02a_Learning_Rate_adam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b0fd06a-f3cb-4732-bc7d-1c7ad2d39c0f",
   "metadata": {},
   "source": [
    "# Comparison Loss Function and Validation Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bce52fe-8793-4ee5-8b75-1932fd4aa55d",
   "metadata": {},
   "source": [
    "1) Import der einzelnen Libraries. Sortieren der Daten nach der Zielkategorie für den ersten Überblick.\n",
    "2) Aufteilen der Trainings- und Testdaten. Erstellen von notwendigen Listen\n",
    "3) Erstellen von Funktionen \n",
    "4) initialisieren der einzelnen MLPClassifier Netze \n",
    "5) Ergebnisse: Textuelle Daten, geplottete Vergleiche, prozentuale Heatmaps und Reports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3990afb-733a-40a5-957e-451abceb2d5e",
   "metadata": {},
   "source": [
    "## imports and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9ce620e-67af-4fcb-8736-ead8893c3523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_covtype\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix, balanced_accuracy_score, roc_auc_score, top_k_accuracy_score, cohen_kappa_score, matthews_corrcoef, hinge_loss\n",
    "from sklearn.metrics import plot_confusion_matrix, multilabel_confusion_matrix\n",
    "from sklearn.metrics import RocCurveDisplay, accuracy_score, recall_score, precision_score, precision_recall_fscore_support\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.figure as fig\n",
    "from matplotlib.colors import ListedColormap\n",
    "from mpl_toolkits import mplot3d\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "covertype = fetch_covtype()\n",
    "forest = fetch_covtype(as_frame=True)\n",
    "covertype.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a574498c-78cc-4eb5-9626-ea65dab5307e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         5\n",
       "1         5\n",
       "2         2\n",
       "3         2\n",
       "4         5\n",
       "         ..\n",
       "581007    3\n",
       "581008    3\n",
       "581009    3\n",
       "581010    3\n",
       "581011    3\n",
       "Name: Cover_Type, Length: 581012, dtype: int32"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a19f2f3-1f0e-43f7-88fd-f3a9b3b7bdf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "forestlist = forest.frame\n",
    "forestlistFiltered = forestlist.filter(items=['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology', 'Horizontal_Distance_To_Roadways', 'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm', 'Horizontal_Distance_To_Fire_Points', 'Wilderness_Area_0', 'Wilderness_Area_1', 'Wilderness_Area_2', 'Wilderness_Area_3', 'Cover_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ec34c71-e117-44eb-a8bb-1d7acdd756f5",
   "metadata": {
    "tags": []
   },
   "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>Elevation</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
       "      <th>Vertical_Distance_To_Hydrology</th>\n",
       "      <th>Horizontal_Distance_To_Roadways</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type_31</th>\n",
       "      <th>Soil_Type_32</th>\n",
       "      <th>Soil_Type_33</th>\n",
       "      <th>Soil_Type_34</th>\n",
       "      <th>Soil_Type_35</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
       "      <th>Soil_Type_38</th>\n",
       "      <th>Soil_Type_39</th>\n",
       "      <th>Cover_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>290505</th>\n",
       "      <td>2928.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1836.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532739</th>\n",
       "      <td>3015.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3378.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532740</th>\n",
       "      <td>3018.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3398.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532741</th>\n",
       "      <td>3021.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3419.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2029.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532742</th>\n",
       "      <td>3023.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2052.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399826</th>\n",
       "      <td>3334.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1642.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399825</th>\n",
       "      <td>3333.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399824</th>\n",
       "      <td>3334.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399812</th>\n",
       "      <td>3331.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293238</th>\n",
       "      <td>3284.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3922.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>3999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>581012 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
       "290505     2928.0    58.0    7.0                             150.0   \n",
       "532739     3015.0   343.0   18.0                             376.0   \n",
       "532740     3018.0   341.0   18.0                             350.0   \n",
       "532741     3021.0   345.0   18.0                             324.0   \n",
       "532742     3023.0   349.0   19.0                             300.0   \n",
       "...           ...     ...    ...                               ...   \n",
       "399826     3334.0   356.0   10.0                             558.0   \n",
       "399825     3333.0     6.0    7.0                             601.0   \n",
       "399824     3334.0     3.0    5.0                             601.0   \n",
       "399812     3331.0   246.0    3.0                             660.0   \n",
       "293238     3284.0   307.0   18.0                              60.0   \n",
       "\n",
       "        Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
       "290505                            -7.0                           1836.0   \n",
       "532739                            50.0                           3378.0   \n",
       "532740                            53.0                           3398.0   \n",
       "532741                            45.0                           3419.0   \n",
       "532742                            47.0                           3439.0   \n",
       "...                                ...                              ...   \n",
       "399826                           100.0                            765.0   \n",
       "399825                            99.0                            806.0   \n",
       "399824                            39.0                            845.0   \n",
       "399812                           117.0                            859.0   \n",
       "293238                            22.0                           3922.0   \n",
       "\n",
       "        Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
       "290505          226.0           225.0          133.0   \n",
       "532739          181.0           211.0          166.0   \n",
       "532740          180.0           211.0          168.0   \n",
       "532741          182.0           209.0          164.0   \n",
       "532742          182.0           206.0          160.0   \n",
       "...               ...             ...            ...   \n",
       "399826          204.0           223.0          156.0   \n",
       "399825          212.0           226.0          152.0   \n",
       "399824          214.0           231.0          155.0   \n",
       "399812          213.0           241.0          166.0   \n",
       "293238          167.0           226.0          198.0   \n",
       "\n",
       "        Horizontal_Distance_To_Fire_Points  ...  Soil_Type_31  Soil_Type_32  \\\n",
       "290505                               190.0  ...           1.0           0.0   \n",
       "532739                              1982.0  ...           0.0           0.0   \n",
       "532740                              2005.0  ...           0.0           0.0   \n",
       "532741                              2029.0  ...           0.0           0.0   \n",
       "532742                              2052.0  ...           0.0           0.0   \n",
       "...                                    ...  ...           ...           ...   \n",
       "399826                              1642.0  ...           0.0           0.0   \n",
       "399825                              1583.0  ...           1.0           0.0   \n",
       "399824                              1524.0  ...           1.0           0.0   \n",
       "399812                              1113.0  ...           1.0           0.0   \n",
       "293238                              3999.0  ...           0.0           0.0   \n",
       "\n",
       "        Soil_Type_33  Soil_Type_34  Soil_Type_35  Soil_Type_36  Soil_Type_37  \\\n",
       "290505           0.0           0.0           0.0           0.0           0.0   \n",
       "532739           0.0           0.0           0.0           0.0           0.0   \n",
       "532740           0.0           0.0           0.0           0.0           0.0   \n",
       "532741           0.0           0.0           0.0           0.0           0.0   \n",
       "532742           0.0           0.0           0.0           0.0           0.0   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "399826           0.0           0.0           0.0           0.0           0.0   \n",
       "399825           0.0           0.0           0.0           0.0           0.0   \n",
       "399824           0.0           0.0           0.0           0.0           0.0   \n",
       "399812           0.0           0.0           0.0           0.0           0.0   \n",
       "293238           0.0           0.0           0.0           0.0           1.0   \n",
       "\n",
       "        Soil_Type_38  Soil_Type_39  Cover_Type  \n",
       "290505           0.0           0.0           1  \n",
       "532739           0.0           0.0           1  \n",
       "532740           0.0           0.0           1  \n",
       "532741           0.0           0.0           1  \n",
       "532742           0.0           0.0           1  \n",
       "...              ...           ...         ...  \n",
       "399826           0.0           0.0           7  \n",
       "399825           0.0           0.0           7  \n",
       "399824           0.0           0.0           7  \n",
       "399812           0.0           0.0           7  \n",
       "293238           0.0           0.0           7  \n",
       "\n",
       "[581012 rows x 55 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forestlist = forest.frame\n",
    "forestlistSorted = forestlist.sort_values(by='Cover_Type')\n",
    "forestlistSorted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad685848-0852-40ec-9224-6479edcfbadf",
   "metadata": {
    "tags": []
   },
   "source": [
    "## training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7374455-6ab6-482b-914d-7d25c028307e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(581012, 54)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covertype['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa2226cd-ac5d-44b9-81d3-86e117c60d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_c = covertype['data']\n",
    "y_c = covertype['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e95d3764-9b2c-4c58-a730-8b7167b298a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(435759, 54)\n",
      "(145253, 54)\n"
     ]
    }
   ],
   "source": [
    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(X_c, y_c)\n",
    "print(X_train_c.shape) \n",
    "print(X_test_c.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e55d24c-b370-4bec-95d4-52c00074d70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_c = StandardScaler()\n",
    "# Fit only to the training data\n",
    "scaler_c.fit(X_train_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2892e8de-0ce9-4e7a-aab7-6b188f2a8c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_c = scaler_c.transform(X_train_c)\n",
    "X_test_c = scaler_c.transform(X_test_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbdcb1ab-db1f-4585-9d11-b7c56775370d",
   "metadata": {},
   "outputs": [],
   "source": [
    "classificationList = []\n",
    "curve = []\n",
    "validation = []\n",
    "iteration = []\n",
    "\n",
    "#validationList = []\n",
    "#curve_test = []\n",
    "#validation_test = []\n",
    "#iteration_test = []\n",
    "\n",
    "accuracyList = []\n",
    "recallList = []\n",
    "precisionList = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad8a3ff-1ec6-4b47-b833-497c31f505d7",
   "metadata": {},
   "source": [
    "## functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00451c60-04e7-4cb1-85f2-cc748c4d0ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotData():\n",
    "    fig, axes = plt.subplots(3,2)\n",
    "    sns.histplot(data=forestlistSorted, x=\"Elevation\", color=\"skyblue\", label=\"Elevation\", kde=True, ax=axes[0,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Aspect\", color=\"red\", label=\"Aspect\", kde=True, ax=axes[0,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Slope\", color=\"orange\", label=\"Slope\", kde=True, ax=axes[0,0])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Horizontal_Distance_To_Hydrology\", color=\"skyblue\", label=\"Horizontal_Distance_To_Hydrology\", kde=True, ax=axes[0,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Vertical_Distance_To_Hydrology\", color=\"red\", label=\"Vertical_Distance_To_Hydrology\", kde=True, ax=axes[0,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Horizontal_Distance_To_Roadways\", color=\"orange\", label=\"Horizontal_Distance_To_Roadways\", kde=True, ax=axes[0,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Horizontal_Distance_To_Fire_Points\", color=\"green\", label=\"Horizontal_Distance_To_Fire_Points\", kde=True, ax=axes[0,1])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Hillshade_9am\", color=\"skyblue\", label=\"Hillshade_9am\", kde=True, ax=axes[1,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Hillshade_Noon\", color=\"red\", label=\"Hillshade_Noon\", kde=True, ax=axes[1,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Hillshade_3pm\", color=\"orange\", label=\"Hillshade_3pm\", kde=True, ax=axes[1,0])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_31\", color=\"skyblue\", label=\"Soil_Type_31\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_32\", color=\"red\", label=\"Soil_Type_32\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_33\", color=\"orange\", label=\"Soil_Type_33\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_34\", color=\"cyan\", label=\"Soil_Type_34\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_35\", color=\"darkgreen\", label=\"Soil_Type_35\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_36\", color=\"gold\", label=\"Soil_Type_36\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_37\", color=\"purple\", label=\"Soil_Type_37\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_38\", color=\"silver\", label=\"Soil_Type_38\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_39\", color=\"navy\", label=\"Soil_Type_39\", kde=True, ax=axes[1,1])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Cover_Type\", color=\"orange\", label=\"Cover_Type\",  ax=axes[2,1], discrete=True, kde=False)\n",
    "    plt.legend() \n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "207ed85a-e659-4bb9-b533-fc3dc2a39ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addToList(mlp, liste):\n",
    "    liste.append(mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b80fe74-be82-444d-ac56-b6a8f4797089",
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeFromList(mlp, liste):\n",
    "    liste.remove(mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8384363b-3a23-4ea7-9ddf-e8e2fb0fcd49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def comparison(List):\n",
    "    for mlp in List:\n",
    "        print(mlp)\n",
    "        print('\\nparamters: ' + str(mlp.get_params(deep=True)))\n",
    "        print('\\nlast loss: ' + str(mlp.loss_))\n",
    "        print('\\nnumber of features seen during fit: ' + str(mlp.n_features_in_))\n",
    "        print('\\nnumber of layers: ' + str(mlp.n_layers_))\n",
    "        print('\\nnumber of outputs: ' + str(mlp.n_outputs_))\n",
    "        print('\\nname of output activation function: ' + str(mlp.out_activation_) )\n",
    "        print('\\nnumber of iterations: ' +str(mlp.n_iter_) )\n",
    "        print('\\nnumber training samples seen by the solver during fitting: ' +str(mlp.t_) )\n",
    "        print('****************************************************************************************')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76d31667-65c3-4d58-89a5-0c8e333620a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def curveValidateIteration(List, c, v, i):\n",
    "    for mlp in List:\n",
    "        c.append(mlp.loss_curve_)\n",
    "        v.append(mlp.validation_scores_)\n",
    "        i.append(np.arange(1, mlp.n_iter_+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecba6578-cde2-4e01-970c-209a788ea8ed",
   "metadata": {},
   "source": [
    "__________________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e8f31d2-0b07-4411-8bc6-1c2c68b140fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = ListedColormap([\"coral\", \"azure\", \"indigo\", \"darkgreen\", \"lightblue\", \"crimson\", \"gold\", \"silver\", \"teal\"])\n",
    "def plotCurvePerInteration(List, curvy, iterate):\n",
    "    print('default: hidden_layer = (100,), activation = relu, solver = adam, alpha = 0.0001, learning_rate = constant, learning_rate_init = 0.001, max_iter = 200, shuffle = true, early_stopping & warm_start = FALSE')\n",
    "    print('Alle Infos, die davon abweichen, werden in der Legende gelistet')\n",
    "    print('****************************************************************************************')\n",
    "    for i,c,cl in zip(iterate, curvy, List):\n",
    "        plt.scatter(i,c, marker = '.', label = str(cl))\n",
    "    plt.title('Losscurve per iteration')\n",
    "    plt.xlabel(\"iteration\")\n",
    "    plt.ylabel(\"loss curve\")\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3ef3f87-42fb-46b9-96f7-7b363ec85fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotValidationPerInteration(List, validate, iterate):\n",
    "    for i,v, cl in zip(iterate, validate, List):\n",
    "        plt.scatter(i,v, marker = '.', label = str(cl))\n",
    "    plt.title('Validation per iteration')\n",
    "    plt.xlabel(\"iteration\")\n",
    "    plt.ylabel(\"validation\")\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25ada7f9-9a81-4a58-80ae-1bea09db70f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotValidationPercurve(List, validate, curvy):\n",
    "    for c,v,cl in zip(validate, curvy, List):\n",
    "        plt.scatter(c,v, marker = '.', label = str(cl))\n",
    "    plt.title('Validation per curve')\n",
    "    plt.xlabel(\"validation\")\n",
    "    plt.ylabel(\"curve\")\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcda0d6-d713-4584-ac38-41b1814e1eec",
   "metadata": {},
   "source": [
    "__________________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ad8cf6d8-5f4f-477f-9b18-8acb297611fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def heatmaping(List):\n",
    "    for mlp in List:\n",
    "        print(mlp)\n",
    "        predictions = mlp.predict(X_test_c)   \n",
    "        con_ma = confusion_matrix(y_test_c,predictions)\n",
    "        con_ma_perc = np.empty([7, 7], dtype=float)\n",
    "\n",
    "        for i in range(0, 7):\n",
    "            number = 0.0\n",
    "            for j in range(0, 7):\n",
    "                number += con_ma[i][j]\n",
    "            for j in range(0, 7):\n",
    "                con_ma_perc[i][j] = 100/number*con_ma[i][j]\n",
    "\n",
    "        tree = ['Spruce', 'Lodgepole Pine', 'PoderosaPine', 'Cottonwood/Willow', 'Aspen', 'Douglas-fir', 'Krummholz']\n",
    "\n",
    "        # ======== Plot confuse matrix prozentual ========\n",
    "\n",
    "        ay = sns.heatmap(con_ma_perc, annot=True, cmap='coolwarm', yticklabels=tree, xticklabels=tree, fmt='.2f', vmin=0, vmax=100)\n",
    "        ay.xaxis.tick_top()\n",
    "        ay.xaxis.set_label_position('top')\n",
    "        plt.xticks(rotation=90)\n",
    "        ay.set(title=\"Confusion Matrix - Recall\",\n",
    "              xlabel=\"- Predicted Tree -\",\n",
    "              ylabel=\"- Actual Tree -\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "633860b1-fd10-4b7a-b001-8bacfb1ce97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reporting(List):\n",
    "    for mlp in List:\n",
    "        print(mlp)\n",
    "        predictions = mlp.predict(X_test_c)\n",
    "        print(classification_report(y_test_c,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e4d2697-5eb8-4deb-a58a-a9cde3fc2599",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracyPlotting(List):\n",
    "        for mlp in List:\n",
    "            print(mlp)\n",
    "            predictions = mlp.predict(X_test_c)\n",
    "            accuracy = accuracy_score(y_test_c, predictions)\n",
    "            recall = recall_score(y_test_c, predictions, average = 'weighted')\n",
    "            precision = precision_score(y_test_c, predictions, average = 'weighted')\n",
    "            \n",
    "            addToList(accuracy, accuracyList)\n",
    "            addToList(recall, recallList)\n",
    "            addToList(precision, precisionList)\n",
    "            \n",
    "            print('Accuracy: \\t{0:.2f}'.format(accuracy * 100.0))\n",
    "            print('Recall: \\t{0:.2f}'.format(recall * 100.0))\n",
    "            print('precision: \\t{0:.2f}'.format(precision * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f628e5a-26a7-4774-8cbc-9913bb044787",
   "metadata": {},
   "source": [
    "## initializing nets\n",
    "### Learning Rates\n",
    "Gewichte updaten"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa91474-4585-43a0-8506-fd28bf269907",
   "metadata": {},
   "source": [
    "### Constant\n",
    "</li> konstante learningrate nach learing_rate_init </li>\n",
    "</li> learing_rate_init-Default Wert  = 0.001. Nur bei sgd oder adam </li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a21184a-dd71-4064-adad-44372e811ed5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.68506341\n",
      "Validation score: 0.744791\n",
      "Iteration 2, loss = 0.56957901\n",
      "Validation score: 0.758376\n",
      "Iteration 3, loss = 0.54150407\n",
      "Validation score: 0.768657\n",
      "Iteration 4, loss = 0.52262737\n",
      "Validation score: 0.776138\n",
      "Iteration 5, loss = 0.51039216\n",
      "Validation score: 0.783665\n",
      "Iteration 6, loss = 0.50157645\n",
      "Validation score: 0.789770\n",
      "Iteration 7, loss = 0.49399290\n",
      "Validation score: 0.790894\n",
      "Iteration 8, loss = 0.48797684\n",
      "Validation score: 0.789448\n",
      "Iteration 9, loss = 0.48369043\n",
      "Validation score: 0.795002\n",
      "Iteration 10, loss = 0.47925334\n",
      "Validation score: 0.798123\n",
      "Iteration 11, loss = 0.47585687\n",
      "Validation score: 0.799982\n",
      "Iteration 12, loss = 0.47261098\n",
      "Validation score: 0.800234\n",
      "Iteration 13, loss = 0.46991638\n",
      "Validation score: 0.802827\n",
      "Iteration 14, loss = 0.46693359\n",
      "Validation score: 0.804732\n",
      "Iteration 15, loss = 0.46412899\n",
      "Validation score: 0.800693\n",
      "Iteration 16, loss = 0.46186169\n",
      "Validation score: 0.803607\n",
      "Iteration 17, loss = 0.45979255\n",
      "Validation score: 0.809138\n",
      "Iteration 18, loss = 0.45733324\n",
      "Validation score: 0.807784\n",
      "Iteration 19, loss = 0.45553603\n",
      "Validation score: 0.808840\n",
      "Iteration 20, loss = 0.45347428\n",
      "Validation score: 0.805466\n",
      "Iteration 21, loss = 0.45120307\n",
      "Validation score: 0.813705\n",
      "Iteration 22, loss = 0.44993451\n",
      "Validation score: 0.810010\n",
      "Iteration 23, loss = 0.44746132\n",
      "Validation score: 0.814646\n",
      "Iteration 24, loss = 0.44598985\n",
      "Validation score: 0.816665\n",
      "Iteration 25, loss = 0.44449323\n",
      "Validation score: 0.814164\n",
      "Iteration 26, loss = 0.44253859\n",
      "Validation score: 0.815724\n",
      "Iteration 27, loss = 0.44133645\n",
      "Validation score: 0.816688\n",
      "Iteration 28, loss = 0.43965869\n",
      "Validation score: 0.816527\n",
      "Iteration 29, loss = 0.43869269\n",
      "Validation score: 0.817698\n",
      "Iteration 30, loss = 0.43739135\n",
      "Validation score: 0.818822\n",
      "Iteration 31, loss = 0.43657257\n",
      "Validation score: 0.821370\n",
      "Iteration 32, loss = 0.43547827\n",
      "Validation score: 0.817560\n",
      "Iteration 33, loss = 0.43441053\n",
      "Validation score: 0.819580\n",
      "Iteration 34, loss = 0.43435576\n",
      "Validation score: 0.819396\n",
      "Iteration 35, loss = 0.43283147\n",
      "Validation score: 0.816390\n",
      "Iteration 36, loss = 0.43216033\n",
      "Validation score: 0.819947\n",
      "Iteration 37, loss = 0.43182637\n",
      "Validation score: 0.820291\n",
      "Iteration 38, loss = 0.43102854\n",
      "Validation score: 0.820268\n",
      "Iteration 39, loss = 0.43002779\n",
      "Validation score: 0.819648\n",
      "Iteration 40, loss = 0.42949804\n",
      "Validation score: 0.818983\n",
      "Iteration 41, loss = 0.42870564\n",
      "Validation score: 0.820498\n",
      "Iteration 42, loss = 0.42792977\n",
      "Validation score: 0.822746\n",
      "Iteration 43, loss = 0.42735540\n",
      "Validation score: 0.825913\n",
      "Iteration 44, loss = 0.42737575\n",
      "Validation score: 0.822953\n",
      "Iteration 45, loss = 0.42611476\n",
      "Validation score: 0.822265\n",
      "Iteration 46, loss = 0.42600170\n",
      "Validation score: 0.824169\n",
      "Iteration 47, loss = 0.42591251\n",
      "Validation score: 0.821553\n",
      "Iteration 48, loss = 0.42480386\n",
      "Validation score: 0.823802\n",
      "Iteration 49, loss = 0.42455387\n",
      "Validation score: 0.821484\n",
      "Iteration 50, loss = 0.42383653\n",
      "Validation score: 0.824353\n",
      "Iteration 51, loss = 0.42356315\n",
      "Validation score: 0.822724\n",
      "Iteration 52, loss = 0.42320997\n",
      "Validation score: 0.825064\n",
      "Iteration 53, loss = 0.42277795\n",
      "Validation score: 0.821209\n",
      "Iteration 54, loss = 0.42228951\n",
      "Validation score: 0.821989\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
     ]
    }
   ],
   "source": [
    "mlp_1 = MLPClassifier(hidden_layer_sizes=(20,20,20), max_iter = 150, early_stopping = True, verbose = True, learning_rate = \"constant\")\n",
    "mlp_1train = mlp_1.fit(X_train_c,y_train_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce68f314-0ae2-4f07-a799-73318f259c59",
   "metadata": {},
   "source": [
    "### invscaling\n",
    "</li> nutzt inverse scaling exponent: power_t </li>\n",
    "</li> power_t - default: 0.5; nur bei sgd </li>\n",
    "<li> $effectiveLearningRate = \\frac{learningRateInit}{t^power_t}$ </li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d4f02a3f-d77b-4d44-81cb-b207614dd4e2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.67330515\n",
      "Validation score: 0.748738\n",
      "Iteration 2, loss = 0.57062144\n",
      "Validation score: 0.766362\n",
      "Iteration 3, loss = 0.54194187\n",
      "Validation score: 0.771893\n",
      "Iteration 4, loss = 0.52395893\n",
      "Validation score: 0.777836\n",
      "Iteration 5, loss = 0.51165858\n",
      "Validation score: 0.781026\n",
      "Iteration 6, loss = 0.50152505\n",
      "Validation score: 0.786213\n",
      "Iteration 7, loss = 0.49384245\n",
      "Validation score: 0.788553\n",
      "Iteration 8, loss = 0.48581089\n",
      "Validation score: 0.794313\n",
      "Iteration 9, loss = 0.47843506\n",
      "Validation score: 0.800005\n",
      "Iteration 10, loss = 0.47155811\n",
      "Validation score: 0.803539\n",
      "Iteration 11, loss = 0.46582155\n",
      "Validation score: 0.801886\n",
      "Iteration 12, loss = 0.46095046\n",
      "Validation score: 0.806820\n",
      "Iteration 13, loss = 0.45660105\n",
      "Validation score: 0.809253\n",
      "Iteration 14, loss = 0.45277021\n",
      "Validation score: 0.809115\n",
      "Iteration 15, loss = 0.44924484\n",
      "Validation score: 0.809023\n",
      "Iteration 16, loss = 0.44607245\n",
      "Validation score: 0.814233\n",
      "Iteration 17, loss = 0.44306717\n",
      "Validation score: 0.813934\n",
      "Iteration 18, loss = 0.44114345\n",
      "Validation score: 0.812879\n",
      "Iteration 19, loss = 0.43854417\n",
      "Validation score: 0.814692\n",
      "Iteration 20, loss = 0.43708714\n",
      "Validation score: 0.812534\n",
      "Iteration 21, loss = 0.43530869\n",
      "Validation score: 0.814003\n",
      "Iteration 22, loss = 0.43419891\n",
      "Validation score: 0.815013\n",
      "Iteration 23, loss = 0.43273136\n",
      "Validation score: 0.817560\n",
      "Iteration 24, loss = 0.43139086\n",
      "Validation score: 0.820520\n",
      "Iteration 25, loss = 0.42992378\n",
      "Validation score: 0.817124\n",
      "Iteration 26, loss = 0.42852683\n",
      "Validation score: 0.820589\n",
      "Iteration 27, loss = 0.42732168\n",
      "Validation score: 0.820750\n",
      "Iteration 28, loss = 0.42618123\n",
      "Validation score: 0.824307\n",
      "Iteration 29, loss = 0.42426400\n",
      "Validation score: 0.822173\n",
      "Iteration 30, loss = 0.42334731\n",
      "Validation score: 0.820520\n",
      "Iteration 31, loss = 0.42294453\n",
      "Validation score: 0.821393\n",
      "Iteration 32, loss = 0.42138121\n",
      "Validation score: 0.817514\n",
      "Iteration 33, loss = 0.42038341\n",
      "Validation score: 0.825776\n",
      "Iteration 34, loss = 0.41973760\n",
      "Validation score: 0.823137\n",
      "Iteration 35, loss = 0.41907534\n",
      "Validation score: 0.823504\n",
      "Iteration 36, loss = 0.41813597\n",
      "Validation score: 0.823894\n",
      "Iteration 37, loss = 0.41718338\n",
      "Validation score: 0.825822\n",
      "Iteration 38, loss = 0.41678577\n",
      "Validation score: 0.818111\n",
      "Iteration 39, loss = 0.41612752\n",
      "Validation score: 0.827198\n",
      "Iteration 40, loss = 0.41549596\n",
      "Validation score: 0.823963\n",
      "Iteration 41, loss = 0.41491591\n",
      "Validation score: 0.824032\n",
      "Iteration 42, loss = 0.41431167\n",
      "Validation score: 0.824995\n",
      "Iteration 43, loss = 0.41330767\n",
      "Validation score: 0.827589\n",
      "Iteration 44, loss = 0.41315783\n",
      "Validation score: 0.827566\n",
      "Iteration 45, loss = 0.41184892\n",
      "Validation score: 0.826762\n",
      "Iteration 46, loss = 0.41140827\n",
      "Validation score: 0.827221\n",
      "Iteration 47, loss = 0.41030631\n",
      "Validation score: 0.829838\n",
      "Iteration 48, loss = 0.41012845\n",
      "Validation score: 0.826166\n",
      "Iteration 49, loss = 0.40934346\n",
      "Validation score: 0.825225\n",
      "Iteration 50, loss = 0.40879823\n",
      "Validation score: 0.828782\n",
      "Iteration 51, loss = 0.40799587\n",
      "Validation score: 0.828988\n",
      "Iteration 52, loss = 0.40761508\n",
      "Validation score: 0.829952\n",
      "Iteration 53, loss = 0.40707607\n",
      "Validation score: 0.830503\n",
      "Iteration 54, loss = 0.40628199\n",
      "Validation score: 0.830136\n",
      "Iteration 55, loss = 0.40606316\n",
      "Validation score: 0.831260\n",
      "Iteration 56, loss = 0.40547554\n",
      "Validation score: 0.828048\n",
      "Iteration 57, loss = 0.40502037\n",
      "Validation score: 0.828897\n",
      "Iteration 58, loss = 0.40447093\n",
      "Validation score: 0.830824\n",
      "Iteration 59, loss = 0.40448808\n",
      "Validation score: 0.831536\n",
      "Iteration 60, loss = 0.40413721\n",
      "Validation score: 0.830457\n",
      "Iteration 61, loss = 0.40350811\n",
      "Validation score: 0.830021\n",
      "Iteration 62, loss = 0.40305579\n",
      "Validation score: 0.832821\n",
      "Iteration 63, loss = 0.40316378\n",
      "Validation score: 0.830778\n",
      "Iteration 64, loss = 0.40258315\n",
      "Validation score: 0.831903\n",
      "Iteration 65, loss = 0.40276843\n",
      "Validation score: 0.831352\n",
      "Iteration 66, loss = 0.40217489\n",
      "Validation score: 0.829356\n",
      "Iteration 67, loss = 0.40158485\n",
      "Validation score: 0.831352\n",
      "Iteration 68, loss = 0.40097198\n",
      "Validation score: 0.831146\n",
      "Iteration 69, loss = 0.40094550\n",
      "Validation score: 0.834657\n",
      "Iteration 70, loss = 0.40094651\n",
      "Validation score: 0.830434\n",
      "Iteration 71, loss = 0.40037149\n",
      "Validation score: 0.833853\n",
      "Iteration 72, loss = 0.39997417\n",
      "Validation score: 0.832155\n",
      "Iteration 73, loss = 0.40000847\n",
      "Validation score: 0.829103\n",
      "Iteration 74, loss = 0.39970575\n",
      "Validation score: 0.830251\n",
      "Iteration 75, loss = 0.39915530\n",
      "Validation score: 0.830434\n",
      "Iteration 76, loss = 0.39912989\n",
      "Validation score: 0.833395\n",
      "Iteration 77, loss = 0.39890269\n",
      "Validation score: 0.832270\n",
      "Iteration 78, loss = 0.39877488\n",
      "Validation score: 0.834267\n",
      "Iteration 79, loss = 0.39835834\n",
      "Validation score: 0.835689\n",
      "Iteration 80, loss = 0.39824487\n",
      "Validation score: 0.828851\n",
      "Iteration 81, loss = 0.39777140\n",
      "Validation score: 0.834496\n",
      "Iteration 82, loss = 0.39777071\n",
      "Validation score: 0.835070\n",
      "Iteration 83, loss = 0.39768378\n",
      "Validation score: 0.834175\n",
      "Iteration 84, loss = 0.39762303\n",
      "Validation score: 0.836997\n",
      "Iteration 85, loss = 0.39737549\n",
      "Validation score: 0.832477\n",
      "Iteration 86, loss = 0.39684874\n",
      "Validation score: 0.834611\n",
      "Iteration 87, loss = 0.39691503\n",
      "Validation score: 0.831146\n",
      "Iteration 88, loss = 0.39638858\n",
      "Validation score: 0.835001\n",
      "Iteration 89, loss = 0.39659955\n",
      "Validation score: 0.836034\n",
      "Iteration 90, loss = 0.39605091\n",
      "Validation score: 0.836561\n",
      "Iteration 91, loss = 0.39549006\n",
      "Validation score: 0.837296\n",
      "Iteration 92, loss = 0.39551837\n",
      "Validation score: 0.834932\n",
      "Iteration 93, loss = 0.39502093\n",
      "Validation score: 0.835506\n",
      "Iteration 94, loss = 0.39547683\n",
      "Validation score: 0.835552\n",
      "Iteration 95, loss = 0.39468813\n",
      "Validation score: 0.834955\n",
      "Iteration 96, loss = 0.39450993\n",
      "Validation score: 0.837043\n",
      "Iteration 97, loss = 0.39441727\n",
      "Validation score: 0.831352\n",
      "Iteration 98, loss = 0.39410933\n",
      "Validation score: 0.838076\n",
      "Iteration 99, loss = 0.39379802\n",
      "Validation score: 0.833096\n",
      "Iteration 100, loss = 0.39295957\n",
      "Validation score: 0.837273\n",
      "Iteration 101, loss = 0.39371751\n",
      "Validation score: 0.834496\n",
      "Iteration 102, loss = 0.39339860\n",
      "Validation score: 0.833165\n",
      "Iteration 103, loss = 0.39253192\n",
      "Validation score: 0.836768\n",
      "Iteration 104, loss = 0.39261323\n",
      "Validation score: 0.833670\n",
      "Iteration 105, loss = 0.39202406\n",
      "Validation score: 0.838305\n",
      "Iteration 106, loss = 0.39208399\n",
      "Validation score: 0.837296\n",
      "Iteration 107, loss = 0.39217653\n",
      "Validation score: 0.833785\n",
      "Iteration 108, loss = 0.39164328\n",
      "Validation score: 0.836929\n",
      "Iteration 109, loss = 0.39166510\n",
      "Validation score: 0.834886\n",
      "Iteration 110, loss = 0.39096252\n",
      "Validation score: 0.836814\n",
      "Iteration 111, loss = 0.39112115\n",
      "Validation score: 0.837043\n",
      "Iteration 112, loss = 0.39080868\n",
      "Validation score: 0.838604\n",
      "Iteration 113, loss = 0.39069844\n",
      "Validation score: 0.834680\n",
      "Iteration 114, loss = 0.39056682\n",
      "Validation score: 0.838627\n",
      "Iteration 115, loss = 0.39033736\n",
      "Validation score: 0.837502\n",
      "Iteration 116, loss = 0.39002627\n",
      "Validation score: 0.833647\n",
      "Iteration 117, loss = 0.39018103\n",
      "Validation score: 0.837204\n",
      "Iteration 118, loss = 0.38941044\n",
      "Validation score: 0.834840\n",
      "Iteration 119, loss = 0.38967092\n",
      "Validation score: 0.839453\n",
      "Iteration 120, loss = 0.38979373\n",
      "Validation score: 0.832637\n",
      "Iteration 121, loss = 0.38963675\n",
      "Validation score: 0.836309\n",
      "Iteration 122, loss = 0.38909339\n",
      "Validation score: 0.832247\n",
      "Iteration 123, loss = 0.38884050\n",
      "Validation score: 0.838260\n",
      "Iteration 124, loss = 0.38936098\n",
      "Validation score: 0.835896\n",
      "Iteration 125, loss = 0.38891659\n",
      "Validation score: 0.838305\n",
      "Iteration 126, loss = 0.38878864\n",
      "Validation score: 0.837709\n",
      "Iteration 127, loss = 0.38850318\n",
      "Validation score: 0.838122\n",
      "Iteration 128, loss = 0.38832887\n",
      "Validation score: 0.836699\n",
      "Iteration 129, loss = 0.38878013\n",
      "Validation score: 0.835483\n",
      "Iteration 130, loss = 0.38839732\n",
      "Validation score: 0.835070\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
     ]
    }
   ],
   "source": [
    "mlp_2 = MLPClassifier(hidden_layer_sizes=(20,20,20),  max_iter = 150, early_stopping = True, verbose = True, learning_rate = \"invscaling\")\n",
    "mlp_2train = mlp_2.fit(X_train_c,y_train_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0530faff-8bc6-4be7-b876-7108acd7e694",
   "metadata": {},
   "source": [
    "### adaptive\n",
    "</li> constant, solange learningrate fällt. </li>\n",
    "</li> Wenn learningrate zweimal nicht fällt um tol, dann  $\\frac{learningrate}{5}$</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e0a4874c-1d2a-4cd8-bce9-17f0a2a68efd",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.67171091\n",
      "Validation score: 0.751882\n",
      "Iteration 2, loss = 0.55745033\n",
      "Validation score: 0.767005\n",
      "Iteration 3, loss = 0.53127835\n",
      "Validation score: 0.775633\n",
      "Iteration 4, loss = 0.51661674\n",
      "Validation score: 0.779558\n",
      "Iteration 5, loss = 0.50692923\n",
      "Validation score: 0.781944\n",
      "Iteration 6, loss = 0.49935867\n",
      "Validation score: 0.788806\n",
      "Iteration 7, loss = 0.49330700\n",
      "Validation score: 0.791101\n",
      "Iteration 8, loss = 0.48784528\n",
      "Validation score: 0.794864\n",
      "Iteration 9, loss = 0.48312523\n",
      "Validation score: 0.795576\n",
      "Iteration 10, loss = 0.47919168\n",
      "Validation score: 0.795966\n",
      "Iteration 11, loss = 0.47498869\n",
      "Validation score: 0.800762\n",
      "Iteration 12, loss = 0.47097568\n",
      "Validation score: 0.801588\n",
      "Iteration 13, loss = 0.46737506\n",
      "Validation score: 0.808059\n",
      "Iteration 14, loss = 0.46435371\n",
      "Validation score: 0.805742\n",
      "Iteration 15, loss = 0.46181042\n",
      "Validation score: 0.805971\n",
      "Iteration 16, loss = 0.45900947\n",
      "Validation score: 0.808427\n",
      "Iteration 17, loss = 0.45660347\n",
      "Validation score: 0.809276\n",
      "Iteration 18, loss = 0.45427422\n",
      "Validation score: 0.809023\n",
      "Iteration 19, loss = 0.45208373\n",
      "Validation score: 0.811616\n",
      "Iteration 20, loss = 0.45054634\n",
      "Validation score: 0.810905\n",
      "Iteration 21, loss = 0.44918615\n",
      "Validation score: 0.814279\n",
      "Iteration 22, loss = 0.44713139\n",
      "Validation score: 0.811456\n",
      "Iteration 23, loss = 0.44561704\n",
      "Validation score: 0.815311\n",
      "Iteration 24, loss = 0.44419456\n",
      "Validation score: 0.814256\n",
      "Iteration 25, loss = 0.44289419\n",
      "Validation score: 0.814233\n",
      "Iteration 26, loss = 0.44206857\n",
      "Validation score: 0.813154\n",
      "Iteration 27, loss = 0.44056864\n",
      "Validation score: 0.815219\n",
      "Iteration 28, loss = 0.43980351\n",
      "Validation score: 0.818134\n",
      "Iteration 29, loss = 0.43896405\n",
      "Validation score: 0.817032\n",
      "Iteration 30, loss = 0.43792300\n",
      "Validation score: 0.818294\n",
      "Iteration 31, loss = 0.43748334\n",
      "Validation score: 0.815151\n",
      "Iteration 32, loss = 0.43590679\n",
      "Validation score: 0.817583\n",
      "Iteration 33, loss = 0.43591052\n",
      "Validation score: 0.817193\n",
      "Iteration 34, loss = 0.43497987\n",
      "Validation score: 0.816298\n",
      "Iteration 35, loss = 0.43372721\n",
      "Validation score: 0.816734\n",
      "Iteration 36, loss = 0.43335539\n",
      "Validation score: 0.817583\n",
      "Iteration 37, loss = 0.43266277\n",
      "Validation score: 0.819029\n",
      "Iteration 38, loss = 0.43236451\n",
      "Validation score: 0.819373\n",
      "Iteration 39, loss = 0.43114449\n",
      "Validation score: 0.816573\n",
      "Iteration 40, loss = 0.43082334\n",
      "Validation score: 0.817055\n",
      "Iteration 41, loss = 0.43018465\n",
      "Validation score: 0.820130\n",
      "Iteration 42, loss = 0.42905126\n",
      "Validation score: 0.819052\n",
      "Iteration 43, loss = 0.42892863\n",
      "Validation score: 0.817422\n",
      "Iteration 44, loss = 0.42825948\n",
      "Validation score: 0.824559\n",
      "Iteration 45, loss = 0.42732435\n",
      "Validation score: 0.823205\n",
      "Iteration 46, loss = 0.42717363\n",
      "Validation score: 0.821806\n",
      "Iteration 47, loss = 0.42671992\n",
      "Validation score: 0.819373\n",
      "Iteration 48, loss = 0.42547382\n",
      "Validation score: 0.823825\n",
      "Iteration 49, loss = 0.42560617\n",
      "Validation score: 0.824055\n",
      "Iteration 50, loss = 0.42515541\n",
      "Validation score: 0.820704\n",
      "Iteration 51, loss = 0.42418877\n",
      "Validation score: 0.824582\n",
      "Iteration 52, loss = 0.42398760\n",
      "Validation score: 0.821370\n",
      "Iteration 53, loss = 0.42319824\n",
      "Validation score: 0.821117\n",
      "Iteration 54, loss = 0.42279602\n",
      "Validation score: 0.820566\n",
      "Iteration 55, loss = 0.42272900\n",
      "Validation score: 0.824904\n",
      "Iteration 56, loss = 0.42200699\n",
      "Validation score: 0.822012\n",
      "Iteration 57, loss = 0.42221756\n",
      "Validation score: 0.824215\n",
      "Iteration 58, loss = 0.42117071\n",
      "Validation score: 0.805696\n",
      "Iteration 59, loss = 0.42119681\n",
      "Validation score: 0.826005\n",
      "Iteration 60, loss = 0.42047136\n",
      "Validation score: 0.824215\n",
      "Iteration 61, loss = 0.42001576\n",
      "Validation score: 0.824330\n",
      "Iteration 62, loss = 0.41981253\n",
      "Validation score: 0.824330\n",
      "Iteration 63, loss = 0.41955430\n",
      "Validation score: 0.819625\n",
      "Iteration 64, loss = 0.41902505\n",
      "Validation score: 0.824743\n",
      "Iteration 65, loss = 0.41897608\n",
      "Validation score: 0.821691\n",
      "Iteration 66, loss = 0.41893389\n",
      "Validation score: 0.822173\n",
      "Iteration 67, loss = 0.41806382\n",
      "Validation score: 0.826051\n",
      "Iteration 68, loss = 0.41707738\n",
      "Validation score: 0.829631\n",
      "Iteration 69, loss = 0.41694466\n",
      "Validation score: 0.823963\n",
      "Iteration 70, loss = 0.41663310\n",
      "Validation score: 0.829218\n",
      "Iteration 71, loss = 0.41649582\n",
      "Validation score: 0.828369\n",
      "Iteration 72, loss = 0.41546095\n",
      "Validation score: 0.825569\n",
      "Iteration 73, loss = 0.41612350\n",
      "Validation score: 0.825179\n",
      "Iteration 74, loss = 0.41497710\n",
      "Validation score: 0.826739\n",
      "Iteration 75, loss = 0.41544508\n",
      "Validation score: 0.826441\n",
      "Iteration 76, loss = 0.41426217\n",
      "Validation score: 0.822884\n",
      "Iteration 77, loss = 0.41413133\n",
      "Validation score: 0.827657\n",
      "Iteration 78, loss = 0.41395969\n",
      "Validation score: 0.828897\n",
      "Iteration 79, loss = 0.41324207\n",
      "Validation score: 0.827382\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
     ]
    }
   ],
   "source": [
    "mlp_3 = MLPClassifier(hidden_layer_sizes=(20,20,20), max_iter = 150, early_stopping = True, verbose = True, learning_rate = \"adaptive\")\n",
    "mlp_3train = mlp_3.fit(X_train_c,y_train_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b323c8-2457-480e-958d-2acc38eec75b",
   "metadata": {},
   "source": [
    "__________________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4ac5986c-d3f6-4858-8305-52a9e57b4518",
   "metadata": {},
   "outputs": [],
   "source": [
    "addToList(mlp_1train, classificationList)\n",
    "addToList(mlp_2train, classificationList)\n",
    "addToList(mlp_3train, classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "56044537-957c-4290-a83a-73c02e499a82",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
       "               max_iter=150, verbose=True),\n",
       " MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
       "               learning_rate='invscaling', max_iter=150, verbose=True),\n",
       " MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
       "               learning_rate='adaptive', max_iter=150, verbose=True)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classificationList"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d98e013-6397-47bf-9663-2415ad6c92f8",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2ad41f1b-5588-42f7-8797-cd2f50093c01",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, verbose=True)\n",
      "\n",
      "paramters: {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': True, 'epsilon': 1e-08, 'hidden_layer_sizes': (20, 20, 20), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 150, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 4.2228950765935896e-01\n",
      "\n",
      "number of features seen during fit: 54\n",
      "\n",
      "number of layers: 5\n",
      "\n",
      "number of outputs: 7\n",
      "\n",
      "name of output activation function: softmax\n",
      "\n",
      "number of iterations: 54\n",
      "\n",
      "number training samples seen by the solver during fitting: 21177882\n",
      "****************************************************************************************\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='invscaling', max_iter=150, verbose=True)\n",
      "\n",
      "paramters: {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': True, 'epsilon': 1e-08, 'hidden_layer_sizes': (20, 20, 20), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 150, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 0.3883973232432725\n",
      "\n",
      "number of features seen during fit: 54\n",
      "\n",
      "number of layers: 5\n",
      "\n",
      "number of outputs: 7\n",
      "\n",
      "name of output activation function: softmax\n",
      "\n",
      "number of iterations: 130\n",
      "\n",
      "number training samples seen by the solver during fitting: 50983790\n",
      "****************************************************************************************\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='adaptive', max_iter=150, verbose=True)\n",
      "\n",
      "paramters: {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': True, 'epsilon': 1e-08, 'hidden_layer_sizes': (20, 20, 20), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 150, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 0.41324207134684476\n",
      "\n",
      "number of features seen during fit: 54\n",
      "\n",
      "number of layers: 5\n",
      "\n",
      "number of outputs: 7\n",
      "\n",
      "name of output activation function: softmax\n",
      "\n",
      "number of iterations: 79\n",
      "\n",
      "number training samples seen by the solver during fitting: 30982457\n",
      "****************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "comparison(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "daad5cc5-0f3e-416d-bf86-f53792ab7ba2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default: hidden_layer = (100,), activation = relu, solver = adam, alpha = 0.0001, learning_rate = constant, learning_rate_init = 0.001, max_iter = 200, shuffle = true, early_stopping & warm_start = FALSE\n",
      "Alle Infos, die davon abweichen, werden in der Legende gelistet\n",
      "****************************************************************************************\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxkAAAEWCAYAAAAKKloDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAACoR0lEQVR4nOydd1xT1///XzcJI5Ehe08hJGEXRHGPauHnqBWps07cWke139bVT9WPH1vHR3FXq9ZR9eOqq63WWke1akERIQQERREQlBF2yLi/P8JNQ0wgKtTanqePPOTee+77nHvvCZz3fS+KpmkQCAQCgUAgEAgEQkvBet0DIBAIBAKBQCAQCH8viJJBIBAIBAKBQCAQWhSiZBAIBAKBQCAQCIQWhSgZBAKBQCAQCAQCoUUhSgaBQCAQCAQCgUBoUYiSQSAQCAQCgUAgEFoUomQQ/nJQFEVTFOXX8PNWiqIWG9P2JfoZSVHUuZcd5+virzDu5p4LgUAgEAiEfzYUqZNBaGkoijoL4AZN00t09r8LYBsAd5qmFU2cTwPwp2k624i+jGpLUZQ3gAcATJrq+03kRe7XS8ofCyCBpukurSGfQCAQCATC3w9iySC0BrsBfEBRFKWz/wMA+/9ui/zWhKIozpssn0AgEAgEwj8TomQQWoPvANgC6MrsoCjKBkB/AHsoioqiKOo3iqLKKYoqpChqI0VRpvoEURS1m6Ko5Vrb8xvOKaAoarxO234URd2mKKqCoqg8iqL+pXX4csP/5RRFVVEUFU1R1FiKon7VOr8TRVG/UxQlbfi/k9axixRFLaMo6ipFUZUURZ2jKMrewJh7UBT1mKKoBRRFPaMoKpeiqJFax80oilpNUdQjiqKKGlyPuDrn/h9FUU8A7NIjXzNuiqKY67rTcF1DG/b3pygqpeEeX6MoKkTr/NwG+akAqimK4lAU9QlFUTkN1yamKOq9hrZCAFsBRDfILzfwXCZSFJVNUVQpRVEnKYpy1TpGUxQ1haKoexRFlVEUtUmPAkogEAgEAuFvBFEyCC0OTdO1AP4HYLTW7vcBSGiavgNACWAOAHsA0QB6A5jWnFyKomIAzAPQB4A/gLd1mlQ39NkWQD8AUymKGtRwrFvD/21pmragafo3Hdm2AM4ASARgB2AtgDMURdlpNRsBYBwARwCmDWMxhHPD9bkBGAPgK4qiAhqOfQGADyAMgF9DmyU659oC8AIwqYk+QNM0c12hDdd1iKKotwDsBDC54Vq2AThJUZSZ1qnDob5HbRssSzlQK4XWAD4HsI+iKBeapjMATAHwW4P8trpjoCiqF4D/QP2MXQA8BHBQp1l/AO0BhDa0e6ep6yIQCAQCgfBmQ5QMQmvxDYB45g091Iv/bwCApulkmqav0zStoGk6F+pFcHcjZL4PYBdN02k0TVcD+Jf2QZqmL9I0fZemaRVN06kADhgpF1AvuO/RNL23YVwHAEgADNBqs4um6SwtJSqsGZmLaZqW0TR9CWoF5v2GN/gTAcyhabqUpulKACsADNM6TwXgs4Zza40cvzYTAWyjafoGTdNKmqa/ASAD0FGrTSJN03mMfJqmD9M0XdBw7w4BuAcgysj+RgLYSdP0LZqmZQA+hdry4a3VZiVN0+U0TT8C8Auav3cEAoFAIBDeYIg/NqFVoGn6V4qingJ4l6Kom1C/xR4MABRF8aG2FEQC4EE9D5ONEOuq0+6h9kGKojoAWAkgCGpLgxmAw0YO2VVXXsO2m9b2E62fawBYNCGvrEER0pblCsAB6mtO1vIYogCwtdo+pWm6zshx68MLwBiKomZq7TNt6J8hT/sEiqJGA5gLwLthlwXUlhhjcAVwi9mgabqKoqgSqO9dbsPuF7l3BAKBQCAQ3nCIJYPQmuyB2oLxAYBzNE0XNezfArWVwJ+maSsAC6BeaDdHIQAPrW1PnePfAjgJwIOmaWuoYwkYuc2lUSuAenGujSeAfCPGpQ8biqLa6MgqAPAMQC2AQJqm2zZ8rGma1l50v2rKtzwA/9aS35amaV6Ddea5PiiK8gKwHcAMAHYNLlFpeMl713Dddnj5e0cgEAgEAuENhygZhNZkD9RxExPR4CrVgCWACgBVFEUJAEw1Ut7/AIylKEpEURQPwGc6xy0BlNI0XUdRVBTUMRQMT6F2Q/I1IPt7AHyKokY0BEIPBSACcNrIsenjc4qiTCmK6gp1TMJhmqZVUC/o/0tRlCMAUBTlRlHUq8QoFKHxdW0HMIWiqA6UmjYNQfGWBs5vA7Ui8bRhPOOgtgZpy3c3FJwPtXI3jqKosIa4jxVQpzDOfflLIhAIBAKB8CZDlAxCq9GwyLwG9SL2pNaheVArAJVQL4gPGSnvBwDrAFwAkN3wvzbTACylKKoS6kDq/2mdWwPg3wCuNmRc0o5PAE3TJVArAh8BKAHwMYD+NE0/M2ZsengCoAzqt/z7AUyhaVrScOz/GsZ/naKoCgDnAQTolWIc/wLwTcN1vU/TdBLUit3GhjFkAxhr6GSapsUA1gD4DWqFIhjAVa0mFwCkA3hCUdRz94Om6Z8BLAZwFGprUzs0jjEhEAgEAoHwD4MU4yMQWhiKonoA2EfTtPtrHgqBQCAQCATCa4FYMggEAoFAIBAIBEKLQpQMAoFAIBAIBAKB0KIQdykCgUAgEAgEAoHQohBLBoFAIBAIBAKBQGhR/lbF+Ozt7Wlvb+/XPQwCgUB4Y0hOTn5G07TD6x4HgUAgEP5e/K2UDG9vbyQlJb3uYRAIBMIbA0VRupXuCQQCgUB4ZYi7FIFAIBAIBAKBQGhRiJJBIBAIBAKBQCAQWhSiZBAIBAKBQCAQCIQW5W8Vk0EgEAiEvwbJycmOHA5nB4AgkBdaBAKB8HdDBSBNoVAkREREFOtr0KpKBkVRMQDWA2AD2EHT9Eqd49YA9gHwbBjLapqmd2kdZwNIApBP03T/1hwrgUAgEFoODoezw9nZWejg4FDGYrFIQSYCgUD4G6FSqainT5+Knjx5sgPAQH1tWu3tUoOCsAlALAARgOEURYl0mk0HIKZpOhRADwBrKIoy1To+C0BGa42RQCAQCK1GkIODQwVRMAgEAuHvB4vFoh0cHKRQW6v1t2nF/qMAZNM0fZ+m6XoABwG8q9OGBmBJURQFwAJAKQAFAFAU5Q6gH4AdrThGAoFAaB3ybgJX1qj//2fCIgoGgUAg/H1p+B1vUJdoTXcpNwB5WtuPAXTQabMRwEkABQAsAQylaVrVcGwdgI8b9huEoqhJACYBgKen5ysPmkAgEF6ZvJvANwMBZT3ANgXGnAQ8ol73qAgEAoFA+NNoTUsGpWef7lutdwCkAHAFEAZgI0VRVhRF9QdQTNN0cnOd0DT9FU3TkTRNRzo4kKK1BAKhhdC1RLyIZSL3ilrBoJXq/3OvtO5YCQQCgUD4i9GaSsZjAB5a2+5QWyy0GQfgGK0mG8ADAAIAnQEMpCgqF2o3q14URe1rxbESCIS/Eq/qatQS538zELjwb/X/Sbsbbzcn17ur2oJBsdX/e3c1fqz6xk5cr14KiqIiBg0a5MNsy+Vy2NjYhPbs2dMPABITE+1Gjx79nAnczc0tmM/niwICAkSdO3f2f/ToEQcApFIpa8SIEV4eHh5Bfn5+gZGRkQEXLlxoAwA8Hi+8pcb95ZdfOmzcuNEOAG7fvm0uEAhEQqFQlJ6ebhYeHi54UXkqlQodO3bkl5aWtsjf/Li4OO9du3bZtISsxMREu9zcXJOWkKXN5cuXeWPHjvVovuWLc/ToUSuBQCASCAQiHo8X7u3tHSQQCETvvfeed2v0BxieX9pzRZvMzExTf3//QH3nREVFBVy+fJnXUmMz9D16XbzMd+RVuHr1Knfo0KFeALBlyxZbPp8v4vP5ovDwcMFvv/3GZdodOXLEytvbO8jT0zNowYIFzs3Jfd2yJk2a5H7y5MkmvYmaozXdpX4H4E9RlA+AfADDAIzQafMIQG8AVyiKcgIQAOA+TdOfAvgUACiK6gFgHk3To1pxrAQC4a/Cq7oatYSrkq4lIuNE4+0736rbeHfVL9sjSt1vU230jTVmJfDjJ423n6QAt78FVArievWCcLlcVWZmJreqqoqysLCgjx8/buXk5CQ35txLly5lubi4KGbMmOG2ZMkSl927d+eNHDnS28vLS5abm5vGZrMhFotNU1NTuc1LezE+/vjjp8zPhw8fbhsbG1v+3//+twAAbt++LTFWjkqlAk3TOHz4sHVgYGCtra2tqvmzmkahULyqiEbs27fPPiwsrNbb29uo52Is3bp1q+nWrVtNS8pkiIuLq4iLixMD6gX76tWr83T7UigU4HBav0qA9lz5uyKXy2FiYrwe+iLfkZZg+fLlLkuWLCkEAD8/P9nVq1czHRwclP/73/+sJk+e7JWamipRKBSYM2eO59mzZ7N8fX3loaGhwri4uPKIiIg6Q3Jft6x58+YVjxs3zmvgwIGVL3tvWs2SQdO0AsAMAGehzhD1P5qm0ymKmkJR1JSGZssAdKIo6i6AnwH8H03Tz1prTAQCwQj+bCuCbnt9rkav4qp051vjz2X64do1tkQI3/1jm8VWL/q1rRr6xucRBXT9qGmFoCllRiEDvv9IbUVRyojr1UvSu3dv6eHDh9sCwIEDB2zj4uJKX+T8Hj16VD548MAsPT3d7Pbt223Wr1+fz2azAQAikah+2LBhUu32UqmUFR0dzReJREI+ny/at29fWwCoqKhg9ejRwy8gIEDk7+8fuH37dhsAmDZtmlu7du0C+Xy+aNKkSe4AMHfuXNclS5Y4HTp0yPqrr75y2r9/v32HDh34QOM32osXL3YKCgoS8vl80Zw5c1wB9RtsX1/fwFGjRnkGBgaKcnJyTPfv32/73nvvlTPnbd682TY4OFgoEAhEI0aM8GIUh5EjR3oGBQUJ/fz8Ahl5gNqyM2/ePJeIiIiAnTt3aiwYJ06csOzTp087Zvv48eNWffv21Wxro1AoEBcX5+3v7x/I5/NFn3/+ueOuXbts0tLSeKNHj/YVCASiqqoq6sSJE5ZCoVDE5/NF8fHx3rW1tRQzhqlTp7oFBwcLg4ODhWlpaWaA2qoyYsQIz4iIiABvb++gAwcOWAPA6dOnLRmL1dy5c13j4+O9o6KiAtzd3YOXL1/uyIxr/vz5Lj4+PoGdOnXyHzBggM+SJUucjJ0buujeJ22LQWFhIcfNzS2YuReTJ092Z57dqlWr7I2RP3PmTLeAgABRaGioIC8vj8NcGzPmK1eu8AICAkRhYWGCtWvXaq6xqqqK6t+/vy+fzxf169fPt66uTuPOfuzYMauwsDCBSCQSxsbG+kqlUhZzLXPmzHFl5vHt27fNjRnjt99+ax0SEiIQCoWiTp068fPy8jhKpRJeXl5BBQUFHABQKpXw9PQMKiws5BQUFHDeeeeddkFBQcKgoCDhuXPn2jDXNXz4cK/OnTv7Dx482EdfX0lJSebMPObz+aK7d++aAX98R2bPnu3KWJwcHR1DhgwZ4g3on//65qcx11tWVsbKyMjgRUdH1wJAnz59qh0cHJQA0LNnz+onT56YAsDFixfbeHl5yUQiUb25uTk9ePDg0iNHjrRtSvbrlsXn8+vLy8s5jCX3ZWhVNZum6e8BfK+zb6vWzwUA+jYj4yKAi60wPAKBoMurWAHybqoX9C/y1l1ff4yrEbOPa9f0mPJuNrYYaJ/PKATa4wGetzDoG3vMSqC25I92TiL1edLHQPI3jZWYlIMvd890r1X4LvDwN/U2RQG0Cn+EslHq65E+Vo+XWDOM4oMPPij97LPPXIYOHVqekZHBmzBhQsm1a9csjD3/5MmTbUUiUW1KSoq5SCSqae7tNI/HU505cybb1tZWVVhYyOnQoYNgxIgR5ceOHbNydnaWX7x4MRsASkpK2EVFRezvv//e5v79+2ksFgvPnj1ja8saOnSo9MaNG08tLCyUS5cuLdI+duzYMavs7Gzz1NTUDJqm8fbbb/v98MMPFr6+vvW5ubnm27dvz923b98jAEhOTrbo3LnzQwC4deuW+ZEjR2yTkpIkZmZm9KhRozy3bt1qN2PGjJK1a9fmOzk5KRUKBTp16hRw48YNbocOHWoBwNzcXJWcnJwJAGfPnrUGgAEDBlTOnj3bs6CggOPq6qrYuXOn3dixY/W+KPztt994hYWFJvfu3UsHgGfPnrHt7e2VW7ZscWQsATU1NdTkyZN9zp07lxkSEiJ77733vFetWuWwZMmSYgCwsrJS3r17N2Pjxo12M2fO9Pjll1+yASAvL8/s5s2bmWKx2Oztt98OePfdd+/q9p+dnW1+7dq1zPLycrZQKAyaP3/+0xs3bnBPnTplc/fuXbFcLqfCwsJE4eHhNYBagTt8+PBzbkgdO3as3L17d57ufgbt+7Rjxw69C9V169bZW1tbK9PS0jJqa2up9u3bCwYMGFAhEAjqDcmtra1lRUdHV23YsCF/ypQp7hs2bHD48ssvC7XbTJgwwfu///3vo379+lVNnjzZndm/evVqRy6Xq8rKyhLfuHGD27lzZxGgVnxWrFjhcvny5SwrKyvVwoULnZctW+a0evXqQgCwt7dXiMXijJUrVzqsXLnS6dChQw8NjY+hT58+VcOGDZOwWCysXbvWfunSpc7bt29/PGTIkJIdO3bYLlmypPjEiRNWQqGw1sXFRTFgwACfuXPnFr3zzjtV9+7dM33nnXf879+/nw4AqampvBs3bkgsLCz0ZqnbsGGDw7Rp04qmTp1aWldXR+la2datW1cAoKCkpIQdHR0dMGvWrGJD8z80NLRWd34Czc+DX3/9tU1AQECtgfHZ9+zZUwoAeXl5pm5ubprn6+7uXn/jxg2jfw+9LlnBwcE1Fy5csBg7dmy5sfK1IRW/CQTCH+izIjSnJOReUSsCP34CKOqgWRQbc76+/rp+1NjVqKkxGVKKmPONUQgAtQzdsdeWqMfC4BGl/uTdBFIO/CEDlPH3TFch0udWxSgzzD1llCX/vsC9n9TXk3KAuE0ZSYcOHWofP35stn37dtu3335b2vwZarp3785nsVgQCoU1a9euzT979qxRf8RVKhU1e/Zs9+vXr1uwWCwUFxebPn78mPPWW2/VLly40GPq1Klu7777rjQmJqZKLpfDzMxMNWzYMK9+/fpJhw4davT4fvzxR6vLly9biUQiEQDU1NSwJBKJua+vb72Li0t97969q5m2UqmUY2Njo2o4zzItLY0XGhoqBIC6ujqWo6OjAgC++eYb2927d9srFArq6dOnJnfu3DFnlIzRo0eX6Y6BxWLh/fffL9m+fbvt9OnTS27dumVx7NixB/rGKxAIZHl5eWZjxozxGDBggPS9996r0G1z584dc3d3d1lISIgMAMaOHVuyadMmRwDFADBmzJhSAJg4cWLpokWLNPEWcXFxpWw2G8HBwTIPDw9ZSkrKc2/d+/btW87lcmkul6uwtbWVP378mHPx4kWL2NjY8oZFLN2nT59ypv2yZcuKli1bVqQrpzn03Sddzp8/byWRSHgnT560AYDKykq2WCw2b0rJMDExoRmrWURERPX58+ettI+XlJSwKysr2f369asCgPHjx5dcuHDBGgB+/fVXiw8//LAYUH8f+Hx+DaB+i52Tk2MeFRUlAAC5XE5FRERUMTJHjBhRBgBRUVE1zFib48GDB6aDBg1yf/r0qUl9fT3Lw8NDBgBTp059NnDgQL8lS5YU79y5055RRq9evWp17949jcthVVUVu6ysjAUAMTEx5YYUDACIjo6uXr16tcvjx49Nhw0bVhYcHCzTbaNSqTBkyBCf6dOnF3Xt2rVmxYoVDvrm/9ChQ8v1zc/m5kF+fr6JnZ3dc65+p06dsty3b5/9tWvXJABA089fBkVRRqX4fp2yHBwcFPn5+abPNTISomQQCH93dBe2TaH7Zr25gGVmga/vrbv2+drKiLZ1wFB/zAKcwdCYDCkgL6IQAOqfDY1dF13FAGjch6HzDClE2mO9skZ9PqPcMAoHo2xl/mC8AkjQEBMTU/7ZZ595nDt3LrO4uNiov3tMTAazHRYWVpeRkcFTKpVg3KX0sW3bNtuSkhLO3bt3M8zMzGg3N7fg2tpaVkhIiOzWrVvio0ePWi9cuNDt/PnzFatXry5MSUnJOHnypNXBgwdttmzZ4nj9+vUsY8ZH0zRmz55dOH/+/EaWg8zMTFMej9co9oLNZtPMuGmapuLj40s2bdqUr91GIpGYbty40Sk5OTnDwcFBGRcX511XV6dxqba0tNQbzzF16tSSfv36+Zmbm9MDBgwoM+Q77+DgoExLSxMfP37cavPmzY6HDh2yPXz4cK7uNTUFi/WHh7f2QkhdagswtA0AZmZmmvZsNhsKhYJqqr+XtWRo3ycOh0MrlUoAQE1NjWZQNE1Ta9aseRQXF/ecomUIDodDM9fP4XCgUCgaXSRN03qvm0HfMZqm0aVLl4pTp07pVQzNzc1ppm/d/gwxY8YMz1mzZj0ZOXKk9PTp05ZLly51BQA/Pz+5vb294uTJk5a3b99u8913391nxpCUlJShT5lo06ZNkzFEU6ZMKe3atWv18ePHrWNjY/mbN2/O1Y0f+Oijj1xdXFzqZ82aVdLQn975DwD65mdz84DH46lkMlmj0IMbN25wp02b5nXmzJl7zs7OSgDw9PSs116sP3782NTV1bXZOKTXLauuro7icrkvHcvVmtmlCATC60Y3S5JuXIJuLAGzgO61sPk35doLfJUKoFh/xDBEjvvjfGYMPy8HTs8CLiz/YyzG9NdUm+ayOOmeGzr8+fa6MrTHbgjteAtj71lTaW0NPSftfnTHybVTP7uk3STzVDNMnTr12UcffVQQFRWl163BGAIDA2UhISHVc+fOdVWp1H9z7969a8bEXDBIpVK2vb293MzMjD516pRlQUGBKQDk5uaaWFpaqqZNm1Y6e/bsopSUFJ5UKmWVlpayhw4dKt26dWteRkaG0Rl/YmNjK/bu3WvP+NA/ePDAJD8/X68C5ePjU5eRkWEGADExMRWnT5+2YdoWFRWxs7KyTMvKythcLldla2urzMvL41y8eNHamHF4e3vLnZyc5GvWrHGZOHGiwZjKwsJCjlKpxNixY8uXL1+ef/fuXR4AWFhYKKVSKRtQK3L5+fmmTLzFnj177Lp27apZNO7Zs8cWAL7++mub8PBwjaXm2LFjNkqlEunp6WZ5eXlmoaGhBgNgtenRo0fV2bNnrWtqaiipVMo6f/58W+bYsmXLiiQSiVj305SCoYuHh4fs5s2bbQBg//79GktAnz59pFu2bHGQyWQUAKSmpppVVFSwAMDHx0dvRqjmsLe3V1pYWCgZi9vu3bttmWNdunSp2rdvny0A/P777+ZZWVm8huuvTkpKsmDud2VlJSs1NdXsZfpnqKysZHt6esobxtBocT5+/PinCQkJPgMHDixl3A67dOlS8cUXX2jcyq5du2Z0IgWxWGwqFAplixYtKu7bt295SkpKo3MPHDhgffHiRaudO3dqnpmh+W9ofjY3D4KDg+tyc3M19+zevXum8fHx7Xbu3PmAscgBQPfu3atzc3PNJRKJaV1dHXXs2DHbuLi4cgBYsWKFw4oVK56rwfC6ZQFATk6OeWhoaC0ATJ8+3W3Pnj1tjX0+ALFkEAivnxexNLwoL+NqpGtF0De+vJuANA9gcQAV9McwMAqM9HGDpaDhZQitej5Dk7Zbkj50x6S9v7ksTrrn6lohcq88P3ZtjHk+2n0Yat+UlcgYNzXta9W4p8kANCh4bDPiQmWAdu3ayRcvXlys79iRI0fszp4925bZvnbtWoYhOfv27cudNm2ah5eXVxCXy1W1bdtWuWrVqkaLzoSEhNLY2Fi/oKAgYWBgYI2Pj08dACQnJ3M//fRTdxaLBQ6HQ2/evPlheXk5u3///n7MYnP58uVGL2AHDx5ckZ6ebt6+fXsBoI4F2b9//wMOh/PcG+G+fftKz507ZxkUFCSLiIioW7RoUX7v3r35KpUKJiYmdGJi4qPevXtXBwUF1fj7+wd6enrKtN1mmmPYsGElmzZt4jSV3SY3N9dkwoQJ3iqVigKApUuXPgaA0aNHP5s5c6bX/PnzVUlJSRlbt27NjY+Pb6dUKhEaGlozb948TfYkmUxGhYSECFQqFXXw4MH7zH4/Pz9ZVFRUQElJicm6dese8ng8o1xHunfvXhMTEyMViUSBbm5uspCQkGpra2ulsdfdHJ988knR0KFDfQ8ePGjXtWtXjdVizpw5z3Jzc82Cg4OFNE1Ttra28u+//z6nsLCQQ9O0URYDfXz99de5CQkJ3lwuV9WrVy9Nf/PmzSseNmyYD5/PFwUGBtYEBwdXA4Crq6ti27ZtucOGDfOtr6+nAOCzzz7L116EvigLFy4sGD58eDsnJ6f6yMjI6kePHmkW4MOHD5fOmDGDPWnSpBJm31dffZWXkJDgyefzRUqlkurQoUNlp06dHhnT1969e20PHz5sx+FwaAcHB/l//vOfAu3j69atcyouLjYJCwsTAmqL5rp16wr0zX8ej6fSNz+bIzw8vK6yspJdVlbGsrGxUS1atMilvLycM3PmTC9AbQVKS0vLMDExwZo1ax7FxMTwlUolRowY8SwyMrIOACQSCbdz587Pfd9etyyZTEbl5uaadevWrRoAxGIxVzuBhDE0aS5804iMjKSTkpJe9zAIBONprcrQurESuvLzbgIX/wPcv9jg5sQC2vUAenz6vIKhOz7gj30sNhA+Sm0hMHQeiw21m5IcmgUxi6Pe9zJpWVtKKTPm3r/o82muvaGxv2g/V9aorR601nqIYqutKc0pbDpQFJVM03TkC51kBHfu3MkNDQ0l2QL/Ajx8+NBk+PDh3teuXbvXGvJHjx7tGR4eXjNnzpxWe95ubm7BSUlJGdoubIA6u1T//v2l48aNazYWQh9SqZRlbW2tqqysZEVHRwds3br1YZcuXWpaZtQvxoEDB6xzcnLMFi1apFchftO5fPkyb86cOR5MYPzfhc8//9zR0tJSNXfu3Jea/z179vT74Ycfchj3tFehJWXt2bOnbXJyMm/9+vUFANClSxf/X3/99bnfIXfu3LEPDQ311ieDWDIIhNeJoTfYr7KQ1ld7QdfC8M3AP96Cg1L/f/+iOrOR9gLXkIuPxk0KgLX782Ns5EoFIGI0YO3xR0yGbkC2sfEFL7uI13fMGOtBc21eVOarWGS0YawijSwZzcTQvAH8mv2szdXsZ5ad/ewru/jZVzd/BsEYvLy85OPHj39WWlrKaolaGdoEBgYKuVyuatu2bUZbYf5KjBo1yuvevXtcmUxGDRs2rOR1KRiA+k3/6+q7tVmwYIHz7t27HXbt2qU3/uNNZv78+U9fpTglkyWtJWhJWQqFglq8eLEm6F2fgtEcRMkgEF4n+lxoXsa6ob3Y1V3o6mZJYo6jwYJh6w2U5f7hxqS9MDbk4tNccLjueaEjnl+cGxMsrYv2tSlkamsMY31p6r4ZkyrXmOvQbvOyMg1hSAEx1FbbdcqQq9cbxK/Zz9qM3/07X6FUsb7+9YFq59j2WUTRaDkSEhJe6k1/c6Snpz/nXhYSEiKor69vFPO5Z8+eB68SE5Ofn/9cWloAOHr0aO7LygQAQ0HPhOdZv3693ZYtWxrVEWnfvn3V3r17m3VvWrFixZMVK1Y8eZl+jx49arVw4UJ37X0eHh6yn376Kedl5LU0PB6Pnj59+gvV33kTGD9+/Cv/ziBKBoHwqryK1UHfG+wra148jayu5aKpha7uQrjTrMYuVdrtDb1hNyYOoqk2L1IRW18NDOYNvrb1pSkrgjGpcl/0Ol5EZmvE3byIUvIGcDX7maVCqWKpaEChVLGuZj+zJErGm0lqauqfWnGZ8Ocwa9asEiZL05+JdoV1wpsFUTIIhFehJWIqdBeLusXkmiu+pvt2P+NE04HMTdVmMLQw1vXzN2aB21yb5o43VQNDO56EWeA3ZUUwNlXui4zTWJmG4lpaK9j/DaWzn33l178+UCmUKhaHzVJ19rOvbP4sAoFAIPxVISlsCYRXQd/bbN20sC8Ks5COGA2AUscu6Es/y8AsdsGC5u3+j580nw2JSY2qb7u51Ld/BobiQTyi1C5SbLPGqWibSiX7Iql5jeVlU9fe+fb139u/IF387Kt3jm2fNbl7u3ziKtW6zJ492/W7776zBIClS5c6VlZWtvha4MmTJ+wOHTrweTxe+OjRoz21j0VFRQV4e3sHCQQCkUAgEDHpRGtra6l+/fr5enp6BoWEhAgyMzNNW3pcuri5uQUXFha+lheuffr0aScQCESenp5BlpaWYcz9+Omnn9q8itxJkya5nzx50rKlxkkgvCzEkkH4Z2OsG4uxaUm5dk0HXRvbJxNErFI07zbV1Nv9l11Mv2jl75bAkGvUi7hxNWWZaA33ImNk6l7Hi1QI/4fRxc++migXrc+6desKmJ+3bdvmNHHixFJDxfb0oVAowNQ5MASPx6OXLl1acOfOHW5aWtpztQ/27Nlzv1u3bjXa+9avX29vbW2tePToUdpXX31lM3fuXPczZ87c1z23pVAoFM03akWYmILTp09brlmzxkk3aFcul8NQccOmmDdvXvG4ceO8dAvTEQh/NsSSQfjnYmyhuqTdhttpv82OWal2VVLK/nBd+v6jxudp97m7H3B6TvMWCkOF5rQx9Hb/ZTHU96taaQyh71k0ZynQtb78VdG9Dn0FAQn/eBITE+3efvvtdr169fJzc3MLXrFihcO//vUvJ6FQKAoNDRUUFRWxAWDNmjX2QUFBwoCAANE777zTjrFC9O7du93GjRvtAGDVqlX2AwcO9DHUV1xcnPeuXbtsli9f7lhcXGzSvXt3focOHfgAcOzYMauwsDCBSCQSxsbG+jLF/tzc3ILnzZvnEhEREbBz585mM+lYWVmp3nnnnSpzc3OjlZfTp0+3HT9+fAkAjBs3ruzatWuWTOFDfXzxxRcOU6ZM0QQEJyYm2o0ZM8YDADZv3mwbHBwsFAgEohEjRngxCgWPxwufPXu2a0hIiODnn3+2AIClS5c6BQcHC4ODg4VMYbqsrCzT6OhoPp/PF0VHR/Pv3btnCgA7d+608ff3DwwICBBFRkYGAGplZfLkye5BQUFCPp8vWrVqlb2x16xLYmKiXWxsrG+vXr38unbtyj99+rRlz549/Zjjo0eP9kxMTLQDgCtXrvDat28fEBgYKOzSpYv/w4cPTQCAz+fXl5eXcx49ekReJBNeK2QCEv45vEi6UW0/eopSWwdo1fMZjYA//tdOC0ux1B/mPH3pX5VKIGmXOsuSdv0K7TE2FUSsm1XoRdOgNoU+Wa1V0wMw/Cz+LsHNutfRUs+J8LciKyuLe+fOHXFtbS0rICAgaPHixfkZGRniCRMmeGzbts1uyZIlxSNHjiz76KOPngHAhx9+6JqYmGi/cOHC4t27dz/s3LmzwM/PT7Zp0ybnGzduGCwqyLBo0aLiLVu2OF26dCnLxcVFUVhYyFmxYoXL5cuXs6ysrFQLFy50XrZsmdPq1asLAcDc3FzF1DhYvHix0+HDh+10ZXbs2LHSmKrYCQkJ3iwWCwMGDCj74osvClksFoqKikx9fHzqAcDExAQWFhbKoqIijm5tDIYPPvigrGPHjgIAjwHgyJEjtgsXLiy8deuW+ZEjR2yTkpIkZmZm9KhRozy3bt1qN2PGjJLa2lpWUFBQrbY1x8rKSnn37t2MjRs32s2cOdPjl19+yZ4yZYrniBEjSmbOnFmybt06u6lTp3qcP38+Z+XKlS7nzp3L8vHxkT979owNAOvWrbO3trZWpqWlZdTW1lLt27cXDBgwoMLJyUkRHR0t0Df2/fv33zdUvPDWrVsWqamp6U5OTsrTp0/rdXuSyWTUhx9+6HnmzJlsV1dXxfbt223mzZvndvjw4VwACA4Orrlw4YLF2LFjy5t7FgRCa0GUDMKbx4tm6sm7qfaDv/3tH8XfYlY+X7HaUAVmmgWwWAANaGIecn9tXIRONy2sbw9A+K7+rE1sU0BRB7VAurECYqgCt/a16Coz2pWeW3JRriurNV2oXiX165vI30V5IrQonTp1qrSxsVHZ2NioLCwslPHx8eWAesGYmprKA9SVw5csWeJWWVnJrq6uZnfv3l0KAB4eHooFCxYU9O/fP2DPnj3ZTk5Oyhft/+LFi21ycnLMo6KiBAAgl8sp7crfo0eP1qS0XLZsWdGyZcuK9MlpjkOHDt338fGRl5WVsfr3799u8+bNdjNmzCjRVxyYoiiDRcVcXV0VHh4esp9//rlNYGBg3f3798379OlTtXLlSoe0tDReaGioEADq6upYjo6OCgBgs9kYO3Zso9ScY8aMKQWAiRMnli5atMgDAG7fvt3mhx9+yAGAqVOnln7++efuABAZGVk1cuRI77i4uLKRI0eWAcD58+etJBIJ7+TJkzYAUFlZyRaLxeYCgaBCIpGIX/T+dO3ataK555eammp27949bq9evfgAoFKp4ODgIGeOOzg4KPLz81s9poVAaAqiZBDeLF62ArNmUY8/3JhoWp29KWLM8xWrdRe9jCuUdsyDthVCtz1j6WCyNnHt/lCMxpwE7nyLlPTDSDJjI7JehTB99S2aKg6HBheCloi/MJbWVARa0gpDILyhmJqaahbULBYLTNVeFosFhUJBAcCkSZN8jhw5kh0dHV2bmJhod+nSJc2b7rt373Ktra0V+fn5L+7ID4CmaXTp0qXCUO0I7biNV7Fk+Pj4yAHAxsZGNXTo0NKbN2+2AVDi7Oxc/+DBA9N27drJ5XI5qqqq2I6Ojk0utocMGVJ24MABG4FAUBcbG1vGYrFA0zQVHx9fsmnTpnzd9qampirdeBIW6w/P8aaUGgD49ttvH124cKHNyZMnrcPCwgJTUlLSaZqm1qxZ8yguLq5Cu21ZWRnrZSwZPB5Pc59NTExobZcxmUxGAQBN05Sfn19tSkqK3nTBdXV1FJfLbdHCiwTCi0JiMghvFoYyDukj76batUkpg0bBANVglVA1VKNWPl+xmrGUxKz8w48+cuwfMQ+gGhrSjRf4+uIHmADmHz/5I94AQErUWEx0dcIGG2tMdHFEipl60Z7C5WFHW2ukcHnPL+Lzbv5hfWG+ugYqPacUp2DH3R1IKU5pfH5LZL1qyQxNuvLfhBgLAuE1UlNTw/L09JTLZDLq4MGDtsz+X375hffzzz9bJycnizdu3OgskUiMeovdpk0bJRN30aNHj+qkpCQLJi6hsrKSlZqaaqbvvGXLlhVJJBKx7qc5BUMul4PJ5iSTyajvv//eOigoqBYA+vXrV75z5047ANi1a5dNdHR0JaMA+Pj4BOqTN2rUqLIff/zR5vDhw7YjRowoBYCYmJiK06dP2zBZq4qKithZWVkG78eePXtsAeDrr7+2CQ8PrwaA8PDw6h07dtgAwLZt22wjIyOrACA9Pd2sV69e1evWrSuwsbFR3L9/37RPnz7SLVu2ODAKQGpqqllFRQXLxsZGpe8eSSQSsSEFQ5d27drJsrOzubW1tVRJSQn7119/tQKAkJCQutLSUs758+fbMPcyKSnJnDkvJyfHPDQ09KWLHxIILQGxZBDeLJp7m64dr/DjJ43dilgctYuTc6jh4nNNWUqYRbau61VzNRf0KEZJba1Rr1JCBUBOq5BUlAQ4RWKiiyPqVXJwKDbeffwjBpqZIswxrPG4WGy10uMcqrcWRkpxCiaem4h6ZT1M2abY3nc7wmT1LRNPQdx8CITXyieffFIQFRUldHNzqxcKhTVVVVXs2tpaasqUKd5ff/11rre3t3zFihV5Y8aM8f7tt9+ytN/S62PMmDHPYmNj/R0dHeU3btzI2rZtW+6wYcN86+vrKQD47LPP8kNCQmQvM1Y3N7fgqqoqtlwup86ePdv2+++/z/L3969/++23/eVyOaVSqaiuXbtWzJ079ykAzJo161lcXJyPp6dnkLW1tfLQoUM5AFBYWMihaZrS14eDg4PS39+/9t69e9yePXvWAEBERETdokWL8nv37s1XqVQwMTGhExMTH/H5/Hp9MmQyGRUSEiJQqVTUwYMH7wPAli1bHo0ZM8Z7/fr1znZ2doo9e/bkAsCcOXPcc3NzzWiaprp06VLRsWPH2g4dOtTm5uaaBQcHC2mapmxtbeXff/99i1Sj9vPzkw8YMKBMKBQG+vj41AUGBtYAgLm5OX3w4MGcDz/80LOyspKtVCqpqVOnFkVGRtbJZDIqNzfXrFu3biRTG+G1QunzgXxTiYyMpJOSkl73MAitjaGYDEPB2mAB7Xo0DtbWJ4OxfDAuURRb/dZetxBdU2MwNF6dBX6KmSkmnpsIuUoOE5YJtvfdjqSiJGy4tQGqBlcoChTM2GZqJSHzZ7UlhFY2PS4AO+7u0MhhU2zMCJ+BhHKp0ecT/llQFJVM03RkS8u9c+dObmho6LOWlkv453HgwAHrnJwcs0WLFhW/7rG8CezZs6dtcnIyb/369QXNtyYQXo07d+7Yh4aGeus7RiwZhL8WhjInaWOMxYAJ1gbVOEbCkAy9AdVNxB28yBt9PfEGYYBGsYh0ilRbKwCYsk0hU8pAN/yTq+RIKkpSx2wYEQ+RUpyCwqpCcFgcKGklTFgmsDa1xg7kIZLLQ1htzT8jsJpAIPxtGD58uPR1j+FNQqFQUIsXL36poHwCoSUhSgbhz8GYN//NZU5qToa+YG1Dioou+rJD6Somr4IepSTMMUyjXDDb2/tux6mcU/gu+zuNkhDpFAk4hjUbGK3tJsVhcTDYfzCEtkJ8+fuXatcpF0dsd+6DMEEccXkiEP4BfPDBB56///67hfa+qVOnFs2aNavkdY2J0PqMHz++rPlWBELrQ5QMQutjbEYoQ5mT7nz7fByEoeJsL5uhyFB2qD8ZRvEY0G7Ac1YORlFJKU5B0t0dsDa1hrReqmmTVJSEemU9VFBBSSvhauEKab1Us09OU0hy9kcYUTAIhH8Ee/fuffS6x0AgEP65ECWD0PoYW1+BWeg3CtZmq5ULZT00GaK0ZehaN142MLkZBSWlOOX5Rf9LYowsXSuH9rkTz03UuFSxwNIEd0c6RcKUbaqJ84h0UrvZ69tHIBAIBAKB0JoQJYPwYhgb8Kzdztj6Ch5RkLyzD2XiC3BxcYM3tw6QPgaSv0GjFLSMjJauQG1AQdGbreklFY0XlaWrkDDWCrrhfqig0sRtJAQn6I3z0LePQCAQCAQCoTUhSgbBeIxd1Otr15wbU95N5Kecw79uWuKmojNMs1nYn9AREax76oJ3TOpW7SrbV9b8YSFRyNSZofS4OSU/LMP1+yXo6GuHCC+bF75sbTckTSD2Sy7WX0SWrkLycfuPNUHdCpUCKqjAAquRhUKfBcSQVYRAIBAIBAKhtSBKBsF4jHV70teuqSJrDUqJi0KGXSwORmIB7nLMsPl2KmZ2egdhhhQUXfeq+xeBh79plJ+U4hSckFzBwSsmkFV5wJTToLhoKRrGuC4ZckMyRFMyX0SWtkJSr6zHihsroKJV4LA4iOPHQWgrbBSTQSAQCAQCgfBXgVT8/qfzIlWgmUU9xW7a7cnYdkzfd9SWChZUMIECfm1uwsxzB36XfouJ5yaqq2F3/QjJKn9s+iUbyQ8bEmcwcRTteiDF3Bw7rCyQwgF+u/AdDqVewcRzE3H0wQ6wXb8CzB9CrlDh+v0/kqowloINtzao+2mojq1bLZvJ+jQjfAY+bv8xkoqScDjzsKaNdntDMhm0ZTXnKsUoJGyKDRbFgopWNQrqjg+IR0JwAlEwCAQDUBQVMWjQIB9mWy6Xw8bGJrRnz55+AJCYmGg3evRoT93z3Nzcgvl8viggIEDUuXNn/0ePHnEAQCqVskaMGOHl4eER5OfnFxgZGRlw4cKFNgDA4/HCW2rcX375pcPGjRvtAOD27dvmAoFAJBQKRenp6Wbh4eGCF5WnUqnQsWNHfmlpaYv8zY+Li/PetWvXi5uF9ZCYmGiXm5tr0hKytLl8+TJv7NixHi0tFwCOHj1qJRAIRAKBQMTj8cK9vb2DBAKB6L333vNujf4Aw/NLe65ok5mZaerv76+3SnpUVFTA5cuXeS01NkPfo9fFy3xHXoWrV69yhw4d6gUAW7ZsseXz+SI+ny8KDw8X/Pbbb1ym3ZEjR6y8vb2DPD09gxYsWODcnNzXLWvSpEnuJ0+etHyRe6ELsWT8k3nRmIbmsjdpx2E01047WxSLra7GrQJYbBPI/JzAkolBa7kUKWu9MHLHddQrVI0sEofKZPiB3RZ3nR2gAGBCA44PaeSWnATHrh40VABFw4R3H5TCBx19//hdrM91CYDemAlmIa8bdM1hqb9CCpUCpmxTDGg3oFl3KF33JUOWD0YhSSpKgrWpNb78/UsSwE0gvABcLleVmZnJraqqoiwsLOjjx49bOTk5yY0599KlS1kuLi6KGTNmuC1ZssRl9+7deSNHjvT28vKS5ebmprHZbIjFYtPU1FRu89JejI8//vgp8/Phw4fbxsbGlv/3v/8tAIDbt29LjJWjUqlA0zQOHz5sHRgYWGtra6t61bEpFIpXFdGIffv22YeFhdV6e3sb9VyMpVu3bjXdunWraUmZDHFxcRVxcXFiQL1gX716dZ5uXwqFAhxO6y+xtOfK3xW5XA4TE+P10Bf5jrQEy5cvd1myZEkhAPj5+cmuXr2a6eDgoPzf//5nNXnyZK/U1FSJQqHAnDlzPM+ePZvl6+srDw0NFcbFxZVHRETUGZL7umXNmzeveNy4cV4DBw6sfNl7QywZ/2T0uTU1h0fUH65PjCUiaTdwejawu7+6qvQ3A9Vt9blIMYpN0m5AKVP3rVIC4SOAXgvBGnsK43qNhVnDG3ymmNzm219BwXkAFQ2NReJQ6hUsS56FpPok1IOCiqIgo1jI5sohr/QBi+KATbFhxjbF+8E9nnOV0rYUMAt3Q4oHAINB13KVXPMzBeo5mU1hjOUjITgB8QHxRltACATCH/Tu3Vt6+PDhtgBw4MAB27i4uNIXOb9Hjx6VDx48MEtPTze7fft2m/Xr1+ez2WwAgEgkqh82bFijQnFSqZQVHR3NF4lEQj6fL9q3b19bAKioqGD16NHDLyAgQOTv7x+4fft2GwCYNm2aW7t27QL5fL5o0qRJ7gAwd+5c1yVLljgdOnTI+quvvnLav3+/fYcOHfhA4zfaixcvdgoKChLy+XzRnDlzXAH1G2xfX9/AUaNGeQYGBopycnJM9+/fb/vee++VM+dt3rzZNjg4WCgQCEQjRozwYhSHkSNHegYFBQn9/PwCGXmA2rIzb948l4iIiICdO3dqfomeOHHCsk+fPu2Y7ePHj1v17dtXs62NQqFAXFyct7+/fyCfzxd9/vnnjrt27bJJS0vjjR492lcgEIiqqqqoEydOWAqFQhGfzxfFx8d719bWUswYpk6d6hYcHCwMDg4WpqWlmQFqq8qIESM8IyIiAry9vYMOHDhgDQCnT5+2ZCxWc+fOdY2Pj/eOiooKcHd3D16+fLkjM6758+e7+Pj4BHbq1Ml/wIABPkuWLHEydm7oonuftC0GhYWFHDc3t2DmXkyePNmdeXarVq2yN0b+zJkz3QICAkShoaGCvLw8DnNtzJivXLnCCwgIEIWFhQnWrl2rucaqqiqqf//+vnw+X9SvXz/furo6ijl27Ngxq7CwMIFIJBLGxsb6SqVSFnMtc+bMcWXm8e3bt82NGeO3335rHRISIhAKhaJOnTrx8/LyOEqlEl5eXkEFBQUcAFAqlfD09AwqLCzkFBQUcN555512QUFBwqCgIOG5c+faMNc1fPhwr86dO/sPHjzYR19fSUlJ5sw85vP5ort375oBf3xHZs+e7cpYnBwdHUOGDBniDeif//rmpzHXW1ZWxsrIyOBFR0fXAkCfPn2qHRwclADQs2fP6idPnpgCwMWLF9t4eXnJRCJRvbm5OT148ODSI0eOtG1K9uuWxefz68vLyzmMJfdlIErGPxlj3Zr0wSgLPy8HTs9qrDQ0pbBoamE0zhYlceqPTYp3kazyR5hjGOaFrEWE1XAMazcDX/7+JX6Xfgtzzx0w4T6ECYeFjr52OHf/GkApQFGMNAo0zQFd4wu2wgcjPFcgwmo4Pg79L5bF9H8u6Fuf65I+xYOBOcZq+NowQdcmLBNN+wHtBryQMtCUUqMLo3AYSm2r7eJFIBDUfPDBB6WHDh2yqampoRoWA9Uvcv7JkyfbikSi2pSUFHORSFTT3NtpHo+nOnPmTLZYLM64dOlS1oIFC9xVKhWOHTtm5ezsLM/MzBTfu3cvffDgwRVFRUXs77//3ubevXvpWVlZ4hUrVhRqyxo6dKh09OjRT6dMmVJ048aNLO1jx44ds8rOzjZPTU3NyMjIEKekpPB++OEHCwDIzc01HzduXElGRoaYz+fXJycnW3Tu3LkaAG7dumV+5MgR26SkJIlEIhGzWCx669atdgCwdu3a/LS0tAyJRJJ+9epVyxs3bmisNObm5qrk5OTMSZMmaQq9DRgwoDI7O9ucWTzu3LnTbuzYsc/03ZfffvuNV1hYaMJc6/Tp00vGjRtXFhQUVLNnz577DWPB5MmTfQ4dOpSTlZUlVigUWLVqlQMjw8rKSnn37t2MyZMnF8+cOVPjCpWXl2d28+bNzFOnTt2bPXu2V01NDaXbf3Z2tvmlS5eyfv/994zVq1e7ymQy6vLly7xTp07Z3L17V3zmzJmc1NTUNkz7xYsXOzELVO1Pcy5Y+u6TLuvWrbO3trZWpqWlZdy5cyfjm2++cZBIJKZNya2trWVFR0dXZWZmiqOjo6s2bNjgoNtmwoQJ3mvXrn2UkpLS6E3+6tWrHblcriorK0u8ZMmSQrFY3AZQKz4rVqxwuXz5cpZYLM546623apYtW6ZRsuzt7RVisThj/PjxT1euXGmU8tWnT5+qlJQUSUZGhnjIkCGlS5cudWaz2RgyZEjJjh07bAHgxIkTVkKhsNbFxUUxefJkj7lz5xalpaVlHD9+PGfKlCnejKzU1FTe2bNns0+dOvVAX18bNmxwmDZtWpFEIhGnpqZm+Pj41GsfX7duXYFEIhFfvXo1s23btopZs2YVG5r/+uYn0Pw8+PXXX9sEBATUGhiffc+ePaUAkJeXZ+rm5qYZn7u7e31+fn6Tz/yvICs4OLjmwoULFvrkGANxl/ono+v+BKgtEy9cIRvQm2JWH9rpbBuyRUmc+mHQSTnqFZkw5bCwpH8glp6uQr0iCL85XNS4PbFYQLfQSkwLV1sk+ko74UbyIdBQADQLPV37obNzDJ55u8CGZ4qlp9NRrwjC1eQq+FmX6c0sZajqtjHuS0zQNYDn2usqAoZcol40qFwfLZlil0D4u9GhQ4fax48fm23fvt327bffljZ/hpru3bvzWSwWhEJhzdq1a/PPnj1r1B9alUpFzZ492/369esWLBYLxcXFpo8fP+a89dZbtQsXLvSYOnWq27vvviuNiYmpksvlMDMzUw0bNsyrX79+0qFDhxo9vh9//NHq8uXLViKRSAQANTU1LIlEYu7r61vv4uJS37t3b40yJZVKOTY2NqqG8yzT0tJ4oaGhQgCoq6tjOTo6KgDgm2++sd29e7e9QqGgnj59anLnzh3zDh061ALA6NGjn1s0s1gsvP/++yXbt2+3nT59esmtW7csjh07pndBKBAIZHl5eWZjxozxGDBggPS9996r0G1z584dc3d3d1lISIgMAMaOHVuyadMmRwDFADBmzJhSAJg4cWLpokWLNIv9uLi4UjabjeDgYJmHh4csJSXlubfuffv2LedyuTSXy1XY2trKHz9+zLl48aJFbGxsuYWFBQ2A7tOnTznTftmyZUXLli0ravZB6KDvPuly/vx5K4lEwjt58qQNAFRWVrLFYrG5QCCoN3SOiYkJzVjNIiIiqs+fP2+lfbykpIRdWVnJ7tevXxUAjB8/vuTChQvWAPDrr79afPjhh8WA+vvA5/NrAPVb7JycHPOoqCgBAMjlcioiIqKKkTlixIgyAIiKiqphxtocDx48MB00aJD706dPTerr61keHh4yAJg6deqzgQMH+i1ZsqR4586d9owyevXqVat79+5plNmqqip2WVkZCwBiYmKYZ6OX6Ojo6tWrV7s8fvzYdNiwYWXBwcEy3TYqlQpDhgzxmT59elHXrl1rVqxY4aBv/g8dOrRc3/xsbh7k5+eb2NnZPefqd+rUKct9+/bZX7t2TQIANP38ZVAUZfDa/iqyHBwcFC+iwOhClIx/OkxtiBeNz9BbOI/TOMWsof504jV+/iUb9YpMjSvUD2mFqFeooKKB+kofsGzYAEWDpim04VWAzX0IwAZDQ7oCWI9z96+hr2+nhm01m37J1shg3Ku0lQx9aW21FYGE4AS9wzeUDrapNLSnck7hu+zvNHEb2kpAU0qNsbRkil0C4e9ITExM+WeffeZx7ty5zOLiYqP+7jExGcx2WFhYXUZGBk+pVIJxl9LHtm3bbEtKSjh3797NMDMzo93c3IJra2tZISEhslu3bomPHj1qvXDhQrfz589XrF69ujAlJSXj5MmTVgcPHrTZsmWL4/Xr17MMCteCpmnMnj27cP78+Y0sB5mZmaY8Hq9R7AWbzaaZcdM0TcXHx5ds2rQpX7uNRCIx3bhxo1NycnKGg4ODMi4uzruurk7j7WBpaak3nmPq1Kkl/fr18zM3N6cHDBhQZsh33sHBQZmWliY+fvy41ebNmx0PHTpke/jw4Vzda2oKFusP5wvthRBFNTZc6G4DgJmZmaY9m82GQqGgmupv8eLFTocPH34uoLpjx46Vu3fvzjN0nvZ94nA4tFKpBABoW1domqbWrFnzKC4u7jlFyxAcDodmrp/D4UChUDS6SJqm9V43g75jNE2jS5cuFYYsBebm5jTTt25/hpgxY4bnrFmznowcOVJ6+vRpy6VLl7oCgJ+fn9ze3l5x8uRJy9u3b7f57rvv7jNjSEpKytCnTLRp06bJGKIpU6aUdu3atfr48ePWsbGx/M2bN+fqxg989NFHri4uLvWzZs0qaehP7/wHAH3zs7l5wOPxVDKZrJFX0I0bN7jTpk3zOnPmzD1nZ2clAHh6ejayEDx+/NjU1dW12Tik1y2rrq6O4nK5Lx3LRdylCGoMxGdIfj+P375ZAMnv5xu3Z5SF3ouA/uuBXouAsWeA/v9t3gqiHdcBoKOvHUw5LLApwITDQmyQi2YbMm/UPUqAvCwKAIWLhacx4WwCFv94GskPyzA0pCu+HvR/8LMOapR5SlumqUUenrF/wKHUK9j0Sza+vfEII3dcx5pzmRi54zqSH5Y1GxvxMjAyD2cdRr3KsEtUU25QxtCUixeBQFC/Rf3oo48KoqKi9Lo1GENgYKAsJCSkeu7cua4qlfpv7t27d82YmAsGqVTKtre3l5uZmdGnTp2yLCgoMAWA3NxcE0tLS9W0adNKZ8+eXZSSksKTSqWs0tJS9tChQ6Vbt27Ny8jIMDrjT2xsbMXevXvtGR/6Bw8emOTn5+tVoHx8fOoyMjLMACAmJqbi9OnTNkzboqIidlZWlmlZWRmby+WqbG1tlXl5eZyLFy9aGzMOb29vuZOTk3zNmjUuEydO1OsqBahdc5RKJcaOHVu+fPny/Lt37/IAwMLCQimVStmAWpHLz883ZeIt9uzZY9e1a1fNonHPnj22APD111/bhIeHayw1x44ds1EqlUhPTzfLy8szCw0NNRgAq02PHj2qzp49a11TU0NJpVLW+fPn2zLHli1bViSRSMS6n6YUDF08PDxkN2/ebAMA+/fv17zl6tOnj3TLli0OMpmMAoDU1FSziooKFgD4+PjozQjVHPb29koLCwslY3HbvXu3LXOsS5cuVfv27bMFgN9//908KyuL13D91UlJSRbM/a6srGSlpqaavUz/DJWVlWxPT095wxgaLc7Hjx//NCEhwWfgwIGljNthly5dKr744gtN/MO1a9eMTqQgFotNhUKhbNGiRcV9+/YtT0lJaXTugQMHrC9evGi1c+dOzTMzNP8Nzc/m5kFwcHBdbm6u5p7du3fPND4+vt3OnTsfMBY5AOjevXt1bm6uuUQiMa2rq6OOHTtmGxcXVw4AK1ascFixYsVz7m+vWxYA5OTkmIeGhtYCwPTp09327NnT1tjnAxBLxj8X3crdeqpyS34/D6/Tw+EHBeT3t0OCAxC0f/sPGQYqZL8oEV422J/QsZFlIcDZEtfvlzS4PVGgFfcBSgkaNGTKevzv7kUcvsrG/oSOAKDJPGVmkYdhXeV4V9AV+xM64ruMq/j+6Q4cfSCHSkVBIY2EqiICCoVnIyuHmX3LWwN0A8UpUK2iBLSENYRA+DvTrl07+eLFi4v1HTty5Ijd2bNn2zLb165dyzAkZ9++fbnTpk3z8PLyCuJyuaq2bdsqV61a1WjRmZCQUBobG+sXFBQkDAwMrPHx8akDgOTkZO6nn37qzmKxwOFw6M2bNz8sLy9n9+/f349ZbC5fvtzoBezgwYMr0tPTzdu3by8A1LEg+/fvf8DhcJ57I9y3b1/puXPnLIOCgmQRERF1ixYtyu/duzdfpVLBxMSETkxMfNS7d+/qoKCgGn9//0BPT0+ZtttMcwwbNqxk06ZNnKay2+Tm5ppMmDDBW6VSUQCwdOnSxwAwevToZzNnzvSaP3++KikpKWPr1q258fHx7ZRKJUJDQ2vmzZunyZ4kk8mokJAQgUqlog4ePHif2e/n5yeLiooKKCkpMVm3bt1DHo9nlOtI9+7da2JiYqQikSjQzc1NFhISUm1tba009rqb45NPPikaOnSo78GDB+26du2qsVrMmTPnWW5urllwcLCQpmnK1tZW/v333+cUFhZyaJo2ymKgj6+//jo3ISHBm8vlqnr16qXpb968ecXDhg3z4fP5osDAwJrg4OBqAHB1dVVs27Ytd9iwYb719fUUAHz22Wf52ovQF2XhwoUFw4cPb+fk5FQfGRlZ/ejRI80CfPjw4dIZM2awJ02apMkl/9VXX+UlJCR48vl8kVKppDp06FDZqVOnR8b0tXfvXtvDhw/bcTgc2sHBQf6f//ynQPv4unXrnIqLi03CwsKEgNqiuW7dugJ985/H46n0zc/mCA8Pr6usrGSXlZWxbGxsVIsWLXIpLy/nzJw50wtQW4HS0tIyTExMsGbNmkcxMTF8pVKJESNGPIuMjKwDAIlEwu3cufNz37fXLUsmk1G5ublm3bp1qwYAsVjM1U4gYQxNmgtfFYqiYgCsB8AGsIOm6ZU6x60B7APgCbXCs5qm6V0URXkA2APAGWqn/69oml7fXH+RkZF0UpLhwNl/HLqKBLNPO32stmsU055rB9SW4K44HcKC4+BQKihoFn73nYroMSuM6ppxR7LhmaKspl5vtW1jK3EnPyxrUBb+pc7kpGKj5lECqDovzO0bAABYcy4TMH8InucOsFhKmDW4JSUVJWHDrQ1QQQXNVKdNIC8aALCrwar3w75RQ8HmPsTEcxM1sREtEdfAWDLkKjnYFBvv+r2Lge0GEiWA8JeCoqhkmqZb3Px1586d3NDQUINvtgl/Hg8fPjQZPny497Vr1+61hvzRo0d7hoeH18yZM6fVnrebm1twUlJShrYLG6DOLtW/f3/puHHjmo2F0IdUKmVZW1urKisrWdHR0QFbt2592KVLl1ZJfdscBw4csM7JyTFbtGiRXoX4Tefy5cu8OXPmeCQnJ2e+7rG0JJ9//rmjpaWlau7cuS81/3v27On3ww8/5DDuaa9CS8ras2dP2+TkZN769esLAKBLly7+v/7663O/Q+7cuWMfGhrqrU9Gq1kyKIpiA9gEoA+AxwB+pyjqJE3TYq1m0wGIaZoeQFGUA4BMiqL2A1AA+Iim6VsURVkCSKYo6iedcwlNoS/GAlDvU9RBE6itXbmbUUQazguk2JCDBdCAHBzYiHo12aW2YrH0dDpkchVoACwKz1XbTn5YhlH7DkFlmo2N19QLfUOKRoSXDSK8+iOu2F1TwZuq89BkmQLU8mnefYBSgAatsUYwrkRMbQt1Jio5zF1OgAYNE9ZFsLkdW8UaQCwMBMILcv9iG9y/aAnfHpXw7fFCWaAIhvHy8pKPHz/+WWlpKaslamVoExgYKORyuapt27YZbYX5KzFq1Cive/fucWUyGTVs2LCS16VgAOo3/a+r79ZmwYIFzrt373bYtWuX3viPN5n58+c/fZXilL/88kt2S42lJWUpFApq8eLFmqB3fQpGc7SaJYOiqGgA/6Jp+p2G7U8BgKbp/2i1+RSAB9TKhjeAnwDwaZpW6cg6AWAjTdM/NdUnsWRocWWNumYFrVSnqO21UL2f2QcAoACOeeMgb53znvKHIrveBjaiXhC0f7uR9QFAo58ZlyUWRUFF01DRAIv7EBzefahqfBHtHoHZb/MR4WWDxT+exvHCJQClAGgO3nNZimUx/Y26tEOpV54L9ta2dihpRSNrBBN8fezecShpJVgUBZqmoYIKbIqNGeEzDAZ6vwiGMkgRCH9l/jKWjPsX22D/+3yo5CywTFQY+b8somi8mYSEhAjq6+sbxXzu2bPnwavExBBeP+vXr7fbsmVLo1S27du3r9q7d69R7k0vy9GjR60WLlzorr3Pw8ND9tNPP+W0Zr8E43gtlgwAbgC032w8BtBBp81GACcBFACwBDBUj4LhDSAcwA19nVAUNQnAJADw9PzLVLV//eiJsQDwXPrY5zJBeXeFimUCKAGwTODQZRwcGo4nPyzTKBIcFgVQFBRKdQXuuLfcNdmcQNPg8B6BZZkMTttkgFICNAe/PQJ+31GKJf0DISlPaahxQYOGAlLWdey4+6TZBXpKcQpWp85FvbIeqalHEOCsViS0rR360smGOYZhQLsBrVY9m6SRJRBekfsXLaGSs0CrAJWc1WDRIErGG0hqauqfWnGZ8Ocwa9asEiZL05+JdoV1wptFa2aX0he8pGs2eQdACgBXAGEANlIUpcn9TFGUBYCjAGbTNK031RtN01/RNB1J03Skg8NzAfX/XJjsT70W/mGp0N5nIBNUssofg1gTMNYyGoNYE5Cs8tccu36/5I+0sEoacq0UsTSgyeZkZpmHNt47YGJ7ExRLrUiAUoDFu496uQpLTqThdqYtQHMAsGDC4uBq0Q9GZXZqrnhdU5maWrN69osU1SMQCHrw7VEJlolKnQ7bRAXfHpXNn/T3QbtCdWsydOhQr+TkZKOqN7c0n3zyiXNLyJk7d65rYmKiHaCu6vzdd99ZtoTclyEuLs6bcZV5lXubmZlpGhUVFdCyo3t59u/fb71gwQJnANi7d2/b1poz8fHx3ra2tqH+/v6NsmrNnTvX1dHRMYQpfnfo0CFNtrNPP/3U2dPTM8jb2zvo6NGjVs9LbVm0n/Gfzf/93/85M/eAzWZHMD9rV61/GQ4cOGA9Z84c15YapyFa05LxGGpXKAZ3qC0W2owDsJJW+2xlUxT1AIAAwE2KokygVjD20zR9rBXH+fdFX/YnnX26Lj7fZVzFfdcfG9yYCvFdRidEeKndmJi0sHKFCuwGS4ZSqYJJgyUj7i13XL9fgmfs+zj2QIE/6nADFM0CXeMLFqvBlarWC7JHCegWWgkvJxmOZh01KrNTSxSvAwzXu3hZWmpcBMI/Ft8e1Rj5vywSk/FqKBQKNFWV/NChQw9fV9+JiYkuK1eufNKSfa5bt053XfHaaM17+2czcuRIKQApAHz33XdtFQqFtKnsYbrI5XIYqpmizfjx45/NmjWreNy4cT66x6ZMmVK0dOnSRoXwkpOTzY8dO2abmZmZ/vDhQ5M+ffrw33333bSm5t2rIJc3W36iVfniiy+efPHFF08AgMfjhUskkkYWHZVKBZqmm6zdo4+hQ4dKly5d6rZ06dInhmrgtAStacn4HYA/RVE+FEWZAhgGtWuUNo8A9AYAiqKcAAQAuE+pq8Z8DSCDpum1rTjGvyd5N9WxFXk3m2ymWxviUOqVRm5MoBTg8NQxWicu7cCJi5Mwt30O5vYNwIFJ0TgwsSPm9g3QBHSzuQ9hZn8RIidndd0GsGCqojGkogrfFD/Ff0KdsfTdII3Fg63wwbTwSRjYbqCmzgObYqOgqsCgNYMJpm5JK0RL8FcdF4HwRuHboxpv/+vJP13BqKioYMXHx3sHBQUJhUKhiKnFkZmZaRoREREgEomEIpFI+NNPP7UB1FaQDh068AcMGOATEBAQePr0acuoqKiAmJgYXx8fn8CBAwf6MLU9oqKiAi5fvswD1IuWmTNnugUEBIhCQ0MFeXl5HABIT083Cw0NFQQFBQlnz57tyuPxwg2NVbdvAHj77bfbBQYGCv38/AJXr15tDwDTpk1zk8lkLIFAIBo4cKAPAGzevNk2ODhYKBAIRCNGjPBSKBSGummEhYWFkikQpv2W2c3NLXjOnDmuIpFIyOfzRbdv3zZXKpVwc3MLfvbsmWYV5unpGZSXl8fZuXOnjb+/f2BAQIAoMjIyAFArSpMmTXLn8/kiPp8v+ve//+0IAPPmzXMJCgoS+vv7Bw4fPtyLuZ/avMq9ZbPZdNu2bZu8AZmZmaY+Pj6BQ4cO9fL39w8cOHCgz3fffWf51ltvCby8vIJ++eUXHgD88ssvvPDwcIFQKBSFh4cL7ty5YwYA//rXv5zi4+O9AeDmzZtcf3//wMrKSr3rwMTERLvRo0d7/vTTT23Onz/fdtGiRe4CgUCUnp5ulp6ebta1a1f/wMBAYURERMDt27fNmWeRkJDg3qFDB/60adPc9cnVJTY2tsrBwcG4Bw/gyJEjbQcPHlzK5XJpgUBQ7+XlJbt48WIbQ+1v3bplHhwcLNS+h3w+XwQAV65c4bVv3z4gMDBQ2KVLF/+HDx+aAOrnOGPGDLf27dsHLF++3AkAfvrpJ8uIiIgAb2/voAMHDlgD6gKLQ4YM8ebz+SKhUCg6deqUJQAkJSWZM/Oaz+eL7t69awa8/HzXJTMz09TX1zdw1KhRnoGBgaKcnBxT7e/orl27bOLi4rwBoKCggPPOO++0CwoKEgYFBQnPnTvXBlAXtuzUqVOltoWoNWg1JYOmaQWAGQDOAsgA8D+aptMpippCUdSUhmbLAHSiKOougJ8B/B9N088AdAbwAYBeFEWlNHz+X2uN9W8Fk1Xqwr/V/+fdNKh0aLv41Cvr8fnFb3C3MBeg2QBYMGOb4l1BV5y4tAPL7q/DCToDW0u2IqdiFdjch4jwssH0nn6I8LJppLB8+fuX+Lj9x5hhE4avi55hSUkpwutqMcTuAUZ08MTn8Rbo0SENn8dbIMLLRrNAj/OPAwAczTrapNvUqxavay3+quMiEAhvFgsWLHDp2bNnRVpaWsaVK1cyFy1a5F5RUcFydXVVXLlyJUssFmccOnTo/pw5czSBiKmpqW1WrVqVn5OTkw4AGRkZ3E2bNuVlZ2enP3r0yOynn36y0O2ntraWFR0dXZWZmSmOjo6u2rBhgwMAzJgxw2PatGnFaWlpGcZUEtbte//+/bnp6ekZKSkp4m3btjk9efKEvXnz5nwzMzOVRCIRnzx58sGtW7fMjxw5YpuUlCSRSCRiFotFb9261Q4A+vXr58u4hWh/Nm7caAcAS5cuLZo4caLelLX29vYKsVicMX78+KcrV650YrPZ6Nu3b/n+/fvbAsCFCxfauLu713t4eChWrlzpcu7cuazMzEzxjz/+mA0Aa9ascXj48KFZenq6OCsrS5yQkFACAPPnzy9OS0vLuHfvXnptbS3r4MGDTS7OXvTe+vn5yc+dO9dsIHNeXp75Rx99VCyRSNJzcnLM9+/fb5eUlCT597///fjf//63CwCEhobW3bx5U5KRkSH+7LPP8j/++GN3AFi8eHHRgwcPzPbs2dN2/Pjx3ps2bcpt7i12nz59qt9+++3y5cuXP5ZIJOLAwEBZQkKC1+bNmx+lp6dnrFq16vHUqVM18zAnJ8f86tWrWdu3b3986tQpS33PMTw8XNDcdQLA119/7cjn80Xx8fHeT58+ZQNAfn6+qYeHRz3TxtXVtT4vL8/UkIy33nqrTi6XU2Kx2BRQF3QcNGhQmUwmoz788EPPEydO5KSnp2eMGTPm2bx589yY88rLy9m///575ueff17UcN/Nbt68mXnq1Kl7s2fP9qqpqaGYIoJZWVnib7/99v6kSZO8a2pqqA0bNjhMmzatSCKRiFNTUzN8fHzqX2W+6yM3N9d83LhxJRkZGWI+n19vqN3kyZM95s6dW5SWlpZx/PjxnClTpngzxyIjI6uvXLny3O+FlqRVi/HRNP09gO919m3V+rkAQF895/0K/TEdhObQqtxNK+uR9v1WBBafAUslb1QTI/lhGR7mO4NFcUDTCtBggWWZDBalBGgWRBZ98GnXUQhzDMOxH/4NOQWoKAoq0Pip/BqunLuleWN/KPUKdou/0qSJlavkkNZLkRA+HUg9C1BspHB5SEIFrDMPY3Xql88Fboc5hiGpKAkKlaJFC+IRCATCm8bFixetzp492zYxMdEZUBfFys7ONvXy8pJPmDDBSywWc1ksFh4+fKgpdBYSElItEAg0i43g4ODqdu3ayQEgMDCwJicn57mFmImJCT1s2DApAERERFSfP3/eCgBu375tce7cuWwASEhIKPnXv/7V5Ftp3b6/+OILpzNnzrQFgCdPnpikp6ebOzs7N7JO/fjjj5ZpaWm80NBQIQDU1dWxHB0dFQBw5syZ+3hJRowYUQYAUVFRNSdPnrRp2Fe6dOlS11mzZpXs37/fNi4urhQAIiMjq0aOHOkdFxdXNnLkyDIAuHDhgtWUKVOeMq4+Tk5OSgD44YcfLNeuXetcV1fHKi8v54hEolo0uBPpo6XurS5ubm4yJksXn8+v7dWrVwWLxcJbb71Vs3z5clcAaKgi75Obm2tOURQtl8spAGCz2dizZ8+DyMjIwJEjRz7t27fvC1sMpVIp6/bt2xbx8fHtmH1MIT8AGDx4cBnjujRgwIDKAQMGvFTA9pw5c4q//PLLAoqiMHv2bLdp06Z5HD58OFdfRlSKoppMkzpo0KDSffv22a5YseLJ8ePHbQ4dOnQ/NTXV7N69e9xevXrxAbXbkYODg0bpGz58eKm2jLi4uFI2m43g4GCZh4eHLCUlxfzatWsWM2fOLAbURflcXV3r7969ax4dHV29evVql8ePH5sOGzasLDg4WNbS893FxaW+d+/ezT6/q1evWt27d09TCb2qqkpTONDZ2Vnx5MkTgwpaS0Aqfv/daMgqRSvrUadi405eOYTserAolaYmRrLKHyN3XIdMToHiToAJ7z5YpuVgW99syPakgsjBS7PAj/B+B9/fz8AftasBuUqOUzmnsDPlCC7kn1HHcABgUaw/YhIcw4AxJ5EiOYqJT35C/f3vwHpwEipapVeRIHENBAKBANA0jSNHjmSHhoY2qrzcEAwrP3r06AOVSgUulxvBHOPxeI3eSJuZmWkWXmw2GwqF4rkXdxwOh2axWMzPetsYg3bfp0+ftrx06ZJlUlKSxNLSUhUVFRVQW1v7nNcETdNUfHx8yaZNm/J1j/Xr1883JyfnuUDjGTNmFM2YMaPJ7EZMETIOh0Mz19O7d+/qCRMmmBUUFHB+/PHHtv/+978LAODbb799dOHChTYnT560DgsLC0xJSUmnafq5RWtNTQ310Ucfed24cUPs5+cnnzt3rmtdXV2TniAtdW91MTU11YyNxWJprpfNZkOpVFIA8H//939u3bt3r/zpp59yMjMzTXv16qUJKM/IyDDn8XiqJ0+eNB8woQelUglLS0uFbmwAg4WFhWYunDp1ynL+/Pkeum24XK7q9u3bTWYg8/Dw0PgSzZgx42n//v39AcDd3b2R5aKgoMDU3d29SWvbBx98UBYfH+87bNiwMoqiEBwcLLt58ybXz8+vNiUlRe84dC08ai/+xtuGSkBMmTKltGvXrtXHjx+3jo2N5W/evDm3pee77vdde3y1tbWaDZqmkZSUlGFhYfHcYGtraylzc/NWi8cAWjcmg/A6aMggdd17CkbJF+CYsivk4EAFNsA2hcQ8FOvOZ6G+ISOUqtYLspKeUJS/BTZlAkrLTYrh3e4JWOw7G10pb5iwTDSxE99lf4dfCk82xHCo27qZhTSOSfCIQpKzP+pVSqiggopWgUWxwKbYzykSJK6BQCAQgJ49e1asWbPGifH7v3r1KhcApFIp28XFRc5ms7F582Y7pVLZpJyXJSwsrGr37t02ALBz507bFzm3vLycbW1trbS0tFTdvn3b/M6dOxp/eQ6HQ8tkMgoAYmJiKk6fPm2Tn5/PAYCioiJ2VlaWKaB+syuRSMS6n+YUDEOwWCzExsaWT5s2zcPPz6/W2dlZCajjI3r16lW9bt26AhsbG8X9+/dN33777YqtW7c6MAG/RUVF7JqaGhYAODs7K6RSKevUqVMvnWnImHv74MEDk+joaP7L9lFRUcF2d3evB4Bt27bZM/tLSkrY8+bN87hw4YKktLSUY2zGJAsLC2VFRQULAGxtbVXu7u71O3futAHUFoDffvuNq++8AQMGVOp7js0pGIC6Sj3z88GDB9sGBATUAkBcXFz5sWPHbGtraymJRGKam5tr3qOHOobrvffe82biUrQJDAyUsVgsLFmyxPW9994rBYCQkJC60tJSzvnz59sAamthUlKSwQxax44ds1EqlUhPTzfLy8szCw0NrevSpUvVvn37bAEgNTXVrLCw0DQkJKROLBabCoVC2aJFi4r79u1bnpKSwm3t+W5nZye/deuWuVKpxIkTJzTPtUuXLhWMWxcAXLt2TfOsMjMzzQMDA1u1dg2xZPxdyLupdpXy7ooUM1Oc83GB+LEJ6qs8ME61CGuiKlHp3BGDTsohkz8DxX0IU959KGp8gTovsBU+WBC+DpVUZqMaE5rsU8JIbOqeoNkuqCrA0ayjYDJI0TQA2gRjRZOeUw50LRQft/8Y0nqp3poYLZ31iUAgEN40Vq5cWTBp0iRPgUAgommacnd3l/3yyy/Zs2fPLo6Li2v33Xff2XTp0qWSCX5uaTZs2JA3cuRIn8TEROe+ffuWW1hYGK3NxMXFSb/66isHPp8vateuXV1oaKjGpWPkyJFPhUKhKCgoqObkyZMPFi1alN+7d2++SqWCiYkJnZiY+Kgp//JXYeTIkaXdu3cXJiYm5jL75syZ456bm2tG0zTVpUuXio4dO9a2b9++Nisry0wgEARyOBx6zJgxTxcsWPB05MiRT0UiUaC7u3u99jW9KMbc27y8PBM2m/3SlZL/7//+70lCQoJPYmKic9euXTXp/6dMmeIxYcKEpyEhIbJvvvkmt1evXgF9+/atdHNzazICeeTIkaVTp0713rp1q9ORI0dyDhw4cH/ixIleX3zxhYtCoaDee++90ujo6JdarA4YMMDn+vXrlmVlZRwnJ6eQTz75pGDOnDnPZs2a5S4Wi7mA2nqxa9euhwAQGRlZN2jQoFI+nx/IZrOxdu3ah4x7VkZGBs/Dw0OvVWPw4MGly5Ytc//iiy/yAbXF6+DBgzkffvihZ2VlJVupVFJTp04tioyM1JtBy8/PTxYVFRVQUlJism7duoc8Ho/++OOPiz/44AMvPp8vYrPZ2LZtWy6Xy6X37t1re/jwYTsOh0M7ODjI//Of/xQ4OTkpW3O+f/755/nvvvuun4uLi1wgENRWV1ezAOCrr77KS0hI8OTz+SKlUkl16NChslOnTo8A4PLly5bM/WgtWq3i9+vgb1/xW0uRaJSalgn2VtYjhcvDBGdHyGkl2BQH/R0/xyBhZ0R42WDTL9lYcy4TMH8InucOUJQCHJZJozbaNFVgjjkmV8lBgQV+m14Y7P+upgK3LqQaNoHw1+QvU/Gb8JehsrKS1aZNGxWLxcJXX31lc+jQIduff/6ZVFduAYy5tytWrHDw8vKqb0gjSzCC0tJS1siRI71/+OGHl47n+SeRl5fHef/9931/++23rFeV9boqfhNeBiMUCe0A7pS0b5F0axsiOTTCFEr8bsqCXCUHTQFyVT2y64/hYkEJNt9+goC2YTDlsEDz7qtjKCgaNJTwcnvynIIB6C8wp11Fe3vf7UYrDsRCQSAQCG8GV69e5c2aNcuTpmlYWVkpd+/enfu6x/R3wZh7u2DBgqevYWhvNLa2tiqiYBjP/fv3TdesWZPX2v0QJeOvhK4iEbMSqC1RKxxaWaOYAO4UaTYSfl8BOQWYOtlje9EzhNcpAZoNGmoLbFrZ77hb+jsACjfKDmF87/+gsq4Hvn96EUpa0WSAdXOB2ERxIBAIhL8fMTExVZmZmY0Ce2/evMkdPXp0o4JppqamqtTU1Gb96wl/oO/evk7Wr19vt2XLFiftfe3bt6/au3fvo9c1JkLr071795o/ox+iZPyV0FYkFDLg+4/UwQ6MwsE2/UMB8e6Kn39bp0ktKwfwqwMfb4d8Bvm5bKDtObDbZENTdZuiQUOBpGc/oW+AAJ/4/J/BuAiGF7VWEAgEAuHvSVRUVK2hjEKEN5dZs2aVzJo166UC6gmE5iBKxl+JhvSzUNYDFAXQKvVHIQMyTjS2bADwLlfAxISGAgCHBuTWQyBo/zb2OUbguwx3fP/0X5Cr1IlnaZoCaBbu1VxA5q2fnouxMASxVhAIBAKBQCAQXhSiZPyVaEg/i9wrANcO+PETtYIBFXD/IvDwN/VxAPhmIAYrZfA0McM3ZgI8quuEbqNGAwAivGwQ4dUfccXuSCpKgrTKBOKiJ2jDq8ClwtOk2B2BQCAQCAQCoVUhSsZfDY+oPwK+nUTAxf+oFQxahRQOkHR7EyK5zghT1oOiVYiU10Ph1gmmPeY/F7yta4VIKU7Bb8XnSLE7AoFAIBAIBEKrQpSMvzIeUUCPT4GHvyGFA0x0skd92W2YSjnYzuUhrLYGFNsU0b0GIcXsIXbcPUpiLAgEAoFAIBAIrx1S8fuvToMLVZLoHdSzWFCBhpxW4VT4IOwIH4CUQWuRYmaKiecmYsOtDZh4biJSilMMigtzDENCcAJRMAgEwt8eiqIiBg0apMmIJJfLYWNjE9qzZ08/AEhMTLQbPXq0p+55bm5uwXw+XxQQECDq3Lmz/6NHjzgAIJVKWSNGjPDy8PAI8vPzC4yMjAy4cOFCGwDg8XjhLTXuL7/80mHjxo12AHD79m1zgUAgEgqFovT0dLPw8HDBi8pTqVTo2LEjv7S0tEX+5sfFxXkbWy26ORITE+1yc3NNmm/5Yly+fJk3duxYj5aWCwBHjx61EggEIoFAIOLxeOHe3t5BAoFA9N5773m3Rn+A4fmlPVe0yczMNPX39w/Ud05UVFTA5cuXn6uM/bIY+h69Ll7mO/IqXL16lTt06FAvANiyZYstn88X8fl8UXh4uEC7GvqRI0esvL29gzw9PYMWLFjg3Jzc1y1r0qRJ7idPnrR8kXuhC7FkvAl4RCHSzBSmDcXv2BQb3z35DQqVAqYpYgxoN8BgPQsCgUD4p8LlclWZmZncqqoqysLCgj5+/LiVk5OT3orAuly6dCnLxcVFMWPGDLclS5a47N69O2/kyJHeXl5estzc3DQ2mw2xWGyamprKbV7ai/Hxxx9r6iQcPny4bWxsbPl///vfAgC4ffu20SljVSoVaJrG4cOHrQMDA2ttbW1fuUK4QtFkcegXZt++ffZhYWG13t7eRj0XY+nWrVtNt27dWiVNZ1xcXEVcXJwYUC/YV69enafbl0KhAFOJujXRnit/V+RyOUxMjNdDX+Q70hIsX77cZcmSJYWAujL41atXMx0cHJT/+9//rCZPnuyVmpoqUSgUmDNnjufZs2ezfH195aGhocK4uLjyiIgIvRXG/wqy5s2bVzxu3DivgQMHVr7svSGWjDcExtVpRvgMvOv3LhQqhUapoECBwzIBBRbYFIfEWhAIBEIDvXv3lh4+fLgtABw4cMA2Li6u9EXO79GjR+WDBw/M0tPTzW7fvt1m/fr1+Ww2GwAgEonqhw0b1qgqs1QqZUVHR/NFIpGQz+eL9u3b1xYAKioqWD169PALCAgQ+fv7B27fvt0GAKZNm+bWrl27QD6fL5o0aZI7AMydO9d1yZIlTocOHbL+6quvnPbv32/foUMHPtD4jfbixYudgoKChHw+XzRnzhxXQP0G29fXN3DUqFGegYGBopycHNP9+/fbvvfee+XMeZs3b7YNDg4WCgQC0YgRI7wYxWHkyJGeQUFBQj8/v0BGHqC27MybN88lIiIiYOfOnRoLxokTJyz79OnTjtk+fvy4Vd++fTXb2igUCsTFxXn7+/sH8vl80eeff+64a9cum7S0NN7o0aN9BQKBqKqqijpx4oSlUCgU8fl8UXx8vHdtbS3FjGHq1KluwcHBwuDgYGFaWpoZoLaqjBgxwjMiIiLA29s76MCBA9YAcPr0aUvGYjV37lzX+Ph476ioqAB3d/fg5cuXOzLjmj9/vouPj09gp06d/AcMGOCzZMkSJ33jNwbd+6RtMSgsLOS4ubkFM/di8uTJ7syzW7Vqlb0x8mfOnOkWEBAgCg0NFeTl5XGYa2PGfOXKFV5AQIAoLCxMsHbtWs01VlVVUf379/fl8/mifv36+dbV1VHMsWPHjlmFhYUJRCKRMDY21lcqlbKYa5kzZ44rM49v375tbswYv/32W+uQkBCBUCgUderUiZ+Xl8dRKpXw8vIKKigo4ACAUqmEp6dnUGFhIaegoIDzzjvvtAsKChIGBQUJz50714a5ruHDh3t17tzZf/DgwT76+kpKSjJn5jGfzxfdvXvXDPjjOzJ79mxXxuLk6OgYMmTIEG9A//zXNz+Nud6ysjJWRkYGLzo6uhYA+vTpU+3g4KAEgJ49e1Y/efLEFAAuXrzYxsvLSyYSierNzc3pwYMHlx45cqRtU7Jftyw+n19fXl7OYSy5LwNRMl4HeTeBK2vU/zdBSnEKdtzdoXF/YlydBrYbCFO2KdgUGyYsE/Db9EDNowTIivug5lEClLVef8JFEAgEwl+fDz74oPTQoUM2NTU1VMNioPpFzj958mRbkUhUm5KSYi4SiWqaezvN4/FUZ86cyRaLxRmXLl3KWrBggbtKpcKxY8esnJ2d5ZmZmeJ79+6lDx48uKKoqIj9/fff29y7dy89KytLvGLFikJtWUOHDpWOHj366ZQpU4pu3LiRpX3s2LFjVtnZ2eapqakZGRkZ4pSUFN4PP/xgAQC5ubnm48aNK8nIyBDz+fz65ORki86dO1cDwK1bt8yPHDlim5SUJJFIJGIWi0Vv3brVDgDWrl2bn5aWliGRSNKvXr1qeePGDY2VxtzcXJWcnJw5adKkMmbfgAEDKrOzs82ZxePOnTvtxo4d+0zfffntt994hYWFJsy1Tp8+vWTcuHFlQUFBNXv27LnfMBZMnjzZ59ChQzlZWVlihUKBVatWOTAyrKyslHfv3s2YPHly8cyZMzWuUHl5eWY3b97MPHXq1L3Zs2d71dTUULr9Z2dnm1+6dCnr999/z1i9erWrTCajLl++zDt16pTN3bt3xWfOnMlJTU1tw7RfvHixE7NA1f4054Kl7z7psm7dOntra2tlWlpaxp07dzK++eYbB4lEYtqU3NraWlZ0dHRVZmamODo6umrDhg0Oum0mTJjgvXbt2kcpKSmN3uSvXr3akcvlqrKyssRLliwpFIvFbQC14rNixQqXy5cvZ4nF4oy33nqrZtmyZRoly97eXiEWizPGjx//dOXKlUYpX3369KlKSUmRZGRkiIcMGVK6dOlSZzabjSFDhpTs2LHDFgBOnDhhJRQKa11cXBSTJ0/2mDt3blFaWlrG8ePHc6ZMmeLNyEpNTeWdPXs2+9SpUw/09bVhwwaHadOmFUkkEnFqamqGj49PvfbxdevWFUgkEvHVq1cz27Ztq5g1a1axofmvb34Czc+DX3/9tU1AQECtgfHZ9+zZUwoAeXl5pm5ubprxubu71+fn5zf5zP8KsoKDg2suXLhgYaxsXYi71J+NdlVvFhsIHwWEDlcfy72iroHhEYWU4hRMOJugcY96z38QAtr0xLMSF3T09WoUwH013QKyqgqoaA+wKeD6/ZLnMk0RCATCP5EOHTrUPn782Gz79u22b7/9trT5M9R0796dz2KxIBQKa9auXZt/9uxZo/7QqlQqavbs2e7Xr1+3YLFYKC4uNn38+DHnrbfeql24cKHH1KlT3d59911pTExMlVwuh5mZmWrYsGFe/fr1kw4dOtTo8f34449Wly9fthKJRCIAqKmpYUkkEnNfX996FxeX+t69e2uUKalUyrGxsVE1nGeZlpbGCw0NFQJAXV0dy9HRUQEA33zzje3u3bvtFQoF9fTpU5M7d+6Yd+jQoRYARo8e/dyimcVi4f333y/Zvn277fTp00tu3bplcezYMb0LQoFAIMvLyzMbM2aMx4ABA6TvvfdehW6bO3fumLu7u8tCQkJkADB27NiSTZs2OQIoBoAxY8aUAsDEiRNLFy1apFnsx8XFlbLZbAQHB8s8PDxkKSkpz71179u3bzmXy6W5XK7C1tZW/vjxY87FixctYmNjyy0sLGgAdJ8+fcqZ9suWLStatmxZUbMPQgd990mX8+fPW0kkEt7JkydtAKCyspItFovNBQJBvaFzTExMaMZqFhERUX3+/Hkr7eMlJSXsyspKdr9+/aoAYPz48SUXLlywBoBff/3V4sMPPywG1N8HPp9fA6jfYufk5JhHRUUJAEAul1MRERFVjMwRI0aUAUBUVFQNM9bmePDggemgQYPcnz59alJfX8/y8PCQAcDUqVOfDRw40G/JkiXFO3futGeU0atXr1rdu3dPo8xWVVWxy8rKWAAQExPDPBu9REdHV69evdrl8ePHpsOGDSsLDg6W6bZRqVQYMmSIz/Tp04u6du1as2LFCgd983/o0KHl+uZnc/MgPz/fxM7O7jlXv1OnTlnu27fP/tq1axIAoOnnL4OiKIPX9leR5eDgoHgRBUYXomT82WhX9VYqgaRdwO19AChApUAKl4ek6AlIrVVApqwHRdGQq1Q4nHUYtOo71D1KAOeCD/YndERCcBgAQFlbBlMOC3KFCiYcFjr6PhcDRiAQCP9YYmJiyj/77DOPc+fOZRYXFxv1d4+JyWC2w8LC6jIyMnhKpRKMu5Q+tm3bZltSUsK5e/duhpmZGe3m5hZcW1vLCgkJkd26dUt89OhR64ULF7qdP3++YvXq1YUpKSkZJ0+etDp48KDNli1bHK9fv55lULgWNE1j9uzZhfPnz29kOcjMzDTl8XiNYi/YbDbNjJumaSo+Pr5k06ZN+dptJBKJ6caNG52Sk5MzHBwclHFxcd51dXUabwdLS0u98RxTp04t6devn5+5uTk9YMCAMkO+8w4ODsq0tDTx8ePHrTZv3ux46NAh28OHD+fqXlNTsFh/OF9oL4QoqrHhQncbAMzMzDTt2Ww2FAoF1VR/ixcvdjp8+PBzf0w7duxYuXv37jxD52nfJw6HQyuVSgCAtnWFpmlqzZo1j+Li4p5TtAzB4XBo5vo5HA4UCkWji6RpWu91M+g7RtM0unTpUmHIUmBubk4zfev2Z4gZM2Z4zpo168nIkSOlp0+ftly6dKkrAPj5+cnt7e0VJ0+etLx9+3ab77777j4zhqSkpAx9ykSbNm2ajCGaMmVKadeuXauPHz9uHRsby9+8eXOubvzARx995Ori4lLPVDU3NP8BQN/8bG4e8Hg8lUwma+QVdOPGDe60adO8zpw5c8/Z2VkJAJ6eno0sBI8fPzZ1dXVtNg7pdcuqq6ujuFzuS8dyEXepPxvvrkjh8rDD2gopZqYAaEApB5T1SDFlY6KDNTbcP4ZLhd8DNBuNfgdSCrB49yFXqHD9folmd4SXDfYndMTcvgHYn9CRWDEIBAJBi6lTpz776KOPCqKiovS6NRhDYGCgLCQkpHru3LmuKpX6b+7du3fNmJgLBqlUyra3t5ebmZnRp06dsiwoKDAFgNzcXBNLS0vVtGnTSmfPnl2UkpLCk0qlrNLSUvbQoUOlW7duzcvIyDA6409sbGzF3r177Rkf+gcPHpjk5+frVaB8fHzqMjIyzAAgJiam4vTp0zZM26KiInZWVpZpWVkZm8vlqmxtbZV5eXmcixcvWhszDm9vb7mTk5N8zZo1LhMnTtTrKgWoXXOUSiXGjh1bvnz58vy7d+/yAMDCwkIplUrZgFqRy8/PN2XiLfbs2WPXtWtXzaJxz549tgDw9ddf24SHh2ssNceOHbNRKpVIT083y8vLMwsNDTUYAKtNjx49qs6ePWtdU1NDSaVS1vnz59syx5YtW1YkkUjEup+mFAxdPDw8ZDdv3mwDAPv379f8Ye7Tp490y5YtDjKZjAKA1NRUs4qKChYA+Pj46M0I1Rz29vZKCwsLJWNx2717ty1zrEuXLlX79u2zBYDff//dPCsri9dw/dVJSUkWzP2urKxkpaammr1M/wyVlZVsT09PecMYGi3Ox48f/zQhIcFn4MCBpYzbYZcuXSq++OILTfzDtWvXjE6kIBaLTYVCoWzRokXFffv2LU9JSWl07oEDB6wvXrxotXPnTs0zMzT/Dc3P5uZBcHBwXW5uruae3bt3zzQ+Pr7dzp07HzAWOQDo3r17dW5urrlEIjGtq6ujjh07ZhsXF1cOACtWrHBYsWLFc+5vr1sWAOTk5JiHhobWAsD06dPd9uzZ09bY5wMQS8afToqZKSa6OKJeJYepisb2omcIkysBUEjimqOeoqACwIIKqsr2UCkBTttk0FACNAeqGl+91gp1lW+iXBAIBIIu7dq1ky9evLhY37EjR47YnT17ti2zfe3atQxDcvbt25c7bdo0Dy8vryAul6tq27atctWqVY0WnQkJCaWxsbF+QUFBwsDAwBofH586AEhOTuZ++umn7iwWCxwOh968efPD8vJydv/+/f2Yxeby5cuNXsAOHjy4Ij093bx9+/YCQB0Lsn///gccDue5N8J9+/aVnjt3zjIoKEgWERFRt2jRovzevXvzVSoVTExM6MTExEe9e/euDgoKqvH39w/09PSUabvNNMewYcNKNm3axGkqu01ubq7JhAkTvFUqFQUAS5cufQwAo0ePfjZz5kyv+fPnq5KSkjK2bt2aGx8f306pVCI0NLRm3rx5muxJMpmMCgkJEahUKurgwYP3mf1+fn6yqKiogJKSEpN169Y95PF4RrmOdO/evSYmJkYqEokC3dzcZCEhIdXW1tZKY6+7OT755JOioUOH+h48eNCua9euGqvFnDlznuXm5poFBwcLaZqmbG1t5d9//31OYWEhh6ZpoywG+vj6669zExISvLlcrqpXr16a/ubNm1c8bNgwHz6fLwoMDKwJDg6uBgBXV1fFtm3bcocNG+ZbX19PAcBnn32Wr70IfVEWLlxYMHz48HZOTk71kZGR1Y8ePdIswIcPHy6dMWMGe9KkSZq3pF999VVeQkKCJ5/PFymVSqpDhw6VnTp1emRMX3v37rU9fPiwHYfDoR0cHOT/+c9/CrSPr1u3zqm4uNgkLCxMCKgtmuvWrSvQN/95PJ5K3/xsjvDw8LrKykp2WVkZy8bGRrVo0SKX8vJyzsyZM70AtRUoLS0tw8TEBGvWrHkUExPDVyqVGDFixLPIyMg6AJBIJNzOnTs/93173bJkMhmVm5tr1q1bt2oAEIvFXO0EEsbQpLnwTSMyMpJOSkp63cMwTN5N7Li9CRvKbkMFGmywMMMmDAnh0wEAKZKjmPjkJ8hpFUxYJpgXshanfzfFb4+TweLdh6rGF9HuEZj9Np8oFAQCoUWgKCqZpukWT0l3586d3NDQUINvtgl/Hg8fPjQZPny497Vr1+61hvzRo0d7hoeH18yZM6fVnrebm1twUlJShrYLG6DOLtW/f3/puHHjmo2F0IdUKmVZW1urKisrWdHR0QFbt2592KVLl1ZJfdscBw4csM7JyTFbtGiRXoX4Tefy5cu8OXPmeCQnJ2e+7rG0JJ9//rmjpaWlau7cuS81/3v27On3ww8/5DDuaa9CS8ras2dP2+TkZN769esLAKBLly7+v/7663O/Q+7cuWMfGhrqrU8GsWT8WTQEfEdyAFMne8hZbJiwTRHZcQ7QUNMizCMK24uHN6rI7Wddht93lEJe6gUTDosoGAQC4W/L9YLrba4XXrfs6NKxsqNrxxfKAkUwjJeXl3z8+PHPSktLWS1RK0ObwMBAIZfLVW3bts1oK8xfiVGjRnndu3ePK5PJqGHDhpW8LgUDUL/pf119tzYLFixw3r17t8OuXbv0xn+8ycyfP//pqxSn/OWXX7JbaiwtKUuhUFCLFy/WBL3rUzCag1gy/iyurAEu/BuglUgx5yJJ9A4iO87RFM1LKU5ppFxok/ywDNfvl6Cjrx1RMAgEQovyV7FkXC+43mb6z9P5CpWCxWFxVJt6b8oiisabSUhIiKC+vr5RzOeePXsevEpMDOH1s379erstW7Y0SmXbvn37qr179xrl3vSyHD161GrhwoXu2vs8PDxkP/30U05r9kswDmLJ+Cvg3RVgmwLKeoQpgLDw6UgBsOPuDlibWuPL379EvbIepmxTbO+7vZGioS/egigerU9Tih+BQGhZrhdet1SoFCwVVFCoFKzrhdctiZLxZpKamvqnVlwm/DnMmjWrhMnS9GeiXWGd8GZBskv9WXhEAWNOAr0WAmNOqgPAz03EhlsbsOLGCtQr6zUVvJOKmrbGJD8sw8gd17HmXCZG7riO5Icv5YpKaIKU4hTN85l4bqKmICKBQGgdOrp0rOSwOCoWWOCwOKqOLh0rmz/r74N2herWZOjQoV7JyclGVW9uaT755BPnlpAzd+5c18TExBfK1a5dKf1FWbp0qWNlZaVmvdS9e3e/Z8+eGc5j3ARM1e+/ApcvX+YxReVOnz5t+dNPP7Vp7pyXYebMmW7Ozs4hus8gMTHRzsbGJpQpcLd27VpN5fMNGzbYeXl5BXl5eQVt2LCh1fPya1dO/7NZv369HXMPTExM3uLz+SKBQCCaNm2a26vIvXnzJjcuLs67hYb5UhBLxp+JR5T6AyDp7g7IlPWgoQJN02BTbFCgYMIyQaRT054L1++XoF6hgoqGJp0tsWa0LElFSc8pfsSaQSC0Hh1dO1Zv6r0pi8RkvBoKhQJNVSU/dOjQw9fVd2JiosvKlSuftFb/rcW2bducJk6cWMrUwLh06VKL+b2/Trp161bTrVu3GgC4cOGCpYWFhbJPnz5Gf+/kcjkM1UXRZtCgQeXz5s0rFgqFQbrHBgwYULZnz55G7lZFRUXsL774wjU5OVnMYrEQHh4uGjZsWLmDg0OLZf7SRi5vtsREq6JtIXJzcwvWrdEDNP/d0kdUVFRtYWGh6b1790z9/f0NFnpsTYgl4zVhSQdApWKDpimoVByM8p+NGeEznnOV0kdHXzuYclhgUyDF91qJSKdImLJNwabYRil+BALh1eno2rF6dsTsJ/90BaOiooIVHx/vHRQUJBQKhSKmFkdmZqZpREREgEgkEopEIiHz5vn06dOWHTp04A8YMMAnICAg8PTp05ZRUVEBMTExvj4+PoEDBw70YWp7REVFBVy+fJkHqN/uz5w50y0gIEAUGhoqyMvL4wBAenq6WWhoqCAoKEg4e/Zs16asALp9A8Dbb7/dLjAwUOjn5xe4evVqewCYNm2am0wmYwkEAtHAgQN9AGDz5s22wcHBQoFAIBoxYoSXQqEw1E0jLCwslEyBsDVr1tgHBQUJAwICRO+88047xuIgkUhMw8LCBEFBQcJZs2a5MudKpVJWdHQ0XyQSCfl8fqN76+PjEzh48GBvPp8viomJ8a2srGQtX77csbi42KR79+78Dh068AH1QrCwsJAzdepUt5UrV2pqEsydO9f1s88+cwLUxfyCgoKEfD5fNGfOHE3/NjY2zV4kj8cLnzp1qltgYKCwU6dO/F9++YUXFRUV4O7uHrx//35rZrz65sKePXvadurUia9SqfDw4UMTb2/voEePHuldnTLWs8zMTNM9e/Y4bN261UkgEIh+/PFHi4KCAs4777zTLigoSBgUFCQ8d+5cG+Yahw8f7tW5c2f/wYMH+xjzvHr37l3t5eVl9Er+u+++s+7WrVuFk5OT0sHBQdmtW7eKY8eOGazbUlJSwnZzcwtmCh9WVlaynJ2dQ2QyGZWenm7WtWtX/8DAQGFERETA7du3zQF1VrKEhAT3Dh068KdNm+YOAKmpqbyOHTvyvby8gtasWWMPqCuGT5482d3f3z+Qz+eLtm/fbgOoM7ZFRkYGCAQCkb+/f+CPP/5oAQDHjh2zCgsLE4hEImFsbKwvU8fmZeDxeOGzZ892DQkJEfz8888WzLwD1FaoqKioAMDw7wsAiI2NLf/mm29e21toomS0Fnk31cHeeTf1Hn5W4oK6Rwmof9oXskcJMK/rjITgBChrvbDpl+wmXaBI8b3WJ8wxDNv7bjda8SMQCISWYsGCBS49e/asSEtLy7hy5UrmokWL3CsqKliurq6KK1euZInF4oxDhw7dnzNnjidzTmpqaptVq1bl5+TkpANARkYGd9OmTXnZ2dnpjx49Mvvpp58sdPupra1lRUdHV2VmZoqjo6OrNmzY4AAAM2bM8Jg2bVpxWlpahjGVhHX73r9/f256enpGSkqKeNu2bU5Pnjxhb968Od/MzEwlkUjEJ0+efHDr1i3zI0eO2CYlJUkkEomYxWLRW7dutQOAfv36+TLuI9qfjRs32gHA0qVLiyZOnFgGACNHjixLS0vLyMzMFAcEBNQmJiYySo1nQkLC07S0tAxnZ2fNNfB4PNWZM2eyxWJxxqVLl7IWLFjgzihgubm55lOmTHmalZUltrS0VK1atcph0aJFxY6OjvJLly5l3bhxo1E19lGjRpUePXpUU/TuxIkTNqNGjSo7duyYVXZ2tnlqampGRkaGOCUlhffDDz9YAEBaWprBOizaz6Vnz56V6enpGW3atFEuWrTI7cqVK1mHDx/OXrZsmRugrnGhby6MHj263MHBQb5y5UqHsWPHen366acFnp6eTSo2AQEB9aNHj346ZcqUIolEIo6JiamaPHmyx9y5c4vS0tIyjh8/njNlyhRvrefNO3v2bPapU6ce3Llzx0zfsxIIBCJjXMp++OGHtoxSl52dbQIA+fn5Ju7u7po3725ubvX5+fkGTSZ2dnZKgUBQ8/3331sCwMGDB627d+8uNTMzoxMSErw2b978KD09PWPVqlWPp06dqvnO5OTkmF+9ejVr+/btjwH1d+b8+fP3rl+/Llm1apVrbm6uyZ49e9revXuXm5GRkf7zzz9nLVmyxP3hw4cmO3futO3du7dUIpGIMzIy0jt06FBTWFjIWbFihcvly5ezxGJxxltvvVWzbNkyJwCYMGGCh757tGDBAoMuhLW1taygoKDa1NRUyTvvvGOwbo2h3xcA0KFDh+pr165ZNvccWgviLtUaNKSrhbJeHew95qTGTYqho68dOBd8NKlpO/raaWIt6hUqmHJYTSoQpPhe6xPmGEaUCwKB8Kdz8eJFq7Nnz7ZNTEx0BtRFsbKzs029vLzkEyZM8BKLxVwWi4WHDx9qCp2FhIRUCwQCzcIsODi4ul27dnIACAwMrMnJyTHV7cfExIQeNmyYFAAiIiKqz58/bwUAt2/ftjh37lw2ACQkJJT861//ctc9Vxvdvr/44gunM2fOtAWAJ0+emKSnp5s7Ozs3sk79+OOPlmlpabzQ0FAhANTV1bEcHR0VAHDmzJn7MJLk5GTukiVL3CorK9nV1dXs7t27SwHg1q1bFj/88EMOAEyePLlk2bJl7gCgUqmo2bNnu1+/ft2CxWKhuLjY9PHjxxwAcHZ2ru/bt281AHzwwQcliYmJjgCKDHSNzp0715aUlHByc3NNCgsLOdbW1kp/f//6VatWOV6+fNlKJBKJAKCmpoYlkUjMY2NjjSpwaGJiQg8ZMqQCAAIDA2vNzMxUZmZmdFRUVG1+fr4pANTX11OG5sKOHTseBQYGBoaHh1dPnjy51Nh7qc3Vq1et7t27p6mgXVVVxS4rK2MB6qJ2FhYWNACEhobKJBLJSwVlv//+++UTJ04s5XK59JdffukwatQon+vXr2fpy3pKUU3XKIyPjy87cOCAzYABAyr/97//2U6bNu2pVCpl3b592yI+Pr4d044pOggAgwcPLtN2QYqNjS23sLCgLSwsFNHR0RVXrlxpc+XKFcv333+/lMPhwMPDQ9GhQ4eqX3/9ldexY8fqyZMne8vlctaQIUPKOnXqVHvgwAHLnJwc86ioKAEAyOVyiilq+fXXX79wimc2m42xY8c2G3Rr6PfFW2+9Vefi4qIoKipq3qetlSBKRmuQe0WtYNBK9f+5VxopGUxmqCX9A1FWU6/JELXpl2wSa0EgEAj/cGiaxpEjR7JDQ0MbVV6eO3euq6Ojo/zo0aMPVCoVuFxuBHOMx+M1qn9hZmamWamx2WwoFIrnVmkcDodmsVjMz3rbGIN236dPn7a8dOmSZVJSksTS0lIVFRUVUFtb+5zXBE3TVHx8fMmmTZvydY/169fPNycn57ng9BkzZhTNmDGjUXajSZMm+Rw5ciQ7Ojq6NjEx0e7SpUuat7YsFuu51eq2bdtsS0pKOHfv3s0wMzOj3dzcgpnx6S5km1vYAuqYgn379tk8efLEJC4urrTh2jB79uzC+fPnv1RxNu3nwmKxNM+SzWZDqVRSAPDvf//bydBcyM3NNWGxWHj27BlHqVSCzX7xGHWappGUlJTBKBPatGnTRvO879y5YzZ06NB2um0A4Ndff820t7c3GEfh7OysOTZ37tynjJXG3d1drv0c8/PzTbt3795kIojhw4eXL1261K2oqIidlpbGGzBgQEVFRQXL0tJSYUgJsrCwaPSd0ff8DZV5iI2Nrbp8+XLm0aNHrceOHevz4YcfFtna2iq6dOlScerUqedqgUyYMMHj6tWrz1kUBg8eXLpixQq9cUqmpqYqbSWIzWbTjNVN+ztl6PcF087c3LxFa+O8CMRdqjVg0tVSbPX/3l01h7QzQy09nd4oBS2JtSAQCARCz549K9asWePELCiuXr3KBQCpVMp2cXGRs9lsbN682Y7xQW9pwsLCqnbv3m0DADt37rRtrr025eXlbGtra6WlpaXq9u3b5nfu3NFkLOJwOLRMJqMAICYmpuL06dM2+fn5HEAd7JuVlWUKqC0ZEolErPvRVTAAtZXA09NTLpPJqIMHD2rG+tZbb1Vt377dFgC2b9+u+WMqlUrZ9vb2cjMzM/rUqVOWBQUFGgtPYWGh6fnz59sAwLfffmvbqVOnKgBo06aN0pBv/QcffFB69OhR29OnT9uMGjWqDABiY2Mr9u7da8+c8+DBAxPmOrXx8fEJfJF7q42huSCXyzFu3Dif3bt33/f396/7/PPPjcqYZGlpqaysrNRoI126dKn44osvHJnta9eucfWdx1gy9H2aUjAAdVwD8/O3337b1tfXtw4ABg0aJL106ZLV06dP2U+fPmVfunTJatCgQVIAmD59utuePXva6sqytrZWhYaGVk+ePNmzd+/eUg6HA1tbW5W7u3v9zp07bQB1fMVvv/2m9zoAtetWTU0N9eTJE/b169ctu3TpUt29e/fKI0eO2CoUChQUFHBu3rxp0bVr1+qsrCxTNzc3+UcfffRs1KhRz27dusXr0aNHdVJSkkVaWpoZoI4NSU1NNQPUlgx998iQgqEPd3f3+qtXr/IA4H//+5/mDbSh3xcAIBaLzQICAl5bfRqiZLQG2ulqY1aqLRl5N5FSnILNt7+CgvOgkbWCgcRaEAgEAmHlypUFCoWCYoJKFy1a5AYAs2fPLj5w4IBdaGioICsry5wJfm5pNmzYkLdhwwan4OBgYWFhoYmFhYXR2kxcXJxUoVBQfD5ftGDBAtfQ0FCNm9TIkSOfCoVC0cCBA30iIiLqFi1alN+7d28+n88X9erVi5+Xl/fCbh2ffPJJQVRUlLBr1658f3//Omb/5s2bH3311VeOQUFBQqlUqlk8JyQklN65c6dNUFCQcN++fbY+Pj6ac3x9fet27txpx+fzRWVlZZx58+Y9BYAxY8Y8i42N9WcCv7WJjIysq66uZjk5OdUzwc2DBw+uiI+PL23fvr2Az+eL3nvvvXbl5eWNzAmFhYUcmqZfynIEGJ4Ln376qUvHjh0rY2JiqrZs2ZK3d+9e+1u3bjWbsjguLq78zJkzbZnA76+++irv1q1bbfh8vqhdu3aBGzdudGhOhiGmTJni7uTkFFJXV8dycnIKmTt3risAfPnll45+fn6BAQEBok2bNjnu3r07FwCcnJyU8+fPL4iIiBBGREQIP/744wInJyclAIjFYq6hOKH333+/7MSJE7bDhw/XuIgdOHDg/q5du+wDAgJE/v7+gUePHm1raJzh4eHVvXv39u/QoYNw3rx5hd7e3vIPPvigPDAwsFYoFAb26NGD//nnnz/29PRUnD171lIkEgUKhULRiRMnbD7++OMiV1dXxbZt23KHDRvmy+fzRRH/v737D5K7rvM8/nxPzwwQMBBiEhUmCVlZOH7IYMY4uMR1wYW4F0GP2wPEksUNrL/R25VF3dLzdr1yC907ao+VIhHxyiy6BYrsFSt4iBpxQ5jAyA8hGseERCAJIQJCSM9Mv++P7mgTZiadpDvdnXk+qlI931/drwkZ4JXv5/v5zJ9//IMPPli36aI//elPP37FFVfMnj9//nGFQuG3t1jG+/cFwPe+972pixcvbtpK8rtd8TsiDgLOA+ZSNbwqM/97Q5PthZZb8bvq2YzBQ6Zw6atnsqM0SqlUYMdjSyiMHGOZkNRUrbLit1rHc88913HooYeWOjo6uO6666Z94xvfOPLOO+88oFdXXrNmTffixYuP/fnPf/7w/vi8G2+88fBf/OIXB/3N3/zN5v3xeQeK008//dgf/ehHP292jnawffv26O/vP25gYODRWqYa3lv7uuL3t4FngNXAy8Z7TSQiFgFXAwVgWWZ+fpfjhwNfA2ZXsnwhM79Sy7VtYd0KBjth4NBDebyzk2JpmAQKHfDmU57jA6f+rmC4grckqRXcfffdUy6//PLZmcnUqVNHd/4Ns+rnwgsvbNrfLrczC0bt1q5d2/25z33uV40sGLtTS8k4OjMX7ekbR0QBuAb4Y2AjcG9E3JqZ1Q/gfBD4aWa+PSJmAGsiYjkwWsO1LW9w2qu5dNYrKQZ0JhSik8wSnR1dfPhNZ9M783cFo9ZZpSRJaqRFixb9Zs2aNS/57+2qVasOec973vOSdRG6u7tLDzzwwKP7N11jHHfcccX9dRdjf7v55punfupTn3rJDGE9PT07vvvd7x7Qd6cmu5NPPnnHySefvEc3B+qtlpLx44g4OTMf3MP3XgCszcwhgIj4OnAuUP0vrgReEeVH+g8DngZGgDfWcG1r2rCq/AzG3IUM5AsUOzookQxHMPLr+YzuOILh4msZ3T7nt5e4grckqZUtWLBg+95OVarmOu+8854977zz/Gen/a6WknE68GcR8UvKw6UCyMx83W6uOwqonhd4I+XyUO1/A7cCjwOvAM7PzFJE1HJt69llfYwZC/+O0VInxAiZnRS3vZ6R7XMoBC8pEjtnlRoeKTmrlKQDRalUKsVY04hKktpfqVQKYNwJKGopGW/by88ea9aEXf9jczYwCJwB/B7w3YhYUeO15Q+JuAy4DGD27NljnbL/7LI+xsx1Q7z42BI6pgxRemEe7JhLIfJlRWLnrFI+kyHpAPLQli1bTpgxY8YzFg1JOrCUSqXYsmXL4cBD452z25KRmesj4hRg52IPKzLzJzV8/kagp2r7aMp3LKpdAnw+y1Ncra3cLTm+xmt35rsOuA7Ks0vVkKtxdq6PUbmTMe2EM+hcO/zbVb0/fe5LF9+r5grekg4kIyMjS5588sllTz755Ek4XbokHWhKwEMjIyNLxjuhlilsLwcuBb5Z2fVO4LrM/MfdXNcJ/Aw4E/gVcC/wrsx8uOqcLwGbMvO/RcQs4D7gFODXu7t2LC0xhW3lmYxHDz6FO38zl2lTusctFpLUbI2awlaSNLnVMlzqz4E3ZubzABHx98C/AxOWjMwciYgPAbdTnob2+sx8OCLeVzl+LfC3wA0R8SDlIVJ/nZlPVT7nZdfuzTe43/UsYHXp2MpsUWucLUqSJEmTTi0lIyhPKbvTKGM/M/EymXkbcNsu+66t+vpx4Kxar20XzhYlSZKkyayWkvEV4J6I+FZl+x3AlxuW6ADgbFGSJEmazGp58PsfIuL7lKeyDeCSzLy/0cHa2fw50/jsnx7GHUM/5qx5b/IuhiRJkiaVcUtGREzNzGcj4khgXeXXzmNHZubTjY/XfgY3D/Kvv/hXbll7CyOlER544CaOe9VSemf2NjuaJEmStF9MdCfjn4HFwGpeukZFVLbnNTBXWxncPMjApgEO7z6cz6/6e4qlIjt/y4ZLwwxsGrBkSJIkadIYt2Rk5uLK6zH7L077Gdw8yKV3XEpxtEhEMFIqEZFkQkTQ1dFF3yxnh5QkSdLksdsFkiLizlr2TVYDmwYoju6gRInRLE+8lRmQBU447CyWnuVQKUmSJE0uEz2TcTAwBXhlREzjd9PWTgVesx+ytYW+mEJ3qcRwQGfCkU+dxnoOo6P4Wq4443x6Z/rQtyRJkiaXiZ7J+Avgo5QLxWp+VzKeBa5pbKz20bvtCZZueoqBg7ro2zHMjBOP5pbDLnCFb0mSJE1aEz2TcTVwdUR8ODMnXN17Upu7kN4fXEXvjueh0A29Z/HBntc2O5UkSZLUNLWsk/GPEXEScAJwcNX+/9PIYG2jZwFcfCusWwFzF5a3JUmSpElstyUjIj4DvIVyybgNeBvwI8CSsVPPAsuFJEmSVLHb2aWA/wycCTyZmZcApwAHNTRVmxjcPMiyB5cxuHmw2VEkSZKklrHbOxnA9swsRcRIREwFNuNCfAxuHuTPb1/CcGmYro4uvnz2MqeqlSRJkqjtTsZARBwBLKU8y9R9wKpGhmoH3350BTtGiyQldowW+fajK5odSZIkSWoJtTz4/YHKl9dGxHeAqZn5QGNjtbbV67fxk59Ph45OkhHITkZecGF0SZIkCSZejO/1Ex3LzPsaE6m1rV6/jYuWrWTH8OHEIUvomjIEL/4epSPmsHr9NtfGkCRJ0qQ30Z2ML1ZeDwb6gJ9QXpDvdcA9wOmNjdaaVg5tZaTzl3RNHaL0wjyOP+QdPPLMc9y46jFuvm8jy5f0WzQkSZI0qU20GN8fAUTE14HLMvPByvZJwF/tn3it55XTn+Dg2csgRujIDv5k+A10lk5gdf4+wyMlVg5ttWRIkiRpUqvlwe/jdxYMgMx8COhtWKIW91ysoaNjlIikI0boeOF2vtb1P+jr+BldnR30z5ve7IiSJElSU9Uyhe0jEbEM+BqQwLuBRxqaqoX1zerjoEI3w6M76MoSb9i+nYM7RvnL47bQ/ZY/8y6GJEmSJr1aSsYlwPuByyvbPwS+1LBELa53Zi9Lz1rKwJpv0ffvX6a3OAqFbk474x3QY8GQJEmSapnC9kXgf1Z+CejdUaSXqXDG38H2rTB3IfQsaHYsSZIkqSVMNIXtv2Tmf4mIBykPk3qJzHxdQ5O1qg2r4KvnwGgRCt1w8a0WDEmSJKnKRHcydg6PWrw/grSNdSvKBSNHy6/rVlgyJEmSpCoTTWH7ROV1/f6L0wbmLizfwdh5J2PuwmYnkiRJklrKRMOlnmOMYVKUF+TLzJzasFStrGdBeYjUuhU+iyFJkiSNYaI7Ga/Yn0HaSs8Cy4UkSZI0jlqmsAUgImYCB+/czszHGpKoRa1ev42VQ1vpnzfdtTAkSZKkCey2ZETEOcAXgdcAm4E5lBfjO7Gx0VrH6vXbuGjZSoojJbo7O1i+pN+iIUmSJI2jo4Zz/hboB36WmccAZwJ3NzRVi1k5tJXiSIlSwvBIiZVDW5sdSZIkSWpZtZSM4czcCnREREdm3gX0NjZWa+mfN52DDtvAQdPvovuwDfTPm97sSJIkSVLLquWZjF9HxGHACmB5RGwGRhobq7UUDlnPlNnLGC4N09XxfQqH9AMOl5IkSZLGUsudjB8CR1BenO87wC+AtzcwU8sZ2DTASKlIUmK0NMzApoFmR5IkSZJaVi0lI4Dbge8DhwHfqAyf2v2FEYsiYk1ErI2IK8c4/vGIGKz8eigiRiPiyMqxj0XEw5X9N0bEwS//hP2jL6bQXSpRyKSrNEpfTGlWFEmSJKnl7bZkZOZnM/NE4IOUZ5j6QUT8v91dFxEF4BrgbcAJwIURccIu731VZvZmZi/wCeAHmfl0RBwFfAToy8yTgAJwwZ59a/XTu+0Jlm56ig9te4alm56id9sTzYoiSZIktbya18mgPH3tk8BWYGYN5y8A1mbmEEBEfB04F/jpOOdfCNy4S7ZDImIYmAI8vgdZ62Zw8yADPEtfRydLnn0eCt3llb4lSZIkjamWdTLeD5wPzABuAi7NzPGKQrWjgA1V2xuBN47zGVOARcCHADLzVxHxBeAxYDtwR2beMc61lwGXAcyePbuGWLUb3DzIpXdcSnG0SPerZ7L0VX9M7/Hnudq3JEmSNIFansmYA3w0M0/MzM/UWDCg/CzHrnKcc98O3J2ZTwNExDTKdz2OoTxE69CIePdYF2bmdZnZl5l9M2bMqDFabQY2DVAcLVKixHCWGHjVsRYMSZIkaTdqeSbjyswc3Iv33gj0VG0fzfhDni7gpUOl3gr8MjO3ZOYw8E3gTXuRYZ/0zeqju9BNIQp0dXTRN6tvf0eQJEmS2s6ePJOxp+4Fjo2IY4BfUS4S79r1pIg4HPhDoPpOxWNAf2UY1XbKq4zv93lje2f2srT3YwwM3U7fvLPpndm7vyNIkiRJbadhJSMzRyLiQ5Snvy0A12fmwxHxvsrxayunvpPyMxfPV117T0TcBNxHeeG/+4HrGpV1XBtW0XvLf6V3tAgP3A6Hv9bhUpIkSdJuNPJOBpl5G3DbLvuu3WX7BuCGMa79DPCZBsbbvXUrYLQIOVp+XbfCkiFJkiTtRkNLRtubu5BSRxeMAh1ddDh1rSRJkrRblowJrC4dy1XFTzI/H2b16Il8vHQs85sdSpIkSWpxloxxDG4e5J/uv52Bzlewcvu5FAJWDm1l/pxpzY4mSZIktTRLxhh2LsK3Y7TIwbML7HhsCYWRY+ifN73Z0SRJkqSWZ8kYw85F+JIShQ548ynP8YFT+72LIUmSJNXAkjGGnYvwDZeG6ero4sNvOpvemRYMSZIkqRaWjDH0zuxl6VlLGdg0QN+sPhfhkyRJkvaAJWMcvTN7LReSJEnSXuhodgBJkiRJBxZLxng2rIIVXyy/SpIkSaqZw6XGsmEVfPUcGC1CoRsuvhV6FjQ7lSRJktQWvJMxlnUrygUjR8uv61Y0O5EkSZLUNiwZY5m7sHwHIwrl17kLm51IkiRJahsOlxpLz4LyEKl1K8oFw6FSkiRJUs0sGePpWWC5kCRJkvaCw6UkSZIk1ZUlYxyr12/jmrvWsnr9tmZHkSRJktqKw6XGsHr9Ni5atpLiSInuzg6WL+ln/pxpzY4lSZIktQXvZIxh5dBWiiMlSgnDIyVWDm1tdiRJkiSpbVgyxtA/bzrdnR0UAro6O+ifN73ZkSRJkqS24XCpKoObBxnYNEDfrD6WL+ln5dBW+udNd6iUJEmStAcsGRWDmwe59Pb3UiwN093RxdKzr+eDf9Tb7FiSJElS23G4VMXAmm9RHC1SAoZHiwys+VazI0mSJEltyZJR0ffii3RnUsikK5O+F19sdiRJkiSpLTlcqqL3+PNYev9yBro76CuW6H3rec2OJEmSJLUlS8ZOPQvovfCb9K5bAXMXQs+CZieSJEmS2pIlo1rPAsuFJEmStI98JkOSJElSXVkyJEmSJNWVJUOSJElSXVkyJEmSJNWVJUOSJElSXVkyJEmSJNVVQ0tGRCyKiDURsTYirhzj+McjYrDy66GIGI2IIyvHjoiImyLi0Yh4JCJOa2RWSZIkSfXRsJIREQXgGuBtwAnAhRFxQvU5mXlVZvZmZi/wCeAHmfl05fDVwHcy83jgFOCRRmWVJEmSVD+NvJOxAFibmUOZWQS+Dpw7wfkXAjcCRMRU4M3AlwEys5iZv25gVkmSJEl10siScRSwoWp7Y2Xfy0TEFGARcHNl1zxgC/CViLg/IpZFxKHjXHtZRAxExMCWLVvql16SJEnSXmlkyYgx9uU4574duLtqqFQn8HrgS5l5KvA88LJnOgAy87rM7MvMvhkzZuxrZkmSJEn7qJElYyPQU7V9NPD4OOdeQGWoVNW1GzPznsr2TZRLR0OtXr+Na+5ay+r12xr9UZIkSdIBq7OB730vcGxEHAP8inKReNeuJ0XE4cAfAu/euS8zn4yIDRFxXGauAc4EftrArKxev42Llq2kOFKiu7OD5Uv6mT9nWiM/UpIkSTogNaxkZOZIRHwIuB0oANdn5sMR8b7K8Wsrp74TuCMzn9/lLT4MLI+IbmAIuKRRWQFWDm2lOFKilDA8UmLl0FZLhiRJkrQXGnkng8y8Dbhtl33X7rJ9A3DDGNcOAn2NS/dS/fOm093ZwfBIia7ODvrnTd9fHy1JkiQdUBpaMtrJ/DnTWL6kn5VDW+mfN927GJIkSdJesmRUmT9nmuVCkiRJ2keNnF1KkiRJ0iRkyZAkSZJUV5YMSZIkSXVlyZAkSZJUV5YMSZIkSXVlyZAkSZJUV5YMSZIkSXVlyQAGNw+y7MFlDG4ebHYUSZIkqe1N+sX4BjcPcukdl1IcLdJd6GbpWUvpndnb7FiSJElS25r0dzIGNg1QHC1SosRwaZiBTQPNjiRJkiS1tUlfMvpm9dFd6KYQBbo6uuib1dfsSJIkSVJbm/TDpXpn9rL0rKUMbBqgb1afQ6UkSZKkfTTpSwaUi4blQpIkSaqPST9cSpIkSVJ9WTIANqyCFV8sv0qSJEnaJw6X2rAKvnoOjBah0A0X3wo9C5qdSpIkSWpb3slYt6JcMHK0/LpuRbMTSZIkSW3NkjF3YfkORhTKr3MXNjuRJEmS1NYcLtWzoDxEat2KcsFwqJQkSZK0TywZUC4WlgtJkiSpLhwuJUmSJKmuLBmSJEmS6sqSAaxev41r7lrL6vXbmh1FkiRJanuT/pmM1eu3cdGylRRHSnR3drB8ST/z50xrdixJkiSpbU36Oxkrh7ZSHClRShgeKbFyaGuzI0mSJEltbdKXjP550+nu7KAQ0NXZQf+86c2OJEmSJLW1ST9cav6caSxf0s/Koa30z5vuUClJkiRpH036kgHlomG5kCRJkupj0g+XkiRJklRflgxJkiRJdWXJkCRJklRXlgxJkiRJdWXJkCRJklRXlgxJkiRJdRWZ2ewMdRMRW4D1e3DJK4GnGhRnf2jn/GZvDrM3T6vmn5OZM5odQpJ0YDmgSsaeioiBzOxrdo691c75zd4cZm+eds8vSdKecLiUJEmSpLqyZEiSJEmqq8leMq5rdoB91M75zd4cZm+eds8vSVLNJvUzGZIkSZLqb7LfyZAkSZJUZ5YMSZIkSXU1aUtGRCyKiDURsTYirmx2nolERE9E3BURj0TEwxFxeWX/kRHx3Yj4eeV1WrOzjiciChFxf0T838p2W2SPiCMi4qaIeLTy+39aG2X/WOXPy0MRcWNEHNzK2SPi+ojYHBEPVe0bN29EfKLy87smIs5uTurfZhkr+1WVPzcPRMS3IuKIqmMtk12SpEaYlCUjIgrANcDbgBOACyPihOammtAI8JeZ+R+AfuCDlbxXAndm5rHAnZXtVnU58EjVdrtkvxr4TmYeD5xC+Xto+ewRcRTwEaAvM08CCsAFtHb2G4BFu+wbM2/lz/8FwImVa/6p8nPdLDfw8uzfBU7KzNcBPwM+AS2ZXZKkupuUJQNYAKzNzKHMLAJfB85tcqZxZeYTmXlf5evnKP+P7lGUM3+1ctpXgXc0JeBuRMTRwH8EllXtbvnsETEVeDPwZYDMLGbmr2mD7BWdwCER0QlMAR6nhbNn5g+Bp3fZPV7ec4GvZ+aOzPwlsJbyz3VTjJU9M+/IzJHK5krg6MrXLZVdkqRGmKwl4yhgQ9X2xsq+lhcRc4FTgXuAWZn5BJSLCDCzidEm8r+AK4BS1b52yD4P2AJ8pTLUa1lEHEobZM/MXwFfAB4DngCeycw7aIPsuxgvb7v9DL8X+LfK1+2WXZKkPTZZS0aMsa/l5/KNiMOAm4GPZuazzc5Ti4hYDGzOzNXNzrIXOoHXA1/KzFOB52mt4UXjqjy7cC5wDPAa4NCIeHdzU9VV2/wMR8SnKA95XL5z1xintWR2SZL21mQtGRuBnqrtoykPJWlZEdFFuWAsz8xvVnZviohXV46/GtjcrHwT+APgnIhYR3lY2hkR8TXaI/tGYGNm3lPZvoly6WiH7G8FfpmZWzJzGPgm8CbaI3u18fK2xc9wRFwMLAYuyt8tStQW2SVJ2heTtWTcCxwbEcdERDflhzBvbXKmcUVEUH4u4JHM/IeqQ7cCF1e+vhj49v7OtjuZ+YnMPDoz51L+ff5eZr6b9sj+JLAhIo6r7DoT+CltkJ3yMKn+iJhS+fNzJuVnedohe7Xx8t4KXBARB0XEMcCxwKom5BtXRCwC/ho4JzNfqDrU8tklSdpXk3bF74j4E8rPChSA6zPzc81NNL6IOB1YATzI755r+CTl5zL+BZhN+X8q/zQzd31wtmVExFuAv8rMxRExnTbIHhG9lB9Y7waGgEsol/N2yP5Z4HzKQ3XuB5YAh9Gi2SPiRuAtwCuBTcBngFsYJ29lGNJ7KX9/H83Mf3v5u+4f42T/BHAQsLVy2srMfF/l/JbJLklSI0zakiFJkiSpMSbrcClJkiRJDWLJkCRJklRXlgxJkiRJdWXJkCRJklRXlgxJkiRJdWXJkMYQET+uvM6NiHfV+b0/OdZnSZIkHSicwlaaQPXaHntwTSEzRyc4/pvMPKwO8SRJklqSdzKkMUTEbypffh5YGBGDEfGxiChExFURcW9EPBARf1E5/y0RcVdE/DPlRROJiFsiYnVEPBwRl1X2fR44pPJ+y6s/K8quioiHIuLBiDi/6r2/HxE3RcSjEbG8soq3JElSS+psdgCpxV1J1Z2MSll4JjPfEBEHAXdHxB2VcxcAJ2XmLyvb783MpyPiEODeiLg5M6+MiA9lZu8Yn/WfgF7gFMorR98bET+sHDsVOBF4HLgb+APgR/X+ZiVJkurBOxnSnjkLeE9EDAL3ANOBYyvHVlUVDICPRMRPgJVAT9V54zkduDEzRzNzE/AD4A1V770xM0vAIDC3Dt+LJElSQ3gnQ9ozAXw4M29/yc7ysxvP77L9VuC0zHwhIr4PHFzDe49nR9XXo/izK0mSWph3MqSJPQe8omr7duD9EdEFEBG/HxGHjnHd4cC2SsE4HuivOja88/pd/BA4v/LcxwzgzcCqunwXkiRJ+5F/GypN7AFgpDLs6QbgaspDle6rPHy9BXjHGNd9B3hfRDwArKE8ZGqn64AHIuK+zLyoav+3gNOAnwAJXJGZT1ZKiiRJUttwCltJkiRJdeVwKUmSJEl1ZcmQJEmSVFeWDEmSJEl1ZcmQJEmSVFeWDEmSJEl1ZcmQJEmSVFeWDEmSJEl19f8BtbIysnJK3v4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "curveValidateIteration(classificationList, curve, validation, iteration)\n",
    "plotCurvePerInteration(classificationList, curve, iteration)\n",
    "plotValidationPerInteration(classificationList, validation, iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "715808d2-3450-4561-b72a-fe134258b439",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "plotValidationPercurve(classificationList, validation, curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc09c0c7-abb7-44d0-bbe0-36da87b460e4",
   "metadata": {},
   "source": [
    "## Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ef05d5a2-ac58-4035-aa6c-dfa6abd61000",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, verbose=True)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='invscaling', max_iter=150, verbose=True)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='adaptive', max_iter=150, verbose=True)\n"
     ]
    },
    {
     "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": [
    "heatmaping(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6f083132-b371-422e-9d69-2489a9f2c6db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.84      0.81      0.82     52835\n",
      "           2       0.84      0.88      0.86     70951\n",
      "           3       0.75      0.84      0.79      8918\n",
      "           4       0.69      0.57      0.62       679\n",
      "           5       0.70      0.30      0.42      2394\n",
      "           6       0.64      0.43      0.52      4343\n",
      "           7       0.84      0.81      0.83      5133\n",
      "\n",
      "    accuracy                           0.83    145253\n",
      "   macro avg       0.76      0.66      0.69    145253\n",
      "weighted avg       0.82      0.83      0.82    145253\n",
      "\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='invscaling', max_iter=150, verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.85      0.82      0.84     52835\n",
      "           2       0.85      0.89      0.87     70951\n",
      "           3       0.80      0.85      0.82      8918\n",
      "           4       0.75      0.60      0.67       679\n",
      "           5       0.71      0.38      0.49      2394\n",
      "           6       0.67      0.55      0.60      4343\n",
      "           7       0.85      0.85      0.85      5133\n",
      "\n",
      "    accuracy                           0.84    145253\n",
      "   macro avg       0.78      0.70      0.73    145253\n",
      "weighted avg       0.84      0.84      0.84    145253\n",
      "\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='adaptive', max_iter=150, verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.81      0.84      0.83     52835\n",
      "           2       0.85      0.86      0.86     70951\n",
      "           3       0.79      0.84      0.81      8918\n",
      "           4       0.76      0.59      0.66       679\n",
      "           5       0.73      0.35      0.47      2394\n",
      "           6       0.64      0.55      0.59      4343\n",
      "           7       0.87      0.75      0.81      5133\n",
      "\n",
      "    accuracy                           0.83    145253\n",
      "   macro avg       0.78      0.68      0.72    145253\n",
      "weighted avg       0.83      0.83      0.83    145253\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reporting(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "74a399f7-9f58-4c18-9122-6cd52a869c2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, verbose=True)\n",
      "Accuracy: \t82.64\n",
      "Recall: \t82.64\n",
      "precision: \t82.37\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='invscaling', max_iter=150, verbose=True)\n",
      "Accuracy: \t84.02\n",
      "Recall: \t84.02\n",
      "precision: \t83.84\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              learning_rate='adaptive', max_iter=150, verbose=True)\n",
      "Accuracy: \t82.82\n",
      "Recall: \t82.82\n",
      "precision: \t82.69\n"
     ]
    }
   ],
   "source": [
    "accuracyPlotting(classificationList)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254cdfe5-bcc2-4d94-94d9-e4977915308d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Fazit\n",
    "<li> Die learning rate invscaling liefert die besten Ergebnisse, braucht aber auch die meisten Iterationen </li>\n",
    "<li> der default-Wert hat die schlechtesten End-Ergebnisse abgeliegert (constant). Dabei ist diese learning rate abhängig von learning_rate_init. Dies sollte weiter untersucht werden. </li>\n",
    "<li> adaptive verzeichnet die stärksten Ausreisser in der Validierung </li>"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}