Course_workbook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../assets/packt-banner.png\" alt=\"\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Classification Models\n",
    "\n",
    "We continue where `Course_workbook.ipynb` left off, by loading the processed Human Resource Analytics dataset and getting started on training models to predict the target variable \"left\" (modeling if an employee has left the company or not).\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import time\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "%config InlineBackend.figure_format='retina'\n",
    "sns.set() # Revert to matplotlib defaults\n",
    "plt.rcParams['figure.figsize'] = (8, 8)\n",
    "plt.rcParams['axes.labelpad'] = 10\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-02-13 \n",
      "\n",
      "CPython 3.7.5\n",
      "IPython 7.10.1\n",
      "\n",
      "requests 2.22.0\n",
      "numpy 1.17.4\n",
      "pandas 0.25.3\n",
      "matplotlib 3.1.1\n",
      "seaborn 0.9.0\n",
      "sklearn 0.21.3\n",
      "\n",
      "compiler   : Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 18.7.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -d -v -m -p requests,numpy,pandas,matplotlib,seaborn,sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training classification models   \n",
    "We train SVMs, random forests and K-nearest neighbors classification models.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction to classification algorithms\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the processed data\n",
    "\n",
    "df = pd.read_csv('../data/hr-analytics/hr_data_processed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 421,
       "width": 421
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The two features we'll use for training in this section\n",
    "\n",
    "sns.jointplot(\n",
    "    x='satisfaction_level',\n",
    "    y='last_evaluation',\n",
    "    data=df,\n",
    "    kind='hex'\n",
    ")\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-jointplot.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 490,
       "width": 507
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Segementing by the target variable\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plot_args = dict(shade=True, shade_lowest=False)\n",
    "for i, c in zip((0, 1), ('Reds', 'Blues')):\n",
    "    sns.kdeplot(\n",
    "        df.loc[df.left==i, 'satisfaction_level'],\n",
    "        df.loc[df.left==i, 'last_evaluation'],\n",
    "        cmap=c,\n",
    "        **plot_args\n",
    "    )\n",
    "\n",
    "ax.text(\n",
    "    0.05, 1.05, 'left = 0',\n",
    "    size=16,\n",
    "    color=sns.color_palette('Reds')[-2]\n",
    ")\n",
    "ax.text(\n",
    "    0.25, 1.05, 'left = 1',\n",
    "    size=16, color=sns.color_palette('Blues')[-2]\n",
    ")\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-bivariate-segmented.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "features = ['satisfaction_level', 'last_evaluation']\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df[features].values,\n",
    "    df['left'].values,\n",
    "    test_size=0.3,\n",
    "    random_state=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data for SVMs and K-Nearest Neighbors\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_std = scaler.fit_transform(X_train)\n",
    "X_test_std = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "    max_iter=-1, probability=False, random_state=1, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a support vector machine classifier\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel='linear', C=1, random_state=1, gamma='scale')\n",
    "svm.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 75.9%\n"
     ]
    }
   ],
   "source": [
    "# What is the classification accuracy?\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = svm.predict(X_test_std)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print('accuracy = {:.1f}%'.format(acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3416,    0],\n",
       "       [1084,    0]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What does the confusion matrix look like?\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percent accuracy score per class:\n",
      "left = 0 : 100.00%\n",
      "left = 1 : 0.00%\n"
     ]
    }
   ],
   "source": [
    "# What is the accuracy by class?\n",
    "\n",
    "print('percent accuracy score per class:')\n",
    "cmat = confusion_matrix(y_test, y_pred)\n",
    "scores = cmat.diagonal() / cmat.sum(axis=1) * 100\n",
    "print('left = 0 : {:.2f}%'.format(scores[0]))\n",
    "print('left = 1 : {:.2f}%'.format(scores[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/mlxtend/plotting/decision_regions.py:247: UserWarning: No contour levels were found within the data range.\n",
      "  antialiased=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 482
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the resulting decision regions\n",
    "\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "# Note: If getting ModuleNotFoundError,\n",
    "# then you can try installing mlxtend with pip.\n",
    "# Open the terminal and run: \"pip install mlxtend\"\n",
    "\n",
    "N_samples = 200\n",
    "X, y = X_train_std[:N_samples], y_train[:N_samples]\n",
    "plot_decision_regions(X, y, clf=svm);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rbf'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdegree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgamma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto_deprecated'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcoef0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshrinking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mprobability\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcache_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdecision_function_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ovr'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "C-Support Vector Classification.\n",
       "\n",
       "The implementation is based on libsvm. The fit time scales at least\n",
       "quadratically with the number of samples and may be impractical\n",
       "beyond tens of thousands of samples. For large datasets\n",
       "consider using :class:`sklearn.linear_model.LinearSVC` or\n",
       ":class:`sklearn.linear_model.SGDClassifier` instead, possibly after a\n",
       ":class:`sklearn.kernel_approximation.Nystroem` transformer.\n",
       "\n",
       "The multiclass support is handled according to a one-vs-one scheme.\n",
       "\n",
       "For details on the precise mathematical formulation of the provided\n",
       "kernel functions and how `gamma`, `coef0` and `degree` affect each\n",
       "other, see the corresponding section in the narrative documentation:\n",
       ":ref:`svm_kernels`.\n",
       "\n",
       "Read more in the :ref:`User Guide <svm_classification>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "C : float, optional (default=1.0)\n",
       "    Penalty parameter C of the error term.\n",
       "\n",
       "kernel : string, optional (default='rbf')\n",
       "    Specifies the kernel type to be used in the algorithm.\n",
       "    It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or\n",
       "    a callable.\n",
       "    If none is given, 'rbf' will be used. If a callable is given it is\n",
       "    used to pre-compute the kernel matrix from data matrices; that matrix\n",
       "    should be an array of shape ``(n_samples, n_samples)``.\n",
       "\n",
       "degree : int, optional (default=3)\n",
       "    Degree of the polynomial kernel function ('poly').\n",
       "    Ignored by all other kernels.\n",
       "\n",
       "gamma : float, optional (default='auto')\n",
       "    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.\n",
       "\n",
       "    Current default is 'auto' which uses 1 / n_features,\n",
       "    if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.var())\n",
       "    as value of gamma. The current default of gamma, 'auto', will change\n",
       "    to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of\n",
       "    'auto' is used as a default indicating that no explicit value of gamma\n",
       "    was passed.\n",
       "\n",
       "coef0 : float, optional (default=0.0)\n",
       "    Independent term in kernel function.\n",
       "    It is only significant in 'poly' and 'sigmoid'.\n",
       "\n",
       "shrinking : boolean, optional (default=True)\n",
       "    Whether to use the shrinking heuristic.\n",
       "\n",
       "probability : boolean, optional (default=False)\n",
       "    Whether to enable probability estimates. This must be enabled prior\n",
       "    to calling `fit`, and will slow down that method.\n",
       "\n",
       "tol : float, optional (default=1e-3)\n",
       "    Tolerance for stopping criterion.\n",
       "\n",
       "cache_size : float, optional\n",
       "    Specify the size of the kernel cache (in MB).\n",
       "\n",
       "class_weight : {dict, 'balanced'}, optional\n",
       "    Set the parameter C of class i to class_weight[i]*C for\n",
       "    SVC. If not given, all classes are supposed to have\n",
       "    weight one.\n",
       "    The \"balanced\" mode uses the values of y to automatically adjust\n",
       "    weights inversely proportional to class frequencies in the input data\n",
       "    as ``n_samples / (n_classes * np.bincount(y))``\n",
       "\n",
       "verbose : bool, default: False\n",
       "    Enable verbose output. Note that this setting takes advantage of a\n",
       "    per-process runtime setting in libsvm that, if enabled, may not work\n",
       "    properly in a multithreaded context.\n",
       "\n",
       "max_iter : int, optional (default=-1)\n",
       "    Hard limit on iterations within solver, or -1 for no limit.\n",
       "\n",
       "decision_function_shape : 'ovo', 'ovr', default='ovr'\n",
       "    Whether to return a one-vs-rest ('ovr') decision function of shape\n",
       "    (n_samples, n_classes) as all other classifiers, or the original\n",
       "    one-vs-one ('ovo') decision function of libsvm which has shape\n",
       "    (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one\n",
       "    ('ovo') is always used as multi-class strategy.\n",
       "\n",
       "    .. versionchanged:: 0.19\n",
       "        decision_function_shape is 'ovr' by default.\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       *decision_function_shape='ovr'* is recommended.\n",
       "\n",
       "    .. versionchanged:: 0.17\n",
       "       Deprecated *decision_function_shape='ovo' and None*.\n",
       "\n",
       "random_state : int, RandomState instance or None, optional (default=None)\n",
       "    The seed of the pseudo random number generator used when shuffling\n",
       "    the data for probability estimates. If int, random_state is the\n",
       "    seed used by the random number generator; If RandomState instance,\n",
       "    random_state is the random number generator; If None, the random\n",
       "    number generator is the RandomState instance used by `np.random`.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "support_ : array-like, shape = [n_SV]\n",
       "    Indices of support vectors.\n",
       "\n",
       "support_vectors_ : array-like, shape = [n_SV, n_features]\n",
       "    Support vectors.\n",
       "\n",
       "n_support_ : array-like, dtype=int32, shape = [n_class]\n",
       "    Number of support vectors for each class.\n",
       "\n",
       "dual_coef_ : array, shape = [n_class-1, n_SV]\n",
       "    Coefficients of the support vector in the decision function.\n",
       "    For multiclass, coefficient for all 1-vs-1 classifiers.\n",
       "    The layout of the coefficients in the multiclass case is somewhat\n",
       "    non-trivial. See the section about multi-class classification in the\n",
       "    SVM section of the User Guide for details.\n",
       "\n",
       "coef_ : array, shape = [n_class * (n_class-1) / 2, n_features]\n",
       "    Weights assigned to the features (coefficients in the primal\n",
       "    problem). This is only available in the case of a linear kernel.\n",
       "\n",
       "    `coef_` is a readonly property derived from `dual_coef_` and\n",
       "    `support_vectors_`.\n",
       "\n",
       "intercept_ : array, shape = [n_class * (n_class-1) / 2]\n",
       "    Constants in decision function.\n",
       "\n",
       "fit_status_ : int\n",
       "    0 if correctly fitted, 1 otherwise (will raise warning)\n",
       "\n",
       "probA_ : array, shape = [n_class * (n_class-1) / 2]\n",
       "probB_ : array, shape = [n_class * (n_class-1) / 2]\n",
       "    If probability=True, the parameters learned in Platt scaling to\n",
       "    produce probability estimates from decision values. If\n",
       "    probability=False, an empty array. Platt scaling uses the logistic\n",
       "    function\n",
       "    ``1 / (1 + exp(decision_value * probA_ + probB_))``\n",
       "    where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For\n",
       "    more information on the multiclass case and training procedure see\n",
       "    section 8 of [1]_.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> import numpy as np\n",
       ">>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])\n",
       ">>> y = np.array([1, 1, 2, 2])\n",
       ">>> from sklearn.svm import SVC\n",
       ">>> clf = SVC(gamma='auto')\n",
       ">>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE\n",
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)\n",
       ">>> print(clf.predict([[-0.8, -1]]))\n",
       "[1]\n",
       "\n",
       "See also\n",
       "--------\n",
       "SVR\n",
       "    Support Vector Machine for Regression implemented using libsvm.\n",
       "\n",
       "LinearSVC\n",
       "    Scalable Linear Support Vector Machine for classification\n",
       "    implemented using liblinear. Check the See also section of\n",
       "    LinearSVC for more comparison element.\n",
       "\n",
       "References\n",
       "----------\n",
       ".. [1] `LIBSVM: A Library for Support Vector Machines\n",
       "    <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_\n",
       "\n",
       ".. [2] `Platt, John (1999). \"Probabilistic outputs for support vector\n",
       "    machines and comparison to regularizedlikelihood methods.\"\n",
       "    <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_\n",
       "\u001b[0;31mFile:\u001b[0m           /anaconda3/lib/python3.7/site-packages/sklearn/svm/classes.py\n",
       "\u001b[0;31mType:\u001b[0m           ABCMeta\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The docstring for scikit-learn's SVM\n",
    "\n",
    "SVC?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=1, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a kernel SVM\n",
    "\n",
    "svm = SVC(kernel='rbf', C=1, random_state=1, gamma='scale')\n",
    "svm.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from IPython.display import display\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "def check_model_fit(clf, X_test, y_test):\n",
    "    # Print overall test-set accuracy\n",
    "    y_pred = clf.predict(X_test)\n",
    "    acc = accuracy_score(y_test, y_pred, normalize=True) * 100\n",
    "    print('total accuracy = {:.1f}%'.format(acc))\n",
    "    \n",
    "    # Print confusion matrix\n",
    "    cmat = confusion_matrix(y_test, y_pred)\n",
    "    cols = pd.MultiIndex.from_tuples(\n",
    "        [('predictions', 0), ('predictions', 1)]\n",
    "    )\n",
    "    indx = pd.MultiIndex.from_tuples(\n",
    "        [('actual', 0), ('actual', 1)]\n",
    "    )\n",
    "    display(pd.DataFrame(cmat, columns=cols, index=indx))\n",
    "    print()\n",
    "    \n",
    "    # Print test-set accuracy grouped by the target variable \n",
    "    print('percent accuracy score per class:')\n",
    "    cmat = confusion_matrix(y_test, y_pred)\n",
    "    scores = cmat.diagonal() / cmat.sum(axis=1) * 100\n",
    "    print('left = 0 : {:.2f}%'.format(scores[0]))\n",
    "    print('left = 1 : {:.2f}%'.format(scores[1]))\n",
    "    print()\n",
    "    \n",
    "    # Plot decision regions\n",
    "    fig = plt.figure(figsize=(8, 8))\n",
    "    N_samples = 200\n",
    "    X, y = X_test[:N_samples], y_test[:N_samples]\n",
    "    plot_decision_regions(X, y, clf=clf)\n",
    "    \n",
    "    plt.xlabel('satisfaction_level')\n",
    "    plt.ylabel('last_evaluation')\n",
    "    plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total accuracy = 89.7%\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">predictions</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>3308</td>\n",
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      "text/plain": [
       "         predictions     \n",
       "                   0    1\n",
       "actual 0        3308  108\n",
       "       1         354  730"
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     "text": [
      "\n",
      "percent accuracy score per class:\n",
      "left = 0 : 96.84%\n",
      "left = 1 : 67.34%\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 490,
       "width": 504
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_model_fit(svm, X_test_std, y_test)\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-svm.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'uniform'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0malgorithm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mleaf_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'minkowski'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmetric_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Classifier implementing the k-nearest neighbors vote.\n",
       "\n",
       "Read more in the :ref:`User Guide <classification>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "n_neighbors : int, optional (default = 5)\n",
       "    Number of neighbors to use by default for :meth:`kneighbors` queries.\n",
       "\n",
       "weights : str or callable, optional (default = 'uniform')\n",
       "    weight function used in prediction.  Possible values:\n",
       "\n",
       "    - 'uniform' : uniform weights.  All points in each neighborhood\n",
       "      are weighted equally.\n",
       "    - 'distance' : weight points by the inverse of their distance.\n",
       "      in this case, closer neighbors of a query point will have a\n",
       "      greater influence than neighbors which are further away.\n",
       "    - [callable] : a user-defined function which accepts an\n",
       "      array of distances, and returns an array of the same shape\n",
       "      containing the weights.\n",
       "\n",
       "algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional\n",
       "    Algorithm used to compute the nearest neighbors:\n",
       "\n",
       "    - 'ball_tree' will use :class:`BallTree`\n",
       "    - 'kd_tree' will use :class:`KDTree`\n",
       "    - 'brute' will use a brute-force search.\n",
       "    - 'auto' will attempt to decide the most appropriate algorithm\n",
       "      based on the values passed to :meth:`fit` method.\n",
       "\n",
       "    Note: fitting on sparse input will override the setting of\n",
       "    this parameter, using brute force.\n",
       "\n",
       "leaf_size : int, optional (default = 30)\n",
       "    Leaf size passed to BallTree or KDTree.  This can affect the\n",
       "    speed of the construction and query, as well as the memory\n",
       "    required to store the tree.  The optimal value depends on the\n",
       "    nature of the problem.\n",
       "\n",
       "p : integer, optional (default = 2)\n",
       "    Power parameter for the Minkowski metric. When p = 1, this is\n",
       "    equivalent to using manhattan_distance (l1), and euclidean_distance\n",
       "    (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.\n",
       "\n",
       "metric : string or callable, default 'minkowski'\n",
       "    the distance metric to use for the tree.  The default metric is\n",
       "    minkowski, and with p=2 is equivalent to the standard Euclidean\n",
       "    metric. See the documentation of the DistanceMetric class for a\n",
       "    list of available metrics.\n",
       "\n",
       "metric_params : dict, optional (default = None)\n",
       "    Additional keyword arguments for the metric function.\n",
       "\n",
       "n_jobs : int or None, optional (default=None)\n",
       "    The number of parallel jobs to run for neighbors search.\n",
       "    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
       "    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
       "    for more details.\n",
       "    Doesn't affect :meth:`fit` method.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> X = [[0], [1], [2], [3]]\n",
       ">>> y = [0, 0, 1, 1]\n",
       ">>> from sklearn.neighbors import KNeighborsClassifier\n",
       ">>> neigh = KNeighborsClassifier(n_neighbors=3)\n",
       ">>> neigh.fit(X, y) # doctest: +ELLIPSIS\n",
       "KNeighborsClassifier(...)\n",
       ">>> print(neigh.predict([[1.1]]))\n",
       "[0]\n",
       ">>> print(neigh.predict_proba([[0.9]]))\n",
       "[[0.66666667 0.33333333]]\n",
       "\n",
       "See also\n",
       "--------\n",
       "RadiusNeighborsClassifier\n",
       "KNeighborsRegressor\n",
       "RadiusNeighborsRegressor\n",
       "NearestNeighbors\n",
       "\n",
       "Notes\n",
       "-----\n",
       "See :ref:`Nearest Neighbors <neighbors>` in the online documentation\n",
       "for a discussion of the choice of ``algorithm`` and ``leaf_size``.\n",
       "\n",
       ".. warning::\n",
       "\n",
       "   Regarding the Nearest Neighbors algorithms, if it is found that two\n",
       "   neighbors, neighbor `k+1` and `k`, have identical distances\n",
       "   but different labels, the results will depend on the ordering of the\n",
       "   training data.\n",
       "\n",
       "https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm\n",
       "\u001b[0;31mFile:\u001b[0m           /anaconda3/lib/python3.7/site-packages/sklearn/neighbors/classification.py\n",
       "\u001b[0;31mType:\u001b[0m           ABCMeta\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the K-Nearest Neighbors class\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNeighborsClassifier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total accuracy = 90.9%\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">predictions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">actual</th>\n",
       "      <th>0</th>\n",
       "      <td>3203</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>198</td>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         predictions     \n",
       "                   0    1\n",
       "actual 0        3203  213\n",
       "       1         198  886"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "percent accuracy score per class:\n",
      "left = 0 : 93.76%\n",
      "left = 1 : 81.73%\n",
      "\n"
     ]
    },
    {
     "data": {
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4p3Hf9CU4mZErOg4RERERkU9gAU/kQ3omNENB+nHsWPWD6Cho160fYhK6YG9qpugoREREREQ+gQU8kQ+JDAnAgufGYNeqBbiQflp0HCgUfIshIiIiInIXfrom8jGNwwIR1SgYNqv4U+mJiIiIiMh9WMATuUCWZdERiIiIiIiogWMB7+UkWQZkGU4WmHVKBgBZLnm9vUCwXovT+7eKjgG1Pggb95+F0+kdrxsRERERkSdjAe/l1E4TIDtgtdtFR/FpVpsdkB0lr7cXePf+63FqyzLsWv2T0By9Rk/AwVzg39+sE5qDiIiIiMgXsID3cv62PEh2CwqMZpitNjhlmad7u4l88cwGs9WGApMZkt0Cf1ue6FguaRwWiP/c3Q+nd20QmkPjp8fgCa/g9+3HheYgIiIiIvIFKtEB6OoEWrJgUgXDDCDPYQMkJSBJomP5DlkGZAckuwV+1lwEWrJEJ3KZWqkUHQEAoPCQHERERERE3o4FvJdTwIlIwzEU2SNhUIfCptBBvljAKy8u4eVwOkVG9GqSLEPtNMHflodASxYU4GtZGw6nE06nDIWCXy4REREREdUWC3gfoIATwZZMBFsyL7s9IEALACgutoiIRQQA0AUEIbJZLJ77YjXeHj+w7IslIiIiIiKqGX6SJvJR/n5qXMjKRFFejtAcCoUSIx5/G3sybPhp/X6hWYiIiIiIvBkLeCIfldQyCvcP7oCF7z4BQ6HYyfc0Wh0at+2IQiPPBiEiIiIiqi0W8EQ+7KGh3dA6MgAZJw6KjkJERERERFeJBTyRj9OoOdUFEREREZEvYAFP1AA4PWQlAq6IQERERERUeyzgiXzcgA7NsfmXeSi8cF5ojuYJ3fHl6n3YezKz+sFERERERFQOC3giH3fngA64t38cfv/4P0JztEzqhv73TMEDMxazE09EREREVAss4IkagCFdW8NYlC86Btp07g2DyQKnUxYdhYiIiIjI67CAJyIiIiIiIvICLOCJqF6p1WocPJ0lOgYRERERkddhAU/UQMiyZ5y2PnTCS3hw1m+czI6IiIiIqIZYwBM1AJEh/vBTOrF12XzRUdC6cy+0H3gLft18RHQUIiIiIiKvwgKeqAHQadX47rkxSN28DMd2/SU6DrR+/pAhiY5BRERERORVWMATNRCRIQHondgcxfk5oqMQEREREVEtsIAnIiIiIiIi8gIq0QG8gcPhwIIFC7Bo0SKcPHkSDocDzZs3x7BhwzBhwgRotVrREYlcolYqkJdxSnQMKFQqnMzIhcPphFLB7xGJiIiIiFzBT87VcDgcmDRpEt544w2cPHkSnTp1Qo8ePZCVlYXZs2dj3LhxMJlMomMSueThYV2Re3gLUpZ+JTRHYq9ByJED8fQnf3jM7PhERERERJ6OBXw1fvrpJ6xbtw5xcXFYuXIlvvzyS3z66adYtWoVunTpgj179uDDDz8UHZPIJZEhAfjq6ZuRsuIHoTk0Wh2GP/YW/jqQhvQLRUKzEBERERF5Cxbw1Vi0aBEA4MUXX0RUVFTZ7WFhYXjttdcAAMuWLRMRjahWQgJ0gAd0vdUaLTRaLWSIz0JERERE5A1YwFcjNDQUrVq1QseOHcvd17JlSwBAVlZWPaci8h0e8F0CEREREZFXYAFfjXnz5mHFihXQ6/Xl7tu3bx8AoHHjxvUdi8gnRLVoi+kLt8DucIqOQkRERETk8VjA15Isy5g9ezYAYMiQIYLTELlOIQFOWUbBhfOio2DQAy/jeLEW037ZLDoKEREREZHH4zJytfTee+9h69atCA8Px4QJE6odv3DhwrLr6V0xpa8OHTt0uJqIZQICuMwdXUqLl+/ujznTn8KdL72P0Mgmbt9DgL+Lx5y/Fl0GjcG5Ld/X6DiVFIC/XuP6fqhBKD0e/Pw0UKmUXvve52/QQKGQeHy7mVKpgM5P7dJxoZAk6F14j2nIf0eSJEHvr3H590yrVUFjUbn8mlmVTkgSP8NcyZ2vh1qtgkajRIC/FlqNCupq3jf9TRpIEt+bGhr+fXseFvC1MGvWLHz88cfQaDSYOXMmwsLCqt3m3Llz2Lp1q8v7KOzQ+WoiElXpweHdcSozD7vWLcP1tz4oOg4REREREbmABXwN2O12vP766/jhhx+g1Wrx/vvvIzk52aVtmzZtih49eri8L62fGsXFltpGBfDPt7RX+zjkm0L0WljNNhQb3Hd8lH5LW5PHNFtssNudNTpOZSdgMFohaXhsU/njzmy2wm53eO17n8FohdMpu/V3kwCHwwmT2ebSceGUZRiNVmgq+TuozXudr5FlGUaDFcV+rr0GFosdVpvd5dfMarFAlvkZplRdfKaz2eywWh0oNlhgsdphq+Z902C0Qpb53tRQ8H3Oc7GAd5HBYMATTzyBjRs3IigoCB9++KHLxTsAjBkzBmPGjHF5fPHv02oTk4iIiIiIiHwUJ7FzQUFBAcaNG4eNGzciOjoa3377bY2KdyJP1KZJIxzb+gfys9OF5giJbIodR9Kw85jYHEREREREno4FfDWsViseeughHDhwAG3atMH333+Pdu3aiY5FdNUGd2uDx4Z1wsJpk2G3WYXliGzeGoMeeBEPv78M6RcKheUgIiIiIvJ0LOCrMXv2bOzevRvR0dGYP38+13wnn3LngI6wmo2wWcxCc8S274HwqCbIyC0SmoOIiIiIyJPxGvgq5OfnY/78+QCAsLAwTJ06tdKx06bxmnUiIiIiIiKqOyzgq7B3716YzSWdyQMHDuDAgQOVjmUBT3SVFApk5xtEpyAiIiIi8lgs4KvQr18/HDlyRHQMogah6/D78Mqnb6JRkB7Jcc1ExyEiIiIi8ji8Bp6ogWsRFYrdq3+GLMtCc7RM6o5rb38c7/yyVWgOIiIiIiJPxQKeqIH75IkRyNyzBnvWLhQdBcHhjWF3OkXHICIiIiLySCzgiRq4RkF6PDC4Ey6c4eUiRERERESejAU8EUGSRCcgIiIiIqLqsIAnIkgAjMWFwq+DlyQJxUYz7A6eRk9EREREdCUW8ESE/p1bwZF7Fn//8rHQIj4ypg38wmPwxLyVsNodwnIQEREREXkiFvBEhNAAHb55dhQOb16JvPNnheVQqtQYNukNHLlgx4a9qcJyEBERERF5IhbwRASgpIgPCfSHw24TmkOl1iAkPIodeKq9i5diOJ1iLwkhIiIicjcW8ERE5FOatEqERR2MF79cAweXJSQiIiIfwgKeiDyOPiQSS1KOswtPtaLW+mHkE+9g84lcbD0s7pIQIiIiIndjAU9El7GYDKIjoNeYh5DhDMWzn60RHYW8lMZPh5DwKFwoNIqOQkREROQ2LOCJqMz9gzvg90/eRG6m2K6lSq1Bvzsex/aj54TmIO+WcN0YvLFgE/acyBAdhYiIiMgtWMATUZlb+7XHA4MSsfmnD0RHASRJdALycq07XYO+tz+Ol+avFx2FiIiIyC1YwBPRZRJiIuC0W0XHIHKLiGatUFhsht3ByeyIiIjI+7GAJyIinxUS2RRBjVtg8se/w8ZJEYmIiMjLsYAnosuEBuiQefaU8OvgNX56GM1WbDtyeY4th9Jgsdmh1voJSkbeRKlSYfhjU3H4ggPLtx4VHYeIiIjoqrCAJ6LLJLaIxLNjemLh9KdQlJcjLIefPgDDH/kPHp2zEntPZgIA9pzIwP/NXYXhk16HVucvLBt5F5Vag/CmLZFXbBIdhYiIiOiqsIAnonJu6dce7ZqEIefcSaE5YhK6oG3yAOw8lg4A2H70HOJ6DkJMfGehucj7tO4xBB/+tgMph9NERyEiIiKqNRbwRFQhhcIzZoHXBYVi3f40WG12AIBCwbctqrmY+M4YPOFlPDFvlegoRERERLXGT8JEVCm7Vfxs9N1vvAMF2saY+P5yFBjMouOQF4tulQCjyQwrJ7MjIiIiL8UCnogqNLZXW6xfMAs56aeF5lCqVLjxoVdRqG+GL1fvRetu1wnNQ95L46dH6/bdMemD5bBcPKODiIiIyJuwgCeiCt3UKx5P3dQVa794S3QUKJRKDH7gRdw/dT6atE4UHYe8lCRJuOGhV5HhCMI3q3eLjkNERERUYyzgiahS3ds2hc1iFB0DAKBQKOEfHCY6Bnk5pUqFiNgkGC020VGIiIiIaowFPBEREREREZEXYAFPRJXy99OgMD9f+HXwRO6k1fkj5WgGr4MnIiIir8MCnogqFd0oEK/e1Q+/vvc08s6fEx2HyC26XD8KRv/mmPjBcsiyLDoOERERkctYwBNRlW7unYA+ic1w9uhe0VGI3EKpUuGGB1/B9sNpMJh5LTwRERF5DxbwRFStsAAt0o/uhux0io5C5BYKpRJ6f39sPZImOgoRERGRy1jAE1G1nhzVE4qco1i/YJboKERuM2zif/D8F+uwcd8p0VGIiIiIXMICnoiqFaDT4sNHh+LAlrWioxC5TZPWieg2fByW70gVHYWIiIjIJSzgicglWrVKdAQit9MFBOPg6SyuC09ERERegQU8EblMlp28Dp58SlzydfBrmoT731sCq90hOg4RERFRlVjAE5FL/P00iI+Jwpqvp7GIJ5+hUCgx8N4pOJNrRFp2geg4RERERFViAU9ELlEoJHz+1Eg4Mw9i9/qlouMQuY2kUEDvH4i9JzJERyEiIiKqEgt4InKZv58GfRObwliYLzoKkVsNuO95vP1zCpZsPiw6ChEREVGlWMATUY30iGuGg+sXI+MkCx3yHZHNW2PU5OmY+tMW/LLpgOg4RERERBXitNJEVCO9EmPwzv0DMHnW83ho+k9QqtSiIxG5RXjTlhg1eTr+9+6TSIqJRHxMhOhIRC5ZNG8qUg/uqtE2sYldMHrii3WUqP4YzFasSjmEG3smQO+nER3HJ5W+xp3aNqvT/TTk45ioJljAE1GN9e/UCg77CthtVhbw5FMaRccgqmkMLhQZRUchclnqwV3INVprtlENCyVP9f7P67F88z4cTcvC83cPFh3HJ5W+xv26xNXpfhrycUxUEyzgiahWeiS0wJ/zp2Hw/S9BqeJbCfmO6LhuePvHVfjqmUiEBepExyFyWdIjc1wad2Duo3WcpH6cy87HypQDkCwFWJlyAONuSEbTiBDRsXzKpa/x+l1H4dDU/evb0I5jopriNfBEVCsfTLoRgcZz2LbsG9FRyIdYLSbREdB9+N0Ib98fd/1vIXIKDKLjEFEl5q/aBtlqgkbhhGw1Yf6qbaIj+ZzLXmObGWaz+PdoooaOBTwR1YpWrcKNXWNhKsgWHYV8xLbl32LeU2Nx5vBuoTkkSULvMRPQtPsNuON/C3GhkKfTE3ma0s6wwmHCtBGRUDhMWJlyAOeyuUqKu1z5GksOMywWK/JzMkVHI2rQWMATUa21j43CsV1/I+3IHtFRyIvJsoyUJV/h1Jbf8NDQrji8YbHoSACAniPvhX9UK2zcd0p0FCK6QmlneHi8Hte00GF4vJ5deDe78jUe2FoHyE6k/L5QdDSiBo0FPBHVWvuWUZg9cQiWzf03DIV5ouOQlzqybR3Stq/Ed8+ORnzzCORlpcNuq+FERnVEFxiKo+dyRccgoktc2hkenxwMABifHMwuvBtV9Br/q1MQJMg4lLKWXXgigVjAE9FV6ZUYg9AAPcyGItFRyEsV5WXjug4xCA/2R/9OsUiI1GDZBy/BYbeJjobuI+/Fkp1pmLdsu+goVJ9kWXQCqsKlneFmISUroTQLUbML70YVvcbRQSro1BK0tgJ24YkEYgFPRFetc+vG+PvnuR7TNSXvkXf+HPatXYgurRsDANQqJWY8NATmC2nISjshOB0QFBaJsVNm4ru/TmL24hTILOx83uerdsEGFfTBoaKjUAUq6gyXutou/KEzWfhpw353RfVaVb3Geo0Cekchu/BEAnHtJyK6am/dNwBPfrQKa76ZhRvGTxEdh7zI4lnP4Y6+bTA0uV3ZbSqlAgF6P8BDauWAkHCMeWYmFr43GTa7A5PH9IIkSaJjXbVF86YitYZrKMcmdsHoiS/WUSLx5i3bjm83HsOYZ2ZAo+USgiKUHpeyLEO2WtHvsVmX3Z9xoRAKuxGj41UwFObiSCHgr9OiWURIWRd+6bGSLnxN1oXfezITD876DUqtDucuFOGJUde49HtuMOPioh0AACAASURBVFuxKuUQbuyZAL2fpsbP1xOVdt9HXOy+O2UZBcUmOGUtVAoJ18cH4ddjJV34G+6cJDouUYPDDjwRXTW1Solb+sSjOCdddBTyMsnD7sb3Gw7i2Lkc0VGq5B8citHPzMSyvVl456e/RMdxi9SDu5BrtNboT00Lfk9T2QkUsixj9uIUfPfXCYx+ZiaCGkXVbzAvJdfoWzbXxpYel3kmK2QAJwyasj/HChW4UGxGkFbGiHg/WJwKWJwKGEyWsu1r04XfcewcJsxcigH3PodbX5iDxTvTMe3nv1064+b9n9dj5o9rMPvn9S7ty5Nc9vQu/lBR9z0rrwhZeUVly2rekRzBLjyRQOzAE5FbxDYORdqJtTi2ewui23YRHYe8RFLfG6FQqXHPu3PwxeSbEB8TITpSpfSBwRg1eTrmPX0LnhrTCxqVUnQkt0h6ZI5L4w7MfbSOk9StRi3i8e4vm7B27+ly92XlG7A/3YAxz8yEf3CYgHTeJ7plO7zwxZ9o3cS1Sw1W7zyB6A5h2LBgdpXjZKsBOjjRqONAmM/sRtNGTaDSBwEACs4eg0NpQfcYGa1blXzJkpdxDj8eUUF58p8iPjQ4CCfzbZj0/hJ0i28JAJAAjOkdh6SWJduZrXbMW74d+cVmLN96DIMnvIyWSd0BAKOfmYHFM57ByczliG4UWGlWg8mC31P2A3YrftiwH3mWkrMBriQBGNGjDbq0aVLuPqdTxlerdyEtp7jC7W6+ph06tmpc9Wsmy/h+/X4cS3d9MllZlrFi2zHEdmuKDQtm4+TezRg6qltZ971D0wB8stsJp9OEAoMdgApnipxIiNCiSYgGQ9iFJxKGBTwRuUXLxqH4asoo3PPO6xj77GyENW4uOhJ5iYRrBkKhUuHe6TPx2VMj0b6l53Y/dQFBkCQFJznzQr1GTcDB6FikVzDhphShwOg7B0AfGFzBlt6jPr9kGTThFRzc/AfSXZz7pEXPdsjY+QfuHpCEqk5MP6SwwQwltLIJFiiA6ETYZRl2UzGK83IQLNuw6ZwWX2y+AJ1awp/HJRgdElqFO8oeQ6tRQpLNOHreiJBOCdAFBMFiMmD8e79gzqND0To6DI9/tApF2mhEx/XCiMcfQHRsfNn2uoBgjJr8Hg6lrEW601FRTADA4R2bYHcCfpIMsxNIOa9AfLee5cbZLGY8NOsH/Pb6nYgKDbjsvk9X7cTPO86jVfLA8tuZTZgw8yfMeXQoBnRrXWEGWZbxzk9/YeX+LMT1GV7FK1teq+SWKDr6N27pE4eYO3ujXdMwzFiwCjabFRvTA5EsOWC3WuC02yBBhk6yoZXOjvlbSs6n8LMX4FDKWvQcMgYh4VV/yVAT3v5lIVFdYwFPRG7TPa4p4mIiUZSXzQKeaiSu+3VQKlV4YMY0fPT48IZdHzfoJ193JIUCSb1cvybam8QmdgFqMZ/B1VBrtOh03QiXx6es+B5DusbiviFdqxz32ZKNsFlViL5mBHIPvYyADiV/Z7n71sMJwCKpAQcwf58NEgCbXYJsMyLXdun15xIktQ5KpQIBYVHo3G8oACA6Ng6PzHkLVqsVST36Y9i4yVAoKj6Txs8/EF2uv7nSnPk5mdi49DuEoghTb26OF5ekITv9DIY/0LvCYvbMzrXIKTCUK+DPZBeiba8b0bn/yAr3E906AZPmvI6PnxqBazu0vOw+WZbx5oKNWHc0D6Offg+6gJp9AbXxp49wU882uPfi38nC9XvgdDjgUPrB5gC2nbXBUJAPhWxHiF6FfKMdG0+q4B+shkKpBFTBkJw2nDq4q+w1vhoijmMib8QCnojcqkPLCKxb/g2iWyVwEiiqkTZd+kChVOG2N1+CQqHAgEaRoiPVO7vDiRm/bkVUTBvRUciL+PLEgqWC2yXDabfCabfCvn8lAtp0hVIXiKwtS+BQB6HzyAfLbaPSaBHfvV/Zz7Hte+DhGYvckifl94XQ2gowJD4I3VoE1Nkp5S0SumLoI69j4qxXMaZP/GVf8J06X4jj+TJGPf0e/PQBVTyKawYnx8FstWF36nlsOWOBRaFFkXE3OkYCgxNC8MehfOzOArRN4pHUoz+A8q/x1WgIxzGRO7CAJyK3evXufsj+YDnWfvE2bpz4H9FxyMu06tgTE6d9DwAeey2yUqFAWk4hWke7L5/d4YTV7sBzn69BqsEPwx59xW2PTeQLlFo9wruUdOOzTvyF6Pa94BcWjexty6HT6tFr6K31liU/JxOHUtYi1FGIO5JLTm2/IzkCvx8+USenlDdv1xG3PTcTJ/Ztg9X6zyn9yrYq3Nz3Rmh1/m7ZT6DeD3cO7o6wbUdxqDgDJ0+eRLTegbdHt0aTEA0Gxodg/PwTyMvNQkJyP7c+RyJyHWehJyK3UioVGD+oEwqyzoqOQl4qICQcASHhomNUqv/tk3DPu7/iREauWx5v3Z6T6PboXCQ/Ng9ptmAMf+y/UGvKT4RFRJ7h0u57k5CSU/dLJ3bT2kq68O4WHdsOfW+6Cz2G3lb2p9vgsW4r3q+Uk5le78+RiFzDDjwREXk0p9OB3WuXwFiYi+hWiWjduZfQPB36jYBCocK4dz7GLX3jER6kx10DO0GpcP078UKjGfNX70aB0YJFfx/D2KenI7pVQh2mJiJ3qKj7Xqouu/D1KbfQiPzcHDSC7z5HIm/GAp6I3C79QhGkSiYGIqqpLb9+BuPxzeiX1AwLvlsBpfoFtEzqJjRTUt8boQ9phP2njuDctq3Ynboa7zwwCCpl1UW8LMvIN5hx3/QlUES2RWiTOIx+eiIimrWqp+Suyc/OhMVsvOw2u9WC8xfS8N7j/6pwm9jELj5xDeuieVOrXe9eqSyZR93hKLke2Veeuy+xmI04tHU9Env0h8bPffOxVNR9L3Xp8mp/L/8BTVq2c/v+S5WuUS9JVc3pXzsbdh+HwmnDkMSqnyOXkCMSgwU8EblVyqE0vPjlWgx6gB9myT0KM09j4pDOGNytDbILLTh/Pk14AQ8Ase2TEds+GbYh/8KKD1/GUx+twnsPDYG6kvXhP1+1E+/+uBGyLKPnkLHoc8vDdfLh2x0sZiPszstnw5cBOJwyco2VLBtWw9mjPVXqwV2VP8eLpIsLocm4+Br5yHP3JesWfolDm//A+bOpbisyq+q+lyrtUG//YyH0AYFu3T9QckbSum9nYuf6FVAqlRh895Nof+3VzwBf6lx2PnYdT4NStuOO5KYVjmEXnkgsFvBE5Fbv/pKCXrdMQmz7HqKjkI8IatwSX67egj7tW6BNdAjWr1+K+OT+0AeGiI4GoGQ5rWGP/hcr5v4b/zd3JWZPvAEadck/r2lZ+Vi65QjOXSjC+iPZmPD2NwgK857Z9XURMWX/r1BpoI6IQdIjc8qN88V1myt6nqUUFy+XcDqdPvnc60Kjxs3x3WfzoVZI0KgrP0Or2CpDDTvObfgZ6tDoCseoQ6ORkbIMfmHRUMAJ2WHH5qXfoGmbJMQkdCkrtIMsGbUuMg2Fedi/aQWcDmfZbcf2pkAy5yM6LBDLjpgBmCvctnGoP84bTXAU5+LAxuWQHFYU5OYg8uIScg6nEz+u34cLhSas3Z2Kgd3urvBxTh/aifTjBwEAGk3Ja5Z+bD8CLOexc+6jSMsuwF3vfOTWAn7+qm1Q2i2QlBrkmoEmFYxhF55ILBbwRORWDoeMoLAI0THIh1wz6n6s/ToP989Ygk+fGIkTGXn47f0XcOuLc0VHK6NSazBs0htY9fHrmDRnJeY8eiPO5hTi3mm/olmnftAFt8HYKcMRENJIdNRyKipA7VYLZJQU7UTu0KZLH/g98Tb27N8OVHGCg0HeBRkOpB/ZBSvUyKrwCxIZWtggpR6GAkronWa0dxzEj5/+gqGT/ot9KeugtRXAT+mExVa7InPxjGfQvbk/mjYKBAAUGc3YffYQNA4TFLIa+87kV7qtw26DTiWj0OKEv60IOJ2Cr58ZhYjgkgnn3lu4BX8czkeT+O7oN244YuI7l3uMc8cPYPWnb+DWaxOgkCRonCUFfNc2KowbNAI6rRrRYYGQnc5y29bWuex8rEw5AI3TiG7NFJj4cxZGxqnQJLD8pUEyAD97AbvwRAKwgCciIo+mUCgx8J5nsOmnD9Ft0hyolEr0u2WC6FjlKFVq3PDQq1j9xVvoPHEOlEoFBt31JJL63ig6WoViE7tUeur3+QtpcDhlqC/pwANAQExSfUQjH9WsbQc0a9uhyjFZ5zPK5iCoen71iys1mAuR3LYxJo/tg4x8M84e21d2mvt/borBi0vSalVkZp49g7dfe7xscsqF6/fgD5UTkiQhs9Be6XZ2pwM5+QZAdiJSp4TFYkZ2jgaBOnXZmMPn8tB16LgqJ+TMzTyDfh1jMXlsHwBAQEDJ8y0utrj8HGoq5eBpOB0OSCod9mY6oIWMHw/I0KokKBUVXO6jCobktOHUwV3o3M99ZwEQUdVYwBMRkceTFApce9tj6D32YQASlCrP/OdLqVLhhgdfwaDxNkiSAgql507mWNWka+89/i/kGq1VnkZOVBdqOhng0hlPY8KN/1yPfmL/jrJJ5rq1CHDbqd6Dk+NgttpgtlVevAPAyi2HkG84hY6REsZ2DMSRLCt+TzVh/qpteP7uwbXef32o6DkePJODv07kI+G60RVuo9JoEd+9X31FJCKwgCciIi+iVKmrH+QBvCUnkS8xma3IOHkMjS6ZZM5dE64F6v1w5+DuVY45l52Pr1dsQaRexgejG6NZiBpn821YfTwDK1MOYNwNyWjcKAh5hUZEVNTRriGFJMFmt8NsKIKff2CNtzcW5kIR/E+Oip7jruPp2J+zE72G3nrVeYnIPVxftJaIiIiIyEOdPJcFlcN02RJvpROuaS9eC1+X5q/aBtlqwvB4PZqFlHyJ1yxEjeHxeljMZgx9+Rt0eeRDFDj9sP7bmXj/0eFYPOMZWM2mWu0vUK/Fndd3wryn/4U5j43ArtW/VLtNbuZZfP3SOLw/aRgKT+3FyF7xtdo3EYnDAp6IiIiIvNq57Hycy86D1mnCHcmXT6R6R3IE9I5CHEpZi/yczDrb/8qUA1A4TBifHHzZfeOTg1FkV8ImqXH7S3MRHBaOBwcl4u8ZExAuFeLItnW13u8Lt/XFtg8m4tdXb8f6nz+B3Vb1Eoi7VszHv65pgc2zHsSKN+9EZEhArfdNRGLwFHoiqlRWfjGmL1iLKXdej/Bg/iNPROSJFs2bWjbxm6tiE7vU+HpzT9v3peav2gY47UiK1pV130vVx7Jnpd33EZd030s1C1EjyE8Jg92IneuXQ3bY0bJxJAJ0GjSLCEKeo+rr6quj16rRsnEolAqp2lnpnQ4HgvRa+Pu5tsKEJEkwGg1w2O1umXukvo4XTzkuieoCC3giqtT4qd9g19HT2J+ajmXvTBQdh4iIKpB6cBdyjVV3XsupYXHjifsulVNQjJUpByDJdvSMDalwjLuuha/I5d33iteuD9Mr4cgvwqGUtWgZ09xt+66p9tePxfuzX8AHS7chsUVjzJl0IwL12krHJ7aIRNtIPeY9OQpanQ4D752C2PY9ar3/+jpePOG4JKorLOCJqEI7Dp/BziNn0NTfiZ1HzmDH4TPoFh9T/YZERCSEq6sGHKhwbfW637cl/QjOLJ4O2ebErj8XVzlWqVQhvsf10Pjpqn3cNduPwGYxQ6HSID6q4mLUXV14h9OJ37YcQbH5n+Lw962HYDBZ0TEyAMG6ij9atwpTwObwh7E4Hxcu+GP2r9nIuFCEjftOo3fHqHLjgxpFYf3SMziTlY+YyJIvJWRZxrKUI8g3mMuNz843QKcPqHYCzehWCXjgnR9gs5ixdemXuGf6YtzSJw4xEcG4tkPLcuM1KiU+fWIE8g1m7D6egVe+nYnYt76rch+uqK9jVeTvBFFdYQFPRBX69+fLEaR2QqcGgtRO/Pvz5ezCEzUwDflDbVXPXULJzN0y5PqK4/XMZw8if808qOGAn2xDeMamKsenZRdg+bbVGPX0jCrH2WxW/H3sNIw2NbpHSziaXoij6YUVjpUB+NkLrqoLP/X7TVh/vABRLeIAABazEQdOXIDGIeNkkQaDvi7CXQlOXFnHdwoB0vNkKB0O5GWdQ6uBN2P5WTu63vwQWnUo39FukdAVnYbeg7ve+RpfPzMKSa2i8Pxnq7Hu0AVEt04qH0zhj9FPP+zS0pVqjRZqjRbX3fE4dq/5BcvT0pC6YiOeH2PGTRVMaidJEkIDdIhrHg6n8+pO9yeiq8cCnojKKe2+xwTKmH6DP/5vuYFdeKIGJDaxS41PJ41N7FJHaeqXK89dqSwp4B0O+Z9tqEq27FSEtUtG6u6/EKS14vVx/ascbzBb0euJT6p93KDmCTh59BA0SgUOXJBw4IKt6g1UwZCcNpw6uAud+w2twTMosSs1G31vexpN25QU0bs3rMDhPVuhVGiRb5Fhc0iYvl0BCYC/0g5/paNsW1kGoFBBIUsIjIxB94E3V7mvTv1vglKpxvCXZyDQX4/wJi0w5tlZ0Or8a5y7IpIkocugWwAAgX/8gt2pWyss4InIs7CAJ6JySrvvw9qqMaCVBsPaWvHdPju78EQNREOeyMmV5x7gX3KadrHBUtdxfEsNlj6XJNcG9735XgRHNoXd6vrfhUqjRXz3fq6HucKl2eK794PNZr1s/1azCTaLCfvXLcL4oV2g06jhlGWs2J4Ka5GMrgNGo32vQS7tq/21Q9G2ez9olE74B4fCaKqjDriLrzcRiccCnoguc2n3fWJyybWHE5N1WH6skF14IiLyKH56fyRX08kWtf+TW3/HbQO6IjzYH//5dgOMfhEY98xbLl3Xfymtzr/sSyOAp7ATNXRcB56ILnNp971VWMm1dK3ClBjWVl12LTwRERG5btuxDPS+ZVKNi3cioiuxgCeiMqXd9xC/f7rvpSYm6xDiJ5d14YmIiMh1rl4WQERUFRbwRFSmou57KXbhiYiIXGO3WWG3VzOhHhFRLfAaeCKCwWzF3EUbsePwabQIKt99L3XptfCb9pxAVn4xbuyZAL2fpp4TN2yL5k1Fai1mCG/IE5ORe/DY833THh2N7PTTLo+3Wy1Q+AXUYSL3c+U5hoeFInrUy2U/t2kajr/nTq72sY/u2IBtv34Kk8mIvkkt0ChIX6uMDrsd67+bgXNH9gAAFAoJcT0GoNuI8V7dyc/PzoDZaHB5iUpz9hnk690z6z6Rr2ABT0R4/+f1mLNwHRSyA8Paasp130uVduG/22fHxOk/ICzQD0fTsvD83YPrOXHDlnpwF3KN1pptVMOii6giPPZ8X3b6aSAw0uXxKlmGveA8AMBhyIPssEEVVH57hS4I+QfXQgUHzHYZy1OOVPm4p7PyERgcUrPwVbiQfhrZZ08CAApzMqANDocsA1ZZQkXT4zthhzo4Cs7SPNn5WLjpANIyL6Cnf1CF+zi8ZQ3+/mkOZjw8BI1DAxATGVLrYvvcsX3YuWEVxt/QFYF6LRxOGR8uWYDYbgPRqEmLGj2WLMs4dWA7LMbiSsec2bMJnRIrfl7uZDEboQyOgsMpuzReGRwFS1FWHaci8i4s4IkauHPZ+Vi4fhfCtXakW2UUmGVM/8tY6XinDARpZZzIzkeApMXKlAMYd0Mymkb880HLajHVR/QGL+mROS6Nc7XTQeQqHnueyZ2vd8yDc10ad+bjkqVFD819BAEwA5BhgB8cuPKLYBka2KGCE0V2Fb7YUVDl40pKDUY+/nYtkpeXdeY4fp0xBT3jmwMS4KdRwynJ0DVqDGXzzlB3G1NuG2feWWj2rQBkJwwndkDSaLFgrwHX3/sswho3Kzf+wF+rsHXRR/jqmZvRrln4VWeOSeiCmx56Adt3bYCUC6hUSox99JUaF+8AsH3ZNzixZRkSYir/Yub61kF4cGi3Kh/HaHbfJQGNRjzt0rjsX9+6qv3wPYh8EQt4ogZu/qptMBuKEKgGAjUSvt9vQ6DWUeU2DhnQqgCz1Qat1YT5q7aVdeHvvj4Rr86fgZCIJghv2rIengERUcMWm9ilxmc6xCZ2cdv+lXAiWDIjQA0oJAlKixmQFIBSCaj9L+lCa8v2PeTh+rusIv3EQQzu2gpT77seAPDjivWwB/ojrM89MG9ZAFRQwCtCm0HT70EAQObRR4BiA4Y8/J8KH3/fhmXY+dvnmP/saLSODnNb7nY9BqJdj4EAULaMXLHB9fXuS507tA1vjrsOvRJrtwTsxyt2YOHfR5FbUIzOA8fW6jFKaf30sAJQKVw7M0ECoPGr+WUIon8niOoSC3iiBuxcdj5WphxAmA54sV8wXl1ThAyTGt3at6r0unaj2Yq/9p1EmN6KOWOi8PKqvLIufFxAFMZemwSL2Y7/vvc0bnryHUQ2b13Pz4rqW3baSeTnZAAo+bDVPK4TtHrvuiaWyJsJnWNAkhASGooX7rgO9w4uKYAyc4uQbzDjtW83IKLnLejYb5i4fBcpFRXM21zRbTVkKMjDhh/nYvFrd6BFlPtO+Xc3hYsF85UOn8nGZ7/vxfBJb8LPPwChUeXPPqiJkIho5BqtiGreyqXxORotQkICa7wfzrtBvowFPFEDNn/VNshWE0Ym+OPmzuHYm63A0mMyOrZuWul17W9/8wfOpWdgZFs9BrULxObTFiw9VtKFf3PiCADATb3ioVJKeHXGFNzy7OwKTzck33Bs5yasmz8Nnds2BQAUGS3YtgS4efI06AM998MsEbmPBKBTq8ZlPzcOC0TjsEAkNI9AprXmHWNvYrdbEeiv8+ji/WqYrDaENopAdKt40VGI6CIW8EQNVGn3XeEwYXxyNABgfHIwlh3OqPC6dle2mTimD5pFhgIAhvWIw6qdp5CReogFvA8ouHAe6776HyyGorLbZMgozr+ALybfhKSWUSW3yTJmLNqCb197AMEhVZ9KKikUSB71IFomda/T7ERERES+ggU8UQNV2n0fEa9HsxA1AKBZiBrD4/VlHfUru/DVbfP5b1vw7/uHlo1XKhVlM/iSd9u0YCYGxPrhhu7tL7s9OizwsmWSJEnC5DG9MLJnW1hsVc+lkJlbhMkfvor/m7OsTjITERER+RoW8EQNUEWd9FKVdeFd2ea3v/bj/hHXIERfu3VvyXM5LCb0ToxD+4ud9uq0bVr9LMztW0bBYlkKu80KlbriOReIyDtYbPZyt1kruI28S3VfxBJR/WMBT9QAVdRJL1VZF96VbX47YcDnv23B5Fuvr3C/i+ZNRWotZoXlZDS+a1ivJCz74EUMm/Qm1Fo/0XGI3Mob3/McdhuUsgxT9hnXNpBlmMwmvPDFn5g/ZRR0GhVe/HodsvKNOJtTiLGDn6zbwFRnzuYU4IUv1yJp0B2ioxDRJVjAEzUwVXXSS13ZhQfg0jbLj5R04W8b0LXc9fMAkHpwF3KN1poFruGHX/Iu7z4wCM9/vga/vf8Chj82FRo/nehIRG7jje95stMJQAJk2cUtJBgMBsRffytGvPI5AKDDdSPRcfC16BcRDf9g9y2rVltafQB2n8hAkdGCQL3WpW1kpx3OrBOA7IRacgIqFU4f2llunN1qQ5HBhJXbjiLYv+IvIY0WG85fKKh0ObQL6adRXHDhstv8gxshvBZrvl+pICcTOZnnEKSv2RJpZ7LyMe7dX5E0+A50un70VecgIvdhAU/UwFTVSS91ZRcegEvbjEjwx5Jjhss69w6brdzYpEfmuJT1wNxHXRpH3kulVOB/DwzES1+uxdJZz2L0MzOhUCpFxyJyK298z1OH12zN8C6DxsI/OAx2qwkJvYdesva7ePHJ/XH+xD7cM30xvpx80z93OCo/xd+28TPIeecg6YKgV8qQ9H5IXTGv3Lj0nHw0j++MOevOVPmlR9/bHkNoVNNyt5/ctxVrPp+Kts0jL7v9WFoWBtz7LNp07u3CM6xYflY6Fr03GY+O6IqEmMjqN7goNTMP495dhC7D70XH626qfgMiqlcs4IkakNLuu2Q3QYYen2zJr3SsDECym7Dkr72ADGhc2UYGYDOVde5v7BqLV+Z/hsaxcVwPvg540of92tp+9CxmLN6OgmIzoBPfqSPX+MKxR1VL//KJGm/TLnlAHSS5epJCgevueBybfvwQ/Z/5HAFBIZCVdhQtfxcmhwKmTx4pt0242o4cmxJANgAlVEUF+HbKzeXGncrMw53TlmL8/36oVbbTuzfisZHdcc/gyzvkC/7cgyW7N9S6gM/NTMOi957GEyO74o7+Haod73A68e7Pm7E7NRup6Tm4ZsxDaN93aLXb1RbfQ4hqjwU8UQOScvA0nA4HJJUOvx2TAZTvjl9GpYPRaIZDlhGmq34bSQFArYPT4UDKwdMYc10nmK02vDvvVdzz32/c+VQatNjELjU+zTY2sWanT9aXh2YuRb87n0DrsEg0jo1n993D+dKxRxVTqTWwF2RCHdK4+sEAbAWZXjEJpSRJ6HvrJCReOwJfvPEY8nPOQ5Zl2B2VTNIWFgpVUWbZj20qmZhTrbr69yy1SlH+NqUSJV+l184fn76Bx4d3dql4B4DFfx/C6sO56DX2UXQKDEF405a13ndV+B5CdPVYwBM1IIOT42C22mCuyczApacEunA6pJ/24luKs2RfANAnqQVe+3bjxesqyR18aVI/g8mMpF6Dqx9IHsGXjj2qWFRMG+QarTU67T9M7/kFPFBSxDdq0gLPzF1a7dj3HhyMjF/fhFJRvrj2BlaTAb0TXb8MoshoQXSrRMTEd67DVHwPIXIHFvBEDUig3g93Du5eZ48fEFAyOVBxsaXstpAAP7Rr2ghrvnoXssuTIhERERER0ZW882tFIvIaSoUCn0++CY7MQ4CjhrMxExERERFRGRbwRFTn9Fo1esRF12BZIiIiIu/gdDq8/gwzh9O78xM1JDyFnoiIiHzWonlTkVqLSbN4rS65IiLEH1HBOmz84QNce9tjHrV8nqsOnDqPZ3FAqgAAIABJREFUj1fuwoD7XhAdpUL8HSa6HAt4IiIi8lmpB3ch11jDy3dqWCyQWPnZGbCaDAAAhUqNRtEx5QppU3EhinKzLrtNrdVVuDY7AGi0Wuw+kYFubSu+v2ycSon5U0Zh/HtLse7bmeh/5xOQKpn4Lu/8OdgsprKfZcjITjsBXbvYy8bJsozdJ89DqWlR5XOtiqRU4a8Dp2Gy2tAyKvT/2bvv8KiqrYHDvzM1k56QBAg1oYWEKoQiTUBAOqJcBSt2bChFvPrday+goFi5FkRRFEGaCAKCAiIERERIACmhQwohffqc74+YwDDplcB6n8fn3uzZZ581YWYy66yz98Zk1Bfab/fhMzzwzg/0uWMKkW27lDhuTZD3sBDuJIEXQlQLuT1PCFGTyrKquqg99m9bz8Zv3qFusD8A5zNzaNa5r1s1PH9P9GAfo9uGKslpmXQbOZ52fUd5jDv0oeeY8O4rvPfwYLpENSw2Bn9vLz6fPIJ73vqeX756i753TPbos3vDUuJWzCPsnzjzdW9Rn2Fdowp+VlWVl7/ezNZjOYx8Yrz7GBtX8dOXF55rUcwWG5m5NuZtOc5H6w/gr3fx9dM34Wty3y1g58FTTHh3Ff3H/5vIdl2LHfNyIO9hIfJIAi+EqHLb959k6Zb9oNEBLvnjKoS4qtTGz7zaEvPBrat47e5+DOjUHIDMXAt3z1zB0jcm4u2Xl+iePLyfp27qwk09Y9yO3XHgJM98/UOhCXxEm1gGPfAcj3zwAp1aNiy4GDCqazMGdW7h0d/XZOTTJ4bT5bE5hSbwB7b8wIePDaFzS/eLATaHkxmLfuPEuWwAzmflcs5uYNSkmXh5+xb027l+OXFLP2HhMzfTrH5wsb8Tp8tFn6mfM+C+/xAY1oBFrz5E/LEkukY1wmJzMP3bLZxJz+WPv08x4L7/o2lM1e1OI4SofJLACyGqVI7FxoR3VzJkwovs3Li6XPPYhBCiNoqI7ljmW3lr+jOvNsZ88e3h/t5efDFlFFsTjuP6Z2G5+kMG0y6yXrHHFaZx647cNHU2584cA8Bhs/HsF+/RsmEIEfWCyjyeyeD5+Affb+e3U05a9xoDgJ+ioVd0JwxepoI+Z4/+zW/ffcTyF8YS6udd7Dkgb/cXfx8vThzYjd1qISczHS+9jlyrnQfeWYnZtzGRXYZx44jGhDRoWuJ4QojLiyTwQogqlWu1o9HqaNy6I41bSzIuhLh61MZFtGpjzJfyNRkKKvIVVSe8CXXCL8xF3/fLd6RnmwHPBB4AVUV1uTzmwbuKWKU+NctCkw59aNmpd5ExmLMziGpSj4j6QWRnW0sV9/TxfXnm82/Y/5OTsb1b0Sy8DvfMWoEa2oqBd01Bo9GWahwhxOVHEnghRJWx2R08O+9nmkjifkX68/AZfj94mnsHXVMrV14WQojKpNUodGjZmKUzn8QvsE5Bu81qwWnOolFYQLXFEhboS0yTUMw2J4fOZDDm1cUENruGfrc9Wegie6qq8uf6xaQm7sPLP5huN96P3mCstniFEKUnCbwQokpYbA4efm8V6cZ6DLyn9ld0hLsdB07yyPurMfr4E+zrxehL5paWxr7jyej18mdICHFlUBSFT58czi9/Hrlk4VYfukV3wN/bq9pieXzOGnRNOlG3Vd4Cee06m2jWvnuRF1sP7PiFI5uX8vDQa/hxZwK/LZ5Dn3ETqy1eIUTpyTcnIUSly7XaefCdHzD7NWbQPc+g0cqteleC/FtAtyYc5/E5axj0wH85kbCTlPSTZR7rryNnuX/2Sgbf92xlhymEEDXG26hnSNdWlTqmWsTt98VJSs9hyF0jCapb/Ar6+XIyz9M9qiEjurdGp9Uwd0dqmc8phKgeksALISpVttnKvW99j7NOCwbcPVXm2V0h6rbowEtfryWmyUE27TnK4Akv0qhlO04k7Cz1GDa7g9nLt5OUnsumvxLpd9c0mnXoXoVRi8q2dM6r5VqI8kqYVy1ETcjNyiBu6ceM69G01Mf8b9XvOBQ9Phfdxl+S+hFRLPruI1IzzcQfS6b19beUI1ohRHWQBF4IUammfbIBV2grrr9zSqHz7ETt1GX4XdRp1AJLbjajB7UqWLlYo9Nz4FQaZ9Oy8DUZPfYZBkjPtpBjsfGf+Rs5pw2haYcbGN43gnpNW1bzsxAVlZiwi7RcW9kOKmPCL0RZaHR64o8l0Tw8mGA/E4YqmpaTm5WONTebjPNpGHRFX5hWVZWUjBxcLveqeVigLxrNhdvXM3Mt5JjtHDlznpAmF1ant1stWHKyALBZcvnxo+cZ3iGciTd2Kejjcqkkp2cXev5vNyfwXdxRbpr6NgajqdA+hQlvFs34lz/n+P4/6XptEJFtu5R8kBCiRkgCL4SoVKfTsmk56HZJ3q8wiqLQvGMPj/aO/Uez5qO/GPXyd6gOO588OZy2ERe2a1oVd4Bn5m3Ay2QiIqYzQ+6YIlMqrgAxE94vVb/aspe4qL263fww3/zveeb+FE+gt54F00ZTx7/krdbKYtdP3/Hbsrn4mLy4uUdropuEFdn31YW/snhzPEbjhfnuDoeDtk3D+Ojxoeh1Wn7de4zHP1yNwehFg+YxtO7aD4D05NN89+YTaFVnwbHj+rbl4WGdC+auO5wuJry3ij8OnUGv99yWLjC0HjdOmY1PQBGr5BfDv05d2vQYVObjhBDVSxJ4IYQQ5Wby9WfUpFkAHNq1hVtffYlBsa0wGfXY7A42xZ/kX9PeIbRRZA1HKq52cjHhylSnfmPGPT8XgB/ef5aNfyWWa1HN4vy57lu++fcYohqHFtvP4XTx5dqdPDx7KV7evgXtLqeT+f+5k4OnzhHdJIx5G+LpfetjxFw70O34fds3MLhjI567vV+R50g8m0b8yfPc/+ZitLqr62u8vIeFyHN1vfOFEEJUmeYde3D3S59xLGEn+TdZ3zz0GgLDwms0LnF1i4juWObb+COiZevL2sjL25dyrPdWCir+PqXcUk1R3JJ3AI1Wi8lkKliMTlVVvLz9CjsNASWsVK+qYDR6XVXJu7yHhXB39bz7hRBCVLnA0PoE9hlW02EIUUAW0BOidpP3sBDuZJKqEEIIIYQQQghRC0gCL4QQQgghhBBC1AKSwAshhBBCCFGEqplXL4QQ5SNz4IUQQgjhZumcV0ksZNGopBOHUfzCSDpxxOMxo5c3gaH1PNqFqAxWSy77tm8kust1GLxKv795Rf25YSl6xUWQb9WfM2HHRv5MSWTJhh0ej+XvIW9zuEizKMx6fAyQt1jb5ThHvKjPkOJcrs9FiMuNJPBCCCGEcJOYsIu0XJtHu9OlogUcrkJKkpbcqg9M1AoupxOn0+HWptXp0Gi0ZR7LYbehqiobFn3K/rj1nDl2iOtveaDwzhotx5PTsdjcz63TatBpPW861Wk0ZGdlYrPkYvDy3Ds+ce92fv3uY777z60oioLF5sCo1xbsyX4pRcn7L+3sCfyCL+wVb7dayM7KRPtPDHqNwvnkU9htVnQ6PYomrz095Qw2u4Yku+f+7gp559TgxICTrPz35yVJcv7vqzLo9IYin2tJivoMKVYZE34hrlaSwAshhBCiUDET3nf7Of7DR3C6VEyhjd3azSnHqzMscRk7c2Q/y2Y/jd1mdWv3CwjkxsmzCAytX+qxti6bS9zqhSiKgt3hAFTiNy7n7y0/eCSWKioh9Rqw2Gph7hr3RDDA15vPp4ykWXgdt/aWDUPo17YhK96eyvDHp2O8ZPu3+pHRhDdtwY0vfA2Ay+Xiuo4tePvBgeh1nhcjtBoNz4y9jukveF5guKl3W1o2CAHgiZGx3P/ON2xZ+gl1wxsz8sk3Mfn6g86IYrfhp3UV+TtR9EaCrn+Ixo3aeOyL/se6RWz67lM05Uy6L+ZyuWjVoSsD7/8PWp3nBYXSuvQzpCiyx7sQpScJvBCiUkU3rsOWH7+ifmRr9Mbi97MVQtROJz+b6Pazy2FDAVINpdwrW1yx/lq7gEmjYrn9+g5u7a8s+IW9m1bS86b7SzWO3Woh7sdv2fzW/Xy0/FdWb/4dg5qLTfFmcK/OPH37ALf+qqpyz1vf49/5Ltr0GOj22JZln/HF+j28cMd1bu0ajcJLd/blxQWbWP7WFEY88QZePhf2Z/fy9mX01NkFPzvsNhY8fw8Jx5Jp36zwCxG392/P7f3bF/vcohqHsvnN8aiqykPv/sDfOzfRvs8wFEVDNiZiHvBMejX/VOldrqKT+y3L5rH6lTtoHBZY7PlLw+Zw8viHP7Lqg/8weMKL6PSGCo8phKgcsoidEKJSvXDndbQKsLPy3X9js5hrOhwhRCXybRyDVqN4/OfMSELNSibY2+DxX0R0x5oOW1QjVXURGujj0R4a4IOqFp18eoyDilajwWyx8mNcPBqnmTeHhaFxmvkxLp5TKelu/RVFIaSIc/gE1KGwWR+Ql8Q/d1tv+rYMYPUH/1dsTDq9AR9fP1yVdIu6oiiEBvpU2i3vqqoSGuD5uy8Pg07LuxNuoIkxm41fzqqUMYUQlUMq8EKISqXTaphx7/U8O28D389+iuETpxc6t1AIUfs0Gfpwoe3xHz5CsLeBSe8squaIxJVu/podqDYzw6K86dbExNAob74/aGb+mh0eVfjyUhSF+wd3Ytlz31TKeFcKvU7LPQPb8+9v/qjpUIQQF5EKvBCi0mk1Gl69uz91dGYO795a0+EIIYSohVTUgur7+NgAAMbHBhRZhRdCiKuBJPBCiCpT3tVrhRBCCKfDiWozMzTKm4aBeQupNQzUMzTKG9WWV4UXQoirjSTwQohK53KpPPv5BlLsXjRr372mwxFCCFHLZJxLwulyuVXf80kVXghxNZM58EIIciw21sTt44aurfH2qvhKs3+fSuWXvSe5/cXPMXiZKiFCUVWWznmVxDLuvRsR3ZEbH3qmiiIS4spRG99fl0vMv69fAahu1fd8+VX4ypoLr6oqn/z4B3XqNSz0casll33bNxLd5boKnac2qhvky7nkM/y6bB5e3r606zMUg1H+rgtRkySBF0Lw7uKNrNq6h79PJFfKokBJ57Px9QuQ5L0WSEzYRVqurWwHlfHLvRBXq9r4/qpozIqiITUjx6NLSkYOiq50N36mp55l/46NKOBRfc83PjaAH/af4ce4eO4YFEt4SACpmTn4K57nUDQaUjJyUFXVY2qXy6Xy0oJNbD6SwYiJbxR6rl+WzGPf1nUknUwsVfylpaoqqRm56BpWznQzRVFIzcihUSVsI5cvvI4/nz4xnLU7/+bA32msPbCTYY+9VmnjCyHKThJ4Ia5yp1LS+TEuHsWaUfBFqEFo+f/47zp4mqc+Xc91d0ypxChFVYuZ4LnvcGHiP3ykiiMR4spTG99f5Y253cCxvPn2NKZ/+6tbu49fAN5+Gbzz8FK3dm9vH4Y+/BL1I6MK2uLWLsFoz8Cm4FF9z5dfhV9ywMLQ/3yF06VSr1FTRnS81qNvy069+OG3VUTd8zZGg/t4LlWlQZPmjHpyJkZvX49j01PPsi9uA/7WM+yL20D9kOL/Pn65fjdvLt5S7H7t+VQVwsIbMrJTrxL7lkaPkXdzw7OfotWU/oKARqNhys09it27vl1kPdpF1uP0uUxufm1pkf2EENVDEnghrnL5W/QYNC5storfjvjqwq10GXU/zTv2qMQohRA14XJKKEXtUD+yNQ+9tRSnw+7WvmvDcryObeL7px/g4vRyxdZ9fLn8I0Y8mbfXeEHC7MgErR+3LMzAzwAxdVR8L5nhpQJZDg0OjZY7//Medeo1QtF4VuBNvgHcNO09HPbC7yzQ6Q2FHgcXLiZ4aV1Y7RlkpBe9Z7vT5eLVr3/h7hfn4hsYUmS/ks5d2PtO+ee3plL0+a8ZOIZ2fUeiluLiQb6s86l8OPspjp49T4MQPwDq+HszvFtUpSxEK58hQlQ+SeCFuIrlV981TjNvjgjjiRUpFa7CO5wuAkPrV3KkQojqFBHdscy3ckdEd6yiaERto9Fq0Wi1bm0qKo3C/PE2ulfAm9QNQnWeLfj5aMIuFJcdqz4AVVX5PUlBBdYfVwnRWbk0p1QVLQouTh5KICS8SZExKRoNeqNXmZ5H/sWEIGcmL4xozDMrTnAuSyElPbvQ/qqa919Q3cLn0pekuPedVpv3xJ1O9wT+0vedTl+2dWyC6zVk9NS3+Wv9Ug6ccwBwcssfnErLYcLQzmUayyMu+QwRokpIAi/EVSy/+j4syptuTUyVuiiQEKL2kkUKRU2J6twbu92Gw2Z1a9++Yi63DumCl8H9q+uCTQcIbN2LqM69Kz2W/Or7wCh/OjXxZWCUP98kOPkxbh8DO7es9PMV977z9TECkJ1jLbJPefkHh9FzzIMFPx/4fSN7fl9coTHlM0SIqiMJvBBXqYur7+Nj8yrmly4KVJG58EIIIURJwgJ9SDp1jB1rFuHl7Ut09+uJ7T/So9/R7WtJzXEQ6etL3w6RNAjxB+BQspm96WdI2LaO+hFR1I+I8jgW4PThBM4e+9utzWA0Ed3teo+7BcC9+j42thkAY2ND+Tb+NNvij3AqJb3S/kYe3r2NjHNn3dr8g0KLnIqWkXqWw39tc2tTFIVWna/D26/wRf+KY83N5kziAZrGdMofrcxjCCGqj+wDL8RVKr/6fvEWPfmLAqn/zIUXQgghqlJk/WBmPziQ8LQdpGxbzIbPpxfab9jjr3OQpixP1HDLa4vJzLUA8NxtvejXWEPo2S2sfOdpzh792+PY04cT+OG9Zwg7u4W6Sb8V/Hdq09ds/mZ2oee7uPoeHph3W3p4oIFAkxbVbq20v5F7f11F3MJZbnHVTfqN3797lz0bV3j0t+RksXjG4/gd+8WtPwfWsmzWJFwuZ5lj+P6dp1n81jT2xW2ojKckhKhiUoEX4ipUWPU9n1ThhRBCVKdrY5pwbUwTjpxJ4+7Zawrt4xcUQu9b8xZE++q/d5J0Pht/by+Meh1P3NgdALPNScrJI9Rr6n57e/LxQwzs3JwX7rjOrX1P4lkmfe5eyYbCq+/5Qnx1pJzLqrS/kSmJ+7h/UAfG9XNfBX7Rpr0sOZgAfUa4tWelpxJg0jPjPvdpbqqq0u7B93Ha7WiMnncUFKdB1DUcO3yAkIYR5XsSQohqJQm8EFehi+e+X7pFT34VXubCCyEuN0vnvEpiORbGqur5uEXFlXTiME6XWuhK3L6NY2gy9OEqjas2UVWV737dh19waIl9y7M6ulLIbeGXjmO15LJv+0ZOH/27oPqeZoFte/IWrbM6VM5kuRjUzMjGY5X3N7Kwp1PWZ6goSqHjlEb3UffQfdQ95TtYCFHtJIEX4ipTXPU9n1ThhRCXo8SEXaTlFr4VWJHKmPCXR1FxKX5haAGny3Prr+zj8VUeV22hqiqvf7uFdQmpjHzyzXKPk5lrwavQbFgpuOXerX+OxS17/mXJPPZsXkVudhZNvC1E1G3IkytSGBB54evyB0O8aOpvZPMRz7+RmTmWStl6Le+5WAFjpYwlhLiySAIvxFWmuOp7PqnCCyEuZzET3i9Vv+reg/rSuJJOHMHhUjGFNnZrP/nZxOoM67K1J/EsUz5ZT2auBZ+gUEZNmonJ179MYxw+k8bEOWs4n2VG6+XL6Nu7ePRp3qE7S9cvovOj/8PL68I2a9k5FvrelvdvkX/bvOP8KbxQaVkvmDnbMvlilIm2YRduSU9IcXL391YyNIGYs10M/e8CfH1MAOSarXQZcFN5fhVuftl9hA9X7WLYo69UeCwhxJVHEnghriL51XfFYUbFm4+3pRfZVwUUh1mq8FeJ6k50hLiamFOOu/3sctiwZyRd1u+7yo7NN7AOKzYcoEmoPwadllybg9nLttNr7ETCm0XjExCMVlf4ReXivPbtVup1GkLfbv3x9g8qdB9038AQbnv+M7LTU93adQYj3n55f9vi1i5BY07DbLXTwE9h22mFe9o42HYki21HLhzzeYKGyGAdrYMVNh7KIksJoP/9z+MfFIpGq8U3MKRM8ZsCQ/hi/WqMeh0KcC7LzEc/7mb4Y69SP7J1mX8fQogrnyTwQlxF4hKO4XI6UXQmVh5UAXvxB+hMqE4ncQnHGN1HEvgrUUR0xzLfYhwR3bGKohHiymL08gZLrke7Amg1CsHenslmTb+/quozIbrb9eg0sCR+B+AAYMB9/7lo67LysTqcNG7aEv86dYvtp9Fqi+yTX31XzOnUD9CTabaj6mFJggX9JVvMpdoNJOc60WoU0PujwUnq6RM0bN6mXPF3GXI7e7z9WLhvPwCKYmTExOnUbdKiXOMJIa58ksALcRUZENsKi82Oxe4o9TFeeh0DYltVYVSiJlX14l5CXM0CQ+sV2p5qMBLcqBmT3llUzRGVrKo+ExRFoVXXAbTqevlNycrfMm5IuyCahZlY+Vcae9OhQf1wBnePdus7b0MCYW2vK7gYoDMYiercu9zn1mi1tO8/ukLxCyGuLpLAC3EV8fP2YtyAzjUdhhBCCHFZuHjLuLu6NyM80EDPZv4Mm5dE8vkMBsVGuU0hW7nrFG279ye8WXQxowohRNXR1HQAQgghhBBC1IT86vvAKH/CA/OmNIQHGlAA1WZh/podNRugEEJcQhJ4IYQQ5Waz5LJ6zvMc/OPXmg5FCCHKJL/67u3MZGys+/7ziqKgceYt5HoqpegFX4UQorrJLfRCCCHKbee6xfjlnmDTVzPJSD3rsUhUg+Yx+AQE11B0QghRtMKq7xcbEuXND4dkO1UhxOVFEnghhBDl5rRZubZ1Q565tTfvfr+RrMNqwWNWu4Mti9K5cfIsAkPDazBKIURtsXTOqySWsAq+asngzpe/RKeBcxaVvadnc9+LH2HwMpX6PBfPfR8b26zQPrd29Gf1gbP8GBfP8eTz7D1yhnNmlYRZz6JoS/4KHRHdsUoWBbTZHbhcKhqNUtBmtTtwutRijiobi81zlxqzrfQL4F6uSvP6ulRV/TsKUV6SwAshhKiwVo1CeO/hGzzaF/z8F7NeeZjA4Dpu7WFNo+g97km0OvkzJERNSj5+iE0L3sJps7i1N4yO5drRD6BoKj7b8vBf2zi8/Seuu2MyBmPxSXZiwi7Scm0A6HBgwg64J6ZONJyzuHChoDpdpB1J4OclnzFo3MOFjulyOtn8zTucPby3oO18WhpqbibdmvkVWn0fGePNs79YGdDCmzVHzKzbcYBcrxBMWMm12nHiKvmJlzFRLI2gsAZ41wmn28SPCAu+sLheZq6Z1tdci85grPA5GrZsy+8rLFz75CcE+/sVtKdmZBHWrC0LX7oP1Iv+TRSF9gPHEdW1X4XPXdUufn2VWhX8OwpREfLNSQghRJUZ17cdPaIbk2O58IVJVVVmL9/B6jn/pXWvEYUeF96sNSbfgOoKU9Qy8R8+UtMhFOpyjas46z97jXv7RNKx+YW7ZJwuF9M+28DhZm1p3rFHhcY/+MevbPxyJi3Cg9i5dhHdh99ZquOiH3ibM3Mfoemg+9F5+xe0u+wWEld/SvCQJ3GYc/j7y/8Q4e9kX9wGug4cTWCI59Z9++LWYz6+m9nje6MoCsnns3j2oxWAmbjTJuZvS/Y4pomPyqFklVXH9GjsGWTnOtDo7Rh8fKg/6hn0QcXfVVRVrwWd3sDISbNIPX0Ul/NCRVxRNIQ0aIqiKMUcXTo+/kGMfe5TUk4dcWu326wse2sacx4fRoCPV0H7L7sTmfPFTFp06oVWp6/w+atDzIT3S9WvNr6nxZVPEvhyWLJkCf/+97/56quv6NxZtuQS4lJ2u7WmQxCXkSZ1Az3a3ntkMDMW/Ubipi88HrPY7Py2yMK/nvkQo7dvdYQoaomI6I5lroZFRHesomguOcdlGFdp2K1merZp6vE+bdEgBPslVfmyOrJnO5u+msncJ4ezJf44v2abS32s6sqrcPs38dyuTe8dgMbgTfq+OIKMLrx1gD2DuLVLCq3C260WohuHEtM0b42O/UfPoMWFUavlnBk++sPzdnHIu9hod2pQCUCvz8SoceDX/dYSk/eqpigKoQ0iqvQcGq2Wuo1buLVlnkvCz9dE9+jGBW17Es/y2U9/MfzhF2tN8i5EbScJfBnt2rWLl156qabDEOKyNaZXK1778i2CJ88kqG7Dmg5HXKYMOi3/N7ZXkY8Pf34haWdPUj8yqhqjEpe7y3Ue6uUaV007sXcbDw7uSEzTumyJP16pY9uz0sg6sJUGXi6m9PThzbjMYqvwFxsQ2wqLzc7R5HSW7zxJTL8xRfZVVZXje7biOLiXbJeeRlE9K/V51HZeBh1ajYLVnF3ToQhx1ZAEvgzWrl3L008/TW5ubk2HIsRla1y/dljMNma9+SSjJs2kTv3GJR8kxCW0WtnlVIgrgbYS5tAX5vy+LfipmfRpaqBdPT0Do7xYdrDoKvzF/Ly9GDegM3uPJvHzwWy6D/5Xsf27D/4Xsx4fg8NceKX+ataiQQjzJo3kltdm0CS6E15y15QQVU6+IZXC2bNneeqpp3jsscdwuVyEhITUdEhCXNb+1actT42OZenMSaSnnK7pcIQQQlxBXC4HOYm7CSSbka3zFm0bGxuKtzOvCp+eerZSz6coSqXMLb9StWhYB71Oi9NexsXhhBDlIgl8Kbz99tssX76cNm3asHDhQiIjI2s6JCEue3anC41Gg6LR1nQoQgghriAOcw4+ZNOvpR91ffO+yoYHGhgY5Y/xn7nwono4nC6mfLKOsEbNMfnJwqNCVAdJ4EshMjKS6dOns2jRIlq1alXT4Qhx2TuWlM4bi7dx4+RZBNSpW9PhCCGEqAF6ky/bDpzG4SzFlmulZM44h9Nmxo9cRncMdnusKqrwWedTOXFgN6rLWSnjWXKzOXFgN2eO7EdVK2/f9tKwWXI5cWA3pw8nVPjcx5LS2b7/JE/+bw2HsowMe+w1NHLBXohqIXPgS+GBBx6o8BhLlixh6dKlpe4/taeJdm0l8FYLAAAgAElEQVTbVvi8AL6+Fd8TVIiysLqc1AkNpVFks5oORVQxvUGHwair9M8ZjUaDyaTH16f045alrxCV4Up4zWk0CnqD1uM9rAJGY9neg5e67ubxLJm1n6fm/kTL8EAMel2J42m1CgoKGjUv6c+fQ++wWUjev5PMs8fQ4KJrEx/qBRgxJwOKgkaj0DDYyKDW/iw/mMGfP69g2N2PAWA06kDx/D6k02vRaJUiYzp1eD+L3pxCZHgdsGZiwIimlHP6FRS0l4ydmZbCd68/RqivluTzWbToMoB+tz1aptvzy/vvkZN5nqVvTsRf5yA9K5dGbbszcPzUUj8fAIfZiMvlYuuBE0z8YA31GjYmsF4EYx6djL4S9p+vDgWvrwr8O15trubnfrmSBL6anDp1iu3bt5e6f2bbDlUYjRBVJyU9h0lz1tK825CaDkUIIUQJWsVex/D/+4q2zS/sGuJ0uTh0Oo07xrWp0NgGoxc3TZ7B0rf/zQ+Lt3D3f25xezw9NYkNX8zCYb2wOLAzNxMfXKQseZHAZhe+Cx3ZvIKU+C2Yc3II8TNwfeugQs85rksoa/cfZs9vP9FjyM0EhdWnaXRH5n0zhwMnz+HtdSEZSTyTSrv+NxU6zomD8Sx+8yneeuh6BsW2pMPdM3Dm2MiJ/xmfmL5l/l2kpybx9SuPMr5/ax4b1ZXzWWZueWUx676wM+DOJ8uUxMdv/Yn4X5aX6fzJp09ya+9WPH1rT3IsNm57fSmrP36Nwfc/XerKuV9gHUwBIdz9+mLue2kODZvHlCkGIUTlkAS+mjRo0IAuXbqUur/RS092dsX20s6/0lzRcYQoLV9fIy99uRGHb33aDxhLdo689q40dquFlJNH0Or0hDVujt3mwKZzVPrnjMvlwmy2l+o1lF8dkNebqC5X0muu84j7aNr5enLS09zar2nQFL13cKU8xxseeplBqopWpysYLz3lDMtmTuLWni24pvmFtYUm/b2bM04djXrfhE/9SJwuF+aMc6Ts30GoM4XjViem4BBCfHSQfxu4quJy5f3/ev4GBrTyZ9nB82xY/g2Dxj2MKbA+90z/mjNH9rvFFeFlIrxZdKHP8ZvpU3jr/uvp0boJ2dlWNIAZPRlxi9HVbY4+uEGxz1lFxelUC8b+4YMXGNejOeOv70B2thW9ouHzSSMZP2sF3//vNaKuHYxfUCh+waGFjpf/mjt9/DirP32D18f3x9ur9Puu+3hF0aFZfXJy8haa+/jxYdz15jK2rVlOu95DSz3Ov/79IZC3T3xtfP07nSoqKi5X6aZ1XPrveDW5kj7nrjSSwFeT0aNHM3r06FL3z177ZhVGI0TVmXZrT2564Vt2rP6KLkNur+lwRCWy5GSx/O2p6O1ZZJutRFzTF0VnqOmwhBAVoCgKoQ0iCG0QUWXn0GjdK7yZackseeMJHhnSntv7t3d7zKhVcKJl9/JPCtqsWecJULPw8nERZFCx2l2s3HGUIGNeEqZRFPxPJxf0VwEvR4bbvvBGkw9NYzqVOmZzbg492zZxa1PRYDNnc3jhSzgp23xva24Wvdq4T4308zYyb/JInvn8Z/789g1OJJ9n6MMv0bBF0VMorZZcQgL9GNCpeZnOfylvo57OLepxKKds+7df+m9ZW8V/+EhNhyBEuUkCL4SoVA1C/Fkw7UbueGMZLrudriPulu13rgC5WRksf2sK/VvX4ZlbhnEmLYsRLywkpmfpKzdCCAFw8sBfXBMZ6pG8A3SLaQrxRwt+ttkdHLNnU8fPxaTuRqb/auWcQ2VRggMNeQm8Tm/A2++SPdp1ASguO0cTdtGh9+AKx5wf1zkz6PR6FG3JX6EjojuW2MfXZOCdhwYBsCX+GE988F9uePA5GkfJVMqqEBHdERJ2lf0YIS4jksALISpdWKAvXz11I7e8voSwiGgi23Wt6ZBEBW36ahb9o4J55paeKIqCv7cRu83O6YN/YeoiOw0IIcrGoC/8K+iMCSPdfn79y3Ws3vw7w5rDkDbexKdlsOCgBr/mHWnbY1Cx59AZjER17l0p8ebHNfqV72g/ZgrhzaIrZdyL9YhpwrsTBvH4hy8w4L7/K9MdA6J0bnzomZoOQYgKkwReCFElQgJ8aNO0LuaczJoORVQCS9Z5+vVtXXA3ha/JyGv39OfXhFPcel3l7JghhBAXO5WSzo9x8WicZu7pUp9gfz2P9DSw8FAOORlptI7tTWBIvZoOs1J1a92IDx4dzMPvvcTAB/5Lk9bX1HRIQojLjCTwQogq8eOOv9macIKbhrWu6VBEBe3+eRlZqadp0aCnW/uQLq0Y2KkF7y6Po3+HCNpFXllfpIUQNWv+mh2oNjPDorxpGJi3YFvDQD2+Rg1WexZxa5cwaNzDABzevY2DW1aSNwMeghq2oMuwO0k9c4z9W9dy7ah70OpKv+hbTXC5VN5ZEcf+k+fRajRYsjJqOiQhxGVIEnghRKX7ftt+Xv7mN0Y++QbB9RqWfIC4bP2xbhEJ67/lq2mjCQnw8Xj8i592sezPs3z9y15m3NufhiEBNK0XhE5b+r2Fy8qcnYmXj5+srSBENbLkZGE0+aCUYd/wougMRvafOkeu1Y63sfCk+uLq+/jY+m6PBZq0ZGRmkbBtPS3bdyH7fCrbl/6Pp8dcWzDep+s2sepUIqcP7SE80JvtOj3dR91T4diritPl4t+fbeCvZCdt+9/C4EEh1I+UC+BCCE+SwAshyiU5PZuZX29g6rh+hAT4FrQv2RzPywu3MmrSTEIaNK25AEWF/bFuEQd+XszXT48mvI5/oX3SMi20iO1HWOMWvPTdh5hzc4gKD+SDR4fgZaj8PzGnDyfw3ayptL12AH3GPl4pyYSouKVzXiWxHAtDyXzU2uH04QSWvj2NVtf0pN9dU0q9b3hRrwtVVcGeS+zD7xPklbeKfL5uMU2ZMWFkodX3fHqNwjWNfPj5eDpr/vc8jerVYd7kkUQ1urAF2zUtwnlw9gr+71/Xkm2xsfL4+TI/b5dL5dJrkaXdfsxzrKKPczhdTP10HQfSdQyfOB2D0VTsWGr+9nk1zGrJZd/2jUR3uQ6DV/ExCyEqjyTw5TB//vyaDkGIGjf+1S/Z9fcx9iae5ocZDxW0z9+QwHW3T5Lk/QqQsGkF7z00sMjk/WIRbWKJaBOLy+lk3aev8MA7K/nfY0MxFVFdK6+DcWsZP6A9vx3YzYb5M+l3x+RKHV+UT2LCLtJybWU7qIwJv6hedpuVbUs/xpyewtH9fzH9nv58sWEvS9+YiF9QSKnGSNwVh8WZl7i60GBBD+Qn6zpMuLDmOnFxIYFfvTORtNkr2bH3EP4uK+NjPRfJ7NlYyxe7nXhjwVdReP+hgTQIDXTr88n3v5GSdIaEIyGM6NWet5Ytx56bCRddLNAZvek2+kG8/QI8ztEsuj2D/+9LWje9UP232uwkZ1oIqlv8HvCX2rlmIS5rNo3CPM9jdziZ9NFajppNDHvsZfQGY7FjBdSpS65Tw71vryTQx8jjI2JpUjfvuR8+k8b7K3did7on+EM7R3BD5xZlirkoJw7sZt+mZaCqnDp6hPPnktj149c0aBqJKSCU7qPvR6eX7UWFqEqSwAshymzn/uP8ceA4DXxc/HHgODv3H6dTVOOCx0uqHojLX1ZaClmZmQT5le3fUqPVMuC+Z1k/bwb3vv09L93RB1+TkbpBviUfXAKnw07qqaM07F6Pz4bE8sA7K/lp3nR63XwPJh9/0MrrrqbFTHi/VP1kD+bL37alH+N9LoFbujSn2fC86nafdhH8svsITlfpKsAv7HaQ69TRcsA4Uvf+ij6iC77tL6wcr6oqlmO7Ue0WAE7+9BkGrY6j9kCcqopZ6+9RfQd4ro+BYS21/Bhv5fuDFuav2cHTtw8oeDz/9nvFmsGPcfHcMSiWr6eN5sDJVLdx4g6cYsNnrzLs8eke5xjyyCucOZxAblZ6QZsBGHNbW0y+nol4UXas+oojv33PgqdG4+/t5fH4Byt3kGj2ZugjL5cq8TV4eXPztHc5sf9P0pNOcNuMJcx+aBAul8rE/60hus9NBISFA5B1PoWczPM8+cFiYl6/m0ZhgSWMXryj8b+z9pOXeXJUF5xOJ+/tPo2fKwdnhpMRzVvw6769bF3yMb1ukfe3EFVJEnghRJn9d+4q/PUuTHrw17v479xVblV4UbtlpJ5l6cxJPDKsMw1DSv9FNZ9Go+X6u6exeeF7jH9nDVlZmTxzS0/G9G5T7phcLier5zxHUx8bI69tjUGv49MnhjPl0/Usf+MJ0rPM3PCgrNgsRGWxZKYxpnMzhnZtVdDmZdBxQ2zLUo8xff6POKxaglpcgz0rjaxc90XZFEXB1PTCfuf2n+bjROHM0YP4uzLJdPnx8bb0S4ctoAKKw1yQpOdX4fNvvzdoXNhs5oIEv3mDOm7HN6kbyNZ5WwsdW28w0rh1xfb/3rFyPke3r2LBtBsJCyz8ImZKppkmHfqUqWrt4x9EVJe+APgF1+PJuXl3hnYf81hB+66fvmPnD/MJ8PPhgWHdaBha9s/yix3+axsbPnudOY8NoVOLBrz+5Tq8VAsGnRWbquVU0jmGxjbjy7/OVeg8QoiSSQIvhCiT/Op7Yz+VmYN8eGxVTkEVvk8l3aInak568mmWzprEg4PacNf15f/yqmg09B77OABpZ08ya9Zk7E4X4/q2K9d4meeSSE7cx+I3x6PX5c2/NRn1vP/wDfj6GtmWcJx73nyR6+99hog2XTyOdzocbF/5OTnnzhLYIJJOg26VRfCEuAzZzLkYtSYsOn8cdpWP/7AXf4DOhOp0EpdwjNF9At0Wv3tzRBhPrEjxSPCry64NS1n079FFJu+VIabHQGJ6DPRo3/vzEj6feiNtmnpOQSirg7u2sHH+m3w8cRjtm9Uv8ndcPzS4wucSQpRMEnghRJnkV9+HtNDTN9LAkBY2Fuxx8N+5q9jYeWJNhycqaMU703h0SPtyJ9qFCa7XkNFT3+L9mZMxWx0Mjm1OkK+pTPPjU04m4ufjVZC8X6pbdGP+9/hQHnznVXqNe4Lwi1ZvVoEt375HoC2ZsV2bM++n1fxl8qN9n2EVfWpCiEJk5FjIsdhwulQUVGxZaZjPnUHxDy/xWC8fX64ddRfW3Gx+X/kFd428tuRj9DoGxObdKfDpD9twWM1cH+lNoyAjfSK9WX/U7HGbfXXxNRVfWTfqtJw7cYjMc0nu7SYfjN7lT/y1Oj2HT6cR7GciLNC33DuDnE86xYbPZ/D55JHE/HMx4OIFBrs1MTE0ypvvD5r5dc8R8Issd8xCiNKRBF4IUWoXV98fis2bb/xQrIlVBzP548BxdiQcrdkARYVlpqcxoltUpY8bGBrOjVPfZuH/nueTdXswaFS+nDqqVHMyj8bv5Jcv3uCDRwcX269j83A+eWI4Uz/9iDir+4JqsS0bMP3BIRj0Ok6kZLAzXW7zFKIqbE04zqPvr8JoMpFmARM2Di59F11gfYJ6DSrxeI1GS2z/kdisZnat/op7hnQr9bnX/v43CzbuQ8HAT6e1/LLIjMPlxTm7kxW/7a2RKnxJJgztxKMfrmHpDPd542aLnRGPv0Z4s+hyjdvn9inM/Hw6lm+3ElE3gHmTRpRrUVFzdgaN69UpSN4L295vfGwAP+w/w1+HTlK/dckXaYQQFSMJvBCi1C6uvkcG51VCI4O1DGmhZ8EeB9M+XEFASP0SRhFXq4A6dRnzzIcA7P5lBeNmfMEXU0YRUS+o2OP2bVzCtDHX0rllwxLP0S6yHmteua1S4hVClN38XxLoMeZh2vYazKzHx5CZayPmjlnVcu63l2xFr4UxUVomD7iwUvz4BSf5O9VRY1X44oQE+PDN06M92j9bs5O1m78vdwLfoEUb7nh5PqrLxcKXH2BPYhJdokr+DC1JYdv7NQzUMzTKm2/320g+c7LC5xBCFE820BVClEp+9T3Q60L1Pd9DsSYCvVR+33eMrBxzDUUoKoNOq+Pw6aqvTre/bgTXDLuX22Ys4dCpC+dLz7ZwIjmd1PQstLq8a8wK4O9d/NZKQojLg6qC0dun2s97KiWdUynnMbrMjI0NdXusR6Qviurkx7h4TqUUvSheZdPpdBw6nVauY/1MRvImAFWMotHgZfJGrYSx3Kvv7ovijY8NQHHZOX8umfTUsxU+lxCiaFKBF0KUSmHV93wFVfi9DhJPJdO2hmIUFdfvzik88M4sPp44jHaR9UrsX5EvhW16DUaj1XLHG3OYN3kkWxJOMHvpVoxeRmKuHURoo2blHltUzNI5r5JYhn3ak04cRtV5bpElrj6qWvJnQnrKWayWXI92h81K0rkTzHp8DKqqotps9H50NgDdYpoyY8LIIsecv2YHuBzE1DcRHug+7zzApKNxoI70HPe58KWJtSKuu30yj3/4Gh88OoTOLcu2d3xFI3N7D1symTh7Hwat+8KdWTYXZuL5dfViACKiO3LjQ8+4x3HR76iw6nu+hoF6OoQb+PWMnbi1Sxg07uEKPgMhRFEkgRdClKiwue+Xyp8Ln5qTS9LJxApvvyNqRstOvdBqddz2+osM7Nyy2AWYVGDdH4fpN/7mcp8v+tqBaHR6xr0+C5OvP3e+OBf/OhVfNVlUTGLCLtJybSV3/IfiF4YzI6nkjuKK8N2vCfyVmOzR7nS52Pn3KW4a1bTY462WXByF7CWvAk6X+s9rTyUAOJzzz2dQ/NGCfpm5Fj5c+Tu5VgcAOWYrP+3Yh9WlEOLvxesbzruNqygqZ806bHY7CzfFk5rrwsdkJP5YCnUj2xf0SzlxhPjN36MoCu3730xgmOd8bpvFzI4fvsRuyfZ4TNHo6DBgDAEheRc/I9t1RXPff5jw3ku8O+EGurVu5HGMy6Xy6Zo/OJma5db+a/xxYgaWfzrQxe9hExocFieOi268VQAtkIuKw/7Pe/2ii3aHdm3hZMJ2ko4e4LoWYYXOfb9Yco6LozlGjGou++I20HXgaAJDSr4ILIQoO0nghRAlKq76nq+gCn9I5beVC4gd4DmnT9QOzTp0584X55K4ZzslTYgY8MAdNGpZsRXro7r0xS84jICQevgG1vF4XNHqSDx7vpAjy0en1ZB55gSqqspWciWImfB+qfrtnn1fFUciLhfvf7+Dhb8dJqbfTSh4vn9G9XmYOvUbl2osU6h7P43OgD60MTET3sdlt3J23mP0eGA6Wz6Y5tbvyY/WkeHThLCmefPD9/2+Caeq0ixQQ3KWneEt3S8OLNvvYEBLE4nJKn+lqOw8p6d15540bGqidbf+ANisZpbMmszd/dtgsTtY/OaT3PP61yga99mmP38xg3BS6NrSM4k9mZrJslmTuevVrwramsZ04oYHn+exD57j+xfGUi/Yz+2Yj1bvZMmfKTTv4j43v3N0KM07lLwCf0liJryPqrqwnkzAkeF+a7s+uBGGus1QHVb2zZ1a0H7myD42fTmTCUOvwb95M4Z3i+KNBeuLrL6fzXZx63dmbojyIStLZdnBDKnCC1GFJIEXQhQrv/reyFfFpcLMLZ63PeZTVfBVs8jM0vPr8i/oOfLOaoxUVKbA0Pp07Ff07aqVrUHzmCIf6zz8Hua+NYVgf29u7lV0v9K6tW87fpy5gs0L36PXLY9KEl+J4j98pOROolZSVZXZy+NYuuM4N055u9CLbUW59HXhsFlRyUvYy+NkahbXjR5LSIOmpKeeZePy+QSombjwp67Rjs3sfvdImFHhwFmVNqEKickZJJ86xpB7urpViO0WMzoNPDoyb9X7T1f9jorqcYki+9xZxt96DR2be1bnHU4X89a+69HeOKoDgXVCOZeZ65HAnzyXRYtug+hw3fBy/S5KQ1E0eDVqA43auMebfpakr6aiOqz4Y0Z15aUFmWkpdGzRgDsH5N1Jl199VxxmVLz5eNuFdQQyrDA/QUNMmBYTVjIBL0eGVOGFqEKSwAshivXtz3+iVVQyrArf7HUU3/mfbzoW13n++HEBwWH1ie5+ea34K2qfOuFNGDLhRd7/5PlKSeCDfE3MnzqSu2eu4Jev3ua6cRM9qmyibBSNBq1GIdi79AlZRLRMs6kNDp06x7eb4zmTlsPeM7ncOOVtfPyL3zkiX0R0R7fbsvMlnTuB06WiD/Ws1Ps2Ltt7/GjCLhSXHYsugIxMF6lZTnacdLr1yXVqMasaDqcroMtbbO1owi469C5+a8orXc6+TQS36Ej4tSOJ+2AKJmsWyccPefSLSziGy+lE0ZlYeVBFVe3kurQ4VQWLS4tOq7ArSWVXkj3vAPkdC1GlJIEXQhTriX/1IddqxWy1l9hXp8u7vV6v1TBuQCyTP/0Ql8NBm17yB1xUjMkvoFIXnPL39uKLKSMZ/9b3/L7mG2IHj6u0sa9GWp2e0EbNmPTOopoORVQWjZY1Ow/y6sIttOw5DFOzQG68rT8m34CSj/3HpQui5Zv1+BjScm2lnqJRnKjOvbHbbThsVo78voEOoSrtIsLc+uw6fJb480aaXtMbAJ3BSFTn3hU+d+2nojP5AuBAi7/BwfK3n6LDoFtJPpdJZq4FnVZLz3aRZOb2xGLPu4j/y54THD5nI6xhC0x+AQTXb+IxsvyOhag6ksALIYpVv04A7z4xplR9fX3ztvrKzrYC8NVTo7nzzU9xOmy071t9t2MLURq+JiNjerRi2RHZt1iIS3Uaeicr3/8/eo57gpadLt9EzMvbh9j+eX9fcs8eplcrLTddcqfOgg27STvhRffB/6qJEGsNL53Cc3dfx7S5C2jQLJrYRz7EaPC8q6ZhZEvueuFljN6+NRClEEISeCFElYmoF8SCp27k9je+xOV00PH6m2o6JFFLefsFYnGoLNq0lzG925R8gBCiQoLrNeLOlz6v6TBENYtt1YDQAG/CIqMZ9tirNR2OEKIQMulPCFGlGoUFsmDaaPZtWMjBP36t6XBELWXw8mb0lLeYtfwPNu85WmnjnkrNBI1cyxZC1F6VM71IxaWq3PHGcupEdSd2SPm3sBNCVC351iKEKFJyejYzv97A1HH9CAko/61yDUL8Gdy5GX8nya3KovyC6jYksn03jiWl06ttxcf7asNuFm09zKhJsnK6uHwsnfMqiYUs/FaciOiORc43r2pWSy77tm8kust1GLxMNRLD1ey3JZ+QuHsLI554A7+gkHKNkfLnz/hgJSUXmkf15Nqb7q+R3Tlq22tfiJoiCbwQokjjX/2SXX8fY2/iaX6Y8VBNhyNEpX2pnLtmF5/8FM+NU94mMNRzP2chakpiwi7Scm0ld7xYGZOeyvTLknns27qOpJOJlbLvt/38acwH41A1ClqTP4k/fIQBO9k2F+8s/Y1si53z2RZ8Akq3En5Jkk8c5uAfv5J+5igtGpQvAc6nURQa1a3D2nlv4BPkvpCeKTCUp+f+xICOEQVtKrB+1xGu73R7uc5nzc1m50/fcc8NnVj8xkRunDwL/zp1PfqpTgc5e9fjsuZe0m7nzNbV6PzDyMREkLcXPW5+oFyxVIba9toXoqZIAi+EKFT+/u8NfFz8ceA4O/cfp1OU55Y/QlQrRcvZ81kVGmLV9gPM3bCPm6a+XeiXXSEuB6Vdof3SPdarU3rqWfbFbcDfeqZS9v12WXM4t/x1glteg8Ocg1XvhaZJJ5zH/iYXPbtoBV4wevIjha6Gr2jK9vlw+nACK997ln/1bEVAUyNj+1ZsxxSNRuHLp0axeFM8LldGQbvZ5mDhkQN0GX4Xu7Iz3Y7pe/coGkd1qNB57+zfngAfLz5580lGTZrpdlFSddo5v+4DtPYcfOpHXjhIhbSjfxDQ6w5MkZ1J/fzyqWLXhte+EDVJEnghRKH+O3cV/noXJj346138d+4qqcKLGteu32i+mzmJ8JAAxvVtV64xDp9Oo3nXgZK8l4J8QRbFiVu7BKM9Ay+tC6s9g7i1S8pUhb/09aXBhS8WEndvA9S827o3fQcY8NEbuHbEncWO127ALXzx1lTeXbaNFo3q8tmkEYQE+BTa98Tff7H6w+eYef/19G7btNQxlyQs0JeHR3T1aN+YcJrwZjHUbdKi0s5l9Pal04CbGTdjKfOnjkKv1fDq/92Fy6WCVg/oOPbRQyioWNDDyeNuxxuwk7n5G6ybl1RaTEKIqicJvBDCQ371vbGfysxBPjy2KqdCVfiTqRms2nGYLv8aVgXRiqtJUN0GjJo0k9dfvL/EBN7pcrFo417OZ5vp0Kw+3aPlDpLSiojuWOZbUyOiO1ZRNOJylF99D3Jm8sKIxjyz4kSpq/BFvb5UlxPFaqGZtw1VVUnOhWBvw4VjShAS3oR7ZywEVWX7yvmMnb6UL6eOKnjcbrWwe+NKLLnZJGxcxjsPDar1nwvX3ngv23V6xv3zXMf1bUe2xcqAafNQc8wYNRBgBEWxexybZXNh/ufiCMh7WIjaQhJ4IYSH/Or7kBZ6+kYaGNLCxoI9jnJV4Y8lpXPHG0tpO+h2WnTsUUURi6uJf50wnK7iV112OF08PXc9fyXZqd+qI/M+Ws1/x/ZgaNdWJKfnogTKJizFkUWhREnyq+8Do/zp1MSXgVH+LDtYuip8Ua+v80mnWDLjUX6cfhcp6TmMeH4Bj76zqExxKYoCikLXEXeh0esZN30JsS3q4tQ05vt3n6ah0UL7RsE8OXEYHZpdGetfdBl+JxqdjttnLOXHV27D39uL9TPuYeW2/dzcuw06beGfd9O/3cwh7450GXxLNUcshKgISeCFEG4urr4/FJu3ovBDsSZWHcwscxX+8Jk07npzGR2H3k27PsOrMmwhCtgdTqZ8so5DWQZGTXodvdGLVl3788JbU5n26VqCgkMZNXlQTYcpRK11cfV9bGwzAMbGhrJ2/+EKzYUPDAuneafr6PjQe+i0WvqMqdiCarGDx6HV6lnzw5cEhljo1sSXl+8ailZz5V3A6zx4HL99/yU2uxOTUYOvycCt5ZxmJKhL/iwAACAASURBVIS4vEkCL4Rwc3H1PTJYC0BksJYhLfRlqsIfPJXKXW8up8uoB4jpeUNVhy2uIhqtloDAIL7asJvb+rV3e8zmcDJxzhpO2v0Y9tiL6P65NTS0QQT3zliIy+lEo9Wi0WhrInQhrggXV9/DA/PeY+GBhjJV4QujKAp9xk2kx5gJKIqCVqevcKzXDByDzsubnF0reOWufmg01b89WnUJCavHZ+v+5OFhsTUdihCiCl15lyCFEOWWX30P9LpQfc/3UKyJQC+1oApfnLTMXO58Yxndbn5EkndR6TQaLTdOnskHq/ewavuBgnar3cGj76/mjCuQIRMuJO8XH6fTGyR5F6IC8qvv3s5MxsaGuj02NjYUb2cm++I2kJ56ttzn0OkNlZK853M5nTQI8b+ik3eAEU/M4JvfEpm9bBuqWvw0IyFE7SUVeCFEgUur7y5VJcPsIsCkKVMV/uz5bLwDgmndrX81Ri+uJoGh4UT1Gk7Csb2s3LqfaZ/8hNlipUX7btxw77NodfLnTYiqUFj1PV9lVOFr0lMfLmdb/FEAXKrKWxNvyZtTfzFLBhNmHsKgzWvvFtOUGRNGVnOkhfMNDGH0lLdZ9tZkPlszh2B/H+Y8NoSWDSu2v70Q4vIi33CEEEDhc9+Tspykm51YHFrq++tKNRfe5VL56ue9BIRUz+JALqeT+K3rsFvMHo8pWg3R3a7HaCp8GyFRuwWG1ufjj+byzSZfbpk2E1NAXQwmH88v3EKISpFfffdx5ODjE8bXuzz3XO8SGcja/cdLnAt/JnE/Zw7vw+jtQ3S361FqaF76saR0Nv6VCMCGPxNJtWlRUNGhkGm2Ae6fJ94o2C3g+Ocm1tw/E/liXfE7Npitds6ey8TbP6jQxw/9+RuZqUnFjmEw5n1lV9ER3X1AkRcpfQKCuPU/H2O3mjm8eyu3z3iPMT1bUzfI162fqqqs23WUDqMGFzpO0vGDnPp7r1ubVq8n5tqBHnc3CSGqlyTwQgjAs/puc6ikm51A3v/W8daWqgr/8oLN7DxtY/jEl6ol7s3fzMZ6cg9tmoR5PHYyNYPlW39kxMQZePn4VUs8ovpEdemLf526BIfWIaR+Y7JzrDUdkhBXtLi1S9Ba0rFpTFitVlJs7smtw6XyyXYHPSL8+Pl40VX45OOH+P6dp/l/9u47OoqqfwP4s7vJJtn0HlJJKAkJIAFCr9I7oYhgRVEEpEi1vBZQUaQICIKKHbsSehdBBAnF0BI6SagppJK6bX5/5JdITCEbZnd2k+dzjue8zNyZeWbfnc1+987cO6hdU5y8moLsW1fReZRhM5yIITO3AGPf/w0BLTrCWmmDPL01AD3cWvaCfYveCHCq+HdFrylGwbkD0OWmIePMfuTprbE71em+xxr84rtwdK3YE372rx34Z/M69G7duNrtldYlj/6cupqCtCun0Ovpl6tsK1coYKNyQHjHPnBrEIRTh3cAqRV/2Gze/2mEtu1eYfm1c3HY+el8DIxqAvk9P6xcvpWJveeOov8LC6rNSkTGxQKeiMp63wMcBOgFYOmhAhRq9NDqBcgACACs5BrYWcuhFwBnG6HKXvi4K6loO3QilLYqk2RPSzyPJU90QcuQir08giDg3R8PYuOymRg6YwlUjs4myUSm49soHA72NlLHIBJd/JopUkcoJ/tOChL+2glBp8fwRloE2ukrbedlJ0d2sQy22pwqe+Hv3ExC5+YN8dbj3fF3wjW8u+WcKU6hgluZd6FydkPvp+YAAE78fRCFWjWcO4+rchu5tQ0cWvYFANw48zfslEp0Hzut1hm8gpqiWCcgqok3BrYLrbKdg0PJ59yJczfxwid/1Hj/Pg2bwqdh0xq3Tzx7DHs/fxerJw9AuzD/cutu3snF6Pdjaryv2jK39z6RuWEBT0T4+Y+TUMgE5BTL8ONZLQQAOp0eCjngbCtDTpEAnR5QKOT/fzOhDAqZgJ//OFnpbfQymPYW5qrumJbJZHjt0a6w+fUwNi59CRE9R8DB1QONWnYwaT4iopoKDo8EEqq/JbvSbYwsKSEOOnUBPKw1OHxdwOHrlRfwWVprJOUqYGXlDJleg6SEOLTqVvE27dKPbakfeTH136v/8gpohOEzluCt5XOg0eoxrFOzatsb8+W6cuoI9n31PtZMHYg2TfxMemzAfN/7ROaGBTwRYcYj3VFQXIzCYg0A4GxiCtLvZKCFjwx9Gtthz+VCnEwR4ObhjubBJT0pdjbWmPFIxVvvzI1MJsPsUZ3g634GCdf34+9d13Hn2mC0H/yE1NGIiCqIfuFVqSNUKqxtN+z9ZimeGda92qL7t8OXoQh4CB5+IbBS2iCsbTcTprRMngEhiJ61DO99OBtOKhv0bBVSZVuZDCguKoROqxV1sM6bl87ij68XYd2MIZXe0WYK5vreJzI3LOCJCA3cnfHRjNEAgJvp2XhswVcIdFHg09EN4O9ijdGRGoz69jYEaznmPzMQfp4uEic2jEwmw2MPtwQApGXn4cklm6DXatBh2HjJe3+IiCyBrapkgMjxAzpUOx3bscRsOER2QtM2LNwN4eEbhGbdhuLU1fPVFvDBPm6ICHDDZ7NHwdr636n2ZDI52g8dX+upW1OSL2JguyaSFe9EVHMs4ImonG93HYOgLsTgMBX8XUq+HPi7WGNQmApbLhXi213H8PLjfSROWXteLg6YFd0eL33yMx7qNZLPxRMRkVmoye38Vgo5Ppk6CGnZeeWWp2XnY9z7yxHa/uFajxLPH7SJLIM0c3YQkVm6mZ6NnbHxkOsKMT6qfGE7PsoZcl0hdsbG42Z6tkQJH9yRc9fx2tf7MezFd1m8ExGRWblbeP/ZNORyGXzcHMv91zLEB3KZDIK+8rEJaiKvgDN5EFkC9sATUZnKet9L1YVe+L/OJuOlT/eg/8Q3ERjWSuo4RCShmLULkViLAbP4nC4ZS2hUD2xYugnNAuMxqmuESY/dJLIzft39E2at3Yq3n+4LlS3neicyVyzgiQjAf3vfG1TaZnyUM7adv42dsfF4ol+UWTwLb+fsho+3HMPAqCbVtsu8W4BVW//BwMkL4N+khYnSEdUN6TcTcefG1XLLlDYqhDzUwWJvu01MiENmgdqwjQws+OsaJ2dnvPfjAbQMrvw5aZ1ej9NXbqFPL7dq92Pv7IrfE5Kxfu9JnEpKh52ze6Xt1EUFuHo6FoJQvlc5sFlr2Du5VrrN9QunkJd9p9yyooI87Dt5Bc0D3aCy+bcwPXrxFlQulR9bbIJejyunj0BTXFhuuad/I3j4NQQAuPn4Y8SsZViybBZuZeQi2Pvfc7S1LfnKLhNkeLhVSLXjENSGk7s3PEOjsO3ILqRk3MV3r40Vdf9EJB4W8EQEoPre91I17YVXFxUYM2o5vca/gtN7fsa3p25X31Amx5Cp76FBSJhpghHVESlJF7F5xVx0jAgq94TuyevpSL1yGp1GTpQsmxgiJq2uUTvOTQ1Ez1yKiwc34dSpqh+jaj9iIvyaNK92P0HhbdDj8ZnYfGwfrO2D0H3McxXaCHo9Nn04G142Grg7qcqW5xep8evmLzH29U+htFWV2ybh8C4c3/QZWv9nCrTzF26gYeue+O1cJiDTlC23svVFr/ETqs0qBkGvxx/rlyH7ahya+nv8uxzAhh+TMeylxfAOLPkR2s0nACNnL8ehbV/jr5T8f7NalTz1evtaIg6fv4k3xok7SGD2nRRcOX0EzsjDiSvA+z8dxMtjupZrk1+kLglNRJJiAU9EZb3vMm0hBKjw2ZGqv5wJAGTawip74Z/qHYFXv1wGZ88G8PSveiRdsdg5OKN9dMUvf0QkjttXz6F/28Z4+8me5ZafunIbc747KlEqkoKHbxA8xkwTZV8hkV0REtm1yvUaTTFuX0vErk+mVLjLo89r3yE3I62s57rU7Qv/YNrQKIzuVv4HhO9+P4ntNzTo8/ybNcom1o811y+exqEfV6CwIB9B7vb45dVRcLArf2v6q1/tw+2r58sKeABw8fJF7/GvlGvnYG8DAEg8fw4H1r0uSr57xe7eABtNDlRWOhRDwE9/nsOek0lwVtli8YRecLBVYvKqHWjRc7joxyYiw7CAJyLEJiRDr9NBZmWHrZcEAJrqN7Cyg6DTITYhGSO6ly/gh3cOh7pIi7c+nI2h0xeV+1JCRJZJXslt8pZ66zxZDplMVun7rLL3Y3XravpeDQ6PNPgxieDwyEqXXzsXh12fLsA7T3ZHI183BHq5wNpKUTGvobfCi3zrPFDS+34udh9cdbmYPzQQr26+jkw4o/24OchNvYkhr6+CTqdHj5HPou0A3lpPJDUW8ESEPlGhKFJrUKTR1ngbW2sr9IkKrXTdwPahsFLI8b/l8zBy7gq4+QSIFZWIiMgoxBqgMPHsMez5/F2snjwA7cL8RdmnMZX2vvcNc0KbIAf0DXPCxku5uHgyFv3GTYaLtx906iKEtOosdVQiAgt4IgLgqLLFuD5tRd1n37ZNsO1EIm4nnmcBT0RE9cKVU0ew76v38cnUQWjdxFfqOPd1b+/72KhGAICxUZ7Yff4KzsXuQ/u+IxAU3kbilER0L84DT0RGozDCrX5ERETmSBAE7Pt2KSb0a2URxTtQvvfd16Xk+XxfFyX6hjnBRpOD2N0bJE5IRP/FAp6IiIiI6AHJZDIMnrwA63afxp4Tl6WOc1+lve8qXS7GRnmWWzc2yhMqXS7Oxe5D9p0UiRISUWVYwBMRERERiaBBSDMMnb4Ir317ANuPXpA6TrUq630vxV54IvPFZ+CJiIioSrb2DjgUfw27j1+CnY112fJDCdehVDmaNEvM2oVIrMUo4WINTkYPJjvtFrLSbpZbZqtyQIOQZqLsX6lywsa/T8DTxR73PsD1Z/wNKH0qHy3eGLyDmmD4jCWYv2Iu0rLz0aiBGx5q5AMnlW2Fts4qJc7EH4Wzh0+55Q7O7vAMqDgVq42tCulZufjxj9Pw83AqW56ekw+5wgpyRcWR7oGS6SCLCvLK/p2fm4WEv/fA7Z5n3//rv8/Cu/wnIxFJgwU8ERmVTnOfKemIyKyFtXsY6rtZWLX/b9w7G5eVrT0efnq2SbMkJsQhs0Bt2EYGFvxkHNcvnsaONW8iLMgLsnvK66u37uChfmPRqtfICtvoBT00Wl2l069Vpv3wZ3B8M7B099Vyyx08GqHLkKce7AQM5BkQguEzl2LjhrUoPp4KoeAQvp0zHJ7O9uXaTR3aHtk/HMT1vZ+XW37hWiq6PzYTTdp0Lbfcyd0b/Sb8Dz8f2ACd7s6/K2RyDJ36LhRW1vivuL2/4vTuHxHi61G2LPlWGvSFuWgb4lih971UaS/8xkslvfD9xk029GUgIiNgAU9ERjOobQhe+XodvIOawDuI88ETWSKZTIaHeo/CQ71HSR2lTMSk1TVqF79miihtqHbUxYX487sPcTf9JtJTbmLlC/3QMTywXJu/zibj/W1/VijgrZW2aBTRBlPX7MTKF/pBaX3/r6xKGzt0Gm0+/396+AZh0IvvAQBit36Dce9vwPq50fB2dShrY6u0wrtP9ayw7Y9/nMamM4cqFPAAENKyPUJatq9xjuS4P7HomYfROSIIAHAzPRuPLfgKdijG7XxbfHskrcptBQC22hz2whOZERbwRGQ0vSIb4S21Fgs/eQtPLfxO6jhERGWCwyMN7p0PDjfdbdh1wfHt38O9+AbmjGoFH7fO8PdwrtDGSlH5cEwymQwDXngLuz5dgEmrd+DjKQNgU4Mi3ly1H/wkFAorjH1/A9bPHQ5fd6dq25e8LoJox7/3dY5NSIZepwPkVoi/A1zKus+dclbOkOk1SEqIQ6tuA0TLRES1Y7mfhERkEVo38UVx4UGpYxARlcPn4o1PU5SPnqG+aNvUr1bbK6ys0e/5N7Hni3cxceU2fPHSUMgteHrStgPGQW5ljXHv/4xf/je6wu30ptInKhRFag12nLiCVGt/+Ie2uu82VkobhLXtZoJ0RHQ/LOCJyGjyCosx/ZPdaMo/+kREVAsKKyv0ffZ/+Hj6UOQVFVc6EJwlad1nNG6cPYIziSl4uFXlg8cZm6PKFuP6tMX1rELYqh5CuwGPSJKDiGqH08gRkVHkFhThqaWbofBtju5jp0sdh4iILJRcoYBc/u9XVntba1w5eRg3L8ej+J6R1S2FVSUDzRER1RQLeCISXXZeEZ5YvAl2Ddugx2MvQSbnRw0RET0YrVYPAFjybC8UX9iPf378AD+/NxmFebkSJyMiMh1+qyYi0Xy1Kw5jFm7A8AU/wSW0E7o9+iJkMst9XpGIiMxD01Yd8dKne1BYrEFIAzd8O2cYRnRsCp1GA71eJ3U8IiKT4TPwRFQr+UVq7Io9h/7tm0Flq8Sabcfx46Er6DFuKvwVtvBr0pzFO5GFilm7EIkGjNCenX4bAODi2aDG2wSHR3IguTrO2sYOxy9fRGTjFHi5OJSbPs1QDz81D79/tQhPLd2E2SM64NjFW/jh0GVEz1kOeydXEVMbn8JGhSPnb8LT2R7+ns5wdbCTOpJZMPRzB+DnCNVPLOCJqFY++vUAtv99Bhevp8HZ1RU/HLqKx19fDUdXD+TlF0sdj4geQGJCHDIL1DVur7V2gDYnFXoDtjF0CjeyPG0HPoZ9X93ArG9ikZWRhjUvDkDbpv7l2uj0+hrtS65QoNf4eTix9Ru88v1BqJzcMGLOCji6ehgjulF1fmQK/vh6Ef74+ggKcjPxzexhaOTrXq5Nyesi3o/gOl3F11mnE2+aOjEY+rkDgJ8jVC+xgCcig91Mz8bO2HjIinOwMzYeHSKbw1blAGsb9iIQ1SURk1bXqN2pFRMMah+/ZkqtM5HlUNqq0P+F+QCA5IQTmLTqbXw0qT86NAvAV3tP4fC5m7h4PR3Nuo+o0f7kcgWiho5H1NDxxoxdrfzcLBz+5WNoi/KrbOPkHYiO0c9BrlBUut7BxR1Dpn8AAIg/tBuDX1+CLg81wb03rZ25ehudH5kmSuaA5h0xedV6tAtvWLZMEICTl29hyFTpXsuq8HOEqHos4InIYN/uOgZBXQilXA+1uhDO1np0amiPn96bhj7PzIVepoSLp6/UMYmojuIXd8sTFN4G/Se+halr5iPI0x7pRQq0GzYB3Xo5ISD0Ianj1dgfX3+A1h4adG4bUGWb7/bHYddnb6PvhP9BYVX9V+2Izn3h17QF0m9cLbd8wAB3NAgOEyVzmwFjEdi8PbLv3C63fOSIALg3CBTlGERkOizgicggpb3vcl0hlgz1wozN6dh1NAHrX38KbrGXsGvt60jNzEG7wU8isvcoqeMSUR0SHB5p8C2zweGRRkpDhgoMa4VBk9/G+b+2InrkRIt7dh0AivOyMXRwa0Q2rvpH6m4tGuLFj3di5ydvYeDkt+87HoyLZwODxo+oDc+AEHgGhBj1GERkGizgicggpb3vg8NU6BBkh0FhKmy5VIj1u4/jnRcG49WxXXExOR2Pf/ALdFoN2vYfK3VkIqojOFiV5fNr0hx+TZpLHcOolNZWWDVlADpO/wwFudmwd7a8HyqIyHxxGjkiqrF7e9/HRzkDAMZHOUOuK8TO2HjcSMsCAPi6O+GHl0fgwoEYJMUflzIyERGRySmtFLC6z+3zRES1wQKeiGqstPd9UJgK/i7WAAB/F2sMClNBUBfii61Hytp6uzqgVeMGyM/NkiouERGRJARBgCDUbIR9IiJDsIAnohqprPe9VGkv/NZDZ8t64fefuopDZ6/BO6ipFHGJiIgkIQgCFv96GPYuHrC1d5Q6DhHVMby3h4hq5N5n30t730uV9sJvvZKPL7YeQcvGgXj16wMYMvU9ePgGSZSYSHwxaxcisRaDqPHZbaqP6uP1IggC3vnhIPZfzET0rGX3HYWeiMhQ/FQhovsq3/te+Ui546Ocsf3Cbfyy/wy+/+sKhs9YDO+gJiZOSmRciQlxyCxQG7aRgQUMUV1RV68XQah8uV4v4K3vDuDvpDwMn/UhbFUOpg1GRPUCC3giuq/qet9L+btYY3Aze3wRr0WDRhEs3qlOi5i0ukbtOF85Ud26Xnwat8SU1dvQ7aFGFdbdzshFarENhr20BDZ29hKkI6L6gAU8EVWrtPddpi2EABU+O5JdZVtBAGR6HW5cPI3sOylw8fAxYVIiEpuhBZUlFGBED6LTyIkIaNEROem3K6xzbaRA+zZdobS1kyBZ3cHPEaLqsYAnomrFJiRDr9NBZmWHrZcEAJoq28rkgJNShgxNIQ5vWY+B42ebLigRiSY4PNKgW5mz8zOgVNnDRaU07BhEFkYmlyMwrBUQ1krqKHWOoZ87ZdsQ1TMs4ImoWn2iQlGk1qBIo71vW1ubko+U9KwC/HToMM78uR0tug00dkQiEpklDyJGRJaJnztENcMCnoiq5aiyxbg+bWvU1sHBBgCQl1eMYV1a4PHFX8DF2w8BoQ8ZMyIRERERUb3AeeCJyCga+riiXZg/cjPTpI5CRERERFQnsIAnIiIiIiIisgC8hZ6IiCyGoNdDJudvz5YkZu1CJNZiYCpjPw8rZi5zPceqWFre6tSlcyEiqgkW8EREZPb0eh3++GYpUpPOY9hLS2Hv7Cp1JKqhxIQ4ZBaoDdvIwIKsNsTMZa7nWBVLy1udunQuREQ1wQKeiIjMQmFeLv7Z+yv0mopTFWbevAIXfSaGRPpiw5IZiJ61FA4uHhKkpNqKmLS6Ru1MPQe0mLnM9RyrYml5q1OXzoWIqDos4ImIyCzsXPM6ItwFNPVzq7DOxt0Jj3TvDFulFexsrPHT8jl47K0vJUhZgkUAUc3xeiEiEg8LeCIiMguZaTcxe+JoeLs6VNvu2b6tsGrj3yZKVV5weKTBt98Gh0caKQ2ReeP1QkQkPhbwREQkudMHtsBKJsBJZXPftlYKBXw9XXBsx/eIGjDOBOn+xYGviGqO1wsRkfg4lC8RGY1CLsPdDM4DT9U7uS8Gp3d8g+/mjoCdjfV928vlMqyfE43Ew5sRu+VrCIJggpRERERE0mMBT0RG83z/Vji3/zfEH9oldRQyMzl3UvD1a49jyYTeOP/7j/huXjSCvF1qvL23qwN+mDcCKXF7MH9cN6yYMgx3biUbMTERERGR9HgLPREZTRM/D3w7NxpPLv4UqdcuQeXkhuad+8HBxV3qaCSx039sxMCHfPHq2LG13oeHsz22zh8LBwcbrPjtMPbs+gG9xr8sYsq64fLJw/AObAJHN0+poxAREdEDYgFPREbVqIEbfnh5BGIOJeD2ncvYsHgHomctYzFRjwmCgLzMNPg2dhRtn74ejhCSC0TbX11xYvfPiNvxHaxs7RE9axmcPXykjkREFiZm7UIk1mIwwsrGQBBzX0T1FQt4IjK6QC8XTI/uBABYt+MEvlw8ncVEPfZ3zDoU3r6AIY8PlzpKnXZsx/e48tcmxLz5KPadTMTqxTMQPXMpXL39pI5GRBYkMSEOmQVqwzaqokgXc19E9RULeCIyqQkD2sDaWo5PlsxA9MxlcPHylToSmVB+ThZO/rEJvy96Gm6OdqLtN8jLBUnnDuHOzSR4+DUUbb+WJjvtFi6eOIjc9BvIvHQM388bAW9XBzze6yGkZefh+O4f0fOJWVLHJCILFDFpdY3axa+ZYtJ9EdU3LOCJyOSe6h0JpZUVViwp6RF08wmQOhKZiF6vg52tjajFOwC0axaA1x7piHeXzcLQGR/AK6CRqPu3BHduJmHT8jno2yoQzZxs8eS8EfBwti9bH+DhhOM5WsnymesXcTFzmes5VsXS8lanLp0LEVF1WMATkSTG9mgBa4Uc8+c/D7lCjoj2D6P7YzMglyukjkYWamjHMFhbyfHGh3MwdNr78GnYVOpIJpN2/Qo2L5+L18Z0wtCOYVLHKSc4PNLgW2CDwyONlOY/xxApl7meY7XHtqC81alL50JEVBMs4IlIMqO6RmBw+1AUFKnx4tpd+P3LRej19DzIFSzi6yJBEHDmjxgEeDob7RgDopoiO68QP276DIOnLzbaccyBTqtF/OHdKCq4i5O7f8KCx7tiQJT5/WhhroNPiZnLXM+xKpaWtzp16VyIiGqC88ATkaRslVZwc1Lh8+mDIc+4hIM/1+y5OLI8cbt+wp2zB7DmxYFGPU6Qlwv0OuluFTcFnVaDnZ+8hfTYXxGY8w8+fK63WRbvREREJC72wBORWbh0MwOJKdno3ru11FHISO4kn8MLA1uL/vx7faPVqLFjzRvwtcrFytnDobTiHStERET1BQt4IpJc3OVbmLhyG3o+NReNW3WSOg4Zib17A/x2OA69WzeCjbVx/vxodXr8/Nd5OLjVvanSblw8gzs3E5H0zwE0clRj6XP9Yc3inYiIqF4xyjcovV6PPXv24OTJk8jLy4NOp4MgCJW2lclkWLhwoTFiENEDyi9SY1fsOfRv3wwqW2WN1xniyu1MTFy5Db2ffRXBzds9aGQyYx2GT8Cez9/FzE/3YPWUAUY5xpvrD+BKng0GTJ5hlP1LJTnhBH7//F30ah2C8GaOmDiwDawUfAqOiIiovhG9gM/Ly8PTTz+N+Ph4AKiycC/FAp7IfH306wFs//sMLl5Pw8uP96nxOkNcvpmBgCbNWbzXAworK3QePRm/vveC0Y7xz5UUdBn/FqyVNkY7hhRSki5gROdQzBndReooREREJCHRC/i1a9fi7NmzkMlk6NChA4KDg2FjU7e+SBHVBzfTs7EzNh6y4hzsjI3HE/2i4Ofpct9195NfpMbu45eg1Zf8uLc7LhEq93CjnQeZGZnMaLs+fvEG0rLuws6xZu9FS+Lg6omNG87Dz92x1rfNbz56GXaN+AMAERGRJRO9gN+1axdkMhk++ugj9O7dW+zdE5GJfLvrGAR1IZRyPdTqQny761hZT3t166qTV1iMZz7cgnxrNzi5eQEAbH3boNOQp4x6LmQ+igvyjLLfhOQ0TFq1A/2fFSiGeQAAIABJREFUewOOrh5GOYaUwjv0hpVCji0Jx2u9D/vGXRE16HERUxFRfRK/ZopZ7ouovhG9gE9JSUFQUBCLdyILVtrDLtcVYslQL8zYnF7W0w6gynWhDt5V7jMnvwjjl22GbWArDBs3HTI5n9+tb3IzUrH94//hmb6tRN93fFIqQpq3RcOINqLv2xzIZDKEtuuF0Ha9pI5CRPVMcHgkkBBn+DZG3hdRfSV6Ae/s7AyFgqPiElmy0h72wWEqdAiyw6AwFbZcKulpB1DlundeGFxhX38nXENqdh6+3HMGTo2j0HXMi5AZ8TZqMl+xG9dheNtATOhvrKkC+b4iIhJb9AuvmuW+iOor0bvAOnXqhOTkZNy4cUPsXRORCdzb+z4+yhkAMD7KGXJdITb/dRqbD52udN3O2HjcSMsqt6+f/4zHrC8P4IczRfBtN5jFez0naNUI8HSSOgYRERGRxRK9gJ8+fTrs7OwwZ84cpKeni717IjKy0t73QWEq+LtYAwD8XawxKEyFO1k5KLibU+k6QV2IL7YeKbevIxduIWrIM+g94XW07vsIi/d6rmmXoVjy2xEcv3hT6ihEREREFkn0W+h3796N3r17IyYmBj179kRoaCi8vb1hbW1daXuZTIbly5eLHYOIaqF873uDcuv6h9pj2YFMuDrL8Xjr8s+6j49yxrbzt7H10Fk8M7gDXFQqpGTexT+XbqFzezdTngKZsYYRbdBnwuuYvOodrJzUDx2aBUgdiYiIiMiiiF7AL1q0qKyXTavVIj4+vmxO+MqwR47IfNz77HtpD3upnRfy4e0gR89gK9jJigH8Oz1kaS/81iv5+GLrETzaqy0e/2AjQrtHo2FEWxOfBZmzhhFt0G/im5i6Zj6WT+yDzhFBUkciIgsQs3YhEmsx+BmfuSaiukb0An7KlCksyoksUHW97zeyNdh2Lg/OtsCIZgrk5BXC3cm+3HzU46Ocsf3CbWw8eAYbYpPQsu9YRPYeZerTIAsQGNYKgya/jRkfv44lz/ZC94eCpY5ERGYuMSEOmQVqwzYysOAnIrIEohfwU6dOFXuXRGQC1fW+f3ksB4JejyGhSoR5WCO7SI+M3Hz4uP07IJm/izUGN7PHV/FauHr5sXinavk1aY4hUxdi9kev4r2ne6B368Y13jY+KRW3M/MQ4OmM0IC6N+c7EVUtYtLqGrXjPONEVFeJXsBXRa1WQ6lUmupwRGSA0t53mbYQAlT47Eh22bqsAh2+Pp4DJ6WA/GI9vj2tRqFaD8g08HTRQnHPfO56PaDX65F2KxnZd1Lg4uEjxemQhWgQ0gxDpy/Cqx+9DI1OjwFRTe+7zabD5/Duz4fhHxKK65f/wHtP98DwbhEmSEtEREQkPaMV8BkZGfjqq6+wf/9+JCYmQqfTQaFQIDAwEF26dMGzzz4Lb2/v+++IiIwuNiEZep0OMis7bL0kANCUrcvKL0Z2kQC1FvjujPbfjWQyOKkKobIt+WFOEIA8vRX0MgXkEJCUEIdW3QaY+EzI0vg0bIphMxbjreVzodHqMbRjWJVtfz0YjyUxxxA960N4+AYhJekiXv3oZSisFVVuQ0RERFSXGKWAP3HiBKZOnYqsrCwIglC2XKvV4urVq0hMTMTmzZuxcuVKtGvXzhgRiMgAfaJCUaTWoEijrbCuSK3B6Su3oNXqyi23UijwUGNf2CitIQgCfj91DVcyitG6xzDIbewR1rabqeKThfMKaIThM5di4fI5UGt1GNW1Yo/6D/vPYMWWfxA960O4+fgDKCn+h89YgldWzkWwlwOUAW1MHZ2IiIjIpEQv4NPS0jB58mTk5OQgIiIC48aNQ0REBFQqFe7evYv4+Hj8+OOPSEhIwIwZM7Bx40Z4eXmJHYOIDOCossW4PrUbLV6vFzD/uwNI06owcdE62Dk4Ii+/WOSEVNd5+DVE9KxlWLpsFrR6PR7t3qJs3Td7T2LNzjMYOXs5XLx8y23nGRCCsa+sxIm9v6H9sAmmjk1ERERkUqIX8OvWrUNOTg4GDRqExYsXQ37P87EAEBERgVGjRmH27NnYsWMHvvvuO7z00ktixyAiE9DrBfzv6304cUuN4S8tgZ2Do9SRyIK5+QRgxOzl+GjZLHy2Iw4ujvbQ6fVIv6vGyDnL4VzFmApeAcEYMH42fzgiIiKiOk/0Av7AgQOwtbXF/PnzKxTvpeRyOebPn4/ff/8de/fuZQFPZKG2xV5AbHIeRsxZAaWtndRxqA5w8fLFE+98g5Ski2XL3HwCYOfgVM1WRERERPWD6AX87du30bRpUzg4OFTbztHREY0bN8bVq1fFjkBEJpKVVwjvhk1ZvJOoFFbW8GvMkeXrupi1C5Fo4DzdweGRiH7hVSMlIiKx8PomMh7RC3ilUom7d+/WqG1eXh6srEw2kx0Riej01RSs3noCvZ55ReooRGSBEhPikFmgNmwjAwsCIpIGr28i4xG9eg4NDcU///yD06dPo2XLllW2O3XqFJKTk9GmDUcNJrI0cZdvYeLKbXj4qXkIbs6ZJIio9iImra5Ru/g1U4ychIjExuubSHyiF/DR0dE4ceIEpk+fjpUrV6JFixYV2pw+fRrTp0+HTCbD8OHDxY5AREZ0+moKJq7cht7PvsrinYiITIqFHhHVd6IX8CNHjsSWLVsQGxuLRx55BOHh4QgPD4e9vT3y8vKQkJCAc+fOQRAEdOjQASNHjhQ7AhEZ0R+nriKs6xAW70REZDLB4ZEG32IdHB5ppDRERNIRvYCXyWT49NNP8dZbb2HTpk2Ij49HfHw8ZDIZBEEAUDIKfXR0NF5//fUqR6onIvOj1urwz5U02EXwSxEREZkOBzcjIiphlBHkbGxs8N577+HFF1/EwYMHkZSUhPz8fKhUKgQHB6Nr167w8/MzxqGJyEjUGi2mrtmFbGsv9O85VOo4RERERET1jlGHgPfz88Ojjz5qzEMQkQkUqbWYvHoHsqy9MOC516GwspY6EhERERFRvfNABXxhYSEAwM7OrsIyQ9y7PRGZn5WbYpFl7YX+z78JuUJRZbviogJkpd5CQWHJ1DHO7t6wUTmYKiYRERERUZ32QAV8ZGQk5HI5tm3bhuDgYABA69atDdqHTCZDQkLCg8QgIiNLzy1EQPO+1RbvedkZ2Lx8Nmyghlwmh14QcLdYj1HzVsLBxcOEaYmIiIiI6qYHvoVer9eX+3fpQHU1ZWh7IjKtfSevYP+pJIzo06zKNncz07Fx2Uw82aMpZozsiLy8YgDApFXbkXwuDhEd+5gqLhERUa3ErF2IRANHutfrtJArDPs6HRweaVGD8tXmdclOvw3YuxspEVH99kAF/O+//w4A8Pb2rrCMiCxfalYeZn+2F8NnfAAPv4ZVttv79SI0cldiUPsmuHTjDgoK1NALAm5m3EVTaxvTBSYis5ebmQZNUSEEvQ5y6KHJugUAkMnkUDh7QyaTSZyQ6qvEhDhkFqgN2kaTcR3W7gGGHcjAYlhqtXpdigpgzQKeyCgeqICvbCR5ji5PVHfcLSiGk4sLGoSEVduuY/TzOPTjcjy2ZAcAQK8vubPGPywSTVp3NnpOIrIM8X/twJ8/fwx3F0fIiu/CHsDtH18DAMggQAMrFEIJgEU8SSdi0uoatYtfM+WBtrE0tTlHSz5fInMl+ij0Tz75JEJDQ/Haa6/dt+20adNw/vx57N69W+wYRGRCPg2bYuTLH8PBvqS3PS+/WOJERGSOzv25CasmD0CniCDMXbMJR+KTytbpBSCrSAdbuRawVlXaEx8cHmnCtERUWza2KriolAZtw+ubqGZEL+CPHj0KnU5Xo7YXLlxASkqK2BGISCTnrqXDirfAE5FoBNjblnyp/2DSsApr8wrVeGbZZri3GYzWfUaaOhwRicTFswFmrvxF6hhEddIDFfBXr17FihUrKl0+ffr0KrcTBAG3b99GcnIyGjRo8CARiMhIdh+/hLd/PITBUxdKHYWI6gkHOyV6twrEXzkZUkchIiIySw9UwIeEhCAnJwdHjhwpWyaTyZCVlYVdu3bVaB9jx459kAhEJKKsvEKs2HgUOQVqHEq4hqEzFsE7sInUsYiIiIiICCLcQj9//nxs2bKl7N+rVq2Cr68vRowYUeU2MpkM9vb2CA0NRceOHR80AhGJICO3AE8s2QjnkLbwDG2GUYNbwNWbg1ISEREREZmLBy7gg4KC8OKLL5b9e9WqVWjQoEG5ZURk3tKy8/DE4o3wb9MX7Yc+zWmciEgygiB1AiIiIvMl+iB258+fF3uXRGQEgiDguz/O4PyNDByOv45GnQej3aAnpI5VYzFrFyLRwLl0g8MjEf3Cq0ZKVH9I+doXFxXg3NEDCG/XA0pbuwfeH5kHvV7Aul3/4Fp6Lv44mYjO4/oa/Zj8DDGMJb5ehmZOvX4Fcmcfg46h02ogUxcj9frVGrXXqouRnV93xnjITk9BcVFBuWVadTFSM65j2bTRlW4j9fuCyNKJXsAbIjc3F/v27cPw4cOljEFUrxSptSgoUmPdrjjsOJ2C8B7RaNfCE41adnjgfev1OhTm5aKgQA0AsFU5Qq5QPPB+K5OYEIfM/z9OjRn45ZMqJ+Vrv3/DVzj39x6k3khEv3GTRdknmY7cSomzyWkI8HQuWyYAWPTLYZxK1aJx+754uPVjCGxm/Omk+BliGEt8vQzNrNMLkOn1Bh1D0OshANDqa3briABUKHgtWXFRQYVzF1DyWlb52tfj64hIDEYp4I8fP47PPvsMly9fRlFREfT/+TDU6XQoLi6GWl1yYbOAJzKN+KRUPPvhZmgFwNM3ENGzP4Sdg/P9N6wBdVEhNiyeh/SbSZDL5NALeji5eWP4zKWwc3AS5RiViZi0ukbt4tdMMVqG+srUr332nRSci90Hp+LbOBe7D+37joCLh2G9ZSStjqOnYO26d/DhxqPllvuFNMPQGW9AaWP6uyr4GWIYS3y9apr5xIIhtT6GnWdgrbetC+49f7mVEtaegZW+7ub0viCyVKIX8AkJCRg/fjy0Wi2E+zzIplAo0KJFC7EjENE91FodPt1+Ahm5hdh+7BK6Pz4bTVp3Ef04BblZSLmeiDXTBqFDaACOnr+B51dswd2sdKMW8FR/xO7eABtNDmwVehRrchC7ewN74S1Mg+AwPPnueqljEFWrNkXmjS+rnj65rqjsddGqiyGgpGgnItMQvYD/4osvoNFoEBYWhmeeeQa2traYNm0a+vbtizFjxiAlJQUxMTE4fvw42rRpg2+++UbsCET0/9QaLV78eCduaR0R0LwjBkx+Cn6NI4xyLBcvX4x9+UNMWzIXQ9uFYHPsFQyesgBeAY2McjyqX0p73111uZg/NBCvbr5e1gvvYB8kdTwiqgPkSjsId9PgZsDfrVRrJXQ5qbCuYQ+8JicVKpV9bSNKIjg8ssrb3lMzrkOnFyqcv0Ogcb5rEJERCvgTJ07AysoKq1atgr+/PwDA398f165dQ+fOnQEAI0eOxMyZM7Fjxw7ExMQgOjpa7BhE9V6RWovJq7Yj28YHg194HQor4w95EdAkAmPmLcOm1fPRf9J8BDRtafRjUv1Q2vveN8wJbYIc0DfMCRsvlfTC+z/3ktTxiKgOUDp7wk2lxMyVv9R4m2XTRiOzQG3QowUuKsvqra5uwDlDz5+IHpxc7B1mZGTAz8+vrHgHgLCwMFy6dAnFxcVly1555RUoFAps2LBB7AhE9dr+U1fx3k8H8cTiGOSq/NHvuTdMUryX8g0JxRMLvmTxTqIp7X1X6XIxNsoTADA2yhMqXS7Oxe5DVtptiRMSERERmYboBbxCoYCjo2O5ZYGBgdDr9UhMTCxb5unpiYYNG+LSpUtiRyCqd/R6AcUaLTb8FY95Xx3AFftW8On0CPo8+6rRRoEnMpV7e999XUp6rnxdlOgb5gQbTQ4Obf9V4oREREREpiF6t5y3tzdu3boFQRAgk8kAlBTwQMkc8WFhYf8e3MoKeXl5YkcgqlfSsvPw1JJNuJaaCRdXN0TPXAp3Xz4TTHXDvc++j40q/1zq2ChP7D5/BWcO70XngaNgbe8mUUoiIiIi0xC9gG/bti1+++03fP3113j66acBAE2bNoUgCNizZ0/ZlHEZGRlITEyEl5eX2BGI6jxBELDhrwQkp2Vj+/GrCO44CCMGPSF1LCLRVdb7Xqq0F37z5Swc2v4reox+XqKURCSWmLULkWjAPOHZ6SWP0Lh4NqiwLvX6Fej0QoXR0x0CIxA0iDNYEJFlEr2Af+KJJxATE4NFixZh165d+PrrrxEZGYmgoCDs27cPr732Gpo1a4ZffvkFarUazZs3FzsCUZ0lCAL0goClv/2NHadTENK2F1oN74fQtt2ljkYkuup630uNjfLEngslvfCteg7lvPD1hCAIQBVT1crkoj8dSCaUmBCHzAJ1jdsXFeTDytm70m1kjl5QANDpy79X8q7FP2hMIiLJiF7Ah4aG4p133sH8+fNx/vx5KJUlPSazZs3C9OnTywatEwQBSqUS06fX/XkzicSQkJyGZ5ZtQvbdfAQEN0b07A9h5+AsdSwio6mu971UaS/8pktZnBe+nrh1JQGbVr6KgoL8CutkADoNfgwdhj1t8lwkrpqOan5iwZAq26devwqtXoDdPVOc1Yf52omobjPK0NTR0dHo1q0bYmNjy5b17dsXn332Gb788kvcuHEDwcHBmDRpEho14hzRRDWxdkccHur/ONr2HSV1FLPy31sjyXSM+dqX9r47a3MgwBnfHkmrsq0AwEaTWzYvPHvh664bF89g+5o38OHzvdG9ZXCF9Rm5Begx+wu07vcIlLaqGu2TnyGGscTXqzD9Wtn/1mvV0OSkin4elvi6iKm+nz+RKRltbil3d3cMHDiw3LIuXbqgS5cuxjqkUR0+fBhr167FhQsXoNFoEBERgeeffx5du3aVOhrVE4KAsoEhCQgOjwQMeE6ybBt6YKZ47ZMS4iDTa1Bk5YxNlwBAU2VbuQyAtRNkeg2SEuLQqtsAg45FliE77Ra2r3kdKyb2RaeIygfqdHdSwcpKAUGvv+/++BliGEt8vWxsVUBRQbllMgAKuQxuVczFbmhmS3xdxFTfz59ICqabHNqCbdiwAa+88gqUSiU6dOgAvV6P2NhYTJgwAQsWLMCYMWOkjkj1wAsDWuHZ5evh4OqJ0LbdpI4juegXXpU6Qr1litc+rG03aDRqaNXF921royz5U6aDAmG8Nuqs3IxUNPH3qrJ4NxQ/Qwxjia+Xi2fFu3HuKG3gFtAIM1f+IsoxLPF1EVN9P38iKYhewK9atcqg9jKZDFOmmO9tN2lpaXjzzTfh6OiI77//Hk2bNgUAnD59GuPHj8e7776LHj16wNvbW+KkVNdFNPTGVzOHYfyy5UhNPA87JxdEdOoHlSOfg6e6x1Zlj6hew2rU1sHeBgCQl3//Yl9MyQknkJJ0ESonFzTv1I+DpxmZo5snLt1Iwxtf7YW/p1Olbe7kFsLWzh5WShsTpyMiIjINoxTwNb3Nt3SueHMu4NevXw+1Wo2JEyeWFe8A0LJlS0yYMAHLly/HTz/9hGnTpkmYkuqLsEBPrJ87HDGHzuPm9buIWboDw19aBntnV6mjEdUrCX/vwZHf1iC6YygOx91E+tV49Hx8Jot4I3L19seoeR8h4dBOJOVVMQK9lQwj5syAwsraxOmIiIhMQ/QCvm/fvlWuKyoqQnp6Oi5cuABBEPDYY48hMDCwyvbm4ODBgwCA3r17V1jXp08fLF++HH/++ScLeDKZRr7umD26MwRBwEebj+KTeWOhUCjQfsCjiBr0OJ+TJzKyswd34Njmz/Dt7OFo7OeOvEI1nl+5DSunDIL8/6+/Rs1bo+9zb8DKuvLnbKl23HwC0GXkc1LHICIikozoBfzKlSvv2+bGjRuYNm0adu7ciY0bN4odQTSCIODy5cuQy+UICQmpsL5hw4aQy+W4fPly2d0ERKYik8kwbVh7PD+gDTLvFuDZD7cg504qXH0bIqRlB7j5+EsdkajOyMu+g/NH9yM/+w6ST+zFd3NHINin5M4XBzsl1s8ZjiKNFgCg1+vx2tf7sXnFXAS27AKvwMYIDGslZXyzFLN2IRJrMfgVn7mlmspOT0Hxfwax06qLkZpxHcumja50G77HjIvXPdGDk2QQO39/fyxfvhwDBgzARx99hAULFkgR475ycnKgVqvh5uZWNp/9vaysrODq6oqMjAzk5+fDwcGhyn1t2LABMTExNT72nC52aNmiRa1y/5eDA58FrMscYAMPN3tsnP8o1m49jpyso4hZ+iPGzPsQPkGNpclkz/ccmZ6x3nfZd1IRs2QGOjf1QqiLCivmP4ogb5cK7e59KvvTlwbjk63HkJIVh02f/4CBk99CSPO2Rslnqa5dOImsgqpnF6iM4sJJs/p8MacsdYVCIYMMMsgNfBylsvbqogJo9eUftxAA6PRCle89c3uP/Zc5Z6uJunDd1zd87c2PZKPQBwYGIiQkBPv375cqwn0VFhYCAOzs7KpsY2trCwD3LeBv3ryJo0eP1vjYuS3YW0OGcXdW4bXHSkbg7triPF566wUorKwQ1rojBjz3GhRWnHSCyFCZqbfww8KpmDKwJZ4bVPMCXKGQY/Kw9mX/vp50mQV8FZpP+bhG7c6unmzkJGTptLnpyNj+IbR5mQBK7oYBALmjF5QPT4HM3g1yKyWUnkGVvu/4HjMdXvdEtSfpN3q1Wo3s7GwpI1SrJr/+CkLlA+n8l5+fH9q1a1fjY9vYWiMv78FGVC7teX/Q/ZDl6dkiGIeXP4fCYg3mffE7fl48B/7NO8I7qAl8G4Ub7bhSjQZO9ZvY7ztBr8f5Y3+gMC8XJ3f9iKmDW2Fs9xa1/izVarUoVmt5XfyHTidAgFBWZN2PAAE6nWAWryM/64yn9H1xZvUkg7Y7s3oSVCgGIED9/19vi9VaCADs1beQ/8trKBBK7qas6n1nTu+x/6or7zlLvu7rm7rynquLJCvgd+/ejeTkZAQFiTOfqzGoVCoAQHFx1W/c0nXV9dIDwIgRIzBixIgaHztv95IatyWqjMrGGioba3z84kCs2XoMGSl/YfvWrzBwyrtGLeKJLJmg1+OP9cuQn3wKkY188MajHTAgqun9NyQiUQSHRwIGPCOdrbIHNHlwcXGEoFbDUa6DnXXJLdrnM1JRrJdB5ukDGQDF/49V5BAYYYzoREQmIXoB/8EHH1S5ThAEaDQaJCYm4siRI5DJZBg4cKDYEUTj4OAAlUqFrKwsaLVaWP3nFmStVousrCzY2NjAyanyOWmJpKa0UmD68A4ASq7B1GuXWMATVUKv1+H3rz6AIuMyvp83Ag52HEGeyNQeZLCy3798D6NDFRjZtaRA7/biClzJV6JRtyHIvnMHLt2fEismEZFkRC/gv/jii/uOxl5623nLli3x/PPPix1BNDKZDI0bN8bp06eRlJSExo3LDwiWmJgIvV5fbn54InMjCAJ+j7uCjNwCHIq/jnYRXlJHIjI7ep0Oe75YCLu71/DJS0OgsuE84kRERGR+RC/ghw8fXm0Br1Ao4OTkhNatW+Phhx82eJRRU+vatStOnz6NvXv3Vijg9+7dCwDo3r27FNGI7ksQBHzwyyFsP3ULfo0j0GLgU2jUqqPUsYjMik6rwe5178ClOAUfTx8MWyUHfCQiIiLzJPq3lPfff1/sXUpqxIgRWLduHT777DN06dIFzZs3BwCcOXMG69atg62tLcaNGydxSqKKBEHAOz8cxP6LWRg5dyXsHJyljkRkdrQaNXZ+Mh/eyMSqqQOhtGbxTkREROaL31Tuw9/fH/PmzcOCBQvw6KOPokOHDhAEAbGxsdBqtVi0aBHc3d2ljklU5sqtDJxJTMXhczcRd7sYw2ctg62q6ikOyTRi1i5EogEDMwElgzk9yPOgdH8Xju2HbcFtrJozHEorhdRxiIhEwb85RHXXAxXwly9fFiXEf29NNzePPfYYfH19sW7dOpw4cQJKpRKtW7fGpEmT0LEjb0cm8/HPpVt44aNtaBjRFkr7EAx76VnY2NlLHYsAJCbEIbNAbdhGBn75IsPptBqENHAzavGeX6gBOM4pkQnIkFdYceYgvbpIgizS4t8corrrgQr4IUOGPHAAmUyGhISEB96PsfXs2RM9e/aUOgZRBUVqLeZ8/jvik9OQnVeA/s+9juDmUVLHoipETFpdo3bxa6YYOQmJ7cqtDMz8bC/uFv77pVkQBBRqBYwc0EXCZET1Q2jnQVi15nU08XNHp4iSaYoV0CHt1H64DZghcTpp8G8OUd3zQAV86WjyUu+DqL44cu46MnILyi374c8EFDk1RL+pc2Fr7wCVo4tE6Ygsj72TG45euIGtR87DzdGu7Et/bbz1/V/waj0Andt0LX8MZzcobVUPGrXOYuFAYvFv2gIDJ72N6WvewLjuzZCvEWADDbILFcjYsEzqeHQPXvdEtfdABfz58+fFykFE9/HJ9uP4+o9z8AsJK7fcKaQTug59CnI5n98lMlSjVh1xN/M2vo47jduJJzCq021MH9b+vtOh/pcgCMjKK0Sbxs3h6u1vpLR1S3B4pMG37AaHRxopDdUV/k1bYOj0RYg7tAVyBw8oCorgLK/5112+x4yL1z3Rg+MgdkRmSK8X8P4vh3DgzDUAgE6vhxrWGDVvFRxdPSROR1S3tHp4BPDwCOTnZmHTslnYErseTipbLBrfE03973+96fUCFnz/J4oVDvAKMu8xXcwJB8siY/Fp2BQ+DWeh62NSJ6H/4nVP9OAkL+CPHz+Otm3bSh2DSDLnrqXhRnpuuWW74xJxOk2HbhMWlPWsO7l7Q2lrJ0VEonrB3skVo1/5GDl3UnD76jk8ueRTTBwQCX+P6qdg3HsyEXG3NRg2YzGUNrxGiYiIyHiMUsAnJSXhm2++weXLl1FUVAS9Xl9uvU6nQ3FxMe7cuYO8vDzTBCDcAAAgAElEQVSLGMSOyBh2Hb+E1789gIDG4eWW2zgGYOj0ySzYiUzM2sYWHn4N4eHXEM4ePthxcBN0F9Oq3cbGMQDDXprE59yJiIjI6EQv4K9du4bRo0cjLy+vbIA6mUxW5WB1Xl5eYkcgMlvf/n4KPx38d+yItOx8DJuxGF6BvO2WyNwENotEYDM+e0lERETmQ/QCft26dbh79y58fHwwZswY2NraYtGiRejWrRt69+6NlJQUbNu2DcnJyejYsSO+/PJLsSMQSe56eg6u3Moot+yfK6nYEJuIXk+/DGsbWwCAo5snR42nKmWn38ayaaMN2iY4PJLPGBKZiZi1C5FYiwG7LOkarg/nSERkTkQv4P/++2/I5XJ88sknCA0NBQB8+eWXyMnJwSOPPAIAmDhxIiZOnIgjR45g79696N27t9gxiCRz6sptPLdiKxo0bFJuJGsrGxVGzFnBQeioxoqLCpBZoL5/w3sZ+EWaiIwnMSGuzl/D9eEciYjMiegFfHp6Onx9fcuKdwAIDw/HoUOHoNFoYG1tDRsbGyxYsAD9+vXDTz/9xAKeLNLdgmK88tUfuJmZV2759dRM9H7mFTRq2UGiZFTXRExaXaN2nFeXyDzVh2u4PpwjEZE5MMogdq6uruX+HRQUhAMHDiA5ORmNG5c86xsYGIjAwECcO3fOGBGIRJd5txDnr5UMZqUXgCUbjsC+YWu06t23XLvOzm5w9vCRIiJZAH55JSIiU+HfHKK6R/QC3t3dHampqeWWBQQEAAAuXrxYVsADgL29PW7evCl2BCLR3biTg8c/2AiVqzcUViWXjU/LXoga/GS52+SJqhIcHmnwbaPZ+Sro79+MiIionNr8zQkO56CdRJZA9AI+MjIS27Ztw/bt2zFw4EAAQOPGjSEIAg4ePFi2LC8vD0lJSXBzcxM7AlGtabQ6vPvTXzh3PbPc8uSUDLQZ9CRaPRwtUTKydLUZsGnZtNGGP1tKRET1HgcJJKq7RC/gx44di61bt2Lu3LnYt28f3nvvPbRp0wbe3t7YuHEjAgIC0KxZM6xfvx6FhYVo06aN2BGIaqywWIOzSf/eMfL57lO4qXVC5NCp5do9pHKEZ0CIqeMRERERlclOvwVnjwa8+4+oHhO9gG/Tpg2mTZuGVatWYc+ePViyZAkAYPLkyXjzzTfx0UcfAQAEQYBcLsfkyZPFjkBUI2qtDk8s3ogsjQI2tioAgKtvIwwcMxVW1kqJ0xERERH96+iuX7H9qxXoPvwpRA1+Quo4RCQRowxiN3nyZPTq1QuHDx8uWzZmzBjIZDJ8/vnnuHnzJoKDgzFt2jRERvJ5G5KIIECr10NhZQtrGzu0HTYBPg2bSp2KiIiIqJwj23/Amd0/YeWUwXh/4+8s4InqMaMU8AAQGhpabio5AHjkkUfK5oInkprS2grfzY3GySu3cT0tB0tXzEPPJ2bB3rlkXAaVkwtcPH0lTklERATErF2IxFoMSsZnoS3fsR3fI/HwZsS8NQapd+6WW8f3BVH9I3oBP3PmTAwbNgxdunSBQqEQe/dEorK3VaJzRBAQATRwd8TKLV9ALwgASuZzf/ipuWgS2VnilEREVN8lJsQZPqilgYUdmRdBEHB0yzdIPrYDMW+NgY+bY4UCnu8LovpH9AJ++/bt2LFjB1xdXTFo0CAMHToULVq0EPswRKLr3jIY3VsGl/37bFIqJixfggsHN8HG3hldxkyFnYOThAmJiKi+i5i0ukbtOP+35bpzMwlHN32G4vy7EPIz8MO8EfBxc6x2G74viOoP0Qv4yZMnY9u2bUhOTsa3336L9evXIygoCMOHD8fgwYPh7+8v9iGJjKJ5Q2/8+r/RuHQjA3+evYaNS2eixxOzIP//eeCd3X1ga1/9H1QiMfALF5Flqw/XcH04R1NIu3YZm1fMwwsDWqGhtw/aNO0OJ5Wt1LGIyIyIXsBPmzYN06ZNw9mzZ7F161bs2LEDSUlJWLFiBVasWIE2bdpg2LBh6N+/PxwdWfyQefP3cIa/hzN6PBSMj7cew47v3gMEQC8IyCrQIHrmUrh6+0kdk+qo4PBIg291DA7nwKBE5qI+XMP14RxNJf3GVWxcPhcLHu+KAVEcVJeIKme0QeyaN2+O5s2bY968eTh27Bi2bt2K3bt34/jx4zhx4gTefvtt9OjRA0OHDkXv3r2NFYNIFDKZDFOGtMOUIe3Klv104AyWLZ4O/5BQuPg1RvshT0Eml0uYkuoaDjJEZNnqwzVcH87RVC7HHcaIjo1ZvBNRtYxWwJeSyWRo164d2rVrhzfeeAOHDx/Gnj17sG3bNuzZswd79+5FQkKCsWMQiW5M9xYI8/dAalYevthzEL9npqJV30dh5+AMe2dXqeMRmY3cjFSoiwqrbySTwc3HH3I5Bz8lovrL7v/Yu/O4qKr+D+CfOysMCCgiIrK5IrimKJpLaS65IVpPZYtZVm65L2nLz6zMnhTF3MpcnsfUnhZ3TcslKzNNc0lQc0HBDREFZZ3t/v4gyJFBZmCGO8vn/Xr5Ks6ce893Lvde5jvnnnNUSqlDICIHZ/cE/l4nT57EwYMHcfjwYeTl5QEAPDw4roecV4v6QQCAjs3CMWX5bvz42ZvIzM7F48NnIDSypcTREUnvz5+2Y/+3S1HT78FDpvIKClEjpDH6jHyPT7IQERERlcHuCXxycjK2bduG7777DteuXYMoipDJZIiNjUVcXBx69Ohh7xCI7E6jVmLhyF4AgIOn0/D64hkIa9ISau/qaD/wVag8PCWOkKjqHd+7Ccd3rMY3b/0LEbUf/FSKVm/Aw+OXIyf7FqpVr1lFERIRERE5F7sk8OfPn8e2bduwfft2XLp0CUDRWpYNGjRA//79ERcXh8DAQHs0TSS5dpEhWDt1IE6nZWDviQvYkjgFjzw/CWqNF7z9mJiQa9MW5CM3+xbOH/sZp/d+i7VT4hFSy6/c7VQKOZTKKn0ojIjIociVSpw6lwmD0Qg5n0QiojLY/NNS//79cfbsWQBFSXvNmjXRp08fxMXFISoqytbNETmkBsH+aBDsj95tG+PDr37BzoVTkJ2Th4cHvYLmXfpLHR6RXdzJTMe3H4+FSiaipo8G694YiDr+PlKHVSEbls5CSgVm1uaEXu6J5wtZo6zzRRRFoPAuWr26AL7qonmkinVqWR+J4wbBKIpVGapDKOt4ZWVcQ2FBnvmNRBG45/gVU3to4BcQZHYTXpPkLGyewP/111/w8PBAt27d0L9/f3Ts2BFyOSclIvckkwl48+lOePPpTki9kYXnP16NK8mHodZUQ5u+L8DHn0+ikOs4d/wAOjQKxJxXnH9oVEryUdzK01q3kZUJHLkOni9kjQefL2p4oQA5eQIKoQBQlITKTlxAbr4WM9f9guBGLaosVkdQ1vHSK70BpbfZbXSZaVD6h5Qq1wJlH3tek+QkbJ7Af/DBB+jVqxe8vLxsvWsipxZayw9fTX8CvyZdwvlrt/Htx+MwcNI8+NasLXVoRDbj7amWOgSbih6xyKJ6SUtG2TkScgZVcb7wXHMdZZ0vol6LW98vgr9gRJ2H43BkzcfQG0U8PetbGP0bocszY0pt4w7nxf3HKz3tAvRGEZ4BoSbll1eOhUyhgkyhQt2hiSXl+RmpUMgEBIbUK7Vvdzh+5DpsnsAPGjTIbHlubi6TenJ7gdW9Ed8xGgAQ5O+NRR+PxYAJCageGCxxZERE5MgiolpZ3UMYEdXKTtGQPQkKFWr0fB139q/BX9/Ohye0uJkHNI5ojXbxw01W6uB5QeR+7DZj0PXr17Fq1Sr89NNPuHTpEkRRRHJyMtLT0zFx4kS89NJL6Nq1q72aJ3J4z3ZtAYVchnlzxqFes7aoFhCM1j2f5hJaRERUCsfmuhdBroBv5yHw7TwESUtGIcBbjcdeGI+c3EKTejwviNyPXRL4n3/+GRMmTEBOTk7RhBz4ZyKOy5cv4/Dhwzhy5AhGjRqF0aNH2yMEIqfwVJdmCKvlh0vpt7H+wC7svp6K9gNfhVLtyaXnyOnI5QqkXM9CRnYuNGolvDxUUodERERE5FJs3tWXmpqKMWPG4O7du+jVqxcWLlxoMvt8eHg44uPjIYoiFi1ahB9//NHWIRA5ldgmIXjqkeZYOaE/fPIuYd27L+PzKU8h9fQxqUMjskpU7GPIVtZE73e+ROeJK3AgOVXqkIiIiIhcis174D/99FPk5+dj3LhxGD58OABgxYoVJa/7+/vjww8/RP369TFnzhysXbsWjzzyiK3DIHI6GrUSy8b0BQAcPJ2G1xfPQIOHOkHt5YM2vZ9jjzw5PKXaA31GzwIApJ05jjFLZ2DuK4+hc7NwaQMjIiIichE274Hfv38/fH198corrzyw3tChQ+Hn54cTJ07YOgQip9cuMgT/mRSH3nUL4HPzOLYkTkFhfq7UYRFZLKRxC/QZ9T4mLtuFPcfOSx0OERERkUuweQ98ZmYmGjduXO7a73K5HHXr1sXp06dtHQKRS2gSWgtNQmthcNcW+L8vfsSmeZPQf9zH8NCYX/PU3WxYOgspFZh5lxP+mLLncQxuEI1+Yz7EG59Mx6whIh57qEFFwyRyCMXXi1xeNK+PwVA0z0962nkI1WohPe2CSX2DTgcAkCuVJuV6bSHSM9OQMObJUm3Y8j7F+yQRkeuxeQLv4+ODq1evWlQ3PT0dfn5+tg6ByKXIZAJmPv8I3l/3MzZ8PA7BkQ+hVlhjRMZ2kzo0SaUkH8WtPK11G1n5QdYd2Ps4BkVEouuQKVi8/VMm8OT0iq8XAUUJvIiiBN5gFCEHoDeKJvVFowGQKyHeX/73NmavPRvep3ifJCJyPTZP4Fu2bIk9e/Zgx44d6NWrV5n1tm/fjoyMDHTr5t5JCJElBEHAW890QovfTiMjKwOrN/2InOwMtHhkAOQKJWTlPPHiyqJHLLKoXtKSUXaOxLnZ8zh6+/njZnYOcvK18PYsPTN9oU6P23fzkZdfCJnMfc9lch5NRy0GABiNRgBF14XBKMIzINSkXt7VswBQqlymUEEZEFrqurPXfYr3SSIi12HzBP7FF1/E7t278fbbb6OgoACPP/64yet6vR4bN27EBx98AEEQ8Oyzz9o6BCKXJAgC+rdvAgDo064xXpq/BQc2/QfePr4YMH4OqgfWlThCIvNqhdRHnSZtMXTeZqwc3w/enuqS1xZtOYTFmw9CLpOh7eNPwcu3uoSRlsaEhqxxeeVYk5+NukIAAmRKLqlIpfH+Yp37j5deWwixjLpGvRZGvRapy0aYlAsAbqrU5jcichI2T+BjYmIwZswYLFiwANOmTcObb75ZsgZ8//79kZaWhoKCAoiiiKFDh6JDhw62DoHI5dWuUQ3bZw4GAPxv35+YN2c8GrXrDi+/mmjVdQAEmc3npySqMEEmQ9fnJ2LfugV4Yc6moiUTNWrM3/AbNh65jFc+Wgsv3xpSh2kiIqqV1Y8SR0S1slM05Oi8Q6ORk5pUqrzw1mUIciU8a4WZ3YbcU0XuL/WbtrZTNI6vrOOVlZuJwoI8s9solSrg7o1S5SoPDfz8qpXdDpETsHkCDwAjR45EgwYNkJiYiPPn/5l9+K+//gIABAcHY+TIkRg0aJA9midyK091aYba1b1xOvUKdh8+gF2pf+HR5ydALlcwkSeHIchk6DJ4LH7+ahGe/3gjmobVxIGUO4ifNA9ePo7V6w6Ak3iRVcL6jDRbfmRmPyh8Ay1+hJ3cQ0XuL95e7ttrzPsxkSm7JPAA0KNHD/To0QNpaWk4d+4ccnJy4OnpifDwcDRowImMiGypS/MIdGkegee7t8LIRd9hwai+UKvV6DdyJkKb8BtlcgyCIKDTv0bh129VOHTmD8RPTICnt6/UYRERERE5Dbsl8MVCQkIQEhJicf3Bgwfj2LFjSE5OtmNURK5Jo1Zi1YT+AIBDpy9j9OJ30X3YWwiPbiNxZERFBEHAw0+8KnUYRERERE7JIZ+vFcWypqQgIku1jayLpa/3wQ+fv4/zJ36TOhwiIiIiIqoku/fAE5F0HmpYB5+P64eX583Cptx8hDVojD6vz4aHxlvq0IiIiMgJbFg6CykVmNSTY9eJ7IMJPJGLa16vNg4mvgqjKOLD//2CjXPHI/yhR+EfFIqGD3WUOjwiIiKyI6PBgOM/bUVhXq5Jea2Q+qjXvF2526ckH8WtPK11jVqZ8BOR5ZjAE7kBmUyADALeeqYTGu37E5dvnsTWbzbiTsYVtO75lNThERERkZ388tUi6NKOIaZh7ZIyURSx8Yv1wHOTLUriAVi8mgLXtyeyLybwRG5EEAQ89UhzAMCzXZvj+TkbsH/jKvjW8Ef/MbNRPbCuxBFajx8UbIPHkchyJxcVLRsnwrI5e6S+vqRun6R16/I5vBPfFu0iTSeVNogizl25aHECT0SOgQk8kZuqXaMadrz/LHR6IzYdOIV5c8ZjwPg58K8TJnVoFomIamX1I3oRUVxS7348jkSWK75e5HIBAGAwPDiBz9J4Aboc+PlVs64NG+H1TUTkepjAE7kxuUwGuUqGp7o0g0ohx+yEiYgb/zECgiOkDq1cnBzHNngciSxXfL14e6kBADm5hVKGUy5e30RErocJPBEBAOIfjoJKIce0D0ZBrpCjUauH0fWFyZDJ5VKHRkREREREYAJPRPfo064xurash/xCHSZ9vgvblryN4CYxCIqIRFC9JlKHR0RERETk1mRSB0BEjsVTrUQNHw2Wvt4b3cLkqJX+K7Z+Mg3nj/8mdWhERERERG6NPfBEZJZKqcDYAbEAgEEPR+KVxI9gfG4i144nbFg6CykVmBiL43GJiJxPVsY16JXeSE+7YFF9vbYQWbmZdo6KyH0xgSeicjWvVxsrxvfDy/PnwqDXIbLto1KHRBJKST6KW3la6zayMuEnIiLb8KoRiPkbfkNc7O2SMhEidh+/hGZ9e5a7fWFBHqD0ht5o2bKJYvE2RGQXkifwt27dQo0aNUp+FkURomjZDYKIqk50eCBWTYzD0LmfwGjQI6p9d6lDIolFj1hkUT2uQU1EJJ0uz01A0o+bsPnSZZPyxt2eseoLec+AUFuHRkQVYPMEvlu3bmjevDnmzZtXbt3Bgwfj8uXL+Omnn0rK1q1bZ+uQiMhGIkMCsHryAAyZuxRGvR5NOz1ul3ayb17Hzk//DznZtxEU3giPvfwWlGoPu7RFRETkylRqT7Tq+XSl93N55VgbRENElWXzSeyuXLmCGzdulFtPr9fj+vXryMrKsnUIRGRHDYL98cWUeOxemwid1vZrIN9Ov4JvPx6HZ2ND8O30eNT3zMGWBVOhLci3eVtERET0YGoPDQzZ6ZDLBIv+GbLTofbQSB02kcuqVA/8+fPn8cYbb5R65P306dN44oknytxOFEVkZGQgIyMDoaF8HIfI2UTUrg65TAbRaLTpfvPuZuPbOeMwrn9rPPNIMwDARy89hmkrd2HrwmkYOGm+TdsjIiKiB/MLCIIxT2vVsCk/jcrOURG5r0ol8PXr10dwcDB27NhRUiYIAnJzc3Hy5MlytxcEAa+99lplQiAiF6Ly8ETtkHr4YM1efPrdP5Oe5eYVIJJj7omIiIjIzVV6DPxbb72FLl26ACjqWZ8+fTrCw8MfmJgLggAvLy80bNgQ4eHhlQ2BiFyEQqlC3zEfISvjGnDPkz2CXA6fGrUkjIyIiIiISHqVTuBr1qyJ+Pj4kp8XLlyIyMhIkzIiImv4BQRJHQIRERERkcOx+Sz0e/bssfUuicjF6HVa/PhFAgLrN0Wzzn2lDoeIiIiIyCnYbR34q1ev4u7du2jcuHFJ2erVq7F582YYDAZ06dIFr7zyCjQazlJJ5G6SD/wAY/ppHD9zBHptAVo9Vvakl0REREREVMTmy8gBwEcffYTu3btj+fLlJWVLlizBrFmz8OeffyI5ORlLly7FkCFDoNPp7BECETkwvVaLpuGBWDt1IE7t+QqHd3wpdUhERERERA7P5j3w27dvx8qVKyEIQknvekFBAT7//HMAQHx8PNq1a4fly5fj5MmTWLNmDV588UVbh0FETiC4pg/WTR2I5z7eCKNBh7Z9npc6JLJC0pJRUodARERVhPd8Isdg8wR+/fr1EAQBs2fPRlxcHADg559/Rm5uLurUqYMPP/wQANC2bVv06NED3333HRN4IjdWu0Y1rJ0ajxfmbIJBp0Ns3FAIgiB1WPQAEVGtgOSj5Ve8fxsiInI6vOcTORabJ/DJycmoXbt2SfIOAPv27YMgCOjatWtJWZ06dRAWFoYLFy7YOgQicjK1/LyxZko8np+zEYcVCsT0fUHqkOgB4odPlzoEIiKqIrznEzkWm4+Bv3v3LmrWrGlStn//fgBA+/btTcpVKhXy8/NtHQIROSF/Hw1mPtcFqX/+KnUoREREREQOyeYJfO3atXHz5s2Sn8+dO4dr165BLpejXbt2JeUFBQVITU2Fv7+/rUMgIidVvZonbt64jtvpl6UOhYiIiIjI4dg8gW/UqBGuX7+OnTt3AgBWrFgBAGjXrh28vb1L6iUkJCA3NxctWrSwdQhE5KTqB9XA1CfaY/2c8ci8eknqcIiIiIiIHIrNx8C/8MIL2Lt3L8aNGwcfHx/cuXMHgiCUTFR3+vRpTJo0CefPn4dMJsOQIUNsHQIRObEnOzeFUiHDhwkTETfu3wioW0/qkOg+hQV5OHVoH6LaPgKVh6fU4RAR2d2GpbOQUoGJ3Dh+nIhszeYJfLt27fDRRx9h9uzZyMzMRLVq1TBmzBh06tQJACCXy3Hu3Dl4eXnh3//+N1q3bm3rEIjIyQ3oEAXRKCLxs5l4duYqqcNxeXqdFtdSTkM0Gi2q//vuTbiUdBjn/zyEmG7/TFharXpNVA+sa68w3ZZBr8e1C6dgNBpMymuHN4LKQyNRVI7LaDTg2vlTMBj0JuUBdevB09tHoqjI2aUkH8WtPK11G1mZ8BMRWcLmCTwA9OvXD3379kVmZiaqV68OuVxe8lpYWBgSEhLwyCOPlKwTT0R0v9gmofjwm98gGo0QZDYf7UN/E0UR2xe9BX3WFfh6l39P1up0uJxyBXKjDpeP/wLNnRSolEoAwPkrGej5ytsIi+IXs7YiiiJ2fPp/yL+RgurVvErKCwq12G9QYdDUBVCp+RTEvX5cnYAbZ48gwK9aSZlOb0D6XS3+NW0RPL19JYyOnF30iEUW1eOa6URkL3ZJ4AFAEIRSs9EDRTPP9+7d217NEpGLqOmrQXgtX+z9IgGPPjeBSbydaAvycOmvk/hj8Qgo5OUf49lf/IDMtHNQiXnQChq0DffDG891BwCs2HEEe479zATehowGPc4c/x0nPh0NpeKfL8NFUUSPt9YiK/0KaoU2kDBCx3Pq8E/YPfsF+PuYfiH11Oz1SL90DuHRPD+JiMh52f0TcWFhIfLz803+5eTkIDMzE6dOncKiRZZ9k0lE7kWpkGPlhP4QbpzCrlUflXp8mGxHJpNZlLxfycjCjoNJkBnyMadvLcgM+dhxMAlXMrIAAKp7EkyyHZkgmCTvQNGX5DzeZTN3bJQKu/VZEBERVRm7/DXbvn07Fi9ejJSUFBgtGFM5ahQfMyJyJgajEaIo2r0dLw8Vlo/rh9c+2YbdK2ej+8tv2r1NKtvqnb9D1Oajb6QGsWGe6BOpwZaz+Vi98/eSXniDgV+0EBEREdmLzXvgDx48iIkTJ+LcuXMwGAwQRbHMf76+vnycnsjJ6A1GTP58F0IaNIFSpbZ7e55qJT59vQ9O/Paj3duist3b+z40pmgM8dAYX5Ne+JjGdXH+j5+xNXEydq38EIV5ORJHTURERORabJ7Ar169GqIookOHDlizZg2+/fZbCIKAuLg47NixA6tWrUKfPn0AAEFBQZg9e7atQyAiO/pi9zEk3xLQZ/SHVTYuXa3ko69SK+597xOpQV2/oknr6vop0SdSA1Fb1AvfOKQmvn3rSUzoFoZQpOOXrzhEioiIiMiWbP6p+NixY1CpVJgzZw5q1KgBAIiIiMCJEycQHh6O8PBwxMbGwt/fH6tXr8a6devwwgsv2DoMIrKTzDt5CIpsXSW978X0Fi5vRvZh2vseZPLa0BhfbDt9DTsOJuH5njEIqeWHkFp+0OoNWPrrDYkiJiIiInJNNk/gs7KyEBYWVpK8A0CjRo3w/fffIzc3F15eRcvgjB49Gl9++SW2bdvGBJ7ISRz+6wr+99MpPD5ycJW1qdMbMHHZD2jczHTm6A1LZyHFyjV2I6JaIX74dFuG5xbuHfte3PterLgX/v6x8ORe3P16rIr37+7HuCJ4zIjIFdk8gVer1VCrTXvmQkNDIYoizp8/j+bNmwMAfHx8EB4ejpSUFFuHQER2UKjT49XELXj8tRmo27BZlbSp1RswZskOXNX74PERM01eS0k+ilt5Wut2aOUHOXcgkytgMBrxa9Il+Pt4lXr9xu072PLrSSj1pXvfi93bCz+gU3MIMjl+P3sVMpW3vcMnB+FI16NKpcKOw2fRPKJ2SZnOYEBa+i00sNOTQ1Xx/h3pGDsLHjMickU2T+Dr1KmDy5cvQ6fTQaks6qkJCQkBAJw5c6YkgQeK1rHNz8+3dQhEZAf5hXoYRdhsDeXyVqgwGI0YufA7ZMprovfIdyBXKM3Wix5h2TjrpCVc7cIcpUqNni9Nxdv/+4/ZlQWybt9GfoGI6mo/aNTmly0r7oXf8FcBnv7wG1SvWQsan+p47OUR9g7fLYgQYTSKkMkEqUMplyNcjz1feRsrNn6Gwh9Om5Q37NAHdRpE261doGrevyMcY2fDY0ZErsTmCXxsbCy++OILJCQkYMqUKRAEAdHR0RBFEZs3b8YTTzwBQRCQmpqKlJQU1KlTx9YhEJGNaXV6vLFyN+pHP2ST/dVpEIXN2/6LzLuFCPD1xJQn2sNTbZqg37idi+MXruPljz+BnOs321XjNl3QuE2XUuVZN69j1d39vYsAACAASURBVHuj4Ye7CPT2QffVOXi+iRFeZr5LEVH0xEShXETv12fDr2bt0pXIajK5AmENmmDwvzeglp8Xhj7WDK0a8O/mg4RGtkToG4ulDoNcFJN8IpKazT8VDxkyBF9//TVWrVqFHTt2YOfOnYiKikJUVBQOHz6MYcOGoVGjRti+fTsMBgNat7ZNbx4R2c+CzYdw1eCL3q++ZZP91Q5vjIGT5uHmlRScOP4LXpq3BcvG9oW3p6qkTtKldHhovJi8S+hi8lEIRh0Klb64mC1CZxCQ+IcMfnItBAFQCKY99qJMCYgiLiYfRcvOj0sUtWsRBAF9x8xGyp+/I+/Obby2YBU+GdkT7SJDpA6NyK1ERLWy+vH6iKhWdoqGiNyZzT8Z161bFwsXLsS0adOQk5MDlaroA/m0adPw8ssvY//+/fj1119L1oEfM2aMrUMgIhtLz8pHeMvuZT7GXhE1g8NRMzgcjdt0wZ7VczF03masHN8P3p5q/Hj8Aqat+hG9R84sf0dkN5FtOkOn00KvLSwpu3rmGDIunoJWW4jW9WuhU3RdCELRo91Hzl3DmRxvRLbpLFXILkml9kTjv4+pf1AoRi9+F4mv9YDRzJAHIrIPTmxHRI7CLl1bHTt2xO7du5GUlFRSFhMTg2+++QarV6/G1atXER4ejhdffBFBQeYnRSIix/DLyUv48XgK4rs1tsv+BZkMXZ+fiH3rFqDb1FWIjY7AgeQ09Ht9FoLqNbFLm2QZD40XYrrFmRY+/i8AQN7dLGxMmIgmuQZMefJhCIIA1a5jyL7mBQ9N6cnwyDZCm7RC75HvYehH46HReMGHQxWIiIjcit2eTVWpVGjVyvTRoUaNGuG9996zV5NEZGM3snIwdukO9B39AQJC6tmtHUEmQ5fBY9GwXXdkZVzFoL5NUKN2Xbu1R5WnqeaH+IkJ2DRvMnL+uxev9m6N5LSbkKmrSx2ay6vbsBnGLNwMAFB5aCSOxr5ysm7CoNejWo0AyGTmJ1EkIiJyJ5VK4MeOHVvpAARBwPz58yu9HyKyvezcAlTz9auSZeMEQUBwg2gE23mWaLIdT29fDJiYgN0rPsBTszfCr1Ywer72rNRhuQVXT9wB4NCW/+LID19DpVSgZt0G6D3yPSjVHlKHRUREJKlKJfA7d+6sdADFYyeJyLHkF+rwwZf7Uadh8/Irk9vy0Hijz+gPpQ6DXIgoijiwcQWuHduNnbOeR3VvT0xZvgtbPpmGvq/PgkrtKXWIREREkqlUAj969GhbxUFEDua9dT8jxzsU3QePt3tbG5bOQoqVs/tmZVwDvPztFBERSeXUb7tx/dhurJ0Sjxo+RU8afDzsMbyxcjd+WjMPj71UejKxitxDIqJaSToxWVbGNSSMedJseWFBXqlyXWEBAODIzH6lXpOpPKHyDTApK8hIRVYZ81E44/Ei11fWeVnWNQEAag8N/AJKz6fF85VcGRN4IjLryq1cNOr2FGRy+487TUk+ilt5Wqu20RXkQckEnsjl3L2dga4twkqSdwCQy2QY2L4xPth62uw2FbmHWLskmK0VFuSZjVmv9AaU3qXKFd4iDFnpkPsGmt2fwWi6KoHcNxCFd2+YreuMx4tcX1nnZVnXBADkZafDaO5c5vlKLowLLBORw4gesciieklLRpn9fyKSltTXY0XuIbZUkf3eH3N62gXojSI8A0JNylM/GwEIAmoPMZ03SH/7GmDQQVCqTcozNpY/tEXq41VVnD1+d2PpNXF55VgoA0JL1efvm1wdE3giKiW/UIfrmXcQInfcW4TaQwM/jcqqbSKiWpVfiYisFhHVyuoeL1e6Hivy/rNyNTBas4Fg8p/7XhOgkJm+IsA9Jjt8EHc/L4nINTnup3MikkSBVo+XE7fCJywaIY1aSB1OmfwCgjBhwddSh0FEgNuPNa3I+08Y86T1j7EDUKpMe9r1MhkUCjUC71vq86ZKDT+/albv35W4+3lJRK5JJnUARORY/jh7Bel5Ah4b+kaVjH8nIiIiIiLLMIEnIhMX07Pg7VMdMhmTdyIiIiIiR8IEnohK7Pj9L8zffBgxA16ROhQiIiIiIroPE3giKrFgyx/o8fKbCAxrKHUoREQl8gp1WLTtCGrU5b2JiIjcGyexIyIAQE5+IbLu5sLbr6bUoVSpwoI8nDq0D1FtH4HKw1PqcIjcxoals5BiZoZwUVeAQ8YCbP7xcNHPAG4XAAaZAuK5m/h9/75S26SnnYdQrRbS0y6Uek3toYFfQG2bx+/MsjKuQa/0Nnu8zNFrC5GVm2nnqIiIyBJM4IkId/IKMDRhC8JbdkSNoNDyN3AhP65fhVMHfkD65RT0HDxS6nCI3EZK8lGzs7ALEOAFAYV5Roh/l2mhQL5RCV1mKpT+IaW2MRhFyAHojWKp11CQZ9vAXUBhQR6g9DZ/vMwQi7chIiLJMYEncmPf/f4XzqTdxO7jl+DbsB06Pz0agmB2lWGXlHXzOk4d3AOfwms4dXAP2vUYCL+a7KkjqkrRIxZZVC9pyagyt0laMgoGowjPANMvIPMzUisfoAu7/3gREZHjYwJP5KaW7/gDK/acQuMOvRHRtROaPtxT8uT93g/oVeHg9+uh1mXDQ25EoS4bB79fz154Iid2eeVYk5+Nei0EFK2J7qjuv+/ptYUw2y8uigBEpC4bUeqlir7H+48XkSOw+JoAoMtOr/LPDkRSYwJP5EYuXr+NF+duwrWbtxEYFIyBk+fDp0YtqcNCRFQrwMxY2HK3qYTi3vfqhjt4t38opm9OYy88kZPyDo1GTmpSqXJddjrkMgE1QuqXeq2y95DKKuu+l5WbafZxdYNeBwEA7t4o9ZrKQwM/v2rm2zBD7aFBXnY6lBb2wOuy06HReFlUl6iirL0mAECj8YKfRmV+X0Quigk8kYszGkV8te9PZN7Nw9ofk9G630sY3Kk3AEje414sfvj0Km+zuPe9R6QPWod5o0ekDzaeZS88kTMK62P+mk1aMgo1NCpMWPB1FUdUPinue8X8AoJgzNNaNXzBXJJEZEtSXhNEzoTLyBG5MIPRiKkrd2HF/ss4VBiOTs9OQrPOfSAIgsMk71Io7n3XGO7gmZgAAMAzMQHQGO7g1ME9yLp5XeIIiYiIiIhKYw88kQs6kJyKMUt2IDc/H/UimyNuwodQqblEWrF7e9/r+BX1KtXxU7EXnoiIiIgcGhN4Ihfz858XMWHZLvQa/i7q1I+CXKF06972+9079v2ZGNNxsc/EBOD70+c5Fp6IiIiIHBITeCIXsvfYBUxZsQd9Rr2P4AbRUofjkMz1vhdjLzy5kw1LZyGlApNHcpyqa8vKuF5qwjC9thDpmWlIGPOk2W14XhARVR0m8ERO7k5eAV77ZDtOXUyHQqlE/9c/RFC9SKnDckgP6n0vxl5455Z6+hh2Lnsf2sICAIBKrUb3l6YjPLq1xJE5npTko7iVp7VuIysTfnI+hQV50BtNF+0SARiMYtnni4OfF9qCfOxY+g7Szp0qs45CqUTX5yei4UMdqzAyIiLrMYEnclJ7j11A6o0srD9wFj4NYjBs5FDIlSoolJwpuCwP6n0vxl5453Ux6Qh++Px9zHutO1rUK/ri5eTFGxiz5D007fYEPLyqIbp9d6g8NDZv+0bqOciVKvgHWbYsV0VpC/ORduoY6rWItdnQGGtmIif34XnPEnMyhQrKgFCz54oznBenD+2FP7Kxdu5LKOuy+TMlHROWL2ACT0QOjwk8kRPacuA03vvqABq37oy6HQaixaNxHOdejuLed199NkT4YvVvpddSLiYC8NBnsxfeifx19AB++Pw9LB7dG20aBZeUxzYJwfLx/bDxwJ+4lJKNTb/tRP+x/4Za422ztlNPH8OOT2dAkMnQ7/XZqB3eyGb7vpe2IA9bFryB9NTz6DDwZbTsOtAu7VS1iiSAzpA0OgNzx1GvLYSIoqTdVRgNeoQE+KKaRl1mnQbB/jAY9FUYFRFRxTCBJ3JCTUIDoIAR/qGRiH64h9ThOIWLyUchGHUoUPhi01kA0D14A4UvBKMOF5OPomXnx6siRKqgU7//hJ3LZ2PZ2L5oUT+o1OvNImqjWURtiKKImWt/xmeTn4JapUK7/kPQ4tEB1rf322788vViGA0GAIBoNGDR6D7IyS/EtMSp6Dv6A9SpH1Xp93WvwrwcbE6cgrYhnnioUxf8L7nsR4GdRURUK6sfvTaqwyCTW/fRJSKqlVX13cGDjn16ZhoMRhHKANOnSbxDOa8KEZEjYAJP5IQaBPtj9eQBeGHOp7idngbv6kVrmXv7+aNBq4cljs4xRbbpDJ1OC7220OJtFCo1Itt0tmNUVFlnDu/D/i8TsW7aINQLrP7AuoIg4P+e7YyxcW2RkZ2DVxLXIuvGVfjWqmtxe7lZmfjr1y1YPrYPgmv6AgA8VApo1EoAgEIuw+SFb6LPyPcQ3LBpxd/YPfJz7mBz4mR0qu+HdwZ3wqEzl5F6Zg9up19B9cDg8nfgoDjpmXQedOwTxjyJW3lai4dWEBFR1WICT+Sk6tfxx5op8Vi16wQMV08DAA7sTkPGpTOIjRvKR+rv46HxQky3OKnDIBs6dWAXDnyzGF++NQjR4YHIybHsyxk/bw/4eXtg3dSBWLbjD+iupljcZoBMwP9NGYiI2ua/LHikRT3Mf02OcYvfQq/XZiA0sqXF+zYn7242NiVMRPfomnjjXx0hCALaRYZgXP+HMG/ueAx+53N4evtUqg0iIiJyHkzgiZxYeO3qmPFcl5KfM+/k4YU5m/D5z1shk8kAAPWat0eXZ8dBJpNLFSaRzSX9sgOHNn2G/06OQ3R4YIX2EeRfDe8826X8ilZ6ODoMn4zohdeXzECPYW9XeAb83Ozb2DhvAvq2rIOJA9ubfCn3VJdm+M/uJNy9dYMJPBERkRthAk/kQvx9NNjwzr9w+24+AEBvMGLaqj3Y8em7CGnWHgAgkyvQ8KGOUKk9pQyVqMJO7NuCo9tW4YvJ8agXVEPqcMyKbRKCJaN7Y+TC99Cy12B4evuhUeuO5c6AL4oiLpw4iLw7t3Hs+y/xZGwYXu/fzmxdPmRDRETkfpjAE7kYlUKOwOr/zLC9bGxfLNh4EBnnfwAAXLl5B2d+2Yq+r39ol+W0iOzp2J4N+HPnGqyZMhBhgX5Sh/NAbRoF4/Nx/bD8+59x+PQlnNz7LQZMmAuPMmbAF0URBzYsR9ofu9C8XiBiw6vh5R6Vn4Btw9JZSDEzYdnVlNMwGo04MrNfqddkKk+ofANMygoyUpGl8ap0PFWtrPf/IBFRrawao18VbdjSg+JNTzsPg1EsNUO9d2g0wvo417Kaxe9T1BVCZczHvkPHy6yrN4q4U1C0DednICJHxgSeyMWplQpMfvKfie2MRhFv/3cvVrwxGJ4aDVo//jyaduIs684mK+Mavv/sXeTdzQIAyORydHhyFBq07CBxZLaXlXENPyybidw7t6EUjFj7xkDU/XsCOUfXvF5tnLtwESkXLyFYq8Wq6c/Cw8P80y9GoxH+3ip8NW0QPtu8H9sPnMKCbxR447nulYohJfkobuVpS5XL/epADgEw05Ovz0qHwSia1vcNROHdspdfdFRlvf8HsjIZr4o2bOlB8QrVakEOlPr956QmVUFktlX8PgWI8AaQn2uE2RMegAARBVBa/UUMEVFVYwJP5GZkMgHvD3kUo/q1wc3sPIxevBzZmddRPTAEAODhVQ0RzdpyEjwHdjv9CjYkTMTwnk3RtWXRLPlXbt7B2KUfw2gYj0atXWfm/OL3+lqPpujWqhNq+mjg+feM787gyOlU/HEmFcFeRly+cg3/efMFNIkovdRdsUA/L2Rk5WDHwSQIhdnYcTAJz/eMQXBA5Z82uH9W8cvnTwEyBVQBYSbl1/4zDqqaIag7NNGkPHXZiErHICVLZ1WvzBrzVdGGLZmLNz3tAvRGEZ73LCN3eeXYqgzL5qJGLIFo0MOQe7vMOoJcgdP/fQvO94wJEbkbJvBEbkgQBNTx90Edfx+smToQC7cchjaz6NHC31Ou49pfj6LDoFeZxDugzGup2JgwEePjWuOpLs1KykMCfLFqYn+8lDAfBp0OTWK7SRilbWReS8WmeZMwrv9DJu/VmbyzYjt8lEZ4KgEfpRGJ3/yIbf8e/sBtVu/8HaI2HyqZEVptPlbv/L3SvfAPYtSb9sSKogjRoEN+Rqrd2iTHd+/v36jXQped7jBfPFSEIFdA4RNQfkUiIgcnkzoAIpJWSIAvPnqpG+a92h3zXu2Or998ArdP78d/pw/G/957BemXzkodIv0t40oKNsydgCkDY8wmtE1Ca+E/k+Lw2zeLkLR/Z5XGduHEQWxdOB35OXdssr/i9zopvo3TJu/Fve9+HiLm9vSCn4eIP86k4sjpshPjKxlZ2HEwCTJDPub0rQWZIR87DibhSkaWzeOTCUUfAYQy/ilkgsk/AYCa82a4PLWHxuzvXi4TUEOjMvsvIqryczUQEZFl2ANPRCaqe3viq+mDcPnmHZxMSccHiVMRO/AVeHj5oF6ztpArnOfxZVdyI/UcNidOxVtPd0C/2Mgy6zWqWxOrp8RjwIz5aNi6U5VMVHj26H78+N+P0blpKDbOnYC4CXOhqVa5Meq/rJ2Hsf0ewqCO0TaKsuoV9773bqjEo/VU6N1Qi7V/6vHOiu1l9sIX9773jdQgNswTfSI12HLWPr3wcqUSolGEUqU2KRcEAXKVGoEh9UzKb6rU8POrZtMYyPH4BdQuVXZTpUaNkPqYsOBrCSIiIqJ7sQeeiEpRKxWoH1QDcR2aYM6wbjAkbcdf2z/Dd0v/D3qdlRM1UaVdv3gGm+ZPwbvPdnxg8l6sflANqFVKFOTetXtsZw7vw09fzMGKCf3x8Ss90LtFIDbMGVfp88SgLUTTCq7v7gju7X0fHlM0ad3wGM8H9sLf2/s+NKboC5ChMb527YUnIiIi58IEnogeqHOzcCwa2QtfTx+EEOUdbFv0JnTaQqnDchtXzydjc+IbmDXkEfSKaWTxdk91aYZti95C3t1su8V26sAu7F+XiFUT49A0PBCCIGDiwPYwFNxFTlam3dp1Bvf2vterIQcA1KshR++GSvgojXhnxfZS2xT3vveJ1KCuX9GTLnX9lOgTqYH491h4IiIicm9M4InIIkqFHPNf64mG1bTYtnAatIX5Uofk8i6f/RNbF76JOcO64bGH6lu17aRB7dG7eS1smDsOudllz7xsrfycbJw7uh+Htq/Db+sX47+T4xAZ8s/EUIIglIytrgy1ty9+OHqh0vuRgrne92Jl9cKb630vxl54IiIiKsYx8ERkMYVchjnDHsO0lXuw7ZNpGDAhAYKM3wPaw/WLZ7B98dtIfK0HOkSHlb/BfYp7w5Xyg/h2zjjET5wLb7+aFm9fWJCHU4f2IartI1D9vW753ds3sTFhAsKqe8DHS40vJsejXlANs9tX9hH6ri9Ow8aECRCEAxgfH+tUKyKY630vVtwLf/9Y+HvHvhf3vhcr7oW311h4d3Vp22Kza5sXZKQiXSYgYcyTpV6LiGqF+OHTqyI8IiIis/jJm4isIpfJMHtoN1y+cIaP0ttR2pnjiIttVKHkvZggCBg7IBbPPFwP6z8ejzu3bli87Y/rV+HHrz/D3vUrAQB3MtOx/uOxeLZjfayeHIdFI3uVmbwP6hiJnZ/NqFTPv5dvdcRPmo+tx67jo6/2QxTFCu+rKj2o973Y/b3wD+p9L8ZeeNvLSU2CwSiW+if3DYRQrRZu5WlL/UtJPip12ERE5ObYA09EVpPJBPh4a3Dylx2oXjsYdepHQ+3pJXVYLkcut813rCP6tIFOp8fedYnoPeqDcutn3byOUwf3oFrBNST/+j1qB4fhj+++wLDHovFSz/KXi3q9f1vk5Bfi128Wo/vLb1Y4bk01XwyYmIBvZo9Cp6apeLgSX2ZUlQf1vhe7vxe+U/P6Zfa+F7u/F37Qo62QfusOVLzuKq3u0ESTn/MzUqGQCaVm4XfmNdCJiMh1MIEnogpZNrYv5m74AX8dzsdvWgXixs+Bt1ctqcOiMrRpGITvkpMtqnvw+/VQabOgFZQQC+7iz+3LMSEuFk90smxJN0EQENOwDn7bd6UyIQMAPL19EFAnFHkFukrvy96Ke99DvEUYRWDu/rwy6xpFwFct4sjpS7hx6w68xHyI0GDZb2X3rosABH0+tvx6EusPXUTnp8fALyDI4vjuT0D12kKU9VyDLjvd5RLW+99PQUYq5L6ByM8ovSKArdpwdM4Wb0W5y/skIvfABJ6IKqRJaC18PrYPRFHEnG9+xbqZw1CzVtGyXwaDsaSeTKFE+ydHIzCsoVShEoCosEDoc37CwS3/Qdu+L5Q5pjzr5nUk/7YbSr0WIdU9cDfrFoRCGdpHBlvVXssGQbjz5S848v1XaN3jX7Z4Cw7vq73HIBdEZBcK+PKk3oItBMggIvNODjTVPbH1rAjgwV9U6GQa3MoHmnZ+BE1iu1kUV0RUK8DMo99ZuZkoLDD/JYNG4wU/jcr8vpxMWe8/XSZAAKCQlb4W1B4am7RR7jYScbZ4K8pd3icRuRcm8ERUKYIgYNITHdA7pgFkiqJHvvPy/5nA7EzaTSz+dAaGzFojVYhOSe3pjSN/XEdeoQ4atfnHqq3h5+2BtVMH4oWPN+KATof28S+bTeJ/2/kthIJs+HkqsOlpL8zbV1ChydMCfL3w8bDH8Nzsz9Dgoc7wrVm70u/B0Y37VxfkFRYiv9DypwUUchma168DD3XpZNmcJduPoUm7LujyxCsWt+Huk66V9f4TxjyJW3naUo/K27INR+Vs8VaUu7xPInIvTOCJqNIEQUB0eCC8vdUAgJycfya3Cw+sjnkbuX61tZp27Ikb50/gpYTN+GJKPBQ2GA8f4OuFNVPi8cLcTTgolyM2bqjJ67fSryD5561QGHVYE+8Nb5WAoTG+2Hb6GnYcTMLzPWMQHOBnUVuX0rMwcdkPeGzwaLdI3gEgyN8Xn4wrPXO5LS3d8SceeWIYPDQc+05EROSOOAs9EZEDs+XyaV/sOYFRS7/HndzSqwcY9HpsSJwGhaEQzzYxomFAUY9w8eRporaoF94S56/dwrP/Xo+WfYaiZdcBNoufiIiIyN0xgSciu1Ir5dDptEi/dFbqUJzKyV92wHD9NJaP72eT3vcTF65j4dY/ENHzVTz22ky06/+iyeu/bvkvcjOvwV/IxivtTJcys2QJM4PRiOPnr+HH4xfw/L83oE3cq2jWuU+l4yYiIiKif/AReiKyK29PNT4e1gNvJk7FgPFzEGCD8abuoDA/F60b1rbJ+HcAyMkvREBQXUQ0jTH7ekrSH1CKOvRtUnops/uXMLt/LLzeYMSU5btw6PxN+Pj6of1TYxAZ84hN4ran3AItdh48hV7tmkDjYdkYdFdqX2obls6yel31iKhWbjuumcfLfW1YOgupZ44BAAyGstaOMOUOv3teE+SumMATkd11b90Av525ggsnDzGBd0BZN68j8/pliEYZWtT1NFunrLHwOr0BEz77HhfzPTF4xgooVeqqDL1SPvlmH7Yf+BN/pd2waoI+V2lfainJR3ErT1t+xXtZ+WHdlfB4ua+U5KO4nVc0OaZY5uKP93GD3z2vCXJXTOCJqEqolXKpQ3AqSrUnTpy4gRMXrpeUNQz2h6cFPfIZ2bm4lnnXpGx/choUavNLYx38fj08jLmIDZfhjb169GwoQq0wHXtvrhdeq9NjzNKduGrwRe9R70KhtH0vcn7OHdy4cgkaD+uWsSvPlYws7DiYBKEw2+oJ+lyhfUcSPWKRRfW4lncRHi/31XTUYhiNxnLrudvvntcEuRsm8EREDqhZx8fx8+W/MOmLgwAAnVYLX5UR66YOfGAS/2fKdQybvwW+/rVMytUaHzw6ZGqp+lk3r+PUwT3w1Wcj0MsXWr2IT3/LhqeZvw4iAEFfNBb+qa6t8P5XB3BLHoDeI96BXGGbR/3vlXc3C5sSJqFPqxB0iAq16b5X7/wdojYfKpkRWq31y+Q5e/v0D36oJyIiZ8IEnojIAckVCjzy3MSSn0VRxLoZQ3H2Siaa1zO/LNux89fwauJWPDpkChq07GBROxeTj0Iw6lCg8MWms4ARIhb8AVSX62BuAvwCmRfy8kU8P2cTAhu2QM+X3oRcYds/Jcf3rMfV5ENIv3IJg2LrY3x8rE1n4y/u/ZYZ8jGnfy2M25xRpb3gUrdPRSKiWln9OG1EVCs7RUNERGQZJvBW+uSTT7Bw4ULs27cPtWu7x9rGRCQ9QRDg4e2DYxeumSTwOr0B56/dwuWMO5i+ag+6vTQd9Zq1tXi/kW06Q6fTQq8tWlrOaDTi7MHvEeFjRJdmISZ1ky5lYNeJy6jfqgsatIhFw1YdIJPbbmiEKIo4uPk/SDuyE5MHxsLXKwJtI+vabP/Finu/+0ZqEBvm+cAJ+uxB6vapCCeyIiIiZ8QE3gq7du3C0qVLpQ6DyCkZjBZOvENl6vLcJCyZNwleHmoM6hiF/EIdhn+yHedu3IVG440er7yNsKjWVu3TQ+OFmG5xJmVeGg18L/+El3rHlpRtPnAK+3edxtPTF6FmnTCbvJ/7Hdy8CleP7sLaKfGo6etllzbu7f0eGhMEoOwJ+lyxfSIiInJuXAfeQmvWrMG4ceOg1+ulDoXI6fx+5jLW7z+Nug2bSR2KU/MPCsUjz0/CV7+cQW6BFi8nbkW+TzheeP8L/OvtZVYn7+ZkXkvFke2r0TGqjkn5tdu5UHt6wdPLp9JtlCX3VjoCfDXwsuOSasW9330i/1kur3iCPvHvsej2JHX7RERE5NyYwJfj/PnzePXVVzFz5kx4e3vDy8s+vUJEriq3QIvhC7ai56vvILhBtNThOD1Pbx+cS0tH/MwvYazZCN2GTrXZY+wZV1KwYe4ETB3UFr3bNjZ57dXHW+OJdqFYP2cscrJumXxfWQAAIABJREFU2qS9+z36wmTk+4RjWOJWnL1yE9dv3S1/IyuY9n77mrw2NMYXMkPRBH1XMrJs2q6jtE9ERETOj4/Ql2PGjBk4dOgQHn74Ybz//vt47rnnkJubK3VYRE4jr1AHmVyBsCYPSR2KS6gd3hhtej0DvTYf7foPhSCzzfewN1LPYVPiFLzz9MPoGxtZ6nVBEPB6/3ZQyuX44uNxiJ+YAJ8atczsqeJkcjkeG/oGfvnfIgxbtAt3s7Px5jMdMaijbb74uXfseXHvdzFzy+TZmtTtk3PYsHQWUsxMrpeedh5CtVpIT7tQ6rU7B9dDm5FiUlaQkYp0mYCEMU+abSciqhXnASAickJM4MvRtGlTDB06FF27dpU6FCKno9Xp8fZ/f0RYk5ZSh+IyBEFAm96DbbrP6xfPYMuCaZj5XCf0imn0wLrD+7SBSiHD5x+9jroNo1EjpBEe6vGUzWaJl8nk6PzMGADAretpmJMwEXqDEU91qdzwC3Njz+9nz7HoUrdPziMl+Shu5WlLlRuMIuQA9GbmE8m7chqCwnToidw3EAJgdl8ArJ6Bn4iIHAMT+HJMnVp63eSKWL9+PTZs2GBx/ckdPdG8mW3GC3t7q22yHyJLFRj0uJunxZsr9+K2shbiXn8XCjusE06Vl3Y2CVsWTMP84d3RI6ahRduMGdQerRvXwfVbd/HZdztw4G4mur8w3qZLvQGAd/0GeO6dxZg/cwRaNAhCm8bBD67/gHvd/776A9Dno1+UF8L8zY+xD/NXoV+UFzafzcf/9v6Bd156vFLx26N9tUqBzLQzUIQ2MCn38Q9wumtMLhcgQIDMwqdIBAiQywV4eznO3zR7xFJ8XJqOWmxSfnLRSOiNRngGhJqU52eklvx/3aGJJuVKmYDaYabnSvG+HO1YUtnk8n/urZZcL454rdiDK9xDnAGPl+NxqwR+4sSJSEpKKrde9+7dMXHixHLrWePKlSs4dOiQxfXvNGOPJTmnTftPYfJnP8C7mjfColoj7uUpkMvd6lbjNNL+Oolv5k7BwtG90LVVfau2fbhp0Uz0j7VugGdmrceO5R+h50tTLP4gZakagcEIqR+JjKycCu/j8o3b2Lr/JKDLhyh64bMDZY8xF0UAunxs3X8SL/WNRd1a1Svcrj3aTxzRE9NWJkKrN5SU6fUGePrVxDPTF8JD43zztJxcNFLqEJzK5ZVjTX426rXQZ12Hwq+2STJProfXink8LuRu3OpT9dWrV5GSklJuvYyMDJu3HRwcjLZtLV+bWe2hRE5OYaXaLO6Nqux+iCz13ZGzeHfNfjwxZT4C6tYDAOQXGAAYHrwhSeLqxRSolXLUqV6twvcJOQSsGNcPw+ZvwZr3X4evfyAiO/VDnfpRNovTYDCioEBfZozl3ev2/H4Wep0OgtwTm/8yAijjkeJick/odTrs+f0sBnZpUZnQbd7+Q/XrYOf7z5qUiaKIpz5cjzPHD6N+81g4i9DGLWGw8jHu0MYtkZMr/d+04h4pe8RiMIgQIcJoNJqUe4VGISe1dCeELjsdMOggAFDITJ+CUXloSu0HAESIMBhEhziWVL7Qxi2BM8cAFJ0flm7j6r9fZ76HOAN73ueoctwqgV+3bp1kbQ8cOBADBw60uH7O93PsGA2R7X3z00nM3XQEg99cAE8/82N8ybFEP9wD2oIcvDzva+z84LkK78fbU4UVE/rj+8NncTsnH4sXTkev12YgNNIxniTqHtMYBVodCnSWLwPqoVSge0zj8is6QPuCIMBb43yPOHICNeuE9THfy5i0ZBR0mWlQqNQIDKlXxVFRVYgfPp3JlBm8h5C7cqsEnojsY93eE0jcehTPvbUQ/kEh/IDhRKI69MSvG1ZUej8atRIDHi7qdY8Kq4XRi2egx7C3EB7dptL7rqxqGg8M7i5dHFK3T0RERK6D68ATUaWs+v4YPtl+HIMmzYd/UIjU4ZADaBcZgiWje+OHz9/H+RO/SR0OERERkctgAk9EFbbsuyNYtisJAycnwq9WHanDoQq4dS0NSoXc5vtt0ygYy8b2xZ6Vs3H26H6b75+IiIjIHfEReiKqkCXbDmPtL+cwaMoCVKteU+pwqALSL53F1kXT8f6QR+2y/xb1g7BiQn8Mmz8HotGARq0726Ud+nv2enIJWRnXUJCXi6QloyyqX5CRChj1MLeI4KVti81OfFeQkYp0mYCEMU+Wei0iqhXHFruADUtnIcXKCd74uydyDkzgiahCPt16CM/NWM7k3Ykl7duIYd2boWcby9Z/r4im4YH4aGg3zNq8lgm8jeXkF2Lp9iO4k1eIkynX0eQpDmFxBYUFeZD7BsJgtOxbGblvIPSZaWZfy0lNMrsfuW8gBAC38sysiGBl0keOKSX5qPnf74Pwd0/kFJjAE1GFiKIIT29fqcOgShBkcmTlFiIn37JJBxVyOTxU1v/ZqF7Nk13EdjBp+W7ckNdGrXqtET9xJKoHBksdEtlQ3aGJFtVLXTbC6n3lZ6RCIRNKzVpvaa+/KxFFEdqCPJMyQZBB5eEpUUS2FT1ikUX13PF3T+SsmMBbac+ePVKHQOQQmI85v4d6DcbWT6Zh7V7LZqE3Go34aFh3PB7TyM6RkSXSMu6gw4vjUSu0gdShkB3kZ6Ravc39SVhBRirkvoEV2pc7MBoM2PnZuzj35++Qyf6ZC0Rv0KNdr6cRG/eidMEREZWBCTwRWW3RlkPw8w+AQqmSOhSqBN+atfHsuystrn8j7TxmzJ8Cnd6A/u2b2DEyIvel9tBAC0AhEyyqLwBQKFWooSl9P06XCUWvm9mX2kNTuUBdQMaVC7iddgaHFw6HSvnPR+LMO3noOO4ztO33vEliT0TkCJjAE5HFRFFE4sbfsOHwZcRPTIBMzg827qRWSH0MmDAXs+ZPhlZnQL/2kVDK5ZBZmGgQUfn8AoJwK09b6vH2stxUqVEjtAEmLPi61GsJY560al/uRjSK8NZ4mCTvAODvwy83iMhxcRk5IrKIKIqY882v2PTHVcRPmgdvP3+pQyIJ1AwOR/zEBMzbegIPjVyC3u+sRfrtHKnDIiIiInIL7IEnonKJoogPvvwZe8/cQvykeZy8zs3VqB2CFz9cAwD4/bu1GPzRenwxOR5B/tVK1dXq9Fi87QhqBLMHkIgcQ9qZ47iUdBgZl04hum4NqcMhIrIKe+CJqEyiKMJgNGLGF/uw79wdDJiQwOSdTMQ8PhiNujyBZ2Z/i7SMbIj3zG5YoNVj+MLvkC74o8tzEySMkoioyM2rl7B+/lS0lJ3HU8198cGQR6UOiYjIKuyBJyKz/jh7Fa8mbsbd3HzUaxyNuPFzoPb0kjosckAP9XgSMoUSPactgcFgwLPdW2PSoPYY8cl25HgFo+dLb0Ku4J8bIpKeX0AQIiJb4Nz1O5gXFwuVgnO5EJFz4ScqIirl0OnLGLX4O/R45W1ENG1rs/1uWDoLKclHrdomIqoV4odPt1kMZB8tuw5Ay64DUJiXg82JU7Bm+EK0aN8VPV6cyskOidyUI97zFUoVeo98Dzs+fRejF32HhSN7lZrEjqThiOcLkSPiHYuITBxITsWYpTvR89V3ENbkIZvuOyX5KG7laa3byMo/5iQttcYbcePnIPNqKgLDG3IJJiI35qj3fIVShceHz8DOZe9h+CfbsXh0b3io/vlInH47BxC4ukZVc9TzhcjRMIEnohI//XkRE5ftwuMjZiKkUXO7tRM9YpFF9ZKWjLJbDGQ/Kg8NgupFSh0GkVOz5f1P6nupI97z5Qoler36f/j5y0S0eO0TyOWm00J1in/RJb6AlPp3XxGOeL4QORIm8EQEANhz7DymrtiLPqPeR3CDaKnDof9n7z4DoyrTNo5fM0kmnRRCTSgBaaEZNQI2FARULCR2VJQir2ClqYusdcVVAYFIUVB3l6KoC0gVGyqKVBEhgCIEQqihBEidzGTeD2zAmMIkJDNzJv/fJ3LqnXAmmWvuc54HQI0UGxdf4a5ibFx8tR/LG5l9fNT1vuG66u4nii03mUyGf/SH/3vAexHgAWjFhp36+6zvdcsTr6lBLJ1TAHCXqnyel2eDneONg2zyfw94L+/7jQXAKSeycjV48lL9nnZYloBA3fbU66rXuIW7ywIAAABQBgI8UMN8ufEP7T96Sh//uEN123bV4CcfkI+vn1d2IABv1rhOLW1YPkd1Y9uqcZt41W3U3N0lAQCAamY+/yYAvMWUxev08icb9PXRCF103b264vaHZQkIJLwDBvTmwOt1df0CNTi2VgsnjNSJw+nuLgkAAFQz3rUDNYDD4dDEBWu0cGO6bn96koLDIt1dEoALFBJo0fCkLpKkvRlZOn44XRH1YtxcFQAAqE4EeMCLpR46odUpe7Ut7ZhWp55U0qiJCgoNd3dZQLXIzrNqxdrtuqFTGwUFWDzmWK7AlNWobgumj1VqBUc1z8w4KAXXLrF879KpykpLKbE8LyNNh80mTXjizhLrYuPiGZgNAESAB7xWyp7DGjhxsZp26CJLYAsljuirwJAwd5cFVIjD4VBO9mmZTHXPu23yp99p2U9b9Pu+I3r2/h4XdN6qPBbgDVK3bdLxHGuF9inIy5FfKQE+Ky1F9kJHieU+YfVkkko/TwU/PAAAb0WAB7zQr7sP6eFJS3TNfcPV8tKr3V0OUCmOwkJ9/1GyAgpOqnObRuVum37khD5fmyJT/kl9vjZFD/RKUHSdyt1tsj8js8qOBXibtkOmOLVdyrRHz7tNTP9Jxb7OzUiTr9mkeo2aVfhYAFBTEOABA8vKzdeKDX+osLDw7LJcq01vL96gbg89q+YdO7uxurLxZgzn4ygs1MrZE2Q7mKL/jLhNIYH+5W7//pI1clhzZTEXymrN1awV6yvdOZ+1Yn2VHcsVfk8/qm17DqnZbSU7nYAn+Ovv/LyMNPmE1VNuRpqbKoIn4z0CUD4CPGBQJ7Pz9NCERbIF11No+Lk37g5Z1GvwC2rcJt6N1ZUuNi6+wrdBxsZ53veB6lVYaNfX/3pTPsd26oNhtyoksPxn0NOPnNCSH7fKbM/VuFvr6qlFGZXunBd136viWK6wPe2IBkxYrCvuelz1GrdwdzlAMf4BQQoPKvn6PWw2ySTJ11xy8Ab/gCAXVAZPxHsEwDkEeMCD7diXoWHvfqms3JLPA+bk5qntlTfoqruGymSQEayMMABRVuYxrXjnBZ06niFJMpnMSrjlIbW9spebK/N+e7f/rO9nT1BeXq5aRUfqnWG3KMjf77z7vb9kjQqt2bq5dZA6NwlU79ZBWryzcp3zou57VRyrum1JPaRBE5fomvuGqeWl17i7HKCE8DoNNHzyJyWWT3jiTh3PsZa4VR41mxHeIwCegAAPeIDc/AJ99fMu2f90K3x+gU1vLVynzrcPUUyrjiX2MZt9FBJe2zDh3Sg2LJ2lLo0DNGhwH0nS0ZPZenTqu7LbrOrQ9RY3V+fdVv7nTf39zsvVPra+6keGyMdsPu8++zMyz3Tfbbnqn9BAktQ/IUxLdxyscOf8z933Cz2WKzw/e5W63DGE8A4AQA1CgAfcLCvXqocnLVGmQlQrsvhI29c++Iyad/DM59i9lcOWr7jmUYqOqiVJio6qpTlPJ6nfuP+o0G7Txd0S3Vyh97LbChR/UUPViwhxep9ZK9ar0JqtW9sEKyb8TLc+JtyvUp3zP3ffL/RYrmC12VUnhg4mAAA1CQEecKF3lm3Qx6u2F1uWlZOnZhdfpcT7h8vkRMcRrtekXrjmPJOo+9/4UHZbgS7teZe7S4L+1DG35WrA5Q2Lrato57y07ntljwUAAFBdCPBAFbIW2PTt5lTZ7IUl1v2865C+TDmiHoNfkZ8l8Oxys9mssDoNuBXew8VEhenDZ5J0/5v/VaGtQAk33efukjzGoT2/KfPIgWLLgmpFqHHri6v1vGc75v/rvtvt5+aVrmjnvLTue2WPBdcb92iiMg7srdA+dRo20cgpC0osXzB9rFLPM5CWj8+Z39dF11xsXDzP79ZQzlwvf1Vot8nsU7G34C07XqZ7nni+Qvt4osr8vHh9AcUR4IEqkme1aciUZdqfZVZY7bol1vv411bSqNEKCqV758ny8/MklT5lWYPaoZr7TJIeGLdQvhaL4q+/07XFeaAd61bqh48mK6F18XnaN+w+qFbX9Km2Dzr+3DH/a/e9iLOd8/K67xU9lqtZ83PdXYJHyDiwVwot+Xv3vPuUInXbJh3PKTlw6J+dGUNdcuh/HxpVMJDAezhzvfxVwbF98qvd6Pwb/smurRsrtL2nqszPi9cXUBwBHqiEWV9t1pxvU+T407KsnDw1aHmxbn90tMw+Pm6rDZW36atPdXT3Vl199+1lblMvIkTP3NFFyd+ur/EBftvqL7Rm/nT9Z2QftWoUVWzd4RNZemDcQtkLCtTp1ger/A6T8jrmRZztnFflsVzp3q5xmvbeWPUZMV7hdUr/EKOmafzwNKe2S5sx5LzbtB0ypcx15v897lRYWMic1ZBU/vXyZ3++Xiqzj7eoyd87cKEI8EA50o5k6rd9R4st27zniBau26MeA5+TX8C5W+FNJrNq12/Ec+wGteHzj/T79/M195kk1Y8MLXfbqFpBOrQvVZkZBxVep/SOrbfKzTqp9N+36PjBNG379lPNGtlHF0XXLrFdvYgQzX06UQ+M+0yrbQW6ImlQuSE+/fctsubnOzVtXFHH3GTLlUNBevenTElSYaGjxLYOSSZbbpmd878ea8aazDLPe75judr93TrIbJKS3xymxBHjFVEvxq31oGaoykBFOAOAiiPAA2XYvOugBk9equhmbWQynwseZkuAkkZNUmhEVDl7w0h++fq/2v3DQn307O1OjYDesXkDPdY7XlPGDVPi8PGKqBftgird7/SJo1owfphiowIVFhSgOU8nKbZ+RJnbR4UFa87TiXpw3EL9YC/QVXcOKTXEH0zdoaVTx2jKozcpNKj0xxf+bO22vSq022XyDdSSnQ6ZzGdux3SUHHriDN9AOex2rd22V0ldi4fuvx5LKij/5OUcyx36XtdBfj5mjR83THeNnsbvJVSb2Lj4Ct/KHBsXX+3HAoCahgCPGu90Tr6efv9r7Tt6utjyg8dOqseAvzGNWw2QvvUn/e3uKyo0fdn93TvKz9est8Y9pT7Dx6t2g8bVWKH7nTx2WAvHD9eD17bSwzde6vR+kaGBmv10oh6asEhznl+jgKAQXXXvk6rftNXZbfwDg2X28dWRzGynjtkjoZXyrAXKK7BJkgL8z/wpy8u3lblPgJ+veiS0KrH8r8dyRlnHcpc7r2mnxet368i+PwjwqDZVOYgYA5IBQOUR4FEjHDx2Wr+nHy2x3CGHJi3aoKAm8ep8U+9i6wJDw0rMyw7v5VOJRx/u7tpeP2xN0+r/Ttctj42thqo8x9rP/qVmtQM06IZLKrxvWHCAPnwmSbsOHFPK3iNKnvEP9Xt11tn1kfUbKXHEBD0/dqiu6dBUESGB5RxNCg0KUN8el539OiTkTNc+Kyu/wrX99VhG5evDozsAANQEBHh4vR37MtR//CLVbdSs2K3wRep2uF6X9b6fadxQYVMWr9fm9NPqM+Jld5dS7a668xF99tZIvTbvB/3t7qsq/HoJsPiqbdN6Cg3y14TPNpRYH1k/Rn5+FlkL7FVVMgAAgNchwMNr5OQX6Ll/f6u9GaeKLd93+Liu6fuUWidc657C4HUcDocmLlijhRvTlTRqkoLDIt1dUrULCg1T4ogJ+mziKBXM+V5/73uNzKV8IFaeE1m5enzaCrXpXHwE90K7XV++P1ZtGkUpKiyoKssGAADwKgR4GNKvuw8pK/fc7bIOScmLNqgwqqU63jm42LadQ8JqzCBjqF6Hjp/W7oPHtfLXvfpqW4YSR76l4FplD+LmbQKCQ9Vn2DgtnvyMnp+1Ui8/cJ3TIf7YqRz1G/eZouKuUpekc69Ru82mL2a+ovD8Q5r65M2VepShOmTnWbVi7Xbd0KmNggIs7i6nXIdPZOmP9KNqHOj8GA6ebMH0sUqt4ABndluBvH3yzsr8XGLj4qv9eXNPrcvd9i6dqqy0lBLL8zLS5LBZZc9IKzEKf0jjtmrSe6irSgRgUAR4GIrD4dDbi9dp7nc7FFW/eCiPuqiLuvQZyDRuqASTCmzl37q9be8RDXhrkeo0bCL/0AgljnxLgSFhLqrPc/gHhejWp97U4snP6tkPvtZr/budN3Rn5ebrvjcWqGH89ep8W/9it9+vmPGS6tiP6u3Hb5LFz3P+JCV/+p2W/bRFv+874hFzvpflwLFTuv+NhWpzXZKiW7RzdzlVInXbJh3PsVZoH0dhWVMQeI/K/FwqOtJ7ZXhqXe6WlZYieylTW/qE1ZMcDslkKrG+tMAPAH/lOe+WgL/YuHO/JixcL5v93B+4/IICZeabdfdz02vEbctwjdhLu2nMf97VjC+2qFPLBhqW2KlYyPx19yE9PGmJut4/Qi0uucqNlXoGS0CQbnnydS19+znd/MJHiggN0hO3XKrObRqVuv3ugydU4BOoLn0GlFi3beNP2jLjCVl8q7Z/OuurzVqyYXep61rFROrv91wlvzLOeXZu+PyTHjHn+5rt+zR58cZSw0DaoeOKv7Gv4q+/ww2VVa+2Q6Y4tV1Nm0vcU38unlqXu8X0n1Ts69z/deBNvhYF1jk3e0n6B0+6ujQABkWAh1vt2JdR7Fb4IoeOZ+mluat05Z1DVat2vWLr6ja+SP6Bwa4qETVA26tuVN2mrZSbdUqL5yWrTaOd6h7fXFtSD+lIZrZemPWduj34jJpf3MXdpXoMi3+gbnninzqU+ptOnziix6cla8LDPXR1+6Yltv1p+75S71ZI275JQQH+VR7ef919SFOWbVL3h56Vj2/JP3Prl/xbc77erId6lT6i/qwV6+Ww5spiLpTVmqtZK9a7rQtvsxfqkcmL1e2BEQoJr11ifYegENVt1NwNlXmmtBlDquxY5QVNk858wOdQyQ9VUDP99XrJy0iTT1g95WaklbnPn9cV2qwqOHm4xnzAUVO+T6A6EODhNu+v2KTpy39WVL2GJdaZTD664f9eVOPWF7uhMtREdWKaSZIaNG2pQ8dP65HkZdp1NFchoWHqMWiMmrY1/lRjVc3Xz6KYlu0lSbVq19fwKWP05sBuurZjs7PbzFy+UbO+36k+IyYU23ff779qxbsv6d2nbq3yurJy81WnQYyati19vvq0HR11OndnqeuKuu9me67G3VpXTy3KcGsXvtDhUEGBTW06dXP5uY3E188inT5SoX3qNGxS6vLYuPjz3uLt43MmwNv/d4dYbFx8hc4N71HW9XLYfOZjHt9Sxglx+FlKzORhkuRjNikyqPQxN5q3K/33mdE48/oqdR8AZxHgUe1S9hzW+IXrVWA/94xiQYFd6SdydffoaSU67IA7Nepwlf455QW179RVfV8YLbOPtw+NVTWiL2qrWx5/TU9OGKmWjeoqwN9P1gKbDmRalTRyokIj6xTbPnXTKg3s2VEJrWLcVHHpirrvN7cOUucmgerdOkiLd7q3C4/zq9f4Ig2f/EmVHMuZwdVCgv0lSVnZJe8gQ81S1vUy4Yk7dTzHqnqNmpW6/q+OWvwV2ah5mddx0TVndN4+eCHgCgR4VJk8q02/7csotizjZLae+/e3uuzWAWpQp0GxdVc0aVEjBwGDZ7so/ko9NW2ZzD4+MpsJ7xXRoFlrPfzGRzqYuuPssiubtFRgSK1St7f4Vc/PNyjAoqOHDmjXLz8pqFbJrnnG7q0KbhdaYvmfu+/9E878vuqfEKalOw66rQtvNpnk6+ujnT//UOot9EUi6zeSf5B3jELvKpkZB5V7OlOhkXXL/dkCAOBJCPCoEqdz8tV/wiIdy5Ms/uc+JTaZzOr20LNq1qGTG6sDKsbXz7OnDfNk/kEhTj9uYLNXz8jhFzdvoBGJCXr/symljmzfPqa27u/escTyP3ffY8L9JEkx4X5u7cL7+piV/GhvTVr0vuxljLRutxcqM9+hO555W0GhfCjqjN1b1unL915VTN0I7T96SonDxqmOk51SAADciQCPCnE4HJrx+c9a+9uhYsv3HD6uBnFddO+9T5R4rgsA/qpxu06a+d6r6tymkdrH1q/y499xVRvdcVUbp7cvrftexN1d+GvaN9U1pQwO+Gd931iog6nb1bxDZ9cUZXCpG77S8MRO6tuto8Z+9J12bf6JAA8AMAQCPEqw2uzadeBYqesWrP5NX207qoTbBshkOjf3c0OLvxq1vpjwDsApTdtepmv7jdKACf/U+uT/c3c5pXbfi7i7C++MAAt/ziuq6GcWUMpdGoCzFkwfq9TSBrHbt0um0Lo6vK/kdJb+AUEKr+P8B5cLpo9V2m+/SDo3cOL5xMbFu+R587K+/8yMg8rPyyl1nzPff4MSy11VM2B0/NVCMTn5BRo8eYn2Hs2Vf0BAifXBEXXVZ8SEMp9pBQBnXXTxFVqYVfobPFcqr/texN1deFQ9e+GZIFRYyFRwqLzUbZt0PMdaYrm90CEfSbbSrq8ygm155ziRUyCpAlMXVnCk98oq6/vPy8mWT1jpgxRbpVL3cVXNgNER4Guw2d/8qh+3Hyi2LPXgMYU17aD7nxrFAF4AaoTyuu9FXN2FX52yV7O/2y4fk0mP33KpWsZEVev5aprouMv1wvtv6Jst6dr4W7puGnqfu0uCwbUdMqXY1ynTHpW90KHAOo2LLS9vXvjzaffoVBWWMRbGX8/tamV9/zH9JxVbnpuRJl+zqcTo/MwLDziPAF8DHD+Vo+Onc4stW7Lud81fn6bLbxtY7Lb3eD9/xbZLkMls/uthAMDrFHXfTbZcORSkGWsyy9zWIclky632Lvzx07l6bOpyXXXXUOVln9aACfP0w4QB1XIuT1adb+hbd7pedZu01tH9u9XnlkaqEx1bbeeqap4adDy1LndEc6BLAAAgAElEQVRL/+DJYl8X2qwy6cy0cQBQGQR4L7du+z49+MZCBdcqPjJxSHiUkkZOUnBYhJsqA4AzCgsdMpvdM37G2m17VWi3y+QbqCU7HZIKyt/BN1AOu11rt+1VUtfqCfDZeVYFBgWr/VU3ym4r0I/z3zvvPoUO77kNPDYuvsK30sbGxVf4PJH1YxRZP6bC+7mLq34uFeWpdblbSOO2ykpLKbG84ORh+ZhNimzUvMS6mvBzAXDhCPBeIjMrT2/+9yedzj335tPX16wft+xRz4efV9O2l7qxOgAo3UVt2uuZD77SP/t3l48b7vzpkdBKedYC5RXYnN4nwM9XPRJaVWNVFfPZ6u3annZU7Q3URS4Pg1iVzlN/Lp5al7s16T201OUp0x5VZJBFwyd/4uKKAHgLAryBnMrJU2ZWXonleVabRsz4UiGxl6pBh3NzGwf4++neW2MVXNs4HQYANctNj43VsinPaeTML/XmwB7y9XFtiA8NClDfHs7NW++JPv1+q95cuEF9ho9XrdqlDxgFAAC8BwHeILbuOayBby2SxT+oxDqHSWrduZcuv6VfsefZQ4LPPF+VlZ3vsjoBoCIs/oHq/dhrWj7173rqnRVKHnID01E66cOVv2rykl+UNOItQ90KDgAAKo8A76FGvff12X87HA6t2rJHXR8YpRbxV7qxKgCoer5+FkU1bqk/Nn+jAnuhLL7MgFGW/UdPacqSDTqZY9Wm1AwljXxL4XUburssAADgIgR4D2Vt0avY1zdf11T1m3rOM5cAUBUcDodW/3eGMlK+15ynE70yvGfnWbVi7Xbd0KmNggIslT7OviOZuv/NhWqa0FPhzRrrzrsvVUg408vBuyyYPlaplRgUj2fxAdQUBHgP1e7KXuffCAAM7sdPpunEbz9p9tN9FBES6O5yqkXyp99p2U9b9Pu+IxWeP97xv9Hl9x3JVN83FqjjDQ+o43W3VUeZgEdI3bZJx3OsFdupgoEfAIyMAA8AcJs1X8zXureHKCw4wN2llGtHWoZmrdwqs0l6qEdHNW8Q6dR+Z+eZzz/p9PzxUbWCZDEX6pPXn5DF4q+OLRop/egp5eQVqH7zuKr4duCknRtXKT1lTYnlfn5n7hQpsEsX97hLEfUYg6CqtR0yxantmH8eQE1DgAcAuI2Pj4+Onsz26ACfZ7XpwXEL1a77nSq02/TAGwu0anx/p6a9m7VivRzWXFnMhbJaczVrxfrzduED/f208Pm79cPWvcrNL9CNl1+h4ACLXnvoOo1+62kN+OeH8vP33J+Xt9i6arnWL5qhR2++TL7m4gMr+vuf+XrPwRP6cNww9Rk2TlENm7ijTHgIV3yQsHXKUDnkqPbzVMZfv/+8jDT5hNVT2owhJbY1STpq8XdRZYD3IcADANymZ78ReuDN6fpg+G1q1cgzn+fOyS+Q3SF1vvk+SdK65R/Kbi88b4Av6r6b7bkad2tdPbUow+kufFhwgHp3Kj7uSc/LWmjMf75VgTWPAF/Ntny/RD8v+UCzRyWqWSl3W4SE/G+Wl6x8xdaP0Kvjhytx+HhFRTd1caVwt9i4+Arfwh8bF1/h7X1++0WSZLc7F+Areo7KKuv7zwwKVv7pI6XuYwkIUnh4aOnHAnBeBHgA8GDZp07o588/UqHtzDOhfoEhSrjpPq8JcG2v7CkfPz89OH6yPhh+q9o0ruvukqpMUff95tZB6twkUL1bB2nxTue68HCvTZ/P1dRHbyo1vP/VbVe00e6Dx/XzD8t0zd1DXVAdPIkrBs9LfGS0x04NzOCBgOsR4AHgAhRY82W3FVTLsfOyT2lJ8t/UtVWUWjSMkCSt/e13LU7eopsfe02WAO8Y9K315dfJ7OOrh8aN1zdvPKjgCxip3VP8ufveP6GBJKl/QpiW7jjodBcebuSQwkOc/5AsIiRQyiusxoIAADiDAA8AlbR7yzotf+dlmf/yfGxVMUkadNNlGtL7srPL+nbrqDH/XqlFk57WLU/8U/6BwdVybldreenVWvXRJJ3OyfeKAP/n7ntMuJ8kKSbcjy48AAC4IAR4AKiA/JwsbfjiY1mzT+uPDSv1wYjbFH9RQ5ed38ds1qsPdtOLc77TgnFPqWHLeNWLjVOry691WQ3VpV7jFho/f43+OaC7UwPEearSuu9F6MIDAIALQYAHgAr4YuYrah6Uo/ZN6uiFUYluGXjNbDbppfu76rOftuvYyQz9Z/4U5Zw6pvjrb3d5LVWp5+DntWzKcxox40uNG9RDvj5VE+KPZGZp/IffaFTfbooKC6mSY5antO57kQvtwvuYTTpxKF1+lgD5WvxlMlXP3R+AsxZMH6vUSgzixrPTAFA5BHgAqICTR/br8Sd6OTW4VXUymUzqc8WZOcFvSGip+9+cp0K7TZf2ututdV0Ii3+gej/2mpZPe15PTv9cb/1fL1l8fS74uP3Hztam3/dqa+oBLX3jkSqotGzldd/P1nMBXfi/33u1xiT/TfnWAsVddqWu7z9aZp8L/xkBlZW6bZOO51grtlMFAz8A4BwCPAA4KeXHL1SQl6OosCB3l1JMdFQt/WdkH9005t8lAvzuLet0YOdm+QeF6ZLrk+Tj69m/9v0s/rpp6Cv6/J0X9fjU5UoecoMsfpWveeOONP38W5qigwv1829p2rgjTZe2blyhY4QE+CkkwE8LJ49WUEgtxdSNkG8Zobm87nuRC+nC39SplW7q1Er5BTYNSV6mz999Sb0e/rt8fEs/FyonqlEzPT51ma5pV/rc7n6WM///BVa7JGn5xt1qe8ODLqvPE7UdMsWp7VwxXzoAeDPPficHAB4ibccvWr/wHc0a1Ue1gjxvCre64cFyOIqPgr1t9RdaO3+67r+undZuX6fPd21Wz4efl6+vn0we/Iy5r59FNz7yklbMeFnPz/5e/+zfrdLHev79ZarlV6hAP6mWX6Gef39ZhbvwFj9fffzcHVr5y26dzM5T4l2JpQ5cWNR9N9ly5VCQZqzJLPOYDkkmW26ln4X39/PV9Mdv0uPTPtfy6S/qhv97Qb5+xh/8z1P0GDhGOzf9qG3HDpe63vK/AG/VmQDf8baeap1wravKqxEI+gBQOgI8ADjhxOF0de0Qq+YNa7u7FKds+X6pNi5+X7NG9VHzhrU1yGbXyJlfKfnRm+Xr56cbB41Wi0uucneZZfLx9VOH7ndp+8KJlT5GUfe9cahD43sF6/Fl2ZXuwtcND9Hd13Yod5u12/aq0G6XyTdQS3Y6JJ1nekHfQDnsdq3dtldJXSs+mJ3Fz1fJQ2/UsHdWaNnUMbpp6D8I8VXEzz9AcZ27l7neU+fk9gaxcfEVvsU+Ni6+mqoBAM9DgAeA88g5fVLbvvtM/a5s6u5SSuVwODR18XrVa9hIkrR55UJt/ny25jydqKb1z8wfb/H10eRHekmP9FLKnsMaNHGC7HabR3cNQ2vX1e6Dx7Rqyx5d3b5phfcv6r7f1MJP1zWz6KYWVs3dYqtUF94ZPRJaKc9aoLwCm9P7BPj5qkdCq0qf0+Lro0mP3KCkf3yifb/9oth2l1f6WIAnYHA7ACgfAR4AzmNJ8rO6sX0dPdijo7tLKcHhcOjNT1dr+ZbD6jN8vDZ99alSvpqnuU8nqlHd0ru6bZvW0wcjblX/8ZNVWGBV3BU9XVy1c8Jq19PNj76q4VPGaNao29S6UR2n9/1z9/2RhEBJ0iMJgVq281Slu/DnExoUoL49LqvSYzrD18esqLBgFRYWnn9jAABgaJ77ECQAeIjD+9M09OYEj5uyy+Fw6B8frtKKlKNKHDFB21d/rh3ffKIPn00qM7wXad2ojmaN6qO1C6Zr66rlLqq44qIvaqvGLeK078jJCu335+57s8gzzys3i/TRTS38zj4LDwAAYDQEeAA4jzr1ozXj85/lcDjcXYokaUdaht5Zsk5PTf9c3+86pT4jJsjPP1Cr5n+gD59NUsPatZw6zkXRtTXn6ST9vOQ9rfxoitYu/0hZmUerufqKOZi6Q/v+2KaYOs59T9K57nt4wLnue5FHEgIVHuA424UHAAAwEgI8AJzHLU++rnk//qEPV/7q7lK0ced+9Ru3UN+frKvTDS7XbcPGKSAoRJJkMptULyKkQseLrR+huc8k6bKA/Yo6skb/feNJnSxj5G1XO3XssBZP/pte799NbRrXdXq/0rrvRejCAwAAIyPAA8B57NmyXiZbvhJaRbu1jnU70jUkeZmuH/icrr3nUXW5pZ/8A4Mv+LiN6oRp+O1X6p8Demhg9zZa8OZTysw4UAUVX5hTx44otkGkrru4mdP7lNd9L+IpXfjsPKvmf7dZOXlWt9UAAACMhUHsAOA8tn47XzcmXKQW0VEuPe9P29K0PS1DkpRrLdC/vtqiGwa/oMZtqm/KpP494+Xv56PkN4epfffbFRgSrrZdrnf5vPF2m01bv52vZnXDKrRfed33IkVd+Oockd4ZyZ9+p2U/bdHv+47o2ft7VPo4v+4+pK27D6pPovN3KQAAAGMiwAPAefR+9FUtnDBczVduUd/r2rvknAt+3KZ/frpWLROuk3Rm8LybH7tXDZvHVfu5+17XQVGhgdrwx0atXX9Qh//YrG4PjHBZiLfbCrRixiuKKDiifwy60en9irrvjUIcKnRI43/MKXPbQocU5u+othHpz2d/RqY+X5siU/5Jfb42RQ/0SlB0nYrPBX/g2Ck9OG6Begz4m+rEOH+nAlDVUqY96u4SAKBGIMADwHmEhNdW++53auveb11yvo+/26IJn21U4ogJqt3AtcGySM/LWqjnZS2UnWfVw5OW6Kt/vaHuD42S2Vx6V7uq2Aqs+vydl1TfdFzJj90oi5/zf6Y+XvmLfEwOncw36aOtzszFbpKPyaGPV/7i8gA/a8V6Oay5spgLZbXmataK9ZXqwteuFaT2zRpo9/qv1ax9J/n48mcdrhUbFy9t21TxfQAAlcJfegDwIHO+2ay3l25W0si3FFEvxt3lKDjAoplP3aL/m7xUX743Vj0GjJbZp3pCfIE1X8un/l1NAnP01uAb5edbsfM8dVdX5eTnKze/wOl9Av399NRdXSta6gUp6r6b7bkad2tdPbUoo9JdeH8/X8148mYNeXu5lk37u6LjLlf9pq1ccqcGIEmJj4x2dwkAUKMQ4AHACSaTSZnZudV6jsVrdmjq8i1KGjVR4XUaVOu5KiLI308znrxZQ99epu/mTNB1/UZVy3m+nPmKmofkadygXvL1qfjt+g1qhyn5qTurobKqVdR9v7l1kDo3CVTv1kFavLPyXXh/P19Nf+xGTV+2UcePrNbSJf/WzY+/pgbN2lRD9QAAwJ0YhR4AnNC84xVKOZCliQvXVNt88Fv2HFHb65I8KrwXCbD4auTtnXVkz45qO8fB1B169s4rKhXejeLP3ff+CWcG6OufECazPVefr03R/ozMSh3X4uerJ27rpBfvv1bd45vryL5dVVk2AADwEN77LgkAqlBwWIQSR07UrK+3KP3oqSo//uETWfpm817Vql2vyo9dVUym6jv2rl9+kr2gQCGB/tV3Eg9Q1H3v3TpIMeF+kqSYcD/1bh0kx/+eha8Mh8OhLzbs1Ecrf9XqbWkKjWREegAAvBEBHgCcFFwrQiEhISqw2av0uIeOn9Z9byxQ7BU3q9Vl11TpsY3g943fa+V/3tB7w25VSKDF3eVUm9K670UupAvvcDj06kerNHbhZi3a46tLbh2kZu0vr8rSAQCAh+AZeABwo/SjJ/XAmwvVquvturTX3e4ux+V2rP1GP36crH+NuFVtGnt31/jPz74Xdd+LFHXhK/osfGGhQy/N+U6r92Tp9qcnKyA4tDpKBwAAHoIADwAVEBxeRwt/+k3DEjvLVM495Rt37tfew5mKiQrT5a3LHk3+rQXr1CSh1wWHd7PZR4GBQfrHnJWKa1I9QXjjzkMKCouq0mP+tHCmunVorB1pGaofGaqIkMAqPb6rpB46IYuvj6KjapW6fn9Gppat2ar8Arvqhofpk5SSI+XXDQ9Woe2Y0yPSFxY6NObf32jjwQLdNmyc/AODq+R7AQAAnosADwAV0HPw8/pswgjZ7as18o4rSg3x839I0ev/XafYtpdo7/av9cTN8br3ug6lHu/Y6VzVbtfsgusy+/goacQE7fxpmbbuqNxAaOfjY2mk6wcOqNJj3vr4a/rli4+0bWOm3vl8vha9cHeF5n73BGlHMnXPa/+Vr49ZC1+4W3XCSgbp95au0fGcQtUOCtaW437S8ZLHsTn8dMpUS0F5eeftwtsLC/Xs+19r61GHbn3yDVkCjPnBBwAAqBhjvUsCADcLrhWhPiPe0qKJI7Vo7b8VFhygNwd2O3v798ffb9WEhRuUOGKCajdorMyMA3p7/AhZbYV6sMfFZ4/jcDj01oI1Sj2Wr/g2l1RJbVHRTRV1x9AqOZar1G7YRN0fekaSNHPE7TqRlad6ESHVes6sXKtGzPxSv6cfU3RULU36v56qXSuo0sf7bd9RRTdro9ysk9p7+ESJAL9j72HNW7VDvg6brmtiUf1aZY+hkB1t0vdpflq0emupXfiftqXphTmrlJWbp4gGsbrliVfl5x9Q6doBAICxEOABoIKCQsN0x7NTdPp4hg7u3q6Hxk/RQ9e3l9VeqHk/7FTSyImKqBctSQqv01CJoybq3XHDdCQzS20anbkFfcPOg/p+Z6aSRk1UUGhYeaerMUzmyg1zby2w68uNO3U6K1+SVCs4QFe3a1LmIw5zv/lFBx21deOTz2n76s/V9/X5mjWqj+qGV/yDA5u9UEs37FJgeFPl5RSfnWDN9n3KyMzWuP+ulsPkI7OfRYv+MEkqefv8OSZZfM06nitNmv+jrrukha7r2EwBFl99v2WPRsz4St37P6PIeo0UFlVfZh+fCtcMAACMiwAPAJXg62dRRL1oRdSLVq2oevr+h8UqdEiJIyeWmMc9rHY93T5qktZ99p7WH82SJPkE1FefEc8rMKT0Z6bhHGuBTUOmLFNqpkO1Is9MwXdw7ybdcul+jSrjEYfjp3MVGdNWEfWidUXiQK31s6jv6ws0e1Qf1Y90fhC4AptdI2d+pT9O+6n3A0O1ZNLIs+veXbZB//pmm2Kax6nF1X3UMiBU9gKr08fOOpGhXacOa/MXf2jut9t07zVt9MKcVer96D8UfVFbp48DAAC8CwEeAC5QTIv2imnRvtxtQiPrqHv/Z11UUc2QZ7VpyNvLlB0crftfeEm5+WduTc/NOqnP3hqp5Rtmyc+3ZIc6MztficPPPWrQ6eYH5OPrp5tfmKvIWmV34eMa19E/+3dTgMVXVptdT72zQvvyQ3Tz46/I1+/c9HdTFq/TR6t3645npyg04sIG/SsstOvrf72p5/7zrRKHvaEGsa0v6HgAAMDYCPAAAMPJyS/Q/01eqtzQxurz2Ivy8fGV/hfgA0POPOKQmXGw1H2Da0WUuPPhshvu0UWXdpWtnC75xqX/0cOTluj+69rq0x9/0xFF6qahL5wN7wEh4Xrtox90NN9Ht4+apOCwyAv+Ps1mH13/0NO6Oi9HAUHVOzYAAADwfAR4AIChZOVa9fCkJbLXbqEeD406E97/wtfPoqiGTSp03L8++vBXPQaO1trPPtC7a/YotF4H3Zj4sHx8z5372n6jtHbJLCX1vl9BoeVPAVcRJrOZ8A4AACQR4AGgTAumj1Xqtk0V2ic2Ll6Jj4yupopwOidfA95aLN+GbXX9/cNlMptddm6z2UddEgeVuT4wJEzX3vOYy+rBObxWAQA1BQEeAMqQum2Tjuc4P/CYJKmCIQLnBAbX0oxl63Vl29I75w45lLx4o4KaXqKu9zzu0vAOz8ZrFQBQUxDgAeA82g6Z4tR2KdMereZKvNuNQ17WryvmauO36WVuU6fD9bqs9/1lThGHmo3XKgDA2xHgAQAeIaJejLr2e9rdZQAAAHgs7j8EAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAG4OvuAgDA06VMe9TdJQBwAq9VAIC3I8ADQBli4+KlbZsqvg8Al+K1CgCoKQjwAFCGxEdGu7sEAE7gtQoAqCl4Bh4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIBn4AEANdaC6WOVWonBz3jmGqg8XncAUHkEeABAjZW6bZOO51grtlMFgweA4njdAUDlEeABADVe2yFTnNqOecaBqsPrDgAqjmfgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAF93FwAAgLulTHvU3SUANQ6vOwCoOAI8AKDGio2Ll7Ztqvg+ACqN1x0AVB4BHgBQYyU+MtrdJQA1Dq87AKg8noEHAAAAAMAACPAAAAAAABgAAR4AAAAAAAPgGXgAAAAPt2D6WKVWYuA3njcHAO9CgAcAAPBwqds26XiOtWI7VTDwAwA8HwEeAADAINoOmeLUdsyxDgDeiWfgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAF93FwAAAADnpEx71N0lAADciAAPAADg4WLj4qVtmyq+DwDAqxDgAQAAPFziI6PdXQIAwAMQ4M8jLy9P77//vpYvX660tDSZTCY1a9ZMiYmJuu+++2Q2M4wAAAAAAKD6EeDLkZ2drX79+mnr1q0KDw9XQkKCCgoKtHnzZv3jH//QTz/9pOTkZPn4+Li7VAAAAACAlyPAl2P69OnaunWrunTposmTJ6tWrVqSpPT0dA0cOFBff/21PvnkE91zzz1urhQAAAAA4O24/7scCxYskCS98sorZ8O7JMXExGjUqFGSpKVLl7qlNgAAAABAzUIHvgzZ2dlq2rSpGjRooEaNGpVYHxsbK0k6cuSIq0sDAAAAANRABPgyBAcHa/bs2WWu37JliySpfv36rioJAAAAAFCDcQt9JVitVk2bNk2S1LNnTzdXAwAAAACoCWpUB37EiBFKSUk573Y9evTQiBEjSl3ncDg0evRo7dmzR82bN9edd97p1Lnnz59/9pl6Z0R0vkPt27d3evvyhAT7V8lxAGdxzcEduO7galxzcDWuObga15znqVEB/sCBA0pNTT3vdhkZGaUut9vtGjNmjBYvXqywsDAlJyfLYrE4de79+/dr3bp1TtfauU0Pp7cFAAAAAHi/GhXgP/zww0rvm5OTo+HDh2vlypUKDw/Xe++9p+bNmzu9f3R0tC6//HKnt/cPCFJWdn5lSj2r6BOzCz0O4CyuObgD1x1cjWsOrsY1B1fjmvNcNSrAV9bRo0c1ePBgpaSkqH79+po5c6ZatGhRoWMkJSUpKSnJ6e3fWr69omUCAAAAALwYAf489u/fr379+ik9PV0tW7bUjBkzGHkeAAAAAOByjEJfjszMTPXv31/p6elKSEjQ3LlzCe8AAAAAALegA1+Ol156SXv37lVcXJxmzpypgIAAd5cEAAAAAKihCPBl2LVrl5YvXy5JCg4O1pgxY0rdLjIyUqNHj3ZlaQAAAACAGogAX4Z169bJ4XBIktavX1/mdtHR0QR4AAAAAEC1I8CX4d5779W9997r7jIAAAAAAJDEIHYAAAAAABgCAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeEJ3+xkAAB3GSURBVAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAADwAAAACAARDgAQAAAAAwAAI8AAAAAAAGQIAHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAC+7i4ApWsRFXzBxwgLD5QknczkvxmuwTUHd+C6g6txzcHVuObgalxznsvkcDgc7i4C1WP+/Pnav3+/oqOjlZSU5O5yUANwzcEduO7galxzcDWuObga15zn4hZ6L7ZgwQK9/fbbWrBggbtLQQ3BNQd34LqDq3HNwdW45uBqXHOeiwAPAAAAAIABEOABAAAAADAAAjwAAAAAAAZAgAcAAAAAwAAI8AAAAAAAGAABHgAAAAAAAyDAAwAAAABgAAR4AAAAAAAMgAAPAAAAAIABEOABAAAAADAAX3cXgOqTmJioyy+/XNHR0e4uBTUE1xzcgesOrsY1B1fjmoOrcc15LpPD4XC4uwgAAAAAAFA+bqEHAAAAAMAACPAAAAAAABgAAR4AAAAAAAMgwAMAAAAAYAAEeAAAAAAADIAAX0Pk5eXp7bffVq9evdSuXTt17txZQ4cO1c8//+zu0uCl8vLyNHXqVN1yyy3q2LGjLr74YiUlJWnWrFkqLCx0d3moAZKTk9WqVSsdOnTI3aXAi6xevVr9+vVTp06ddMkll+iBBx7QqlWr3F0WaoD58+erVatW2rBhg7tLgRez2+2aPXu2br/9dsXHx6tDhw7q3bu3pkyZovz8fHeXBzGNXI1gs9k0cOBArVmzRpGRkerYsaNOnz6tjRs3ymQyaezYsUpMTHR3mfAi2dnZ6tevn7Zu3arw8HC1b99eBQUF2rx5s3Jzc9W9e3clJyfLx8fH3aXCS3311Vd68sknZbPZ9N1336l+/fruLgleYP78+frb3/4mi8Wizp07q7CwUGvXrlVBQYFefvll3X333e4uEV5q06ZNGjBggHJycjRnzhxddtll7i4JXshut2vo0KH69ttvFRQUpI4dO8rX11ebN2/WqVOn1LFjR/373/9WYGCgu0ut0XzdXQCq3wcffKA1a9YoISFB06dPV0hIiCRpzZo1GjRokF5++WV1795dtWrVcnOl8BbTp0/X1q1b1aVLF02ePPnstZWenq6BAwfq66+/1ieffKJ77rnHzZXCG82ZM0evvfaabDabu0uBFzly5IheeOEFhYaGau7cuWrZsqUk6ddff1X//v316quv6tprr1W9evXcXCm8zRdffKFnn31WOTk57i4FXu6TTz7Rt99+q1atWmnGjBlnf58dP35cQ4cO1aZNmzR16lSNGDHCzZXWbNxCXwMsWrRIkvTcc8+dDe+S1LlzZ11xxRXKycnhVnpUqQULFkiSXnnllWIfDMXExGjUqFGSpKVLl7qlNnivXbt2afDgwXr55ZcVEhKi4OBgd5cELzJ79mxZrVY99NBDZ8O7JHXo0EGDBg1Sfn6+5s2b58YK4W0OHTqkp59+Wo8//rgKCwsVFRXl7pLg5Yrev40ePbrYh5GRkZF68cUXJfH+zRMQ4GuAjz/+WAsXLlSbNm1KrMvOzpYkbmVGlcnOzlbTpk3VoUMHNWrUqMT62NhYSWe6WUBVevHFF/Xdd9/pyiuv1Pz58xUeHu7ukuBFip5zv/7660us69GjhyTp+++/d2lN8G4TJ07UZ599pnbt2mnevHlq1qyZu0uCl4uIiFCzZs3UoUOHEuuaNm0qifdvnoBb6GuAwMDAEuHd4XDo008/1c8//6z69eurU6dObqoO3iY4OFizZ88uc/2WLVskiWeSUeXatWun/v37q1u3bu4uBV7G4XDojz/+kNlsLjVENW3aVGazWX/88YccDodMJpMbqoS3adasmV5//XXdeuutMpvpuaH6TZ8+vcx1vH/zHAT4GubAgQN69dVXtWPHDqWnp6tFixZ66623ZLFY3F0aagCr1app06ZJknr27OnmauBtnnnmGXeXAC918uRJWa1WRUZGlvr30tfXVxERETp27Jiys7OLPa4GVNbgwYPdXQIg6cyHmJMnT5bE+zdPQIA3oBEjRiglJeW82/Xo0aPEIBO7du3SV199dfbrwsJC7dy5Uy1atKjyOuE9LuSaK+JwODR69Gjt2bNHzZs315133lnVZcKLVMU1B1SV3NxcSSp35OWAgABJIsAD8DoTJkzQunXrFBUVpUGDBrm7nBqPAG9ABw4cUGpq6nm3y8jIKLGsY8eO2rhxo/Lz8/X111/r9ddf17Bhw+RwONS7d+/qKBde4EKuOenMtCRjxozR4sWLFRYWpuTkZO76QLku9JoDqpIzty8zKy8AbzRp0iS9++67slgsmjhxoiIjI91dUo1HgDegDz/8sNL7Fo0IHhISorvuukshISEaNmyYJk+eTIBHmS7kmsvJydHw4cO1cuVKhYeH67333lPz5s2rsDp4owu55oCqFhQUJEnKz88vc5uidcyPDMAb2Gw2vfzyy5o3b578/f2VnJyshIQEd5cFEeBrvF69esnf31979uxRTk7O2TcpQFU4evSoBg8erJSUFNWvX18zZ87kcQ0AhhMSEqKgoCCdOHFCNptNvr7F3z7ZbDadOHFC/v7+xabOBAAjys7O1pNPPqlVq1apVq1amjp1KuHdgzCkpZc7deqUxo4dq+eff77U9Waz+ewbEbvd7srS4OX279+vu+++WykpKWrZsqXmzZtHeAdgSCaTSRdddJHsdrv27NlTYn1qaqoKCwuLzQ8PAEZ08uRJPfDAA1q1apUaNGigOXPmEN49DAHeywUEBOiTTz7RvHnz9Mcff5RYv3HjRmVnZ6tRo0YKDQ11Q4XwRpmZmerfv7/S09OVkJCguXPnMu0IAEO7+uqrJanYQLBFipZ17drVpTUBQFWyWq1n75y86KKL9NFHH/HBpAciwHs5i8WixMRESdJzzz2nzMzMs+t27dql0aNHS5IGDBjglvrgnV566SXt3btXcXFxmjlzJh8OATC8pKQk+fv7a8aMGdq6devZ5Vu2bNHMmTMVEBCgvn37urFCALgwkydP1i+//KIGDRpo1qxZNF88FM/A1wDDhw/XL7/8ol9++UU9e/bUJZdcotOnT+vXX3+V1WrVXXfdxZsOVJldu3Zp+fLlkqTg4GCNGTOm1O0iIyPPfoAEAJ4uJiZGzzzzjF5++WXdc8896ty5sxwOh9auXSubzabXX39dtWvXdneZAFApmZmZmjVrlqQz79HGjh1b5rbjxo1zVVkoBQG+BggJCdHcuXP13nvvaenSpfrhhx/k7++viy++WH379tWNN97o7hLhRdatW3d2OqX169eXuV10dDQBHoCh3HfffWrYsKFmzpypjRs3ymKx6JJLLtGQIUPUpUsXd5cHAJX266+/Ki8vT5KUkpKi/2/v3oOyKhM4jn+RF9GCRTMWTEG8HQRXLSvQJTUlbwnrLcc2FU3NGA13ZseZLquZU84yTbnZiNq6OehqbbigrLe8YUwqoClgixcuCoIlXtdRSBHes38w74l3AC+R0Vu/z1/Mc57znOd5cUZ+73M5eXl5jdZVgG9ebqZeXCoiIiIiIiLys6c98CIiIiIiIiIuQAFeRERERERExAUowIuIiIiIiIi4AAV4ERERERERERegAC8iIiIiIiLiAhTgRURERERERFyAAryIiIiIiIiIC1CAFxEREREREXEBCvAiIiIiIiIiLkABXkRERERERMQFKMCLiIiIiIiIuAAFeBEREREREREXoAAvIiIiIiIi4gIU4EVERERERERcgAK8iIiIiIiIiAtQgBcRERERERFxAQrwIiIiIiIiIi5AAV5ERERERETEBSjAi4iIiIiIiLgABXgRERERERERF6AALyIiIiIiIuICFOBFREREREREXIACvIiIiIiIiIgLUIAXERH5GaiqqqK0tNSpLCUlheDgYMaNG9fk9jMyMpg4cSKPPfYYjz/+OLGxsU1u817Y7XZOnz7tVJaVlUVwcDDh4eE/aV+aKjg4mODgYPLz85u7K3dlyJAhBAcHs3fv3ubuioiINJECvIiISDPbv38/UVFRpKen35f2z5w5w0svvUROTg6tWrUiKCiIjh073pdnNeTrr7/mueeeIykp6Sd7poiIyC+Rrbk7ICIi8mu3cuVKSkpK6pUPHTqUPn360KpVqya1v3v3bm7dukVgYCBbtmzB09OzSe3dq/Xr15OXl1dvpr13795s27YNm01/joiIiNwN/Y8pIiLyM+Xt7Y23t3eT27l8+TIAvXr1+snD++20bt2arl27Nnc3REREXIaW0IuIiPzC1dTUANCyZctm7omIiIg0hZtpmmZzd0JEROTnrLS0lI8++oicnBzKysqw2Wx07tyZ4cOHM2nSJFq3bu1Uf+/evaSkpHD06FEuX76MzWbD39+fgQMHMnPmTHx9fYHaQ9xiYmLqPe+VV14hLi6OlJQUXn/9dXr27ElKSop1vaqqivXr17Njxw5KSkqoqKjgt7/9Lf369ePFF1+0ZrUd9zfk5MmT1s9fffUV//rXvzhy5AiXLl0CwNfXl/79+zNz5kw6depU7/5bt26RlJTEf/7zH06fPs2NGzfo1KkTUVFRTJs2DU9PT8rKyoiMjKx379ixY4mPj7fG36ZNG7Kyspzq3Lx5k08++YStW7dSVFSE3W4nICCAZ555hhdffBEfHx+n+q+99hobN25k6dKlBAUFkZCQwKFDh6ioqCAwMJDo6GimT5/+o3yJERwcDMDmzZsxDMPp2okTJ1i1ahUHDx7kypUr+Pj48OSTTzJz5kx+97vfWfUyMjKYNm0abdq0Yd++fXh4eNR7TnJyMm+88Qa9e/dmw4YNVvn169dJTExkx44dnDlzBnd3dwzDYNy4cYwfPx53d3endoYMGcLZs2dZuXIlgwcPbvL4RUSk+WgJvYiIyG0UFRXxxz/+katXr+Lj40OXLl2orKzk66+/5ujRo+zatYt169ZZAewvf/kL//73vwF45JFHMAyDixcvcurUKU6dOsX27dtJTU2lbdu2eHt707dvX/Lz87l+/ToBAQH4+vrSvn37RvtjmiavvPIK6enp2Gw2OnXqhL+/P8XFxWzYsIEtW7awZs0a+vTpQ7t27ejbty9nz56lvLycdu3a1Qvjy5cvZ+nSpUBtaO/WrRtXrlyhrKyMpKQktm3bxoYNG+jSpYt1z9WrV4mNjeXIkSMAdOnSBXd3d4qKiliyZAkZGRmsWrUKT09P+vbtS0lJCZcuXcLPz48OHToQFBR028/88uXLTJs2jZMnT+Lm5kaXLl1o2bIlBQUFrFixgtTUVP7xj380uPw+MzOTefPmAdC5c2dat25NYWEhf/vb38jNzWXFihV3+I3/cCkpKSxYsIDq6mq8vb0xDINz586xfft2du7cyaJFi5gwYQIA/fr1o3379nz77bfs37+fp59+ul57W7duBWD06NFWWVlZGdOnT6ekpASbzUZQUBB2u53s7Gyys7PZuXMny5cv12oLEZFfKlNEREQaFRcXZxqGYb7zzjtmVVWVVZ6Xl2eGh4ebhmGYGzduNE3TNPfs2WMahmE++uijZkZGhlM7WVlZ5qOPPmoahmH+/e9/d7o2efJk0zAM85///KdTeXJysmkYhjl27FirbO/evaZhGOawYcPMb7/91iq/du2aOXv2bNMwDDMmJsapnfj4eNMwDPPVV191Kj9+/LjZo0cPMyQkxNy8eXO9awMHDjQNwzAXLlzodO3VV1+1+nDy5Emr/MSJE2ZERIRpGIa5fPnyevXj4+Od2snMzDQNwzDDwsKcymNiYkzDMMyoqCizsLDQKj937px1bdiwYeaNGzfqPcMwDHPWrFnmhQsXrGtr1qyxruXm5ppN5Wir7thzc3PN0NBQMzQ01Fy3bp1ZU1NjmqZp2u12MykpyezZs6cZGhpqHj161LrnvffeMw3DMOfNm1fvGRcuXDBDQkLM0NBQ89KlS6ZpmmZ1dbU5ZswY0zAMMzY21mmMBQUF5siRI61/q3UNHjzYNAzDTEtLa/LYRUSkeWkPvIiIyG043vU9btw4p2XOoaGhxMXFMXz4cOtguAMHDuDh4cHkyZPp16+fUzthYWE8++yzQO2sflP7M3DgQPz9/a1yLy8vXn/9dSIiIujevftdtXXgwAFsNhsjRowgKirK6VqPHj2YOHFivf6Wl5ezadMm3NzcWLZsmdMS8uDgYObPnw9AamrqDxrfV199RWZmJp6enqxcudJplt3Pz4+EhARrxUFycnK9+9u0acPSpUt5+OGHrbKYmBgCAwMByMnJ+UH9upNly5ZRXV3Nyy+/zKRJk2jRovZPLDc3NyZMmMDUqVOprq5m5cqV1j2OmfU9e/Zw8+ZNp/a2b99OTU0NAwYM4KGHHgJg586dHDt2jKCgID744AOnMXbr1o0PPviAFi1a8Omnn1pbIURE5JdFAV5EROQ2HMHvrbfe4uDBg1RXV1vXJk2axIcffsjIkSMBmD9/Prm5ucTFxTXYlmOv/HffffeD+xMQEADU7o/esGED//vf/6xrHTt2ZPXq1VaIvpPp06dz9OhR4uPj77q/6enpmKZJnz59GvyiIDIyktTUVDZt2nTXY6rriy++AGDQoEF06NCh3nUvLy/GjRvnVLeusLCwBl+717lzZ6B2//iP7ebNmxw4cACAUaNGNVjH8QXJgQMHrH9D3bp1o2fPnlRUVNQbi2P5/B/+8AerLC0tDah9vWBDbxMwDAPDMLh16xaZmZlNG5SIiPwsaQ+8iIjIbcyZM4fMzExycnKYMmUK3t7e9OvXj4EDBzJkyBCnWVAAd3d3bt68SWZmJkVFRZSVlVFcXExeXh5XrlwBavex/1CRkZH06dOH3Nxc5s+fz5tvvkmvXr146qmnGDx4ML169bqn9tzc3IDaQ9UKCwspLS2lpKSEY8eOcf78eQDsdrtV/8yZMwCNzvJ7eHjQo0ePHzI0AIqLiwEICQlptE7Pnj2d6tbl5+fX4D2OUF93LD+W4uJibt26BcAbb7xhzb7X5XhuZWUl5eXl1pcTo0ePJi8vj61btzJ8+HCgdp97dnY2Xl5eTocAOlZCfP755xw+fLjBvpw7dw6A06dP/0ijExGRnxMFeBERkdvo06cPGzduZMWKFaSlpXHt2jV27drFrl27eOutt3j22WdZuHAh3t7e2O12PvroIxITE51mxj09Penduzd2u73R4HW3WrZsydq1a1m9ejWbNm2ipKSE3NxccnNzSUhIoHv37ixatIjHH3/8rtr77LPPSEhIoLy83Crz8PAgJCSE4OBgvvzyS6f6jnE98MADTRpHYyoqKgB48MEHG63jeLajbl0NneZeV1O+PGlM3Vn9u1mif+3aNevnqKgo3n33XdLT06moqODBBx+0Zt/rbs+o+5zS0lJKS0vv+hkiIvLLoQAvIiJyB127duW9996jqqqKnJwcMjIySE9PJy8vj82bN/Pdd9+RkJDA0qVLWblyJTabjcmTJxMWFkb37t0JDAzEZrOxZMmSJgd4qJ1Nnj17NrNnz+b06dNkZGSwf/9+vvzySwoKCpg5cyaff/55o7PRDklJSbz55ptA7UzwoEGD6N69O0FBQbRs2ZLPPvusXoB3zGQ3ZRvA7TjC+e2WujvC6f36EuFeOfrxwAMPkJ2dfU/3tmvXjoiICNLT00lLSyM6OpotW7YAzsvn4fstDR9++KE1Wy8iIr8u2gMvIiLSCLvdTmlpKQcPHgRqZ7/DwsL405/+REpKCosXLwZg9+7dXL9+nbVr1wLwzjvvsGDBAoYPH06XLl2w2Wq/L3csb26KK1eucPjwYS5fvgzU7u1+4YUXSEhIYNeuXfj6+lJZWcnu3bvv2NbHH38MQFxcHO+++y6jRo3CMAzrFWQN9dexl7ywsLDBNqurq3n++eeZO3cuFy9evOfxOdo/fvx4o3Xy8vKA788naG4BAQG4u7tTWVnZ6O/4+vXrZGVlUVZWVm8VgOMwu127dlFUVER+fj7t27cnPDzcqZ7jFYC3OwQxOzub/Px8bty40ZQhiYjIz5QCvIiISCMuXLjA0KFDmTp1qtMSc4ff//731s9Xr16lsrISaHj/9qVLl6yDyuoehAff70O/m+Xd8+bN44UXXrDeNV+Xn5+f9b72mpqaO7Z19uzZRvt748YNayl33bYiIiJwc3MjJyenwT3oGRkZZGdnk5WVRdu2bYF7G5/jfejp6elW/+q6fv26dUDeU089dcf2fgpeXl7WloVPP/20wTqJiYnExMQQExNTbx9+ZGQkXl5e7Nu3z5p9j46Otj43B8dns2nTpnqn1kPt0vrJkycTHR19zysBRETENSjAi4iINMLPz4+wsDDsdjvz5s1zCvEVFRUsWbIEgMceeww/Pz9+85vfALB69WqqqqqsusePH2fGjBlcvXoVoF74cizB/uabb+7Yp+joaABWrFjBvn37nK5t376dw4cP06JFCyIiIu7YVlBQEADr1q1zWrJ+5swZYmNjKSkpqdffoKAgRowYgd1uJy4uzqoDcOLECRYuXAjAlClTcHd3B77fz34343viiScIDw+nqqqK2NjYeq+wmzNnDuXl5QQEBDBhwoQ7tvdTmT17Nm5ubqxatYq1a9daX3qYpklqaqr1+ripU6dan4tDq1atGD58OBUVFSQmJgL1l89D7X75oKAgSkpKiIuL48KFC9a14uJiZs+eTXV1NSEhIfTv3/8+jVRERJqT9sCLiIjcxuLFi3nuuec4ePAgkZGRBAYG4uHhwZkzZ6isrKRNmza8/fbb2Gw25syZw1//+ldSU1P54osv6NixI1evXqWsrAyA8PBwsrKyrNPdHYKDg9m7dy9r1qwhIyODkSNH8vLLLzfYn9GjR5OWlsaOHTuYMWMG/v7+PPzww5w/f95q989//rPT+9MbM3fuXObOncuBAwcYMGAAnTt3pqKigpKSEkzTpF+/fmRmZnLx4kVM07RmhBctWkRpaSn//e9/GTFiBN26dePWrVuUlJRgt9sZMGAAs2bNchofwI4dO4iOjiYsLIwFCxY02q/333+f6dOnk5+fz6hRo+jatSseHh4UFBRQXV1Nhw4dWLZsGV5eXncc40+lf//+vPbaa8THx7N48WISEhIICAjg3LlzVtAeM2YMMTExDd4/evRokpOTqaysJDQ0tMFT/lu2bElCQgIzZswgPT2dp59+2vrsi4uLqampwd/fn+XLl9/XsYqISPNRgBcREbmNgIAAkpOTWbVqFRkZGZSWltKiRQvat2/PoEGDmDlzJr6+vgBMmzaNTp068fHHH1t7mdu2bcszzzzDlClT6NWrF+Hh4RQUFFBaWmq9033WrFmUl5eTlpbGqVOnyM/Pb7Q/bm5uvP/++zzxxBNs3bqVwsJCLl68SNu2bRk6dCiTJk2669nXYcOGsX79epYvX87Jkyc5efIkPj4+REREMHHiRIYNG8aAAQM4f/48hw4dIiwsDAAfHx8++eQT1q9fz5YtWzh16hR2u52QkBDGjx/P888/7zTLPHbsWAoLC9m8eTPFxcXWSoXG+Pr6kpSUxPr169m2bZv1SrSuXbsyYsQIJk2ahI+Pz12N8ac0bdo0+vbtS2JiIocOHeLEiRN4enry5JNPMn78eMaMGVNvWbxDWFgYjzzyCN98802Ds+8O3bp1IzU1lcTERPbs2WMF98DAQAYPHsxLL73EQw89dL+GKCIizczNvB/vUxERERERERGRH5X2wIuIiIiIiIi4AAV4ERERERERERegPfAiIiLyq3Ls2DHefvvte74vNDT0tofviYiI3G8K8CIiIvKrcu3aNY4cOXLP99ls+rNJRESalw6xExEREREREXEB2gMvIiIiIiIi4gIU4EVERERERERcgAK8iIiIiIiIiAtQgBcRERERERFxAQrwIiIiIiIiIi5AAV5ERERERETEBSjAi4iIiIiIiLgABXgRERERERERF6AALyIiIiIiIuICFOBFREREREREXIACvIiIiIiIiIgLUIAXERERERERcQEK8CIiIiIiIiIu4P8gV8w2lgJ/fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 490,
       "width": 504
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train a K-Nearest Neighbors model\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_std, y_train)\n",
    "\n",
    "check_model_fit(knn, X_test_std, y_test)\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-knn-overfit.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total accuracy = 91.6%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">predictions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">actual</th>\n",
       "      <th>0</th>\n",
       "      <td>3290</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>254</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         predictions     \n",
       "                   0    1\n",
       "actual 0        3290  126\n",
       "       1         254  830"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "percent accuracy score per class:\n",
      "left = 0 : 96.31%\n",
      "left = 1 : 76.57%\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 490,
       "width": 504
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Increase the number of \"nearest neighbors\" to reduce overfitting\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=25)\n",
    "knn.fit(X_train_std, y_train)\n",
    "\n",
    "check_model_fit(knn, X_test_std, y_test)\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-knn.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'warn'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcriterion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gini'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_samples_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_samples_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_weight_fraction_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_leaf_nodes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_impurity_decrease\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_impurity_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbootstrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0moob_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwarm_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "A random forest classifier.\n",
       "\n",
       "A random forest is a meta estimator that fits a number of decision tree\n",
       "classifiers on various sub-samples of the dataset and uses averaging to\n",
       "improve the predictive accuracy and control over-fitting.\n",
       "The sub-sample size is always the same as the original\n",
       "input sample size but the samples are drawn with replacement if\n",
       "`bootstrap=True` (default).\n",
       "\n",
       "Read more in the :ref:`User Guide <forest>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "n_estimators : integer, optional (default=10)\n",
       "    The number of trees in the forest.\n",
       "\n",
       "    .. versionchanged:: 0.20\n",
       "       The default value of ``n_estimators`` will change from 10 in\n",
       "       version 0.20 to 100 in version 0.22.\n",
       "\n",
       "criterion : string, optional (default=\"gini\")\n",
       "    The function to measure the quality of a split. Supported criteria are\n",
       "    \"gini\" for the Gini impurity and \"entropy\" for the information gain.\n",
       "    Note: this parameter is tree-specific.\n",
       "\n",
       "max_depth : integer or None, optional (default=None)\n",
       "    The maximum depth of the tree. If None, then nodes are expanded until\n",
       "    all leaves are pure or until all leaves contain less than\n",
       "    min_samples_split samples.\n",
       "\n",
       "min_samples_split : int, float, optional (default=2)\n",
       "    The minimum number of samples required to split an internal node:\n",
       "\n",
       "    - If int, then consider `min_samples_split` as the minimum number.\n",
       "    - If float, then `min_samples_split` is a fraction and\n",
       "      `ceil(min_samples_split * n_samples)` are the minimum\n",
       "      number of samples for each split.\n",
       "\n",
       "    .. versionchanged:: 0.18\n",
       "       Added float values for fractions.\n",
       "\n",
       "min_samples_leaf : int, float, optional (default=1)\n",
       "    The minimum number of samples required to be at a leaf node.\n",
       "    A split point at any depth will only be considered if it leaves at\n",
       "    least ``min_samples_leaf`` training samples in each of the left and\n",
       "    right branches.  This may have the effect of smoothing the model,\n",
       "    especially in regression.\n",
       "\n",
       "    - If int, then consider `min_samples_leaf` as the minimum number.\n",
       "    - If float, then `min_samples_leaf` is a fraction and\n",
       "      `ceil(min_samples_leaf * n_samples)` are the minimum\n",
       "      number of samples for each node.\n",
       "\n",
       "    .. versionchanged:: 0.18\n",
       "       Added float values for fractions.\n",
       "\n",
       "min_weight_fraction_leaf : float, optional (default=0.)\n",
       "    The minimum weighted fraction of the sum total of weights (of all\n",
       "    the input samples) required to be at a leaf node. Samples have\n",
       "    equal weight when sample_weight is not provided.\n",
       "\n",
       "max_features : int, float, string or None, optional (default=\"auto\")\n",
       "    The number of features to consider when looking for the best split:\n",
       "\n",
       "    - If int, then consider `max_features` features at each split.\n",
       "    - If float, then `max_features` is a fraction and\n",
       "      `int(max_features * n_features)` features are considered at each\n",
       "      split.\n",
       "    - If \"auto\", then `max_features=sqrt(n_features)`.\n",
       "    - If \"sqrt\", then `max_features=sqrt(n_features)` (same as \"auto\").\n",
       "    - If \"log2\", then `max_features=log2(n_features)`.\n",
       "    - If None, then `max_features=n_features`.\n",
       "\n",
       "    Note: the search for a split does not stop until at least one\n",
       "    valid partition of the node samples is found, even if it requires to\n",
       "    effectively inspect more than ``max_features`` features.\n",
       "\n",
       "max_leaf_nodes : int or None, optional (default=None)\n",
       "    Grow trees with ``max_leaf_nodes`` in best-first fashion.\n",
       "    Best nodes are defined as relative reduction in impurity.\n",
       "    If None then unlimited number of leaf nodes.\n",
       "\n",
       "min_impurity_decrease : float, optional (default=0.)\n",
       "    A node will be split if this split induces a decrease of the impurity\n",
       "    greater than or equal to this value.\n",
       "\n",
       "    The weighted impurity decrease equation is the following::\n",
       "\n",
       "        N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
       "                            - N_t_L / N_t * left_impurity)\n",
       "\n",
       "    where ``N`` is the total number of samples, ``N_t`` is the number of\n",
       "    samples at the current node, ``N_t_L`` is the number of samples in the\n",
       "    left child, and ``N_t_R`` is the number of samples in the right child.\n",
       "\n",
       "    ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
       "    if ``sample_weight`` is passed.\n",
       "\n",
       "    .. versionadded:: 0.19\n",
       "\n",
       "min_impurity_split : float, (default=1e-7)\n",
       "    Threshold for early stopping in tree growth. A node will split\n",
       "    if its impurity is above the threshold, otherwise it is a leaf.\n",
       "\n",
       "    .. deprecated:: 0.19\n",
       "       ``min_impurity_split`` has been deprecated in favor of\n",
       "       ``min_impurity_decrease`` in 0.19. The default value of\n",
       "       ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it\n",
       "       will be removed in 0.25. Use ``min_impurity_decrease`` instead.\n",
       "\n",
       "\n",
       "bootstrap : boolean, optional (default=True)\n",
       "    Whether bootstrap samples are used when building trees. If False, the\n",
       "    whole datset is used to build each tree.\n",
       "\n",
       "oob_score : bool (default=False)\n",
       "    Whether to use out-of-bag samples to estimate\n",
       "    the generalization accuracy.\n",
       "\n",
       "n_jobs : int or None, optional (default=None)\n",
       "    The number of jobs to run in parallel for both `fit` and `predict`.\n",
       "    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
       "    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
       "    for more details.\n",
       "\n",
       "random_state : int, RandomState instance or None, optional (default=None)\n",
       "    If int, random_state is the seed used by the random number generator;\n",
       "    If RandomState instance, random_state is the random number generator;\n",
       "    If None, the random number generator is the RandomState instance used\n",
       "    by `np.random`.\n",
       "\n",
       "verbose : int, optional (default=0)\n",
       "    Controls the verbosity when fitting and predicting.\n",
       "\n",
       "warm_start : bool, optional (default=False)\n",
       "    When set to ``True``, reuse the solution of the previous call to fit\n",
       "    and add more estimators to the ensemble, otherwise, just fit a whole\n",
       "    new forest. See :term:`the Glossary <warm_start>`.\n",
       "\n",
       "class_weight : dict, list of dicts, \"balanced\", \"balanced_subsample\" or     None, optional (default=None)\n",
       "    Weights associated with classes in the form ``{class_label: weight}``.\n",
       "    If not given, all classes are supposed to have weight one. For\n",
       "    multi-output problems, a list of dicts can be provided in the same\n",
       "    order as the columns of y.\n",
       "\n",
       "    Note that for multioutput (including multilabel) weights should be\n",
       "    defined for each class of every column in its own dict. For example,\n",
       "    for four-class multilabel classification weights should be\n",
       "    [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of\n",
       "    [{1:1}, {2:5}, {3:1}, {4:1}].\n",
       "\n",
       "    The \"balanced\" mode uses the values of y to automatically adjust\n",
       "    weights inversely proportional to class frequencies in the input data\n",
       "    as ``n_samples / (n_classes * np.bincount(y))``\n",
       "\n",
       "    The \"balanced_subsample\" mode is the same as \"balanced\" except that\n",
       "    weights are computed based on the bootstrap sample for every tree\n",
       "    grown.\n",
       "\n",
       "    For multi-output, the weights of each column of y will be multiplied.\n",
       "\n",
       "    Note that these weights will be multiplied with sample_weight (passed\n",
       "    through the fit method) if sample_weight is specified.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "estimators_ : list of DecisionTreeClassifier\n",
       "    The collection of fitted sub-estimators.\n",
       "\n",
       "classes_ : array of shape = [n_classes] or a list of such arrays\n",
       "    The classes labels (single output problem), or a list of arrays of\n",
       "    class labels (multi-output problem).\n",
       "\n",
       "n_classes_ : int or list\n",
       "    The number of classes (single output problem), or a list containing the\n",
       "    number of classes for each output (multi-output problem).\n",
       "\n",
       "n_features_ : int\n",
       "    The number of features when ``fit`` is performed.\n",
       "\n",
       "n_outputs_ : int\n",
       "    The number of outputs when ``fit`` is performed.\n",
       "\n",
       "feature_importances_ : array of shape = [n_features]\n",
       "    The feature importances (the higher, the more important the feature).\n",
       "\n",
       "oob_score_ : float\n",
       "    Score of the training dataset obtained using an out-of-bag estimate.\n",
       "\n",
       "oob_decision_function_ : array of shape = [n_samples, n_classes]\n",
       "    Decision function computed with out-of-bag estimate on the training\n",
       "    set. If n_estimators is small it might be possible that a data point\n",
       "    was never left out during the bootstrap. In this case,\n",
       "    `oob_decision_function_` might contain NaN.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn.ensemble import RandomForestClassifier\n",
       ">>> from sklearn.datasets import make_classification\n",
       "\n",
       ">>> X, y = make_classification(n_samples=1000, n_features=4,\n",
       "...                            n_informative=2, n_redundant=0,\n",
       "...                            random_state=0, shuffle=False)\n",
       ">>> clf = RandomForestClassifier(n_estimators=100, max_depth=2,\n",
       "...                              random_state=0)\n",
       ">>> clf.fit(X, y)  # doctest: +NORMALIZE_WHITESPACE\n",
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)\n",
       ">>> print(clf.feature_importances_)\n",
       "[0.14205973 0.76664038 0.0282433  0.06305659]\n",
       ">>> print(clf.predict([[0, 0, 0, 0]]))\n",
       "[1]\n",
       "\n",
       "Notes\n",
       "-----\n",
       "The default values for the parameters controlling the size of the trees\n",
       "(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n",
       "unpruned trees which can potentially be very large on some data sets. To\n",
       "reduce memory consumption, the complexity and size of the trees should be\n",
       "controlled by setting those parameter values.\n",
       "\n",
       "The features are always randomly permuted at each split. Therefore,\n",
       "the best found split may vary, even with the same training data,\n",
       "``max_features=n_features`` and ``bootstrap=False``, if the improvement\n",
       "of the criterion is identical for several splits enumerated during the\n",
       "search of the best split. To obtain a deterministic behaviour during\n",
       "fitting, ``random_state`` has to be fixed.\n",
       "\n",
       "References\n",
       "----------\n",
       "\n",
       ".. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n",
       "\n",
       "See also\n",
       "--------\n",
       "DecisionTreeClassifier, ExtraTreesClassifier\n",
       "\u001b[0;31mFile:\u001b[0m           /anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py\n",
       "\u001b[0;31mType:\u001b[0m           ABCMeta\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RandomForestClassifier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total accuracy = 92.0%\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">predictions</th>\n",
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       "      <td>3371</td>\n",
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      "text/plain": [
       "         predictions     \n",
       "                   0    1\n",
       "actual 0        3371   45\n",
       "       1         317  767"
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     "text": [
      "\n",
      "percent accuracy score per class:\n",
      "left = 0 : 98.68%\n",
      "left = 1 : 70.76%\n",
      "\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 494,
       "width": 514
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train a Random Forest\n",
    "\n",
    "forest = RandomForestClassifier(\n",
    "    n_estimators=50,\n",
    "    max_depth=5,\n",
    "    random_state=1,\n",
    ")\n",
    "forest.fit(X_train, y_train)\n",
    "\n",
    "check_model_fit(forest, X_test, y_test)\n",
    "plt.xlim(-0.1, 1.2)\n",
    "plt.ylim(0.2, 1.2)\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-forest.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "\n",
    "dot_data = export_graphviz(\n",
    "    forest.estimators_[0],\n",
    "    out_file=None,\n",
    "    feature_names=features,  \n",
    "    class_names=['no', 'yes'],  \n",
    "    filled=True, rounded=True,  \n",
    "    special_characters=True,\n",
    ")\n",
    "graph = graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../figures/hr-analytics-tree-graph.png'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.render(\n",
    "    filename='../figures/hr-analytics-tree-graph',   \n",
    "    format='png',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<title>11</title>\n",
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       "<text text-anchor=\"start\" x=\"278\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.229</text>\n",
       "<text text-anchor=\"start\" x=\"272.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 24</text>\n",
       "<text text-anchor=\"start\" x=\"270\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [33, 5]</text>\n",
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       "<!-- 8&#45;&gt;9 -->\n",
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       "<title>8&#45;&gt;9</title>\n",
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       "<!-- 10 -->\n",
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       "<title>10</title>\n",
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       "<text text-anchor=\"start\" x=\"408.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"395.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"393\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [26, 0]</text>\n",
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       "<!-- 8&#45;&gt;10 -->\n",
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       "<title>8&#45;&gt;10</title>\n",
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       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>12</title>\n",
       "<path fill=\"#f6d4bc\" stroke=\"#000000\" d=\"M762.5,-187C762.5,-187 612.5,-187 612.5,-187 606.5,-187 600.5,-181 600.5,-175 600.5,-175 600.5,-116 600.5,-116 600.5,-110 606.5,-104 612.5,-104 612.5,-104 762.5,-104 762.5,-104 768.5,-104 774.5,-110 774.5,-116 774.5,-116 774.5,-175 774.5,-175 774.5,-181 768.5,-187 762.5,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"608.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">satisfaction_level ≤ 0.465</text>\n",
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       "<text text-anchor=\"start\" x=\"637.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1202</text>\n",
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       "<!-- 11&#45;&gt;12 -->\n",
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       "<title>11&#45;&gt;12</title>\n",
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       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<path fill=\"#f2c19e\" stroke=\"#000000\" d=\"M954.5,-187C954.5,-187 804.5,-187 804.5,-187 798.5,-187 792.5,-181 792.5,-175 792.5,-175 792.5,-116 792.5,-116 792.5,-110 798.5,-104 804.5,-104 804.5,-104 954.5,-104 954.5,-104 960.5,-104 966.5,-110 966.5,-116 966.5,-116 966.5,-175 966.5,-175 966.5,-181 960.5,-187 954.5,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"800.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">satisfaction_level ≤ 0.465</text>\n",
       "<text text-anchor=\"start\" x=\"843\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.447</text>\n",
       "<text text-anchor=\"start\" x=\"833.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 294</text>\n",
       "<text text-anchor=\"start\" x=\"823.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [305, 155]</text>\n",
       "<text text-anchor=\"start\" x=\"847.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
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       "<!-- 11&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>11&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M791.0662,-222.8796C801.6679,-213.6031 813.0002,-203.6874 823.8933,-194.1559\"/>\n",
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       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>13</title>\n",
       "<path fill=\"#5fb0ea\" stroke=\"#000000\" d=\"M624.5,-68C624.5,-68 520.5,-68 520.5,-68 514.5,-68 508.5,-62 508.5,-56 508.5,-56 508.5,-12 508.5,-12 508.5,-6 514.5,0 520.5,0 520.5,0 624.5,0 624.5,0 630.5,0 636.5,-6 636.5,-12 636.5,-12 636.5,-56 636.5,-56 636.5,-62 630.5,-68 624.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"536\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.271</text>\n",
       "<text text-anchor=\"start\" x=\"526.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 553</text>\n",
       "<text text-anchor=\"start\" x=\"516.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [140, 727]</text>\n",
       "<text text-anchor=\"start\" x=\"537.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = yes</text>\n",
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       "<!-- 12&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>12&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M644.6782,-103.9815C635.0095,-94.607 624.7473,-84.6572 615.0882,-75.2921\"/>\n",
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       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<path fill=\"#e5833d\" stroke=\"#000000\" d=\"M762.5,-68C762.5,-68 666.5,-68 666.5,-68 660.5,-68 654.5,-62 654.5,-56 654.5,-56 654.5,-12 654.5,-12 654.5,-6 660.5,0 666.5,0 666.5,0 762.5,0 762.5,0 768.5,0 774.5,-6 774.5,-12 774.5,-12 774.5,-56 774.5,-56 774.5,-62 768.5,-68 762.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"678\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.035</text>\n",
       "<text text-anchor=\"start\" x=\"668.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 649</text>\n",
       "<text text-anchor=\"start\" x=\"662.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [990, 18]</text>\n",
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       "<!-- 12&#45;&gt;14 -->\n",
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       "<title>12&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M697.5538,-103.9815C699.6013,-95.5261 701.7621,-86.6026 703.8302,-78.0623\"/>\n",
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       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"#6eb7ec\" stroke=\"#000000\" d=\"M900.5,-68C900.5,-68 804.5,-68 804.5,-68 798.5,-68 792.5,-62 792.5,-56 792.5,-56 792.5,-12 792.5,-12 792.5,-6 798.5,0 804.5,0 804.5,0 900.5,0 900.5,0 906.5,0 912.5,-6 912.5,-12 912.5,-12 912.5,-56 912.5,-56 912.5,-62 906.5,-68 900.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"816\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.333</text>\n",
       "<text text-anchor=\"start\" x=\"806.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 120</text>\n",
       "<text text-anchor=\"start\" x=\"800.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [41, 153]</text>\n",
       "<text text-anchor=\"start\" x=\"817.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = yes</text>\n",
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       "<!-- 15&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>15&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M869.4462,-103.9815C867.3987,-95.5261 865.2379,-86.6026 863.1698,-78.0623\"/>\n",
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       "<!-- 17 -->\n",
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       "<title>17</title>\n",
       "<path fill=\"#e5823b\" stroke=\"#000000\" d=\"M1032,-68C1032,-68 943,-68 943,-68 937,-68 931,-62 931,-56 931,-56 931,-12 931,-12 931,-6 937,0 943,0 943,0 1032,0 1032,0 1038,0 1044,-6 1044,-12 1044,-12 1044,-56 1044,-56 1044,-62 1038,-68 1032,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"951\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.015</text>\n",
       "<text text-anchor=\"start\" x=\"941.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 174</text>\n",
       "<text text-anchor=\"start\" x=\"939\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [264, 2]</text>\n",
       "<text text-anchor=\"start\" x=\"955.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
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       "<!-- 15&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>15&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M919.7152,-103.9815C928.7064,-94.6989 938.244,-84.8522 947.2371,-75.5677\"/>\n",
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       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1085,-417.5C1085,-417.5 996,-417.5 996,-417.5 990,-417.5 984,-411.5 984,-405.5 984,-405.5 984,-361.5 984,-361.5 984,-355.5 990,-349.5 996,-349.5 996,-349.5 1085,-349.5 1085,-349.5 1091,-349.5 1097,-355.5 1097,-361.5 1097,-361.5 1097,-405.5 1097,-405.5 1097,-411.5 1091,-417.5 1085,-417.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1011.5\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"994.5\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 399</text>\n",
       "<text text-anchor=\"start\" x=\"992\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 620]</text>\n",
       "<text text-anchor=\"start\" x=\"1005.5\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = yes</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>18&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1040.5,-460.8796C1040.5,-450.2134 1040.5,-438.7021 1040.5,-427.9015\"/>\n",
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       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>20</title>\n",
       "<path fill=\"#e89152\" stroke=\"#000000\" d=\"M1399.5,-425C1399.5,-425 1263.5,-425 1263.5,-425 1257.5,-425 1251.5,-419 1251.5,-413 1251.5,-413 1251.5,-354 1251.5,-354 1251.5,-348 1257.5,-342 1263.5,-342 1263.5,-342 1399.5,-342 1399.5,-342 1405.5,-342 1411.5,-348 1411.5,-354 1411.5,-354 1411.5,-413 1411.5,-413 1411.5,-419 1405.5,-425 1399.5,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"1259.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">last_evaluation ≤ 0.825</text>\n",
       "<text text-anchor=\"start\" x=\"1295\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.199</text>\n",
       "<text text-anchor=\"start\" x=\"1281.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 4362</text>\n",
       "<text text-anchor=\"start\" x=\"1271.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [6103, 770]</text>\n",
       "<text text-anchor=\"start\" x=\"1299.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
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       "<!-- 18&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>18&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1127.8096,-466.7961C1163.7709,-452.0903 1205.4101,-435.0625 1241.6334,-420.2496\"/>\n",
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       "<!-- 21 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>21</title>\n",
       "<path fill=\"#e6843e\" stroke=\"#000000\" d=\"M1406.5,-306C1406.5,-306 1256.5,-306 1256.5,-306 1250.5,-306 1244.5,-300 1244.5,-294 1244.5,-294 1244.5,-235 1244.5,-235 1244.5,-229 1250.5,-223 1256.5,-223 1256.5,-223 1406.5,-223 1406.5,-223 1412.5,-223 1418.5,-229 1418.5,-235 1418.5,-235 1418.5,-294 1418.5,-294 1418.5,-300 1412.5,-306 1406.5,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"1252.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">satisfaction_level ≤ 0.465</text>\n",
       "<text text-anchor=\"start\" x=\"1295\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.047</text>\n",
       "<text text-anchor=\"start\" x=\"1281.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2447</text>\n",
       "<text text-anchor=\"start\" x=\"1275.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3798, 93]</text>\n",
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       "<!-- 20&#45;&gt;21 -->\n",
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       "<title>20&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1331.5,-341.8796C1331.5,-333.6838 1331.5,-324.9891 1331.5,-316.5013\"/>\n",
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       "<g id=\"node29\" class=\"node\">\n",
       "<title>28</title>\n",
       "<path fill=\"#eda673\" stroke=\"#000000\" d=\"M1804.5,-306C1804.5,-306 1668.5,-306 1668.5,-306 1662.5,-306 1656.5,-300 1656.5,-294 1656.5,-294 1656.5,-235 1656.5,-235 1656.5,-229 1662.5,-223 1668.5,-223 1668.5,-223 1804.5,-223 1804.5,-223 1810.5,-223 1816.5,-229 1816.5,-235 1816.5,-235 1816.5,-294 1816.5,-294 1816.5,-300 1810.5,-306 1804.5,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"1664.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">last_evaluation ≤ 0.995</text>\n",
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       "<!-- 20&#45;&gt;28 -->\n",
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       "<title>20&#45;&gt;28</title>\n",
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       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>22</title>\n",
       "<path fill=\"#e78c4a\" stroke=\"#000000\" d=\"M1310.5,-187C1310.5,-187 1174.5,-187 1174.5,-187 1168.5,-187 1162.5,-181 1162.5,-175 1162.5,-175 1162.5,-116 1162.5,-116 1162.5,-110 1168.5,-104 1174.5,-104 1174.5,-104 1310.5,-104 1310.5,-104 1316.5,-104 1322.5,-110 1322.5,-116 1322.5,-116 1322.5,-175 1322.5,-175 1322.5,-181 1316.5,-187 1310.5,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"1170.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">last_evaluation ≤ 0.625</text>\n",
       "<text text-anchor=\"start\" x=\"1206\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.143</text>\n",
       "<text text-anchor=\"start\" x=\"1196.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 340</text>\n",
       "<text text-anchor=\"start\" x=\"1190.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [490, 41]</text>\n",
       "<text text-anchor=\"start\" x=\"1210.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>21&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1300.3721,-222.8796C1293.771,-214.0534 1286.7371,-204.6485 1279.9298,-195.5466\"/>\n",
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       "<!-- 25 -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>25</title>\n",
       "<path fill=\"#e5833c\" stroke=\"#000000\" d=\"M1488.5,-187C1488.5,-187 1352.5,-187 1352.5,-187 1346.5,-187 1340.5,-181 1340.5,-175 1340.5,-175 1340.5,-116 1340.5,-116 1340.5,-110 1346.5,-104 1352.5,-104 1352.5,-104 1488.5,-104 1488.5,-104 1494.5,-104 1500.5,-110 1500.5,-116 1500.5,-116 1500.5,-175 1500.5,-175 1500.5,-181 1494.5,-187 1488.5,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"1348.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">last_evaluation ≤ 0.805</text>\n",
       "<text text-anchor=\"start\" x=\"1388\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.03</text>\n",
       "<text text-anchor=\"start\" x=\"1370.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2107</text>\n",
       "<text text-anchor=\"start\" x=\"1364.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3308, 52]</text>\n",
       "<text text-anchor=\"start\" x=\"1388.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>21&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1362.6279,-222.8796C1369.229,-214.0534 1376.2629,-204.6485 1383.0702,-195.5466\"/>\n",
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       "<!-- 23 -->\n",
       "<g id=\"node24\" class=\"node\">\n",
       "<title>23</title>\n",
       "<path fill=\"#e99456\" stroke=\"#000000\" d=\"M1170.5,-68C1170.5,-68 1074.5,-68 1074.5,-68 1068.5,-68 1062.5,-62 1062.5,-56 1062.5,-56 1062.5,-12 1062.5,-12 1062.5,-6 1068.5,0 1074.5,0 1074.5,0 1170.5,0 1170.5,0 1176.5,0 1182.5,-6 1182.5,-12 1182.5,-12 1182.5,-56 1182.5,-56 1182.5,-62 1176.5,-68 1170.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1086\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.224</text>\n",
       "<text text-anchor=\"start\" x=\"1080.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 77</text>\n",
       "<text text-anchor=\"start\" x=\"1070.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [102, 15]</text>\n",
       "<text text-anchor=\"start\" x=\"1090.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 22&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>22&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1197.8164,-103.9815C1187.7273,-94.607 1177.019,-84.6572 1166.9399,-75.2921\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1169.0839,-72.5065 1159.3757,-68.2637 1164.3191,-77.6346 1169.0839,-72.5065\"/>\n",
       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node25\" class=\"node\">\n",
       "<title>24</title>\n",
       "<path fill=\"#e78946\" stroke=\"#000000\" d=\"M1308.5,-68C1308.5,-68 1212.5,-68 1212.5,-68 1206.5,-68 1200.5,-62 1200.5,-56 1200.5,-56 1200.5,-12 1200.5,-12 1200.5,-6 1206.5,0 1212.5,0 1212.5,0 1308.5,0 1308.5,0 1314.5,0 1320.5,-6 1320.5,-12 1320.5,-12 1320.5,-56 1320.5,-56 1320.5,-62 1314.5,-68 1308.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1224\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.118</text>\n",
       "<text text-anchor=\"start\" x=\"1214.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 263</text>\n",
       "<text text-anchor=\"start\" x=\"1208.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [388, 26]</text>\n",
       "<text text-anchor=\"start\" x=\"1228.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 22&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>22&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1249.2025,-103.9815C1250.5527,-95.618 1251.9768,-86.7965 1253.3418,-78.3409\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1256.8301,-78.6937 1254.9686,-68.2637 1249.9196,-77.578 1256.8301,-78.6937\"/>\n",
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       "<!-- 26 -->\n",
       "<g id=\"node27\" class=\"node\">\n",
       "<title>26</title>\n",
       "<path fill=\"#e5823b\" stroke=\"#000000\" d=\"M1454.5,-68C1454.5,-68 1350.5,-68 1350.5,-68 1344.5,-68 1338.5,-62 1338.5,-56 1338.5,-56 1338.5,-12 1338.5,-12 1338.5,-6 1344.5,0 1350.5,0 1350.5,0 1454.5,0 1454.5,0 1460.5,0 1466.5,-6 1466.5,-12 1466.5,-12 1466.5,-56 1466.5,-56 1466.5,-62 1460.5,-68 1454.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1366\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.019</text>\n",
       "<text text-anchor=\"start\" x=\"1352.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1944</text>\n",
       "<text text-anchor=\"start\" x=\"1346.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3055, 29]</text>\n",
       "<text text-anchor=\"start\" x=\"1370.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>25&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1413.7975,-103.9815C1412.4473,-95.618 1411.0232,-86.7965 1409.6582,-78.3409\"/>\n",
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       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>27</title>\n",
       "<path fill=\"#e78c4b\" stroke=\"#000000\" d=\"M1592.5,-68C1592.5,-68 1496.5,-68 1496.5,-68 1490.5,-68 1484.5,-62 1484.5,-56 1484.5,-56 1484.5,-12 1484.5,-12 1484.5,-6 1490.5,0 1496.5,0 1496.5,0 1592.5,0 1592.5,0 1598.5,0 1604.5,-6 1604.5,-12 1604.5,-12 1604.5,-56 1604.5,-56 1604.5,-62 1598.5,-68 1592.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1508\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.153</text>\n",
       "<text text-anchor=\"start\" x=\"1498.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 163</text>\n",
       "<text text-anchor=\"start\" x=\"1492.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [253, 23]</text>\n",
       "<text text-anchor=\"start\" x=\"1512.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>25&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1466.673,-103.9815C1477.2007,-94.5151 1488.3808,-84.462 1498.8849,-75.0168\"/>\n",
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       "<!-- 29 -->\n",
       "<g id=\"node30\" class=\"node\">\n",
       "<title>29</title>\n",
       "<path fill=\"#eba06a\" stroke=\"#000000\" d=\"M1811.5,-187C1811.5,-187 1661.5,-187 1661.5,-187 1655.5,-187 1649.5,-181 1649.5,-175 1649.5,-175 1649.5,-116 1649.5,-116 1649.5,-110 1655.5,-104 1661.5,-104 1661.5,-104 1811.5,-104 1811.5,-104 1817.5,-104 1823.5,-110 1823.5,-116 1823.5,-116 1823.5,-175 1823.5,-175 1823.5,-181 1817.5,-187 1811.5,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"1657.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">satisfaction_level ≤ 0.725</text>\n",
       "<text text-anchor=\"start\" x=\"1700\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.317</text>\n",
       "<text text-anchor=\"start\" x=\"1686.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1795</text>\n",
       "<text text-anchor=\"start\" x=\"1676.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2238, 550]</text>\n",
       "<text text-anchor=\"start\" x=\"1704.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 28&#45;&gt;29 -->\n",
       "<g id=\"edge29\" class=\"edge\">\n",
       "<title>28&#45;&gt;29</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1736.5,-222.8796C1736.5,-214.6838 1736.5,-205.9891 1736.5,-197.5013\"/>\n",
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       "<!-- 32 -->\n",
       "<g id=\"node33\" class=\"node\">\n",
       "<title>32</title>\n",
       "<path fill=\"#a1d1f3\" stroke=\"#000000\" d=\"M2054.5,-187C2054.5,-187 1904.5,-187 1904.5,-187 1898.5,-187 1892.5,-181 1892.5,-175 1892.5,-175 1892.5,-116 1892.5,-116 1892.5,-110 1898.5,-104 1904.5,-104 1904.5,-104 2054.5,-104 2054.5,-104 2060.5,-104 2066.5,-110 2066.5,-116 2066.5,-116 2066.5,-175 2066.5,-175 2066.5,-181 2060.5,-187 2054.5,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"1900.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">satisfaction_level ≤ 0.715</text>\n",
       "<text text-anchor=\"start\" x=\"1943\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.452</text>\n",
       "<text text-anchor=\"start\" x=\"1933.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 120</text>\n",
       "<text text-anchor=\"start\" x=\"1927.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [67, 127]</text>\n",
       "<text text-anchor=\"start\" x=\"1944.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = yes</text>\n",
       "</g>\n",
       "<!-- 28&#45;&gt;32 -->\n",
       "<g id=\"edge32\" class=\"edge\">\n",
       "<title>28&#45;&gt;32</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1816.7407,-225.2052C1838.8305,-214.3875 1862.9849,-202.5588 1885.7638,-191.4037\"/>\n",
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       "<!-- 30 -->\n",
       "<g id=\"node31\" class=\"node\">\n",
       "<title>30</title>\n",
       "<path fill=\"#e68743\" stroke=\"#000000\" d=\"M1738.5,-68C1738.5,-68 1634.5,-68 1634.5,-68 1628.5,-68 1622.5,-62 1622.5,-56 1622.5,-56 1622.5,-12 1622.5,-12 1622.5,-6 1628.5,0 1634.5,0 1634.5,0 1738.5,0 1738.5,0 1744.5,0 1750.5,-6 1750.5,-12 1750.5,-12 1750.5,-56 1750.5,-56 1750.5,-62 1744.5,-68 1738.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1650\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.093</text>\n",
       "<text text-anchor=\"start\" x=\"1640.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 829</text>\n",
       "<text text-anchor=\"start\" x=\"1630.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1206, 62]</text>\n",
       "<text text-anchor=\"start\" x=\"1654.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 29&#45;&gt;30 -->\n",
       "<g id=\"edge30\" class=\"edge\">\n",
       "<title>29&#45;&gt;30</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1717.8818,-103.9815C1714.0078,-95.3423 1709.9145,-86.2144 1706.0094,-77.5059\"/>\n",
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       "<!-- 31 -->\n",
       "<g id=\"node32\" class=\"node\">\n",
       "<title>31</title>\n",
       "<path fill=\"#f1bd97\" stroke=\"#000000\" d=\"M1892.5,-68C1892.5,-68 1780.5,-68 1780.5,-68 1774.5,-68 1768.5,-62 1768.5,-56 1768.5,-56 1768.5,-12 1768.5,-12 1768.5,-6 1774.5,0 1780.5,0 1780.5,0 1892.5,0 1892.5,0 1898.5,0 1904.5,-6 1904.5,-12 1904.5,-12 1904.5,-56 1904.5,-56 1904.5,-62 1898.5,-68 1892.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1800\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.436</text>\n",
       "<text text-anchor=\"start\" x=\"1790.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 966</text>\n",
       "<text text-anchor=\"start\" x=\"1776.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1032, 488]</text>\n",
       "<text text-anchor=\"start\" x=\"1804.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
       "</g>\n",
       "<!-- 29&#45;&gt;31 -->\n",
       "<g id=\"edge31\" class=\"edge\">\n",
       "<title>29&#45;&gt;31</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1773.7363,-103.9815C1781.9791,-94.7908 1790.7178,-85.0472 1798.9721,-75.8436\"/>\n",
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       "<!-- 33 -->\n",
       "<g id=\"node34\" class=\"node\">\n",
       "<title>33</title>\n",
       "<path fill=\"#eda876\" stroke=\"#000000\" d=\"M2024,-68C2024,-68 1935,-68 1935,-68 1929,-68 1923,-62 1923,-56 1923,-56 1923,-12 1923,-12 1923,-6 1929,0 1935,0 1935,0 2024,0 2024,0 2030,0 2036,-6 2036,-12 2036,-12 2036,-56 2036,-56 2036,-62 2030,-68 2024,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1943\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.359</text>\n",
       "<text text-anchor=\"start\" x=\"1937.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 33</text>\n",
       "<text text-anchor=\"start\" x=\"1931\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [36, 11]</text>\n",
       "<text text-anchor=\"start\" x=\"1947.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n",
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       "<!-- 32&#45;&gt;33 -->\n",
       "<g id=\"edge33\" class=\"edge\">\n",
       "<title>32&#45;&gt;33</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1979.5,-103.9815C1979.5,-95.618 1979.5,-86.7965 1979.5,-78.3409\"/>\n",
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       "<!-- 34 -->\n",
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       "<title>34</title>\n",
       "<path fill=\"#6eb7ec\" stroke=\"#000000\" d=\"M2162.5,-68C2162.5,-68 2066.5,-68 2066.5,-68 2060.5,-68 2054.5,-62 2054.5,-56 2054.5,-56 2054.5,-12 2054.5,-12 2054.5,-6 2060.5,0 2066.5,0 2066.5,0 2162.5,0 2162.5,0 2168.5,0 2174.5,-6 2174.5,-12 2174.5,-12 2174.5,-56 2174.5,-56 2174.5,-62 2168.5,-68 2162.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"2078\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.333</text>\n",
       "<text text-anchor=\"start\" x=\"2072.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 87</text>\n",
       "<text text-anchor=\"start\" x=\"2062.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [31, 116]</text>\n",
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       "<!-- 32&#45;&gt;34 -->\n",
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       "<title>32&#45;&gt;34</title>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1a229e9050>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activity: Training and Visualizing SVM Models with Scikit-learn\n",
    "\n",
    "_Note: If desired, the following code can be removed from the student version of the notebook and replaced with empty cells._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-load the processed data\n",
    "\n",
    "df = pd.read_csv('../data/hr-analytics/hr_data_processed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.803054</td>\n",
       "      <td>200.992489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.492423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       number_project  average_montly_hours\n",
       "count    14999.000000          14999.000000\n",
       "mean         3.803054            200.992489\n",
       "std          1.232592             49.492423\n",
       "min          2.000000             96.000000\n",
       "25%          3.000000            156.000000\n",
       "50%          4.000000            200.000000\n",
       "75%          5.000000            244.000000\n",
       "max          7.000000            310.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the features \"number_project\" and \"average_monthly_hours\"\n",
    "\n",
    "df[['number_project', 'average_montly_hours']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do a train test split on the features \"number_project\" and \"average_monthly_hours\"\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "features = ['number_project', 'average_montly_hours']\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df[features].values,\n",
    "    df['left'].values,\n",
    "    test_size=0.3,\n",
    "    random_state=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data using a MinMaxScaler\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4231614367047082, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    X_train_scaled.flatten().mean(),\n",
    "    X_train_scaled.flatten().min(),\n",
    "    X_train_scaled.flatten().max()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=1, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a support vector machine classifier with the rbf kernel\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel='rbf', C=1, random_state=1, gamma='scale')\n",
    "svm.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.84444444444445"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the classification accuracy?\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = svm.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([95.98946136, 66.32841328])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the accuracy by class?\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cmat = confusion_matrix(y_test, y_pred)\n",
    "cmat.diagonal() / cmat.sum(axis=1) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 472,
       "width": 499
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the resulting decision regions\n",
    "\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "N_samples = 200\n",
    "X, y = X_train_scaled[:N_samples], y_train[:N_samples]\n",
    "plot_decision_regions(X, y, clf=svm)\n",
    "plt.xlim(-0.2, 1.2)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-num-proj-hours-svm-c-1.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 472,
       "width": 499
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm = SVC(kernel='rbf', C=50, random_state=1, gamma='scale')\n",
    "svm.fit(X_train_scaled, y_train)\n",
    "X, y = X_train_scaled[:N_samples], y_train[:N_samples]\n",
    "plot_decision_regions(X, y, clf=svm)\n",
    "plt.xlim(-0.2, 1.2)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-num-proj-hours-svm-c-50.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "kernelspec": {
   "display_name": "packt",
   "language": "python",
   "name": "packt"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
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    "builtin_function_or_method",
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}