Code/orpheus/analysis/chart_svc_prev_seen.ipynb

{
 "cells": [
  {
   "source": [
    "Copyright (C) 2020 Ralph \"Blake\" Vente and Anthony Chen\n",
    "\n",
    "orpheus is free software: you can redistribute it and/or modify it under the\n",
    "terms of the GNU General Public License as published by the Free Software\n",
    "Foundation, either version 3 of the License, or (at your option) any later\n",
    "version.\n",
    "\n",
    "This program queries the filesystems's logs and produces charts based on the\n",
    "experiments in those logs.\n",
    "\n",
    "This one in particular reads content for our \"previously seen\" experiment."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "FileSystemExperimentLoader(\"../prev_seen_logs\")"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('.')\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import incense\n",
    "from incense.experiment_loader import FileSystemExperimentLoader\n",
    "\n",
    "basedir = Path(\"../prev_seen_logs/\")\n",
    "loader = FileSystemExperimentLoader(basedir)\n",
    "loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pmap({'name': 'svc__min_df_0.003_review_contents', 'dataset': pmap({'min_doc_freq': 0.003, 'test_set_prop': 0.1, 'feature_column': 'review_contents', 'filename': './data/100A50D__doc+pos.pkl', 'name': '100 author 50 docs each pos_tags'}), 'seed': 123096581, 'naivebayes': pmap({'alpha': 0.01, 'name': 'MultinomialNB'})})"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "exp = loader.find_by_id(1)\n",
    "print(dir(exp))\n",
    "exp.config\n",
    "#float(exp.metrics['test.accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hack: only run this cell _after_ generating some other graph first.\n",
    "matplotlib.use(\"pgf\")\n",
    "matplotlib.rcParams.update({\n",
    "    \"pgf.texsystem\": \"pdflatex\",\n",
    "    'font.family': 'serif',\n",
    "    'text.usetex': True,\n",
    "    'pgf.rcfonts': False,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(dataframe, title=\"Title\", xlabel=\"xlabel\", ylabel=\"ylabel\"):\n",
    "    graph = dataframe.rename(columns={\"review_contents\": \"review contents\", \"pos_tags\": \"POS tags L0\", \"path_pos_bigrams\": \"POS paths L1\", \"nested_pos_bigrams\" : 'POS nested pairs', 'path_pos_trigrams': 'POS paths L2'}).plot(title=title)\n",
    "    graph.set_xlabel(xlabel)\n",
    "    graph.set_ylabel(ylabel)\n",
    "    return graph.get_figure()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     review_contents  pos_tags  nested_pos_bigrams  path_pos_bigrams  \\\n",
       "0.1         0.898000  0.720000            0.770000          0.782000   \n",
       "0.2         0.880000  0.757000            0.774000          0.784000   \n",
       "0.3         0.866000  0.741333            0.752000          0.773333   \n",
       "0.4         0.850000  0.715500            0.741500          0.758000   \n",
       "0.5         0.824800  0.692400            0.713200          0.736000   \n",
       "0.6         0.785667  0.651000            0.666667          0.692667   \n",
       "0.7         0.730857  0.600571            0.606571          0.633714   \n",
       "0.8         0.631250  0.508000            0.515750          0.537500   \n",
       "0.9         0.435111  0.337111            0.347556          0.373111   \n",
       "\n",
       "     path_pos_trigrams  \n",
       "0.1           0.698000  \n",
       "0.2           0.694000  \n",
       "0.3           0.674000  \n",
       "0.4           0.652500  \n",
       "0.5           0.628000  \n",
       "0.6           0.565000  \n",
       "0.7           0.493143  \n",
       "0.8           0.405250  \n",
       "0.9           0.259111  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>review_contents</th>\n      <th>pos_tags</th>\n      <th>nested_pos_bigrams</th>\n      <th>path_pos_bigrams</th>\n      <th>path_pos_trigrams</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0.1</th>\n      <td>0.898000</td>\n      <td>0.720000</td>\n      <td>0.770000</td>\n      <td>0.782000</td>\n      <td>0.698000</td>\n    </tr>\n    <tr>\n      <th>0.2</th>\n      <td>0.880000</td>\n      <td>0.757000</td>\n      <td>0.774000</td>\n      <td>0.784000</td>\n      <td>0.694000</td>\n    </tr>\n    <tr>\n      <th>0.3</th>\n      <td>0.866000</td>\n      <td>0.741333</td>\n      <td>0.752000</td>\n      <td>0.773333</td>\n      <td>0.674000</td>\n    </tr>\n    <tr>\n      <th>0.4</th>\n      <td>0.850000</td>\n      <td>0.715500</td>\n      <td>0.741500</td>\n      <td>0.758000</td>\n      <td>0.652500</td>\n    </tr>\n    <tr>\n      <th>0.5</th>\n      <td>0.824800</td>\n      <td>0.692400</td>\n      <td>0.713200</td>\n      <td>0.736000</td>\n      <td>0.628000</td>\n    </tr>\n    <tr>\n      <th>0.6</th>\n      <td>0.785667</td>\n      <td>0.651000</td>\n      <td>0.666667</td>\n      <td>0.692667</td>\n      <td>0.565000</td>\n    </tr>\n    <tr>\n      <th>0.7</th>\n      <td>0.730857</td>\n      <td>0.600571</td>\n      <td>0.606571</td>\n      <td>0.633714</td>\n      <td>0.493143</td>\n    </tr>\n    <tr>\n      <th>0.8</th>\n      <td>0.631250</td>\n      <td>0.508000</td>\n      <td>0.515750</td>\n      <td>0.537500</td>\n      <td>0.405250</td>\n    </tr>\n    <tr>\n      <th>0.9</th>\n      <td>0.435111</td>\n      <td>0.337111</td>\n      <td>0.347556</td>\n      <td>0.373111</td>\n      <td>0.259111</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "start = 1\n",
    "end = 46\n",
    "df = pd.DataFrame()\n",
    "for i in range(start,end):\n",
    "    exp = loader.find_by_id(i)\n",
    "    cfg = exp.config\n",
    "    feat = cfg['dataset']['feature_column']\n",
    "    alpha = cfg['dataset']['test_set_prop']\n",
    "    df.loc[alpha, feat] = exp.result.value\n",
    "\n",
    "df.to_pickle('../results/svm_x_prev_seen.pkl')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "dataplot = plot(df, title=\"Linear SVC TfIDF(min\\_df=.003)\", xlabel=\"Test-Train Split Percentage\", ylabel=\"Test Accuracy\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataplot.savefig('../figures/svm_x_prev_seen.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     review_contents  pos_tags  nested_pos_bigrams  path_pos_bigrams  \\\n",
       "0.1         0.782000  0.674000            0.616000          0.676000   \n",
       "0.2         0.772000  0.669000            0.630000          0.680000   \n",
       "0.3         0.764000  0.638667            0.589333          0.646667   \n",
       "0.4         0.744000  0.613500            0.574500          0.619000   \n",
       "0.5         0.722400  0.546000            0.519200          0.548400   \n",
       "0.6         0.677333  0.414667            0.426333          0.431667   \n",
       "0.7         0.618000  0.365429            0.363714          0.363143   \n",
       "0.8         0.496500  0.195500            0.213500          0.195250   \n",
       "0.9         0.330889  0.148000            0.163556          0.156444   \n",
       "\n",
       "     path_pos_trigrams  \n",
       "0.1           0.552000  \n",
       "0.2           0.543000  \n",
       "0.3           0.472667  \n",
       "0.4           0.443000  \n",
       "0.5           0.358800  \n",
       "0.6           0.263000  \n",
       "0.7           0.233714  \n",
       "0.8           0.097750  \n",
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     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "# the next 45 experiments are for naive bayes\n",
    "start = 46\n",
    "end = 91\n",
    "df2 = pd.DataFrame()\n",
    "for i in range(start,end):\n",
    "    exp = loader.find_by_id(i)\n",
    "    cfg = exp.config\n",
    "    feat = cfg['dataset']['feature_column']\n",
    "    alpha = cfg['dataset']['test_set_prop']\n",
    "    df2.loc[alpha, feat] = exp.result.value\n",
    "\n",
    "df2.to_pickle('../results/svm_x_prev_seen.pkl')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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WhSeb6mFj4c6yIGDhHgEQKD2QXqBZoa1oVFSIWyvFiopGRbm4tapJYVqyhEGjHBgkkwx5sC0GsajuLAr0PoFA6YH0Cnoj0iBVmQZrJkt2+2m1NjO2B9vCk031tqVZW8A5fgiUHkjvYJpWKxHKy4StJUJ5EV+u0D4xY9NJONNUmkmMPNlUFpUAawwCpQfS62JU06ws5MuaFdo2MWqzYZtUyZFBhvP7ECg9kF6hbU7NNEwrFsrFco3pKyoR62JNaS9GcLURlB4oPZDeorFVYzIVlYjk5aLWEmFrY+sTMaIQsB42FoPYFj4c2jhfW3sGCVYXlB4oPZDeQqrUlYrkpaLWMlFrqVBeJlLwW1QoFBjmxHjT327iEFs2jQhrCUoPlB5Ir9MgVd0oEFx7zM+ulqBRqGFOlhP9OZP97WygrRpKD5QeyH9AnUSVWCj4g1ufX9+CQaOGOlpO9OdMDbS3osDJeyg9z0dWVpZarQYAREVFtSVWV1f/9ttv9vb2KBTK1tZ2xowZUHogXVIilF/L41/KbahsVGDQqHA362nD7Mf52kK/Myg9zyIvLy89PX3x4sW7d+8eN26cl5eXKf3mzZsCgYBAIPD5fAcHh5iYGCg9kOfRoPiH9dVNSgIWHTmIOXEIZ7yfLVxRDaWnC77++usRI0a8/vrrCQkJPB5v7dq1pvTGxkYmkwkAiI2NnTNnDhxwQV5Ig+K59X88rBfK1EQcJsLDeuIQzsQhHBicqJ9JT7c8Bvl8PolEAgBQqVQ+n9+WbtKdxMTE6Ojo9vm3bNmCQqFQcIkH5Ol4sqnrJnilrR99fln47FDHx3Utn557FPrtrTXncm8VCXUGI6yi/kG3erM4HE6j0QAANBpNh91HAQAJCQlvvPFGB+nZsmWLSQhh1UOeARqFCnGxCnGx2jTJh1sjMdmD/uDW00m4Md6siUM4Iz1ZWAz8FQ1U6QkMDJRKpQAAiUTi5+cHADAajWg0GgAglUrLy8th/UK6CQb9jwalVTT9wa1LLBD+wa1nkPGjvGwmDuGMGsyCsYcGnPTExMRs3749IiIiLy9v48aNra2t06dPT0xMBACUlZVRqVRYv5Ae1KBID2akB1OjN94vFV/L49/MF/7BrbelEScMsZ04hBPsbAU702ZEd829Op2uvLzcw8Ojw4ALQRCJRGJlZdX1WaGZGdJt1DpDSlnjtTz+9TyBSmewsySN82VPHMIJcbGCldOHJAYuKewSo1KJ6PWIRoOo1Yheb1QqAYIYZHKMpSVxsCfAwFkVM0Cu1icWCq7l8e+VNOoMRgcGaawPe8YwBz97OqwcKD0vLz0GicTY2mrSBcRoNMpbAQDGVjliNBqVSqDXG9VqRKNF9DqjUgmMiEEuN2UARsSoVCJ6HaLRGtuUxWg0yuUAAENrKzA+a7oEQ6OSg0PIYWHk0FDiYE+AhvFD+zoSpfZ6vuDKo4aMymaDEfGypU4JtH87xBEukobS8zLSU/9BjCwt//nsARgMhQIAQNNoAIXCUCgAg0ETiSgCHoXDoUlkgEahLagmWQEoFJpCQWGwKCIBTSCgcDgUiYRCo00ZDJJmRUamMjNTW1UFAMDQ6eSQYHJYGCUsjDBoEIBGhb6NWK65lse/8pifXd1MxGKmD3NYFOXqyqTAmoHS8wLnVfz4tj4/BYXRo1AAjUUAABi8EaAAGougUAgKC1AYBIVG0Ji/CyRYADQWEC0BGgsIFgBLBFgiIFABGvPvRBLAUwAGB4h0gMYAAg1g8ABHBngywOCBBRvgSAAAvUikyMhQZmYqMzK1NTUAAAyDQQ4JoYSFksPCCO7uUIb6MlVNiuMPquIyazV64wh36/kRLq97s2G1QOl5ERAEqFuAUQe0CqBTAb0GaFuBUQ9UUmDUA20r0KuBTg20rcCoA+oWYDQAjQzotUCnBDolMGiBugUY9UAjB3oN0Kn+n9OhsYDtC5yGA8cw4BQOaHYAAH1jozI7W/EgTcXN0ZSVAwAwVlYkf39yUBBlRDjRxwfKUN+kWaE9m117LLVKKFP72tEWRLpOCbTHwil5KD2vBq0SGLRAIwNGPVDLgOFvkdJrgUYGWupAbTqoywZaBQAA0O2BUzhwDAOOwwHbF6AxerFYmZOjeJCmePBAV1cHAMBaW5NDQkjDhpKDgqAM9UG0emPC44bfkstLRa0sKuHdMKcPIlzpJBysGSg9fQ+jATSWgNoMUJMGGnKBmAcAAHgysPUHTsOB43DgNByQGHqRSMnlKh6kKVJSdA0NbTJEGRFOGjaM4OEBf399BwQBqeWNR1MrbxeJKATsrGCHxVFudpYwcCKUnr6MnA8ackFtOqhJB/VcYNACNAZYDwJ2gU/6RCxvXW2tMoer5HIV9+/r+HwAAJbJJAcHQxnqaxQ2yA6nVFx+1GA0glFeNitGDRrqZAmrBUpP3+++K4HgEajJADVpoDYDqCQAAGDBBnZDgVM4cAoDdsN0fJEiLU2Zk6PMzNTxBQAArI2NyTBEGTEC5+AAf5evHJFcczqj+mhqVYtKF+zM+CDSdbyvLfTMgNJjJpjGZfxcUJMOatKfjMtwJMAJAJxA4BQOXF/TNSlMMqRIz9ALhf+SochInJ0d/I2+QhQa/dns2t9TKuslKhdryrwRzrNDnWCYDig95karENRzn4zLGh4CvQYAABguwGk4cAoHDqEaKUaRmanMzFRmZRukUgAA3tmZHBJM8vcnDvEneA5CwVXUr+QNgiB3eKJfksq5NRIrCv7tYMf5ES4wdj2UHvNEpwL83CcWosp7QNkMAAAWLGA3DNgFAocwtcZamf1ImZGhfPjQIJEAAFBEItHHhzTEjzRkCNHfH+/kBH++/zFZVc3HUqtuFAgwaNSb/pxl0e6ebOgLDaXHfEGMQFwMajNATTqozQDNFU/GZXZDgWMYcAjR4xzUNc2qggIVl6t6mGtUqQAAaAsLgqcnydeXFDSMHByMZTLhr/m/obpJeexBZVxmrVpviHBnwuWIUHr6zbhM9ESG6jJBQy4waAEAgMQAHH9gGwDYfhqtlbquVV1YpCooUOfnI1otAABrY0P09SX6+ZJ8fUnDhmHo0EOydzEtRzyeWiWQqX3saAsjXacE2MMoZVB6+gt6NRDkAf5jIHgM+I+AqAjo1U/6RGxfYDsEYflrFDRlVYuaV6IuKNCUVwAEAQDgHB3Jw4YSfX2Jvr4kPz8UAW5o1TvjZoPx8qOGA3crSoRyGyphDlyOCKWnmyj1SoFCIFaKhUqhUCkUK8UChUCsEuPR+GjH6JGOI93obq/gsox60FgKxMVAXAQackF9NlA0AgAAGgPojsDGy2jlq1ZZq5tQqvxSZXa2vrERAIDCYPCuLkRfX3JQEGnYMIKbG/S273Gyqpp/u1t+hyci47Gzgh0WRbrBfZyh9DylFSPGJnWTSCkSKUUmcREqhEKlUKwSCxQChU7RPrMlwZJJYnIsOBK1pKCxAAGIM815tNPokY4jA20C0ahX15JNqxn5uUDMAyIeEBcDgAAAANUWcAL1BGd1C0UlRtSVQtXDh4aWFgAAmkIhDB5M8vU1DdDgasYepIgvO5lWfZFbpzMgo7xsPhrlMcyJAatlIEqP1qCVaqSNqsY6eZ1IJRIrxWKVWKwU17XWCRQCvVHfPjMNT3OgOtiQbGzINjYkGxaZ5UB1YJKYthRbC5xFWzaJWnK//n5iVWIaP01r0FoSLKMcokY6jIywj6DgXnX4BXULEBU+EaOGXNBUCowGAAAg0gDLR4cfpGymqkU6VUWDuqAQ0Wg6GokCAzEM2FS6i1iuic2oPvagSqqEyxH7tfTItDKRUtSoamz7X6wS18nrxEpxo6oRAf8USMAQTLJiQ7ZxsHCwIduwSCwmmckisews7F6086LWq9P56Xfr7ibVJjWpmggYwlDW0GjH6Dec32CRWX3i8Rq0oKn8iQzxcwH/0RPXfAweobupEXd1K00l0KnK+NrqamA0AhQK7+xMCQuzXrIYZ28Pm0d3UGj0Z7Nqj6RW1klUrkzK0mj3t4Mdoe9wf5Cendk7/6r+S6wSa01zQAAAANAoNJPEZJPZpt4Ki8xikVlsMtuGbMOhcEjYXhl+GxBDtiA7uTY5qTapvrUejUIH2ASMdBw52nG0C92lDz1tox6Ii4Hg8RO7tSAPqFsAAACNNVIHqQzOailZxdcocgoRg5ExezZz2VKMFYxn3C30RuRGvuDQ/YpHtdJZwY7fTR8Cuz9mLz3HC47zmnkmcbGl2NqQbGwpttYkawzqVS75LZGUJNUmJdUkFTYVIgBxobuMdhw9ynGUv43/qzQJPQ1J1d8y9BgI8oCsAQBgMJCbpGHNyRUoLJYxdw5zyRI03ESk2+y6VbrrVsk4X9s9s4cSsNDM32vSk5WVpVarAQBRUVHt06VSaWpqKpVKHTFiROfdAfvT5PrTTEKRDpFkLLmvDhIageAxeHwO5J3Tqijiah/Zw1oMnW69cKHVvPfgDH03OZlWvflyQYir1e/vB1sQsFB6el568vLy0tPTFy9evHv37nHjxnl5eT3pfOr1K1as+PXXX48dOzZs2LCAgIB+LD2dTUJ3au40q5vbTELjXMbZkGz6qnBWg5SfwMOTKilFXO6mKBbhOBzm8mWWM2bA3Ti6w+VHDWvO5Xrb0o59EGptgYfS08PS8/XXX48YMeL1119PSEjg8Xhr1641pcfFxfH5/PHjxzs5OVEolG4OuMwOI2LMFeferb17u+Z2tawajUJ7WXlFO0SPcxnnbuneF69YzAMpP4PH5xTNVqIijrqmieDhzlyxgjZ+PBSRlya1rHHJyRxrCv7kwjBnazKUng50ayzK5/NJJBIAgEql8vn89r2h+vp6FxeXTZs2tU/fsmULCoXq9xuuo1HoYaxhq4NWX5l2JX5K/CfDPiFgCL89+m3qpakT/pjwfeb36fx0A2LoQ1ds4wWmHQDLUymRka7h+fajdIissX7V6qp3ZiuzsqGIvBwRHszTi8NaNfqY3x7w+DJYIT0pPTgcTqPRAAA0Gk17gw4Oh/Py8iKRSN7e3seOHWsvPQiCDCgXCg9LjwV+C05MOJH8dvK3kd+6093Pl5xfnLh41NlRG1I2JFYlKvXKvnKtLB8QcxwsT6GNjnAbxeNE6nWVJdXvvVezYIGmuBg2lZcgwMHy7NJwDBr19sH0nGoJrJD2dMsGFhgYKJVKAQASicTPzw8AYDQa0Wi0t7d3a2uryejT2cY8MLEiWr3l/tZb7m+1altTGlLu1NxJrk1OKE8gYUkj7EaMchwVxgljkVmvfnbM1g/EHEcNz7RM+pbGuSup5jTl5lRMm0574w3WZ5/CyIovyiCWxYVlI+b+nvHe7xm/zg2K9rSBddIDth65XL59+/aPP/54z549GzduNBqN06dPT0xM1Ol0GzZsWLJkye7du//3v/9ZW1sPKFvPc6I36rOF2SYN4iv4AAAsGssis2zJtrYUWzaFzSazORSO6YBJehWxMmrSQNK3huLUpgpOcyEWIIA+bZrNJyuxnR4o5Nk0tmreP5JZImz9aVbA5ICBFZGytybXdTpdeXm5h4dHh96NTqcrLS318PDA4/HPfzUDlqLmosfixyKliK/gCxQCkVIkUAg0Bk1bBjwGb1oz2SZGpgNbsi2D2MsuERXJ4PbXutKHjaV2LcUIikhizHmXuWwZmgI383wBFFr9spM5KWWNX07yWRDpCqUHBs3ou7Q5jtTJ6/7xGlGJG1obVHpVB1Xq4DVi8kqzIdugAKrHBOjWZi0vX1xiLyvXYxgM6w8+sHp/HgoPtzN/XrR646qzudfy+Muj3ddN8ILSA6Wnn6hSZ1/ZLn3ZTKr0Mt5nCAJKboDkbap8nqjQVlmvx9nbM5cusZw5E0bneE4MRmRjfF5cVu28cOctb/miB4CvF5SeAYERMTaqGgUKgUApECqEfAVfqBSKlKKG1oZGVaMRMbblpOAoHpYe0wdNn+Q2iYB5keXLiBEUXgZJ3yjya0SFLLXIQPD0ZK1ZbTFyJKz/5xTw7Td4v90tnxJotzMmsN+CTQ1JAAAgAElEQVQHPITSM+Dft4hBrBSbxEioEAoUgkxBZomkhE6gT/WY+vbgtx2pji8sQLe/lj2qFxewtFI9ZUQ4a80aop8frOrn4Uhq5dYrhRHuzAPzgij4/jwLDKUH0gWFTYWnik5dr7xuRIyhtqExnjGvO7/+AhP8Bh3Iv4AkbW/JEYoLrfRKhDZunM3qVXhnZ1i3/y8XuXXrLjz2tacf+yCEQe63JjMoPZCn0qRqulR+KY4Xx1fwHagOMZ4x0wZNYxCee+LMoAO5scbb2yU5skaeJaJH0WfMsFnxEZbFgnX7bP4qFK44zXWyIp9cFGbbTzf8gtID+X8wIsZ7dfdii2Iz+Bk4DG6k48j3vN8LZAU+twBpQe5pw43vmrKVzaVUFAbHmDfPevESDA0G4ngW6RVNi45nW5JxJxeGuTL74XoFKD2Q56WqpSq+LP58yXm5Vu5j7RPjGTPZffLzmqJ1KpBzTHfz58ZMjbSSgqFQrJcstXpvLooI9/B8Knn1LfOPZqIA6viCUF87GpSeviI99SUSZYuWQMYSyDgCBUsk4wgULApGoOxlFDrF9crrsUWxZdIyK6KVyRRtZ/F8K3G1SpB5UHN1d2M2kNUSsTbWNh+vpM+YATd0fho1zcr3fs9oVmh/fz8k1LVfRYw0Y+m5fiCv4qG4QyIGhyaQsUQy7okkkbEEypODvxOffCTT8VCnugNXxI0tir1dfRsBSKht6BzvOdGO0c+1UlHTCrIOKy/uFmViVE04gqsja90GOAf/NERyzbwjmRXi1j3vDB3vZzugpaf3nD9fSHrUCp1KrtModRqFXq3UaRR6jVKnUerVCp1G+a8Ug97Y+c/RaNQ/qkTBtTt48j+BjCVQcKbOFAZGtHxaw1CKrlRciS2KFSlFzjTn6YOmzxg0g054jr1Plc0gdbf84lHxQ6KmBWPz4XLmypWwPrukRaVbcCwrt1a6bdqQt0McB6703Lx5U6FQTJ8+3VxsPTqNwaRBT0Tqb3lqJ1umj3q1QtdlCVg8xiRJVnaUkEkuDFvopvTvGjbqkmqSzpecT+enk7HkiW4TZ3vN9mR4Psf4TYzc3SnYFyetIFlOn2q79Rs4+OpaqLWG5ady7pWKv5jgvfQ1twEqPTwer6mpSalUYjCYESNGEHvOUtgXzMx/q5Jeo9B17kzVFUt0GoN3OCfkTRcLBjSRdqSoqehcybkrFVfUerWPtc8c7zkTXSdi0f9fH5l7omn7RtFjC8rwMIe9+9AWFrAmu9B3g3H12UdXHjcsj3b/fLyXuVsLXkZ6mpubGQxGfn7+L7/8olKp5s2bFxAQYN0TARP6/gyXWqF7mFjz+E4tggCvcNuwt9xIVOgn2ZFWbeuNqhsnC09WtFQwScy33N+a7TXblvJMO8XDky271/EzLfEeg5wOHcSy2bAaO4MgYOvVwiMplTOCHLbP8Mea8746LyM9iYmJ27dvDw0NXbp0qYuLCwDgr7/+ev3117tvtTWXyfVWiSb7WmVRKh+DQw8Z6RA0wRlPhJHPOmJEjJmCzPMl529V38KgMKOcRsV4xoRxwp5qin54UnFwbd0Da4y1rdPhQ3g3N1iHXfLr3fLt13ljfdj73h1mvvvqvIz0pKamenl5WVtbazQaUyzUrVu3btu2bSD0etojESgzEyrKuCIiBTd0rFPAGEdoje6SWnnthZILf5T+IdVIXemuswbPmjFoRtfbMT48pTmxuibFFkGTHfb/Qg4OgrXXJSfSqrdcLghzszo0z1z31XkZ6SkvLz9w4MCOHTtUKtXNmzenTp3an2w9L4qwSpYeX15XLKFaEYMmuPhEcFBwe8mu0Bq0ybXJJwpPPBI/ssBZjHcdP8d7joelR8d8ubG62E9qHjjo5Ijd9u9pEybAquuSS7kNn57P9eHQjn0QakXBDwjpSU1NjYiIMB1nZ2cPHjyY2kO7U5rvaubaoua0+HJxjdzKjhIyydUjCLopPZXCpsLzJecTyhO0Bm0YJyzGM2aM85h/7Rmbf9EQt7Qu00lZq2Z99qn1woWw0rrkDk/0USzXzpJ0cmGonSWp/0tPQUHB5cuXR48e3dLScvPmzZ07dw7kXs8/IKCMK8q4VCEVKW3d6eFT3e0GWcLm8TSaVE0XSy+eKz4nVAqdac5bI7YOZQ1trz7I+aUNee6yQpnVe++x138Bo451SW6t9IOjWTgM6sTCMC9baj+XHgBAenr6iRMnrKysVq9ebd1zwcD7gQ+X0YAUPeBnXalUtGgcva1GzPBgOsCp4qdiQAxJNUk/5fxU31q/eMjiZQHL/pmJz78ILiwRlHhIuDLqG2/Y/7AD7rzcJSVC+bzfM9V6w5H5IcOcGP1cekzU1dWlpaXNmDED3UNvpH7jPqrXGh4n1XFvVmtVBvdhNuHT3GlMEmwkT0OtV+/i7ootivW19v0+6nsXusvf6vMH+GNxs9BLmCQlBwc77NsH/d27bokS1dzfM0Qy9W9zg14zk311XkZ6JBLJ/PnzW1tbcTgck8k8depU5zxZWVlqtRoAEBUV1T5948aNPj4+Mpls6dKlnQWrn3mud1gEFDrZjUyDi4Ceyu2a2/978D+lXrkqaNUc7zlP5uAL4sHFRTKlX8P1Fryjo+OhgzgOB9ZVZxpbNfOOZJYKW39+O/BNf475Ss+zejFpaWkXLlxYt27djRs3PvroI52uo/NBXl5ebm5uVFQUl8vl8XjtvxKJRBqNZtasWegBMHQnUnDh09znbg33CrctSuWf+jItLb5cq9bDdtIlY5zGxE+JH84Zvj1z+/K/lotVYgAA8J0GZhymkfOdpjP0IkHVrLfVRTxYV51hWhDiFg8f6mT5SdzD0xk15nsjz9IFV1fXAwcOEInEQ4cOpaSkdJau+Ph4V1dXAICbm1tCQkL7ryZMmDBy5Eg6nT5wfhMUS8LIOV6zN4c5+1lzE6tPbkrj3qzu0qMVYk2y3jtm7+bwzVwRd+blmUm1SW3qQ0Y9do6hozCY6jlzFCkpsK46QyPhTi0MG+vD3hCft/06rx9Kj7e39+TJk1977TUqlerg4NB5Mz8+n08ikQAAVCqVz+e3/0ogEGAwmDVr1igUirbELVu2oFCo/h3CwpJNHrfYL2ZdMNPBIi2+PPar9IL7DYgRxkXr1A8HqJmeM+PejGNT2CvvrNyQskGlVwHfaWDG7wTVQ5fpWLyTQ+3yD1v+/UqDmMBj0fveHTZ9mP2vd8u3Xik0R+vFs6SnoqKiuLgYAPDOO+/Mnj27cwbTEmcAgEajaR9eQ6PRLF++3NnZ2c7O7vz58+2lB0GQgRCfkOVCm7Jq6FufBBItcMmxvLitmWU5IthgOuNGdzs98fTygOVXK65Ovzw9V5QLfKeCmUewUq7zeBU5JLjh83XifftgRXUGi0btjAlcEOn6e0rlb/fK+5X06HQ6Dw+PNrtP5wyBgYFSqRQAIJFI/Pz8AABGoxEAEBcXl56eDgBQKBS9FPHHLHD0tpq1PmTcYj+jEbl5KP/ijpyGUilsM52aEPbDwA+Pjz8OAJh/Y/4vub8YvSeDmUfQYq5jeB19wvjGffv5X36JGAywrjr2HFHgqzd9JgfY/XCjOLlY3H+kp6GhYcaMGZMmTZo9e/aKFSs6Z4iJieFyuUKhMC8vb+bMma2trePHjwcATJw4USAQFBcXi0SimTNnDvBxhUcQa/bmsJFzvORN6vid3Mu7cxvrWmGz6fgaYwWen3x+otvEXx/9+t7192och4IZv6MEXDvfAubyxdLzF+pXfmJUq2FFdeaHmf4+drRP4h5WNynNqWU8Y/iTkpLi7+9Po9EAAHfv3o2Oju6yZ1ReXu7h4dGhd9Pa2lpXV+fp6dnlDNfADAsPFwE9D4lViV+nf20wGj4N/nSmHg8uLgD2QVLSPME33xH9fB1//RVjZQVrqQP1EtXkfSksGvGP5SPI+L4Vg627sZnr6uocHBx6+2oGAhqlnnuzGi4CegZ8BX9jysYsQdZY57FfWYda/rkC2A2T269s+OJLLIvleOgg3GKwi45CWeP7RzLH+rB/nRPUpyZyXkZ6MjIy4uLiTMfZ2dlXrlzpqclyuBmOQqrJugojAT0VBCCxRbE/5/xMw9O2ukyNTPwWcAJVAV/XrvwUGI2Ov/5CGjoU1lIHfk0u336Dt3GS9+KoPhQC6WWkJyEhgc1ms9ns5ubmrKysRYsWQUeKnkUqVGZcrijjikgW+IDRDvaDGTZOVBgMqI0yadm6e+tKJaUz2GHrci4Tbf210XtqP1qtF4nsf/4Jbm7RUa8R8PEZ7vV8wdH5IX3HzaK7A64HDx4MGTIEBs3oDUTV8vQ/y2uLmgEAGCzaxplq60rnuNPZrjSK5UB3pNQYNL/k/nKs4JgbifVdRYEXY5B+0tHaT9api4psv9zEeOcd+Pv5V29aq5++/4FQrr68ItLJimyu0nP16tW9e/eSSCQ8Hu/s7Lxjx47evpqBjFalF1bJ+OUtgvIWfnmLXmsAAJDpeJYTjeNB57jTWS60AdshSmtI25S6Sapu/kginU90AjPP1m/Y0pp813rxItaaNQDus9aOOolq8t4UWzrxjw9HkHAYs5SetLQ0Pz8/KpVqCpDag95YUHqejdGISAVKfnkLv0wqrpE38xUAACweY+NoYeNM5bhb2ntaDrQw9RKNZMuDLXdq7oSptd+iWezZ8YIf90vi4uhTp3K+2YrCQkvZP9wvbZx/NHNygN2utwPNUnpggNQ+grJFK6qWmZRIVCM36IymDpGdh6WtO93Ow5LpaDFAdli9XH752wdfow2qTXrqpDnXm05dFP30EyV8uMOePXBrnfbsvVO2M7H4q8k+CyJczU96YIDUvtghMiBN9a0NZVJxtbyhVCpvVgMAcAQM08GC42HJcafbutOJFFw/roGG1ob1fy3nyireMOI2z/gTucPlb/oS7+YGt9ZpD4KAFae5NwsEpxaFDXezNjPpgQFS+z4KqYZf3sIvl4qr5aJquclRnsYkcdzpNs7U/tohMiCG4/e+3Ft1mYVgto3e7VVHqFu5EkOnOx06CLfW+ee3odVP2/9ALNdcXhHh+OpMzjBAav9HpzE01spFNXJBeUt9iUQl1wEA8EQs25Vm605nOVE5HpYEcv+xieTl/r4+Z2ctFjN70IyPGTENyz5CVCqHX+DWOv9Q2aiYsj/VgUG6uPyVmZy7NbkOzcxm3SHil7U01rYiCIJCoxhsso0T1TQ0s+JQgJn3hzQlN37+66NYC7IvY/B3nmuNa77W1dXZff89bSLcWucJt4qES07kTAm0+/kVmZyhmXlAo1XpBZUyQUWLoKJFWCEzRVC0ZJGj3vF08jFzl6jyOynx879kMuRY7Kdey0fsSlbmcOHWOu3Zdatk163S/73l+/4IF/OQHmhm7pcgCNLcoBBUtDy+U9fMVwwOs42YOYhENWfLdPmd5vNzv2LZ3sXoXmOFf3oDp7l5B26t0+6Jgw9jc/4qEsYuDAv7z03O0MwM6YjRgOTeqslMqMTi0eHTPHwj7cx4/FV+B4l79yLbaQcJIWFIOwsDKef+glvr/DP61uin7k+VKHUJH0dy6MS+Lj2gnZl5yZIlTk5OUHr6H1KRMjm2uL5YYudpOWqOlyWbbK53Up4E4mZX2Lh+YcspkpSuK/MKulBADhpmu3kzYdAg+KArxIop+1PcbSzOLQ3H/4fL4rvrw/Xzzz+vXr0aSk8/7ZGDgpSGBxfLjAZjyJuuQ8c6met28tWpIHamzsptX+CkYyVn36q0eveKHChUtIkTbT5eAUNt/FUoXHIye/pQh52zAvq69PB4vLi4uAsXLshkspqamt6+GsgrRNmivXe2pJwrYjpYjJrrxXKhma36xACGS/bEbzZkfS+TCj8sdwtJrAEqNe2NN1ifrsE5Og7kp7wzsXjvnbJvp/nNCfuPhPjFpKe6uvrs2bNnz56lUCheXl7btm2rqakZNmwYlJ5+T9XjxrtnipUtWr+R9sOnuOMIGPO7h+oHIHYmoDu0vht3rOraad5plFS+vNBu2D0B2ojQp02z+XgF1sZmYD5fI4IsOp59r1R8etHwUFerviU9Op1u/vz5TU1N+/bt8/DwuHTp0pQpU/6bq4H0BbQqfUZCRV5SPdWaOHLOYEdvM5x9/1t9wPsJSiL1j9I/juYf1QuEi/KYQWlNGAzWMibGetlSrLX1AHy+rRr91P2pLSpdwseRtjRiH5KeJ91vpTIpKUmpVBYWFm7YsKGoqMjf3x9Kz8CBXyZNOlUsESg8gljRswcTLcxt9r3yPjgzC9DsQfjHwHeaDk+6Xnn94OODyrrq97MtQrJlGAKRMedd5pIlaOqA29+9XNw6ZV/qILbF2SW9bnJ+eTOzVCq9f//+zz//fOfOHSg9A6tz/vfsO56EjZjhMXi4rbn1fVLB1U+BqAjgSMDrTRD4rtEl6l5D6i+PfpEVF87JJgY9UmJoNObCRVbvzUURiQPq4SYWCJaeynk31PnbaX59VHpMJCUljRo1qnN6VlaWWq0GAERFRXX+NjY2ds6cOVB6zJrmBsWdk0XCSpmzn3X0u4OpVubWREVF4HEceHgKKBoBlQN8piBD597VNR3KOyQtfPRuGm5YgRptxWB+sMBq3nsDahHQjpvFvySVfTd9yOxQp74rPV2Sl5eXnp6+ePHi3bt3jxs3zsvLq/23d+/e/fHHHxO62rgWSo95gSBIYQo/9WIpYgQhk1zMcvbdoAVlt8HjOMC7Cgw6YBcI/N/h2nkdKb3YkJk89x7Ku1KPsWWzPvyQPmMGCoMZCI/ViCALj2XfLxOfWTw8xMXKnKTn66+/HjFixOuvv56QkMDj8dauXfuPpYDPF4lEmzZtgtLTb1BINffiSipyxTaO1FHvedk4maeJRCUBhZdA9hHAfwSwBOA2ijdo5Al1ddm9q3PuIp41BrSLk+2yD+mT3wQDQIBaVLop+1KVWn3Cx5Hs3jE5P62xd8vCxOfzSSQSAIBKpfL5/PZf5efnm7ZCbs+WLVtQKBQKRtI1TyiWhAnLhkz60F8p1174Pvv+uRKdxgw3IyYxQNB8sPQe+DAdDF8OGrheV7/YlvHHjwHRuevGbHsXX6Gubfjii+I3J8lu3AD9/QVJJ+F+ey+oVaP/MJarMxj/y1M/S3oUCoXpICcnp7a2tnMGHA6n0WgAABqNpv3uoykpKSNGjOicf8uWLQiCwP6OWePiz5z9VZh3BOdxUt3ZbzJNG2mYJSxv8Pr/wBoemHcJOIc7PTy7+f6x7Uz8o0/8980g1LfU1K9aXTRzSmtSUv9+oF621J9mBXJrJN9cLeor0pOcnGwwGAAAQUFBRUVF6k47XgcGBkqlUgCARCIx9XGMxifCeevWrbNnzwoEgszMTNhc+xkEMnbkHK9pa4ah0KjLe3JvHS1UK3TmejNoDHAbCWKOg89KweTddkSrLx5d/4ZUX7yE+dsMorChrHb5hwUxU5VZWf34gY73s10S5Xb8QdXZrNr/7KRdD8PUanVsbOyJEycsLS1NEcKsra0PHz7cIZtcLt++ffvHH3+8Z8+ejRs3Go3G6dOnJyYmmjTo4MGDv/7664EDB4YPHw5tPf0Svc7IvVnNvVFNIGNHTDfD2fcuEReDR6dB7hmFUvSnpU1xFWXcPY1VK9AO8/LcuJXk69cvH6URQT44mpVe0XRuWXiAg+V/YOv5f/bhGj9+POaZxjadTldeXu7h4YF9kd1IoPT0J5rqW5NO8oRVMuch1tGzzXD2vUsQI6i8Bx6d0RZeuonFFNQwRqYZaCqgDPH23fAtycu7/z1HqVL31r4UnQG58nGktUWPbbX0MtJTWVmZn58fFRW1Z8+eMWPGtIUN672rgZhrOzXNvl8oBQAET3QxY9/3LoYALaD4mv7RmRv1OaWVtNcyAUkDpMO9/L/6wcLVo589xyK+bPovD3ztaGeWDMdheneX82dJQHp6uo+Pz86dOyMiIiwtLYOCgjA9NN0IpadfIm9SJ8fyagqbbd3po+Z4WdlR+tXtNZYY8y6kPDxTlKcLyUHh9aAx0j1k8y8Ue6f+dJeXchs+iXu4INL1qzd9Xpn03L9/f/jw4dHR0ampqUlJScOHDyeTyb16NZB+QFmO6F5csUapD3zdKXSya3/brBkxgtqMh/f2Ft7k+j1EoREgCmIO3bDdymtEv7nFrVcKf0+p/GGmf0ywY+819mdJgEwm279/f2hoqIWFxdmzZ3/88cee2pQCSk//RqPUp8WXFaQ00JmkUXO97Acz+uNNyov+2s07dd7zkVaHBTXBpND5H9iOWARwJHO/M4MRmX80K7Oy6fyyEf4O9FcgPSaUSiUWi8Xje3KHbyg9A4GGEmlSLE8qUg4OtY2cNai/7olalnGhYO8PHly5Eo+UD0XCJ0U7T9sLMHizvimJUvvWvlSDEbnycaQVBd8bjf1ZElBfX//DDz8wmcw1a9bcunXrrbfegtIDeSH0OmPWlcqHf9WQqLjX3vZ0H8bqr3dazU0u2/alXX6jxAIQPhoW9EGsud9RYYNsxq8PhjpZnlgYhu3GpMHLOFLweLxdu3aNHTuWTCZzOBy5XA7bEuSFwOLQ4dPcZ64LJtPwNw7m3ziYb8aLD5+J87CRYy7cpx/7hWhANR99WHbvJ3O/Ix872rbpQx6UN+24weuN8p8lPQwG4/jx43V1dVwuNyEhgTrwIipBegSWMzVmfUj4NPeqx41nv8msL5H01zu1Gz6K/eMOu0bw4OdDwqpkc7+daUPt549wOXiv4mJOXY8X3nVf6LvvvvPw8AgODi4qKjpx4gSbzV6/fr2tbY+tVYUDroFJc4Mi8feCpoZW/1EOETMGoTH905G4ZMdGw5E/rozFLP/2Lwsax7yHzEZkzuGMR7XSC8vC/ezpPdjYu049dOjQ4sWL26eUlpYO6rnNjKD0DFj0OmNafNnjO3VsF9rYhb50G1I/vEkEyXt/Giq7+MIci41fpOLM3OTc2KqZvDcFjUYlrHgZk/OL2XoIBILm36SmpsJmA+k+WBw6apbnhKVDWsSqc99mlWQK++FNolC+v8YaWRbjL7buPDXT3O+GaUE4/H5IU6t25ZmHBmOP9Ri6FqTx48dXV1e3T5FKpR0i8sBeD6Q7yJvVt44UNpRJB4fZRr872Cx33Xkm2oqK4mmTK5iIYP2kpa//YO638we3fs253OUj3deN9+rFAdfFixdnzJjRPiU+Pn7atGlQeiA9iNGIZF+ryr5WRbchvbHI18axv81jyK5fqVuz9kYQ2m3d2qlD5pv77Wz6Mz82o3rv7GFv+nO639i7HnB16PIAALoM/QWBdAc0GhX6puuUVYF6reHi9hzuzep+9kKiTXiTMeutCdnG5OM/pNbdNffb2TLZN8TFat2Fx8XCHlhn07UgnTp1Co/Hh4SEuLq69s5YuON5lUpla2trW6QxyEuDw+EsLS0xZhVXWKvSJ8cWl2YLHb2tXv/Ah0zD95vHgRgMlbMmK4orv5lH+N+iOC8rL7O+HZFcM3lvCgmHubQigk7CvVxjf5b09DYdrkahUDQ0NFhZWfWUj9hARq1Wq9VqFxcXs4uBXZwuSD5djCNgxrzv7ezXf/YF1Tc1lU96vRFRb19i+dvbF+0s7Mz6drg1kg9PcQ+9Hzzk+eba+7T0CAQCPB5vZWUFhaNHKCsrc3Jy6lm3u/8GiUBx89CThT8jpnv0G6931cOHVXPnFDqC2IVOx6aco+FpZn07Wr3x+fcs7ZUdKXoKo9GIGRg7H/03YLFYU1Bts4NhS4n5Ith/lMPjO3V//JDTIlb1jydCGjrUdu0qnyokKLH2k1srtAatWd9Oj+yVDAc4kL4FxrTwZ9kQWaP67LeZxRmC/nFfjPlLLMe/Ni3FiKQ93JS6CQEDfYYXSs+/EAgEu3fv7u2zdN7bozvZ+iVugTZvbwqxcaTeOlp481C+RqnvBzdlu30PyZn12WXdQ+71Pdw9UHq6RVZW1v379+/fv98hPScn5+7duzdu3DCv6qDT6WFhYb19ltjY2B7M1l+xYBCnrhkaOWtQRa747LeZgooWc78jFIHgcOQMHoPbel53knv4DO/MgDYLdOeP8/LycnNzTXuu29jYtO25npaWlpiYuHnz5nHjxnE4nICAgOcv8w5PpNK9vJ3CkUHuMq5aamqqVqu1s7PD4/Gurq5cLlcikXh5eVVXV7e2tgYHB4vF4urqajqdLpE88atuy2Nvb5+amqpQKBwcHOrr652cnDQajUAgGDt2bNssUmFhoUQi4XA4bm5uAIDi4uKmpiYmk+np6Zmeni6Xy52dncvLy0ePHn379u2TJ0/a2tqOHDmSQqG0naW2tvZp2YxGY25uLhaLtbW17aXlDn20raJQAaMdbV3pib8XxP/IDZroEjLJxax3r8XZ2dnv3mtY+uFXV/Rf4r9nk9mjnUZD6Xlh4uPjTUsN3dzcEhIS2qQnKCjIxcUFAIAgiLu7+wuVuenP/AbpyxsXZwU77pjp3zl9yJAh0dHRFy9eLCkp4fF4IpHo/fffX7Vq1caNG99+++3bt2/X1dW1tLRERka+8847EyZMuH79elueXbt2ubi4LFu27MKFC19++eXBgwdNmdvaQEZGxp07d9avX//555/v2LHj7t275eXlCxYsOHz4cFNT05AhQ6Kios6fP89gMPbu3bt69eoDBw6Ydhlqf5Zvvvnmadm+++67zz77TKVS3b17d0BJjwm2K+3tjSHJp4uzrlTyy6Svz/ehWBLM93YoUSNtFs8FB05+kIl8jv78yLgj/jb+UHpejKftuY7H4ykUynfffffRRx9ZWFi8UJkXloXru+GiZkHo+o5oNJqpS+Lm5vbOO+/ExMRkZGQwmUwKhRIQEFBcXCwUCmNiYgAAFAoFAHD8+PG2PEql0t7enk6nazQaT0/Pv/76y8rKypTZxPHjx+fNmwcA2LFjBwDg4MGDGzduBABER0dv37798J46baAAACAASURBVOHDtra2Hh4eOByuoaEBg8Gg0WjTjF77s6DR6KdlCw0NjY6OfuONN1atWjUw35B4EvaNhb7OvtZ3zxSf/TZzzDwf5yFmvPCHuWq9+hF3/O38CjvCitsrTk486UxzhraeF+k9PmXPdVNTX79+/enTp9u7vG/ZsgWFQj27w2xnSXKyIr/0v2c49dPpTwZibDbb1dU1LCzs008/xWKxS5cuPXjwYMc37b/zAACmT5++devWFStWJCcnd8hsZWUlk8kAACqVSqPRMBgM06iNz+czGAwAAJH4ZFe89gsccnJyOpzladksLCxSU1PDw8P37BnQtsnBw21j1odQLAlXfnl0/1yJQW+2a99RKLv9x/Es2ofnZAypavmt5RK1BErPC/C0Pdf37t17/PhxAICnp2dSUlJ76UEQ5JUsYrx7925JSUlaWhoAYP369X/88Ud2dnZiYiIej/fy8qqqqjJZl9PS0oqLi4uLizvkAQBMmjSpvr7e3t7e2tq6w6hn9erVN2/ezMnJuXnzJg6HW79+/Z07d7Kysu7evbt27drU1NTS0tJHjx5du3aNx+M1NDRYW1snJiayWKz2Z8nKynpatrNnzyYnJzMYjDFjxoCBDcOWPPPzYP9RDo+T6i7uyJEKleba8CgUh99jUQj2u9MtErnwo9sfqfUDbEIT6QYymWzjxo0CgWDDhg0KhUIul48dOxZBkLS0tEuXLvF4vKlTp1ZVVXX+ww7nra+vl0qlyH+LTCZrO9br9f9vHgRBDAbDMzK3tLS0//jsO1KpVE87S+dsBoNBoVDodLrnvLXKykqlUon0a2oKmo6svf/biqTc2zXmexeyhAuFXl5Zs3z9j/uvSV5jMBr635N6msh015HiaXuuG43G4uJiNzc3AoHQ5cxF+/M2NDRQKJS2ARGkm1RVVbHZbJMZrh+jlGlvHyusKWx2H8YaNdeLQMaa410IN37SfDGx8i2rdb6yeT7z1oas7WePqU/7cEHpgdLzsp128Cip9sHFMgqdMHahL8fdDH9CBkNNzHhlce29Fd57yWXrQtfN9Z47EKQHrmaGmPXvGgSMdpyxNgiNQf25k5t5pdL8Iv5gMHYHz2AsCKMPF0wm+/+Q9cNf1X9BMzMEYgawXGizNoZ4BLOzrlRe2pWrkGrM6/qxTKbDLwf1asyS33KGWnqtv7/+oeghlB4IxAzAE7FjF/i8Pt9HVCWL+yazKq/RvK6fFBTGXrNcVY/a+mclx8J25Z2VVS1VUHoGgMWgUy8dho42RwYPt521IYTKIF795XFyLE+vM6eFP4yFKy3HBLU+kO8rpmHQmGW3ljWpmvrzWHmAmJkzMjJ27NgRFhbm7u5eVVUVHBwcHR0NAJBKpUeOHAkKClKpVDU1NYsWLdLr9T/++KOvr69GoykpKdm0aVP7ctRqddvCvxclPT39559/Hjp06NSpU9ucTgAAWVlZJif1qKioHrnZAWRm7gqD3pgWX/7oTi3TweKNhX4MW7LZvAI1murJIzUNEsO3c+e3Xnajux0df5SENe/nONDNzGFhYTQaLSIiYsaMGWvWrJk7d25jYyMA4L333ps9e3Z0dPT48eNZLNY333xz8eLFgICAKVOmzJo1q/PC6+54kw8fPhyPx4eHh7fXHZMLblRUFJfL5fF4ANJtMFh0ZMygNz8KUEi1Z/6XcebrjKRTvKIHfIlA0ceD5KAIBPsj51B4LPG7Ezu9P+I18z67+5kBMfTLx9T3lkLknQdaxcv/ubUHcIn8f2UYAKDT6dLS0rRaLYfzZGePyZMnL1iw4MKFC7t27QoICHBwcPjggw/a/+G1a9faO50XFRVVVVWFh4dbWloCACoqKiorK9lstlgsDg4Ofk5f86e54EK6ibOf9TtfhhamNAgqWioeigtTGgAABDLW1o3OdqWZ/scT+9zvH+fgaP/jDzUr1rht+XHT9+v+l7X9m/RvNodvhtLT+9zaAlq6sbf80PeeIT05OTlqtbqkpOTAgQMcDufatWuOjo7/vC0xGCqV6uTkFBkZGRYWxmKxjh07Zmf3TxDvcePGtXmTV1dX3759e+nSpTNnzrx06VJRUVF8fPyKFSvWr1+/devWffv2Paev+dNccCHdh0zDB090MR0rpBp+eQu/XCqulmdfqzIaEAAAjUniuNNtnKl2HpZMR4s+Eo6DMnoC6/37oqPxow/H1s5ZcCT/iCPVcYHfAig9vcyCm8DYjZB0+Gc5yvv6+o4ZM6bNE8rR0fHixYtt3xqNRrFYbGlp+fnnn3/++edZWVkbN268cuVKe21q8yZ3dnb29/dPSkpqbW0FACiVSiqVisPhtFqtlZXV8/iaFxQU+Pr6PsMFF9KT7dmS4BHE8ghiAQB0GkNjrVxUIxeUt9QUNplisOKIGKa9BcfDkuNOt3WnEym4V3i11p9/q378SHS9fElAo9h98q6cXTYkm8nuk6H09CZ0h//sVK+99trGjRulUqlpxHTnzp2ZM2cmJibOnDnTtA2ZKb3L3lNpaalcLl+8ePFPP/1UV1fH4XCoVGpKSsrOnTsBACZf88TExD179nz11VddFpKSkuLr69vZBRfS64MaAobjYcnxsAwY7dihQ/Todi33ZvWr7xChUJyD5zQTIht+vvDV4R/EHPHmB5tZZFYYJwxKj5mRmZlZUlJy8+ZNPz8/NpttSiQSiceOHdu2bdvSpUtFItH169f37Nlz6tSpQ4cOjRw5srS01DQL9q/XkbV1YmKit7d3cXGxVCpNS0uztrZOS0ubOHHi5cuXWSxWcnLyqlWrzp49q1QqO/iap6WlFRYW3rp1SyQSpaSkmH7NMTEx27dvj4iIyMvLM0X5gcAOETC5th86VRkTI1jz+U8X/pyfvnFV0qrjE457Mjz7R7VDHy4AAKioqLCwsGCxWAAAlUpFIpFqamrQaLSDQxddsLb5da1Wi0KhcDic0Wj87rvvVq5cabLX/PTTT9u3b1er1Xg8/nnGUE9zwX1pBvjkeg/SvkMkqpabIgT9lx0i+YWjdV/uYARaoA9emnPzfSNiPD3pNJvMNieJge6jvcq1a9eMRqONjY1AICCTyWPHjn2FFwOlpzdo3yGqL5Gq5Nr/pkMk+nxx0+UUzpzgxo83/197Zx7WxLX38ZOEhEgWWSUsAgoim4CoFQTZFAQsiChqb2uv1bYUq9LaXnt9bd/Wvq232uuC3qqPSh/AqiBSdhQElEdShBZkX0RAQLawCVkgk2XeP6bNjWERIYREzufhD55wOL8zk5nvzJwz399v5+2dBhSDWL9YKokKpQdKz38Ri8XKULsZSo8CeM7idTcPdTUNdjUN9rdzxWIU4IA2g2K0VMtlszlRXX4lLUWi1s2evMYesx8OVKxa+dHdj5z0nS6sv0DEE1ViR0HpmUNA6VH8DRGrZaircbCreailqs9oiebGfQ5qRLldhER9Pc0B3kCILIr/5Q6edfjB4eAlwd+u+ValpQd6uCCQ6UJUJxhZaq3wN9u41953j217w/PM8xUi+TnICDp6xv/5ScjHt4e/t5Hh9pHDR0kNSRfLL6r0ToPSA4HIE4sVC7x3Wj2rG8i8WCnHxPXkVe6MvTu5baKeg1vDHcODLYLPl51PfpIMpUe1gc51iByxcjHwfGdpa01fdlS1WCy3A0lz7/9orl3ae/8Z5/yhr12+XmO45mjhUWYHU0X3EnSuv5pzffJM7HE/evSojo7Ovn37Jt9hV1dXfHx8RETES1vCuR5loCLv2YObjy1WLPDZY4vHy2cBHuXzWwJc+SyO2YXvhav9dt3Z1cZui/aLttJWXusfdK7Lx7k+eSb2uG/atInLfTWXrGLqwUPkhb23sVvokiclrHtX6+R1gcepqxv9HI9Twz/7/Mt5fV0/rfuJRqKF54RfrbmqcpW8pvsO23i5ZrKysvr7+ykUSlBQ0Ct1GF8fzxVM3bluqWXpZjRTznWZ6ulYsQ1J0XQ6nS5xq9fW1o5ZWN3IyOjZs2dYoXeZUTGZTB6Pp6ur29vb6+Xlhb1eKG2Ox8q6A6n68Xw+f3BwcA7WYlcVHNYtRIaFxenNOBzw3mkN5HHrQzQ1Nzr2v20Hv+kI2278a+GF9Re+Zn594vcTp0pOuRu7B1sEuxu7E3CE11x6sFwzH3zwQWRkpJ6eniThQ1paGoPB2LBhQ0hIiJaW1itlwIqqjOrkTt3AvXnJ5gmkZ5rOdTs7O+my6J9//rl00XR9fX2JWz0gIGDMwuq7d+/OyMg4fPjwjRs3ZC+S9vaenp45OTn6+vo7duy4deuWjDne2tr66NGj/v7+kvrxYWFhd+/enbO12FWCVW8uEonQkttPieqEtdvl44Gg+O/Q+6OQdS27/6t3LY7duLbxWvNgc/KT5NTG1LzWvPnq831MfbYv3a7MT2HTlZ7xcs2QyWQs/4ONjU1lZeUrSc+vQb+KwdTXBUh40gR/naZznUqlSpdFBy8WTbezs5O41ccrrH7mzJmPP/4Y22Otra3SY8NUT0tLS0tLq7+/v62tTcYcr6GhgdWDl9SP/+qrr+Z4LXaVwHnTYlSMlma14PA4t9AlculT58szI1XerKRH5OVnNEI/WTR/0acrPo1wiijuKk54nJDyJOXW41vmmuZB5kGbl2zWUtdSwt0yrbme8XLN+Pj4BAUFiUSi4uLikJAQyeeTqblOJVHpJPqUf8hqr5C91N3dvaenBzOOAynnOoIgAIAxnesyZdGli6ZramqOrowuU1hdR0cH21FYhWjZScS/ZgT6+/vpdHpcXFx9fb2vry+RSHz27JnMvA/4yx8Pa7ErPy7B5ss8jctz24rTmuXTIw5ncCWZpENsP3ZRUP3nIhceh3c2cD7pcfL+9vtfu3xNIpBOl5xen7B+X+6+7KfZypbtcFrSM3GumYsXL544cYLBYEhLz2zVXJc417u7u6V1BHOuNzY2FhYWYs71/v7+y5cvV1dXJycnyzjXR1dPly6anpSUJF0ZfczC6ocOHSouLq6qqnrw4MGDBw96enqk+2exWCUlJQkJCeHh4fPnzxeLxdLmeEk9eEn9eFiLXWXAAfftlnbuRr9nNP9x+6l8Tl3afKP/XBQLce0ff8RLuYQKEMmf6CT6VsutN9+8mbQp6R3rdyp6Kz7L/8z3lu/pktMtQy3KskumIwQ///yzpqZmSEhIXFwcgiDvvvuuxMSUm5trZmZmbm5eUlKyYsWK0RO9qutcHw2bzabRaGKxWMatLr2+jrWR/E4mk4eHh+l0+gsTVZs337x5E0VREunPx0Zpc/xod9joiBhwcV1JQcG9a3U1BR0um82dNpjKpUv2tbPtxy6gIkBQRyk2C6l+wdTAtwja2tJtBGIBs52Z1pSW15onFAttdGxCLUP9F/lTiBRFSMxMeLjYbPbx48f3799/9uzZI0eOiMXikJCQ7Ozs+/fv7927V0dHRyAQfPvtt76+vkouPcpAfn7+F198ERUVZWtrO82uoPQor/iI0ZzomsfF3Wu2WCz3MZFLn2LO4HBGNOd28lBFh5CHBzhAXqhD8fCm+m3ScHICUvMbPcM9WU+zkp8k1/fXqxPUPRZ6hFqGrjZYjQM4FZMeMNVcM1B6ZhQoPUquPtlR1U9KWR5vLbVzN5Jn1yOD/NwYTvotbu0zXjcJRXFqdA3K2rVUHz+qmxue+t88GzV9NamNqRlNGc/5zxkURsCigFDLUGOasSpJj1xGA6UHSs+cQiQU37lU9bSy1+ttKxs3Q/kHGGwXFV7l3onj1A1wOsgiBI/D48kODjRvb9o6b9LixVgrvoif35af2pj6oP0BAMBBzyHIPGjj4o3yrfwFpQdKD0S51Of2xcrWmv71u2ws35ixrIOsWlB2YyQvnv2Ey+mkjPThAQDEhQtpnp5ULy+NVStxRCIAgMVjpTelJz5ObGW3UolULxOvIPMgeT2IQemB0gNRLoQCcfp/yjsanvvstlmyciZznopF4FkxKI8TFCZy20Tsbk1uJwEViPDz5mmsXk3z9qJ6eqotWIA9iCU8TshsyuQJeWbzzfzN/IMtgg2phlB6ZuzxG0Vl3jYa/QmUHoj81QcRpZ0r72oa9A9bZmavO+PxBMPgcRYov4HW5fBYBM6AAbuNKOjnAjyebG1NWeNC9fTUcHLii5H8tvyExwlFnUU4HO4NxhuB5oG+pr6v9NIclJ4/UZhzXbKgnpOTc+XKlbi4uAkaj1eFvaen58aNGwcOHIDS89qDjAhTz5T1PuP4hy0zXaajoKi8flCbCspvgNaHfLY6h2fF7aXyalpRkUhNR4fi5kr18qK6uXXj2Lebb9+sv9nOaaeRaBvMNgSaBzotcILS82q8995777//vqurK4qiJiYmjx490tXVDQwMvHTpEuYgTU5OrqioWLJkCZ1O37hxIwDg+++/f9UCNVFRUXv27MF+37Fjx8TSAwDYuXPn+++/L/3u4sjIyB9//HH48OEHDx5A6ZkT6jMsTD79aKCT++Y+B6OlijU99NSD6l9BRTzobxaJNbi4lRzWfE5Jg2hoCEcgkB0caF5eGl4epRqslCcpea15fBHfUssy2CI4yDxovvr86UiP0tXhGrhxQ8yZunNdfakl1d39pfsCTMm5/lJzuUxRdhwO19TUVF9f7+3tjSDIJKuwk8lkNzc3WIl07kCapxZ0wDHpVGn6TxWB+xwMLTUVF1tvKfA8DDwPg44yQvkNemUCXb8PhDJGND3ZLC1O6RPWyZPg5En9hQs/dXE5tPb/8o2GUloyTvx+YqX+yklKz3go3fHdd+myYBqlxzW3bJlAeqbpXH+puVy6KDsAoKGhQSQSYTZ3gUAwySrskDkImUoM/nR58qlHGecrgj5x1DejK3oEho7A0BH4fAsa80BNMrkmhSwY1vNeKngrhDOgzy6qHExJQW/etKPRnF1d+as/MSVO9yUgpZMei3t5M9f5NJ3rLzWXSxvWAQB6enpLliwhkUgdHR0bN26cZBV2eB7OTebRSJs+XZ58qjQ1smzTJ8sXmNJmYRBq6mCpP1jqD/xPgPpMUB5HrPpJC4fTWvMG+u4ensiS81sJO+eu4M4dgYMLwcZmOqHmdG7mKTjXJ2kuLykpAS/a3CfjMi8oKIBn4FxGg04KinAkU4ipkY962tizehs2Hzi8Bd5NAZ9Wgw3HAMLF5X1JYe7UX1RucfqDRQlxZGvraUaYK9IjF+c6eJm5HEgZ1mVs7qdPnx7tMpdUYU9ISIiIiKitrQUAiESilJSU1tbWxMRENpsNT8g5BVWLHHxwOYmslna2rL+TO/sDmm8EnMPBRwXgw/tg5W7QUQqSw8mZm0D7H9PsGL7XA8CrONcnYy4fMyH8eC7zmQCucKk6z1m85JOPUBQNPuikxdBQopGJhaDxHqj+Ffj/CNSpUzjZ4QPXCyxevBjTHQAAdsaamJiM1p38/PzOzs7Hjx9LdAcAQCKRiEQiAECS1GLMQhR4PF5DQwOuW0Emg+YCjeCDywEAqZGPhnqHlWhkeDWwxAcEX5ik7sC7nrkFvOt5Pehr5ySfekRUJ2z+zImmQ1bRrYB3PRCIiqFjRN30yXIBX5Ry5hH3Of812zooPRCI8qK7kLpxnwOPjSSffsQbQqD0QCAQBcFYRA/a78h9zk+NLBvhCqD0vFbAmusQpVYf8/kB4faDLF7KmUevjfpA57oyOtenXLsVA04zv5a01fRnXKjQNaYGRTiSyCqzVAprriuo5rqk1Pr69etf2tjZ2ZlEIrm4uEjrTlpamra29ltvvRUdHT1l8zrk9WOhjXbAR8t62zhp58oFfJGqb47SaWfpnRb+sHDK/77AlGbutOClMgyU27k+5dqtkNcbE1sd3z22WZer0s6VB+53IKoT5q70/P777yMjIwAAmTNkysmuHv/ezRkYmfJ4BHzGBNKjEs51Hx8fAABWuzU2NhaebxBpFi/X89ljmx1Vffti5ca99gQifi5KT2VlZVlZ2QcffBAZGamnpyd5ahgZGamvr09ISJiC9Oz46o2Z21oVcq6Prt0KgWBYrFggRER5sXWZFysDwpcR1FRSfaY16KSkJOwCvnjx4rS0NOlHBpVIdqW0zvXc3Fw/Pz9HR0esHwhEBisXA6+dVq01fdlXqsVilVyNnZY6dHZ2YssoNBqtcxL5vb755pujR4/OynZKnOt2dnb6+voSicSc62FhYSwWC3Ou//LLL5cvX/b09GxoaBjPud7U1DSmcz00NBRzrltbW0uc67/99hvmXA8LCxvPuc5isQoKCrBJqPv37+/fv19SuxWeZpAxsV5jgIwIC242ZF2uMrF5IakziUzA4V9YIVHXeOFMx+NxpHkvfkLAycwcqZEIMvdTRDIBj5dbrYRpLa4fOHAgODjY29s7KysrNzf3xIkT0n/18vK6d+/e2FGhc30mgYvrc4fS7JbCpEag8Pue0H+uXDC5VIozkpvZ0dERe1oZGBiws7PDTjCJgVuFWPxXPUYg5Vwf3UziXJfOJSjRoMk41+GpApEvTr6my9yNsWcuAV8oFr1wkvOHhdKqhKKAz3vhjUSxCJVZpxcJxUJELP3J6G6RYSFFU302H7hCQ0OPHz/u6upaWVl55MgRDocTEhKSnZ0tEonS09OxZFe+vr40Gu31+Jo9PDwePnwID3eIUkEkE8Z8qlJypvs2s0AgaGxstLCweKXnCJl7MGzOaPScLmRqNDc3GxgYjHnzBYEoWmJm6G1mIpFoZWU1zfkLIpE4PDwMvyS5IBQKEQTBspdBIK/tXY9chFAkErW1tQkEAtWtNaw8iEQiPT09bW1tuCsgynzXoxTSIzlnxGIx/KqmCfZaI9wPECg9rzAaCAQyR6QHXh4hEMgsAKUHAoHMArP2IgCcUYZA5vSDmErMuSh+bghGhBFhxBmNCB+4IBDILAClBwKBzAKEb775RvlHOTIyMplUxzAijAgjqkpE+H4NBAKBD1wQCGRuoKQu+zGzzU851fyUI4Jpl8SaQsSSkhIOhzM8POzn56eYiBjXrl17++23FRPxyJEjNjY2Q0NDYWFh8rV9jBfx+fPnTCaTRqOtWbNGvtnaxozY0tJy8eJFIyMjHA7HYDC2bNmigG2sra1lsVhqamqurq6K+R7v3r2LoiiFQplCRGW868Gyza9du7a0tLSurk7yeImlmldYRDCTJbHGi1hYWJienu7h4XH69Ony8nIFRMTIz8+fuFqhfCOyWCw+n79t2zb56s54EYVC4T//+c+AgIDGxsbq6moFRKyrq7OystLV1UUQRL7OxPEiVldX9/T0eHh4dHd3V1VVKSBiTk5OQUGBr69vRkZGa2vr6yA9Y2abn9FU8xPkt5cuiaWAiCtWrPjwww8BACiKmpubKyAiAKCzs3MmkiVNENHf39/T01PuKXHHi3jr1q2lS5fW1dVt27bNwcFBMd/j3//+9x07dixYsCA0NFQBEQUCwb///e/e3l42mz06se9MRMzJycGK0JiZmUkXblFh6XnVbPMzF9HHxycoKAgriRUSEqKAiCQSiUKh/Otf//r444+pVKpi9mpVVRWW31Zh32NXVxeBQDh48CCXy1VAxMrKyvb2djMzsy+//FK+R9R4EXV1dQEA2dnZoysLzFBEx7/o6+uT74VkvIi2trYsFgsA0NbWJl2ORYWlh0gk8vl8AACfz1dMRZ2JI85ESawJItLp9MOHD1+/fp3JZCogYkFBwZo1axS5V/l8fnh4uKmpqaGhoXyfoMeLiCW0mzdvnrW1dXR0tMKOnLS0NPnegEwQsb6+3tHRsaCgICMjIyMjQwER3377bXV1dSxlMCa1Ki89Y2abn62IM1QSa7yI586di4mJAQBYWlqOV89D7tuYk5MTHx/f1dVVXFysgIhxcXHY8crlcuV7aRkvorW1NeYZFAqFiokIAHj+/HljY6PCjtXExMSAgAAzM7PExETFHKscDmfz5s3Ozs4IgkiXeFJh6QkNDS0tLe3u7q6srNy6dSuHw/Hz8xOJRCkpKViqeTabrYCI4K+SWLt27XJ2du7r61NAxFWrVmlpadXX11dVVe3cuVMBEd3c3AIDA4eGhuQ+ITpexICAgK6urvr6ehaLtXXrVgVEDAkJqaura2hoqKmp2b17twIiAgCePHkyEwURJtirly5damhoSEhIeOeddxQQsaOj4+rVq0VFRYaGhlOZQUOVEgRBamtrBQLBHIwoEolqampGRkZe421ks9m1tbUikUhhEREEqa6u5vP5CosoFov7+voUuVfFYnFtbe3w8LDCIvb29ra3t0+tT/g2MwQCmQXg28wQCARKDwQCgdIDgUAgUHogqgGcPYRMBtXI1wMZzS+//HL37t2Ojo6DBw+KxeKioqKsrKwxfaEYIyMjMq+0vGoPXV1dUVFRzs7O4zVAEOT48eMDAwMVFRVJSUnu7u4yDfLy8g4dOrRt2zbprkYPrKioKCIioqWlpaur6/bt2wKBwMzMTC47bXQsyGxeoyCqSHx8PPaLlZUVthIv+WRMrly5Ms0eeDxeYWHhBA2uX7+enp6O/f7dd9+N2Wb79u0yXY0eGIqiu3btKigowBaMjY2Ne3p65LLTxowFmRXgFUBVWbVq1ZiflJaWDgwMWFlZ0en0srIyNTU1BoNRW1t79epVBoPh6elJoVAm6IHJZCIIYmhoSCKRFi1aVFtb+/TpUxcXF01NzZqamoGBgYcPH7LZbFNT08bGRm9vb3V1dcm/Gxoanjx50sHBwdjY+L333gMAMJlMHo+nq6vb29vr5eWlpqaGvVWMdQUAyMzMHD0wabD2AoFAsl1GRkYyg8R6MzAwWLx4sXQzmaHm5uZKx5LeNABAU1NTc3Ozvr5+T0+Pl5eXdD/wYIN3PZAxkNyzoCiamZkZHR2NomhERMSxY8cQBBkcHExNTRUKhUFBQUKh8KU9DA4OOjo6NjY23rlz5+nTp+fOnUMQJCgoCEVRLpcbGBjIZrMdHR0bGhoKQToveQAAA4NJREFUCwt//PFHmVfajh8/bmho6OjoWFZWhqLo0NCQk5NTf39/e3v7li1bUBTdsWOHpCsURccb2K5duyIjI3Nycs6fP5+RkSG9XTKDfPjw4bFjx1AU/cc//iHTTGao0rFkNq2mpub7778fHBzcu3dvX1+fTD+QmQBOM79WxMTEUKnUoqIiXV1dOzs7Dw+PU6dOrV27FivETiAQAABRUVGRkZGJiYlj9kCn07Hbhw0bNpiamtrb29+7d4/D4QAANDQ0KBQKlUplMBgWFhYGBgYdHR3S/8vj8Q4dOtTe3n7p0qUjR44AAGg0momJiZaWlqGhYX9/f1tbG9YS6wr8VSEeG5gMtra269atCw8PDwgIkN4uHo8nPciYmBgvLy8AwIkTJ2SayQxVOpbMpvF4PBqNRiQSEQTR1taW6QceV3CFC/IS9PX1Fy1atHr16s8++0xTU5PJZLq4uJw9e1bSoKSkZN26dW+++aaLi8t4nUjS6MTFxdXX1/v6+hKJROmsCGQyeczFrJSUFARBsAc3Sd4GSZv+/n46nT5e0Ikdj9Lbhc0TSwapra09NDQEABgeHtbU1JRpNuZQS0pKZDbNwMCARqMVFBScPHlyzHAQuMIF+S/d3d05OTmJiYkMBkNDQ0NHR2f58uUxMTHYLM+dO3fU1dXpdLqpqamJiUlOTg6ZTDYzMzM1NdXW1sacjaN7yM/Pv379ur29/cKFCysqKnp6evB4fFVVFZVKHRoaio2NJRKJGRkZrq6ueXl5TCbT29tbYpLMz88vLS2lUCi//fabnp7eihUrAABnz551dHRkMpl2dnZ4PD4mJsbV1bWpqSk2NtbT01NXV1cyMImaFBcXX79+HUEQe3t7LGmR9HbZ2tpKD9LBwSE6OlpXV7e0tPRvf/tbbGyspBmTycTCSYZaVFSExWppaZHetGXLlv3www+tra2lpaX29vaurq7S4eCRBud6IJNiaGgIs6FyuVxpy98UjIV8Ph9BEKy3lzbm8Xgoira0tLS1tUk+DA4ORhBkYt/mJAeGbdeYDA4OTqaZdCzpTfvuu++w/+ro6Pj8888n0w9kmkD7KGQGyc/P/+KLL6KiopT83iEzM1MsFuvp6XV1dWloaPj4+MDvbqaB0gOB/IlYLJZvmnoIlB4IBKJcQI2HQCBQeiAQCJQeCAQCgdIDgUCg9EAgEAiUHggEolr8P7e7wpDcf84yAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "dataplot2 = plot(df2, title=\"Naive Bayes $\\\\alpha = .01$ TfIDF(min\\_df=.003)\", xlabel=\"Test-Train Split Percentage\", ylabel=\"Test Accuracy\")\n",
    "dataplot2.savefig('../figures/nb_x_prev_seen.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python38264bit94fc5f5a5f1c4911908bbdb2db56aaaf",
   "display_name": "Python 3.8.2 64-bit",
   "language": "python"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}