classify.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian classifiers on the Adult dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The autoreload extension is already loaded. To reload it, use:\n  %reload_ext autoreload\n"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "from pgmpy.models import NaiveBayes, BayesianModel\n",
    "from pgmpy.inference import VariableElimination\n",
    "\n",
    "from estimators import TreeAugmentedNaiveBayesSearch, BNAugmentedNaiveBayesSearch, ForestAugmentedNaiveBayesSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   age          workclass   fnlwgt   education   education-num  \\\n0   39          State-gov    77516   Bachelors              13   \n1   50   Self-emp-not-inc    83311   Bachelors              13   \n2   38            Private   215646     HS-grad               9   \n3   53            Private   234721        11th               7   \n4   28            Private   338409   Bachelors              13   \n\n        marital-status          occupation    relationship    race      sex  \\\n0        Never-married        Adm-clerical   Not-in-family   White     Male   \n1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n\n    capital-gain   capital-loss   hours-per-week  native-country  high-income  \n0           2174              0               40   United-States        <=50K  \n1              0              0               13   United-States        <=50K  \n2              0              0               40   United-States        <=50K  \n3              0              0               40   United-States        <=50K  \n4              0              0               40            Cuba        <=50K  ",
      "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>age</th>\n      <th>workclass</th>\n      <th>fnlwgt</th>\n      <th>education</th>\n      <th>education-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capital-gain</th>\n      <th>capital-loss</th>\n      <th>hours-per-week</th>\n      <th>native-country</th>\n      <th>high-income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>77516</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>83311</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>38</td>\n      <td>Private</td>\n      <td>215646</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>53</td>\n      <td>Private</td>\n      <td>234721</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>28</td>\n      <td>Private</td>\n      <td>338409</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 59
    }
   ],
   "source": [
    "df = pd.read_csv('dataset/adult.data', na_values=[' ?'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                age        fnlwgt   education-num   capital-gain  \\\ncount  32561.000000  3.256100e+04    32561.000000   32561.000000   \nmean      38.581647  1.897784e+05       10.080679    1077.648844   \nstd       13.640433  1.055500e+05        2.572720    7385.292085   \nmin       17.000000  1.228500e+04        1.000000       0.000000   \n25%       28.000000  1.178270e+05        9.000000       0.000000   \n50%       37.000000  1.783560e+05       10.000000       0.000000   \n75%       48.000000  2.370510e+05       12.000000       0.000000   \nmax       90.000000  1.484705e+06       16.000000   99999.000000   \n\n        capital-loss   hours-per-week  \ncount   32561.000000     32561.000000  \nmean       87.303830        40.437456  \nstd       402.960219        12.347429  \nmin         0.000000         1.000000  \n25%         0.000000        40.000000  \n50%         0.000000        40.000000  \n75%         0.000000        45.000000  \nmax      4356.000000        99.000000  ",
      "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>age</th>\n      <th>fnlwgt</th>\n      <th>education-num</th>\n      <th>capital-gain</th>\n      <th>capital-loss</th>\n      <th>hours-per-week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32561.000000</td>\n      <td>3.256100e+04</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>38.581647</td>\n      <td>1.897784e+05</td>\n      <td>10.080679</td>\n      <td>1077.648844</td>\n      <td>87.303830</td>\n      <td>40.437456</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>13.640433</td>\n      <td>1.055500e+05</td>\n      <td>2.572720</td>\n      <td>7385.292085</td>\n      <td>402.960219</td>\n      <td>12.347429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.000000</td>\n      <td>1.228500e+04</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>28.000000</td>\n      <td>1.178270e+05</td>\n      <td>9.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>37.000000</td>\n      <td>1.783560e+05</td>\n      <td>10.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>48.000000</td>\n      <td>2.370510e+05</td>\n      <td>12.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>45.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>90.000000</td>\n      <td>1.484705e+06</td>\n      <td>16.000000</td>\n      <td>99999.000000</td>\n      <td>4356.000000</td>\n      <td>99.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strip leading and trailing spaces from column names and cell values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array(['age', 'workclass', 'fnlwgt', 'education', 'education-num',\n       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n       'high-income'], dtype=object)"
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "df.columns = df.columns.str.strip()\n",
    "for column in df.columns.values:\n",
    "    if df[column].dtype == 'object':\n",
    "        df[column] = df[column].str.strip()\n",
    "df.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing redundant or useless features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array(['age', 'workclass', 'education', 'marital-status', 'occupation',\n       'relationship', 'race', 'sex', 'capital-gain', 'capital-loss',\n       'hours-per-week', 'native-country', 'high-income'], dtype=object)"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "useless_features = ['fnlwgt', 'education-num']\n",
    "df.drop(columns=useless_features, inplace=True)\n",
    "df.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Record variable names and the target variable (`high-income`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables_names = df.columns.values\n",
    "target_variable = variables_names[-1]\n",
    "feature_variables = variables_names[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "age                  0\nworkclass         1836\neducation            0\nmarital-status       0\noccupation        1843\nrelationship         0\nrace                 0\nsex                  0\ncapital-gain         0\ncapital-loss         0\nhours-per-week       0\nnative-country     583\nhigh-income          0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "source": [
    "np.sum(pd.isnull(df), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can drop NA values, since they are only a few, w.r.t. the size of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "age               0\nworkclass         0\neducation         0\nmarital-status    0\noccupation        0\nrelationship      0\nrace              0\nsex               0\ncapital-gain      0\ncapital-loss      0\nhours-per-week    0\nnative-country    0\nhigh-income       0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 65
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "np.sum(pd.isnull(df), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning continuos variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binning(column, bins_edges, categories=None, right=False):\n",
    "    '''\n",
    "    Bin the given pandas Series values into specific categories names associated \n",
    "    with numerical bins. If the categories list is not given, natural numbers \n",
    "    will be used as categories.\n",
    "    '''\n",
    "    if column.dtype == 'object':\n",
    "        return column\n",
    "    bins = [(bins_edges[i], bins_edges[i + 1]) for i in range(len(bins_edges) - 1)]\n",
    "    if categories is None:\n",
    "        categories = list(range(len(bins)))\n",
    "    if len(categories) != len(bins):\n",
    "        raise Exception(\n",
    "            'The lenght of the bin edges list should be one element more than the lenght of the categories list.'\n",
    "        )\n",
    "    _categories = dict(zip(bins, categories))\n",
    "    indices = np.digitize(column, bins=bins_edges, right=right)\n",
    "    values = []\n",
    "    for i in range(column.size):\n",
    "        values.append(\n",
    "            _categories[bins[indices[i] - 1]]\n",
    "        )\n",
    "    return pd.Series(values, index=column.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `age` variable will be divided as follows:\n",
    "* `age` < 18: `child`\n",
    "* 18 <= `age` < 30: `young adult`\n",
    "* 30 <= `age` < 60: `adult`\n",
    "* `age` >= 60: `senior`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "count     30162\nunique        4\ntop       adult\nfreq      19296\nName: age, dtype: object"
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "source": [
    "_age_bins_edges = [0, 18, 30, 60, 100]\n",
    "_age_categories = ['child', 'young adult', 'adult', 'senior']\n",
    "df['age'] = binning(column=df['age'].copy(), bins_edges=_age_bins_edges, categories=_age_categories)\n",
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `hours-per-week` variable will be divided as follows:\n",
    "* `hours-per-week` < 25: `part time`\n",
    "* 25 <= `hours-per-week` < 40: `full time`\n",
    "* 40 <= `hours-per-week` < 60: `over time`\n",
    "* `hours-per-week` >= 60: `too much time`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "count         30162\nunique            4\ntop       over time\nfreq          20991\nName: hours-per-week, dtype: object"
     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "source": [
    "_hours_bins_edges = [0, 25, 40, 60, 100]\n",
    "_hours_categories = ['part time', 'full time', 'over time', 'too much time']\n",
    "df['hours-per-week'] = binning(column=df['hours-per-week'].copy(), bins_edges=_hours_bins_edges, categories=_hours_categories)\n",
    "df['hours-per-week'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discretizing other continuos variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `capital-gain` and `capital-loss` variables will be quantile-based discretized. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x151921040>"
     },
     "metadata": {},
     "execution_count": 69
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['capital-gain'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "count                 30162\nunique                    9\ntop       (-0.001, 2012.24]\nfreq                  27749\nName: capital-gain, dtype: object"
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "df['capital-gain'], capital_gain_bins_edges = pd.qcut(df['capital-gain'], q=100, duplicates='drop', retbins=True)\n",
    "df['capital-gain'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x151aa9dc0>"
     },
     "metadata": {},
     "execution_count": 71
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['capital-loss'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "count                30162\nunique                   5\ntop       (-0.001, 1590.0]\nfreq                 28963\nName: capital-loss, dtype: object"
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "source": [
    "df['capital-loss'], capital_loss_bins_edges = pd.qcut(df['capital-loss'], q=100, duplicates='drop', retbins=True)\n",
    "df['capital-loss'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/test split & utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Training set shape: (24168, 13)\nTest set shape: (5994, 13)\n"
    }
   ],
   "source": [
    "mask = np.random.rand(len(df)) < 0.8\n",
    "train = df[mask]\n",
    "test = df[~mask]\n",
    "test_no_target = test.copy().drop(target_variable, axis=1).reset_index()\n",
    "print(f\"Training set shape: {train.shape}\")\n",
    "print(f\"Test set shape: {test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store every possible state for each variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_names = {name: column.unique() for name, column in df.iteritems()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(data, inf, target_variable):\n",
    "    '''\n",
    "    Given a Dataframe, an inference object and a target variable,\n",
    "    perform prediction and return the obtained results\n",
    "    '''\n",
    "    results = defaultdict(list)\n",
    "    for _, data_point in data.iterrows():\n",
    "        if 'index' in data_point:\n",
    "            del data_point['index']\n",
    "        result = inf.query(\n",
    "            variables=[target_variable],\n",
    "            evidence=data_point.to_dict(),\n",
    "            show_progress=False,\n",
    "        )\n",
    "        values = result.state_names[target_variable]\n",
    "        for i, val in enumerate(values):\n",
    "            results[val].append(result.values[i])\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perf_measure(test_data, predicted_data, positive_class, negative_class):\n",
    "    '''\n",
    "    Return the number of true positives, false positives, true negatives and\n",
    "    false negatives\n",
    "    '''\n",
    "    tp, fp, tn, fn = 0, 0, 0, 0\n",
    "    predicted_column = []\n",
    "    keys = list(predicted_data.keys())\n",
    "    for val in zip(*predicted_data.values()):\n",
    "        max_index = np.argmax(val)\n",
    "        predicted_column.append(keys[max_index])\n",
    "    predicted_column = pd.Series(predicted_column, index=test_data.index)\n",
    "    for actual, pred in zip(test_data, predicted_column): \n",
    "        if actual == pred == positive_class:\n",
    "           tp += 1\n",
    "        if pred == positive_class and actual != pred:\n",
    "           fp += 1\n",
    "        if actual == pred == negative_class:\n",
    "           tn += 1\n",
    "        if pred == negative_class and actual != pred:\n",
    "           fn += 1\n",
    "\n",
    "    return (tp, fp, tn, fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(test_data, predicted_data):\n",
    "    '''\n",
    "    Return a percentage representing the classification accuracy\n",
    "    over the test set\n",
    "    '''\n",
    "    predicted_column = []\n",
    "    keys = list(predicted_data.keys())\n",
    "    for val in zip(*predicted_data.values()):\n",
    "        max_index = np.argmax(val)\n",
    "        predicted_column.append(keys[max_index])\n",
    "    predicted_column = pd.Series(predicted_column, index=test_data.index)\n",
    "    equality = predicted_column.eq(test_data)\n",
    "    true_equality = equality[equality == True]\n",
    "    return (len(true_equality) / len(test_data)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def precision(test_data, predicted_data, positive_class, negative_class):\n",
    "    '''\n",
    "    Return a percentage representing the classification precision\n",
    "    over the test set\n",
    "    '''\n",
    "    tp, fp, _, _ = perf_measure(test_data, predicted_data, positive_class, negative_class)\n",
    "    return (tp / (tp + fp)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recall(test_data, predicted_data, positive_class, negative_class):\n",
    "    '''\n",
    "    Return a percentage representing the classification recall\n",
    "    over the test set\n",
    "    '''\n",
    "    tp, _, _, fn = perf_measure(test_data, predicted_data, positive_class, negative_class)\n",
    "    return (tp / (tp + fn)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_measure(test_data, predicted_data, positive_class, negative_class):\n",
    "    '''\n",
    "    Return a percentage representing the classification F-score\n",
    "    over the test set\n",
    "    '''\n",
    "    p = precision(test_data, predicted_data, positive_class, negative_class)\n",
    "    r = recall(test_data, predicted_data, positive_class, negative_class)\n",
    "    return 2 * ((p * r) / (p + r))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes model"
   ]
  },
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CqYi0+4rX09eJ63fyyn20Av99w1ZrCezsxsSuz3Fs67ccP36cjIwMnJ2duXr1KufPn0elUqFQKFAoFOzbtw8XF5dy60NFEwXUhcepHfMaQqW5N+3+UZxaP1uuo73Hpd3LsoxGozFkbNZkCoWCiV09COzsRq5Gi0b7+EotJaHR6snV3A16HigUCubMmYOjoyM2NjYkJCSg1+txcHDA1tYWvV5P3759adKkZiW/3C2gXpFTxVA07anWSiz75WKFXkcof2KqUyi10N0XKj3tPrOgEG6cJOtgBHZ2dmi1WjQaDRqNhsLCQrTaoqLN5ubmHD16FBubmr8vzZCRuPcSam3pMxLvbSdbrcPMWMn0F32KTc3JssyAAQOIi4vD2NiY27dvY2Vlhbm5OY0aNWL37t1YWFiU59OqUHEpOUyIOIF1GV+r0pJlmRy1jjXBHcQ+vxpEjPiEUhvevgkKqLSstrtp9y91cCU+Pp7o6Ghu3LiBRqPBxMQEa2trHB0dMTIyol27drUi6EHRyO/Flg1ZP6EzndwdyFHryCwofOxo+y69JJNZUEiOWkcndwfWT+h833qUQqEgJCQESZIwMzPD3d0djUZDSkoKixcvrlFBD0QBdaFkVPPnz59f1Z0QahZHK1Nik7K5lpZXbLNvRcn+7xv33IAXim3F+Pvvv9FqtZiamqJSqdBoNCxbtqzGTc09joWJEb18G9LFsx46vcyllBy0eon8Qh1avYQkFwU5rSSj1uopKNSjl2R0kkzflg0J6dOc0R1dsTB58JK+q6sre/fuJSkpCVNTU2RZZtSoUWzevJnGjRvj4eFRyc+4bHI1OhbvvICFiRHKStxjZ6RScDkll+H+TTAxEmOJmkBMdQplkparIWDNEZCp8LR7FLB+QmfqWZkiSRKvv/46e/fuxcbGhszMTNLT01Eqlfj4+HD48OFak5L/MHfrTMal5HLqZiapOWoK9RImKiUNrM1o52KHVynrTB46dIhx48YhSRL9+/fn888/58KFC8yaNYtnnnmGt99+u0pLupVE9I10pm8+W+EF1B+kPAuoCxVPBD6hzKoqZTw/P59BgwYRHx+PnZ0dsiyTlJSEu7s77u7uBAUF0bNnT1Sqih+N1hayLDNo0CAyMzPZvXs3VlZF2bM5OTksWrSI27dvExoaWq2zO0uy1UaWZZDl+07+eFJ1ZatNbSECn1BmFbVJGIqn3T9ok/DNmzfp378/Op0OhUKBpaUlhw4d4ujRo0RGRpKUlERAQACDBw+ucetUVSUhIQGVSkXjxo2LfV+WZX744Qe+/fZbpk+fTq9evaqoh482c8sZjl5Px9qseNKVOiuNmE2fYt3IndyUBKwbupOXdgtJp6Wetz9uXQYDRaeAXPv9e/SFGhQqI9qMfg+lkQnXD24hK+ESsl6Lc7vncfbrft+1c9RaOrk7smx4m0p5rsKTEfv4hDK7m3YPEHX0BmZGqnKZ9tRo9ah1ekPa/YO4uLjw9ddfM3bsWLRaLTNnzsTExISuXbvStWtXzp8/T0REBGFhYQwbNoxRo0bh6Oj4xH2rzVxdHzxaUSgUjBw5kjZt2hASEsKJEyd49913q+wIp4dJydY8dI9pQWYq3v3HY9OoGdqCPIzNLZEliXMbPyI39SYWjs5c3P4NzQe9irWzOzpNAUojY5LP/oGRqTntgucg6bScWR+KvXvL+yoNGamUpOaoH3htofqp3YshQoW7G/zm9vMFBWQVaMuc7SnLMlkFWlDA3H6+TOrW7JHZec8++yyzZs3C0dGRESNGFLutZcuWLFu2jLVr15KTk8NLL73E4sWLiY+PL1PfBGjevDnr1q0jJyeHV155hYSEhKruUjFaSXrobWY2jtg0Kpo5SLt0nJP/t5BT/7eA/LRECu4kUZCejImVraGsnpGpOQqlioz486TG/MXJtQs4HfUhuoI8CtJT7mtfARTqH359oXoRIz7hid1Nu2/nZm84+kVCxrqEa396SSZHo0WJgk6lPPpl/PjxDB8+/KHTmU2aNGHmzJm8+uqrbNq0iUmTJtGqVSuCg4Np27atqLBfSpaWlnz44Yds3bqV8ePH895779GnT5+q7hZQVED9YZTGRet+6qw0bh3bg1/QHIzNLbm0KxxJp31ku816voy9e6tH3kcGTKqoopFQeiLwCeWmnpUpK0a0fehhn0YqZdH+P0Cnl9BLsmFqqn8r5zId9qlQKEpUWNrOzo5JkyYRHBzMzp07WbBgAXZ2dgQFBdGjR49anwlanhQKBcOHD6d169aGqc/p06dX+dSnk40p8XcevcVGp8lHZWyKkak5hXnZZFyPwc7FB3OHhhTmZpGTdB1rZ3f0hWqURsbYN21J0qn92Lo2R6kyoiA9BRMrO1QmxZ+rTi/RwNqsop+iUE5EcssTuptafjkll9M3M0jJ1qCVJIyVSpxsTPFzsce7lKnltUVFpN2XF0mS2L9/P5GRkWRmZjJmzBgGDhxY5W/eNU1+fj5LlizhypUrLFu2jKZNm1ZZXx6W1anOSuP8ls/xH78QgEu7wsm5fRVTG3tUpuY4NvPDqfWz5CRd5+q+9Ug6LUojY1qPehelkQk3Dm3jzpUzgIyxhTW+Q9/AyLT4DIPI6qxZROAro4eNau5dXNf+Y1TTy9epTKMaoeLIssyZM2eIiIggJiaGESNGMHLkyCo/nqgmkWWZbdu28dVXX/HOO+/Qr1+/KumH2McnlJQIfKWUlqsxrGPJgJWpUYnXsXI1OhRAx1KuYwmVIz4+nqioKH7//Xd69+7NmDFjal0VmIoUFxdHSEgIbdu2ZcaMGZiZVe7UX65Gx6AvD2FmXHGnMjyITpLQaCW2v9mlzs3q1FQi8JWQoWDwr5dQ68qvYHBPXyeRYFHN3Llzh40bN7J161b8/f0JDg6mZcuWVd2tGiE/P5/Q0FAuXrxIaGhopZc7q6oC6v1bORPSt0WlXVN4MqJWZwnc3aj95f4rGCmVWJqWvfK7QqHAzFiFXi/z+6VUdHqZ9q72IvhVIxYWFjz11FOMGDGCrKwsPv30U/bv34+dnR0uLi7iZ/UIxsbG9OjRAzMzM+bMmYO9vT0+Pj6Vdn0nGzN2nEnExEhZaaczaHUys/o2x8FSzODUFGLE9xhVWZ1EqB70ej379u0jIiKCwsJCgoKC6NOnT7WvXVnVrl27xsyZM/H19SUkJARzc/NKue70zUUVXCpj1JdVoKWTuwMrRrSt8GsJ5UcEvseoqnqUQvUjyzLHjx8nMjKSuLg4Ro8ezbBhw2rNMUgVoaCggOXLl3Pu3DmWLVtGs2YV/wGvqgqoCzWHmOp8hLRcDe9uPoORUolxBR03olQoUACHr92hbyvnhx4dI1Q9hUJB48aN6devH506deLgwYMsX76cO3fu4O7ubijsLPzP3alPCwsL5syZg62tLT4+PhU6DWlhYkRDazN+v5SKsUpZIUcU6SWZfK2OWX1a0LqJyACuacSI7xHElInwOKmpqWzYsIGffvqJZ555huDgYLy9vau6W9XStWvXCAkJwcfHh1mzZlVo8XCxRCE8iihX8RBxKTkcu56OjVnljMBszIw4dj2duJScSrmeUD4aNGjA1KlT2b59O97e3kybNo3XX3+dI0eOVNoJ9TWFh4cHERERmJiYEBgYSFxcXIVd624N2cDObuRqtEXTkuVAo9WTq9YS0NHloQXUhepPTHU+xLd/XCMuNbdSThiHoj/UAp0enV6mi1f9SrmmUH5MTExo27YtI0eORKlU8s033/Djjz9ibm6Ou7u7KIn2X0ZGRnTv3h1ra2vmzJmDlZUVzZs3r5CpT4VCQXtXe1zsLDh87Q55Gj2mZcz2vLsNyUilwCn5CGs/eJUGDRrg7e2NsXHlbZ0QyoeY6nwAsRFWeFKyLPPXX38RERHBzZs3CQgIYOjQoeJswHvEx8cTEhKCh4cH77//PpaWlhV2rXsLT5S1gPrdwhMxJ/5iwIABWFhY4OTkxMSJEwkKCsLe3r7C+i+ULxH4HuCfpY80ORlc/W0DvkNeJzc1gcLcTBw8Hn3gZGbCJW4f30PL4W+V6toPKn0UGxvLzp07mT59eumfjFDlYmNjiYqK4ujRowwZMoTRo0dTv74Y1QNoNBo+/vhjTpw4wdKlSyt8z19ZCqj/s9Rgeno67du3Jz09nfr166NQKFCpVIwZM4YJEybQqFGjCn0OwpMTge8BHlbsFiDl3J/kpMTj2XPMI9soa+ATxW5rr8TERNatW8fu3bvp0aMHgYGBlV7ZpLras2cPK1asYPLkyQwfPrzCN58/aQH1Tp06kZGRQXJyMk2bNkWpVJKTk4NSqWTAgAG89957951kL1QfdSLw7dy5k8jISBQKBV5eXvTs2ZOwsDC0Wi12dnYsXrwYBwcHVq9eza1bt/jx0BmSUu/Q9Ol+OLftZqju3m7sB5xYPQtJp8XE2h6XTn0xs6vP1d82IOm0qIxN8Or7ChYODR8Z+NKvneXa75tQGZtg09gTdVYaLYe/RU7SdS7+ug4HMyWdvRoyb9483NzciI6OJjIyks8++4zVq1eTnJzMrVu3SE5OJiAggNGjR1fBqyqUVVZWFps3b2bjxo20aNGC4OBg2rdvX+crwiQkJBASEoKrq6th/a+6eu2119i3bx+SJJGRkWEIfnl5eWRnZ/PNN98wYMCAqu6m8BC1fiHp2rVrhIWFER4ejp2dHdnZ2QCsXbsWhULBtm3b+L//+z/efvttoKjQrt/o6TimZRK7bgkOHq0NbSlVRrh1GVJsxKfTFNA2YOZ/T2uOJf7gVnyHvP7Q/kg6LXF7ImkbMBMz23pc3P6N4TZzh4a0HD0DV0crJvvCV199xfLly+9rIz4+nlWrVpGfn8+wYcMYMWIERka1/kdZa9ja2vKvf/2LoKAgdu7cyZIlS7CysiIoKIjnn38elapyEqqqG1dXV9auXcsnn3xCYGAgS5cupUWL6ln/slOnTuzZswcHBwfUajWJiYlYWVlhZmbG+vXr6d69e1V3UXiEWv9uefz4cXr27Gk4ZsbGxoYrV64wa9Ys0tLS0Gq1xaYkunfvzh8qI4zNrbFz9SEnOR6rBi4PbV+vKeDyrnAKMlIABbL06LTp/PQkzO3qY2ZbD4D6vp1IPnPQ0NaVveu4kptGvkc9dDrdA9vo0qULJiYmmJiY4ODgQHp6Og0aNCjNyyJUAyYmJgwdOpTBgwdz8OBBIiIi+OKLLwgMDGTgwIGVVuKrOjExMSEkJIS9e/cyZcoUJk2axEsvvVTtRsNt2rQxlKxr0KABV65cwcHBgZ07d+Lm5lbFvRMep07mWC9fvpxRo0axceNG3n//fTQajeE2hUKB8T2p54/7c4s/tA1bVx/8xy+k5fApSHrtffc5t+lTTq5dwOVf1j62LWsXH55/YymffvophYWFD7zfvenTSqXyoQFSqBmUSiU9evQgPDycxYsXc/z4cQYNGsSqVatIT0+v6u5ViRdffJHvvvuO7du3M3PmTHJyqtf+1ubNm6PVaiksLCQrK4sRI0bQqFEjkpOTq7prQgnU+sD31FNPsW/fPrKysgDIzs4mNzfXkFX3888/F7v/gQMHqGeupCA3m6yES1g1bFrsdpWJGXqN2vC1XlOAqVVRGnNKzOEH9qH1yLdpP24e3n3GYW7fkILMv1FnpQHw98XjxdpSWdjSwNqsWL+0Wi1paWmsW7eO9evXM2/ePD7//PMyviJCddamTRtWrFhBWFgY6enpjBgxgg8//JCEhISq7lqlc3FxITw8nHr16jFmzBhiY2OruksGFhYWuLi4kJ2dzaxZswgPD2fZsmXMnTuXxMTEqu6e8Bi1fqrTw8OD8ePHM3HiRFQqFT4+Prz66qvMnDkTGxsbnnrqKW7fvm24v6enJweilnMhPhH3ZwZiam1vCFIAtq7NuXl0NyfXLsClU1+adOzN5V3fkfDXz4/d4gCgMjbB88UxxPzwGSpjk2KBtUnH3pzfEcZ/Lv9GQStPrly5woABAzhz5gy5ubn89ddf5OTkIEmSOCC1lnN1dWX27NlMnjyZTZs28a9//Qs/Pz+CgoJo0+bxv2e1hYmJCTNmzKBDhw5MnTqV8ePHM3r06Gox9fnuu+9Sv359OnfuDIC/vz/jxo3jvb+uHoYAACAASURBVPfeIzw8vNIP4hVKrk5kdZbU6tWrMTc3x7db/2L7+MqbvlCNysQMWZa5uncd5g5ONO7wIgC5ai1nvp2O+mYsZmZmWFtbY2dnZ5jelGWZ7Oxsfv/9d1xcHr72KNQuBQUF7Nixg6ioKOrXr09wcDBdu3atUxVhbt++zaxZs2jQoAEffPBBtTwVQ5Zl5s6di1KpZMGCBdUiQAv3qzt/NaXg07DoD0ovVcxnguSzf3By7QJOhn+ArrCAhm27AUWVWxQKBTvXfUvTpk0xNjYmPz+fa9eucf36dVJSUsjIyMDKykqM+OoYc3NzRo4cyY8//sjo0aMJCwtjxIgRbN269aFrwbVN48aNWbNmDc7OzgQGBhITE1PVXbqPQqFgzpw5XLlyhY0bN1Z1d4SHECO+hwjdfYFdMcmVcjLDXZkFhfRv5UxI3xYkJiYSHBzM9evXsbW1RaPRkJeXZwh83bt3p0OHDvj7+9OuXbtq+elXqDiyLHPy5EkiIiK4cOECo0ePZsSIEXXm92D//v0sWbKEcePGERAQUO1GVrdv3+aVV14hNDSU9u3bV3V3hH8Qge8h4lJymBBxAmszo0r5o5JlmRy1jrCxHfBsUFQaKScnh9dee43Dhw9jZ2eHUqkkOzub0NBQPD09OXHiBNHR0Zw7dw4XFxf8/f3p0KEDfn5+deYNUCjaqxoZGcmBAwfo27cvY8aMqRNlsxITE5k1axaOjo7Mnz+/2v3O//XXXyxYsICIiAix3aiaEYHvEarDeXw6nY4PPviAjRs3YmVlRX5+Pv/5z3+KTXVqtVouXLjAiRMnOHHiBDExMbi6uuLv728YEVpbW1f4cxCqVmpqKhs3bmTbtm106tSJoKCgarsBvLxotVq+/PJLfvvtNz788MNql/jz3XffceDAAVavXm3Y91eZ7pZmu5ySy+mbGaRka9BKEsZKJU42pvi52OP9iNJstZUIfI+QlqshYM0RkMG0Ao8n0mj1oID1EzpTz8r0vttlWSYsLIwlS5bg6OjI8ePHHzkK1Wq1xMbGGkaEMTExuLm5FQuE1bkclPBk8vLy2LZtG+vXr8fFxYXg4GCefvrpajcdWJ4OHjzI4sWLCQwMJDAwsNok/ciyzMyZM7G1teX999+vtOs+rBj33cLbd7/3qGLctZkIfI+x93wyi3bFYlXCY0xKSy/J5Gq0zO3ny4stGz66L3v3kpiYyNixY0t1jcLCwvsCobu7uyEQ+vn5iUBYC+l0On799VciIyORJImgoCB69+5da8+PS05OZtasWdjY2LBgwQJDtaaqlp+fz9ixYw1HU1Wke49fkgErU6MSH7+Uq9GhAMPxSw/6EF5biMD3GLIs8+0f14g6cgMrs/INfnpJJletJbCzG5O6NSu3dh+nsLCQ8+fPGwLh+fPncXd3NyTL+Pn5VejZaELlkmWZo0ePEhERQXx8PKNHj2bYsGG18sOOTqdj5cqV7NmzhyVLluDn51fVXQLgxo0bTJgwgU8//ZRWrVqVe/uyLLMvNoUVv15CrZOwKWNuwt0Dd82MlUx/0Yeevk61cqZABL4SMAS/ozcwM1KVy7SnRqtHrdMT2MmNiV09qvSXq7CwkJiYGEMgjI2NxcPDo1iyjDhAtXa4dOkSkZGRHD58mMGDB/Pyyy/XysSLQ4cOsXDhQgICAggODq4WU58HDx4kNDSUqKgoHBwcHv+AEqrt708VQQS+EjJ8otp7CbW2/D5RPW56syrcGwhPnDjBhQsXaNasWbGpUREIa7akpCQ2bNjAzz//TNeuXQkKCsLT07Oqu1WuUlJSmD17NpaWlixYsKBanJC+atUqoqOj+frrr8vlRJXaOCNVGUTgK6V759AlZKxLuPanl2RyNFqUKGrcHLpGoyEmJobo6GhDIPT09DQEwrZt24pAWENlZ2ezZcsWNm7ciLe3N4GBgTz11FO15hO+Tqdj1apV7Nq1i8WLF1f5njpJknj77bdxcXHhvffee+L2qlMOQk0iAl8ZPSxrykilRAHIgK6WZk1pNBrOnTtnCIQXL17Ey8urWCCsi0fq1GSFhYX88ssvREZGYmpqSlBQED179qw1ZwMePnyYBQsWMGrUKMaNG1elU5/Z2dkEBwczadIk+vXrV+Z2qkvWeU0kAt8TurtPJi4ll1M3M0nNUVOolzBRKWlgbUY7Fzu8avk+GbVaXSwQXrp0CS8vL0OyTJs2bUQgrCEkSeLPP/8kMjKSpKQkAgICGDx4cK0Y0aempvL+++9jYmLCokWLynWdrbSuXr3Kq6++yldffYWPj0+Z2qgO+4xrKhH4hHKnVqs5e/asIRBevnwZb29vQ7JMmzZtROX6GiAmJobIyEiio6MZNmwYo0aNwtHRsaq79UT0ej3ffPMNO3bsYNGiRXTo0KHK+rJ3716++OILIiMjsbW1LdVjq6qy1JrgDjV+xgpE4BMqQUFBAefOnTMky8TFxeHj41NsatTUtHZModRGt27dYt26dezZs4fnn3+ewMBAmjZtWtXdeiJHjx5l3rx5DB8+nH/9619VNvX573//m8uXL/PFF1+Uqg9VXUu4phOBT6h0BQUFxUaEcXFxNG/e3BAI27RpIwJhNZSZmcmmTZvYvHkzrVq1Ijg4mLZt29bYRJi0tDTef/99lEolixcvrpLRrF6vZ8qUKbRo0YIpU6aU6DG5Gh2DvjyEmbGqQhJaclMTKMzNNJwveufKafLTEnHu2AeNVmL7m11q/LKNCHxClcvPzy8WCK9cuUKLFi2KBcKqqHMoPJharebnn38mKioKe3t7goKC6NGjR7XYK1dakiSxZs0atm7dysKFC+nYsWOl9yEzM5OgoCCmTZvGCy+88Nj7R99Ir9DzQlPO/UlOSjyePcfcd1ueRseKEW3wd6u69dHyIAKfUO3k5+dz5swZQyC8evUqLVq0MCTLtG7dWgTCakCSJPbv309ERARZWVkEBgYyYMCAGjlaP3bsGB988AFDhw5l4sSJlR7EL1y4wEsvvYSbmxuWlpYMGTKEgIAAdu7cSWRkJAqFAi8vLxYuXMi3e8+yYNFi5Lx0ADx7BWJiZcf5LZ/jP34hALeO/YJeW4jbs4M4u2E5lg1cyLp5GVnS4933Fayd3clJus7V3zYg6bSojE3w6vsKZrb1OLF6FpJOi4m1PS6d+iLptIZAmJKciE3sduqZ6LC3t2fevHk0bNiQ+fPnY2lpyYULF0hLS2Pq1KklCuJVpWaPV4VaycLCgqeffpqnn34aKAqEp0+fJjo6ms8//5xr167h6+trSJZp1aqVCIRVQKlU8vzzz/Pcc89x+vRpIiMj+eabbxgxYgQjR46sNrUyS6Jjx46sW7eOuXPn8tprr7F48WLq169fqX2wt7fHyMiIL774gilTptCyZUvCwsIIDw/Hzs6O7OxsAMK+/jfWLt54Pt0HWZLQa9Xo1PmPbFvSFtJ+3Dyybl7i8u7v8B+/EHOHhrQNmIlCqSIjPpb4g1vxHfI6bl2GFBvxpZz709BO4sFNuDzbne+XTmH79u2sWLGCjz/+GCiaOl6zZg3x8fG88847IvAJwpOwsLDgmWee4ZlnngGKTh84c+YMJ06c4LPPPuP69eu0bNnSMDUqAmHlUigUtGvXjnbt2nH9+nWioqIYOnQoffr0YcyYMcWO0KrOHB0d+fLLLwkPDycwMJAFCxbQuXPnSrn26dOnGTNmjOG8ze7duxMbG0vPnj0NHyDunjeYcPEcLV55GQCFUomRqcVjA1/9FkVTuLYuPug1BejU+egL1VzeFU5BRgqgQJb0j+1nfvJ1rDzfAaBfv378+9//Ntx2d7rbw8OD9PT0Ur8GlUkEPqHGsbS0vC8Q3h0RfvbZZ8THxxcLhC1bthSBsJK4u7szd+5cXn/9dTZu3MjYsWPp0KEDwcHBtGzZsqq791hKpZIJEybQrl075s6dy4ABA3j11VcrbSP/22+/zeTJkzl8+DADBgx44H0k7l+dUihVcM+qlaTX/eMO/0iCUUD8oW3YuvrgO/QN1FlpnP1+RYn6WKiXHvj9e//GqvsKWs1bjRaEf7C0tOTZZ5/lrbfeIiIigl27djFmzBjy8vL45JNP6NmzJ6+//jphYWGcPn0arVZb1V2u9RwdHXn99dfZsWMHfn5+hISEMGnSJP744w8k6cFvnNWJv78/UVFRnD9/nsmTJ5Oamlqh12vXrh379+9Hr9ezYMECfv/9d9RqNfv27SMrKwvAMNXZwKMlqWcOACBLEjpNPsYW1hTmZ6MtyEXSaUm/cqZY+39fOAZA1q04VKYWGJlaoNcUYGpVVL80Jeaw4b4qEzP0GvUD+2nVqBlpF44DsHv3btq1a1eOr0LlEcktQq2Xk5NjGBGeOHGChIQEWrVqZUiW8fX1rbVn1FUXer2effv2ERERQWFhIUFBQfTp06faj8QlSWLt2rV8//33zJ8/3zDLUBHWrVvHTz/9BED79u357bffCAgIYPfu3ahUKnx8fJg/fz5vrT3I1vAvkHLTUSiVeL44BpvGntyO3kdi9G+YWNlhZlcfM9t69yS3uJJ181Kx5Jbs21e4vOs7lMYmOHi0IfXCETq+ugxtQR4xP3yKLOnvS25JS02m4MhGmjuo7ktu6dq1q2Fdr2vXrvzxxx8V9lo9KRH4hDonJyeHU6dOGQLhzZs3ad26tSFZxtfXt1wq5wv3k2WZ48ePExkZSVxcHKNHj2b48OFYW1fvaiAnT55kzpw59O3bl9dee61Sfj/uFg9fu3ZtsZJxG44lsHL/FewsSvah4eyG5bj3eAlrZ/cn7lNmfiFv9PBkdEfXJ26rKonAJ9R52dnZnD592lBZ5tatW7Rp08awRigCYcWIi4sjMjKSQ4cOMWDAAAICAmjYsPqeAJCRkcG8efPIzc1l6dKlODk5Vej1ZFlm0aJF5OXlERoaaigUUNp9fOUZ+MQ+PkGopbKzs4uNCG/dukXbtm0NgbBFixYiEJajlJQUNmzYwPbt23nmmWcIDg7G29u7qrv1QJIkERkZybp16/jggw/o0qVLhV6vsLCQCRMm0LNnT4KDg4GKr9zyMDpJEpVbBKGuyMrKKhYIb9++jZ+fn2FqtHnz5rXm+J6qlJuby9atW9mwYQPu7u4EBwfTqVOnalkS7fTp07z//vv06tWLN954o0I/CKWkpBAcHMzChQvp1KkTIGp1PikR+AShlLKysjh58qQhECYlJdG2bVs6dOhAhw4d8PHxEYHwCWi1WsPZgEZGRgQGBtKrV69qN8rOzMxk/vz5ZGVl8eGHH+Ls7Fxh14qOjmbWrFmsXbuWRo0aVdnpDGFjO+DZoHqvx5aECHyC8IQyMzOLBcLk5ORiI0IRCMtGlmUOHz5MREQEt27dIiAggKFDh1arswElSWLdunVERkYyZ84cunXrVmHXWr9+PTt37iQ8PBxTU1NxHt8TEIFPEMpZRkYGp06dMiTLpKam3hcIa2JB56oUGxtLVFQUR48eZciQIYwePbrSS4o9ytmzZ5k9ezbPP/88U6ZMqZDtMbIsM3fuXJRKJQsWLOBOXqE4gb2MROAThAqWnp5ebI0wNTWVdu3aGQKht7e3CIQllJiYyLp169i9ezc9evQgMDAQDw+Pqu4WUJQUNX/+fO7cucPSpUtp1KhRuV9DrVbzyiuvMGTIEEaNGsXe88ks2hWLlalxhSS66CWZXI2Wuf18ebFl9c24La1qH/hyNTouJWdzOSWX0zczSMnWoJUkjJVKnGxM8XOxx9vJCp+GNjU+00ioG9LT04tNjaalpRULhF5eXiIQPkZWVhabN29m48aN+Pr6EhwcTLt27ao8EUaWZTZs2MDatWuZPXs2PXr0KPdr3L59m1deeYVly5bh5+fHt39cI+rIDazMyjf46SWZXLWWwM5uTOrWrNzarQ6qbeCLS8lhy8lb/BqbAoBWL6FSKjBW/e8NQauX0Euy4Xu9fJ0Y3r4JXk41f/FVqDvS09OJjo42BML09HT8/PwMyTKenp4iED5EYWGh4WxAKysrgoKCeP7556t8TTUmJobZs2fTrVs33nrrrXKvUHP48GEWLlxIREQE9evXLwp+R29gZqQql2lPjVaPWqcnsJMbE7t6VPkHivJW7QJfWq6GZb9c5Nj1dGTAytSoRJ9iiobkOhRAR3cHZvZpXmvmo4W65c6dO8UCYUZGRrERoQiE95MkiYMHDxIREUFaWhqBgYEMHDgQc3PzKutTdnY2CxcuJCUlhdDQUBo3blyu7YeHh3Pw4EFWr16NsbEx+2JTWLH3EmqthE0Zsz1lWSZbrcPMWMn0F31q1fTmvapN4JNluegH9+sl1Lry+8H19HWqdZ9WhLolLS2NkydPGpJlMjMzad++vSEQNmvWTATCe5w9e5aIiAjOnDnD8OHDGTlyJA4OVVNpRJZlNm7cSFhYGLNmzeL5558v17ZnzJiBvb09s2fPBooPHCRkrEu49qeXZHI0WpQo6sTAoVoEPlmWxVBdEEro77//LrZGmJWVVSwQenh4iEAIJCQkEBUVxd69e3nxxRcJDAzE1bVqakzGxsYSEhJCly5dmDZtWrlNfebl5TF27FgCAwMZMmSI4fsPWyoyUilRADKgu2epKDs7i06NLZg2uHOdWCqq8sBnCHpicVYQyiQ1NdUQCKOjo8nKyjKUV+vQoQPu7u51OhCmp6ezadMmtmzZgp+fH0FBQbRp06bS+5GTk8OiRYu4ffs2oaGhuLi4lEu7N27cYMKECXz66ae0atWq2G13kwPjUnI5dTOT1Bw1hXoJE5WSBtZmtHOxw8vJirfGvsSlmDPs2bMHf3//culXdVblgU+k4wpC+UpNTTUEwejoaHJycmjfvr0hWcbd3b1OzoAUFBSwY8cOoqKiqF+/PsHBwXTt2rVSPxTIsswPP/zA6tWrmTFjBr169SqXdg8cOMDy5cuJjIws07Rujx49OHr0KC4uLnz77bd07969XPpVXVVp4EvL1YgNmIJQwVJSUgzToidPniQvL6/Y1GhdC4R6vZ7ff/+dyMhI8vLyCAwMpH///pV6NuDFixcJCQmhU6dOvPvuu+Vy7a+//ppTp06xcuXKUpd3a9u2LYmJiVhYWGBiYsJnn31G//79n7hP1VWVBj5RckcQKl9ycnKxZBm1Wl0sEDZt2rROBEJZljl58iQRERFcuHCB0aNHM2LECGxsbCrl+nl5eSxevJgbN24QGhr6xOuPkiQxbdo03NzcePfdd0v8OK1Wi7e3N0ZGRmRlZeHk5ER+fj6LFi0iICDgifpUXVVZ4KuqIqtrgjvUicVbQSippKSkYskyarXaEAT9/f1xc3Or9YHw2rVrREZGcuDAAfr160dAQECFVF75J1mW2bp1K19//TXTp0+nd+/eT9RednY2wcHBTJo0iX79+pXoMYmJiXTv3h0bGxvi4uLw8PBAkiRyc3OZMWMGr776aq37+VdZ4BPHaghC9ZSYmFhsRFhYWGhYH/T398fV1bXWvRHelZqayvfff8+2bdvo3LkzQUFBtGhR8e8Xly9fJiQkBH9/f9577z1MTcu+JHPlyhUmT57MypUrS3Su4ZkzZxg1ahRWVlYkJSVhbGxMvXr10Ol0ZGZm8tZbb/HOO++UuT/VUZUEPnGQoiDUHImJiYbR4IkTJ9DpdIYRYYcOHXBxcal1gTAvL48ff/yR9evX4+rqSnBwME8//XSFPs/8/HyWLFnC1atXCQ0NpWnTpmVu69dff+XLL78kMjISW1vbx953ypQpWFtbU1BQwO3bt3F0dESlUmFhYcHkyZOZPHlymftSHVVI4Nu/fz+urq6G4rGrVq2iffv2dOzYEYDoG+lM33wWyyoIPnkaHStGtMHf7cGZT+Hh4YwfP76SeyUINYMsyyQlJRmC4IkTJ5AkqdjUaG0KhDqdjl9//ZWIiAhkWSYoKIjevXtXyOkLUPT6btu2ja+++op33nmnxNOVD/LZZ59x5coVPv/880dmrq5bt445c+ZgamqKXq8nKSmJ/v37M2XKFJ5++ukqL/9WESok8M2fP5+uXbvywgsvPPD2DccSWLn/CnYWlZdFdVdmfiFv9PBkdMcHLyR37dqVzz77DC8vr2KL3LIsI8tynd4PJQj/JMvyfSNCWZaLBcImTZrU+EAoyzJHjhwhMjKS+Ph4Ro8ezbBhw7CysqqQ68XFxRESEkLbtm2ZMWMGZmZmpW5Dr9fz5ptv0rJlS958882H3u/jjz/mk08+oV27dgQFBZGWlkZ2djazZs16kqdQrZUo8CUmJvLWW2/h5+fH2bNnqV+/Pp988gm7du3ixx9/RKvV4uLiwsKFC7l8+TLTpk3DysoKKysrli9fzpo1a+jatSvm5ub89NNP0DGAo9fT0ade4/bxPbQc/hYZ189z48+fkPQ6zO3q4933FVQm9/+wbx7dTer5IygUCuw9WuHefQS5qQlc2ROJpCvEzK4BXn3GYWxuydkNy3Hv8RLWzu5o83M4FbmYFmMX43jnHB66m6jVam7dusVzzz1HYGAgb7zxBjt27ECSJIYNG8by5ct58803adWqFRcvXqRnz57k5OQYMqZ+/PFHrl+/XuvmvwWhrGRZ5vbt24ZAGB0dDVAsEDZu3LhGB8JLly4RGRnJ4cOHGTx4MC+//DINGjQo9+vk5+cTGhrKxYsXCQ0NLdPxSxkZGQQHB/P2228/tFxacnIyBQUFuLu7G74OCAjgl19+qdQtHpWpxIFvyJAhREVF4e3tTUhICN26dePZZ581zB+vXLkSR0dHRo0add+I7+7XPXr0YNCgQdgPmklKvsyt/6zHurEnDu6tiN22klYjpqEyMeXm0d1IOi1uzw4q1o/0a+e4+dfPtBr5DipjU7QFeRibW3Lyu3k06xmArYsPNw5tQ6dR0+yF0Q8MfK3Hfwg3TmBx7QBRUVGcO3eOMWPGoFQqUSqVJCYm4ubmRlRUFM7OzgwePJjw8HBat25Nfn4+L7/8Mlu2bMHIyIjx48cze/ZsPD09K+BHIwg1nyzL3Lp1q1ggVCgUxZJlGjVqVCMDYVJSEhs2bODnn3+ma9euBAUFlft7gSzL7Nixg88//5ypU6cycODAUrdx4cIFpkyZwurVq0scPF977TWGDx9Oz549S329mqDEi2yNGzc2ZAi1aNGCpKQkrl69ysqVK8nJyaGgoIDOnTs/sg2VSsUzzzzDb5dOoWzcmvSrZ3Hv8RKZCZfIv5PImfWhAMh6HdaN7v8BZcbH0qDVs6iMizKejM0t0Wny0anzsXXxAaBBy2e4sH3VQ/ugAAoKddgYGdG/f3+Sk5PJyMigYcOGWFtbk5KSgizL2NnZAeDs7Ezr1q0BsLCw4KmnnuKPP/7A3d0dnU4ngp4gPIJCocDFxQUXFxeGDBmCLMvcvHmT6Ohojh49ysqVK1GpVMWSZZydnWtEIHR2duadd95hwoQJbNmyhTfffBNvb2+CgoLo0KFDuTwHhULBoEGDaNmyJSEhIURHRzNz5sxSnTrRokULpk6dynvvvUdERESJpmcHDhzIjh07ROC7dzFXqVSi1+uZP38+H330Ed7e3uzYscMwrfEovXr1YsMH/8ZCb4yVc9P/TmfK2Lv50nzQq8Xum514jSu/RgLg1mVwSbtqoFD+b1FW0uuAouKscRdj+evAT5iZmWFqaopWqyU9PZ38/Hy0Wi1ZWVmcO3cOCwsL9Ho9mZmZ2NjYoFQqGTJkCOHh4TRt2rRMn74EoS5TKBS4urri6urK0KFDDYHwxIkTHDlyhK+++gpjY2PDaPDuiLA6s7Gx4ZVXXmHMmDHs3r2bZcuWYWZmRlBQED179iyX5JBmzZoRERHBsmXLCA4OJjQ0lGbNSl5/eODAgcTGxvLBBx/w0UcfPTZX4bnnnmPFihWkpaVRr169J+1+tfNEaZV5eXmG/R67d+82zHNbWFiQl5f3wMf4+/uj/vsm2eo/aNSyKMvTulEzru5dR0FGCub2TugLNRTmZmLTyIP24+YZHqtQqrj518808O1UbKrTyMySrJuXsXXxJjX2CHYuRSNTU1tHcpLjsXZ2J+3SCaCoInmn9m3J1F0nISEBKJpOMDMzMwT3goICDhw4QHJyMmfOnGHYsGHk5uZiY2ODvb09p06dQpIkAgMD+fvvv7G3t8fOzu6+/59kL44g1AX3BsJhw4YhyzIJCQlER0fz119/8cUXX2BqalosEDo7O1d1tx/IxMSEwYMHM3DgQA4dOkRkZCRffvklAQEBDB48GAsLiydq39zcnPnz57Njxw5effVVpkyZwqBBg0o8snznnXeYPHkyYWFhTJw48bHXeu6559i9ezdBQUFP1O/q6IkC32uvvca4ceOws7OjVatW5OfnA9C7d28WL17M999/z/Lly4s9RqlU0vHpZ9n58w5aDZoAgImFNd79xnNxx7fIei0Abl2HYu7gVOyxDh6tyUu9yemIxShURjh4tKZpt2F49x//v+QW2/p49X0FgCZP9ebi9lUknzmIg0dRNXa9JOPjbEf9AQPo3bs3M2fO5K+//sLU1BQ7Ozs0Gg25ublYWlry8ccfM23aNDZt2oRerycrK4vMzEwiIiK4fPkybdq0ITMzk1u3bhETE0NGRgaZmZlkZmaSkZGBkZGRIQje/XdvcPxnoLSyshJZo0KdplAocHNzw83NzRAIb9y4wYkTJ/jzzz/5/PPPMTMzKxYIGzasXsXnlUol3bp1o1u3bsTExBAREUFYWBjDhg1j1KhRODo6PlH7AwcOLDb1GRISUqKgamxsbBgxNm/enK5duz7y/gMGDGDZsmUEBgbWiKnn0qiSDezVaR+fXq9nw4YNLF26FLVajZmZGXZ2dvz5558PbWPatGkEBAQY9iU+iCzLFBQUGILhvf/fGyDvvS0/Px9bW9sHjh7/+f/df7U160oQHkSWZeLj4w2JMtHR0ZibmxdLlnFycnp8Q5Xs5s2brFu3jj179vDCCy8Q792hhAAAIABJREFUGBj4RBvUAdRqNcuXL+fMmTOEhobi5eVVosedPXuWd999l7CwsEfWB5UkiSFDhrB8+XKaN2/+RH2tbkTllv9KS0vjww8/5IcffsDPz48dO3bc9/icnBzGjh2Ll5cXy5YtK//+/bdE0D8D5aO+Z2Ji8shR5L0jTXt7eywtLWvdpzeh7pJlmevXrxcLhJaWloYg2KFDhwrZalBWGRkZ/PDDD/zwww+0bt2a4OBg2rZt+0R/k7t27eKTTz7h9ddfZ+jQoSVqa8uWLWzcuJG1a9c+crS4evVqsrKymD59epn7Vx2JWp3/EB0dzZ07d8rtnKyKJMsyubm59wXDf44s7/2/sLDwgdOuj/peaY84EYSqcm8gvHsMk5WVVbGp0eoQCNVqNTt27GDdunXY29sTFBREjx49yrzUER8fT0hICM2aNeP9999/7NSnLMssWrSIvLw8QkNDHxosb9++zdixY9m9e3eFVaupCnXudIawsR3wbFB3T2coLCx84DTrw6Zis7KysLCweOQo8p8B09zcXIwqhWpBkqT7RoS2traGIOjv70/9+vWrtH/79+8nIiKCrKwsAgMDGTBgQJkS4zQaDR999BHR0dGEhoY+tkB1YWEhEyZMoGfPngQHBz/0fhMnTmTMmDH06NGj1H2qrsR5fMIj3T2e5GHB8UEjTb1e/9CEnn8GTDs7O2xtbWtlPUCh+pEkiWvXrhULhHZ2dsVGhFWRvi/LMqdPnyYyMpKYmBheeuklXnrpJcN+4tLYs2cPK1asYPLkyQwfPvyRH0JTUlIIDg5m4cKFdOrU6YH32b59OwcOHODjjz8udV+qK3ECu1DuNBpNiRN6MjIyyM7OxsrK6pGjyH8G0dJs4BWEh5EkiatXrxoC4cmTJ7G3tzesD7Zv377SA+H16//P3pnHRVXv//85C4Lsi4oICJiI4g6jpuWS10rTzNLSFLjZ17K9X7e87ktpRlm3W/fe9lxmwH3L3NIWy7RUFFcUXFBQZFGWYRhmPef3B5e5oqCAwAxwno+HD3XOmXM+M3NmXufz/rzfr3c6CQkJ/PzzzwwfPpxJkyYRFBRUo2NkZGQwY8YMQkJCmDNnDm5ublXue/jwYWbOnMny5csrrZksKSlh5MiRbNq0CR8fnxq/HkfErsIHsPtUNgu3p+Du7FQviS5WQURnNDP3kUge7OpYac8SZQiCgFarrVZCT7mIymSy284ib95WbkAgIXE7BEHg3LlzNiFMTk7G19e3Qmj0bssRqsu1a9dYs2YNGzduRKVSERcXR9euXav9fJPJxEcffcSBAweIj4+/bWbmypUr2bZtG0uXLq00zDp37ly6du3KhAkTavVaHA27C58oiny99wIJf17C3aVuxc8qiOgMZmLuDeH5QdV3OZBwfG4sFalMJG8WzBsNCO6U0CMZEEiUc6MQJiUlkZycTKtWrSoIoa9v5S3O6gq9Xs/mzZtZuXIl7dq1IzY2lvvuu6/aN3K7d+/m/fff5/nnn+fJJ5+sNPQpiiJz585FLpfz9ttv37LPwYMH+eSTT0hMTKyT12Rv7C58cIP4HbiEi1JRJ2FPo9mKwWIlpl8Izw3sICVbNHNuNCCobBZZ2UxTMiCQuBlBEEhLS7OtDyYnJ9O6dWubCEZFRdWbEFosFn788UfUajVms5nY2FiGDx9erVrezMxMZsyYQWBgIHPnzsXD49YEP4PBwOTJkxkzZgzjx4+vsE0QBEaNGsWnn37aJPyJHUL4oEz8fkzJYcnuVAxmAc9aZnuKoojWYMHFSc60ByOk8KZErajMgOB2CT0FBQWUlpZKBgTNDEEQSE1NrSCEbdq0sSXLREVF1fm6mCiKHDp0CLVazblz55gwYQJjx46tVMxuxGQy8c9//pN9+/bx3nvvERkZecs+V65cYfLkybz//vv07t27wrb//Oc/mM1mprz4CqnZWtJydBzNLCBHa8QsCDjJ5fh7OtMr2IdO/u5EtPW8pV7aUXAY4Svnms7I+zvPcDA9HwERj2qu/VkFkWKjGTky+ob5Mn14ZymRRaJBkQwIJARB4MyZMzYhPHr0KP7+/hWSZWqTqVkVaWlpJCQk8PvvvzNq1CgmTpx4Rwu3n376ifj4eJ599lkmTJhwy/W0f/9+3nnnHdRqdYWax1+TU/l/n6zCu/tgZMgwWwUUchlOiv9FOcxWAasg2h57KNKfsVFBhPs7VgmZwwlfOWdzitlw5DK7UnIAbG+yUiFHRlmXBUsjeZMlJCpDMiBo+litVlJTU23JMkePHiUgIKBCaLQuhDAnJ4dVq1axZcsW7rvvPmJjY29bx3flyhVmzpxJmzZtmDdvHp6enhW2L126lN9++42vvvoKrUm0TUZycnNp4+uJ1x1ml1CeWGhBBg43GXFY4StHZ7SQmq3lbI6O5MxCcosNmKwCLRRy2ni40DvYm3AHn1ZLSNQVkgFB48ZqtdpmhElJSRw7dox27dpVEMLy5t61obi4mE2bNrFq1SrCwsKIi4ujX79+lX6eJpOJTz/9lN9++4333nuvQsaoKIpM+/vf0bqHcNGzOwZL2fJTYWEhJSUlNSqvuHn5aVikv92vL4cXPgkJidojGRA4NlarldOnT1cIjQYFBVUQwptnY9XBbDazc+dONBoNSqWSmJgYHnrooUpn/7/88guLFy/mmWeeYeLEichkMkRR5D8/neHTHUfx9XKnta+Pbbznzp2jY8eONf7MHSnhUBI+CQmJClRmQHC79UrJgKDusFgsFYTw2LFjBAUF2ZJlevfuXSMhFEWR/fv3o1aruXz5MhMnTuTxxx+/xcszKyuLmTNn4ufnx/z581lz7BoJf16ihVwgMyOD4OBg22d25cplXF3dapW04yglZpLwSUhI3BW3MyCobIZZlQHB7dYrm6sBgcViISUlxSaEx48fJzg42JYs07t37ztmc5aTkpKCRqPh4MGDjBkzhgkTJlTwKTWbzfzrX/9ic9IFTJGj8PV0QyGXUVxcTHZ2NmFhYSiVSnQ6HXl5eYSFhdXqNTmCqYgkfBISEg1OaWlptVx6yv++nQFBVTPNpmhAYDabOX36tC1Z5sSJE7Rv394WGq2OEF65coXExER27tzJkCFDiImJoUOHDkBZVv1jn/xMbm4urX298fX1RSaTkZeXi15fauvfd/bsWUJCQmr9HtvbRlISPgkJCYfnRgOC6qxXlhsQVMelpzEbEJjNZtuMMCkpiZMnTxISElJBCN3d3St9blFREevWrWPt2rVERkYSFxfHygsKDqbn46osC2kqFEratQtALleQmZmJs3ML/P3bkpOTg0wmu6sWT/ZsHCAJn4SERJOjMgOCO61X6vV6mwFBddYrHdGAwGQyVQiNnjx5ktDQ0ApCeLNhtdFoZNu2bXy9divpwQ/RxtvDto6Ym5uLVqslMDAQZ2dn0tPTad26NS4uLmRkZNCxY8daJ6mUt4r7Jk7V4CVokvBJSEhIULaeVlRUVC2XnqoMCO5kmO7u7t6g2Ywmk4lTp07ZhPDUqVOEhobakmV69eplE8L3tqewMekiBm0+FosFX19fvL290ev1ZGVl4evri7u7OxkZGbRv356rV7No08b/tp0f7kR1moPXB5LwSUhISNQCURQpKSmpUVcRk8mEl5dXtVx6amNAkJWVxf/7f/+PtWvXVnj8iy++ICoqil69enHy5MkKQtihQwe691bx2S9ncXf3IKT/CEpL9Vy/dh19aSk+Pj54eHiQnZ2NXC5HuH6Ja5nnCR0wCqPRSGBgYK3fQ4sgYDQLbHnl/gatw5aET0JCQqKBuNmA4HYuPeUGBC1btqx2VxGDwcDMmTNvEb7bjefkyZNs/v0432zbh1UQaN1jCG5ubri6uqJUKm0lK56enjaxd3FxwWq1YjQaCQ/viFxe+zrOEqOFJeN6EB1Sv10ubkQSPgkJCQkHpSoDgqoaPGdnZ3P+/Hn8/PwwGAx4enryyCOPkJycTI8ePejfvz9Xrlzhu+++w9PTk969e5Ofn8+9k95k3vv/RF5aiO56NvrCPDw69cM5pBcuLs64uLTEYrFQUlKCIeMkJXkZ+EU/wvUD3+Hp2wprUQ4mXRFhQ8bRKkIFQOaBHeSe+hOZTIZPh26EDR6HLjeDcz9oECwmXLzbED78GUpEJ1rs/4aRg1QkJydTWlrKO++8w7Jlyzh37hwPPvggL730EgDbt29n9erVWCwWunXrxowZM2qVkCR5fElISEg4KHK5HE9PTzw9PW2lBLcjKyuLxx57jE8++QRfX18WL16Mu7s7np6etgzQ5cuX079/f0wmE8uWLcNgMPCzri1abRGmq+cJHj6F1qKVCxs/IrL3AwhiWfao0WhEEAQsVguiIGI0GjGbzRTlXWXAX2eiz88mZeO/aBWhIv/CCfLPHaVX7CwUTs6YS0sASNv2LfcMm4hXcASXft9Mxv7vaXPfWK6VmFAqlWg0GlatWsXf/vY3EhIS8PT0ZMyYMUyaNIn8/Hx2797N0qVLUSqVxMfHs2PHDkaOHFnj91USPgkJCYkmRFBQEAMGDADgL3/5CxaLBUEQGDhwIMHBwVy+fJmvvvoKgN9++40NGzZwvdsgDhbkoPBW4evXCovFglNLD4y6QmjhhsViRhQFZDJsx7NYLAiiCD4hXMnKIigoCLO+GIDCiym06XYfCqeyGj2nlm5YjHosBj1ewREAtOk6gNNbvqCdQo7BbGHw4MEAdOzYkQ4dOtCqVSsAAgMDycnJ4ejRo5w+fZq4uDigrH9gbXsfSsInISEh0YRwcnKy/Vsul2O1Wm3/FwQBo9FIeno6BQUFHDlyhPPnz1Pil4PBYECuUFBQUIDFYsFsNlNYUEBp1kH0F48hA1oPehqlQolZJkMmkyEDUCj+5wBTi5UzGWAV/zduuVxeoUxEJpNhsVgQRZFRo0bxyiuv1OJdqYgkfBISEhKNFKPRWGHN79y5c2RkZPDZZ59RWFjI77//TnFxMdevX+e7775DoVBw8eJF8vLyaNeuHSdPnkQul+MsWJEr5Dg7u+Dr64tCoSCvZUta+flhbRtMSURZhweFQoEekCvkODk5oVAo8PXxvcXBxTs0ksw/ttImsp8t1OnU0g2lixtFmWl4BXciN+VPvIM7IQKKalR49OnThzfffJOJEyfi6+uLVqulpKSEgICAGr9vkvBJSEhIOADliSy3y/K8+TFBECpkeSoUCkpLS3F2diYiIoLi4mIUCgXFxcUMGTKE0aNHs2/fPj755BMMBgOjR49Gr9dj6taJzOP7EEWRoqKisiSW0lIMRiOenn4olUq0Wi1yuRylUolVoUCpVKJQKvHwvLX43LdDd0pyMzmqXoRMocS3Q3dCBz1Bp5HP/i+5xas14SMmY7AKuDjdWYo6dOjAiy++yCuvvIIgCCiVSqZPn14r4ZOyOiUkJCTqgRtLF6qTkVneO7G6fRPLu1zUtCBer9fTsmVLMjIymD17NjqdjquuHchvq8LTRYmrqytubq6AjPz8fAoLC3F3d8fNzY1r167h5uaGXq/H3d2d4uJiOna8h7KAZe0o1Jt4eUhHJvS9c/JOXSHN+CQkJCTugCiKttlYVY2AbxY3k8lUpd1Zx44db9nm5eVVo2L1mo4/MzOTw4cPs2LFCg4ePIgoinTs2JHXX38d99AefPBrFm7OSgwGA3l5eRQX6/D29qZDhzCKi4vJzc2lbdu2FBcX07JlS0RR/G/T3LtzonFSyAn3r9xPtL6QZnwSEhLNDrPZbLMnq47rSlFREc7OzlXOwCqbnbm5udmt2aooily+fNnm0HL48GEAm1VZdHQ0gYGBtvEVG8w8/NFP6IryMRkN+Pj42vrtZWVlYbFYCAoKRKstRqston37EM6fP09YWChOTrX3K7WXc4s045OQkGjUiKKIXq+/Yyjxxm2lpaU2Q+qbhSw0NPQWhxQvLy+HM6S+EVEUuXLlik3kkpKSEEXRJnTPP/88QUFBtwix1Wrlxx9/RK1WI7h3wymgO+2Dg5DJ5JSW6rly5QoeHp4EBQWh1+u5fv06YWFhlJSU4OzsfFeiB6AzWhjZLaBBRQ8k4ZOQkHAwbmxBdKf2Qze2IKrKzqt9+/a3+F82xhZENyKKIllZWTaRO3z4MFar1SZ0U6ZMITg4uMoZp16vZ/PmzaxatYqAgABeeOEF2oT34HnNEUQR8vOvc/36dQICAvDw8MBsNnPlyhUCAwNxcnIiOzv7v2HOu3sNcmSMiw66q+PUBkn4JCQk6g1RFDEYDNVuOFtYWIhOp6swG7sxlBgUFET37t1v2dYUm87eTFZWlk3kDh8+jNlsrrbQlXP9+nVWr17Nxo0bUalUxMfH07VrV9v2XoHu7D52EbnFQFhYGE5OTgiCwOXLmfj5+eHm5obFYkGv19+VOTWA1mChX5gvHds0bEsikIRPQkKiBgiCgFarvWMo8cZ/y2SyKrMS27Vrd8tjnp6ejXo2VldcvXq1gtAZjUZUKhUqlYpnn32W9u3bV3sN8eLFi2g0Gn7++WeGDx/OihUrCAqqONM6duwYZ1a/T8seE/FtG4yTkxIQyc7OpkULZ/z8ylxStNoiPDzubsZsNFtxcZIzfXjnWh/jbpCET0KiGWM0Gu/YyfzGv7VaLe7u7pUKWUBAAF26dLkl0cPFxcXeL7NRkJ2dXUHoDAaDbUb3zDPPEBISUqNkGVEUOXr0KBqNhpMnTzJu3Dg2bdqEt7d3hf0EQUCj0ZCYmMjcuXMx+IazcHsKTgqRosICDIZSQkPDKM/eLCwswt/fv9av0yqIGCxW5j4SSSt3+8zUJeGTkGgi3FgAXd2u4xaL5Za1sfL/R0RE3BJS9PLyQqGofQsaif+Rk5NjE7qkpCQMBoMt4zIuLo7Q0NBaZYUKgsCePXtQq9UUFhYSExPD4sWLK70BKSgoYP78+eh0OjQaDf7+/oiiSPr1Er797SwF164TFhpim90ZDAasViuurq61es1WQURnMBNzbwgPdm1bq2PUBQ5fzqAzWkjN1pKWo+NoZgE5WiNmQcBJLsff05lewT508ncnoq1ng2cGSUjUJzf3brtTokd5AXR1+rbdTQG0RO3IycmpkIyi1+ttQhcdHU1YWNhdfRZGo5Hvv/+exMREvLy8iIuLY8iQIVWGJJOTk5k9ezYjRozgxRdfrFBDmJuby6i3PobwgXi7u+HspLC9BplMRps2bWo+PrMVg8VKTL8QnhvYwa7XncMK39mcYjYcucyulBwAzFYBhVyGk+J/H6LZKmAVRNtjD0X6MzYqiHD/hl8slZC4HeUF0FVlJVY2OysvgK6sK3dlafj1WQAtUXNyc3MrhC51Op1N5FQq1V0LXTmFhYWsXbuW9evX061bN+Li4ujZs2eVxxYEgeXLl7N69Wrmz5/PfffdV2G72Wxm6tSpDBhwHyH9R7JkdyoGs4CHs4Jz584RGhpao9IOURTRGiy4OMmZ9mCEXWd65Tic8F3TGXl/5xkOpucjAu7OShTyO18cVkFEZ7QgA/qG+TJ9eGe7xY8lmj4Wi6VS4apqdnZzAXRVjh43PmbPAmiJmpObm1uhYFyr1dpErnxGV5dJO5cvXyYxMZEffviBoUOHEhMTQ2ho6G2fk5+fz7x58zAYDCxevLjSmdt7773HtWvXWLJkCXK53Pab/OvpK2i1xXQIblft3+Rioxk5Mof7TXYY4RNFkR9TcliyKxWDRcDTRVmrL/3NdxfDIv2lHw+J2yKKIqWlpTUyB765ALqqUOKNfxy5AFqi5uTl5VUQuqKiogqhyw4dOtRLduqpU6dQq9UkJSUxduxYxo8fj5+f3x2fd/jwYebOncuoUaOYOnVqpWu1W7ZsYcWKFajVatzc3Cpsm/r3BZgDe3PRUla/Vx6FUyrkyAARsDSSKJxDCJ8oiny99wIJBy7holTY4sl3gyPFkyUalqoKoKuanRUWFqJQKGpkDtzYC6Alas61a9cqOKMUFhZWELp77rmn3q4JQRDYt28fGo2Gq1evMnHiRB577LFqJZkIgsDSpUtZt24dCxYsoH///pXul5KSwmuvvcbXX39NWFhYhW1arZbRo0fz/fffI2vRktRsLWdzdCRnFpJbbMBkFWihkNPGw4Xewd6EO3jehd2FzyZ6f17C3cWpWlPo6nJjBtHzg+6ps+NKNCzls7HqJnrodDo8PT3vGEq88e/mUAAtUTOuX79eIRmloKCAqKgom9B17Nix3m9+TCYTO3bsICEhgRYtWhAXF8ewYcOqnVmbn5/PnDlzsFgsvPvuu/9rGFvJfrGxsbz11ls88MADt2xft24dycnJLF68+K5ej6Ngdzn+MSWHhAN1L3oACrkMdxcnEg5cIszPzSEWVZs7NxZAV8ccuLCwEFEUbbOvmwUrICDgltmZVAAtURvy8/MrJKPk5+fTu3dvoqOjGTduXIMIXTlarZYNGzawZs0awsPDmTZtGn369KlR5OrQoUPMmzePxx57jOeee65KsbRarcycOZORI0dWKnoA33//PS+88EKtXosjYlfhu6YzsmR3Ki5KRZ2LXjkKuQwXpYIlu1PpHeLjMIurTYU7FUDfvO12BdBt27a9pQC6POVeQqKuyc/Pr7BGd+3aNXr37o1KpeKJJ54gPDy8wW+gsrOzWblyJVu3bmXgwIH861//Ijw8vEbHEASBr7/+mk2bNvHOO+/Qt2/f2+7/ySef0KJFiyqFLT09ndzcXO69994ajcORsavwvb/zDAazgFdLp3o9j7OTgqJSM+/vPMOScT3r9VyNmfIC6Nul2FenALr8b6kAWsKRyM/P58iRIzahy8vLs83oxowZQ6dOnewWKUhLS0OtVrN//34ee+wxVq9eXatauWvXrjFnzhwAEhISaNWq1W3337lzJ7/++isajabK175161YeeeSRJhVFsZvwnc0p5mB6Pp4uDTMETxclB9PzOZtT7HAZRvXF7QqgK0v0uF0BdKtWrQgPD79F3KQCaAlHpaCgwCZ0SUlJ5Obm2oRu9OjRRERE2PXHXBRFDhw4gFqtJj09naeffpoZM2bg7l67pqwHDhxg/vz5PPHEE0yZMuWOry0tLY0PP/yQzz77DE9Pz0r3EQSB7du385///KdWY3JU7CZ8G45cRoQG+9GUyWQIiGw4cpkZI7pUus+1a9d49913kcvlfPTRRw0yrupS1wXQHTp0uCX9XiqAlmjMFBYWcuTIEds6XU5ODr169SI6OpoFCxYQERHhENEGi8XCrl27SEhIwGq1Ehsby8MPP4yTU+0iX1arlS+//JLvv/+eRYsWoVKp7vgcrVbLtGnTmDZtGp06dapyvwMHDtC6dWs6dOhQq7E5Knb5ldMZLexKyWnwVFd3ZyW7UnJ4ZWh4hXNbrVYSExN5//330Wq1dOlSuTDWJbUpgG7RokWVKfZhYWFSAbREs6KoqKjCGt3Vq1dtQjd//nyHEbpy9Ho9mzZtYuXKlQQHB/PKK6/Qv3//u/qO5uXlMXv2bJRKJYmJifj6+t7xOYIgMHv2bAYPHszDDz982323bt3KqFGjaj0+R6XBlGfgwIHs3bsXgNRsLUCFhBaLQU/u6QO0612WVWQsLuD8T6uIHPNSnY0hZc2HtB3wBKnZPYgOKbtAjh49yksvvURqaqqtRUp+fn6NjntjAXR1ne5vVwAdEhJCr169pAJoCYkb0Gq1FYQuKyuLnj17olKpmDt3Lp07d3YooSsnLy+P1atXs3nzZvr168eSJUuIjIy86+P+8ccfLFiwgKeeeorJkydXO2z7xRdfYDKZeP3112+7n06nY9++ffz973+/67E6GnUqfKIolnXVvVNsOUeH2SpUeMxi1HM1+Reb8Dl7+NSp6NnOIwiczdER5gHx8fFs3LgRuVxuc0IXBIGioiLy8/Or1TSzOgXQ5R2gpQJoCYnqo9VqKySjXL582Tajmz17Np07d3bo0PyFCxdISEhgz549jBgxAo1GQ7t27e76uFarlc8//5zt27fz3nvvERUVVe3n/vLLL2zfvh21Wn3Hm4Tdu3fTt2/fu+607ojc9VWTlZXFK6+8Qrdu3Thz5gzDhg3j999/x2Qy8cADDzB16tQK++v1ej6Y+xZZefkoEAkdOAa/8N5c/HUDhsI8jix/G+/QSNr1foBTGz4l+tl3ECxmzu1KoDj7IjK5nA5Dx+PdvjM5J/Zx/fxRBLOJ0sI8WoVHETZkHKIgkLZzObrsi4CMtj3uJ1D1IABF55N5c8pS8s8extnZmZYtW2IymcjMzMTFxYXS0lLMZjNdu3ZFLpcTGRlJ7969K3SA7tatm1QALSFRx2i1WpKTk23JKJcvX6Znz55ER0czc+ZMunTp4tBCB2U3/0eOHEGtVnP69GnGjx/Ppk2b6kw8cnNzmTVrFi4uLiQkJFQrtFlOeno67777Lp9++mm1nvf999/zzDPP3MVoHZc6uYoyMzN5++23KSkp4aeffmLFihWIosjf/vY3jhw5UuGOxNnZmcjHX6ZVKSjMeo4mLMa3Yy9CB4+l5NoVop6ZD4Ch6JrtOVnJP4MMop99G/31q5xc+w9Uz5U5CJTkZNL7mXnIFU4kfTObdlFDMem1mIoLiH72HaAsjFqOHBHfQZMoyTiJ0WjEw8MDJycnRFEkKCjINpP7448/8Pb2ZuLEiUybNq1KxwMJCYnaUVxcTHJysi0ZJTMzkx49ehAdHc2MGTOIjIx0eKErx2q18vPPP6PRaNDpdMTGxrJkyZI6XZ7Yt28fb7/9Nk8//TR//etfaxQxKikp4a233uK1116rVpg1IyODy5cvM2DAgLsZssNSJ1dVQEAA3bt355///Cd//vknkyZNAspmd5mZmRWETxRFUn5cx9XzKSgUCky6Qsx67W2Pr718jnZRQwFw9QvA2csPfX42AN4hXVA6l/nVubZqh0F7HVe/dhiKrnHux0R8O/TAJ7Sr7Vi+HXtzT5eubHkzmbFjx+Lq6sqlS5eHO4HUAAAgAElEQVSwWq0oFArkcjlubm7o9XpCQ0NRqVScOnWKIUOG1MVbJSHRbCkXuvLQZUZGBt27dyc6Oprp06fTpUuXWmc22ovS0lK+//57W83cs88+y6BBg+p0GcNisfDZZ5+xc+dOPvjgA3r16lWj5wuCwLx581CpVIwePbpaz9m2bRvDhw9vNDceNaVOXlV5Z19RFJk8eTJPPPFElfvu2LEDS2kxXSfNxtXFmYNfTkewmGt9btkNH4xMJkcUBJxauhH1zHwK0k+RffRXrqUm0WnE5LKdFEpaKOT4+PgQGBjId999h0aj4R//+AclJSWUlpZitVopLCy84bhSZqSERE3R6XQVhO7SpUt069YNlUrFtGnTiIyMbHRCV05+fj5r165lw4YN9OrVi0WLFtGjR486P092djazZs3C3d2dlStX4u3tXeNjLFu2jIKCAuLj46u1vyAIbNu2jX/84x81PldjoU7lvH///nz++ecMHz4cV1dXcnNzUSqVFeLJOp0OP18frMgpzDiDseg6AIoWLlhNhkqP6xkUTm7Kn3iHdKE0PwejNh9X37aU5GRUur9ZX4xMoaRVRDQtfduSuu0b2zarINDGw8X2f7lcTrdu3XjggQeYO3cub7zxBj/88AOiKNrSpV999dW6eHskJJo0JSUlFYTu4sWLdOvWjejoaN58800iIyMbfWZyRkYGCQkJ/PjjjwwbNoxvv/2W9u3b18u5fvvtNxYtWkRMTAwxMTG1mkXu37+f9evXo1arq7zJ2LFjB0ePHmXs2LF06tSJw4cP4+npedv6vsZOnQrfvffeS3p6OpMnl82uXF1dWbhwYQXhGzFiBF8kbiJF8w6+gR1o6VdmHO3U0h3PwI4cXjoPnw7dbdmdAO16P8C5XQkcXjofmVxOp0eeRa6s+k7RqCsgbcdy+G/jidBB/5uBCoJI7+DK75r8/Py4//77CQ0N5ZtvvqGwsJApU6ZI63sSEpVQUlLC0aNHbckoFy9epGvXrkRHR/PGG2/QtWvXRi905Rw/fhyNRmMTiPXr19cosaQmmM1m/v3vf/PTTz/x4Ycf1nommZmZyfz581myZMltf8POnz/PP/7xD5YuXUpoaChubm488sgjtR1+o8AubYkOX8pn2vrjuNmhV1OJ0cKScf+r47uZr776ipYtWxIbG9vAI5OQcGz0ej1Hjx61JaOkp6cTGRlpa9PTrVu3JiN0UBby27t3L2q1mry8PGJiYnj00Ufr1TQ9KyuLWbNm4ePjw4IFC2qdDVpaWmpbdnrqqaduu+/GjRuZPn06np6elJSUcOnSJQICAhgyZAiTJk1i4MCBTepzBTs5t0S0LfOFswpivXVlqAyLIFQ4v4SERNWUC1156PL8+fM2oXv99debnNCVYzKZ2LZtGwkJCbi5uREbG8vQoUPrvTj+119/5d133yUuLo5JkybVOrdAFEUWLlxI586defLJJ++4f+vWrVEoFMhkMqxWK56ennh7e7N37152797NoEGDSExMrNVYHBW7CJ+7s5KHIv3ZfjK73jsz3IjOaGFkt4DbWqU9//zzDTYeCQlHQq/Xc+zYMZvQnTt3ji5duqBSqXj11Vfp3r17kxS6crRaLevXr2fNmjV07tyZWbNmERUVVe/JbWazmU8//ZQ9e/bw0Ucf0b1797s6XmJiIhkZGXz77bfVGru/v7/t34WFhfj6+trWE/38/Jg9e/ZdjccRsVuu6tioIHaczEYUxQbJmhRFETkyxkUH1fu5JCQaA6WlpRWE7uzZs3Tp0oXo6Ghefvllunfv3iyMGbKysli5ciXbt29n8ODBfP755w1mypyVlcWMGTNo3bo1iYmJVXZJqC6HDh1CrVazYsWKan92rVu3xmKxYDabMRqNuLu7U1RUhI+PD6tXr25yBtVgR+EL9/egb5gvB9LzG2TWpzVY6BfmS8c2zaMlkYTEzZSWlnL8+HFbMsrZs2fp3Lkz0dHRvPjii/To0aNZCF05p0+fRqPRcODAAcaMGVPrHni15eeff+a9995j8uTJPP3003c9AcjOzmbOnDm8++67BAQEVPt55SUShYWFeHh4UFRURGBgIKtWraoTizVHxC7JLeVc0xmZ+M2fIJY1i60vjGYryGDllHulDuwSzQaDwcDx48dtyShpaWlERETYklF69Ohhq8FtLoiiyB9//IFarSYzM5OJEycyZswY3NzcGmwMJpOJf/7zn/z+++/Ex8fXiWG10WhkypQpPPTQQ7VKzLv33ntJSUnBy8uLrl27kpiYiJ+f312Py1Gxa1l+K3dnpj0YwcLtKSgV8npJdLEKIgaLlbmPREqiJ9GkMRqNFUKXqampdOrUiejoaKZOnUr37t3rNSPRkTGbzfzwww+2TuOxsbE89NBDDe5McvnyZWbOnElAQACJiYl4eNx9BEoUReLj4wkODiYmJqZWx2jZsiVms5m+ffuybNmyuw65Ojp296MZFulP+vUSEv68hLuLU52Kn1UQ0RnMxNwbwoNd29bZcSUkHAGj0WgLXR4+fJgzZ84QHh6OSqXiueeeo0ePHs1W6MrR6XRs3LiR1atXExoayhtvvEG/fv3s4sb0448/8v777zNlyhSeeuqpOhvD+vXrSUlJYfny5bU+plwuR6VSkZCQ0CyuGbuGOssRRZGv914g4cAlXJSKOgl7Gs1WDBYrMf1CeG5gB8l2TKLRYzKZKgjd6dOnCQ8Pt4Uue/bs2Sx+tKpDbm4uq1at4rvvvmPAgAHExsYSERFhl7GYTCY+/vhj9u/fT3x8fJ02uj527BhvvfUWS5cuJTg4+JbtOqOF1GwtaTk6jmYWkKM1YhYEnORy/D2d6RXsQyd/d9q5QmsfzybrzXkzDiF8UCZ+P6bksGR3KgazgKeLslZiJYoiWoMFFyc50x6MkGZ6Eo0Wk8nEyZMnSUpKIikpidOnT9OxY8cKQufq6mrvYToU586dQ6PRsHfvXkaNGsXTTz9do0SPuiYjI4MZM2bQvn175syZg7u7e50dOy8vj7i4OObMmcN9991XYdvZnGI2HLnMrpQcAMxWAYVchpPif7ZnZquAVRBtjz0U6c/YqCDC/Zt+AqDDCF8513RG3t95hoPp+QiIeDhXL/xpFUSKjWbkyOgb5sv04Z2lNT2JRsWNQnf48GFSUlK45557bELXq1cvSegqQRRFkpKS0Gg0pKWlMX78eMaOHWv3dapdu3bxwQcfMHXqVMaNG1enUSez2czUqVMZMGAAU6ZMsT1+4++nSFnNdHV/P3VGCzJoFr+fDid85VR1x6JUyJEBImBpxncsEo0fk8nEqVOnKghdWFgY0dHRqFQqevbs2aDZho0Nq9XKTz/9hFqtxmg0Ehsby/Dhw+1eZG80Gvnoo484dOgQ8fHx9RJijY+PJzc3lw8//BC5XP6/iNmuVAyWuouYDYv0b5LLRA4rfOWUx6jP5uhIziwkt9iAySrQQiGnjYcLvYO9Cfd3J6Kt520dWSQk7I3JZCIlJcUmdKdOnbL1fCyf0UlCd2f0ej1btmxh5cqVtG3bltjYWO6777467YFXWy5dusSMGTMICwtj9uzZ9fJ5btmyheXLl6NWq3F3d5dyJGqBwwufhERjxWw2VxC6kydPEhoaWiF0WZdrPk2d69evs2bNGjZu3Eh0dDSxsbF069bN3sOysWPHDj766CNeeuklHn/88XoRi5SUFF577TW+/vprwsLC/id69ZwV//yge+rsuI6AJHwSEnWE2Wzm9OnTtmSUkydPEhISUkHo6qJuq7lx6dIlNBoNP//8Mw8//DCTJk0iKMhxrAcNBgNLliwhOTmZ+Pj4eutjl5+fT1xcHH/7298YOnQoALtPZbNwewru1cyFqClla39m5j4S2aQSBSXhk5CoJRaLpcKM7sSJE7Rv394mdL1795aE7i44duwYK1as4OTJk4wbN46nnnqqVh3I65P09HRmzJhBp06dmDlzZr0lH1mtVl566SV69OjByy+/DEjOV3eDJHwSEtXEYrHYZnSHDx/m+PHjBAcH25JRevXqZfdMwsaOIAjs2bOHhIQE8vPziYmJYdSoUQ5prbZt2zY+/vhjXn31VUaPHl2v62Aff/wxFy5c4JNPPrGtZU5bf6zBvI6LSs30C/Nlybie9X6uhkDKBpGQqAKLxcKZM2dsQnfs2DGCgoJQqVQ8+eSTLF68WBK6OsJoNLJ161Zbh4K4uDiGDBniEAkrN1NaWsoHH3zAiRMn+OKLL+jYsWO9nu+HH37gl19+ISEhwfZ+nM0p5mB6Pp4uDfMT7umi5GB6PmdziptE1rw045OQ+C9Wq/UWoQsMDLSFLqOioiShq2MKCwtZt24d69ato1u3bsTGxtKrVy+HzSK8cOECM2bMoEuXLkyfPr3e6yrPnj3Liy++yGeffVZh7TB+x+kG72daWGpiZLcAZoyoO+cZeyEJn0SzxWq1kpqaaktGOXbsGO3atasgdF5eXvYeZpPkypUrJCYmsnPnToYOHUpMTAyhoaH2HlaViKLI999/z6effsrrr7/Oo48+Wu/n1Gq1xMbG8uKLLzJ8+HDb4zqjhdH//h0XJ0W9JLRUhUUQMJoFtrxyf6MvHWvco5eQqAE3Cl35jK5t27ZER0czZswY3nnnHYdLnmhqpKSkoFarOXToEGPHjmXdunUO3/5Gr9cTHx/PmTNn+OqrrxqkMasgCMyZM4fBgwdXED2A1GwtQIOKHoBSLseIQGq2lugQ3wY9d10jCZ9Ek0UQhApCd/ToUfz9/YmOjuaxxx5jwYIF+Pj42HuYTR5BENi/fz9qtZqrV68yceJE5s2b1yjs186dO8eMGTPo0aMHK1asaDAT8C+++AKDwcBrr712y7a0HB1mq1Dlc1M2/htjcT6C1UK7qL8Q0Gsw2cf3cvnAThTOLXFvE4xMqaTjsEmY9MWc26XBqM0H4J6hE/AMqnrN0mwVOJujk4RPQsJREASBtLS0CkLXunVrVCoVjz76KPPmzcPXt3F/YRsTJpOJnTt3kpCQgJOTE3FxcQwbNgyFov5S7+sKURT57rvv+Pe//80bb7zByJEjG+zce/bsYdu2bWg0mkq7JRzNLLjtbC98xGScWrphNZs4qlmE7z09yPhjK73j5qFo4cyJNR/i1qask8OFn1YRqHoQr6BwDNrrnFz3Mar/W1TlsRVyGcmZhUzo2/7uX6gdkYRPotEiCAJnz561CV1ycjKtWrVCpVIxcuRI5s6dKwmdHSguLmbDhg2sWbOGjh078tZbb9GnTx+HTVi5Gb1ez+LFizl79qzNIaWhuHjxIosWLeKTTz6p8trN0RordFm4mawjP3E97QgAxuJ8ck/9gVdQJ5xaltmntYpQUVpQ5oFceOk0+utXbc+1Gg1YTQYULSovH1Eq5OQWG2r12hwJSfgkGg2CIHDu3DlbMsrRo0fx9fVFpVIxYsQI5syZIwmdHcnOzmblypVs3bqV+++/n08//ZTw8HB7D6tGpKWlMWPGDKKiolixYkWD1g+WlJTw5ptv8uqrr9K1a9cq9zMLVYc5CzNSKbyYQs+YmSicnDm+6gNa+gVUELcbEUWBXjGzkCurlx0qA0y3CbM2FiThk3BYyoXu8OHDJCUlkZycjI+PDyqViuHDhzN79myHT4xoDqSlpaHRaNi3bx+jR49m1apV+Pv723tYNUIURTZt2sRnn33Gm2++yYgRIxr0/IIgMH/+fNv68+1wuk1to9WoR+niisLJGf31qxRfTUcwGSm6nIa5tASlswvX047g2joQAJ/QrmQd/omgfmUJNLrcDNzbVB3GFIEWt5ltNhakcgYJh0EQBM6fP28TuiNHjuDt7Y1KpUKlUhEVFUWrVq3sPUwJyoTi4MGDqNVqLly4wNNPP80TTzzRKE23S0pKePfdd0lPTyc+Pp6QkJAGH8PSpUvZu3cvX3755R3bKk3fUObY4uFy6yxNsJhJ2fwfDEXXcPVpi8Wop/19j1Gan83lQz/g5OJGS9+2OHv4Ejroccz6Ys79mEjp9auIgoBnUDjhD8dVee5ig5l+YX68P7bHXb9meyIJn4TdEASBCxcuVBA6Ly8vW5ueqKgoWrdube9hStyAxWJh9+7daDQaLBaLrQeek1PDFVLXJampqcyYMYM+ffrw5ptv4uzc8F6U+/fvZ+HChajV6mpd76sOZvDZnnN4u1a/72D5up0oWEnZ9B/8u99Pq05RNR5rod7Ey0M6SsktEhLVRRAE0tPTbUJ3+PBhPD09UalU/OUvf2HatGm0adPG3sOUqAS9Xs/mzZtZuXIlgYGBvPzyywwYMKDRJKzcjCiKrF+/ni+//JK///3vPPTQQ3YZR2ZmJvPnz2fJkiXVvsnr5O9+2+SWyri0bwuFl04jWMz4hEbiF967NsPFSSEn3L/xzepvRprxSdQboiiSnp5uS0Y5cuQI7u7uthlddHS0JHQOzrVr11i9ejWbNm2iX79+xMTEEBkZae9h3RU6nY6FCxeSmZlJfHw87dvbZ/ZSWlrK5MmTeeKJJ3jqqaeq/TzJueXuadyjl3AoyoXuxtClq6srKpWKIUOG8Oabbza6pIfmyoULF0hISGDPnj2MGDECjUZDu3bt7D2su+b06dPMmDGDAQMGsHDhwjuup9UXoiiycOFCIiIiePLJJ2v0XHdnJQ9F+je4V6fOaGFkt4BGL3ogCd9dozNaSM3Wkpaj42hmATlaI2ZBwEkux9/TmV7BPnTydyeirWeTuGBuRBRFLl68WEHoXFxcUKlUDB48mDfeeIO2bZtO88qmjiiKJCcno1arSUlJYfz48WzatKlJ+JWKosjatWv55ptvmD59OsOGDbPreBITE8nIyODbb7+tVbh4bFQQO05mI4pig4SbRVFEjoxx0Y7TAPhukEKdteRsTjEbjlxmV0pZIajZKqCQyyrE3s1WAasg2h57KNKfsVFBjbathyiKXLp0qcIaXbnQlSejBAQE2HuYEjVEEAR+/vln1Go1Op3O1gPPXrOhuqa4uJiFCxeSlZVFfHy83bu3Hzp0iNmzZ7NixYq7+r5I/fhqjyR8NeSazsj7O89wMD0fkbKwQ3Xi7FZBRGe0IAP6hvkyfXhnh+9mLIoiGRkZNqFLSkqiRYsWFcoLmkL4q7liMBjYsmULiYmJ+Pn5ERcXx6BBgxyyB15tOXXqFDNnzmTgwIG8/vrrdhfz7Oxs4uLiWLRoEX379r2rY0kd2GuPJHzVRBRFfkzJYcmuVAwWAU8XZa1CDKIoojVYcHGSM+3BCIZF+jtMZpwoimRmZtpE7vDhwyiVSpvQRUdHS0LXBCgoKGDt2rWsX7+enj17EhcXR48ejbsu62ZEUWTVqlUsW7aMmTNnMnToUHsPCaPRyJQpU3jwwQeJi6u6Vq4m7D6VzcLtKbg7O9VLokvZDbuZuY9E8mDXprNsIQlfNRBFka/3XiDhwCVclIo6ubsymq0YLFZi+oXw3MAOdhG/cqG7MXQpl8ttQqdSqQgICHAYYZa4OzIyMkhMTGT37t0MGzaMmJgYu2U01idarZa3336bvLw83nvvPQIDA+09JERR5J133sFgMLB48eI6+07Zfpv+vIS7S92Kn1UQ0RnMxNwbwvOD7qmz4zoCkvDdAUe6sARBwGg01ro1iiiKXL58uYLQyWSyW2Z0ktA1LU6cOIFGoyE5OZmxY8fy1FNPNVlP0xMnTjBr1iweeOABXn31VYcprF+3bh3r169n2bJldd6OqanemNcnkvDdAUcJJZSUlPDKK68gCAIrVqyo1rFFUeTKlSsVhA4gOjraJnSBgYFN7qKWKLtJ+v3331Gr1eTm5hITE8Ojjz7aYP3kGhpRFElMTEStVjN79mwGDx5s7yHZOHbsGG+99RZLly4lODi4Xs5hW4rZnYrBXHdLMU0pvHkjkvDdBkdZPM7JyeGvf/0rqamptGjRghMnTlS6SC+KIllZWRWSUQRBqDCjCwoKkoSuCWMymdi2bRsJCQm4ubkRGxvL0KFDG0UPvNpSVFTEggULKCgoYPHixQ61Dp2Xl0dcXByzZ8/m/vvvr/fz3Zh8JyDiUc0bdqsgUmw0I0fWaJLv7gZJ+G6DI6QLnzlzhtjYWAoKCvDy8qKkpIRVq1bRu3eZ5VBWVpZtNpeUlITVarW5oqhUKoKDgyWhawZotVrWr1/PmjVr6Ny5M3FxcURFRTX5z/748ePMmjWLYcOG8fLLLztMaBPAbDYzdepU+vfvz3PPPdeg566q3EqpkCOjrMuCpYmVW9UESfiq4GxOMVPUSXjUMmRQU0RRpNhg4Zs4le3C+/XXX3nhhRewWq14enoCkJuby6OPPkrr1q05fPgwJpPJJnKS0DU/srKyWLlyJdu3b2fw4MHExsbSoUMHew+r3hEEgYSEBBISEpgzZw6DBg2y95BuIT4+ntzcXD788EO7lYiUG2yczdGRnFlIbrEBk1WghUJOGw8Xegd7E95EDTZuhyR8VRC/43SDWwIVlpoY2S2AGSO6kJCQwIIFC1AoFAiCQElJCXq9HpPJREhICIsWLUKlUtG+fXtJ6JohZ86cQa1W8+effzJmzBgmTJjQbHxPCwsLmTdvHjqdjsWLFzukO9CWLVtYvnw5arW6UbZqaupIwlcJ9jaBDTihYdvmDQj/7bTs5uaGq6srbm5uKBQKSkpKOHnypN2LcSUaFlEU+eOPP9BoNFy6dImJEyfy+OOP4+bmZu+hNRhHjx5l1qxZDB8+nJdeegml0vFmKSkpKbz22mt89dVXzWL23RhxvKvGAUjN1gJUED1jcQHnf1pF5JiX0OVmYNIV4tvh9kW/hRmpXDn0A13HvnbLtuOrPiBsyJN4BITZHlPK5RhEKylXCvHw8ECpVOLk5ITFYsHFxQUnJydkMhkymYyTJ08SFVXzfloSjQ+z2cyuXbvQaDQAxMXF8dBDDznkj359UZ7NvGrVKubPn899991n7yFV6pOZn5/P3//+d2bNmiWJngPTfL45NSAtR4fZKlR4zNnDh8gxLwFQkpNJcc7FOwpfbbAIItPf/ZjxfYLJyMjg+PHjHDhwgD/++IOLFy+iVCopLCzkwIEDkvA1cXQ6HRs3bmT16tWEhoby+uuvc++99za70HZ+fj7z589Hr9eTkJDgMCHd1157DblcznvvvYerqytWq5WZM2cyfPhwh3CKkaiaZiN827ZtQ6PRIJPJCA8PZ9iwYXz77beYzWa8vb1ZtGgRvr6+fPXVVyzffZgLFy+BSU9Q3+EE9ByEoegapzZ8Su+/zuPS75sRLGa0l88R3G8ELt6tOf/TKgSLGYVTC8JHTMbVt/rrDrmnD5D5x3ZAxLV9V5LveY6nVEEsW7aMlJQUZDIZ//d//8eYMWP4+OOP2bRpE+vXryczM5PFixfX35smYRdyc3NZtWoV3333HQMGDODjjz8mIiLC3sOyC0eOHGHOnDmMHDmSF154wWHKMkRRZO/evRQWFnL69Gm+/fZb1q9fj5OTEy+99JK9hydxB5qF8F24cIFvv/2WpUuX4u3tjVZbFspcvnw5MpmMzZs3s2LFCt544w0Armak0/WpaTiJFo6seAffDt1tx5IrlITcP4binIt0HDYJAIuxlJ4TpyOTKyi4mMLF3zbaZod3wlhcwMU96+n113k4ubhydPVHHDu0j9RIJ3Jzc1m7di1Q5jDv6urK3r172b9/Py1atKC4uLgu3yYJO3Pu3Dk0Gg179+5l5MiRJCYmNttuF4IgsGzZMtauXcuCBQvo37+/vYdUgezsbEpKSvDz8+PChQsMHDiQwMBAtm/f3qRMvpsqzUL4Dh06xLBhw/D29gbA09OTc+fOMXPmTK5du4bZbK7g59e6Uy/kyhY4ObXEu30ExdkXcW9TteOC1VhK2vallBbkADJEwVrtsemyL+LVPoIWrmUlDH5d+pGbfobAwElcuXKFDz74gPvvv597770XgPDwcObMmcOQIUMYMmRIzd8MCYdCFEWSkpLQaDSkpaUxfvx43nzzTVv5SnMkPz+fOXPmYLFY0Gg0DhPavJGUlBQUCgUymYyWLVty4cIFBEEgMTGRF154QRI/B6fZfjoffPAB48ePZ82aNcyePRuj0Wjbprjpor3TisrF3zfj1T6C6GffoevYVxGs5lv2ObH2Y44sf5u0ncvvODalXIanpyerVq1CpVKxYcMGFi1aBMAnn3zCU089xZkzZ4iLi8Nqrb7ISjgOVquVXbt2ERsby/vvv89f/vIXtmzZwuTJk5u16CUlJTFp0iS6d+/O559/7pCiB2XZpUajEavVSmZmJu3atcPX15ePPvqIF154AZ1OZ+8hStyGZjHj69OnD2+99RaTJk3Cy8sLrVaLTqejdevWAGzdurXC/kUXjuHUZSgKi0BRRiqhg8ZWmMUpWrhgNRps/7caS3F29wEg5+T+SsfQ/ak3Kn3cIyCM8z+twqwvRunixrXTBxk0/DEKCwtxcnJi6NChhISEMHfuXARBIDs7G5VKRa9evdi1axd6vR4Pj6bvtNBU0Ov1bNmyhZUrV+Lv78/zzz/P/fff3+xnCIIg8M0337Bx40befvtt+vXrZ+8h3ZZ9+/bh7OxMVlYWHh4eeHl5IQgCoijazAQmTZpk72FKVEGzEL4OHTrw7LPP8txzz6FQKIiIiGDq1KlMnz4dT09P+vTpw5UrV2z7d+7UiX3r/4HMrCd4wKM4e/hgKLpm2+7VvjOZB3ZwZPnbBPcbQVDfh0nbvoyMP7bWONOzhbs3oYPHcnz1h4BIy+BIHhs+jNzcXN5++23KyyzLDarnzp2LTqdDFEUmTJggiV4j4fr166xZs4aNGzcSFRXF4sWL6datm72H5RBcv36dOXPm2NxYWrVqVe/nLHc0ScvRcTSzgBytEbMg4CSX4+/pTK9gHzpV4WgiCAIpKSmUlJQgCAJ+fn7k5+cjl8ttDW/LLQUlHBOpgP0mvvrqK7JLrOwRuuBmBwufEqOFJbLlycQAABW7SURBVON6EB3SNNvGNDcuXbqERqPhp59+4uGHH2bSpEn15tDfGDl48CDz5s3jiSeeYMqUKfU+863Kw7Lcr7L8sdt5WF64cIHBgwdTWFhIq1atUCqVPProo7z44ouEh4fX6/gl6oZmMeOrKQFeLaGgzLG8oZ1bACLaNt81nqaAKIocO3YMtVrNiRMnePLJJ9m4cSM+Pj72HprDIAgCX331Fd999x0LFy6kT58+9Xq+G7sWiIC7s7LK77bLDZ1YrILI9pPZ7DiZbeta8PPPP5Obm0u7du2YPHkyU6ZMcaiOEBJ3RprxVYG9vTolGh+CILBnzx40Gg0FBQVMmjSJRx99FBcXF3sPzaHIy8tj9uzZKJVKFi5ciJ+fX72dy9anblcqBkvd9KmLUmZRkvYH77z9tnQz00iRZnxVMDYqiB0nsyu1JaoPRFFEjoxx0UH1fi6JusVoNLJ161YSExPx8PAgLi6OBx54oNknrFTGn3/+yfz583nyySd59tln6/U9urkz+d3cxMpkMrxaOmE0W9lf6k/MuNdt5VESjQ9J+Kog3N+DvmG+DdaPT2uw0C/Ml45tpGSVxkJhYSHr1q1j3bp1dOvWjTlz5tC7d+9mZylWHaxWK19++SVbt25l8eLFREdH1+v5bKL35yXcXarXjLU6ODspUCrkJPx5CYDnB91TJ8eVaFikUOdtcJQO7BKOxZUrV0hMTGTnzp088MADxMTEEBYWducnNlNyc3OZPXs2LVq0YOHChfj61n/i1u5T2SzcnoJ7NTuQ1xSrIKIzmpn7SCQPdnW8tkgSt0cSvjsgfYEkyklJSUGtVnPo0CGeeOIJxo8f3yCp942Z/fv3s2DBAiZMmMAzzzzTIOFf6YZV4k5IwncH6itkAv8VPYOZmHtDpJCJgyIIAvv370etVpOVlcXEiRMZM2YMrq6u9h6aQ2OxWPj888/ZsWMHixYtatBOItPWH2uwJYqiUjP9wnxZMq5nvZ9Lou6Q1vjugEwm47mBZX21yhfJ6+Iu0mi2YrBYibk3xHZ8CcfBZDKxc+dOEhISUCqVxMXFMWzYsGbVA6+25OTkMHPmTNzd3UlMTGzQzMezOcUcTM/H06VhPidPFyUH0/M5m1Nsq/OTcHykb3E1KBe/MD83luxOpajUXCdp0VJ40/EoLi5mw4YNrF69mo4dO/LWW2/Rp08fKWGlmuzdu5eFCxcyadIkYmNjGzyzdcORy4jQYJ+XTCZDQGTDkctSGVIjQgp11pAbC2EFRDyqufZnFUSKjWbkyGyFsNK6gOOQnZ3NypUr2bp1K/fffz8xMTF06tTJ3sNqNFgsFv7zn/+wa9cuFi9eTM+eDR/60xktjP7377g4KRrceMJoFtjyyv232JtJOCbSp1RDWrk7s2Rczyqtj5QKOTJABCw3WR+N7BZQwfpIwv6kpaWh0WjYt28fo0ePZtWqVfj7+9t7WI2Kq1evMmvWLLy8vFi5ciVeXl52GUdqdlmfzYYUPQClXI4RgdRsrWQ12EiQZnx3SbnZ7dkcHcmZheQWGzBZBVoo5LTxcKF3sDfhVZjdStgHURQ5ePAgarWaCxcu8PTTT/P4449Lht+14LfffmPRokXExsYyadIkuxbtrzqYwWd7zuHt2qLBz12oN/HykI5M6Nu+wc8tUXOkX+K7xN1ZSXSIL9EhvtJFfxN344BfH1gsFnbv3o1Go8FisRAbG8vw4cNxcmo4W7qmgtls5l//+he//PILH374IT161KwrSX1wNLPgjrM9q8nI6S1fYCouQBQF2vcfhYtPG9J/WYvVZEDZ0oOIRyYjd2rBUfW7RI59FVfftpzZ8iVeIV0I6Dmo0uMq5DKSMwul34BGgiR8EnVOdRzwL14vYf/561U64FeX/Px8du/ezfjx46vcR6/Xs3nzZlauXElgYCAvv/wy/fv3lyzFaklWVhYzZ87Ez8+PxMREh2mcm6M1VrjGKqMg/STO7t50G/c6ABajnlPrP6HL46/QwtWDvNMHubh3E51GTOaeByeStn0pgdHDsBj1VYoegFIhJ7fYUOV2CcdCEj6JOqMuHfCrk/hjNpt5/vnnOXjwINHR0XTs2LHC9ry8PFavXs3mzZvp27cvH3zwAZGRkXf1Gps7v/zyC4sXL2by5Mk8/fTTDpXtav5vd5Pb4dY6iAt71pK+Zz2+9/RA6eJGSd4VTq79BwCiINDCvWyN0ie0K9dSD3Nu9/9v796jqqzTBY5/952rXGSBispFERFEN5Zg95uNmE1zlFXePZa2Tmc8p846y6VNNePRs6bMsrWaGbPJSyJaKU5WDl0c0mNaJiZqBSklKpGCCMLesG/vfvf5Yx84YWmCsGGzn89/wF6/d+93Ac/7Pu/veZ4tZM1fdtV1NYDT/cvHF72DBD5x3a63A75O620A7PF4+LyynpnrDrJ4Yir3jIq76jrLly+ntLQUvV7P2rVreeGFFwDvvLSCggL27NlDbm4u+fn5xMfHX/fnDGROp5OXX36Zffv28dJLL/XKIbqGa7iDD46Owzz399SfOs6Z/TuJGDqSkJhBjJ39u5+81qOqtFz8Aa3BiGJvxhR+5XpED2D8hbtN0XtI4BPXpbs64K8oKqPyYjMLb03+2eC3fft2tm7d2pZme/fdd5k0aRK7du2irKyMBx98kJ07d/bYDkN/9NZbb6GqKjNmzGj3/erqapYuXUpcXBwFBQW9JrV5ubh+Jk5fbG6XTbicw9KAITiMuPQJ6E0hnDu6B1eLhabqb+kXPxzVrWBrqCU0ZhDVX+wmpP8gEm+dysn3NzJm1pNodT//L1Nxq8SGy/gpfyGBT3RaT3XAP3bsGE899RShoaHodDqampo4d+4cTzzxBMuXL2flypWYTFIj2RFWq5UVK1bgcDhIS0tj7NixABQXF/Pcc8/xyCOP8NBDD/Wq1Oblxg6J4tPvLl71NS111VTu3Q4aLRqtjuETZ6HR6fjuH2/gdtjweFQGjbsHjVbL+eOfYJ7zNDpjEBGDU6j67O8k3PLAz67rVj2Yh8iYIn8h5Qyi03qigXdtbS25ublYrVYUReHixYvo9XoiIiIwmUwcOnSIsLCwLn8vfd2aNWt48cUXMRgMREREsGvXLjZu3MiBAwd49tln/eLZ6Bdn6llceJzQHigbanYorMrLlDo+PyF3fKJT6qwOVu0+QZC++7pk6LQagvQ6Vu0+gTkhin5GDfPmzaOqqgq73U5ISAjx8fEEBwcD3vl427Zt4+GHH+6W99NbdHWZiMViYc2aNYSGhmI0Gjl//jw5OTlMnTqVgoICv6lvTB3gTcG6VY/PO7f8+Pii95PAJzpl5QffYHep3d4B32TQ0WhzsfKDbzi7/Y8UFxcTHh5OZGQkGo2G5uZmbDYbGo0Gp9PJK6+8wpw5c/pkbV53lYnk5+djs9mIioqiqamJ+vp6QkJCSEtL85ugB95dxPeOiqPoq/M+mczQyupQuC9joDSo8COS6hQdVlFjYUH+YcI72ai7ozweDxa7wrwkG0Z7PUajEbvdjt1up6WlhZaWFpqbm2lpaUGr1bJixYo+NTaoI2UiP+ZNFSto4IplIhaLhQkTJqDRaGhoaMBqtTJ48GB0Oh02m43CwsJeUZx+rXrqd3P9vBsYHus/FwmBTi5RRIf1VAf82tAkluZN9skxewNflIls2rQJi8WC1WrFaDSSnJyMVqtFVVWcTicLFy6kuLjYb56bpsSFMz4p2mfz+JrsCtlJ0RL0/IwUnogOsToUtm5/m9r927p03bqKUprrfmj7+sz+nTScLmv7Osyk56OyGqwOpUuP21u17phdUeQ9BxHBhk5faLSWieCBFUVlvPbJKe94rKYmVq1aRV1dHUFBQZhMJqxWKxaLhcbGRhITE8nJycHlcnXlR+t2SyaNJMig9U5I70YOl5sgg5Ylk0Z263FE15M7PtEhrR3wu/pm72JFKf2HZRIaMwiAhFt+0+7ngdQB31dlIpXvr+PChQskJCQwZswYsrKySE9PJyUlhcTERIxG3zd77goxYSYWT0xlRVEZep2223Yc2xU3z0weJePF/JA84xM/UVRUxJtvvomiKGRkZLB06VJ27drF66+/zgW7hrPuCEKCTQy/ZxYnijbQf1gmMak3APDpS7/lpv/4CwBVn79P7dcH0Wg0RCVnkHR7HueO7eP8sX143ApBUbGk3vcIzbVVfL3jT+hNwehMwaQ98BhnP9vVtm7D6TIq927H6VK4IzuLLX9+FqPRyP3338+UKVPYt28fiqKwcuVKEhMTe/DMdQ1flYn8552JZA8OYcCAvjcMubsuHuD/zp/dxeychJ/UmAr/IKlO0U5lZSW7d+9mw4YNbN26Fa1WS1FREa+++irr168ne96TOBvO/eI69ae+pP7bo4yd8zuy5i9j8PhcAGJGZGGe+zRZ85cREj2QmuP76Rc/nOjhY0i6I4+sf/4DwVGxbeuoiouT728k7df/Qvqc31PdYKWwsLDt55GRkWzZsoW8vDw2b97c9SfEx3xZJvKX/VXow67chsufaTQaFt6azOycBKwOV5elPR0uN1aHN+gtvDW5S9YUviepTtFOSUkJ5eXlzJ07FwC73c7x48cZN24cUVFR1LWoxIy8EaXpwlXXuXS6jNiMm9EZvGkgQ3Ao4O2ccfqTt1HsNlSXg8jEqxdGt9SfIygihuDoOGwuN2FpOZSWljJz5kwA7rzzTgDS0tLYs2fPdX323qAnykRW5fl+WrovtAa/pP6hrNp9gkabq8MbhFp5PB6a7ApBBm27hgrCP0ngE+14PB6mTJnCokWL2r63d+9ePv74Y+CnHfA1Wi2t2XKPqqKqV998crJoI2n/9K+ExQ6l5ssDXKo6cc3vTYO3du3HWp9DabVaFMW/N75U1Fg4VFlPvyDf/Fn2C9JzqLKeihpLh8dB+QuNRsPE9AGYE6LaSkJUPIRfYxrZrXqwOFxo0ZDdgckhoneTVKdo58Ybb6S4uJj6+noAmpqaSE1N5ciRIzQ2NqLzqNSf/KLt9UERMVjPezdLXPzuGB63N6UUmTiK2q8O4HY5AHDZmgFQnDaMoZGoboXasoNt6+iMJhTnT+eZhUQPxNFYh62hBg9Q8/VBsrKyuuWz97SeKhPZceR7nxyvJ8WEmViVN4Z1c2/gvoyB2F1umh0Kl1qcWOwubC43dpcbm8uNxe7iUouTZoeC3eXmvoyBrJt7A6vyxkjQ6yPkjk+0k5yczGOPPcaiRYtQVRW9Xs+SJUt49NFHmT9/PicvujBG/X+aZ0DmbZT97U8c2biMqKSMttRmdPJommurOJr/32h0eqKTR5N421QSbvkNxwr+iD44jPBBSbid3sAYOzKbig838cMXxaQ98Fjb+lq9gZTc+ZS/sxaXojBiZBrTpk3z7UnxAatD4aOyml/s/nHo1SWY5zyNIeSX79DOHHgXncHI4PGTrvia1jKRRXelBETnkZS4cJbmprHorhROnG+iosZKadUlai12nG4Vo05LbHgQ5iGRpHSg7ZvwL7KrU3TIG4fOsmbvt0SG+H6r+6UWJ7+9YzjTxw/1+bG727U0WPaoKiWvPdmlgQ+kwbIIPHIpIzpkRFxYu96QvmTQaUmJ848OIpfLz8/HaDQyffp0Vq9ezcmTJ1m7di0lJSW88847KLGpfJm/AYNOQ3RyJkl35AHe8pABY27j0plyhk2c1bae2+Wk/J019E/JYuCY26j56lOqSz4ENITGDib1vgXtjv9zZSQ6g4kL3xzmu307+bcPI0hPiOW1117j1KlTLFu2DEVRUFWV559/nqFD+97FhghcEvhEh0gH/M4xm80UFBQwffp0ysrKcDqdKIpCaWkpQ4cOZeWrf2XEtMVERUbw5baXqKsoJSbFjNvlIHxQMsl3PdS2ltvl4Jv3/kps+gTiMm6iue4Hqj77O2NmLcUQEt72PPXHYkZ4AyTA6X1vU3N8P4PG3c3ZT98jZeq/c+vo4Tx9bxIAhYWFzJgxg9zcXFwuF+plG5qE8HeyuUV0SGsHfF+3DrM6FO4dFee3z1vS0tIoLy+nubkZo9FIZmYmZWVlHD16lPDwcMLiRxA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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "CPU times: user 805 ms, sys: 98.2 ms, total: 903 ms\nWall time: 326 ms\n"
    }
   ],
   "source": [
    "%%time\n",
    "nb_model = NaiveBayes(\n",
    "    feature_vars=feature_variables,\n",
    "    dependent_var=target_variable\n",
    ")\n",
    "nx.draw_circular(nb_model, with_labels=True, arrowsize=20, node_size=1000, font_size=10, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate CPDs from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The NB model has no errors.\n"
    }
   ],
   "source": [
    "nb_model.fit(train)\n",
    "nb_cpds = nb_model.get_cpds()\n",
    "if not nb_model.check_model():\n",
    "    print('The NB model has errors.')\n",
    "else:\n",
    "    print('The NB model has no errors.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "{'<=50K': 0.6814657267519727, '>50K': 0.31853427324802736}\nCPU times: user 1min 47s, sys: 393 ms, total: 1min 47s\nWall time: 1min 47s\n"
    }
   ],
   "source": [
    "%%time\n",
    "nb_inference = VariableElimination(nb_model)\n",
    "nb_results = predict(test_no_target, nb_inference, target_variable)\n",
    "nb_mean_results = {}\n",
    "for k, v in nb_results.items():\n",
    "    nb_mean_results[k] = np.nanmean(v)\n",
    "print(nb_mean_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The Naive Bayes model has 82.799 % accuracy over the test set.\nThe Naive Bayes model has 63.297 % precision over the test set.\nThe Naive Bayes model has 79.08 % recall over the test set.\nThe Naive Bayes model has 70.314 % F-score over the test set.\n"
    }
   ],
   "source": [
    "nb_accuracy = accuracy(test[target_variable], nb_results)\n",
    "print(f'The Naive Bayes model has {round(nb_accuracy, 3)} % accuracy over the test set.')\n",
    "nb_precision = precision(test[target_variable], nb_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The Naive Bayes model has {round(nb_precision, 3)} % precision over the test set.')\n",
    "nb_recall = recall(test[target_variable], nb_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The Naive Bayes model has {round(nb_recall, 3)} % recall over the test set.')\n",
    "nb_f_score = f_measure(test[target_variable], nb_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The Naive Bayes model has {round(nb_f_score, 3)} % F-score over the test set.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TAN (Tree-Augmented Naive Bayes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "CPU times: user 13.1 s, sys: 195 ms, total: 13.3 s\nWall time: 12.7 s\n"
    }
   ],
   "source": [
    "%%time\n",
    "tan_estimator = TreeAugmentedNaiveBayesSearch(data=df, class_node=target_variable)\n",
    "tan_structure = tan_estimator.estimate()\n",
    "tan_model = BayesianModel(tan_structure.edges)\n",
    "nx.draw_circular(tan_model, with_labels=True, arrowsize=20, node_size=1000, font_size=10, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The TAN model, without the target variable, is a tree.\n"
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "tree_check = tan_structure.copy()\n",
    "tree_check.remove_node(target_variable)\n",
    "if nx.is_tree(tree_check):\n",
    "    print('The TAN model, without the target variable, is a tree.')\n",
    "else:\n",
    "    print('The TAN model, without the target variable, is not a tree.')\n",
    "nx.draw_circular(tree_check, with_labels=True, arrowsize=20, node_size=1000, font_size=10, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate CPDs from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The TAN model has no errors.\n"
    }
   ],
   "source": [
    "tan_model.fit(train, state_names=state_names)\n",
    "tan_cpds = tan_model.get_cpds()\n",
    "if not tan_model.check_model():\n",
    "    print('The TAN model has errors.')\n",
    "else:\n",
    "    print('The TAN model has no errors.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "{'<=50K': 0.7398133593008539, '>50K': 0.2601866406991461}\nCPU times: user 9min 34s, sys: 1.53 s, total: 9min 35s\nWall time: 9min 35s\n"
    }
   ],
   "source": [
    "%%time\n",
    "tan_inference = VariableElimination(tan_model)\n",
    "tan_results = predict(test_no_target, tan_inference, target_variable)\n",
    "tan_mean_results = {}\n",
    "for k, v in tan_results.items():\n",
    "    tan_mean_results[k] = np.nanmean(v)\n",
    "print(tan_mean_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The TAN model has 85.385 % accuracy over the test set.\nThe TAN model has 76.012 % precision over the test set.\nThe TAN model has 63.212 % recall over the test set.\nThe TAN model has 69.024 % F-score over the test set.\n"
    }
   ],
   "source": [
    "tan_accuracy = accuracy(test[target_variable], tan_results)\n",
    "print(f'The TAN model has {round(tan_accuracy, 3)} % accuracy over the test set.')\n",
    "tan_precision = precision(test[target_variable], tan_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The TAN model has {round(tan_precision, 3)} % precision over the test set.')\n",
    "tan_recall = recall(test[target_variable], tan_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The TAN model has {round(tan_recall, 3)} % recall over the test set.')\n",
    "tan_f_score = f_measure(test[target_variable], tan_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The TAN model has {round(tan_f_score, 3)} % F-score over the test set.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BAN (BN-Augmented Naive Bayes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the current implementation of the BAN model estimator is slow and buggy, we report the results obtained in a single run.\n",
    "\n",
    "The estimated structure is given by the following edges:\n",
    "\n",
    "('hours-per-week', 'capital-gain'), ('relationship', 'hours-per-week'), ('age', 'marital-status'), ('marital-status', 'relationship'), ('relationship', 'occupation'), ('occupation', 'sex'), ('occupation', 'workclass'), ('workclass', 'education'), ('education', 'native-country'), ('native-country', 'race'), ('high-income', 'capital-gain'), ('high-income', 'hours-per-week'), ('high-income', 'sex'), ('high-income', 'race'), ('high-income', 'native-country'), ('high-income', 'education'), ('high-income', 'workclass'), ('high-income', 'occupation'), ('high-income', 'relationship'), ('high-income', 'marital-status'), ('high-income', 'age'), ('high-income', 'capital-loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "CPU times: user 917 ms, sys: 107 ms, total: 1.02 s\nWall time: 381 ms\n"
    }
   ],
   "source": [
    "%%time\n",
    "ban_estimator = BNAugmentedNaiveBayesSearch(data=df, class_node=target_variable)\n",
    "ban_structure = ban_estimator.estimate()\n",
    "ban_model = BayesianModel(ban_structure.edges)\n",
    "nx.draw_circular(ban_model, with_labels=True, arrowsize=20, node_size=1000, font_size=10, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The BAN model, without the target variable, is not a tree.\n"
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "tree_check = ban_structure.copy()\n",
    "tree_check.remove_node(target_variable)\n",
    "if nx.is_tree(tree_check):\n",
    "    print('The BAN model, without the target variable, is a tree.')\n",
    "else:\n",
    "    print('The BAN model, without the target variable, is not a tree.')\n",
    "nx.draw_circular(tree_check, with_labels=True, arrowsize=20, node_size=1000, font_size=10, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate CPDs from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The BAN model has no errors.\n"
    }
   ],
   "source": [
    "ban_model.fit(train, state_names=state_names)\n",
    "ban_cpds = ban_model.get_cpds()\n",
    "if not ban_model.check_model():\n",
    "    print('The BAN model has errors.')\n",
    "else:\n",
    "    print('The BAN model has no errors.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "{'<=50K': 0.7285330252103173, '>50K': 0.27146697478968285}\nCPU times: user 8min 45s, sys: 1.43 s, total: 8min 47s\nWall time: 8min 46s\n"
    }
   ],
   "source": [
    "%%time\n",
    "ban_inference = VariableElimination(ban_model)\n",
    "ban_results = predict(test_no_target, ban_inference, target_variable)\n",
    "ban_mean_results = {}\n",
    "for k, v in ban_results.items():\n",
    "    ban_mean_results[k] = np.nanmean(v)\n",
    "print(ban_mean_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The BAN model has 84.381 % accuracy over the test set.\nThe BAN model has 69.685 % precision over the test set.\nThe BAN model has 67.511 % recall over the test set.\nThe BAN model has 68.581 % F-score over the test set.\n"
    }
   ],
   "source": [
    "ban_accuracy = accuracy(test[target_variable], ban_results)\n",
    "print(f'The BAN model has {round(ban_accuracy, 3)} % accuracy over the test set.')\n",
    "ban_precision = precision(test[target_variable], ban_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The BAN model has {round(ban_precision, 3)} % precision over the test set.')\n",
    "ban_recall = recall(test[target_variable], ban_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The BAN model has {round(ban_recall, 3)} % recall over the test set.')\n",
    "ban_f_score = f_measure(test[target_variable], ban_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The BAN model has {round(ban_f_score, 3)} % F-score over the test set.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAN (Forest-Augmented Naive Bayes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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66afpNmAEb3x3EXeNErPZTHJyEiqVE02aNEGhUJCZmYFOV0DLli3t8uFT/OGy5pkeBDaqfx8ut65BExBxL3HdSkQQBCwWqy0YFgdGs8WCwVI0RetuSMM3+SC6zJvo9Xo8PT3vmNDj6emJXq/nvvvuw8vLq2gZSinS0tL46quv2Lp1K//4xz8YP3487dq1q74X528W/Xi+2tdCVvRLmaTmkAKfpFSCIPDLL78QFRVFfn4+YWFhjBgxArW66FpGxOZT7ItLQpuZhq+vLz4+3oAMnU7HzZs3CQhohbICW+JUhDSdVKSsqiNKhRwZRbssWKwCVkG8Y9URs9lMXl5ehRJ6iv+r0WhuC463Xqt0cnLi+PHj7Nmzh3bt2vHss8/Sq1eval3rpjNaGPXfA2hUVXdtrzTlTcNLah4p8ElKMBgMbN26lfXr19OgQQPGjx/Pww8/XGJqzGQysXDpx2zMaEzjxn54uLrYbr927RrNmjXDxcXFLv35e9q95K86k5fSdPyZmEtiZh5/njpD7wd60MhDQ9fmXrS1Y51JURTR6XQVCpJZWVkkJCSQmpqKk5MTQUFBBAUF0aBBgzsuHSleNH8vjl/PJmLzaVwdEHwKjBYiQzrVyQLqdZEU+CRA0ULwr7/+ms2bN9OpUyfCw8Pp3Pn2EdaNGzeYMWMGLVq0oM+/XmDpz1dxU6uQIXLtWsL/RgL2efNLKeMVM2fOHBYvXsz333/PsGHDHN0dAIxGI3v27CE6Oprk5GT69u1Lhw4d0Ov1pY408/LycHFxKTfr9daRprOzc4kRZWlLbarL35faSGo2KfDVc4mJicTExLB7924GDRpEWFgYLVu2LPWxu3btIjIykueff56QkBAA2yJhbU46Cpmcpk2bULSn972RFglXTHZ2Nr169SI5OZmePXuyd+/eGrfOLi4ujujoaI4ePcoTTzzBmDFjaNiwYYnHCIKATqcrdRRZkQIEXl5eXPTsTqbcGxeVDIVCeVuij0KhQDCbOL91Jab8HERRoMWDI9F4NyLhl6+xmgwond0JGj4RucqJk1ELCX7qVVx8GnNh62d4tuxAk879Sj3HfIOZXgENWPxUp+p4SSX3SJqQrqfOnDlDdHQ0J06c4Mknn2TLli34+JQ+UjMajXzwwQccO3aMTz75hKCgINt9zz3UmuPHj/OrBZo3bYQ9gp4t7b53Udq9pGyrVq3CbDajVCpJSkpi165dNWbUVyw4OJj333+f5ORk1q9fz+jRo+nfvz9hYWG0bl307yuXy/Hw8MDDw6PML15/ZzQaSwTIBb9loy60IAhWzGZDiUSf4h9DykVMJgG/ARNQKhQYBQtXtn9BwNBncfVqgD4xjmu/fUe7YRNpMziU+Ni1+HcfhMVYWGbQA1Aq5KTnl15qT1LzSCO+ekQQBA4cOEBUVBTp6emEhoYyatSoO16Pu379OjNmzCAgIICZM2fi6upa4v6jR48ya/ZsJs/5iNXH0jGYK18IuJgoimgNFjQqORGDg6TpzXJkZ2fTp08fFAoFSUlJNG3aFD8/vxo56rtVXl4emzdvZtOmTQQHBxMeHk7Xrl3vORFm3JrDpGuNJXZXuJUoihRkpXD264/wbtsV9+YdQKXm0rcfo3IvSs5SOznh5ObJ/f8qqvxzaVcUmReP023iXNTu3mUe22C20shDzfpJve/pHCTVQxrx1QMmk4nY2FhiYmLQaDSEh4czcOBAFIo714KMjY1l2bJlvPTSS/zzn/+87YPp5s2bzJo1i4ULF/LAAx14pHPrO6Tdl80qiOQbzciR0Uva+qXCikd7xWXRXFxcauyo71aenp5MmjSJsLAwduzYwYIFC3BzcyM8PJxHHnnkroO2qpznyWQy3Hz96T5xLtlXT5P25x48W7THs0lLuoS9fdvjRUGgMCsFucoJi6HgjoFPBJzuYiNhiWNII746TKvV2r5ZBwUFMX78eHr06FHuN2uDwUBkZCR//vknixYtKnVdltFoZNKkSQwdOpSwsLAS91U27b6wsJDs07+wYeFr9GjXzF6nX6cVj/bUajUymYyEhATatm1LQUFBrRj13UoQBPbv309UVBRZWVmMGzeOUaNGVbrO6fQtRRVb3DVlL6Mx5uegcnZDrlSRdfkUN0/+gj47jaARk/DwD0SwWtDnpOPq25SkY7vQZ6fRKLg3V3/ZROdxbyFXlD5WkK7x1S7SiO8eFaeWx6fpOJmYQ5rWiFkQUMnl+Hmo6dLcm3Z2TC2viJSUFDZs2EBsbCz9+vVjxYoVtGlTsQSRhIQEZsyYQdu2bYmJiSl1GlQURd5//31atGjBuHHjbru/rZ87M4Z14JVH2pZIu0/PN2CyCjgp5DRy/yvt/tIf+wl/92NeTTrAt99+S5Mm9qn2UpcVj/bc3NywWP7axqi2jPpuJZfL6d+/P/379+f06dNERUWxatUqQkJC+Ne//oW3d9kjrVt1ae7NoStZd3xMYWYyCfu+AZkcmVxB4OBxyBQKrvz0FVajHlEUaNp9EDK5nNTTv9F1/CwUTho8m7Ul8fcdtOz7eKntWgWRrs3vvTSfpHpII767VNaoRnXLdIe5AouJK8JoNCKKYrnfgC9cuEBUVBSHDx+2Zc81atSowsfZvn07H330Ea+88gqPP/54mSPDr7/+mm+//ZYvvvjCtmv7vVi7di3Tpk1DqVQSEBDApk2baNWq1T23W1dZLBa6du2KXq9HpVJhtVpJTk6mRYuiVHq9Xk/37t3ZsmWLg3t6965fv8769evZs2cPQ4YMYdy4cbbzK4u0jk9SUVLgq6Rby0eJgJtaWeHrWDqjBRnQsxLXsUwmE6NHj6ZNmzYsXbr0tvtFUeT3338nOjqa69evM3bsWP75z3/i5uZWSmul0+v1LFmyhDNnzrBo0SICAwPLfOzJkyd58803Wbt2Lc2a2Wdact68eaxevZr8/HwaNmyIm5sbGzZsoEMHqQRUWS5fvoxerweK1mDOmDGDzz77zHa/j48P/v7+juqe3WRnZ7Np0ya2bNlCt27dCAsLo1On0qcTpcotkoqSAl8FiaLIT3FpRO6+iMFiv8zFQcF+ZbYjiiJvv/02GzduRKVS8dtvv+Hn5wcUlZ3avXs30dHRiKJIeHg4Q4YMKbOmYlmuXr3KjBkzaN++PTNmzLhjhmdGRgbh4eHMnj2bPn36VOo4d/Lss89y4MABMjMzady4MVarFZVKRVRUFN26dbPbceqqzMxMxo0bx65duxzdlSqj1+ttFYUaNmxIeHg4Dz300G3XMaVanZKKqB1Xvx2seDfnd2PjAPB0Vt116rVMJit6U4rwbmwcq367SlnfPb766is2bdqEt7c3FouFdevWUVBQQHR0NI8//jjbt29n6tSpbNy4kREjRlQq6ImiyNatW5kyZQphYWHMmzfvjkHPbDYzffp0QkJC7Br0AJKTk1GpVHh6epKXl4e7uztWq5XQ0FD2799v12NJaidnZ2dGjx7Nd999x+jRo1m9ejVPP/003333HSaTyfa4p7o1K0qgqqbv86IoIkdGSHcpKas2kUZ85SgOelW9Kejfq5OcOHGCMWPGoNFocHJyQq/Xk5WVRbt27ejbty/h4eG0b9/+ro5bWFjIokWLuHDhAosWLbItIr6T999/n8zMTCIjI+2eLdilSxdEUUQmk3HlyhXatWuHIAhkZGTg4uLCzz//TPPmze16zLqkPoz4/k4URY4fP05UVBQXL15k9OjRhISE4OHhIe3HJymXNCFdjp/i0og5Yv+gB0W7OLtpVMQcuU5AA1fbgu3U1FQmTZqEQqFAFEVSUlLQ6XQ4OTnxxBNP8MYbb9z1MS9fvsz06dPp3LkzX375ZYWSU7Zu3coff/xBVFSU3YOexWIhLy/PVjVGpVJx8+ZNXF1dGT58OKGhoXa7liipO2QyGT169KBHjx5cuXKF6OhonnjiCYYPH84zTzzNqaRcjGYr6jIWs9uD0WxFo5IzfejdfQGVOI404ruDTJ2R0NWHQaTK30DFOxC4KUWefPJJzpw5g8lkwmAw2Arzms1m1Go1hw8frvS1PFEU+f777/nkk094/fXXGTFiRIWeFxcXx9SpU1m1ahUBAQF3c3p3lJqayoMPPohMJkOhUNjqLn7zzTd22bm9PqiPI77SpKens3HjRr7//nua9RrGJbdOeLu7VEmii1RAvXaTrvHdweKdFzCYhSoNelAUVA1mgUU7LzBu3Dj27dtHXl4ezs7OtGrVCm9vb+RyORqNhry8PH788cdKtV9YWMisWbPYuHEjq1atqnDQy87OJiIigpkzZ1ZJ0IOiHccDAgJ48cUX2blzJwcOHMBqtWI0GqvkeJK6q1GjRkydOpVt27YxOLgJ1ou/kpB0E21+PkVlE+zDdomiV0sp6NVSUuArw6W0fI4mZOOhqZ7ZYA+NkiNXMzmfnENwcDCdOnWyrcEr3solMzMTs9nMtm3bKtxufHw8YWFhODs78+WXX1Y4gFmtVt566y1GjBjBgAED7uqcKsLf35+9e/cSERFBmzZtUKvVDBo0iNjY2Co7pqRuc3V1Zfz4MA6snsvQQFdSs/K4nHCdvLy8e056MZqt6AxmQns2lwqo12LSNb4ybDmRhAjVtoO0TCYDmYzwdz4pMy3aYrFgNBorNM0piiLffvstn376Kf/3f/9X6SoeH3/8MU5OTrzwwguVep49jBw5krlz5zJhwoRq3cFbUrc4OTmx7LnhDItL5d0fTpKZm09aejoNfHzw8vIqt1btrW5dhuSfcYS5Y0JxXryYp59+2m6bLkuqjzTiK4XOaGF3XFq1L0Z1UyvZHZeGzmgp9X6lUomrqytOTnfeaLOgoIC3336bzZs3s2bNmkoHvZ07d/Lrr7+ycOFCh9R7vP/++wE4e/ZstR9bUrfIZDKG3NeE76Y+woieQfj4NSXPYObS5SukpaVhNpvv+HyrIJKrN5FvsNArwIcNk3vz/IgH0el0RERE0KtXL5YvX05OTk41nZHEHqTAV4qLqVoyzx0iYe8Gu7abeelPCjJTbL9fP/A9OdfibL8r/xdkLqZq7/oYFy4UXSd0d3dn3bp1Fd7brFh8fDxLly4lMjISDw+Pu+7HvZDJZIwcOZLt27c75PiSusfXTU1kSGe+mNibtk75NGnWApMg49KNFK4lp6EtKMRgtqI3W8k3mMktNFFgtGAwWxnRsQmrw3sQGdIZXzc1nTp1wtfXF4PBQGFhIcuXL6d3797Mnz+flJSU8jsjcThpqrMU8Wk6LIJg93azLv1JgzadcPVtCkDLvk/c9hizVeBSmq7SNf9EUeSbb77h888/580332TIkCGV7p9WqyUiIoKIiIhSd2SoTsVLGf7v//6v3BGuRFIRJpOJd6e9xK5vv+X0+Xi0MlfOXM/gh4Onib8Wj7ObOy2aNaVti0Z0be5N2zKKy/v4+ODn54eTkxOpqam0atUKuVzOl19+SXR0NCNHjmTatGl1omRcXVVvA19sbCwbN27EYrHQsWNHZsyYwfbt21m3bh3xWWb0zg3RaIpqaV6MXUuDNp3wDeoBwKEPX6bP658AkHjkR9LPHUYmk+HduiMBD4dw89R+Uk/tR7Ra0Hg3ImjEJArSE8m+fAptYjw3ft9Bh8df5Mbv223t5lyLI2HfN5gtFv57ogNPRn2Ek5MTjz32GCNHjmT//v1YLBYWL158WwFnnU7H/PnzSUpKYu3ateUW8y2NIAjMnDmThx9+mEcfffTeXlw7aNy4Me3bt+fXX39l8ODBju6OpJYrLCzkueeeY9euXbi7u6PX5tC9U3O6t/RhQr8g256V0dErSXZ2ZmB4OF26l71nZbdu3fjpp59o2LAhSUlJtuzrgoICvv76awYOHCgFvhqsXk51JiQksGfPHtauXcuGDRuQy+XExsby2WefsWbNGrqFvYUxJ7XcdrKvniH78km6jH+bbhPn0qznMARBwCewC13DZ9Ft4lxcfJqQdvoAHv6B+AR2JqB/CN0mvIOz91+7JggWM/E/fkGHUS/Q6Zm56PRGNm/ebLvfy8uL9evXExISQnR0dIk+xMXFMW7cOBo0aMC6devuKugBrFy5EpPJxGuvvXZXz68K0nSnxB60Wi2hoaEcPnwYk8mEu7s76enpJR5TXBzimzAyvYMAACAASURBVG++YcqUKXzzzTf885//ZOPGjRQWFt7WZq9evbBarXh7e+Ps7ExKSgq5ubkoFAo2bNjAyJEjq+v0JHehXo74jh07xvnz5wkPDweKNl49ffo03bt3x9vbG6tcToOgHli0GXdsJ/daHI06/gOFqmhkqHJ2JSMjg8LUBHLP7MVi0COYjXi1Cr5jO4XZN9F4+uLs44fBbMWvYx/+/PNPQkNDAWzLCTp06MAvv/wCFE1tbty4kTVr1jBjxgwGDRp016/H3r172bFjB9HR0ZXKdKtqAwYMIDIykszMTHx9fR3dHUktlJGRQWhoKFevXkWlUtkyov8e+IrJ5XL69etHv379OHv2LFFRUaxevZonn3ySMWPG2CoMderUyTYF36hRIy5fvoyPjw87duyo9HV1SfWrl4FPFEVGjhzJK6+8Yrtt37597N27FwDV3zIZZXK5bf2PKAgIQulZl8VtJ+3bSOfR/8atUQvSzhwkN/FixfsGJfb0A2xvMLlcjsViQavVMn/+fFJTU1m3bt09lfS6evUq7733HsuXL7e9qWsKZ2dnBgwYwI8//sj48eMd3R1JLZOcnMzo0aNJTU3Fy8uLlJQUvLy8EASBpKSkcp/fsWNHlixZQmJiom3GZeDAgYSFhdG+fXvMZjMmkwmdTkdISAiJiYmkpqZKga8WqJdTnQ888AA///wz2dnZQNFUSFBQECdOnCAvL4+GrgoyLx63PV7j6Ysu9ToAWVdOIVqtAHi1Cib97EGs5qIqI2Z9AQBWswEnVy8Eq4X0uMO2dhROaiwmw239cfFpgjEvE31OGharQM6Fo2Vux5OTk0NYWBh+fn73vCeeTqdj2rRpTJ06leDgO49KHaV4ulOqrCepjBs3bvD444/bgp4oiuh0Ojw8PFAqlVy/fr3CbTVv3pwZM2awZcsWGjZsyOTJk5k1axbe3t5otVreeust1q5dy+LFi5k9e7aU2VkL1MsRX+vWrXnxxRd55ZVXEAQBpVLJ9OnTmTJlChMnTiTDIEPt/Vcposad+hH37X848cVcvAM62qY2fVrfT0F6IiejFiBTKPFpfT8u7fvi12Mop2LeQ+nshnvTAKymosDYqH0vLu36kpTjP9Ph8Rdt7cuVKtoOm8j5H1ZiMlvo36sbTz31VIk+i6LI9u3b+f3334mKiuKRRx65p9dAEATmzJlDz549GTVq1D21VZW6dOmCXq/nwoUL0sa0kgrLz8/Hy8uLvLw8srOzEQQBV1dXFAoFKpWqQiO+v/P29mbKlCmEh4ezbds2Dh48SMeOHWnTpg2iKNK9e3cmTJjAtGnTWLt2LRqNpgrOTGIPUpHqUhy/nk3E5tO43sUC9vT0dORy+V1fkyowWogM6VRiOYNWq2Xu3LlkZmby/vvv2yVbbPXq1Rw6dIjPPvus0gWvq9vnn39OXl4eERERju5KjSQVqS6dKIrEx8ezZcsWli5diqurK2q1Go1Gg6urK0eOHLmn9gVB4JdffiEqKor8/HzGjRvHiBEjWLBgAXK5nHnz5kmVh2qoejnVWZ6gxkULt61C9X4nKF47WHx8gNOnT9u25lmzZo1dgt7BgwfZsmULixcvrvFBD2DEiBHs2rWr3CobEsmtZDIZQUFBhIWF0a1bN6Kiohg0aBAGg8EudTvlcjkDBw5k3bp1zJo1iwMHDjBq1CiaNm1KXFwcmzZtstOZSOytXk51lsdNrWRIsB+xZ1OrZTPLYjqjhREdm+CmViIIAjExMURHRzNz5kz69+9vl2MkJiYyd+5cli5dSsOGDe3SZlXz9/endevWHDhwoEoLZkvqph07djB8+HAGDBjAgAEDyMvLIy4uzm6jMZlMRrdu3ejWrRtXr14lJiaGlJQU5syZg5eXF0OHDrXLcST2I434yvBUt2bIoNqSKkRRRI6MkO7NyM3N5Y033mDv3r18+eWXdgt6hYWFTJs2jSlTptC5c+3aMVpa0ye5G4IgsGPHjhLr6jw9PXnwwQer5HitW7dmzpw5bN26lccee4xx48bx6quvEhcXV/6TJdVGGvGVoa2fOz0DfDiSkF0toz7t/4rgFty8ymtvv83gwYN5+eWX7TYVKYoi7777LsHBwYSEhNilzeo0cOBAli1bRk5ODt7e3o7ujqSWOH78OB4eHtVegs/X15cPPviAtm3bsmHDBqZNm0azZs0IDw+nT58+1Vb8XWe0cDFVS3yajpOJOaRpjZgFAZVcjp+Hmi7NvWlXRmm2uqz+nOldmD60PaGrD2M0W6t8B3aNSk7L3JNERKxn1qxZ9OvXz67HiImJISkpiTVr1tTKC+6urq7069ePnTt3MnbsWEd3R1JLbN++3aFVVJ5//nmuXr2Km5sbPXv2ZMWKFSxfvpywsDCGDh1aZXVoL6Xls+VEErvj0oCiGsAKuazEGuFrWQUcupJlu21IsB9PdWtGWz/3KulTTSJNdd6Br5uaiMFBGCzWKkt0sQoihUYzntf3c+LQr0RFRdk96B09epSYmBgiIyNrdcFnabpTUhmFhYXs37/fodfYZDIZc+fO5cyZM+j1etavX8+0adPYvXs3jz/+OOvWrSM/P99ux8vUGYnYfIrJUX8QezYVjUqBq1qJl4sT7hoVGpXC9uOuUeHl4oSrWolGpSD2bCqTo/4gYvMpMnVGu/WpJpICXzkGBfsR1qslOoPZ7sHPKohkaQswxe3loQBPPv/8cxo3blz+Eyvh5s2bzJo1iwULFti97erWo0cPcnJyuHTpkqO7IqkFfv75Z7p27erwikQuLi4sXbqUFStWcO7cOXr27Ml///tfPv74Y65evcrjjz/OsmXLSE0tvz5wWURRZM+5VEJXHeZIQjbuGiWezioU8orN7ijkMjydVbhrlBxJyCZ09WH2nEuts4UjpMBXDplMxnMPtSasd0t0RjNGs9Uu7RrMFm5mZGO9sI9lU4YzdepUlEr7zjwbjUYiIiIIDw/ngQcesGvbjiCXy6VRn6TCHD3NeauWLVsye/Zs3nzzTVvFqHbt2jF//ny++uorZDIZoaGhzJ49m/j4+Eq1LYoiq367yruxRQk0ns6qu76cIZMVBUBEeDc2jlW/Xa2TwU8KfBVQHPxmDw8GGeTpzXf9xyCKIjk6AzdvpnLjh4/o39hSJeXCRFHk/fffp0WLFowbN87u7TvKiBEj+PHHH7Fa7fMFRFI3paSkcOXKFfr27evortj069ePUaNGMX36dCyWv+r9+vn58frrr/PDDz8QGBjIa6+9xssvv8yRI0fK/ZwpDnoxh6/jplbZLRdBrVLgplYRc/g6q367apc2axIp8FWQTCZj8H2N2TC5N70CfMg3WMjVmyo8/WkVRHL1JjLzCtBd/ZPwZjk0FTNZsWIFvXv35p133iE5Odlu/f3mm2+4cOECs2fPrpXJLGVp2bIl/v7+/P77747uiqQG27FjB0OGDKlx17SnTJmCi4sLH3300W33ubu788wzz/DDDz/w6KOP8sEHHzBu3Dh+/PHHEoHyVj/FpRFz5DpumopPa1aUQi7DTaMi5sh19py7+2nYmkgKfJXk66YmMqQzq8N7MKJjEwxmKwVGC7mFJvINZsyiDLMAerOVfIOZ3EITBUYLBrOVZtZ0NMe+5NOJfXlz6ouMGDECjUZDbm4uMTEx9O/fn6lTp1Z6quPvTp48yerVq1m6dCnOzs52OvOa47HHHmPbtm2O7oakhiqua1tTpjlvJZfLeffddzlw4ACxsbGlPsbJyYlRo0axceNGXnrpJb777jueeOIJNmzYUGJvwEydkcg9F9EoFXYPesUUchkapYLIPRfrVMKLFPjuUls/d2YM68DWV/oSGdKJl/sH0iugAV5KC+5KgebezvQKaMDL/QOZNbgVgVe24H51L1+v+ti2eLZnz574+PhgtVoRBAE3Nze2b9/O8OHDCQ8P58SJE5XuV0ZGBm+99RZz5869p50barLBgwdz5MgRtFqto7siqYFOnjyJWq2usUXNPTw8+OCDD1i2bBkXL5a9ZZlcLqdv3758/vnnLFmyhNOnT/PYY4/x3//+l8zMTBbvvIDBLFTpUisomvY0mAUW77xQpcepTlLgu0duaiXdW/owpmcLFj/ViZCGGYxvlssXE3qy+KlOtJGlseTNF+h6fzCffvopjRr9tfN6x44dUSgUNGvWjOzsbAwGAz4+Pnh4eLBr1y5efPHFStWnNJvNTJ8+nZCQEPr06VMVp1sjuLu78+CDD7J7925Hd0VSAxWP9mryFH+bNm2YPn06ERER5OXllfv44OBgFi1aRFRUFIWFhTzxzAvsPHEZtax6rnV7aJQcTcjmUpr9ll44khT4qoggCHz22WfMnj2befPm8eKLL962u3nr1q2Bom92/v7+JCcnYzQaycvLo3PnzmzevLlSlVuWLl2Kt7c3EydOtOu51ETSdKekNAaDgb179zJs2DBHd6VcgwcPZuDAgbz99tsI/ytQXx5/f3/efPNNBj83C4VCyY0bN0hMTPzfFGjVZV/KZDIERLacqPx2TjWRFPiqQH5+Pi+++CJ//vknMTEx9OrVq9THKZVKgoKCMBgMuLi44OXlRUJCAkOHDuX777+nefPmFT7m1q1b+eOPP5g/f361lUNypF69epGWlsa1a9cc3RVJDfLLL79w//3315oC7K+88gqiKPLJJ59U+Dk6o4U9p66hPbSJwMBAFIY8rhz/lYSEa2i12jIzQXNvXOTcluUVPk7mpT8pyPxrU103tZLdcWnojCUTbfbt28fVq7Ur87Puf0JWs4SEBFatWkX37t1ZsWJFufvyPfjggxgMBgoKCnBycmLAgAH4+PhUKiklLi6O5cuX2/Ycqw8UCgXDhg2T1vRJSqipSS1lUSgUvPfee+zevZuff/65Qs+JS87Byc2L+/75EnK5HIUhD2dDNr6+vmRnZ3PlyhXb5rv3IuvSn+iz/gp8yv99ob6YWvLa+r59+0hISLinY1U3qVannVitVj777DNiY2N58sknmTJlSoWe161bN0wmE56enqxbt47OnTszceJENm3axJgxY8p9fnZ2NhEREcycOZOAgIB7PY1aZeTIkbz66qu89NJL9WKUK7mz9PR0zp8/z7JlyxzdlVKlpKTw6quvcv/993P69GmCg4MZNWoUK1euRK/XM3PmTMxmM5s2bcJoNKLRaHjnnXdo2bIl27Zt45dffqGwsJD4VC26jk9wIXYlXZ+Zw/UD3yNYzGiTL9O81zBEtRvnv/kAi8mIxsWV4Meew73RnRPdEn7dTPblU8jkcrxa3Ydvu25kXz6FNjGeG7/voMPjL5J74wI3Tuzj1V3O9O0SxPz584mPj2f//v2cOHGCNWvWsGTJEubPn8+///1vgoODyc3NZfz48Wzbto2rV68yd+5cLBYLgiCwZMkSWrRoUU2vfklS4LOD9PR0Zs2ahVKpZMKECTRo0KDCz+3evTtPPvkkb7/9Nk2aNAGKrtVNnDiRdu3a0a1btzKfa7VaeeuttxgxYkS93KeuTZs2NGjQgKNHj9K7d29Hd0fiYDt27GDQoEGo1WpHd6VMiYmJLF68mNatWxMeHs7OnTtZs2YN+/fv56OPPmLFihVERUXh5eXF0aNH+eSTT1iyZAkAFy5cYOPGjSzck8D+k0VLnuQKJS37PkF+2jUCBxUVqrAY9fR5bj5mi4UbZ45yevs6Wg+bhKqMRDmzXkdW/J90n7wAmUyGxVCIUuOCT2BnGrTphG9QDwCUGhfcgh6kV0ADWqYd4IcffmD06NH069ePhx56iIEDB97x3Ddv3szYsWMZNmwYZrP5nkek90IKfPfo0KFDzJ07l3/96188++yzrFy5slLP9/X15T//+U+J2/z9/Zk3bx5vv/02UVFRJTJBb/Xxxx/j5OTECy+8cNf9r+1GjhzJtm3bpMBXzxWv3XvnnXcc3ZU78vf3JzAwEChKbnvggQeQyWQEBgbi7OxMmzZtGDhwIK1bt0Yul5dYuN6rVy88PDxI0xpL7LLwd1ajnvjYtehz0gAZTlYzCoWClJspFObnYzabSyTNKdXOyJUqLu1ch0/rTvgElr5XZ2FmMld+/Zbrgon7GztX+j3XqVMn1q5dS3p6OgMGDHDYaA+ka3x3zWq18t///pcFCxbw/vvvM3nyZLtOtz344IOMHj2aN998E5PJdNv9O3fu5Ndff2XhwoX1eprv0Ucf5eDBg+h0Okd3ReJA586dQxRF7r//fkd35Y5uDThyudxWWUYmk9mCnIeHB4MHD+bDDz8s8d4vvu5vLmekdO3A93i2CKL7s/O576lXEQUrjRo1onnz5qhUKqxWK2e+/pAT6+YRv3MdMrmCLuNn4hvUneyrpzn7ze1VZQDiY7+g5YCx9Hn+XZ577rlSP5egKGmvOMHm1scMHTqUZcuWoVaree211zh27Fh5L1eVqb+fmPcgPT2d559/ngsXLhATE0P37t2r5DgTJkygUaNGREZGlrg9Pj6epUuXEhkZiYeHR5Ucu7bw8vLigQce4KeffnJ0VyQOtG3bthq/dq8i9Ho9U6dO5fvvv+fDDz8s9TGqv33RVThpsBoNtt+tRj1qt6LNmtPOHrLdLpPJ0Wg0aDQa7v/X63Sb8A7thk7AajJgMerxad2J1o+MpiA98X/tqrGY/mrXYtKjcvVEicCPP/5ou93FxYWCggLb702aNOH8+fMAJd6XycnJ+Pv7M2bMGB5++GGH7rIiBb5KOnjwIGFhYfTp04fly5dX6ZYnxXt5nTx5ku+++w4ArVZLREQEERER1b6rdE1VPN0pqZ9MJhN79uxh+PDhju7KPQsPDyc6OhooCuZ6vf62x/h5qDFb/xr1ebZoT2HWTU6sm0fG+aM06/ko1/Z/y4l18xArUMzdajJwbstyTnzxDqfWL6b1I/8CoFH7XiQf3cWJdfPQ56TTsu8TnPvqfY5HvU+rVq1sz3/00UeJjo4mNDSUpKQkxo8fz+bNmwkNDSU3N9f2uD179jB69GhCQ0O5cuWKQ7NvZWJd3HOiClgsFlasWMHOnTtZsGBBmUknK1asQKPR8Oyzz9rt2NevX2fy5Ml88MEHrFq1ioCAAN544w27tV/bWSwWhg0bxtq1ayu19rGuyMzMZNy4cezatcvRXXGIn376iW+//ZYVK1Y4uit2tWXLFjZt2sS6detwcXGx3f7V0Rus2HcZL5fqL8CdW2ji5f6BjOnpuOtz9iCN+CogNTWVKVOmcPnyZdavX3/HTMuqULyX19ixY9Fqtbz22mvVevyaTqlUMnToUHbs2OHorkgcoHias6558skn6dixI/PmzSuxKL2dn9sdk1uqkkohp62fm0OObU9S4CvH/v37CQ8P5+GHH+ajjz7C29vbIf2wWq3I5XJEUayTG0Peq+INah2ZIi2pfllZWZw+fbpOLueRyWTMmDGDmzdv2qY/AYIaF13Xr+iWaPZi+d97q/j4tZkU+MpgNpv58MMPWbx4MZGRkTzzzDMOy55MSEhg4cKFfPXVV3h7e5e6l1d9165dO9zd3e9qRwtJ7fXjjz8yYMCAOrn9FhRtURQZGcn69es5cuQIUFQ6bEiw322lw6qazmhhSLAfburavwpOCnylSElJ4bnnnuP69ets2LCBzp1LX9dSHQoKCpg2bRpTp06lY8eO5e7lVV/JZDLbqE9SP4iiWGenOW/l5+fHe++9x+zZs0lJKSoh9lS3Zsig2mZ/RFFEjoyQ7nVjq7N6Gfi0Wi179+4t9b59+/bxzDPPMHDgQJYtW4anp2c19+4vgiAwZ84cevTowahRo4CK7+VVHw0bNox9+/aV2KxTUnddvHgRvV5Ply5dHN2VKte9e3cmTJhAREQERqORtn7u9AzwQWuonlGf1mChZ4APgY3cq+V4Va1eBr5PPvmEZ555hkOH/lrjYjab+eCDD2xBZfz48Q5fGP7FF1+Qk5PDtGnTStxe2b286gsfHx+6du1a5pcaSd1SXJDa0e/T6jJ27FgCAgJYuHAhoigyfWh7NCo5RnPV7slnNFvRqORMH9q+So9TnerHX8wtMjMz+fLLL3F2duall14iPT2dlJQUJk2aRHJyMuvXr68R1R8OHTrE5s2bWbx4cal78t3NXl71gTTdWT+YzWZ27tzJiBEjHN2VaiOTyZg1axaXLl3i66+/xtdNTcTgIAwWa5UlulgFEYPFSsTgIHzdam4N1Mqq8VcpdUYLF1O1xKfpOJmYQ5rWiFkQUMnl+Hmo6dLcm3Z+bgQ19qjQRdfPPvsMs9mMt7c3OTk5jB49GoVCwbPPPsvYsWNrROWHxMRE3nnnHSIjI++4r9grr7zCq6++yieffMKrr75ajT2suR566CHee+89UlJSaNq0qaO7I6kiBw8eJCAgAH9/f0d3pVppNJoSRewHdelCQlYBMYev46ZRoZDb7/PLKojoDGbCerdk8H2N7dZuTVBjA9+ltHy2nEhid1waAGargEIuK7F+5VpWAYeuZNluGxLsx1PdmtHWr/R56MzMTKKionB3d0cURQwGA0eOHGHq1KmEhoZW/UlVgF6vJyIigueee67caxfFe3mNHz+e4ODgcquj1wdOTk4MGTKE2NhYJk+e7OjuSKrItm3beOyxxxzdDYfw9/dn7ty5vPXWW0RFRfHcQ60pLCzk2zMZaJQK1CrFPR/DaLZisFgJ692S5x5qbYde1yw1bqozU2ckYvMpJkf9QezZVDQqBa5qJV4uTrhrVGhUCtuPu0aFl4sTrmolGpWC2LOpTI76g4jNp8jUGW9re+XKlba9oK5du4bVaqVNmzZs3ryZw4cPO+BsSxJFkXfffZf27dvz9NNPV+g5Xl5eLFmyhPfff7/W7YJcVYqnO6X1jnVTTk4Ox48fr9df9Pr06UNISAjPPPMMffv2ZdGEwcweHgwyyNOb7/pvXxRF8vRmkMHs4cFM6demRsyC2VuNCXyiKLLnXCqhqw5zJCEbd40ST+eKD90VchmezircNUqOJGQTuvowe86l2v4AMjIybItAr127hqenJ82aNUOtVqNSqXjppZfIzMyssvOriPXr13Pjxg3eeuutSv2xdejQgX//+99MmzaN/Pz8Kuxh7RAcHIxSqeTUqVOO7oqkCuzatYuHHnoIV1dXR3fFISwWCzt27OCrr77i0KFDHDlyhB49ejD4vsZsmNybXgE+5Bss5OpNFb72ZxVEcvUm8g0WegX4sGFy7zo3vXmrGhH4RFFk1W9XeTc2DgBPZ9Vdf8uQyYoCICK8GxvHqt+uIooin3zyCRkZGWRmZtK0aVOcnJzIysoiPz8fQRDQarUcPHjQnqdVKceOHSMqKorIyMi72khz5MiR9O7dmzlz5tT7ZBeZTMZjjz0mJbnUUfV1mlOv17Nhwwb+8Y9/8Nprr5GUlAQUlewrvtbp66YmMqQzq8N7MKJjEwxmKwVGC7mFJvINZvRmKwazFb3ZSr7BTG6hiQKjhfSsbDp5Wlkd3oPIkM51KpGlNA6/xlcc9Ox9cVatUqBUyIk5fJ2cnBw++OADFAoFPj4+ODs70759e7p168Z9991HYGAgrVq1su2NVd1SU1OZNWsWCxcutO3Cfjdef/11XnjhBVavXs2UKVPs2MPaZ9iwYYwZM4Zp06ah0Wgc3R2JnVy+fJmcnBx69Ojh6K5UG0EQ+Pzzz1mxYgWFhYWo1Wq8vLxITEzE3d0dX19fTpw4wdmzZ+nYsSMAbf3cmTGsA6880paLqVoupen4MzGX9HwDJquAk0JOI3cNXZt70dbPjanPPM2ms6d4tvsu8KuabdZqEocHvp/i0og5Yv+MJCia/nTTqNh6PoeQ1+bzwmN9aNu2bZVuJVRZRqORiIgIxo8fzwMPPHBPbalUKpYsWcL48ePp0KEDDz30kJ16Wfs0atSI4OBg9u3bx9ChQx3dHYmdbN++nREjRtSbtXtQ9Bnx9ddfk5ubS8OGDZHJZGRmZmK1WvH39ycvL485c+Ywffp0oqOjS3y+uamVdG/pQ/eWPnfcUUGFlYKCAsaOHcuqVat4+OGHq+PUHMahfz2ZOiORey6iUSrsHvSKKeQyXNVOJPt2o819XWpU0BNFkUWLFtG8eXPGjRtnlzYbNGjAokWLmD9/Pjdu3LBLm7WVNN1Zt1itVmJjY+t8ibK/c3Z2ZuvWrQwdOpScnBxyc3PJycmhWbNmWCwWfH19eeyxxxg5ciQzZsyw7eReGTk5Obi5uWE0Gpk8eXKd3+nEoYFv8c4LGMyCXdJv70StUmAwCyzeeaFKj1NZmzdv5vz588yePduumVOdOnXihRdeYNq0afW6fFf//v2Ji4sjPT3d0V2R2MHvv/+Ov78/LVu2dHRXqp2bmxsrV65k0qRJpKSk4Ovri1KpRK/X27ZJe/7559FoNHz88ceVattsNqPVavHy8kKv16PRaJg6dSobNmyoilOpERwW+C6l5XM0IRsPTfXMtnpolBxNyOZSWs3Iejx16hSrVq1i6dKlVVJZvqy9vOoTtVrNI488IhX0riOKS5TVVwaDgdOnTzNjxgxcXFzIzc1FEAR69eoFgFwuZ8GCBfz222+V+pvPyMhAqVTi4eGBwWBAoVDg6urKrFmzWLlyZZ38/HBY4NtyIgkRqm2NiEwmQ0Bky4mkajnenWRkZDBjxgzmzp1Ls2ZVU+28rL286pvi6c66+OatT7RaLb///juDBw92dFcconiNb3BwMLNnz2bHjh0EBARgNBrp1KmT7XEeHh4sXbqUZcuWER8fX6G2MzIyUCgUyGQy3N3dyc3NxcnJCQ8PDxYtWsSHH35YVaflMA4JfDqjhd1xadW+r5ObWsnuuLRq38fqVmazmenTpxMSEkKfPn2q9Fil7eVV33Tq1Amr1UpcXJyjuyK5B7t376ZPnz54eNT+TVDvRkxMDElJSbY1vi1atOCHH35g3rx5tkzOYoGBgbz55ptMmzatQkXs09LSbF8Mvby8bNcQCwsL8fLy3ru0LgAAIABJREFUwsXFpUrOyZEcEvgupmoByk1oOfrZdMyFFZuavH5wK0lHd97xMcr/ZYIVH98Rli5dire3NxMnTqyW45W2l1d9UrxP37Zt2xzdFck92L59e71cuwdw9OhRYmJiiIyMLLHkysXFhUmTJpW6DGvIkCE88sgjzJw5s9x1vRkZGZhMJnQ6HSaTCYvFQo8ePVi7di3Hjx/nhRdesPs5OZpDAl98mg6z9c7/GGIVLcI2WwUupemqpO3ybN26lT/++IP58+dXazr23/fyqm+GDx/Onj17MJlMju6K5C5cu3aN1NRU27Ws+uTmzZvMmjWLBQsW0Lhx5SqpvPrqq1itVlasWHHHx6WmpmIymQgMDGTBggUsXryYLl260LdvXxSKqk08dJQqmWuMiorCycmJMWPG2OaaV65cybFjx/jhhx+Il/lzcdvXKOUyfFp3IqB/CACHPnyZxp37kXv9PG0G/5XebzWbOP/DChq07UaTzv1IO3uI5GO7ABmujZoRNKJkMeKbp/aTemo/otWCxrsRQSMmoVCpybjwB1d/+555W535uUMzVq1axdWrV5k7d66thueSJUto0aLs9S53Ky4ujuXLl7Nq1SqHlFoaO3YscXFxLFy4kHnz5tXJ+ntladKkCW3btuW3336r1/Uda6vt27czbNiwOvshXJbiNb7h4eF3tca3uIh9eHg4wcHBPPLII6U+bty4cTz55JMEBPw/e+cd1uTZ9uEzCYGwhwhu1IIKTtS6Ohx1K1brqBXUah111AkIFBWLOEB8W9tii8UBqK2jVkWcX9XXljqpE1frAAcoIhtCSJ7vDyqv1oWa5AnIeRwch4bkuX4E8lz3fd3XqAeUOMJhw4Yxc+ZM0Zp66BqdbDvc3d35888/gZIbfn5+PsXFxfz555/UqVOHhG2xuA2eScuP55KTeo30yyXPVauUWNaoT8tRQVjXcil9LOnnr6naqA3Vm79LXvotUv7YQdMPvWk5Koj6XT56zL59g5a4jwik5aggzOyqk3b6NwCSE7bTaOA02o2Zx9KlS4GSkoKPPvqIdevWERMTg6Ojo9bfj4yMDHx8fPj8889L/7j0zb9neb1ueHh4VIY7yyEajea1rN0TBIGFCxdSp06dV6rxtbW1JTQ0lAULFjy1iX21atUeuS9Vq1aNhg0b8t///vel7Ro6OnF8rq6unD9/nry8PIyNjWnWrBlJSUmcPHkSS0tLbOs0RG5miUQqw8GtLdkpF0teKJFi3+DRdjlJP3+DY5O3cGxSkgiSdf089g1bIzcrGT0kN31895SffpNT6xZxYuVc7p4/Ql76TQCsar7B1d2r+fvYr6Vx72bNmrFq1SrWrFnD7du3X6pP5rNQq9X4+/vTp08fOnfurNVrvygPZnlFRUWVLkxeFzp37sypU6fIyMgQW0olL8DRo0epUqUKb7zxhthS9MrGjRu5cOGCVmp8XV1dmTp1Kt7e3uTmlu2Yp6IvFHXi+B40Td2+fTvNmjXD3d2d48ePk5KSQo0aNZA+5RcpM5Ij+dfZl1VNZ+5fO/tC6eiX4lfxRtdhtBo9jzodPNCoS7I4XXqMoOZb76PKzcTLy4usrCx69uzJ0qVLMTExYerUqRw7duzlf/An8NVXX2FsbGwwB8QPz/J6nQq7zczM6NixIzt37hRbSiUvwOtYu3fy5El++OEHrdb4enh40LZt2zI3se/cuTOnT58WfWKNrtBZhoW7uzsxMTG0bNkSd3d3Nm/eTMOGDWncuDG5Ny9TkJuNoNFw9/xRrGs3fOp1nN5+HyMTM/7euxYAaydX0i8eR1VQsnJRFeQ99priogKMzW3QqIu5k/S/OXsF9++gcKjLu/2GYWtrS1paGjdv3qRmzZoMHTqUjh07cvnyZa29B7t27eLgwYOEhIQYVG/BDh06MGTIEHx9fV+rhI8Hq9jKmr7yQW5uLr/99hs9evQQW4reuHv3Lv7+/jqp8Z0xYwZZWVlERUU997mmpqZ07ty5wi4UdXY3btGiBenp6TRt2hQ7OztMTExwd3fH3t6ewSPHcnHTUhJXB2Hh6EQVF/dnXqv+ex+hKVZx9cAmzO1rULt9b06vDyVxVRBX9v/02POd3u7PqdgFnFq7CNMq/8uEunpwI+divuDg8gCaNWuGi4sLe/fu5cMPP2TYsGH8/fffWltdXrp0iSVLlhAWFmaQtUejRo2iatWqLFmyRGwpesPd3Z28vLwyF/ZWIi779u3jzTffxMbGRmwpekHXNb5yuZzFixezZcsWDh069NznV+SBzhJBhJ/qxPUMfDadxlzPBewAecpiwgY1o5WTbppVR0REIAgCe/bsYeLEiQa9Ws3Ly2PkyJF4eXnRv39/seXohe+++468vDxmzpwpthStkZ6ejqenJ7t37xZbilYZM2YMw4cPr/CTAh6wcOFC0tPTCQsL02mE6PTp08ycOZOoqKhnZrBrNBr69+9PaGgojRo10pkeMRAl/tawWskOqKzTgbVF8T+x7Qf2dYEgCPz888907NjRoJ0egLm5OeHh4Xz77becPXtWbDl6oW/fvuzatQuVSiW2lEqeQUpKCsnJybz11ltiS9EL+qzxLWsTe6lUWmGbP4ji+CxMjOju5qj31mG5ymK6uznqtFXaoUOHKC4uZurUqTqzoU2cnJwIDAxk1qxZr0XGY61atXByciIhIUFsKZU8gx07dtCjRw+MjEQfGapzHtT4LlmyRG81vmVtYt+nTx92795d4RaKomVcDGxZCwnoLX4sCAJSJAxqpZum0AD79+/n7NmzDBo0qFwV23bs2PGVZnmVNx6cXVRimGg0Gnbs2PFaZHOKVeNb1ib2NWvWpF69evz+++9606YPRHN8Lo6WtKlnR3ahfm602YXFtKlnh7ODpU6uf/XqVUJCQhgwYIAonVlelZed5VUe6dq1K8eOHSMzM1NsKZU8gcTERCwsLGjQoIHYUnSK2DW+ZW1iXxFr+kTNsZ/VsxEKuRSlSq1TO4WqYjRFhUzpqJsBlnl5eXh7ezNlyhSqV6+uExu65mVneZVHLCwsePvttytcMkhF4UHtXkVvq2cINb5laWL/3nvvceLECe7fv69ndbpDVMdnb2GCT7eGFBardZbootYI5OQXcnbdQlq6udCuXTvGjBlDREQEv/76K8nJyWUq6HwaGo2GOXPm0Lp1a/r166dF5frnZWZ5lVcqw52GSX5+PgcOHKBXr15iS9EphlTj+7wm9ubm5rzzzjsVaqEoelV1VzdHvNo6kVuo0rrzU2sEcgtVjH7HBY+WThQWFvLXX39x4MABwsLCmDhxIl27dsXV1ZXevXu/VBx71apV3L9/H29vb61qF4sXneVVXmnTpg337t3j77//FltKJQ/x66+/4u7ujp2dbsqNDAFDrPH96KOPqFevHiEhIU/Mu6ho4U7RHZ9EImHsO/XxaudErlKltbCnUqUmV6nCq50TY9+pz+zZs3FwcEChUJCeno6ZmRkWFhZYWVkhl8u5cOECVapUeSEbCQkJbNq0icWLFyOXy7Wi2xB4MMsrICDglXbDhoxUKqV3796Vuz4Do6K3KMvOzsbHxwcfHx+DOsN8XhP71q1bc//+ff766y8R1Gkf0R0f/M/5ze7tBhLIKlC9dLanIAhkFahAArN7uzHu3TeQSCQ0aNCA7t27Y2xsTLVq1UhJSeHevXtoNBoKCgoIDg5+oSLNlJQU5s6dy8KFC6latepLaTVkPvvsMzQaDd9++63YUnRG3759iY+PR63W7RlzJWXj9u3bXL58mXfeeUdsKTpBo9Hw+eefG2yN77Oa2EulUvr06UNcXBy5ymJOXM9g/dFkZm0+xcerjuIZdZiPVx1l1uZTrD+azInrGXovV3sRDKZIRiKR0K1xNdydbFm86wJHr2agQcDSRP7cSe3wz1meUoUUCW3r2TGrZyPsLR6dtDBjxgz27t2LmZkZ9erV4+bNm2RkZDBo0CCGDRtWZq0FBQX4+PgwduxYWrRo8cI/a3ngwSyv4cOH4+bmViHn2NWtW5fq1atz+PDh16ZQ2pDZsWNH6eK0IvLdd99RVFRk0DW+Dzexj46OxsHBofR7TTp0ZdpX69n6zSEkSFCpNcikEuSy/+2frt3LI+Hve6WPdXdzZGDLWrg46iab/mUxiB3fw9hbmBA2qDk/jGhNnybVKVSpyVMWk5lfRE6higKVmkKVmgKVmpxCFZn5ReQpiylUqenTpDo/jGhN2KDmjzk9ABcXF7p160ZWVhZyuRw7Ozvs7OxISUnh1KlTZdInCELp7nDw4MHa/vENCltbW8LCwli4cOFTZ3mVdyqTXAwDQRAqdO3e/v37iY+PZ+HChQZf4/vvJvbpuUp8Np1izr5bFFR1RV1UiLmJETZmxlgq5CjkstIvS4UcGzNjzE2MUMhlxJ9NZUz0cXw2nSI99/HEGbEwmB3fv3FxtMSvlyuTu7hwMTWby2m5/JmSyZ2cQorUGoxlUhwsFbjXtsHF0YKG1azK1JFl5syZ7N27tzR7KS4ujszMTHx9ffnoo48YOXLkM7Os1q5dS3JyMlFRURU+3RpKZnlNmzYNb29v1qxZg6WlYa3cXpXu3bvz9ddfk52dbTCJBq8jp0+fxsjICDc3N7GlaJ0HNb7Lli0rN0k7o0aNIun8eSYvjOSaVVMKizVYKYxQW5mRk5WFdRnuAzKpBGtTOYIgcORqBsN+OIxPt4Z0dXMU/d5psI7vARYmRrRysqOVkx1D2zy9oWpZcXZ2pmfPnmzatInQ0FAaN24MQGxsLP7+/iQmJjJv3rwn/oEeO3aM6Oho1qxZo/WBtYZM3759SUpKYs6cOYSHh4uefq1NrKysaN++PXv37mXgwIFiy3lt2b59e4Ws3Xu4xre8OfW6PUazbOdJ7CTZVLWzBUo+L3fu3EGtVpd55yqRlDhApUpNcHwSV+/lMfad+qL+rivOHewF8PX1xdfXl+HDh5c+5uDgQGRkJI0aNcLLy4vExMRHXpOamkpgYCAhISHltkj9VZg+fTrZ2dn88MMPYkvROpXhTnFRKpX8+uuvFa52r7zW+AqCwIpDV9iQeJs61atyP/0uBQUFQMnZv4WFOdnZ2S98XRO5DAsTObGHr7PikLhHJ6+l46tTpw4zZsx4bMUhk8mYNGkSs2fPxt/fn6ioKDQaDUqlEh8fH7y8vHjzzTdFUi0ucrmc0NBQfvnllzLN8ipPtG/fnps3b3L9+nWxpbyWHDhwgMaNGz+SSFERKK81vvuS0og9ch0LhRxThYLq1atz48aN0j6+1tY2L93uTyaVYKGQE3vkOnvPpWpT9gvxWjq+59G+fXtiYmI4cuQIkyZNYs6cOdSuXRsvLy+xpYlKlSpVWLRoEV988QXJycliy9EaMpmssqZPRB6EOSsS5bXGNz1XSdjeiyiMZKXZ9JaWltjYWHPz5k0EQcDc3ByVSvXELi9lQSaVoDCSEbb3omgJL5WO7yk4ODiwfPlyiouLiY6Opnfv3hXu/OFlKOssr/JG37592bFjR4Ut2DdU7ty5Q1JSEp06dRJbitZISUkhKCiIRYsWlbsa38W7LlCo0mAif/T8zt6+KhKJhDt30krO7KytX6mzk4lcRqFKw+JdF15V8ktR6fiewdmzZ7l27Rrff/898+fPJzIysvLGSNlneZUnnJ2dsbW15dixY2JLea3YuXMn7733XoVJFnu4xrd58+Ziy3khLqflcPRqBlaKx3MeJRIJNWvWJCcnl6ysLGxsbMjKynqlz7+VwoijVzO4nJbzKrJfikrH9xTu3r2Ln58fQUFB9OvXj9jYWE6cOMGkSZO4d++e2PJEpayzvMobHh4eleFOPSIIQoUKcz6o8XV1dWXQoEFiy3lhNifeQICnRrZkMhm1a9ciLS0NQRAwMpK9UtRHIpGgQWBz4o2XvsbLUun4noBKpWLWrFkMHDiwtKOHvb09y5cvx93dHU9PT44ePSqySnEp6yyv8kSPHj04dOgQeXl5Ykt5LUhKSkKtVtOsWTOxpWiFtWvXkpKSgr+/f7k4Fnm4NVyuspg9SWmP1EIXF+Zz68/9pf9X5tzn750rqVatGjdu3MDS0uqVZ1pe2/Iftvx65LH2ZklJSYSFhb3StZ9FpeN7AuHh4djY2DB69OhHHpdKpYwbN47g4GDmzp3Ld99991qHPssyy6s8YWtrS6tWrdi3b5/YUl4LKtLcvWPHjhETE0NYWJhBtVwTBKFM96iLqSXlCQ+3hyxW5nP7IcdnYmmLW/+JWFlZYWlpSW5uLrm5uWg0L9/rVvrP7/6B/Qe4ubnh4+Pz0td9HgZfwK5vtm3bxtGjR4mOjn5qofabb75JbGwss2fP5tNPPyUkJKTcHWJri4dnea1cubLcn9V4eHiwdu1a3n//fbGlVGiKiorYs2cPsbGxYkt5ZR7U+M6fP59q1aqJLYdbt24xefJkmjRpwoULF+jatSu//fYbRUVFdO7cmfHjxz/y/Pz8fGbNmMrZK7cxkgjUfac/VVzcuXZwM4WZd0lcPQ+bum7UcO/Muc3LaDX6C+ztbDmxaR35d5LJPGhGw57DsanTiLQzv3Pv75NoVEUUZN7F3qUl9ToNQtBouLRrNbmp1wAJ1Zq9Tc3W3QBIv3Sc6RO2Y68QmD17Nu7u7pw4cYKYmBi+/PJLIiMjuXHjBikpKWRmZjJixAgGDBjwSu9R5Y7vIZKSkli2bBnh4eFYWFg887lVqlThm2++oU2bNnh5efHHH3/oSaXh8bxZXuWJt956i2vXrnHjhv7PHV4nDh06hIuLS7lvBvGgxnf48OEGVeObkpLC4MGDmTFjBnfv3mXNmjWsW7eO8+fPP9acw8TEhKYfTMLN83OaDfXmyv4NCIJA3Y4DUdhUpeXHc6nf6dG+xLdP7sfKyopqPcZj2aovl3ZEoSlWAZCXlkKjfuNpNWoedy8cRZmdQe6dZIpy7tNq9Be0Gj0Pxyb/awovETS0/2QuM2fOZMWKFU/8eS5fvsx3333HqlWrWLFiBXfv3n2l96fS8f1DRkYGvr6+BAQEUK9evTK9RiqVMmbMGBYsWEBwcDARERGvZejzebO8yhNyuZzu3buzY8cOsaVUaLZv346Hh4fYMl4JQRBYtGgRtWvXxtPTU2w5j1C9enWaNm3K4cOHOXz4MJ6ennh5eXHt2jVSUlIeea4gCBzaupYLa4M5s2EpRbmZqPKf3Zkl+8ZfVGvSASenOhTJTDGysCE/o6Qg3cbJFSMTM6RGcszsa1CYfQ+FdVUKs9L5a99aMq6cQWasKL1WlQatuJNTiKur61OPTDp27IiJiQk2Nja0bt2ac+fOvdL7U+n4ALVajb+/P71796ZLly4v/PpWrVoRGxtLUlIS69evf6l2PuWdZ83yKm94eHhU1vTpkIyMDE6dOkXnzp3FlvJKbNq0ifPnzzN79myDO6dUKEociyAIjBo1inXr1rFu3Tp++eWXx8L4O3fupCA3i8aen9Py47nIza1Kd2/Pw9TUDBcXF2TS/9X9SYz+d4ImkUgRNBrkpua0/HguNrUbkXryIJd3ryl9jlRmRJFag1QqfepszH+/v6/6flc6PmDZsmUYGxvz6aefvvQ17OzsWLZsGfXq1WPFihUkJCRoUWH54OFZXnfu3BFbzkvTsGFDTE1NOXnypNhSKiQ7d+6kY8eOmJmZiS3lpTl16hQrVqxgyZIlmJqaii3nqbRv356tW7eWlh3cuXOHjIyMR56Tm5uLqYU1UpkRmckXUGaVlGvJjBWoiwqfeF2rWi7cSToMgFSZizInAzO7p59vqvJzEAQB+4atcHpnALlp/+v8JADGsme7ooMHD1JUVERWVhYnTpx45Ybfr31yy+7duzlw4AAxMTGvPHVAKpXSoUMH3njjDebPn0/Pnj2ZOHEiRkavz9v88CyvyMhIg8pwKysSiYS+ffuyfft2WrZsKbacCsWD2j1dZuzpmodrfGvVqiW2nGfSrl07rl69yqhRowAwMzMjODj4kekzvXr1YsGKHzm1JgjbGvUxrVLiwOSmFljVdObEyjnY1m9KDff/7dBruHfmrz2xnFg5F4lUSoPeo5EaPb01mzL3Ppd2roZ/cgDqvvtB6ffUGg0OloqnvLIEZ2dnxo8fT2ZmJmPGjHnlZEKJUN6zEV6By5cvM2HCBCIiImjQoIFWrhkREYFCoWDAgAHMnTuX3NxcFi5ciKOjo1auXx4QBAFfX19sbW0JCAgQW85LkZ6ezuDBg4mPjzfoFT2UaPX09GT37t1iS3kuFy9exNvbm61bt5bL8VYqlYrx48fz1ltv8cknn4gtR2usP5pMxIG/sDHT/0I1M7+ISZ2cnzp2LjIyElNT00em6bwq5e8vT0tkZ2fj7e2Nj4+P1pzew9ja2vLll1/SsWNHhg8fXuEmGjwLiURCUFAQiYmJ/PLLL2LLeSns7e1p1qwZ+/fvf/6TKykzcXFx9OnTp1w6PSip8bW1tS3dQVUUGjhaIH9OuFFXyGVSXByfnUWvbV6fGNxDaDQaAgMD6dixIz169NCZHalUysiRI2nRogUBAQGcOHGCyZMnvxahT3Nzc8LDwxkzZgzOzs40adJEbEkvTN++ffn555/p3bu32FIqBCqVil27drFq1SqxpbwU27ZtKx1GXV4d99NoWM0KALVGeKSIXdcU/5NA9sD+kxg3bpzW7Vas394TWLduHQMHDnwk2eK7775DqVQydepUvWho3rw569at49q1a4wdO5bbt2/rxa7YODk5ERgYyKxZsx47UC8PdOzYkYsXL5KaKt7csIpEQkICTk5OBn8u9iSSkpL4+uuvWbJkCebm5mLL0ToWJkZ0d3N8rHWYrslVFtPdzfGRVmn6oMI7vl9//ZWEhAR69uxJYmIiBw4cID4+noULFyKTyZ5/AS1hbW3N0qVL6dKlCyNHjuTgwYN6sy0mHTt2pG/fvvj5+ZUOsiwvGBsb061bN+Lj48WWUiGIi4srl7V7L1PjWx4Z2LIWEtBbEwpBEJAiYVAr/S+EKrzj+/PPP6latSr5+fkMHDiQyZMns3jx4keymvSFVCpl+PDhhIeHs2TJEpYuXYpKVbZ6mfLM+PHjUSgUfPXVV2JLeWH69u1LXFxcue9IIzaZmZkcO3aMrl27ii3lhXi4xre81x0+DxdHS9rUsyO7UD8L1OzCYtrUs8PZwVIv9h6mQju+O3fukJWVhVwux8zMjIyMDNLT01m1ahWFhU+uT9EHTZs2Ze3atdy4cYNPPvmkQjR4fhZSqZT58+dz6NChcrd7atKkCRKJhDNnzogtpVyze/du3n777XIXJtRGja8hIwgCd+/eJSEhgfnz51Mz/TgKuRSl6uUbT5cFpUqNQi5lVs9GOrXzNCq04zt37hwymQyJRMKtW7ewtLSkRo0abNmyhYEDB4rqcKysrAgPD6dnz56MHDmywmcPWllZle5yL126JLacMvOgpq9yTt+rUR7DnA9qfENCQipEMotSqSQpKYlt27axYMECBg0aRLNmzWjXrh2DBg0iODiY/Iw0fLo1pLBYjVqjmyiHWiNQWKzGp1tD7C3EaWpfodMLT58+jVKpRKlUUlxcTM2aNYGSs5vExETCw8MJDw8XTZ9EImHYsGE0b94cf39/jh8/ztSpU8tl0XdZcHZ2xtfXF29vb2JiYrC2thZbUpno06cPQ4cOZebMmeV++oQY/P3339y7d8+gmjg/j8uXLxMWFkZERARWVk/POCwv5Ofn065dOwoKCpBIJKhUKkxMTJDL5eTk5FBUVISrqyve3t7I5XKu3ssj9vB1LBRyrWZ5qjUCuYUqvNo50a2xeJMsDH4Zk6ss5sT1DNYfTWbW5lN8vOoonlGH+XjVUWZtPsX6o8mcuJ7xxGykhIQENBoN9+/fp2bNmuTk5JCdnU2NGjWIjIxk0aJFIvxEj9O4cWNiY2NJS0vjk08+qdCTAbp3706XLl34/PPPy00vTAcHB1xdXV+bhCRtExcXR+/evcvNrknXNb5iYGZmhpeXFxqNBktLS6pUqYJUKiU5ORmFQkGVKlXw8/PD2NgYiUTC2Hfq49XOiVylSmthT6VKTa6yxOmNfae+Vq75shhs55bLaTlsTrzBnqQ0AFRqDTKp5JEiS5Vag1ojlD7W3c2RgS1r4eJoiUajoWHDhty+fRsbGxsUCgVNmjRhxowZvP322zr7ED7o3PLvIbZlQRAEfvrpJ6KiovDz8+O9997TgULxUavVTJ48mcaNGzN58mSx5ZSJnTt3snPnTpYtWya2lMcw5M4tarWa3r178/3331O3bl2x5TwXjUbDtGnTqFu3LjNmzBBbjlYpLi5m8ODBnD59GrVaTUZGBjVq1EAmk2FmZsbvv//+SLRJEAT2JaURtvcihSoNVgqjl2oOLQgC2YXFKORSfLo1FHWn9wCDC3Wm5ypZvOsCR69mIFBSX/K0rbZC/r9yBLVGIP5sKjvPptKmnh2DnI24efMmVlZWdOvWjSlTptCiRQuD66L+MBKJhKFDh9KsWbPS0Of06dMrXOhTJpOxYMECRowYgZub20tNxNA3nTt3JjQ0lLt37762Q4dfhsOHD1O9evVy4fRA/zW++sTIyIiQkBDeffdd1Go19evXx8jIiMzMTObMmfPYfUYikdCtcTXcnWxL78kaBCxNyhb+VGsEcpQqpEhoW8+OWT0biXam928MJvYgCAJ7z6UybMVhjlzNwFJhhLVp2ePLMqkEa1M5lgojjlzNwHvnDd7s/wkHDx5k1apVuLu7G7TTexg3NzfWrl1LRkYGo0aNIjk5+fkvKmfY2toSGhrKggULuHLlithynotCoaBLly7s3LlTbCnliri4OPr27Su2jDIhVo2vvkhMTMTb2xtPT0/s7OyQSCQUFBRgZ2f3zInm9hYmhA1qzg8jWtOnSXUKVWrylMVk5heRU6iiQKWmUKWmQKUmp1BFZn4RecpiClVq+jSpzg8jWhPJ1PuSAAAgAElEQVQ2qLnBOD0wkB2fIAisOHSF2CPXURjJsDZ9epfv5yGRlDhApUpKofsADqYZ0aCBUG6c3gMsLCxYtGgRGzduZPTo0fj6+tK9e3exZWkVV1dXpk6dire3N9HR0c+dei82ffv2ZeHChQwfPrzc/T2JQXZ2Nn/88Qf+/v5iS3ku165dIyQkhK+++kqUGl9dotFoWLlyJRs3biQoKIj27dvj6OhIVFQUEomEuXPnlimq5OJoiV8vVyZ3ceFiajaX03L5MyWTOzmFFKk1GMukOFgqcK9tg4ujBQ2rWem9I0tZEV1VqdPTcgaRiVyGkUxK7OHrAIx79w2tXFefSCQShgwZ8kjos6JlFnp4eJCUlMScOXNYsmSJQSdAtGjRAqVSyfnz5195HtjrwN69e2nXrp3BZ0Xm5eUxc+ZMPvvsswr3e83IyCAwMJDi4mJiY2NLw/Q+Pj4cOXKE1NRU+vfv/0LXtDAxopWTHa2c7J46UcHQEf0usy8pjdgj2k+bhZLwp4VCTuyR6+w9V377LTZq1IjY2Fhyc3P5+OOPuX79utiStMqMGTPIysoiKipKbCnPpLKm78UoD2FOjUbD3Llzad26Nf369RNbjlY5duwYnp6eNG3alOXLlz9yNi2Xy1m5ciXR0dEVLoegLIjq+NJzlYTtvYjCSKazjuAyqQSFkYywvRdJz1XqxIY+MDc3JyQkhEGDBvHJJ5+wa9cusSVpDblczuLFi9myZQu//fab2HKeSZ8+fdi9ezdFRUViSzForl+/zq1bt2jfvr3YUp7J6tWrycjIwNvbW2wpWkOj0RAZGcns2bOZN28eEyZMeOKZZZUqVWjYsKEICsVHVMe3eNcFClUaTOS6PUg2kcsoVGlYvOuCTu3oGolEwsCBA4mIiCAyMpL58+ejVJZfZ/4w9vb2LFq0iHnz5hl0Mk+NGjVwdnY2eActNnFxcfTq1cugk0QSEhLYuHEjixcvRi5/+bwCQyI9PZ2JEyeSmJhIbGwsbdq0EVuSQSKa47uclsPRqxlYKfRzzGilMOLo1Qwup+XoxZ4uadCgAbGxsRQUFDBixAiuXbsmtiSt0KxZM8aPH4+3tzf5+fliy3kqleHOZ6PRaNixY4dBhzlTUlKYO3cuCxcurDDlKUeOHMHLy4uWLVsSERGBvb292JIMFtEc3+bEGwigt+w4iUSCBoHNiRWjK4qZmRnz589n6NChjBkzhh07dogtSSsMHDiQJk2aEBwcbLATEd577z0SExPL5YxBfXDs2DHs7OxwdnYWW8oTKSgowMfHh7Fjx9KiRQux5bwyarWa5cuXExQURHBwMOPGjTPoJDFDQJR3J1dZzJ6kNL2nulqYGLEnKU3vwxZ1hUQiYcCAASxfvpyVK1fyxRdfiDp1QhtIJBL8/Py4efMmMTExYst5ImZmZnTs2LFCnbNqE0NOahEEgeDgYBo1asTgwYPFlvPK3L17lwkTJnDmzBnWrl1brvqhiskzHd+tW7cYMmSI1o1eTM0G0OuIewCjf1ZBD+y/CpGRkQZzY3ZxcSEmJobi4mJGjBhRLgrCn4WxsTGhoaGsXbuWo0ePii3niVSGO59MXl4ehw4dokePHmJLeSJr164lOTkZf3//cl+L+ccff+Dl5UWbNm345ptvKlz9oS7R+45PrVZzKS0XlVq3DYqFpzRAVqk1XE7L1altMTAzM2PevHl4eXkxbtw4tm/fLrakV6JatWqEhIQQGBhokPMKW7VqRXZ2drkasaQP9u3bR+vWrbG1tRVbymMcO3aM6OhowsLCynUtrFqt5ttvvyU4OJiFCxcyZsyYytDmC/LcWKNGo2H+/PmcPn2aqlWrsnTpUq5fv86CBQsoLCykVq1azJkzBysrK8aNG8e0adNwc3MjMzOT4cOHs337drZv387+/fvJz89Ho9Fg0n4Yf20OR6ouQtCoce7mhXXtR7ugp535nfTLiaiVBShzM3Fwa4fTWyV1Nmnn/uDWif9D0KixrF4P525eSKRSEv4ziWrN3yXz+nne6OaJdS0XAHJuXyXlSDxu/SeRc/U0M6NmMPDsMQRBYNCgQWzbto0bN26wePFi7t+/j0KhIDAwkLp163L//n0WLlxIampJHeDMmTNp3rz5I1q3bNnC/v37CQsL08ov5WWRSCT069ePJk2aMGvWLI4fP46fnx+mpqai6npZWrduzciRI/Hx8WHlypUGdbOSSqX06dOHuLi4CtfM+FWIi4vD09NTbBmPkZqaSmBgICEhIVSvXl1sOS/NnTt3CAgIQKFQEBsbW7nLe0meu0xITk5myJAhbNiwAUtLS/7v//6POXPmMGXKFH788UecnZ1ZsWLFcw1duHCB0NBQIiMjOfnHQWzrNaHlx3Np+XEQFo5Prv7PvX0V1/4TaflxEOkXj5Nz+yr56bdIv3CM5p5+tPx4LhKJlDtJhwFQq5RY1qhPy1FBpU4PwMKxDnlpKQDk3/4LU/saJCUlcfbsWZo0aQJASEgIPj4+xMbGMm3atNKRRUuWLGHYsGFER0cTGhpKcHDwIxo3bNjAoUOHWLJkicHcmOvXr090dDRSqZThw4fz999/iy3ppRk2bBhOTk6EhIQYXLJLnz592LVrF8XFFePM+FW5ceMG165d46233hJbyiMolUp8fHzw8vIq12dgv//+O15eXrz11lssW7as0um9As/d8dWsWbN0JpWrqys3btwgJyeHli1bAiVnHbNmzXquobZt25a2LrKoVpeLf6zASCJQxaUFFg5Pdnw2dd2Qm5b0b7Rv0JLsm38hkcrITbvOyZgQADSqIuTm/7REkkixb9DqsetIpDIUNlXJT79FXuo1GrTvTmJiIhqNBnd3d/Lz8zl16hR+fn6lr3lQoHz06FGuXr1a+nheXl5pqv2OHTtwdHQkPDwcIyPRu789gqmpKXPnziUuLo7x48fz2Wef0a9fv3J3riGRSJg9ezajR49mw4YNfPjhh2JLKqVOnTrUqlWLhIQE3n33XbHliM6OHTvo0aOHQdXECYLAokWLqF27Nl5eXmLLeSmKi4uJiIhg165dLF68GHd3d7EllXuee7d++I9YKpWSm/v08zEjI6PSVfm/O1s8HG5zrOeK6xAf8pLPcSl+FTVbd0NmrCA5oeRcyqXHiH+e+e+btAQEAYfGHajXceBj9mVGciT/xLrPbPgPqvxsLKo50aDnx1jXbkDG1bNIZDKqOzfh5MntaDQapk6diiAIWFpasm7duseuqdFoWL169RPb+jg7O3Pp0iXu3LlDjRo1nvq+iEnfvn1xc3PDz8+P48eP4+/vj5mZmdiyXghTU1PCwsIYPXo0DRo0MKgPvoeHB3Fxca+943tQuxcaGiq2lEfYtGkTSUlJrF69utwt+qAkRBsQEICFhQXr1q3DxsZGbEkVghc+EbWwsMDKyoo///wTgPj4eFq1KtllVa9enfPnzwMlh9xPw1ydDSYWVG/+LtWavUNuWjL2DVr+E/qci2X1egBkXk9CVZCHWlXEvct/YlXzDaydXEm/dIKivJLMTFVBHoVZ6Y/ZaDpkOi0/nkuDnh8DYFWrAbeO78XUsR61qzmQlZXF9evXeeONNzA3N6dmzZqlmgVBKE1aaNeuHT/++GPpdR9OZmjYsCEBAQFMnz6du3fvvuhbqTcehD6NjY3x8vLi8uXLYkt6YWrVqkVQUBD+/v7cuXNHbDmldOvWjaNHj5KVlSW2FFE5efIkpqamBtUC69SpU0RGRrJkyZJyec793//+lxEjRtCpUye+/PLLSqenRV4qFWjevHl89dVXDB06lIsXLzJmzBgAhg8fzqZNmxg2bBiZmZlPfb1ZdgpJscEkrp7H3QtHqdm66xOfZ1mtHue3RpC4OogqDVphWb0e5vY1qPt2f85u/A+Jq+ZydkM4RXnPv+lYVq9HUX4OZtWdS8ZmuLjg7OxcugqcP38+W7du5aOPPmLIkCEcPHgQKOlifv78eYYOHcrgwYPZtGnTI9dt0aIF06ZNY+rUqc/8mcVGoVAwe/ZsxowZw4QJE/j5558N7szseXTo0IEhQ4bg6+trML0yLSws6NChg0FOP9cn27dvx8PDw2B2VXfv3sXPz4+goCBq164ttpwXQqVS8Z///IfFixcTFhbGiBEjKrM2tYxEEOHud+J6Bj6bTmP+jAL2tDO/k5N2Deeu2s0Qy1MWEzaoGa2cdHMwHBERgUKhYPTo0Tq5vja4du0afn5+1K9fn88//xxzc3OxJZUZQRDw9fXF1taWgIAAseUAJfVUy5cvJzo6WhT76enpeHp6iuZ8CwoK6N27N5s2baJKlSqiaHgYlUrF+PHj6dChQ+mivLxw+/Zt/P39sbW1JSgoCGtra7ElVUhEWUY0rFaSjKLW6NfnFv9T2/fA/utK3bp1WbNmDRYWFnh5eXHx4kWxJZUZiURCUFAQiYmJ/PLLL2LLAUoSt+7evVvuGwe8LPv376dZs2YG4fQAwsPDsbW1NejF55M4ePAgI0eOpGvXrixdurTS6ekQUVIRLUyM6O7mSPzZ1KdOW3ds+haOTbWbFp2rLKZPk+oYSzRkZ2ejVCqf+NWwYUODLMDVJiYmJgQEBLBnzx4mTZrEp59+ysCBAw0mVPUszM3NCQ8PZ8yYMTg7O5eWpIiFVCqlV69exMXFMWXKFFG1iMH27dsZOPDxZDMx2LZtW2mhenkJD6pUKpYtW8aBAwcIDw+nadOmYkuq8IiWgz+wZS12nk1FEAS93GwFQSA3J5dFnw7gP4X3MTc3RyaTIZFISj8gEomE3NxcvvzyS4P5IOua7t2706hRo9Ksz8DAQCwsLMSW9VycnJwIDAxk1qxZxMTEiF7T1LdvXyZOnMikSZMMehSPtklNTeXixYsGkdWalJTE119/TWRkZLkJ39+6dQs/Pz/s7e1Zu3atwU+rryiItiRycbSkTT07sgv1U/ybXVhMlya1ebNBbQoLC7l37x7FxcVYWFiUfsnlcqytrenVq5deNBkKderUYfXq1djY2ODl5VWamWvodOzYkb59++Ln5yd6EXn9+vVxcHDgyJEjourQN/Hx8XTr1k30Kd4ZGRn4+voSEBBAvXr1RNVSVn799VdGjhxJz549CQ8Pr3R6ekTUWMCsno1QyKUoVWqd2lGq1CjkUvx7u/HTTz/h6uqKra0tSqWSv/76i7S0NFQqFXl5eYwaNarc1blpA2NjY/z8/Jg0aRJTpkxhw4YN5SLrc/z48SgUCr766iuxpbx2jasFQSAuLg4PDw9RdajVagICAujduzedO3cWVUtZKCoqIjQ0lC+//JIvv/ySYcOGlYsjhoqEqI7P3sIEn24NKSxW6yzRRa0RKCxW49OtIfYWJtjZ2bFq1SoUCgV2dnalq8MrV66QmZlpcO2W9E23bt1YuXIl27ZtY9asWeTkGPbgXqlUyvz58zl06BDx8fGiaunRowe///67wb9n2uLMmTNIJBIaN24sqo5ly5Yhl8v59NNPRdVRFm7cuMEnn3zC3bt3Wbt2rejv3euK6Ke/Xd0c8WrrRG6hSuvOT60RyC1U4dXWiW6Nq5U+7urqSnh4OAUFBUilUhwdHbG3t6d9+/bMmzePSZMmceTIkXKx49EFtWvXZuXKldjb2+Pp6UlSUpLYkp6JlZUVS5YsYenSpaJOS7C2tqZt27bs3btXNA365MHcPTF3K7t37+bAgQOEhIQYfDLLvn37GDVqFH379iU0NBRLS0uxJb22iN5gUiKRMPad+gDEHrmOwkiGifzVkwOUKjWFxWq82jmVXv9h+vTpw5kzZ4iMjMTGxgaZTMayZcuoXbs2u3btKu2/OXz4cLp162ZwvTh1jbGxMb6+vrRu3ZqpU6cyevRohg4darAhGWdnZ3x9ffH29iYmJka0VPC+ffuyevVqPvjgA1Hs6wulUsm+ffse6Wqkby5fvkxYWBgREREGfT5WVFTEf/7zHxISEli2bBmurq56s52rLOZiajaX0nI5mXKftGwlKo0GuVSKo5UJLWrb0sDRgobVrPQ+GFxMRClgfxKCILAvKY2wvRcpVGmwUhi91E1WEASyC4tRyKX4dGv4yE7v36jVaj7++GN2795Nnz59WLVq1SPXSUhIIDo6mps3bzJs2DD69+//3PO/8lDA/qLcvHkTf39/HBwcSkdQGSpffvklf/31F8uWLRNlB1BcXEzv3r354YcfqFPnyc3XtY0YBex79uxh69atfPvtt3qz+TDZ2dkMHz6ciRMnGuzQWyiZbuPn50ft2rWZPXu23jKmL6flsDnxBnuS0oCSOaQyqQS57H+fCZVag1ojlD7W3c2RgS1r4eJY8XeiBhMbkEgkdGtcjXVj2tG2nh05hcVkFhSVOfyp1ghkFhSRU1hM23p2rBvT7plOD0Amk/Htt9/SoUMHpk2b9piet956i++//57Q0FDOnDmDh4cHX3/9tUH35dQFNWvWJCoqiho1auDl5cXZs2fFlvRUPvvsM9RqNREREaLYNzIyomfPnhU+yeVBmFMMNBoNgYGBdOzY0aCd3p49exg9ejQDBgxg0aJFenF66blKfDadYkz0ceLPpqKQyzA3McLGzBhLhRyFXFb6ZamQY2NmjLmJEQq5jPizqYyJPo7PplOk5yp1rlVMDGbH92+etmIxkkmRAAJQrOcVy82bN1m7di27du2iU6dOeHl5Ub/+o2HUirjje5gDBw6wYMECRo4cabDZaPfv32fEiBFMnz6dLl266N3+pUuXmD59Otu3b9fLrlPfO767d+8yZMgQdu7ciUKh0IvNh4mIiODUqVNEREQYZM2kUqkkPDycY8eOsWjRIr007i6NmO25SGGx9iJmXd0cDfIz/qoYbFDXxdESv16uTO7iwsXUbC6n5fJnSiZ3cgopUmswlklxsFSUNJzWU4y6Zs2a+Pr6Mn78eDZu3Minn36Km5sbI0aMwN3dvUL+gfybTp060aBBAwICAjhx4gRBQUEGF/q0tbUlNDSUzz77jLp16z62ONE1DRo0wNramuPHj9OmTRu92tYHO3fupEuXLqI4vQMHDhAfH090dLRBOr3r16/j5+dHvXr1iI2N1UshvSAIrDh0pTRH4mndsMqCRCLB2lSOUqUmOD6Jq/fyGPtO/Qp3bzPYHV95QKlUsmPHDmJjY7GwsGDEiBFcuHABMzOzCrvje4BKpeLbb79l3759LFiwgGbNmokt6TG2b9/OqlWriI6O1ns3mnXr1nHhwgW++OILndvS545PEAQ+/PBD/P399T4X8dq1a4wdO5avvvoKNzc3vdouCzt37iQ8PJyJEycyYMAAvXWkWnHoCrGHr2OhkCOTas9maVZ8OyfGvfuG1q5rCBjMGV95xMTEhA8++IBNmzYxevRo1q1bR2RkJMeOHaOwsFBseTpFLpczbdq00kzK6OhoNP80ATcUPDw8aNu2LXPmzNG7tp49e/Lf//6X/Px8vdrVNefPn0epVNKiRQu92s3Ly2PmzJl89tlnBuf0CgsLCQ4OZsWKFURERPDBBx/obYe0LymN2CPad3oAMqkEC4Wc2CPX2XsuVavXFptKx6cFpFIpnTp1YuXKlXh4eHDlyhU8PDz4/vvvuX//vtjydMq7775LdHQ0Bw4cYPr06QY3k3DGjBlkZWURFRWlV7t2dna0bNnymQOZyyNi1O5pNBrmzp1L69at6devn97sloWrV68ycuRIlEolsbGxNGjQQG+203OVhO29iMJIpnWn9wCZVILCSEbY3osVKuGl0vFpmZo1a/Lhhx/yww8/cO/ePT744AMWLlxIcnKy2NJ0RrVq1YiMjOSNN97A09OTkydPii2pFLlczuLFi9myZQu//fabXm1XtBZmRUVFpaU/+mT16tVkZGTg7e2tV7vPY8eOHYwdO5aPPvqI4OBgvbc6XLzrAoUqjVbqnp+FiVxGoUrD4l0XdGpHn1Q6Ph3h5OREQEAAmzdvxsbGhtGjR+Pr68vp06fFlqYTjIyMmDJlCv7+/vj6+rJq1SqDCX3a29uzaNEi5s2bp9cFyNtvv82VK1e4deuW3mzqkt9++w1nZ2dq1KihN5sJCQls3LiRxYsXI5e/fNKGNikoKGDevHmsWrWK7777jv79++s9+eNyWg5Hr2ZgpdBPfqKVwoijVzO4nFYx2vFVOj4dY2dnx4QJE9i+fTutWrUiMDCQTz75hIMHDxqMY9Amb7/9NrGxsfz2229MnTrVYEK9zZo1Y/z48Xh7e+vt3M3Y2Jju3btXmF2fvmv3bty4QVBQEAsXLqRq1ap6s/ssrly5wsiRI1Gr1URHR+Ps7CyKjs2JNxBAbw5XIpGgQWBz4g292NM1lY5PT5iamvLhhx+yZcuW0lDo4MGD2bJlC0VFRWLL0yoODg5ERkbSqFEjPD09SUxMFFsSAAMHDqRJkyYEBwfrrQ/rg3BneV/kZGRkkJiYyHvvvacXewUFBXh7ezNmzBi9J9I8CUEQ2LZtG+PGjcPLy4t58+aJNsUlV1nMnqS0R8q3lDn3SfqlpGlD7p1kMq48P7KUmXyRc5uXPfF7p9eHknP76iOPWZgYsScpjVyluCPAtEGl49MzMpmM7t27Ex0djb+/P/v378fDw4OVK1eSnZ0ttjytIZPJmDRpEoGBgfj7+xMVFSX6zV8ikeDn58fNmzeJiYnRi01XV1cUCoVBnXu+DLt27aJjx456udkLgkBwcDCNGjVi8ODBOrf3PPLz85k7dy6xsbFERkbSr18/UevaLqaW3CceTmgxsbTFrf9EAPLSUsi4ckbrdo3+acbwwH55xmAL2MsLxcXFpKWllf4/MzMTExMTbt68WfpYtWrVHiu2lUgktG7dmtatW/P3338TExND//796d27N8OGDdPrOYou6dChAzExMQQGBnLixAnmz58v6rR0Y2NjQkNDGTlyJI0aNdJ5gblEIind9bVs2VKntnRJXFwc06dP14utdevWkZycTFRUlOiF03/99Rd+fn40a9aMNWvWYGpqqhM7O3bsICYmBolEgouLC127diUqKgqVSoWNjU3p5yYyMpJdR85x7tRFJKp8arXpSfXm71KYlc65zctwHzmH67/9gqZYRfaNv6jdthcKm6r8/X/r0RSrkMmNcek1CjO7Z7dzfJg754+Q8kc8IGBa243L79THvbYNX3zxBUlJSUgkEt5//32GDRvGjz/+yObNm5HJZNSvX58FCxbo5P16VSod3yuyZcsWfHx8SidQZ2VlIZFI+P7774GSTLhvv/32mZlwb7zxBkFBQdy5c4cff/wRLy8v2rVrx4gRI2jUqJFefg5d4uDgwPLly4mMjMTLy4svvviC1q1bi6anWrVqhISEEBAQwOrVq3W+yOjduzeDBw/Gx8dHZzdOXXLp0iWys7Np1aqVzm0dP36c6OhoVq9ejYmJic7tPQ1BENi6dSvffPMN06dP12km65UrV4iKimLlypXY2NiURn5Wr16NRCLhl19+Yc2aNaULj/MXL9Fw0HTMpBoS13yBXf2mpdeSyoxwers/OWnXcO7qCUCxsoDmw2Yhkcq4fy2Ja//9uXR3+DyUOfe5dmATLUbOQa4wI3F9OFt37qW5ZQfu3LnDhg0bAEpnUK5evZpt27ZhbGxs0HMpK0Odr0j37t0xMzNDoVBgZWWFQqEo/beJiQlWVlZlngrt4ODAlClT2L59O25ubsyYMYMJEyaQkJBQ7mcDymQyJkyYwNy5cwkMDCQyMlLU0Gfr1q0ZOXIkPj4+KJW6rU+yt7enadOm7N+/X6d2dEVcXBx9+vTRed/R1NRUPv/8c+bPn0/16tV1autZ5OfnM3v2bNavX8+KFSt0Xr5x7Ngxunbtio2NDVAyX/LOnTtMnjyZDz/8kOjoaK5cuVL6fJv6zTExUSA3s8SmTkNyUq898/pqZQHnt37HiZVzuPLrT+Snlz3LODf1GtZ1GmJsZolEKqOqW1v+vnCGmjVrcvPmTUJDQ0lISChtzebi4kJgYCDx8fEG2VLuAZWO7xWxtrZm3Lhx5OXlPfa9goICpkyZ8sLnIubm5nh5ebF161Y8PDxYtmwZQ4cOJS4uDpVKpS3potC2bVtiY2M5ceIEkyZN4t69e6JpGTZsGE5OTixYsEDnC4vyWtNXXFzMrl27dH7zLyoqwtfXFy8vL958802d2noWly5dwsvLC4VCwZo1a6hXr54oOkJDQ/nwww/56aef+Pzzzx9ZnKl59G/1ecHga7/9gnWdhrQa/QWNB36GRv34PeTMhv+QuHoel3atfq62Yo2AlZUV69evp3Xr1mzevJn58+cD8NVXXzFkyBAuXLjAiBEjUKvVz72eGFQ6Pi0watQoTExMHsnOVCqVmJmZMWzYsJe+rlwup3fv3qxfv55p06YRHx/P+++/T3R0NLm5udqQLgr29vYsX74cd3d3PD09OXr0qCg6JBIJs2fP5tKlS2zcuFGntjp16sSFCxceOQ8uDyQkJFCrVi2dzhYUBIFFixZRq1YtvLy8dGbneRp+/vlnJk6cyNixYwkMDNRbE+4333yTffv2kZWVBZTMGszNzS0t4fj3gin90kk0xSpUBblkJV/EolrdR74vM1agVv6vZaJaWYCJhS0AaWcTnqih6ZDptPx4Lg16fvzI45bV65GVcglVfg6CRsO9C8dwrOdKZmYmgiDQpUsXJk6cyIULF9BoNKSmptK6dWumTJlCbm6uwbbsqzzj0wIPdn1ff/116WMFBQX4+flpJQtOIpHQvn172rdvz4ULF4iJiaFfv368//77fPTRRzg4OLyyDX0jlUoZN24cLVq0YM6cOfTv359x48bpfXisqakpYWFhjB49GhcXF501XjY2NqZr167s2LGjXDUwj4uLw8PDQ6c2Nm/ezLlz50rPtPRNXl4eISEhXL16laioKJycnPRqv379+owePZqxY8cik8lo2LAh48ePZ9asWVhZWfHmm28+mixXuy7nNiyBonxqd/DAxKjK4B4AACAASURBVNKWwqz00u9b12lEypGdJK6eR+22vajVpgeX4leR/EccdvVfrJm8sYUNdTsO5PSPSwABszqNafZmyfnevHnzSiMlkydPRqPRMHv2bHJzcxEEgaFDh2JpaZhDbSunM2iJrKws2rdvX7pqq1KlCocPH9ZZ+vetW7dYv349O3bs4J133mH48OGiFdO+Kvfu3WP27NkUFxcTEhIiSrFyQkICwcHBrFmzRmcLidOnTxMUFMTmzZu1eoPX1XSGrKws3n//feLi4nQ23eL06dN4e3sTFRVF7dq1dWLjWVy8eBE/Pz/efPNNZs6cKWpCTVmIjIzk5K18zpg1w8bMWO/2M/OLmNTJmaFtdBcB0AeVoU4t8WDXV1RUhEqleqmzvRehRo0azJw5k19++QUnJycmTZrElClTOHbsWLlLhKlSpQrffPMNbdq0wcvLi8OHD+tdQ4cOHRg8eDCzZs3SWUOBpk1Lsu8MeYL9w+zevZsOHTrozOmlp6fj5+fH3Llz9e70BEFg48aNTJo0iQkTJhAQEGDwTu8B1axNSodv6xu5TIqLo35HfOmCyh2fFsnKyqJRo0bIZDIuXbqk184ORUVF7Ny5k5iYGBQKBSNGjOC9994z6MyqJ3HixAlmz55N3759GT9+vF71azQafH19sbOzIyAgQCc2Vq5cSWpqqlavr6sd34gRI5gwYQLt27fX6nWhZJ7j+PHj6dChA2PGjNH69Z9Fbm4uwcHBpKSksGjRIp2eX74oV65cITMzE4VCgYmJySNfCoUCuVxOXpGaft/8hkKuu6kMT6JYo0Gp0rBt8ts6H/qtayp3fFrE2tqajz76iFGjRum9nZGxsTHvv/8+GzZsYNy4cWzcuJEBAwbw448/GuwB85No1aoVsbGxnDt3jk8//ZQ7d+7ozbZUKmXevHkkJiaydetWndjo06cP+/btM/g2dVeuXOHu3bu0bdtWJ9dfunRpafN2fZKUlISnpyd2dnasXr3aoJwewOeff86AAQP44IMP6N27N126dKF9+/a4u7vj7OyMvb09b9SpSVdXB723DstVFtPdzbHcOz2odHyvTK6ymBPXM1h/NJlZm0+R0XQoV+r24eNVR5m1+RTrjyZz4nqG3v5IpVIp7777LitWrGDBggUkJibSr18/IiIiRC0deBHs7Oz4+uuvad++PV5eXiQkPDkTTReYm5uzZMkSvv76a86dO6f16zs6OtKoUSMOHjyo9Wtrk7i4OHr16qWTZKPt27dz9OhRvvjiC70lMwmCwE8//cSUKVP47LPPmDVrVmnTCUNi5MiRGBsbY2FhgaWlJVZWVpiZmVFYWEhGRgaCIODv78/gVrWRgN6ONQRBQIqEQa1q6cWerqkMdb4kl9Ny2Jx4gz1JJenpKrUGmVTySOxdpdag1gilj3V3c2Rgy1q4OOo30yklJYW1a9eye/du3nvvPby8vKhbt65eNbwsiYmJBAYG0qtXLyZMmICRkX5WmwcOHCAsLIyYmBitt1iLj49n165dLFv25AbBL4q2Q51qtZo+ffoQERFB/fr1tXLNByQlJTF16lQiIyP1ViOXk5NDcHAwt27dKi2bMFTUajVvvfUWOTk5CILAvXv3KCgowMbGBqlUyuDBgwkNDUUikeCz6RRHrmZgbar7cU1ZBSra1rMjbFBzndvSB5U7vhckPVeJz6ZTjIk+TvzZVBRyGeYmRtiYGWOpkKOQy0q/LBVybMyMMTcxQiGXEX82lTHRx/HZdEqv04xr166Nn58fP//8Mw4ODowdO5YZM2Zw8uRJg0+EadmyJWvXruXy5cuMGzdOb3VwnTp1ok+fPvj5+VFcrN3deufOnTlz5gzp6enPf7IIHDlyBAcHB607vfv37+Pr64u/v7/enN65c+fw9PSkatWqrFy50qCdHpSULnXp0oXk5GRu376NhYUFLi4uGBsb06xZM+bPn1+aETyrZyMUcilKlW6LxJUqNQq5lFk9y3/7xAdUOr4yIggCe8+lMmzFYY5czcBSYYS1qbzMh8syqQRrUzmWCiOOXM1g2A+H2XsuVa+Ox9bWlnHjxrF9+3bat29PUFAQo0eP5tdffxV9csKzsLW15csvv6Rjx44MHz6cQ4cO6cXup59+ikKh0NrO7AGmpqZ07tyZnTt3avW62kIXc/fUajUBAQH06tWLLl26aPXaT0IQBNatW8e0adOYNm3aI/10DRGlUsmmTZsYOHAgKSkp2Nvb/3975x1f4/n+8fc5J3sPO4kMm0iUGKV0fI3YWopmUVU1anzVCErtWS1+qqqo5iTUKjVi+7aKaiVFbCGCyJI9TnLm8/sjzalUIusklOf9evWlSZ7nfu5zcvJ8nvu6r+tz4eLigr29Pbm5udjY2PDtt98WyTytYWXK1G5NyNdo0eqq5j6i1Qnka7RM7daEGlb/jqzXsiCbO3fu3Gc9iecdQRD49tcY1v58GyOpFEtTowrXYUkkEsyMZWi1AidvJqPRCrSub1+thbtGRka0aNGCwYMHY2NjQ0hICHK5HGNjYxo0aFBt4cTyIJFIaNWqFV5eXsybN4+UlBR8fHyqdI9IIpHQqVMnvvzyS+zt7Q1aJ2ltbc3mzZsZNGhQpX/3CoWCH3/8kcDAwErPKzs7m2XLljF79myDOpesWbOGzMxMZs+eXeWf9aysLGbNmkVUVBRr167F2/v5Dc9lZGQQEhLC7NmzUSqVTJw4kTFjxgAFHe9lMhlqtZrQ0NBiP38eNS3RaAUiYtMwlkmRGvC91eoEcvLVBHRwZXDb5ysJqLKIK75SKBS90HP3sDI1xtTYMOn1psYyrEyNCT13j29/jSn9hCpAKpXyn//8hy1btjB79mzOnDlD3759+fbbb8nIyHgmcyoNb29vtm7dyt27d/nwww9JSEio0uvZ2Njw+eef8/nnn3Pr1i2DjduqVSvy8/O5ceOGwcY0BMeOHaN9+/bY2toabMyjR49y8uRJFi1aVOXJLJcvX8bf3x8nJyc2bdqEk5NTlV6vohSWUrz99tskJyezYcMGvvjiC1q1aoVEIiEwMBAjIyNycnJYuHBhic14JRIJH3b2IKCDKzlKtcHCnkq1lhxlgeh92NmwIe/nAXHFVwrHryWx9ufbWJmVPaxZVqSSgmSYiHtpONuZ06DWsykMlUgk1K1blx49evDaa69x9uxZli5dSlJSEm5ubtjY2DyTeZWEmZkZ3bt3Jycnh3nz5lG/fv0qTdZxdHSkTp06LFy4kN69exuk0FkikZCdnc3ly5fp1KlTpcYy5IpvxYoVDBkyxGC2XdHR0cycOZPVq1dXafsnQRAICwtj+fLlTJ8+nffee++5rGG9evUqK1euZN26dbzyyivMnz8fX19ffWeGQiwsLEhPT8fb25uxY8c+dZUskUhoXd8eFzsLzsakkqvUYmokrdDKWhAEsvI1GMkkzPBtxuC29Z95T8SqQMzqfAopOUr8Np4DAYOt9IpDqdaCBLaO7PDcxNFTUlLYvn07P/74Iz4+PgQFBdGiRYtnPa0niIqKYubMmbz11luMHz8eY+Oqy3BbtWoVt2/fZs2aNQZZuTx8+JBhw4Zx6NChSs3bUFmd9+/fZ+TIkYSHhxsk3J2VlUVgYCBjxozB19e30uOVRGZmJnPnziU9PZ3Fixc/d02cdTodp0+fJjQ0lISEBPz8/Ojfv3+ptb6CIJRbdFJylCw7fIM/7qahQ8DatGwP7FqdQLZSjRQJ7dwdmO7b9Lm5F1UFovA9BTFduGA1sXfvXrZu3Uq9evUIDAykU6dO1W4m/TSysrKYO3cuqampLFmypMpufFqtlo8//hhPT0/GjRtnkDFHjRrFe++9V+aejcVhKOFbt24d+fn5TJ48uVLjQMHNftKkSbi5uRlkvJIofPDp2rUr48aNq9IHn/JS6KYUGhqKiYkJQUFBdO3atVpWoiWVWxnJpAX1f4DmOSm3ehaIwlcC0UnZjAyJwNqs4oks5UEQBLLzNWwM8nkuP3gajYbjx48TEhKCWq0mMDAQX1/f5yZTThAEtm3bxnfffcfMmTMrJSRPIz09naCgIP773/8aJDtx3759/PLLL6xcubLCYxhC+HQ6HX379mXVqlU0atSowuMUsm7dOi5dusRXX31VJclSOp0OuVxOWFgYn376KV26dDH4NSpKVlYWu3fvZvv27TRu3FjfY/BZhAxzlBpuJmYRnZTDhQcZJGfno9LqMJFJqWVtxisudjSqbUWTOjYvhCNLWXl5Xmk52f1nHAJU24dVIpGgQ2D3n3EE92xWLdcsD0ZGRvj6+tKjRw/Onz9PSEgI69atY+jQoQwcOPCZtx+RSCT4+fnh5eXFzJkziYyMZMKECQYXZnt7e5YvX86ECRNwc3OrdK1b165d+eKLL0hPT8fe3t5Asyw/ERER2NraGkT0fv75Z8LDwwkJCakS0cvIyGDOnDnk5OQQEhJCnTp1DH6NipCQkMC2bds4cOAAnTt3Zu3atc+8Y4qVqRFtXB1o4+rwr++oYEien3jVc0SOUsPRa0nV/gRkZWrE0WtJ1e7BVx4kEgnt2rVj7dq1rFmzhpiYGPr3788XX3xBYmLis54enp6ehIaGkpiYyAcffFCkj5mhaNasGRMmTGDKlCmVbghsYWFBly5dOHz4sIFmVzEMVbsXGxvLokWLWL58ucEdbwAuXLiAn58fDRs2ZMOGDc+F6N26dYtPP/0Uf39/ZDIZP/zwA/PmzXvmoidSMi+l8P3888/ExPxdQrB+/foiXcBvJmYBVKvzOYDRX/tmhdcvjs2bN1fXdEqlcePGzJ8/n23btiGVSvHz89N3NH+W2NjYsGLFCnr37s3w4cM5ceKEwa/Rt29f2rdvz5w5cypd/N+nTx/2799voJmVH4VCwalTpyqdgJKbm8snn3zC+PHjad68uYFmV4BOp2Pz5s1Mnz6dWbNmMWHChGdabyoIAufOnWPs2LFMmjSJxo0bs2/fPiZOnPivbAz9svHSCt/du3f1X48ePZp27drpv76VlINa+2ycTNRaHdFJJa8ivvvuu2K/LwjCM3NfqV27NpMmTeKnn36iUaNGTJw4kbFjx3Lu3LlnZokmkUgYOnQoq1evZvXq1SxbtszgHREmT55MZmZmpR9GfHx8yMzMJDo62kAzKx/Hjx+ndevWlVqh6XQ6PvvsM3x8fOjXr58BZwdpaWlMmDCBM2fOEBoaWunyj8qg0WgIDw/H39+fL7/8kl69evHTTz8RFBRUZX0LRQzPC5HcEh8fz4QJE2jVqhVRUVHUrFmTL774gvDwcPbs2YNarcbFxYX58+dz69YtJk2ahJWVFVZWVixfvpyNGzfSuXNnzM3NC9rRtPPj97tpaJNjeHj+CC0GTiD97lXunfkJnVaDuV1NGvd8H5nJk84WD34/RPLVc0gkEuw9PHF/fRA5yfe5fUSOTqPCzK4WjXyHY2xuSdS25bi/8S7Wdd1RK7K5IF9Is2ELcUy9jIfmAfn5+cTFxfHmm28yYcIE/u///g+5XE7Dhg3x8PBg7Nix+izDGzdu0LVrV7Kzs/nkk08A2LNnD3fv3q3SrLriUKvVHD58GLlcjpGREQEBAXTv3v2ZPaEXmhQ/fPiQpUuXGrTpaUpKCkFBQcyaNatSN+R169ahVCr573//W6E5VCa5ZdSoUQwdOrRSyTqbN2/m9OnTfPPNNwbNrCw0Ke/duzejR49+ZrV5ubm57Nmzh23btuHi4kJQUBCvvvrqC1nj9jLwwqz47t+/z+DBg9mxYwfW1tacOHGCt956i5CQELZt24abmxs//fQTXl5edOnShYkTJ7J169YiprXt27fnypUrPEzJxFgmJeXGH9Ro2ha1Ipv7vx2g5eBPaD1sDlZ13Ig7f/SJOaTFXCY1+gJe/tNp/f5cnNv1BODWwU24vzGI1u/Pw7KmE/fPlhzWMpJJycxTc/PmTZYsWcL27ds5evQoSUlJjB8/HlNTU7Zu3crChQuBAgeId999lx07dhAQEMCpU6f0psr79+83+NN3WTA2NqZv375s376dcePG8dNPP9G/f3/CwsKeSW9Aa2trli1bRv/+/Xn//fc5evTJ311FqVGjBkuXLmXevHk8ePCgwuP07t2bQ4cOGdwQuzTi4+OJiYmhc+fOFR7j7Nmz7Ny5k2XLlhlM9HQ6HRs3bmTGjBnMnj2bcePGPRPRe/ToEWvWrKFv375cu3aNzz//nPXr19OxY0dR9P7FvDBZnU5OTjRu3BgoSD5ISEjgzp07rFu3juzsbPLy8ujQocNTx5DJZHTs2JETNy8gdWpJ2p0o3N94l4z7N1GkxnNp61IABK0G63pPZvNlxF7DtmEbYmIfYGdnh4ODAxqlAk2+AluXJgDUatGR6/vWlzgHCaDS6mjXrp0+dOLh4UFCQgK1a9d+4vi6devSsmVLoCBRom3btvz666+4u7uj0Wie6QZ7oddlp06duHbtGnK5nM2bNzNgwACGDh1KzZo1q3UugwcPxsvLi+DgYCIiIvjkk08M4sLi5eXFqFGjmDJlCt99912FmhC7urri7OzMb7/9VikRKi8HDhyge/fuFRasuLg45s6dy/Llyw32+0xLS+PTTz9Fo9Egl8ufyZ5ZTEwMcrmcX375hZ49exIaGvrcFcaLVJwXZsX3+B+uVCpFq9Uyd+5cpk2bxvbt2/nwww/LtMfTvXt3kq+fJ+v+Dazquv0VzhSwd21O6+Gf0Xr4Z7T5YAGNe75PVnwMf26Zx59b5pF6+yIAZubmeHh4IJEU/PHExyegLWHvTSL9+wlWpy140hcAY5m0SBp+4espjn8aCQ8YMID9+/ezb98++vbtW+rrrS6aN2/OkiVLCAkJIS8vjyFDhjB//vwiSUbVQdOmTQkLCyM7O5v333+f+/fvG2TcgQMH0qJFCxYsWFDhfc0+ffpw4MABg8ynLOh0ukplc+bl5TFlyhRGjhxZopdkeTl//jz+/v60bNmSr7/+ulpFTxAEIiMjmThxIqNHj8bZ2Zk9e/YwdepUUfReMF4Y4SuO3NxcatSogUajKdICxsLCgtzc3GLPadOmDfmPHpAY9Ss1mxYkvFjXa0DWw9vkpRe4IGhVSvLSkrCp56EXQ8eGrbBza07ylTNI0VGrVm1cnetiaWOHSgc3/viF3Jwckq/9hp1LwcrU1NaR7MRYAFJuRgAFbgr/dIrRarVER0cTGhpKTEwMbdq0YfXq1cXO39PTk6SkJA4fPkyPHj0q/uZVEU5OTkybNo09e/ZQr149Ro8ezaRJk/jzzz+rLRHG0tKSxYsXM3DgQEaMGGGQUgKJREJwcDAPHz4kNDS0QmN069aNc+fOkZVVclavIbl48SJmZmY0a1b+ulFBEFiwYAFNmzbl3XffrfRcdDodGzZsYPbs2cydO5cxY8ZUW2hTq9Vy7Ngxhg0bxqJFi3jjjTc4cOAAH3zwgUHNukWeH16YUGdxjBkzhuHDh2NnZ4enp6d+f6lHjx4sXLiQH374geXLlxc5RyqV0u7VThw8sB/PfiMBMLGwpnGvEdzY/y2CVg2Aa+e3MXcoGnp08GhJbvIDLoYsRCIzwsGjJW5d3qH14IlcP7iZyN/3YWTlQJNeIxAEAee2Pbixbz2Jl07h4OEFFHjm1TYvqA1avHgxZ86c4cyZM+zfvx+ZTIZKpeL69ev8+uuvJd5wunXrxq1bt547c+nHsbW1ZeTIkQQGBnLw4EEWLlyIlZUVQUFBvPnmm1V+05NIJAwcOJCWLVvqQ59Tp06tVOjTxMSE5cuXM2zYMJo0aVIkU7gsWFtb06lTJ44cOWIQMSmNwtVeRfaqtm7dyv3799m0aVOl97pSUlL49NNPAQgNDaVGjRqVGq8s5Cg1RN1LYffxs5y8FIPU0p7aXn7UcrTnnM6M/IuJNH4JHU1eFl6IrE5DE3kvjam7orA0+AdeICcnl9TUVFQqFQ4ODtjb2yF9LOSZo1QTtWEaivtXMDc3x9raGjs7O30oVxAEsrKyOHHiBPXrF+/EMGnSJPz8/Mp9432W6HQ6Tp06RUhICCkpKQQEBNCvXz+D9oQrCYVCwaJFi7hz5w5Lly6tdKeHiIgIZs2axZYtW6hbt265zj179izffPMN33//fZnPqUhWZ15eHr169WLnzp3lFprKvL5/8scffzBnzhzeeecdRo4cWeUesNFJ2YSdvc2+P++RnZ2NsZk5djZWWJqb649Rv8Qeli8LovAVQ45SQ7+1pzEzllVZEXt+fj6pqank5uboE2GQylCqdSx5054RgX6kpKQgCAJ5eXkYGxtjYWGBiYkJNjY2XLhw4Ykn7ezsbIYNG0ajRo1YtmxZlcy7Orh06RIhISFERUUxaNAgBg8eXOV2XoIgsHfvXr766ismT55Mr169KjVeWFgYhw4dYtOmTeVaRWq1Wnr37s3XX3+Nu7t7mc6piPCFh4dz+PDhcneXT0xMZNiwYSxcuJC2bduW69zH0Wq1bNiwgX379jF//vxKjVUWUnKUzN4VwZlbSSgUCmwsTKlZw7FUSzutTiBHqUECL0XXgpcFUfhKYOmh64RfSazyzgxqtYrU1DQyMzMxtbKjt3c9lg59lYSEBIYNG8adO3ews7MjPz8fhUJBeno6lpaWdOnShTZt2uDj40Pr1q2f67BmRbl37x5hYWEcO3aM7t274+/vX+Iq11BER0cTHByMt7c306ZNq/CKUxAEZs2ahYmJCZ999lm5woFr1qxBIpEwfvz4Mh1fEeEbO3YsAwYMoHv37mU+R6VSMXLkSLp161ap3n+PHj1i1qxZGBkZsWDBAhwdHSs8VmkIgsC3h35n7al7KJQaHK0tcHCwL3dNaWGfOjNjKVO7NaFr89piOcO/mBc6uaUyDGztXNC+o4qfC4yNTahTpw4NGjTAyNiIc9tWM3HiROLj49m9ezevvfYa6enpmJqa4ujoSK1atVi9ejWzZs2iZs2a7N69mz59+uDn58fKlSv55Zdfqi05oqpxdXVl5syZ7N69Gzs7O0aMGMHUqVOJioqqsms2atQIuVyORqMhKCiowlmnEomE2bNnc/PmTXbu3Fmuc/v06UN4eHiVOfEkJSVx48YN3njjjTKfIwgCS5cuxdnZmYCAgApf+7fffiMgIEDv91pVoqfT6fjf//5H14+X8vmJGMzMzGni7kKtWjUrZKQgkUgKHoIFWBB+jW9/jXlmrkQilUdc8T2FZ9GPb1G/Zhw8eJDQ0FAsLS3x8/Pj119/Zdu2bVhbW6NQKDh58mQR9xGNRsP169eJjIwkIiKCqKgonJ2d8fHxwcfHh1atWr0QK8K8vDz27dtHWFgYNWvWJCgoiM6dO1fJvpAgCOzfv581a9YwceLECpeGxMXFMWLECJYvX16ulP/AwEDGjh3Lq6++Wuqx5V3xbd68mcTERGbOnFnm+ezatYudO3eyZcsWzB/bDysrWq2W9evXc/DgQRYsWECbNm3KPUZZUKlUHDhwAHloKBl12pBduxU17awxkhnuM6LVCeTkqwno4MqoLg0MNq5I9SEK31N4lh3YdTodv/76KyEhISQnJ+Ps7MyhQ4eoUaMG58+ff2qYRaPRcO3aNb0QXr58GRcXF31o9JVXXnnmbYQqg1ar5cSJE8jlchQKBQEBAfTu3btKegPeuXOH4OBgmjdvTnBwcIVu+mfPnmXBggWEhISUuch7x44dXLp0iUWLFpV6bHmETxAEBg4cyNy5c/Hy8irTXKKiopgyZQqbNm2qkN1bcnIyM2fOxMzMjPnz51dJ14asrCx27tzJ9u3badasGU3/M5htN5VYlbEDeXkp2PtTM7tXc7q1ePYdIkTKhyh8pXDsaiILwq890z+gqKgoQkNDOX78OC1btuTLL78s181DrVZz/fp1IiIiiIiI4MqVK9SvX7+IEP4bDXYLC45DQkK4efMmQ4YMYdCgQQZf3ebl5bFs2TKuXLnCsmXLaNCg/E/55fWyzMzMpH///hw4cKDU3015hO/y5ct89tln7N69u0x7VJX1Ij1z5gzz5s1j6NChDB8+3OCr8/j4eH0i0euvv05gYCA2tZye2QOryL8DUfhKQRAEvv01htBz97AyM6z4lTdkcv/+fX2yR9euXQkICKhQsodarS6yIrxy5Qqurq56IWzVqtW/Tgjv3LmDXC7n1KlT9OrVCz8/P4O7bRw4cIBVq1Yxfvx4+vXrV67kBp1Ox7Rp03B0dGTGjBllOmfq1Kl07NiRt99++6nHlUf4Fi9eTJ06dRgxYkSpx6rVaj766CM6duzIyJEjyzTnQjQaDevWrePw4cMsXLiQ1q1bl+v80rh+/TpyuZzff/+dAQMGMGTIEL3Ly7PYolgxyLvKryViOEThKwN68fv9HmZGMoM8RSrVWvI1WgLau/JhZ49y3UTT0tLYuXMnu3btwtvbm6CgoDKHrYpDpVIVEcKrV6/i5uaGj48Pbdq0oVWrVlhaWlZ4/OokOTmZH374gb1799KhQweCgoJo2rSpwcaPiYkhODiYJk2aMGPGjHL5cubm5jJs2DCCgoLKZB5+6tQpvv/+ezZt2vTU48oqfCqVCl9fX7Zt21as7+s/WbZsGUlJSXz++eflWqkV7h9aWVkxb948g5WiCILAb7/9RkhICA8ePMDPz48BAwYU+WxGJ2UzMiQCazOjasm6FASB7HwNG4N8xDq/fxGi8JURQRA4fi2JFcdukq/WYVPBP6x/pkVXZn8gLy+P/fv3690ugoKC6NKlS6XDSSqViqtXrxIREUFkZCRXr17F3d29SLJMRYyYq5PCNjJbt27F1dWVwMBAg7WRyc/PZ/ny5Vy8eJFly5bRqFGjMp8bGxvLhx9+yOrVq0tt1qrRaOjVq1epe2tlFb5jx46xZ88e1q1bV+o89+/fz5YtW/j+++/Ltfo/deoUCxcuxN/fn8DAQIOENtVqNUeOHEEulyOVSgkMDCyxzVV1lSE9Tkaeit6edQnuWX7rN5Fn30Ys7gAAIABJREFUgyh85SQlR8mywzf4424aOgSsy7j3p9UJZCvVSJEYvBBWp9Nx8uRJQkJCyMnJISAggD59+hgs2UOlUnHlyhW9EF67dg0PD48iK8LnVQjVajXHjh0jJCQEQRAIDAykR48eBmmfEx4ezhdffMHYsWN5++23yyyqP//8MytWrEAul5e6V7ty5UosLCwYM2ZMiceUVfgmTJiAr69vqcX5165dY+LEiWzYsKHMRfQajYa1a9dy7NgxFi9ejLd35UN/OTk5/Pjjj/zwww+4ubkRFBRE+/btS3yfq8N4ojg0Oh1KtY59H78m2pv9SxCFr4JEJ2Wz+884jl4rMK5Wa3XIpBKMZNKC+j8KDKer0/pIEAQuXLhASEgI165d0yd7GNpo93EhjIiI4Pr16zRo0EAvhN7e3s+dEAqCwLlz55DL5cTGxjJ06FDeeeedSu9lxsbGEhwcjIeHB7NmzSpzSHjdunVcunSJdevWPdWX9NatW0yePJl9+/aVuHoqi/ClpKTw7rvvEh4e/tTM1PT0dAIDA5k8eXKZG9MmJCQwY8YM7OzsmDdvXqU/b8nJyWzdupV9+/bRsWNHAgMDadKkSann/dNqUJmdzp0T22g+YCw5yfdR5WToPXFLIuP+TX3z6fKQq9SwYpAXbVz/fpC5du0aBw8eZOrUqeUaS6TqEYWvkuQoNdxMzCI6KYcLDzJIzs5HpdVhIpNSy9qMV1zsaPQMzG5jYmIIDQ3l559/pmfPnvj7+1dZaxWlUllkRXj9+nUaNmyoF0IvL6/nSghv3LiBXC7nt99+o3///rz33nuVan+jVCr5/PPPiYyMZMmSJWW6Set0OiZNmoSbmxuTJ09+6rHvvfcen3zyCT4+PsX+vCzCJ5fLuXv3LnPmzCnxGK1Wy8cff4ynpyfjxo0r9TUA/PLLLyxatIjAwED8/f0rFdqMjo5GLpdz+vRp+vTpw3vvvVcuL9Btf9xn3c+3sbN4MtKRdPkM2UmxNOzq/9QxKip8GQoV495oyNB2VessJGIYROF7wXn06JE+2aNdu3YEBgaWurdUWZRKJZcvX+bMH5GcvhzD7Uc5mDk1w9yhNpbWttjb2lDXzpxWLvbP1AE/Pj6ebdu2cfDgQTp37kxgYGClGvceOXKEFStWMHr0aAYOHFhq6DMrK4ugoCBGjx6Nr69viceFhYVx69Yt5s2bV+zPSxM+QRAYMmQIwcHBT82uXLVqFXfu3GH16tWlCphareb//u//+N///sfixYv1zZDLiyAInD9/HrlcTnR0NEOGDGHgwIHY2Nhw8OBB5HI5EomERo0a0bVrVzZt2oRarcbOzo6FCxfi4ODAhg0biIuLY8/pSyQkp+L2ai/qenchPzOFq7vX8MqwOURsmIFOo8bE2h6X9j0xs6vJnRPb0GnUyIxNaNTzfSwc6jxV+NJioog5uQOZsQk2Tg3Jz0yhxcAJZCfc5cbRMBzMpHRoVIfPPvsMV1dXIiMjkcvlrFq1ig0bNpCYmEhcXByJiYn4+fkxdOjQCr1nIpVHDEi/4NSsWZPx48fzwQcf6JtqOjs7ExQURMeOHUu8OWdmZnLgwAH8/PzKnRByP0PF8UeWHM1rAg2bYOmmRaNWkanMJzE+BVVsPMamZhy+YIaluRmmpqb0aFGn2h3w69WrxyeffMKHH37Irl27GDduHE2aNCEwMBAfH59yv+4ePXrQrFkzfZujTz/99KmhVBsbG1asWMGYMWPw8PCgcePGxR7Xs2dPvv32WxQKRYVWzjdu3CA/P/+pzjFHjx7l5MmThIaGlip68fHxBAcHU6NGDcLCwipUN6nVajl+/DhyuRylUklgYCArV67U70vHxMSwadMmNm/ejJ2dnd6Gb8uWLUgkEvbu3cv333/Pf//7X6Bgtdhq6FQcUzK4FrYIB4+/hVgqM8L1tQFFVnwaZR7eftORSGWkx14j9tSPNB8wtsT56jRqoo/I8fabjpltDW7s+0b/M3OHOrQYOo36jlaMbg5fffXVE+3OoCAsvn79ehQKBe+88w6DBg2qkH2aSOUR3/WXBAsLC/z9/RkyZAhHjx5l7dq1rF69msDAQHx9fZ9I9tiyZQtLlixBrVYzfPjwMl3j8cQfAbAyNUImlfy152IKFIiaTqcjLy+P3NxcstNTSM5XsiUpmR/O3qRVPSsWDW6Hk2P1WazZ2NgwYsQIAgICCA8PZ+nSpZibmxMUFMR//vOfcvUGrF+/Plu2bGHlypUEBASwZMmSpzZ6bdSoEdOmTWPKlCmEhoYWKyIODg688sornDx5skLd0gv77pUkaNHR0Sxfvpx169aVKmInT55kyZIlvP/++7z33nvlfjhQKBT89NNPbN26lbp16/LRRx/RqVOnJ+Z2/vx5unbtip2dHVDwO7p9+zYzZswgJSUFtVqNk5OT/vjXX3+dX2VGGJtbY1e/CdmJsVjVKjkTVqvM41b45r+aS0sQdNqnzzstAXO7mpjZFrRwqtm8PYmXTunHun0sjNs5KSg8ChpfF8drr72GiYkJJiYmODg4kJaWVq0d5kX+RhS+lwwjIyN69epFz549+f333wkJCeHrr78ukuyRl5fHxo0bsbe3Z+HChTRt2pQOHTqUOKa+1OPoTfI1pZd6SKVSLC0t9YkghUKYk5PL7zEpvD7vR5prbtOzpTM+PgV7hJVpEFtWTExMGDBgAP369eP06dPI5XLWrl2Ln58f/fr1K/Nqy8TEhBkzZnDs2DHGjx/PqFGjePfdd0t8T7p3787Vq1eZOXMma9asKVag+vbty/bt28stfIWlACX198vKymLq1KlMmTKlxBUnFCQ0rVq1itOnT7Nq1SpatGhRrnmkpqayfft2fvzxR9q0acOSJUvw9PQs1xjLly8nICCALl26EBkZyTff/L3qkkgkGD/2vpUmx7Gn92JbvwnN3x5HfmYKUT+seOKYyzu+RK3IwqqOK/Val5zoE3t6L9YuTWj71ics6u7MRx99VOxxjz9cSqXSEgVSpOoRhe8lRSKR0KFDBzp06MDNmzeRy+X069ePfv36YWpqSl5eHnZ2digUCj766CMOHjyIs7PzE+P8s7i/IvVTjwthbSBPpSExtzbnMlL44+uvuR0dTdOmTfXJMi1btqxSIZRKpXTp0oUuXbpw5coVQkJC2LhxI++88w5Dhgwpc0eBbt260bRpU33oc/bs2SV6pE6YMIFx48bx9ddfF5tY8tprr7Fo0SLi4+PLlaR0+vRpPDw8iqyOCtHpdHz66ad06dLlqXuMcXFxzJgxg9q1a5e4Ki2J2NhYQkNDOXHiBL6+vmzZsqXYz9E/adu2LVOmTMHf3x+tVsvw4cO5c+cO3t7euLi4sH///iLH//LLL9Tr14bbcVlk3r+JW5eBRVZxMhMztMp8/ddaZR6mVgWF9UlXzhY7h5aD//v38WoVeRmPyM9Mwcy2Bo9unC8ylszCllrWZhw4cKBsb4zIM0VsSyRCkyZNWLhwIWFhYWg0GoKDg8nMzESpVGJhYUFubi4jRoxAoVAUOa+InZupscF8Ec1NjKhha8VdmROvDp/FkSNH+OCDD1Cr1Xz11Vd07dqVUaNGsWHDBiIjI1GpVAa5bnF4enqyfPlyvvvuO7Kyshg0aBALFy7k3r17ZTrfxcWF7777jho1auDv78+1a9eKPU4mk7FkyRIOHTrEyZMnn/i5iYkJ3bt3Jzw8vFzzLwxzFsc333xDfn4+EyaUnMF4/Phx3n//fXr37s2KFSvKJHqFZTWTJ09m1KhR1KpViz179jB9+vQyiR6Ah4cHI0aM4MMPP2TMmDGcPn2a1NRUpk+fjre3N+vXr+fkyZN8/fXXxMTE4OLiwvnQ5VzfvgyXjn0xtS7qFmNbvymK1AT+3DKPR9f/wLldD2JP/cifW+YhaJ8e5gSQGZvQsJs/V3au4sL385EZmyIzLSgLcW7Xg4en93J6w2xxFfcvQczqFCnCkSNHGDt2LDqdjrS0NExNTXFwcEClUtGzZ0/Wrl2rD9k9KwNvhULBpUuX9OUTd+7coXnz5nqvUU9Pzyrp1AAFdW47d+5k586dtGzZkqCgILy9vcu013XixAmWLl3KiBEjGDp0aLHnXLt2jQkTJrBhwwY8PDye+NnMmTPZs2dPkXNLyupMT0/n7bffJjw8/IkwbWlF9CqVii+//JKzZ8+yZMmSMmUC63Q6fv75Z0JCQsjIyNAbKVS0me/jDB48mKioKH2ykEajITc3l5ycHHJyclCpVIyYtpCbdm31dXyGRqvKR2ZihiAI3DkWhrlDbZx8ugHF1/GJPL+IwieiRxAE+vTpw+3bt7G2tkYQBDIzM0lLSwPA1NSUOXPmMGbMmGfasumfKBQKLl68qBfCu3fv6oWwTZs2VSKE+fn5ers4BwcHAgMDeeONN0rNiHz48CEzZsygVq1azJkzp9gV1L59+9iyZQshISFFskIFQWDw4MFMnjYDk1pu3ErK4eKDdOJSs7lw6TLtfNpQ28ZUXyZy+dcjxNy6zvz584uMX2ibVtJe3f379wkODsbZ2fmp4dlClEol+/fvJywsDFtbW4KCgsr0XpSHb775hiVLlmBmZoZCoSA3NxeVSoWpqSlKpZLGjRtz4PAx/EOiqsy55WHEMZKunEXQarCsXZ9GPYKQGZuKzi3/QkThE9ETERFB//79MTMreKoVBAGdTodEIkGlUpGVlYVOp2Pbtm2cVrs9tw74ubm5+hVhREQEsbGxtGjRQi+ELVq0MJgQFnb6DgkJITs7G39/f/r06fPUPUiVSsWaNWs4deoUixcvLjbJY+nSpSQnJxcxiI5OymZeyGEup0txdHTUuwVJBIG4uAe4ubmhfswtKD4hgR7Na/Nx77b6MpFCo+zAwED69+//xHWPHj3K8uXLS03IAcjIyGDHjh3s2rULT09PAgMDadWqlcHMoXNzc7l48SKRkZH6cgsrKyssLCywsLBAJpOhUCgYPnw4s2bNQiqVil6dImVCFD4RPZGRkXz33Xf6G4uFhQWWlpaYm5tjZlZQb/fo0SOatX+TOcfj/zUO+IU30MIVYWxsLJ6enkWEsLLenYX7WnK5nKtXr/Luu+/y7rvv6tPxi6Ow+Hv48OFP1Es+3hJowNBAfZmIRqs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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "CPU times: user 14.6 s, sys: 150 ms, total: 14.7 s\nWall time: 14.1 s\n"
    }
   ],
   "source": [
    "%%time\n",
    "fan_estimator = ForestAugmentedNaiveBayesSearch(data=df, class_node=target_variable)\n",
    "fan_structure = fan_estimator.estimate()\n",
    "fan_model = BayesianModel(fan_structure.edges)\n",
    "nx.draw_circular(fan_model, with_labels=True, arrowsize=20, node_size=1000, font_size=10, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The FAN model, without the target variable, is a forest.\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "forest_check = fan_structure.copy()\n",
    "forest_check.remove_node(target_variable)\n",
    "if nx.is_forest(forest_check):\n",
    "    print('The FAN model, without the target variable, is a forest.')\n",
    "else:\n",
    "    print('The FAN model, without the target variable, is not a forest.')\n",
    "nx.draw_circular(forest_check, with_labels=True, arrowsize=20, node_size=1000, font_size=10, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate CPDs from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The FAN model has no errors.\n"
    }
   ],
   "source": [
    "fan_model.fit(train, state_names=state_names)\n",
    "fan_cpds = fan_model.get_cpds()\n",
    "if not fan_model.check_model():\n",
    "    print('The FAN model has errors.')\n",
    "else:\n",
    "    print('The FAN model has no errors.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "{'<=50K': 0.7361518588598222, '>50K': 0.2638481411401779}\nCPU times: user 8min 45s, sys: 1.18 s, total: 8min 46s\nWall time: 8min 45s\n"
    }
   ],
   "source": [
    "%%time\n",
    "fan_inference = VariableElimination(fan_model)\n",
    "fan_results = predict(test_no_target, fan_inference, target_variable)\n",
    "fan_mean_results = {}\n",
    "for k, v in fan_results.items():\n",
    "    fan_mean_results[k] = np.nanmean(v)\n",
    "print(fan_mean_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The FAN model has 84.685 % accuracy over the test set.\nThe FAN model has 73.358 % precision over the test set.\nThe FAN model has 63.666 % recall over the test set.\nThe FAN model has 68.169 % F-score over the test set.\n"
    }
   ],
   "source": [
    "fan_accuracy = accuracy(test[target_variable], fan_results)\n",
    "print(f'The FAN model has {round(fan_accuracy, 3)} % accuracy over the test set.')\n",
    "fan_precision = precision(test[target_variable], fan_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The FAN model has {round(fan_precision, 3)} % precision over the test set.')\n",
    "fan_recall = recall(test[target_variable], fan_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The FAN model has {round(fan_recall, 3)} % recall over the test set.')\n",
    "fan_f_score = f_measure(test[target_variable], fan_results, positive_class='>50K', negative_class='<=50K')\n",
    "print(f'The FAN model has {round(fan_f_score, 3)} % F-score over the test set.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "NB     82.799466\nTAN    85.385385\nBAN    84.380640\nFAN    84.684685\nName: accuracy, dtype: float64"
     },
     "metadata": {},
     "execution_count": 95
    }
   ],
   "source": [
    "indexes = ['NB', 'TAN', 'BAN', 'FAN']\n",
    "accuracies = [nb_accuracy, tan_accuracy, ban_accuracy, fan_accuracy]\n",
    "accuracy_df = pd.Series(data=accuracies, index=indexes, name='accuracy')\n",
    "accuracy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "NB     63.297045\nTAN    76.012461\nBAN    69.684775\nFAN    73.358209\nName: precision, dtype: float64"
     },
     "metadata": {},
     "execution_count": 96
    }
   ],
   "source": [
    "precisions = [nb_precision, tan_precision, ban_precision, fan_precision]\n",
    "precision_df = pd.Series(data=precisions, index=indexes, name='precision')\n",
    "precision_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "NB     79.080311\nTAN    63.212435\nBAN    67.511371\nFAN    63.665803\nName: recall, dtype: float64"
     },
     "metadata": {},
     "execution_count": 97
    }
   ],
   "source": [
    "recalls = [nb_recall, tan_recall, ban_recall, fan_recall]\n",
    "recall_df = pd.Series(data=recalls, index=indexes, name='recall')\n",
    "recall_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "NB     70.313850\nTAN    69.024045\nBAN    68.580858\nFAN    68.169209\nName: f_score, dtype: float64"
     },
     "metadata": {},
     "execution_count": 98
    }
   ],
   "source": [
    "f_scores = [nb_f_score, tan_f_score, ban_f_score, fan_f_score]\n",
    "f_score_df = pd.Series(data=f_scores, index=indexes, name='f_score')\n",
    "f_score_df"
   ]
  }
 ],
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