Different types of feature selection techniques/Different types of feature selection techniques.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork/labs/boston_housing.csv'\n",
    "boston_df = pd.read_csv(boston_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading top 5 records of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  \\\n",
       "0           0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0   \n",
       "1           1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0   \n",
       "2           2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0   \n",
       "3           3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0   \n",
       "4           4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0   \n",
       "\n",
       "     TAX  PTRATIO  LSTAT  MEDV  \n",
       "0  296.0     15.3   4.98  24.0  \n",
       "1  242.0     17.8   9.14  21.6  \n",
       "2  242.0     17.8   4.03  34.7  \n",
       "3  222.0     18.7   2.94  33.4  \n",
       "4  222.0     18.7   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS',\n",
       "       'RAD', 'TAX', 'PTRATIO', 'LSTAT', 'MEDV'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting Unwanted Unnamed column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO  LSTAT  MEDV  \n",
       "0       15.3   4.98  24.0  \n",
       "1       17.8   9.14  21.6  \n",
       "2       17.8   4.03  34.7  \n",
       "3       18.7   2.94  33.4  \n",
       "4       18.7   5.33  36.2  \n",
       "..       ...    ...   ...  \n",
       "501     21.0   9.67  22.4  \n",
       "502     21.0   9.08  20.6  \n",
       "503     21.0   5.64  23.9  \n",
       "504     21.0   6.48  22.0  \n",
       "505     21.0   7.88  11.9  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1 = boston_df.drop(['Unnamed: 0'], axis = 1)\n",
    "boston_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking no of rows and columns of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 13 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    float64\n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  LSTAT    506 non-null    float64\n",
      " 12  MEDV     506 non-null    float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 51.5 KB\n"
     ]
    }
   ],
   "source": [
    "boston_df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</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>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</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>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>16.955000</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>22.000000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO       LSTAT  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534   12.653063   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946    7.141062   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    1.730000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000    6.950000   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000   11.360000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000   16.955000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000   37.970000   \n",
       "\n",
       "             MEDV  \n",
       "count  506.000000  \n",
       "mean    22.532806  \n",
       "std      9.197104  \n",
       "min      5.000000  \n",
       "25%     17.025000  \n",
       "50%     21.200000  \n",
       "75%     25.000000  \n",
       "max     50.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation between all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.388305</td>\n",
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       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.360445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.483725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.055892</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.175260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.427321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.695360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.376955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.249929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>-0.381626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>-0.468536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.737663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDV</th>\n",
       "      <td>-0.388305</td>\n",
       "      <td>0.360445</td>\n",
       "      <td>-0.483725</td>\n",
       "      <td>0.175260</td>\n",
       "      <td>-0.427321</td>\n",
       "      <td>0.695360</td>\n",
       "      <td>-0.376955</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>-0.381626</td>\n",
       "      <td>-0.468536</td>\n",
       "      <td>-0.507787</td>\n",
       "      <td>-0.737663</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "ZN      -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "INDUS    0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "CHAS    -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "NOX      0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "RM      -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "AGE      0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "DIS     -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "RAD      0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "TAX      0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "PTRATIO  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "LSTAT    0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "MEDV    -0.388305  0.360445 -0.483725  0.175260 -0.427321  0.695360 -0.376955   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO     LSTAT      MEDV  \n",
       "CRIM    -0.379670  0.625505  0.582764  0.289946  0.455621 -0.388305  \n",
       "ZN       0.664408 -0.311948 -0.314563 -0.391679 -0.412995  0.360445  \n",
       "INDUS   -0.708027  0.595129  0.720760  0.383248  0.603800 -0.483725  \n",
       "CHAS    -0.099176 -0.007368 -0.035587 -0.121515 -0.053929  0.175260  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933  0.590879 -0.427321  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501 -0.613808  0.695360  \n",
       "AGE     -0.747881  0.456022  0.506456  0.261515  0.602339 -0.376955  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471 -0.496996  0.249929  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741  0.488676 -0.381626  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853  0.543993 -0.468536  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000  0.374044 -0.507787  \n",
       "LSTAT   -0.496996  0.488676  0.543993  0.374044  1.000000 -0.737663  \n",
       "MEDV     0.249929 -0.381626 -0.468536 -0.507787 -0.737663  1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = boston_df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.695360\n",
       "ZN         0.360445\n",
       "DIS        0.249929\n",
       "CHAS       0.175260\n",
       "AGE       -0.376955\n",
       "RAD       -0.381626\n",
       "CRIM      -0.388305\n",
       "NOX       -0.427321\n",
       "TAX       -0.468536\n",
       "INDUS     -0.483725\n",
       "PTRATIO   -0.507787\n",
       "LSTAT     -0.737663\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that RM is (positively) strongly correlated with MEDV and then LSTAT is (negatively) strongly correlated with MEDV."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap showing correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5EFtWLvXGX+Hq1F3vlHXj1XMo+cu/Kns78fLB5/Kx5C/5vOQT6mrKOMtjR59nRnBkzQ6OnnLsqNOpCc2u787y6Z9VdIqV7MzWh23bGrJevpvsj2Y4xk8okP32w2S/MYmcuVPx6jQAS72LXJlspTGcHGPLqgx4b9KbTOhwO8f2HKH9YMfT1Iu7teTQtv1MaH8bjw+ayE1P3YZvgJ9Lc3YVZ+U+dT+y2e1sPxjPG3dfy1v3Xs/sH1dyMN5x8+xhsTDv8VH8POMuthyIY8/R0k9nqxtnW8Kpm0yTOhEsePwW5k0eytBuzZjwrmO8mvSsHJZu2c+Pj93CL0+NIDvXyo/rzoFrE6ctTpzvMx4XtMCr6wByv5oDgD3+MHkL5+E/4Vn875mG/ci+wsr/c9nzy3dyT5fGeFhKrjyr3c6OxAyubVaHz27oiJ+XB++tO/OWL1XaGVy//mPOpDe5p8PtxO05QoeCY+sPM7+hRnANnvrpBfoMH8TBrfux2c6NihapOsobM+FLYGPBD5Q8/JnA184WMgxjNDAa4K0Xp3LbLcOchYkLNb+lDxcP6wlAwqZ9BMQU1UQGRIeSmZDmdLkO9w7BLzSQ36a8VxlpVgoz4zhGUNGTdSMwFDMzrUSMJaY+PleMccz3D8SzYQty7XZsu9dXZqouUWdkP2rd1AuA9I178a1VtC34xoSSG59aIj4/JQPPIH8MDwumzY5vbFGMrVhTuuTfNtLk2VF4hQaSE5dCbtxxTqzfA0DC/NXUH3+5q4tWocyMki0RjICQ0ttJdF28L3OMnWH4BeDRoBl5dhu2PRvB4oHP5WOwbl9Vrbeblrf0oXnBsSN+0z4Cix07Ak9z7OhUcOz45ZRjR/iFdeg/4za+uuV5ctIyXZZ3ZTDTUzCCi/q+G0GhJVo9ncp+YBuW0CjwD4SsjMKWL+bJdGxbV+NRpxH2A9tcnrcr9Lp5AN2H9QFg/997CC02JkBIdBhpCWWvF9NuZ80PKxkw+gpWfLGErtf24seZjgHkEg/Gk3w4kZiGtdj/9x7XFsIFokICiT9e1AUqITWDiJoBp8QEUTPAHz8fb/x8vGnTuA47jyRSt9gTwyB/X9qefx4rt+yjUa3q/dQ5KjiA+NSifT8hLZOIoJJdFQJ8i5qtX3JRPaZ9sZzUzGzW7jlKrdAgQgsql3o3b8DG/fFc2vaCykneRczU5BKtCYyQCOxppfcZS636+N0ygazXHsY8WdTCJX/FQvJXLATAZ8hI7KnJpZatTiIDfEjILGqJkJCZS0SNkq0btyWmM2WhoxVPWk4+Kw4k42kxaBYdTGSAD82iHc33+zSKYu5fByot94rW+5Rja1hsOP8MZR0aHUZqOcfW1T+sZNDoK/j9iyXkZGYzZ1JR67gXVswk6XCiK9M/p5kagNGp8ioTrgauB5oD3wH/M02z3LO7aZqzgdkA+cn7/nuPequATR8uYtOHiwCo16slLYb3Zdf3fxLdqiG5GVlkJaaVWqbp0B6c160ZXw+bfk49obfH7ccSEoURHI6ZkYpHkw7kfl/yzR7ZMycW/u596W3Y9mys1jeExR2e+wuH5/4CQHifVpx3a3/iv/mD4DaNsGZkkedkWzi+chtRgzsQ/+2fxF7XjaSFBW+2iAgmL+kEAEGtGoLFIP+44wInJy4F/4YxZO09RtglF3Ny19HKKWAFsccfwCi2nXhe2J7cH98pEZPzTtHAR94DRmLbt8lRkQB49x+O/fgxrH/9WplpV7iNHy5iY8Gxo0GvlrQa3pcd3/9JTMGx46ST7aXZ0B7U69aML045dgTGhnHF7Hv56d63Sd0fX1lFcBn7kT1YwmMwQiIx04/j2aIruZ+9UiLGCIvGTHGU1RJbHzw8HQMuevk4njLl5YCXDx6NW5C3+As3lKJiLP5oIYs/ctzMNO/Zmt7DB7L6+xU0aNWY7IwsTiSllVomsm40iQcd66Zl77bE73UcI1LikrmoSzN2r91OUHgw0Q1iSTqUUGllqUhN68VyKDGVo0lpRIYE8vPa7Uy7rWTFao+WjXn201+w2uzkW21s3h/HTX3bczwjC08PC0H+vuTk5bN6+wFGFjRRrs6anhfJoeQTHE1JJzK4Bj9v2M20m0t280lOP0lYoD+GYbD5YAKmaVKzhi8xNQPYdDCe7Lx8fL08Wb37CE3rRLqpJBXHdmAnlshaGOHRmKnJeLXrTvacZ0vEGKER+I99jOz3ZmBPKHk+NQJrYmakYYRG4NmqKyefvacy069wTaOCOJSWxdET2UQG+PDz7nim929WIubHEZcU/v7Yr1u4pF4EPRs6toXoQF8OpJ6kXkgN1hw+ToPQ6juuxm8fLeS3gmNri56t6TN8IKu+X0HDMzy2turdlmMFx1b/IH9ys/Ow5VvpPrQPu1ZvKzG+gkhFOG1lgmma3wDfGIZRA7gCeNEwjDDgYdM0l1VGgv/WpMefZe2GTaSlpdP7ypsYO+pmrh7c391pVaoDizdSr2cLhv/+ItbsPH6dOLtw3hXvT2TRA3M4mZBGr2kjST+azPXfPgHAnoVrWfPqt+5JuiKZdvJ+/QjfoZMcr/zbtBwz+SierRxPX60bTv8KMp8rxmA570IMvwD8xr1M/u/fYN20vDIyr3DJizYQ3rslXVe/ii07l633FFWqtPrkAbbdN5vchFR2T/2U5rPuptGU60nffIAjnzrWUdTgjtQZ3gfTZseWk8emO14rXH7HQ3Np9tZdWLw9yT6YyJZ7qtmrWE07eb99is/V9zpeIbp5JWZKHJ4tugNg/bvsQ56lViM8m3bGnnQEj1seAyDv92+wn/JmkOpm3+KN1O/Zgtt+f5H87DwWFjt2XPX+RH4uOHb0LTh23FBw7Ni9cC1/vvotne4Zgl9IAH2mjgDAbrPx8WWPuaEkFcRuJ/f7Ofjd+igYFvLXLcaeeBjP9v0AsK75Bc+mHfFs3QNsVrDmkfO/lwDHeBu+N092/B2LB9aNv2PbtdE95ahgm5asp3nP1jy37E3ysnN5t9iTsAlzH2buA29xIimN214cj1+AHxgGh7cf4MNHHNvT/Ne+YNQLd/H0wpfAMPji2Y/JTK2eb7zw9LAw5Ya+jHnlM+ymyRVdmtOoVgRfLHVUTl/bozUNYsLpfHEDrntyDoZhMOSSFjSqFcGuI4k8+t4P2O127KZJv7ZN6NaisZtL9O95eliYcvUljHn7e+x2kys6NKFRTBhfrNwCwLVdLmbR33uZt3ILnhYLPl6ePDu8H4Zh0KxeNH1aNGTYC/PwsFi4sHY4V3du6uYSVQC7nZxP38D/3mkYhoW8lT9jjzuIV/dLAchf9iM+l92EUSMI3xvHO5ax2Tj5zF0A+I15FKNGENis5Hz6euFAjdWVp8XCAz0uYOx36x3bSNNYGoYF8MVmx2sMr21W57TLP9D9Qh76eTNWm0mtYD+e7HMObCPA3wXH1ueXvUludm6JVgb3zX2Y9wqOraNfHI9vgB+GYXBo+wE+KDi2xjSqzegX78ZutxO3+zDvTn7LXUWRc5hxJoMcGYbhAQwAhgIXA1NM0/z5TL5ALRNKeqt1Nb6QdpHbxni4O4UqZ+UrueUH/Yd0nRRQftB/zFuvVf8xGSranTfqiUtx4//n7gyqnrc+HOzuFKqeU95g9F+X//UP7k6hyvFseb67U6hyxjxfvV/97AofHPjqHHj/TNlyd//h1ntan8adq+T6PW3LBMMwegLDgPbAIuBV0zTXVUZiIiIiIiIiIlI1lTdmwm/AJmAF4APcYhjGLf/MNE3zbhfmJiIiIiIiIiJVUHmVCbdS1rtqRERERERERM51epuDU+UNwPh+JeUhIiIiIiIiItVEeWMmzOc0LRNM06xeL5IXERERERERORt2m7szqJLK6+bwQqVkISIiIiIiIiLVRnndHMp8sbphGF0qPh0RERERERERqerK6+bgAVwH1AIWmqa5xTCMy4CHAD+gletTFBEREREREXETDcDoVHndHN4F6gBrgNcMwzgIdAKmmKb5rYtzExEREREREZEqqLzKhLZAc9M07YZh+ALJQCPTNONdn5qIiIiIiIiIm9nVMsEZSznz80zT0abDNM0cYJcqEkRERERERET+28prmXChYRibCn43gIbFPmOaZnOXZSYiIiIiIiIiVVJ5lQktgCjg8CnT6wJxLslIREREREREpKrQAIxOldfN4WUg3TTNg8V/gKyCeSIiIiIiIiLyH1Ney4R6pmluOnWiaZrrDMOo55qURERERERERKoIDcDoVHktE3xPM8+vIhMRERERERERkeqhvMqEtYZh3H7qRMMwRgF/uSYlEREREREREanKyuvmcC/wjWEYN1JUedAW8AaGuDAvEREREREREbczTZu7U6iSTluZYJpmAtDZMIyewMUFk380TXOxyzMTERERERERkSqpvJYJAJimuQRY4uJcRERERERERKoWvRrSqTOqTPg33mr9mKu/oloZu/4pd6dQ5XRrMcrdKVQ5U4l0dwpVyoCX97s7hSrnxyE+7k6hyrlk7gl3p1Cl/Dn/PnenUOU8dO0X7k5BqrgkM8DdKVQ51l+OuDuFKmfOTV7uTkGkSihvAEYRERERERERkRJc3jJBREREREREpNqyq5uDM2qZICIiIiIiIiJnRS0TRERERERERMqiARidUssEERERERERETkrqkwQERERERERkbOibg4iIiIiIiIiZbHb3J1BlaSWCSIiIiIiIiJyVlSZICIiIiIiIiJnRd0cRERERERERMqitzk4pZYJIiIiIiIiInJW1DJBREREREREpCx2tUxwRi0TREREREREROSsqDJBRERERERERM6KujmIiIiIiIiIlEUDMDqllgkiIiIiIiIiclbUMkFERERERESkLBqA0Sm1TBARERERERGRs6LKBBERERERERE5K+rmICIiIiIiIlIWdXNw6pysTOj+5M3U69kSa3Yuv9w/m6QtB0rF9H91DFHNG2CzWknYuI/FD76H3Wqr/GTd4JFpL7F85RpCQ2ry7cdvuzudSjPhqfF07tWBnOwcnp7wHLu27C4V88jLD9CqYwsyM04CMHXCs+zeupdL+nVh9KSR2E0Tm9XGK4+/waa1Wyq7CBXi/GdGENa7FbbsXLbfPZOMzftLxfieF8HFs+7Bq2YAGZv3s3XcG5j5RftHYMuGtPtpKltGv0LiD6ux+HjR+rsnsHh7YXhYSPxhNfuf/6Iyi1Vh7n5qHB17dSA3O5fpE2Y43U4efHkyLTs2L9xOpk+YwZ6te+k7pDc3jB0KQHZWNi8++Ap7t+2r1PwrkkeTNvhecwdYLOT/8TN5v5b8P/Vs1hHvy252jHBst5P75Sxs+7YB4HvjvXhc3B4zI42saWPdkb7LTJ56L116dyInO4fH73mGHZt3lYp58tWHadOpJZnpjm3ksXueYdfW3dwy9gYGXdUPAA9PD+o3rkuvppeSnpZRqWWoSCv/3sVzH/2A3W5nSI92jLq8e6mYtdv28fzHP5JvsxES6M97j4wGIP1kNk/O+Zo9RxIwDIMnb7+aFo3Pq+wiVLghjw+nSc9W5GXn8r+JMzm69UCpmOufu4M6zRsAkLQ/nv9NfIu8rFwiG8Yy9Pk7qd20Pj+98DlL3/mhkrN3Da2T0m5+YhQterYmNzuX2RPf4OCW0ueL22aMpX6zRmBA/P5jzL7/dXKzcvAPqsHtz99FZN0o8nPzmTPpTY7sOuSGUlSc4U/cRsuebcjLzmXmxNc44GR9/GPEk7fT/dpejLxoGACxDWtxxwvjqd+0IZ+/8DE/zv6ustJ2GY/GLfG+dCRYLFjX/Ub+8m9Lzm/SFu8+QzFNE+w28n58H/vBHY6Zvv74DBmDJaoOmCa5X8/Efrj0uUrk3zrnKhPq9WxBzXrRfNDtfqJbNaTXMyP4/IonSsXt/PYPfr5nJgADXh9H06E92Pzxb5WcrXtcOagvN1x9OQ89/YK7U6k0nXp1oE79Wlzb9Saatm7C5OkTuG2w8xucN6a+zZIfl5eYtm7FX/z+y0oAGjZpwDNvP87Q7sNdnndFC+vdEr/60fzZ8R6C2jTmghmjWDfwkVJxjR65kcOzfiLh2z+4YMZtxN7Qi6Mf/OqYaTFo9OgNpCz5uzDenpvPhquewpaVi+HpQZv5T5KyeCPpf5W+Ea/KOvZqT+36tbmh6y1c1LoJ902/hzsH3+U09q2ps1l2ynZy7PAxxl8zgcwTmXTo2Z5Jz91X5vJVnmHB97qxZL3xMGZaMv6TXsG6eRX2+MOFIdadG7FuXgWAJbYevrc+SNbUOwDIX7WIvGXz8b3lfrek7ypde3fivAa1uaLT9TRr3ZSHnpvILYNGO4195ak3WfTD0hLTPnzrUz5861MAuvXtwo13XF+tKxJsdjvTPvieWVNuJSo0iBsee4sebS6kYa2owpj0k9lMe/873po8kpjwmqScyCycN+OjH+jS/HxevOdG8q1WsnPz3VGMCtWkR0vC68cwrce91G3ViGueuY1Xryx9nP326Q/JzcwG4PJHbqbr8P4snvk9WWmZfPPE+1zcr11lp+4yWieltejZmqj6MUzsPo6Grc5n5NTRPHHllFJxHz81l5yCdXLDoyPoO3wgP8z8hsvvuppD2/bz6h3PEdOwFsOfvp1nb3iikktRcVr2bEN0/RgmdB9Do1bnM2rqnTx65WSnsQ2aNcQ/qEaJaZlpmXzw+Bza9u9QGem6nmHBe/AocuY+jZl+HN8x07FuX4eZdKQwxLZ3C9nbJzrCo87Dd9h9ZL9yLwDel47EtnsDuf97ETw8wcvbHaU4p5jmf+Oh89k658ZMaNCvDdu/WgFA/Ia9+ATVwD+yZqm4A8VuhBI27iUgJrSyUnS7ti2bERwU6O40KlW3/l1Y8OUvAGxdv52A4BqERZ75/3l2Vk7h737+vo5a4GooYkA74r9w3ACn/7Ubz6AaeDvZP0K6NiVxvuMm8di8ZUQMLLqAq3PbQJJ+WE1e8okSy9iycgEwvDwwPD2hGq6jrv278HPBdrJt/XYCggPOajvZsm4bmQU3SlvXbyMiJsIleVYGS73zsSfHYabEg82Kdf1yPJt3KhmUV7Rf4OMLFP2f2/ZuwcyqvjfJZenevys/zFsIwOb1WwkMCiQ8Muz/9bcGDOnDwm9+rcj0Kt2WvUeoExVG7chQvDw9GdCxOUv/2l4iZsEff9O7XVNiwmsCEBYcAEBmVg5/7TzAkB5tAfDy9CSohl+l5u8KF/dry7qvHcfZgxv24BfoT2BEzVJx/9w0A3j5ehfuPpkp6RzetO+cai2pdVJa677tWfHVUgD2btiFf1ANgiNDSsXlFFsn3j7ehafWWo3rsHXlJgCO7T1KeO1IgsKDXZ63q7Tp257fC9bHnoL1UdPJ+jAsFm54eASfTv+gxPT0lBPs27QHW/65sY1YajfCfjweMzURbFZsm1bi2aRtyaBi52DD27fousvHD496F2Fdt9jx2WaFnKxKylz+a865yoSA6BAyj6UUfs6MP05AdOmD0T8snh5ceFVXDi7bVBnpiZtERIeTEJdY+DnpWDIR0eFOY+94YBQf/TqHe54Yi5e3V+H07gO68tmyD3jxg+k8c/8Ml+fsCj4xIeQcLdo/co+l4HNKRZpXaCDW9CxMm6NvWG7c8cIYn+gQIga248gHTm6ALAbtf3uOS7a+w/Flm0hfv8d1BXGR8OhwEuOSCj8nHUsivIzt5PYHbmXur+9w1xNjSmwn/7hs6EBWL1njslxdzRIchj01ufCzPTUZI7j0TbNn8074PzIL/zufJOeTVyoxQ/eIjIkgvtixJOFYIpFlVBqNm3IHny/+gPufvLvUNuLr50Pnnh357celrkzX5RJTTxAdWnQDExkaTEJqeomYg/HJpJ/MZtTUdxj6yBvM/309AEeSjhMSWIPHZn/FdQ+/zhPvfE1WTl6l5u8KQVGhpMUVHWfT4o8THO28UnLo83fy5Nq3iWoYy+/vL6ysFCud1klpIdGhHI8rOsYej08hNMr5Orn9+bt4Y917xDSqxa/v/wjAoW0HaDuwIwANWjQivFYEodH/v4rNqiA0OpSUM1gf/YcP4q9f15CWmFqZ6VU6IygU80TRPmOmH3d6Dva4qD1+976C7y0Pkvu1o8W1JTQKMysd76vH4TtuBt5D7gQvn0rLXf5bTluZYBjGLaf7qawkz45RetJpnpD2fGYER9fsIG7NThfmJG5nlN4unG0WM6e/w9Buw7n10jEE1Qzi5rHDCuctW7iCod2H88CoRxk96VZXZutCZ7B/OAn5J6bx0yPYM/VTsDtZeXaTNb0fYGXLMQS3bkSNC+v8+3QrmZPNxGkrlNnT53BTtxGMvnQsQTWDCsdJ+Eerzi25dNhA3p72jqtSdT1nK4PS68K66U+ypt5B9uyn8bn0Ztfn5WaG02NJ6fXy+jNvM6TrMG4acBvBIUGMvOumEvO79evKxrWbqnUXB3B+HD11DVntdrbtj+P1icOZ+cBIZn+7hAPHkrHZ7Ow4EMe1vTsw75nx+Pl48d78ZZWStys533WcX4d8NultnugwhoQ9R2k5uJPTmHOB1klpZ3osAXhn0huMb38bcXuO0mFwVwDmz/yaGkEBTP3pRfqOGMTBrfux26rvAHHO10fJzyGRIXS4tDM/F1SonNPOcJ+xbVtD9iv3kvPJDLz7XO+YaLFgiamPdfXP5Lw5GfJy8ep+pUvT/U+w2937U0WVN2aCs85pBjAYqAV86GwhwzBGA6MBrgtpT+eAxv8mx3I1v6UPFw/rCUDCpn0ExBTV3AVEh5KZkOZ0uQ73DsEvNJDfprzn0vzEPa4efiWX33gpANs37iAqNrJwXkRMOMkJyaWWSUk8DkB+Xj4/fL6AG++8vlTMxtWbqFU3luCQIE6c8gSuKqo9sh+xN/UGIH3jXnxrhfFPBwWfmDBy40vW7uenZOAZ5I/hYcG02fGJDS2MCWrZgIvfvhsAr7Agwvu0wm6zkbxgXeHy1vQsUlduI6xnC07uOExVN2T4FVx24yAAdmzcSWRs0VPmiJgIUhJSSi1TfDv56fOFDL3zusJ5DZo0YPLz9zPp5gdJrwbbR1nsacl4hRS1yrCEhGOeOF5mvG3vFizhMRg1gjBPVt9yO3PdyKu46sbLAdi6cTvRxY4lUTGRJMWXPpYkJzq2m/y8fL777EduGTOsxPz+V/Rm4TeLXJh15YgKDSb+eFGXp8TjJ4gMCSoZExJMSHN//H298ff1pvWF9dh16BitL6hHVGgQzRs5Kh77tr+Y9+aXHIekuuhycz86DusFwOG/91Iztug6pGZ0KCcSyn6KatpNNv7wJz1GD2btF9W/MuUfWiel9bllAD2G9gVg36Y9hMYWHWNDo8NIPc3TdtNuZ/X8FQy640p+/2IxOZnZvDPpjcL5L614m8TDCa5L3gX63jKQXkMdA9Lu27SbsFLro+Q5p97FDYiuG8MryxyDh3v7+fDysplM6D6m8pKuJOaJki0RjKBQzPSyz8H2A9sxQqPBPxDzxHHM9BTsRxwtRK1b/sSr+xCX5yz/TaetTDBNc/w/vxuOKsMbgQeAVcAzp1luNjAb4NXzbnJ5x+lNHy5i04eOi7J6vVrSYnhfdn3/J9GtGpKbkUVWYlqpZZoO7cF53Zrx9bDp1bJvt5Tvqw++5asPvgWgc++OXDPiSn79bjFNWzfhZPrJwhvC4sIiQwundx/Qlb07HG86qF0vliMH4gA4/+LGeHl5VouKBIAjc3/hyFzHOABhfVpR+9b+JHzzB0FtGmPNyCLPyf6RunIbkYM7kvDtH8Rc152khY7Kgj/aFR4SaPLqGFJ+XU/ygnV4hQVi5tuwpmdh8fUitNvFHHjj+0op37/1zQff8c0HjlGfO/buwFUjruS375Zw0RluJ5cM6ML+gu0kMjaSqe88wTP3TOfIviOllqtO7Ad3YYmIxQiLwkxLwbN1N3LeL9m9xwiPwUw+BoCldkPw9DznKhIA5s39mnlzvwaga59ODL31ahZ+u4hmrZuSmZFZWHFQXHhkWOH0ngO6sXdH0ajkAYE1aNOpFQ/f9VTlFMCFmjaoxaH4ZI4kHicqNIiFqzYxfWzJStiebZow/YPvsdps5FttbN57mJsGdCG8ZiBRocEciEuiXmwEq7fupUGtyDK+qWpb+dEvrPzIcZxt0rMVXYf3Z8P3f1C3VSNyMrLISEortUx43SiSDzpu/i7q3YbEvXGVmbLLaZ2UtujDhSz60NF1o0WvNvQdPpBV36+gYavzycrI4oSTyoTIutEkHowHoFWfdhzbexQA/yB/crPzsOVb6TG0DzvXbCsxvkJ18OuHC/j1wwUAtOrVhn7DB/HH97/TqNX5ZGWcLNWVYcPivxjTbmTh57nb/ndOViQA2I/uwRIWgxESiZl+HI/mXcid92qJGCM0GvO4Y9uwxNYHT0/IysAEzBMpGOGxmMlxeDRshj2xel+TVAlm1W0d4E7lvs3BMAxPYARwP7AauMY0zSrbJ+DA4o3U69mC4b+/iDU7j18nzi6cd8X7E1n0wBxOJqTRa9pI0o8mc/23TwCwZ+Fa1rz6rXuSrmSTHn+WtRs2kZaWTu8rb2LsqJu5enB/d6flUn/8torOvTrwxcqPyc3OZep9zxXOe/HD6Uyf9ALJCSk88cbDhITWBMNg99Y9zJjyEgA9BnVj4DX9sVqt5Obk8siY6nkTkLJoA+G9W9Fp9avYs/PYVvBGE4AWn0xh+32zyEtIZc/UT7h41j00mHI9GZsPEPfp4tP+XZ+oEC56bSx4WDAsFhK/+5OUX9e7ujgVbtVvq+nUqwP/W/kRudk5TL/v+cJ5Mz6cxnOTXiQlIYVH33iImqHBYBjs2bqXF6e8DMCICTcTHBLEhGn3AGCz2hg9qJq+FtFuJ2feTPzHTQXDQv6qX7DHH8Krq6MVR/6Kn/Bq2QXPDr0dgzvl55Hz3rOFi/uOmIxH4+YYAUHUePpD8n76mPw/f3FXaSrMikV/0rV3J75fNY+c7ByeuHda4bzXP3mBp+57lqSEZJ5563FCwmpiGAY7t+zmmclF21LPQd1ZtWwNOcUGdq2uPD08eHD45YyZMRe73eTK7m1oVDuKeb+tBuC63h1oUCuSLs3P59oHX8OwGFzVox2N60QDMGX4YB6cOY98q43akSE8NfoadxanQmxfsoEmPVvy0LJXyc/O5X+Til7BfPvcB/j8gdlkJKUx7MWx+Ab4gWEQt/0gXz7yLgCBEcFM+H4avgF+mKZJt1sH8lzfiSUGJ6xutE5K+3vxX7Ts2ZoXlr9FXnYu70wsamUw8f2HmTP5LU4kpXHHS3fjF+CHYRgc2n6AuQ/PAiC2UW3ueOlu7DY7R/ccYc6kN91VlAqxYfFftOzZhleWv01udi6zJr5WOG/y+4/yzuQ3TttyIziiJs/MfwG/AH9Mu8nAWwczqc94sqvrNmK3kzf/XXxHPAyGBev6JZiJR/Bs72jZYl3zK55NO+DZqjum3Qb5eeR+9nLh4nk/vIfPdXdjeHhiP55A7ldvuaskco4zTjcqvWEY44B7gN+AZ03TPHi2X1AZLROqk7Hrq+dNqCt1azHK3SlUOVOt1fPpnKs86Vm6Gfl/3Y9DNJjSqbp9eaL8oP+QP+ff5+4UqpyHrv3C3SlIFZdkVv9BQCuaFT2RPdWcm0oPvPxfV+OZL5yN9HDOyF4yx633tH49b6uS67e8lgmvA4lAV2B+scFRDMA0TbO5C3MTERERERERca8qPAjiPwzDGAC8CngAc0zTfNZJTA/gFcALSDZNs/u/+c7yKhPGAgtxNoQ3lB6ZTkREREREREQqjWEYHsCbQF/gCLDWMIzvTdPcViymJvAWMMA0zUOGYfzrptCnfTVkQULvAVbTNA8W/wGGlrOsiIiIiIiIiLhWe2CPaZr7TNPMAz4Drjgl5gbga9M0DwGYppn4b7+0vMqETcD/gFWGYVx7yrwq2W9DREREREREpMKYdvf+lK8WUPyd7EcKphV3PhBiGMZSwzD+Mgzjln+7WsqrTDBN03wH6A1MNgxjrmEY/v/M+7dfLiIiIiIiIiJlMwxjtGEY64r9jD41xMlip96vewJtgEuB/sCjhmGc/2/yKvfVkACmae4yDKMTMBXYUBG1GCIiIiIiIiJVnpsHYDRNczYw+zQhR4A6xT7XBuKcxCSbpnkSOGkYxnKgBbDr/5tXeS0TCms4TNO0mqY5BbgDR9eHxv/fLxURERERERGRCrEWaGwYRn3DMLxxjG/4/Skx3wGXGIbhWdDboAOw/d98aXktE548dYJpmksNw2iDo1JBRERERERERNzENE2rYRh3AT/jeDXke6ZpbjUM486C+W+bprndMIyFOMZFtON4feSWf/O9p61MME3z2zKmpwKl3lspIiIiIiIick45s0EQ3co0zZ+An06Z9vYpn58Hnq+o7yyvm4OIiIiIiIiISAlnNACjiIiIiIiIyH+SmwdgrKrUMkFEREREREREzooqE0RERERERETkrKibg4iIiIiIiEhZ1M3BKbVMEBEREREREZGzopYJIiIiIiIiImWpBq+GdAe1TBARERERERGRs6LKBBERERERERE5K+rmICIiIiIiIlIWDcDolFomiIiIiIiIiMhZcXnLhNvGeLj6K6qVbi1GuTuFKmf53++6O4UqZ3eH8e5OoUpZtOwVd6dQ5Yzq8ri7U6hy/vzmZnenUKVE9Zzs7hSqnPiv73d3ClWOpV5zd6dQpVg/m+nuFKocS+dL3J1ClXPFyG/dnUKV88sz7s7AxTQAo1NqmSAiIiIiIiIiZ0WVCSIiIiIiIiJyVjQAo4iIiIiIiEhZNACjU2qZICIiIiIiIiJnRS0TRERERERERMqiARidUssEERERERERETkrqkwQERERERERkbOibg4iIiIiIiIiZdEAjE6pZYKIiIiIiIiInBVVJoiIiIiIiIjIWVE3BxEREREREZGyqJuDU2qZICIiIiIiIiJnRS0TRERERERERMpimu7OoEpSywQREREREREROSuqTBARERERERGRs6JuDiIiIiIiIiJl0QCMTqllgoiIiIiIiIicFbVMEBERERERESmLWiY4pZYJIiIiIiIiInJWVJkgIiIiIiIiImdF3RxEREREREREymKqm4Mz50RlgkeDZnj3uREsFqwbl5G/6kencZaY+vje8hi5376Jbec6ALwHjcKzUUvMrHSy5zxcmWm73ISnxtO5VwdysnN4esJz7Nqyu1TMIy8/QKuOLcjMOAnA1AnPsnvrXi7p14XRk0ZiN01sVhuvPP4Gm9ZuqewiVJpHpr3E8pVrCA2pybcfv+3udFymRrc2RD86GsPDQurnv5Ay64tSMVGP3UFgj7bYs3OJm/wyOVv3AmAJrEHs9LvxOb8umBA35RWyN+wgYsJNBPbpCHYTa0oacZNfxpp4vLKLViFWrNnAc2/OxWa3c9Wg3tw2bEiJ+XM//44ff/sdAJvNxr5DR1n+1bsEBwWSnnmSJ16Yye4DhzAMg6cmjqVl0wvcUYwKdfMTo2jRszW52bnMnvgGB7fsKxVz24yx1G/WCAyI33+M2fe/Tm5WDv5BNbj9+buIrBtFfm4+cya9yZFdh9xQioqzctNunvt0IXa7nSHdWjPqsktKxazdvp/nP11Ivs1OSKA/7z04snCezW5n2BOziQwJ5I0JN1Zm6i713POP0a9fD7Kysxl7x2T+/ntrqZgFv3xGQEANACIiwvhr3SZuHHYnjc9vwFszn6NFy6Y8/eRLvP7anMpO36VWbjvAjK+WYbebDOnUlFv7tSsVs3b3EZ7/ahlWm52QAD/evecaN2TqOiv+2sxz7/wPu93kqr6XMOraQSXmz/16IT8tXQWA1WZj/5FjLPv4FbJz8nj45Tkkp6ZjMQyuHtCNmy7v644iVDiPhs3x7n+z49p1w1LyV84vOf/8Nnj3vAbTNMFuI+/nj7Af3gWAZ4cBeLXqCZjYEw+T+91ssOW7oRSus3LrAWZ8udRxrO1yMbf2a18qZu2uwzz/5TKsNptjv5lwnRsyda2xT46hXa925Gbn8sJ9L7Jny56yY58aQ//r+nHFhY5rF/9Af6a8OpmIWpF4eHjw5ewv+WXer5WVuvxHVP/KBMPAu98t5Hw2AzP9OL4jnsC6ewNmSlzpuB7XYdu/ucRk6+YVWP9ahM/g0ZWYtOt16tWBOvVrcW3Xm2jaugmTp0/gtsFjnca+MfVtlvy4vMS0dSv+4vdfVgLQsEkDnnn7cYZ2H+7yvN3lykF9ueHqy3no6RfcnYrrWCzEPDGGg8MfIT8+mQbfvEzGb6vI23O4MCSgR1t86sWyp9ft+LW8gJinxrH/6vsAiH5sNJnL/+LIXdPByxOLrw8AKe98RdLLHwMQOnww4eOHEf/om5Vfvn/JZrPxzGtzmD3jMaIjQhk6dgo9O7WlYb06hTEjr7+CkddfAcDSP9bx0Vc/EBwUCMBzb7xHl3YteemJieTn55Odm+eWclSkFj1bE1U/hondx9Gw1fmMnDqaJ66cUiru46fmkpOZDcANj46g7/CB/DDzGy6/62oObdvPq3c8R0zDWgx/+naeveGJSi5FxbHZ7Uz76CdmTbqZqNAgbnjyHXq0uoCGtSILY9JPZjPtox956/6biAmrSUp6Zom/8ckvq2gQG05mdm5lp+8yffv1oGHDerRq0Yu27Vry0itP0bvn1aXiBvYbWvj7R5+8yY8/LAIgNfUED0x6iksH96u0nCuLzW5n+hdLeXvcEKJqBnDj85/RvVkDGsaEFcakZ+Uyfd4S3hxzBTGhQRzPyHJjxhXPZrMz7e1PmP30/USFhTDsvqfp0aElDc+LLYwZedUARl41AIClazby0Xe/EhwYQF5+Gvffej0XNarLyaxshk54mk4tm5ZYtloyDLwHjiDn4+mOa9fbnsa6cz1m8tHCENv+LWTv+ssRHlkH32vuJvutSRiBIXi170/2zMlgzcfn6vF4XtwJ69/Ly/q2asdmtzN93mLeHn8VUTUDuXHGp3Rv1vCU/SaH6Z8v5s1xQ87J/QagXc921Kofy8hLbuXCVhdy97S7uPvye53GNm7emICggBLTLh8+mIO7D/HYrU8QHBrMu8vmsPibJVjzra5P/lykARidOqsxEwzD8DIMo5VhGJHlR1cOS2wD7KkJmGlJYLdh274az/Nbl4rzbNsX6851mCfTS0y3H96JmXOystKtNN36d2HBl78AsHX9dgKCaxAWGXrGy2dn5RT+7ufv66gZP4e1bdms8KbwXOXX4nzyDsaRfzge8q2c+GG5o0VBMYF9OpL2zWIAsjfuxBJUA8+IECwBfvi3u5i0eY5tinwr9oLWLPaCm0gAw88Xqum2snnHHs6rFU2d2Ci8vLwY2LMLS/5YW2b8T0tWMLBXFwAyT2bx1+btXDWoNwBeXl4EFTyBrc5a923Piq+WArB3wy78g2oQHBlSKi6n2Dbg7eNduAnUalyHrSs3AXBs71HCa0cSFB7s8rxdZcu+o9SJCqV2ZChenp4M6HAxSzfsLBGzYNVmerdpQkxYTQDCil3cJRw/we9/72ZIt9LnqOrs0sv68L//fQPAurUbCQ4OIioqosz4gIAadOvWiR9/cDwhS05KYf36zeTnn1tPVgG2HEygTngwtcOD8fL0oH+b81m6uWTrngXrdtCrRUNiQoMACA30d0eqLrNl9z7Oi4mkdnQEXl6eDOjWniWrN5QZv2DZGgZ2czyFjgityUWN6gJQw9+P+nViSExJrZS8XclSq2HJa9etq/C8oE3JoPyiCkfD26fkudXiAZ7eYFjAywczo/qvk+K2HIinTkRNaofXLNhvLmDppr0lYhas20mvlo3O2f0GoHO/Tvz61W8A7NiwgxpBAYQ6uZa3WCzc/vBtzJn2bskZJvgH+AHgV8OXjLQMbFaby/OW/5bTViYYhvG2YRhNC34PBv4GPgQ2GIYxrBLyK5cREIKZXtSk2sw4jhEYUirG8/w2WDcsruz03CYiOpyEuMTCz0nHkomIDncae8cDo/jo1znc88RYvLy9Cqd3H9CVz5Z9wIsfTOeZ+2e4PGdxLc+oMPKPJRd+tsYn4xUVVjomLqlEjGd0GF51YrAdP0HsjAnU//41YqbdjeHnUxgXcf8tNF7xPsFX9CDplY9dXxgXSEw+TnRE0T4SFRFGQrLz7hrZObmsXLuRvpc4KmOOHEsgJDiIR2a8ybV3TOTxF2aSlZ3jdNnqJCQ6lONxRdvM8fgUQqOcV0re/vxdvLHuPWIa1eLX9x1dzQ5tO0DbgY511KBFI8JrRRAaHeZ0+eogMTWd6IILV4DIkCASUktWUB+MTyH9ZA6jps9l6OOzmL9yY+G8GZ8uZML1fbEYRmWlXCliYqI4eqSoNWBcXDyxsdFlxl82uB/Llv1BRkZmmTHnisS0TKJDiiqqo2oGkJhWstwHk9JIz8pl1KtfMmzG/5i/entlp+lSCSlpRIUXHTeiwkJITElzGpudk8vK9Zvp27lNqXlHE5LZsfcQzS5o4KpUK40RGIp5IqXws5le+toVwOOCtviNfR7fYZPInT/bEZuRSv6fP+J/72v43/cm5GZh27e51LLV2RntN4mpjv3mlS8Y9uwnzF+9rbLTdLmw6DCSil2TJR9LIszJOfTyEYNZ9esqjp/SxfS797+nTqPz+N+6T5n169vMfPztc/7hoFS+8lomXGKa5j8dH0cCu0zTbAa0ASaXtZBhGKMNw1hnGMa699bsqqBUy/yy0tNO2VG8+9xA3pJ51faJ6f+Lk/XirPgzp7/D0G7DufXSMQTVDOLmsUV1RMsWrmBo9+E8MOpRRk+61ZXZSmU4kxsYp/sTGJ4WfJs2IvWTn9h/+d3Ys3MIv/PawpCkFz9kd9cRnPhuKaE3D67ApCuPSekdxChjnS37cx2tml5Q2JrFZrOxffc+rr+8H1/MegE/Xx/e/ewbl+ZbGZyVv6wLkXcmvcH49rcRt+coHQZ3BWD+zK+pERTA1J9epO+IQRzcuh+7rfo2E3RWdIOS68hqt7PtQByv33cjMyfexOzvl3MgPpllG3cSGlSDi+pV8+bZTpzNdgJwzbWD+fKL+WXOP5c4Wwunri+bzc72w4m8cecVvDX2Smb/vJqDiefQk2Yn20JZp6Nla/+mZZPGBAeWbK6dlZ3DfdPfYvLtQwnw93NFllVA6fVk27mO7LcmkfP5y3j3KDjn+vrjeUEbsl67l6yX7wIvHzyadankXF3rjPebQwm8MeZK3rrrKmYvWM3BhHNov6GMa5BT9qfQqFC6XdqNb+d+Vyq0bfc27Nu2l2Ftb2DMgLHc9fRY/APOvRYclcY03ftTRZU3ZkLxTr99gS8ATNOML+siu2D+bGA2wMnpw11aejPjOEZQUY23ERiKmZlWIsYSUx+fK8Y45vsH4tmwBbl2O7bd612ZWqW7eviVXH7jpQBs37iDqNii3igRMeEkJySXWialoBYzPy+fHz5fwI13Xl8qZuPqTdSqG0twSBAnTnkKJ9WHNT4Zr5iiJ++e0eHkJ6SUjomNIPuvohhrQgqmCfnxyWT/7WjSnbFgJWHFKhP+kf79Uuq8+wRJr37iuoK4SFR4GPFJRftIQlIKkWGlnxQBLFiykoG9uhYtGxFGVEQYzZucD0Dfbh1597NvXZqvq/S5ZQA9hjoGONu3aQ+hsUXbTGh0GKmnuckx7XZWz1/BoDuu5PcvFpOTmc07k94onP/SirdJPJzguuRdLCo0iPjjRcfAxNR0IkNKdo+KCgkiJMAffx9v/H28aX1+XXYdSmD7wWMs3bCTFX/vJjffysmcXB6c9RXT7yg9tkB1cNvomxg+wnG+2PDXZmrVjgUcB47Y2GiOHXP+/xwSWpM2bZpz47A7KytVt4qqGUB8akbh54S0TCKCa5SKqRngh5+PF34+XrRpWIudR5Op66RLUXUUFR5SopVXQkoqEaE1ncYuXF7UxeEf+VYr901/i0t7dKCPkxYL1ZGZcRwjuOgJsxEUipmRVma8/dAOjJBI8AvAo95F2NOSIMuxXdl2rMWjdmNsm1e6Ou1Kc0b7Tcgp+02jWuw8mkTdqOq93wwePphBwxzjh+z8excRsUVdxsJjIkhJKNn6oFHTRsTWi+H93+cC4OPnw9zf32PkJbfS77p+fP7W5wDEHThG/OF46jSqzc6NLn7QK/8p5bVMSDMM4zLDMFoBXYCFAIZheAJVomrYHrcfS0gURnA4WDzwaNIB6+6SffGyZ04s/LHuWEvuzx+ccxUJAF998C3D+93O8H63s/znlQy8xjGYVdPWTTiZfrKw4qC44uModB/Qlb079gNQu9jTs/MvboyXl6cqEqq57E278K5XC6/aUeDlSfBl3cj8bXWJmIxFq6k5pBcAfi0vwJ5xEmtSKrbkVKzHkvCuXwuAGp1bkLvHMSq/d7FtJaBPR/L2HqmkElWsiy9sxMGjxzhyLIH8/HwWLFlJj86lR13PyDzJuk3b6FlsXnhoCNERYew/7Bg8a/WGzTSsW7vScq9Iiz5cyCOD7ueRQffz1y9r6Hp1DwAatjqfrIwsTjipTIisW9SkvVWfdhzb61gP/kH+eHg56qx7DO3DzjXbSoyvUN00rR/LoYQUjiSlkm+1snD1Frq3KvnGjp6tL2T9rkNYbTayc/PYvO8I9WPDuefaPvz68v0seHECz425hnZN6lfbigSAObM/5pLOg7mk82B++OEXhhW8+aRtu5akp2eQkJDkdLkhQwaycOEScs+BAUrPRNPzojiUlMbR5BPkW238/Ncuujcr2Uy/R/OGbNh7FKvNTnZePpsPJtCgmt8QFde0cX0OxiVwJD6J/HwrC5evoUf7lqXiMk5msW7LTnp2bFU4zTRNHn/tferXieGWK/tXYtauZT+6D0toNEbNCMe1a9OOWAsGW/yHERJV+Lsluh54eEJ2JmZ6Ch61GjnGTAAs9ZtiTz5l0PFqrmndaA4lphbbb3Y632/2FNtvDsTTIPrMxwarquZ/MJ8xA8YxZsA4/vj5T/pe7RiL6cJWF3Iy42SprgxrFq9haJsbuKXzcG7pPJzc7FxGXuJoTZwYl0irLo79qWZ4TWo3rM2xg/GVW6Bzid3u3p8qqryWCXcArwHRwL2maf6zBfYGnL9/sbKZdvJ+/QjfoZPAsGDdtBwz+SierXoCYN2w5LSL+1wxBst5F2L4BeA37mXyf/8G66bqPyLuH7+tonOvDnyx8mNys3OZet9zhfNe/HA60ye9QHJCCk+88TAhoTXBMNi9dQ8zprwEQI9B3Rh4TX+sViu5Obk8MuYpN5Wkckx6/FnWbthEWlo6va+8ibGjbubqwefOhQsANjvxT87kvPefxrBYSPvyV3J3HyJk2EAAUv+3gMylawno0ZZGi+dgz8kl7oGXCxc/9uQsar08CcPLk7zD8cRNfgWAyEkj8G5QC+wm+UcTOVYN3+QA4OnhwUPjb+POB6Zis9sZMrAXjerVYd78nwG4rmB7+G3FGjq3aY6/n2+J5R8cP4op014lP99K7Zgonp48rtLLUNH+XvwXLXu25oXlb5GXncs7E4taGUx8/2HmTH6LE0lp3PHS3fgF+GEYBoe2H2Duw7MAiG1Umzteuhu7zc7RPUeYM6l6bhv/8PTw4MGbBjHmhY+w202uvKQVjWpFMm+xY6DO63q1o0FsBF2aNeLaR2diGAZXdWtN49pR5fzl6u2Xn5fSr38PNm5aTFZ2DuPufKBw3hdfvcv4cQ8SH+8Yw+eqay7j5RdnlVg+MjKcpb9/S2BgAHa7yZhxI+jQdsA5MaaCp4eFKdf2YMxb32I3Ta7oeBGNYsL4YoVjYNJruzanQXQonZvU47pnP8EwDIZ0akqjWOdjHFVHnh4ePHTnjYx5/GVsdjtX9ulKo7q1mLdgKQDXDewBwOI/19O5VVP8fYvG49mwbQ8/LPmTxvVqc+3dTwBw9y1XcUnb5pVcigpm2slb8D6+Nz7guHbduAwz6SiebRw3jta/fsOzSTs8m1+CabeBNY/cr14HwH50L9bta/Ab/QzYbdjjD2Jdf26NCebpYWHKdb0Y8+bX2O0mVxTsE1/8/jcA117SggbRYXS+qB7XTfvIsd90vvic2m/AUVHQvlc73l/xnuPVkPe/VDhv6gdP8dLkVzh+SkuF4j559VMmvXQ/s351nI/enfYe6XowKBXMcPVAHK7u5lDd9Hmjer9j3RWW//1u+UH/Mbs7jHd3ClVKo2XPujuFKmdUl8fdnUKV885npbve/JdF9X3E3SlUOfFf3+/uFKocS71qfmNewayfzXR3ClWOpfMl7k6hyrli5LfuTqHK+eXwwnNrdOFTZM+d7NZ7Wr+RM6rk+j1tywTDMF6n5DgoJpAMLDFNc4UrExMRERERERFxuyrc1cCdyuvmsM7JtFDgecMwPjdN85WKT0lEREREREREqrLTViaYpvmBs+mGYbwN/AG84oKcRERERERERKoGUy0TnCnvbQ5OmaZZfYfjFhEREREREZF/pbxuDqUUvBbyZqB6vv9NRERERERERP6V8gZgzMAx6GLx0SOzgGU4XhspIiIiIiIics4y7XpBoTPljZkQWFmJiIiIiIiIiEj1UG43h4JuDQOBCwsmbQN+Nk3T6srERERERERERKRqKq+bQyywBDgGbMDR3eEy4CXDMHqaphnn+hRFRERERERE3MSutzk4U17LhGnATNM0Xyk+0TCMu4HpwHAX5SUiIiIiIiIiVVR5lQkdTdMccepE0zRfMwxjp2tSEhEREREREakiTLVMcMZSzvzs08zLqshERERERERERKR6KK9lQrBhGFc5mW4AQS7IR0RERERERESquPIqE5YBg8uYt7yCcxERERERERGpWuymuzOokk5bmWCa5sjKSkREREREREREqofyXg15y2lmm6ZpflTB+YiIiIiIiIhUHXo1pFPldXNo52SagaPrQy1AlQkiIiIiIiIi/zHldXMY/8/vhmEYwI3AA8Aq4BnXpiYiIiIiIiIiVVF5LRMwDMMTGAHcD6wGrjFNc6eL8xIRERERERFxP3VzcKq8MRPGAfcAvwEDTNM8WClZiYiIiIiIiEiVZZhm2a+5MAzDDiQCSUDxQAPHAIzNy/uCX6KG6j0axXig1XGqmIBMd6dQ5TRe/bq7U6hSDnYb4+4UpBqwm4a7U6hStqSGujuFKifayHV3ClVOjt3D3SlUKQkeXu5OocoJs9ncnUKV0+KiY+5OocqJWrLsnD4JZ71yh1tv4vzvnVUl12953RzqV0oWIiIiIiIiIlJtlDcAo7o1iIiIiIiIiEgJ5Y2ZkAFO2+X/080hyCVZiYiIiIiIiFQFGoDRqfJaJgRWViIiIiIiIiIiUj2U+2pIERERERERkf8suwbRd8bi7gREREREREREpHpRZYKIiIiIiIiInBV1cxAREREREREpi6kBGJ1RywQREREREREROSuqTBARERERERGRs6JuDiIiIiIiIiJl0dscnFLLBBEREREREZFqzDCMAYZh7DQMY49hGFNOE9fOMAybYRjX/NvvVMsEERERERERkTKY9qo9AKNhGB7Am0Bf4Aiw1jCM703T3OYk7jng54r4XrVMEBEREREREam+2gN7TNPcZ5pmHvAZcIWTuPHAV0BiRXypKhNEREREREREqq9awOFin48UTCtkGEYtYAjwdkV9qbo5iIiIiIiIiJTFzQMwGoYxGhhdbNJs0zRnFw9xstipSb8CPGCaps0wnIWfPVUmiIiIiIiIiFRRBRUHs08TcgSoU+xzbSDulJi2wGcFFQnhwCDDMKymaX77/81LlQkiIiIiIiIiZTGr9gCMwFqgsWEY9YGjwFDghuIBpmnW/+d3wzDeB374NxUJoMoEERERERERkWrLNE2rYRh34XhLgwfwnmmaWw3DuLNgfoWNk1CcKhNEREREREREqjHTNH8CfjplmtNKBNM0R1TEd1bryoQLnhlORO9W2LJz2XL3TDI2HygV43deBM1n3YNnzRpkbD7A5nFvYObbCOl8ES0/mEj2IcdbMRJ/XMO+l74GwDPIn6Yv3UHAhbUxTdg64W1OrNtdmUX7fzv/mRGEFayT7XfPJGPz/lIxvudFcPGse/CqGUDG5v1sLVgn/whs2ZB2P01ly+hXSPxhNRYfL1p/9wQWby8MDwuJP6xm//NfVGax/l9qdGtD9KOjMTwspH7+CymzSucc9dgdBPZoiz07l7jJL5OzdS8AlsAaxE6/G5/z64IJcVNeIXvDDiIm3ERgn45gN7GmpBE3+WWsiccru2iV4pFpL7F85RpCQ2ry7ccuqcx0O/+ubQl/8E7w8CD9ywWkzZlXKib8oTH4d2uPmZ1D4kMvkrt9DwDBN11J0LUDwTBI/2IBJz76BoAa/S8hdNzNeDeow5Hr7yZ3a/U4dvxD66Q0/65tiHxoDFgsnPhyIalO1knEQ2Oo0a0dZk4u8Q+9SO42xzqpOXwIwdcMANMkd9cBEh56ETMvn5iXHsSrXm0APIICsKVncuiqcZVarn+r+dRbiO7dElt2Hn/d8zZpTs7B/udF0P7t8XjXDCBt837W3vUWZr4Nr+AatHl5NDXqRWHLzWf9hFmk7zgCQMPbBlDvpp4YhsH+jxez952FlVyyM1f/6Vup2bs19uw89tz7OiednHN96kRy/tsT8KwZyMnN+9g9/jXMfGuZy/s2jOWCt+8rWr5uFIef/4xj7/xYOC32zsup9/hw1jQdgfV4husL+v/U+JmRhPVuhT07l213v0VmGdckTWfdW3hNsm3c65j5NsIHtKXBA9dj2k1Mq43dj77PiTU7Acd12oUv3UmNC+uAabJ9wkzSq8l1WnGtn76F2F4tsGXnsWrCLFKd7EONR/blgtsGEFg/mq8uvoO845kA1B3SmYvGDQYgPyuHdVPmkrbtUGWmXyFcdS0PgMWg4y/TyI1PZcNNMyqpRBXHu117Au8aDx4Wsn/8kaz/fVpivked8wh6YApejRuT+e4csuZ9XjjP/5pr8bv0UjBNrPv2c+K5ZyE/r7KLcG5x8wCMVVW1fTVkeO+W1Kgfw4qO97Jt4jtcNOM2p3GNH7mBg7N+ZGWnCeSnZVLrhl6F89JW72BV7yms6j2lxMHnwqnDSV6ykZVd7+fPXpM5ueuoy8tTEcJ6t8SvfjR/dryHHRPf4YIZo5zGNXrkRg7P+ok/O91LftpJYoutEywGjR69gZQlfxdOsufms+Gqp1jTazJrej9AWK8WBLVp7Ori/DsWCzFPjOHQrY+zp/8Yggd3w7tRnRIhAT3a4lMvlj29bufYw68T81TRhXz0Y6PJXP4Xe/vdyd7L7iJ3j+NNKynvfMW+S+9i3+DxZC5ZQ/j4YZVarMp05aC+vP3SVHen4ToWCxGPjCPujkc4NPh2Agf1xKvheSVC/Lu1w6tuLQ4NGEni468S8fh4ALwb1SXo2oEcuf5uDg+5kxo9OuBVNxaAvN0HiL/7KXLWba70Iv1rWielWSxEPjqOo6Mf4cDg0QRd2gPvU9ZJjW7t8K4by4EBt5Lw+KtEPnYXAJ6RYYTcdAWHrhnPwcvvxLBYCBzUA4Bj903n0FXjOHTVODJ+WUHmopWVXbJ/Jap3SwIaRPNLp/tYP3EOLZ+71WncxY8MY8+sBfzS+T7y0k5S74aeAFxwzxWkbT3Ib72msG78TJo/fQsAQRfWpt5NPVk68FF+6zWFmL6tqVE/utLKdTZq9mqNb4MYNnS+i72TZtLg2dFO4+o+cjNxs39gQ5e7sJ7IJHJY79Mun7M3jr/7TnT89J+MPTuX4wvWFP4979gwgru3IPdIkusL+S+E9W6Ff/1oVnW8mx0TZ3NBGddpDR+5icOzfmRVp3uwFrsmSV2+mTU9J7G292S2T5jJhS/dWbhM46kjSVmykdVdJ7Cm1ySyqsl1WnExvVoQWD+aH7rcz5rJ79J2+kincclrd7Hk+ulkHi75/33ycBKLrn6aBX0eZOvL39KujGu+qsyV1/IAdW8fyMndp44/V01YLATecy9pUyaTMmI4vr1741G3bokQe0Y6Ga+/xslilQgAlvBw/K+6mpQ7RpNy60jwsODbqxcirlBtKxMiBrQl7ovlAJz4aw+eQf54R9YsFRfatSkJ81cDEDdvOZED257273oE+BHSqQlHP1kCgJlvw5qeVbHJu0jEgHbEF6yT9L924xlUw+k6CenalMT5qwA4Nm8ZEQPbFc6rc9tAkn5YTV7yiRLL2LJyATC8PDA8PcGs2rVzfi3OJ+9gHPmH4yHfyokfljtaFBQT2Kcjad8sBiB7404sQTXwjAjBEuCHf7uLSZv3iyMw34o94yQA9szswuUNP98qvx7+jbYtmxEcFOjuNFzGt9kF5B+Kw3rEsY1kLlhKQK9OJWJq9OpExneLAMjdtANLYA08wkPxangeOX9vx8zJBZud7LWbqNG7CwD5+w6Tf+BIpZenImidlObb/ALyDx0jv2CdpP+0jBpO1kn6d78BkPP3DjyCAvCICHXM9PDA8PUGDwuGnw/WxJRS3xE4oBsZPy51dVEqVGz/Nhya9zsAqev34BXkj6+T801El6Yc/cFxDj4073diBzjOwUHn1yLp960AZO6Jw79OBD7hQQQ2rkXqX3uwZedh2uwk/7md2EGnP2+7S+iAdiR9sQyAzPWOc66Xk3UQ3PViUn74E4DEeUsJHdj+jJcPvqQZOQcSSlQc1H9yJAef/hCzip9/wge0PeNrkqTCa5KlhBdck/xz3QHg4e9TeL71CPCjZqcmHPvEcf6uTtdpxdXu34YDXzr2oZT1e/AOdr4PpW45yMkjyaWmJ6/bTf4JR7mT1+/GPybUpfm6gquu5QF8YkIJ79uaowXbSXXjdWETbHFHsR07BlYrOYsX49Ola4kYMy0N684dYLWW/gMeHhg+PmBx/GtPKb0NyVmy2937U0WdtjLBMIzbDcNoXPC7YRjGXMMw0g3D2GQYRuvKSdE535hQco4WXZTlHDuO7ykHUq/QQKzpWZg2x39ATlzJmOA2jem0+DlafzqFGhc4mpv6140kLyWdpq+OoeOi6Vz00mjHSawa8IkJKbFOco+l4FPOOsmNO14Y4xMdQsTAdhz54NfSf9xi0P6357hk6zscX7aJ9PV7XFeQCuAZFUb+saIDpzU+Ga+osNIxcUklYjyjw/CqE4Pt+AliZ0yg/vevETPtbgy/om0g4v5baLzifYKv6EHSKx+7vjDiEh5RYeTHl/z/94gMLxHjGRmOtXhMQjKeUWHk7T6AX9tmWIIDMXx9qNGtHZ4xEZWWu6tonZTmGRlWqrxOjyUl1luSY7nEFFLnfkmD3z6iwfJPsWecJOuP9SWW9Wt7MbaUVPIPVq+nZ74xIWTHFXXxyj52HN+YkBIx3qGB5KefLDzfZB9LKYw5sfUQsYMcN40hrRriXzscv9gw0nccJqzjhXiHBODh501U75b4x5Zc31WFd3QouXFF55ncYyl4x5yybYQGYj1xEgrWQd6xFHyiQ894+fArupD87YrCzyH92pIbf5ysbQcrvDwVzScmlJyjJct3NtckAOED29Fhxcu0+PhBtk+YCYBf3UjyU9Jp8upY2i16jgtfugNLNblOK84vOpSTcUXXbFlxx/GPDjnNEmVrOKwHx4q1KK0uXHUtD3Dh08PZ9dQnmNW0abolPBx7YmLhZ3tSEh7h4adZoog9OZmT8z4j/PN5RHz1NfaTJ8lbt85Vqcp/XHktE+4BDhT8PgxoDtQH7gNeLWshwzBGG4axzjCMdT9l762IPM9IqVp6o+yY9E37+b3NXfzZ6wEOvbuQlu/f71jE04PAZvU58sGvrOrzILasXOqNv8LVqVcQpwUuN+SfmMZPj2DP1E+d9wmym6zp/QArW44huHUjRz/FqsxwVtAziDHB8LTg27QRqZ/8xP7L78aenUP4ndcWhiS9+CG7u47gxHdLCb15cAUmLZXK6TZyZvtL/r7DpM6ZR+y704md/Qy5O/eD1eYkuJrROinNyTopfa5xfuy1BAUQ0KsT+/uOYF/3G7H4+RI4uGRT08BLe1S7VgkARhnHz5JBThYsWHc7X/8e75o16LVoGg1v7ceJLQcwrTYydsex6435dP38Qbp8+gAnth7EXkW3I+froPxt45/tp7zlDS9PQvu3I2X+HwBY/Lypfc/VHJ7x2f8/6Ur1/70mKfo1ecFaVnedwOYRz9Pggesdi3h6ENCsPkc/+IW1fR7AlpVL3fFXVljWleY016hnI7LzRTQY1oONz1SX7eL0KuJaPrxva/KST5CxqfQYHdXGmRxjy1o0IADfzl1JHjaUpGuuwvD1xbdP34rNT6RAeQMwWk3TzC/4/TLgQ9M0U4BFhmGUOZKJaZqzgdkAv0QNrbAqwToj+1HrJseFWPrGvfjWKqrB940JJTc+tUR8fkoGnkH+GB4WTJsd39iiGFux5urJv22kybOj8AoNJCcuhdy445woePKeMH819cdfXlFFqHC1R/Yj9iZH/8t/1sk/HRR8YsLKXSc+xdZJUMsGXPz23QB4hQUR3qcVdpuN5AVFtZnW9CxSV24jrGcLTu447PoC/j9Z45PxiimqwfWMDic/IaV0TGwE2X8VxVgTUjBNyI9PJvtvx0BPGQtWElasMuEf6d8vpc67T5D06ieuK4i4jC0+Ga/ooifnntHh2E5pgm5NSMazeExUeOGAmxlf/0zG1z8DEHrvyBJPr6srrZPSTlfewpiC9ZbzT0x0BNak4/h3akX+0QRsqY6jcsailfi1akLG/IJmtx4WAvp04dA14yujKP9ag5F9qXejY8yD1I378IstejroFxNKzinnm7yUDLyCahSeb/xiwsiJTwPAmpnNX/fOKoztv/ZVTh5ybC8H/7eUg/9bCkDTB68n+1jpriHuEj1iAFE39gEg8+89+MSG88/whz4xYeTFn7JtpKTjGVwDPCxgs+MdE0ZegmM95R5LOe3yNXu14uTmfeQXdDv0rRuN73lRtPjtxcL4Fr88z6aBU8hPSnNZmc9GrZH9C69JMjbuxbdWOCdwnEvP/Jqk9KDGaau241cvGq/QQHLjUsiNSylsIZk4f1W1qUxoPKIvDQv2oZSN+6gRG8Y/bTf8Y0PJTkg7q79Xs0kd2r9wG8tumkFeambFJusilXEtX7P9+UT0b0N471ZYfL3wDPDj4jfHsWXcm5VQwophT0rCEhlZ+NkSEYHtDLsqeLdpiy3+GOYJx7Ej9/ff8br4YnIWOWl5LGeumrZycbXyWibYDcOIMQzDF+gNLCo2z891aTl3eO4vhYOsJC5YR+y13QAIbtMIa0YWeYlppZY5vnIbUYM7ABB7XTeSFjpujL0jggtjglo1BItB/vEM8pJOkBOXgn/DGADCLrm4Sg/AeGTuL6zp/QBrej9A0oK1RBesk6A2jctcJ6krtxE52DF+QMx13QvXyR/txhf+JM5fxc4H3iV5wTq8wgLxDPIHwOLrRWi3izm5p2o3yc3etAvverXwqh0FXp4EX9aNzN9Wl4jJWLSamkMcJzS/lhdgzziJNSkVW3Iq1mNJeNevBUCNzi3I3eMYIdm7Xmzh8gF9OpK3t3r2AxfI2bITr7q18Kzl2EYCBvbg5JJVJWJOLl5F4BWOGwef5hdiz8jCluy40PUIdRxDPGMiCOjThcyfllZq/q6gdVJazuadeNWNLVwnQYO6l14nS1YRdIXjBsq3xYXYM05iSzqO9Vgivi0uxPB1NMH279iSvL1FlbD+nVqRt/8w1oTq0Zd139xfWdznIRb3eYhjC9dx3nWXABDSuhH5GdnkODnfJP2xjVqXOc7B5113Ccd+dpxvvIL8Mbw8AKh3Y0+SV+3AWnBj4BMeBIBfrTBiB7Xj8Dd/urpoZyz+/YWFgyMeX7CGiGu7AxDQ2nHOzXeyDk6s3ELYZY5xNiKv60HqQsdgiqk/rz3t8hFXdiX5m6IuDlk7DrG22a2sbz+G9e3HkHsshb/7TaoyFQkAR+f+zNrek1nbezJJC9aUuCaxlXFNkrZyKxGF1yQ9SC64JvGrF1UYE9CsPhYvz8LrtNxi12mhlzTj5K7qcS7e/f6vLOz7EAv7PsTRheuod41jHwpr3Yj8dOf7UFn8a4XRdc69rLp7Jhn74l2UccWrjGv5Pc98xvJW4/i93Xg23fEax1durVYVCQD5O3bgUas2luho8PTEt1cvcv84s4F6bYkJeF10Efg4zj3erVtjPVj1u0ZJ9VRey4THgHWAB/C9aZpbAQzD6A7sc3Fup5W8aAPhvVvSdfWr2LJz2XpP0avrWn3yANvum01uQiq7p35K81l302jK9aRvPsCRTx0DK0YN7kid4X0wbXZsOXlsuuO1wuV3PDSXZm/dhcXbk+yDiWwp9rerspRFGwjv3YpOq1/Fnp3HtntmFs5r8ckUtt83i7yEVPZM/YSLZ91DgynXk7H5AHGfnn5wGp+oEC56baxjADGLhcTv/iTl1/WnXcbtbHbin5zJee8/jWGxkPblr+TuPkTIsIEApP5vAZlL1xLQoy2NFs/BnpNL3AMvFy5+7MlZ1Hp5EoaXJ3mH44mb/AoAkZNG4N2gFthN8o8mcuzR6nVyOhuTHn+WtRs2kZaWTu8rb2LsqJu5enB/d6dVcWx2kp55k9h3pmFYLKR/8wt5ew4SdP2lAKR//iNZy9fg360ddRfOxZ6TS+LDLxYuHv3qY3jUDMTMt5E09Q3s6Y6nQjV6dybi4bF4hAYTM/Np8nbsJW70w24p4lnTOinNZidp6lvUnvMMWCykf+1YJ8HXDwLgxOc/cXLZGmp0a0e9n98reDXkSwDkbNpJ5s+/U/erNzBtNnK37+XEvAWFfzpwUPXs4gAQv2gjUb1b0m/Vy9iyc0u0Muj8yWTW3zebnIQ0tjz9P9rPGs9FU64lbctBDny6FIDAxrVo+/oYTJud9F1HWH/fO4XLd5hzL96hAdjzbWx8cC75J05WdvHOSOpv66nZuzWt/3wTW3YueyYUnQ+afPwwe+5/i/yEVA5O/Zjz357AeQ8M4+SW/ST877dyl7f4eRPcrQV7J88q9b3VRcqiDYT1bk2n1a9hy85j+z1vFc5r/skUdpS4JrmXBlOGkrl5f+E1ScRlHYm+thum1YY9J48to4vO0bseeo+L3rq78Dqt+N+uLuJ+20hM75Zc9sdL2LLzWD2h6P+6+0eTWDPxHbIT0jh/VH+ajLkM38hgBi56lmOLN7Jm4hwunjAEn5DAwrdA2K02fhn4qLuK8//iymv5as9uI+O1VwiZ8QJYLOQs+AnbgQP4DXa0ls6e/z2WkFBCZ83C8K8Bph3/a64hZcRwrNu3k7NsGWGz3wGbjfzde8j+Yb6bC3QOMKvuIIjuZJTXP8swDE8g0DTN1GLT/AEP0zTLfblxRXZzOBd4nGmHp/+QmIDq0TSvMjVe/bq7U6hSDnYb4+4UpBqwm2cwVsp/yJbU6je6u6tFG7nlB/3H5Ng93J1ClZLg4eXuFKqcMFvVHLfEnVpcdMzdKVQ5UUuWndMn4ZOPXufWm7gaT8+rkuu33FdDmqZp/acioeCNDr2A14CqPZy/iIiIiIiIiLhEed0cADAMowNwAzAECAXGAZNcmJeIiIiIiIiI+2kARqdO2zLBMIxnDMPYDUwDNgOtgCTTND8o3u1BRERERERERP47ymuZMBrYCcwEfjBNM8cwDFXLiIiIiIiIyH+CadcAjM6UN2ZCNPAMcDmwxzCMjwC/gkEZRUREREREROQ/6LSVAqZp2oAFwALDMHyBywB/4KhhGL+ZpnlDJeQoIiIiIiIiIlXIGbcwME0zB/gS+NIwjEDgKpdlJSIiIiIiIlIVaABGp05bmWAYxn2VlYiIiIiIiIiIVA/ljZkQWOxn4imfA1ybmoiIiIiIiIhUReWNmfDkP78bhnFl8c8iIiIiIiIi5zx1c3CqvJYJxWkNioiIiIiIiMiZD8AoIiIiIiIi8p9j2t2dQZVU3gCMmylqkdDIMIxNxeebptncVYmJiIiIiIiISNVUXsuEq4Ao4PAp0+sCcS7JSERERERERESqtPLGTHgZSDdN82DxHyCrYJ6IiIiIiIjIuctuuveniiqvMqGeaZqbTp1omuY6oJ5LMhIRERERERGRKq28bg6+p5nnV5GJiIiIiIiIiFQ1ZhVuHeBO5bVMWGsYxu2nTjQMYxTwl2tSEhEREREREZGqrLyWCfcC3xiGcSNFlQdtAW9giAvzEhEREREREZEq6rSVCaZpJgCdDcPoCVxcMPlH0zQXuzwzEREREREREXdTNwenymuZAIBpmkuAJf+fL+g6KeD/s9g5a8DL+92dQpWzaNkr7k6hyjnYbYy7U6hS6i6f6e4UqpyH2z7s7hSqnKd/He/uFKqUi5pe6+4UqpyMn590dwpVjhEU7u4UqpSMKXpZ2akCJupYcqqbR//i7hSqnC/dnYC4xRlVJoiIiIiIiIj8J9nt7s6gSipvAEYRERERERERkRJUmSAiIiIiIiIiZ0XdHERERERERETKogEYnVLLBBERERERERE5K2qZICIiIiIiIlIWtUxwSi0TREREREREROSsqDJBRERERERERM6KujmIiIiIiIiIlME01c3BGbVMEBEREREREZGzopYJIiIiIiIiImXRAIxOqWWCiIiIiIiIiJwVVSaIiIiIiIiIyFlRNwcRERERERGRsqibg1NqmSAiIiIiIiIiZ0WVCSIiIiIiIiJyVtTNQURERERERKQMpro5OKWWCSIiIiIiIiJyVtQyQURERERERKQsapnglFomiIiIiIiIiMhZOSdaJljqNcW71zAwLFg3/451zQLncdH18LnhIfJ+mIVt118YgSF4DxyFUSMYTDvWTcuxrv+tkrN3nbufGkfHXh3Izc5l+oQZ7Nqyu1TMgy9PpmXH5mRmnARg+oQZ7Nm6l75DenPD2KEAZGdl8+KDr7B3275Kzb8irVizgefenIvNbueqQb25bdiQEvPnfv4dP/72OwA2m419h46y/Kt3CQ4KJD3zJE+8MJPdBw5hGAZPTRxLy6YXuKMY/5p/17aEP3gneHiQ/uUC0ubMKxUT/tAY/Lu1x8zOIfGhF8ndvgeA4JuuJOjagWAYpH+xgBMffQNAjf6XEDruZrwb1OHI9XeTu7X0dnYueGTaSyxfuYbQkJp8+/Hb7k7HbS5/fDgX9mxJfnYe8ybO5OjWA6VirnluNLWbN8DAIGn/MeZNnEleVm7lJ+sCK/7azHOzP8Vut3NVv26MuvbSEvPnfrWAn5b+CYDVZmf/kTiWffIavj4+jHxgOnn5Vmx2G326tGXcjUOcfUW19PJLTzFwQC+ysrMZNWoCGzZuKRWzdPHXBAQGABAZEcbadRu5+ppR3H/fnQwbdhUAnp4eNLmwMdGxzUlNTavMIlSYlVv3M2Peb9jtJkO6NOfWAR1KxazdeYjnv1iM1WYnJMCPd+8fBsDjHy5g+eZ9hAb689VjIys7dZdZ+fdOnvvwe8c66dmOUZf3LBWzdttenv9oPvlWGyGBNXjvsTs5EJfE5Nc/KYw5knicsdf05aaBl1Rm+i7h1bY9AWPHY1gsZC/4kezPPy0x36POeQROnIJno8acnDuH7C8/L5znN+RqfAdeBoZBzk8/kP3Nl5WdfoVbue0gM75e7thGOl3ErX3blpi/dvcRJrzzI7FhQQD0bt6QOwa2B+CTpRv5+s+tmCZc1akpN/VsWdnpu8ytT9xOq55tycvO5Y2Jr7B/S9nX4rc+OZqe1/bm5ouuB6Bd3w4Mvf9G7HY7dpuNuU/OYce67ZWVuvxHVP/KBMPAu8+N5H7xEmZGKr43PYJt70bMlGOl4ry6XY39wNbCSabdTt7SeZiJh8DLB9+bH8V2cFvpZauhjr3aU7t+bW7oegsXtW7CfdPv4c7BdzmNfWvqbJb9uLzEtGOHjzH+mglknsikQ8/2THruvjKXr+psNhvPvDaH2TMeIzoilKFjp9CzU1sa1qtTGDPy+isYef0VACz9Yx0fffUDwUGBADz3xnt0adeSl56YSH5+Ptm5eW4px79msRDxyDiO3vYg1oRk6nz+OieXrCJ/76HCEP9u7fCqW4tDA0bi0/xCIh4fz5Gh9+DdqC5B1w7kyPV3Y+bnEzt7GlnLV5N/MI683QeIv/spIp+4242Fc70rB/Xlhqsv56GnX3B3Km5zYY+WhNePZkaPCZzXqhFDnhnFG1c+Wipu/tMfkZuZDcBlj9xE5+H9WTrz+8pOt8LZbHamzfyI2VMnEhUWyrAJT9GjQ0sanlerMGbk1QMZefVAAJau3shH3/1McGAApmkyZ9pk/P18ybdaGT55Ol3bNKfFhQ3dVZwKM3BALxo3qs+FF3WlQ/vWvPnGdDp3HVwqrkevqwp/n/f5bL6f/wsAL770Ni++5Kigu+zSvtxz9+3VtiLBZrcz/X+/8vY91xEVEsiN0z+ie/OGNIwNL4xJz8ph+v8W8ebd1xATGsTx9JOF8y7vdDFDe7Tmkfd/ckf6LmGz25k291tmPXgbUWHB3PDIG/RofRENa0cVxqSfzGba3G9564FbiQkPIeVEJgD1YiOYN/3ewr/Td9wz9Gp7sTuKUbEsFgLH30vaA/djT04i5I1Z5P25Etuhg4Uh9ox0Mt98De8uXUss6lGvPr4DLyN1/J2QbyV4+gzy1vyJ7ejRyi5FhbHZ7Uz/Yilvj7uSqJoB3PjC53S/uAENY0JLxLVqGMvrd5Q8tuyJS+HrP7fy8f3X4eXhwbiZ33FJ03rUjaxZiSVwjVY92xBTP5bx3e+gcasLGD11DA9eOclpbMNmjagRVKPEtM0r/2btr6sBqHthPe57czL39B7r8rzPWXZ3J1A1VftuDpbo+pipiZgnksFuw7pjDR4NW5aK82zVG9uu9ZhZ6UUTT55wVCQA5OdiP34MIyCkchJ3sa79u/Dzl44LtW3rtxMQHEBYZGg5SxXZsm4bmQUn863rtxERE+GSPCvD5h17OK9WNHVio/Dy8mJgzy4s+WNtmfE/LVnBwF5dAMg8mcVfm7dz1aDeAHh5eREUUKPMZasy32YXkH8oDuuReMi3krlgKQG9OpWIqdGrExnfLQIgd9MOLIE18AgPxavheeT8vR0zJxdsdrLXbqJGb8c6yt93mPwDRyq9PJWtbctmhRVM/1UX9WvD+q8dLXgObdiDX6A/gRE1S8X9U5EA4OXrDea50c9wy659nBcTSe3oSLy8PBnQrT1LVm0oM37B8lUM7NYRAMMw8PfzBcBqtWG1WTGMSknb5QYP7s9HnziejK5es57gmsFER0eWGR8QUIOePbrw3XcLS827/vor+Ozzb12VqsttOXCMOpEh1I6oiZenB/3bXcjSTXtKxCxYs51erRoTE+p4whpa7AagTeM6BPn7VmrOrrZlz2HqRIVROyoML09PBnRqwdK/tpWIWfDHRnq3u5iYcMc1WFhwQKm/s3rLHupEhREbUf2v0zwvaIIt7ij2+GNgtZKzdDHenUtWGphpaVh37QCrtcR0j/Pqkr9jG+Tmgt1G/qa/8e7SrTLTr3BbDiZQJ6ImtcODHftN6/NZuvnMWsPuSzhO87rR+Hl74elhoU2jWizetNfFGVeOdn07sPSrJQDs3rAT/6Aa1Iwsvf1bLBZufngEH01/v8T0nKycwt99/H0wOTfOxVK1/L8rEwzDqBKtGozAEMyM1MLPZmYqRmDJHc0IqIlH41ZY/15a9t8JCsMSeR72Y9W3KX9x4dHhJMYlFX5OOpZEeHS409jbH7iVub++w11PjMHL26vU/MuGDmT1kjUuy9XVEpOPEx1RVPaoiDASko87jc3OyWXl2o30vcRxA3DkWAIhwUE8MuNNrr1jIo+/MJOs7Byny1Z1HlFh5McXbRPW+GQ8IktuE56R4ViLxyQk4xkVRt7uA/i1bYYlOBDD14ca3drhWY0rmOT/JzgqlLS4lMLPafHHCY52Xkl57fN38Ojat4lsGMvK93+urBRdKiEllaiIovJGhYeSmJLqNDY7J5eVf22hb5c2hdNsNjvXjn+MHjfdQ6eWTWl+QfVvlQBQKzaaI4fjCj8fPXKMWrHRZcZfeeVAFi9ZSUZGZonpfn6+9O/Xg6+/qb5P5RNTM4kOKap0jKoZSGJqyXIeTEwlPSuHUS9+xrBpHzJ/VekuIeeSxNQTRIfVLPwcGRpMwvETJWIOHksi/WQ2o56exdCHXmP+8r9K/Z2Ff/7NgE4tXZxt5bCEh2NLSiz8bE9OwiPc+TXaqWwH9uPVrAVGYBD4+ODdviMeEWVX3lUHiWknia5ZVIEUVTOAxBOZpeI27Y/numc/ZdzM79hzzHEuahQTxl9740g7mU12Xj4rth0kIa30stVRWHQYKcWu5Y/HpxAWFVYqbsDwS1n36xrSEkufj9r378irv73Fg3Mf461Jr7k033OdaTfd+lNVnbYywTCM+YZh1HUyvQ+w8TTLjTYMY51hGOveW7Xj32d5tk55CubVcyj5y78q++mYlw8+l48lf8nnkFc9bxRP5eyJl+mk/LOnz+GmbiMYfelYgmoGFY6T8I9WnVty6bCBvD3tHVel6nLOamKNMh4JLvtzHa2aXlD4BNpms7F99z6uv7wfX8x6AT9fH9797BuX5usyTst8yrpxGmKSv+8wqXPmEfvudGJnP0Puzv1gtbkiS6nKnGxDzo4rAF9MmsXUDmNI2BNHi8GdnMacC8o8lqzZSMsmjQgOLLpA9vCw8MXrT/Hr+y+xZdd+dp8jLXqcrYOytguAodc5b31w2WX9+OPPddW2iwOUOqICpXcbm83O9kMJvHHXVbx19zXM/vFPDiY4r+A+FzjbFE7dZqw2O9v2H+H1SSOZOWUUs7/5jQPHim6i8q1Wlv21jX4dm7k63crh9CLtzBa1HTpI9uefEvzciwRPex7rvj2YNmv5C1ZhZ3Kd1qR2JAueHM68KTcwtFsLJsz5EYAG0aGM7NOaO9/8jnEzv+f8WuF4WKp9w2uHM7iWD4kMpdOlXfjp/R+c/ok1P6/int5jmXH7NIbef6MrspT/uPJaF3wGLDEM411gBhABvAKcBwwvayHTNGcDswGyXrjNpVUpZkbJlghGQAhmZlqJGEt0XbwvG+2Y7xeAR4Nm5Nlt2PZsBIsHPpePwbp9Fbbd612ZqssNGX4Fl904CIAdG3cSGVv05DgiJoKUhJRSy6QkOi5g8vPy+enzhQy987rCeQ2aNGDy8/cz6eYHSU9NL7VsdREVHkZ8UnLh54SkFCLDnDeTXLBkJQN7FTU1jIoIIyoijOZNzgegb7eOvPvZty7N11Vs8cl4RRdtE57R4dgSS24T1oRkPIvHRIVjLdhGMr7+mYyvHU+YQ+8dWaIFg5y7Ot3clw7DegFw+O991IwteipSMzqU9ATnT+bBUYu/6Yc/6T76MtZ9sczlubpaVFgICUlFN30JyceJCK3pNHbh8jUM7F564D2AoAB/2ja7gJXrN9O4Xm1XpOpyY+4czqhRjgvTdes2UrtObOG8WrVjiDuW4HS50NAQ2rVrxdXX3lZq3vXXXV6tuzgARIUEEJ+aUfg5IS2DiJoBp8QEUjPADz8fb/x8vGnTuA47jyRRN+rMuyJWJ1GhwcSnpBV+Tjx+gsiQoJIxYcGEBNbA39cbf19vWjepz66Dx6hX0AJuxcadXFi/FmHB50ZXM3tSUonWBJbwCGwpyadZoqSchT+Rs9DRgqfGrbdjS6re5+OomgHEF2tNkJCWScQp/f8D/LwLf7+kaT2mfbGU1MxsQgL8GNKpKUM6NQXgtfl/EFWzdDeZ6mLALYPoPbQfAHs37SYsNgJwDJoYGh3G8cSSFY/1L25AdN0Y3lg2CwAfPx9eXzaL8d3vKBG3fc1WourGEBgSSEaxY5TIv3XaqjvTND8BWuGoPNgO/AksAjqZplm6DZob2OMPYIREYQSHg8UDzwvbY9v7d4mYnHceJOedKeS8MwXbrr/IW/SJoyIB8O4/HPvxY1j/+tUN2Vesbz74jlH97mBUvzv4/eeV9L/GcTC6qHUTTqafLKw4KK74OAqXDOjC/h37AYiMjWTqO0/wzD3TObKvej89u/jCRhw8eowjxxLIz89nwZKV9OjcrlRcRuZJ1m3aRs9i88JDQ4iOCGP/YcfARqs3bKZh3ep58Z+zZSdedWvhWSsKvDwJGNiDk0tWlYg5uXgVgVf0AcCn+YXYM7KwFXQJ8QgNBsAzJoKAPl3I/GlppeYv7vHnR7/yyqAHeWXQg2z9ZR2tr3KMon5eq0ZkZ2SRkZRWapmwukUDqzXp3ZrEvXGlYqqjpufX52BcIkfik8jPt7Jw+Rp6dGhVKi7jZBbrtuykZ8fWhdOOn0gnPTMLgJzcPFZt3Eb92jGVlntFm/n2B7Rt14+27frx/fc/c/ON1wDQoX1r0k+kEx+f6HS5a66+jB9/WkRubsm3ewQFBdLtko58/3317hLTtG4MhxJTOZqcRr7Vxs9rd9C9eaMSMT1aNGLDniNYbXay8/LZfOAYDcroLnQuaNqwNofiUziSeJx8q5WFf/5N9zZNSsT0bHMR63fux2qzkZ2bx+Y9h6lfq+hme8EfGxnYqUVlp+4y1p078KhVG0t0NHh64tujF3l/rjzj5Y2aNQGwRETi3eUScpcsclGmlaPpeVEcSkrjaMoJx36zfhfdm9UvEZOcfrLwqfzmg/GYpknNGo7xRY5nOI6tx45nsPjvvQxsc37lFqACLfzwJyYNupdJg+5lzS+r6XG1480njVtdQFZGVqmuDOsXr+P2dsMZ2/V2xna9ndzs3MKKhOi6ReeY+hc3wNPLUxUJ/4bddO9PFXUm4x5cBLQH1gBtgaiC5fJdmNeZM+3k/fYpPlffCxYL1s0rMVPi8GzRHQDr32U/DbPUaoRn087Yk47gcctjAOT9/g32/ZsrI3OXWvXbajr16sD/Vn5EbnYO0+97vnDejA+n8dykF0lJSOHRNx6iZmgwGAZ7tu7lxSkvAzBiws0EhwQxYdo9ANisNkYPqp4jwHp6ePDQ+Nu484Gp2Ox2hgzsRaN6dZg333HRet3g/gD8tmINnds0Lxwk7R8Pjh/FlGmvkp9vpXZMFE9PHlfpZagQNjtJz7xJ7DvTMCwW0r/5hbw9Bwm63vFqu/TPfyRr+Rr8u7Wj7sK52HNySXz4xcLFo199DI+agZj5NpKmvoE93fEUoUbvzkQ8PBaP0GBiZj5N3o69xI1+2C1FdKVJjz/L2g2bSEtLp/eVNzF21M1cXbDt/FfsWLKBC3u25IFlr5CXncsXk2YVzrt17mS+fOAdMpLSuP7FMfgE+GEYBse2H+TrR95zY9YVx9PDg4fuvJExj72IzW7nyr6X0KhuLeb95Bgg67pBjou+xX+up3Orpvj7+hQum3z8BI+8PAeb3Y7dbtL/knZ0b9/SHcWocD8t+I0BA3qxc/tKsrKzue22+wrnzf/uQ0bfOYljBS0Vrr/ucmY8/2apv3HlFQP5ddFysrKyS82rTjw9LEy5vg9jXvsSu93OFZ2b0Sg2nC+WbwTg2m4taRATRuem9bnu6fcxLAZDujSjUS3HE/gpc+azbtdh0jKz6TdlJmMGd2FIl+ZuLNG/5+nhwYMjrmDMs+9it9u5skc7GtWOZt4iR2X2dX060qBWFF2aX8C1U17BMAyu6tmOxnUc425k5+axasseHr3tqtN9TfVit5H5xisET38Bw2Ih5+efsB08gO9llwOQ88P3GCGhhLw5C8O/Bph2/K66htTbhmNmZRH82NMYQUFgtZL5xiuYmdV7jABPDwtTrunOmLe+d+w3HS+iUUwYX6xwXI9f27UZizbuYd6KLXhaDHy8PHl2+IDCrhD3v/sTJ07m4Olh4cFre5wzg5iuX7yO1j3b8MbyWeRm5/LWxKIxDx56/zFmTn6DVCcPCv/RcWAnul/dC2u+lbzcPF4eN6My0pb/GON0/RoNw5gDtAbGmab5p2EYNYAngUHAvaZp/lLeF7i6m0N1M+Dl/e5OocpZtPoVd6dQ5RzqN9HdKVQpdZfPdHcKVc7Dbc+9Cpt/6+lfx7s7hSqlRtNr3Z1ClZPx85PuTqHKMYLObOC//4qMgocqUiRgoo4lp7p5dLm3QP85Xx78/hx5R5Fzadf3dOs9bc3Pl1TJ9VveCCVbgXamaf4JYJrmSdM0JwLXA6VfLi4iIiIiIiIi57zTdnMwTfNlwzAiDcMYBzTFMdbsNuAt0zQvqYwERURERERERKRqKe/VkF2AtQUfPwQ+Lvh9dcE8ERERERERkXOWaTfd+lNVlTcA44vAlaZpbig27TvDML4BZgHO33slIiIiIiIiIues8ioTgk6pSADANM2NhmGcGy/7FRERERERESmL3d0JVE3lDcBoGIYR4mRi6BksKyIiIiIiIiLnoPIqBF4GfjEMo7thGIEFPz2ABQXzREREREREROQ/pry3Ocw2DCMOeBrH2xzA8brIqaZpznd1ciIiIiIiIiLuVJUHQXSn8sZMwDTNH4AfKiEXEREREREREakGTluZ8H/s3Xd4FOXXxvHvpBDSC+l0AgrSOwhC6EWRJggiIkUQOwpS9CciAipiVxALKnYs2GgiTbDQe5HeUyghCem78/6xMSFkA+aV7Cbx/njlYmfnzO6ZdXZm9pnzPGMYxlNXmG2apjn1GucjIiIiIiIiIsXc1SoTLtp5zhsYDpTD1v1BREREREREpHTS3RzsutqYCbP+fpx9K8iHgaHA58CsgpYTERERERERkdLrqmMmZN8G8lFgEPAh0Mg0zfNFnZiIiIiIiIiIs5mqTLDramMmzAT6AHOBuqZpJjskKxEREREREREptlyuMv8xIBJ4EjhlGEZi9l+SYRiJRZ+eiIiIiIiIiBQ3Vxsz4WqNDSIiIiIiIiKll7o52KXGAhEREREREZESzDCMroZh7DMM44BhGBPszB9kGMb27L/fDMOo/2/f86oDMIqIiIiIiIj8VxX3ARgNw3AF3gQ6ASeADYZhfG+a5u5Lwg4DbU3TPG8YRjds4yI2/zfvq8oEERERERERkZKrGXDANM1DpmlmAJ8DPS8NME3zt0vuyvgHUOHfvqkaE0RERERERESKKcMwRhqGsfGSv5GXhZQHjl8yfSL7uYIMBxb/27yKvJvDW6+lFfVblCg/9fZwdgrFzvBWk52dQrEz2dvZGRQvTzR5wtkpFDvTNk5zdgrFzouNn3J2CsVKTIfqzk6h2JkzZJWzUyh2Ylwszk6hWEkg1NkpFDsr7/zA2SkUOzt+0TH4P8fJ3RxM05yLrVtCQQx7i9kNNIx22BoTWv/bvDRmgoiIiIiIiEjJdQKoeMl0BeDU5UGGYdQD3gW6maZ59t++qRoTRERERERERApQ3AdgBDYANQzDqAqcBAYAd1waYBhGJeAbYLBpmn9dizdVY4KIiIiIiIhICWWaZpZhGA8ASwFX4H3TNHcZhnFv9vw5wFNAOeAtwzAAskzTbPJv3leNCSIiIiIiIiIlmGmai4BFlz0355LHI4AR1/I91ZggIiIiIiIiUoAS0M3BKXRrSBEREREREREpFFUmiIiIiIiIiBRAlQn2qTJBRERERERERApFjQkiIiIiIiIiUijq5iAiIiIiIiJSENNwdgbFkioTRERERERERKRQVJkgIiIiIiIiUgANwGifKhNEREREREREpFDUmCAiIiIiIiIihaJuDiIiIiIiIiIFMK0agNEeVSaIiIiIiIiISKGoMUFERERERERECkXdHEREREREREQKoLs52KfKBBEREREREREpFFUmiIiIiIiIiBTANDUAoz2lpjGh/ZTBVG3XgKzUdBY/Npe4nUfyxXR/dTTh9aphzcri9NZD/DzxfaxZFmr1upFmo28BIONiGsuf+ID4PcccvAbXlmutxpS9bRS4uJD521Iyfl6QZ75b3RaUuWWwrWbHaiX9q7exHNoNQNlBj+BapxlmUgIp0+9zRvpFYvDTw6nfrhHpqenMHfsGR3ceyhcz4oX7qFq3OhgQc/g0cx97nfSUNLz8vLln5gOEVg4jMz2Td8e9yYm/St424tW6CcET7wVXVxK/WkzCu1/miwmeNBqvNs0wU9OImzSL9D0HAPC/sxd+/bqBYZC4YDEX5n8LgHeXmwi6fzBlqlXkxO0Pkb5rv0PXqSjdOnkINds1IDM1gy/HzubkriP5Ym57fiQV6lXDwCD+8Gm+HDubjJR0xyfrBE9Of4k169YTFBjAwo/nODsdh+n09GCi2jUgMzWdH8fOJdbO8ebWV0cTXtd2vDm17RBLso83f4uoV427Fj7NwgdeZ9+iDQ7M/tpzb9wM73sfxHBxIW3JT6Qu+DTPfI92HfHsdwcAZmoqyW+8hOXwQQB8xoynTLOWWBPOkzB6qMNzLyrR2eckmanpLCvgnKTrq6MJyz4nidl6iF+yt5HAqAg6vziS0DpV+G3mAjbNXeT4FSgCt04ewvWX7E9P2dmfDnjlfirUrYYly8LxbQf5ZtK7WLMsePp5c9vMUZSrFEZWegYLHn+b2L9OOH4lrrHbJw+lTrtGZKSm88HYNzm+63C+mMHPj6Zy9jEm9vBpPhz7JukpaQBc1+IG+j81FFc3V5LPJzHr9smOXoVr6snpY2nbsRWpKWlMeOhpdm/fZzduzKT76HprB6wWK59+8BXz3/mCatUrM+O1ydSuV5OXpr/F+2997ODsi9baLbt5ft43WK1W+nRoyfDenfLMn/fdLyz6dSMAWVYrh0/EsPq96fj7ejsjXfkPKRXdHKq2q09glXDea/MYyya8R6dpd9uN27PwN95vN44POk3ErWwZ6g6IBuDC8Xg+7/8sH3aZxB+vLaTzc8Mcl3xRMFwo2/8+Ut56iovP3otb47a4hFfME5K1byspM+4n5bkHSfv4ZTzueDhnXuYfy0l983+OzrpI1W/XiLCqEYxtez/vT5zD0GdH2o37+Jl5PNHtUZ7o+ihnT8XTaUg3AG59oC/Hdh/mia6P8vajr3Hn0yVwG3FxIeTJ+zk16kmO9bgH3+7tcI+qlCfEq01T3CuX51jXocRNfpWQyQ8CUKZ6Zfz6dePE7Q9xvPe9eEc3x71yJAAZ+48Q89AzpG3c4fBVKko1oxsQXDWcF6LH8PWkd+g9bbjduB+mzueVbhN4udt4Ek6d4cYhXRycqfP06t6JOS896+w0HCqqXX0Cq4Yzp+1jLJ74Hl2fvdtu3K6FvzG3/Tje7TwRd48y1M8+3gAYLgbRE2/n8Jrtjkm6KLm44HP/IyT+73HOjxqCR3QHXCtVzhNiiTnNhccfIuG+YaR89hE+D43NmZf282IuPDnO0VkXqSrt6hNQJZx5bR5j+YT3aF/AOcnehb/xYbtxzM8+J6mTvY2kJVxk1eT5paYRAeD67P3pzOgxfHOF/emWhet4scNjvNzlcdzLlqHZgHYAtLu/J6d3H+WVbuP54rHZ3Dp5iCPTLxJ1ohsSWjWC/0U/yMeT3mbQtHvsxi2Y+gHPdhvH1G5jOX/qDNFDugLg6efFwKn38OaI55nS+VHm3jfLkelfc207tqJKtYp0atab/z02jSkvTLQb12dgDyIiw+ja8ja6terHT98uAyAhIZFnJ73Ie6WsEQHAYrEy/b0FzH7iXha+PInF6zZx8PjpPDFDe3ZgwYvjWfDieB6+4xYa31BdDQniEKWiMaF658bs+notAKe3HMTDzxvv0IB8cYdXbst5HLP1IL4RQQCc2rSf9AsptsdbDuCT/XxJ5VLlOqxnTmGejQFLFlmb1+BWr2XeoIy03MceZQEzZ9JycCdmSpJjknWQRp2asfbrVQAc3PIXXn7e+IcG5otLS07NeVzGowxm9sdSvkZFdq2znfifPniS4Aqh+AX7F3ne11LZuteTeewUWSdiIDOL5MWr8Gmfd7vwbt+SpO+WA5C+fS8uvt64BgfhHlWJtG17MNPSwWIldcN2vDu0AiDz0HEyj5T8K0SXu6FzYzZ/8ysAx7YcwNPXC9+QgHxx6ZdsM+5ly5Cz0fwHNGlQF38/X2en4VA1OjVmZ/bx5tQVjjcHLznenNqWe7wBaHJ3Z/Yt3sDFM4lFnm9Rc7uuFpZTJ7HGnIasLNJXr6BMi9Z5YrL27MJMTrY93rsLl+CQ3Hk7t2Mmla7jTVTnxuzJ3kZirrCNHLnsnOTvc4/Us4nEbj+Up5KlpKvduTGb/sH+dN+qrTmPj287gH+47TMJrVGBA+t2AhB/8BSBFULwKWHH4MvV79yUP75ZDcDhLfvx9PXGz85nklbAMabZra3ZuuRPzp86A0DS2ZK9P+nQtS3ffmFrQNu2aSe+/r6EhJXLF3fH3bfxxqx3MLM/h3Nnzuf8u2PrbrIysxyXtIPsPHCUSuEhVAgLxt3dja6tGrHyChdwFq/dTLfWjR2Y4X+DaXXuX3FVKhoTfMIDSTp9Nmc6KeYcPuH5fyj+zcXNlRv6tObw6vxXhereHs3hlSX7apGLfzms58/kTFvPn8Hwz79DdqvXEq8n38br3imkffKKAzN0vMDwIM6dyv1MzsWcJSjMfqPRPTMf4I2N7xNRvTw/f/ATAMd2H6FJtxYAVKtfneDyIQSF5/9MizPXsHJkxsTnTGfFnME1NDhPjFtoMFmXxsSewS2sHBn7j+DZpC4u/r4YZT3wbtMUt4gQSjP/sCASTuXuVxJizuWc2F6u38xR/G/DHEKjIln3wVJHpShO4BseSOKpvMcb37ArH2/q9GnNoVW244pPWCDXdWnClo9/KfJcHcElOBhrfFzOtPVMPC7lgguML9vlZjI3/umI1Jzm8nOS5H9wTlKrT2uO2jknKS38woK4cMn35kLMOfwK2J+C7TNp1Psm9q22Nbic3nOUOl2bAlChfhQB5YML3B+XFAFhQZzLc4w5S2AB6zRk5n3M3PAO4VGRrPhgMQBh1SLx8vfm0c+fZtIPz9OiTxuH5F1UwiJCiDkVkzMdeyqWsPDQfHEVq5Sne6/OfP3zR7z7+atUrlYxX0xpE3sugbByATnTYUEBxJ29YDc2NT2DdVv30Kl5fQdlJ/91VxwzwTCM16403zTNhwpYbiQwEqBvYDNa+NT4fyf4TxjYGRDjClcHO067mxPr93Jyfd6+WBVb1qLu7W35rO/Ua52iYxn2BgjJ/3lkbf+drO2/4xpVB4+bB5P6xhNFn5uTGHY+E7OAbeSdcW9guLhw1zMjaN6jNb8uWMEPs79h8OThPLtoFsf3HeXorsNYLcW4mdCef7Jd2A0xyTx0nPPvfknkezMwU9JI33cYStFVM7sKsc0sGPc2hotBzylDqd+jJRsXrC7q7MRZ7H2PrnC86fLs3Rz/cy8nNtiONx0n38nK5z7HtJaWCpZ/PiCVe72GeHS+mQtjHyjCfIqDf77vAGg/7W5O2jknKVUK+b3pPXUYh9fv5Uj292bV7O+5dfJdPLxoBjF7j3Nq1xGslpJ9DLJ/XmI/9sNxb2G4uDBgyjCa9riR3xaswsXVlUp1q/HyHc/gXrYM47+ZxqEt+4k7fNr+ixRz//Q8rYxHGTLS0unb6S4639yOGa8+xR097HcRKc3sfV4AqzfupEHNquriUARMqwZgtOdqAzDeC+wEvgRO8Q/PGkzTnAvMBXix0p1FcsbU4K6O1Bto60sXs/0QvhG5V4l9w4NIjk2wu1zLR3rjGeTLsgnv53k+uGZFurwwgq/vmklaQnJRpOww1oQzuAfmXhlyCQzGvHCuwHjLwZ24BEdgePthXizZZXKX6nhXV6IH2AaoObT9AEGRuZ9JUHg5zsedL3BZ02rlzx/W0n1UL35dsIK05FTeGfdGzvyX1s4h7nhs0SVfBCwxZ3APz60mcAsPxhJ3Nk9MVuwZ3C6NCQsmK8627SR9s5Skb2xX3YMeGZqngqG0aDm4E80Htgfg+LZDBETm7lcCwoNIjL3SNmOy/cffaTvyFjUmlDKN7upIg+y+26e3H8IvMu/xJikuwe5yrR/ujVeQL19PzD3eRNSrSs/XbT+mvYJ8iWpXH2uWlf3LNhXdChQh65l4XEJyrx66BIdgPXsmX5xrlWr4PDKOC/97HDOp9Bxn/lb/ro7UyT4nib3snMQnPIiLBZyTtMg+J1l+2TlJadBycCeaZe9PT2w7hP8l3xv/K+xPOz7cF+9yvnwz6t2c59KTU1kw7u2c6fFrX+Pc8ZJ3DIoe3IXWAzsCcGTbAYIiy3Ewe15AeDkSYgs+VzOtVjb++BudR97KbwtWcT7mLMnnE8lITScjNZ396/dQoVblEtWYMGhYP/oP7gXAji27CY8MB2zVKGGRYcTF5v9/HHsqjqU/rgBg2U8rmfFayR508p8ICwog9mxCznTsuQRCgvzsxi5Zt5lurdTFQRznat0cIrA1CnQBBgPuwPemaX5omuaHRZ3clWz9aDkfdXuCj7o9wYGlm6jd19ZHM6JhFOlJKVy0c3JXd0A0VdrU5acH3szT/OsbWY6ecx9h0SNzOH84Jt9yJY316F+4hERilAsDVzfcGrUha/sfeWKM4Iicxy4VosDNrVQ1JAAs/2gJT3Z/jCe7P8amZetp3TcagKiG15GSlMIFO40JoZXDcx437NiU0wdPAuDl54Wru63tLXpAR/at352nH2NJkLZzH+6Vy+NWPgzc3fDpFs3FlXm3i4sr/sC3p+1Ex6NeTaxJKVjO2E5uXINs/VPdIkLw6diK5EWrHJq/I/w+/2de6T6RV7pPZNeyjTTqcxMAlRpWJzUphaT4hHzLlKsclvO4VodGxB085ah0xUE2f7Sc97s/wfvdn+CvZZuok328ibzC8ab+gGiqtq3Ldw/mPd7Mbv0os1uPYXbrMexdtJ6l//ugxDYkAGT9tRfXyAq4hIWDmxsebduT8ce6PDEuIaH4/W8qSTOnYT1Z+sZXAdj20XI+6fYEn3R7goNLN1ErexsJbxhFRgHbSJ0B0VRuU5dFl52TlBa/z/+ZV7tP5NXs/WnjS/anaQXsT5ve3o7r2tTj0wdfz3NVuqyfF67urgA0G9Cew3/uyTNeTUmxav5Snu0+jme7j2Prsg206NMWgKoNa5CalEKinc8k5JLzknodGhOTfV6ybdkGajSthYurC+5ly1C1QXViDpx0yHpcK5+8v4Ce7QbRs90gli9eRe/buwNQv3EdkhOTiY89m2+Z5YtX0eImW5eXZjc25sjBow7N2RlqV6/E0dPxnIg9S2ZmFkvWbSa6Sd18cUkXU9m4+wDtmuafJ1JUrliZYJrmWWAOMMcwjPLAQGCXYRjjTdOc74gE/4lDK7ZStV19Rvw6i8zUDJaMnZszr88HY1k6/l0uxibQafpQEk+e4Y6FTwOwf8kGfn91IS0f7o1noA8ds0fltlosfHzLU05Yk2vEaiXty9l43f8sGC5k/rEMa8wx3FvbdtKZaxfh3qAVbs07gCULMjNIe/+5nMXL3v04rjXqYfj44T31IzIWfUzm78uctTbXxLYVm2jQrhEvrnmLjNR03hmbW2Uw9oMnePfxt7gQn8Colx7C08cTwzA4tucI856wXQmJrF6BUS89hNVi5eSBE7w77k1nrcr/n8VK/LQ3iXxnOoaLC4nfLiPjwFH8br8ZgMQvfiJlzXq82jSl8pJ5WNPSiXsid3To8FefwjXAFzPTQvyzb2BNtFXweHe4kZAn7sM1yJ+I2VPJ2HuQUyNLfpeZvSu3ULNdA8avfoWM1PQ8V8WGzXucr8a/Q1J8ArfPGo1H9jZzes9Rvnmy9F1hLMi4yc+xYct2EhIS6dDrTu4bPpi+PUr33SwOrthKVLv63LvGdrz56ZLjTf8PxrLo8XdJjkug67ShXDh5hru+fRqAfUs2sO61hc5JuihZLSTPfgX/Z18EVxfSli3CcuwIZbvfCkDaou/xumMIhq8/PvePAcC0WLjw8CgAfMc/hXu9Bhh+/gTOX0DK/HmkLyvZdzE4vGIrVdrVZ+ivs8hKzWDZJdtIrw/G8nP2OUmH7HOSAdnnJAeWbODPVxfiFeLPHT9OpYyPJ6bVSsPhXfmow3gySuCP57/tXbmF69s14HE7+9Ohf+9P487Te9pwEk6e4f5vnwFg55IN/PLaN4RWL8/ts0ZjtVqJ23+Srx6fW9BblRg7V26mbruGPLv6dTJSM/jwkvOKB+ZNZP74OSTGJ3D3rPvx9PECA07sOcqnT74DQMzBk+xavZX/LZmFabWy7otfOPXXcWetzr+26ud1tO3YiuXrF5KamsbEh6bkzHvns1d54pGpxMWe4e1XP2DWnGe5e9QdpFxM4YkxtjsKBYeW45ufP8LH1xur1eTuUQPp1qo/F5MvOmuVrhk3V1cmDb+N0dPewmK10qtdC6pXjODLZbaBXvt3tjVerli/nRvr18SrrIcz0y21SmGb7zVhXKkfX06QYTTC1pDQCdgEzDJNc/c/eYOi6uZQUo26teAStv+q+34o6+wUip3J3iX/4HctzU0p2QNtFYVpG6c5O4Vi58XGJbgRuAjcc0PJ/WFRVObvKv2DtRVWjEvJHnvgWkug9N0N4N9aefGws1Modnb8omPw5TzqdSnVgwoca9LBqb9pK238pVh+vlcbgHEKcAuwB/gcmGiapvayIiIiIiIi8p+gARjtu9oAjP8DDgH1s/+mZ48eagCmaZr1ijY9ERERERERESlurtaYUNUhWYiIiIiIiIhIiXG1ARjtDpFqGIYrMAAo/UOoioiIiIiIyH+WujnYd8VbQxqG4WcYxkTDMN4wDKOzYfMgtq4P/R2TooiIiIiIiIgUJ1fr5jAfOA/8DowAxgFlgJ6maW4t2tREREREREREnEu3hrTvao0J1UzTrAtgGMa7wBmgkmmaSUWemYiIiIiIiIgUS1fs5gBk/v3ANE0LcFgNCSIiIiIiIiL/bVerTKhvGEZi9mMD8Mye/vvWkH5Fmp2IiIiIiIiIE2kARvuudjcHV0clIiIiIiIiIiIlw9UqE0RERERERET+s0xTlQn2XG3MBBERERERERGRPNSYICIiIiIiIiKFom4OIiIiIiIiIgUwrc7OoHhSZYKIiIiIiIiIFIoaE0RERERERESkUNTNQURERERERKQAVt3NwS5VJoiIiIiIiIhIoagyQURERERERKQApioT7FJlgoiIiIiIiIgUSpFXJtw7KLWo36JEuWneBWenUOz8/u1gZ6dQ7Bwb9qGzUyhWpv78oLNTKHZebPyUs1ModsZuesbZKRQrPhXaOjuFYuf8rLrOTqHYMfz9nZ1CsZKx9A9np1DslBkywtkpFDv33jrX2SkUO/OOdHF2CuIE6uYgIiIiIiIiUgDTqm4O9qibg4iIiIiIiIgUiioTRERERERERApgms7OoHhSZYKIiIiIiIiIFIoaE0RERERERESkUNTNQURERERERKQAGoDRPlUmiIiIiIiIiEihqDJBREREREREpABWU5UJ9qgyQUREREREREQKRY0JIiIiIiIiIlIo6uYgIiIiIiIiUgBT3RzsUmWCiIiIiIiIiBSKKhNERERERERECmCazs6geFJlgoiIiIiIiIgUihoTRERERERERKRQ1M1BREREREREpABWDcBolyoTRERERERERKRQVJkgIiIiIiIiUgDdGtI+VSaIiIiIiIiISKGoMUFERERERERECqVUdHNwva4BHrcMAxcXMjf8Qubqb/POr9WUMp0GgmkFq4X0H+dhPboXAK/HZ2Omp4LVNi/1zfHOWIUi8fizj9CqQ0vSUtOY/PA09u74K1/MlFefoHHLBiQnXgTgqYen8deu/dx13x1079MZAFc3V6rWqEz72jeTmJDk0HW4VtZt38/zny7BarXSu00jht9yU76YDXsOM/PTJWRarAT6evH+xKE58yxWKwOfnktooC9vjBnkyNSvKa/WjQmdNBpcXLjw1RLOv/tlvpiQSaPxbtMUMy2dmEmzSN99AICAIb3xv60rmCbpfx0hdtIszIxMIl6aiHuVCgC4+vlgSUzmWJ/7Hbpe18raTTt4fu6nWK1W+nRuw/B+N+eZP+/rxSxa9TsAWRYrh0+cYvUnr1HWw4Oh42eQkZmFxWqhY6sm3D+otzNW4Zrr9PRgoto1IDM1nR/HziV255F8Mbe+OprwutWwZmVxatshlkx8H2uWJWd+RL1q3LXwaRY+8Dr7Fm1wYPaO9+T0l1izbj1BgQEs/HiOs9NxmJdmTaFr1/akpKQy4p5H2bp1Z76YX375Gl8fbwBCQoLZuHEr/fqPoE2bFny14D2OHDkOwMLvFjN9+qsOzf9aWnfkDDPX7MNqmvSqXZ5hTarajdsVe4G7vlzPc13r0alGGABJ6ZlMWb6bg+eSMTCY3PEG6kcEODD7orHuQAwvLN2K1TTp3bAqw1rVzDN/w5E4xnz5G5EBtu2jQ83yjGpzAwCTv9/Imv2nCfL24Ot7Ozs896LiVrcpZQffbzt3XbWI9B8/zzPf/cYOeNw8AAAzPZXUD17BeuyQbaaXN17Dx+JSoQqYJqnvvojlwG4Hr0HRWrfrCC98tcp23taqDsM6N8sXs+Gv48z8ajVZFguBPp68N6a/EzItWndMHka9do3ISM3gvbGvc3TX4XwxQ5+/jyr1ojAwiDl8ivfGvkF6Shqevl6MfPlhgsoH4+rqypJ3vmPtgpVOWIvSwTSdnUHxVPIbEwwXPG69h9T3nsFMPIvn/c+TtWcDZtyJnBDLwR2k7rGdwLqEV6bswMdIefmhnPmp70yGlJL5I7kgrTu0pFK1CvRseTt1G9Vm0vNjuav7SLuxrzzzJst/XJXnuY/e+pSP3voUgDadWjFo1O0ltiHBYrUyff4i3h43mLAgP+6Y8g7RDa8nqnxoTkzixVSmz/+Jtx67k4hyAZxNTM7zGp8s+4NqkcEkp6Y7Ov1rx8WF0P/dz8nhk8iMPUPlL1/j4so/yDh4LCfEu01TylSO5EjXYZStX5PQpx7g+IBHcAstR+CdPTlyy0jM9AwiXpqEb/doEhf+zOlHZ+QsH/z4PViTLzpj7f41i8XK9NnzmfvsWMLKBTFwzDNEN29AVKXyOTFD+3ZjaN9uAKz6cyvzv1uKv68Ppmny7vTH8fIsS2ZWFkMen0HrxvWoXzPKWatzTUS1q09g1XDmtH2MyIZRdH32bj7s9XS+uF0Lf+P7h2cD0PO1+6k/IJotH/8CgOFiED3xdg6v2e7I1J2mV/dO3NH3ViZNfdHZqThM1y7tqF69KjfUvolmzRry+mvTuanNrfniOnTom/P488/e5ocfl+VMr1u3nt59huZbpqSxWE2eW7WX2b0bEeZTlkFf/EnbqiFElfPJF/fquv20rFQuz/MvrN7HjZXL8eLN9cm0WEm7pFGupLJYTWYs2cKcQTcR5ufFoHd/oe11kUSF+OWJa1gpmNcHtM63/K31KzOgaRRPfleKGiINF8oOeYiLzz+OeS4en2feInPz71hPHc0JscafJnnaGEhJxq1eMzyHPcrFpx8AwPPOB8jcvoHM16eAqxt4eDhrTYqExWplxpcrmPNgH8ICfBn0wqe0rRtFVETu9yUxJY0ZX6zgzft7ExHkx7mkFCdmXDTqRTcirGoEE6IfoFrDGgyeNpJne03MF/fZ1HmkJacCMODJu+kwpBuLZn9L+8FdOXXgOK+OmIFvkB/TV7zG7wt/xZKZ5ehVkVKsxHdzcKlYHevZGMzzsWDJImvbWtxqNc0blJGW+7iMB1D6m5badmnNj18uAWDH5l34+vkSHFruKkvZ17V3R5Z8+/O1TM+hdh46ScWwICqEBuHu5kbX5nVYtWVfnpjFf+ygQ+NaRJQLAKCcX+6JX+y5C/y6bT+92zRyZNrXXNl615N57DSZJ2IgM4vERavxbt8yT4x3+5Ykfmf7EZi2bS+ufj64hgTZZrq6YpQtA64uGJ4eZMWdzfcevl3bkPTTqqJelSKx869DVIoIpUJ4KO7ubnRt04yVf2wpMH7xmj/o1qYFAIZh4OVZFoCsLAtZliyMUjBOT41Ojdn59VoATm05iIefN96hAfniDq7clvP41LaD+EYE5Uw3ubsz+xZv4OKZxCLPtzho0qAu/n6+zk7DoXr06MzHn3wNwPr1WwgI8CM8PLTAeB8fb6Kjb+T775c6KkWH2Rl7gYoBXlTw98Ld1YUuNcJZdSg+X9zn247RISqMIK8yOc8lp2ex+dR5ete2NWC6u7rg6+HusNyLys5T56gY6EOFQB/bZ1K7Iqv2nfrHyzeuHIKfZ5mrB5YgrlE1scaexIw/DZYsMv9YiXvjG/PEWPbvhhTbhY2sA7txCQyxzSjrhVvNumSuXpQdmAUpJbMRvyA7j8RQMSSACsEBuLu50qXx9azafjBPzOKN+2jfoDoRQbZGqSBfL2ekWqQadm7Kb9+sBuDQlv14+XrjHxKQL+7vhgQA97JlLrmEblLW2xMAD6+yXExIzlM1KHItFNiYYBiG3xXmVSqadArP8AvCvHAmZ9pMPIfhn/9Hs+sNzfAa8xqeQyaR9vWbuTNME89hT+H5wAu4Ne3kiJQdIjQihJhTcTnTsafjCI0IsRt7/4RRfLHiQx6b8hDuZfKeuJT19ODGdi34pYT+QASIO59IeFDu5hwa6Efs+bw/bI7GnCXxYhrDZ8xjwOS3+WHd1px5L3y6hDG3d8KlhP86dAstR1ZM7kltVuwZ3MPyflfcwsqReWlMTLxtubiznJ/3FdV+mU+1NZ9iTbpIym+b8yzr2aQOlrPnyTz6z08Si5PYs+cJC8n9ERwWHETc2fN2Y1PT0lm3aSedWjXOec5isdLvwaeIvvNhWjaoTb3rS3ZVAoBveCCJp3IbjZJizuEbFlhgvIubK3X6tObQKlsVgk9YINd1aZJTpSClU2RkOCdO5H7vT548TWRkeIHxPXt2ZeXKdSQl5VaANW/emA3rl/L9dx9Rq9Z1RZpvUYpLTifMJ/cqcZiPB/EX0y+LSWPFwThuq1shz/MnE1MJ9CzD5OW7GPDpH0xZvovUzJJ/4h+XmEq4n2fOdJifJ3FJqfnitp84R/+3f+b+T3/lQNwFR6bocEZgMOa53GOt9Vw8RmBwgfFloruRtX09AC6hEVgTL+A58nF8ps7Bc/hj4FG2yHN2pLiEZMIDcxtlwwJ8iEvIWzF6NO48iSnpDH9lAQOf+4Qf/ixd3TwAAsKCOHcq9zfO+ZizBIbbvzA4bOb9vLLhPSKiyrP8A1tD0y8fLiaiegVeXv8uU5e+xKdT3sdUrf7/m9U0nPpXXF2pMmHV3w8Mw7j8THBhUSTz/2Pnw7XzRbHsXk/Kyw+ROv8F2/gJ2VLnPEHqG+NIm/cs7i274lLlhqJM1mEMOz987e1AXp82h96tB3Jn1xH4B/ox9IE788xv07k1WzdsL7FdHMB+Hyfjsu0my2pl95FTvP7oIGaPvZO536/hSMwZVm/dR5CfNzdUiXRQtkXon2wT9hpMTBMXPx982rfkcKe7OdR2EC6eZfHt0T5PmO/N0SW2KqEg9r5HAKvXb6VBrer4++ZWsLi6urDg9Wf4+YOX2PnXYfYfOWF32RKlgO2hIF2evZvjf+7lxAZb5U/HyXey8rnPMa06eSnN/unx5m+39+/JF19+lzO9ZctOalzXgqbNuvDWW/P4asG7RZJncTFzzT4eblUDV5f8x6G9cUn0q1uRz+9ogae7K+9vzN8/uqSxtyVcvsnUighk8UPd+XJUJwY0rc6YBb87JDensXdoKeA741qrAWXadCPti3dsi7q64lqlBhm/fE/y/+7FTE/D45YBRZis49nfZvJ+aBaLlT3HYnljdC/eeqAPcxf/ydFY+xcASqrC7FvfH/cmY5rfw+kDJ2jWoxUAddo04Njuw4xpNoLJ3cdy5zMjKOvjaXd5kf+vK42ZcOkWHHSFefkXNIyRwEiAV7s2ZFgD+4MPXQtm4lkM/9zWXMMvCDPxXIHx1iO7cQkKAy9fSEnCTLLteMyLiVh2/YlrxepYj5TM1s3+Q/vQZ5Ctn+qurXsIj8wtMw2LCCU+5ky+Zc5kl6pnZmTy3ec/cdfogXnmd+nZgSXfLi/CrIteWJAfMedyKxHizicSGpi3DDks0I9AHy+8PMrg5VGGRtdV5q9jsew5eppVW/axdtt+0jOzuJiWzsS3v2bGqL6Xv02xlxV7Brfw3OoUt7BgsuLyfleyYs7gHh7C3x2D3MJDyIo/h1fLhmSejMVy3na1KGn5Ojwb1iLphxW2QFcXfDq24thtDzpiVYpEWLlAYuNzP4/YM+cICQqwG7tkzXq6tW1ud56fjxdN6l7Pus07qFGlgt2Y4qzRXR1pMKAdAKe3H8IvMvcqiG94EElxCXaXa/1wb7yCfPl64vs5z0XUq0rP1219fL2CfIlqVx9rlpX9yzYV3QqIQ9w7agjDhtmOFxs3baNChdwG1/LlIzh9OtbuckFBATRp0oB+/e/Jee7SCoUlS1fy6mvTKFcukLMFVAYVZ6E+HsQm51YixCanE+Kdtz/77rhEJizZAUBCWiZrj5zBzcWgbrg/oT4e1A33B6Bj9TDmbTrisNyLSpifJzGJuZUIsYmphFz2g8bnku4cN9WIYPriLZxPSSfQq3SNBfA389wZjKDc47FLUAhmQv6ugy4Vq+E5/DFSXpyImWw7j7Gei8c8F4/loG0g8cz1a/DoUboaE8ICfIg5n3sRKzYhmRB/77wxgT4E+Hji6eGOp4c7jauXZ9/JeCpfoXquJGg/uCttB3YE4PC2AwRF5v7GCQwvR0Jswb9xTKuV9T+uo+vInqxdsJLW/drz02zboPRxR2M4czyOiKjyHN52oGhXopQyi3F1gDNdqTLBLOCxvem8M01zrmmaTUzTbFKUDQkA1hMHcAmOwAgMBVc33Oq3xrJnY54Yo1xuuaVLZFXbYDUpSeDuAWWyS8PcPXCtUR9r7DFKqi/nfcOAjnczoOPdrFyyhlv6dwWgbqPaJCcl5zQcXOrScRTadW3Dwb2HcqZ9fL1p3LIhq5b+WvTJF6HaVSM5FnuWE/HnyczKYsmfO2nb8Po8Me0a1WTzX8fIslhITc9gx6ETVI0M5uF+Hfn55cdYPGsMz4++jaa1qpbIhgSAtB37cK8ciVv5MHB3w697Wy6u/CNPzMWVf+DXswMAZevXxJp0EUv8ObJOx1G2fk2MsrYTO68WDcg4eDxnOa+WDck4fJys2PwNViVF7euqcvRUHCdi4snMzGLJmvVEN2+YLy7pYgobd+6jXYvcMTTOXUgkMdk2+FNaegZ/bN1N1QoRDsv9Wtr80XLe7/4E73d/gr+WbaJOX9uAaJENo0hPSuGincaE+gOiqdq2Lt89+Gaeq2uzWz/K7NZjmN16DHsXrWfp/z5QQ0IpMeftD2nWvCvNmnflh++Xcucg236xWbOGXLiQRExMnN3l+va5hUWLl5OenvuDOyws90dVkyYNcHFxKZENCQC1w/w4lpDCyQupZFqsLN0fQ3S1vF0Mf7r7JhYNtf11rB7KxOhatIsKJdjbg3Dfshw5b+v/vv74OaoFedt7mxKldmQgx84lc/L8Rdtnsus4ba/Lu388k5yWc8V1x8lzmKZJQCkbJ+FSlkN7cQ0vjxESDq5uuLdoR+bm3/LEGOVC8Xr4aVLfnoE1JrfSzbxwHuu5eFzCbY3VbrUbYj15lNKkduVwjsWd5+SZC2RmWVi6aR9t61bLExNdL4otB06SZbGSmpHJjiMxVAu//NpnybNi/hImdx/L5O5j2bxsPTf2aQtAtYY1SE1K4UJ8Qr5lQivn/s5p0KEJMQdPAnD21BluaFUXAL9gf8KrRRJ/zH5Dr8j/15UqE0INw3gUWxXC34/Jnrbf+d4ZrFbSv38Xz2H/A8OFzI0rsMYdx62Z7fZBWeuX4Va7BW6Nom2D1GRlkPbZSwAYPgGUHfy47XVcXMna+iuWv7Y6Zz2usbXLf6d1h5Z8/8eXpKWm8fQj03Pmvf7Jizzz6HPEx55h2luTCSwXgGEY7Nu5n2mPz8yJa9e9LX+sXk9aSpq9tygx3FxdmXhnd0a/OB+r1aTXTQ2pXj6UL1fYRobu374p1SJDaFW3Ov3+NxvDMOjTphE1KoQ5OfNrzGIl/tm3qPDuNHBxIfGbZWQcOIr/7d0BuPDFIi6uXo93m6ZUWfp+9q0hbd+VtO37SF76K5W/fgPTYiF9z0EufLk456V9u5f8Lg5urq5MuncQo5+ahcVqpVenm6heuTxfLrLdRql/d9vV+hW/b+bGhrXxKpt7xezMuQs8+fK7WKxWrFaTLjc1pW2zBs5YjWvq4IqtRLWrz71rZpGZmsFPY+fmzOv/wVgWPf4uyXEJdJ02lAsnz3DXt08DsG/JBta9ttA5STvZuMnPsWHLdhISEunQ607uGz6Yvj26ODutIrV4yQq6dm3Pnt1rSUlJ5Z6Rj+XM+27hh9w7+vGcSoV+/W/lxZlv5Vm+T+/ujBw5mKwsC6mpaQweXDJvLQvg5uLC+Ojrue+7zVitJj1rRxJVzocFO2yNr/3qVrzi8uPb1mTS0h1kWUzK+3sypWNtR6RdpNxcXJjQtQGjP/0Vq2nSs34Vqof6s2CTbUC9fo2jWL7nBF9uPISbi4GHuyvP9WmeU+I94Zs/2Xg0noSUdDq/8hOj295A74ZFe6GqyFmtpH70Ot7jnrfdGnLNYqwnj1Km/S0AZKz4kbK9BuPi44fnkIcBMC0WLk6+D4DUj17Hc/QkDDd3rPGnSZn7gtNWpSi4ubowoX97Rr/5je171LI21SODWfCrbbDffjfVp1p4OW68oQr9p8/HMAx631iH6pEFjztREm1fuZl67Rrx/Oo3yUhN571xuWO+jZn3BPPGv8WF+ARGzHoQTx9PMAyO7znCR0/ajtU/vLaA4S8+wNQlL4FhsOC5j0k+X3K7LUvxZBTU98YwjMlXWtA0zSn/5A2SJ/ZVZ9lL3DQvxtkpFDu/f/uAs1Modo4N+9DZKRQrlb9/ytkpFDsvdXzr6kH/MWM3PePsFIoVnwptnZ1CsXN+Vk9np1DsGP7+zk6hWMlY+sfVg/5jygzp5+wUip37RpTcu5wVlXlHvi7V/QD+jOzj1N+0zU99c9XP1zCMrsCrgCvwrmmaz10238ie3x1IAe42TXNzvhcqhAIrE/5pY4GIiIiIiIiIOIdhGK7Am0An4ASwwTCM703TvHQwwG5Ajey/5sDs7H//3wpsTDAMozYQZZrm99nTLwN/N1+/8W9bMURERERERESKuxJQat8MOGCa5iEAwzA+B3oClzYm9AQ+Mm1dE/4wDCPAMIwI0zRP/3/f9EoDMD4HXDqaWhfgJ2AloJpjERERERERkSJmGMZIwzA2XvI38rKQ8sDxS6ZPZD9X2JhCudIAjBGmaV46tGyiaZpfAxiGMerfvKmIiIiIiIiIXJ1pmnOBuVcIsTemwuUFFf8kplCu1Jjgm+ddTLPFJZOh/+ZNRUREREREREoCq1nsx5c8AVx6y6AKwKn/R0yhXKmbwynDMPINyGAYRot/+6YiIiIiIiIick1sAGoYhlHVMIwywADg+8tivgfuMmxaABf+zXgJcOXKhPHAF4ZhfAD8PdhiY2AIcPu/eVMRERERERGRksAs5pUJpmlmGYbxALAU260h3zdNc5dhGPdmz58DLMJ2W8gD2G4NOfTfvu+Vbg25PrvF4n7gbmz9KXYBg7E1KKz/t28uIiIiIiIiIv+OaZqLsDUYXPrcnEsem9h+218zV6pMwDTNWOApwzAaAgOxNSK0Ab6+lkmIiIiIiIiISMlRYGOCYRjXYetrMRA4C3wBGKZptnNQbiIiIiIiIiJOZXV2AsXUlSoT9gK/Aj1M0zwAYBjGGIdkJSIiIiIiIiLF1pUaE/piq0xYaRjGEuBz7N+bUkRERERERKRUMvUz2K4Cbw1pmua3pmneDtQEVgFjgDDDMGYbhtHZQfmJiIiIiIiISDFTYGPC30zTvGia5iemad4CVAC2AhOKOjERERERERERKZ6ueDeHy5mmeQ54O/tPREREREREpFSzms7OoHi6amWCiIiIiIiIiMil1JggIiIiIiIiIoVSqG4OIiIiIiIiIv8lVt3NwS5VJoiIiIiIiIhIoagyQURERERERKQApioT7CryxoQHPyvqdyhZfv/hUWenUOyEtXvc2SkUO/N8mjs7hWLlhtr9nJ1CsRPTobqzUyh2fCq0dXYKxUryidXOTqHY6drgXmenUOykmJnOTqFY6elW3tkpFDs/r/zG2SkUO1/fpO+NCKibg4iIiIiIiIgUkro5iIiIiIiIiBTA6uwEiilVJoiIiIiIiIhIoagyQURERERERKQAGoDRPlUmiIiIiIiIiEihqDFBRERERERERApF3RxERERERERECqABGO1TZYKIiIiIiIiIFIoqE0REREREREQKoMoE+1SZICIiIiIiIiKFosYEERERERERESkUdXMQERERERERKYCJ4ewUiiVVJoiIiIiIiIhIoagyQURERERERKQAVhUm2KXKBBEREREREREpFDUmiIiIiIiIiEihqJuDiIiIiIiISAGsGoDRLlUmiIiIiIiIiEihqDJBREREREREpACmsxMoplSZICIiIiIiIiKFosYEERERERERESmUUtPN4Y7Jw6jXrhEZqRm8N/Z1ju46nC9m6PP3UaVeFAYGMYdP8d7YN0hPScPT14uRLz9MUPlgXF1dWfLOd6xdsNIJa3HtrNv2F8/P/xGr1Urv6KYMv7VtvpgNuw8x8+OfyLRYCPT14v0nRwKQeDGVKe9+w4ETsRiGwZR7+lK/RiVHr8I19/zMp+jcOZqU1FTuG/U427btyhezeNnn+Ph4AxASUo5NG7czaOC91LiuGm/Nfp76DWozdcpLvP7au45O/5qp9+xdhHdogCU1g00PzyFhx5F8MV6VQmg250HKBPiQsOMwGx54CzPTgru/N41fHol3lTAs6ZlsHvM2iXtPABA1oitV7myHYRgc/ngFB99Z4uA1uzZefukZunVtT0pqKsOHj2HL1p35Ylat+AYfXx8AQkPKsWHjVvreNpzHHr2XgQP7AODm5kqtmjUIj6zH+fMJjlyFa8a9cTO8730Qw8WFtCU/kbrg0zzzPdp1xLPfHQCYqakkv/ESlsMHAfAZM54yzVpiTThPwuihDs+9KL00awpdu7YnJSWVEfc8ylY728gvv3yNb86+JJiNG7fSr/8I2rRpwVcL3uPIkeMALPxuMdOnv+rQ/B3pyekvsWbdeoICA1j48Rxnp+Mw9z9zH83bNyU9NZ0XxrzI/p0HCox9YOp9dO3fhVuu7wmAj78P42Y9RmTlCDLSM5j52Esc2XfEQZkXnTHPPMiN7ZuTlprG1DHP89fO/flinnx5PA1b1Cc56SIAz455jv27DnJT51aMHDcUq2liybLwyuQ32L4h//eupOn49GCi2jUgMzWdn8bOJXbnkXwxPV4dTXjdalizsji97RBLJr6PNctCjU6NuOmx2zCtJlaLhV+mfMyJjX85fiWK0D/5Hj3+0ljqtajHxext5oUxMzm4+5CjUy0SbvWb4nnXA+DiSsbKn0j//rM8891bdaTsrQMAMNNSSXnvFazHbMdgv9c+w0xNAasV02oh+Yl7HZ5/aWN1dgLFVKloTKgX3YiwqhFMiH6Aag1rMHjaSJ7tNTFf3GdT55GWnArAgCfvpsOQbiya/S3tB3fl1IHjvDpiBr5Bfkxf8Rq/L/wVS2aWo1flmrBYrUz/8HvenjCMsCA/7njqLaIb1ySqfFhOTOLFVKZ/8B1vPT6UiOAAzl5Izpn3wvwfaVXvOmY9PIjMrCxS0zOdsRrXVKfO0URFVaFh/fY0adqAl155hg7t+uaL69Z5QM7j+Z+8yU8/Lgfg/PkLjB/3DDf36OywnItCWIcG+FQLZ1nLRwlsVJ0Gzw9jVfen8sXVeXIgB95ezInvfqfB88Oockc7Dn+4nOsf7knCrqP8MexlfKpH0mDG3aztNx2/mhWocmc7VnX7H9aMLFp9NoGY5Vu5eDjGCWv5/9eta3tqVK9KzRta07xZI958YwY3tu6RLy66fZ+cx19+MZfvf1gGwKyX5jDrJdsPpltu7sTDD91TYhsScHHB5/5HuDDpMaxn4gl49W0y/lyH5djRnBBLzGkuPP4QZnIy7k2a4/PQWC6MGQ1A2s+LSf3+G3zHTnLWGhSJrl3aUb16VW6ofRPNmjXk9demc1ObW/PFdeiQu3/5/LO3+eHHZTnT69atp3ef0tXAUpBe3TtxR99bmTT1RWen4jDN2jelQtXy3NV6KLUa1eThGQ/xQI+H7MZeV68GPn4+eZ6748GBHNh1kMkjplAxqiIPTXuAcQPGOyL1ItOyfXMqVi1Pv9Z3UrtRLR6fMYYRPe6zG/vGs3NY+dOaPM9tXLuJX5etAyCqVjWmzZnMgLZDijzvolStXX0Cq4bzdtvHiGwYRZdn7+ajXk/ni9u18Dd+eHg2ALe+dj/1B0Sz5eNfOLJuF/t/3gxASM2K9HrzQd7p8LgjV6FIFeZ7NHfaO6z56VcHZ1jEDBc8hz7MxenjsJ6Nx3faHDI3/Yb1ZO4x2Bp3muRnHsG8mIxb/WZ43fMYyf/L/V4lPzsGMynRGdnLf0ihujkYhlHOMIzehmE0LqqE/j8adm7Kb9+sBuDQlv14+XrjHxKQL+7vhgQA97JlwPx7KA2Tst6eAHh4leViQjLWLEtRp11kdh48QcWwclQIDcLdzY2uLeqxatOePDGLf9tGh6a1iQgOAKCcv+1kJjkljU37jtA7ugkA7m5u+GV/NiXZzbd05LPPvgVg44at+Pv7ERYWUmC8j483bdq05KcffwbgTPxZNm/eQWZmyW5YiezSmGNf2g645zcfwN3Pi7KhAfniQlrV5uSPfwJw7Mtfiexq2x78ritP/K+2io7kA6fwqhiCR7AfvjXKc37TASypGZgWK2d+30Nk9yaOWalrqEePLsz/5CsA/ly/Gf8Af8LDQwuM9/Hxpl10K777Ln8Vxu239+TzLxYWVapFzu26WlhOncQacxqyskhfvYIyLVrnicnaswsz2dYQmbV3Fy7Bud+prJ3bMZOSHJqzI/To0ZmPP/kagPXrtxAQ4HfVbSQ6+ka+/36po1IsVpo0qIu/n6+z03CoVp1vZNlXtmPHns178fHzJig0KF+ci4sLo568h7nT8la6Va5RiS1rtwBw/OBxwiuEEZh9rC6p2nRpxeKvbA1quzbvwcffm3J2PpOCpKak5Tz29CqLaZb8odBqdGrMzq/XAnBqy0E8/LzxtnM8PrRyW87j09sO4hth+9wyU9Jznnf38sAsZcPD/dPvUWnlWr0m1phTWONOgyWLjN9X4N6kVZ4Yy/5dmBdtx2DLgd24BAU7I1X5j7tiY4JhGD8ahlEn+3EEsBMYBsw3DOORok/vnwkIC+LcqTM50+djzhIYXs5u7LCZ9/PKhveIiCrP8g8WAfDLh4uJqF6Bl9e/y9SlL/HplPdL9IEq7vwFwoP8c6ZDg/yJPZ+3ZfJozBkSL6Yy/Nl3GPDkG/zwq611+0T8OQJ9vXlq7tf0f+J1nn7nG1LSMhyaf1GIiAjj5IlTOdOnTsUQGRleYPwtPTqzevVvJCUlFxhTEpWNCCT11Lmc6dTT5ygbEZgnpkyQL5mJFzEt1uyYszkxF3YdI7J7UwACG0bhVSEYz8hyJO49TrkWNSkT6IOrZxnCOjTAK9L+d7A4Kx8ZzonjudvJyROnKX+F7aRXr26sWLku33bi6VmWLp2j+ebbRUWWa1FzCQ7GGh+XM209E49LuYJPVMp2uZnMjX86IjWniowM58Ql+5KTJ09fcV/Ss2dXVl62jTRv3pgN65fy/XcfUavWdUWarzhecHg54k/F50zHnz5DsJ1zkl5Db+W3ZX9wLu5cnucP7T7ETd1sDXfXN7iesAphBEcU3PhdEoSEBxN7Knd/En/6DCHh9vcno8YPZ/7P7/Lw0/fhXsY95/m2XVvz+eoPmfXhDKY99kKR51zUfMMDSTp1Nmc6KeYcvmGBBca7uLlSu09rDq3anvPcdV2acM8vL9Bv3lgWjXunSPN1tH/6PQIY9vjdvPPzHEZPvjfPNlOSuQQGYz17yTH4bDwugQUfg8tEdydz6/qcadM08Z44E59pb1Om/S1Fmut/hdUwnPpXXF2tMqGqaZp/d0obCvxsmmYPoDm2RgW7DMMYaRjGRsMwNu5Lyj92wbVm2PmAC2oMeH/cm4xpfg+nD5ygWQ9bC1+dNg04tvswY5qNYHL3sdz5zAjK+pTcq/H2Vv3yTyjLamX34VO8PnYIs8cPZe7ClRw5fQaLxcreI6fo16E5X057EE8Pd97/YbVD8i5KhdlGAG7r14OvFvxQlCk5hb3PId/FDHv7q+zPat/r31MmwJv2y6cTNawzF3YewcyykLT/FH+98QOtv5hIq0/Hc2HX0RJZ3VPY7WRAf/vVB7fc0pnfft9Ycrs4APY3BPvc6zXEo/PNXHz/7SLMp3go7DZye/+efPHldznTW7bspMZ1LWjarAtvvTWPrxaU3PFXpAB2t5G80+XCgmhzcxu+nbcwX+xnb36Bj78Pby+dTe+hPdm/8wCWErg/zeMffCYAs2e8w4A2Qxh282j8AvwYfN/AnHmrl6xlQNshjB/+P0aOK/AUtOQo5L6k87N3c/zPvZzYsC/nub+WbuSdDo/zzT0v0+ax24okTaf5h9vMu8+9z91th3PfzQ/iF+DLgPv6OyA5B7D749H+9uF2QwPKtOtO2mdzc55LfvpBkieN4uLz4/Ho3AvXmvWKKFH5r7vamAmX1nR3AN4BME0zyTCMAsehME1zLjAXYGiVvkVyib/94K60HdgRgMPbDhAUmdtaFxhejoTYcwUtimm1sv7HdXQd2ZO1C1bSul97fpptK4GPOxrDmeNxRESV5/C2ggdMKs7CgvyJOXchZzru3AVCA/3yxgT6E1jPC6+yZfAqW4ZGNavw17HTNLq+CmFBftSrXhGATs3q8P4PefsulhQjRt7JkLtvB2DLph2UrxAJbAJsVxdPn461u1xgUACNG9dj0MDSMVhNtaGdqDKoHQDntx7CMzK3TNAzIoi0mPN54jPOJuHu543h6oJpseIZUY60mAQAspJT2fRI7g/GLhte5eIx25WDo5+t4uhnqwCoPfF2Uk+fpSQYfe8Qhg8fBMDGjVupUDEyZ175ChGcKmA7CQoKpGnThvTtNyLfvNv731qiuzhAdiVCSG75vktwCNazZ/LFuVaphs8j47jwv8dLbd/Me0cNYdgw24+ajZu2UaHCJdtI+YgC9yVBQQE0adKAfv3vyXnu0gqFJUtX8upr0yhXLpCzZ8/bewkpIXoO6UH3O7oDsG/bPkIicysJQiKCORubd39YvXZ1yleJZP7aDwDw8PTgo7XzuKv1UFKSU5j52Kyc2E9+/4iY4yVr/BmAvkN6ceugmwHYs3UvYZG5+5OQiGDOxObfn5zNrtLIzMjkxy8WM+je2/PFbP1zO+UrR+If6MeF8yVrn9Poro7UH2A7Hp/efgjfSyr4fMODSI5LsLtcq4d74xXkyzcT37c7//j6fQRUDsUz0IfU8yW3orKw3yMgp7InMyOTJV8upf+o0tGoYj0Xj0u5S47B5UKwns+//i6VquE5ciwXn5uAmZz7fTCzY83EBDI3/IpbVE0se7fnW17+uZJbs160rlaZcNwwjAcNw+gNNAKWABiG4Qk4tY5oxfwlTO4+lsndx7J52Xpu7GO7W0G1hjVITUrhQnxCvmVCK+eWojbo0ISYgycBOHvqDDe0qguAX7A/4dUiiT9m/+SwJKhdrTzHYs5wIu4cmVlZLPljO20b1coT065xLTbvO0KWxUJqegY7Dh6namQIwQG+hAX5cyS7tOzPXQepVr7g/sDF2btzP+amG3tw0409+PHHZQwc2BuAJk0bkJiYRGxsvN3levfuxpIlK0lPL/ndOwAOzfuZFR0nsaLjJE4v2Uil/jcBENioOplJqaTZOXmJ/2035W9pDkCl/jdxeulGANz9vDDcXQGoMqgdZ/7YS1b2WCQewbYGK8/y5Yjs3pTj3/5e1Kt2Tcye8yFNmnamSdPOfP/9UgYPsp2ING/WiMQLicTExNld7ra+t/DTouWkp6fned7Pz5c2N7Uo8X3ks/7ai2tkBVzCwsHNDY+27cn4Y12eGJeQUPz+N5WkmdOwnjzhpEyL3py3P6RZ8640a96VH75fyp2DbIMrNmvWkAsXkgrcRvr2uYVFi/NuI5eO1dKkSQNcXFzUkFAKfPfhD4zqMppRXUazbslvdL6tEwC1GtXkYtLFfF0Z/lyxnn6NBjCo5V0MankX6anp3NXaNiint583bu62az3d7+jG9j93kJKc4tgVuga+/nAhQzrfw5DO97Bm6Tq63WYbwLh2o1pcTLyY03BwqUvHUWjbtTUH99qqWytUyW3Au65ODdzd3UpcQwLA5o+WM6/7E8zr/gT7l22iTl9bd5bIhlGkJ6Vw0c7xuN6AaKq2rcv3D76Z59J8QOXcQbXD6lTB1d2tRDckQOG/R0CecRRadbmRw6XgzicAloN7cQkvj0tIOLi6UaZlezI3/ZYnxigXiveYZ0h5cwbWmEuOwR5loaxnzmO3ek2wnCj6SnH5b7paZcJw4BmgI3C7aZoJ2c+3AOYVYV6Fsn3lZuq1a8Tzq98kIzWd98a9mTNvzLwnmDf+LS7EJzBi1oN4+niCYXB8zxE+etJWDvTDawsY/uIDTF3yEhgGC577mOTzJXfgMDdXVyYOuZXRL8zDajXp1bYx1SuE8eUvtv7M/Ts0p1r5UFrVu45+E1/DcDHoE92UGhVtjS0ThvRg4uwvycyyUCE0kGdGlvxW3mVLV9G5SzRbt68gJTWN++/NHRl7wdfv8eD9E3N+EPS57RZenpW3XDs0NJhVvy7E19cHq9Vk9P1307xJ1xI3pkLM8q2EdWhA5z9expKanqfK4MZPHmfzo3NJi01g59TPaPb2g9wwoR8JO49y5NNVAPjWKE+T10djWqwk/nWCzY/m9tFs/u4jlAnywZppYevEeWReuOjo1fvXFi3+ha5d27NvzzpSUlMZMeLRnHk/fPcRI+8dl3MV+vb+t/LCzDfzvUavnt34efkaUlJS880rUawWkme/gv+zL4KrC2nLFmE5doSy3W13Lkhb9D1edwzB8PXH5/4xAJgWCxceHgWA7/incK/XAMPPn8D5C0iZP4/0ZSV3DIm/LV6ygq5d27Nn91pSUlK5Z+RjOfO+W/gh945+PGcb6df/Vl6c+Vae5fv07s7IkYPJyrKQmprG4MH3OzR/Rxs3+Tk2bNlOQkIiHXrdyX3DB9O3Rxdnp1Wk/lyxnubtmzF/7QekpaUz89HcO1lM/+hZZo17ibNXqJ6sXL0S4199HKvFytH9R3lx7EuOSLtI/fbLH9zYvjkL1n1Memo6zz76fM68WR/NYMa4FzkTe5an33iCwKAAMAz27zrACxNs6x7dvQ3dbutCVlYW6WnpPDn6GSetybVzcMVWqrWrz6g1s8hMzWDR2NwS9X4fjGXx4++SHJdA12lDuXDyDIO/fRqAv5ZsYN1rC7m+W1Pq9G2NNdNCVnoG393/hpPWpGj80+/RpNcn4F/OHwODg7sP8vKEUnKrXauV1A9ew3viC+DiQsaqxVhPHKFMR9sdpjKW/0DZPndh+PjhNewRgJxbQLr4B+L96FTb67i6krFuOVnbNjhpRaS0M4p6oMGi6uZQUs1ecIezUyh2wtqVnlsZXSvzfJo7O4Vipf+5kj9ux7UW06G6s1ModsqvLB33Fr9Wkk/oe3O5rg1KR/e1aynFLNl3KbrWerqVd3YKxc7PFvvVV/9lX9+k783lAj5bWXxHCbwGvogY5NTftLef/qRYfr5XrEwwDOMHrtBFxDTN/DfXFhEREREREZFS7WrdHF68ynwRERERERGRUstaLOsCnO+KjQmmaebUSBqGEZL9nP1R60RERERERETkP+Fqd3PAMIzJhmGcAfYCfxmGEW8YxlNFn5qIiIiIiIiIFEdXbEwwDGMM0BpoappmOdM0A4HmQKvseSIiIiIiIiKllhXDqX/F1dUqE+4CBpqmmXNzUtM0DwF3Zs8TERERERERkf+Yqw3A6G6a5pnLnzRNM94wDPciyklERERERESkWHDqfSGLsatVJmT8P+eJiIiIiIiISCl1tcqE+oZhJNp53gDKFkE+IiIiIiIiIlLMXe3WkK6OSkRERERERESkuLEW3zEQneqqt4YUEREREREREbnU1bo5iIiIiIiIiPxnWZ2dQDGlygQRERERERERKRQ1JoiIiIiIiIhIoaibg4iIiIiIiEgBTGcnUEypMkFERERERERECkWVCSIiIiIiIiIF0K0h7VNlgoiIiIiIiIgUihoTRERERERERKRQirybw1sf9SjqtyhRJvVb4OwUip2Ybx5zdgrFzpYhvzg7hWIlaekUZ6dQ7MwZssrZKRQ752fVdXYKxUrXBvc6O4ViZ8nWOc5OQUqAzK9edXYKxcrDHpHOTqHYuW/KQWenUOzMc3YCRczq7ASKKVUmiIiIiIighgQRkcJQY4KIiIiIiIiIFIru5iAiIiIiIiJSAHVzsE+VCSIiIiIiIiJSKKpMEBERERERESmAaTg7g+JJlQkiIiIiIiIiUihqTBARERERERGRQlE3BxEREREREZECaABG+1SZICIiIiIiIiKFosoEERERERERkQKoMsE+VSaIiIiIiIiISKGoMUFERERERESklDIMI8gwjJ8Nw9if/W+gnZiKhmGsNAxjj2EYuwzDePhqr6vGBBEREREREZECmE7+uwYmAL+YplkD+CV7+nJZwGOmadYCWgD3G4Zxw5VeVI0JIiIiIiIiIqVXT+DD7McfAr0uDzBN87RpmpuzHycBe4DyV3pRDcAoIiIiIiIiUgCr4dz3NwxjJDDykqfmmqY5txAvEWaa5mmwNRoYhhF6lferAjQE/rxSnBoTRERERERERIqp7IaDKzYeGIaxHAi3M+uJwryXYRg+wNfAI6ZpJl4pVo0JIiIiIiIiIiWYaZodC5pnGEasYRgR2VUJEUBcAXHu2BoSPjFN85urvafGTBAREREREREpgNXJf9fA98CQ7MdDgO8uDzAMwwDeA/aYpvnSP3lRNSaIiIiIiIiIlF7PAZ0Mw9gPdMqexjCMSMMwFmXHtAIGA+0Nw9ia/df9Si+qbg4iIiIiIiIiBbhG1QFOY5rmWaCDnedPAd2zH68FCjXUpCoTRERERERERKRQSkVlwrqdB3nh8+VYrVZ639SAYd1a5ovZsO8oMz9fTpbFSqCvJ++Nu5P0zCyGvfAxmVkWsixWOja+nvt6tnHCGhSN3pOHUKtdQzJS0/ls7GxO7jqSL+b250dRsV41AOIPx/DZ2LfISEknNCqSATPvpULtqix68QtWvfOjg7MvWut2H+GFr1djtZr0blmbYZ2b5ovZsP8EM79ebdtmfDx57+HbnJDptVF16jACOjTCmprBgUde5+KOw/liPCqGct2cMbgF+HJxxyH2P/gaZmZWgcuXjYrk+jmP5i5fOYzjMz/n9Ds/5TwXee+tVJk8hPW17ybrXFLRr+i/tG7XYV748hfbdtGqHsO6Ns8Xs2HfMWYuWJG7XTw2EIDJHy1mzY5DBPl68fVTQx2depGJnjKYqu0akJmazrLH5hK380i+mK6vjiasXjWsWVnEbD3ELxPfx5plITAqgs4vjiS0ThV+m7mATXMX5X+DEmbdkTPMXLMPq2nSq3Z5hjWpajduV+wF7vpyPc91rUenGmEAJKVnMmX5bg6eS8bAYHLHG6gfEeDA7IvO/c/cR/P2TUlPTeeFMS+yf+eBAmMfmHofXft34ZbrewLg4+/DuFmPEVk5goz0DGY+9hJH9h1xUOaO9+T0l1izbj1BgQEs/HiOs9NxiLV/bOS5V+ZgsVrp26MrIwb3zzP/QmIS/5vxMsdPnsajTBmmThpDjWpVgNL7ea07Es/MVXuwWqFXnQoMa1bNbtyumAvc9fnvPNe9AZ2usw3SnpSWyZSfd3LwbDKGAZM71aF+ZKAj07/m1h2K5YXlO7BaoXf9SgxreZ3duJ2nz3PXR2t4vmdTOtWMBOCTDQf5ZttRTKBP/crc2TTKgZkXrTsmD6Neu0ZkpGbw3tjXObor//nb0Ofvo0q9KAwMYg6f4r2xb5CekoanrxcjX36YoPLBuLq6suSd71i7YKUT1kJKsxLfmGCxWpnx6TLmjBlAWKAfg6Z9QNv6NYiKDM6JSUxJY8YnS3nz4duJKOfPucSLAJRxc+Wdx+7Aq2wZMrMsDH1hPq3rRFEvqryzVueaqRXdgOCqEUyPfoTKDatz27QRvNrryXxxC6d+RHpyKgC3PjmY1kO6sGL296QkJPPt0x9Qx86P7JLOYrUyY8Eq5tzfm7AAHwbN/Jy2dasRFVEuJyYxJZ0ZX67kzdE9iQjy41xSihMz/ncC2jeibLUIttz4AD6NalDtuZHsuHlivrjKTw7m1NwfOfvdOqo9P5LQgR2I/WhpgcunHTzFtk5jbQu7uNBky1zOLV6f83plIsvh37Y+6SfiHbWq/4rFamXGZz8z5+H+hAX6MmjGfNrWi8q/L/lsOW8+dJttu8jelwDc2rIOA6Ib8eQHJf8H89+qtKtPQJVw5rV5jPCGUbSfdjef93w6X9zehb+x5OHZAHR7/X7qDIhm+8e/kJZwkVWT5xPVpbGDMy8aFqvJc6v2Mrt3I8J8yjLoiz9pWzWEqHI++eJeXbeflpXK5Xn+hdX7uLFyOV68uT6ZFitpWRZHpl9kmrVvSoWq5bmr9VBqNarJwzMe4oEeD9mNva5eDXz88n5edzw4kAO7DjJ5xBQqRlXkoWkPMG7AeEek7hS9unfijr63Mmnqi85OxSEsFgvPznqTd16ZTnhoMLePeJh2rZsTVbVyTsw7H31BzRpRvDbjKQ4dPc60WW/y3mvPAaXz87JYTZ5bsZvZfZoS5luWQZ/+TtuoUPv7krX7aFk5OM/zL6zaw41VgnmxR0PbviSzZO9LLFaTGcu2M2fAjYT5ejLog9W0rRFOVLBfvrhXV+6mZdXQnOcOxCfyzbajfDykDe6uLtz/xe/cFBVG5SCfy9+mxKkX3YiwqhFMiH6Aag1rMHjaSJ7tlf/87bOp80jLPpcf8OTddBjSjUWzv6X94K6cOnCcV0fMwDfIj+krXuP3hb9iyb5QJIVjOjuBYqrEd3PYefgUFUMCqRASiLubK12a1mLV1r/yxCz+cxftG15PRDl/AIL8vAEwDAOvsmUAyLJYybJYMQrVS6T4qtO5CRu/WQPA0S0H8PT1wjckIF/c3w0JAO5ly+R8U5LPJnJ8+yGspeRk91I7j8ZSMdifCsH+tm2m8XWs2nEoT8zijXtpXz+KiCDbgSzI18sZqV4TQV2bEr9gNQDJm/fj5ueNe2hAvjj/1nU4++PvAMR9uYqgbs3+8fL+N9Ul7UhsnoaDqlOGcnTqR5hmydj97jxymoqhgVQICcjel9Rk1fa8V1cXr99D+4Y1creL7H0JQOMaFfHzKuvQnItaVOfG7Pl6LQAxWw7i4eeNt51t58jKbTmPY7YexCciCIDUs4nElqL9yM7YC1QM8KKCvxfuri50qRHOqkP5G8s+33aMDlFhBHmVyXkuOT2LzafO07u2rbHa3dUFXw93h+VelFp1vpFlX/0MwJ7Ne/Hx8yYoNChfnIuLC6OevIe5097N83zlGpXYsnYLAMcPHie8QhiBwQFFnrezNGlQF38/X2en4TA79vxFpQqRVCwfgbu7O906tGXFr3/kiTl45BgtGtcHoFrlipw8HcuZc+eB0vl57YxJsO1LArL3JdeHs+pgbL64z7cepUN1O/uSk+fpXacCkL0vKVuy9yU7T5+nYqA3FQK8bZ/HDeVZtT8mX9xnmw7R4foIgrw8cp47dDaJepGBeLq74ebiQuNKwaz467Qj0y8yDTs35bdvbOdfh7bsx8vXG3875/Jp+c7l/z7vMinr7QmAh1dZLiYkl5rjsRQfV21MMAzDzTCMHoZhjMv+u8UwjGJT0RCXkEx4UG7LZVigL3EJecupj8aeIzEljeEzP2Hg1Hn88NuOnHkWq5X+U96j/WOv0qJWVepWK/lVCQB+YUEknDqbM50Qcw7/8PwndwADZt7LlA1zCIuK5NcPljgqRaeJS0gmPDD3xCQswIe4hOQ8MUfjE0hMSWf4q18x8IXP+OHPPY5O85opEx5E+qkzOdPpp89SJiLvFVO3IF+yLlwEi214mYzTZ/HI3l7+yfLBPVtxZuHanOnAzk1IjzlHyu6j13x9ikrc+cu3C1/izl+2XcSdt+1LZn3OwOkf8cMfOx2dpkP5hAeSdDp3P5Iccw6f8IJLaV3cXKnVpzVHV293RHoOF5ecTphP7klsmI8H8RfTL4tJY8XBOG6rWyHP8ycTUwn0LMPk5bsY8OkfTFm+i9QSfjXxb8Hh5Yg/lduoEn/6DMHh5fLF9Rp6K78t+4NzcefyPH9o9yFu6tYagOsbXE9YhTCCI0KKNmlxmLj4M4SH5v7/DAsNJi7+bJ6Y66tXY/nq3wDYsXsfp2PjiI07Q2kVl5xOmK9nznSYT1nik+3sSw7Eclu9SnmeP3khxbYvWbaDAR+vY8rPO0kt4Vea45LSCL/08/D1JC4pLU9MbFIqK/86Tb+GebuWVQ/2Y9PxsySkZpCamcXag7HEJqZSGgSEBXHukvOv8zFnCbSzbwUYNvN+XtnwHhFR5VmeXSH5y4eLiahegZfXv8vUpS/x6ZT3S8wFHik5rtiYYBhGJLALeAyIBMoD44Bd2fMKWm6kYRgbDcPY+N73q65huvnZ+1IYlw1CabFa2XM0hjce6sdbj9zO3J/WcTTGdiBzdXHhy8nDWfrCA+w8cooDJ0tGSfbV2K2wKGAH8vm4OTzdfDSxB07SoEf+8SZKG3ufgnHZB2axWNlzPI437u3JW/f1Yu7SPzkad94xCV5jl68bkH9bsBPz93frassb7m4EdWnK2R9sJ4IunmWo8HBfjr/w+f8/aSewv13knbZYrOw5FssbD/ThrYduY+5Pv3M09pydJUuLgrcLe9pPu5uT6/dycv2+okyqWJu5Zh8Pt6qBq0vezy7LamVvXBL96lbk8zta4Onuyvsb8/d9LZHs7j/yTpcLC6LNzW34dt7CfLGfvfkFPv4+vL10Nr2H9mT/zgNYdPWs1LC3y7h8kxkxuB+JScn0HXI/n3z1PTVrROHq6uqYBIuLyz6Tmav28PBN19vZl5jsjUukX71KfH5nKzzdXHl/Q8nel5h2jsCX71VmLt/Jw9E35Ps8qgX7MrRFDe79/Dfu/+IPrgv1zxdTUtk7/yroGPz+uDcZ0/weTh84QbMerQCo06YBx3YfZkyzEUzuPpY7nxlBWR9Pu8vL1VkN5/4VV1erMJgOzDZN85VLnzQM4yFgBjDE3kKmac4F5gKkrvmgSJvAwgJ9iTmXmDMdez6JkACfy2L8CPDxwtOjDJ4eZWhcoyL7TsRR+ZLWPT+vsjS5rhLrdh6ievmSeUWk1eDOtBjYHoDj2w4SEJm7fgHhQVyILfjHsGk12frj70SP7MGG7JL20ioswIeY87nVK7EJyYT4e+eLCfDxxNPDHU8PdxpHlWffyTNUDi0ZAxyF392VsEEdAUjedgCPyGD+XmOPiHJkxOT9AZx1NhE3f29wdQGLlTIR5cjI3l7ST5+94vIB7RtyccchMs9cAKBs5XDKVgqj/i+zcuLrL5vJ9m4TyIxPKLJ1/rfCAi/fLuztS3yzt4tL9yXxVA6zX/VTEtW/qyN1BrYDIHb7IXwvqULxCQ/iYmyC3eVaPNIbzyBflk943xFpOkWojwexl1w9jE1OJ8TbI0/M7rhEJiyxVb8lpGWy9sgZ3FwM6ob7E+rjQd1wW3e7jtXDmLfpiMNyv9Z6DulB9ztst57et20fIZG5x82QiGDOxua98ly9dnXKV4lk/toPAPDw9OCjtfO4q/VQUpJTmPnYrJzYT37/iJjj+UucpWQKCw0mJi73Qk1s3BlCgvNeXfXx9ubZJ2wD+pqmSZfb7qZCZJhD83SkUB8PYpNyr57HJqfl35fEJjJh0VYAElIzWXs4e18SEUCorwd1swdv7VgjjHklvGEyzNeTmEs/j6RUQnzzdhvcHZPA+O82ApCQmsHaQ7G4uhi0vy6C3vUr07u+bQyO11bvzlP1UdK0H9yVtgNt52+Htx0g6JJxmwLDy5FwhQsYptXK+h/X0XVkT9YuWEnrfu35afa3AMQdjeHM8TgiospzeFvBA+SKFNbVujm0uLwhAcA0zdeAFkWSUSHVrhLJsbjznIxPIDPLwtINe2hbv0aemOgGNdiy/zhZFiup6ZnsOHyKahHBnEtKITHFVkaVlpHJn3uOULWArgAlwbr5y5jVfQKzuk9gx7KNNOljuzNF5YbVSUtKIcnOD7ngyrkH6xs6NCbu4ClHpes0tSuFcSw+gZNnLti2mU1/0bZu3lGUo+tFseXgSds2k5HJjqOxVAsrGQ0JADEfLGFbp7Fs6zSWc4vXE9KvLQA+jWqQlZRCZlxCvmUurNtJuVtslSmh/aM5v8Q2mOL5pRuuuHxIr9ac+Ta3i0PK3mNsqDuMzc1Gs7nZaNJPn2Vb53HFuiEBoHblCNu+5Mzf+5K9tK1XPU9MdP3qbDlwIne7OHKaaiV4n2HPto+W80m3J/ik2xMcXLqJWn1t5efhDaPISErhop1tp86AaCq3qcuiB94ssAKqNKgd5sexhBROXkgl02Jl6f4YoqvlbXz+6e6bWDTU9texeigTo2vRLiqUYG8Pwn3LcuS8bdDO9cfPUS3I297blAjfffgDo7qMZlSX0axb8hudb+sEQK1GNbmYdDFfV4Y/V6ynX6MBDGp5F4Na3kV6ajp3tbbd9cTbzxs3d9u1je53dGP7nztISS65g95KXnVqXsexE6c4cSqGzMxMFv+ymnat855CJiYlk5mZCcDXPyyhcYO6+HiX3O/H1dQO9+fY+RROXkix7Uv2xRBdLTRPzE/D27JoeDSLhkfTsUYYE9vfQLvqYbZ9iY8nR87ZuuGtP362RO9LAGpHBHDs3EVOJly0fR67T9K2eniemEWjO7H4vs4svq8zHa+PZFLn+rS/LgKAc9ndzU5fSGHFvtN0u6HkdlleMX8Jk7uPZXL3sWxetp4b+9jOv6o1rEFqUgoX7JxLhVbO/awadGhCzMGTAJw9dYYbWtUFwC/Yn/BqkcQfyz82h/wzVif/FVdXq0y4UqejYnGkd3N1YcIdnRj9yudYTZOerepRvXwIC1ZtBqBfdCOqRQRzY51q9J/yLoZh0Pum+lQvH8JfJ+L43/s/YrVasZomnZvUos1lDREl1Z6VW6jVrgGTVr9KZmo6n43LvZ3SPfPG88X4uSTFJzBw1n22kifD4NSeo3z15HsA+Ib4M+b76ZT18cQ0TdoM68bzncbmGbCxpHJzdWFCv2hGv7XQts20uIHqEeVYsNbWz7tf63pUCw/ixlpV6P/cJ7ZtpmVtqkcGX+WVi6fzv2wmoEMjGv3+JpbUdA6MeTNnXq2Pn+DAY2+RGXueo89+zHVzxlBp/EAu7jxM7Ge/XHV5F88y+Lepz8HH33b4el1rbq4uTLi9I6Nf+wqr1UrPG+tSPTKYBWu2AtCvTQOqRZTjxtpV6T/1AwwXg96t6uZUMk149wc2/nWchORUOk+Yzegerejdqp4T1+jfO7xiK1Xa1Wfor7PISs1g2di5OfN6fTCWn8e/y8XYBDpMH0riyTMMWPg0AAeWbODPVxfiFeLPHT9OpYyPJ6bVSsPhXfmow3gySuh+xM3FhfHR13Pfd5uxWk161o4kqpwPC3YcB6Bf3YpXXH5825pMWrqDLItJeX9PpnSs7Yi0i9yfK9bTvH0z5q/9gLS0dGY+mjvq/vSPnmXWuJc4e4WraZWrV2L8q49jtVg5uv8oL459yRFpO824yc+xYct2EhIS6dDrTu4bPpi+Pbo4O60i4+bmyqQxoxn16JNYLBZ639KZ6tUq88W3ttsI3977Zg4dPc6kqS/i6uJCtSqVeGbiIznLl8bPy83FhfHtb+C+bzbazkNqVyAq2JcF244B0K9+pSsuP75dLSYt3k6W1Up5fy+mdK7riLSLjJuLCxM612P0F7/bPo96lage4seCLbaKi8vHSbjcY9+u50JqBm4uLkzsXA+/smWuGF9SbF+5mXrtGvH86jfJSE3nvXG5519j5j3BvPFvcSE+gRGzHsQz+1z++J4jfPSk7Vj9w2sLGP7iA0xd8hIYBgue+5jk88X/Nt1SshhX6v9qGMYhYKy9WcALpmle9UauRd3NoaR54q7SP8BhYU2b28bZKRQ7W4b84uwUipWGn3ZzdgrFzpwhq5ydQrEzanyAs1MoVnrM+OvqQf8xS7bOuXqQ/KdlfvWqs1ModgwPj6sH/cfcN+Wgs1ModuYd+boY9+z/956rfKdTf9NOOPpxsfx8r1aZsBroUcC8Ndc4FxEREREREZFiRVfH7btiY4JpmkMLmmcYRukdGUdERERERERECnS1yoQ8DMPwB/oCdwC1sN0qUkRERERERKRUsqo2wa6rNiYYhuEJ3IqtAaER4Av0Qt0cRERERERERP6TrnhrSMMwPgH+AjoDbwBVgPOmaa4yTbM436VCRERERERERIrI1SoT6gDngT3AXtM0LYZhqMZDRERERERE/hN0Fd2+K1YmmKZZH+gP+AHLDcP4FfA1DCPcEcmJiIiIiIiISPFzxcYEANM095qm+ZRpmtcDY4D5wHrDMH4r8uxEREREREREnMh08l9xddXGhEuZprnRNM1HgerAW0WTkoiIiIiIiIgUZ1cbgNHPMIyJhmG8YRhGZ8PmAWyDMvZzTIoiIiIiIiIiUpxcbQDG+dgGYPwdGAGMA8oAvUzT3Fq0qYmIiIiIiIg4lwZgtO9qjQnVTNOsC2AYxrvAGaCSaZpJRZ6ZiIiIiIiIiBRLV2tMyPz7QfZtIQ+rIUFERERERET+K6yGszMonq7WmFDfMIzE7McG4Jk9bQCmaZp+RZqdiIiIiIiIiBQ7V2xMME3T1VGJiIiIiIiIiEjJcLXKBBEREREREZH/LCums1Molq54a0gRERERERERkcupMkFERERERESkAKpLsE+VCSIiIiIiIiJSKGpMEBEREREREZFCKfpuDhcTrx4j/2kuVeo5O4ViJ826ytkpFCuGX7CzUyh2Ylwszk6h2DH8/Z2dQrGSYmY6OwWRkse0OjuD4sc/wNkZFDux1lRnpyAOpj2DfapMEBEREREREZFCUWOCiIiIiIiIiBSK7uYgIiIiIiIiUgCr7udglyoTRERERERERKRQVJkgIiIiIiIiUgDVJdinygQRERERERERKRQ1JoiIiIiIiIhIoaibg4iIiIiIiEgBrM5OoJhSZYKIiIiIiIiIFIoqE0REREREREQKoFtD2qfKBBEREREREREpFDUmiIiIiIiIiEihqJuDiIiIiIiISAHUycE+VSaIiIiIiIiISKGoMkFERERERESkALo1pH2qTBARERERERGRQlFjgoiIiIiIiIgUiro5iIiIiIiIiBTA1BCMdqkyQUREREREREQKRZUJIiIiIiIiIgXQAIz2lYrGhHV7jvLCN2uxmlZ6t7iBYR0b55m/Yf9Jxry3iMggXwA61ItiVNemAMxftZVv/9iNgUGNiHJMuaM9Hu6l4mOh9+Qh1GrXkIzUdD4bO5uTu47ki7n9+VFUrFcNgPjDMXw29i0yUtIJjYpkwMx7qVC7Kote/IJV7/zo4OyvrbWbdvD8O59htZr06XQTw/t1zzN/3jdLWLTqDwCyLBYOnzjN6o9fITUtgydefpcz5xNxMQz6dm3Dnbd2csYqXDM1pg2lXIeGWFPT2f3QWyTvOJwvpmylEGq//QjuAT4k7TjM7vtfx8y0ENy1CdXG345pNTGzLOz/3wdcWL8PADc/L2q+dC/eNSuCabJnzGwSN+539Or9K+u27eP5j77HajXp3a4pw29tly9mw+6DzJz/A5lZFgJ9vXn/qXs5ciqex1//JCfmRNw57rutE3d2u8mR6ReJWycP4fp2DchMzeDLsbM5ZWc/MuCV+6lQtxqWLAvHtx3km0nvYs2y4OnnzW0zR1GuUhhZ6RksePxtYv864fiVuIbWHYjhhaVbsZomvRtWZVirmnnmbzgSx5gvfyMywBuADjXLM6rNDQBM/n4ja/afJsjbg6/v7ezw3IvSmGce5Mb2zUlLTWPqmOf5a2f+7/6TL4+nYYv6JCddBODZMc+xf9dBburcipHjhmI1TSxZFl6Z/AbbN+x09CpcM2v/2Mhzr8zBYrXSt0dXRgzun2f+hcQk/jfjZY6fPI1HmTJMnTSGGtWqAPDk9JdYs249QYEBLPx4jhOyd47/4nqvO3KGmav3YrWa9KpTgWFNq9qN2xVzgbu++JPnutejU41wAJLSMpmyfBcHzyZjYDC5U23qRwY4MPtrb92+k7zw43rb8bdpDYZF180zf8OhGMZ8tILIIB8AOtSuzKgO9XPmW6xW7njjJ0L9vHj97g4Ozb0ojZoyiqbtmpKems5Lj73EwZ0HC4y9d8q9dOrfib61+gLQd1RfontFA+Dq5krF6hUZ2GAgyReSHZG6/EeU+F/NFquVGV+tYc7oWwkL8GHQSwtoW6cqUeFBeeIaVovg9ZG35HkuNiGZz9Zs55sJd1C2jBvjPljCks376dm8liNXoUjUim5AcNUIpkc/QuWG1blt2ghe7fVkvriFUz8iPTkVgFufHEzrIV1YMft7UhKS+fbpD6jTuamjU7/mLBYr0+d8wtypjxFWLpCBj04lunkDoipF5sQM7dOVoX26ArBq/Vbmf/cz/r4+ZGQm8Niw27mhemUupqQyYMxUWjaonWfZkqRch4Z4VQ3njxYP4de4Bte/MIJN3Z7IFxf15J0cf/sn4hb+xvUv3EPkHe05+eHPnF+zg/VLNgLgfUMl6swdw5+txwBQ49mhnF25lZ0jXsJwd8XV08Oh6/ZvWaxWps9byNsTRxBWzp87nnyD6EY3EFUhLCcm8WIq0+ct5K3xw4gIDuRs9gG5SmQIX854JOd1Ot0/jfZN6jhjNa6p66MbEFw1nJnRY6jUsDq9pw3nzV7/yxe3ZeE6Pn/kTQAGvvYgzQa044+Pl9Pu/p6c3n2U+aNeIiQqkl7PDOWdQdMcvRrXjMVqMmPJFuYMuokwPy8GvfsLba+LJCrEL09cw0rBvD6gdb7lb61fmQFNo3jyuw2OStkhWrZvTsWq5enX+k5qN6rF4zPGMKLHfXZj33h2Dit/WpPnuY1rN/HrsnUARNWqxrQ5kxnQdkiR510ULBYLz856k3demU54aDC3j3iYdq2bE1W1ck7MOx99Qc0aUbw24ykOHT3OtFlv8t5rzwHQq3sn7uh7K5OmvuisVXCK/9p6W6wmz63cw+w+jQnzKcugz/6gbbUQosr55It7de1ftKwcnOf5F1bv5cYqwbx4SwMyLVbSMi2OTP+as1itzPj+D+YM72zbt775E21rVSQqLCBPXMMqYQU2FHy6bg9VQ/25mJbpgIwdo0m7JpSvUp4RbUZwfcPreWDaA4zpOcZubI16NfDxz7v9fP3213z99tcANOvYjN7De6shQa65AsdMMAyjwF+RhmEMLpp0Cm/n0TgqBvtTIdgfdzdXujSswSo7V1oLYrGapGdmkWWxkpaRRYi/dxFm6zh1Ojdh4ze2E7ajWw7g6euFb0hAvri/GxIA3MuW4e+xRZLPJnJ8+yGsWSX7AAWwc/8hKkWEUiE8BHd3N7q2acbKP7cUGL949Xq6tWkGQEhQADdUt50Eent5UrViBHFnzzsk76IQ3LUJMQts20Xipv24+XlTJjQgX1xg69rE/2Cr1Dj95SqCu9l2B5aU9JwYVy8PMG0bjKuPJwEta3H6kxUAmJkWshJTinJVrrmdB45TMawcFcLK4e7mRteW9Vm1aXeemMW/baVD0zpEBAcCUO6yAzfAnzsPUDGsHJEhgQ7JuyjV7tyYTd/8CsCxK+xH9q3amvP4+LYD+Gc35obWqMCBdbYrzPEHTxFYIQSfYP8iz7uo7Dx1joqBPlQI9MHd1YUutSuyat+pf7x848oh+HmWKcIMnaNNl1Ys/moZALs278HH35tyoUFXWSpXakpazmNPr7KYZskd5GrHnr+oVCGSiuUjcHd3p1uHtqz49Y88MQePHKNFY9sV1WqVK3LydCxnztmOK00a1MXfz9fheTvbf229d8ZcoKK/FxX8vWz7kuvCWXUwLl/c51uP0aFGGEGX7DeS07PYfPI8vWuXB8Dd1QXfsu4Oy70o7Dx+horl/KgQ5Gs7l69flVV7jv/j5WMvXOTXfSfo07RGEWbpeC06t+CXr38BYN+WfXj7eRMYmv/cwsXFhWGThvHe9PcKfK3oW6NZ9f2qokr1P8GK6dS/4upKAzC+ZxjGbMMwAv5+wjCMOoZhrAH6Fnlm/1DchWTCA3NP6MMCfIi7cDFf3PYjMfR/4XPun/MDB06fzYm9q10Duk75kE5PzcPHsww31qzksNyLkl9YEAmnzuZMJ8ScyznBv9yAmfcyZcMcwqIi+fWDJY5K0WFizyYQFpy77mHlAok7m2A3NjUtnXWbd9Dpxsb55p2MPcPeg8eoe321okq1yHlEBJF28kzOdPrps3hE5N0u3IN8yUpMwbTYeoelnzqXJya4W1Oar32Z+h9PZM+Y2QB4Vg4l82witV69j6bLn6fmS6Nw8SpZlQlx5y8QXi4gZzo0yJ/YcxfyxBw9HU/ixVSGT32bAZNe44c1m/K9zpLft9G1ZYMiztYx/MKCuHDJfuRCzDn8CtiPALi4udKo903sW70NgNN7jlInu0tZhfpRBJQPLnA/VBLEJaYS7ueZMx3m50lcUmq+uO0nztH/7Z+5/9NfORB3Id/80iYkPJjYU7k/hOJPnyEkPNhu7Kjxw5n/87s8/PR9uJfJ/QHUtmtrPl/9IbM+nMG0x14o8pyLSlz8GcJDQ3Kmw0KDiYs/myfm+urVWL76NwB27N7H6dg4YuPOIP8dcRfTCPMtmzMd5luW+IvpeWOS01hxMI7b6lbM8/zJCykEepZh8rJdDPjkd6b8vIvUzCyH5F1U4hJTCL/kYl6Yn5f9c/lj8fR/9Xvun7ecA7G5F3Zm/riBR7o1wTAMh+TrKMHhwcSfjs+ZPhNzhmA7+9Yed/fgz5//5Hyc/YtdHmU9aBzdmHWL1hVZrvLfdaXGhEbAMWCLYRjDDMN4GfgaeME0zV5XelHDMEYahrHRMIyN7y3+7dpla4e9dprL9yW1KoawePJdfPn4AAa0qcuY9xYDkJiSxqqdh/npqbtY9szdpKZn8dPGfUWar6PY3Z8WcLXn83FzeLr5aGIPnKRBj5ZFm5gz2Fnvgo43qzdso0GtGvj75r3inJKaxqMz3uLxewbg4+Vpf+ESwc6KX/752N12ch+eWbyBP1uPYcfdM6k2/nbbIm6u+NStyskPl7Gh43gsKelUfrDXNcvaEex9PS4/McmyWNl9+ASvjxvK7AnDmfvtLxy55ECfmZXF6k276dyi7uUvVTLZ+6Jc4apx76nDOLx+L0c22Pajq2Z/j6e/Nw8vmkGrIV04tesIVkvJrXb6R8ebiEAWP9SdL0d1YkDT6oxZ8LtDcnMqO9uJvc1k9ox3GNBmCMNuHo1fgB+D7xuYM2/1krUMaDuE8cP/x8hxw4oy2yJlfz+Sd3rE4H4kJiXTd8j9fPLV99SsEYWrq6tjEpTi4R9cZJy5eh8Pt66Bq8tlxyHTZG9cEv3qVeDzQS3xdHfl/Q1HiiZPB7G/b8273rUig1g8vi9fPnwrA1rWZMz8lQCs2XOcQO+y3FC+nAMydb7LK7eCwoJofXNrvv/g+wKXad6pObs37lYXh3/JdPJfcVXgmAmmaWYBMwzDyALeBU4BzUzTvGpNp2mac4G5AKmLXyvS9Q/z9yHmfO6XIzYhmRC/vF0VfMrmlofddEMVpi9Yw/nkVDYcOEn5ID+CfGw/DjvUq8bWwzHc3OT6oky5yLQa3JkWA9sDcHzbQQIic3esAeFBXLikFfdyptVk64+/Ez2yBxsWrC7yXB0pLDiQ2DPncqZjz54nJCjAbuySNbldHP6WmZXFozPe4ubo5nS0U7FQ3JUf2oXIO219DJO2HqRs+WAuYPux5xFRjvSYvNtF5tkk3Py8MFxdMC1WPCKDSI85l+91E/7Yg2eVcNyDfEk/dZb0U2dJ3HwAgLgf/ihxjQlhQf7EXFKxEnfuAqGBefvCh5XzJ9DXG6+yZfAqW4ZGtary19HTVImwXYlcu3UfNauWp5x/yS3XbTm4E82y9yMnth3C/5L9iH94EIkF7Ec6PtwX73K+fDPq3Zzn0pNTWTDu7Zzp8Wtf49zxeHuLlwhhfp7EJOZWIsQmphLik7dx0ccj92r7TTUimL54C+dT0gksYZU6V9N3SC9uHXQzAHu27iUsMjRnXkhEMGdi819pPxtn249kZmTy4xeLGXTv7flitv65nfKVI/EP9OPC+cQiyr7ohIUGExOXu43Hxp0hJDjvjxwfb2+efeJRwPbDoMttd1MhMgz57wj1KUtsUm73ntikNEK88+4jdsdeYMKi7QAkpGWy9kg8boYLdSP8CfXxoG5EAAAda4Qxb8M/795bHIX5eRFzSSVCbGIKIX5eeWLynMvXrMD07/7g/MU0th6NY/We46zdd4KMLAsX0zOZ9MWvTL+9ZA6AfMtdt9BlYBcA9m/fT0hEbqVTcHgwZ2PzVjpF1Y4ionIE762xdXHw8PTg3TXvMqLNiJyYNj3asPq70nVuL8XHlcZMiDIMYynQDqgFvAisMQxjqKOS+ydqVwrl2JkLnDybSGaWhaVb9tO2TpU8MWcSL+a05O04GotpmgR4lyUiwIftR2NIzcjENE3+3H+CamElt5/zuvnLmNV9ArO6T2DHso006dMGgMoNq5OWlEJSfEK+ZYIr557A3NChMXEH/3n/35Kido2qHD0Vy4mYeDIzs1iyZj3RzRrki0u6mMLGnfto16JhznOmaTL5tQ+oWjGCu3p1cWDW187JeUvZ0OFxNnR4nPjF6wnvZ9su/BrXwJKUQkZcQr5lEtbtIqRHCwAi+kdzJnvQRc8quduLT92quLi7kXkuiYz4C6SfOotXVAQAQTfV5WIJG7W/dlQFjsWc5UTcOTKzsljy+zbaNs47GGu7xjewed9hsiwWUtMz2HHgOFXL5/6IWvzbVrq1rH/5S5cov8//mVe7T+TV7hPZtWwjjfvYTsgqXWE/0vT2dlzXph6fPvh6nqsmZf28cHW3XXFtNqA9h//ck2eclpKmdmQgx84lc/L8RTItVpbuOk7b6yLyxJxJTss93pw8ZzvelMJxEr7+cCFDOt/DkM73sGbpOrrdZrs7Re1GtbiYeDGn4eBSl46j0LZraw7utf0AqlAld0Db6+rUwN3drUQ2JADUqXkdx06c4sSpGDIzM1n8y2ratW6RJyYxKZnMTNsgcV//sITGDeri4106xmuSf6Z2uB/HElI4eSHFti/5K4boqNA8MT8Na8Oi4ba/jtXDmNi+Fu2qhxLs7UG4b1mOnLP9+F5/7CzVypXs7ad2hWCOnUnk5Lkk27n8tsO0rVUhT8yZpNTcfevxeEwTArw8eKhrY5ZN7Mfi8bfx3MC2NK0WUWIbEgB+/OhHHuz2IA92e5Dfl/5Oh762i0HXN7yei0kX83Vl2LBiA3c2uZOhrYYytNVQ0lPT8zQkePl6UbdFXX5f9h+okhOnuNLdHJYCE0zT/Cp7ep9hGF8CLxmGMcI0zVZFn97Vubm6MKHvTYyeY7udW8/mtageUY4F2YN+9WtVh+XbDvLlup24ubjg4e7Gc0M6YxgGdauE07F+FANf/BJXFxdqVgim7421nbxG18aelVuo1a4Bk1a/SmZqOp+Ny73V0j3zxvPF+LkkxScwcNZ9lPXxBMPg1J6jfPWkrWXTN8SfMd9Pp6yPJ6Zp0mZYN57vNLZE/hBwc3Vl0r2DGD35ZSxWK706tqZ65fJ8uXgVAP27RQOw4vfN3NiwNl5lc68ObNl9gB9X/k6NKhXo99DTADx0Vx9ualLPwWtxbZxdvoVyHRrR8s/XsKRmsOfht3Lm1ftkAnsffZuM2PMcePYT6rz9CNUmDCB5x2FOfWobWDHklhaE92uDmWXBmpbBzpEv5yz/16T3ueGth3Ap40bq0bg8r10SuLm6MvHunox+7j2sViu9optSvUI4Xy63DZ7Wv2MLqpUPo1W96+k34RUMw6BPu6bUqGi7VVdqegZ/7DzA/0b0ceZqXFN7V27h+nYNeHz1K2SkpuepMhg673G+Gv8OSXHn6T1tOAknz3D/t88AsHPJBn557RtCq5fn9lmjsVqtxO0/yVePz3XWqlwTbi4uTOjagNGf/orVNOlZvwrVQ/1ZsMl2q65+jaNYvucEX248hJuLgYe7K8/1aZ5Trjvhmz/ZeDSehJR0Or/yE6Pb3kDvhvZvB1eS/PbLH9zYvjkL1n1Memo6zz76fM68WR/NYMa4FzkTe5an33iCwKAAMAz27zrACxNeAiC6exu63daFrKws0tPSeXL0M05ak3/Pzc2VSWNGM+rRJ7FYLPS+pTPVq1Xmi29/AuD23jdz6OhxJk19EVcXF6pVqcQzEx/JWX7c5OfYsGU7CQmJdOh1J/cNH0zfHiWzIbsw/mvr7ebiwvh2Nbnv2822fUnt8kSV82HBdtugg/3qVbzi8uOjazJpyQ6yrFbK+3kypXPJvnuQm6sLE25tzuj3l2M1rfRsUoPqYYEs+NNWRdmv+fUs33GEL//cl30u78pzA9uUujESLrdhxQaatmvKe7++R3pqOi+PzT3nmvLBFF4d/yrnYvM33F7qxi43snnNZtJT068YJ1dXnAdBdCajoFGTDcPwMU3TbucawzA6mqa5/J+8QVF3cyhpnhhdtGNIlEQzlj3g7BSKnXU3vebsFIqVGxfd4ewUip3Jfb9wdgrFztPPltzBUYtC+/E63lxuzbaCRzsXAchc8PLVg/5jjLCIqwf9x/R9ZK2zUyh2Fh1bVKpbd0ZV6efU37RvH1lQLD/fK42ZkK8hwTCMKGAgMAAo2c2gIiIiIiIiIvL/cqW7OQBgGEaEYRiPGIaxHtgFuGJrUBAREREREREp1axO/iuurjQA4z2GYawAVgPBwAjgtGmaU0zT3OGoBEVERERERESkeLnSAIxvAr8Dd5imuRHAMAyNfyAiIiIiIiL/GaYGYLTrSo0JFYC+2O7eEAZ8CbhfIV5ERERERERE/gOuNGbCEtM0Z5um2QboAFwA4gzD2GMYxnTHpCciIiIiIiIixc2VGhNybj9hmuYJ0zRfNE2zMdAT0M1KRUREREREpNTTAIz2XambQ4hhGI8WMC+pKJIRERERERERkeLvSo0JroAPl1QoXEIjUIiIiIiIiEippwEY7btSY8Jp0zSfcVgmIiIiIiIiIlIi/KMxE0RERERERERE/nalyoQODstCREREREREpBgqzoMgOlOBlQmmaZ5zZCIiIiIiIiIiUjJcqTJBRERERERE5D/NamoARnuuNGaCiIiIiIiIiJRghmEEGYbxs2EY+7P/DbxCrKthGFsMw/jxaq+rxgQRERERERGR0msC8ItpmjWAX7KnC/IwsOefvKgaE0REREREREQKYDr57xroCXyY/fhDoJe9IMMwKgA3A+/+kxdVY4KIiIiIiIhIMWUYxkjDMDZe8jeykC8RZprmaYDsf0MLiHsFeJx/eAMLDcAoIiIiIiIiUgDrtaoP+H8yTXMuMPdKMYZhLAfC7cx64p+8h2EYtwBxpmluMgwj+p8so8YEERERERERkRLMNM2OBc0zDCPWMIwI0zRPG4YRAcTZCWsF3GoYRnegLOBnGMbHpmneWdDrFnljQuY3Vx0E8j8l3vRxdgrFTtbns52dQrET6+ru7BSKlaQJLzs7hWInocDqtP+ujKV/ODuFYqWnW3lnp1DsZH71qrNTKH7Mf1TJ+p/h3m+Ms1ModjJem+TsFIqdFGuGs1MQKazvgSHAc9n/fnd5gGmaE4GJANmVCWOv1JAAGjNBREREREREpECmk/+7Bp4DOhmGsR/olD2NYRiRhmEs+v++qLo5iIiIiIiIiJRSpmmeBTrYef4U0N3O86uAVVd7XVUmiIiIiIiIiEihqDJBREREREREpAAaXcY+VSaIiIiIiIiISKGoMuH/2rvv+CiK/4/jr7kUkpCEEEpCE2lK7yACSgdFUSwg2AAbCqKioFi+P7siWLEgNhC/9q+9gIIIKipFpBcp0gmEEgik383vjztSLwlo7i4J76cPHt7uzh6fHXZn9mZnZkVEREREREQK4SqZSRDLHfVMEBEREREREZGTosYEERERERERETkpGuYgIiIiIiIiUgirYQ5eqWeCiIiIiIiIiJwU9UwQERERERERKYReDemdeiaIiIiIiIiIyElRY4KIiIiIiIiInBQNcxAREREREREphLWagNEb9UwQERERERERkZOingkiIiIiIiIihXDp1ZBeqWeCiIiIiIiIiJwUNSaIiIiIiIiIyEnRMAcRERERERGRQrgCHUAppZ4JIiIiIiIiInJSykXPhKBm7QkbcgvG4SDj59lkzP4wz/bgs3pS4bzBANi0VNLefRHXzi0AhPYaSMg5/cFA5k+zyPjhM7/H7yvXPHQ9rXq0JT01ndfGvcS21VsKpLlh0ijqtWgIBhL+3sNrd71IekoaEdEVuXHyj0GiTAAAUy9JREFUrVSvG0dmeiZvjH+ZnX9tD8BRlIygBi0J7XcNOBxk/TmfzIVf5d1+RjtCe1zufu2Ly0nGd+/g2vEXAMFnnUdImx6AxbVvB+lfvAbOzAAcRclr++i11OzZCmdqBr+PncahVVsLpGk0og9n3nAeUfXi+aT5SDIOHgWg7iWdaTp6AACZKWksnTCdpLVl9xwBCGnfkchRYzAOB6mzviH1w/fybA+qcxpR4yYQ3LARx6a/Qer/csqa8EsuI+z8C8EY0r79mtTP/ufv8H3iigdH0LxHWzJS05kx7mV2rPm7QJprnrqFui3rYzDs/XsPb497mfSUNADO6NSUwf83gqDgII4eSuaZKx709yGUqOAWHQi7ZjQ4HGTO/5b0rz/Isz2kcy8qXDAEAJueSuqM53Ft95S9ERWJuH4cjtqng7WkvvE0zk1r/XwEvtH7oWto0KM1manpfDPuNfau3logzYAXbiG+RX1cWVnsWbGF2fe+hSvLSaM+bTnnrsuxLovL6eSHh//LzqV/+f8gSsjCrYlMnr8OlwsGNq/NdR3re023JuEw137wGxP7t6bPGfEAJKdl8vCc1Ww+cBRj4ME+zWlVs7I/w/eJhVv3M3nBelwu686TDvW8pluTcJhrP1zExP4t6dMoV57MXePOEwwP9mlGq5oxfoze/x544ll+WriY2MoxfP7fVwMdjl8ENWxFaP9hYBxkLZtH5s9f5t3euB2hPQfn3KfNmolr+wZMlRpUGHx7djpH5epk/PgxWb/N8vch+MSYR0bTqWdH0lLTmTh2EhtXbyqQZsKz42nVqSXHko8BMHHsZDat3cxpDepwz7PjadS8IW9Oms6H0z72d/jlitUEjF6V/cYE4yD8yls59twE7KH9VLz/RbJW/IZrT86PGrs/gWOTx0HKUYKbdyD8mjs49uRtOGqeTsg5/Tn2xBjIyiTi9ifIWrUI177dATygktGqR1vi6tVgXLfRNGhzBiMeu4mHBk4okO6/j0wn7WgqAFf+Zzh9hp3P11M/46JbL2P72r95YeRT1GhQi2GP3sjEKx/y81GUEGMIPX84af99EnvkIGE3PErWhmXY/buykzj/Xk3qX3+4k1evQ9jlt5H6ynhMVGVCOvYjderdkJVJhcvGENz8bLJW/BSooykxNXq2IqpePF93uYsqbRvS/skRzLmw4A+9/Uv+YvecP+n5yQN51h/bkcjcyx4l83AKNXq0osOk673uX2Y4HESNuYOke+7CtT+Ryi9NI+O3hTi3b8tO4ko+wtGXpxDapWueXYNOr0fY+RdyaMzNkJlFpScnkbH4N5y7duX/W8qU5t3bUL1eDf7TfQz12jTiqsdvZOLA+wqk+/jRGdnlyKAHhtF92Hl8N/VzwqMjGProjUwZ9jiHdu8nqkq0vw+hZBkHYcNu49hTd2MPJhL5yCtkLvsN1+5c50jiHo4+PtZd37TsSPh1d3LsoVsBCL/6VjJXLiHzxYchKBgqVAjUkZSo+j1aUblePNO63UXNNg3o99hwZg58qEC6NZ//yle3TwXgoimjaTWkO3/+9we2LlzDxjnLAKjWuA4DXx7D673u9uchlBinyzJx3lqmXtqBuKgwrnrvN7o1qE6DKpEF0r3wywbOrls1z/pJ89fR+fSqPD2gDZlOF2mZTn+G7xNOl2Xij+uYemk74iLDuOr93+lWv1ohefJXwTxZsN6dJxe2Ljd5UpyB/ftw5WUXcd+jTwc6FP8whtALryPt7cexRw4QNvIJstb/gU3MdZ+2ZTWp6z33aXGnETb4dlJfvAt7YA9pUydkf0/4uKk41y4JxFGUuLN6dqR2vVpc1XUYTds2YeyTtzNqwBivaV99/DUWfPNznnVHkpKZ8n8v07VfZ3+EK6eoMj/MIajembgSd2P3J4Azi8wlCwhunfeicW5eCynup6lZW9ZhKrsrKkeNOji3rIOMdHC5yPprFcFtuvj9GHyhbZ+O/PLJfAA2//kXEdEVqVS94NON4z8AAEIrhGI9jW61GtVhzcKVAOzZvIuqtasTXbWSz+P2BUetBrgO7cUmJYLLiXPN7wSf2S5vosz07I8mtALZGQHgCILgUDAOCKmATT7kp8h9q3a/dmz9n7viObBsE6GVIgirHlMg3aHV2zi2c3+B9fuXbiTzcIr787KNRNSI9Wm8vhZ8ZhOcu3fhStgDWVmkzZ9HaOe8jQY2KYmsv9ZDVlae9UGn1SVz/VpITweXk8yVKwjtcq4/w/eJVn078PunCwD4+8+NhEdVJLpaTIF0ucuRkLDQ7Oun40VdWT57EYd2u8+f5ANHfB+0DwU1aIxr7y5s4h53ffP7j4S0y1ffbMxV32xai6NyNfeGsAiCG7cgc8G3noRZkHLMn+H7TKM+7Vj9yS8A7P5zMxWiK1LRS1my5ccV2Z/3rNhMlKfMyEzJKX9DIiqU6ac/qxOSqBMTQe2YCEKCHPQ7M575m/cWSPfB8m30ahhHbERo9rqj6Vks23WIS5rXBiAkyEFUWIjfYveV1QmHqVMpgtqVPHlyRjzzN+8rkO6D5dvp1SiO2HAvedKsFlB+8qQ47Vu3oFJ0VKDD8BtH7Ya4DiZgD+0DpxPnql8Jbtw+b6KMfPdpXgTVb4E9tBd7uOA9S1nUpW9nvvvfHADWLltHZHQksdVP/F4r6UASG1ZswJlV/hvgJHCKbUwwxlQ3xjxsjPmfMeZjz+c4fwR3IkxMVVwHE7OX7aFEHDFVCk0f2vU8sla7Wyxdu7YSdEYLTMUoCK1AcIsOOGKr+Txmf6gcH8vB3TmF6cGEA8TGeS+Abpx8Ky8tfYsaDWsxZ8Y3AGxfu5X253cCoH6rhlStVY3Y+MLztTQzUbHYwweyl+2Rg5iogg0rQWe2J3zUZMKGjif9q9fcaZMPkfnbN0TcMYWIO1+G9BScW1b5LXZfCo+P5djunHxJ2X2QiPh/1p22wdDu7Mn1Q6EsclStijMx5wbXtT+RoKpVi9gjh3Pr34S0aIWJioYKFQjt2ImgatV9FarfxMTFcjDXOZKUcIDK8d7LkWGTRzF5yevEN6jJvBnu7qVx9WsSUakid37wEPd99RSdLi3bDSymclVsrvrGdTAxu3Ham9Du55O1cjEAjuo1cB05TPhNdxP56KuEX38XVAjzecz+EBVfmeRc50lywkGi4govSxzBQTS7tCtb5q/MXndGv/bc+MMkBk0fx7fjX/dpvL6072g6cVHh2ctxkWEkHk3PlyaNeZv2cnnL0/Ks33U4hcrhoTz4/SqG/HchD89ZTWpm3obLsmjfsTTionLO9bioMBKPecmTzfu4vEWdPOtz8mQNQ979jYfnrCkXeSJ5eb1Piy5Y1wQ16UD4mGcIu+oe0j8vOPwjqMXZZK381aex+lO1+Kok7s6pcxL3JFIt3nudc/3d1/HmnNcY/eAthISW/wa3QHBhA/qntCqyMcEY0wU43ldoJvBfz+dFnm2F7XeTMWapMWbp9PU7SybSQv8ybyu9Z3jQma0I6Xoe6Z+8AYArYQcZsz8iYuxEIm5/AtfOLVhn+Zir05iCGWOt93x5ffxLjOl4A7s37eKsAe4nsV9N/ZSK0ZE89u0z9Bnen21r/sZVTvLGrWBeODcsJfWV8aR9+Byh3Qe5V4ZFEHxmO1Km3EHKc7dCSAWCWpSP3iverp3CzpGiVO/clPpDu7P88Q+KT1yaeblmTrTsdm7fRuqH71HpqWeo9MRksrZswjrL/g2v93LEe9q3x7/C3WeNZM+mXXQY4H5a7wgK4rQW9XlpxJO8cO1j9B9zOdXr1fBlyL7lrb4pJEOCmrQm9NzzSfvQ/cPYBAURdHojMn74kqP/uRmbnkaFC4f4MFg/Oon6BqDvY8PZsWg9O5dsyF7313dLeb3X3Xx643Oce9flPgkzYPJlz+T567j9nDMJcuTdkOWyrN93hEEtT+ODq7sQHhzEW0sKzlFS5pxAOTp5wQZu79qoYJ5Yy/p9yQxqWZsPrjqb8JAg3lqy1TdxSuCcYNnqXLeE1BfvIu39pwntOTjvxqAggs9sR9aa330TYyCcYNn62sQ3ubbbCG6+YDRRMVEMHXWFP6ITAYqfM+EZYKC19s9c674wxnwGTAPO8raTtfY14DWAIzf29WlTij20P09vAlO5Gq6kgwXSOWrVI/zasaRMuR97LDl7feYvs8n8ZTYAFS4ZgetQ2e0a1fva8+g+pA8AW1ZuIrZmTutlbHwVDu0rvHu+dblY9NUv9B85kJ8/nkfa0VReH/9S9vZnf3mVfTsKdtUsC2zyQUylnF4VJjoWm5xUaHrX9vWYytUhPJKg05viSkqEFPc541y/hKDajXCuWujrsH2i0fA+NLiqBwAHlm+hYs0qHD/jI2rGkro36aS+L6ZJHTo+fQMLrp5ExqGjJRusn7kSE/P0JnBUrYbzwImXB2mzvyVttrsLe8XrbsSZmFjMHqVT92v60XVobwC2rthEbM0qbPZsi4mvQtLeguXrcdblYunXv9L3pov49eP5HEo4wNFDR8hITScjNZ2Ni9dRu0ld9v29xw9HUvLswf2YXPWNI7YaNulAgXSOOvUJv/4uUp6+F3vUPbTDdTARezAR5+b1AGQu/okKA8puY0Lba3vTaoi7LNmzcgtRNXPK2Kj4WI7uS/K6X5fbLyEiNopP733L6/YdizcQU7c64ZUjSS2DZUr1yArsTc4Z9rP3aBrVKubtkr127xEmfLscgKTUTH75ez/BDkOLGjFUj6pAixoxAPRuFMf0pWW/MaF6ZBh7k9Oyl/cme8uTw0z41t1TJSktk1+2JhJsHLSoUYnqkfnypDw0sEge9oi3+7TC71ld29ZjYuMgIir7/iyoUWtce7bCscO+DtenBg67iAuv7A/A+hV/Ua1mTp1TrUY19u8tWOcc3OeulzMzMpn90XdcMXKQf4I9xfyTB26nguKGOUTna0gAwFq7HCgVg7mcWzfgqF4LUzUegoIJ6dCNrBW/5UljYqsRMer/SH1rEq69eSdEM1Ex2WmC23Qlc/GP/gq9xM2dOZsH+t/FA/3v4o/vF9P1su4ANGhzBinJKRz20phQvW589uc2vTuwZ7M7fyKiIwgKcbc1dR/Smw2L1+YZF12WuHZtwREbj4mpBo4ggpp1Issz2eJxpnLOyB1H/OnuydFSj2KPHCCoVkP3nAmAo14zXPvL7gSdG2fMYXaf+5jd5z52zV7K6ZefA0CVtg3JPJJKWiE/ALyJqFWFrm/cwe+3TSV5S4KPIvafrA3rCapVG0d8PAQHE9a9Jxm/nXijkYmJAcBRrTqhXc4h/ce5PorUt+a/8x2P9R/PY/3Hs/z7JXS6tBsA9do0IjU5hSOJSQX2qZarHGnZqx0JnnJkxfdLaNShCY4gByFhodRr3ZCETWV3UkrnlvUExdfCVPPUN516kLksb5daU6U6Ebc/ROq0J3El5PTMs4cP4TqYiCPePR4+uFkbXLu2UVYtmzmX6f3vZ3r/+9n4/R80v8zdq61mmwakJ6dwzEtZ0nJId+p1a8GXY17O89Qxpm5O+RvX/HSCQoLLZEMCQLP4Smw/lMKuwylkOl18tyGB7vXzDnn65vpufHt9d769vju9G8Vxb8+m9GgYR9WKFYiPDGer5405i3ccoH5sxUAcRolqFh/N9qRcefJXAt0b5MuT687l2+vdf3o3jOPenk3o0bC6O0+iwth60D2/yOLtB6hfpeznieTl2rU55z4tKIigFp3JWp/vPi02131ajdPd92kpOQ8Hg1t0IauMPujJ7fO3v+SGfjdzQ7+b+WX2Qvpd7n5I2LRtE44lH8tuOMgt9zwKXft15u8NW/0VrkixPROMMaaytfZQvpWxlJbJG10u0t57iYg7nsAYBxkLv8O1exsh3S4AIHPBN1S48GpMxWjCrvLMgOp0cuxxz+zat/wHUzEanFmkvfdi9sRZZd2KeX/Qukdbnv7pFTJS03l9XE4vg3Ez7ueNu1/hcGISI5+9jfDIcIwxbF+3len3TwOgZsPajHz2NlxOF7s27eSN8S8H6lD+PesiY9YMwq66x/3KoeULsIm7CG7XC4CsP34guEkHglueg3U5ISuD9E9eBNwVXNa6xYTf9Di4nLgStpG1bF4gj6bE7P5hOTV6tebCX5/FmZrBorHTsrd1e2c8i8e9TureJM64vh9NbrmQsOqVOH/uRPbMW87icW/QfOwlVKgcRfsnRwDgynLy/fn/CdTh/HsuJ0dfep5KTz6NcThI++5bnNu2EnbhRQCkff0lpnIslV+ehomoCNZF+KWXc+iGYdiUFCr936OY6GjIyuLoS89jj5b9smT1j8to0aMNjy14kYzUDN7OVQ7cOv1e3rnnVY4kJjH8mdGER0aAgZ3rtvHeA+6u/Qmbd7FmwXL+M/sZrMvFwg9/YPdfOwJ1OP+ey0XqzBepOP4p96shf5qFa9c2QnteCEDGvK8JG3gNjshowoe5X1VmnU6OPTgKgNSZLxJ+y32Y4BBciXtIeW1SwA6lJG2et5z6PVox8qdnyEzN4Ntxr2VvGzRjHLPufoOj+5I47/ERHN61n2s+ewiAv2YvYeGUzznz/A40v6wrrkwnWekZfDH6pUL+ptIv2OHgnp5NGfXpUlzWcnGz2jSoGsXHK9xvmBrU6rQi97+nRxPum7WSLJeLWpUieLhvC3+E7VPBDgf39GjMqM+WefKkFg2qRPLxSndZMKhlnSL3v6d7Y+6bvcqdJ9HhPNy3uT/CDqjxD05kyZ8rSUo6Qq+BVzPq+mu4bEC/QIflOy4XGd9MJ+za+9yv8F72IzZxJ8Ht3b3kspbOJbjpWQS3Pgfr9NynffRCzv4hoQQ1aEH6l2V3vhVvfp+3iLN6duTdX2aSnpbOU3dOzt42cebjTB7/LAf2HuCBF+8lpkoMBti0djPPTngegNhqlZn27StEREZgXZbLb7iUYT2uJ+VoSmAOSMolU1SXDWPMTcCNwDhgmWd1O+Ap4C1r7bTC9j3O18McyprR30cWn+gU8+r14cUnOsV8eWq8VvqE9W7m47lXyqAHNpb9CR5L2qRzCh+CcSqa+lMZnp/CR267t2xOJOxTtjzNh/TvhQwaG+gQSp2MKQVfCXyqu+Ctsjss2lfm75zrdSa78qJfnfMD+pv2ux2zSmX+FtkzwVr7mjFmN/Ao0Myzeg3wmLX2K18HJyIiIiIiIiKlT3HDHLDWfg187YdYRERERERERKQMKLIxwRjzIkW81Mdae1uJRyQiIiIiIiJSStgTfV/4Kaa4nglL/RKFiIiIiIiIiJQZxTUmnGmt1awrIiIiIiIickpyqWeCV8W93vE8v0QhIiIiIiIiImVGcT0TgowxlQGvr6Kw1uo9XCIiIiIiIiKnmOIaExoDf+C9McEC9Us8IhEREREREZFSwloNc/CmuMaEtdbaNn6JRERERERERETKhOIaEwpljImz1u4tyWBEREREREREShNNwOhdcRMwvpB7wRhTyRhznTFmLrDMd2GJiIiIiIiISGlVZM8Ea+0MY0w4cBFwJdAWiAIGAj/5PDoRERERERERKXWK7JlgjHkX+AvoC7wEnA4cstbOt9a6fB+eiIiIiIiISODYAP9XWhU3zKE5cAhYB6y31jqhFB+NiIiIiIiIiPhcccMcWhljGuMe4jDXGLMPiDLGxFtrE/wSoYiIiIiIiEiAuPRqSK+K65mAtXa9tfb/rLVnAmOBd4DFxphffR6diIiIiIiIiJQ6xTYm5GatXWqtvRNoCLzim5BEREREREREpDQrbgLGaGPMvcaYl4wxfY3brbgnZRzknxBFREREREREAsMG+E9pVeScCbiHNBwCfgNuAMYDocBAa+1y34YmIiIiIiIiIqVRcY0J9a21LQCMMW8A+4HTrLXJPo9MREREREREJMBcpbp/QOAU15iQefyDtdZpjPn7ZBsSgluf8Y8CK6+yvt8Z6BBKHUfncwIdQqlT5eX5gQ6hVIkcp1FV+f149YxAh1DqhA67IdAhlCpzfvw00CGUOrdXqBnoEEqfSjGBjqBUyZhyX6BDKHVCb3si0CGUOosm9wh0CCKlQnGNCa2MMUc8nw0Q7lk2gLXWRvs0OhEREREREREpdYprTAiz1mYWk0ZERERERESkXNIwB++KezXkIr9EISIiIiIiIiJlRnE9E4xfohAREREREREphaxVzwRvimtMqGaMubOwjdbaZ0s4HhEREREREREp5YprTAgCIlEPBRERERERERHxKK4xYY+19hG/RCIiIiIiIiJSymgCRu+Km4BRPRJEREREREREJI/ieiZcYIy5A2gIrALetNZm+TwqERERERERESm1imtMeA7IBH4GzgeaArf7OigRERERERGR0sBqmINXxTUmNLXWtgAwxrwJLPZ9SCIiIiIiIiJSmhXXmJB5/IO1NssYTaEgIiIiIiIipw5r1TPBm+IaE1oZY454Phsg3LNsAGutjfZpdCIiIiIiIiJS6hTZmGCtDfJXICIiIiIiIiJSNhTXM0FERERERETklOXSBIxeOQIdgIiIiIiIiIiULeqZICIiIiIiIlIITcDonXomiIiIiIiIiMhJUWOCiIiIiIiIiJwUDXMQERERERERKYQmYPSuXDQmLNy6n8k/bcBlLQOb1eK69vW8pluz9zDXfrSYiee1pE+jOACS0zN5eO5aNh88isHwYO+mtKoR48fofWfYQzfQukc7MlLTmTpuCltXbyk07fCHb6TboJ6MaDoUgJoNajHy6THUa9aAD5/+L9+89oW/wvaLhWu2Mul/83G5XFzSpTnX9e1YIM2Sv3Yw+X8LyHI6qRwZzptjBwcg0pJ35uPDqNarDc7UdFbfNpXkVVsLpAk/rRotp91OcExFkldtZdXol7CZTip3bkrrt8eRun0fAPu+WcyWZz/N2dFh6PT9E6QnHOLPqyf56YhKzsK125j06U+4XJZLzm7KdX3a59m+ZONOxr7+DTWrRAPQq2UDRp7vPnfenb+cT39bg7Vw6dnNuLpHa3+H7xMPPDGObr27kJqSxoTbHmLtyg1e0429bxTnXdQLl9PFezP+xzuvf0j9hnV5csqDNGvZmGefeIW3Xvmvn6P3rVO5HCnK6EdGcVbPDqSnpjNp7NNsXL2pQJq7nx1Hy04tOZZ8DIBJYyezeW3hdVRZsnDLXibNXYXLBZe0Oo3rzj7Da7rVew5x7cyfeOriDvRpXBOAd5ds5tMV27DApa3qcnWHBn6M3HcWbtjFpK8Xu8vWDo24rnuLPNuXbElg7Mx51IyNBKBXs7qM7NUqe7vT5eLKl76henQELw7v5dfYfSWoYStC+w8D4yBr2Twyf/4y7/bG7QjtOdg9TtvlJGPWTFzbN2Cq1KDC4Nuz0zkqVyfjx4/J+m2Wvw/Brx544ll+WriY2MoxfP7fVwMdjt8888xD9OvXg5SUVG66aRzLl68ukGbu3I+JjKwIQPXqVVm6dDmDB9+Uvb1du5YsWPA511xzK5999q3fYpdTQ5lvTHC6LBPnr2fqJW2Jiwzjqg8X0a1eNRpUiSyQ7oWFGzn7tCp51k9asIHOdavw9AWtyHS6SMty+jN8n2ndox3x9WowttstNGxzBtc/djP/GXi317T1WzQgIrpinnVHk47y9oNv0L7fWf4I16+cLhdPfjSPV8dcSlxMFFdNeo9uLRrQoEbOuXEkJY0nP5zHy6MvoUZsNAeTUwIYccmp2qs1FevV4JdOd1CpXUOaTrqBRec/UCBdoweuZNu0b0j4/DeaTLqeWlf2ZOfbcwBIWrS+0IaCujeez7GNuwmOCvfpcfiC0+XiyY/n8+rogcTFRHLV0x/SrXl9GtSIzZOuTYOavDhyQJ51m3Yf4NPf1vDfuwYTEhTE6KlfcE6z06lbPcaPR1DyuvXuwun169Cn4yW0atechyfdy6DzhhdId+nQAdSoGcd5Z1+OtZbYqpUBSEo6wmP3PU3v/t39G7gfnMrlSFE69uxA7Xq1uLbrCJq0bcztT97GrQNu85r2tcdf56dvfvZzhL7ldFme/H4lrw7pTFxUOFfNWEC3RvE0qBpdIN0LP67l7HrVs9dtSjzCpyu28d9h5xIS5GD0h79xToM46sZG5v9ryhSny8WTX/7Oq9f3JS46gqte/oZuTerQIC4mT7o2p8cV2lDw3sJ11KteiWNpmX6I2A+MIfTC60h7+3HskQOEjXyCrPV/YBN3ZSdxbllN6vo/3MnjTiNs8O2kvngX9sAe0qZOyP6e8HFTca5dEoij8KuB/ftw5WUXcd+jTwc6FL/p168HDRrUo3nzbnTs2IYpUx7j3HMHFkjXu/eg7M/vv/8qX331ffayw+HgscfuZc6cn/wRcrlm1TPBqyLnTDDGnOavQP6p1XsPUycmgtqVIggJctCvUTzztyQWSPfBiu30ahBHbERo9rqj6Vks232IS5rVAiAkyEFUhRC/xe5L7fp05OdP5gOw6c+/iIiuSEz1ygXSGYeDK+8fzntPvp1n/ZEDh9mychPOzPLRuJLb6q0J1KkWQ+2qMYQEB9Gv3ZnMX7k5T5pZSzfQs3VDasS6bwBjoyICEWqJq3Zee3Z/7K5QDv+xieDoCEK9/OCN7dqMvV8tAmD3Rz9R/fz2BdLkV6FGLFX7tGXXu/NKNGZ/Wb1tr+e8qOQ+L9qewfxVJ/akdMveg7SsG094aAjBQQ7aNazFvHznVFnU67xufPah+ynGij9WE1UpimpxVQqku3L45bz0zOvZMx0f3H8o+/+rlq8lKzPLf0H7yalcjhSlS9/OfP8/d8PjumXriYyuSGz12GL2Kj9W7zlEncoVqR1T0X1P0rQW8zcmFEj3/h9b6HVmDWIjKmSv23IgmZY1KxMeEkyww0G706oy7689/gzfJ1bv2E+dKtHUjo1yXyut6jF/3Y4T3n/v4WP8vGEnl3Zo5MMo/ctRuyGugwnYQ/vA6cS56leCG+erZzPSsz+a0Ap4E1S/BfbQXuzh/b4Mt1Ro37oFlaKjAh2GX114YR/ee+8TABYv/pNKlaKJj69eaPrIyIp069Y5T2PCqFHD+fzzWSQmlv9zRAKjuAkYP/dHEP/GvqPpxEXmFLJxkRVIPJaeL00a8zbv4/IWtfOs33UklcrhoTw4dw1D3vudh+euIbWc/HiOjY/lwO6cguNgwgFi4wre0PUb1p8/5iwmad8hf4YXUPuSjhJfOadCiouJZF/S0Txptu07xJGUdK5//mOGTnyXrxat9XeYPhFWI5a0XQeyl9P2HCQs35P3kNgoso6kYJ0ud5rdedNUateIs+c9Rdv3JlDxzJxrqvGjw/jrkXexrrLZcrsv6RjxMTlPAONiItl3+GiBdCv/TmDwxPcYPfULNu1x52XDGlX4Y/Nuko6lkpqRyS9rt7E3qeC+ZU1cjWok7M75IbR3917ivNzI1Dm9Fv0H9uWTOTN544MXqFu/jj/DDIhTuRwpStX4KiTuzmnQT9yzn6rxBRugAK67ezivz3mVWx68mZDQ8tGQvy85jfhcPbPiosLZl5yWJ83e5FR+/GsPg9rkHZLZsGo0f+w4QFJqBqmZWfyyeS97j6T6JW5f2nckhfhKOb0f46Ij2Hf4WIF0K7cnMviFLxk9fS6b9ubck0z+egl3nN8eY4xf4vUHExWLPZxTF9sjBzHRBe/Rgpp0IHzMM4RddQ/pnxfs2h/U4myyVv7q01glcGrWjGfnzt3Zy7t2JVCzZlyh6S+6qB/z5y8kOfmoZ/84LrqoH6+/Xr6GGErpUlxjQrkouSf/tIHbuzQiyJH3cLJcLtbvS2ZQizp8cGUnwkOCeGvp3wGKsmR5q3Tzvx61cvXKnHVBZ76b8Y2foiodvP3UzZ9fTqeLddv38tItA3nl1kt5bdYitu0tnw0uBd6b6+WqP57myMq/+bndrfzW8x62vzmb1jPuAqBqn7Zk7D9M8sqye/14676W/7xoUrs6sx4exkcTrmTIua0Y+4b72qkfH8uI3m25+eUvGD31S86oVZUgR9l/WY73cqRgPoVWCCUjLZ3L+lzLR+98zpMv/J8/wgsolSOFOIG6B+CNiW8xvNv1jLpgDNExUQwZVT7mkvBajuRbnjx3Nbd3b1rgnqR+1ShGdGrEzR/8yugPf+eM6pUKpCmLTuRaaVIzlln3XMZHt1/EkLMbM/adHwH4ad0OKlcMo2kt7w1SZZa3f1YvF4pz3RJSX7yLtPefJrRnvmskKIjgM9uRteZ338QoAXeidfBxgwdfzEcf5cy9MXnygzzwwERcLpdP4jvVuKwN6J/Sqrg5E2oZY6YUttFa63UgpDHmJuAmgBeHdOe6rs3+eYTFqB5Zgb1Hc3oi7D2aTrWKebuDrd13hAmzVwGQlJbJL1v3E+wwtIivRPXICrSIrwRA74ZxTP9jq89i9bU+155PzyF9AdiyciNValbN3hYbX4VD+w7mSX968/rE163B8wvcrd2h4RV4bsFUxna7xX9BB0BcTCQJh5Kzl/cmHaVapbxzRsRVjiQmMpzwCiGEVwihXcNabNiVSN24gkNFSrs6I/pS6+qeABxZvpmwXDdlYTViSU/I++Mm80AywdERmCAH1ukirGZOGufRnKdk+39YTpOJ1xMSG0VMxzOo1q8dVXu1wREWQnBkOM1fHs3q0S/74QhLRlxMJAm5nizvTTpKtXxziUSG5wyTOqfZ6Tzx8XwOHU2lcmQ4l5zdjEvOdpd1U776lbiYsjnO+arrBjH4moEArPpzLfE144EVAMTVjGPf3oLDyPbu3sd3X7uHt3z/zY88OeVBf4UbMKdaOVKUi4cNoP+V/QHYsGID1WpWy95WrUZVDuw9UGCfg576KDMjk9kffcfgkZf7J1gfi4sKJyE5p5zcm5xKtaiwPGnWJiRxzxdLAUhKzeCXLXsJchh6nlGDS1rV5ZJWdQGYsmAtcWVw/pn84qIjSMjVE2HvkRSqRecd8hMZlqtsbVybJ774nUPH0li+bR8L1u3glw07ychyciw9k/s+/JknrjjHb/H7gj1yEFMppy420bHY5MIbGl3b1mNi4yAiClLc5U5Qo9a49myFY4d9Ha740ciR1zJixBAA/vhjJbVr18zeVqtWPHv27PO6X2xsDO3bt+KKK3ImXmzbtiUzZ74IQJUqsfTr14OsrKw8wyBE/q3iHp2lAn8U8ccra+1r1tr21tr2vmxIAGgWF832pBR2HU4l0+niu40JdK9fLU+ab4afw7cj3H96N6zOvd2b0KNBdapWrEB8VBhbD7krucU7DlI/tqK3v6ZMmDNzFvf2H8u9/cey9PtFnHNZdwAatjmDlORjBYYy/DnvD27pMILbut7EbV1vIiM1vdw3JAA0qxvP9n2H2LX/MJlZTr77YwPdWtTPk6Z7ywb8uWkXWU4XqRmZrNqaQP34sjnud8f07/m91wR+7zWBfbOWUnPQuQBUateQrOQUMvYlFdjn4MK1xA1wT75Zc/C5JM523/iGVquUnSa6TQNwGDIPJrPp8Q/4qc1ofu4whpUjp3Bw4Zoy1ZAA0Oy0OLYnJrHrgOe8WPYX3Vrk7Ya8/8ix7KcCq7YlYK0lpqL7h8LxyfX2HExm3orNnN/O+wzupd27b33MxT2u4uIeVzF31nwuucL9I7FVu+YcPXKURC8/DOfOmk+nczoA0LFzO7Zu3ubXmAPhVCtHivLF218xst8tjOx3Cwtn/0rfy/sA0KRtY44lH8tuOMgt9zwKXfp15u8NW/0Vrk81qxHD9oPH2JV0zH1PsnYX3RrG50nz7S19mDWqL7NG9aX3mTW5r28rep5RA4CDnmGaew6nMG/DHs5vWsvvx1DSmtWuyvb9R9h1MNl9raz4m25N8g473Z+cmlO27kjEWoiJqMBt57Xj+3sHMeuey5k4tBsd6tco8w0JAK5dm3HExmNiqkFQEEEtOpO1Pu9ttYnN6c7uqHE6BAVnNyQABLfoQtaqhf4KWfxk2rSZdOrUn06d+vPVV99z5ZWXAdCxYxuOHEkmIcF7Y8Kll17ArFk/kJ6e84C1SZOuNG7s/vPZZ99yxx3/UUPCv2AD/N+/ZYyJNcbMMcZs9Pzf65MNY0yMMeZ/xpj1xph1xpizi/re4nomHLDWvl1MmoAKdji4p/uZjPpiGS6X5eJmNWlQJZKPV7kn9xnUouixu/d0a8x9360iy2mpVSmch3v7tvHDX/6c9wete7Tj+Z9eJT01nWnjcjqY3D3jP7x+90scKmKehErVYnj8q6cJj4zAuiznXzeA8b3HkHq07I/fDA5yMGFwT255+VP3OXN2MxrWrMrHP7ufvg46pxX146vQuenpDH7iHYwxXNK5OQ1z9fQoq/bP/ZOqvVrTddELOFPTWXN7zhjMNu/ew9o7XyN97yE2PvYeLafdRsMJV3Bk1VZ2vufucho3oBN1hvXGOl040zJYObLQjktlTnCQgwmXd+OWV77E5XJxcaemNKxRhY9/cfdqGtS1BXOXb+KjX1YT7DBUCAlm4rDzsrsh3vXmtxw+lkZwkIN7B3UnOiKsqL+uTJg/ZyHdendh7uLPSU1N497bHs7e9vr7L3D/HY+yb+9+pr0wg2defYzhI68k5VgK9499DICq1avw6ZyZREZVxOWyDB85lPO7DObY0YLjpcuaU7kcKcqieYs5q2dH3vllBmlp6Uy+M2fm9SdmPsYz45/lwN6D3PfiBCpVqYTBsHntZp6b8EIAoy45wQ4HE/q25JYPf8NlLRe3PI2G1aL5+E/3ELD88yTkd9dnizmcmkGww8G9fVsSneuJfVkVHORgwkVncctbc3FZFxe3b0TDuMp8vMj9mtlBZ53J3FVb+WjRBoIdDiqEBDFx6Lnlao6EAlwuMr6ZTti194HDQdayH7GJOwlu3xuArKVzCW56FsGtz8E6nZCVQfpHua6RkFCCGrQg/cvXA3QA/jf+wYks+XMlSUlH6DXwakZdfw2XDegX6LB8avbsefTr14M1a34iJSWVkSPHZW/77LMZjBp1d3ZPhUGDBvD001MDFaqUDROAH6y1E40xEzzL93hJ9wIw21p7uTEmFChy9mhT1NgbY8zv1tpOXtZ3Aa601o4uLuqUl28tvYM8AuD6STsDHUKp89ab5wU6hFLn56vmBzqEUuWcmV0DHUKp0+rqGYEOodRZ8f4NgQ6hVLlw+KeBDqHU+frR1oEOofSpFBPoCEoV1/KVgQ6h1Am97YlAh1DqRNfpEegQSp3U1G3luAUQmsWdFdDftGv2LvpX+WuM2QB0t9buMcbUAOZba8/MlyYa99jW+raoRoJciuyZkLshwRjTGrgSGAz8DeguRURERERERMq1QE+CmHtOQo/XrLWvncRXxFlr9wB4GhS8vWe0PpAITDfGtMI9rcHt1tpCu5QW2ZhgjDkDGAIMBQ4AH+LuzaDmOBEREREREREf8zQcFNl4YIyZC8R72XT/Cf41wUBbYIy1dpEx5gXcwyH+U9QORVkP/AwMsNZu8gQ59gSDERERERERESnTSmISRF+z1vYubJsxZq8xpkauYQ7eZvPcCey01i7yLP8Pd2NCoYp7m8NlQALwozHmdWNML7y/HVdERERERERESp8vgWGez8OAL/InsNYmADuMMcfnUugFrC3qS4tsTLDWfmatvQJoDMwHxgJxxpipxpi+JxW+iIiIiIiIiPjbRKCPMWYj0MezjDGmpjHm21zpxgDvGmNWAq2BImdgLW7OhBnW2uGeSRfe9XxxLDAId5cHvaxUREREREREyq1AT8D4b1lrD+DuaZB//W6gf67l5UD7E/3e4oY5tPTyFx601k6z1vY80b9ERERERERERMqP4iZgjDDGtKGQeRKstctKPiQRERERERERKc2Ka0yoBTyD98YEC6h3goiIiIiIiJRbZeFtDoFQXGPCJg1nEBEREREREZHcimtMEBERERERETlllfUJGH2luAkY78m9YIwJMca0McZU92FMIiIiIiIiIlKKFdeYcKkxphmAMaYSsAKYCfxpjBnq6+BEREREREREpPQprjHhHGvtGs/nEcBf1toWQDvgbp9GJiIiIiIiIhJgNsD/lVbFNSZk5PrcB/gcwFqb4KuARERERERERKR0K24CxiRjzIXALqALcD2AMSYYCPdxbCIiIiIiIiIBZa0r0CGUSsU1JowEpgDxwB25eiT0Ar7xZWAiIiIiIiIiUjoV2Zhgrf0LOM/L+u+MMU18FpWIiIiIiIiIlFrFzZlQlDtLLAoRERERERGRUsiFDeif0urfNCaYEotCRERERERERMoMY+0/a+kwxmy31p5WXLphp19WeptSAuCVq/5N+035dMnM5ECHUOq80zA10CGUKqO3VAp0CKXOu1+NDnQIpc7NF70W6BBKlefOPhjoEEqdsb/FBjqEUmevS/VNbimujOITnWIWHdgY6BBKnSM7fgx0CKVOSNX65fpB82mxLQL6m3b7wVWlMn+LnDPBGJMMXvtVGPQ2BxEREREREZFTUnETMEb5KxARERERERERKRuKezWkiIiIiIiIyCmrNE+CGEgawC8iIiIiIiIiJ0U9E0REREREREQK8U9fWlDeqWeCiIiIiIiIiJwUNSaIiIiIiIiIyEnRMAcRERERERGRQrg0zMEr9UwQERERERERkZOixgQREREREREROSka5iAiIiIiIiJSCIuGOXijngkiIiIiIiIiclLUM0FERERERESkEFYTMHqlngkiIiIiIiIiclLUmCAiIiIiIiIiJ0XDHEREREREREQK4dIEjF6pZ4KIiIiIiIiInBT1TBAREREREREphCZg9E49E0RERERERETkpKgxQUREREREREROSrkZ5nDVg9fRqkdbMlIzeH3ci2xb83eBNNc9NYp6LRtgMCT8vZvXx71EekoaEdEVuWHyaKqfFk9megZv3P0yu/7aEYCjKDlBjVoTesEIcDjIWvoDmT99nnd7k/aE9h7i7rLjcpLxzQxc29a7N4ZFUOGSW3DE1QFrSf90Kq4df/n/IErYqIdvoUPPDqSnpvP0nc+wafWmwtM+cgv9Bvfl4saXABARFcGEF+6mWq3qBAUF8b/X/sf3H83xV+g+EdqhI1G3joEgB6nffEPK++/l2R5U5zSi75lASKNGHH3zDVI++jB7W8Tlgwi/4AKwlqwtf3P4qYmQmeHvQ/CJ6x66kTY92pORms5L457n79VbCk/78E30GNSLa5peAUCHPmcx5K6rcLlcuJxOpj/8BuuXrvNX6D73y59reWr6p7hcLi7tdTbXX9Inz/bpX/zAtz8vBSDL5eLvnQksePMJKkVVDES4PnPlg9fR0lPfvFlIfTPiqVGcnqu+edNT34RHRXDTc7cTW6sqQUFBzH79C375+McAHEXJCW7VgfBrbwVHEBk/fkP6l+/n2R7SpTdhFw0BwKalkvLm87i2bwYgesr72NQUcLmwLidH77/Z7/H7gs6RgkY+PJIOPdx18LN3Pcvm1ZsLTXvzwzfTZ3AfLmtyGQCXjbyM7gO7AxAUHESdhnUY2nooRw8f9UfoPjPmkdF06tmRtNR0Jo6dxEYv9yUTnh1Pq04tOZZ8DICJYyezae1mTmtQh3ueHU+j5g15c9J0Ppz2sb/DL3HPPPMQ/fr1ICUllZtuGsfy5asLpJk792MiI911SvXqVVm6dDmDB9+Uvb1du5YsWPA511xzK5999q3fYg+EB554lp8WLia2cgyf//fVQIdT7rk0zMGrctGY0LJ7W+Lr1eDu7rfSoE0jhj1+E48MvLdAuvcenU7a0VQAhj4wnN7DzuebqZ8xYPRlbF/7N1NGTqJGg1pc88gNTLrqYX8fRskxDkIHXE/a9EexRw4SdsuTZK1bik3cmZ3EuXk1qevGuZPHnUbY0DtJff4OAEIvGIFz45+kv/8MBAVDSGggjqJEdejRgVr1ajLinOto3KYxtz1xK7dddIfXtI1aNiIyOjLPuouGDWDbxu3833UPUSm2Em8ueIN5n/1IVmaW74P3BYeDqNvvIGn8XTgTE4l9dRrpvy7EuW1bdhJX8hGSX5xCha5d8+5atSoRl17G/uHXQkYGlR58iLCePUn7bra/j6LEtenRjhr1ajKm20gatTmTmx67hXsHjveatkGLhlSMzvsjedXCFSyZswiAuo1P586X7+b2XqN8Hrc/OJ0unnjzY177z2jiYmMYeu/TdG/fnAZ1amSnGXFxL0Zc3AuA+UtX8c7X88tdQ0LL7m2Jq1eDCd1vpX6bRlzz+E085qW+eT9XfTPkgeH0GnY+3079jJ7XnMfuTTt44YYniYqN5ol5U/jt859xltWyxDgIH3E7x54Yj+tAIlGPv0rmH7/i2pWrLNm3h6OP3IE9dpTgVh2JuPEujv4n57o4+thYbPKRQETvEzpHCmrfoz21Tq/FDefewJltzuTWx29l7MVjvaZt1LIRkZXy1sGfTPuET6Z9AkDH3h255PpLynxDwlk9O1K7Xi2u6jqMpm2bMPbJ2xk1YIzXtK8+/hoLvvk5z7ojSclM+b+X6dqvsz/C9bl+/XrQoEE9mjfvRseObZgy5THOPXdggXS9ew/K/vz++6/y1VffZy87HA4ee+xe5sz5yR8hB9zA/n248rKLuO/RpwMdipzCysUwh7Z9O7Dw0wUAbP5zIxFRFalULaZAuuOVNkBoWGj2RBo1G9VmzcJVAOzZvItqtasTXbWS7wP3EUfthrgOJmAP7QNnFs6VCwlu0j5vooy07I8mNAyOt7ZVCCfo9KZkLZ3nXnZmQVqKnyL3nc59z2bOJz8AsP7P9VSMjiS2emyBdA6Hgxvvv4E3nngz7wYLEZHhAIRXDCM5KRlnltPncftKSOMmOHfvwrlnD2RlkTZvHhW65G00sElJZG1YD1lebmCDgjAVKoDD/X/Xgf1+ity3OvQ5i/mfuJ8AbvxzAxHRFYmpXrlAOofDwTX3D+edJ2fkWZ+WknNdVYiogC1HrxFavWkbp8VXo3ZcVUJCgjmvS1t+XLqq0PSzflnG+V3b+TFC/2jTtwO/euqbLSdY34SEheaUsVjCKrrLkgoRYRxLOoqrDJclQQ0b40rYjWvfHnBmkfHbPELad8mTxrlxDfaY+4efc9NaHLFVAxGq3+gcKahT30784KmDN/y5gYrRFalcSNl63X3X8Wb+OjiX7hd1Z/6X830Vqt906duZ7/7n7uG4dtk6Igu5LylM0oEkNqzYUKbvRXK78MI+vPeeu8Fo8eI/qVQpmvj46oWmj4ysSLdunfM0JowaNZzPP59FYmL5uCcpTvvWLagUHRXoME4Z1tqA/imtimxMMMbE+SuQf6NyXCwHducUHAcTDlA5vorXtDdMHs2UJW9So0Et5s5wd3/asW4r7c/rBED9Vg2pUqsasYXsXxaY6Fjs4QPZy/bIQUylgscT1LQj4Xc8T9i195L+6VQAHLFx2JQjhF42mrDRkwi95GYIqeC32H2lSnwVEncnZi/v35NIFS//xhcNH8Dvc37n4L6DedZ/MeNL6jQ8jfeXvse0Oa8y9cFXS/WFXRxH1aq49u3LXnYlJhJU9cRu8F3793Psow+o+uFHVPvkU1zHjpGxdKmvQvWrKvFVOJDrPDmYcIAqcQXPk/OGXcDSOYtJ2neowLaO/Trxwg+vcO/0/+OV8VN8Gq8/7T2YRFyVmOzluNgY9h047DVtanoGC5evo89ZrfwUnf/ExMVyMFd9c6iI+ua6yaN5Pl9988Pbs6jRsDbPLX6DR797lvcefqtslyWVq+I6kKssOZCIo3LhZUlo9/5kLl+cvWytpeK9k4l8fBqhPS/0aaz+onOkoKrxVUnck6sOTthP1fiC58mA4QNYNGcRh7yUrQAVwirQrns7Fn670Gex+ku1+Kp57ksS9yRSzUueAFx/93W8Oec1Rj94CyGhIf4K0a9q1oxn587d2cu7diVQs2bhP0Muuqgf8+cvJDn5qGf/OC66qB+vv/5fn8cqIjmK65mwwhgzxxhznTGm9D6qN6bgukIq3jfGv8ztZ93I7k07OWuA++nJ11M/o2Klijzy7dP0HtafbWv+xukswy29XrLDW3441y4m9fk7SHt3EqG93WO+cThw1KhH1qLvSHv5bshIJ6TbQJ+G6w/mBM6R2LhYzr3gXD6f/kWBpO27tWPL2s0MbX8lt5w3ilsfHUVEZISvwvU9r/lxgrtGRhLWuSv7hw4h8fJLMWFhhPXuU/yOZYHX0yRvxlSuHsvZF3Th2xlfe/2Kxd/9zu29RjHpxicYctdVvoiy1PB6XQELlq6mdeN65W6IA3g/5sJ+6L01/mXGnnUjezbtpKOnvml+bmu2r/2bsR1v4MH+47j6kRsI8/R6KpO8ngPe8yO4aWtCe/Qn7f3XstcdfWgMR+8bybGn7qFC34EENW7po0D9R+fIicmfJ7FxsXS9oCtfzviy0H3O6nMWa5euLfNDHACv14638+S1iW9ybbcR3HzBaKJiohg66gp/ROd3J3PdAAwefDEffZRzrkye/CAPPDARl8vlk/hExLvi5kyoBfQGhgBPGmN+A94HvrTWpha2kzHmJuAmgE6xbTgjql4JhZuj1zXn0W1obwD+XrGJKjWrstGzLTa+Cof2Hix0X+tysejrhfS/6WJ+/vhH0o6m8sb4l7O3P/3LVBJ37Ct0/9LOHs7bE8FEx2KPFJ4frq3rMLHxEBGFPXwQe+QArp3uSYCyVv9GSLdLfB6zLwwYNoD+Q88DYMOKv6hWs1r2tqo1qnEg3znSsFlDap5egxk/TwegQngFpv/8FiPOuY6+g/vy4SvuCQh3b91Dwo4E6jSszYblZXNiSldiIo7qOd0HHdWq4TzBoQqh7drjTNiDPex+Kp3+88+ENG9O2tyyOSHledf2p9eQvgBsXrmRKjWrAe5JE2PjqxTopVKveX3i69bgpQXTAPd58uKCaYzpNjJPunWL1xBXtwZRlaNIPpTs+wPxsbjYGPYeSMpe3nswiWqx0V7Tzl64jPO7lJ8hDj3z1TexNXOeHlaOr0JSMfXN4q8Xct5NF/PLxz/SdVBPvpn6GQD7tiWwf8c+ajSoxd8rCp8QtjRzHUzEUSVXWVKlGq5DBwqkc5xWn/CbxnFs4gTs0Zz5EawnrT2SROaSnwlu0Bjn+pW+D7yE6Rwp6MJrL6Tf0H4AbFy5kWo1ctXB8VU5sDfvedKgWQNq1K3Bmz+5hzhUCK/AGz+9wQ3n3pCd5twB57LgiwV+iN43Bg67iAuv7A/A+nz3JdVqVGP/3oLXzvE6KDMjk9kffccVIwcVSFNWjRx5LSNGuCdn/eOPldSuXTN7W61a8ezZ4/1ePDY2hvbtW3HFFTkTL7Zt25KZM18EoEqVWPr160FWVlaeYRAi/4arHA1dLUlF9kyw1jqttd9Za0cAdYDpwEDgb2PMu0Xs95q1tr21tr0vGhIAfnhnNv/Xfxz/138cy75fTJdLuwHQoE0jUpNTOJyYVGCf6nXjsz+36dWePZt3ARARHUFQiLtdpduQ3vy1aG2esYxljWvXJhxVamAqV4egYIJadiFrfd5u6CY2Jy8cNetBcDCkJGOPJmEPH8BUdRfoQQ1a4Nq3k7Loq7e/4pbzRnPLeaP59bvf6HOZe2K4xm0acyz5WIEfiYvnLWZIuyu5tvMwru08jPTUdEaccx0A+3bvo02XNgDEVI2hdoPa7NmW4N8DKkGZ69cTVKs2jvh4CA4mrGdP0n89sW6jzn17CWnaFCq4h7+Etm1LVq6JG8ua2TO/ZXz/Oxjf/w4Wf7+I7pf1AKBRmzNJSU4pMJRh2byl3NhhGKO63siorjeSnpqe3ZAQXzdnMsJ6zesTHBJcLhoSAJo1PI1texLZufcAmZlZzF64jO7tWxRIl3wslaVrN9GjQ8FtZdW8d2bzYP9xPOipbzp76pv6J1jftO7VngRPfXNg936adnHnTXTVSsTXr0ni9r2+PwgfcW5ejyO+Fo5q8RAUTOjZPcn849c8aUyV6lQc+wgpLz+JKyFXfVIhDMLCsz8Ht2yPc2fBtx6UBTpHCvp65teMOX8MY84fw2/f/UYvTx18ZpszOZZ8rMBQhiXzlnB1+6sZ0WUEI7qMID01PU9DQkRUBC06teC373/z63GUpM/f/pIb+t3MDf1u5pfZC+l3ubtXX9O2TbzelwB55lHo2q8zf2/Y6q9wfW7atJl06tSfTp3689VX33Plle63d3Ts2IYjR5JJSPDemHDppRcwa9YPpKenZ69r0qQrjRu7/3z22bfcccd/1JAg4gcn/DYHa22GMWYt7kd27YCmPovqJK34cRkte7Rl8oKXSU9Nz9PL4M7p9/PWPa9wODGJm54ZQ1hkOMYYtq/bytsPuLta1mhYm5ueuQ2Xy8XujTt48+5XAnUoJcPlIuOrNwkbfj8YB1nLfsTu20lwR3ellbV4DsHNziK4TTesywmZGaR/8Fz27hlfv0WFwbdhgoJxHdxL+idlPD9wNxR07NmBGb+85X415F3PZm977O1HePbu5zlYxJOjd194j/HP3sW0OVMxxvDmE29x5FAZnn3c5SR5yvNUnvQ0OBykzfoW59athA+4CIDUr77EUTmW2GnTMBEVwbqIuPxyDgwfRta6daQtWECV114Hp5PMjZtI/fqrAB9QyVg2bylte7TjpZ+mkZ6azivjcuY8uG/G/zH17pc45OVm77hO559Nt8t6kpWZRUZ6Bs+NnuSPsP0iOCiI+66/nFsefwWny8XAHp1oWKcGH33/CwCD+7on8Jy3eCWdWzUmIqzsz7XizUpPffPUgpfJSE3nzVz1zdjp9zPdU9/c8MwYwiPDwRh2rNvKTE9989WUj7n+6Vt5dPazYAwfT/wvR8tyg5PLReqMKVS8dxI4HGTMn4Vr51ZCew8AIGPuV4Rdei0mMpqI6+4AyH4FpKNSZSre+aj7e4KCyFg4l6wVSwJ0ICVH50hBS+YtoUOPDrz585ukp6bz3Lice46HZzzMC/e8UGQdDNC5X2eW/bSM9NT0ItOVFb/PW8RZPTvy7i8zSU9L56k7J2dvmzjzcSaPf5YDew/wwIv3ElMlBgNsWruZZyc8D0BstcpM+/YVIiIjsC7L5TdcyrAe15NytGxOmj179jz69evBmjU/kZKSysiR47K3ffbZDEaNuju7p8KgQQN4+umpgQq11Bj/4ESW/LmSpKQj9Bp4NaOuv4bLBvQLdFjlVlmfu8ZXTHEZY4w5DbgCGApUBD4APrDWntDL04edfplyPpdXrioXL9AoUZfMLNs3Sb7wTsOy2zPGF0ZvKb1TtgTKu1+NDnQIpc7NF71WfKJTyHNnF/3j7FQ09rcTny3/VLHXpfomtxRXRqBDKHUWHdhYfKJTzJEdPwY6hFInpGp97xMplRPRFesH9DftkWNbSmX+FtkzwRjzK+55E/4H3GStLR9TtouIiIiIiIjIP1bcMId7gZ+s+nWIiIiIiIjIKciln8NeFTcB4wLgWmPMH8aYY54/S40x1/opPhEREREREREpZYob5nAtcAdwJ7AM91vY2wKTjTFYa2f6PEIRERERERGRALF6NaRXxc0GOAq4xFr7o7X2sLU2yVo7D7jMs01ERERERERETjHFNSZEW2u35l/pWRfti4BEREREREREpHQrbgLGot4XpHcJiYiIiIiISLmmCRi9K64xoYkxZqWX9Qao74N4RERERERERKSUK7YxwS9RiIiIiIiIiEiZUVxjQri1dj2AMaaCtTb9+AZjTCdgmy+DExEREREREQkkq2EOXhU3AeN7uT7/lm/bKyUci4iIiIiIiIiUAcX1TDCFfPa2LCIiIiIiIlKuWNQzwZvieibYQj57WxYRERERERGRU0BxPRNqG2Om4O6FcPwznuVaPo1MREREREREREql4hoTxuf6vDTftvzLIiIiIiIiIuWKJmD0rsjGBGvt2/4KRERERERERETKhiIbE4wxXxa13Vp7UcmGIyIiIiIiIlJ6qGeCd8UNczgb2AG8DyxCb3AQEREREREROeUV15gQD/QBhgJXAt8A71tr1/g6MBEREREREREpnYp8NaS11mmtnW2tHQZ0AjYB840xY/wSnYiIiIiIiEgA2QD/Ka2K65mAMaYCcAHu3gmnA1OAT30bloiIiIiIiIiUVqaoySSMMW8DzYFZwAfW2tX+CswXjDE3WWtfC3QcpYXyoyDlSUHKk7yUHwUpTwpSnuSl/ChIeVKQ8iQv5UdBypOClCcSSMU1JriAY57F3AkNYK210T6MrcQZY5Zaa9sHOo7SQvlRkPKkIOVJXsqPgpQnBSlP8lJ+FKQ8KUh5kpfyoyDlSUHKEwmkIoc5WGuLnFNBRERERERERE49aiwQERERERERkZNyqjUmaDxRXsqPgpQnBSlP8lJ+FKQ8KUh5kpfyoyDlSUHKk7yUHwUpTwpSnkjAFDlngoiIiIiIiIhIfqdazwQRERERERER+ZfUmCAiIiIiIiIiJ6VMNyYYY+KNMR8YYzYbY9YaY741xpxhjEk1xiz3rJtpjAnxpO9ujPna83m4McYaY3rl+r5LPOsuD9Qx+YLnuJbn++MyxtziOd4xudK+ZIwZHsBw/zVjzFHP/08v6viMMTOMMX8bY1YYY/7ynCu18n9PruXhxpiXPJ/PNMbM9+TlOmNMmRivVsQ1szpfuoeMMeNyLQcbY/YbY57Ml+5CY8yfnjxca4wZ6a9jKWmec+WZXMvjjDEP5Vq+yRiz3vNnsTGmq2f9ncaYN3Olu8oY841fg/cDY4zTc76vNsZ8ZYyJ8aw/fp09mittVWNM5vHrpbzJVVc0zrWuo6dM2GiMWWaM+cYY08Kz7SFjzK58ZXBMwA7AR3KdI2s8ZcKdxhiHZ1vu+jfOGPN1rnLj28BGXrIKu1ZybV9hjHk/37oi66OyzhhTJde5n5DveojzlBcjc6WP8tRTjTzLIcaYVcaYswJ3FCcm37//x8aYWkUce+jJnC/GmBG59s3w5MlyY8xEk+sexZPWa51VWph891iedQXurYwx/XId81FjzAbP55meffKUx8aYRZ7t240xibn2Pd3Ph3jSPMfxTq7lYM8x5P7tkvuYlhtjmhp3PZxq3Pdj6zz/3sM8+3Q3xvyW7+8JNsbsNcbU8O8RSnlUZhsTjDEG+AyYb61tYK1tCtwHxAGbrbWtgRZAbWBwIV+zChiaa3kIsMJnQQeItfYza23r43+AV4Cfge+AfcDtxpjQQMboQ8Ud33hrbSvgTOBP4McTzIspwHOePG0CvFgy4fpOMddMcfoCG4DBnu/BuBvpXgMGePKwDTDfF7H7STpwqTGmav4NxpgLgZFAV2ttY+Bm4D1jTDzuc6GdMaaL5ybwMWBM/u8oB1I953tz4CAwOte2LcCFuZYHAWv8GZyfDQV+wV1nYIyJAz4C7rPWNrLWtgWeBBrk2ue53OWwtTbJ30H7wfFzpBnQB+gPPOgl3SPAHGttK085NMGfQfpBodeKMaYJ7nuvc40xFfPt90/ro1LPWnsg1z3Iq+S6HoDLgN/JdT9mrU0G7gVe9qwaB/xqrV3k18D/mdz//hnAFYUdu7U2g5M4X6y103N9126gh2c5zzVUTJ1VmhW4t7LWfpfrmJcCV3mWr/Xsk6c8ttae5Un7f8CHufJ6q78P5h84BjQ3xoR7lvsAu/KlyX1Mra21az3rN1tr23jybQgw1hgzAvgJqJ2vMaU3sNpau8d3hyKnijLbmAD0ADKtta8eX2GtXQ7syLXsBBYDhbXu/wx09LR4RwINgeW+Crg0MMacgbuAvQZwAYnAD8CwQMblQyd0fNbtOSABOP8EvrcGsDPX/qv+TZB+Uuw1U4ShwAvAdqCTZ10UEAwc8HxXurV2Q0kG7GdZuBtHxnrZdg/uG/39ANbaZcDbwGhrbRYwCvdN7yTgLWvtFv+EHDC/kbdcTQXWGWPae5avwP3jutzx1BVdgOvx3LwCtwJvW2t/PZ7OWvuLtfZz/0dYOlhr9wE3Abceb4DMJX/5udKfsflZ/mvlSuAd4HvgIm87/IP6qKwbCtyF+wdPdl5Zaz8CXMaYu3H/GL43QPH9Gz/jvrc8USd9vhSi0DrrJL4jEE7q3qqQ8rismwVc4Pk8FHi/iLReee5B7gRus9a6gI9x18vHDfkn3yviTVluTGgO/FFUAmNMGHAWMLuQJBaYC/QDLga+LMkASxvPk+T3gHHW2u25Nk0E7jLGBAUmMp87meNbBjQuNhU8B8wzxswyxozN3y2xlCrqmmmQu9sc7hs3ADwt5L2Ar3FXPkMBrLUHcV8z24wx7xt39/6yXKaAu0HgKmNMpXzrm1Ew75Z61uP5EbkOd2v/JF8HGUie66gXBcvLD4AhxpjagBP3U7PyaCAw21r7F3DQGNMW93mwrJj9xua6xn70dZClgeeG1gFUz7fpZeBNY8yPxpj7jTE1/R+d7xVyrVwBfEiusrQIJ1oflVnGmDpAvLV2Me4GyCvyJbkDeAp4zFPnlBnGmGDcjUEn9LChBM6X3Iqss0qxk723GkjB8risO16XhgEtgfy9ca7IN8whvOBXAHnLj/fJ6UlXAXevsU9KPnQ5FZX1G//CNPD8IDoAbC/mqccHuC+wU6GV7lFgjbX2g9wrrbV/4+7BcWVAovKxkzy+/E/QCnyd5zunA01wt/Z2B373FNBl1eZ8Q2FezbXtQuBHa20K7srnkuMNM9baG3Df/CzG3Q31Lf+GXbKstUeAmcBtJ5Dc4DkfPE9H2gMhQDWfBRhY4bnK1VhgTr7ts3F3yRyK++a3vBqKu97A8/8CN/ieMbvrjDEv5Fqdu2tzD38EWkoUKFOttd8B9YHXcd/s/mmMKU/XjddrxRjTAUi01m7D3WOurTGmchHfU1x9VB4MIacXk7fr6TxgD+7G8LLi+L//Uty9+d4sOnmJnS/Fya6zSqt/cG9VbHlc1nh+s5yO+1i8zSeTf5hDaiFflV1+WGuXAJHGmDNxN3D9bq09VMKhyymqLDcmrAHaFbLt+JwJDYFOxphCu4Z5WsObA1U9LZvlkjGmO+5xibcWkuQJ3N3iyvI5UZQTPb42uJ8wA6TmG68aC+w/vmCt3W2tfctaezHuLvKl/WanqGumKEOB3saYrbifdFTBPWQCcHdD9HTJ7YP7HCvrnsfdZTL3eOa1FMy7tp71AA8D/wUex/1kpTxK9ZSrdYFQ8nWX9Yz9/QN3d+Vy+cTDGFMF6Am84bkexuN+crgG9/kAuMfsAv8B8vdwOaUYY+rj7qWyL/82a+1Ba+171tprgCXAuf6Oz4cKu1aGAo09585mIJqiy8zc9VF5NRQY7smTL4FWJmfSxZq4G3Y7Av2NMS0DFuXJSc31Q2+Mp2wsNj3//nzJrbg6q9Q60XurwspjL8OqyqIvgaf5dw8585cfp9LDU/GjsvzDcR5QwRhz4/EVnlbcuseXPROLTKD4cXb34p6IrlzytGRPB6617kmNCrDWrsddyVzobXtZV9zxGbfbcI/XOz4sZgFwtWd7OO6JPH/0LJ9nct4SEo/7B3b+SXJKm2KvmfyMMdFAV+A0a+3p1trTcd/oDDXGRHoaqY5rDWwr+bD9y9OV9iPcDQrHTQKe8ty8YIxpDQwHXjHuGfsvwN0V9zWgrjGmjz9j9idr7WHcN/jjjl8DuTwD3GOtPeD/yPzicmCmtbau53qoA/yNezzzcGNM51xpIwISYSnh6WnwKvCStdbm29bTGBPh+RyFe6LK7QW/pWzLd61UwD0xactcZenFeO/Z4q0+Knc8T0krWmtr5cqTJ8kZ+/4c8IS1difu8d8vl5Mfil790/OlEIXWWSUcdok6yXurwsrjUvXWin/oLeCRfzofl3FPtvg0eScHfx/3PW1PyvmwbvGv4EAH8E9Za60x5hLgeWPMBCAN2Ip7fF1unwMPGWPOKeK7ZvkozNLiZtxjVqfmq4fzt0w+jnsG6fLK2/FNNsb8B/eN/++4Z0Y+/hThdmCa56bO4K60fvJs6wu8YIxJ8yyPt9Ym+Db8f+ckrpncLgXmWWvTc637AveNyp3A3caYabgn4DuG+2alPHiGXL14rLVfGvfEYL8aYyyQjLtSTsDdHXOstTYNwBgzCphpjGlti38iVSZZa/80xqzAfdP/c671ayj/b3GYmG/dJ7iHUF2B++a9Fu4n8ftxv7XguLHGmKtzLQ+0ZWN28ZNxvLt2CO4niu8Az3pJ1w54yRiThfuhxhuebrjlTq5rZTCwy1qb+4fRT0BTk/N6tqLqo/JoKO43DOX2CfCBMeZ34DQ8QwSstV95GsKvxT2RYLl0oueLLWYW/sLqrOL287MIY8zOXMvP4n4D24neWxVVHv9cMHnZ4WlAe6GQzVeYvK/5HIV7jqIGxpg/gTDc/94veoaNHP/OtcaYFOAPa+0xH4UupyCT74GBiIiIiIiIiEiRyvIwBxEREREREREJADUmiIiIiIiIiMhJUWOCiIiIiIiIiJwUNSaIiIiIiIiIyElRY4KIiIiIiIiInBQ1JoiIiIiIiIjISVFjgoiIiIiIiIiclP8HVl8YbM5KvgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_heatmap = boston_df1.corr()\n",
    "f , ax = plt.subplots(figsize=(20,12))\n",
    "sb.heatmap(boston_df1.corr(),vmax=0.8, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RM         0.695360\n",
       "PTRATIO    0.507787\n",
       "LSTAT      0.737663\n",
       "MEDV       1.000000\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation with output variable\n",
    "cor_target = abs(boston_df1.corr()[\"MEDV\"])\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target>0.5]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            LSTAT   PTRATIO\n",
      "LSTAT    1.000000  0.374044\n",
      "PTRATIO  0.374044  1.000000\n",
      "             RM     LSTAT\n",
      "RM     1.000000 -0.613808\n",
      "LSTAT -0.613808  1.000000\n"
     ]
    }
   ],
   "source": [
    "print(boston_df[[\"LSTAT\",\"PTRATIO\"]].corr())\n",
    "print(boston_df[[\"RM\",\"LSTAT\"]].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrapper Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>MEDV</td>       <th>  R-squared:         </th> <td>   0.734</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.728</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   113.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 21 Jun 2021</td> <th>  Prob (F-statistic):</th> <td>2.23e-133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:57:34</td>     <th>  Log-Likelihood:    </th> <td> -1504.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3036.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   493</td>      <th>  BIC:               </th> <td>   3091.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>   41.6173</td> <td>    4.936</td> <td>    8.431</td> <td> 0.000</td> <td>   31.919</td> <td>   51.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CRIM</th>    <td>   -0.1214</td> <td>    0.033</td> <td>   -3.678</td> <td> 0.000</td> <td>   -0.186</td> <td>   -0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ZN</th>      <td>    0.0470</td> <td>    0.014</td> <td>    3.384</td> <td> 0.001</td> <td>    0.020</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INDUS</th>   <td>    0.0135</td> <td>    0.062</td> <td>    0.217</td> <td> 0.829</td> <td>   -0.109</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHAS</th>    <td>    2.8400</td> <td>    0.870</td> <td>    3.264</td> <td> 0.001</td> <td>    1.131</td> <td>    4.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NOX</th>     <td>  -18.7580</td> <td>    3.851</td> <td>   -4.870</td> <td> 0.000</td> <td>  -26.325</td> <td>  -11.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RM</th>      <td>    3.6581</td> <td>    0.420</td> <td>    8.705</td> <td> 0.000</td> <td>    2.832</td> <td>    4.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AGE</th>     <td>    0.0036</td> <td>    0.013</td> <td>    0.271</td> <td> 0.787</td> <td>   -0.023</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIS</th>     <td>   -1.4908</td> <td>    0.202</td> <td>   -7.394</td> <td> 0.000</td> <td>   -1.887</td> <td>   -1.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RAD</th>     <td>    0.2894</td> <td>    0.067</td> <td>    4.325</td> <td> 0.000</td> <td>    0.158</td> <td>    0.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TAX</th>     <td>   -0.0127</td> <td>    0.004</td> <td>   -3.337</td> <td> 0.001</td> <td>   -0.020</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PTRATIO</th> <td>   -0.9375</td> <td>    0.132</td> <td>   -7.091</td> <td> 0.000</td> <td>   -1.197</td> <td>   -0.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LSTAT</th>   <td>   -0.5520</td> <td>    0.051</td> <td>  -10.897</td> <td> 0.000</td> <td>   -0.652</td> <td>   -0.452</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>171.096</td> <th>  Durbin-Watson:     </th> <td>   1.077</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 709.937</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.477</td>  <th>  Prob(JB):          </th> <td>6.90e-155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.995</td>  <th>  Cond. No.          </th> <td>1.17e+04</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.17e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   MEDV   R-squared:                       0.734\n",
       "Model:                            OLS   Adj. R-squared:                  0.728\n",
       "Method:                 Least Squares   F-statistic:                     113.5\n",
       "Date:                Mon, 21 Jun 2021   Prob (F-statistic):          2.23e-133\n",
       "Time:                        08:57:34   Log-Likelihood:                -1504.9\n",
       "No. Observations:                 506   AIC:                             3036.\n",
       "Df Residuals:                     493   BIC:                             3091.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         41.6173      4.936      8.431      0.000      31.919      51.316\n",
       "CRIM          -0.1214      0.033     -3.678      0.000      -0.186      -0.057\n",
       "ZN             0.0470      0.014      3.384      0.001       0.020       0.074\n",
       "INDUS          0.0135      0.062      0.217      0.829      -0.109       0.136\n",
       "CHAS           2.8400      0.870      3.264      0.001       1.131       4.549\n",
       "NOX          -18.7580      3.851     -4.870      0.000     -26.325     -11.191\n",
       "RM             3.6581      0.420      8.705      0.000       2.832       4.484\n",
       "AGE            0.0036      0.013      0.271      0.787      -0.023       0.030\n",
       "DIS           -1.4908      0.202     -7.394      0.000      -1.887      -1.095\n",
       "RAD            0.2894      0.067      4.325      0.000       0.158       0.421\n",
       "TAX           -0.0127      0.004     -3.337      0.001      -0.020      -0.005\n",
       "PTRATIO       -0.9375      0.132     -7.091      0.000      -1.197      -0.678\n",
       "LSTAT         -0.5520      0.051    -10.897      0.000      -0.652      -0.452\n",
       "==============================================================================\n",
       "Omnibus:                      171.096   Durbin-Watson:                   1.077\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              709.937\n",
       "Skew:                           1.477   Prob(JB):                    6.90e-155\n",
       "Kurtosis:                       7.995   Cond. No.                     1.17e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.17e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = boston_df1[['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS','RAD', 'TAX', 'PTRATIO', 'LSTAT']]\n",
    "Y = boston_df1[['MEDV']]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2, random_state = 30)\n",
    "import statsmodels.api as sm\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y,X).fit()\n",
    "predictions = model.predict(X)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table displays all the statistics regarding the independent variables. But right now, we are only interested in looking at the column with the p-values in it. We can clearly see that the 'CRIM', 'ZN','CHAS', 'NOX', 'RM', 'DIS','RAD', 'TAX', 'PTRATIO', 'LSTAT' have a p-value less than 0.05!. And the other variables like 'INDUS' and 'AGE' have a P-value greater than 0.05.\n",
    "\n",
    "But the question is, what does this p-value mean in a regression model? For that, let’s understand what’s the hypothesis for which these p-values are calculated:\n",
    "\n",
    "Null Hypothesis: The independent variable has no significant effect over the target variable\n",
    "\n",
    "Alternate Hypothesis: The independent variables have a significant effect on the target variable\n",
    "\n",
    "Now, the above results show that 'CRIM', 'ZN', 'CHAS', 'NOX', 'RM','DIS','RAD', 'TAX', 'PTRATIO', 'LSTAT' have a significant effect over the “MEDV”. So we have to consider all the variables in our model.And 'INDUS' , 'AGE' have not a significant over 'MEDV'.So we have to reject those variable from our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Embedded Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harshita\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha using built-in LassoCV: 0.724820\n",
      "Best score using built-in LassoCV: 0.694230\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "reg = LassoCV()\n",
    "reg.fit(X, Y)\n",
    "print(\"Best alpha using built-in LassoCV: %f\" % reg.alpha_)\n",
    "print(\"Best score using built-in LassoCV: %f\" %reg.score(X,Y))\n",
    "coef = pd.Series(reg.coef_, index = X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 10 variables and eliminated the other 3 variables\n"
     ]
    }
   ],
   "source": [
    "print(\"Lasso picked \" + str(sum(coef != 0)) + \" variables and eliminated the other \" +  str(sum(coef == 0)) + \" variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature importance using Lasso Model')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J42pVhX6/JL8J/AXwDODnwCxwDvCsUdYlSdK4WpWhn2R74F3AsVX17b6mjcCLk2y3lW2PSjKTZGZ2dnalS5UkaWysytAH/ga4uKpO719ZVd8EPg8csdCGVbWpqqaranpqamqFy5QkaXysuv9wJ8nBwJOBAxfo8mrgfcAnh1SSJEkTYVWN9JPcEXgH8Myq+uF8farqq8AlwCHDrE2SpHG32kb6zwPuBLw1Sf/60wb6vQq4cFhFSZI0CVZV6FfVa4DXLND82r5+F7HKZikkSVrtDE5Jkhph6EuS1AhDX5KkRhj6kiQ1YlVdyCeNmy0b14+6BElaMkf6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEv3QJrjzuTtcedOeoyJGlJDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJasTIQj/JE5NUkv371j0wyblJ/l+SC5KcmeQ+XdvxSa5Msrnvtsuo6pckadysGeFjbwA+DRwOHJ9kD+A9wBFVdR5AkoOAuwFf6rY5oar+9yiKlSRp3I1kpJ9kZ+AhwHPohT7AC4CT5wIfoKo+XVUfHH6FkiRNnlFN7z8B+EhVXQpcl+RA4N7ABYts96K+qf1PzNchyVFJZpLMzM7OLm/VkiSNsVGF/gbg9O7+6d3yr0jyuSRfSfJ3fatPqKp13e2h8+24qjZV1XRVTU9NTS1/5ZIkjamhn9NPshvwMOCAJAVsBxRwMnAg8CGAqnpQkkOBQ4ZdoyRJk2gUI/1DgVOqat+qWltVdwG+CXwMODLJ7/T1vc0I6pMkaSKN4ur9DcDGgXXvB44ADgNem2Qf4GrgGuCv+/q9KMnT+5afUFVbVrBWSZImxtBDv6oOnmfdm/oWf3+B7Y4Hjl+RoiRJaoDfyCdJUiMMfUmSGmHoS5LUCENfkqRGjPK796Wxt2Xj+lGXIElL5khfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRFDD/0kleT1fcvHJjm+b/moJF/tbp9PclC3/pgkb+/r97QkZw61eEmSxtgoRvo/AZ6UZPfBhiSHAM8FDqqq/YHnAe9KsifwJuD+SR6SZBfglcCfDq9sSZLG2yhC/0ZgE/CiedpeBrykqq4BqKoLgJOB51fVjcCfAG8B/hY4saq+MZySJUkaf6M6p/8W4GlJ7jCw/t7A+QPrZrr1VNV5wFeAR9AL/l/TnR6YSTIzOzu7vFVLkjTGRhL6VfUD4BTg6CV0D1AASXYGpoHtgakF9r2pqqaranpqat4ukiQ1aZRX778ReA5w2751lwD3H+h3YLce4K+AdwKvAk5Y4fokSZooIwv9qroOeA+94J/zt8Brk+wGkGQdcCTwD0nuA6wHXkvvmoB9kzxymDVLkjTO1oz48V8PvGBuoarOSLIPcF6SAn4IPB34LvBe4EVV9WOAJH8CnJJkXVX9dPilS5I0XoYe+lW1c9/97wG3GWh/K/DWeTY9aKDfDHCvlahRkqRJ5DfySZLUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1IgVC/0keyY5PcllSS5JclaS30zy5YF+xyc5tm95TZJrkrxmoN8hSS5MclG3v+euVO2SJE2iNSux0yQBPgCcXFWHd+vWAXssYfM/AL4GPDXJy6uqkmwPbAIeWFVXJNkRWLsStUuSNKlWaqT/UOBnVfW2uRVVtRm4fAnbbgD+Dvg28OBu3e3ovUG5ttvXT6rqa8tZsCRJk26lQv8A4PwF2u6WZPPcDXjeXEOSnYCHAx8GTqP3BoCqug44A/hWktOSPC3JvLUnOSrJTJKZ2dnZ5XtGkiSNuVFcyHdZVa2buwFv62s7BPhEVf038H7giUm2A6iqP6L3huDzwLHAifPtvKo2VdV0VU1PTU2t5POQJGmsrFToXwzc/2ZstwF4RJIt9GYKdqN3qgCAqvpSVZ0APBJ48jLUKUlSM1Yq9D8O7Jjkj+dWJHkAsO9CGyS5PXAQcNeqWltVa4HnAxuS7Jzk4L7u64BvLX/ZkiRNrhUJ/aoq4InAI7uP7F0MHA9ctZXNngR8vKp+0rfuQ8DjgO2Alyb5WncdwF8BR65A6ZIkTaz08nkyTU9P18zMzKjLkCRpaJKcX1XT87X5jXySJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1YixDP8nLR12DJEnjZixDHzD0JUnaRisa+kmemeSLSS5KcmqSfZOc0607J8ldu34nJXlTkvOSfCPJod36vZJ8MsnmJF9O8rtJNgI7dev+eSXrlyRpkqxZqR0nuTfwCuAhVXVNkl2Bk4FTqurkJP8DeBPwhG6TvYCDgP2BM4D3AUcAH62qVyXZDrhNVX0qyQuqat0Cj3sUcBTAXe9615V6epIkjZ2VHOk/DHhfVV0DUFXXAb8NvKtrP5VeyM/5YFX9vKouAfbo1n0BeHaS44H7VNUPF3vQqtpUVdNVNT01NbVMT0WSpPG3kqEfoBbp09/+k4FtqapPAr8HXAmcmuSZy1qhJEkNWcnQPwd4apLdALrp/fOAw7v2pwGf3toOkuwLXF1V/wi8HTiwa/pZku1XpGpJkibUip3Tr6qLk7wK+I8kNwEXAkcDJyZ5CTALPHuR3RwMvCTJz4AfAXMj/U3AF5NcUFVPW5EnIEnShEnVYjPw42t6erpmZmZGXYYkSUOT5Pyqmp6vbVw/py9JkraRoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS/dTGuPO3PUJUjSNjH0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRyxb6SX7U/VybpJL8aV/bm5Mc2d0/Kck3k1yU5NIkpyTZZ3A/fctHJnlzd3+/JOcm2ZzkK0k2LVf9kiRNupUa6V8NvDDJDgu0v6Sq7gfsB1wIfGIrffu9CTihqtZV1T2Bv1+eciVJmnwrFfqzwDnAs7bWqXpOAL4LPHoJ+90LuKJv+y/dkiIlSWrJSp7T3wi8OMl2S+h7AbD/EvqdAHw8yb8leVGSXQY7JDkqyUySmdnZ2W2rWJKkCbZioV9V3wQ+DxyxhO5ZbHfdPt8B3BN4L3Aw8NkkOw487qaqmq6q6ampqW2uW5KkSbXSV++/GnjZEh7nt4CvdPdvGDi/vytwzdxCVV1VVSdW1eOBG4EDlrFeSZIm1oqGflV9FbgEOGS+9vQcTe9c/Ue61f8BPL1r3wl4KvCJbvlRSbbv7u8J7AZcuZLPQZKkSTGMz+m/CrjzwLrXJbkIuBR4APDQqvpp1/ZC4ElJNgOfBd5bVZ/s2v4A+HK37UfpfQrguyv9BCRJmgRrlmtHVbVz93MLfVPuVXURfW8uqurIRfZzJQvMDFTVMcAxt7xaSZLa4zfySZLUCENfkqRGGPqSJDXC0JckqRGGvnQzbdm4ftQlSNI2MfQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktSINaMuQBo3a4878xf3t2xcP8JKJGnbONKXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEaMJPST7JZkc3f7bpIr+5b3SPKzJM/t63+7JJcluUe3vH2SLyV50CjqlyRpHI0k9Kvq2qpaV1XrgLcBJ/QtPxn4LLChr/8PgT8H3tKtOhY4r6o+N9TCJUkaY6txen8D8GLgzkn2mVtZVe8Bfp7kpcDz6L0JkCRJS7SqQj/JXYA9q+rzwHuAwwa6/BnwWuCVVXXdAvs4KslMkpnZ2dkVrVeSpHGyqkIfOJxe2AOcTt8Uf+dRwHeAAxbaQVVtqqrpqpqemppamSolSRpDqy30NwBHJtkCnAHcr+/ivb2Bo4EHAo9Jct+RVSlJ0hhaNaGfZD/gtlW1T1Wtraq1wGvojf4BTgBeXVVXAMcAb0mS0VQrSdL4WTWhT2+U/4GBde8HNiR5JHBX4O0AVfWvwH8CzxxqhZIkjbE1oy6gqo7fStsXgXt1i2cPtD1uBcuSJGnirKaRviRJWkGGviRJjTD0JUlqhKEvSVIjRn4hnzRutmxcP+oSJOlmcaQvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGrBl1AdKorT3uzJu97ZaN65exEklaWY70JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1Yiihn2TPJKcnuSzJJUnOSvKbSW5Isrlbd0qS7bv+Byf5cHf/yCSV5OF9+3tit+7QYdQvSdIkWPHQTxLgA8C5VXW3qroX8HJgD+CyqloH3Ae4M/DUBXbzJWBD3/LhwEUrVrQkSRNoGCP9hwI/q6q3za2oqs3A5X3LNwGfB/ZZYB+fAh6YZPskOwN3BzavVMGSJE2iYYT+AcD5W+uQ5NbAg4CPLNClgH8H/hB4PHDGVvZ1VJKZJDOzs7M3r2JJkibQqC/ku1uSzcC1wLer6otb6Xs6vWn9w4HTFupUVZuqarqqpqemppa1WEmSxtkwQv9i4P4LtM2d07878OAkj1toJ1X1eXqzBrtX1aXLXqUkSRNuGKH/cWDHJH88tyLJA4B955ar6jvAccCfL7KvP6d3EaAkSdpGKx76VVXAE4FHdh/Zuxg4HrhqoOsHgdsk+d2t7OvfquoTK1WrJEmTbCj/tW5VXcX8H8c7oK9PAffrazu3W38ScNI8+zxyGUuUJGnijfpCPkmSNCSGviRJjTD0JUlqhKEvSVIjDH1JkhoxlKv3pdVsy8b1oy5BkobCkb4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1JkhqxZtQFtGLtcWeOugStgC0b14+6BElaMkf6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIasehH9pLcBHyp6/sV4M+Auc+f7QncBMx2yw8Ebujr/03gGVV1fd/+LgIuqaoNSZ4NvLBruhfwtW5/HwG+CkxX1Qu67Y4Cjun6/gA4pqo+vc3PWJKkRi1lpH9DVa2rqgOAnwKHdcvrgLcBJ8wtV9VPB/pfBzx/bkdJ7tk95u8luW1VvaNvX1cBD+2Wj+svIMkhwHOBg6pqf+B5wLuS7HlLD4AkSa3Y1un9TwF334b+nwH26Vs+AjgV+BjwuG3Yz8uAl1TVNQBVdQFwMn1vKCRJ0tYtOfSTrAEeTW/qfin9twMeDpzRt/ow4N3AacCGpZfJvYHzB9bNdOsHH/eoJDNJZmZnZwebJUlq1lJCf6ckm+mF7LeBty+x/7XArsDZAEkeAMxW1beAc4ADk9zxZtYNEKAGV1bVpqqarqrpqampW7B7SZImy7ac019XVX/anbdftD+wL7ADv5yC3wDsn2QLcBlwe+DJS6zzEuD+A+sO7NZLkqQlWLGP7FXV94GjgWOT7Ag8BbhvVa2tqrXA41n6FP/fAq9NshtAknXAkcA/LHPZkiRNrBX9X/aq6sLuI3pPBa6sqiv7mj8J3CvJXlX1nUX2c0aSfYDzkhTwQ+Dpi20nSZJ+adHQr6qdt9J2/GL9q+qx3d1TB9bfBOzVt7x2oP0k4KS+5bcCb12sXkmSND+/kU+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWrEin5kT7+0ZeP6UZcgSWqcI31Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSIwx9SZIaYehLktQIQ1+SpEYY+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDVizagLmHRrjztz1CVoBW3ZuH7UJUjSkjnSlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUiGUP/SQ/mmfdfknOTbI5yVeSbEryh93y5iQ/SvK17v4p3TZPTFJJ9u+WP9e1fzvJbN+2a5f7OUiSNImG9Tn9NwEnVNWHAJLcp6q+BHy0Wz4XOLaqZvq22QB8GjgcOL6qHtT1PRKYrqoXDKl2SZImwrCm9/cCrphb6AJ/QUl2Bh4CPIde6EuSpFtoWKF/AvDxJP+W5EVJdlmk/xOAj1TVpcB1SQ5c6gMlOSrJTJKZ2dnZm1+xJEkTZiihX1XvAO4JvBc4GPhskh23sskG4PTu/und8lIfa1NVTVfV9NTU1M2sWJKkyTO0796vqquAE4ETk3wZOAA4f7Bfkt2AhwEHJClgO6CSvLSqalj1SpI0aYYy0k/yqCTbd/f3BHYDrlyg+6HAKVW1b1Wtraq7AN8EDhpGrZIkTaqVGOnfJskVfctvAO4M/F2SH3frXlJV311g+w3AxoF17weOAD61rJVKktSQZQ/9qlpo9uCYrWxz8Hz3+9a9qe/+ScBJN7c+SZJa5TfySZLUCENfkqRGGPqSJDXC0JckqRGGviRJjRjal/O0asvG9aMuQZIkwJG+JEnNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSI9aMuoBxs/a4M0ddglaRLRvXj7oESVoyR/qSJDXC0JckqRGGviRJjTD0JUlqhKEvSVIjDH1Jkhph6EuS1IhVEfpJbkqyOcnFSS5KckySW3VtByf5cHd/jyQf7vpckuSs0VYuSdL4WC1fznNDVa0DSHIn4F3AHYC/HOj318DZVfV3Xd/7DrNISZLG2aoY6ferqquBo4AXJMlA817AFX19vzjM2iRJGmerLvQBquob9Gq700DTW4C3J/lEklck2Xtw2yRHJZlJMjM7OzuMciVJGgurMvQ7g6N8quqjwG8A/wjsD1yYZGqgz6aqmq6q6ampqcFdSJLUrFUZ+kl+A7gJuHqwraquq6p3VdUzgC8Avzfs+iRJGkerLvS7kfvbgDdXVQ20PSzJbbr7twPuBnx7+FVKkjR+VsvV+zsl2QxsD9wInAq8YZ5+9wfenORGem9Y/qmqvjC0KiVJGmOrIvSraruttJ0LnNvdfx3wuuFUJUnSZFl10/uSJGllGPqSJDXC0JckqRGGviRJjVgVF/KNky0b14+6BEmSbhZH+pIkNcLQlySpEYa+JEmNMPQlSWqEoS9JUiMMfUmSGmHoS5LUCENfkqRGGPqSJDXC0JckqRGpqlHXsGKSzALfGnUdq9TuwDWjLmICeBxvOY/h8vA4Lo9JOI77VtXUfA0THfpaWJKZqpoedR3jzuN4y3kMl4fHcXlM+nF0el+SpEYY+pIkNcLQb9emURcwITyOt5zHcHl4HJfHRB9Hz+lLktQIR/qSJDXC0JckqRGGfiOS7Jrk7CT/r/t5xwX6bUnypSSbk8wMu87VKMmjknwtydeTHDdPe5K8qWv/YpIDR1HnareE43hwku93r73NSf5iFHWuZklOTHJ1ki8v0O5rcQmWcBwn9rVo6LfjOOCcqroHcE63vJCHVtW6Sf6s6lIl2Q54C/Bo4F7AhiT3Guj2aOAe3e0o4K1DLXIMLPE4Anyqe+2tq6q/HmqR4+Ek4FFbafe1uDQnsfXjCBP6WjT02/F44OTu/snAE0ZXylh5IPD1qvpGVf0UOJ3esez3eOCU6vkssEuSvYZd6Cq3lOOoRVTVJ4HrttLF1+ISLOE4TixDvx17VNV3ALqfd1qgXwEfS3J+kqOGVt3qtQ9wed/yFd26be3TuqUeo99OclGSf0ty7+GUNlF8LS6fiXwtrhl1AVo+Sf4d2HOepldsw24eUlVXJbkTcHaSr3bviluVedYNfs51KX1at5RjdAG97wz/UZLHAB+kN02tpfO1uDwm9rXoSH+CVNUjquqAeW4fAr43N83X/bx6gX1c1f28GvgAvWnZll0B3KVv+c7AVTejT+sWPUZV9YOq+lF3/yxg+yS7D6/EieBrcRlM8mvR0G/HGcCzuvvPAj402CHJbZPcbu4+8AfAvFe3NuQLwD2S/H9JdgAOp3cs+50BPLO7cvrBwPfnTqXoFxY9jkn2TJLu/gPp/X26duiVjjdfi8tgkl+LTu+3YyPwniTPAb4NPAUgyd7AP1XVY4A9gA90r/U1wLuq6iMjqndVqKobk7wA+CiwHXBiVV2c5Hld+9uAs4DHAF8H/ht49qjqXa2WeBwPBf5nkhuBG4DDy68M/RVJTgMOBnZPcgXwl8D24GtxWyzhOE7sa9Gv4ZUkqRFO70uS1AhDX5KkRhj6kiQ1wtCXJKkRhr4kSY0w9CVJaoShL0lSI/5/H9WwMrJsZfAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_coef = coef.sort_values()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n",
    "imp_coef.plot(kind = \"barh\")\n",
    "plt.title(\"Feature importance using Lasso Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-79d6890a5705>:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model = RF.fit(X_train,Y_train)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = boston_df1[['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS','RAD', 'TAX', 'PTRATIO', 'LSTAT']]\n",
    "Y = boston_df1[['MEDV']]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2, random_state = 30)\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RF = RandomForestRegressor(n_estimators=100)\n",
    "model = RF.fit(X_train,Y_train)\n",
    "plt.barh(X.columns,model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "clf = DecisionTreeRegressor()\n",
    "clf = clf.fit(X_train,Y_train)\n",
    "\n",
    "from sklearn import tree\n",
    "s = plt.figure(figsize=(20,10))\n",
    "tree.plot_tree(clf, max_depth=2, filled=True, fontsize=10)\n",
    "plt.title(\"Decision Tree for Boston Housing data\", fontsize=30)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Chi-Square Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"C:\\\\Users\\\\Harshita\\\\Desktop\\\\Data Science\\\\Hands-on-ML-Basic-to-Advance-\\\\Different types of feature selection techniques\\\\tips.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoker  No  Yes\n",
      "sex            \n",
      "Female  54   33\n",
      "Male    97   60\n"
     ]
    }
   ],
   "source": [
    "dataset_table=pd.crosstab(dataset['sex'],dataset['smoker'])\n",
    "print(dataset_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54, 33],\n",
       "       [97, 60]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_table.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed Values :-\n",
      " [[54 33]\n",
      " [97 60]]\n"
     ]
    }
   ],
   "source": [
    "#Observed Values\n",
    "Observed_Values = dataset_table.values \n",
    "print(\"Observed Values :-\\n\",Observed_Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "val=stats.chi2_contingency(dataset_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.008763290531773594,\n",
       " 0.925417020494423,\n",
       " 1,\n",
       " array([[53.84016393, 33.15983607],\n",
       "        [97.15983607, 59.84016393]]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "Expected_Values=val[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree of Freedom:- 1\n"
     ]
    }
   ],
   "source": [
    "no_of_rows=len(dataset_table.iloc[0:2,0])\n",
    "no_of_columns=len(dataset_table.iloc[0,0:2])\n",
    "ddof=(no_of_rows-1)*(no_of_columns-1)\n",
    "print(\"Degree of Freedom:-\",ddof)\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2\n",
    "chi_square=sum([(o-e)**2./e for o,e in zip(Observed_Values,Expected_Values)])\n",
    "chi_square_statistic=chi_square[0]+chi_square[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square statistic:- 0.001934818536627623\n"
     ]
    }
   ],
   "source": [
    "print(\"chi-square statistic:-\",chi_square_statistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "critical_value: 3.841458820694124\n"
     ]
    }
   ],
   "source": [
    "critical_value=chi2.ppf(q=1-alpha,df=ddof)\n",
    "print('critical_value:',critical_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.964915107315732\n",
      "Significance level:  0.05\n",
      "Degree of Freedom:  1\n",
      "p-value: 0.964915107315732\n"
     ]
    }
   ],
   "source": [
    "#p-value\n",
    "p_value=1-chi2.cdf(x=chi_square_statistic,df=ddof)\n",
    "print('p-value:',p_value)\n",
    "print('Significance level: ',alpha)\n",
    "print('Degree of Freedom: ',ddof)\n",
    "print('p-value:',p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retain H0,There is no relationship between 2 categorical variables\n",
      "Retain H0,There is no relationship between 2 categorical variables\n"
     ]
    }
   ],
   "source": [
    "if chi_square_statistic>=critical_value:\n",
    "    print(\"Reject H0,There is a relationship between 2 categorical variables\")\n",
    "else:\n",
    "    print(\"Retain H0,There is no relationship between 2 categorical variables\")\n",
    "    \n",
    "if p_value<=alpha:\n",
    "    print(\"Reject H0,There is a relationship between 2 categorical variables\")\n",
    "else:\n",
    "    print(\"Retain H0,There is no relationship between 2 categorical variables\")"
   ]
  }
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