Abalone+Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Sex', 'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings']\n",
    "abalone = pd.read_csv('abalone.data', names=cols, header=None)\n",
    "abalone.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex               0\n",
       "Length            0\n",
       "Diameter          0\n",
       "Height            0\n",
       "Whole weight      0\n",
       "Shucked weight    0\n",
       "Viscera weight    0\n",
       "Shell weight      0\n",
       "Rings             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirmation that there is indeed no null values present\n",
    "abalone.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
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       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings     Age  \n",
       "0         0.150     15     old  \n",
       "1         0.070      7   young  \n",
       "2         0.210      9  medium  \n",
       "3         0.155     10  medium  \n",
       "4         0.055      7   young  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 8, 10, abalone['Rings'].max()]\n",
    "group_names = ['young', 'medium', 'old']\n",
    "age = pd.cut(abalone['Rings'], bins, labels=group_names)\n",
    "abalone['Age'] = age\n",
    "abalone.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_Encode</th>\n",
       "      <th>Sex_Encode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "      <td>old</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "      <td>young</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "      <td>medium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "      <td>medium</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "      <td>young</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings     Age  Age_Encode  Sex_Encode  \n",
       "0         0.150     15     old           1           2  \n",
       "1         0.070      7   young           2           2  \n",
       "2         0.210      9  medium           0           0  \n",
       "3         0.155     10  medium           0           2  \n",
       "4         0.055      7   young           2           1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "abalone['Age_Encode'] = le.fit_transform(abalone['Age'])\n",
    "abalone['Sex_Encode'] = le.fit_transform(abalone['Sex'])\n",
    "abalone.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the scatterplots below, age groups ['young', 'medium', 'old'] correspond to ['green', 'blue', 'red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nnIW27rc4ebTGcYQ63lD8YiQCqlpsERQ5syWqxSml3vgEwlddC8ROGvL7veWs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\nSjlIgGv8GAIPPYp32OBixt1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Ds1r1eB00DdeaVSA7n4IqO7bbyuScHOStW0jq8xTq5k0H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Xo1Xq+XMWPG0KRJk7j33X///dSqVYvbbrutKr9ClVNdUqRiJcnFZhUvwUx2Bz0tDUh8/VIy3uNx\nzRKN9h3Q1q9PPJaSPTTJxHr2puCRxwm89Bza6lVotWtR3LkrRQ+O3eeNMdRf12PXysKu57J3JpFg\nsGQvjTiszJ07l6eeeopQKIRt21iWRTgcZtEil8quNcgC/RPWawl6vSkwNfAiOnpKm53rumubTRSX\nG0z27gO4bPKzGsf/vbObeXQTjI6diFxwMb6Z08ucamN/aU3oxlvwvzrV+fM9HozTzyDWuQvezxaU\nPfZRDSi+8RbHsXvTF31J2pgH0HOWohgGRsvjKL7uH0QGS/Gn6q7Cz8A7dOjAuHHj+OKLL1i8eHHp\nP0IIcbCpu6qA7V5NEI1GS19zMm/ePKLRKDNnzmTEiBGMHx+/z2PGjBmsWeN8d/ZQ4tgo2IYjzCMO\n7mT2nULpEs7E0tIsrL32LpUZC0R6nIVVu7bjeKtetuPdZgX3RsR7i/W9gPy33mPn0h9g1WqKHnl8\nV/ZZwv/0k9TqcRpZXU4i69T2pF99Ocqfm8t1bHH4evjhh7nnnnto3rw5jzzyCBdddBHnnHNOqqdV\nbbwaeNnxJlG+mseJsQ5V+vn7Vvrf13nnJUkubeflBwpQOOYRx7ObpWpELhuMWTfxzaDYGd0BKJww\niaIHxxLp3pNo5y4UX/N38qe/id2gIZG/XYaZlfhLxDqfBsXFqH/8Hj+33Fz0nKUJRu3zvoJ80m+5\nEc9335YWmtLXrCJt9P3oDv33RPVR4Sd4339f8sj5hx/2bApXFEX6ugghDrqzzz6bm2++mby8PKZM\nmcLs2bPp06eP65glS5bQtWtXANq1a8eKFWX3Vy1dupRly5YxYMAAfvklwd3lQ8wGxaEQiAK/aEmK\nhByUEuTOB9i+XcU6pinazz8lHGU3blxmL/i+zNbHo/3uvM7KPOqoikwUfD6UfW4g+GZOJ23saJRI\nydNQJS8X/3uzUfPzyH9jdkmjP1EjZWZm0qlTJ5YuXUpBQQHDhg3joosuSvW0qo2/GK2Zbb+d8BTg\ntb0s03PczzEHpE2C8xt++MF9majdsDFs+iNxTFVRdMW5TUKnzthHHEmsW3fUt99A2XUeswHryCMp\nvmHXE31VJTz0RsJDb4w7htWkKcXDby1pk5CXV/q50bPOJXz9cPwvPJOwkqdaHML35gxivc5y/X6+\nl55HX/dz/Pj8fPyvT6fw9DNcx4vUqnCCN23atKqYhxBCVNh1113H559/ToMGDdi0aRPDhg3js88+\ncx1TWFhI+l79hzRNwzAMdF1ny5YtPPXUUzz55JN88EHiBrSJZGUF0fU9FwPZ2c7V1Q6234m/g7vb\nB8HZ7oOTbYMpV9xtD57mugfPsjQ89epA/DUGAOk+DQoKHMdnzP8QevWEBQmKgGVnkzn0GpRdTwjt\nP/6A//4XGjSAvn3jErmyQ/f897XnzIJI/FJX79dfUS/na5TevR2PU1Wq05+/RGrK/Px+P+vWraN5\n8+Z8++23dOrUiQKXP681zS3Ft/FUcAKF++4FtqF97CS+0Be6JmeKomIr+7+JNz0dduxwfkNenvvN\nmcIbbyLryssSfoRtWWgbf3Mcq4YjKDt34Fn0RWlyt3u62ubN+F94hvCwW8C28b4+Hd+8jyAUwmjT\nlvD1w7FrlaxcCF8/nOiZPfG/Pp2gYlFwYkeifc4HRUF1qeKpJqiuGfeerc7jlW3O1ZlF9VDhBO+7\n777jhRdeKLOm/I8//mD+/PlVMT8hhHDVtWvX0idyACNGjGDUqFGO709PT6eoqKj095Zloe9qWPvh\nhx+yc+dOrrvuOrZu3Uo4HKZZs2ZJ77rv3Llno1h2dgZbt1afizi7ru24GN8wzZIrCqdcxqAkSXO6\nzrFw70Vl7f5X4g9o0CDGxo2aYzwryyBWGCLRIk0byGt2HLUsy/ku+arVxBocTSDR1MJhcnN+xGrS\nlODIu/HNeh1t+zZsRcE44USK/vlvjJPiq2Tu+9+39q+/Jf6LNBajcNG3hE88uD1iq9ufv30dSvOr\nbKJ38803M2HCBB5++GGeffZZZs6cSf/+/Q/ENA8LKirnhfsxMzC9zDnEY3t4qHAcZ9Z2L2JkkyS5\nS/b5qvsm4KIi93itG65zPPeogNH2BGxFKZPA7WbVr4/vlaloDk8AvQs/JTzsFtLuuR3/lOdLK2X6\n5s3F++l88qfPwq5bUiHY+stfCT0whrTsDKJ7/b9lHHec4/NJ02X/X+l7mrd0jFmNj046XqRWhffg\n3XffffTs2RPTNBk0aBBNmjShZ8+eVTE3IYSoMLclewDt27dn4cKFAOTk5NCy5Z6/xIYMGcJbb73F\ntGnTuO666+jTp88hv6RKd7mPV9dyLwaSYddyP3iSIinJ4k2agKY5vcmme3cLJT8vYVQBPEu/w3Z4\n0mYD5rHHoTvss1MLCvD+9y38z00m8PzTaLv63Sm2jSdnKem331yeTuuYDRom/nyvF6Ntu6TjxeGr\nY8eOPP7443i9XmbNmsW8efO48847Uz2taiNfyeMz34K4DCSmxngyOKHKPz8ScT9BJa0UH4uvTrk3\ns2kzxyXatqK6tipQwsVoy5clbIPgyVlK4IlH3ecGRC8eQOzk+JtU5hFHEr7qmqTjI5cNJnZi/D5I\no2FDiq+6Nul4kVoVTvD8fj8XX3wxHTt2JDMzkzFjxkiRFSFEtZHsL+VevXrh9XoZOHAg48aN4+67\n72bOnDnMnDnzIM3w4PLZzt1661h1XJcwFaiJk6uKcf6AlStVNM0prpCfr6Dkxjcp303/YQVWRmbC\nmAJEu/csqWTnxOvFO/eDuAuo3cf2vv2G89hdIpdcihWI71cX63xamX56oua4//77ARg8eDBDhgwp\n/WfYsGEMGTLEcZxlWYwcOZIBAwYwePBgNmzYUCY+d+5cLr74Yvr378/Uqc4VFA8V7/hmsVlLXIly\nnvdj0q30hLEDZdMm90vgBN0FyjCOO84xZgO+Oe8kPLcAaOt+Jtr7bMcbVGb9I/C+Nwc1VJQwrn+f\n4z45AF2n4IVXCF90CWbDRpj1somc0Z2CiU9jnnBi8vFeL/kvTCN8wcWYDRqWjD+zJ4VPPCttXA4B\nFV6i6fP5yM3N5ZhjjmHZsmV07tyZUMiljrUQQhxggwcPTpjI2bZNJEHPoL2pqsro0aPLvNa8eXxD\n2UP9yd1uesIFjiWiqvvPqqrbJESjEI06H2TpUrW0elsils/v+peY/sNyop274Pn267iY2aAhkUsv\nxzfLObFXNyevhBm9+G8UFeTjf2UK+prVWJm1iHU5naJxj0iBlRpqwIABAPTu3Zvs7Gx8Ph87duyg\ncePGruP2rvCbk5PD+PHjmTx5MgCmafLoo48ya9YsgsEg5557Ln379qVOslKQ1dgvinMRqwI1H9Ws\n+mbnbrxecLu8NU/oAN8n7nVne72ov//qOFbdtgV16xbnXjG2DQ4VhAHQXWJ7H+aIIymc/ELJagTD\ngECiBesu4xs2ovCZl/Z7vEidCid4V155JbfccgsTJ06kf//+zJkzhzZtKtAwUQghKmnYsGGpnsIh\nI4zzMiC/ldq/rJN0tKCwEGy3IguWBQ6NfgEoDlF8653oK5bjnf9x6V4Ys249QiPuxK6dhdn8WDzL\n4y/SbH8Ao8tp5fkaRK68hsjgq1A3b8LOzMR2eKooaoajjjqKQYMG8dNPP9G0aVMA1q1bR7t27Xj0\nUeeldW4VfjVN4/3330fXdbZv345lWXgP8YbTG/T1jjELC0ur3B67yjrvPINXX3WuJGW7JddZWdiZ\nzi1cMAz0pd8l3J8HoG35k6J//hv/i8+iJSiIEj21fOemUh6Pe8JY1ePFQVfhBO+cc87h7LPPRlEU\n3nrrLdavX89xLo+phRDiQOvYMX5fgUisWHFO8BI2Gd5bFbdJSJbg2cmOHw27LsFUwhHw+yl45XW8\ns99GX/wNpKUTHjQEq0lTAMJXX4dn0Zdo+/Sti/Y6C6NDBf6caRpWw0blf784bD300EN06NCBKVOm\n4Nl1URyNRpk4cSJjx45N2HsT3Cv8Aui6zkcffcTo0aPp1q0bgXI8TanOFX6PwHkPsKIoqJqKienw\nBtyr+JYj7vVqLsswFZYtc17eDgppxzsvU9TaHk+wKN857vGQ1ty50InniPrUO74V9n33wYOjYOeu\npeqqCv36kf7g/WQkSLiq03/fRGR+lVOR+VU4wcvLy+Phhx/m119/5fHHH2fatGncdddd1KqVZDO+\nEEKIKhMjxiLPlwSsACeZHSntwOSSIO1UnPe3JRtbrngSLqsvgV0JnuryIboHsrJKHvUlYB7315Jf\nqCrRCy4mesHF8XM4pTMFTz1P4PnJaKt+xM5IJ3b6mYTuur+c30KIslavXs2ECWWLhHi9Xm699VbO\nP/98x3FuFX536927Nz179uSuu+7inXfe4eKL4/9M7606V/g92t8cHK5XbdvmisL/48WMZx3eQEmB\nXqckzoZ20Q7k+JY4xlu2jLFihdNTKZtt29w/YNtZ51O7dVv0lcvLRjSdwrP7oP6+kTSH0UYgSG6f\nS6j1xJN4Vv9YdrzXS+FZfYhsLYBLr0I9+TT8r70CkWKMzqcRPbcv5IaBsu1Zqtt/333J/CqnohV+\nK7zA+f7776dt27bk5uaSlpZG/fr1uf322ys+UyGEEAfEq75pdK/dhf61+9E36yzOqd2DhfqnSccp\nShXvcUmSACZL8ABIcy60YNWpS/TMxFWcbb+fSL8Ly/EBYHQ9nYKpr5H7TQ55874gNPKhkg04QuwH\nny/xkx9FUVBdHlu7VfgtLCzk8ssvJxqNoqoqgUDA9ViHgu8190IhLwYdkrty2pBkhcKPP7r//HJz\nk3yAx0OsVau4YsFW7dpEz+iB0f5kx0LC5l9bg99P0cOPEzuxQ2mxFbNBQ0LDbyXyt0v3HK/FsYTu\nf5DQmH8TPa+f7O0V5VLhs8PGjRsZMGAAqqri9Xq55ZZb2FyOjehCCCEOvEX6l4xKv4fVnlUAWIrF\nUs933Jo5jB3KdtdWBX6XCpvlYuN6/IAdX11yb6bD6qvd6ta1MVo77/E2Tu5IaNitxFr9pey0FIXw\ngEFS6U2khFslX7eYW4Xf9PR0+vbty6BBg7j00ktRFIV+/fpVxfQPGtulD51a8cvTOG7L0yH5+SfZ\nPR517U/4Pvow7j6Wtn0bwScnYDZp4twmYVdCZ5zSibz3PyF/+psUTHyG3M++pvj2e9w/WIhyqPAS\nTU3TKCgoKD1JrV+//pC/iySEEIeqGf5XyUvQzuBXbQMv+p9DtVUsh4bAQTuNIhKX4QYqvcfOTtII\nL0nLQmrXhthJnfB+Oj9uGpbHS7T/QPB4yJ/xFsGHRuJZshg7kEZ40GAi1/6jzPvVH1bi/fhD7Kw6\nJXfH/f6KfyEhyuGnn36iR48eca/bts3WrfEFM3ZLVuF3wIABpRU6Dwd3FN3D2743E55jAlaACBEM\nyvOYP7Ew7lWCMzMh33mbHCeeaPDZZ84b+bz/nYXqsDxcf/e/+OrVcy6ismKvwk6qSsxhJYIQ+6vC\nCd6wYcMYPHgwmzZt4vrrrycnJ4exY8dWxdyEEEIksV3d5hjbpm7Fj58QiStNptnpbGWL88HLswfP\n5T3hJHfQdd2919Sffyr4Zr+V8COUWBTvrNeJ/u1Sgo/+C99HH5RebClTnsdqegyx3ueAZZE2Yhi+\n2W+XxgPPPEnRg+OI9eyd5AsKUXFz585N9RQOCT+rax1jNnalkrvdR3GTluae4KWlOZ/cVBU8H33o\nGNd2bEdb/K1zfPOfrnMTorIqnOC1adOGnj17smDBAjZt2kSvXr1YsWIFZ5xxRhVMTwghhJvG5tGO\nsWZmCzy2FxwSvEZmQ9brSSppVkaSJ3TJtpJ4vTbaHxsTjwX8r72CunMH/lemlEkC9Z/XkjbybnK7\nnI7/pefwT59WNr72J9JG3kVul67S10kccA0bNkz1FKqVXGUnU/wvsEPdwfGxE7gw2h8NjRmBVx1v\nEIXUEA4LDw6Yo4+22LTJ+Qnd9u3OJyjLAtXlaaxiWY5NykviSdaHClFJFV5beRYsQj8AACAASURB\nVO211/LHH39w5pln0qNHD7Kzs6tiXkIIIcrhmuKhHJ0gyTs+dgJDwle5PmELqS495CBpgpY0Xkl5\neQq2S0NfOy0N7/x5Cb+ivu4XfK9Nw7vAIf7zWnwzXj1gcxVCxPvYM5ceWaczNn00Twef5PrMa+lf\nqx/5Sh4hitzPIUn2+FY2npbm/oaSIizOcbuRc+N6W9exmjZzjFtSeV5UsQo/wQNkSaYQQlQTza0W\nPJX/HE8EHyNH/x8ePJwcO4X7ih7Ej5+wEnYcu0nd5H7wSrZJ0NCdulgBEIu5jw+HwehyEtonH8fF\nbFWlaNzDZAz9P8fxal4uSkHiPTIAam6SNhFCiP1mYDAmbRS/aRv2vKjAl97PeSjtAbz43M8hSZaA\nJ4urioqlOD8GbNnSZv58pwPsHuf8AfnTZlK31dFx++xswDjhRArHP4p39luokUhcPHrWuc4TF+IA\nqPATvJ49e/LGG2/w22+/8ccff5T+I4QQIjVOMTrzav4bLN2xksU7vue5gik0sUqa6Jq28z4Wxa5k\nuW0bcDlGffsI9+FJngBqGhRMeQ0zK6vMfXQbCF/YH6vJMRitjks41goEiZ3RHaNVq8TxYBrR7lLY\nQIiq8oH3XX70rEwY+1pfRBSXDbgHQNB26kJX4uOP3TqhqzRokGSNaK1aFN5+N7ailp6fbMBs0Ij8\n9z+B9HSiJ58S30YhECQklTJFFavwE7yCggKeffZZsrKySl9TFIVPPvnkgE5MCCFExfiIb3tgKs7P\n0KKqe5W55FU0FbLM2uxUEz8JuzQ8iMdcRuu6+1M8rxc8n85HiUbLTEOhZAkmpknx0GF4F32Jtn5d\nmbHRc87D6NARK5iG95tFaBvWl42f1xfzhBPdvpwQohLylPjqvrtFlTCmneQRfiXVsmtRiHPj6oIC\n9xtcLVvarFrl/hmRoTei//EH3tlvo4SLMU44keKbbgNAXfUj3pyl8W0UikMEn3iMovGPludrCLFf\nKpzgffTRRyxatAi/lJgWQohqo1Ap4L/etwnaQc6L9sNLSRMnDc2xGl3SLXRJH/DZFGnOhQQ+9n4I\njHM8UK1asG2bcxbZqpWN/+UXUIviP0Nf+h3et98g2n8geVOmE5j0BPqqH7CDacROP5Pim0YAYP2l\nNfkvvYr/6Sf3xM/oQfHwW5N9OSFEJfSLXsAj5nj+0H6Pi7U12rFGX12ln28mqdKSrMPX//7n9oQP\nsG0yrr0C315LyL2Lv0G76R8UPDsVzzdfOrdRWL6szHH0rz5H3baNWI9e2OkZ7p8rRDlUOMFr3Lgx\neXl5kuAJIUQ1Mdn/JM8FJ7NR+w2AlrHjuDN0L32j52O7rIP025U8jysQU5zvwpfMxzlLjEZL2tGF\nE24TtDnllBj62zlOH43+zSKi/Qdi/aU1RROfcfwcs3Vb17gQ4sDLtGtxZfH/8Vjav8vsBW5qHMPw\n0C3clH59lX6+maTNQrI9wAXOD/8A0D+dj3fhp3Gva9u34Z/6PEa79o5jbV/Jagv9u29JG3k3es5S\nFNPEbNSY8KArKL71DvcPFyKJCid4iqJw3nnnceyxx+Lx7Klu9vLLLx/QiQkhhEhuvucT/pX2T0Lq\nnqdcazyruDf9Dk7e2RFbcU7wipP0qUvK3tXM3CGH02wdRbGxHfbpBQJgOq4gVcjJ8bhehanbt5dr\nmtr3OQQmT0Rb9SOkpRHt1p3iW24vWSMqhKgyNxffRgvzWP7re4s8JY9jzGZcV3w9za0WNLIas5IV\nVfbZAdxvYHm9ScYHIDfXOe5Z+h2Kw/lJ27CBon//h8Bzk9E2xdepiJ3aFSIR0kYMx7Pqhz3jNv5G\ncMLDmEcfTbT/QPcJCuGiwn+7DR06tCrmIYQQYj/M8s0sk9zttlnbxEuB512ryBWTJMFLugfPXcAO\noGk2hsON9GbNLH780XkZlMdjY9Wpi7ZzR8K40fb4pHPQli8j8+rL0X77dc9xF3+D9staCie/kHS8\nEKJy+kTPp0/0/LjXNyubXc8xiq2UrEBwOgft7nLgEP9T2eIaL7k/5PyGksUPznGzUSOHiYGVVRs7\nqw6hO+4lOPZBtK1bSo6p60R7nU3xTSPwvTq1THK3mxKJ4Jv9tiR4olIqnOB17NixKuYhhBBiP+Q6\nFDgByFNyXS9wDJKsUSpHmwQVFctpr4sCPp/imOClp9u0bWvy+efxm2EUxeb++6Nwa+K78DZgtkxc\nQXNvgWcmlUnudvN++B760u8w2p+U9BhCiAMvrIZczzGa4sFQnCttevC4LhFXFMX1+IoCbm9o2tRi\n82bnG1CxHr2xNR3FjD/B2btWuEUuG0z0jO74X5mCUhQi1qUrsV5ngaKgbt7seGx1R+KbWkKUV4Xb\nJAghhKg+nAqoAGRZdVwvcNwujsrLMbkDNEvf1Uw4Ma9XYerUMPXrW7BPI4RzzonRrp2FujNxAqtQ\nsn8lGW1N4kIOaiiE57MFSccLIapGzHbrkglGkjYKyc5fTY1jXOONGrkXYcnKcj53eTw2vjdmJEzu\nALSNG0t/bTdoSPEd9xJ68J/Eep+9O7PEOLG9Y6Ers757ixkhkpEETwghDmEePI6xIsW5wiWQvIxm\neeIuCeQ2bStHHOF8kBNOsEhPh++/L+If/4jSurVBx44wfXqIKVN2tXDId94Eozskb2WmmOFckc7K\nqpN0vBCiagQJVunxz4n1cYwptsLllzsnmH6/TYcOzueu7Gwbbe9KmPvQ1v2cdH7WkUc6l/JMVuJT\niCRS/ido+/btdOvWjZ9/Tv4/gxBCiLIUl9N4zGV5E4CqJPkroDxLNG3nY/htv0shA5u6dUvuoKsq\nPPhglAULivnmG+jZc68765bzXXY7WZUEINqjV8I81WjRksjAQUnHCyEq72XfS9ycdgPfq3uq4vaI\n9nQdoya5RPVZ8X0/y4x3OX95bC/nn2/gdBerVSuLfv1i1KqV+PzTs6cZ11tzb4rTuvS9eOd+iOJw\nftP+dF6+KUR5pDTBi8VijBw5UlouCCHEfmpjtE0csOHU2Gm4tYIKWpW/g17HcngKZsN5kX54HB8w\nKqxfX46/gtLSHUPleQIXHnoj4cFXYdWqVfqa0eo4iv7575IeDUKIKrNQ/5Rj6zbmtoybmB6cRq86\n3eha+xQMDGrZWY7jNFvDst2XUPqTVMlcp6x3jBlKjA8+cN5ft3GjQtOmNr17xyeBdeuaDBsWxapb\nz3G85Ut+brFdzj92OcYL4SalNaL/9a9/MXDgQJ599tmk783KCqLrSZpOCrKzpUHmgSA/xwOjOv4c\nLcti1KhRrF69Gq/Xy5gxY2jSpElpfO7cuTz77LMoikLfvn254oorUjjb5DLMTMelkq2NtkmKDFSi\nRCaADUOLb2RM+qj4z7HhwdA/GV7feZlTo0ZJW61jtG6D9un8hDHzpHIU/VJVih55nOK/34D3ow+w\n6tQhevGA5DXShRCVYmFxda3LyVfzS1+zFZvV+o9cknk+Z0XPdRzr1n5lt5DqXgV4p+ZSqESBefN0\nnD6ksFDFMGDVKi3uPbm5KvPnaxwzZjy+ue8nPIJ1ZPI9dJFBVxB44Wm0BMVWol27JR0vhJuUJXhv\nvfUWderUoWvXruVK8HbuDB2EWR36tm5N0plTlIv8HA+MZD/HVCSA8+bNIxqNMnPmTHJychg/fjyT\nJ08GwDRNHn30UWbNmkUwGOTcc8+lb9++1KlTffdqTQ28mPgaRYHb0oe7ji0gyZ/zJHvs/AT4xPNR\n4veo8Ip/Kh06DGP27PgLKY/H4rLL9hRJWLtW4YMPdBo2hLPPhuCuh4uRQVfg/forlH26oRttTyBy\nSfnLiFvHtiR8bMtyv18IUTnP+SeRr+THBxRY4vmOJmZTx3OMrdglqw9czkGWbbrG2xhtWeCdlzAe\nsIK0bGmxYEHiAwQCNm+9pbN8efyDBdNUeP99nb/7P3c8PVoNGjtE9rDr1iV0x30Ex43e00bB4yF6\nTh/CN9yUdLwQblKW4M2aNQtFUVi0aBE//vgjd955J5MnTyY7OztVUxJC1ABLliyha9euALRr144V\nK/Y02tU0jffffx9d19m+fTuWZeEtx5OefVcYHMzEtcglScv1ObdQACDZoggNFJSSu+kJ1NeyWaw5\nV7J8I2M69qw7EsZiMZW3387gxhth+HCYNg3y8kpiLVpk8PDDcMEFwP8NwS7YDs88A6tXg88Hp3ZB\nnzCB7AapSbyr45Ppvcn8Kqe6z+9QsUT/zjH5iihhtqpbHOOlT/BcbjDpio6pOBdKuSw0hInB/ySM\nxZQoDzwQZepUD+Fw/PKD7t0N/vjDeQn5zp0q2qZNjnG1IEFim0Bk0BCiPXrhf2UqSqiIaNczMM7o\nXlppU4j9lbIE79VXXy399eDBgxk1apQkd0KIKldYWEh6+p59XZqmYRgGul5yOtR1nY8++ojRo0fT\nrVs3AoFA0mPuvcIgOzvjoD4Brlc7my2eLQljR4ePYYVvhfNFkknJTmzH29C7Ejw1cYJnGAaGZjiO\nX2v+jLnGwmm79wsvGBQVGUya5MOy9hxk7VoYPtzk+ONDZGQAg66B/oPRlyzGqlsXq9VfSt6Ygift\nB/u/b0XJ/Cpn7/lJolc5xxgtHGOKrbBSXeEYL49ke/SG1r7a8dwUVaLoOpgmJHoM2KKFSceOFqrq\nLXNu2i072yJ2ckdsrxclGl/MymzarJzfAuwjj6L4trvK/X4hyiPlVTSFEOJgSk9Pp6hoT/sAy7JK\nk7vdevfuzcKFC4nFYrzzzjsHe4oV8kDhGFQ7/lGc3wowoeCpyh3cdu9zZ9nue+gMYrsuoBLLzVWY\nN09LeAH1228aL7+8V4UWnw/j1NP2JHdCiGqtid3EMZZupSet8ptMsj3EyZagT5qkE4slekyo8Nhj\nPurXtx27FSgKGKd1I9qte1zMrH8E4auudf1sIapatUjwpk2bRvPmzVM9DSFEDdC+fXsWLlwIQE5O\nDi1b7tmXVVhYyOWXX040GkVVVQKBAGo170fU1ehGppmxb59wmhhNqU1t98HlaYPg8teEHz+q5Rxv\naDbCpQ0dXboYFBU5TyI/X5YpCXGo6he9gAZmw4Sx7kYvaiU7PyVhJWnU2SXaxTGm2CoTJ3pxOgka\nhsJ773kwjMTxrVtVUBQKnp1C8dXXYbQ6DrNhIyI9e1Pw5DMYnTqX+3sIURVSWkVTCCEOtl69evHl\nl18ycOBAbNtm7NixzJkzh1AoxIABA+jbty+DBg1C13VatWpFv379Uj1lV8PSh5Kr5Za9TlFgtedH\nZnhfdRxXHpqtYarOj+Dy1TxaWC1Zo62KD9pwQbQ/C5pY7NiRKAm06dzZRNMUvv46Pur12nTpkryX\nlBCiekq3M7gw3J9JwSdKiqbskmFlcGPoJi7McG5EDiVJmO2ygiDNDpJHnmP8ROskpjE1YayR2Qgr\nSSeCtDTnBNLv3xULBika90jJr21b9s6JakMSPCFEjaKqKqNHjy7z2t4rCAYMGMCAAQMO9rT228fe\nuY5VNCcFJ7oPTlIl08RlfSWQTz5TCl7joqzzMJSyyVg9K5s7iu/mC4/TRZLChg0a118f5auvNH7+\nuewy07POMjj9dPc9NkKI6svGZqnnuzLJHUCBWsCHvvcpUgvdD6C4P6G7PHwlT6U/7hi/MNyfu9Nu\nI6JG4mKNrSY8/HqILl3SSXQSDAYtBg6M8fTTHn79NX4J/GmnJTg3SnInqpHqvfZICCGEq4JEZch3\n+VVd7z64HEs03RgY/NX6K82MfZbY29A7cjYqKl6v80EMA5o3t5kypZiBA6O0bWvSpQvcemuEp58O\nO44TQlR/X3gW8q0nweN54EPv+9i2vW8P8TKSxb/xfOUan+edS0SJT+4AokqEY4+F44/ft5G5jaJY\nfP11iPR0uO++CA0a7EnmPB6bc8+Nccstlds/KERVkyd4QghxCHNrY6BX8SleVVSeCPyHNZ7V+06K\nt/2zuDxyBe3ancYXX8TPIxi06dOnpA9eq1Y2TzxRciFWUsVQLp6EONSt0VY5tjFYp/2SdAVBsjYJ\nO9QdrvFftLWO8Z+1tQB8/HGYe+/1MGWKB9OE7Gybt94Kc+SRJe+74AKTbt1CTJ3qxTR9tGtXTPfu\npjysE9WePMETQohDmN923kiSadeq/Ae43CH32l6W6f9LGCtWQ3zgfY/hw6OcdFLZ5ZuaZnPppTGO\nP959CZYQ4tDl1sYgYocdb0ztpiVp1Bm00l3jhUqRYyxGyc2l++7z8eKLPgxDw7Y1tmzRueKKAKtX\n78ngsrLg5pujjB8PPXpIcicODZLgCSHEIayFeaxj7IxoD/fBSfIrxVZc75B78Lg+JfTgoXZteP31\nYm6/PcLZZ8e44IIYTzwRZuzYxEunhBCHh21sc4wZavICSh48rvH61HeN6wnax+xmY7FuncLrr+tx\nbVp+/lnjqae8SecnRHUmSzSFEOIQVseqlzhgw5Hmka7LoIKkEbKLHONpdhohO4SlJL4T78HDqbHT\n+MT3cfyxrSADw4MASE+H22+XZZdCuLEsi1GjRrF69Wq8Xi9jxoyhSZM9veTeffddpk6diqZptGzZ\nklGjRlXrNi7z/fMqNd5n+wjjvBe3dawN8xOcewCwS5aQO7Gxefddnby8xO9ZudL96aEQ1V31PTMI\nIYRIKsezNHFAgc+9n7mO1W3N9QldWA2TYTs0srOhdawtXtuX+Emg7b58VAhR1rx584hGo8ycOZMR\nI0Ywfvz40lg4HGbChAm8/PLLzJgxg8LCQhYsWJDC2SaXYWUkXSXgKsnY/3rfdg4qEMZ9lUBmpnOs\ntA2CEIcoSfCEEOIQlqfkOsaW6f9zTeDyXSpwAhi2wf2FDya80NLQeKXgdT73fprwM0JaiGn+Ka7H\nF0LssWTJErp27QpAu3btWLFiRWnM6/UyY8YMAoEAAIZh4PP5UjLP8uodO9v1/BMg4DpeU9yXaG7S\nfneNp9nOe/SiSpS//S1Gs2aJi8B07ereIkaI6k6WaAohxCHAwGCx/i0+28uJZgeUXVdO+/aY2ltU\nSbIsMkmxAFVRGRK9mq2FW3g87THCSslyqUy7FnNy5xIgQDHFjuN3v18IkVxhYSHp6XuSEk3TMAwD\nXddRVZV69UqWY0+bNo1QKESXLl2SHjMrK4iu71lumJ3t8ES+CixmkXNQgTQtzfn8oUCx7lwkBQX8\nmr+0WEqi+A/pyxyH26rN0Udn8MgjcMstsGFDyeteL/TpA+PG+fB64xPog/nz2x8yv8o5nOYnCZ4Q\nQlRzb/hm8FTgCX7QV6Ch0c5oz12F99HNONN1nGVbyZM4S8XSEu+xq28eAcCI8F2MCN+V8D1tzLZ8\nTvxSUK/t5Yxod/cPF0KUSk9Pp6hoT1JjWRa6rpf5/cMPP8y6deuYOHEiSjnKOe7cGSr9dUkLkoID\nO2kXv9b6DZxqldiQaWSyzeNQiMUu+b6OhTRt6BjtlHD/7+54OBoFh4eciqWwdXsBp50Gn3wCL7/s\nIS9PoUsXk27dTPLy4scc7J9fRcn8KudQml95Ej1ZoimEENXYEn0x96bfyQ+eFaCAqZgs8Szm1szh\nbFOcq9QBSZM7BYW/GH91jA+KDAFgq7KV+9Pu5sJa5zEg80KeDEzApGQJ0w2hm2kbOz5ubJ/I+XQ1\nurlPQAhRqn379ixcuBCAnJwcWrZsWSY+cuRIIpEIkyZNKl2qWZ11jHV0jW9Ncv7yWO5LNAcVD3Hc\np1ffOoKLii9xjNez9xSnysyEG2+Mce+9Uc44Q9ogiMODPMETQohq7FX/y+SqO+Ne/03bwAuBZ5If\nwKWKps+pQErpUJttyjYurXUR33v2LHda4PuE7/VlPFPwIvXt+kzPm8WTwf+wXP8en+2jW+xM/l58\nQ/K5CSFK9erViy+//JKBAwdi2zZjx45lzpw5hEIh2rRpw5tvvslJJ53EFVdcAcCQIUPo1atXimft\nrJ55pGvcdqjOu5tXcW9V0Cd2Po3NJvymb9jnwPD30D/oHxvAWHM0G/XfyoQVW2Fo0TDXYwtxqJME\nTwghqrFtqvNd7m3KVtexuu3BUB32qABhJcKPnh8c49N904gokTLJ3W7v++bwaXg+Z8Z6cIR9BA8V\njU9wBCFEeamqyujRo8u81rx589Jfr1q16mBPqVxsbOZ7PmGdtpYzoj1oYZX05vzBs8J1XLZVn0Kt\n0DGeYWeygx2ux/h85zdcXKsvK/XlGIpBbas214T+zrDwrQB8sXMxF9Y6jx/1lbviWfw9dAPXRyTB\nE4c3SfCEEKIaa2Q2cow1tZrhsb3EHIqp1Lfr8wduleZsLNX5Ed42bSsr9eUJY1ElymfeBZwZS9JM\nXQhx2FqjrmZExnC+83yLqZjUsmpxVuQ8JhQ+yWp1lesKgqgddY3XteqwgfWunx8kyAd5n7jG5+ZV\n73YSQlQF2YMnhBDV2LXF/6CxeXTc662NNlxVfA2a7Xwar2XVdj+4TWk1zkS8tg8/zr3spM+dEDWX\njc3tGTfzjXcRplKyJzdPzeP1wHTGB8fwh/a76z7gqBpxjZ8V7VOuefysrmVU8F5GpA3nJf/zRElS\nPViIGkCe4AkhRDV2jNWMSfnPMSH4CP/Tl6ChcUrsVO4rGkWQoGsrgvXaL67H9uLjSOMoftXXx19o\n2XBX4f34VC9zvR/EtWOoa9bj8vAV+/mthBCHukX6F3zn+TZhbL73Y5rGjuF7LccxidtJrusTvFX6\nD47x3TemXvO9wui0+9mubS+NveV7g5fzXyPLrlOBbyPE4UWe4AkhRDWXp+RRoBQQUkOElGIK1QLy\n1V11vF3ugEeUCEErmDhoQxvzeL7OXYpma2WLrdhQ18xmaPR6rgz/H4PCQwjsdZxssz53hO6hkdW4\n8l9OCHFI+kX7hZiSeI9vrppHf2OA6/nJUkzXeL6S5xi3sSmiiMeC/y6T3AF8413E+OCYZNMX4rAm\nT/CEEKIa+1H9gVszbmSLtqXkBQU+9c5no7qRubnzXe+Aq7ZKbbM2IS0UH1TguNhx5OhLqW1nsX3v\nYi4KHEF9iikmQIDHCidyWXgwH3k/xGf7GBQewpH2UQf8uwohDh09or2oa9ZjuxZfCKq50YIVunOj\ncYDaZhY79knO9ubYxHyXN/0z2aCvTxhb7PBkUYiaQp7gCSFENfZC8Jk9yd1e1upreN7/jOs+u1Oj\nXdmsb3aMv+efwzT/lIQXaD/oK5nmn1L6+1bmcTQ1j+EvRmuy7foV+xJCiMPOUXYD+kUuiGu1Utuq\nzVXh/yNEghtLe6nPEa7xutRzjKmorgmgtatPpxA1lSR4QghRjX2tf+Uc8yxCderKa4OhGFguvaaK\nlRCb1E2O8V+19QA8EXiMblmduTnzBq6sdRk9a5/OfM/H5Zq/EOLwNa7oEe4suo9mRgvqmHU4JdKJ\nJ/Inc260b9KxDa2GrvGRRQ+i2InPb0cbTbg4cglHOvTaa2ecmHzyQhzGJMETQohqbKcS3+R8t63q\nFgqUgsRBBVZ6lrs3MrdtTAzHeD0rm/e8s3kkbTwbtd/KHPeO9BEle2SEEDXWT+oa5ns/Zr32Czu0\nHaz0rGC2/x1ixMhV8lzPP7Xt2q7xhlZjekXOjnuP1/LxWOFEsuw69ImcH5cE1jazuDl0eyW+lRCH\nPknwhBA1imVZjBw5kgEDBjB48GA2bNhQJv7uu+9yySWXMHDgQEaOHIllOT8BOxhUxfk0raJgKM4J\nWsSlwiZApl3LtU1ChChv+2YlrNT5q76eKf4XXI8vhDh87W6TsNj7TelKgUK1kDf9MxkXfIh8Nde1\niMoJRnvn/cNWyXnvlYKZPFD4EMfEmnGEeSQnRTuyYOdXnGacjonJYs+3cRV+c9WdvO1784B8RyEO\nVZLgCSFqlHnz5hGNRpk5cyYjRoxg/PjxpbFwOMyECRN4+eWXmTFjBoWFhSxYkNomufWsbMdYI7Ox\n6x1w0zbRbedaWj7bh+ZSa6tAzSdPyXWMlynMIoSoUb7SP+drT+Il5LN979DMaOF6fooRdYyrilb6\n6xvCN/FNbg7Ld6zh/bx5HGsdC8B73tks0/8XP1iBed6Pyv09hDgcSYInhKhRlixZQteuXQFo164d\nK1asKI15vV5mzJhBIBAAwDAMfD5fSua521FmA8dYc/NY1zvkpmKi7XWhtC9bgWZmM4cgtDHacozl\nHG8bO8H5w4UQh7WV2krH88+v2gZamX9xPz/h3CbBSFJBE3BtpC7Lx0VNJ20ShBA1SmFhIenp6aW/\n1zQNwzDQdR1VValXr6Ry27Rp0wiFQnTp0iXpMbOyguj6nkQqOzvjgM3XdtkjZ6ZF3MeqNhlkECHx\n+47WG3GB3pcpvFBysbWXdCWdazOvZANn8hnzWce6MvFuSjeuq3UVGs4J5P46kD+/qiDzqxyZ3+Hh\nQ+/7LlGbFwPPuo7/wPeec3CfxK2IIgrVArKt+qi7nk2cGelJRnAcBWr8PuQW5rGuny3E4U4SPCFE\njZKenk5RUVHp7y3LQtf1Mr9/+OGHWbduHRMnTkRxqlK5l50795QDz87OYOtWh8In+2Ft1s+OZ+pv\no0tKYg5rMQJmgGyrPts8iZdSnlh0MjPVNzED8SXFCynkqfxnGRK5iqf1F5kYmMByfRlefHSKdWZk\n0Wh22O5l0PfHgf75HWgyv8o5lOYniZ47xwq+u0Rw3wNs2M43r3YrUPK5O/12Pvd8Sp6STwuzBYPC\nV3BV+BpaWcdxXqQfMwKvlhmTbdbn6uK/J/8CQhzGJMETQtQo7du3Z8GCBZx77rnk5OTQsmXLMvGR\nI0fi9XqZNGkSqpr6VewePI6xoB1At3UMh6d86VamYwzgd+03VuorHONfehcyJHIVJxodeLFgWvkn\nLYQ47E3Mf5oT6h2XcJmkx/bgs92Xt58VO4flPvdm6P/IuIaPfB+W/v57dRlr9HtJs9P4W+RS/lP4\nJI2sxsz3ziNfyeNYsxXXhIbS1Th9v76TEIcLSfCEEDVKr169+PLLLxk4Efd8/gAAIABJREFUcCC2\nbTN27FjmzJlDKBSiTZs2vPnmm5x00klcccUVAAwZMoRevXqlbL5ZZh3HM3UzszmLPF86jjXVGDvY\n4Rj/Xf2dYsX5KdxmxblHnhCiZstX81FRsYivNNzAbEh9qz5b7a2O++QCdrCkyEqCuNf28p3+LQu9\nn8XFwkoxr/te42+RS9HQuCN0D3eE7qnktxHi8CIJnhCiRlFVldGjR5d5rXnz5qW/XrVq1cGekqvf\n1d8dY8v1713HKijEXJ7g2dgcZTZgk5Y4kWtjHl++SQohapxvPV+XtkfYl6mavJE7m6b1EjciV2yl\nZC+dQ/IXtaMs1ZcQVooTxn/XnM+LQgipoimEENWaoUYdYwVKPrbtXIdcs3QKnRqhA5vUPzg72sdh\nrMZFkUvKP1EhRI1ygnEiXtubMJZlZfF04CnHsRo6n3jmOrdRUKG10RrVTnyZmmVlVXS6QtQokuAJ\nIUQ1VtelD97RZhPHO+gAETWM7dKIKk/Jo4HZIOFFlg9fSZ89IYRI4DjzL+h24j3CKhrLtP85t0FQ\nYmzVtiVthO5xPL5cvgrhJmX/h8RiMW6//XYuu+wy+vfvzyeffJKqqQghRLV1dejaxHe5bfhXwaOu\nCVyYMLrLSvwMO6OkgEGCi6yQGmKq/8X9mLEQoib4wPsuIbUoYWyzsonf1Y2u45NV0XzH/yYRNXGL\nl83K5vJNUogaKmUJ3uzZs6lduzbTp0/n+eef56GHHkrVVIQQotp6NfBy4rvcCjyYMRJbcU7wDMXg\nBONExwTxzqL7KHBZwpmv5ld8wkKIGiFXzXWM5at5hFwKOAGkE3SNb1MSt3cB2KFud5+cEDVcyhK8\ns88+m5tuugkA27bRtAPfLFcIIQ51P2s/Ocb+py1xHWthcWGkv2P8RLM9Lc2WCWOKrXByrGP5JimE\nqHmc7y0RUSIUKYWuw3tFznU9to7zdWHy7qRC1Gwpq6KZlpYGQGFhIcOHD+fmm292fX9WVhBdlyQw\nGWnMemDIz/HAkJ9j+USJ8nzgaRZ7vkWzVbrGzmBw+EpUVGJKzHFcsZq4wtxuCgrv+v7r+ARwhu8V\nrg8N51PPfFZ7ylYPPSPag77RC/bn6wghaoA/lc2ObQ5sbNc2CAABAo5xHQ+NzSaO8cbW0fs/cSFq\ngJS2Sdi0aRM33HADl112GX379nV9786d7o/6RYmtW52XW4nyk5/jgZHs5ygJYElyNzhzIAt880pf\nm+17h688X/B0wQv4bT9hwgnH1rHqskPZ7lhoJd1OZ4O63vGzv/fk0CDUkJfzZ/BE8FGW6Tl48dEp\neip3hu6VQgZCCEfrtfWuj9L+arbmF+9ax7gXr+N41VY4L9qPNkZbVniWx8V7Rs+q4GyFqFlSluBt\n27aNq6++mpEjR9K5c+dUTUMIIVLqJf9zZZI7ABT4r+8t+kUuJNs6glwt8V6XhlZDtqpb2OnQzLyJ\n0YxCLc/xs2vbJaXGj7Ga8Z9C55LmQgixr97Rs3kzMMOhUbmPxmZj1yd4MaLOT/AUHR2dMYX/4q70\nEazy/AiA3w7QO3K2NDYXIomUJXhPP/00+fn5TJo0iUmTJgHw3HPP4ff7UzUlIYQ46BZ7vkn4uqVY\nfOr9hFOMTvzkWZ3wPR1jnahn1eeNwGsJ431iffnZWssv+i8O4+XmmhBi/3SP9XSMqbbCkXYD1yd8\nQdIc40G7pADLqcZpfJy7kDf8M9iibKFrrCsnG50qM20haoSUJXj33Xcf9913X6o+XgghqgW3NgYe\n28NF4Ut4zf8KpmKWifmtAFeFr2GlvpxZ/plxyzT9lp+rwtfws7aWed6P2K6VrUjXMnYcVxVfc+C+\niBDisFWg5LND2UEDqyEeSnrT/Sv4T8cELayGWa7luB6zd/RsxptjyNV2xsU6GHsKPPnwcXn4iv2f\nvBA1kGywEEKIFDoz2gPFjr9K8tk+zov05VPfJ3HJHUBYKWau9wM+9SxIuAcvrIT5yPMh7Y2T+Hfh\nf+gQOxnd1gkS5IxodyYVPEsaaVXynYQQh4ciihiWPpQuWSfRuU57zqzdhQmBR0piSuIeeFBSZGWH\nEp+47a2R1ZgLIxfHVePMNutzbWhopecuRE2W0iIrQghR0/0tchlfeD5nlv91DKWk8a/f9nN16Dq6\nGKfzTHBy4oEKLNWXsNSz2DE+z/sRl0QH0jd6Pn2i/fhV3UDDuvXQ89Kr6NsIIfaXZVmMGjWK1atX\n4/V6GTNmDE2aNCnznuLiYq666ir++c9/0rx58yqf0/CMoczx/7f092s8q/i3PpagHaSN0dZxnILK\nFm1L0uOPK3qERlZjPvLOpcibT5PwMVxd/He6GqcfkPkLUVNJgieEECmkoPBE4WQuiFzMPN9cFFvl\n/MiFnGKU7I9L9HRvN5/tY4vyp2P8F+3nMp/TxGpKNhlsRarEClHdzJs3j2g0ysyZM8nJyWH8+PFM\nnrznBs/y5ct54IEH+PNP5//nD6Sf1DV86p0f97qhGLztn8XA4kGORVI8tk4LowUrPMtc9+GpqAwr\nvoVhxbeQnZ3B1gI5NwlxIEiCJ4QQKaag0CPWix6xXnExc9dTvUR8theD+OWbpWyXTsRCiGplyZIl\ndO3aFYB27dqxYsWKMvFoNMpTTz3FHXfccXDm41lMgZo44fpD/b3kFw7JmxcvTxRN5p3ArIRxvxU4\nEFMUQjiQBE8IIaoxFc0xlq/mU4tabGdbwngWdatqWkKIA6ywsJD09D3LpzVNwzAMdL3kUq1Dhw4V\nPmZWVhBd33MOqUjv0Z50I4MMChI88T9aa8xZmd25Gx2D+JtQzdXmLM92WD4ONNeaJZxLde+NKvOr\nHJlf5VRkfpLgCSFENVbXck7S6tn1ODHWgV/0nxPGT451TPi6EKL6SU9Pp6hoT+ESy7JKk7v9tXNn\nqPTX2dkZbN1a/iWQ9WjE6Zln8p5vdpnXdVvn3MJ+ZIWPwlfXh6EmWGUQVfkhsgYcrkcLjSK27iw7\nl4rO72CT+VWOzK9y9p5feRI9qaIphBDV2KDwYGpbteNeb2w25urivzMkfBWZVq0E8SZcE/77wZii\nEOIAaN++PQsXLgQgJyeHli1bpnhGMDF/MpeEB1DfrI9qq7QwjuW2orsYGr6RD7zvUqQmrqSZr+Zy\nVuRc6pnZCeOtzOOqctpC1HjyBE8IIaqxk4xTODXalbm+90vbJXhsL+eFzyfbzibbyObBwrE8E3iS\nVZ4fUW2VE4wTubfwAbLsOimevRCivHr16sWXX37JwIEDsW2bsWPHMmfOHEKhEAMGDEjJnNLJ4KmC\n58hX8timbKOR1Rgv3nKNPdI+iosil/B84OkyrVyyzfpcWyxtEISoSpLgCSFENfaxdy7zfR+X6YUX\nU6K84X+Nq8LXcIzVjEGRwfwtMpCvPF8QtNI4yTwZxa10nRCi2lFVldGjR5d5LVErhGnTph2sKZXK\ntGuRaZddKXBOtA8tjGNZq/8U9/4Ou5aHP1Q0jkZmI+b6PiBXyaWZ2Yyriq/jNKPrQZm3EDWVJHhC\nCFGNveOdRVgJx72+XdvOy/6XeCD0EAAePHSLnXmwpyeEqKF8+BgeGsGotHvYoe0off2vRmvuCN0L\nlFQIHhq+kaHhG1M1TSFqJEnwhBA1SnVsJuzGqUw5QKFSeBBnIoQQZQ2MXEZb43he8U8lT82lmdmc\nvxdfT4admeqpCVGjSYInhKhRqlsz4WRaGq340PdewtgJRruDPBshhCirtdmGcUUPp3oaQoi9SBVN\nIUSNUt5mws2aNUvF9OJcXzycv8baxL1+avQ0BkQuS8GMhBBCCFGdyRM8IUSNUt2aCSeTTQbv8y5j\nGMN3fIeOThe6MNo7mszs/VsGdTg1c00FmV/lyPyEEKJqSYInhKhRqlsz4fIIUoexPFbmtQiwlYp/\nzqHUzLU6kvlVzqE0P0n0hBCHKlmiKYSoUapjM2EhhEgVA4OZ3uk8Fvg3C/VPsbFTPSUhRCXJEzwh\nRI2SqmbCa9WfeNv3JioqAyKX0chqXGWfJYQQ5bFcW8bNGTey3LMMAK/t5fTomTyXP4U00lI8OyHE\n/pIETwhRo6SimfA/g6OYEniBPDUPgOcCT3N98XCGF99ywD5DCCEqwsbmnvQ7SpM7gKgSZZ5vLg+m\n3ce/i/6TwtkJISpDlmgKIUQV+sj7IZODT5YmdwA7tO1MCD7C//QlKZyZEKImW6x/zRLP4oSxz70L\nZammEIcwSfCEEKIKzfH+l6gSjXu9UC3gDd/MFMxICCFgk7oJQzESxgqVAiysgzwjIcSBIgmeEEJU\nobAScowVu8SEEKIqdY/1pIHZMGHsL8Zf0dASxoQQ1Z8keEIIUYXaGCc4xk6KdTyIMxFCiD0y7Ewu\nK74cj+0p83o9M5tri/+RolkJIQ4EKbIihBBV6NrioXzk/YDF3m/KvN4t0p0BkctSNCshhIA7iu+l\ngdWIOb532KFup6nZjCuK/4/TjK6pnpoQohIkwRNCiCoUJMhr+W/yn8AjLPV8h4rKKbFO3BS6DV1O\nwUKIFLs8cgWXR65I9TSEEAeQXF0IIUQVy7Rr8UDooVRPQwghhBA1gOzBE0IIIYQQQojDhCR4Qggh\nhBBCCHGYkARPCCGEEEIIIQ4TkuAJIYQQQgghxGFCEjwhhBBCCCGEOExIgieEEEIIIYQQhwlJ8IQQ\nQgghhBDiMCEJnhBCCCGEEEIcJlLW6NyyLEaNGsXq1avxer2MGTOGJk2apGo6QgghhBBCCHHIS9kT\nvHnz5hGNRpk5cyYjRoxg/PjxqZqKEEIIIYQQQhwWFNu27VR88Lhx4zj++OM577zzAOjatSuff/55\nKqYihBBCCCGEEIeFlD3BKywsJD09vfT3mqZhGEaqpiOEEEIIIYQQh7yUJXjp6ekUFRWV/t6yLHQ9\nZVsChRBCCCGEEOKQl7IEr3379ixcuBCAnJwcWrZsmaqpCCGEEEIIIcRhIWV78HZX0VyzZg22bTN2\n7FiaN2+eiqkIIYQQQgghxGEhZQmeEEIIIYQQQogDSxqdCyGEEEIIIcRhQhI8IYQQQgghhDhMSNnK\nSvrmm28YMmQIjz32WGlPP4C+ffvSunVraeBeARs3bqRfv360bt269LVTTjmFG2+8MYWzOnRt3LiR\nW/+fvXsPs6n6Hzj+3ud+zlwMmYpEIlGS1K9EupAoTWTcNYSukqSofFFypwthiL4lUkiKIUouySVR\nIXyRkih3xsyZOXNue//+OByO2WcGcznH+Lyex/PMrHXW3p+9zezZn73WXqt3b2bPnh3pUC4Jp94r\n3rFjBxaLhSFDhlCpUqVIhxXi0UcfDS5PU6FCBYYPHx7hiGDTpk289dZbTJ8+nT179vDqq6+iKArX\nXXcdr7/+OgZDZJ9Dnhnftm3bePrpp7nmmmsAaN++PQ899FBE4vJ6vfTr149//vkHj8fDs88+S9Wq\nVaPm/OnFV65cuag5f36/n/79+7N7924URWHQoEFYrdaoOX+FSa5NF06uTxdGrk8FUxjXJ0nwCsG1\n117LwoULgwnejh07cLlcEY7q4lS1alWmT58e6TCEOG/fffcdHo+HWbNmsXHjRkaMGMHEiRMjHVaQ\n2+1G07So+v2aMmUK8+fPx263AzB8+HB69erFHXfcwcCBA1m6dCmNGzeOmvi2bt1Kly5d6Nq1a8Ri\nOmX+/PkkJCQwevRo0tPTadGiBdWrV4+a86cX33PPPRc152/58uUAzJw5k3Xr1vHuu++iaVrUnL/C\nJNemCyPXpwsn16eCKYzr08X/aCoKVK9enX///ZfMzEwg8IOTlJQU4aiEEMXp559/pkGDBgDUrl2b\nLVu2RDiiUNu3b8flctG1a1c6derExo0bIx0SFStWZNy4ccHvt27dyu233w7A3XffzZo1ayIVGpA7\nvi1btrBixQo6duxIv379cDqdEYutadOmvPDCCwBomobRaIyq86cXXzSdv/vvv5/BgwcD8O+//xIf\nHx9V568wybXpwsj16cLJ9algCuP6JAleIXnggQf49ttv0TSNzZs3c8stt0Q6pIvSrl27SElJCf47\nePBgpEMS4pw4nc7gECMAo9GIz+eLYEShbDYb3bp147///S+DBg3i5Zdfjnh8TZo0wWQ6PZBE0zQU\nRQEgJiYm+NAsUs6Or1atWvTt25cZM2Zw9dVXM2HChIjFFhMTQ2xsLE6nk549e9KrV6+oOn968UXT\n+QMwmUy88sorDB48mKSkpKg6f4VJrk0XRq5PF06uTwVX0OuTJHiFJCkpia+//pr169dz2223RTqc\ni9apIZqn/l1xxRWRDkmIcxIbG0tWVlbwe1VVQ/74RlrlypV55JFHUBSFypUrk5CQwOHDhyMdVogz\n3yfIysoiPj4+gtHk1rhxY2rWrBn8etu2bRGNZ//+/XTq1InmzZuTlJQUdefv7Pii7fwBjBw5km++\n+YYBAwbgdruD5dFw/gqLXJsKR7T9fp0t2n6/5PpUcAW5PkmCV0iuvvpqsrOzmT59Oo888kikwxFC\nFLM6deqwcuVKADZu3Ei1atUiHFGoOXPmBCd9OnjwIE6nk8TExAhHFeqGG25g3bp1AKxcuTLqHpZ1\n69aNzZs3A7B27dqQCaGK25EjR+jatSt9+vShVatWQHSdP734oun8ffXVV7z//vsA2O12FEWhZs2a\nUXP+CpNcmwpHNP1+6Ymm3y+5PhVMYVyfoucRTgnw0EMPMW/ePCpXrszevXsjHY4Qohg1btyY1atX\n065dOzRNY9iwYZEOKUSrVq147bXXaN++PYqiMGzYsKh6ig/wyiuvMGDAAN555x2uvfZamjRpEumQ\nQrzxxhsMHjwYs9lM2bJlg+9IRMKkSZPIyMggNTWV1NRUAP7zn/8wZMiQqDh/evG9+uqrDBs2LCrO\n3wMPPMBrr71Gx44d8fl89OvXjypVqkT1z9+FkmtT4ZDr07mT61PBFMb1SdE0TSumeIUQQgghhBBC\nFCEZoimEEEIIIYQQJYQkeEIIIYQQQghRQkiCJ4QQQgghhBAlhCR4QgghhBBCCFFCSIInhBBCCCGE\nECVE9M1DK0q8xYsXM3nyZHw+H5qm0bx5c5544olIhyWEuMTs27ePpk2bUqVKFSCwAHRWVhYtWrTg\nvvvuY+bMmQwdOjTCUQohLjVybRIFJQmeKFYHDx5k5MiRzJ07l9KlS5OVlUVKSgqVK1emUaNGkQ5P\nCHGJufzyy5k3b17w+4MHD9KkSROaNWsmN1BCiIiRa5MoCBmiKYrV8ePH8Xq95OTkABATE8OIESOo\nWrUqmzdvpn379jz66KN07dqVvXv34nQ6adiwIWvXrgWgW7duzJgxI5KHIIQowQ4fPoymaWzZsoWU\nlBQAUlJSGDVqFG3btqVx48Z8//33ABw4cIDHHnuMpKQkXnrpJe6++24A1q5dS8uWLWnZsiVdunTh\n2LFjETseIUTJINcmcT6kB08Uq+rVq9OoUSPuv/9+atSowR133EFSUhLlypXj+eefZ9KkSZQvX54f\nfviBAQMGMHXqVIYOHcobb7xBp06dUBSFjh07RvowhBAlxKFDh2jevDlut5vjx49z0003MX78eCwW\nS8jnvF4vs2bNYtmyZYwdO5Z77rmHoUOH8uCDD9KxY0eWLFnCggULAEhNTeWNN96gVq1aTJs2jW3b\ntnHXXXdF4vCEEBcpuTaJgpAETxS7QYMG0b17d1atWsWqVato06YNTz31FHv37uXZZ58Nfs7pdAJw\n5513UrduXd555x0WLVoUqbCFECXQqWFQqqoyYsQIduzYQd26dfnll19CPtegQQMArrvuOtLT0wFY\nvXo1w4cPB6Bx48bEx8cD0KhRI3r06MH9999Po0aNqF+/fjEekRCiJJBrkygIGaIpitWKFSv4+uuv\nueKKK0hOTubdd9+lf//+rFu3jgoVKjBv3jzmzZvH3Llz+fTTTwHQNI3du3djt9v566+/InsAQogS\nyWAw0LdvX44ePcqHH36Yq95qtQKgKEqwzGg0omlars8+/vjjTJ8+nYoVKzJ69GgmTpxYdIELIUo0\nuTaJCyEJnihWNpuNt99+m3379gGB5G3Xrl3Url2bEydOsGHDBgC++OILXn75ZQA+/fRTHA4Hqamp\n9O/fn+zs7IjFL4QouUwmE3379mXSpEkcOXIk38/Xq1ePtLQ0AL7//nsyMjIAaN26NVlZWTz++OM8\n/vjjbNu2rUjjFkKUbHJtEudLhmiKYlW3bl169OjBM888g9frBQLDC55//nkaNmzI0KFDcbvdxMbG\nMnLkSPbu3cvEiRP5/PPPKVeuHHfddRejRo3ijTfeiOyBCCFKpLvvvpvatWszZswYrrzyyjw/269f\nP1555RVmz55N9erVg8OgevfuzauvvorJZMJqtTJo0KDiCF0IUYLJtUmcD0XT68MVQgghRJ6mTZtG\nvXr1qFq1Klu3bmXAgAHMnTs30mEJIS5xcm0S0oMnhBBCXIBKlSrRu3dvDAYDVquVwYMHRzokIYSQ\na5OQHjwhhBBCCCGEKClkkhUhhBBCCCGEKCEkwRNCCCGEEEKIEkISPCGEEEIIIYQoISTBE0IIIYQQ\nQogSQhI8IYQQQgghhCghJMETQgghhBBCiBJCEjwhhBBCCCGEKCEkwRNCCCGEEEKIEkISPCGEEEII\nIYQoISTBE0IIIYQQQogSQhI8IYQQQgghhCghJMETQgghhBBCiBJCEjwhhBBCCCGEKCEkwRNCCCGE\nEEKIEqJIE7xNmzaRkpKSq3zBggW0bt2adu3aMXDgQFRVLcowhBBCCCGEEOKSUGQJ3pQpU+jfvz9u\ntzukPCcnhzFjxjBt2jRmzpyJ0+lk+fLlRRWGEEIIIYQQQlwyTEW14YoVKzJu3Dj69u0bUm6xWJg5\ncyZ2ux0An8+H1WrNd3uHD2eGfF+6tIPjx7MLL+BCFM2xgcRXUBJfwZwZX2JiXISjKRxnXp8upvMf\njSS+gpH4CqakXZ/k2lR4JL6CkfgK5nyvTUWW4DVp0oR9+/blKjcYDJQtWxaA6dOnk52dTf369fPd\nXunSDkwmY0hZNF98ozk2kPgKSuIrmGiPryDOvk5FG4mvYCS+gpH4Iifaj03iKxiJr2BKWnxFluDl\nRVVVRo8eze7duxk3bhyKouTb5uysOjExLlevXrSI5thA4isoia9gzoyvJCd6QgghhBCREJEEb+DA\ngVgsFlJTUzEYZCJPIYQQQgghhCgMxZbgpaWlkZ2dTc2aNZkzZw633XYbnTt3BqBTp040bty4uEIR\nQgghhBBCiBKpSBO8ChUqMHv2bACSkpKC5du3by/K3QohhBBCCCHEJUnGRwohhBBCCCFECSEJnhBC\nCCGEEEKUEBGZZEUIIYS4lJjW/IB5zSrUMmVxt38MTq4FK4QQQhQ2SfCEEEKIouLxEPdsNyxLFqO4\n3QDY/zsJ5/C38N19X4SDE0IUlJKZgXX6RyiZmXjvaYivbr1IhySEJHgXk28si/jc+hmHDIe4yn81\nnXIe505f/ovECyGEiAzHqGFYF8wLKTPt+p2YAa9x4rsfwGyOUGRCiIIyp31F7Bv9Me77GwBtwnu4\nmyXhHPc+mOQWW0SOvIN3kZhq+y/PxHVjvu0rfrSs4Qv7LLrGp/C1ZUGkQxNCCBGG+YcV+uXbt2GZ\nN7d4gxFCFJ4sJ7FvDgwmdwCKOwfb3M+xj3sngoEJIQneRcGLlw9tU8gyOEPKjxqPMNmeGqGohBAi\nOhg3/kLs011IuPt2SjW9D/vQN8DjiXRYACjZ2WHrDMeOFmMkQojCZP10Osa//9KtM//wffEGI8RZ\npP/4IvCbcRPbzdt067aafuO4cozSWplijkoIISLPuGUz8d06hTxFN//6M6bdf5L5wbQIRhbgu+FG\nTDtzr/3qL10Gz0NJOi2EEBcDg9MZti6vBztCFAfpwbsIlNISsGk23TqbZsMapk4IIUo62+TUkOTu\nFMu3izGtXR2BiEK5numBv0LFkDLNYMDdqi1qhasjFJUQoqDcjZuixsbp1vluqFnM0QgRShK8i0AV\ntSr/571Dt66utz4OHMUckRBCRAfT7zt1yxV3DuY1PxRzNLn5b7mVjKkzcLVpj7fObbjvbYRz6Ciy\nB4+IdGhCiAJQa96Eu0VLtLPKfVWuw9W9Z0RiEuIUGaJ5kXjTOYzn455hi/m3QIEGt3vv4E3nsMgG\nJoQQEaTGx4evK1O2GCMJz3/TzWSNez/SYQghClnW6LH4r6+BedkSDFlZ+K6vgat7T9Rrq0Q6NHGJ\nkwTvInGj/yYWpy/nM9sn7DX8zfW+GrT0tMKIMdKhCSFExHgeeBDLyhUoqhpS7qtaDXe7jhGKSghx\nSTAYyHmqOzlPdY90JEKEkATvImLBQuecrpEOQwghooa761MY//wD2xezMBw/DoC3+g1kvTkM7PYI\nRyeEEEIUP0nwhBBCXLwUheyho8h5+jksX6ehXlYWT4tkWUBcCCHEJUsSPCGEEBc9tWIlcp7pEekw\nhBBCiIiTBE8IIUR0U1UsX32B6ad1YLeR0/4x1GrVIx2VEEIIEZUkwRNCCBG93G7iuj2G5btvUbTA\nhOS2GdPI7vMaOU8+G+HghBBCiOgj6+AJIYSIWvb33sa65JtgcgdgOJGOfezbKIcORjAyIYrGpk2b\nSElJCVs/YMAA3nrrrWKMSAhxsZEETwghRNQyr12tW248fAjrZ58UczRCFK0pU6bQv39/3G63bv3M\nmTPZuXNnMUclhLjYSIInhBAiaik+X/g6r6cYIxGi6FWsWJFx48bp1v3yyy9s2rSJtm3bFnNUQoiL\njbyDJ4QQImp5a92Ced3aXOVqfDzuRx6NQERCFJ0mTZqwb9++XOWHDh1iwoQJjB8/nkWLFp3z9kqX\ndmAyGYPfJybGFUqcRUXiKxiJr2BKUnyS4AkhhIharl4vY17/I+aNvwTLNJOJnA6dZCZNcclYvHgx\nx48f56mnnuLw4cPk5ORw7bXX0rJlyzzbHT+eHfw6MTGOw4czizodOTJeAAAgAElEQVTUCybxFYzE\nVzAXU3znkuhJgieEECJqaWXLkvH5PGwTx2HaugXNYcfzwEN4WraOdGhCFJtOnTrRqVMnAObOncuf\nf/6Zb3InhLh0SYInhBAiunm8KE4nyoEDKLGxKC4XaBooSqHtwrBzO5Yl36BeVjaQPFoshbZtIS5U\nWloa2dnZ8t7dxSwnB+vnn6GcSMfzwEOo1a4/XadpWD/7BMt334ArG3+NmrieewHtsssiF68oESTB\nE0IIEbWUgwdIeOAejAf2B8vMq1diXrEU55SPC74DVSXm5Rewzp+LITMw/MWb+h5ZQ0fia3Bvwbcv\nxHmqUKECs2fPBiApKSlXvfTcXTzMS74h5vXXMP2xCwB17Nu4H21F1sh3QVGI6dcH29QPUFQ10GDZ\nd1hWLufEp3PQLr8igpGLi53MoimEECJqxb7wbEhyB6AA1rSvMGz8tcDbt6WOxTbj42ByB2De8T9i\n/9MXwkxVf8nxeLB+9AGO117G/tYIlGNHIx2RENEvO5uYAX2DyR2AISMD27SPsE2ZiGHrFqyzPj2d\n3J1k+m0TjrFvF3e0ooSRHjwhhBBRy/Trz7rliqbheHsEzumzUI4cwZ76HsYd28DuwN30ITzJbc9p\nCKdl6RL0PmXasR3rnJm4O3Yu4BFc3JT9/xLf9THMv2wIltlmzsA5egze+xpFMDIhopv102mYdu/O\nVa5oGpbvvkE5kY4hy6nb1vTbpqIOT5RwkuAJIYSIWorfH74uOwvl4AFKdWiFacvmYLll0QJcmzeR\n/ebw/LefGX7WNOXIkfMLNhxNw7xkMeaffkRNKI27c1e0uPjC2XYRixnyRkhyB2DcuwfHsEGcuOc+\nMMhAICH0GNKPh61TnE40izVsvWaWd4BFwciVWQhxSfF6vfTp04cOHTrQqlUrli5dGlI/depUmjVr\nRkpKCikpKfz5558RilQA+Msm6pZrgLtVOxxjRockdxBYHN028xMMv+8EwLRyOXEdW1P61htJuPsO\nYvr1BZcrsJ0wN1maouC9576CH4DLRdxjbYh/vAOOce8SO3ggCQ3rY/723NcyixhNw7z+R90q02+b\nMK9aWcwBCXHx8N59H5rVplvnq1Ydd4dO+MO8Z+et36AoQxOXAOnBE0JcUubPn09CQgKjR48mPT2d\nFi1a0KjR6aFmW7ZsYeTIkdSsWTOCUYpT/BUrY96tn2SrV5bD9MlU3TrDiRNY077CW78Bcc89hfHQ\nwWCdacf/MOz7m8xpM9EdnwmgaRj3/o2/dp0Cxe8YMRjrd9+ElBn/3kPM4IGk39soumfrVFXwenWr\nFE2DrKxiDkiIi4fv9rq4H3oY25dzQsr911yL65keaImJZL/0Ko6RQzCefK9VMxrxPPAgrudfjETI\nogQp0gRv06ZNvPXWW0yfPj2kfNmyZUyYMAGTyURycjJt2rQpyjCEECKoadOmNGnSBABN0zAajSH1\nW7duZfLkyRw+fJh7772Xp59+OhJhipOMWfpDKBXAvP5HNLM5bFvNYsH24ZSQ5O4Uy4qlmFatRHHn\nhN2+4c8/LiDiUOY1q3TLTTt3YPlyDp62HQq8jyJjNOKrVRvjv//kqvJVuQ5vw/sjEJQQFw/n+Mn4\nq1yHZeXyk8sg3Ijr2edRq9cAwP14N7z3NcL66TQUVza+eg3wNHmoUJeAEZemIkvwpkyZwvz587Hb\n7SHlXq+X4cOHM2fOHOx2O+3bt6dhw4aULVu2qEIRQoigmJgYAJxOJz179qRXr14h9c2aNaNDhw7E\nxsbSo0cPli9fzn335T1Ur3RpBybT6UQxMTGu8AMvRBdTfFpi+PWgYipdBfb7YO3q3JXlyxPbszs8\n2FS3reJ2k7DtV7j2Wvhtc+4PmM3E3l2POJ1zdT7nT/Pr94ABxKtulCL4vyjM/1/t9QGwYxucOVlE\nfDymF3uRWOHC/m5fTD9/QhSIyYSrz2u4+rwW9iNqpWtwvTawGIMSl4IiS/AqVqzIuHHj6Nu3b0j5\nH3/8QcWKFSlVqhQAt956K+vXr+fBBx/Mc3tn30BBdF+Eozk2kPgKSuIrmEjHt3//fp577jk6dOgQ\nss6Upml07tyZuLhAfPfccw/btm3LN8E7fjw7+HViYhyHD4efuCPSLrb4rA2bEvvtt7kmW/FVvY70\npDZgNBL30wYs330bnG7cf9llZPd+BbdqIS62FOGmMsiIvwytdUfiVizHcOJESJ2nfgMyateFs87V\n+Z6/2Oo3Ytu6NVe5v2wi6fc1RSvk/4tC//+99gYMn32JfUoqhj170MpcRk6b9vga3JPr3EQkvkJ2\nZnyRvk4JIcSFKrIEr0mTJuzbty9XudPpDN48QeBputOpP03smc68gYLo/iMRzbGBxFdQEl/BRPoG\n6siRI3Tt2pWBAwdy5513htQ5nU4efvhhvv76axwOB+vWrSM5ObnYYxSnuVMex7j7D6yzP8N45DAA\n3htqkjVoKDgcAGROm4VlwTxM69aAI5acjp1QK10DgOehJCyrvkfx+UK267uxZmApBbMZ58h3sH04\nBdOO/6HFxuOtfxdZQ0YWyjAp13MvYP5lA8Yz3iPUTCbc7R5Du7JcgbdfHNTK15I17K1IhyGEEOIc\nFfskK7GxsWSd8WJ2VlZWSMInhBBFadKkSWRkZJCamkpqaioArVu3xuVy0bZtW1588UU6deqExWLh\nzjvv5J577olwxJc4RSH79SG4nn4O68L5qJeVxfNwczCZQj7jSWqBJ6lFrubuTl0w7tuLdfanGA/s\nRzMY8N1yG84hI+Dk+3ueR1vjadEKJeMEms0O1vDTl58v/403ceKTz7FPGo/xj9/R4kvheehh3G07\nFto+hBBCiDMVe4JXpUoV9uzZQ3p6Og6Hgw0bNtCtW7fiDkMIcYnq378//fv3D1vfokULWrTInSiI\nyNKuLEdON/0Jb5RDB7FPGItx+//A4cDTuCnu9o8FeuAUhez/vI6r+/NYFi9ELVce7z0Nc/fOKQpa\nqYQiiV2teh1Zb40tkm0LIYQQZyu2BC8tLY3s7Gzatm3Lq6++Srdu3dA0jeTkZK64Qn8dECGEEJeG\nn34ysHSpicREaNkSypQ5t3bKv/8Q37E15m1bgmWWRQswbv2N7KGjgmVa6TK426cUdthCCCFE1CnS\nBK9ChQrMnj0bIGQig4YNG9KwYcOi3LUQQoiLgKpCz55W5s83k5MT6FUbNy6Gfv3ctG3ry6c1ON4d\nHZLcQWCNNtsnU8np1AX1+hpFErcQomRYs8bAnDlmsrIUatRQeeIJD7GxkY5KiIKRhc6FEEJEzKRJ\nZmbPNnPmiuP79xsYNsxK48a+fHvyLGctIn6KIScH6xef4+on048LIfSNH2/mrbesZGcHrj9ffglf\nf21kxgwXiYnntg3D/7Zh+2w6SnY2vltuxd22Q+g7wkJEgPwECiGEiJjvvzdyZnJ3yv79BqZPt/DC\nC568N5AVfhZm01k9e0IIccqRIzBpkiWY3J2ycaOJt9+2MGJEPtcewPbBJByjhp5eZmX6R1i/nEPG\ntJnBWX4t8+ZiWbQwsJD5DTeS80yPc3/fV9OwzP0cy5JFKK4cfDVvCrSPiz+vYxWXHknwhBBCFCmf\nD2bPNrF+vRGrVaNlSx+33x5Ys87lCr8UgcsV+r2SmYFmsYbOcumIgbPWsAvut8YNBY5dCFEyff65\nmUOHDLp1v/xiAvJO8JQjR7CPfTvXGpqWH1bgeGsE2QPfxDFoAPYpqSheLwDWxQuxLF1Cxow5aOfQ\nRejo/wr2DycH1/gMtv/sC7TS5/iisrgk6f9kCyGEEIUgJwcee8xGr152Zsyw8OGHVlq3dvDOOxYA\nbrjBr9vOZtO4//7AO3jmr9OIb/EgpW+7idJ1bya2+xMoh0+uiXd7Xd32msGA55GWRXBEQoiSwJDH\nHbCiaPm2t878BOOhg7p15p9+xLDrd2wzpgaTu2Ddpl9xjBmdf3xbfsP+8X+DyV2w/a8/Y397ZL7t\nxaVNEjwhhBBFZvx4C8uWmUPKXC6FiRPN7N6t0KOHlxo1zk7yNJo393LbbSqmVT8Q+1JPLGtXY0g/\njvHff7F9MZu4pzqDqqKFuUvTDIawdUII0aaNlyuvVHXr6tTRf/AUwp/HZ1Q/1nlzc/XunWLa9Gu+\nm7dP/yhXcniK9esF+ccnLmny108IIUSR+ekn/T8zJ04Y+PxzMxUqaMyY4aJrVzd16/po0gQGDXIz\ndqwbANu0DzEeO5qrvfnHNVgWzsf09x7d7Rt8PqyLFxbegQghSpTSpeH55z3Ex4cmebff7qNv3/zf\nv/M82gq1dGndOl/tOmgWS9i22jlMwmLYuT18pTMz3/bi0ibv4AkhhCgyqv4DcuD0A/AKFbTghAaJ\niXEcPnz6qbXx3326bRVVxbhtC8qRw2G3fy43UUKIS9eTT3r5v//zM2uWCadT4aabVDp18mKz5d9W\nrVgJ1+NP4pgwBsVzOiH03nwL2S/2BcA+OVV3GKe3br18t++vWQvWrNKt00qVyj9AcUmTv35CCCGK\nTJ06KitX5i6PjdVo0UJ/+NGZ1LLhJyLwV6yUZ1vNEH4Cl8Jk+uF77B9MwrhzB1p8PJ77GuF6+TWZ\nKl2Ii0Dt2iq1a+ffY6fH9Wp//FddhX3cGBRXFr5bbydrbGpwlkzXi31wjByKIf04AJqi4GnYGFev\nPvluO+fxJ7B9/F8MbneuOndzeb9Y5E2GaAohhCgyPXt6uPPO0AXLTSaNTp081KiR/0QG7uQ2qHZH\nrnLvTTfjadUuz7aGnJxzitGwcwcxvXtQqtn9xLV9FNvkVNDyjw3AtHoVcd2fwLp4IaY/d2He+Asx\n744mtlf3c2ovxNk2bdpESkpKrvIFCxbQunVr2rVrx8CBA1Hz6h4XxcI2ahixr76Eec9uTIcOYV20\ngPimDeFkUpbT9SnS074l+7kXyO7yJJkTppD5yWzOpYtQrVIV1/MvoplDh3p66jfAdbKHUIhw5PGi\nEEKIIuNwQMWKKuvXa/h8gR61uDiV6tVDb04PHFBYsMBEpUpw771gPjkviyepBdn7/8X2wfuY9uxG\nM5nw3l4X57DRYDaj6LyfB6ABuPNP8Aw7thP/eDtMf/4ZLLN8vwzjrp1kjRqTb3v7h5N0h2BZFi3E\nsHUL6o01892GEKdMmTKF+fPnY7fbQ8pzcnIYM2YMaWlp2O12evfuzfLly2nUqFGEIhXKkcPEjH0b\nxXf6AZYCmP/cRewTKTinzwZArXY92QMHX9A+XH364a1bD+v8L1FcLry33Io7pQvk8X6fECAJnhBC\niCL04YdmZs0yc+Zi5sePGxk2zEqjRn7KltV4800Ls2aZOXIkMKikenUHb7zhpmFDP2gaxt1/Yjge\nSOQUnw/DoUMY9+1DrXFjyM3VmRTA9NtvgW/cbqyfTMW8eSOaIwZ363b46twGgH3C2JDkDkDRNKxf\nzMb1xDOo1arneXzGXb/rlhucmVi+X0aOJHjiPFSsWJFx48bRt29oD43FYmHmzJnBxM/n82E9cz1I\nUewc/+mL4tMfZm5e92Oh7cfX4F58De4ttO2JS4MkeEIIIYrMsmVGzkzuTjlwwMCMGWbKllV5/31L\nsHcPYPt2I6+9ZmXp0mwumzUZ29QPQtaCMu3aScyAvqTXq4+Wx1BKzW5Hycwg/rG2mH9cHSy3zvqU\n7JdfJad7T0zr9W/EDE4nlsVfk5NPgqeefNcm174BtfxVebYV4mxNmjRh377cEwsZDAbKli0LwPTp\n08nOzqZ+/fr5bq90aQcmkzH4fWJiXOEFWwSiLT5Ng8mTYeFCyMyEG26I4+WXoXJl0DLSw7Yz+rwR\nOZZoO39nk/gK5nzikwRPCCFEkXG5wk90kpUF69ebQpK7U3bvNvLJJ2b6rFica6FfANPu3dg++Rgl\nj7Xu1Kuuwv7WiJDkDsCQ5cQx5i3crdphSA9/k6YcPx627hTP/U0wr1ubK4X11aqNJ6lF4JuTPYim\nrVvQ4uPJ6dgZ9bpq+W5biDOpqsro0aPZvXs348aNQ1Hyn0To+PHs4NeBGWqjd3r9aIyvXz8rH35o\nRlUD53rFCliyxM+0aS5qPNySuGVLdR5fga/s5aQX87FE4/k7k8RXMGfGdy6JnkyyIoQQoshUr66/\nGLDVqnHffT5+/z38n6EDBxQMx46FrVdOpKMZjWHrDfsPYF0wT7/uRDrWGR/nOdOlFhsbtu6UnB69\nyOncNbgelqYoeGvXwTnyHTAaUY4dI77Vw8T164N9xsc4Jo6j1CNNsH46Pd9ti9NMP67BljoW85Jv\nznkCnJJm4MCBuN1uUlNTc72jJy6MpsFnn5no3NlGcrKd116z8PffgZRt1y6Fzz83BZO7U37/3cj4\n8RY8HR7Df3XF3Ns0Gsn6z8BiiV+IcKQHTwghRJF5/nkva9aY+N//QhOxZs281KuncuJE+LbHjoHh\nH/118DTAX/ZysNkDY6d0qOXKoawM30Nn2vgL/uuq6U6SAqBefXX44E4xGMgaNQbXc72wLFmMv9xV\neB9sBid7Fh0jh2D5aV1IE+OxozjeHRWY6jwmJv99XMKUzAxin+mG5YcVKG43mtGI9//qkjn+fTSd\nm+uSJi0tjezsbGrWrMmcOXO47bbb6Ny5MwCdOnWicePGEY7w4vb66xamTLHg9weSuB9+MLFypYmp\nU118/bWZEyf0H0Bt2WIEReHEstWUatoQ4+4/QVPRYmLIemUA3ubJ5xWHaflSDP/sw9MiGc7hwVKh\ncruxfPUFBmcm7ubJaCeHAouLmyR4Qgghikz58hozZrgYP97M1q1GbDaNBg1UnnsusO6U1xt+mNmh\nQwZwuXTrFMC6KA1frdoYl36bq14DfPUboH05BwiTAF5bFV/58pjXrs41DNR3Y008j7Y+p2MEUCtd\nQ84Tz+QqD/eOn/HvPVi/mI27U5dz3selKKb/K1i/+yb4veL3Y/lxNbGvvkTmjM8jGFnRqVChArNn\nB2ZgTEpKCpZv3749UiGVSHv2KMycaQ4md6f8/ruRCRMs1KwZfhkKmy3Qi2wfORTj7j+C1w/F6cQ2\n42O8TR5CrZT3Op0ApkULiX/hWZQT6SiA9tpLuBs3xfnhJxd+YOfBvHA+McMHY/p9BwD2MW+R06kL\nrj79imX/oujIEE0hhBBFqkIFjREjPKSlufj88xx69vRwamSl1xt+uJ3ZrKHktVi5qqLGx+tWaWYz\n3tvuQE0orV8PeO5rSM4Tz+B68ln8ZS4LlCsK3ltuJXP0mNNrNRSEP/xNouI9YwY+vx/Dvr0ozuh9\nB6TYud2Yf1ipW2VeuwrDnr+KNx5RoqSlmUhP178N/u03I+3aealUSX+Ief36fgw7t2Ob/Wmuh0Pm\n7duwT8h/iRWys4l/tiuGk8kdgOLxYF04H/ug/udzKBdEOXaU2AGvBpM7AOOhgzjGvYvlqy+KfP+i\naEmCJ4QQImLOfnp+pmPHFPyVq+jWaYCre0+M+/fr1hu8XqyzZkCYd5UUwLRlMygKWqmEwFBPAKMR\nzRGDFqufOJ4v3y236pb7y5XHnRzoIbRNmUipxg0oXa8OCfVuJbb7EygZeYxd1aEcPIDl88/Q1q3L\n/8MXCSU7CyUzQ7fOkJWFcvBAMUckSpK8RkdbrRqxsdCihReT6cyHUBoVKqi88IIH68I0DBn6P5+m\nzb8GvzZ/u5jYns8Q90Qn7O+MgixnYP+vvYRBZ4SCAti+mH0hh3RebFP/i1FnCLzidod9d1lcPCTB\nE0IIETF5zZfhdCq4G9yL3kc0gwHfPQ1R/Prr4MHJHrK8FgT2+rDOmIZjzGiM/wZudBSfD8vqlcS9\n8Cz49Z/en4/sl17Bd9ZaeKojBtdT3dESSmP97BNiBr+OeesWFLcb48ED2L6YTeyzT5zbDlSV2Cc6\nUfr/biK+x9NQ704S7vo/DH/tLnDskaYllMYfZrZRX+Uq+G+6uZgjEiVJ27ZerrlG/3e8Xj0/OTmw\ncKH5rFl+FfbtMzBligUtjxl8DYePAGAf/ibx3R7DNutTrGlfETNyCKVaPYJy7BjGs9bfPJPidF7Q\nMZ0PJY8XoM/3AZOIPpLgCSGEiJg8JsEkJgbsc2bqTkOuqCqONwfgL11Gt62mKLhbJOMPM1GKZrbg\nebQVlvlfong8uepNv/6MZf6X53IIedKursiJuQvJ6t0X98PNcXVIIWPaTHK69wTAOmcWijsnVzvL\nD99j2pB/b1xM7+expn2Fwe0+uUMN0+87iE9++OKfbVJRyOncDTU2dEpwzWLB3bZD2N5ZIc6FwwH/\n+Y+bcuVOJ3kmk8YDD3jp08fD7Nlmdu3Sv0AtWxaYZCUcTVMx/LUb+0cf5Lq+mH/ZgP3dUfhq19F9\neAWEHVpemHy31EELcwz+KlWLfP+iaMkkK0IIISKmUiWN33/Xr2vUyIfhnSO6dQpgXvUDuPUnYUHT\nMP36M/j1b6E0TcVw5DCGo4fDbt/w9558oj83WkJpXK/ov1Nj+Pcf/f27czD98gu+2+7Ic9uWr+fr\nJsDGfXsxf7sIb5OHzjfcqOJu2wHVEYPts+kY/9mLmng57ubJuFMej3RoogRo3txPgwbZTJ1qwemE\nO+7w88ADfhQlMEQ8HKdTQcmjh18rm4h17ucYTujP4mtes4oT36zA9tk0lLNmAdYA15O5J2wqbJ5H\nHsX76TQs3y8PKfddWxXXM88X+f5F0ZIETwghRMTceqv/5Fp4oTdTRqNGSooX7R1FN4EB0BQwhRmK\nqADWz6ZjyNCftMTg82H+fjnq1ZXgt825t22x4Kuj//7cedM0LF+nYdqwHjUhAXenLmgnex7VcuXg\nz125m1it+GrlPwTR4MzSLVcAy7y5F32CB+BNao43qXmkwxAlVJky0Lt37l78Ro28jBxp0X1PuEIF\nFXfTZthHD8Ogk+j5y5VH2a//8AbAcHA/mEykz/+W+JQ2GP/9JzBpVFw8OY89jvvZYkiwDAYyPvqU\nmGGDMK1bg+L24Kt1M9k9eqFWuqbo9y+KlCR4QgghitTWrQrjxlnZutWAzQb16vl47TUPNhtkZCic\nndxBYPKVxYvNVLv88rC9XJ5HmmPekjs5O8X41595TpaiJpQmp1OXwIyMx4+HbrvBPfga3HtOx5en\n7GziuqVgWbE0ONuefdqHOIeMxPvgw7gfbY15/bpcw7i89Rrgq1sv382rVgvG7NzvIWqA94782wsh\n9Fmt4V/D3bVLwZB+DCXMMGjl5Jp4YRkCt9/qDTeS/vNWOH4MQ0YG6tUVg2toFouYGLKGjiq+/Yli\nI+/gCSGEKDI7dyp07Wpn7lwzO3YY2bTJyMSJVrp1s6FpsG9f+GFQ+/Yp+C+/QrdOAzxJj+a5b81k\nBlV/mQINUC8ri/e++8kc/R6e+g3wl03EX6kyOR07kTl56jkeYd4cIwZjXbYkZCp14769xAx5A9xu\n3CmPk/XqAHzVqgcWby9dhpykFmROmHJO2/fVvVO3XCtdBo8MYxTigg0ZYkXv4RPAP/8YMK/JvX7m\nKYaDB1ErVAy7bbX8laEFpcsEes2KM7kTJZr8JAkhhCgykydb2L0790QFy5eb+PZb48kePD0aoGHe\nqb+4swLYRwwGU/iBKOrlV4DNFra96Y/Ay39afByawwE2G6rDgRoTC+Y8Zt88D+a1q3XLTbt2Yvky\nsFB3znMvkL50FcdX/0z6mp9xfjAN7bLLzmn7mf+dgffGmiGTNfgTSpMx+WO5WRSiAPKaRFfTTl5f\nwlBLJeC/pnLYSVQu9vmPRPSTIZpCCCGKzK5d+kmGz6ewfr0xXAcboKCqCkp2dthtm39ej69iJcx/\n/qFb72nZBsvSb3TrNAIzxZnWriaux9MYDx0EwAiY/7cV4769ZH40I1D22ybs70/AuHMHWpkEbA0a\nkvPs8+eUQOnNkHmK4czJFSwW1KrX5bu9XBwOTixdjfm7bzBtWE9M5as53rytzDApxDnavVth+nQz\nWVkKt9zip1UrHyYTvPiimyVLTOj14lWqpOFu3Y6Y1/thyApd0kAD/NWqY/55fdj3h88eEp6nLCe2\nT6ejOJ24mzZDrXHDubcVlyxJ8IQQQhSZuLjwj6oTEjRywuc//P23EpiKPMzjbs1sJef5FzH1fj7X\nuzC+ayrj7pCC8Y/f0ch9i6bFxuJ5KInY7t2Cyd2ZLEuXYPrpRzSbjfiuj2Hc+3ewLmb5cox//kHW\n2++FD/4kf4WKmHbk7oX02+24H8l7iOk5UxS8jZvibdyU2MQ4OKw/sYwQItSMGSaGDLFy9GjgYc1H\nH2nMmePn449d3HabRqlSGidO5Lp60K2bG9O6tboPcBTAcOggWnr4JO5c17mzzPuCmCGDMP79FwD2\n8e/iTm5D1sh381ymQQgZvyGEEKLIPPigD5Mpd4JWsaKfzp294UZQAlC+vIbqCD9Rgffue3B36IT3\n9rrBoVAaoFptZL/YBywWjH/t1l9HLysLy9dpGMP0/inuHExrV2OfOC4kuYOTM3TOn4shzPDRXB8+\n92IhRDHJzIS337YEk7sAhZUrTYwaZWHNGkOYIeQKM2eaMW/4CcWXe4IjAONfu1GvuirsvjWHI9/4\nlPTjxAwaEEzuAAxOJ7ZpH2H98Nze0RWXLknwhBBCFJn27X0884yHsmVPj8WsVs3PsGFu4uLyHuVY\ntqyKVibMQuaAv/atWP87GfO6tcGESQEM7hxihrwOXi+Ggwd02yuahvGPXWh5LCisliuPUaf3DcCQ\nkYHl20Xhgz/p7OQw2N7lwloIC6kLIS7MrFlm9u3TX8h83TojM2aY0TT9RzE7dhjxlysXdtua34f/\n5jpoYS5wvlvyX4LF+slUjP/sy1WuaBqWpd/m215c2opsiKaqqrzxxhvs2LEDi8XCkCFDqFSpUrB+\n/vz5fPTRRxgMBpKTk+nQoUNRhSKEEEFer5d+/frxzz//4PF4ePbZZ2nUqFGwftmyZUyYMAGTyURy\ncjJt2rSJYLQXP0WBgQM9PPWUl/nzTSQkaLRo4cNycg6Tf3tUfiAAACAASURBVP4J35f1yy8GlIwM\n/e0C5tUrA8OkdOqNhw9jmzQetfxVsOnXXPWa0YjvhhtR4+Mxr/4h12x4vpo34WnZGvu0D8PGp15W\nNvi14Y9dWJYsRi1XHs/DzcEYuHHUrOG7KNW48Es4XEyUw4cD7yju3YNaugyulC6oN9aMdFhC5Mnt\nDl/n8ym4XOHr/X4gJlZ3+DcnyzwPNsN7x51YzppoSY2LIyelS77xGTLDD+NUss5tiKe4dBVZgvfd\nd9/h8XiYNWsWGzduZMSIEUycODFYP2rUKBYsWIDD4aBZs2Y0a9aMUqVKFVU4QggBBB4uJSQkMHr0\naNLT02nRokUwwfN6vQwfPpw5c+Zgt9tp3749DRs2pGzZsvlsVeTnyis1nnrKm6vc6w2f4K1aZQo7\nBArAsG8vhsOHw9ZbVizF9fRzmFevxHBWouitWw/vAw9i3PQrqiMGozP0vTX/5eXAZMJzbyPM69fl\n2ravWnU8LduAqhLbIRnrD9+j+HyBIaJlE8mc8jG+enfhrXcX5s0bc7e/rhqeR1sBoOz9G8f4MZg2\n/4pa9nLc7R/D81BS2OOKJsatvxH3ZGdMf5xerN0y/0uyBo/AkywPR0T0Sk72MX68etYQzYDatf3c\nfLOPBQvM6KVwFouGcdfOsEOtT72bp+mMQdeMJrS4uHzj8zS4B3vq2FxrZAL4q8tEKyJvRZbg/fzz\nzzRo0ACA2rVrs2XLlpD666+/nszMTEwmE5qmocjLokKIYtC0aVOaNGkCgKZpGI2nh+j88ccfVKxY\nMfiw6dZbb2X9+vU8+OCDeW6zdGkHJtPp7SQm5v/HO5KiPb5T/H4jhsSyEOZptaXRfaCTfAXrzUas\nHdugqW5InQBbtkBcHNxzD5ax75F4eTzatCngzD0piXXdaqz7dsHQQbD/b5g7F07N6Fm9OqaxY0ms\nUBatSxdYvjTYTgGMRw6T0KUDHDoMb4+CPX/Ct9+cnne9UiVMo0cH2m/dCk3ugaNHT+/7u2/gxd4o\no0ef/0mjeP9/tXFvwxnJHYDx6BHiJ46FJzqj6CxjEe0/f9EenygcV16p0aWLl/fes+DxnL4HvfFG\nP716efjyS/0ZNAFKlQJNVcP34DkzMf3wPZbVP+SqM6Yfx/7f93GOn5xnfL677sbz4MNY580NLa96\nHa7uPfM7PHGJK7IEz+l0EhsbG/zeaDTi8/kwnbzYX3fddSQnJ2O322ncuDHx8XkPVTn7Bgqi+yIc\nzbGBxFdQEl/BRDK+mJjApB1Op5OePXvSq1evYJ3T6STujCerMTExOM9htrPjx09P5Z+YGMfhKJ7F\nMNriMxpj8Pv131OpVs1LZqvuxPbrm+smSi2VwLFnXuSyYcPDPkV3O7PJPJwJTVug1KmLbdwY/Ndf\nj6fj44Gxo4czKbV6LWa9xllZOL+YR06FqvB2KsaUJ7AsX0rMNVdx5MGWgfX1DmdSet489N7i0dLT\nyXp9MDm9XoaPPsPyzdeY1q9DLZWA+/FuaPGlAvvv+BjmM5K7wMGpaO+9x9EneoR9BzGcYv3/9fko\n/eOP+sf/229kfLkA772NQsqj7efvbGfGF+3XUVFwfft6qFnTT1qaGacTqldXeeYZD5ddlucSmyQm\naiiKIey1R7PYMK9bo9v7BmD8Q39yp7P5atyI5es0FG9g9ENghEBZ1CuuzLuhuOQVWYIXGxtLVlZW\n8HtVVYPJ3fbt21mxYgVLly7F4XDQp08fFi1alOdT8jNvoCC6/0hEc2wg8RWUxFcw0XADtX//fp57\n7jk6dOhAUtLpoXBnX7eysrJCEj5R+C6/XGP/fr0ajTZtvPhq1tZdKkGLiQWzGc1iRgnzMs2pYVBx\nyQ9jWbMK5eQTd21gPzLHTsSb1AJDxomwsSnHjwW/9teug6t2nVzLECjZWXpNA+8IbviJHABFwdO0\nGZ6mzXJ9zhhuIXevB/vbI8geOipsfFEhj9E3Wl53yEJEiYce8vPQQ7lXNW/b1suQIVbdYeSJiRpq\nufJht+mvXBk1MfxC6JoWdgHQIMOev7C/Pz6Y3EHgumL5cS32ce/g6tMv322IS1eRzaJZp04dVq5c\nCcDGjRupVq1asC4uLg6bzYbVasVoNFKmTBkywrxIL4QQhenIkSN07dqVPn360KpVq5C6KlWqsGfP\nHtLT0/F4PGzYsIFbbrklQpFeGq64Itw6eQqHDhmJ6/5krjXuAAz/7sPy+Uzdd1xO8dW6hZjuT2JZ\ntTI4iYoCGLKyiH+mW2CWBbMlbHstn5ElAFqs/gMADfDUvzvf9nms9I7h2LGwdVHBZMJ36//pVvlu\nqoXvzruKOSAhCs+vvxqDo6rP5vWCu3U7vNdcm6tOAzwPPox6+eWEvbqF6dk7k/XzmWEXRDevW5tv\ne3FpK7LHa40bN2b16tW0a9cOTdMYNmwYaWlpZGdn07ZtW9q2bUuHDh0wm81UrFiRRx8tpAVfhRAi\nD5MmTSIjI4PU1FRSU1MBaN26NS6Xi7Zt2/Lqq6/SrVs3NE0jOTmZK64I/xRWFJzDEX4hdJMpMJGK\nHoXAor/qNVUwbvpF/zMWM9aF8/SHUfm8xLz0PL7rr8d4MHcXogaoOjdvZ3M/8ij2qR/kXki9zGW4\nn+6eb3u1bCKGf//JvX9FwdXtyXzbR1pW3/9g+H0n5u3bgmX+K64gu/crwZlEhbgY/fabEVXV76E+\neNAABgPGQwd16w37/8W4/X9hh3AaDuq3C6GGyS6BsJmnECcVWYJnMBh48803Q8qqVKkS/Lp9+/a0\nb9++qHYvhBC6+vfvT//+/cPWN2zYkIYNGxZjRJc2ny/sWyzUrevLs4eLjAx8t/4fZp0ET7U78N53\nP45Rw3SbKoDpp3X4b75Fd6IEzWY/p5kss0e8jWnLJsw/b0DRtMAQ0JgYTnw8M+9F/k617/MacS/1\nzLVMg7d2Hfy33XHyGy/WmZ9g3PIbWkICOV2eRLsy/BpcxUmtdj0ZC77FNmUSxr/+RC1zGTldnkSt\ndE2kQxOiQKpW9UOYaVTMZhX78MG6Q7QVwPbZdHw31wm7bSXr9DBv0+qVWL+ai5LjwlurNu5OXcFq\nxf1IS+yTUzFk5n7lwnfLbRdySOISIgudCyGEiJicnHA1CqtXm8I+AQfA7sD1VHdUnWGWvmrV8NXJ\n+yZItVoxHDuquw9DjgvL/Lk6NaHMS5dg+mNXcBjpqSGgjknj8m0L4OnQCefosfgqVUa1WPDHl8KV\n3JaMuQsD2zt2jPjkh4l7+QUcUz8gZsxbJDxwD5a06FkkXYuLx9W7L873JpH9xlBJ7gpo06ZNpKSk\n5CpftmwZycnJtG3bltmzZ0cgsktLQkL40QWapmDa+HP4ZRIyMvJ5wBNoaX97JPEd22Cf9iG22Z8R\n1/8V4tu1hKws1Oo1yEnpimYOnQbKc3tdXL1eOs+jEZcaeQNaCCFEkVJV+OorE+vWGbFaNVq18lKr\nVuDmadeu8DdB27YpBG6EwtxoKQr2UUMweHO/z2LatBEl/XjgJitcL2DpMign9N9xATAcOBC27hTb\n9A9135MxL/sO0y8b8k0yAdyPdcbdsRNKZgaa3QFn3NA5hg3Cctb7NsaDB3CMHIqnSTOCK8aLEmHK\nlCnMnz8fu90eUi5rdBa/9evDL5OQnq7gu70m1lUrdes1uwPfNZWxhtm2ZrYEJlGZkorBFTqJoGXN\nDzjeHU12/zfIfn0wvv+7A8uiNBRXoIcv54lnwOEowJGJS4EkeEIIIYqMxwPdutn49lsTmha4WZox\nw8KLL7rp0cNLdnb4PrqdOw1oBgVF1U/wVLsN69LvdOsMQEzPp1FjYjFm6k/ipZUrhx8w/7Y5d53N\nju/OenkfHGD8a7f+/l3ZmH5cfU4JHgCKElg64Szmn37U/bjp951Y5n+Jp1XbQPPDhzH/sALttpuh\nYjXdNmFpGqaVKzBv+Al/+fJ4WrULSTLF+Xv//fd5+umnQ8reeecdevfunWe7ihUrMm7cOPr27RtS\nLmt0Fr+8XnNzOo3EdH8aJqfq1hvLlyfGES69A6PFRJlFX0GYSVQcv/5EzKlz0bl94B9gA/I6Q9F0\n/vRIfAVzPvFJgieEEKLITJhg4ZtvQpOFzEyF996zkJTky7Ptvn0KGE2ghplx7spyKNu2hm1v3PY/\nfLfehnHFslx1msFA1qBhGP/6C/Oa1RgPhfbWeRo9gO//6uYZH4Ba5jLdcs1gwH8Ok7TkK/wYVhRX\nNqgqMf/piyXtK4yHD4HNRvwdd+Ic/R5qpUr5bz8ri7inHseycnlwZj/flElkvv0e/ltuLXj8l5i3\n3nqLo0ePsmzZMv76669guc/nY/PmzfkmeE2aNGHfvn25ymWNzuJnMFggTB+c2ezHOXMOsbq14DVb\n8O3Zhz1Mvd/twf3PQcL1w/n/3M3x8zwX0Xb+zibxFcz5LjEl7+AJIYQoMmvX6v+ZSU83MGtW3r1E\nfj9otvBPwdXYOLQ8ZmpUr7sO3836y1yoMbFocXH47qiLp34DVGtguQUN8F9ZjuznXsgztlM8DyXp\nxuC75Va8OuvenT/93kvNaMTTpBn2d0dj+3ByILkDyMnB8v1yYnv3yLV2oJ6YNwdg/e6bkGnbTVt/\nI2bAq+fUXoR64IEHuP3223E4HNx+++3Bfw0aNOD999+/4O3KGp3Fr0qVU5Os5Hb99SrYw6VvoFlt\nKD5v2HqMxjx/v7Q8Xz4uZNnZWKd+gC11LMo/uR8uiIuT9OAJIYQoMn5/+DuV/Gb6ttuB7Oyw9ZrV\nhr9sWQw6K6VrQE7nJ7BPfE+3rTEzA+tnn4DJhC3tSxRfoDdRAYwH9hP7am9OLFoWWKshDzlPPIPh\nwAGsX8zCuP9fNIsF72134Bzx9ulJFlQVy7y5mH79GS2+FDmdu6ElJga3oRw6hH3SeIx//I5WqhQ5\nj7bGd1+jk231bwIVvx/LsiVYvl2k+5aQ+ae1mH5cg+/O+nnGbw7zDpH5158xrV2Fr16DPNtfDJRD\nB7FPnojhwL+ol19BTtenUCtcXST7qlWrFrVq1eL+++8v1ATszDU6HQ4HGzZsoFu3boW2fZHbww/7\nufVWPz//HHoNsFo1Hn3Uh/uhFOyTUzHqJEXeevVRyyZim6c/UZMWE4uWkBB232r5CgUL/hxZvppD\nzPDBwaHm9nFjcLdPIXvgm/m0FNFOevCEEEIUmdq19bO4mBiNZs3yeMINxMQEEplwLMuWQOkyunUK\nYNq6GeNff4Ztbzh6FOvCtGBydybT5o1Yvvri9Gd37sCW+h7aJ58EVjkO7kghe8Ag0lesJWPyVNK/\nWkzGlwtRr68eqM/KIr7to8Q92w3H+xOIGT2MhMYNMKd9FdjuH7uIb5WEY8IYrIsXYpv1KfFdH8M2\nYWxg80799wcB8PswHDuqf/weD6ad28O3BdA03WneARSfTzdxvtiYNvxEQtIDOMa9g+3zmTgmjKXU\nI00w6QzbLUyLFy/mzjvvpEaNGtSoUYPq1atTo0aN895OWloas2bNwmw2B9fobNeunazRWYi8Xpg9\n20Rqqpnffz/9uMRohDffzOHyy1VO9eRZLBpNmnhp186HFl8Kd/PkkB58DfBdXQlXrz4oWfq/WwBK\nVhbu5DaopfSTPN/tdxbKseVFOXSImNf7hbxHbDx2FPvkCVjmzCzy/YuiJT14QogSxePxYJGZBaNG\njx4ePvjAjMt15vNEjcqV/dSqpWG1arjd+r18116rouQxkaVy/BiKKY9hnh4vuMO8vwfg8aAcPaK/\nbQgsQK6qxLz8Atb5c4PrUZW64Uayho7GV++u00eUUBpP85a5tuMY/iaWlctDyoz7/yVm+JukN26K\n493RmHf8L6TekJ2F/YOJuFMeB6/+e4oaoJYug/+ayhj/3pOrXo0vhefu+8IfO4Ci4KteA6POQuv+\n/2fvzONsKv84/j7bXWa3lYai7CERsmTJVpEoska0KKTSomSpH7K0KEm0iJTsS0r2LSFk32Ps+z7r\n3c/5/XGZMe45Z6ZmGMN5v14tznOf5/me695zn+/zfL+fb2whfA0fMe+fCwj7eHCIEI507Chhnw4l\noc7DIFybWLjRo0czYcIESpQo8a/7Fi5cOLUMQtOmabUYrRqd2c/q1RLvvWdn586gkzZ8uMoTT/j5\n5BMPggDDhjk4fTrt2eX1CixfrrBwoZ9GtZKwLfg93SaUAEhHDuEY9y3K/LmG8wrxF1DvvgfX810I\nGzUCweNJm+PB6rh6vpX9N3sVjgnfI+koBQs+H/bf5wbFlixyLdYJnoWFRa6ldevW6f6sqiotWrTI\nIWss9OjTx36VcwcgsHOnxIEDAmXLGp3QabRsaS7CgiiiCSZ5YgIIycZCFEJSIoKBiIkGBMqVwzHy\nMxwTf0hXbFjZuYPw3m+mneS53TjGfEnEK10If+8txCtUOZW1q3XHl+P2YZ81DXnLJt126fhxbDOn\nI7hdRreGtGcX7vbPokaEhgJ6H22CenfGIi/uDp1C6mxpgLdWHV1Vz9yEcP4c8sYNum3Kpg2IJqe7\nWSVfvnz/ybmzuH54vdC7d5pzB5CQIPLTTwqjRiksXSqxenVofm1CgsDEiQr2KROR4/aGtAuAbeZU\npP1xhnNfrpvpeqcv8eMn42rfEfeTLUl6fxAJU2ajRUZl/QYzQNApoJ7aZhY5cCOhadhmzyDita5E\n9HgJ25SJxmVxbjGsEzwLC4tcR8eOHVm3bh0ApUuXTr0uy7K1w32DsWKFvgiKqgr07m3n2Wf9bNwY\nWm/Kbtdo08YPZqKDqmpYQgEIOmBe4xM8efMmNFnfPgGQ4uKwz5ymn+O2exe2WdPwNXiUqCb1Ua5Y\nzNknjCO53wA8L3VPJ14SMkdSEppZjp/DjmCyWBFPnMDdsxdJqorjx3FI++OQ8ucjpU59Unr3T5vn\n9CmcY0YhHYhDjcmDp3U7/NWCJSBsS5cg+NKHygoEyzCgadfshOu6oKX+S6ft2gjIzJ4dDL2NjY2l\na9eu1K9fH/mKv+PmzZtfk3kt/j0zZsjs2qX3/RdYvFgmEAjg9+t//o8eFQ03DwCkPXtAyeQSW5GD\n8aCSHNxsMS2Qnn34q1RF+0bUfcYESpS6LjZkCU0jvGd3HFN+Tr0H+9RJeBctIPHrccH39BbGcvAs\nLCxyHRMmTABg0KBB9O3bN4etsTDD5TJ2EBISBL780oZeMWGPR2DJEpG2ZoP7/eYOiCwblCkOIp4+\niZb/NuMXeNxIx44YD79xA/aZ09M5dwCiz0f4wPfxtuuAv3wF5N27QvoGCtyGp3lLpL3/oOzcHtLu\nv6cYnuYtCX+jh/E9XFrUeJ96Gu+TLSEpkfxFCpJyPk2YRty9k6jOzyDv35d6zf7LDJL7D8TT6QWU\n1X/q39vmjch//oG/Vh3D+7/R0fLlw1exMvYVofl2/ooPoGZHGYurWLt2LQBhYWGEhYWxYUN6J8By\n8G4czp41fzbdc89lFc3Q14mihnTA5ITO68VXrBjSjtDv9pU4Px1K2BefpZ7UO6ZPxv77byT8NAXC\njYowZA/eJk8QKHgH8lUh2qrDSUqnF67p3NmBbf5cHFMnpXNQBcD+62y89Rrgadcx54y7AbAcPAsL\ni1xLr169WLFiBReuKhZrLaJuHIwPoDRKlAgwZYpRvqRA794OcwcP0EwcPM1uCzqABqc1aoHbCZQv\nj7I1NExSzZMHb9MncY741Hh8RUFZpa9CKfq8OD4agqfRY9hnTU8n5KIBgSJF0AoUIKV3P6TdO7Gt\nXZPaHihwGylv9QaHA+2228BAJdT7VEsA7BMnBMNI98dB/nyE16pL8geDwW4nbEC/dM4dgJicjPPj\nIXhatUNw66uUCqqKeOaU4b3nFlxvvoN8cD/SoYOp1wKxhUh5851rcjo5ZMiQbB/T4trw8MN+hg/X\nSE4O/RyULKkSZlSkjuDeklkooICGv2Zt7AYOnhoWhnjoIM5vRoeEYdtWryTss09I6ftBZm7jPyOv\n+wvpbGgOsuh24Zg1DVevPtd0/qyiLFlkKMKlrFxhOXg5bYCFhYXFf+X111/nzJkzFCtWDOGKxZrl\n4F1/AgTYI+0iXIugiFo09bpxtJHAkSOiabqEztojBPHsGeO202dRFQXJIEzS3aIV/kebIG/4G2VX\nWsF0TVFwt+2AWqQogaL3IG3dHNJXAwIPVEH4drTh/MqaP5GOHwtR6RQAafs2hOPH0GILkTDjN+w/\n/4i8Y1uwjELHzqh3BYuUu55/mfBB74ecIfhLlcZfoxb2qZMI79ML0XXJUbtwHufevQgXLpI0Ziy2\nNQY5gGfPYPt1Nv57yyEd0xFZKXwnvkaPGd5bbsH/YHUuzllwqUzCMdQCl8okFCl6Tedt1KgRgSvF\nNwQBh8PBPffcwzvvvEOhQoWu6fwWGVOuXFDJd+rU9JtMsbEqXbp4mT8/NHT8MocPi6hVjE/YNE2D\nCJP2qGjs0ycjXryg266sXpnxDWQR24LfEbwe3TZ5k3H46Q1Dbg4fvw5YDp6FhUWuZf/+/cyfPz+n\nzbjl+dn+IyPDPiNO2oeMTBXfg3yYNIxygftM0yB85lUS0KleEIJeiYPLKMsWIwSMPUjl73V4X+pG\nwoTJRPTsjhy3D83hwN26Pe6ebwPgbdUWZduWVFGEVNsqVMTbtDla9xcNbQgUL4Ft7hzdNsntxj5z\nGu5XXgdFwfPsc+gttYT4i/pLTEUBVcU+eWKac3cFtsXzEf/ZY7iAA5B2bsf1Ug/kbVuRTqadEmp2\nO+52HdF0xFtyI1rBO657Xa/atWtTuHBhWrYMnrLOmTOHbdu2Ua9ePfr06cP48eOvqz0W+owY4aFo\nUY358yUSEgTKl1fp2tXLAw+oDB5s7EAkJ4Nw/qLxwJpmWsNTcLsQTxlLBIvXo+C4ZOICmLXdIHgb\nNMLx84SQ568G+HJxaHl2YaloWlhY5Fruuusujh8/ntNm3NIsk5fyduTrxMn7QAC/4GeNbRXto1vj\nxk2ePEZiFhpPPWXu4eXVL3GXfhQTQQItTwxCwNgBlPbsREiIJ6rHy9j//APpxHHkA/sJ+2oEjksn\nc+4XXsbd7CnUK4Qy/LcXJOmTz0GSUPMX0B1bA1xvvINoIrJi+/3XDO4ObCuW6V6Xd+5AWbRAt0QC\ngJiYiG31n2h2h+HYgfIV8NeqTcK4ibifbouvajU8jzxG4vCRuN58J0PbLIzZsGEDnTp1IiIigoiI\nCNq1a8eePXto2LAh8fHxOW2exSWOHxfYvFlk/36Jgwcldu8WWbcuuCt17Jjx7pSqCqbPFgGQDh80\nntjvR1NMyvlc4VuK++MIG/QBYf3ewTbvt2wTCHK3bIUapa/W6buiBMyNiq/RY7hbtw+pQ+ht1gJP\nm2dyzrAbhBvfRbewsLC4ig4dOiAIAufPn6dp06aULl0a6YqH/GURFotrz5CI/+ETQh21E9IxxjhG\ncued/Th4UF+pzucTiY5WiY/Xa9do1coHX5rPH7j7bsS9oVLlGpDcbyAxTzUx7CsEVJwfD0H5a1W6\n62JCAs6Rn+Fp0Qppx3Ycv6cvhi6fOkn4e71I+G0RWsE74KR+QXDpqLFAC5jLlKe9Rl+uXFBVpGNH\nUQsUQDoS6uRpdjv+smXx1q6LY95vIe2B2wvibRoMZQ5UqkxSpcoZ2mKReURRZOXKldSqVQuAlStX\nYrPZOHv2LP7MHE1bXHM0Dbp3d/DXX2lL4b17JYYMEcmfX8NmM3ek1AL6mztwKTfYYZLEJ8mo+fIb\njx0RdLwcY78m7OPBiJfyzLWx3+B9tElQJVIxqQF6BeK+vTgmTwSvB1/th/HVbwiCgFqqDK6ur+L8\n8nPES+VkNFHE27gp7i7dMjV2jiIIJH/6Bb76DVEWLUDQVLy16uJ96unrpkR6I2M5eBYWFrmOHj16\n5LQJFpc4LOqfIAEsti9E8vc3bD91SqBSpQDLlunvlHfo4Dd38EQRT4u2yEMHhIQxqjYbgSoPmnQG\nZBnl73W6TdKpk9inTsI+fqxuqQNl/Vqk9X+Z16nbvQNNlg1DOD3NnjS3DwiUKIl8ILReWyBPXryN\nHoWUJORNG0JCSH3VauKvUo2UfvmR9u9D2bM7tU2Njg6WUbDbM5zf4r8xZMgQ3n33Xd56K1iwukiR\nIgwZMoQpU6bw3HPP5bB1FgALF0qpp3VX4vEIzJghU7x4gN27jZbJGmKCca04QdPwVq2GfeZU/RDr\n8DBwGJ+uo2kIJ0/gHD4s1bkDEAIB7HPn4Bs1AvfrGRdDd4z5krDhHyHGB8NJtbFf43m8GUlffQeS\nhOuNXnjrN8QxfWrQAaxVB2+TJ3JPfpsg4G3yRNBmi3RYDp6FhUWuo2rVqgCsX78+3XVBELDb7SQk\nJBBlEHpikb3IGO8iR6iRxNxhlAOnUb58gDFjjBY5Au+/byP07OkKVBXb7Om6CyjJ68U+5Wez3mia\nFuIYpSMQQDYIsxIA5+efEihUWLcMgmaz4b+/EoGCBRGPhubTaJCpXXLXCy8jb9qIdOZ0ur7eZk+h\nFr4Td/fXEc9fwD57OtKxoxARgaf6QyR9MgIAtVhxEuYswPHtaKT9cWjRMbjbdyRQvkKGc1v8d0qW\nLMnMmTOJj49HkiQiLgludO/ePYcts7jMnj0SqqrvyJw+LRIfb+bkCAg66rZXIl04Z1jiRA2PQjh/\nznh0nw/HxAm6KpcQVNrMyMETDx3E+fknqc4dBHOWHbNn4L+/Eu6uwY3SQIWKJFeoaDpWhiQnIfj9\naNExWRvHItuwHDwLC4tcy6hRo9i+fTvVq1dH0zTWrVtHoUKFSEpK4rXXXuPxxx/PaRNveqp7a/KL\nc2bIdUETeNHdlRHHjRdJsbEqPp9x+x9/ZFyo9uoSAFei/P4LmiAYOnFagdsIlCyJoqMYFyhQAM/T\nbQgf+L7h+OKpk7hf7Yny12rE5OR0bd6H6uCvUQuiY0DHwRMA++yZeDp2Rpn3G45x3yLv2YOaJwZf\nvUak9O4HioK/Tj0SR3+H8/tvkfbvQ42KwdvoUdzdYnmYSAAAIABJREFUX7s0kEBK/wG4er6FvPFv\noivcS2JMwfT3GZMH19vvGd6HRfbRr18/Bg4cmBpGfjVW+PiNQ/nyxnXufD6NCxcyOMWKNBch0kyE\nSrR8eU1FVoSzZ8xVqExyey9jn/QT0oXzum3KyhWpDl5WEPftJfzD95HXr0PwevHfV4GUHj3x16mX\n5bEtsobl4FlYWORaNE1jzpw5xMbGAnDq1Cnee+89fvzxRzp06GA5eNeBQclD2ais54icPt+svqcR\n9XwNeGGbkZMmMHSoeYigx1gAMg2TfCbp+Ak0RTFcDPkrVsLTpgP2yT8jpqQ5aBrgL1cB7bbbUW0K\nkoEh3noN8D7xFMmJiTgmjEPa+w9aVCS+mrVJHvxx8EUmKp6Cz4uycB6RLz2H6HEHbT55HGXXTsQj\nh0j69oegnbXqklirrsmbAFpkFL469RAKRMKZjHP7shvh5AmUtWvwlyyNWube6z7/jULr1q0BK4w8\nN3DKpMzjmTMCEREaV+3bpCNQoADynl26p3SaIOJp3Q7nN18h6ZRy8VephvSXfgkTAMHjxtu4Cc6v\nR6V7NqX2v+9+Y8Muj+EzdgLN2jKNy0XUS52Qt29LvWRbuQJp7z/ET551Sz8HbgSsLEQLC4tcy+nT\np1OdO4Dbb7+d06dPExEREaxDZHHNuV0ryG8XF/F8ShcqeStT01OLvkn/Y0LiJAQEXC7jXfC9e81/\ngjKpIWCM3W5aRkHavw/n91+HLKAEQN6+FeHMGdQ8+XT7aoD38WYAeNo/S/y8pVz4azMXVm8iadS3\nqaFK/gr6C7FAvnx4WrQi7P0+qc5dOtPnzEL8Z7dOzxsMv5/wN3oQU68GUV06EfNYPSKfeRrh9OmM\n+96ElCtXDgiGkUuSRFxcHPfffz+CIKSGllvcGMyerWBU587lEskoyl8IqIYhmAIaWsE78Je7j6t/\nidSoKFwvvISYZJzDBxC4ryKe1m1DlIJ9FSrievVNc+MAb8NHDVV0/eUzdhAzwv7juHTO3WWkkydw\njvs2y+NbZA3rBM/CwiLXUrFiRd58802aNm2KqqrMnTuXihUrsnz5csLCTBTMLLKVO7RYhiR/otsW\ndLT1l0F2u7kT7nQCob5PekwqpfvKlEXessmwXdq2FXQUOAGkM6exT/tZd/ccLoVYzp2N6/JOuiii\n3XZbyOu0/LfpBoFpDidaVDSSQYipADi+/ZqUjz8ztD/TeL2E9+2FvGkTar68JH30OdqlQupZJWzw\n/3BO/CH1z6IrBfuiBfDGKyT+NDVb5siN/PDDDyxevJjTp0/z6KOP0r9/f1q2bMnzzz+f06ZZXKJQ\nIeNnh6ZppjU8AUQD9dxLAyAePYK8dXPId19MSMD59Sj8lR5A2bVTv3tkNADJQz7FX6EStsULwO3C\nX7Y87m6vosXkMTcO8FergbtFKxyTfkwXpu6rVBnXqz0z7A9AIIDjuzEoK1cEQzDLV8DV43W0mDxI\nBw8YdhOPmSsIW1x7LAfPwsIi1zJgwAAmT57MlClTkCSJGjVq0KpVK1atWsVHH32U0+ZZAHnzapwz\n0BKoVEnl8GERIwcwKgq4oNuUilmWjH1OaG5gOhxOpH3/GI99+Ih5ofB/9J3DK1HWrdEXgTl+DGXB\n76b2SxvXm7RmDvHAfmIa1U6n+GerVpHkXu9lSoXPFE0LLjx1UFb/ifjPHtSSpbI2Ry5l1qxZTJ06\nlVatWpEnTx6mT5/O008/bTl4NxAVKqj8+KN+m6JoREVlUCbBab6JaJs2GclASEXZsJ7ET0fimDJJ\nN8rA2/hSeoEg4Gn7DJ62/62uW/LwkfgrPoBt2WLwuPGXr4C7+2toUdEZd9Y0Irq/iGPW9LR7WrEU\nZc2fJEyeiXrb7YZdjeqDWlw/LAfPwsIi13HmzBkKFCjA6dOnqVevHvXqpSV0nz59mjp16uSgdRZX\nUq1agLlz9evc9e7tZfZs458hQTDeYc8UKSmoYeFIBqdw3oaPEmYSBmlf+LupXLj//qDynLJ4IY7v\nxiDv3IEaHYWvwSPBMgQ2G8JFfQ9V0DTDIuWpZKbSewZEtX86RM5dCPgJ++wj3N1eBZtJseWM8HoR\nzumLOIjJSUj79t6yDp4oitiueG/tdnu6Wp0W15fj4jFOC6cpE7gXO8Hc37g4sxDxjMoEaMg69SfT\nD2E+hlqiJN469bAtWZhuNjU6mpQ+H2QwfyYRBDwdO+Pp2Plfd1WWLsb+2y+h1zesx/HVF7i7v4Zj\nykTk/XHp2tWYPHjadfzPJltkD1YOnoWFRa6jb9++ADzzzDN06NAh5L8W15czwhn6h/emRXRT2ka1\nZIzjS1SCztmBA8Y/MwsWyJgtpI4fz+JPlKyg3l5Qt0mDYB05EwSXC81sl15WkFcsJfLFZ7EvW4x0\n6gTKP3sI++oLIl8ILnACxYrrdlUjIvHWqosaHmFon/uFl03tywySUZkHtxvn8CyecttsqEWL6jYF\nCtyOv2q1rI2fi6latSrDhg3D5XKxePFiunbtSrVqt+77kVMcFY7QPqoVD+WpQqO8daibpwZfOINh\nzxdMogM8HoE8JlGQkiSAyek+gOfpNgTy6ufw+ipXRbhwHnnn9tAQzvh47D9fpbYaCGRSdSr7sC1f\nimCg5Clv24IWGUXSiNF4q9dEUxQ0wFf+PpIHDcP/YPXraqtFKNYJnoWFRa7j66+/BmDp0qU5bInF\nGeEMbaOfYquyJfXaEvtCtslbGJX0LQcPGjlpAnPmmJ9oeDxkvJFuQqB4CaQz+lJ5AqAsWWjYVyOY\nq6JsMAmT9PsJH/i+bp6ebeF8pC2bcD/7PPK6tSGhWt5HHkO9tyxq8RJIOnmCmqzgq/2w8dyZxSBH\nUQDEM1kUQhEE3G2eQd62FeEqoRjv403R8ufP2vi5mF69ejF16lRKlSrF7NmzqVOnDm3atMlps24p\nNDReiXqJ1bY/U6/FyXv5OHww+dT8JCa+YNxX06hYMcCCBfpKTw6HhuYzjzDQChXGX7kq4sJ56U/o\nIqNwdXsV+08/IJ04rtvX9sdy3D16Ipw/T/j7vVH+Wg0pLvzlyuF++RV8DzcwnTs70OwmKse2YJu/\nyoO4Oz6HFhmJ4PHgqd8IT7OnrrltFhmTqe3Ry4upKxk+fHi2G2NhYWHxb4iPj6dv37507NiRCxcu\n0Lt3bxISzJXJLLKXUc7P0zl3l5njmM0q+Q/Tck3mhYQzgWj+Eya43WDyeVB27kAzCFEUgECZe8Hl\nMp5AVZH2hBY5BxA0Fcf4sfjqNcTTtDmBiAg0QJNlfCVLkzxgSPCFBrvyot+H/ZcZxnNnEqNcGE2U\nSHmnT5bH93ToRNLgj/FVqUbg9oL47i1H8utvkfzhx1keOzczaNAgYmNj+eSTT/jyyy955plnkGVr\nT/16skhZwFplTch1j+Bhln16UMTJAEGAQMD4+RQIaIanW6ljnD2LsnJ56AldYgKOEZ8gJBqXMxGP\nHQFVJfL5DjimTkI6fAjp7Gnsy5cS8WpX5L/Xms4dMt7xY0j/7DEVpboad5v2qDqFyzVBwFu3PgDh\nb79OZPcXsS+cj23FMiL79yayQ+vrftpoEYrpr+Mnn3xC7969+f777+ndu3fqP2+//TYLFugnVltY\nWFhcL/r160f58uW5ePEi4eHh3Hbbbbz1VhaFIyz+FdvlUJlsCC6iltqWmJWpI8uVLDJYrAgXziGa\nLMK0pEQ0h76MOIB46hSC37i/tGeXaRkG8eAB7NOnBhdoSUkIgOD3o/yzm4hXuwZtDASM7XddcSqW\nkoK0dg3isdCi6WYkv9MX7arcLw3w1q2HZiKS8G/wPPMs8b8t5MKmXcQvW42rd38ylCC8yalcuTK/\n//47jRs3plu3bkybNo3Tt2jpiJxir7yHgKD//TojniI21vgBpKoCK1caL5G9XhEU8/xVx8hPEQ02\niBxzZoFm8vzy+rD9OhtlzZ8hTdLpUzjGf28692XEXTuIat2cmBoPEFO7KtGP1ME+5edM9VWLlyCl\n59uoV8SqanY77tbt8XTsjPznHzimTES46jlsX74ExzdfZWoOi2uH6XZSo0aNiIuL46+//kpXv0WS\nJLp3737NjbOwsLAw4+jRo7Ru3ZpJkyZhs9no2bMnTzzxRE6bdUvhwNhBsmt2zGIsT57M4gleRgjm\nToYctxctXz7DU77AvWURppsUKne70QTB8A4DxYtjm/ozoislpM22cjnyX6vx318Ree+e0L6xhfA8\n2QI0DeewD3HMnIp06CBqZCS+GrVI+vhzNIP8wivxtu9IfIECwVDS0yfBGY67xdO4+g3IsO+/5hZ3\n6q6kcePGNG7cGL/fz/Tp0xk5ciT9+/dn1y79E18AVVX54IMP2LNnDzabjUGDBlGkSFo5izlz5jBu\n3DhEUaRFixa0a9fuetxKrqWCryI2zYZXCA0jiFULsWeP8fNH0zQSE43bVRXU225HMikHIG/fatgm\nJCaBZLwE16KikHdsS1fe4EqkA3G619PhdhPV7UXkndtTLylbtyD1e5fAHbH4a9fNeIiuPfA+9jj2\nyT8heL14Gz6Cv/pDANgXzEMwCNFQ1q/NsMKNxbXF1MG77777uO+++2jQoAGRkZHXyyYLCwuLTCFJ\nEomJiQiX1MoOHjyImEHYnkX2Usf7MAtt80P8uPyB/Dzjfhb96nhBTA6vsgfJ3IEUEpPwPNUK+etR\nIW2BiEg8bdoT/oFJGKMrBU1RdG9EI1iHyrZ4kf7cXi/y5o2kvP428pZNyP+kOXmqMwzXc13QYvLg\n+HoUYV98mnrSJyYmYl/wO4LbTcLU2ab3dxl/o8eIb/RYpl5rkT189913rF+/nr1791KmTBleeOGF\nDEVWFi9ejNfrZcqUKWzevJmhQ4cyevTo1PaPPvqI3377jbCwMJo0aUKTJk2Ijs6E3P0tykP+2jzk\nrc1S++J018PVCNq6O/DlKePfClUVOKWfvpv2mqgoOGbcHihXAf5cqdumRUYSuL2gbo1MAHxeVIMi\n5QBcvGhuHGCf+EM65+4yYvxFHJN/IikTDh6AWvRuXO/2C7muiSbPV+t3OMfJ1N/A/PnzqV69OmXK\nlKFMmTKULl2aMmXKmPZRVZX+/fvTunVrOnTowKFD6eVkt27dSrt27Wjbti2vvvoqHite18LC4l/S\no0cPOnTowPHjx+nWrRvt2rXj9ddfz1TfLVu26Cpujh8/niZNmtChQwc6dOjA/v37s9vsm4rn3S9R\n21sXQUv7sbdpdl5K6U6sVsi0b4S+gGS2Ifgy8CBtNoRjR9HbIxdTktEio0y7Szu2Ge5gC4Dy50qE\nJP3TQQ1QIyJQ7ymGr1JlAo5gQpAmCATuKoL3kcYA2H+drRvGqfy1CnltML9Inj+X6IeqkKd4YbRC\nsTj79c6G+FeLrLBkyRJ27dpF48aNadu2LU8//TQlS5Y07bNhwwZq1aoFwP3338/27ekX56VKlSIx\nMRGv14umaakbWxbGfJM4jqfdrYkNFCJCjaCi7wE+TBrGE97m3HGHyem8AOfPm7+/0lnzkFvXa2+i\nGoSAe55siXT2jHF8QyBgnuOnZJzPKR01DucWT53MsH9GeJ9ojmqQyOitWSvL41tkjUxl/I4ePZoJ\nEyZQokSJTA9sthOlaRr9+vXjiy++oEiRIkybNo1jx45xzz33/Le7sLCwuCWpWbMm5cqVY+vWrQQC\nAQYMGED+TCj3ffvtt8yZMwenzo/T9u3bGTZsGOXKlbsWJt90bJE3sUfejSakORRewcMC+zy6u18z\n7RsIXFsnJBAZjpgQD7ouHPhLlcF+lcJdKqpKWC/zzQJR00LyT9K1796JYLB5KQDi8eM4Rn6GY/LE\nVBsETUPZs4uIN3uQMGcBwpkz+v09HuTt2xAP7iey5ytpTmBiAmHfjELetZ3E6b+a2m9x7Zg0aRIp\nKSmsX7+eNWvWMHjwYKKiopg8ebJhn6SkJCKu2PWQJAm/358qzlKiRAlatGiB0+mkYcOGREWZb0AA\n5MkThiynhc4WKHBjR2Nlt30FiGQwgxjNaM5znppiTTopnZCRueMO436yLCBJEm7DOEMB2ST/VgDy\nFSts2B52VyHYscOwXTl9CiXC+ARPuf023ffqymvaffca9rfdXTTr7/Wj9dEeqgWLrlIjLl6CyF5v\nEKUjKnSrff6ym39jX6YcvHz58v0r5w7Md6IOHDhATEwM48ePZ+/evdSpU8dy7iwsLP419evXp2LF\nitStW5c6deoQExOq+KXHXXfdxciRI+nVq1dI244dO/jmm284c+YMdevW5aWXXspus28qxju+45QU\nuhv8t7KO6fYpgPH7l5CQxRMISTKN8xRdblSHA8mtL3TgafYUtvV/6bYJgLIqVODgSgIFbkc8e9aw\nXRAE02LH0vGjyHv/0XUwlQ3rURYvRDX4TKuiiPfB6kS3bxlywicAtlUrEQ4dRCtS1PQeLK4Nl527\n1atXs3btWqKioqhdu7Zpn4iICJKT00puqKqa6tzt3r2b5cuXs2TJEsLCwnj77beZN28ejz1mHnp7\n4UJa/meBApGcOWOs3JjTXAv7Jtl/YkB4f85Jwe/peMYzzjuBHxMmoWn5wCCHWBRVXnrJw4gRDvSD\nKFV8TieKQTE9DUh+pw8RBh6i/8svUQsVxkimRfV4uNigCTGfDUdMSgppTyl7PylXvVch71+TFkRX\nGINyVRmWQL78JD7dHn9m3mtNwzZ9MrYlixC8XnwVHwjW53Q6EQ/sJ3rDBq7OvNUOHiDxux/wtmgF\nXBKbmjKRcJvExToN8VeqnPG8OUBu+n5kxtEzdfBmzw7G98fGxtK1a1fq16+fTua3efPmhn3NdqIu\nXLjApk2b6N+/P3fddRcvv/wy5cqVo3p148KIV+9CwY3tad/ItoFlX1ax7Msa2WXf4sWL2bBhA3/8\n8Qfjx4/H6XRSt25dunTpYtrvkUce4ahB+EqTJk1o164dERERvPLKKyxbtoyHHzavR3az75JvZzvD\nGMZGNuLAQW1qM4hBhBPOSfTrOCHAkaj9mImsqGpGohySaRk8KYMkPtnvA5sCBg5eVNxu0/5KRFjQ\nQTMId7TdXQQOHYCUUBEVAFu9unDyOBzTT9RxPtIANqzTbRMCAaLPHYeD+iHCoqqSt1JZOGtwwqeq\n5Bs1HGHcODRNg8WLYc0aiI2Fjh0RrigPoU2fBt98C3H7IH9+eLwp9OmDcI3yaG6274ceDRo0oHr1\n6tSuXZuXXnqJvHnzZtinUqVKLFu2jMaNG7N58+Z0IZ2RkZE4HA7sdjuSJJE3b16rJEwGJJHEJ+HD\nUp27y6y2rWRo2CA8nhGGfVUV4uONP/+iCMLZc4btALa1q437nz9HoLjxwYnmcKKWKo0akyfEwVMV\nBU+DRqZzBw2wkfj1OMI/6Iu8bg2C24W/3H24unTPdCHy8LdewzHxh1SxF/vcOdiWLSbhp2k4Jk4I\nqe8JQaVg+4K5eFu0wvHl54R9+RniJUc4asQIPK3bkjx0uOnml0XWMXXw1q4N1tkICwsjLCyMDRs2\npGs3c/DMdqJiYmIoUqQIxYoVA6BWrVps377d1MG7chcKbmxP+0a2DSz7soplX9b4t7tQZsiyTIkS\nJbhw4QJut5slS5awYMGCDB08IzRN49lnn00VlapTpw47d+7M0MG7mXfJD4oHaRv9FHHyvtRrG9nI\nZs9WJifMJDoyr9EmONGJ+cFYRgBVDRBMBTfUoTTpDRn19kVGI588btjuPnUWxRmGpKNyCZD0aFPC\n9u1DNNiFTy5THkkVsM+fGzKHGhbG+dd7E5bgIuzrL0P6+m8vyMUGTYkaNVp3F1+12bhYqTp54uMN\n7Xd1eh67KBom0yeHReE6dIrILp2wrViami/o++xzkj8Zgb9yVWyzZxDx1quIl2tyHTiAtn497kNH\nSB7yqcHI/53c9P3IyvPpzz///NeCTw0bNmTVqlW0adMGTdMYPHgwv/76KykpKbRu3ZrWrVvTrl07\nFEXhrrvu4sknn/zP9t0KTHNM4oh0SLftT2Ul5UxS3Gw2WLRIxvjZJSB4zbUj9GrIpeLz4er+KrYV\ny3RnCJQqjW3eXKTjoZtDos+HY/JEkmsE1SyF06dx/DQeTVKRq9VJ57ypd99D4viJKIsXIJw9h7fZ\nkxAWZmr3ZeRVf+CYNilEydO2aiXOMV+CwXMTgOQUxB3bCRvxCeIVGxGiKwXHhHH4HqiCt1XuV4EV\nEuJxDv8YeePfIAr4Kz9Iyhu9Mv0eX0tMHbwhQ4b854HNdqLuvPNOkpOTOXToEEWKFOHvv/+mZcuW\n/3kuCwuLW5PGjRuTkJBA48aNqV69Oq+99lqm8lKMSEpK4vHHH+f3338nLCyMtWvX0qJFi2y0OPcx\nxjkynXN3mT9sy/nV9gvVvTWYbZ8JQvpFgFMNo727I32vsX1mDqC/WHHkE8Yyd+KxI/geeQxpdmhB\ncU2U8D3yGNqEsRgl4mgRkSSNm4j0aD3krZsQNC1YzDwykoQRo0GWQdJf5GuKcslI/TweIRBAi8lr\nKpYiHT5EoOg9iP+EnkSqdjuud/oQPqAf9kXz07Upu3YQ3u9d4n9fgmPCuDTn7vLcgO3X2aS8/nam\nSjFYhPJf1HxFUWTAgPTlKy5vhAO0bduWtm3bZtm2W4XzwnnDtpPicV6rH2DaNP0nSGysxjnzA7oM\nEa445AghEMBfpz6+GjWxrV6VrkmNiibpizE4pvxsmOMrXzrZt/80nrBhHyKdDkp+RtuH42n+FEmf\nfwWiiLxmFeED+yNv2oCgqvhHfIy70wu4X34lQ/ttC+cb5hDLG9bjeboN2tivdUs5BMqUxTFtUjrn\n7jKCqmJfvCDzDl4ggLJqJfh8+GrXhcvPzpzG5SKy/dPY1qWF+dvWrELetIGEyTNz3M5MPYEaNWpE\n/fr1U/9p0KABjz/+OK+++irHDEJPGjZsiM1mo02bNgwZMoTevXvz66+/MmXKFGw2Gx9++CFvvvkm\nLVq0oGDBgtStWzc778vCwuIWoFOnTlSpUoV169Yxb9485s2bx8GDB//1OJefTZGRkfTs2ZOOHTvS\nrl07ihcvTp06dbLf8FxEnBTq3AGogspG5W+2K9tDnDsAl5DCItt8nZ7Zi1mQj3zkcDA80QAtPBx/\n0bt1JVi06GgCZcsb6bMEHTlnMITTW/MhVEeaYE+gaDHUkqUBUFbr5/EpR49g+2Umko5zBkEHzz5j\nCppNP0tHA1xdu+Nt8IiuieodseB0oqz6Q7e/vHkjyrIlSPv1/36lM2dQ/liu2/Zvsc2aTmSr5sRU\nux+t1kM4vvrCUvm0uOZoRl9egt+fZs38lCql50BpdO7sMy2EnikSTEoZXApPTJj+G0m9+uC/pxiB\n2Fjcjzfj4sIVqPcUI2BWAiMlGeHUScKGDkp17gAEjxv7lJ9xfDcGkpOJeKMHyob1qY6ifGA/YcM+\nRJk/N91w4pHDSLt3BWNTL2NW11KW8D7xJN76oaGi/nL34er2Kpip47szp5yvLJxH9CN1iGrVjOh2\nLYipXxP75ImZ6nutcXz/TTrn7jK2P1dg//nHHLAoPZkSWalduzaFCxdOPWWbM2cO27Zto169evTp\n04fx48eH9MloJ6p69epMnz49C6ZbWFjc6rRq1YpWrVqhqipz5szhq6++4oMPPjAtJnyZwoULM3Xq\nVACaNm2aer158+am4ee3GlGa8YlotBrNRtvf+o0CbJW3XCOrModw8YKpI6HedhvK5k26TqJw4Ty2\nObMMFykCIJ49jWPEJ4R99UWaCiagbNtMVNsWXFy3BeG0cTEt8dxZBJd+fiCAtHMH7hatcU76MTQE\nNE9efM1aEPb1V7r2SydOIG9Yb3iKIKgq4qkTqDF5kE6E5lFqdjuBYsUNbcss9ik/E977LcTkS3lE\nB/YTvmYN4vnzpPT9IMvj32gcP26Qk3qJ2NjY62SJRV4tn2FbQbUgkhRMHyJEJgTuukvlrrtUtoeW\nkbtExs6fYLL9lPq8SEpE3r4F8fgxBL8fac8upP37UO++B8FvnGMspKTg+OkHpDOhpRoEQFm+FPx+\nZJ0NHDElGcfMqfgebYK0bTPhA95HWbcGPB785e/D3aU7nqfb4H6qFY4fxuqKvHhr1gZRJPG7CdC5\nPfLmjaAGUO8uTsKYsWh58+KvWBmNb/RDUAtm/D0Qjh0lolfPdM8nec9uwt9/j0DJUjku1qJs3WzY\nJm/agOfZ566jNaFk6gRvw4YNdOrUiYiICCIiImjXrh179uyhYcOGxMfHX2sbLSwsLHSZPHkyr732\nGg0aNGDBggU899xzLFu2LKfNypVoaOwX93FCTL9AbeJphk0LPUUqHChMJ/fznBX0RT4AUgSTHA3A\nbv9vtmYWNW8+c5GWf/YgnjMQKQHEw4fMd6ETEnCO+1bfwTpyCGXWdASDEEwN8N9dzNQ+wecl5fNR\nuFu2Rr10kqcJAv57inPxz6BjLZ7Wr8UleNzI27biL1NWtz1Q8A68jz2Or15D3XZf1WoEsrqA0jQc\nP45Pc+4u26aq2GdNQ0i8+URCnnnmGTp06ECrVq2oX78+nTp14rnnnqNBgwZ07tw5p827pWjlacOd\ngTt12x7y1WbFCpG9e/XOOQReecXOoUOm384M59cijXM4NUkCTSO6djUcv/+G6HYj+P0oe/8h6plW\nyKtWomw02DwDhPiLpptDwoXziDrOX2r7uXPgchHR/SVsfyxDcLuDJVq2biG8Ty/kVStRy5XH9fIr\nqM60fDJNFPE0bY7nuS6gaUS82QPbssVIF84jxcejbN5A5OvdITkZ8dQJw3dJ8OvXD70S57jvdDef\nxIsXbogTsivflxAM6gNeTzJ1gieKIitXrkwte7By5UpsNhtnz57Fb1IHxMLCwuJasm/fPlq2bMnH\nH3+MzSCUzSJjZttn8JVzJNvkLdg0Ow/6q9E/aQDlAvfxlLcle1N286PjB05LwdOoEv6SvJfcn7xa\nPlIE4zyTAOa/DxmIYGYCAbOd9IxUIKUjR1Amgl2GAAAgAElEQVSviCy5Eg3wV6qM4DdWYhDPnEIw\nKJMgAPZfZxl6sQIg79+HGhmJZLBR6rsklpA86luSh3+JdGA/aoHb0PKlnUwE7roL6UiokIQaGYm3\nRk38xYqj/LUa8QpnShME3E+3QYvJQ0rvfohnT2ObNxcxIR7NZsNX5UGSPvnC8L4zjcuFuH+vbpN0\n9AjyurX46us7mLmVpUuXAtCzZ0/at29P5cpBJ3nr1q189913OWnaLUeEFknP5F4MDH+fC1JaPl51\nb03eTelL4z52jBy1ixdFvBn7IKYEMhBZsU34HulkqAMjqCqR3Z7HV6WayegCaky0YQ6y4Hbhv7es\nYXugSFEcP45D2RMa7SLGX8Tx0ziSatbC9fZ7+Oo1wD5rBni9+GrVwdvkCRBFlEULsM+ZFTK+7a9V\nOMd8iWDiYIoG9T3T3YOOQmdqf5O264X3iSdxzJwakqeohkfgadkqh6xKI1MO3pAhQ3j33Xd56623\nAChSpAhDhgxhypQpPPdczh5BWlhY3Lr07XutJTxuftbKa3g3/C3OS8EfTJeQwnLbUk5GnmT+xaWE\nEcY7KX3p7OrCHPtMIrRInvS0xE7QcfEKxg7QPkl/cX+ZrO8PmodJCUeOmHeXZdNUMDH+oq6AQGr7\n+fOmN6HmKwAGDqIGBO4pjr9seSSdPD1NVvC0bp92wW4nULpMyOs8rdqhrFuL4Eu/GvXWa4RasjTK\npo0hp5CCpiGdPBH8g6KQ9MUYxAP7sa1Yhr9kSfzVH8oeCXO7HS0qBj21CtUZRuCuIlmf4wYlLi4u\n1bkDuO+++zhw4EAOWnRzc0E4zwXhAnepRZCvWNo+43mW+30VeT/iPeKFBB7y1aJPygcoKBmc0AX3\nZgwqoGQK2+qVhm0C4Bw90vCESzxzBl+NWjh+na3b7i91L6KJwi5h4XifaoXvxx9CyjX4Ywvh7vwi\njlHGmzjyFar5/geq4n+gashrbCuWIPj0n2/y5o34TCIA1NtvN2y7TMCkPvaN8Ozw1WtASvfXcI79\nBjE+mG8ZyJsPV9ceuu/X9SZTDl7JkiWZOXMm8fHxSJKUWt+ue/fu19Q4CwsLC4try0THD6nO3ZXs\nVnbyg+N7urqDamu3abfxgvvlkNf5MXbwYrTMFZ6/VgjuFFOVTU+VKigG9RAFQIzbhxoWjpSkL+sf\nKFYc2wrjkGDx+FG4aCy0IJw+hXTksH6j34d47ChqiUsK1G430t5/UAvegVagQJoNd9+DKqTPItII\n5ugB2Cf/hKgj525bOB/x4AHUoncDQTl1993GC6r/hCThq/Mw8oG4kCZ/9Zpp93YTUrBgQUaMGEHj\nxo1Tc4SLFi2a02bddJwVzvJOxBv8qfxBvHiRUv7StHU/w8uXnltLlcX0iXibODn4GfxH2c1R6Sij\nE7/DZjOPwC5WTOXvvzOq1WmMYPLdBxBUk92lQABP22cIH9Qf8ao8Wg3wNnrEUOESguHpSBKJ3/9E\n+IC+KH+tAbcLf/kKuLq9RqB8BcRz+tEHQLoSEMrCedjm/Ybg9eGr8iCe9h1BUdAUk6gZRcHzfBcc\n0yYhx6XPAwzkL4C7Q8bhyu7OXXDMmoG8fWu66/6778HVpVuG/a8Hrnf64mndHvuMqcHw1VZt0QoV\nznR/8dBBnJ8ORdmyCU2U8Fd5kJR3+6Flom5mRpg6eP369WPgwIF06NABQWc3b8KECVk2wMLCwsIi\n5zgpGouAHJMyOAEDwrRw4tEPMRR1xAuuN6Yqm2fPIxooQWtAoFBh/NUfQlo0L6RdtTtI6fM/nN9/\nazi+dPw4okGejADYF8xFvHySptPuHDmc5C/G4Px0GPbpU4IhnVHReGvVIfmjz9Hy5yf8zVeRrool\nEwDHzz/gercP0qGDuuOL8RdRVv2B55KDd61I/t9gxPPnUJYuRkxKBJsNb9VqJH78+TWdN6f5+OOP\n+eKLL3jjjTcAqFGjRpZKT1mEoqHRLfJ5ltvTNll2KTv5UP4fMVoenvS0pH9471TnDsAtuJnjmEXx\nQHH8fvO/D5MUukwaaB5hEIiMMlyEa4CyaqWuSJIAyDu3k/K/wTh/GIt0LP0mlSZJeBs9Fvz/vHnx\nla+AcP48gttNoGSp1EiAQLny8If+BpVaOJi7GNbv3WCe8aWTOsf0ydgW/E7i+J9xt2iNY8L3ISIs\nGuCt/TBaVDRJI8cQ9uH/UP5ej+D34bvvflzdXyVQoaLpewNAeDgJ300gbOhAlPXrQPXjr1iZlNff\nQostlHH/64Ra9G5cb77zr/sJ584R+WxblF07Uq8pO7cj7dpOwoy5wWKMWcDUwWvdujUAPXr0yNIk\nFhYWFtcCr9fL2LFjOXDgAP3792f8+PF06dLFysf7F8SqxmpmRQJFM+wfrkYYthkJHFwvNEkyTfST\njh9FcJs4YL//SuK34xEb1UH+Z3eqs6ja7SQPGAwREcFQRoOFnO/ee5F37zScX1VsCGaJiIEAjrFf\nEzZ8WKpYi5gQj2PuHERXCgmTZiIf0g/7E71eHF8MR82XP2QBCEGVTH/Z8sZzZxcOB4nf/oC4Yzu2\nv1YR8eADJJR9IHtCQG9goqOjefPNNzl8+DAlS5bE7XYTdgMUP76Z+FP+g9W2VSHXPYKH6fapaGj8\no+zR7bvEthi329zBO3cui2US7HbDGpoAkkmhPQGwLfjdcIPK9scKkvPkxdWsBeGjv0gNJdcI1v/0\nPPMsABFdn8c+e0bqOLaVy7Etmk/8LwuCDtq4b3U3oTyNHkPesA7Hj+NDwjDtSxfh++Yr3D164ur+\nOs5Rn6c6eZrNhqdZCzwdgyd0/geqkjBzLuLhQ+QNl4nPG/uvvvvq3feQ9PW44HNc04K1RW8SnGO+\nTOfcXca2bi32n8YHhWyygGkGerly5QCoWrUqkiQRFxfH/fffjyAIVK2a8/GlFv+epCQ4cMA8LMHC\nIrcwYMAAXC4XO3fuRJIkDh8+TJ8+fXLarFxFR3dnCgRuC7le1l+ODu6Mw2hEwfhnxKwO1fVAyKDQ\nrOr3my42BJcLnE7i/1hL4jfjcbXrCL3e4cLfO/B0eiE4htn4pUqhmQm9REejGRQyBlCLFcc2e6au\nEqeyaiXy+r9MHVghKRk1Xz79OnkRkQTur2RifRryX6uJePk5oh+rR2S7ltin/JypfunmK1sO9/Mv\nITz88E3v3AGsWbOGZs2a0a1bN86dO0e9evX480/9mogW/43tyla8gr4SyknpOCdF/dNxgP3ivgwP\nSHbvzloEgmYmsgJomvF3VwPEwweNO7tdkJhI2FWFxgVA/mcPjs8/RV65AvsvM0OcRHnvP4QN6BtU\nyXyxK5rdkW5eT8NHcL/8CrZff0F06SchKmvXAOB6oxcX5ywkpftruLp0I37CFJJGjoFLzz0hMYHw\nPr2IfL4DtGlNeM/uiAZRBaZI0k3l3AFIcf8Ytsk7DetzZJpMlUn44Ycf+Pzzzxk/fjzJycn079+f\nsWPHZnlyi+uH2w1vvGGnZs0wSpaEunXDGDrUZtWatcjV7NixgzfeeANZlnE6nQwbNixTNfAs0qjk\nr8zHSZ/zoLcaTjWMaDWahp5HGRP/PQ7SfviTSWa2bQbLlMWoV7g1fhORFbdgvHt9PTCVsQZEVwqa\n17iMgevSLjSCgLfZUyR/9iXC0KFot6U5xGY/otKOnWCSp6Lefod5mYStWxAN6ugJHg/ytm1oBose\nDfC0bI148aK+yl5SEtK2rTot6VGWLiLyhY44Zk1H2fg39iULiXj7NZyfDsuw763M8OHD+fnnn4mK\niqJAgQL89NNPfPTRRzlt1k3Ffb4KuiVcAO4IFOKMYKzi6BJcBkXO08iqyq9m4kFqYPjdhqCj5q9w\nv3H//PmJ6PU6oif0GSsAzh+/x/nNKEORKNvyoNqrr3Y9/IULp24Cqfny4atRK+PwwCseKmrZcqT0\nH0jywKH4H66ftoHj9xP5bFuc340J1ozbuhXnpJ+I7NgGIRMqmjc7aqRxjVktyqTIfSbJlIM3a9Ys\nxo4di9PpJE+ePEyfPp0ZM2ZkeXKL68dbb9n56ScbJ05I+P0QFyfx2Wc2PvvMCmWzyL0IgoDX603N\nEb5w4YJuvrCFOY29jzMnfgF/XdjI2vObmZgwlVJq6dT2UY4vqJunOl2iO9MmugWPxNTlD3k5ABcw\nFhI4LRgvYK4LGTh4WnQ0oqrv4AmAc/gVToymQXIS2tWqcSa7ZNKxo2h2/WesBgTuNE/Gt23bglpI\nP9dEDQvHV7kKWrh+iKwAyHt2GC4iRY8beesm0/kBHN98FVJMWfB4cPz8IySHFkC2CKKqKgWuEMMp\nXjzrReMt0lPTX5sa3loh1x2ag6c9rdkt7zbsqwoqsbHmO9wZVFnJEPHCecM2AcDk9B7A06o9msHv\nma/Kg0hxoUXMU8c/dw7hvPH8eDwIiQlEvPIiSty+VH9NOneOsMH/Q1k4H2/TZoabZJdLuJhhn/oz\ntlWhSqLK7p04R4/MsH92IR7Yj/PjwTiHDUL8Rz9kNyfwPNUKNSw85HogX/5MidBkRKY+vqIopstp\nsdvtSFLOJ89bZI6TJwUWLw7d5dU0gV9/lTN6xlhY3LB07NiRzp07c+bMGT788ENatGjBs88+m9Nm\n5UoEBO5QY8mr5Ut3fZ5tLh+FD+aQfBAATdDYomzmrcjXSRQSuCAa55FkVCbhWiMmJhoGiWpAyovm\nSmzyrmD+nG3mVKJrVSFvqaJQID8RXTpnqki3/84iGP3MCoCYaO4gBW4viKdlm3QhVJfx1XmYwH33\no95xh25fTRBQY+8kUEg/D1KNjMRXNYNFWiCQ+h5cjXT0MMrSxeb9b2EKFizIsmXLEASBhIQERo8e\nTWyscb6rxX9jTOJYmrtbUCBwG4qmUNZXjv5JA2jlaUu4Frp4voyGht9vvBkoSWBQASDzZLROzqBd\n3vi34QaSvH1bhiHwvuoPGbapsYWwf/MVso7Ik+jz4fhyBP4HquJ+9rmQk0hPg0dwd0lT0ZdXLif8\nnTcIf6MHtulTUh1XaesWw/mlvdfH0XJ+OpSYR+oS/slQwod/REyT+jgH/++6zJ0R/joPk/J2bwJX\nPBcCRe8m5f1BqNmgaJypgNaqVasydOhQXC4XixcvZsqUKTz44INZntzi+rB9u8D58/qLjBMnBFJS\ngloBFha5jebNm1OuXDnWrl1LIBBg9OjRlC5dOuOOFplmpn0aLjE0D+OgvJ9xjrGmMpUHpP3X0LJM\nkGhcJ0oAnKNGmPeXJJTffiGy24tpoU4+L45fZiDt2U38ijWmZRgEUUAIGNfB01QVNSICKSnU0dOA\n5M9HoZYsjeByYZ/0I1LcPrSYGHy165I0+GMA/GXLo+wIzddQCxXCV68B4rEjKBvXh+Txees1zLhM\ngSiiGQiDaLKMlolaVrcqAwYM4MMPP+TEiRM0bNiQBx98kIEDB+a0WTcdebW8fJM4jnjhIvFCPIXU\nwkiX1HtL+EuwwKCfoikUK+a/tPkd+g3OangmQEaV0tW8+ZHOGEc52Cf9aPhskeL24e7QCduWzbrt\ngYIFcfd4HedXIxCvuhkN8DzVAttCo3cH5P3BzbmU/w3GV6sutnm/Ini9+KpWx9P2mdR8uLD/9cM5\ndkxqyQbHzxPw/v4rid+MhwhjGVLNJDwxu5DXrMI58vN0eYRiQgJho0fir1odX4NG19yGjHB3exXP\nM88GyyzYbHiefBqySYwpUw7eO++8w5QpUyhdujS//PILdevWpU2bNtligMW1p1w5jbx5VV0nr2BB\nLbs+SxYW143Zs9MXfw0PD+7U7t69m927d9O8efOcMOum5KJwwbDtnGhcRwlyXmQlI8Rz50wdNF+N\nhwh/503dPBZ59w7kpYsQZNmw2LmYnGR4DCAA4rmzJH71HdHPtg2Zw/dgNdSSwc0K9/NdcHd+AeHM\n6eDC6IqHtnjiuP74Fy+CyxU8wbtqbI1LCqMZIQj4atRC3h9ax85XqTL+KtUyHuMWRRRFhg8fnu7a\nsmXLePjhh3PIopubaC2G6KvqbkZjLHISqUUyY4YN80IqWSQDNTsxWb++Zmp7gn75GQB8XvwP1kAb\n86XuHfgfqo0yf66uSq8ASHFxaGYh7HLw+SAkxGNbNB95w9/B2nheL77KVVDLlA2qbI77Nl09PkHT\nsM+dg/f7b3B37Ix98o9IV+XbaXYH3mZPmt16tmCfPUNXJEbwerH99ssN4eBBMN/O3fnFbB/X1MEr\nXbp0unwW7dKPxKJFixg0aJAlZpBLKFhQo359P9OmpT9mFwSNpk39WY4zt7C43qxdu9a03XLwso8i\nalH9Bg3u9ZUFp3Ffh+Y0KYN+AyBLCD6TGHXFhnRWX6hBAJwfD0GNjkHSKRisAd76j6D8sdxweCE5\nGX+jx3A/9jj2hfMQ/f5gbl7snSR9clWOiiii3V4w1MQNf+uOLSYlYZ88kbCvRoQs8gTA8etsUoZ8\nghaTx9A+gOQPBqGsXI506GBamYjoGJL7fHB91TBTUpCOHkEtWDBbBAiuNZ07d+b7778nb968nDlz\nhoEDB7Jv3z7LwfsP/C2vZbJjEolCAiX8JXnJ3Y1ILeMToJKBUgiagCaEbtCU8pdhnXkd8qwjSqZ5\ndmr+AkiHDxm2+0uWQdm+TbdNczjxVa4aDPPUOaFT8+ZD3r3L+ATw8CHcT7XCvnyJbruv8oMQCBDZ\nqV26PDo5bh/y1i0kTJmFbc5sQ5VN2+o/8XTpRkq/gYR9OjS1HmegwO24O7+A95HG6V4vJCWCqmbr\nd9uoBA6AoCNOc7Nh6uDt3p2WoNq8efOQXXOL3MOnn3qw2WDpUplTp0TuvjtA06Z+3njDPITAwuJG\n5MqCwT6fjwMHDhAIBChRogTyTSalnNO8mPIyy2xLOCIdTne9mq8GLb2t6cHLhn29Qs7XYzE9oXug\nMva/1hj2ldeYy9rLu3YSuK8C4rmzoXOIIp52HQj78H3QqTOlAf7qNXGMGoFj3m+pJ3gCIB8/QsTb\nr5Ewe16GTpTgNX6P5Z3bjAup+3w4Ph6C60NzZUfH+LFIhw+luz8x/iLOsV+TVK2Gad9sQVUJG9gf\n+6+/IB05ROD2gvgaNCJp8CfgCM1NvFHo2rUrzz33HM2bN+e7776jbdu2fPrppzltVq7jO8fXDAkf\nQKKYdto1z/4bE+InU0hLEymKE/dxXDpKJV8VwglGdDzibUxF3wNstKXfBBE1kWbeJ9mkGB6+Zw+q\neZynt0lTlNFf6rYF8uTFX7ESzJyq2+6vWAnH6JGGJ3T2WdNxv2D8bFbz5cf7dBvU995CvKpWn8b/\n2TvPACmKrQ0/HaZnZiNLVEBQQNR7VZIJAQMmxISiAhL0M2DOARVRVBBEvJhQUVFEURBEgoIoWRCQ\nICCKBAkqSnRhw8Seru/HbGS6ehZh2QX6uT8uOzVVXbPu9vapc877QrBzN4zPx+CxEUnRN6zHP+wN\nUB3+1hYEtuFONxC+/Cq8n48hXYfdF12BKCE+pK75ldT+fdGXLoaYiXlqM4L3P4LZSt4/WFaizU7D\nN3qU7ZhZFqP1Q5wy525cZbpDG58PhgwJM39+PmvXwqxZAZ58MnIk2BG5HMasWrWKSy65hMcff5wn\nnniC8847jxUr5I3dLvvOCdZJ3J5/N1mxLBCgCIVjzPoMyH25qNdFRrQS5O+cbnEeiYBI0dyQ8wGY\n8HoTgp8iLAtj4niwUUkr2lckgvfrr2xLQD1LfsAzy/50vdRlJOsLIHKOc7ZIKUOZpnfyBNv9GbNn\noG4s/x5L/8Dn8b/5Gtof8UyHtm0rvlEjSev1YLlfe3+45JJLePTRR3nttdcYPHgwd999N54kvowu\npclTcnkr5fVSwR3AT56VvJQaP+TbrG6mc0ZH2ma1pmOVKzm3aksG+V9AIFBR0UXi91wRKtWsGuXv\nB5xEwS7a/kppEXusQUPCnbpiGd6EMQHETjoZY9pX0rXVbVsJdf8/zEaJfbZWairhazthjPkExcaI\nXQFSXnsZ/acV0vunumED4UsuRUhsYKJnlNDpUBRQ1LgsqVYi7MjPI/22G/FOm4K2cwdadjbeOTNJ\nv/c2VJuy8H0l3LWH7T0w2rIVoQIf08OZMh91C9cw7bAgLQ1q1ADXgsTlcKBfv34MGTKEJk2aALB8\n+XKef/55xo0bV8E7O3z4SVvBgNTnydfiQiACwR/6Zm7N6M7c3c6lsgYGFVoIY3jBIcPFHoceFwBd\nBYcYL3b00Xg22Ac5CmB8/RXCQWhA/WcXyi57FVLFNNHWryPa9kKUrX/jHza0SGQldPV1cb8pINLu\nMvyfJRqPx+rWI3r5VcT6PYNuUwYmDIPAo0/KPxyAaUp7/NScHPSVy4kcALU3p+t7p3xp+5DpmT4N\nZedORPXq5Xf9f0Hbtm2LDsSFEAghuOeee8jIyEBRFGbMSB60u8QZ7x2XUDlQyDJ9KQLBfel3sMCY\nX/T679omXkkdTHVRnRPNk/jBSMzQx1STt/yvYVk3lNvey4IxdrQ0gNLXrMY7ZTKqzf1LAdQ/f3dW\n4RRAairhK65Ce+NVlGj8RiYUBfO/pxK9qB1VWjaVXt+z4kfMNufJl09LxTztDERaGspedhCWYRBp\nEw+sjHFjSBk8AL3gMKhKzf6Ebu5J8MFH8Q1/B8+axFYvbcsW/MOGkv/i/xLG9gmPh5yRo/G/9jKe\nxYtACKItTid4/8OVOvt/oChzgOdm8FxcXCobgUCgKLgDaNq0KeFyP5Y9sngm9ami4K4kv3nW84H3\nPccayFDFhndxpTenAM9BIAVApKdDwL7HBECNRMBBSCZWuw5YAt1GEtzy+4lcejmeOTPRNyT6WVlp\n6URan4O6dg0ZN3crtYbx5UQCjz5J6M57CfR6Mp4FzClWDBWqRqhrd9A0cocMJbPrtaXKsISiEOzS\nHdLlwScAuo7w2zdZWoaB2bS58/z9RMnZg7rNvsRU27kT9bd1xCpZgPfRRx9V9BYOG1SH/LsCzPRM\nZ7En8ZDJVEwmeSewUf1Nem9aqa08QLt0QsHp/uCZLlexVPPy0Jcvk89dvIjQFVfjkfi6xY4+GmX7\ndnwff1gU3EFcBMWzeCHej0eg7JYfcCmRCGbzFvLbu+HFO/J9W68/NRLBP+pDglWrktr3qVJKodr2\nbaS8Mjiu/jt/rvT6+oL50rF9wu8n2Osp5N14hy+OJZpt27blggsu4IILLmDdunVF/y583cXFxaUi\nyczMZPr0Yi+u6dOnU6WKXDnNZd/5VZeXMU7yfeFcA1nR54Ix5wYbK6uq1EgYINriDOf5mVURPvsA\nSACx/56C9ou9SIISDGIdXZtQ1xuxbE7io2echfWf/5LyyksJAaKan4//vbdQ8nJJGTIYNae0HYRi\nxfBOmgCmSaz1OeyeMZ/w+Rdg1qtP9OQm5Lw7ksCgIY6frXgxyfdHVeMBcDkiMjKxjrY3eo/VrIXV\n+IRyvf6/oU6dOtSpU4f8/HwGDx5MnTp1CIVCPPbYY+7h0z5ydeg66sXq2461MM9grb4GU7H/Hd+p\n7mC5Zm8hABBVD4L+QIqDAhWg5iVR0fxZHoQqu3YSfPo5rIxEsRmhKAQffQLfh++h7UgUiVKEwDtu\nDCJTLmgiNA3P3NlyC5jt29E2bZLvfcsf+EZ+YGsDoYSCeCeOR82WKzQrDgdrLmXDMYPnnkS5uLhU\nZp5//nkeffRRevfujRCCevXqMWiQs2iESyJRooz0fcBSzw94hMElkfZcGrkMpeB/MpL14FW4S4Lp\nLHKgRsJYVbJsT6EFELm2E76vv5L20og6RyOyd8KeRDm+uA3CDvRNG23nKkDqg3cj/Cm2Qgnazz+B\nENJTfO3PPzEmfI7nB3uRGM/qn/FM/ZLoFR2wGh1P7ugvbN/nSCyGYiMQA6CGQnjmzSVyZTnKnet6\nvMRs7a8JfYCRi9ohsqqW37X3k6eeeoq7746bQTds2JC77rqL3r178+mnn1bwzg4dUknlvvyH6Zf2\nDLvV4mCgabQ5vfJ784e2GZ/wE1ISf0brxeqzVlt7MLebgJWejuYQqFhH1UbLyZGOa1u3SseUWAy8\nXqKNT8BYsrg4ew9YHoPIVR1Jv0legqpt2EC4Q0f0d4bajscaHo/m0Aenbd5EtPU50nGreg2wCS4L\nUX/fhHlKEzwrfrS/foNyLP0+QnAM8OrUsT85c3FxcakMHHvssYwdO5ZAIIBlWaSlpVX0lg45woTp\nkdGZWd7i3qCxvtHcHLyNfvkv0sxszjfa14kTBXQJdmOBR15Ko6FxIPyC/zVJMohCgCIJ3hRAW7sG\n4fGU8nkqSaxhI6nQiEDBbH56ggddSTzzv0MJBGy3qW3bivHZp6DLhTmE1+fotaXmyh8ey4SqIjKr\nwLbEB03h9RKrf+z+rV8Ggo88AbEY3skT0P74HavmUUQuuoT8Z18o92vvD8FgkHPPPbfo61atWvHS\nSy85zrEsi759+7JmzRoMw6Bfv37Ur1+cwVq5ciUDBw5ECEGNGjV46aWX8HoTRTgOJ3qEb6KFeRqf\n+EayR8nhxNiJ3BzsSQop1DJrcV7kfL72Tik1J81K54ZQd17wP1dBu46j5DtnoaJnnY2+9lfb33+h\nqs7aF4qCtmhhqeAOCu5bkTDpV7VD1HZ4htdUQt1vxPf+O6hmohhWuFNX9BXyElF0jfDNt+H79GP0\njaUDQSsri3CX7vjfek2+/dwcwtd1xjdmFMpeXqECiFzVUX5tlzLhOqC5uLgcsqxcuZIHHniAO++8\nk7vuuosePXrQo0ePit7WIcXb/jdKBXcQ72H5yPchS/QfeCrvWXQr8SywhlWT6yNdHNe2cFaRK3eS\naKArwaA0eAPwLP3BOQsZjqL9+YdkUCT4UyVs75hjUPMS+xuhQOr8m6lETz/TdtxsdHw8e6baZ1GF\nx0Pk8qscr58URSFyXlvboeiZLYkdDKlxRSH4eB92z1pA9vfLyJ67iPwBg8GwV++rLFStWpVPP/2U\n/Px88vPzGTt2LNWqVXOcM336dCKRCLgCKpgAACAASURBVGPGjOHhhx9m4MCBRWNCCPr06cOAAQP4\n9NNPadOmDVu2bCnvj1Ep+G/sZPrnD+KNvLe5J/gAKRQbdL+VM5xuwRupbx5LViyLMyJnMTBvMJdH\nrkKnYlVLFYlHXCGeaV/Jz6AsC+vEk6RzhcdD2j23OYuknCCfb2VkIDKrIDyJ93YBWFUysWrUkl8/\nIxORnkHeq28SPb4xomCeWeso8p/pj3nW2cRsfDuLrl/zKMxTm2LZlImK1FSiZ7eRznUpG65hlIuL\nyyFLr1696NatG40aNXKFoP4lP3gW2r4eVAN85Z2Ez/JhqomBUrb6Dz9rqxzXtjMYrlREIwhN3idj\nNjwez4xvpeP6xt9QJCVWCuB74xWEqkqzhLEzW2L98jNarr2dROS0M4hcfwPamtUYi4vFJGI1a8UV\nML3eBAW7IqJR9AXfE73kUun+y0Kgz3Noa9fimTe7yIjdbHwCeYNe2a919xnDwKp7zMG95n4wYMAA\nnn32WQYNGoTH4+H000+nf//+jnOWLl1KmzbxB9umTZuyalXx79fGjRupUqUKI0aMYN26dZx77rk0\ncMvYSCWV/+W9TpQoYSVEqkgrKivPU5x73CoaNSAXoVIgaYClOsihK5aFapN5Lxrfk4NvxHtoNiXY\nCuCdMD5eZimbvzNeful/ZTD6urVFgaa2bSv+N4YQvq4z1rHyn0+rQcP49XfuTBhT8/PxDX+bQL8X\npfNdkuMGeC4uLocsPp+Prl27VvQ2DmmceuwQCuN8EqNdxWSo/1XnxSt5fAeA14CQfZ+ZyMpy9LIy\nMzMxHMbV3dlY9eqjSvrwrIbHE21zPtqUSQljscwqhG+7E3SdnPFf4f1kJPrqXxAZmQRvugVRJ27y\nrObLM4D6ov0P8PTFP6Cv+QW1IBuqANoff2BM/ZLQXfft19qHM7Vr12bYsGH7NCcvL69UmbmmaZim\nia7rZGdn8+OPP/L0009Tr1497rjjDk4++WRatmzpuGZWVgq6XpzlrVGjfIVx9pd93Z+JSV/68jVf\ns4c9nMRJ3MM9XMzFRHAQtVEAxx5iJel40tk+H+Tny8fbngcTJ9qP6zr+o2tK19esGAj5vUcBUn6z\nV9gE0CNh9J/lnrHelT+CwwGCHgxRff4MmFn6AEwB9PXrqP7kgzD0TfjiM1i5l1jMMcfgf/QheFX+\n9yNl+9+kVsDP6uH0++EGeC4uLoccf/0V9+Y66aSTGDFiBBdccAFaCSXC2rVrV9TWDjnOjJ7NN97E\nHju/lcJV4Wv4xCcX29qol7/RdXmjOHjhqX//7djG5/1maty8VxLkRU87E89C+x5FS9MIX9WRWM2j\nML6ZUhRAQTwujp5+RtzGAeL/r3tQt/xJTFFK2xs42TxUyXLYfdnwv/kq2l5eeGowgH/E8LhZcEqK\nZOaRTUk/vJI4+eClpaWRXyIgsCwLveBnoEqVKtSvX5+GDRsC0KZNG1atWpU0wMvOLi4TrFEjnR07\nKm9W69/s7560nnzmH1309XrWsyC2kLdzhvNPxj/yGE0AxHB+g+U4nnR2OOI4ntvuKtInTrS9x0Tr\nHANffSUtMrV27UJ4PI7rh4+qi8ztzUxNgeXLpUGAtWcP0YwqyDo8Y34/sef7ISuUjn3zDdn5MdQ3\n3ye1f1/0JT+gmVHCpzYjeO+DmNXq4E9JJ1UyP5iWSf5B/lk9lH4/yhLouQGei4vLIUe3bt2K/r1w\n4UJGjhxZ9LVrJrxv3B68i0We7/nG+LpIlMQQBrcEe9I01oyqVlWyNfsywBOiJ/GjvlS++CFQNeu0\nRWPcaIdRULKzsTKroNmoaAKI6tXQ/vrTfm4shuerifgmTywV3BXuyViyGHXzJqxq1ahyzhloW7YU\n7dX3yUjyBrxMpGsPqYiLANRCg3MhMKZ+hb7iR2LVqxPudhNI/O1KEYuhS6Tatc0b8cyaTvSyK5Ov\nUwbUNasxFnyP+d//Yp5+1gFZsyIpqUJumibffvstkYizNH/z5s2ZNWsW7du3Z/ny5TRu3Lho7Jhj\njiE/P5/NmzdTv359lixZwrXXXltu+z8UWK3+whTvlwmv79R2MDzlnQrY0V7YiJeURJ8/V3r/Ubf9\n7Xh4o4CzCAsQvPtevBM/T1CgBYhcegXejz+Ur29ZBG+/B2PWDNs9httdhuf7efKLR+N7FzVqYNU6\nCqtadTQRQ9SsiXV0/AA2dPPt+MaOTuhjtqpUIdy1u+Nnc0mOG+C5uLgccowcOZK6detW9DYOCwwM\nRuR8wljvaBZ4vsfAw2XhKzk/Gvc67RrqwXOepxPnWQb3Bx9itP9j+eKVvUQzidG5IhFAKcIwiNWp\naxvgCeIlmE49et4pk9FsTNAhXt5pTJuKMWEc+l5iGmo4TNpTj/HP9XKRGwVQgvmQl0vGzd3xzJtT\nZMfgHzGcvMGvYrZs5fz5VBXhs88BCFWNl7DuL6EQaffdiTFjGmpeHsLrJXpWK3JffxvhINJQ2dlb\nhfzWW2/lmmuu4a677pLOueiii5g/fz6dO3dGCMELL7zA5MmTCQQCdOrUif79+/Pwww8jhKBZs2ac\nd9555fwpKg+7lWx2K7upax2DXvDoOts7kzzV/nd0jfYrjWMn8Iv288HcZmkcsvsAhk1pdiFKKIRQ\nnXUQrawsNEkfngCEbkgPgJT8/OIKAQnmeW0Jd7gG74TxpYK86En/JfBEH/yv/g/9559sA0Dz+MYQ\njZJxYxc8C78vet23Zg36ih/ZM24yotZR5A1+jZSBz6OvXI5iWURP/A+hO+/FTOJB6pIcN8BzcXE5\n5OjSpQspKSm0atWKVq1aceaZZ7oWCfuBjk6XcDe6hLsljN0RuoeP/CPYqG0oTncJaBe+jIZWI+eF\nKzqD5/U62ghYaemou+Vmu8mINWho66FXiHlKk/geJJkbs/GJaL//bjsmgFiduug/2xulq4EAvsED\nEIaBYrO+AEJdupP6/DMYc2aWGtPXryX1mSfZ8/Ws+EOoDEXBPKMl+sbEHkKzaXPMlq3lc8tI6jNP\n4Jv4efElw2GMOTNJe+QBcj9yzqBWZhYvXlz0byEE69atS2p0rqoqzz1XWtq/sCQToGXLlowbN+7A\nbrSSk638Q6+0h5nnmcNudTfHm43pHOrGnaF7qGpVjf+g29xncpU91LZOPOj7LYmoXh22y73glFx5\nOaACiNR0yJWXkCuG3CJDAVL7PSO9BRszvokHiDn264uClgd92dKENbRNG1CiUYK9euP77JOEEm4r\nNY28N97BO+aTUsFdIfraNfjfep1A3/5EzzqbaOtzIRJGiZqYLc8mcsHF0s/lUnbcAM/FxeWQ47vv\nvuP3339nyZIlTJ8+ncGDB5OVlcXZZ59N69atadq0aUVv8bBhtHcUm7VNpR+iFFhozGer8rfz5IrO\n4DmcngOoYbmKHZA0QFSiJupu+/JMBfD/70WizVrgnTs7cWuGQejeh/BKVDqFrhNteyGKQ4ZR37QR\nYdgHkAqgb1iP5/vv7OeuXI5n5nSiFzo/TOX3fQHt983oC78vKvUyGx1P/jP9YH+Va6NRPHNm2Q55\n5s+Nl6geBK+98uC114o9wBRFISsrq5TtgUvZuDP9NmZ6i39HVnt+4QX9WTJEJiEl4HCIpPCLXoHZ\nO0BILEyKxn1+iMrLOCMtz8L/zTTbMUtRpOq8EA/Q1O0OKpq7sxGnNIHNm+znV6mCMWwoWmGZdwnU\nYJD0664i59u5ZM9fSvrt/4e+dDHETGLHn0Deq28h6h6DMUuuQOyZPRNiMTJu6oIxu/gASl+3Bn3Z\nUnLGTUJkJFoo/FuU7dtBWId0VcC+4gZ4Li4uhyT16tWjXr16XHPNNeTk5DBjxgzef/993n777VLy\n4i77x7feaVhK4oPEdm07I/0fVMCODiBJAlDL50d1CvB27Ugw6S01np8v9zkvGFAkD2GqaWJMHI+V\nVRVtR2IWQADB2+7EMzWxB6lwPFa7LoqNDDqAIgTqTrnMetE6VauyZ/xXGF+MQ/9lFVaNmoR63HxA\nxFWUUBA12z6Dqubnof2x+ZAN8Er24Ln8OxbpC5hvzE14PayE+dw7hisiHaRzq1vV2axsKsfdJcfJ\nYxPAqlMXfv1FOq6GHXr4fH4iLc7A/+UE22GRng6S3mAAwmGCt92BZ/Eie5GXZqeR+ubr0vhZ/3V1\n/B+pqeR+/BnKrl0o0QhWraOKDn7UP+37jwHUnD0YY0fHA7298Kz4Ed+woQQffVK+/zKi/7AQ/0sv\n4Fm6BISF2ew0Ag/1wmx9+PvslVuAZ1kWffv2Zc2aNRiGQb9+/ahfv37C+/r06UNmZiaPPPJIeW3F\nxcXlMMM0TZYuXcp3333HvHnzCIVCnH322dx///2cddahL9BQmdioypUydymJHkaVCaHrjifkVt1j\nUNevlY6rSUQMUDUsXUOzEVMQQOSMs0h90d77TI1G8b3+P9Td8hIs/eefiBkGKomJCgHETjsDVfL5\nFMAzby7mf09G+yOxDDRWuw6Ry66QXrv0ZlUiHa8n0vH6sr2/jIi09HiZ67Ilifurewxm0+YH9HoH\nk5UrV/L++++TnZ1dSgyjpCCUizPL9R8JK/ZB0t/aX1wVvpreab0wlcTfAR2dgOJsNF7eKJLDi0JU\nhxJNwLE/GEUh/9U38U6ZhLpXJk8AgfseImXIYPl0IYhe1ZHoO2/iWbK41P0lVrUa+W8Mo0qr0+TX\nj8X3pq7+mbR+fdEXL4SoiXnKqQTvvp/oJe2J1a6LZ/ky2+mWYeBZtkQeQP6y/9lXZft20u+9Ha2E\nTY0xfy7a5o3smTAV65h6+32Nyky5BXjTp08nEokwZswYli9fzsCBA3nrrbdKvWf06NGsXbuW008/\nvby24eLichhy+umn06xZM9q1a8cbb7zxrwRXVqxYweDBgxNO2mfOnMnQoUPRdZ2OHTty/fUH9qH2\nUCOMvIwxR7EXECmignvwlFAIy+tFk5ykhzvfgN6vr3R+7OijUSU9KgAYcg89BVCjYVTJOIBnzRpH\noQOrRk30LX/afhsVwHjjFccySXV3NsE770P7aSX6luLTdOH1Eup2IyI9Qzr3oKAohLr2QFv9C2qw\n+GFcqCrhq69DpFVuTyonevXqRbdu3WjUqJGtXYJLcpqazfAKr22Qd3SsNrM8M2yDO4AozgqWB4ck\nB0RJVFUL1SZtUQDLslXIBFD+yU6q4okQiFpHJx4e1aoFZgxLdfD6i8UgECDjjlvQS2QhjUUL0DZu\nIKd2bZR/5AeASjCI8Mp7CA9Edb/vvbdLBXeFaH/+gW/4MAJ97Q/fDhfKLcBbunQpbdrEU6BNmzZN\nKJlatmwZK1asoFOnTmzYcOh7Kbm4uBw8OnfuzIIFC/j888/ZunUrrVq1olmzZqhJVMcKeffdd5k0\naRL+vaTio9EoAwYMYNy4cfj9frp06ULbtm2pXr16eXyMQwLh8KfWkLokFU2uWIRw3kM4guXxoEmy\nYJErOuBZ49A3FQiiBOyzBAJQNm9GWJZtgCaAyIknoWzdgrEs0WpCKApm63OkMbICpLzzFpbfj2aj\n9imAwIO9EPXrk/vhJ/jeG4a+cQNWVlXCV3Qgcm0n+ec6iIS73QSGF++YUWi/b8aqUZPwZVce8ibq\nPp+Prl27VvQ2DmnONFvSKnJOqR48AK/w0jHcic1aYn9YIX+pf0nHDhpJVDQLs2AylC1/yMfCEdJu\n7mob4CmAf8S7CK8fJCXaAvBMn4YxbUrCmL76F/xvvooqUQAuvIZvxHulgrtCtO3b8H34PqqDx6gS\ni8V7EGXjVkw6Vla0v7c4jFWCn49yptwCvLy8vFKqdpqmYZomuq6zfft2hg4dyhtvvMHUqVPLtF5W\nVgq6XvosoTI7zlfmvYG7v/3F3d/+sb/769WrFwDbtm1j/vz5jBo1iscff5zGjRvTunVrunSRy8dD\nvH/v9ddf57HHHiv1+m+//Ua9evXIzIw3d7do0YLFixdz6aWX7td+D2Xs+u8KyVUcsluVBDUi74Mx\nJo4ndsJJaKvsvd6sOkkyw7GoYwBmzP9O+oCnAJ41v4Jmb2UsdA9mk2aOlxfRKGRWAYmdg7b9b8z6\n9Ymd0pT8V9+yfU9lIHx9F8IOlg+HEn/9FX9wPOmkkxgxYgQXXHABmlb87FK7tkNWxiWBt3LfpZd4\npEBFM5vjzRPoEupK13B3XvQ7ZWAEXryEce6DK1e8XmmABaA49cgRF1GSYkbxrLW3WAFQAgGsOtVg\nt1zl1zP/O6mIk75yOWg6OHz/HHvs/v4LUasmrLYvtRQZmegSixgAdYs8OCsrVq2jpWOxI0BspdwC\nvLS0NPLz84u+tiwLvaAU5euvvyY7O5uePXuyY8cOQqEQDRo04JprrpGul51d+pS0MjvOV+a9gbu/\n/cXd3/5Rcn/7G+jVqlWLyy+/nPr167Ns2TImTpzIihUrkgZ4l1xyCX/a/HHKy8sjPb14T6mpqeQl\n80Ij8QDqcAqws6jC79iflDfyNXCerIFznaazyhxo+znbGePP3+MPYRIymvzHcb5esybYWAgU4sl1\nLmH1ZaXDd/Yqkmo0QvVJnznO99SrC+vW2Y4pQJUNa1DaX4TYsQNu/j9YuxYyM2HgQJS2FziuXZ4c\nTr8fe9OtW7HVyMKFC0v13CmKwowZM/Zrb4cjS/RFjPZ9QpgAx6Qcx+3Bu8gUVQDIElV5J/d99ii7\nyVayS/ngHSXkD/DVrRr8ocozYAeFJCIrcgWmOIqDBYsCxFL88nucEIiUVPniqoriILKkbNlCtElT\n1AXzbe/BlseDVVv+/bdq1iJ85dV45sy2zTKGrr0e79eJ2cOi6xcGv+Ew/tf+h2fRgrhISpPmBB98\npEzl5cHb7sA78fMEJdBY7TqEbrk96fxDnXIL8Jo3b86sWbNo3749y5cvp3HjxkVjPXr0oEePHgCM\nHz+eDRs2OAZ3Li4uLiWZPn06P/74I0uXLuXPP/+kSZMmtGzZkiFDhnD88cf/63X3PpjKz88vFfDJ\nKHkAdSgF2GVhQ9UN0khpU+AP8COP4WIgNaoqeoOdhEjx+P7NTjJumqiBoHQ88viTeBzmB09uim/j\nRvn4iSfj27ZNOh76ezvenBzpeHj8BAyH6wdObY7v11+RFSbn7tiNOXUGmddegVoikyAuuojALbcT\n6D9IMrP8OJR+P/5NoDdzZqIqoIucD3zv0j/1WXLUgsOQVJjq/ZIRez6lvlUszJcpqhQFfYV0DF3P\nyykvslVLtGs5N3o+H3lHlOfWk5PEpgVFBRzek6R302x0Ih7JAZMwDNR8h98zVUOxUectGt72N/gM\n+QGbYRD6v9vwjf4kIRMXq1adcLebMFucTqhzN3yfflRqnXDrcwjd+xDa+t/wrPjRdnnr6DoQi5H+\nf13xzvim+LLz5qIvXkjOmAlJlXxFraPIffUtUl56Ac+yJSAE0WYtCDz42CGrzrsvlK1h5V9w0UUX\nYRgGnTt3ZsCAATzxxBNMnjyZMWPGlNclXVxcjhA++eQTMjMzefLJJ5k7dy5Dhw6lW7du+xXcQdxU\nePPmzezevZtIJMKSJUto1sy5TO5wJ0+RZzBneyt5NsJBwAQAnw+nJj11/TqER1JCCYgaNRyNwq0m\nTZy3t3K58/527IgrgUqINWmK5XCSLWrVJP2uW0sFdxBX0PONfB9KHGa4HDhmzZrFH3/Es0fTp0/n\njjvu4LXXXsN0UkU8Asknn6EprxUHdwX8rK9icMqApPNTSSWITQ+siPfpWU7BU2XA43x/cszAAZpT\nhYDPj3XscfK101LjQY8ENRBA3blLOq6Ew5CaRu7Qd4iccz6WPyXu3dn8NPJffBmzRVw8UV+daFmk\nbfgtHmy1v0x6941cfAnGuNEYJYK7QowfFuJ7723p3kpint2anC+mkP39MrLnLyVnwlTM89qWae6h\nTrll8FRV5bnnniv1WsOGDRPe52buXFxc9pX333//gK43efJkAoEAnTp14vHHH+eWW25BCEHHjh2p\nVavWAb3WoYZQ5AFQvpIkQKho8UCPx1Fq3ErLcBQScEKBeAmWwym9vlrucQWAsOKn9JJSLVGvPvyy\nSv4ZIhHM89qijx2dMGQeexyRK64m/YG7baeqkQj+Qf0JPvuC8x5d9onhw4czZcoUXnzxRX799Vce\neeQRevfuzfr163nxxRfp3bt3RW+x0vCFdxy/S4RSlnkShYf2Zqwxmj2qTR+wAuN8n1X+AE9xzrGI\nKlVgl70SpVBVYjVrypfOzyPvqWfJane+vc/dmWdjzJQbkUM8CygfjP9frEkzckaPxxg7GiVnD+Gu\nN0JqPDA1PnwfffmPCdfX/9pC6j23kz/sfSJXXYN34vhS45HW5xC69U5Sn3pM+ifEs3K5g75zIkn7\nqQ9DXKNzFxeXI5K6devy2WfxHqcrrij2A2vbti1t2x4ZJ3yFmJiM8X7CQs8CDDxcFr6KttGCHi2H\nGklDGEQlMuWVAgeBAwDVoccFkvvokaTHju3bnNfXPMRqHYW+NbHETAD5fZ7F+NreyBziPTqBR59E\n+/knPCV8o6wqVQje+1BBhlKO4iBA4/LvmDhxImPGjMHv9zN48GDatm3LddddhxCC9u3bV/T2KhWa\nQ5esKpKfDk32TZTem/aoux2rHysFyYzQj6kPv623H9R1jBnyAE0xTfQ/5T2IokYNhKqBk51E1CHj\nXKBy6fl2GikDnkX/eVVc2Xf4MIK33kHotjvxvzNUGqAZs6eTryhEzr8A/bs5aP/Es4VWWhqRVufE\n710O9y/hdb63uZRjiaaLi4uLS+UnQoQbM7rwYMY9jPGP4iP/CLpnXs+zKX3ib3B4zkoVziVEFU4y\n24xgEiNkr9dZ5MWhhCk+LhcxAMDvI2ZT2QLES0P9KXG/KRnpGVj1jyVnwlTyH3mc0DXXwZ13sWfs\nRMLdbgTAkqjFCV0n8NhTzvtz2WcURSmyX1m0aFGRXZTrhZdIh3BHjjPthZpamGcknX9i9CTpmCUq\ne3RHUZAkxekzCCH14CzEM2WyPMCa8pWjz57QdZSQw/1RUVB27iSt10N4CoI7AG3TRlIGPI8+awbq\nPw4iMcEQ6sYNpD7Tuyi4A1Dz8kgdMgh95gzCHTth2XhhCo+HSPvL5XtzAdwAz8XFxeWIZpj/Tb71\nTiv1WlSJ8q7/LZZrP6I4hDhBxfkBo6KJ1di/8lp1p9yoF0Ddk+18/SRS3NZxDdH+3mq/djSKd9IX\n0gBPAKLAg09kViH46JPkvTUcZehQYqcW943mDnwZa69SKwGEOnSErCwA9O/nkXbbjWRefA7pnTrg\nHfl+UoU/F3s0TSMnJ4etW7eyevVqWrVqBcCWLVuKlMRd4vjx80DgEbJiVUu93iTajMfzk5eyKkKR\nttDG2H8ftf1lf4U8tN9/lw9Go44+cgCexQulY0peDpELL5aOxxo1RmRkOmxOw/fBu2g2Xn1qfh6+\ncaMxHT6/yMjAP/xtVBurCCUSwT/iHcxmLQje+yCWv1hMxTK8BG+6lchlV8r35gK4AZ6Li4vLEc1k\nY4Lt6xE1whjvJ2hCXkaVZVUrr22VGacwROyv51g45JgFjB11FJaDCEvo/oexqlSRjoc73YCyVW64\na6WmSDM/CuD5+Sfp3ELMiy9l9/R5RFqcTuzo2kQbNSb35dfIH/ouAJ4Z35De8yZ8k77As2I53tkz\nSXviEfwDnk+6tksiPXv2pEOHDlx//fVce+211KxZkylTpnDTTTdxyy23VPT2Kh1dwt2YuHsqPQN3\n8n/8H33ynmXi7qmOFgiFjPGPklcYVIaEaShJCXTSCoMkB2ipzhUUokpV+ZiqEXr8KWI2QZxQVAK9\nejsGqCI1DW39Wum49tNKwvc8IL0/h27ogb74B/n81b+AEGhrf0UpUWmhRsJxkRan0nkXwA3wXFxc\nXI5o/tbkAcYqzwoaxOxLCBFwdbjiRbIcSyjX23vEFaE6O+UJVcXS5O8xm5/mvH5+HuapTe3X9niI\nXNEhQeGyaBxQf1uP5bc/pRdA5MyWAOgL5sczcBe0Qlx4Ab533yqVgbNOOJGcKTPIXv4re+YvIdLt\npqIx33tvo+0ll66YJr7PPklqxOySSLt27fj0009555136Nu3LxD30+zXrx8dOnSo2M1VUk60TuK5\n/AG8yZvcE3yAFJzl7wtRkvTpeZF7XB4M1O322flCrNp1nMdr1HAcV5Ko4Iau6yQNsKwTTkSkZ5D7\n/keYdeoWvS+Wnk7+U32Jtr8C80S5D2js+MYo/ziobIaCRC67kkj7xExbpFkLgg88gjAc/vtoOsaU\nyXgnfJ5wj/fO+CZeZeDiiBvgubi4uBzBOPXR1bbqMDj3Vds0mU/4eDzYpxx3tv8oyURQVOcHRMXj\nQYnZ98EIgHAYVXKSrAD+N19DX7vGfjwaxfPNVGkppAJ4p3+DqGafJRWqSqTHzehzZxVn4Fb9BDNn\nkvr0E6T0LYNaoxDov662HdL+/gvPt9Nsx1ycqVWrFieeeGLR1+eeey5nnnlmBe6o8hImzJOpj9Iq\n63TqU58rM9sx1puoCmvH9eEu8kERX7tCSVLmrIacdSCV3fIScAUcbWAEED2rtXQsVqD+qy//ETU3\ntyiIUgMB9J9WQCyGVa++7XyIl3BaJfytE8aPqQeKQqTthcQyi7OElt9P9OzWkJpK9JzzpPOjZ7bE\nM2cmiqRE3Vj4ffwfwSD+gc+T0fFyMq5uT8pzTye/7x8huAGei4uLyxHMmdGz7AcEnBtpyyJjgTRN\n9qtmHxwcMiTrMxMCRSKEoADG9ESPppKoOTkoEqEBBTAmjnc0M460OB2Ram+2rVgWxrQp+N99G20v\ntU7FsvB+/hnKjiQiL4qCSE2zHRK6jlVQ4urv35es/zSgWp1qVG1Ym/Ru1zuLv7i4lJF70+/gvZRh\n/KavYzvbWWQs4LG0h5hgfJ50bsdwJ2mNdk1LbiFw0EhWQhlwFnlStzlnAJ1EohQgo8vVtrduBTBW\nrURdv46UIS+h5hRbTSixGL4J82pMAQAAIABJREFUn+MdPoxQj5uJNkz0lo1lZBDu1oNIq/OlGcJY\n3XqoGzeQMuBZtD3F66vBICnvvY0xZTKh2+/CtPHqi1WrTvCOexwrLISmgWmScWNnUoe8hDFvLsb3\n80gZ+grpXTomL28tJBDAP+A5MjpejrjwAvwvPHvY+IO6AZ6Li4vLEYwh5F5HNa1aTPfYZ3FCaojP\nvZ+V17YODpZzgGfVcu4D0jb+5jhuNj7JMUtoHV0bIenDEUDszLNQ8u2N5hVA3bULfY0kA7djO8bU\nSY77A4i2Ocf+9WYtMFu2JqXP46S89j+0XTtRzChqXh7Gt1+TcemRZSXicuBZrf7CdCPx/pKv5jHK\n91HS+bONmdLDp5hS8QcQVnXnIFNJEuA5eXjGL+CsFKpmO2QAhcD3yUeokvuLd/JE8HhQ7LzwPB6s\n9Ay8n4+Wq3R+Pw/fxx+i7Uos41TCYYzJExEZmeS+NZzIOedhpaQgDIPo6WeSN+QNrJP+Q7j95bZe\nfAKInHs+3k8+wpgzK/Haixfhe+9N6WcvIhIho/v1pL4yGGPe3Hj1w6svk9H9+qQWFocCboDn4uLi\ncgTzg2eR/YAC07xfs0qXC3ks0RaX064ODKJqEhEYYTmKtEQuuNB5vq47zreOqhk/aba7NBBpezFI\nxhVAX7ZEmuETmkb4kvZYaZIMnKpi1T7GYXdx8vs8T/jiS0s9SJmnNCH/hZdAUfCOHpXwEKcAnp9W\noG7ckHR9FxcZ3xvzyFPtA4zftU1F/w4S5D3fMAb5X2CuPrvo9c3qpoR5hfyjOHtcHgxUhx41ILkQ\njETAqXi+8wIiyQW0n5bLxzZtwPvpx+irf04c27UL//B38CxcIJ2v/r0FJMEjgJKXC0Cs+WnkjJ3E\nni+msmfsJPZMmkb0krhfpHnO+QRvuhXhLe7VE5pG+LrORDp1xfOlvUAYgPHVZOlYId6PPogHdnvP\nnf8d3o8+SDq/suNq9rq4uLgcwTg9JM3X5zhaIWzSN5bDjg4cSizJCTjyZywB0uxYIbGjauPJte+x\nA9D+2ooiOYWPl3h+HX+Ii0Rs32NVrS5dW4nF8CycT7TNeXh+XpUwbjZpRvSCixz3D0BKCrkfjUGf\nNxfP4kXE6tYlcvV18f6ecBg1N9f++paF74N3CDw3MPk1XFxsaBw7AY/wEFUS+1irWfGf/bn6bB5P\nf4T1elyx0RAGF0YuZljOB6zU5QGKUISzxO7BIFkvWJIS8eh/T0VbZn+IJgB8fkc1SevYY+OKk7L5\nThnCYBBt3a/SYfX3zc4+fLEY5n9PQWB/j7WOipd/60t/IGVAP/QlP6CYUcwmzQnc+wDRdpcBEHh+\nIJGLL8U7ZTLEYkTaXhgPABUFbau8hNXJg68Qz49LHcaWVXQH537jZvBcXFxcXCQoWIq8DGiPkkRl\nscIfsOyDkyIcTsAVQF8jD94AqQBK0RrBgGOvmrJnN7HjT7BfW9eJXnQJSo78Myg5e4i2bIXlSfS5\nM4+pn/SEvyRm63MIPvgokeu6FIs3eDzI/iMKIPqfk8u8vovL3rSOnsMZNj3AqlBpH74CE5On054s\nCu4AIkqEKd4vGZDyPL+rmw/mdg86+u/yAzQFEDYm4IUIFGJVsuS3YEVFcbQaEI4lpqJqNWLHHisd\nt1JSUYJBeQ7RiqHszibt3jswvpuNGgzEhaeWLCLt0QdQfynOHJptziV/wGDyBw2JB34F9zXruAby\n6ydRKAUQEoViAOHzJZ1f2XEDPBcXF5cjmBpC/ke8kZnYYF+SIEl6SCqaZAFOkj/iIkmJlKLqCIdr\nmPXkJZICMM9oSbDnXbYPE5GL2hFr0gynAMuqeRS+kR+gRktnABXAmDcHZevfjvsvRF+0gLQ7biaz\n3fmkd+mI95Pk/U8AVMkq2/tcyoRlWTz99NN06tSJ7t27s3mzfQDTp08fBg8efJB3d+BRUHgt9y3a\nhi/Ab8Ufto+O1eaOwD3cHbqPycYEfvEkZqcB5htzyVfkJYCVAfOEE6VjZTn7Uv6R99ABWNUdbBQ8\nOp5fVsltAoXlLJQUixH+v1uJ1Um8h1l+P6HruhC54BL59BNPRNuwXjqubv0L3/B30H9LfI+2fRv+\nEe/J91ZAuKPcBiLc4drk86+8xtYsXnh9hK+qeAug/cUN8FxcXFyOYHoG7rR92vBYBoPyhzhPTpYg\nqmizYW+SAE53DuBiNWs5zycmNSIHUHPyUBxsEIzZM4h0vJ68/oOINmuBlZFBrG49Ql17kFtgRI7k\nlFkhbmWgr/jRdlz7ZxfGN1Mc9w/gmT2T9Ft74PtiHJ4fl+Kd+S1pjz2Af1B/5+wjoDuUOLnsO9On\nTycSiTBmzBgefvhhBg5MLH8dPXo0a9fKDaYPNY6x6jE65wum7Z7FJCYxJ3sBfQP9UFDYpe6UzstX\nAoQUZ5uBikbV5QJWCiQ3Opco+BYiajpk2CwrqVCIsmObfCwUQnh9iGhi+biIxbBq1kD/Te4zqu3c\nibJTruKrbNqAulXuwapul++tkMgVVxHueH3Cn69wu/aEu92YdL7Z5lwC99yPVcLs3crIJHD3fZiF\nFg6miXf4MNJv60H6bTfiHfHeIaMg7Pbgubi4uBzB3Ba+k1+11Yzxf0pEjT8QpFvp9Mt7kaPEURW8\nu/3DyqqKGpBLXhc2+svQ161BKIo0SBMpqQhVRZGp2RkehKKiIHkgKCiFDHe7iXDXG1F2ZyNSUqGE\nqIDZsBHaX1sSplo+H5HWbfD3f8Z+b4Aw5CVIhfiGDU20WYhG8X36MaHb70b4fCg2kuOFGUiXA8fS\npUtp06YNAE2bNmXVqtLZq2XLlrFixQo6derEhg2Hl8DNibH/UIMz2SGKfycvC1/JyykvsktLFCv5\nj/lfdujb2MOehLHKgrpRnsECiDZojLbevs+tLBk+zeGARYnF4uIkDkGeum27fHHLwvf2G+g2gZYW\niZD67FOoDjYsSm6uY4+b/tdfRI6Wl1E6ZieLLqIQPu9CPFOnoAXi2dyYP4XI2ec4egSWJPjok4Sv\n7Yx33GhSU7zsbncVVsNG8UHTJP3W7ninflX0fu+kLzC+m0PuOyOkAlmVBTfAc3FxcTnCeTnwGr2D\nz/CF93PSRTodwh0xKDh9lnXJCzgu2oiNhvNDTEUikkldK4qz0EE0Gj9ll5zYipTU+EOUjViBIH6S\n7Pl2GpiJvS4CCHbuWmovIqtq4kXsZMoBYXixGjRCUeyzAAqghJN4QQmB/usvtkPaX1vwTP+G6Nlt\n8M5I9Puz6h5D9PwLnNd32Sfy8vJIK6GKqmkapmmi6zrbt29n6NChvPHGG0ydOrXMa2ZlpaDrxQ+i\nNWrI+7YqAyX3V4MTuJEbeZVXiZU4JKlHPZ709cKHh8+QWLUogNPzt0IZ3uA8nnS2g5G5Avgse3El\nKCiv03WpEIoC6A4qnSpAjRrw55/y/dlk50rOT/vmK+m4d+VyaNRIOq7FTHA4QFMjEdJOkPfQ+atm\nkJLkZ1WsWQMP31NKpEoLBkh/tjfp57VCaW1v9J5AjSZwRhMASnZVi/eHw9TE74H3y4l4Z3yJ0rVb\n2dY/gOzL768b4Lm4uLi4UFVU45ZQz4TXVVQsbDJUCngcjGgrA9oe5x4WUa067JCfYgtFQdF0eYBX\nuw5UqWJrjKsA2vbtiGpVwSaLqACe1b8QO9fZT079276PTs3ZgzFlErGaNVFt1PqEohBrIH8Ai29C\niWcMbRCahlWrFnmDX0Xt3gnPqpVFY7GaNckbNCR5iZnLPpGWlkZ+iZ8ly7LQCzIRX3/9NdnZ2fTs\n2ZMdO3YQCoVo0KAB11zj3CuUnV3cJ1ujRjo7diQRHqpA7PbXi2fI94eY5PuCoBKitlWb/nmDaGD+\nh+N8jSENiUwj8VMU2S1KAMSSvMFyHE8627Kcx//4w3E8punokgAv+e7ANGPIitAtFISioMmqEwBr\n8+/S9a3cXMyoiawINSYE6Lp8vqoSnjMfWY1BeN1GcpP8rKbfcSdeOwXiWIzo/Q+w5+tEjzwn9v75\nS/t6OrIi/9BXX5N38VX7tP7+UnJ/ZQn03ADPxcXF5QhHIPjWM43PvZ/hJ4XbA3dxkvUfAEcVzc2V\nXcUu5mwELNLSHAM8BRBeAyL2mUDL8GLVqIW2JbGEEkCkp8e96P74I3FMVYk1aBj/IhLBO2ok+s8/\nITIzCfW4Gav+sfE9SKTIFUD9J1suJKPrxE45FYhLmvveeRNt0wZElSxCHTthFmTfoq3PQV+f2NNl\nNmuB2eocUBT2TJmB9+MRaGvXIKpXJ3TTbYgaZSihctknmjdvzqxZs2jfvj3Lly+ncePGRWM9evSg\nR48eAIwfP54NGzYkDe4OB4b5hvJRyocE1Hjgu1vL5sm0xxiZM5qp3i/lfb4KFa/iW5YKAafpkvtO\nWVEdKhgUBFZqutTKQVAGkSmJvQsAuofw6WeR8vWXtsOxmjWl1QkAwpvEAxBQbe6rRWPb5BYKZcah\nBFPmb1qZcAM8FxcXlyOYGDGuyLyEJZ4fih6WxvhG0T14U1KRFTv/qsqF8xOe6uCjBGDVOxZ1m4MQ\ngc9L9OzWeJYvSxgzGzQk3LETyp49eBYvTOjTi7Y4nehF7VD++YfMC1ujbykupfK/PZTc/71OpNMN\nxE4+Bd3GyypWqxbhKzvgf/NV+71FoxhfTcQ8tRnpPW9EL9GzZUz5ksATfQjddif5Tz+P+sfvGHNn\nFcmmm/89mbx+LxYHj14v4Vtul3+jXA4IF110EfPnz6dz584IIXjhhReYPHkygUCATp06VfT2Djq5\nSg5DUgYXBXeFrPb8zEsp/dmhOPSQVQaS+NwlDQCTjRuG1EMT4lYr0jFVRdSpC5ISbXSPrcJkEaqK\nefwJeH5JNEKHgv5nJ5Gt9EzCV3TA996wuKJnyb0B0dMT7TP2RlStCpJWVCujStL5yYhceDHecaNR\n9qrgELpOpMCMvTLjBnguLi4uRzB9Up9gifFDqddiSowP/e/TNdjDca4mVCylEiuKycRPColGiFWv\ngW6j9iaAvFeGknm1wx/yqEmg11Noa37FmD2j6EEgVqcO+b37gs9H6M57UbdtxTtqJFp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P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TJGjTJkyfPvo+dhDt3xPWGPvIyVs2EalwEeLunXExDQh2uMswgQLA7kTIMxYrEY4fQzh+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\nIziv4m+8HRkZeLpSYq/hpZzntenjUlFbtt2QTTA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TMeRW1o39jLVTZ1D24uu43XtkH7cZ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\nEjwREREREZFa4v8D1ZYRpYk13zIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db1ab50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,15])\n",
    "subplt = 331\n",
    "for col in cols:\n",
    "    if col=='Rings':\n",
    "        break\n",
    "    if col=='Sex':\n",
    "        plt.subplot(subplt)\n",
    "        sns.barplot(x=col, y='Rings', data=abalone)\n",
    "        plt.plot()\n",
    "        subplt += 1\n",
    "    else:\n",
    "        plt.subplot(subplt)\n",
    "        plt.scatter(abalone['Rings'], abalone[col], c=abalone['Age_Encode'], cmap=plt.cm.brg)\n",
    "        plt.xlabel('Rings')\n",
    "        plt.ylabel(col)\n",
    "        subplt += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If an abalone is an infant, it is more likely to have less rings than males or females.\n",
    "* Length and Diameter seem to be features that help identify abalone in the young category due to the large tail ends where there is no overlap between the other age groups.\n",
    "* Height seems to be a bad indicator of age as there is not much spread between the age groups.\n",
    "* The other features, on visual inspection alone, seem to have similar importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QsgYAAAAABqJlDQCmEIbpN1+o19We23VpjJMAAKxGyxoAAAAAGIhiDQAAAAAM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\nFaVvf/vb6t+/v3tdnz591L17d3Xo0EE9e/Z0V+UAmOGhhx7Sxo0bNXH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db2029a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Frequency counts on y axis\n",
    "o = abalone[abalone['Age']=='old']\n",
    "m = abalone[abalone['Age']=='medium']\n",
    "y = abalone[abalone['Age']=='young']\n",
    "plt.figure(figsize=[15,15])\n",
    "sub = 331\n",
    "for col in cols[1:-1]:\n",
    "    plt.subplot(sub)\n",
    "    sns.distplot(o[col], bins=40, kde=False, color=\"red\")\n",
    "    sns.distplot(m[col], bins=40, kde=False, color=\"blue\")\n",
    "    sns.distplot(y[col], bins=40, kde=False, color=\"green\")\n",
    "    sub+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34642087622695716"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null accuracy (accuracy obtained using the most common age group as a prediction for each sample)\n",
    "pd.value_counts(abalone['Age']).max()/len(abalone['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>I</th>\n",
       "      <th>M</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_Encode</th>\n",
       "      <th>Sex_Encode</th>\n",
       "      <th>medium</th>\n",
       "      <th>old</th>\n",
       "      <th>young</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "      <td>old</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "      <td>young</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "      <td>medium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "      <td>medium</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "      <td>young</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F  I  M Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "0  0  0  1   M   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1  0  0  1   M   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2  1  0  0   F   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3  0  0  1   M   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4  0  1  0   I   0.330     0.255   0.080        0.2050          0.0895   \n",
       "\n",
       "   Viscera weight  Shell weight  Rings     Age  Age_Encode  Sex_Encode  \\\n",
       "0          0.1010         0.150     15     old           1           2   \n",
       "1          0.0485         0.070      7   young           2           2   \n",
       "2          0.1415         0.210      9  medium           0           0   \n",
       "3          0.1140         0.155     10  medium           0           2   \n",
       "4          0.0395         0.055      7   young           2           1   \n",
       "\n",
       "   medium  old  young  \n",
       "0       0    1      0  \n",
       "1       0    0      1  \n",
       "2       1    0      0  \n",
       "3       1    0      0  \n",
       "4       0    0      1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Binarizing the categorical data\n",
    "abalone = pd.concat([pd.get_dummies(abalone['Sex']), abalone], axis=1)\n",
    "abalone = pd.concat([abalone, pd.get_dummies(abalone['Age']),], axis=1)\n",
    "abalone.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filtering out data that will be used\n",
    "features = abalone[['F', 'I', 'M', 'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight']]\n",
    "labels = abalone[['young', 'medium', 'old']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   F  I  M  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
      "0  0  0  1   0.455     0.365   0.095        0.5140          0.2245   \n",
      "1  0  0  1   0.350     0.265   0.090        0.2255          0.0995   \n",
      "2  1  0  0   0.530     0.420   0.135        0.6770          0.2565   \n",
      "3  0  0  1   0.440     0.365   0.125        0.5160          0.2155   \n",
      "4  0  1  0   0.330     0.255   0.080        0.2050          0.0895   \n",
      "\n",
      "   Viscera weight  Shell weight  \n",
      "0          0.1010         0.150  \n",
      "1          0.0485         0.070  \n",
      "2          0.1415         0.210  \n",
      "3          0.1140         0.155  \n",
      "4          0.0395         0.055  \n",
      "   young  medium  old\n",
      "0      0       0    1\n",
      "1      1       0    0\n",
      "2      0       1    0\n",
      "3      0       1    0\n",
      "4      1       0    0\n"
     ]
    }
   ],
   "source": [
    "print(features[:5])\n",
    "print(labels[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Conversion to numpy arrays for faster computation\n",
    "features = np.asarray(features)\n",
    "labels = np.asarray(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.      0.      1.      0.455   0.365   0.095   0.514   0.2245  0.101\n",
      "   0.15  ]\n",
      " [ 0.      0.      1.      0.35    0.265   0.09    0.2255  0.0995  0.0485\n",
      "   0.07  ]\n",
      " [ 1.      0.      0.      0.53    0.42    0.135   0.677   0.2565  0.1415\n",
      "   0.21  ]\n",
      " [ 0.      0.      1.      0.44    0.365   0.125   0.516   0.2155  0.114\n",
      "   0.155 ]\n",
      " [ 0.      1.      0.      0.33    0.255   0.08    0.205   0.0895  0.0395\n",
      "   0.055 ]]\n",
      "[[0 0 1]\n",
      " [1 0 0]\n",
      " [0 1 0]\n",
      " [0 1 0]\n",
      " [1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(features[:5])\n",
    "print(labels[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class multi_class_classification():\n",
    "\n",
    "    \n",
    "    def __init__(self, X, y):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "#         ops.reset_default_graph()\n",
    "        \n",
    "    \n",
    "    def split_data(self, test_size=0.25, random_state=5):\n",
    "        self.Xtrain, self.Xtest, self.ytrain, self.ytest = train_test_split(self.X, \n",
    "                                                                            self.y, \n",
    "                                                                            test_size=test_size, \n",
    "                                                                            random_state=random_state)\n",
    "\n",
    "    \n",
    "    def setup_graph(self, optimizer, learning_rate):\n",
    "        self.graph = tf.Graph()\n",
    "        with self.graph.as_default():\n",
    "            # Placeholders\n",
    "            self.x_data = tf.placeholder(shape=[None, self.X.shape[1]], dtype=tf.float32, name='X')\n",
    "            self.y_target = tf.placeholder(shape=[None, self.y.shape[1]], dtype=tf.float32, name='Y')\n",
    "\n",
    "            self.W = tf.Variable(tf.random_normal(shape=[self.X.shape[1], self.y.shape[1]]), name='weights')\n",
    "            self.b = tf.Variable(tf.random_normal(shape=[self.y.shape[1]]), name='biases')\n",
    "\n",
    "            \n",
    "            self.y_pred = tf.add(tf.matmul(self.x_data, self.W), self.b)\n",
    "\n",
    "            self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.y_pred, \n",
    "                                                                               labels=self.y_target))\n",
    "\n",
    "            self.optimizer = optimizer(learning_rate)\n",
    "            self.train_step = self.optimizer.minimize(self.loss)\n",
    "            self.accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(self.y_pred,1), \n",
    "                                                            tf.argmax(self.y_target,1)), tf.float32))\n",
    "            \n",
    "    '''In order to test this out on unseen data, just process it as shown above and then substitute the feature\n",
    "       columns into newX to generate predictions once trained on all of the previously seen data.'''\n",
    "    def run(self, epochs=1000, newX=None):\n",
    "        \n",
    "        # Just makes it to where the entire dataset is used to train the network\n",
    "        if newX is not None:\n",
    "            self.Xtrain, self.ytrain = self.X, self.y\n",
    "            self.Xtest, self.ytest = self.X, self.y\n",
    "\n",
    "        with tf.Session(graph=self.graph) as sess:\n",
    "            init = tf.global_variables_initializer()\n",
    "        sess.run(init)\n",
    "\n",
    "        self.train_loss_vec = []\n",
    "        self.test_loss_vec = []\n",
    "        self.test_acc = []\n",
    "        \n",
    "        for i in range(epochs):\n",
    "\n",
    "            sess.run(self.train_step, feed_dict={self.x_data:self.Xtrain, self.y_target:self.ytrain})\n",
    "        \n",
    "            temp_loss = sess.run(self.loss, feed_dict={self.x_data:self.Xtrain, self.y_target:self.ytrain})\n",
    "            self.train_loss_vec.append(temp_loss)\n",
    "            \n",
    "            test_temp_loss = sess.run(self.loss, feed_dict={self.x_data:self.Xtest, self.y_target:self.ytest})\n",
    "            self.test_loss_vec.append(test_temp_loss)\n",
    "            \n",
    "            test_accuracy = sess.run(self.accuracy, feed_dict={self.x_data:self.Xtest, self.y_target:self.ytest})\n",
    "            self.test_acc.append(test_accuracy)\n",
    "            if (i+1)%50==0:\n",
    "                print('Epoch: {0} | Loss: {1}'.format(i+1, temp_loss))\n",
    "                \n",
    "            if (i+1)%epochs==0:\n",
    "                # When predicting labels for a new dataset\n",
    "                if newX is not None:\n",
    "                    test_preds = sess.run(self.y_pred, feed_dict={self.x_data:newX})\n",
    "                    self.test_preds = test_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = multi_class_classification(X=features, y=labels)\n",
    "predictor.split_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50 | Loss: 0.6233932375907898\n",
      "Epoch: 100 | Loss: 0.5680698752403259\n",
      "Epoch: 150 | Loss: 0.5437891483306885\n",
      "Epoch: 200 | Loss: 0.5310794711112976\n",
      "Epoch: 250 | Loss: 0.5228580236434937\n",
      "Epoch: 300 | Loss: 0.5169237852096558\n",
      "Epoch: 350 | Loss: 0.512389600276947\n",
      "Epoch: 400 | Loss: 0.5087708830833435\n",
      "Epoch: 450 | Loss: 0.5057691931724548\n",
      "Epoch: 500 | Loss: 0.5031906366348267\n",
      "Epoch: 550 | Loss: 0.5009095072746277\n",
      "Epoch: 600 | Loss: 0.49884316325187683\n",
      "Epoch: 650 | Loss: 0.4969349503517151\n",
      "Epoch: 700 | Loss: 0.4951491951942444\n",
      "Epoch: 750 | Loss: 0.493460476398468\n",
      "Epoch: 800 | Loss: 0.49185293912887573\n",
      "Epoch: 850 | Loss: 0.4903164803981781\n",
      "Epoch: 900 | Loss: 0.4888438582420349\n",
      "Epoch: 950 | Loss: 0.48742908239364624\n",
      "Epoch: 1000 | Loss: 0.4860701560974121\n",
      "Epoch: 1050 | Loss: 0.4847630262374878\n",
      "Epoch: 1100 | Loss: 0.4835064113140106\n",
      "Epoch: 1150 | Loss: 0.4822976887226105\n",
      "Epoch: 1200 | Loss: 0.48113515973091125\n",
      "Epoch: 1250 | Loss: 0.48001763224601746\n",
      "Epoch: 1300 | Loss: 0.4789431691169739\n",
      "Epoch: 1350 | Loss: 0.47791051864624023\n",
      "Epoch: 1400 | Loss: 0.47691774368286133\n",
      "Epoch: 1450 | Loss: 0.47596341371536255\n",
      "Epoch: 1500 | Loss: 0.475046306848526\n",
      "Epoch: 1550 | Loss: 0.4741653800010681\n",
      "Epoch: 1600 | Loss: 0.473318874835968\n",
      "Epoch: 1650 | Loss: 0.47250527143478394\n",
      "Epoch: 1700 | Loss: 0.4717242121696472\n",
      "Epoch: 1750 | Loss: 0.4709743857383728\n",
      "Epoch: 1800 | Loss: 0.4702540338039398\n",
      "Epoch: 1850 | Loss: 0.46956291794776917\n",
      "Epoch: 1900 | Loss: 0.4688994586467743\n",
      "Epoch: 1950 | Loss: 0.4682629108428955\n",
      "Epoch: 2000 | Loss: 0.46765246987342834\n",
      "Epoch: 50 | Loss: 1.2995527982711792\n",
      "Epoch: 100 | Loss: 1.2150647640228271\n",
      "Epoch: 150 | Loss: 1.1407270431518555\n",
      "Epoch: 200 | Loss: 1.0763022899627686\n",
      "Epoch: 250 | Loss: 1.021225929260254\n",
      "Epoch: 300 | Loss: 0.9746420979499817\n",
      "Epoch: 350 | Loss: 0.9355105757713318\n",
      "Epoch: 400 | Loss: 0.9027251601219177\n",
      "Epoch: 450 | Loss: 0.8752147555351257\n",
      "Epoch: 500 | Loss: 0.8520165085792542\n",
      "Epoch: 550 | Loss: 0.832306981086731\n",
      "Epoch: 600 | Loss: 0.8154030442237854\n",
      "Epoch: 650 | Loss: 0.8007510304450989\n",
      "Epoch: 700 | Loss: 0.7879106402397156\n",
      "Epoch: 750 | Loss: 0.7765332460403442\n",
      "Epoch: 800 | Loss: 0.766343891620636\n",
      "Epoch: 850 | Loss: 0.7571258544921875\n",
      "Epoch: 900 | Loss: 0.7487077713012695\n",
      "Epoch: 950 | Loss: 0.7409554719924927\n",
      "Epoch: 1000 | Loss: 0.7337601184844971\n",
      "Epoch: 1050 | Loss: 0.7270379066467285\n",
      "Epoch: 1100 | Loss: 0.7207202911376953\n",
      "Epoch: 1150 | Loss: 0.7147523164749146\n",
      "Epoch: 1200 | Loss: 0.7090895175933838\n",
      "Epoch: 1250 | Loss: 0.7036960124969482\n",
      "Epoch: 1300 | Loss: 0.6985421180725098\n",
      "Epoch: 1350 | Loss: 0.6936046481132507\n",
      "Epoch: 1400 | Loss: 0.688862144947052\n",
      "Epoch: 1450 | Loss: 0.6842993497848511\n",
      "Epoch: 1500 | Loss: 0.6799024343490601\n",
      "Epoch: 1550 | Loss: 0.6756579875946045\n",
      "Epoch: 1600 | Loss: 0.6715574264526367\n",
      "Epoch: 1650 | Loss: 0.6675918698310852\n",
      "Epoch: 1700 | Loss: 0.6637543439865112\n",
      "Epoch: 1750 | Loss: 0.660037636756897\n",
      "Epoch: 1800 | Loss: 0.6564364433288574\n",
      "Epoch: 1850 | Loss: 0.6529455184936523\n",
      "Epoch: 1900 | Loss: 0.6495606899261475\n",
      "Epoch: 1950 | Loss: 0.6462773680686951\n",
      "Epoch: 2000 | Loss: 0.6430917382240295\n",
      "Epoch: 50 | Loss: 0.5118685960769653\n",
      "Epoch: 100 | Loss: 0.49723368883132935\n",
      "Epoch: 150 | Loss: 0.4867374300956726\n",
      "Epoch: 200 | Loss: 0.4787253737449646\n",
      "Epoch: 250 | Loss: 0.47283029556274414\n",
      "Epoch: 300 | Loss: 0.4685472249984741\n",
      "Epoch: 350 | Loss: 0.46542441844940186\n",
      "Epoch: 400 | Loss: 0.4631231129169464\n",
      "Epoch: 450 | Loss: 0.46140623092651367\n",
      "Epoch: 500 | Loss: 0.46011143922805786\n",
      "Epoch: 550 | Loss: 0.45912665128707886\n",
      "Epoch: 600 | Loss: 0.4583740234375\n",
      "Epoch: 650 | Loss: 0.45779672265052795\n",
      "Epoch: 700 | Loss: 0.4573524594306946\n",
      "Epoch: 750 | Loss: 0.4570086896419525\n",
      "Epoch: 800 | Loss: 0.4567411243915558\n",
      "Epoch: 850 | Loss: 0.45653051137924194\n",
      "Epoch: 900 | Loss: 0.4563625454902649\n",
      "Epoch: 950 | Loss: 0.45622628927230835\n",
      "Epoch: 1000 | Loss: 0.45611393451690674\n",
      "Epoch: 1050 | Loss: 0.4560193121433258\n",
      "Epoch: 1100 | Loss: 0.45593827962875366\n",
      "Epoch: 1150 | Loss: 0.4558669924736023\n",
      "Epoch: 1200 | Loss: 0.4558039903640747\n",
      "Epoch: 1250 | Loss: 0.4557473659515381\n",
      "Epoch: 1300 | Loss: 0.45569533109664917\n",
      "Epoch: 1350 | Loss: 0.4556475281715393\n",
      "Epoch: 1400 | Loss: 0.4556034207344055\n",
      "Epoch: 1450 | Loss: 0.4555620849132538\n",
      "Epoch: 1500 | Loss: 0.4555233120918274\n",
      "Epoch: 1550 | Loss: 0.45548680424690247\n",
      "Epoch: 1600 | Loss: 0.4554525911808014\n",
      "Epoch: 1650 | Loss: 0.455420583486557\n",
      "Epoch: 1700 | Loss: 0.4553898870944977\n",
      "Epoch: 1750 | Loss: 0.45536112785339355\n",
      "Epoch: 1800 | Loss: 0.45533373951911926\n",
      "Epoch: 1850 | Loss: 0.4553079605102539\n",
      "Epoch: 1900 | Loss: 0.45536285638809204\n",
      "Epoch: 1950 | Loss: 0.45526084303855896\n",
      "Epoch: 2000 | Loss: 0.45523926615715027\n",
      "Epoch: 50 | Loss: 0.7687684893608093\n",
      "Epoch: 100 | Loss: 0.6248205304145813\n",
      "Epoch: 150 | Loss: 0.5865975618362427\n",
      "Epoch: 200 | Loss: 0.5714969038963318\n",
      "Epoch: 250 | Loss: 0.5617393851280212\n",
      "Epoch: 300 | Loss: 0.5542635917663574\n",
      "Epoch: 350 | Loss: 0.5482977628707886\n",
      "Epoch: 400 | Loss: 0.5434661507606506\n",
      "Epoch: 450 | Loss: 0.5395102500915527\n",
      "Epoch: 500 | Loss: 0.5362368226051331\n",
      "Epoch: 550 | Loss: 0.5335003137588501\n",
      "Epoch: 600 | Loss: 0.5311877131462097\n",
      "Epoch: 650 | Loss: 0.5292136669158936\n",
      "Epoch: 700 | Loss: 0.5275111794471741\n",
      "Epoch: 750 | Loss: 0.5260293483734131\n",
      "Epoch: 800 | Loss: 0.5247269868850708\n",
      "Epoch: 850 | Loss: 0.5235729217529297\n",
      "Epoch: 900 | Loss: 0.5225422382354736\n",
      "Epoch: 950 | Loss: 0.5216147303581238\n",
      "Epoch: 1000 | Loss: 0.5207746028900146\n",
      "Epoch: 1050 | Loss: 0.520009458065033\n",
      "Epoch: 1100 | Loss: 0.5193079710006714\n",
      "Epoch: 1150 | Loss: 0.5186616778373718\n",
      "Epoch: 1200 | Loss: 0.5180644392967224\n",
      "Epoch: 1250 | Loss: 0.5175093412399292\n",
      "Epoch: 1300 | Loss: 0.516991913318634\n",
      "Epoch: 1350 | Loss: 0.5165088176727295\n",
      "Epoch: 1400 | Loss: 0.516054630279541\n",
      "Epoch: 1450 | Loss: 0.515628457069397\n",
      "Epoch: 1500 | Loss: 0.5152266025543213\n",
      "Epoch: 1550 | Loss: 0.5148470401763916\n",
      "Epoch: 1600 | Loss: 0.5144873857498169\n",
      "Epoch: 1650 | Loss: 0.5141464471817017\n",
      "Epoch: 1700 | Loss: 0.5138226747512817\n",
      "Epoch: 1750 | Loss: 0.5135144591331482\n",
      "Epoch: 1800 | Loss: 0.5132208466529846\n",
      "Epoch: 1850 | Loss: 0.5129405856132507\n",
      "Epoch: 1900 | Loss: 0.5126726627349854\n",
      "Epoch: 1950 | Loss: 0.5124157071113586\n",
      "Epoch: 2000 | Loss: 0.512170135974884\n",
      "Epoch: 50 | Loss: 0.4895777106285095\n",
      "Epoch: 100 | Loss: 0.4673595726490021\n",
      "Epoch: 150 | Loss: 0.4609662890434265\n",
      "Epoch: 200 | Loss: 0.45833438634872437\n",
      "Epoch: 250 | Loss: 0.4570466876029968\n",
      "Epoch: 300 | Loss: 0.4563484191894531\n",
      "Epoch: 350 | Loss: 0.4559342861175537\n",
      "Epoch: 400 | Loss: 0.455668568611145\n",
      "Epoch: 450 | Loss: 0.4554850161075592\n",
      "Epoch: 500 | Loss: 0.4553526043891907\n",
      "Epoch: 550 | Loss: 0.4552532434463501\n",
      "Epoch: 600 | Loss: 0.4551781117916107\n",
      "Epoch: 650 | Loss: 0.4551209509372711\n",
      "Epoch: 700 | Loss: 0.45509424805641174\n",
      "Epoch: 750 | Loss: 0.4550563395023346\n",
      "Epoch: 800 | Loss: 0.45501944422721863\n",
      "Epoch: 850 | Loss: 0.4549996554851532\n",
      "Epoch: 900 | Loss: 0.45714879035949707\n",
      "Epoch: 950 | Loss: 0.4549967050552368\n",
      "Epoch: 1000 | Loss: 0.45496416091918945\n",
      "Epoch: 1050 | Loss: 0.4559910297393799\n",
      "Epoch: 1100 | Loss: 0.45498406887054443\n",
      "Epoch: 1150 | Loss: 0.45561251044273376\n",
      "Epoch: 1200 | Loss: 0.4550315737724304\n",
      "Epoch: 1250 | Loss: 0.4551812410354614\n",
      "Epoch: 1300 | Loss: 0.45494934916496277\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 700 | Loss: 0.5052153468132019\n",
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      "Epoch: 2000 | Loss: 3.859037399291992\n"
     ]
    }
   ],
   "source": [
    "learning_rates = [0.01, 0.1, 1.0, 10, 100]\n",
    "\n",
    "adam_acc_scores = []\n",
    "adam_train_loss_scores = []\n",
    "adam_test_loss_scores = []\n",
    "\n",
    "gdo_acc_scores = []\n",
    "gdo_train_loss_scores = []\n",
    "gdo_test_loss_scores = []\n",
    "\n",
    "for rate in learning_rates:\n",
    "    \n",
    "    predictor.setup_graph(optimizer=tf.train.AdamOptimizer, learning_rate=rate)\n",
    "    predictor.run(epochs=2000)\n",
    "    adam_acc_scores.append(predictor.test_acc)\n",
    "    adam_train_loss_scores.append(predictor.train_loss_vec)\n",
    "    adam_test_loss_scores.append(predictor.train_loss_vec)\n",
    "    \n",
    "    predictor.setup_graph(optimizer=tf.train.GradientDescentOptimizer, learning_rate=rate)\n",
    "    predictor.run(epochs=2000)\n",
    "    gdo_acc_scores.append(predictor.test_acc)\n",
    "    gdo_train_loss_scores.append(predictor.train_loss_vec)\n",
    "    gdo_test_loss_scores.append(predictor.test_loss_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nmE3BM/ABS7jDnZnTE9MiB1uxx/9u1BonLufY+FxPSJCnpqZSXV3t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\nJvkmiqfDa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db29fcb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GTMoJ/l1SbsZr71I01mwDCy6ZgD5FTapJR0urI7g1Z/4FxSgUOjTGIpxt+9Gn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\naIqLoRAyS69DAGp62F4G0dLxxRgAhN6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Qmp+KUZ/cpS9RFDB0rGef7MRV3i6Xi/LyrhCd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HAAAg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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db47a5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = range(0, 2000)\n",
    "for x in range(len(learning_rates)):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, adam_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Adam Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, adam_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "for x in range(len(learning_rates)):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, gdo_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Gradient Descent Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, gdo_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x0SF6CuyUd1iv3hTuL1TctyWpLK/j8N4CEuK9v74XglDeHijdZZumULFvHwQp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\ny+WdFhV1dlqTJpEUFjoAuPnmW7Ba67at3Dhb3jptLiIS0u7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WL1/OCy+8AJR3OepwOEhI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\nRERCzP8HMZWuprw7f3oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db1b80d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uHyy9c3EMGw4Gwwl1L1hyc1t1njU9AbOxZzQFtlWRp4QiTwl21YY37GNv3QHK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\nRs1bRETEZNS8RURETOYvArjxN4ccnUsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db1b80c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = range(0, 2000)\n",
    "for x in range(len(learning_rates)-1):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, adam_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Adam Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, adam_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "for x in range(len(learning_rates)-1):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, gdo_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Gradient Descent Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, gdo_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cL7/8sjlia0L+VW9J3kIIIYJJwOQ9Z84cPvzwQ0wmE/Hx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\nV7sHVH3L3u/K2i4WIYQQooWiFm9d11m9ejVr165l2LBhrF2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Zll7SB292GkG7FWWxoHTQ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\n/w9USnJRFd46JgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db1b80c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+vp5wOExCQkJXxNVtvL3zX6zY/1/obyXOHcP/jJtBeGgu/++TJ8isCLJ5oI1n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LAAAC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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db4868320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = range(0, 2000)\n",
    "for x in range(len(learning_rates)-2):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, adam_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Adam Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, adam_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "for x in range(len(learning_rates)-2):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, gdo_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Gradient Descent Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, gdo_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KuKigSkWouh8JcXHhZGdXfE90XZG2L/t0y7+iDq2iiAmv2P2782AuRdbK55o7tY4iNiII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\nAmD79u18+9vfTom2AHz22WdMmTIlupyqxwVg7NixXT4ORUVFfPLJJ9G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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db42d1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+vp5wOExCQkJXxNVtvL3zX6zY/1/obyXOHcP/jJtBeGgu/++TJ8isCLJ5oI1n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LAAAC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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db47e07b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = range(0, 2000)\n",
    "for x in range(len(learning_rates)-3):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, adam_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Adam Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, adam_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "for x in range(len(learning_rates)-2):\n",
    "    plt.subplot(211)\n",
    "    plt.plot(k, gdo_acc_scores[x], label=learning_rates[x])\n",
    "    plt.title('Gradient Descent Optimizer')\n",
    "    plt.ylabel('Test Accuracy Score')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(k, gdo_test_loss_scores[x], label=learning_rates[x])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test Loss Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From above, it seems that the best learning rate for the Adam Optimizer and Gradient Descent Optimizer is 0.1 and 1.0 respectively.\n",
    "* The Adam Optimizer's accuracy has stagnated, remaining practically the same since around epoch 500.\n",
    "* Gradient Descent Optimizer on the other hand still seems as it can learn more from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50 | Loss: 0.5386370420455933\n",
      "Epoch: 100 | Loss: 0.5246683955192566\n",
      "Epoch: 150 | Loss: 0.5196877717971802\n",
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      "Epoch: 500 | Loss: 0.5097251534461975\n",
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      "Epoch: 600 | Loss: 0.5080156326293945\n",
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      "Epoch: 750 | Loss: 0.5056335926055908\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10500 | Loss: 0.4624166786670685\n",
      "Epoch: 10550 | Loss: 0.4623662829399109\n",
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      "Epoch: 14650 | Loss: 0.4594708979129791\n",
      "Epoch: 14700 | Loss: 0.4594469368457794\n",
      "Epoch: 14750 | Loss: 0.4594224989414215\n",
      "Epoch: 14800 | Loss: 0.45939865708351135\n",
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      "Epoch: 14900 | Loss: 0.4593512713909149\n",
      "Epoch: 14950 | Loss: 0.45932817459106445\n",
      "Epoch: 15000 | Loss: 0.45930516719818115\n"
     ]
    }
   ],
   "source": [
    "# Searching for when gradient descent optimizer stops increasing in accuracy\n",
    "predictor.setup_graph(optimizer=tf.train.GradientDescentOptimizer, learning_rate=1.0)\n",
    "predictor.run(epochs=15000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kEhw6dAhnzpzB3LlzERsbC09Pz2qvk5+vqte4PT2dkJ1dXK/nbAysNa+dR67i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\nExERmcT/APWQ2sv2zLU+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db1fa2710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wY6jXN2nvwWbta2jRV3vbL5o7msMhZXuTo2Ge5fUo25usrHSPsrweZaUnK8vr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db40c9ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = range(0, 15000)\n",
    "plt.plot(k, predictor.test_acc)\n",
    "plt.title('Gradient Descent Optimizer | Learning Rate = 1.0')\n",
    "plt.ylabel('Test Accuracy Score')\n",
    "plt.show()\n",
    "plt.plot(k, predictor.test_loss_vec)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Test Loss Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50 | Loss: 0.5192819237709045\n",
      "Epoch: 100 | Loss: 0.5021727085113525\n",
      "Epoch: 150 | Loss: 0.4912886917591095\n",
      "Epoch: 200 | Loss: 0.48316338658332825\n",
      "Epoch: 250 | Loss: 0.4771360158920288\n",
      "Epoch: 300 | Loss: 0.47263389825820923\n",
      "Epoch: 350 | Loss: 0.4692341387271881\n",
      "Epoch: 400 | Loss: 0.466632604598999\n",
      "Epoch: 450 | Loss: 0.4646107852458954\n",
      "Epoch: 500 | Loss: 0.4630136787891388\n",
      "Epoch: 550 | Loss: 0.46173086762428284\n",
      "Epoch: 600 | Loss: 0.460684210062027\n",
      "Epoch: 650 | Loss: 0.459818959236145\n",
      "Epoch: 700 | Loss: 0.45909616351127625\n",
      "Epoch: 750 | Loss: 0.45848727226257324\n",
      "Epoch: 800 | Loss: 0.4579715430736542\n",
      "Epoch: 850 | Loss: 0.4575330317020416\n",
      "Epoch: 900 | Loss: 0.45715969800949097\n",
      "Epoch: 950 | Loss: 0.45684176683425903\n",
      "Epoch: 1000 | Loss: 0.4565715491771698\n",
      "Epoch: 1050 | Loss: 0.4563412368297577\n",
      "Epoch: 1100 | Loss: 0.4561452269554138\n",
      "Epoch: 1150 | Loss: 0.45597901940345764\n",
      "Epoch: 1200 | Loss: 0.45583751797676086\n",
      "Epoch: 1250 | Loss: 0.45571696758270264\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "predictor.setup_graph(optimizer=tf.train.AdamOptimizer, learning_rate=0.1)\n",
    "predictor.run(epochs=15000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X6fXS9t/svSiNrKyb+ZzRuh+jrmLnb1eldXohAAEolQr88scNhB8smlhTcj60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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db47ff4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cnqJQyI7eb8v6GkIh7drbdu2R1+QHuw+3WieFXzfbWzzWkfWuNm7X9LZtrff2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\nBgDAMIQ3AACGIbwBADDM/wcPwuXA0a2UOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db4865dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = range(0, 15000)\n",
    "plt.plot(k, predictor.test_acc)\n",
    "plt.title('Adam Optimizer | Learning Rate = 0.1')\n",
    "plt.ylabel('Test Accuracy Score')\n",
    "plt.show()\n",
    "plt.plot(k, predictor.test_loss_vec)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Test Loss Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Gradient Descent Optimizer seems more reliable than the Adam Optimizer (improper epoch selection can lead to variable test accuracy), but training time is sacrificed in terms of the number of epochs required to obtain similar testing accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50 | Loss: 0.5471810102462769\n",
      "Epoch: 100 | Loss: 0.5314507484436035\n",
      "Epoch: 150 | Loss: 0.525094747543335\n",
      "Epoch: 200 | Loss: 0.5213460326194763\n",
      "Epoch: 250 | Loss: 0.5187799334526062\n",
      "Epoch: 300 | Loss: 0.5168573260307312\n",
      "Epoch: 350 | Loss: 0.515317440032959\n",
      "Epoch: 400 | Loss: 0.5140169262886047\n",
      "Epoch: 450 | Loss: 0.512872576713562\n",
      "Epoch: 500 | Loss: 0.5118365287780762\n",
      "Epoch: 550 | Loss: 0.5108758211135864\n",
      "Epoch: 600 | Loss: 0.5099716186523438\n",
      "Epoch: 650 | Loss: 0.5091102123260498\n",
      "Epoch: 700 | Loss: 0.5082833766937256\n",
      "Epoch: 750 | Loss: 0.5074844360351562\n",
      "Epoch: 800 | Loss: 0.5067099928855896\n",
      "Epoch: 850 | Loss: 0.5059565901756287\n",
      "Epoch: 900 | Loss: 0.5052224397659302\n",
      "Epoch: 950 | Loss: 0.5045046806335449\n",
      "Epoch: 1000 | Loss: 0.5038032531738281\n",
      "Epoch: 1050 | Loss: 0.5031170845031738\n",
      "Epoch: 1100 | Loss: 0.5024449229240417\n",
      "Epoch: 1150 | Loss: 0.5017864108085632\n",
      "Epoch: 1200 | Loss: 0.5011412501335144\n",
      "Epoch: 1250 | Loss: 0.5005085468292236\n",
      "Epoch: 1300 | Loss: 0.49988853931427\n",
      "Epoch: 1350 | Loss: 0.49927952885627747\n",
      "Epoch: 1400 | Loss: 0.49868273735046387\n",
      "Epoch: 1450 | Loss: 0.4980970621109009\n",
      "Epoch: 1500 | Loss: 0.4975224733352661\n",
      "Epoch: 1550 | Loss: 0.4969579577445984\n",
      "Epoch: 1600 | Loss: 0.4964045584201813\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10500 | Loss: 0.4626713693141937\n",
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      "Epoch: 15000 | Loss: 0.4593338072299957\n"
     ]
    }
   ],
   "source": [
    "# Demonstrating how to load in new data to test\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(features, labels, random_state=5)\n",
    "\n",
    "submission = multi_class_classification(X=Xtrain, y=ytrain)\n",
    "submission.split_data()\n",
    "submission.setup_graph(optimizer=tf.train.GradientDescentOptimizer, learning_rate=1.0)\n",
    "submission.run(epochs=15000, newX=Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.10617781, -0.71348834, -1.63334656],\n",
       "       [-1.24506283, -0.7011857 , -0.69872928],\n",
       "       [-4.18883801, -0.17448997,  0.73274589],\n",
       "       ..., \n",
       "       [-1.08786154, -0.2987442 , -0.87510741],\n",
       "       [-2.20393562, -0.23061395, -0.60532403],\n",
       "       [-1.65710545, -0.02623272, -0.53092837]], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.test_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       ..., \n",
       "       [1, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6555023923444976\n"
     ]
    }
   ],
   "source": [
    "preds = []\n",
    "true = []\n",
    "for pred in submission.test_preds:\n",
    "    max_index = np.argmax(pred)\n",
    "    preds.append(max_index)\n",
    "for val in ytest:\n",
    "    max_index = np.argmax(val)\n",
    "    true.append(max_index)  \n",
    "print('Accuracy: {0}'.format(accuracy_score(preds, true)))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow]",
   "language": "python",
   "name": "conda-env-tensorflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
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 },
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}