MLPCLassifier-Breast-Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94169a3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5397f074",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cancer_Disease=load_breast_cancer(as_frame=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439c73ec",
   "metadata": {},
   "source": [
    "# Description et Exploration des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f273fa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "#DEscription générale de la base de données \n",
    "print(Cancer_Disease['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6eed58c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nom classes à prédire\n",
    "Cancer_Disease.target_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a903f9c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#les attributs du Dataframe\n",
    "Cancer_Disease.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "443f755f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   mean radius              569 non-null    float64\n",
      " 1   mean texture             569 non-null    float64\n",
      " 2   mean perimeter           569 non-null    float64\n",
      " 3   mean area                569 non-null    float64\n",
      " 4   mean smoothness          569 non-null    float64\n",
      " 5   mean compactness         569 non-null    float64\n",
      " 6   mean concavity           569 non-null    float64\n",
      " 7   mean concave points      569 non-null    float64\n",
      " 8   mean symmetry            569 non-null    float64\n",
      " 9   mean fractal dimension   569 non-null    float64\n",
      " 10  radius error             569 non-null    float64\n",
      " 11  texture error            569 non-null    float64\n",
      " 12  perimeter error          569 non-null    float64\n",
      " 13  area error               569 non-null    float64\n",
      " 14  smoothness error         569 non-null    float64\n",
      " 15  compactness error        569 non-null    float64\n",
      " 16  concavity error          569 non-null    float64\n",
      " 17  concave points error     569 non-null    float64\n",
      " 18  symmetry error           569 non-null    float64\n",
      " 19  fractal dimension error  569 non-null    float64\n",
      " 20  worst radius             569 non-null    float64\n",
      " 21  worst texture            569 non-null    float64\n",
      " 22  worst perimeter          569 non-null    float64\n",
      " 23  worst area               569 non-null    float64\n",
      " 24  worst smoothness         569 non-null    float64\n",
      " 25  worst compactness        569 non-null    float64\n",
      " 26  worst concavity          569 non-null    float64\n",
      " 27  worst concave points     569 non-null    float64\n",
      " 28  worst symmetry           569 non-null    float64\n",
      " 29  worst fractal dimension  569 non-null    float64\n",
      " 30  target                   569 non-null    int32  \n",
      "dtypes: float64(30), int32(1)\n",
      "memory usage: 135.7 KB\n"
     ]
    }
   ],
   "source": [
    "#Etude des différents types de données\n",
    "#on remarque que la majorité des données est de type réel alors que pour notre target le type est entier\n",
    "Cancer_Disease.frame.info()\n",
    "#pas de valeurs nulles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a34a58ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
       "count     569.000000              569.000000  ...     569.000000   \n",
       "mean        0.181162                0.062798  ...      25.677223   \n",
       "std         0.027414                0.007060  ...       6.146258   \n",
       "min         0.106000                0.049960  ...      12.020000   \n",
       "25%         0.161900                0.057700  ...      21.080000   \n",
       "50%         0.179200                0.061540  ...      25.410000   \n",
       "75%         0.195700                0.066120  ...      29.720000   \n",
       "max         0.304000                0.097440  ...      49.540000   \n",
       "\n",
       "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
       "count       569.000000   569.000000        569.000000         569.000000   \n",
       "mean        107.261213   880.583128          0.132369           0.254265   \n",
       "std          33.602542   569.356993          0.022832           0.157336   \n",
       "min          50.410000   185.200000          0.071170           0.027290   \n",
       "25%          84.110000   515.300000          0.116600           0.147200   \n",
       "50%          97.660000   686.500000          0.131300           0.211900   \n",
       "75%         125.400000  1084.000000          0.146000           0.339100   \n",
       "max         251.200000  4254.000000          0.222600           1.058000   \n",
       "\n",
       "       worst concavity  worst concave points  worst symmetry  \\\n",
       "count       569.000000            569.000000      569.000000   \n",
       "mean          0.272188              0.114606        0.290076   \n",
       "std           0.208624              0.065732        0.061867   \n",
       "min           0.000000              0.000000        0.156500   \n",
       "25%           0.114500              0.064930        0.250400   \n",
       "50%           0.226700              0.099930        0.282200   \n",
       "75%           0.382900              0.161400        0.317900   \n",
       "max           1.252000              0.291000        0.663800   \n",
       "\n",
       "       worst fractal dimension      target  \n",
       "count               569.000000  569.000000  \n",
       "mean                  0.083946    0.627417  \n",
       "std                   0.018061    0.483918  \n",
       "min                   0.055040    0.000000  \n",
       "25%                   0.071460    0.000000  \n",
       "50%                   0.080040    1.000000  \n",
       "75%                   0.092080    1.000000  \n",
       "max                   0.207500    1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#statistiques pour chaque colonne \n",
    "Cancer_Disease.frame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f289bb31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ub+GQ9v5rrhTsM+er5JB12/3AoI0eY8Pvsc1OTueYvX74N2T9qLsT27sMHBC00fht+Df1hP89kdjevV/4s5SG5fdITj2Z+tpbie0AJ3c5L9jnv2cvWdWU6uNfHJjaMSz5l+trcvoW5bcikUgk0oJajoJMS3RukUgkEmlBLe+lpSU6t0gkEom0pBPsuUXnFolEIpEWpAncq3Wic4tEIpFICzpDtOSC/wxqFEkbSrrY/71NQVczEolEah11qU991Cpx5tZGmNkLwAv+4Ta4vLenKrU37Y3xie09VkkOiwf4+o1weaQu3RsS23uvvnJiO4TD/AE+fSpZVb7/2mFF/xX2XTyx3ZYNj3XmEyOCfRo2npNs48OwknuXKWHF+DlDd09s7zcwnLYw/YPSGpBlCPwqb+oa1kro2TvcZ+qzyVUoeq61YdDGlAkfJLZ36xeuTtBnw/WCfQYMXiSx/fM3Pw3a6NIt/HWqQUsktvdZM7EZgC9GJVePcCRXHwjRGQJKcp251UAVgZUlPeIV+F+StJIc5/vxjCtS5W/VpqSNvOr/GEnPSeqTMLabJO1SNIarJf3A279H0mCc6PNJkkZL2lLSRDVXL+grV90g2atEIpFIOyHVpT5qlbaYua0M7AscjVMAKVQR2ANXRWAvmqsIHCmpP/CcpEdoriIwU9IqwA1A4efdesBaOPX+J3FVBEozL4fhSpXf7tU/6oC9cfJW6wKLAs9Leqw1m5KeA27CVQ54XlJfXLWB1sZ2I67iwX1yVWa3x6mQbAxgZu9KugyYZmYXAEgaidPNvAOnLXmbF4KORCKRjifO3MrSIVUEJPUBljaz28FJcJnZdJxjvcHMGs3sU5yW5EYJNlfD6WE+7+184yvJtja2+4Ht5OrFfQ94zMxmBF6jK4Aj/N9HAFeV66SiqgD/fSm5aGMkEonkRd5VASR9V9KbkiZIOq2VPtv41a1XJY0q1ycLbTFz66gqApVUAihnU0A56ZmTyo3Nz+RGAjvjZnA3JNwPf82Tfplza6DezF5ppd+8qgCfn3lE1EmLRCLtQp57bpLqgX8AOwKTcKtnd/mSYYU+/XFaxN81s/clLVbtfTtqwTT3KgJm9g0wSdJe3mY3ST1xhUb3l1QvaRCuhM5zCabeAJaStJG300dOZDlpbDfiZmBb+udWSrmKA//FOcKys7ZIJBLpKFRfn/pIwXeACWb2jpnNxn1f7lnS50Dgf2b2PoCZfVbtc+go53Y2bplvrKRX/GNwnvswSc8Aq5KtigA4p3O8pLG4yMQlgNtxRU/HAI8Cp5pZq+Ft/sXfH/i7pDHAw7hl06SxPYRzmo/460u5G/h+IaDEnxsGDCDFTC8SiUTalbq61Efx9ok/ji6xtjRQHPY6yZ8rZlVggA/ye1HSodU+hVyXJc3sXWDtoseHl2vze1L/V+b68cCQolOn+/MjcXtzhX7HtnL/8ZQp+gmc4o/ivq3a9Pttm5TYKDs2338O0CKWuNi+r/xdfC24vcBbzWxKuedSyuTXJyW2L7NH+HfK5Dc/DvZp6JEctNlr8LJBG2kU/UOh/lNemRa0oW7dk20MWiVoo27KXcE+M5uS7zN7atlC6i1onBVObZje1Cuxfek1Vg3aoFc4RH/Wm8kh+p839A/a6LFcOGZ98jV3JrYvmyLS7ovxyWkWadT606QC9FoiORUgzd7SpBeS0xYArGu35A7LrBC0MfWLKcE+1VImGL1VirdPWjNX7rKSx12ADXABeT1wpcOe8d+dFRHz3DoASX/HBZ/sEuobiUQi7U6+CiWTgOJfxcvgItRL+0w2s2+Bb31E+7pAxc6tdpMUOjFmdpyZrVzNr5JIJBJpK3KuxP08sIqkFXy61A+B0iWTO4EtJXXxsRIbA69X8xzizC0SiUQiLckxOdvM5ko6FhdsVw9caWavSjrGt19mZq9LegAXH9EEXNFaFHlaonOLRCKRSAtSRkGmxszuA+4rOXdZyePzgfPzumd0bpFIJBJpSVQoiZTDJ2gf2NHjiEQikUrIW6GkI5BTxorkiaRtgJPNbLcybV28nFcmJr31SuIb9W19v6CN/rPDeZFz6pNDlQeMCKflNX4bTk9sWCJZ0T8U5g8w/IeXJbZvP+zHQRtzl18t2OeLRZJD8Bf9PLzvPav3ouGx1Cc/574TX0hsB2jqE67IUDcz8P5Mnxq+z6DSNKX5mTpwcHJ71/BYB8xITl+Z0yWcCtB/wjPBPswIpHOk+DymiTCcMurJxPb+W24atPEv/TTY54Tdq6s2Ov3K36Z2DD2P/F1NTvNycbvq+GoAx0t6TdJYSTdKqvP3HeTb67ym2aJetf9SSSMkvSNpaz+m1yVdXWRzmqTzfELhI5K+48fyjqQ9fJ96uYoDz/t7F3L3zsVF/oyWdJKkwyXdIulu4CFJ1xaeu7czrGAzEolEOpwMSdy1Sp4jWxn4Gy5ZeXWaqwGcjKsGAM3VADYCtgXOl9SLZsX99XHqIBcX2V0POBEnVLwirhpAKacB65nZEOAYL5F1HXCQb98BGGNmk/3jAbhk75Nw6iEX4aoDrCNpqO/TCxhpZhvg5LP+gNNG+z7we9/nKOBr/3w2An4saQU/nsfNbKiZXeT7bgocZmbbUSScLKkfsBklm62RSCTSUeQsv9Uh5OncOqQagGcsMEzSwUBhye9KoCDhciQtNRzvLhrbpyXjLtifDTzg/x4HjPJKJKXP51D/fJ7FqZS0JovxsJl9CWBmo4CVvTjoAbiSN/MtVRbL2gy76ZZWzEYikUjOqC79UaPkGS3ZUdUAwNVG2wpXM+7XktYysw8kfSppO1xC4EFF/YvHVjrugv051rwhOa+fmTV5IeXC8znOzFqIJfs9t1JKNzqu9WP6Ic75zkexrE1ozy0SiURyI0ZLZib3agBypWCXNbMRwKlAf6AgsHcFbnnyZjNrzOUZtORB4Cdqrqq9ql9mLVcFoJSrccutmNmrbTC2SCQSqYhYiTs7ZwN/xVUDEPAusBtOcf82SfsCI8hWDaAeuM7vXQm4qEiM+C7ccmRblZW5ArdE+ZJ/Pp/jKo2PBeb6qgJXA1+VXmhmn0p6HVeNOxKJRGqHTjBz69SpAJI2xDm7LYOd2xmvnzYOWN/Mvg71v+S+5DdqneVDxb/h7U/Dav1zAkkKB755SnIHQA3h30x1a62f2P51CkX/AWMfSWwfftC/gzaWfPWpYJ8l7MPE9i/rw3UVP58RTtXY9KPrE9vnTpwQtNFliSWDfZoWCfRJ8Z0w7f57gn3u2/m/ie2rLBau/PDBlORKCXNTrMf84L1zgn3UM/k+TYuGX9dvFgt/Zvt+8U5i+2dXJb9mAFN+eUWwz9BVBlXlnWbefEFqx9B9v5Nr0hN2WoUSuVLmP6HlXltNIGkHXMDLhWkcWyQSibQrNRwFmZZO69zM7FxcvlnNYWaP4KJEI5FIpPao4b20tHRa5xaJRCKRCukEe27RuUUikUikJXHmFolEIpFOR3XSlDXBgu+eW8HrUt7j/97DB5hEIpFIJETO2pKSvivpTa/x2+p3saSNJDVK2qfap7BAzdx8Lpl8sndqzOwu5i9r3iaUqv6nrQIgqT4p0Xy/J49IvL774gcEx7b6g9cF+8yZPiuxXQcfErRR/3a4gO7MJ0YkttdNCb9dc/feL7E9TZj/x2ttFuyz3MtXJ7Z/uNn2QRsNfcLRZ18G3p8B3cKpHE0NXYN99E5yFYOZQ8KZM10OPDrYZ6crkz+TDT8+KWhjhVsuSmyfOyP58wow+Rd/CPbpP+XdxPaGbyYntgP0feqOYJ+Ptj48sX32aeGqAH8PF+bgP78O90mkLr9oSUn1wD9w2ryTgOcl3WVmr5Xpdx5OHKNqan7m5isGvC7pn8BLwLJe1f8FSa9K+l1R3+/66gFPAHsXnT9c0iX+76uLfxVImub/XVLSY17J/xVJ8/0Pl7SBpFG+UsCDkpb050dK+pOkUcAJZR5vL1cFYZyvQNDNX/eupN/48e7bFq9fJBKJZCbfmdt3gAlm9o6ZzQZuBPYs0+844DackH7VLCgzt9WAI8zspwCSzjCzL72nHy5pCPAW8G+c2v8E4KaM9zgQeNDM/ujttviZ7CW2/g7saWafS9of+CPNupD9zWxr33f3wmNJ3YHxwPZm9pak/+Ly7/7qr5tpZltkHGskEom0HfnuuS0NfFD0eBJO77fodloaV3FlO1yFlapZUJzbe2ZWXHFwP0lH48a/JK6KQB2uMsF4AEnXAeG1k2aeB670TuwOMxtd0r4asDbwsJfGrAeKKymWOtObiq6baGZv+cfXAD+j2blldcKRSCTStmSIlvTfxcXftZd70fd5XcpcVqqA8lfgl2bWqJwc64Li3OZpTfp6aScDG5nZV3IFRgtlctNIxszFL8f6PbyuAGb2mKStcBUGrpV0vpkVa+EIeNXMWlsUL9XDLDwOvVOt6mgWf2jO/96mHLpeckXoSCQSyYUMDqa4ekkrTAKWLXq8DPBRSZ8NgRu9Y1sU2EXSXDO7I/VASqj5Pbcy9MU5hK8lLQ58z59/A1hB0kr+cWu72e8CG/i/98TVkkPS8sBnZvZv4D9Aqfjhm8AgSZv6/g2S1kox3jeAwZJW9o8PAUaluA4zu9zMNjSzDaNji0Qi7YXV16c+UvA8sIqkFSR1xZX5ahExZmYrmNlgMxsM3Ar8tBrHBgvOzG0eZjZG0su4wqLvAE/68zP9TOdeSZOBJ3DLiKX8G7hT0nPAcJpnTtsAp0iaA0yjudBp4b6zfSDKxb4CQRfcVDqxXI0f1xHALb4O3PPAZZmfeCQSibQXOSZxm9lcScfioiDrgSvN7FVJx/j2Nvk+7NRVAToTw8fNTHyjNv/kxqCNp5fYP9invi45y2Lx7l8Gbcy18G+mBs1JbJ/Z1D2xHaCf5qsk1PIejeEw8a5zwtWVnl3v8MT2TV/6T9DG1z2XCPZZ8u3HE9unL7FyYjtAY5duwT71c5Nfl56TklMFAOYsFpZGfaZh28T2LT++NmjjiaWSU0+6BD6vAFL4O66pKXkZLvT/AmDm3IZgn8bAffp1n5nYDtCjS/hzvd4qi1a1cTVj5A2pHUOPbQ6oyYzvBW7mFolEIpG2xTqBQkl0bpFIJBJpSdSWjEQikUinI87cIpFIJNLZSBkFWdNE5xaJRCKRlnSCZcmafgaSjpF0aLhnKlu/ysNOJBKJdHZMdamPWqVmUwHSqulnsDfNzHpnvCZRqb+VayqtCpDY7/Mzj0h8o7rvFQ7zn37LsGCfuTNnJ7b3PSj8W6NuzNPBPjM//CSxffbU6UEbffdJrgowcdAmQRsfbhxW9N9o5L8S259e/6igjd4r9wj2WenO5KoAi04IVzmwHr2CfZreei2xvXGjbcI26sJh77NuSE6RqDvsuKCN2f/6S2K7NYW/vxpPODvYZ9BHYxLb66aGU2AaJ30Q7PPpDkcmtjemWEw778bwe3zFGdWlAkx79u7UjqH3xrvX5AZdmy1LShoMPAA8C6yHEzY+1MymS9oAuBDoDUwGDjezjyWNBJ4CNgfuktQHmGZmF/i2l3HqIoNwSdanA+sAN5nZmf6+BwPH42S1ngV+ihM47iFpNE5C66By/byu2TQ/tp2BX+CSwQvPaSVc6YZBwHTgx2b2hpcA+9I/z5ckLVLy+Fpc4nZP4G3gSC8d1uL5Asn/kyORSKQdqOUZWVra+hmshhPRHAJ8A/y0SF1/HzPbALgS53wK9Dezrc2s3Bf9bDPbCuco7sQJEK8NHC5pEUlrAPsDm5vZUKAROMjMTgNmmNlQ79jK9vP36AW8YmYbm9kTtORy4Dg/7pOBfxa1rQrsYGa/KPP4vzhR0CHAOOC3KZ9vJBKJtD919emPGqWtA0o+MLMn/d/X4WZKD5BNXb+Ygh7ZONwM7GMASe/ghDm3wM3snve2e1C+NtD2Cf0acTWFWiCpN7AZTkarcLpYDuKWkiXMW/xMsB/OgRX0JK8Bbkn5fCORSKTdiUncYUrXbY3s6vrFFHRnmor+Ljzu4m1fY2anB8aV1G9mK/tsdcAUP9MrR2tVAUKkqgpwwfc25dD1V0tpMhKJRKogLksGWa6goo9T6X+CytX10zAc2EfSYt72QK/2DzDHL4mG+pXFzL4BJkra118jSeuGBmRmXwNfFVX2rqwqQHRskUiknTCU+qhV2tq5vQ4cJmksMBC41JcZ3wc4T9IYYDRuua9qzOw14EzgIX/Ph3HFTMHtl42VNCzQL4mDgKP8uF+lfKn0chwGnO/vNRT4fcrrIpFIpN3pDKkAbb0s2WRmx5Se9FWutypzfpuSx2eVazOzkcDIVtpuosw+lpn9Evhlin6tpguY2UTgu2XOHx54PBqYLy699PkmMWvKtMT2rnXhtzJNeP2Mr5JXU3t1DYchNywyKNiny5SvE9sbZyWnJADM6r1oYvvnM/oFbTT0CW+IhxT904T5T5swI9jn2/rk8fZLocRvKTb4uy6b/B5/0WeZ8H3SfKl9NTWxuWfYAnNnJCvgz/gq/JkOvzvQ2LNvYnvdrPB91CX82s9xtZFbZUaKahj19e3gUGrYaaUlKpREIpFIpAVNNRwFmZY2c89m9q6ZlSsWGolEIpFaRkp/pDKn70p6U9IESaeVaT9I0lh/PJUmniFEnLlFIpFIpAV57qVJqseJX+wITMKlYN3lYx8KTAS29uIW38PFSGxczX2jc4tEIpFIC3KOgvwOMMHM3gGQdCMuGG+eczOzYm25Z4Dwxm+A6NwikUgk0oKcoyCXBoqFNyeRPCs7Cri/2pvWbEiMpMGSDix6fLikSzpyTJFIJLJQkGHPTdLRkl4oOo4utVbmDmWFmSVti3NuvyzXnukp1HBVgG2Ak81sN//4cGBDMzu2A4cVpIqqAIkVCK5/IvmNWmPxL4JjGz95YLBP1y7Jn4edZt8RtDF5sTWDfebUd0tsn94UTjlYfHayCnvfD5KV3gG+HLxRsM/A915MbJ+cwkYozB9gwuo7JLZv++gfgjbm9uof7NNlxjeJ7XVfhz9LNIWLZdzab74soBYsOzCcHvHRlORA/m4N4e+vnRvvCvYZ3S/5tU+zTDdzbnghLPR1u2KfD4M25lhyOgHA6istU9W64qevv5jaMSy+xgaJ9/KCHWeZ2c7+8ekAZnZOSb8hwO3A98zsrcyDLiHTzE1SL0n3Shoj6RVJ+/vz70r6k6SnvedeX9KDkt6WdIzvI0nn++vGFV1b9jxwLrClpNGSTvLnlpL0gKTxkv5cNK5pkv7ox/WMpMX9+UGSbpP0vD829+e39nZHS3pZUh9JS0p6zJ97pUhRpPj5byBplKQX/fNb0p8f6Z//KOCEMo+39/cZJ+lKSd2KXrffSHoC2DfLexGJRCJtRc5J3M8Dq0haQVJX4Ic06wQDIGk54H/AIXk4Nsi+5/Zd4CMz29UPqPjn6Admtqmki4CrcWVcuuOUPC4D9sapc6wLLIqLmHkMp05S7vxpzD9zG4orIzMLeFPS383sA5yS/zNmdoZ3ej8G/gD8DbjIzJ7wL96DwBo4Rf+fmdmTcoLIM3Eajg+a2R99dE+LHFM1VzPY08w+9074j0ChQFN/M9va99298FhSd2A8sL2ZvSXpv8BPgL/662aa2RZZ3oRIJBJpS/IMKDGzuZKOxX3/1gNXmtmrhYmPmV0G/AZYBPinXHrBXDPbsJr7ZnVu44ALJJ0H3GNmjxe1FSv29zazqcBUSTMl9ccp9t/gl94+9bOajRLOl1s7Ge61GpH0GrA8bqNyNnCP7/MiLuQUYAdgTTXnYvSVqxH3JHChpGHA/8xskqTngSu9E7vDq4oUsxrZqhncVHTdxKJfI9fgSvX8tZXrIpFIpEPJW1bLzO4D7is5d1nR3z8CfpTnPTM9A/8FvQHOgZ0j6TdFzWkU+8uR5SdCsd1Gmp3zHGvePCw+Xwds6uu4DTWzpc1sqpmdi3shewDPSFrdzB7DSYJ9CFwrqbTkdKGaQcHWOma2U1F7a1UBQs8vsSpAYZP20bsuD5iJRCKRfDAp9VGrZN1zWwqYbmbXARcA62e4/DFgf0n1kgbhHMlzCeenAn2yjK8MDwHzAlAkDfX/rmRm48zsPOAFYHW5qgCfmdm/gf+UeW6VVjN4AxgsaWX/uKKqANvtURqAFIlEIm1Dk+pTH7VK1mXJdXDq9k3AHNzeUVpuBzYFxuDCQE81s08ktXb+C2CunAL/1cBXGccKrjjqP+TU+LvgHOkxwIlyIaeNuETC+3GbnKdImgNMA1rM3MxstqR9gIv9XmMX3NLiq0kDMLOZko7AFTntgttcvSzpmkgkEulIarmUTVpqNhUg0pLPX3028Y2yFL+gus5KDgEHmNuQHHrdY+qnQRtfDRsW7NN9YLIKe+81Vg3aoG9yeP3cD94LmqhffUiwz4yByWIJPSe9HrQxJ4Wif/2MZBX9EdudGbSxyj4rBPv0GJCcZjFor92CNt6/KrxVPODM3yW2z+waXpjpO/WjxPbZ3ZM/RwD1jeEKE3NvvCKxvaFnuLZAj5XDr/3slZMlE/XEQ0Ebw1b/S7DPMTtX553em/Bmasew/Mqr1aQnjAolkUgkEmlBZ5i5RecWiUQikRbUchHStETnFolEIpEWxJlbJBKJRDodTbUrO5ya6NwikUgk0gLrBM5twX8GGZD0e0k7+L9PlNQzdE0kEoksbBhKfdQqC9XMzcyKFVVOBK4Dpud5D5Wo+5c+TntdKaFQ/1kNYT/dpXFmsE9oI3lOj/5BG/3XXCnYZ/oHySHe9OodtNHUJ7nKQZclwiHgTQ1hhfXGLskVDKxHuIKB1YVTNUKK/mnC/MffOjHYZ81DVkseR99FgjZ6LhoOwW+qS/56mV3fPWijsSG5T5oZxqyG8GdpwJDkShbfvp5Cy7d7+P/grB4DEtv7rLBi+DbJH8dcqGWnlZY2n7lJOlTSWK/Yf60/t7yk4f78cC9qjKSrJV0s6SlJ7/ik6YKdU72q/hhJ5/pzP/Zq/2O8+n9PSf282n6d79NT0gdeUeRqSftIOh5YChghaYSko7zgM0V2LyzzXHaSq3zwkqRbvOjyfOr+ZR4f4Mf+itflLNib5meTz+IS2SORSKTD6QwztzZ1bl6e6gxgOzNbFzjBN10C/NfMhgDDgIuLLlsSJ6a8G67sDZK+B+wFbOztFMrd/M/MNvLnXgeO8sLKY4CtfZ/dcWr/cwo3MLOLgY+Abc1sW+BGYA8vmgxwBHBVyXNZFDgT2MHM1sfJdv28qMtMM9vCzG4sfoxTRTkP2A5X1WAjSXv5Pr2AV8xsYzN7IvHFjEQikXYiOrcw2wG3mtlkADP70p/fFLje/30tzpkVuMPMmszsNWBxf24H4Cozm15iZ21Jj0saBxwEFLQebwIKdeF+SEB538y+BR4FdpO0OtBgZuNKum0CrAk8KWk0cBiuKkGB1qoCbASMNLPPfdHSYTj9THDyX7cljS0SiUTamyarS33UKm09MtFKOfESivsUK/+r6N9ydq4GjjWzdYDf4erHgSu/8z1JA3FVDB5NMYYrgMMpM2srGsPDRVUB1jSzo4raK6kKMDNpn624KsB/b/5f+BlEIpFIDsSZW5jhwH6SFgHwzgbgKdyMCtyMK7Qk9xBwZCG6schOH+Bjv5x4UKGzmU3DVRb4G67uXDkH0qLqgJk9CywLHAjcUKb/M8DmBXV/v5eXQgCRZ4GtJS0qVwT1ACqoCnDofnunuSQSiUSqJm/nJum7kt6UNEHSaWXa5eMtJvhYjCwVZ8rSptGSvtrqH4FRkhqBl3Gzo+NxhUFPAT7HzZaS7Dzgy9W8IGk2rujdr4Bf45zHe7gac8VKrDcBtwDbtGL2cuB+SR/7fTeAm4GhZjZfBQJffftw4AZJhXilM4HEMCoz+1jS6cAI3CzuPjO7M+maSCQS6UjM8puR+R/1/8AVkZ4EPC/pLr/1VOB7wCr+2Bi41P9b+X1jVYBmJN0DXGRmwzt6LKXMePTaxDfqq6WT1cYBBk58LtjHuibHGSvF52XGM08H+8yZPiOxvVv/sGJ8t3WTf9w19QiHgOvdN4N9Zqy7dWJ7t6cfCNqoX3b5YJ+mfskh+F8Muz6xHWDuzDnBPq9dm/yct378/KCNxifC/0W6brhJYvukZTcL2lj27eQdhabu4TSMUFUHgF4TXkhsb/z8s6CNxqnTgn2+3j25LuOALyYEbVz49i7BPr85qEtV3mn0+M9TO4ahqwxKvJevgXmWme3sH58OYGbnFPX5Fy424Qb/+E1gGzP7uILhAwtZEndrSOov6S1gRi06tkgkEmlPcg4oWRr4oOjxJH8ua59MLFRJ3K1hZlOANPtnkUgk0unJEigi6WigeEp6uZldXtyl7C1KzKTok4no3CKRSCTSgix7bt6RXZ7QZRIuWK/AMrg846x9MhGXJSORSCTSgpyjJZ8HVpG0gqSuuEj5u0r63AUc6qMmNwG+rma/DeLMLRKJRCIl5BktaWZzJR0LPAjUA1f6SPpjfPtluAj4XYAJOL3fxAj6NCw0zs1LXr1VEn4aiUQikRKacrZnZvfhHFjxucuK/jbgZ3nec6FJBZB0NS6h+9YybV28NFYe92lhK63tUL9Hxs5KfKMW6TE1OLavZ4XDpqXkz8OaeiVoo37urGCf2V2Tw/SnNfQP2lhm3N2J7U0DlwjamNVv8WCfHh8mh87PXiKs1j+lTzgcfbHxjyd36NKQ3E46Rf8Qo7Y8Jdhnu4d+E+wzsv9+ie09u4bTFuY0JldTSPP1NaB7uPBHry7JfRotXNVh2twewT5DP7g9sf39FbcL2vi2KVx9YL1VFq1q6vXU61NTO4bN1uhTkzIlbTJzk9QLlxC9DG4aejYwGSeV9X3fZ0fgJ2a2t6RpuCS/HYCvcAnafwaWA040s7t8AvVe3t7awF+ArsAhOMmuXczsS0kreVuDcNPbHwMDgT1wSiFnAj8A/oNTStkceNTbX9XM5kjqC4wFVikWXJY0CLjMjws/ticlnYWrMjAYmOzTCoofnw5c6cf0OXCEmb3vHe6XwHrAS8AvKn7RI5FIJCfyXJbsKNpqWfK7wEdmtiuApH7AN8A/JA0ys4IqSUHDsRcuge+Xkm4H/oDLZl8TuIbmzce1cY6gO25t9pdmtp4vV3Mo8Fdc1M4xZjZe0sbAP81sO0l3UTRzkwTQ38y29o8HA7sCd+A2PG8rdmyev+GSvJ+QK9PzILCGb9sA2MLMZnhnV/z4blwVhGskHYmrgrCXv25VXKWBYM23SCQSaQ9qWTMyLW3l3MYBF/jaZfeY2eMAcvXcDpZ0Fa4ywKG+/2zggaJrZ/kZ1Djc7KfACDObCkyV9DVwd9E1Q3x9tc2AW7zzAkiS3ChW8r8COBXn3I7AzfhK2QFYs8h2X0kFKY27zKxYdqP48aZAQRzyWppL9gDcEh1bJBKpJZo6wW5Vm6QCmNlbuJnLOOAcSYUF+quAg3HiwbcU7THNsebNvyZ8ZQAza6KlAy7ezGkqelzoVwdMKVLuH2pma9A685T8zexJYLCkrYF6Myu3uVQHbFpke2nvbFvYauVxMcUfnVb7FVcFuPfWKxLMRSKRSH7EqgCtIGkpYLqZXQdcAKwPYGYf4RLzzsSVq8kVM/sGmChpXz8OSSqILraoAtAK/8VVBChX8gZcdYJjCw+8mHMaslZBAFpWBdh1nx+lvFUkEolUh5lSH7VKWyVxrwM854t6noHbQyswDPigDUPyDwKOkjQGeBXY05+/EThF0ss+6KQcw4ABlC95A66awYa+JMNrwDEpx3Q8cISksbgAmBMC/SORSKTDaDSlPmqVdk8FkHQJ8LKZ/addb5wCSfsAe5rZIR09llKefv2bxDeqf9dwKsD0xnCockgIdfWpzwRtdHnjpWAf9U5OBZi13JpBGw0zvk5sn3ZPuLJQlwOTVdoBGmYlq703dume2A7wde8lg30WfyNZAf/9YeHn03PRvsE+fZZPHkvXDb4TtPHoTr8P9hk49vnk+9SHs28UkBdME6K/jL0b7DPgg9HJHerC84Bpi68S7DO1x6DE9kXvuTRo46Udzg722XqtnlV5nVDqUTE7DOlWkx6uXZO4Jb2I22OquZB3SX/H1RQK15OIRCKRTkxnSH9uV+dmZhu05/2yYGbHdfQYIpFIpBao5UCRtCw08luRSCQSSUdnSAWIzi0SiUQiLWhqijO3SCQSiXQymjrBsuRCWc9N0l6SwuF42WyOlLSh//s+Sf3ztB+JRCLthVn6o1bp1DM3SfWtSFvtBdwDJObaVVotwMxyj7icts1mie3r3nxm0MaT+/0h2EcNyb/YZj2QVHDXUT87XBVg6rNvJLZPviYc9r7In/6c2H7/zv8N2tjpygOCfV48/OrE9iHXp0iw/yqcqnHrIcmK8TufuX7QRlNd+L9010/fSmwf2W+foI2BY3cP9vlyyEaJ7ds9fFbQxqM7Jvep7xH+fb7iUymyjr6anNg89/PPgia+vuamYJ9Ffp5cceHT3Y5NbAf49cnhVJvHbt8i2CeJWk7OTktNztwknSrpeP/3RZIe9X9vL+k6//cBksZJesVrWBaunSbp95KeBTaVdK6k13zi9QWSNsNVCDhf0ujShG5JV0u6UNII4DxJ35H0lE/+fkrSar5fD0k3ers3AT2KbLwraVFJg6XmGjGSTvaiykg6vmhcN7bNKxmJRCLZabL0R61SqzO3x3C5cBcDGwLdJDUAWwCPe3mv83D6lV8BD0nay8zuwFUYeMXMfiNpIK60zepmZpL6m9mU0goBZZin1O/L32zlq8nuAPwJVzLnJziJsSGShuBK1mThNGAFM5sVlzAjkUgt0V7Ljf47+iacQP67wH5m9lVJn2Vx0ohL4HSELzezv4Vs1+TMDXgR2MAr7s8CnsY5uS2Bx4GNcCVyPvfLhsOArfy1jcBt/u9vgJnAFZL2xtV3S0OxUn8/XJWBV4CLgLX8+a2A6wDMbCyu/lsWxgLDJB0M5FIoNRKJRPKgHeW3TgOGm9kqwHD/uJS5wC+8CP4mwM/SxEzUpHPzddTexZWeeQrn0LYFVgJeh8RQnpkFx+Qd33dwzm4vmsvqhChW6j8bV2pnbWB3XC25eUMN2JlLy9e4+NpdcUVVNwBelDTfLLpFVYAZX6YceiQSiVRHOwaU7Imr2Yn/d6/5x2Ifm9lL/u+pOB+wdMhwTTo3z2PAyf7fx3EixaN9aZxncVW1F5VUjyuhM6rUgK/v1s/M7gNOBIb6pjQVAgr0Az70fx9eMr6D/H3WBoaUufZTYDFJi0jqBuzm+9cBy5rZCFwNuf7AfGKLLaoC9BiYcriRSCRSHVmcW/GPcH+EBVubWdzMPnb3tI+BxZI6+6LS6+F8QCK1uucGzqGdATxtZt9KmunPYWYfSzodGIGbxd1nZuXC6/oAd0rq7vud5M/fCPzbB63sY2ZvJ4zjz8A1kn4OFCvbXgpc5ZX+RwPPlV7oC67+HvdGTAQKIYL1wHVyFcqFq+49JfHViEQikXaiKcNyo5ldDrQaRi3pEdx+WSlnZBmTn6zcBpzoy5sl92/vqgCRypj63L2Jb9S4n54VtDHkH78J9rG6ZJX19waEw9GXmZYc5g/QZVZyaLwpvKjw6SLJy+6fzkpWYAdYNTkbBIA+7ydvp05drtykPTuv2dqJ7St2fTdoY3Z9uEJBU0BJ/6OZ4detd8PMYJ81P3owsT0U5g+w3UPJn1mrbwjamDlgqWCfHl98kNzBmoI2SPGZnTFwmeTbBP7/AZz/dPj/4Lk/7l7VZtgNT6Z3DAdsrorvJelNYBs/YVkSF0uxWpl+Dbj0rQfN7MI0tmt5WTISiUQiHUA77rndBRzm/z4MmG8FTpJwUe+vp3VsEJ1bJBKJREpobFLqo0rOBXaUNB7Y0T9G0lKS7vN9NscVed7O5yaPlhQUyqjlPbdIJBKJdADttVtlZl8A25c5/xG+tqaZPUFyhHxZonOLRCKRSAtqWXkkLdG5RSKRSKQFnSHOcIHac2sLNf8S+0/lZGcbr2EZiUQiCxyxKkAbUa2af6X3M7O8HNI2wDScukraMSRWIHhm918mXr/FTeGUkSf2OD08jkBVgA3uvSRoo37U3cE+UyYkh15/Mf6ToI1lLjg3sf2FbwYHbaxwy0XBPk8ccXVi+xr/OjRoY+6McKWEjw5LrgowtMdHQRuNDeFUgJ4fj09sf2+JA4M21BD+VguF+ofC/AEe3en3ie1d+oa/wr7z6D+Dfezd5EoJcz5PrhoA8PFzbwb7LHV6clWAr/sEhTd48p7ng3348ZbhPgk0psh8qHVynbnVgJr/ZZIel/SWpIIaSL2k8yU97239nz+/jaQRkq4HxhXGUNQ2StLN3ta5kg6S9Jwf+0q+3yBJt3nbz0va3GfQHwOc5Me5Zbl+/vqzJF0u6SGcMGgkEol0OE1N6Y9aJe+ZW0er+Q8GtsZpUI6QtDJwKPC1mW3kJbCe9M4EnO7k2mY2sYytdYE1gC+Bd4ArzOw7kk4AjsPJef0Npy7yhKTlcAmGa0i6DJhmZhcAeAfaop+3jX8ttjCzGalf5UgkEmlDanm5MS15O7dSNf+XaFbzP54iNX8ASQU1/ztoXc3/XtxSZBpuNrMmYLykd4DVgZ2AIZIKFRj7AasAs4HnWnFsAM8XNM8kvQ0UHOI4nIgzwA7AmmpO0O/rn3spSf3uio4tEonUEtG5leC1FN+lWc1/LC3V/FdNuLyFmr+k7+DyH34IHAtsl2YIZR4LOM7MWmgBSdqGlur/pRRvkjQVPW6i+XWrAzYtdU6aX40mqV+rY/ACpEcDnNBnSaJ4ciQSaQ86QypAW0RLdqSa/76S6vye2IrAm7glwJ/45VEkrSqpV9XP0vEQzvEWxt3aOFvrl0isChCJRDoCM0t91Cpt4dweB5bEqfl/iltenKfmDxTU/McALyWo+d/jFfdH0VLN/xRJL5cGlHje9P3vB44xs5nAFbjoypfkCo7+i/xmrMcDG/pAlddwjhzgbuD7hYCShH6RSCRSczQ2pj9qldxTAcxsONBQ9HjVkvbrgevLXNe76O+PccEepX2eBJLy3J40s5OKT/g9uF/5o5iR/phvDGbWos3Mtin6e16bmU0G9i8zzreYv75buX5ntfpMSug6MPmtSqMmHrIBMGdq8qd1bpdwqHn3gQOCfbr1Sy6+2r1fj6CNOV2S+8xN8R8vTYh+l7rkkDBLsYYz46twEfhugfD62d37Bm1Yit+rTd2TFy7S/BhvDFQWAKjvkTyWNIr+oVD/ud+Ei9g31ncN9qkbuGhie0OKb/Feg8IlImd3na9sYwtm1CW3A3Tv3fZb9DU8IUtNTea5RSKRSKTj6Ax7bp3GuZnZ4R09hkgkEukMxJlbJBKJRDodaZbam6m67E2bEJ1bJBKJRFoQ5bcikUgk0uloarLURzVIGijpYUnj/b+tRqN5KcWXJaUS9ehw59bWSv8ZxnG4lwcrPH5XUnIIVSQSiXRC2rEqwGnAcDNbBRjuH7fGCTgxkFSovZLw1IrSv6SrSdaLbBckjQRONrMX/ON3gQ19uH+H8+6EtxLfqNlKoQbf+E2wz9y65LDpWWccF7Sx9LbrB/t0WWLJYJ+wkeRQcpvyVdDE5I33CvZ5Z/bgxPbB3ZIrHKRl0c+T/9/O6h3+rTWrIRxK3nVuclrCxIY1EtsBlrb3qr6PLLz2pabkEPw0Yf7Prnd4sM/Wf/t+Yntdr55BGzZ7drBPfe/kdIFZkyYFbVy4+IXBPr87NFDeI8Afb2xM7RjO+GF9xfeS9CawjZl9LGlJnDzjamX6LQNcA/wR+LmZ7RayHZy5qWOV/vf1NsdIesyfO1zSHZLuljRR0rGSfu6nq8940WUkDfWPx0q6vTDdLXfe605uCAzz4ygkUB0n6SX/3Fb3158l6UpJIyW9U3htfNvBcpUDRkv6l59G18tVLHjF2znJ9z2+6LW4MfQ+RCKRSHvRZJb6qJLFCxq+/t/FWun3V+BUnPxhKtIsSz6GEz4G5wB6q7zS/3Y4mayNJO3l+xeU/jfGqYR8H1jLzIYAfzCzp4C7gFPMbKiZvV1y798AO5vZujgnWGBt4EBcovcfgelmth7wNK4KALgSMr/09xoH/La1837W+AJwkB9HIUtyspmtD1yKkxQrsDqws7//byU1SFoDl6i9uZkNxQlBH+Rfk6XNbG0zWwe4yts4DVjPjyMqlkQikZrBmtIfko6W9ELRcXSxLUmP+B/3pceeacYiV77sMzN7MctzSBMt2ZFK/08CV0u6Gfhf0fkRZjYVmCrpa5zcFThnNURSP6C/mRV0K68BbmntfML9C/d8Edi76Py9ZjYLmCXpM2BxnMjzBsDzcoLIPYDP/NhWlPR34F6aqwuMxc0U78C9VpFIJFITNKZflcTMLgcuT2jfobU2SZ9KWrJoWfKzMt02B/aQtAvQHVdV5TozOzhpXMGZm5nNAd6lWen/cVoq/Sett7ZQ+sfNdG7DVdR+IMW9jwHOBJYFRktaxDelUezPg4LdxhK7xfcvtAm4xs/8hprZamZ2lpl9hasNNxL4GU7rEmBX4B84h/iipPnGXfyL6Pobb8rxaUUikUjrtKNw8l3AYf7vw4D5tIbN7HQzW8bMBuOqxDwacmyQPlqyQ5T+Ja1kZs+a2W+AyTgnF8TMvga+khMtBjgEGNXa+dA4UjIc2EfSYn7sAyUt7yMu68zsNuDXwPqS6oBlzWwEbh25PzBfJEBxVYADfzifNGUkEom0CU2W/qiSc4EdJY0HdvSPkbSUpPuqMZx2lvM4cAZO6f9bSS2U/iUVlP4F3Jeg9H+npO6+X7HS/799YMY+Jftu50taxfcfjqskMDTlmA8DLpPUE1dJ+4jA+av9+RnApinvMQ8ze03Smbjq4nXAHNxMbQZwlT8HripCPXCdXyYVrkr3lKz3jEQikbYgm0JJFfcx+wK3pVN6/iNglzLnR1IieN8a7ZYKEKmOmfddnpwKsPjyQRtd33sjfKM+/ZLbp3wRNPHGv+8I9hkweJHE9l5LJLcD9F4nkB5Zn6IKwrKrBPu80H3bxPaNv7o7sR2gsWdY0X9szy0S21cZdmxiO0DfISlSRrslp41MXOcHQRMrvPNw+D5fJWfR2GJLB03Yu28ltofU/AHmTgqnaow64fbE9sU2Dle66Lds/2CfxTeYL8q9BZ88H/4/+sdVrw72+e/ZS1aVCvCr/8xK7Rj+dFS3mtTfivJbkUgkEmlBtcojtUB0bpFIJBJpQVOGaMlaJTq3SCQSibQgh+TsDic6t0gkEom0oDPEYkTnFolEIpEWdIY9tw6vCpAV1UgVgRA+T+NW//dQn10fiUQiNU87VgVoM2p25qZWqgjg1E3uwWlV1iw+T2Mf/3AoTrKs4qTEps8+TmyvWySssj/3s0+Cfeq/nZrYrn7hkOju/XoE+3z+5qfJ96kL/+7qHQhpb+o/KGij4Ztw0Yf6nslarXVTvwzaqJuVrJAPYD2TI6obeoZf129fTw6dB+i+9BKJ7Y1WH7RBivdn7ufllJSaqR8U/szO+Tz5/WloTK4aAOkU/UOh/p89G64w0bV3uEJBw8D+ie0DVlw8aGPa198G+1RLYyeoVpr7zE0dW0Vgca/0P8Yfm/nzPy8S6zzRnxss6XVJ/5b0qqSH5KsBSFrZi32OkasKsJKk3pKGq7lKwJ6+73mSflo0hrMk/cLbf0VSV+D3wP5+zPvLFeYb5PvXSZqgWDsuEonUCNZkqY9apS2WJTuyisDFOJmtdYH1gVclbYBTIdkY2AT4saT1fP9VgH+Y2VrAFKCQvTrMn18X2Az4GCf6/H1fJWBb4C9yCsk34qoBFNiPIjFmM5uNq25wkx/zTcB1uIoBADsAY2qlblwkEolE51ae0ioCT9NcReBxiqoIeDHlQhUBaL2KwN5AeF3HOcxLAcys0WtJbgHcbmbfmtk0nNJ/wflONLPRReMe7Me9tJnd7u3MNLPpOJmsP0kaCzwCLI2rRfQysJjfY1sX+MrM3g+M80qaS/McSXMZnEgkEulw2lFbss3I3bl1ZBWBVki6X2vq/uU4CBgEbODrtX2KK78AcCtuf21/3EwuETP7APhU0na4GeX9ZQdeVBXgypEvhMxGIpFILsSZW+t0SBUBnLjyT/z19ZL6+jHsJamnpF64pc7HWxu4mX0DTCoslUrq5kWW++EK5s2RtC1QLOZ4I64Uwz44R1dKuTFfgVuevLmVwJkWVQGO3GbD1oYciUQiudKOJW/ajLZybo8DS+KqCHyKW16cV0UAp4w/Aqfy/1JCFYF7/DLgKFpWEThF0sulASXACcC2ksbhlhnXMrOXcIr/z+Ec6xV+KTGJQ4Dj/b2fApbALZ9uKOkF3CxunsKpmb3qx/uhf36ljADWLASU+HN34crcxCXJSCRSUzQ2NqU+apU2SQUws+FAQ9HjVUvarweuL3Nd76K/P8YtS5b2eRIom+fmHel8pcvN7ELgwpJz7wJrFz2+oOjv8bj9u1JaLYVjZuu0Zt/MvsTtNRazLi6QJIVUP3zx3NjE9v5rhmd2k597Ndina5/kcPOBG60btJGGLt2SP3qTXggruS+2W/Jvs28WCyv+933qjmCfmYs2JLY3plCdV5dweP3MxZNfkx4rrxC0Qfdw2Pvstycktk+bG045mLZ4+LX9+prkArtLrbVeYjvAx8+9mdjea1C4BOPA9dYI9gkp+qcJ8580PDm9BWDVQ5PTV/qsuWpiO8BnN38Y7AMrp+jTOrW83JiWBS6JuzMg6TTcXuLpHT2WSCQSKaW99tx8UeeHfXrUw5LKJhxK6i/pVklv+BSuYM3N6Nw6ADM718yWN7MnOnoskUgkUkqTWeqjSk4DhpvZKriYidNa6fc34AEzWx236vV6yHB0bpFIJBJpQTtGS+4JXOP/vgYXGd8CHxi4FfAfcLnDZjYlZDg6t0gkEom0oB2jJRcvBOH5fxcr02dF4HPgKh9IeIWPfE8kOrdIJBKJtKBxblPqozgf1x9HF9vyUoavlDnmC/5rhS44xalLzWw94FtaX75scdECgc87e8vMalowGdzmJ3Cgmf2zo8cSiUQiWckyIzOzy4HLE9p3aK1N0qeSljSzjyUtCZRT254ETDKzZ/3jW0nh3FRrSXhqpRqApKuBe8ysXJJ0TSFpMG6sa5dpa63aQSIfvTk28Y2a2SU4S6f3rLCy+ayG5FDyQROfTWwHsE/DocoalKxMb127BW18ff+Die199/p+0MZHS6wf7DP+m2US21frG1JbgzmEQ8nfn1ZuRaaZjeeODNqY1SNctWFmQ+/E9kHvPhe08fGKWwT7DJg6KbG9qS6cHtFl7szE9tldk58LQO9xjwX7zPrwo8T2kJo/gLonh/kDPHrY1Ynt2162f2I7wHkNvw72+d2hDcklJgIccOr7qR3DDX9eruJ7STof+MLMzvVR5APN7NQy/R4HfmRmb0o6C+hlZqck2c5tWVIdWw1gX29zjKTHCi+GpKFFfZ6UNMSr9l8jVwXgXUl7S/qzH9cDXuQZ3/YnSU/7qfb6kh6U9LakY4rsniLpeT/W3/nT5wIr+bGeL2kbSSMkXQ+Mk3S2pBOKbPyx8NpFIpFIR9PUZKmPKjkX2FHSeGBH/7hQD7O4RNhxwDAvrDEU+FPIcJ7Lko8Bv8Ap828IdFP5agAbAF8BD0nay8zuoLkawG8kDcRFxaxuZiapv5lNkXQXrc/cfgPsbGYf+iVBcPJWhwMnSloV6GZmY+VEmFfC6V2uiRN2/oGZnSrpdmBX4A5v4wMz21TSRTiVk81xepKvApdJ2glXWeA7OE3KuyRthZsyr+01KJG0je+ztplN9DO7/wF/k1SHk+6aL2E9EolEOoL2WtEzsy+A7cuc/wjYpejxaJxfSU2eASUdWQ3gSeBqST8GCmsdtwC7eQd7JM45FbjfCzyP8/0LoszjgMFF/e4qOv+smU01s8+Bmd6J7uSPl4GXgNVxzq4cz5nZRJinXvKFXOmdnYCX/ZsciUQiHU7T3KbUR62Sm3PryGoAZnYMcCawLDBa0iK+TM3DuDyK/Wgp9zXLX9cEzLHmnylNtJzNzio6X1xBoNBPwDnm6rQNNbOVzew/rQyztHxuYWZ5BK4EznwURyFdd1PNbzVGIpFOQpM1pT5qlbyjJQvVAI7EzXYuBF70y4vP4pbhFsUtSx4A/L3UgFw1gJ5mdp+kZ4CCEF6r1QAkreQjaZ6VtDvOyX2BcyB3A497fce8eRA4W9IwM5smaWlgTtJYi7gdV6G7ATiwXIfiKKRQQEkkEonkRWfQlszbuT0OnIGrBvCtpBbVACQVqgEIuC+hGsCdkrr7fsXVAP7tAy/2sZZVuM+XtIrvPxxXbQAze1HSN7SR8r6ZPSRpDeBpSQDTgIPN7G0fwPIKrlbbvWWunS1pBDClkujJSCQSaSs6g3OruVSAPPFBLCNxwSk1NX/2gSQvAfv6KgSJvDx+cuIbNUDhiel0wmHTM5uSQ/DXeKHVdJZ5TB0XlH0Lq58vE1bA19Qpie2f3PVQ0Mb00y4N9pkyJ3kSPqhr+LX/tims1t+nfmpi+2J3/yNoo2GFFYN95i62bGL7R4OGBm0sec/fgn0+3e3YxPYBM8pVh2rJ9G79E9tn1IU/04v/7/xgn09eeCuxfcCKiwdtpFH0t9mzE9tHHJNcSQHgnTvChUSO21VVpQLs+ZM3UzuGOy9drap7tRWdVqFE0qG4+m1n1KBjWxO33Do8jWOLRCKR9qSpqSn1UassMAolWTGz/wL/7ehxlMOrrIR/YkcikUgH0NS44O+UdFrnFolEIpHK6Ax7btG5RSKRSKQF0blFIpFIpNNRy/lraem0ASXVIFfS/KftcJ+9fHBJJBKJ1AztWKy0zejUqQCVkqTq30p/4V7LTD93lKHSwU6HvJz4Rm2+c3ioj987Jtina4/kVIDtdlsraGPsy58G+3zxcbLa2NQvpgRt/ODIzRPbt159ctDG328MduHYHyZHOv/j5rCN+vrw78iT9k1Wmhs5Ibk6AUD3cDEF3v9wbmL77t+ZErTxzexwasOvz3wpsX2zXcJSgU/e83xie/fe4XFstvOQYJ+J45M/K9O+LhUYmp/PJoarYWy/72aJ7YsuEl5MW3Gv1YN9dp3zZlXh+Tse9GJqx/DwsA1qMhUgLkuWZ56qPy7pfAgwAKcmcqaZ3ekd4P2+fVNgL59+cBDwATAZp85yga9i8A9gEE4r88fAQFylg60lnYkTby5OTI9EIpEOIUZLdl7mqfpL6oKTA/vGS4c94ysUAKwGHGFmP5W0IfADYD3c6/oSTkwanITWMWY2XtLGwD/NbLtApYNIJBLpEHIoZdPhROcWRsCffCmbJmBpoCBX8J6ZPeP/3gK408xmAEi62//bG9gMuEXNogEpFo8ikUikY7AaTs5OS3RuYQ7CLSduYGZzJL2Lq+kGLZX+W1t3rsPpRw7NemNJRwNHA6yx8Rkss8oPspqIRCKRzNRyoEhaYrRkeYpV/fsBn3nHti2wfCvXPAHsLqm7n63tCmBm3wATJe0LLvhE0rpl7jMfZna5mW1oZhtGxxaJRNoLs6bUR60SnVsZfOHQgqr/UGBDSS/gZnFlVUvN7HlccdMxuCrbLwBf++aDgKMkjcFV8d7Tn78ROEXSyz7oJBKJRDqcprmNqY+axczikdMB9Pb/9sQ5t/Xb8F5H14KNWhpLrdiopbHE5xNfk4X1iDO3fLncpw+8BNxmZsmJPtVxdI3YyMtOZ7KRl51asZGXnc5kIy87tWKj0xEDSnLEzMpW1I5EIpFI+xJnbpFIJBLpdETntuASLondPjbystOZbORlp1Zs5GWnM9nIy06t2Oh0RG3JSCQSiXQ64swtEolEIp2O6NwikUgk0umIzm0hQVK9pPM7ehwFJNXnYGM3SRV/hiXVSdovh3HUSzqpShtVj8XbSK6pks5O7p8VP7a+FYzjuo4eR9G1A/McS6Rtic5tAUHSnyX1ldQgabikyZIOTnu9mTUCG6hIvbmjxuKZIOn8Kou1/hAY78ezRtaLzWkHHVvF/Qt2GmlWnanURtVj8Tb+Uo0NbyeXz4qk6/3npBfwGvCmpFMyjmOQpK4dOY4inpV0i6RdKn1tJB0raUAl1xbZWCHNuYWd6NwWHHYyp1O5GzAJWBXI+h/0ZeBOSYdI2rtwdNBYhgBvAVdIekbS0Vl/UZvZwbgSQ28DV0l62ttpVa+zDA9LOlnSspIGFo4s4/A8KekSSVtKWr9wZLSRx1gekvSDHH7E5PFZWdN/TvYC7gOWAw7JaONd3Gv7a0k/LxwdMA5wn/PL/bUTJP1J0qoZbSwBPC/pZknfrfB9uq3MuVg2q4QYLbmAIOlVM1tL0r9x6icPSBpjZusGL262cVWZ02ZmR7b3WErsbQXcAPTH/Sc928wmZLh+UeBg4ETgdWBl4GIz+3uKayeWOW1mtmLa+3s7I1qxs10GG1WPRdJUoBfQCMzAVaswM8u6JFj1Z0XSqzht1uuBS8xsVAWf2d+WO29mv2vPcZSxuS1wHe61HgOcZmZPp7xWwE7AEcCGwM3AfyxQrFjS6sBawJ9p+WOyL3CKma2V9Xl0ZqJCyYLD3ZLewH1h/VTSIGBmFgNmdkStjEVuz21X3H/wwbjltGHAlrhf18FfxJL28NevBFwLfMfMPpPUE+fkgs7NzHJZzjGzbXOwUfVYzCzLrDXJTh6flX/hZl5jgMckLQ98k3EcvwOQ1MvMvg31b6tx+DEsgvsRdQjwKXAcTix9KHALkOr9MzOT9AnwCTAXGADcKulhMzs14dLVcKsl/YHdi85PBX6c5bksFHS0uGU80h+4/wT1/u+ewBIZr18VGA684h8PAc7soLG8A/wH2KxM28UpbVwDbNVK2/YpbTQAx+NmjLfi9r0aKng9+gEX4gSzX8A5634ZbeQ1lj2AC/yxW4Xv7zLA7cBnuC/y24BlKv3sFtntkrH/prh9svf943VxlezbdRz+mreAX5d7HYBfprRxPPAi8CCwb+H9xW0RvZ32Nan2+S8MR1yWXECQdGi582b23ww2RuGWM/5lZuv5c6+Y2doZx7Iv8ICZTZV0JrA+8AfLIBQtaQsze6Lk3OZm9mTK6+uBB81shyxjL2PnCpxTucafOgRoNLMfZbRzG/BKiZ11zSz1PlUeY5F0LrARbhYMcADwopmdltaGt/MwbhnvWn/qYOAgM9sxg43flDtvZr/PYONZYB/grko/s5JOAK7CzXCuwO3TnmZmD6W14e3sZ2Y3l5zb18xuyWDjd8CVZvZembY1zOz1FDZWBS4FFjeztSUNAfYwsz+kHcdCQUd713ikO3BLbIXj37iZz60ZbTzv/3256NzoCsYy1v+7BfA4LlLw2Yw2XkpzLmDjLjLOjsrYGJPmXAo7872OWV/bPMYCjAXqih7XF96vDng+vyg6zgCexn2xZ7HxrP/35SpekzH+3539Z2bdrJ81f31Vn1nc7OyVrPctY2cU8J2S16Rqu53tiHtuCwhmdlzxY0n9aP5VnZbJckVRzdvYB/i4guEUKhTuClxqZndKOivNhZI2BTbDhXgXR731xX0RZ2EmMM7PMubtx5jZ8RlsNEpayfxmvqQVaX5+WZhRPBuVtDluTzILeY2lP/Cl/7tfBdeD+6wcjAv0ATcD/CKLATNrkZYg6QKcc8nCB3K5eyaXEnA8bj81C4WIxF2Aq8xsTJYoRUnf89cuLenioqa+uD2zVJhZk6QxkpYzs/fTXleGnmb2XMlTSD2OhYXo3BZcpgOrZLzmZ7hQ5tUlfQhMxFUJz8qHkv4F7ACcJ6kb6dNKugK9cZ+94uCHb3DLT1m41x/FZF1nPxkYIekd3Jfg8rgglawcA/zX/+gA+Ao4rAPG8ifgZR+9KWAr4PSMNgCOBC4BLsK9pk/5c9XQE8gUhYp7Xf8GLI1LO3kI+GlGGy9KeggX8HG6XKpIU4brP8Lto+6B2y8rMBXImry/JPCqpOdo+YNsjww28vqR2qmJzm0BQdLdNH9x1wFr4kKIs2BmtoNcMmuduT2zSiL09gO+C1xgZlMkLUnKPDczGwWMknS1ldl3yEh/M/tb8Qm/v5IKv2+3Lu5Hwmo4Z/CGmc3KMghv52AzW1c+V89cXlVWG1WNRU6tpQnYBLfvJlygwycVjOVPGb9wy9kZR/Nnth4YBKTeb/OsZmYtfoD5WXGqvVnPUbiIxnfMbLqPekz9o8HMxgBjJA0zs2pnSKlTGBIo9yM1q4hCpycGlCwgSNq66OFc4D0zm5TRxktmtn7JuRfNbIMKxrMFsIqZXeVTAXqbWbk8rdLr/mpmJ5Y463lk+UJt5fm8bD7wIKWNEZZDGL+kRy1DTltbjUXSY2a2VTU2vJ0Hgd3NbHYVNpYvejgX+DSrc2jlPZ7vXMCGcCsUK5rZ7yUth4vufS7l9Teb2X4lznoeZjYkw1jOM7Nfhs6ltDXvR2rWaxcG4sxtAcHPeCqiKPmzn1qqTPQFuldg77e45NPVcFFoDbiE1s1TXF7YJ7wg632L7n8AcCCwgqTiPZw+ZNwXAp6SdAlwEy2XiVJHfnpe9mO5pcTO/9p5LA9LOrmMjS9bv6Qs7+KUQe4qsXNh6EJJff3MtfRLt6+kVGPJeW/2n7gZ7Xa4meNUXGrDRimvL6wG7JbxvuXYESh1ZN8rc65VSl4P/N7b17io2NFVjq/TEJ1bjSPpCTPbQk55ovhXYxblibyTP7+PC6d+CTeIj5RS8srMCnsWA4H7si4Bep7C7TEsSkstxam4aMEsFISGi5fLDPdFmIWBOMdafJ0BWZxbHmMp7Iv9rMRG1r2uj/xRR8u90TRcj/u8vejvXRz5kHYsee7Nbmxm60t6GcDMvlIGvUozK+xn7Q3cbGYfZrw/kn6C2ytcUVLxZ7QP7vOchQ39cbd/vCvwPHCMpFvM7M9Zx9cp6ehwzXi030FOyZ/Ac/7fl/y/vcgYbo6b8b2Hm8ntSgVJtd7O8sAO/u8eQJ8M19YDJ+XwetQD5+dgo6qx4BzR/jk9n+vy+KzkMJbl/b+9qrDxrH9Ohc/rIIrC6DPY+S3wKi795We4PLO01/bDKfHc4D+zhWNgBeN4ELcNUHjcG3jAf/5f6+j3rFaOKJxc46hIQLfckdHcF3Iq/q9420PkkrCzcrOPluwv6cfAI7jcu9SYk3daGbeMdyDwtlwSc2r8vW/FySuBU9W4I8MYGnERcFXh7WQVSc59LOaqAvws2DHdWCpW41eRcHS5I6O5pSS9hg//l7SupH9mtHExTm1lMUl/BJ7ARZVmwsx+Z06/8WfAUrjAqEdSXvu1mb1rZgcAywLbmQuoqqsgqGs5oHgvdA7uR8AMoJKVkE5JDCipceTEdAtLO8vhQsyFW2J83zLoESonhRJ/3Y448VfhlEIezmrD22nARV4eAWxpZoMyXDsal8z6bNHzGWdm62Sw8Ufcr+qq9twk/QUX6VjxnlseY5H0a1x+XVV7bv7Hy/q4vLSse24j/J/dcctnY3CfkyG492qLDOOoWqHEX7M6sL0fx3BLoQSSYGsJnHTWD3ErBVkCSubtV5vZqpKWAm4xszT71QUbv8ZtDdzpT+2Oe5/+AlxuJdGlCytxz63GKTgvSZfh/oPf5x9/D5dnloXckj+9M6vIoQFI+i7uy2FbYCROFilrsc5ZZja78HwkdSF7nlvccytPxXtu5iM+Jd0IHG1m4/zjtXG5fJkwsw9KPrOVJLaPx+3XdfFjyZxI7ffN9scta94K/NjMXss4jor3q/0YBFyNExffAuesjzGzF3yX6Ng80bktOGxkZscUHpjZ/ZLOzmgjl+RPH3F5HrAY7j9XJWVVDgduBP7PKgsqAbcs9Cugh59J/pTmTfZUWA5pAN5O1Sr6eYwly0w+YCcPNf7VC47N23xF0tCMNqpWKJF0HG6/7FOcYxTu/0DqGZdneeBEqy4icbaZmaTC/8FeWS72195hLn3nxeAFCzMdvekXj3QHbhP5TNym9PI4rb4HM9pYEbc/Nh34ELf3MLiCsUwA1qiB16QOF+15C/6XNH6pPYONxXHVCe73j9cEjqpgLFVXXMhjLDgVkDNxy1PglkozVwYgBzV+XPDEFcA2wNa4fdkbMtpYFCcC/SmuQsF1wCIVfF4zXROwtxhui2A5YLmM156M2yN+x39enwaOy2jjH7gfu7k8n856dPgA4pHyjXLLXn/DVUh+2f+dOdLK2+pFhqjCMtc/mcPz2QQXvjwNtzneCHzTAa/r/bjl0IK4bhdgXAV2qhazzWMsuL22U4ucbA8qE8d+Fhf4UM3z6Y6Tp7rdHycB3TvgPR5BhdG4JXZ2xy1vfotTBWkCXq3Azo7A+bhczx0ruP413HbC27jUl3FUII7d2Y+4LLmAYC4gILW0VDkk9QcOxc3+uhT2MSyb0DDAC5JuwkUmzltStGwJy5fg9txuwW2wH4qLnkyNpN2As3Ez2S5Utjy6qJndLOl03MVzJVWyp5PHfmYeY1nJzPb3ie6Y2QwpvUhwMVblXpeZzcRpU15Uyf0BfCThcfjPbJHtLJGl7wAjJd1Ly89rMDimhD/gfpQ9YmbryVXjPiCjDczsYR8oU9j/G2jZAn6+l/WeCyPRuS0gyElcnYpTGpmnKmLZJJ/uA57B/dLLIhxbSl/c0uZOReeyBk9gZhMk1ZsLPb9KUtZk1r/iEmvHmf9JWwHfymkNFvZANsGpPWQlj/3MPMYyW1KPIhsrUVl4eB57XasA5+CWV4s/s1mCW+7ALdXeTeWf2ff90dUflTLHzL6QVCepzsxGSDoviwFJ/4cLGJqBez6F/b/Ur4l5TVZJi1GBwtDCQnRuCw7DcEtOu+GU0g8DPs9oo7uZ/TzcLRnLIXgCmO6/NEdL+jPOEWTaXAc+wC2VVZPP8nNcGPVKkp7ERcJlVcCAfCou5DGW3+ISepeVNAwniXZ4RhtQXo0/aw7dVX48F+GiYo+gpVpJGmaa2cXhbq1jPjgmB6ZI6g08BgyT9BnZZ+cnA2uZ2eRKByFpD1zY/1K4fcjlcT881qrUZmck5rktIMgLHEsaaz6vRtIoM9s6g42TcHtc99ByeSZrDtQg3Gb4YFouFaUuiSInqvsp7pf0Sbj8rn+a2YQMNjbCLUuOoorlJp9CUFDif9PM5mS5vsRWVWK2eYzFz/428TaeqeaLtBqKPrPzcg8lPW5mW2awcSAuKOYhWr7HWXL/VsU5lcG0/LxmSvfw7+0MXCDTQbjP7DAzS61nKukBYG8zm57l3iU2xuDSQ1osj5rZ0ZXa7IzEmduCQ+FL7mNJu+JykJbJaGM2biP7DJrzwSrJgboTJ0H0CJXlHAFMxoVFzwR+J1dmpVtGG3/EOevuVLHcZE6p/tVKry+xVWnYfG5j8V+2pXXuOoKZcmV4xks6Fhehu1hGG+sAh+C+zAvLkllz/24BLsNFblb6eQU39o/9Z/Yav/y7ONnEuk/HCWQ/S0tnnWXfu+rl0YWB6NwWHP4gVwjzF8DfcfteWQsl/hxYOYdf8j2tghIdJQzHJaFP84974H6db9bqFfMz0Mx2CneLdBAn4lITjsfNsLclewHX7+NK1VRcegeYa2aXVnF9gVto+fls9OfSVhcAlwbwKNXte5dbHq14taGzEp3bAoCf1axiZvfgAgwqTfZ9FRcIUi33SNrFvFpKhXQ3s4Jjw8ymSeqZ0cYjknYys4eqGEfV+NnJJmaWNSCmZikK9Kn4emA/MzsF9wOm0n3aMTipuc8qGENBe/VuST/FpSNUvByPSyeY52TNqeNkXTGYm8O+9xjc/+OTaF4e7V2lzU5HdG4LAGbW6DeRKw6p9jTiAjhGUMGSiJrL7gj4laRZuF+MlYTgfytp/cLeiaQNcPsZWfgZcGo145Cr6jzazL6VdDBOT/FvlqFKuJk1yWlLbppt+PmPRdIFwFVmVu0y6wRJt3pbWSWmCp/ZDSSpyoCfxYE3JD1Py89smlSA0pI7xdXiK1mO/1zSHmZ2F4CkPXHL61kYIeloXPRnpY52W3Mi2U3ANX4sWUs9dXpiQMkCgvIR1S27JGRm11Q9wIz4YJAbcXuHAEviyrW0q6SQ/1JYF6coci0u7HzvLIE63s7vcAm1/6v0yzyPsUj6EW6W1AUXrXiDmWVObZDTO/yht1UHXAncaK4IaVobeYhJl33ulqF4r6Tufp8s8VwKOyvhopaXwjnMD4BDMwZBTSxz2tKkR6i5JtxKONWVAn1wwgoHpx3HwkB0bgsIalZaL8ayRnzlNJbhZrZ96FwKOw00Rwa+UWFk4BDmj4LL8uX5krlClr8BPjSz/xTOZRzHVFwqQyNuBlrJLDKXsXhbq+Ec0wHAk8C/zazcZyiNra1wUlr9cTJnZ6f5Qpd0VZnTliWqNg/KvYaVvq7+2t64786KImIrxe+5D8DlDp5W1DS1giXWTk9cllxAsCpEdSXdbGb7SRpHGdV8S1myQ1J33Bf4opIG0Lzc0xf3azYrG9HsmNaThJn9N+3Fkq7EzXJepWUkXZZk8qlyiiAHA1v5vaKGDNe7m5plrVbdZmPx163uj8m4PZqfS/o/M/thBhu74hzkYFxe1TBgS5wYwKohG9XkQyqHCvRypWmWxglrr0fLz2vW/V0kdQN+wPwKP79PuKzURuF1HUzLH2TB9BU/A/+aClRRFkaic1s4KMh27Valnf/DRcAthS/Z4fkGJ+aaGknX4pZXRtMcnm1AaueGC+JYM8t9y7A/rljqUWb2iaTlcOkSmZD7pjsIWMHMzpa0LLCkmT3XnmORdCFOA/FR4E9F9z9P0psZTI3HaTKeXxIoc6ufybUp5mu+VfmjYWdcAvsyQLHzmAr8qgJ7d+Kcy4tUXhT0bmAm1asERQLEZcmFBP+L8UEzy1oDrpyt48zs71XaeB1Ys5pgA0n/Af5SScBD3ki6FPdltZ2ZreFntg+ZWeowcZ8kPNMHY6yKm3ndn2W5VtKRuL2x+aJiJfVLs//mPytnZJmRtAU+CnWsVVBMt8TOD8zsthzGU1Fh3xIb80QYIm1LXUcPINI++LDu6X7dvlr+Jel4Sbf641i/f5aFV4AlqhzHNcDTkt6UNFbSuKxRY5KmSvrGHzMlNUqqRFtyYzP7Ge5XOWb2FdkTyx8DuklaGpcHeASuMGUWDip1bJKG+zGlel7+s1J1bTk50ePguYRxNAFj/Ay2GoZLulDSC/74S4X/D56SlLrKeyvcLynmZrYDcVlyAUJOyHYwLdfqsyzjzQTGSXqYltFrWasC/BO3F/RP//gQ4FLgRxlsLAq8Juk5sod4F7jS37viJZ7SZS9Je+FK12Rljp/xFASLB1UwJpnZdElHAX83sz9LGp3qQrcf2pP89kOfknQJVUTnArfh0hmKuRXYIIONJYFX/eekeBxZPif/wf2YKlR6PwQXSbp3BhvgKl8f7iMeZ9G8/5dlJvYMcLuflVaaRhNJQXRuCwg57VHdSz6yTBuZ2bpFjx+V07vLwlk5jOP9Qs5RXpjZHZJOC/ecj4txScKL+bSNfYBfZ7QhSZvi9u6O8ufqU16b236op6DEUbw0mUr2StLqOBHffnJV2wv0JbuKfR6ixyuZ2Q+Kbab90VBCHqVmCvmQ1VSyiKQgOrcFhw2pco/KzK6R08NbzsyyBBeU0ihpJTN7G0DSimSv9TVK0uI0Sxc9Z2ZZVSjekHQ98yfEZkkFKP7yrcO9zplfYzMbJulFYHvcr/G9zCxTiRicczoduN3MXvWva6rwfTP7G/C3PPZDPUeZ2TvFJ/x40rAaLnipPy64pcBUnOB2avznZHmcQs8jcio2aR1+gRmStjCzJ2BesnxWwQDM7D1J6+IiRgEeN7OsP+rGU30li0gKYkDJAoKkW4DjzSxrjbBiG7vjqv92NbMVJA0Ffp9xiQdJ2+OWdd7BfZEvDxyRJY9K0n64SMCR3saWwClmdmsGG1XnUZXYmAu8i8sJy+RoJV1rZoeEzqW01csyCjBL2s7MHi1x1vPI4vC9vXK5YS+aWeolRUmbmtnTWe5bxsaPgaNxOqIrydWIuyxLTqX/nF+DE0EQ8CVwmJll3Z89AeecC6/l94HLs/yYkHQ1ThnlfqornBoJEGduCw557FGdhdtPGumvHZ1lg7/onsP9l0xxAnbW0OgzcMubn8G8PapHcHsyaflFtcmr1eRildCilpbff8uyt4RfkvwPTidwOT9L+D8z+2mKy7fGhf/vXqYtde5fzkuK35f0Km6W9ABOfeVEM7sug42f4T6zzwKY2Xi5Ip2pMbPRwLqS+vrHqVVWSjgKFzj0LYCcEv/TOCHztEz0R7WFUyMBonNbcDgrBxtzzexrqUW9yMxTdx8Z+X9AId9ppKR/ZQlZx9U8K54dfUH26N1n/d7JVbiQ+dTPRdKpPmDj75RPbE+rt3k6Lmeqh6RvaA7kmI0rXpqFv+Jys+7yYxijlDllZvZb/+ePrArBY3JcUgR2MrNTJX0fV/B0X9wyaxbnNsucQDFAod5dps+sj4z8Lf7zKmkUbsUia1SsaLn83giZi6/eZmavZLwmUgHRuS0gWAYtvQRekSv+WO9nXscDlSjZX0r10ZIPSHoQJ+sELoH5/ozjWBVXNudI4O+SbgKuNrO3Ulxb2A97IeM9W2Bm5wDnSDrHzE6vxpa390HJj4+sjmqiXEHMm4BHs+7tmNmdwJ15LCnSrK6yC07j8suS55aGUZIKPx52xGkr3p3RxpXkEy15Fe4H1e3+8V64mXYWLpOrJHA1cL2ZTcl4fSQlcc9tAUHSJrjljzVwyxn1wLdZQoj9ZvwZwE64X5wP4nQCswrIjimJlix7LoWdvXHh1QIeM7PbA5ck2doWNyPohZObOi2HL+cs96/DqYtUrFAip8J/IXAJrpL28cCGllIyy9vogZtx/RAXhn8PLqn7idRPxtlZFfeDZXEzW1tOw3MPM/tDBhvn4PalZuCWFvsD95jZxhls1OGWA4s/s1dknKWPNrOhoXMpba1Py8/syxXYWBWXw7gv8BzuB1mHlm3qlJhZPBaAAzfDWBl4GefYjsDJK1Viqy/Qp4qxvIQLry48XhF4KaONFXA13QqPewCDM9pYBCct9gIuxWFv3GrEhsDEwLV345b/yh4VvCaX4kLuX/ePBwDPZ7SxKE6/8VNc/bLrgEWqeJ8G4FJFGiu4dhTOIb1cdO6VDNfX4dIJBgD1/lwvYIkKxtIVpyG6Di4YKuv1TwNbFD3eHHi6AjubFP+/wanxb1zhe1OP06n8ELeK8AauAkRF73U85j/isuQChJlNUHMRyaskZVpSlCszcyXuPyVyShxHWvYyM6fg6lK1iJbMaCOPqsZP40rD7GVmk4rOvyDpssC1F/h/98YppRT2gQ7ARUxmZWNziv4vg1MoUcZCluYqpB9Uwb1bIFcmZn9cXtbzNC/HZaGnmT1Xsow4N+3F5mvcmdmmRee+pSgROw2SdgUuA97GfdZWkBOAzrKE/RPgGr/3VoiWPDzLODyX0jIp/dsy5xLxM+AjcOLJDwO7m9lLkpbCfZ4zRbVGWic6twWH6f7LcrSkPwMf434JZ+E/wE/N7HEASVvg9hEyad1ZPtGSeVQ1Xs38z2C/fNXbfCScmZ2XdKH5PUxJZ5tZcdDG3ZIeyzgOyEGhxF/zY+ZXocmS2jARl+h/My61IpMzKWKyXP2ywvPZB/eZy8JDkn5AFTXucEnP25ovsePHdC8Z9mctv2jJFoVXvQPP+h16CfBv4FdmNi/Xzsw+knRmheOKlCE6twWHQ3BLPcfiyssvi1vWyMLUgmMDMLMn5EqKZMJ/ie9M85fw9nLlarLk6uRR1XiYpGNws74XceHrF5pZFiX9QZJWNJ+w7FMjBmUcB5RXKMn6ZXUn8DguJaLSiMd1q/jyLuZnuGjP1SV9iAtfz1oM8+f4GneSKqpxB3xmLWvHvYNbsk2NpP7Aocxfqiar7Nw7ko7HzdbABbe8k9B/Pop/SMnJpC1rPt/OzK7NOJ5IAjGgZAFCVaqLSLoIpz94A+4X+f7AVzgNQCylbqCk+yhTtsPMUkslqWVVY3Ch4oeYVz1JaWO0mQ2VdBAup+yXwIuWQetP0ndxX+KFL6nBuNyyB9PaKLK1Os0KJcMto0JJpUEOJTa64wIw1qIoLy3L7K/EXi9c2ka7FuYsuv+luGXvm3Gf2X2BN3EFWLEUyel++f4Z5v+8ZqpA7/PrLsZJkBlO3PpEy5DwL2kksAfuR+Fo4HNglJn9PMtYImGic1tAUA7qIipfzbuAWcqq3sqxbIeqqGrsE4SHAtcDl5iTaqokarMbrrwMVLbEWrAzADejLl5STC00LOkPwFNmdl8l9/c2bsEFJxyI04U8CBfkckLihfPb6U/RbKdwPutsR9IeFOVDmtk9Ga8vp0JTNJyw01YVVbfzRtLLZraepB/hZm2/zfP/U6SZ6NwWEOR0C7fDfUGs5891yH8KOWWG4dbB4ct+ieiXuND/XYHlgOvMbMvEC+e3szawJi1nOlkEqZF0Ni5I4W2ak4xT/2DwNqbilvFmUaFifNGX51gzGyKXcP9glnF4O1XPdiSdiwsQGuZPHYCbWVciTF0xkk4CpuHSIorVfapSt6lwLONwaQ3X4GrmPR+dW9sQ99wWHMqpi3QUNVG2w8wuxi0TASDpfTLWIZP0W2AbnHO7Dxdh+ATZqi2Ai0hcqThIJitWXdXpAgWVmCneaX+Cm31lpXsOS2W7AEPN1WVD0jW4VJZ2dW44tZjzcTme83544FJY2pvf43L1nvCObUWcmHIkZ6JzW3DIS10kD2qybIcfS+pwdc8+OM3Dl83sCLlKBVdUcPtXcEnKWSsbtECuUOnytFwKzBK9eblfHj0Tl7PXG/hNBUO5Vk60uNrZTn9c6D044eKO4OfAyj7VomIkrWBmE0PnkjCzW3ApL4XH75A9MCySgujcFhyOw/3ynIULCHkQOLuDxpJL2Q5VX3w1D2b4kO65PlT8Myr7RX8O8LKkV6hQ2Nov9+4PvEbLmn2pnZuZFRzzY1Q3M8ljtlN4TUbgZvdb4Ur6pCYPhwK8CkwP9gqTR/HVSDsRndsCgplNx33RnFGNnZwcysc4seSKy3Yon+KrefCCD574Ny6dYBpOEikr1wDnUUVVcJxW4WqVBrQASPoT8GfzmoV+FvcLM8uallD1bMfMbvDRgRvhnNsvzeyTjGbycCiNuPzQEbT8vKYVx86zUkKknYjObQFB0oY49fnBtHRMWcLe83IoeZTtqLr4KlTnrOU2MM/xjuAyOcHhvpaxzpdnst8DrIZ3cGLDFTs34Htm9qvCA3NKKbuQPeeu6tmO/7w9hivq+UbGa/N0KHf4o1Jyq5SQ00w0koIYLbmAIOlNnOxVafTaexlsvE4ODqXEZgtlkAzX5VF8tayzzhKurowFOBPsXIhzSnfRcnYQTAVQc9mdpXH7f8OpYIbhbY3F1cmb5R/3AF4ws7WSr5zPzu0451LRbMfb2A4nMrwlbjlzNE5s+G8prt0TN5PdA18CyDMVJwRd0X5zaeJ0xmvzKL5adRHYSDrizG3B4XPzah5V8ApOR7FihwIg6XqgWmWQPIqv5jH7e0bSRmb2fBU2ANbz/25SdM5w6RshCmV3XqTlF3nBRhauA4b7/DDDlQPKlKzsuYPqZjuYqww+CrcsuS3uM7MWEHRulmPpnXKJ05IqSZz+viosvhqXNtufOHNbQJC0PS5PqPRXfWqhVb/nMBS3p1SpQ8lLGWTrcuctQ926nGZ/r+Hqwr2HE8ItpDV0RP7gCaWzmnLnUtj5Hs1KKQ9ZBWoreSBpOC5v72mcrNgTlkHNw9v4M/AHqqjmnVfidNHn/vu4WeVJwAhLIRrQVjPRSOvEmduCwxE4FY0GmpcljWwq4mflNJYGnxy8F04ZZI6krEUx8yi+msfs73vVDEDSwWZ2naSys4AsQTbAYcw/qzm8zLlEzCnmZy382gJJu+GicQtpCZXkMo7F/fhZG/gal3v3tBUJBqcgj2reXSQtictFrCYgq+Liq3nORCPpiM5twWFdM1unGgM5ORSAf+HKwowBHpO0PJB1z63q4qvk4Kyz7Fm2QqEyQ8UJ2JIOwBc6lVT8q74v8EVGW3vjojYXwzmkShPs/4orB1RxLqOZneTH1Bv34+wq3LJ4twxm8qjmnVfi9F2S3sDNIn8qV8UhU6FfqljajGQjLksuIEj6N3CRmb1WhY08HEo5u8IVpEydQC3pBVy16Ftwe2eHAqsUR/otKMhVSTjezC6q8PrlccVbz6GlesdUYGzG13UCrkZYJtHmMnZGANubVxep0MaxuGCSDXDLvoXIyUcz2Ki6mnce+MCpTXCFRb8xs0Y5Uek+WdIbqlnajGQjztwWHLYADpOr1zWLyvaGLqGMQ6l2YP6XfVZlkDyKr7aJs86K/6LbA6jIufnZ43vApnIKKYWCra9ncWyeT6t1bJ5Tgft8QEhFuYy46uoX4vZjM38+vEO5G/gzzQ5lOrBnVlvVYjkVXyWfmWgkBdG5LTh8Nw8j1TqUHMmj+GqbOOsKeUrSJcBNFH3hpUkFKCBpX1zlh5G4Hy9/l3SKmd2aYRwvSLoJF+lYUeCR54+4hPbuVJjLmDF6ttz1eTmUvMij+GoeS5uRFMRlyYUIuQrTO+C0Ez/BOZTDO2JJxC/FfYr74jwJpzv4T2tZmDJk4wUz27A48k3SU2a2WZsMOnks5coJmWWrCjAG2LEQUei/+B7J8v6ofIkYs4z13AqvbZZr2gJJv8MFplTsUPJKnFZz1YZGnHPKtJ+Z19JmJB3RuS1E5OFQimxVLeOl6ouv1oyzzgNJ44qDhvyX4ZhqA4kqHMu5wKPW8WWNqnIo3kbNJE77aNFNwz0j1RKXJRcizOw971CWtAxVs0tRDjJeKiq+iosSHErG4qvAIUAdcCzOWS9LByms+72yPwFLmdn3JK0JbGpm/8lg5gFJD+KEscGJKGcqXCppGdw+5Oa49+QJ4AQzm5TFDvAz4FRJFdeWywOrogxQWyROq8riq+SztBlJQZy5LUQoh2re3k7VMl7KqfhqtbO/vJATkb4KV4ByXUldcGV0Ms26/JfwFjhn8piZ3Z7x+odxlcmv9acOBg4ysx2z2KklKnUoeSdOK4fiq3nMRCPpiDO3hYuzcOHUIwHMbLSkwRXYyUPGq+riqznN/vJiUTO7WdLpAGY2V1Jj6KIyPImbKRmVVScYZGbF+25XSzoxqxFJtwJXAg9Ukw5QLWUcygmStkjjUCz/xOmqi69WMxONZKOuowcQaVfmmtnXOdgpKIM8KOmuwpHRRoviq3LiwVkjN8/COesp4Jw1lVWdzoNvJS2C14L0aQqZXmtJ++Ec2j44NY1nJe2TcRyTJR0sqd4fB5MxEdxzGXAQMF7SuX6JryPYBRdkc6WZXYmLGt4lo43vS+orqUHScEmT/etSCf2L/q6o+KqkPSRd4I/dKhxHJECcuS1c5FXN+6wcxpJH8dWqZ3858nPc0tdKkp4EBuGcVBbOwCn6t4iWxNUvS8uRuBSJi3CO9il/LhNm9gjwiKR+uOW3hyV9gKt7d52Zzclqswr6U1017zwkvCCf4qsVz0Qj2Yh7bgsRknrivkB3wv3nfBA428wWyDwbSf/BCUmfhgskOR5oMLNjOmg8XXC1vwS8mdUB1FK0pL//Irg9u0OAj3BfyFsA65jZNu00hgOAc3HOaJ5DMbMbM9h41czWklP5uc3MHpA0ppKoWjmNykLx1WezhvDLlSQqXtqsx+3NtrtQd2cnOrdIZpSDMojyKb5aM85aUnfgp7gvf8Op4F+WZSySzgeG0DJacpyZnZrBxjW46Mgp/vEA4C8V5Ln9DyfUfS1wtRVVXmjvHLgcHEouEl6qovhqkY2xwDZm9qV/PBAXJBOdW85E57YQkYdD8Xaq1oVUDsVXawlJN+Oi8ApLXQcAA8xs34x2foAL4680WvLlQvRp0rkUdrazDBqQbUW1DkU5Jk6riuKrRTaqnolG0hGd20JEXg5FOSiDSHrCzLbIct8yNnJx1nlQbpmriqWvvrR8Pl8mdJ9vHLiZwVf+8UBgVAUpCfviIiWnSjoTWB/4g2WQE8uDnBxKbonTfhmxuPjqDDPLFGxT7Uw0ko4YULJwkUc1b8hHF/K3kq6giuKruD2g+Zx1B/GypE3M7BkASRvjwvpTI+n/cOVZZuCej3BLnCtmMPMXnM7lrf7a/XA6kVn5tZndImkLYGdcysWlQLuq8VsV1byLyCVxWvMXX50X/JPBRtVLm5F0xJnbQoRyqObt7eShC3kdbk/nVYqKr2bZG8pj9pcXcontqwHv+1PL4ZbCmkhZvUHSeJyqyeQqx7ImLkFewHCroEySmqtXn4Pb97u+kuXNainjUCqp5p1L4rSki3Dle2bhfrg8BmQqvprHTDSSjujcFiLycChFtqrVhWwRGVihjVycdR54h98qaZZ+JT0A7G1m03MbWIVIugf4EKfduQHOKTxXyTJrleOo2qG0wZgKxVdPBpYwsyzFV3NZ2oyEic5tISIPh+LtVC3jpXyKr+bmrGsBSevhJLyepaWzPr4DxtITlzA9zszG+32idayDhJRzcCjVakKifIqvVj0TjaQj7rktXDwjac1qHIrnLKqX8cqj+Oq6HZUD1kb8C3iUGthD9LPH/xU9/pjq5NYqooxDuRLnFLLYyCtxuqriq56xuOeyNk7BZooPeOmwmWhnJc7cFiL8vtBKQDUOBUnPmtnGxXswyih63NoyXpbIzTxmf7VE1ojThQFJp+BmSBU7lFpMnK52JhoJE2duCxe5VPMmBxmvnPLZ8pj91RIjJB0N3E3LZcnUqQCdDauymncR/alOwisX8piJRtIRZ26RzNSKMkges79awjvpUszMsqQCREqopcTpPGaikXRE5xaJRDo9MXF64SOWvIlkRtKGkv4n6SVJYwtHR49rQUfSvpL6+L/P9K9xu+aVdUZ84vRuwFtmdmd0bAsHceYWyUxn04WsFQpBOV4V5BxcusWvLKPAb6QlMXF64SQ6t0hmakkZpDNRK6ognZGYOL3wEZ1bJDO1pAzSmagVVZDORkycXjiJqQCRSjgCpwzSQJEyCEVJv5GK2A+XrnGBmU3xQRCndPCYOgMxcXohJM7cIpnJS8YrEmlPYuL0wkWcuUUqIS8Zr0ikzYmJ0wsnceYWyUxeMl6RSHsQE6cXTqJzi2SmsymDRCKRzkd0bpFIJBLpdESFkkgkEol0OqJzi0QikUinIzq3SCQSiXQ6onOLRCKRSKcjOrdIJBKJdDr+H24MeuCSmXwwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Etablissement d'une matrice de corrélation entre les attributs \n",
    "Corrmatrix=Cancer_Disease.frame.corr()\n",
    "correlations = Cancer_Disease.frame.corrwith(Cancer_Disease.frame.target)\n",
    "sns.heatmap(Corrmatrix, cmap=\"coolwarm\",)\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(correlations.index, correlations)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Variable')\n",
    "plt.ylabel('Corrélation')\n",
    "plt.title('Corrélation entre les variables et le cancer')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d58c095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='worst radius', ylabel='mean perimeter'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vérification de la corrélation entre worst radius et mean perimeter\n",
    "sns.scatterplot(data=Cancer_Disease,x=Cancer_Disease.frame['worst radius'],y=Cancer_Disease.frame['mean perimeter'],hue=Cancer_Disease.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072552b4",
   "metadata": {},
   "source": [
    "# Création du modèle MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af715e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9dc3c746",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=Cancer_Disease.data\n",
    "y=Cancer_Disease.target\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38911b71",
   "metadata": {},
   "outputs": [],
   "source": [
    "    #préparation des ensembles train et test#normalisation des données pour obtenir un meilleur score après l'entrainement\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "Scaler=StandardScaler()\n",
    "Scaler.fit(X_train)\n",
    "X_train=Scaler.transform(X_train)\n",
    "X_test=Scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c41c9f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalisation des données pour obtenir un meilleur score après l'entrainement\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "Scaler=StandardScaler()\n",
    "Scaler.fit(X_train)\n",
    "X_train=Scaler.transform(X_train)\n",
    "X_test=Scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5da040e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(40, 40, 40))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Entrainement du modèle \n",
    "MLP=MLPClassifier(hidden_layer_sizes=(40,40,40))\n",
    "MLP.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a10205cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Evaluation du modèle avec la matrice de confusion et le rapport de classification\n",
    "Y_pred=MLP.predict(X_test)\n",
    "cm=confusion_matrix(Y_pred,y_test)\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\")\n",
    "plt.xlabel('valeur prédite')\n",
    "plt.ylabel('Valeur réelle')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2596203c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99        49\n",
      "           1       0.98      1.00      0.99        65\n",
      "\n",
      "    accuracy                           0.99       114\n",
      "   macro avg       0.99      0.99      0.99       114\n",
      "weighted avg       0.99      0.99      0.99       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_pred,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2480429",
   "metadata": {},
   "source": [
    "# Optimisation des  hyperparamètres avec GridsearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aec0bd51",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36daa659",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'hidden_layer_sizes': [(50,), (100,), (50, 50), (100, 100),(30,30,30)],\n",
    "    'activation': ['relu', 'tanh', 'logistic'],\n",
    "    'solver': ['adam', 'sgd'],\n",
    "    'learning_rate': ['constant', 'adaptive']\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8db1c12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(random_state=42)\n",
    "grid_search = GridSearchCV(mlp, param_grid, cv=5, n_jobs=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cb46031",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cf0cb22",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_mlp = MLPClassifier(**best_params, random_state=42)\n",
    "print(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75459f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_mlp.fit(X_train, y_train)\n",
    "accuracy = best_mlp.score(X_test, y_test)\n",
    "print(accuracy)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}