04_momentum SGD.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": [
    {
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       "0         5\n",
       "1         5\n",
       "2         2\n",
       "3         2\n",
       "4         5\n",
       "         ..\n",
       "581007    3\n",
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       "581009    3\n",
       "581010    3\n",
       "581011    3\n",
       "Name: Cover_Type, Length: 581012, dtype: int32"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "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": [
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       "  <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",
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       "      <th>Soil_Type_39</th>\n",
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       "      <td>45.0</td>\n",
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      "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",
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       "532742           0.0           0.0           0.0           0.0           0.0   \n",
       "...              ...           ...           ...           ...           ...   \n",
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       "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]"
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     },
     "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",
    "accuracyList = []\n",
    "recallList = []\n",
    "precisionList = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad8a3ff-1ec6-4b47-b833-497c31f505d7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dab39c2-f220-4cae-acbc-4002ded2e75a",
   "metadata": {},
   "source": [
    "###  Nesterov Momentum\n",
    "Gradient Descent (SDG): $$\\lambda_{t+1} = \\lambda _t - \\eta\\nabla l(\\lambda)$$\n",
    "Gradient Descent mit Momentum: $$v_{t+1} - \\mu*v_t - \\eta\\nabla l(\\lambda)$$\n",
    " $$\\lambda _{t+1} = \\lambda _t + v_{t+1}$$\n",
    "Gradient Descent mit Nesterov-Momentum: $$v_(t+1) - \\mu*v_t - \\eta\\nabla l(\\lambda + \\mu*v_t)$$\n",
    "$$\\lambda _{t+1} = \\lambda _t + v_{t+1}$$\n",
    "Ändern der Geschiwindigkeit anstatt Änderung der Postion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a21184a-dd71-4064-adad-44372e811ed5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.91901706\n",
      "Validation score: 0.698045\n",
      "Iteration 2, loss = 0.69496201\n",
      "Validation score: 0.724298\n",
      "Iteration 3, loss = 0.65646399\n",
      "Validation score: 0.734280\n",
      "Iteration 4, loss = 0.63667082\n",
      "Validation score: 0.739581\n",
      "Iteration 5, loss = 0.62236177\n",
      "Validation score: 0.744951\n",
      "Iteration 6, loss = 0.61147997\n",
      "Validation score: 0.747086\n",
      "Iteration 7, loss = 0.60228354\n",
      "Validation score: 0.750597\n",
      "Iteration 8, loss = 0.59456839\n",
      "Validation score: 0.752639\n",
      "Iteration 9, loss = 0.58751957\n",
      "Validation score: 0.752754\n",
      "Iteration 10, loss = 0.58100832\n",
      "Validation score: 0.758147\n",
      "Iteration 11, loss = 0.57530320\n",
      "Validation score: 0.759386\n",
      "Iteration 12, loss = 0.56982024\n",
      "Validation score: 0.761314\n",
      "Iteration 13, loss = 0.56424213\n",
      "Validation score: 0.763746\n",
      "Iteration 14, loss = 0.55896524\n",
      "Validation score: 0.765490\n",
      "Iteration 15, loss = 0.55387134\n",
      "Validation score: 0.768611\n",
      "Iteration 16, loss = 0.54919479\n",
      "Validation score: 0.769162\n",
      "Iteration 17, loss = 0.54484688\n",
      "Validation score: 0.771870\n",
      "Iteration 18, loss = 0.54085062\n",
      "Validation score: 0.772398\n",
      "Iteration 19, loss = 0.53718898\n",
      "Validation score: 0.775060\n",
      "Iteration 20, loss = 0.53345628\n",
      "Validation score: 0.775702\n",
      "Iteration 21, loss = 0.53036207\n",
      "Validation score: 0.776643\n",
      "Iteration 22, loss = 0.52709682\n",
      "Validation score: 0.777653\n",
      "Iteration 23, loss = 0.52378452\n",
      "Validation score: 0.779925\n",
      "Iteration 24, loss = 0.52060063\n",
      "Validation score: 0.778525\n",
      "Iteration 25, loss = 0.51783445\n",
      "Validation score: 0.782128\n",
      "Iteration 26, loss = 0.51498622\n",
      "Validation score: 0.782449\n",
      "Iteration 27, loss = 0.51199225\n",
      "Validation score: 0.783046\n",
      "Iteration 28, loss = 0.50897020\n",
      "Validation score: 0.784193\n",
      "Iteration 29, loss = 0.50627643\n",
      "Validation score: 0.786281\n",
      "Iteration 30, loss = 0.50363674\n",
      "Validation score: 0.785409\n",
      "Iteration 31, loss = 0.50093010\n",
      "Validation score: 0.788576\n",
      "Iteration 32, loss = 0.49823731\n",
      "Validation score: 0.790504\n",
      "Iteration 33, loss = 0.49578917\n",
      "Validation score: 0.791169\n",
      "Iteration 34, loss = 0.49345400\n",
      "Validation score: 0.793120\n",
      "Iteration 35, loss = 0.49132963\n",
      "Validation score: 0.791927\n",
      "Iteration 36, loss = 0.48919972\n",
      "Validation score: 0.795162\n",
      "Iteration 37, loss = 0.48744765\n",
      "Validation score: 0.795736\n",
      "Iteration 38, loss = 0.48545703\n",
      "Validation score: 0.794336\n",
      "Iteration 39, loss = 0.48331565\n",
      "Validation score: 0.795851\n",
      "Iteration 40, loss = 0.48144078\n",
      "Validation score: 0.797297\n",
      "Iteration 41, loss = 0.47963715\n",
      "Validation score: 0.798169\n",
      "Iteration 42, loss = 0.47784176\n",
      "Validation score: 0.800303\n",
      "Iteration 43, loss = 0.47612453\n",
      "Validation score: 0.801152\n",
      "Iteration 44, loss = 0.47480888\n",
      "Validation score: 0.800968\n",
      "Iteration 45, loss = 0.47304286\n",
      "Validation score: 0.801313\n",
      "Iteration 46, loss = 0.47169522\n",
      "Validation score: 0.803447\n",
      "Iteration 47, loss = 0.47040757\n",
      "Validation score: 0.802781\n",
      "Iteration 48, loss = 0.46919893\n",
      "Validation score: 0.800762\n",
      "Iteration 49, loss = 0.46797644\n",
      "Validation score: 0.801611\n",
      "Iteration 50, loss = 0.46661853\n",
      "Validation score: 0.806040\n",
      "Iteration 51, loss = 0.46545301\n",
      "Validation score: 0.805994\n",
      "Iteration 52, loss = 0.46435197\n",
      "Validation score: 0.805397\n",
      "Iteration 53, loss = 0.46333878\n",
      "Validation score: 0.808082\n",
      "Iteration 54, loss = 0.46246234\n",
      "Validation score: 0.807256\n",
      "Iteration 55, loss = 0.46134017\n",
      "Validation score: 0.807899\n",
      "Iteration 56, loss = 0.46034301\n",
      "Validation score: 0.807233\n",
      "Iteration 57, loss = 0.45942651\n",
      "Validation score: 0.807922\n",
      "Iteration 58, loss = 0.45829066\n",
      "Validation score: 0.810240\n",
      "Iteration 59, loss = 0.45745964\n",
      "Validation score: 0.811158\n",
      "Iteration 60, loss = 0.45679497\n",
      "Validation score: 0.809735\n",
      "Iteration 61, loss = 0.45592600\n",
      "Validation score: 0.808197\n",
      "Iteration 62, loss = 0.45531328\n",
      "Validation score: 0.808725\n",
      "Iteration 63, loss = 0.45451786\n",
      "Validation score: 0.808473\n",
      "Iteration 64, loss = 0.45349860\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/jupyterhub/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "mlp_1 = MLPClassifier(hidden_layer_sizes=(20,20,20), max_iter = 150, early_stopping = True, verbose = True, solver = \"sgd\")\n",
    "mlp_1train = mlp_1.fit(X_train_c,y_train_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4f02a3f-d77b-4d44-81cb-b207614dd4e2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.43912277\n",
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      "Iteration 148, loss = 0.52412025\n",
      "Validation score: 0.776184\n",
      "Iteration 149, loss = 0.52359227\n",
      "Validation score: 0.775610\n",
      "Iteration 150, loss = 0.52314273\n",
      "Validation score: 0.776414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/jupyterhub/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "mlp_2 = MLPClassifier(hidden_layer_sizes=(20,20,20),  max_iter = 150, early_stopping = True, verbose = True, solver = \"sgd\", momentum = 0.0, nesterovs_momentum = False)\n",
    "mlp_2train = mlp_2.fit(X_train_c,y_train_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e0a4874c-1d2a-4cd8-bce9-17f0a2a68efd",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.28301464\n",
      "Validation score: 0.615821\n",
      "Iteration 2, loss = 0.92111954\n",
      "Validation score: 0.665986\n",
      "Iteration 3, loss = 0.80252653\n",
      "Validation score: 0.688384\n",
      "Iteration 4, loss = 0.74259424\n",
      "Validation score: 0.702612\n",
      "Iteration 5, loss = 0.71116340\n",
      "Validation score: 0.712548\n",
      "Iteration 6, loss = 0.69283093\n",
      "Validation score: 0.721705\n",
      "Iteration 7, loss = 0.68053177\n",
      "Validation score: 0.726891\n",
      "Iteration 8, loss = 0.67131834\n",
      "Validation score: 0.730448\n",
      "Iteration 9, loss = 0.66359309\n",
      "Validation score: 0.732812\n",
      "Iteration 10, loss = 0.65702330\n",
      "Validation score: 0.734234\n",
      "Iteration 11, loss = 0.65131538\n",
      "Validation score: 0.735956\n",
      "Iteration 12, loss = 0.64623831\n",
      "Validation score: 0.735979\n",
      "Iteration 13, loss = 0.64168637\n",
      "Validation score: 0.737424\n",
      "Iteration 14, loss = 0.63749160\n",
      "Validation score: 0.738342\n",
      "Iteration 15, loss = 0.63361334\n",
      "Validation score: 0.739513\n",
      "Iteration 16, loss = 0.63003732\n",
      "Validation score: 0.740270\n",
      "Iteration 17, loss = 0.62663584\n",
      "Validation score: 0.740935\n",
      "Iteration 18, loss = 0.62343463\n",
      "Validation score: 0.742060\n",
      "Iteration 19, loss = 0.62032946\n",
      "Validation score: 0.743804\n",
      "Iteration 20, loss = 0.61743960\n",
      "Validation score: 0.744332\n",
      "Iteration 21, loss = 0.61470404\n",
      "Validation score: 0.745204\n",
      "Iteration 22, loss = 0.61206757\n",
      "Validation score: 0.746305\n",
      "Iteration 23, loss = 0.60951541\n",
      "Validation score: 0.746099\n",
      "Iteration 24, loss = 0.60701580\n",
      "Validation score: 0.747613\n",
      "Iteration 25, loss = 0.60458510\n",
      "Validation score: 0.748692\n",
      "Iteration 26, loss = 0.60217462\n",
      "Validation score: 0.749541\n",
      "Iteration 27, loss = 0.59979820\n",
      "Validation score: 0.750734\n",
      "Iteration 28, loss = 0.59754456\n",
      "Validation score: 0.751606\n",
      "Iteration 29, loss = 0.59534327\n",
      "Validation score: 0.751859\n",
      "Iteration 30, loss = 0.59324121\n",
      "Validation score: 0.752180\n",
      "Iteration 31, loss = 0.59122693\n",
      "Validation score: 0.753305\n",
      "Iteration 32, loss = 0.58928259\n",
      "Validation score: 0.753786\n",
      "Iteration 33, loss = 0.58734654\n",
      "Validation score: 0.754865\n",
      "Iteration 34, loss = 0.58551619\n",
      "Validation score: 0.755255\n",
      "Iteration 35, loss = 0.58371151\n",
      "Validation score: 0.756127\n",
      "Iteration 36, loss = 0.58189531\n",
      "Validation score: 0.756540\n",
      "Iteration 37, loss = 0.58011098\n",
      "Validation score: 0.757137\n",
      "Iteration 38, loss = 0.57833897\n",
      "Validation score: 0.757298\n",
      "Iteration 39, loss = 0.57660006\n",
      "Validation score: 0.757940\n",
      "Iteration 40, loss = 0.57488873\n",
      "Validation score: 0.758743\n",
      "Iteration 41, loss = 0.57315276\n",
      "Validation score: 0.758858\n",
      "Iteration 42, loss = 0.57156456\n",
      "Validation score: 0.759592\n",
      "Iteration 43, loss = 0.57001420\n",
      "Validation score: 0.760028\n",
      "Iteration 44, loss = 0.56852062\n",
      "Validation score: 0.760281\n",
      "Iteration 45, loss = 0.56706151\n",
      "Validation score: 0.761130\n",
      "Iteration 46, loss = 0.56550455\n",
      "Validation score: 0.761337\n",
      "Iteration 47, loss = 0.56405536\n",
      "Validation score: 0.761750\n",
      "Iteration 48, loss = 0.56261347\n",
      "Validation score: 0.761704\n",
      "Iteration 49, loss = 0.56121510\n",
      "Validation score: 0.762851\n",
      "Iteration 50, loss = 0.55981986\n",
      "Validation score: 0.762484\n",
      "Iteration 51, loss = 0.55849082\n",
      "Validation score: 0.764205\n",
      "Iteration 52, loss = 0.55720136\n",
      "Validation score: 0.764457\n",
      "Iteration 53, loss = 0.55591856\n",
      "Validation score: 0.765123\n",
      "Iteration 54, loss = 0.55469887\n",
      "Validation score: 0.765605\n",
      "Iteration 55, loss = 0.55347595\n",
      "Validation score: 0.766133\n",
      "Iteration 56, loss = 0.55223161\n",
      "Validation score: 0.766202\n",
      "Iteration 57, loss = 0.55100655\n",
      "Validation score: 0.766385\n",
      "Iteration 58, loss = 0.54984931\n",
      "Validation score: 0.767693\n",
      "Iteration 59, loss = 0.54862987\n",
      "Validation score: 0.768428\n",
      "Iteration 60, loss = 0.54748333\n",
      "Validation score: 0.768955\n",
      "Iteration 61, loss = 0.54627844\n",
      "Validation score: 0.769116\n",
      "Iteration 62, loss = 0.54516478\n",
      "Validation score: 0.770195\n",
      "Iteration 63, loss = 0.54405670\n",
      "Validation score: 0.769988\n",
      "Iteration 64, loss = 0.54293239\n",
      "Validation score: 0.770677\n",
      "Iteration 65, loss = 0.54193455\n",
      "Validation score: 0.771319\n",
      "Iteration 66, loss = 0.54087665\n",
      "Validation score: 0.772008\n",
      "Iteration 67, loss = 0.53985669\n",
      "Validation score: 0.771962\n",
      "Iteration 68, loss = 0.53881222\n",
      "Validation score: 0.773063\n",
      "Iteration 69, loss = 0.53784554\n",
      "Validation score: 0.772948\n",
      "Iteration 70, loss = 0.53682166\n",
      "Validation score: 0.774050\n",
      "Iteration 71, loss = 0.53580160\n",
      "Validation score: 0.774968\n",
      "Iteration 72, loss = 0.53482644\n",
      "Validation score: 0.775220\n",
      "Iteration 73, loss = 0.53379166\n",
      "Validation score: 0.775702\n",
      "Iteration 74, loss = 0.53285491\n",
      "Validation score: 0.775840\n",
      "Iteration 75, loss = 0.53191128\n",
      "Validation score: 0.776046\n",
      "Iteration 76, loss = 0.53107815\n",
      "Validation score: 0.776551\n",
      "Iteration 77, loss = 0.53011697\n",
      "Validation score: 0.776414\n",
      "Iteration 78, loss = 0.52928948\n",
      "Validation score: 0.776804\n",
      "Iteration 79, loss = 0.52839011\n",
      "Validation score: 0.777859\n",
      "Iteration 80, loss = 0.52759738\n",
      "Validation score: 0.777492\n",
      "Iteration 81, loss = 0.52674287\n",
      "Validation score: 0.778846\n",
      "Iteration 82, loss = 0.52588249\n",
      "Validation score: 0.779030\n",
      "Iteration 83, loss = 0.52515252\n",
      "Validation score: 0.779007\n",
      "Iteration 84, loss = 0.52427166\n",
      "Validation score: 0.778456\n",
      "Iteration 85, loss = 0.52352233\n",
      "Validation score: 0.779581\n",
      "Iteration 86, loss = 0.52274350\n",
      "Validation score: 0.779856\n",
      "Iteration 87, loss = 0.52198835\n",
      "Validation score: 0.780292\n",
      "Iteration 88, loss = 0.52128459\n",
      "Validation score: 0.780774\n",
      "Iteration 89, loss = 0.52052190\n",
      "Validation score: 0.779695\n",
      "Iteration 90, loss = 0.51978148\n",
      "Validation score: 0.780705\n",
      "Iteration 91, loss = 0.51912276\n",
      "Validation score: 0.779994\n",
      "Iteration 92, loss = 0.51836357\n",
      "Validation score: 0.781072\n",
      "Iteration 93, loss = 0.51774613\n",
      "Validation score: 0.780682\n",
      "Iteration 94, loss = 0.51703753\n",
      "Validation score: 0.781852\n",
      "Iteration 95, loss = 0.51638538\n",
      "Validation score: 0.782105\n",
      "Iteration 96, loss = 0.51570952\n",
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      "Iteration 97, loss = 0.51503891\n",
      "Validation score: 0.780843\n",
      "Iteration 98, loss = 0.51436885\n",
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      "Iteration 99, loss = 0.51370040\n",
      "Validation score: 0.781990\n",
      "Iteration 100, loss = 0.51297980\n",
      "Validation score: 0.782679\n",
      "Iteration 101, loss = 0.51235582\n",
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      "Iteration 102, loss = 0.51171304\n",
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      "Iteration 103, loss = 0.51106641\n",
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      "Iteration 104, loss = 0.51053525\n",
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      "Iteration 105, loss = 0.51000520\n",
      "Validation score: 0.784790\n",
      "Iteration 106, loss = 0.50929497\n",
      "Validation score: 0.782679\n",
      "Iteration 107, loss = 0.50883440\n",
      "Validation score: 0.783229\n",
      "Iteration 108, loss = 0.50824764\n",
      "Validation score: 0.783046\n",
      "Iteration 109, loss = 0.50762332\n",
      "Validation score: 0.785547\n",
      "Iteration 110, loss = 0.50702879\n",
      "Validation score: 0.784698\n",
      "Iteration 111, loss = 0.50650502\n",
      "Validation score: 0.785180\n",
      "Iteration 112, loss = 0.50599009\n",
      "Validation score: 0.785868\n",
      "Iteration 113, loss = 0.50554142\n",
      "Validation score: 0.785501\n",
      "Iteration 114, loss = 0.50493958\n",
      "Validation score: 0.787360\n",
      "Iteration 115, loss = 0.50451595\n",
      "Validation score: 0.787062\n",
      "Iteration 116, loss = 0.50397393\n",
      "Validation score: 0.785868\n",
      "Iteration 117, loss = 0.50348810\n",
      "Validation score: 0.786832\n",
      "Iteration 118, loss = 0.50292134\n",
      "Validation score: 0.785501\n",
      "Iteration 119, loss = 0.50244027\n",
      "Validation score: 0.786327\n",
      "Iteration 120, loss = 0.50195218\n",
      "Validation score: 0.787980\n",
      "Iteration 121, loss = 0.50139590\n",
      "Validation score: 0.788003\n",
      "Iteration 122, loss = 0.50075738\n",
      "Validation score: 0.787681\n",
      "Iteration 123, loss = 0.50024748\n",
      "Validation score: 0.788232\n",
      "Iteration 124, loss = 0.49965221\n",
      "Validation score: 0.788714\n",
      "Iteration 125, loss = 0.49910920\n",
      "Validation score: 0.787589\n",
      "Iteration 126, loss = 0.49860330\n",
      "Validation score: 0.788760\n",
      "Iteration 127, loss = 0.49817973\n",
      "Validation score: 0.787727\n",
      "Iteration 128, loss = 0.49761086\n",
      "Validation score: 0.789678\n",
      "Iteration 129, loss = 0.49701629\n",
      "Validation score: 0.789471\n",
      "Iteration 130, loss = 0.49648335\n",
      "Validation score: 0.790183\n",
      "Iteration 131, loss = 0.49600046\n",
      "Validation score: 0.789563\n",
      "Iteration 132, loss = 0.49544764\n",
      "Validation score: 0.790274\n",
      "Iteration 133, loss = 0.49493420\n",
      "Validation score: 0.790710\n",
      "Iteration 134, loss = 0.49449885\n",
      "Validation score: 0.791307\n",
      "Iteration 135, loss = 0.49404279\n",
      "Validation score: 0.791858\n",
      "Iteration 136, loss = 0.49356859\n",
      "Validation score: 0.792064\n",
      "Iteration 137, loss = 0.49315324\n",
      "Validation score: 0.790389\n",
      "Iteration 138, loss = 0.49272384\n",
      "Validation score: 0.792569\n",
      "Iteration 139, loss = 0.49232200\n",
      "Validation score: 0.791353\n",
      "Iteration 140, loss = 0.49197457\n",
      "Validation score: 0.792638\n",
      "Iteration 141, loss = 0.49157738\n",
      "Validation score: 0.792500\n",
      "Iteration 142, loss = 0.49112375\n",
      "Validation score: 0.793372\n",
      "Iteration 143, loss = 0.49074215\n",
      "Validation score: 0.791147\n",
      "Iteration 144, loss = 0.49041547\n",
      "Validation score: 0.793304\n",
      "Iteration 145, loss = 0.48993661\n",
      "Validation score: 0.790252\n",
      "Iteration 146, loss = 0.48967996\n",
      "Validation score: 0.793074\n",
      "Iteration 147, loss = 0.48925375\n",
      "Validation score: 0.792753\n",
      "Iteration 148, loss = 0.48887704\n",
      "Validation score: 0.793143\n",
      "Iteration 149, loss = 0.48844041\n",
      "Validation score: 0.794015\n",
      "Iteration 150, loss = 0.48805498\n",
      "Validation score: 0.794497\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/jupyterhub/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "mlp_3 = MLPClassifier(hidden_layer_sizes=(20,20,20), max_iter = 150, early_stopping = True, verbose = True,solver = \"sgd\", momentum = 0.5)\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": 26,
   "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": 27,
   "id": "56044537-957c-4290-a83a-73c02e499a82",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
       "               max_iter=150, solver='sgd', verbose=True),\n",
       " MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
       "               max_iter=150, momentum=0.0, nesterovs_momentum=False,\n",
       "               solver='sgd', verbose=True),\n",
       " MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
       "               max_iter=150, momentum=0.5, solver='sgd', verbose=True)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classificationList"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d98e013-6397-47bf-9663-2415ad6c92f8",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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, solver='sgd', 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': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 4.1663815955508277e-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: 150\n",
      "\n",
      "number training samples seen by the solver during fitting: 58827450\n",
      "****************************************************************************************\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, momentum=0.0, nesterovs_momentum=False,\n",
      "              solver='sgd', 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': 0.0, 'n_iter_no_change': 10, 'nesterovs_momentum': False, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 0.5231427320832721\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: 150\n",
      "\n",
      "number training samples seen by the solver during fitting: 58827450\n",
      "****************************************************************************************\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, momentum=0.5, solver='sgd', 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': 0.5, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 0.4880549759312877\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: 150\n",
      "\n",
      "number training samples seen by the solver during fitting: 58827450\n",
      "****************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "comparison(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 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bO+YrV67wXVxchB07dnTdsGFD/TWWl5dTAQEB7QQCgXD48OHtqqur639Cjhw5YtixY0dXoVDo5u/v304kEtHstcyfP9+ancd37tzR0WSMv/zyi5GXl5erm5ubsGfPnoLs7GyuTCaDg4ODR25uLhcAZDIZ7O3tPfLy8ri5ubncYcOGtffw8HDz8PBwO3funB57XWPHjnXo1atXh9GjRzupOldcXJwOO48FAoEwMTGRB/z1HZk3b541a+GysLDwGjNmjCOgev6rmp+aXG9xcTGdkpLC79GjRxUADBkypMLc3FwGAAMGDKh49uyZNgBcunRJz8HBQSwUCmt0dHSY0aNHFx06dKhNY32/6b4EAkFNSUkJl7Uc/50QywmBQHgllK0W6t4b87VRXFmj0rpx+PZTlZYF5WNVWSGCOtu+FIcxb7CgPj6DXeirW/CvOHEfKc/KIKlrS1MAh6ZwP0fUQHzce1q79qRQ61KVVVgJpu49o9BfxLEkyORMg/Ox/1cWD8qWErYvmgK4NIX+Lha4lP4cUqm83o3rz8yiegsKe99kMjk4Kqw3hL/46KOPipYvX24VEhJSkpKSwv/kk08Kr1+/rq/p8cePH28jFAqr7t69qyMUCiubehrO5/Plv/3220MTExN5Xl4et1u3bq7jxo0rOXLkiGHbtm0lly5deggAhYWFnPz8fM6pU6eMHz16lETTNF68eMFR7CskJEQUGxv7XF9fX7ZixYp8xX1HjhwxfPjwoU5CQkIKwzAYPHiw8+nTp/XbtWtXk5mZqbNr167Mffv2PQGA+Ph4/V69emUBwO3bt3UOHTpkEhcXl8rj8ZgJEybYb9++3TQ8PLxww4YNOZaWljKpVIqePXu6xMbG6nbr1q0KAHR0dOTx8fFpAHD27FkjAAgMDCybN2+efW5uLtfa2lq6e/du00mTJr1QdV9u3LjBz8vL03rw4MF9AHjx4gXHzMxMtm3bNgvW8lBZWUnNmDHD6dy5c2leXl7iUaNGOa5du9Y8IiKiAAAMDQ1liYmJKZs3bzadM2eO3cWLFx8CQHZ2Nu/WrVtpycnJvMGDB7uMHDkyUfn8Dx8+1Ll+/XpaSUkJx83NzWPRokXPY2NjdU+cOGGcmJiYLJFIqI4dOwo7depUCdQKv5iYmJfUfvfu3cv27t2bre7zV7xPP/zwg8oF7saNG82MjIxkSUlJKVVVVVTXrl1dAwMDS11dXWvU9VtVVUX36NGjfNOmTTkzZ8603bRpk/m3336bp9jmk08+cfzuu++eDB8+vHzGjBm27PZ169ZZ6OrqytPT05NjY2N1e/XqJQRqBdPq1autLl++nG5oaChfsmRJ25UrV1quW7cuDwDMzMykycnJKd988435N998Y3nw4MEsdeNjGTJkSHloaGgqTdPYsGGD2YoVK9ru2rXr6ZgxYwp/+OEHk4iIiIJjx44Zurm5VVlZWUkDAwOdFixYkD9s2LDyBw8eaA8bNqzDo0eP7gNAQkICPzY2NlVfX19l1r9NmzaZz5o1Kz8sLKyourqaUrbqbdy4MRdAbmFhIadHjx4uc+fOLVA3/729vauU5yfQ9Dy4evWqnouLS5Wa8ZkNGDBABADZ2dnaNjY29Z+vra1tTWxsrMa/Q2+qL09Pz8oLFy7oT5o0qUTT/lsCIk4IhH8YmrhCqRMT6lyl6hfTsoauT+zim6bwkvhQdmNirRbswpsVDezCnX1l2zKo7bNG8tcCPvZRIfq7WIDLoesX9uoW/AlPRfX90QA8bYyQ8qysfrvi+WgA9qZ8PCmqhJypa29b214mk4OiKMgZ5iUhxJ5bUTwoWmsUBRQNoJezGeYNFjRox7pxsZaX2QOc4eNgjKDOtio/R0JDunXrVvX06VPerl27TAYPHixq+oha+vXrJ6BpGm5ubpUbNmzIOXv2rEZ//OVyOTVv3jzbmzdv6tM0jYKCAu2nT59yO3fuXLVkyRK7sLAwm5EjR4r8/PzKJRIJeDyePDQ01GH48OGikJAQjcd35swZw8uXLxsKhUIhAFRWVtKpqak67dq1q7GysqoZNGhQBdtWJBJxjY2N5XXHGSQlJfG9vb3dAKC6upq2sLCQAsBPP/1ksnfvXjOpVEo9f/5c6969ezqsOJk4ceJLpk2apvHhhx8W7tq1y2T27NmFt2/f1j9y5MhjVeN1dXUVZ2dn8z7++GO7wMBA0ahRo0qV29y7d0/H1tZW7OXlJQaASZMmFW7ZssUCQAEAfPzxx0UAMG3atKKlS5fWx5MEBQUVcTgceHp6iu3s7MR379596Sn/0KFDS3R1dRldXV2piYmJ5OnTp9xLly7p+/v7l9QtfpkhQ4aUsO1XrlyZv3LlynzlfppC1X1S5vz584apqan848ePGwNAWVkZJzk5WacxcaKlpcWwVjofH5+K8+fPGyruLyws5JSVlXGGDx9eDgBTpkwpvHDhghEAXL16Vf/TTz8tAGq/DwKBoBKofWqekZGh4+vr6woAEomE8vHxKWf7HDduXDEA+Pr6VrJjbYrHjx9rf/DBB7bPnz/Xqqmpoe3s7MQAEBYW9mLEiBHOERERBbt37zZjRey1a9cMHzx4UO8aWV5ezikuLqYBwM/Pr0SdMAGAHj16VKxbt87q6dOn2qGhocWenp5i5TZyuRxjxoxxmj17dn6fPn0qV69eba5q/oeEhJSomp9NzYOcnBwtU1PTl1wST5w4YbBv3z6z69evpwIAw7x8GRRFaZRq/U32ZW5uLs3JydHWpO+WhIgTAuEdRlFoAGhysaouJa2yWGhKTCgu3JVjHxRdn9g2TVk3KNRaLRRFCYuyBYS1QgR1tkVQZ9uX4jD+l5wPLQ6Fsd3s4W5t1EBoKS/42f60tWh42BghMUdULxQUxYcWl8b0vu2x4uR9SKS17yMC3evvuTFfu34fa8lQPrfi/VdlMdHm0vXCBAB8HIwxb7AAf2YW1Z9T0W3Lx8GYiBIN8fPzK1m+fLnduXPn0goKCjT6u8fGnLDvO3bsWJ2SksKXyWRg3bpUsWPHDpPCwkJuYmJiCo/HY2xsbDyrqqpoLy8v8e3bt5MPHz5stGTJEpvz58+Xrlu3Lu/u3bspx48fNzxw4IDxtm3bLG7evJmuyfgYhsG8efPyFi1a1MBSkZaWps3n8xvElnA4HIYdN8MwVHBwcOGWLVtyFNukpqZqb9682TI+Pj7F3NxcFhQU5FhdXV3v+m1gYKAyXiUsLKxw+PDhzjo6OkxgYGCxutgAc3NzWVJSUvLRo0cNt27danHw4EGTmJiYTOVragya/ssTXXEBRVENHR2V3wMAj8erb8/hcCCVSqnGzveqlhPF+8TlchmZTAYAqKysrB8UwzDU+vXrnwQFBb0k0NTB5XIZ9vq5XC6kUmmDi2QYRuV1s6jaxzAMevfuXXrixAmVglJHR4dhz618PnWEh4fbz50799n48eNFJ0+eNFixYoU1ADg7O0vMzMykx48fN7hz547er7/++ogdQ1xcXIoqEaKnp9dojNTMmTOL+vTpU3H06FEjf39/wdatWzOV4yM+++wzaysrq5q5c+cW1p1P5fwHAFXzs6l5wOfz5WKxuEGIRGxsrO6sWbMcfvvttwdt27aVAYC9vX2N4iL/6dOn2tbW1k3GWb3pvqqrqyldXd3XjlVrLkScEAjvAE25OXFpCqAolUHfiseeTsrTyBVK8Um9j4Nxk65SiqhyfWJdopStG4puSQCw/9aTl/pWdnliF/6sEGMX8OyCnwEgkzOwbqOLcd0aJpBRXvArukQBwOHbT1WKD1ZguLQ1eEkAsq+q9il/hooWExpArw5m8PewUmmpYvuOntqdWEhek7CwsBdGRkYyX1/fqpMnT75S9hl3d3exl5dXxYIFC6y/++67XJqmkZiYyLt3757uhAkTSth2IpGIY2ZmJuHxeMyJEycMcnNztQEgMzNTy8LCQjpr1qwiAwMD+U8//WQqEono8vJyOiQkRNS/f/9ygUDgqel4/P39S7/88kvr6dOnFxkZGckfP36spa2trfKr6eTkVJ2SksLz8PAQ+/n5lY4ePdp58eLF+TY2NtL8/HyOSCTiFBcXc3R1deUmJiay7Oxs7qVLl4z69evXZCCso6OjxNLSUrJ+/Xqr06dPqxVWeXl5XB6PJ580aVKJQCAQT5kyxQkA9PX1ZSKRiAPUCsCcnBztpKQknoeHhzgqKsq0T58+9WOIiooyWb169bMff/zRuFOnTvWWoSNHjhiHh4cXpqam8rKzs3ne3t7VFy5caNLS1b9///KwsDCHysrKPIlEQp0/f77NxIkTnwOvbjlRxM7OTnzr1i29AQMGVEZHR9d/eYcMGSLatm2beUBAQBmPx2MSEhJ4jo6OEkNDQ7mTk5P748eP7zf3XGZmZjJ9fX3Z2bNn9YcNG1a+d+9eE3Zf7969y/ft22cSGBhY9ueff+qkp6fz666/4rPPPrNn73dZWRn9+PFjLdZy9SqUlZVx7O3tJQCwd+/eBov6KVOmPJ86dapTUFBQIese2bt379I1a9ZYsPf6+vXruj179lTpJqVMcnKytpubm9jd3b3g0aNHvLt37+oqipP9+/cbXbp0yfDGjRtp7DZ189/AwECuan42NQ88PT2rv//+ex77/sGDB9rBwcHtd+/e/VjxPvbr168iMzNTJzU1VdvR0VFy5MgRk+jo6EcAsHr1anMAWLx4cYPkBm+6LwDIyMjQCQ0NLQaA2bNn23Tr1q1i4sSJJU1+OK8JEScEwluMYlxGo25OstplOYO/grRj4rLrXZlUHduUKxSHppBbUoVfYp8gp6SqUVcpVmS4WRkiMUdU7/rELr5PJ+WptG6E+to3EFGsOFC2Pii6mgF4yfqjHIfRWHB4Ywt+VdsV9zdmrWhsnzrLlKKlRB3EQvL6tG/fXrJs2bICVfsOHTpkevbs2Tbs++vXr6eo62ffvn2Zs2bNsnNwcPDQ1dWVt2nTRrZ27doGT9GnTp1a5O/v7+zh4eHm7u5e6eTkVA0A8fHxuv/+979taZoGl8tltm7dmlVSUsIJCAhwFovFFACsWrVK7RN5ZUaPHl16//59na5du7oCtbEu0dHRj7lc7ksCZejQoaJz584ZeHh4iH18fKqXLl2aM2jQIIFcLoeWlhYTGRn5ZNCgQRUeHh6VHTp0cLe3txcruvc0RWhoaOGWLVu4jWULyszM1Prkk08c5XI5BQArVqx4CgATJ058MWfOHIdFixbJ4+LiUrZv354ZHBzcXiaTwdvbu3LhwoX1iyyxWEx5eXm5yuVy6sCBA/ULKGdnZ7Gvr69LYWGh1saNG7P4fL5GLi79+vWr9PPzEwmFQncbGxuxl5dXhZGRkUzT626KL774Ij8kJKTdgQMHTPv06VNvJZk/f/6LzMxMnqenpxvDMJSJiYnk1KlTGXl5eVyGYTSyUKjixx9/zJw6daqjrq6ufODAgfXnW7hwYUFoaKiTQCAQuru7V3p6elYAgLW1tXTHjh2ZoaGh7WpqaigAWL58ec7riJMlS5bkjh07tr2lpWVNly5dKp48eVK/cB87dqwoPDycM3369PrMIDt37syeOnWqvUAgEMpkMqpbt25lPXv2fKLJuX7++WeTmJgYUy6Xy5ibm0u+/vrrXMX9GzdutCwoKNDq2LGjG1BrQd24cWOuqvnP5/PlquZnU3Tq1Km6rKyMU1xcTBsbG8uXLl1qVVJSwp0zZ44DUGt1SkpKStHS0sL69euf+Pn5CWQyGcaNG/eiS5cu1QCQmpqq26tXr5e+b2+6L7FYTGVmZvL69u1bAQDJycm6iok1WpNGzZrvGl26dGHi4uLe9DAIhNdGlShRBwVAi1NrOWmqLfBy/IRiH6wgYK0yisHiXDXuSsquZeN/uFlvfWgsxoRDAQuGumB2bUKd+utuykqw5eJDrD+XBjnzch+aHP8mUBwzK9o0ESZ/BxRFxTMM06Wl+713716mt7e3yuBowt9LVlaW1tixYx2vX7/+oDX6nzhxon2nTp0q58+f32qft42NjWdcXFyKoqsdUJutKyAgQDR58uRXKvIjEoloIyMjeVlZGd2jRw+X7du3Z/Xu3btVUhA3xf79+40yMjJ4S5cuVSmk33UuX77Mnz9/vh2bMOB94auvvrIwMDCQL1iw4JXm/4ABA5xPnz6dwbrRvQ4t2VdUVFSb+Ph4/vfff58LAL179+5w9erVFv0NuXfvnpm3t7ej8nZiOSEQWgBNg8yVXxtb4Csu5BVR5eak6JbUmKBhn9iz8RPqXKG2XHwIqayhm1djrlKaWB80sW5oYiVgCxK+S3EYymN+W4TJ28bVhy/0rj18YdDL2ayst7NZRdNHEDTBwcFBMmXKlBdFRUV0S9Q6UcTd3d1NV1dXvmPHDo2tPm8TEyZMcHjw4IGuWCymQkNDC9+UMAFqLQtv6tytzeLFi9vu3bvXfM+ePSrjW95lFi1a9Px1ipKyWedagpbsSyqVUsuWLat3aWtpYdIYxHJCILwm6or4NeWO9VIWqbp4EUXXKOBlq4YqcaM8HlWpZlVZPJTFgrKlg82EpRwg/7r363WtG2+rhaQx3tYxvy2Wk6sPX+hN2funQCqT01wOLd89qWs6ESjvJl5eXq41NTUNgoSjoqIe+/r6ahRLQHg7+f777023bdvWoA5M165dy3/++WeN3LBelcOHDxsuWbLEVnGbnZ2d+H//+19Ga56X0Pqos5wQcUIgNEJTtTyAl1122CxPmrhYNYaqNLSvM/bmtmsqtTDh/eBtESdrzqS23fFHho2cqZ37M/q1z/mXn+uzlh4XgUAgEN4OiFsX4R9DcxfVjS3OWYuIKgsHKxpYlx3WyqBYP0MRZcuJYkC7Mso1L14FTV2c1LV7W12kCO8nvZzNyn68+ljOWk56OZs1mSmKQCAQCO8fRJwQ3itUZUVSl1pXVeaniAD3erepBml3lWt5KGTECu5ih4gA9wYZqYDG3bEUUwGzrleKma9IXALhn0ZvZ7OK3ZO6ppOYk9Zn3rx51v379y/74IMPylasWGExf/78F+rqmLwqz54944wcObJ9YmKi3pgxYwqjoqLqXX98fX1dCgoKtHR0dOQA8Pvvv6fb2NhIq6qqqDFjxjglJiby27RpI42JiXnk4uKitjBhc3nd4PnmcPLkSYM9e/aYHj58OPN1+1m/fr3lxYsXH0ZGRppmZmbyNmzYkNv0ka9/vtY6hzpu3bqlO3HiRCcAyMvL09bX15cZGBjITExMpNevX9eo/o8qqqurqd69ewtu3LiRpq4OD+HtgogTwjtDUy5Wqipvs8JCObWuYnyHYt2PiGNJkMkZjSwciml7eVq1wkZV/YzGBIZyle+3NS6BQPg76O1sVkFESeuzcePG+sXtjh07LKdNm1bUHHEilUrB1qlQB5/PZ1asWJF779493aSkJF3l/VFRUY/69u3bIPj8+++/NzMyMpI+efIkaefOncYLFiyw/e233x4pH/um0eT630Ukkibr+LUqvr6+VampqcmAeiEpkUjQXIGho6PD9OvXr/SHH34wCQsLK2rBIRNaCbrpJgTC30t8VjG2XHyIX2KfYMvFh4jPKq63iKw/l4axO29gelQcxu56+f3VB7WWCzZRPPvKColzyfkNrCH3ntbV5KAAmqYgZ5gGhQZpAF62RtDm0uBQgDaHwlChJbS5dIO+JVI5iitrED21OxYMdcH+6T3wn1GeGtWwmD3AuUFWK8X3BALhn0FkZKTp4MGD2w8cONDZxsbGc/Xq1eZffvmlpZubm9Db29s1Pz+fAwDr16838/DwcHNxcREOGzasfVlZGQ0AgwYNar9582ZTAFi7dq3ZiBEjnNSdKygoyHHPnj3Gq1atsigoKNDq16+foFu3bgIAOHLkiGHHjh1dhUKhm7+/fzuRSEQDtal8Fy5caOXj4+Oye/fuJn+gDA0N5cOGDStnrSOacPLkyTZTpkwpBIDJkycXX79+3UAuV3+4VCpFUFCQY4cOHdwFAoHwq6++sgBqC/l5e3u7CgQC4ZAhQ9o/f/6co3jcf//7X8P/+7//a6dwXoOBAwc6t+T183g8uaGhoQwAfvvtN31XV1ehq6ur0M3NTVhcXEzLZDJMmDDB3tnZ2X3AgAHO/fr1c2YzPh06dMjQycnJ3cfHx+XQoUNt2D51dXXl+vr6jdZh8fLyco2Li9Nh3/v6+rpcuXKFX1paSgcHBzt6eHi4ubm5Cfft29cGqJ13/v7+7QYOHOjcp08fAVBbSHHIkCHt27dv7z5u3Dh7tsr9jh07TAQCgbBDhw7uYWFhNo19Bvfv3+f16dOng7u7u5uPj4/LnTt3dJTHqim+vr4u4eHhNl27dnVZtWqVJTt/2f18Pr8T+/9ly5ZZenh4uAkEAuH8+fOt2e1jxowpOXDggIly34S3k/dP+hPeadS5ZTWwcKhwsVJ8r1j8T7lehyrY+A5/DyusOHn/pQxVqiqFq8p0xe4jwoJAILwK6enpuvfu3UuuqqqiXVxcPJYtW5aTkpKS/Mknn9jt2LHDNCIiomD8+PHFn3322QsA+PTTT60jIyPNlixZUrB3796sXr16uTo7O4u3bNnSNjY2Vm0xSZalS5cWbNu2zfKPP/5It7Kykubl5XFXr15tdfny5XRDQ0P5kiVL2q5cudJy3bp1eQCgo6MjZ2tULFu2zDImJualSqfdu3cv27t3b5NphadOnepI0zQCAwOL16xZk0fTNPLz87WdnJxqAEBLSwv6+vqy/Px8rnJtE5YbN27w8/LytB48eHAfAF68eMEBgEmTJjl99913T4YPH14+b94863/961/Wu3fvrh/TqFGjSufOnetQWlpKGxoayvfv3288ZsyYopa8/iFDhlQMGTKkAgDWr1/fNjIyMmvo0KEVIpGI5vP58qioKOPs7GzttLS0+zk5OVwPDw+PSZMmFVZWVlLh4eGO//vf/9Lc3d3FAQEB9SJq2rRpTbqjBQUFFUVHR5t06dIlNysrS6ugoECrT58+leHh4TYDBgwojYmJyXzx4gWnS5cubiNGjCgFgNu3b+snJCTct7S0lJ08edIgMTFR786dO0kCgaCmb9++HaKioowHDBhQ/uWXX9rEx8enmJubS/v06SP4+eef2zg6Otao+gymTp3qsHPnzixPT0/xhQsX9MLCwuxv3ryZvm3bNpPvv/++rfK4HR0dq8+cOaPWSlZSUsL5888/0+qu0VFVmyNHjhg+fPhQJyEhIYVhGAwePNj59OnT+v7+/uVdu3atSkhI0Gvq/hHeDog4IfztNOa6dPNRoUq3rHtPa9O/NxZEzu7X1moYrxHU2fal1Lrq4jtc2hqoDaZXVSlc2S2LQCAQXpWePXuWGRsby42NjeX6+vqy4ODgEgDw9PSsTEhI4AO1leYjIiJsysrKOBUVFZx+/fqJAMDOzk66ePHi3ICAAJeoqKiHlpaWza50funSJb2MjAwdX19fVwCQSCSUYqX4iRMn1i+OV65cmb9y5cp8Vf00xcGDBx85OTlJiouL6YCAgPZbt241DQ8PL1SVPZSiKLU/+a6uruLs7Gzexx9/bBcYGCgaNWpUaWFhIaesrIwzfPjwcgCYNm1aYXBwcDvF47S0tNC/f//SAwcOGE2ePLn4woULRps3b3565swZg9a4/u7du5cvXLjQ7sMPPywaO3Zscfv27eVXrlzRHz16dDGHw4G9vb20e/fuZQBw9+5dHVtbW7Gnp6cYAMaPH1/4ww8/mGtyHnaMgwcPFnz33Xe5UVFRxoGBgcUAcOnSJcOzZ8+2iYyMbAvUVv9++PChNgD06dOnVHG+eHp6VgiFwhoA+PDDD4uuXLmir6WlxXTv3r3M2tpaCgAhISFFf/zxh76fn1+e8mcgEonoO3fu6AcHB7dn+2Qr0IeFhRW9imvV2LFjmzzmzJkzhpcvXzYUCoVCAKisrKRTU1N1/P39y7lcLrS0tBi2kntzz0/4eyHihPC3opwBSzkuQznzlaIYUUzTq0pkqIvzUCckVImk5lo+iKWEQCC0FNra2vULcZqmwVZ5pmkaUqmUAoDp06c7HTp06GGPHj2qIiMjTf/44w8D9pjExERdIyMjaU5OzitF/TIMg969e5eeOHFCZaE8xbiU17GcODk5SQDA2NhYHhISUnTr1i09AIVt27atefz4sXb79u0lEokE5eXlHAsLC7Uiy9zcXJaUlJR89OhRw61bt1ocPHjQZPv27RoVgwwNDS3asmWLhZmZmczLy6vS2NhY3lrXv3r16mcffPCB6NixY0Y9e/Z0O3PmTHpjZRwoilK7rymcnJwkbdq0kcbGxuoeOXLEZMeOHVlA7Wd76NChh97e3mLF9levXtXj8/kNFuvK56coCurGq+oz2LFjxxMDAwMpGz+iyKtaThTvPZfLZVhXM7lcDolEQrHXOG/evLxFixapTGEukUgoPp///tTPeI9p1ZgTiqL8KIpKoyjqIUVRX6jYb0RR1AmKou5RFHWfoqjJmh5LePtRjh35JfZJfcC6YqD62J03sORoIn6JfYKbjwoREeCOz4a5YPUoT4ztZv9XvIdWrYvV/ml/xXXsnNilwfvG4jxIfAeBQHjXqayspO3t7SVisZhS9KG/ePEi//fffzeKj49P3rx5c9vU1FRtTfrT09OTsXEV/fv3r4iLi9NPSkriAUBZWRmdkJDAU3XcypUr81NTU5OV/zUlTCQSCfLy8rhA7dP7U6dOGXl4eFQBwPDhw0t2795tCgB79uwx7tGjRxlN1y5TnJyc3JX7ysvL48pkMkyaNKlk1apVOYmJiXxTU1OZoaGh7MyZM/oA8OOPP5r26NGjXPnY4cOHl92/f5+/a9cus+Dg4KLWvP779+/zfH19q/7zn/888/T0rEhKStLp06dP+a+//mosk8mQnZ3NjY2NNQCAjh07Vj99+lT7/v37PABQFycRFRXVZvbs2Taq9o0ZM6Zo9erVbcvKyjhs4csBAwaUrl+/3pKN4bl27dpLSQpYEhMT9VJTU7VlMhkOHTpk0qdPn7K+fftWxMbGGuTl5XGlUiliYmJM+vfvX67qMzAxMZHb2trWsLE5crkcN27c0AVqLSeq7ltjwkQZBweHmvj4eD4AREdHt2GFu7+/f+nPP/9sxs7nx48fa+Xk5HCB2uxxxsbGUh6PR8TJO0CrWU4oiuIA2AJgCICnAP6kKOo4wzCKSno2gGSGYQIpijIHkEZRVDQAmQbHEt5SFOMx1FVHZ1/ZQPXo2Nosk6oqkatynVJlGSEQCIT3nS+++CLX19fXzcbGpsbNza2yvLycU1VVRc2cOdPxxx9/zHR0dJSsXr06++OPP3a8ceNGOru4V8fHH3/8wt/fv4OFhYUkNjY2fceOHZmhoaHtWDec5cuX53h5eYkb7UQNNjY2nuXl5RyJREKdPXu2zalTp9I7dOhQM3jw4A4SiYSSy+VUnz59ShcsWPAcAObOnfsiKCjIyd7e3sPIyEh28ODBDKBWhDAM85I5ITMzU+uTTz5xlMvlFACsWLHiKQDs2bPncVhYmMOnn35K29vbi/fv35+pfCyXy8WgQYNEhw4dMv3vf/+bCQDW1tbSlrx+lm+//dbi+vXrhjRNMwKBoGrMmDEibW1t5vz58wYCgcDdycmp2tvbu6JNmzYyPp/PbNq0KSsgIMDZxMRE2q1bt/KUlJSXhMTDhw95bMC9MhMmTChetmyZ/dy5c+uzsn3zzTe506dPt3d1dRUyDEPZ2tqK1aUL7tixY/lnn31mm5qaqtutW7eyjz76qITD4SAiIiKnX79+AoZhqEGDBokmTJhQcuPGDV1Vn8H+/fsfTZs2zWHNmjVWUqmUGjVqVFGPHj2qXuc+ssyZM+d5QECAs6enp1vfvn1LdXV15QAwevTo0vv37+t07drVFQD4fL48Ojr6sY2NjfT06dOGgwYNErXE+QmtT6tViKcoqgeALxmGGVb3/t8AwDDM1wpt/g3ADrUixRHA/wAIAHRr6lhVkArxbxZVokQVTVVR51DAgqEumD3AuZVHTCAQ3pYK8QSCOvbv32+UkZHBW7p0acGbHktLIhKJaCMjI/mzZ884Xbt2dbt27Vqqvb29yuB/ZUaOHOm0bdu2bDYGhNA4Q4cObb927dqnym5thDfLm6gQbwNA0bz5FLWiQ5HNAI4DyAVgACCEYRg5RVGaHAsAoChqOoDpAGBvb98yIydojGI19hUn79dn2VKGtZQoZ8BixYxU+leGLDbzFYFAIBAIY8eOfS+feA8ZMqRDaWkpRyKRUIsWLcrTVJgAwLFjx1TGxRBeprq6mhoxYkQJESbvDq0pTlRFdCmvW4cBuAtgIID2AP5HUdQVDY+t3cgwOwHsBGotJ686WIJqFMWHYvYqRSuJVCYHTTWsEdJYdXRl9yzWbUvVfgKBQCC8Gh999JH9n3/+qa+4LSwsLH/u3LmFb2pMhL+4detW2psewz8BHR0dJjw8nMz5d4jWFCdPUeuyxWKLWguJIpMBfMPU+pY9pCjqMQBXDY8ltDLqao6wGbIauGQxDGiaAgVG4+roLCRmhEBoPncL7iIuPw5G2kYQ1YjQxbILOlp0fNPDIrxF/Pzzz0/e9BgIBAKhubSmOPkTQAeKopwA5AAIBTBOqc0TAIMAXKEoyhKAC4BHAEo0OJbQCiim11VXc0Sx4CHwV22RiAB3Yvkg/ONhRQMrFtS9Z0WFsrjQpH1qUSp+ffgrJHIJGDCgQUObo41dQ3cRgUIgEAiEd5pWEycMw0gpigoHcBYAB8BuhmHuUxQ1s27/dgArAeylKCoRtWvcfzEM8wIAVB3bWmMl4CU3LdZCwuXQ9fEgygUQFV23NLWSEAjvA6qsFgBwIuMEfn34K6RyKbQ52vi86+f49s9vUSOrAZfmordNb1zNuVovKmptjbXiQnG/8vFimbhBe/aVRQ45JHIJ4vLjiDghEAgEwjtNqxZhZBjmFIBTStu2K/w/F8BQTY8ltDzqMmzVyBj8LzkfWhwKY7vZw93aCEm5ogZV1okoIbxvqLNqNGW14NK1P6XsNgCokdVg7/29qJHVQA45auQ1uJB9ocH52Laq9isez7ZTfmWhQUOL1qoXSQQCgUAgvKuQCvH/UDRJ+8sAkMkZWLfRxbhutZnQVNUcIRDeFtTFYTS1nV3UTzs3Ta2VoimrBYCXtmeXZas8hkVdn+qOV7a0jHQeCTcTNxJzQiAQCIT3BiJO/oEoB7qzsG5abMC7TCZ/Ka0vCV4n/J1oIirYBfndgrsNxIWyq5SypUPRhYpLcyEwETRqpWjMasFaTmSMDByKA4GJAMkvkiGHHDRouJu5I60orX6/oqhQtMaoO767dXcMth+sMkaFoB6KonxGjhxZ9Ouvvz4GaquTW1hYeHfs2LHi4sWLDyMjI03j4uL0oqKiGgSO29jYeOrp6ckoioKZmZlk//79j+3t7aUikYgOCwuzu3LligGPx2PatGkj/fbbb58OHDiwgs/nd6qsrLzTEuP+9ttvzfl8vjw8PLzwzp07OmPHjm1HURQOHTqUMWHCBKc7d+6kNqc/uVyOnj17Ck6dOvXQxMRE/rrjCwoKcgwICBBNnjy5+HX7ioyMNB0xYkSpo6Oj5HX7UuTy5cv83bt3mzZVsf5VOHz4sOGSJUtsAeDJkyc8CwsLiY6OjtzNza3y6NGjmS19PgBQN78U54ri9rS0NO2AgIAODx48eMkl3tfX12XdunXZffv2rWyJsan7Hr0pOnXq5Nrc78jrcO3aNd3IyEiLgwcPZm3bts3ku+++awsAenp68q1bt2axxScPHTpkuHDhQnu5XI4JEya8WL169bPG+n3TfU2fPt02ICBANGLEiLLXvUfNhYiTfwiKKYFPJ+U1CHRXFTuiGBhPxAihJWksYBxAvRhR5z6lLCrYhf75J+cbiAt1rlQqXajkNUh6kQTgZWuGplYLduyKVhiJXAItWgufd/28wX5VoiKwfaDa48O8w4gQeQV0dXXlaWlpuuXl5ZS+vj5z9OhRQ0tLS40WwX/88Ue6lZWVNDw83CYiIsJq79692ePHj3d0cHAQZ2ZmJnE4HCQnJ2snJCS8VL37dfn888+fs/+PiYlp4+/vX/Ldd9/lAkBzFl1yuRwMwyAmJsbI3d29qiWEiVTasjX/9u3bZ9axY8eqlhYnffv2rWypxbcyQUFBpUFBQcmA+oW+VCoFl9v6SyzFufK+IpFIoKWlpXH7v1OYAMCqVausIiIi8gDA2dlZfO3atTRzc3PZf//7X8MZM2Y4JCQkpEqlUsyfP9/+7Nmz6e3atZN4e3u7BQUFlfj4+FSr6/dN97Vw4cKCyZMnOxBxQmhxVLlvKRZE5KqJHSEWEkJTNBWfoWoRzlo3lAPEWaEB4KVgcRZ1oiImPQaAelGhjLrtANRaKTS5NgANtu0auuslMdKYwOho0bHJ4wnNZ9CgQaKYmJg2kydPLt6/f79JUFBQ0fXr1/WbPrKW/v37l23atMni/v37vDt37uj9+uuvjzgcDgBAKBTWCIXCGsX2IpGI9vPzcxaJRBypVEpFRETkTpgwoaS0tJQeMWJEu7y8PG25XE59/vnnudOmTSueNWuWzdmzZ9twOBymf//+pTt37ny6YMECa319fZm7u3v1zp07LWmaZq5fv64fGxubrvgEfdmyZZZHjx41qampoYYPH17y3Xff5aalpWn7+/t36NmzZ1l8fLz+sWPHHkZHR5vMmDHjBTvGrVu3mmzbts1SIpFQnTt3roiKisricrkYP368/b179/Sqq6vpwMDAYlYQ2djYeI4dO/bFxYsXDWfMmFFfpf3YsWMGmzdvtvjf//6XAQBHjx413LZtm/m5c+cylO+jVCpFSEiIY0JCgh5FUcz48eNf2NvbS5KSkvgTJ05sp6OjI4+Li0v5/fff9b/44gs7mUwGb2/vyqioqCxdXV3GxsbGc8SIEUVXr141BID9+/c/8vDwEAcFBTnyeDx5WlqabmFhodbXX3+dPXbsWNHJkycN1q9fb3nx4sWHCxYssM7OztbOysri5ebmas+cOTOfrTa/aNEiq0OHDplYWVnVmJqaSjt16lS5YsWKfOXxa4Lyffrhhx8sWOGSl5fH7dKli1tOTk6iVCrF7Nmzba9du2ZQU1NDTZs2rWDRokUvmup/zpw5NufOnTPS0dGRnzx58qGdnZ2UnSsrVqzIv3LlCn/q1KmOurq68m7dupWzx5WXl1OhoaFO6enpOh06dKiurq6uryV35MgRwxUrVljX1NRQDg4O4gMHDmQaGRnJbWxsPD/88MPCs2fPGkmlUurgwYOPOnXqpHbxy/LLL78YffPNN1YSiYQ2NjaWHjx48JG1tbW0Xbt2Hjdu3Ei1traWymQyODk5ecTGxqYyDIPJkyc75OTkaAPAhg0bngwdOrRiwYIF1nl5eVpPnjzRNjExkZ44ceKlopNxcXE6kydPdpJIJJRcLsfhw4czPD09xex3ZN68edZnzpxpAwBFRUXcvn37lh46dChT1fwH8NL8XL58eYHyOZUpLi6mU1JS+KwVYsiQIRXsvgEDBlSEh4drA8ClS5f0HBwcxOzvxejRo4sOHTrUxsfHR63F4033JRAIakpKSrhPnjzhNqdAaEtAxMl7hqKFhA1gV44pYQDQAHo5m2HeYAERIQSVNJbSVtmqoYl1wUjbqN66oUpoKMdtKAuIxkQF215RXCi7SrFjUd7OCiQZI2tRK4Wy2Pi7jyfU8tFHHxUtX77cKiQkpCQlJYX/ySefFDZHnBw/fryNUCisunv3ro5QKKxs6mk4n8+X//bbbw9NTEzkeXl53G7durmOGzeu5MiRI4Zt27aVXLp06SEAFBYWcvLz8zmnTp0yfvToURJN03jx4gVHsa+QkBBRbGzsc3bxqbjvyJEjhg8fPtRJSEhIYRgGgwcPdj59+rR+u3btajIzM3V27dqVuW/fvicAEB8fr9+rV68sALh9+7bOoUOHTOLi4lJ5PB4zYcIE++3bt5uGh4cXbtiwIcfS0lImlUrRs2dPl9jYWN1u3bpVAYCOjo48Pj4+DQDOnj1rBACBgYFl8+bNs8/NzeVaW1tLd+/ebTpp0iSVi+wbN27w8/LytFg3oxcvXnDMzMxk27Ztq1/AV1ZWUjNmzHA6d+5cmpeXl3jUqFGOa9euNY+IiCgAAENDQ1liYmLK5s2bTefMmWN38eLFhwCQnZ3Nu3XrVlpycjJv8ODBLiNHjkxUPv/Dhw91rl+/nlZSUsJxc3PzWLRo0fPY2FjdEydOGCcmJiZLJBKqY8eOwk6dOlUCtcIvJibGVLmf7t27lzXmKqZ4n3744QcLVW02btxoZmRkJEtKSkqpqqqiunbt6hoYGFjq6upao6o9AFRVVdE9evQo37RpU87MmTNtN23aZP7tt9/mKbb55JNPHL/77rsnw4cPL58xY4Ytu33dunUWurq68vT09OTY2FjdXr16CQEgLy+Pu3r1aqvLly+nGxoaypcsWdJ25cqVluvWrcsDADMzM2lycnLKN998Y/7NN99YHjx4MEvd+FiGDBlSHhoamkrTNDZs2GC2YsWKtrt27Xo6ZsyYwh9++MEkIiKi4NixY4Zubm5VVlZW0sDAQKcFCxbkDxs2rPzBgwfaw4YN6/Do0aP7AJCQkMCPjY1N1dfXV/nDv2nTJvNZs2blh4WFFVVXV1PKVr2NGzfmAsgtLCzk9OjRw2Xu3LkF6ua/t7d3lfL8BJqeB1evXtVzcXGpUjM+swEDBogAIDs7W9vGxqb+87W1ta2JjY3V+HfoTfXl6elZeeHCBf1JkyaVaNp/S0DEyXtCYxYSZWgK0ObSRJj8w2hOvQ0AKi0c6qwayoKCFR+aWjUoUNCia832Urm0PtZCUeCoExWK7bU52g3EhaKrlOJCX3m7qhgWwvtBt27dqp4+fcrbtWuXyeDBg0WaHtevXz8BTdNwc3Or3LBhQ87Zs2c1+uMvl8upefPm2d68eVOfpmkUFBRoP336lNu5c+eqJUuW2IWFhdmMHDlS5OfnVy6RSMDj8eShoaEOw4cPF4WEhGg8vjNnzhhevnzZUCgUCgGgsrKSTk1N1WnXrl2NlZVVzaBBg+qflIpEIq6xsbG87jiDpKQkvre3txsAVFdX0xYWFlIA+Omnn0z27t1rJpVKqefPn2vdu3dPhxUnEydOfCm+hKZpfPjhh4W7du0ymT17duHt27f1jxw58tITbgBwdXUVZ2dn8z7++GO7wMBA0ahRo0qV29y7d0/H1tZW7OXlJQaASZMmFW7ZssUCQAEAfPzxx0UAMG3atKKlS5fWF2oOCgoq4nA48PT0FNvZ2Ynv3r2ro9z30KFDS3R1dRldXV2piYmJ5OnTp9xLly7p+/v7l9QtfpkhQ4aUsO1XrlyZv3LlymZbUFTdJ2XOnz9vmJqayj9+/LgxAJSVlXGSk5N1GhMnWlpaTGhoqAgAfHx8Ks6fP2+ouL+wsJBTVlbGGT58eDkATJkypfDChQtGAHD16lX9Tz/9tACo/T4IBIJKoPapeUZGho6vr68rAEgkEsrHx6fe4jJu3LhiAPD19a1kx9oUjx8/1v7ggw9snz9/rlVTU0Pb2dmJASAsLOzFiBEjnCMiIgp2795txorYa9euGT548KDeNbK8vJxTXFxMA4Cfn1+JOmECAD169KhYt26d1dOnT7VDQ0OLPT09xcpt5HI5xowZ4zR79uz8Pn36VK5evdpc1fwPCQkpUTU/m5oHOTk5Wqampi+5JJ44ccJg3759ZtevX08FgNpa4w2hKEr907a3pC9zc3Mpa9X6OyHi5B1FMSYEgMoAd1UxJe7WRqRQ4jtMU4JCnZsVu7Bn3aeaqrfBBoc3lQKXpSnxoShe1AWIj2g/4qVr0FRUqGuvzvqgvJ1YKd5v/Pz8SpYvX2537ty5tIKCAo3+7rExJ+z7jh07VqekpPBlMhlYty5V7Nixw6SwsJCbmJiYwuPxGBsbG8+qqiray8tLfPv27eTDhw8bLVmyxOb8+fOl69aty7t7927K8ePHDQ8cOGC8bds2i5s3b6ZrMj6GYTBv3rw8ZXegtLQ0bT6f3yC2hMPhMOy4GYahgoODC7ds2ZKj2CY1NVV78+bNlvHx8Snm5uayoKAgx+rqaprdb2BgoDJeJSwsrHD48OHOOjo6TGBgYLG62ABzc3NZUlJS8tGjRw23bt1qcfDgQZOYmJhM5WtqDJquH06DBRRFUQ3aKb8HAB6PV9+ew+FAKpVSjZ3vVS0niveJy+UyMpkMAFBZWVk/KIZhqPXr1z8JCgp6SaCpg8vlMuz1c7lcSKXSBhfJMIzK62ZRtY9hGPTu3btUlcsUAOjo6DDsuZXPp47w8HD7uXPnPhs/frzo5MmTBitWrLAGAGdnZ4mZmZn0+PHjBqx7JDuGuLi4FFUiRE9Pr9EYqZkzZxb16dOn4ujRo0b+/v6CrVu3ZirHR3z22WfWVlZWNXPnzi2sO5/K+Q8AquZnU/OAz+fLxWIxrbgvNjZWd9asWQ6//fbbg7Zt28oAwN7evkZxkf/06VNta2vrJuOs3nRf1dXVlK6u7mvHqjUXIk7eQdhsWzXS2mKJblaGDQLcWRqLKSG8nTQWx6HOlUpVnY3GxIIm9TaUg8OVUee2pUoMySFvMFa2uCCgOkC8OXEZRFQQNCEsLOyFkZGRzNfXt+rkyZMGr9KHu7u72MvLq2LBggXW3333XS5N00hMTOTdu3dPd8KECSVsO5FIxDEzM5PweDzmxIkTBrm5udoAkJmZqWVhYSGdNWtWkYGBgfynn34yFYlEdHl5OR0SEiLq379/uUAg8NR0PP7+/qVffvml9fTp04uMjIzkjx8/1tLW1la52nZycqpOSUnheXh4iP38/EpHjx7tvHjx4nwbGxtpfn4+RyQScYqLizm6urpyExMTWXZ2NvfSpUtG/fr1azIQ1tHRUWJpaSlZv3691enTp9UKq7y8PC6Px5NPmjSpRCAQiKdMmeIEAPr6+jKRSMQBagVgTk6OdlJSEs/Dw0McFRVl2qdPn/oxREVFmaxevfrZjz/+aNypU6d6y9CRI0eMw8PDC1NTU3nZ2dk8b2/v6gsXLjRp6erfv395WFiYQ2VlZZ5EIqHOnz/fZuLEic+BV7ecKGJnZye+deuW3oABAyqjo6Pr/wAPGTJEtG3bNvOAgIAyHo/HJCQk8BwdHSWGhoZyJycn98ePHze76LSZmZlMX19fdvbsWf1hw4aV792714Td17t37/J9+/aZBAYGlv3555866enp/Lrrr/jss8/s2ftdVlZGP378WIu1XL0KZWVlHHt7ewkA7N27t8GifsqUKc+nTp3qFBQUVMi6R/bu3bt0zZo1Fuy9vn79um7Pnj1Vukkpk5ycrO3m5iZ2d3cvePToEe/u3bu6iuJk//79RpcuXTK8ceNGGrtN3fw3MDCQq5qfTc0DT0/P6u+//57Hvn/w4IF2cHBw+927dz9WvI/9+vWryMzM1ElNTdV2dHSUHDlyxCQ6OvoRAKxevdocABYvXtwgucGb7gsAMjIydEJDQ4sBYPbs2TbdunWrmDhxYkmTH85rQsTJO0Z8VjE2nk9HjVQOOVNbLPHe01pPAEUxQiwkbzeqREhTcRzqrBGq6mw0lf5WGVXnaCoFblMuUKyVo7FgciIsCH8H7du3lyxbtkxlcOuhQ4dMz54924Z9f/369RR1/ezbty9z1qxZdg4ODh66urryNm3ayNauXdvgKfrUqVOL/P39nT08PNzc3d0rnZycqgEgPj5e99///rctTdPgcrnM1q1bs0pKSjgBAQHOYrGYAoBVq1ZpnPZ29OjRpffv39fp2rWrK1Ab6xIdHf2Yy+W+9GUfOnSo6Ny5cwYeHh5iHx+f6qVLl+YMGjRIIJfLoaWlxURGRj4ZNGhQhYeHR2WHDh3c7e3txYruPU0RGhpauGXLFm5j2YIyMzO1PvnkE0e5XE4BwIoVK54CwMSJE1/MmTPHYdGiRfK4uLiU7du3ZwYHB7dnA+IXLlxYv8gSi8WUl5eXq1wupw4cOFC/gHJ2dhb7+vq6FBYWam3cuDGLz+dr5OLSr1+/Sj8/P5FQKHS3sbERe3l5VRgZGck0ve6m+OKLL/JDQkLaHThwwLRPnz71VpL58+e/yMzM5Hl6eroxDEOZmJhITp06lZGXl8dlGEYjC4Uqfvzxx0w2IH7gwIH151u4cGFBaGiok0AgELq7u1d6enpWAIC1tbV0x44dmaGhoe1qamooAFi+fHnO64iTJUuW5I4dO7a9paVlTZcuXSqePHlSv3AfO3asKDw8nDN9+vT6tMc7d+7Mnjp1qr1AIBDKZDKqW7duZT179tQoJfHPP/9sEhMTY8rlchlzc3PJ119/nau4f+PGjZYFBQVaHTt2dANqLagbN27MVTX/+Xy+XNX8bIpOnTpVl5WVcYqLi2ljY2P50qVLrUpKSrhz5sxxAGqtTklJSSlaWlpYv379Ez8/P4FMJsO4ceNedOnSpRoAUlNTdXv16vXS9+1N9yUWi6nMzExe3759KwAgOTlZd9SoUSWa3JfXpVGz5rtGly5dmLi4uDc9jBalsQB3xZgSGkCvDiTA/U3zKhXGm8oupQ5VlhNlS4VyG8Xg76bqbWjRWtg1dBeAxlPgElqJ7FtA5hXAsQ9g5/vXe11ToKrw5Ve2XTOhKCqeYZgWLy1/7969TG9v7yYzEBFan6ysLK2xY8c6Xr9+/UFr9D9x4kT7Tp06Vc6fP7/VPm8bGxvPuLi4FEVXO+D1a66IRCLayMhIXlZWRvfo0cNl+/btWb17926VFMRNsX//fqOMjAwem0nsfePy5cv8+fPn27EJA94XvvrqKwsDAwP5ggULXmn+DxgwwPn06dMZrBvd69CSfUVFRbWJj4/nf//997kA0Lt37w5Xr15t0d+Qe/fumXl7ezsqbyeWk7eUpgLcaQCetkZIeVZWXyyRCJO/j9exfDQVTM6iaQYsTWJOmipgqIiqIHIiSloRVaLj2V3gzi+AXArQHKDDUODB/wCZBIAcf/0a1L1SNMDhAR8ffyWBQni/cXBwkEyZMuVFUVER3RK1ThRxd3d309XVle/YsaPFix3+HUyYMMHhwYMHumKxmAoNDS18U8IEqLUsvKlztzaLFy9uu3fvXvM9e/aojG95l1m0aNHzPXv2vPLii8061xK0ZF9SqZRatmxZvUtbSwuTxiCWk7cQdRXcWSgAPC0a0VO7AwAplvg30Jw0us1FE/FBqoK/QzRl4VAlQtSJjuZAcYCBS4A+nzXvMGI5IbQwXl5erjU1NQ2ChKOioh77+vpqFEtAeDv5/vvvTbdt22apuK1r167lP//8c6tWhj98+LDhkiVLbBW32dnZidn6OoR3F3WWEyJO3gKUM29tPJ+Oaw9fQK700ZAA99anORaRpmhOhXEiPt4R1LlaOfap3X/vlybEhrrX5tJylhMiTggEAoHwJiBuXW8pypm3QFH1blwkwL11aCwu5Ns/v4VYJtbIDYulOZYPIkLeEZrjasW+BwXIatBQbDAavrIoiA2a+/I5Ok0A2nq3aMwJ4Z/HvHnzrPv371/2wQcflK1YscJi/vz5L9SlCn5Vnj17xhk5cmT7xMREvTFjxhRGRUXVP1339fV1KSgo0NLR0ZEDwO+//55uY2MjraqqosaMGeOUmJjIb9OmjTQmJuaRi4uL2tofbytpaWnaFy9e1J85c2bR33XOK1eu8D/55BPH6upqeuDAgaLdu3dnK6ZeZvn3v//dNjo62oymaTQ3nfHr8MUXX7T95ptv1FYwJ7yZeWNjY+Opp6cnY+fKpk2bshSrySu3VRX31RoQcfIGUAxyP52UV595SyJjwC59SQX316c5VhAaNGiKhpyRN5npilg+3gMac73SxNVKJgNST/7Vn4xd16kRG+peWRGiSnSoss4Q8UFoAeoqZwMAduzYYTlt2rSi5ogTqVQKNhWsOvh8PrNixYrce/fu6SYlJekq74+KinrUt2/fBvEd33//vZmRkZH0yZMnSTt37jResGCB7W+//fZI+di3nQcPHvAOHjxo8ncuMmfNmuWwdevWrIEDB1b079+/w6FDhww//PDDBsIjPj5e58iRIyZpaWn3s7KytIYMGSIYOXJkUlOfZUsQGRlp1Vxxosk8e594E/MGeLmm09vAP+dTf0tQjidhlyqslQQURQLcX5O7BXdxIuOERnEhiul4wQA0RQMMXqrNoSxGiAh5y1EWH4oLfY1dr1jUWTlYKICjVfuqiYWjuRYPO18iSv4BREZGmh4/fryNXC6n0tLSdGfPnv2spqaGPnjwoKm2trb83LlzDywtLWXr168327Nnj7lEIqEcHR3Fhw4demxgYCAfNGhQ+1GjRpWEh4cXrl271uzKlSsGx48fVxl8zGa4ysnJ0SooKNDq16+fwNjYWBobG5t+5MgRwxUrVljX1NRQDg4O4gMHDmQaGRnJbWxsPMeOHfvi4sWLhjNmzCiYPn16o9mxDA0N5cOGDStPS0vjNdZOkZMnT7b58ssvcwFg8uTJxf/617/s5XI5VFkAWPh8fqePP/644PLly4ZGRkay//znP0//9a9/2eXm5mqvWbPmyfjx40WVlZXUxIkTHRISEvgcDgfffvttdmBgYJmm9/z+/fu8mTNn2hcVFXF1dHTkP/zwQ1anTp2qg4KCHA0MDGT37t3Te/78udbKlSufTp48uXjJkiU2jx490nF1dRWOHTv2hbGxsSwuLk6PtR4NGDDA+bPPPssPCAgo02T8Td23rKwsrfLycnrw4MEVADB+/PjCX3/91VhZnBw6dKjN6NGji3R1dRlXV9caBwcH8aVLl/TY41Th6+vr4uPjU3716lXDsrIyzvbt2zP9/PzKpVIpZs+ebXvt2jWDmpoaatq0aQWLFi16kZWVpRUUFNSuvLycI5PJqE2bNmUdP37cSCwW066urkKBQFB1/Pjxx1u3bjXZtm2bpUQioTp37lwRFRWVxeVywefzO02fPj3/woULhmvXrn1648YNvejoaDMA+Oijj55HREQUhIWF2Tg4ONR88cUXzwFgwYIF1gYGBrIpU6YUKZ/bz89PZUpsMm/UM3jw4PZ5eXnaYrGYnjlzZv7ChQsbuOeWlpbSI0aMaJeXl6ctl8upzz//PHfatGnFV65c4S9YsMCusrKSNjY2lkZHR2c6ODg0WRxSFUSc/M3cfFTYoGCispWEbUPct9TTnEKFLE1lxFIsDkjcsN5BFMWIsuVD2UVKY9crFg1crbzH1jYlFo7X49ElPTy6ZIB2/cvQrr/aBdP7Snp6uu69e/eSq6qqaBcXF49ly5blpKSkJH/yySd2O3bsMI2IiCgYP3588WefffYCAD799FPryMhIsyVLlhTs3bs3q1evXq7Ozs7iLVu2tI2NjVVbr4Vl6dKlBdu2bbNkn5zm5eVxV69ebXX58uV0Q0ND+ZIlS9quXLnSct26dXkAoKOjI2fTwL5qBXWWqVOnOtI0jcDAwOI1a9bk0TSN/Px8bScnpxoA0NLSgr6+viw/P5/b2FPdqqoqesCAAWXbtm3LGTJkSPulS5faXLlyJf327ds6kydPdho/frxozZo1FnX3N/nOnTs6//d//9chIyMjSdN7PnXqVIedO3dmeXp6ii9cuKAXFhZmf/PmzXQAyM/P14qLi0u9e/euzqhRo5wnT55c/J///Cdn/fr1lmzmpMjIyJfuU3PGf+/ePV5ISEh7VcdfvXo1LSsrS8vKyqp+Eejg4FCTl5enpdw2JydHu3v37vWLdWtr65rs7GxtAI1+16RSKZWYmJhy8OBBoxUrVlj7+fmlb9y40czIyEiWlJSUUlVVRXXt2tU1MDCwdP/+/caDBg0SrVmz5plUKkVZWRnt5+dXvnfvXovU1NRkALh9+7bOoUOHTOLi4lJ5PB4zYcIE++3bt5uGh4cXVlVV0R4eHlUbN27MvXLlCv+XX34xjY+PT2EYBj4+Pm6DBg0qmzBhQtG8efPsWXFy7Ngx4zNnzjzYvXu3ifK5ybxRP2/MzMxkANCvXz8BTdPQ1taWJyQkpEZHR2daWlrKysvLqU6dOgknTJhQzFaUB4AjR44Ytm3bVnLp0qWHAFBYWMgRi8XUp59+av/bb789tLa2lu7atct44cKFNjExMZmNzS11EHHyNxKfVYyckipwOTSkUjnkqLWYaCtZSYgoqaUlChWykGD0txhVbkvNqfGh1g2rDkZeK0gU3bAa0IKuVkSUvDqPLukh+kMB5BIaN7bKMf6/6f80gdKzZ88yY2NjubGxsVxfX18WHBxcAgCenp6VCQkJfKC2mGNERIRNWVkZp6KigtOvXz8RANjZ2UkXL16cGxAQ4BIVFfXQ0tKy2cUEL126pJeRkaHj6+vrCgASiYRSLMY4ceLEemvJ61RQP3jw4CMnJydJcXExHRAQ0H7r1q2m4eHhhaoS9FAU1WjGCC0tLWbMmDGlAODu7l7F4/HkPB6P8fX1rcrJydEGgOvXr+vPmTOnAKgtmmdtbV2TmJioAzR9z0UiEX3nzh394ODg+kUeW7AQAEaMGFHC4XDg4+NTXVhY+JIgaApNxu/t7S1mF/aqUHPfNG3XZEaO4ODgYgDo2bNnxaJFi7QB4Pz584apqan848ePGwO1leGTk5N1unfvXjFjxgxHiURCjxkzplhVtfczZ84YJCUl8b29vd0AoLq6mrawsJACAIfDwaRJk4oB4NKlS/r/93//V2JoaCgHgOHDhxdfvHjRYOnSpQWFhYXczMxMrby8PK6RkZGsQ4cONZqcm4XMm79Qdutas2aN5W+//dYGAJ49e6Z1//59nbZt29b/Fnfu3LlqyZIldmFhYTYjR44U+fn5lf/55586Dx480B04cKAAAORyOczNzV/JagIQcfK3oFizRCqrDXwf282eBLkroVh/AwCmnZumcXC68isFClq0FhEhbwPqhAWb4eqnEbXigaMN+H3T/Bofat2wWJT317lhvU5wOXG1ankeXTKAXEKDkQNyCV1nQflHiRNtbe36iUrTNNhCajRNQyqVUgAwffp0p0OHDj3s0aNHVWRkpOkff/xhwB6TmJioa2RkJM3JyWn2YgeoXbz27t279MSJEyrdwRTjUl7HcuLk5CQBAGNjY3lISEjRrVu39AAUtm3btubx48fa7du3l0gkEpSXl3MsLCwaFVlcLpdh3b5omgaPx2OA2kWuTCaj2OtSR1P3XCaTwcDAQKpukadY7E7debhcLiOX/xXSIxaLacV9TY2/qSfgjo6OEkVLSVZWlnbbtm1fWhja2tqylhIAQG5urratrW2TC0j2GrlcruI9pdQF1F++fDnt8OHDRpMmTXL69NNP88PDwwsV9zMMQwUHBxdu2bIlR/lYbW1tORtn0tjnFhgYWLxv3z7jZ8+eaQUFBRUBgL+/f3lT52Yh8+Yvy4kiJ0+eNPjjjz8M4uLiUg0MDOS+vr4uVVVVDSxQXl5e4tu3bycfPnzYaMmSJTbnz58v/fDDD0ucnZ2r7t69m6ryYpoJESetjKqaJTI5A+s2uhjXzf6Nju1No6p2iFQuBZfmQmAiQI2spsng9MbS9I5oP4KIkJakOfU7mrJqsNaJtp6ATFxr3ZCKgVOfAXIZ1AaeA2jSDUvZ8qEsdlg3LCIu3i7a9S/Dja1yyCU0aC052vUve9NDehuprKyk7e3tJWKxmDpw4IAJ685z8eJF/u+//24UHx+f3K9fP5fAwMBSV1fXJjNd6enpyUQiEW1lZYX+/ftXfPbZZ/ZJSUk8Dw8PcVlZGf348WMtLy8vsfJxr2o5kUgkePHiBdfKykoqFoupU6dOGQ0cOLAMAIYPH16ye/du08GDB1fs2bPHuEePHmXsAszJycn98ePH95t7PgDo3bt3+b59+0xGjBhRlpCQwMvLy9P28vKqjo2N5Td1rImJidzW1rZm9+7dxlOmTCmWy+WIjY3V7dGjh9qn8kZGRrLy8nIO+759+/Y1u3bt4stkMjx+/FgrISFBrznjb+oJuJmZmUxPT0/++++/6w0YMKAiOjradPbs2S9VmQ8KCioZP358u4iIiPysrCytzMxMnf79ax8AjBo1yvHTTz8tGDBggEZFKIcMGSLatm2beUBAQBmPx2MSEhJ4jo6OkmfPnnGdnJxqPvvssxcVFRX07du3+QAKuVwuIxaLKR6Px/j5+ZWOHj3aefHixfk2NjbS/Px8jkgk4ggEggbzdeDAgeVTpkxxXLly5TOGYXDq1CnjvXv3PgKAjz76qGjatGmOxcXF3D/++CMNANLT07VVnVvzO92Q933eqKKkpIRjZGQkMzAwkN+5c0fn3r17L50zMzNTy8LCQjpr1qwiAwMD+U8//WS6atWqZ0VFRdzz58/rDR48uEIsFlOJiYm8Ll26VK9evdocABYvXvxc03EQcdKKxGcVY+P59AYxJhQALS5dX9PkfaY5sSGKFpEaeQ2SXiQBaF6VdGIZaSEaS6Pb7PodaqwarKtVTnzte4qu/cfI8bLlQxkN3LCULR/e40g8yNtOu/4VGP/f9H9yzIkmfPHFF7m+vr5uNjY2NW5ubpXl5eWcqqoqaubMmY4//vhjpqOjo2T16tXZH3/8seONGzfSGwsmB4CPP/74hb+/fwcLCwtJbGxs+o4dOzJDQ0PbsS4oy5cvz1ElTjTBxsbGs7y8nCORSKizZ8+2OXXqVHqHDh1qBg8e3EEikVByuZzq06dP6YIFC54DwNy5c18EBQU52dvbexgZGckOHjyYAQB5eXlchmFe9lPSkM8//7zgo48+chAIBEIOh4MdO3Zk6urqalxgaP/+/Y+mTZvmsGbNGiupVEqNGjWqqLFFpq+vbxWXy2VcXFyE48aNe7Fs2bKCLVu2iF1cXNxdXFyqhEJhi1eh37p1a9Ynn3ziVF1dTQ0YMKA0ODhYBADR0dFGf/75p97GjRtzu3TpUv3BBx8UCQQCdw6Hgw0bNmSxVoqUlBS+nZ2dxm448+fPf5GZmcnz9PR0YxiGMjExkZw6dSrj7NmzBpGRkW25XC7D5/Nl0dHRjwFg/Pjxz93c3IQeHh6Vx48ff7x06dKcQYMGCeRyObS0tJjIyMgnyuKkd+/elePGjSvs3LmzG1AbEN+rV68qAOjSpUt1RUUFbWlpWcMGXas796vyT5g3ygQFBYl27txpLhAIhO3bt6/29vZ+6Xc4Pj5e99///rctTdPgcrnM1q1bs3R0dJgDBw5kfPrpp/ZlZWUcmUxGhYWF5Xfp0qU6NTVVt1evXioTE6iDFGFsBRTduFTVLHmfCigqu2JpWkVdk0KGNGh0t+6OwfaDifhoDV5JhLwuqvqjgfb9AbeRwJkvakWLqjoipMZHq0CKMBLedvbv32+UkZHBW7p06UvWAMLrU1RURI8fP97x9OnT71zaZsLbz4ABA5xPnz6doejKxkKKMP4NqBIlwPtVs6QxVywAaquoa1o7pLdNb1zNuQoZI4MWrYUw7zAiRJqLJu5XGouQJjJYvWodD0VXK4420P/fteLCUqi+AjsRHwTCP5KxY8e+clpUQtOYmJjIiTAhtBZs9rHmQMRJC6EqtgSoXaJpa717NUuakykLqBUlwKvFhijXDlG0xhBhogZVdTyAZtTwaKYIaSyD1avW8VDlaqUcaE4CzwmEV+ajjz6y//PPP/UVt4WFheXPnTv3lf3wCYR3CS8vL9eampoGvo1RUVGPfX191bpXEd48RJy0EMr1SygAWpx3x41LlUVE0wKGbGYsAJDKpZBD/lqxIR0tOv5zRcmrWD1Y4QCqGTU8XkGEtLT1gggPAqFV+fnnn5+86TEQCG+ShISEFskeRfh7IeKkhejezhTaXBoSqRycdyC2RBMxwtKUKxabGQtAA2sLsXxoQGPFAzW1ejDyumMAjd2v/k4RQiAQWhw+n9+psrLyzt9xrqCgIMfJkycXBgQEvFYGNbYy/eTJk4t9fX1dfv7558cuLi5NZhRrifO11jnU8a9//avtsWPHTADgwYMHuh06dKgCgAkTJrx4ndiZ/fv3G926dUvvu+++y22psRIIbxtEnLwG8VnFuPmoEMZ8bRRX1iAiwP2trVvyKmKERRNXLBYiRpR4neKBmlo9FC0nmgSPExFCIBAASKVSsNma3jckkleu/9YirFmz5tmaNWueAbVCUjmlq1wuB8Mw4HA4qjtQQ0hIiGjFihU2K1aseKZYd4ZAeJ9o1V8liqL8AHwPgAPgB4ZhvlHavwjAeIWxuAEwZximiKKoTABlAGQApK2RTeZVUZeNS5tLI3pq97dCmLyOGCFV1DVAkwrmGltCWF7D6sHGnJDgcQLhnSQuLk5n8uTJTnUpdnH48OEMT09P8ZdffmkZHR1tBtSmUo2IiGjw1H348OHtJk6cWBgSEiICaq0FgYGBJRMmTCiZPXu27bVr1wxqamqoadOmFSxatOjFyZMnDVauXGllYWEhSU5O5mdkZDRZO8TQ0FDG4/HkADBr1iybs2fPtuFwOEz//v1Ld+7c+fT+/fu8cePGOclkMmrw4MGinTt3WlZWVt6Ry+WYNGmS/bVr1wzs7OzEitlB27RpI+VwOGpTAN6+fVvn448/dkpMTEwBgLS0NO3AwEDn9PT05CtXrvAXLFhgV1lZSRsbG0ujo6MzHRwcJL6+vi6+vr7lsbGx+v/3f/9XAgD/+9//DDZv3mxRWFio9fXXX2ePHTtWVFlZSU2cONEhISGBz+Fw8O2332YHBgaWqfsMtm7darJt2zZLiURCde7cuSIqKirrVURdWlqatr+/f4eePXuWxcfH6x87duxhx44d3VkL2J49e4xPnjxpdPjw4czc3Fzu5MmTHdhq3xs2bHgydOjQCpqm0bNnz7KDBw8aTZ069W+3CBEIfwetJk4oiuIA2AJgCICnAP6kKOo4wzD1Tw8YhlkLYG1d+0AA8xmGKVLoZgDDMG9VKkp1ge9yBpBI5bj5qPCNipO7BXdxIuPEa4sRIkLUkH3rr6BzjSuYszSzeOCrWD2IKCEQ3kk2bdpkPmvWrPywsLCi6upqSiqV4sqVK/xffvnFND4+PoVhGPj4+LgNGjSojK31AAAhISFFBw8eNA4JCRFVV1dT165dM/zpp5+yNm7caGZkZCRLSkpKqaqqorp27eoaGBhYCgAJCQl6d+7cuc8WafTx8XGpqKh46RH+N998k/3BBx+U7dmzJxsA8vPzOadOnTJ+9OhREk3TePHiBQcAwsPD7WbNmlUwY8aMom+//dacPf7nn39u8/DhQ15aWtr9p0+fanl6erpPmjSpEADOnTuX0dj96Ny5c7VEIqGSk5O1hUJhTVRUlMkHH3xQLBaLqU8//dT+t99+e2htbS3dtWuX8cKFC21iYmIygdoicn/++WcaUCvUsrOzebdu3UpLTk7mDR482GXkyJGJa9assQCA9PT05Dt37uj83//9X4eMjIwkVZ/B7du3dQ4dOmQSFxeXyuPxmAkTJthv377dNDw8vHD48OHtMjIydJTHHh4errYyeWZmps6uXbsy9+3b12g80IwZM+wWLFiQP2zYsPIHDx5oDxs2rMOjR4/uA0CXLl0qrly5ok/ECeF9pTUtJ74AHjIM8wgAKIo6AGAkAHXVKscC2N+K42kRlAPfWWjqzRRXbMxCwkLESDPQyA1LIehcowrmLK9QPJBAIPwj6NGjR8W6deusnj59qh0aGlrs6ekpvnTpkv7//d//lRgaGsoBYPjw4cUXL140UBQnY8aMEX3++ef2VVVV1OHDh418fX3L9PX1mfPnzxumpqbyjx8/bgwAZWVlnOTkZB1tbW3Gy8urQrF6fHx8fJomYzQxMZHxeDx5aGiow/Dhw0WstebOnTv6586dewgAU6dOLfzyyy9tAeCPP/4w+PDDD4u4XC4cHR0lPXr0aFbMygcffFC0b98+k9WrVz87evSo8cGDBx8lJCTwHjx4oDtw4EABUOseZW5uXu/DNXbsWMUHnAgKCiricDjw9PQU29nZie/evatz/fp1/Tlz5hQAQKdOnaqtra1rEhMTdVR9BmfOnDFISkrie3t7uwFAdXU1bWFhIQWA3377rdnpd62srGoGDRrUZIHRa9euGT548ECXfV9eXs4pLi6mjY2N5W3btpU+e/ZMu7nnJhDeFVpTnNgAyFZ4/xRAN1UNKYriA/ADEK6wmQFwjqIoBsAOhmF2qjl2OoDpAGBvb98Cw1ZPfFYxckqqwOXQkMn+Cnx3tzb622JNmuOuxfKPFyOq0u6qc8U68wUgFUMzNyx1NCP9LhEjBAIBwMyZM4v69OlTcfToUSN/f3/B1q1bMzUpkszn85nu3buXHTlyxPDgwYPG7OKcYRhq/fr1T4KCgkoV2588edKAz+c3iFVoynLCvtfS0sLdu3dTjh8/bnjgwAHjbdu2Wdy8eTO9sfFR1CsXdsdHH31UHBwc3C40NLSYoih4enqKb926pevs7Fx19+5dlVmYlOMwlM9PURTU3Vc1nwEVHBxcuGXLlhzl9q9iOVG+94rjq6qqqn/DMAzi4uJS9PX1XxpsVVUVpaOjQ+JNCO8trSlOVP0iqfulDQRwTcmlqxfDMLkURVkA+B9FUakMw1x+qcNa0bITqK0Q/7qDVoVijIlUJgeXphDqa/+3ZuNqjrsWm9r3vRcjTcV9sHEYP434S3CwgkHZFYuia/8x8tr3ANS7YVEAR0uzCuZEfBAIBA1ITk7WdnNzE7u7uxc8evSId/fuXd2BAweWT5kyxXHlypXPGIbBqVOnjPfu3fvS0/rQ0NCiH3/80SwxMVGPdW8aMmSIaNu2beYBAQFlPB6PSUhI4Dk6OqqMEtfUciISiejy8nI6JCRE1L9//3KBQOAJAB07dizfu3ev8bRp04p3795twrbv169f2a5du8xnz55dmJOTo3Xz5k0DZcsGAIwaNcrx008/LRgwYECl4nZ3d3cxTdOIiIiwHjVqVBEAeHl5VRcVFXHPnz+vN3jw4AqxWEwlJibyunTpUq1qzEeOHDEODw8vTE1N5WVnZ/O8vb2re/fuXb5v3z6TESNGlCUkJPDy8vK0vby8qlV9BsOHDy8dPXq08+LFi/NtbGyk+fn5HJFIxBEIBDWvYjlRxtTUVHL79m0db2/v6mPHjhnr6+vLAKB3796la9assVi5cmU+AFy/fl23Z8+eVQCQlpam4+7uTup0EN5bWlOcPAVgp/DeFoC61HehUHLpYhgmt+61gKKoo6h1E3tJnLQ2qmJMZHIG1m10W1WYvK671oj2I959MdJYdfDsW7WiQ1ajOu6DFSFtPQEZawlBXdrdmpddsRh5rfag6ToNosJyomgJ8R5LKpgTCIQW4+effzaJiYkx5XK5jLm5ueTrr7/OtbS0lI0bN66wc+fObkBtQLyiSxfLqFGjSmfOnOk0ePDgEh0dHQYA5s+f/yIzM5Pn6enpxjAMZWJiIjl16lSjcR5NUVJSwgkICHAWi8UUAKxatSobADZt2pQ9fvx4p8jIyLZDhw4tYRfYH330Ucnvv/9u6OLi4u7k5FTt6+ur0q0rJSWFb2dnp1I4jR49umjlypW2a9asyQEAHR0d5sCBAxmffvqpfVlZGUcmk1FhYWH56sSJs7Oz2NfX16WwsFBr48aNWXw+n/n8888LPvroIweBQCDkcDjYsWNHpq6uLqPuM1i6dGnOoEGDBHK5HFpaWkxkZOQTgUDQIimQv/rqq5yRI0c6W1lZSVxdXasqKipoANi5c2f21KlT7QUCgVAmk1HdunUr69mz5xMAuHz5sgF7PwiE9xFKE7PxK3VMUVwA6QAGAcgB8CeAcQzD3FdqZwTgMQA7hmEq6rbpAaAZhimr+///AKxgGOZMY+fs0qULExcX16LXseXiQ6w/lwZ53W2iAPC0Wi8rlyYWEpb3xl1LlQVEOehc2SqRcgx4dKnO0qEJTbhmUTTA4QF+35D0u4R/FBRFxbdGNsR79+5lent7v1UJTQitQ1lZGa2npyenaRo7d+40PnjwoMnvv/+ukRAqKiqix48f73j69OnXtkL8E8jOzuZ++OGH7W7cuNGoOx2B8C5w7949M29vb0fl7a1mOWEYRkpRVDiAs6hNJbybYZj7FEXNrNu/va7pKADnWGFShyWAo3W+mFwAvzQlTFqLv6O4oqKV5Ns/v4VYJn4/3bUaS7f7UuYrpaDzuN11nWgaB6K4nQba9wfcRr58PhKMTiAQCK/FtWvX+HPnzrVnGAaGhoayvXv3Zmp6rImJiZwIE8159OiR9vr167ObbkkgvLu0muXkTdDSlhPlIostFfCuymVLKpeCpmjIGTnkaGgNeGfctV6p4KCmQeaqoAGbTsCzpJfFhqII4WgDHx//S3gQVywCoR5iOSEQCATCm+Bvt5y867CxJjVSeYsVV2zSZYsBaIoGBQocivN2W0heO9OVunS7LEpB58qiRtENC1AtNrzHqd5u50tECYFAIBAIBMJbCBEnamDrmbREcUVVooRF0WVLm6ONz7t+/vaLEWUrSLMyXbEoBZmrynylHHTeWPyHuoKERIQQCAQCgUAgvDMQcaIGxViT5hZX1CTTFstb67LVmBhRtoI0N9OVunS76tytiMggEAgEAoFA+EdAxIkK2FiTiAD3ZsWaaJJp660Nam+OGFEWWS2V6YqIEAKBQCAQCIR/NEScKPEqsSaaum2xouSNWEheKVj9r6togCorCAkuJxAIBAKBQCC8JvSbHsDbhqpYk8a4W3AX085NQ0x6DGrkNSoLI2rT2hgjGIMfh/2IiB4Rf48wyb4FXFkPxO0FTs4D9gYAv68CTs4Ffl/Z8DVub8NChSotI9pAlylAwPfAwKXApN+AgO+ALpOAPp8RYUIgEN46KIry+eCDD5zY9xKJBMbGxt4DBgxwBoDIyEjTiRMn2isfZ2Nj4ykQCIQuLi7CXr16dXjy5AkXqK2QPm7cOAc7OzsPZ2dn9y5durhcuHBBDwD4fH6nlhr3t99+a75582ZTALhz546Oq6ur0M3NTXj//n1ep06dXJvbn1wuR/fu3QVFRUUt8jc/KCjIcc+ePS2STz8yMtI0MzNTqyX6UuTy5cv8SZMm2TXdsvkcPnzY0NXVVejq6irk8/mdHB0dPVxdXYWjRo1ybI3zAernl+JcUSQtLU27Q4cO7qqO8fX1dbl8+TK/pcam7nv0pniV78jrcO3aNd2QkBAHANi2bZuJQCAQCgQCYadOnVxv3Lihy7Y7dOiQoaOjo4e9vb3H4sWL2zbV75vua/r06bbHjx83aM69aCmI5USJ5sSa3C24i233tqFGVqPSQvJG3LYUCxg2aQlRE6xOLCMEAuE9QFdXV56WlqZbXl5O6evrM0ePHjW0tLRUWYlcmT/++CPdyspKGh4ebhMREWG1d+/e7PHjxzs6ODiIMzMzkzgcDpKTk7UTEhJ0m+6teXz++efP2f/HxMS08ff3L/nuu+9yAeDOnTupmvYjl8vBMAxiYmKM3N3dq0xMTDStWqsWqVT6ul00YN++fWYdO3ascnR01Ohz0ZS+fftW9u3bt7Il+2QJCgoqDQoKSgZqF/rr1q3LVj6XVCoFl9v6SyzFufK+IpFIoKWluX5tznekJVi1apVVREREHgA4OzuLr127lmZubi7773//azhjxgyHhISEVKlUivnz59ufPXs2vV27dhJvb2+3oKCgEh8fn2p1/b7pvhYuXFgwefJkhxEjRpS1xn1rDGI5UcLHwRjRU7tjwVCXRl26WIvJjdwbkEOu0kIS7BKMqZ5TW1eYqLKQNGUJAdXwlVhGCATCe8qgQYNEMTExbQBg//79JkFBQUXNOb5///5ljx8/5t2/f593584dve+//z6Hw+EAAIRCYU1oaKhIsb1IJKJ79OghEAqFbgKBQLhv3742AFBaWkr379/f2cXFRdihQwf3Xbt2GQPArFmzbNq3b+8uEAiE06dPtwWABQsWWEdERFgePHjQaOfOnZbR0dFm3bp1EwANn6AvW7bM0sPDw00gEAjnz59vDdQ+MW/Xrp37hAkT7N3d3YUZGRna0dHRJqNGjSphj9u6dauJp6enm6urq3DcuHEOrOAYP368vYeHh5uzs7M72x9Qa0lauHChlY+Pj8vu3bvr/ygeO3bMYMiQIe3Z90ePHjUcOnRo/XtFpFIpgoKCHDt06OAuEAiEX331lcWePXuMk5KS+BMnTmzn6uoqLC8vp44dO2bg5uYmFAgEwuDgYMeqqiqKHUNYWJiNp6enm6enp1tSUhIPqLXijBs3zt7Hx8fF0dHRY//+/UYAcPLkSQPWQrZgwQLr4OBgR19fXxdbW1vPVatWWbDjWrRokZWTk5N7z549OwQGBjpFRERYajo3lFG+T4oWiry8PK6NjY0ney9mzJhhy352a9euNdOk/zlz5ti4uLgIvb29XbOzs7nstbFjvnLlCt/FxUXYsWNH1w0bNtRfY3l5ORUQENBOIBAIhw8f3q66uppdBODIkSOGHTt2dBUKhW7+/v7tRCIRzV7L/Pnzrdl5fOfOHR1NxvjLL78YeXl5ubq5uQl79uwpyM7O5spkMjg4OHjk5uZyAUAmk8He3t4jLy+Pm5ubyx02bFh7Dw8PNw8PD7dz587psdc1duxYh169enUYPXq0k6pzxcXF6bDzWCAQCBMTE3nAX9+RefPmWbMWLgsLC68xY8Y4Aqrnv6r5qcn1FhcX0ykpKfwePXpUAcCQIUMqzM3NZQAwYMCAimfPnmkDwKVLl/QcHBzEQqGwRkdHhxk9enTRoUOH2jTW95vuSyAQ1JSUlHBZy/HfCbGcqMDHwbjROBNliwkNGt2tuyPMO+wts5DU0VimLE2D1QkEAuEd5KOPPipavny5VUhISElKSgr/k08+Kbx+/bq+pscfP368jVAorLp7966OUCisbOppOJ/Pl//2228PTUxM5Hl5edxu3bq5jhs3ruTIkSOGbdu2lVy6dOkhABQWFnLy8/M5p06dMn706FESTdN48eIFR7GvkJAQUWxs7HN9fX3ZihUr8hX3HTlyxPDhw4c6CQkJKQzDYPDgwc6nT5/Wb9euXU1mZqbOrl27Mvft2/cEAOLj4/V79eqVBQC3b9/WOXTokElcXFwqj8djJkyYYL99+3bT8PDwwg0bNuRYWlrKpFIpevbs6RIbG6vbrVu3KgDQ0dGRx8fHpwHA2bNnjQAgMDCwbN68efa5ublca2tr6e7du00nTZqksvDmjRs3+Hl5eVoPHjy4DwAvXrzgmJmZybZt22bBWh4qKyupGTNmOJ07dy7Ny8tLPGrUKMe1a9eaR0REFACAoaGhLDExMWXz5s2mc+bMsbt48eJDAMjOzubdunUrLTk5mTd48GCXkSNHJiqf/+HDhzrXr19PKykp4bi5uXksWrToeWxsrO6JEyeMExMTkyUSCdWxY0dhp06dKoFa4RcTE/OS60T37t3L9u7dq7ZCu+J9+uGHH1QucDdu3GhmZGQkS0pKSqmqqqK6du3qGhgYWOrq6lqjrt+qqiq6R48e5Zs2bcqZOXOm7aZNm8y//fbbPMU2n3zyieN33333ZPjw4eUzZsywZbevW7fOQldXV56enp4cGxur26tXLyFQK5hWr15tdfny5XRDQ0P5kiVL2q5cudJy3bp1eQBgZmYmTU5OTvnmm2/Mv/nmG8uDBw9mqRsfy5AhQ8pDQ0NTaZrGhg0bzFasWNF2165dT8eMGVP4ww8/mERERBQcO3bM0M3NrcrKykoaGBjotGDBgvxhw4aVP3jwQHvYsGEdHj16dB8AEhIS+LGxsan6+voqC6Jt2rTJfNasWflhYWFF1dXVlLJVb+PGjbkAcgsLCzk9evRwmTt3boG6+e/t7V2lPD+BpufB1atX9VxcXKrUjM9swIABIgDIzs7WtrGxqf98bW1ta2JjYzX+HXpTfXl6elZeuHBBf9KkSSWa9t8SEHHSTFiLiVgmrhcm2hztv0+YNBAlNVDtrlVXwJC4ZREIhH843bp1q3r69Clv165dJoMHDxY1fUQt/fr1E9A0DTc3t8oNGzbknD17VqM//nK5nJo3b57tzZs39WmaRkFBgfbTp0+5nTt3rlqyZIldWFiYzciRI0V+fn7lEokEPB5PHhoa6jB8+HBRSEiIxuM7c+aM4eXLlw2FQqEQACorK+nU1FSddu3a1VhZWdUMGjSogm0rEom4xsbG8rrjDJKSkvje3t5uAFBdXU1bWFhIAeCnn34y2bt3r5lUKqWeP3+ude/ePR1WnEycOLFYeQw0TePDDz8s3LVrl8ns2bMLb9++rX/kyJHHqsbr6uoqzs7O5n388cd2gYGBolGjRpUqt7l3756Ora2t2MvLSwwAkyZNKtyyZYsFgAIA+Pjjj4sAYNq0aUVLly6tjycJCgoq4nA48PT0FNvZ2Ynv3r370lP+oUOHlujq6jK6urpSExMTydOnT7mXLl3S9/f3L6lb/DJDhgwpYduvXLkyf+XKlfnK/TSFqvukzPnz5w1TU1P5x48fNwaAsrIyTnJysk5j4kRLS4thrXQ+Pj4V58+fN1TcX1hYyCkrK+MMHz68HACmTJlSeOHCBSMAuHr1qv6nn35aANR+HwQCQSVQ+9Q8IyNDx9fX1xUAJBIJ5ePjU872OW7cuGIA8PX1rWTH2hSPHz/W/uCDD2yfP3+uVVNTQ9vZ2YkBICws7MWIESOcIyIiCnbv3m3Githr164ZPnjwoN41sry8nFNcXEwDgJ+fX4k6YQIAPXr0qFi3bp3V06dPtUNDQ4s9PT3Fym3kcjnGjBnjNHv27Pw+ffpUrl692lzV/A8JCSlRNT+bmgc5OTlapqamL7kknjhxwmDfvn1m169fTwUAhnn5MiiKUnttb0tf5ubm0pycHG1N+m5JiDhRgE0h3Fjq4Lj8uL/XYqIyxa+yKKlD0ULCFjAkEAiEfzh+fn4ly5cvtzt37lxaQUGBRn/32JgT9n3Hjh2rU1JS+DKZDKxblyp27NhhUlhYyE1MTEzh8XiMjY2NZ1VVFe3l5SW+fft28uHDh42WLFlic/78+dJ169bl3b17N+X48eOGBw4cMN62bZvFzZs30zUZH8MwmDdvXt6iRYsaWCrS0tK0+Xx+g9gSDofDsONmGIYKDg4u3LJlS45im9TUVO3NmzdbxsfHp5ibm8uCgoIcq6ur612/DQwMVMarhIWFFQ4fPtxZR0eHCQwMLFYXG2Bubi5LSkpKPnr0qOHWrVstDh48aBITE5OpfE2NQdN/eaIrLqAoimrQTvk9APB4vPr2HA4HUqmUaux8r2o5UbxPXC6XkclkAIDKysr6QTEMQ61fv/5JUFDQSwJNHVwul2Gvn8vlQiqVNrhIhmFUXjeLqn0Mw6B3796lJ06cUCkodXR0GPbcyudTR3h4uP3cuXOfjR8/XnTy5EmDFStWWAOAs7OzxMzMTHr8+HGDO3fu6P3666+P2DHExcWlqBIhenp6jcZIzZw5s6hPnz4VR48eNfL39xds3bo1Uzk+4rPPPrO2srKqmTt3bmHd+VTOfwBQNT+bmgd8Pl8uFosbhEjExsbqzpo1y+G333570LZtWxkA2Nvb1ygu8p8+faptbW3dZJzVm+6rurqa0tXVfe1YteZCYk7qYFMIrz+XhvE/3ER8luqHH10su0Cbow0OxWkdi0ljWbbqY0kULSRqYkWIMCEQCAQAtU9tP/vss1xfX1+V7hea4O7uLvby8qpYsGCBtVxe+7c6MTGRx8aUsIhEIo6ZmZmEx+MxJ06cMMjNzdUGgMzMTC0DAwP5rFmziubNm5d/9+5dvkgkoouKijghISGi7du3Z6ekpGicQcnf37/0559/NmNjBB4/fqyVk5OjUng5OTlVp6Sk8ADAz8+v9OTJk8Zs2/z8fE56erp2cXExR1dXV25iYiLLzs7mXrp0yUiTcTg6OkosLS0l69evt5o2bZpKly6g1oVIJpNh0qRJJatWrcpJTEzkA4C+vr5MJBJxgFoBmJOTo83Gk0RFRZn26dOnfrEZFRVlAgA//vijcadOneotQ0eOHDGWyWS4f/8+Lzs7m+ft7a02MFiR/v37l589e9aosrKSEolE9Pnz59uw+1auXJmfmpqarPyvMWGijJ2dnfjWrVt6ABAdHV3/xHPIkCGibdu2mYvFYgoAEhISeKWlpTQAODk5qcyw1RRmZmYyfX19GWvh27t3rwm7r3fv3uX79u0zAYA///xTJz09nV93/RVxcXH67P0uKyujExISeK9yfpaysjKOvb29pG4MDRb1U6ZMeT516lSnESNGFLHukb179y5ds2ZNvfvb9evXNU4wkZycrO3m5iZeunRpwdChQ0vu3r3b4Nj9+/cbXbp0yXD37t31n5m6+a9ufjY1Dzw9PaszMzPr79mDBw+0g4OD2+/evfsxawEEgH79+lVkZmbqpKamaldXV1NHjhwxCQoKKgGA1atXm69evdpc+fredF8AkJGRoePt7V0FALNnz7aJiopqo+nn8zoQy0kdqlIIK1tP2Mrvn3f9vGWzcClaR858AUjZYHZ1WbYU3LaIhYRAIBAapX379pJly5YVqNp36NAh07Nnz7Zh31+/fj1FXT/79u3LnDVrlp2Dg4OHrq6uvE2bNrK1a9c2WKxOnTq1yN/f39nDw8PN3d290snJqRoA4uPjdf/973/b0jQNLpfLbN26NaukpIQTEBDgzC5SV61apfHCd/To0aX379/X6dq1qytQG+sSHR39mMvlvvQEeujQoaJz584ZeHh4iH18fKqXLl2aM2jQIIFcLoeWlhYTGRn5ZNCgQRUeHh6VHTp0cLe3txcruvc0RWhoaOGWLVu4jWULyszM1Prkk08c5XI5BQArVqx4CgATJ058MWfOHIdFixbJ4+LiUrZv354ZHBzcXiaTwdvbu3LhwoX12ajEYjHl5eXlKpfLqQMHDjxitzs7O4t9fX1dCgsLtTZu3JjF5/M1cnHp169fpZ+fn0goFLrb2NiIvby8KoyMjGSaXndTfPHFF/khISHtDhw4YNqnT596K8n8+fNfZGZm8jw9Pd0YhqFMTEwkp06dysjLy+MyDKORhUIVP/74Y+bUqVMddXV15QMHDqw/38KFCwtCQ0OdBAKB0N3dvdLT07MCAKytraU7duzIDA0NbVdTU0MBwPLly3MUF6/NZcmSJbljx45tb2lpWdOlS5eKJ0+e1C/cx44dKwoPD+dMnz69vkbDzp07s6dOnWovEAiEMpmM6tatW1nPnj2faHKun3/+2SQmJsaUy+Uy5ubmkq+//jpXcf/GjRstCwoKtDp27OgG1FpQN27cmKtq/vP5fLmq+dkUnTp1qi4rK+MUFxfTxsbG8qVLl1qVlJRw58yZ4wDUWp2SkpJStLS0sH79+id+fn4CmUyGcePGvejSpUs1AKSmpur26tXrpe/bm+5LLBZTmZmZvL59+1YAQHJysq5iYo3WpFGzJgBQFMUDEATAEQpihmGYFa06slegS5cuTFxc3Csdy1pO2BTCypm62FiTGlkNtDna2DV01+sLE8X4EbkUoCiAkdf+UwVx2yIQCC0MRVHxDMN0ael+7927l+nt7a32STrh7yMrK0tr7NixjtevX3/QGv1PnDjRvlOnTpXz589vtc/bxsbGMy4uLkXR1Q6ozdYVEBAgmjx5cpOxHqoQiUS0kZGRvKysjO7Ro4fL9u3bs3r37t0qKYibYv/+/UYZGRm8pUuXqhTS7zqXL1/mz58/345NGPC+8NVXX1kYGBjIFyxY8Erzf8CAAc6nT5/OYN3oXoeW7CsqKqpNfHw8//vvv88FgN69e3e4evVqi/6G3Lt3z8zb29tRebsmlpNjAEQA4gG8spp+22FTCKuLOWFjTeSQQyKXIC4/7tXFibqgdoYGaLrurZzUGyEQCP8obube1LuZd9Ogu1X3su7W3SuaPoKgCQ4ODpIpU6a8KCoqolui1oki7u7ubrq6uvIdO3ZobPV5m5gwYYLDgwcPdMViMRUaGlr4poQJUGtZeFPnbm0WL17cdu/eveZ79uxRGd/yLrNo0aLnr1OUlM061xK0ZF9SqZRatmxZfTKAlhYmjaGJ5SSJYRiPv2k8r8XrWE6agrWcSOQSaNFar2Y5aTTTFgVwdQC/b0iKXwKB8LfxtlhObube1Jv9+2yBVC6luTRXvmXQlnQiUN5NvLy8XGtqahrEtEZFRT1+nZgfwpvn+++/N922bVuDOjBdu3Yt//nnnzVyw3pVDh8+bLhkyRJbxW12dnbi//3vfxmteV5C6/M6lpPrFEV5MgzzUt7wfxIdLTpi19BdiMuPe7VYk+xbwE8jAGk1XhIlJH6EQCD8w7mZd9NAKpfScsghlUvpm3k3DYg4eTdJSEj4Wyt0E/4e5s6dW8hmvfo7CQoKKg0KCkr+u89LeHNokq2rN4B4iqLSKIpKoCgqkaKohNYe2NtIR4uOr1bxPfsWcOlrNZm2JpMMWwQC4R9Pd6vuZVyaK6dBg0tz5d2tupc1fRThVZg3b571r7/+agAAK1assCgrK2vxzJ3Pnj3jdOvWTcDn8ztNnDjRXnGfr6+vi6OjowdbvZvNnFRVVUUNHz68nb29vYeXl5drWlra315foSVIS0vT3r59u0nTLVuOK1eu8AUCgdDe3t5j0qRJdmxGOeVx6ejodGbv+7hx4+xVdPXKREZGmip/1q2JjY2NZ0v0w1aUT0tL0/b19XVpiT41Od+bwMvLy9XV1VVoZWXlaWxs7M3Ohdf9rgUEBLRLTEx8rUxvimhiOfFvqZP941BVxZ0EtRMIBMJLdLfuXrFl0JZ0EnPS+tRVzgYA7Nixw3LatGlF6uqYqEIqlYJNBasOPp/PrFixIvfevXu6SUlJL6WHjYqKetS3b98G8R3ff/+9mZGRkfTJkydJO3fuNF6wYIHtb7/99kj52LedBw8e8A4ePGgyc+bMor/rnLNmzXLYunVr1sCBAyv69+/f4dChQ4YffvjhS3VU7OzsxKmpqW+1FUIikUBdvZx3Gblc3mQtn9aGtWpGRkaaxsXF6UVFRTVwyXvVex8WFlbwn//8p+2BAweyWmKcTT4tYRgmC0AbAIF1/9rUbfvHcLfgLn5I/AF3C+5qfhDrxlVfm0QOgAba9SeWEgKBQFBBd+vuFfN85j37JwqTyMhI08GDB7cfOHCgs42Njefq1avNv/zyS0s3Nzeht7e3a35+PgcA1q9fb+bh4eHm4uIiHDZsWHvW6jFo0KD2mzdvNgWAtWvXmo0YMcJJ3bmCgoIc9+zZY7xq1SqLgoICrX79+gm6desmAIAjR44YduzY0VUoFLr5+/u3Y+uo2NjYeC5cuNDKx8fHZffu3U0G/xoaGsqHDRtWrqOjo7HoOXnyZJspU6YUAsDkyZOLr1+/bqDKAqAIn8/vFBYWZuPu7u7Ws2dPwcWLF/m+vr4utra2ntHR0UZAbQHEMWPGOAoEAqGbm5vwxIkTBoDm9/z+/fu8Pn36dHB3d3fz8fFxuXPnjg57HydNmmTXqVMnV1tbW082KHrJkiU2cXFx+q6ursKvvvrKQtmiMGDAAOeTJ08aaDr+psjKytIqLy+nBw8eXEHTNMaPH1/466+/vnKAtiKrVq2yaN++vbtAIBAGBAS0A2prgwwePLi9QCAQent7u8bGxjYQn4WFhRwbGxtPtgBlWVkZ3bZtWy+xWEw1di+nTp1q261bN8GsWbNsXxqICoyNjaXs9Xfp0sXF1dVV2KFDB/czZ87oA8B3331n5ujo6OHr6+sSGhrqwH4Gqamp2h07dnT18PBwmzt3rjXbH4fDYdq0aSNVfbZawsLCbL755pv6OiILFiywXr58uSVQW7jTw8PDTSAQCOfPn28N1Fpj2rVr5z5hwgR7d3d3YUZGhjYATJs2zVYoFLr16NFDkJubywVqa7x4e3u7CgQC4ZAhQ9o/f/6co+4zKC0tpYODgx09PDzc3NzchMr1lprDggULrMeOHevQq1evDqNHj3ZqbL6q+33w8/Mrv3LliqFE0mQtSI1oUpxQFDUXQDQAi7p/+yiKmtMiZ38HYAPhN93ehGnnpmkmUNS5cXF5QP9/E1FCIBAIhJdIT0/XPXz48KM///wz5euvv7bh8/nylJSU5C5dulTs2LHDFADGjx9fnJSUlJKWlpbs4uJSFRkZaQYAe/fuzfr222+tzpw5o79ly5a2u3btajJIeenSpQUWFhaSP/74Iz02NjY9Ly+Pu3r1aqvLly+nJycnp3Tu3Lly5cqV9QHQOjo68vj4+LTp06cXL1u2zJJ1CVH8N2nSJDtNrnXq1KmOrq6uwkWLFlmxAiQ/P1/bycmpBgC0tLSgr68vy8/Pb9REU1VVRQ8YMKDs/v37KXp6erKlS5faXLlyJT0mJubhypUrbQCALfKXnp6e/MsvvzyaPn26I1uxXZN7PnXqVIetW7c+uX//fsratWufhoWF1S/c8vPzteLi4lKPHTv2YPny5TYA8J///CenS5cu5ampqcnLly9vNC2wJuO/d+8eT9W9dnV1Fb548YKTlZWlZWVlVb8qdHBwqMnLy1P5+Pvp06fabm5uwq5du7qwi/jGiIyMbJuUlJScnp6evHfv3iwA+Pzzz629vb0r09PTk1euXJnz8ccfNxDCpqamMldX18pTp04ZAMCBAweM+vXrJ+LxeExj9zIjI0Pn2rVr6bt27Xp64sQJA1XX26lTJ1e2fVJSUgoA7N6922TQoEGi1NTU5JSUlPvdunWrzMzM1Fq3bp1VbGxsypUrV9IfPHigwx43a9Ys+6lTpz5PSkpKadu2bf19c3Z2lpw7d67RIPsJEyYUHT58uN5l79ixY8YTJkwoPnLkiOHDhw91EhISUlJSUpLv3r3LP336tD4AZGZm6kyePLkwJSUlWSAQ1FRVVdGdO3euTE5OTunVq1fZF198YQ0AkyZNclq9evXT9PT0ZHd396p//etf1uo+g8WLF1sNGDCgNCkpKeXKlStpS5cutS0tLaWbmivqrishIYF/9uzZhydOnFCbTa2x3wcOhwMHB4fqmzdvalxItjE0cev6BEA3hmEqAICiqDUAbgDY1BIDeNtpdgrh+sD3OmsJceMiEAgEggb07NmzzNjYWG5sbCzX19eXBQcHlwCAp6dnZUJCAh+oLeYYERFhU1ZWxqmoqOD069dPBAB2dnbSxYsX5wYEBLhERUU9tLS0bHYxwUuXLullZGTo+Pr6ugKARCKhFIsxTpw4sb6WyMqVK/NXrlyZr6qfpjh48OAjJycnSXFxMR0QENB+69atpuHh4YWqXF4oimrUD0ZLS4sZM2ZMKQC4u7tX8Xg8OY/HY3x9fatycnK0AeD69ev6c+bMKQBqi+ZZW1vXJCYm6gBN33ORSETfuXNHPzg4uD17TrZgIQCMGDGihMPhwMfHp7qwsLDZ/jCajN/b27tRVyw19+2lbfb29pLHjx8ntG3bVnblyhV+cHCwc3JyclJj6aVdXFyqRo0a5TRixIiS8ePHlwDArVu3DA4fPvwQAEaMGFE2ffp0bmFhYYOFb3BwcPH+/fuNAwMDy/773/+azJo163lT93L06NHFrLtgYGBgWWBgoEbuZ927d6+YMWOGo0QioceMGVPcs2fPqpMnTxp269atjP0ejBo1qjg9PV0HAG7fvq1/+vTpDACYMWNG4cqVKzWy1ABAr169qgoLC7mZmZlaeXl5XCMjI1mHDh1q1q5da3H58mVDoVAoBIDKyko6NTVVp127djVWVlY1gwYNqrcG0zSNqVOnFgHAlClTCkePHu1cWFjIKSsr4wwfPrwcAKZNm1YYHBzcTt1ncOnSJcOzZ8+2iYyMbAvUFkx8+PChdufOnatfxW3Pz8+vRF9fv9HvWlO/D2ZmZtLs7OwW8cfTRJxQABR/5GR12/4RdLHsAm2Odn0K4S6WTWTczLxSlyZYwY2LWEsIBAKB0ATa2tr1iwOapsEWUqNpGlKplAKA6dOnOx06dOhhjx49qiIjI03/+OMPA/aYxMREXSMjI2lOTs4rLRAYhkHv3r1L1T09VYxLWbZsmWVMTIypcpvu3buX7d27t9GaJ05OThIAMDY2loeEhBTdunVLD0Bh27Ztax4/fqzdvn17iUQiQXl5OcfCwqJRkcXlchmarnUCoWkaPB6PAWqf5MpkMoq9LnU0dc9lMhkMDAyk6hZ8isXu1J2Hy+Uyiu5pYrGYVtzX1Pjv3bvHCwkJaQ8VXL16Nc3R0VGiaCnJysrSVrQIsOjq6jK6uroyAOjTp0+lvb29OCkpSUc59keRixcvPjh9+rTBr7/+2ubbb7+1fvDgQZImInLs2LElK1assMnPz+ckJSXxAwMDS0tLS+nG7qW+vn79TTpx4oTBokWLXrLC6erqyu/cudMgG5y/v3/55cuX0w4fPmw0adIkp08//TTf0NCw0XlD0/QrB38EBgYW79u3z/jZs2daQUFBRUDtZz9v3ry8RYsWNUifnpaWps3n8xv1TVQlJBVR9xkcOnToobe3d4P6g03NFTMzM5X3RU9Pr36M6uZrU78PYrGYbupaNUWTDB17AMRSFPUlRVFfArgJ4MeWOPm7AJtCOLxTeNO1TbJvAaLsWksJxSFuXAQCgUBoUSorK2l7e3uJWCymDhw4UO9ecvHiRf7vv/9uFB8fn7x58+a2qampGmXf0dPTk7F+4/3796+Ii4vTT0pK4gG1sQIJCQkqM/CsXLkyPzU1NVn5X1PCRCKRIC8vjwvUPu09deqUkYeHRxUADB8+vGT37t2mAP6/vXuPrrK+8z3++bBDFKhELkIrBIIdwNtB1JiB0zJqvVTnKI7taYuI0puoHVtrb8fepqvr9Lh09TijPUumCt6OpdiuqiPLVYUuRwvjUSDYiCDVQYiSohIvRaBKLvt7/thPdDfshB3Mzn5I3q+1WE+e296fnWSHfPO76c477xwxc+bMXR2/uE+aNOm4Yl5PIR//+Md3/+IXvxgpSevXrz/klVdeqZw2bdq7xdw7cuTI7Pjx41s6xtlks1k9+eST+wzwz1dVVdW+e/fu91oSPvrRj7Zs3LhxaHt7uzZv3jx4/fr1w3qSv6PlpNC/0aNHt0+cOLF12LBh2UcffXRYNpvVkiVLRl1wwQV/7vw427dvr2hryw2peO655yobGxsPmTp16l5JuvDCC2see+yxv+qS097erhdffLHy/PPP37Vw4cKmXbt2ZXbu3JmZMWPGrjvvvHOUJD300EOHjRgxoq1z60tVVVX2hBNO2HP55ZdPOOOMM3ZWVFT06HN5/vnn7yr0ejsXJpL0wgsvVI4bN671m9/85uvz5s17/emnnx46a9asPatXrz6subk509raqgcffPC9MTgnnXTS7kWLFo2UpEWLFu1TYEvS1q1bB8+cOXNKoXOXXHLJm/fdd9/Ihx56aMS8efPekqRzzz337XvuuWd0x3tp69atgztmoessm82qY3zSXXfdNaqurm7XqFGj2ocPH97e0dXu9ttvHzVz5szdXX0NTj/99LdvvPHGsR1FxBNPPDFE2v/3SqE8nXX1/bq/nw9bt2495MQTTyzqfbU/+205iYh/tv24clMKW9IXIuIPvfHkB4vpY6bvf/rgju5c7S3SoIx08ny6cQEAetW11167va6u7phx48a1HHPMMX/ZvXt35p133vEVV1xRc/vttzfW1NS0Xnfdddvmz59f8+STT77Q8ct9V+bPn//6ueeeO3nMmDGtq1evfuHWW29tnDNnzlEd3W1+9KMf/WnatGl7u32QLowbN+6/7N69O9Pa2urly5cf/tvf/vaFyZMnt5x55pmTW1tbnc1mPWvWrLe/8Y1vNEvS1Vdf/fqnP/3pSRMmTDi+qqqq/Ve/+tWLUq6ve0QccI+N73znOzsuueSSiVOmTDk2k8no1ltvbRwyZEjRfzlfunTplssuu2ziDTfc8JG2tjZfeOGFb86cObPLBSXr6ureqaioiKlTpx47d+7c13/4wx/uuOWWW/ZOnTr1uKlTp75z7LHH9voq9AsXLnzpS1/60qR3333Xp59++tuf+cxndkrSkiVLqtauXTvspptu2r5ixYoP/eQnPxmXyWQik8nETTfd9FJHt6dNmzYNra6u/qvWlra2Ns+dO3fSrl27MhHhyy+//LXRo0e333DDDdvnzp1bM2XKlGOHDBmSveuuuwr+Jf2zn/3sW1/84hePeuihh57vONbTz2Uxli9fftjPfvazD1dUVMTQoUPblyxZsnXSpEmt11xzzSunnHLKMWPGjGmdMmXKO1VVVe3J5+rlOXPmHLVw4cKxs2fPfqvQY27btm1wJpMp+D1SW1v77p49ewaNHTu2ZeLEia2S9KlPfertjRs3HnrKKaccLUlDhw7NLlmyZGtFRcU+jzFkyJDsxo0bhxx33HEfPuyww9rvv//+LZJ05513br3yyisnfu1rXxs0YcKEvUuXLm3s6mtw/fXXb1+wYMGEo48++tiI8Pjx4/f21urwZ5111u5C369HHnlkW1c/H7Zt21ZxyCGHRMfn44PqcoV428Mj4m3bBefqjog+myKvWKVcIX6/Vt0o/fv/kqI912ryie9Ls75ZniwAUKS0rBAPdGXp0qVVL7744iE/+MEPuh1cjgPz5ptvDrr44otrHn744YNu2ubu7Ny5c1BVVVW2tbVVn/zkJ//m85///OuXXnrpn4u597rrrjti4sSJLRdffPHOEsfsF3784x+PGT58ePaaa67p0c/8A1kh/peSzpO0Tvssaa6QdNT+ntT2OZJulpSRtDgiru90/tuSLs7LcoykIyLizf3d2xcadjQUvyJ8zazcoortLbltzaw+yQgAQH920UUX8QtiCY0cOTLb3woTSfr2t7995MqVK4fv3bvXp5566tvz5s37c7H3fu9732suYbR+5/DDD2//yle+8kZvPV6XLScf+IHtjKQXJJ0lqUnSWkkXRUTBgVC2z5d0TUR8oqf3dujNlpOOKYRb2ltUmansfrzJtjW5gfBDRknvvJErTOjOBeAgQMtJ/3XJJZdMWLt27V9NF3vllVe+dvXVV/faLxEAcKAOpOVEkmT70Yg4Y3/HCqiTtDkitiT33CvpAkldFRgXSVp6gPf2uqKnEM4fa5KplOYvozABACmbzWb9QWbFwQdzzz337HetEwAoh2w2a+Wmtt1HlyPlbB+ajDcZbXuE7ZHJvxpJR3Z1X55xkvJn7WhKjhV6rqGSzpF0X0/v7Q3rXnpLtzy2Weteen9cVMcUwhlnup9CuGPq4GjPbRtXlSomABxMNjQ3N1cl/wEBACApV5g0NzdXSdpQ6Hx3LSeXS/q6coXIOr2/tsnbkm4p4rkL/YfU1V/Qzpf0RN4g+6Lvtb1A0gJJmjBhQqFLurXupbd08eKn1NKWVWXFIC358gydPHHEe1MI73fMCWNNAGAfbW1tX3711VcXv/rqq8eruGnrAQADQ1bShra2ti8XOtllcRIRN0u62fZXI+JAVoNvkpS/gM54Sdu7uHaO3u/S1aN7I+I2SbdJuTEnPQ351JY31NKWVTak1rasntryhk6emJsOu6gphKvrcl25Glcx1gQAEieffPIOSbPLnQMAcHApZp2T/2P7eEnHSjo07/j/3c+tayVNtj1J0p+UK0Dmdr7IdpWkUyXN6+m9vWHGUaNUWTFIrW1ZDa4YpBlHFVyPp3vVdRQlAAAAwAdUzID4H0k6Tbni5LeSzpX0H5K6LU4ios32VZKWKzcd8B0RsdH2Fcn5nyeXXihpRUTs2d+9PXxtRTl54ggt+fIMPbXlDc04atR7rSZF6ZilixYTAAAA4APb71TCtp+VdIKkP0TECbbHKrfuyPl9EbAn+nQRRmbpAtAPlGoqYQAADkQxgxTfiYispDbbwyXtUBELMPZ7zNIFAAAA9Kr9duuSVG/7cEmLlJu1a7ekNaUMVW5FrQzPLF0AAABAr+rRCvHJGifDI2J9yRJ9AL3RreuAVoZnzAmAgxTdugAAadJly4ntk7o7FxFPlyZSeRW9MrzELF0AAABAL+quW9eNyfZQSbWSnlFuccRpklZL+nhpo5VHx8rwrdnW7leGBwAAANCruluE8XRJsn2vpAUR8Wyyf7ykb/VNvL5X1MrwdOcCAAAAel0xA+KP7ihMJCkiNtieXrpI5dftyvBMIQwAAACURDFTCW+yvdj2abZPtb1I0qZSB0stphAGAAAASqKYlpMvSLpS0tXJ/kpJ/1qyRGnHFMIAAABASey3OImIdyX9S/IP1XW5rlyMOQEAAAB6VXdTCf86Ij5r+1lJ+yyGEhHTSposzZhCGAAAAOh13bWcdHTjOq8vggAAAAAY2LqbSviVZPtS38UBAAAAMFB1161rlwp051JuIcaIiOElSwUAAABgwOmu5eSwvgxyUGDxRQAAAKBkiplKWJJke4ykQzv2I+LlkiRKKxZfBAAAAEpqv4sw2p5t+z8lbZX0e0mNkh4uca70YfFFAAAAoKSKWSH+f0qaIemFiJgk6QxJT5Q0VZk07GjQ4mcXq2FHw74nOxZfdIbFFwEAAIASKKZbV2tEvGF7kO1BEfGY7RtKnqyPNexo0GUrLlNLe4sqM5VadPYiTR8z/f0LWHwRAAAAKKliipM/2/6QpFWSltjeIamttLH6Xv1r9Wppb1FWWbVmW1X/Wv1fFycSiy8CAAAAJVRMt66Vkg5XblHGRyS9KOn8EmYqi9qxtarMVCrjjAYPGqzasbXljgQAAAAMKMW0nFjScklvSrpX0q8i4o2SpiqD6WOma9HZi1T/Wr1qx9bu22oCAAAAoKQcUWidxQIX2tMkfU7SpyU1RcSZpQx2IGpra6O+vr73H5j1TQD0U7bXRQRNxQCAVCh6nRNJOyS9KukNSWNKEyeFWN8EAAAA6BPFrHNype3HJT0qabSkyyJiWqmDpQbrmwAAAAB9opiWk4mSvh4RDSXOkk4d65t0tJywvgkAAABQEvstTiLi2r4IklqsbwIAAAD0iZ6MOekx2+dIullSRtLiiLi+wDWnSbpJ0mBJr0fEqcnxRkm7JLVLaivrgE3WNwEAAABKrmTFie2MpFsknSWpSdJa28si4rm8aw6XtFDSORHxsu3OA+1Pj4jXS5URAAAAQHoUswjjgaqTtDkitkREi3JrpFzQ6Zq5ku6PiJclKSJ2lDAPAAAAgBQrZXEyTtK2vP2m5Fi+KZJG2H7c9jrbl+adC0krkuMLSpgTAAAAQAqUcsyJCxzrvOJjhaSTJZ0haYikJ20/FREvSPpYRGxPunr9zvYfI2LlPk+SK1wWSNKECRN69QUAAAAA6DulbDlpklSdtz9e0vYC1zwSEXuSsSUrJZ0gSRGxPdnukPSAct3E9hERt0VEbUTUHnHEEb38EgAAAAD0lVIWJ2slTbY9yXalpDmSlnW65kFJs2xX2B4q6W8lbbI9zPZhkmR7mKSzJW0oYVYAAAAAZVaybl0R0Wb7KknLlZtK+I6I2Gj7iuT8zyNik+1HJK2XlFVuuuENto+S9IDtjoy/jIhHSpW1S9vWsL4JAAAA0Ecc0XkYyMGrtrY26uvre+fBtq2R7p79/srw85dRoADod2yvK+s6UgAA5Cllt66DW+OqXGES7blt46pyJwIAAAD6NYqTrtTMyrWYOJPb1swqdyIAAACgXyvlVMIHt+q6XFcuxpwAAAAAfYLipDvVdRQlAAAAQB+hWxcAAACAVKA4AQAAAJAKFCcAAAAAUoHiBAAAAEAqUJwAAAAASAWKEwAAAACpQHECAAAAIBUoTgAAAACkAsUJAAAAgFSgOAEAAACQChQnAAAAAFKB4gQAAABAKlCcAAAAAEgFihNJDTsatPjZxWrY0VDuKAAAAMCAVVHuAOXWsKNBl624TC3tLarMVGrR2Ys0fcz0cscCAAAABpwBX5zUv1avlvYWZZVVa7ZV9a/Va/reFqlxlVQzS6quK3dEAAAAYEAY8MVJ7dhaVWYq1Zpt1eBBg1XrodLds6X2FilTKc1fRoECAAAA9IEBX5xMHzNdi85epPrX6lU7tlbTn380V5hEe27buIriBAAAAOgDA744kXIFynvjTPYmLSYdLSc1s8qaDQAAABgoKE46q67LdeVizAkAAADQpyhOCqmuoygBAAAA+hjrnAAAAABIBYoTAAAAAKlQ0uLE9jm2n7e92fa1XVxzmu0G2xtt/74n9wIAAADoP0o25sR2RtItks6S1CRpre1lEfFc3jWHS1oo6ZyIeNn2mGLvBQAAANC/lLLlpE7S5ojYEhEtku6VdEGna+ZKuj8iXpakiNjRg3sBAAAA9COlLE7GSdqWt9+UHMs3RdII24/bXmf70h7cK0myvcB2ve365ubmXooOAAAAoK+VciphFzgWBZ7/ZElnSBoi6UnbTxV5b+5gxG2SbpOk2tragtcAAAAASL9SFidNkqrz9sdL2l7gmtcjYo+kPbZXSjqhyHsBAAAA9COl7Na1VtJk25NsV0qaI2lZp2selDTLdoXtoZL+VtKmIu8FAAAA0I+UrOUkItpsXyVpuaSMpDsiYqPtK5LzP4+ITbYfkbReUlbS4ojYIEmF7i1VVgAAAADl54j+M0yjtrY26uvryx0DAA4attdFRG25cwAAILFCPAAAAICUoDjJt22NtOrG3BYAAABAnyrlbF0Hl21rpLtnS+0tUqZSmr9Mqq4rdyoAAABgwKDlpEPjqlxhEu25beOqcicCAAAABhSKkw41s3ItJs7ktjWzyp0IAAAAGFDo1tWhui7XlatxVa4woUsXAAAA0KcoTvJV11GUAAAAAGVCty4AAAAAqUBxAgAAACAVKE4AAAAApALFCQAAAIBUoDgBAAAAkAoUJwAAAABSgeIEAAAAQCpQnAAAAABIBYoTAAAAAKlAcQIAAAAgFShOAAAAAKQCxQkAAACAVKA4AQAAAJAKFCcAAAAAUoHiBAAAAEAqUJwAAAAASAWKEwAAAACpQHEiSdvWSKtuzG0BAAAAlEVFuQOU3bY10t2zpfYWKVMpzV8mVdeVOxUAAAAw4NBy0rgqV5hEe27buKrciQAAAIABqaTFie1zbD9ve7PtawucP832TtsNyb9/yjvXaPvZ5Hh9yULWzMq1mDiT29bMKtlTAQAAAOhaybp12c5IukXSWZKaJK21vSwinut06aqIOK+Lhzk9Il4vVUZJuS5c85flWkxqZtGlCwAAACiTUo45qZO0OSK2SJLteyVdIKlzcVJ+1XUUJQAAAECZlbJb1zhJ2/L2m5Jjnc20/Yzth20fl3c8JK2wvc72gq6exPYC2/W265ubm3snOQAAAIA+V8qWExc4Fp32n5Y0MSJ22/57Sf8maXJy7mMRsd32GEm/s/3HiFi5zwNG3CbpNkmqra3t/PgAAAAADhKlbDlpklSdtz9e0vb8CyLi7YjYnXz8W0mDbY9O9rcn2x2SHlCumxgAAACAfqqUxclaSZNtT7JdKWmOpGX5F9j+sG0nH9cled6wPcz2YcnxYZLOlrShhFkBAAAAlFnJunVFRJvtqyQtl5SRdEdEbLR9RXL+55L+u6QrbbdJekfSnIgI22MlPZDULRWSfhkRj5QqKwAAAIDyc0T/GaZRW1sb9fWlWxIFAPob2+siorbcOQAAkPpZcWK7WdJLPbxttKTSrqXywaU9Y9rzSWTsLWTsHWnKODEijih3CAAApH5WnBwI2/Vp/6th2jOmPZ9Ext5Cxt5xMGQEAKAcSjkgHgAAAACKRnECAAAAIBUoTpIFHFMu7RnTnk8iY28hY+84GDICANDnBvyYEwAAAADpQMsJAAAAgFSgOAEAAACQCgO2OLF9ju3nbW+2fW2580iS7Wrbj9neZHuj7auT4yNt/872fybbESnImrH9B9sPpTGj7cNt/8b2H5PP58w0ZbR9TfI13mB7qe1D05DP9h22d9jekHesy1y2v5u8h563/cky5ftp8nVeb/sB24eXK19XGfPOfct22B5dzowAAKTVgCxObGck3SLpXEnHSrrI9rHlTSVJapP0zYg4RtIMSf+Y5LpW0qMRMVnSo8l+uV0taVPeftoy3izpkYg4WtIJymVNRUbb4yR9TVJtRBwvKSNpTkry3SXpnE7HCuZKvjfnSDouuWdh8t7q63y/k3R8REyT9IKk75YxX1cZZbta0lmSXs47Vq6MAACk0oAsTiTVSdocEVsiokXSvZIuKHMmRcQrEfF08vEu5X6hHqdctruTy+6W9A9lCZiwPV7Sf5O0OO9wajLaHi7p7yTdLkkR0RIRf1aKMkqqkDTEdoWkoZK2KwX5ImKlpDc7He4q1wWS7o2IvRGxVdJm5d5bfZovIlZERFuy+5Sk8eXK11XGxL9I+o6k/FlIypIRAIC0GqjFyThJ2/L2m5JjqWG7RtKJklZLGhsRr0i5AkbSmDJGk6SblPslK5t3LE0Zj5LULOnOpOvZYtvD0pIxIv4k6X8r9xf0VyTtjIgVaclXQFe50vg++qKkh5OPU5PP9mxJf4qIZzqdSk1GAADSYKAWJy5wLDVzKtv+kKT7JH09It4ud558ts+TtCMi1pU7SzcqJJ0k6V8j4kRJe1T+bmbvScZsXCBpkqQjJQ2zPa+8qQ5Iqt5Htr+vXNfIJR2HClzW5/lsD5X0fUn/VOh0gWOp+VkEAEBfG6jFSZOk6rz98cp1qyk724OVK0yWRMT9yeHXbH8kOf8RSTvKlU/SxyTNtt2oXHe4T9j+hdKVsUlSU0SsTvZ/o1yxkpaMZ0raGhHNEdEq6X5J/zVF+TrrKldq3ke250s6T9LF8f7iTWnJ91HlCtFnkvfNeElP2/6w0pMRAIBUGKjFyVpJk21Psl2p3IDUZWXOJNtWbpzEpoj457xTyyTNTz6eL+nBvs7WISK+GxHjI6JGuc/bv0fEPKUr46uSttmemhw6Q9JzSk/GlyXNsD00+Zqfodz4orTk66yrXMskzbF9iO1JkiZLWtPX4WyfI+l/SJodEX/JO5WKfBHxbESMiYia5H3TJOmk5Ps0FRkBAEiLinIHKIeIaLN9laTlys2UdEdEbCxzLCnXKnGJpGdtNyTHvifpekm/tv0l5X6x/Ux54nUrbRm/KmlJUnxukfQF5YrxsmeMiNW2fyPpaeW6If1B0m2SPlTufLaXSjpN0mjbTZJ+pC6+thGx0favlSv82iT9Y0S0lyHfdyUdIul3uVpPT0XEFeXI11XGiLi90LXlyggAQFr5/R4QAAAAAFA+A7VbFwAAAICUoTgBAAAAkAoUJwAAAABSgeIEAAAAQCpQnAAAAABIBYoToADb/y/Z1tie28uP/b1CzwUAADDQMZUw0A3bp0n6VkSc14N7Mt2tVWF7d0R8qBfiAQAA9Cu0nAAF2N6dfHi9pFm2G2xfYztj+6e219peb/vy5PrTbD9m+5eSnk2O/ZvtdbY32l6QHLte0pDk8ZbkP5dzfmp7g+1nbX8u77Eft/0b23+0vSRZWR4AAKBfGZArxAM9cK3yWk6SImNnRJxi+xBJT9hekVxbJ+n4iNia7H8xIt60PUTSWtv3RcS1tq+KiOkFnutTkqZLOkHS6OSelcm5EyUdJ2m7pCckfUzSf/T2iwUAACgnWk6Anjlb0qW2GyStljRK0uTk3Jq8wkSSvmb7GUlPSarOu64rH5e0NCLaI+I1Sb+XdEreYzdFRFZSg6SaXngtAAAAqULLCdAzlvTViFj+VwdzY1P2dNo/U9LMiPiL7cclHVrEY3dlb97H7eK9CwAA+iFaToDu7ZJ0WN7+cklX2h4sSban2B5W4L4qSW8lhcnRkmbknWvtuL+TlZI+l4xrOULS30la0yuvAgAA4CDAX1+B7q2X1JZ0z7pL0s3Kdal6OhmU3izpHwrc94ikK2yvl/S8cl27Otwmab3tpyPi4rzjD0iaKekZSSHpOxHxalLcAAAA9HtMJQwAAAAgFejWBQAAACAVKE4AAAAApALFCQAAAIBUoDgBAAAAkAoUJwAAAABSgeIEAAAAQCpQnAAAAABIhf8PGU0m9pI6EcQAAAAASUVORK5CYII=\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": 30,
   "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": 31,
   "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, solver='sgd', 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",
      "              max_iter=150, momentum=0.0, nesterovs_momentum=False,\n",
      "              solver='sgd', 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",
      "              max_iter=150, momentum=0.5, solver='sgd', 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": 32,
   "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, solver='sgd', verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.83      0.82      0.83     53296\n",
      "           2       0.84      0.88      0.86     70578\n",
      "           3       0.74      0.88      0.80      8845\n",
      "           4       0.85      0.57      0.68       735\n",
      "           5       0.73      0.28      0.41      2361\n",
      "           6       0.64      0.44      0.52      4354\n",
      "           7       0.87      0.75      0.81      5084\n",
      "\n",
      "    accuracy                           0.83    145253\n",
      "   macro avg       0.79      0.66      0.70    145253\n",
      "weighted avg       0.83      0.83      0.82    145253\n",
      "\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, momentum=0.0, nesterovs_momentum=False,\n",
      "              solver='sgd', verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.79      0.75      0.77     53296\n",
      "           2       0.79      0.85      0.82     70578\n",
      "           3       0.70      0.85      0.76      8845\n",
      "           4       0.71      0.46      0.56       735\n",
      "           5       0.76      0.14      0.24      2361\n",
      "           6       0.54      0.30      0.39      4354\n",
      "           7       0.81      0.71      0.76      5084\n",
      "\n",
      "    accuracy                           0.78    145253\n",
      "   macro avg       0.73      0.58      0.61    145253\n",
      "weighted avg       0.78      0.78      0.77    145253\n",
      "\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, momentum=0.5, solver='sgd', verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.82      0.77      0.79     53296\n",
      "           2       0.80      0.87      0.83     70578\n",
      "           3       0.71      0.84      0.77      8845\n",
      "           4       0.76      0.44      0.56       735\n",
      "           5       0.63      0.15      0.24      2361\n",
      "           6       0.57      0.28      0.37      4354\n",
      "           7       0.81      0.75      0.78      5084\n",
      "\n",
      "    accuracy                           0.80    145253\n",
      "   macro avg       0.73      0.58      0.62    145253\n",
      "weighted avg       0.79      0.80      0.79    145253\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reporting(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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, solver='sgd', verbose=True)\n",
      "Accuracy: \t82.79\n",
      "Recall: \t82.79\n",
      "precision: \t82.62\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, momentum=0.0, nesterovs_momentum=False,\n",
      "              solver='sgd', verbose=True)\n",
      "Accuracy: \t77.89\n",
      "Recall: \t77.89\n",
      "precision: \t77.65\n",
      "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20, 20, 20),\n",
      "              max_iter=150, momentum=0.5, solver='sgd', verbose=True)\n",
      "Accuracy: \t79.56\n",
      "Recall: \t79.56\n",
      "precision: \t79.12\n"
     ]
    }
   ],
   "source": [
    "accuracyPlotting(classificationList)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370241e7-2747-4199-9131-cd19819794e9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Fazit\n",
    "<li> Hier wurde das Maximum der Iterationen immer überschritten.</li>\n",
    "<li> Von den angegebenen Netzen, lieferte das erste ohne Veränderung des Momentums oder des nesterovs_momentum am besten </li>\n",
    "<li> Insgesamt lieferte das sgd immer schlechtere Ergebnisse als Adam. </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
}