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ExplainableAI/Explainable_AI_Adult_Census_Income.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explainable AI Projektaufgabe - Adult Census Income Datensatz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Abhängigkeiten installieren"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:22.499947Z",
"start_time": "2025-03-20T12:17:19.734970Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: scikit-learn in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (1.6.1)\n",
"Requirement already satisfied: matplotlib in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (3.10.1)\n",
"Requirement already satisfied: seaborn in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (0.13.2)\n",
"Requirement already satisfied: pandas in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (2.2.3)\n",
"Requirement already satisfied: numpy in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (2.0.2)\n",
"Requirement already satisfied: lime in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (0.2.0.1)\n",
"Requirement already satisfied: scipy>=1.6.0 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from scikit-learn) (1.15.2)\n",
"Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from scikit-learn) (3.5.0)\n",
"Requirement already satisfied: joblib>=1.2.0 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from scikit-learn) (1.4.2)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (4.56.0)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (1.4.8)\n",
"Requirement already satisfied: pillow>=8 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (11.1.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (1.3.1)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (3.2.1)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (2.9.0.post0)\n",
"Requirement already satisfied: cycler>=0.10 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n",
"Requirement already satisfied: packaging>=20.0 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from matplotlib) (24.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from pandas) (2025.1)\n",
"Requirement already satisfied: tzdata>=2022.7 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from pandas) (2025.1)\n",
"Requirement already satisfied: tqdm in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from lime) (4.67.1)\n",
"Requirement already satisfied: scikit-image>=0.12 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from lime) (0.25.2)\n",
"Requirement already satisfied: six>=1.5 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
"Requirement already satisfied: imageio!=2.35.0,>=2.33 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from scikit-image>=0.12->lime) (2.37.0)\n",
"Requirement already satisfied: lazy-loader>=0.4 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from scikit-image>=0.12->lime) (0.4)\n",
"Requirement already satisfied: networkx>=3.0 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from scikit-image>=0.12->lime) (3.4.2)\n",
"Requirement already satisfied: tifffile>=2022.8.12 in /Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages (from scikit-image>=0.12->lime) (2025.2.18)\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install scikit-learn matplotlib seaborn pandas numpy lime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Daten einlesen und erkunden"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Einlesen der Daten aus einer csv Datei"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:27.087445Z",
"start_time": "2025-03-20T12:18:23.163620Z"
}
},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
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" <th>education.num</th>\n",
" <th>marital.status</th>\n",
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" <th>relationship</th>\n",
" <th>race</th>\n",
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" <th>capital.gain</th>\n",
" <th>capital.loss</th>\n",
" <th>hours.per.week</th>\n",
" <th>native.country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>90</td>\n",
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" <td>77053</td>\n",
" <td>HS-grad</td>\n",
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" <td>82</td>\n",
" <td>Private</td>\n",
" <td>132870</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Widowed</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>4356</td>\n",
" <td>18</td>\n",
" <td>United-States</td>\n",
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" <td>66</td>\n",
" <td>?</td>\n",
" <td>186061</td>\n",
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" <td>10</td>\n",
" <td>Widowed</td>\n",
" <td>?</td>\n",
" <td>Unmarried</td>\n",
" <td>Black</td>\n",
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" <td>0</td>\n",
" <td>4356</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>54</td>\n",
" <td>Private</td>\n",
" <td>140359</td>\n",
" <td>7th-8th</td>\n",
" <td>4</td>\n",
" <td>Divorced</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3900</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>41</td>\n",
" <td>Private</td>\n",
" <td>264663</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Separated</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3900</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>34</td>\n",
" <td>Private</td>\n",
" <td>216864</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Other-service</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3770</td>\n",
" <td>45</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>150601</td>\n",
" <td>10th</td>\n",
" <td>6</td>\n",
" <td>Separated</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>3770</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>74</td>\n",
" <td>State-gov</td>\n",
" <td>88638</td>\n",
" <td>Doctorate</td>\n",
" <td>16</td>\n",
" <td>Never-married</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Other-relative</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3683</td>\n",
" <td>20</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>68</td>\n",
" <td>Federal-gov</td>\n",
" <td>422013</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3683</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>41</td>\n",
" <td>Private</td>\n",
" <td>70037</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Never-married</td>\n",
" <td>Craft-repair</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>3004</td>\n",
" <td>60</td>\n",
" <td>?</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" </tbody>\n",
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],
"text/plain": [
" age workclass fnlwgt education education.num marital.status \\\n",
"0 90 ? 77053 HS-grad 9 Widowed \n",
"1 82 Private 132870 HS-grad 9 Widowed \n",
"2 66 ? 186061 Some-college 10 Widowed \n",
"3 54 Private 140359 7th-8th 4 Divorced \n",
"4 41 Private 264663 Some-college 10 Separated \n",
"5 34 Private 216864 HS-grad 9 Divorced \n",
"6 38 Private 150601 10th 6 Separated \n",
"7 74 State-gov 88638 Doctorate 16 Never-married \n",
"8 68 Federal-gov 422013 HS-grad 9 Divorced \n",
"9 41 Private 70037 Some-college 10 Never-married \n",
"\n",
" occupation relationship race sex capital.gain \\\n",
"0 ? Not-in-family White Female 0 \n",
"1 Exec-managerial Not-in-family White Female 0 \n",
"2 ? Unmarried Black Female 0 \n",
"3 Machine-op-inspct Unmarried White Female 0 \n",
"4 Prof-specialty Own-child White Female 0 \n",
"5 Other-service Unmarried White Female 0 \n",
"6 Adm-clerical Unmarried White Male 0 \n",
"7 Prof-specialty Other-relative White Female 0 \n",
"8 Prof-specialty Not-in-family White Female 0 \n",
"9 Craft-repair Unmarried White Male 0 \n",
"\n",
" capital.loss hours.per.week native.country income \n",
"0 4356 40 United-States <=50K \n",
"1 4356 18 United-States <=50K \n",
"2 4356 40 United-States <=50K \n",
"3 3900 40 United-States <=50K \n",
"4 3900 40 United-States <=50K \n",
"5 3770 45 United-States <=50K \n",
"6 3770 40 United-States <=50K \n",
"7 3683 20 United-States >50K \n",
"8 3683 40 United-States <=50K \n",
"9 3004 60 ? >50K "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.tree import plot_tree\n",
"\n",
"# Daten einlesen\n",
"df = pd.read_csv(\"./sample_data/adult_census_income/adult.csv\")\n",
"\n",
"# Anzeigen der ersten Zeilen des Datensatzes\n",
"df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ausgabe verschiedenster Informationen über den Datensatz"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:33.125325Z",
"start_time": "2025-03-20T12:18:33.112378Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Datensatzgröße: (32561, 15)\n"
]
}
],
"source": [
"# Informationen über den Datensatz\n",
"print(\"Datensatzgröße:\", df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:33.306881Z",
"start_time": "2025-03-20T12:18:33.232246Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Datentypen:\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 32561 entries, 0 to 32560\n",
"Data columns (total 15 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 32561 non-null int64 \n",
" 1 workclass 32561 non-null object\n",
" 2 fnlwgt 32561 non-null int64 \n",
" 3 education 32561 non-null object\n",
" 4 education.num 32561 non-null int64 \n",
" 5 marital.status 32561 non-null object\n",
" 6 occupation 32561 non-null object\n",
" 7 relationship 32561 non-null object\n",
" 8 race 32561 non-null object\n",
" 9 sex 32561 non-null object\n",
" 10 capital.gain 32561 non-null int64 \n",
" 11 capital.loss 32561 non-null int64 \n",
" 12 hours.per.week 32561 non-null int64 \n",
" 13 native.country 32561 non-null object\n",
" 14 income 32561 non-null object\n",
"dtypes: int64(6), object(9)\n",
"memory usage: 3.7+ MB\n"
]
}
],
"source": [
"print(\"\\nDatentypen:\")\n",
"#print(df.dtypes)\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:34.030250Z",
"start_time": "2025-03-20T12:18:33.974088Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Fehlende Werte:\n"
]
},
{
"data": {
"text/plain": [
"age 0\n",
"workclass 0\n",
"fnlwgt 0\n",
"education 0\n",
"education.num 0\n",
"marital.status 0\n",
"occupation 0\n",
"relationship 0\n",
"race 0\n",
"sex 0\n",
"capital.gain 0\n",
"capital.loss 0\n",
"hours.per.week 0\n",
"native.country 0\n",
"income 0\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"\\nFehlende Werte:\")\n",
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:34.431804Z",
"start_time": "2025-03-20T12:18:34.372676Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Statistische Zusammenfassung:\n"
]
},
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>fnlwgt</th>\n",
" <th>education.num</th>\n",
" <th>capital.gain</th>\n",
" <th>capital.loss</th>\n",
" <th>hours.per.week</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>32561.000000</td>\n",
" <td>3.256100e+04</td>\n",
" <td>32561.000000</td>\n",
" <td>32561.000000</td>\n",
" <td>32561.000000</td>\n",
" <td>32561.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>38.581647</td>\n",
" <td>1.897784e+05</td>\n",
" <td>10.080679</td>\n",
" <td>1077.648844</td>\n",
" <td>87.303830</td>\n",
" <td>40.437456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>13.640433</td>\n",
" <td>1.055500e+05</td>\n",
" <td>2.572720</td>\n",
" <td>7385.292085</td>\n",
" <td>402.960219</td>\n",
" <td>12.347429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>17.000000</td>\n",
" <td>1.228500e+04</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>28.000000</td>\n",
" <td>1.178270e+05</td>\n",
" <td>9.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>37.000000</td>\n",
" <td>1.783560e+05</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>48.000000</td>\n",
" <td>2.370510e+05</td>\n",
" <td>12.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>45.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>90.000000</td>\n",
" <td>1.484705e+06</td>\n",
" <td>16.000000</td>\n",
" <td>99999.000000</td>\n",
" <td>4356.000000</td>\n",
" <td>99.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education.num capital.gain capital.loss \\\n",
"count 32561.000000 3.256100e+04 32561.000000 32561.000000 32561.000000 \n",
"mean 38.581647 1.897784e+05 10.080679 1077.648844 87.303830 \n",
"std 13.640433 1.055500e+05 2.572720 7385.292085 402.960219 \n",
"min 17.000000 1.228500e+04 1.000000 0.000000 0.000000 \n",
"25% 28.000000 1.178270e+05 9.000000 0.000000 0.000000 \n",
"50% 37.000000 1.783560e+05 10.000000 0.000000 0.000000 \n",
"75% 48.000000 2.370510e+05 12.000000 0.000000 0.000000 \n",
"max 90.000000 1.484705e+06 16.000000 99999.000000 4356.000000 \n",
"\n",
" hours.per.week \n",
"count 32561.000000 \n",
"mean 40.437456 \n",
"std 12.347429 \n",
"min 1.000000 \n",
"25% 40.000000 \n",
"50% 40.000000 \n",
"75% 45.000000 \n",
"max 99.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"\\nStatistische Zusammenfassung:\")\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:35.616458Z",
"start_time": "2025-03-20T12:18:34.680587Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prozentuale Verteilung der Einkommensklassen:\n",
"income\n",
"<=50K 75.919044\n",
">50K 24.080956\n",
"Name: proportion, dtype: float64\n"
]
}
],
"source": [
"# Überprüfen der Verteilung der Zielklasse\n",
"plt.figure(figsize=(8, 6))\n",
"sns.countplot(x='income', data=df)\n",
"plt.title('Verteilung der Einkommensklassen')\n",
"plt.xlabel('Einkommen')\n",
"plt.ylabel('Anzahl')\n",
"plt.savefig('output/Verteilung_Einkommensklassen.png', dpi=300)\n",
"plt.show()\n",
"\n",
"# Prozentuale Verteilung berechnen\n",
"income_counts = df['income'].value_counts(normalize=True) * 100\n",
"print(\"Prozentuale Verteilung der Einkommensklassen:\")\n",
"print(income_counts)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:35.950107Z",
"start_time": "2025-03-20T12:18:35.872971Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Spalte 'workclass' hat 1836 Einträge mit '?'\n",
"Spalte 'occupation' hat 1843 Einträge mit '?'\n",
"Spalte 'native.country' hat 583 Einträge mit '?'\n"
]
}
],
"source": [
"# Überprüfen auf fehlende Werte oder '?'\n",
"for col in df.columns:\n",
" missing_count = df[df[col] == '?'].shape[0]\n",
" if missing_count > 0:\n",
" print(f\"Spalte '{col}' hat {missing_count} Einträge mit '?'\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Datenvorverarbeitung"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Leere Werte durch häufigsten Wert der Spalte ersetzen\n",
"\n",
"So stellen wir sicher, dass die Daten vollständig sind und die Daten so nah als möglich an den realen Daten liegen."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:36.357635Z",
"start_time": "2025-03-20T12:18:36.201432Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education.num</th>\n",
" <th>marital.status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital.gain</th>\n",
" <th>capital.loss</th>\n",
" <th>hours.per.week</th>\n",
" <th>native.country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>90</td>\n",
" <td>Private</td>\n",
" <td>77053</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Widowed</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>4356</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>82</td>\n",
" <td>Private</td>\n",
" <td>132870</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Widowed</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>4356</td>\n",
" <td>18</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>66</td>\n",
" <td>Private</td>\n",
" <td>186061</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Widowed</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Unmarried</td>\n",
" <td>Black</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>4356</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>54</td>\n",
" <td>Private</td>\n",
" <td>140359</td>\n",
" <td>7th-8th</td>\n",
" <td>4</td>\n",
" <td>Divorced</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3900</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>41</td>\n",
" <td>Private</td>\n",
" <td>264663</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Separated</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3900</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>34</td>\n",
" <td>Private</td>\n",
" <td>216864</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Other-service</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3770</td>\n",
" <td>45</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>150601</td>\n",
" <td>10th</td>\n",
" <td>6</td>\n",
" <td>Separated</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>3770</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>74</td>\n",
" <td>State-gov</td>\n",
" <td>88638</td>\n",
" <td>Doctorate</td>\n",
" <td>16</td>\n",
" <td>Never-married</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Other-relative</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3683</td>\n",
" <td>20</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>68</td>\n",
" <td>Federal-gov</td>\n",
" <td>422013</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>3683</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>41</td>\n",
" <td>Private</td>\n",
" <td>70037</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Never-married</td>\n",
" <td>Craft-repair</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>3004</td>\n",
" <td>60</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education.num marital.status \\\n",
"0 90 Private 77053 HS-grad 9 Widowed \n",
"1 82 Private 132870 HS-grad 9 Widowed \n",
"2 66 Private 186061 Some-college 10 Widowed \n",
"3 54 Private 140359 7th-8th 4 Divorced \n",
"4 41 Private 264663 Some-college 10 Separated \n",
"5 34 Private 216864 HS-grad 9 Divorced \n",
"6 38 Private 150601 10th 6 Separated \n",
"7 74 State-gov 88638 Doctorate 16 Never-married \n",
"8 68 Federal-gov 422013 HS-grad 9 Divorced \n",
"9 41 Private 70037 Some-college 10 Never-married \n",
"\n",
" occupation relationship race sex capital.gain \\\n",
"0 Prof-specialty Not-in-family White Female 0 \n",
"1 Exec-managerial Not-in-family White Female 0 \n",
"2 Prof-specialty Unmarried Black Female 0 \n",
"3 Machine-op-inspct Unmarried White Female 0 \n",
"4 Prof-specialty Own-child White Female 0 \n",
"5 Other-service Unmarried White Female 0 \n",
"6 Adm-clerical Unmarried White Male 0 \n",
"7 Prof-specialty Other-relative White Female 0 \n",
"8 Prof-specialty Not-in-family White Female 0 \n",
"9 Craft-repair Unmarried White Male 0 \n",
"\n",
" capital.loss hours.per.week native.country income \n",
"0 4356 40 United-States <=50K \n",
"1 4356 18 United-States <=50K \n",
"2 4356 40 United-States <=50K \n",
"3 3900 40 United-States <=50K \n",
"4 3900 40 United-States <=50K \n",
"5 3770 45 United-States <=50K \n",
"6 3770 40 United-States <=50K \n",
"7 3683 20 United-States >50K \n",
"8 3683 40 United-States <=50K \n",
"9 3004 60 United-States >50K "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Ersetzen von '?' durch NaN und dann durch den häufigsten Wert\n",
"df_clean = df.copy()\n",
"\n",
"for col in df_clean.columns:\n",
" if df_clean[col].dtype == 'object':\n",
" # Ersetze '?' durch NaN\n",
" df_clean[col] = df_clean[col].replace('?', np.nan)\n",
" \n",
" # Ersetze NaN durch den häufigsten Wert\n",
" most_frequent = df_clean[col].mode()[0]\n",
" df_clean[col] = df_clean[col].fillna(most_frequent)\n",
"\n",
"df_clean.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Umwandlung von Kategorischen Daten in Numerische\n",
"\n",
"**Label Encoding**\n",
"- Wandelt Kategorien in Zahlen um => Jede Kategorie bekommt eindeutige Zahl\n",
"- Income => *('>50K', '≤50K') in 0 und 1*\n",
"\n",
" \n",
"**One-Hot Encoding**\n",
"- Kategorisch geordnete Daten in Binärformat darstellen\n",
"- Für jede Kategorie werden neue Spalten erstellt, wobei 1 bedeutet, dass die Kategorie vorhanden ist, und 0 bedeutet, dass sie nicht vorhanden ist\n",
"- Z.B. education => *education_Bachelors education_Masters education_PhD*\n",
"\n",
"\n",
"**Zweck:**\n",
"- ML-Algorithmen arbeiten mit mathematischen Operationen\n",
"- Können nur mit Zahlen rechnen, nicht mit Text\n",
"- Benötigen numerische Eingaben für Berechnungen"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:36.714586Z",
"start_time": "2025-03-20T12:18:36.582665Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Kategorische Spalten: ['workclass', 'education', 'marital.status', 'occupation', 'relationship', 'race', 'sex', 'native.country', 'income']\n",
"\n",
"Label Encoding für 'income':\n",
"<=50K -> 0\n",
">50K -> 1\n",
"\n",
"Neue Spalten durch One-Hot Encoding:\n",
"['age', 'fnlwgt', 'education.num', 'capital.gain', 'capital.loss', 'hours.per.week', 'income', 'income_encoded', 'workclass_Federal-gov', 'workclass_Local-gov']\n",
"\n",
"Datensatz nach Vorverarbeitung: (32561, 107)\n"
]
}
],
"source": [
"# Kategorische Variablen in numerische umwandeln\n",
"categorical_cols = df_clean.select_dtypes(include=['object']).columns\n",
"print(\"Kategorische Spalten:\", categorical_cols.tolist())\n",
"\n",
"# Label Encoding für die Zielvariable\n",
"label_encoder = LabelEncoder()\n",
"df_clean['income_encoded'] = label_encoder.fit_transform(df_clean['income'])\n",
"print(\"\\nLabel Encoding für 'income':\")\n",
"for i, label in enumerate(label_encoder.classes_):\n",
" print(f\"{label} -> {i}\")\n",
"\n",
"# One-Hot Encoding für kategorische Variablen (außer der Zielvariable)\n",
"categorical_cols = categorical_cols.drop('income')\n",
"df_encoded = pd.get_dummies(df_clean, columns=categorical_cols, drop_first=False)\n",
"\n",
"\n",
"print(\"\\nNeue Spalten durch One-Hot Encoding:\")\n",
"print(df_encoded.columns[:10].tolist())\n",
"\n",
"print(\"\\nDatensatz nach Vorverarbeitung:\", df_encoded.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Daten aufteilen und Modell trainieren"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Datensatz aufteilen in Test und Trainingsdaten\n",
"\n",
"**In den Testdaten wird das Ergebnis *(income, income_encoded)* entfernt**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:37.018743Z",
"start_time": "2025-03-20T12:18:36.962517Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trainingsdaten: 26048 Beispiele\n",
"Testdaten: 6513 Beispiele\n",
"Features: 105 Merkmale\n"
]
}
],
"source": [
"# Features und Zielwerte definieren\n",
"X = df_encoded.drop(['income', 'income_encoded'], axis=1)\n",
"y = df_encoded['income_encoded']\n",
"\n",
"\n",
"# Daten in Trainings- und Testdaten aufteilen\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
"\n",
"print(f\"Trainingsdaten: {X_train.shape[0]} Beispiele\")\n",
"print(f\"Testdaten: {X_test.shape[0]} Beispiele\")\n",
"print(f\"Features: {X_train.shape[1]} Merkmale\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Instanz des Random Forest Classifier erstellen\n",
"\n",
"1. Trainieren mit Trainingsdaten\n",
"2. Vorhersagen für Testdaten\n",
"3. Ausgabe der Modellgüte durch Vergleich von Testdaten und Vorhersagen"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:45.888131Z",
"start_time": "2025-03-20T12:18:37.243261Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.8531\n",
"Precision: 0.7313\n",
"Recall: 0.6161\n",
"F1 Score: 0.6687\n"
]
}
],
"source": [
"# Random Forest Modell trainieren\n",
"rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"rf_model.fit(X_train, y_train)\n",
"\n",
"# Vorhersagen für die Testdaten\n",
"y_pred = rf_model.predict(X_test)\n",
"\n",
"# Modellgüte evaluieren\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"precision = precision_score(y_test, y_pred)\n",
"recall = recall_score(y_test, y_pred)\n",
"f1 = f1_score(y_test, y_pred)\n",
"\n",
"print(f\"Accuracy: {accuracy:.4f}\")\n",
"print(f\"Precision: {precision:.4f}\")\n",
"print(f\"Recall: {recall:.4f}\")\n",
"print(f\"F1 Score: {f1:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Berechnung und Darstellung der Konfusionsmatrix"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Klassifikationsbericht:\n",
" precision recall f1-score support\n",
"\n",
" <=50K 0.88 0.93 0.91 4945\n",
" >50K 0.73 0.62 0.67 1568\n",
"\n",
" accuracy 0.85 6513\n",
" macro avg 0.81 0.77 0.79 6513\n",
"weighted avg 0.85 0.85 0.85 6513\n",
"\n"
]
}
],
"source": [
"\n",
"# Confusion Matrix\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"plt.figure(figsize=(8, 6))\n",
"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
" xticklabels=['<=50K', '>50K'],\n",
" yticklabels=['<=50K', '>50K'])\n",
"plt.xlabel('Vorhergesagt')\n",
"plt.ylabel('Tatsächlich')\n",
"plt.title('Confusion Matrix')\n",
"plt.savefig('output/Confusions_Matrix.png', dpi=300)\n",
"plt.show()\n",
"\n",
"# Klassifikationsbericht\n",
"print(\"\\nKlassifikationsbericht:\")\n",
"print(classification_report(y_test, y_pred, target_names=['<=50K', '>50K']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ausgabe eines Entscheidungsbaums des Random Forest zur Veranschaulichung"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:52.630675Z",
"start_time": "2025-03-20T12:18:46.231962Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2500x1200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Visualisierung eines einzelnen Entscheidungsbaums aus dem Random Forest\n",
"plt.figure(figsize=(25, 12))\n",
"tree_to_plot = rf_model.estimators_[0] # Ersten Baum aus dem Forest auswählen\n",
"plot_tree(tree_to_plot, \n",
" feature_names=X_train.columns,\n",
" class_names=['<=50K', '>50K'],\n",
" filled=True,\n",
" rounded=True,\n",
" fontsize=10,\n",
" max_depth=3)\n",
"plt.savefig('output/Random_Forest_Tree_Example.png', dpi=300)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Random Forest Feature Importance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Visualisierung Feature Importance vom Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:18:54.946339Z",
"start_time": "2025-03-20T12:18:52.770982Z"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1200x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Feature Importance\n",
"feature_importance = pd.DataFrame({\n",
" 'Feature': X_train.columns,\n",
" 'Importance': rf_model.feature_importances_\n",
"}).sort_values('Importance', ascending=False)\n",
"\n",
"plt.figure(figsize=(12, 8))\n",
"sns.barplot(x='Importance', y='Feature', data=feature_importance.head(15))\n",
"plt.title('Feature Importance')\n",
"plt.tight_layout()\n",
"plt.savefig('output/Feature_Importance.png', dpi=300)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Hyperparameter-Tuning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Evaluation optimaler Hyperparameter\n",
"\n",
"1. Durch ausprobieren verschiedener Parameter aus *param_grid*\n",
"2. Random Forest mit besten Parametern in *best_rf_model* speichern"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:21:30.012732Z",
"start_time": "2025-03-20T12:18:54.965340Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Beste Parameter:\n",
"{'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
"\n",
"Bestes Modell:\n",
"Accuracy: 0.8595\n",
"Precision: 0.7585\n",
"Recall: 0.6110\n",
"F1 Score: 0.6768\n"
]
}
],
"source": [
"# Hyperparameter-Grid definieren\n",
"param_grid = {\n",
" 'n_estimators': [50, 100],\n",
" 'max_depth': [None, 10, 20],\n",
" 'min_samples_split': [2, 5],\n",
" 'min_samples_leaf': [1, 2]\n",
"}\n",
"\n",
"# GridSearchCV\n",
"grid_search = GridSearchCV(RandomForestClassifier(random_state=42), param_grid, cv=3, scoring='accuracy')\n",
"grid_search.fit(X_train, y_train)\n",
"\n",
"# Beste Parameter\n",
"print(\"Beste Parameter:\")\n",
"print(grid_search.best_params_)\n",
"\n",
"# Bestes Modell\n",
"best_rf_model = grid_search.best_estimator_\n",
"\n",
"# Vorhersagen mit dem besten Modell\n",
"y_pred_best = best_rf_model.predict(X_test)\n",
"\n",
"# Modellgüte evaluieren\n",
"accuracy_best = accuracy_score(y_test, y_pred_best)\n",
"precision_best = precision_score(y_test, y_pred_best)\n",
"recall_best = recall_score(y_test, y_pred_best)\n",
"f1_best = f1_score(y_test, y_pred_best)\n",
"\n",
"print(f\"\\nBestes Modell:\")\n",
"print(f\"Accuracy: {accuracy_best:.4f}\")\n",
"print(f\"Precision: {precision_best:.4f}\")\n",
"print(f\"Recall: {recall_best:.4f}\")\n",
"print(f\"F1 Score: {f1_best:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. LIME für Erklärbarkeit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Initialisierung des LimeExplainer \n",
"\n",
"1. Initialisieren des LimeExplainer mit Trainingsdaten\n",
"2. Auswahl eines zufälligen Datenbeispiels"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:21:34.627276Z",
"start_time": "2025-03-20T12:21:30.211876Z"
}
},
"outputs": [],
"source": [
"# LIME für Erklärbarkeit\n",
"from lime import lime_tabular\n",
"import random\n",
"\n",
"# Erstelle einen LIME-Erklärer\n",
"lime_explainer = lime_tabular.LimeTabularExplainer(\n",
" X_train.values,\n",
" feature_names=X_train.columns,\n",
" class_names=['<=50K', '>50K'],\n",
" mode='classification',\n",
" random_state=42\n",
")\n",
"\n",
"# Wähle ein zufälliges Beispiel aus den Testdaten\n",
"random_idx = random.randint(0, len(X_test) - 1)\n",
"instance_df = X_test.iloc[random_idx:random_idx+1]\n",
"instance = instance_df.values[0] # Für LIME benötigen wir das Array\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### LIME-Erklärung erstellen für Datenbeispiel\n",
"\n",
"1. Berechnung der LIME-Erklärung\n",
"2. Ausgabe der Resultierenden Feature Importance"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:21:35.766182Z",
"start_time": "2025-03-20T12:21:34.636343Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/nick.starzmann/.pyenv/versions/3.10.13/lib/python3.10/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
" warnings.warn(\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"LIME Feature-Wichtigkeiten Analyse:\n",
"--------------------------------------------------\n",
"Random Forest Vorhersage für diese Instanz: 0.2749\n",
"\n",
"Feature-Einflüsse:\n",
"capital.gain > 0.00: +0.4438\n",
"native.country_Greece <= 0.00: +0.1138\n",
"education.num > 12.00: +0.1044\n",
"marital.status_Married-civ-spouse <= 0.00: -0.0963\n",
"capital.loss <= 0.00: -0.0891\n",
"native.country_Thailand <= 0.00: -0.0723\n",
"native.country_Haiti <= 0.00: -0.0699\n",
"relationship_Husband <= 0.00: -0.0684\n",
"native.country_Iran <= 0.00: -0.0679\n",
"relationship_Wife <= 0.00: -0.0665\n",
"native.country_Poland <= 0.00: -0.0627\n",
"education_Prof-school <= 0.00: -0.0619\n",
"occupation_Exec-managerial <= 0.00: -0.0614\n",
"education_Doctorate <= 0.00: -0.0614\n",
"native.country_El-Salvador <= 0.00: -0.0571\n",
"native.country_Italy <= 0.00: +0.0560\n",
"native.country_Cambodia <= 0.00: -0.0537\n",
"education_Masters <= 0.00: -0.0524\n",
"marital.status_Married-AF-spouse <= 0.00: +0.0376\n",
"native.country_Ecuador <= 0.00: -0.0372\n"
]
}
],
"source": [
"# Erkläre die Vorhersage mit LIME\n",
"\n",
"def analyze_lime_feature_importance(instance, rf_model, lime_explainer, num_features=5):\n",
" \"\"\"\n",
" Analysiert die Feature-Wichtigkeiten der LIME-Erklärung.\n",
" \"\"\"\n",
" # LIME-Erklärung generieren\n",
" exp = lime_explainer.explain_instance(\n",
" instance,\n",
" rf_model.predict_proba,\n",
" num_features=num_features\n",
" )\n",
" \n",
" # Random Forest Vorhersage (nur zur Information)\n",
" feature_names = rf_model.feature_names_in_\n",
" instance_df = pd.DataFrame([instance], columns=feature_names)\n",
" rf_prediction = rf_model.predict_proba(instance_df)[0, 1]\n",
" \n",
" # Feature-Wichtigkeiten aus LIME extrahieren\n",
" feature_importance = exp.as_list()\n",
" \n",
" # Visualisierung der Feature-Wichtigkeiten\n",
" plt.figure(figsize=(10, 6))\n",
" \n",
" # Features und ihre Wichtigkeiten trennen\n",
" features, importances = zip(*feature_importance)\n",
" \n",
" # Balkendiagramm erstellen\n",
" colors = ['red' if imp < 0 else 'green' for imp in importances]\n",
" y_pos = np.arange(len(features))\n",
" \n",
" plt.barh(y_pos, importances, color=colors)\n",
" plt.yticks(y_pos, features)\n",
" \n",
" plt.xlabel('Feature-Einfluss')\n",
" plt.title('LIME Feature-Wichtigkeiten')\n",
" \n",
" # Vertikale Linie bei 0 für bessere Lesbarkeit\n",
" plt.axvline(x=0, color='black', linestyle='-', alpha=0.3)\n",
" \n",
" plt.grid(alpha=0.3)\n",
" plt.tight_layout()\n",
" plt.savefig('output/lime_feature_importance.png', dpi=300)\n",
" plt.show()\n",
" \n",
" # Ausgabe der Ergebnisse\n",
" print(\"\\nLIME Feature-Wichtigkeiten Analyse:\")\n",
" print(\"-\" * 50)\n",
" print(f\"Random Forest Vorhersage für diese Instanz: {rf_prediction:.4f}\")\n",
" print(\"\\nFeature-Einflüsse:\")\n",
" for feature, importance in feature_importance:\n",
" print(f\"{feature}: {importance:+.4f}\")\n",
" \n",
" return {\n",
" 'rf_prediction': rf_prediction,\n",
" 'feature_importance': dict(feature_importance)\n",
" }\n",
"\n",
"# Analysiere wie gut LIME die RF-Vorhersage erklärt\n",
"importance_analysis = analyze_lime_feature_importance(\n",
" instance=instance,\n",
" rf_model=best_rf_model,\n",
" lime_explainer=lime_explainer,\n",
" num_features=20\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Visualisierung des Einfluss einzelner Features auf die Vorhersage\n",
"\n",
"Berechnung der Feature Importance für Veränderte Werte um nachzuvollziehen, wie diese die Vorhersage beeinflussen.\n",
"\n",
"Es fehlt:\n",
"\n",
"- Zufälligkeit der Störungen/Perturbationen/Variationen,\n",
"- Gewichtung nach Nähe zum ursprünglichen Wert,\n",
"- Lineare Modellanpassung,\n",
"- Perbutation mehrerer Features zeitgleich"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Wählen wir zwei Features zur Variation:\n",
"less_important_feature_education = \"education.num\"\n",
"less_important_feature_fnlwgt = \"fnlwgt\" \n",
"\n",
"# Festlegen derRange für die Variation der zwei Features\n",
"education_range = np.linspace(instance_df[less_important_feature_education].values[0] - 10, instance_df[less_important_feature_education].values[0] + 10, 50)\n",
"fnlwgt_range = np.linspace(instance_df[less_important_feature_fnlwgt].values[0] - 100000, instance_df[less_important_feature_fnlwgt].values[0] + 100000, 50)\n",
"\n",
"# Erstellen von Instanzen für LIME\n",
"instances_education = pd.DataFrame([instance] * len(education_range), columns=X_train.columns)\n",
"instances_fnlwgt = pd.DataFrame([instance] * len(fnlwgt_range), columns=X_train.columns)\n",
"\n",
"# Ändern der Feature-Werte in den Instanzen\n",
"instances_education[less_important_feature_education] = education_range\n",
"instances_fnlwgt[less_important_feature_fnlwgt] = fnlwgt_range\n",
"\n",
"# Vorhersagen mit dem Modell (Wahrscheinlichkeiten)\n",
"instances_education[\"prediction\"] = best_rf_model.predict_proba(instances_education)[:, 1]\n",
"instances_fnlwgt[\"prediction\"] = best_rf_model.predict_proba(instances_fnlwgt)[:, 1]\n",
"\n",
"# Bestimmen der y-Achsen-Grenzen (min/max für alle Vorhersagen)\n",
"y_min = min(instances_education[\"prediction\"].min(), instances_fnlwgt[\"prediction\"].min())\n",
"y_max = max(instances_education[\"prediction\"].max(), instances_fnlwgt[\"prediction\"].max())\n",
"\n",
"# Visualisierung der Variation von 'education-num' (moderater Einfluss)\n",
"plt.figure(figsize=(8,5))\n",
"plt.plot(education_range, instances_education[\"prediction\"], label=\"Moderater Einfluss auf die Vorhersage\", color='green')\n",
"plt.axvline(instance_df[less_important_feature_education].values[0], color=\"red\", linestyle=\"dashed\", label=\"Originalwert\")\n",
"plt.xlabel(\"Bildungsniveau (education-num)\")\n",
"plt.ylabel(\"Vorhersage (0 = <=50K, 1 = >50K)\")\n",
"plt.title(f\"Einfluss von {less_important_feature_education} auf die Vorhersage\")\n",
"plt.ylim([y_min, y_max]) # Einheitliche y-Achse\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"# Visualisierung der Variation von 'fnlwgt' (wenig Einfluss)\n",
"plt.figure(figsize=(8,5))\n",
"plt.plot(fnlwgt_range, instances_fnlwgt[\"prediction\"], label=\"Wenig Einfluss auf die Vorhersage\", color='orange')\n",
"plt.axvline(instance_df[less_important_feature_fnlwgt].values[0], color=\"red\", linestyle=\"dashed\", label=\"Originalwert\")\n",
"plt.xlabel(\"Finales Gewicht (fnlwgt)\")\n",
"plt.ylabel(\"Vorhersage (0 = <=50K, 1 = >50K)\")\n",
"plt.title(f\"Einfluss von {less_important_feature_fnlwgt} auf die Vorhersage\")\n",
"plt.ylim([y_min, y_max]) # Einheitliche y-Achse\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Approximation der LIME-Funktionswiese \"von Hand\"\n",
"\n",
"Manuelle Perbutation und lineare Regression zur Berechnung des Einflusses der Features\n",
"\n",
"Ergänzt:\n",
"- Zufälligkeit der Störungen/Perturbationen/Variationen,\n",
"- Gewichtung nach Nähe zum ursprünglichen Wert,\n",
"- Lineare Modellanpassung,\n",
"- Perbutation mehrerer Features zeitgleich"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics.pairwise import euclidean_distances\n",
"\n",
"# Anzahl der zu erzeugenden Perturbationen\n",
"num_samples = 500 \n",
"\n",
"# Die Originalinstanz, die wir erklären wollen\n",
"original_instance = instance_df.iloc[0].copy()\n",
"\n",
"# Erstelle perturbierte Instanzen durch zufällige Variation aller Features\n",
"perturbed_instances = pd.DataFrame(\n",
" np.random.normal(loc=original_instance, scale=1.0, size=(num_samples, len(original_instance))),\n",
" columns=original_instance.index\n",
")\n",
"\n",
"# Vorhersagen für die perturbierten Instanzen mit dem Random-Forest-Modell\n",
"perturbed_instances[\"prediction\"] = best_rf_model.predict_proba(perturbed_instances)[:, 1]\n",
"\n",
"# Berechnung der Gewichte nach Distanz zur Originalinstanz\n",
"distances = euclidean_distances(perturbed_instances.drop(columns=[\"prediction\"]), [original_instance])\n",
"kernel_width = np.sqrt(len(original_instance)) # Kernel-Bandbreite\n",
"weights = np.exp(- (distances ** 2) / (2 * (kernel_width ** 2)))\n",
"\n",
"# Gewichtete lokale lineare Regression zum Erklären der Vorhersage\n",
"lin_reg = LinearRegression()\n",
"lin_reg.fit(perturbed_instances.drop(columns=[\"prediction\"]), perturbed_instances[\"prediction\"], sample_weight=weights.flatten())\n",
"\n",
"# Anzeige der Feature-Wichtigkeiten\n",
"feature_importances = pd.Series(lin_reg.coef_, index=original_instance.index).sort_values(key=abs, ascending=False)\n",
"\n",
"# Visualisierung der wichtigsten Features\n",
"plt.figure(figsize=(8, 5))\n",
"feature_importances[:10].plot(kind=\"barh\", color=\"skyblue\")\n",
"plt.xlabel(\"Einfluss auf die Vorhersage\")\n",
"plt.title(\"Erklärungsmodell (Nachbildung von LIME)\")\n",
"plt.gca().invert_yaxis()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Berechnung Konsistenz LIME Erklärungen\n",
"\n",
"1. Berechnung von 100 Erklärungen auf *gleichem Datenpunkt*\n",
"2. Varianz der resultierenden Feature Importance Werte berechnen"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Varianz der Feature-Wichtigkeiten:\n",
"capital.gain > 0.00: 0.000075\n",
"education.num > 12.00: 0.000010\n",
"capital.loss <= 0.00: 0.000015\n",
"marital.status_Married-civ-spouse <= 0.00: 0.000009\n",
"education_Preschool <= 0.00: 0.000000\n",
"education_Doctorate <= 0.00: 0.000012\n",
"relationship_Wife <= 0.00: 0.000009\n",
"relationship_Husband <= 0.00: 0.000003\n",
"education_Prof-school <= 0.00: 0.000071\n",
"native.country_Hong <= 0.00: 0.003236\n",
"marital.status_Married-AF-spouse <= 0.00: 0.000000\n",
"native.country_Ecuador <= 0.00: 0.002583\n",
"education_Masters <= 0.00: 0.000009\n",
"occupation_Exec-managerial <= 0.00: 0.000006\n",
"native.country_South <= 0.00: 0.002072\n",
"native.country_El-Salvador <= 0.00: 0.001839\n",
"native.country_Laos <= 0.00: 0.001499\n",
"native.country_Peru <= 0.00: 0.001286\n",
"native.country_Thailand <= 0.00: 0.000615\n",
"native.country_Yugoslavia <= 0.00: 0.000134\n"
]
}
],
"source": [
"def predict_fn(x):\n",
" # Konvertiere das NumPy-Array zurück in ein DataFrame mit den richtigen Feature-Namen\n",
" df = pd.DataFrame(x, columns=X_train.columns)\n",
" return best_rf_model.predict_proba(df)\n",
"\n",
"# Speichere mehrere Erklärungen\n",
"explanations = []\n",
"for i in range(100):\n",
" exp = lime_explainer.explain_instance(\n",
" instance, \n",
" predict_fn,\n",
" num_features=20\n",
" )\n",
" explanations.append(exp.as_list())\n",
"\n",
"# Berechne die Varianz der Feature-Wichtigkeiten\n",
"feature_variances = {}\n",
"for i in range(len(explanations[0])):\n",
" feature = explanations[0][i][0]\n",
" values = [exp[i][1] for exp in explanations if len(exp) > i and exp[i][0] == feature]\n",
" feature_variances[feature] = np.var(values)\n",
"\n",
"print(\"Varianz der Feature-Wichtigkeiten:\")\n",
"for feature, variance in feature_variances.items():\n",
" print(f\"{feature}: {variance:.6f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ausgabe der 5 Features mit höchster Varianz als Diagramm"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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YSb7//vvgyCOPDGrVquW2i9b32GOPDT7++OPIMpvi+6lT0en0cXoNxUCv8euvvxb7dxi9LeI/l56rU5bp+7LVVlsFp512WjBu3Dj3+IIFC9xp5vSbodMFark99tjDneoOQGbK0T/paAAAAADIZDoVlebtq3d+c/TMAwDKLuaIAwAAAACQQiTiAAAAAACkEIk4AAAAAAApxBxxAAAAAABSiB5xAAAAAABSiEQcAAAAAIAUKpfKNwNQthUVFdmcOXNsiy22sJycnHSvDgAAALJcEAT277//WsOGDS03t+z0M5OIAyg1JeGNGzdO92oAAAAAMf744w9r1KiRlRUk4gBKTT3hMmPGDKtZs2a6VyfrrVmzxr7//nvbZZddrFw5fs7TjXj4hXj4hXj4hXj4hXhsnCVLlriOovA4tawg0gBKLRyOXrVqVXdB+nfclStXdrFgx51+xMMvxMMvxMMvxMMvxGPTKGvTJsvOIHoAAAAAADIAiTgAlGF5eXnpXgVEIR5+IR5+IR5+IR5+IR7ZJydQmTkAKOUcnGrVqtnixYsZmg4AAIC0W1JGj0/pEQeQNNrv/InDokWLiIcniIdfiIdfiIdfiIdfiEd2IhEHkLTCwsJ0rwLWxmHKlCnEwxPEwy/Ewy/Ewy/Ewy/EIzuRiAMAAAAAkEIk4gAAAAAApBCJOICMP09jJsehoKCAeHiCePiFePiFePiFePiFeGQnqqYDyPiqlAAAAMhMS8ro8Sk94gCSVlRUlO5VwNo4zJs3j3h4gnj4hXj4hXj4hXj4hXhkJxJxAEljR+FPHKZPn048PEE8/EI8/EI8/EI8/EI8shOJOAAAAAAAKUQiDgAAAABACpGIA0gaVT39iYOKkxAPPxAPvxAPvxAPvxAPvxCP7ETVdAAZX5USAAAAmWlJGT0+pUccQNIoJuJPHGbNmkU8PEE8/EI8/EI8/EI8/EI8shOJOICksaPwAztuvxAPvxAPvxAPvxAPvxCP7EQiDgAAAABACpGIAwAAAACQQiTiAJKWm8tPhy9xqFOnDvHwBPHwC/HwC/HwC/HwC/HITlRNB5B8VUozKzs1KQFkLQ5xACDjLaFqOoBsUZSXl+5VgOJQrpxN69bNXSP9iIeH8Zg2jeJHnlAciIc/iIdfiEd2IhEHkLQihk55E4f5bdoQD08QDw/jMX8+B7aeUByIhz+Ih1+IR3biaAEAAAAAgBQiEQcAAAAAIIVIxAEkLbewMN2rgLVxaDRqFPHwBPHwMB6NGlGF2BOKA/HwB/HwC/HITlRNB1BqVE0HUKZwiAMAGW8JVdMBZItCqkJ7obB8eZvcs6e7RvoRDw/jMXmyFTJCwQuKA/HwB/HwC/HITiTiAJIW5OSkexWwNg6LW7QgHp4gHh7GY/FiY+CfHxQH4uEP4uEX4pGdSMQBAAAAAEghEnEAAAAAAFKIRBxA0nLXrEn3KmBtHFqMHEk8PEE8PIxHixZUIfaE4kA8/EE8/EI8shNV0wGUGlXTAZQpHOIAQMZbQtV0ANmCqun+VIWe0KsXVbo9QTw8jMeECVQh9oTiQDz8QTz8QjyyE4k4gKRRFdqfOCyvXZt4eIJ4eBiP5cupQuwJxYF4+IN4+IV4ZCcScQAAAAAAUohEHAAAAACAFKJYG4Cki2Esysmxavx0pF2Qm2uLmzWzajNnWk5RUbpXJ+sRDw/jsXCh+83KYbpA2ulwU4WUiIcfiIdfiEd2FmsjEQdQalRNB1CmcIgDABlvSRlNxBmaDiBpa6gK7YU1+fk2tk8fd430Ix4exmPsWFvDed29oDgQD38QD78Qj+xEIg4AZVghSZ9XiIdfOBWQX4iHX4iHX4hH9iERBwAAAAAghUjEAQAAAABIIYq1AUi+arqZVUv3ysCCnBxbXquWFSxcqB/zdK9O1iMeHsZj6VIrKCigCrEHdLi5fPly4uEJ4uEX4rFxKNYGAEitILD8JUuoDO0L4uFfPJiz7xXi4Rfi4RfikX1IxAEkrZCq6d4UBhvXpw8FwjxBPDyMx7hxFEDyhOJAPPxBPPxCPLITiTgAAAAAAClEIg4AAAAAQAqRiAMAAAAAkEJUTQdQalRN90uwdh5s3qpVRo3V9CMeHsZj9WrLy8ujCrEHdLip+a/Eww/Ewy/EY+NQNR0AkFo5ObZKOxx22n4gHv7FY9WqdK8FohAPvxAPvxCP7EMiDiBpVE33Jw4Te/UiHp4gHh7GY+JEqhB7QnEgHv4gHn4hHtmJRBwAAGSNBx980Jo1a2YVK1a0PfbYw7755psSl3/ppZds2223dcvvuOOO9s4776wzpHTgwIHWoEEDKygosC5dutgvv/wSs8xNN91ke++9t1WqVMmqV69e4vstXLjQGjVq5IanLlq0aJ1132677dz7tGrVyoYNGxbz+OrVq+3666+3rbbayq3vzjvvbO+9914ptwwAIJVIxDcB7dDvueeedK8GAAAowYgRI+yyyy6zQYMG2XfffecS1a5du9q8efMSLv/VV19Zz5497cwzz7Tvv//eDj/8cHf58ccfI8vcfvvtdt9999kjjzxiY8aMscqVK7vXXLFiRcyQ02OOOcbOO++89a6j3munnXZa5/6HH37YrrrqKrv22mvtp59+suuuu84uuOACe+uttyLL9O/f3x599FG7//77bdKkSXbuuefaEUcc4dYdAOAZFWtD6QwZMiSoVq3aOvfPmzcvWLZsWZDpPv30U9W+Cf7555/N/l6nnnpq0KNHjyCbPfDAA0HTpk2DChUqBO3atQvGjBmz3ue8+OKLQatWrdxzdthhh2DkyJExjxcVFQUDBgwI6tevH1SsWDHYf//9g59//rnU67R48WL3HVhYvryqPHJJ82V1fn7wTZ8+7jrd68KFeHgZj2++CVavXh35DdNv6QUXXBC5XVhYGDRs2DC45ZZbEv7mHXvssUG3bt1i7ttjjz2CXr16RX5T9Xs6ePDgyOOLFi1yv8HDhw8v9XFE6KGHHgo6d+4cfPzxx+vsb/faa6/giiuuiFn+sssuC9q3bx+53aBBA7fviHbkkUcGJ554YpBuikN8PJA+xMMvxGPjhMenui5L6BHfBOrUqeOGmyF7i00UFRXZ7Nmz09Zrsyl7bkqj3OrVG/X5sGmUW7XK2g4e7K6RfsTDLy4ebdtauXLlIvumb7/91g0dD+Xm5rrbo0ePTvgauj96edFvZrj8jBkzbO7cuTHLqHKvhrwX95rFUQ+2hpVruLnWK97KlSvdcPNoGqKuofUakl7SMv/73/8s3RSH6HggvYiHX4hHdsqaRHyfffaxiy66yK688kqrWbOm1a9f3w3vinbXXXe5+V9KTho3bmznn3++LV261D322Wef2emnn+7K4mveli7h86OHpp9wwgl23HHHxbyudpC1a9eOzOVS0nbLLbdY8+bN3Q5SSdbLL79c4vpr59q3b1+3XhUqVLCWLVvak08+GXn8888/t3bt2rnHNE+tX79+tmbNmhKHz7dp0yZmG+gzPfHEE24YmxoWtt56a3vzzTfdYzNnzrR9993X/b9GjRpu2dNOOy2ybXv37m2XXHKJ+5w6SDnjjDPs0EMPXWc71K1bN2a9N1RJsQq98sortv3227ttos9/5513xjz+0EMPuc+og5Z69erZ0UcfnfR6TJkyxQ0VbNKkid1xxx22qejznX322e4717p1a5c4KyZPPfVUsc+599577aCDDrI+ffq4OYQ33HCD7brrrvbAAw9E5jHqO6Chiz169HBDH/WdnDNnjr3++utJrV9AVWgvBLm5tqhFC3eN9CMeHsZj0SL32ycLFixwhZD0ex9Nt5VMJ6L7S1o+vE7mNYvbx6shdfDgwW5/koj2rdpHqzFBn2ncuHHutvat+mzhMtp/aI66jjU+/PBDe/XVV+3PP/+0dNM6R8cD6UU8/EI8slNWHS08/fTTLnFTT6B6BtXyrJ1USC3Q6i3U3Cst+8knn7jEXVRkRUmMzk2nHZouV1xxxTrvceKJJ7r5WtFJ4fvvv2///fefS3BFSbgSICVXeq9LL73UTjrpJJdMF+eUU06x4cOHu/WbPHmymwNWpUoV95h6Yg855BDXkjZhwgQ3j0zJ7o033pj0NtKcs2OPPdZVbtRr6vP8/fffLtlVYitTp051n1+JX/S2zc/Pty+//NJ9rrPOOssViIne+b/99ttuO8Q3VGyIkmIlOlDR5zj++OPthx9+cA0OAwYMsKFDh7rHdQCjhhl9B/R5tK6dOnUq1Xv/888/bhvvueeetsMOO7ge61tvvdUV4wndfPPNLj4lXX7//feEr78hvTabq+dGB4c6N2P0RQppsfWC4jClZ0/i4Qni4WE8pkwpE1WI1aCrBlQdCxRH+7CDDz7Y7XvKly/vGlRPPfVU91jYg679shqYVVxO+2Q1kqtBN1EPe6opDmUlHtmAePiFeGSpIEtozlWHDh1i7mvbtm3Qt2/fYp/z0ksvBbVq1Vrv3C7N47377rvd/zW3o3bt2sGwYcMij/fs2TM47rjj3P9XrFgRVKpUKfjqq69iXuPMM890yyUydepUN+/hww8/TPj41Vdf7eYFa65a6MEHHwyqVKni5r/Fr2No5513DgYNGhS5rffo379/5PbSpUvdfe+++26Jc8S1bXfZZZd11qt169bBbbfdFrl92GGHBaeddlqwOeaIx8fqhBNOCA444ICYZfr06ePWSV555ZWgatWqwZIlS0r1+tqOb7/9dnDMMce4uX877rhjcPvttwdz5sxJuPzChQuDX375pcRLcfOAZs+e7bZz/HdE66/5jcUpX7588Pzzz8fcp+9B3bp13f+//PJL97rx66zPpHmQiej7oefEX5gj7s8c2NHXXMOcZE8uxMPDeIweHfmtXblyZZCXlxe89tprMb9zp5xyStC9e/eEv4GNGzdeZ985cODAYKeddnL/nzZtmvtN/P7772OW6dSpU3DRRRet83rFHUdof5ybm+vWTxf9X6+r/+v9oq1atSr4448/gjVr1rg55VtssUVkXx9avnx5MGvWLHdccOWVV0b2femkOETHA+lFPPxCPDYOc8TLgPgqpBrCHT3n9qOPPrL999/fttxyS9tiiy3s5JNPdqcRUS9uaWluh3pin3vuOXd72bJl9sYbb7ieZfn111/d6x1wwAExvaPqIZ82bVrC1xw/frzl5eVZ586dEz6uHvK99trLDRcPtW/f3vXKz5o1yzZ0G2n0gEYAlDQvObTbbrutc596xYcMGeL+/9dff9m7777rhqxvCuuLlbaJtkE03dZwPbU2avs3bdrUWrRo4Z6reJUUZ/Vea6i93lcjEzRiQEPA9R1KRNMfNH2gpEtZmAekXhpNxwgvf/zxR7pXCQA2iHqIta/6+OOPI/dp+LZuax+aiO6PXl40ki5cXlPMNNUtehmNHNLIu+JeMxGNONOINu3vddGQcxk1apSrjB5NveE6vZmOC1544QW3b4rv8daUK+0fNUVNr63ecwCAX7IqEdfOK5oSV+2EwznQ2pkpEdVOS0ODdb7ODSk+pqRbO2UlsJp7q3ngmrsr4ZD1kSNHRna4uqhIS3HzxPX8jaWddPy8k7C4S2m3UUmUtCcaTj99+nQ37PnZZ591BywdO3a0jbUpYqXkXUPKlVQrmdY5YDVXP/6crSEd9GhZDeNWQ4uGsT/++OPFLr8xQ9M1z14HWGq8iKbbOuArjh4r6TnhdTKvq/n1aoyJvkhO3HcJ6aE4FCxYQDw8QTw8jEdBQUwjtYpg6rdbU5rUYKvTianBXMO3w/2WGiBDF198sZu6pBojGjaqaU6a2qQh3+49cnJcfRRNBVNNFU2F0ms0bNjQFcsM6fde+3pdqzE43PeHxwQ677emOoUX7S9Fw9VVW0V+/vlnty9Vg7IKtGnqlYpxan8TUgOA5oRr36skXsce2odHT91KF22r+HggfYiHX4hHdvK/Sy5FlMxpZ6Wdbdiy/OKLL67Tml6auRuaT6451ap8rV5gnTs0THBVeEvJjXbGxfVwx1NRMq2b5pDHzwEOd9RKSJVoh3/AmqutZFMJZFjZPXq+tlrsNWc4Gfr8Utr5K7Vq1XIHIuoVVzIeHuikIlbaJtoG0XR7m222cUmuqEda21MXVSevXr26m2t+5JFHrvOeWlYHPbpoOz7zzDOuZsCFF15ohx12mOtV19y9MM46d6sS9pLoQG19vTbhgVzYaxMe/JXUc6ODwvX13KhQX3TPTWnObRstL6oQINInb/Vq2/nRR9O9GliLeHgYj513jrlPNUrmz5/vGl9VM0O/hUq0w2Jr2jdH9y5rf/7888+7IpdXX321m3+tBnYlyyEluUrmzznnHNc426FDB/ea0dXL9X5K/kO77LKLu/70009dwdPS0L5X+z3VNdG+RgVUdbYMFSMN6QwYWlcl4mrwVa0X7a+0f0s37Xvj44H0IR5+IR5ZKsgSmsd88cUXx9ynOciaiyzjx493cwvuueceN+dLc7y33HLLmDnR4Rzbjz76KJg/f37k3OGJ5l9fc801bk5WuXLlglGjRq3zmOYzDx06NPj111+Db7/9Nrjvvvvc7ZDmfL/66quR25pbrblqmts2ffp0N197xIgR7jHNA9O8c50bdfLkycHrr7/u5qlHz//u16+fO9fpF198EUycODE4/PDD3Rzy+Dni8XPnNJdNc9rC98nJyXHrqXOn//vvv8Vu29AHH3wQ5Ofnu3lumvtcWorLPvvs4+bdRV9+//33UsVK21Rz7K6//no3x17rXFBQEPksb731VnDvvfe615w5c6abZ6flf/zxxyAZY8eOddtd8dT5XDeVF154wc1F13pPmjQpOOecc4Lq1asHc+fOjSxz8sknu7iG9P3U9+2OO+5w3wPFVvPGf/jhh8gyt956q3udN954w30P9DfQvHlzN58wmTk4/+S4Pj8uab4U5uYGf7Vp467TvS5ciIeX8fjrr3XmTyM9FAfi4Q/i4RfikZ1zxEnE1ybictdddwUNGjRwCVvXrl1dghdfnOzcc891SZfuD5PYRIm4kicto8eii6iJbiuJVLKtRKlOnTru/T7//PPIMnpumDSKEqVLL73UrZ8S25YtWwZPPfVU5PHPPvvMFZ/TY0q4VYQuuuCDvpgqGKcCZUroleAlKtZWUiIuSmz1+krIw21XUiKuz6ptcMghhwTJ0GsnKhKmonaljdXLL7/sGkO0jZs0aRIMHjw48pgaR7TeNWrUcK+hwjthw8aGUBEgNQpsSvfff79bb8VURdq+/vrrmMe1/tHfX3nxxReDbbbZxj1n++23D0aOHLlOPAYMGBDUq1fPJfr777+/a6hI9oeOYm1+XCgO5teFePhdrA3pRTEqvxAPvxCP7EzEc/RPunvlkbk0/00FYzQ8PdGQb5QtGsquU54tLF/eaiaoMYDUWpOfb+P69LHdBw+2cknWssCmRzw8jMfnn9vuu+9eJopjZjoVjtP8euLhB+LhF+KxaY5PVVg4rGdUFhBpbBaa07xgwQI3n01z07p3757uVQIAAAAAL5CIY7NQwRsVB1OxuKFDh8a07ukxFa0rjirIN2nSJEVrig1BVWh/4lBt+nTi4Qni4WE8qlWjCrEnFAfi4Q/i4RfikZ0Ymo60DL/RKciKowqwDMvxfOiPmZWdgT8AshaHOACQ8ZYwNB0oHSXZLVu2TPdqYCMU6fQ+pTi/PDavorw8m9O+vTX88kvLLeVpBbH5EA8P4zFrljtVZPQpyZC+KWtz5swhHp4gHn4hHtmJSAPYoANc+BGHWR07Eg9PEA8P4zFrljvARfopDsTDH8TDL8QjO5GIAwAAAACQQiTiAAAAAACkEIk4gKTlMnTKmzjUGT+eeHiCeHgYjzp1mG/pCcWBePiDePiFeGQnqqYDKDWqpgMoUzjEAYCMt6SMVk2n2QVA0ihG5YeicuVsWrdu7hrpRzw8jMe0aRQ/8oTiQDz8QTz8QjyyE4k4gA07fRm8iMP8Nm2IhyeIh4fxmD+fA1tPKA7Ewx/Ewy/EIztxtAAAAAAAQAqRiAMAAAAAkEIk4gCSlltYmO5VwNo4NBo1inh4gnh4GI9GjahC7AnFgXj4g3j4hXhkJ6qmAyg1qqYDKFM4xAGAjLeEqukAskUhVaG9UFi+vE3u2dNdI/2Ih4fxmDzZChmh4AXFgXj4g3j4hXhkJxJxAEkLcnLSvQpYG4fFLVoQD08QDw/jsXixMfDPD4oD8fAH8fAL8chOJOIAAAAAAKQQiTgAAAAAAClEIg4gablr1qR7FbA2Di1GjiQeniAeHsajRQuqEHtCcSAe/iAefiEe2Ymq6QBKjarpAMoUDnEAIOMtoWo6gGxB1XR/qkJP6NWLKt2eIB4exmPCBKoQe0JxIB7+IB5+IR7ZiUQcQNKoCu1PHJbXrk08PEE8PIzH8uVUIfaE4kA8/EE8/EI8shOJOAAAAAAAKUQiDgAAAABAClGsDUDSxTAW5eRYNX460i7IzbXFzZpZtZkzLaeoKN2rk/WIh4fxWLjQ/WblMF0g7XS4qUJKxMMPxMMvxCM7i7WRiAMoNaqmAyhTOMQBgIy3pIwm4gxNB5C0NVSF9sKa/Hwb26ePu0b6EQ8P4zF2rK3hvO5eUByIhz+Ih1+IR3YiEQeAMqyQpM8rxMMvnArIL8TDL8TDL8Qj+5CIAwAAAACQQiTiAAAAAACkEMXaACRfNd3MqqV7ZWBBTo4tr1XLChYu1I95ulcn6xEPD+OxdKkVFBRQhdgDOtxcvnw58fAE8fAL8cjOYm3l0r0CAMqgRYvMqpGKp10QWL7mlOXlmbHjTj/i4Wc84I18aih4hXj4hXhkH4amA0gaBUX8icO4ceOIhyeIh1+Ih1+Ih1+Ih1+IR3YiEQcAAAAAIIVIxAEAAAAASCEScQAAAAAAUoiq6QCSr5q+aJG7Rnrp51vzyfLy8qiy6gHi4Rfi4Rfi4Rfi4RfikZ1V0+kRB4AybNWqVeleBUQhHn4hHn4hHn4hHn4hHtmHRBxA0qjq6U8cJk6cSDw8QTz8Qjz8Qjz8Qjz8QjyyE4k4AAAAAAApRCIOAAAAAEAKkYgDQBmmwi7wB/HwC/HwC/HwC/HwC/HIPlRNB5DxVSkBAACQmZaU0eNTesQBJI32O3/ioFPJEQ8/EA+/EA+/EA+/EA+/EI/sRCIOIGlU9fQnDlOmTCEeniAefiEefiEefiEefiEe2YlEHAAAAACAFCIRBwAAAAAghUjEASQtJycn3auAtXEoKCggHp4gHn4hHn4hHn4hHn4hHtmJqukAMr4qJQAAADLTkjJ6fEqPOICkFRUVpXsVsDYO8+bNIx6eIB5+IR5+IR5+IR5+IR7ZiUQcQNKK6tbVOCouab4UFRTY9Pvuc9fpXhcuxMO3C/FIcEnnfqOoyKZPn06i4Qni4RfikZ1IxAEAAAAASCEScQAAAAAAUohEHEDScqjx6E0cqk2fTjw8QTz8Qjz8omrQKqZEVWg/EA+/EI/sRNV0AMlXpTSzslOTEgDgcMgHIAMtoWo6gGxRlMtPhw+K8vJsVqdO7hrpRzz8Qjz8oiJUs2bNohiVJ4iHX4hHduJoGkDSOLD1KNHo2JF4eIJ4+IV4+IVEwy/Ewy/EIzuRiAMAAAAAkEIk4gAAAAAApBCJOICk5TJ0yps41Bk/nnh4gnj4hXj4JTc31+rUqeOukX7Ewy/EIztRNR1AqVE1HQDKMA75AGSgJVRNB5AtKH7kh6Jy5Wxat27uGulHPPxCPPyiIlTTpk2jGJUniIdfiEd2IhEHkDROX+ZPHOa3aUM8PEE8/EI8/KIEY/78+SQaniAefiEe2Ym9EwAAAAAAKUQiDgAAAABACpGIA0habmFhulcBa+PQaNQo4uEJ4uEX4uEXVYNu1KgRVaE9QTz8QjyyE1XTAZQaVdMBoAzjkA9ABlpC1XQA2aKQKsReKCxf3ib37OmukX7Ewy/Ewy+FhYU2efJkd430Ix5+IR7ZiUQcQNKCnJx0rwLWxmFxixbEwxPEwy/Ewy8agKneKgZi+oF4+IV4ZCcScQAAAAAAUohEHAAAAACAFCIRB5C03DVr0r0KWBuHFiNHEg9PEA+/EA+/qBp0ixYtqArtCeLhF+KRnaiaDqDUqJoOAGUYh3wAMtASqqYDyBZUTfeDqkFP6NWLqtCeIB5+IR5+UTXoCRMmUBXaE8TDL8QjO5GIA0gaVYj9icPy2rWJhyeIh1+Ih180AHP58uVUhfYE8fAL8chOJOIAAAAAAKQQiTgAAAAAAClEsTYASRfDWJSTY9X46Ui7IDfXFjdrZtVmzrScoqJ0r07WIx5+IR4JpPF3W4ebKqSkfUgO0wXSjnj4hXhkZ7E2EnEApUbVdAAowzjkA5CBlpTRRJyh6QCStoYqxF5Yk59vY/v0cddIP+LhF+LhlzVr1tjYsWPdNdKPePiFeGQnEnEAKMMKSTK8Qjz8Qjz8wqmZ/EI8/EI8sg+JOAAAAAAAKUQiDgAAkKUefPBBa9asmVWsWNH22GMP++abb0pc/qWXXrJtt93WLb/jjjvaO++8E/O4Sg8NHDjQGjRoYAUFBdalSxf75ZdfYpa56aabbO+997ZKlSpZ9erVE76Phunuv//+7vEaNWpY165dbcKECZHHp06davvuu6/Vq1fPrUuLFi2sf//+tnr16sgy+v/1119vW221lVtm5513tvfee28DtxQAZGgirp3APffck+7VyDqqzPj6669nRHxnqjJuTo6NHz9+s74PzPKiDnSQ3jjs9OijxMMTxMMvxGP9RowYYZdddpkNGjTIvvvuO5eoKuGdN29ewuW/+uor69mzp5155pn2/fff2+GHH+4uP/74Y2SZ22+/3e677z575JFHbMyYMVa5cmX3mkqKd9ppJ8vLy7NVq1bZMcccY+edd17C91m6dKkddNBB1qRJE/ca//vf/2yLLbaIvI6UL1/eTjnlFPvggw9cUq5jjMcff9x9lpAS80cffdTuv/9+mzRpkp177rl2xBFHuHXPdopDGA+kH/HIUkGKDRkyJKhWrdo698+bNy9YtmxZkOk+/fRTlSwN/vnnn83+Xqeeeqp7r/hL165dI8vo9muvvVbi67z66qvBHnvsEVStWjWoUqVK0Lp16+Diiy9Oal2aNm0a3H333cHmNGPGDPd5vv/++6CsW758eXD++ecHNWvWDCpXrhwceeSRwdy5c0t8TlFRUTBgwICgfv36QcWKFYP9998/+Pnnn2OWWbhwYXDCCScEW2yxhfs7POOMM4J///231Ou1ePFit40X/V/tXS5pvhSZBavz8911uteFC/Hw7UI8ElzitGvXLrjgggsitwsLC4OGDRsGt9xyS8J9wLHHHht069Yt5j4dH/Tq1SuyH9I+aPDgwZHHFy1aFFSoUCF4/vnng9WrV7tl1ndMOHbsWLev+f333yP3TZw40d33yy+/FLuPuvTSS4MOHTpEbjdo0CB44IEHYpbR/vTEE08Msp3iEB8PpA/x2Djh8amuyxJvesTr1Knjhijh/6i1eFNQi/Kff/4Zcxk+fHipn//xxx/bcccdZ0cddZQbrvbtt9+6IWXRQ78yyYZu92XLltnChQs32Xpceuml9tZbb7khgJ9//rnNmTPHjjzyyBKfU1wvxIoVKyLLnHjiifbTTz/Zhx9+aG+//bZ98cUXds455yS9foVUTfemENW4Pn0oSOUJ4uEX4rH+/Z326Ro6HsrNzXW3R48enfA5uj96edF+Jlx+xowZNnfu3JhldEohDXlXb/q4ceNKVZCqVatWVqtWLXvyySfdei5fvtz9f7vttnMj7BL59ddf3bDzzp07R+5buXKlG5IeTcPl1cOe7RSH0sYDmx/xyE5JJeL77LOPXXTRRXbllVdazZo1rX79+nbttdfGLHPXXXe5OUNKAho3bmznn3++G2Ikn332mZ1++unuHG8aQqxL+PzoocsnnHCCS/6iKfGrXbu2DRs2zN0uKiqyW265xZo3b+5+VDWc6uWXXy5x/fWD3LdvX7deFSpUsJYtW7of9pASnnbt2rnHNLepX79+MacRSDS8uk2bNjHbQJ/piSeecEOf1LCw9dZb25tvvhkZOq35TKL5Tlr2tNNOi2zb3r172yWXXOI+p3ZsZ5xxhh166KHrbIe6devGrHdJ9FkUp+iL3ru0lAy2b9/e+vTp43aM22yzjRuGpjlloWnTplmPHj3cPK0qVapY27Zt7aOPPir2NUsTX+1MO3To4OaGaWes7aD3iaaGgV122cXtZHffffeEQ83WF9NE2720NKBAr6/vtLbrptqx6+9D8dXf0n777We77babDRkyxB3EfP3118Wui76bGoanWGh4k7alEvhw6sHkyZPddtX3UwdF2r4arvfCCy+45QAA2WPBggXuoF/77mi6rWQ6Ed1f0vLhdaJl/vrrr1Kvm4ah65jx2Wefdcd4OrbQ/uvdd9+1cuXKxSyrueY6DtDxVseOHd2c8JD26dqXao66jhvVCP3qq6+6TgkASLeke8Sffvppl2Srx009cPrB0w9b5AVzc12vnHrdtOwnn3ziEvfwx1LJgk60HvbOXnHFFeu8h3rtlACGCby8//779t9//7kEV5SEK9FQ75/eSz2IJ510kkuMiqO5ROoN1vopKdG8If24y+zZs+2QQw5xSaSKgTz88MMuGbrxxhuT3UR23XXX2bHHHmsTJ050r6nP8/fff7sGgFdeecUto/lM+vz33ntvzLbNz8+3L7/80n2us846y+14oncY6sXUdohPZDcXJZjavtHzv+IpTvqc6j1XMqxe+MMOO8x+//33hMuXJr7qYda8NbUO6nX1vdJj2pGG76nkvHXr1q5FX40h8d+l0sY0fruvz/Tp090cNBWG6datmzuQee2119xnDh188MHuu1XcZfvtty/29fV51DAR3aOgwjiaK1dcL0VJvRDhc3Sthg01WoS0vLat/p6La7xasmRJzAUAgM1JPeCah66OADVAa/+8ww47uH2uHouf56757c8//7yNHDnS7rjjjshjOsZSgq59qPbzanhX47n2ewCQdsmMY+/cuXPM3Btp27Zt0Ldv32Kf89JLLwW1atVa73yg6DnEmiNRu3btYNiwYZHHe/bsGRx33HHu/ytWrAgqVaoUfPXVVzGvceaZZ7rlEpk6daqbO/Dhhx8mfPzqq68OWrVqFTM348EHH3RzojVnKn4dQzvvvHMwaNCgyG29R//+/SO3ly5d6u579913S5wjrm27yy67rLNemo992223RW4fdthhwWmnnRaUdo54Xl6em2McfbnppptKPUdc63/IIYe45fT5FYMnn3zSxaAk22+/fXD//fdvUHwTmT9/vluHH374wd1+9NFH3fdKc6lDDz/8cMwc8dLEtLjtHk/zqJ944omgY8eObpt26dLFrb+2TyKzZs1y89iKu8ycObPY93ruueeC/Pz8de7X39qVV16Z8Dlffvml++xz5syJuf+YY45xc/pEcd9mm23WeW6dOnWChx56KOHr6rudqM7AwvLl0z/XkYub/zr6mmvcdbrXhQvx8O1CPBJcoqxcudLtz+KPAU455ZSge/fuCfcJjRs3Xuc4aODAgcFOO+3k/j9t2rSEtVo6deoU9O7dOxg9erQ7BljfMaH2t3Xr1o3sq8P11bHf8OHDg+I888wzQUFBQbBmzZqY+3WsoP2yjge0H9WxVbZTHOLjgfQhHhsna+aIa8hrNA33ja6uqSHJOt3Elltu6YYWnXzyyW7urHo7S0vDjtSj/Nxzz0V6R9944w3XkxrOA9LrHXDAATG9jOohjx++HFIlbVUijJ47FE095HvttZcbLh5SS6x6XmfNmmUbuo00ekAjAIqrQBpNQ5DjqVdcw5JFw7o0LEtD1ktLQ+H12aMvqhqaSHQvbthjq/VXC7O2uYY967HLL7/cDfcOY6ptpN5ozd1Sj6uW0fYsrkd8ffEVDSNTZVb1Omv7hXPCwtfU62s7R8/9Uvw2JKaJtns8TXtQLP755x/Xu65RIPpua/skou+/pj4Ud2natKmVBVdddZUbKh9e/vjjD3c/VYj9kLdqle0+eLC7RvoRD78Qj5Kph1j7P406C2nUmW7H709Duj96edH+MFxe0wU1ki56GY2k0qgrjYrUiKzSVIXW8YV6raP33+HtcGRcInpMI8ril9GxgvbLmpqmkYmawpXtFIfSxgObH/HITrETbUpBp4uIFv2jqDnQGi6s01GooJfmkWverIYXqdhGMsXYlJQpaVYCqx95zRHSkGcJhzQrQdQPazTNBU5Ez99Y2gn8Xyfy/5eoaFlJ26gkiZI6DafXvGYNK9YcYe3kNAeqtPSaSvxKQ3OHwyFf8Z9B5+DURcnoNddc4+aKaziYhngpCVeMNBxM76VtffTRR5dY+Kyk+IqGeStZ1alIGjZs6LafhqVtqiJ20YpLpqNpp3333Xe7Yew6cNH6KRFX40X8thLdP2rUqGJfT59NQ/4T0UGMPueiRYtizq+qhhg9VtxzwmXUOBb9HNUxCJeJbxDSQYmmTRT3uvp7Ku5vCh7IybFVVatagQoFxv02IQ2Ih1+Ix3ppCtipp57qEgA1sGv6oBrHtW8Pj0F0nKXpgHLxxRe7ffedd97phomrxoimkD322GOR4x3VXNEUMA0J1zHLgAED3H5c9WW0b9P+Xo3q2vfoWtO7wtOO6hhCjfnqaFFtmgsuuMAuvPBCdwxw6623uob8sNaOGvO1/1VdIu2ntB5qPNbUvXC/rAYATVPTflDXmsam1wqnTGa7MB7wA/HIPkkn4iXR3Fb9wOkHOpx/8+KLL67TAluaioBqOdWcaiV76gXW+SbDH1bNC9aPrn7Ai+vhjqcfaq2b5pDHV/wU9eaqlVSJdtgCqzlJ6tVv1KhRpLJ79HxttfJqbm4y9PmltFURVahMOy/1iisZD3eOm0N8o0Zx1DutRhXtrMPtpKJz4fxuNZSoUWZD46sRFJpDryQ8bHSIL4SmeD3zzDOuInjYKx5fyKw0MS0tFbjTwYUumvs/dOhQV21ciezxxx/vknLNx07UqJFIouQ9pERfj6tHQdXqRdtD3/fieimieyHCxDvshQjP06rnKrnX32k4CkA1HPR3Eb3upa6aTq942ikOE3v1cr1+5ej1Szvi4RfisX5KWufPn28DBw50dUa0/1BtmrDYmvY70fOpte/WXGyNkLv66qtdsq2CoGooDynJ1fGB9pHa56gwqF5T+zUly0r69X5q2A6p8Kp8+umnroiq5nSrloxq7mjfpXXQMnqdsLFZSfltt91mP//8s9vPq4Fbc8BVMyikYwStq2q7KMFX3RgdO0Q3cmcrHYfqeEbxiC+Ah9QjHlkqmXHsmk8bf/7oHj16uLnIMn78eDc+/5577nHzhDSHdsstt4yZEx3OZf3oo4/cvN/w3OGJ5l9fc801bh5PuXLlglGjRq3zmOYIDx06NPj111+Db7/9Nrjvvvvc7ZDmB+sc2CHNrdb8Js2Hmj59upuvPWLECPeY5g5p7pHOpzl58uTg9ddfd/OYo+d/9+vXz50f84svvnDnszz88MPdfOP4OeLx8600/0nzoML3ycnJceupc6eH53BOtG1DH3zwgZszrLlcs2fPDkpLcTnooIOCP//8M+ai7V7S+kbTZ+vTp4/bVtpm3333nduOmoM1ZcoUt8wRRxwRtGnTxs0J03dA89h1nuroz5NMfDUnTLE96aST3Hzqjz/+2M2Pjl5XbTfFR8v89NNPwciRI4OWLVvGzE0rTUxL2u7ro3k8b731VnDUUUe5+Lz55pvBpnLuuecGTZo0CT755JNg3LhxwV577eUu0eK/37feemtQvXr14I033nDfT/1tNm/ePGYevb4PmhM/ZsyY4H//+1+w9dZbF1tXoaQ5OMwR9+PCHFi/LsTDrwvxSHBJI+bA+oV4+IV4ZOcc8U2aiMtdd90VNGjQwCVqXbt2dcl4fHEyJRlKtHR/mBQlStQmTZoUKRIWf4J73VbCr2SkfPnyruCU3u/zzz///x/OLJIAixKSSy+91K2fEiclbk899VTk8c8++8wlfHpMCbeK0EX/QSi4KihWtWpVl9ArmU5UrK2kRFyuv/569/pKyMNtV1JCqM+qbaCiacnQaycqtKVtVtL6RlMiqERTn1fbpV69ei6Zi06cZ8yYEey7774u5lrugQceWOfzJBtfFdXbbrvtggoVKrgiMIpN/LrqB0vbX+ulhoBXXnllnSIx64vpxiTi0RYuXBj89ddfwaai7+r5558f1KhRwzUmqLFDjSjR4r/f2oYDBgxwMdJ223///V2Rwvj1VOKtBiR9j08//fRIY1BpkIj7dSHR8OtCPPy6EI8ElzQi0fAL8fAL8cjORDxH/6S7Vx4l01BvDRvX8PQjjzwy3auDLKbh7jot2sLy5a0mQ9PTbk1+vn1/8cW2y733MvTWA8TDL8QjgTQe8mkql05xqiHmDL1NP+LhF+KxaY5PVVhYRZ7LChJxj2nu7oIFC9ycexVEUUV4/jjhxQ+dmZWdnzkAgMMhH4AMtKSMJuJJn74MqaMiKSqYosIoTz31VEwSrseiT90Wfynu1GHAphBEnVIG6RPk5tqiFi3cNdKPePiFePhF/T4q3kb/jx+Ih1+IR3Zi7+QxVSfXH6TO3axzs0fTqUDizw8efdHjwOZSyMgMb+IwpWdP4uEJ4uEX4uFfVegpU6aU+qwx2LyIh1+IR3Zi71RGqXe8tOcHBwAAAAD4gx5xAAAAAABSiEQcQNJymMPkTRwKFiwgHp4gHn4hHn7JycmxgoICd430Ix5+IR7ZiarpAEqNqukAUIZxyAcgAy2hajqAbFFEi60XinJzbV6bNu4a6Uc8/EI8/Dsl67x589w10o94+IV4ZCf2TgCSVkQVYm/iML1bN+LhCeLhF+LhFyUY06dPJ9HwBPHwC/HITiTiAAAAAACkEIk4AAAAAAApRCIOIGlUIfYnDtWmTyceniAefiEeflE1aBVToiq0H4iHX4hHdqJqOoBSo2o6AJRhHPIByEBLqJoOIFtQhdgPRXl5NqtTJ3eN9CMefiEeflERqlmzZlGMyhPEwy/EIztxNA0gaRzYepRodOxIPDxBPPxCPPxCouEX4uEX4pGdSMQBAAAAAEghEnEAAAAAAFKIRBxA0nIZOuVNHOqMH088PEE8/EI8/JKbm2t16tRx10g/4uEX4pGdqJoOoNSomg4AZRiHfAAy0BKqpgPIFhQ/8kNRuXI2rVs3d430Ix5+IR5+URGqadOmUYzKE8TDL8QjO5GIA0gapy/zJw7z27QhHp4gHn4hHn5RgjF//nwSDU8QD78Qj+zE3gkAAAAAgBQiEQcAAAAAIIVIxAEkLbewMN2rgLVxaDRqFPHwBPHwC/Hwi6pBN2rUiKrQniAefiEe2Ymq6QBKjarpAFCGccgHIAMtoWo6gGxRSBViLxSWL2+Te/Z010g/4uEX4uGXwsJCmzx5srtG+hEPvxCP7EQiDiBpQU5OulcBa+OwuEUL4uEJ4uEX4uEXDcBUbxUDMf1APPxCPLITiTgAAAAAAClEIg4AAAAAQAqRiANIWu6aNeleBayNQ4uRI4mHJ4iHX4iHX1QNukWLFlSF9gTx8AvxyE5UTQdQalRNB4AyjEM+ABloCVXTAWSLwr///r8DOi5pvRSuWWMTxo931+leFy7Ew7cL8UhwSed+o7DQJkyYQFVoTxAPvxCP7EQiDiBpDKTxJw7Lly8nHp4gHn4hHn4hHn4hHn4hHtmJRBwAAAAAgBQiEQcAAAAAIIVIxAEkLS8vL92rgLVx2HbbbYmHJ4iHX4iHX4iHX4iHX4hHdiqX7hUAUPbk5OSkexWwNg7Vq1dP92pgLeLhF+LhF+LhF+LhF+KRnegRB5C0NZyX15s4jB07lnh4gnj4hXj4hXj4hXj4hXhkJxJxACjDONWJX4iHX4iHX4iHX4iHX4hH9iERBwAAAAAghUjEAQAAAABIoZyAM8cDKKUlS5ZYtWrVbNGiRe4a6aWf7+XLl1tBQQEF9DxAPPxCPPxCPPxCPPxCPDbN8enixYutatWqVlbQIw4AZVh+fn66VwFRiIdfiIdfiIdfiIdfiEf2IREHkDQKivgTh3HjxhEPTxAPvxAPvxAPvxAPvxCP7EQiDgAAAABACpGIAwAAAACQQiTiAAAAAACkEFXTAZQaVdP9op9vzSfLy8ujyqoHiIdfiIdfiIdfiIdfiMfGoWo6ACDlVq1ale5VQBTi4Rfi4Rfi4Rfi4RfikX1IxAEkjaqe/sRh4sSJxMMTxMMvxMMvxMMvxMMvxCM7lUv3CgAog+rXN1u9Ot1rAZ1ztE8fs86d1ZSe7rUB8fAL8ciseDCTEkCGoUccAAAAAIAUIhEHgDIsj54+rxAPvxAPvxAPv6gwGPxBPLIPVdMBJF+V0szKTk1KAECZx+EqgGJQNR1A1gg4tYYXgtxcW9SihbtG+hEPvxAPvxAPv6gfTqcipT/OD8QjO/FrCCBpheWo8+hLHKb07Ek8PEE8/EI8/EI8/KLq3FOmTKFKtyeIR3YiEQcAAAAAIIVIxAEAAAAASCEScQBJy2EOkzdxKFiwgHh4gnj4hXj4hXj4JScnxwoKCtw10o94ZCeqpgMoNaqmAwDSgsNVAMWgajqArFFEi60XinJzbV6bNu4a6Uc8/EI8/EI8/FJUVGTz5s1z10g/4pGd+DUEkLQiqt56E4fp3boRD08QD78QD78QD78o4Zs+fTqJnyeIR3YiEQcAAAAAIIVIxAEAAAAASCEScQBJo+qtP3GoNn068fAE8fAL8fAL8fCLqnOruBVVuv1APLITVdMBlBpV0wEAacHhKoBiUDUdQNag6q0fivLybFanTu4a6Uc8/EI8/EI8/KKiYLNmzaI4mCeIR3biaBpA0jiQ8ujAtmNH4uEJ4uEX4uEX4uEXEj+/EI/sRCIOAAAAAEAKkYgDAAAAAJBCJOIAkpbL0Clv4lBn/Hji4Qni4Rfi4Rfi4Zfc3FyrU6eOu0b6EY/sRNV0AKVG1XQAQFpwuAqgGFRNB5A1KLbjh6Jy5Wxat27uGulHPPxCPPxCPPyiomDTpk2jOJgniEd2IhEHkDROX+ZPHOa3aUM8PEE8/EI8/EI8/KKEb/78+SR+niAe2YlfQwAAAAAAUohEHAAAAACAFCIRB5C03MLCdK8C1sah0ahRxMMTxMMvxMMvxMMvqs7dqFEjqnR7gnhkJ6qmAyg1qqYDANKCw1UAxaBqOoCsUUjVWy8Uli9vk3v2dNdIP+LhF+LhF+Lhl8LCQps8ebK7RvoRj+xEIg4gaUFOTrpXAWvjsLhFC+LhCeLhF+LhF+LhFw2IVe8hA2P9QDyyE4k4AAAAAAApRCIOAAAAAEAKkYgDSFrumjXpXgWsjUOLkSOJhyeIh1+Ih1+Ih19UnbtFixZU6fYE8chORHs9mjVrZvfcc0+6VwObyGeffWY5OTm2aNGidK9KmZbLHCYv5BYVWd3x49010o94+IV4ZHY8HnzwQXeMVrFiRdtjjz3sm2++KXH5l156ybbddlu3/I477mjvvPNOzOOamztw4EBr0KCBFRQUWJcuXeyXX36JPD5z5kw788wzrXnz5u7xrbbaygYNGmSrVq2KeZ2JEydax44d3fs0btzYbr/99pjH99lnH3ccEn/p1q2be3z16tXWt29ft46VK1e2hg0b2imnnGJz5syxTUkJX926dUn8PEE8shPRXmvo0KFWvXr1de4fO3asnXPOOZbpUpmgzp8/38477zxr0qSJVahQwerXr29du3a1L7/8cpO+j3Z2l1xyiZXVeOy6665u+7Rs2dJ9P9dnfTv/0hyIlBZV0/2g6sMTevWiCrEniIdfiEfmxmPEiBF22WWXuUT4u+++s5133tkdR8ybNy/h8l999ZX17NnTJdLff/+9HX744e7y448/RpbRPvO+++6zRx55xMaMGeOSYL3mihUr3ONTpkyxoqIie/TRR+2nn36yu+++2y179dVXx5xC6cADD7SmTZvat99+a4MHD7Zrr73WHnvsscgyr776qv3555+Ri9YhLy/PjjnmGPf4f//95z7TgAED3LWWnzp1qnXv3t02JVXnnjBhAlW6PUE8spTOI44gGDJkSFCtWrUgW3366afq4gz++eefEpdbuXLlRr9Xx44dgz322CP45JNPgpkzZwZjxowJbr755uCNN94INqXOnTsHF1988QZ9zmT99ttvm+y1pk+fHlSqVCm47LLLgkmTJgX3339/kJeXF7z33nvFPmfx4sVBvXr1ghNPPDH48ccfg+HDhwcFBQXBo48+Glnmyy+/dK9z++23u9ft379/UL58+eCHH34o9brpfbT9FpYvrz5xLmm+rM7PD0Zfc427Tve6cCEevl2IR4bFI0q7du2CCy64IHK7sLAwaNiwYXDLLbck3Hcde+yxQbdu3WLu03FIr1693P+LioqC+vXrB4MHD448vmjRoqBChQpuf1oc7U+bN28euf3QQw8FNWrUiDlW6tu3b9CqVatiX+Puu+8Otthii2Dp0qXFLvPNN9+4fe+mPNZYvXp1MHr0aHeN9CMeGyc8PtV1WZIRPeLq+bzooovsyiuvtJo1a7oeVrVARrvrrrsiw3zUW3j++efb0qVLI72Pp59+ujttQDhEKHx+9ND0E044wY477riY19UQotq1a9uwYcPcbbWW3nLLLZGhS2qlffnll0tc/5UrV7phSFqvsAf0ySefjDz++eefW7t27dxjGjLVr18/WxM1xyrR8Pk2bdrEbAN9pieeeMKOOOIIq1Spkm299db25ptvRoZb7bvvvu7/NWrUcMuedtppkW3bu3dv17Osz6nW4TPOOMMOPfTQdbaDhtREr3ci6nEfNWqU3Xbbbe491Wqsz3bVVVfFtPb+/vvv1qNHD6tSpYpVrVrVjj32WPvrr78ij2v91JodTeuo9Q0f13a79957IzHV5wyppXr33Xd322Lvvfd2rc0b0rOv11fPtdZ1U1ELu74/d955p2233XZu+x999NGu9b04zz33nBse99RTT9n2229vxx9/vPub0Pc+pHU96KCDrE+fPu51b7jhBrfuDzzwwCZbdwAANift67QP19DxkIbz6vbo0aMTPkf3Ry8vOp4Jl58xY4bNnTs3Zplq1aq5Ie/FvabouFHHndHv06lTJ8vPz495Hx1j/PPPPwlfQ8dN2mfr+LSk99FxTKKRmwDKroxIxOXpp592P2IaTqThRddff719+OGHMT/SGnKk4URa9pNPPnGJuygRUyKrhC8cKnTFFVes8x4nnniivfXWW5EEXt5//303jEgJrigJV1KuZErvdemll9pJJ53kksLiaO7P8OHD3fpNnjzZDXtSAiqzZ8+2Qw45xNq2beuGrDz88MPuR/vGG29Mehtdd911LqHVEGa9pj7P33//7RoAXnnlFbeMdhb6/EraoretdioaOq7PddZZZ9l7773nlgu9/fbbbjvEN1TE0+fS5fXXX3cNEImoMUOJrdZN201xnD59+npfO5rWf6+99rKzzz47ElN9ztA111zjEt1x48ZZuXLlXONCaQ8AXnvtNdcIsOWWW7rEV9sxeoi3GhrCz1ncRYlzcdZ3wFDcc9a389+Q1wUAwCcLFixww3fr1asXc79uK5lORPeXtHx4ncxr/vrrr3b//fdbr1691vs+0e8RTfPaNTRdx1XF0dB4ddZoaL2OUwFkjoyZ6LnTTju5uUKi3l718n388cd2wAEHuPui5wqrB1mJ7LnnnmsPPfSQS17U8qnWRvWmF0dJi5J9JWInn3yyu+/55593PblbbLGFSyxvvvlm++ijj1wSKKqA+L///c8l1507d17nNX/++Wd78cUXXbIZJkl6TkjrpwRSn0frp/m9KtihH2UVFUmmqIN6ifVDLlpPJf7aCaiXNGzRVa92fIurtmf8fONWrVrZM888E2nMGDJkiJvfFDYgFEdJr+Y7K0FWUq8eWW0XtQYrhqK4/fDDD66FOkye1bihnl7N2VejxPoonoqrerwTxfSmm26KxEMjDFQkRTs7zZ1ORPO0tN6Kt15XoyOUxIfrHE097ePHjy9x/eJ31NGK25Fr7tny5cvdSItEz1EveqL30GMa6bC+A5FE9J2ObjDROkgeVW+9oDhsO3w48fAE8fAL8fBLJsVDnSQ6dtJxj45nNpQ6VjRaUyMDE9FoQ3WgqJCcOmI2Jc1L1zGlrpF+xCM7ZUyPeHxCpCHc0UU7lBzvv//+rhdTSbMS6YULF7pe3NJSEqkfxLA3c9myZfbGG2+4HtGwdVSvp+Q/uvdTSeS0adMSvqYSNv3RJUrSRT3kSuqVhIfat2/veuVnzZplG7qN1KCgltXiCptE22233da5T623Sr5FQ8bffffdUvcqH3XUUa4xQUPjtSMLC5OFBcn0mZWAR/dgt27d2jUQ6LFNIXpb6LsiJW2LI4880jWKXHjhhfbHH3/YHXfckTAJFyXKml5Q0kXfwbJAIzzUqBFewpjkaMYe0i6nqMiqT5/urpF+xMMvxCMz46Fpcjpuip6uJrpdXGeK7i9p+fC6NK+p4xdNrdNoyugibCW9T/R7hHQM+cILL7gCciUl4b/99pvrrNnUveHhUPfo40ukD/HIThmTiJePq8KpL7KGOIvmBmtOsxInDcHW3CKd9kLiTzuxPkq61WOrpE3Dq5V0KZmUcMj6yJEjXYIdXiZNmlTsPPFEvZvJUq+4Wkvjf8CT2UYlSTRvScPpNVxcw5qfffZZ1xurit2lpZ5nNVioKqiqmaq3PhzRsCk/c3Git0X4o1fStlDvvxpvlICH86vVY5/Ixg5NL25Hrp1wcd+X0uz813cgkojm7mtuWnhRI4SsoQqxF9bk59vYPn3cNdKPePiFeGRmPDQqTR0EOhYLaf+t2+FoxHi6P3p5UXIbLq9jGO0Lo5fRCDBNd4x+TfWEqxaN3l+dEfGjErXsF198EXM8ovfRKEKNTIs/i4lGnGn6YnFJuE6fpo6kWrVq2aamWkMaZRhdcwjpQzyyU8Yk4iVR4q0fac0J3nPPPW2bbbZZ53yM+mEvzSkD1AKqXkGdOkPJlIYlhUmdem1VUE2FxuJ7QKN7d6NpSJLWrbg55Er6lOxGJ52aq60e1UaNGrnbderUiZmvrZ1HcUliccK5xaU9bYJ2CponrR2RerJV7G5jaNupdTj8zEr4wqRP1JihQm9aLtFnlvjh4KWNaWmokUGfVcO4+/fv73rxNWRf87Iff/zxmNO+hUPTS7qUdBqS9R0wFPec9e38N+R19X1WA0D0BX4pJMnwCvHwC/HIzHjo1GXa96qGjUbK6ZSoOoYIj0XUWaCG5NDFF1/satvoOFCnIVMxW00vUzHUsEFeUxg1bVGj9TQ9Tq+hc3iHhWHDJFynXlWjvAq26pggenqXpq3p2EO93KoTpGNF1azR+iYalq7Xjk+ytR9XgVatn44zdRwTvk+ynUfrw6my/EI8slCQARKdpqpHjx7Bqaee6v4/fvx4V9L+nnvuCaZNmxYMGzYs2HLLLWNOY6VTO+n2Rx99FMyfPz9YtmyZu79p06bu1BLRrrnmmqB169ZBuXLlglGjRq3zWK1atYKhQ4cGv/76a/Dtt98G9913n7sd0mksXn311cjt0047LWjcuHHw2muvuVNX6RRbI0aMcI/NmjXLncpKp+mYPHly8Prrrwe1a9cOBg0aFHl+v3793Gk3vvjii2DixInB4YcfHlSpUiVmGX02vX40na5Np20L3ycnJ8et57x584J///232G0b+uCDD4L8/Hx3SqzZs2eXKlYLFiwI9t133+CZZ54JJkyY4D7viy++6E69dcYZZ0ROI9KmTRt3mjNtP53ebLfddnPrEtKpvLS+Tz/9dPDzzz8HAwcODKpWrRqzzNlnnx20bds2mDFjhoupTm+S6PRl33//vbtPyyVDpxG54YYbgq233jrYddddg019+rI+ffq4mD/44IPrnL5MpzTbb7/9Yk6zom148sknu9OXvfDCC+414k9fpu/sHXfc4V5X3w9OX1a2L5yeya8L8fDrQjwy9/Rl4X6wSZMm7jhEpzP7+uuvI4/pWCA8BgzpWGObbbZxy2+//fbByJEjYx7XsceAAQPcvlSnLdt///2DqVOnRh7X8ZL2f4ku0XRs06FDB/caOta89dZb11n3KVOmuOfpOCqejkWKex8dw2wqnC7LL8QjO09flhWJuNx1111BgwYN3LmVu3bt6pLx+ITs3HPPdUm07g+T2ESJuM7BrGX0mH64o+m2En4l20py6tSp497v888/jyyj54YJsCxfvjy49NJL3fppB9GyZcvgqaeeijz+2WefuYRSjynh1jkpo/9Q9aU77rjjXCKqhF7J9M4775xUIi7XX3+9e30luOG2KykR12fVNjjkkEOC0lqxYoVrOFDiqvdXsqhtpXNa//fffzFJbvfu3YPKlSu782sec8wxwdy5c2NeS8m3dph6HW2/3r17xyTi2oHuueeeLuZhor0pE/FoSmw3Ja2nGiMU8xYtWsTESRRbbftkd/7rOxBZHxJxvy4kGn5diIdfF+KR2Yk4Ng6Jn1+IR3Ym4jn6J9298iibNCdexe80ZFvFzJD5NO1BRds0EL9aulcGFuTk2PJataxg4UIK6HmAePiFeGRYPIjhJqXD//BMLBQISz/isWmOT1XPqCxNo8yY05chdTSnXefx1FwrVXgsab4zgM0oCCxfp5TjANUPxMMvxMMvxMM7YX0g+IF4ZJ+sKNaGTUvF6HT+aZ1T+6mnnnKndYt+rKRq4XocZV8hVdO9KXw0rk8fClJ5gnj4hXj4hXj4VxhMBeEoEOYH4pGd6BFH0po1a7bOqcNCqjAaX708/nEAAAAAyGYk4tik1Duu07UBAAAAABJjaDoAAAAAAClE1XQApUbVdL/ox1vzLfNWrTJqrKYf8fAL8ciweHC4uknp8F/zkfPy8qjS7QHikZ1V0+kRB4CyKifHVmmHw07bD8TDL8TDL8TDO6tWrUr3KiAK8cg+JOIAkkbVdH/iMLFXL+LhCeLhF+LhF+LhF/W+Tpw4kSrdniAe2YlEHAAAAACAFCIRBwAAAAAghUjEAaAMU+Ej+IN4+IV4+IV4+EWFweAP4pF9qJoOIPmqlGZWdmpSAgDKPA5XARSDqukAskZA1VsvBLm5tqhFC3eN9CMefiEefiEeflE/3KJFi9w10o94ZCd+DQEkrbBcuXSvAtbGYUrPnsTDE8TDL8TDL8TDL6rOPWXKFKp0e4J4ZCcScQAAAAAAUohEHAAAAACAFCIRB5C0HOYweROHggULiIcniIdfiIdfiIdfcnJyrKCgwF0j/YhHdqJqOoBSo2o6ACAtOFwFUAyqpgPIGkW02HqhKDfX5rVp466RfsTDL8TDL8TDL0VFRTZv3jx3jfQjHtmJX0MASSui6q03cZjerRvx8ATx8Avx8Avx8IsSvunTp5P4eYJ4ZCcScQAAAAAAUohEHAAAAACAFCIRB5A0qt76E4dq06cTD08QD78QD78QD7+oOreKW1Gl2w/EIztRNR1AqVE1HQCQFhyuAigGVdMBZA2q3vqhKC/PZnXq5K6RfsTDL8TDL8TDLyoKNmvWLIqDeYJ4ZCeOpgEkjQMpjw5sO3YkHp4gHn4hHn4hHn4h8fML8chOJOIAAAAAAKQQiTgAAAAAAClEIg4gabkMnfImDnXGjyceniAefiEefiEefsnNzbU6deq4a6Qf8chOVE0HUGpUTQcApAWHqwCKQdV0AFmDYjt+KCpXzqZ16+aukX7Ewy/Ewy/Ewy8qCjZt2jSKg3mCeGQnEnEASeP0Zf7EYX6bNsTDE8TDL8TDL8TDL0r45s+fT+LnCeKRnWiWBJC8uXPNatZM91pgzRqzcePMrr3WjF6m9CMefiEefiEeABCDZkkAAAAAAFKIRBxA0qjq6U8cGjVqRDw8QTz8Qjz8Qjz8Qjz8QjyyE1XTAWR8VUoAAABkpiVl9PiUZhcASSssLEz3KmBtHCZPnkw8PEE8/EI8/EI8/EI8/EI8shOJOICkMZDGnzio9Zd4+IF4+IV4+IV4+IV4+IV4ZCcScQAAAAAAUohEHAAAAACAFCIRB5A0qnr6E4cWLVoQD08QD78QD78QD78QD78Qj+xE1XQAGV+VEgAAAJlpSRk9PqXZBUDSqOrpTxwmTJhAPDxBPPxCPPxCPPxCPPxCPLITiTiApDGQxp84LF++nHh4gnj4hXj4hXj4hXj4hXhkJxJxAAAAAABSiEQcAAAAAIAUIhEHkLS8vLx0rwLWxmHbbbclHp4gHn4hHn4hHn4hHn4hHtmpXLpXAEDZk5OTk+5VwNo4VK9ePd2rgbWIh1+Ih1+Ih1+Ih1+IR3aiRxxA0tasWZPuVcDaOIwdO5Z4eIJ4+IV4+IV4+IV4+IV4ZCcScQAowzjViV+Ih1+Ih1+Ih1+Ih1+IR/YhEQcAAAAAIIWYIw4gefXrm61ene61QH6+WZ8+Zp07m61ale61AfHwC/HwC/HIzHhw3mtgg+UEnDkeQCktWbLEqlWrZovMrFq6VwYW5OTY8lq1rGDhQv2Yp3t1sh7x8Avx8AvxyNB4EMtNQunY8uXLraCggIK4G3F8unjxYqtataqVFQxNB4CyKggsf8kSDoR8QTz8Qjz8Qjz8Qjy8k69RCsgqJOIAklZYvny6VwGKQ36+jevTx10j/YiHX4iHX4iHX4iHf4Xaxo0bR8G2LEMiDgAAAABACpGIAwAAAACQQiTiAAAAAACkEFXTAZQaVdP9oh9vze/LW7XKqLGafsTDL8TDL8QjQ+NBGrFJKB3T/PC8vDyqpm8AqqYDAFIrJ8dWaYfDTtsPxMMvxMMvxMMvxMM7qzbmfO4ok0jEASSNqun+xGFir17EwxPEwy/Ewy/Ewy/Ewy/qDZ84cSJV07MMiTgAAAAAAClEIg4AAAAAQAqRiANAGaZCO/AH8fAL8fAL8fAL8fCLCrUhu1A1HUDyVSnNrOzUpAQAAJsFaQQ8sISq6QCyRUCVVS8Eubm2qEULd430Ix5+IR5+IR5+IR5+Ub/ookWL3DWyB399AJJWWK5culcBa+MwpWdP4uEJ4uEX4uEX4uEX4uEXVUufMmUKVdOzDIk4AAAAAAApRCIOAAAAAEAKkYgDSFoOc5i8iUPBggXEwxPEwy/Ewy/Ewy/Ewy85OTlWUFDgrpE9qJoOoNSomg4AACJII+CBJVRNB5Atimix9UJRbq7Na9PGXSP9iIdfiIdfiIdfiIdfioqKbN68ee4a2YO/PgBJK6LKqjdxmN6tG/HwBPHwC/HwC/HwC/HwixLw6dOnk4hnGRJxAAAAAABSiEQcAAAAAIAUIhEHkDSqrPoTh2rTpxMPTxAPvxAPvxAPvxAPv6hauoqNUTU9u1A1HUCpUTUdAABEkEbAA0uomg4gW1Bl1Q9FeXk2q1Mnd430Ix5+IR5+IR5+IR5+UZG2WbNmUawty3A0DSBp7Lg9OpDq2JF4eIJ4+IV4+IV4+IV4+IVEPDuRiAMAAAAAkEIk4gAAAAAApBCJOICk5TJ0yps41Bk/nnh4gnj4hXj4hXj4hXj4JTc31+rUqeOukT2omg6g1KiaDgAAIkgj4IElVE0HkC0o7uKHonLlbFq3bu4a6Uc8/EI8/EI8siceDz74oDVr1swqVqxoe+yxh33zzTclLv/SSy/Ztttu65bfcccd7Z133ol5XH2GAwcOtAYNGlhBQYF16dLFfvnll8jjM2fOtDPPPNOaN2/uHt9qq61s0KBBtmrVqphldI7u+MvXX38d81733HOPtWrVyr1O48aN7dJLL7UVK1ZEHn/44Ydtp512csmeLnvttZe9++67G73NVKRt2rRpFGvLMhmfiOuHQH9UyA76UX399dfTvRoZj9OX+ROH+W3aEA9PEA+/EA+/EI/siMeIESPssssuc4nwd999ZzvvvLN17drV5s2bl3D5r776ynr27OkS6e+//94OP/xwd/nxxx8jy9x+++1233332SOPPGJjxoyxypUru9cME+QpU6a4BPbRRx+1n376ye6++2637NVXX73O+3300Uf2559/Ri677bZb5LHnn3/e+vXr59Z98uTJ9uSTT7rPE/06jRo1sltvvdW+/fZbGzdunO23337Wo0cP974bQ+s/f/58EvFsE2SIIUOGBNWqVVvn/nnz5gXLli0LMt2nn36qsUHBP//8s9nf69RTT3XvFX/p2rVrkG5aj9deey0o64qKioIBAwYE9evXDypWrBjsv//+wc8//7ze5z3wwANB06ZNgwoVKgTt2rULxowZE/P48uXLg/PPPz+oWbNmULly5eDII48M5s6dW+r1Wrx4sdvGC8uX18bmkubL6vz8YPQ117jrdK8LF+Lh24V4+HUhHhkajzg69rjgggsitwsLC4OGDRsGt9xyS8LjimOPPTbo1q1bzH177LFH0KtXr8jxkI6FBg8eHHl80aJF7jhn+PDhxR6v3H777UHz5s0jt2fMmOGOX77//vtin6P13m+//WLuu+yyy4L27dsHJalRo0bwxBNPBBtj9erVwejRo901khcen+q6LMn4ZkkVPqhUqVK6V8Mb0cN0NsZBBx0U06Koy/Dhwy1bt4+eN3fu3E22Hutr/d3QVmgNsXrrrbfcMLDPP//c5syZY0ceeeQmW28AAJCddCyknmINHQ+p+Jhujx49OuFzdH/08qJjl3D5GTNmuOOr6GU0F1hD3ot7TdFc4Zo1a65zf/fu3a1u3brWoUMHe/PNN2Me23vvvd36h0Ppp0+f7obJH3LIIQnfo7Cw0F544QVbtmyZG6IOJC3wQOfOnYMLL7ww6NOnj2tVqlevXjBo0KCYZe68885ghx12CCpVqhQ0atQoOO+884J///03pjc4+hI+X72Dd999t/t/z549XctbtFWrVgW1atUKnn766UjL3c033xw0a9bM9UTutNNOwUsvvVTi+q9YsSK48sor3Xrl5+cHW221VUzL2GeffRa0bdvWPaZWvb59+8a0eEWvY2jnnXeO2Qb6TI8//nhw+OGHBwUFBUHLli2DN954I6aVL/qiXutw26qF7+KLL3afc5999glOP/30dVoftR3q1KlTqhY9vXaPHj1KXEY98+ecc05Qt25d12q5/fbbB2+99ZZ7TJ9Lny+aPr+2Q+ibb74JunTp4ta5atWqQadOnYJvv/025jnqIe7YsaN7/e222y744IMP1ukRnzhxYrDvvvu6WKoX+Oyzz458b6I/y4033hg0aNDAxT0Z48aNC3r37u3W85577gk2hQ1t/V1fK7Reo3z58jHf58mTJ7ttplbYZFoc/8nNTXtrPhcLCvPygj86dXLX6V4XLsTDtwvx8OtCPDI0HlFmz57tjhG++uqrmPt1fK9jlER0XPL888/H3Pfggw+640f58ssv3WvOmTMnZpljjjlmnWP60C+//OKOHR977LHIffPnz3e5xNdff+2OMXUsnpOTEzmWDt17771uncqVK+fe99xzz13n9XVsqVGFeXl5bjTuyJEjg42lY7Y//vjDXSN59IhvpKefftr1+qn3T72B119/vX344YcxLWrqIdQcDC37ySef2JVXXhlpwdI8cBVNCHtnr7jiinXe48QTT3S9gUuXLo3c9/7779t///1nRxxxhLt9yy232LBhw1xPpN5LPYgnnXSS6z0szimnnOJ6g7V+mlOiOSpVqlRxj82ePdu1pLVt29YmTJjgijxozsmNN96Y9Da67rrr7Nhjj7WJEye619Tn+fvvv10xiVdeecUtM3XqVPf577333phtm5+fb19++aX7XGeddZa99957brnQ22+/7bbDcccdZxtL81sOPvhg937PPvusTZo0yc2nyUuiwNe///5rp556qv3vf/9zhTS23npr95l1f/ge6snV59J3Rp+rb9++Ma+hFkq1qtaoUcPGjh3reoE1N6h3794xy3388cduu+n7pu2wPtpugwcPth122MF99xTjJ554ws4///zIMueee677DpR0Kc6GtP6WphVaj69evTpmGRVHadKkSbGvu3LlSleJMvriXps5TF7ILSy0Rl984a6RfsTDL8TDL8TDL5kaDx2TadTmMcccY2effXbk/tq1a7tRgzqW0jG5jkt1fK/judBnn31mN998sz300ENuZOGrr75qI0eOtBtuuCHmPVTMbfz48e7487zzznPHqzrW3Rg6ZtP8c05flmUCD6jXtkOHDjH3qQdZrVXFUa+eeiHXN0c8urdZvdC1a9cOhg0bFnlcveTHHXdcpGdbPe7xLXlnnnmmWy6RqVOnuhaYDz/8MOHjV199ddCqVSvXyxnd0lelSpVIq1dpe8T79+8fub106VJ337vvvlviHHFt21122WWd9WrdunVw2223RW4fdthhwWmnnRaUhnqR1Qqo1sDoy0033eQef//994Pc3Fy3bRIpTY94PG2rLbbYItKrrvdQa6VaX0PaFtE94moJ1QgLbauQWi21buG8aH0WjcBYuXJliZ9Zj7/wwgvBwQcf7N53zz33DB566KHg77//Trj8X3/95VpkS7oUZ0Naf0vTCv3cc8+5URnx9LemER3FxSpRPYC/y5VLe2s+FwvWlC8fTOrZ012ne124EA/fLsTDrwvxyNB4xB0r6fgwvlbPKaecEnTv3j3hcUbjxo3XOQYeOHCgG5Eq06ZNSzi3WyMlL7roonWOhbbeeuvg5JNPLlXPsurqaARiSLnIFVdcEbPMM88840ailvR6quOjUaAbY82aNcGkSZPcNbKnR9ybc0joVADRdIqC6Lmt6slUb7UqI6pXbs2aNW6+rHpxSzsHvFy5cq5H+bnnnrOTTz7Z9Zi+8cYbbn6H/Prrr+71DjjggHV6G3fZZZeEr6kWMfX0du7cOeHj6iHXvBFV8w61b9/e9crPmjXL9UZuyDbS6AGNACiuCmW06IqQIfWKP/bYY25UwV9//eVOvaBRBqW17777ut79aOFcHG0Ttepts802tqG0Tv3793etk/qMmoej2Pz++++R7aqRAA0bNow8J35+jpbRPGltq+htr9509YDXq1fP3adTZahnvSSq6nn88ce799R26tixY4nLa/6RLmXdVVdd5VqQQ/rb0zYIor7PSB/FYXGLFsTDE8TDL8TDL8Qj8+OhYykdc2qkoSqfi465dDt+NGL0sZsev+SSSyL3aYRieEynU5LVr1/fLdOmTZvIsUjYGx3dE65jU73/kCFDStWzrONV5RshHWfGPy8czfl/fWKJ6TNqBOHG0OtrXntJ74PM400iXr58+ZjbSlzDEv4699+hhx7q/uBuuukml/BpyLJOdaAkOZlibBrOraRZyZ3+0HWeQA1hkXDIuoahbLnlljHPq1ChQsLX0/M3lv7o4//wNIQ4mW1UkuhENHo4vU7RoCHJSjL1Q7e+5DL+NVu2bLlB26Q0n1fDfBYuXOiG2Ddt2tRtf/0ob6pic+vbPvHatWtnjz/+uBvmr1NVaHi3GnO0o0n0/dPQdA3LL0n0FIlo2uGEjRHROwjdDndC8TTkSjsLLRNNt8PX07W236JFi6x69eoJl4mn7V7cdx8AACCaGu91DLf77ru7YydNHVXH1+mnnx45/tQxtjrX5OKLL3bH5Xfeead169bNdY7ptGDqLAqPdZWka0qnpinqeHXAgAGuIyZM9pWE77PPPu548Y477nCnAQuFxzfhNM2wY03Dzp966ik3tTB02GGH2V133eWW0RB2ddDpvXR/mJCrg0LTL9WRpumSOuWZOo001RUos4l4STS3VQmn/kjDlqoXX3wxZhn9canXdH00p1c9eqowrV5gzSEJE9zWrVu7pEO9rsX1cMdTb6rWTXPI46s+ynbbbefmbyvxDHvFNXd6iy22cL3GYWX36PnaaunTPOFkhD26pdkGUqtWLfcDplZDJePhD+SmoJ579fb//PPPCXvF9Xk1Bzp6m6hVMpq2kebohJUq//jjD1uwYEHMdtV92m5hsqq55NG0zNChQ90OIEy29br6Dml+TzKUbGsUgS7Tpk1zP+jXXHONS7iPOuool5RrJxB+P1XjIFGdgtIobetvsq3Qelzfdd2ndRaNDND3nWqfAABgY6nWkBLhgQMHumM9HceoLlE4ClHHHNG9zjouVzKrUZA6X7eS7ddff93V4Qlp9KaO5c455xzXmaCK53rNihUrusfVsaakWZfw2DoU3fGjud6//fabGyGrGjnKBY4++ujI41oHHZfqWsm9jleVhKsTMKSOPDUm6PhT9Xt0zKskPH40LVAqgQc0j1lVvaOpknVY+Xv8+PFu3L+qUmuuiOZ4b7nlljFzosN5tR999JGrjBieOzzR/OtrrrnGzZHWXN9Ro0at85jmng8dOjT49ddfXaXu++67z90Oac73q6++GrmtudWa46I5MdOnT3fztUeMGOEemzVrlpt3rmrWqlD9+uuvu3nq0fO/+/Xr5+aofPHFF64Soyqjaw55/Bzx+Dk3mhOvufHh+6j6o9ZT504PK4Mn2rYhVRnXnGHN54mea70+istBBx0U/PnnnzEXbfeQqrOryr3eQ9vknXfeicxn1xwYreutt97qtrHm6Ggud/Qccc1rP+CAA9yyqnCp6uiaoxPGUnN1FEMto++Htt1uu+0Ws530HVAl9KOOOir44Ycfgk8++SRo0aJF5HtV2grwxdG8f8Var6F46XuyqWjbVK9e3VXz1HdC66jzYeo84CGd6/L++++P3NYcdlVW13dA203zlfQa0ecJV/XPJk2auG2hiu977bWXu5RWpGp6Tk7a57dxsaAwNzf4q00bd53udeFCPHy7EA+/LsQjQ+OBTULHtaovRNX07JojXiYScbnrrrtcUqVkrGvXri4Zjy9OpiRDSXRxpy8LKUnRMnosuoia6LYSfiXbOn2BTuml9/v8888jy+i5YQIsSo4uvfRSt35KbHVqsaeeeqrUpy/Tl0YF43SqBSX0SqQSFWsrKRGX66+/3r2+ktzo05cVl4jrs2obHHLIIUEy9NqJCnhpm4UWLlzoTpOmeOjUYUrK33777cjjDz/8sPusKvKmIh4q9BadiH/33XfB7rvv7p6rwhsqzhcfSxWDU2ENbddtttkmeO+99zb49GUbSwXhfv/992BTUWwGDBjgCskpuVYhkPjid9oe8af5U2KuRFvbREXa1IgRTd/V888/3zV8qIHoiCOOcI0oSf/QeXAQwYULFy5cuHBJ8wXwwOIymojn6J/S9Z0j02iOsubpaHi6TgUGrI+GyGso1t/lylmNNWvSvTpZr7B8efvxjDNsh6eesrwEdSWQWsTDL8TDL8QjQ+NBGrFJaGrpjz/+6IbkJ3O6X8Qen6rgnYpZlxVlYo44Ni3NHdZ8a825V9Gu7t27p3uVUMZQ9dafOCyvXZt4eIJ4+IV4+IV4+IV4+EX9osuXL6dqepYhEc9CKpShgmAqaKFiZipaEf2YitYVZ9KkSUmdcg0AAAAAEItEPAs1a9as2BY3nQ4ivoJ5/OMAAAAAgA3HHHEASc/BWZSTY9X46Ui7IDfXFjdrZtVmzrScoqJ0r07WIx5+IR5+IR4ZGg+OBTYJpWOa36xjrPDUvsj8OeIk4gCS/6Ezs7LzMwcAADYL0gh4YEkZTcRz070CAMqeNeXLp3sVoDjk59vYPn3cNdKPePiFePiFePiFePhlzZo1NnbsWHeN7EEiDgBlWCEHUV4hHn4hHn4hHn4hHv6dwgzZhUQcAAAAAIAUIhEHAAAAACCFKNYGIPmq6WZWLd0rAwtycmx5rVpWsHChfszTvTpZj3j4hXj4hXhkaDyI5SahdGz58uVWUFBA1fQNQLE2AEBqBYHlL1nCgZAviIdfiIdfiIdfiId38pmzn3VIxAEkrZCq6d4U2hnXpw8FdzxBPPxCPPxCPPxCPPwr1DZu3DgKtmUZEnEAAAAAAFKIRBwAAAAAgBQiEQcAAAAAIIWomg6g1Kia7hf9eGt+X96qVUaN1fQjHn4hHn4hHhkaD9KITULpmOaH5+XlUTV9A1A1HQCQWjk5tko7HHbafiAefiEefiEefiEe3lm1alW6VwEpRiIOIGlUTfcnDhN79SIeniAefiEefiEefiEeflFv+MSJE6manmVIxAEAAAAASCEScQAAAAAAUohEHADKMBXagT+Ih1+Ih1+Ih1+Ih19UqA3ZharpAJKvSmlmZacmJQAA2CxII+CBJVRNB5AtAqqseiHIzbVFLVq4a6Qf8fAL8fAL8fAL8fCL+kUXLVrkrpE9+OsDkLTCcuXSvQpYG4cpPXsSD08QD78QD78QD78QD7+oWvqUKVOomp5lSMQBAAAAAEghEnEAAAAAAFKIRBxA0nKYw+RNHAoWLCAeniAefiEefiEefiEefsnJybGCggJ3jexB1XQApUbVdAAAEEEaAQ8soWo6gGxRRIutF4pyc21emzbuGulHPPxCPPxCPPxCPPxSVFRk8+bNc9fIHvz1AUhaEVVWvYnD9G7diIcniIdfiIdfiIdfiIdflIBPnz6dRDzLkIgDAAAAAJBCNIMBSN7cuWY1a6Z7LbBmjdm4cWbXXmtGr0b6EQ+/EA+/EA+/EA8g7egRB5A0qnr6EwcVJyEefiAefiEefiEefiEefiEe2Ymq6QAyviolAAAAMtOSMnp8So84gKRRTMSfOMyaNYt4eIJ4+IV4+IV4+IV4+IV4ZCcScQBJY0fhB3bcfiEefiEefiEefiEefiEe2YlEHAAAAACAFCIRBwAAAAAghUjEASQtN5efDl/iUKdOHeLhCeLhF+LhF+LhF+LhF+KRnaiaDiDjq1ICAAAgMy0po8enNLsASBrFRPyJw7Rp04iHJ4iHX4iHX4iHX4iHX4hHdiIRB5A0dhT+xGH+/PnEwxPEwy/Ewy/Ewy/Ewy/EIzuRiAMAAAAAkEIk4gAAAAAApBCJOICkUdXTnzg0atSIeHiCePiFePiFePiFePiFeGQnqqYDyPiqlAAAAMhMS8ro8SnNLgCSVlhYmO5VwNo4TJ48mXh4gnj4hXj4hXj4hXj4hXhkJxJxAEljII0/cVDrL/HwA/HwC/HwC/HwC/HwC/HITiTiAAAAAACkEIk4AAAAAAApVC6VbwYgM+TWrq1xVOlejayn6qotdtrJcidONCsqSvfqZD3i4Rfi4Rfi4Rfi4ZeMiAfHhUmjajqA5KtSmlnZqUkJAACAzSqNKeUSqqYDyBaF5RhM44PC8uVtQq9e7hrpRzz8Qjz8Qjz8Qjz8QjyyE4k4gKQFOTnpXgWsjcPy2rWJhyeIh1+Ih1+Ih1+Ih1+IR3YiEQcAAAAAIIVIxAEAAAAASCGKtQFIuhjGopwcq8ZPR9oFubm2uFkzqzZzpuWU1SqrGYR4+IV4+IV4+IV4+CUj4kGxtqSRiAMoNaqmAwAAYB0k4kljaDqApK2hqqcX1uTn29g+fdw10o94+IV4+IV4+IV4+IV4ZCcScQAowwrZaXuFePiFePiFePiFePiFeGQfEnEAAAAAAFKIRBwAAAAAgBSiWBuA5Kumm1m1dK8MLMjJseW1alnBwoX6MU/36mQ94uEX4uEX4uEX4uGXjIgHxdqSRo84AJRVQWD5S5akdeeHKMTDL8TDL8TDL8TDL8QjK5GIA0haIVXTvSnsMq5PHwq8eIJ4+IV4+IV4+IV4+IV4ZCcScQAAAAAAUohEHAAAAACAFCIRBwAAAAAghaiaDqDUqJruF/14az5Z3qpVlpPulQHx8Azx8Avx8Avx8EtGxIOq6UmjRxwAyqqcHFulHU5Omd1tZxbi4Rfi4Rfi4Rfi4RfikZVIxAEkjarp/sRhYq9exMMTxMMvxMMvxMMvxMMvxCM7kYgDAAAAAJBCJOIAAAAAAKQQiTgAlGEq7AJ/EA+/EA+/EA+/EA+/EI/sQ9V0AMlXpTSzslOTEgAAAJsVVdOTVi75pwDIdoGqetKGl3ZBbq4tbtbMqs2caTlFRelenaxHPPxCPPxCPIpXWKmSra5dO6UVsxWPf7fc0raYPZt4eCAj4rFixWZ76fLly1teXp5lGhJxAEkrLFfObPXqdK9G1lMcpvTsabsPHmzlGNKWdsTDL8TDL8QjcaP23NNPt0Xdu5vl56c2ETezVdWqWf7ixWX3vNUZJCPiMWPGZn356tWrW/369S0ng07xRiIOAAAApJhLwnv2tLrVq1slzRdNcSPAf7VrW6X8fM1TTeE7I2Pj0bz5ZnnZIAjsv//+s3nz5rnbDRo0sExBIg4AAACkUGHlyq4nXEl4rTS8f7B2lELFFDcAIIPjUVFrv3kUFBS4ayXjdevWzZhh6lRNB5C0Mttam4FxKFiwgHh4gnj4hXj4hXjEWl2rlhuOrp7wdMldsyaN7454xKNklSr931/L6gyaGknVdAClRtV0AAA23oqmTW3GI49Y89q1XS8oUObtvvtmffkVK1bYjBkzrHnz5lYxrve9rFZNp0ccQNKKMqhQRllWlJtr89q0cddIP+LhF+LhF+LhF/XCra5UyV0j/YhHduLXEEDSilQ1HV7EYXq3bsTDE8TDL8TDL8TDMzk5trJatZRWag9d+9hj1uaEE8xXQ996y6rvu29Sn+G0a6+1w6+4Yr2vffLAgXbzkCFexSOd+vXrZxdeeKFlKxLxJDVr1szuueeedK8GAAAAMkzOyLYpueS+vbtt8USjpNdv/j//2Hm33mpNDj3UKuy9t9Xv2tW6XnihfTlhgvlmcyb8V5x0kn380ENJPWfCzz/bO199ZRcdd9wmWw/NMB74yCPW4KCDrKBDB+ty/vn2y++/r/d5D774ojXr3t0qtm9ve5x2mn3z008xj69YudIuuO02q9Wli1Xp1MmOuvJK+2vhwphlfp8717pdcolV6tDB6h54oPXp08fWRM1z/+GHH+yQQw6xpk2bWm5url1wwQXrrMcVV1xhTz/9tE2fPt3+/vtv+/HHH+3bb7+1n376yQ0zj/+sixYtso4dO7ribV26dLFffvklZpnjjz/emjRp4oauq7r6ySefbHPmzIk8/tlnn1mPHj3cY5UrV7Y2bdrYc889F/Mar776qu2+++7udGnhMs8888w66z558mTr3r27GxKv5dq2bWu/l2LbRyMRL8bQoUNdAOKNHTvWzjnnHMt0+qLqPH36wm9up512mh1++OFpXQff6A+5W7durjCFqkPG/7gloh+wE0880c2N0Xf3zDPPtKVLl8YsM3HiRPcDph+oxo0b2+23376ZPwkAAMgUR/Xta99PnWpPX3ut/fzKK/bmXXfZPrvuags387HaKs8KdFWpVMlqJcgTSnL/iy/aMfvv755bWn8uWFDi8d/tw4bZfSNG2CNXXWVjhgyxygUFrmFEiXRxRnzwgV12zz026Kyz7LtnnrGdt97aPWfe339Hlrn07rvtrVGj7KVbbrHPH33U5ixYYEdeeWXk8cLCQpeEKy5fPfmkPT1okMudBg4cGDkmnTlzpjVq1MgGDBhg2223nf3zzz/rFFqrXbu2de3a1e69916XjOt269at3XHsr7/+asuXL48sO3/+fDcX/Nprr7UxY8a45FfP1dzxkI5xX3zxRZs6daq98sorNm3aNDv66KMjj3/11Ve20047ucd0THz66afbKaecYm+//XZkmZo1a9o111xjo0ePjiyjy/vvvx9ZRq/boUMH23bbbV2+ouX0OePnrq8PiXiS6tSpE6naB7NVq1alexW88Oeff643US4t9+PWrZvbtvrBUEth9I9bcZSEqwXxww8/dD8oX3zxRUyjkX68DjzwQNcyqdbGwYMHux+zxx57LOl1pOqtHxSHatOnEw9PEA+/EA+/EI+yb9G//9qo77+323r3tn13392aNmhg7bbf3q46/XTr3rlzzHJn3Xij1TngAKu6zz6233nnud7geM+8847rla22zz52/NVX27/LlkUe26dXL+t9++12yZ13Wu0uXVyimMhn335r7U491Sp37OiGk7c/80z77c8/3fDy6x5/3Cb88ovltG3rLrpP7nruOdvx+OPdcxp362bn33qrLf3vv3Ve+/XPPrOtjzzS9Rrr/f+YO3eDe9t1bPfyxx/bYR07FrtM3trkWUm0kuWDL7rIGh96qC2LSjTje4jvGT7c+p9xhvXo3Nl22nprG3bddS5pfv3zz4t9n7uef97OPvxwO717d2vdooVL4itVrGhPvfmme3zx0qX25Btv2F2XXmr7tW1ru223nQ0ZONC+mjjRvv7hB7fMB19/bZNmzLBnr7/e2rRqZQe3b2833HCDPfjgg+749a+//rKdd97ZHWOeddZZLn9S59qCBQvWWZ/DDjvMJc/qWa5fv77r7d5yyy1dvhWeO1yfVcm9EvT999/fJdPDhg1zvd2vv/565LXU677nnnu6Y929997bDX3/+uuvIw0AV199tVtPPbbVVlvZxRdfbAcddJDrBQ/ts88+dsQRR7jGg3AZvd///ve/yDJK1NXbrw6tXXbZxS2n3nF1nlm2J+LagBdddJFdeeWVrlVDQVXCEe2uu+6yHXfc0bWmqGfw/PPPj/QeqmVDLR8aEqEvjS7h86OHpp9wwgl2XNzwEgVarTn6ckhRUZHdcsstrsKfvlj6Ur788sslrv/KlSutb9++br0qVKhgLVu2tCeffDLy+Oeff27t2rVzj2lohb5k0UlgouHzGlYRvQ30mZ544gn3RdMXfeutt7Y31/4BqgVr37VzY2rUqOGWVa91uG179+5tl1xySaQV64wzzrBDDz10ne2gL2P0em8KasHafvvt3WfX57zzzjtjHtd9N998s1unLbbYwg1PiU80ldxqe6jVSkNP9Aeszzh+/Pik1kUtcCNGjLCDDz7YxWpZ1A5kY3zwwQc2adIke/bZZ9166vWjf9wS0fCY9957z8V0jz32cK10999/v73wwguRITkaeqPnP/XUU24baviO/k70t5CsPE6x4YW81attu+HD3TXSj3j4hXj4hXj4KZmGkSoFBa43V0neyhI6Yo7p18/1rr5777327bBhtmurVrb/+efb31FDjafNnu0S3bfvusvevvtu+/y77+zWp5+OeZ2nR460/PLl7csnnrBH+vVb53107Kt52Z133dUmDh9uo596ys454gh3THfcAQfY5SeeaNu3aGF/vvuuu+g+yc3JsfuuuMJ+GjHC9ex/Mm6cXXnffTGv/d+KFXbTU0/ZsGuvde+vxoXjr7nGNtTEX391Ce7u221XbBzGf/aZnXfzzdbg4INdj/UOW21l45991qpVqZLwOTNmz7a5Cxdal3btIvdp2T22395GT5yY8Dnqwf52ypSY52jYuG6PXptkfzt5sq1esyZmmW2bNbMm9etHltH1jlttZfV0Gr61lBOo00dD0nVMrOPwaPn5+QmPldu1a2dz585dZwSsRniGuZmOXxXv6B5nJe465lXPdSJK3HXsq6S7fPnyVhzle8oXE1EDwMcff+x62Dt16hTJ7UaOHGnbbLON+8zKd7Qe0Q0CWZ2Ii3oRlWRr6IJaK66//nrXUxj9pbvvvvtcD6KW/eSTT1ziLgqYEll9AdTTqYvmMCTqgXzrrbdihv9q2MJ///3nElxREq6k/JFHHnHvdemll9pJJ53kkuniaIjE8OHD3fopwXr00Uetyto/wtmzZ7sWGM1DmDBhgj388MMu2b3xxhuT3kbXXXedHXvssW44hV5Tn0dfWiWVSnhFXzx9fg0Zid62+mP68ssv3edSS5eSQC0XUo+stkN8Q8XGUC+u1lcJpP7I1bCgYSDqLY6m5FwJ9vfff+8aWM477zz3OUQ/EGp5UyPMd9995xJcNXokQ3/w5557rmsEueyyy2yHHXZwSbx+EEKKV0kXPb+k19f61atXb50fN32HinuOWgn1uUOaO6Pvuf4GwmX0I6LYRb+uto2GCyWDqrd+KMrLs1mdOrlrpB/x8Avx8Avx8FMy4xPKlStnQwcNcgly9f32c73PVz/4oE2Mmqf7v/Hj3Xzjl2691XZv3dq2btLE7rjkEqu+xRauRzikZEavtUPLltZxl13s5EMOsY/Hjo15v601he6ii6xVs2buEm/JsmUuuT20QwfbqlEj2655czv10ENdwlhQsaJrNCiXl2f1a9d2F90nl5xwguvRb9awoevxvfG88+zFjz6KeW0log9ceaXttdNOrkdYCbt6hOPnUpeWeunz8vKsblzCN+uvv1zCv81RR1mX3r1dz/yIm2+2P95+2wZffLHbPsVREi7RyXB4O3ws3oJFi1zvfL249dDt8Dm6VgOIYlbSMuu879rjVuUqEp/86vMnOgd4w4YN3XV0HhE+P1w+vNZrxL+nkvhoOq5XDlirVi031fONN96w4qgnXtOO1QEbn5zreF3HzBqlqs6tA9Y25KiXXrnfrbfe6nrT1YGmvO/II48sMb9LJGNLV2oIwaBBg9z/1dv7wAMPuBaNcCOqRze6F1WJrJKjhx56yG10JVVqUVNvenGUxCjQr732misGIM8//7wbmqBWIPVsq3f2o48+sr322ss93qJFCze0Qcl156hhPKGff/7ZfSnUaKBEKnxOSOunRFmfR+unuQnq8dSXTkOXlXiVlnq5e/bs6f6v9VTi/80337gvVdgypFae+Lny2p7xc4tbtWrlChmEjRlDhgyxY445JtKAsD5K3OOX1Q9FNPXcajiKkm9RS5R6jjXEOuyxFzUqKAEXbZe7777bPv30U7eOio+22+OPP+5a1TQPRT8YZ599donrN2vWLNcAoYuW1x+cesPDZDfe+nrXSzrHoX5QopNwCW/H/9hEPyd+OIx2mIpj+Bxda2RGca+r0Q/x9B3WJaTGAHEHUkVFJX5GpOjAtmNHq//115Yb9/eC1CMefiEefiEenlKV7iR6xY/abz/r1r69jRo/3g1Tfverr+z2Z56xJ665xk477DA3BH3p8uWuyFe05StXul7wULMGDWyLypUjtxvUrh0zR1l223bbEtelZrVqdtqhh1rXiy6yA9q1cz24xx5wgHutknw0ZozdMnSoTfntN5fMryksdMPB1QuuIdqiBL5t69YxPcJKTCfPmOGG4ydLn79C+fLuGDRa/4cfdg0bR+67r300cqQ1Xrky66ZvFBQUuOvo+eAbQ3WVVCfpt99+c52O4Rzw+G2v3EAJuHICjRSNpjxOx/JKuJU/quNN+ZhGBqsRSVT0TR2sohGsGnGrDspE+V1WJuLR1HsZzjMQJcfqrZ4yZYpLLjTcQUON1Ytb2jngSnTUQ6thD0rENdxCrS4aDiwqMqDXC5P/kIZXaD5BIgq6WnuKC6J6yJXUR3+Z2rdv774oShY1FHtDtpEaFJQcRm+j4uy2227r3KdecQ0BVyKueSHvvvuuG2VQWhoKr979aOrJ1eiB6M+uL300fXaNXlDSHraSRX+usDEl/Fzq/dXj0UNbNCRmffr37++ScLV2aa5JcUNYQppOkAn0N6IfMQAAAKlYoYIdsMce7jLgrLPcfPBBjz3mEnEl4UqEP3vkkXWeF93DWj7uNHY6qi2KS0BVeGx9hgwaZBcdf7y999VXNuLDD63/I4/Yhw88YHvuuGPC5WfOmWOHXnaZnXfUUXbT+edbzapV7X8TJtiZN9zghm2HifimVrt6dZfo6z3U2xzqf+aZbns98+67tkunTnZ8ly52yiGH2B477LDe16y/tkda1cyjGx90u8022xS7Hjpe/iuu0UO3w9fTtdZTw/GjYxa/TPzoAB3/i+Z3a0h3fO+3jtUTDRH/e+26xHf86fnh8uF1fCed3lNJcMxnrF3bXdRhp3ne6sDUsXvYKSrqudYIWXXWKVGPp0628Fher68cRMfESsT12soB1ZkXTe8VPY+8NDJ2fGl8oJWQhS0YmgOtOc1h1TwNedb82w0pPqbh3GopUaKnuQFq1VGPsoRD1jWPQAl2eFEvbnHzxMNWoY2hL4/+AKIlGgpS0jYqiZL2ePoSq9qhhj9rbrN6XlW5sLT0mvrCR1/0h7whNvRzrS8R11x8NQ7oD1vz5MMh34lszNB0NRyEP2ah8HZxIzSiGxtCalzSj1v4nA153auuusoNzwkvf/zxR7HrDQAAsk/r5s1t2drezF233dYNW1aPcsvGjWMuSgI3h11atXIF47566ik3r/r5tdWtlfAWxh3/aX60jgnvvOQSl6xv07SpzZk/f53XVC/5uMmTI7enzpzpElMNf98QYWI8afr0mPu1XW7p3dt+e/tte+qBB9x77HvuuW6o+g1PPOHmgRen+ZZbuoQ4ekj/kqVLbcxPP7kh9Ylom2ikQfRztD10e6+1jRcaiq+Gkuhl9Pl1urJwGV3/MG1azCgGjeZVp56mbOq4/t9//415b+VYiXKIH3/80R27h0PUI59lyZLIaFmNVlbyG10hXY/rWDw6wY4XHv9Hj+5ULTANN7/ttttKfSYsvU74GloXTREOp71Gj2pWkbhkZGyPeEmUeLs/wjvvjAwr1nDwaNrI8a0uiWg+uVpaNExZvcAajh0mgmopUVExzU8o7TAFzQ3WuqmlJhyaHt/aosYDJdphr7jmamsIhU4RIKpMGD3PQl/UGTNmWDLCecSl2QaieRg6BZmGpCsZj59rsSnos+uzRtNtJcbxc0aKo+HpaijQH5NiI5obsj5qGFBL2E033eRGU6h3XL342uYaDaGe++hh3xszNF0/KHofJdbhcPPwxy2+9S36OSpyoe92OGJBIxL0XVIBiXAZVXmMbmHU62qbJBqWLtpG4XaKlsuwdC8oDnXGjyceniAefiEefiEeZZ9OUXbMVVfZGYcd5ip0b1GpkktWdQotVe0WDQ9XkqYiaprfvU2TJi7RHfnll3bEPvu4eeObipLUx157zbp36mQN69Sxqb/95s6hrR7lcPj7jDlzbPzUqdaoXj23vi0bNXLzv+8fMcJVMP9y4kR7JKpidkiJ6IWDB7uibmpU6D14sEvcN2RYutSpUcM1Uqj3XVXG4ykf6dq2rXVv1colsJqzriHr1z7+uP3z8cdWNcFUT+UBl/TsaTc+9ZSbT6/EfMAjj1jD2rXt8Ki8Y//zzrMj9t3Xeh97rLt92Qkn2KnXXecKx+nzqPK6GlJOP+ywSMG3M3v0sMvuvtuNGKhaubLbFoprONLgwD33dA0wJw8aZLdfeKFrfOl/ww2uarmOGzX1UbmHTvOlTkZ1DGnEsUbvqi6R7itfvrw7jh41apTLp5Soa6qkpgdrGY0q1vTh8LNqNKpGMuv4VrmXpqoqeVf+EXakanSu8icd1+q9tYwqmofJuoajqzNWldCPOuqoyPRN5T3haFcd76vmkp6nfOGdd95x02+jR+5q+LvqYKn2kvIB1cpS3TAl+cnIykRcSZWSEU2817CEsOhYNAU+nBegSuf68hQ3ZF3V0/V8tYQowCElxyrypvkDSohUyVq9ino/JVWnnnqqW07zvBV0zTvW++p+Vf3WnG29t+Y4KCnTMHjNfdZQ7AsvvND1yqo1RnPhNXchbFTYb7/9XAEzfTYN89Dc8dImqiG16OhLrzkVmnOtP5j1zffW8HR9uZW8h59tU7r88stdC5QKrOnLr4Rfc+U1b760FCslo2oBUw+3GknuuOMO91j83JFEtI11CjBd1MChBhwl5Socpx+NMMHemKHpem0l3ErwNRdfPxLqkQ9/3ERz+TUKQd9PjRxQI4VGYmiuu76L+n7r+6HCdmELoz67hplr3ozmzqsFUkX4NCwnWczv80PumjW21ciR6V4NrEU8/EI8/EI8SifoNjbFb5hE1fRKlVxF7ruHD7dps2a5hLZxvXruVFhXr+2A0bHUO/fcY9c8/LCdfv31Nv+ff1yvbadddlmnQNjG0jByzfN+um9fW7h4sRuefcExx1ivI4+MzGd/9dNPbd/zznM9zToFl4bP67Rctw0bZlc9+KBbr1suuMBOWVtXKvq1+55yip3Qv7/Nnj/fOrZpY0+urVG0oc7q0cOGvfNOJCGOpnnhFddWlVfSfdbhh7uLtnNYZC6RK085xSXR59x8sy1autQ67LyzvXfffW76QEhz81WkLXTcgQfa/EWLbOCjj7oEWr31ek508bW7L73UVZfXeeNVIb/rnnvaQ1HFjZVXqNr9ebfeanudcYabRnDqmWe64tiipFYjM6NrHqnQsjotlWNEn5P7hRdecMfR6tBS3SvVYtIUUh1LR48UVkejHlMOpGNY5VVKgLVsmIjrDFDKqTRdWNOSdWysY+jw+FnH7ErwtYwuIXWYhkm0nqt8S40Gen/laerEiy5ArZxNx9t6DZ2BSJ1a6ijVOiUjJ4gfw5wBNH5f4/mjT+Gl1hIlpWGFbSUfKvKlXkS1ZmiIuRIbJVPhHAVV237ppZds4cKFLtnVl0SJsgq9RRd707wBJU76Yqn1Jzqh0+ZVQq1WFA3d1mvvuuuu7jx2YRl8La+e5LDgmIZd6HF9MfXemvet22Evs3rL1RKjqun6oivpVbE5DdkQJYhKNPVlV6uSEld9Xm2D8BRmek8VmdN9Ia2btlm4HnqeklwNX9a20bZLtG2jP6v+iFTwQMPxS0vvpzjEl/3XH4RamaJjoi+5GhZ++eUX9wemP8boivaJ4qP1jf7sKqag2KpVTSMQlOArSdVt/SFtCLW6KU4lnR4hGWp80TpqG2gYj2Ks6oxhjMNto+9b2Fqo1kYl32qRU4OBWvr03YtuQFGFfCX0GgWgOS7afslUjdd3y7VU5uVZdZLxtCsqV85mdO1qzd9/3x3kIr2Ih1+Ih1+IR6wVOmZ85BFrXru2bZ5ZySULcnJsZdWqVmHJkqwrDpYuy1essFZHH+2qoscPHc+IeESduae03n33XXccruPT8Bi3OMqPdNyrXCO61lP08ak6PEsadeqbjEzEkR4aQaDeWTUqqKhZWaFie+F54zfFHP1MFv7QLSxf3mpyLti0W5Ofb+P69LHdBw+2cknWt8CmRzz8Qjz8Qjz8S8SX1a9vlefOLbuJXxn02bff2r/LltlhazvjMioeG5CIv/zyy26YeTiNMtsS8awcmo5NS8PuFyxY4Obcq+dap2/zmc7rrlMQqNFAowrUI6xh/yThAAAA2Fz2SXDmoWx29NFHWzYjEcdG0zxrtU6p4IKGr0cPLdFjxRUYE1WQT+aUa5uC5lxreLuuNbxdBfZUHA0AAAAAUoFEHBtNc5SLm+GgQmElVRCPP1VBKuhc57pgw1GszZ84NBo1inh4gnj4hXj4hXh4JggsX6eXKqvDoDMN8chKJOLYrNQ7vjEVxOEnTj/j0YHtF1+kezWwFvHwC/HwC/Hwi8oK5y9dmu7VwFrEIzv93/muACAJheupbInUKCxf3ib37OmukX7Ewy/Ewy/Ewy8qDra8Zk13jfQjHtmJRBxA0thR+BOHxS1aEA9PEA+/EA+/EA//FEadaxrpRzyyD4k4AAAAAAApRCIOAAAAYLOovu++NvStt9w5tHPatrVFKkoW5fe5c+2IPn2sVpcuVq9rVzv/1lttxcqVkcf1XL2Gj/R5Xv/ss2Ifnzlnjltm/NSp7nZx2yDex998Y9sdc4wVZkFxw1WrVrnCz+PGjbNsQyIOIGm5a9akexWwNg4tRo4kHp4gHn4hHn4hHp4JAquweHHSVbpPu/Zal0iee8st6zx2wW23uce0TLSfX3nFjjvgANt7p53sz3fftWpVqsQ83vOaa2zBokX26cMP27v33mtf//ij3T5sWORxPVevsbnEJ8ubUuN69dxn3mGrrZKKx5X332/9zzjD8vLyNtm6qBFg15NOsgp7720tjzjCNXCsz8RffrGOZ59tFdu3t8bdusXEJfTSRx/ZtkcfbRUrVrQdd9zR3nnnnZjHX331VTvwwAOtVq1alpOTs87ZlPLz8+2KK66wvn37Wrah4hKApOVyeg1vqtfXLeH0gEgt4uEX4uEX4lFKP7dNydtopr4rm7fN2A1KLl/44AO7+9JLraBiRXeferCff/99a1K//jrL161ZM/L/+rVrxzz277Jl9tXEifbRgw/aTltvbUv/+8/23GEHe2/0aBt49tluGb1H+D5ljRLp+M9cbDz++8/9/3/jx9u0WbPsqP32K/X7rFq92v5evLjY95oxe7Z1u+QSO/fII+25G26wj8eOtbNuuska1K5tXffaK+Fzlixdagf27m1d2rWzR/r1sx+mTbMzrr/eqlepYucceaRb5qsJE6xn//52ywUX2KHnnmvPP/+8HX744fbdd9/ZDjvs4JZZtmyZdejQwY499lg7e21M45144ol2+eWX208//WTbb7+9ZQt6xAEkjarpflD14Qm9elGF2BPEwy/Ewy/Ew08bUjxv1223dcn4q59+GrlP/1cSvkurVjHLvvXFF9b+zDPd0HINPT/00ktdkhlaqF5gM6tVrZrN+/tv2+H4492pbwdffHGJQ9NvfPJJq3vggbZF58521o03Wr/777c2J5xQ7Dr/s2SJndi/v9U54AAr6NDBtj7ySBvy5pvuseY9erjrXU46yfWM79Orl7s99qef7IALLrDaXbpYtX32sc7nnGPfTZmyzmv/uWCBHXzRRe51W/ToYS9//HHSve2Kw3916rhrNXIcsMceVrEUxdu+nTzZLhw82BoefLCN+PDDYpd75NVXrXnDhnbnpZfads2bW+9jj7Wj99vP7n7++WKf89x779mqNWvsqYEDbfuttrLjDzzQLjruOLsr6jn3vvCCHbTXXtbn5JNtu+22sxtuuMF23XVXe+CBByLLnHzyyTZw4EDr0qVLse9Vo0YNa9++vb3wwguWTUjEASSNqrcene6kdm3i4Qni4Rfi4RfikVnO6N7dhkQNbX7qzTft9EMPXWe5/1ascEnauGHDXK93bk6Omw9eVFS0zrLD33/fqlaubPddcYW133nnYt/7uXfftZuGDLHbeve2b4cNsyb16tnD6xm6PuCRR2zSjBlu6PvkF1+0h/v2tdrVq7vHvhk61F1r/TSM/NXbb3e3//3vPzu1Wzf73xNP2NdDhtjWTZrYIRdf7Hrx419bvdcTnnvOTjzoIDv+mmts8owZlqyitZ0co8aPt923267Y5ZT4D37mGdvhuONs7zPPtNnz5tkT/fvb+cccU+xzRv/wg+vZjtZ1zz3d/SU9p9Muu1h+VOOZes+n/vaba9iIvG7b2FEcXbt2tdGjR1uy2rVrZ6NGjbJsQrcWAAAAgFI76eCD7aoHH7Tf/vzT3f5y4kR74eab7bPvvotZ7rgDD4y5rd5V9UpPmj7ddmjZMuaxmtWq2S9//OGGZndo06bY977/xRftzO7d7fTu3d1tDWH/YMwYN6y9OCoIp9763Vu3drebNWwYeaxOjRqRXvnood37xSWYj119tVXfbz/7/Lvv7NCOHSP3H9Oli511+OHu/zecd559+M03dv+IEfZQv362IbRNG9aps87Q89c+/dSeHjnSPhwzxn2OC445xvVS16hadb2vOXfhQqsXNUVA6tWqZUuWLbPlK1YkHPqv56gXPeY5a19Dj+l93evWqhW7TL16NnfuXEtWw4YN7bfffrNsQiIOAAAAoNSUvHZr396Gvv22BUHg/h/2MEf75fffbeCjj9qYH3+0BYsXR3rCf//rr3US8RO6dnVJ7j7nnmsDzzwzMkc8nnpkzz/66Jj72m2/vX0ytvj57ucddZQd1bevG1p+4J572uGdO9veJfS6y18LF1r/hx92jQsaNl9YVOR6+JXUR9trxx3XuT3+559tQy1fudIq5ufH3Kd59Opp15SATx5+2DrusotlmoKCAvuvhMaUTMTQdABJy6PqrTdx2Hb4cOLhCeLhF+LhF+LhqY0ovqrh6UrE1Uur/ydy2GWX2d9Lltjj11xjY4YMcZewhzdRYTMNsX7jjjts8LPP2p3PPmubysHt29tvb71ll55wgs2ZP9/2v+ACu+Kee0p8zqnXXusS6nsvv9y+evJJG//cc67XPNG6b7QgsIp//+2u1aARDv2ObmjQNmzaoIHtd955bk768++95xoGSqN+rVr2l14/rqFBUwGKK4SX8Dlrb+uxyDILF8Yu89dfVj9B0b71+fvvv61O3EiATEciDiBpOVRN90JOUZFVnz7dXSP9iIdfiIdfiIefNmbGvop0KSldvWaNm28cb+GiRa73Wqfh2r9dO1ck7J/1nD9bunXoYEfuu6+r7J1Iq6ZNbeykSTH3xd8urhf/1EMPtWdvuMHuuewye+z119394Rxo9XhH03D7i44/3g5p394VK6tQvrw7zVq8r+PmWev0a9s1a2bJxqHcypXuWkPoNZ89WqWKFd3w91GPP25TXn7Z2rZubdc8/LDVP+ggO/2669xogETz7qN76eO3p4bQx/fmxz/ni++/d/GNPGfMGLf9w+HwCV/3ww9tr2IqsZfkxx9/tF0ysKe/JCTiAJK2hqq3XliTn29j+/Rx10g/4uEX4uEX4uGnjSmepx5sFT6bNGJEwvNdK1lTD/Jjr71mv/7xh0sWL7v77oSvtXL1anvlk0/caba++eknN0+8zTbbJFz2wmOPtSffeMOefvttN/RdFdR1vmudo7o4Ax95xN74/HO3Hj9Nm2ZvjxoVSZbr1qhhBRUquFOmqXd38dKl7v6tGze2Z955xxVe09D6EwcOdMvFe+njj12xup9/+80GPfqoW39VJU82Dsvq13fXatT434QJxS67VaNGdv2559r011+3N++8000N6HHFFfbgSy8V+xydtmz67Nl25X332ZSZM+2hl16yFz/6yI0QCD3w4ou2/3nnRW6fcNBBll+unJ15ww1um4344ANXJf2yqOdcfPzxbrtp9MKUKVPs2muvtXHjxlnv3r1jerp17vBJaxtLpk6d6m7PjRvir0JtOt94NiERB4AyrJCDWq8QD78QD78Qj8xTtUoVd0kkNzfXXrjpJvt2yhR3WrJL777bBl90UcJlCwsL7YYnnrDtjj3WDWffb/fdrf+ZZyZc9kQVijvtNLvi3ntt15NPthlz5thphx5a4um+1Out4nI79expnXr1cg0HWjfR6dJUqf3RV1+1hoccYj0uv9zd/+SAAW6IuN7j5EGD3Km7os+JHrrunHPcKcd2OuEEG/bOOzb8xhutdYsWtqGNIqq8/tP06TZ15swSl1fDwz677WZDr73W5r73npv3XpzmW25pI++5x/Vo73zCCXbnc8/ZE9dcE3MOcfX2T5s9O3K7WpUq9sEDD7jtu9spp9jl995rA886K3IOcdE8++dvvNE1tuy888728ssv2+uvvx45h7i8+eabrqe7W7du7vbxxx/vbj/yyCORZVRlffHixXZ03Nz/TJcTqBkFAEphyZIlVq1aNVtYvrzV3BxzpJAU9SyN69PHdh882MqtWpXu1cl6xMMvxMMvxCPWiqZNbcYjj1jz2rUt8QzdzSvsga08d25GTDfT+b41X/mZ66+3sig+Hn3uvddVNH/06qutzNh99w1+6nHHHecS+atL+LwrVqywGTNmWPPmza1i3Lz28PhUyXzVUlSR9wU94gAAAADKBBUou+u559xwaQ2z1nDwj775xp3zO1Ncc8YZrjBbSfO+M8WqVatsxx13tEsvvdSyDT3iAEotbHFUqZJq6V4ZuBb05bVqWcHChRnRo1HWEQ+/EA+/EA/PesTNrKhcOctds2ajCralg857reHr3//8s61YudIVD1NBuCP328/KqrIcj03RI14amdgjznnEAaCsCgLL1ylOOKj1A/HwC/HwC/HwTm5hoZVFOt3WRw89ZJmmrMYDG46h6QCSVkjVdG8KH2nOJQWQ/EA8/EI8/EI8PLN2TrKu4QHikZVIxAEAAAAASCEScQAAAAAAUohEHAAAAACAFKJqOoBSo2q6X/TjrfmWeatWld0qqxmEePiFePiFePhXNd3NRw4C4uGBjIgHVdOTRo84AJRVOTm2Sjscirv4gXj4hXj4hXh4pygvL92rgCjEI/uQiANIGlXT/YnDxF69iIcniIdfiIdfiIdndF73OnVoGPEF8chKnEccAAAA8EHbtil5G6V7VTQketw4K4v26dXL2myzjd1z+eXFLtOse3e75Pjj7ZITTnC3c9q2tdcGD7bD99mn2OcsXLTItjv2WPtm6FBr1rChZbrjr77a2rZubZefdFK6VyUr0SMOAAAAYL1Ou/ZaO/yKKzbZ6ylZvuf5521zGPv003bOkUcm9ZybhgyxHp06bdIk/Pe5c63bJZdYpQ4drO6BB1qfe++1NWvWlPicvxcvthP797eq++xj1ffd18684QZb+t9/MctM/OUX63j22VaxfXtr3K2b3T5sWMzjP02bZkddeaXbxmqESLSd+59xhvvMi5cu3USfFskgEQeAMkyFj+AP4uEX4uEX4oFUqlOjhlWKK+pVkv9WrLAn33jDzuzRI3KfalovX7Fig9ehsLDQJeGrVq+2r5580p4eNMiGvv22DXz00XWWzYmqn33igAH20/Tp9uEDD9jbd99tX3z/vZ1z882Rx5csXWoH9u5tTevXt2+HDbPBF19s1z72mD326qsxn6fFllvarb17W/1atRKu3w4tW9pWW25pz77zzgZ/RmwEVU0HgNJYvHix9hLuGgAAbJjly5cHkyZNctcxXN3sFF6SdOqppwY9evSI3O7cuXNw4YUXBn369Alq1KgR1KtXLxg0aFDk8aKiIne7cePGQX5+ftCgQQO3fPjctQXDIxdZsGBBcPzxxwcNGzYMCgoKgh122CF4/vnnY9ZDz73gggvcpWrVqkGtWrWC/v37u/cLNW3aNLj77rujNq0Fr732WrGf7aWXXgrq1KmzTpy0DgcffHBw//33B9OmTUtqe73zzjtBbm5uMHfu3Mh9Dz/8sFvnlStXJnyOvhda17Fjx0bue/fdd4OcnJxg9uzZ7vZDDz3ktnf0a/Tt2zdo1apVwteM3xbRrrvuuqBDhw5Bmf2bCcru8Sk94gCSxlkP/YnDokWLiIcniIdfiIdfiIefNkU8nn76aatcubKNGTPGbr/9drv++uvtww8/dI+98sordvfdd9ujjz5qv/zyi73++uu24447usdeffVVa9SokVv+zz//dJfwNFW77babjRw50n788Uc755xz7OSTT7ZvvvlmnfctV66cu//ee++1u+66y5544okN/hyjRo1y7xtNp8n64osvbI899rBhw4ZZy5YtbbvttrPLL7/cPv74Y1u1nlEeo0ePdp+3Xr16kfu6du3qTrf1008/xcRBw9V1redUr17ddo86HViXLl0sNzfXbePwdTt16mT5+fkxrzt16lT7559/kvrc7dq1c9tw5cqVST0PG49EHMAGDbWCH3GYMmUK8fAE8fAL8fAL8chcO+20kw0aNMi23nprO+WUU1wCqSRVfv/9d6tfv75LJJs0aeKSvrPPPts9VrNmTcvLy7MtttjCLaOLbLnllnbFFVdYmzZtrEWLFnbhhRfaQQcdZC+++GLM+zZu3Ngl+a1atbITTzzRLafbG+q3336zhgnmhuvz6PMpWZ07d67169fP/vjjDzviiCOsVq1adtNNNxX7mlo+OgmX8LYei6YGiPD+unXrxjymBgdtr/A5ybzu+ugzq0Eh2edh45GIAwAAANjgRDxagwYNbN68ee7/xxxzjC1fvtwl1ErAX3vttfUWKlNjzQ033OB6kpV8VqlSxd5//32X1Efbc889LSfqdF977bWX63Xf0MYerad6wEsyZ84cd1Hv/X///ec+ax2ddqwMKygocNf6PEgtEnEAAAAAG6R83LnhlRwXFRVFeq01XPqhhx5yCd/555/vhlSvXr262NcbPHiwG2ret29f+/TTT238+PFu2PX6hoFvrNq1a68zrFtDxTW8/qyzznI99Ur2P//8c9fAoM/1888/u6HzxVEv/19//RVzX3g7HAGQ6DlhQ0ZIjRd///135Dkb8rrF0etKWW9QKItIxAEkLboFGumNgw5siIcfiIdfiIdfiEf2UtwPO+wwu+++++yzzz5z85t/+OEH95jmOMf3YH/55ZfWo0cPO+mkk2znnXd2velKeOOF86VDX3/9tRser+HuG2KXXXaxSZMmxdynedOaD67GhkceecQWLlxo7733nl100UW21VZbrfc1lbjrs0Yn1po/X7VqVWvdunXMspoDHj5H9RS+/fbbyGOffPKJa9zQXPVwGc1dj27Q0OtqmH6NGjWS+tyah6+5+mqIQGqRiANI2obu5LDp46CDFOLhB+LhF+LhF+Lhp83dMDJ06FB78sknXbI3ffp0e/bZZ11i3rRpU/d4s2bNXEI5e/ZsW7BggbtPybSSyq+++somT55svXr1Wqf3VzRU/bLLLnM908OHD7f777/fLr744g1eV/W6q4BadK94hQoVXOKt19V66T1V6yD6oqHqxTnwwANdwq1icxMmTHBD7Pv3728XXHCBe23R3HMVgNP7Kh76v+bEayi/HlPDRO/eve3444+PzGE/4YQTXCPGmWee6dZ5xIgRbhSBtkdIIwg0mkAX/V/bWP//9ddf1ylSp/VE6pVLw3sCKOPCIWdIfxx04KJW7LAlHelDPPxCPPxCPEopRVXlwyrd5YJgsybjqv596623ugRRPd+a9/3WW2+5ImeiiulKtNW7rN5nrZcSVSXtSowrVarkhn4ffvjhtnjx4pjXVmE4zetWATg18ChZLmmY+Ppo3XbddVdXFE7rJFonJcYlOe644+yFF15I+JjW6+2337bzzjvP9WKruvypp57qPndIc7PVmKDrYG08nnvuOZd877///u7v5aijjnIjCkLVqlWzDz74wCX0qvSuv6uBAwfGfH41EKiXP3THHXe4S+fOnd3IhLBAnCrZq7EBqZejc5il4X0BlEE63YZ+/DU0SwVUkF46iBo3bpyr6KqKqkgv4uEX4uEX4hFLCdCMGTOsefPm6y0Qtjno8H/ZsmUuMWS6wP+nU6b16dPH9eCnssEoXfF4+OGHXQE9JfVl+W9mydrjUzXWaNh/WcEvIQAAAICs161bN1d5XcO4VWgu02nuu4b0Iz1IxAEAAADAzC655BLLFqoGj/Rhkg6ApDGMzZ84aCgW8fAD8fAL8fAL8fAPhfP8QjyyDz3iAJLGzsKfOKyviAxSh3j4hXj4hXj4eTo5+IF4ZCd6xAEkjarp/sRh1qxZxMMTxMMvxMMvxCOxdNVM1vvqlFbUbPYD8Vi/TNw2JOIAksaBlB84sPUL8fAL8fAL8Vi3SJbolFXposQP/iAeJQv/VsK/nUzA0HQAAAAgxUP1dY7tefPmuds6X3Yq58+rd1HnyNZ6MG8//YhHydtGSbj+VvQ3k0nTI0nEAQAAgBSrX7++uw6T8XQMhc7Pzyfx8wDxWD8l4eHfTKYgEQeQtNxcZrX4Eoc6deoQD08QD78QD78Qj3Up4WrQoIHVrVvXVq9enZapAo0aNSImHiAeJdNw9EzqCQ/lBJk48x3AZrFkyRJ3+pnFixdb1apV0706AAAAyHJLyujxKU0uAJJGsR1/4jBt2jTi4Qni4Rfi4Rfi4Rfi4RfikZ1IxAEkjR2FP3GYP38+8fAE8fAL8fAL8fAL8fAL8chOJOIAAAAAAKQQxdoAlFpYUkJzccqV4+cj3dasWWPLli0jHp4gHn4hHn4hHn4hHn4hHhtH203KWukzIg2g1BYuXOiumzdvnu5VAQAAACL+/fdfV7StrCARB1BqNWvWdNe///57mfqhy+QW4MaNG9sff/xRpqqEZiri4Rfi4Rfi4Rfi4RfisXHUE64kvGHDhlaWkIgDKLXw3JZKwtlR+EOxIB7+IB5+IR5+IR5+IR5+IR4brix2EFGsDQAAAACAFCIRBwAAAAAghUjEAZRahQoVbNCgQe4a6Uc8/EI8/EI8/EI8/EI8/EI8slNOUNbqvAMAAAAAUIbRIw4AAAAAQAqRiAMAAAAAkEIk4gAAAAAApBCJOJDBHnzwQWvWrJlVrFjR9thjD/vmm29KXP6ll16ybbfd1i2/44472jvvvBPzuEpKDBw40Bo0aGAFBQXWpUsX++WXX2KW+fvvv+3EE09058GsXr26nXnmmbZ06dKYZSZOnGgdO3Z079O4cWO7/fbbLRv4GI+ZM2daTk7OOpevv/7aMl064nHTTTfZ3nvvbZUqVXLxSOT333+3bt26uWXq1q1rffr0sTVr1lim8zUeif4+XnjhBct0qY6Hfov0+9S8eXP3+FZbbeWKV61atSrmddh/+BMP9h+p/b3q3r27NWnSxL2Gljv55JNtzpw5Mctk699HmaVibQAyzwsvvBDk5+cHTz31VPDTTz8FZ599dlC9evXgr7/+Srj8l19+GeTl5QW33357MGnSpKB///5B+fLlgx9++CGyzK233hpUq1YteP3114MJEyYE3bt3D5o3bx4sX748ssxBBx0U7LzzzsHXX38djBo1KmjZsmXQs2fPyOOLFy8O6tWrF5x44onBjz/+GAwfPjwoKCgIHn300SCT+RqPGTNmqGBn8NFHHwV//vln5LJq1aogk6UrHgMHDgzuuuuu4LLLLnPLxluzZk2www47BF26dAm+//774J133glq164dXHXVVUEm8zUeor+PIUOGxPx9RL9GJkpHPN59993gtNNOC95///1g2rRpwRtvvBHUrVs3uPzyyyOvwf7Dr3iw/0jt75V+q0aPHh3MnDnTveZee+3lLtn+91GWkYgDGapdu3bBBRdcELldWFgYNGzYMLjlllsSLn/ssccG3bp1i7lvjz32CHr16uX+X1RUFNSvXz8YPHhw5PFFixYFFSpUcD/2oh2Mdspjx46NLKOdeU5OTjB79mx3+6GHHgpq1KgRrFy5MrJM3759g1atWgWZzNd4hAdSSvqySTriEU2JXaLET4l3bm5uMHfu3Mh9Dz/8cFC1atWYv5lM42s8RH8fr732WpBN0h2PkBIXJSMh9h9+xYP9R3rjocYR7c/Dho9s/fsoyxiaDmQgDR379ttv3dCmUG5urrs9evTohM/R/dHLS9euXSPLz5gxw+bOnRuzTLVq1dyQrHAZXWt45+677x5ZRsvrvceMGRNZplOnTpafnx/zPlOnTrV//vnHMpHP8Yge8qZh0B06dLA333zTMlm64lEaWlbDFuvVqxfzPkuWLLGffvrJMpHP8QhdcMEFVrt2bWvXrp099dRTbhhppvIpHosXL7aaNWvGvA/7D3/iEWL/kfp4aNrZc88956bWlC9fPmv/Pso6EnEgAy1YsMAKCwtjDuZFt/Vjn4juL2n58Hp9y2hnHK1cuXJuxx29TKLXiH6PTONzPKpUqWJ33nmnm782cuRIdyB1+OGHZ/TBVLriURr8ffgVD7n++uvtxRdftA8//NCOOuooO//88+3++++3TOVLPH799Ve3nXv16rXe94l+j0zjczzYf6Q+Hn379rXKlStbrVq1XD2RN954Y73vE/0e8Eu5dK8AACB91Mt32WWXRW63bdvWFX8ZPHiw6+UAst2AAQMi/99ll11s2bJl7u/joosuSut6ZbLZs2fbQQcdZMccc4ydffbZ6V6drFdcPNh/pJ6Kd6qI3m+//WbXXXednXLKKfb222+7Inkoe+gRBzKQdo55eXn2119/xdyv2/Xr10/4HN1f0vLh9fqWmTdvXszjqvasIVTRyyR6jej3yDQ+xyMRDYdT70emSlc8SoO/D7/iUdzfx6xZs2zlypWWidIdDyVy++67rxty+9hjj5XqfaLfI9P4HI9E2H9s3njo/bfZZhs74IAD3NkbVH09rFKfjX8fZR2JOJCBND9ot912s48//jhyX1FRkbu91157JXyO7o9eXjQUM1xepzDRD3n0Mpq3qrnG4TK6XrRokZs/Ffrkk0/ce2vnHC7zxRdf2OrVq2Pep1WrVlajRg3LRD7HI5Hx48e7U6NkqnTFozS07A8//BDTgKL30ennWrdubZnI53gU9/eh36oKFSpYJkpnPNTzus8++7j3HzJkiJt7G/8+7D/8iUci7D9S93ul95WwUTAb/z7KvHRXiwOw+U6voYqbQ4cOddWzzznnHHd6jbAa88knnxz069cvsrxOhVGuXLngjjvuCCZPnhwMGjQo4ek19Bqq1Dlx4sSgR48eCU+XtcsuuwRjxowJ/ve//wVbb711zOmyVAlUp9fQ++v0GlrPSpUqZfzpNXyNh9bn+eefd++hy0033eSqduu0LJksXfH47bffXIXh6667LqhSpYr7vy7//vtvzOnLDjzwwGD8+PHBe++9F9SpUycrTl/mYzzefPPN4PHHH3ev+8svv7iqxPq90mnPMlk64jFr1ix3esX999/f/T/6dFgh9h9+xYP9R+rioVOQ3n///e73Sacv+/jjj4O999472GqrrYIVK1Zk9d9HWUYiDmQw/Wg3adLEne9Sp9vQD3moc+fOwamnnhqz/Isvvhhss802bvntt98+GDlyZMzjOsXGgAED3A+9dkLaQU+dOjVmmYULF7pETwe1OuXS6aefHjmoDekcmR06dHCvseWWW7odUDbwMR46kNhuu+3czlqPa71eeumlIBukIx56TbWBx18+/fTTyDI6yDr44IPd+V91DnGdt3f16tX/r707C/Fx/+MA/nGylLFkUJZkIrKEyXLjwnJlyZYLS5ZEiVK4IaEkSRFxNaWsZZclklwIWZKQJIlCZLlRSPY5fb81mjnm+Df/c85jnPN61TPTPM/z+z5P8zTz6/37fJfKf7v6+DzScn/l5eX576ekpKSyX79+lRUVFXm5on+7op9HWkKutmfxx5qR94/68zy8fxT3PFI4Hz58eGVpaWk+XlZWVjlv3rz8IUl1/9W/j19Vg/TlZ1flAQAA4L/CGHEAAAAokCAOAAAABRLEAQAAoECCOAAAABRIEAcAAIACCeIAAABQIEEcAAAACiSIAwAAQIEEcQCAgjRo0CCOHj36s28DgJ9MEAcAqGbs2LExcuTIWo9duHAhh+lbt279X20/e/YsRo0a9RfvEIBfnSAOAFDNnDlz4syZM/HkyZPvjm3fvj0GDhwYffv2rVObHz9+zN/btWsXTZo0+dvuFYBfkyAOAFDNmDFjom3btrFjx44a+9++fRsHDx6MCRMmxNSpU6Njx47RtGnT6NOnT+zdu7fGucOGDYsFCxbEokWLok2bNjFixIhau6YvXbo0unfvntvp0qVLrFy5Mj59+vTt+KpVq6K8vDx2794dZWVl0bJly5gyZUq8efMmH3/48GFu849buj4A9ZcgDgBQTcOGDWPmzJk5iFdWVn7bn0L4ly9fYvr06TFgwIA4efJk3L59O+bOnRszZsyIq1ev1mhn586d0bhx47h48WJUVFTUeq3mzZvn69y5cyc2b94cW7dujU2bNtU458GDBzm8nzhxIm/nzp2LdevW5WOdOnXK3d2rths3bkTr1q1jyJAh/8jvBoC/R4PK6u8wAADE3bt3o2fPnnH27Nlv1eUUbjt37pyr07VV0Xv06BEbNmzIP6fXvH79Oq5fv17jvFStPnLkSK6q1ya9ft++fXHt2rVvFfH169fH8+fPc2hPlixZEufPn48rV67UeO379+/zdVM1/9ixY/Hbb+otAPVVw599AwAA9U0K1YMHD45t27blcHv//v08Udvq1atzVXzt2rVx4MCBePr0aR7//eHDh9y9vLpUNf9f9u/fH1u2bMlV79T1/fPnz9GiRYsa56Qu6VUhPGnfvn28fPnyu7Zmz56du6yn8e1COED95r80AMCfTNp2+PDhHG7TJG1du3aNoUOH5gp16kaexnenivnNmzfzGPCqCdmqlJSU/LD9y5cvx7Rp02L06NG5y3nqVr58+fLv2mnUqNF3VfWvX7/W2LdmzZo4ffp0HD9+vEZoB6B+UhEHAKjFpEmTYuHChbFnz57YtWtXzJ8/P4fgNOZ7/Pjxeax4kkLxvXv3olevXnVq/9KlS7mrewrfVR49elTn+0wfFqRK/alTp/KHBQDUf4I4AEAtmjVrFpMnT45ly5bl8d6zZs3K+7t16xaHDh3KQbpVq1axcePGePHiRZ2DeGrn8ePHeUz4oEGD8uRvafx4XaTJ4tLEcqk637t37zyWPEmTxJWWltapLQCKo2s6AMAPuqe/evUqdz3v0KFD3rdixYro379/3pfGj6e1wf9s8rUfGTduXCxevDgvc5aWKEvBPi1fVhdpUrd3797lrulp7HjVNnHixDrfDwDFMWs6AAAAFEhFHAAAAAokiAMAAECBBHEAAAAokCAOAAAABRLEAQAAoECCOAAAABRIEAcAAIACCeIAAABQIEEcAAAACiSIAwAAQIEEcQAAACiQIA4AAABRnN8Bn0yD+Lha9IwAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Erstelle nur das farbcodierte Stabilitätsdiagramm für die Top-5 Features\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from matplotlib.patches import Patch\n",
"\n",
"# Anzahl der anzuzeigenden Features (Top-N mit höchster Varianz)\n",
"num_features_to_show = 5 # Top-5 Features\n",
"\n",
"# Sortiere die Features nach Varianz (absteigend)\n",
"sorted_features = sorted(feature_variances.items(), key=lambda x: x[1], reverse=True)\n",
"# Beschränke auf die Top-N Features\n",
"sorted_features = sorted_features[:num_features_to_show]\n",
"features = [item[0] for item in sorted_features]\n",
"variances = [item[1] for item in sorted_features]\n",
"\n",
"# Definiere Schwellenwerte für die Farbkodierung\n",
"low_threshold = 0.0001\n",
"medium_threshold = 0.001\n",
"\n",
"# Farbkodierung basierend auf Varianzwerten\n",
"colors = []\n",
"for v in variances:\n",
" if v < low_threshold:\n",
" colors.append('green') # Niedrige Varianz - sehr stabil\n",
" elif v < medium_threshold:\n",
" colors.append('orange') # Mittlere Varianz - mäßig stabil\n",
" else:\n",
" colors.append('red') # Hohe Varianz - instabil\n",
"\n",
"# Erstelle das Balkendiagramm mit Farbkodierung\n",
"plt.figure(figsize=(10, 6))\n",
"bars = plt.barh(features, variances, color=colors)\n",
"\n",
"# Füge Werte am Ende der Balken hinzu\n",
"for i, v in enumerate(variances):\n",
" plt.text(v + 0.00001, i, f\"{v:.6f}\", va='center')\n",
"\n",
"# Füge eine Legende hinzu\n",
"legend_elements = [\n",
" Patch(facecolor='green', label='Sehr stabil (< 0.0001)'),\n",
" Patch(facecolor='orange', label='Mäßig stabil (< 0.001)'),\n",
" Patch(facecolor='red', label='Instabil (≥ 0.001)')\n",
"]\n",
"plt.legend(handles=legend_elements, loc='lower right')\n",
"\n",
"plt.xlabel('Varianz')\n",
"plt.title('Stabilität der Top-5 LIME-Features')\n",
"plt.grid(axis='x', linestyle='--', alpha=0.7)\n",
"plt.tight_layout()\n",
"\n",
"# Speichere die Abbildung\n",
"plt.savefig('output/Lime_Varianz_Features.png', dpi=300)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Regelbasierte Erklärungen"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Initialisierung DecisionTreeClassifier\n",
"\n",
"**Vorghehen**\n",
"1. DecisionTreeClassifier mit **Trainingsdaten** und **Vorhersagen von RF** erstellen (**Da Globales Verfahren ganze Daten und Vorhersagen**)\n",
"2. Genauigkeit berechnen\n",
"\n",
"**Ziel: Random Forest nachbilden**\n",
"- Random Forest nachbilden\n",
"- Je näher, desto besser kann der RF vom Surrogate-Modell erklärt werden\n",
"\n",
"**Genauigkeit**\n",
"- Wie nah sind die Vorhersagen des Surrogate-Modell an denen des Random Forest\n",
"- Berechnung: Mittelwert über alle Vorhersagen, ob sie gleich sind"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Genauigkeit des Surrogate-Modells: 0.9070\n"
]
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier, export_text\n",
"\n",
"# Random Forest-Vorhersagen verwenden als Ziel\n",
"rf_predictions = rf_model.predict(X_train)\n",
"\n",
"# Einfachen Entscheidungsbaum auf die Vorhersagen des Random Forests trainieren\n",
"surrogate_tree = DecisionTreeClassifier(max_depth=5)\n",
"surrogate_tree.fit(X_train, rf_predictions)\n",
"\n",
"# Evaluieren, wie gut der Baum den Random Forest approximiert\n",
"surrogate_predictions = surrogate_tree.predict(X_test)\n",
"rf_test_predictions = rf_model.predict(X_test)\n",
"surrogate_accuracy = np.mean(surrogate_predictions == rf_test_predictions)\n",
"print(f\"Genauigkeit des Surrogate-Modells: {surrogate_accuracy:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ausgabe eines generierten DecisionTree"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--- marital.status_Married-civ-spouse <= 0.50\n",
"| |--- capital.gain <= 7073.50\n",
"| | |--- education.num <= 12.50\n",
"| | | |--- capital.loss <= 2218.50\n",
"| | | | |--- hours.per.week <= 40.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- hours.per.week > 40.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | |--- capital.loss > 2218.50\n",
"| | | | |--- fnlwgt <= 125450.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | | |--- fnlwgt > 125450.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | |--- education.num > 12.50\n",
"| | | |--- hours.per.week <= 43.50\n",
"| | | | |--- capital.loss <= 2365.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- capital.loss > 2365.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | |--- hours.per.week > 43.50\n",
"| | | | |--- age <= 27.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- age > 27.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| |--- capital.gain > 7073.50\n",
"| | |--- age <= 21.00\n",
"| | | |--- Einkommen ≤ 50K\n",
"| | |--- age > 21.00\n",
"| | | |--- capital.gain <= 8296.00\n",
"| | | | |--- capital.gain <= 7436.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | | |--- capital.gain > 7436.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | |--- capital.gain > 8296.00\n",
"| | | | |--- capital.gain <= 30961.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | | |--- capital.gain > 30961.50\n",
"| | | | | |--- Einkommen > 50K\n",
"|--- marital.status_Married-civ-spouse > 0.50\n",
"| |--- education.num <= 12.50\n",
"| | |--- capital.gain <= 5095.50\n",
"| | | |--- education.num <= 7.50\n",
"| | | | |--- capital.loss <= 1791.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- capital.loss > 1791.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | |--- education.num > 7.50\n",
"| | | | |--- capital.loss <= 1782.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- capital.loss > 1782.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | |--- capital.gain > 5095.50\n",
"| | | |--- age <= 60.50\n",
"| | | | |--- education.num <= 1.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- education.num > 1.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | |--- age > 60.50\n",
"| | | | |--- hours.per.week <= 8.00\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- hours.per.week > 8.00\n",
"| | | | | |--- Einkommen > 50K\n",
"| |--- education.num > 12.50\n",
"| | |--- capital.gain <= 5095.50\n",
"| | | |--- capital.loss <= 1794.00\n",
"| | | | |--- hours.per.week <= 34.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | | | |--- hours.per.week > 34.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | |--- capital.loss > 1794.00\n",
"| | | | |--- native.country_Iran <= 0.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | | |--- native.country_Iran > 0.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"| | |--- capital.gain > 5095.50\n",
"| | | |--- age <= 85.00\n",
"| | | | |--- occupation_Farming-fishing <= 0.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | | |--- occupation_Farming-fishing > 0.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | |--- age > 85.00\n",
"| | | | |--- workclass_Self-emp-not-inc <= 0.50\n",
"| | | | | |--- Einkommen > 50K\n",
"| | | | |--- workclass_Self-emp-not-inc > 0.50\n",
"| | | | | |--- Einkommen ≤ 50K\n",
"\n"
]
}
],
"source": [
"# Formatierte Regeln anzeigen\n",
"def format_rules(tree_rules):\n",
" \"\"\"Formatiert die Baumregeln mit menschenlesbaren Klassennamen\"\"\"\n",
" # Ersetze 'class: 0' durch 'Einkommen ≤ 50K'\n",
" formatted_rules = tree_rules.replace('class: 0', 'Einkommen ≤ 50K')\n",
" # Ersetze 'class: 1' durch 'Einkommen > 50K'\n",
" formatted_rules = formatted_rules.replace('class: 1', 'Einkommen > 50K')\n",
" return formatted_rules\n",
"\n",
"# Regeln aus dem Surrogate-Baum extrahieren\n",
"tree_rules = export_text(surrogate_tree, feature_names=X_train.columns.tolist())\n",
"#print(tree_rules)\n",
"print(format_rules(tree_rules))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Extraktion einer \"WENN, DANN\" - Regel aus DecisionTrees"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Einzelne Regel aus dem Surrogate-Modell:\n",
"WENN marital.status_Married-civ-spouse ≤ 0.50 UND capital.gain ≤ 7073.50 UND education.num ≤ 12.50 UND capital.loss > 2218.50 UND fnlwgt ≤ 125450.50 DANN Einkommen > 50K\n"
]
}
],
"source": [
"from sklearn.tree import _tree\n",
"\n",
"def extract_single_rule(tree, feature_names, class_to_extract=1):\n",
" \"\"\"\n",
" Extrahiert eine einzelne Regel aus einem Decision Tree für eine bestimmte Klasse.\n",
" \n",
" Parameters:\n",
" -----------\n",
" tree : DecisionTreeClassifier\n",
" Der trainierte Entscheidungsbaum\n",
" feature_names : list\n",
" Liste der Feature-Namen\n",
" class_to_extract : int, default=1\n",
" Die Klasse, für die eine Regel extrahiert werden soll (0=≤50K, 1=>50K)\n",
" \n",
" Returns:\n",
" --------\n",
" rule : str\n",
" Eine lesbare Regel als String\n",
" \"\"\"\n",
" tree_ = tree.tree_\n",
" \n",
" # Funktion zum rekursiven Extrahieren einer Regel\n",
" def tree_to_rule(node, depth, conditions):\n",
" # Wenn wir einen Blattknoten erreicht haben\n",
" if tree_.children_left[node] == _tree.TREE_LEAF:\n",
" # Prüfe, ob dieser Blattknoten die gewünschte Klasse vorhersagt\n",
" if np.argmax(tree_.value[node][0]) == class_to_extract:\n",
" # Formatiere die Bedingungen als Regel\n",
" if conditions:\n",
" rule = \" UND \".join(conditions)\n",
" return rule\n",
" else:\n",
" return \"Keine Bedingungen (Wurzelklasse)\"\n",
" return None\n",
" \n",
" # Feature und Schwellenwert am aktuellen Knoten\n",
" feature = feature_names[tree_.feature[node]]\n",
" threshold = tree_.threshold[node]\n",
" \n",
" # Linkspfad (≤)\n",
" left_conditions = conditions + [f\"{feature} ≤ {threshold:.2f}\"]\n",
" left_rule = tree_to_rule(tree_.children_left[node], depth + 1, left_conditions)\n",
" if left_rule is not None:\n",
" return left_rule\n",
" \n",
" # Rechtspfad (>)\n",
" right_conditions = conditions + [f\"{feature} > {threshold:.2f}\"]\n",
" right_rule = tree_to_rule(tree_.children_right[node], depth + 1, right_conditions)\n",
" if right_rule is not None:\n",
" return right_rule\n",
" \n",
" # Keine passende Regel gefunden\n",
" return None\n",
" \n",
" # Starte die Suche vom Wurzelknoten\n",
" rule = tree_to_rule(0, 1, [])\n",
" \n",
" # Formatiere die Ausgabe\n",
" class_name = \"Einkommen > 50K\" if class_to_extract == 1 else \"Einkommen ≤ 50K\"\n",
" if rule:\n",
" return f\"WENN {rule} DANN {class_name}\"\n",
" else:\n",
" return f\"Keine Regel für {class_name} gefunden.\"\n",
"\n",
"# Anwendung für die Extraktion einer Regel für hohes Einkommen (Klasse 1)\n",
"single_rule = extract_single_rule(surrogate_tree, X_train.columns.tolist(), class_to_extract=1)\n",
"print(\"Einzelne Regel aus dem Surrogate-Modell:\")\n",
"print(single_rule)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Berechnung der Genauigkeit des Surrogate-Modells mit unterschiedlichen Baumtiefen\n",
"\n",
"**Vorgehen (Ähnlich zu Hyper-Parameter Tuning)**\n",
"- Vorhersagen erstellen mit unterschieldichen Baumtiefen\n",
"- Vergleich der Vorhersagen von RF und Surrogate-Modell nach Gleichheit\n",
"\n",
"**Ziel: Beste Baumtiefe finden**\n",
"- Mit der Random Forest am besten nachgebbildet wird"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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vujdhd9Kmxno8eNteTeDmbVs06642WdfuC1n1uc6KNkPO2I9UmwL60hRZt0mbMbrTpobOsVrzut7aRF+H1XPn7AqhzT8DdVz5Ki/Hv5M2m9Tmts8884zJ9n62x3Yg6L7W3723Y9P9fKHNP7W5sjbxfvnllzPN7+xTfzbrq03MtQm1Nud2/l60u4M2q9VzmfaFP9t9l1W2fW0irt1qtLm0Nt/WZuy6DzIm1tPmyCo3zV81c3ZGOoSkyniMZ5Tb/a77S/MqaJ4P7aOfkW5Txv3l3Jf6N8rbcJba/Fm7GujDfX/oeUObo+e0Xc7la/Npb+cG57rr/+7dSfScrX3mM9LvcOZO8ZU2658wYYJ5rucMbf6szfP1b4N2fdFtd+/6ou9rrgh9T7tuaF4Od/4412hz/YwjJTgz/Ws3Av074+29s/3uvJaHP5flz7IFzgWCbiCIacKc66+/3jyUJkzRvorjx483/fB27tyZ637IGbn3mctIg0dvidycGae9fVbn1+m6bhnnd+8vntP3a783vVDWgEqDWf2sXgTrhZj2ecxN9nZNCKS0n6YvMgaGSvu6eQu6dZ20v5z2KdebI9rvWvtaZ+yr7E73h/Z39MZ5UeSe1VvLPav5ve0/5/Zq39bsaN/b3NKg25l0y0n75TmDbuf6eLuo033o7DOoF1oZA7W8rrcmhfJGy0CH9gnUceWrvBz/7km+9CaW9j/3x7EdCBpEZQyc3LdNj1+lfS81WNFzg7ff2Nkcl04ayOr36k0V7aOs/VY1z4AGPBqQ+2PfZVVmep7Wftaak0GDM71BqudDvZGmySqdvxHn/tDh23zl7Rh3nmMyHuMZ5Xa/Z3e85nTO0W3Ojn6He9Dt63Y5l6/95vWR0zY4ZZX5Xr9Db+DllfPvnJ779YaC3iAfOXKkK7mlrq/enNH+/npjTftY698PPR715qPeSPPHuSa7/Zfde2c7rnhey8OfywpU2QKBQvZyoADRPzJaS6cJT7TGQGsv3C8Cssoa7rzI88ZbUJ3Te84/5npnOiO9uNPp7n/wnc/1zn9G3pahSYM0MNIkMJqIRy9k9MJY74JrbVJuOb9fLyZPd6vx+nDfhowPb9mAnfQGiP6R1wQu+r++zml9vO0LZ22u+zo7y12T8Hjjbf85P6v7MLvt9ZY5PCeaDCfjctyT71x88cXmf01AlVuBXO+8Hld5/V152y5fj393WqOotVWdO3c2N3XO9tgOBD0XebvIdW6b8+LYua5aa5vduubl2HHSoEYzRuvvSG+AKa311psCGccGzuu+y+6cqQnE9KaUBrpaw6lZ+PU3o+XnrLl13rxzvzEZSLnd79kdrzmdc/RvUnbfoX+7zmYbNJlgdsvXljPnmjPBl55fnLT1kQbcmjxRk+bpeutzPddoS4iMnDetvJ1rfD3PnEv+LI9gLlvAnwi6gQJGL/i0qbG3ZmbeLuK0KXBOzQ9zS4cDUt6GRdImdnrn3725sbNJ5++//55pfm+BhLN5omZZ1Ytod5oZ3RudL6saH+dFkbMZm79pM0z9Dt3/ekFVr169HPefNvl0NjN159ynGfef3uXPODRZVvvDub350cROa5K1hca0adM8hqbxRaDXOy/HlT9+V7k9/jMeK5qRW1u0aOCWcRn+Orad+yOnWlNvNFDwVmbOfeo8X+hxob8NzZzs73OSO2eN9kcffWQybmsQrLXNGWuWA3le0G3V8tKhwvSmlAaqem5Uzm4lc+bM8fv3ZrUuudnvGgRps13Ndu28CZjf55z8PKflRG+wKPcbT85zjd6E8WX/ObO6ezvXOLtnBJIG/bn57fuzPIK5bAF/IugGgpCO5+t+19ydNl/UiyetLXHv36RN2TQQcG/6q/1XtW+4v2mNkTbh0maU7s2I9fueeuop89y99lNrEfWPujaxdB/LVANJb007nbUhWkPg7p9//jFN+LzRoct02RrwZ3TfffeZ9dUhWpzD+7jTC9GzvbDRvtw6LmvG/tXeOO/Y65Ay7s38NEDQfarrqsP7ZAwitF+v+4WR1ippLV5GeqGn/fx0Wdo8OSP9zoz71l/04vHVV181TSd1vGIdsssbbxf/gV7vvBxX/vhd5fb49xa0a+Ct49BqIOe+/v46tvX34zwG80K7veh+cdKhf7S5vq6zeysCHStbbzhpc1xvTU+1L7G3sadzQ/ty640w/T3quVRryTI2LQ/EeUGPWW+Bi7PG2Dmsl+bi0H6nelNA+5NnFIga8Nzud91fWp461J07vVHgrf/77bffboJ7PUfp7ykj/e6zubmhNyo0ONP++prPJCMNeDN2ezkXtLz1OFc6DGFO55pPPvlEvvvuu0zL0eHGdP/pkGLO5tZKb9Z4y+Xgb/r7199sfpRHsJYt4G/06QaCkDZLvOeee6RWrVqmP5gmwNELJb0A1LvkegGv/br1gtZJgwC9INK+n9q8UpukapIXDc6d/YT9pWbNmiYhj47J3bhxY9OsWmvftemuJsHS4En7urlfUDz99NNmbFtNZqTz68WuJh3S1zrOrHufUP0jrA8db3T37t2m9lgvivWCRGsptY9mRjqeuCab00BPE/1ozaBeBOlDb07o/rr33nvNuug+0m3QMXI3bdpk/qDrTQJnkpy80It8ffhCL2h127Vfn+4/TbrmHKdbL7g0OHNPyKNBul6saVI9rTXUbdT59CJF+7B+8803HsvX40L3kc6nTbF13+h+1lYSmtBHjyEdCz5QCbY04ZAmKNMbMJrwTPtua9NWrUHT8cX1ezVA0b7U7mOvBnq983Jc+eN3ldvj3xs9TjTw1uRcun/0wl2Pc38d27qvdfs1cZsuXwNEDfZ13Omc6H7Q41fXUed3jtOtZa3jEbvXMOuxocHXlClTTK299nXV85sGF1quWhusx3p23Tl8/Y3pTS0d21rLTLcrI3+fFzSw1ZuQmjBP11+PWw26tEWLHmvORHp6nOv+0Rsouq/1f93X2sxd+/tqgOrv2s3c7vcBAwaY41O7YGgQredRvSGj662/lYx9b0uXLm3OR9qvXbdFt6lu3brm5pvzppV2PdFzWF7p8rXFgt7E0RwaenMlJibG/Ia1llS74Hi76eov+vfFPdGX3kzR36T+zdObhYMHD/Y4/vRvpN7Q0Wb7GoRrQkW9YdGzZ0+zb93p3yudV88Rul36N1SPQ/2bqudCb8np/El//1q2mtRS/8Zoyxe9OaS/6XNRHvldtsA5cU5zpQPwyZo1axyvvPKK44orrjBDqhQqVMg8dMgwHTbEfTxVd9OmTTPjWepQL+XKlXM8+OCDZiil7IYM0+FIvMluCC738cR1mBEdR1PH49Xv1uFQ3McMdTd+/HhHvXr1zPrp+OA6Zq6OSescb9Pdvn37zBAiFSpUMNuuyx43bpwZIsTb8Cm6nc5xU3W8T2/DryxdutRx4403mmVGRkaaIWx0KJunn37a8d9//znyMmRYTrwNGaZ0H+lQSrpduu90H+q+1H3qjQ79MmDAADMOrs6vYxHr2LvZDTWj4yM//PDDjvPOO898RseT1f3fr18/x7x58zzm9deQYe7Wr1/veOSRR8xwRfrdOoRXyZIlzVjuur4Zx/PNy3rndkiv3B5Xuf1dZTcWbW6O/6yG8NJhc8qUKWOGf/rll19yfWxnNfyQHo96fFWpUsWUU1b7Iqt9fOjQIcddd93lKFu2rCkzHftah0PLbtisDh06OEqUKGHWV4/ryy67zJw/9u/ff9bHoA6bpMPL6Wd1GLHs+LrvsvutOYcL1KHJ9DxduHBhMxaz7gcdWjHjkHZqw4YNZjg4PRb0e7VcdR+4D7uV16Gksjp3+7rf1cGDB02Z6tBp+lvR4dN0WL/sykT/duk26ffrca7fo78dHZ5N93NO657T71qPMx3Hu2HDhmZMdh0WS88TvXv3Nuvm637IzRB5WQ0ZpvtEf89PPvlkpnHelQ5P17FjR7MPnOd3HUc6q/2nQ6fp8IKVK1c2+6527dpmSM+szk3ZbV9W+y+r/a5Dw+mxq8e+83fjXL/sjsHclEd258b8KlvgXLLpP+cmvAeAzDTZ0RVXXGFqVrwNZwNYGcc/AADWR59uAOeENg/L2N9R+0xqE1DlHKsZsCKOfwAAQhd9ugGcEzr2so6bq33HtC+h9qnV/n3aL077TWYcsxmwEo5/AABCF0E3gHNCk+hoMi1tTqtJwDRRiw5jM2TIEJNFGLAyjn8AAEJXUPXp1my2OtSMDjGjtQA63EdOTe50TFvNLqvZNStXrmyyR7oPVaSZJocPH+7xGc1SGqisvQAAAAAABGWfbh1mRIeaGDdunE/z67iSOnSFDjOgw2w88sgj0q9fv0zjXjZo0MAE8c5HoManBQAAAAAgaJuX63iV+vCVjp1ZvXp1M6at0qZ6GlC/8cYb0qlTJ9d8Oh5quXLlArLOAAAAAAAUiKA7txYtWiQdOnTwmKbBttZ4u1u/fr1JXFOoUCGTrGbkyJFSpUqVLJeblJRkHk52u930wStZsqTYbLYAbAkAAAAAoCDRntrHjh0zsWZYWJg1g+49e/ZI2bJlPabp66NHj8rJkyclJiZGWrVqJZMnTzb9uLVpufbvvvTSS2X16tVSrFgxr8vVoDxjP3AAAAAAADLavn27VKpUSSwZdPvCvbl648aNTRBetWpV+fzzz+XOO+/0+hkdN1WTszklJCSYmvGtW7dKbGzsOVlvZE1bHhw4cEBKlSqV7R0lFCyUq/VQptZEuVoT5Wo9lKk1Ua7BRSt7NbbMqjLXEkG39tPeu3evxzR9rYGx1nJ7U7x4caldu7Zs2LAhy+VGR0ebh7fPEnQHx8kmOTnZlAcnG+ugXK2HMrUmytWaKFfroUytiXINLs4yyKkLcoEuKe2fPW/ePI9pP/74o5melcTERNm4caOUL1/+HKwhAAAAACCUBVXQrQGxDv2lD+eQYPp827Ztrmbfffr0cc1/zz33yKZNm2TAgAFm3O3x48ebZuOPPvqoa54nnnhCfvnlF9myZYssXLhQrrnmGgkPD5ebbropH7YQAAAAABBKgqp5+bJly8yY207OftV9+/Y1ydA0EZozAFc6XNi3335rguwxY8aYzuvvvfeex3BhO3bsMAH2wYMHpXTp0tK6dWtZvHixeQ4AAAAAQMgE3ZdddplJu54VDby9fWblypVZfuazzz7z2/oBAAAAAFBgm5cDAAAAAGAlBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAChEHT/+uuv0r17d6lQoYLYbDaZOXNmjp+ZP3++nH/++RIdHS21atWSyZMnZ5pn3LhxUq1aNSlUqJC0atVKli5dGqAtAAAAAAAgSIPu48ePS5MmTUyQ7IvNmzdL165dpV27drJq1Sp55JFHpF+/fvLDDz+45pk6dao89thjMmzYMFmxYoVZfqdOnWTfvn0B3BIAAAAAAEQiJIh06dLFPHw1YcIEqV69urz++uvmdb169WTBggXyxhtvmMBajRo1Svr37y+333676zPffvutTJw4UZ5++ukAbQkAAAAAAEFW051bixYtkg4dOnhM02Bbp6vk5GRZvny5xzxhYWHmtXMeAAAAAABCoqY7t/bs2SNly5b1mKavjx49KidPnpTDhw9LWlqa13nWrFmT5XKTkpLMw0mXp+x2u3kgf2kZOBwOysJiKFfroUytiXK1JsrVeihTa6Jcg4uv5VCgg+5AGTlypAwfPjzT9P3798upU6fyZZ3geXAnJCSYE462XIA1UK7WQ5laE+VqTZSr9VCm1kS5Bpdjx45ZP+guV66c7N2712Oavo6NjZWYmBgJDw83D2/z6GezMnDgQJN8zb2mu3LlylK6dGmzbOT/yUaz22t5cLKxDsrVeihTa6JcrYlytR7K1Joo1+Cio2NZPui+6KKL5LvvvvOY9uOPP5rpKioqSpo3by7z5s2THj16uA5Uff3AAw9kuVwdfkwfGemBzcEdHPRkQ3lYD+VqPZSpNVGu1kS5Wg9lak2Ua/DwtQyCqqQSExPN0F/6cA4Jps+3bdvmqoHu06ePa/577rlHNm3aJAMGDDB9tMePHy+ff/65PProo655tMb63XfflSlTpsh///0n9957rxmazJnNHAAAAACAQAmqmu5ly5aZMbednE28+/btK5MnT5bdu3e7AnClw4Xp8F8aZI8ZM0YqVaok7733nmu4MNWrVy/TF3vo0KEm8VrTpk1l9uzZmZKrAQAAAADgbzaH9sJHtrRPd1xcnElaQJ/u/KddBPbt2ydlypShWY2FUK7WQ5laE+VqTZSr9VCm1kS5Fsw4kZICAAAAACBACLoBAAAAAAgQgm4AAAAAAAKEoBsAAAAAgAAh6AYAAAAAIEAIugEAAAAACBCCbgAAAAAAAoSgGwAAAACAACHoBgAAAAAgQAi6AQAAAAAIEIJuAAAAAAAChKAbAAAAAIAAIegGAAAAACBACLoBAAAAAAgQgm4AAAAAAAKEoBsAAAAAgAAh6AYAAAAAIEAIugEAAAAACBCCbgAAAAAAAoSgGwAAAACAACHoBgAAAAAgQAi6AQAAAAAIEIJuAAAAAAAChKAbAAAAAIAAIegGAAAAACBACLoBAAAAAAgQgm4AAAAAAAKEoBsAAAAAgAAh6AYAAAAAIEAIugEAAAAACBCCbgAAAAAAAoSgGwAAAACAACHoBgAAAAAgQAi6AQAAAAAIEIJuAAAAAAAChKAbAAAAAIAAIegGAAAAACBACLoBAAAAAAgQgm4AAAAAAAKEoBsAAAAAgAAh6AYAAAAAIEAIugEAAAAACBCCbgAAAAAAAoSgGwAAAACAACHoBgAAAAAgQAi6AQAAAAAIEIJuAAAAAAAChKAbAAAAAIAAIegGAAAAACBACLoBAAAAAAgQgm4AAAAAAAKEoBsAAAAAgAAh6AYAAAAAIEAIugEAAAAACJCIs/nwsWPHZOvWrXL48GFxOByZ3m/Tps3ZLB4AAAAAgNALug8ePCgPPPCAfPnll5KWlpbpfQ3AbTab1/cAAAAAAAgVeQq6+/fvL7NmzZKHHnpILr30UilRooT/1wwAAAAAgFAMuufMmSOPPvqovPLKK/5fIwAAAAAAQjmRWuHChaVatWr+XxsAAAAAAEI96L7llltkxowZ/l8bAAAAAABCvXn5ddddJ7/88ot07txZ7rrrLqlcubKEh4dnmu/888/3xzoCAAAAABA6QXfr1q1dz3/88cdM75O9HAAAAACAPAbdkyZN8v+aAAAAAABgMXkKuvv27ev/NQEAAAAAwGLylEjN3e7du+XPP/+U48eP+2eNAAAAAAAI9aD7q6++krp160qlSpVMwrQlS5aY6QcOHJBmzZqR3RwAAAAAEPLyFHTPmjVLevbsKaVKlZJhw4aZxGlOOq1ixYoyefJkf64nAAAAAAChEXQ/99xz0qZNG1mwYIHcf//9md6/6KKLZOXKlf5YPwAAAAAAQivoXr16tdxwww1Zvl+2bFnZt2/f2awXAAAAAAChGXQXLlw428RpmzZtkpIlS57NegEAAAAAEJpBd7t27WTKlCmSmpqa6b09e/bIu+++Kx07dvTH+gEAAAAAEFpB94svvig7duyQCy64QN5++22x2Wzyww8/yODBg6VRo0YmsZomWAMAAAAAIJTlKeiuU6eOSaKmTciHDBliguxXX31VRowYYYLu3377TapVq5anFRo3bpz5bKFChaRVq1aydOnSLOdNSUkxSd1q1qxp5m/SpInMnj3bY55nn33W3BRwf+hQZwAAAAAABFpEXj/YoEEDmTt3rhw+fFg2bNggdrtdatSoIaVLl87zykydOlUee+wxmTBhggm4R48eLZ06dZK1a9dKmTJlMs2vNesfffSRac6ugbTWtl9zzTWycOFCM1Z4xnV1iojI82YDAAAAABD4IcM0g7kqUaKEaWauQbIz4P7nn3/MPLk1atQo6d+/v9x+++1Sv359E3xr0raJEyd6nf/DDz+UQYMGyZVXXmkC/nvvvdc8f/311z3m0yC7XLlyroeOJQ4AAAAAQKDlqcpXm2zXqlVLGjZs6PV9DciHDx8uQ4cO9XmZycnJsnz5chk4cKBrWlhYmHTo0EEWLVrk9TNJSUmmWbm7mJgY0/Td3fr166VChQpmXh1DfOTIkVKlSpUs10WXqw+no0ePmv+1Nl8fyF9aBtqlgbKwFsrVeihTa6JcrYlytR7K1Joo1+DiazkEpJ31oUOHJCoqKlefOXDggKSlpZkxvt3p6zVr1nj9jDY919rxNm3amH7d8+bNk+nTp5vlOGkN/OTJk00/9N27d5ubAZdeeqm5MVCsWDGvy9WgXOfLaP/+/XLq1KlcbRcCc3AnJCSYE47emIE1UK7WQ5laE+VqTZSr9VCm1kS5Bpdjx475N+j+9ddfZf78+a7XGtxqX+6Mjhw5Yvpma0K1QBszZoxpjq79uTVBmgbe2jTdvTl6ly5dXM8bN25sgvCqVavK559/LnfeeafX5Wptu/Ytd6/prly5smk+HxsbG+Ctgi8nGy1vLQ9ONtZBuVoPZWpNlKs1Ua7WQ5laE+UaXDK2uj7roPvnn3921f5qQWvQrQ9vtD/22LFjJTe0n3V4eLjs3bvXY7q+1n7Y3ujBNnPmTFP7fPDgQdOE/Omnnzb9u7NSvHhxqV27ttcbBk7R0dHmkZEe2BzcwUGPQcrDeihX66FMrYlytSbK1XooU2uiXIOHr2Xgc0kNGDDANK/et2+fac6gSc70tftDm4ifOHHCNN3WGuXc0ObozZs3N03E3e/k6Gvth53THYaKFStKamqqfPnll3L11VdnOW9iYqJs3LhRypcvn6v1AwAAAAAgt3yu6dYEZfpQmzdvNrXMmlncn7RJd9++faVFixbSsmVLM2TY8ePHTZNx1adPHxNca59rtWTJEtm5c6c0bdrU/K8J3jRQ1xsETk888YR0797dNCnftWuXDBs2zNSo33TTTX5ddwAAAAAA/JJITQPYQOjVq5epMdes53v27DHB9OzZs13J1bZt2+ZRha/NynWs7k2bNknRokXNcGE6jJg2IXfasWOHCbC1+bneKGjdurUsXrz4rMYTBwAAAADAFzaHthXPg7/++sv0216xYoXJoJcxXbr2NdBm3FagidTi4uLMdpJILf/psabdHMqUKUNfFguhXK2HMrUmytWaKFfroUytiXItmHFinkpKs5hr8+9vvvnGJC/TmmZNXqbPt27damqddRgvAAAAAABCWZ6Cbm3+rUH22rVrZdKkSWbaoEGDZMGCBbJw4ULTpPuGG27w97oCAAAAAFCg5Cno1iblOsa1VqFrUjKVlpZm/tes5XfffbcMGTLEv2sKAAAAAEAoBN0RERFSrFgx81yTlkVGRpq+BU5aC/7vv//6by0BAAAAAAiVoLtWrVqyfv16V8K0unXryowZM1zvf/vtt1KuXDn/rSUAAAAAAKESdOvQXJ9++qmkpqa6xteePn26nHfeeebx9ddfmybmAAAAAACEsjyN0639tR9++GFXf+6+ffua519++aX5/5lnnpHbbrvN3+sKAAAAAID1g27tw12yZEmPabfccot5AAAAAACAdIyoDgAAAABAftZ0t2/fPtcL1gRr8+bNy8s6AQAAAAAQOkG33W43QbS77du3y6ZNmyQuLs4MEaY2b94sR44ckZo1a0rlypUDs8YAAAAAAFgp6J4/f77H6wULFshVV10l7777rkmipuN2K81mPmnSJHnqqadk8uTJgVljAAAAAACsnEjtiSeekNtvv13uvPNOz4VFREj//v1lzZo1ZhixJUuW+Gs9AQAAAAAIjURqf/31l6tJuTfVq1eXv//++2zWCwAAAACA0Ay6K1SoIFOnTjXNyTPSafqezgMAAAAAQCjLU/PyAQMGyD333CMXXnih+b9WrVpm+vr162XChAmyatUqGT9+vL/XFQAAAAAA6wfdd911l4SHh8szzzxjnjszmzscDildurQJvLVvNwAAAAAAoSxPzcuVJlHbsWOHLFy4UD755BPz0Oc7d+4k4AYAADiHxo0bJ9WqVZNChQpJq1atZOnSpVnOm5KSIs8995wZ4lXnb9KkicyePdtjnl9//VW6d+9uugtq5crMmTMzLWfv3r1y2223mXkKFy4snTt3Nq0eAQB+CLo/+OAD2bJli8lWrk3Me/XqZR76XKfpezoPAAAAAktz6eioMcOGDZMVK1aYILpTp06yb98+r/MPHjxY3n77bRk7dqz8+++/pqvgtdde65EE9/jx42Y5Gsx7o60be/ToIZs2bZKvvvpKVq5cKVWrVpUOHTqYzwIAzrA59KyZS9q0/MMPP5TevXtnefLX99LS0sQKjh49KnFxcZKQkCCxsbH5vTohz263mwuJMmXKSFhYnhtrIMhQrtZDmVoT5Rp8tGb7ggsukDfffNNVRpUrV5YHH3xQnn766Uzza820dhG8//77XdN69uxparSnTZuWqVx1+owZM0yQ7bRu3TqpU6eOrF69Who0aOD63nLlysmIESOkX79+Adxi+ILfqjVRrgUzTsxTSeUUp+sdTq3xBgAAQOAkJyfL8uXLTQ2zk16I6+tFixZ5/UxSUpJpVu4uJiYm2ybp3pah3Jej3xsdHS0LFizIw5YAgHVF5GZsbs1K7vTbb795HTLsyJEjJpFa7dq1/beWAAAAyOTAgQOmZWHZsmU9puvrNWvWeP2MNj0fNWqUtGnTxvTrnjdvnqnJzk0Lxbp160qVKlVk4MCBpql6kSJF5I033jD5fnbv3n3W2wUAIRl068l4+PDhrmZGeoLVhzfFixenTzcAAEAQGjNmjEl6q4GzXtNp4K0J0SZNmuTzMiIjI2X69OkmsW58fLzpeqi16126dMmxRSQAhBqfg24dGqxbt27mRNqyZUuT9VJPrO70xK13OvXkTfNyAACAwCpVqpQJeDWTuDt9rf2rvdHhXTUb+alTp+TgwYOmj/dTTz1laq5zo3nz5qYVpPZl1GbuulztX96iRYuz2iYAsBqfI+Py5cubh/r555+lXr16pgM/AAAA8kdUVJQJfrWJuDPRmSZa0tcPPPBAtp/V/tgVK1Y0Q4hprfWVV16Zp3XQJEJKhwtbtmyZPP/883laDgBYVZ6qo9u2bZtpmtaAazCuiTVat24txYoV88f6AQAAIBs6XFjfvn1NDbO2Rhw9erRJanv77beb9/v06WOC65EjR5rXS5YskZ07d0rTpk3N/88++6wJ1N2zmScmJsqGDRtcrzdv3mxqtbUpubNGXDOda+22vtbhxh5++GET+Hfs2PGc7wMAsFzQrcNMLFy40ATZzoBbT7A//fSTea4nX73Dqs3MAQAAEDi9evWS/fv3y9ChQ2XPnj0mmJ49e7Yrudq2bds8hhbSZuU6VreOsV20aFFTwz1lyhSProFaY92uXTuPwF5pcD958mTzXBOm6XRtyq6tITW4HzJkyDnccgAoGPI0ZNiXX35p7qQ6ffHFFybIfuGFF+Sbb74x2S/1rikAAAACT5uSb9261bQ41Jps7VvtNH/+fFeg7Gyx+O+//5rgW7Ofa/Jb7dft7rLLLjMVKRkf7st56KGHZPv27aY/t363NivX5u6BNm7cOKlWrZppHq/bmd1QZ9p0XvMQaUWQzt+kSRNzQ8Ldr7/+Kt27dzf7QPMTaX/3jPS6VhPPae6iEiVKmKRxup8BIGBBtzZFqlWrluu19gOqX7++GTZC75bee++95gQPAAAA+MvUqVNN7fqwYcNkxYoVJojWIdD27dvndX6t0dfRdsaOHWtuNNxzzz1yzTXXyMqVK13zaFN8XY4G81nRoXDffPNN04xexyHXoF9beWoLAwAISNCtzY/0TqrSu55ay925c2fX+9qcSe+cAgAKrnNdm6TL0AzKjRo1MrVJOp82V921a5cEWihtK1CQ6fjiOtyZ9lfXCp8JEyZI4cKFZeLEiV7n//DDD2XQoEGmUqhGjRqmYkifv/766655dDQeba2pwXhWevfubWq3dRkNGjQw63H06FH566+/ArKdAKwlT0F3w4YN5aOPPpLDhw+bMR11uImuXbu63tcmRjqEBQAgdGuTrr32WlMr5Gtt0okTJ8x3aZ9Q/V9bUa1du1auuuoqsVrNWX5tq+IGAwoqbca+fPlyE/w6aV91fb1o0SKvn9FKIj123cXExJja6rNZj3feecdkbdffBM4e5yVYniMP5syZ44iOjnaEhYWZx6WXXurx/vnnn++45pprHFaRkJDg0F2l/yP/paWlOXbv3m3+h3VQrsGlZcuWjvvvv9/1WsulQoUKjpEjR3qdv3z58o4333zTY5r+HejZs6fXMtVz6owZM3Jcj6VLl5p5t27d6gjmbdXtvPnmm73OH0zb+tlnnzmioqIcEydOdPzzzz+O/v37O4oXL+7Yu3ev1/kHDBhg9sW3337r2Lhxo2P8+PGOQoUKmesAZ7l+9913jmeeecYxffp0r9t65MgRR4cOHRxTp051rFmzxrFo0SKzz5s3bx6w7YQ1z8E7d+40x9jChQs9pj/55JPmmPLmpptuctSvX9+xbt06s2167MbExJjfQW5/r7NmzXIUKVLEYbPZzO9Cf7PBrCCUqT/PSytWrHDNY+XzUkEp11CR4GOcmKegW+mPYvTo0Y7Jkyc7Tp486Zp+6NAhxyOPPOL4+eefHVZB0B1cONlYE+UaPJKSkhzh4eGZLlL69OnjuOqqq7x+Jj4+3vHee+95TOvdu7ejUqVKZxV0//jjj+YCN1DnX39tqwbcVatW9Tp/sGxrqN1MgfXOwXkJuvft2+e4+uqrTSWR/tZr167tuO+++0yQ5k12x3BiYqJj/fr1JkC74447HNWqVcsyMPQH/e3peUUrunT7lixZkuW8ycnJjuHDhztq1Khh5m/cuLEJSt3L9JdffnF069bN/K6z2k673e4YMmSIo1y5cmYfXX755eaGRSCF0o3PUPmthpIEH+PEPDUvV9qPRsdj1KEj3JvtaEbHN954w2S9BAAUPJqTQ0ehcA435KSvdTgib7Q5tvZxXL9+vRnv98cff5QZM2Zk2UTbF5pZWZv/3XTTTRIbGyvBvK3aPFyHTwrmbfVn09zsmn76IiEhwTT5LF68uFgty7Ve4+vQXTqElu4r3b96rODsadfF8PBwM0SZO31drlw5r5/RccS1nLTLh3Z/XLNmjRkmTftm55Y2Q9ZEwhdeeKG8//77JseR/m+VLj7qlVdekf/973+mr7xmZ9dt1u/Vc5SVuwycy/MSQlOeg261ePFiGTlypDz66KOuPyjOfmqJiYn+WkcAQJAbM2aMnHfeeWZIHR0ySIcvuu222zzGBs4NDYBuuOEGE8C89dZbEuzbqkmdgn1bQ+lmSn5muT7XQUso0d9b8+bNTQJfJz0u9fVFF12U7Wc1SKtYsaKkpqaaoW+vvvrqs14f/W5nYuFgTBinCeL0c74mjNNz0OjRo81vQfdP48aNzXBy2s/Z2w0mfwilG58IbWF5vSvVs2dPueSSS+SZZ54xf1x0nEazwLAwM4SCXpQAAEK7NqlKlSp5DkJ1OXoxFcgLoPyuOTuX2xpqN1PyI8t1fgQtoUZvpLz77rsyZcoU+e+//0w56W9Ry1lpMiwdwtZJb3xoQLZp0yb57bffzGg7GqgNGDDANY9WFK1atco81ObNm83zbdu2mde6fD02tLJJf6taM3vHHXeYIXSvv/56y7RK0e3WQNf9ezVZnLYSyep780NBvfGJ0Jano1OzrX7zzTfmwNRsq+ndJdLpD15PQF999ZU/1xMAUMBqk/RCV2sk8nLxozUYc+fOlZIlS4pVa87O9baG0s2U/GqyWlCClkz0Ou7AAQnXChQd8tXtui7Y9OrVS1577TXThL9p06YmONZuAM6aUg2U3Ws8tQZTb4LojRe9WaK/WS1T9ybEy5Ytk2bNmpmHM7DX5/odSn83euxrc20dr1u7GOjIPRrE6/BhVmmV4lx2br73bHHjE6EiIi8f+vTTT82dxbvuusucdDKqV6+eTJs2zR/rBwDIB3rRqTk7WrRoIS1btjS1dxlrk/TiVbsYOWuTtNZHL4L1/2effdZc+N1///0etUkbNmxwvXbWJsXHx5sgTi9+rrvuOtMUWG/s6kWn80JP59EAOZi3NWPNWbBtq/sNhh49enjcYNCaIl9uMOh6680UrQHO6w2Gn3/+OeA3GLILWvQCPbugpU2bNqZft+4X3VZdTjAHLWflyBGRKVNExo6VsI0bpbRzes2aIg8+KNK3r0gQ9m/V4zWrY3b+/Pker9u2bWu6C2RH8xC5VyB5O/71WAhmWvurLTu09lf7JesxrK1SdGjfYOav85Le+NRzTDCflxDa8lTTrXfNdFy7rOgdK+3bDQBW5O/kTDktc8uWLeYiytsjUDc4/VGbpEmntJbP19okDWC//vpr2bFjh/lOTUTlfCxcuDAg25lfNWf5ta3+apqb8WZKdk1znTcYdJ98/PHHrhsM+tAaaas2WQ16P/wgUqmSyKOPimza5Pmevtbp+r7Oh3Mqv1qlOJedm+8tqF0GCsp5CRaSl9TotWrVcjz++OPm+YEDB8wQJ/PmzfMYE7FRo0YOq2DIsODCUAnWFMrjiea0zNTUVLNv3B86NEzRokUdx44dcwSrglKmoWbs2LGOKlWqmGNOh+pZvHix6722bds6+vbt63o9f/58R7169cwQRCVLlnTceuutju3bt3uUqw4Rqn8jMz6cy9m8ebPX9/URqOFF8zIUnJMOg7pjxw4zdJL+fnWMZ1+HIdLfuE5fuXKlx/Q2bdo4HnroIUfQmD3b4QgPdzjCwnRDsn7o+zqfzo9zSn+bDzzwgOu1/t4qVqyY5TBa3oYQq1mzpuPBBx/0eXg/PeZ1qLDXXnvNNU2vffX3/+mnnzqC+bykw8m5C8bzkr/wt9Xi43TrmHUnTpwwz4cOHWou9nScRGfQ/dNPP5n33nnnHfOH7tVXX3VYBUF3cOFkY00FpVwDMZ5obpepmjZtasaJDWYFpUxhzXL1V9AycOBAr+8HW9Dis8OHHY4iRXIOuN0Db51fP4dzRm/G6nEzefJkx7///uu46667zM3YPXv2mPc10Hz66add82uQ+uWXX5obP7/++qujffv2jurVqzvWrFnj+q3qTVq9IaQPPX5HjRplnruPS/3SSy+Z7/nqq68cf/31lxnjXJejN6MQnOOv+7pMjdvatWvnKFy4sKNYsWKOSy+91BXbIYiC7rCwMMfHH3/suoPcsWNHR0REhKnR1veaNGniqFy5sgnAu3btampmrIKgO7gUlAs+WK9c81J7Fh8f73jvvfc8pmnArX8c87rMZcuWmXPS77//7ghmBaFMYd1y9VfQctgt2LRE0DJ6tMNhs/kWcDsfOv+YMfm95iHnXLdKcd44GjJkiKNs2bJmWZdffrlj7dq153jLrctfreXmzJnjKldflqkBd2xsrLnpuHr1anMzZurUqY5Tp06ds223Kr8H3RpMO4Nu54/yww8/NH9MtOlV3bp1TbA9ZcoU856VEHQHl4JywQfrlas2X9Nzgf7xcvfkk0+aCyJvtLuNniPXrVtntk3/UMbExJg/kHld5r333msuroJdQShTWLtcz3WT1aAPWvT6rGbNvAXd+jmLXd9ZXUH6rYYKf7SWu+aaa0yLOWe5+rLMVq1aOQYPHhyALUKCj3FinrKXK03gc8stt5gHAMD3jLKaHCarsYJzcvLkSfnkk0/M0I0AgivLtdLfuSZP1EfQ0RFnNm7M/ed0mzdulNQ1yyT8vKZii4gMxNoBluYcytA9KVxehzLURKW+LlMTYGvyuZtvvlkuvvhi2bhxo7kmefHFF6V169YB2lpklKugW/+QAECoOpuMsprxWodYrFChgjz99NOu8URzu8wvvvjCjA6h2VwBICcOu13S9qyRlA2LJXXxHCl2FstKfOs2sccXkfBSVSW8TE0JL1NLwsvqo6aEFa8oNqtmegeCaChDHX/dOZShL8vULO9Kh7fUkTp0xIwPPvhALr/8clm9erUZtQFBFnQ/8sgj8swzz/gcoOudFACwikCMJ5rbZb7//vty1VVXmWAeALyxH90vKRsXS8qGReZ/x4kjZrrtROpZLdcRFS5iT5W0fRvNQ2TOmTcjC0l46RomADfBeJmaElG2ltiKlabSBsjH8df1mkLdfffdrmHYdAhLvc7QVncjR44M2Pojj0G3XjDqAwBClY4n2rdvX2nRooW0bNlSRo8enWk8UT1POv+IaZMuHZNZ7yzr/3qnOeN4ojkt02nDhg2mSdl33313jrcaQDBzpJyS1K2rTgfZiyRt7wbv88WES1rJGAk7dFJs2beS9/yczSaOcvES0bS9pB3cKvbDO0Ts6TVtLimnJG3Xv+bhzlaoWHqtuNaIu2rHa0pY4TPj2gOhwF+t5Z566inX+Ou+LLN8+fLm//r163vMU69ePde45QiyoPuJJ56Q3r17B25tACDI9erVS/bv3y9Dhw6VPXv2mGB69uzZrqZd+gdM+1M56R/KwYMHm+ZdRYsWlSuvvFI+/PBDKV68uM/LdNI70pUqVZKOHTuewy0GEGy0X3na/k2Ssn6RpG5cJClbVoikJnmfOTJGIio3koiq50tEjQvEVvQ7kYGaE8KRq+973RYtp9bGSNdOD0rj+nXFcWSXpB7YIvb9myRN/9dg/MieTMt1nDomqdtWmYc7W7FSbjXip4Px0jXEFl04bzsFCJHWctOnTzfXEr4us1q1aiZYX7t2rccy161bJ126dAnQ1iIjm2ZTEx/oReRHH30UkkH30aNHJS4uThISEiQ2Nja/Vyfk6clEk0KUKVPGI7hBwUa5Wg9lak2Ua/6wHz8sKZuWnmkyfnRfFnPaJKxsTYmskh5kR1SoL7ZwtzqWIwliq99E5OQpsZ1udpodrc8+KSKVtE93eJikpdmlbJky0qNbF+nWpZO0u/QSk9jJWeOedmi7pO3fbB72A1tNzbgj8YDP2xlWouLpGnG32vFS1cQWEeXzMpCO32rwmTp1qmnZ9vbbb7tatn3++eem/7XeaPeltdzmzZvl+++/N32xtVxzWqbSacOGDTNd1HRZU6ZMMf27tU+3NllH4OPEPGcvB5CzcePGyauvvmpqL5s0aSJjx441J0Rv9O6lnmT1RKgn1jp16sjLL78snTt3ztUytWb18ccfl88++8xkvdQkHOPHj89Uaxps26qvzz///AKxrQAQaI60FEnd/pdJgKaBdtru/9KziHthKxIvEVWbSUS15hJRrYWEF47LesHF48TxwUSx3XCzOMLCsg28NeDWb+wpIglmQvq8e/ftk/c/+FjenjhFoqOj5Yp2bU0A3q1zRylf7jyJKOuZmMl+KtHUhKfu3yT2/VtcNeNaC56R/fBO80hZm56d2QiLkPCSVTz6i2tArgG6LSxccsNhT5PUrSvFfuyAhBUrZfZbbpcB5GdrOb12ioiIyFVrOc3Lpct69NFH5dChQ+aa6scffyTgPoeo6fYBNd3BpaDcudU7j3rHcsKECdKqVStzl3HatGmmeY+ue0baR0d/Y++++65JmPHDDz+Yvr4LFy40CS98Xea9994r3377rUyePNkct9q8SPfT77//HvTb+vXXX5tsms47t8G4rbDebxW5Q7kGTtrBbWcSoG1eJpJ03PuMEVESXqG+RGqQXeMCCS9ZLffJyub+JLY+d4icOOm8IPTsw+1wyInTAfePOSwq/HQNuGrcsIFc0/1K6dqpo5zftHGW66XL1wRvGoBrU3kTiOvzg9tM/3CfRERLeOnqmfuLx5b1+r3J/86T49+96tFKwBZbRopc+aRE1b9crIbfqjUVpHINhcqnoz7GiT4H3Vu3bjWd+QsXDr2+NgTdwaWgnGw0ULzgggvkzTffdK135cqV5cEHHzRDRmWk/W10dID777/fNe3aa681zfY0QPVlmXqM6u9Ux3G+7rrrzDzavEiTZeh4jRdeeGHQbmvPnj3NRZIG1lquwbqt8L02KXnzcknYtUniKtSQqOrNLVubFEo1Z6FUrueCXfs7b16WHmRvWJyeoCwLYSWrmmMrsloLiajSxD/NrY8kiHz2udgmvCu2zVtckx3Vq8m/7drKxROnyNE8LFYTO+kwRtWqVJG/Fv8iRYoU8fmzDoddHEf3S+qBzWLXZuoaiGtAfkiTt/mWfd0WXfR0bbiziXotSTu8S07MHO6lL3t6cF70xlcsF3gXlOulsxVK5+CCVK6hUvl01N/Ny6tWreqvdQMsLzk5WZYvXy4DBw50TdMffIcOHUxA6I3ejdNEGe404F6wYIHPy9T39U6hTnPSE5dmuQxUIOrPbdXM3MG8rfCNe22S/pHRuroTFq1NCqWas1Aq10AGB2m7/jsdZC+S1B1/Z84CfpotJlYiqjSViKrNJaJ6CwkvVsr/K1Q8TuSe/uK4u5+kHTwkB3fslJKVKkpYyXg5Ly1Nor/+RuTAwVwvVgNuvYlauWKFXFfW2GxhYosrK1FxZUVqXuix77TZuWfN+DaxJ+zK1OzekZQoqdv/NI+cpX/2+DcvSXjlJhJWpISlAzarCaVzcEGj44vrcGfOkVgmTJhggmFNCuutQkaTzGqFjDNJnAbPc+fOlddff91V+ZTTMjXw1X7rWiHTvn17M48Or6YVMosXL87Xa0P6dAMBcODAAXPRkbEpi77W2lhvtPmLnkzatGlj+tho5knNUKnL8XWZ2tRGM1m6Z8Z2zqPvBfO2zpgxI+i3Fb5dACV+psOhZbgIPrrfTLdSbRLbas1t9Td7wl4zjFd6ArSl4jhpekdnFhYu4eXrpddm12hpamg1AD0ntCl2fLykaa1vfAnzWvuM9rmpl4wZ/7aknj4356amu3HD+jJr2id+G6NbA2HTr7tkFZE6bVzTHanJruRtrppx7S9+bH+ulu9IPCgJr3Y0224rFCu2wsUlrHCc+T/9+en/ixQXW4zbc52u8wdZjaPepEjZvFzCdm2SlOPWbJXCeSl4hVLlk68IuoEgMWbMGHP3Tk8OepGiwajeydO7d6Gwrbfddpu5G4mCSy/ytMbB+1BE6dMSZzwr0Tv/PXfBRIBoE9ikJZ/nsK3DpdD+zWILi9CIwTxMAKIX5/q/Lfz0/zr99LSwM9PSHzaxuc3n+l8TYLnN476s9M/oPJk/ZwIDj8851yvDNNd8NlOLePzbV7LZVpsc/+41iax7meUu6vPCkXxSUraucNVmayCYlbDi5dOH8qrWXCKrni+2qPQs4MHi1ptukNfHjs91wF2rRnX5YeY0KVasqASaNrOP0GHHyngmhHIkHTfBd9q+zZK8foGkbV3h2wK1r/nJBPOw+1rJr7+hGGeg7hmkZwretSZdn0cXDVigbuVWKfp3RlKSxJ50XI5/8zLnpSAVSpVPviLoBgKgVKlS5sJj7969HtP1dbly5bx+Rvsnz5w50ySAOHjwoOn3rE1latSo4fMy9X+9E3jkyBGPE0523xss26p9efROZDBvK9I50lLFnrDHJDyyaw3T6f9T96zPZhij05KOS9JvIXJzJSlRTs3LXcBSMDnEcXSvHJ3YX8LjK4utUFHzCCtU7PTzYukBRsbXEZFihX6iZszsvetd/bJTt60USU32vtCowhJRuXF6ArTqF0h48fISzBo1qC8N69eTf/5bY7YzJxHh4VKhfDmZ980MKRkfL/nJFl3EDJemj7D4SnLch6A7rGxtsenxfOqY6W+fZSK7jLQf+okj5pHzIGzOLwvPIVB3+99Zo66/mxxaDuRX7a9Ds+CnJplh4/ShyfDOPPc+3ZFyMsN8p8SRfPq527LcP5flbyuL89LJn96WqMadTAsJW3hwn3NC3RiLVz75FHTHx8fLO++840pW9Nxzz5mkRw0bNgz0+gEFkt5la968ublL16NHD1fiC32tCR2yo01rdIxGbR7z5Zdfyg033ODzMvX9yMhIM02TsClNLqFDUFx00UVBva16N9PZjydYtzXkAusju0yzTfvB7W7/bxP74V0+JzRC6Ejbtso8fBZZKD0Ij84qSPcSqMe4vRdV5JzUFHrrJ2pPPJSeZdxkGl+c9TjUNpuElz1PIlxjZtcrcLVud9zaWx4fNDTH+fRGaXx8Cfnp25lSvlxwDdsYUamh2IqWyna8cFux0lLs5tEe5aPnQQ3A02u+j4pd/z+R4PrfOd0E6eb/oyLJ6dngc2RPE8fxw+bhe6AeIbbCcV6C8/RpEhMrJ38YnW0LHK0dtsVVEElLzhzweg2M3V4newbDGT8TjE79+p55SHiEhJc6nem+7Hnmfx3aTn/X/uoCgdCsfPJr0J2YmCgnTujAEel0YPZatWoRdAPZ0IyLffv2lRYtWpihDDTD4vHjx13JHzT7ogacOjyCWrJkiRkiQcdX1P/1d6aB5oABA3xepmZPvPPOO818erNMsyhqtm8NQgPZj8Vf2+qezTxYt9Vq4wDbj+x2q7HWwHpbeoB9ZHfuA2ttRu3DZwq16SfhZdL/iBZUafs2pV/I5SD6kj4SHl/F1ISZmhf9X2sMzdjI+n9a+muH8z3nQ6d5vj7zfvr8HvPZ7eIQ92W7L+P0Z53f6bHs9OemFjPTd6f/r8GEff8m/+9E5wX7sfRAKHe9htMDWq3NzDZId70+HdRnCNx1yKmMF9xZ1xTuk8TPnpSw4hXFfmRn1qtVNL1WPLJ6C9N0PCymYI96ctP1PeWJZ4blXNPtcEjpUqWkaFHfM5WfKxpIx7S/V058/XyW88S0uyfTDRFbeITYipQQ0UcuzqsmENffjQbmpxLS/3cP0k8H6PaTx9IDdV8DVnuq6Xuelpj75Hau9Us8IMfevlkKhPBIsUVEi0RGp2fr19+rvo6IFkfqKbHvWef7stJSTYsUfYh875qs54j0QPx0MF5Gg/Fa6ecH5FkoVT75NejW6v0vvvhCLr30UlcqdL341cHVs6MXwkCo6tWrl+zfv1+GDh1q+pFogDl79mxXXxQ9AbgP9aB39gYPHiybNm2SokWLmlpfzeTofqcup2WqN954wyxXTzbu4xMG+7bquIyauCfYt7WgcaRqYL0zPaB2awp+JrDOZagTES1hceVMX9Tw4hUkrIQ+Kkt4fCWRwnFy7N3bcqxNim7Rs8DV9mWkWaWTVszMcVsLtbqxwG+rNrM++k6f7Le1aCkp2nu0SPIJcSQfF/up4+k1hNo8NynR/J/+0Ocnzvyv8+s82sw01yumzYATzeOsLurdgnSJLippJuN11gFmpoA7IloiKjY0/bLNmNnaxN5CNWdlSpeWTpe3kzk/zXf1rXSn52DdXn1vzbr10vqKrqZ5eaWKFSSYRNVuLXLVEDn501sex7L+TjXgNu/7gTZhthUtKVK0pPj6y9dkcOnBuNaia7B+JD1AP5ExSE//33HymKlxzjd6g/V0IOwMgk1QbKadDozN80Kuaa73InVaoTPT9Lnz4fG5qGxzf/h0XoqJlchmV4njwNb0bPeHd56+Aeq2HB22b+tK8/DYRP0bZ8Z9P1MzHl6qKk3UcyGUKp984dM43Xrhrxvj45DeLt5OzgUR43QHl4IyPiFyJ1TKNRDjieoFmxlK59CZmmpnkG0C6wwXGTmK1MC6vFtgXdE8TDBRJD7bgCJ53YJsa5MKXzXEbxe3+Y1t9d+26u/CGYCbhwbTziDdPHcP3DWQP/3c+ZnkEyJpKXKu2OLKSVStS8xQXqbpsj/GzM5ndrtD9h06LGXiS0hYmOdvfNqMr+XG2/pl+oyeCyIjI6RY0WJyJCHBXPdp889yZcrIT9/OkFo1awTnOXjHanEcP2TOZ6b8CuCNMdNKRAN0vbmlfclPJEjqrn8lZdWsHD8bUe0CCStR3iMoTg+GT/9vXmsAXEjCzGsNik9P0/eCZH/l9rxk/lZq/hHNcr9vo9hPjwGvx4JPvDRR10dYbNlzeqOtIF0vvfnmm/Lqq6+6Kk/+97//mfG11WWXXSbVqlUz42mrX375xQwT5l4h89JLL5lm5r4u01mx8/jjj8unn37qUSETqOblvsaJPgXdzqr5+fPnmzbxeufhmmuukcaNG2f7mWHDhokVEHQHl4J0soHvQqFcz2Y8UYdmaz1dY50eUG87E1gn7MlDYF3IBNaayCm9tloD60qmxtpWuMRZXUDohVCga5OCBdsaPNtqagvdAnO71oCbYP30Q2vf3d7XGvn05+m181pLn3G856wU7vq0RNVrJ1aSXdCtF7Fla9aTxETPxGIaYH819SOpU6umtO/aQ3bv2WuGF9PpJYrHydxZ000yNpwbPtX+Fistsf2nBE3gHAznJdO64MBWSd2/Uez70seA11ZhvrbAOddN1EPheqkg8XvQ7a569eomw9xVV10loYCgO7hwsrEmq5drVv1EdVgTpRllI8+7RNIO70xPWObeDFyfH93rc0DgEhVzJrA2NdaVTIBtaqw1+U4A78ybMWK3/y1H9m6X4mUrS2TlRpa5yLNqzVmol6teDqVu/kOOTx+S47xFbnhFIqs0kVAJutW9jzwhEz/42DVmt54/Ppn4jtzQ82rzeueu3dKhe0/ZuHmLq8a7SOEYmT1zmrRq0fycb0+oCqUWOIE8B+v5QP/u6vjvafs3ucaA99ZEPSuBaqJu9eulgiagQXeoIegOLpxsrMnK5aoXBEdGdc1+OC3tu5bb2moVVfhMM3BTa11RwuIrS3iJimKLicvXvqU5XcSjYLJyuYZiTaGv5bpwyVK5tGM31+t3xr4hd/bxTMh14OBB6Xj1dbL63zWnA+8wiYyMkm+mfSLt2lgr0Atmwd4qpSDzaKLuFoyfqybqeo5K3rxcEnZtkrgKNSSqenPLnYusGifmaZzuX3/91af5dHBzAAjlJGZp+zZI0l/f5zx+dXYBd3SRM4G19rWO1xrrihJeolL6OK8WStoEFMQs16HgopYXSNUqlWXrtu3y2ovDMwXcqlTJkjL/u6+l63U3yeI/lklammbGT5YuPXvJ5x+8L1dd2Tlf1j3UaGAdWesiy7ZKyU+avyG8TE3zyLKJugnIN3tvop6LLOrhZWuaURe8dVHTAE47e5zwsYsa8l+earqdmSpzQiI1BIKVa0RDWUEvV+1zrQF26q7/JG3Xf+n/79tg/sD6yhZbTiLK1zndFLyCq8a6oA45ZOUa0VAWCuUaijWFvpTr7Lk/yY6du6Rf31uyXdbJkyfl2ptvkx9/mi92h8NcM+pjyjvjpPf16cP4IPBC4bcazPzZRF1ryFP++9nLHGe6qBF4W7Cm++eff/YaYG/ZskXeeecdc/Gs2eYAwIo0a2zanvWSulsD7DWnA+yNuR/XOoPCnR+zXD9RoCDXFIZKX31fde7Q3qf5YmJiZOZnH0qf/vfJFzO/NsGHPvR1QsJRubdf+vA+gJXpjabwuHLmIbUuylMTdR2BxIxCkqX0utPj37wsEbUvlTALjKRgVXkKutu2bZvle7fddpsZz1sznbdv79vJGQCClSP5pKTuWSdpu0/XXutj/2Yfxre2pSctK1NLwsrUkORl0824q1nOXay0uagHEBw0wOYmWN5FRUXJxxPflti4WHl/ykdmmgbeDzz+lBw9ekyeeuyh/F5FoGA2UfdCW+UceaG1hJepkb7s0qf/L1PDdEcL9RuGBTbozo42C73xxhvNQOfPPfecvxcPAAGjQwel7ll7pnn47jXpAXZOzcBsGmBXSv8Dp32xytWWiLLniS0qxjWL9r+mnyiAUKIZzN8e87rExcbKqLHjXdMHDX9BjhxNkBHDBpOTAjhNu5KFVW4kEZUbuabpjaqkFTPl1M8Tcl6APVXStJJgzzrP6RHREl66enpAXjo9ENfrFe3KZiuA3fkKKr8H3erQoUNy5MiRQCwaAPzCcSpRUnevddVg68N+cGvOw3LZwtL7WpfVALu2CbLNeJyRhbL9mOkHetWQkOsnCiC0aVD9yvPDpERcnAx5YaRr+itvjDVNzd98/eUCmccDOGdN1EvX8G3eoqXSm6hnrChITUqvRNi9xnN6ZKHTwbizZvx0MB5XnmA8WILubdu2eZ2ugbZmNn/11VdNE3MACAb2k8fMH5v02msNsNekB9g5CQuXsPgq6QF2GWcNdi3TNCwv6CcKIFQDh0FPPipxcbHy0JMDXdPfmTTFNDWfNGGsREae3djFgFWZ6wQNqH0YylC7vmmittQDm8V+YIukHdgq9oPbxJ6wO3OlguanOd1tzkNUTPqwZqebp7uaqWswTsuUcxt0V6tWLcudrs0gLrzwQnn77bfzvlYAkEf2EwkZAuz/xH5oR84fDIuQsJJVTFCttddhGmCXrpHnADsr9BMFEKruv+tOKVasqNxx70Oua8bPvpwhRxOPyedT3pdChbJvMQSEolwNZRgWnl57Xbq6x/uu5G2aPd0jGN/jSsbmknxS0nb9ax4eogp71Ii7asZ9HGM81OUp6J44cWKmnauvS5QoITVr1pT69evneYXGjRtnasr37NkjTZo0kbFjx0rLli29zpuSkmL6jk+ZMkV27twpderUkZdfflk6d+6c52UCwcJhT5PUrSvFfuyAhBUrJRFVm1m2RlS3NWXzcgnbtUlSjteQqOrNfdpW+/HDrr7XziRn9iO7cv7C8EgJK1n1dIBdS8LL1TF/oGzh1LQAQCD1uamXFCtaVG68rb8Z7UYf38+ZZ8by/nrqxyYoB+DfLmpZJW8zw50e2i5pB7eIff/pYPyQBuN7vQTjJyRt52rz8Fh2dFEJK11dIjxqxmuadTubYNxhsevgPI3THShTp06VPn36yIQJE6RVq1YyevRomTZtmqxdu9aM3ZvRU089JR999JG8++67UrduXfnhhx/ksccek4ULF0qzZs3ytExvGKc7uBT08Zx9kfzvPDn+3aviOLrPNc0WW0aKXPmk5cZh9HVb7YmHzoyBbYbq+u/0Hdoc6B+aUtVMFnGT5Kx8HQkvWVVs4QFJaQE3jBFrTZSrNZ3rctUxvHvceKskp6SYv+vh4WHStFEjmT3jc4mPLxHw7w8F/FYtWkmx/W85sne7FC9bWSIrNwpIIGqGRtVg/MCZYDzt0DZxHNVg3De2QkU9EreFnw7Ktal8TsF4QboO9jVODKqgW4PiCy64QN58803zWk/ClStXlgcffFCefvrpTPNXqFBBnnnmGbn//vtd06699lozPqQG43lZpjcE3cHF6kG3nmgSPxuQ+Q6jpJ+git74StCdcPy/remiGnYUR2qSCbbdT7xZcmXoPF2D7QywC/Cd0YKMCz5rolytKT/KdeGSpXJlzxvlxMmTkpaWZrKd165VQ+bOmi7lypY9J+tgZfxWrSk/y9WRrMH4Nkk7oEOabTH5cXRYM8ex/T4vwxYT6zak2ZmgXPPcaDBe0K6DfY0T81zVo02133//fVmxYoX5Eg2E3OlOmzdvns/LS05OluXLl8vAgWcSbGhA1aFDB1m0aJHXzyQlJWXq/6MB94IFC/K8TARebpv7a+uEt956yyTwK1WqlLmx8sgjj7je1z/Uzz77rLnRosvUmzE6XvzgwWeGItHX2g3BXadOnWT27NkSbHcw9c6e9yA0fVrijGcletd/YrMV7BsODoddkhZPzTLgVsmr52S9AJN18/RJ21mDHV+ZABsACoiLW7WUX2Z/LR26XysJR4+av+frN2ySiy/vIj99O1OqVa2S36sIwI0tqpBEaM6bcrU9pju06flBDca3mKFWtb+4Cca9JH9znDwqqdtWmYfHsgsXl7BS1U9nWc/qOtgmx797TSLrXlbgrvfyFHT/9ddfctlll8nJkydNP+q///7b9OPW7OXat1r7dWttcm4cOHDAnGzLZrizqa/XrMmQ4t4taBo1apS0adPGfKcG+dOnTzfLyesyncG8PtzvYChn3yPknTb31y4A48ePN60QxowZY8rxv//+89rc/5NPPjEtEt577z25+OKLZd26dXLHHXeYY0+Dd/XSSy+ZoHzSpEnSoEEDWbZsmdx5553mbpO2aFDaoEO/R/MROEVHRwddeWq/5hxrdJOOS9KvZ7YjJGgmzdI1JMxZg122roTFV8x040FPxw570DTeCXl2h8P89vR/Ca6fGs4C5WpN+VWujRo0kN/mfCsdul0j+w8ckNS0NNm5a7dc3KGLzJ01Q+rWPu/crYzF8Fu1pqAs14gYCStbxzzcs+M4ko6nB+MHNZu6Jm9LT+BmhjbLwHHiiKRtW5nDFzlME/fkzcslsnoLCQa+xhJ5Cro1CCpatKisWrVKChcubIIlDZ7at29v+kvfe++98vHHH0ug6Xf279/f9OfWGk0NvG+//XaPwCovNDnb8OHDM03fv3+/nDp16qyWHeq0hvvmm2+Wrl27mte6n7/55htT2+0MkN399NNPpnuAtk5QTZs2le7du5vAWpuYa8uF+fPnS8eOHc18Sm/C6EOHr+vVq5eZ5iw39+bomohPlxEUkk9I2LqfJWz5VCnY9df+l9LiFklr0MWMj+3hcEI+rRF8pTdAEhITzd0QG00bLYNytab8LNfi8fHy1RefynW9+8rOXbtM4H3g4CFpfcWVMvXDSdKoYYNzuj5WwW/VmgpcuRYqJ1JRHxedmZZ0XGwJOyXs0DaxHdkhtoQdEnZkp9hOHvFpkQm7Nom9SHC0hDl27Fjggu7ff/9dBgwYIFWqVJFDhw55RPnXX3+9ad795JNPyi+//OLzMrXZsPbl2bvXs4O+vi5XrpzXz5QuXVpmzpxpAqqDBw+aZsV6Q6BGjRp5XqbS5uhaG+te06019/p99OnOO23ur60ktNm3e632FVdcYVpLeKvp1hs52nphy5Ytpgn6pk2bTDDdo0cPV59ubXWhyfS0pUXt2rXlzz//NEH5a6+95lqmdkPQRHuNGzc2WfbbtWsnzz//vJQsWVLyU9reDZL0xxeS/Nd3JvD2VdSld5qa34Isbf8mSf7t/Rzni6veSCLyuZyQN+YuvE2ktPY7YzgRy6BcrSm/y1X7py6c+510vPo6Wbt+g2mpmJiYKNfedKt888Vn0vqiVud8nQq6/C5TBIY1yrWESPlKmn3LY2rKxsVy8qtnc/x0XIUaEuljQuxA83WowzwF3RpgO5tsFy9e3AS2zuBbNWrUyPT3zo2oqChp3ry5aSKuAZXze/T1Aw88kOPGVqxY0dRcfvnll3LDDTec1TK12bE+MtIAz4qJu84VPUb0j2j58uU99qPeANFs8t727S233GI+pzXX2pQmNTVV7r77bnn44Ydd5aE3SfQuk3Zx0GNRv+PFF1+UW2+91bWcLl26mL7g1atXl40bN8qgQYNMbbv27dfPnEuO1BSTJCLpj2lmKIRMwiJE7KlZft4MD3HBtQWuL0tGjmrNJGXlV177+7hva3pmzoL6RyXE2dPze+hFAUl8LIRytaYgKNfy5crKr7NnSZdre8mKlX9Kmt0uJ0+dks49rpPpn34gnTu0z5f1KrCCoEwRABYu16iaLeVU0VLZXBvaTBbz9KFlgyMm8zU2zNPaauCyefNm1xfp67lz57re1yG7NBjPLa1d1hpLTXilfXy1mfrx48dNk3GlQ3+5J0VbsmSJqQXV2s/ffvvNjM+tQbXWwvu6TAQ3bTo+YsQI0wdck/ZpeX/33XemL7/T559/brozaP9vnUfLWmu53ROn3XjjjXLVVVeZG0J6A0abtP/xxx9m+edK2pHdcmLum3Lk9S5y/ItBngF3RLTJ1F301jelcLczx7g3Oh5jQQ+4lW5DTPt7Q2JbAQC+KVGiuMybNV3atr7EXGPqdZ0OK3bVDTfLtBlf5/fqAci3a0Ob+bfIlU8UyGvDPNV0a/9Z7buttYlKA9nHH3/cBL9aG6mBjL7OLe1/q/2mhw4darJQa/9dzS7trFXX7NXudxO0Wbk2Vdbv1T7mV155pXz44YceAX9Oy8S5k5fm/kOGDDE11v369TOvNWjWWm2t7dZgXI8H7cqg3Qo0sHbOs3XrVtM3v2/fvl6Xq10QdH02bNggl18euGEHHHa7pGxcZJqQp6z9TSd4vB9WoqJENekqUQ07SVihoukTy54nctUQOfnTWx53+kwNd7t7JKp2a7EKsy0hsq0AAN8UKVJEvvniE7nx9v4y67sfzLWltmK76fb+ciwxUe64tXd+ryKAc31tGKvjdD8RVMOFBTzo1rGxb7rpJtOcOzIy0gzfpLXH2rRbgyoNlLT5bl5os++smn5nrJVs27at/Pvvv2e1TJw7eWnuf+LEiUzNNpzNwZ1DzGc1T3bZBHfs2GHyAGhT90CwnzgiSSu+lqRlX4j90A7PN8PCJaJGK4lu2l0iqjZzDWuW8YQTWesiSd2x2mR41LELIyo1LJB39nLi3NaU7X/Lkb3bpXjZyqeblFtvWwEAvtFuftM+mCj9HnhEPvz0c9ff/f4PPGJuvj983935vYoAAiTKeR28dYXYjx+WiPJ10q+ZC/C1YZ6Cbk1EpcGTkwYNWuOsDyA72txfa59btGhhEqPpGNwZuxBo/3ytpVaaqVybkjdr1swMMaY108OGDTOtLZzBt86jrS40sZ8OGbZy5UrzGR1aTGkiFs2Srn26tUZd+3RrF4RatWqZYcT8xdyJ37FaTv0xLX186dRkj/c1cI5q1EmimnST8GKlclyenlgiqzSRUKDbGlG5iclEGRFfgj7cAACJiIiQieP/J7HFism4d87kCnps4BA5fCRBhg180uuNawAWuTas1EgDTQmP16RrBVuegm4gr3LbhUBv5Dhv6ugY8JpBvlu3biaRmpMON6atK+677z4zBJhmsdfm5/odSoNzzZqufbw1w7m+r0G7Zi/3ljAvtxzJJyX57x/k1NLPJW135vHfwys3lugm3SSydusCfYcOAIBzTa8JxrwyQorHxcmLr57J5/L8y69JwtGj8vqI50hyCyDo2RzONrrIkg4ZFhcXJwkJCQwZFgS02bgG184hw/JL2oEtcmrpNEleNUscpxI934wuIlH12ktUs+4SUbJqfq1igWK3O2TfocNm2BirZeMMVZSpNVGu1lQQynXU2PHy5GDP4YT69r5R3hk7ytSKw1NBKFPkXiiVqyPlVNDXdPsaJ3KGAnLBkZYqKWt/McF26qalmd4PK10jvVa7fnsJi4rJl3UEAMCKHnvwPilWrJjc+8gTrrwuH3w61dR4fzLxbb+0XgOAQCDoBnxgP7pfkpZPl1PLpovj2H7PN8MjTdPxqGZXS2SFevm1igAAWF7/2241fbxv7X+vafmmwffX3802Q4pN/2SKyXwOAMGGoBvIgv4hT928zCRGS/nvZxF7msf7YXHlJKrxlRLZqIuEF6bbAQAA50Kva3tIsWJF5dqb+0pqapoJvn/6dYFccfV18v2XUyUujr/JAIJLnjrEPvfcc7J69eos3//nn3/MPEBBZD95TE4t/lQSxl4rxybfLSn/zD0TcNvCJKL6BVK453NSrN8kKdSqFwE3AADn2JUdO8jsGdOkUKFoCQ8PM4H3shWrpG2X7rJvf4YWaQBQEIPuZ5991mSDzooG5DpEE1CQpO5eI8e/el6OvNZJTnz3qtgPbHG9Z4uJk+gLrpdi/SZL0WtfkKgarcRmI1sqAAD5pW3ri+Xnb7+SokWLmpFK0tLS5N8166T1FV1l+46d+b16ABDY5uWHDh2SqKioQCwa8CtHSpIk//OjSYyWtuPvTO+HV6gvUU27SVSdNmILj8yXdQQAAN61OL+pLJjzrbTv2kMOHT5iAu+t27bLxR26yE/fzpTzatbI71UEAN+D7l9//VXmz5/vej19+nTZsGFDpvl0HOSpU6dKo0aN/LeWgJ+lHdohSX98IUkrvxbHiSOeb0bGSFTdyySq2VUSUYY/1gAABLP6devIwrnfy+XdrpGdu/dIalqa7N23Xy7pcKXMnfWlNG7YIL9XEUCI8zno/vnnn11Nxm02mwm69eFN/fr1ZezYsf5bS8APHPY0SVm3wATbKRsWaqY0j/fDSlaRqCbdJKpBBwmLJvspAAAFRY3q1eT3ud9Lh+49ZcOmzabG+0hCgrTp1F02/r1MSsbH5/cqAghhPgfdAwYMkAceeMBkdC5TpoxMmDBBrr32Wo95NBgvXLiwFCpUKBDrCuSJPfGQJK2YKUnLvhT7kd2eb4aFS0StiyW66VUSUbmROYYBAEDBU6F8OfltzjfS8err5O/V/5ppNWtUk2JFi+b3qgEIcT4H3TExMeahNm/eLKVLlzYBNhC0w31tW2VqtbXPtqSlerxvK1Zaohp1lqgmV0p4Ee5+AwBgBVqjrcnVetx0q+w/cFDmfPUFeYYAFMxEalWrVvX/mgC+NhHfvFzCdm2SlOM1JKp6c7GFhZ95P+m4JP31vSQt/VzS9mbMOWCTiKpNTRPyyFoXeXwOAABYQ2xsMZn3zQxzAz4sjJFGABSQoLt69ermpLVmzRqJjIw0r3Nqhqvvb9y40V/rCUjyv/Pk+HeviuPoPnPgHheRE7FlpMiVT0pYyaqS9Mc0SfrzO5EkfecMW6FiEtmgg0Q36Sbh8ZXybf0BAMC5odehdBkDUKCC7rZt25oTl/NuofM1cC4D7sTPBmhdtsd0DcATP3vS62fCy9WWqCZdJapee7FF0LQMAAAAQJAG3ZMnT872NRDoJuVaw50x4PYqIloi67SRaB3uq1ztc7F6AAAAAJAlOrog6KVuXWlqtHMS2eRKib37YynS5QkCbgAAkO+Gj3xFzm/dzhLfc/u9D8o1vfvk6jO/L14iTS5qK9ElK+T6s4CV5DnoPnr0qLz00kvSqVMnadasmSxdutRMP3TokIwaNUo2bMiYxArIG/uxAz7NF1mpsYTFFAv4+gAAAGvYvmOn3Hn/w1KpTiMpVKqiVG94vjzy1DNy8NChXC8rPK6MzPzmO49pjz94n/z49ZcS7HTds3toUD/6pRdl0vixuVruE4OGSZNGDWTjX8ty/dns/Pr7Irmq1y2m3Lztd6WJ9Ia9+JJUrN1QipStIh2vulbWb9zkt3UAAh5079ixwwTaQ4cONc//+usvSUxMNO/Fx8fL22+/LWPH+u+HhdAWVqyUT/PZGPoLAAD4aNPmLdLysitkw8ZN8vH7b8u6lUtk/Buvyk+//CaXdLhSDh06fNbfUbRoUTOMWbDbue5v1+ONl14wGeDdp+nNg7i4WClePC5Xy924eYu0a3OpVKpYIdefzc7xEyekScMGMva1l7Kc59XRY2Xs2++ZMl0073spXKSIdLnmBjl16pTf1gMIaND95JNPyrFjx2TVqlXyyy+/mDtJ7nr06CFz587Ny6KBTCKqNhNbbJls59FxtyMqNTxn6wQAAAq2B5542ozhPXvG59K29cVSpXIl6XLF5TLnq2myc/ceGfz8CNe8NRo1lxdeeV1633G3FCtfTSrXbSzj333f43117c23mZpX5+uMzb6dTbRHvjZayteqL/FVasnzL78mqampMmDws1Kqam2pUq+JTProU491fXroc1L3/AulaLmqUqtxCxn6wkuSkpKS7fa9/8FH0rbjlVK0XBWp3+JieevdiVnOW65sWdcjNjbWJEx2n6Y3DzI2L7fb7fLS62OkZqMWpia52SWXyRczZ5n3tmzdZvaDthjod//D5vnkjz8z763+9z+58tobJbZCNbMP+tx1nxw4eFByQ8vp+SED5ZruXb2+r7HJmLfekWeeeFSu7tpFGjdsIFMmvCm79uyVmd98n6vvAvIt6J4zZ4489NBDUr9+fa9ZzGvUqCHbt2/3x/oBZjztwl28Zyh3iml3D+NuAwAAn2gt9px5P8u9d94uMTExHu9pkNn7+mvl8+lfeVQsvfa/caZ2dflv8+SpRx+SR54aLD/+NN+8t+TnH8z/74//n6kZdr725udfF8iuPXtk/vdfy2svPifPjnhFut9ws5QoXlwW/TRb7r6jr9z7yBOyY+cu12eKFSsqE9/6n6xe8pu88fKL8t6UD2X0uAlZfsfHn39hAv6nHn9E/l7ym7wwdJAMffFlmfJJeuDrDxpwf/jZ5zL+jVfk78W/ysP33WMC6F8WLJTKlSqa/aA15lpzrs979bxajhxJkA7dr5VmjRvJ0vk/yndfTpW9+/bLjX37u5arwbkG6Wdj85atsmfvPrn8sjauaVpT36rF+bL4j2VntWwgYNnLMzp58qSULl06y/e1Fhzwp/Asarq1hlsD7qjarc/5OgEAgIJp/aZNJqCuW+c8r+/Xq3OeHD5yRPYfOCBlTl/zXtyqpTz12EPmee1aNeX3xUtl9Pi35Yr2l0npUuld4YrHxZqgPTvxJUrImFdGmKF465xXS14b86a5th74xCPm/acfe1hefuN/smDRErnxumvMtGeefMz1+WpVq8i6B++TqV/OlCcfedDrdwwf8Yq88vyz0r59OykTX0JqVq8m/61dJ+9O+kD69r5RzlZSUpKMHDXGtAq4qOUFZlqN6tXk90VL5J1JH5iWA7oftHJOa86d+2TUm29J08YN5cVhz7iW9f64MVK1flNZt2Gj2a9xscXMfjkbe/alJ+AtW8bz+lHLUoNxoEAE3VrD/euvv8rdd9/t9f2ZM2eaPt+Av5xa9oXreeT518jxohWleNnKElm5ETXcAAAgTzJ2kczORS1bZHo9Zvw7uf7O+nXrmIDbqWyZ0tKgXl3X6/DwcNMPXAN+Jw2w33z7XdNHOvH4cUlNTZPYYt6Txx4/ftzMd9dDj5mg19koVT+jAa0/bNi0WU6cOCGdelzvMT05OcXUYmflz7//kfm//W6almek66xBtzYZz6rZOBBSQfcjjzwiffv2lcaNG8v111/v6tehGcuHDx8uixYtki+/DP5MjSgY7CePSvLfc9JfRBWW6Iv7yLHEUxIRX0JsYZm7NwAAAGSnVo3qJiBds3a9SPfM7/+3dr1p7u2swfanyEjPy29dj8jIyEzT9NpaLVr6h9za/155duAA6Xh5O9NMeuqXM0ytsTcalKsJo1+TmrVqScniceK8XNKA3h+c3zHr80+kYvlyHu9FR0dn+7lunTvKS8OHZHqvfLnsWwjkRrnTNdx79+3zWO6+/fulSSNyAKGABN233HKLbN26VQYPHizPPJPePKRz587mbqHeuRsxYoRJpgb4Q/Kf34qkJpnnUfXaSViU9r0i8yQAAMgbrUnu0K6tvPX+JHnk/rs9+nXv2btXPpn2pdx64/UeuYsW/7HcYxn62r15ugbOaWlpfl/XRUv+kKqVK8mgJx91Tdu6bUeW82uT6grly8nmrdukQ4fLTfPyMD9XUtSvU8cE19t27DBNyX11fpPGMv3rb0wT+YiIPIUhPqleraqUK1vGZKJverrm/ejRY7Jk2Qq5+47bAva9QFbyfLRrsH3rrbeaGm2t4da7cTVr1pSePXuaRGqAP+iNnFN/nGlaHtXUy+1oAACAXNLhplpf0VW69Owlzw0eKNWrVpF/1qyVp4YMN7W3LwwZ5DH/wiVLzTBUV3e7Uub+PF++mPm1zPr8Y9f71apUTh9u7MKWEh0VLSVKFPfLetaqWUO27dgpn30xQy44v6l8N2eu13Gp3Q0bOMCMNx4WHi7XXtVNUlKSZdnKP+XIkSPy6AP3nvU6aWI3HUbs8YFDTQzQ+sJWknD0mNlH+l5W/cbv63+HvDflI5MF/smHH5D4EsVNU/Wp02fKu2PfMDXxM2Z9K88Mf1H+XbYwy+/XoYr1c06aLX3VX3+b/vKahV5vljx8713y4qtvmP2nZTv0xZekQrmy0qNbl7PefuCcBN3aZ1trsqtUqSKPPnrmrpu7p556Sl5++eW8LB5wSd26Quz700+q4RXqSUTp6mK3+97/CgAAwJvzataQpfPnyLMjX5Ebb+snhw4fMbWjOsTU0KefkPj4Eh7zP/bAvSZwfe7l1yW2WFF5fcRz0qlDe9f7r744XJ4YNNQElRUrlJdNf3vWjOfVVVd2lkfuu1seenKgJCUnyZUdr5DBAx6T4S+9muVn+vW9RWIKFZKXR4+VF15+TYoULiyNGtSTh+69S/zlucFPS+lSJeXlUf+Tu7dsleJxcdKsSSMZ+Hh6QjhvtAb+tznfmCHQOl9zgyQlJ5tafN2Pzn7uGryvXb8h2+/Wcri8W3qSOfX4oKHm/z69e8mkt8aa55pkTsfzvufhx+VIwlFpfWFL+W76VClUqJCf9gDgO5sjNxkkTtODVQNvbVLuzT333CPvvvtuQJrY5IejR49KXFycJCQkmAyMOHcSpw2S5L9nm+cxnR+X6IYdTdC979DhgDSXQv6hXK2HMrUmytWaKNfs6bjbWnP68H3ekwgHI8rUmkKpXB0ppzTBgYTHV5KCHifmaZzuPn36mGbk8+bN85iuzUt69+5tAu5x48blZdGAi/34YUn+d655bitUTKLqXpbfqwQAAAAAgW9e/s4775jx+a6++mr5/vvv5dJLL5Xk5GSTyVxff/DBB3LzzTfnZdGAS9LKr0XSUs3zyPqXiy0iKr9XCQAAAADOTSK1SZMmmcC7a9eu8sUXX8grr7wiv//+u0ybNs0E48DZcNjtkrTszLBzUU265ev6AACA0OWv/tkAQlOeg25NdvDxxx/LddddJ126dJEiRYrIt99+K+3bn0koAeRV6qalYj+UPhxGeKVGElGycn6vEgAAAAAEJugeNWpUlu+1atXK9O3WpGqrVq0yD6Wp+rPKbA7k5NSyM8OERTfpmq/rAgAAEAwmf/yZPDZwsBzaln12b3d33PeQJBxNkBmffJCn7xw+8hX56tvvZcWCn/P0eQA+Bt1PPPFEjvNoE3N9OBF0I6/sR/dLyppfzHNb4eISWfvS/F4lAABgQXv27pWXRv1PvvvhR9mxa7fExRaTWjWqS+8brpO+vXtJ4cKFJZj06nm1XNnx8nP6nToe9wN393O9vv3eB+VIQt6C+EOHDstDAwbKN7PnmFazPbt3k9EvvyBFixbN8jMbN22WJ4c8K78vWmqGTOt0eXv536sjpGyZMq55Rrz6hnw350dZ9fc/EhUV6fWmxLz5v8qwF1+Sv//9zwyh1uemXvLC0EESEZE5HNqwcZM0b3O5GTc8Nzc4gLMKujdvPjP4PBBoSSu+ErGnDzcX1eAKsYXnuRcEAACAV5s2b5FLO3Uz40u/MPQZM451dFSUCcrenfyhGWtbx8gOJjExMeZxLmlAnHVInDu39L/X3Oj4YeY0SUlJkTvve1jufvgJ+fj9CV7nP378uBnPu3HDBjJ3Vnqun6EvviRX97pVFs773jW2d3JKslzX4yq5sGULmfjhJ5mW8+ffq6Xb9b1l0BOPyOQJb8rO3XvkvkefNMMb6/jq7nS9br7zHml90YWyaOkfftpyhDqfhgyrWrVqnh5AbjnsaZK0fPrpVzaJakrTcgAA4H/3P/6UqeVcOn+O3NDzaqlXp7bUqF5Nru7aRb6Z9ol079LJNe+RIwnS/4FHpWyNelK8Ug3p0K2nCeTcm2Cf37qdfPjZ52ZM7xKVa8pNt98lx44luuaZPfcnadOpm8RXqSWlq9WR7jfcbGpxneb/9ruEx5Ux3+W06q+/zbQtW7e5mpfr5929+OooKVezvsRVrG7WceCw5826ZOWP5SvNdrzyxv9ytW3O5x98MlW+/na2WS996Hr74r+16+SHuT/JO/97Q1q1aG6C2jGvjpSpX86QXbv3eP3M74uXypZt22XSW2OlUYP65jH5rTdl2cpV8tMvv7nme3bQU/LI/fdIw/r1vS7n8+lfSeMG9WXIU09IrZo1pG3ri+Wl54bK+PcmeZSRGvL8SKlTu5Zcfw2JoeE/eRqnGwiUlPW/iz0h/cQbUbWZhMeVz+9VAgAAFnPw0CH58af5cl+/O0wyYG+0q6TTDX3vlH0HDsi3X3wqf/wyV5o1aSRXXHWdaS7ttHHzFvnqm+/l66kfyddTP5Zff18oL58ObJ21thoYLv15jvz49RemlvbaW24Tu92e5+34+PMvZMRro2Xk8CFmvapUrigTJk7Ocn4NVDtdc708P2SgDHj0IZ+3zb2puQajnTq0l53r/jaPi1tdYN5r37WHaXqelUVLl5lWBS3Ob+qa1uGyNmY/LFnmPTt8UnKyKYfo6DPDxhYqFG0+8/viJT7uJV1Okvmcu5hCheTUqVOyfNWfHvvni5lfy5uvvezzsgFf5KndbvXq1T1ORBnpe4UKFZJKlSpJu3bt5O6775YSJUrk5asQYpL+OJMXIIoEagAAIAA2bNosDodDap9X02N6mep15VTSKfNcA3KtDV2waLH8sWKl7Nnwr0RHpwdu2iRZk4t98dUsuev2Pmaa3e4wNbLFiqU3xr6l1/UetbHXXt3d47veHzfa1C7/u2atNKxfL0/bMe7t9+WOW3vL7bfcZF5rTa7eTEg8fjzTvDNmfSu33fOAqWnudW0PM83XbXNvah4TU8gEseXKlvV4r3KlilI+wzR3e/fukzKlS3lM05YG8SVKyJ69+7x+5sILmkuRIoXl6WHPy4tDB5kyG/jsC6ZZ+O49e33eTx3bt5Mx49+RT7+YLjdcc7X5vhdeed28t3vvXteNGE0698E74yU2tpjPywYCVtPdtm1b86PbsmWLFCtWTJo1a2Ye+lyn6Xv169eXffv2yaBBg6RRo0b0C0eO0o7sMjXdyla0pETWuii/VwkAAISQxT/NlhW//SQN6tY1tazqr9X/SGLicSldvY7EVqjmemzeus30C3eqVqWyK+BW5cuVlX3797ter9+4SXrfcbfUatzCNOPWZuhq246deV7ftes3yAXNm3lMy/haLV22Qnr17SdT3h7nCrhzs22+0GWPeHaw+FPpUqVk6uT35Jvvf5DYCtWlROVaJonb+U0au/pz+6Lj5e3kleeHmX7cMaUrSd3mF0mXKzqY98Js6cu568HH5KbrekqbS7j+RJDUdPfo0UNmzZolv/zyi1x6qWdmaZ127bXXyogRI6Rr164yf/588//AgQPls88+89d6w4KSls8UcTjM86iGncUWFp7fqwQAACxIM5Rry8x16zd6TNc+3Uprc500KNUA+qdvZmRaTvHica7nkZGel9W6fPvp6xp1da9bpErlSvL2/0ZJhfLlTLPyxhe2keTTwb0ziNTaXKeUlFQ/bK1IzerVpGR8vEz66FPp2ukKiYyMzNW2+UPZsmVk3/4DHtNSU1Pl0OHDUq7smUzk3gLm9X/+IQcOHpSI8AizXhXOayC9qp25eeCLRx+41zTv1xryEsXjTF/xQcNfkBrV0vNQ/fzbApn1/Q/y+tjxrnLQMoqKLy8TxrxuWhQA57Sme+jQofLggw9mCridteD333+/qeFWl112mWlePnfu3DyvJKzPkZaSHnQrW5hENemS36sEAAAsSgPQDu3ayrh33zd9rbPTrElj0xxZm0JrEi73R6mSJX36Pm26rLXSzzz5mFx+WRuTtO3wkSMe85QuVdKjubNa5ZbQzJs659WSZStWeUzL+Npsb8l4k/1bE7f1uq2fydCd122LioyStLTc90O/qGULU0u9fKVnH2oNbDWxWk50fTTg1s9o8N49D5nl9UaI3vDQDPCffTHdNIk/v2lj897vP34nKxb85HoMH/SUabmgz6/pdmWuvws466B7/fr12fbRjo+PN/M41atXL8cTGkJbyppfxZGYfvczovoFEl6sdH6vEgAAsLBxr79salpbXtZRpn4502TX1sD4o6nTZM269RIenn6ZrMG5Bow9b+4rc+b9bDKJL1yyVAY/N8JrgOtNieLFTaD/7uQPzBjQGjg+MWhYptp3DQKHj3zVNEX/9ocf5Y0338p2uffffacZImvKJ5+Zz2gm87/++Vdskjn3UpnSpU3gvXbdBtPMXbc9L9umzej//udfs6+09tkZwPe9+34Z9OwLWa6r3mjQBGx3P/SYLF2+wiRCe+jJgdLr2mtMIKx27tot9VtcbN530tr5xX8sMzcMtGy0mfwj999tbjg4bdu+w2R6375jh+nvrc/1kZh4JjP5a2PeNOv9z39rTH/ul98YK6NfftGMxe1cP+1b73xUqFDetD7Q5yVKFM+2HICABN01atSQKVOmyMmTJzO9d+LECZk0aZJJtua0a9cuKV2aIApZO+WeQK1pt3xdFwAAYH01a1SX5b/9ZGqenxn+gjS7pJ20vOwKk5xMs3Q/98zTrtrRb6Z9KpdefJHcef/Dpj+wBq1bt2+XsmV8u77V4O2TiW/LilV/SeOL2srjg4bIy897Bt3a5FvHq167fr00vfgyeXX0WHlu8MBsl3vzDdfJ0489JAMGD5cWbS6XzVu2Sd/eN2bK1O2kyc/mzpouq//9T27pd6+pZc7ttvW77RapXaum2VeaCE6H9VLbd+z0qKX35qN335I6tc+TK6661oybfclFreTtMa+53tcAXoP5EyfOxBjr1m+Qnr37SoOWreWFl183Y22/+oLn2NrDRrwszS+9XJ4d8YppMq/P9bHMrVZ99o8/SdsuV5mbLN/9MFdmfPqB9KAGG+eIzeHeccRH06ZNkxtvvNFkJ+/bt6/UrJme+XHDhg3ywQcfyM6dO+XTTz+V66+/3txtqlu3rrRo0cJMK4iOHj0qcXFxkpCQILGxsfm9OpaTdnCbJIxJ75djiy0rsf0ni+10UgtvNDvovkOHpUx8CQkLyzqLPgoWytV6KFNrolytiXK1jo5XX2f6SE+eMI4ytaBQ+q06Uk7pXS8Jj68kBT1OzFMiNQ2mCxcubJKjvfCCZzOShg0byrhx46Rbt/TaSo3ptT83Q4YhK0nLvnQ9j27UOduAGwAAAGdamL49cYpJNqbNpLWf8rz5v8oPM6fl96oBONugW2lGcn3s3r1btm7daqZVrVpVypcv7zGfJmbQ6YA3jtRkSVo5K/1FWIRENiaBGgAAgC+06fv3c+bKiNdGy6mkJKlTq6ZM+3Ci6autNaIACnjQ7aRBdsZAG/BV8r/zxHEiPXtnRM0LJbwILSIAAAB8oVm453x9psUggAIcdGs/bXXrrbeaO2rO1znp06fP2a0dLC/JLYFadNPu+bouAAAAAJAvQfdtt91mgm1NnhYVFWVe50TnJ+hGdlL3bZTUrSvN87ASFSWiSpP8XiUAAAAAOPdB9+bNm83/GnC7vwbORtIfZ5pDRTXuYm7UAAAAAEDIBd0ZE6GRGA1ny5F8UpL//Cb9RXikRDXslN+rBAAAAAB+x9hMyBfJq+eI41SieR5Zu7WExTD+OQAAAADryVP28vbt2+c4jzYVnjdvXl4WjxBwyr1pOQnUAAAAAFhUnoJuu92eqf9tWlqaGa97+/btUqtWLalYsaK/1hEWk7rrP0nbudo8DytVTSIq1M/vVQIAAACA4Am658+fn+V733zzjdx1110yatSos1kvWFjSMvcEaleSQA0AAACAZfm9T3e3bt3klltukUceecTfi4YFOJKOS9Jfs9NfRBaSqAYd8nuVAAAAAKBgJVKrWbOm/PHHH4FYNAq4pL++F0k+YZ5H1WkjYdFF8nuVAAAAAKDgBN2pqany+eefS6lSpfy9aBRwDodDkv74wvWaBGoAAAAArC5PfbrvuOMOr9OPHDkiixcvlj179tCnG5mk7fhb0vasM8/Dy54nEeVq5/cqAQAAAEDwBd0//fRTpuRX+rpEiRLSunVr6devn3Ts2NFf6wgrDhPW+Mp8XRcAAAAACNqge8uWLf5fE1ia/eRRSV49J/1FdBGJrNcuv1cJAAAAAApmIjUgo+RV34ikJpnnUXUvk7ComPxeJQAAAAAIzppup5SUFFmzZo0kJCSI3W7P9H6bNm3OZvGwUAK1UyRQAwAAABCC8hR0a4A9cOBAGT9+vJw4kT78kzdpaWlns26wiNQtK8R+IL1LQniFehJRunp+rxIAAAAABG/z8hEjRsirr74qt9xyi3zwwQemJvOll16SCRMmSOPGjaVJkybyww8/+H9tUSAlLXOr5W7cNV/XBQAAAACCPuiePHmy3HDDDfLWW29J586dzbTmzZtL//79ZcmSJSaTuWY4B+yJhyT533nmua1QMYmq2za/VwkAAAAAgjvo3rFjh7Rv3948j46ONv+fOnXK/B8VFWVqwD/88EN/ricKqKSVX4ukpZrnkfUvF1tEVH6vEgAAAAAEd9BdsmRJSUxMNM+LFi0qsbGxsmnTJo95Dh8+7J81RIHlsNsladl01+uoJt3ydX0AAAAAoEAkUmvWrJn88ccfrtft2rWT0aNHm+maZO1///uf6deN0Ja6aYnYD+8wz8MrN5aIkpXze5UAAAAAIPhruu+66y5JSkoyD/Xiiy/KkSNHzBBhbdu2laNHj8rrr7/u73VFAeM+TFh0ExKoAQAAAAg9earpvuqqq8zDqX79+rJx40aZP3++hIeHy8UXXyzx8fH+XE8UMPaj+yVl7a/mua1wCYk8r3V+rxIAAAAAFIyabm/i4uLk6quvlm7dup1VwD1u3DipVq2aFCpUSFq1aiVLly7Ndn5t1l6nTh2JiYmRypUry6OPPupK6qaeffZZk03d/VG3bt08rx98k7Ripog9fZz2qAYdxBaep/s7AAAAAFCgnVUkdOzYMdm6datJmqZjdWekzc1zY+rUqfLYY4+Z8b414NaAulOnTrJ27VopU6ZMpvk/+eQTefrpp2XixImmdn3dunVy2223mcB61KhRrvkaNGggc+fOdb2OiCAADCRHWuqZBGq2MIlqSgI1AAAAAKEpT9HnwYMH5YEHHpAvv/xS0tLSazPdaQCuga+397KjgbKO9X377beb1xp8f/vttyao1uA6o4ULF8oll1wivXv3Nq+1hvymm24yY4W70yC7XLlyudxK5FXK+t/FfnSveR5RtZmEx7HvAQAAAISmPAXdGhjPmjVLHnroIbn00kulRIkSZ70iycnJsnz5chk4cKBrWlhYmHTo0EEWLVrk9TNau/3RRx+ZJugtW7Y0w5Z99913cuutt3rMt379eqlQoYJpsn7RRRfJyJEjpUqVKlmui3uSOKWJ4ZRmZtcHfE+gFtmkq9jtmVtBnA27w2Fu7Oj/QnFYBuVqPZSpNVGu1kS5Wg9lak2hVK6O0y2pbUEcf/kaG+Yp6J4zZ47pO/3KK6+Ivxw4cMDUjJctW9Zjur5es2aN189oDbd+rnXr1qZQUlNT5Z577pFBgwa55tFm6pMnTzb9vnfv3i3Dhw83NwpWr14txYoV87pcDcp1voz279/v0V8cXhzbK5HrF4pND8LC8XI4ro7IIf+O2e6wOyRBx4l3aOt1/SZYAeVqPZSpNVGu1kS5Wg9lak0hVa6pySI2m4SlREqw0u7WAQu6CxcubJpy5zfNlj5ixAgZP368Ca43bNggDz/8sDz//PMyZMgQM0+XLl1c8zdu3NjMV7VqVfn888/lzjvv9LpcrW3XvuXuNd2apK106dISGxt7Dras4Dr59xeSpGcBESnUuLMUL1XS799h7uzZRErHl5Awm8VPNiGEcrUeytSaKFdrolythzK1plAqV0fqKRN0h5fInNsrWGhL6oAF3bfccovMmDFD7rvvPvGXUqVKmeHG9u5N7wvspK+z6o+tgbU2Je/Xr5953ahRIzl+/LgZR/yZZ54xzdMzKl68uNSuXdsE6FmJjo42j4x0ed6WiXSOtBRJXvlV+gtbmBmbOywQd+Ds+vuzmRNNQJaP/EG5Wg9lak2UqzVRrtZDmVpTCJWrQ28qmO0M3vjL13XL0xZcd911cujQIencubNMnz5d/vjjD1mxYkWmR25ERUVJ8+bNZd68eR5t5PW19sP25sSJE5k2VAN35S2bukpMTDRjipcvXz5X64ecpaz5RRyJB83ziBotJbxYqfxeJQAAAADIV3mq6dY+1E4//vij37KXa5Puvn37SosWLUxiNB0yTGuundnM+/TpIxUrVjR9rlX37t1NxvNmzZq5mpdr7bdOdwbfTzzxhHmtTcp37dolw4YNM+9plnMELoFadBOGCQMAAACAPAXdkyZN8v+aiEivXr1MsrKhQ4fKnj17pGnTpjJ79mxXcrVt27Z51GwPHjzYBPf6/86dO02faw2wX3zxRdc8O3bsMAG2DnOm7+sNg8WLF5vn8J+0g9skddNS8zwsrpxEVG+e36sEAAAAAPnO5siqHTY8EqnFxcVJQkICidSycOKHN+TU7x+a54Uu6SuFLkofOz0QdAiyfYcOSxlNIGHxviyhhHK1HsrUmihXa6JcrYcytaZQKldHyulEavGVpKDHiWfdK12H4frzzz9NM3CEJkdKkiStnJX+IixCIhufyRgPAAAAAKEsz0H3V199JXXr1pVKlSrJ+eefL0uWLDHTddxs7WOt2c0RGpL/nSeOE0fM84haF0l4kRL5vUoAAAAAUHCD7lmzZknPnj3NMF+amMy9hbpO02RnkydP9ud6IoglkUANAAAAAPwXdD/33HPSpk0bWbBggdx///2Z3tchvlauXJmXRaOASd23UVK3rTLPw0pUkogqTfJ7lQAAAACgYAfdq1evlhtuuCHL9zXb+L59+85mvVBAJP3xpet5VOPOJps8AAAAAOAsgu7ChQtnmzht06ZNUrJkybwsGgWII/mkJP/5TfqLiCiJatgpv1cJAAAAAAp+0N2uXTuZMmWKpKamZnpPx9d+9913pWPHjv5YPwSx5NU/iONUonkeeV5rCYthODUAAAAAOOug+8UXX5QdO3bIBRdcIG+//bZpUvzDDz/I4MGDpVGjRiaxmiZYg7Wdcm9a3rR7vq4LAAAAAFgm6K5Tp45JoqZNyIcMGWKC7FdffVVGjBhhgu7ffvtNqlWr5v+1RdBI3fWfpO38xzwPK1VdIirUy+9VAgAAAICgE5HXDzZo0EDmzp0rhw8flg0bNojdbpcaNWpI6dKl/buGCEpJy9wTqHUhgRoAAAAA+DPodipRooRpZo7Qof24k/76Pv1FZCGJatAhv1cJAAAAAAp+0L19+3YJCwuTihUrmtenTp2S8ePHZ5qvUqVK2Q4phoLNBNzJJ83zqDptJSy6SH6vEgAAAAAU7KD777//lmbNmsno0aPlgQceMNN02LAnnngi07zh4eFSr149078b1qL995P+mOZ6HdWMBGoAAAAAcNaJ1DRLedWqVeW+++7L9N7HH38smzdvNo+NGzdKhQoVzPywntTtf0na3g3meXjZ8ySi7Hn5vUoAAAAAUPBrun/++Wfp2bOnaV6eUdmyZU1A7tS7d2/5+uuv/beWCBpJy6a7nkc1vjJf1wUAAAAALFPTvWXLFqlbt67HtIiICGnSpIkUK1bMY3r16tVl69at/ltLBAX7iQRJXj0n/UV0EYms1y6/VwkAAAAArJNITYcFcxcXFycrV67MNJ8OH6V9f2Etyau+EUlNMs+j6rWTsKiY/F4lAAAAALBGTbdmJP/zzz99mlfn0/lhHXoT5ZT72NxNuuXr+gAAAACApYLuK664wiRM27dvX7bz6fs6n84P60jdslzsB7aY5+EV6ktE6er5vUoAAAAAYJ2gW4cGS0lJkcsvv1yWLVvmdR6d3qFDBzPf448/7s/1RD5L8qjl7pqv6wIAAAAAluvTXa1aNfnss8/kpptuklatWkmtWrWkYcOGUrRoUUlMTJTVq1fLhg0bJCYmRj755BOTTA3WYE88JMn/zjPPbYWKSVSdNvm9SgAAAABgvURq3bp1M/21X375Zfn2229lxowZrvfKly8vd955pwwYMMAE5LCOpJVfi6SlmueRDTqILSIqv1cJAAAAAKwXdKsaNWrI22+/bZ4fO3ZMjh49aoYMi42NDcT6IZ857PYMTctJoAYAAAAAAQu63WmwnXGMblhLysbFYj+80zwPr9xEIuLJSg8AAAAAfk+khtDkXssdTQI1AAAAAMgVgm5kyX50n6Ss/dU8txUuIZHnXZLfqwQAAAAABQpBN7KUtHymiD3NPI9qeIXYws+qNwIAAAAAhByCbnjlSEuVpOWns9PbwhibGwAAAADygKAbXqWs/13sR/ea5xFVz5fwuHL5vUoAAAAAUOAQdMOrpD/chglrSi03AAAAAOQFQTcySTu8S1I2/G6e24qWksgarfJ7lQAAAACgQCLoRiZJy6eLOBzmeVSjTmILC8/vVQIAAACAAomgGx4cqSmStOKr9Bdh4RLV+Mr8XiUAAAAAKLAIuuEhZc18cSQeNM8jqreU8GKl8nuVAAAAAKDAIuiGh1PLziRQiyaBGgAAAACcFYJuuKQd2Cqpm5aa52Fx5SSiWvP8XiUAAAAAKNAIuuGStGy663lUo85is3F4AAAAAMDZIKqC4UhJkqSVX6e/CIuQyEZd8nuVAAAAAKDAI+iGkfzvXHGcTDDPI8+7WMKLFM/vVQIAAACAAo+gG0bSH2cSqEU16Zav6wIAAAAAVkHQDUndu0FSt60yz8NKVJKIyo3ze5UAAAAAwBIIuiFJbsOERTXuIjabLV/XBwAAAACsgqA7xDmST0ryqm/TX0RESVTDTvm9SgAAAABgGQTdIS757x/EkZRonkfWvlTCYorl9yoBAAAAgGUQdIe4U+5Ny5t2z9d1AQAAAACrIegOYam7/pW0nf+Y52GlqktE+br5vUoAAAAAYCkE3SHMfZiwaBKoAQAAAIDfEXSHKPupY5L09+z0F5ExEtmgQ36vEgAAAABYDkF3iEr+63uR5JPmeVTdNhIWXSS/VwkAAAAALIegOwQ5HA5J+uML12sSqAEAAABAYBB0h6DU7X9J2t4N5nl42fMkoux5+b1KAAAAAGBJBN0hKMl9mLAmXfN1XQAAAADAygi6Q4z9RIIkr56T/iK6iETWbZffqwQAAAAAlkXQHWKSV80SSU02z6PqtZewqEL5vUoAAAAAYFkE3UFi3LhxUq1aNSlUqJC0atVKli5dmuW8l112mRlTO+Oja9czTcX37t0rt912m1SoUEEKFy4snTt3lnXr1smpZdPN+9uOJEmxa5+T8LgymR7TZnx9TrYZAAAAAKyOoDsITJ06VR577DEZNmyYrFixQpo0aSKdOnWSffv2eZ1/+vTpsnv3btdj9erVEh4eLtdff70rO3mPHj1k06ZN8tVXX8nKlSulatWqckW7tnJs10YzT5W6TWTnur89Hs8OGiBFixaRLle0P6fbDwAAAABWRdAdBEaNGiX9+/eX22+/XerXry8TJkwwtdMTJ070On98fLyUK1fO9fjxxx/N/M6ge/369bJ48WJ566235IILLpA6deqY5ycSj8r0fw6beWKadZdyZct6PGbO+k6u73G1FC1a9JxuPwAAAABYFUF3PktOTpbly5dLhw4dXNPCwsLM60WLFvm0jPfff19uvPFGKVKkiHmdlJRk/tem6i4nDkuUpMjibcfFFhMrUXXaeCxj+co/ZdXfq+WOPr39s2EAAAAAAILu/HbgwAFJS0uTsmXLekzX13v27Mnx89r3W5uX9+vXzzWtbt26UqVKFRk4cKAcPnzYBPYjnrhHdh1Nkb2JKRJV/3KxRUR5LGfihx9LvTq15eJWLf24dQAAAAAQ2gi6Czit5W7UqJG0bHkmWI6MjDT9vjVxmjZF16bn8+f/Ih1qxUqYTcfm7uaxjJMnT8qnX0yXO26llhsAAAAA/ImgO5+VKlXKJEHTbOPu9LX2187O8ePH5bPPPpM777wz03vNmzeXVatWyZEjR2Tr71/J5zdUkkMnUqV65YoSHl/JY94vvpolJ06clFtvusFPWwUAAAAAUATd+SwqKsoEyPPmzXNNs9vt5vVFF12U7WenTZtm+m/fcsstWc4TFxcnsZt+ko0HT8mq3Sfk6muuyzTPpA8/ke5dOknpUqXOcmsAAAAAAO4iPF4hX+hwYX379pUWLVqYZuKjR482tdiazVz16dNHKlasKCNHjszUtFyHBitZsqTXgLx06dJSqURhWfzVVzJo9na5sn5p6XLrfR7zbdi4SX79fZF888WnAd5KAAAAAAg9BN1BoFevXrJ//34ZOnSoSZ7WtGlTmT17tiu52rZt20xGc3dr166VBQsWyJw5c7wuU8fv1mB+757dUrZImPRqFC/PPPag2MI9i3zSR59KpYoVpGP7ywK4hQAAAAAQmmwOh8OR3ysR7I4ePWqaaSckJEhsbKwUFI60VDnyRjdxHN0nYguTYv0mS3icZ5b0gshud8i+Q4elTHwJCdPMcLAEytV6KFNrolytiXK1HsrUmkKpXB0pp0Rstkz5qApinEifbgtLWf97esCtTRqqnW+JgBsAAAAAChKCbgtL+uML1/OoJl3zdV0AAAAAIBQRdFtU2uGdkrJhoXluK1ZaImu0yu9VAgAAAICQQ9BtUUnLpouc7q4f1bCT2MLC83uVAAAAACDkEHRbkCM1RZJWfJX+Iixcopp0ye9VAgAAAICQRNBtQSlr5ovj+CHzPKJGSwkvWiq/VwkAAAAAQlLQBd3jxo2TatWqSaFChaRVq1aydOnSbOcfPXq01KlTR2JiYqRy5cry6KOPyqlTp85qmQXdKbcEatEkUAMAAACAfBNUQffUqVPlsccek2HDhsmKFSukSZMm0qlTJ9m3L33Yq4w++eQTefrpp838//33n7z//vtmGYMGDcrzMgu6tANbJHXzH+Z5WFx5iajWPL9XCQAAAABCVlAF3aNGjZL+/fvL7bffLvXr15cJEyZI4cKFZeLEiV7nX7hwoVxyySXSu3dvU5PdsWNHuemmmzxqsnO7TEskUDstqlFnsdmCqogBAAAAIKQETUSWnJwsy5cvlw4dOrimhYWFmdeLFi3y+pmLL77YfMYZZG/atEm+++47ufLKK/O8zILMkZIkSStnpb8Ij5TIRp3ze5UAAAAAIKRFSJA4cOCApKWlSdmyZT2m6+s1a9Z4/YzWcOvnWrduLQ6HQ1JTU+Wee+5xNS/PyzJVUlKSeTgdPXrU/G+3280j2DjsaZK6daWk/DNXHCcTzLSIWheJLSZO7Pb0YcOsxO5wmPLW/yX4igN5RLlaD2VqTZSrNVGu1kOZWlMolavj9PDHtiCMv5x8jQ2DJujOi/nz58uIESNk/PjxJkHahg0b5OGHH5bnn39ehgwZkufljhw5UoYPH55p+v79+zMlactvto2/S8SCt8V2/IDH9JMxZSTx0GGxIofdIQmJiSIOEVuYLb9XB35CuVoPZWpNlKs1Ua7WQ5laU0iVa2qyiM0mYSmREqyOHTtWsILuUqVKSXh4uOzdu9djur4uV66c189oYH3rrbdKv379zOtGjRrJ8ePH5a677pJnnnkmT8tUAwcONMnX3Gu6NTN66dKlJTY2VoJF8r8/yYkfXvT6XuSqLyS2cl2JPK+1WI25s2cTKR1fQsJsFj/ZhBDK1XooU2uiXK2JcrUeytSaQqlcHamnTNAdXqKMBCsdHatABd1RUVHSvHlzmTdvnvTo0cNVXa+vH3jgAa+fOXHihOmj7U6DbGdzhLwsU0VHR5tHRvpdGb8vP5uUn5z9WrbznJr/tkSdd7HYwtL3iWXY9fdnMyeaMKvf4QsllKv1UKbWRLlaE+VqPZSpNYVQuTr0poLZzuCIv7zxdd2CJuhWWrvct29fadGihbRs2dKMwa0115p5XPXp00cqVqxomn+r7t27m+zkzZo1czUv19pvne4MvnNaZkGlfbgdR7Mf9sxxbL+k7lgtkVWanLP1AgAAAAAEadDdq1cv02966NChsmfPHmnatKnMnj3blQht27ZtHncTBg8ebO706P87d+40zb814H7xxRd9XmZBZT/m2Yc7K47jhwK+LgAAAAAA72wOZ1o4ZEn7dMfFxUlCQkLQ9OlO2bxMjk26K8f5itzwiuVqujUj+75Dh6WM9mWxeLOaUEK5Wg9lak2UqzVRrtZDmVpTKJWrI+V0n+74SlLQ48TgbSCPbEVUbSa2WE0qkPWPzVastERUanhO1wsAAAAAcAZBdwGlydGKXPmk85XXeWLa3WO9JGoAAAAAUIAQdBdgUfUvl6I3viK22NKZargLXzVEompbb7gwAAAAAChIgiqRGvIWeEfWvcxkM0/dvVbCCpeQiGrnU8MNAAAAAEGAoNsCNMCOrN5CwuLKidgdBNwAAAAAECRoXg4AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAAAECEE3AAAAAAABQtANAAAAAECAEHQDAAAAABAgBN0AAAAAAAQIQTcAAAAAAAFC0A0AAAAAQIAQdAMAAAAAECAE3QAAAAAABAhBNwAAAAAAAULQDQAAAABAgBB0AwAAAAAQIATdAAAAAACEUtA9btw4qVatmhQqVEhatWolS5cuzXLeyy67TGw2W6ZH165dXfPcdtttmd7v3LnzOdoaAAAAAECoipAgM3XqVHnsscdkwoQJJuAePXq0dOrUSdauXStlypTJNP/06dMlOTnZ9frgwYPSpEkTuf766z3m0yB70qRJrtfR0dEB3hIAAAAAQKgLupruUaNGSf/+/eX222+X+vXrm+C7cOHCMnHiRK/zx8fHS7ly5VyPH3/80cyfMejWINt9vhIlSpyjLQIAAAAAhKqgqunWGuvly5fLwIEDXdPCwsKkQ4cOsmjRIp+W8f7778uNN94oRYoU8Zg+f/58U1OuwXb79u3lhRdekJIlS3pdRlJSknk4HT161Pxvt9vNI1jZHQ4Rh0NsdodYmW6nw+FI397gLQ7kEuVqPZSpNVGu1kS5Wg9lak2hVK4O3UYRsQVz/OXjugVV0H3gwAFJS0uTsmXLekzX12vWrMnx89r3e/Xq1Sbwzti0vGfPnlK9enXZuHGjDBo0SLp06WIC+fDw8EzLGTlypAwfPjzT9P3798upU6ckWNmPJZigWyJOipU57A5JSEwUcYjYwmz5vTrwE8rVeihTa6JcrYlytR7K1JpCqlxTk0VsNglLiZRgdezYsYIXdJ8tDbYbNWokLVu29JiuNd9O+n7jxo2lZs2apvb78ssvz7QcrWnXfuXuNd2VK1eW0qVLS2xsrASrtMgUvf0ltshCYmXmzp5NpHR8CQmzWfxkE0IoV+uhTK2JcrUmytV6KFNrCqVydaSeMkF3eInMeb2ChSb+LnBBd6lSpUzN8969ez2m62vth52d48ePy2effSbPPfdcjt9To0YN810bNmzwGnRr/29vida0qbs+gpVDf3i2ELjrZdffn82caMKsvq2hhHK1HsrUmihXa6JcrYcytaYQKleHiW1sQR1/+bpuQbUFUVFR0rx5c5k3b55HO3l9fdFFF2X72WnTppl+2LfcckuO37Njxw6T5bx8+fJ+WW8AAAAAAIK+pltps+6+fftKixYtTDNxHTJMa7E1m7nq06ePVKxY0fS7zti0vEePHpmSoyUmJpr+2ddee62pLdc+3QMGDJBatWqZochy04nfmVAtWKUdPSaO1CSxhQdvvwd/Nas5duSYREuK5ZvVhBLK1XooU2uiXK2JcrUeytSaQqlcHWkpYouIlvCI4I3BnPGhM14sMEF3r169TMKyoUOHyp49e6Rp06Yye/ZsV3K1bdu2ZarG1zG8FyxYIHPmzMm0PG2u/tdff8mUKVPkyJEjUqFCBenYsaM8//zzPo/V7ewgr/26AQAAAABwjxfj4uIkKzZHTmE5TBP3Xbt2SbFixUwfCuQvZ2K7/7d3H8BRVW0Yxw/SB+lKADGhiPSmIAqIQigiVSmCgBFQFOlNQERUOtKlK4IjTVBAQBlEpCq9CaIUiYhIVSQiUnO/ec43m8mGBALZZXOX/29mJ7t37+6evSfJ7nvf95xz5MiRZD2xHW4O/Rp86NPgRL8GJ/o1+NCnwYl+TV4USivgVmL3euO7k12mOznSAcyTJ0+gm4E49I+GfzbBh34NPvRpcKJfgxP9Gnzo0+BEvyYf18twJ8uJ1AAAAAAACCYE3QAAAAAA+AlBN1xHE+D1798/0RPhwR3o1+BDnwYn+jU40a/Bhz4NTvSrOzGRGgAAAAAAfkKmGwAAAAAAPyHoBgAAAADATwi6AQAAAADwE4JuuMaQIUNMuXLlTMaMGU2OHDlMgwYNzL59+wLdLPjQ0KFDTYoUKUyXLl0C3RQk0dGjR02LFi1M9uzZTfr06U2JEiXM1q1bA90sJMHVq1dNv379TL58+WyfFihQwAwYMMAwNYx7rF271tStW9fkzp3b/q9dtGiR1/3qy7feesvkypXL9nG1atXMgQMHAtZeJL1fL1++bHr16mX/B2fIkMHu88ILL5g//vgjoG1G0v9eY3v11VftPmPGjLmtbUTiEXTDNdasWWPat29vNm7caFasWGE/SGrUqGH+/fffQDcNPrBlyxYzZcoUU7JkyUA3BUl05swZU7FiRZM6dWqzbNkys3fvXjNy5EiTNWvWQDcNSTBs2DAzadIkM378ePPTTz/Z28OHDzfvv/9+oJuGRNLnZalSpcyECRPivV/9OW7cODN58mSzadMmG6TVrFnTXLhw4ba3Fb7p1/Pnz5vt27fbE2b6uWDBApuwqFevXkDaCt/9vXosXLjQfjdWcI7ki9nL4VqnTp2yGW8F45UrVw50c5AE586dMw899JCZOHGiGThwoCldujRna12sd+/e5rvvvjPr1q0LdFPgQ3Xq1DEhISFm2rRpMdsaNmxoM6IzZ84MaNtw85QV05d1VY2Jvg7qS3v37t1Njx497LazZ8/aPp8xY4Zp2rRpgFuMW+nXhE5yP/LII+bw4cMmNDT0trYPvu1XVZWVL1/eLF++3NSuXdtWClItmDyR6YZr6cuAZMuWLdBNQRKpgkEfFiplhPstXrzYlC1b1jRu3NieGCtTpoz54IMPAt0sJFGFChXMypUrzf79++3tXbt2mfXr15tatWoFumnwgcjISHP8+HGv/8OZM2e2X+g3bNgQ0LbB99+fFMRlyZIl0E1BEkRHR5uWLVuanj17mmLFigW6ObiBVDfaAUiu/2h0Jk8lrMWLFw90c5AEc+fOtSVvOvOO4HDo0CFbhtytWzfzxhtv2L7t1KmTSZMmjYmIiAh085CECoaoqChTuHBhkzJlSjvGe9CgQaZ58+aBbhp8QAG3KLMdm2577oP7aaiAxng3a9bMZMqUKdDNQRJoiE+qVKns5yuSP4JuuDYzumfPHptlgXsdOXLEdO7c2Y7RT5cuXaCbAx+eFFOme/Dgwfa2Mt36e9U4UYJu95o3b56ZNWuWmT17ts2q7Ny50578VEky/Qokf5oLp0mTJnYogU6Mwr22bdtmxo4da5MWqlpA8kd5OVynQ4cOZunSpWbVqlUmT548gW4OkvihcfLkSTueW2drddEYfU3ko+vKpMF9NPNx0aJFvbYVKVLE/PbbbwFrE5JOJYzKdmtsr2ZCVllj165d7coScL+cOXPanydOnPDartue++D+gFvjuHWimyy3u2nOFH1/0ph8z/cn9a3mZMibN2+gm4d4kOmGa+jMbMeOHe1EEqtXr7bL1sDdwsPDze7du722tWrVypavqvxNJaxwHw37iLucn8YBh4WFBaxNSDrNgnzXXd7n6vU3qsoGuJ8+UxVca9y+JrMUDSfQLObt2rULdPPgg4Bby78pYaGlHOFuOukZdx4crTSg7foeheSHoBuuKilXWeMXX3xh1+r2jDHTRC+aPRfuo36MOyZfS9ToCwFj9d1L2U9NuqXycn3R27x5s5k6daq9wL20XqzGcCuzovLyHTt2mFGjRpnWrVsHumm4iZUiDh486DV5moYJaEJS9auGC2gFiYIFC9ogXMtMafjA9WbCRvLuV1UeNWrUyJYhq0pQFWSe70+6X3NtwJ1/r3FPnmiZTp04K1SoUABaixvSkmGAG+jXNb7L9OnTA900+NATTzzhdO7cOdDNQBItWbLEKV68uJM2bVqncOHCztSpUwPdJCRRVFSU/dsMDQ110qVL5+TPn9/p27evc/HixUA3DYm0atWqeD9HIyIi7P3R0dFOv379nJCQEPu3Gx4e7uzbty/QzUYS+jUyMjLB7096HNz79xpXWFiYM3r06NveTiQO63QDAAAAAOAnTKQGAAAAAICfEHQDAAAAAOAnBN0AAAAAAPgJQTcAAAAAAH5C0A0AAAAAgJ8QdAMAAAAA4CcE3QAAAAAA+AlBNwAAAAAAfkLQDQDAbZIiRQrz9ttv33Gv7Sa//vqrPVYzZsy4pccfOHDA1KhRw2TOnNk+z6JFi3zeRgCAuxB0AwCCmoInBT+6rF+//pr7Hccx999/v72/Tp06AWmjG+XNmzfmuOqSLl06U7BgQdOzZ0/z119/meRu9uzZZsyYMT5/3oiICLN7924zaNAg88knn5iyZcv6/DUAAO6SKtANAADgdlBQqECrUqVKXtvXrFljfv/9d5M2bVq/t+G///4zqVIFz0dv6dKlTffu3e31CxcumG3bttlAVsd08+bNJjnT78KePXtMly5dvLaHhYXZfkqdOvVNP6cet2HDBtO3b1/ToUMHH7YWAOBmwfPJDwDAdTz99NNm/vz5Zty4cV6Br4Kvhx9+2Jw+ffq2BP7B5L777jMtWrSIuf3SSy+Zu+++24wYMcKWWSvz7TaerP2tOHXqlP2ZJUsWH7cKAOBmlJcDAO4IzZo1M3/++adZsWJFzLZLly6Zzz77zDz//PPxPkbBY4UKFUz27NlN+vTpbXCu/WObPn26DdQ++ugjr+2DBw+227/66qsEx1Xrurbt37/fBq8aB3zvvfeafv362bL3I0eOmPr165tMmTKZnDlzmpEjR3q9htr/1ltv2XbpsRkyZDCPP/64WbVqVaKOydGjR03r1q1NSEiIzfQXK1bsmvdxs9ROiX1i44cffjAvvviiyZ8/vw1otY9eV/0Rm/ZR2XpcnuMUm24rm6wTKUWLFrX989hjj9nSbpkyZYp54IEH7Os9+eSTdqy2h25/+eWX5vDhwzHl8Z7XTWhM988//2waNWpksmXLZp9TZeOLFy/2aqOy5KIS+9jP6a9jDQBwBzLdAIA7ggIgBWVz5swxtWrVstuWLVtmzp49a5o2bWoz4HGNHTvW1KtXzzRv3twGuHPnzjWNGzc2S5cuNbVr17b7tGrVyixYsMB069bNVK9e3Y4PV+D3zjvvmDZt2tgM+40899xzpkiRImbo0KE2GBw4cKAN7hQ4Vq1a1QwbNszMmjXL9OjRw5QrV85UrlzZPi4qKsp8+OGH9oTCyy+/bP755x8zbdo0U7NmTVverfLvhJw4ccI8+uijMcGrgn0dD7VZzxu37Do+ly9fjqkQUHn5jh07zKhRo2z78uXLF7OfTnQcOnTIHisF3D/++KOZOnWq/blx48ZrAurEWrdunQ1827dvb28PGTLEjst//fXXzcSJE81rr71mzpw5Y4YPH24D3m+//dbup/Jv9buGFYwePdpuU4Y+IWpnxYoVbWa/d+/e9uTGvHnzTIMGDcznn39unnnmGfPss8/aDHfXrl1tf6jfPc/pi2MNAHAxBwCAIDZ9+nRHH3dbtmxxxo8f72TMmNE5f/68va9x48ZOlSpV7PWwsDCndu3aXo/17Odx6dIlp3jx4k7VqlW9th87dszJli2bU716defixYtOmTJlnNDQUOfs2bNe+6kd/fv3j7mt69rWtm3bmG1Xrlxx8uTJ46RIkcIZOnRozPYzZ8446dOndyIiIrz21evFpv1CQkKc1q1bX/e127Rp4+TKlcs5ffq0135NmzZ1MmfOfM17j0vHS88Z91KxYsVrnjO+55ozZ47df+3atTHb9N70vHF5jlPc95M2bVonMjIyZtuUKVPs9pw5czpRUVEx2/v06WO3x95XfR3fa2kf7avfG4/w8HCnRIkSzoULF2K2RUdHOxUqVHAKFix4zWPfe+89r+dM6rEGALgb5eUAgDtGkyZN7GRXylQrK6yfCZWWi0qWPZQxVXZU5dvbt2/32k/Z2wkTJtiMru7fuXOnLR1WWXhiaCy0R8qUKW3psuJKZUI9lEUtVKiQzRjH3jdNmjT2enR0tJ01/MqVK/bxcdsYm55bGdq6deva68pWey7Kkut9Xu/xHuXLl7fvWRcdS83YraywqgN0nOM7jsqI63WU+ZXEvE5CwsPDvUq41R5p2LChyZgx4zXbYx+7xNIxVYZcvzv6nfEcJ5XG61hp7LpKx/19rAEA7kV5OQDgjqGy3mrVqtnJ086fP2+uXr1qx+kmRIGkSr0VRF+8eDFme3zl0CpRnzlzpi0Pb9u2rQ0IEys0NNTrtsZna9zwPffcc832uOOgP/74YzvWW2OOVe7tEbu8O74Jv/7++29b4q1LfE6ePHnDdqt9Op4eKrnXiQEdU5W9d+zYMSZwVbm9yvPjPq+CzlsV33ETlfjHt10nTm7WwYMHbbCscfa6xEfvSaXn/jzWAAD3IugGANxRlNnW+Ofjx4/bsd0JzTSt8cLK2Gp8ssYH58qVyy4jpYnTFLTHpWB469at9vrevXtt5vmuuxJXUKaMdWK2yf8rq/9PQb4mH9PYYk3elSNHDvs4jW3+5ZdfEnw9tU00eZvWlY5PyZIlza3wnGxYu3ZtTNCtLPH3339v26hx5hrrrDY89dRTMW2RhMZ26+RIfBI6Rok5donlaZ/G0yszHR9N2BaIYw0AcAeCbgDAHUWTXr3yyit2Aq9PP/00wf1UEqxs8/Lly73W8FbQHR9N5qXyYwW8ffr0setVa3I1f9JM6poRXBO5xQ5Y+/fvf8OMv8qvFczGzlT7gsrb5dy5czHZ5ZUrV9pMt2Za91BZdlxZs2a1WeG4NMu4ryV28jYdX9EJl1s5Vv481gAAd2BMNwDgjqIs66RJk+wSTxpnmxBlSxWYxc6yajmpRYsWxRv8KoDX7OOa3Vql5m+++aZdCsyfPBnd2BncTZs2mQ0bNtzwcRr3rBMLe/bsSXC96VuxZMkS+7NUqVIJtlF0UiKuAgUK2HJzLTHmcezYMbNw4ULja5qBPDGl7aoe0BJjmklebbnZY+XPYw0AcAcy3QCAO05CZb6xaXyylr9SCbRK0jXuVpOlqZQ4dlCo7e3atTNVqlSxy0HJ+PHj7VrZKv1ev359osvMb5aWx1KWW9l7tTcyMtJMnjzZrlvtyTQnRCcI1EZNMqZyez1GY681qdc333xjr9+IJhBTibtoSbVdu3bZ4FRjvT2l5ZpMTiX6WrZLY8419vnrr7+2bY1LJyt69epl30+nTp3suHudIHnwwQd9PtmY1jbXiRJVI2gZNp2MSegkjPq9UqVKpkSJEvZYKfutZcB0ckPLjul9+/tYAwDci6AbAIB4aH1srXmtgEnrKGtiMq2XrWx37KBbAbcmWVPZuadkOXv27HbSrPr165sRI0bYdaP9QUG9xqYr0FUZvII5BcHz5883q1evvu5jQ0JC7Fre7777rg3cNW5d7S5WrJh9n4mhCeZatmxpr+vEgoJtrVc9YMAAr4nFNAZeQbiCV2W8a9SoYdepzp07t9fz6fWV1VYgrGOmY65yfZWi+zro1hrear/6TWt1h4WFJRh067hqvL5K5GfMmGHH7ysDXqZMGa+SeX8eawCAe6XQumGBbgQAAAAAAMGIMd0AAAAAAPgJQTcAAAAAAH5C0A0AAAAAgJ8QdAMAAAAA4CcE3QAAAAAA+AlBNwAAAAAAfkLQDQAAAACAnxB0AwAAAADgJwTdAAAAAAD4CUE3AAAAAAB+QtANAAAAAICfEHQDAAAAAOAnBN0AAAAAABj/+B9FTgdArlpupgAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimale Baumtiefe: 10 mit einer Genauigkeit von 0.9194\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"def plot_surrogate_accuracy_vs_depth_test_only(rf_model, X_train, X_test, max_depths=range(1, 16)):\n",
" \"\"\"\n",
" Visualisiert die Genauigkeit des Surrogate-Modells für verschiedene Baumtiefen,\n",
" fokussiert nur auf die Testdaten.\n",
" \"\"\"\n",
" # Random Forest-Vorhersagen (nur einmal berechnen)\n",
" rf_train_predictions = rf_model.predict(X_train)\n",
" rf_test_predictions = rf_model.predict(X_test)\n",
" \n",
" # Ergebnisse für verschiedene Baumtiefen\n",
" test_accuracies = []\n",
" \n",
" for depth in max_depths:\n",
" # Surrogate-Baum mit aktueller Tiefe trainieren\n",
" surrogate_tree = DecisionTreeClassifier(max_depth=depth, random_state=42)\n",
" surrogate_tree.fit(X_train, rf_train_predictions)\n",
" \n",
" # Vorhersagen\n",
" surrogate_test_pred = surrogate_tree.predict(X_test)\n",
" \n",
" # Genauigkeit berechnen\n",
" test_acc = np.mean(surrogate_test_pred == rf_test_predictions)\n",
" test_accuracies.append(test_acc)\n",
" \n",
" # Visualisierung\n",
" plt.figure(figsize=(10, 6))\n",
" plt.plot(max_depths, test_accuracies, 'o-', color='#ED7D31', linewidth=2)\n",
" \n",
" # Finde die beste Tiefe\n",
" best_depth = max_depths[np.argmax(test_accuracies)]\n",
" best_acc = max(test_accuracies)\n",
" \n",
" # Markiere den besten Punkt\n",
" plt.scatter([best_depth], [best_acc], s=100, c='red', zorder=5)\n",
" plt.annotate(f'Optimale Tiefe: {best_depth}\\nGenauigkeit: {best_acc:.4f}',\n",
" xy=(best_depth, best_acc), xytext=(best_depth+1, best_acc-0.05),\n",
" arrowprops=dict(facecolor='black', shrink=0.05, width=1.5))\n",
" \n",
" # Beschriftungen und Layout\n",
" plt.grid(alpha=0.3)\n",
" plt.title('Surrogate-Modell-Genauigkeit bei verschiedenen Baumtiefen', fontsize=14)\n",
" plt.xlabel('Maximale Baumtiefe', fontsize=12)\n",
" plt.ylabel('Genauigkeit auf Testdaten', fontsize=12)\n",
" \n",
" # Füge Werte über den Punkten hinzu\n",
" for i, acc in enumerate(test_accuracies):\n",
" plt.text(max_depths[i], acc + 0.01, f'{acc:.3f}', ha='center')\n",
" \n",
" # Y-Achse anpassen (je nach Daten)\n",
" y_min = max(0, min(test_accuracies) - 0.05)\n",
" plt.ylim(y_min, 1.05)\n",
" \n",
" # Verbesserte visuelle Elemente\n",
" plt.fill_between(max_depths, test_accuracies, y_min, alpha=0.1, color='#ED7D31')\n",
" \n",
" plt.tight_layout()\n",
" plt.savefig('output/surrogate_accuracy.png', dpi=300)\n",
" plt.show()\n",
" \n",
" return best_depth, best_acc\n",
"\n",
"# Aufruf der Funktion\n",
"best_depth, best_accuracy = plot_surrogate_accuracy_vs_depth_test_only(rf_model, X_train, X_test)\n",
"print(f\"Optimale Baumtiefe: {best_depth} mit einer Genauigkeit von {best_accuracy:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-20T12:21:48.896773Z",
"start_time": "2025-03-20T12:21:48.874715Z"
}
},
"outputs": [],
"source": [
"# Für Quarto Präsentationen\n",
"import extract_cells\n",
"extract_cells.extractToFiles(\"Explainable_AI_Adult_Census_Income.ipynb\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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