Finalfinal.ipynb

{
 "cells": [
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   "metadata": {
    "id": "UzfdnviCTipV"
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   "outputs": [],
   "source": [
    "#variedad\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import interp\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "import plotly.figure_factory as ff\n",
    "import re\n",
    "import nltk\n",
    "import mlxtend  \n",
    "from collections import Counter\n",
    "import warnings\n",
    "from itertools import cycle\n",
    "from yellowbrick.text import TSNEVisualizer\n",
    "import pickle\n",
    "from pickle import dump\n",
    "from numpy import sqrt\n",
    "from numpy import argmax\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "\n",
    "#Separar datos\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "\n",
    "#modelos\n",
    "from sklearn.tree import DecisionTreeClassifier         # Arbol decisión\n",
    "from sklearn.naive_bayes import GaussianNB              # Naive bayes\n",
    "from sklearn.linear_model import LogisticRegression     # Logística con regularización\n",
    "from sklearn.svm import SVC                             # Maquina de vector de soportes\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn import svm\n",
    "\n",
    "#tf-df\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "#metricas\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import  classification_report\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from yellowbrick.model_selection import LearningCurve\n",
    "from sklearn.model_selection import learning_curve\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "DFx9fBup1G_C",
    "outputId": "5159efc7-5dc9-4ed9-b877-3decb75835fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "KTB78997Tipf",
    "outputId": "b53c9128-fff8-4bce-d4eb-bc30bd04ccb3"
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   "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>created_at</th>\n",
       "      <th>id</th>\n",
       "      <th>user id</th>\n",
       "      <th>full_text</th>\n",
       "      <th>localidad</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>clasification</th>\n",
       "      <th>tidy_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12/10/2020 01:31</td>\n",
       "      <td>1.320000e+18</td>\n",
       "      <td>9.884670e+17</td>\n",
       "      <td>Slim es de los principales beneficiados por AM...</td>\n",
       "      <td>Xochimilco, Distrito Federal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>slim principales beneficiados amlo siempre mas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/10/2020 02:30</td>\n",
       "      <td>1.320000e+18</td>\n",
       "      <td>1.147230e+18</td>\n",
       "      <td>¡ESTO NO LO VISTE DE @lopezobrador_  EN VERDAD...</td>\n",
       "      <td>Aquí! en mi México Querido.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>viste @lopezobrador_ verdad ll0ro dar mensaje ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19/10/2020 12:08</td>\n",
       "      <td>1.320000e+18</td>\n",
       "      <td>1.102780e+18</td>\n",
       "      <td>▶️ Sigue la conferencia diaria del presidente ...</td>\n",
       "      <td>Querétaro, Querétaro Arteaga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>▶️ sigue conferencia diaria presidente lopez o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19/10/2020 12:08</td>\n",
       "      <td>1.320000e+18</td>\n",
       "      <td>2.374583e+09</td>\n",
       "      <td>#EnPortada | Ante represión violenta en Tabasc...</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>#enportada represion violenta tabasco amlo: pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19/10/2020 12:08</td>\n",
       "      <td>1.320000e+18</td>\n",
       "      <td>1.204230e+18</td>\n",
       "      <td>@Vale___Mx @PartidoMorenaMx Por eso de que vot...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>@vale___mx @partidomorenamx voto masivo morena...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_at  ...                                          tidy_text\n",
       "0  12/10/2020 01:31  ...  slim principales beneficiados amlo siempre mas...\n",
       "1  12/10/2020 02:30  ...  viste @lopezobrador_ verdad ll0ro dar mensaje ...\n",
       "2  19/10/2020 12:08  ...  ▶️ sigue conferencia diaria presidente lopez o...\n",
       "3  19/10/2020 12:08  ...  #enportada represion violenta tabasco amlo: pr...\n",
       "4  19/10/2020 12:08  ...  @vale___mx @partidomorenamx voto masivo morena...\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
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    }
   ],
   "source": [
    "#Leer base madre\n",
    "df = pd.read_csv('tweets_base_madre.csv', encoding ='utf-8')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
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   "source": [
    "def adjusted_classes(y_scores, t):\n",
    "    \"\"\"\n",
    "    This function adjusts class predictions based on the prediction threshold (t).\n",
    "    Will only work for binary classification problems.\n",
    "    \"\"\"\n",
    "    return [1 if y >= t else 0 for y in y_scores]\n",
    "\n",
    "def precision_recall_threshold(y_test,y_scores,nombre, model, p, r, thresholds, t):\n",
    "    \"\"\"\n",
    "    plots the precision recall curve and shows the current value for each\n",
    "    by identifying the classifier's threshold (t).\n",
    "    \"\"\"\n",
    "    \n",
    "    # generate new class predictions based on the adjusted_classes\n",
    "    # function above and view the resulting confusion matrix.\n",
    "    y_pred_adj = adjusted_classes(y_scores, t)\n",
    "    #print(y_test)\n",
    "    #print(y_pred_adj)\n",
    "    #print(pd.DataFrame(confusion_matrix(y_test, y_pred_adj),\n",
    "    #                   columns=['pred_neg', 'pred_pos'], \n",
    "    #                   index=['neg', 'pos']))\n",
    "    matrix = pd.DataFrame(confusion_matrix(y_test, y_pred_adj),\n",
    "                       columns=['pred_neg', 'pred_pos'], \n",
    "                       index=['neg', 'pos'])\n",
    "    matrix = np.array(matrix)\n",
    "    #print(type(matrix))\n",
    "    # plot the curve\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.title(\"Precision and Recall curve ^ = current threshold\")\n",
    "    plt.step(r, p, color='b', alpha=0.2,\n",
    "             where='post')\n",
    "    plt.fill_between(r, p, step='post', alpha=0.2,\n",
    "                     color='b')\n",
    "    plt.ylim([0.5, 1.01]);\n",
    "    plt.xlim([0.5, 1.01]);\n",
    "    plt.xlabel('Recall');\n",
    "    plt.ylabel('Precision');\n",
    "    \n",
    "    # plot the current threshold on the line\n",
    "    close_default_clf = np.argmin(np.abs(thresholds - t))\n",
    "    plt.plot(r[close_default_clf], p[close_default_clf], '^', c='k',\n",
    "            markersize=15)\n",
    "\n",
    "    return matrix\n",
    "    \n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    \"\"\"\n",
    "    Modified from:\n",
    "    Hands-On Machine learning with Scikit-Learn\n",
    "    and TensorFlow; p.89\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.title(\"Precision and Recall Scores as a function of the decision threshold\")\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\")\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    plt.xlabel(\"Decision Threshold\")\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()\n",
    "\n",
    "def prediccionesModelo(modelo,Test_X_Tfidf,Test_Y, nombre):\n",
    "    with warnings.catch_warnings():\n",
    "        # ignore all caught warnings\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        # execute code that will generate warnings\n",
    "        predicciones = modelo.predict(Test_X_Tfidf)\n",
    "        print(classification_report(Test_Y,predicciones))\n",
    "        classes = [\"Liberales\", \"Conservadores\"]\n",
    "        visualizer = ClassificationReport(modelo, classes=classes, support=True, cmap='PuOr')\n",
    "        visualizer.score(Test_X_Tfidf, Test_Y)        # Evaluate the model on the test data\n",
    "        plt.show()                       # Finalize and show the figure\n",
    "    return predicciones\n",
    "\n",
    "def curvaRocAuc(Test_Y,predicciones):\n",
    "    # y_true must be your labels\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(Test_Y, predicciones, pos_label=None)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "    plt.figure()\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Curva roc-auc modelSVC')\n",
    "    plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.show()\n",
    "\n",
    "def curvaRocAucSalida(Test_Y,predicciones,clase):\n",
    "    with warnings.catch_warnings():\n",
    "        # ignore all caught warnings\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        # execute code that will generate warnings\n",
    "        import plotly.graph_objs as go\n",
    "        n_classes = len(np.unique(Test_Y))\n",
    "        #print(n_classes)\n",
    "        # y_true must be your labels\n",
    "        fpr = dict()\n",
    "        tpr = dict()\n",
    "        roc_auc = dict()\n",
    "        for i in range(n_classes):\n",
    "            fpr[i], tpr[i], thresholds = metrics.roc_curve(Test_Y, predicciones, i)\n",
    "            roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
    "\n",
    "        # Compute micro-average ROC curve and ROC area\n",
    "        fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(Test_Y.ravel(), predicciones.ravel())\n",
    "        roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "        #print(roc_auc)\n",
    "\n",
    "        lw = 2\n",
    "\n",
    "        trace1 = go.Scatter(x=fpr[1], y=tpr[1], \n",
    "                            mode='lines', \n",
    "                            line=dict(color='darkorange', width=lw),\n",
    "                            name='ROC curve (area = %0.2f)' % roc_auc[clase]\n",
    "                           )\n",
    "\n",
    "        trace2 = go.Scatter(x=[0, 1], y=[0, 1], \n",
    "                            mode='lines', \n",
    "                            line=dict(color='navy', width=lw, dash='dash'),\n",
    "                            showlegend=False)\n",
    "\n",
    "        layout = go.Layout(title='Receiver operating characteristic example',\n",
    "                           xaxis=dict(title='False Positive Rate'),\n",
    "                           yaxis=dict(title='True Positive Rate'))\n",
    "\n",
    "        fig = go.Figure(data=[trace1, trace2], layout=layout)\n",
    "        fig.show()\n",
    "\n",
    "def curvasRocAucSalida(Test_Y,predicciones, nombre):\n",
    "    with warnings.catch_warnings():\n",
    "        # ignore all caught warnings\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        # execute code that will generate warnings\n",
    "        import plotly.graph_objs as go\n",
    "        n_classes = len(np.unique(Test_Y))\n",
    "        #print(n_classes)\n",
    "        # y_true must be your labels\n",
    "        fpr = dict()\n",
    "        tpr = dict()\n",
    "        roc_auc = dict()\n",
    "        for i in range(n_classes):\n",
    "            fpr[i], tpr[i], thresholds = metrics.roc_curve(Test_Y, predicciones, i)\n",
    "            roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
    "\n",
    "        # Compute micro-average ROC curve and ROC area\n",
    "        fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(Test_Y.ravel(), predicciones.ravel())\n",
    "        roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "        #print(roc_auc)\n",
    "\n",
    "        lw = 2\n",
    "        \n",
    "        # First aggregate all false positive rates\n",
    "        all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "        \n",
    "        # Then interpolate all ROC curves at this points\n",
    "        mean_tpr = np.zeros_like(all_fpr)\n",
    "        for i in range(n_classes):\n",
    "            mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "        \n",
    "        # Finally average it and compute AUC\n",
    "        mean_tpr /= n_classes\n",
    "\n",
    "        fpr[\"macro\"] = all_fpr\n",
    "        tpr[\"macro\"] = mean_tpr\n",
    "        roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "        \n",
    "        # Plot all ROC curves\n",
    "        data = []\n",
    "        trace1 = go.Scatter(x=fpr[\"micro\"], y=tpr[\"micro\"],\n",
    "                            mode='lines', \n",
    "                            line=dict(color='deeppink', width=lw, dash='dot'),\n",
    "                            name='micro-average ROC curve (area = {0:0.2f})'\n",
    "                                   ''.format(roc_auc[\"micro\"]))\n",
    "        data.append(trace1)\n",
    "\n",
    "        trace2 = go.Scatter(x=fpr[\"macro\"], y=tpr[\"macro\"],\n",
    "                            mode='lines', \n",
    "                            line=dict(color='navy', width=lw, dash='dot'),\n",
    "                            name='macro-average ROC curve (area = {0:0.2f})'\n",
    "                                  ''.format(roc_auc[\"macro\"]))\n",
    "        data.append(trace2)\n",
    "        \n",
    "        colors = cycle(['aqua', 'darkorange'])\n",
    "        for i, color in zip(range(n_classes), colors):\n",
    "            trace3 = go.Scatter(x=fpr[i], y=tpr[i],\n",
    "                                mode='lines', \n",
    "                                line=dict(color=color, width=lw),\n",
    "                                name='ROC curve of class {0} (area = {1:0.2f})'\n",
    "                                ''.format(i, roc_auc[i]))\n",
    "            data.append(trace3)\n",
    "\n",
    "        trace4 = go.Scatter(x=[0, 1], y=[0, 1], \n",
    "                            mode='lines', \n",
    "                            line=dict(color='black', width=lw, dash='dash'),\n",
    "                            showlegend=False)\n",
    "        data.append(trace4)\n",
    "        layout = go.Layout(title='Some extension of Receiver operating characteristic to multi-class',\n",
    "                   xaxis=dict(title='False Positive Rate'),\n",
    "                   yaxis=dict(title='True Positive Rate'))\n",
    "\n",
    "        fig = go.Figure(data=data, layout=layout)\n",
    "        fig.write_html('rocauc' + \"\".join(nombre) + \".html\")\n",
    "        fig.show()\n",
    "\n",
    "def plot_confusion_matrixPlt(model,Test_X_Tfidf,Test_Y):\n",
    "    from sklearn.metrics import plot_confusion_matrix\n",
    "    # Plot non-normalized confusion matrix\n",
    "    title = \"Matriz de confusion\"\n",
    "    class_names = np.unique(Test_Y)\n",
    "    #class_names = [df.clasification]\n",
    "    disp = plot_confusion_matrix(model, Test_X_Tfidf, Test_Y, display_labels=class_names, cmap=plt.cm.Blues)\n",
    "    #disp.ax_.set_title(title)\n",
    "    plt.close()\n",
    "    #print(title)\n",
    "    #print(disp.confusion_matrix)\n",
    "\n",
    "    #plt.show()\n",
    "    print(disp.confusion_matrix)\n",
    "    print(type(disp.confusion_matrix))\n",
    "    return disp\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, class_names, zl, nombre):\n",
    "    import plotly.figure_factory as ff\n",
    "    confusion_matrix = metrics.confusion_matrix(y_true, y_pred)\n",
    "    confusion_matrix = confusion_matrix.astype(int)\n",
    "        \n",
    "    x = class_names\n",
    "    y = class_names\n",
    "    z = zl\n",
    "    \n",
    "    z_text = zl\n",
    "    \n",
    "    fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, \n",
    "                                      colorscale=[[0, \"rgb(102,102,102)\"],\n",
    "                                                  [0.5, \"rgb(117,112,179)\"],\n",
    "                                                  [1, \"rgb(230,171,2)\"]])\n",
    "    fig.update_layout(title_text='Confusion Matrix')\n",
    "    # add custom xaxis title\n",
    "    fig.add_annotation(dict(font=dict(color=\"black\",size=14),\n",
    "                            x=0.5,\n",
    "                            y=-0.15,\n",
    "                            showarrow=False,\n",
    "                            text=\"Predicted value\",\n",
    "                            xref=\"paper\",\n",
    "                            yref=\"paper\"))\n",
    "\n",
    "    # add custom yaxis title\n",
    "    fig.add_annotation(dict(font=dict(color=\"black\",size=14),\n",
    "                            x=-0.15,\n",
    "                            y=0.5,\n",
    "                            showarrow=False,\n",
    "                            text=\"Real value\",\n",
    "                            textangle=-90,\n",
    "                            xref=\"paper\",\n",
    "                            yref=\"paper\"))\n",
    "\n",
    "    # adjust margins to make room for yaxis title\n",
    "    fig.update_layout(margin=dict(t=50, l=100))\n",
    "\n",
    "    # add colorbar\n",
    "    fig['data'][0]['showscale'] = True\n",
    "    fig.write_html(\"matriz\" + \"\".join(nombre) + '.html')\n",
    "    fig.show()\n",
    "\n",
    "#funcion que limpia los tweets\n",
    "def cleanTxt(text):\n",
    "    # Cambia texto a minusculas\n",
    "    text = text.lower()\n",
    "    text = re.sub(r'@', '', text) #quita las @menciones\n",
    "    text = re.sub(r'#', '', text) #quita los # simbolos\n",
    "    text = re.sub('(?:(?:https?|ftp):\\/\\/)?[\\w/\\-?=%.]+\\.[\\w/\\-?=%.]+','',text) #quita los hyper textos\n",
    "    # Expresiones regulares para remplazar simbolos por espacios \n",
    "    text = re.sub('[\\n(){}\\[\\]\\|,;\\\"\\“\\”\\‘\\’\\'\\«\\»!¡?¿]', '', text)\n",
    "    # Elimina Manejo de puntos suspensivos\n",
    "    text = re.sub('\\.[\\.]+', '', text)\n",
    "    # Elimina Manejo de caracteres aislados\n",
    "    text = re.sub('\\s.\\s', '', text)\n",
    "    # Elimina Manejo de números aislados\n",
    "    text = re.sub('\\s[0-9]+\\s', '', text)\n",
    "    # Elimina Manejo de fechas\n",
    "    text = re.sub('\\d\\d/\\d\\d/\\d\\d|\\d\\d/\\d\\d/\\d\\d\\d\\d', '', text)\n",
    "    # Se establece token de risa\n",
    "    text = re.sub(r'\\s([jJ][AaEeIi])+\\s', r' <risa> ', text)\n",
    "    text = re.sub(r'(ja|je|ji|JA|JE|JI|\\s a|\\s e|\\s i)([jJ][AaEeIi])(\\w?)', r' <risa> ', text)\n",
    "    text = re.sub(r'\\s((ha|Ha|he|HE)[hH][AaEeIi])+\\w', r' <risa> ', text)\n",
    "    return text\n",
    "\n",
    "def separaData(df,nombreCol,tam):\n",
    "    Train_X, Test_X, Train_Y, Test_Y = model_selection.train_test_split(df[nombreCol],df['clasification'],test_size=tam)\n",
    "    \n",
    "    Encoder = LabelEncoder()\n",
    "    Train_Y = Encoder.fit_transform(Train_Y)\n",
    "    Test_Y = Encoder.fit_transform(Test_Y)\n",
    "    \n",
    "    Tfidf_vect = TfidfVectorizer(max_features=None, ngram_range = (1,3), max_df = 0.9, min_df = 5)\n",
    "    Tfidf_vect.fit(df[nombreCol])\n",
    "    Train_X_Tfidf = Tfidf_vect.transform(Train_X)\n",
    "    Test_X_Tfidf = Tfidf_vect.transform(Test_X)\n",
    "    #print(Tfidf_vect.vocabulary_)\n",
    "    len(Tfidf_vect.vocabulary_)\n",
    "    return Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y, Tfidf_vect\n",
    "\n",
    "def GraficaData(df,nombreCol):\n",
    "    with warnings.catch_warnings():\n",
    "        # ignore all caught warnings\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        # execute code that will generate warnings\n",
    "        Train_X, Test_X, Train_Y, Test_Y = model_selection.train_test_split(df[nombreCol],df['clasification'],test_size=1)\n",
    "        y = Train_Y\n",
    "        Encoder = LabelEncoder()\n",
    "        Train_Y = Encoder.fit_transform(Train_Y)\n",
    "        Test_Y = Encoder.fit_transform(Test_Y)\n",
    "\n",
    "        Tfidf_vect = TfidfVectorizer(max_features=None, ngram_range = (1,3), max_df = 0.9, min_df = 5)\n",
    "        Tfidf_vect.fit(df[nombreCol])\n",
    "\n",
    "        Train_X_Tfidf = Tfidf_vect.transform(Train_X)\n",
    "        Test_X_Tfidf = Tfidf_vect.transform(Test_X)\n",
    "        #print(Tfidf_vect.vocabulary_)\n",
    "        len(Tfidf_vect.vocabulary_)\n",
    "        #print(Train_X_Tfidf)\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        tsne = TSNEVisualizer(cmap='PuOr')\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        tsne.fit(Train_X_Tfidf, y)\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        #tsne.show()\n",
    "\n",
    "def ModeloClasificador(modeloToTrain, df,nombre):\n",
    "    Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y, Tfidf_vect = separaData(df,'TokenizeTweetsTidy_text',0.2)\n",
    "    \n",
    "    if modeloToTrain == LogisticRegression:\n",
    "        myModelo = modeloToTrain(solver='saga',max_iter=100, C=5)\n",
    "    elif modeloToTrain == SVC:\n",
    "            myModelo = modeloToTrain(gamma=0.01, C=100,probability=True, kernel= 'rbf') \n",
    "        \n",
    "    myModelo.fit(Train_X_Tfidf,Train_Y)\n",
    "    \n",
    "    # predict the labels on validation dataset\n",
    "    # predictionsModel = myModelo.predict(Test_X_Tfidf)\n",
    "    # Use accuracy_score function to get the accuracy    \n",
    "    predicciones = prediccionesModelo(myModelo,Test_X_Tfidf,Test_Y,nombre)\n",
    "    print(nombre,\"Accuracy Score -> \",accuracy_score(predicciones, Test_Y)*100)\n",
    "    disp1 = plot_confusion_matrixPlt(myModelo,Test_X_Tfidf,Test_Y)\n",
    "    class_names = ['Liberales','Conservadores']\n",
    "    plot_confusion_matrix(Test_Y,predicciones,class_names,disp1.confusion_matrix,nombre)\n",
    "    #curvaRocAuc(Test_Y,predicciones)\n",
    "    #curvaRocAucSalida(Test_Y,predicciones,0)\n",
    "    curvasRocAucSalida(Test_Y,predicciones,nombre)\n",
    "    curvaRocAuc(Test_Y,predicciones)\n",
    "\n",
    "    return myModelo, Tfidf_vect\n",
    "\n",
    "def ModeloClasificadorKFoldCV(modeloToTrain, df,nombre):\n",
    "    X, _, y, _, Tfidf_vect = separaData(df,'TokenizeTweetsTidy_text',1)\n",
    "\n",
    "    if modeloToTrain == LogisticRegression:\n",
    "        myModelo = modeloToTrain(solver='saga',max_iter=100 , C=5)\n",
    "    elif modeloToTrain == SVC:\n",
    "        myModelo = modeloToTrain(gamma=0.01, C=100,probability=True, kernel= 'rbf') \n",
    "\n",
    "    cv = KFold(n_splits=10, random_state=None, shuffle=False)\n",
    "    scores = []\n",
    "\n",
    "    for train_index, test_index in cv.split(X):\n",
    "        #print(\"Train Index: \", train_index, \"\\n\")\n",
    "        #print(\"Test Index: \", test_index)\n",
    "\n",
    "        Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y = X[train_index], X[test_index], y[train_index], y[test_index]\n",
    "        myModelo.fit(Train_X_Tfidf, Train_Y)\n",
    "        scores.append(myModelo.score(Test_X_Tfidf, Test_Y))\n",
    "\n",
    "    print(np.mean(scores))\n",
    "    predicciones = prediccionesModelo(myModelo,Test_X_Tfidf,Test_Y, nombre)\n",
    "   \n",
    "    disp1 = plot_confusion_matrixPlt(myModelo,Test_X_Tfidf,Test_Y)\n",
    "    class_names = ['Liberales','Conservadores']\n",
    "    plot_confusion_matrix(Test_Y,predicciones,class_names,disp1.confusion_matrix, nombre)\n",
    "    #curvaRocAuc(Test_Y,predicciones)\n",
    "    #curvaRocAucSalida(Test_Y,predicciones,0)\n",
    "    curvasRocAucSalida(Test_Y,predicciones, nombre)\n",
    "    curvaRocAuc(Test_Y,predicciones)\n",
    "\n",
    "    prediccionesModelo2(myModelo,predicciones,Test_Y, nombre)\n",
    "    return myModelo, Tfidf_vect\n",
    "\n",
    "def ModeloClasificadorFinal(modeloToTrain, df,nombre):\n",
    "    Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y, Tfidf_vect = separaData(df,'TokenizeTweetsTidy_text',1)\n",
    "    \n",
    "    if modeloToTrain == LogisticRegression:\n",
    "        myModelo = modeloToTrain(solver='saga',max_iter=100, C=5)\n",
    "    elif modeloToTrain == SVC:\n",
    "            myModelo = modeloToTrain(gamma=0.01, C=100,probability=True, kernel= 'rbf',) \n",
    "        \n",
    "    myModelo.fit(Train_X_Tfidf,Train_Y)\n",
    "    \n",
    "    return myModelo, Tfidf_vect\n",
    "  \n",
    "def ModeloClasificadorKFoldCVThreshold(modeloToTrain, df,nombre):\n",
    "    X, _, y, _, Tfidf_vect = separaData(df,'TokenizeTweetsTidy_text',1)\n",
    "\n",
    "    if modeloToTrain == LogisticRegression:\n",
    "        myModelo = modeloToTrain(solver='saga',max_iter=100 , C=5)\n",
    "    elif modeloToTrain == SVC:\n",
    "        myModelo = modeloToTrain(gamma=0.01, C=100,probability=True, kernel= 'rbf') \n",
    "\n",
    "    cv = KFold(n_splits=10, random_state=None, shuffle=False)\n",
    "    #scores = []\n",
    "\n",
    "    for train_index, test_index in cv.split(X):\n",
    "        #print(\"Train Index: \", train_index, \"\\n\")\n",
    "        #print(\"Test Index: \", test_index)\n",
    "\n",
    "        Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y = X[train_index], X[test_index], y[train_index], y[test_index]\n",
    "        myModelo.fit(Train_X_Tfidf, Train_Y)\n",
    "        \n",
    "        #scores.append(myModelo.score(Test_X_Tfidf, Test_Y))\n",
    "    #Sacar el mejor thresholds \n",
    "    yhat = myModelo.predict_proba(Test_X_Tfidf)\n",
    "    # keep probabilities for the positive outcome only\n",
    "    yhat = yhat[:, 1]\n",
    "    \n",
    "    # calculate roc curves\n",
    "    fpr, tpr, thresholds = roc_curve(Test_Y, yhat)\n",
    "    # calculate the g-mean for each threshold\n",
    "    gmeans = sqrt(tpr * (1-fpr))\n",
    "    # locate the index of the largest g-mean\n",
    "    ix = argmax(gmeans)\n",
    "    print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))\n",
    "    pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')\n",
    "    pyplot.plot(fpr, tpr, marker='.', label='Logistic')\n",
    "    pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')\n",
    "    # axis labels\n",
    "    pyplot.xlabel('False Positive Rate')\n",
    "    pyplot.ylabel('True Positive Rate')\n",
    "    pyplot.legend()\n",
    "    # show the plot\n",
    "    pyplot.show()\n",
    "\n",
    "    y_scores = myModelo.decision_function(Test_X_Tfidf)\n",
    "\n",
    "    y_pred_adj = adjusted_classes(y_scores, t = thresholds[ix])\n",
    "    y_pred_adj = np.array(y_pred_adj)\n",
    "\n",
    "    p, r, thresholds = precision_recall_curve(Test_Y, y_scores)\n",
    "    disp1 = precision_recall_threshold(Test_Y, y_scores, nombre, myModelo, p, r, thresholds, t = thresholds[ix])\n",
    "    plot_precision_recall_vs_threshold(p, r, thresholds)  \n",
    "\n",
    "    #print(np.mean(scores))\n",
    "    #predicciones = prediccionesModelo(myModelo,Test_X_Tfidf,Test_Y, nombre)\n",
    "    #disp1 = plot_confusion_matrixPlt(myModelo,Test_X_Tfidf,Test_Y)\n",
    "    class_names = ['Liberales','Conservadores']\n",
    "    plot_confusion_matrix(Test_Y,y_pred_adj,class_names,disp1, nombre)\n",
    "    #myModelo.predict(y_pred_adj)\n",
    "    #curvaRocAuc(Test_Y,predicciones)\n",
    "    #curvaRocAucSalida(Test_Y,predicciones,0)\n",
    "    ####curvasRocAucSalida(Test_Y,predicciones, nombre)\n",
    "    ####curvaRocAuc(Test_Y,predicciones)\n",
    "    prediccionesModelo2(myModelo,y_pred_adj,Test_Y, nombre)\n",
    "\n",
    "    return myModelo, Tfidf_vect\n",
    "    \n",
    "def prediccionesModelo2(modelo,y_pred_adj,Test_Y, nombre):\n",
    "    with warnings.catch_warnings():\n",
    "        # ignore all caught warnings\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        # execute code that will generate warnings\n",
    "        #predicciones = modelo.predict(Test_X_Tfidf)\n",
    "        class_names = ['Liberales','Conservadores']\n",
    "        clf_report = classification_report(Test_Y,y_pred_adj,target_names=class_names,output_dict=True)\n",
    "        print(clf_report)\n",
    "        #classes = [\"Liberales\", \"Conservadores\"]\n",
    "        #visualizer = ClassificationReport(modelo, classes=classes, support=True, cmap='PuOr')\n",
    "        #visualizer.score(Test_X_Tfidf, Test_Y)        # Evaluate the model on the test data\n",
    "        sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, annot=True, cmap='PuOr')\n",
    "        plt.show()                       # Finalize and show the figure\n",
    "    #return predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "rJWFUAr5Tipq"
   },
   "outputs": [],
   "source": [
    "#Lematizando\n",
    "#limpiando el texto\n",
    "df['CleanTweets'] = df['tidy_text'].apply(cleanTxt)\n",
    "df['CleanTweets'].dropna(inplace=True)\n",
    "#Tokenizar\n",
    "df['TokenizeTweetsTidy_text'] = df.apply(lambda row: nltk.word_tokenize(row['CleanTweets']), axis=1)\n",
    "#comillas a las palabras\n",
    "df['TokenizeTweetsTidy_text'] = df.TokenizeTweetsTidy_text.astype(str)\n",
    "\n",
    "liberales = df[df[\"clasification\"]==0]\n",
    "conservadores  = df[df[\"clasification\"]==1]\n",
    "neutral = df[df[\"clasification\"]==2]\n",
    "noAplica = df[df[\"clasification\"]==3]\n",
    "\n",
    "libCon = pd.concat([liberales,conservadores]).reset_index()\n",
    "libConNeu = pd.concat([liberales,conservadores,neutral]).reset_index()\n",
    "libConNo = pd.concat([liberales,conservadores,noAplica]).reset_index()\n",
    "libConNeuNo = pd.concat([liberales,conservadores,neutral,noAplica]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "ATIi9OOh1-4s"
   },
   "outputs": [],
   "source": [
    "df['CleanTweetsNo'] = df['full_text'].apply(cleanTxt)\n",
    "df['CleanTweetsNo'].dropna(inplace=True)\n",
    "#Tokenizar\n",
    "df['TokenizeTweetsTidy_textNo'] = df.apply(lambda row: nltk.word_tokenize(row['CleanTweetsNo']), axis=1)\n",
    "#comillas a las palabras\n",
    "df['TokenizeTweetsTidy_textNo'] = df.TokenizeTweetsTidy_text.astype(str)\n",
    "\n",
    "liberales = df[df[\"clasification\"]==0]\n",
    "conservadores  = df[df[\"clasification\"]==1]\n",
    "neutral = df[df[\"clasification\"]==2]\n",
    "noAplica = df[df[\"clasification\"]==3]\n",
    "\n",
    "libConNoLem = pd.concat([liberales,conservadores]).reset_index()\n",
    "libConNeuNoLem = pd.concat([liberales,conservadores,neutral]).reset_index()\n",
    "libConNoNoLem = pd.concat([liberales,conservadores,noAplica]).reset_index()\n",
    "libConNeuNoNOLem = pd.concat([liberales,conservadores,neutral,noAplica]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bL8UIBRYqRPt",
    "outputId": "2228a5b9-8cbd-439f-8225-51948a89b4f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for f1_weighted\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 5, 'max_iter': 100, 'solver': 'saga'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.660 (+/-0.020) for {'C': 100, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.655 (+/-0.018) for {'C': 1000, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.649 (+/-0.010) for {'C': 1000, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.650 (+/-0.009) for {'C': 1000, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.650 (+/-0.009) for {'C': 1000, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.650 (+/-0.009) for {'C': 1000, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.650 (+/-0.009) for {'C': 1000, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.650 (+/-0.009) for {'C': 1000, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.675 (+/-0.027) for {'C': 5, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.666 (+/-0.031) for {'C': 10, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.658 (+/-0.017) for {'C': 100, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.658 (+/-0.015) for {'C': 100, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.651 (+/-0.014) for {'C': 1000, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.651 (+/-0.013) for {'C': 1000, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.652 (+/-0.010) for {'C': 1000, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.651 (+/-0.008) for {'C': 1000, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.653 (+/-0.010) for {'C': 1000, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.001, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.523 (+/-0.002) for {'C': 0.01, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.524 (+/-0.004) for {'C': 0.1, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.647 (+/-0.028) for {'C': 1, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.677 (+/-0.026) for {'C': 5, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.677 (+/-0.026) for {'C': 5, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.677 (+/-0.026) for {'C': 5, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.677 (+/-0.026) for {'C': 5, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.677 (+/-0.026) for {'C': 5, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.666 (+/-0.029) for {'C': 10, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.666 (+/-0.029) for {'C': 10, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.666 (+/-0.030) for {'C': 10, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.666 (+/-0.030) for {'C': 10, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.666 (+/-0.029) for {'C': 10, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.659 (+/-0.015) for {'C': 100, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.658 (+/-0.016) for {'C': 100, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.658 (+/-0.015) for {'C': 100, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.658 (+/-0.015) for {'C': 100, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.657 (+/-0.020) for {'C': 1000, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.651 (+/-0.015) for {'C': 1000, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.652 (+/-0.012) for {'C': 1000, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.653 (+/-0.012) for {'C': 1000, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.652 (+/-0.010) for {'C': 1000, 'max_iter': 10000, 'solver': 'saga'}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.62      0.55       491\n",
      "           1       0.77      0.68      0.72       950\n",
      "\n",
      "    accuracy                           0.66      1441\n",
      "   macro avg       0.64      0.65      0.64      1441\n",
      "weighted avg       0.68      0.66      0.66      1441\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y, Tfidf_vect = separaData(libCon,'TokenizeTweetsTidy_text',0.3)\n",
    "# Set the parameters by cross-validation\n",
    "tuned_parameters = [{'solver': ['newton-cg'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['lbfgs'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['liblinear'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['sag'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['saga'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    ]\n",
    "scores = ['f1_weighted']\n",
    "\n",
    "for score in scores:\n",
    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "    print()\n",
    "\n",
    "    clf = GridSearchCV(\n",
    "        LogisticRegression(), tuned_parameters, scoring='%s' % score\n",
    "    )\n",
    "    clf.fit(Train_X_Tfidf, Train_Y)\n",
    "\n",
    "    print(\"Best parameters set found on development set:\")\n",
    "    print()\n",
    "    print(clf.best_params_)\n",
    "    print()\n",
    "    print(\"Grid scores on development set:\")\n",
    "    print()\n",
    "    means = clf.cv_results_['mean_test_score']\n",
    "    stds = clf.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean, std * 2, params))\n",
    "    print()\n",
    "\n",
    "    print(\"Detailed classification report:\")\n",
    "    print()\n",
    "    print(\"The model is trained on the full development set.\")\n",
    "    print(\"The scores are computed on the full evaluation set.\")\n",
    "    print()\n",
    "\n",
    "    yhat = clf.predict_proba(Test_X_Tfidf)\n",
    "    # keep probabilities for the positive outcome only\n",
    "    yhat = yhat[:, 1]\n",
    "    \n",
    "    # calculate roc curves\n",
    "    fpr, tpr, thresholds = roc_curve(Test_Y, yhat)\n",
    "    # calculate the g-mean for each threshold\n",
    "    gmeans = sqrt(tpr * (1-fpr))\n",
    "    # locate the index of the largest g-mean\n",
    "    ix = argmax(gmeans)\n",
    "    y_scores = clf.decision_function(Test_X_Tfidf)\n",
    "    y_pred_adj = adjusted_classes(y_scores, t = thresholds[ix])\n",
    "    y_pred_adj = np.array(y_pred_adj)\n",
    "\n",
    "    y_true, y_pred = Test_Y, y_pred_adj\n",
    "    print(classification_report(y_true, y_pred))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "je1bT7JmMr67",
    "outputId": "f180cd21-725d-4a01-8318-2a4396375938"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for f1_weighted\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
      "\n",
      "The max_iter was reached which means the coef_ did not converge\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 5, 'max_iter': 100, 'solver': 'saga'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.644 (+/-0.021) for {'C': 1000, 'max_iter': 100, 'solver': 'newton-cg'}\n",
      "0.644 (+/-0.021) for {'C': 1000, 'max_iter': 500, 'solver': 'newton-cg'}\n",
      "0.644 (+/-0.021) for {'C': 1000, 'max_iter': 1000, 'solver': 'newton-cg'}\n",
      "0.644 (+/-0.021) for {'C': 1000, 'max_iter': 5000, 'solver': 'newton-cg'}\n",
      "0.644 (+/-0.021) for {'C': 1000, 'max_iter': 10000, 'solver': 'newton-cg'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.656 (+/-0.019) for {'C': 100, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.648 (+/-0.032) for {'C': 1000, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "0.645 (+/-0.021) for {'C': 1000, 'max_iter': 500, 'solver': 'lbfgs'}\n",
      "0.645 (+/-0.021) for {'C': 1000, 'max_iter': 1000, 'solver': 'lbfgs'}\n",
      "0.645 (+/-0.021) for {'C': 1000, 'max_iter': 5000, 'solver': 'lbfgs'}\n",
      "0.645 (+/-0.021) for {'C': 1000, 'max_iter': 10000, 'solver': 'lbfgs'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.003) for {'C': 0.1, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.003) for {'C': 0.1, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.003) for {'C': 0.1, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.003) for {'C': 0.1, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.003) for {'C': 0.1, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.648 (+/-0.016) for {'C': 1, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.648 (+/-0.016) for {'C': 1, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.648 (+/-0.016) for {'C': 1, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.648 (+/-0.016) for {'C': 1, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.648 (+/-0.016) for {'C': 1, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.673 (+/-0.012) for {'C': 5, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.673 (+/-0.012) for {'C': 5, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.673 (+/-0.012) for {'C': 5, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.673 (+/-0.012) for {'C': 5, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.673 (+/-0.012) for {'C': 5, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.644 (+/-0.019) for {'C': 1000, 'max_iter': 100, 'solver': 'liblinear'}\n",
      "0.644 (+/-0.019) for {'C': 1000, 'max_iter': 500, 'solver': 'liblinear'}\n",
      "0.644 (+/-0.019) for {'C': 1000, 'max_iter': 1000, 'solver': 'liblinear'}\n",
      "0.644 (+/-0.019) for {'C': 1000, 'max_iter': 5000, 'solver': 'liblinear'}\n",
      "0.644 (+/-0.019) for {'C': 1000, 'max_iter': 10000, 'solver': 'liblinear'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.647 (+/-0.020) for {'C': 1, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.672 (+/-0.012) for {'C': 5, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.655 (+/-0.021) for {'C': 100, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.653 (+/-0.022) for {'C': 100, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.653 (+/-0.021) for {'C': 100, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.653 (+/-0.020) for {'C': 100, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.654 (+/-0.022) for {'C': 100, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.651 (+/-0.025) for {'C': 1000, 'max_iter': 100, 'solver': 'sag'}\n",
      "0.643 (+/-0.027) for {'C': 1000, 'max_iter': 500, 'solver': 'sag'}\n",
      "0.645 (+/-0.023) for {'C': 1000, 'max_iter': 1000, 'solver': 'sag'}\n",
      "0.645 (+/-0.021) for {'C': 1000, 'max_iter': 5000, 'solver': 'sag'}\n",
      "0.645 (+/-0.023) for {'C': 1000, 'max_iter': 10000, 'solver': 'sag'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.0001, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.001, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.01, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.515 (+/-0.001) for {'C': 0.1, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.648 (+/-0.017) for {'C': 1, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.648 (+/-0.017) for {'C': 1, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.648 (+/-0.016) for {'C': 1, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.648 (+/-0.016) for {'C': 1, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.647 (+/-0.017) for {'C': 1, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.674 (+/-0.013) for {'C': 5, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.674 (+/-0.013) for {'C': 5, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.674 (+/-0.013) for {'C': 5, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.674 (+/-0.013) for {'C': 5, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.674 (+/-0.013) for {'C': 5, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.669 (+/-0.021) for {'C': 10, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.669 (+/-0.021) for {'C': 10, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.669 (+/-0.022) for {'C': 10, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.670 (+/-0.021) for {'C': 10, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.659 (+/-0.016) for {'C': 100, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.654 (+/-0.022) for {'C': 100, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.654 (+/-0.021) for {'C': 100, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.654 (+/-0.021) for {'C': 100, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.654 (+/-0.021) for {'C': 100, 'max_iter': 10000, 'solver': 'saga'}\n",
      "0.657 (+/-0.020) for {'C': 1000, 'max_iter': 100, 'solver': 'saga'}\n",
      "0.645 (+/-0.024) for {'C': 1000, 'max_iter': 500, 'solver': 'saga'}\n",
      "0.645 (+/-0.026) for {'C': 1000, 'max_iter': 1000, 'solver': 'saga'}\n",
      "0.644 (+/-0.024) for {'C': 1000, 'max_iter': 5000, 'solver': 'saga'}\n",
      "0.644 (+/-0.026) for {'C': 1000, 'max_iter': 10000, 'solver': 'saga'}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.42      0.49       469\n",
      "           1       0.75      0.85      0.80       972\n",
      "\n",
      "    accuracy                           0.71      1441\n",
      "   macro avg       0.66      0.63      0.64      1441\n",
      "weighted avg       0.69      0.71      0.70      1441\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y, Tfidf_vect = separaData(libCon,'TokenizeTweetsTidy_text',0.3)\n",
    "# Set the parameters by cross-validation\n",
    "tuned_parameters = [{'solver': ['newton-cg'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['lbfgs'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['liblinear'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['sag'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'solver': ['saga'], 'max_iter': [100, 500, 1000, 5000,10000],'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    ]\n",
    "scores = ['f1_weighted']\n",
    "\n",
    "for score in scores:\n",
    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "    print()\n",
    "\n",
    "    clf = GridSearchCV(\n",
    "        LogisticRegression(), tuned_parameters, scoring='%s' % score\n",
    "    )\n",
    "    clf.fit(Train_X_Tfidf, Train_Y)\n",
    "\n",
    "    print(\"Best parameters set found on development set:\")\n",
    "    print()\n",
    "    print(clf.best_params_)\n",
    "    print()\n",
    "    print(\"Grid scores on development set:\")\n",
    "    print()\n",
    "    means = clf.cv_results_['mean_test_score']\n",
    "    stds = clf.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean, std * 2, params))\n",
    "    print()\n",
    "\n",
    "    print(\"Detailed classification report:\")\n",
    "    print()\n",
    "    print(\"The model is trained on the full development set.\")\n",
    "    print(\"The scores are computed on the full evaluation set.\")\n",
    "    print()\n",
    "    y_true, y_pred = Test_Y, clf.predict(Test_X_Tfidf)\n",
    "    print(classification_report(y_true, y_pred))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NepqfpcaqSMy",
    "outputId": "7547ca53-6901-45fb-effa-93253cdc943a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for f1_weighted\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 100, 'gamma': 1, 'kernel': 'rbf'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.540 (+/-0.009) for {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.630 (+/-0.021) for {'C': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.557 (+/-0.006) for {'C': 1, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.557 (+/-0.006) for {'C': 1, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.006) for {'C': 1, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.006) for {'C': 1, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 5, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 5, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 5, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.667 (+/-0.018) for {'C': 5, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.673 (+/-0.044) for {'C': 5, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.560 (+/-0.006) for {'C': 5, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.557 (+/-0.006) for {'C': 5, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.553 (+/-0.009) for {'C': 5, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.006) for {'C': 5, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.547 (+/-0.010) for {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.666 (+/-0.045) for {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.673 (+/-0.041) for {'C': 10, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.560 (+/-0.006) for {'C': 10, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.558 (+/-0.011) for {'C': 10, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.553 (+/-0.009) for {'C': 10, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.006) for {'C': 10, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.547 (+/-0.010) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.663 (+/-0.048) for {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.644 (+/-0.046) for {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.673 (+/-0.031) for {'C': 100, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.560 (+/-0.012) for {'C': 100, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.557 (+/-0.011) for {'C': 100, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.553 (+/-0.009) for {'C': 100, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.006) for {'C': 100, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.547 (+/-0.010) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.662 (+/-0.049) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.635 (+/-0.032) for {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.643 (+/-0.046) for {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.671 (+/-0.030) for {'C': 1000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.560 (+/-0.012) for {'C': 1000, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.557 (+/-0.011) for {'C': 1000, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.553 (+/-0.009) for {'C': 1000, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.006) for {'C': 1000, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'kernel': 'linear'}\n",
      "0.669 (+/-0.021) for {'C': 1, 'kernel': 'linear'}\n",
      "0.650 (+/-0.051) for {'C': 5, 'kernel': 'linear'}\n",
      "0.640 (+/-0.043) for {'C': 10, 'kernel': 'linear'}\n",
      "0.631 (+/-0.035) for {'C': 100, 'kernel': 'linear'}\n",
      "0.615 (+/-0.019) for {'C': 1000, 'kernel': 'linear'}\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.90      0.57       504\n",
      "           1       0.85      0.31      0.45       937\n",
      "\n",
      "    accuracy                           0.52      1441\n",
      "   macro avg       0.63      0.61      0.51      1441\n",
      "weighted avg       0.70      0.52      0.49      1441\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y, Tfidf_vect = separaData(libCon,'TokenizeTweetsTidy_text',0.3)\n",
    "# Set the parameters by cross-validation\n",
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [0.0001,0.001,0.01,0.1,1,5,10,100,1000],\n",
    "                     'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'kernel': ['linear'], 'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]}]\n",
    "\n",
    "scores = ['f1_weighted']\n",
    "\n",
    "for score in scores:\n",
    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "    print()\n",
    "\n",
    "    clf = GridSearchCV(\n",
    "        SVC(probability=True), tuned_parameters, scoring='%s' % score\n",
    "    )\n",
    "    clf.fit(Train_X_Tfidf, Train_Y)\n",
    "\n",
    "    print(\"Best parameters set found on development set:\")\n",
    "    print()\n",
    "    print(clf.best_params_)\n",
    "    print()\n",
    "    print(\"Grid scores on development set:\")\n",
    "    print()\n",
    "    means = clf.cv_results_['mean_test_score']\n",
    "    stds = clf.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean, std * 2, params))\n",
    "    print()\n",
    "\n",
    "    yhat = clf.predict_proba(Test_X_Tfidf)\n",
    "    # keep probabilities for the positive outcome only\n",
    "    yhat = yhat[:, 1]\n",
    "    \n",
    "    # calculate roc curves\n",
    "    fpr, tpr, thresholds = roc_curve(Test_Y, yhat)\n",
    "    # calculate the g-mean for each threshold\n",
    "    gmeans = sqrt(tpr * (1-fpr))\n",
    "    # locate the index of the largest g-mean\n",
    "    ix = argmax(gmeans)\n",
    "    y_scores = clf.decision_function(Test_X_Tfidf)\n",
    "    y_pred_adj = adjusted_classes(y_scores, t = thresholds[ix])\n",
    "    y_pred_adj = np.array(y_pred_adj)\n",
    "\n",
    "    y_true, y_pred = Test_Y, y_pred_adj\n",
    "    print(classification_report(y_true, y_pred))\n",
    "\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EnZX42NlA_rY",
    "outputId": "c3f499b1-2d55-49bf-b94d-a3f214815cea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for f1_weighted\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.534 (+/-0.008) for {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.624 (+/-0.019) for {'C': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.557 (+/-0.014) for {'C': 1, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.558 (+/-0.013) for {'C': 1, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.549 (+/-0.015) for {'C': 1, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.016) for {'C': 1, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 5, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 5, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 5, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.657 (+/-0.040) for {'C': 5, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.671 (+/-0.028) for {'C': 5, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.562 (+/-0.017) for {'C': 5, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.556 (+/-0.013) for {'C': 5, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.550 (+/-0.014) for {'C': 5, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.016) for {'C': 5, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.541 (+/-0.013) for {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.672 (+/-0.027) for {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.672 (+/-0.026) for {'C': 10, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.562 (+/-0.018) for {'C': 10, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.553 (+/-0.010) for {'C': 10, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.550 (+/-0.014) for {'C': 10, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.016) for {'C': 10, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.541 (+/-0.012) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.674 (+/-0.023) for {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.651 (+/-0.026) for {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.670 (+/-0.027) for {'C': 100, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.558 (+/-0.016) for {'C': 100, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.554 (+/-0.011) for {'C': 100, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.550 (+/-0.014) for {'C': 100, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.016) for {'C': 100, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.541 (+/-0.012) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.674 (+/-0.022) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.641 (+/-0.028) for {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.654 (+/-0.019) for {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.669 (+/-0.024) for {'C': 1000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.558 (+/-0.016) for {'C': 1000, 'gamma': 5, 'kernel': 'rbf'}\n",
      "0.554 (+/-0.011) for {'C': 1000, 'gamma': 10, 'kernel': 'rbf'}\n",
      "0.550 (+/-0.014) for {'C': 1000, 'gamma': 100, 'kernel': 'rbf'}\n",
      "0.548 (+/-0.016) for {'C': 1000, 'gamma': 1000, 'kernel': 'rbf'}\n",
      "0.528 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}\n",
      "0.528 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}\n",
      "0.528 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}\n",
      "0.528 (+/-0.001) for {'C': 0.1, 'kernel': 'linear'}\n",
      "0.658 (+/-0.037) for {'C': 1, 'kernel': 'linear'}\n",
      "0.655 (+/-0.038) for {'C': 5, 'kernel': 'linear'}\n",
      "0.647 (+/-0.044) for {'C': 10, 'kernel': 'linear'}\n",
      "0.634 (+/-0.023) for {'C': 100, 'kernel': 'linear'}\n",
      "0.626 (+/-0.013) for {'C': 1000, 'kernel': 'linear'}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.45      0.50       505\n",
      "           1       0.73      0.82      0.77       936\n",
      "\n",
      "    accuracy                           0.69      1441\n",
      "   macro avg       0.65      0.63      0.64      1441\n",
      "weighted avg       0.68      0.69      0.68      1441\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Train_X_Tfidf, Test_X_Tfidf, Train_Y, Test_Y, Tfidf_vect = separaData(libCon,'TokenizeTweetsTidy_text',0.3)\n",
    "# Set the parameters by cross-validation\n",
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [0.0001,0.001,0.01,0.1,1,5,10,100,1000],\n",
    "                     'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]},\n",
    "                    {'kernel': ['linear'], 'C': [0.0001,0.001,0.01,0.1,1,5,10,100,1000]}]\n",
    "\n",
    "scores = ['f1_weighted']\n",
    "\n",
    "for score in scores:\n",
    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "    print()\n",
    "\n",
    "    clf = GridSearchCV(\n",
    "        SVC(), tuned_parameters, scoring='%s' % score\n",
    "    )\n",
    "    clf.fit(Train_X_Tfidf, Train_Y)\n",
    "\n",
    "    print(\"Best parameters set found on development set:\")\n",
    "    print()\n",
    "    print(clf.best_params_)\n",
    "    print()\n",
    "    print(\"Grid scores on development set:\")\n",
    "    print()\n",
    "    means = clf.cv_results_['mean_test_score']\n",
    "    stds = clf.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean, std * 2, params))\n",
    "    print()\n",
    "\n",
    "    print(\"Detailed classification report:\")\n",
    "    print()\n",
    "    print(\"The model is trained on the full development set.\")\n",
    "    print(\"The scores are computed on the full evaluation set.\")\n",
    "    print()\n",
    "    y_true, y_pred = Test_Y, clf.predict(Test_X_Tfidf)\n",
    "    print(classification_report(y_true, y_pred))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 401
    },
    "id": "MrV7LBveTipx",
    "outputId": "76884e29-534d-49ae-fa7f-a43a814ac5a1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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89YzHWfE9OnYepxrOFba8JXjPrRsveIPXT/lNjRpynUgbPnh9DxvftZ5Ih7h9RwlCn/FSP25YJW6Bm46UtYEfHGqjbyxLxVdknYi1HVXuW9uOH0ZYUlMNJKEGR9U6o3XcZV3Pd2TsWsc2sKzV490rJ7GVYX1XhWqo8MPYGCRjGywp2bg8y2+8+0M89O2XMZg5WYejvsWGm25+01ugkuvOlcGZjtHzvNMGoucsyJs2beK3fuu3+PjHP87U1BSVSoVNmzbx5JNPcv/99/PUU0+xefPmc335hMuYUIc89cr/YLhyoPHYkbFXAfjILQ/Rkmm/WEubFyUtFjWv5NDIPkphJ6H26crFUWzJVyDAqtUoUyoiNAJtBEoYepo9ru+q8IE1Pl5gQFAzsDAMTx0hiDwcO934XVprYqtGyTtX/ST5dCspKzunkat3eAw3MITz7ITSxvB0bz//9503nFEAsk6K21d28HTfyCwB0cawaWXnRem2rm8T2npwmImqR1smxR2rOvnFdywh0mHjvTgxvo/2fA8ldxw3LGO0JtIQ4fODQy3sGmpCCrAVBJFiz3AL+bRDyfeZdG1CLfCi+D1ylEEb3WjSEyKOsIUAhSHQgnKgyIuQ3tEsRyYy+JFECEFLSvPTGwwfuUHQlLLwwuiMKfekwSvhzXDOgrxo0SLuvfdefuZnfgaA3/md3+HGG2/kN3/zN3n00UdZunQpH/7whxdsoQmXD1t7H50lxjP5px1/zC9t+m8XeEVnJtKafzvYxr/uaeXkVJmhUoqMrenO+XRkA0bKDiVfoYThmhYXYwQGw/rOMveuGUdJmykvQ94JmX2trm/JMRhj5tSQd53YwrvXPzhvV/W67g5SSuDOE91KEd8QvB4B+N0PvB94im2Hxyi6mqa0ZNPKztrjFx4l42EMH7ttDaNll9GpV5iovMxz+58jZWdY1LySNYvfBcDg1GGyqVZast105HsoDUqOmxfZP5at3WDUz41AScn39kcEkUIbETtqibgL3qDJ2lGtNgxKGISIt0rlUxF5JyJnR/z1iz30juSxVVyrBkGgJXtHNJH2yDkh7dkUlSCcc1yna+ZLSHgjvKka8gMPPMADDzww67GvfvWrb2pBCZc3fuhybKz3jM85OX7ooqSvT8eXt77GI8/vZ6oaEUQ2fqipBtCUbqcj42HLAgPFEEvFVc2sE7K2o8K7V06y9UgHB8ZylHxJ3tGs7XR573XFWgSmsFQaozWhni3GUiiGp46wtfdR7r7+5xpGGSBY0pyhLZvlbT0ZthyszOovMgZaMpLmlCDvnL1Pw1YWv/8TH7xk9iHXSVmKQnknw4VehJAoaRFGASfG9wFw/dLbWbv4XXhhpZFBeGFoO1NVTTWwa4I5TaQN41WLlW0GWYkoemCruAYshWBNV5bD4y4VHywVx8r5VER7JmBtR4UgEhwcz1Lvy5Kivj3KsGdIEGiH5nQTm1d1z5ty37y6O3HsSnjTJD37CQtK0R3H4J/xOQdHdlwwQdYmouIX5k0La6PZdXw7X3r2ECOlWCwFsViGGvqnqixp7qAj387bew5y6zUnKXmQtUNsZfi3Q+3sGsxhKQslQ6ohvDyQItQB96wex5IOTekOLNnE/tFD5GyBreKLvWPFaeyhqWN8/vs7+buXDjNccpFC0tOS5f9452r+9698iHf+98c4NBagTSws+VRET5NLqzPME6/+mK6mZWxe97NnPQ9ZJ8WqzrP3dFwo6j7VolYzNxh0zUp0cOowaxe/CyUtsk5z42eMiGhNW+SdaE6jnTESKQSW0ixtVmhjCLXBkgIls3zmzg7c6g7+tVezdzSHH0nyTsTajgp3r5rgxFQKL4p7B4yBahjNGjDx1ZcqvO8GPW/KffPq7sSxK2FBSAQ5YUFpSrcjcM4oyqu7zn9Dhzaavf3PctT/Mcd37WTcTXPTkpXcsvL2RuNU78DzvNrfy2hp+uJuACE0KUsgCfmD+97GhiVNvHBwgP5JaBUBCAgiQd9oFiEMkQ6pp0+lgL6xDHeuEAgM3+m1OTqZY7BwLTknYl2nx/uuizuDtYaHt7ewa+gVvFAgpcGS4IUejzzXhxSCXQ/9DH+6ZTdP9/YzVhohpaqs73Z53+oS2sDg5EG29j5KMwszkzfS4ayo9HzhhRX8sIqSFiVvAi+oNATZUWmqQYl8qnXWzygculsWccPiAi+diOcdg0BKhZIOy9payDo+XlBBYEgpRcrO0plvJ2f3k7fbuXfNAe5eNUk5UOTsqBFpd2YDUkoTmXi/cqglQkw3gR0YhYcee5Q//3e/0Ei5153Gksg4YaFIBDlhQXGsNMs61nF0bNdpnzMzOh4pnODo2G6Wd2ygq/maBVmDNpof7vsH+icO8/D2Ng6NK3wdkbYO8vaeIf75Y/ejZFyjhLqN5cxJQgYh4m03HTmLrJOiK7+c42P70GgwUPQtyn4c8WpjiLRA1QwmSr5isJimbyzP/vEcUnjYCvxIsXMwCwLuXVPiu315dg5k8HRtAKCOLTtBM1wssOXgEB+7bQ0Pveet/Nrtq/n6C18ia/uz6tRCCkaKx8iKN5dx0EbTO/A8Q4XDeEG1Uc+d2fm9kKSsLCk7w2RlGNcvNZrhDBovrHJ0dBc39MxuCpVCsbh5FR+9YR8pS7BrUFD2BTnbsGlVB93NK/nuvpPkUq0NcdcGblvRBuYguvZO1/cyzyTraFa3V9g7nG+Icd3BK+dE2ApePF6h6JZpSudIWSpp4EpYcBJBTlhwNq/7WaqvVOdt7PrILQ8BUPQKfOvFP6QeWe7u3wIIfvqdv01TqnnOz71eIh3y6vEtDE0d4ssvdLBvJIOUcZNPoOGFYxV+/u+38T8efDt+WKUtA20Zw3jFnjVD1xjozEa0Z+P1hcafMT3XkLMjsnbEyWKacqCIdC1iM7G38T/sWsRwySHvRHRkAxAmHiGIoHckzeblJbYczjf8suu/WhNPLrJlxHCx0mjc8sMpmhwXOY/5RBj5BFb5nM8ZxNmCE+P7TlvPXWiUtOjKL2Nw8vApNXJDxs4xXDjG+iXhnCh93ZLbAPiofZh717i4UYo1XSt5S887cYN4i9Szh0aZqGraMjabV3fz8dtW8dyBPbhBpbZhrS7Ns/n4O/v58vPXsGe4iah2kyRFXG8eKVu0pkOOTwzzliUrF/x8JCRAIsgJ5wFLWnzw7R874z7kmWI8jeFbL/4hv7TpD9/w76xHeINTBxmYPMRUNWT/aJqZ+mWMQQrD88dG8EKLlJ1BCo/NKwp8/2Cesh9HVFJAztHcc52iOd1EpEMmyoOk7SzGaCKjgSoABddCyvhnvFAQRIqmVIQS8XaaSVdgjGJJi0UYeWijKfmCgWKagmfN8YMSxDcDQhhaM6LRuduUbsdSDtqENdOdOKoXQmApB5vcGz5ndU6t5zbWIuSseu5Cs7zrRvYNvkAQubV5x5KMnSOXasMPXbywMquGDHH9fWbDlzY2Pz76Ik/ufpTJSoW1bVnuvHsVnS23z5pRvah5JcfH96KkItKGuKAwe0+ZoxT/6Y4JfudphwnXaWyNqg+eUMJwbdulU4dPuPJIBDnhvOFYadb1vIt1Pe+a9fhI4QSn9ww2jBROvOH0dT3Cq48wHK1YeJHAmifbWvE1Ryeqjf2uP7VeIymyZzjN8Uk7nn9sInYNBvzu40/y0D2344cuKStLyZtAm4ggisf6NadDyn7cBBZpiSXjvcPagKqN6yv6ko7QQwqBkjb5lOR913+UP932DJbS6Ehial5e9bNiS80dy7saguJYaTrz19I/MX2MQggEkp62lVjVc5+qNrOeeyqnE8aFIGPn6cgvIQjjGxUpZOOmwFHpWTaic5F89Ucn+dfde9g9OIUbgGMplreW2LD4FT61ydDTsqnx7HpkHU/cGmmcQykUAkHGaaY500GoFVIFcWQsZpYxwFKpRjNeQsL5IDFfTbjgHB07s13q2b5/KjMjPCkkUioW5TUpNddVQwpFzrFY1dHEuiW3cU37etpy3Xzoes26ziod2ZDlbT6Lm0LcUPPE3hN89l+/PcvcwxhDOVBUA0VXLmBZq8vifJzS9rWkHFgcGMvgBYJIE5tUhLom5AErW8c5MfYELekAW8bie+pAxpIveWT7y3z6H7+GF/r4kc9w4SiRCTFoDPHrWcqhu3n5GzpfEAvtWOlk40YjZWfmfZ5jnU0Yz526CUj960bHtdEsbll5xqj8kef6+M5rJ9g7XKDoCUIjKPuKEwWLHx2XfGnb7lqzXUw9sv7gTb/GW699Dz2ta0hZORwrQ1Omg9ZsN1Ioih50ZNJ0ZC2kMLXudkNHVrG8vZvxindezkVCAiQRcsJFYHnHhlrN+PTffyPMjPCEkKStLDpVZHW7y76RbDwXF4EUCiksblveSXM6jijr6c+JygRfev4Jso6PNjP3C8OLJ8p8YF0GNyhjKRslFTnbJevERhRSwHDZJqqNAoR4qL0XSUSkEQJOFFK0ZQLuWDbJu1dOUHYdbl+W45kj7ZR9BYHG1xIQpJQmZcFE1eKxPT5CPMpdK8Zww+KcY490yEjxGHmz5nWeK5d/2/N3jJdPIjDYVpqupmV05q+hf6L3DQvjm6UeuQ5OHcYPXRwr3bARPf0xxFOjtA4puNORrKg11LVnAnYOaMbKY3Q3LZr1s7ZyuGnZe4h0yO4TzzBUOIwU011yeUezqKmZpW2thFGEG/ikbQdLKbKWlZh/JJxXEkFOuODE6ejZU3ymEW84XV2P8OLZuZBLtQHwqY0TPLwdjkw2EUSSXCrNxuVdfO3n7pj180paFFxDwdWIGXVFY2IjkLIPR0ZP0JqJRxQEYYCtDGs7KuwaakIbqAaK2BNK1Mb0xSltg2RR1qMjG9ZMKmKRN0Tcs3oCKeC1kRz7R3NEJh5kkFLTXd/lQPKjYyU2dA0xj2MjofZwgxKZs+z91kaz69gzvHz8u7MeD3yP42N7GS4ewZZpQu2jpENbdhFL2ladURgXglNrwq9nu1V9alSkI6KaK1edSAsiLSgH8Mqxl3j/DT8x72soaXHjtXfhDKRn3Qys6lrJfW9p44l9J7GUIq8ytddNzD8Szj+JIF+iHByd4qneAe5a3U1rNn3F7Xf86Xf+9jyNXXGX9Rtlpv+xELEHcT7dTtZp5r/enuK2m36aoxNVVnU0NSLjU1nc3EJzSlD06naX07OGsrYm50QEYSzOGVshZMjdqyYAeHkgH9s1SpAm9rMO9XSs3JSa3u/aN5blzhWT2CpqzPi9aXGJR168huGyjTplaEGkBZOupByoOVt16hijUZy5hrzv5PY5YlxHE+IFFdpaFiOEJNIhi5qXn5fu6tNxqgnImWjPxjaVk5UIS5pZM46VNChpSFkaPxxq+GPPx+luBtYu1gghEvOPhAtOIsiXGFOVKiv/4DGmvOmLrxLwobf0cM/apVfM3NWmVDO/tOkPF2wf8nypz562tZQ9h7ZslrbsmeugljS849oc/7bfrUWwMdoY1nZW+f6hXDx714s7sNd0lLl71QT3rJ7gHUsL/N6W1WgtEAIiDbqWvhbE4lCnEqg54tqeEXTnFaNlOHUOr5KGppTGDSSjkU1LOpxjG7moeSWMn/5mreqX2HV86xmPXxtNqAMclcaSNsPFY6w/g5hdTFKWYvOqbr7z2gmaHJh0mfamduJyw/pOF0ukXldD2qk3AzP9thPzj4QLyaX313aV4oURfcOTbPzz71I9ZcRPZAzffq2f8co4xhh+Y/P1c352//AUxybLvHNZJ135+Rt0LkW6mq9ZEEOQ00U7OwbOPJM01CFbex9lpHiM69s9jrQ3cWQyTRDFhhPrOqtEBnYP5bCkxFYRfiTZNRTPEb5n9QQtmYg17WX2juRr1pvxP61jU4mZnd5ZOx5kUEcbwZbDeQaLBi+K0622NDhWLDDaGA5PpPijrSswBlprdeh6uluiWLP4nfSOHTztsQ1NHcYNS2c5gwZrhviez+7qhaAerepokt2Dk7ihJGVHLG3yWd9V5SfXC1J29k01pF2q5h9nc1Pzwii5kbhMSQT5IhNpzRe37uVz391JwY9O+e50JKSNoVAt8/hre/n4xrWkLEXFD/iT7+/hz555jYIXYQBLCm5Y3MKzn7qXjHPuW04ucGQAACAASURBVGEuV95I6hPiyVQnJw7yvUNN9I20UvIVKStgfUeVD64roaTkKy90YDWyEqLmXKUb6WclDavaKxwYz1D0LHStfqyIR/6NlG06swEGWNtRmRXh/uBQGzsH8+QdQ2dWM1GtTSgKDVlbE2lBqFVc2xaGyarDM0fakQLeu3qKTKqLf9n5TezAJ38ynOWstbX3UQYnD87Zbzs/gopfJF+rvy9kd3XVLzFeHqA9t4SMk1+Q16xHsb9y62q+8+qjTJUO40WCfMrQnM6RdVrOe0PaheZsbmqR1jzyXB9bDw0zWnLJpSzuvm4xn9q8/orIql0NXDmf1suUv3i2l//8nZfxo5lpyPn36E65YKkyvYOj/N2OQ3xv/wivDUwxU8Yjbdh1cpJNDz/Jjs/85Hld++WOH7qMFI/xvUNN7BzI1ubrGrSxODCR48X+Fjat0FQC02ioqoudEKKRft7R38TekSaWt/oMFQ1TnoUwYKm4FjnlKgSGTSsmG3VniI1Eth1tYcqz0ToebNCdl7Sk4+YuS8KBsdisBCQYDQLKvsWB8XYEKQ6Opyn5kLUVN554hU9t0tx47abGsQkpwMTd26ff+w0pK4MXVMilWsGYBREzP/J5YucjFKrDRCZCCUVzppv7bvoEjlqYm8WMbfPRtz/I3pPPcnLyIFEUkLKzZ+3Uvhw4NRI+m5vaw1v38e09xxksukxWfcLI8NKxMbYfGeHrv7A5EeXLgESQLyIVP+C/fW/3DDE+8zi9rBVweFxxx5eexA1iB6HTsW+owEipelmlry80RXecqu/TO9Ia140bjVyxXeJrw4oPrHPIOWXcMH58GkHWjnBkHClLEZuBVCPVEG+BIGNFlENFwbM4MJZFCcPdq+J089MH2hmrxo1cQsQd2ZNufCHO2gGhFviRQArRMM0QxCWMoxOKkpchZYGtwIskL50QfGnbHv7iZ2+j6I4TRj5Sxk1uSlhEJpj3POScNiIdEOkQgaCnfd2CiNkTOx9hsjLQ2B9uMExWBnhi5yPc//ZPv+nXryOF5IaezaxfsvGCDMY439Qj4WPjBxkpVejKZ7mmbQXDhaPzuqn1TxziX/fl+fNneplyA0KtcZQibUkCbdhyYIDPPPYCv/yu5azp7rgkxm8mzM/l+6m9AviTH+xhpOy+7ucfGFcEM0bCzRVw0Xgk0JoXj43ywbdc++YXeomxUBOJmtLtuFGasq+wZDTH33ii6pPPrGHD4n386DjIGR3Q2sTbnnwtqfixCEdaEEaxuGLi1HMQCSwVe1RXfMWPTzYTGcHdKyc4OpVujPsDU3OPMhR9QVcuQgBjVbtWS66nnQ0SgxcalNTM9PaRAl4d1ExUJoE8JwpZOrMuWQcsZSO0INRB7TVslLRQShFqD2puVRuv+2itQUrDmxgqETtiDc8rIIXqMFW/dE7paz90KbrjNKXbARpfO1b6DZcrLlX2nHiOh7e9yp5BSckX5J0ya7t2svFan2va2nEU+BEUPcjZ8MfPBPSN7GW86lO/t/ejkIpPrVhhePjZ/Xz52T7aMpL/89Y2PvfBD2Cr5PJ/qZG8IxcJL4z40fFxpBBEDW/iM+PrszVoTMd32sBfbd/P4fHyFdOZrY1m59Ft7B3qw+gCSrWxunMZNy/fjH0OKVDHSrOio4ecU6hFwLPJ2AEnx7dz1wrwghS9oxkqgSRrx65ed62cItSGrBPhh4pJ18ILBZERtRgbQm1QWiOFoL+QIjSCwaJD1RdUayMAi55VG2xRG2QRCVa0VcnZMFB0KPkWQtS3Y0HGjoU7dpLSs6YxlV3FL379JfYMlplyF2OLgOs6XT556xiWspEoMqkmDAYvKAOxsYYXaMbKBb79yl/Rmm0hbWff1LSn8fIAkYnm/dnIRIyXB+hxXp+ZCcSGJofcZ9j/0hNoExLpEEPc1ObY6cZcaOsyjowhnt/9pW17eOlEbDhjSTgxZbF31OGp/Wmu6xQYowlCgacFxSr0FxRSTItx/XN0aueAQTBZNfzVC2NY8il+/yc+eCEPLeF1cHl/ei9jxiserh/OMDU4c43vjZJ3LDTw+N5+AD65aWHm5V4sgijkM//76zx/bIoTU2n8qAXHCrm2+TVuWtrLpzffxFt6Nr5h8bjnLQ/w7de+xrYj1VkGE9rA2s4qkS4jpM196zT3rK5Q8iUppfEiBdg4KmRtR4Uth9spuBaGaTGGuKygtQI0kVaxvSaC7cdbSSlNSyq+Eyj7isjEW9zaswHvWz1Wi7ojnj3WyqRrY4i7rG+7pkTfaDre+yxmf2YGyjaDlSmUlGTsFH4Ie4clX34e/uOmKRa1LeO26z7C07v/hkAogijgyf0Z+sayVHxF1olY2zHO/W8pEISxTeTZ9iPP19XbnluCEmreqUpKKNpzSxr/f6aMhzY63kN97GkC48KcGyeDNmFjLvTd1//cGdd6qVMOK7w6OH2TNVpWcU+CgEog2D2gKXqy5rVt8Gfsdz8bBkAYSh5sOThGxfeS9PUlRiLIF4mMJfjxiTG8aOFEePq1NRtXxCk9JWODg4/dtuay3gLxXx9/gmcOlxirpKkEtQuULzlZTOMdD3jkuV4+uUm8YTMLS1r8v+9/Dw/986PsH8vWImDD2s4K71k1gcEgULXnCl44nqZ3NEM5kORszfVdPneuKPBEn8KLJJE5XV1fEtVGM4JgyrXRGCZcg6Xi7VCt6RApDTctLpKy4s/Fe6+b4F09JQ5P2rSmI/rG8hwczzJUTlEJJHknoisXAnE3th/ZZJ3pjvB4GILhRCHLe97y83Tmmyl5kzVXM8OT+zPsGmpCClASKr7FzsEmoMhHbhjn2PhBsunraU5Dc7pplmDO7OqtC/LmVbGBRsbJ05zpbtSQ6xijacnG3dZn6hoOIsN4xWN48mUODG0niOYv7WgitNZIKRkpHmvsQb9cKfi6VgIxaANFXzXGglYDCGpZssjM2Cz/Bm/kjYbhosdgYYpVncn0qkuJRJAvEvd85XsUvPmdl86V7gwsbYW0JbFm1Icmql5jru7lhjaance28f39J4A4kqxfoISAcqDoIODVQTg2fuicRgV25BfxgbUV3u1PUQlUbe9wfWauQEpJELo8ub+Vlwfy2MpgS0OoLXYOWrw6mCHQgpSKJz2Z0zTbGQRam9g8pGYbkncC3EhS8Cwspdm8fLoTO9Tw1y/2cHA8ixcJjAFbGpa3enRmfUbKNiUv7sK+tiVgVUeKE4X5jl3ghpoTUwGdeTg6ugsvqOCFIX1jLbV5v3ZtrnMszFOuItIFDk8WcYNv0ZxRvG2pxcduXdXIRDzyXB+P7+1HSUHaVlSCcFZG5r6bPjGny7olu4T7bvoEMP8M5qOj+/j6y5PsH8szXnEJwyGWtyg2r2BWBuPUMwvxXOiiO05Hfukbev8vJTpSWVoykoofUQ3ADeKeBCVozM5+swgBLemIrvy5j+xMOD8kgnwRGClV2TdcxFYSP5pZ6Xm9aeu5z5OAp+HYJLRlbdpn9Mu0ZVKXrSl+78Dz7B3YRzmIBSkysy/Mde/iomcYL3vzmllEOqTojuOHHq3ZrjkRlGOlWdJyHf0TvThWhDYRfiQoBxbNThzZPrG/maf2txFogZLQZGuWNNuEJuTImMCWkmoozvruaQAjapJtaE0HOJYhiAS20my8dqpxfH/9Yg/7RvJIGV+Qy4GkCvSOSjKOicVTgC0ln3ibT2tLwA8PKTRzMyHZ2oQrL3TZfWIrXlRh0lNUfMWkazfq2LLmeDVccthyuI3FTT5ShlR9eP6YIor6+PVNglXdt7L14DDqFJWcnZFxuP/tn553H/LpZjA/3it58cQYbTmFJWHCc3l5IEWo27hn9QTzI/BDQyVwUPLyFpm0ZbNx+SK+tG0U96w9I2+UeBxozol465IQJU/1PUi42CSCfBF4pX+cMIqQUqJkHDVBXWLPVhMSp/w3piWtaclICm7878h4mZUd+cvaFL9+0W7JWDQ5HuUgTvmaGaem7l3cnJK05+xZZhbaaEa8Xr754ndxgwpgsJTDkpbruHP9A7MagO5c/wDP7PtfHB/v5d8ONs+qqWIERT/ucI8FS1DwLShCR84mMJpmBwqeQc4aTzEfdSmOGStbOHYc6QcR/M2OHm7oLnHrNVMcHM8iZez4VQ1FY5ubrxWRFxuHGAQTVXjqUJpfuU2ypjNi77BCSkm9c9tow9uXtSMo88N9j1ENikghyNmatB1RLqSZaaFtTFz7roYSY+JtV9pEWMKwe0hybPwQTdkbGa96pOeZeHFqRibj5Oc0cNUndEXGouhBUyoea/nyyZAg8hgvn8QYTaTjPotpD/DZfxfawOO9OfaOZPEjxTd2/wt3rOzgdz/w/su2i/jpA+Dq+tqnB41M//+5ImhKBXz4Bo+ffWvzeRurmXDuXJ6f2Mucm3vasZRCGxMPE5BgZoiyDeRSCi/S+FG871QiGmJkmBYlAzSlLN61vAMlFUcnyoyXfUZLLuu7Wrhr3aLL1hS/ftFOWRY3LDb86DizupJnehe/dTEsa181K1297+R2hvQejJ6OBMLIo3+id04DkCUtFres5Gs7Rtg1lKqZhIAXKo5Npsk5EUoatBYEURypl30ouqC1ZFGTzWjFJdISTltHPhXBqOsgXHCUIW1ptBHsHGyiv+BQDSS2gmoIkZkdSUZGUA0kWcdgKcPhSYUfwWc2+Xz15UU8e2SIqh/iKM3q9go/sWYn33rph8SJ8zhdnrJgeYtL72gOdUpDmxKxrac2ClXv3jeGki8ZL3vknJD2bIpKMLfs8noyMpZM83hvilcGQkqeoCkFi/JVRisWWUuAEVSDAFmbkDWfB7g28Jc/6qFvLIc2AktCwfOZck9izFP8wYcuvy7i8apP71BhxiOv97N0eiQgJdgS1nUqcnaGJa2rLuu92lcqyTtyEejKZ1jf3cTugck4Sq4VibTWXL+olXXdzTx/dBTph7RlJW9d2sLf/uxGSr7h6zsO8+rJScYrLpE2DJZc1nQ1NyKcFe15lrUZyl7I5++/hRXtC2NVeDGYOVbxozdk0LrCq4MukUnjh5KUrVna5HHrtU386u3XzTKziHRI3+CLGGan5QwGbSJGikdnNQBFOuS1ky+xd8SemxI3cQSbsyPGKjZhbYiENlAK4juk1wY9pATH0mgDlbDujnU24jS3H0HagrGKzXjVZt9IlsgI3IjTvk5kBNoYmlOaaigpetCdT/Mbtw1y78qDDJcFHVmfrFOP2eu7Uk2cNheS9183yY6TzUx5NpEWtWxDRDWMLw1KTkdkQgjyjqE9l6I53cTmVd2NGvL0+Tp7RkYbzee++z22HqmCCTEGDo7ZvHIy3TBCAYGSKZQ05OyIzqyPIzVBJBrvxQ8OtbJ/LEdsZxq/H1NubGv6w0MnL8su4v2THoE+U45lZgbt9XVY10eCOsqAUOwazrDlcDsb3ryFfMICkwjyRWLbp+5l08NPsm+4SBhFWErxliWtbKt5UBdcn0NjxVkjAxcBn733psY2k5xj8avfeH5OlCKFYFE+w5Lmy9ula+ZYRUtJHrg5x0dCzVjJpbOpldWL7qS7KTen+xeg4hcpe5Pzvq5BE0TerAagil9guFiiHLRjzxChOCUeC3NzKmKiasdirKl5h0PKEvihxhjwtSDjaJpkSNG3gThCOXWn+cwuAFH754USN5SN0Y2KevPX/BggY0W0ZSCrDHlH09nUw96Tz5FxQq61z1QjjF83bcPmFQV2DjSRT3ciREgUuQyWJFpHjZsTKRTaCDYsiljWvgYlrUbm5Y2OKdx1fDs/PNjfmB89WrEaWY9IQ2AkQhgsIwgiQclTFDzJH29bjiCugWZtzUDRJqw1ocXmKfHrFz3JVDWgf3KcNd1LzriWS401rSlsJYnCs/mPi1O+PrMoCwSLW/J0NjUjhGDboZGGJ37CpUMiyBeJjOOw4zM/yUipyiv949zc0z7L5rI57XBzT8e8PztzCs25RimXC6eOVcw6aVatXP86DCtMLS6YHyXthttTjCCf0uRsjR9Nn0sp4jR5yY/9qJWMfa3LviCtIG3Fz7UVLM67THkWtjRMuhb1KGZmh8B80a4g7nwNtAAxPUowY2tKwfyRdm33FIFWHJkQdGQ1z59YxC0rVvNq9IN59/+e+kttFUeP77+uSlt2MUPlbiaqHkoUecc15dgXfUhR9gQ5R3HzUsHHb1vbeE9ez5hCL4wYKVUQVJisBDhWmj0Dhyi48Xk0texD/Zi1kVgqItKyFi2DJTVBJJmoOAgRT7EShExWHSIDgYYgko3udolhoipoTk+vZabD16W8Lao947C+u5lXTs5/M3k66jc3p6Mta7O6IxZjuLx3XlzJvClBdl2XD33oQ/z6r/86Gzdu5KGHHiKKIrq6uvj85z+PcxVOG3qjdOUzvG9dzzn//LlGKZcLpxureDayTjP5TCvj5SrzRQ/dTctmXZizThOt2RbWdVbZOZidlbZuzwQsbXLj1F/NiEPJCFtSM+eILTIHSim8QOAog5CxaOrGb599sYxdpmopdATS0LDcjCNv3djuUt+qrhpuXXHjmFWbPNWUiWhLaX54qERbboxrsqmGk9V8CCQSgTFxk9vilmU8cNvPEmnREFZLGrywAqQYr/jknHDeTATMP6Yw0pqvPNvLd/a8xq6BApNufIuUUtCaiTAI2tKaUMfZh/oMaiEMjoQIjRdJ0ioeqFENJZbUyNpWt9Z0iFIGzxdEZlp4BXGKtuRZfGPnKL92R3tjvGYY+VjKueRdvbZ96l7u+OKT7BqYfF1zugBSlqEazi/ItoQNi9sbYgyX986LK5k39Yn8yle+QktLCwBf/OIXefDBB7nvvvv4whe+wDe/+U0efPDBBVlkwum5Woapv1GfYiUtruu+hR8f/j4aF92oJQtaM4u5c/2D8z7/Q+ueQ5sSfWMZKoEia0fc0F3hPatim8wn97fTN9rEiaKDMbGoFX2DMbEoR4CODOY0F8dTiWufcTxrS1Pb1hVPetIzhFiIeMtK3PWswQhWt3mkHQ1oNIZC1efrL73IL789S8ZyUY291NNY0iGXauW6RW9naevaWRGjJZklrPXzPV8dNtIhBbdI2bfoymfnfOYeea6Pb+7cw5GxAmNVSaRlwx50vBIPvAij+GZHCoOpGV0IYaiEEl1rjCuH069bCRRSxFmKE4UUbiDnNLuZ2vmSQvDDA0Osbfsx48VDCBnvJ78cXL0yjsOP/584e/bcoRG+d7Cf7+0dYv9Y8bQ3WW0ZRco3uFG8fz3e3hRvQ3NUCmfGUO4rKYN2pXHOgnzw4EEOHDjAXXfdBcALL7zA7/3e7wFw991387d/+7eJIF9ALtVh6heT9Us3cuL4cazmgIpXQklBW76Hm5fdM290tH7pRozRaJ6h6E5RDhTNKYGjBOAghMtPrJukKWVRPCwZq1iEkahZXtZMREzcbCVqHl9zt61Mo4nrxClL0+SE3NBd4qX+FtxINby1tYmbcTqyAW2ZAD+MbRNPllKkHY1Ao41htGIzXrUIIsl/fkrRmctzx/Ip7lo50oj2BRZN6Q7WLHoH65ee3mb0TAPutdG81r+dv3nhEK+cjKgEiq5cjvvecj2/dsc6lJR4YcQzBwfxgipTbrxPvB6chTqO2ARQ9iUVvy6s8TmMzmisEp/bKKr3ic9FAJaMW/kGi2WOjPXTkp79TCHFZeHq1ZXPcP9bl/GhDdfw5217+ezjr+BGc2NmJeJpX82ZEFONu+cFsLrDkLGzLG7uQiCuyAzalcY5C/If/dEf8dnPfpbHHnsMgGq12khRd3R0MDIysjArTEg4R6SQdNhr2LD2Brbu+wfGKwNMVIY4NrZ7TtqyLkLXLb4dBOwb2E7Gr2DQRCZsTFtSAt69cgw3DNh2pJWhcgrq6V+p4yYsM9PP+nSRskECK1qrVENFNZDsGcoT6Pi1MHE9WQpNShk6swFCgOVoIi1oTQeN6Hq0YjNatuMbARF3Q49VFM8dX8Jblt7Ox29bgZIKJW2yzvxpZ5hrhdmcttiwJM9v3HE9gZa0Z1McGn6Bv3yujxf7FVJILGmYqBb55s49CCH45Kb1jFc8xiouXhTW0u3T58CYWErdMO7oXtbigoH+okPRt2Y89/Q3MgBhrftdzKjTx5mEWLQlmtaMIK1cmKeX4HJy9VJS8h/evpLHdh3nxyfGCLXBGNMYGKONwRiLJU0OEpcp15BPCTpzeT5Yu1EKtbmiM2hXCuckyI899hg333wz1147/2g/Y06XWJnL7t27z2UJF4QdO3Zc7CWcd66GY/zOj/6akh6h7o8VRS7Hx3r5l+e/wjX2Rr51YJxXhisUvIjmlOLmrizvXnENVb2fkIC46qrQhGgi/u1gG3tH8rRmIqY8jRtJjI7TrZaAKDq9kEwTx9BFT1EJLJrTIWVfUe+5ljLel5yv2XhaSuOG8aSpt3YHBMZnz3CcUi77qtHQUxclgImyz9O7Bnh3s4Wjzm67+I2+MZ7rLyGA46Uyk17Ev+6BP/3+HtrTirWteZa1DbNvTBFpPWtDWRgUeOylvbzdKQIgvSpSx6n3mf5l9bS1NpKUjMV0ylOEWjbyCUrEPs5nuooYE58pW+qGv3P9cWMgbUWszWgUhmCevdICwYG9RzkiB856Xi408/1N+pEmFVbJWfVpYtO3K35kUCYk8tOsaspzw0qb917bRkfGwVEVXnn55cbrnLwwh3BWrobrzrkc4zkJ8pYtWzh+/DhbtmxhcHAQx3HIZrO4rks6nWZoaIju7tdnWr5hwwZSqUuvuWDHjh3ccsstF3sZ55Ur/RgjHbL9pWcIVBFH2XO+H4oSz1dS7C4pUvlmumpbtneXDddEN7OhU+OFVSCuMY+XTlJ0K/SNxU1fplbPtLQhFIJQSzKWJozMGbcr1TEIxqs2WTui2Qln7XE2NWEu+RY5J+Tnb5yis7mL1oyFo2CwcAxHObwyaOPXfK4taeK9pjU0gsBOsWL9DWctZxTdMn2vbKO1tZl9QyMMVSKCKHYHCzWMVDWOU2UsTDFWkfS0nHIsxkA60/hd9/tN/OMru2muFqiGplFDtmScyo80uCEcHE8RaImYIdumZo966tyV6TLA9PmLjEQKTWSmB3soDKs7FJ/7d/fw4uECg5MHETO69Iw2LG5dza3Xbzzre3ShOdPf5If9Jgae62W05DUatIwxLG5J8fHb1vIzb1t+WUTAV/p1B858jJ7nnTYQPSdB/rM/+7PG1w8//DA9PT28/PLLPPnkk9x///089dRTbN68+VxeOiHhTTNzitCJaj9jgUveNqSUaNQhK6GDI31+cOAkUlg1i0aFEAIlBc8cHGBlk0vanhby9vxSRkrHKPuKKTcexuCGEl0XURPXhTNOLGaOMrihnHcoQFzr1NjKYJBMehaRphHd1h3ZpIj3J2dSaaY8SS4Vv25P63X85PVDvLPnJH/9Ug9HJjOcmuK1pKQ7lz1jN22oQ57Z93V6h/s5NNqOpQQnp2yCSPL/s/fe4ZZe1Znnb+/9hZNuDnUrqFS5ShGBhFAGIZARYI8xxgGPn2m7HRjG6jDdbXvmaXump3ume+y2n7E9OIwDg9NYDW4b2wiQAaFIIVUplVThVk43h5PP+cLee/7Y3zk31C1JJYStUO8fULrn3Hu+84W99lrrXe9rspqCxfV+J6sxg2OKduoy2OVMdCkkg/lc97M+ddsurLX83UsHSSerlNsaYx3LusOKXj5e1sn4OvPactVorcQipfOXdhUEwGRENyQSSyFw5LC+XEIzgX/7pb/kp9+zFWstc/WzSyzrfteueL3xcr331wOfum0Xxlo+9/RxzleaYGFjX4F/cvN2Pp317y/jzY3Xjfd///338wu/8As88MADbNiwge///u9/vf70ZVzGJeHI5F5Ozx3mwSOS/Wf7qcYlCoFmx2ATARxdKGQWd1BuzzPWo7FWI6Ui9AqUwgEqbU1kcuRIiTXMNy1xmhJr1+vsCFmEyhLjgrwnnUzlVcMtTizmiLQk1YLztZAoE/zQVqCExZOW0HN9aWOFm3MWFtPtA7sApTW0reR/emiARDtHqWvGfP7uZz7O6dl9NOOvce26OrONgHrsrQjo/XmP9+5YhzYNtFnba/hLz/025eYUvhDk/V5qkSLpkLCygNgJm4kRJKkh77tgmuv+OYvvFbhr+9iyQGT4yfds5Cffs42FZtydQ/6L56b5w73H0SaitYbZmSctxnTkO03mqiUwGaNdkQm1WNe/lsISa5mNiFl6wpTREviez/MThlPz42wb2cMdu37wuzaH/HI2lK9nkFRScv+dV/Ezt+5isurG+db3Xshwv4w3L77jgHz//fd3//3Zz372O/1zl/EmQjN2nqpjvX1vGInCjiHFg0ckT50TtFMNCKJU8eipQayF0VKCkhClgko7RiBZ1yOx1tJO6gCMlkbYOriFP/z2Ub4ybpisKVIdoOQYBpAIQs/1a0PPYoylN5fyz25ZJB8YvjxueWGqSOBZSoHGZoE4MXQDmcCxYxPd6f8axzS2Igs2kFjQWmJ9ga9cBv78pOUTn32Qv/zJD3J67iAf3b2ItYs8caY/EyRxQiE/cF2Oq4ee4bEjT67wGu6wq1889wiLzUmc6QbsHKqz73wvK0hYuGy1O8IqYM9oiR2DTV6c0jRT1SUPuQzu4h7HwyXLC5PjaCDvB7TTqPsZHeQ8N7m9rhhzzWidD++eJ0oFXxkf4kw15ORiAV9BwYsz/2iPM+UQC2zsSyiFYZcrUI+hHkumKifZNXbzd43A9Uo2lK83Qk+9qSVxL+PieGNOxl/GGxqJTvnlB7/KY8cXaCaagbzitjeIw06UNqlHLfafs5yvQCMJuzaFUerKvbMNMu9fgTaW6TqMFJ0APwhacZPbtw3z5FnBl46cZKJquv1JnRGIJAapBZ6yKAG9uZTBHMw2fdariA9sr4GFI/N5+nMpnnQa1+W2T2bEUbjw9QAAIABJREFUhLECX9rMVlK4oC0tI0VNf96lj4dnQ3xlu0EGXIB8ZqLJC2f3U48XMCR8aIfho3sEbd2Lkj55r0FvHqxVXa/hcwuHAbhqw21ok3J0ej/Lw+Hd2xbRBs5WQmKtkFnPVmYz0EH2Xe+9ajv//M5r15xDPjTx5AUex53P7S/dQDNJ8ZUkxeApl/U6hS6X+YKbx75pY4UP7FjMTD4sP3jdLM1Y8gf7N2ZiLO5a+Spwil9YfNGRYXHnqhRYekKn0rWWLefrc7/pV2FDuXYGq016SWI3l/HWx+W74DIuSVZQG8P3/f4XefpsPcvkJOW2pR7PAg/x7z/y4VXv/4dddHyV43xljpOLQzSTJY9fbSHWksS4DFRls7BSOOnFmYZlqODME65ep/nRd47xqc8/z1yjM1e8BNfnFOQ8wZUDBk/BTN1nomb40+cG6A01u0ba3LN9gbu31WnGPj05Q5xGfOXYAEdmC5yv5VzPNCNj9QcJfTlDM5WMlVKiRGFxATBY47S1E80zZ4+xoTdHO06xWCwt+nOKQtBLO3X608shxFK22IxrRElzxetSwL07F7EIHjk5SDORRFp0nZTWlQy3Xenzc7dfjZIeA4UBBpZxxS7mcdz53CuHb2SokGOw0Gam1iZUilaSdhXJRgoxfbmUd6yvce+OC72PC4HhmtEGB6aXskNrE3KeY00jLFqneMrHWMF1Y5ZAgZK512Q1uPze7Xhqr35GFpoRC62I0JMYq7vn3FjNQitloRkxWgpWPAMvV0W4jLc3LgfktzGMMTx86M8uSVbwNx59iafO1Lq+QYmGhSaA4PGT812Hnc6iM1U5QSupk/dLjPVtexUa1N8Zxie/jTCtbl+xA4HLgJ09oiZK3b9dBugmVX/uVsNIqVNO9ZmsNYj12jIVFkFiLEoqym2fajuhNzSEniXSkucnC/gq5IevLzFY2sBc/Syz1TN8dLcLNGcrztlIZyYVURqw2HajTu/eUOfeHZqhnlE+/deGdA1SWCAtG/o8SsEAAGlaAQSxjriy90qmq6eXHavtBotOtggWKWQ2zrXShOKDOxbIeT6HZnNUI0Up0Owahh+6LmDn2HYCb21J3I5d5lobrzhtAxF3bhul2k4AmKu3SLJZ5YKv2dDbZvdwk7u3XRiMO1fx/dvKSCEZnyvQSAShSnj/9jYSODKXpxlrQs9y00afj+wBaw1jfVsvaTO4PGC2kybV1hyJjsCC74XdZ0QAgWrhiSoLjSbGaIx151IKRc5XPH/6L1EyReuUXFBgXa8jmZ1fPLJmFQHeGK2fy/jHweWA/DbG2WQvUXnxVcsKRqnmb188RGqdMIOT6BMIYZmpQ1/OMFWtsG14lMMT3+LI1LeJ0xbGampinnJzBmstV2+8/bvyfbRJmawcR1ufwDM046Wg7DJlS4ogMY4UJFxShSdhril45KTlR9/hFvCRUoGBfG4Zz3gJIvvfYhDQmx9gulGlL99mrOQhhMJaixCCo3PQShJ2r7+Za+QdPDr+F1hj+N7d59h3roeFlpPf1JkIiDaCOJUcWyiwblrx6Z1bedfGGfaeaXQZzRZX4t0zmlDKhHhK4QBpW9BTKmEt7Fh3I5XWDEkaUYsWiOIWBo0SHoWwF1/lCD1JIezFGE1q4hVBOVAhv/0jP8vhiX0cmTlB0Uso5QqM9b18FrfcLnM1As9lqcu112eLIdX2DNeORFw3doqeQOMre8HvgpP8tEbg+R4/9q5+ys0q09UKfXlF6ClCv4DvhczUKgSyxVBxmMB75WNeC0cm93bL7pXmDFHScPPR0us+I1967rfpKwwxX59kXcFjolIi0RZfGTwJGstVIxWmKzNIIfG9kFxSIIqbRLp1Qfm8U0Uo2Z2XdKyX8dbC5YD8NkWctmmaeTx/VVlzlaxgp2wHIYemq1RaEdrQtQgUWS8v1lBpC8Z6+9Am5djMfqKkkbkXCSyGKGlwbGY/u9e/57tSvo7SJqlJ6M3BFb0RE7WlXrGSlqFCymLTcyQqIRBC4CvXV5bCcmJBMdq7s5vFf2DXBh49PkkrudA6Me/Dv3rftXxg93r+9Rf30UramSCO6M6I1mNo67Bbqhzp2cKzp79K4nhmXSMEsSIFd2Sul6Y9zi2e5gs/8XF+4LNf4pmzNSINgTLsGI749HsWsXZD97MEAiU8lPLJ+SWM0UxXTpLapeCYAImOePb0Q9yw+QPsGL2RI1N7idIWUZzSiD0GCgHv2Hw7eb/AO6+8i+uvuO1VtRzitM1cfQ7LeqL0FOEy7eTVWeqnbttFkmr+9qVDxGnM4bmY1PRcNDMuhYOkOiI2CXm/iBQevbkSnjAU8/1uXC3bJl3R309qSrx764fpL4xe0n0WpZrz5RrPnTtOX86ZWURpy90r4DJgoTBWU2lOk/PyVJotnpsc5Oi8ItESgaUUaj60c5E7rpxzGzQM1hpacR1jNVHaJh/0IFbVXuK0jSZ+1cd7GW89XA7Ib1PU2gtZVnQh4STVMdXWHLO1M0yUT/DA8zHPTfpYAs5XDYkRGNMhQXVgCTwPJT2acZVGVFkVaAABjahKM67Rkxt43b9T6BXI+QWKYYHdw00iLRki6QZkgLGS4dRiL1JKZBaUrbUMlwICv8Bo/zu7JfX779zDEydn+Mrhs1lQduXrnCe4d89G/uX7riY1luFSnplaocvQdu9z/eidI9u7QeHA2W8AlkbiUfI1cZDSai6Vfz1pUNKSaEsjFiw0IoRI+MX3RhyfOcNsM2CkEFMMBamOma+fZ7hnyWXeWsO63i08Pv55pqunu8E40YJGoij6GlTCwYnHOTX3AsOlzWwbfjefefIgL04ZGrGiL6+4a77C/zKW4ivvoqYenZnb3pziW0c/z//37AKHZr2sXy545waPD+5sUQqL3VZFB7/75Dh//eIh4rRBMfBJteXAdA8A92xfhMzFqoNi2E+i25A2yfmOtLa+fzsztdNofeHsVOjlLykYa2P47SeO8Lmnj3Ou3CDRhqGC4J7thps3GpQUWOuMOhLdxuJIaA+80OKLh9ZTzYh6nrT4ypJoybH5kA/uoFvCjtIWUkjSqNPj1ggEqUnwpI8UisDLoaLLDnlvZ1wOyG9T9OQGkWsEYwBPBUxVjnN67jj/8kuS04seFosUEUqCMU7aERwpCaAnNGzqG2B8+gRDhdV7f7pl3CXph9cfSnqs691KO27wgR1nAcdybiaKnGe5fr3gQzsV/+GRIjO1mMQYPCEYLIVcOVCi6HurRDQMv/3xq/mRz03z1NkoI0pZcr5mXfE8hya+xdUbb+XObaN86WBMlDSJdSvTvZa8e1OeHWM3cWKuzEzlPK0kwVfOX7kYajwFrVS52WOWZo+VNOS8mMFiHoFkpnqanK9Z31MFLFEKILBpK3NNSpEoNg3uQZuU6cpJZ5Jh4eETA4zPu7nrQqDZNeR6tKlOmK6c4IHnGzw/lUcK6wK9EXzt6CxCrCTodSolnszx+3tP8OiJKeYbbZJ0hnqrgSbEk+ApQyM2PH7KkBrFJ65fea07xhNJ2qJT/PdUgLGao/NF3relRiEMnHKXtazr28ItO76f0CvwzDPPcM2ePd1sXU6obmm5g9fSM/7dJ8f5vSfHma7VMdaZdczU4UuHBY14gHu2LaxyzbI8fGKAZyc96pHX3Zh2qkahZzm+UKAZSwqB6f6Ouy80wkjma+dJdAxYhJAEKsd1m95Hu3l5pvjtjMsB+W2KwMtRkEPUk0UaqUcp0G58xFiG+zYxWzvH//mo5NSi6xG7MR3QKa7xCuQ9NxLTm7OMFAyN9im+fexxXBXcdpWvXJbgep+5oETO/+7NUO5efwvGaKrtee7b3eZ9UQMjexnrHSDnSZT0+Ymbd/DgoQmMtfjKZcrLLemMNRye+BbHZvbzlwdixmf7kNKjEIAUgkQL/u5girTjfPoOwaduu4WpygkeO66J0oBSYLlmnaWdxLzrVx9gtmEw1tCf38Ltm8vcs32RXUNNDkz3UAp0V2TEWiiFLqPaMVRn08Aeau0F2kkDu4p4hbO9oCc3zLWb7uSlFw6za+xdfOPQn2Izyt3DJwY4MN2TjQ5BolU3E/2enU2MVTw/mSKlU+vuQIolgl7O91cwgh8cD3nqjNtSTVQ1lZahlRTwpJvDHi0leNICmoMzAa1Erxi36hhPGKtXBNLAy9GMBPVEkgsMvgovIBhKoVZk652se6pysttiudSecZRqvnl8mrlGo5vNgtMlr8eCw7N57ryS7J52cqaJhvH5AqmWy0aw6BpbWGuJjWCu6bNexd3qRKc/npr26ktJoiOmq6fo46pXfeyX8dbD5YD8JsSljCldDNoY9p7cxb7FGebqbQIv5drRhP/2xkFu2vYRvv7SX3JwWoF1Yy8rTN8sKAxjpTbF0GV3idFcNVLrjugYa9DW+faKrK8qpcSXAcem93HVhtu+09OwplShFJJrNt0JAs7OH8LXTfr7+91hZ9nTe7bvpBalPHt+kWorvsCS7sjkXo5M7aXWanBodohGqpbJRLpMvxbBC1OSMwsn2DpyAzetP8augSq1NuT9hG+e7OUbJ/qoRm6RFwjKrYBHTw0iBd1+6ZG5AsbmiFJJ4Bs29MTsHmny4Z1ukX7h7MNrBOMl1FqzhF4BKRRR2kTrBCEkiTZdze3lkMIFk7u2lGkkikYsKAYGIVdmZrW2I+hF8bFuFqqtx/6zbSYqgvmWTzuVmWeVI6TNNgPmmj4FP2Vzf0S1ram2BcPFpXGrwULIUDFHK1arTGgEA4WQ3et28K4r30d/YfQV720pJFdtuI1dYze/5tG6hWbETK1FlGqnT74sE9ZWUI08GomiX7nSuMXSSBTNWBF4JqsULXezcpsVX1lemCzwV4dGiFNJKVyqTqy+Jkp6eMpnrn6W0F7BfH3iu6IodhlvfFwOyG8ipCblsSMPXNKY0sXwu0+O8+REnYrtY6GdI041kzVFy45y1+4ic62QKNV0pBYuOBYraKWCYuChVJOrRprcuaVKuaUoBgZPSmymhuzJACkVeb9IMRzoLs6Xunh2AnBfzuezTx3nkePTzNTbjJZyvHf7uhVShVdtuB0pFIfrz6CNJvByjPRs4atHSzzy5a9TiTQD+Ty3bu3nX733WnpyeSBjapePE6Ut6omiFjlS2OpF1Il8WBYaES+ee4RGtIivJL25hHaiOTgTstDySI0zfuhIOzZixeE5FxDv2b7YDYyhBG19ekLozfcghcdM9TTaXDwYA7TiVka6y3roQYFcUmCh2aKZePjywqvXTFQ3aysEjmUdyPyK9/TkJCOlIvtOurniWMPpRc2JBUkzUURarHlfWASNxOfQrEd/LqHSPE1O9ZPze7viHHdtG+Pzz80Rpw06wcxYuHad4cqh3Yz2bn4Vd8MSLtbnfjUYLIQMFz0n2WmXO9U5ZbW+0DKQ8wm9HKXcIPX2PIN5KASaKFXkPU0jXipbdyodWMs3To44n2cJxUjTTtymp3PNO1mzMRqNJDItTvINTh/45nf0bF/GmxeXr/SbCI8deaDrXPNqxpQuho660HQzpaY1Qgg8pTDA4ydm+M3HxtnZP0LgTdBO1UUWXsBaPnn9aUphzOOn+/nD/WPZQmPYNdzgfVsXCD2fgcIYvgq6Jcq1lJNeLutfrRU832h3tXxjrfGkx4tTixhruf9OV/LrZE/1CY9rdu9ByYB/81ef55GTDYxxLk3lhuDUvMdi/TCfunU7u9ffQpQ2XYnYGnoCQW+omW7YzMd3CVJAf04wWAyotmazkRg3h9pIJGcrIYle0qUG12Nsp5LasqzLV5Z+lWYkppiecJCC30OkW0ihVrCk14KnVFf0otNDj5M2I0XoDVhTL7rg624w2DXU4sC0h8+SGpixlju2DqOkphW3+PIRxXOTKYsty0IrQAgXQF8egkbk8Uf7R/j0eyaBpePsGE88ePAQc40GBU/zjg2Kn75l1z+4OEboKd63YwPPnpuk3F7iN7j2QcqekSY9uRLtpIGUTrtMScAKzpRzaANSGkw2Kx56hoF8TKXlu+c0C9C1yMNaeOx0P0fmirQSuaynX8aa2LGxswpMqmMmy8cu+dm+jDc3LgfkNwnitM1s7cwKGzkABMzWTnd7aK8GC82IuUabSqxR3nJbQkstTvm/HjnEFf0Ga+zKUvWqD55pBmhrePx0/1KvUjpFrBem3EzsR3bHK4IxLM2kwqvL+pdrBftScnS2TCuxSGFwpnwJi62IX/nGS/zT9+wg9ES3hNnpOz74wp/wt4daVKLASWlKR64aLqQ8c67NiVnX59w1djP5oEi1LfE9y56RFhO1oNvnBbfA9ubg+jHDWO8VTFVOEPoFWlEVay2+1M5ScPWlymaNi0FKX6BY13MFvhdSay8QpQ2M0ZTyQ4z2bGaycgJwhCeBZO0rIdg4uGtFpaET0PzKSW7Y0Gb/hE/Oy7mZ2tYMxsKuoSa+skgU37OzjRSS0+U89dhlxndsHeaXP3QvUsBDx0K+dSYCDEp0lM3Wkkq5EJ5y5KZGDDm/3v25kpKfu/MqfvrWXczWmxSDlN5czytWTIzVNOPq66769unb9zBVOcVfPDvFgnPbZCCfcvvmMnduqTPf6mMg10fOK1BF8vXjPURaUQw0jUQhDWA1V/S1+KkbJ/jc8xsot4MVxEYhYLHlMumewGRsbLWCXS5RaBJ0mtCRAD27cJBmXKcQXNaufjvgckB+g2O23uK58wtc0ZeQ6hiZ1cZSnXTJUnEKL5x9mHdt+R6sNa/YTxsshBRDj9TaFTzrZqxJtSXWhlTHbB2E56dWetAuR6wlj5/uY7oRruixkqlfHZsvIWXroixYbVK+efBPmKmdITWCRuxTCldm/au1ghebC7RTF5xSK1HCZoHPMF1r8Yt/8xV+4Noq7aSJJ32S2GexsZE/3r/IfLMHJVmRtbhjstQiOL84zvbRdzHWt51yc4Z20uB7drawwGOnepcZN2g+vCfgv7txM/ncdqiepxR6WGtJ2jGxEYTK0MTJdC4P5Eoatg0kDJT68T3H6O7JDVJiAIHgrt0/gpIei81JUp0ghST0i9lI1cq0tD8/xjUb71rxMykku8ZuZtPgVbxjc8qvPfwU3zq1QLWtCZRl93CLD+5oo0Shu7n7yJ6Ee6/9GAvNaIVRSJRqjswVAFcSVxJyvoFMUvOVIHCuUHNNn4G8C6Y9ucHu66Gn2NTfc9Hf7zC7fZXj2PQ+zsXPMH/keTzps75vO1dtvP11UX1TUvK/3vcB3rnudzlbKWO0pjeneexUH3+wb5RGrOjP+9y1fYiP7hnjxFMTKNFipJgwZJfG6kLPkFpJrCVKrqyqWOs2MgXPdEfwwN2LR+dLvHdL1fEvVvTVncLawwf/mA9d/zOX9a7fBrh8hd+gaMUxd/zWVzk8UyPVGk9JBvMb+ee3TjNUiFGy01t0C/5U+Rz/9Zk/pzcH1rQJvJDB4kau3ngHvlo52xh6iju3jvL48UmMNUjh+pyxNgSexJMCKTRWWEIF7Yu2MQXPTvbSG6aEK+4kx7C2ooeh3l1IO72CBbtz7GYOTTzJxOI4ZxbHefhEP+PzRZqJohRY9gxHvH/bGU4vLGLxWGhF5HxHAmomrcx32C12nfXLlYYNe0/Pce1ohYLXwNJGm5QvPnOU4wsjeMpijOjaDwgBjVixvqdFkk4zUzU8Ov4XbOjbya517+HYzDM04wr37WrzfVd7FMI9bBt9F4MFyW888m1+4cEzVNonKQQpe0ZiPn7tIBZLnC5wRZ8bEVto+t2M0leGsRL8k5tGCNSqUrS1bBzc3a1yrOvd2iVTDZU2sFCfoJ00EQICL09/YTPXXPExEm275361RnKUNrlpfcJtVwxSjwVJUiY1daxVIPys1wkj/ZvpL/TRn8k9dwLhuXJMpe3jZtWNG43ydXYODca4TdHacKVdT1qGCykIj9V+zRfD6u9Ra9dZbKX4tkm74d4zVzvHZPkY773qxy64v18L2kmDQhhwzdgG6lGZL74EL0wXkMK5bKXG4+tH52hEg8S2gBCRs+wUIDP2dDPrEZeClKLvrayqACDoCXR38+p61h75wkZSW8WzlTWPbbZ+hr999rfYMnw912y683X5vpfxxsTlgPwGxR2/9VVenCy7XjHQTjUTtYBffGgDG3tjbtxQ475di3jS8NipdRxdgGq7yUDBZ+dQm/dtnWSqfILT8y+yc91N7Fh3E+2kgTaGP943ydePjmNsTLmp8ZSg4Et8Jcl7iqFiCNRItMZTCmmcT+9qBNJQizwGCzFkbFspPLozl3aBs3PjXDGwldt2foxC0IuSHocmnuTs/CHm6ud4+MTycrelncDXjpd45FSJLc89wXCph/lGxPreHKcW6kxWTTcYQxaYLQhrkVjG5wX/+dEC/Xmf3cN17t62SDVKaSXufa1UriBZedJyZV8rG2tRWGM4v3iETYN7+OgNn6YZVwFBIVgqqf7Slx7k60fnkEIQeJLU+DxzHgQRH7umjyQfsWekTWJ8RooRxiqE8CgGBX74Xddx3/V7Mp3vi4/rrB7pGe3dwkjPJkZ6tvHAc2X++sgiC0/s7Xrv3hik7D/5MMfnTtGbU/hK0YprtJOUttZs6OknIaQa1dHWaSsLJH35ddy+6xPuXGaB8Hz5OA88V+XFaY8jsyFCKHoCyXDRlfgBvEQRKE2l5dE2ktXBNshcrLYPNikGhkLQQyG4eDa8HEcm93J2/iCJtnzhhYjnp30iHVIKwmVMZcH58lEefP532DX27teskb6kuX6canMOIQRC5Dk6n8dbpnzjNnGW5ybKrO/ZyLlyDaNXUh4LvqYvl7JrqNklcHWU4gSW3jBluJBgLcw1fZqJh7GK2WaF54ZGuXlj5QLyYAeV1gwHzj3M6fkX2TJ83Zob7ct48+NyQH4DYrbe4vBMDSklqXFCBR1YFBPVHDP1kG+f62NDb4KxBVIdIYSh2tY8fU4SpTnu3VGj0a5waPJJDpx7BG1THhrv4fmpAkoKtg9I5iNFpWXJ+Sm+8hksBFw5UGCm6npe7fTCRS7LTQmUKxdv629xslxECjDWLdjGCnYNtxDETJSPse/kl7n7qh/rOgI1ojKNOGJ8fnDFIjTX9KlFXlYeDWmnmkhrnjm3kJ2Hjh3CypXLItC4caQoCZhrepyvBjQTxW1XlFnMGM9KWDSuIqARFJTlQ7tqWOtY4J3y+mT5OBv6dyKlWhGMm3HE4yfnkSsaxILACxifF9y07T768r3sHnuC3997khenBK3UY7hY5MNXX8V/f/vuZWXlPawO9h1cbKTnM48f5qvjM0veu3HKf3n2AH8eV7AyoRkriqFhx5AkSn3GZ/tYbPsM5GKuWad5/zaBFAJPBUghsWiOZ2NoHQ3nzx9os/+8hxTQE2qqkaLcdhndcBGGCilXjyxw8xU1PGH45qlBjs3lOL6YJ0oVSjgW8o7BJj/97gmEUFw5fO2rKrkmOmZ8ah+NqMpvPOlETUxmS1n0vW6gu2f7IhZLpVnh+bMHiVPLng23XDAG90pYrlud8wu0kjqLzRb1uECgyMQ8IE5dc7kRC7YNnOa0cfdiopdK0J3efGekbXy+QCNW+Mpy1UgDieXFmR7mmwH1OOgKweQ9zanyBprRAu/fPn+RIxVoqyk3p3nx3GJ3o/3dNmuBS3NsW2sU8TJePS4H5Dcgnju/QKq1Y3WuwWY1CGIjmKqHTNVDJJa873cX0OFCwvhcnls3lYkN9CQxnjIkacgLUyHGphjtCEZDectI0UOQsGuowvh8LwuNClN1qETK+f4KQYoLsu7RdyL6QkB/LuFDuxZ4/LTh6HyJRiIo+pZdww3ev63c1bGeqTriWWqcolU7bdBMPJqxoiOnbawrITsWr0Jbd4Nu7i8xUWmR9xVR6kr00toVmXIHAktqBPVYUY0UXzw0zN8eGs4CuMATrtfX+c1SmCIF5LwihaCPVpwwU68jKTNd/R1SLdH0ccOmd3L95tuYqlaotTWBd+EiWGsbyq2U2eo+Zmrnuf1Kzb07ffrym9g4dAP7zy1yvlKn1nieycpxUpOQ8wtd6721FtblIz3L++nW2i7J6Wy5SbnlsWVAs9hWnK1K9p8TJHYdne3T2Uqew3NFEgMf3lVFZXPHcdpiqnKC7aPvctm4Ebw4JbubpOGi22DVYkktVoz1xN3KQ+c9H909R7LDyXN6KMqRz0ghoRSCkkXyQe8Fve6L4eD5x2lEi3xlvJej83kyY0W0EVTbbrk6PFfgunV19k/0cGy+hLEejeQEPbkFhoq5btVg+RjcWlhtF1kMM+cs3aTgaxKdGVdD9zzm/ZRbrpjkyNwmjs4FtFJLqCzbB5vctrlMue1R9PWKkbair0m0YKru8+JUgZm6h8nGoUqhpT8XMVc/w2Izx51bxJoGGx1SnwWkFO66zx8EeF1m+tfCpTi2xWnMbz12gCdPLVKLDIOF3Ku6BpexEpcD8hsQN2wcxFMKbcwqyb61YXCLocp6opW2JKcMv7tvPcZICr57mBMjOTofYrK+nycsSlmKfkJ/LuHdGxfpyRX5yrhguuY5gQNp8ZVGaklbq0z0wNkMFn3N7ZvLhJ7lnu1l3r+tSSUy9AR2xaJirSXVMbX2Av2FUTzPx1pLKTAUA0Osl8QltHVKUYUgjxKQpBGpcWpae0Z7mKs1OVUGgSHSglhLpFgqqXe8ipdOm1ghq5FaQZpIPImb+Q0ErSQg57X4m8OKQzM+jThH3h9BCYkQgkYs6c0d5H07qvz8PXfTl1O00guvS09OUq4d4XP7TvDitKQWh+Q9y3MTp2il57q9675czH++7zwFX9H2CyRpBLzywrrQjJhrtmhGFbRpkxiNMQmVdoCxMFN341QAyRq93Ugr/u7wKPfuaOBnUqbGGlpJg1p7gThtUW27LLCzSRLAcDFhqADtVPKp9wgkq00gBKEn8ZUmUD671o0hhMRYt/G5YujqV1Ve1SbjRPsKAAAgAElEQVRloTGBsR4vzeZJDaSmY5PpjqXd8Ki0i/yHb26lnUiktChhsRiKbWeXmfMVDx46D8D/cMeei37eartIIQSl3CDFsJ9rRhs8O+nBMna7sZY9w20eP1WklRi2DgqacRshLGcrOf7To1voDfUKidJSkPL7T2/k2HzBzbRngV3hWiyphtkGDBc1UTYf3hEhWQuZyGqmrc1rnul/NXg5x7Yd625kujpFPZa0Wif5vb2HefqcI6k5m88+/uZAk4Vmk5+7Y+urYtFfxuWA/IbESCnP7tEeXposr2H+dzEIjLVdYYZGAvXEoy/LAGuxk8c01kk/Atis9VeNPDxpKPgxqWmT93wCT6BE5tRkJf25FEtKPVasK8X051P2DDe65bnecASpJErOXXDEIiuR9uQGUdJjfd925qrnCD23cL0w3YMUHRIQCOGT89rM1k52daElPouNMqBJTZ7ULFUPlFje4341xCFBKDXaCBqJR86P+PJ4iRdn8oAbSZmshVTbHv15w0gxpZVYvjY+z2jvSW7bOsTfj8+uKFsba7ltyxB/8swZnj7vlL0CBU+fEyRGLTsyS7nt86+/spHPfO8E7diNA73SwprolN985GGePz9LPXbfVwlBoDxi7fSTm4l0m581xpI6Aa2VKs5XBFcOtLHWLZ6edKNhoZ+n18aUgnQFi1pkxMH+nDNdWGyuvCs750Eg8L0QIT2M0QRe/pKkLF2AbJPYInHqbCnTbHxMAMZklRNjlimEQZJ9u6SV0IxrAGwZLPHY8Rl+6padFy2drraLjDXUIugJJZ+4voQQdV6cljQTt6ndOdhmz3DEnz0/iKcsAku57bHQ8rOZc4s2CZ6EA9M9KOFzYNrnyGwJbUEvkyfVWLACtKGeKAZt0p0Pfzko6WWa8M6AY62Z/tcDF3NsaycN9p16iP/493s5OJunrQVFXzPbCOjPaQwWbTTH58o0khpPn53kyy/t491X5PnpW7Zx9cZbX9fjfKvhLReQnz41zZ/sP8mP37iVd29Z9499OJeMTpnof/9gmfv/JuXMolzWL10p07caq8eTIq2YaWZkK6CZyO77BJZYS9JM4aEqFfUI9p+TeMoQqKUgJwS0tMfmvhYbVZsfvnaGvpzbxVcjj/7Q4+6rP8lU5QTPn/kGcdrsqmFY6xjXo71XdhnEV224nZnqGabKJ7hnRx2EYHyuSDtRDBV8Um3oD5uOfIVAG83WgYhIKxZafsZOJftebrToks8zWdnbpkSJ4ch8Pvup7ZbOpYR6LBkqZKNSaB49Pslnf/Qe4Os8fnKeWtt0Z3fvv+tmfvCP/o7UuAw80XbZzK5dYncDlZbHXB2GSxAlTSK/9bIL6//2lYf4xrE5tDak2h2b86N2esrWGtpakhr5iqIdCy3FFf0uK9fWMFU+yd+/9EeUwkF8YbhmzLDv3JJcqBTKKWmNxeSDHOWm7Mp5iu43cp6+Q8UN3LHrh0hNfEHP8ZV6kZ0Aua7HozeXuIDVOXvLmfVZYFvaFiwx7rWxTNdaCAHre/IsNCPW9xbWPA8dIZXTc4d58IjkwJSgEglyynDn9hF+5AaPqcppKm3BE6cLfOVYP//lRY/Uusnw0HMkxtSITKHLSW0KAUP5hL851Eclvhi73B19aiSpNiRacN26ZreytNyhq/MzKZz7lrW2y3cIlN+d6X890YxrKx3brLuDozTid749xvh8sdvbz3tOjz01gpFiwlzTp9L2ulKk9djyxKkIY5z2O4Qv99Fva7xlAvJUuc4V//6vugWmzzx5FAmc/aWPMdb/5hmq75BMfCX53Y8JJhZj/p+nm5xazDPf9on1Urb1ajNn4AJZieXBWwlnvferT2yhGnkEyoJxPWNPucXPWNDG46ZNhvW9AV87OsCh+RzNWFIKLM9O/z0/9Z6tXLvpLg5OPEE7rmOxeCpgfd8O7tz9w93Pk0Ly3j0/yqHzTzBZOc6P3pBg8CmGV3L9phv5N3/1/3Jo1qcZS/KB4aqRKu/dusg3Tgzy1fEhN3ssjZOlRGTzni+/WVkNiaUn1BSDlNmmRzPxCLII5ErnbpZ66d8WJRSVtqYWaf79Rz5MM46YqlYY6+0j9Hz+09de4KUZhbFOtMQVy/01P98iODwbcHsxJkpTbFtxsYWqQyTLdij4amkTYhEU/JRIK1IjXjYY2+x7b8mCMWQBVUAjqqB1Sn9xHT90fQDWsaybiWKgEHL7liF++B29zNVPE3h5orRBR9fb/XFL4OVZ37+DwMsRsCRSs3qMKfTza/bNOwHy3MJh9oz4HJxxu47UXkymc/V/u3tACMFCI2b3aN8q964LsXv9Lfz5s2WeOjvPdMNQjwTG+pwut3hhosA/vbHA148LvjzeRz32utm6RdJKLapTHehuXtxmbq7hEZtXJjVZ62w337G+xt3bFjEWvnZsgENzReJU0hvCzqEGd28r40nPkc+8AsVw4DU5W716ZMpt1pKaTPPAWh462sfR+SIdrQFrBfXYQ1tBLVIYA/Mt10IRArzsOTBW89yEz+n54wzZ3d+F431r4C0TkJcH4w5M9vPk1378H+OQLhnLSSaptvzXl1ocmBSU2znW9Ua8e2OVp8/3MNPMrTmGdOmw5D1DkvXpmonClzbLuFyP2WZl8EBZ7tqW53v3eHzpcJEXZjQCTaAgtT7fPmsx5iifvmM3H7/p31BvLxKlrYuaBHRMIPZsuHVF1jRfn+D92xa440pLLbIUfI2nNMY6IZLUugVX4KwQQ2VJjOuPX8KZZstgG0+CrzTDhZhS4HxsLaCk7Qo7qKxHKYUi9AsM5nPdRb4QhGwbHgXgM48f5vFTc/hKEacxxkKq1z6mTJyRTb11vnykh/H5ApE2/MkzX+K+jIm9nAjTIZJZnOFB6HWui8OGnpgT5dwF8p5rffLGnoi+vEEgl4Jp9lqsnRzpXbs/wW07Y7RRzNYbK8RCtLmFWnuRp459kZn6WbRJEEAu6OHqDXesWZ5ezmRW0iPVyQoHqOXo/P7HrzvOYyc18y2X9brjc/aQL1cRSY2l2nZl4xvWD7wi0zfRlqNzJRppQjNuZRtQZ6bywqTm2+d3c3h+kmailvSqM5a+Yz5LZHYlPOmIjq1YEl90PrvzNyCfcTs+uH2Oe3e4YPx7T23k6Hyxq4Fdjwyxdu2en7h5A9XWHEmmaHepzlaXgkLQSyHspdKczaohjkh6aK6Y8TwyI41M8CQxgnbq0Ygdz6TzuhGw0PIZKSZUopRz5Tn681u/K8f8VsBbIiA/fWr6ohKPJnv9zVC+Xk4y+cKLLb46HtBMpDM3kD4z9Rx3bq1wdD7lwFSRdMXC9FoCtPsdV2J1gbgUaBqxIrGSloZQWqS07B5R/OePfYLxyf28OHMCQQwIpFR40mWBL04796NdYzfTVxh5VUew2higJzcIAqRI6M+7XMRY+NrxQQ5OF11fGxecrXUlVykc+9u8yixZCZGNaLlRlUJgunaIMuvLlnxNpe3Rmzf4yqMY9pP3+7sWjcvRjCO+eew8nhSMlIqcKyeAcZrHq47Jdo/B8Hv7NpEayWjRkPc9Fls1vvD8SwghVpCRxnr76MspGonBk8b5J4uOuIkbPfOlRVpN26iLnAPLpp42v/T+s0jUBVKcxjoCYStpkJq4e016csUV7xmfeorp6kmsgA392ynlhti57iZ680Mo6V2gSb6aydyBEHLNvvnyca+fmt3P55875BZ2GzNbV0zW1644LH1LS2oMofIoBK+8vHVkZBeaMXKV41Vq4cB0gDFjIBazXrm7dxLdOX8Ct2W1+NL1uWO7VMa/WB1LCosUll1DDb5nl+NhfO34IOMrsk+oxj5KCQ7OCq7e+H5CT71mZ6tLgZIe20Zu4LkzX8NmlpKN2KOdSAQQZT1+bbq5NAKbTUg4PkugDIFys9hDNiHva/JeC2Evj0NdDG+JgPwn+0++4utvhoDc6aHVozZfP+5nSj+225+qRT57z/Xx7+4R/MqjMak2eMpwtpyjnry2S6mtE8cIlAu8o8WUM6minWZZsgf9OYhSw//x0Df49J3vQ8oqnprKyo1Li389Fiw0ou+IZKKkh69CtI7d8RnL3x8b4KFjQyRGdsuyHXGPDrnLVwZjBEmWuVwMUpD1Jg3XreuQ0gR3b1tESZ9DswHaFLh6LE/e70UIQbVtKAY+d+8c61o0wlIp9uDUCU7OpYS+YjAfUmtpZpuSqDvDvTIoe9Kwa6jF+VoOYwRSGDb2ueNO0haPHp9aQUYqBGGXSNYTCMrtJVehntAgJQzkEyqRTxJ3Pm8JoTJs6G1z/y0zBJ5E4ZHaKPsOrhdstPv/hfoEQqx9L3Uy3VhDtQ29uRRtppmqHKcnP8TDh/5smSa5z2BhPTdt+yhR2nT2m0Ihlp2HlyMkKenxL977TibK4zw/oVlsCjzlxu0cK3/1NV4qHwNs6i/w7dNzRKl+2Sy5KyOrLXKVKocvHcdisJjDk0u9eb1qFrHgOSJdasgmGDokt86RrUTBT9k22OKa0QYf2L6QBXjBodniCn9loGv1GachlbZmfW/4uhO4LoYtI9czPvU0cdoiNZr9E73MNkKXJFjZ3Yp0GkZKWAq+JdICbQSBWtJvdz3yBjnPw4qXJ669nfGWCMg/fuNWPvPk0Zd9/c0AYw2JTjg2NcFicyNCXNglW2h6NGPNQF7QiC2zDf819VCX/cnu55QC0y1jFrIplR1Dgs7I7eMn5/kf74bBQi6bCbUrFo9SYBkshitIJpciKgCuStCbH3YkpaTBN0708/xUL0lmgehLV7q02bFjnT50oCDCIrRbKJbPKAsc4zlQGWkLy49cN8NYT9w9CQLLD16XY6RnB+sHb2akVMCTgv/7sSN849gUzSRh7+k5BEf4+Ds2M1LKcfD8ExyaPEwh8OgJFZG2xGkTbTWJXnLJUmJpES8Fmu2DbZJs0ZICapGgGTcJlI+SHgut9gVkpF/+0L3AQzx2Yp7j8wntRJALLJv7LDuGWiQ64ZGTg7Ri1b0NXH/SMlBIGchrNg2MYk0ThKTeTtBrOEmlJuKhA3/Af/Ouf7bi59qknFs4zucPtHlpSlCPJaUg4ZoxyyeuO85k+RjTlVMIKTBW005izpWPMPnsCaRUSCG7Zf9SNu+73GRkLQRewM/eup0Ts4c5X27yB/sCEg3Tdf+CGfQOE7zTyljXE7DYil6W1AVORvbuHWPsOzO/omZgrWWwFDJcyHHLlmGen1hgoRk7trHpUMksodKOQW3BGEGgDD2hsy6Nte1S3jptil4/ZV1PTKAMatnz3UjUmhrYAoGxkqHS8Cv2w19v5P0Sg6UxEh3z1y9ZjswrCn5KNerYnXR4DK7i0wnNobLE2SvGumfW9cjL9Bf3oJqXFcYuhrdEQH73lnVZyfJCyOz1NzI6mdbR6X002oukpCucgpzOtCsRWQt/uC+hGOZR0tKINYmB11aytkjpRkcElpGizsZKXKDoz8Ny/Ytqy/Cbj77EifkGp+YFUlp6QxgtuYfy2nWGzYM7MyvCV0fkWY3QK5DzC6jSBqYq5zg6X8RXbiSqkxWHnkVotyPvqCRZK9BGEnqGQLnzVM/irRSQ8wEkxmgG8wlDhZXByGJY17eVd155d/f4PvP4YR4an3CqWJ7Hwekyj52Y5neeOIy1LRLdoBRYesIIhCLVAcYm1GO1xFDPlmJ3fwraqcrcppb61Nq6a5CKBCHEij51B77yVhDJenM5njv7bZrtkzSiOnGSAoKvHB2kGjmRGF9ZBvMJg/mUXcNtCsEgvurl1h0f48DZR3hp4pELzr9AUm3N0Irr5Jc5DEVpkwdeqPJMpuDlK4i0ZN85MLrCB3aWEVKQ6gRt0u78vLYJWicIJKGf7455FYJeRooX9z3uKD5tGXk3AM3kOK24xWLLRwiJtBc+73kPpHQtidDz6Q2DVxXE7r9zD986NcvjJ2ZIrcGTgqFijk19Re7c7sQthIBfe/ggs402BqfuXQpS8r6mHnsoaTHWVbNSneBJhbEmK+k6BNJw5UCUPXPO6Ukg+MgeTaJrlAI39uQqYwJP+njKRyB4/86xf3Dlqw7J7sTsYQ5OK5QQDOQ1s013f1vjrnIgLaFniTRdwZOcZ9jU10YbwTvGqty7YxGJx3DpCtLW5ZL1xfAdBeRf+ZVfYf/+/aRpys/+7M9y3XXX8fM///NorRkZGeFXf/VXCYJ/mN3Q2V/62AXErg7L+o2OI5N7ObtwiFZcwwJ9uZT+XMJi5j3bIbMA+NKgrcXYgNGefo7NTb2sHV5fmNKMRSYUsfJ9JT9lc3+MkpJKW+LLkCh1O9qenGWkKIi1C8pullmy93SZDX15Ym2YqddZaGmUNNy3W67ws70UIs9ydEdR5l+iFomu9ODSQuXe50n3s9CzJNopc3kSCr6PsYa2tijh+oDaQjuBgm8phs5Wz1eWJd0xixQeu8Zu6gbj1S5TpxfrzNTatBLNbK2NxSBFQMFP2TIQYWxKzkswVhBrr5sRWVzA7cAYJ7ARLPtOStDdWMRpwo6hBr5a+5p2iGTapChmyAc+rVghheZ7r4p439ZTPHikn1PlPIkRlALXp7xvpyNfjfVtJecX2DCwIwvInY2D6FZHtNVMV07Rkx9c1gtWHJx2y0Wi6bpmSQEvzSpu35yQC2QWjC/cGltc9ceTnhunQTBZPs5ic3LFRm219/VgIeS2Kwf4vmvuoMd7CkOS9ebFClnZbqfCwkDRRwixZr9/7XtO8rlPvof/+W+/wmOnnJnLQD7ixo0eP3PrDpSU/PO7ruZTt+1mfKbCv/irvVTaM6Q64kw5JDFLm2WBxVMWT0p6PUOiHTM+STU7h1srKkpSwJH5Au+P58j7Pu8Yk8QJBErRSDwsHhLBHdtGuf/OiwucfDexe/0tzNZT6vFpPKnxlHuOdBaMY+0sV4WAvGcoBpp6rMj5mry/JJACgp78IFdvvIMXZg/8o3yXNwNec0Deu3cvR48e5YEHHmBxcZGPfexj3HrrrXzyk5/kvvvu49d//df5whe+wCc/+cnX83gvirH+Esmv/fibbg65Q3qx2K6doq8st28u882TgzQSRWqdOILEMlhIEcKS6BapVuwakew/v3ICuVNQyyn4t++d5dmpgAeP9FCOfDoLsBKaUuA8bhebilqsCL2EUpCybVAyWfM5seCRGheQi4GlEAT42QK3dajE5oEicarpCSX/8fvuXMbEvTQiz2rsXn8LxmjmarMUgpREK4azjLYRO0lNX1ru2VbjI3ssml4eOQlfPAhWeJjU6QuXAkktitBG4CkYyMPtm6vctbWcnSfbLaX2hMMrenMLzajrMmWsZaER00o0UZqiccEeAY3EY65pGCkmGCw/cNUMDxwY5dBcqdtfW35dDDDTCDJhD1cq7w0TtHXm9lePRNx2xQRHJve+7MZlOQEw9AvErnlMTxjy/VcvEusFmolHyTfkAsVw7xY2DezubpgGi+tRwuMCJbjMAmrv8b/GWN31p1438F7OlD2aqVjhJ90XGhqRZLEFQzJaMxgv/9u+yhEnTXJ+ac2N2nLv69CTnF04zedmDvPsmQZjfQIzPdS1IPWk69u6P23xhKAv53HDhjHeu8P1+19JW9lYw8Hz3+K3HnuRA1OaduxRCAR7hi23XzHFsemnutch9BTXbRjk+6/fwp/umyFK3Oaqs1n2ZMYGTx0v4OrRHLFuoo1grmEzUtjK891MJJXIMFQUfHBnC6VL/P/svXmw5edZ3/l53/e3nP3u9/a+b2q19sXaJUu2bHBiGQJoCCEhGRzACRkqlaSSqUxqSCoUBWGqmAzEBJchA4QIDB4WybYQsuyWZC1ubd1S7/ty93vPPetve993/njPOfee3qTYIMu4H5Wqu+9yzm877/M+z/NdFswGFtspeV/x8LbV/NP7+1H372dIIfnQlnvZMmaotdtUW9MM5AQLbYMEcspSCh0QNO9r1lQitg63uX1tjUpgKYQBggICwY5Vd1wzxHiX+JYT8h133MGNN94IQKVSod1u8/LLL/PzP//zAHz4wx/m85///PuWkHvHtWniuyIRd6O7sLo5m+rNGh/euogQcGC6xNH5gquoAqdTLVAIIVhst7lurMQ3z9f7Fn6HvnVcyZ2r72DnxDGOLRjenta0Uyf4oCQ0Eo8o9dB0xfstUgZMNzwaiePSuj6DQAoPSz99SUlBPvBoJpqlSPfmziuThTEZmcnwpIfsoHDfDfTVpUQlOmbnyBsdGzyc/2whJdWCm1fV+f5dEQjJeHGQH7lR4CvJwblBDs/USHRCZlICZankU0ZKgkro8Yldkij1QAiUUD1TgR2rbu/bJAwXQoYLIa00I9WGRGtSfXkp03qsXIs8VfzuW6topcvz4+5kv/tv0WOAd2bKoeau9VXu2dCkGBiKYY7AG7zsxkWbrOc+FXr5nspUKRyi3W53ZueGfFDihonbOTn9Bo2kSqpjLiweoxktsXXidgIVkA9KVPLjVFuTfRsn002owiKRaJMyWT3G7+6r0UpzvfOJUkEtUkzVXaX3/7y8jns3LPLhDkhpZSyLXGQYu4gQsueNbLFok3F6/m1WD17f15VYaFxwgDDg8FyOH79xiq+fKVOPgl6nJFQWX8FY0eO5f/oJhPAZLoQINL+69y1ePl1lsZVeUd/68ORLfPbFI7x6zoHOQs9Vf6+c03jK4wf8S+/DZ+7dxdTScb5y6Iwb9wg3Pgl6crEGbQz5sMKgVGwearJ/ShJn7gPiWtlug1n0DeUAlJCkWYMHN6d88s7HWYr0B8agIfQUD2xZxZPvnENKxUTJkBlNPZYUAs3aSsyWoRa3r6tRDizFIETKXGf+7WbLEwObuW7Nvd/pU/nAx7eckJVSFAoOLPGFL3yBBx54gOeff77Xoh4ZGWF2dvav5ij/BsdK+b7QLxAlDSQKhBOov3v9Er+5bw3WdgX/3cIBgoKn+WcP3sYTb32tUyl0hRHAEwJfCa5bezv/9YUWh2fnUBICD4cI1W7G1TaSYggDOUvghxhjaSbCuUGNCIrhKDnfVdYHJqsd0Qu3GhprSbWhku+f1YVeAV+FzNZOE2XtDvxDkvPyjJY3vmdloT3rHuBTe95GiBaH5kJaqaLgG26caPHw1iWEyGGtcZ7OwM/cs50/PVTmwIU5tNE98X43486oRRahRllXHscYTZy1yfkFVg9uvYTPGXqKezeP8uTbp7HWoGRHmtS6TkU3IbiRgqQWu01NI/FYaPWrM3UFOYRwvO/V5bizKXKguJPVAh/dWiP0FDm/iED0bVyMNRy68A2OzexjqVWjnkrGiwUG8gNYNFJ4BKJIpVRBm4z1Q7s4NvM6C+3F3jXzlabanuKL3/xP/PCd/xopJA9f/2m+8M3PYfUMUmbIzuYrUO55NFZjrSHRcGAmoxRqapEi0aIDrHMTcl9aqnHI106NgLA8snWx83zAV084x6ZWonoazx/d1qSd1HtSmV2HsN/9xq9yfG4zqwbWsMyLdtFKFJlVfGJnjWePl2kmPk5A021U716/wOELf879Ox/n6NTL/Nrzx3nhdEbO88gHBVrJ0CX61tpknFk4wYFpJ/zSH5r9Ux6Pbr90A6mk5D984hNsLP/f/LtnUtqpx8oCNlCCgRz80t+6lT2rh/Gk5Vf3HuCZw3MoKUh1xGz9HNo4XvhiJDA2IfDAkpDpOqsro+/pM/J+RZdd8OTbNWbqNdZWUrYNtbht7RKlICPnexTDIXxZYO3wdqqtaeKsjdeRyr1u7b1/7a5UfxPi2wZ1PfPMM3zhC1/g85//PI8++mjv6/ZyNkWXiQMHDny7h/DXFvv27Xtf3idJFTU9ixDSmcejoCNNWAgMe8ab7J92XrIChTWCWKfsWZURTV5gcyXgTC3p2Qp2d+yrC4qzRw7ywpE2mXFo30BCkrkUqTtiGlGmKfsRSepmxokOEQjacYZnaqRtByyRJmN2oUqoJJPNhFqsSTLNWMHnf3/iGR7fMdKrbqbaZ0lo9M7RomlnDaYXz/LG62++t+uiDUlznEc3TvLgxjkaqaDk28781ydLNRZLs9ampMaIopAHyy2eHI5oJstzTqxA4FH0LBPpVgqtEGM1mgSVBrTaitcnX18+VmuZS44wwFHG8nmOzBfAhhgbOJCbtT0OuCct2rg5GtYy3/TRVtDfnHTHbOncG2n7wHKtVNFIFQUVkkaWalRF4fH2W4eQQjGfHmUmPc7TJ0KOzA/RTGQnuc3y0S1tAhGgREhjqUlRjrLQsHzhSMSR+dV9ifDDWxZppzWefPH3eP7MBG/MtKjFayj5a7l+zPC3tua4oF8m1VGnE+DOYClyUohD+QRrfWYaASuHJJ40WOuq4ENzZR7Y5Gb0XzkyzBtTFXKewVeQatV5jgWPbF2Ai9gvoddGUOPcfEpBldDWCVIA5Do6zw9vqmJMypG5MrVYUg411422eXhzjXPzi3zxxV/nz45ZnjpaJjUCJVNK/iJrii1CWeJP9tW5NagTKElq25yuzlFtlR3Aru9oLIutjJn5uHcfLo4dwS1sHHybC/XlUYoSUAlhbSmieeEQB2bc5vO+gmW6bHh9psVSlLAQSWqJ5NRiwLMnBxjMOWzDw1sX2fvGU6wKrn9Pn5H3M+7Kwy03r+NU6xixPIWnDG5FKuCbIiaWpGiy2QolKuRJUAREkeL16df7Xuv9Wlu/k/GtnOO3lZD37t3LZz/7WT73uc9RLpcpFApEUUQul2N6eprx8fF3fY09e/YQhh88bdN9+/Zx2223vS/vZewtKwzrSwReyHBxDdsmbqcZL7Fr9QF+55vneWfG69BNLHsmDD91zx72rLuLzyQD/MaLR1hoJSTaECjJcCHg03dv54VmxrGaJdYKZ01o6Z84uwrv9FLAluEEXzmREItFyITIVlHSCWPcMTLBvZsn+J19x6kmiQOTKY9qJvkfR2qcSj1+/+/dDxgOvfqnoC+emQmsirnhpusvq9pRmIMAACAASURBVN7VjX5gzyi+CFlbmeb+DdP4nk/OL1IKhzBWM1Fxcp3LfsU1bj79Ts/PN01TfN/pX9+0VnPbrXvelcd58MKLHDtzHGsiHtmadIBaklYiiTIfJV1V2DkjkFAOM5qpwnQ6FP2ru+iZNWgD52o5in5n/CCcsX0lFGiZYIRDJK8a2MRt192OtYavvnOAp/cFvda9r5yq2P7pEp70+V9uKmGaOR790I+ipMd/fHov+6dLPTT0ciJ0PsJfOhdzqi4JSxVGiw67cKQleXauzc6hZYR0t8I9NFdgqh46TqkWXGx0FaWCvG+xQtCIJUux4r+/OcHbM+XOrNyd44bBCCngyHyeBzYt2wymWlBPFN88X2G26bHQkpRy4ImA0WKGtXDdeIyvwPMUH9m6xEe3xjQzRSnQHWcqp/H850dSXp8qdxzCbEdfWgExW0fzCG+ATbuuZ3WlgDYZtYOnGCpomikYs9JlSVDJe9y2/Tpu2nDnFZ7TjLsvnOEbp3Wn9e7GJgB3rPe49467+1rdd97hwIK//NUD/NcXXuu1uwEW2z5fOzWEFJJ/eKfk5p03fWDdke7hTt4+v5ez8wc7ZhcdcKQ1rBvexXVrLn+9uvF+rq3fqbjaOcZxfMVC9Fu+4/V6nV/6pV/it3/7txkcHATgnnvu4Stf+QqPPfYYTz/9NPfff/+3+vLfU3ElM3qAYlhhtLyWgfxLnFk4wUIzZrgYsmF4OztX30WcaX7ghg1oY3n+xDTTjRYjBZ9HdqwDBHuPzyClIFCCKL00GbsQNBOPzMT40lDwdccJyH1Xm4xae5F7N+X56Xt38PyJGZpJxkIzRkpJZhKiTPPc0XP88y8+wd+/bZhMpytaVMsc6UynVFszjFeuTHn57ItH+PN3znXUfiStxLJ/qoQxmke317DGoKTH2oEdl9CoQq/AD+4JUCJj/5SgFUNJWW5YZfnk7ssL8a/kSgNcWDxKoiOEkPzl8UHennHJbctwwnwL6oki76esrcSsK0ecq+WIMsVS7F3Wv3o5nAa11oK6cfd3pJCyY6SNEHEPoARwZuEQzx38Pe7c+rc5Xz3F4fnhvtms0xYXHJjxnXShXQLcgv/GhfQSAJFLhAXuXr/E29OKUt7QiJaIsxbGOK3h544lbL51OVF+9cRQh5rjNh712Lvs86ORtFNDKWcoBZr//OJ6LtRzPZ6qsc5b+8RCjk1DEa3UYynyGC6kvZb22WqOdubMFCq5DG0ttSRACcODWxp8dGsDrd2YJPTyWAxDFzkjZVpweC7EV6ZDQ3JfF8KZhDSTBnlfMpBTtJIaoVdg/dAW9kwc5tXzPkgwRvdQ9/dvHeeG9Vd2J1LS4yc/tAWtj3BgWtJIBHnPbZY/fdeO3md4JbAM4M1zVSLtI8Qy9U4IaKUeR+ZKNONvT1zn/Yjr1tyLFKpTREQEXu6vVcrzeyW+5YT81FNPsbi4yM/93M/1vvaLv/iL/Nt/+2954oknWLNmDZ/61Kf+Sg7yeyUulpHsxuUSNkj+ywvL9BBP1FlTnuWhTfMMBIZCeIjP7VuHrwYZLoRk2tBOE64k5WcQLEWK8VLGA5sWkAiOLhSIUkUhMOwca/PAxnlmGy0W2zG1KEVK2ZkBuoXRWnj1bMTDm06t0PCBfrqVIPDyV7wGrSTlt145xlS9TZxpBClFX7OqLDi+UKYYFlAiY7yysQ+FvFKycc3gFv72dYf4+E7Jhdkma8Yq+NKwdmhLX9VhrGHfyRd5c/IkI7mYoWKeocJqmnGtY2MoOTJX6CVC0QGWjRYd0v1/vXWaQqB55tggb0yVnGiJ6Gj8uivSd+5OIAQSA4GytFPJnvEWD2+pXvaOnF04xFBpNUuRppUofOVee67l00xVr8L6o/2W79ucEGctliKPpUjjyYDMxH2v2EoVi+0CrdRDqpqz1uvQnbTOaGuIsgBfOeGSI/Pu3Gebfs+T+EqhkeRVisZyoR5e5med1eXbM0V8afgf+8dRAqJMITp/gjMqKIcZN68dJtWWJJvmI1tn0SZBo1EiT+gVaSZVPLVi+bKQmCLNRBF4UAwyapG3LCAjBEkGI/lZ9h75A7AxoZ9nvLyJf3z3duTLJ3nzgqKVBQwXcnz/7uv4J/ftfte55+61d/OZ+8RlN8uXo3DduGaQmWYbCBBCd0Z77jnRFhqJTzsLSLOYulmgEFRWdH9iTi8uEKg86wbL31HA19WKiGvxrce3fAUff/xxHn/88Uu+/lu/9Vvf1gFdiyvHyoT9a88f6tFDMl2jGi0yVYMoybNnVYzfrDO5tMBw0bJpeJBUG+ZayVVfX+K8bZUQPLx1iYdMjVjnKYcGT1pSnacYZD2pQWPTXjJ2x2dpJhlnlqYYzNne97rzN2stuaBEOTd0xWP4lecOcGJuEcty+3AhhThLGS06RPNQXjK9dIpdq+/GAnsPP7FCsjFgtLSeNYM7mKmfZjCXkff9S3bvSZbx2Of+hNfON4i1IFQB142n/Iv7T2JshhCSZiJpptLNhzshOpqVWksy4wzsH95axWKpxYp2KjFCOGvHFclY0nW/gZxnWVOOkULw0FaBEJenClk05+cPUfANhcCpf821/B4fWwqnsrZ/WuLbIn/nngLDBdFB525isnq8B5gCKPmwe9UqXjhXYKndortZsJ3/Cr4m73XoZamilSiUdH8XHe7v1WL3eJ3Jer5z3lf6WUFqJFP1HM3UtZwHc1nPsEAI937WOt1u4Y+zemgNrfYhqrU2wh9AyAwpFJlOUdJHCkkYFCjly1TCjChzG5f2CkoSWM4tOaGOs0uaG1d5fGJXyvnFw6wb3sX/9YM/Si2q00w8xkqF95zsrpaYfu2F5c9ozle00oy9x2eoxym+Elicb/Tya0EpiEjSGZ7a/1mEFRTCCptGbuH336zyx28tMNt09MHV5YB/fM8N/JP7dpEZewm1q9v1cRuzS60w/6riSkXEtfjW4tqW5rswVopWWGuJ0xbaGE5XA96ZCfmTg06/GAF5v0UxHGTtYIHjc/XOYnlp/aLQDBWcxeJbnXnjo9sbFMNusnD0oEqu3JMajLL+lmExyNz/ftb3dWN1h1JSYM+aBwB6LUN3Pi18leOd86/y5YMHQUjoCC0k2jlRtbOAWGc8eXCRj+9oooTkwLmvM9+4wPTSCYQUSCkxNnP/FoIHdj7Oq6+9xB077+pbjJIs4of/27O8fKaJEG7GmRl4c1Lyn/bCP79P4auQgt+m6He1k10EXp5UR4SeZiAvESgg45Gti9y3scrTx0b45vkys82AWCtkt1cgbE/K0qIIVMBgXjFatFRbV77XWaYphiFbh9u8fqFMM1G9uaO1TorTkx5H5otkRhB6ivu3jPPUwfOsGdxGPZqnnTYwVnDnBsWu1Vv5yM48T7z2Zo/+IxAYA9vHWnjK3dOirykEmlbioY24UmNl+fkRhlvW1Dj5zntB0AvmWx6+cmCooXzaU2ID14q/UD2NpwyhgpMzkzx/epB3ZnO0kpRCoNk5kufBzQ2K4VoKoaEU5lk1sJmHti/yu9+cZr7lNM6V6FpVChqJYCnyKOcEr5wTgOGTu5e58UOFIYb+J6yFL5aFDb1C79+ZEX0Urm54yr3vQM5noWU7bXIncFLyDTtHm0iRue2MsDTjKr/y3Ms8e2KQRtJ9hg0XajH/+etv8MLJWWc32UnI920e46HNC5xfOsHZ+VlyXpucHzBSWs2qgS3vqpR3Lb6zcS0hfxdGv2iFRtuMU1Wfeux2x1IaNJIohSOzhuGSZrre7jkccZmkPFGEvF8g1TFKGI7Ol/jItgjoVLYdepCSHj97/y72npzkuaPn3KInLaXAMJhL2THSNVkXKOmhOxXA6oFtrB1y1Im9R54gStskmctEgcqT6DazDU0jKlL03bwy1g6QROdo26niuRMVPCn5+I4WU0vHmWucR13k0iOkYLZ+Bm0yfJHvJePMZOw9/ASn58/wwokJul7K3QVKCjg4I9GmwK5Vuzky/Qo7Rlq8NV1ESUnoFRgsTLDYnGHn6FwHTORjrUectch7gk/uWuDj2+eZafh89pV1NFKPpONV7EtBzpeEXkA5D3smGu9aiQVBkafe8Tk2r1loOXs7JS05z1AKNWPFjHw4RNQUPd3mLkVl7/EZ4myQ8fIYH9o0yM/cvRNLys+M+pyYPcBr5w1xJij4CTeuavDQ5sXe+/rKsmOkxeuTZYSwHSnQK+ul+9Ly7PFRZpvBFX9mZWRG4nV0oK0VfUpsK2VFt4/Uee5kgf3TYQ+klmSKr54cZO/pAUYKis2jG3lg62ru27GDH7z+G/z+69OkWlyid50ayWRdIoVhomx4Y1Jw53oYzL07N35lXCwL63sB2jjqXZxF5IMiSm3sfUbBXbpGvOh41UZz1wbFS2cCzi854wVPKUIv6zNOAYhSzTuzOZrpchK1HYnOybpm74lpbl0/0qvA//CNt/nD1+tEacx8q8hgPmD3WJvv23GKNHMjjKsJzlyL72xcS8jfhbFStMK1gz2aiek4AC3DbkIFqZX4UjHfjMn7HlGmMbbjedz5ufFSwM3rHe/RWE0rrrHUXqKR+AypjEIwyPaJ23otXyUlv//37ueff/EJXj0b0U4dF3TbcL0jkwdKePgqcLaOxnDThkeYrp3gzNw7eNKnmVRpRw0SDakpkPMipDHk/YBhBJmGZhL0jlF2hBfqicfXTpb4/l3uWDOdIEXuIm9fSLOI4zNvkOjlan3v4SeYqh5nqhGQaNGzRzTWLEtmaphrh3xyw0PcuOEhPnL9PL/50nG+duwC8602rWiSO9crHtykSLVCmxRPBeT9imuhKulmmKHhI9uavDM3SiU3wYVak6V2RqINq8p5fuTmrTy0eYHZ+klHZbuYA4TTlX7h7ARvTVXRpsHqSkpqXHIvBpqxYkolP0Y5N4yf1XqgISUl/+S+XfzkXdtZaMUM5n1Ozb7Kyyf+mFbc5uljIW9PGZqJpugLNg82eXR7E62dLaPF9BJDM1EkmZNude4+XSXnlcdpGMhlGCTlQBOl6iJr0EvDIEg6bliLba9PiS3vaXKeYedoxD0bqnxu30QfoK3btpfSMpyPiTLLlw9NMlM7xfVjU7TTZQT8xSA0A0w1YClyA/hf+KpmVTnHyfo5PnPvrvekiNWVhbXW0owXadRqGJsCovPch0g5gyc2Aw7w2ogXidIGIKjkBD+0R5OTLV6bLFIMB0l1lVZc583pPJGG799RI+d71GNLPV7Wl++dh7Ek2hJrQz1KKQQCTyrOLLa5UBOA80w/Wws5Pp8nM/P88I3V96SUdy2+c3HtrnwXxsq2pBOsyGGsE1Hw5LJohRQgEXz67h0stt+mGHicrbaYb8YkmcGajBTBDauHeglNCY9yfpjx8ig/csdjhJ7sA5Z0I/ACfubuTXxs20Fmmy182UAbxzuWKDzlfGuFECjp89aZZ7lQPYrpJJ6LRSNyfoH1lYhEG05Xi2TaLdoCl4yVWEbNViOfWA8xIJcdZnrzUGuJszZgefnEnwCCmdde45Hrf4LZ+hmEFIwXU0JlVySN5W5BqCw3r1kGf42UJnhs93FuGKvRSCTlEHyVoU2e1QM3sG54J+XcMFJ6y7Ns42bZP3brGO8s3MqLJ+aQQrB7VcAta4f4uQeuoxC466PNh3jrzHMcmnyRWC/3riWKscou/uxoHsE8oV9wqlyBoRYrB8wSgmI4gDaWW8b7557aZGjTYrxU4MjUKz1d8aeOKF442UQI0wFVwVvTAZDn0e2GQjhElDT50pEcB2ZKDOU1lVybuabPYtvRx6TQnXawq2Z9ZZkouYQ6VkxpJJJ6cnXfYueUZFHSOLpYCzYMJGwdanLvxoxyTuNJy2xD0kq8HvLbdDYJXVu/zFhn5qACnj85z9aBKokeQEpWzI/7IzMOYFYMNIFKacSSLx+aRArZ50N9udAmY7J6gmZcpREvoU3akwtdakvO10I2DqYMFw1rSrOcXCo6D+PUzeyNhRtWuXM5OCtRIsbYGknaYrahqKc+Z5dynKnm2DXW5t4NEZVQM9PsukDZjg+4Q5w34pg3zk/hK0E5gAt1yKxECdsRK3FAua8cG+bjOybJ+60PPIL7ezmuJeTv0ljZliznK3gyQgjTAyC5ROiR90Pu3DDK6kqBVpr1NKhTbWg36rREcElVoI3lga2rGC1dXi2o27KbqZ8m0TVKQYoncq7azSI8L+gleGucz+xM/XQvGUM/paYa+TRrOd68UOkAYm1vnmhxlVpqBUlHyEQCTx8x/NgtklxQIssSuroN3WQMDnBjrKXamuQr+3/DVdNSUghg22jEwZlCz3rSYsFKbl1b5LbN9/SBYqaWThJ6ktBzlc5Co0UthvnGHJ7yGCquQgrJAzsfpx4tkGQxg4UxAi/HR4CfuvvKespKety08WFyQYELi0dpJnVyXo61QzsZLN9K7cUXkFI5rWYVsHbAohqWpbaknSlynsfD29dwR9jouzfTtZNEaQslHMWnGA6SaNh3ts1FzYSOyUGeD29pEqdNPFnk+EKhp16lBEyUMsaKGQstxVgpJsoUgTJsGmhzdKHYp1y2YSDmyLwkNd3n6uLE6MzrB/JZpzJ2M/afvSvG97JloKAVTk400L05vjaiBwDzpJvJesrHWE090kw1EkJlaF6lvd41/MgpjRSQmQQpLHuPz/T5UF8u4qzFYmuKJG11AHOWRMMvPLeZqUboKllpKfmarcMJtbRBKzUIo9k8LLhxteUTu6AaQT0BT2a0kyaTdclSx//cWKdx/eZUAWsNu0ZbTNZz1GPRc33rbjasda2MTFvmmpCZS49dSqjHHrMNwerBy1P/rsUHI64l5O/SuLgt+bN/9Ap7T0yvWBgFxlju3jjGWCnfV1G7/xVt4Cfu3IoUDoCy2I4Zyoc9y7krxUonp4GOd7E2GduGbme+cb4P8TxSWcN883xfS/liSk099txC0yVKWfCUIenYJF08C7TA109JBnKan7rnPmbrZ5itnyHNIpatNZY3GUJIGtEiOb/YkYO0fOZDc/z6y6Mcm8uRaEkln+fujeP8vz92T0ek5QStpI6SHs2oSik3SD2q8eQhyZG5YRqJohhk3HLmCD99j8BT8opWk6GnrurJeyWkbpxphgs5Ml0g6gCzUi0wxiDIsEYz3zjP1FKEHRvruzfNZKkD9stIdUyUNtGM00jAv0y+aaWKemzxVUY1SUj0CDlfYqzu8ZQFLon+6A0zeB23KoDP7csTZ5JMC6qRRyvr2k8KfGEQwpB1aFNSWEYLKWPFZdcmgQMTLsUR2yqrma+fRUgBAkJPdub4Zdcpkc6K0xgohYacl3d2jAjKoWC0ELO2EtFKJWly9fZzuzeXtSy150iykXf1T/Zk4HSouw8qgl94bhPnazmkdMnPGEk1lrw9I3lwWwkhFFO1iN0Tmk/u7txzFglEnvNLHvXE0kicZ7TTxNbOxhA4Ml/kH948S5SFPHO81EH220473qKtopkq5IoOEoCxAtGZNbuPnqCd+qzp4ECuxQczrt2Z7/LoLvb//cfv48d/7wVeOj1HK8koBIq7tozyOz/mBN1XVtTdxHvd2hKfudc5yXQT+7sJ2l/s5GQ7hgZK+cw3zvPAzsfRJutxguvRAl/Z/5t9VVkfpSZx79Xb8eOqH21kZ7J66aIaKGgmgqPzFbZO3MV1a+6mES1yfuEIr5z688uiSC1u07BSH/ln7jhPkgVYbwuP3/4pKrmAd86/wKHJl2glS2Qmo+u6W48X+MvjQ+yfzndsBw3NxOPV8x7Z82/zd/YopPQucTBaN3wTZxdnWD80TjlXvOq9XEkh6bac7908ypfeSTi3lLLYSmkkGm0kpcDZ2zXiFl86GLOwsMjtt9/C1NJJmsmS8x0WbmMmhCDOWlh7nkIw0gPKrYyCryn4LhEU/YzRUhHfG6YZV4nTFomOMdb9zEAu62shYy1nqjmiTHZmnQaswBNOvUsKZ6KR9zRxphgvpT1d9u4zlPNTcirm1o2Psv/cc71Nna9yfOr6HIOFIq+cbdBKBQNhhjaKtQMBw6U1veO4Z/MopeAsUgislYTKdBTSLq2UJZa2Vj03sySLKAX1q/ond59rJXw0rrKvtgVTjbCnZd3loAsg0oosg1woGcgVODhTd9K02SJaN9A2ZDFyCdadgyUzDvB2rpZzbmBW8pv7PCLtpuEj+YRSoDlVDbEsAx61vXTTanH0O3Csi5vWrblm8PABj2sJ+W9IBJ7HE//gQWpRwon5OltGylRyy1ZnF1fUw4WQA2++0WtXv1sV142VTk6NeJE4bfVoTYHK0U4blMJBRkprnGdvx2RdmwyJwqD7KTUrdvV9S4p11dTlvi+EkylcaGteOfEi1p6jEbVppT7tRJIYRSkwvaTRe0lrkUL1WqIGTTnv83fu+FE86aFNxrGZfbSSJbRx7chupNpJPgpgrunRSBVaS5SCmbrk7vUwXnGbBYBMwy8/+00Oz75FIxGUArh9fYFf+tTjhF5wRVvAixG8u0dyPGXzNGKJMQJjhQPKWcFMUzFWTAHDa7MZi60qcdZy88oVnRIpHPhMkLBjpMX+6fIlql87RloUwxBrLWOVdXzi+uv58qFJSrmhDugoYnrpLDtHGgRKIISrnr96YogoUxQD3XO5yoxEAHnf9ARS1lViAgVTDVflh57te//tIw2kjPnqoT9gYuA2PnbDJ4nTKoEXUs4N89it8MKr32D91uvwZJsv7q/y4qnFvq7OP757G88eXESIGpVcRiNWxPpSH3AlnPa2MQ5Y5gUuhW4faXYsFPvj4jGAtglSKCSKczW/06bue3R777gUJ+RCn1I4xGLT0EzaxEmdp4+VObGYJzPOzrKbyLuGHVhL1PFBn25KBnMZmZXoTNLOJAZ1BQJjf1jcxuiGVSlbx3aQastCq/WBcZK6Fv1xLSH/DYtKLuDmtSNX/H7oKUYKkno0TWauLhRyufBkgJIe9WiBOG32TAiMde5Jp+f2s2PVhzhw9mvMNc6irXaLtE4RUoHRPUrNm1NllHT6zrC8uHR3+9rKnsTEyhllZpxEoS+XmFw8y4tnR3hr0uf4gqadbiHwDOsrETtH2zy0pYpzMRAopVC4eayjjohOaz8D6dFK6jSi6gqxk+XlrlvVVyOfWuSRms48MxUsRSE/96Tm5tXLM8I/fKvGa5MBnhT4yqG3955s8S+/+ASbxm/hq0cmaWeG0WK+zxZw5TjAta0zorTOxiFFM9acqhpU52I0U8WITZHC0kgt+06/ghKeay+vaEko6RSrjMm4b2OVdqY43aloCz0T+SpCFBBI1gxs4+HrdiGF7HVUSmHIzavbPLh5qffamVYcmS+iJAzmMpYir6fjHWXL805jnX73YF6wbUSwZ8Jn3/kmrRTyvmbrcIubVtd5+tgwJxYCouwIw4XT3LgaHt0Wo1SIUqvI2RKbRx2u4Z89uJ6fuvfSTc2e9T+AUk+yulxnEk2mBa0VAHZJ19jDtXiltBR9j1vWWj62Pbks4GnlPfGkT+Dlacd1irkBrhszHWOK5eu9rE8nGMwtV9zlnKLga/7snQL7zudJtPPAthZayfJoRlvhkq51dLJmqhjOp04O1Aha6XtLxk7oB25eY/g3DwX8xjdOMNlMWGjFDOUD7lhf5Gfvv5F8cGXlvGvx/sb3dEJuJTFTtSVWVQYoBB88g4tvNa5UfXV5uN12oNGW1sET3L/zcbx3mSt1q4Sziyc4szCPNfO9atCFIFQ5jk7t48C5rxN1ZBmVVOT9ComIeh6wAB/esoixcL4W0Er9Zd1h+mUnxQpZQQGIzp85lbFztMVXjiremYXpuvNnFcLSSiQX6jlirbBYfuB6STttLh9pJxEDZDqhHi0wUnKWf4LLO5UVfU3eN5yvK9JOW9GuoI61EsnJBU2kPVKd8c6s37OpXH5f+O9vpFj7GgbwpWCo4FOL3Mbop+/Z1jcOAKjHUI8FQrQJPQ/vIgENbQRSOZWtOD5LKZd3oio4J57ueeb9QZ467HF0vsRcM6UQJOwcafLRbQuEniVQ7vcmBjb3rPJWdlQGcopn3n6HWlt1wEwuMbRShd9xr/JUBwnc0RFJtascXYtf0UwCJiqr+MXHbuSP9/0ai62UV84VOTpf4C+PDtHWioGcZqSQMtfIeP5UnlfPWrANGskJSj7cM73A47d8iB3jFSq54JKuzmgxx/qhVdSiIU4uTuL74FnboW25e6CtIFROhONn7spYVckTKFAydwng6eIRDUApdEpzSRaxZnCUtRXDmaqzYBTu9DEWCj4o5VDgzbjGjRMNmvEiR+byBGqZa93lXutOZ2HFEwPCoDudkaKvWYq8Dne+n7p4uQg9zZ6JjP/jEZ8vH4YXT8cE3hmSLGKhYTg6A4cmX+bHbxt5T2vAtfjrj+/JO5DqjH//5ad58eQ8S5FmIKe4Z/MI/+7jj+Kr795Lcjnt3JXVV5eH21W10jpjqnqcvYef4MPX/dgVXtOhjY9NvcFvvnScA9OSxXaF0At6ln5OblORmoSoPY1E9hKBNhnNeBEpFaEqUM4NU23NkOE8gdeUE0JlmWv5TiQCgcSQ9wyxlq7lJk1P6MHi7P4e2LzAXesW+fxrq0l0RC0OeojprlD/uLAcnx/m4d0/wtMHfr1PRrIbngoo54YBKAQVCuEASSu+hBfsK8uGwYiDM0Uy052fu5C4XUI1FkxYyxsXBI1EEio6fs1OCWyx7VGLFYXAooRAW8tsMwHm2Hs84O/eOtEbB3SjHELOj2klGQJD0VeXCGh0W86+F7LQXGQp9vFEHV+BkopiMMBfHB/i2RMetchgrIcnc8w0ChR9j8d2a8qFUdYMbu2YBqw061geZWwbv43Dky+7lr7NKAaGUmBJtQfC9sQ9uhfHUwZtZCdZe4yXCggEx6YOECrB65NDfP1UiYW212stN1PFTMMn5xu0sVjrsWPUoCQcnPXZNznPr73wFq7OWAAAIABJREFUJcZKBe7a6DASgef1He/9W8b5ozdPkBpXc+Y9Syt1IjDdzcq24Tb/8v6E4ZK799YaVg1svgTwtHJEszJK4RCZn3HH5u/n9X8xyF2/+qecXIjJtEBJQyVn2DaYcGHxNHlfs3OszYObYa4Z00oLeNIZcdQi97rdbpAUFq9DJXMzeYn0NEo6MJxTr/NdJ8JcOjteGYmWbBpYohkrXr9QAgxJpjsdIJfUD854nJ2/+hpwLd6/+O7NPt9G/PsvP81fHJlFCkHgSdqZ5S+OzAJP8x8+8f2X/PyVKs7vVFws2df9WtcE3VOyp9zTNWX/9F2bejzcldFVteo6tnSjb26WtPjDt9qOhkGMFOYiS78q2i7PXA0aYd2cUQjhkM3GopTC90LyfomFdsKR+QK+gqF8xvQKhSeDJNHOfD7tzEwD5RKPsTCcT/jwlnmWIo9qpPAEPSpMNzIDidakBr55+hVGS+t7EpvdsMYyNrihd95CSMq5EZZaM5etPD66dZ6Xz5aZbrifd7V7p2lvBa1EcmwOSr5grOS+P9fyaCRO2CHqtCGt6ZB4O2e80EqZb7dpJh6hnyfTy52EJFtk+zC8PpnDk+ISAY3Q0+wYafPQ5jp/dKDI29M+rbSCZJjd400+tr1KlMEX3w5YbBuHvsUl8kR7vHJ+Ff/xk/cyXh6/qiUmwK41dyOEYGrpBO20jq9yPLh1iGePVcl0m9GC28TUYoWUkPctBS9hohwyWhxESMtiFHF8fhalinz9VIn5tkfWN+cVZFaQapdsjBFMNxRxpqgn0qGYrftMPndsih//vRd44h882PeZ+Ol7dnBm4Rj7LxhSI1ESVpUzBnMpqZZUQsvPP1oi9CokWXxVp6LQK1xyT5a/l2ewMI6SHn/09zew/9w7HF+A1cUllKySZIZ2FlDJgbUx7VQRKkk5sESZ6CXYeqI66ly296x3Pc1TI8h7uvdsjxZTVlVimrFish5cle8thWHXeJ25ZkgtcnzozKR917qZQiNVl10DrsX7H99zCbmVxDx/cv6SdqIUgudPztNK4l77+moVZzPJLgue+naP7Wot9G6SdAtig7xfYqKyGYBz1ZP86ZsZqXWa0cVwsAe22nt8hh/YU+qoWslOClluDve3bV2snJu1U8Ebk34HzLJs/aek5dBcgZtWNTrI2+Vjdbxes2KW6eZsrWSJdtbozWR9BScWclwMvEmtottCjjJX2dChvVQjn19/eR2eND2fXidNuTxrVp02auhpovg0m0Z3IoToo2SNDW7g/p3LBimHJ19Cm4RSbphaNMfFUzop4PY1Nb560sdYQbvjf9wN0TniZgarpeB8TfX0h61dnpWnxiKluzYCJ26RU4KxUoF6ZXPvulucTvlHtrlEd3A2INaCNZWIrUMtbl9XpxxoAiV45vggb05Kzi6FtDKBMSHvzAQ8f6rArvEWc80MKV171FhBpgWJhhMLCf/mz57hJ24PWDPotI6tNZd18LkcPevBnYbmH/8er5zTtFLJmkrEPQNtjswXXbdDgLUZsw3Hkw4VtKJ5hFxLte1Q2Zf6R0PSQUd3NzXGLitV2Y66mpKKb5ye5dUTz9OIT/U+E+PljXxsW5X5WsTL55yoiPtdV61/ZOdaPn7D377sxvbiUNJjYsU96T3fKyrqblt7uOgxVDDMN9skmcH3wPcSfBmSGoG1BiUN20aavDnlaH9jxZShfMrpao5yqBHQcfIS5DxDwbesqUTEWvXm/Q9uXuQ3X13LVCMkkKbHz18ZgTQUA8N4MSXvCUohaKvAJH2ftYKvKQUZmeaSNeBavP/xPZeQp2pL1CNN4JAdvXaiJ6HWNrx1YYZb1rmH8rMvHrnEreXJd87x+ZePMtdMaCYZxcC7pHX2Xj7oK+O9ttAPXfgGh6deJsnaaJtRF/NML50i9Aokdpha4kzZ43SRZlx1SFCpqJIjynZjsSQ6wtiuYYTFZholfYrhYO99uguMsZInD8JLZwyH50I8uQywyjoCDQCf2+cxkMvYMdLqgYaKvgNvYZfpTGBpJw2MzSgHgkJgaHVQ0ZcPgS/dsaYWfGHJeW6xPr5QoBJmFHxDM1GdqkmQ852oSDl0YLIdI218L8ds/Qwf2vpDzDZa+LLJaGm0rxpYOSsshgPEaYNmktBIXYXy/OlBjswXaMQeEttDFK8MC8SZYbTougVKGFJNRyCjh58l1ZJAOTAcApSQfHjHGkJP9aq0qaWTtNMGUarJbJGPbdfcs+EczXTFte1E0uF1n10KaSTLs0xjBefqeSYbIRbHS+8muW4ONBbeuGD4/w6kfHL3ISarxzAYsiwlFxT6+NTdWEnPUhL+0Z3ruHHiqzRT2Tu24LjtdVCWaT1Om9rYJo3WNLDWXRcBwvYDo7p/l9CZ1QuMtSjhkAXd42lEEXtP7GNtueHEQcQ8C41JAH7oxlGkanNgynUvCoFh94ThXz1y3yXncaXQJmP9yG6M0cysqCJXVtQr29rGmo6D04qr3Jmh6051+tDmBYzVTqUudYl212iTKHNUwBGbdjYqlptW1XlgU7XvvqfaDao3DkZkRnByIU+00gBFGgJl2TrcohgI1g5u4PZ1Pi+dNaxMxsbCjtE2vnJqet3RzbX4zsX3REJeqbpUyUkqOUU7tcw0oBY7SktmACv5N08eYKJ8krWqzbmscYlby1sXFplvxQzkfDwJcZbx7NFJ/u7vPM8f/MQDfZSViwUirhQrW+iekizFlq8c7m+ha5NxdOabNONqT9yipRMaqaQSprx6YYjzS4YoEygpKfuG1RW3k/dUk6NTX0YJj9RGfe9trCYQOU7OvtETne8uME8e8nj5rGCy7izzosyJOyhhkFg39xLQTDzynuW5k8M8f3qQUuBoTcszZnCKRsscYF8pdo1G7D1V5mohBWTW6U5rK9HaYA1kAmZbPqFy1ChhAWERGELPMFFK2D3W5pHNTawd5Qv7E3795edZikyn09HozdZXnrOSHtZKnj42wMFZj2YiqSeKTHfPVdFKJfoiyU7BsgjDUD5hKRbEWXd+6uroRDvKj5t1u42DwHLbhiL/2/27O+cr2Th6G8gNfO4br/LSKUstFngq47rRQT6ybZGLHkmaqXRymplrFXelFbvHpq1EsoziXfnrvjR4KuONScsdayeJs5g4DRkoSMphgSR1z8vVDAn2rH+QI9Ov4qta72sf7ng8d5NO0TfsGG3z8JYlBIKc12S0YGgtLR/Tyg2OgB6quHvcFkcRyvsOrOZobBlFVXN3Qrj5cKIj4kyT2hE+dX2Bx3YbapGhkvMpBIGj05nsqpvli+lnoZ9nvLKBjaM3kPdLfb+7sq0thURKBT3Zzs7GQiisAItGIXhk62JfolXSLkvJpopCkPV9fgbVMv5hZXcpULBzrM3JxZBWqjDGIde3j7T49B0XCL0ia4d38em7LMYcYd95QSOxFHzDjpEmH9naAOsxVt5wSbv6f7awuBbffvyNvsorW7wLzSkyHSOlYttIha8czlFLPOLMdAAlAJaXTs+xbSThBCl1G7F9bDlhpFqz2E4wxtJKE7SxPUGLLx06z7/+ky/x8JZpPCUvEYi40oLWbaELBNN1t0FwFbvgiTfn+VePtCnn8rSSOvX2PNqkaGv7dKDrsWIpsiTGzd6EUESJxJAxVjTsmcioNqsMFSeYXmr3SVhKFEOF1X2i86FXQMgc+6echm499pxFYed3tJW9VxDWstD2sJZOdWYZyOmLZsyLXLzkWmv56PYm8004vXRl2kXBz1iKl+dkeT+jlnjoTptOCEsgO8QmYdk4GBNrDyUlUiiUCHjqkOC184rRMpfM1rvaxSsX1acOS/ZPlzA2QUmoRT5RtryhWm5Td5vU3f/c8zDX9FlVipm1QR8/NVghprGmnJAPLDdOZPzKD/4tlJR9nZIjcynNBIQNscKirc/pxTzHFwr81J3nkcKhmJupIpAGr8PNBvoqzW4oabAdtazuNkJgGS5kCCzzrZQ/2F9iujlElKqOxWHMY7uX3tWQwFcBN6x7kDfPPkuSuU2XEqxIOh7lwPaqemsFgYKP7NA88YakkUoHNDTLxxZ47m+xcaC5/tGApJWkxJkzofhvb4ywczTmoc0NFtuSF86UOVP1yIwzcrhhleQTuyStZAFtPF48+sfvulm+mH6W6ZQLi0eRQl3yWb64rZ3zCqRZ3KOfpTrGWoMUHp50vOr55nl8ZfsS7QObqty0yrX2V4qvXBxdHn+qVW/ssXko7qCxLT952zkG8oa8X+b6tff3KvnP3Cc4s3Cc47PnyCnnyhZ4BcYrG7lz6w8xWXP8ZF+JqxYW2mS0khrGaKRUl9W6/6DFd8vm4oN7ZH8F0f1Q1aNFGtECXRH421Yv8fTRTSSZILlItSjRlpMLNVYVfBJh0caghKQRLzLbaBFlnblrj8Lr2mipNnz58AJRKnvyeOCAQldb0Lot9MW2ZDGig7R0i/Zsw/IrXzvA//mxO9Am6wAybE8HGlwbcLYZkBqJJx0oJDOCDMFiW/HApkXu3VAjziyZyfC9EKybw2Va43seCEfh6HIwlfTI+RtYik5Sj1xF50vnLnMx31IKSDLJfNsj5/VTcaSAo/NFHtpcx5MGkB2daqfvlWSah7Ys8ezJYWJ96bUROKH8bi73pWG0mFKN/WVecudnM+vkF5UMycuMTCv2z1RA5zlVh3xQuIibK/q0i5X0GCmt5fj0O7w1WcBXPnGWok3ntTv+zCuPrYPp7m0zHGXK6QaPF2NCz7XkV3KoA+Wcsf7R7S0mik08ZXnzzFdYNbCF33utyjNHZwFopYI4EyRa4itD6LnJ/5H5Ik8fG8aXtrchKwQa33MYdHMZZbPusQXKbT6lcHex6GeMFjJmmx6LbcW5pRyecgu+J+Gt6QJCtvnRm65sSNAFPG4evxNjDG+c/Ys+fns36axMekIIQq/ALz/2I5Rzz/BHb1VZbDtgk0MXgxCGKHOt/UC5DVyUic61yVDCeUGvH4hop/AXx0r82aGKk8w07vfWVAJCX/PyWUOcRXx8hyXvlxFCXHWzfDmqkzvuK3+WV44acn4FIRSNeJFMu5mtkj6FYIAka5PquCOS45LxxUYrl3aX+iNUHrvHMt6cCnrPv68CJirreXj7MD9y68cpBCVKuaG+4+zO/+/PWp1NTQ0lK/z6iyf4L6+8RLWdMFwI2T7S4N71U5cUFl064NHpb1KP5jFGk2lF25RZN3Qdj1z3CKXwg8Vpvlyn4710Lb9T8Tc2IXc/VEBfMga3YAbSKTmlxhmYL0OPINGGxTiiEFimliJCBZMNw/mly1MMXHvQstBKeO2c5WM7PEJv+WdXJruLY1VlgFKoOF29tK7xpODAhQZx1p1JufnR4bkC8y2fRqJIMifRJ4HUiJ4hAJ0Z4s2r6libYoFaaw6DRgnP7ay1cTrAQuKpsI+DecfmuxkqzHBisdmbGyvZae13QoqOs5RwidjartbwcopqppJmIhnIGaR0c0yL4C+PD3J0vkQzEYyVUiZrEm37PyCBcjMvr4M2NUC1HaCEUzDylRP3wLqqPfQ8hkqrSLMl0szNyt+eFXh+gdH80CXXfrEds9CKGSsFHX72aaZrCQvtCXzl5pRKOvFObS6uPFfqMS3LFwosqYG5ps+acsx0w1GwMuNasOVQs6acMFao4StnjqFNxvHZg3ztuEWKgEQbt6myzqggM4Kgo0tsrOCF04MM5TM86XSpU+2eg6txUoWw5Dz3DPnKUgk1o4WMuZbnuK2IDi+WHnVprJhyaDZE412Gn9sPeBzMe4zlT/KhNZJA5R3OQGdom9J/ZBaBYqy8gbML+/m+nU1uW7XAbDNDyYDNI6P82TuWvzyWMlnv7zCMlxKG8oYz1RxrKzGh5xLabNNnvuX3KEDd7sFkXeOpMhuHchyZn+UxP+yTcL1Sgr0S1Qmu/Fm+GPDmyYDnj/whiXYtfyU9BIJGtECUtgi8HFHmquHuBrvr96xNwP5p996uu9R5D1RPnOTRbS2K4SAHpgTNBEaKFT5x/Ya+Mczlojs318bw+VfO8fmXX/v/2XvvKLmv687z894vVurcjQwiAwRAglEMYBJJZdmyZGslHa801pyV17LHx16v7N31zNhaza7nHO9s8EqyPbaPvR6PZHNsK1iyEkWJmWAmSBC5kRudqyvXL763f7xfVXejGyBEgZSs8f1DOiS7qn7x3Xfv/QbOVpq4tsVA3sW1BA8enabc0PzMLkWUQrWdUPIExyafI05DmmGVVKc8NNrPE2f6qLQdhJiiz/8rPnzdaj7znnf+2NBHF3Y6Um0zWY9phoeJEs1I3/U/NsyZTvx4XLU3IDovlVLJEsu/IzMFJpse7chakKYXhIZIaTYUQnaPVHn0VIGJurtIxvHCEGhqocWJuZSpepV1/fMgKddeKjjQibzrcf3qfl46X87E9rND0NBfcKiHCTPNgMm5/YCZH52t+FQCG6Vl95hUdtyd5AkmeT55psT7ds5mp6XQWpPoGNty0Gj87Lgu5GDmHIe3rM+x/3zLJBqhs2RvbBs0845MtjRWhkprSk66aFdfcDQF1yCK09QI6T91tsTBqQK2NA5Kg5ZCZdW/LTW9XsLZmofOzs+3jVUfQtCIJQVHgZDZTNGIT1hSUPIcPNsi5wyitVEPmynXGC4NLJus+nPGV/qxI3/T5Wf35S1KrrElRAhcy6WDEL/wjl+YlCFFYLijk00fhGAgFzOUT0i16DCd2DbYwrMtfLfQFZmoh5paoCh4GluajdVCo/rOL0lhRgh9fmeTZp7rmZaDvkh1bJ4nwYpiwM7hBp6tODFXpBFJ2rGk5CVdNDjZ89OMLAbzMa3YwrVXL0lOHcCjFNAMJig32hxVCbV2jrdtrmFbLo7tQgKpjrvVlSUdVvRsYLi0rrtQDhYHGSiaue6avnW8f/c5wiThKwdtolQazq6XMpSPjcCHSHGyVm+iMH7BF3QwtBYEMZwqN6kGEY5UVNswXIQoNYIrJQ8stTTBXorqdKl32ZyfSXitqEachthyMS2p4PWTak3J62emMUYrCjg6W+iyPgQS1/aRaczx2R7u2lDBtQSO5SGEwLcLCCFox03evSPip3YWyLnruXnjbeScS1teXnj/vv7qOSbqbYPTSFPOV2vU2rMM5No8d05RaQeMln2a3ao96II2nzk3wGOn+ruceIC5tsN/fmEC21qePvpmhynKDB/9m0cEByYk9VBQCwSJPsHq3iorSjluXDe4yBL1Rxk/sQm581I1g3mgyfdP9LN/ooTWomukrhehgLMGpDAcwF0r2ty7qcarkw4zTfsSaGCz72/HkigVfPe45qM3KGxLXlRwYGH8u/e+jQePfYXJRkSizILcX3DYPjxMwXWYqb7EyZmXALCFohzYy9qsLToebWT3ztV94lQaAIiTJ0lDI2OJhY2gJzfCqozusjBSlXDfpjmeOqU4MCFJtMK3Mw1gbcQ5OouILU2yLrlJlyfbOYarR2KESPnu8QGOzuaohxaTDZeiJ1nd0zGWiFlRTLFlygd3TaBR/OWLqzJZTVO5ddr4QSLYPRJyrt6ffdYkqdMVi8GCN7+wCWMW3+vbvHXLSh48Or4IoJcqzZ2bRxDEi/jZjgXbhgP2j+cBhRAuUXKxndi8G7P5XwvR6RoAtcBmRSnCFsZNKecIrl/tcPcGTX9xDWJBMu/xLQpunAGVJD2eohVfQKnShqbSTqxuNTvTcmhmOIJLhdJw7co679hazirIChN1n785MIIjNe3EQql5BLbCXPuSJ7ht812LvitMUh4bncKSgtn6mDGuwNyjo7N57tpQASJc6XeTcl9+Ba2ojtYp1dYU5dY4np3L/JzNhtmSNhO1U0RJTN7Nz/sAL7ziQtPnxwg6TmGWQdlnM3xLZOOF7J7EqaIRJrhS8ujJFMeCVyYE9UhQcjXXrnJ569WLwUyXQ3V6rbhYUhdCMFBYyd6tP0uiIiZrEV848BQ5x6EdN2i0qoCxlGzH0I4dfFsjpYVv5yl4xru8JzfMzZveS94t/cAz0TBJeXR0klpQoR0FGBEc8zxVAk3JFZyv+dRDC98xHZUokXz/xACPne6j4KRM1D2CRC6iOgphAIaPjk4voo++2ZGqhEq7ygunnmW8epLvjxbZP1HAkhanKg7VtkChOV2ZxRLw7cNj/P3+0/zLW7a+ZofhjY6f2ITceanORK8CkjDRPH66j2poxOAtobGlIk0X01cMvULR58fctWGSucCmkSXa1woj5K/ZP5Gn56DiA9dIVvdvXVZwYGEUPY9P3rGHrx04S6wTPMvBtiSp0uzdNMRs43miJAAED432dwFNy0WnSrCEosdLMnlDm0EHenNDoDVxGnP71g9w+OARbt5x67IvdJi0SFXA/3KPzVcPKl4aF7RCRTWMiVJJv59gWx0aj+DalXXszlwztsg7iquHIj60p5//8pLg5Uknk1A0NKBqILCEYEVJmSpC2kSppjfXi0WlC1qRC4AtUkCvn/L+nSH7xjSvjGta2mKoUGTDwACNaLG6Vqo014/k+dU7d+BYclmLyUprouuT3Im3bTatxMNTPoFySTHgIZWB+BYCjBypDMd0Yfs6m4VKaSOFxydumiBMBL05iz3rbme67mXmFfPhWQZ89NK4AUStKgnqYUojsrCkyjSXU/r8mEZkNkPTTZt6aHSqyVr3F4seP+G29bWuy5JrCa7qF5RcRZgItNK0MyqXgGzmD/duXb9kLlhuhZTbIY7UhEkrO2fz463Y2AE6VkIUh0hpNqVREhAlLaJUM9c2M+b+XItae6b7vZa08e0ij5wa5plzguGiRaximpFFLbSwpeK29VXCRLLvbC+N7NwNbcqcV6rVkm5GmChyvuSh0ZS+nEG+u5m++P6JHH+670QX3NeJhTPh5ahOrxWvldRd28fFZ3VfyopSL604oSQHCNqBwQJoTV/OY9uKrWjVwraceYc1rVjVv42Sv3QMczlRboWMVaZIVQshUmOykemOp0pwZCajEabGVWwoHzPTcgxoU2gKjsoApIYrv9AopGP6MlGrsmlo5HUd3+sNpRUHx57iz54+wXNn29RD8J1BphsOvTnFWFUz1178mVRr2gkcna7w9QPnAJY8C29m/MQmZJh/qeZaU3z9sMts28HK7OCMJCM4VkIzktkia+odR2oiJfnu8X5uWtOgHcluJX2psKXCsxSWsDk4ZfPuOGK6fgY5brFh+GZmmwEFN6HHX7qrvdAescezuXPzCB+7aRWPHXkUtNk8nKwY+7+Ltc81GA9aadrbcV3yzLke3r9LmhpCgGt75N0SjshdMDtbqEg2v8P/wG5479WmVRqEE3x31OXobK7LobxmhQGgWEJy94YaUZpnqJijvzDAzRvfz//+vb/FkmYX7ljzbkXVIGVdX5E4bZt2t2dx3brrmK4dY9tgZVlXoh1DAav6VvN/vuWDNCOjbjVczGNLYWaaC5Lu7Rv62eNPkKpoidNVZ25U8gewLXeRrKaU8I6tDe7fHHDVip/ig//fk3TEVMIkoBXNV20b+gPOVHOESSb7KUxF79sis5H0KPhrWJsz44JNI9djSWfZhfpdO7ZyfKZMI26TKM3WQbN6JMoiTDVFV7FtqIlWcGhmiGZMV1RjvrZdGgLFut5217/YnKOFLQVXD0d8d7SI0tLoKWetX42gx3f4N++4d8n39fgWSTJBOWgtMOIwkXfS7u8oEpQC3y7QCKt853hxwcwR+vyYvesr3L+lihSCVMVU23VePN9Hkio0muGCoj8XEyaSIBYcmclRa9tMNw09bf4cs/b9BW17KcC3JZawmGtDr68yxLPEc8zIYCG4b/5zS2fCiYrQ2oxLlosLkbyXk9Q7Up8dvQNXFOgp9pCkCe/euY63796RMUVe38Zguej1LVwrMJ0IRzPTdLL7bp4fpTuc+vl1rxlbmQ64+Q5bQtTpRuj5rpAlNQM5i5U9va/7+F5vHBnfx3988ijPjEnSVONY0Aol4w2byaYgTi58P3T3/9sxVNtzPHxc8PG3bMSzrR8JKvsnOiF3Xqo1A3v4o2f+DkeqromB1mSOPQuTMYDh3E41XL58aIT9Ez3MtjtKXBdf9BxphCgiJThdEZyrCb78qsPP7Y75w8eP8OrUKI3ILFjXrbb4727ZxM41t3WRfsvZI3q2RaoScm6BWmDRSnTWflakyfItSo15WSzLzNFyTsrB6SL9Jz3u3zJHEDVxbJ8nj/89UWyh9PVozbKKZHdv2MB45QhCSFwLBvOg/FV89EbB+NxZmrFF0dVI2ZntgWvDQMHDc/rwnE2MVerUApW1tszfFB1FNZQkGiyrQCnXT5zEbBuY4Pjkk6Qq5r5NJkEurLi3DwW8dwes6ttsKgzbp3/BOK9z/aYaLQ6e+yrV1hOcjlpMvvAcwyWjynWhGYFr+wyX1ndnyN3rqDSr+9Zz8/pVrOnLc67SQgjwbI9Up4RximtrenyfFalkrAqeY5F3bENzQdPjQY+n6fEllpRY0sGz80sW6kbg8t/+rUWUjs0fl7T5/E/F5N06UaqptI12tGMpwMGSA4yWrcwRSFJ0FI2ow49eHCU3ZddIawGNRhv6nIq5fX3IwycL2FYHuKcpuoKVJYEjbdpxG99Z3NJ9ZvTvuKp3hpfauUW/1tXUXkTXESjt8LXDPvvO9tGIFs4cXR4+OYAUgvu3VDPqHFTaGltaaJ0w23ZoxhbtSBIrmGp6Gc9aIrLNs2drwkRk0qodKhq4Elzb6GAHicKxXEZ6BgCFFFYXdd8B9y1nPyqE5PTMgUXqeCt7Ny1C6S5E8jaCNqHy2TK0kWvW3bZE2Wy5xX3hZnxaaQqOw5071vBLt29bVh3th08QIZsHajx3zmIgpyi3bCNTCtk1VRk+xVzLemTGGZY03RvH0hTdlCARJKmcl0DRUHQVd21e+aa1qxdqTJwpj/LSeEKaKhJlfMPLgUWqJKnINIos8R2kAAAgAElEQVQuUciU2xWaUZMv7PsTVpQMJcx/k1HZP9EJuRPNSOC5q1nd2+R8tQ5o2okiUXLZSrPT8kqURbltWq2GxykyENhitK0jVSYRKAzXNKvCD0wKjs8qWlEKRLi2T5RK9p3RpOlRfvkOsYRycaEvsSVtVvZuptKaouTWKXopJSclWCYhS1IUFloYAFEpB4M5c7wvnY+5ZU2Top/HtkpM1CN0VOXI+D6+d2JgiSLZNw6NofVq7tu8Y9HufE3/NrasuInHjz5AlITZQypohAY9qjQ8cmoFx2cLxDqk4B6iHgn6/fld9FDBVFDN2LS8co7FsP8q1606b8BUGPOLt22uc8/GJkHsUvQUffk+tq648ZLVgWdbHDv/FWZrJ7q3KUljxivHefTwF9m77eeXVMl3bv/QIhcs23IYKK3i9q0/i2dbfOymTXzusf3MtRMSpXGlwPcla/pL9ORd1g969M80GK+2STGiJ3knZqgguGZlh7azeP64cJEd+jdfI0oXwwsjBb/y9UG++CGFEG36cmH2uEkgYe9Vo7w6vZogNg5QFg4TzYTJhruopZ6zEt6+dYa3bppjuWjGUHQT+nMajY1n2905fCuRNCN70aYnSgKm62d42xazYTo4bXc7JR2qzvzz6PHQiRKHp/McmfVpR6YKd7PkLwS0EotDM3nu3ljDlpJ950qM120SLUlSIyHpWLorDxktuEwaQaQEHim+kzGsdYpnK9qxjexusIxK2lBBZ/PB+dZvksb05HwG8ssnkIXqeB0lsLnmJGHS4pq192BJmyPj+zg9c9jw1ycc6pGi5B7jjk1VPv2ud76mItjCzfjD+57lnltvXoL8vRxVscsNW7rct6lGkvq8NOEgpSZnpYSJxLM0Upo2f5wlWzK0v9aComdAmx0d7kpgd7sT/bmED1+3mt9559uvyHFeKi6kM1nSZnT6PNX2IJXAohE7JKlpuwtALWFJLI3RWRfH0vzZsxY7R0LeuqlCuZXiOS/QCAJu3rS0W3Sl47+KhDyQN2ha1xK0wjpzLd1VTJrfTy8f7dhwZz1b4KNYUQyZa9u0s4TY5ydEqSBMLeblFjQFNyFNEw7O2qztjbOErbM5MxyYlJwpn7ik4EInjL6w5vD402wbbNEMLWbazhIajm1ZpClsGRRIjGOR0mb/Wg8lrSjH905IjszE1EONZ+W46fxxplqrluz+LCl4/MQ0n7ht77K785W9mxe1XUv+IAW3l++fGuFYOY9tSywgUZpUOUw1E1YUTSIWAgYLKW9bnfLp99zJK2e+xGTt/KJz6ehwl3yfn7nh41mb/dICBGaG9Bjnyoe7yPo4FZQDTc5O+dazE/z+o19C0bdIl9yWNm+9+ucJ4hYHzj1CtTVBmIQ8eexvWdm7mbduVBybCHh5wsy+e33FtSsTPv6WAVYN3MBA3sOWgs89doTvHR+nHSfYosHWwSbv2JpiyeXbjJa0OV1OCNNlsf5EKTjODfTlz1FunCdOQ6IkQGHASTuGWrwyWTJ0KCKEcMg5ioKTUPJTbKGxMx5vyeunGS1NykZkQiFF/gLerWaoUGS4uLhqrAfl7sz97Vtr3L4+XCSZOR+C75/s4+XJHGlKBhgTmZCOwstuY6oE9dBlsp7n1WmXg1N58k5KJTBKZ6brdOnKpCOZqjRs6m+jtKTcNgpyKkOq716luHZlxPE5hSUF5cZ5orRNohQ3FCKePHZ+iQVhqhKOTz1PGDdBGIxIK4qohyGV9mPMNccZ6dnAVO003zgieeacRIrOfFrw4NEZhouv8kt7t1IPypT8gUuaN3i2xVDOecNpOImKcGyHt2+tc8uamD95fiWpEpyt+d0WtWuZUYclzHimx4tRWnZBm0IYo4u7NszxljUhG0au4/6r73/TeMgXCrconeLIBrVokFpodTd8nSLqtZIxGMGfRMFYzeHojM8XXzZtfIFPyTvJ+3b8EZ/78MfJOW+cAcd/FQm5M6f5h1dOkqQhhllqHvp5PObyNyyIDaLV0GsU1cAmTG2kgJKXMJhPmKg7hInZyVtSU3RS+v2YWBkwWKpAWuYFty3T/q6FgtGZkLcEdfrzlwZnSCHZuWYvq/u30Yj+kKMzhSztzx+zJYxhgG8rLBGSqIRUyQyhrPGdiEdOJbw6laOctQFTBcfKCYJz7Bixybt5Q8PpthTnW3kX7s6Xm48Nl7ZyopxgW/NzRSEEmwaLnK3UsGVCEBtN4d0rFZ+8fQe2mKbSPLXseSutMmEFfVk6u0fG93Fq5gCKdInYQiOySJRkKD9D3g3w7TVL1LpOTL3I2fJBwqSN1opaIJlrTmJJh/ft6uVdOzT1UFPywLUE1fZp9hRvxZLmWfq1u6/ml/Zuo9wKKbg2tSBchBlIVUIrbiza2Hw1A5JcLA5MDfCb995BO25wbPI59p/5bve/3bupghCSozO5jMJk0eebZ7JjRQlwfLYHKRNgaUJ2Ldg+2OJEdRCtElQ2X3XsPO/eeTW21NSDMiDIu6ULZu4CzxY41jwFCwx/O0oMX96WEoGRhpyn40ncTE/dKJvZ/NX+PqabHjln/t+ry1hEwUirgmIgF/OLN4/x5JlMdzyyydmCt6z1eN8uQaoiDpX7+fqBAzTCwDyHIwH3bW4wUakssSBsRXWaYdVseBR8+1iOIzO57gbkxjUR795+iEZY4+WJoS4tMFExSqVoFH/zwhP0WH+PI8Gxve7o5EfpPezZeQYKK42rWa7B7pEmL08Wu/aZnfs0kE/oz8Vsy3yzHz/dx7HZAu3YIe8qdgwH3Lepxa7Ve7l+w9vetONfTrilEZhne9ETo1mUjC2hL2lXCWZGPtl0uzgKiUZIqIU2XzoosP/uL/mPH/nvr/AZzccVfyp+7/d+j/379yOE4Ld/+7e59tprr/RPvK74pdu38bcvPkMtMi1WSxq6jDEuV8YJZZlQCCyhUAhSZTHdkni2ot83FB8hYEUpJlKSVcUIu+ssI7DQXSNygehqUE82DJn/z59zeeTMfu7atPKy4PYlv58nTq8kTKHkaRrR/AOWakM/EgiOz1hIyyZVZubj2Sm3r21waKZAue1QCww61RhEmKrlwIRmpNjgqn5NKWeSX4enu1wsN9uaakTMtZ/AdxZfy5I/wKqS5pdvC0hVi8FCniiucGxyH6+OPUKiwoucsUYI67KScZxGHJt8jrnmBLBYbMGSUA0dVCYtOSIapmLJDXQBPbbUHJt8niBuGtGUDC0VxE1SnZL3enAtweAF7dvOuXf+35aSL718ZtE8/o6Nw9yzscxM49QStaD37V7Lv/7m/oue1/t2r8WSNkWvj5HSVZABxzQGsHT/5gp3XVXhfN3mbw+szirPBTKfWtGOLRrZJe6IfM6vXIJ7N1W5Nr6XZ882KLcDBnI+d25awd0bZvnH/Q/SCmtooSm4vWwZubFrZTnP+ZpH23e0mduxQyuSmY63puQpwkR2pWZ19vdaG+1lWwpiJWi1HHT2LF9OCDS+ZTpXu1c0seW8ZGc7dunzLXpyRaToJ0ib7Bl5hZ5rj3aP07MFSlto7XB06hw3bWxS8gvZt+suse3B0SL7J3wj3iGN1/Cz5yRRGnDtSJ25VgnHMn/bmammSlOOYKapGMwLapEmiH703sNmDLaJJI0oeH18+DpF6bDghbGYM1UD5nIdxapSwPahecWwd2xt8lNXO5RbdfJOgmdLcu4Au9fd/aYe/4XCLRpNnIa0Y5uCm5KohHLbIUoXsx/UZQBzDeZh/p8UApSh27UTixfG2lRaVfrybwxo7Yom5GeeeYbTp0/zwAMPMDo6ym//9m/zwAMPXMmfeN3RitpESZsNfZpEaSqB3VUkitOLJ0INJKlpRVlCkWqBLeZnYGAWRs9W3UWy80kNbB5oEat5qceJhqbSFvTnND25Au1YLanULhaJEpypDSModxOwVmQet4ajnGiBSi0sbUAWgTLt6m+NDpGkMqsm5h9MkR1zlMJcSyBos9PXxInihrWljJ5z8RbawtnWQF4wkPdoxYspPQhY07eC9113CxCy7/hXmKvPISSvCZQY6bkKS9q0otolAS0Hxx7vKrLFmftR51505TyFEbxI8wlh0qSo+7tdgKIb0Iqqi+Q1wRxfkkamu3GByINre5yc3s90/UwXzPPcWD/7xz2EUHi2mcf/3f5XOTJe5327xLIa574lCZZpW/uW5OqV85uRvvyKbGNn/razbtgWrCopenOCIF6cyKSQrOgd4D177uahV/8ka78uOEetyXsl/oe915Eo0d1EnJh6msPjT89vUIBWWOXIxNNsXXEzQgjG5o4u243IuymbB9oUXE2sLBKddmeO5bZt9NbRWELQn08YzBslOYlJ0gpBrC4vIWuMaUc7khyeMo5XnVn2cNH4cQdJC6WNKl2lNYVjKfos1X02vnuyl2OzRVqRxTeOPcoNa4f4jXt2k3d7yHs9VFoVDk75xj5T6u5zlaiAl8ZhSz/4dkLc2Wx0+OGxg9Lw2X1rEAJKjqbH1+wYnuXWzW1y7o9OZnJhh0vpgJ+71udjN63n+dPPYomQdqoy/XEjeau00cbuzQ/QXzAjJYFg3cDVONaVsZ+93LiQ4610itIpJU9QdDMzlqwzGMSSeXOS1xfmthvuciWwOTFzjhvW/xNIyE899RT3338/AJs3b6ZardJoNCgWi1fyZ15XnJ2bohmRSSLqRUbvSmeu51l0NIF195/EgvYHIOaVjDov59qegC2DbU6U84tALvdsqvP8+AZeHldUAwMwG8gL1vfNqzRdqKt8MSH0cisk1SV684pktkGiOjMOughvNBRcTZSa81TCNF4SZWXJeHF0ChzHMnSfubbi2PQkWqdM18d47NjhZa0gl4sLKRyd6Ihw5F2PIE4Zr54w6k2JRl/iNbGly4rSRh47+sAldWhTlTDbGOu2sBa64ZjrqzMhDWMZaUT4FUqn9GeAnigJlwfQC5DSNnSXhddNG0zq6NQxvnJQMlp2qAYpZytT5BxYXRI4toVr54iTNgcmJe/aYea5sFi28ezvvJ+1n/nyolmyb0nO/s77F/3mmdkDmfzF0nAti+tXOTx1VoGebyE7Vp737NrFUHGYXWvu4sC5R4jSAJ0lKNfOsXP1HVjSxpKwqidPqhLGK6OESXvxBkVAlLSZqp9iqLCWsbkjwFLpxzi1ODhVxLMilPaRwkLplOFCZFqgAwE3rYUvvlzCsZLM9cpsYRc6Ul3uAppoST1yGMgvNjV597YITYbdsC2Kfi+z9fOLPvu9E328MllECsFcYHH+1DSPjE7zxecP899cN8j7d+7hiy/u58iMQXdb0szdB3Mx5bZNPbL4k+dWESUCpSX9+YTqgs2+FJpqYBJWqhJcO+GF8y6ffexlfuu+Wy7zDK98LNfhApisHaIRxBS08ZzW2YLn2yW2r7qF6frZbESV+6HpV683LuR4S2EhhYXvwPahhKMzZjP+gzxDF4t5WKApXPr8hE1Da3/Ib714XNGEPDMzw65du7r/PDAwwPT09I9FQl7XP0LRNWIAQkgsAcMFszs36lyKJLURmWvOeN2jFQtiZc1rNgMos4vH0l0TBaVh+1CL+zdXSFSNVmSTc2JcG0Dwvp2Cd2+3CNL1fH5fTMF1l1Ric+2QmWZArbn/okLoHXBaLegFXc98YjtQMrqbhjA1LbWOS45kXuZyudCAb6ds6E+ZqFvkrIica9Co7UTz4NHFVpCXigv51AtFOFKV8OLp7xhxfSGyZLz8gVnCxXdKHJ18mp7c0CXds8KkRZyGeE6BMGktcsMB8yJ15mOWyGhhwvBY924aIlVNoxrl9tIKqxeC6CnlBlg/uJOp2rwf7mBhHX/29Gm+ddRiqiFIlNGwTpRFJYDpRsqKYspgvo4QKc3IpdzSrCyZbkQt1OTsBvWgxl+/NMVP717H2UqdRpDw9h2D/Ov7d1Ly5yuPjgygbflEaeuC6ybIMcgnbt2MlKd56XxCK5YMFYu8Z+dOfvG2zbSiGttX3YIlLcYrx2lFDfJukVV9W5YsqmHSMr7VKjUbnwXXQ2nFXGOc2fp5EhUv6UZ0wnD9NdsH6xwtF4kSRcmFPSsD7tnYpBX5FJw0E1UhA191JEN/kNXUiPw0Y4uZlsNwIc4Uwwp80O/Hc8yCnWbzcbPVtlCki459ummSq5WNOMptY4H62IlZ6mEeS2TgIG20vhuhJEgsEgXNMNd1P5tuGl1w14K+XEw74+9CpzsTY0nBs+dahEn6I9dRvhC9vWXkRo5M7CNM2iiVdo1Adqy6jZ1r9v7YuCYtrPCbYUA76SHvRLxjWw8Pn5DUI9WlcXUKq8uN5T6jAd9KuWGN/Ya1q+ENBnXpS2WBLA4cOPBGHsKi2DkIz07MSz523vjdK4wyU2eXb0tN3kmphy4dn1uYV2AS2d8oNI41T/fQmGqs11fZImME/Wq1Oj1yBf2iByc+T60dLDk2zxa8eOAfCJjsJusGTWZmpzl75gyDzlYArnICHho3utc2iijtqCsZ5bGOgg7QXdTUReuq+XAzPekwkUgUcbxY8OHhI+M8MbQP335tvddbc3DD1TlqkUuPa+HIBg8981+op9M09AQGRX3p40l1RCOcBQTtIMATxe51OVx7gcZ5GynMYqZ0SisKUNpCYHXvyUJhkU7L1LE0SkscLdiUm2SVPMw/PhthC0MXUkqiiIlSTSu2KTmSflYQTOQp6q2kRFihy396cZrHzobMNm3iVBN2KCJZJNpirA5jdWPmYUvF559IsluS0IwFvpPyvz34JTQeeauA0i0c0eLBw+NMzT3NuzbH9NsbGLS3kBAwHU6Q6hgLF5X1a+LUgLlsJ+bc+dPcvarCzcMBQewz5As860X+8ZmHSLQ5x4Icos/aTl7EWIlLq23x4viL3ePWWjOTHKOcTJASZTNUgcyWCoGgmTS7dWwzlou6EfPfA2M1n1iFREmMa2s29cWQOnz+6QFqgSRIDIZhKJ9mXQvTtk4X9KYuhY7tbDj1Ar5sp2vViiXjs00GcwJfDzMVtsi5MalQmBGM7h67lYnomHGA+e1UQRAmjM4I1pYERUdTDWUXLNeI7At08DMsR/bdkYJK20EBXrZmdLozUsDZyVke3vcsQ7ml79Pzzz9/0XN+o0Nrj0KyBp1Ok+gAW/gUGaY17vH8xJU7ritxjqly+eaxIV6calKPevGdgM39TdaUJEGqCWI4V3OJteq6+lnobPPU2fGJbGynsYWiPxcxkEs5W/VoxlZXaMaWivdua/ALG2+57GN/Ped4RRPyyMgIMzPzUnhTU1MMDw9f8jO7d+/G894cEvnn9lzDb33lAZ4926IZgZ+ZdN+7qTM7FF0FquFCSDO2MotGiZQmCbtZRXzXVXPcvalCwTEUDtPxlniWl/GQPVw7R9Hv5+7tH+nSHX46PrxsS/edO1ZRGHgRX/UtOW5Lply3fQ+WtLnuesUfPPwK//6h57PZnkIpQOhMUnM+9V4MxHDhQifRrCjGpNro5nquzYU7ynaiWbV50+uSwzt0/knmyi1EEKKD9LU/sCg0wkqxXUUxA3elKmXX9h2LdvbF8wnnyofppZeJ6okuH3ZeWCTlvs0t3rvDZaR3D72+xXT9KELMd2+USumV/XztkOal8ZhW5NCfy3PDug38T/ftoj9v7mGYpPwfrz4KUpBobSwiLxoWqQaVwitTDraEHs9c3bG6RyM0VoEjxSYDuRCExhaCY+U892+ZoeWcZ/2q9WxbeSvVgyc5ORNTcBMcAQ+OFjk67dOILAqO4vo1xmxAxglOXKOeTNJQkrzXQ19ugDiVlIMWK1Yk7Fl/x5IjTVXCS2e+R1KZo5CUaEbVrErSCKGxpYNj+Sg0SRoglKbgxIu6EZ2YaTlGilMbAxGB5JHTBRqRRErRBRwqrZHCouil5t9JjYVRgOooRoFZMC/2PHfaiUrPW38WHFg7PMS+c4McnS0wNufiyCZbh1zu2TCLlA79KHp8myBxUYpFoEohYC60qEeCE1Wz2RXGdRulxaJN7nLGI1ob206tIQI8W2fVt/lMLt9k5fqUa9a9ZdH45fnnn+fGG2+8xPP0ZsRNb2glfKXO8fOPH+bVZpVcqY/ONH48UqwaFLQjTSuqMt1u4WhNBoDHtwVRarqcBU8ykLPo9WNSpblpdYiQKS+N59i50kYKl0T5CJHygWs38Zv3Xn9FzjEMw4sWolf0Su/du5fPfvazfPjDH+bVV19lZGTkx6Jd3QnPdvmDn/so9aDJqdlxzs48wkxjgk7yeduWKndvqNCIJa5U/OVLqwliyXTTIUjMwiqFaUW9c9ssBdfBEi4IkyQsYTNYWo0R2rdAa9b2b1/EPbxYS/djN63iyWNPvqblmyUlv3HvHkZnXubREy2kTDkz59GM7a5K03LdvkwKwbSwBUhh/jpRkqKXknNSdo0oDk4JtLaWtNRLvnxdcnhdG0whaIW11/7ARSJIWhR0Xzb3XOq4s7CFNdJzFe2ozs/sjGm1Wnj5lVw1sJqrV99AwTPn8OiRB7jQ71ZKi68d0hwrryTvKo7P1jg4FfDIyYP86b5j3L9tFX/183sptyLOV2awhBGcWJg4LhYaaCcCz1LMtmwcCUKadnCsYLaVkqTGjQgwVK3YwrXbjM2N8t3RPr51sMBYdZiCE6PQGSUv8xBONU+dSWhGDa4bqXGiUmRNT4DnKPLxHP9wSDBa7qGdePR4J3j/nn5+ee8OLGkAO4fGnuTA2CO04/rSY9cCdEouN8DGoes4MzvK2eYURVfg2Uu7EUpDI7IouilWhn5USjPXtomVJO+orlmI0gb38LE9k/zRM2uoBAYAqbUm1RDEZsNSdBPKgdOlo3TvWfeZNk5hljT34+a1OU7UbuSF8+NYMmWgMEgjlLwyYd7L+7fUKPouN67J8dK4j2MF3epYa6OOWQ/nQWNhpjtQcBJWlSJOlH0MU/fimwQjamKAoEppevwUKcxGY+tgk6naEY6MLxUH+nGIKylE8kbEQoOThWFbEltYvH37MPtOuUw2ylTbEat7TdE314qQScJA3mGkZPAj/TmHWzb08a/2GiP7zz1xkKdPVai040UjtzcjrmhCvuGGG9i1axcf/vCHEULwu7/7u1fy669YlPwC16zZgi2mqLXPEyat7uLsWtBvmZbz9sE2L08WWFmKUTomVUZMZM/KeldQXUgjzO/bBXpyA4BAK4XtuBcRg7i4ROYPYvn2f33gQ/zmlx9g35kaCEHBTQ2iMJu/6Wx3LkRngdFdbnSYGNlBz9Zs7G/ykT1T9HqKgudjW0Ndg4NOKK25Y+PQ65LD61AUDGDHVBg/KNTCnIMyHFmWWkXCPEhl7cAejk6OoXHoyZWYOHmIvTfetujvW1FtWb/bKIX951MKOc3+8zVmm2E215S045TvH5/go194gr/4yK2ZFrCg5KW04sufAyZKdkF0YsGoVINpuWqjhJR3U4quQTB/6UDIycpZHKuPgYKmHlQYnZWU3JThorG2NFrnmr9+eYC/UkPZdV6BJVJWFIzo9UhBYVkx7djmGwfPIoV5Fo+M7+OVcw8TJs1lj9kSFr5dYk3fLv78mbM8dzalFvaTd1KuHo64d3MZmO9G2NIIsawogmPnSdOYIF2AQgairF2tNYw3PB452U/JBaUTGpktqi2M9KXSgnZiZXNpYwjQSYYSjS1TfMv8pm9rtg+1ecfWPH/x0vn58ZSAot9PwetjLlrNu669FUum/NR1Pn+67wR/+MR+zldDbAlFzyTjKDUb2YU2mM3YphGqzLLSueSj3AFMWkLR40f0+imuZSRg37ohBLF6WT/mf47Xjo7ByYUUS4BKO+JD12/gk3u3M90I+Lv9p9l3aoa5dsiOFX1cv6afX7/raiwpl6j2AfzG3dcR7k2X/W9vdFzxp+BTn/rUlf7KNyy2r7oVpRQHxh4liJsYtSuXVb1bCOfgZ3dXEbQ4POuZtqebsH2wzX2bGxgv3gSlNKAI04BmZIQain4/a/u3X1L/dDmJzB/E8s2zXf7fD36U77z6EJ/6+hlsKRgta+JUkyjZXUCMZSLcu3mWt2Yt9jg1LcVeL0EIK9NI1iiV8Iu3buErByMePzlLPVCUfMkdG4detxxeh6IQxMYVSCyATLxWWNLB7C5MHWRL56LuWXGa8OlvfJsH9k9yviZJlcC2NKsLFr8aDvIrd+zotiQ7xxQnYVcIQwhJPTSzxJwnmGtFi7oECgM62nd6hlpQ5+qRiKdOp4wUNI0woR7Zl5x3dqIj1dqKdMbRFRnb1VSESaqxLcH24QDHMpS7Q9MeRd/uJhXbKmFXykQKNDHoENAcmvGX8OlTbXG+YawGBQkjxRTHdrEtm8dGp/j4WzYyXjlOlBpE9XK4D6VT2kmdv3j2DM+POUjh4dtmzv7iuEeqSrxzW5MP5QZZ3X8bA4Ue/uXfPEyQ2VaaZ9cIiQhh2oUd7XOz2RKcrhZoJzBSVAzqlDQ14KqZlsVUw8WyNbbSJMiMhGfaEr6jGMwl3LimyR0bGvT7KVImnKlEnJrV+I6NZ88L3gghqAUp1UAxkJ/fIP+Lmzfyb//xQV4Ym6PSUlQDw/f2PUGYaBJFFxgppeJtmyt8+/gQ9ejSmAqlBSsKIb9152kSLenxJDnHjISUThd1v650vJ62848LaOu1ogNwXUKxZF4/wbMt1vYV+PW7d140wS6nXw5L1+c3K358r/ibEFJIdq29gx2rb6UelImSkL78MK7t89xzz5Fb2ebdyaPcEZQNwMeFvOcALmgPpVOSNO7KNBqKhaLWnuaF0w8SpSF71r/1sh/s12P5due2u1jX9zXK7SaWjDOIgtHpBjP37vFjPrAzwLU9oiTAkorRconDMz6tTCBh21DAz+y0uXb9Xdy40aUVhUzUqqzs6f2hhOI7G42z5UNdtSqBtcQlaLmQWNjSiJi4Vg4pL75T/cy3vsN/fmGKmaYxqydb+M81Uj732P7uwgsGZa81zNwtJMEAACAASURBVDbOm4ankMZr1ullpNhDMzGSowsTspnOQytKGKtqfvpqi1ZY4/CMz7remGqQMF53M1DPa4VR/0mUxJKKgq0ga+F6TszVwwH3b26jtSBSBeLUX3QsjiWJUk2YpLTiFFtKXOlcVNwGJGlqrBwHVEjRNt831zb3uBnVskR8sY2SIEyML3bHggDM+yMFnK4O05cfwrUg51TZMLSLvRsHefDoNDITWTGKXiqjnonFQEmpGCr2Ml4PESIlZwtwDEp7EKgFSdeNCmWczGwpQaes6w8pOJpjs3nG6x5bBpvcs7FMyVP0eJIw1QSxAW0W/f6sYq1zcOyraBUsYjL83x94L60o5HR5jk997RWeOjWBwDh3pcpAzSxhWs+3rK9jW/Cto4PUQmfZ1rUlTPs8VpKnz/Vy3+Y5PDufYT8M+tu23CXdrx82LtR5vhhd8If9zI8yXotieWFV+6NKsD9oWJ/+9Kc//aP44TRNmZqaYmRkBNv+0e4LpJD4ToGC19tNnuPj4+zZfitaK6qt8xQ9C6dzkzUgBFHSMfdeGhrFVO00ldYkQdxgsLhmyVz2whBCMFxax/rBnawZ2MbmkesZ6bnqkp9zLItKIDhbUTTCJu1YY1tgZ0buq0ox79iW53982yfYPHI9juXx5QMxL43nDNpYSpS2mG7mKeY2cP/2Hdn32vTnC6/JPb5Y1IKIQ5MV8q7N6r71xGlIPZwjitsYBS4bKWz0BVjV7EpgCQspHRCCnFugJzfU3ezEacBwaV33r1tRyL//7nOcqyqCtEsCM5KHmmw2KHnfNesRKA6ce4RaMI1GE8YplUCCjhgqDdNbuJbR6Trnqs1FFa9rSVzbwnMEn7xtmGrzFIO5c1y3qsY1K5rcs7HG+bqbLcCC9JJAL3Ncxj1Hs7E/oMdLuXltjQ/tnmLLYAulYyPq0bOW0coqkgU8+dPlJpV2ZJyeLOP/24ysS8oCdqQrez3NcGkVQkh6PJefu24DRydHaUdzSKGJU6hHNpbQWF2wvqQWSfadKQFJRiFKSbUgiAWptnnLOkHeFbSiGusHd3LXls2cm5vgfK1NO9LkPU2fHxDEsqsDL4Q5/5EiXDWwGknMzuGIeigIEoFvwWQjphrahIkZx/i2YvNARH/eXL+8IzJ2g7nuZ6su7dji+jUerdjnXM1sCIxrWol6MMeOoRpXj2ikMPPzhc+UY9kMF4vMNCKePDmxhA1Q9FIG8zG3rauxdajNPRvnjPpe00VmXQ5BJqUjDHthXW9INbS5fmULL2Mp+G4B1/JY3b+VkZ6rut8/Pj7O6tWrL/nsvFYcHn+Kc+XDaL38OXYiVQlB3EAKi6MTT3OufJh6kHKmYgxzgmhmyWeWi4XfcznJ+0qcI8CNaweoBjHj1TaNMKbHc7l/26rMJevyaU5vRFzqHC+V+36iK+TF/r6vbw6wa+1dzDTGmKyezMBaEtfJEUQNXovbplEEcXNZ7uyl4gcFVPzS7dtMyzwYQ2mLVqzxXcXGAYc9qxzeuS0mURElf4Adq+/hXL2OY80RZ/NqS9oUvD6OzhR+aG5klCR89AtPsO/0DM0ooeDa3HrVEH/183vZsuImDo49zmzjHEHSJucU0FoTJW2EkChtqudUxazu38Zs41yGB5+/zgsFNTqbp4lalXIryaQY56uvTsRKcb7W4AtPf4+R4gT19iniRPKd0QHOVvtpRlBwBVuHFJ+4ZZLjU5O8OJZSCSykBM+yyTkWYRywsa/FE0f/jFQrJMZTu9dPqAQ2QSJZWYoZyiccnvYvUS1nk2OtkVIjpWL3SIs7rpozko4YowaJTTuaYuvAIC+Oe1hSGDnGVkjesZEeRHFMrF57Ki8wuADbMpVnkmpiK+VjX3iCmaaPUKtJM0ERM56Zp/NJkVJwBHk3Jk6NYcPJsksrtlFaYFvw+ScV//M9YMn5Fuy/e8+7aUUhY5UKXzs4xXcOH2G8XkVg1K58R9GXs9k8uIJUa9b0jfBrd61monaScjPks08p5to2UmizwAozUjhdgfV9MZ7TS0/OZq5ZZaahaafGMnCi4eG7grduhEQpDk0J6qFhSOxZ2ebd2xc/IMs9U//qzu08cXKSh0+MkSQKSwhybsxALmb7YBvXkmhSPFvzgd0zFL2U58/3Ml73DJcac4t7/ARLQiu2iFLT5XGkS29uuGvjeCVjOZ3nC89RCLmoGnZtn5naLP/PU3mOTluEqYVnabYNp/zbe0cvOuNWWrH/9OMcnT5B3okp+fk3taq+GB7nn3L8xCXkMEnnB/mnZ5hpBBQ8m7duWcmv3mnmiD/InEQKyd07PsKhsScYr46SqARLWgRR86LV8cJohlX68iNvKHjDkpJfu3M3u4cOMNdK8G0IEpGZIIAl50FhlXZMqkuM9PRSqZYplYyDkhCCSju6qC/s5cZHv/AEDx+fQEqZtVYVD2dgqAf+xd3sWX/vouvfWRzGKyeYa02Sqghbusw0z1Jvl+nJDS75jQvnbit7ehnI24zVEsK0I5Uyn6JakeL5s3PsH4O8oyh6KwEj42gJkNIArfaPpzx64hTv3A5//DPwHx5LOV4WJGmKJmH7UJ1P3jKTVR4iM7+QNGMbR86LkXSO4NIhSLRAas1Hrx3nxYke/vyF1TQiG89S7Bhu8Z7tbYK4yQ0rj7Oi936eODHDRL2F0pqRks9V/QVmGiHnKjE1BFG0/C9ZIqU/n2BLM9PM2TYHJ6eYqsWkaGwpgCJRktDjJwwX4kWKV/dtnsOxNFsGmrwyWeL4rD9f5WLazq9MSn7/UcXv3j//rIVJSjVI+cahMg8dm8Kx+9nQ38YSEeWWBCWoBYKXxys4UnLHphF2r72N3dzCRG2WX/v6t5BS4wpYOIZpxZJNfSn7J21eOh/TitwM6DUv6vCfXoAvvwrXrIRdI5q7Nlncd/Uenj956jWZDGDeqb/+2F189rHDfO/YOM0owrVabOqv8M5tEs8epNw4T5AB4e7fMgdC8J1jg8TK2J8WvDRTBBSUXMEHb/4EvbleEhW9YTPaC3WelzvH0zMHFjklRWnAf3jc5vC0say0pDHseHVS8pnvtblrx9IZd5wmfOrLf81zZxs0IknR1exaWeUDu36w4uNKxD+VdvTlxE9MQk6V4g8eO8j3j45zbLZJrR2TKmXQvVrw7JlZHjo2zv96v0ctOP0DzUnMrPlOdqy+jTBpMTr1ArP1MaSwM9ebi0eiIjT6DQVvgKly1/VvQmjzohUykacLQWELwRASG9uaB6VczEzicufJtSBi3+kZk+AWhJQyA0NF9Pjukg7A1atvR6mUKDULiciQ6lHSphHOdSVGO3Eh6jzvety5aZCT5UmCGGKluw49RkDDQmlIUghSSTnwsISi4GqCRJOk4NoG+VwOJI+dMpXVZ94OzUgz2VA4nCPnpllFbxb9h0b7Mv1mI2qP1sbZS4jMUey1I0otPvPwJgquoQIZFy7BybkcJ8sBv3rbLGFS5RduXskv3rad8VqL3/raC7TihChVOFaOINFIkdLjJjRjuahdLkm5dqTJNati7tnYINVFXpmp8fDxCC0yIQ6lacZmLttOXLQ2blFSwOGZArtGmthSc8dVcxyczneTMWSgLCBO4dCUoOhdBUg+//hhHjsxxUyjzdGZGn2+R9GtEiUthgtQbrvUYguZKKSIGMh5VNsRf/zkUX7ljh2crUSEieiq5Hm2wNWG/RDGgufOFxivt7sI9U7Mjxk09RBOz5nN6UBxkJ+7sf+iTIZYuRyaDNk6bJ5RMEn51+/eySf3bqfcCunLOZyafraL8ejNryConTDXWcDbt5TNxm6iZJzfhEQIC7C4d+tqhksrzPPLG2ff1wEsNqOIWpDS41t4GWXCtX1s6S6poFuRYLSc40JvGyHg2LRDK4L8BXLVn/7mN3nsZMs4g2V2k8+ds4CAj1z3z8jx1xs/EVcsThN++k+/ynNnW8TZ4rKciMA3D53n0HjKu3fAe3ZcXIrxYmFJg9icqY/hO3m0VoTpxROyQKK1EWJ37dwVB29cGJcDClsIhlgYy4Eh4jThM9/6Dk+enKUapPT61iV1rU/M1mlGCY61NBm1ooQTs3WuW2Mq3loQcWK2zqbBEgVXMlU/s8i8QQiJ7+QJoiYFr6/btr4Y6vx33vl2lPo2f/PSJON1SaI6EinLt7BSLYmT1CDSpZG91FqQaDhZhvM1A2D66Z2wutSg2oqy7zLH8dBo7wL9Zk2USlJlJEhjZWb48WW5y0A9cmhGRvwil3F0QXBkJse3jhV517YmIPBsi3V9BQTw4rkyiTKVejuR2ChKfsLGgYAwEbRiiSMVH7l2ij4fhos2Rb+I7w7xZ8/Xu5x66IARIVFkKH0jLzrdcii3HI7M5JFAnx8TxDJTNupQgUw3IskU4ix3B3/85FG+cXCMdlyhHraoB5pG2KboRowUjLxkR9rVFkYzea4d8fTZGaabIR9/y2a2jwyScwRRMp9whYA4McIcQWIhJGh1KTAaVALFVQM9HJ0pkCixhMkQJfD7j2iOzVqE6fcWjVjcbL63sAJbqP8skHztpc+Sqrjrdf7eHSGOJTkykyNOPUq+zR3ZO/NmhNLw5QOK584GWeWasGul4gO7fNb0byRR0ZIKerxmnt9lXlsiJTg+U2Wo2JN9v2lTf+/YOcg4+AKRaUrDgQlJI2wvKj6iJLgsP+h/jp+QhPw73/g2z55tkmpFKxZZMr7wJTXL83hd8ugpU1G/f/fy86NLRaclVMiqtiRISPXyvULPySGQCMSySeRKx3KC8cv9Zofk/tXn64RxelHy+2e+9Z0uUta1X1vX2iRXm2gZ56K8a7NpsLTsjPmm9X38wp42OddGo2mEKf8/e28eL9lR3Xl+I+Lem/vb36t6te+lKq1oRSqVQCAWsxpjTHvRtD3usd1u4x7jaXqMZzy2uz3j8cfucc90e2jbuBsbxCIwYIwAARJIhSQklUpSSbUvqnr1qt6+5HIz7xIR/Udk5ltrkVRgJHT+AWW9zLx58+Y9Eeec3/c3UlX05bqpJoIOLVBCX3Dq3Fcef/Tud/Jbd47zD/s+z0wY8rEnShyfPn9Pqa7d7HTLOtgKl2ikdBrUR4cESlp+8soitWgGi2P7no/frJqj2P/8umH+9uk17B/JXfJO2eLKhLEWTY27wzgeGMvx3p2KfODKxx975AiVKKEnn2EqjEi0xRhB4AVs6slRT6pIPyXjGWbqHt86sYoo9SllBFetMLz7yhU0krN4Urb9VNwYnOOdS+H62qdmslQih4dMjdP7nkuDdtlYW+eAo6QbmrK4SsXargJ/cvwA9WSGelxB0qJsQTWWlIKEqbrr9bohqCaOUkCUGIZmavz5Qwf56F3X8LpVeR47XcP57LhFQ2IEpazE4CNJHaHuAg10bcBTxXY7ZvGi9U8eCnhuVOB7Pr5iSYtluZhf4ekvrWNk5ni7KiQEvH1ryM++boBtg29/2SqFFxt/+PX7eey0wVofX+n2zjXn+/w/V7sh1cDLEusGUjhcaH9BNpGyi3vrgqwn2T7Q206qw1OHefL0XqpRoY1LtVhsUz4YxpIwcRuX1KQ8fPizjFdOk+oYTwX0l9ZRND8cyMYrMV7xCTmMI757fIrUWuKUC0yaul9tQ7sd0Lmy07a+dydoc+nl5PnWX8VsD/mgk2o0RbVRxpDiy0xbnmONIZ/tZG3Pjh+qK8rFhsJawxDXBxU2XHHlssMQYRyx5+TkkmlFKQR7Tk4SxtGSG01HNuD16/vaPeRWpFpz9aAjZC3XY95zfILJSoZ/dcsE/+F7OY6OB9QSlyZyfh9v2rqG2zZ18xu7dhJ4F7Z6685301dMaKQNjL3Y9zkHqgCXZIx1iSHjGayN2XtG8e4rt9JVkExVTrQHi5bjN4Mb3rFC4ElD1rM0UnNJSbl1DKkRBNaVaaWAxHj0lXagpNemE3lKsrG3yLruAok2DM+GjM9WKeX76KAPbVJemKqhVASk+MoQa8n+sQIb+1fSnRtlpmGZrs/tLpV0+vVikDIV+k2OL20SmUYuWeM6YmuzNGstr9/QT2osU/U61caM2zliyPs+s3WPRio41Mi3z7vB1bubRPImaS5l35kpolTzxV9+N+/7+Fd46mxIlNgmYMPjprUDPHN2pnmOBFYvndW3zQl0T1miZJrVnavoyWcWLFrHqzOcmP4Ovrfwg0kpefTUOCPlCfqLXRdcSO/e/sGlSadrHbu3fxDvMi/AW0OqnVkFREsW3K3frJMBBYBt79yfHXH/PjS5j0pjimpjGoHHgy90c3SiQJoaaqkikJB18EGMhetW5Xj61BcZr5wmSRskJgIryAeZRbhUdw7zgWZb/yaU9Hjw4KcYmTmOkAIp3dDmyMxxMnaKm7jpsp6bV0u84hPySHmWMNYkGqIL+BovCOtuUI8NSTxpeN9VS0lY54sl1l9S0ZHrp5jpRkgPJRSJjvFVQG9xNTtX3/5D9wu91AiUPO8wxEh5lkpDE3hLz2mlYRgpzy7Ltf67n9/F3Z/6Ho+cHKMSxcTa3RCeGpriyv/7H5gKI0qZRZQtKTk4Dn/yUIajk1liTXPnBLUEHjs1QT01ZNSJi3pGt6IUGHoKKSdnXuzuxFlX+soyUVPUYo/f+doYq7sGWVOy3L72NHm/QT4wy/po531DT7aT/tJaRqqTlCPrhsKsJdKiCatfiH6U0iXipljLcRsRdOcEqzs7uXmTa6csphMpKVBSsam3SFhvoISgkWg6sgFZv8Gm3n6EcAAKKRwO9fHTM9y8vpeZ+lm0dnQwY50BwkChQX8h4chkvmmJeH6nnBbH2ViBspadKzu55xdux5l9lklNTOsm3ZdPmAy9806du92y20kX/JSR2fH2cOHXfu39TIchh8cmmR06zd+cTDk4Mks1dtWBVtd4fk1M4KArvoSir0l1zK5NfQsWnUp6jFQEYayXtFhSE1NPDH//1JfYufLCcyae9Lhzx8//QMuy2hg+9sgRHjoxyvD0GJ5ssLE74QPX+KzumrvHLP3NirZkstLQ7Dn6ANKeI+eXMCblywcl+4YlQ2VDbJ3ePzIQNSw9OcHNa/P85q1Vzs284BJqc9njK7sEl+qO0/K6dQVKhW3MhLOMV04jFpWRhBSEyWS7pfZaLIxXfEJe2dHpytRmOT3reUI4nF2gJPtH4Jdv2fCiysnL9WpXd29j+6ArCb0SSDcXi5UdnXRmFfV0aT3wQlxrp3mcIEob1GLHGfalxVM+jVTTSA3GpnRmvbahBzhnlmNTDhHfoji1YroeYy0LPKOXi+kw5NnhIVKTo5TTXDUQc2QsQyW52PcwNxdtEfhSE2tJJXL2eqemI4ZmJ/BklpHw9fzy9Se5ZmXEE2fc0NHcZ4ed/QlBsJIDYzVqscAY8JSl4Gv6CgmjVR9jYLIeoKSTTWWUdOfKOs543s/Skw9Y21XgbVesYaaeLLDfXEonsmzqEvynn7qWJ194hmp0hv+4RzMdlhdQqgAmaw1+643XM14e4sCYoRppAs+yrbfMXVummAp9/urJVYxVM1TiuZ75/BC48nxGGTyp+I3br+T/ePu1bQXDtt46I+W5HrW27ju9UBjrNLs9+QhIKGXm/r47n+f1G/LsnRwDW+XsbBlPGDLKtRYM7vfsPK/BNucBpDQMdiRcv9rjF29aveQ9l2uxpCbGGE1GwerOS58zCbwsvcWXr61dLj72yBHuOzhMGM1wthxSiQT7zkoeOhmxa90z3LHxWW5Yfz1bV960zG/WkuqEQKWMzT5F4AmyXh7f6+aFacFQBSoN5daBzC1qOjKW39pVYaR8FLBYzQLCnjNvmTPjyXmGnJ/jwKjgn33i6xQzsL7T521b0yXDYgZNpTH1Aztfr+R45WaMZijp4akMvmwQX1JOdoYAhcCghCI1Bfo6r3tR73nBXq2QP9JQ9kuNfJDhtnm0pVZcjGv9vo9/hcdOOymIS3NuYGqmHtGZyyCARBui1HkiSyGb+u6UpIlLpOWH23yV1BjGqnVirZeVZTWSmPd9/CvsOxtSTyyBVGzpK7Kp16Mra6gmF7bxawE9hQDR5Ew30tZdxKKtcyAyFh49OcW23kHef9Uo2lQ4MOYRJrLJdU74mWs6+M6pbsr1aSyOxGURVGMPIWBdV8QvXz/Fx/cOcmwqA3hk/YDBTh9jLL5SrChm6Slk0Mby7aPn+MrzQ/QVs+zeNMCujf18/dBZlHSYy6nqWepJHV8m/Mx/+yxh7NGVz1COBL1qjlJVyHTxwlSNciPhd766D6MzXLvKcvs6TWcOpmtu0Kszm9KVSwkTj3K8/MKndVsuBIatfYr/9a6dbSxplIa8ZWvIbL3G4YkctVgwFaqLglIELiFrA1t6KhwffZRr171pwd/E2mCtoTNrmW2AlBbPGlLT5GJrgacM3fmUvkKCsYItPTXesjkh6+eWvOfSFovFGIOxsGPAtJUKlzJn8oNCTrbaFFLA6ZmQmYZLmwIYqSi+criTb5/QrO96npvWnuKW9T08cGzCcb+tRZuU1GiuHXTadoB6XGUihOl6L7VIYFse1PN+JUOzlv/2xCRv3aabNqcLh+ekcHK4W9fOMhF6HBrr5PhMESUtgSeJUsMzIwUA3r6t5d/tduwSRanp3NaKSqPG0PQYa7sHKGULl+38vdLiFZ+Qp8KINV091OJRwllzkRuv++F35yQDRUFnfoCOTI6+wksrnfyoO6K83HCTofcvy7VeDroyHYY8dTZECjex29p3CiA1DgCihBteSrVBSYitG6Pd1hdyYtLdwDUw1xR03+f+czN4UvKhLzzOPXff3p6AhblFgPOyhjBVPHHGY+8ZQzErWFGImQx9kgskBb9JstKmRfYCISyBak2RuuPQ1vL8aI5/du127r7hJGPlEabqCd1Zj4GOQXqLGzn+VJ1SVjNbd4zs1jByGAu291VZ3dXPH709x7dPdPP8aI56nNJbyLJ78wC/dPNmxqt1/ucvfp/HT8+QGse27qk2KDdifmLHat6xYzUPHx/jxOQpFFUynqUeO4tQJVNqEaQ6y0gVBkuCKAkZr3mMVRqs6MhRCAKmaop9Zy2+FLzzihRPemidtMuRlYaHLz0Sc4GhuETiGNUR4BZobkde5B3bJ3njxgpfPVzkwHiBsZo9rzOSOz2uQrC9r8Fbt4RM1c6iTboguZVjzWTYIDWa1Eri1M5L9A7qGWvFRCiJtGJ9V4PjUzkaaZ1jo08uu8P9z++/iZ/75B4OjsxSTw1KGq4esHzkjoV/F6cNyo0KjTSz4Jo31vDlp7/L3z97hlvXRuxcdXmRk602hZKGcsM2e7uWWDsvbE8Z6qmikWoePhnytm1d3LJW8sRQSBhDzk+5bjDlXTtKnJv1yagGSqYkSUycFtAmt+yMurXw7FiOOzcLfLWUt2AsPHjCyf6qscdoJSDnJ/QXDBkvixAST0oOTWS4Y8OMs69sIkPzor9dro7SmI986bM8ORRSjaEYwI1r8/zJT36QzEXmRV6N8YpPyK6Ml+Wqlauox+eoxAmxXtino/lfLVlJb0E5Fi5qWe7pa+HCV16bttTSIWc8n489coSHT4y1E/LuTW5C+/DYJI3EkhqHYJzrNLqoxk0rPyBM525WCsOJyQzlRMJ5BqAE4EnBwydGF0zAzl8EAETpXHlUI9HaUjMeOV9jEve4Jy2JEe2pUoltu3eV8inFIKUaKcqxj0A0d0+t5wrqccpA1+vZPHAlY+VT9BZX43uOSTxWjSlHexgsKURTC+t8fiEfSN61o487rngn+aCDu670FixsfCXYP/Qof/7wCR464ZYzbhjGZ6wSAoYHjw7zp++5kQ9et4ov7H0UX8b89ZN9zbJgazpbM1CyTIXgS6hEltlGzIqOHOu7i01db55GUmX/iOBt25xeNtWKaiK4ff0M2sBYLaAWO+Z263tsLXg9YenJi+buaa5aoqTHys5NzIRjNJKUoXKWQIEUBnMe1rbCks9otvTUeef2WXKZEnEaLRm0zCrBvjMzzEatHdvSa0XghsWqsWIi9OnJaRC9S3a4i6f9c77i+tXd/MtbxihlFiYgbeC+wwF/s+8ZpsOkfc2//6o1rPv3X2xDSe55NkCS8Hc/ffngGK02xVTYcAzw5jR8atwCTOBsHrVxk9J7Xhjh375R8tZtAdNhTJpO8t2TJf70IU2l0UvGaylCLNN12Zxfn9sdt86qaPpP1xJFl1oq7XzwRHe7hyxxvO40cgu0/kINTwYIoQgTj1os6cy6EUFfZSjY/vbrfORLn23qmWnqmeHhkyEf+dJn+Y8/fffLPn+vtHjFJ+T5utoVHR2Y2TKBNNSS5u5MWHxp6c4744VI+1hr6cwVeefONT80n8t/qrgcpbR8kGkPcP3nPYfaQPesrwiTtK1p/rnr12GMG5hbbi80J6tYyLLSSMrJ+VbDc6WunK/mOS45gMPhsUk3gdscAk7N3LOAto0fCAp+Sq1pD+imqF0pWwrHVA48w2Cpwfa+kNQIvn60DyUDPOmOzVpLTyFLV8Hne4f/mno8hrYaJRQduQF+4tpfay8QU51nZanKQFG0db05z7B9xdYF5bqWxlUbw+/+49d48Og4hyYk9cTt2n2VkKQO1HFiosGZ6Wne9zfDrChlWF0MuGm1phYrlGxZW7Y+vaEzq/ifbnY7qM/u7yAfzOm8W7CVahQyE6Z8+3gHB8a6qcWSfJCyrTfkri2TPHuuxHTDYzL0wboxr2IGtvU5swdtssw29AJwhJulsDx28nHCZh86UBar9bIGGFnfUPI1Vw006MiVKGa6UdJfMmj5h4+dpRKnizhs57lqrBtYW9sJHVm5BMyzcNpfkGjNM2dn+MvH83z49tkF4Ix/PGjZP5ajI2cWXPP/+otPLDkOg+Tuz8P9/+LywDFa97d/PHCGQCkSnbaxnC0+uRLu/wNUGpZKZOkvCFYUfe55uoNnRnJIYfAUnK1kKDc8OrIpK0tNTrhu7NLzhgAAIABJREFU+2c1P4P7zdQTSc5bujteLPtTTR9qax1TvTefNIf6HKFsU/96lEhQMkAKSWV2Em1SwjjiiaGQxfJBKeCJoZBKo/ZjV75+xSdkmNPVPnRsFG0MM/UQTzlyUjEDK4oWT7me3I1rJB+8bj03bbyVnH9h67RXSiyXdJ3pfAv3mZD1Xz5n9nym4EoKvnt0hG8eOUs9FRe9WV4KWHJhWLTR1GJLMZNZABnZPtBL1hckujnQM+8d5vZQzhR+XWdEJVJ055w3bykDG7vK3LimQlYZYiMp+LqpyYQTUwWGZgto6/CSPYUsa7vy9ARHqEXnmjAE1wOfCc/xtWc+xnuv/023QDzgaFBREiKkARwa8uq1ty74ZO7GVON//MxeHjw6RWJcMrbWUjeOMtWavjYIfJUiCZmqNRirZEm1pRho6mnL2LK1gHE4w+6c4erurXzrREI1itvT1gjnftRf7OVMWOTg+EGMjdwCoInNvGqgwg2ryxyeKDHb8N1OX7jveyIUrOsqMFDqoyefWTJlvHP1Ltb2Xsd/3XsP1VgTeI6jrbUmSgUpAiXcMffmU968Keanr+3GkxJrDT2FDYxVY3ryDohSbsTsn6ijhNvVGbtwyGjJFSbAGMH2PrkEHzufKOeGuAythd8z5zSdua3U4tPEaQNEhuPTeTqyXQtevxYnzXdfegwGycFzDW7ZfHnIfK3721ilwQtTM3jC4HtuitxaKGYcTEYKRS5I6Mj6WGtopJqjE/kmW9sBXyqRQgioxo4IFyhX/l7Y6rMI6+YovvtCtyOQzf/si2R/UkDB123dujYCqSzGWrb2hmS9XiwSYzUWgbYxURoyND1JLWZZ+WAthqHpMXYObnzZ5++VFK+KhLwYMl4IPKbDBp97+hhPDpWZqSd05Xxu2dB1SVrWV0rMt0xrJCGe9Bns3Mz2Vbfy8OHPNg0xDFIoGn6eJI0A2sNol2KBOD8uZAr+8MlRIq2b41GXP5yPriHVMfkgy6ZeB8rozufnABKLJkWVsEjZKjW717hj4yx3ba4TJj4FX2NtvW2fmZ+nZpUCfu3ms4w0buKxU1XCZp/3lg2d9KjvLAvvL9fHqMfV9g304eM+U/UGnVnFHZsH+fVdV7QXQ/O/u8/sa/DAEYW21uE+rWzfIJfjULlyokYKODLls7mnwoGxIp5UWGtQUmEMXLNGsrFvCwCDhSEendBIqRAEdOQ6kcLj1i0r+d7Jc4DGa8rzjE2RQnB8qsQv3zCMsD7nKjnqiYfvBVhrqcWC8dDjfdf288jRz3B25jSzdU1nTrGqqcMtZQvcsXmQrx86R9E3zEYeSkFeQX8hw5quLm7b0MX7r4yZqQ+5BIjHQ6d6ODKZMh1+r10efv36PhJt2naOCkFqzAI3JrcgcVegLw19hZT3XaWW0N1aRDkhUoyZ/xuwhEnKcDnhPdd+kCgNmQoFqX2sWWWZi7Mz9fa7LpeUHxkK+NXLROZr3d9+6ebN/PlDB9k7NMGTp89QiTSljKG/oJHSQ6C4ZmVKkkwzHc8yWbPMNgbxJEyGPpVYUotbqm9nuKGku9YXwlUEiYXp0Of+o71gLXdtmW7vZANp2iCR1mOO2e121QaLr5w5ya51ZY6Ox5QCg7Ha/e4E+CrL2u4BioErUy+OQgBru5fKKl/t8apIyK2Yj7jryAb8mzddf1kcn35U4/C5xzgzdYhaPEuUuAQ7URni4NnHiNKaG6Jo6lDDaBZjDUdHn2Rk9jhxGhHGDYpn00veNZ9PdpOkhnKkKQYKKTWSZmK5TJ9zfiE20YZb1vW2ecPAAoCEbPa+unJQ9Js93Ob08OsGa9y1pUYxKLG2ZwUDpfV05gf41oH/usx7Sjzl8Ru7t/LhOzva19BE5QTffF7jlLN2wTO01UzVzrG6e+tFXWha311iJHvPGsJUYYzEKVbOX0Gop46w1boRhonixtVllLAcm+xAU2R97xpu3eBAKsfHnuTM1CHeukXyxFDC4XFDrFMCVeOKfsOV/Yr7DqZIqbDW4qmA1AiM0dQTj0Za5Fy4imtW9XB6psFUGJFa10vPKMXG0l7+9skJjkx0UY4UWc+wrXeM0co97Fi5mTs3TjNajnl+RGHKgtgElAKP69as5A2bV/Jrt21bYPjy8e+f4amz51BSk/EklajBVw+coRFrsp7CaNoyJU9KtDHNHq5tDvVpurKa/qLmpjUppWwXPYUNdBSubTuZObmTohotJexlFPjStWDyQQdK6mWv+VVdOY5PVc/7Pb3/mjWXXfaYD3w+etc1RKlmtFLnLx76Dt8/PUklUhQCxe0be7lj/SQjs8NYDMVAUAgMw+WASuS5WQhBm9AGboe9PExJoJtktGdGOlDSSZ2+daybgxMFRsoBDa0oBs5AQwjozSfsHKhy/WAFY+HpkRIff2olYexRSzwErm2T9eC52fv53966mxvW5NjzQn1B2dpYuGlt/seuXA2vsoS8XLyanEDmR8tmrRbP0oirzvRARzgHqtqyz4nrDQSSQOXwlI9BvyiW9/lMwSux+0G6fpzjA1+ecL1Qi9MDKwFXrUj5y59ZKFPL+kEbIHFgZIJHTs7w5JlphmfGSNIJ1nXVuWvzBIXAZ2XHRm7e8l5yfoFjo09ydPRxPBmQGrfCb01US6EoZbsBiydt+xrqKQwihcJavaBkKnASrp7C4ILztdy1N98ib6Q8w/BsAW1E80Z54XJ+aiSxFmSbQ2h5X9MR+Ny0usEHrlnB9Rt/kv5inoynFrzPlw6EhLFgTWeCNq7/GyaSz+w7iC9Wtoe8QDimuPTIehGBX2A6bJDxR+kv5FnX1UNiDL6SJDrlnn0zHBrPMxUqKrGkkUqeHcny1SMJVw48z/WrfT5wdYH3XWmYrads7l/Huv6b2osUbVIqjVnAomSB752YQAlBtTHtqjjG7eq/fbTMzt4sz025JJpoh6mQQrBzRRevX5eyb3iGRENnLsNtG3r47Ttv5RNPDvHw8RGq8R568rn2AOKN67p54Ei4JBHsGDD4Urf7zee75guBj6S16Fw4G6EEvPe6pdjNyyWNyniKdd1F/vi971o0cKn47uFPI4VEW4OvDJt7Qo5M5JZYkrqjFefFjraG48CBQA6O59k/UuTUbM55eQvAWqqRA8us6WywpSdEAPc+P8DQTJZ6osj57gzVEkWcCidVk3Di+2M8+sLf89E3ekQp7B9xVZdC4JLxn/zkB1/y+Xklx6s+Ib9aI0pDojSkEddITdJMKBcvFlsMM+EofaU1wItnec+VY8eYrkd05zLcceUAQ9M1Ym0pBhkqpkHjxVXDmfv5z5W8BSBpsY4NKwqKG9Z4lLLFZV+hO59n16Z17Nq0jijVfOWZTzNbPUc+4yHIgYDxymn2nryPlZ1ztLVCpotqYxprDUJKfJVp2RXz8JHPkfOLbe/aXFAk4+WpJ+VF59WS9QrkguWPbX60eOj1pILVZSLd0XyNS+utJ1qS9TTauCnqv3mqn2ok6c4b3ls+zr/efc2C90mt4rkR0Szpu/6e21FaDo5JXre6xskZt5BwlRZDalJetyphdWeJUkYQ6TlNczHrBsI8aTkx5TFZU8xEikQ7PbAQECYep2ctkdYgFO/aYegv+YTxGQaKu2gkNZ48+W3Gyi/QSKsIK2joToZmVpLxZHtxIIQzaBmvlPlX1ynuO7OWx05PUItSsr7i1g393PMLTgbXInptH+illA347b//NHteqGOswRhBpZ6j3HAJ/ZM/t4t3/Jd7OTBmibTbGe8YMHzkjqVuYstd87s3D/DxD9zC+n//ReY4HC4ZD//eTy2oOM1vT7wYl7lLifkDl2FcppHUkFKhcFz4XesSvvOCoZ44qaFEIqW9KKil/fqeZjL0mQhzpLoprRMWodzio5RJ6StE/PPrzvHokDNcsRZmGx6xUZTbRQjXTJDWtROsFTw/Ivji85o7N/v84s0b6MxvfE2H/E99AK/FS4uMl8eTPomJ0CblUpJxK2JdX9A/fjHWkOczBX9iaLI5tSoIPGfHh4BCkBJrOQ+0sTgsvrAUMpo7N0xyxUCNp8+WeOBkn4M9NAeyJQIlDUcnO0iNWNaZZn4IEoweIlBzGmJw6L6x8ims1e2bPU3er7EWa0zbIKqRVKknZSpikplwDGst2wdvob+0lrMzR5uTpC486VCplcZUs9R5/p9WxssTeFmmqqPcf7yTeiIvwGBfGsaCrzS+hEaq8JTEU5Aan28dniCjnIVhi7s+Ol1npi4JPD0P8uIWPmEsuGVtyOYVfTz+QsBUvUEpI1ldPMc7r1AoCVevtDx+RiCF0zQXMl0YCzev6+cvTw0zEXqu9IkzjGhFNVb02Yi9ZxJuWjVOxlMo4fMP+/5fyvUJ5gjUgkDlkEwjbIZqQ+G13cQcacpXCZEc4S2bprhttQWRZ8tAjs19AULQtnucDBvkfZ/BwhmeHq4yXvWoJkFTepbSURmiGHj8i9dv5S/ev5nDI4cYqUoGS65vuZyb2PmueYDoT+/mmTPjfPbp03zwunVcu8ZJeubvho+MPM7Q5AE3ZCjki3aZu9TIeHlyfpGKmMSpvgWlrGZdZ0wjEaRGcbYS0EhFW41wvrCAJzRKQrnhoefR81rJPONZarGiIysJE9WevB6p+kRNX/C5cNebtoJGCoGEhoUvPGd54oyiKzfM+65dya/vWgpw+XGK1xLyKzQcU3sDZ6cd2u7FhLGO79uKxTuCS4nF5dgWw/rRU2OEkaMoFQJD3rfUYkui7aKkY8mplHVdEUo6P5/bN5Yp+JqvHsnhK0vQnhmmybX2SHRxWVrX4qg0phZ8xvmRmphaXCbnF6lF0zTSGp7yUdLDGEu1EVFPFJ05SeAJLIYoqXFsbC9re3eQ6IiVnZvQJiXRDXyVoZ5UGa8O8fDhz5HPlC64A1LSo6ewir99cpzDEzkCZS6Zw67Q/IsbhunMpnzi6UEn97IWgSLj5/GUbCNGPSn59vFuHjqeMFJVIJoIz2bPz1pL1rfkvYQP3bwTs0sxFUb4ssbjx48h8NAG3rHdHdv+EUElMngi5U3b15IY6/yTMe3ZJtPcBUlJ04PaUjOSWuIReJrp+gy1RBJIiI3XnmqPdR1P+mzqnuXZ0UJzsMwlvURrlLD8nw+vZqbhkn9PXnPXlgrvv+oAf7t3gmfO5RmaqTFZazQhNJpYOw10y6jDWpipw96hswucnzqbCFwlz+8mBudvQVy7pr+diBfvhn2VYbwyhLUabVOU8DE2z2zcTaSPX1bf4Pka8CipQVPbu7WnyrOjpabW3i5gsM8fSWvNX0gsSlj6CqkzS8ENRbbK20LQXCy7tB8ol93D2C3gavHiZLwwEqOcZzmCEENqGtSTDPcdGEIKecm8+ldjvJaQX8GxceA69p95iFiHL/q5jaQG+Of1F36xEXgen/3nb2A6DPnCvi/wvRNVvn28xUOGQmDRxhAlgqwPSoE2ktFalryvWVWKKfqSakLTm9XpGhfuGwPygUdP/uKGEaVsD54K0Lqx5N88GVAIOtAmpZGG7R20sfCNYwUOT2QIY0UhsGzvq/PWrSFKCmpR2XGOm25fSnooWaQaTdOIqwgh8FVwSTugzStu5dj0CTypKWYMleXXDktisBSzohRTjry29EQIQWBzbW3xdD1ieKbC3+49yuNDHqlRFDMR5YZq7nZgoJgAgu197rMdG32S7YOvZ6b6NOdmT7RJWa4M6/GG9UVuX6+Yries6kzI+OP8h4fzDJSK1KYqzUWBC4PAw7jSprAoqfGF5uuHCxwY7+LMbJZYSwLPsLajwba+kDs3TZOahDducru7I5MFwkRT8C1ZT3BoPEs1UZjmNRHOKj6/X1KPZzgxlXC2EjAbtfTvomnm4QbvMvPcnIRwMw9ZbykC180SxFhrmgYfLz5aw3pCSLT1GJoYw9oKvnKyo796oo/jU3liI8h5htv3fZdP/8IbFpDnXmzM3423NODHxvZSi8pgLW/eXEFJn+fGAoz18aXGasfQczpmR9HKeAZtoSuTIJus8lqi6Mi4xNxIZZsz35IYKmHZ0VejM5uSDzRh7BHpS6n2uL9JjWAqdC0YT3kX5dW/2uO1hPwKjpxfpL9zLSPTx9H2xU1SNZIaWXpY03PFi7aGdNrZMiDIB6UFybw7n2fXxs0M5A/x0AuWln2fFNBf8KinAVNhQlcQkBABlmrskQs81vau4+z0EMUgpRh4lBteu0zWQvHduXXlJf1YAy9Lf2kdQ5OHFzxujWVF13pWdm7k1MRzrm8s3A38G0dyPDuab1ODYi14ZiSPsYZ3bI/AWlKT0p0fZHT2BFK6Pl2UhFgsOa/QlkNdrDdfiSzWduKrGgMFGKsulp4sjY5MSmdO8+CJbu7YMEMhsCAKCMA3rkdureXM7BS/eM9XeG7EIoWhEBh6s5pKQ1DXinoYEGnJlp4G79ouKWa6GZk9iTGa4ZnDTFXPLSjHp1ZTiZ1kbqA4QKB8Pvt0yhOnqyCc3/f8yV1HVbPNY8mS8TT/7sFBDAJrDWHivtcwlpytZNtgijdvnm4zku/YMEMtUQTS8Cd7NlKNvXaBuwk2pRp7PHSyRCmrqcSLFm/zyqstO0twiSTnwXRYpTvvdrvGGvaf2cPo7HGk9chmcwx2bmbHql0vqsfbGqIzVvLVg7B/BMaqOQJvNTv6ahybzHN4otjuw6ZGsef4xALynLGaMC5f0uDX+XrTV6y6le2DtxDGFbRJ+e6hT/GWLbNcNWD4q72DZD3LRM2jHHlIQTvBDpZidg5UeduWScD1gT/33ApSIxmvOX2xwDmx0aw6bO9rtCVR23pD9p0rLfFVPn9YPOkUBq32xXQ9uqQK2Ks1XkvIr+BQ0mNV5xYmy8PYRX3hi4WvsqwU17yoHpaxhkNnH+XY2F7CqIwVlkLQyZaBG7hi1a3tm9f2wdczXk3pyZ+ir6DRVlIIchQzPTx9dorAU22urRCarixgJdoIBjvWcOVAnShNsdZNZ1qryHpuQvZDuy+9nLV7+wf5h8f+f1JRXeJVK4UDFZQbjhrUSDSHJ3PLUoOOTOa4I5rGV/DtA/8VTwYo5chVngow1pDzixSaO9RWXKg335PPsLp7gPHKBBBSCixhIuYZpMxlOF8aAmVZ0xHhSTgymefOTTWuGRQ8OyKbfWF37g+PjpGYmJzX6tnBbENQbnhYJDnPARsGizGRlnzzRJYPXA2NOOTI6JOE0YzzvF0UiRbN72KWbx/v5pkRt4l0n701We8mn41xqExXlBQ0UsVUopqWju7vodWGUPTiLB/v2DDTNkDwlaVLpUzUfKbralkJnUUwXffozKYORjHvuxM4vKds6s9Nk2zVkbFs6vFY2dGJsYbnh/aw9/TXFpxvQhiZeoGx8mnecMXPXnJSbg3RffWQx/eHJCOVhEqcRRs4OZWnngqyyh2LgLZP8GOnJpipNzg3/RRn4qeYOvzsJQ1+zd+NK7nUmaqU7SbRMVFaJ9UxHVlDZ9aBX/oLqTv/sYexgsATvGGjz+71DeLmyrCvkLC9L2T/aLGtM641F0Y9Wcvbtgt2rR1v96Pv3DRNPZGcmMqhL3Arks30667rFvpTYaymO5e9pArYqzVeS8iv8Nixahdj5dMMTx1ucmkvRf0r0DZBiRcHSDl87jEOjzxGI2lqnIEwmuXwyPcRQrSTuxSSWzbtYlO/WUCHaiSaVFs6Mj7XrOomNYY4LZOkIdXYEiYeVw5u47/87M38pz3P852jZ6jGimIm4E1bBvmN3dvbrkKXEp70WJ+5jauvvXJZr9orV+8GC6enDnJycqJZAl66TQ0T1Wb6GquJ0zpZqcgHHfSX1pMPCmiz9A40vzc/X56SD9xQ0B2bVnDfwZRCpotKXGG8GlFuJG5IS2qqkcVXhozXKv3a5vF4IHp4/1V5MkpzfLqT6TJklABSVhZFu++XGtv+DDnf7RQ9afGU0+3uPyd4y+ZpPCWox+UllZYoFXzzWA+nZrM0mjKW8VpMZ0ajdcbpS5vXgkWQVYZskJI0+deOqNUczkOQWkWUGrK+Oy53MxYLzvGSuEDlIDWCTT0hw5UsCyVjgryvQQjWdUZNe0eFpzLcvqmXfJDh4NlHeOr0N5Z9g5QGo7MnOTj8Pa5cs/v8BzAvMl4eIbPsH9GM1yyzDdnuX6caUqOoGXfeBeAZjSctYZzy4KFHKPovYNDLJtfFMV/SNj8WV2YODO9B6xgpFQFmgY/xiqLBkpJqwe0bPN53lQLWMF5pQVosb948ixSSwxM5enKadZ2wY4XHr9+2iSvX3Mg39v8lM2HS7PnD27ZOcWomx9Gm6cTiM+tL3WabQ+v8WAJPYa38sfcWeC0hv8JDCskbrvhZDgzv4ejoXmqR68VJWobii2cdnZQkUNmmZvnSQpuUczPHidL6gqllBMRpnZHZEwvKsy7hrOS+g8Ptv/eVbCIoAzwlnMxHFOjIdVDMSN5x9e1tW8ffesO1/Pquqy4L1OVCXrU7Vu8iNQnjlVEKgSFepv+V9zUF350r25zIbk0bz4SjDDSH6+bfHFu9eWPh9796H4+cnGS2oenMKm7b2Mvvvf2tC+Q0qzryZDyP8VqDRqKd+5KJ8Jrj5F1ZS87Pk5qEQgYGigGB5/Prt29kQ/9NPPT9vazbsoG7P3mq7TBVCFJm6qKZGB3HCgvFQM8Di0jGqw1WdeXaVQNoJuLjvTxxpsR0w8eTNMlmKZNhwEzdYKzEk4bUuClxi0B4lv5CyulyhihVqCZL3p0T956tgSAh5jjIGW/uHCdaMNvw2ue+K5tSPY+ntQXesnmW0WqWo5N5tBUoYSllND05zYqOLqJEUo2hlHXwjN97+1vRJmVo4sAFfwNJmnBu9jhXrLr1kmYslPTI+uuYbZykHLFA+9uSRrUtHIRTIlSjiJ5CDl8OXzS5zo/Wbny542pVZjJensnqMEIqPCRK+ty1pYoQkiMTeRqpYrCzhxtW53jjxim0iQi8LNeufRPapBwfe4owrvL2bXXevSOgt7SDa9ZeS1eus/2+W1fcyOnJA0yH55w3vDJc0V8j1pKpekAlUkSpk8MpacgqSzGj6c0lTNZ9arEi52m6850/Ft4CF4uXlJDTNOV3f/d3OX36NFprPvKRj3DjjTdy6NAhfv/3fx+A7du38wd/8AeX81hfi/OEFJKr1tzBjlW3UYtmOT62j+naOWZqo1SjGRDzAYyCjJenu7ASVbv0HXKUhjSSWrvnOj+MNdST2pLy7HL6zds3DVBuxDw5NMlMPUYbgyclVwx04MmFSfeHAXVpnbup2lmuXjnLE2dgMSxiW2/Y3jkb63y0jTXN3XKD9X1XI4VipDmtG3gZegqr2LLiRv7w6/e3PaUDT1JPLd88Mg7cz7975zuWyGm0Mfz5QwfZNzTN86PTTNZqFDOG/oJtsorz/NQ1V7Br21pqsUd/ce78rOzomGdQb+nNOwhINVZI4XTHLbJSK/KBoSfvEXhZqo3ppq1eNw+f6mIyDEi0dM/FUonc7UJJSxi7HXfGA98YwkS2z5sQ1kEzjBvw0k1nrRb7WTcHglqLA3DnWEnLN491873TXczUfYSArmxCdy5hpJZrlkYXLjCVVHzvdD+/dvMw3zrWzYGJPLGWFAOHbnzflRFvuvKXmKrV29UJcJrdqXDkItcGpM2BqUtlUt+08Va682O8MF2nVa2y1g0wLoZsCgGxtrxuVQe+nGG52/H52h4tSVuqkyXPaVVmojQk0RFZL089qTYHtwLesa3BmzbWWN17K3dsu6sNaFkMLdm5ehdhXAHseaV8rfmTwMs5OiCSu28I+PKBlOdGBJVIoGTCpq5pblpb5qnhEsen84SJYlVHg83dIT+xvYefvuk9rxpvgZcTLykhf/nLXyaXy/HpT3+ao0eP8ju/8zt8/vOf54/+6I/46Ec/yjXXXMNv//Zv893vfpc3vGEpsea1+MGEkh4duV5et/4utEmpJ1UeP/4PjJVPO12iEGS8AsVMFys7NxGGl77rzHh5ckGBcmMxMtIltZxfWCKdWp4xHnP7//d1xipu+lkARsDp6Ro/98k9fP4X37jkvbVJKTcq7QS0eLf8cvGoLbnI+686RKpnOTDuESaKvO9u6nduml7wWVv/K4XCUwE5v8iOVbexZcWNHBjew1RtmKGpExwYGeaBYxFyUWtACsGek5OEcdQuX88tPBT/9k07KTcqlBuKL+0f5nsvjDA8M06gInb0Vzkx/gy/8HfHiE2RqTB2Zeg0Yu2xCEuORNfwmvCP/kJKdy5BCSc78+ZtwiyK61d7DHSsxFhn4PHgiW6eGSlRbvjIZu+1NaTT0p1mlaZqFXZuv9eGmoSJ4kw5h2g2UFps6Zakxn1+gycN2XnuWi0s43dO9lCLPVqdiel6QJwqOjMwVZ8n0WkS1LKe4cS0BwS8ZesUb9w0TS1RFHxNoATlxgwPHvgr3vO631yQUDzpmNwXioxfJOPlXpQkMOf7vPuqnRwae55Yx25q251sAmXaCxPbHHQsBJb/6507GZsdv2ByXRxO9jgHt2nFfNVEK2m35GONNGwuqCXdhR7euP1OfKXar7c46SvpNWl154/Fk+oZL48QkrU9j3F66gRTtYiufIbx8izGat66dZpEz7S/o6zn84GbPkT2tWQMvMSE/J73vId3vetdAPT09DAzM0McxwwPD3PNNY4SdOedd/Loo4++lpD/iUJJj2Kmizfu+IV5rk8pGS/X1lruO7fvRb3eys7NTZ/b2twu2ULg51jZuem8ZT1PCv7+2dM8fGKMp85McK5cb7rTOBhHqg0zDcN9B4b5428+y79581UoKTHWcGD4Uf76+yd4+qwmTBT9hQI/sXMH/3LXdoAF3syljMe2/g4+8qYr6c5nlz2W80Vrpf8/3HiC42OHqCa0NbLzQwgBFjJBHqxdIBk7Nvok52Ze4L7Dkv0jPlOh5vSspDOrnT/yvJ13pWEYKc+2KUuw/NTsXZtIO9RNAAAgAElEQVTXcevalEOjFaRQPHpK8thpi7EVZuoNZpsLJGEtQ+EYcWrwlU8x0OSUs1K8ekXIGzZO8+CJDo5Mut1Jwbfs6K9z65pZqlE3vswSa8ORyTzWOqRiS/8N83Wn0J1LqcSqmbBFExRh2ztka8GYhRacQjhTAk9aOjMp/8vtL2CFoOBrlLR863gP3zjWSyVyzGPVHPgRAupakvfr5Dyvfd213qsQ6KYcpwNfjRMol/hak/Ngma2P8Z2Dn2JV91a2rLiRRDfQJiXj54n08phZgM5870uSBH5o9xU8+sI4e06MNqfPo7Y7UymT0JNLSIxHzld0ZAM29PYj7MZ2z7gVF5Mktq7ZucrMQh31/KRdzPZQsF0Y64h4a3t34qvzV8jqcZWp2jl6CoOXRJ9bnNAXJ+mHDn+Wc9PHmrS4lGLG2eAOdm0h6/94TlQvFy8pIfvzVjOf+MQneNe73sX09DQdHXNfSG9vL+Pj4y//CF+LlxVSSK5cs5srVt36sjm6LY3j0dG9hPEsIChkOtgycMMFpVMfe+RI2zN5Ooznhn2spV1MtxBpw589dJB9Z6f56w/ezPdP7uELz55h31mFFBJPWqbrFT7/zPPtG/N9B4eRAg6MzDBdj/nH58/wl48e5a5tg/zdz+96UeepdRO5fsMsDzz/CSrRZPMGJpDCI+eXMKT4KqAz19/GacLckM19hyWPn3Hl21zgFh0zdYEUlpWluRRVykpWdnQuOIbFU7Mz4RjDUye4/1iOY5NFqrHl5HTQ3rXGeuEAVJeweNLH4rO+p5cd/XXeunmc1EgaaY6f2FbnzqRGmChKgSUTeGS9Ejm/xHhlqG2r5/q6LrE6eY47bqc7ddCXrb0hiZFgBcOVDCBItMRXrjcsnDKGYsZNyzfSZp/ZOJ/ibx7r4d07JvCVdVPb50pzwArhmN1gyHhu8CvvayRQTRw1Sso5yEngGQJVdRAZ4fS11poFrPFKfYrnag/x/PAe8plOsn6BxDQQeFiWDpKt6tjOmp7tL1oSCK4ydM/du/lPDx/mgWPnCOOEFybP0ki1c2YSHhlfIYXH7RvdgFnrfQ6Vn0IbvSS5LhfL7U4X/7aXJu3cBV831jFfe+ZjlOtLvb6DCyTw5c/DXJLevf2DPHz4s4xXTiN0ipI+/SWneHgt5kLYi9Rt7r33Xu69994Fj33oQx9i9+7dfOpTn+KBBx7gYx/7GFNTU/zqr/4qX/rSlwB45JFH+MIXvsCf/dmfLfu6URTx3HPPXaaP8Vr8MMNYTWKd/Zwv5kpiy0WsDf/7I84qbjSMOVvV5x2jEUBn4LjJKwopuSBipBpQCCwD8xfRVhCIThAWieTwdIPZSM/tQC3kPcENK4v88e61C46lHGs6AkVwMfYmDjEaMUuGTjwZoIkRVmGFRhEs+NyJrXMifJy/eLJjARhhrAazkUQJwYYu3S4D3zqY5Zd2zkm4jNWciR9vW0HGtkZqI755vNTURktGKx5jtaCdZpb+cK0rpQMdGUV/1uOP70zRcorUREjhURC91OwUhrQpOxMYY6gxRqLhr/euItGK8Zrf7hnH2vWAA2nxPU1HJqUQaGqxIjWCmbrXLIW7gTdtnQgr0oKunGa27hE3Nayt1B54hr58ws1rZjkyUcBYwenZbNsesKWNzfsGKS2be0K29oY8N1rEWoGStn0ur15R4c2bW22FeRfBvGjJtuaqHrL99woP3UzKioBebxP93vYLXteXGq1rLq8Enzl2jKfH69QSKPmCa/uz3L19W3twD9x1oImXXF8vNy71dQ/X7ydmdsnjAZ1sz731ZR9HamISavgU8OSrwwb3pcZVV11FJrNQ4nXRrdIHPvABPvCBDyx5/N577+WBBx7gL/7iL/B9v126bsXo6CgDAxf3s1zuoH4UYu/evdxwww3/1IfxA40f9GfUxvDH336OZydCqrFBm9aeZXlwgK8EkYXYWKYjST7jyqHlSCKEYbCkGK0KxmsQpyEWyPuKMNFtn1xwRg+eH3CkrKnGKbffctOC0nbLY7dl/Xd5PmvK8NMv0LCGYF47bFWnRVQslUiRWkFPXrYnfX019/ML4zJTh59FNWEjU9UqaI+jUwWHfUQw0/AuImpzLQAhJEp5TMTwXOMq/u2bdrZ3UFEa8vDhzy3YSUVpnVoFAiXaspg53amTgvXmNCtKPqXMKmbqw6RGE+S0Y5Zj6c3PLTa0cQlzaCZgKvRIjVyQHhUWiWCkkuGrh/pIjTMtSE0LjiKQljZUpuhpruirceemaZSw7bJ7xlvY43fQx4WJuDWkdmQyTxgr8sHcc6RwzlZKengohJDk/A7ecu0HL1jOfamx+9ZblsjfFsfevXu56YbbL/t7X0rU4yrPPf6FZf8tocLOq7dfUvn6YvHjfm+90Gb0JdUuh4aG+MxnPsMnP/nJdjL1fZ9Nmzbx5JNPcuONN3L//fdz9913v5SXfy1eJfGxR47wjUNHMGh3oxUt6UuLUD0XAvdv9cT1uBqpRRs3DOTgFmBsymToEWvZRvdV4jk5klJuFyaFg0SEccrZWtIumSspyPqKMEnbJfTLxc1V0mNL30ZKwdE2l9pYl2Q29JToL/bwh2/fwfqe7mVvxPOnZo3VGKupxg6P6UnNaMWjcYm8a20t5UZCxpM8NTRFakS7dLh4OrfSmCJslHHLGMudm9yi+shknq4crCw1WFVMuGZlzDeP9zLbONu2qgRHNPOa3GrZ1NzKJibSfcuLU6Qzoagmrf2ybD04L1rWCNCTi7l9/UwzgS6keC3u8QcqR8bLUo4m2zvix4dKHBgvtelriVbsHy0BLTKYoDu/AiFkU/Zlm3zyH8zubb47049ajFfOYM+z5LMYxitnWNf748uZ/mHES0rI9957LzMzM/zKr/xK+7GPf/zjfPSjH+X3fu/3MMZw7bXXctttl8/J5NUY5UbMickKm3pLdGRfXeWbKNU8dHwErevkPE254cqjSoIxDnSBlWjAEy2aUtNdVjjgw3A5IDWCWDuAQJRaEuN2gZKFTF0DGO2Sua/ce+UDRV9W8eXjYwu8bAGUFJedm3v12lu5fdMs3zwyyVjVUIkkxiqynmZFSbKhp5MwnsSTCwEl7njcAM6hkYOcK1tyykl3CkFKlAZMhJe+k3d4Q4u2lgNjs0tQhN35QUZmTzATjlJt1KkkUPAFvnILoLs2z3DHhllmI8nn9g/yyOkuHjgpiVNBIdCs65ob4AKaEqMaQ+Vsezp9XUeDY5M5DMsd9/nRiq00raSmvxDzkd2n8JWlHM0ZUbQoXosj0nXA49vHezgymaMae4xWAnK+aRtqQIu+1iSDSdBGN68ZSaD8i05VXy5f4x+1KGQuLO262L+/Fi8/XtLV9OEPf5gPf/jDSx7fsmUL99xzz8s+qFd7xGnK3Z/6Ho+dmqAWpxQCj9ev7+Pvfn7Xy4LM/yjFVBgxGTYwVtNfSJiuByQGZxghoD+fsqLkMVrLMlmLnYRGuERrrdOvVhMP2XwcWNCbNTh59dz+qLUfg1QbalHCHTtWYxBM1SOy/tKkezm4uXHaWEAB+/2feDuHxr/Lianh5g4/JesZjo8f4zc+9wxv3jztEJ7NgRaveUNvJDG/9Q/n2DesqCeKQK1ic0+dK1fCN44oUnv+RYNCo1ms4ZbkfY9a5K6v+RPcjThkujbGfUd8jkx2EsaSfKDZ3B1y45oKpUCT8QRfeGoNR5uWer6EGEs1VpyeybKhe860Ix9o3rZ1ikQLJkKfvnzC14/2kDQXUpJL48eB+xalcMNjxUDzwMkehmYzy5Sbl3u24WtHfPY3eeQSSIwkjdyioL8wt7NvkcF8FTFRHWrCVDKs7d19XnvPH6Sv8Y9C/Hf23jvKrus68/ydc+69L9erjJwzGMUg5iSRNEmllmwN1VZrltc4tKatdk+Peo098ozaub3cM7Ldtjwa2/K0x7YsWVbbo0BJpCSKOSfkAlDIqJxevXTTOWf+uO+9qkIVEgEKJFHfH1iFwsN9N++z9/7297Vne3FUiljPl051ZIr27Nszs3834d3x9n+H4VN/90zDO1jiKkmoDT8+ODRHZP6djs5simLaY6oKYOjK6saITsI47s2HQMz71kf8qF9QjxTaWvxIoGfRf2CWytSczGr2FOzM75I/Ba4S/OXDN7Nrxw46sylq0fyMqiOTOi/d3NmZkYUWa7Slk11YzXs3/AzVYJJ1nZrYWFwpiHUdY2P2jDjcvV4ibczg1EF+vOdvuGf7p1DS4aNf/hYvHKsihcRVYJH0jeWQIkaJGDh9QPYcS6g1xiZl17SryHnJWEnOU1TDmJMTL89icCsePZhl53AKKZKS88B0mgNjOX58pJM17RFXLElxcNxpLIYECItqqHLVItXwF05K1Rs7azx5pJ3941mqoaIcKMYa5LPEzu/8Yayg7Cv2j2VxZPJdtdDhjaGZcvOpiLRg/3i6FaybKmDWCqqhoisbtf6tqb6WTEkLfthfZP9YFm2Psbbrx9y5YRn/5ratczgGZ9OOfqdDSYdrV97L68cfm+f1fe2qe99V1YC3KxbP8E8Q2sSMVqZ47ugosvGgW9t0QxI8f3SMaT885/L1hQpivFWIdMzvfP9Rdg2MM1QGpTxybmIzWAkc0k5MyjFs7w358PYs/eMhfqyIYjg+7RHEEDZGbUwrCC+UEs0EYwHkE78HJOA5ivFahKckN63u5Fu7j5F2vRajVRt7zrq5p2aXjuNSqZeohSVkwyDA2JihqX6+88bXGJyOUEIjsMSahhyloBpBOVC0pZLKwcmpgzyx7yvkUmt4daA2L+tTUnJw3GNpm2aiDtGsNLM5MpZzNes6qxyayKGNwFEOKZXMCHfm0mzvLVJMK/adnNE99iPN3lG39X1jtYRRLQT4scKPNC8cd6iEkoxrGq0Ei6eSKxIbQS2SdGYjNnfVsJaWPnLJdyn5SZ9fCoO2ctY1PBckAiLaCkZrKYSMsBZqsWqRxcqB4vY1U3NsFWuh5PBkmulAkW78Xork/JSDhAynjUAq21Jfy6eyCAHf359n13C2IWZS5fjkcb7yygAj00f4zYceQArZGmtLzl9MJZS0pSWuhBOT+9nQe928NsQ7sbS9feVtKKU4OdFHNSiTSxVY8SbHvxZx/nhn3CU/IcwOcKf7/ZsJfMYa3jj6NPtHDzE0HTHtp3GkINKKyBiMscmLPYg5MDrNlcs6Wt/nSDvvodbG8H/+eBeP9Q2gLfTmsxedNXwhaMpFdmYFQWyZDmC85iKFpZCK6c0HbOio89AWRc5TbOmp8urJFON1h1okmRnEm5H7PBsktJx9wIK1RFrz/+zZx56SoX/c4GtJ3nW4ftUy7tqy9LS6ubNfpLERvHDoGaZrB4h1iXJQY6IKkQnoSFuyKW8Ou7tcO0jGWUqoVYvUJRsC+lnXkHZ8jI1pWsP7cY19w33UIw93gWsXGkg7kp48jJQTFrKxyXG6wvLxqwwPbZF883WXIb0MKQRhnJgWCKG4c+MSIJijezxSLlMN07gN56FqOCNaok0SDJuOTNbYhvRq8p0pBzJo/ofrBujORURa8BevrACSbVUaY0tSJuIgKrFLOOv1m8GMxGZsBaMVF4Ql4ybn0VrBRN3lsYOdfHDrOLER/MVLy+mfyOLHgthIsq5hTbuPEILevMFaQ6AzFDKddGUdbl5TZE3+RwghiTT0jaVbymIzumOWpw+NsPP4c1yz+jbqUYWx8iCP7HfoG/Uohw4pZbii1+e+jSWe7PsqKzo2twJX3+DzDE4daAW1Ze2b3hGl7XOZbV7EW4fFM00S4GaPxRTTHoW4wkPqMC8fG2fXcAk/0nTn0+cd+CId8x/+6e95+XiFSihJOxZjNHUtibVtZcrGWCyG33n0DRwFJT/GFVU2dVe5f2NANpX0q9Z0X8cNX/hnDo1HGGtRQtCWlpTqSd/nYrGG3yxqYcDTh8cbloCW7lyENqLRl7MsyUdIIdk9miOz3/Dg5gluWz3G4/3rGKt5aNv0tW2WqM8sb9iEBUKdBAxrBcuLWf7q+Wd4btAn5bmsKCZksFCHbOiqLXieZmfCtaDOI30pdg2nKAc+GccQ6zQjtQKT9STAtKVj3ruiyk9tnMRRYYvZu7q9wjNHO6hFikgnIhZ5V3PXugmkCAnjpOyb8wyOdFleFKSkWZAAlXYFd63v4KnDEwgE0z5ExqKEYWtvxMPXZFnRsZ3l2uWlIM8je/ZSqlfJOpprVyjuWpvBVetbzGprDa6qkPVcIq0aAZg5ZV5HWtJumqwTEluJnEWBNgY299RYUgh5/FAHu0ZyHBzP4DbK17VIta6HQSDfVMEamouwyCbKaCLWpBrKXUrA0VKaWCv+4uVl7BvNISWNBUaSQR+dSrOmPaBvLIUfSwSaNwaqbO5p4zO3X8f3dz2NsTGVUFENFUrq1vc278BKKDkwepgrV97E0dGdPNLn8PpQhvGaohImdqH9Ex59Yy7/8V7NkbFd+FENR7rsPvkkoQ6w1jBZk4xMH8MYwxUrL81I0/liISnNRbz1WAzIzChJSQGDpRpPHBwitvDXe59ufabgOaxszzLtJ72Vcw18v/Hd7/PU4VqrN5gEHE0QC4SwGG0b6/HkRfPY/gG292qE0FhrGa+5GOPwwJaIN07s45e+3k//WKJrKAQYLJN1zasnRiimUxeVNfxmMDRdouxrPCeZCTXWUo2cRr9RYK0Ckcys7hyGm1aO8/ihDqYDl6xrCHQSvK1tBuNzKXcm6lBhLPAUdGUVP3vdWr69+/U5ZWApIO0Inj86oyM9G80eoTaC333csnc0QpsYJSzGSvwog5SSlGMJYhgqp/jWPo+XTmRZkk8UyMqBYqquqAQKjUA3ZnDDWLJzKI820D+Zpew7dGQEN65SfGArbOoJ2TvizDO2uG55lv/04Qf5re89ytOHxynVDWlXcsf6Lj57z82EOkVPPsuOgVe4Y80ga9smqYaKQgo8pRmc6kNJ0ZJQNDaRr9zcVWfncH6OKpdtmT0YasEUm7oNSqboG7WEOmFhr+2s83PXDfD4oY7G+JDAVeBHMrlujfPclF87v3L16RFriSCR08ynNH6sGK44HBjPzJEkbZbVa1FiGRhoiUSgpMDYRNHtff/Xj/g/HlrN0FR/i8nutygGilAnfsx5D9IqoBaWOTF1hL6xNONVyXSgWraKINg/luFvXx3n/o2TjEwfaY0ONYVXIKly7Tr5JFuX37yYcS7itLjs74wg1jzVPwJYDoxO0z9ebejPzkU5jDk8XibUmkf3DfCpG9aftddbCwOePTzWeslqYxirOTTLcrYhti+wOA2pwUDDYBmW5ONkMtRYvtfn8sYglHzF3pFEoH6OGbuAkm8YrdYumDV8oVjaVmw5DgkEuiH2IAUNdaWZRmgtkpR8h71juZZ2sqcSqlaoxSzS1tle6gKsBhQCSVumjWm/znRdL5ifLaQj3eoRCsHXdtTYO5LFIog01Bv2ggDSWIhpyUkiBCNVj7GKh4aGD/DM/jYlMUIt2T1aYO9oDk8lQXCsrhiqJpn5r96Z5g+fSbFnJCSMLSlXcN3yLP/08x/CVQ6//YGHWqISPfkC/+/LR/mVf9rDRC3AFWV6U6Ncv2qUrAtC5Smk2hu7l1j43b45EfcZnOpHCsV9GysIkvGfjJPohBdSiRSlQDT6rBXu2zTOiamYJ4+0MVrzCGLJX726nLGqR3tao6Sk4EIlaBxzcwyttaC6OLAN16h8JqIrEzEVKP5+xxLKQWJE4UhIOQLRkNsMY0tkFI4QrSoUgJSSfSNltq74MPBNRsvH2NId8OpAmsm6QzVSCTteGrb1aFJOGrCcmByjHGapRHM1ySG5v/eMJhaGs+eik+VIU3lM4kdVyv7EHLbyO7HPvIi3Dpf1HRDEmp0DE7x47CRlP6YUzDjSLARfW/rHqxwer/LJv3mSB7atPGP5emi6xHSQ9KSsNYzVXKZ9pxVipEiIOa60jZJrsp3hisBaSU8uYqSmmA4MaSdEW4WxTfbqqTaByQzp+bCG3wpkvRS3rutqWQ66SjVmj6GQMo2xpKR8nXWb3rtJdhWbpH/c9MlN7PbOnmEJLAZFznO4ae0SHCV48ViJSqho8+YLdS6kI12PKkxWh6gGdd4Y7ERb8CNBfMr9YKxojV8l7WqLbgz22AV6padmiAaJryGFwbVQqsPXdlheHwjxY8PWHsXG7jy//6F76S3MLRk2RSW++PS+ltBJEJXYO16mEnj8U98yHClIOYZ1HRHXrXD5wFbQxifSfqs3uOvEkwyVDvHRK6eZqo0y6RtePlmgfyKDH7lkXMvGzhqVQPGFpzsZrrj4sSLXsG70I4fxuktsJEvylsgkRKnmQkQ1jlo0zkrzKl0YLCnH0JGJGa+7iVtSSiNl8jRFOik0pxzZWNgJfJ1w040xc4JyrDW7Bqe5b9snCWOfu7eO89/99Q84XkoWu0pa8p6lFhq+vQ/u2pol7fikVCYhhp1KvhPJs1uN1Lz56KZ/dvMYwjhpLZ1uhMraZJHf5K0U0woIFgP2ZYLL8grP7hm/ePQEA9MxUjRl+2av6hd+iRjgqUOjrZftp2/d2FrlAq2fl7YVybgxpUT2uSUj2DQrb2Zds+drk9/DVM3BWKjHqiHob8BapHDRVjTMGRp7KUAJwX1bVrwt2Naff+B+ICmxhrFDezomjJOX07FSKhmZEbCpq0ohFVOP1JzjMec6tDoLStKQz0z+7joKx/GITchsYzdjLbev655Xrj46tpMwrlMOk+w8NkkwPlOeN1cG9PzOe1KWh0BbolBSixVpB2Jj2TNc5k+efJrf/sBD8/5fs6IjheDQ+DSHxyvJQs4KLJKca4iN4thU3FhMWD565YyFn5IOV626G89JM1Q6TNot0JXz6c4MgQioBIpnjnk8sr+D6YaWtbXgSItuVAW6shGOhFrkMFzR1CLZKHtbrAVPJqIknkNDzNJSDi/MXq8Z7D0nxpGS9nSiXZ51EkUzKS2RAcdAPaJBRGt0vy2gDY5IMmRHKa5d0dkQ5qmytNCGEJINXTGh1ihhE7U36bBjCKb9MmnXZXtPnf4Jj9nvBWuTEnreS8ao5u+3bfWkHeXRnu0BTj9CVffT/M5jLq8cG2GkMklaBWztjfjYlR5Li2tY030VGTe/GJzfpbgsr+qM+5ClHOiGEP5Cnzz96r4SxsSx5ps7d1N0nqctFQKJWILnZHFVmh8d6mCoLBmppoh0MwM+l0xBUDeKejWhxOS9GEHyUnRlMkoze++she6s5LN3X3nO5+CtxKkl1q5cnof+779jz4hqZSA5N+kDPnGkI6kSCNAY9BwBjHMteSY+v66SRNqgZLKNFcUOVngRR2qWsm8opCW3r+tuLBhmoE3MyPQx0m6WXFwm55o5X31xi68zW1XSRUdxi1fcxKl+ybMxUQuYqAccnRhnuOwTxqLFQQBBPZJkPUMlVFhr2TkEP3/T2jkvcCkkW5bdjLGawVI/1iR2hUJEvHAiwxOHi5RDp6WEZkgWKFJCPZJ4jkMxLRivQsUmJVxHWEIjcJXBURDFyX/OpzWdmYgD4zKRPH0TmXKz7H/76knet2GKv35tWWvhtaY94FgJqpHCGEGkk5zcGMWpz29swTGGrUuLfOYbL7aEeVJK4EeGTd0uOc+ZFUQFZd8wWYvpzC3lI9tH6BurcWA8i7YN8ZJUcnybu2rzrDrnXm3JsuJGPCfdao/M9jHWBr6zT/LtvTUm/R1Iocm6MQXXMF5zmayVeHDTE/QNvkhnfum7SpBkETO47AJyM8NQUlALQiKtiYzlzBnO3D5m8oK2vHbyBJGB45MRxYxmc1fIT22qk01pHtnn8MLxUYqpkCBSDFXmrqzPDQIpDMZKxmouXdkIV0FsRTJOg0VIQTEluX39cmJjCeIzi9cvhLeqj9UssZb9KtYa1nWYRsCckV7cO5oj62hK0mDime8WLeGPM0NgG+MwABHOrHpiZybNL23bwjXXXn3GcxLENcK4Ti6VmLGvaQ/ZM5JNWgr21HD55jBbrWpmSCr5M/Hwnfv5hfrckAiuSMpM1AICPb8krhEtApa2gthk6S5eO29/+gaf58jYASqhpJBK4ak000GNPaMZqpHTah0IIRA22fdYg1YCYxVLcgatY6ZDhbWQcgwpJ7lmxgqUsOS8uCVZ2ZWNmao7LZ3vZqA/O5Kz35aO+Mj2hI+R9TSRnhmvWtsREJtkxMyTgt0j2dZ5PvXabV3SxvruwhxhHm0tlVDSP2HZ1D3DsoakvbGivZM4Xk+kAz5z03G+eyDD3rEcoZYN2dBEPWy+o5TAkR6ek6KnsKZlNdi832Y/a9/ZB88c1UwFCjCUA8GU3+SoWPaMZOkbtVy1JOAT7wneVYIki5jBZReQkwyjjrCaSlAGm/Qfzw4776d6lLz4PMcQxII3hnIA3Lexxo6hDrQJsBja0pbhqneK1OPZIQFHASKZFW1LaSyC9kyKzpxHV9Yl73m4jqLkR/zaN7/LjoFJSr6mmFbcuoCr0Jwjspa9A8++5VKAxydHqIbJWMqpCpaRFrhOkqElrkYzYVif5WQpYVvs4EALRBixZ+gE6zoLZL127tjSi6eqZxX0n224kE938sn3aF4ZMAyVaQhcgBIGbRLmXVcmIjaWCf/c+/UWi8S2RpuSsqjGldCdt40xsRks1OeGpEqysljl2SMWYxYuqcdGkHYg62VY3raE7txcwYowDvnSs/28OqAYrwm6spZrlq5ga/cA5UCiTXOeu9ErFzOa4UIkBERr4f5NEbuHIyphk7CXlOFjA2lHs6mrSv9k4sy0rOCzJA+HJ7LUY9VwiTo1IJ9K4LMNkRfD+s56Q2ubliPVqQz67T1VXh9qaxlTzHxDsl0l4I8+ciM/99Xn5vSUhRB4jqAcJLwFp/FPs9sbW5bdTBj7jJVPcv+mKe5ZP2NwoaSd5yi1pcvnV+58D+t6trdkVZto3m+1MBqAg+4AACAASURBVKIcQNqBnUOCSGvqUXJu5i5GBdoqDo5n8GOFlHV+9j05hkqH2bz0vYvl63cRLqsrGZuY1458g/7RgClfEpukX3hmnI7laxIlIE8Dlkgn+rd9YxmuXx4wWQtnzTbOkLj0GUhjC0GJpLxbjSSREXjK0p33WNfZNoftebI0yVTNx5ECz5HUY8tj+0eBRxfsRQJMxAeZnKi95VKAqzp6yXvNtsDc+eKcp1meC9g/mm9IZM4YDMg5YzNzr4NkhkikhEVhcCVM1sErVXj42gyfvnUzr7/22ln3r2ns0Ozp5VLJKNJTh2NGq4ZqKBoZosFTlqWFkJHKufVElUgKysYKHGVRjZnepKwa0ZvXtKU0sXFwZLLN0/W5IcmuPrjF57E+GGztwynVG5sExDD2uW199zxewR8/uZu/fW3muA6NC/YOKz525XLa03XGarKRXTfOdfNyWUtaRUg0N67O8nPv3cYf/fhl3hjKNoKjbRDyBFt7arx/wySRnuvM9MXnV7BvNI/jQi2ar0U+G4KkBN6ejvFjxeOHOnj/hsmW3WLThjHrGjZ3VblzbZmDE4UFr4Mg2a/pIKIaxriniFXnPY+SHzRG1Oa3N6SQXLnyTiaqJxmvDFEPp/GUwWL5YX9Ha4GQOEo57Bpp4593h/zOB5cvcA0t/9/uIi+fGKMcSFKO4ehkjBDJbPPpKkOVyOFYSTBSgZRr+dBWnyCuLc4Lv4twWQXkp/q+xtffGCPSbWgrkSIpF4ZnJBGdLoAKAm0p+5JqmEY3eqMZRzeUjqJGzzhxp3GUIdLylCBzZgiREGnqsUSQlKaXFhzy6fycYBzFmigOcVJzt3umXqQ2MVUzRkHM9TdtjslczJV3IZ3jhlVZnjxcm5O7GGvZ3hPjqg6kFGCazYDmCJQg1HYWZ3cGjkhe/q6T5ENKWlYVQ6yFYibF7WsmOHdLA1oKS0Olw4Sxz4e3p2jL9PLKSZ9jk5NoHXJFb5lcSrJvNBl/OdNIlhRJvz/lJAuHnJvMMxczCSEqkbmUuDKFqyzWhtRDS3s2tWCfu4mUkyWfcrlr3STf2ldE22Q0S1uJsYkys6MMK9tirl8u+bkbV8z5/0Gs+ZOn+6mEyb3ZjLXlEP55j+TDWxXDlYYaWDzjUewpw3UrLJ9+L7RnXTwVcXT0Dd63bgpj9azgONejOKVEK3BFOiHdtWcSQQ4lGtd2Vvm6uT+yUSuJjKAcONTjZLa7KZl559oprllaAaCY1mRcB2sl23un2TOSQlt1yjIFunNpbl3XS85zCPUp94YQ9OSzfOsX7qcW1hdsbyjpsLS4IXnWpKIeVQnC5NibftwCgRQKR3k8c2Tus9ckk/7VCwfYPzpNbBTG2kYQbi6uzlQWSp6D0MALxwWe8vipqy7diOMiLj4um4Acxj4DU8foG2unJx9DNekbIURSjrTnW6IVREYSGYknLWk36TfWIsWOoTY2d1XZMZxvecR2pBMvXyETkQN9lqCcZDqJoJ+1go6swHU8hEhTSLkIBJP1gI5Mim0rCoxMn4QFpm5P14sM4hqxDed9vnmuLvbK+w/+xcP8L//8NV48VqEaWbKuZXtPxEeuKPKHT8uZl2RzNrvBeu/OpQjixA3K2uR3WkN7JmH3Nhcmec80So2CeiSYqAYEce2c928hycDbNxs+8pffYqTi4seSsVqKDZ11PnFVib942WFwGmqxXPDecaXFa5B8pIB6nFCT2jMxUliksggMjkperP/uNoWvJR+46mMU0rnT7qeSDsuKG3hg8xP0jbnsH882yv2ajKPpyEZs66nx8FWS7kIbWW9mW2Hs8/qJ44xV/AVL3dO+4CNXbmJJ0efrO8YZr1g8R9CWinhgk+CjV4rWIs1YTdkfJeWmuG9j+bQexUmZW4CVlHzFdODQnY3pyRlGq4JKqNDGUI1kqwWR/L+5s/pNyczvH+gk55k55eGregV3rhvB2Ij7NkxyYDTDjuE8sU1IlEpAV87js/dspyef4eY13a0echPGGG5e38vStgKwcJYNcxdu6ajGhEjkYbOeN4cMBvOfvS89u59v7z5B/3iFWCes8LlM/VN/ng9J0h7wpODAeO60zlSLeGfisgnIZX+CUl1TbRi+d2c1nZkkUzHWsm8sx5shXQENM/akyVZMWw5N5vjUNUNY4MB4llokWd7ms6ItCSiVwKEWSsqhwiCpR83sxjRylsbLiIR9KZShN+eQdvPkUx0IBH/+8M1Uw5jObAptYp48uI96PH91fbpeZMrJ4oiFhU08Z2ZM5mIh5Xj88c98ilK9zHP9T2LNGFLElEOP46VE0CPrauqRxjQCrxKC7nya7qxDPaokLx8BY1WYDmSjDZCoS3XnkvlPKRU5FzpzqTd1DLMlAz/459/gxeNVpABPqWR2dCzH13e7LGvLEGoQdUMQ20Ym2RhvEYk4xWzoRvav5HwuQiUUBFrSkdZz2hynw7YVtzFSPsav3HKI7+z32TuaIdKJV/Hmrjof3iopZoosLa5LWhEm5qm+rzEyfYQ9wzGxWdvYkphTabFW0FO8gt+9Zgm/fn/A0ckJjNYcHvkeaXfuAcUmQluNahhPnM6j2GKwVvGjQx3sGckwOJ3CUZJiWrCiTTBStUzUZtjQzft+NpqdciXglYE2imndYNVDpBWvDEoCneP+TRWsNfybmwf5wcF2do7kqYcO25dv4P4tK1ra5X/zydta9qe1MCbrOdy8vpe/+eRtZz33py7ctFF8+aX/1hLCmY3Zz16TTBoaQxAnRFJ7nnRBJQxCQN41pL0C2hYuuRDQIi4uLpuAXEh3Uswosl5M0HhvSAFCWobPsR94OhgEGEVvm8fmnm4CHZNPG+7dcJC7101TDSU5L3nZzmZiApT8ZN74lYEC+8dzDJU96pEirTSVyEkINEgqUYElxUR9abIeUA3jWQ+imiPG0dqvM/QilXTIyW6src0Zv7DWtF7kbwWKmQIPXPmBFrM7iB3i73wXKQ1ZT5JxHbRplKOlpDefJue5CAFBVMNYw9KCZElbnit6PV49OQbESalQKqRwuXKJZnXnpgs6hslabZ4DkxASJeDQRJrP3nklPz48zkvHBqnZpPypsAiZCLQEsSDlzOShUliK6YhTRSW0sbgyIfac60JICsldW/8leweeobfYTy0IODkxTmfBo5huJ+3lWFpc18rmnur7GkNT/QRxwJ6RNrRNisLAnOinBHzjjZe5ftVPkfVSbFuyLHEom04Ib7PhSBclVKOSMz+wiEZzBuCxg0WePNJONVKERlKPJX5sMdawok3SlXM4PJ7Ycy4UonwtyQpDLhVTClzaUmbOjgss+8ez3LchwFGJFvcHttZ5YEtA2Rf8i+seYnn7SiC5rn0j43zp4zeipMOh8TLruwrn7LDWOlezFm7n8uw1x9WaxM4zB2OLJCE6NqsvShhyrqGQNqwsehTSneRc95ILAS3i4uKyCciek2ZZcQWbOodapWRIrOdK/oWfBl+LltNNVybLQ9d8gu/u+GOCqEoxHSOEIOUUSDsGV1Vpdsu6G6bpD26e4K5wir94ZTm28RAeKymsFQghmaprtLEoKRb08Z0txnGmmdvZ6HQ2kusMW31Tz0nPeZG/lWi+0Ep+jaznENSCxiyswFECay2FjEs+5WKBfLqDXKodYzVSKHKuyx//9C385fN9PLJnLxM1n6yjuWa54Bdv3nzBx9A3Mk4Q2QXLgUEMt23owvFcXjkxSNZNdMmL6YRQNVkTaJvUOpJM37K5u8rGrhq7RwoNNrJlrOZQjwS5lOCPnrbcvr6DO7ecW/1RCskVK+5g67JbCOIau3fs47rrrps3vhbGPqPlYwn56FAbzx7vnBUq56ItZXnq0Bi/9b0ZIuBswhuANgmJUQpJW6aXqj/flxga41IotHZ46eQyykEi5JFSlhBNpCUjFViWr3HLqhy3rengi89OsVCVShsopqHNsy1G96xvAiy1SFHyNW3pmIRclvRyu3JpuvPd+FHIR7/8LV4bqOFHlrQreE9DmjTtnl8wPhXn8ux1ZpNnthxEuFI0WOYLQwFZz7Kus8KKfEjK0ewbK5ByJCknTdrNYSznbB+6iHcOLpuADHDj+g8yMPWn2MaKuhoq6pHEVYZAL7w6P1dYLOMVn1dPTPDr911NWzrH1Svv5tj4brTVONJFCoXWMX5coxJMEOkAKSSek6aYWcLB0UFiLVujQXlPUw7cRtnREmkDyAUfxFPFOM5lDlkIccmt1jqzKa5e3s7e4RIT1ZDIGFwp6cyn2NZb5Ja1PTy2fxAlk2CthNPyMs56Lr9y55X861u3MVqpkfNi2tKFi3IMW3q7SLuCaIHZq5Qr2L60m2Vtaf72RZ2oP0nRmoe2JqIcKla0CfKeZWPnJO/fOI61Sb9431iaY1NJJaQ9A0sLAiELvHoyxZee3X9ejl3NhY0UakGHnrI/QaxDamHMk0faGau5s6hcM8HPkYb1nWJBIuCmpe9lcOoQg6WDxDpEIEh7ebYuv5Wx6eMMTO2fY2gvhcRai5IO03GGsapulcabFo6eNRgsH79ygmWFESbC1TgqGbmyrSwyydqthRXtHZggoCMzdUqVITmOrBuTdkNmK2NpE1FId+M5aR780jd4/li1oakOkbY8f6zKR7/8Lb776Z8+5/O9EM7l2Us5ijvW9/LI3pMsK6Tpn6iednudmZDb15V4YOMUnrLE1pI9BEdLS6gEGZTKcG1Phvs2LyGI9WJQfhfhsgrIWa+Npe2reXDTEe5cO8Vg2ePvdyxhuJLGU7ZhaHAqZl5csvHzqdmFnPWy8WPNz163FpjP3FXKbXmmhrHPrhNPMF0fJdIRnpPmjo3v5zsHBinVq1gsK4sO4zWXapgI3hdTHndvWnJaH9/kGM88c7sQLqXVWspR3Ll+CZUgZnVHjkgbXJVoWt+5ITlWV0me6h9pkdju2NA75xykHMXK9tMTcd4MOrJZ3rM823qJN9F0YOrIZkk5io6MmtO7F0KwpADLhM8v35qmKysII0nZVwgpuH9TmTvWVvnSS0vIuHny6SJSqJbe8VP9IxfVsauQ7kRJl+8dzDFe87BWtNydmmsNiSWlmrrTYh4Z6cDQi5Tqw0ghESJNNXIQIqZ/+BW2LruZWzd9jO+8/kUqQTNbFiipkCiK6Z5ZvPkZCJFkgkoahBRk5QBt6VVEcaOYayE0lkg3jEnENNuWVthkq+wYzrXGrCCRG13fGSBamuKWKJbEtsBSr4Ph6QlePVmd1y6QAl4dqDFZq9GRvfA+7NmeveY9+7hSHJuqNQSJ5sKRhnvWT/DAlgopJwnqUhscmWfHoEM5DACf546O8SdPHWRTt8unb7uGX75969vCC30RF4bLKiA3GaqjpWO4CpYVItKuaYx1JH7ETYMHAE8aVhd9hqse1VCRcQ3aJKMYTQJPxgHPoaFslIgjHJ2s0pFNn9HsO+1muWHdg/NUsj505T6+s+cEQhikUHQXEjnAO9b38tm7r3hXroabL6qn+keYjAPyrtsKukpKfvn2rfzCzZuYqAV0ZlM/sXPwTz//IT765W/x6kCNIJrrwATzjTSaEMLlptUpVhYlYexTzPZQzPZijCaI6zhOFk+lKWa65lVoJ+vBRSXqeE6afGY5R6equMrOK5UKaHgJW5zGC302GUmbmMGpfuphnUf6CuwcTmQjiynDlp6QQrqfzUvfy0eu//c8ue/vGS0fI9YRrpOip7Ca7SvupeOH32Sy7jJn6thCRyaiI6MBScqJuHqpx8sngqTSICyesDgCNvdE/OpdivHyIDT0yvvGMlRD2Rjfgn0jOQams2zuDrAWDo6nCUyWjKqzrPjP1KPEglHAHM5EEFn6Rsa5ee1bT4yafS//wY928eXndlAKYrRJFgcZx3DP+kke2lJFSa9hTCF58mg3/7i7QC2a2zs3wP6xkP/y5OutbS/inY3LKiADbFt+G8OlI5yY6CPlWLZ11zg6mcWS9LdMQyDfkZa0Y8h4hrWez4lSqjGSkQzyVyNBxpEoOTOmA4Ks57C+a262dqYM9NR/mxOcmhnhlt4zukq903EuQTflqJ84mzTtenz30z/dIgJt6e2al0mdqX8oBXMWW83FF6T4xp4XqEXzWckL8QMuFFes/AhR/I8UUho/TjyahQBpGwaBAgqpZG59NhkpiDUnpyYYq1T53KO9DJZTGJIyctbVVCNIuwG3bkpG5N63/VOEsU/Zn8BziuwfqfL9viluXml4+mgibhMbWlrmt60u4aqknzrtp/nDj9zNZ7/5FK8P1vFDg6cMm5dqPne3gxQRFoMSkgc217h3Y51v7yuwd9RrjbuFWvGj/jYslu5cjLUBVWPZP2owVqEaubq1pqVEl3IFW3q7Lur5PhtSjuJz915FR8bjW7v2MFyuUkhprl2ueGDFMtatuYWh0iH8qIaUWfonstSjGgvNKFsEY1Wfxw8MXnIv9EVcOC67gCyF5O5tn+THe/+W4ekjfGCrz6HJOvvHshiSQGxtMkea8zRSJGXKBzePc8uqKSZqGVYUFX/5Sg97Rua+OAWCW9b0nDdjczYuZUZ4qXEpgu65oCObPW0Gdbb+4ezF1uzFV7OfqGbVUZu98dnXu2nDdyH3wbK2Nla0d5GuTSMFjFVtS6HOlYaleUtHNmGF376um1+79x6+8MTrvHB0islayGvH00z6ojVTb4FKqDg2laKQ1sDM8Srp8V9fnuDzjzyO3xLfyJJxYrb1BFRCyKditnZXef+GKo8e6OCJI3lKfhrviSdZ3pbhM7etoui8zuqOxOxhojpAENWxxI0RQ8B6HJ1yWzKXALFJOBd+LCnVE7UxVyXB35WJ9GlC0rMYqzEGtvbEHBt7nvyK23HVhZG7zgdKSj5zxzZ+8ZbNc/gPr7/2BttXXM+WZTcRxDUmaoJS8ASW08/UR1owUCotjkC9C3DZBWRoBuV/xd6TzzBY6ud/f3/MI30Oe0Y8pusBE3UfiyXvaVyVKA+9b32JjJtlfc8SBILPvz/Ff/ox7BlJytxZV3H7uuXnNMt4Lni7BqdFLIzz7d0vWAmZ1RufbRHaDMh3rO/lF29eT2z8eQS82ISMVwbm6CbPboc8uH0b//jGbla01VmaN0RGoqTLJ96znU/ftoWh6RK9hQLHx1/j17/9jzx3VOMohRJpymHDUcraOeSsWqQo1y0TtbC1CPnSs/v53x55nfAUMlw9djgy5fCHH6yCKSFExGMH2vnRoTaqUWLAAHCyVOOrrw9y38Ys25dqxisn8aNqQ1wk2ZYxEdO+pB45KGmwFiIN43WX6bDBLm+sBXwN5dABLAU3JowlsiHasrazzn9/7TA7jh/jyPhONi+58SfuoHQ6/kNz8aakZmUxy2snF2azQ9JuyHvh4gjUuwCXZUCGxtjIyjvYujwZG3ngqiyxEUzUArKu5gcv/wOhp4jiAVwlSLtd5FOdLfKNkpJH/8efZqJW4WTJsqmneEGZ8SIuL5ytEtK0CFVSkHYVtTDm66/vom/wBe7bWCaXKrCsfRMbltzAM/u/zsngAP07BY7y6M6voqdtFcOlw8QmIu1muXvdWuAKnjo0zETdpzOT5s4NS1utkPXdvewdeJZDo/t4Y0ChZMKULvlVtE1EWAwCMSsoG5scR85LSu9BrPnu7oF5wbiJqTq8d/1H2NDdQy0M+Ic9T6Ep4816CwkhmKpH7BvJcu+GCYK41nrmEvezZL/SbkDONQyU05RDSy0SRPpMgVRQjhwUhhSGXFoTGcmXX1nCmnafBzdNcNzdC7y9HJRSjuK+Lct5/OAglXD+rJTAUkhp7t2y9rKppL2bcdkG5CZmlxGVpJWVrs1ey+Yr1vFU3z/gKm8OEQSS+c7YhCxt62bporb7It4kFqqEzLYIbaLsT1Dxx3nxeMzVvYN4zhAj08fYN/AifjSNxSKlwpiY4xN7OD6xF89JIYXCd7NEccD7N2zlF2+5Y8EFQNOjtxJKyqFo2UGm3MS8Q8+zoUx0s9+72qEtnWR4E7WAY6XyaY/VAK+erLO516EShpR8QWzmO13FxuLrHI6TxtrDLX6GQOKpNNYaLD5Il0lfgdVocy5eywKNoqYVUVUz7RukEuwby/HyyTYe2gofveLQ285B6dO3biaKNb/+yIv4euaaCSwrCj53rJ3ms/fccAn3cBEXC2+fu+5tiKzXRjZVmKdSBG+NvOQiFgEzqk7pxkC6tZayP4GxiX53LVJ4jsaPa/hxlbQzo1cd6XCW7IfAYvDDCtZaTkzuZ0PvdQu2QpoevYWUQ8GzBI0esyMFOS/x5xW2qTWdzDGvKsb88m1bW8GrM5tidXuB3cPTCx6XBG5f19v6bG8+zcGxMvqU8R9HCnpyGW7beAPf3fEG2iRKbFHczMQjHj3Yzv5RSagNxsjzdlGLjCIyioLQKAGlwOHF4xKIWiS1twuUlPxPd1/Bp967ki/96A8Zqko6M5pCytCRtjx8y6+9awmflxsuKCCPjY3x4IMP8qd/+qfcdNNN7Nu3j9/4jd8AYMuWLfzmb/7mxdjHS4ZTbfmaeKvlJRdxeaOp6lQNowb5SGOsJhHAMOQ805KrjLSgGoAjwEQKVxqaia+1BhCJs1d1koFShUh/lXXdm+f1Smd7Ql+11PLiCdGa293cregfF9SjGAE4SrO1x/BnH9vC9hW3zNqG4sHty/nhwYXL1svaMqxsz7c+e9eGJewZnmKsErTK0tZaOrIp7t60hEI6R09hDUNT/S2lz9hE/PBQgTeG2ogMpB2LMZZ6LFv2neeDIBak3abjkmDPiGI2Se3thK5skV//4G8wURni+MQ+VnVupTO/9FLv1iIuIi4oovzBH/wBq1atav39d3/3d/nc5z7H1VdfzWc/+1meeOIJ7rrrrgveyUuJU8U9fpLykou4POEqwaauCj84MJ6UY60liJOe7bbuKkomv/tBfyf7RhP980ArPGVZ2VZna0+Ve9ZPEpsQY+CHh9oa7kgObak6Vyx9nX97u+GqVbe3vnP24vMDWxOBjZ1DgkoIPfk8//HG7Tx87Sr6RkZY25llaVvXggvST9+6GT+O+fwjb8xiWcPytgx7fvWD8z5rrOWvX+rnZKkGFlYUs/zceze0yG13bHmYp/q+1pD/DAkjy4HxPClH4siGTaSYUfc6X2grGqpiFkFErIuUfE32bUwH6cwvXQzE71K86YD83HPPkcvl2Lw5eXDCMOTkyZNcffXVANxzzz0899xz7/iAfCZxj0Us4q1A3+Dz3LZqiImK4Lt9huGKJDYZlEwywdhC31iOI5MZ/FgSG9nonlqm/ByD5RSxEdy8qsILJ/LsGck3HKsg0JKXT8CfPr2bP3v45jn38uzF5we2+nxgW4qMt5ob191Cxk0MWLrz686470pKPnv3lXzm9m3sGhhn70iZuzcuaWXGp372396xjV+6ZTOD03XAsqwtO6e37UiHe7Z9kjD2efbVH3IsPtaQu01cj0qBQ6g5Z4/xU6GtwFgoekkve1l79yJbeRGXDG8qsoRhyBe/+EX+7M/+jN/7vd8DYHJykra2mb5LV1cXo6OjF2cv3wa4lPKSi3h7I4g1RybKlOoxW5e0XRDbvkmucpQkMjUi4+FI0fIYHqmk+G+7ezGneF83fXVDrRisSL6xu5dXTuaZqKfIuIaeXNxqu0gBO4YMk7UpuvPdrW1czMVnylFcv7qX61effRQs5SjWds4P2LPhOWlysofetnGyXoUgFnRmNQbLcOXNn28BFNOGZW2JkcvNazsW2cqLuGQ469P29a9/na9//etzfnfnnXfy8Y9/fE4APhULWbIthF27dp3T5y4FXnnllUu9C285Fo/xzUMby9f2j/GVvRNM+rqh9gbX9Tr89q2ryXrp895mZOuMBcPEWvL6QCLZKhvuRkEsiLQ4h2wwkXY9XsoghGhl0N25mbGZaih49tUdrMjN98p+u0IKRW26xMZOn50NPev2dMyU7+BHEiUMsT0XtjV4UmMtSCkoeImv8xXdhlszwSV/Ji719/8ksHiMC+OsAfnjH/84H//4x+f87hOf+ATGGP7u7/6OY8eOsWPHDr7whS8wNTXV+szw8DC9vWdfHV955ZWkUm+/EtErr7zC9ddff6l34y3F4jFeGL749D7+fv8hJgINIvEn8jW8MBTwPz/zKp+9o40PvecT5M7iujUb2sT4+48yWKpTCXSjR5oEmPA8Hckik/g3h0AlUnRZ3RqlKqYV995821kdwd5OeOnlF2lv7+Aj24aRokbfWIZQC1LK4imDsZIwhqCx7mi6PjvSJNfHCmIjUNKQ8SwFz7I0H/LzNwasKLaxvucKti2/6VIe4uIz+S7BmY4xCILTJqJvqh711a9+tfXzr/3ar/HRj36UrVu3sn79el5++WVuuOEGHn30UT71qU+9mc0vYhFvewSx5od9Q0zVI2bMAhPERrBjuMBv/ajGn7/0D/z0tTeesxZ5k1xV8fdQTBuGqxZjk+z4fElLUoAQhkgnmuuxsQhhsDbxAk43+sLvFGhCIu3TnuviX11n8CNNObA8eRiePx4yUVeUkRgE2ggyjqE9EyOEJYwlfqzIeZpiKsZRSYn+mmWGTT3dLC2uXyRqLuKS46Kykz73uc/x+c9/HmMM11xzDbfe+vZRvFnEIi4mJmoBg+Uq2s514LG2QTCyYK1gql7hO3uOA5yzG08zMFy9bCcnpjXTvkNszp+0ZCwIJAYIY8F4zWFVMeTqZZaHtkT0DT6/oCrVqQ5kC+FcPnOxofBao1lCSDKeJOPBXWtPUAs9DoznKacEnjJs6qpyy+oKOTfRECiHipdPtHF4Mo8fe7RnXD589bX8wk1ryXq5RaLmIt4WuOC78Pd///dbP2/cuJGvfOUrF7rJRSzibY/ObIplbTnkwARNXYtmMBbQYDUnYz/Ghuflc9wkV33hY9egv/HXPLZfUCvPzq7n+wsvDAENC8OebEQxHbOqWGdt67pD6wAAIABJREFU0WeiFpIqzVWlMtbQN/g8w9OHCaI6KTfDkrZ1c2aW/ajGjuOPM1UbxlhN2s3O+8xbFaylUPN0AYyJiXSd+zeF3LvRZ9q3ZNwIJRMREU9lwUJnNs223iIGl3IA25Zu5JrV11y0fVvEIi4GFpeFi1jEm0DKUbx/81KeOzLMWNXn1Gp0ztUNZ6EkS30zPsdCaH7mSpc710zxvz5aZKzqos+RtAQghW3tS3cu4thUmv6JDN87YEg7sKW7xnfWVGjPtgPJuFUz2CnpEOuIExP7ANi09L08ue+rnJjch7FxY/sOhXQnURwASWZ/toB+oThVF8CS+Bs7qjmWJbDWA1yMsdyx5WGm6yOMTB8jjH3STor1PYs6Aot4e2IxIC9iEW8Sn751M7Ex/Najz1H2k7EZ2RD7X93uNz4lEELSkT5/n+OUkyXUdR4/pJKsW4Ke7y8AgCMMKce2StuRFmRcQ1vK0J0NODqVphIm2bmSAm1h14jLz//DK/zNz96IIz2GSofnabYLIRkqHWZw6iAnJvZjmPFwNjam7I8jhGCodBhjNQOTBxYM6BfLsOHU0SyB5Fuv/0lrkZDsswAEruOwpG0Nqzq3sHXZT77EvohFnC8W78xFLOJNQknJv7tzO5+6YQV//sQXqEWCvtEsfeP5luxk4rGr5vkcnyv8CJ44nKcWqaQvfcq/C2zyXUKQ92J68yHGwonpNGuKESnHox4JapFqKVo182sp4On+E3x/52EKaYdSfYy2dBeJX7BBCokQEj+sUqqPYDGJVGekEo9hlfgK+1GVlFPh+PheQKAkrcAuhOTYxCEK2avoyWcv2ozvbF2AnsJqhqb6EbPMOKyx9LSvbllRvlt0BC5F734RPzksXtFFLOIC0Zkt8uGr72f3yWfY2FklfUhyYDxHPXbpzHXwge0rW1KQ54MgrvHI/hzjdZn0pB2LjhLnJYFFAkqZxOnIQi2SxFogpWVTZxVLgciAH0t0gwjuKsNsc6UgFgxVJBkvIghrTMQ+ViQ62EJI0k6WlFsg1BE/6G9n/3iGWqjIeolP+D3rJwljn7HKSbSJEEiUVOS8Ihmvk0f6BDuGYhznGbpyaW5a085nbtuO51w8bcrZ8pqxDnGUR0/7au7Y8vBF+45LCW1ixqvjvH70ZSIzjEKTctN05lawfcXtjUXfIt4NWAzIi3jX4FJmD1uX34IQgsGpfh4u1JAySza1Zo7s5PkjxeEJB0dqjJWNMSYamXJC2PIkhBhiLamGimOlFMV0hJvV1GKPEyVNoNOYRjndWoExtmnYhBKacm2YEScCGxKaGE9lkFIClmpQxg9rPHYwyxtDeawVKGmJtGLncGK7+P4NkyjhNvrEFm0iKuEU3z2QYcdQHiUEQlQ4MTnKkbEjHBvbyb++ZcNF6y3Pltcs+xMU0p2tzPidDGMNO489xRef3c2OQUM5kHiOYUtnjVtWV2nPHOLo+C42LbnhovbpF3HpsBiQF8C0H/LSYIUTO45x6/oeevKZS71LlxVmB1bgrEH2TOzgc/mOixG83wrN85KviW2GvDtNKZAIAY4EbZLStZIWKQUYi8SilEUJGK+lGKmkcVWEpySeoyj7Go0gMhA1nI0AJIbfe3IJHZmY21ZP8r4Nk4TaR1kXV6WwREwHIU8f7aUUOGiTlLozjqYnF7F/PMvda8sokajzNa0f/Uizc1AghAEUQVwFBFIKXjsZc3B4F2Hsc+XKOy/a4slz0nTll1+UbV1qGGv48d6v8F9fGuSNoRwT9RSVQOHHkt1Deb53ULO5u8bmroD7Nu1kul7lhnX3LJax3+FYvHqzEMYxH/nLH/KDAyON18oxBHD1snae+ZWfIuMtlobeSjQD61Cpn1pYJdaJLZ+nMqS9+eM1TZyJHXyqld65jPZcCC5mr7Izm2JlxxKsCaAUUA4VjrTEUmIMeFIgaTgTqiT7tSSGCQhBPU4CdyHlkfXqVEMzTwM70b8WTNZdfny4EyGSjNcYjeulCOMaj/V3MFHzEMISadBGUg0UpcChmI4phYJOFSVZeuSSdWOqkaQSCbqyWSIdYIEormOsoWIs/eMDTFaPMlw6woqOTWxbcdtihjcLewee4cjYAfrGuxmvuYzXHUItaTIAyqFg70iO/WM5fnRYs7q4n2uW9fOZ26/kipW3Lp7LdygWA3IDZb/O8v/4j9TiubQZC7wxOMmt/+V7vPYfPnxpdu4dhDebeWoTs/P4ExwafY1Q+9SCkHIIhRR0ZIo4yl2Qsds0YzgdOzhvN835/ZmC98ViAl8spBzFneuX8Igfk/ImqYc1Im1QUtKRyeC57UzWAg6MlqkEPilPYK3EWFqz0X5sEQQ40iKEIOcY6nEyK93sJcdG4ilDLVbsG8tx59opXKWp+BOEGo5OZXCUpR4lnxWN0nlsJPVQ8dKJAo60DYvHpL+8oaNGzjVkvAK1yjSxaSqaQdbV5NzEGGKwdJCJ6gAj5WPctfVfLgYSknt6YOog06GlEigmag6hOZUMJ6jFyfNVjyU92YiXTsCfPL2Df3+XfNvdy4s4NywG5Aau/c/zg/Fs7BqcZLRSXyxfnwZ+VGPXiSeYrv//7b1pnBzVee//PedU9To9+4w0WtFISEICITazCAnJgAmYa+NFhuTiODf29cY1IeYfbAMX8HWST+I4TuyL/XcSlMQfBwcQJI4TE+OwCyMwCFsgtAwaCTSa0ezTMz291Xbui+rpmdEKQmIWne8baaqrq8/pqq6nnuc8z+/pxfWLWNImEa1ixdz3k4we2WMc9Yr3sL+/BS/weHJPNS19VeWb+5L6Ar+zMsBSoZEdK2ZR9HI4Xv6wxt/xCvg45b+PZbzHHneyMJIMtqnVpj9foCqmWLOwiS+uWooXaA4M5fhfj7zIc3vaEQiKfugpj8XxddlAC3F4SREN+IEgU1RkXUW18tAEZF2LvCtJ2B6ZYqRsxEf6D8dtnxfaqkhFfSwJtgLXV2zvSRFVmv5cN4H2GTHGgYbFdbly9ypNgNYBXYN72dHxS5bPXn3iv8QpRnhNF0jaPpYKKB5ijMfjBZK30jEW1jps65Ts62+dlNey4diYMwa0pwdoGww4mvpRgOCXe/Zz3YrTj7jPqYgXeGza9SAd6Tdw/SKl1CEoBf1bOn9FdWImV5/9eSKHyQbd0f5L2vp3kHUGKXguj+2uo6U3Me7m/mpnkshrOW5YWYHjFSh6uXJYOGolynKKBxOxYqji6Gcey3iPPe5kQUnJTZcu5TMXnU5/rkhtIlouHVISTqtNccXiWbzw5gECNF5QztcahxeELRwFYCt9iBRnWOcc1lAn7dFi55QNiYiH1tCXs/G1KK1hh5neeU+RLlgk7QLI0U+VQiCFYHHdILv7ogw5oWc8kpk9lpHrpSPdytKmi095QxK1EiQiKeK2YlZFgW1dx74m857C15BzJD3DuUl5LRuOjYkPAS++uT8sCzkGUZk5+YOZYmza9SAHBnbjBy5SSDQ+I8YYQg9oIHeAR39zL34wVlQi4PX2Tbze8Rzdmf385HXN3748i8d317J/KEZP1makg6cUsK0r9P4iVqyc7AWjzRi0Dkqfp/G1R6B9ZlYtQIpR72LEeB+Og4872YhaiqbKw9fxfmn1Us5pSCGRoTiJ1FhCowizqAUBSmhq4w6piEtt3EUJHXq5utQNCUhYPkvrs2Xv1fUFRT/O6bVFpNREraBsjCH8N+soPF+SLow1ogIpJHnf4sI5Q9xyiWb98i4+tfIAly8cQB703CtK4im+71L0cifj65tSKGkxs6qZRKSKM+qdY+4vCM+j40sSkaBU7z15r2XDkTm1H0VLXHjaHCyxBU8f2UMWaBbUz34PRzX5cbwCPZl9CAn6KOF+0KTz3Ty940fMqllSllhs69uBH7g83prita4kfhAmJWktyBTDS7MhGXq+OVeSzrk0z1tyiAe1pOkitNbs7t5C1hlCBwGJSAo/8EoyiiEjxnusFrJG4wceTVULp6xnpqTkT1bN4ZlMhG8+uRUvCLOxY1aRqqiHlBolAz59bgcv7q9iV28SX8NQwcLXEFGamrjLqnlp1jUPEGh4ak9NaU3YIhHRZIo2meJodvYIvg7bV+RcRaDdkrENr4WqqM3mtgR7+jVDxZkkIl7ZQx4xyhIFglK9szEkI4xc01lnC/JFfdQe2FKE4jC21Jw5M2Be7dS9lk91zFkDZlfXMK9asmfgyPvMrfRYUF//3g1qCpAp9OP5DkIIhBAEOjjq/jkny/7+nQTap3toXympStLSGw9v0FKjZElNSkDWVdQGLoEWxGyfhQ0LWTTjfHLO0CElUUIIoiqOqxxcXSDvZti2/xnsoIpz9bnlZKFRLeQ9pLNduEERiUWX2ovsUFO2nlNJwR+9fwX92TYe29WLLTVuKYwfaDizMUtFVHP5wgHWnJbGDVJUxqI4XoKskyZmZbBLzTCeaK1ha2eqXHPcMWSTzltH1NAOEDheuAYtS961F/g4Xo7Xu5NIAVELXN8aV7sskNgqRtxOkohUMbNqgTEkJaSQLJu9iiVNF/KXz/0Hr3WNjc6NPvwKwkhHRdTnonnwvy5dYXS6pzDm6i/xyv+3nuY/foD+/PiQoEKTjAT8wdr3nTDZv+lCKlaLpSIE2kMKdUyDHFFRhJB0pFvxAxdL2ri6gqyjsFX4lJ+0/bJ3XPAk+wZj+IGgLuHyT1vayeT/GcfP4/lFtNZIqUhGUmSdIYLAw/Fy+IFXTiQq6BxP7fgR6874JFLIcr2wH3j0ZzvwAx8fj+7MPgbzPWitWTZ71Xvw7Z0c/s81V2HJX/BMaxc5d2TdNs/lzYOlPlQCWwXMSFWQKCXb9Q33k3cDQFL0NJveqmaoaJdrjnOuIAhGS24OJQyTRywPx1fELJ/5lUX2D8axpAIhCAK/9P3D3oFaljadQ6bQge+7RO0EjZXzmFu3DD/wjFEeg5IWm2+5hlX/9zFeP5AmDESF5yEqoTYRYcWsKu67/jxmVNaa726KY85eiVQsTsfXP8mt//ofPPJamuFiKFI/s9Lmi6tWvu1etqcSEStW1hEeuRF4weHXvCwZQZU68vi+W+7OMyNVRWW0SL4U8q5PhF5df87CD0ITUhXzqI55PPemx2A+w+UL04z1EtJZRUCAJSOEOswegdblffb3t/D0jvtZe8Z/RwqJH3i0dv8GxysghCitpfrknWHe6NrCkqYLp+yNzVYW3/jgNfQN9/FPL3yHhO2VPd+xftW8+jMZyB6g4ObwAgcpLALt81+tdfTnIigZGuOCJ8JSp0PSxMbjBQoCgSU0aMGOniSdmSiV0YBZlQplWWU5zoIHjVUruWDB+8m7w7zV8xo9mX3s7991UjpETXXikQiv3Prf6BnO89K+XuZVJ5lRGad9MEdzXYrKmNFHmC5MzbvOScJWFt/9+HX82YeKPPb886w48xzmVKeMZ3wUxuoIa62Jqgp87eAHfinBKzTGDRXzy++J2gkaU/PoSL9B1JKcNyfG8295BNpFCKhLuAwXFVUxh8YKt7zeKICWvkSpTnZ0DGGWrsYLRrK8D/bUdVhW0/5Lls9ZTc4ZIucMorXGC5xQSpKwTneo0Eu2mKYyXj+lhfzrKuqYmYqRd4cOec33FUMFiwsXfpzt7U8ykO0gYlkUXJ+30jEspQkCUapnFqWs7aOtYYaZ2weGo2Qdi8qYR13CRUoYKir0kEtDcqRloyIekTSmUihp0da3nY70ye0QNV1oqIhzzbK54/42TC+m1l3mPSIRiTIvVcnC+uqJHsqkZ6yOcNdQJ1lXMa+mEYHLb/Y9zmC2G2tMIwGtA2ZWLWDRjPPxApf+bAe/tbiIEFFa+mpI511cL03c9qlNeId8Xs4drZM9HIca41KtK5r9A7s4feYFJUPs4o8RqxAlgxMEHq3dvyZmJ4+p5jWZDbYfeNRVzGb/wDAjWe9hslYtrf0p3OAtFtT7NCV7uWi2BQTkXJu8O9qoQjNqkAEiIsDR471WSbjMIISm4CmkDNf+63DLyw9ZR1GX8JBC4wU+ZzS4tPX9msUz3zfl6sINhpOJudoN7xrX9/jGY0/y/N4+Bgs+VTHFJQvquPOqa9jZ8RJv9OwlpookozFmVJ6G1prnWh4k72SJ2XFm1yzgWx+5lO3tL/F6xy5+3uLz5oBN22AMJTVJ26c+EXrPMcvH8wWuL8rlOcARPONRvMClb3g/z7Y8gKSUOTYmDKtLvQ0tFWFf/+vErQqktA7rtZ1s+c0TQdHL4fh5olasJOIR8PjuKl7vTiAFWMonUyjwVp9P3q3h/Qv6SNoBnZkoOUcR6PFesUQjJAhfl2uQIUy+K3qCyqhH3lMoGSZ3+YEoLz8MO4qiJ6iKBSypz/GRZTV0Du5lTu0ZU64u3GA4mRiDfIrieAXyQdg67912xvk/P/8F/9XSE4YuLch5Af+5o5NNex6kJtFA1m2gKmaxZmET9RX97O5+kaKXR+uAoYJkMN+Lkhb92TZeaJPs6q0gYTsMFWW5BGok89qSih/+umlc+z8p3kYRudYIoXCcPOlcN0pa+IeIiQjikUpyxWFiVsX4V8Z4bS2dv5r08ptRK0HcriAj+tAiwPUVu3pj5fC/EgpL2VhK8UZvkisXFXmyRZY94zDbXZcNc0QFRK2wtMbxBUJoPF+gtcDTgpyncHyJCjRRS6NKUp0NSZeZKYcbVwxQkwiwFUhZjeMVAH1UURdTAmU41TAG+RRjRFmrJ7OPgpOj45WXaEiFvWOt4wgP5pwiz+3tww888r6PwEMK6Mna7OmHedV7kELgevU8ut3nN2/t5rxZeVIxja3C1cmCm+WNri1YqoLXOiMoKZhRIdB4ZB1FAKQLFtUxj+qYjxAc0v5vhDDb2z9knEU/oL9QZLi4b5xn7fqCrKtI2gERBVErTtHNHdbTdbwCOWdoSoRZR8Ql0rluim6WYUeNyWZXRO0EQkiiVoJMYZiiX0NLn8aW4TnxgzAjWwlABCgBrh/WLNfHPGrjLm+lwzVjIUJlrnAfSVR5JaUuiRcEnNWQY0alhlKWtxQSS0VJRCoPqQuH0WWNyfA9GgzvJeaKP8XYtOtBOtOtCCkQSALt0ZluZdOuB1l3xn9/x8drT6fZ3euScyVeEHqMSdsn64RSfiO1qelcDwOFYV4qRnlhfx2VUc3S+iJXLMqgpCDvZFBWDRlHEFGglE19wqUmXsAPBPuHotTEvbKWMoTJRKNJXiVtZB0ghSyXYI0XuSg1PqjNc+6sIV7pqKS1P17evrguxxULDxCLxDlciU8YSRBTJsw6VjDF8YdIRnx8HSEZraYiWgNARbSGmCVQMkvR87FU6B1TCkqPNGmcV50nZkUItMALXAIdGmFbaYKSoE7MCohZ4XbXV6SiPitmOly+MA+EHnfcTgKUDe5oXfjecrRmZtXRW2caDNMVY5BPIUaVtUakD3UoOSkEPZl9hw1fHytx6d+3dYZ1qlogRdjYYLBg4QaCuBWgSvrGvTmbTDEMf0oERU/z6wNRfO1yxcJBBIoFtTOpsDvZNyjJFAVuYKGEJKYChKB8rLEcnOSlS58wwlN7anitKxWum0roGIrR0pvk0V11CCGoiITr0yMetxSKD50Bw8V+UrE6tA4IdKiTNLtmMYlIasqEWceKS+ScDD3OHp54YwBLjUlM05oPLl/Gh1cu4F+2P8ew20Nv1in5siECzbmzPCqiUV5oC8+BH4iyylcq6lId81BSo4TEtmv4yw+9j4RsI53fRzrr4QYOSkWojDfSVN1cNrgno4+0wTBVMVf+KcRYZS0/8Cj6HgMFqIgERJRD52AHWS9ORMWZVZXkzZ6Xjpq4VPR8Nu/rpSKiGSyMtvOTAoJAkIz44f91mHkrRJiVq0rayVLArt44q+cPYCufAwOvg2gkndcgNILQ+8oUFRErKK9/BiXPW0ldbuU3Fq1HtZhb+hLl94UPBaWWda4kbusxEp0elpS09CWJ2jZ+kGW4kKbgDqPRJKNVaK0RQk65MKuSFqlYDbdcdg4xu4VNrd0M5IvUxKOsXtjI5y9ZjJKStYtmk3U0gl76cy5eoFFCcN7cBL9zrsT3fQKteXm/puiH68lx2y+VNIX+dNSOM7dmBufPm0fUWlB+oLNkBC9wjmhwT2QfaYNhqjL57h6Gk8aIslbBzfP47gp29SXIuYq4CnB8uPuJp0kXLIQQ1CXgikUeH14mjpi41J8rMljwmV1pEehiOUytBMRtj1Qk9FpHsm4FocTf2OYCox6uT3+2n4IbJxWNknHCvr5CaCpjPkKEYxws2GRdFYbChWZxXfYQz3kk2zrrKnKOwlbjHwpG6ms1oTpY1lXUaw8E5B3FsCOxpUfcriBqx1AyghSS9oFdCCEOG2atTszFtpvJOUUSkehJPpPHx9E6R8HYVo8R+vJ5YkqwbvEs/mD1Mlo6wz7S1y2XXDR7gN58gV/tr2R7dwXlwLaQRKwkaxbOHNORatTQRnh3yYMGw3THGORThEwhy5t9BwiCOh7YmmVPOk7UCkjnLVpzNkU/VGOKWWEYsns44Gc7NJYUfGhZeIyDE5dqE1FqEzEcN0ZDcpi6hFv2XCPKZ1Fdntb+OI6vsGVA3A5oSPqM3MADrVFCU3AlfkQz7AqyDtTGiwTaZqioKLqCvGsjNGSKPkVfYkuNJTXJiE/BUzy1p2ZcYtdIA8Kk7ZOI+Li+Kj8UyFI/YClGi3r8QOAF4dppKiZCjz+fwfVzFFyfvBelPhmjJlFTnv9ImHW4mOGbTzzPC2++xWBhT7nk667f+gC2mpw/r5HOUQdzNIM99iGkrmIGturmqkVDWAJa+pIUXJsZ1fV8cNkZZcNuMBjeGZPzjmE4YRQ9hz/61wf5VVuGnb0xhosRdMlTEWjChokjnY8EeU/jBWGJS8YR/KYDfmsJREqO1NjEpailWN3cyL9vy5Ip9CEFSBWWyiypz3H5woFyFvOv2irZ0ZMKWzRqQU9W0ZcPy5n+bNNp1MRcLpyTJmH7dGRiZIoWrh82Xx/pdOM64Th9P6Cxwi3XJh+c2DVSX2wrzeK6XKlRAqXyqPBYCdtHl+atRppaoFjZpMg73RS9Ak/tqaWlL07OsUhGAlY05bhuWbQ8fyUtvvXkZp54oxcpBBFLkvc0/9XSA/yCb3zwmpN/gk8ChzPYh1vr9QOPq1f0oLFxg2Sp7Z9RtTMYjpfjVjHYsGEDH/7wh/nYxz7Gq6++CsDOnTu54YYbuOGGG7j77rtP2CANx89tP3mQZ/cO09IbY7holY0QhAbYP+QSELiBIudIhh3J7j6fwfzoqwcnLn3+ksV8cPl8ElGbIJBELcE5TUXWNaeB0CjOTMX4b0tdVjYViVmS7myEvlzY7ziiQpmJgXyEX+6rxdehkASMN8Zj8ZH0Zm16c6Ee9kjY++B5hKHpUL6xbTBKwRUUXUhFPeZXF0lFPQSQtDUJW3DRPMmHllkUvGwpGawC1w9LhRxf8Ks2wWNvRMrzHyn5kmL8GKUQPLe3j5xTfAdnamowEoJW0iJixWisnMuMyplGYtZgOAEcl4f8xhtv8LOf/YxHHnmEXbt28cQTT7BixQr+5E/+hNtvv50VK1Zw66238swzz3DZZZed6DEb3iaZQpaX2rLo0vrpOyFAoNDkXckze3w+dlZodA5OXFJS8qXVy1g9r59X2l7CltlSw/vQaMXsCuKRFBEV4dMX1RKNnMFd/9nKK+09OGMylYWArKNwfEHM8sm74Xr0kXC1pHs4NOqzKgskbX9MTbGPrTRP7alhW1cKP5AoGV7sgRbkXElNTDG/Gppr0lwwO8OMyhRLZi5mds0SfvLKLlr6kuPWuiH0sHf0RPECgZLQOTRIpuATsQ59rs0UAjqHBmmub3xH37vBYDh1OS6D/NRTT3H11VdjWRbLly9n+fLlOI5De3s7K1asAGDdunVs3rzZGOQJpG2gm2EnzDYOFZfeGUoEpKIeWw94rD4tzazqeZw+832H3XfFvEuI2pID6VYKbo6YnaCpeiGnNazkmZ0P8U+vpNnZM8xg8QAHhmI4fliGBJTrXQOg6CmaUg6OL2jpTR7VKAelEqumSnj2zepDao1b+5L058PMaiHGfJ5WLGnIctWiLiwVZmgX3CLbO35JwRkm68pSMtihH+4GcfpzRZoqE8ysrKIqpsqdqsaSiklmVla9g2/bYDCc6hyXQW5vb0cpxac//Wk8z+NrX/saNTU1VFaOli3U1dXR09NzwgZqeOfMrWkkFYFhR5fLj94JlVGfhmSAG8RRVgzPL/JG568OKw95pHrSx7b9E//wUppdvXEsCVEVAB5FT+IIXdKgDg2yJcMHgMX1ebYeSKFEgK+P7NkLEbZm7MlGKHoKS4KtQhWvrQdS9Octip4ql2OFms4aJSzeHIgyqsqsCbRP0cvS0vUyVTFJMhLg+JJxTQuFpL6igtpEmEWdiES5ZEFdSTZ01J0OtObSBfWTNtvaYDBMTo5pkDdu3MjGjRvHbevt7WX16tXcd999bNmyhTvuuIPvf//74/YZqQU9Ftu2bXsHw31v2bJly0QP4V2zrE7wUmdA0vYZKlpv00sOs59npBw0grj0oJBlyIWdQ68w3GEhxdFD4H6geailh02dQ7T0VSIEJO2A+oRHRcRj2LHxtUIJjRChsQx0KL94xcJ+0JqOTASneOTPkSIg6yqKeYsKuwBjyp9spSl6Ei8I650dH/xAlhSmYP+QJONIauPju0b52sGmgtPrsrzWVTE+bB1EaI64vPqbLfg4KCJc29DAQP8AW3sKZFxNyhac2xDj2oaG9/z6mQ7X67Ewc5wemDkenmMa5PXr17N+/fpx27773e/S3NyMEILzzz+f9vZ2amtrSafT5X26urpobDz2+tmZZ55JNDr5PIktW7Zw3nnnTfQw3jX3nn1WOct6e3eMrHusU65LiU4+lgSN5Jw5ivraMPzqBz7Llyw9pojDvZt28GLfG6MqXkCmqJC2XwplAAAcmUlEQVRAbcKhL2ehS/FoTWhAa+MumrAM6bIFabZ1VbCjxzpsYhdoIpKSlKMgXbBorBhZkw5rlGNWQNZRFD1wAslouRX0ZCPcv3UmN124/5C14pqKen73vAQP/CbN9m6bvKeoScT50Flnc9n8Hgbzuxgq5MgUYemMRXz3gt+m4Lp0Dg0ys7JqQjzj6XK9Ho2pPMecU3xb18dUnuPb5VSfY7FYPKIjelwh6zVr1vDAAw9w7bXX0traSlNTE7Zt09zczMsvv8z555/PL37xCz75yU8ez+ENJ5CoFeG76z9JppDl+T17ufHHr5Ep+niBLoVsR4OyCp+IFWoSz6xwiCpY2pDj6iWKkUvl7chDFj2fn23fQcEdLpUUhZKaQkDGkVREJZbSRCyfplSxpN4VqnJlHcVjb9SypaOSvpyNFLoUah9vNQUCT4OtQ2Oe8xSBdkvGVZcyuAMKUuIc9BAiSu/f2Z3k8d01fOD08TXMQipWLb6Oy5ZG6B3uJV1QvNH1Kt1DT7F13wBP7k2UyqEUich2lje+zveu/6xJ4DIcQn92iK/828/5dUeBrBM2MFl7ejXf+vC1k7ZO3TBxHNcVsXLlSp599lmuv/56AO666y4Abr/9du666y6CIODss8/mkksmRys6A6RiSdYuPoP5tW+yP50j7/oUPQ8QpXCyy80XdzO7skjEkmQdRcJyidgSW4Xe8duVh+wZztE5NIASAVKIcqN6ISgnaSkByYhPVGl6c6PqW34A6bxd6q0riFmarBs+NoSJX+GqsxChJ10bD73ioaJVbmQBoUym1jCzwimF6sf09hXh+rMbSLb3JFnXPFrDHLOSxOwEUStBoOF7z23lwd900ZcDSKFEBTEroCHplterX+mAP3j4H/jb3/7CCTxjpyZFzz+sithUo+g5fPlfHuSHW8I+0SFhVuG27kF+/MqP2HPnDaRi8YkbpGHScdyPaDfffDM333zzuG2LFi3ixz/+8bselOHkELUUn7pgIX+zuYV0zkUREI1EqE1EuG65zUXzfTRhgpYQkkyhHyksAq2JKPttd+GJWQXiloPjS6SQJa3jknQlUBHRnF6Xo+DJsr60KLnqWkPGCZtTxCw9puMQ5X1GGk3YMqAm7mFJjZIBlgwTseK2h5QWqYiPX1pDHnkQCFW6BEJIfO2Tdy2yrkWtCojaCWoSM8sPHff87FHuf6WHdF6W1rk1GVeFes8CGpLhw4AU8OqBgHRukOqEyaw+Hvwg4AfPt7BpT3fZIK9uHtXZnmrc+i8P8Pcv+zj+4R4qBP15xdl/8QB7/vf/eM/HZpi8mJjJKcYXVy1BCsHTrV3s7exhwcwG1i6cwWcvXsTurl+N6jMrm6VNF7Foxvm4fuEddeGJWhZLGgpsPRA2dpBCMqMiwPU9Tq/Pcs3pQyAcHm+t5Rdv1IWa0oQe87CjkCJcdy564AWivIYs9KisiR+EXaOkCD3s0BgLIirA9cJyqHQ+DHkLEYBW48LzWgckbJvTaupYVG+hpCZmJ8oPHTmnyLN7+hguivD9pfdqDYEIQ+t1Cbe8/px1BXt693PuPGOQj4cfPN/Cozvaw6iIrcgUCjyydQdFN8+X150z0cN7R2QKWf59ewHHP7p291tpRXt6gNnVNe/RyAyTHWOQTzHG6hU//cJLrL3ognJo8Eht8GwVeUefEbXiXLsE0Dl29sbIO5J4JGBZQ4a1zf0oESZYvW/OEFsPVJQ8Vo0fCAqeKhs+V4txK8cagRLhmnRoqCFdUGgdNq1wPUlPQTFUsPAZkfsUpTVjXQ5bawKU0FTHPc6fV8mFCy8jnfeYXV1XTrjpGBygd7iIG6iy0RVQzgj3RrSxS6HupK1prp/zzk+IgaLns6m1GyUFgQ7oTO8h0GFU5cFf76cx8igfv/DWCR7l22f7gb30F97erXXjlt9wy+XrTvKIDFMFY5BPUaKWoj5uH7JO927a4AU6YNeBF+ga2gt4rFuQ5pJ5mqyrSNkaVepZLEo1uxV2QEUk1K4eWUP2grB1oxBgCY2nRcmtHU1Ai1kByYhPzPJI2AH70jF6cgmCQITdkEWAJTSBACkhZoHrB+U67IilqY17XDJ3kH19XXxkQztOEKchmeTqZWfwhVVLGBzeQSrqoGQMrUseugAlND6iFCbXpXnD2U2WCVcfJ/25Iv35IjFbjTPGEMqi9ubzPPyrP2Vp5EMTOMq3j+c7iLdZ89+TTx97J8MpgzHIhhPGrgMvlPsEe75DQICtNNVKY0kbLxi7tyBihXetoYKFlOFarC2hEIRZ0rYVEAGKvsAuGVdfa5pSRaKWBjSt/TFyTvj+kdaKvpagA6rjLgVPEZQ+a+2Cfi6eO4gUUB3zeebNKl7rSqGET9QOGMhneHjr6xwY3MPZja+yuN6mfWjMGjcj2tsBlTEXX0PM0pzdZPHXH//Ue/xtTx/CrmFRMoXCOGMMlPtde4HHjsKjrAxWYk3CntNjWT5rIbXJlygMiaMK24DmhpVTKxxvOLlM7ivbMGXwA4/Owb0IIRnK95H3MiVtaYuk7SPwkEIRaB+tNUIIXF+ggcqYN66Xcn3CJ+tI4nZA3lMIBEVfoHRAVIVlToGGRbVZtncnGZvzU26piKA24WJJFz+QxG2faxb3lbOpXV/Q0heucWt0aUySvDPIc3syLEjlWLvAxQ98frmvmnTBRgA1cZdL5w1y6WmDCFHNx877feoq6t7rr3taMdI17JGtO8ZtDzQsrsuVz5lPjqd33M8Vyyf3w091ooorFgb85y6P3hx4RzDKKcvlrDmnvbeDM0xqjEE2nBCKXg7HyyOlYjDfz5N7asZpSy+uy/H+5kGkGLGemoyjyLuKhqRLfcIr9SQOZT739EfJFC0sFXqlDmH3p4jyiFo+S+ryLK5z+dmu8MYtSqVMQoea2ACer4goH2TAGQ35cdrUWVeV9KrHzkLj+S4DLgy7muoYXLlogLUL0gyW1gSrYh4RJZBSURmLmjD1CeLzlywmWxzika37ybmKhB1eM+uaB8bt1z6wK0w6tI6eMDXR/N9P/A9uefiH/Lojy0BOsTcdK6Uuhgsv1VGX17/68YkepmGSYQyy4YQQtRJE7TgFJ8uTeyp5rSsVhqBLtbqvdaUA+OhyCyEleSdDhe2QjGgcX6KkRIiw0UOgQcmwTWLWVQRaELND+c/6hMOnVh4gFVMUSsY+7Awlwp7HUpcUNDVSBtgqYEl9gbULcqQLNknbI6JCJbJExMf1FWEttsDzXTQB8VKYdARbaeqTo52ppLSpiFQRtRLl3siGd4eSkk9fOIca61/Hdew6GE1A3/ABmqoXTMAo3z5xO8bf/PbnSOcG2dO7n+b6ObT1D/Dozte5Zuly4xkbDosxyIYTgpIWMyoXsKPjlXIoeCxSQEtfgtrUXC49/Vr6hjt4ac/POH9OjBf2+egxa4euL4iWxDfqtIsfiLLn7AYCJ5D4gYttuSyqzbGzp4KIGK1Z1hpOb8jxh6tsCoVeNrUl+duX6xkqgKUCmqvz/NbifhbX5XitK0VEhX2V/cA9JEzq+mKcdxy3beor5mCpCErax1QtM7x9UrFaIkpiK++o+x3UfnpSU52oKpfCVSeqjCE2HBVjkA0njCVNF9Ha203O6TgoFByScxWV8SUoaVFXMYtkrJKrT88zmO8PpShLocql9Tla+xPlkiM5xlNK2D4V9ujf//OCdv7updm09idwfIGtNAtrc3zhwh4aUwv48a5KXu1O0psV9OUkbiDY0ZVi01u1XHV6H2fOyNHaL8g7ELUCltUNs655gEDDk6015fVjraEq5nLhnDyfPNdCybenWmZ4+0SsGLOrF7E/veuI+0ihqE02vYejMhjeO8zdxHDCkEJy2eIrSTz+D7j+oepKSVuzqHEeMOpRv9j771y+ULPmNDEuVPlEK+Ww9wiBhiX1BSw1mq5tSfjChe3knFD1qz7hkohobBnF8TS7+mL0ZSWdwzLMvib0ogcLFs++Wc91ywR3rPNJ530ctwslPTTwRGsNT++tZdgJfyKOLziQifHvuyJs7bL4xNl13HXG4XtDG46ftcs+yY+fv4eAw3vJc2qWTvr1Y4PheJl6mnSGSU0qluTCuVWH9F4ONLxvbiWpWLK8bWbVIka0s2ylqY555VDxuuYBzpqRwVY+XiCIWYqzZgyzbsEAo3pboyQiAfOqiyQimpiVoKFyHotmXslwUdGTFfhalgRCSuNB0J+TbO0USGkxozJCxApfdX3Bzt4kuVIvZccPO0qF+tmSTDHKy+0R/nbz7hP87RksaXHDxXdRGW046BVBSsxizdLfnpBxGQzvBcZDNpxw/uIj13PbTx7kpbYsWQeSEbhgbpJvXnf9uP06B/cc8RhSwJULs/zWYoFtzWRhw1xauzfjB1GKXoHDGeUQja1izK1dytyaRiIqbCJxMALwtCCd12SKUJcQJOwUBS/HQMEhUwyFSgTglzpVjVD0PQKt2dTazWcuOn1KN0GYjERUhI9ecCt5Z5juwbewrSj1qTm8tvX1SV+DbDC8G8zVbTjhRK0I3/l42PKxbaCbuTWN4zzjEWZVLzrqca5e8VlikQQgiFpx+rNvsb9vL0OOTcJ2D5uFC7CgYSVLmi5CCsmy2ihbu8YpkpQaaIT/T9iQKrWnramYSUNqHtvaf0Vl1Kc7G8p5aj0+kSgiJbaSDOSL9OeKNFWaxK6TQTxSwfyG5RM9DIPhPcMYZMNJIxVLsqzpyOUpVYl6LBnFC4qHvKZEhN7hfXQO7qE/e4CiW+SxN2Ls7K0fV9u8rnkAJSQajUJRmWjg7HnryvXO/3P5Yh57q4X+XEBQ0rWWIvSQLQnnzg41r8PWks1lPe/HWv5/OoZ8Ms6oSpfWYElNQ0UCKQQ18VBhymAwGE4EZg3ZMKF89IKvYMnxRs2SUVbMez/7+3fSNfgmmUIf/9lis7UzgeurcbXNT+2pQRMgkSSi1Zw+4/xxmc+JiMUdV57H/GqbuBUaY4kgqgQXzJZcu9RHSZs5tUvLrSUjVoxbLjuPKxdlqY17KBGgNURUwOyqKPNrK/ADzeqFjSZcbTAYThjGQzZMKAk7wY2XfJ3BXC8d6d3Mql5ERayaZ3c9yHAxTcHLjpO5HMtIbfPa0zLEojFqK5pYOuviQz7jpkuXoqTk6d0H6BzKURWPcsXiWXxx1SK84PCtJc+aeym3XW5xfd9u2gayPL8vyYHhKjQpkrbF6oVhr16DwWA4URiDbJgUVCXqqUrUA5Bzhih6OQrOMKDJutZhZC5Dcq6iGMSYWzELCEPPiPGBn7EtJ/tzRWoT0bJnG+HwrSWlkOPaUf7+pQm8QBzyfoPBYDhRGINsmHRErQSWtAl0mIw1XuZyPAnbpyISoAlwvMJRpSyjlnrHCVhj21EqiUngMhgMJw2zhmyYdChp0VS1EClCnWlbaRbX5Q5b27ykvkDUUkihiFgxI2VpMBimLMYgGyYlZ8xexczqZpQIgzhjhULcAGzlc9aMDFednidqJ0BrI2VpMBimNObuZZiUSCG5bOlvs739Obbt30TBG+byhYNcdtoQedciYfvEIhYJO0VVvIGZVc3lLGmDwWCYihiDbJi0SCE5c84aljRdxLb9z9I73Ibve0TtOLXJWSxpuohAe4fNkjYYDIaphrmLGSY9topwzvwr8AOPopczBthgMExLzF3NMGUYm/FsMBgM0w2T1GUwGAwGwyTAGGSDwWAwGCYBxxWy7urq4vbbb8dxHIIg4Gtf+xpnnnkmzz//PN/+9rdRSrFmzRpuuummEz1eg8FQYuyausFgmPocl0H+x3/8R6688kpuuOEGXnnlFf7qr/6KDRs28Md//Mds2LCBGTNmcOONN3LVVVexaNHRW+wZDIZ3RqADdh14ga6hvRTdPFE7juMqAn1OucuVwWCYehzXr7empoZ0Og3A0NAQNTU1tLW1UVVVRVNTE1JKLrvsMjZv3nxCB2swGGDXgRdo69tO0c2D1gwXBunz3uK1tmfIOUM4XoGcM4QfeBM9VIPB8A44Lg/5937v9/j4xz/OT37yE4aHh/nnf/5nenp6qK2tLe9TW1tLW1vbCRuowWAA13do6XyZXHEQx8+jCcqv/XrfY/x63+PErAS2FaU2ObMsmGI8Z4Nh8iO01vpoO2zcuJGNGzeO27ZmzRqUUnzhC1/gqaee4pFHHuH3f//32bBhA9/73vfK72tra+PLX/7yYY9bLBbZtm3bCZqGwXBq0OVup9drIcDF9QWDBQsvEFhSUxXzsNXIz9nCJkqECqqsJurs0yd03AaDYTxnnnkm0ehBveCP9ab169ezfv36cds+85nPcMsttwCwatUqvv71r9PY2Ehvb295n66uLhobG49rUJOBLVu2cN555030ME4qZo5TCz/weGbXdvzegMdba3jurWq6sxF8X6CkprHCYfX8NO9fOIAUHi4eyhLkI92sOPuj2OrwrSanCtPpXB4JM8fpwdHmeDRn9LjiWPPnz2fr1q0AvPrqq8yfP585c+YwPDzM/v378TyPp556ilWrVh3P4Q0Gw2EoejnSw508uaeSZ9+spWs4gutLfCROoGgfivEfu+p5orWm/B43KJItptne/twEjtxgMLwdjmsN+XOf+xx33HEHP//5zwG44447ALjnnnu49dZbAbjmmmtYsGDBCRqmwWCwZITBwjA7e+sZLiq8QKIRiPIegpxr8dxb1axdkMZWGj/wsO0o/dkO/MAzkqMGwyTmuH6djY2N/N3f/d0h2y+44AIefPDBdz0og8FwKEUvx3BRkilYuAEHGWMYWT0eKNgMFizqk264VWscr0jRyxnpUYNhEmNSLw2GKYMgYfukYh7qML9cAQgBUhy0VQgiVtQIiBgMkxxjkA2GKULUihO1JUvrc6QiPgJd9oo1oUFWQlMTc6mKhTXIlrQJgoDa5CwTrjYYJjnGIBsMUwQvcIjbSdY1p7lsQT9V0TAkrQGJJmIF1MZdVs1Ll8qfBJaKkIxWs2z2pRM8eoPBcCzMI7PBMEWIWglqK5qQUnHloh7WnJbm5y117BlIUPQkqZjH0vos65oHAGhMzceSFnPrlk35kieD4VTAGGSDYYqgpMXMqmY83yFhV9KXbefDy3pxfUHWVSRtvywMErWSxCMpZlYtYEnTRRM8coPB8HYwBtlgmEKMGNfOwb3UVcxBIhkq9BItDqOUwlZR6ivmcEHztSQilWbd2GCYQphfq8EwhZBCcsasS1g8833l1otKWrz40mYWnTGfVKyWiBWb6GEaDIbjwBhkg2EKoqQ1rqbYkhHqKmZN4IgMBsO7xWRZGwwGg8EwCTAG2WAwGAyGSYAxyAaDwWAwTAKMQTYYDAaDYRJgDLLBYDAYDJMAY5ANBoPBYJgEGINsMBgMBsMkYMLqkLUOJf4cx5moIRyTYrE40UM46Zg5Th9OhXmaOU4PTuU5jti8ERs4FqEPt/U9IJPJ0NLSMhEfbTAYDAbDhLJ48WJSqdS4bRNmkIMgIJvNYts2Qohjv8FgMBgMhimO1hrXdUkmk0g5ftV4wgyywWAwGAyGUUxSl8FgMBgMkwBjkA0Gg8FgmAQYg2wwGAwGwyTAGGSDwWAwGCYBph/yQWzYsIGf/vSnWJbF3XffzYoVK9i5cyf33HMPAEuWLOHrX//6xA7yBNDb28vVV1/Nvffey4UXXjit5uh5HnfccQf79u3D931uu+02zj///Gk1xxH+9E//lK1btyKE4Pbbb2fFihUTPaQTxje/+U22bNmC53l87nOf46yzzuK2227D930aGhr4i7/4CyKRyEQP811RKBS49tpr+eIXv8jFF1887eYH8NOf/pT77rsPy7K4+eabWbJkybSaZzab5Stf+QqDg4O4rstNN91EQ0PD8d1rtKFMS0uL/shHPqJd19Xbtm3T3/nOd7TWWt94441669atWmutv/zlL+unn356Iod5QvijP/oj/ZGPfES/8MILWuvpNceHH35Y33333Vrr8Jx+7GMf01pPrzlqrfWLL76oP/vZz2qttd69e7f+xCc+McEjOnFs3rxZf+Yzn9Faa93f368vu+wy/dWvflU/+uijWmut//Iv/1Lff//9EznEE8K3v/1t/dGPflQ/8sgj03J+/f39+gMf+IDOZDK6q6tL33nnndNunj/60Y/0t771La211p2dnfqqq6467nuNCVmP4amnnuLqq6/GsiyWL1/OzTffjOM4tLe3lz2PdevWsXnz5gke6btj8+bNJJNJFi9eDDDt5vihD32Ir33tawDU1taSTqen3RwhPI9XXHEFAAsXLmRwcJDh4eEJHtWJ4YILLuA73/kOAJWVleTzeV588UUuv/xyYHqcv9bWVnbv3s3atWsBpt38ILxGL774YioqKmhsbOQb3/jGtJtnTU0N6XQagKGhIaqrq4/7XmMM8hja29s5cOAAn/70p/nUpz7Fzp07GRgYoLKysrxPXV0dPT09EzjKd4fjOHzve9/jD//wD8vbptscbdsmGo0C8MMf/pBrr7122s0RwmWHmpqa8t+1tbVTfk4jKKVIJBIAPPzww6xZs4Z8Pl8ObU6H8/fnf/7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      "text/plain": [
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   "source": [
    "GraficaData(libCon,'TokenizeTweetsTidy_text')"
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   "source": [
    "# Ploting Learning Curve\n",
    "# Creating CV training and test scores for various training set sizes\n",
    "X, _, y, _, Tfidf_vect = separaData(libCon,'TokenizeTweetsTidy_text',1)\n",
    "cv = StratifiedKFold(n_splits=10)\n",
    "estimator = LogisticRegression(solver='saga',max_iter=100 , C=5)\n",
    "train_sizes, train_scores, test_scores = learning_curve(estimator,X, y, cv=cv, scoring='f1_weighted', n_jobs=-1,train_sizes=np.linspace(0.3, 1.0, 10))\n",
    "\n",
    "# Creating means and standard deviations of training set scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "\n",
    "# Creating means and standard deviations of test set scores\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "layout = go.Layout(title='Learning Curve Logistic Regression',\n",
    "                   xaxis=dict(title='Training Set Size'),\n",
    "                   yaxis=dict(title='Accuracy Score'))\n",
    "\n",
    "fig = go.Figure(layout=layout)\n",
    "\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=train_sizes, y=train_mean,\n",
    "    line_color='rgb(230,171,2)',\n",
    "    line = dict(width=4, dash='dash'),\n",
    "    name='Training score',\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=train_sizes, y=test_mean,\n",
    "    line_color='rgb(117,112,179)',\n",
    "    line = dict(width=4),\n",
    "    mode='lines+markers',\n",
    "    name='Cross-validation score',\n",
    "    \n",
    "))\n",
    "\n",
    "fig.update_traces(mode='lines')\n",
    "fig.write_html(\"LearningCurveRegLogistic.html\")\n",
    "fig.show()"
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    "id": "fxbRmubATipy",
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       "                        {\"responsive\": true}\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "if (outputEl) {{\n",
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       "\n",
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       "        </div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting Learning Curve\n",
    "# Creating CV training and test scores for various training set sizes\n",
    "X, _, y, _, Tfidf_vect = separaData(libCon,'TokenizeTweetsTidy_text',1)\n",
    "cv = StratifiedKFold(n_splits=10)\n",
    "estimator = SVC(gamma=0.01, C=100,probability=True, kernel= 'rbf')\n",
    "train_sizes, train_scores, test_scores = learning_curve(estimator,X, y, cv=cv, scoring='f1_weighted', n_jobs=-1,train_sizes=np.linspace(0.3, 1.0, 10))\n",
    "\n",
    "# Creating means and standard deviations of training set scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "\n",
    "# Creating means and standard deviations of test set scores\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "layout = go.Layout(title='Learning Curve SVM',\n",
    "                   xaxis=dict(title='Training Set Size'),\n",
    "                   yaxis=dict(title='Accuracy Score'))\n",
    "\n",
    "fig = go.Figure(layout=layout)\n",
    "\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=train_sizes, y=train_mean,\n",
    "    line_color='rgb(230,171,2)',\n",
    "    line = dict(width=4, dash='dash'),\n",
    "    name='Training score',\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=train_sizes, y=test_mean,\n",
    "    line_color='rgb(117,112,179)',\n",
    "    line = dict(width=4),\n",
    "    mode='lines+markers',\n",
    "    name='Cross-validation score',\n",
    "    \n",
    "))\n",
    "\n",
    "fig.update_traces(mode='lines')\n",
    "fig.write_html(\"LearningCurveSVM.html\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_OqvJWh5jzCY",
    "outputId": "c7b5af0b-5a80-43fe-b326-4cc08ea6ba0d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.681871, G-Mean=0.708\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.691281, G-Mean=0.659\n"
     ]
    },
    {
     "data": {
      "image/png": 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lS/BW+8/37rvvMmnSJC644AJM02TLli20adOmopx0uVNOOYXly5dz7rnn8u2336K15qabbgrKeYiWLb8km9ziwxXLCdEpTB16e8XvffXyxucMvoFiIy+gOvatncfrqxjGOev0QQA8OWUYidGR9R3W4oRVAoDg/DLv3r2bq6++umL5jjvu4Nlnn8VisZCYmMjs2bOx2+189NFHXH755YwcOZKkpKQqn9GtWzfuvPNO4uPjiYyMZPbs2eTm5vL000/TocMv0ydPmTKFpUuXctVVVxEREcFf/vKXRj0X0fK5vQbZBftZvPkNoFK132qFf2urpdNQSeRw8P3OLG76YDlzLx3NxN7t6ZEcx0vT6h6+2pKFXQJobF26dKl4QFvZP//5zyrLeXl5TJ8+nbPPPpusrKyKu4Hy/fr168fEiROrHJOSksKSJUtqfPZTTz3VWOGLVsLtNcjM3UZm7lZ256zFZ9bsJixw5tQYhhmOV/d1KXC6eOCztcxdtg2rxcLq/UeZ2Ltlj1pqiCSAJhIbG8sXX3zBG2+8gc/n48EHHwx1SKIFKx/Z4zU9FDpzWZAxB6/prveY1jAMM1gWbTnAzR8sJzO/hLQOSbx22RhGdktp+MAWThJAE7Hb7Tz33HOhDkO0ApXfzI3AwbHtXett/OMdbRnb95KKypqiqvfX7Oaq95Zgt1n5f2cN5oEz0oiMsDV8YCsgCUCIFsD/QtVu4hxJFJTmVLyZ68HgUP6OWo+Rhr9u5TMhWiwWLkzrymVDe/DQ5DTSOrYJcWRNSxKAEM1ciauAhWvqekvXwqk9p7Ap80dK3QU4ImI5K+16fKYnbIdtNuRAfgm3frSCM/t15Nbx/YmJjOBfV08IdVghIQlAiGamoPQoO7JW0y6+Bz7Tyzdb36myvW1sF44WZ5YtmbRL6M4lI0aG9Vj9QJimyesrdvC7TzMocLoxTbhlnKq3GkBrJwlAiGakxFXAxxl/rXefod0ms3rP5+SXZuOwxFc0+jKap247cwq58YNlfLsji4QoO69MH8VvR/UN68YfJAGctMzMTKZOnUpaWhrgrzt03333MWLEiIA/4+uvv2bChAlERraul0xE4Nxeg8yj21i15/Mq65NjO5FbfLBiOSYykQ5JPSteaNytD8kVfwP0kXzSn/2cUreX8wZ25uVpo5r9RC1NJewSQElJCYcOHaJjx47ExDROIaeePXtWjOdftWoVr7zyCm+88UbAx7/11luMHj1aEkCYKnQeY2HGHDymq8p6q8XGpP5X8PXmtyhw5hATmcj5Q2+taPBT47uxz5IdipBbBNM0sVgs9EtNYMbQHpylOnLZ0B5hf9VfWdgkAI/Hw6xZs1i4cCH79u2jW7duXHjhhTz99NM1CrOdjJycHNq1a0dWVhYPP/wwbrcbm83GE088QadOnXjiiSfYtGkTXq+Xyy+/HKvVyrp165g5cyZvvfWWJIFWqr4y5DuyMmo0/r1Th5Pe8xxiIhNqvK0r6ufyeJn99Sayi5y8NG0UFouFN389NtRhNUthkwBmzZrF888/X7G8Z8+eiuWTHZ9fXgrCMAyysrJ44403eP7557nuuusYO3Ys33//PS+//DKzZs3iu+++46uvvsLtdjN//nxmzJjBCy+8wGuvvSaNfytT3uj7fCZf//wWLm8pNqud1PhuWC2/TBdY5DxW5TirxVbR+IO8rXs8Vu7L4bf/Wcrmw/l0TYrhiSnDSGpl9XsaU1gkAKfTyYIFC2rdtnDhQv70pz+dVHdQ5S6gnTt3ctddd+HxeNi9ezevvPIKXq+X5ORkkpKS6NGjBzfffDPnnHMOF1100Ql/T9G8ub0G8zOepcSVX2W91+fmcP7Oeo89vX/NSdNF/UpcHv7433U8/8NWfKbJjWP68efzh5EQJY1/fcIiAeTk5LB///5at+3fv59Dhw7Ru3fvRvlevXv3xuFwcPDgQd5++23atas6ecbrr7/O5s2b+eyzz1i4cCFvvvlmo3xf0Xw43cVsOfhTjca/3OSB19ExqVfFsttr8MWGv1eUKe+Q1LOpQm0VDI+XEc9+js4uoE9KPK/OGMOkVl7Dp7GERQJISUmhW7du7Nmzp8a2rl270rFj403nlpeXR3Z2NmeddRZfffUVV1xxBcuWLSMnJ4dhw4bxzTffcM011zBo0CAuueQSwP82YvXy0KJlcnsNPlz1l4oa/NXFOZJpn9gdm/WX/3o2a0TY19w/GY4IGzOG9qDU7eGRc4YQbQ+LZq1RBPVfSik1BxiNvxDtnVrrVZW23QpcBXiB1Vrru4IVR1RUFBdeeGGVZwDlLrzwwpMeDVS5HLRhGPzhD3/glFNO4aGHHuLzzz/HYrEwe/Zs2rVrx9q1a1m0aBF2u51p06YBMHLkSK644greeecdkpOTTyoWETpur8Hu7A21Nv4DO02gY1IvOiT2qrWBl37+4/Pp5v28tWon866ZiM1q5ZFzhoQ6pBYpaAlAKTUJ6Ku1HqOUGgC8CYwp25YA3Af00Vp7lFKLlVKjtdbLgxXP008/Dfj7/Pfv30/Xrl0rRgGdjLrKQQO1DgWdM2dOjXWzZ88+qRhEaLm9BjmFmSzZ9gHFrrwa2xOjUxnWfbJc2TeC7CInv/8pk8V7fybSZmXV/qOM7p4a6rBarGDeAZwBLADQWm9RSrVRSiVorQsAV9mfOKVUERAD5AYxFiIiInjuuef405/+1OjvAYjwtS9nCz9u/zdur1Hr9rF9ptEzdbA0/ifJNE3+tWY3dy9YzdESg9HdU3htxhgGdkhq+GBRp2AmgA5ARqXl7LJ1BVprp1LqUWAXUAr8W2u9raEP3LRp0wkHk5GRUWU5L6/mlVprU/2cw0FTnbPX9FDoPch+94o693FY4snL9LHhwIn/3gYiHH7Ojy8/yKe78oiyWbhneHsu7ZdM6YGdZBwIdWRNJxg/56Z8WlLx+l1ZF9BDQD+gAPhGKTVEa72+vg9IS0vD4Tj+K6nySeHDiZzziavvpa3y7QvWzKHYXftFRFOWYQ6Xn/NVESmU2LYx99LR5O7ZFhbnXNmJ/pwNw6j3wjmYCeAg/iv+cp2AQ2VfDwB2aa1zAJRSPwLpQL0JQIhgc3sNPlr9V5zuImzWCNrEdKzy0haA4Sml2KjZ+EfbE5ioLpP6+41ge3YBDy1ay9+nj6ZtrINpg7sxbXA3LBYLuXtCHV3rEcwEsBh4FJirlBoOHNRaF5Zt2wMMUEpFa61LgRHAoiDGIkRA8kqycLqLAPD6POQUZdaoHWPWMt9ueZ0eeYHr5Hi8Pp77YQv/77/rcXq8TOjZjjsmDpD6PUEStASgtV6qlMpQSi0FfMCtSqlrgXyt9Xyl1F+Bb5VSHmCp1vrHYMUiRCDcXgOP143FYsM0vSREpzB16O01rubdXoNP175IgTNHZt1qRBsOHmPmvGWs3n+UdnFRvHXJOKYPlqGxwRTUZwBa6weqrVpfadtcYG4wv78QgahzGKdZ+/52m0MKtDWyf6zcwU0fLMfjM7l6RC+euWAEbWPl3zXY5JU5EVaqP+CteKBbS59+gTOHvJKsWl/Qkhe3GtfIbin0TI7juYtP5Zz+nUMdTtiQBCDChttrsCDjWYpd+dhtDjol9cVwl9Ta+IP/Ba6kGKkpEwzFhps//nc9V6X3YliXZAZ1SGLz/Rdgs1obPlg0GkkAImzklWRRXFagze012Hu09uFx0q8fXF9tO8SNHyxjT24x+/OKmfd/kwCk8Q8BSQCi1XN7DQ7n7SanKBOrxYbP9BLnSObMtOsA+HLTmxQZudLwB1leqYtZn6zmHyt3YrNauP9Xg/jDWYNDHVZYkwQgWi231+BI/j6+1//C5S2tss1qsRITGY/d5uDC4XfKA90gy9h/lAvf/JZDBaUM7dSG1y4bw/AubUMdVtiTBCBaJZenlA9W/bnOGj2VH/DKA93g650ST7TdxhPnDmXW6YOw26S7pzmQBCBapezCfXU2/iAPeIPNNE3+mbGLaHsElw7pTlJ0JJt+dwGOCFuoQxOVSAIQrVJC9C8lgi1YMfHhiIjlrLTr8Zke6e4Jor25Rdz04QoW64N0axPLRWldsdus0vg3Q5IARKtkt/nngu2Q0JuJ/S+j2MiTRj/IfD6Tvy/dxoOL1lBkeDizX0f+fulo6e5pxiQBiFbDa3rILtxXo2tH+viDL7fE4OI3v2PJ7iO0iY7kzV+P5ZoRvaSGTzPXYAJQSnUHngHaaq1PV0rNBL7TWm8PenRCBMDtNcjK28025xf8vN5JhDWS5Dj/26SHC3by2bqXOH/obXL1H0RJUf47rksGd+PFi0fSISE6xBGJQARyB/Aa8BJwb9myBl4FTg9WUEIEqsRVwCdrn8fpLq5Y5/G5OFKwu2I5vzS7zpIO4sStO5DLT7uPcOv4/litFj6f+SviHPZQhyWOQyCdc3at9Sf4K3qitf4huCEJEZhSVxEfrvxLlca/3MR+V5AQlQLIiJ/G5nR7+f2itYx8bhF3LVjN7qP+Ku/S+Lc8AT0DUEolUVYbUSk1CJD7OxFymblb8eGtsT4hKoWubRVd2yp5wauRLd19hJnzlrH1SAHd28Ty90tH07NtfKjDEicokATwGLAc6KiU2gCkAFcFNSohGlDozCWvNKvKughiOCPtyiqlHKTbp3GYpsk9C1fz4pKtANw+oT9PnDtUrvpbuEASwFpgGJAGGMA2oGMwgxKiPm6vwcern8bkl5m5YiIT6WaZSMek3iGMrPWyWCx4fCYqNYFXZ4xhXM92oQ5JNIJ6E4BSygrMB34FlE9Jbwc+AU4JbmhC/KJyHf+8kqwqjT9AiSsft6PmswBx4nJLDN5csYN7TxuIxWLhz+cNw2ZNJ8ouL3S1FnUmAKXU5fjn9O0DePE/A7Dgfxj8vyaJToS18kY/1pHE5+tfodg4RoQtknYJPWrsmxidisNMbPogW6mPNuzl9o9XklXopHtyHJcO6U6sdPe0OnUmAK31+8D7SqlHtNaPVN6mlJL/aSKoKs/UFWmLrqjm6fG6OHhsW5V9x/aZRs/UwWxYV3t9fxG4wwWl3D5/JR9v2Icjwsrs84ZxUVrXUIclgqTBZwBa60eUUgPxP/wFcAAvAAOCGZgIH9WnaQQ4UrC3Yqau6qWcx/ebwfp9X1PoPEpidCo9UwfLKJ9G8O+1u7nto5UcK3Uxvmc7Xp0xGtVOrvVas0DeBH4OOBvoAOwAegNPBzkuESbcXoNP175IgTOHKHss/TuOxWaNoNiZX2kvC46IGAxPMQlRKXRvO4jubQfJEM9GVuzy4Pb5eOmSkdw4ph9Wq5RxaO0CGQU0Sms9QCn1bVkpiHTg4mAHJsJDXkkWBc4cAJzuYtbt+7KWvUxO638FETZ7lQZfhnieHK/PxxsrdnD5sJ7ER9m5bmQfpgzoTMeEmFCHJppIIAmgvKi6Qyll0VpnKKXkDkA0iqSY9kTZY3G6i3FExDCmz8VEWO14fG5W7vqMElc+idGpMk1jI9uSlc8N85axdE82O3IKeWpqOhaLRRr/MBNIAtBKqVuAH4AvlVIaSApuWKK1K+/3dxolxDmScbqLGdHzXHqk/DK6uHObftLN08jcXh9//XYzjy/egMvr49Ih3bn3tIGhDkuESCAJ4CagDZAH/BpoD8wOZlCiZSspKeHQoUN07NiRmJiqV5Rur0FOYSbfbf0XhqfquP01e7+kR8ovD3SljHPjWn8wl9+8v5T1B4/RIT6av00byUWnyL9vOGvoRbAk/A99t2itfcC/ytaPAjKDH55oSTweD7NmzWLhwoXs27ePbt26ceGFF3L7g9dSaBwlyh7Lil2fYJq+Wo8vdRVI1c4gcrq9bDyUx3Uj+/DU1OG0iZG7qnBX34tgFwMvA4fw1wE6H9gEPAlchP8FMSEqzJo1i+effx5HtJ0e/dtTXJBPfuRWftz+74COj49qK1U7G9mPu7LoEB9N39QERnVPZcsDF9AnJSHUYYlmor47gPuAIVrrI2Ujf14FovC/BTykKYITLUdJSQkLFizAEW3n8bevI7VjYI+J4iKT6d9pDIkxKXRI7CV9/Y2kwOnioc/X8srSbZzWuz1f3XwmFotFGn9RRX0JwNBaHwEoG/kTDVyjtV7dNKGJluTQoUMcyTnMqaf3r78e1BEAACAASURBVLfxj4lMpMSVT7yjLWP7XiKje4Lgiy0HuPnD5ezPK2Fg+0SePG+YTM0oalVfAjCrLWdJ4y/qktKuLU+881uS28VhmiYWi6XG3wlRKZwz+AaZoD1IcksM7l6wmnczdhFhtfD7M0/hocmn4IiQ4m2idvUlAItSyoK/ABwAlZfLHgrXSyk1BxiNP5ncqbVeVWlbV+B9IBJYo7W+6YTOQDQLLgpIbhcHUHG1abFY+OTtn+iTks59D91T0ejHREo3RDC4vT4WbckkvUsyr182lsGd2oQ6JNHM1Tcl5CTAA7jL/pQvl6+rl1JqEtBXaz0GuB5//aDKngGe0VqPBLxKKRn60YIlxbQnwuqfGNzr8V8bZB8ooEdCOn96/GlS47vJFX8Q5JS6Wb43G4D28dF8d+vZLL3jXGn8RUDqqwYayHzB9TkDWFD2WVuUUm2UUgla64KyeQYmAJeXbb/1JL+XCDG7zUGHxF5kHtvKeWm3cij7IL2GDCDxMnlnMBhM0+QfK3dy92c7SYjOZPP9F5AQFcmgDvLvLQIX0JzAJ6gDv0wiA5Bdtq4ASAUKgTlKqeHAj1rrBxv6wE2bTrzcb0ZGRsM7tTJNfc4FRgEA+/YcxGaJZMe2nU36/SE8fs4HilzMXnmIlYeLiY2wco1KQm/agDWMHvSGw8+5umCcczATQHWWal93Bp4H9gCfK6XO01p/Xt8HpKWl4XAcfzdCRkYG6enpx31cSxaKc87/eTMFuQcZMnQIjoimrynT2n/OXp+Pl5Zofv9fTYnLy5QBnblZxTBlwuhQh9akWvvPuTYnes6GYdR74Xyy3Tz1OYj/ir9cJ/wvlQHkAHu11ju11l7ga2BQEGMRQeL2GmQX7sPtNfCVveHr9rpCHFXr5DPhnVU7iY6I4J9XjueT60+nfYzM0iVOXCDzATiA3wJdtdYPlJWBWK+1djZw6GL8U0rOLevmOai1LgTQWnuUUruUUn211tuBdPwjgkQL4fYaZBfs54dt7+N0FxNpi8bj8zf8ize+ztRht8tD30bg8nhZsS+HCb3aY7dZef+aibSJjiQ1LirUoYlWIJAuoJeBfGBc2fJw4G78heHqpLVeqpTKUEotxT+P8K1KqWuBfK31fOAu4K2yB8IbgU9P7BREU3N5nHywajZur/HLukqzdhU4c6SmTyNYtS+HmfOWsfVIARn3nMegDkn0S5UhtKLxBJIA+mutxymlvgXQWr9SNmF8g7TWD1Rbtb7Sth3A+IAjFc3GseLDVRr/co6IWAxPMYnRqVLT5ySUuDw8+r/1PPv9FnymyW9H96FLotTpF40vkATgKfvbBFBKxQLRQYtINHtJMe0qvrZgxcQnb/k2ku92HObGD5azI6eQXm3jmHvpaH7Vt2OowxKtVCAJ4AOl1NdAL6XUC8C5wN+CG5Zozsob97ZxXThj4DVVGn15y/fkvLFiB7uOFnHPpIE8es4QYiKbcqCeCDcN/nZprV9SSq0ATsM/PeSvtdbhNwhX1BAZEUVMZII0+idp1b4cTu2WAsCcC0dw+4T+jCxbFiKYAhkFtBx4B3hDa50b/JCECA/ZRU7uXrCK99fuYd7/TWTa4O6kxEWRIiN8RBMJ5P7yXuAyYK1Sah3wT+ATrbUM9hbiBJimyX/W7eHO+avIKTYY2a0tSkb3iBAIpAvoJ+AnpdSd+AvCXQW8gr+cgwgzbq9BbpH/fT6Xx4nba8gD3+OQmVfMrR+t5LOfM4m223jmgnRun9AfmzWY72QKUbuAnjCVzQ18EXAp0AuYG8ygRPPk9hosWDOHYiMPgKNFmXy27iXOH3qbJIEAfbh+L5/9nMnpfdoz99Ix9E6JD3VIIowF8gzgf/jLNCwAntRaLw16VKJZyivJqmj8y+WXZstLXw3YdbSQzokxOCJs3Da+P52TYpk+uJvM0iVCLpA7gOeB/wYyAYxo3ZJi2mOzROA1PVgsVkzTJy991cPr8/HCj1v5wxfruGfSQB47dygRNiuXDuke6tCEAOpJAEqp57XWdwIPAg8opaps11pPDHJsohlxew3ySrKId7ShwMjlwuF34/KUyEtfddh06Bgz5y1j5b6jpMY5SKtnnmQhQqW+O4A3y/7+fVMEIpovt9dg4ZrnKDKOVaz75ue3pe+/Fi6Pl9lfb2L215twe31cmd6TZy8YIUM7RbNU34xg5XV7fqO1vrbytrLnAt8HMS7RjOSVZFVp/EH6/uuyYl8Ojy3eQJfEGF6ePorzBnYJdUhC1Km+LqArgZuANKXUD5U2RQLtaj9KtCYlrgJyCjOJjUzAbnPg9hoVtX+k7/8XJS4PxS4PqXFRTOjVnrevGMcFg7qQEBUZ6tCEqFd9dwDvKaW+A94D/l+lTT5gc5DjEiFWbOTzwao/U1YDsEK0PYEJ6lJS4rtI9w/w7Y7D3DBvGf3bJfLJ9adjsVi4Kr1XqMMSIiB1vn2ilOqotT4AXAfsr/TnACBPtFqRyrN6lcsu3Ef1xh+gxJ1HhM0e9o1/fqmLGz9YxuRXvmRPbjED2yfi9dX89xKiOavvIfAzwBX4p2s0qTqnr4n/hTDRQpWP6ol1JLFow98pcuYSaYumV+pQrFYbJUZ+xb4WrMRFtaHQeVS6foBPN+/nlg9XcLCglFM6JvH6ZWMZ0bVtqMMS4rjV1wV0RdnfPZsuHNEUXB4n/1n5JF6fu+p6bylbDy+rsb+Jj7F9LiHCZg/7YZ85RU6ufHcJbq+Px84Zwn2nDyIywhbqsIQ4IYG8CXwu0FZr/a5S6j1gJHC/1vrjoEcnGp3ba7DryNoajX+5Mb0vJiW+Cx6vix+3zaPIOEZidGpY9/mbpkl2kZN28dGkxEXx1uXj6N8ugYEdpCdUtGyBvAn8R2BqWSKwAcOAzwBJAM2c22tw8NhOMo9tJTm2AzZrBMt2zMes1LdvwUqsI4kiI5eEqBR6tRta0dBfOPwu8kqywvqqf/+xYm7+aAXbswtYN+t8ou0RXDJYhr6K1iGQBFCitc5RSp0H/FNrXaSU8gY7MHH8yvv17bYoslxbeH/ZfHwVM3rWzsTHuL7Tau3esdscYTvO3+czmbt8Gw9+tpZCw80ZfTtQ4HQTbZcZukTrEchvc5RS6j78U0HOUkr1BRKDG5Y4Xm6vwSdrn6fQWfecPR0T+3Iof3uVdeHevVOb7dkF3DBvGT/sOkJilJ3XZozhNyN7S/E20eoEUoT8BqAz8H9aaydwNvBAUKMSxy2vJKvext9isTK6z4UkRPmnGox3tOXstJlSzqEa0zSZ8fYP/LDrCBemdWXT7y7gulF9pPEXrVIgE8JsVko9BwxXSl2MfzawfcEPTRyPpJj2RFgj8fhqTtTmiIhlypCbSYxOYeqw28O+X782+aUuEqMjsVgsvHjJSA4VlkrJZtHqBTIK6CbgfmAV/juGZ5RSj2qt3w52cCIw5X3/SbGdyCncw5TBt7B+ywpGDj69RsXOcO7Xr43h8fLklxv520+ajHvOo0dyHON7SaUTER4CeQZwNTCgrPsHpVQs8BUgCaAZqK3vf8n2eXSxTSAxOiWEkTV/y/ZkM3PeMrZk5dOtTSyHC0vpkRwX6rCEaDKBJABPeeMPoLUuVkrJhPDNRE5hZo2+/4LSHAxHfh1HiCLDzR++WMeLS7ZimnDLOMWfpgwjPsoe6tCEaFKBJID9SqkXgS/Lls8G5BlAM1BiFPDV5po3YonRqThMGahVl1mfZPDa8u30S03g1RmjmdArvEtbiPAVSAK4AbgD+A3+GkDLgReDGZQIzJ6cDXjNqjdjY/tMo2fqYDas2xSiqJqnUrenYgz/7888hXZxUTw4OU3G9YuwFtB7AFrrPwc9EnHcIu2xVZbjo9rSM3WwjO6pZsHGfdz28Ur+8euxnKk60SUplsfOHRrqsIQIufomhJkAzAPsSqks4AKt9c4mi0w0qHLFToCRPadK419JVmEpd8xfxYfr9+KIsLI7tyjUIQnRrNR3B/AkMLnsPYAzgNnAjKYJS9SlxFXAgdxtxEcl10gAETZ5iAn+l7n+mbGLexas5lipi7E9Unltxhj6t5fnIkJUVl8C8GmtNwNorb9WSv3heD9cKTUHGI3/2cGdWutVtewzGxijtT7teD8/3Li9Bh+t/mutlTwtWEmMSQ1BVM3P26t2cf1/lhIbGcELF5/KzWMVVqu80CVEdfUmgAaW66WUmgT01VqPUUoNAN4ExlTbZyAwEai9NrGoIq8kq0rj3zGpD4fydgD+om7FRh4xkQmhCi+kfD6zYkauy4f3YOW+HH73q0Eyrl+IetRXCyhZKfWr8j9Am2rLDTkDWACgtd5Sdnz11ukZ4OETijwMJcW0x2rxTz6SGJ3KhH4zSIxOrVgO15m69JF8Tn95Me/rowA4Imy8PH2UNP5CNKC+O4BjQOVun7xKyybwTQOf3QHIqLScXbauAEApdS3wPbAn0GA3bTrxoY0ZGRkN79QCRJjReDHobI5ny8btdDbHk+LIx2Em1hj62VrOuS4en8l7W47y2sZsXD6T+B6JrF69Ouzq97T2n3Nt5JwbR31TQp7eyN+r4n+lUioZ/3sFk/FXGg1IWloaDsfxj3LJyMggPT39uI9rjvZlfIfhgZEjRtW7X2s659qszczl1nnLWHsgl/bxUbx4yUh6uHNa9TnXprX/nGsj5xw4wzDqvXAOpBz0iTqI/4q/XCfgUNnXvwJSgR+B+fgrjc4JYiwtnttrkF24D5/Ph8/nxe01Qh1SyPx8OI9Rzy9i7YFcrj21N5t/dwHTBncPdVhCtDjBfA1yMfAoMFcpNRw4qLUuBNBafwh8CKCU6gG8pbW+O4ixtFhur8G+o1tYuesTDE9JxfrP1r0UdrX8fT4Tq9XCwA5J3DpOMWVAZ85UnUIdlhAtVtASgNZ6qVIqQym1FP8IolvL+v3ztdbzg/V9WxO312B+xrOUuGoWdssvzSavJCssSjsXOt08vGgtBYabty4fB8Cci04NcVRCtHyBzAfQHf9onbZa69OVUjOB77TW2xs4FK119ZnD1teyzx7gtICiDTN5JVm1Nv4QPqN+/rf1IDd9uJx9x4oZ0D6RAqeLhKjIUIclRKsQyB3Aa8BLwL1lyxp4FWjsh8SimlhHEpER0bg8pfifoZvEO9oytu8lrX4e39wSg3sXruad1buIsFp4aHIaD08eTJTdFurQhGg1AkkAdq31J0qpuwG01j8opYIclig28vlw1Z8x8b/c5LDFcNqAK1p9ww/gdHtJf/Zz9h0rZniXZF6bMYahnZNDHZYQrU5AzwCUUkn4x/6jlBoERAczKAGH83dVNP4AhreYCJu9VTf+pmlisViIstu4dZzCZ5rcM2kgEbZgDlYTInwFkgAewz8HQEel1AYgBbgqqFEJ4qOqXvEmRKe02j5/0zR5a9VO3l29i//eOBm7zcqs0weFOiwhWr0GE4DW+lul1DAgDTCAbZWniBSNr9RdRHaBf9K19vE9Ses6kQ6JvVrl1f+e3CJu/GA5X207RJwjgg0Hj5HetW2owxIiLAQyCuixWtahtf5jcEIKb26vwQcrZ+MzvQBkFe7GubuI84feFuLIGpfX5+PlnzQPL1pHscvDOf078cr00XRrE9vwwUKIRhFIF5C30teR+Kt3rglOOOHJ7TXIK8nCZo1kc+YPFY1/udY45v+Kd5fw4fq9JMdE8vL0cVw5vGfY1fARItQC6QJ6tPKyUsoGfBS0iMKM22vwydoXKHQerXOf1jjm/+oRvbAAL1x8Ku3iZUyBEKFwIm8C24E+jR1IuMoryaq38S+f5L2l9/+vyTzK/Z+u4d2rxtM+PprzB3bh/IFdQh2WEGEtkGcA+6HSeERIBt4KVkDhJimmPY6ImCp1fsolRqe2+Ma/1O3hsf9t4Jnvf8brM1m4aT83jOkX6rCEEAR2BzC+0tcmUKC1zgtSPGHHbnPQp106mw/+yIR+l+P1uWif2BOXp4SkmPYtuvH/cVcWN8xbzrbsAnomxzH30tGc0a9jqMMSQpQJJAE8pbW+LOiRhDGb1f9jiItKpH1Cj9AG00j+tmQrd8xfhcUCd00cwGPnDCHWIZPWC9GcBJIAdiulrgOWAq7ylVrrXUGLSrR4Z6lOjOjalucvPpXR3WWyeiGao0ASQG1X/ybQq5FjES1YTpGTez/J4JZx/RjVPZW+qQksv/NcGdopRDNWZwJQSl2ptX5Pa92zKQMSLYtpmnywfi93zF9JdpGBicmosit+afyFaN7quwO4HnivqQIJR6ZpcqRgL0cK/WUfPF53iCM6PgfzS7j1oxV8sjmTqAgbT50/nDsnDgh1WEKIAAVzSkjRgEN5O1m8+fWK5aU7PuKi4Xe3iJE/S3cf4fzXvyHf6WZS7/a8OmM0fVISQh2WEOI41JcAxiql9tWy3gKYWuvWU5cgBA7n72LTge+rrCs28lpMyYfBndrQrU0sN49TzBzVF6tVunuEaGnqSwBrgV83VSCtXXm9n6SY9ri9Bv/d+GqNfeKj2jbbkg9en48Xf9xKmxgH/3dqb+Icdtbcc740/EK0YPUlAKfWem+TRdKKVa73E2WPo3NS7W/Cju1zSbPs/tl8OI+Z/1nGin059G4bz5XDexJhs0rjL0QLV18CWNlkUbQy5Vf7FtPKwfztREXEVtT7cbqL2Jlds5hqYnQqKfHNqzaOy+PlL99s5smvNuL2+vj1sB48d9GpMkOXEK1EnQlAa31/UwbSWri9BvMznqXElV/rdgsWTu15Phszv6PUXdhsJ3nPLnJy5t+/ZOOhPDonxvC3aSOZOqhrqMMSQjQiGQXUyPJKsups/AFMTFITunLJiFkVzwSaU8NfLiXWQbu4KGaO7stfzh9OYnRkqEMSQjQySQCNrLaHuBasxEW1odB5tKK2v93maHajfb7bcZhle7J5cPIpWCwWPp95Bnbp7hGi1ZIE0IjK+/4B4hzJnD7gKo4WHaBLssJuczTbK/78Uhf3f7aG15ZvJ8Jq4YrhPemeHCeNvxCtnCSARuD2Ghwp2MeSbfModRcCUGTk8oN+n/OH3lbR4De3K36Az37O5JYPV3Agv4S0Dkm8dtkYuifHhTosIUQTkARwktxeg48znqHUVVBjW3Oey9c0Ta59fynvZuzCbrPyyNlDuP9Xg4iMsIU6NCFEE5EEcJLySrJqbfyhec/la7FYaB8fxchubXltxhjSOrYJdUhCiCYmCeAk+ad0jMbwlFJWJaPZDu08kF/CKz9pHjtnKFarhcfPHUqE1YLNKn39QoQjSQAnqHJphx5th6CzljNJXUFcVGKze9Dr85m8vmI793+2hgKnm6Gdk5k+pDsO6e4RIqwFNQEopeYAo/FPIHOn1npVpW2nA7MBL6CB32qtfcGMpzG4vQY5hZl8r/+F012M3RqFz/QAsHrPIi4aflezavx35BRw47zlfLczi4QoO3MvHc20wc3vmYQQoukFLQEopSYBfbXWY5RSA4A3gTGVdnkVOF1rnamU+gA4B1gUrHgag9tr8MHKP+Pylv6yzues+LrYONasHvrOXbaNexeuptTtZeqgLvxt2ig6J8aEOiwhRDMRzM7fM4AFAFrrLUAbpVTlgvHpWuvMsq+zgbZBjOWkub0Gu7M3VGn8y0XZ/cMmm9tD39jICOIcEbx/9QTm/+Y0afyFEFVYTNMMygcrpV4FPtdaLyxb/hG4Xmu9rdp+HYEfgVFa66O1fVZGRkYPYHdQAg2A1/SgnYvwYtTYFkkcvRyn46YYhyURmyV0j1VcXh/v61wu6dOG+EgbpmlS5PYRHyl9/UKEuZ7p6el7qq9sytaqRu1gpVQ74FPglroa/8rS0tJwOI6/fz0jI4P09PTjPq5cduE+fl5fs/Ef22caPVMHN4s+/xV7s7lx3jI2H84nKimFyzrBiBEjQh1WkzrZn3NLJOccHk70nA3DYNOmTXVuD2YX0EGgQ6XlTsCh8oWy7qAvgN9rrRcHMY6TVnlUj9Xiv5pOjE5tFo1/seHm3oWrGffif9l8OJ+bxvbj92eeEtKYhBAtQzDvABYDjwJzlVLDgYNa68JK258B5mit/xvEGBqF3eagY1If9h3dzHlDbsNnupvFUM9le7K55l9L2HW0iL4p8cydMYZJvZvPMwghRPMWtASgtV6qlMpQSi0FfMCtSqlrgXzgf8A1QF+l1G/LDvmX1rrmPInNRPmVf3RkHDGR8SGOxi8qwsaB/BLuO30Q/+/swUTb5bUOIUTggtpiaK0fqLZqfaWvQ99xHgCXx8m+oz9TbPjLPbi9rpDG88mm/fROiWdQhySGdUlm18OX0CEhOqQxCSFaJqkBUA+312D+mmdZsn0e2YV7APh685u4vTUfCAfbkcJSLv/nD1z8j++49aMVFeul8RdCnCjpM6hHbYXeCpxHm/RlL9M0eW/Nbu5esIrcEheju6fw8rRRTfK9hRCtmySAeiTFtCfKHofTXYQFKya+Jn3Z63BBKb+dt4wvthwgJtLGcxeN4JZxSoq3CSEahSSAethtDgZ2Gs+avf9lTN+LaRPTvklH/9htVjL2H+WMvh2Ye+loerZtHg+fhRCtgySAeri9BsXOPABsVnuTdPtszy5gf14xv+rbkbaxDpbdeS7d28RisdR4j04IIU6K9CXUweVx8tGqv6KzlgOweveioD789Xh9/PWbzQx9+jOufHcJBU7/aKMeyXHS+AshgkLuAOqw7+jPOD1FFculroKgPfxdfzCX3/5nGWsyc2kXF8WLl4wk3mFv9O8jhBCVSQKoQ0xkYpXlhKiURn/46/J4efzLDTz1zWY8PpNrRvTi6QtG0Da2RbwiIYRo4SQB1CHC5r8C75EyGNVhVFCmd7RZLXy17RCdEmP4+/TRnN2/U6N+vhBC1EcSQAPiotrQMal3o31ekeHmh11HmDKgMzarlX9fPZHkGAfxUdLlI4RoWvIQuAl9qQ8y5OlPuejNb9lw8BgA3ZPjpPEXQoSE3AE0gWMlBrM+yeCtVTuxWS3cd/og+qUmNHygEEIEkSSAIJu/cR+3fbSSw4WlDO3UhtcvG8uwLsmhDksIISQB1KbEVcChvB3+r40C3F7jhB8AL/r5AMdKDZ6cMpR7TxuE3Sa9bkKI5kESQCVur0FOYSaLN72BiQ+AXdlrOVqUyflDbwsoCZimyZfbDnFmv45YLBb+ekE69542kP7tExs8VgghmlLYJwC31yCvJItYRxIL1zyP4SmusU9+aXZAL4HtzS3ipg9XsFgf5J0rxnFlei+SoiNJio4MVvhCCHHCwjoBFBv5fLL2eQxPCTaLHa/prnW/hiqA+nwmryzVPPj5WopdHs5SnRjfs12wwhZCiEYRNgmgyMhnd/Y6YhyJRFj9wy735WzG8JQA1Nr4xzvaMrbvJfW+BKaP5DPzP8v4aU82baIjefHXY7lmRC+p3yOEaPbCIgF4TQ8L18zB7XXWs5eFmMgESlz5ATX85b7edpif9mQzbXA3Xrh4pMzQJYRoMcIiARhmfpXGf0DHccRFtaHIeYwth34qW2syod8MImz2Bmv+rzuQS7/UBGIiI7hpbD/6tUtgcr+OQT4LIYRoXGGRAByWRCJskXi8LhKi2jK8x1nYbQ7cXoODedvIL80mMTq1wSt+p9vLY4vX8/R3P3PXxAE8NTUdq9Uijb8QokUKiwRgs0TQMbEX+3O3cmbabysaebvNwflDbyOvJKvBq/4lu44wc94ytmUX0CM5ljOl0RdCtHBhkQAArBYbAHZb1SGZdpuj3uGdhU43Dy1ay8s/aSwWuH1Cf544dyhxUq9fCNHChU0COFEbDh3j5Z80/dsl8NqMMYyV4Z1CiFZCEkAtcksMSt1eOifGMK5nOxZcdxpn9utElN0W6tCEEKLRSGGaSkzT5MP1exn0l0/4zfs/YZomAFMHdZXGXwjR6sgdQJlDBSXc9vFKFmzcT1SEjbNUJ3ymiU1e6BJCtFJhnwBM0+QfK3dy36cZ5JW6mNirHXNnjJF6/UKIVq/VJwC316DEdxSPz1227CLKHluxPbvIyb2frMY04W/TRnHD6L5YrXLVL4Ro/Vp1AnB7DRaueY4i4xgY/nVfbnqdKUNu43Chlx7JcbSLj+b9qycwqH0SXdvE1v+BQgjRirTqh8B5JVn+xr+SAudRLn/7U3718mKKDP9dwTn9O0vjL4QIO0G9A1BKzQFGAyZwp9Z6VaVtk4E/AV5gkdb68cb+/kkx7XFExGJ4irFgxcTHoUIH/9tWwoWn9MLl9TX2txRCiBYjaAlAKTUJ6Ku1HqOUGgC8CYyptMsLwNnAAeB7pdRHWuufGzuO8qGcBYaVV1Z0xelL5v1rxnJhWtfG/lZCCNGiBLML6AxgAYDWegvQRimVAKCU6gXkaq33a619wKKy/RtVTmEmLq+/3n+8w8OUQZ1Zc+/F0vgLIQTB7QLqAGRUWs4uW1dQ9nd2pW1HgN4NfeCmTZuOK4Aib1aV5akdI9j588bj+oyWLCMjo+GdWhk55/Ag59w4mnIUUH1jKwMad5mWlobD0fDE7OXcXoOja3+mwJlDQlQKY4edEdDE7q1BRkYG6enpoQ6jSck5hwc558AZhlHvhXMwE8BB/Ff65ToBh+rY1rlsXaOy2xxMHXY7y9d8z+hhk8Km8RdCiEAE8xnAYmA6gFJqOHBQa10IoLXeAyQopXoopSKA88v2b3R2m4MYa1tp/IUQopqg3QForZcqpTKUUksBH3CrUupaIF9rPR+4GXi/bPf/aK23BSsWIYQQNQX1GYDW+oFqq9ZX2vYDVYeFCiGEaEKt+k1gIYQQdZMEIIQQYUoSgBBChKmWUg3UBuByuU74AwzDaLRgWgo55/Ag5xweTuScK7WZtU5paCmvldOcZWRkjAd+DHUcQgjRQk1IT09fUn1lS7kDWAVMwP8imTfEsQghREthAzrib0NraBF3AEIIIRqfPAQWQogwJQlACCHClCQAIYQIU5IAhBAiJAZRvAAAB1BJREFUTEkCEEKIMNVShoEGLNQT0YdCA+d8OjAb/zlr4Ldl03C2aPWdc6V9ZgNjtNanNXF4ja6Bn3FX/JV1I4E1WuubQhNl42rgnG8FrsL/e71aa31XaKJsfEqpNGAhMEdr/VK1bY3ahrWqO4DKE9ED1+OfeL6yF4BpwDjgLKXUwCYOsdEFcM6vAtO11uOAeOCcJg6x0QVwzpT9bCc2dWzBEMD5PgM8o7UeCXiVUt2aOsbGVt85l80tfh8wQWs9HhiolBodmkgbl1IqFngR+LqOXRq1DWtVCYBmMBF9CNR5zmXStdaZZV9nA22bOL5gaOicwd8oPtzUgQVJfb/XVvwvSX5Stv1WrfW+UAXaiOr7GbvK/sSVTSgVA+SGJMrGZwBTqGWGxGC0Ya0tAVSfbL58Ivrath3B/4ZcS1ffOaO1LgBQSnUEzsL/S9PS1XvOZRMPfQ/sadKogqe+800FCoE5SqklZd1erUGd56y1dgKPAruAvcCK1jKhlNbao7UurWNzo7dhrS0BVHfSE9G3QDXOSynVDvgUuEVrfbTpQwq6inNWSiUDv8F/B9BaWap93Rl4HpgEDFNKnReSqIKr8s84AXgI6Af0BEYppYaEKrAQOuk2rLUlgJBPRB8C9Z1z+X+WL4Dfa62DMu9yCNR3zr/Cf1X8IzAfGF72MLElq+98c4C9WuudWmsv/r7jQU0cXzDUd84DgF1a6xyttQv/zzq9ieMLhUZvw1pbAmgWE9E3sTrPucwz+EcT/DcUwf3/9u49xKoqiuP4d2oqi0qKSSMMJIyfBUaiaFpGPv4ItMBXLwiRwCkFHxUFYjWYA0omBVkYEpL4mFL7QyksE8NplMIHidQqGoMUjMQHRBAzWX+sffN0He9cnXEe96wPzB9zztn77HNmuOvcffZe+zIp9XfeZGZ3m9l9wGR8VMyC7mtqpyh1va1As6Q707HD8NFevV2p/+tfgLskXZt+Hw781OUt7GKX4zOs4pLBSVqKj/44C8wBhpIWopf0ILAsHbrZzJZ3UzM71YWuGdgOnAL2ZA5fb2bvd3kjO1mpv3PmmIHAmgoZBlrq/3oQsAZ/oDsEPFchQ31LXXMt3tXXCjSZ2Uvd19LOI2kY/tA2EGgBjuEv+I9cjs+wigsAIYQQylNpXUAhhBDKFAEghBByKgJACCHkVASAEELIqQgAIYSQUxWXDTT0TmnIpvH/IasA883s4AXK1AHVZraoA+d9CM+8eCBt6gPsx7NPtlxkXQ/juZfqJY0GjptZs6S3gLVmtq8D7azDhz0eSZuqgaNArZmdKVHuNmCwme281HOHyhUBIPQkv3fTmP1DhfNKqgI2ArXAO6UKFUuT7QoT7mYCDfiM1c5KVbw2G+wkLcNTIrxcosxYfOZsBIBwnggAoceTNBhYhU/6uRFPa7E9s78aWA0Izx1/wMzmSLoaWAkMwlNhbzCzkjmCzOwfSY3A4FT3ROBV4M/0M8vMjqVJSuPw7I3HgBnAk8AEYDMwHRghaUEqvwRfl2GemTWlunfgk34OA+/iWS2vBxaa2Y4ybk0TMCvV9QA+QeivVM9sfBJgPVAl6SQe0C7qfoTKFu8AQm9wK/CKmY0H5uIfallDgJFmNsrMRgMHJfUF5uEpBMYCI4EnJN1T6kSS+gCPALslXYcHlqmpjs+AJZJuwmemjjKzMcAWoH+hjjQb+SDwQlHXyzrOpTfohz+Zfw68h+fzHwc8CqxOQa1UO6uBpzjXZVaDzwAehyeGW2hmR/AZwmvNbMWl3I9Q2eIbQOhJbpG0q2jbdDwJ2BuS6vFVr2qKjvkeOCHpUzzr6UdmdiathjYgLS4C3r8/CPiuqPyQovNuNbMGSfcCv2XWU9gFPGtmpyRtB76S9AnQYGZHJbV3fRuBr4Hn8UDwsZn9ndp5g6TX0nEtQD/OT/T1dHrSr8LTIrwNLE37jgPLUwDriz/9Fyv3foSciAAQepI23wFIWo93V3yQlsvblt2f8sOPSUnDJgHfSrof7w5ZbGab2jnvobbOi3cnZVUVtpnZtNQ1NREPBFPbuzgzOy6pWdII4HE8EJDaOcXMTrRTxX/vACRtxbOAthb24S+Ed0qaBLzYRvly70fIiegCCr1Bf7yfHPyD85rsTknDJc0ws/1mthjYh+eKbwQeS8dcIWlFWi+gXD8C/TJLLE4A9kq6Q9ICM/sh9aFvAYrz0Z8FrmqjznX4Eoc3Z0YFZdtZk0YNtWc2UCdpQPq9P3BY0pX4t6bCPcq2o6P3I1SYCAChN3gT+DB1uzQCJyVlX17+DEyT1CRpJ3Aa72pZCfwhaQ+wFzhtZmUvHZhWZnoGaEhdROOBRfjwy6GSvpH0Jb4oyeai4l8AqyRNKdq+Be+735DZNheYLGk3vmJbuyN2zOxX/KVvIbPrslRuK97vf7uk+Xiu/JmSXqeD9yNUnsgGGkIIORXfAEIIIaciAIQQQk5FAAghhJyKABBCCDkVASCEEHIqAkAIIeRUBIAQQsipfwHiZVjoySlMPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8JUnKmY4sdx5CuAl4GtAEXhFj/FrLcz8DvBYoA++NMd6aZS2SJK0WmfW8QwjXAZfGGK8BXgq8qeW5InAr8GzgGcBzQwjnZVWLJEmrSZaHzW8APggQY9wD9IUQNqTPbQEGY4zHYowN4NPAMzOsRZKkVSPLw+ZnA7ta7h9LHxtOb68PIVwKPAT8GPC5DGuRJGnVyPSc9zSFqRsxxmYI4UXAPwJDwIOtz89m9+7d2VUndu3aNf9GOi22bbZs3+zYttk63fbNMrwPkvS0p5wDHJq6E2P8PHAtQAjhDSQ98DldddVVdHd3L26VApIP0M6dO5e7jFXJts2W7Zsd2zZb87VvuVyetdOa5TnvO4AXAIQQrgYOxhhHpp4MIXwihLAthLAWeC7wHxnWIknSqpFZzzvGeFcIYVcI4S6gAdwYQngxMBRjvB14G0nAN4E3xBiPZ1WLJEmrSabnvGOMr5720Ldbnvs34N+yfH9JklYjZ1iTJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLG8JYkKWcMb0mScqYjy52HEG4CngY0gVfEGL/W8tyNwAuBOvD1GOMrs6xFkqTVIrOedwjhOuDSGOM1wEuBN7U8twH4I+DaGOPTgStDCE/LqhZJklaTLA+b3wB8ECDGuAfoS0MboJL+ty6E0AGsAfozrEWSpFUjy/A+GzjWcv9Y+hgxxkngz4AHgIeBr8YY78uwFkmSVo1Mz3lPU5i6kfbAXwNcBgwDnwkhPCXG+O25drB79+5sKzzD7dq1a7lLWLVs22zZvtmxbbN1uu2bZXgfJO1pp84BDqW3rwAeiDEeBwgh3AnsBOYM76uuuoru7u4MStWuXbvYuXPncpexKtm22bJ9s2PbZmu+9i2Xy7N2WrM8bH4H8AKAEMLVwMEY40j63EPAFSGE3vT+DwLfy7AWSZJWjcx63jHGu0IIu0IIdwEN4MYQwouBoRjj7SGEvwQ+G0KoAXfFGO/MqhZJklaTTM95xxhfPe2hb7c89xbgLVm+vyRJq5EzrEmSlDOGtyRJOWN4S5KUM4a3JEk5s5STtCjn9vWPsm9wDIBSocD5fWs5b9Pak8/vHxxjYKJy8n5fb9djnpckLQ7De4VqDcJKrc7B4QkKFCgW4PvO6cs0FFvfu9lscnBonEIBRst1RstVujpK1Bp11vV0ct6mtSe3r9QaHBwap9ZoUms0OHtDr+EtSRkwvFeQ1tCcrNb53rFhuktF6jSpN6BYgGqjwcBEZdFD8ch4lbsPDQBQqTV48MQIpWIRaDJRqVMqJbPbbl3Xw/b1vXznYD8nRie564EjHBiaYLxWo7NYZH13Jxf0rWXPkSFGy4/u0164JC0ew3uZ7R8c49joJLVGk2q9wd7jIxSLBZpN2NTbyY4Nax6z/Tf29/PIwCiHh8fp6ihx8VnrH1coPnB8hCOjExwYqTB0eJBGownAxu5OdmxcM+vrxit1Hh4co7NYpFAosHVtEuqt9g2MM1ltUK03eMLmNYa3JC0Sw3uZxCNDHBwZp1pvcGR48uSqLX29XXOGZr3ZoH+8SmepQIECm9d0LzgU7zs6xMMDowAcH61Qqdc5Ml7lnK4Ozt4w+3u32tjbBUDYtnHWbbpKBS7oW8t3Dw8yXqlz96EBKrU6R0cmKRSgWCxw5fZNhrokLZDhnbHWQ+Hj5RqDk2UA+scq1JvQWSpw1truU3qts1nX1QnAJVs28HD/aNvvPVmtc3B4nGKhwOhk9eR7F4uwtbeH0sbutoMb4Irts4f29OebzSb3Hx9hYLxCrdl4zCmAHRsW/xSAJK12hncGWkNzZLLKgaFxujtKlGs10qPSFIsFNnZ3th3aU+YLzf2DY5wYK9OkSbnW4L6jw/R0Fqk3gSYne7zT37t/QVUsTLFQoKtU5Amb1z3m8e8dH87wXSVp9TK8F8n0Xu596WCzRrPJ2gUcjj7d9z4wNEa1npw3f2hglM5icgn/fIfhl8KVZ29a1veXpNUmV+G9+9AA2zc9vgFa81nItcr7B8c4PjYJTajUG9x3LBlsRhM29pw62GyxDUxUeGRglEcGoH+8SqmYnDnftrYn0y8Li6Vab1CpNRyRLkkL1FZ4hxB+DPhdYDOcHFtFjPEZGdU1owdOjNDVvfABWvNpDexyrc6DJ0bp7ihRqde5ZMupXxamRmhX6w32D47TaCbHwjf1dmUe2K0q9UcHr21e07XgQ/DLbWC8wu7DA3QWi3SWily+faPhLUltaLfn/WbgfwMPZ1jLvLpKpUXb12MnQWnwwIkRSoUC0KSno8Q5G9ew58ggaS6z99gw+4aS2cUOD09SqdfpLBbZ2NO5bL3c9d2PDl7Lo77eLgqFAmHbRr53fJjxSo1v7j/BWWu6uWDa+XFJ0qPaDe+HYozvzLSSRfDIwCj3HRsBmkCBy7au5/y+x4ZA62xg+wZGqTUaAPR2dpzSa56sNtg3kBwaHxyvUK43kxHaBWa8rnmpzTd4baVrPRd+dGSSiUqdArBlXU/b4d36JWx0sspYpQbAjg29PGlH36LXLEkrQbvh/YkQwq8DnwNqUw/GGB/IoqiF2D84xn3Hhqk3GlTqDY6NTdLb0cFEtc7mNV2c37eO/YNj9I8nl2g93J/8se8oFdnQ1cEFfbOHRJMmAxMVJmtJjz+Ph6bzYvOa7pO3R8oV7nzgCADnbljDRVvWP2bb1sA+PjrJifEKnaUik9Xko1lrNKnUG4a3pFWr3fB+Rfrvn7Q81gQuWtxy2rN/cIy9J0aYrNao1psMjFfo6khGV29b25tM33mon/FqnW/uP8Gh4QmOjE6cnA1sS5vXVfd2Js2T18PSeTJ1FGHPkSH6xypAgVqjQbla56It65MR9YPjNGkyNFHl0MgEXR0laDbZ1PvYL1X3ewmapFWurfCOMT4x60LaMThR4aETozwEDE0mA7WAGSc5mazU2X14kLWdJaDA9nW9i35NtbKxsbeLy7Zu4P7jwxwaGeeOeIByrcGx0Ul60nEP29Yt/2kLSVou7Y423wH8OfBDJD3urwCvjTEey7C2U9QajZOhPd8h7HXpYK4rtnuNcZ60fmFa393J4eEaA+PJIfKz1y/8C5gkrUbtHjZ/K/BJ4K9JLhV7JvAPwPMyqmtG67o7qdLeYWwnBsm/7Ysc1vsHxzg0PH7y/o4NLpYiKZ/aDe81Mcb/23J/dwhhSYMb4JIt66HUudRvq5xJZpprnpz85ejIBI1mMl3s4ZFJekpFyo0GnaWi4S0pl9oN77UhhB0xxkMAIYTzgJ7sypIen8MjEzRJLtIfr9ZJh0dwzoakN7/nyOAyVidJj0+74f06YFcI4TDJYfOtwEszq0p6nDpLBc5vs1e9kClxF8NSv5+k1afd0eYfCyFcDFxGMmDtvhjjZKaVSaepnasEqo0GE9UG3zpwgmOjZU6MleksFugoFblohilxH6/9g2M8MjBGo9lkstZg3+BYMgVvrc73n9tneEtakDnDO4TwkhjjP4UQ/tcMzxFj/O/ZlSZlZ6JS57uHB+npKAKPXr2wb2Bszte102tu3eboyCT3HBjmCV39DEyW6Swml7ptTy91u/fI0OL+YJLOCPP1vBvpv/WsC5GW0oaeLqC9Xvr+wTH29Y9SJ1nudd/AGD2dJcq1Oleft5nzNq1lX/8ox8eSWfwODI8zOlmjo1SgWm8wWmsyUast+ZS6e48Pc3QkOUC2bX0PFzvZkLRqzBneMcZ3pP/+WQhhfYxxJISwneTw+ZeWokApC7OFdusypcdGJinX6sksfpPlkwvj7EgHve0+NMDBoQlOjB3kxFiZsUo9ncWvSd+aRycOavR3zXp5Y6XRYLxS5xuPnKBQgK3rehZ8CH36/O6D6e3+8Qq1ZvL9+8joJOPV5Du459il/Gt3kpZbgG+FEG4H7gK+DrwQ+I0Ma5OW3PGxSRrNJp3FAuV64+Qa6TP1mtd2dTA4UaWUzMzL9vUL71lPVmrcc3iIns4STRp8/7lntRWsrdesD01UOTwyQXdHkVoD0pLpKBXo6+6hf7ySjL5vJhMdnb2ht+3wbv1iYOhLK0e7o81/IMb4OyGElwNvjzG+LoTw6SwLk5bD2q4Oao0ml26d/xDzE89aP+8281nfcvh+pjnZp59jPzw8QaPZoFxtcHS0nJ6zh81zzNffP16ht6PEBX1r2XNkiMMjE3xhb7Lwy4V9a09Zwe3h/lGOjiaH2w8OTTCertS2ZZ3hLa0U7YZ3+l2e5wCvTW93z7KtlFtXLfFKZLMdvt/XP0r/eJlKvcGB4QmajeSa9cla/eTRgHM2tjcD3fT3OD4ySa3epFyrA02GylXK1TrHxyYpFKB/ospkpUZnsUixUGDTmi5GylVq9ebj+2ElLZp2w/t7IYR7gGMxxm+FEH4F6M+wLumMM1lrcHBojKMjExwdLTNZSwJ0Y08X52xevB7v+p5k4ZfvHOwnHh2mr7eLWrMJTSgUoMipU9OOlKuL9v6SHr92w/ulwJOBe9L73wU+lElF0hmqp6PI4EQSksUCp7US3nxae+EdxSIdxcU5/C9pabV1nTfwP9KHnh9CaN3E67ylRbLU68af7uI9izFDnLPMSY+P13lLmle13mRwssqdDxyhWm9wYHCcjlKRSq3O5ds2tj1C/vDIBPV6k0q9wSODY3SWklH9l23ZYHhLC9DWdd4ka3n/SIzxToAQwnOBj2Vcm6QVZHC8fPL25jVdnL1hDfceGWLviRFOjJdpNOEp52zi/L51j+lZD4yXGSvXTl4v31FIRshv6u3k7A1riEeGqDcdDCctRLvnvN8MHAfuTO9fDzwfeEkGNUlagaYGurXa2NPJwESFkXKVsUqNvcdLHBga59homeHJKp2lAvXmo9eeL/Usc9Jq1W54XxZj/LWpOzHGPwghfC6bkiStNLNd0rZj4xp2bFwDwH/uO86D/aN0l0pQgL50vnhJi6/Y5na9IYTNU3dCCOfget6SWqzv7qSzVCJs30jYttHgljLUbs/7fwHfDSHsA0rAObiet6QW7SzyImlxtLue90dDCBcBV5Ks531vjHE808okSdKM2jpsHkLoI+l9/16M8RvADSGErZlWJkmSZtTuYfO/Bz4P/Eh6vxt4B/DsLIqSdOaoNhpU68kyrDDzhC2ubiY9VrsD1rbGGN8EVABijB8A1mRWlaQzRqPZ5KH+MR48MUo8OkR/ej35IwOjfOvACb514ATfOTjAnsND3HtkiHj01NXXpDNNuz1vQgidJOe7CSFsB/zqK+lx6ygWaTSbnLtxDXcf6mf/wDhHRybpHy8zVk0WZylQoC9d3Wz/4Bj/fu9+AC7sa39ednvvWk3aDe9bga8BO0IIHwaeCrwis6oknTFa51hf393FULlKMZ32fKbFWSaqdQYnqlTrTUbKAwwdHWPigSNcsClZm7w1pCerNUbKyXrkR0Ynmawmt8/q7T65zZHhCZo0aTbh0i0beOIWF2rRytfuaPP3hxDuAq4BysBvxBgPZVqZpDPOhZvXzfl861Kle44McWxkkv7xKsVjw9QbDQYmKgxPVjg8MkFnqUS1XiddCj1Z6nRdL/3jFfYPjVOtJ0s3lBsNSoUCE9U6lXqD/cPjFNJa7J1rpWorvEMI74sx/iLwrxnXI0ltW7dK6qwAABYWSURBVN/TxfoN3VSB3YeG2LSmi2YzmXt9tkli+scrrO/u5MJpS6HefWiAQ8MTjJRrTFRrdBSLHB+dhAJsWdtjkGtFafew+YMhhF8F7iIdtAYQY3wgk6okaR5Tk8LsGThIZ6nExt4Sl2+bf6KY2SaTSdY3L3LZ1g189eFj3HNkkK5SkSbw/eduNry1orQb3r9IMlit0PJYE7ho0SuSpAVajNndWvexoafr5GN7jzu6XSvPnOEdQtgAvBbYDXwB+JsYY3UpCpOk5eJUr1rp5rvO+2/Tf98CXAH8abblSJKk+cx32PzCGOMLAUIInwA+nX1JkrSytV6OBl43rqU3X3ifPEQeY6yHEJoZ1yNJK9beY8McHZ2kUm9weGSCAlCtN9m2vnfOCWAMey22+cJ7elgb3pLOKCOVGg+eGOWRgTH6xyvUmw06i0U29nRy9oY17DkyxN7jw4xMVqjUG2zs7eT+4yOP2ceJsUnGKnU6SkW6SwUu27bR8NbjMl94/0i6hveUben9AtCMMV6QXWmStPw293Yzkc7M1lkqsLm755RryHu7Sjxh8zqOjEwwPFllePLUcb1b1nazfX0vjwyOLUndWt3mC++wJFVI0gp1Qd/cPeTWkemtM8DNZrJWZ3SyxtcfOcGWNd1ceNY6D6trweYM7xjjw0tViCSdCY6PTjJRSXrym3q7GZwoU6k3eHhgjI5ikWq9wUVbnJpVc2t7VTFJ0uO3qbf75O39Q2OMVZJD7H29XZy9YQ33HhmiXnd4keZmeEvSEjo5reuRIfp6u0+ZEKbaaFCpN7j70AATlRqHRycoUKCjUODJ5/TZIxdgeEvSsphtFrdGs8nuwwNs6Omi2WzSWUrm0mo0mgxMVAxvAYa3JK0oXaUSXaVTF1mJR4eo1JIe+YmxMmPpOuXnb1rD9527eTlK1TIyvCVpBZmtR35ivMx3Dw/SUSpCs0mpWKBab3J4ZIJHBscBuHjLOi7fvum033v/4BjHRidppqfcD49V5n6Blo3hLUk50JcOdAtbN5x87MjIBMfHyoxWqlTrTb780DH2Hh+lSZMrt2/ioi3rebh/lAPpteXFYoHzNq3lvE1rH3N52tGRCWqNBpV6k2Ojk3SWipRrdTonakv/g6othrck5cBMPfLW68pbg3ysUmNo8hj3HBlitFJNgrhYpFyvM1Gt81D/KIeGJ6jUGnSWCtQakJ5a5+x0n/ceGWK+6P7KV77C5OQk119//eL+sJqX4S1Jq0BrkB8aGmd4sspYpUoB2Lo2mRXuP/cdZ//gOB2lIgWgb03XvJPKzOXmm2/m6NGjXHfddRQKhcX5QdQWw1uSVpkdG9ewY4ZT5+u7OwG4rOXQ++m65557uOOOO2g2m3zoQx/iZ3/2Zx/3PtW++dbzliStElds3zjrgLiFuuWWW2imI9te//rXU6t5fnwpGd6SpFM0mk2OjNf4VDzAp+JB9rcsqLJ//35uu+22k/fvv/9+3vWudy1HmWcsw1uSdIpNvV1M1ur0j1c4ODRO/3j55HO33nrrKT3tN/zF/2FiYmKpyzxjGd6SpFOcs3ENF23q4ZItG2jSPDll68e/cS9vf+c7T9n+2NEjvPWtb12GSs9MhrckaU5jlTp3HxrgOwcHePc7/onK5OSM2918880MDg4ucXVnJsNbkjSnjT1ddBZLPGFdJ1/6yAdm3W5wcJCbb755CSs7cxnekqQ5TY1S/8Tt72d4cGDObf/uzW/hq9+9f4kqO3MZ3pKkedWqVW7753+ad7tKeZI//4s38B/xIJ+KB3m4f3QJqjvzGN6SpHl99pMf5eihg21t++VPfph74n0cHBrnyIgj0LNgeEuS5tRsNnn/P72t7e0b9Tqf+Ze30VE0YrKS6fSoIYSbgKcBTeAVMcavpY+fC7y7ZdOLgFfHGP8ly3okSQv3lc9/hof2fm9Br7nzU5/kh5/3S1y69akZVXVmy+xrUQjhOuDSGOM1wEuBN009F2M8EGO8PsZ4PfBMYB/w4axqkSSdvvctoNc9pdls8uF/uCWDagTZ9rxvAD4IEGPcE0LoCyFsiDEOT9vuxcBtMUZHNUjSCvQ373jvab3u7kMDjFdqfHP/CQC2ruvhvE1rF7O0M1aW4X02sKvl/rH0senh/TLgWe3s8P69e6k2motTnU6x5949y13CqmXbZsv2zc7jadsHByaIe2FNR4Fqo8mVZ/Vyad/pL0G6Gu3atWv+jWawlEuCnrLYawjhGuDeGXrjM7rk4ouh1LnohSn5H/SKy69Y7jJWJds2W7Zvdh532x4ZApLrxPccGeTKc/r4vnM2L1J1+bdr1y527tw56/Plcpndu3fP+FyW4X2QpKc95Rzg0LRtngP8R4Y1SJKWyUKWH90/OMbAROXk/aPDEzRIjrRetnUjT9i8btHry7Msw/sO4M+At4QQrgYOxhhHpm3zQ8DpnUyRJOXeA8dHOD42SaXe4NDwxMlDtJV6g1KxwFi5xtHRMncfGqTRbHL1eZs9b06G4R1jvCuEsCuEcBfQAG4MIbwYGIox3p5utgM4mlUNkqSVodponFyZbGSiwuBkFYATYxXqzQadxSIbezrZsXHNY153aGic4XKVsUqViWqdgYmK4U3G57xjjK+e9tC3pz3/5CzfX5K0MoyVa9xzeIjOUpFms0mpmPSxuzoKrO/uYfv6mQey7di4hh3p7T1HXLFsylIOWJMknaE29XYDcNnWDctcyepgeEuSMreQwWuan+EtScqd1tHpA+MVJqpVqvUm67o6OGtdDwAnxso0mk2aTbhky/pVNWLd8JYk5UK51uD46CRfeuAIx8fKjFVqdJYK1BpQLEC13mT/0DjbJ5LBcBPVGqVigYlKnQ09HYa3JElLrdposG9wjM5iiUIBNvV2PWag254jQ/R2lrig77Gj0fccXn0D3QxvSVIurOtKZtgM22Ye9DbbefXWy9QA+nq7cn+5meEtScqF0x301mjCPYeH2djTQbne4PvPzf9EL66ULkla1bo7SvR2lrh4y4ZTF9nIKXvekqRVbTVepmZ4S5LOGJV6g4f7Rzk4NEazCVvX9dLVkRyEztO5cMNbknTG2NTbxVilBiTXgQ9NVuksFunqKMFmDG9JklaaJ/Q9eq13pTZItd7kki3r2D80voxVLZzhLUk6I1159qblLuG0Gd6SpDPe8GSF/QNjHB4e59yNa1d8sBvekqQz3prODobLVWqNJo1m0/CWJGmluzCd9/z+48McHp7k3+/dD8BFZ63n0q0r71IzJ2mRJCm1vjuZgnVwosrxsQqPDK7MgWz2vCVJSm1f33tysZP7jw8vczWzM7wlSZpBtd6kf7zMnQ8cAeAJm9ZywQpZVtTwliRpFsdGy1TrTSaqdXo6iismvD3nLUnSLNZ3d3LZ1g10FVfWkib2vCVJmsFKXtDEnrckSTljeEuSlDOGtyRJOWN4S5KUMw5YkyRpHtVGg7FyjW88cgKAbet7lnXtb8NbkqR5NJtw77Fh1nZ1UKk1eOoTthjekiStZF0dJbooccX2jdx9aIDDwxOcGCvTbDbZ0NNFV0dyFrqvt2tJQt3wliRpHq3XfK/p7KB/vAxA/3iZLWt66CgV6S4VYTNLEt4OWJMkaQEu3rKeS7Zs4JItG1jT1UG53uCCvrVM1OpUag3uPjTA/sGxTGuw5y1J0mm66uy+k7cPDo8zXqlBAbZnPKDN8JYkaRH09XYD0Fkq0Gw2M30vw1uSpEUwdV58z5EhKvUmdx8aAODh/uQQeqPZ5Mk7+njiWY9/ZTLDW5KkRfZQ/ygjk1UAChQoFmGiUuf42KThLUnSStTX203Y9thVyfYcHly0/RvekiQtoqVYStRLxSRJyhl73pIkLYEGcHBonE/fdxAoUB6vnva+DG9JkpbAxp5OhierTNbq1BpN+uZ/yawMb0mSlkDrpC3VRoPO4wOnvS/PeUuSlDOGtyRJOWN4S5KUM4a3JEk5Y3hLkpQzhrckSTljeEuSlDOGtyRJOWN4S5KUM4a3JEk5Y3hLkpQzhrckSTljeEuSlDOGtyRJOWN4S5KUM4a3JEk5Y3hLkpQzhrckSTljeEuSlDOGtyRJOWN4S5KUM4a3JEk5Y3hLkpQzhrckSTljeEuSlDOGtyRJOWN4S5KUM4a3JEk5Y3hLkpQzhrckSTljeEuSlDOGtyRJOWN4S5KUM4a3JEk5Y3hLkpQzHVnuPIRwE/A0oAm8Isb4tZbnzgfeA3QB34gxvjzLWiRJWi0y63mHEK4DLo0xXgO8FHjTtE3eCLwxxvhUoB5CuCCrWiRJWk2yPGx+A/BBgBjjHqAvhLABIIRQBK4FPpw+f2OMcV+GtUiStGpkGd5nA8da7h9LHwPYCowAN4UQvhhCeEOGdUiStKpkes57msK02+cCNwMPAR8LIfx0jPFjc+3g/r17qTaa2VV4httz757lLmHVsm2zZftmx7bNRq3e5Mlb17Br167Ten2W4X2QR3vaAOcAh9Lbx4GHY4x7AUIInwaeBMwZ3pdcfDGUOjMoVXvu3cMVl1+x3GWsSrZttmzf7Ni22ak2GnB8Hzt37px1m3K5zO7du2d8LsvD5ncALwAIIVwNHIwxjgDEGGvAAyGES9NtdwIxw1okSVo1Mut5xxjvCiHsCiHcBTSAG0MILwaGYoy3A68E3p4OXrsb+EhWtUiStJpkes47xvjqaQ99u+W5+4GnZ/n+kiStRs6wJklSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOWM4S1JUs4Y3pIk5YzhLUlSzhjekiTljOEtSVLOGN6SJOVMR5Y7DyHcBDwNaAKviDF+reW5h4BHgHr60C/HGA9kWY8kSatBZuEdQrgOuDTGeE0I4QrgH4Frpm32UzHG0axqkCRpNcrysPkNwAcBYox7gL4QwoYM30+SpDNClofNzwZ2tdw/lj423PLYm0MIFwJfBP4kxticZV8lAOq1xa9SAHQWC1CvLncZq5Jtmy3bNzu2bYYaSdyVy+VZN6lUKlM3S9Ofy/Sc9zSFaff/O/BJoJ+kh/7zwAdmee0OAAaPZFXbGe+STT1wwiEHWbBts2X7Zse2zU5n+u/u3bvb2XwHsLf1gSzD+yBJT3vKOcChqTsxxndO3Q4hfBx4MrOH99eAa9PX12fZRpKk1aREEtxfm/5EluF9B/BnwFtCCFcDB2OMIwAhhI3A+4HnxhgrwHXMHtzs3LmzTHJoXZKkM8nemR4sNJuznWZ+/EIIfwE8A2gANwI/AAzFGG8PIbwCeBEwAXwT+J05znlLkqRUpuEtSZIWnzOsSZKUM4a3JEk5s5SXirXNaVWzM0/bng+8B+gCvhFjfPnyVJlfs7VvCOFc4N0tm14EvDrG+C9LX2U+zfPZvRF4Icnfha/HGF+5PFXm1zzt+zPAa4Ey8N4Y463LU2V+hRCuAj4E3DS9/UIIzwReT/L5/XiM8XXz7W/F9bxbp1UFXgq8aYbNfirGeH36n8Hdpjba9o3AG2OMTwXqIYQLlrrGPJurfWOMB6Y+s8AzgX3Ah5el0Byaq23TmRv/CLg2xvh04MoQwtOWp9J8mqd9i8CtwLNJBiA/N4Rw3rIUmlMhhLXALcCnZ9nkTSRznfwo8KwQwpXz7XPFhTdOq5qlWds2/R/0WtJAiTHeGGPct1yF5lS7n90XA7c5r/+CzNW2lfS/dSGEDmANyeRPat9c7bsFGIwxHosxNkgC6JnLU2ZulUm+/Byc/kQI4SKgP8b4SNq+Hyf5fcxpJYb32SRTqU6Zmla11ZtDCF8MIfxFCGH6zG2a3VxtuxUYAW5K2/YNS13cKtDOZxfgZcA/LElFq8esbRtjnCSZU+IB4GHgqzHG+5a8wnyb67N7DFgfQrg0hNAJ/BiwfYnry7UYYy3GODHL09Pb/ihTs4rOYSWG93QzTav6+8D1wFUkhxp0egrTbp8L3Ewyac4PhBB+elmqWj1O+WIZQrgGuDfGODzD9mrfybZNe4ivAS4Dngj8cAjhKctV2Cpxsn3T+TdeRLIy5O3Ag8zw2daiaattV+KAtcWcVlWPNVfbHgcejjHuBQghfBp4EvCxJa0w3+b87KaeA/zHklW0eszVtlcAD8QYjwOEEO4EdgLfXtIK822+v7ufJzmtRnpU7qGlLG6Vm9725zLD4fXpVmLP+w7gBQAzTasaQvj3EEJXuu11QFuzuguYo21jjDXggRDCpem2O4G4LFXm16zt2+KHMFROx1xt+xBwRQihN73/g8D3lrzCfJvzsxtC+EQIYVs68Oq5+AV00cQYHwI2hBAuTMdsPIfk9zGnFTnDmtOqZmeetr0EeDvJl7q7gd9MB1CoTXO1b/r83cAzY4wukbdA83x2fwN4CVAD7ooxvmr5Ks2nedr3+SSnLJvAX8UY3z37njRdCGEnydU8FwJV4ADJ4OAH0/Z9BvB/0s1vizH+1Xz7XJHhLUmSZrcSD5tLkqQ5GN6SJOWM4S1JUs4Y3pIk5YzhLUlSzqzESVokZSCEcCHJtftfbnm4A3hNjPELi/Qebwe+SHId8BdjjC5gIWXA8JbOLMfSlc0ASFcv+o8QwrnOlyDlh+EtncFijPekM5NtCSH8HsmShL3A54FXxRibIYTXAj9DMnnHP8cYbw0hPJ1kUokyySpevxVj/Mby/BTSmcdz3tIZLITwPJIVja4Hzo0xXpeu534J8JwQwrUk0zU+DXg6yVrDm0iWifzNGOOPkyxm85rlqF86U9nzls4sW0MIn0tvX0CyhOZzgFcC17Q8t5Fkha4u4M4YYx2oA88DCCEcBv4qhNCTbjuwVD+AJMNbOtOcPOcdQvh54HdJFvEoA2+dPqdyCOEPmPkI3T8DvxFj/EwI4TnAH2ZataTH8LC5dIaKMd5G0mP+bZIR4s9PVzUihPDf0xXm7gJuCCF0hhA6QgifDSHsALYD3w0hlIBfALqX56eQzkyGt3RmuxH4E+BbwJeAu0IIXyYJ5wdijF8GbgPuJAn4D8YYD5EMVvsM8BGSlejODyG8cunLl85MriomSVLO2POWJClnDG9JknLG8JYkKWcMb0mScsbwliQpZwxvSZJyxvCWJClnDG9JknLm/wf3ZiNzgUOYwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelLogisticRegression = ModeloClasificadorKFoldCVThreshold(LogisticRegression, libConNoLem, \"LogisticRegressionThresholdNoLem\")\n",
    "modelSVC = ModeloClasificadorKFoldCVThreshold(SVC, libConNoLem, \"SVCThresholdNoLem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "rta0FrRRTiqM",
    "outputId": "37810138-98e8-45a7-aac3-ebc5d28e6f33",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7042831774081775\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.44      0.50       151\n",
      "           1       0.77      0.85      0.81       329\n",
      "\n",
      "    accuracy                           0.72       480\n",
      "   macro avg       0.67      0.65      0.65       480\n",
      "weighted avg       0.71      0.72      0.71       480\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Liberales': {'precision': 0.5726495726495726, 'recall': 0.44370860927152317, 'f1-score': 0.5, 'support': 151}, 'Conservadores': {'precision': 0.768595041322314, 'recall': 0.8480243161094225, 'f1-score': 0.8063583815028901, 'support': 329}, 'accuracy': 0.7208333333333333, 'macro avg': {'precision': 0.6706223069859434, 'recall': 0.6458664626904729, 'f1-score': 0.653179190751445, 'support': 480}, 'weighted avg': {'precision': 0.7069538626356808, 'recall': 0.7208333333333333, 'f1-score': 0.7099831406551059, 'support': 480}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.689505803880804\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.45      0.52       168\n",
      "           1       0.74      0.85      0.79       312\n",
      "\n",
      "    accuracy                           0.71       480\n",
      "   macro avg       0.68      0.65      0.66       480\n",
      "weighted avg       0.70      0.71      0.70       480\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Liberales': {'precision': 0.6129032258064516, 'recall': 0.4523809523809524, 'f1-score': 0.5205479452054794, 'support': 168}, 'Conservadores': {'precision': 0.7415730337078652, 'recall': 0.8461538461538461, 'f1-score': 0.7904191616766467, 'support': 312}, 'accuracy': 0.7083333333333334, 'macro avg': {'precision': 0.6772381297571584, 'recall': 0.6492673992673993, 'f1-score': 0.655483553441063, 'support': 480}, 'weighted avg': {'precision': 0.6965386009423704, 'recall': 0.7083333333333334, 'f1-score': 0.6959642359117382, 'support': 480}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelLogisticRegression = ModeloClasificadorKFoldCV(LogisticRegression, libConNoLem, \"LogisticRegressionNoThresholdNoLem\")\n",
    "modelSVC = ModeloClasificadorKFoldCV(SVC, libConNoLem, \"SVCNoThresholdNoLem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9A1kqkqG2rYr",
    "outputId": "aa08a6d8-69c0-4de4-be10-fa6b9422cdfd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7130444386694387\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.39      0.49       170\n",
      "           1       0.73      0.88      0.80       310\n",
      "\n",
      "    accuracy                           0.71       480\n",
      "   macro avg       0.69      0.64      0.64       480\n",
      "weighted avg       0.70      0.71      0.69       480\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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yJgJkHM7hZy/NZvHWDIZ2acWkm3tSzebzMR7zNAGIyDigO+AH7lPVbwOWjQVuBfKBxap6v5exGBOpdmQdYfD4WazaeYDhF7RlwvXdqVzJntE03vPsr0xE+gLtVbUHMBJ4JmBZXeA3QG9V7QV0FpHuXsViTKTalHmIvs+lsWrnAe7t3ZGXru9hnb8pN17+pQ0EpgKo6hqggdvxA+S6/2qLSBWgJpDpYSzGRJy1uw7Q57mZrM84yMMXn8u/rjyfSpWsoLopP14OATUDfAGv97jvZalqtoj8GdgAHAXeVtV1Je1w5cqVpQ7G5/OVvFIFY22OXJp5lF/M3cL+nHzu7dqEKxrnsWTJklLtK1raXJaszWWjPC8CHz+0cc8EHgQ6AFnAHBHpoqrLg+0gKSmJ+PhTvx/a5/ORkpJyyttFM2tz5Ppi427u+XAOWbn5vDi0G6N6dCj1vqKlzWXJ2hy6nJycoAfOXg4BpeMc8RdqAexwf+4EbFDVvaqaCywAYusbNTHpM03nkgmzOJSbx6Sbe51W52/M6fIyAaQBQwFEJBlIV9WD7rJNQCcRqeG+Ph/43sNYjAm7qSu2MOTlueQX+Hl/WF9uSj4r3CGZGOfZEJCqLhIRn4gsAgqAsSIyHDigqlNE5J/AXBHJAxap6gKvYjEm3N7wbeCOtxdRvUplpozox0Cby99EAE+vAajq74u8tTxg2XhgvJefb0wkeHGRcs8H31C/RjU+uWsA3Vs1DndIxgD2JLAxnvrHnJX84ZOlNKldnRmjB9KlhRVyMZHDEoAxHvD7/Tz86TIen72SxHo1SRuTijSxQi4mslgCMKaMFRT4uX/qtzz/hdKuUR3SRqfSygq5mAhkCcCYMpSXX8Bd737J64s3kNSsPjNHp9Ksbo2SNzQmDCwBGFNGcvLyueWNhUxZsYULz2zIJ3cNJKGmFXIxkcsSgDFl4EhuHtdOnE+aptOvbVOm3tGfOtWrhjssY4KyBGDMaTpw1CnksnDjbn7WqSXvDutDjar2v5aJfPZXasxp2Hsom0tfms2SbZlc16UVr1shFxNFLAEYU0rpB5xCLqt3HWBkt3a8OLSbzeVvooolAGNKYUPGQQb9ZxYbMw9xf59OPDkkhbg4m8vfRJcSE4CItAKeAhqqan8RuQuYp6o2eZuJSat37mfw+FmkZx3lkUHn8vCgc63zN1EplPPVl4DXA9ZVYIJnERkTwZZsy6D/C2mkZx3lySEp/GlwF+v8TdQKJQFUVdVpODN6oqqfexuSMZFp4YbdDHzxMzKO5PCf67rzQN/O4Q7JmNMS0jUAEakP+N2fzwbs0UYTU9I0nWtencex/ALeuKUXN3a1ufxN9AslAfwF+ApoLiLfAY2AWz2NypgI8uF3W7jljQXExcGHI/pxWefEcIdkTJkIJQEsBboCSUAOsA6wahYmJry+eD0j3/6SmtUqM/WO/vRv16zkjYyJEkGvAYhIJWAKkA34gJU4Q0HTvA/NmPB6YaEyYvIi6lWvStroVOv8TYVz0gQgIjcBa4G+QD5wDMgDDgNbyiU6Y8Lkidkr+MWUb2hapzpz7h5EN6viZSqgkw4BqepkYLKIPKqqjwYuExGrbGEqJL/fz0PTl/L3Oas4s0Et0kan0r5x3XCHZYwnSrwGoKqPikhnnIu/APHAM0AnLwMzprwVFPi5d8o3vLhoHe0b1SFtzMWc2aBWuMMyxjOhPAn8b2Aw0Az4AWgLPOlxXMaUq7z8Aka+8yVv+DZwbvMGzBg9kKZ17G5nU7GF8iBYN1XtBCxT1QuAi4Ga3oZlTPnJycvnhkmf84ZvA93ObMScuy+2zt/EhFASQI7733gRiVNVH9DTw5iMKTeHc44x5OW5TF2xlf7tmjJzdCoNrIqXiRGhPAegInI38DnwmYgoUN/bsIzx3v6juQz57xy+2LSHyzsn8s7tfahe1ebyN7EjlAQwBmgA7AduBJoCj3sZlDFe23Mom0snzGbp9kxu7NqaiTf1pGplm8vfxJagCcCdA6gtsEZVC4C33Pe7Adu8D8+Ysrdt/2EGj5/F2t1Z3Nm9HS9ca4VcTGw6aQIQkauBF4AdOPMAXY7zJPBjwFVAu3KJ0JgytH7vQQaN/4xNmYf5Zd/O/OOKZJvO2cSsYIc9vwG6qGoycDlODYAl7jZdyiE2Y8rUqp376fv8TDZlHubPl3Sxzt/EvGBDQDmquhtAVX0iUgO4XVUXl09oxpQd39YMLp0wm4wjOfzryvO5r489x2hMsATgL/J6l3X+Jhot2LCLK/47l8O5ebx0fQ/u6Gajl8ZA8AQQJyJxwPFz5MDX7kXhoERkHNAdJ5ncp6rfBiw7A5gMVAOWqOqYUrXAmCBmrN3O0InzOZZfwJu39uL681qHOyRjIkawawB9cWb/POb+K3xd+F5QItIXaK+qPYCROPMHBXoKeEpVLwTyReTMUw/fmJN7f/lmrnplHn4/TLmjv3X+xhQRbDbQ070vbiAw1d3XGhFpICJ1VTXLrTPQG7jJXT72ND/LmBNM/GY9d73rFHKZNnIAfds2DXdIxkSckGoCl1IznCIyhfa472UBjYGDwDgRSQYWqOofStrhypUrSx2Mz+creaUKJlbb/I5m8pRvJ3WrVeLpvmdQe/82fL6K+9hKrH7PscaLNnuZAIqKK/JzS+BpYBPwiYhcpqqfBNtBUlIS8fGnPk+Lz+cjJSXllLeLZrHY5sWLF/PZ/nie8u2kWZ0azBw9kKTmDcIdlqdi8Xu2NocuJycn6IGzlwkgHeeIv1ALnIfKAPYCm1V1PYCIzAbOBoImAGNOxu/389yy3Uxak0GrBrVIG5NKu0ZWyMWYYEoc5xeReBEZKyJPuK+7iUj1EPadBgx1t0kG0lX1IICq5gEbRKS9u24KoKVpgDEFBX7GfvANk9Zk0KFxXeaPHWydvzEhCOVC7ws48wH1d18nAxNL2khVFwE+EVmEcwfQWBEZ7k4xAXA/8Kq7/ADw0SnGbgzH8gsYNvkLxn+5jvb145k/dhBnWBUvY0ISyhBQR1XtKSJzAVT1RbdgfIlU9fdF3loesOwHoFfIkRpTRPaxfG6a9DnTVm2jR6vG/PWCBJpYIRdjQhbKGUCe+18/gIjUAuz/MhNWTiGXOUxbtY2B7ZsxY/RA6lazufyNORWhJID33Iu0bUTkGWAZ8Ka3YRlzcvuP5jJ4/Gxmf7+TIWcnMm3kAGrHVw13WMZEnRKHgFT1ORH5GuiHUx7yRrcspDHlbvfBo1wyYTbL0/dxU9fWvGqFXIwptRITgIh8BbwOvKyqmd6HZEzxtu5zCrnonixG9WjP89d0o1Ilm87ZmNIK5SLwr4AbgKUisgyYBExT1VxPIzMmwA97sxj0n1ls3neYX/frzBOX21z+xpyuEs+dVfULVb0XaA2MAy4BtnsclzHHrdyxj77PpbF532H+eul51vkbU0ZCehLYrQ18FXAd0AYY72VQxhT6dstefvbSbDKP5PL0VRdwT++O4Q7JmAojlGsAM3GmaZgKPOY+4GWM5+av38WVLzuFXF6+4SKGX9g23CEZU6GEcgbwNDAjlAIwxpSV6Wu2c93E+eT7/Uy+rTdDu7QKd0jGVDgnTQAi8rSq3gf8Afi9iJywXFX7eBybiVHvLd/MrW8soEqlSkwZ0Y9LO7UMd0jGVEjBzgBecf/7x/IIxBiAV77+gdHvfUWtalWYNrI/fayQizGeCVYRrHDenhGqOjxwmXtdYL6HcZkY9Mzna3jgf4tpWDOe6aMGcv4ZDcMdkjEVWrAhoFuAMUCSiHwesKga0MTrwEzs8Pv9PDZrBY/MWE7zujWYOTqVs5vVD3dYxlR4wc4A3hSReTjz/jwSsKgAWOVxXCZG+P1+fvfxEp6at5rWCbVIG30xbRvVCXdYxsSEYGcAzVV1u4jcUczi+oBNC2FOS35BAWM/+IaXvvqejk3qMnN0Kon1bS5/Y8pLsIvATwE3A7NxpoIOfPTSj/NAmDGlciy/gOGTv+DtpZs4r0UDPh010ObyN6acBRsCutn971nlF46JBdnH8rnh9c/5ePU2LmrdmI/uHED9GtXCHZYxMSeUmsCXisit7s9visj3InKN96GZiuhQzjGu+O8cPl69jdQOzZkxaqB1/saESSgTqf8JmCEilwKVga7AvZ5GZSqkfUdyGDx+FnN+2MmVSWcwbWR/alkhF2PCJpQEcERV9wKXAZNU9RCQ721YpqLZdfAoA174jK827+WWlLN49/Y+xFexEo7GhFMoCaC6iPwGuBSYLSLtgXrehmUqki37DtPv+TS+27GPMRd1YOKNPaliVbyMCbtQ/i8cBbQEhqlqNjAY+L2nUZkK4/s9WfR9fibr9mTx2/5n89w1F1oVL2MiRCgFYVYB/waaiMjVONXAZnkemYl6K3bso+/zM9my7zCP/ew8HrdCLsZElFDuAhoDzAVuBG4B5onIMK8DM9Htmy176f98GrsOZvPs1Rfy+4HnhDskY0wRodQDuA3o5A7/ICK1gFnAa14GZqLXvB92cuUrczmSm88rN17EsAuskIsxkSiUawB5hZ0/gKoeBqwgvCnWJ6u3cdlLc8jJK+Dt23tb529MBAvlDGCriDwLfOa+Hgxs8S4kE63eWbqJ299aSNXKlfjfiP4M7tgi3CEZY4II9S6g7cAIYDiw2X3PmONe/vp7bnlzATWrVWHGqFTr/I2JAqGcAVRX1Sc8j8RErX/PX82vpvloWDOeGaMHkpxohVyMiQbBpoPuDbwLVBWRXcAQVV1fbpGZiOf3+/lr2nf8Oe07WriFXDpbIRdjokawIaDHgFRVbYQz98/j5ROSiQZ+v5/ffOTjz2nfcVZCbebfM9g6f2OiTLAEUOA+BIaqzqYUZSBFZJyIfCkii0TkgpOs87hbecxEifyCAka/9xXj5q+hU9N6zL9nMG0aWhUvY6JN0ARQwuugRKQv0F5VewAjgWeKWacz0OdU9mvC61h+Abe+uZCXv/6B5MQE5t09iJb1aoY7LGNMKQS7CJwgIgMCXjcIfK2qc0rY90BgqrvuGhFpICJ1VTUrYJ2ngIeAR08tbBMOR4/lcf1rnzN9zXZ6ndWEaSP7U8/m8jcmagVLAPuAhwNe7w947QdKSgDNAF/A6z3ue1kAIjIcmA9sCjXYlStXhrrqT/h8vpJXqmDKss2Hj+Xz6/lb8e0+Qrdmtfi/CxL4YfWKMtt/WbHvOTZYm8tGsJKQ/cv4s47PAiYiCTjPFaTizDQakqSkJOLj40/5g30+HykpKae8XTQryzZnHsnhspdm49t9hKvPOZM3b+0VkXP52/ccG6zNocvJyQl64OzlpOzpOEf8hVoAO9yfBwCNgQXAFCBZRMZ5GIsppZ1ZRxnwQhrfbMngtvPb8PZtvSOy8zfGnLpQHgQrrTTgz8B4EUkG0lX1IICqvg+8DyAirYGJqvqAh7GYUtiy7zCD/vMZ3+89yNiewr+vusDm8jemAvHsDEBVFwE+EVmEcwfQWBEZ7tYUMBFu3Z4s+jw3g+/3HuQPA5N4+mrr/I2paEo8AxCRVjh36zRU1f4ichcwT1W/L2lbVS1aOWx5MetsAvqFFK0pF9+l72Pw+FnsPpTN45d15bcDksIdkjHGA6GcAbwEvB6wrgITPIvIhNVXm/fQ/4U0dh/K5rlrLrTO35gKLJQEUFVVp+E+CKaqn3sbkgmXOd/vYNB/ZnEw5xgTb+rJz3tKuEMyxngopIvAIlIf595/RORsoIaXQZny99Gqrdzw+uf4/fDO7X24+pwzwx2SMcZjoSSAvwBfAc1F5DugEXCrp1GZcvX20o0Me+sLqlWpxIfD+3Gx2Fz+xsSCEhOAqs4Vka5AEpADrAssEWmi24Qv13H3B19TJ74qH985gJ5nnfKcf8aYKBXKXUB/KeY9VPVP3oRkysu/5q3mNx/5aFQrnhmjUumamBDukIwx5SiUi8D5Af8qA/2Bel4GZbzl9/t5dMZyfvORj5b1ajJv7GDr/I2JQaEMAf058LWIVAY+8Cwi4ym/38+vpi3m6c/X0qZhbdJGp3KWzeVvTEwqzVQQVYF2ZR2I8V5+QQFj3vuaV775gc5N6zFzdCotbC5/Y2JWKNcAtuLeAupKACZ6FZDxRm5ePre/9QXvLd9MSls9hBkAABERSURBVGIC0+8aSKPa1cMdljEmjEI5A+gV8LMfyFLV/R7FYzxw9FgeQyfOZ8badHq3cQq51K1uhVyMiXWhJIB/qOoNnkdiPJGVnctVr8xj/vpdDO7YgveH9aVmNS8ngTXGRItQeoKNInIHsAjILXxTVTd4FpUpExmHnUIu327N4Npzz+SNW3pRzebyN8a4QkkAxR39+4E2ZRyLKUN7jx7jjhfSWLlzP8MuaMuE67pTpbKX9X+MMdHmpAlARG5R1TdV9azyDMicvs2Zhxj92Wa2HsrlF7078q8h59tc/saYnwh2SDiy3KIwZUZ3H6DPczPZeiiXh1LPYdyV1vkbY4pnVwMrkGXbM7lkwiz2HMrhnvOa8JdLzwt3SMaYCBYsAVwkIluKeT8O8KuqzRccQb7ctIfLXppNVs4xnr+2GxfEHwx3SMaYCBcsASwFbiyvQEzpzV63g6tfnUd2Xj6v39yLm5PPwufzhTssY0yEC5YAslV1c7lFYkpl2kqnkAvAe8P6cmXSGWGOyBgTLYIlgG/KLQpTKm8t2cjwyV8QX6USU0b0J7VD83CHZIyJIidNAKr6u/IMxJya8V+uY+wHX1PXLeRykRVyMcacIrsLKAo9OXcVv/t4CY1rO4Vczmtpc/kbY06dJYAo4vf7eWTGch6btYLEejVJG5OKNLHaPMaY0rEEECUKCvz8ctpinl2wlrYN6/DZmFRaJdQOd1jGmChmCSAK5OUXMOq9r3jt2/UkNavPjNEDaV7XCrkYY06PJYAIl5uXz61vLuSD77ZwwRkN+eSugTSsFR/usIwxFYAlgAh2JDePoa/NZ+badPq0acL/rJCLMaYMWQKIUFnZuQx5eS4LNuzm0k4teW9YH2pUta/LGFN2rEeJQBmHc/jZS7NZvDWDoV1aMenmnlbIxRhT5jxNACIyDuiOU0DmPlX9NmBZf+BxIB9Q4E5VLfAynmiQfuAIg8fPYvWuA4y4sC3jr+tO5UpWyMUYU/Y861lEpC/QXlV74NQWeKbIKhOAoaraE6gDXOJVLNFiY8ZB+j4/k9W7DnBv745MuK6Hdf7GGM942bsMBKYCqOoaoIGI1A1YnqKq29yf9wANPYwl4q3ddYC+z6exIeMQD198Lv+yQi7GGI95OQTUDAick3iP+14WgKpmAYhIc2AQ8HBJO1y5cmWpg4nk6ZE18yi/mLuF/Tn53Ne1KVc0zmPJkiWnvd9IbrNXrM2xwdpcNsrzIvBPDmdFpAnwEXC3qmaUtIOkpCTi40/9Hnifz0dKSsopb1cevti4m3s+nENWbj4vDu3GqB4dymS/kdxmr1ibY4O1OXQ5OTlBD5y9TADpOEf8hVoAOwpfuMNBnwIPqWqah3FErM80nWsmziM3r4BJN/fipuSzwh2SMSaGeHkNIA0YCiAiyUC6qgbWKXwKGKeqMzyMIWJNXbGFIS/PJb/Az/vD+1rnb4wpd56dAajqIhHxicgioAAYKyLDgQPATOB2oL2I3Olu8paqTvAqnkjyhm8Dd7y9iOpVKjP1jn4MaG+FXIwx5c/TawCq+vsiby0P+DkmJ7R5cZFyzwffUL9GNT65awDdWzUOd0jGmBhlTwKXo3/MWckfPllKk9rVmTk6lXNbNAh3SMaYGGYJoBz4/X4e/nQZj89eyRn1a5I25mI6NK5b8obGGOMhSwAeKyjwc//Ub3n+C6Vdozp8NuZizmxQK9xhGWOMJQAv5eUXcOe7XzJp8QbOaV6fGaNSaVa3RrjDMsYYwBKAZ3Ly8rnljYVMWbGFC890Crkk1IzJ697GmAhlCcADR3LzuHbifNI0nf7tmjJlRH/qVK8a7rCMMeYElgDK2IGjTiGXhRt387NOLXnXCrkYYyKU9UxlaO+hbC59aTZLtmVy/XmteP3mXlStbNM5G2MikyWAMrLdLeSyZtcBRnZrx4tDu9lc/saYiGYJoAxsyDjIoP/MYmPmIR7o24l/XpFCXJzN5W+MiWyWAE7T6p37GTx+FulZR3l0cBf+ePE51vkbY6KCJYDTsGRbBpdOmM3ewzk8NSSF+/t2DndIxhgTMksApbRww26ueHkOB3OOMf667tzZvX24QzLGmFNiCaAUZq5N59qJ8ziWX8Cbt/Tmhq6twx2SMcacMksAp+jD77Zw8xsLqBQHH47ox2WdE8MdkjHGlIolgFPw+uL1jHz7S2pWq8zUO/rTv12zkjcyxpgIZQkgRC8sVH4x5Rsa1KjG9FEDufDMRuEOyRhjToslgBA8MXsFD01fRtM6TiGXc5pbIRdjTPSzBBCE3+/nwU+W8o+5qzizQS3SRqfS3gq5GGMqCEsAJ1FQ4OcXU77hP4vW0b5RHdKskIsxpoKxBFCMvPwCRr7zJW/4NnBu8wbMGD2QpnWskIsxpmKxBFBETl4+N7+xgKkrttK9VSM+vnMADayQizGmArIEEOBwzjGumTifWet2MKBdM6bc0Y/a8VbIxRhTMVkCcO0/msuQ/87hi017uLxzIu/c3ofqVSuHOyxjjPGMJQBgz6FsLhk/i2Xp+7ixa2sm3tTTCrkYYyq8mE8A2/YfZvD4WazdncVd3dvz/LUXWiEXY0xMiOkEsH7vQQaN/4xNmYf5Vb/O/P3yZJvL3xgTM2I2AaxyC7nsyDrKXy7pwoOpVsjFGBNbYjIB+LY6hVwyjuQw7srzubdPp3CHZIwx5S7mEsDn63cx5OW5HM7N46Xre3BHt3bhDskYY8IiphLAjLXbufbV+eT7/bx1W2+u69Iq3CEZY0zYeJoARGQc0B3wA/ep6rcBy1KB/wPygemq+lcvY3l/+WZufXMhlePimDKiH5d2aunlxxljTMTz7H5HEekLtFfVHsBI4JkiqzwDXAv0BAaJiGcV1T/esJ+bJi2gepXKTB810Dp/Y4zBwwQADASmAqjqGqCBiNQFEJE2QKaqblXVAmC6u36Zm71uB3/5Kp36Nary2ZhU+rZt6sXHGGNM1PFyCKgZ4At4vcd9L8v9756AZbuBtiXtcOXKlaccxN792fRNrMOYcxtTac9mfHs2n/I+opXP5yt5pQrG2hwbrM1lozwvAge7yT6kG/CTkpKIjz+1mTlTgHb1faSkpJzSdtHO57M2xwJrc2wobZtzcnKCHjh7OQSUjnOkX6gFsOMky1q67xljjCknXiaANGAogIgkA+mqehBAVTcBdUWktYhUAS531zfGGFNOPBsCUtVFIuITkUVAATBWRIYDB1R1CvBzYLK7+juqus6rWIwxxvyUp9cAVPX3Rd5aHrDsc6CHl59vjDHm5GzeY2OMiVGWAIwxJkZZAjDGmBgVLZPBVQbIzc0t9Q5ycnLKLJhoYW2ODdbm2FCaNgf0mcUWOI/z+/2nEVL58Pl8vYAF4Y7DGGOiVO+UlJSFRd+MljOAb4HeOA+S5Yc5FmOMiRaVgeY4fehPRMUZgDHGmLJnF4GNMSZGWQIwxpgYZQnAGGNilCUAY4yJUZYAjDEmRkXLbaAhi6RC9OWlhDb3Bx7HabMCd7plOKNasDYHrPM40ENV+5VzeGWuhO/4DJyZdasBS1R1THiiLFsltHkscCvO3/ViVb0/PFGWPRFJAv4HjFPV54osK9M+rEKdAURSIfryEkKbJwBDVbUnUAe4pJxDLHMhtBn3u+1T3rF5IYT2PgU8paoXAvkicmZ5x1jWgrXZrS3+G6C3qvYCOotI9/BEWrZEpBbwLDD7JKuUaR9WoRIAEVKIvpydtM2uFFXd5v68B2hYzvF5oaQ2g9MpPlTegXkk2N91JZyHJKe5y8eq6pZwBVqGgn3Hue6/2m5BqZpAZliiLHs5wM8opkKiF31YRUsARYvNFxaiL27Zbpwn5KJdsDajqlkAItIcGITzRxPtgrbZLTw0H9hUrlF5J1h7GwMHgXEistAd9qoITtpmVc0G/gxsADYDX1eUglKqmqeqR0+yuMz7sIqWAIo67UL0Uegn7RKRJsBHwN2qmlH+IXnueJtFJAEYgXMGUFHFFfm5JfA00BfoKiKXhSUqbwV+x3WBB4EOwFlANxHpEq7Awui0+7CKlgBisRB9sDYX/s/yKfBHVa0odZeDtXkAzlHxAmAKkOxeTIxmwdq7F9isqutVNR9n7Pjsco7PC8Ha3AnYoKp7VTUX57tOKef4wqHM+7CKlgBisRD9SdvsegrnboIZ4QjOI8G+5/dVtbOqdgeuxrkr5oHwhVomgrU3D9ggIu3ddVNw7vaKdsH+rjcBnUSkhvv6fOD7co+wnHnRh1W4yeBE5Amcuz8KgLFAV9xC9CLSB/i7u+oHqvpkmMIsUydrMzAT2Ad8GbD6W6o6odyDLGPBvueAdVoDEyvIbaDB/q7bARNxDuhWAD+vILf6BmvzaJyhvjxgkar+NnyRlh0RScE5aGsNHAO241zg3+hFH1bhEoAxxpjQVLQhIGOMMSGyBGCMMTHKEoAxxsQoSwDGGBOjLAEYY0yMqnCzgZro5N6yqZx4yyrA/aq67CTbPApUUdU/nsbn9sOZeXGp+1Z1YAnO7JPHTnFfl+DMvfSYiFwE7FTVDSLyb2CSqvpOI85HcW573Oi+VQXYBoxW1QNBtmsBdFTVOaX9bFNxWQIwkWRPmO7ZX1H4uSISB7wNjAaeC7ZRUe7DdoUP3I0A3sF5YrWspiqeFJjsROTvOFMi/C7INv1xnpy1BGB+whKAiXgi0hEYj/PQT12caS1mBiyvAvwXEJy545eq6lgRqQY8D7TDmQp7sqoGnSNIVf0ishDo6O77MuBPwBH33yhV3e4+pDQAZ/bG7cAw4CYgFfgAuA64UEQecLf/G05dhvtUdZG771k4D/2sAl7AmdWyNvCgqs4K4VezCBjl7qsXzgNCOe5+7sZ5CPAxIE5EMnES2in9PkzFZtcATDRoBjysqgOBe3E6tUDnAN1UtYeqXgQsE5F6wH04Uwj0B7oBN4rIucE+SESqA1cAC0SkJk5iudbdx6fA30SkAc6TqT1UtTfwIdC0cB/u08jLgF8VGXp5kx+nN2iCc2SeBryIM5//AGAI8F83qQWLswpwMz8OmTXCeQJ4AM7EcA+q6kacJ4Qnqeq/SvP7MBWbnQGYSNJYROYVee86nEnA/ikij+FUvWpUZJ01wF4RmY4z6+m7qnrArYaW6BYXAWd8vx3wXZHtzynyuR+p6jsich6wK6CewjxgjKruE5GZwHwRmQK8o6rbRKSk9r0NfAH8EicRvKeq+W6cdUTkEXe9Y0ATfjrR123ukX4czrQITwNPuMt2Ak+6CaweztF/UaH+PkyMsARgIkmx1wBE5C2c4YpX3HJ5Hwcud+eH7+1OGnY58K2I9MQZDvmLqr5fwueuKO5zcYaTAsUVvqeqQ92hqctwEsG1JTVOVXeKyAYRuRC4AScR4MZ5jaruLWEXx68BiMhHOLOA5hUuw7kgPEdELgd+Xcz2of4+TIywISATDZrijJOD03HGBy4UkfNFZJiqLlHVvwA+nLniFwLXu+tUEpF/ufUCQrUOaBJQYjEV+EpE2ojIA6q61h1D/xAoOh99AVC1mH2+iVPiMCHgrqDAOBu5dw2V5G7gURFJdF83BVaJSGWcs6bC31FgHKf7+zAVjCUAEw2eAl53h10WApkiEnjxcj0wVEQWicgcYD/OUMvzwCER+RL4CtivqiGXDnQrM40E3nGHiAYCf8S5/bKriHwjIrNxipJ8UGTzz4DxInJNkfc/xBm7nxzw3r3A1SKyAKdiW4l37KjqVpyLvoUzu/7d3e4jnHH/M0Tkfpy58keIyF85zd+HqXhsNlBjjIlRdgZgjDExyhKAMcbEKEsAxhgToywBGGNMjLIEYIwxMcoSgDHGxChLAMYYE6P+HzOBIwygYXRVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Liberales': {'precision': 0.6442307692307693, 'recall': 0.3941176470588235, 'f1-score': 0.4890510948905109, 'support': 170}, 'Conservadores': {'precision': 0.726063829787234, 'recall': 0.8806451612903226, 'f1-score': 0.7959183673469388, 'support': 310}, 'accuracy': 0.7083333333333334, 'macro avg': {'precision': 0.6851472995090017, 'recall': 0.637381404174573, 'f1-score': 0.6424847311187248, 'support': 480}, 'weighted avg': {'precision': 0.6970812875068194, 'recall': 0.7083333333333334, 'f1-score': 0.687236208351954, 'support': 480}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6930388080388081\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.37      0.41       151\n",
      "           1       0.73      0.80      0.77       329\n",
      "\n",
      "    accuracy                           0.66       480\n",
      "   macro avg       0.60      0.59      0.59       480\n",
      "weighted avg       0.65      0.66      0.65       480\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                \n",
       "            </script>\n",
       "        </div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Liberales': {'precision': 0.45901639344262296, 'recall': 0.3708609271523179, 'f1-score': 0.4102564102564103, 'support': 151}, 'Conservadores': {'precision': 0.7346368715083799, 'recall': 0.7993920972644377, 'f1-score': 0.7656477438136827, 'support': 329}, 'accuracy': 0.6645833333333333, 'macro avg': {'precision': 0.5968266324755014, 'recall': 0.5851265122083777, 'f1-score': 0.5879520770350465, 'support': 480}, 'weighted avg': {'precision': 0.6479312627835271, 'recall': 0.6645833333333333, 'f1-score': 0.6538475534654574, 'support': 480}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelLogisticRegression = ModeloClasificadorKFoldCV(LogisticRegression, libCon, \"LogisticRegressionNoThresholdConLem\")\n",
    "modelSVC = ModeloClasificadorKFoldCV(SVC, libCon, \"SVCNoThresholdConLem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "DLw-3nOQTiqZ"
   },
   "outputs": [],
   "source": [
    "modelSVCFinal = ModeloClasificadorFinal(SVC, libCon, \"Maquina de vector de soportes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "WTmbjsvNTiqd"
   },
   "outputs": [],
   "source": [
    "filename = 'ModelSVM.pkl'\n",
    "pickle.dump(modelSVCFinal, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WfEImHfnTiqe"
   },
   "outputs": [],
   "source": [
    "df.to_csv('dfFinal.csv', encoding='utf-8')\n",
    "libCon.to_csv('libCon.csv', encoding='utf-8')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Copia de ProyectoFinal.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}