkNVeHrtyr3n9w2nODKWASDitS8qPq1oBvuGU/idKjvEg7xAILhLudni+cYrUG4z5Vr9Yg11e7rfeKwfpHqO83y29wTpDrlw25UF1nLPro/yrQ/GcagKU5kKpmnhd9ooNjoQyhJ0BV24HQoP9YcwLChUdc5N5zg5nUORYE2bF1muF1sVqjpnZwoUqpMMRDzs6Q9xfDKDZli8eirOYKuXQrV+A0w0urrNJ5at4HEo+JbIA70SPmd9f1RZwiOKFB844rkKumnReYMPTQMRD4OtXkzLQjNMFElCkaFQ1nlrKMF4qoREPb1jJFniT18/z0iy1HhgtLArEi0eO5JkMRDx4LaruG0KuarOdw9P8vBAC2dj9Ye80WSJWK5CpqRzdDxz3eLZ57QRz1Vx2xXUZXK57xfm2TwveH27mLvWyJKE17H49vP+hSRHxjMAhD0O4RstEAjuSq5ZPEuSlGdpkSxxj7ltQL2gqCPgZDpboT/iIeC+suicKxK8/L1nN0T5yfF63eTjq8MUqwZf2ztC0GXD61AJuGxISIyly3z5sX7+0xsX+OmpGKZpUjMhV9b4mwMTfOmRXs7G8hwcTXN6OodumvzS7h46Ay5qhkk04CDorjdWmcyUeWTVwpzOg6Mp3jqXwK7KfOmRvkUPAFfi0VVhOgIu/E6VoPvGLb0E9w7jqRLfOTSBZcHHt3RcNTVjOfb0tzCSLHJoNM17F5L0hj18cXcPf/neCKencxQqOh6HQthTt2msagYbOnw4bQq7+4IMtHp5eyiBrpt0BF1UdYPZfBWbInNqKsvB0RQht52w18FAxIMsQbFmsLHj+j2cP7a5ndFkiTa/Y4E95P2KTYa5rtz2JXLFbzU7e0MYhknAbSfqX1yw6G48vMuSJNyGBALBXcv1FAzeV4mBsizxxd09lDRjyUjIlUgXa+wbTtIecLG9J4jbpiJJ0NNSj5Q8NFAXEr0tbv7mwATZsobLpvDdgxP84OgU5ZqBDHjsCpOZMu8MzTIY9dIbctEXdlOoaGzvCeK0KzyxNsKrp2bw2OsNWHb1hdjVt3hKczZfL3ys6Sa5srZi8SxJEgPX2FpXcOOMJUuoinTDEd8bIVvWmhHIbPnapvEThSpeh9pMdbCrMn0tHuK5KqYFyUINSYKJbIVSzUACukNudvUFeflknELVoD2g8Pc/vIbOgIuQ2872niB/+sYFhuJ5Pr6lg7VRL6+cmmEmV8XnsOFQFbZ2BegIOLkwW6A94GLDDYhnmyKzOrp8rm65ZvDu+QQuu8Kjq8LI93CEui3oZDhRz3vuDN2c4y5f0VBleUVi9+h4hnfOJ1FkiZDbvsif+6GBFiI+Bz6HuugzgUAguFu42Wkb9ySyvPQU4pU4E8vx0xMxTNPi9HSenpBr0fRiulRjIl3vLvhLe3o4PJbmbw5McHg8TVkzMCx4cm0rewZa+O6hSbIVjT978zypkoZpWjw8EOZjmzsYT5X40dFpsmWNw2NpPrSulahvaYupRwfDGKZFyG2je5kbo2la/PxMnEShyjPro7QtEQES3FpOTeV4udE6+k62Nd7Q4SdT0tBNc4Hf+NV473yCfcMpfE6VLz3S1xTQPS0u3jlf7xi4rt3Ha6dn0HSD3hYXz6yL8shAC//fn54lV66hGSY9LW4Oj2b4cTZGe8DJujYfVd1s5vp7HQoRr4Pf+9AqLiRKWJbFhdkC711IsqHDd0PCeaV8MJLi+GQWqNdC3MuFhZp+c/2vT0/n+M9vXsRhk/mHH1l7xWvTHKlilVSxPpuQLWuLBHKqWOPgSBq/S+X5DW0PxGyAQCC49xDi+RoZThT5yfEYI4kiNlVmsNWL266SLWskC1X6wnW/5p8cj1Go6pyPF/jCrm5+eHSKM7EcqixhWdARcDEQ8dLX4mFDh599F1PM5CroponTpnBgJMk/+95xaobJ3Oyq06YQdF05rSLgsvGJrR1X3YfpXKUpBvYNp/jUts6rrCG42czlrQPkqzdeuHW9KLLEtp4As/kq1xJQnW64NuQrOoWq3hTPbw0lcKgyiXwN3TRp9TmoGRYtHgfdITenYwWyJY2aYdLptvGRTW38t7eHOTmVpaKbrIt6GJotEnTZSRZryFK9FuDNcwl+7dF+dMPkj18/D8B4qszZWJ4j42k2dgTY0n19ec9XI9RIZZIlacmGLPcSicKlBrGxXG2ZJVfGm2dnmc6WgfpDxie2LH8tsSky6VLdKWWp+oqDo2kmM2UmM7CmzXdPe2oLBIL7FyGer5E5fdEXdrO23cdTa1uRJPj6vjEqmsGWrgBr23ycn80znioTcNnwO1VAQpGluj3X5g5m8lWqmsmF2Ty7eoMcGE4hSfUiHrsiUzMsDo2lkYCA287feqiXz2zvwq7eeCSmxW3H51TJV3T6WkRBzp1gR2+Qqm5gU2Q2tN/66OmVqOkm39g/RrFqsK7dx8e3XP3hC+Dx1RGs8wk6A84FtQDpYo1DY2m6Qy4kJGLZCn6nSnvASX/Ew1C8wJbuAKWazj9+YT3dITf/4bUhKrpJRTM4NplDM0w0w8LvVKnqdSu6Vq+dimaQr+g8vbaVs7E8O/tC/PDoFJphEstW2dzlvyXWZ1u6A7R47ThVmfASdQ/3EjYZ5szqlvLHvlY2dvr4yYlp7OrKjmPToimI0yWNN8/Nohkmn9jaSYvHTk+Lm1PTOVw2Zcm0jVJNZ99wqpnicznZksY75xOEvXYeWRW+4f0TCASCpRDi+TIsy+LsTB5VlpbsTNYf8fDJrR1UNJOg28ZPj8cIumzEsmUKVZ02v4ORZBGbLFOs6mztDpCv6LQHnHQG29jTH+bkVI63h2Y5NZUj7LXz8ECE3rCbRLFKyO3gy4/1E8tWeP3cLOWqQXvD3st5kwpoXHaFX3+0n6puXJMrh+DmYVPkplXinUQ3TUo1g2ShyqunS3QGXStK32gPOPn8ru5F7ztUmYGwB49D5Uwsx5lYno6Ak+29ITZ0+OkOubAsi4pmXEr1CLmZypRx2WRyZQ1VkZGAtoCDUs1kW3eAc/ECp2N5QGJPfwtPrInw9tAsp6dzFKsGH9nYtkA4T6RLVHVz2cjl3MxQb4ubFs/yhbL3i31ji8dOvlZ36Wm9yj6vBFmS2dkbQpElqvrV00AeXtWCLEl4HAq6aTZnME5P53h8dYQNHX56WtzYFAmHuvh69+75JCcas2atPsei/8u7FxKcm8nDDPS2uO9oPYFAILh/EeL5Mk5M5nj19AwAv7CNJQX0XM7jtw6MM5kpM5woUKjoVBs5hGGvnXSxRlfIhduu8vzGKMOzRcbTJd4amuX9CwmmsxUqmkE8XyOeL1PV6+kaXke9g+GvPdrPZ3d281/eHsbrUAl7b64LhipLnEuW8DpU+kWx4AOL267ywuZ2/uvbw4Q9dt44G2dzp3/FuaZjyRITmRJbu4N4HSobOvyUagarWj20eh1MZkpUNJPnNrQB9VmVo+MZbIpMsTrFbz4xwO6+EBXNwGVXUICRVIlkoYZlSZRrBqWqwYGRNDXdZHNXgLFUkcNjac7PFrAs2NLl55HBS1HG8VSJbx+cAOqdQq/0MPDtA+OcjuVp9zv5/efW3NOFgCslOS9tI76E1eW1Evba8btszQLAq+FQFZ5YEwHqsxRuu4JuWgsKlperP5lrE6/IUrPb5ILxNB4I7KqM1ylubwKB4NYgri6XoZmXoieasbwRanfQxWS6TNBtJ1WsMZWrcXG2yD95YR2xviqtPjuqLHNwNM37F1OYlsVYsoQk1S/8ZZuCTZFJFmrYFZmAy1bvmqab/Pj4NOfjBaI+OxYST6yO3NT9/GAkxXsXkgB8YXf3gnbKggeL9e1+nlsfbUaJVyqci1WdF49MYpgWsWyFz+7s5qm1rTw6GMamyBybyDCVKVOs6pyeyrGjN8R/evMC755PYFgW69p8fGxLO21+JyG3HbdD4YnVEdr8Tn5+ZoYTkzk2d/kJuu20BZzM5quEvXbWt3v57qEpAk4bAZeNRwcjrJ1XxFfRjObrcs1YMGbdMNnf8HU/OJomnq8Sy1YwTOuBEM/mPHNn8yb4PG/o8BP22LEpMqFrjGSHPHZ+58lVWNTF8ErY1h3gbCxPT2jp2YKHV4XpaXHjc6piVk3wQNL/By/d0Poj//oTN2kk9zdCPF/G9u4gUI/Mrr+K5+1jjWlGj0PlP795AUmScNrq+cpzlnXxXIVXT8c5E8vR2+LmQ+tbGZopUNUMTjW8b00LBqNeJjNl+sIe/vy9ESbTZXJljfaAi6jPzmtn4nx+ZzdV3WS2UCFX1lnb5rvuHGh93p3TuBl3UcE9zQub23l4VXjF9oZQL6CTJTBggeC2NV6nijWSRY1CRePnZ2Z4fE2EE5NZ7Gr9gTHqc7DvYoqBiAe/y4Yk1X3Tu0IuvvRIP+fjBToCTtx2lX3DSaq6wRtnZvn+kSm8DpV17T7+tw+vI3iZiFod9fLU2laqmsHu/oWWjiemcs2mSC0eGzZFJup3PBDCGUCVZaAeILCpN2efl/JrXinX+r1/c/84r5yawaZI9IXdSzqfiFQNgUBwqxHi+TJkWWLnNbSFnYu2PLY6wrvnE6xq9TSN/ss1g0ypPjW5rs3HYKuHX39sgMlMmdFEkfYLCUYSJVRZIux10B/2UNFNRpNFDNNCkiSms2UM02TvhSTr2urWXwfH0qxr87G7v2XFBV6X89BACw5Vxm1X6QuLtI0HHUmSrpr3ezkuu8IXd/cwna0s2VxlR0+IwVYP8VyF/lYviiSxqTNAVa/nWZ+J5Xl+UzvbeoLYFIlkscZLx6dx2hR+5aFeNncFmMlV+OYHY7T67JycrAvfQqVGTbfhdYZwLDF1L0nSkj7oAF7HpeU/ta2LqmEyEPGsOPJ5rxPy2sml6nnGSzV9uhvRDZPJTJlWn4N8o724blqUavpV1hQIBIJbw20Vz5IkdQI/AjYCXsuydEmS/gjYDRyyLOv3b+d4biaXNy4Zmsnz4+MxHDaZkNvGa6dnGEmWaAu4qOn1rmi/8nAvb56d5cx0nniuwjN7ehiKFwi4bKyKuEmXdGbyFYYTRbJljcdXh9EME8OwyFd0aiso0LkSNkVmd3/L1RcUCC7DNC2KNR2f00bU72xGHo+OZzg4mmZDh59HB8O47Ar/+KPrOTiWZmS2wNvnE/zqw70g1VMrdNPCqcr81fsjvHR8Go9DpT/sJpGvsu9iksdXR/gf743wkxPTmKbFQwMtBFwqyUIVvyTxqa2duOwKo8kiFc1kbZt3kduGeVk6xuqoj8/vUpAkHshUJfu8IrylCvJuNfF8hZdPxPA4VD6xtWNFY3j55AznZvL4nCq/tKcbVZFo8zvZ3BW89QO+z6g701SI+h135P8vENwv3O7Icwp4DvgegCRJO6mL6CclSfpTSZL2WJb1wW0e03Uxkijy+tk4HQEnH9nYvmj6cSRZwrQsEvkqQ/E86VINw7L4P14+Q7ak4XOqfHZnF6+fmeV0LI9DkYnlKkS8dhKFGrIk8aH1rfz5u8OYlkVVN6nqJuva/QRcNpLFGmOpIudiOdZewSIqV9GYzlToj7jFhVJwU7Asi+8cmmAiXWZbT4Bn17c1P9t7MUmpZrD3YpKdvUG++cE4qWKNRKHKwdE0pmUxnS3jcyiYVj1t4sXDE/zkxAyyVE8DqdRMsuUaJc1grNHqO1Go4bWrdAZdWEggSdhkiYl0Cb/LxncPTQJQqLY2H2BN0+LFI5OMpUo8sTrSfFAsVHVKNYO+O9SU5k4zHC82X5+N5W/73z8+kSVRqJEo1Dg5mePweAbNMPnFHV1XbNaULtWLHAtVnZDHwd95ds3tHPJ9xfePTDGeKhH1O/jVh/vu9HAEgnuW29q+ybKsimVZ6XlvPQL8rPH6VeDR2zmeG+HAaJpMSeP0dJ5EcXHV+o7eIO0BJx0BJ2uiXvwuG6ZhMZ2tkK/opIo1jo1nmS1U0QwT07IoVHSivrqVl8suU6kZjZbEMh1+J2uiPvojHp5c24plwQcXk/y/f3SS45OZRX9fN0y+uX+MHx+f5sfHpwE4MZnl5ZMx0sXrb45wcbbQbIogePDQDIuJdP3/P5woLfhsTVvdFm4w6qWim6Qax9lEuky5ZlCuGUyly5yYrPv4Rv1OTkzmKNfq/s0tbjuaaZCraCTyVWbyFTZ0+GjzO+hpcfH5nT18ensnqiQhy3W7s/kFgWPJIj88OsVfvj/M949MciFed+M43RCJlmXx1x+M8+Pj0/zgyNSy+1ms6pyN5RcVHN7rzE90uIGJq+tmIOIhV9YwLYuabpAqVsmVNc7HC81lKpqBPq/74Yc3trG+3cdHN7U37Q2XwjCtBcWit4tCVb9n6kaShfq9KlWoYVn3xpgFgruRO53zHAQuNl5ngU2XLyBJ0leBrwL09vbetoFdjdVRLxPpEmGvY0mLpojXwa881ItlWXwwkmbPQJi/+WCcfFUjXdLoDLp4YUs7qaJGqabjsqlE/U489rrjwMmpLG+cncWuymzpCvJPXljPyaksxyezjRt7louJMoos8S9+eIpv/e6jC6asDcvi3EyBRL6KaVlkSjV+dqpuwVes6nx252KP3qtxaCzNm2dnkST44u4eUZjzAGJXZR4dDDM0k2fPwMK0n2fXt/HYYASHKiNJEo8OhhlLlVjT5uX98wl00yLqdzbdLvpb3Oy7mCLgrjcS2tjp58h4hppuka9q6Ea9kcantnby8a0d9ITd+F02tvUEGUkW+eYHEzw2GOGZda0cHkvzH147R6asISOxocPPqlYPM7kqDzlbsCwLy4JyI0+2eJV82W8fnCBVrNHqc/ClR+6fCJ3KJQF9E/otXTPpUg2PQ2UqU+InJzROTGZxO1Q+vLE+gzE0k+evD4wTcNr4yhMDeB0qbX4nH7tKbUeppvON/eMUKjof3dzG+tvUeGjvxSTvX0gS8dr55Yd6m8Wydysf3dTO8cksGzp8t6ShkODeR7h1rIw7LZ6zwNxVzg9kLl/Asqw/A/4MYPfu3XfNo/L2niAbOnzYZHnZinFJknhooAXTtHj3fIJsWSPk1tnZG2rY1ll4HXXLrY6Ag8lsGVmScNlVKrpJe8DBM+ujbOj081/euchUpsJkpoTCpb+ZLtXQTWtBxzDLArddwe+qR65zFY1MScPnVPBfp4VTqWo0t126TyJyulFv1NDqcywb1RJc4pFV4St2b5v/Hc5f7sMb27ArMm+dm+XASJqZXJmTUznWtvmwsFjb5qOmGVR0k2xFw8rAli6TvrCHT27rxLQsTk3laPc78DpVNMPEpsjM5Cr84o4uXjwyRbasU6oaqErd7tFhU9jQ4Sdf0ZnNV4n6nXxqWxdD8Tybu5Zv5T0nru+3ojSnDQqNbvAu+80RepZlrViIFaoGsgQT6Qp+p06iUKNTVRhLltjRG+LV0zMcn8giS/UuljuvUPg5n1JN5+2hWWZyFVw2hYuzxdsmnkeT9TSYRKFGoaJfs13f7aY/4hG+/gLBTeBOi+f3gd8FvgU8D/zFHR3NNXItecSyLPGPX1jPuViOE1NZkgWN0WQRSZIo1XTSpRoT6RKbOv30hT2sbQtgV2XKNYPuUD3C29/iIZat0OF34rQpKIqETZb5tUf6sCkyharO+xeSBN02dveF2NwVYDJdZn27nx8encZlr7cXfnZ99Lr2d89ACAsLt11hsPX+uAD/5ESM8/ECQbeNLz/a/8BYlt1u5s6VVKHK2ViWml5vP/+ZHV08OhhmZ28LH4ykeP1cnKqm1KeULegIOKloJi+fjAH1pifPro9yMV6gUNUJuFTePBcn6LbRE3Qxli7TGXCypdvPJ7Z08MbZWcJeO8HG7FBv2E3vCvKdP7Wtk3Mz+dsmwm4X3S1uzszU020Gb4KIms6WefHwFA5V5vO7u6/6YP7wQAsSdWvD8WSRsNeOz6nibrigtHoduO0KDlXBt8ImJz86Ns14ssSpqSzdITef2tZ5o7u1Yh5ZFeatoQQ9IdddL5wFAsHN43a7bdiAnwDbgJeB/ydQkSTpbeCIZVn7b+d4bjdeh8rOvhZ6Wjx8fd8ohmVhAbphYZoWNpuKZlhEvE4eHgiTKNRQZImhmQIf39JBe9BJd8hFsWbw7Poon9nehWdeN673zic4OZUDoN3v5PM7u6noBqYFJ6dyOFSFgMt23QLRoSp3RUvpm8lcXm6urKOZJg5ZRJ9vFclClW8fmqSsmUhSvdvghg4/e/rDzOQqHBxN0xV0kyvrhNx2PrQ+yrPr2+rtlhsYponLYWOg1UumVOP9C0k2dPiRJYnff34Nbw0lCLrtrGr1sKM3xPp2P3ZVvmYruu6Q+75045ifmlu7ShOolXBupkBFM6hoBmPJ0lUj+k6bwlNrW3lqbSvpYo3XzsTRDJOH+uszFC9s6cDrVPE7bayOXmqtninV8DrUJRv4aIbZzDtu9Tk4P1tgMHrltuw3k76wh1+bZ/WpGSYXZ4u0+R3NBzaBQHD/cVvFs2VZGvUI83z23c4xXA3NMBlN1quRrze94fLtvXwyRrGq89TaVjoCLlx2Ba9TZSDiRZElVkU8HBhN4XPaeWJ1hKMTWd6/kMDnVLGrCl6HwiunZvDY6+vIksSnLxPOQLPBhSpL+JwqsizhtteX+fT2TibSJTIlja/tG+Xpta0rEgeWZfHG2VmmsmWeWtPabP6yFIZpcWIyy9tDs/S0uPmFrZ13fST3+Y1tHB5LM9jqFY4kN5HJTJk3zsZp8zl5bkMUSZIYT5cb55WKblo8t6GNj25qR5Ikjo7XuxF2BV34HDa6Qq5m/mib38mqVg9tPgfbe0LNAtu3hmaxrHoU89HBMM9tbGeg1UssW2FbT5CxZIm3hmbpCrl4Zt3VZ1v2XUxyLl5gT3+IXFln38UkaxuFagBV3bjnj5H17X7OxeuR5w2dwRveXthj48RkFqdd4Vd9i2tS0sUar5yK4barfHRTO7OFKi8ensTrVPnCrm4+v2th7YXXofLC5oX5za+fiXNkPEOrz8Hfeqh30TXl45s72OdJoShSs3FPRTPuSBrWz07NcDaWx2GT+c3HB5YcQ1U3sCvyHck5PjGZ5fB4hk2d/mvqZyAQCBZyp9M27jp+2pjG9zgUvvL4wA0XgIwkigzNFDgby/HO+QTPrIsymS4Tz1cJuFSeXd+GYZp8aF2U7T0BvnVgkjOxHHZFplgzeKi/BROLg6MpbLJMvqrzmW2deB2L/3V7+lsYT5cxTGtBpM0w6xHurqCLD0bqZif7Lqbo3nV18Zws1jgyngHqxTFXEs+pYo1vHRjn0Gia7pALzbDIVbRloy/ZksZ4usTqqPeO5Rt3BV20+RwrbkktWBn7h5PEc1XiuSqbuwK0N1xndvWFuDhbRJUlYrl6s46hmTx/+f4Is/kqjwyG+ZWHeijVDLpDTnTd4E9fP49uWpxVZTwOG5u7/Ay2eon6nJiWRX/EzRd39wCwqtXLqtZ61PGlY9McGEnx7YNF9p5P8oU93SiyTEfAueh4q+lms139u+eTWJaFbtbzrJ9ZF+WNs3FOTuXY0OFbJO6uhGVZnI8XcNqUZR86r4ZumDft+PS5bM1qiaDrxi//yaLWjDbHG3nl8zkynmEqU/8/r2krcHAkzVvn4tgUma1dAXYsIeDiuQpO+6XajPF0XezP5qtUdKMZEJjD66x3m9za7efIeJYTkzkuzhb50iN9iwIMN5tiVef4ZJauoIueFjfFaj1HvqabC7q4zjFXYNgVcvH5nd23Pbjw1tAsVc3k7XMJdvQEkSTpmnLWBQJBHSGeL6PQuPiVaya6YXGjmi7qd+JQZTIljdVRL8cmsgRcNlo8dh4dXFh4NZ0tY1oW69v9nInliDgU3jgXBwtmC1VcNpmAy0YiV+Grisz2nuCCvzWcLDKeqt9oPhhJ8aG1USq6wdtDCU5N5bArEm67wlSmzEMDK4s6+J31saaKNQaWyZEcT5Uo1wzCXjuFis62nuCykXvDtPjrA2MUq/U25XPi53bz3oUE+y6mWNXq4VPbOm/aTaTuoKI8sDelvrCHkUSJoNtG0F0/DjwOlS890s+x8Szj6TKqIlPV693jTAvCXgcRr4NNXX7+8bePkSrW6A65GU+XKFR0VBnOxwv84s4unlkXZTRZJF6ookgy3z00yTPrW3nl5AzZco0vPdxbb6CSKpIuaRyfyjD1aplVrd4lHTRsikRXyMVkukx/2I3PaWtGnu2qzFDDSu3cTIEXNq/sOzg8nuHNs7MAfH5X9zULaMuy+MHRKS7OFnl4VQuPDUauaf2lODSaYk7S7R9N3fD21rX5ODOdw2lTlixE62lxc2wii8Mm0+531r3nsxXsikxFX1x0fHg0zXcOTeB2qPze04OEPPXZuH3DKVZFPBQqOi8eniLktvGRTe0ossSPj09zcbaI32Wj1Vt/WC/VjKazx3wsy2IsVSLoshNw3/jM4qunZzg1Vd//33lqFc+tj/Lq6Tjr2n1LBjjmjqPJdLnZaOh2MhD2cCaWpz/iRpIk3jhbj+pv6Qrw3Ia2q29AILgKD4pbhxDPl/HhjW0cHsvQH3bjst94NDTgsvHbT65iXbuPkWSJrd0BZvNVdNNia/el/EDLsugIuPjIpjYeGmjh6HiGN8/GMUyLmWwF3bQoVnXSJY1C1eCDi0lyZY2ZXIWn1rbS5ncSctuxKVIjb9rBtw9NMJkuU6jqeB0qmmkRdKu47SpnYgVCbgdHJ9Ls7G1Zsr0y1K3JfvXhXiq6ueTNYI7VUS/vXUjgsin8xtP99F6l5bdlWc0OidU7YTjb4Mx0PZ/24myRqm7elAj43DRzT4ubz+3seiAF9M7eEGvbfDhVeVHU9Ncf62ffcIo1jRmHJ1ZHOD2VI12u8ct7epjKVEgUamTLGhPpJF1BFwGXjXxZI1Oq8fLxaXb0hPiVh3v5t6+c44ORVL21vWbw2pkZdMNiJFGkp8VDu8+JW1Vo9Tkp1nSOT2ToCi20WNQMk9dOz6DKEr+8p4eOhgVjf8TNuViBeK7CI6taODKeZctVcnrnM99zuLqEULwaVb2ePwv14/RmiGfHvP+FS77xaHZ7wMnvPj14xc9XR738zlMDqLKMXZVp9Tpp9dadbXxLXE/evZBsCswLswV2e1oWzCb85Pg0M7kKM7kKGxvF1dPZMmOpEi1uOx/f0o5mWLR47XQtYaX5zvkEB0bS2FWZX3+074bF6/mZAkfGM7jtCoZpcnAsw2SmTLassb7DtyjNZ09/iHfPJxmIuG+7cAZ4YXM7T6yJNK/lJ6dyWI2aGCGeBYKVI8TzZUS8jqbn6M3CrspXvDDphsl3Dk0wk6vy/IY2NnUGOD6ZYSRRwAJ6Qy7iuQpgocoSTptMolDltbMzvH42jiRJTGcr/N3n1pAra2zs9GNTZHpCbl47HQcg5LYhSxIWFulSDbsqE8tW+LO3LjCdrfDehST/9GMbmr7NFc3g5ZMxTMviwxvb8TpUPLLEbL5K0G1bkMoyk6twcbZIf8SNblj4nDbeu5C8qnhWFZlPb+/iYqLI5s4752iwuz/E3otJ1kR9Ny115MJs/eY/niqhGRZ29cETz8AVH7Y2dwXY3BXg3EyeD0ZSbOsO8nvPrMY0LRKFKolCla3dAfYPpxgI+xlNlVEki8lMBbddwW1X+GffO4ZhQLpcL/g0LJNtPQF+eGySZKGGKkPE68TnsvHs+ihD8QJHJ8qkihqGBRPpUjPn/9xMntONh6hWn6Mpnr9/eIpCVefUdJavPjXIrr66r3VFMyjVDFqu4K6QLWucmsrRHXQhraq3KR9svfYCNqdNYXtPkKF4nt39Nyc/VZ9XJKhbt+ehdX6ahSLVv5+qbuKyKwzN5KkZJhs7/I0UApPhRAGHTcG9hJVeX9jD2Zk8HrtKxOsAwDQhX6l3bY14HXxu15U97DOluk9fTTcp14xFAnYyU+btc7O0BZx8aG3rVR982wIO+sMevA4V04RUo2FWoapT1c1F4nl9u/+OOrhkyxpD8QIDEQ8Rr4MdvUGOjGfYKlqdCwTXhBDPd5hUsdbMCTw9naPFY+dfvXSGsVSRqM/BmjYvZ2YKVHWd/pCL0XQFt0Pl5GQOt0NFN0wiPgd7LyR5/2KSw2Npwl4H46kyDw20cD5eYGOnj7fOJpjMlHHbFR5bHSbidfCN/eNAvS2y2eg2NZYs8T/3jjCdrTDY6uHEZJZHVoV5+WSM09N5ov560Y4kSZhmvVVzVTM5PZ1DUSR008K+wu4LPS3uG8oFvRls7Q6ytTt4U7f52GCED0ZSrGnzrvi7eNCYzpZ56Vi982WhqvPYYJhv7h9vtrzvD3v4d1/cxh++dAYJmMpWMUyTil5P/5hMl9EMsxHRlegOuukKuusR5opBPF9FG0vTEXLxzQMTQL0zYnfIhduuLKgJiPqc2BrHbrvPyU9PTBPPV5tez/MfFgtVna/tHaVUM3h6XeuSRVc/OT7NdLaCTZH46lODN3QMPLM+yjPXaS25FBOZS10hh2dLyyx5a9g/kqKsGZQ1g9dOxyk2vONrusmO3hD5ikGLx9HIia+xsRPKNYNzM3m6Qi5aPHZ8DpWw14Gj8b3GcvWurTO5CpZpwTLPwE+tbcWhyrT6HIvyswH2Xkgyna0wna2wqdNP1Ld0y/A5nl4bRZVlelrchDx2nlkf5YPhNL0t7ptScH6zefHwJOmSxuGxNF99apDHBiMLZjQOjqY5NpFha3ew2epeIBAsRojnO0zY62BVq6fhEFCfEq7oBlXdJFXUyJbqHQglScLjstMrywzHi2imSSJfxanKTKRLDTFgUdYMZgtVksUqf+vhXh5fHaGmm/zo6DSTmTLtfie9LR42dwWIeB18MJJia3ewGYX7YCSFaUGyUKMr6KIr6GI2X+W7hyap6SYbOn3NaKokgdKIzNhVmU9v72QqU2m2ab4byVc0jk1k6Qg4m1PBN5uNnX423sFo+r2AItWPH8uqu8MkCzVSxRpmo0ivVDVw2WQUGVRFwqZIlGoWDptMxOtgPFUiX9Gp6gb6/7+9+46O67oPPP79vekNMxj0XtirRFGiRDVakh1LtlzkrliK7Xg3cZxses5JcrLZbHyyyZ6N7RPvbrJxnNiO05y4yoqlWLK6qEJJlEiKnQQIohAdg+n17h9vMBwAAxAswAzI+zlHAmbmYebO48x79937u7+fgrf6p9jQ6CPotnMsM00slSXgtpPJqvxrCZsaq7i5K8hdG+vJZBX7eifY2uynzufg52/vIp1VxFKZwih0Y5WTRCbLZNRMibd7TQ1TsVShQNDQVAJKFD2dWQRmGOZ7vFJGw0mOnQuztt5Lo3/xTt2CVFGDynBd11nrZu8pwWYxaKhy8vLpcbI5VVhcd+eGOg4PhcxKkvmwth8fHKJvIobDZs6oTScyTCcyDEzF6ajx0Bpwkcrk8Ltss95eKYcGQnz9xV46atxsa/HPCylqr3HTNxHD77IVshctpi3o5hO7zn8I6n1O3rt9aQtKy2F4OsGJkQjtCwxavHhijFg6w95TY7rzrGmL0J3nMrMYZtq5Yrd21/CaRegMujkxEsVptWAY0F3rwWaxMDydZDKaIqfAYjGIJjNkcor3bmuib8IckStmtxp88PoWvvLUCcajSUbCCcA/K5Zwxpp6L2cnY9yzqZ4b2qv59wODjIZT1Fc5OBdKsKbu/GiqiPCRna30TZgZM3xOGzX5qdRK9dMjI/SMRTFE+MztnRU5OnQtqK9y8qEdrYTi6UKp4DqfA4/DijsfmvT4oWE8Dgubm6t4x4Y6Xjo1TianGJiM4XZY6az1sPfUOJlsjlOjEV4+PY6IorvOw2gkRXvQzYZGH3dvqOfV3glaAi7uXF9HJJnh6y/2ks0phkIJ3n9dM6dHo7x4coyWahfVbhuTsRQbm3z8YP8ALruF/Wcn2b2mhpaAix3tAcYjKW7uDpZ8b/dvb+LYuTCt1e4rWq75kbcGmY6nOTgQ4nN7ui8plr4x4GRq2IyjLhUTvNw2NlSxudmPy2YpdE4F88If4Pa1tezuCuYvPMz3l8mZx7NsVtFR4+b0aBSv00qdzzzWvGtLoznTU+/FbjE4Mx6lymkrWbTkq8+dom8iSt9ElJdOj8/LW39TZ5D19V7cDuuylNrOZHP0TcSor3IuuoZkuditFqrdduxWo2SWjalEmrcHQmy5iNh+TbsW6c5zBfqZLY1YLQbxVAaHNY7fbWNHe4CP3tTGv+3rx+uwkEhZ8DnA67TlC6okuaU7yKYmH9Fkdl6nsK3GTY3HTjSV5fXeSQYm46xv8HHznDLL21v8hBNplIL9Zyd5/uQYsaRZ5XD3mlretbmxsO1P3j7H4aFpdrRX43PaODMe5bkTY7QGXNy6poZYKltxVbdmOv4WwxzxzOUUI+Ek1R7bqs/hu9oUV/qbiKYYj6So9zmYiqcJJbKsrfcAwqdv66ShyklDlYvJaIqxSIJDA9PEEhnW1nlJZ3NMxFI8f2IMh9UsvLKpyc89G+vZ0V6NYQhrG84viJ3pMMTTWXL5Ec/9fZPEUllePzOJz2GldzxG/0Qv0VSWaDLLZ27vBCCTU9gshhlCsEAaRrfdWjIF2+Wa+ezarYvnCJ6KmXHgpdJE+orS0wXcK3/4d9ottFa7sBqCw2oUwiJslvPfPcucTuu9W5t4eyBEW9BNc8BFOpuj1usoxFJ31XoKmYD2nhzjlZ4JbBbh4d2d80aPu+o8nBqJ4LJbS46+vj0Y4tG3hmipdvKJm9qveArLn+TzQF+pVKgXa6Z4TFvQXfIzVO2ysrOjGpdNh5tp2mJ057kCXdcWoNHv4JE3h/A4o+yu97Kt1c+P3hzipdPjOK0GQY+dzloP79zUgN9t47Y1tYgIP7O5gVd7JuaNqFgEphMZpmIpIskMVovBWGScGzqqsVkMYqkM33tjgN6xKApzsZfLZpDJKoIeO3esr5uVTk4pxeGhaSajKf79wCBdNW5eOT3BWDjJcCjBq73jHOwP0VXr5edv6yKrFN21nrIXTXnnpgbag27qq8yT72MHhzh6LkyN185DN3eUvX3XqpnCJwG3nevbArQF3QxMxdnRVl3oZL93exOPHhjgqWMhhqYSjFoN1tR5uGtjE08eHuat/hA+h5Xb1tTwnu3NhWw5b52d4ntv9GOzGnz29i6a/GZO3h++2c+xc9PYLEJnrYexSJJUJseRiWlGw0k8TitBj52N1W52ddUUnuvVHjPFm9dhvWBFvSvpgR0t9IxGFy0vfnYixvfeGADggzua6ZizcHc6fj7rx2QsszwNnaNvPMYLJ8doC7qoclrpn4zhc9pY3+CjzucgnVVsXCDbD5gZi25da8blPnd8lNfPTCICn7y5ozD6PCMUNxcEzoTgzO08/959m7hzbR3tQfe8fQNmbvA3+iZ5e9Bgz/o62oKXX8K82NuDIfb3TVHttpPKZC+683xyJMLeU6NsbvZzY0fpmY/F3L+ticlYasH8+3s21HOgPzQrE1SxeCrLYMhcO6AHG7Rrme48V6hQPEMkmcEiQg7FdCLFvt4JBBAx2Nbio6vOy5YW/6x8z08eGSGcyPDToyM8dEsHhwZCPH10hBqvnQ2NPg70TxFJZhiYjBH02PnB/n5GwynGoynGwkkmoknsNgtbm/3cvq6OXV01nByJzIt/ExGqHFYefWuQeDpL/2Sc92wzY/28TitHh6ZJZxV9E1G+9sJpar0OdnZUc+f6xct7z1Sa29UVXJYQELvVmNXhGQolmE6kyeaULs9dRg1VTu7d2shENMXOjmqcNgs35h9LZ3M8emCQfT0T2CwGdosFQ8zPYDqreM+2Zvb1TOBxWKl223jkrQH6p+I8dHM7X3ryBK/3ThJKpAv5yvesr+PwYIjj5yJkcopvvXSGm7qCbGmuIpbK8rcv9BBPZWmvcZtZGdJZpuNpM+tM0VT7xRTgGA0nsRiyYIaOpfA6rGxboFNTeJ1IsrD4dzScnNdBNDifbcOyQikU954aK6SXS2WynBqJYhjCkaFpbl+3+PFgrpkQDqXMXPFz3b6uFqvFoMZrp8k/PyzFYbVw9yIp2cKJFD1jUfwuG4Zc/OjrUCjOK6cnaAu6S8YMex02qt12Am4bJZp/QV97/jQ9Y1GePzHOtgf9OC4yQ9A39vby7PFRbu6q5vN3rZv3+IUWUH/r5V56xqJsbKziU7d2znv81GiYN85Mcfva2kLWGk27GunOc4Wq8zqIJDNMxdNUe2wcGQzTHnQzGEpwc1eQgMuML95QNB2tlCKVnZ07+e3BEJmcYng6ya6uIMPTCbb6HMSSWcYiSV4+nR9Fc1rpGYvSGnCxpt7Lh25ooaPGw8mRCH0TUd48O8Unb2lnS7O/8FqDoQQiEE5kmIimiCQy/MKd3ThtFh47NMRjB8/R7HcUpmEnYyn2nhrD77IVnqfYZDTFU0fN9HqxVHbRlFNgxg/2jEWp8zkWrWS4GMOAwck4HTVubFcg76126TY1lV5kOTAZp3cshiHCWCTFtlY/u7trGI0m2LOuHr/LRr3PSbXLRt9EnHAyQyIzznQizYH+KabiqXxqsgwDk3EzN2/fFNOJNDmlGAzFMUQ4PRZlLJzEZbPQHnSxs6Oaiag5khlOZApt9NitWCyy5JjhkyNhfvTWEIYIH7mxdd7f9YxF6Z+MzSssFElmGJyK0x50LzmN4pbmKkbDSZRSJUfFa3wOyMc8r9T6hPYaN0OhBDVeO8PTCXJKIYqSFfgu5La1tXjsVgJue8lFkz6nbdFUo5lsjmPDYWo8jpJ/H0nm8DgsWAwK1QIvxrPHRhkKJegZi7K23jtv5PuW7iDpbI6OGvclxTzPXDDkFth3B/qneP3MJBsbq9i9pmbe488cHzErDJ4Y53N71mBcxDFPKcVLp8aJpbJMRNPzOs+5nOJPf3yUcCLD3lPjfPnj1y/5uTVttdGd5wpV7bHz8RvbsFnM2MBszlxc+OGdrdzcVVOygIuI8MCOFk6ORAq5RLe1BBiLjNBa7eLW7hoS6RznphME3TaeODyM3WqQzuZIpLN0BN24HRamYml6x6J01HgYjyQ5PhwhFE/zr/vO8gf3+7BZDE6PRTk1GsFpNaj3OVhb72VXV7AwGnf/9mbu394MwMH+EGORJPFMllfynfWAe34RA5fdgstuIZ7KUu258EK+J48Mc2QojN0q3L+9mYYqs+xyJJnh+eOjeJ1WbltTi2EIqUyOxw8N0T9ljpB3zozIKfPk7rRb9MhzhWqochJw27AY5gLVDY1Vsz7/uZyi2mPHZbfSUOUgksrithsE8pU8DRHqvA5GI0kS6SzxdIbOWjcj4QRWizkTUeO1s7mpiueOj1Lns5up6pIZ1jV4CXrsrKs/v7B2sbAJMPNGHz0XZnuLn85aDyPhJFOxFIl0jjNjURrzKdIshhBNZnjkzUFySjEynSxcME5EkvzRjw6TU4rb19XyiZtKpPUowWG18O4tjYtsUTTyzCUMfV6CW9fUsqXZj8du4c2zU5wZj+GxW0teQF+Iw2qZt07jYjx7fJQD/SEshvCp3Z3zqgxub/VzLpTA77JSX3XxFxf1VQ6GQgl8TiuuEhc8O9qrLyse/jO3dfLs8VG2tZQedX759DjRZJaXT4+zqys4KyUjwK7OIHtPjbOjPXBRHecZ6+p9DEzF6a6bH86SU2a2p2gyQzxVuijQ62cmOTkSZmdHkLX1lZuVSdMuRHeeK9j6Rh8fu7GNeDrLpsYqskoVYuTOTsR47cwEa+q8s6bZmvyuWdOVc9OmzYzKHB2aJtSV4s71dTQHXBzsDxFOpNmfD5v49mtn8TqtRJIZqt02nDaDao+9MCV8/FyYDY0+mvwu3n9dEx21npLTpEBhqnnvyTEARMxyyHM5bRY+eXM7k9E0rUVV4A70TxGKp7mpM8jZiRjHhyNc1+Ynks8Re3QoTCTRR32Vk4dv6WBfzwRHz5npxpoDLtbUeTk+HOYnh4cZDSfpGY3yR+/fYuYLnorz1NER1tR5sel454rkslv49K2dZHOqsIBrPJLkhZNj1Hkd7OysJqcUPqeVep+d7jovFkOIp3Nc3xagye/iicPDOG0WBPjgjlZaq1189/V+AD55S0dhZNdps5DJmdU+kxlFZ43nouKacznF44fOFSqD/uc7uzk5EubZ46NYLQbVHiuv901iNQw+srMVn9OK1SKkMrPzo+87M0nvWIRkJkdz4BLT0pUwMp0u/H5uOnXFnvdCZkZgnTYL9T6zwmAmm+W//uAg8VSW33/vJoKe5R8JDyfSnJ2I4XFYSefmF4l5+JYOdq8x8+BfymzWXRvq2dzkJ+C2LUuO983NfjYvctGxtt7LW2dDdNd55nWcAX7l7nX84p41l7RQUfIZik6OREpWpLUYwrp6L8eHI2xsmv94KpPjueNmufpIclR3nrVVTXeeK1xxKjmD8wfDp46OMBFNcWY8ZpZBvojYt5FwgscOnQNgU5OTW7pruKkzSCyV4Y9/dJi+iRjJdI5njo5iGMKWFj9dtR46azyFRSJbW/wMTMVZU+flho7ggieK0bCZc3pdvY9bumuo9tipyk+zl+Jz2gpVv5KZLK/1TvDiyXFsFoORcIJ9PZMEXDaGpxN86IYWXj8zSTansBjCVCxNKpujxmue9KyGEMiftOurHGZqJsDtsBTS+b18apyRcIJ4Kks4kcF/ieEfq9mLJ8c4F0pwx7rakoUjKoGIYC264Np7apzTo1FOj0YJeuyks2aYQn2Vgw9c18zfPN+DzWLmQAcz7Vm9z8Gm5iq2tvh57vgo0/lQjJHpZGE0eWuLn4YqJ48eGMRuNQpZHOZKZrK8PThNnddRKPQzEk6QTOcIeuyMhpOFz+Grp821ColUlsGpBDZLCq/DSt9ElHUNPna0BXDaLGxpOX+R67FbEBEMEZqqXJydiPH4oXME3DY+cH3LJXfMiqtdlrqAXW7nphPEUlnSWcXfvdhbCNP6yk9P8kfv37IibVBF/5/LajEuqwKgiFx6Du4r4O6NDdy6prZQQKaU4o5zIr9epbXataRziMNqVvh0WOZvqxTU+Zz4XfaSObJtFnPfnAslZg2OaNpqpDvPq1RDlYOJaIqgx479IkcR7BYDiyFkc2bRCTBHDXxOG5+6tZO3zk6xoz3A08dGSWVyVLvtNFQ5efb4KOsbfOzqCtIWdPOf7uie99wnhsOcGY+xoz2AzWrwL6/2mbl5W+Pcs6lhwbjWUn745iAnhsOcyI80v9Y7yVAozvB0go1NVQTcdu7Z1MDGpir29UzQWevBbbeyvTVghnBYLYVp2Xqfkz9832b294XoqvUURpWGpuJEk+bJPKtWZhq7koxMJwrZI148NcYDOxaPM68U9T4HJ0ciuOwW2oJu7lhXy8BUnN3dNWb6xu4gvWMxAm4bU7E017X5+fCO1kLHxm41yOYUBwamODI0zX1bG/ngjhZEBLvFoD3opsnvWnBR4LPHRnl7cBpDhJ/b3UEik+Xb+86iFNyxvpZ3bKijIX8hcmNnkGjKzKxwXWuAF0+NMRVL43fZ+PITxxkNJ7mpI8gNRQvMtrb4uW1tLQJ013s5NBAikswU4qA7F+jUX3C/VTlgMF8EpiwLuhSheBq7xaCr5vwIauMlhEhcCp/TRnvQjcWQkmscQvE0r/VO0Oh3XlJYSSW4mIGU7+8f4FwoQa3PwcO3dFxw++++3k8kmeHQwPS8mGfDEK5vC/DS6fGS5eRFhI/ubGU6Yc5matpqpjvPq9TPbG7k+rZqqj22i06vFnDb+diNbUxEU/Om37a2+AvT1PVV5ijBugYv//ByH9PxNKPhJNe1+UumKYqlMvz44DlySjEeTfLOTQ2FRUHxdOkYuMVMx9O47VY2NPq4b2sjTx8bJeixYzOMWVW8WgIuWnbMLjTTUGIEtaHKxb1bZ3cYDIuB1TBH+BZahHM1q3LZ8DrM8JyFwm4q0c3dNXTVevA6rbjtVm7sDBaycwD5ssPmVPHx4TANVc5Zac12dQaZiqfY3zdJKJ3m6WMj7NlQT9Bj56ljw/SOxTg4EKI54Cw5fa8KPxUKiCQyzFx7JVK5QsVOgIdu6eDujQ3U+Ry8fHqc6UQGEXMk+ujQNDkFhwZDs54/4Lbz6ds6mYqlWVvnpWc8ysmRCAG37bJGNn0OG/b8iLPHvvKHf7fNyvr8Iud3bWmkvcZNLJnl/XO+v8tlz/o6Gv1Oar2OefHOAM8cG+H0aDRfhdR1WdlRVoPpfGq/mZ8XombG7UsMNGRzijfOTGIR4dWeSba1BOZtY7UYV/0+1S5P5+/+e1lfv/fP3ruk7XTneZUyjMubHmz0Oy/497VeB7X5FfkdQTcHB0K0BFyzRrqLq1RZDQOHzSCeyuJ1mNlA7tvWyMh0ctao2lLdt62JQwMh1jf46Kr1UON10jMWZX2D94oVF7h/exPPnxilo8ZzyRk7VjOnzcLDuzuIJDOFf+vVYikhJnNTE84wDGF3Vy0vnBjj5HCE7lovVU7zcOh1mJ0qm8VYMDziHRvqqPHYqfU6CHrsVLtt7F5TQzydnTfqZrUYhbCQ29bWUuWyUu22U+dzsLXFz0g4WTKFY73PWQhvWlPn5VfuXntJVQWLffrWLk6MRDBEljTSeKXt6gritFvwOqy0Bd2FkJeVYrUYi44o+/KfAbvVWDT0oZKVqhy4kPdsa+Lw0DSblhiq8qEbWjk9Gp21gHaGIeb+m8zPqmja1Ux3nrUluWdTPTd1BfE6rIUD85OHh3l7cJobOgLcsa4Ou9XgwV3tjEwnCtPKGxur2LjY4v9FtARcszJy1Pkc84oiXK4HdrSQzua4Y23tFa8mtlo4bZaLmuq9WvjdNn7hzm6+81o/XqeVaCqL32Vw98Z6umrds6rYzeWwmmXDxyMpcjmFYQi3lMgCkUhn+Y+3z5HK5Hj31kaqnDZ2FhW3+OW71jIVS9NxgQwewGV3nMFchPy+7c1YRFjTsPILtqwWgxuWofrilbJnfT3tQQ81HvtF5fGuFHtPjrGvd5KNTb4LZF0xXewFTPGAylwiwsdvamcoFJ8186JpV6Nrs7egXTQRwe+yFVZwK6U4NBgipxQHB85POftdNtY1+Fa87Oyl+sbeXo4PR/jHV88Su4S8rtrq1jceR0QIJzL0jccAM/5/bb1v0ZmIdDbHP73Sx3de7+eJI8MLbnd8OMzp0Sj9k3EO9YfmPR5wm5VCr0THeCmeODzMM8dH+emxEZ4+Oroir7mamP/2XqpXaWjBzDH58OB0ySIyy81lt9Bd512WTCOaVkn0J1y7JCLCjvZq7FaDHW2VO5J0ITMjrjaLoGukXHs2NPrwOa3Ueu101i59tCyVyRHJX2xNRBdO+dbkd2G3mgt0VzpEoRSHzcAQc4p9tYYlaAu7vs08Jl/fFiiZqk7TtCtj9c1LaRVjz/o69lyg3Hal+/xda3nx5Bibm6pw2vTX4VpT53OUzBpzIR6HlXs2NnBmIspNncEFt6vzOfjs7V0oRcnCRivt7o31AAiwZ8Pq/u5q8+3qCrKra+HPo6ZpV4buLWjXtJaAi4/d2FbuZmir0LZWf6EA0GIqKZ7cYbVw39amC2+oaZqmLUhKpZypVCIyCpwpdzuusFpgrNyNKLOrZR/cALxxmc9xteyLi6Xf9/Ka+9ms9P2t23d5Krl9c9t2JY6bK6GS9+lCdJsvX4dSat403arqPF+NROQ1pdSNF97y6qX3wXnX6r7Q7/vaeN2l0u27PJXcvkpu22JWY7t1m5ePXjGiaZqmaZqmaUukO8+apmmapmmatkS681x+Xy13AyqA3gfnXav7Qr/va+N1l0q37/JUcvsquW2LWY3t1m1eJjrmWdM0TdM0TdOWSI88a5qmaZqmadoS6c6zpmmapmmapi2RLpKiaRVERH5ZKfV/y92O5SQiTUqpIRER4APAJqAH+I5SKlPe1i0fEbEB9wLjSqm9IvIQ4Af+USk1tcyvvRPYDQSAKeBlpdRry/mamqZpVysd87yCRMQCfJA5JzHgB1dzp2EufSI3icjzwMwXUPI/twCHlFJ3lqdVy09EnlJK3S0ifwHEgaeA64EblVIfK2vjlpGIfB/Yh/m53wn8GLMYwM8qpd69jK/7ZcABPAmEgCrgnUBGKfVry/W6SyUigZmLBxG5H9gKnMK8mCr7CarSj9uVvv9gdR7zV1ubK/1zupDVtp9n6M7zChKRbwEHgJ8y+yR2nVLqoXK2baVU+ol8JYnIbwDXAd9QSj2Tv+8xpdR9ZW3YMhORJ5VS75z5WXT/00qpu8rZtuVU/P5E5JBSauvc+5fpdZ8rdTG20P0rrehi6k8xT6A/BG4DWpVSnylr46j84/Yq2H+r7pi/Sttc0Z/TUlbjfp6hwzZWVqdS6uE59+3Pj0BeK3aWOGF/X0SeK0trykgp9WURsQOfFZHPAf9U7jatkG+KyNeAsyLyD8CzwHag4kcbLlNURP4A8ADjIvJbwASQXObXfU1E/hp4ApjGPEHdQ+WVRL5VKbUn//vjIvJMORtTZLUctyt1/63GY/5qbPNq+ZwWW437GdCd55X2iIg8CjzD+ZPYHuCRcjZqha2WE/mKUEqlgL8Skb8BHgbeKnOTlp1S6lsi8lPg3UAD5nHoa0qpq/29fxQz5vkU8MfApwAn8PHlfFGl1G+KyA7gFmAd5gjPV5VS+5fzdS/CDfmT5eaZEAQRMQBfuRuWt9Bx+0flbFSRG/IdpE0Vuv9W4zF/NbZ5NfYvVuN+BnTYxooTkTuBzZixPdOYMZDdSqlXytmulZQ/kd+MOcUYAmqVUl8oa6M0TSsbEdkKZJVSR/K33cB2pdTL5W2ZSUTqgBsxY9VPASeVUvvK2yqTiHwAeEIpFSu6zw2sq5QL0qKLNz/mMf/lCrp4K6mozQHMNr8EWCvl372U1di/EJFdwN2ADcgASin1Z+Vt1YXpzvMKEpEvAvWYH5Ba4OeVUqMzMWvlbd3KKFokJ0V3bwberoT4S03TVlalHxdF5HGl1L0i8uuY8ZiPYsYU9yulfq+sjQNEZBA4AwwD3wceUUpNlrdVs+UXhd2K2RGdpMIXheVH7ufdDTyulHrXSrdnKSr9e1SKiPxt/tcUZtsHMDv99UqpXyhbw5ZAh22srJtmOogish34NxH57TK3aaV9j2twkZymaQuq9OOiPf/zAeAupVQO+H8i8kIZ21TsmFLqLhHpAj6EGTOaBH6olPrLMrdtZlGYHXMh22HMqfnPiMjDFbwoLIKZqaKYYK7NqFSV/j0qZe1MnL6IHFRKfTj/+9PlbdaF6c7zyrKIiF0plVJKHRCRB4B/wExPdk24hhfJaSWISBY4iHks6gEezsdsduZv/4lS6g/y29YCQ8BfK6V+pUxN1q68Sj8ubhaRvwfWYGYGiOfvd5avSfMppXqALwJfFJEGzBzqlWA1Lgo7AjyglAoV3ykiT5SpPUtR6d+jUor7oL9f9LvM3bDS6LCNFZSP7elVSo0U3WcBPqqU+pfytaw8RMSKuUhug1Lqd8vdHm3liUhEKeXN//5N4LhS6k/yneengJBSakf+8V8CfhF4QXeerx6VflwUkY6im4NKqbSIeIE7lFKPlatdM0Tk3Uqp/yh3OxYiIl/CzDAzd1FYUin162Vs2oJEpAmzmFFqzv3WSs2ZXOnfo1JEZAtwVCmVLbrPDtyrlKrkhY6686xpWvnM6Tx/DnOR2OfznedHMfOWfkkp9Vo+9dZPgGbdeda01WM1Lr7TtMXosA1N08ouP0JyD/C3cx76F+ATIjIMZIFBoHmFm6dp2iXKL757i9lpOAV4HKjIxXeadiG686xpWjm5RORNoAUzznBuTOHjwBcwMwl8e2WbpmnaFbAaF99p2qJKpWPRVhkRUflKbTO3rSIymk+Yjoh8On/7zaL/NotIp4jERWS/iBwRkVdF5NP5v9kjIi/NeR2riAyLiB75066UuFLqeqAD84T6y8UP5mMOXwd+C/jOirdO07TLNbP47u6i/+5iFRTCKDcRiZS4b4OIPJM/jx8Rka+KyLuLzu0RETmW//3v83/zwXw/YWP+9iv5x/vm9A06V/gtrlp65PnqEAW2iohLKRXHnAobmLPNt+fGiea/KKeKFmR1A98TEQG+CbSKSIdS6kz+T96JmY95cBnfi3YNUkrFRORXgR+IyNz0Wl8EnlVKTZgfTU3TVpH7OZ+hpJhOT3ppvgJ8WSn1QwAR2aaUOgj8R/72M8Bvz8mj/SDwQv7nf1NK3Zzf9tPAjXoNycXTI89Xjx8D783//iDwzxf7BEqp08BvAr+az2X6r8Anijb5xKU8r6YtRb7i2AHMz2/x/W8rpb5ZnlZpmnY5lFJDc7NW5O+vyKwVq0AT0D9zI99xXlA+M8ztwGeZfT7XLoPuPF89ZhZWOTFjyeaW4/z4nLAN1wLP8wawMf/7P5P/somIA3gP8N0r33TtWjWTaaPo9vuUUt9SSvUqpbaW2P4bepRE07Rr2JeBp0TkMRH5DREJXGD7D2BWRjwOjItZ7VG7TLrzfJVQSh0AOjFH7X5cYpNvK6WuL/qv1DQaFCUnz0/7eEVkA+YU2ytKqYkr3HRN0zRN05ZAKfV1YBPwb8A7gJfzg1sLeRBzcI38zwcX2VZbIt15vro8Avw5lxdasQNzgceMmdFnHbKhado1Q0QemDNb96aI5ETkl/KLr/5L0bb/Z2axtaYtN6XUoFLq75RSHwAywLxZOgARCQJ3A18TkV7gd4CPiV48ctl05/nq8nfAf79QDNRC8gsI/xz430V3/zPwEOYX8IeX20BN07TVQCn1/eLZOuAvgecxF2aNAL+Wr4amaStGRO4VEVv+90aghvkJAmZ8BPiWUqpDKdWplGoDeoA7Vqa1Vy+dbeMqopTqx1yJW8rHReT2otufxyw4sUZE9gNOIAx8RSn1jaLnPCIiUeB1pVR0eVquaZpWuURkPfCHwK2Yg06jwIvAp4C/KWPTtKubW0T6i25/CWgF/kJEEvn7fkcpdW6Bv38Q+J9z7vtu/v7nrmhLrzG6PLemaZqmLSA/yvcS8L+UUt8uKh3/fuAxYDPwF8BrxQMPmqZdvXTYhqZpmqYt7AuY+e1nVbjMp/Z8BfjZsrRK07Sy0WEbmqZpmlaCiLwD+DBwwwKb/A/MypfPrlCTNE2rAHrkWdM0TdPmEJFq4OvAzymlwqW2UUodBQ4D71vJtmmaVl565FnTNE3T5vscUA/81ZzMXnNTdv4JsH+lGqVpWvnpBYOapmmapmmatkQ6bEPTNE3TNE3Tlkh3njVN0zRN0zRtiXTnWdM0TdM0TdOWSHeeNU3TNE3TNG2JdOdZ0zRN0zRN05ZId541TdM0TdM0bYl051nTNE3TNE3Tlkh3njVN0zRN0zRtif4/uaOipK71h84AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attributes = [\"MEDV\", \"RM\", \"ZN\", \"LSTAT\"]\n",
    "scatter_matrix(housing[attributes], figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:03.542519Z",
     "start_time": "2020-10-10T10:04:02.740037Z"
    },
    "_cell_guid": "4ed16331-e1a3-462a-b76f-80f8e0c0af41",
    "_uuid": "c49d3f38-d870-4d0c-a6de-cb90bbc9e6f5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"RM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d693a72f-16e6-4610-b3bc-d93b2229d0e6",
    "_uuid": "5a975271-3d5d-499c-b829-b98fff214777"
   },
   "source": [
    "## Trying out Attribute combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:06.929318Z",
     "start_time": "2020-10-10T10:04:06.884621Z"
    },
    "_cell_guid": "cdac58b9-f48f-457d-9dbc-ad6f0082d779",
    "_uuid": "ee35f539-efdb-4514-b911-782ca5afd8a0"
   },
   "outputs": [],
   "source": [
    "housing[\"TAXRM\"] = housing['TAX']/housing['RM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:10.410303Z",
     "start_time": "2020-10-10T10:04:10.336463Z"
    },
    "_cell_guid": "42e59039-4b8e-4324-8192-7ea5d35ab943",
    "_uuid": "95096d9b-302e-4581-ac7d-bd949d97a6d3"
   },
   "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>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",
       "      <th>TAXRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.17899</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>5.670</td>\n",
       "      <td>28.8</td>\n",
       "      <td>2.7986</td>\n",
       "      <td>6</td>\n",
       "      <td>391</td>\n",
       "      <td>19.2</td>\n",
       "      <td>393.29</td>\n",
       "      <td>17.60</td>\n",
       "      <td>23.1</td>\n",
       "      <td>68.959436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.04819</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>6.108</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.2203</td>\n",
       "      <td>1</td>\n",
       "      <td>315</td>\n",
       "      <td>16.4</td>\n",
       "      <td>392.89</td>\n",
       "      <td>6.57</td>\n",
       "      <td>21.9</td>\n",
       "      <td>51.571709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>4.55587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.718</td>\n",
       "      <td>3.561</td>\n",
       "      <td>87.9</td>\n",
       "      <td>1.6132</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>354.70</td>\n",
       "      <td>7.12</td>\n",
       "      <td>27.5</td>\n",
       "      <td>187.026116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.02187</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.401</td>\n",
       "      <td>6.800</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.2196</td>\n",
       "      <td>1</td>\n",
       "      <td>265</td>\n",
       "      <td>15.6</td>\n",
       "      <td>393.37</td>\n",
       "      <td>5.03</td>\n",
       "      <td>31.1</td>\n",
       "      <td>38.970588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.06417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0.499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.2</td>\n",
       "      <td>3.3603</td>\n",
       "      <td>5</td>\n",
       "      <td>279</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.68</td>\n",
       "      <td>18.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
       "495  0.17899   0.0   9.69     0  0.585  5.670  28.8  2.7986    6  391   \n",
       "254  0.04819  80.0   3.64     0  0.392  6.108  32.0  9.2203    1  315   \n",
       "365  4.55587   0.0  18.10     0  0.718  3.561  87.9  1.6132   24  666   \n",
       "193  0.02187  60.0   2.93     0  0.401  6.800   9.9  6.2196    1  265   \n",
       "35   0.06417   0.0   5.96     0  0.499    NaN  68.2  3.3603    5  279   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV       TAXRM  \n",
       "495     19.2  393.29  17.60  23.1   68.959436  \n",
       "254     16.4  392.89   6.57  21.9   51.571709  \n",
       "365     20.2  354.70   7.12  27.5  187.026116  \n",
       "193     15.6  393.37   5.03  31.1   38.970588  \n",
       "35      19.2  396.90   9.68  18.9         NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:13.933162Z",
     "start_time": "2020-10-10T10:04:13.900457Z"
    },
    "_cell_guid": "76db6ffc-40b3-4a31-ac8c-5dcec9938f53",
    "_uuid": "0b076df1-368f-443a-8a17-2bcf34d57ab9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.681481\n",
       "B          0.350283\n",
       "ZN         0.341767\n",
       "DIS        0.250670\n",
       "CHAS       0.191563\n",
       "AGE       -0.366230\n",
       "RAD       -0.381955\n",
       "CRIM      -0.395849\n",
       "NOX       -0.425104\n",
       "TAX       -0.467171\n",
       "PTRATIO   -0.475870\n",
       "INDUS     -0.481414\n",
       "TAXRM     -0.535069\n",
       "LSTAT     -0.720350\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:17.827964Z",
     "start_time": "2020-10-10T10:04:17.281431Z"
    },
    "_cell_guid": "979427ed-2527-4c37-9ae9-2a44e555c3fb",
    "_uuid": "7f1218a2-dcdf-46cf-a632-080bd313958c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TAXRM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"TAXRM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:21.591878Z",
     "start_time": "2020-10-10T10:04:21.579237Z"
    },
    "_cell_guid": "1fa5ff96-679c-4dd0-95cf-1774a68626bb",
    "_uuid": "99e4db04-f409-4b18-a482-811d57ea5480"
   },
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"MEDV\", axis=1)\n",
    "housing_labels = strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c8296c10-a2c1-4130-9a83-2ad7dd325b75",
    "_uuid": "234c2e70-da9f-4799-9e50-8797c8e26d30"
   },
   "source": [
    "## Missing Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:24.615142Z",
     "start_time": "2020-10-10T10:04:24.609293Z"
    },
    "_cell_guid": "7d7d15ba-c8f9-4f28-97ef-e143aebf18f4",
    "_uuid": "59dbc6ec-2655-4b68-8742-103a4811289e"
   },
   "outputs": [],
   "source": [
    "# To take care of missing attributes, you have three options:\n",
    "#     1. Get rid of the missing data points\n",
    "#     2. Get rid of the whole attribute\n",
    "#     3. Set the value to some value(0, mean or median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:28.254404Z",
     "start_time": "2020-10-10T10:04:28.218001Z"
    },
    "_cell_guid": "e7fff287-7426-49c6-9bf5-feca2273664a",
    "_uuid": "1cc88dd8-40aa-47f9-9139-d89d3f03f62a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(403, 13)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = housing.dropna(subset=[\"RM\"]) #Option 1\n",
    "a.shape\n",
    "# Note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:32.161067Z",
     "start_time": "2020-10-10T10:04:32.141394Z"
    },
    "_cell_guid": "d43284cb-d250-428d-acc6-68f49c22b4e3",
    "_uuid": "d1c884bc-ea61-409e-aaa5-1c509e575735"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(408, 12)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.drop(\"RM\", axis=1).shape # Option 2\n",
    "# Note that there is no RM column and also note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:35.612019Z",
     "start_time": "2020-10-10T10:04:35.602136Z"
    },
    "_cell_guid": "c75ebd11-1269-4359-b91b-2a62ed3ac1ff",
    "_uuid": "c257ef57-997b-4268-a179-25a217c4a1f9"
   },
   "outputs": [],
   "source": [
    "median = housing[\"RM\"].median() # Compute median for Option 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:39.478158Z",
     "start_time": "2020-10-10T10:04:39.438394Z"
    },
    "_cell_guid": "6eea255a-6efd-4003-b607-41b0debaa95a",
    "_uuid": "9245992d-ddcf-4621-a04d-b065452d39de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495    5.670\n",
       "254    6.108\n",
       "365    3.561\n",
       "193    6.800\n",
       "35     6.208\n",
       "       ...  \n",
       "386    4.652\n",
       "243    6.393\n",
       "22     6.142\n",
       "480    6.242\n",
       "455    6.525\n",
       "Name: RM, Length: 408, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"RM\"].fillna(median) # Option 3\n",
    "# Note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:43.900728Z",
     "start_time": "2020-10-10T10:04:43.881276Z"
    },
    "_cell_guid": "863cae44-afdd-45e7-80fc-08346a35fbce",
    "_uuid": "afd600f8-b8d3-47da-b5d4-2a7839764702"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(408, 13)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:47.347662Z",
     "start_time": "2020-10-10T10:04:47.212357Z"
    },
    "_cell_guid": "0d86fc5d-d08e-44ea-ac8a-d384c4ab5170",
    "_uuid": "3f44ae2e-1304-4e04-ad66-8f13c470bc8e"
   },
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.00000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>403.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.574915</td>\n",
       "      <td>10.730392</td>\n",
       "      <td>11.37527</td>\n",
       "      <td>0.068627</td>\n",
       "      <td>0.558200</td>\n",
       "      <td>6.269007</td>\n",
       "      <td>69.169363</td>\n",
       "      <td>3.738277</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>411.227941</td>\n",
       "      <td>18.500245</td>\n",
       "      <td>353.601397</td>\n",
       "      <td>12.920907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.066171</td>\n",
       "      <td>22.067428</td>\n",
       "      <td>6.83855</td>\n",
       "      <td>0.253130</td>\n",
       "      <td>0.116078</td>\n",
       "      <td>0.703808</td>\n",
       "      <td>28.275511</td>\n",
       "      <td>2.085949</td>\n",
       "      <td>8.716483</td>\n",
       "      <td>168.222809</td>\n",
       "      <td>2.143483</td>\n",
       "      <td>95.566677</td>\n",
       "      <td>7.230954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.74000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.087962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.32000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453000</td>\n",
       "      <td>5.876500</td>\n",
       "      <td>45.075000</td>\n",
       "      <td>2.062000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>374.617500</td>\n",
       "      <td>6.927500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.286735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.90000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208000</td>\n",
       "      <td>77.950000</td>\n",
       "      <td>3.107300</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>390.980000</td>\n",
       "      <td>11.705000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.716457</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.10000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625750</td>\n",
       "      <td>6.629500</td>\n",
       "      <td>94.150000</td>\n",
       "      <td>4.978850</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>395.645000</td>\n",
       "      <td>17.272500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.534100</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.74000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.704000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>36.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN      INDUS        CHAS         NOX          RM  \\\n",
       "count  408.000000  408.000000  408.00000  408.000000  408.000000  403.000000   \n",
       "mean     3.574915   10.730392   11.37527    0.068627    0.558200    6.269007   \n",
       "std      8.066171   22.067428    6.83855    0.253130    0.116078    0.703808   \n",
       "min      0.006320    0.000000    0.74000    0.000000    0.389000    3.561000   \n",
       "25%      0.087962    0.000000    5.32000    0.000000    0.453000    5.876500   \n",
       "50%      0.286735    0.000000    9.90000    0.000000    0.538000    6.208000   \n",
       "75%      3.716457   12.500000   18.10000    0.000000    0.625750    6.629500   \n",
       "max     73.534100  100.000000   27.74000    1.000000    0.871000    8.704000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  408.000000  408.000000  408.000000  408.000000  408.000000  408.000000   \n",
       "mean    69.169363    3.738277    9.666667  411.227941   18.500245  353.601397   \n",
       "std     28.275511    2.085949    8.716483  168.222809    2.143483   95.566677   \n",
       "min      2.900000    1.129600    1.000000  187.000000   13.000000    0.320000   \n",
       "25%     45.075000    2.062000    4.000000  284.000000   17.400000  374.617500   \n",
       "50%     77.950000    3.107300    5.000000  335.000000   19.050000  390.980000   \n",
       "75%     94.150000    4.978850   24.000000  666.000000   20.200000  395.645000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   23.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  408.000000  \n",
       "mean    12.920907  \n",
       "std      7.230954  \n",
       "min      1.730000  \n",
       "25%      6.927500  \n",
       "50%     11.705000  \n",
       "75%     17.272500  \n",
       "max     36.980000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() # before we started filling missing attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:52.718392Z",
     "start_time": "2020-10-10T10:04:51.248231Z"
    },
    "_cell_guid": "c1e8403f-c424-4a93-bb59-8b9dc51ec7da",
    "_uuid": "a8a7f0dc-eac8-40a9-8cd0-fc8ede1785b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:04:56.664033Z",
     "start_time": "2020-10-10T10:04:56.644327Z"
    },
    "_cell_guid": "e780048b-719c-47e9-9a28-0ac86a6dc15d",
    "_uuid": "07ccdd8e-3432-42bc-9c67-fc43a768b5ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.86735e-01, 0.00000e+00, 9.90000e+00, 0.00000e+00, 5.38000e-01,\n",
       "       6.20800e+00, 7.79500e+01, 3.10730e+00, 5.00000e+00, 3.35000e+02,\n",
       "       1.90500e+01, 3.90980e+02, 1.17050e+01])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:00.565606Z",
     "start_time": "2020-10-10T10:05:00.541572Z"
    },
    "_cell_guid": "b01f9a5b-66b6-48c6-90ee-82f2809763c3",
    "_uuid": "073a354c-151b-4995-b3de-8b49251c55a8"
   },
   "outputs": [],
   "source": [
    "X = imputer.transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:03.895874Z",
     "start_time": "2020-10-10T10:05:03.888648Z"
    },
    "_cell_guid": "44cea807-452e-45e0-a5c6-e3a458ef4e5f",
    "_uuid": "a45effba-8ac7-4c99-a71d-df9239a283e7"
   },
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:08.466246Z",
     "start_time": "2020-10-10T10:05:08.301826Z"
    },
    "_cell_guid": "663cb43e-4ccc-467b-86ec-306820e54081",
    "_uuid": "07fbdc7c-9b86-41c4-9da2-0b68b99377b2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.00000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.574915</td>\n",
       "      <td>10.730392</td>\n",
       "      <td>11.37527</td>\n",
       "      <td>0.068627</td>\n",
       "      <td>0.558200</td>\n",
       "      <td>6.268260</td>\n",
       "      <td>69.169363</td>\n",
       "      <td>3.738277</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>411.227941</td>\n",
       "      <td>18.500245</td>\n",
       "      <td>353.601397</td>\n",
       "      <td>12.920907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.066171</td>\n",
       "      <td>22.067428</td>\n",
       "      <td>6.83855</td>\n",
       "      <td>0.253130</td>\n",
       "      <td>0.116078</td>\n",
       "      <td>0.699504</td>\n",
       "      <td>28.275511</td>\n",
       "      <td>2.085949</td>\n",
       "      <td>8.716483</td>\n",
       "      <td>168.222809</td>\n",
       "      <td>2.143483</td>\n",
       "      <td>95.566677</td>\n",
       "      <td>7.230954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.74000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.087962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.32000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453000</td>\n",
       "      <td>5.878750</td>\n",
       "      <td>45.075000</td>\n",
       "      <td>2.062000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>374.617500</td>\n",
       "      <td>6.927500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.286735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.90000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208000</td>\n",
       "      <td>77.950000</td>\n",
       "      <td>3.107300</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>390.980000</td>\n",
       "      <td>11.705000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.716457</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.10000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625750</td>\n",
       "      <td>6.626000</td>\n",
       "      <td>94.150000</td>\n",
       "      <td>4.978850</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>395.645000</td>\n",
       "      <td>17.272500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.534100</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.74000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.704000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>36.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN      INDUS        CHAS         NOX          RM  \\\n",
       "count  408.000000  408.000000  408.00000  408.000000  408.000000  408.000000   \n",
       "mean     3.574915   10.730392   11.37527    0.068627    0.558200    6.268260   \n",
       "std      8.066171   22.067428    6.83855    0.253130    0.116078    0.699504   \n",
       "min      0.006320    0.000000    0.74000    0.000000    0.389000    3.561000   \n",
       "25%      0.087962    0.000000    5.32000    0.000000    0.453000    5.878750   \n",
       "50%      0.286735    0.000000    9.90000    0.000000    0.538000    6.208000   \n",
       "75%      3.716457   12.500000   18.10000    0.000000    0.625750    6.626000   \n",
       "max     73.534100  100.000000   27.74000    1.000000    0.871000    8.704000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  408.000000  408.000000  408.000000  408.000000  408.000000  408.000000   \n",
       "mean    69.169363    3.738277    9.666667  411.227941   18.500245  353.601397   \n",
       "std     28.275511    2.085949    8.716483  168.222809    2.143483   95.566677   \n",
       "min      2.900000    1.129600    1.000000  187.000000   13.000000    0.320000   \n",
       "25%     45.075000    2.062000    4.000000  284.000000   17.400000  374.617500   \n",
       "50%     77.950000    3.107300    5.000000  335.000000   19.050000  390.980000   \n",
       "75%     94.150000    4.978850   24.000000  666.000000   20.200000  395.645000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   23.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  408.000000  \n",
       "mean    12.920907  \n",
       "std      7.230954  \n",
       "min      1.730000  \n",
       "25%      6.927500  \n",
       "50%     11.705000  \n",
       "75%     17.272500  \n",
       "max     36.980000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0322ea4b-cfab-4179-b545-b75208bc0472",
    "_uuid": "66584712-72e9-4581-986d-727c272cc234"
   },
   "source": [
    "## Scikit-learn Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0d381ae7-26fd-4b67-b215-2733368ac49e",
    "_uuid": "d28fb528-01f0-47ee-a538-b922fb9617dc"
   },
   "source": [
    "Primarily, three types of objects\n",
    "1. Estimators - It estimates some parameter based on a dataset. Eg. imputer. It has a fit method and transform method. Fit method - Fits the dataset and calculates internal parameters\n",
    "\n",
    "2. Transformers - transform method takes input and returns output based on the learnings from fit(). It also has a convenience function called fit_transform() which fits and then transforms.\n",
    "\n",
    "3. Predictors - LinearRegression model is an example of predictor. fit() and predict() are two common functions. It also gives score() function which will evaluate the predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b6b304b8-5eed-476b-b1d2-8c4480aaded0",
    "_uuid": "e6461a83-0841-4bcb-b289-f3e6eae6cb97"
   },
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b9af6c7e-bea9-4c4c-858d-e27923c2ff13",
    "_uuid": "052860f7-24e0-4ca5-91f6-bfcdd33a06df"
   },
   "source": [
    "Primarily, two types of feature scaling methods:\n",
    "1. Min-max scaling (Normalization)\n",
    "    (value - min)/(max - min)\n",
    "    Sklearn provides a class called MinMaxScaler for this\n",
    "    \n",
    "2. Standardization\n",
    "    (value - mean)/std\n",
    "    Sklearn provides a class called StandardScaler for this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "af33ecca-02d6-46f2-adba-587941b6f616",
    "_uuid": "95798fcf-c2fd-4615-8320-63c880def3cb"
   },
   "source": [
    "## Creating a Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:14.803429Z",
     "start_time": "2020-10-10T10:05:14.763460Z"
    },
    "_cell_guid": "9b00b1e4-4409-4578-846a-a4c9307ad445",
    "_uuid": "eddf30f0-0533-4fc4-a142-1392befe8ef4"
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "my_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    #     ..... add as many as you want in your pipeline\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:19.223118Z",
     "start_time": "2020-10-10T10:05:19.189302Z"
    },
    "_cell_guid": "4ef859a2-c276-4b13-ac3d-328dedb42aea",
    "_uuid": "0a726c3e-8062-4645-ac8e-84a9aed0a920"
   },
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:23.367490Z",
     "start_time": "2020-10-10T10:05:23.350136Z"
    },
    "_cell_guid": "817486e4-d8b5-4173-a467-d09b13f09889",
    "_uuid": "ae1b6698-e9cd-4926-b805-ce27d82b46a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(408, 13)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "be195717-c2a5-4d4a-bb85-55ac66a32419",
    "_uuid": "d105b70d-a47b-47ac-b7fe-6df828ad36bd"
   },
   "source": [
    "## Selecting a desired model for Dragon Real Estates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:29.132277Z",
     "start_time": "2020-10-10T10:05:27.237053Z"
    },
    "_cell_guid": "15fe0e08-7640-4e90-843c-3295129954d0",
    "_uuid": "481d0a12-4e60-4cb3-a0a0-b6df491487c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# model = LinearRegression()\n",
    "# model = DecisionTreeRegressor()\n",
    "model = RandomForestRegressor()\n",
    "model.fit(housing_num_tr, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:33.311935Z",
     "start_time": "2020-10-10T10:05:33.296944Z"
    },
    "_cell_guid": "f2996746-4610-4ad4-9f30-2d226d2c872c",
    "_uuid": "cade1dea-1bc3-458c-8ace-cccb1710fcc1"
   },
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:37.807443Z",
     "start_time": "2020-10-10T10:05:37.793021Z"
    },
    "_cell_guid": "3a195016-6e3c-486c-bf08-3c3365d102c9",
    "_uuid": "1331ac11-a34f-474b-a501-1bafdceaab8f"
   },
   "outputs": [],
   "source": [
    "some_labels = housing_labels.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:53.116801Z",
     "start_time": "2020-10-10T10:05:53.105876Z"
    },
    "_cell_guid": "2cec49f8-ff63-4d16-936e-149968927246",
    "_uuid": "64a0bcbc-b1a3-4e45-a2e1-a11b8e60d9cf"
   },
   "outputs": [],
   "source": [
    "prepared_data = my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:05:58.021643Z",
     "start_time": "2020-10-10T10:05:57.945979Z"
    },
    "_cell_guid": "38a62e77-796a-44d9-81c8-1e846e552d91",
    "_uuid": "fd113ccf-a3e3-4502-86e5-b8dc82dbc6fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.749, 22.381, 25.647, 30.479, 20.227])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:02.795726Z",
     "start_time": "2020-10-10T10:06:02.774674Z"
    },
    "_cell_guid": "19df7faa-43a6-4091-bf9b-018ca66b5495",
    "_uuid": "694c56f4-2977-460c-97b1-c3f8d8fed628"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[23.1, 21.9, 27.5, 31.1, 18.9]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "943bcfd6-8563-4dab-b3ee-02061378e29d",
    "_uuid": "12166fdc-4d65-4574-8e94-153c6f322470"
   },
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:07.512560Z",
     "start_time": "2020-10-10T10:06:07.461833Z"
    },
    "_cell_guid": "d82a2832-4b06-4d6a-8caf-29ac377cd6a1",
    "_uuid": "a8720f26-f289-4ba7-8d73-a46899ef1813"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = model.predict(housing_num_tr)\n",
    "mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:12.518058Z",
     "start_time": "2020-10-10T10:06:12.497453Z"
    },
    "_cell_guid": "c2b4b399-fd4d-4f68-8770-9c05c3a0639d",
    "_uuid": "59260eba-4654-4d98-9c62-5da234e70042"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4874308543823558"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cf50f4f3-0e90-4295-a801-3cf20d295840",
    "_uuid": "31158afe-0bd8-46e4-a3ac-36df911b7b10"
   },
   "source": [
    "## Using better evaluation technique - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:17.617882Z",
     "start_time": "2020-10-10T10:06:17.606138Z"
    },
    "_cell_guid": "b43bce0d-1155-44d7-9295-2c1d86c64f75",
    "_uuid": "199301bd-24aa-4098-bc52-ac77d0bc2ea1"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:31.535467Z",
     "start_time": "2020-10-10T10:06:22.278348Z"
    },
    "_cell_guid": "57ee497b-5c0c-40d1-be93-fc20dc26fa86",
    "_uuid": "2a5cdbc0-f872-4f4d-b1b8-842fac472771"
   },
   "outputs": [],
   "source": [
    "# 1 2 3 4 5 6 7 8 9 10\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(model, housing_num_tr, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:36.678037Z",
     "start_time": "2020-10-10T10:06:36.641511Z"
    },
    "_cell_guid": "1a5578ce-abb7-46c6-8841-de4ddd9934e7",
    "_uuid": "cb0f741e-eac5-437b-abae-7a92fb122385"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.00378445, 3.93729004, 5.10122055, 4.20617159, 3.0242006 ,\n",
       "       2.18922825, 6.93577849, 2.92493718, 3.36886069, 3.64421746])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:42.068154Z",
     "start_time": "2020-10-10T10:06:42.059419Z"
    },
    "_cell_guid": "cbcea417-2fad-4cac-b95d-6491b7e92793",
    "_uuid": "6028e7d6-246f-407c-add2-4e27d4eb4a1f"
   },
   "outputs": [],
   "source": [
    "def print_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean: \", scores.mean())\n",
    "    print(\"Standard deviation: \", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:06:46.847850Z",
     "start_time": "2020-10-10T10:06:46.824637Z"
    },
    "_cell_guid": "00330d45-ce69-4ace-9aaf-89c00e2b885d",
    "_uuid": "eaca6497-0a4d-4e49-8953-83a9e3f466c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [3.00378445 3.93729004 5.10122055 4.20617159 3.0242006  2.18922825\n",
      " 6.93577849 2.92493718 3.36886069 3.64421746]\n",
      "Mean:  3.833568930976701\n",
      "Standard deviation:  1.2847229513852434\n"
     ]
    }
   ],
   "source": [
    "print_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ace72479-f585-4b85-bafb-eb91f221aa11",
    "_uuid": "904773f7-c9dd-409e-b10d-69d42b94acdb"
   },
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:07:06.108336Z",
     "start_time": "2020-10-10T10:07:05.850399Z"
    },
    "_cell_guid": "461338fc-5ed6-4a88-a5d5-a7d2d3ff5043",
    "_uuid": "280c2e27-e461-445a-b8ee-fa13cda594e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dragon.joblib']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, 'Dragon.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c4eaa8f5-bf93-450c-bfb5-4e910aa75d50",
    "_uuid": "6e6a6a31-741f-415d-ac4d-d2eb0bef26b7"
   },
   "source": [
    "## Testing the model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:07:10.630415Z",
     "start_time": "2020-10-10T10:07:10.560054Z"
    },
    "_cell_guid": "c0753efb-c405-47ca-bfaa-9a118a94a4bb",
    "_uuid": "6bafb0fc-486b-4a01-9cab-9e24016b31cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21.666 27.135 24.245 26.35  33.191 23.388 22.427 20.629 22.806 20.979\n",
      " 10.852 22.094 27.259 19.748 14.708 18.621 18.812 19.341 18.621 31.45\n",
      " 25.895 18.695 31.532 15.714 24.71  18.53  14.757 31.182 19.159 20.401\n",
      " 24.545 19.133 44.946 30.496 21.425 41.643 32.817 22.331 26.763 21.881\n",
      " 19.607 13.821 22.624 28.647 24.244 11.463  8.936 33.906 43.058 19.414\n",
      " 23.447 36.597 15.138 25.254 20.17  28.904 19.499 18.767 23.34  20.282\n",
      " 20.358 32.924 10.939 21.21  22.696 22.043 15.393 20.761 19.409 19.245\n",
      " 31.685 22.414 29.025 19.748 47.327 15.75  19.788 38.667 45.116 16.7\n",
      " 24.569 10.422 44.439 20.679 25.296 15.318 37.133 11.802 31.458 20.81\n",
      " 17.687 43.58  20.989 14.591  7.434 12.718 33.23  29.778 34.033 22.24\n",
      " 17.707 33.15  19.475] [22.4, 25.0, 22.2, 24.4, 31.5, 23.3, 25.0, 24.5, 22.0, 20.3, 12.3, 21.2, 28.0, 23.8, 14.6, 20.8, 17.2, 19.5, 14.4, 28.5, 30.1, 19.6, 23.6, 19.4, 24.7, 19.9, 13.5, 30.3, 21.8, 20.4, 27.9, 18.9, 46.0, 23.9, 21.7, 44.0, 33.2, 21.6, 27.0, 19.8, 20.9, 16.7, 22.2, 24.8, 28.1, 12.7, 7.2, 37.3, 43.1, 23.8, 23.8, 50.0, 15.6, 27.1, 20.1, 29.1, 18.6, 19.5, 20.1, 21.1, 19.7, 33.1, 13.1, 20.4, 23.6, 20.6, 14.1, 22.7, 16.8, 21.5, 32.0, 20.3, 31.0, 19.2, 50.0, 13.6, 18.7, 67.0, 41.7, 10.4, 16.5, 7.2, 50.0, 19.6, 36.2, 12.7, 24.0, 10.2, 32.2, 24.3, 19.5, 21.9, 23.4, 11.7, 5.0, 13.8, 34.6, 26.7, 33.3, 23.2, 16.8, 32.0, 19.4]\n"
     ]
    }
   ],
   "source": [
    "X_test = strat_test_set.drop(\"MEDV\", axis=1)\n",
    "Y_test = strat_test_set[\"MEDV\"].copy()\n",
    "X_test_prepared = my_pipeline.transform(X_test)\n",
    "final_predictions = model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(Y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "print(final_predictions, list(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:07:16.480654Z",
     "start_time": "2020-10-10T10:07:16.461377Z"
    },
    "_cell_guid": "e540ac16-fbe4-4c4c-836a-16cf5f969ffc",
    "_uuid": "5cf58848-8644-4dd7-b868-8b08fa0160bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.795643604619121"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:07:22.254984Z",
     "start_time": "2020-10-10T10:07:22.235621Z"
    },
    "_cell_guid": "661c10d0-99c0-4a66-88d6-3db5bf3a8e33",
    "_uuid": "4eb42c31-812a-4411-a1a0-5f2a1b98aea2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.42152521, -0.48685178, -0.24673925, -0.27144836,  0.2311586 ,\n",
       "       -0.85631303, -1.42946756, -0.4510327 , -0.42117544, -0.12039257,\n",
       "        0.3268577 ,  0.41580739,  0.64788652])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7a82a145-3cca-463f-9c2b-be88ab3d901c",
    "_uuid": "9a53a202-6b0c-43b3-bf67-ee30a1132cf7"
   },
   "source": [
    "## Using the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-10T10:07:28.185332Z",
     "start_time": "2020-10-10T10:07:27.985038Z"
    },
    "_cell_guid": "c6b4c1a6-cb1d-4988-b1db-4f354da0c833",
    "_uuid": "569a0451-3c43-45f5-8ed8-04fcd55a2e80",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.796])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "import numpy as np\n",
    "model = load('Dragon.joblib') \n",
    "features = np.array([[-5.43942006, 4.12628155, -1.6165014, -0.67288841, -1.42262747,\n",
    "       -11.44443979304, -49.31238772,  7.61111401, -26.0016879 , -0.5778192 ,\n",
    "       -0.97491834,  0.41164221, -66.86091034]])\n",
    "model.predict(features)"
   ]
  }
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