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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "75c0974e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split, cross_val_score, KFold, cross_val_predict\n",
+ "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score, roc_curve, roc_auc_score, auc\n",
+ "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+ "from sklearn.compose import ColumnTransformer\n",
+ "from sklearn.decomposition import PCA\n",
+ "from imblearn.over_sampling import SMOTE, RandomOverSampler\n",
+ "from imblearn.under_sampling import RandomUnderSampler\n",
+ "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
+ "from sklearn import datasets, svm, metrics\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.model_selection import StratifiedKFold"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6804c79",
+ "metadata": {},
+ "source": [
+ "https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset\n",
+ "\n",
+ "https://www.kaggle.com/datasets/henrysue/online-shoppers-intention/data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66e90b1f",
+ "metadata": {},
+ "source": [
+ "• Administrative: This is the number of pages of this type (administrative) that the user visited.<br>\n",
+ "• Administrative_Duration: This is the amount of time spent in this category of pages.<br>\n",
+ "• Informational: This is the number of pages of this type (informational) that the user visited.<br>\n",
+ "• Informational_Duration: This is the amount of time spent in this category of pages.<br>\n",
+ "• ProductRelated: This is the number of pages of this type (product related) that the user visited.<br>\n",
+ "• ProductRelated_Duration: This is the amount of time spent in this category of pages.<br>\n",
+ "• BounceRates: The percentage of visitors who enter the website through that page and exit without triggering any additional tasks.<br>\n",
+ "• ExitRates: The percentage of pageviews on the website that end at that specific page.<br>\n",
+ "• PageValues: The average value of the page averaged over the value of the target page and/or the completion of an eCommerce transaction.<br>\n",
+ "• SpecialDay: This value represents the closeness of the browsing date to special days or holidays (e.g., Mother's Day or Valentine's Day) in which the transaction is more likely to be finalized.<br>\n",
+ "• Month: Contains the month the pageview occurred, in string form.<br>\n",
+ "• OperatingSystems: An integer value representing the operating system that the user was on when viewing the page.<br>\n",
+ "• Browser: An integer value representing the browser that the user was using to view the page.<br>\n",
+ "• Region: An integer value representing which region the user is located in.<br>\n",
+ "• TrafficType: An integer value representing what type of traffic the user is categorized into.<br>\n",
+ "• VisitorType: A string representing whether a visitor is New Visitor, Returning Visitor, or Other.<br>\n",
+ "• Weekend: A boolean representing whether the session is on a weekend.<br>\n",
+ "Revenue: A boolean representing whether or not the user completed the purchase.<br>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8ac2733",
+ "metadata": {},
+ "source": [
+ "# FUNCTIONS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3475dee6",
+ "metadata": {},
+ "source": [
+ "All functions used in this project are in this section"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "046d5abe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Read dataset, check for null and duplicates\n",
+ "def read_dataset(dataset):\n",
+ " \n",
+ " data = pd.read_csv(dataset)\n",
+ " nrow = len(data.index)\n",
+ " ncol = len(data.columns)\n",
+ "\n",
+ " print(\"The dataset contains\", format(nrow, \",d\"), \"rows and\", ncol, \"columns.\")\n",
+ " \n",
+ " #Check for null values\n",
+ " if ((data.isna().sum()).sum()) > 0:\n",
+ " print(\"There are null items in the dataset\")\n",
+ " else:\n",
+ " print(\"There are no null items in the dataset\")\n",
+ " \n",
+ " #Check for duplicates\n",
+ " \n",
+ " #col_names = [\"Country\", \"Year\", \"Status\"]\n",
+ " #(data.duplicated(subset=col_names)).sum()\n",
+ "\n",
+ " if (data.duplicated().sum()) > 0:\n",
+ " print(\"There are duplicates in the dataset\")\n",
+ " else:\n",
+ " print(\"There are no duplicates in the dataset\")\n",
+ " \n",
+ " return data\n",
+ "\n",
+ "\n",
+ "\n",
+ "#Function to categorize data into numeric and categorical\n",
+ "def categorize_data(data):\n",
+ " \n",
+ " numeric=[]\n",
+ " categorical=[]\n",
+ " numeric_dtypes = [\"int64\", \"int32\", \"float64\", \"float32\"]\n",
+ "\n",
+ " for i in range (len(data.columns)):\n",
+ " if data[data.columns[i]].dtype in numeric_dtypes:\n",
+ " numeric.append(data.columns[i])\n",
+ " else:\n",
+ " categorical.append(data.columns[i])\n",
+ " \n",
+ " return numeric, categorical\n",
+ "\n",
+ " \n",
+ "#Function to check for outliers\n",
+ "def outliers_check(data, numeric_cols):\n",
+ " outliers_sum =[]\n",
+ "\n",
+ " for col in (numeric_cols):\n",
+ " Q1 = data[col].quantile(0.25)\n",
+ " Q3 = data[col].quantile(0.75)\n",
+ " IQR = Q3 - Q1\n",
+ " outliers = (data[col] < (Q1 - 5 * IQR)) | (data[col] > (Q3 + 5 * IQR))\n",
+ " print(col, \"\", outliers.sum())\n",
+ " outliers_sum.append(outliers.sum())\n",
+ "\n",
+ " return outliers.sum()\n",
+ "\n",
+ "\n",
+ "def remove_duplicates(data):\n",
+ " duplicated_sum = data.duplicated().sum()\n",
+ " if duplicated_sum == 0:\n",
+ " print(\"Number of duplicated rows in dataset =\", duplicated_sum)\n",
+ " return data\n",
+ " else:\n",
+ " print(\"Number of duplicated rows in dataset =\", duplicated_sum)\n",
+ " data = data[~data.duplicated()]\n",
+ " print(\"Duplicated rows have been removed\")\n",
+ " return data\n",
+ "\n",
+ " \n",
+ "def remove_outliers(data, numeric_cols):\n",
+ " \n",
+ " for col in (numeric_cols):\n",
+ " median_value = np.median(data[col])\n",
+ " Q1 = data[col].quantile(0.25)\n",
+ " Q3 = data[col].quantile(0.75)\n",
+ " IQR = Q3 - Q1\n",
+ " outliers = (data[col] < (Q1 - 5 * IQR)) | (data[col] > (Q3 + 5 * IQR))\n",
+ " #print(col, \"\", outliers.sum())\n",
+ " data.loc[outliers, col] = median_value\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "# remove special characters from columns\n",
+ "def remove_spec_chars(data, categorical_cols):\n",
+ " for col in categorical_cols:\n",
+ " data[col] = data[col].str.replace(r'\\W+', '').str.strip() #replaces special characters with white sapaces and removes the white spaces\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "def replace_unknown(data, categorical_cols):\n",
+ " for col in categorical_cols:\n",
+ " if \"other\" in data[col].values:\n",
+ " #source: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mode.html\n",
+ " mode = data[col].mode()[0]\n",
+ " data[col] = data[col].replace(\"unknown\", mode)\n",
+ " return data\n",
+ "\n",
+ " \n",
+ "def oneHotEncoding(data, categorical, drop_first):\n",
+ "\n",
+ " data_final = pd.get_dummies(data, columns=categorical, drop_first=drop_first)\n",
+ "\n",
+ " return data_final"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64d45954",
+ "metadata": {},
+ "source": [
+ "# EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c6fafe71",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The dataset contains 12,330 rows and 18 columns.\n",
+ "There are no null items in the dataset\n",
+ "There are duplicates in the dataset\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = read_dataset(\"online_shoppers_intention.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3de6a535",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "125"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.duplicated().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e6802474",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of duplicated rows in dataset = 125\n",
+ "Duplicated rows have been removed\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = remove_duplicates(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "bf44932c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.duplicated().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c54135de",
+ "metadata": {},
+ "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>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>Month</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " <th>VisitorType</th>\n",
+ " <th>Weekend</th>\n",
+ " <th>Revenue</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>64.000000</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.10</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>9</td>\n",
+ " <td>3</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2.666667</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.14</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>10</td>\n",
+ " <td>627.500000</td>\n",
+ " <td>0.02</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "0 0 0.0 0 \n",
+ "1 0 0.0 0 \n",
+ "2 0 0.0 0 \n",
+ "3 0 0.0 0 \n",
+ "4 0 0.0 0 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "0 0.0 1 0.000000 \n",
+ "1 0.0 2 64.000000 \n",
+ "2 0.0 1 0.000000 \n",
+ "3 0.0 2 2.666667 \n",
+ "4 0.0 10 627.500000 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay Month OperatingSystems \\\n",
+ "0 0.20 0.20 0.0 0.0 Feb 1 \n",
+ "1 0.00 0.10 0.0 0.0 Feb 2 \n",
+ "2 0.20 0.20 0.0 0.0 Feb 4 \n",
+ "3 0.05 0.14 0.0 0.0 Feb 3 \n",
+ "4 0.02 0.05 0.0 0.0 Feb 3 \n",
+ "\n",
+ " Browser Region TrafficType VisitorType Weekend Revenue \n",
+ "0 1 1 1 Returning_Visitor False False \n",
+ "1 2 1 2 Returning_Visitor False False \n",
+ "2 1 9 3 Returning_Visitor False False \n",
+ "3 2 2 4 Returning_Visitor False False \n",
+ "4 3 1 4 Returning_Visitor True False "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.set_option('display.max_columns', None)\n",
+ "\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f6148ae5",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "Int64Index: 12205 entries, 0 to 12329\n",
+ "Data columns (total 18 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Administrative 12205 non-null int64 \n",
+ " 1 Administrative_Duration 12205 non-null float64\n",
+ " 2 Informational 12205 non-null int64 \n",
+ " 3 Informational_Duration 12205 non-null float64\n",
+ " 4 ProductRelated 12205 non-null int64 \n",
+ " 5 ProductRelated_Duration 12205 non-null float64\n",
+ " 6 BounceRates 12205 non-null float64\n",
+ " 7 ExitRates 12205 non-null float64\n",
+ " 8 PageValues 12205 non-null float64\n",
+ " 9 SpecialDay 12205 non-null float64\n",
+ " 10 Month 12205 non-null object \n",
+ " 11 OperatingSystems 12205 non-null int64 \n",
+ " 12 Browser 12205 non-null int64 \n",
+ " 13 Region 12205 non-null int64 \n",
+ " 14 TrafficType 12205 non-null int64 \n",
+ " 15 VisitorType 12205 non-null object \n",
+ " 16 Weekend 12205 non-null bool \n",
+ " 17 Revenue 12205 non-null bool \n",
+ "dtypes: bool(2), float64(7), int64(7), object(2)\n",
+ "memory usage: 1.6+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "d3639f64",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.338878</td>\n",
+ " <td>81.646331</td>\n",
+ " <td>0.508726</td>\n",
+ " <td>34.825454</td>\n",
+ " <td>32.045637</td>\n",
+ " <td>1206.982457</td>\n",
+ " <td>0.020370</td>\n",
+ " <td>0.041466</td>\n",
+ " <td>5.949574</td>\n",
+ " <td>0.061942</td>\n",
+ " <td>2.124211</td>\n",
+ " <td>2.357804</td>\n",
+ " <td>3.153298</td>\n",
+ " <td>4.073904</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>3.330436</td>\n",
+ " <td>177.491845</td>\n",
+ " <td>1.275617</td>\n",
+ " <td>141.424807</td>\n",
+ " <td>44.593649</td>\n",
+ " <td>1919.601400</td>\n",
+ " <td>0.045255</td>\n",
+ " <td>0.046163</td>\n",
+ " <td>18.653671</td>\n",
+ " <td>0.199666</td>\n",
+ " <td>0.906823</td>\n",
+ " <td>1.710114</td>\n",
+ " <td>2.402340</td>\n",
+ " <td>4.016654</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>193.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.014231</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>18.000000</td>\n",
+ " <td>608.942857</td>\n",
+ " <td>0.002899</td>\n",
+ " <td>0.025000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>4.000000</td>\n",
+ " <td>94.700000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>38.000000</td>\n",
+ " <td>1477.154762</td>\n",
+ " <td>0.016667</td>\n",
+ " <td>0.048529</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>4.000000</td>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>27.000000</td>\n",
+ " <td>3398.750000</td>\n",
+ " <td>24.000000</td>\n",
+ " <td>2549.375000</td>\n",
+ " <td>705.000000</td>\n",
+ " <td>63973.522230</td>\n",
+ " <td>0.200000</td>\n",
+ " <td>0.200000</td>\n",
+ " <td>361.763742</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>13.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>20.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "count 12205.000000 12205.000000 12205.000000 \n",
+ "mean 2.338878 81.646331 0.508726 \n",
+ "std 3.330436 177.491845 1.275617 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 \n",
+ "50% 1.000000 9.000000 0.000000 \n",
+ "75% 4.000000 94.700000 0.000000 \n",
+ "max 27.000000 3398.750000 24.000000 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "count 12205.000000 12205.000000 12205.000000 \n",
+ "mean 34.825454 32.045637 1206.982457 \n",
+ "std 141.424807 44.593649 1919.601400 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 8.000000 193.000000 \n",
+ "50% 0.000000 18.000000 608.942857 \n",
+ "75% 0.000000 38.000000 1477.154762 \n",
+ "max 2549.375000 705.000000 63973.522230 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay \\\n",
+ "count 12205.000000 12205.000000 12205.000000 12205.000000 \n",
+ "mean 0.020370 0.041466 5.949574 0.061942 \n",
+ "std 0.045255 0.046163 18.653671 0.199666 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.014231 0.000000 0.000000 \n",
+ "50% 0.002899 0.025000 0.000000 0.000000 \n",
+ "75% 0.016667 0.048529 0.000000 0.000000 \n",
+ "max 0.200000 0.200000 361.763742 1.000000 \n",
+ "\n",
+ " OperatingSystems Browser Region TrafficType \n",
+ "count 12205.000000 12205.000000 12205.000000 12205.000000 \n",
+ "mean 2.124211 2.357804 3.153298 4.073904 \n",
+ "std 0.906823 1.710114 2.402340 4.016654 \n",
+ "min 1.000000 1.000000 1.000000 1.000000 \n",
+ "25% 2.000000 2.000000 1.000000 2.000000 \n",
+ "50% 2.000000 2.000000 3.000000 2.000000 \n",
+ "75% 3.000000 2.000000 4.000000 4.000000 \n",
+ "max 8.000000 13.000000 9.000000 20.000000 "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Summary statistics of the dataset\n",
+ "data.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "e33d1922",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False 10297\n",
+ "True 1908\n",
+ "Name: Revenue, dtype: int64"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Class imbalance\n",
+ "data['Revenue'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "277920d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Categorize data into numeric and categorical\n",
+ "\n",
+ "data['Weekend'] = data['Weekend'].astype('str')\n",
+ "data['Revenue'] = data['Revenue'].astype('str')\n",
+ "\n",
+ "numeric_cols, categorical_cols = categorize_data(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "cf7263f0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dtype('O')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[\"Weekend\"].dtype"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b9c4d65",
+ "metadata": {},
+ "source": [
+ "# Visualisations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "77615c11",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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EiIiahqKiIri7u9fqI0gZsLVUdVnYzs6OAUtE1MTV5lYhH3IiIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgG/KhEqjW9Xo+SkpJa11tZWcHGxkbGjoiIHl0MWKoVvV6PVh6eKLieX+tt7B0ccfnSRYYsETVJDFiqlZKSEhRcz8fAxduhtGl+3/oyfSG0S8ahpKSEAUtETRIDlupEadMcFnYOxm6DiOiRx4eciIiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpKB0QP2t99+w7hx4+Do6AgrKyt07doVqamp0nohBCIjI+Hm5gZLS0v06dMH6enpBvsoKytDeHg4nJycYG1tjaFDh+Lq1asGNQUFBQgNDYVKpYJKpUJoaCgKCwsfxhSJiKgJMmrAFhQUoHfv3jAzM8O+fftw9uxZfPDBB2jevLlUs3z5csTExCA2NhYpKSlQq9UICgpCcXGxVBMREYGEhATEx8fjyJEj0Ov1CAkJQUVFhVQzduxYpKWlQavVQqvVIi0tDaGhoQ9zukRE1ISYGvPg77//Ptzd3bF582ZpzNPTU/qzEAKrVq3CokWLMGLECADAli1b4OLigp07d2LKlCnQ6XTYtGkTtm3bhv79+wMAtm/fDnd3dxw8eBADBgxARkYGtFotkpOTERAQAADYuHEjNBoNMjMz4ePj8/AmTURETYJRz2C/+uor9OjRAyNHjoSzszO6deuGjRs3SuuzsrKQk5OD4OBgaUypVCIwMBBHjx4FAKSmpuLWrVsGNW5ubvD19ZVqjh07BpVKJYUrAPTs2RMqlUqquVNZWRmKiooMFiIiotoyasBeuHABa9euhbe3N/bv34+pU6di5syZ2Lp1KwAgJycHAODi4mKwnYuLi7QuJycH5ubmsLe3v2eNs7NzteM7OztLNXeKjo6W7teqVCq4u7s/2GSJiKhJMWrAVlZW4sknn0RUVBS6deuGKVOmYNKkSVi7dq1BnUKhMHgthKg2dqc7a2qqv9d+Fi5cCJ1OJy1Xrlyp7bSIiIiMG7Curq7o2LGjwViHDh1w+fJlAIBarQaAameZeXl50lmtWq1GeXk5CgoK7lmTm5tb7fjXrl2rdnZcRalUws7OzmAhIiKqLaMGbO/evZGZmWkwdu7cOXh4eAAAvLy8oFarkZiYKK0vLy9HUlISevXqBQDo3r07zMzMDGqys7Nx5swZqUaj0UCn0+HEiRNSzfHjx6HT6aQaIiKihmTUp4hnz56NXr16ISoqCqNGjcKJEyewYcMGbNiwAcCfl3UjIiIQFRUFb29veHt7IyoqClZWVhg7diwAQKVSYeLEiZg7dy4cHR3h4OCAefPmwc/PT3qquEOHDhg4cCAmTZqE9evXAwAmT56MkJAQPkFMRESyMGrA+vv7IyEhAQsXLsTSpUvh5eWFVatW4ZVXXpFqFixYgNLSUkybNg0FBQUICAjAgQMHYGtrK9WsXLkSpqamGDVqFEpLS9GvXz/ExcXBxMREqtmxYwdmzpwpPW08dOhQxMbGPrzJEhFRk6IQQghjN9EYFBUVQaVSQafTNcn7sVX3tIet2AsLO4f71t8suo4v54cgNze3xie4iYgao7pkgdE/KpGIiOhxxIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpKBUQM2MjISCoXCYFGr1dJ6IQQiIyPh5uYGS0tL9OnTB+np6Qb7KCsrQ3h4OJycnGBtbY2hQ4fi6tWrBjUFBQUIDQ2FSqWCSqVCaGgoCgsLH8YUiYioiTL6GWynTp2QnZ0tLadPn5bWLV++HDExMYiNjUVKSgrUajWCgoJQXFws1URERCAhIQHx8fE4cuQI9Ho9QkJCUFFRIdWMHTsWaWlp0Gq10Gq1SEtLQ2ho6EOdJxERNS2mRm/A1NTgrLWKEAKrVq3CokWLMGLECADAli1b4OLigp07d2LKlCnQ6XTYtGkTtm3bhv79+wMAtm/fDnd3dxw8eBADBgxARkYGtFotkpOTERAQAADYuHEjNBoNMjMz4ePjU2NfZWVlKCsrk14XFRU19NSJiOgxZvQz2F9++QVubm7w8vLC6NGjceHCBQBAVlYWcnJyEBwcLNUqlUoEBgbi6NGjAIDU1FTcunXLoMbNzQ2+vr5SzbFjx6BSqaRwBYCePXtCpVJJNTWJjo6WLimrVCq4u7s36LyJiOjxZtSADQgIwNatW7F//35s3LgROTk56NWrF/Lz85GTkwMAcHFxMdjGxcVFWpeTkwNzc3PY29vfs8bZ2bnasZ2dnaWamixcuBA6nU5arly58kBzJSKipsWol4gHDRok/dnPzw8ajQZt2rTBli1b0LNnTwCAQqEw2EYIUW3sTnfW1FR/v/0olUoolcpazYOIiOhORr9E/FfW1tbw8/PDL7/8It2XvfMsMy8vTzqrVavVKC8vR0FBwT1rcnNzqx3r2rVr1c6OiYiIGsojFbBlZWXIyMiAq6srvLy8oFarkZiYKK0vLy9HUlISevXqBQDo3r07zMzMDGqys7Nx5swZqUaj0UCn0+HEiRNSzfHjx6HT6aQaIiKihmbUS8Tz5s3DkCFD0KpVK+Tl5eHdd99FUVERwsLCoFAoEBERgaioKHh7e8Pb2xtRUVGwsrLC2LFjAQAqlQoTJ07E3Llz4ejoCAcHB8ybNw9+fn7SU8UdOnTAwIEDMWnSJKxfvx4AMHnyZISEhNz1CWIiIqIHZdSAvXr1KsaMGYM//vgDLVq0QM+ePZGcnAwPDw8AwIIFC1BaWopp06ahoKAAAQEBOHDgAGxtbaV9rFy5Eqamphg1ahRKS0vRr18/xMXFwcTERKrZsWMHZs6cKT1tPHToUMTGxj7cyRIRUZOiEEIIYzfRGBQVFUGlUkGn08HOzs7Y7Tx0Vfe1h63YCws7h/vW3yy6ji/nhyA3N7fGp7iJiBqjumTBI3UPloiI6HHBgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZPDIBGx0dDQUCgUiIiKkMSEEIiMj4ebmBktLS/Tp0wfp6ekG25WVlSE8PBxOTk6wtrbG0KFDcfXqVYOagoIChIaGQqVSQaVSITQ0FIWFhQ9hVkRE1FQ9EgGbkpKCDRs2oHPnzgbjy5cvR0xMDGJjY5GSkgK1Wo2goCAUFxdLNREREUhISEB8fDyOHDkCvV6PkJAQVFRUSDVjx45FWloatFottFot0tLSEBoa+tDmR0RETY/RA1av1+OVV17Bxo0bYW9vL40LIbBq1SosWrQII0aMgK+vL7Zs2YKSkhLs3LkTAKDT6bBp0yZ88MEH6N+/P7p164bt27fj9OnTOHjwIAAgIyMDWq0W//rXv6DRaKDRaLBx40bs3bsXmZmZRpkzERE9/owesNOnT8fgwYPRv39/g/GsrCzk5OQgODhYGlMqlQgMDMTRo0cBAKmpqbh165ZBjZubG3x9faWaY8eOQaVSISAgQKrp2bMnVCqVVFOTsrIyFBUVGSxERES1ZWrMg8fHx+Onn35CSkpKtXU5OTkAABcXF4NxFxcXXLp0SaoxNzc3OPOtqqnaPicnB87OztX27+zsLNXUJDo6GkuWLKnbhIiIiP7HaGewV65cwaxZs7B9+3ZYWFjctU6hUBi8FkJUG7vTnTU11d9vPwsXLoROp5OWK1eu3POYREREf2W0gE1NTUVeXh66d+8OU1NTmJqaIikpCR999BFMTU2lM9c7zzLz8vKkdWq1GuXl5SgoKLhnTW5ubrXjX7t2rdrZ8V8plUrY2dkZLERERLVltIDt168fTp8+jbS0NGnp0aMHXnnlFaSlpaF169ZQq9VITEyUtikvL0dSUhJ69eoFAOjevTvMzMwMarKzs3HmzBmpRqPRQKfT4cSJE1LN8ePHodPppBoiIqKGZrR7sLa2tvD19TUYs7a2hqOjozQeERGBqKgoeHt7w9vbG1FRUbCyssLYsWMBACqVChMnTsTcuXPh6OgIBwcHzJs3D35+ftJDUx06dMDAgQMxadIkrF+/HgAwefJkhISEwMfH5yHOmIiImhKjPuR0PwsWLEBpaSmmTZuGgoICBAQE4MCBA7C1tZVqVq5cCVNTU4waNQqlpaXo168f4uLiYGJiItXs2LEDM2fOlJ42Hjp0KGJjYx/6fIiIqOlQCCGEsZtoDIqKiqBSqaDT6Zrk/diq+9rDVuyFhZ3DfetvFl3Hl/NDkJubW+NT3EREjVFdssDovwdLRET0OKpXwLZu3Rr5+fnVxgsLC9G6desHboqIiKixq1fAXrx40eCzfquUlZXht99+e+CmiIiIGrs6PeT01VdfSX/ev38/VCqV9LqiogKHDh2Cp6dngzVHRETUWNUpYIcPHw7gz09GCgsLM1hnZmYGT09PfPDBBw3WHBERUWNVp4CtrKwEAHh5eSElJQVOTk6yNEVERNTY1ev3YLOyshq6DyIiosdKvT9o4tChQzh06BDy8vKkM9sqn3zyyQM3RkRE1JjVK2CXLFmCpUuXokePHnB1db3vt9sQERE1NfUK2HXr1iEuLg6hoaEN3Q8REdFjoV6/B1teXs5voiEiIrqHegXs66+/jp07dzZ0L0RERI+Nel0ivnnzJjZs2ICDBw+ic+fOMDMzM1gfExPTIM0RERE1VvUK2FOnTqFr164AgDNnzhis4wNPRERE9QzYw4cPN3QfREREjxV+XR0REZEM6nUG27dv33teCv7mm2/q3RAREdHjoF4BW3X/tcqtW7eQlpaGM2fOVPsSACIioqaoXgG7cuXKGscjIyOh1+sfqCEiIqLHQYPegx03bhw/h5iIiAgNHLDHjh2DhYVFQ+6SiIioUarXJeIRI0YYvBZCIDs7Gz/++CP+/ve/N0hjREREjVm9AlalUhm8btasGXx8fLB06VIEBwc3SGNERESNWb0CdvPmzQ3dBxER0WOl3l+4DgCpqanIyMiAQqFAx44d0a1bt4bqi4iIqFGrV8Dm5eVh9OjR+Pbbb9G8eXMIIaDT6dC3b1/Ex8ejRYsWDd0nERFRo1Kvp4jDw8NRVFSE9PR0XL9+HQUFBThz5gyKioowc+bMhu6RiIio0anXGaxWq8XBgwfRoUMHaaxjx474+OOP+ZATERER6nkGW1lZWe07YAHAzMwMlZWVD9wUERFRY1evgH3uuecwa9Ys/P7779LYb7/9htmzZ6Nfv34N1hwREVFjVa+AjY2NRXFxMTw9PdGmTRu0bdsWXl5eKC4uxurVqxu6RyIiokanXvdg3d3d8dNPPyExMRH//e9/IYRAx44d0b9//4buj4iIqFGq0xnsN998g44dO6KoqAgAEBQUhPDwcMycORP+/v7o1KkTvv/+e1kaJSIiakzqFLCrVq3CpEmTYGdnV22dSqXClClTEBMT02DNERERNVZ1Ctiff/4ZAwcOvOv64OBgpKam1np/a9euRefOnWFnZwc7OztoNBrs27dPWi+EQGRkJNzc3GBpaYk+ffogPT3dYB9lZWUIDw+Hk5MTrK2tMXToUFy9etWgpqCgAKGhoVCpVFCpVAgNDUVhYWGt+yQiIqqrOgVsbm5ujb+eU8XU1BTXrl2r9f5atmyJ9957Dz/++CN+/PFHPPfccxg2bJgUosuXL0dMTAxiY2ORkpICtVqNoKAgFBcXS/uIiIhAQkIC4uPjceTIEej1eoSEhKCiokKqGTt2LNLS0qDVaqHVapGWlobQ0NC6TJ2IiKhO6vSQ0xNPPIHTp0+jbdu2Na4/deoUXF1da72/IUOGGLxetmwZ1q5di+TkZHTs2BGrVq3CokWLpK/H27JlC1xcXLBz505MmTIFOp0OmzZtwrZt26QHrLZv3w53d3ccPHgQAwYMQEZGBrRaLZKTkxEQEAAA2LhxIzQaDTIzM+Hj41OXt4CIiKhW6nQG+/zzz+Mf//gHbt68WW1daWkpFi9ejJCQkHo1UlFRgfj4eNy4cQMajQZZWVnIyckx+GQopVKJwMBAHD16FMCfXzZw69Ytgxo3Nzf4+vpKNceOHYNKpZLCFQB69uwJlUol1dSkrKwMRUVFBgsREVFt1ekM9u2338bu3bvRrl07zJgxAz4+PlAoFMjIyMDHH3+MiooKLFq0qE4NnD59GhqNBjdv3oSNjQ0SEhLQsWNHKfxcXFwM6l1cXHDp0iUAQE5ODszNzWFvb1+tJicnR6pxdnaudlxnZ2eppibR0dFYsmRJneZCRERUpU4B6+LigqNHj+Jvf/sbFi5cCCEEAEChUGDAgAFYs2ZNtUC8Hx8fH6SlpaGwsBBffPEFwsLCkJSUJK1XKBQG9UKIamN3urOmpvr77WfhwoWYM2eO9LqoqAju7u73nQ8RERFQjw+a8PDwwNdff42CggKcP38eQgh4e3tXO4usLXNzc+mebo8ePZCSkoIPP/wQb7zxBoA/z0D/el83Ly9PCnG1Wo3y8nIUFBQYHD8vLw+9evWSanJzc6sd99q1a/f8YUCpVEKpVNZrTkRERPX6qEQAsLe3h7+/P5566ql6h2tNhBAoKyuDl5cX1Go1EhMTpXXl5eVISkqSwrN79+4wMzMzqMnOzsaZM2ekGo1GA51OhxMnTkg1x48fh06nk2qIiIgaWr0+KrGhvPXWWxg0aBDc3d1RXFyM+Ph4fPvtt9BqtVAoFIiIiEBUVBS8vb3h7e2NqKgoWFlZYezYsQD+/HCLiRMnYu7cuXB0dISDgwPmzZsHPz8/6aniDh06YODAgZg0aRLWr18PAJg8eTJCQkL4BDEREcnGqAGbm5uL0NBQZGdnQ6VSoXPnztBqtQgKCgIALFiwAKWlpZg2bRoKCgoQEBCAAwcOwNbWVtrHypUrYWpqilGjRqG0tBT9+vVDXFwcTExMpJodO3Zg5syZ0tPGQ4cORWxs7MOdLBERNSkKUfWkEt1TUVERVCoVdDpdjR8V+biruvc9bMVeWNg53Lf+ZtF1fDk/BLm5uTU+xU1E1BjVJQvqfQ+WiIiI7o4BS0REJAMGLBERkQwYsERERDJgwBIREcmAAUtERCQDBiwREZEMGLBEREQyYMASERHJgAFLREQkAwYsERGRDBiwREREMjDqt+k0RXq9HiUlJbWut7Kygo2NjYwdERGRHBiwD5Fer0crD08UXM+v9Tb2Do64fOkiQ/YB8QcbInrYGLAPUUlJCQqu52Pg4u1Q2jS/b32ZvhDaJeNQUlLCf+wfAH+wISJjYMAagdKmea2+U5UaBn+wISJjYMBSk8EfbIjoYeJTxERERDJgwBIREcmAAUtERCQDBiwREZEMGLBEREQyYMASERHJgAFLREQkAwYsERGRDBiwREREMmDAEhERyYABS0REJAMGLBERkQwYsERERDJgwBIREcmAAUtERCQDBiwREZEMGLBEREQyMGrARkdHw9/fH7a2tnB2dsbw4cORmZlpUCOEQGRkJNzc3GBpaYk+ffogPT3doKasrAzh4eFwcnKCtbU1hg4diqtXrxrUFBQUIDQ0FCqVCiqVCqGhoSgsLJR7ikRE1EQZNWCTkpIwffp0JCcnIzExEbdv30ZwcDBu3Lgh1SxfvhwxMTGIjY1FSkoK1Go1goKCUFxcLNVEREQgISEB8fHxOHLkCPR6PUJCQlBRUSHVjB07FmlpadBqtdBqtUhLS0NoaOhDnS8RETUdpsY8uFarNXi9efNmODs7IzU1Fc8++yyEEFi1ahUWLVqEESNGAAC2bNkCFxcX7Ny5E1OmTIFOp8OmTZuwbds29O/fHwCwfft2uLu74+DBgxgwYAAyMjKg1WqRnJyMgIAAAMDGjRuh0WiQmZkJHx+fhztxIiJ67D1S92B1Oh0AwMHBAQCQlZWFnJwcBAcHSzVKpRKBgYE4evQoACA1NRW3bt0yqHFzc4Ovr69Uc+zYMahUKilcAaBnz55QqVRSzZ3KyspQVFRksBAREdXWIxOwQgjMmTMHTz/9NHx9fQEAOTk5AAAXFxeDWhcXF2ldTk4OzM3NYW9vf88aZ2fnasd0dnaWau4UHR0t3a9VqVRwd3d/sAkSEVGT8sgE7IwZM3Dq1Cns2rWr2jqFQmHwWghRbexOd9bUVH+v/SxcuBA6nU5arly5UptpEBERAXhEAjY8PBxfffUVDh8+jJYtW0rjarUaAKqdZebl5UlntWq1GuXl5SgoKLhnTW5ubrXjXrt2rdrZcRWlUgk7OzuDhYiIqLaMGrBCCMyYMQO7d+/GN998Ay8vL4P1Xl5eUKvVSExMlMbKy8uRlJSEXr16AQC6d+8OMzMzg5rs7GycOXNGqtFoNNDpdDhx4oRUc/z4ceh0OqmGiIioIRn1KeLp06dj586d+PLLL2FrayudqapUKlhaWkKhUCAiIgJRUVHw9vaGt7c3oqKiYGVlhbFjx0q1EydOxNy5c+Ho6AgHBwfMmzcPfn5+0lPFHTp0wMCBAzFp0iSsX78eADB58mSEhITwCWIiIpKFUQN27dq1AIA+ffoYjG/evBnjx48HACxYsAClpaWYNm0aCgoKEBAQgAMHDsDW1laqX7lyJUxNTTFq1CiUlpaiX79+iIuLg4mJiVSzY8cOzJw5U3raeOjQoYiNjZV3gkRE1GQZNWCFEPetUSgUiIyMRGRk5F1rLCwssHr1aqxevfquNQ4ODti+fXt92iQiIqqzR+IhJyIioscNA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAZGDdjvvvsOQ4YMgZubGxQKBfbs2WOwXgiByMhIuLm5wdLSEn369EF6erpBTVlZGcLDw+Hk5ARra2sMHToUV69eNagpKChAaGgoVCoVVCoVQkNDUVhYKPPsiIioKTNqwN64cQNdunRBbGxsjeuXL1+OmJgYxMbGIiUlBWq1GkFBQSguLpZqIiIikJCQgPj4eBw5cgR6vR4hISGoqKiQasaOHYu0tDRotVpotVqkpaUhNDRU9vkREVHTZWrMgw8aNAiDBg2qcZ0QAqtWrcKiRYswYsQIAMCWLVvg4uKCnTt3YsqUKdDpdNi0aRO2bduG/v37AwC2b98Od3d3HDx4EAMGDEBGRga0Wi2Sk5MREBAAANi4cSM0Gg0yMzPh4+PzcCZLRERNyiN7DzYrKws5OTkIDg6WxpRKJQIDA3H06FEAQGpqKm7dumVQ4+bmBl9fX6nm2LFjUKlUUrgCQM+ePaFSqaSampSVlaGoqMhgISIiqq1HNmBzcnIAAC4uLgbjLi4u0rqcnByYm5vD3t7+njXOzs7V9u/s7CzV1CQ6Olq6Z6tSqeDu7v5A8yEioqblkQ3YKgqFwuC1EKLa2J3urKmp/n77WbhwIXQ6nbRcuXKljp0TEVFT9sgGrFqtBoBqZ5l5eXnSWa1arUZ5eTkKCgruWZObm1tt/9euXat2dvxXSqUSdnZ2BgsREVFtPbIB6+XlBbVajcTERGmsvLwcSUlJ6NWrFwCge/fuMDMzM6jJzs7GmTNnpBqNRgOdTocTJ05INcePH4dOp5NqiIiIGppRnyLW6/U4f/689DorKwtpaWlwcHBAq1atEBERgaioKHh7e8Pb2xtRUVGwsrLC2LFjAQAqlQoTJ07E3Llz4ejoCAcHB8ybNw9+fn7SU8UdOnTAwIEDMWnSJKxfvx4AMHnyZISEhPAJYiIiko1RA/bHH39E3759pddz5swBAISFhSEuLg4LFixAaWkppk2bhoKCAgQEBODAgQOwtbWVtlm5ciVMTU0xatQolJaWol+/foiLi4OJiYlUs2PHDsycOVN62njo0KF3/d1bIiKihmDUgO3Tpw+EEHddr1AoEBkZicjIyLvWWFhYYPXq1Vi9evVdaxwcHLB9+/YHaZWIiKhOHtl7sERERI0ZA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZGPX7YIno/vR6PUpKSmpdb2VlBRsbGxk7IqLaYMASPcL0ej1aeXii4Hp+rbexd3DE5UsXGbJERsaAJXqElZSUoOB6PgYu3g6lTfP71pfpC6FdMg4lJSUMWCIjY8ASNQJKm+awsHMwdhtEVAd8yImIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZ8OvqiIio0dLr9SgpKal1vZWV1UP7ruQmFbBr1qzBihUrkJ2djU6dOmHVqlV45plnjN0WERHVg16vRysPTxRcz6/1NvYOjrh86eJDCdkmE7CffvopIiIisGbNGvTu3Rvr16/HoEGDcPbsWbRq1crY7RERUR2VlJSg4Ho+Bi7eDqVN8/vWl+kLoV0yDiUlJQzYhhQTE4OJEyfi9ddfBwCsWrUK+/fvx9q1axEdHW3k7ogIeLQv990PezcepU1zWNg5GLuNappEwJaXlyM1NRVvvvmmwXhwcDCOHj1a4zZlZWUoKyuTXut0OgBAUVFRvfsoLi4GANzI/x23bt7/P+byG4UAgKysLGlbY/njjz8AsPeHrTH3Xlc3btzAs4F9oCssqPU2qub2+C7pW1hbW8vY2f2xd+Oo7/8fxcXFsLCwqNcxqzJACHH/YtEE/PbbbwKA+OGHHwzGly1bJtq1a1fjNosXLxYAuHDhwoULl2rLlStX7ps9TeIMtopCoTB4LYSoNlZl4cKFmDNnjvS6srIS169fh6Oj4123uZ+ioiK4u7vjypUrsLOzq9c+HhWPy1w4j0fP4zIXzuPR0xBzEUKguLgYbm5u961tEgHr5OQEExMT5OTkGIzn5eXBxcWlxm2USiWUSqXBWPPmzRukHzs7u0b/H2qVx2UunMej53GZC+fx6HnQuahUqlrVNYkPmjA3N0f37t2RmJhoMJ6YmIhevXoZqSsiInqcNYkzWACYM2cOQkND0aNHD2g0GmzYsAGXL1/G1KlTjd0aERE9hppMwL788svIz8/H0qVLkZ2dDV9fX3z99dfw8PB4aD0olUosXry42qXnxuhxmQvn8eh5XObCeTx6HvZcFELU5lljIiIiqosmcQ+WiIjoYWPAEhERyYABS0REJAMGLBERkQwYsA/Jd999hyFDhsDNzQ0KhQJ79uwxdkt1Fh0dDX9/f9ja2sLZ2RnDhw9HZmamsduql7Vr16Jz587SL5xrNBrs27fP2G09sOjoaCgUCkRERBi7lTqJjIyEQqEwWNRqtbHbqrfffvsN48aNg6OjI6ysrNC1a1ekpqYau6068fT0rPZ3olAoMH36dGO3Vie3b9/G22+/DS8vL1haWqJ169ZYunQpKisrZT92k/k1HWO7ceMGunTpgtdeew0vvviisdupl6SkJEyfPh3+/v64ffs2Fi1ahODgYJw9e9boH/pdVy1btsR7772Htm3bAgC2bNmCYcOG4eTJk+jUqZORu6uflJQUbNiwAZ07dzZ2K/XSqVMnHDx4UHptYmJixG7qr6CgAL1790bfvn2xb98+ODs749dff22wT4J7WFJSUlBRUSG9PnPmDIKCgjBy5EgjdlV377//PtatW4ctW7agU6dO+PHHH/Haa69BpVJh1qxZ8h78gT9Jn+oMgEhISDB2Gw8sLy9PABBJSUnGbqVB2Nvbi3/961/GbqNeiouLhbe3t0hMTBSBgYFi1qxZxm6pThYvXiy6dOli7DYaxBtvvCGefvppY7fR4GbNmiXatGkjKisrjd1KnQwePFhMmDDBYGzEiBFi3Lhxsh+bl4ip3qq+ws/B4dH7Hsa6qKioQHx8PG7cuAGNRmPsdupl+vTpGDx4MPr372/sVurtl19+gZubG7y8vDB69GhcuHDB2C3Vy1dffYUePXpg5MiRcHZ2Rrdu3bBx40Zjt/VAysvLsX37dkyYMKHeX3ZiLE8//TQOHTqEc+fOAQB+/vlnHDlyBM8//7zsx+YlYqoXIQTmzJmDp59+Gr6+vsZup15Onz4NjUaDmzdvwsbGBgkJCejYsaOx26qz+Ph4/PTTT0hJSTF2K/UWEBCArVu3ol27dsjNzcW7776LXr16IT09HY6OjsZur04uXLiAtWvXYs6cOXjrrbdw4sQJzJw5E0qlEq+++qqx26uXPXv2oLCwEOPHjzd2K3X2xhtvQKfToX379jAxMUFFRQWWLVuGMWPGyH9w2c+RqRo8BpeIp02bJjw8PGr1nYiPqrKyMvHLL7+IlJQU8eabbwonJyeRnp5u7Lbq5PLly8LZ2VmkpaVJY43xEvGd9Hq9cHFxER988IGxW6kzMzMzodFoDMbCw8NFz549jdTRgwsODhYhISHGbqNedu3aJVq2bCl27dolTp06JbZu3SocHBxEXFyc7MdmwBpBYw/YGTNmiJYtW4oLFy4Yu5UG1a9fPzF58mRjt1EnCQkJAoAwMTGRFgBCoVAIExMTcfv2bWO3WG/9+/cXU6dONXYbddaqVSsxceJEg7E1a9YINzc3I3X0YC5evCiaNWsm9uzZY+xW6qVly5YiNjbWYOydd94RPj4+sh+bl4ip1oQQCA8PR0JCAr799lt4eXkZu6UGJYRAWVmZsduok379+uH06dMGY6+99hrat2+PN954o9E+iVtWVoaMjAw888wzxm6lznr37l3t19fOnTv3UL9YpCFt3rwZzs7OGDx4sLFbqZeSkhI0a2b4uJGJiQl/Tedxotfrcf78eel1VlYW0tLS4ODggFatWhmxs9qbPn06du7ciS+//BK2trbSF9irVCpYWloaubu6eeuttzBo0CC4u7ujuLgY8fHx+Pbbb6HVao3dWp3Y2tpWuwdubW0NR0fHRnVvfN68eRgyZAhatWqFvLw8vPvuuygqKkJYWJixW6uz2bNno1evXoiKisKoUaNw4sQJbNiwARs2bDB2a3VWWVmJzZs3IywsDKamjTMuhgwZgmXLlqFVq1bo1KkTTp48iZiYGEyYMEH+g8t+jkxCCCEOHz4sAFRbwsLCjN1ardXUPwCxefNmY7dWZxMmTBAeHh7C3NxctGjRQvTr108cOHDA2G01iMZ4D/bll18Wrq6uwszMTLi5uYkRI0Y0uvvhf/Wf//xH+Pr6CqVSKdq3by82bNhg7JbqZf/+/QKAyMzMNHYr9VZUVCRmzZolWrVqJSwsLETr1q3FokWLRFlZmezH5tfVERERyYC/B0tERCQDBiwREZEMGLBEREQyYMASERHJgAFLREQkAwYsERGRDBiwREREMmDAEhERyYABS9TEjB8/HsOHDzd2G0SPPQYsUT1duXIFEydOhJubG8zNzeHh4YFZs2YhPz/f2K0BAC5evAiFQoG0tDSD8Q8//BBxcXENfrzDhw+jb9++cHBwgJWVFby9vREWFobbt283yP4VCgX27NnTIPsiehgYsET1cOHCBfTo0QPnzp3Drl27cP78eaxbtw6HDh2CRqPB9evXZTv2rVu3Hmh7lUqF5s2bN0wz/5Oeno5BgwbB398f3333HU6fPo3Vq1fDzMzsoXxrCdEjSfZPOyZ6DA0cOFC0bNlSlJSUGIxnZ2cLKysr6XtMPTw8xNKlS8WYMWOEtbW1cHV1FR999JHBNoWFhWLSpEmiRYsWwtbWVvTt29fgC9QXL14sunTpIjZt2iS8vLyEQqEQlZWVYt++faJ3795CpVIJBwcHMXjwYHH+/HlpO9zxpQyBgYFCCCHCwsLEsGHDpLrAwEARHh4u5s+fL+zt7YWLi4tYvHixQY8ZGRmid+/eQqlUig4dOojExESD7zVeuXKl8PT0vOv7pdfrha2trfj8888Nxr/66ithZWUlioqKRFlZmZg+fbpQq9VCqVQKDw8PERUVJb2Pf52Lh4eHwT6efPJJoVQqhZeXl4iMjBS3bt0yeB/WrVsnBg8eLCwtLUX79u3F0aNHxS+//CICAwOFlZWV6Nmzp8F7l5aWJvr06SNsbGyEra2tePLJJ0VKSspd50dUEwYsUR3l5+cLhUIh/eN/p0mTJgl7e3tRWVkpPDw8hK2trYiOjhaZmZnio48+EiYmJtI391RWVorevXuLIUOGiJSUFHHu3Dkxd+5c4ejoKPLz84UQfwastbW1GDBggPjpp5/Ezz//LCorK8W///1v8cUXX4hz586JkydPiiFDhgg/Pz9RUVEhhBDixIkTAoA4ePCgyM7OlvZXU8Da2dmJyMhIce7cObFlyxahUCikHisqKoSPj48ICgoSaWlp4vvvvxdPPfWUQcDu2rVLKJVKkZSUdNf3bdKkSeL55583GHvhhRfEq6++KoQQYsWKFcLd3V1899134uLFi+L7778XO3fuFEIIkZeXJ31zU3Z2tsjLyxNCCKHVaoWdnZ2Ii4sTv/76qzhw4IDw9PQUkZGR0jEAiCeeeEJ8+umnIjMzUwwfPlx4enqK5557Tmi1WnH27FnRs2dPMXDgQGmbTp06iXHjxomMjAxx7tw58dlnnxn80ENUGwxYojpKTk42CJc7xcTECAAiNzdXeHh4GPzDLcSfX802aNAgIYQQhw4dEnZ2duLmzZsGNW3atBHr168XQvwZsGZmZlKo3E1VCJ0+fVoIIURWVpYAIE6ePGlQV1PAPv300wY1/v7+4o033hBCCLFv3z5hamoqsrOzpfV3nsHevn1bjB8/XgAQarVaDB8+XKxevVrodDppm+PHjwsTExPx22+/CSGEuHbtmjAzMxPffvutEEKI8PBw8dxzz4nKysoa51fTe/7MM89U+0Fn27ZtwtXV1WC7t99+W3p97NgxAUBs2rRJGtu1a5ewsLCQXtva2oq4uLga+yCqLd6DJWpg4n/fAKlQKAAAGo3GYL1Go0FGRgYAIDU1FXq9Ho6OjrCxsZGWrKws/Prrr9I2Hh4eaNGihcF+fv31V4wdOxatW7eGnZ0dvLy8AACXL1+uc8+dO3c2eO3q6oq8vDwAQGZmJtzd3aFWq6X1Tz31lEG9iYkJNm/ejKtXr2L58uVwc3PDsmXL0KlTJ2RnZ0vbdOrUCVu3bgUAbNu2Da1atcKzzz4L4M+nm9PS0uDj44OZM2fiwIED9+07NTUVS5cuNXjvJk2ahOzsbJSUlNQ4PxcXFwCAn5+fwdjNmzdRVFQEAJgzZw5ef/119O/fH++9957B3wVRbTFgieqobdu2UCgUOHv2bI3r//vf/8Le3h5OTk533UdV+FZWVsLV1RVpaWkGS2ZmJubPny/VW1tbV9vHkCFDkJ+fj40bN+L48eM4fvw4AKC8vLzOczIzM6vWX9XDSUIIqd/7eeKJJxAaGoqPP/4YZ8+exc2bN7Fu3Tpp/euvv47NmzcDADZv3ozXXntN2veTTz6JrKwsvPPOOygtLcWoUaPw0ksv3fN4lZWVWLJkicF7d/r0afzyyy+wsLCocX5Vx6tprGrOkZGRSE9Px+DBg/HNN9+gY8eOSEhIqNV7QFTF1NgNEDU2jo6OCAoKwpo1azB79mxYWlpK63JycrBjxw68+uqr0j/aycnJBtsnJyejffv2AP4MlZycHJiamsLT07PWPeTn5yMjIwPr16/HM888AwA4cuSIQY25uTkAoKKios5z/Kv27dvj8uXLyM3Nlc7+UlJS7rudvb09XF1dcePGDWls3LhxWLBgAT766COkp6cjLCzMYBs7Ozu8/PLLePnll/HSSy9h4MCBuH79OhwcHGBmZlZtLk8++SQyMzPRtm3bB5pjTdq1a4d27dph9uzZGDNmDDZv3owXXnihwY9Djy8GLFE9xMbGolevXhgwYADeffddeHl5IT09HfPnz8cTTzyBZcuWSbU//PADli9fjuHDhyMxMRGff/45/u///g8A0L9/f2g0GgwfPhzvv/8+fHx88Pvvv+Prr7/G8OHD0aNHjxqPb29vD0dHR2zYsAGurq64fPky3nzzTYMaZ2dnWFpaQqvVomXLlrCwsIBKparzXIOCgtCmTRuEhYVh+fLlKC4uxqJFiwD8/zO/9evXIy0tDS+88ALatGmDmzdvYuvWrUhPT8fq1asN+h4xYgTmz5+P4OBgtGzZUlq3cuVKuLq6omvXrmjWrBk+//xzqNVq6VeKPD09cejQIfTu3RtKpRL29vb4xz/+gZCQELi7u2PkyJFo1qwZTp06hdOnT+Pdd9+t81wBoLS0FPPnz8dLL70ELy8vXL16FSkpKXjxxRfrtT9quniJmKgevL298eOPP6JNmzZ4+eWX0aZNG0yePBl9+/bFsWPH4ODgINXOnTsXqamp6NatG9555x188MEHGDBgAIA/A+rrr7/Gs88+iwkTJqBdu3YYPXo0Ll68KJ0t1qRZs2aIj49HamoqfH19MXv2bKxYscKgxtTUFB999BHWr18PNzc3DBs2rF5zNTExwZ49e6DX6+Hv74/XX38db7/9NgBIl2Gfeuop6PV6TJ06FZ06dUJgYCCSk5OxZ88eBAYGGuxv4sSJKC8vx4QJEwzGbWxs8P7776NHjx7w9/fHxYsX8fXXX6NZsz//mfrggw+QmJgId3d3dOvWDQAwYMAA7N27F4mJifD390fPnj0RExMDDw+Pes21ar75+fl49dVX0a5dO4waNQqDBg3CkiVL6r1PapoUouqJDCJqcJ6enoiIiEBERISxW2lQP/zwA55++mmcP38ebdq0qdO2O3bswKxZs/D7779Ll7GJHke8RExE95WQkAAbGxt4e3vj/PnzmDVrFnr37l2ncC0pKUFWVhaio6MxZcoUhis99niJmIjuq7i4GNOmTUP79u0xfvx4+Pv748svv6zTPpYvX46uXbvCxcUFCxculKlTokcHLxETERHJgGewREREMmDAEhERyYABS0REJAMGLBERkQwYsERERDJgwBIREcmAAUtERCQDBiwREZEM/h8pTpNB8EbtFQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Check distributions of numeric columns\n",
+ "for i in range (len(numeric_cols)):\n",
+ " plt.figure(figsize=(5,4))\n",
+ " sns.histplot(data[numeric_cols[i]], bins=30)\n",
+ " plt.title(numeric_cols[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "a64255ad",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AGYEAAGQEAgCQEQgAQEYgAAAZgQAAZAQCAJARCABARiAAABmBAABkBAIAkBEIAEBGIAAAGYEAAGQEAgCQEQgAQEYgAAAZgQAAZAQCAJARCABARiAAABmBAABkBAIAkBEIAEBGIAAAGYEAAGQEAgCQEQgAQEYgAAAZgQAAZAQCAJARCABARiAAABmBAABkBAIAkBEIAEBGIAAAGYEAAGQEAgCQEQgAQEYgAAAZgQAAZAQCAJARCABARiAAABmBAABkqis9gM4U2loi9WbF1nc7/voAU2hrqfQQADiI9dlAGPLk8r3eRt1Tt5dhJABw8PEWAwCQ6VNnEGpra+O+++7bq22klGLXrl0REVFTUxOFQqEcQ6uo2traSg8BgINMnwqEQqEQAwcO3OvtDBo0qAyjAYCDl7cYAICMQAAAMgIBAMgIBAAgIxAAgIxAAAAyAgEAyAgEACAjEACAjEAAADICAQDICAQAICMQAICMQAAAMgIBAMgIBAAgIxAAgIxAAAAyAgEAyAgEACAjEACAjEAAADICAQDICAQAICMQAICMQAAAMgIBAMgIBAAgIxAAgIxAAAAyAgEAyAgEACAjEACATHVvV0wpRUTEjh07yjYYAGDfev95+/3n8c70OhB27twZERFjx47t7SYAgArZuXNn1NfXd7q8kLpLiE60tbXF5s2bo66uLgqFQq8HuKcdO3bE2LFj48UXX4yhQ4eWbbsHK/uzfOzL8rI/y8e+LK8P+v5MKcXOnTtj9OjRUVXV+ZUGvT6DUFVVFWPGjOnt6t0aOnToB/IHUyn2Z/nYl+Vlf5aPfVleH+T92dWZg/e5SBEAyAgEACDT5wKhpqYmFixYEDU1NZUeygeC/Vk+9mV52Z/lY1+Wl/35nl5fpAgAfHD1uTMIAEDlCQQAICMQAICMQAAAMn0uEG666aYYN25c1NbWxoknnhg///nPKz2kA9LChQujUCi0u40cObLSwzog/OxnP4szzjgjRo8eHYVCIe666652y1NKsXDhwhg9enQMHDgwpkyZEs8++2xlBnsA6G5/XnDBBdmxevLJJ1dmsH3cNddcE3/0R38UdXV1cdhhh8XMmTNjw4YN7e7j+CxNKfvyYD82+1Qg3HHHHXHZZZfFV7/61Vi3bl188pOfjOnTp8emTZsqPbQD0jHHHBNbtmwp3tavX1/pIR0Q3nzzzTjuuOPixhtv7HD5t771rVi0aFHceOONsXbt2hg5cmR85jOfKf59Etrrbn9GREybNq3dsbpy5cr9OMIDxyOPPBJ/+Zd/GWvWrIlVq1ZFS0tLTJ06Nd58883ifRyfpSllX0Yc5Mdm6kM+/vGPp7lz57ab95GPfCRdddVVFRrRgWvBggXpuOOOq/QwDngRke68887idFtbWxo5cmS69tpri/Oam5tTfX19Wrx4cQVGeGDZc3+mlNKcOXPSjBkzKjKeA922bdtSRKRHHnkkpeT43Bt77suUHJt95gzCO++8E48//nhMnTq13fypU6fG6tWrKzSqA9tvfvObGD16dIwbNy5mzZoVzz33XKWHdMDbuHFjbN26td1xWlNTE5/+9Kcdp3uhqakpDjvssDj66KPjC1/4Qmzbtq3SQzogvP766xERccghh0SE43Nv7Lkv33cwH5t9JhBeeeWVaG1tjYaGhnbzGxoaYuvWrRUa1YHrpJNOittuuy0eeOCB+N73vhdbt26NyZMnx/bt2ys9tAPa+8ei47R8pk+fHkuXLo2HHnoorr/++li7dm2ceuqpsWvXrkoPrU9LKcXll18en/jEJ2LChAkR4fjsrY72ZYRjs9d/zXFf2fNPR6eUyvrnpA8W06dPL3597LHHxqRJk+LDH/5w3HrrrXH55ZdXcGQfDI7T8mlsbCx+PWHChJg4cWIceeSRce+998ZZZ51VwZH1bfPnz4+nn346fvGLX2TLHJ8909m+PNiPzT5zBmHEiBHRr1+/rHK3bduW1TA9N3jw4Dj22GPjN7/5TaWHckB7/5MgjtN9Z9SoUXHkkUc6Vrtw6aWXxt133x0PP/xwjBkzpjjf8dlzne3Ljhxsx2afCYQBAwbEiSeeGKtWrWo3f9WqVTF58uQKjeqDY9euXfGrX/0qRo0aVemhHNDGjRsXI0eObHecvvPOO/HII484Tstk+/bt8eKLLzpWO5BSivnz58ePf/zjeOihh2LcuHHtljs+S9fdvuzIQXdsVvACycztt9+e+vfvn5YsWZJ++ctfpssuuywNHjw4Pf/885Ue2gHniiuuSE1NTem5555La9asSaeffnqqq6uzL0uwc+fOtG7durRu3boUEWnRokVp3bp16YUXXkgppXTttdem+vr69OMf/zitX78+nXfeeWnUqFFpx44dFR5539TV/ty5c2e64oor0urVq9PGjRvTww8/nCZNmpQOP/xw+7MD8+bNS/X19ampqSlt2bKleHvrrbeK93F8lqa7fenYTKlPBUJKKX37299ORx55ZBowYEA64YQT2n3khNI1NjamUaNGpf79+6fRo0ens846Kz377LOVHtYB4eGHH04Rkd3mzJmTUnrvo2QLFixII0eOTDU1NelTn/pUWr9+fWUH3Yd1tT/feuutNHXq1HTooYem/v37pyOOOCLNmTMnbdq0qdLD7pM62o8RkW655ZbifRyfpeluXzo2U/LnngGATJ+5BgEA6DsEAgCQEQgAQEYgAAAZgQAAZAQCAJARCABARiAAABmBAH3AwoUL42Mf+9hebeP555+PQqEQTz755H79vuXUm8cA7BsCAfaR1atXR79+/WLatGn75fuNHTs2tmzZ0u7v2Xfnr//6r+PBBx8s6b7ljokLLrggZs6c2W5ebx4DsG8IBNhHvv/978ell14av/jFL2LTpk37/Pv169cvRo4cGdXV1SWvM2TIkBg+fHhZx/Huu+/2et3ePAZg3xAIsA+8+eabsWLFipg3b16cfvrp8YMf/KDd8muvvTYaGhqirq4uLrroomhubm63/P1X1//8z/8cDQ0NMWzYsPj6178eLS0tceWVV8YhhxwSY8aMie9///vFdfY8Pd/U1BSFQiEefPDBmDhxYgwaNCgmT54cGzZsKK6z51mBpqam+PjHPx6DBw+OYcOGxR//8R/HCy+8ED/4wQ/i61//ejz11FNRKBSiUCgUH1OhUIjFixfHjBkzYvDgwfGP//iP0draGhdddFGMGzcuBg4cGOPHj48bbrih3fe99dZb4yc/+Ulxe01NTe0eQ1tbW4wZMyYWL17cbt888cQTUSgU4rnnnouIiNdffz0uvvjiOOyww2Lo0KFx6qmnxlNPPdXbHx3wWwIB9oE77rgjxo8fH+PHj4/Pf/7zccstt8T7fxdtxYoVsWDBgvinf/qneOyxx2LUqFFx0003Zdt46KGHYvPmzfGzn/0sFi1aFAsXLozTTz89fu/3fi8effTRmDt3bsydOzdefPHFLsfy1a9+Na6//vp47LHHorq6Oi688MIO79fS0hIzZ86MT3/60/H000/Hf/7nf8bFF18chUIhGhsb44orrohjjjkmtmzZElu2bInGxsbiugsWLIgZM2bE+vXr48ILLyw+ua9YsSJ++ctfxtVXXx1/93d/FytWrIiI997aOPfcc2PatGnF7U2ePLndeKqqqmLWrFmxdOnSdvOXLVsWkyZNig996EORUorPfe5zsXXr1li5cmU8/vjjccIJJ8Sf/MmfxP/93/91/4MCOlfZPyYJH0yTJ09O//Iv/5JSSundd99NI0aMSKtWrUoppTRp0qQ0d+7cdvc/6aST0nHHHVecnjNnTjryyCNTa2trcd748ePTJz/5yeJ0S0tLGjx4cFq+fHlKKaWNGzemiEjr1q1LKf3uzyz/9Kc/La5z7733pohIb7/9dkoppQULFhS/7/bt21NEpKampg4f0+733V1EpMsuu6zbfXLJJZeks88+u91jnDFjRrv77PkYnnjiiVQoFNLzzz+fUkqptbU1HX744enb3/52SimlBx98MA0dOjQ1Nze3286HP/zhdPPNN3c7JqBzziBAmW3YsCH+67/+K2bNmhUREdXV1dHY2Fh8O+BXv/pVTJo0qd06e05HRBxzzDFRVfW7f6INDQ1x7LHHFqf79esXw4cPj23btnU5no9+9KPFr0eNGhUR0eE6hxxySFxwwQXx2c9+Ns4444y44YYbYsuWLd093IiImDhxYjZv8eLFMXHixDj00ENjyJAh8b3vfa/H12Icf/zx8ZGPfCSWL18eERGPPPJIbNu2Lc4999yIiHj88cfjjTfeiOHDh8eQIUOKt40bN8b//M//9Oh7Ae25EgjKbMmSJdHS0hKHH354cV5KKfr37x+vvvpqydvp379/u+lCodDhvLa2tpK3UygUIiI6XeeWW26Jv/qrv4r7778/7rjjjvja174Wq1atipNPPrnL7zF48OB20ytWrIgvf/nLcf3118ekSZOirq4urrvuunj00Ue73E5HZs+eHcuWLYurrroqli1bFp/97GdjxIgRxccxatSoaGpqytYbNmxYj78X8DsCAcqopaUlbrvttrj++utj6tSp7ZadffbZsXTp0vjDP/zDWLNmTfz5n/95cdmaNWv291A7dfzxx8fxxx8ff/u3fxuTJk2KZcuWxcknnxwDBgyI1tbWkrbx85//PCZPnhyXXHJJcd6er+hL3d75558fX/va1+Lxxx+Pf/u3f4vvfOc7xWUnnHBCbN26Naqrq+Ooo44q7QECJREIUEb33HNPvPrqq3HRRRdFfX19u2XnnHNOLFmyJK666qqYM2dOTJw4MT7xiU/E0qVL49lnn40PfehDFRr1ezZu3Bjf/e5348wzz4zRo0fHhg0b4te//nUxZI466qjYuHFjPPnkkzFmzJioq6uLmpqaDrf1+7//+3HbbbfFAw88EOPGjYsf/vCHsXbt2hg3blzxPkcddVQ88MADsWHDhhg+fHi2v943bty4mDx5clx00UXR0tISM2bMKC477bTTYtKkSTFz5sz45je/GePHj4/NmzfHypUrY+bMmR2+9QGUxjUIUEZLliyJ0047rcMnu7PPPjuefPLJ+IM/+IO4+uqr42/+5m/ixBNPjBdeeCHmzZtXgdG2N2jQoPjv//7vOPvss+Poo4+Oiy++OObPnx9f/OIXI+K98U+bNi1OOeWUOPTQQ4vXBXRk7ty5cdZZZ0VjY2OcdNJJsX379nZnEyIivvCFL8T48eOL1yn8x3/8R6fbmz17djz11FNx1llnxcCBA4vzC4VCrFy5Mj71qU/FhRdeGEcffXTMmjUrnn/++WhoaNjLPQIHt0JKv/3sFQDAbzmDAABkBAIAkBEIAEBGIAAAGYEAAGQEAgCQEQgAQEYgAAAZgQAAZAQCAJARCABA5v8DK8daAfgqFPwAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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lUjz44IMN9hdFEZMmTYqePXtG586dY+jQofHyyy9XZrBtyMbm/dRTT/3K+v/mN79ZmcG2EVdeeWUccsghUVVVFbvsskuMGjUq/vOf/zR4jPVefpl5L8d6b1XRcN9998X5558fv/rVr2LevHnxrW99K4YPHx4LFy6s9NDatP322y8WL15cf5s/f36lh9TmfPrpp3HQQQfFjTfe2Oj+q6++OqZMmRI33nhjzJ07N7p37x5HH310/d94oXk2Nu8REccee2yD9f+3v/1tM46w7Xn88cfjJz/5STzzzDMxc+bM+PLLL2PYsGHx6aef1j/Gei+/zLxHlGG9F63IoYceWkyYMKHBtr333rv4xS9+UaERtX0TJ04sDjrooEoPY6sSEcUDDzxQf7+urq7o3r17cdVVV9VvW7lyZVFTU1NMnTq1AiNsm9ad96IoivHjxxcjR46syHi2FkuXLi0ionj88ceLorDeN5d1570oyrPeW82Zhs8//zz+8Y9/xLBhwxpsHzZsWMyZM6dCo9o6LFiwIHr27Bn9+vWLU045Jd58881KD2mr8tZbb8WSJUsarP2OHTvGEUccYe1vBrNnz45ddtklvv71r8cZZ5wRS5curfSQ2pTly5dHRMSOO+4YEdb75rLuvK/R0vXeaqLhgw8+iNWrV0e3bt0abO/WrVssWbKkQqNq+wYNGhR33XVXPPLII3HLLbfEkiVLYsiQIbFs2bJKD22rsWZ9W/ub3/Dhw2PatGnx2GOPxbXXXhtz586NI488MlatWlXpobUJRVHEhRdeGIcffnjsv//+EWG9bw6NzXtEedZ7s//K5aay7p/ZLoqirH96m4aGDx9e//UBBxwQgwcPjj322CPuvPPOuPDCCys4sq2Ptb/5jR49uv7r/fffPwYOHBh9+/aNv/71r3HiiSdWcGRtwznnnBMvvfRSPPXUU1/ZZ71vOuub93Ks91ZzpmGnnXaK9u3bf6U0ly5d+pUiZdPp0qVLHHDAAbFgwYJKD2Wrsea3Vaz9yuvRo0f07dvX+i+Dc889Nx566KGYNWtW9OrVq3679b5prW/eG9Oc9d5qomHbbbeNgw8+OGbOnNlg+8yZM2PIkCEVGtXWZ9WqVfHqq69Gjx49Kj2UrUa/fv2ie/fuDdb+559/Ho8//ri1v5ktW7YsFi1aZP23QFEUcc4558SMGTPisccei379+jXYb71vGhub98Y0a7236DLKMrv33nuLDh06FLfddlvxyiuvFOeff37RpUuX4u2336700Nqsiy66qJg9e3bx5ptvFs8880xx/PHHF1VVVea8zFasWFHMmzevmDdvXhERxZQpU4p58+YV77zzTlEURXHVVVcVNTU1xYwZM4r58+cX3//+94sePXoUtbW1FR75lm1D875ixYrioosuKubMmVO89dZbxaxZs4rBgwcXu+66q3lvgbPOOquoqakpZs+eXSxevLj+9tlnn9U/xnovv43Ne7nWe6uKhqIoit/97ndF3759i2233bYYMGBAg18XofxGjx5d9OjRo+jQoUPRs2fP4sQTTyxefvnlSg+rzZk1a1YREV+5jR8/viiK//0a2sSJE4vu3bsXHTt2LL797W8X8+fPr+yg24ANzftnn31WDBs2rNh5552LDh06FH369CnGjx9fLFy4sNLD3qI1Nt8RUdx+++31j7Hey29j816u9e5PYwMAKa3mmgYAoHUTDQBAimgAAFJEAwCQIhoAgBTRAACkiAYAIEU0AAApogFaoSVLlsTRRx8dXbp0ie23377Sw0m74447KjLeU089NUaNGrXZnxe2NqIBNoOmvqhdd911sXjx4njxxRfjtdde23QDa4Hddtstfvvb3zbYNnr06FY7XqDltqn0AICveuONN+Lggw+O/v37N/sYX3zxRXTo0KGMo9q4zp07R+fOnTfrcwKbjzMNsJkNHTo0fvrTn8Yll1wSO+64Y3Tv3j0mTZpUv3+33XaL+++/P+66664olUpx6qmnRkTEwoULY+TIkdG1a9eorq6Ok08+Od5///3675s0aVJ84xvfiD/84Q+x++67R8eOHaMoiiiVSnHzzTfH8ccfH9ttt13ss88+8fTTT8frr78eQ4cOjS5dusTgwYPjjTfeqD/WG2+8ESNHjoxu3bpF165d45BDDolHH320wc/wzjvvxAUXXBClUilKpVJENP7xxE033RR77LFHbLvttrHXXnvF3Xff3WB/qVSKW2+9NU444YTYbrvton///vHQQw/V71+9enWcfvrp0a9fv+jcuXPstddecf3117f0nwFoBtEAFXDnnXdGly5d4tlnn42rr746Jk+eHDNnzoyIiLlz58axxx4bJ598cixevDiuv/76KIoiRo0aFR9++GE8/vjjMXPmzHjjjTdi9OjRDY77+uuvx/Tp0+P++++PF198sX77FVdcEePGjYsXX3wx9t577xgzZkyceeaZcemll8bzzz8fERHnnHNO/eM/+eSTOO644+LRRx+NefPmxTHHHBMjRoyIhQsXRkTEjBkzolevXjF58uRYvHhxLF68uNGf84EHHojzzjsvLrroovjXv/4VZ555Zpx22mkxa9asBo/7v//7vzj55JPjpZdeiuOOOy7Gjh0bH374YURE1NXVRa9evWL69OnxyiuvxOWXXx6//OUvY/r06S37RwCarsx/nRNoxPjx44uRI0cWRVEURxxxRHH44Yc32H/IIYcUP//5z+vvjxw5sv7PZhdFUfz9738v2rdv3+DP2L788stFRBTPPfdcURRFMXHixKJDhw7F0qVLGxw7Iopf//rX9feffvrpIiKK2267rX7bn/70p6JTp04b/Bn23Xff4oYbbqi/37dv3+K6665r8Jjbb7+9qKmpqb8/ZMiQ4owzzmjwmO9973vFcccdt97xffLJJ0WpVCoefvjh9Y7l7LPPLr773e/W3197foFNx5kGqIADDzywwf0ePXrE0qVL1/v4V199NXr37h29e/eu37bvvvvG9ttvH6+++mr9tr59+8bOO++8wefr1q1bREQccMABDbatXLkyamtrIyLi008/jUsuuaT+Obp27Rr//ve/6880ZL366qtx2GGHNdh22GGHNRjzuuPr0qVLVFVVNZiPqVOnxsCBA2PnnXeOrl27xi233NLksQAt50JIqIB1L1AslUpRV1e33scX///ahI1t79Kly0afb83jG9u2ZgwXX3xxPPLII/Gb3/wm9txzz+jcuXOcdNJJ8fnnn2/sR/uKdcfd2M+yofmYPn16XHDBBXHttdfG4MGDo6qqKq655pp49tlnmzwWoGVEA2wB9t1331i4cGEsWrSo/mzDK6+8EsuXL4999tmn7M/35JNPxqmnnhonnHBCRPzvGoe33367wWO23XbbWL169QaPs88++8RTTz0V48aNq982Z86cJo35ySefjCFDhsTZZ59dv23tizaBzcfHE7AFOOqoo+LAAw+MsWPHxgsvvBDPPfdcjBs3Lo444ogYOHBg2Z9vzz33jBkzZsSLL74Y//znP2PMmDFfOROy2267xRNPPBH//e9/44MPPmj0OBdffHHccccdMXXq1FiwYEFMmTIlZsyYET/72c+aNJbnn38+HnnkkXjttdfisssui7lz57bo5wOaRzTAFqBUKsWDDz4YO+ywQ3z729+Oo446Knbfffe47777NsnzXXfddbHDDjvEkCFDYsSIEXHMMcfEgAEDGjxm8uTJ8fbbb8cee+zR6HUUERGjRo2K66+/Pq655prYb7/94uabb47bb789hg4dmh7LhAkT4sQTT4zRo0fHoEGDYtmyZQ3OOgCbT6koiqLSgwAAWj9nGgCAFNEAAKSIBgAgRTQAACmiAQBIEQ0AQIpoAABSRAMAkCIaAIAU0QAApIgGACDl/wFNR0tDm+vLkgAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Box plots for numeric columns\n",
+ "for i in range (len(numeric_cols)):\n",
+ " plt.figure()\n",
+ " sns.boxplot(x=data[numeric_cols[i]], data=data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "51443624",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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AADVVryN88OBBLVq0SNHR0Xr33Xd13333afLkyfrzn88cIRYWFkqSXC6X1/NcLpf9WGFhoZo1a6YWLVr85Jrw8PBq7x8eHm6vOZ85c+bY3yE7nU5FRUXVfmcBAE1OvY5wVVWVfvOb3yg9PV3dunXTxIkTlZKSokWLFnmtczgcXvcty6q27Vznrjnf+p97nVmzZsnj8di3/Pz8muwWAACS6nmE27Rpo86dO3tt69Spkw4fPixJioiIkKRqR6tFRUX20XFERITKy8vldrt/cs0333xT7f2Li4urHWX/UEBAgEJDQ71uAADUVL2OcO/evbVv3z6vbV988YUuv/xySVL79u0VERGhjRs32o+Xl5crOztbvXr1kiTFxsbK39/fa01BQYF2795tr4mPj5fH49HOnTvtNTt27JDH47HXAABQ12oV4QEDBpz3GtqSkhINGDDgl85ke/jhh5WTk6P09HQdOHBAq1ev1pIlS/TAAw9IOvMRclpamtLT05WZmandu3fr7rvvVlBQkBITEyVJTqdT48eP19SpU7Vp0yZ98skn+u1vf6uuXbtq0KBBks4cXQ8bNkwpKSnKyclRTk6OUlJSlJCQUOMzowEAuFB+tXnS5s2bVV5eXm37999//6OXE9VGjx49lJmZqVmzZumpp55S+/bt9eKLL2rcuHH2munTp6u0tFSpqalyu92Ki4vThg0bFBISYq9ZsGCB/Pz8NGbMGJWWlmrgwIFavny5fH197TWrVq3S5MmT7bOoR44cqYyMjDrbFwAAznVB1wl/9tlnkqTrrrtO77//vlq2bGk/VllZqaysLC1evFhffvllnQ/aEHCdMBoqrhMG6s6FtOCCjoSvu+46ORwOORyO837sHBgYqIULF17YtAAANFEXFOFDhw7JsixdccUV2rlzp8LCwuzHmjVrpvDwcK+PeAEAwI+7oAifPSu5qqrqVxkGAICmpFYnZklnLhXavHmzioqKqkX58ccf/8WDAQDQ2NUqwkuXLtX999+v1q1bKyIiotpPniLCAAD8vFpF+JlnntGzzz5r/+YiAABw4Wr1wzrcbrduv/32up4FAIAmpVYRvv3227Vhw4a6ngUAgCalVh9HX3nllXrssceUk5Ojrl27yt/f3+vxyZMn18lwAAA0ZrWK8JIlS3TJJZcoOztb2dnZXo85HA4iDABADdQqwocOHarrOQAAaHLq9a8yBACgMavVkfA999zzk4+/+uqrtRoGAICmpFYRdrvdXvcrKiq0e/duHTt2rE5/nzAAAI1ZrSKcmZlZbVtVVZVSU1N1xRVX/OKhAABoCursO2EfHx89/PDDWrBgQV29JAAAjVqdnpj173//W6dPn67LlwQAoNGq1cfRU6ZM8bpvWZYKCgq0bt06JScn18lgAAA0drWK8CeffOJ138fHR2FhYXrhhRd+9sxpAABwRq0i/MEHH9T1HAAANDm1ivBZxcXF2rdvnxwOh6666iqFhYXV1VwAADR6tTox6+TJk7rnnnvUpk0b3XjjjerTp48iIyM1fvx4nTp1qq5nBACgUapVhKdMmaLs7Gz9/e9/17Fjx3Ts2DG99dZbys7O1tSpU+t6RgAAGqVafRz9l7/8RW+++ab69etnbxsxYoQCAwM1ZswYLVq0qK7mAwCg0arVkfCpU6fkcrmqbQ8PD+fjaAAAaqhWEY6Pj9cTTzyh77//3t5WWlqqJ598UvHx8XU2HAAAjVmtPo5+8cUXNXz4cLVt21bXXnutHA6H8vLyFBAQoA0bNtT1jAAANEq1inDXrl21f/9+rVy5Unv37pVlWbrjjjs0btw4BQYG1vWMAAA0SrWK8Jw5c+RyuZSSkuK1/dVXX1VxcbFmzJhRJ8MBANCY1eo74cWLF+vqq6+utr1Lly56+eWXf/FQAAA0BbWKcGFhodq0aVNte1hYmAoKCn7xUAAANAW1inBUVJQ+/PDDats//PBDRUZG/uKhAABoCmr1nfC9996rtLQ0VVRUaMCAAZKkTZs2afr06fzELAAAaqhWEZ4+fbq+++47paamqry8XJLUvHlzzZgxQ7NmzarTAQEAaKxqFWGHw6G5c+fqscce0549exQYGKjo6GgFBATU9XwAADRav+hXGV5yySXq0aNHXc0CAECTUqsTswAAwC9HhAEAMIQIAwBgCBEGAMAQIgwAgCFEGAAAQ4gwAACGEGEAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwBAiDACAIUQYAABDiDAAAIYQYQAADCHCAAAYQoQBADCECAMAYAgRBgDAECIMAIAhRBgAAEOIMAAAhhBhAAAMaVARnjNnjhwOh9LS0uxtlmVp9uzZioyMVGBgoPr166fPP//c63llZWWaNGmSWrdureDgYI0cOVJHjhzxWuN2u5WUlCSn0ymn06mkpCQdO3bsIuwVAKCpajAR3rVrl5YsWaJrrrnGa/u8efM0f/58ZWRkaNeuXYqIiNDgwYN1/Phxe01aWpoyMzO1Zs0abd26VSdOnFBCQoIqKyvtNYmJicrLy1NWVpaysrKUl5enpKSki7Z/AICmp0FE+MSJExo3bpyWLl2qFi1a2Nsty9KLL76oRx99VLfeeqtiYmL02muv6dSpU1q9erUkyePx6E9/+pNeeOEFDRo0SN26ddPKlSv1r3/9S++9954kac+ePcrKytIrr7yi+Ph4xcfHa+nSpfrHP/6hffv2GdlnAEDj1yAi/MADD+imm27SoEGDvLYfOnRIhYWFGjJkiL0tICBAffv21bZt2yRJubm5qqio8FoTGRmpmJgYe8327dvldDoVFxdnr+nZs6ecTqe95nzKyspUUlLidQMAoKb8TA/wc9asWaOPP/5Yu3btqvZYYWGhJMnlcnltd7lc+uqrr+w1zZo18zqCPrvm7PMLCwsVHh5e7fXDw8PtNeczZ84cPfnkkxe2QwAA/J96fSScn5+vhx56SCtXrlTz5s1/dJ3D4fC6b1lWtW3nOnfN+db/3OvMmjVLHo/HvuXn5//kewIA8EP1OsK5ubkqKipSbGys/Pz85Ofnp+zsbL300kvy8/Ozj4DPPVotKiqyH4uIiFB5ebncbvdPrvnmm2+qvX9xcXG1o+wfCggIUGhoqNcNAICaqtcRHjhwoP71r38pLy/PvnXv3l3jxo1TXl6errjiCkVERGjjxo32c8rLy5Wdna1evXpJkmJjY+Xv7++1pqCgQLt377bXxMfHy+PxaOfOnfaaHTt2yOPx2GsAAKhr9fo74ZCQEMXExHhtCw4OVqtWreztaWlpSk9PV3R0tKKjo5Wenq6goCAlJiZKkpxOp8aPH6+pU6eqVatWatmypaZNm6auXbvaJ3p16tRJw4YNU0pKihYvXixJmjBhghISEtSxY8eLuMcAgKakXke4JqZPn67S0lKlpqbK7XYrLi5OGzZsUEhIiL1mwYIF8vPz05gxY1RaWqqBAwdq+fLl8vX1tdesWrVKkydPts+iHjlypDIyMi76/gAAmg6HZVmW6SEai5KSEjmdTnk8njr5fjj2kT/XwVTAz8t97i7TIwCNxoW0oF5/JwwAQGNGhAEAMIQIAwBgCBEGAMAQIgwAgCFEGAAAQ4gwAACGEGEAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwBAiDACAIUQYAABDiDAAAIYQYQAADCHCAAAYQoQBADCECAMAYAgRBgDAECIMAIAhRBgAAEOIMAAAhhBhAAAMIcIAABhChAEAMIQIAwBgCBEGAMAQIgwAgCFEGAAAQ4gwAACGEGEAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwBAiDACAIUQYAABDiDAAAIYQYQAADCHCAAAYQoQBADCECAMAYAgRBgDAECIMAIAhRBgAAEOIMAAAhhBhAAAMIcIAABhChAEAMIQIAwBgCBEGAMAQIgwAgCFEGAAAQ4gwAACGEGEAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwJB6HeE5c+aoR48eCgkJUXh4uEaNGqV9+/Z5rbEsS7Nnz1ZkZKQCAwPVr18/ff75515rysrKNGnSJLVu3VrBwcEaOXKkjhw54rXG7XYrKSlJTqdTTqdTSUlJOnbs2K+9iwCAJqxeRzg7O1sPPPCAcnJytHHjRp0+fVpDhgzRyZMn7TXz5s3T/PnzlZGRoV27dikiIkKDBw/W8ePH7TVpaWnKzMzUmjVrtHXrVp04cUIJCQmqrKy01yQmJiovL09ZWVnKyspSXl6ekpKSLur+AgCaFodlWZbpIWqquLhY4eHhys7O1o033ijLshQZGam0tDTNmDFD0pmjXpfLpblz52rixInyeDwKCwvTihUrNHbsWEnS0aNHFRUVpfXr12vo0KHas2ePOnfurJycHMXFxUmScnJyFB8fr71796pjx441mq+kpEROp1Mej0ehoaG/eH9jH/nzL34NoCZyn7vL9AhAo3EhLajXR8Ln8ng8kqSWLVtKkg4dOqTCwkINGTLEXhMQEKC+fftq27ZtkqTc3FxVVFR4rYmMjFRMTIy9Zvv27XI6nXaAJalnz55yOp32mvMpKytTSUmJ1w0AgJpqMBG2LEtTpkzRDTfcoJiYGElSYWGhJMnlcnmtdblc9mOFhYVq1qyZWrRo8ZNrwsPDq71neHi4veZ85syZY3+H7HQ6FRUVVfsdBAA0OQ0mwg8++KA+++wzvf7669UeczgcXvcty6q27Vznrjnf+p97nVmzZsnj8di3/Pz8n9sNAABsDSLCkyZN0ttvv60PPvhAbdu2tbdHRERIUrWj1aKiIvvoOCIiQuXl5XK73T+55ptvvqn2vsXFxdWOsn8oICBAoaGhXjcAAGrKz/QAP8WyLE2aNEmZmZnavHmz2rdv7/V4+/btFRERoY0bN6pbt26SpPLycmVnZ2vu3LmSpNjYWPn7+2vjxo0aM2aMJKmgoEC7d+/WvHnzJEnx8fHyeDzauXOnrr/+eknSjh075PF41KtXr4u1uwDO4/BTXU2PgCai3eP/uujvWa8j/MADD2j16tV66623FBISYh/xOp1OBQYGyuFwKC0tTenp6YqOjlZ0dLTS09MVFBSkxMREe+348eM1depUtWrVSi1bttS0adPUtWtXDRo0SJLUqVMnDRs2TCkpKVq8eLEkacKECUpISKjxmdEAAFyoeh3hRYsWSZL69evntX3ZsmW6++67JUnTp09XaWmpUlNT5Xa7FRcXpw0bNigkJMRev2DBAvn5+WnMmDEqLS3VwIEDtXz5cvn6+tprVq1apcmTJ9tnUY8cOVIZGRm/7g4CAJq0BnWdcH3HdcJoqOrzdcJ8HI2Lpa4+jm601wkDANCYEGEAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwBAiDACAIUQYAABDiDAAAIYQYQAADCHCAAAYQoQBADCECAMAYAgRBgDAECIMAIAhRBgAAEOIMAAAhhBhAAAMIcIAABhChAEAMIQIAwBgCBEGAMAQIgwAgCFEGAAAQ4gwAACGEGEAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwBAiDACAIUQYAABDiDAAAIYQYQAADCHCAAAYQoQBADCECAMAYAgRBgDAECIMAIAhRBgAAEOIMAAAhhBhAAAMIcIAABhChAEAMIQIAwBgCBEGAMAQIgwAgCFEGAAAQ4gwAACGEGEAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwBAiDACAIUQYAABDiDAAAIYQYQAADCHCAAAYQoQBADCECJ/jj3/8o9q3b6/mzZsrNjZW//znP02PBABopIjwD6xdu1ZpaWl69NFH9cknn6hPnz4aPny4Dh8+bHo0AEAjRIR/YP78+Ro/frzuvfdederUSS+++KKioqK0aNEi06MBABohP9MD1Bfl5eXKzc3VzJkzvbYPGTJE27ZtO+9zysrKVFZWZt/3eDySpJKSkjqZqbKstE5eB/g5dfXP7K/h+PeVpkdAE1FXfw/Ovo5lWT+7lgj/n//85z+qrKyUy+Xy2u5yuVRYWHje58yZM0dPPvlkte1RUVG/yozAr8W58D7TIwDmzXHW6csdP35cTudPvyYRPofD4fC6b1lWtW1nzZo1S1OmTLHvV1VV6bvvvlOrVq1+9Dn4dZWUlCgqKkr5+fkKDQ01PQ5w0fF3wDzLsnT8+HFFRkb+7Foi/H9at24tX1/fake9RUVF1Y6OzwoICFBAQIDXtksvvfTXGhEXIDQ0lH8BoUnj74BZP3cEfBYnZv2fZs2aKTY2Vhs3bvTavnHjRvXq1cvQVACAxowj4R+YMmWKkpKS1L17d8XHx2vJkiU6fPiw7ruP78sAAHWPCP/A2LFj9e233+qpp55SQUGBYmJitH79el1++eWmR0MNBQQE6Iknnqj2NQHQVPB3oGFxWDU5hxoAANQ5vhMGAMAQIgwAgCFEGAAAQ4gwAACGEGEAAAwhwgAAGEKEAaAROfeq04KCAkOToCaIMJqEqqoq0yMAF8XZCL/77rsaPXq0xo8fryuvvFIrV640PBnOhwijUfvuu+8kST4+/KOOxq+qqko+Pj768ssvNXHiREVERCg5OVlff/21iouLJUkHDx7UqVOnDE+Ks/g3ExqVysozvwD+8OHDmjdvnkaNGqUOHTpozpw5Ki0ttdfxg+LQmD3++OO65pprlJGRIZfLpUsvvVTJycmSpLffflurV69WeXm54SkhEWE0Mmd/j/PkyZP1zjvvKCUlRW3bttWqVavUvHlzVVRUeK0DGhMfHx9VVFTI7XbrjjvukCTNmDFDycnJatmypaqqqvTVV19p586datasmeFpIRFhNCKWZcnHx0d5eXl67733tHbtWiUlJamgoEAPP/ywHA6H3n33XaWmpurIkSOmxwXqnGVZ8vf3V4cOHbR+/Xpt375d+/bt00MPPSRJKisrU2ZmpoYOHWp4UpxFhNFonD263bJliwYOHKjw8HC99tprqqiosI8KWrdurdzcXI6E0Sid/ed6zJgx2rt3r0aPHq2bb75ZERER+uqrrzR37lxJ0m233WZyTPwAEUajc/311ys3N1cFBQVasGCBJk2apODgYEnSX//6VwUHB+uyyy4zPCVQd849+79Xr15KTU1VcHCw3nzzTSUkJKh79+7aunWrFi5caGhKnA+/yhCNzqlTp5SSkqLi4mJ9/PHH2rJlizp37qxNmzbpzjvv1MqVKzVkyBDTYwJ17tZbb9Vtt92mcePGSZJOnz6tFStWaNeuXerRo4eGDh2qyMhIw1Pih4gwGpWKigr5+/vrgw8+0BNPPKE9e/YoLi5OBw8elJ+fn3r16qWXX37Z9JhAnfvyyy+VmpqqL774QqGhoUpJSdF9991nf0R99vIl1C9EGA1eZWWlfH199eabb6qiokIDBgyQy+VSZWWlVq1apY8++kgtW7bUjTfeqBtuuIGzQtFo5efn6+OPP9Y//vEPbd68Wc2aNdO9996rlJQUXXLJJabHw3kQYTRolmXJ4XCosrJSbdu21fTp0zV+/HiFhobK7XartLSUj9/Q5BQXF2vXrl1asmSJ3n77bQUFBemNN97Q8OHDTY+Gc/iZHgD4JaqqquTr66v09HRdfvnlevjhh3X69Glt3rxZycnJatWqlXr37q3nn39eAQEBpscFfhV79+7V1Vdfbd8PCwvTiBEj1KZNGx09elRDhgzRjTfeaHBC/Bi+IECD5uvrK0natGmTRowYIUl64403NHfuXA0cOFCjR4/Wxo0bdeDAAZNjAr+avXv3qn///rrzzjv1/vvvez3WrVs3xcTEqHv37vYVAqhfiDAatNOnT0uSbrzxRm3ZskVvvPGGHnnkEd1www1avHixfv/73ysoKEj79+83PCnw6ygqKlJqaqq+//57paam6uabb9bf/vY3VVZW6oMPPtDq1at13XXXmR4TP4LvhNHgHDlyRHl5eUpISLC3bdmyRUlJSQoODtaAAQO0cOFCORwObdq0SaNGjdJ3330nf39/g1MDdef06dPy8/PTZ599poULF+rpp5+W2+3Wli1btGnTJn300Uc6fPiw2rdvr/79+2vJkiWmR8aPIMJocP7whz9o0qRJio2NVVpamn1NpCT9+9//Vtu2bRUQEKDs7Gw988wziomJ0YIFCwxODPw6kpOT9f3332vt2rX2ti+//FL//ve/VVRUpJYtW6p3796cGV2PEWE0OAUFBcrNzdVf//pXrVu3TiEhIUpNTdWUKVPsNSdPntTzzz+vjz76SKtWrVJoaKjBiYG6k5OTIx8fH/3mN7/RihUrdOzYMT388MOmx0ItEWE0WN9++60+/fRTZWZm6q233lJlZaUmTJigRx55REFBQZLO/D7hli1bGp4UqDudOnXSc889p61bt2revHnq0KGD1q1bp6uuusprHT+co2Egwmjwvv32W+3du1fr16/XG2+8IbfbrbFjx2rmzJlq27at6fGAOpOVlaVbb71VRUVF2r17txYtWqS1a9fqyiuv1IwZM3TLLbfwqU8DQ4TRaJSUlGj//v16//339dJLL2nQoEFatmyZ6bGAOhMbG6sxY8ZoxowZ9rY9e/boueee0+rVq9WuXTs99NBDGj16tFwul8FJUVNEGA3K2Y/Ytm3bpg0bNignJ0dxcXF68MEHFRYWJunML3DYv3+/oqKi+Cgajcbu3bt1zTXXaOLEiXr66afVunVrr8cPHz6shQsX6oUXXlB8fLw+/PBDQ5PiQhBhNBhnA5yfn6/hw4fr0ksv1eDBg/Xkk0/K399fY8aM0eOPP67o6GjTowJ1btiwYTp8+LBCQkK0b98+3XLLLXr00UerfRdcVFSkwsJCXXPNNYYmxYXgW3s0GGf/e/HRRx9VdHS0tm7dqp49eyoiIkLp6enasGGDOnbsqD59+uj48eOGpwXqzsGDB7Vp0yZlZmZqxYoVevrpp7V//351795dI0eO1M6dO+214eHhBLgB4WdHo147+wsazp7lfPLkSe3cudP+dYT//d//raSkJE2dOlXff/+9Xn/9dfXs2VMhISGGJwfqzoQJE3TTTTepY8eOkqT27dtr+PDh2rp1q1avXq1BgwapR48emjRpkkaNGmX/vUH9x5Ew6rWzR799+/bVihUrVFhYqBtuuEFt2rTRwYMHlZ+fr9tuu02S9F//9V/q0qWLpk2bZnJkoM7dd999euaZZySd+Tvh7++vK6+8UnfddZdeeeUVvfrqqwoODtYDDzyg06dPE+AGhCNh1FuWZcnHx0e5ubnat2+fhg0bprCwMD399NMqKChQVFSUnE6nSktLJUnZ2dn65JNPOCsUjc7o0aPtP/8wsD4+PmrXrp3atWun+Ph4nThxQn5+/Gu9IeH/LdRbZ39N4fTp05WcnGyf/fz1119r5syZWr16tVq2bKmEhARdf/31+uKLL7wu3QCakssuu8z0CKgFzo5GvXT2O61Dhw6pQ4cO+vbbb9WiRQtJUu/evdWjRw+9+OKLKi4u1tKlS/Xpp59q2LBh+t3vfmd4cgCoOY6EUS+d/cht+fLlatasmZYtW6Y777xTp06d0r59+7Ry5UpVVVUpLCxMv//97w1PCwC1w5Ew6rXPPvtMGRkZ2rNnj4KCgnTgwAFdffXVWrdunb2mqqpKkvg5uQAaHCKMes+yLB04cEBLly7Ve++9Jx8fH/Xp00e33nqr+vTpY3o8AKg1IowG5euvv9by5cu1adMmlZWVqWPHjho9erRGjBhhejQAuGBEGA3St99+q//5n//Rq6++qttvv13Tp083PRIAXDAijAbt+PHj8vHxUXBwsOlRAOCCEWEAAAzhdFIAAAwhwgAAGEKEAQAwhAgDAGAIEQYAwBAiDACAIUQYaOTuvvtuORwOORwO+fn5qV27drr//vvldrtNjwY0eUQYaAKGDRumgoICffnll3rllVf097//XampqabHApo8Igw0AQEBAYqIiFDbtm01ZMgQjR07Vhs2bLAfX7ZsmTp16qTmzZvr6quv1h//+Ef7sfj4eM2cOdPr9YqLi+Xv768PPvhAklReXq7p06frsssuU3BwsOLi4rR582Z7/fLly3XppZfq3XffVadOnXTJJZfY/2FwVr9+/ZSWlub1PqNGjdLdd99t3/+59wEaGiIMNDEHDx5UVlaW/P39JUlLly7Vo48+qmeffVZ79uxRenq6HnvsMb322muSpHHjxun111/XD3+43tq1a+VyudS3b19J0u9+9zt9+OGHWrNmjT777DPdfvvtGjZsmPbv328/59SpU3r++ee1YsUKbdmyRYcPH9a0adMuaPaavA/QoFgAGrXk5GTL19fXCg4Otpo3b25JsiRZ8+fPtyzLsqKioqzVq1d7Pefpp5+24uPjLcuyrKKiIsvPz8/asmWL/Xh8fLz1yCOPWJZlWQcOHLAcDof19ddfe73GwIEDrVmzZlmWZVnLli2zJFkHDhywH//DH/5guVwu+37fvn2thx56yOs1brnlFis5ObnG7wM0NH6G/xsAwEXQv39/LVq0SKdOndIrr7yiL774QpMmTVJxcbHy8/M1fvx4paSk2OtPnz4tp9MpSQoLC9PgwYO1atUq9enTR4cOHdL27du1aNEiSdLHH38sy7J01VVXeb1nWVmZWrVqZd8PCgpShw4d7Ptt2rRRUVFRjfehpu8DNCREGGgCgoODdeWVV0qSXnrpJfXv319PPvmkHnzwQUlnPpKOi4vzeo6vr6/953Hjxumhhx7SwoULtXr1anXp0kXXXnutJKmqqkq+vr7Kzc31eo4kXXLJJfafz378fZbD4fD6iNvHx8frviRVVFTYf67p+wANCREGmqAnnnhCw4cP1/3336/LLrtMBw8e1Lhx4350/ahRozRx4kRlZWVp9erVSkpKsh/r1q2bKisrVVRUpD59+tR6prCwMK8TtSorK7V7927179+/Tt8HqE+IMNAE9evXT126dFF6erpmz56tyZMnKzQ0VMOHD1dZWZk++ugjud1uTZkyRdKZI+lbbrlFjz32mPbs2aPExET7ta666iqNGzdOd911l1544QV169ZN//nPf/T++++ra9euGjFiRI1mGjBggKZMmaJ169apQ4cOWrBggY4dO1bn7wPUJ5wdDTRRU6ZM0dKlSzV06FC98sorWr58ubp27aq+fftq+fLlat++vdf6cePG6dNPP1WfPn3Url07r8eWLVumu+66S1OnTlXHjh01cuRI7dixQ1FRUTWe55577lFycrLuuusu9e3bV+3bt7ePguvyfYD6xGGd+yUMAAC4KDgSBgDAECIMAIAhRBgAAEOIMAAAhhBhAAAMIcIAABhChAEAMIQIAwBgCBEGAMAQIgwAgCFEGAAAQ/4/XILKKvqDll0AAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Bar plots for categorical columns\n",
+ "for i in range (len(categorical_cols)):\n",
+ " plt.figure(figsize=(5,5))\n",
+ " sns.countplot(x=data[categorical_cols[i]], data=data)\n",
+ " plt.xticks(rotation=65) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "dd840803",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Administrative 2\n",
+ "Administrative_Duration 273\n",
+ "Informational 2631\n",
+ "Informational_Duration 2405\n",
+ "ProductRelated 173\n",
+ "ProductRelated_Duration 166\n",
+ "BounceRates 691\n",
+ "ExitRates 0\n",
+ "PageValues 2730\n",
+ "SpecialDay 1249\n",
+ "OperatingSystems 0\n",
+ "Browser 4322\n",
+ "Region 0\n",
+ "TrafficType 261\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Check for outliers\n",
+ "outliers = outliers_check(data, numeric_cols)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f49629db",
+ "metadata": {},
+ "source": [
+ "# Correlation Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "dc3d0c9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Local\\Temp\\ipykernel_6664\\3699474598.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+ " correlation_matrix = data.corr()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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zOPvT999/r1OPS5cuiX379hWdnJxEQ0ND0cXFRWzbtq04f/58tYyu7E8JCQlir169RFtbW9HS0lLs3LmzePXqVZ3Zi+bMmSN6e3uLcrlcBMSlS5eKolgx+5MoqjI4ffrpp6Knp6doaGgourq6iu+8846YmZmpJefp6Sl27dq1wvVERESIEREROq/1SXiU/akydGVwun79utihQwfR0tJStLW1Ffv06SPev39fZ4ajCRMmiDVq1BBlMpmW/fTp/vi7x/bLy8sTAwICxKCgIDE/P19L7t133xUNDQ3F06dP/+3r1JeZqzrPRHFxsThu3DixZs2aoomJiRgWFiZu2bJF5z3dv3+/GBoaKhobG4uA+voeZ39KTU3Vkh88eLBobm5eQV9dz/i3334renl5icbGxmJgYKD422+/qdvVZYdffvlF9PX1FQ0NDcWAgABx9erVlZlPQkLiJUEQRVF8Id6MhISEhISEhMQjBEHg3XffZd68eS9aFQkJib+BtKZCQkJCQkJCQkJCQuKZkJwKCQkJCQkJCQkJCYlnQlqoLSEhISEhIfHCkaKxJST+u5FmKiQkJCQkJCQkJCReUo4ePUr37t2pUaMGgiCwZcuWKuscOXKEBg0aYGJigo+PD/Pnz//H9ZScCgkJCQkJCQkJCYmXlPz8fOrXr1/tJAZ3796lS5cutGzZkosXL/LZZ58xZswY/vjjj39UTyn7k4SEhISEhISEhMR/AYIgsHnzZiIjI/XKfPrpp2zbto3o6Gh12ahRo7h06VKFzS+fJ9JMhYSEhISEhISEhMS/SHFxMTk5OVpHcXHxc2n75MmTFTYK7dSpE+fOnaO0tPS5nEMX0kJtCQkJCQkJCQkJiafgT0P/Z6p/9vPXmTJlilbZpEmTmDx58jO1C5CcnIyzs7NWmbOzM2VlZaSlpeHq6vrM59CF5FRISPDsL4fnSdfSGIq2/Pii1QDAJHIMCTevvmg11LjVDuZsTNaLVgOARv42pF898aLVAMA+uBnxt66/aDXUuNcK4rUxt1+0GgBs+tGPFt2PvGg11BzfHsHKoy9aCxVvtoIP5ua+aDXUzP3Akm/WK160GgBM6Ctn6uqyF60GABMHGrw0dgGVbaas+udGu5+GSW8YvrBzC4bCM9WfMGECH330kVaZsbHxM7X5JIKgrd/j1Q7ly58nklMhISEhISEhISEh8RTIDJ6tc25sbPxcnYgncXFxITk5WassJSUFAwMD7O3t/5FzguRUSEhISEhISEhISDwVguHLuyw5PDyc7du3a5Xt3buXhg0bYmj4z83uvLwW+X/C5MmTCQkJeaY27t27hyAIREVF/avnfZ78nWuQkJCQkJCQkPhfJy8vj6ioKHUf6e7du0RFRXH//n1AFUo1aNAgtfyoUaOIi4vjo48+Ijo6miVLlrB48WLGjRv3j+opzVT8A5w4cYKWLVvSoUMHdu/e/Y+fz93dnaSkJBwcHKpdZ9y4cbz//vvVkp08eTJbtmx5bh3+IUOGkJWVpbV5y9+5hn8KuxYN8Rk7HOuwYExqOHGu12gebjtQeZ2WjQj6YTwWQbUoTkwhduYi7i9cqyXj0rMjtSd/gJmvBwWx94mZOJuHW/dXS6d1J6+w7MhF0nIL8HW245PuLQjzrqFXvqRMwYL9Z/nzYgxpuQU4W1vwVtsG9GwUpJbZfyWWn/eeJj49G3d7a97r1JR2wT5V6rL1z92s37SV9MxMvDzcGT1iKPXqBOmUvXItmt+Wr+R+wgOKi0twdnSgW+eO9I7sriX3x9YdbNu1h5TUNKytLGnVLJy3Bg/EyMioSn327dzIzk2ryMpMp6aHN2+89SEBdUJ1yp49cYgDuzYRd/cmpaUluHn48NrrI6gX1lQtc/TADhbO/apC3SUbj2JkVPlU9R+7D7Jm6y7SM7Pwdq/JB0MHEBJUu8pruHzjFu9++S0+HjVZPnOquvzwqXOs2PQnCUkPKVMocHd1pn/3zrzSulmVbW79cxcbNm0hPePxfRpO3WB99+k6i5at5H5Cguo+OTnStXNHeke+qpb5aPwXXL56rULdxg0bMH3yF1XqA9DvFTs6NLPC3FTGrbhiftuQSnxyiV759uFWtG5siYer6jmIjS9m9fZ0bt/XZEgJ8jWhRztbfN2NsbM24NvfkjhzJb9a+gx73ZNXO7liaWHA9Zu5zJp/i7v3C/TKtwp3YFAfD2q6mmJgIJCQWMjaLfHsOZSilqlfx5oBr7nj72uBg70xE6Zd5dip9Er1OHdoNSf3LCYvOxXHGrXo2O8zPGo31Cmbm5XC/g3fkRR3lYyUOBq3fZOO/T+vIBd9fg9Hts4lM/U+to4etI78kICwDtWyS+cmRjQLNsTURCAuWcHGQ8UkZyj1yrvYyegSboSbkxx7KxmbjhRxJKpizH2Leoa0DTPCylwgOV3JpqPF3Emseq1AizoCIT4CJoaQmAF7LyhJy6m8jn9NaBUsw8YCsvLgyFUlNx9ovnd3gCYBMlxswdJUYONxBbcSq1SFiLoywvwETIzgQTrsOqsgNbvyOgHuAm3qy7C1gMw8OBilJCZBk82/QS2BhrVUugKkZsHRq0puJ1ad8f+lsk09GQ38ZI9sI7LzTNW2CXQXaFNfjq0lZObCwUsKbsRrrrthLRkNa8uwMVd9TskWOXqlerb5p3nW8Ken4dy5c7Rp00b9+fFajMGDB7Ns2TKSkpLUDgaAt7c3O3fu5MMPP+Tnn3+mRo0a/Pjjj/Tq1esf1VNyKv4BlixZwvvvv8+iRYu4f/8+Hh4e/+j55HI5Li4uT1XHwsICCwuL56pHaWnp355W+zvX8E8hNzcj53IMCcs30WBD1RvNmHq50Wj7QuIXbyBq8MfYNgsj+KdJlKRmkLx5LwA2TUMIXTObm5Pmkrx1Py492hP2+xxOth5A1pnLlba/+9ItZmw/zueREYR4urDx9DVGL9nO5o8G4GprqbPOx6t3k55byOTebXG3tyYjvxCFQtMpuBSXzCdr9vBuxya0rePDwWt3+GT1Hpa+05N6Hvrvw6Fjf/HLoqWMGTWC4KAAduzey4TJ01jy8xycnRwryJuYGBPZ9RV8vDwxMTHhyvVo5vy8ABMTY7p1VqW723/4KL8tX8XHY96lTqA/CQ8SmTFXZffRI4ZWaptTx/axatFshoz6hNqB9Ti4ezPfT/mQ735ei4Njxeu4ce0iwSGN6TPoHczNLTiyfwczvx7LlO+X4OWrWaxvambO979u0KpblUOx/6/TzF26hnEj3qReQC227D3M2GmzWD1nGi6O+mNY8/ILmPrjbzSoG0hmtnZvwMrCgsG9uuFZ0xUDAwP+OhfF9J8XY2ttSdPQunrbPHT0OL/+toQx74ykTlAAf+7ay4TJX7H4lx/13CcTenTror5PV69fZ868+ZiYmKjv0+TPP6WsTLNwNScnl5Hvf0hEi6odHICe7W3o3saGn1Y9JCm1lN4dbZn0bg3e+zqOomLdHYTgWqYcP5/LjbtFlJaKRLa3ZdLoGnzwzX0yslWdUWMjGfceFHPwVA6fvlX9jCYDe7nTL9KNaXNiiH9QwOB+nsyeWo/X3zlLYaHujm5ubikr1scRl1BIaZmS5o3smfBBAJlZpZy5mAmAqYmc23fz+HN/MtM/q1OlHtfO7mTvum94ZeAk3P3CuHBkLb//OIJRU/7E2r7iwIGirAQzS1tadHmH0/uX6WwzIfYimxZ+SOseH+Af2p6Yi/vZtPA/DP5kDTV96leqT7sGRrQJNWL1viJSs5R0bGTE6J6mTFuRT7GetblGhpCWLXLxVjE9W+n+OwmtZUDPVsZsOFTM3UQFzeoaMqqHKd+syiczV38HsWmAQOPaAjvOKMnIheZBAv0jZCzcpaREzzrqmvYQGS7j6FWRmAci/jUFIsNlrDqoJDFDJWNoAClZIpfvivRqLq/UJo9pFiTQNFBg60kl6TkiLYNlvNFWzs/bFXp1cXOA3i1kHLqs5Ea8SIC7QO+WMpbtVfDgka+ZWwAHopRkPLJDfR8Z/VrJWLir8k75y2Sb5kEywgNkbDmpID1HpFVdOW+2M2DetrJKbCPQu6WcQ5eURMcrCXSX0bulnKV7FDxIV9kip0Bk/0WF2jYhPjL6R8hZsLOsSofln+ZZF2o/Da1bt6aybeWWLVtWoSwiIoILFy78g1pVRAp/es7k5+ezfv163nnnHbp161bhRn/77bc4OztjaWnJ8OHDKSoq0vp+yJAhREZGMn36dJydnbGxsWHKlCmUlZXx8ccfY2dnh5ubG0uWLFHXKR86dPjwYQRB4MCBAzRs2BAzMzOaNWtGTEyMuk758KfDhw/TuHFjzM3NsbGxoXnz5sTFxbFs2TKmTJnCpUuXEAQBQRDU1yQIAvPnz6dHjx6Ym5vz9ddfo1AoGD58ON7e3piamuLv78/cuXO1zrt8+XK2bt2qbu/w4cNa16BUKnFzc6uwpfyFCxcQBIE7d+4AkJ2dzciRI3FycsLKyoq2bdty6dKlv3vr1KTuOcrNSXNI3rKvWvKeI/tTdD+J62Onk3fjDvFLNhK/bBM+Hw1Ty3i/P5i0/SeInbGQ/Jg7xM5YSNrBU3i9P7jK9lcei6Jno0BeaxyEj7Mdn7zaEhdrS9af0p2V6a+YOM7fSeTnYd1oWsudmnZW1HV3JsRL0+FadfwSTf3cGd6mAd5Otgxv04DGfm6sPl65g7Nxy3Ze6dCWrp3a4+nuxrsjhuHkYM/2XXt0ytfy9aFtREu8PD1wcXaiQ5sIGoaFcOWaZkOe6zdiCA4MoF3rlrg4O9EwLIQ2rVoQczu2Stvs2vo7rdu/SpuOPajp7s2bIz7C3sGZAzt17xr65oiP6NbrTXxrBeFSw4N+g0bj4urOxbPHtOQEQcDG1l7rqIq12/fSvW0rXm0fgZdbDf4zbABO9nZs3nOw0nrfLVhOx5ZNCfb3q/BdWHAAEU0a4OVWAzcXJ/p164ivpxuXb9yqtM0/tmyjc4d2dOnUAU93d0aPHK66Tzt1z5yWv0/t27SmYVgIV69pMkpZWVpiZ2urPs5HXcLE2JhW1XQqukXY8MfeDE5fzud+Ugk/rn6IsaFAqwa6HWOAOSsesvt4DvcelPAgpZRff09BkAnUq22mlrkYXcDvf6rafRr6vFqTFevvc/RkGnfvFzBt9g2MjeV0jHDSW+fi1WyOnkonLqGAxOQiNmx/QOy9POoFWatlTp3P4LdV9zh6Mq1aepzet5SQFr0IbdkHB1dfOvb/HCtbF84f+V2nvI2DG536f0G9ZpEYm+q23Zn9y/EJakbzLm/j4OpL8y5v4xXQlNP7l1epT0SoIXvPlnA5toykdCWr9hVhaCjQwF//gNH9h0q2HS/m4s0yyvRMPLQOM+LUtVJOXSvlYaaSzUeLycxT0rxu5QNRjWoJnIgWufkA0nJgxxkRQzkEeejv0DWsJXD3IZy8IZKRq/o37iE0qq2pcycZjl4VtUboq6JJgIxjV1XOQWo2bD2pxNAAgr3069IkQMadZJG/romk58Bf10TuJos0CdB0v24+ELmdqNI1IxcOXVI5BTUdKu+0vlS2CdS2zZYTCgwNoK63/m5mkwAZsUkix68pSc+B49eUKtsE6rfNwUe2cavCNv8GMgPhmY7/RSSn4jmzbt06/P398ff354033mDp0qVq73L9+vVMmjSJadOmce7cOVxdXfnll18qtHHw4EESExM5evQos2bNYvLkyXTr1g1bW1tOnz7NqFGjGDVqFPHx8ZXq8vnnnzNz5kzOnTuHgYEBw4YN0ylXVlZGZGQkERERXL58mZMnTzJy5EgEQaBfv36MHTuWOnXqkJSURFJSEv369VPXnTRpEj169ODKlSsMGzZM7RCsX7+e69evM3HiRD777DPWr18PqMKu+vbtS+fOndXtNWum3SmRyWT079+f1atXa5WvWbOG8PBwfHx8EEWRrl27kpyczM6dOzl//jxhYWG0a9eOjIyMqm/Uc8SmaQip+//SKkvdewzrBsEIBqrJQNumIaTtP64lk7bvGLbhusN0HlNapiD6QSrhtbRnu8Jru3MpLllnncPX7xHk5sTSIxdpP20Z3b9fxcwdf1FUqhkuuhyXTHhtd616zWq7cykuSb8upaXcvB1Lw9AQrfIGofW5Fh2ju1I5bsXe4Vp0DPWCNSO4wUGB3IyN5cZNVUc5MTmZM+cu0LRhWKVtlZWWcvf2DYJDm2iVB4c25taNK9XSR6lUUlRYgLmFtVZ5UWEhHwzvwftDu/HD1I+4F1v59ZWWlhETe4/GIdoj043r1+FKjH7naMfBYzxITmFY3x5V6iqKIucuX+d+YjIhQfpTIOu/TyFcv3GjyvOA7vtUnl1799O6VQtMTUyqbM/Z3gBbawOibmhCi8rK4FpsIf7eVdd/jJGRgFwGuQXPll6zhrMJDnbG6tkFgNIykairWQQHWFW7nQb1bPCoaUbUtb83ZKooKyEp7ho+QS20yn3qNCch9uLfahMg4U5UhTZ967Sssk17KwFrcxk37mveFQoFxCaU4e1avRFrXchl4O4kI+a+9n2LiVNU2q6NOViYCtxN1ozQKpRwP1U1A6CPmvYCdx9qj+reeShS0/7vd+RsLFShQHeStHWJeyji7qi/XTcHgdgkbV1ik0Tc9NQRBKjjKWBoAAmp+kemX0bbxCZpZsMVSrj3UKy08+/uqG1PgNhEEXc9dZ60TXzaiw9/EgyFZzr+F5HCn54zixcv5o033gCgc+fO5OXlceDAAdq3b8+cOXMYNmwYb731FgBff/01+/fvrzBbYWdnx48//ohMJsPf358ZM2ZQUFDAZ599BqgW5Hz77bf89ddf9O/fX68u06ZNIyIiAoDx48fTtWtXioqKMCnXCcjJySE7O5tu3brh6+sLQGBgoPp7CwsLDAwMdIYnDRgwoIKz8uRmLt7e3pw4cYL169fTt29fLCwsMDU1pbi4uNJwp4EDBzJr1izi4uLw9PREqVSydu1atQ0OHTrElStXSElJUadk++GHH9iyZQsbN25k5MiRett+3hg7O1D8UHtUsiQlHZmhIUYOthQnp2Ls4kDxQ+246uKH6Ri7VAxFeZLMgiIUShF7C1OtcnsLU9Jydcd+J2TkcPFeEkYGcmYPeoWs/EKmbzlKdmERU/u0AyAtrwB7CzOtevYWZnrbBMjOyUWpVGJro90Bt7WxISMrq9Lr6DdkBNnZOSiUSga93peundqrv2vbqgXZ2Tl88OkXiKKIQqHg1Vc68Xqf1yptMzcnC6VSgbWNnVa5tbU9WVmnKq37mJ1bVlNcXEiTFu3UZTVqejLygy9x9/KlsCCfPdvXMfXTEUz/cRUuNXSHMmbl5qJQKrGz1u6Q2tlYk5Gle0YpPjGZX1dt5NevJ2Ag19+xyssvoMfIjygpLUMuExg34k0a19ff2VffJ1sbrXJbWxsyLmTprQfQf/BbZGdnP7pP/ejSSXcM/o2Ym9yLu8+4Me9W2t5jbKxUPzVZOdqdyqwcBY521Q+ZfPNVezKyy7gcU1jtOrqws1Wt0cjI0l7PkZlVgrNT5U6OuZmczcvCMTIUUChh1q+3OBeVWWkdfRTkZSIqFZhbac+EmVs6kJed+rfaBMjLTqvYppU9+TmVt2lpruro5BZod9hyC0Rsrf7+GKS5qYBcJpBToL0uI7dQxNJcf7vmj25FvvZPJPlFItbmAqC7Y2lhoquOpr2/g8Wjunnl2s0rQh3v/zS6WJTTxckGhnWUYyCHkjJYf7TytREvl21Uz0152+QXgXUVtskr0tYzr0ik3M8dTjYwvJOB2jbrjihIe8GhT/Dvrqn4b0FyKp4jMTExnDlzhk2bNgFgYGBAv379WLJkCe3btyc6OppRo0Zp1QkPD+fQoUNaZXXq1EEm07xonZ2dCQ4OVn+Wy+XY29uTkpJCZdSrV0/9/8e7J6akpFRY42FnZ8eQIUPo1KkTHTp0oH379vTt27daOy42bFhxMeH8+fNZtGgRcXFxFBYWUlJS8tSZpkJDQwkICOD3339n/PjxHDlyhJSUFPr27QvA+fPnycvLq5BvubCwkNhY/SPDxcXFFBcXa5U9lzzR5WMdH28u82S5LplKYiS1RcttYvPEKcqjFEUE4Jv+HbA0VV3b2G4Kxq3azWeREZgYGmipqGlTrN6mOBUqqs5XGXO+/ZrCoiKiY27y2/JV1HR1oW1ESwCirlxl9fo/GDNqBIH+tUhMSubnhUuwW7uBN/v3qYY65c8uIlSpEZw4sofNvy/iw8+/13JM/ALq4hegWa9QO7A+X3w4iL07NjBo5NiqlNHWRM/9VSiUTJqzgLf6ReJRo/K1RGamJiz/YQoFRcWcu3KdH5etpYazE2HBAZWrUu6zKFZ9f2d/N011n27EsGj5SmrUcFXfpyfZte8AXp4eBPjrX4S++nvNov9pC3Sv8lSpU72/gch2NrQIs2TiTw8oLXu6UcoOEU58/K5G10+mPprJKt+MIFSpTkGhgqEfnMPURE7D+ra8N9yXxORCLl79+70cnc/wM29QpetZrNjmjHc0a+sWbCt8fPqKTT2PgWFdbZT7GxnbU/Pbt/64Urc61dGn/Ou2WgpqM76vxtn//bBCd7tVPzLVes2n5cCCnQpMjCDQQ0aPcBnL9ynUjkUdD4HODTRX8aJtM6Gfpvu45pDumcO/066uOmk5MP/PMkyMBII8BCKbyVm2r+ylcCwktJGciufI4sWLKSsro2bNmuoyURQxNDQkM7P6I1nlFzsLgqCzTKnUn42jfDuPf6D01Vm6dCljxoxh9+7drFu3ji+++IJ9+/bRtGlTnfKPMTfXHoZYv349H374ITNnziQ8PBxLS0u+//57Tp8+XWk7uhg4cCBr1qxh/PjxrFmzhk6dOqmzQymVSlxdXTl8+HCFejY2Nnrb/Oabb7RmUkAVwtXoqbXTUPwwrcKMg5GjHcrSUkrSs1QyyWkYu2jPSRs72VWY4SiPrZkJcplQYQYhI6+wwkzDYxwtzXCyNlc7FAA+jraIIjzMzsPTwQYHHbMSqjZNyzenxtrKEplMRmZmllZ5ZnY2tpXYHMDVxVmlh5cnmVnZLP99vbqzunTVWjq0aaWevfDx8qSwqIjZ8+YzsG8vLQf7SSytbJDJ5GRlas8AZWdnVJi9KM+pY/tY9NM03v90OsEhjSuVlclk+NQKIjlRf7ihjaUlcpmMjCztX7nM7Bzsys3sABQUFXEj9h637t5n1qJVgMoZFEWRln2GM3viWBrWDVKf381VZb/a3h7EJSSyYtMOvU7F4/uUUe4+ZWVlV5hlKk/5+7RizdoKTkVRUTGHjh5nyED9s6QAY7/T2Mvw0YiejZWczCdmK6wt5RVmL3TRo60NvTrYMvnnROIS9WeL0sfxM+lcv3lO/dnoUX55O1sj0jM17dlaG1aYvSiPKMKDJNWQ7O27+Xi6m/FGHw8uXq1eyN2TmFnYIsjk5GVrvwfyc9Mxt/r7mfAsrB3Iz9FusyA3Q2ebM9Zo1qIYyFX3ydJcIOeJ2QpLU6HC7MXTkF8oolCKWJnLAM1vkK52l+zTfC9/9KdffnTdzFggX8/iflCNmJuXe5WZ6Rihr4oFOzXPpsEj/8LCVHtE3ty48nbziqgw8m5uUnFUX6lUZYYCSMpQUsNORpMAGX+eUdnjVqJIYobmml+0beb/qQmRU9vGBPKemEQ0K/dZly6qWQ6NvuYmQoU6GtuIJGWI1LAXaBogY8fpyvtA/zSCXJqpKI+0puI5UVZWxooVK5g5c6Y6l3BUVBSXLl3C09OT1atXExgYyKlT2mEZ5T+/SEJDQ5kwYQInTpwgODiYNWvWAGBkZIRCUb0Y5mPHjtGsWTNGjx5NaGgofn5+FWYOqtvegAEDuHLlCufPn2fjxo0MHDhQ/V1YWBjJyckYGBjg5+endVSWlnbChAlkZ2drHRMmTKjWtekj61QUDu2014U4dmhB9vmriI+y5WSeisKhXXMtGYf2Lcg8WXmMs6GBnMCajpy6pd2hPXUrnvqeuke4Q7xcSc0poKBY0zmKS8tCJgg4W6tGJet5ulRo8+TNeOp76p+dMjQ0pLafL+cvai+GPx91mTqB+mP8yyOKIqWlmjQyxcXFCOUcB7lMhoj+kX4AA0NDvP0CuBp1Rqv8atQZagXoz4x04sgeFsz9itHjviK0UQu9ck/qG3fnZqWLtQ0NDfD39eLMJe2Uq2cvX6euv28FeXNTE1bO/oplM6eoj8iOrfGo4cKymVOoU6tiHY0+UFqmJ50KT9ynqPL36RJBAZXPbpQ/0ZP36TFHjv9FaWkp7dpEVFo9Oa1UfcQnl5CZXUZ9f40jbCCHOr6mxNytvDfTo60NvTvZ8tX8RGLjiyuV1UdhoYIHSUXq4+79AtIyimkUYqvRx0AgJNiGqzeqyMdZDgGNk/K0yA2McPWsw91o7TVZd6+fwM238vVWleHmE8Kd69pt3rl+XGebadmi+kjOUJKdr8TfQzPeKJeBr5sBd5P+/joWhRLiU5T4e2iH+fl7yCu0m5mnOdJyIK9QxMtZ03mTycDDERIqGY95kC7i7azd4fN2FtQZharLk7qkZqvCtXxctXXxdBaIr2TtQ0KaiI+Lti6+rkKl6yVANeMgf+KxKin7b7CNRmGZDLycBRIqWfsQn6ptTwAfV6Fa6yXkshffoZfJhWc6/heRZiqeEzt27CAzM5Phw4djba09Gti7d28WL17M+PHjGTx4MA0bNqRFixasXr2aa9eu4eNT9d4A/yR3795l4cKFvPrqq9SoUYOYmBhu3ryp3kjFy8tLvdGKm5sblpaWekOG/Pz8WLFiBXv27MHb25uVK1dy9uxZvL291TJeXl7s2bOHmJgY7O3tK9jrMd7e3jRr1ozhw4dTVlZGjx6axazt27cnPDycyMhIvvvuO/z9/UlMTGTnzp1ERkbqDMsCVahTVeFOcnMzzP00IWJm3m5Y1Q+gJCObovgk/L/+CJOazlwa+ikAcQvX4jl6IIHfjyd+8XpsmobiPrQXF9/QhMrcm7eCpgdX4TNuBA+3H8C5ezsc2oVzsvWASnUBeLNlCJ+v20+QmyP1PVz448x1krJy6dNUFVc/d9dJUnLymdZPNdLfJaQWCw+cY+KGg7zToTFZ+UXM2nmCyIaB6tCngc3rMWzBZpYcvkCbIG8OXb/L6dsJLH2nZ6W69I7szrezfqR2LV+CAvz5c/c+UlLT6P6KKu3oouWrSEvPYPxHYwDY8ucunBwd8HBTzd5dvX6DDZu3EdntFXWb4Y0bsnHLdvx8vAmsXYsHScksXb2WZo0bIq9krQHAKz1e59fZk/HxC8AvoC6H9mwhPfUh7V5RrcdYt/xnMjNSGfXhZOCRQzFnCm+M+Ag//2D1LIeRkTFm5iqHa9Pvi/DzD8alhrt6TcX9uzcZMurjSnXp370jU3/8jUBfL4L9/di67wgP09KJ7KjKLf7rqg2kZmQxccwIZDIZvh5uWvVtra0wNjLUKl+xaQcBvt7UdHaktEzByQuX2XXkBB+PfLNSXXpFvsp3s+ZS28+XoMAn7lOXTgAsWrZSdZ/GfgDA1h07cXJ0xF19n6JZv3krkd27VGh71979NG/aBGur6i9oBthxJIteHWxJSi0lKbWU1zrYUlwqcvR8rlpmzBtOpGcrWL1ddV8i29nweld7Zi9PJiW9DBtL1fNQVKykqETV8TAxEnBx1MzKOtkb4FXTiLwCJWmZ+p2vDdse8GYfDxISC4hPLGRQXw+KixXsPaIJK/3iQ39S00tYsOIuAG/0dufG7TwSkwoxMBQIb2BP57bO/PCrJhuXqYmMmq6aoWBXZxP8vM3JzSvjYWpFp6hJh6FsXfwJrp7BuPmGcuHoOrIzkgiLUM0EHdw0k9zMh/QYPkNdJ/m+KntaaXE++bkZJN+PRm5giGMNVQaxRu0GseL7NzixayG1Q9pxM+oAd6NPMviTNZXeI4AjF0vp0MiItCwlqVlKOjQyorRU5HyMxsEc2NGE7DwlO06oBi7kMtVeFQAGMrC2kFHTQUZxqcpZATh8oYQ3Oplw/6GCe0lKmtU1xNZSxl9X9OSpfcTZWyLNAgUy81QZgJoFCpQq4Pp9TcezW2OB3EI4ckVVdu6WyBttBJoGCNx8IFK7poCXM6w6qKljaAC2T2RVt7EQcLIRKSqBHD1Ly07fUNKijoz0HFX61xbBMkrL4Oo9Tbs9wmXkFqr2onhcZ0gHOc2CBGISRPzdBLxdBJbt1ThTbevLuJ2oJLsAjA2hjqcMTyeBNYcqH4l/qWwTraRlsIyMXPFRul05pWVw5a7mGiKbycktEDnwhG2GdpTTPEjGjQQlAW4yfFwFlu55wjYhMm4/EMkuEDE2hGBPGV7OAqsPPluyhueB8BI4Ni8bklPxnFi8eDHt27fX2UHu1asX06dPp1atWkycOJFPP/2UoqIievXqxTvvvMOePbrTcf5bmJmZcePGDZYvX056ejqurq689957vP3222r9N23aRJs2bcjKymLp0qUMGTJEZ1ujRo0iKiqKfv36IQgCr7/+OqNHj2bXrl1qmREjRnD48GEaNmxIXl4ehw4dwsvLS2d7AwcO5N1332XQoEGYmmp+qAVBYOfOnXz++ecMGzaM1NRUXFxcaNWqFc7Ozs9kD+sGwYQfWKn+HPSDanF4/IpNXB4+AWNXR0zdNSP6hfcSONt9JEEzJ+D5zkCKE1O49uE09R4VAJknL3Jx4Ef4T/kP/lPGUBAbz8UBH1a5RwVA5/q1yC4oYuGBc6Tm5OPnYs/PQ7tTw1bVqUvLLSA5S9MxMzM2YsFbr/Lt1qMM+GkD1mYmdKzny3udNKFsIV6ufPd6R+btPc3Pe0/jbmfNdwM7VrpHBUCbls3Jycll5doNZGRk4uXpwTeTPsPZSZWGMz0jk5RUzTCZqBRZvHw1yQ9TkMvluLo489bggeq9DwDe6NcbQRBYuup30tIzsLGyomnjhgx/s2qHq2nLDuTmZrN53RKyMtJw8/Th44mzcXBS3Z+szHTSUh+q5Q/u2YJCoWD5/O9ZPv97dXnLtl15+z8TASjIz2Xxz9+QnZmOmbkFnj61+eKbBfjWrnzPgfbNm5Cdm8+SDdtIz8zGx6MmP3z2Ia5Oqpmz9MxsHqZVvglaeQqLivlh4QpSMjIxNjLCs6YLkz4YQfvmTSqt16ZVC3Jyc1m1dr36Pk2f/IX6PmVkZpKSqlm0qxRFFi9fSfLDFGRyOTVcXXhryJta9wkg4cEDrl6P5ruvJj3VdQBs3p+FkaGMkX0cMTdTbX439ZdErT0qHGwNUT4xSNm5hTWGBgKfDNeeQVu3K4N1u1RZ3nw9TPhqjCbkdNhrqlDEg6dzmLda/7qz1X/EY2wk46N3amFpYcj1mzl8OPGy1h4Vzo4mWvqYmsgZ+44fTvbGFJcoiUsoYOrMGxw8rrFlgJ8lP30Tov485i1VR3/ngWSmz6mYRaxOoy4U5mVybMcv5GWn4FijNv3HLMTGXnVNeVmpZGdoZ2Vb9FWk+v9Jcde4dmYH1vY1ef9bVfpid78wXhs5i8Nb5nB464/YOrrz2sjZVe5RAXDgfAmGBtC7jTFmxqrN737dUqi1R4WtpYAoakalrc0FPhmoCYVt18CIdg2MuJVQxrw/VLEsF2+VYW5aTKcmxlibCSSlK1mwtbDSPSoATt0QMZBDpzDVpmqJ6bD2iPY+DFZmgtas5oN02HJKSUSwjFZ1BDLzYctJzT4MAK62MLCNZtCifYjqei7fVfLnWd06nbguYigX6dJYhqkRPEiDVQe196iwNtfWJSEN/jiupE19GW3qQUae6vODJ14F5iaqDreFKRSXwsNMkTWHlNxJ/u+xzV/XlRgYQJfGckyNVDM0Kw+UlbMNiKKmI56QJrLxuIK29eW0qS8jIw82HlNozZpYmAj0bC7Tss3qg4oqbfNvIMilYJ/yCGJl8QUSEv9P+NOw+uE7/zRdS2Mo2vLji1YDAJPIMSTc1J296EXgVjuYszFZL1oNABr525B+9cSLVgMA++BmxN+6XrXgv4R7rSBeG3P7RasBwKYf/WjR/ciLVkPN8e0RrDz6orVQ8WYr+GBubtWC/xJzP7Dkm/UvfgQaYEJfOVNX65/p+jeZONDgpbELqGwzZVXlM0z/FpPe+Hsb7j4PTjWpfD1eVTQ9faZqof8yJDdLQkJCQkJCQkJCQuKZkMKfJCQkJCQkJCQkJJ4CaU1FRSSnQkJCQkJCQkJCQuIp+F/N4PQsSE6FhISEhISEhISExFMg7VNREcmpkJCQkJCQkJCQkHgKyu+vJCE5FRISEhISEhISEhJPhbSmoiJSSlkJCQkJCQkJCQmJp+BCuxbPVD/swPHnpMnLgzRTISEBL82+EKDaG+Jl2Teja2kM124nVS34L1HHz5WT0TkvWg0AwgOtuHxL/+Zq/yb1ajlxN/bl2BcCwNvXjxHTn26jv3+K3z6zp8+Hd1+0Gmo2zPbmt/0vWgsVI9rDJ/MLX7QaamaMMn1p9mOY0FfOtLUvhy6f95e/NHaBl882LwppoXZFJKdCQkJCQkJCQkJC4imQwp8qIjkVEhISEhISEhISEk+BtFC7Ii/EIpMnTyYkJOSZ2rh37x6CIBAVFfWvnvd58neu4X8VyRYSEhISEhIS/y0IMuGZjv9FnttMxYkTJ2jZsiUdOnRg9+7dz6tZvbi7u5OUlISDg0O164wbN47333+/WrKTJ09my5Ytz62TO2TIELKystiyZYu67O9cw7OwbNkyhg4dCoBMJsPKyoratWvTtWtXPvjgA6ytrf8VPV4GW1TFupNXWHbkImm5Bfg62/FJ9xaEedfQK19SpmDB/rP8eTGGtNwCnK0teKttA3o2ClLL7L8Sy897TxOfno27vTXvdWpKu2CfSvWwa9EQn7HDsQ4LxqSGE+d6jebhtgOV12nZiKAfxmMRVIvixBRiZy7i/sK1WjIuPTtSe/IHmPl6UBB7n5iJs3m4tXqB3rt2bGHrprVkZqTj7uHNsJHvERRcT6ds9LXLrFi6kAcJ9ykpLsLRyZmOnV+le88+WnIn/zrC7yuXkJyUiItrDQYMeoumzVpWS58DOzewa8sqsjLTqOnuw4DhH+FfJ1Sn7LmTBzm0+w/u371JaWkpNT18iOw/grqh4TrlTx3by/yZnxPaOIIPPvuhSl32/LmZrZt+JysjHTcPL4aOGENgcH2dsqdPHGHPzi3cu3OLstJS3Dy86TtgKCENmqhlysrK2LxhJUcO7CYjPY0aNd0ZOPQdQp+Q0cf2HTvY+McmMjIy8PT0YNTIkQQHB+uUvXrtGkuWLCU+IYHi4mKcnJzo8kpnXuvZUy2zd98+Zs2eU6Huti2bMTIyqlIfgO4tTWkVYoKZicDdxDLW7MknMU1/bHYNBzmvtjLF08UABxs5a/flc+BskV75V8JNeK2NOfvPFLJuf0GV+vTpZEP7cEssTGXcul/Moj/SSUgu1SvfrqklEY0scHcxBOBOQgm//5nB7fslWnIdm1vSo401NlZyEpJLWbolnRt3ivW2e/Hoas7uX0x+dioOrrVo0/sz3Pwa6pWPv3WGw398S1rSLSysnWjU4S1CWr6u/n7tnDdJuHWmQj3vOhH0Gr1Qb7uP6dDQgCaBBpgaw/0UJVuOlfIws/KcLsHeMjo1MsTeWiA9W2T3mVKu3VOqvx8/0Bg7y4rjmCeulrHluH6bA7SoIxDiI2BiCIkZsPeCkrQqllb514RWwTJsLCArD45cVXLzgeZ7dwdoEiDDxRYsTQU2HldwK7HyNgFaBguE+mp02X2uGrq4QURdGbYWkJkHRy4riXlCl2aBAv5uAvZWUKaAhDQ4eElJRm7V+vyv28bdEcIDZLjYqXTZcEyhpeuLRFpTUZHnNlOxZMkS3n//fY4fP879+/efV7N6kcvluLi4YGBQfb/IwsICe3v756pHaWnlL8PK+DvX8KxYWVmRlJREQkICJ06cYOTIkaxYsYKQkBASE6vx1qiE/zZb6GP3pVvM2H6cEW0bsm5MX8K8XBm9ZDtJmfrf8B+v3s3p2wlM7t2WreMG8u2Ajng72qq/vxSXzCdr9tAtzJ8N/+lPtzB/Plm9h8v3kyvVRW5uRs7lGK59MLVaupt6udFo+0Iyjp/neKNIbn83nzqzP8elZ0e1jE3TEELXzObB6q0ca9CDB6u3Evb7HGwa63YMnuT40YMs/W0evfq9wcwfFxEYXJevJ31CaspDnfLGJqZ06daTr7+by4/zl9O735usWbmYvbu2q2Vioq8x89spRLTtyKx5i4ho25GZ307m5o3rVepz+vhe1iyZRfc+Q5k6axW1g0KY9dUHpKfqtmvMtYvUqd+ED7+cw+SZKwgMbsCcaR8RdyemgmxaShLrls2ldpBuB6U8fx09wNLffqRX3zeZ8eNiAuvUZ9rkj/Xa5vrVS9QPachnk7/nuzmLCK4Xyrdfjedu7E21zNqVv7Fv1zaGvf0fZv+6kg5devD9tM+0ZHRx5MhRFiz8jf79+vHzTz8SXCeYLyZOIiVF98JyExMTunfvxvczvmPhgvm83r8fy1esZOeuXVpyZmZmrFm1UuuorkPRuakJHRqbsGZvPtOWZZOdr+TD160wrqS6kSGkZSnZdLiArDylfkHAy1VOq1AT4h+WVUufHm2t6dbamsV/pDN+diJZOQq+HOWCibH+jkIdPxOOX8hjys/JfD43ibTMMr4Y5YKdtWaxaLMQc4ZG2vPHviw++SGR6DtFfD7SBQcb3QtKb5zfyaGN39C00zsMmrCFmn4N+OPnEeRk6H4fZ6XF88cvI6np14BBE7bQpNMoDm6Yxs2LezTXNuIn3pl+XH0M+XwHgkyOf2jnKu3SOsSAlvUM2HK8hB//KCa3QGREN2OMDfXX8XCWMbCDERduKZi9oZgLtxS80cEIdyeNLX/6o5ipywvVx8LtKifr8p3KF/w2DRBoXFtg7wUly/YryS8S6R8hw6iSn4qa9hAZLuNqnMjivUquxolEhsuoYaeRMTSAlCyRvRcqf66eJDxAoIm/wJ7zSpbuU5JXKDKgTdW6vNZMxtV7Iot2K7l6T6Rnc21dPJwEzt8WWbZPyZrDSmQCDGgtw7CKNcj/H2xjZAAPs0T2nK++LhIvjufiVOTn57N+/XreeecdunXrxrJly7S+//bbb3F2dsbS0pLhw4dTVKQ90jRkyBAiIyOZPn06zs7O2NjYMGXKFMrKyvj444+xs7PDzc2NJUuWqOuUD5c5fPgwgiBw4MABGjZsiJmZGc2aNSMmRtNZKB/+dPjwYRo3boy5uTk2NjY0b96cuLg4li1bxpQpU7h06RKCICAIgvqaBEFg/vz59OjRA3Nzc77++msUCgXDhw/H29sbU1NT/P39mTt3rtZ5ly9fztatW9XtHT58WOsalEolbm5uzJ8/X8s2Fy5cQBAE7ty5A0B2djYjR47EyckJKysr2rZty6VLl6p9rwRBwMXFBVdXVwIDAxk+fDgnTpwgLy+PTz75RC3n5eXFnDlztOqGhIQwefJkrbb+CVs85siRIzRu3BhjY2NcXV0ZP348ZWWaDkPr1q0ZM2YMn3zyCXZ2dri4uGjp93dZeSyKno0Cea1xED7OdnzyaktcrC1Zf+qqTvm/YuI4fyeRn4d1o2ktd2raWVHX3ZkQL1e1zKrjl2jq587wNg3wdrJleJsGNPZzY/Xxy5XqkrrnKDcnzSF5y75q6e45sj9F95O4PnY6eTfuEL9kI/HLNuHz0TC1jPf7g0nbf4LYGQvJj7lD7IyFpB08hdf7g6tsf/vmDbTr2IUOnbrh5uHJ8JHvY+/gxJ6dW3XK+/jWomXrdnh4euPk7EpE246EhDUi+prmurdv3Uj90Ib06jsQN3dPevUdSN36YezYurFKffZsXUOr9j2I6BBJDXdvBr41FjsHZw7u1l134Ftj6fLaIHxq1cGlhge933wXZ1d3os4e1ZJTKhQsmP0lkf1H4uisf4bqSXZsWUfbDl1p16k7bu5eDB05BgcHJ/bu3KxTfujIMfToPRC/2oG41nRnwOC3ca3hxrkzf6lljh7aw2t93ySsUTjOLjXo1KUnIWGN2b55rc42H7Np82Y6dezIK5074eHhwai3R+Lo6MCOP3fqlPfz9aVN69Z4eXri4uxMu7ZtadAgjKtXr2nJCYKAnZ2d1lFd2jU2ZedfhVyMKSExVcHS7XkYGUKTOsZ669xLUrDxYAFnr5dQVqZ/pNzYEN561ZIVO/MpKKpelvSuEVZs2pfFmSsFxCeXMm9NKsZGAi3CLPTW+XFVKnv/yuVeYgmJKaUsWJeGIAgE1zJVy3RrbcXB07kcPJ3Hg5RSlm3JIC2rjI7NrXS2ee7AUuqG96Je8z7Yu/jStvfnWNq6EHXsd53yl46vxcrWlba9P8fexZd6zftQN/w1zh7Q/D6amttgbu2oPuJu/IWhkQm1w6p2KlrUNeDghTKu3lXyMFNk3cFSDA0gxE9/D7dlXTm3EpQculhGapbIoYtl3H6gpGVdTY8yvwjyCjVHoKectGwldxIr7yw2qiVwIlrk5gNIy4EdZ0QM5RDkod/5a1hL4O5DOHlDJCNX9W/cQ2hUW1PnTjIcvSo+1ah3Y3+Bv66JxCRAajZsP63SpY6nfl0a+wvcTYYT0SLpuap/7z1UlT9m7REll++KpOVAShbsOKPE2lzApYo/r/8PtolNgiNXVO2+bEjhTxV5Lk7FunXr8Pf3x9/fnzfeeIOlS5fyePuL9evXM2nSJKZNm8a5c+dwdXXll19+qdDGwYMHSUxM5OjRo8yaNYvJkyfTrVs3bG1tOX36NKNGjWLUqFHEx8dXqsvnn3/OzJkzOXfuHAYGBgwbNkynXFlZGZGRkURERHD58mVOnjzJyJEjEQSBfv36MXbsWOrUqUNSUhJJSUn069dPXXfSpEn06NGDK1euMGzYMLVDsH79eq5fv87EiRP57LPPWL9+PaAKu+rbty+dO3dWt9esWTMtfWQyGf3792f16tVa5WvWrCE8PBwfHx9EUaRr164kJyezc+dOzp8/T1hYGO3atSMjI6PqG6UHJycnBg4cyLZt21Aoni5N3D9hC4AHDx7QpUsXGjVqxKVLl/j1119ZvHgxX3/9tZbc8uXLMTc35/Tp08yYMYOpU6eyb1/1OuC6KC1TEP0glfBaHlrl4bXduRSne/T78PV7BLk5sfTIRdpPW0b371cxc8dfFJVqHKDLccmE13bXqtestjuX4p5vulabpiGk7v9Lqyx17zGsGwQjPJoFsm0aQtp+7fzYafuOYRte+Yh8aWkpsbdjqB/aSKs8JKwRN6Kv6amlzZ3YW8REXyWoriYk6OaNa4SUazM0rHGVbZaVlnIv9gbBIdqhQMEhTbh9o3Jn7TFKpZKiwgLMLbRD/7auX4SllS0RHXpUq53S0lLu3L5J/dDGWuX1QhsRc0O3M6pLl8LCAiwsNJ3P0tJSDMvNBBgZGXPj+pVKdbl1+zZhYdr3Myw0jOjo6Grpcjs2lujoaOrWratVXlhYyKDBQ3jjzUFMnDSZ27Gx1WrPwUaGjYWMa3c1s5llCrh5vwzfms8+OzmgkzmXY0uIvle92VInewNsrQy4FKNJp1qmgOu3i/D31u/klMfISMBABnkFqvemgRx83Iy12gW4HFOIv1fFdhVlJTyMv4ZXoHa+e6/A5iTeuajznEl3ovAKbF5OviUP466iUOi+/isn/yCgQVeMjM0qvR47SwErc4Gb8ZrfAYUS7iQq8XTR313wcJZxK0H7t+NmgkJvHbkMwmrJOXuj8t8bG3OwMBW4m6xxFBVKuJ8KbpVEyta0F7j7UNu5vPNQpKb93+/IPdblTnldUqrW5ck6AHeSRNwc9OvyeFaoqESvyP9b27xMCDLZMx3/izyXWJPFixfzxhtvANC5c2fy8vI4cOAA7du3Z86cOQwbNoy33noLgK+//pr9+/dXmK2ws7Pjxx9/RCaT4e/vz4wZMygoKOCzzz4DYMKECXz77bf89ddf9O/fX68u06ZNIyIiAoDx48fTtWtXioqKMDEx0ZLLyckhOzubbt264evrC0BgYKD6ewsLCwwMDHBxcalwjgEDBlRwVqZMmaL+v7e3NydOnGD9+vX07dsXCwsLTE1NKS4u1tneYwYOHMisWbOIi4vD09MTpVLJ2rVr1TY4dOgQV65cISUlBWNj1Q/UDz/8wJYtW9i4cSMjR47U23ZVBAQEkJubS3p6Ok5OTtWu90/Z4pdffsHd3Z158+YhCAIBAQEkJiby6aefMnHiRGSP/iDr1avHpEmTAKhVqxbz5s3jwIEDdOjQ4WkuX01mQREKpYi9halWub2FKWm5umO0EzJyuHgvCSMDObMHvUJWfiHTtxwlu7CIqX3aAZCWV4C9hfYPur2Fmd42/y7Gzg4UP0zTKitJSUdmaIiRgy3FyakYuzhQ/FB7D4Hih+kYuzhW2nZuTjZKpRIbG1utcmsbW7IyK3dq3xrUm5zsbJRKBX0HDKFDp27q77IyM7C2LdembdVt5uZmoVQqsLLRHs6zsrYnO7N6eyTs3rqa4uIiGjdvry67FX2Jo/u3MXX26kpqltMlR3VtNuWuw8bWlqwL1XP4t29eS3FREc1atlWX1Q9rzI4t6wiqUx9n15pcuXSes6ePo1ToH93NyclBqVRia2OjVW5ra0NGZmalOrzx5iCys7NRKJUMHDCAVzp3Un/n7u7O2I8+xNvLi4KCArZs3cbYcR/zy7yfqFmzZqXtWpur/l5z8rX1zslXYm/9bD+ujYKM8HAxYNrS7GrXsbFUjbpn52p3arPzFDjYVv9ncWA3OzKyFVy5qfo9szSXI5cLZJVrNytXgY1VxZH+wrxMRKUCMyvtsFwzSwfyc1J1njM/Nw0zS+2empmVPUplGYV5mVhYa7+/k+5dJi3xJp0GTqvyeizNVJ25vELtjl5eoYiNpf6OnqWZQPlXWW6Bpr3y1PGWY2IM52MqD1Uzf/SznV9uGU1+kYi1uQDonpWyMNFVR9Pe30GvLsUiVmZV6FJcvk7lurQPlXE/VSS1kkf6/6ttXib+V2cbnoVndipiYmI4c+YMmzZtUjVoYEC/fv1YsmQJ7du3Jzo6mlGjRmnVCQ8P59ChQ1plderUUXcUAZydnbUWFcrlcuzt7fXGBD+mXj1NXLirqyr8JCUlBQ8P7ZFnOzs7hgwZQqdOnejQoQPt27enb9++6jqV0bBhxQV08+fPZ9GiRcTFxVFYWEhJSclTZ5oKDQ0lICCA33//nfHjx3PkyBFSUlLo27cvAOfPnycvL6/CupDCwkJiqzliqI/HM0uC8HR/JP+ULaKjowkPD9fSp3nz5uTl5ZGQkKC+n0/eb1Dd88qekeLiYoqLtd9ijx20JylvBxHQZxqlKCIA3/TvgKWpqq2x3RSMW7WbzyIjMDE0eNSmdj0R8antXS3Eci/wx+d4slyXTPkyPVTQWRT12uYx02b8RFFRITdvXGflsoW4utakZet2mjYpZ+9qtKm3LqL+m/UEp47uYcvahXzw2Q9qx6SwMJ8FsycydPRnWFrZVE+Bctpo6SJW72/q+JH9bFizlE++/AbrJ5y2oSPHsOCnGXzwzhsICDi71qBN+y4c2q87jElbFV02rVyXH76fQWFhETdibrBk6TJq1HClTevWAAQGBBAYEKCWDQoK4r0xY9i6fTujy73jAX4ap3H2flr/aLWmjkesmo+dTmwtZfTvYM7s33Moq2TQu0WYOW/31XTEv/ntoR51BH39nwq82taaFqHmTPo5idLyoVnl/7yo/DrLP8NU8W7Q9Teoux24cmIjDjVq4+qle83UV8M1PbilO0senb2CgtW2i0ZH/dfcKEBOzH0lOTrGVMb21PQD1h9X6tRHqI4+Ou7B0/JxL40u647+/Zj+p3nGOzUQcLKBFfu1z1fHQ6BzA81V/H+0zcuG5FRU5JmdisWLF1NWVqY1UiWKIoaGhmRWMSr2JIaG2qvABEHQWaZUVv7wPlnn8YtXX52lS5cyZswYdu/ezbp16/jiiy/Yt28fTZs2rfQc5ubmWp/Xr1/Phx9+yMyZMwkPD8fS0pLvv/+e06dPV9qOLgYOHMiaNWsYP348a9asoVOnTuqMSEqlEldXVw4fPlyhnk25UcmnJTo6GisrK7XDIpPJ1I7GY3QtxP6nbKGrA6TL8XnaZ+Sbb77RmkkBVQjX+BBVB8jWzAS5TKgwg5CRV1hhpuExjpZmOFmbqx0KAB9HW0QRHmbn4elgg4OOWQlVm6blm3smih+mVZhxMHK0Q1laSkl6lkomOQ1jF+2RTmMnuwozHOWxtLJGJpORWW4GITs7C2ubyoN/nV1Uzrqnlw9ZWRmsW7NM7VTY2NpVmJXIyaq6TUtLG2QyOdlZ2rMSudkZVdY9fXwvS+Z9xehPvqVOfU34VEpSAmkpicyZNlZdJoqq52nYa0359ueNOLm6VdTFyhqZTF7hOrKzMrWcBF38dfQAv/74LR+Nn0q9EG0n3dralk+++IaSkmJyc3Kws3dg9bL5ODnrH/ywsrJ6dJ+0379ZWdkVZi/K83j20Nvbi8zMLFatXqN2Ksojk8moXas2iQ90LyieujhL/X/DR1lSrCxkZOdrev9W5rIKsxdPg6erHCtzGV8M04SvyWUCtTwMaNPQhHe+U92Pc9cKuP2DJkDcwEClj42lnKwcjT7WFjKy8qoOAe3e2orX2lsz9ddk7idp3ou5+QoUCrHCrIS1pbzCrAiAqYUtgkxOfo72315BbnqF2YjHmOuYxSjIzUAmM8DEwkarvLSkkBvn/6R5tzF6r2XOBs0gi8EjtS1NBXILNO9/CxOB3EL9vb/cAhHLcq9HC9OKMx4ANhYCtWrKWLFXd2zPkn2a50Eue3x+7VFwM2OB/GL9+uQVgXm5V6uZjhH6qli0p6Iu5iaq9h9jbiyQX8k6nrwilf5PYm6sW5eOYQK1awqsOKAkt9xG57cSRRIzNOf5/2ablxHJqajIM807l5WVsWLFCmbOnElUVJT6uHTpEp6enqxevZrAwEBOnTqlVa/85xdJaGgoEyZM4MSJEwQHB7NmzRoAjIyMqr2+4NixYzRr1ozRo0cTGhqKn59fhZmD6rY3YMAArly5wvnz59m4cSMDBw5UfxcWFkZycjIGBgb4+flpHc+SijUlJYU1a9YQGRmpni1ydHQkKUkT75+Tk8Pdu3erbOt52SIoKIgTJ05oOTYnTpzA0tKyylCLypgwYQLZ2dlax4QJE9TfGxrICazpyKlb2mt3Tt2Kp76n7nCtEC9XUnMKKCjW/EjGpWUhEwScrVWLPut5ulRo8+TNeOp7Vj0z9jRknYrCoZ32GhXHDi3IPn8V8dEi98xTUTi0047JdmjfgsyTumO4H2NoaIivnz+XLp7TKr908RwBgXWqraMoQmmpxla1A+pwKUq7zaiLZ6ts08DQEC/fAK5FaTus16LO4BegP5PVqaN7WPTjVN7+6GtCGmrHsru6efH13N+ZOnuV+ghp1IqA4AZMnb0KOwdnnW0aGhri41eby1FntcovR53FP0B3GldQzVD8PGc6H4ybSINGFdcWPcbIyBh7B0cUCgWnThyhUZMWemUNDQ2p5efHxYva9/PixYtaIZ5VIYpipRndRFEk9s4dvYu1UzOV6iMxTUFWnpIgb80ggFwGtT0MiH1QvWxNuoi+V8qk37KYujhbfdxLLOP01RKmLs5Wj4IWFYskp5Wpj4TkUjJzyqjnr+ldGcghyM+EmLv6U78CvNrGmt4dbZm24CF34rU7xmUKuJNQTL3a2r22erVNiblXsV25gRHO7nW4d0N7HdS9Gyeo4aN7jZOrTwj3bpzQlo8+jrNnMHK59iBLzPldKMpKCGr0qt7rSc8R1cfDTJGcfJFa7hqnSC4Dnxoy4pL1O3/3Hyqp5abtSNVyk+us0yhATl4h3IjT3V5mnuZIy1E5Jl7Oms6bTAYejqq0q/p4kC7i7azd4fN2FniQ/nTD4rp08XYpp4tTNXRx0dbFx0UgIU1bl05hAgFuAqsOKsnOr9hOSdn/X9tI/PfwTE7Fjh07yMzMZPjw4QQHB2sdvXv3ZvHixXzwwQcsWbKEJUuWcPPmTSZNmsS1a9Vb1PlPcvfuXSZMmMDJkyeJi4tj79693Lx5U/2j6+Xlxd27d4mKiiItLa1CyMyT+Pn5ce7cOfbs2cPNmzf58ssvOXtWu3Ph5eXF5cuXiYmJIS0tTe+Ptbe3N82aNWP48OGUlZXRo4dmoWj79u0JDw8nMjKSPXv2cO/ePU6cOMEXX3zBuXPndLZXHlEUSU5OJikpiejoaJYsWUKzZs2wtrbm22+/Vcu1bduWlStXcuzYMa5evcrgwYORy6vIb/ccbTF69Gji4+N5//33uXHjBlu3bmXSpEl89NFHWmFyT4uxsTFWVlZaR/nwpzdbhrDp7HU2n73OnYcZfL/9OElZufRpqurkzt11ks/XafZ06BJSC2szEyZuOEjswwzO30lk1s4TRDYMVIc+DWxej5O34lly+AJ3UzJZcvgCp28nMLBF5Wlc5eZmWNUPwKq+KuzEzNsNq/oBmLirnBH/rz+i/tLv1PJxC9di6lmDwO/HYxHgg9uQXrgP7cWdWU9kTpu3AocOzfEZNwJzfx98xo3AoV04935aXqX9uvfsw4G9f3Jg704S7sexZOE80lIf0rGLqtOyatlC5s6crpbftWMzZ0+fIPFBAokPEjiwbxfbNq0joo1mzUu3V3sRdeEsmzasISE+jk0b1nA56jzdevSuUp9OPQZwZP9Wju7fRmL8XdYsnkV6WjJtOvUCYMPKeSycM0ktf+roHn6bO4n+Qz7A1z+YrMw0sjLTKMjPA1SddzdPP63DzNwCE1Nz3Dz9MCg3M/Yk3SL7cWDvDg7u/ZOE+Hss++1H0lJT6NglEoDVy+bz00xNooHjR/Yzb9bXDB7+HrUC6pCZmU5mZjr5j3QBuBVzjdMnjvAwOZHoq5eYNnEsolJJj14DKrXLaz17snvPXvbs3cv9+/dZsHAhKampdO3SBYAlS5fx/Q8z1fLbtu/g1OnTPHjwgAcPHrB37z7+2LSJtm3aqGVWrV7DufPnSUpKIjY2ltlz5nLnzh26dnmlqtsEwIEzhXRpZkpobSNqOMoZ2t2CklI4fU3zbh3W3YKerTVD3nIZuDvJcXeSYyAXsLWU4e4kx9FW9Q4oLoHEVIXWUVwqkl+oJDG18oGLP4/k8Fp7axrXNcPdxZB3X3ekuETk+AWN/d8b4MCArpqZplfbWtO/iy2/rE0lNaMMG0s5NpZyTIw0HaMdh3No19SSNo0tqOlkyOBIOxxsDdh7QndK6obthnLlxEaunNhIenIshzZOJzcjifotVGsHj26dyc7lmsx89Vv0JycjkUN/fEN6cqyq7sk/aNSuYlKSKyc34le/PaYWlc+WPcnxK2W0DTWgjpcMZ1uBvm0MKS2DqNsae/ZrY0jnxgZP1FFQy01G6xADHG0EWocYUKumjGNXtB1GAWjoL+f8zTKU1ew3nr0l0ixQoHZNcLCCbo0EShVw/b6mgW6NBSLqau7BuVsi3s6qlKt2lqp/vZzh7E1NHUMDcLJRHaCaQXGyAatK1rKfiRFpHiTgXxMcraF7E5Uu1+I07XZvItC6nqBVx8dFlXLV3lL1r5eLqvwxnRsIBHsJbDmppKRMNeJvbqKZOfr/bBtDA3C2UR0ANuYCzlXo8m8hLdSuyDOFPy1evJj27dvr3DStV69eTJ8+nVq1ajFx4kQ+/fRTioqK6NWrF++88w579uzR0eK/h5mZGTdu3GD58uWkp6fj6urKe++9x9tvv63Wf9OmTbRp04asrCyWLl3KkCFDdLY1atQooqKi6NevH4Ig8PrrrzN69Gh2PZHjfcSIERw+fJiGDRuSl5fHoUOH8PLy0tnewIEDeffddxk0aBCmppoRL0EQ2LlzJ59//jnDhg0jNTUVFxcXWrVqhbOz7hHU8uTk5ODq6oogCFhZWeHv78/gwYP54IMPsLLSZJ2ZMGECd+7coVu3blhbW/PVV19Va6biedmiZs2a7Ny5k48//pj69etjZ2fH8OHD+eKLL6p1nc9C5/q1yC4oYuGBc6Tm5OPnYs/PQ7tTw1Zln7TcApKzNB0EM2MjFrz1Kt9uPcqAnzZgbWZCx3q+vNdJE0YX4uXKd693ZN7e0/y89zTudtZ8N7Aj9Tz0L1YHsG4QTPiBlerPQT+oFu3Hr9jE5eETMHZ1xNRdM9tReC+Bs91HEjRzAp7vDKQ4MYVrH04jefNetUzmyYtcHPgR/lP+g/+UMRTExnNxwIdknak6Y1KLVm3Jzclh/e/LyczIwMPTm8+nfIeTk+o6MjPSSUvV7MugVIqsWr6QlORk5HI5zq41eGPISDq+0l0tExAUzEefTuT3lYtZu2oJzi41GPvpJGoHBFU4f3matOhIXk42W9ctIjszjZoevnz05RwcnFQ2ycpI09qz4tCeTSgUClYunMHKhTPU5c3bdGXEB5OrPF9lNG/VjrzcHDauXabaGNDTm88mz8DxsW0ytW2zb9dWFAoFi36dxaJfZ6nLI9p15r0PPwegpKSE31f+RkpyEiampoQ2aMr7Y7/E3MKyUl0iIlqRk5vD6jW/k5mRgaeXJ19NmYKzs2oRb0ZmBimpmhAaUVSydNlykh/dJ1dXV4YNHUKXVzQOQ35+Hj/++BOZmZmYmZvj6+vL9zO+w9/fv1r22X2qCENDgQGdzTE3EbiTWMbstTk8McGHnZV22KWNpYyJb9moP3dqakqnpqbExJXyw+oqdtWqgq0HszEyFHirtz3mpjJuxxXz9fxkip4IHXGwNdCK+e7U3BJDA4FxQ7Xft+t3Z7JhTxYAJ6LysTCX0buTDbZWBsQnlTB94UPSMnXPyAQ06EJhfiYnd/1Cfk4KDq61eW30QqztVTOy+dmp5GRqZo1tHNzpNXohh/74hqijqzG3dqJtn8+pHdpJq92Mh3d5EHue3u8t4Wk4HFWGoQH0bGmEqTHEpyj5bUcxxU+M/dhYClph+XEPlazZX0KnRoZ0bGRAeo7I6v0lxKdoew5+bjJsLWWcvVFJWqNynLohYiCHTmEyTIwgMV2VgrXkCXNamQlaz82DdNhySklEsIxWdQQy82HLSSWJT0QnutrCwDaaXnv7EFUn7/JdJX+e1e3xnLwhYmAAnRuqdHmQDr8f1tbF2lxQret6QpfNJ5RE1JMRUVcgM0/1+UldGtRSnfvNdtpexPbTqlSz/59t42oHb7bV6NIhTKXLpbtKdpx+sTMa0uZ3FRHE8oHzEhL/Dyna8uOLVkGNSeQY/jSsXkftn6ZraQzXbj/ftLfPQh0/V05GP1tn8nkRHmjF5VuVJ474t6hXy4m7sbdftBpqvH39GDG9ehm4/ml++8yePh9WPSDyb7Fhtje/VW/z+n+cEe3hk/mFVQv+S8wYZco3658urfk/xYS+cqatfTl0+by//KWxC7x8tnlR3BnSrWqhSvBZtuM5afLy8OK3L5aQkJCQkJCQkJD4L+J/NYTpWZAs8j9EnTp1sLCw0HmU31RPQkJCQkJCQkLi7yHtqF0Raabif4idO3fqXQBe3TUXEhISEhISEhISEk+L5FT8D+Hp6fmiVZCQkJCQkJCQ+J/nf3W24VmQnAoJCQkJCQkJCQmJp0BaU1ERySISEhISEhISEhIST8G/vabil19+wdvbGxMTExo0aMCxY8cqlV+9ejX169fHzMwMV1dXhg4dSnr6P5uVT0opKyEhISEhISEhIfEUJLzX55nqu83bUG3ZdevW8eabb/LLL7/QvHlzFixYwKJFi7h+/ToeHh4V5I8fP05ERASzZ8+me/fuPHjwgFGjRlGrVi02b978THpXhuRUSEgACTevvmgV1LjVDn5p9oao4+f60uyZAap9M4rW//Ci1QDApO844m9df9FqAOBeK4jEmKo3L/y3qOFfj/dmZb9oNQCY95E1gycmVy34L7F8qgu/7n7RWqh4pzN8saz6G9H903w9xIjp616O/Q8+6ydn0grdiU/+baYMMmTKqpdDF4BJbxi+VLZ5USS83/eZ6rv9tL7ask2aNCEsLIxff/1VXRYYGEhkZCTffPNNBfkffviBX3/9ldjYWHXZTz/9xIwZM4iPj38mvStDCn+SkJCQkJCQkJCQ+BcpLi4mJydH6yguLq4gV1JSwvnz5+nYsaNWeceOHTlx4oTOtps1a0ZCQgI7d+5EFEUePnzIxo0b6dq16z9yLY+RnAoJCQkJCQkJCQmJp+BZ11R88803WFtbax26Zh3S0tJQKBQVtgZwdnYmOVn3LGyzZs1YvXo1/fr1w8jICBcXF2xsbPjpp5/+EVs8RnIqXnKSk5Pp0KED5ubm2NjYvGh1qs2yZcteiL5DhgwhMjLyXz+vhISEhISExP8fBJnsmY4JEyaQnZ2tdUyYMEH/+QTtxd2iKFYoe8z169cZM2YMEydO5Pz58+zevZu7d+8yatSo52qD8kgpZf9lhgwZQlZWFlu2bKmW/OzZs0lKSiIqKgpra+t/Vrm/iZeXF//5z3/4z3/+oy7r168fXbp0eXFKPSNb/9zN+k1bSc/MxMvDndEjhlKvTpBO2SvXovlt+UruJzyguLgEZ0cHunXuSO/I7lpyf2zdwbZde0hJTcPaypJWzcJ5a/BAjIyMqtRn144tbN20lsyMdNw9vBk28j2CguvplI2+dpkVSxfyIOE+JcVFODo507Hzq3Tvqb2o7ORfR/h95RKSkxJxca3BgEFv0bRZy0r1sGvREJ+xw7EOC8akhhPneo3m4bYDlddp2YigH8ZjEVSL4sQUYmcu4v7CtVoyLj07UnvyB5j5elAQe5+YibN5uHV/lXYBWHf6OsuOXyItrxBfJ1s+eaUpYV6ueuVLyhQsOHSBPy/dJi2vAGcrc96KCKVnA9XakT/O3WB71E1uP8wEIKiGA+93aERdN6cqddn65y42bNpCesbj52Y4dYP1PTfXWbRsJfcTElTPjZMjXTt3pHfkq2qZj8Z/weWr1yrUbdywAdMnf1GpLlt27mHdpq2kZ2bh5eHGe28NpV6dQN26XI9mwbLVxD94QFFxMc6OjnTv3IE+PbqpZcrKyli9cTN7Dx4hNT0D95o1eHvwQBo3CK3SLo/pEm5M87pGmJoIxCUpWHewkOR0pV55F3sZ3ZqZ4O4kx95axsZDhRy+qL0GwLemnPYNjfFwlmNtIWPh1nwux5ZVS5/INha0bmCKuamM2IRSVu7I4UGq/ro1HQ3o2dYCrxqGONrKWb0rh70nCyrI2VrK6NvRknq1jDE0EEhOL2PJlmzuJelu+9Kx1Zw/uJj8nFTsXWoR8dpn1PRtqFePhNtnOLr5W9KTb2Fu7UTDtm9Rr8Xr6u+vnd7EvjUVOyfv/XAZA0PjykwCQNsQOQ1ryzA1goQ0ke2nFKRkVb78MshToH2oAXaWkJEL+y6UEX1fU6dtiJy2IXKtOrmFIt+tqzo2v2UdgRBfARNDSMyAPeeVpOVUXsffDSKCZdhYQFYeHL6i5OYDzffujtDUX4aLHViaCmw8rtD6Xh+t68toUEtjmz9PK0itYvlQoIdA2xC52jYHLiq4Ea/bni2DZbQPk3PyuoLd5/T/bTwmop6MBn4yTIzgQbrIzjPV0MddoE19ObaWkJkLBy9p69OwloyGtWXYmKs+p2SLHL2i5HZi5c/Ay2abf5pn3afC2NgYY+Oq/x4dHByQy+UVZiVSUlL0bmz8zTff0Lx5cz7++GMA6tWrh7m5OS1btuTrr7/G1VX/b+SzIDkVLzmxsbE0aNCAWrVq/e02SktLMTT8dxczmZqaYmpq+q+e83lx6Nhf/LJoKWNGjSA4KIAdu/cyYfI0lvw8B2cnxwryJibGRHZ9BR8vT0xMTLhyPZo5Py/AxMSYbp1VMZD7Dx/lt+Wr+HjMu9QJ9CfhQSIz5s4DYPSIoZXqc/zoQZb+No8Ro/9DYGBd9uzexteTPmHur8txdKr4QjE2MaVLt554evtgYmJC9LUrzJ83C2MTEzq+onJ0YqKvMfPbKbz+5nCahLfg9MnjzPx2MtNm/ETtAN2dYAC5uRk5l2NIWL6JBhvmVWlLUy83Gm1fSPziDUQN/hjbZmEE/zSJktQMkjfvBcCmaQiha2Zzc9Jckrfux6VHe8J+n8PJ1gPIOlP54uPdV2KZseskn3drToiHMxvP3WD0yt1sfr8PrjYWOut8vO4A6XmFTO7ZCnc7KzLyC1EoNT9i5+4m8kpdP+p3dcbYQM7S45d4Z/ku/ni/N85W5np1OXT0OL/+toQx74ykTlAAf+7ay4TJX7H4lx/1PDcm9OjWRf3cXL1+nTnz5mNiYqJ+biZ//illZZqOaE5OLiPf/5CIFs0qtcvBY3/x86Kl/GfUCIID/dm+ex+fTpnGsp9n4+yoQxdjE3p27YyPtyemxsZcuX6DWb8sxMTYmO6dOwCweNVa9h8+ytj3RuHhVpOzF6L48pvvmffdNGr5eleqD0D7Rka0CTNm1Z4CUjKVdG5izPu9zJm6NJdiPf1KIwOBtGwlF2+W8lqEiU4ZY0OBB6kKTl0rYcSr+u9Pebq0MKdzuBm/bc4mOV3BqxHmfDzYlvE/plFUortTY2QIqZkKzl4rYsArljplzEwEPn/Lnht3i5m5MpOcfCVOdnIKinS3GXNhJ0c2f0PbPpOo4R3G5RNr2TJ/BG9O+BMruxoV5LPT49myYCTB4X3o/Ob3JN69wMENUzC1sKNWSCeNriYWDP5ce0V4dRyKlsEymgXJ2HS8jLQcVUdxSEcD5mwqpUSPv+XuKNAvwoADFxVcv68kyENG/9YG/LazjIQ0zXU/zFSydK+mEWU1+oVNAwQa+wvsOK0kIw+aBwm83lrGgp1KvfrUtIee4TKOXBW5mSBS202gZzMZKw8oScxQyRjKISVL5PJdkV4t5LobKkeLOjLCA2VsOaEgPUekVV05gzoY8NOWMr26uDkI9Gkl51CUkuj7SgI9ZPSNkLN4t4IHadrPRA17gQa1ZCRnVC9/TvMgGeEBMrac1OjzZjsD5m2rXJ/eLeUcuqQkOl5JoLuM3i3lLN2j4EG66rw5BSL7LyrIyFV9DvGR0T9CzoKdZXqdhJfNNv8G/9Y+FUZGRjRo0IB9+/bRs2dPdfm+ffvo0aOHzjoFBQUYGGh38eVy1XP+T+ZnksKfXiCtW7dmzJgxfPLJJ9jZ2eHi4sLkyZPV33t5efHHH3+wYsUKBEFgyJAhANy/f58ePXpgYWGBlZUVffv25eHDh+p6kydPJiQkhCVLluDj44OxsbF6mmzBggV069YNMzMzAgMDOXnyJLdv36Z169aYm5sTHh6ulS0gNjaWHj164OzsjIWFBY0aNWL/fs0IcuvWrYmLi+PDDz9EEAT1VJyu8Kdff/0VX19fjIyM8Pf3Z+XKlVrfC4LAokWL6NmzJ2ZmZtSqVYtt27apv1coFAwfPhxvb29MTU3x9/dn7ty5z3obKrBxy3Ze6dCWrp3a4+nuxrsjhuHkYM/2XXt0ytfy9aFtREu8PD1wcXaiQ5sIGoaFcOVatFrm+o0YggMDaNe6JS7OTjQMC6FNqxbE3I7V2eaTbN+8gXYdu9ChUzfcPDwZPvJ97B2c2LNzq055H99atGzdDg9Pb5ycXYlo25GQsEZEX9N00Ldv3Uj90Ib06jsQN3dPevUdSN36YezYurFSXVL3HOXmpDkkb9lXpd4AniP7U3Q/ietjp5N34w7xSzYSv2wTPh8NU8t4vz+YtP0niJ2xkPyYO8TOWEjawVN4vT+4yvZXnrhCzzB/XmsYgI+TLZ90CcfFyoL1Z3RnZfrrVjzn7yXx85udaOpbk5q2ltR1cyLEQ+OcfdOnLf2aBBHgao+3ow2TerREKYqcia18GPOPLdvo3KEdXTp1wNPdndEjh6uem526U/2Uf27at2lNw7AQrl7T6G5laYmdra36OB91CRNjY1pV4VRs2LqDLu3b0rVjOzzd3XhvxFCcHBzYtnOvHl28aRfRAm8P90fPcCsahdbnynXNM7zv8FEG9HmNpg3DqOHiTI8unWgUGsL6Ldsr1eUxbUKN2XOmiEu3y0hKV7JyTyGGBgINA/TP1N1/qGDL0SLOx5RSpicp0PV7Zew4Ucyl29WbnXhMp3Azth3N53x0MQ9SyvhtUzZGhgJN6+l2XgDuJpaxbm8up68WUarndF1bmpORo2DRlhzuPCglLUvB9TslpGTqvoALh5dSp2kvgsP7YOfiS+vXPsfC1oXLf/2uU/7yX2uxtHWl9WufY+fiS3B4H+o0eY3zh5ZoCwoC5laOWkd1aBYk58hlBdfvi6RkifxxTIGhAdT30d9daBYkIzZRNZqdlg1HryiJTRJpFqRdRylCXqHmKKi4JrUCjWsL/HVdJOYBpGbD9tMihnKo46l/lLhRbYG7D+FktEh6rurfew9V5Y+5kwxHrqrarS5NA2Ucu6Ik+r5IShZs/ktlm3re+m0THiTjTpLIsauq2ZVjV5XcSRIJD9SuY2QAvVrK2XZKQaEep7Y8TQJlHLuq5Ea8SGo2bDmh0qduJfo0CZARmyRy/JqS9Bw4fk3J3WSRJk/oc/OByO1EkYxc1ezBwUsqB87NQb/NXzbb/K/x0UcfsWjRIpYsWUJ0dDQffvgh9+/fV4czTZgwgUGDBqnlu3fvzqZNm/j111+5c+cOf/31F2PGjKFx48bUqFFxsOJ5ITkVL5jly5djbm7O6dOnmTFjBlOnTmXfPlWH7ezZs3Tu3Jm+ffuSlJTE3LlzEUWRyMhIMjIyOHLkCPv27SM2NpZ+/fpptXv79m3Wr1/PH3/8QVRUlLr8q6++YtCgQURFRREQEMCAAQN4++23mTBhAufOnQPgvffeU8vn5eXRpUsX9u/fz8WLF+nUqRPdu3fn/v37AGzatAk3NzemTp1KUlISSUm6U6Fu3ryZDz74gLFjx3L16lXefvtthg4dyqFDh7TkpkyZQt++fbl8+TJdunRh4MCBZGSohpaUSiVubm6sX7+e69evM3HiRD777DPWr69+WraqKC0t5ebtWBqGhmiVNwitz7XomGq1cSv2DteiY6gXXEddFhwUyM3YWG7cvAVAYnIyZ85doGnDsCr1ib0dQ/3QRlrlIWGNuBFdMSxGF3dibxETfZWguvXVZTdvXCOkXJuhYY2r3WZ1sWkaQur+v7TKUvcew7pBMMKjURTbpiGk7T+uJZO27xi24ZWH1ZSWKYhOTCPcr6ZWebhfTS7FP9RZ5/CNOIJqOLD0+GXaz1hN9znrmLn7FEX6eohAUWkZZQolVmb6R3n1PzchXL9xo9LreIyu56Y8u/bup3WrFpia6O/4qnS5Q8PQ+lrlDUPrcfVGdZ/hu1y9EUP9J3QpLS3FqNyMp7GREVeiq74+e2sBawsZN+5p7FymgNsJZfjUqN4o8fPE0VaOjaWcq7c1vdoyBcTcK6GWe9XhiJUR6m/CvQelvNvXhp8+cWTqO/ZENNA9a6soKyEl/hqe/i20yj39m5N096LOOsn3ovD0b64tH9CSlPtXUSg0Uz6lxQUsntyGRRNbsXXB26QkVJ3+2NYCLM0ErTAXhRLuJYt4OOnvULo7yridqD3tcPuBEg8n7S6GvaXAJ30NGdvLkL4Rcmx1TyaqsTEHC1OBu8na+txPVc1G6KOmvcCdZO3O551ksdJOcVWobZOkuU6FEuIeirhXYhs3R4HYcmFDsYki7o7adbo2kXMrQdWprg42Fqqwrdhy+tx7WPl1ujsKFc4RmyjirqeOIKgcOEMDiE/TrdvLZpt/i39z87t+/foxZ84cpk6dSkhICEePHmXnzp14enoCkJSUpO6XgSrUftasWcybN4/g4GD69OmDv78/mzZteq42KI8U/vSCqVevHpMmTQKgVq1azJs3jwMHDtChQwccHR0xNjbG1NQUFxcXQDXddfnyZe7evYu7uzsAK1eupE6dOpw9e5ZGjVQdxZKSElauXIljuVCHoUOH0revKrfyp59+Snh4OF9++SWdOqmmzT/44AOGDtWE49SvX5/69TWdk6+//prNmzezbds23nvvPezs7JDL5VhaWqp11MUPP/zAkCFDGD16NKDyuk+dOsUPP/xAmzZt1HJDhgzh9ddVscHTp0/np59+4syZM3Tu3BlDQ0OmTJmilvX29ubEiROsX79efU3PSnZOLkqlElsb7fUrtjY2ZGRlVVq335ARZGfnoFAqGfR6X7p2aq/+rm2rFmRn5/DBp18giiIKhYJXX+nE631eq7TN3JxslEolNja2WuXWNrZkZWZUWvetQb3Jyc5GqVTQd8AQOnTSxMdnZWZgbVuuTduq23xajJ0dKH6YplVWkpKOzNAQIwdbipNTMXZxoPih9i6fxQ/TMXapfGQ1s6AIhVLE3sJMq9zewpS03EKddRIycrl4/yFGBnJmD+hAVkER07f/RXZhMVN7RuisM3fvWZyszGnqU1Pn9/DEc2Nro1Vua2tDxoWsSq+j/+C3yM7OfvTc9KNLpw465W7E3ORe3H3GjXm30vY0z3A5XaxtyKziGe4z9O1Hz7CCwf370rVjO/V3DUPrs2HrDuoHB1HDxZkLl67w1+mzKKsRw2Jlpupc5hZodwpyC0TsrJ4tLvnvYG2h0icnX1v3nHwl9jbP5uQ42spp08iMPSfz2X40Dx83Q97oYkVZmchfl4q0ZAvzMxGVCsystHvIZpYOFOSm6mw/PycNzwAHbXkre5TKMoryMjG3dsLO2YeOA77BoYY/JUV5XDyygvVzXmfgJ1uxdfLSq7uFqepe5BVq36e8QhEbC/33ycJUNfOgXUdV/pj4VCUbjytJz1aVt64vZ2RXQ37cUkqhnhkL80e+c7622cgvErE2EwDdnUwLE111NO39HR7bJl/HdeqJtFTrklcu9C2vSNSyTbCXgKudwMI/q79Hh4XJo3ul4zqtK4kCrI4+AE42MLyTAQZyKCmDdUcUpOkJfXrZbPNv8axrKp6W0aNHq/tQ5Vm2bFmFsvfff5/333//H9ZKG8mpeMHUq6e92NbV1ZWUlBS98tHR0bi7u6sdCoCgoCBsbGyIjo5WOxWenp4VHIry53u8wKdu3bpaZUVFReTk5GBlZUV+fj5Tpkxhx44dJCYmUlZWRmFhoZZHXB2io6MZOXKkVlnz5s0rhC89qZ+5uTmWlpZa9pg/fz6LFi0iLi6OwsJCSkpKCAkJqbYexcXFFfJA61woVT6jgihS1etjzrdfU1hURHTMTX5bvoqari60jVAtfI66cpXV6/9gzKgRBPrXIjEpmZ8XLsFu7Qbe7F/1rpwVMjyIYgUVyzNtxk8UFRVy88Z1Vi5biKtrTVq21nQShXJXJFajzb9F+fjNxyd5slyXTDXjPsurLIoVb99jlI/u4zd92mJpohqRHttZwbh1+/msW3NMDLVfiUuPXWLXlVgWD+uKsWHVr8uKuujPzvGY2d9NUz03N2JYtHwlNWq4qp+bJ9m17wBenh4E+NeuUg/QZQNRh4ba/PjNVAqLirgec4vfVqympqsL7SJUo+jvjxjKD/MWMHj0B4BATVdnOrdvw+79h3S2NfM9K/X/f92Sr9ZAW0l93cLnS3g9E4Z01+gza7VqEb6uR+xZw41lAtxNLGXj/jwA7ieXUdPJgLaNzSo4FRoqPDk6yp4Ur/g+eLLc1SsEV68Q9dc1vMNY/UNPLh1bRete2gv8vxyomX1aub9Mffbyp3tas5Svc+vBE2srsuB+ahkf9TIk1FfGiesa527ca5rZjfXHlDr10VdWlT5Py2eva/7mVx9U6DxvdV5VOp+zR/9amcErjeSs2F9GWSX+eV0vgW5NNA7vmkO6O9l/5xWuq05aDsz/swwTI4EgD4HIZnKW7StTOxYvk21eGP/Smor/JiSn4gVTfgG1IAiVjvzp66SULzc31z1U8eT5HsvrKnusw8cff8yePXv44Ycf8PPzw9TUlN69e1NS8vS7sFYnHVpl9li/fj0ffvghM2fOJDw8HEtLS77//ntOnz5dbR2++eYbrdkOgEmTJvHWgN4AWFtZIpPJyMzM0pLJzM6uMPJbHlcXlZPm4+VJZlY2y39fr+4cLl21lg5tWqlnL3y8PCksKmL2vPkM7NsLmZ6Xk6WV9SN9tGcQsrOzsLaxq1QfZxdVdgdPLx+ysjJYt2aZ2qmwsbWrMCuRk1V1m09L8cO0CjMORo52KEtLKUnPUskkp2Hsoj3yauxkV2GGozy2ZibIZQJpedrZdzLyC7EvP+z2CEdLM5yszNUOBYCPow2iCA9z8vG018xQLT9+mcVHo1gwpAu1XSqJtUDz3GSUe26ysrIrzHqVp/xzs2LN2gpORVFRMYeOHmfIwP6VtlWZLqpn+Ol0Wb52vdqpsLG25uvPP6GkpITs3Fwc7OxYuHw1Ls66s2J9sypP/X+DR30hKzOBnHxNL8LSVCA3/593Ky7eKCY2QTMbZihXvXesLWRk52net1bmMnLynq33kpWnJLFcBqmk1DIaBVUcJjc1t0WQySnI0X7WC3LTMbN0qCAPYG7lQEFOajn5DGQyA0zMbXTWEWQyXDzqkpl6r8J3P2/ThEwZPLKLpamgNVthbiKQX6j/PpWflVDVqThy/SSlZfAwU8S+3EzV4r0a+8sfvRbLzzyYmwjk61n4DqrR+/KzEmbGFWcvqmL+Ds19VOtSblbGXMesSHldLE21Z1UsTAS1bWrYC1iYCrzdVdMdk8sEPJ1FGgfI+Gq1SoeYBJGENI0+j/+mLEy09TEzqThrVF4f1SyH9v0tX0ephMw8AJGkDJEa9gJNA2TsOK26Py+TbV4UVQ0Y/X9EcrP+ywgKCuL+/fta26xfv36d7OxsAgN1p4t8Fo4dO8aQIUPo2bMndevWxcXFhXv37mnJGBkZoVBUPjUZGBjI8ePacfMnTpx4Kp2PHTtGs2bNGD16NKGhofj5+WktKq8OVeWFNjQ0pLafL+cvXtKqdz7qMnUC/at9HlEUKS3V/FgXFxdXyBQhl8kQqTwTg6GhIb5+/ly6eE6r/NLFcwQE6o+9r6gPlJZqHMHaAXW4FKXdZtTFs0/VZnXIOhWFQzvtRcWOHVqQff4q4qOsRpmnonBopx0j7tC+BZkndceUP8bQQE5gDQdOlVtAfSr2AfXddafZC/FwJjU3n4In0g3FpWcjEwStzE7Ljl9i4eEL/DKoM3VqVr3AVf3cRJV/bi4RFBBQZX015Z6bxxw5/helpaW0a6M7RKuiLj6ci9LOnHU+6jLBAU/3DJfoWGtiZGSEo709CoWCoydO0bxJIx21IS1LqT6S05Vk5ykJ8HyycwB+bgbcSfznwxqKSkRSMhTq40FqGVm5CoL9NLOUcjn4exlxK/7pB0ye5Nb9ElwctMfrXOwNSMuqeJ1yAyOc3OtwP0Z73dH9mBO4euteU+TiFcL9GO1ddONijuPkEYxcrjvLnyiKpCZE61ys/XgxbkauKhtSboGIbw1NZ0kuAy8Xgfsp+t9T8alKfGtov9/8asi4n6LfQZPLwNFaqBBqlZmnOdJyVKFX3i4afWQy8HCEB+nlW9TwIF3E21m7w+fjImhloqoOT9omNVsVrufrqrlOuQw8nQXiK7FNQqqIj6u2Lr41BOJTVXXuJIn8vK2U+TvK1MeDNCVX7ojM31GmHskvKdO2TWq2KiWvzxP6yGTg5Vz5dcbr0MfHVdC7XuJJ5E+E+7xMtnlRPOs+Ff+L/G9e1f8w7du3p169egwcOJALFy5w5swZBg0aREREBA0b6s9r/nfx8/Nj06ZNREVFcenSJQYMGFBhJsXLy4ujR4/y4MED0tJ0jy5//PHHLFu2jPnz53Pr1i1mzZrFpk2bGDdu3FPpcu7cOfbs2cPNmzf58ssvOXv27FNdj7GxMVZWVlpH+fCn3pHd2bnvALv2HSAuPoFffltKSmoa3V9RpflctHwV3876US2/5c9dnDhzloTERBISE9m9/yAbNm+jfetWapnwxg3ZvnMPB48eJyn5IecuXmLp6rU0a9xQneZNH9179uHA3j85sHcnCffjWLJwHmmpD+nYRbWfwaplC5k7c7pafteOzZw9fYLEBwkkPkjgwL5dbNu0jog2mlj9bq/2IurCWTZtWENCfBybNqzhctR5uvXoXakucnMzrOoHYFVf1VE283bDqn4AJu6qWRH/rz+i/tLv1PJxC9di6lmDwO/HYxHgg9uQXrgP7cWdWZpMNffmrcChQ3N8xo3A3N8Hn3EjcGgXzr2flleqC8Cbzeqy6XwMm8/HcCclk+93niQpO48+jVXO6ty9Z/h8oyY8p0s9P6xNTZi4+QixKZmcv5fErD2niQyrrQ59WnrsEvP2n2NKzwhq2FiSlltAWm6BliOii16Rr7Jr73527d1PXHw8v/y2RPXcdFGtV1q0bCXfztSE+23dsZOTp8+S8CCRhAeJ7N53gPWbt+p0HHbt3U/zpk2wtrKq8J0u+vToxs59B9i57yBx8Qn8vGgZD594hn9bvprpszU7q27+czcnzpwjITGJhMQkdu0/xPot2+jQWjNjcj3mFkdPnCYx+SGXr0XzyeRpiKLI66/pTmlYnkMXi+nY2IR6fga42st4s7MppWUi525oOvFvdjbl1RZPdPRlUNNRRk1HGQZysLFU/d/BRvPTZWSokQGwt1b939ay8lHEPScL6NbSnAaBxtR0MmBET2tKSkVOXdYMrY58zZo+7TVB4XI5eLgY4OGiijW3tZTj4WKAk53mb3jPiXx83Qzp1socJzs5Teua0LqhKQfOVNzPAiCs9VCuntrItVMbyUiO5cim6eRmJlGvuWpW6vj2mexZ9Ylavl7z/uRkJnJk8zdkJMdy7dRGrp36gwZtNBnVTu2ax73oY2SnxZOSEM2+3z8j9cEN6jV/vcL5y3PiuoKIenICPQScbAReayGntAwu3dG893u1kNMhTP5EHSV+NQRaBstwsFalpfWtIWiFNXVuKMfLWcDWQpVF6PU2BhgbwsXblc8Mnbkp0ixQoHZNcLSG7o0FShVwLU7To+zeRKB1Xc39PntTxMdFlY7W3lL1r5ezqvwxhgaqdQNONqrP1uYCTjaqkBt9nIpW0rKujAB3lWxkc5VtLt/VXEPP5nLah8q06vjWEGhRR4aDlSr1qo+rwMloVZ2SMkjJ0j5KyqCgWJVFqTJORytpGazSx9EaIsNV+lx5Qp/IZnLahWj0OX1Dia+rQPMgGfZWqrS0Pq4Cp6M1ddqGyPBwFLA2V9mnbX0ZXs6CVrsvu20kXgxS+NN/GYIgsGXLFt5//31atWqFTCajc+fO/9jW67Nnz2bYsGE0a9YMBwcHPv30U3JytHcdmjp1Km+//Ta+vr4UFxfrHHmPjIxk7ty5fP/994wZMwZvb2+WLl1K69atq63LqFGjiIqKol+/fgiCwOuvv87o0aPZtWvXs16mFm1aNicnJ5eVazeQkZGJl6cH30z6DGcnVZhHekYmKaka50lUiixevprkhynI5XJcXZx5a/BA9V4DAG/0640gCCxd9Ttp6RnYWFnRtHFDhr85oEp9WrRqS25ODut/X05mRgYent58PuU7nJxUC+MzM9JJS9VkO1IqRVYtX0hKcjJyuRxn1xq8MWSkeo8KgICgYD76dCK/r1zM2lVLcHapwdhPJ1W6RwWAdYNgwg9oUgEH/fAZAPErNnF5+ASMXR0xdddsqlN4L4Gz3UcSNHMCnu8MpDgxhWsfTlPvUQGQefIiFwd+hP+U/+A/ZQwFsfFcHPBhlXtUAHSu60t2QTELD18gNbcAP2c7fn6zMzVsVHsIpOUVkJydr5Y3MzZkwZAufPvnCQbM34y1qQkdg314r73GIV9/5jqlCiVj12pvvjeqTRjvtG2gV5c2rVqQk5vLqrXr1c/N9MlfqJ+bjMxMUlI1YStKUWTx8pUkP0xBJpdTw9WFt4a8qfXcACQ8eMDV69F899WkKu3xmLYtm5OTm8eKdRsf6eLOtxM/w+XRfhnpmeWeYVHktxVrHj3DMmq4uDBi0ED1HhUAJaUlLFn9O4nJKZiamNCkYSifffg+FhbV2xti/9kSjAwE+rU1xcxE4F6ygnl/5GvtUWFnKdMafbS2EJjwpmY/iPYNjWnf0Jhb8WXM3aC6r57Ocj7oq+n492qtisM5da2EVXv0x4HsPJ6PkaHAoG5WmJnIuPOglO9XZGrtUWFnLeeJLUywtZTz1WhNWFKXFuZ0aWFO9N0Svl2qCie8m1jGj79n0aeDBT0iLEjLUrB6Vy4nL+uOA/EP60JRfian9vxCQXYK9q616fH2QqzsVIkB8nNSycnUZNWztncn8u2FHNn8DZePrcbc2onWr32utUdFcWEOB9ZNpCAnFSNTSxzdgug9ZhUunro3zHySY1eVGBoIvNrUABNj1Wjysr3aew3YWAiIT4SsxKeKrD9SRvswA9qFysnIhXWHtfeosDKHvhEGmBlDQZGqzoI/S8nKp1JO3VClkO3cQLXBW2I6rD2ivUeFlZmg9bvzIB22nFQSUVdGRLBAZj5sOaHZowLA1RbeaKtxjDo86uxevqtkxxndQ+DHrykxMIBuTeSYGMODVJGV+7VtY20OoqhxcOJTRTYeVdA2VE6bEBmZubDhaMV9GP4Of11X6dOlsVy94dzKA5Xrk5AmsvG4grb15bSpLyMjDzYe0+xRAaoQpJ7NZViYQnGpKkxt9UFFhYxaL7Nt/g3+7YXa/w0I4j+5C4aExH8JCTevvmgV1LjVDubabd2pef9t6vi58qdh9UNm/mm6lsZQtP6HF60G/8feeYdHVXSP/7O76b33nkAKLRB6DR0RpSn1lSqgvCqCCIKFogIvvahUKSIISO8QWui9Q0ggAdJ778nu/v5Y2M0mu5sgCnz93c/z3Acy98zcc8+9uzsz58wZAKN+E4l7WH2azleBe60gEiOrH4S9Klz86/PJwmq20n1F/DTBkqHfJVcv+IrYMNOJ5Zq3LnnlfNwNvln/cuFefyc/DDNg1tY3I8vP1P4Spv1W/W7fr4IZQ/SZ8fuboQvAtP/ov1G2eV1kzBxdvZAObL9b9Tdp8uYgeCoEBAQEBAQEBAQEXgDBU1EVYVAhICAgICAgICAg8AKIRMKy5MoIgwoBAQEBAQEBAQGBF0HwVFRBGGYJCAgICAgICAgICLwUgqdCQEBAQEBAQEBA4AX4t+418TIIgwoBAQEBAQEBAQGBF0BYqF0VIaWsgICAgICAgICAwAuQM3/cS9W3nLikeqH/YwieCgEB4Epk9utWQUkTfysuRORWL/gKaBFo8cbsCwGKvSHelH0z3i6L5ObDtOoFXwHBteyJiY5+3Woo8fH1ZfIq7RvPvUr+N9qYTxe/GZ8ngGWfW7Bk35sxlzfuHdEbt//Bm7RPxfd/lFcv+Ar4dqAeMze9GboAfDf4zdHnu8GvrxsreCqqIgwqBAQEBAQEBAQEBF4EYU1FFQSLCAgICAgICAgICAi8FG/coCI5OZnOnTtjamqKlZXV61anxqxfv/616Dts2DB69er1yq/7d/O67CcgICAgICAg8KKIRKKXOv6N/OPhT8OGDSM7O5vdu3fXSH7RokUkJSVx8+ZNLC0t/1nl/iJeXl58/vnnfP7558qy/v37071799enVA0YNmwYGzZsAEBPTw8bGxvq16/PwIEDGTZsGOJX5Mr7v2C/sIPbObjzd7KzMnD18OY/H44noE5DjbJXzp/k+KGdPH0cRVlZKW4ePvQZOIr6jZorZU4f38+qJd9Xqbt2+2kMDAyr1ef4wT85tPt3srPScXX3YdDICfhr0efqhROcPLyD2MdRlJWV4erhQ68Bo6jXsIVG+YtnjrJiwdc0bNqOcVOrXz+x9dJ91p+9RXp+Eb4O1kx6qzmNvJy1ypeWS1l58joHbj0iPb8QRwtTPmzXkN4hirURO64+YN/NKB6lZAEQ5GLHp52bUM/NQaceNq0b4/PFSCwb1cXIxYGrfceSsve47jptmhA0/yvMgmpRkphK9II1xK7aoibj1LsLtaePw8TXg8LoWCK/W0TKnmPV2gXgyIGd7Nv5B9mZGbh5eDF01DgC6zbQKHvpfDhhB3fxJOYR5WWluHl4896gEQSHNFOTO7BnG2EHd5GeloKFhRXNWoUycOiYat+b/fv3s33HDjIzM/H09GTM6NHUrVtXo+zde/dYt3YtcfHxlJSU4ODgQPe33qJ3795KmbCwMBYuWlSl7p7duzEwMKjONAB0CtGjWYAexoYQmypjz7kyUrJ0ry+o6y2mS2N9bC1EZOTKOXKljHtPZMrzkwcaYmNe9bvr/L1y9pzTvV7greaGtKqrj7GRiKfJUradKCY5U6ZV3slGzNstDHF3lGBrIWZHeDGnbpRWkWtTX5+OIYZYmIpIypCxM7yY6ETtawXuntvMjVO/UpiXho2jH616TsXFp7FW+YToy5zfO4fMlEeYWjgQHPohdVsOUJ6PvnOU68dXkpMei0xajqW9J8HthuMf0lOnPZ7Trr6YED8xRgaQkCHn4GUpaTm66wS6i2jfQIK1OWTlwYlbUh7EqZ5t41piGtcWY2Wq+Ds1R87pOzIeJVa/vqRNHRHBviKM9CExE45ck5FezTIZfzdoV1eMlRlk58OpOzKiElTn3e2hub8YJxswNxax/axU7bw22tYV08hX9Mw2cPiqlLRqdAlwExFaX4y1GWTlw8nbMiLjVffdKkhEgJsYWwsol0J8upzjN2Vk5FWvT7t6Yhr5qfQ5dKX6ZxXgLqJ9A5U+J26q6xNSS0TjWgrbAaRlw+m71T+rN0mXV4IQ/lSFN25NRXR0NCEhIdSqVesvt1FWVoa+vv7fqFX1GBsbY2xs/Eqv+Vfo1q0b69atQyqVkpKSwuHDhxk3bhzbt29n79696On9tVdCLpcjlUr/cv03yX4Xz4Tx+5pFDPtoErUD63Pi8C7mzRjP/37egp29UxX5B/duUDe4Ke8P+RhTUzPCj+1nwQ9fMGPeWrx8VYuKjU1Mmbf8T7W6NRlQXDp7lM1rFzJkzGRqBTTg5JGdLPx+HLOWbcNWgz6R925Qp0Ez+v5nLCam5pw9vo/FP07gu7nr8fRRX+ScnprE1vVLqB2keYBSmcN3opl76AJf92hFsIcj268+YOzGw+z69H2cn3/rV+LLrcfJyC9ieu+2uNtYkFlQhFSm+kG4+jiRt+r50eBtRwz1JKw7e4uPNxxix6fv4WhhqlUXiakJubcjid+wk5A/f6pWd2MvN5rsW0Xcr39yc+iXWLdsRN1l0yhNyyR511EArJoH03DzIqKmLSF5zzGcenai0R+LuRA6iOzLt3W2f/70cTasXsrIj7/AP6gexw7tYfb0iSz8ZSN2DlWfU8Tdm9QLbsKAIWMwNTXj1LGDzP1+Mj8uWIW3b20Azpw8yh/rV/DRuK+oHViPpIQ4li/+EYChoz7Tqkt4eDgrV63iv2PHEhQUxMFDh/j2u+9YuWIFDg5VB2tGRka88847eHt7Y2RkxL1791i6bBmGRkZ0f+stpZyJiQmrV61Sq1vTAUW7Bnq0qafHtlOlpOfI6dhIjw+7GzJvWzGlWvr+Hg5iBnU04OjVcu49llLHW8LgTgYs31NCXJriHfppVwkVJ/2cbMSMetuQOzG6F/x2amxA+4YGbDpaRGq2jK5NDfmkjwnfb8inRIs+BvqQniPjxsMy+rQz0ijTqLYefdoZse1EMTGJUlrV1+fjXib8uDGfrLyqHaGHNw9ydu9s2vb5DievRty/uJX9a0Yz8Mv9mFu7VJHPzYjnwJoxBDV/n46D5pH85Dqnd87E2Mwa3/pdATAytiSk40dYOfggkejzJOIUJ7ZOxdjMBg//Njrt0ipITIsAMbsvSMnIldO2noQPOurx095ySrWszXWzE/FeGwknb8mIiJMR6C7mvTYS1h2RkpChuOfcQjnHbkjJfGaDYB8xA9pJWHmwXGfHs3mAiKb+IvZfkpGZr+iADwwVs/KgTKs+rrbQu4WY8LtyouLl1HYT0bulmI3HZSRmKmT0JZCaLef2Yzl9W0t02uQ5LQNFNA8QsfeijIw8OW3qiBncXsIvB6Q6denbSsypOzIexMkJcBfRt5WY9cekJGYoZDwcRFx5KCMpQ45YDKH1xQxqL2HFASllOl7jlkEimgeK2HNBRkaunDZ1xfyng4Sf92nXx80O3mst5uRtlT7vtRGz/qiUhGf65BXC8Zsy5bNq4COmf1sxqw5pHyS8Sbq8KoSF2lV5pcOs0NBQPvvsMyZNmoSNjQ1OTk5Mnz5ded7Ly4sdO3bw22+/IRKJGDZsGACxsbH07NkTMzMzLCws6NevHykpKcp606dPJzg4mLVr1+Lj44OhoSFyuRyRSMTKlSvp0aMHJiYmBAYGcuHCBR49ekRoaCimpqa0aNGC6ApZU6Kjo+nZsyeOjo6YmZnRpEkTjh1TzVKGhoby9OlTxo8fr+bC0hS+s3z5cnx9fTEwMMDf35+NGzeqnReJRKxZs4bevXtjYmJCrVq12Lt3r/K8VCpl5MiReHt7Y2xsjL+/P0uWvFwKMkNDQ5ycnHB1daVRo0ZMnTqVPXv2cOjQIdavXw/AkydPEIlE3Lx5U1kvOzsbkUjEqVOnADh16hQikYgjR47QuHFjDA0NOXPmzBtlv7/KoT1/ENrpXdp36YmruzcfjJqArZ0jxw/u0Cj/wagJ9Oj7Ab61gnBy8aD/kLE4Obtz48qZKvpaWduqHTXhyJ7NtO3Uk3ade+Hi7s3gD7/Axs6RE4e3a5Qf/OEXdO8zBJ9adXBy8eC9D/6Lo7M7N6+cVpOTSaWsXPQtvQaMxt6xaudFExvP36F3I3/6NA7Ax8GaSd1b4GRhxrbL9zXKn3sYx7UnSfz8QVea+7riam1OPTcHgj0clTKz3+9A/2ZBBDjb4m1vxbSebZDJ5VyO1j1tmHbkNFHTFpO8O6xGunuOHkBxbBL3v5hF/oMY4tZuJ279TnwmjFDKeH86lPRj54meu4qCyBii564i/cRFvD4dWm37B3ZvoUPnHnTs+g5u7l4MGz0OWzsHjh7crVF+2Ohx9HxvMH61A3F2dWfg0DE4u7hx7fI5pczDB3fxD6xH69AuODg606BRU1q27UTMwwc6ddm1axddunShW7dueHh48NGYMdjb23PgwAGN8n6+voSGhuLp6YmjoyMdOnQgJCSEe3fvqsmJRCJsbGzUjprSup4eJ26Uc++JjJQsOVtPlqGvBw39tHfoWteT8Chexqmb5aTlyDl1s5xHCTJa11NNXhQUQ36R6gj0kJCeIyMmSbvHASC0oQFHr5RwK7qcpAwZvx8tQl9fROMA7RNSsSky9pwt4XpUOeVaOnvtGxly4V4ZF+6VkZIlY2d4CVn5MlrX1zz4uhW+nsCmfQlq9j42jr607jkVMysn7l74Q6P8vQtbMLN2pnXPqdg4+hLU7H0CmvThZvhapYyrXzN86nXGxtEXSzsPGrQZgq2zP0mPr+u0CUCzQDFn7io6eWk5sPu8FH09qOetvbvQLEBMdJKcs/dkZOTC2XsyHifLaRaoqhOVIOdRopzMPMjMgxO3FIMCNzvdHbOmtUWcuy8nMgHScmDfJTn6Eqjjqb1ek9oiHqfAhQg5GXmKf5+kKMqfE5MM4XcV7daUpv5izt6T8SBeYZs9F2Xo60FdHbo08xcTkyzn3H2FLufuy3mcIqeZv8o2f5yScfuxnLRcSMmGfZdkWJmKcK7m49UsQP1Z7bnwTB8vHfoEPNPnnpyMXDh3T654VgHan9XJZ8/KVcezepN0eWWIxC93/At55Xe1YcMGTE1NuXTpEnPnzmXmzJmEhSk6BVeuXKFbt27069ePpKQklixZglwup1evXmRmZhIeHk5YWBjR0dH0799frd1Hjx6xbds2duzYodYZ/v777xkyZAg3b94kICCAQYMGMWbMGKZMmcLVq1cB+OSTT5Ty+fn5dO/enWPHjnHjxg26du3KO++8Q2xsLAA7d+7Ezc2NmTNnkpSURFJSksb73LVrF+PGjeOLL77g7t27jBkzhuHDh3Py5Ek1uRkzZtCvXz9u375N9+7dGTx4MJmZiqkUmUyGm5sb27Zt4/79+3z33XdMnTqVbdu2vdxDqESHDh1o0KABO3fufOG6kyZNYvbs2URERFC/fv03yn5/hfKyMh4/ekDdhuohKHUbNuXhgzs1akMmk1FcVIipmXr4XnFREeNG9uTT4T2YP3MCT6Ija6TPk+gH1A2upE9wMx490D1zXp0+e7atwdzCmnadaxYSUVYuJSIxnRZ+rmrlLfxcuRWXorHOqQdPCXKxY93Z23Sau4l3Fm9lweGLFJdpT0dYXFZOuVSGhUn1XpwXwap5MGnHzqmVpR09g2VIXUTPPGzWzYNJP3ZWTSY97AzWLXR7csrLyoh5FEX9hk3Uyhs0bELUg7taaqkjk8koKirEzMxCWeYfVJ+Y6EgeRSoGbSnJCdy4epGGTTSHsoHCU/vw0SMaNWqkVt6oYUPuR0TUSJdH0dFERERQr149tfKioiKGDh3Kfz74gGnTpvGohmlsbcxFWJiIeBiv6olLZRCTJMPTUfvPkKejmKgE9d57VLxUax2JGBrWknA1UreXwtZChKWpmAdPVe9huRQexZfj7VyzWWtt13d3UG8X4MFTze1Ky0tJS7iHe+1WauXutVuR8uSGxmskP71ZRd7DvzVpcfeQSqu6WORyOfEPL5Cd+lhnSBWAlZkiFCi6woBMKoMnKXKdnX93exExSepemOhEOe5a6ohEikGBvh7EpWsPY7EyBTNjEY+TVTJSGcSmKTwA2nC1FRGTrN5uTLLue6gOK1OFbWIq6fI0VY6bvfZ23ew06JKkWxfDZ+PaoqqRdSp9nj2rinaXyuBpihz3avSJrvyskrTfQ8VnFZ+m+Vm9Sbq8UsSilzv+hbzy8Kf69eszbdo0AGrVqsVPP/3E8ePH6dy5M/b29hgaGmJsbIyTkyJcICwsjNu3b/P48WPc3d0B2LhxI3Xq1OHKlSs0aaL4ES8tLWXjxo3Y29urXW/48OH069cPgMmTJ9OiRQu+/fZbunZVuInHjRvH8OHDlfINGjSgQQNVDPQPP/zArl272Lt3L5988gk2NjZIJBLMzc2VOmpi/vz5DBs2jLFjxwIwYcIELl68yPz582nfvr1SbtiwYQwcOBCAWbNmsWzZMi5fvky3bt3Q19dnxowZSllvb2/Onz/Ptm3blPf0dxEQEMDt2zXrpFZk5syZdO7cWfm3ra3tG2O/v0JebjYymRRLK/UpIktLW7KzL9aojYO7N1FSUkSz1h2VZS6unowe9y3uXr4UFRZwZN9WZk4exaylv+Pk4qFdnzyFPhaV9LGwtCUnK6NG+hzes4mSkmKatuqkLHsYcYvTx/Yyc9GmGrUBkFVYjFQmx9bMRK3c1syY9DzNexLEZ+ZxIzYFAz0JiwZ1JruwmFn7zpFTVMLM3u001lly9AoOFqY093HVeP6vYuhoR0lKulpZaWoGYn19DOysKUlOw9DJjpIUdbuWpGRg6KT+vVKZ3NwcxXtjXem9sbYh+3rNntP+XVsoKS6mRZsOyrJW7TqRm5vNd5PHwrMQw87de9Hr/Q906JKLTCbDupLnz8ramqysLJ06/OeDD8jJyUEmkzF40CC1z5GbuztfTJiAl5cXhYWF7N6zh4kTJ/LzTz/h6qr7WZmbKH5A84rUOwL5RXKszbT/uJoZi8gvVC/LL1S1V5k6XhKMDOBqlO4c+hamikFJbqG6PnmFcmws/vpcm6mxCIlYRJ6Gdi006FxckIVcJsXYXL2HbGJuS1xeehV5gMK8NEzMW6uVGZvbIpOVU1yQhamFIrytpCiPDd+3Q1Zeikgspm2faVUGI5UxM1LomF+sXl5QDJbaIxExM4L84krPtliOWaWIVgcrGNlVDz0JlJbD1nAp6TpCWEyNVNdX10eOpYkI0NyxNDPSVEfV3l/h+b38Fdto0sVMhy5dGoqJTZXrDO95Xr+yPvnFKNetvIw+DlYwootE+ay2nda+juVN0kXg9fJaBhUVcXZ2JjU1Vat8REQE7u7uygEFQFBQEFZWVkRERCgHFZ6enlUGFJWv5+ioCLmoOPvm6OhIcXExubm5WFhYUFBQwIwZM9i/fz+JiYmUl5dTVFSknGmvKREREYwePVqtrFWrVlXClyrqZ2pqirm5uZo9VqxYwZo1a3j69ClFRUWUlpYSHBz8QrrUhOfhYi9K48bqM19vmv0qU1JSQklJiVqZoWHVGfGqtpAjonr7nA8/wq4/1jD+63lqAxO/gHr4Bajeu9qBDfhm/BCO7v+TIaO/qLbdyteWI4caPK+Lp4+we8sqxk2drxyYFBUVsHLRdwwfOxVzC6tq26iqizpyHarI5HJEKEKczI0U4R9fdJMycesxpvZohZG++lfQujO3OHQnml9HvI2h/j/w9SSv1Al5rnjFck0ylcu0UOU51fBzdS48jO2b1zLx29lYWlkry+/dvs6urb8x8uMvqOUfRHJiPOtXL2HHH+vpO3CYbl1EL67L/HnzKCoq4kFkJOvWrcPFxYXQ0FAAAgMCCAwIUMoGBQXx6WefsXffPj7+6KMqbc0cruoZrDv8bMq1smmrFlWhynkdj6OJv4TIOBl5lQYijf31GNBR1cNdsadQuz5/wwToizZR9b15ro32GpouWLEdA0NT+k/YRVlJIfEPL3Bu7xwsbNxw9VP3ek7pr/qcbT6p2cPzV+ZUNdVJz4UVB8oxMhAR5CGiV0sJ68PK1QYWE/uoBnXbzig8Jprs+cI2/gs3Mfk9lXfpj3Cp1gu/8Dusg24hYhysRKw/pv4s6nqJ6NG0YsiUZn1EohroUwOF0nNh5UEpRgYQ6CGmZwsxG8Kkys78V/0q2OY16/K6EP1LQ5hehlc+qKi8gFokEiGTaY991fZDWLnc1FTzcLji9Z7Layp7rsOXX37JkSNHmD9/Pn5+fhgbG/Pee+9RWqrDD6mFmvyo67LHtm3bGD9+PAsWLKBFixaYm5szb948Ll269MK6VEdERATe3t4AyixQ8gqf9rIyzSsXK9v9TbKfJmbPnq3m/QGYNm0abw/8HABzCyvEYgnZlbwAOTmZVbwXlbl4Jow1y37k08mzqBvcVKesWCzGp1YQyYlxOuXMzRX65GSr65NXA30unT3K2p++Z+ykOdRpoOpIpCbFk56ayOIfVYMZuVxhsxF9mjPn5+04OLtVac/axAiJWER6panjzIIibCtPST7D3twEBwtT5YACwMfeCrkcUnIL8LRVhWRtOHubX0/fZOWw7tR2qtl6kxehJCW9isfBwN4GWVkZpRnZCpnkdAyd7NRkDB1sqng4KmNhYanxvcnNzqr2OZ0/fZwVS+cw/qvvqR+sHj617fc1tO3QlY5d3wHAw8uXkpJiVv00l979h2jM2GZhYYFYLCazklciJzu72rTNz72H3t7eZGdl8fumTcpBRWXEYjG1a9UiMUFzUPqSHarBu96z/oe5iUjNW2FqLCK/SHuvIr9Ijrm6YwwzYzTWsTIT4ecqZmNY1e+aOzHlPEnOr6CP4nvEwlSk5q0wMxGRV6h7LYYuCorkSGVVvRLmJqIqXhEAI1NrRGIJhZW8EkX5GZiYa/4MmJjba5QXi/UwNLVSlonEYiztPAGwcw0kKzWG6ydWVRlUrDig8uo8f05mRor1KcprVvq7MvnFz70cFZ6tkahKHZlMkeEH5CRlynGxFdE8QMz+Syqb/3pU9X+JWKVPxRltUyMRBcU63hsNXgkTw6qz4tWx6rCqY6/3XBdj9Rl5Uw2z7ZV1qTzzbmpUdVYfoGuImNquIn47LqWy8zcqXs7K9Ar6PH9WlfWp5j7zi6niQdKkj+pZQVKmDBcbMc0CxBy4rHg+Kw++Obq8Nv6lIUwvwxs/zAoKCiI2Npa4OFXn6/79++Tk5BAYGPi3X+/MmTMMGzaM3r17U69ePZycnHjy5ImajIGBAVKp7pjdwMBAzp5Vj80+f/78C+l85swZWrZsydixY2nYsCF+fn5qi8r/Lk6cOMGdO3fo27cvgNLjU3G9Q8V1KtXp/KbYTxNTpkwhJydH7ZgyZYryvJ6+Pt5+Ady9eVmt3t2bl6kVUK9ycyrdwo+wcsn3jJ34PQ2btNYq9xy5XM7TmKhqF2vr6evj5RvAvZvqA8l7Ny/jF1BfSy2Fh2LN0pmMmfADwY3V9XF28+KHJX8wc9HvyiO4SVsC6oYwc9Hv2Ng5amxTX09CoIsdFystoL4YnUADd811gj0cScsroLBCOp2nGTmIRSK1zE7rz95i1anr/DKkG3VcdYca/VWyL97ErmNLtTL7zq3JuXYXebmiY5V18SZ2HdVDROw6tSbrgub49ufo6evj41eb2zevqJXfvnmV2gGa07iCwkPxy+If+WziNBo1aVnlfElJcZWBtFgsVgz4tUzx6evrU8vPjxs31HW+fuMGQS/w+ZHL5VonE56fj46JwVrLYu2MXLnySMmSk1sop5abanZTIgYfZzFPU7R3DJ6myKjlqr4WobarRGOdxv4S8ovhQWzVcyVlkJ4jVx7JmTJyCmT4e6jm1SRi8HPT43GS7u8mXUhlEJcqI8BDfb7O30NzuxI9A+xd6xAXdV6tPD7qPI5emtfxOHkGE19JPi7qHPbudZBItC8ylyNHWl51wJWVrzrSchQhaj7Oqq6BWAxejiLidax9iEuT4+Os/p76OIt0rpd4jqRSx6yiPum5igGkt5NKRiwGD3uU2YE0kZAhx9uxkj5Ouu9BE2q2yVXYprIung4infH98enqdbTp0i1ETICbiN9PSMkuqNpOabm2Z1VJH0eRMjOaNn18Kunj66z7HkDhdZBU6DG+Sbq8LkRi8Usd/0be+Lvq1KkT9evXZ/DgwVy/fp3Lly8zZMgQ2rVrVyX05u/Az8+PnTt3cvPmTW7dusWgQYOqzHx7eXlx+vRpEhISSE/XPIP55Zdfsn79elasWMHDhw9ZuHAhO3fuZOLEiS+ky9WrVzly5AhRUVF8++23XLlypfqKOigpKSE5OZmEhASuX7/OrFmz6NmzJz169GDIkCGAIr1r8+bNmTNnDvfv3+f06dN88803Ndb5TbGfJgwNDbGwsFA7Koc/vdVzIKfC9hAetpeEuMf8vmYRGWkpdHyrDwBbN/zMikXTlfLnw4+wcvEMBo34DD//umRnZZCdlUFhgWpmdOcfa7h9/SKpyQk8jYli9dIfiH0cpWxTF117DiL82B5OH9tLYtxjNv+6kIz0ZNp3VQwC/9z4E6sWT1PKXzx9hNVLpjFg2Dh8/euSnZVOdla6Uh8DA0PcPP3UDhNTM4yMTXHz9ENPRzrmD1rWY+e1SHZdiyQmNYt5By+QlJPP+00VndUlRy/z9XbVYvru9f2wNDbiu13hRKdmce1JEguPXKJXo9rK0Kd1Z27x07GrzOjdDhcrc9LzCknPK1QbiGhCYmqCRYMALBooQnJMvN2waBCAkbtizwz/HybQYN3/lPJPV23B2NOFwHlfYRbgg9uwvrgP70vMQlXWnCc//YZd51b4TByFqb8PPhNHYdexBU+Wbaj2Ob3dawAnju7n5NH9xMc9YcPqpaSnpdC5ey8ANq9fwU8LVHuVnAsP4+eFP/DByE+oFVBH43sT0rQVYQd3cy78GKnJidy+cYWtv6+hcbPWiCXaFxT37t2bI0eOcOToUWJjY1m5ahVpaWnKvWDWrVvH/PmqPUn27dvHxUuXSEhIICEhgaNHj7Jj5046VFi/tGnTJq5du0ZSUhLR0dEsWryYmJgY3q7h/jJn75TTPliPOl5iHK1FvB+qT1k53Hik6mz3C9WnWxNVh/zcXSm13MS0a6CHvaWIdg308HMTc/aO+poJEdC4toRrUeXIathvPHWjlC5NDanvq4ezrZj/dDGmrEzO1Qeq9+6DLka800r1/SARg6u9GFd7MXpisDQV4Wovxs5S1TE6eb2EFnX1aR6kj6O1mD5tFftonL2t2VvboN0wIi5vJ+LyDjJTojm7ZzZ52UnUba7Yd+LCwQUc+2OyUr5OiwHkZSVybu9sMlOiibi8g4jLOwhup8pidu34SuKizpGTEUdWagw3w9cRdXUPtUPerdYulyJktKkrJsBdhL0l9Gohoawc7jxWfY/3aimhY7Cq+3DpgQxfZxGtghR7LbQKEuPjLOJShKpOh2AxHvYiLE0VMfIdGojxchSptauJy1FyWgaKqO0K9pbwTlMRZVK491T1oN9pJiK0nuoZXImS4+OkSEdra67418tRUf4cfT2FHg5Wir8tTUU4WIFFJc+Ymi6RMloHifF3U9imZzMxZeVwt4IuPZuL6dBAZZvLUTJ8nUS0DFTo0jJQhLeTiEuRqvt+q7GYel4idp2XUlKumK03NVJ5ALRx6YGM1nUq6NPimT5PKujTQkwHDc+qZZAIWwtFKlhvJxGXHlR4Vg3EeNijfFbtG4jxdBCptfsm6/LKEIle7vgX8sbtU1EZkUjE7t27+fTTT2nbti1isZhu3bqxbNmyf+R6ixYtYsSIEbRs2RI7OzsmT55Mbq564N7MmTMZM2YMvr6+lJSUqIUJPadXr14sWbKEefPm8dlnn+Ht7c26deu0hhJo4qOPPuLmzZv0798fkUjEwIEDGTt2LIcOHfrL93f48GGcnZ3R09PD2tqaBg0asHTpUoYOHaoWSrF27VpGjBhB48aN8ff3Z+7cuXTp0qXa9t8k+/1VmrfpTF5eDru2riU7Mx03Tx++/G4Rdg6Kzmp2VgbpaapsRyeO7EYqlbJhxTw2rJinLG/T4W3GfP4dAIUFefz682xysjIwMTXD06c238xeiW/tOtXq06x1F/Jzc9izdQ05Wem4evgy4dvFKn0y08lIS1bKnzyyE6lUysZVc9m4aq6yvFX7txk1bvpL2aZbPV9yCktYdeo6aXmF+Dna8PMH3XCxMgcgPb+Q5BzVNJuJoT4rh3VnzoHzDFqxC0tjI7rU9eGTTqoJgW2X71MmlfHFFvUN5j5q34iPO4Ro1cUypC4tjqvSDAfNnwpA3G87uT1yCobO9hi7qzblK3oSz5V3RhO0YAqeHw+mJDGVe+N/VO5RAZB14QY3Bk/Af8bn+M/4jMLoOG4MGl/tHhUALdt2JC8vhx1b1pOVmYG7pzdfTZ+H/bM9KrKzMsio8N4cO7QHqVTK2uULWbt8obK8Xce3GDv+awD6DBgKIhFbf19NZkYaFpZWhDRtxYAP1NcbVaZdu3bk5eWxefNmMjMz8fLyYuaMGcp1ZZlZWaSmpSnlZXI569evJzk5GYlEgrOzM8OHD1fboyK/oIClS5eSmZWFqakpvr6+zJs7F39//yrX10T4rXL09aBXawOMDRQz+msOlqjtUWFlJlJzwDxNkfHH8VK6NNGnS2M9MnPlbDpWWmX2089VjLW5mKuRNQ+zPHa1FH09Ef06GGFiKOJJspSfdxWq7VFhbSFGjqqDY2km4qvBqv1YOjU2pFNjQx7Gl7N0uyIs8HpUOaZGxXRrboiFiWLzu+V7CjXuUQFQK7g7JQXZXA37mYLcNGydatFj5ErMbRSL3wtz08jPSlTKW9i68faHKzm3dw53zm3G1MKB1j2/Vu5RAVBeWsTpnTPJz05GT98IKwdvOg6aS63g6geA5+7L0NOD7k0lGBsoZpM3Hlffo8LSFORyVacoPl3O9rNSOjSQ0L6BmMx82H5GtUcFKMKjercSY2as8BylZMnZdEJaJTNSZS4+UKSQ7Rai2IwvMQO2hKvvUWFhIlL7HUnIgN0XZLSrJ6ZdXRFZBbD7vGqPCgBna/hPB1WvvXNDxe/f7ccy9l/WrNP5CDl6EjlvNRZj/GyDt02npDp1iU+HnedlhNYXE1pPMbO/85xMuUcFKDYGBBjaSb1LtueilNuPtdvn/H05+hI53Zs+0ycdfj8hrfSsquqz46yM9g3EtK8PmfmKvyt6fkyNFAPHis9q80mZzmf1JunyyviXehteBpFcU49OQOD/M65EZr9uFZQ08bfiQsSbkdqiRaAFxduq32X7VWHUbyIH9GvWif2nebsskpsP06oXfAUE17In5h8Ijfyr+Pj6MnmVjiD8V8j/Rhvz6eI34/MEsOxzC5bsezN+dse9I2LG77o9gq+Saf/RZ9bWvx5+9ncytb+E7//QnUXsVfHtQD1mbnozdAH4bvCbo893g1/f3Hjh+hnVC+nAZNi06oX+j/HGeyoEBAQEBAQEBAQE3ij+pSFML4MwqPiXEBsbS1BQkNbz9+/fx8ND+34IAgICAgICAgICNePfutj6ZRAGFf8SXFxcdGZocnFxeXXKCAgICAgICAj8mxH2qaiCMKj4l6Cnp4efn9/rVkNAQEBAQEBA4N+PsE9FFYRBhYCAgICAgICAgMALIOyoXRXBIgICAgICAgICAgICL4WQUlZAQEBAQEBAQEDgBSjeOrd6IR0Y9Z/0N2ny5iCEPwkIABl3z79uFZTY1m3J7Yepr1sNAOrXciDu4f3XrYYS91pBb9TeEG/SnhmR/btWL/iK8N96hP4Tn75uNQDYOt+T0PcuvG41lJza3oIVR163Fgo+6sobs98AKPYcmLtD9w7br4pJfcVv1J4Z322o+caO/zQzhxq8MfrMHGrw+i4uhD9VQbCIgICAgICAgICAwIsgEr3c8YL88ssveHt7Y2RkREhICGfOnNEpX1JSwtdff42npyeGhob4+vqydu3av3q3NULwVAgICAgICAgICAi8CK9wn4qtW7fy+eef88svv9CqVStWrlzJW2+9pXMPsn79+pGSksKvv/6Kn58fqamplJf/s55JwVPxf4Dp06cTHBz8utWoEU+ePEEkEuncM+OfZNiwYfTq1eu1XFtAQEBAQEDg/xNE4pc7XoCFCxcycuRIPvzwQwIDA1m8eDHu7u4sX75co/zhw4cJDw/n4MGDdOrUCS8vL5o2bUrLli3/jjvXiuCpeAmGDRvGhg0bAMU+Ee7u7vTp04cZM2Zgamr6mrXTzqlTp2jfvj1ZWVlYWVkpyyvej0QiwcXFhbfffptZs2ZhbW39j+kzbNgwsrOz2b179z92jRdlx+ETbN5ziIysbLzdXRk3fBDBQbWrrXf7wUP+++0cfDxc2bBgprL81MWr/LbzAPFJKZRLpbg7OzLgnW68FVqzD/iRA7vYs/MPsjMzcPPwYviozwis20Cj7KXz4Rw5uJsnMQ8pLyvDzcObfoOGExzSTClTXl7Orj83En78MJkZ6bi4ujN4+Mc0rCCjjT0HDvHnzt1kZGbh5eHO2FEjqVdX827ud+7dZ836jcTGx1NSUoqjgz1vd+vCe73eVcpM+Oobbt+9V6Vu08YhzJr+TbX6HDmwk30VbDN01Didtgk7uIsnMY8oLyvFzcOb9waNULMNwIE92wg7uIv0tBQsLKxo1iqUgUPHYGBgqFUPm9aN8fliJJaN6mLk4sDVvmNJ2Xtcp+42bZoQNP8rzIJqUZKYSvSCNcSu2qIm49S7C7Wnj8PE14PC6Fgiv1tEyp5j1drFqksPrN95Hz0rG0rjn5K6YQVFD+5qlTdv3R6bd/th4OSCrLCAglvXSN24Cll+nlJGbGKK3YBhmDdthdjUnLK0ZNJ+W0XBzSvV6gPwXhdLOjYzw8xEzMPYUtbuzCQ+pUyrvJujPv26WuLtZoiDjR4b9mRy8EyemoyRoYj+Xa1oUs8ESzMxjxNK2bAni+i46mO+h/Vzo0cnR8xN9Yh4lMfi1Y95El+kVf7tTg50bWePt7sJAFEx+azeHMeDR/lKGWMjMSMHeNC6mQ3WFvo8fFLAsrWPiYwu0NrurTObuHr8Vwpy07B1qkW7vlNx822sVT7+4WXCd80hI/khppYONO74IQ1aD1Sev3dpJ0c3TalS79MFt9HT1/4OP6ddPTGN/EQYGUBCBhy6IiUtR3edAHcR7RuIsTaDrHw4cVNGZLwqD0xILRGNa4mxMlP8nZYNp+/KeJSoO1fM/YubuXNmLUV5aVg5+NH87Sk4eWu3TVLMZS4d/B/ZqY8wMXegXtuRBDYboCbz+O5RroctJTczFgsbD0K6jMOrTmfdN/iMNnVEBPuKMNKHxEw4ck1Geq7uOv5u0K6u4t6z8+HUHRlRCarz7vbQ3F+Mkw2YG4vYflaqdl4X7RtICKktxtgA4tPl7L8kJS1bt02DPER0aKiHjTlk5sHxG+VExGqu06aumM4hely4L+XQFd1rTN4kXf4vUFJSQklJiVqZoaEhhobqn9HS0lKuXbvGV199pVbepUsXzp/XvB507969NG7cmLlz57Jx40ZMTU159913+f777zE2Nv57b6QCgqfiJenWrRtJSUnExMTwww8/8MsvvzBx4sQqcmVl2n843ySe38+TJ09Ys2YN+/btY+zYsa9brVfKsXOXWLJuM0P79mD9/Bk0CKzNFz8uJDktQ2e9/IJCZi5dTUi9wCrnLMzMGNq3B6tmf8NvC7+ne/vWzPr5Vy7euFOtPudOH2fd6qX07fcBc5f+SmCdBvw4/UvSUlM0yt+/e4sGwY2ZOn0e/1u8hrr1GzLn+694HB2llNmycTVhh/YyYsznLFq+kc7dezLvx6lqMpo4efosy1evZVC/91ixdAH16gQxZfr3pKRqXjxtZGREzx7dWTTnR9YuX8bg/u+xfuNm9h8+qpSZ/vVktm1cqzzW/LwEsVhMu9bVD7jOnz7OhtVL6d1vCHOWriWgTgNmT59IemqyRvmIuzepF9yEr6bPY/biX6lTvxFzv5+sdt9nTh7lj/UreG/gcBYu38SYz77iwpnj/LFhpU5dJKYm5N6O5N64mTrlnmPs5UaTfavIPHuNs0168eh/K6iz6GucendRylg1D6bh5kUkbNrDmZCeJGzaQ6M/FmPVtL7Ots1btMNh6Edk7vqDp1+NpfDBXdym/ICerb1mXfzr4PzfL8k5cZgnX4wmcdGPGPnWxmnM+Ao3qIfbN7PRt3ckcdEPPB4/kpSViynP0v25eM677S14u60F63ZlMnVJMjm5Ur4e7YCRofbYYkMDESmZ5fxxMIusXM1u+zHv21KvthE//5HOxPlJ3I4q5pvRjlhbSHTqM7CXC+/3cGbJr4/56KvbZGaXMf+7IIyNtP8sBtex4PjZdMZPv8d/p94hJb2U+d8GYmejWiz65ce+hDSwZNbSh4z44hZXb2Wz4LsgNZmKRF4/yKmds2na5WMGT9qNq28Iu5ePIjczUaN8TkYcu1aOxtU3hMGTdtO080ec2vEjD2+qr/42MDJj9A9n1Y6aDChaBoloHiji0FUZaw5LyS+S858OEgx0TEG62cF7rcXcfixj5UEptx/LeK+NGFdblUxeIRy/KWP1ISmrD0l5nCKnf1sx9pba2425fZBLB+YQHDqGXp/sxMkrhCMbxpCfrdk2eZnxHN3wEU5eIfT6ZCcNQkdzcf8sHt9Vfd+kxN7g5JYJ+DV8l96f7sav4buc+GMCqXG3qrVN8wARTf1FHL0mY/0xGQXFcgaGinXaxtUWercQc+epnF+PyLjzVE7vlmJcbFQy+hJIzZZz9NqLLVJvXVdMiyAxBy6Vs/JAOflFcoZ21tOpj7u9iPfb6XErWsove8u4FS2lXzs93Oyqfg5dbEU0ri0hObN6vd4kXV4ZYtFLHbNnz8bS0lLtmD17dpXLpKenI5VKcXR0VCt3dHQkOVnzb11MTAxnz57l7t277Nq1i8WLF7N9+3b++9///iOmeI4wqHhJDA0NcXJywt3dnUGDBjF48GB2796tDFlau3YtPj4+GBoaIpfLiY2NpWfPnpiZmWFhYaGMeavInDlzcHR0xNzcnJEjR1JcXKx2PjQ0lM8//1ytrFevXgwbNkz5d0lJCZMmTcLd3R1DQ0Nq1arFr7/+ypMnT2jfvj0A1tbWiEQitXrP78fNzY0uXbrQv39/jh49qnatdevWERgYiJGREQEBAfzyyy9a7SOVShk5ciTe3t4YGxvj7+/PkiVLlOenT5/Ohg0b2LNnDyKRCJFIxKlTpwBISEigf//+WFtbY2trS8+ePXny5Ila2xMmTMDKygpbW1smTZrE35Ehecu+o7zToS3vdmqHl5sLn48YhIOtDbuOnNBZ738rN9ClTXPq+lfd2bxR3QDaNQvBy80FNycH+vfogq+nG7cfPKxWn/27t9Kh89t07PoObu5eDB/9GXZ2Dhw9uEuj/PDRn9HzvcH41Q7E2dWdQUPH4OzixtXL55Qyp08eoU+/D2jUpAWOTi507d6b4EZN2bdri8Y2n7Nj9166de5I966d8XR3Z+zokTjY2bLv4GGN8rV8fejQrg1enh44OTrQqX0ojRsFc/eeKqOUhbk5NtbWyuPazVsYGRrStgaDigO7t9Chcw+lbYaNHoetnQNHD+7WKD9s9Dg12wx8ZptrFWzz8MFd/APr0Tq0Cw6OzjRo1JSWbTsR8/CBTl3SjpwmatpikneHVas3gOfoARTHJnH/i1nkP4ghbu124tbvxGfCCKWM96dDST92nui5qyiIjCF67irST1zE69OhOtu2frsPOSeOkHPiMKUJcaRtWEFZRhpWXXpolDeqFUhZagrZh/dQlpZCUeQ9so8dwMhH5Z2zbN8Viak5CfNnUBR5n/L0VIoi71HyNKZG99u9jTm7judw+W4Rccll/LwlHUMDMa0bavfqRseVsml/NudvFlKmYUyhryeiWT0TNh3IJiKmhJSMcrYfzSE1s5wuLc116vPe2878vjOBM5cyeRxXxOxljzAyFNOpjZ3WOj8uecSeIyk8elJIbGIx81dEIxJBo3oWABgYiGnX3JaVG59yOyKPhORi1m+LJzm1hJ5dHDW2ef3kOuo270u9lu9j6+RLaN+vMbd24vbZPzTK3z67BQtrZ0L7fo2tky/1Wr5PneZ9uHZCfQGmSCTC1MJe7agJzQLEnLkr40GcnLQc2HNBhr4e1PXSPvhrFiAmJlnOuXtyMnLh3D05j5PlNAtQdTGiEuQ8SpSTmaeYkT55S0ZpObhq6EA+5+7ZDdQO6YN/k/excvCleY+pmFo6EXFJ8/dUxOUtmFo507zHVKwcfPFv8j61Q/pw54zKNvfO/YarX0sahI7GysGHBqGjcfFtzr1zv1Vrm6a1RZy7LycyAdJyYN8lOfoSqOOp/R6a1BbxOAUuRMjJyFP8+yRFUf6cmGQIv6to90VoESjh9B0pEbFyUrPl7DwrRV8P6vto79o1DxQTkyjnzF2Fh+XMXRkxSXKaB6nXMdCD99rosedCOUU1SPT0JunyynjJ8KcpU6aQk5OjdkyZUtXDqLxcpcXdcrm8StlzZDIZIpGITZs20bRpU7p3787ChQtZv349RUXavbEvizCo+JsxNjZWeiUePXrEtm3b2LFjh3KNQa9evcjMzCQ8PJywsDCio6Pp37+/sv62bduYNm0aP/74I1evXsXZ2Vlnp10bQ4YMYcuWLSxdupSIiAhWrFiBmZkZ7u7u7NixA4DIyEiSkpLUOvkViYmJ4fDhw+jr6yvLVq9ezddff82PP/5IREQEs2bN4ttvv1WGTVVGJpPh5ubGtm3buH//Pt999x1Tp05l27ZtAEycOJF+/fopPSRJSUm0bNmSwsJC2rdvj5mZGadPn+bs2bOYmZnRrVs3SksV3yoLFixg7dq1/Prrr5w9e5bMzEx27dLc0a4pZWXlREY/oWlwHbXypg3qcCcyWmu9/SfOkJCcyoh+Pau9hlwu5+rt+8QmJhMcpDslaVlZGTGPomjQsKlaef2GTYjUEcpSEZlMRlFRIWZmFmrt6huoz5waGBjy4L52z0lZWRlRj6Jp3DBYrTykYTD3H+jucD/nYXQM9yIiqV+3jlaZQ0ePEdq2NcZGRjrbKn9mm/oNm6iVN2jYhKiXsI1/UH1ioiN5FKkY+KQkJ3Dj6kUaNmlRozZrilXzYNKOnVMrSzt6BsuQuoj0FNN71s2DST92Vk0mPewM1i0aam9YooeRTy0Kbl9TKy68dQ3j2prD1Iqi7qNna4dpsMKWEksrzJu1oeDGZaWMWePmFD+MwHHEJ/iu3ILX/JXY9BpQo9hgBxs9rC30uB2p+jErl8L96GJqe1U/e64NiQQkEhFlZeqTCaVlcvy9tbfr7GCIrbUBV25lK8vKyuXcvJ9LHX/dg5GKGBqI0ZOIyctXjHgkYoU+pWXqs6klpTLqBVZtV1peSkrcPTwDWquVewS0IvHxDY3XTHpyE4+AVmplXgFtSIm9i1Sq8oiXlhSyZlp7Vn/blt0rx5AaV31qaCszRfhNTJLKnlIZPE2R426vvePsZiciOkn9GUQnyXHTUkckUnTE9fUgPk3zRJC0vJT0xHu41lK/V1e/VqQ+1Wyb1NibuPpVkq/VivSEe8ie2SY19haufuoTFm61WpESq7nN51iZgpmxiMfJ6raJTUPNI1MZV1sRMcnq9xiTLNc4G/8iWJuBuYlILXxMKoMnybqflbu9mEdJ6u/no0QZHvbqn+O3m0mISpCpvQv/F3R5pbxk9idDQ0MsLCzUjsqhTwB2dnZIJJIqXonU1NQq3ovnODs74+rqiqWlyhUYGBiIXC4nPj7+77VDBYQ1FX8jly9fZvPmzXTs2BFQxMFt3LgRe3vFDFFYWBi3b9/m8ePHuLu7A7Bx40bq1KnDlStXaNKkCYsXL2bEiBF8+OGHAPzwww8cO3asirdCF1FRUWzbto2wsDA6deoEgI+Pj/K8jY3C7+rg4KC2pgJg//79mJmZIZVKlddcuHCh8vz333/PggUL6NOnDwDe3t7cv3+flStXMnRo1dlTfX19ZsyYofzb29ub8+fPs23bNvr164eZmRnGxsaUlJTg5OSklPv9998Ri8WsWbNGORJft24dVlZWnDp1ii5durB48WKmTJlC3759AVixYgVHjrxcAvjsvDykMhk2lhZq5TZWlmRma+6oxiUms/z37Sz/YQp6Eu0hF/kFhfQcPYHSsnIkYhETR31A0wbaO9cAebk5yGRSrCqtabGytib7emaN7mnfri2UFBfTsk0HZVmDRk3Zv3srQXUa4Ojsyp1b17hy6SwyqXbXck5uHjKZDGtrK7Vya2srMq9n69RhwNAPycnJQSqTMWRgf7p31Ry//CAyiidPY5n4WfUu2txntrG0tlErt7S2Ift6zUJy9j+zTYsKtmnVrhO5udl8N3ksyOVIpVI6d+9Fr/c/qFGbNcXQ0Y6SlHS1stLUDMT6+hjYWVOSnIahkx0lKer3UpKSgaGT9llniYUFIomE8pxstfLynGxMrTSvjSqOuk/Ssv/h8vlURPoGiPT0yLtygZR1Pytl9B2c0a8TTO7ZE8TP+QYDZ1ccR3yCSCIhY8cmnfdqZa74XOTkq79fOflS7K3/+s9QcYmcyCfF9OlsSUJqGdl5Ulo1NMXPw4DkdO1ZTmysFRMlWdnqYalZ2WU42td8kDP6P56kZ5Zy7bZiwUFRsYy7kXkMec+Np/EPycopo2MrOwJrmRGfVPU7vKggC7lMiom5eq/U1NyOp3maQwoLctPxMlf3ppiY2yKTlVOUn4WZpQM2Dj50HTwbOxd/SorzuXHqN7YuHsh/Ju/B2sFL6/2YPRvH51dSNb9Y0anWVa+gUp2CYlV7z3GwghFdJOhJoLQctp3Wvh6huDAbuUyKsZn6vRqb21L0MF1jnaK8dIxrq9vS2MwOuayc4oIsTCwcKMpPr9qmmR1FeZrbfI6pkeq+1O9TjqWJCNDc4dVmG1PdcybVYmas+F0sKFK/bkGxHCtT7R15M2PIrzRRnV+kKH9OXS8xLrYiVu6vWaagN0mXV8oryv5kYGBASEgIYWFh9O7dW1keFhZGz56aJzJbtWrFn3/+SX5+PmZmioVMUVFRiMVi3Nzc/jFdhUHFS/K8E15eXk5ZWRk9e/Zk2bJl/PLLL3h6eioHFAARERG4u7srBxQAQUFBWFlZERERQZMmTYiIiOCjjz5Su0aLFi04efJkjXW6efMmEomEdu3avfD9tG/fnuXLl1NYWMiaNWuIiori008/BSAtLY24uDhGjhzJqFGjlHXKy8vVRsOVWbFiBWvWrOHp06cUFRVRWlpabTara9eu8ejRI8zN1Wf3iouLiY6OJicnh6SkJFq0UM0e6+np0bhxY50hUNoWRlVBg5tRE1KpjGmLV/Jh/154uDhplHmOibERG+bPoLC4hKt37rN0/RZcHB1oVDdAZ71nClXSp6orVBNnw4/x5+Z1TPp2NpYVOpTDR3/GymVzGffxfxAhwtHZhfadunPy2MEX1ES3C/Y5i/73I0XFxUQ8iGTNho24uDjToV2bKnKHwo7j5elBgH/1i+JV+tTcJVyRc+FhbN+8lomVbHPv9nV2bf2NkR9/QS3/IJIT41m/egk7/lhP34HDaqxXjaj8Xj3Xu2K5JpmahPlpqKetmoGrB47DxpK+YxOFt64hsbbBYfCHOH74GSkrFz2rLkKam03KqiUgl1Hy+BF61rbYvPOexkHFhh9V33Nzfk3VrJKGshfl5z8y+KifLSu+c0MqlfM4oZRzNwrwdlN54jq1seOL0aqJla9mP9Csj/a+YRUG9HShYys7Pp9+j9IKnpJZSx8yaawfO1Y3RiqVExVTwPGz6dTy1tEr1/h9o+sdriT/TOnn772zdzDO3sHK867ejdg0rzc3T/9O+/fUkx981U81EfLHKenzBquoV51ZavIc03Nh5UEpRgYQ6CGmZwsxG8Kkuhc6V/3C0VBYUbxKhWcnKpRXtjeavzMm9lF1GredkVVsTdMVasxf2KKA+t5i3mmhelabjpf/ffpUqGNhAt2bStgQVk65juULXw9SRTC8bl1eG3/lQf5FJkyYwAcffEDjxo1p0aIFq1atIjY2VtlfnDJlCgkJCfz2myKMb9CgQXz//fcMHz6cGTNmkJ6ezpdffsmIESP+0YXawqDiJXneCdfX18fFxUUtVKhyBihtnZ2adoKeIxaLq3RyKy4Ef5kXxtTUFD8/xZqApUuX0r59e2bMmMH333+PTKb4VK9evZpmzdSz5Ui0zNBv27aN8ePHs2DBAlq0aIG5uTnz5s3j0qVLOvWQyWSEhISwaVPVzkrFgdqLMnv2bDXPCcC0adP49D3F4lgrc3MkYjGZ2eqpTrJycrGxqjpwKiwu5kH0Ex4+jmXhmt8VusvlyOVy2rw/kkXffUHjeoqQE7FYjJuzwlVZ29uDp/GJ/LZzv85BhbmFJWKxhOwsda9ETnaWWkdYE+dOH2f50jlM+Gom9YPVs6VYWloz6ZvZlJaWkJebi42tHZvWr8DB0Vlre5YW5ojFYjKzstXKs7NzsNZgm4o4Oynu28fLk6zsHH7bvKXKoKK4uISTp88ybPAATU1UwUJpG/WZ/NzsLCytbLTUUnD+9HFWLJ3D+K++p36wevjUtt/X0LZDVzp2fQcADy9fSkqKWfXTXHr3H4L4b5qdKklJr+JxMLC3QVZWRmlGtkImOR1DJ/VZVUMHmyoejopIc3ORS6XoVXo/9CwskeZkaaxj06s/RVH3yNq3XVEQ+5iU4mI8Zi4kfesGpNmZlGdnIpdKQa76dS9NiEXP2hYkeiBVn0mctDBJ+X99PcX3m5W5mOw8VdYWCzMJOfkvl8UlJaOcGctTMDQQYWyoaH/cf+xIzVTpc+5KJhEPVRmanutjY61PZgVvhZWlPpk51Qdt93/Xmf/0ceWLmfeJeVqodi4xpYTPp93DyFCMibGEzOwyvhtfi6TUkirtGJtaIxJLKMxVf56F+RmYmGte22FqYUdBJS9GUV4mYrEeRqZWGuuIxGIcPeqRnfakyrmVB1X213v2NW5mrO6tMDWsOttekfxi9dllUMzEV/Z4yGSKzFAASZkyXGzENAsQc+By1R6jkYkVIrGkigehKD8TYzPN8UbG5nYUVpHPQCTWw8jESiFjZkdRJfsV52dgpKHNX4+q9JI8+9hX9jyYGokoKNbedc7X4JUwqcaemngQJyM+vYI+EsU7bGYsIr+Ch8DUSP3vKvoUaXhWxlDwzGPgYivCzFjERz1U3UOJWISno5ymAWJm/q74vCzfp/rcvG5d/n+gf//+ZGRkMHPmTJKSkqhbty4HDx7E09MTgKSkJGJjY5XyZmZmhIWF8emnn9K4cWNsbW3p168fP/zwwz+qpzCoeEkqdsKrIygoiNjYWOLi4pTeivv375OTk0NgoCJjUGBgIBcvXmTIkCHKehcvXlRrx97enqQk1Q+2VCrl7t27ygXY9erVQyaTER4ergx/qojBs1h6qbT6H/Np06bx1ltv8fHHH+Pi4oKrqysxMTEMHjy4Rvd85swZWrZsqZZBKjpafW2CgYFBFV0aNWrE1q1bcXBwwMJCPRTpOc7Ozly8eJG2bdsCCo/JtWvXaNSokVZ9pkyZwoQJE9TKDA0NyX+oiD/X19fD39eLy7fu0a5ZiFLmyu37tGkSXKU9U2MjNi76Xq1s5+ETXLsTwY9f/hcXB+0DILkcyqrZiEZfXx8fv9rcvnmFZi3bKstv37xCk2attdY7G36MX5bM5vMvpxHSRPuCZwMDQ2zt7CkvL+fi+XBatm6vU5fafr5cu3mL1i2bK8uv3bxFy2ZNtdarglyuMRta+NlzlJWV0bF9zTxsehVs07Slqs7tm1dprMM258LDWL5kNuO+nE4jDbYpKSmuMshXDuT/hkQAz8m+eBOHt9Xtbd+5NTnX7iJ/9l5kXbyJXcdWPF6iWrNk16k1WRd0xH9LyymOeYhJ/UbkX1GlGzSp34j8qxc0VhEbGikGDBWQP5tEeD7xWxR5H4tWoWqeEn1nN8ozM6oMKEDR2a9IVm459Wsb8yRR8ewlEgjyNWLzAc0DnRelpFROSakUU2MxDfyN2bRf1W5RsYyEZPVeXEZWKY3rW/HosWJQoKcnIjjIgpW/P9V5nf7vuvBBX1cm/RChM01scYmM4hIZZqYSmgZbsWJj1XYlegY4utfhaeQ5/BqoQgJjH5zHt15Hje06ewUTc1fdc/30wVkcPeoikehrrCOXy0lLiMDOuaoHMCtf/e+8Ijk+ziKSsxTPWCwGT0cRx25onyqOT5fj4yTi0gPV58PXWaR1vcRzRCJVZ70yEj0D7FzqkPDovFq618RH5/EI6qCxjoNHMHERp9TKEh6ew861DuJntnHwaEDCo/PUbT1MJfPoPI4eVdcpVbZNfpEcbycRKdkq23jYw8nb2u8xIUOOt6OIK1EqW/g4iYhPf7HvktJyxQJ3FXLyCuX4OYtIzlS0JRGDl5OIMB1ZpOLSZPg6i7lwXyXj5ywmNk3xd0ySnJ/2qH8/924lIS1Hztm7MuVX4Juky2vjBfeaeFnGjh2rNRvn+vXrq5QFBAQQFlazxCF/F8JC7VdIp06dqF+/PoMHD+b69etcvnyZIUOG0K5dOxo3Vswkjxs3jrVr17J27VqioqKYNm0a9+6p5/Dv0KEDBw4c4MCBAzx48ICxY8eSnZ2tPO/l5cXQoUMZMWIEu3fv5vHjx5w6dUq5ONrT0xORSMT+/ftJS0sjP7/SN2cFQkNDqVOnDrNmzQIU2Zpmz57NkiVLiIqK4s6dO6xbt05t3UVF/Pz8uHr1KkeOHCEqKopvv/2WK1fUc9p7eXlx+/ZtIiMjSU9Pp6ysjMGDB2NnZ0fPnj05c+YMjx8/Jjw8nHHjxikXGY0bN445c+awa9cujXbQRE0WRg14pwv7jp9m//HTPIlPZMm6P0hJz6BXF0UHcPnvfzJz6WpA0dn09XBTO6wtLTA00MfXww1jI0Xbv+3cz+Vb90hITuVJfBJ/7D3CofDzdG1b/eLfHr36c/zofk4cPUB83BPWr15KeloqXbr3AmDT+hUsW6CafTgbfoyfFv7A0JGfUCugDllZGWRlZVBQoHrODyPvcel8OCnJiUTcvcWP332BXCajZ99BOnXp2+tdDh09xqGjx3gaF8cvq9eSmpbOO927ArBm/UbmLFAt/N+z/yAXLl0hPiGR+IREDocdZ9uuPRoHDoeOHqNV82ZYahlEauLtXgM4cXQ/J4/uJz7uCRtWLyU9LYXOz2yzef0KflqgGvSdCw/j54U/8MEz22RnZZCdlUFhBduENG1F2MHdnAs/RmpyIrdvXGHr72to3Kw1Yh1rZiSmJlg0CMCigcLzZOLthkWDAIzcFd4f/x8m0GDd/5TyT1dtwdjThcB5X2EW4IPbsL64D+9LzEJVpponP/2GXedW+Ewcham/Dz4TR2HXsQVPlmlOjPCcrAM7serQDYvQLhi4umM/ZAz6dg5khx0AwG7gcJz++6VSPv/aRcybtsKqcw/0HZww9g/CYfjHFD18gPSZlyw7bD8Scwschn2MvrMrpg2bYttrAFlH9+nU5TkHz+TRq6MlTeoa4+6kz9j+dpSUyjh7Q9Ux/+8AWwa+ZaWyqQQ8XfTxdNFHTwLWlhI8XfRxtFXNhzWobUQDfyPsbfSoV8uI7z5yJDGtjFNXtH+vAWw/kMR/+rjSuqkN3u7GfPVfP4pLZBw7o5rpnvKpH6MGqXarHdDThZED3Zn7SzTJaSXYWOljY6Wvloa2SQNLmgZb4eRgSEh9SxZPr0NsYhGHTmpeI9Go/XDuXtjO3QvbyUiO5tTOWeRlJVG/tcJjd3bvAg5vnKSUr996ALlZiYTvnE1GcrSi7sUdhHRQZQ27cOgnnkScITs9jtT4CMI2TyUt/gH1K+xloY1LD2S0riPG302EvSX0bCGmrBzuPlH14Hq2ENMhWKxWx9dZRMsgEbYWirS03k4iLj1QdRY7NBDjYQ+Wpoq1Fe0biPF0EKm1W5m6rYcSdXUHUVd3kJ0azcUDs8nPSSKgqSK5yZUjCwn/c7JSPrDpAPKzE7l4YA7ZqdGKutd2Uq+NyjZ1Wg4h4dF5boWvJjs1hlvhq0l4dIE6rYZUuX5lLkfJaRkoorYr2FvCO01FlEnh3lPVPbzTTERoPdWkxJUoOT5OinS0tuaKf70cURtk6OspbOJgpfjb0lSEg5Ui/EcXFyKktKkvIdBDhIOViN6tJJSVw+0Yld37tJbQqZHqe+tihAxfFxGt64qxs1CkgvVxEXHxWce+tFyR3rbiUVoORSWK8v8LurwyxOKXO/6FCJ6KV4hIJGL37t18+umntG3bFrFYTLdu3Vi2bJlSpn///kRHRzN58mSKi4vp27cvH3/8sdoC5BEjRnDr1i2GDBmCnp4e48ePV3opnrN8+XKmTp3K2LFjycjIwMPDg6lTpwLg6urKjBkz+Oqrrxg+fDhDhgzROMp9zoQJExg+fDiTJ0/mww8/xMTEhHnz5jFp0iRMTU2pV69elRS3z/noo4+4efMm/fv3RyQSMXDgQMaOHcuhQ4eUMqNGjeLUqVM0btyY/Px8Tp48SWhoKKdPn2by5Mn06dOHvLw8XF1d6dixo9Jz8cUXX5CUlMSwYcMQi8WMGDGC3r17k5NTzS5N1dCpVTNy8gpY++deMrJy8PFwZf7U8Tg7KMIRMrJySEmv2ULg5xQVlzB/1W+kZmZhaGCAp6sT08aNolOr6jeba9W2I/l5uWzfsp6szAzcPb2ZOn0u9g6KNRxZWRmkp6nSEocd2oNUKmXN8oWsWa4a7LXr2I1Pxn8NKJII/LFxNanJSRgZG9MwpDmffvEtpma6M9+0b9ua3Lw8ft+yjczMLLw8PZg1/RscHRwAyMzKIjVN1XmSyeX8umEjySmpiCUSXJyd+HDYB/To1kWt3fiEBO7ej+B/30+r1h4Vadm2I3l5OeyoYJuvps9T2iY7K4OMCrY59sw2a5cvZK2abd5i7DPb9BkwFEQitv6+msyMNCwsrQhp2ooBH4zWqYtlSF1aHN+o/DtovuLzFvfbTm6PnIKhsz3G7qrwsqIn8Vx5ZzRBC6bg+fFgShJTuTf+R5J3qVI4Z124wY3BE/Cf8Tn+Mz6jMDqOG4PGk31Zx9QokHchHIm5OXZ9ByOxtqE07inxc76hPF2xtkHPygb9CntW5IaHITY2xqrru9h/MApZQQGF926StulXpUx5RhpxP07FYegYvOauoDwznaxDu8ncs02nLs/ZezIXA30RI/vYYGos4VFsCbNWp1Jcouoc2FrrIavQV7CxkDB3govy73dDLXk31JJ70cXMXK54rsbGYga+ZYWtlR75hVIu3Slky6FsdOQcAOCP3YkYGogZP8obc1M97j/M58vv71NUrKroaGeAvIJCvbo6YqAvZuaX6lnb1m+LY/02xWSHqYkeowZ7YG9rQF5+OacvZrLmj1ikUs2dIP9G3SkuyOLSkV8oyEnF1rk2vT5ahYWNKwAFuWnkZak805a27vQes4rwXbO5dWYTppYOhPb9mlrBXZUyJUW5HNvyHYW5aRgYm+PgFsT7437HyVP3/iYA5+/L0ZfI6d5UsYlZQjr8fkJKaQXHk6WpSC0ENz4ddpyV0b6BmPb1ITNf8XdCha9JUyPo1VKCmTGUlEFKlpzNJ2VVMiNVxKd+d4oLs7lx4hcK89KwdqxFl6ErMLdW2KYoL438bJVtzG3c6DJ0BZcOziHi4mZMLBxo3mMq3nVV3zeOng1p338B18KWcP3YMsxt3OkwYAEO7po3zKzIxQeKFLLdQsQYGUBiBmwJl6nZxsJE3TYJGbD7gox29cS0qysiqwB2n5eRWCGi1dka/tNB1dnu3FDR4bz9WMb+y9rtc/auDH2JiB7N9DAyhIQ0Ob+Flet8VnFpcv48XU7Hhnp0CJaQlQfbwstf2HPyJuvyyniFayr+ryCS/x2J/QUE/o+TcVfzrpSvA9u6Lbn9MPV1qwFA/VoOxD2sPhXlq8K9VhA3H2qe8X3VBNey54C+7pTAr4q3yyKJ7N+1esFXhP/WI/SfqDuM6FWxdb4noe9pDvt6HZza3oIVL5ek7m/jo64wc9Obk1Xnu8F6zN3xZqzIndRXzKytb8auzVP7S/huw5uzQcPMoQZvjD4zh2reVPJVUHx03UvVN+oy/G/S5M1B8FQICAgICAgICAgIvAiCp6IK/86gLgEBAQEBAQEBAQGBV4bgqRAQEBAQEBAQEBB4Ef6li61fBmFQISAgICAgICAgIPACyIXwpyoIgwoBAQEBAQEBAQGBF+EV71PxfwFhUCEgICAgICAgICDwIgiDiioIKWUFBAQEBAQEBAQEXoDC8C0vVd+k3YC/SZM3B8FTISAAb9xeDI+jH71uNQDw9vUjMVL3RmuvEhf/+sRER79uNQDw8fV9Y/aG8N965I3ZMwMU+2YMnpLwutUAYNNsV7qPuPO61VBycG09lh9+3Voo+LgbTPut7HWroWTGEP03ap+K2dvejH0qpvSTvHHP6U3RZ8YQ/detgkAFhEGFgICAgICAgICAwIsghD9VQRhUCAgICAgICAgICLwIQvanKryRw6zp06cTHBz8utWoEU+ePEEkEnHz5s3Xcv1hw4bRq1ev13Ltv5P169djZWX1utUQEBAQEBAQEKgesfjljn8hL+SpGDZsGBs2bFBU1NPD3d2dPn36MGPGDExNTf8RBf8OTp06Rfv27cnKylLruFa8H4lEgouLC2+//TazZs3C2tr6H9Nn2LBhZGdns3v37n/sGtquW/H52djYUL9+fQYOHMiwYcMQv6KX3MvLi88//5zPP/9cWda/f3+6d+/+Sq5fE/YcOMSfO3eTkZmFl4c7Y0eNpF7dII2yd+7dZ836jcTGx1NSUoqjgz1vd+vCe73eVcpM+Oobbt+9V6Vu08YhzJr+TbX67Nu/n+07dpKZmYmnpwcfjR5N3bp1NcrevXePtWvXERcfT0lJCQ4ODnR/qxt9evdWyhwNC2PhosVV6u7dvQsDAwOduuw+eIStO/eQkZWNl4cbn3w4nPp1AjXK3rkfwcr1m4hLSKC4pARHe3ve6daZ93v2UMqUl5ezafsujp4IJy0jE3dXF8YMHUzTkIbV2gVg//79bN+x45ltPBlTjW3WrV1byTZv0buCbcLCwli4aFGVunt2767WNlZdemD9zvvoWdlQGv+U1A0rKHpwV6u8eev22LzbDwMnF2SFBRTcukbqxlXI8vOUMmITU+wGDMO8aSvEpuaUpSWT9tsqCm5e0dquTevG+HwxEstGdTFyceBq37Gk7D2uU3ebNk0Imv8VZkG1KElMJXrBGmJXqS9EdOrdhdrTx2Hi60FhdCyR3y0iZc8xne1WpE9Hczo0NcXUWMyjuFLW78kmIbVcq7yrgx7vdbbA21Ufe2s9Nu7P5vC5AjWZxZMcsbeu+lMWdiGf9XtzdOozuKcD3drZYGYiITKmkF9+TyQ2sUSrfNe21nRsaY2nqxEAj54WsWFHMlGPi9TaHNzTUa1eZk4Z/xn/QGu7t85s4tqJXynITcPWqRbt+kzF1bexVvn4R5c5vWsOGckPMbV0oHGHD6nfeqDy/L1LOwnbPKVKvU/m30ZP31Bru88JbSAmpJYYYwOIT5dz4JKUNN2mJNBDRIdgCTbmkJkHx29IeRCnOQ9Mm7piOjWScOG+lMNXda+huH9xM3fOrKUoLw0rBz+avz0FJ2/ttkmKucylg/8jO/URJuYO1Gs7ksBm6gtiH989yvWwpeRmxmJh40FIl3F41ems+waf0bqOiGAfEUb6kJgJR6/LSM/VXcffFdrWFWNlBtn5EH5XRlSFJUfudtAsQIyTNZgbi9h+VsrDxBqp8488qya1xTT2F2P1rFuXliPn1C0ZjxJ15/V5k96bV4GwT0VVXjj8qVu3bqxbt46ysjLOnDnDhx9+SEFBAcuXL1eTKysrQ1//zV9A8/x+ysvLuX//PiNGjCA7O5s//vjjdav2j/D8fqVSKSkpKRw+fJhx48axfft29u7di57eX4uIk8vlSKXSv1zf2NgYY2Pjv1T37+bk6bMsX72Wzz4eTZ2gAA4cOsqU6d/z6y9LcXSwryJvZGREzx7d8fHyxMjIiLv377P4pxUYGRnRo1sXAKZ/PZnyclXnKTc3j9Gfjqdd65bV6hMefpqVq1bz37FjqRMUyMFDh/nmu2msWrEcBwcHjfq8804PvL29MTIy4t69eyxd9hNGRkZ0f+stpZyJiQlrVq1Uq1tdp/nEmXP8vGYdn380irqB/uw7HMbkGT+y/udFONprsI2hEb3f7oaPtyfGhobcuf+Ahb+swsjQkHe6KX7Ef/19C8dOneaLTz7Cw82VK9dv8u3sefz0vx+p5etdjW3CWblqFf8dO5agoCAOHjrEt999x8oVK3TY5p1KtlmGoQbbrF616oVsY96iHQ5DPyLl158oiryHZae3cZvyA48njKI8I62KvLF/HZz/+yWpG1ZScO0iejZ2OI76DKcx40lcMFMhJNHD7ZvZSHOySVz0A2UZ6ejb2iMrLqrSXkUkpibk3o4kfsNOQv78SacsgLGXG032rSLu1z+5OfRLrFs2ou6yaZSmZZK86ygAVs2Dabh5EVHTlpC85xhOPTvR6I/FXAgdRPbl6hfz92hrRvfWZqzYnkVyejm92pszZaQdExekUFyquRNhaCAiNbOcS3eK+M/blhplvv05DXGF33Y3R32mfmjHpTu6bfTeW3b07mLHwl/jSUgpYUAPB36c6M3oqVEUFWvusNT3NyP8UjYRjwopLZPz3lt2/PCFNx9/E0VGturz/SS+mK/nP1b+LdWRZDHy+kHCd82mw/vTcPFuxO3zW9i9YhQfTDmAhY1LFfmcjDh2rxxN3Rbv0+2DeSQ+vs6JP2dgbGZDrWBV4gADIzOGfq2+IrwmA4rWdcS0CBSz+7yUjFw5betJGNJZj2W7yynVMv5zsxPxflsJJ2/KiIiVEeghpl87Cb8elpKQrn7vLrYiQmqJSc6sPvFkzO2DXDowh5bvfoujZyMeXN7KkQ1j6Pv5PsysqtomLzOeoxs+wr/Je4T2m0vK0+uc3/s9RqY2eNdVfBenxN7g5JYJhHT6DM+gTjy9f4wTf0ygx5jfcXBvoFOf5gEimtYWsf+yjMw8aBUkYkA7MasOybTaxtUWerUQc/qunMgEOf6uInq1EPP7CRmJmQoZfT1IzZZz+7Gcvq0k1drlOf/Us8oplHPsupTMXMXfwb5iBraXsGJ/udZBwpv03rwyhDUVVXhhixgaGuLk5IS7uzuDBg1i8ODB7N69WxmytHbtWnx8fDA0NEQulxMbG0vPnj0xMzPDwsKCfv36kZKSotbmnDlzcHR0xNzcnJEjR1JcXKx2PjQ0VG1WG6BXr14MGzZM+XdJSQmTJk3C3d0dQ0NDatWqxa+//sqTJ09o3749ANbW1ohEIrV6z+/Hzc2NLl260L9/f44ePap2rXXr1hEYGIiRkREBAQH88ssvWu0jlUoZOXIk3t7eGBsb4+/vz5IlS5Tnp0+fzoYNG9izZw8ikQiRSMSpU6cASEhIoH///lhbW2Nra0vPnj158uSJWtsTJkzAysoKW1tbJk2axItmBH5+v66urjRq1IipU6eyZ88eDh06xPr16wHNIV3Z2dlqup46dQqRSMSRI0do3LgxhoaGnDlzhujoaHr27ImjoyNmZmY0adKEY8dUM5mhoaE8ffqU8ePHK+8fNIc/LV++HF9fXwwMDPD392fjxo1q50UiEWvWrKF3796YmJhQq1Yt9u7d+0L20MSO3Xvp1rkj3bt2xtPdnbGjR+JgZ8u+g5pTttTy9aFDuzZ4eXrg5OhAp/ahNG4UzN17qoxSFubm2FhbK49rN29hZGhI2xoMKnbu2kXXLl14q1tXPDw8+GjMaOzt7dh/4KBGeT9fX9qHhuLl6YmToyMdO3QgJKQRdyt5SkQiETY2NmpHdfy5Zz/dO3Xg7S4d8XR345NRw3Gws2PvwaMa5Wv5etOxXWu8PdxxcnSgc/u2NGnYgDv3I5QyYadOM+j9PjRv3AgXJ0d6du9Kk4bBbNu9r1p9du3aRZcuXejWrdsz24zB3t6eAwcOaLVNaGgonp6eODo60qFDB0JCQrh3V92b8FdsY/12H3JOHCHnxGFKE+JI27CCsow0rLr00ChvVCuQstQUsg/voSwthaLIe2QfO4CRT22ljGX7rkhMzUmYP4OiyPuUp6dSFHmPkqcxOnVJO3KaqGmLSd4dVq3eAJ6jB1Acm8T9L2aR/yCGuLXbiVu/E58JI5Qy3p8OJf3YeaLnrqIgMobouatIP3ERr0+H1uga3VqZsftkHlfvFROfUs6KP7Mw0BfRMlj7ZEJMfBl/HMrl4u0iyqWav+vyCmTk5KuOhoFGJGeUE/G4VKc+vTrbsWV/Kuev5/I0oYQFv8ZjaCAmtJmV1jrzVsdx4GQmMXHFxCeXsHR9AmIRNAgyU5OTyuRk5ZYrj9w87VmErp9aR53mfanb4n1snHwJ7fM1ZtZO3D6neWLr9rktmFs7E9rna2ycfKnb4n3qNOvDtZNr1QVFIkwt7NWOmtA8UMyZOzIiYuWkZsOuc1L09aC+t/buQosgMTFJcs7cVczan7krIyZJTotA9ToGetC3jYS9F6UUaRlIVuTu2Q3UDumDf5P3sXLwpXmPqZhaOhFxSXMqz4jLWzC1cqZ5j6lYOfji3+R9aof04c4ZlW3unfsNV7+WNAgdjZWDDw1CR+Pi25x7536rVp8mtUScj5ATlQDpubD/shx9CQR5aJ+xblxLxOMUuPBATmae4t+nKdCktqpOTDKcvitX817UhH/qWUXFy3mYICcjDzLy4PhNxaDJ3V77fb5J743A6+Olh1nGxsaUlSlSiz169Iht27axY8cOZYe0V69eZGZmEh4eTlhYGNHR0fTv319Zf9u2bUybNo0ff/yRq1ev4uzsrLPTro0hQ4awZcsWli5dSkREBCtWrMDMzAx3d3d27NgBQGRkJElJSWqd/IrExMRw+PBhNQ/L6tWr+frrr/nxxx+JiIhg1qxZfPvtt8owosrIZDLc3NzYtm0b9+/f57vvvmPq1Kls27YNgIkTJ9KvXz+6detGUlISSUlJtGzZksLCQtq3b4+ZmRmnT5/m7NmzmJmZ0a1bN0pLFT+OCxYsYO3atfz666+cPXuWzMxMdu3a9cK2qkyHDh1o0KABO3fufOG6kyZNYvbs2URERFC/fn3y8/Pp3r07x44d48aNG3Tt2pV33nmH2NhYAHbu3ImbmxszZ85U3r8mdu3axbhx4/jiiy+4e/cuY8aMYfjw4Zw8eVJNbsaMGfTr14/bt2/TvXt3Bg8eTGZm5osb4RllZWVEPYqmccNgtfKQhsHcf6A9fKEiD6NjuBcRSf26dbTKHDp6jNC2rTE2MqpWn4ePHtGokXooUKOGjYiIiNBSS51H0dFERERQr149tfKioiKGDB3Gfz4YwnfTpvOomlStCtvE0Lih+mxe44b1ufsgska6PIx+zN0HkTSoYJuysjIMKnk1DQ0MuBOh294q2zRSK2/UsCH3/wbbDB06lP988AHTpk2r1jZI9DDyqUXB7WtqxYW3rmFcW3PYXFHUffRs7TANbqJowtIK82ZtKLhxWSlj1rg5xQ8jcBzxCb4rt+A1fyU2vQb87TNkVs2DSTt2Tq0s7egZLEPqInrmfbRuHkz6sbNqMulhZ7BuUX2Ymr21BGsLCXceqkKLyqXw4HEJtTyrnz2vKRIJtA42JvxqgU45J3t9bKz0uX4vX6VPuZw7kQUE+pnU+HqGhmIkEhH5BeqDBldHQzYuDGDt//yZPMYdJ3vNXntpeSmpcffw9G+tVu7p34qkxzc01kl+chNP/1bq8gFtSI29i1SqSvNZVlLIr9Pbs+a7tuxZOYbU+OrTZlubgbmJiEdJKk+NVAZPU+S4O2jvULrZi4iuFBoTnSiv0gl9u5mEh/GKjmN1SMtLSU+8h2st9Xt19WtF6lPNtkmNvYmrXyX5Wq1IT7iH7JltUmNv4eqnPpnjVqsVKbGa23yOlSmYGYt4nKzSXSqD2DRws9Nez9VWxOMU9fuNSZHjavtyoTP/9LN6jkgEdb1EGOhBXJrm5/YmvTevErlI/FLHv5GXyv50+fJlNm/eTMeOHQEoLS1l48aN2D8LgwgLC+P27ds8fvwYd3d3ADZu3EidOnW4cuUKTZo0YfHixYwYMYIPP/wQgB9++IFjx45V8VboIioqim3bthEWFkanTp0A8PHxUZ5/Psvo4OBQZTZ8//79mJmZIZVKlddcuHCh8vz333/PggUL6NOnDwDe3t7cv3+flStXMnRo1Rk6fX19ZsyYofzb29ub8+fPs23bNvr164eZmRnGxsaUlJTg5OSklPv9998Ri8WsWbNGOXu/bt06rKysOHXqFF26dGHx4sVMmTKFvn37ArBixQqOHDlSYzvpIiAggNu3X3w/gpkzZ9K5syoW1dbWlgYNVJ3OH374gV27drF3714++eQTbGxskEgkmJubq91/ZebPn8+wYcMYO3YsABMmTODixYvMnz9f6XkCxTqRgQMVscSzZs1i2bJlXL58mW7dur3wvQDk5OYhk8mwtrZSK7e2tiLzerbOugOGfkhOTg5SmYwhA/vTvavmGN0HkVE8eRrLxM/+W60+ubm5Cn0qvbfW1lZkZmXprPufD4Yo9Rk8aBBvdVOFRri7u/PFhPF4e3lRWFjI7j17+WLil/zy0zJcXV01tqe0TWVdLK3Iys7Wqcv7w8eQk5OLVCZl6IB+vN2lo/Jc44YN+HPPfhrUDcLFyZHrt+5w7tIVZDLdMbPabGNlbU1Wtbb5gJycHGTPbFPxfXFzd+eLCRPwUtpmDxMnTuTnn37SahuJhQUiiYTyHHU7lOdkY2qleX1WcdR9kpb9D5fPpyLSN0Ckp0felQukrPtZKaPv4Ix+nWByz54gfs43GDi74jjiE0QSCRk7Num8xxfB0NGOkpR0tbLS1AzE+voY2FlTkpyGoZMdJSkZajIlKRkYOlU/A25lrgjpyMlX73zn5Muws6p5uEd1NA4yxsRIzOlrhTrlrC0UnfzsXPW4jOzcchxsax62O/w9JzKyyrhRYXASGVPIgjVxJCSXYGWpx4AeDsyf6svH3zwkr9Lgo6ggC7lMiomFrVq5ibkdhXlVQ+YACnLT8QxQ78WaWNgik5VTnJ+FqaUDNo4+dBk0GzsXf0qL87kR/hvbFg9k8KQ9WDt4ab0fM2PFb09Bpcix/CKwMtNQ4Xk9I8gvVu/w5RfLMavghKrrJcLZRsSqAzXb+6G4MBu5TIqxmfq9GpvbUvQwXWOdorx0jGur29LYzA65rJzigixMLBwoyk+v2qaZHUV5mtt8jumz+Z+CSl2TgmI5lqYiQHOH18xIUx1Ve3+Vf/JZAThYwYdv6aEngdJy2HJK+/qIN+m9eaUIayqq8MKDiued8PLycsrKyujZsyfLli3jl19+wdPTUzmgAIiIiMDd3V05oAAICgrCysqKiIgImjRpQkREBB999JHaNVq0aFFlRloXN2/eRCKR0K5duxe9Hdq3b8/y5cspLCxkzZo1REVF8emnnwKQlpZGXFwcI0eOZNSoUco65eXlWFpqju8FRWd/zZo1PH36lKKiIkpLS6vNZnXt2jUePXqEubm5WnlxcTHR0dHk5OSQlJREixYtlOf09PRo3LjxC4dAaUIulysHMy9C48bqC+YKCgqYMWMG+/fvJzExkfLycoqKipSeipoSERHB6NGj1cpatWpVxctUv3595f9NTU0xNzcnNTVVa7slJSWUlKgvxDQ0rDpTWtkSNbHPov/9SFFxMREPIlmzYSMuLs50aNemityhsON4eXoQ4F9bQytaqHTtmugzf95cioqKeRD5gLXr1uPi4kz70FAAAgMCCAwIUMoGBQXxyWefsWffPsZW+jxWowqKH1PduiydPZOi4mLuRz5k9W+bcHV2omM7xezsp6OGM/+nlQwdOw4Q4ersSLdO7Tl8rGbfAZXtUDPbzKOoqIgHkZGsW7cOFxcXQnXY5tPPPmPvvn18XI1tqPxZFImqFD3HwNUDx2FjSd+xicJb15BY2+Aw+EMcP/yMlJWLlPcmzc0mZdUSkMsoefwIPWtbbN55728dVGjTvUq5JhktN/jrdGfl/+dtyNAoo70r9tcIbWzCrahisvPUB6Shza34dIgqBn/a4qeAhtvRUKaN97rZ0a6pJZPnPqasXFXp6h3VAIOEEiIePeHX//nTqZU1u45q67hW+cbRUFZRvNK550o/K3f2CsbZK1h52sW7EZvm9+bWmd8J7aueGGLqQFU3YNMJqfLqlS9XnV00nX9eZGECbzWR8NuxcspfdH1t1S9jDYUVxTXZEnWbVf7OQPN3xhe9VbPJ287KKram3lR174yG9+xFqect4p3mqgH4P/WsnpORCyv2l2NkICLIQ0TvVhLWHVGtqXjj35tXwL/V2/AyvPCg4nknXF9fHxcXF7VQocoZoLT9uL9oB1YsFlfpOD8PuQJeaoGvqakpfn5+ACxdupT27dszY8YMvv/+e+VM6erVq2nWrJlaPYlE8+zatm3bGD9+PAsWLKBFixaYm5szb948Ll26pFMPmUxGSEgImzZV7SjYa1gA+3cTERGBt7diUezzLFAVbV7R3hWp/My//PJLjhw5wvz58/Hz88PY2Jj33ntPGcL1ItSks1g5GYBIJNI5wz179mw1TxLAtGnTGDm4HwCWFuaIxWIys7LVZLKzc7C20j6QBHB2UmR88fHyJCs7h982b6kyqCguLuHk6bMMGzxAUxNVsLCwQCwWV5l5V+hjpbPuc0+Qt7cXWVnZ/L5ps3JQURmxWEztWrVJTNCeckSbbbJyXtw2G7ZsUw4qrCwt+eHrSZSWlpKTl4edjQ2rNmzCybHqQuuKPLdNZY9NTnZ2temJVbbxJjsri983bVIOKiqjsE0tEhO0BzxLc3ORS6XoVfJK6FlYIs3R7DWx6dWfoqh7ZO3briiIfUxKcTEeMxeSvnUD0uxMyrMzkUulIFe906UJsehZ24JED6TaMye9CCUp6VU8Dgb2NsjKyijNyFbIJKdj6KQ+w2voYFPFw/GcqctUg3s9ieJza2kmUevwW5iJycn/e3oLdlYS6voZsvj3quGPl27mEhmj8l7o6yn0sbbUIytHZUNLC70q3gtN9OlqR78eDnw9/zFP4nV71UtK5TyNL8bFsepCf2NTa0RiCYW56jYszMvAxFxzTI2phR2FuWmV5DMRi/UwMrXSWEckFuPkUY+stCdVzq3Yr7pfybM+kpmxYpZZeU0Ns+0VyS9WZC2q2K00MxIpZ65dbEWYGYsY87aqyyERi/B0lNM0QMz3m8qrdC6NTKwQiSVVPAhF+ZkYm6l7I55jbG5HYRX5DERiPYxMrBQyZnYUVfICFednYKShzbVhqndTaZtKtjAxFFFQor3nnF8MppW6KCbV2FMTkXFyEtL/+Wf1HKlMkY0J5CRmyHGxE9E8UMy+iwqbvCnvzWtF8FRU4YWHWc874Z6entVmdwoKCiI2Npa4uDhl2f3798nJySEwUJGCMjAwkIsXL6rVq/y3vb29Wuy9VCrlboWFlfXq1UMmkxEeHq5Rj+dZW6TS6t1n06ZNY/78+SQmJuLo6IirqysxMTH4+fmpHc874JU5c+YMLVu2ZOzYsTRs2BA/Pz+iK8VjGxgYVNGlUaNGPHz4EAcHhyrXsrS0xNLSEmdnZzXblJeXc+2aegz3X+HEiRPcuXNHGVb1fBBT0eY13YfjzJkzDBs2jN69e1OvXj2cnJzUFpuD5vuvTGBgIGfPqsdvnz9/Xvne/FWmTJlCTk6O2jFliir1or6+PrX9fLl285ZavWs3bxFUYfa6WuRyjQOx8LPnKCsro2P7mnnV9PX1qeXnx40b6vG+N27ceCFbyLXoU/F8dEyMzgXJCtv4cPWmepjctZu3qRvg/0K6lJZV/TEwMDDA3tYWqVTK6fMXadWsic52tNnm+o0bBP0DtrHWtVhbWk5xzENM6quv7zCp34iiKM2x7GJDI+Qy9c6I/PmA+NlvVVHkfQwcndV+vPSd3SjPzPjbBhQA2RdvYtdRPc7cvnNrcq7dRf4sa1nWxZvYdVSPV7fr1JqsC5pj0VMypMojIbWcrFwp9WqpvIISCQR4G/LwqfYUri9C2xATcvJl3Iis2ospKpaRlFqqPGITS8jMLqNRhQXWehIR9fxNiXikO3Sqbzc7Br7jwLcLH/Pwie4MUwB6eiLcnY3IzK76vCR6Bji41yE2Un09S2zkeZy9Na9VcfIKJjbyvFrZ08izOHjURSLR/Jssl8tJi4/QuFg7M091pOVAXqEcX2dV10AiBk9HEXGp2jvO8WlyfJzVO1i+LiJlDH5Mkpyf95axYn+58khIl3EnRs6K/VUHFKCwjZ1LHRIeqd9r4qPzOHhqto2DRzCJleQTHp7DzrUO4me2cfBoUKXNhEfncfSo2mZWvupIz4X8Ijlejqr7FIvBwx7idUROJWTI8XZUt423o4iEjBfz0ZWWv5pnpQ0Rig79c97U9+aVIhK/3PEv5B+9q06dOlG/fn0GDx7M9evXuXz5MkOGDKFdu3bKsJlx48axdu1a1q5dS1RUFNOmTePePfUsNR06dODAgQMcOHCABw8eMHbsWLIrxHB7eXkxdOhQRowYwe7du3n8+DGnTp1SLo729PREJBKxf/9+0tLSyM/PRxuhoaHUqVOHWbNmAYpsTbNnz2bJkiVERUVx584d1q1bp7buoiJ+fn5cvXqVI0eOEBUVxbfffsuVK+r55L28vLh9+zaRkZGkp6dTVlbG4MGDsbOzo2fPnpw5c4bHjx8THh7OuHHjiI+PV9pqzpw57Nq1S6MdakJJSQnJyckkJCRw/fp1Zs2aRc+ePenRowdDhgwBFJ6f5s2bM2fOHO7fv8/p06f55pvq91J4fv87d+7k5s2b3Lp1i0GDBlXxHHh5eXH69GkSEhJIT9f8bfzll1+yfv16VqxYwcOHD1m4cCE7d+5k4sSJL3S/lTE0NMTCwkLtqBz+1LfXuxw6eoxDR4/xNC6OX1avJTUtnXe6K9YkrFm/kTkLVGFYe/Yf5MKlK8QnJBKfkMjhsONs27VH48Dh0NFjtGreDEsLixrr3Kd3bw4fOcqRo0eJjY1l5apVpKal8fazfT3WrlvPvPkLlPJ79+3n4qVLJCQkkJCQwNGjYezYuZMOFdai/L5pM1evXSMpKYno6GgWLV5CTEwMb3d/q8r1K/J+zx4cDDvOwbATPI2L5+c160lJS+edtxTpGldv2MSsRcuU8rsOHOb85avEJyYRn5jEoWMn2bZ7L51DVR6c+5EPOX3+EonJKdy+F8Gk6T8il8sZ2Kdntbbp3bs3R44cUbNNWlqacs+TdevWMX/+fKX8vn37KtnmaBXbbNq0iWtqtln8zDa691HJOrATqw7dsAjtgoGrO/ZDxqBv50B2mCITld3A4Tj990ulfP61i5g3bYVV5x7oOzhh7B+Ew/CPKXr4AGmWYrY9O2w/EnMLHIZ9jL6zK6YNm2LbawBZR3VnxpKYmmDRIACLBoqBsIm3GxYNAjByV4Qk+f8wgQbr/qeUf7pqC8aeLgTO+wqzAB/chvXFfXhfYhaqsuY8+ek37Dq3wmfiKEz9ffCZOAq7ji14skxz0orKHD6Xz7uh5jQOMsLNUY+P3rOmtEzO+ZuqjvlH71vTv6vqsyGRgKezPp7O+uhJRFhbSPB01sfRVt1TLBJBuxATzlwvpJqlOEp2h6XTr4cDLRpZ4OlqyISRbpSUyjh1KVsp88WHbgzrq9pz4r1udgzp7cjidfGkppdhbaGHtYUeRoaqn9KR/ZyoW9sURzt9/H2M+XqsBybGYo6f1+yxahQ6nLsXt3Pv4nYyk6MJ3zmLvKwk6rdSeDPP7lvAkd8nKeXrtxpAblYi4btmk5kczb2L27l3cQch7VWZui4e+oknEWfISY8jNT6CsD+mkpbwgPqtBla5fmUuRshoU09MgLsIByvo1UpCWTncfqwybO9WEjo1FKvV8XUR0bqOGDsLRXpRH2cRFyIUdUrLITVb/Sgth8ISRaYgbdRtPZSoqzuIurqD7NRoLh6YTX5OEgFNFclerhxZSPifk5XygU0HkJ+dyMUDc8hOjVbUvbaTem1UtqnTcggJj85zK3w12akx3ApfTcKjC9RpNaRa21x5KKdloIjarmBnAT2aiCiTwv1YVe+2R1MR7eqpOspXH8rxdlSko7UxV/zr5QhXolR19PUUaxgcrBR/W5kpbG9RTc6Af+JZAXRsKMbDQYSVqUKnjsFivBxFau2+Cl3+6nsj8Pp4qYXa1SESidi9ezeffvopbdu2RSwW061bN5YtU3U6+vfvT3R0NJMnT6a4uJi+ffvy8ccfqy1AHjFiBLdu3WLIkCHo6ekxfvx4tcW6oEg/OnXqVMaOHUtGRgYeHh5MnToVAFdXV2bMmMFXX33F8OHDGTJkiDJ9qiYmTJjA8OHDmTx5Mh9++CEmJibMmzePSZMmYWpqSr169aqkuH3ORx99xM2bN+nfvz8ikYiBAwcyduxYDh06pJQZNWoUp06donHjxuTn53Py5ElCQ0M5ffo0kydPpk+fPuTl5eHq6krHjh2xeNYB/eKLL0hKSlJuVDdixAh69+5NTk41u8tU4PDhwzg7O6Onp4e1tTUNGjRg6dKlDB06VG3zu7Vr1zJixAgaN26Mv78/c+fOpUuXLtW2v2jRIkaMGEHLli2xs7Nj8uTJ5Oaq7ww0c+ZMxowZg6+vLyUlJRrXhPTq1YslS5Ywb948PvvsM7y9vVm3bp3WEJW/k/ZtW5Obl8fvW7aRmZmFl6cHs6Z/g+OzfQ8ys7JITVO5z2VyOb9u2EhySipiiQQXZyc+HPaBco+K58QnJHD3fgT/+37aC+nTrl1bcvNy2bT5D7IyM/H08uT7GTNwdHyuT6aaPnK5jHXrN5CcnIxEIsHZ2ZkRw4ep7cNQUJDP0qXLyMrKwsTUFF9fX+bN/R/+/ro9Dh3atCI3L5/ftm5/Zht35nw3Fadn+3dkZGWRmqYaKMrlclb/tpnklFQkEjEuTk6MGjJYuUcFQGlZKWs3/UFicirGRkY0a9yQqeM/xcys+g0127VrR15eHps3byYzMxMvLy9mzpiBo6PjM9tUfVbr169Xs83w4cPVbJNfUMDSpUvJzMrCVGmbudXaJu9COBJzc+z6DkZibUNp3FPi53xDeboiDEjPygZ9W9VMcW54GGJjY6y6vov9B6OQFRRQeO8maZt+VcqUZ6QR9+NUHIaOwWvuCsoz08k6tJvMPdt06mIZUpcWx1UpmIPmK74L437bye2RUzB0tsfYXbXmoehJPFfeGU3Qgil4fjyYksRU7o3/UblHBUDWhRvcGDwB/xmf4z/jMwqj47gxaHyN9qgA2H86HwN9EcN6WmFqLCY6rpQ5a9PV9qiwtZKofR9Ym0uY9ZkqDK5HW3N6tDXnfkwJP65WvWd1/Qyxs9YjvJoF2hXZfigdQwMx//2PC2amis3vvlnwWG2PCnsbfbVBytsdbNHXF/P1fz3V2tq0J4VNexTP2c5an8kfuWNhJiEnT0pkdCHjf4wmNUOzN8y/UXeKC7K4eOQXCnNSsXWuTc8xq7CwUSQFKMhNIzdL5TW2tHWn15hVhO+aze0zmzC1dCC0z9dqe1SUFOVyfOt3FOamYWBsjr1bEO999jtOnvWrXL8yZ+/J0NODHs0kGBlCQpqcjcfU9xqwNAW5XNVxjkuTs/20lA4NJbQPFpOVB3+errrXwIviU787xYXZ3DjxC4V5aVg71qLL0BWYWytsU5SXRn62yjbmNm50GbqCSwfnEHFxMyYWDjTvMVW5RwWAo2dD2vdfwLWwJVw/tgxzG3c6DFhQ7R4VABcfyNGTQNdGYowMIDEDtoSr71FhYSJSe4cTMmD3RRnt6oppW0dEVgHsVIKlrQABAABJREFUvqDaowLA2RoGt1cNlDsFK36Lbz+WceCKdhv+U8/KzEhEn9ZizI2huBRSsuVsPC7VmX3pTXpvXhXC5ndVEcn/jlW+AgL/x4l7WH26xVeFe60gHkc/et1qAODt60di5ItnBfuncPGvT0x16V1fET6+vkT271q94CvAf+sRDujXPATtn+btskgGT3nBpPv/EJtmu9J9xJ3XrYaSg2vrsVzzljevnI+7wbTftIf+vWpmDNFn7o43Y0XupL5iZm97MzIOTekneeOe05uiz4whr2+T5dzrNdsHSBsWjWq2i/v/Jf5RT4WAgICAgICAgIDAvw35X8rj9e9GGFT8S4iNjSUoSPMmW6BYIO/h4fEKNRIQEBAQEBAQ+HcipJStijCo+Jfg4uKiM0OTi4uL1nMCAgICAgICAgICL4MwqPiXoKenp9xvQ0BAQEBAQEBA4B9E8FRUQRhUCAgICAgICAgICLwAQvanqgiDCgEBAQEBAQEBAYEXQFhTURUhpayAgICAgICAgIDAC5B55+xL1bep1/pv0uTNQfBUCAgAfT57M/aFANi51I9RszJetxoArJ5qyycLa7654j/NTxMsmbyqqHrBV8D/RhvTf+LT160GAFvne74x+0KAYm+IN2XfjLfLIrnZpU31gq+I4KNn+O/87NetBgA/T7Tit/DXrYWKIe1gyb43Y55z3DsiftzyZuxT8fUAyRuzfwco9vB4U96bIe1e37UFT0VVBIsICAgICAgICAgICLwUgqdCQEBAQEBAQEBA4AUQNr+rijCoEBAQEBAQEBAQEHgBhPCnqgiDitfEsGHD2LBhg/JvGxsbmjRpwty5c6lfv/5r1OzF8fLy4ulTRWy5kZERnp6ejBw5kokTJyJ6gZRrXl5efP7553z++ef/kKYvTv+3bOjc0gJTYzEPn5aw+s804pJLtcp3amFBaFNzPJwNAIiOK2HTvgwexZYoZYJ8jejZ0Rpfd0NsLPWYszqJy3cKqtXlnTbGtA02wsRIxOPEcjYfKSAxXXvMr4udhHfbGuPppIedlYQtYQUcv1KsVf6tFkb0aW/KsctFbD1WqFOX7i0MaVXPAGMjEU+TpGw9UURyhvaYXydbMT1aGuHuIMHWUsz2k0WcuqFuR19XCZ0aG+LhKMHSTMyqPQXcji7XqcdzOoXo0SxAD2NDiE2VsedcGSlZumOz63qL6dJYH1sLERm5co5cKePeE9U9TB5oiI151R+N8/fK2XOuTGu773WxpGMzM8xMxDyMLWXtzkziU7TLuznq06+rJd5uhjjY6LFhTyYHz+SpyRgZiujf1Yom9UywNBPzOKGUDXuyiI7T/i4+p09Hczo0NcXUWMyjuFLW78kmIVW7XV0d9HivswXervrYW+uxcX82h8+pv5+LJzlib1315yPsQj7r91Zdg2PTujE+X4zEslFdjFwcuNp3LCl7j+vU26ZNE4Lmf4VZUC1KElOJXrCG2FVb1GSceneh9vRxmPh6UBgdS+R3i0jZc0xnuwC27/TC4f2B6NvYUvz0CQnLl1Jw97ZWebt3emPXsw8Gjs6UpqaQ8sdvZB07oiZj2bodzkM/xMDZhdKkRJLWryLn3JlqdXlO95ZGtKpvgImhiCfJUrYdKyRJx2fK2VbM262M8HDUU3ymThRx8nqJmkyXpoYE19bH0UZCWbmcmAQpu08XkZqlvd2rpzZx8civ5OekYe9Si879p+JRq7FW+aeRlzn25xzSEh9ibuVA864fEtJuoPK8tLyM84dXcvv8bvKyU7B18qZDn4n41m1bI7vcPbeZG6d+pTAvDRtHP1r1nIqLj3Z9EqIvc37vHDJTHmFq4UBw6IfUbTlAeT76zlGuH19JTnosMmk5lvaeBLcbjn9Izxrp06auiIa+Ioz0ITETDl+VkZ6ru46/G7SrJ8baDLLyIfy2jMgKS6BaBorwdxNhawHlUohPhxO3ZGTmaW/z/sXN3DmzlqK8NKwc/Gj+9hScvLXbJSnmMpcO/o/s1EeYmDtQr+1IApsNUJN5fPco18OWkpsZi4WNByFdxuFVp3NNzPLGvTevhFecUvaXX35h3rx5JCUlUadOHRYvXkybNtWvFzt37hzt2rWjbt26OjdJ/jsQhlmvkW7dupGUlERSUhLHjx9HT0+PHj16vG61/hIzZ84kKSmJiIgIJk6cyNSpU1m1atXrVuul6N3JinfaW7H6zzQmL4gnO7ecaf91wchQ+xdJ3VrGnL2Wx3fLEpiyMJ70rHKmjXXBxlKilDE0EPMkQTFAqSndmhvRuakRm48W8OP6HHIKZIwfaIGhgfY6BvqQni1j56lCsvN1L/LzcpbQtqERcSnVd+I7NTGgfSNDtp0oYt6mfHILZHza1xRDfR266IlIz5Gx92wxOVp0MdQXkZAmZduJF1uI3a6BHm3q6bH7XCnLdpWQXyTnw+6GGOjQx8NBzKCOBlx/KGXx9hKuP5QyuJMB7vaqZ/vTrhK+31ikPFYfUHTY7sRoH8i9296Ct9tasG5XJlOXJJOTK+Xr0Q463xlDAxEpmeX8cTCLrFzN9h/zvi31ahvx8x/pTJyfxO2oYr4Z7Yi1hUSj/HN6tDWje2sz1u/N5tufU8nJkzJlpB1GBrr1Sc0sZ8vhXLJyNd/rtz+nMfbHJOUxa006AJfuaH52ElMTcm9Hcm/cTJ36PsfYy40m+1aRefYaZ5v04tH/VlBn0dc49e6ilLFqHkzDzYtI2LSHMyE9Sdi0h0Z/LMaqqe5JGat2HXD96DNSNm8k8uORFNy5hc+P89C3d9Aob9ujF84jxpC8cR0PRn1A8sa1uH0yAYvmLZUyJoF18Pp6OpnHjxD58XAyjx/B6+uZmAQE1eh+Ozc1pEOIIduOFzF3Ux65BTI+ed9M52dKX19ERo6MPaeLtH6marnrcfpGKfM35bHsz3zEYvj0fTOtn437Vw4StnU2rbp/zIff7sa9Vghblo4iJyNRo3x2ehxbl43GvVYIH367m5ZvfcTRLT/y4JpqwBW+ZzHXT2+l68BvGTPjII3aDmD78k9Ijr1frV0e3jzI2b2zCen0Ee+P34WzT2P2rxlNXpZmfXIz4jmwZgzOPo15f/wuGnUcw9k9PxJ9W6WPkbElIR0/os+nW+j/xR4CmvThxNapxEZWPwBsESCimb+II9dkrAuTkV8kZ1B7MQY6pmddbaFPSzF3n8hZc1jG3SdyercS42KjkvFwEHHtkZz1YTI2n5IhFsGgUDH6Wj7eMbcPcunAHIJDx9Drk504eYVwZMMY8rM12yUvM56jGz7CySuEXp/spEHoaC7un8Xju0eVMimxNzi5ZQJ+Dd+l96e78Wv4Lif+mEBq3K1q7fKmvTevCjnilzpehK3/j73zjo+i6P/4+0rKJXfplTRIgRBa6CX0DtKkCIIoIgL29igCNmyggqI+iqACIggoICIdpBM6hBIChISQ3uulX/n9cXCXzd0lQXwQ/e379doXZPc7s5+d2dmbme+U9et58cUXmTt3LufOnaNHjx4MGTKE5OTkOsMVFRXx6KOP0q9fv7t51AYjNir+Ruzs7PDx8cHHx4fIyEhmzZpFSkoKOTmGyubFixfp27cvCoUCd3d3pk+fjlqtNobv3bu3Wa/+qFGjmDJlivHvxo0b8+GHHzJ16lRUKhWBgYFmlf3U1FQmTJiAm5sbjo6OdOjQgRMnThiv//7777Rv3x57e3uCg4OZN28eGo2w8qNSqfDx8aFx48ZMmzaN1q1bs3u36YOVkJDAyJEj8fb2RqlU0rFjR/buNfUo9u7dm5s3b/LSSy8hkUgEHo7o6Gh69uyJQqEgICCA559/ntJSU8/p119/TVhYGPb29nh7ezN27Ng7yAXrDOvlwsbd+Zy4UEpyRhVfrMnCzkZCz/Yqq2EWr8pi55FiktKqSMuuZsnabCRSCa2bOhhtzsWVsXabId6G0q+Tgu1Hyzl3tYr0HC0rfldjawOdW9hZDZOUoWXDvjJOXa5Co7Hea29nA9NGqFi1vZSyivpXXunT1o5dJys4f11DRp6OH3eVYyOX0CHcegsnOUvL5kMVnLlajcZKnfxykoat0ZWcv94w78RtureSs++chtgkHVkFetbvr8ZGDm1DrVe4u7eScT1Vx4EYDTlFeg7EaLiepqN7K1PtoLQC1OWmo3mgjNwiHYkZ1htoQ3uo+PWPIk5eKicls5qv1uViZyule1tHq2ESUqpYs7WQ6Jgyqi08uo1cQudWDqzZVkhcYiVZeRo27C4iO1/DwG7W30WAwVFKNu8v4XRsBalZGr75pQBbGwndIhVWwySmVrN2RzHHL5Sj0Vp+H0pKdRSpTUfb5vZk5mmIu2HZc5Kz6xDX3l5M5uY9deq9TdD0CVQkZ3D5lQ9RX0kkZfkGUlZuIvjlqUabJs89Ru7eaBI+Xkbp1UQSPl5G7r7jNH7usTrj9hwznvyd28jfuZXKlJukffMl1TnZeAx/0KK9W7+B5G3fQuHBfVRlZlB44A/yd27F66FJpjgfHEfJ2dNkr1tNZUoy2etWU3LuDJ4PjmvQ8/ZpZ8euExWcj68mI1fHjzvKsJVL6Ni8jjKVqeXXg3WXqa82lnI8toqMPB1pOTpW7yzDzUlKoLflsnFizwoiu4+hbY9xePiGMHD8XJxcfTh7cK1F+7MH1+Hk5svA8XPx8A2hbY9xtIkazfE9y402F4//RtSQmYS26oWrZwDte08kOKI7J2rYWOP8wZU07zSGiM7jcPMOofvIOShdfLh0zLKe2GPrULr60n3kHNy8Q4joPI7wjqOJOWi6l19oZ4JbDcDNOwRnj0Da9HgUd99mZNw4W6+eTs0kHI3VczUVcorg9xN6bGTQIsh6I71TMwk3MiE6Tk9eieHfpCzD+dusO6jjwg09ucWQXQhbT+pwdpTg42Y5zktHfqBp+9E06zgOF68Qugybg6OzD3En1lm0jzu5DkcXX7oMm4OLVwjNOo6jafvRXDxsSpfYo6vwC+1Gm97TcfEKpk3v6TQK6ULs0VX1psv99t7cK/QSyV0dd8Knn37KE088wbRp02jevDmLFy8mICCAJUuW1BluxowZTJw4ka5du97NozYYsVFxn6BWq1mzZg2hoaG4u7tTVlbG4MGDcXV15dSpU/zyyy/s3buXZ5999o7jXrRoER06dODcuXM8/fTTPPXUU1y5csV43169epGens6WLVs4f/48r732GjqdoeK0a9cuHnnkEZ5//nkuX77M0qVLWblyJR988IHFe+n1eg4cOEBcXBw2NqbuMLVazdChQ9m7dy/nzp1j0KBBDB8+3NjK3rRpE/7+/kaPR0ZGBmBoWA0aNIjRo0dz4cIF1q9fz5EjR4zpcPr0aZ5//nneffddrl69ys6dO+nZ8+7do97uclyd5cRcMQ0D0mggNqGcZk3sGxyPra0EmRRKyv780oQeLlJclFJib5iG0Gi0cC1ZQ4jf3Y9gnDjIkQsJVcQlWR+icxt3ZwnOSilXkky1X40WrqdqCG5Ud6/5/wI3lQQnBwnxqab01eogMUNHkLf1z1uQt5RracI8uZaqtRpGJoW2YTJOX7Wej15uclyd5Fy4auqt12jhckIFTRtbb/zVh0wGMpmE6mphBb+qWk+zJtbj9XSV4eok42K8aUiMRgtXblQSFvTn9VjS1z1SwcHTDW8k14dLl0hy9h4VnMvZfRjn9i2RyA3vvGuXSHL3CteJz91zGNeuba3GK5HLcQhrSsnZk4LzJWdO4RjR0nIYW1t0VcJhRbqqKhyaNTc8POAY0ZKSM6dqxXnSapw1cXeW4qyUEmehTDX5C8p3TRS3PGalFjoPtJoqMpJjaRIhXDs/OCKK1IRzFuNLTYwhOCJKaN+iBxlJl9Bqqm/FW43cRtg4ktvak3K97kq8VlNFTlosAU2F8Qc0jSIrybKezJsxZvaBzbqTkxKLVmv+fdPr9aTGH6Mw+0adQ6oAXBxBqZCQmGlKO60OkrPB38N6OD93YRiAxAw9/h51eAtv/XRWWGijazVV5KbH4hcmfE6/0Ciyb1pOl+zkGPxCa9mHRZGbFovuVrpkJ5/HL7SbwMY/LIqsZMtx1tRzP703/yQqKyspLi4WHJWVlWZ2VVVVnDlzhoEDBwrODxw4kOjoaKvxr1ixgoSEBN5+++2/XLs1xEbF38jWrVtRKpUolUpUKhVbtmxh/fr1SKVS1qxZQ3l5OatWraJly5b07duX//73v/z4449kZWXd0X2GDh3K008/TWhoKLNmzcLDw4MDBw4A8NNPP5GTk8PmzZvp3r07oaGhPPTQQ8ZW7QcffMDrr7/OY489RnBwMAMGDOC9995j6dKlgnvMmjULpVKJnZ0dffr0Qa/X8/zzzxuvt2nThhkzZtCqVSvCwsJ4//33CQ4OZsuWLYBhTolMJjN6PHx8fAD45JNPmDhxIi+++CJhYWF069aNL774glWrVlFRUUFycjKOjo4MGzaMoKAg2rZtK7jvn8XFyfBjXlhr+EdhsdZ4rSFMHuFOfpFGUNG8U5wdDcW0uFTYQ15cqsNZeXdFuGOELYE+cjbtr3sOxW2cHAz3KykT/kiWlOlxcrz3K2GoHAz3LCkX6lGX61EprOtRKiSoaz2yuswUX21aNJZhbwunr1n3orioDBXM2kNRitRa47U/Q0WlnqtJFYwe4IyrkwyJBLq3cyQ00BbXOuI16RG+w0VqHS53+d7UpEOEAgd7KYfONOwdagh23h5UZuUKzlVl5yG1scHWw9Vg4+NBZZZwP5fKrDzsfDytxitzckYik1NdUCA4X11QgNzVcrdwyemTuA8ejiKsKQCKsGa4DRqK1MYGubMLAHJXN6oL8mvFmW81zprcLjclFsq3k5X38c8yureC66kaMnLNvW1l6gL0Oi1KJ3fBeUcnD9TFlodqlhbl4ugkrFErndzR6TSUqQ1pHNyiOyf2rCQ/Kwm9Tkfi5aNci/kDdVF2nVorSg16FCqhHgeVO2UluRbDlJXk4FDLXqEy6KkoNeV5ZXkJy+a0Y+msVmz7fgY9HnzDrDFSG8dbfUmltaallVbqcbSv41tjD6W16omllab4LNG/rZTkHD05FrYIqigrNKSLUpjuCpU75WrL6VJekmuWjgqlB/oa6VKuzjWPU+lBuZW0vs399t7cS/QS6V0d8+fPx9nZWXDMnz/f7D65ublotVq8vb0F5729vcnMzLSoLT4+ntdff501a9Ygl9+76dPiRO2/kT59+hhdV/n5+Xz99dcMGTKEkydPEhcXR5s2bXB0NA2biIqKQqfTcfXqVbOXqy5qTvyWSCT4+PiQnW0omDExMbRt2xY3N8s/fmfOnOHUqVMCz4RWq6WiooKysjIcHAzDel599VWmTJlCTk4Oc+fOpW/fvnTrZur1KC0tZd68eWzdupX09HQ0Gg3l5eX1jgc8c+YM169fZ82aNcZzer0enU7HjRs3GDBgAEFBQQQHBzN48GAGDx7Mgw8+aNRVm8rKSrOeADs7O3p2UDJjvGlM9QdLLY8FNXgsG7Y506h+LnRvp+KtL9OormP4kSW+/I8pP778+dYsQAtR6O9inyhXlZQJAxz5bG2x1eETAIuedTL+f8nmUstSJA1Nlbvn3cdNv8Yrdt7qyqt1c4n5KTMsPoOVQB2bybiaoqOkVr35hw8CjP9f8L2hTNWOQ2Lh3J3y1do8Zj7kzjdv+aPV6rmRVsXRc6U08Tf15HWLVPDEKBfj35/8YHkDxYakzZ3Qu4MD569VUFjyF2/OZZaQEvPzlmwakti1bOoq15lrViJ3daPp50tBYmiA5O/egff4SaCrUXDM4rSe0p8+72z8/9ebDENaa1v+1XNAH+qnwM9Txqdr65j9a7iz4C+9Xo+krqUzJeb2htOG8wPGz2X7qjf45q0hIJHg6hlAm6jRnD+6qUG6a9/bEH1diVPrmt48Hls7R8a//CvVlWWkxh/j6JYFOLn54xfaWRD01TGmxvf6Q3/+/b6T8j+ovQQvF1i1t5771U4Cvd7CyZrmZgFuXahxvnZeor+DxVbur/fmXnC3S8rOnj2bl19+WXDOzs66F7l2Xuj1lvNHq9UyceJE5s2bR9OmTe9K450iNir+RhwdHQkNDTX+3b59e5ydnfn222+tvixgerGkUqmxIN6mutrcxVtzGNLt8LeHNykU1sdWA+h0OubNm8fo0aPNrtnbmyp3Hh4ehIaGEhoaysaNGwkNDaVLly70798fMDQ6du3axcKFCwkNDUWhUDB27FiqqupevUan0zFjxgyL3ofAwEBsbW05e/YsBw4cYPfu3bz11lu88847nDp1ChcXF7Mw8+fPZ968eYJzb7/9NtdKJ3MtKcV4zkZuSGMXJ5lgsqqzSmbmvbDEyL4ujBngyjtfpXMzvf4Vemrz7veFJi0ygxYnpZSiUtO9nRylZt6LOyHIV4aTo5Q3ppoqODKphLBAOX062PPUR4ae1/mrTfN45Lc6xp0cJBSXmt49lUJCSem9aVZ8vtHUKLytR+UgEXgrHBUS1OXW9ajL9ahqtTuVCiyGcVFKCPWT8uMe83x87dMM4/+N74xKSmFJjXxSysy8BXdKVp6GeUuysLOVoLAzxP/CIx5k55s8J2cvV5CQYurFk996b5yVMkGF30kptTqx907xcJHRMtSOxavz6ze+Ayqzcs08Draebuiqq6nKKzTYZOZi5yPs7bTzcjPzcNREW1yEXqvBplYnitzFFU0t78Vt9FVVpHy6gJTPP8HG1Y3q/Dzch45AW1qKpsjQlawpyMfGTdhTW1ec81eZKvbGMuUopbhG+VY5SCku+2vK1Li+ClqH2PDZejWFastxOihdkUhlqIuF6VdWkmfWq3wbR2cPSouEvdGlJflIpXIUji4GG5Ub4575Gk11JWXqQlQuXuzftBAXD/86Nds7GvTU9kqUq/PMvBHGZ1B5WrSXSuXY3dIDIJFKcfYIAsDDrzkF2Ymc3bfMrFHx3S5TOZHdal842oO6hrfC0U5icTjZbdQVBm9FTRztzD0eAAPbSWjqJ2HVHzpKrDi37R1ckEhlZh6EcnU+CqXldFGoPCymi0Qqx97BxWCj9KC8RJiXFeo87K3EeZv77b25l9ztkrJ2dnZ1NiJu4+HhgUwmM/NKZGdnW+xgLikp4fTp05w7d844VFyn06HX65HL5ezevZu+ffvelXZriMOf7iMkEglSqZTy8nIiIiKIiYkRTEg+evQoUqnU2PL09PQ0zj0AQ+v00qVLd3TP1q1bExMTQ36+5UpBu3btuHr1qrHBUPOQSi2/Pq6urjz33HP85z//MTZ6Dh8+zJQpU3jwwQdp1aoVPj4+JCUlCcLZ2tqi1QorX+3atSM2Ntbi/W1tDb20crmc/v378/HHH3PhwgWSkpLYt2+fRW2zZ8+mqKhIcMyePZuKSj2ZudXGIyWzioIiDW2amWqechm0CFFw9Yb1ZVnB0KAYO8iV975JJyHFfHxkQ8gp0BmP9FwthWodEU1MjUOZFJoGyklIu7NJzTWJS6rm7W8Leff7IuORlK7hxKUq3v2+yNi7lluoMx6ZeYaJueFBpv4ImRRC/eUkpt9dxbmh5BXrjUdWgZ7iMj1h/qZhQDIpBPtKuZllveJ8M0tHmJ9w6FBTP5nFMB2ayVBXwJVk82tZeRrjkZpVTUGxhtZNTQ11mcywhPC1pD/3HtSmskpPYYkWR4WUNs0UnL5kqnlUVOnJytMaj7RsDQXFWlqFmX60ZDIIb2JH/M2/Rk/P9g4UqXWcu1p3mbhTCo/H4NFPOL7bc0B3is5cQn9rkYiC4zF49BMOWfHo352CY9bHgOs1Gsrir6Fq11FwXtWuI6WX6/l2arVU5+aATodr734Un4g2dkGXXr6Eqp1wTL6qvfU4cwp1xiOjjjJ14y7K920e6qcgMsyGz39Wk1dkvUzI5Lb4BrbgxmXhXJYbcdH4h1iep+IfHMmNOOGY7huXj+DbuCUyubAzS25jh5OrNzqthitnd9M0su7VaGRyWzz9WpByTRh/6rVovBtb1uMTFElqLfuUa0fxDGiBTGZ9KS09erQa806DArXpyC02dDo08TF19kmlEOhlWALWGml5wjAAwT4SUnOFDZFB7SSE+0tYvU9HUR3Tk2RyWzwatSDtuvA5069H4xVkOV28AiNJr2WfFn8UD78WSG+li1dgG7M4065H4x1ofY7SbT3303tzL7lXE7VtbW1p3749e/YIF7rYs2ePYETIbZycnLh48SIxMTHGY+bMmTRr1oyYmBg6d+5sFuavQmxU/I1UVlaSmZlJZmYmcXFxPPfcc6jVaoYPH86kSZOwt7fnscce49KlS+zfv5/nnnuOyZMnG1umffv2Zdu2bWzbto0rV67w9NNPU1hYeEcaHn74YXx8fBg1ahRHjx4lMTGRjRs3cuzYMQDeeustVq1axTvvvENsbCxxcXGsX7+eN954o854n3nmGa5evcrGjRsBCA0NZdOmTcTExHD+/HkmTpxo9JbcpnHjxhw6dIi0tDRycw1f6VmzZnHs2DGeeeYZYmJiiI+PZ8uWLTz33HOAYV7KF198QUxMDDdv3mTVqlXodDqaNWtmUZednR1OTk6Cw1pPwdaDhYwZ4Ern1o4E+try7CRvKqv1HDpj6mV8/hEvJg039eSM6ufCxGHufPVTNtl5GlxUMlxUMsESnva2Ehr72dLYz9Ao8nKX09jPFg8L6/7f5o+T5QztpqBtU1saecp4fLiSqmo4EWuqHE4druTB3qZGkEwKAV4yArxkyGUSXFVSArxkeLoain1lFaTnaAVHZbWe0nId6TnWGwj7z1UysJM9rUPl+LpLmTxYQbVGz+krph/lyYMVjOheozIrBT9PKX6eUuQyQ2++n6cUDxfTJ8jWxmQDhgmsfp5SXFV1f3yPXNTQJ1JOi8ZSvF0ljOttQ7UGzl03PcNDvW0Y3NGUvkcvaQnzl9KrjRxPZwm92sgJ9Zdy5KKwEicBOjSVceaaBl0DOo23Hy5hVD9nOrZUEOBjw9PjPais0nHknKmW8MwEdx4e4mJKGxkENbIhqJENchm4OssIamSDt7tJb5um9rRpZo+nm5xWYfa8NdOb9JxqDpxSUxc7j6oZ0VtFhwh7/L3lzBzrSlW1nugYU2Nk5jhXxg8yDXGTySDI14YgXxvDe+MkI8jXBm93YSNMIoFe7R04fLYMXT2OD5mjA05twnFqEw6AQxN/nNqEYx/gC0Cz91+mzYqPjPY3l61DEdSI5p+8jjI8GP8pYwh4fAyJn5pWfkn67yo8BkQR/J8ncWwWTPB/nsSjX1eSvvyBusjZuB63wcNwGzQUu4AgGs18DhsvL3K3bgbAd+oMAl+da7S38wvAtd9AbBv549CsOUFz3sG+cRMyVphW0cvZvAFV+454PTQRu4BAvB6aiKptB3J+/aXuhLnF/rOVDOpsT5tQG3w9pEwe4kCVRs+pOFOZenSIAyN6mLq8ZVLw95Th7ylDJgMXlQR/TxmeNcrU+P4KOja3ZcW2Uiqr9Dg5GBY2sLHyqek84HFijmwg5sgGcjMS2LP+Q4ryM2jXy7Cfwf5Ni9iy/DWjfbteEyjKS2fPz/PJzUi4FXYjXQaYVulKSzzPlbO7KchJITn+NOu+mIZer6ProGn1pkubXlOIO7mBuJMbyc9K4Mhv8ykpzKBlF4OeY9sXsXftLKN9i64TKClI5+iW+eRnJRB3ciNxJzcS2cuk58wfS0m5dpSivBQKshOJObiCa6d/o2n7EfXqOXlVT1SEhGZ+4OkMwztLqNZC7E3Tx2F4Zwm9W0sEYYJ9DMvRuqsM/zb2MZy/zeD2Elo2lrD5mI4qjcEb4mhv8mLVpmX3x7h2eiPXTm+kMDuB49vmoy7KILzTeABO7fqUg7+Y0qV5pwmoC9M5vm0BhdkJhrBnNtGqhyldWnR7lLTr0Zw/+C2F2YmcP/gtadeP0SLq0XrT5X57b+4VeiR3ddwJL7/8Mt999x3Lly8nLi6Ol156ieTkZGbOnAkYOkwffdSQV1KplJYtWwoOLy8v7O3tadmypWBY/V+NOPzpb2Tnzp34+hp+VFUqFeHh4fzyyy/07t0bMKy89MILL9CxY0ccHBwYM2YMn376qTH81KlTOX/+PI8++ihyuZyXXnqJPn363JEGW1tbdu/ezSuvvMLQoUPRaDRERETw1VdfATBo0CC2bt3Ku+++y8cff4yNjQ3h4eFMm1Z3wfb09GTy5Mm88847jB49ms8++4ypU6fSrVs3PDw8mDVrFsXFwh2D3n33XWbMmEFISAiVlZXo9Xpat27NwYMHmTt3Lj169ECv1xMSEsL48YaPp4uLC5s2beKdd96hoqKCsLAw1q5dS4sWLe4oHSzx695CbG2kTB/niaODYfO7d79Op6LS9GPg4WojqGwO7u6MjVzCa0/4CuJavyOf9TsM3qCQQHvee97PeG3qaMMwj30nivnvGsuT0HYer8DGRsLEwY442ktITNfw2bpiKmt0rrk5CYfDuaikvDXNxfj3oC4KBnVRcPVmNQvX1LNbUx3sPVWFrVzC+L4KHOwNG3X9d2MplTVG3rmppIJxxM5KCbMnm5Y/7d/Bjv4d7IhP0fD5L4YKd5C3jBceUhptxvQ29Pgfj61i9S7rE90PntdgI4dR3W1R2EJKto7vtldSVUOPi1Ii0HMzS8faP6oY2NGGgR3k5BfrWbO3ipQcYcsh1E+Kq0rK6asNG8K2ZX8xtjYSnhjthqNCxvXkSj78Nlvwzri7ygXvjJuTjI9fbmT8e0RvZ0b0diY2oYJ3lxgWZVAopDw8xAV3FznqMi0nLpaxbkch2noq81sPqbG1kTBlpAuOCikJKVUsWJ5LRVUNPS4ywXvjqpLx4fOm+UXDeqoY1lPF5cRKPvjW1CXbMtQOD1c5BxswQdu5fUu6/vGj8e+IhXMASFm1iQtPzMbO1xNFgKnMlCelcmr4dCIWzSboqUlUpmcT+9IHZP5qWqa64Ng5zk16mWbzXqTZvOcpS0jh3MSXKDxpfRM7gMKD+5A5OeEzaQpyN3cqbt4g8Y3XqM42pLWNmzu2XjWGFEileI4ZT4B/IHqtBvX5c8S/+BRVWaahCGWXL5H04Tx8p0zD57FpVGWkkfTB25Rdadia+ntOVmIjlzC+/60ylaHlvxvUgjLl6lS7TEmZ/ViNMtXRnv4d7bmWouHz9YbGZs9IQ8P+pQnCpYd/3FHG8Vjzdzqi41DKSgs4su1r1EXZeDZqyoTnluHsbvheqYtyKMo3ecddPAIY/9wy9vw8nzMH1qB09mLghLmEtx9ktNFUV3Lwt8UU5KRga+dAaKtejJj6MfYOTmb3r01Y5FAqSws5vecrSotzcPcJY9gTS1G5GfSUFeegrrFnhZO7Pw9MW8rRLQu4ePQnHJ286D5yLiGta+ipKufQpndRF2Yit7HHxasJ/SZ+TFjk0Hr1HLuiRy6HwR2k2NtCWh6sPWBoCNzG2VGCvsYMmbQ8+DVaR6/WUnq1klCgNvydXmNwQPswQ0Nwcj9hK+L3E4alZmsT3HooFWWFnNv3NWUlObh6hzHwsW9QuRrSpbwkB3WhKZ9Ubv4MfOwbTmxfQNzxn3Bw8qLLsDk0aWlaTcg7qC19xi/izJ7PObv3S1RuAfSdsAivgDb1psv99t78Gxk/fjx5eXnGFTJbtmzJ9u3bCQoyDOPLyMiod47qvUCirz0oX0Tk/yGjn7/+d0swsumLUJ780PIk23vNt3PcefZTC0uQ/E3892VnZi378ytp/ZV8NF3B+P/c/LtlALB+YRCTZqfVb3iPWDPfj202lr2F95oHqq8SM7D+XWfvFZG7D/PMwsK/WwYAX/3HhVUH/24VJh7tBZ//fn9USV4YLuGDdfdmSGd9zJ0g4+ONf/FCCHfBa2Ok981782ivv+/eKfF3txFfQFjDNsf8JyF6KkRERERERERERETugLtd/enfiNioEBEREREREREREbkD7nb1p38jYqNCRERERERERERE5A4QPRXmiI0KERERERERERERkTtA9FSYI6aIiIiIiIiIiIiIiMhdIXoqRERERERERERERO4AcfiTOeKSsiIiIiIiIiIiIiJ3QEJi4l2FDwkO/ouU3D+IngoREaD78Ptk0W3gyO+9GPfSjb9bBgC/fNaEx97KrN/wHvHDuz48t/jPb9z3V/Lli070Hnvs75YBwIENXRk69eLfLcPI9uWt7pu9ISJ3H75v9swAw74Z98ueImvm+7Eu+v7pV5zQTcKizfeHnldGSfhow/2xN8SssdL7RgsY9Kw88HerMDCl9993b71e9FTURmxUiIiIiIiIiIiIiNwBenFashlio0JERERERERERETkDhDnVJgjNrNErNK7d29efPHFv1uGiIiIiIiIiIjIfY7oqfgXM2XKFH744Qez84MGDWLnzp31ht+0aRM2NjbGvxs3bsyLL74oaGgcOHCAPn36GP92c3OjTZs2vPfee0RFRTVY6+14CgoKcHFxaXC4/zVTHw5ixCBfVEo5l6+V8Ok38dxILrNq37OrB4+OC8TPV4FcLiE1vZx1m1PYtT/baNOmhTMTRwfQLESJh7sdsz+4xOHjefVqGTfIhf5dVSgVUuKTK/luYx6pmdVW7ft1UdGro5IAH0MeJqZWsXZbPteTqwR2A6NUjOzjjIuTjNTMalZszuNKYmWdWkb1UdK7vQJHhZSE1Gp+3FpMWo7Gqr2fp5wH+ypp3MgGT1cZa3YUs/uYeTq6qqQ8NFBF6zA7bOQSMvM0LN9cRFKG9bgBhnSxI6qlDQp7CTcztfy8r4LMfOtjkH3cpDzQ1Y4AbxnuTlI2HqzgwLkqM7serW3o194OJ0cJGXk6Nh2sICFdW6eWKQ/5M6y/NypHOXHXS1j87Q2SUsut2j/Q34tBvTxpEuAAwLVENd/+lMKV62qjjcJeyhMTAune2Q1XJxvik0r5cvkNriaU1qkFYNJILwb3ckPpIONqYhlfr04nOd16/g7q6Uq/bq4E+dkDcP1mOT9szOTaDdMzTBrpxaSR3oJw+UXVPPLSFavxug8fhde4h7Fxc6fiZhJpS76g9NIFq/Yewx/EY+RobL19qcrOImvtKgr27hLYOHfvhe9j07D1bURVRjoZK5dRdPRwnenh1r0Dwa88gXO7ltg38uL0mKfJ2vJH3WF6dCRi4esoI8KoTM8mYdF3JC9bJ7DxeXAgTd95AYeQQMoSkrn61mdk/ba3znhrMrqfir6dHHFUSLmeUsXK3wpJy66jTHnJGTvAiSZ+Nni6yvlxayE7jwrfh8WveePpav4zv+eYmpVbiizGe3LfTxzd8T3qwhw8/UIZMnEOQU07WLQtKcxm17qPSL8ZS37WTTr3n8yQiXMENqcP/sz5o7+RnRYPQKPGLeg35iX8g1vXmR63iT32ExcOfk9ZSQ6u3qF0HT4H3yaW9QCkJ57k+NYFFGRdx8HJiza9phHRZYLxetyJn4k/+xv5WQY9nn4t6Dj4JbwC6tej1+s5t+8rrp76maryYjwDWtN1+Ju4eofVGS7p0m7O7v2C4vxknNwCaTfgBRq3GCCwiTv+ExePLKe8JAcXr1A6PzAbn8bWn/N+03PmwBpO7P4edVEOno3C6P/QHALCrNsnXzvJH78sICc9HpWLF50HTqNdr4eN17Xaao7tWMrFY5spKczC3acJvR/8DyEte9b5bPcS0VNhjuip+JczePBgMjIyBMfatWsbFNbNzQ2VStUg26tXr5KRkcGBAwfw9PTkgQceIDs7u/6A9zGTxgQwfpQ/ny69zrSXz5JXUMVn77ZGoZBZDVNSUs2qn28y89VzPPbcabbvzWT2C+F0autqtFHYy7h+Q82nS683WMvIvs4M6+3M9xvzeP2zdAqLtbw50wd7O+sftRah9hw5q2beV5nM/TyD3AINb8z0wc3ZpL9bpCOPj3Jn455CXluYTlxiBXOn++DhYv0Zh3Z3ZHBXB37cVsw7S/MoUmt59TFX7G2ta7G1gZwCLb/sKaGwxHKl3MFewtxp7mi1ehb9WMCc/+ayblcJZRV1T9zs38GWPm1t+WV/BQvXllJcqufZ0Q7Y2VgPY2sDuUU6thypoKjUcuOjXVM5o3vZs+tkJR+tKSUhXcNToxxwVVl/zodHNWLcMF8+//4GM1+/QH5hNQvfikBhb/1TG9nCiT+O5PLSO7E8M+ciWblVLHyzOR5utkabV58KoX0bZz78Ip6pr5zn9PlCFr0VIbCxxNghHjw40IMlq9N58b3rFBRp+OA/TerU07qZkoMnCpn9cSKvfJBATl4V77/SBHcXYeU0KbWCSS/GGY+n34q3GqdLr774zXyerJ9+5OpTT1B68TzBH3yCjaeXRXv3YaPwnTqDzB9XcOXJyWT+uBz/Z1/GqUs3o41D8xY0nvsO+X/s4upTj5P/xy4az30Xh/CIOtNE5uhA8YWrxL7wbp12t1E09qfj78vIP3KGIx1Hcf2jb2jx2Vx8Hhxoer4ukbT96TPS1vzG4fYjSVvzG+3WLsalU8MqzsN6KhnaXcnKLYW8+VU2RSVaZj/hUWeZsrOVkJ2vYd3OYgqKLZepN7/K4ekPMozHh9/lAnDiouVG7qUT29n503x6DpvJzHm/EtS0A6s/nU5hXrpFe42mCgeVGz2HzcQ7INyiTdKVk7Tq8gBTZv3AtDfW4ezmy48Ln6C4IKuuJAEg4fx2jv0+n7Z9ZzL6+V/xadyBHcunoy6wrKc4P5Wdy2fg07gDo5//lbZ9ZhC95QMSL5oaoxmJJwmJfIBh039g1NPrcHTxZft3T1BaVL+ei4e/I/boSroOf4MRT/+MQunBzhVPUF1pvXGfnXyO/etfJqTtCEY9t5mQtiPYv+5lslPOG20SL2znxPYFtOk1g5HPbMK7cXt2/zADdaHl57zf9Fw+tZ29P8+n29CnmPrGZvxD27P+yycpyrdsX5ibws9fTsc/tD1T39hM1yEz2bP+A66cNeXToc2LOXd4PQMmvMmT72ynbc8JbPrmWTKTL9eZJvcSPZK7Ov6NiI2Kfzl2dnb4+PgIDldXVw4cOICtrS2HD5t69RYtWoSHhwcZGRmAcPhT7969uXnzJi+99BISiQSJRFggvLy88PHxoVWrVrzxxhsUFRVx4sQJ4/XVq1fToUMHVCoVPj4+TJw40djoSEpKMno7XF1dkUgkTJkyBTD0xHz88ccEBwejUCho06YNGzZsMMZbUFDApEmT8PT0RKFQEBYWxooVK/6StBs3wo9VPydz6FguN5LL+OCzK9jZyRjYy3JFCODcpSIOHc/jZmoZ6ZkV/PJ7GglJalpHOBttjp/J59vVSRw6lttgLQ/0cmLTnkJOXiwjJbOa//6Ug52thO7tlFbDfLE6h91HS0hKryI9u5ql63ORSCS0DFMYbYb1dmLfiRL2nVCTll3Nys355BZqGBjlZDXeQV0d2HKolDNxlaRla/h2UxG2NhK6tLa3GuZGuob1u0s4camCaiudrw/0cCS/WMt3m4tJTKsmt1DL5cQqsgvq9gz0bmvL7lOVnE/QkJGnY/XucmxsJHQIt96qSM7S8duRSs5e06CxEn2fdnYci63mWGw1WQU6Nh2spECto3tr6xX5sQ/4snpTGodP5HMjpZz5X17H3k5K/x4eVsN88Pl1ftuVxfWkMpLTK1j4TQISCbRrZcgDW1spvbq4s/THm1yIKyEts4KVP6eSmV3JyIHeVuMFGDXAg3Vbs4k+W8zNtEoWfZ+Kna2U3p1drIb55NsUtu3PJzGlgtTMSr5YmYZUAm0ihO+aVqenoFhjPIqtNBYBPMeMJ3/nNvJ3bqUy5SZp33xJdU42HsMftGjv1m8gedu3UHhwH1WZGRQe+IP8nVvxemiSKc4Hx1Fy9jTZ61ZTmZJM9rrVlJw7g+eD4+pMk5xdh7j29mIyN++p0+42QdMnUJGcweVXPkR9JZGU5RtIWbmJ4JenGm2aPPcYuXujSfh4GaVXE0n4eBm5+47T+LnHGnSPwVFKNu8v4XRsBalZGr75pQBbGwndIhVWwySmVrN2RzHHL5Sj0VpueJeU6ihSm462ze3JzNMQd8PcKwcQvXslbXuOoX2vcXg2CmHIxDk4uflwap/ljihXD3+GTppLZNQo7BWWv0VjZyykU9+J+AY2x9M3mBGPv4deryPxcv0rp104vJJmHccQ3mkcrt4hdBsxB6WzD5ePW9YTd3wdShdfuo2Yg6t3COGdxtGsw2guHFputOn78EJadJ2IR6PmuHgF03OMQU/a9br16PV6Yo+uok3vGTRuMRBX76b0HLsAbXUFCee3Wg0XG72KRiHdaNNrOi6ewbTpNZ1GIV2IjV5ltLl09Aeath9Ns47jcPEKocsDc3B09uHKiXVW472f9Jzcu4I2UWOI7D4OD98QBoyfi5OrD+cOWs6ncwfX4eTmy4Dxc/HwDSGy+zjaRI3mxG5TPl068RvdBs8ktFUvXD0DaNdrIk0iunNyz3KLcf4diI0Kc8RGxf9TbjcYJk+eTFFREefPn2fu3Ll8++23+Pr6mtlv2rQJf39/3n33XaPHwxJlZWXGSn3NoVNVVVW89957nD9/ns2bN3Pjxg1jwyEgIICNGzcCJo/H559/DsAbb7zBihUrWLJkCbGxsbz00ks88sgjHDxoWAL2zTff5PLly+zYsYO4uDiWLFmCh4f1ClxDaeRtj4ebHSfPFRjPVWv0xFwqpGW49Qp3bdq3diHQz4GYWMtDDRqCl7scVyc556+aehc1Wrh8vYJmTewaHI+trQS5FNRlhsqfXAbB/naCeAEuXC2nWWPL8Xq6ynBRybh03TR8RqOFq0lVhAXU3WteH22b2ZOUVs0zD7nw5WuevPuUO73aW69UAbg7SXB2lHLlpqmlotHC9VQNTXyte1vqQyaFAC9hvABXblqP19fLDndXW06dLzSeq9boiblcTItmDfP4AdjZSpHLpJSoNUYtMpmEqmqhR6WySker5tbj9fG0wc3FhrOxpmFUGo2ei1dLaR7q0HA9dlJkMgnqUmGjwc/bjh8/DWf5R82YNSMAH0/LjTiJXI5DWFNKzp4UnC85cwrHiJaWw9jaoqsSDtHSVVXh0Kw5yAzp7xjRkpIzp2rFedJqnH8Wly6R5Ow9KjiXs/swzu1bIpEbvDeuXSLJ3XtEYJO75zCuXdvWG7+nqwxXJxkX44Vl6sqNSsKCGl6+60Mmg+6RCg6ettyLrdFUkZEUS2gL4bDVkBZRpCSc+8t0VFeWo9VqUDg612mn1VSRmxaLf5hQj3/TKLJuWtaTlRyDf9Pa9t3JSY1Fp7U8VFRTXY5Oq8HOoW49JQWplKtz8Qs1xS+T2+LTuCPZydbTJzv5PH5h3QTn/MKijGG0miry0mNpFCrU7RcaVWe894seraaKzORYmkR0F5xvEhFFqpX3Ji0xhiYRUbXse5B58xLaW/mk0VQjtxH+psht7UlNOGv12e41er3kro5/I+Kcin85W7duRakU9iDNmjWLN998k/fff5+9e/cyffp0YmNjmTx5Mg8+aKXn0M0NmUxm9DTUxt/fHzA0KvR6Pe3bt6dfv37G61Onmnr1goOD+eKLL+jUqRNqtRqlUombmxtg8HjcnlNRWlrKp59+yr59++jatasx7JEjR1i6dCm9evUiOTmZtm3b0qGDYexm48aN/1xC1X5eV8PHLL9Q2KNXUFiFt5f1HnkARwcZv67siq2NBK0OPl0Sz+mYgjrD1IWLylCJKqrVE1yk1uJhYby0NSYNcyO/SMvFaxUAqBxlyGQSs+FIhSVaXJwsV5ydlYZ+iOJaQ4aKS3W41zFkqiF4usro09GBXcdK+f2QmmB/Gx4Z6oRGo+fo+QqLYZwcb+kpE/bUlpTpcXP6830mjgoJMqmEEgvxOjlY/jFwczVUqgsKhZWXgsJqvD0bXjmc/kgQuflVnLlgaIiWV+i4dLWER8f6czM1noKiavpFedA8TElqhuV0AXB1MugpLBY2jAqLNXi51zE2rBaPj/Uhr6CaczUaJ1cTy1j0XQppmZW4OMuZMMyLhXNCeOqNeEpqNT5kTs5IZHKqC4RloLqgAJWrm8V7lpw+ifvg4RRFH6Y8/hqKsGa4DRqK1MYGubMLmvw85K5uVBfk14ozH7mVOP8sdt4eVGYJvYpV2XlIbWyw9XClMjMHOx8PKrOE86Iqs/Kw8/GsN35j+VbXLt+6Ooch3ikdIhQ42Es5dMbynLCykgJ0Oi2OTu6C80pnd9SXGu5VrY89Gz7FydWb4Bbd6rSrKCtAr9OiUAr1KJTulJVY1lNekoNC2d3MXq/TUFFagIOTuZf55I5PcXT2xi+0bj3lt+6pUAo7reyV7pTWMUypXJ1rFkah9DDGV1lWeOs5a9u4U6a2nu73i54ytSGfar83jioPSotzLGpQF+cSrBLG7+jkjk6noVxdgNLZi+CI7pzcu5KAsI64egaSdOUY8TF/oNfX7bm+l/xbvQ13g9io+JfTp08flixZIjh3uwJva2vL6tWrad26NUFBQSxevPhP3+fw4cM4Ojpy7tw5Zs2axcqVKwWeinPnzvHOO+8QExNDfn4+Op2hUpqcnExEhOUx0JcvX6aiooIBA4QTyKqqqmjb1tAD+NRTTzFmzBjOnj3LwIEDGTVqFN26Wf9xqKyspLJS2ANqZ2fHgF5evPpMU+O51969tZFY7VEFEon5uVqUlWt5/IXTKOxldGjjyrNPhJCeWc65Sw33Vvy4IMj4//nfZlmUAvVruc2Ivs50b+vI219lUK2pFajWnxJAX+Pc0rmmH+JP1xgqhnoL97V07k6QSuBGejUb9hoqr8mZGvy85PTt5CBoVCx82tQ7/81vZQ16hj9LfVHs+LGT8f+vzzdMUq59X4mkARHdYsLIRvSL8uDFd2KpqjYF+vCLeF57OpSN33ZAq9VzLbGUP47kEtbE0WjTu4sLzz3ayPj324tvWtZj4Zw1xg72oFcnZ2Z9fEPw3py+aGpgkFZJ3PUkvv+oGf2jXPl1t5WKUK2bGkZQWhaSuWYlclc3mn6+FCSGBkj+7h14j58EuhqVCrM47yCx7wSLmVrrvCUbKwn9/Tsmb/AnP1hepOGvfpLeHRw4f62CwpK6N1GrPbRVrwfJX1R5OrL9Oy6d2MaUWauwsWlYQ9tMj+FkXQEafD7mwHckxGxj2IxVyGvp2bJlC6vmvW38e8CjS2oHNymqS4/h5rWC6M2eq3YUer1ekO4JMb9z9Ld37hs99cWpR29+n7pvIIhnwPi5bP/xDZa9PQQkElw9A2jdbTQXojfVoUHk70ZsVPzLcXR0JDQ01Or16OhoAPLz88nPz8fR0dGqbV00adIEFxcXmjZtSkVFBQ8++CCXLl3Czs6O0tJSBg4cyMCBA1m9ejWenp4kJyczaNAgqqosj+0FjA2Pbdu24efnJ7hmZ2f4ARgyZAg3b95k27Zt7N27l379+vHMM8+wcOFCi3HOnz+fefPmCc69/fbbHL3cn8vXThvP2doYerndXG3JKzBpdHW2MfNe1Eavh7RbPcjXb5QSFODAI+MCOXep4Tsev7rQtOOuXG74yLqoZBTWmJDprJRSqK6/12Z4bydG93fm3SWZJGeYetFLSrVotXozr4SzSibwiry5xFTpsZFJjPcuUpsqJ06OUorVd7fja6FaR3qtFaQycjR0jBB6hhasMVVo5bf0ODlKBN4KpYOEkrI/r6e0XI9WZ+6VUDkI7zPtVdPqRTa38snN1Yb8Gt4KF2cb8ovqfmcAxo/w5ZHRfrzy7mUSbwp7k9OzKnnx7Vjs7aQ4KGTkF1bz1kthZGSbGsgnYoq5mmgKd1uPq7OcgiJTujo7yc28F5YYPciDh4Z5MXfhDZJSrXtEACqr9NxMraCRt/kQOG1xEXqtBhs3oQdB7uKKppb34jb6qipSPl1AyuefYOPqRnV+Hu5DR6AtLUVTZGicawrysXET9o7WFeefpTIr18zjYOvphq66mqq8QoNNZi52PsKeVzsvNzMPx23mfGlaxEJuLFMyQYXfqVYZuxs8XGS0DLVj8ep8qzYOKlekUhnqIqHm0uI8HJ3drYRqOEd3fM/hrUt59NXl+ATUv8O5vYMrEqnMzCtRoc7DQWlZj0Llaexxv025Og+JVI69g4vg/PmD3xOzfykPPLkcd19zPX379mXUs62Mf2s1VbfiyxV4PCrU+WbeFIEmpQflamGPfXlpHva3wtg5uFh+zlJhvIHN++JZY4Wqv1vPbRyUhnwqLRbal5Xk4ehkeSiy0snci1Fako9UKkehdDHEq3Jj7NNfo6mupFxdiNLFiwObFuLi4W/12e41oqfCHHFOxf9jEhISeOmll/j222/p0qULjz76qLEibwlbW1u02vorsZMnT0an0/H1118DcOXKFXJzc1mwYAE9evQgPDzcbGUoW1tDZaRm/BEREdjZ2ZGcnExoaKjgCAgIMNp5enoyZcoUVq9ezeLFi1m2bJlVbbNnz6aoqEhwzJ49m/JyLWkZFcbjRnIZufmVdIw0rdokl0uIbOnCpSvF9aZBTSSYGikNJTNXYzxSM6spKNbQuplpfoFcBhGh9ly9UffSryP6ODN2oCsfLM0iMUVYsdVoITG1ktZNhfMWWjdVcDXJFG92vtZ4pOVoKCzR0jLU1Ksnk0GzxrbEp9Rfca6L+OQqfDyE/Rw+7nJyC4XvXG6R3nhk5usoKtXRLNAUTiaFUH85NzL+vJtcq4OUbB3hgUI9zQKF8aZlVhiPpNRy8gqq6NDaxXhdLpcQGeFE7NWSOu83fkQjJo/x57X34+pcJraiUkd+YTVKRxmdIl04espUSSyv0JGRXWU8ktMryS+spl2NCdZymYRWzRyJu259WWSAMYM9eHi4F29+eoP4JOvL4dZ8zgBfe/ILzRsreo2GsvhrqNp1FJxXtetI6eVLdUes1VKdmwM6Ha69+1F8ItrYo1l6+RKqdsIlK1XtGxDnHVJ4PAaPfkLvp+eA7hSduYReY3jeguMxePQTjhH36N+dgmNWxv7naY1HWraGgmItrcKEZSq8iR3xN+su3w2lZ3sHitQ6zl213jiUy23xbdyChNhowfnEy9EEhNQ/N6Qujuz4noO/L+GRV77Fr0mr+gNgmB/g4deCtHihntT4aLyDLOvxDowk1cz+KJ7+LZDKTJ7z8we/5+wfSxgy9Vs8/S3rUSqVOLkHGQ8Xr1AUSg/Srpvi12qqyEw6hVeg9fTxCmxD+nWhprT4aGMYmdwW90YtzGzSr0cL4rWxc7yv9NxGJrfFJ7AFN+KE845uxEXjb+W98QuO5EacMP4bl4/gE9QSmUw4NFNuY4fK1RudTsOVc7sJa9OP+wVxorY5oqfiX05lZSWZmZmCc3K5HFdXVyZPnszAgQN5/PHHGTJkCK1atWLRokW8+uqrFuNq3Lgxhw4dYsKECdjZ2VmdEC2VSnnxxRd5//33mTFjBoGBgdja2vLll18yc+ZMLl26xHvvvScIExQUhEQiYevWrQwdOhSFQoFKpeI///kPL730Ejqdju7du1NcXEx0dDRKpZLHHnuMt956i/bt29OiRQsqKyvZunUrzZs3t5oednZ2Ri9HffyyJY3J4wJJTS8jJb2cRx8KpLJSy+6DpgbRGy81IyeviqWrbgDwyNgArlxXk55RjtxGQtf27gzu683CJablNhX2Uvx8TRV5X297Qps4UqLWkJVjuRKx7WAxo/s7k5lTTUZONaP7u1BZpefIWVOv/bMTPcgv0vLTNkNP7Yi+zkwY4srnP2aTk68xjt2uqNRRUWWomG09UMxzkzxJSKnkWlIl/bup8HCVszvaeiV417EyhvVwJCtPQ2aeluE9Hamq1nP8gqnCMn20MwXFWn65NZRJJjPsVQGGBpGrSkagj5yKKj3Z+YZK+q7oUt540p1hPR05eamCYD8bendQsGJL3Y24A+eqGNjJjpxCHTmFOgZ2tKO6Ws/pKyZvweSB9hSW6vn9qCF9ZVLwcTc09ORScHaU4OcppbLK0FgB2H+2ksmDFCRnabmRoSWqlQ1uKilHLlhvPG3YlsEjo/1IzaggLaOcSaP9qajUsfewqRdv9nOh5OZV8e1PyYBhyNPUCQG8vziezJxK3FwMP6rlFVrKKwyN/I5tnJFIJCSnl+PnY89Tk4NITi9nx37LY5Zvs3lPLg8N8yItu4r0rErGP+BFZZWOAycKjTavTPMnr6CalRsNw+zGDvZg8oPefLwshezcalydDPlWXqmjotKg54mHfDgRU0JOfhUuToY5FQ4KKX9EW/YS5GxcT+Brb1B27Qqll2Nxf2AENl5e5G7dDIDv1BnYuHuQ/MkHANj5BeAQ3pzSuMvIVSo8x4zHvnET43WAnM0bCFv0JV4PTaTo2BGcu3ZH1bYD8S8/U2eayBwdcAwNNP7t0MQfpzbhVOUXUZGSQbP3X8bez5vzj88C4OaydQQ9PYnmn7xOyvc/49KlLQGPj+HcI68Y40j67yq67FtN8H+eJOv3P/Ae3g+Pfl051ntinVpus/OomhG9VYaOhDwNI3urqKrWEx1jatDNHOdKQbGW9bsM5UEmA38vw7sil0lwdZIR5GtDRZWOrDxTw1cigV7tHTh8tow6+owA6DZwCpu+nUWjxi0JCI3k9MGfKcrLoGMfwz4Pe35ZRElhNqOf/MgYJiM5DoCqyjJKS/LJSI5DJrPBy8/gIT+y/Tv2/fo5Y2csxMXDj5Iiwztra+eAnX3dnvHWPaawf/0sPPxb4h0YSdzJn1EXZtD81r4TJ3csorQ4mz7jDXqad5lAbPQajv0+n/BOD5GVHMPVUxvp+7DJcx1z4DtO7/6cvg8vROXmR1mJQY+NrQM2dtb1SCQSWkQ9yoWDy3ByD8LZI4jzB5Yhs7EnpM0wo93BX2bh6ORNh0EvAxDR9VG2fzeZC4e+JbB5P5Lj/iA94RgPTF9tDNMy6jEObXgdD7+WeAVGcvXUz6iLMgjvNP4foadT/8f5fcVr+Aa1xC+4LTGH11Ocn0HbnoZ8OvDrIkoKsxj++McAtO01gTMH1rD35/lE9niItMRznD+6kZHTFhnjTLtxHnVBFl4BzVEXZnH49y9Br6PLoGlW0+Re82+dbH03iI2Kfzk7d+40W82pWbNmTJw4kaSkJH7//XcAfHx8+O6773jooYcYMGAAkZGRZnG9++67zJgxg5CQECorK9HXMTB76tSpvP322/z3v//ltddeY+XKlcyZM4cvvviCdu3asXDhQkaMGGG09/PzY968ebz++us8/vjjPProo6xcuZL33nsPLy8v5s+fT2JiIi4uLrRr1445cwwbLNna2jJ79mySkpJQKBT06NGDdeusL8N3J6zZmIKdrZSXnwpDpbTh8rViXnrrAuXlph9sb097dDWSQWEv45WnQvFyt6OySsfN1DLeXXSFfUdMlb/wUBVfzo80/v38NMOP7/Y/Mvlw8VWLWn7bZ1i2ddpYd8PmWDcref+bTCoqTTf3cJULhnAPilJhI5fwn8eFy47+vLOAX3YVAhAdU4rSUcrYQS64OslJyajiw2VZ5BZYHx6z/UgptjYSHh3mhIO9lMS0aj5ZVWBsqAC4OcsE6eKqkvHe06ZG6NDujgzt7kjcjSoWrDD0tt9I1/DF2kLGDVAyspeS3EIta3aUcOxC3UNv9p6uwkYu4aG+9jjYSUjK1PLVr2VU1pgv7eokRY+pRuWslPD6JFMPfv8OdvTvYEd8qoYvNhh68c9e0+BoX8HgLnY4ORg2v1vyWxkFJdbf+7Wb07GzlfLSk01QOcq5HK/m1fcuGxsHAN4etuhrJM6oQd7Y2kh591XhEIyVP6ew8udUABwd5Dw5KRBPd1tK1BoOHc/nu7XJaK0sJXqbDTtysbOV8swjjVA6Gja/e2PRDYEeTzcbQWXzgb7u2NhImftMkCCuNb9lseY3Q4Paw9WGWTMDcFIahspdTSjjpQ8SyM6zvMJO4cF9yJyc8Jk0BbmbOxU3b5D4xmtUZxsaMjZu7th61XhPpVI8x4wnwD8QvVaD+vw54l98iqosUwdJ2eVLJH04D98p0/B5bBpVGWkkffA2ZVfqXsfeuX1Luv7xo/HviIWGb0nKqk1ceGI2dr6eKAJM38zypFRODZ9OxKLZBD01icr0bGJf+oDMX3cbbQqOnePcpJdpNu9Fms17nrKEFM5NfInCk9Y396vJ1kNqbG0kTBnpYthQMqWKBctzBWXK3UUm+Oa6qmR8+Lxp2MuwniqG9VRxObGSD741NWJbhtrh4SrnoJUJ2jVp2XkoZaWFHNzyFSVFOXj5hTHppaW4eBiGn6qLciiqtWfFN2+bFvdIT4rl4vGtuLg34qWF+wA4te8ntJpq1n/1giBc75HP0GfUc3XqCWkzlIqyQs7+8RVlxTm4+YQx5PGlqFwNespKcgR7Jzi5+TN46lKO/b6A2GM/4ejkRbcRcwluNchoc/n4T+i01exdLdTTrv8zdBhQt55WPaahqa7k2JZ3qaooxtO/NYMf/07QGCktykAiMXmmvYPa0nv8Is7u+Zyze79E5RZAnwmL8ApoY7QJbj2UyrJCYvZ/fWuTvzAGPvoNSlfhsN/7VU9Ex6GUlxZwdNvXqIuy8WzUlIeeXYazu+m9Kc43rRjp4hHAQ88tY+/P8zl7cA1KZy8GjJ9LeDtTPmmrKzm4ZTGFOSnY2jkQ0qoXw6d+jL1Dw1df/F+j+5d6G+4Gib6umqGIyP8Tug8/+HdLMHLk916Me+nG3y0DgF8+a8Jjb2XWb3iP+OFdH55bfGfDz/5XfPmiE73H1r/W/r3gwIauDJ3a8Dk7/2u2L29FzMAef7cMACJ3H2abTf1j+O8VD1RfZdLstPoN7wFr5vuxLvr+qQJM6CZh0eb7Q88royR8tOGvmdNyt8waK71vtIBBz8oDf7cKA1N6/333Phd/d6uitQ27++Xv7zfEORUiIiIiIiIiIiIiIneFOPxJRERERERERERE5A4Q51SYIzYqRERERERERERERO6Af+sKTneD2KgQERERERERERERuQNET4U5YqNCRERERERERERE5A4QPRXmiI0KERERERERERERkTtA9FSYIy4pKyIiIiIiIiIiInIHnLxSdFfhO4U7/0VK7h9ET4WICPDjob9bgYnJPeHbvX+3CgNP9oclO/9uFSaeGgyf/35/9IO8MFzCN7v+bhUGZg66//LpmYWFf7cMAL76j8t9sy8EGPaGuF/2zXig+iqvf1v35pL3kgVP2vPpb/dH+X55pISvdvzdKgw8MwQWbrp/9qn4z2jpfbWfyN/F/ZMj9w9io0JERERERERERETkDhCHP5kjNipERERERERERERE7gBxorY54o7aIveUxo0bs3jx4r9bhoiIiIiIiIjIn0avl9zV8W9E9FT8Q5gyZQo//PADAHK5nICAAEaPHs28efNwdHT8n9+/VatWdO7cme+++87s2tq1a3n00UdJTU3F29v7f67lXnB6/xqO7foedVEOno3CGDh+DoFNO1i0LSnMZu8vH5Fx8xL52Tfp1HcyAyfMNbOLO7OLg799TkFOMq6egfQe9RLh7QY0SM+5Q2s4tfd7Soty8PANo8/YOfiHWtYDkBJ/kgMbF5CbEY/S2YuOA6YR2eNh4/V1iyeTGn/SLFyTFr0Y8/SyOrWcP7yGM/u+p7Q4B3efMHqNnoNfiHUtqddPcujXBeRlxuPo7EWHvtNo3d2kJfbEJvb8NNss3LMLLyC3satTC8Cloz9x7sD3lJXk4OYdStTIOTQKtq4nLeEk0VsWkJ91HUcnLyJ7T6NltwnG6wkXd3P2j6UU5Saj02pw9gwistfjNGs/sl4t5w+v4fQfNdJmzBz860qb+JMcrJk2/abRplba7F5jnjbPLao/be63fAIY2s2eqNa2ONhJSMrU8vPeMjLyrI9M9nWX8kCUPYHectydpWzYV87+s5UCm4Gd7IhsaoO3m4xqjZ7ENC2bD5WTXVD/iOfR/VT07eSIo0LK9ZQqVv5WSFq2xqq9n5ecsQOcaOJng6ernB+3FrLzaKnAZvFr3ni6mv+07jmmZuUW4cROt+4dCH7lCZzbtcS+kRenxzxN1pY/6tTs1qMjEQtfRxkRRmV6NgmLviN52TqBjc+DA2n6zgs4hARSlpDM1bc+I+u3hk/U6t9OTqdwGQo7SMnWszm6muyCusfQt2wsZUAHOe5OEvKK9ew+rSE2SZgHTg4wpJMNTQOk2Mght0jPxkPVpOVajzs2+ifOHzSUb1fvULqNmINvE+vvcXrCSY5tXUBB1nUcnLyI7DWNiK6m8h134meunfmN/Kx4ADz9WtBp8Et4BbauN10uHFnD2Vtlys0njJ4P1l+mDm9eQP6tMtW+7zRaRZnK1OUTm9i71rxMPf1J/WXq8rGfOH94OeUlObh6hdJl2Ow60yUj8STHt31EQfZ1HFRetO71BBGdTemSnxXPmT1fkpsWi7ownS4PvE6r7o/VqaEmscd+4kKNfOo6vJ58SjzJ8Rr51KbXNCK61NCTGc/pPV8Y9BSk03XYbFr1aLiee4HoqTBHbFT8gxg8eDArVqygurqaw4cPM23aNEpLS1myZMn//N5PPPEEb731Fl988QUODg6Ca8uXL2fYsGH/mgZF7Knt7F4/nyGT3iYgtB1nD65j7RdPMnPeNpzdG5nZazVVOKhc6T70KU7sXWkxztSEc2xa9hK9R75As7b9uXpuL5uWvchjr/2EX3CbOvVcObOd/Rvm03/82/iFtOP8kXVs/OpJHn9zG05u5noKc1PY+PV0WkeNY+iUT0hLOMve9fNwULrRtO0gAEY++SU6TbUxTHlpIT/MH0mztoPr1HL17HYO/jqfvuPeplGTdlyIXsfmb55k8mzLWoryUti8dDotu45j8ORPSL9xln2/zEOhdCMscpDRztZeyWNzhTONG1JRjY/ZzpEt8+k5+i18Grfj8vH1bP1uOg+/uhWVq7me4rxUtn03g4gu4+g38RMyk85yaNO7KJSuhLQ26LFXONO+30xcvIKRyWxIijvAvvVzUCjdCGzWo860ObDpVtoEt+Pi0XVsXvIkj86xnja/Lp1Oq67jGPzoJ6QnGtLGwULaTHnjztLmfssngAGd7Ojb3o4fd5aRXaBlcBd7nh2n5N3vi6msthzGxkZCXpGOc1fLGdNHYdEmLEDOoXNV3MzUIJXC8O4Knhun5L0VxVRZiRdgWE8lQ7sr+WZDAZm5Gkb1UTH7CQ/+syiLiirLlVw7WwnZ+RpOXCznkQcsr9zy5lc5SGvUNfy9bZgzzYMTF8vNbGWODhRfuErqD5to/8t/rYu9haKxPx1/X0bK978Q89iruHZrR8sv36YqJ5/MX3cD4NIlkrY/fca1tz8n87e9+IzsT7u1iznWeyKFJy/Ue49ebWR0byXjl4PV5Bbp6dtWzrQhtiz8pdJqegZ6SXi4nw17bjUkWjSWMrGfDd9sqSIlx5CWClt4aoQdCelaVuysorQc3JwklFdajhPgesx2on+fT/dRt8r3ifVs/346D71ipXznp7Jj+QzCO4+j7wRD+T6y+V3sla4EtzK8x+kJJwmNfADvxm2Rye04f+A7tn33BA+9shVHZ+u/YdfObufQr/PpPdZQpi5Fr2PL0id5ZPY2i1qK8lLYsmw6LbuMY9AjhjJ1YIOhTIW2EZapyXPurEwlXNjOsW0LiBr5Jt5B7bhyYj07V85g3Eu/o3SxnC47V84kvONYeo//mKybZzn623soHN1o0nIgANqqCpzcAghuNYhj2xbUeX8zPee3c+xWPnkHtSPuxHp2LJ/OQy9vRWkln3Yun0F4p3H0Gf8JWTdv5ZOjKZ801bf1DObY1jvTI/L3IQ5/+gdhZ2eHj48PAQEBTJw4kUmTJrF582ZWr15Nhw4dUKlU+Pj4MHHiRLKzswVht2zZQlhYGAqFgj59+vDDDz8gkUgoLCw02kRHR9OzZ08UCgUBAQE8//zzlJYaeuEmT55MZWUlv/zyiyDe5ORk9u3bxxNPPEFCQgIjR47E29sbpVJJx44d2bvXeu9YUlISEomEmJgY47nCwkIkEgkHDhwwnrt8+TJDhw5FqVTi7e3N5MmTyc3NNV7fsGEDrVq1QqFQ4O7uTv/+/Y26/wwn9qwgsvsY2vYYh4dvCAMnzMXJ1YczB9datHfx8GfQhDdo3W0UdgqVRZuTe38gOKIbUUNn4OEbQtTQGTQO78KJvT/Uq+f0Hyto1XUMraPG4e4TQt+xc1G5+hBz2LKe80fW4eTqS9+xc3H3CaF11DhadR3NqT+WG20Uji44Onsaj5tXjmJja0/TdnU3Ks4eWEGLLmNo2XUcbj4h9B49F6WrDxeOWtZy4eg6VK6+9B49FzefEFp2HUeLzqM5s3+50FAiwdHJU3A0hPMHV9K80xgiOo/DzTuE7iPnoHTx4dIxy3pij61D6epL95FzcPMOIaLzOMI7jibmoEmPX2hnglsNwM07BGePQNr0eBR332Zk3Dhbd9rsX0HLLmNo1c2QT73HGPLpwhEraXMrn3qPMeRTq27jaNFlNGf2CdNG8ifS5n7LJ4A+7ezYdaKC8/HVZOTq+HFHGbZyCR2b21oNk5yp5deDFZy5Wo1Ga9nmq42lHI+tIiNPR1qOjtU7y3BzkhLoLatTz+AoJZv3l3A6toLULA3f/FKArY2EbpGWGy8AianVrN1RzPEL5Wi0lhseJaU6itSmo21zezLzNMTdqDKzzdl1iGtvLyZz8546td4maPoEKpIzuPzKh6ivJJKyfAMpKzcR/PJUo02T5x4jd280CR8vo/RqIgkfLyN333EaP9ewXt6olnL2xxgaB1kFen4+UI2NHCJDrKdnVEs519N0HDivJadIz4HzWq6n6Yhqaeq37NVGTmGpng2HNKTm6ClQ60lI15FfYt1LcfHwSsI7jqF553G4eocQNcJQvi8ft/weXz5uKN9RI+bg6h1C887jaNZhNOdrlO9+ExfSottEPBo1x9UrmJ5j30Ov15F2/Vid6XLuwApadDaVqZ6j56J0sV6+Lx1dh8rFl541ylRE59GcrVW+4c7L1MXDP9Csw2jCO47D1SuErsPnoHT24fLxdRbt406sQ+niS9fhc3D1CiG84ziath/NhUMmLZ4Breg89FVC2jyATGa9TFriwuGVNOs4hvBOhnzqNuK2HstpE3fcoKfbrXwK72TIp5p6vAJa0eWB1wiNfACZ3OaO9NwrdPq7O/6NiI2KfzAKhYLq6mqqqqp47733OH/+PJs3b+bGjRtMmTLFaJeUlMTYsWMZNWoUMTExzJgxg7lzhcNzLl68yKBBgxg9ejQXLlxg/fr1HDlyhGeffRYAd3d3Ro4cyYoVKwThVqxYgbe3N0OGDEGtVjN06FD27t3LuXPnGDRoEMOHDyc5OflPP2NGRga9evUiMjKS06dPs3PnTrKysnjooYeM1x9++GGmTp1KXFwcBw4cYPTo0fzZ7Ve0mioybsYSHNFdcD64RRSpCef+9HOkJsaYxRnSoke9cWo1VWSlxNK4uTBs4+ZRpCdaDpuRGEPj5lG17HuQdfMSWq3lrsaLxzYS3v4BbO0cLF6/rSU7JZagZkItQc2iyLhhWUtmUgxBzYRagsJ7kJ0s1FJdWcb37/Thu7d68tvSGWSnXraqo6aenLRYApoK4w9oGkVWkhU9N2PM7AObdScnJdZi2uj1elLjj1GYfaPOIVW38ykoXJg2geFRpFtJm4ykGALDa+VTeA+yaqVNVWUZ373dh2/f7MnmpTPITqk7be63fAJwd5birJQSl2QaWqTRwvVUDU38/lqHucLO4CYorbD+DfB0leHqJONivKmbXKOFKzcqCQtqmOelIchk0D1SwcHTf76ToyYuXSLJ2XtUcC5n92Gc27dEIjeko2uXSHL3HhHY5O45jGvXtvXG76aS4OQgIT7VNGxJq4MbGTqCvK1XF4K8pYIwAPGpwjDNg6Sk5eiY2M+GNx6x4/kHbenYzHpD5Xb59q9VXv3DrJfvrJsx+IfV+h40605uquXyDaCpKken1WCnsL5ngFZTRXZqLIEWyneGFS2WyndQeA+yU2qVqaoyVszrw/dv92TLsvrLlFZTRW56LH61ntMvLIqsZMtaspNjzOz9m0aRkxaLzkq6NBStporctFizdPdvGkXWTSv5lBxjnq9Nu5OTevd67iV6JHd13Clff/01TZo0wd7envbt23P48GGrtps2bWLAgAF4enri5ORE165d2bXrf78Gujj86R/KyZMn+emnn+jXrx9Tp5p6qYKDg/niiy/o1KkTarUapVLJN998Q7Nmzfjkk08AaNasGZcuXeKDDz4whvvkk0+YOHEiL774IgBhYWF88cUX9OrViyVLlmBvb8/UqVMZOnQoiYmJBAcHo9frWblyJVOmTEEmk9GmTRvatDEN5Xn//ff59ddf2bJli7FxcqcsWbKEdu3a8eGHHxrPLV++nICAAK5du4ZarUaj0TB69GiCgoIAw/yPP0uZugC9Toujk7vgvKPKA3VRzp+OV12Uax6nkzulxXXHWX5Lj0OtsA4qD6thS0tycVB5CO2d3NHpNJSrC1A6ewmuZSRdIDf9GoMmfUBdlJda11JWYkVLcS5B4Za1VKgLcHT2ws07mIET5+PRqBlVFWrOHVzFz4sfZtJrv+Hq1diqnopbehSq2nrcSSnJtRimrCQHB5WwYqBQ3dJTWoCjkyFtKstL+OG9Xug0VUikUnqOftusMVITY9qozN+bm3WkTePa+aQS5pObVzCDJhnSprJCzbkDq1i/+GEemWU9be63fAJwcjT8gJaUCiuexaU63Jz+2r6t0b0VXE/VkJFrfU6Fi8pQmS1SC90fRWodHi51ezjuhA4RChzspRw6U/aXxGfn7UFllvDdrsrOQ2pjg62HK5WZOdj5eFCZlSewqczKw86n/h5w5S0nTUm5sEFWUq7HVWW9EqRUWA6jqtFH4aaS0Lm5jCMXtRyIqcLfU8qIbnK0Oj1n483zyli+lcL3WKFyp8xK+S4vyUFRu3wrzct3TU7s+BRHZ2/8wrpZfT5r5dtB5UGZle9wmaXvsEpYply9gxkwcT7uvoYyFXNoFRs+f5iJr/2Gi2dji/FWlBUatCiFcSuU7pRb/e7l4l8rHR2UHuhvpYuDhXRpKBVlVvJJWU8+Kc3z6a/Qcy+5l5Ot169fz4svvsjXX39NVFQUS5cuZciQIVy+fJnAwEAz+0OHDjFgwAA+/PBDXFxcWLFiBcOHD+fEiRO0bVt/B8OfRWxU/IPYunUrSqUSjUZDdXU1I0eO5Msvv+TcuXO88847xMTEkJ+fj05n+EAnJycTERHB1atX6dixoyCuTp06Cf4+c+YM169fZ82aNcZzer0enU7HjRs3aN68OQMHDsTf358VK1bw3nvvsW/fPpKSknj88ccBKC0tZd68eWzdupX09HQ0Gg3l5eV35ak4c+YM+/fvR6lUml1LSEhg4MCB9OvXj1atWjFo0CAGDhzI2LFjcXV1tRhfZWUllZXCQbx2dnaAsHdSIqn9sdBbOHenCMMbvCkNi1NiZle3HrNrtzw35vHAxegNeDRqim/j+icq3lZTW0udz2FFy+3zvo0j8W0cabzcqEk71ix8kPOHV9N7zBsNUFM7XS1pFIYQBjCPx9bOkfEv/0p1ZRmp8cc4umUBTm7++IV2rkfMneZxLftbYm7nn2+TSHybRBqv+zVpx5pPHiTm0Gr6jK0vbf7efPr0eVPP79eb1EYFdd3ybnmonwI/Txmfri0RnO8WqeCJUS7Gvz/5IQ9LSCxovBt6d3Dg/LUKCkv+wm2yanthbydizfOWbKx4b+dNMX37Vu68NUSr4cGtIqklQyKBtFw9u04bvFXpeVq8XSV0bi7nbLz50DBBwJroLX2fa97X8jVLYWIOfEdCzDaGz1zVwLlB5mWk7t8FS98D6ixTaxc+yPlDq+nVgG9fLTF1FqjaOmtruVvM4q8vbmvX/uqPwv+QPzkg4k/x6aef8sQTTzBt2jQAFi9ezK5du1iyZAnz5883s6+9yuaHH37Ib7/9xu+//y42KkQM9OnThyVLlmBjY0OjRo2wsbGhtLSUgQMHMnDgQFavXo2npyfJyckMGjSIqirDh1pv4cNXe3iQTqdjxowZPP/882b3vd0KlkqlTJkyhZUrVzJv3jxWrFhBz549CQsLA+DVV19l165dLFy4kNDQUBQKBWPHjjXqqI1UKjXTUl0tdH3qdDqGDx/ORx99ZBbe19cXmUzGnj17iI6OZvfu3Xz55ZfMnTuXEydO0KRJE7Mw8+fPZ968eYJzb7/9NiF93wHAQemKRCpDXSTsYSktycPRSdgzdCconT0oLRbGWVaSX2+cilt6zMPmmfWC3cbRghejrCQfqVSOvdJFcL66qpwrZ7YRNcw83820OBq0lN2JFifznjyjFkcXi2EkUik+ga0oyEmqU4/9bT21esPK1XlmPYq3cVB5WrSXSuXY1dAjkUpx9jB4vjz8mlOQncjZfcusNiqspo267rQpreU5KG9A2ngHtqKwjrS5X/Jp/ipTxV5+q/PfyVFKcanJO6BykFJc9tf8Mo/rq6B1iA2frVdTqBbGefZyBQkppnlmcpnhe+islAkq/E5KKUXqv6YB4OEio2WoHYtX5/8l8QFUZuWaeRxsPd3QVVdTlVdosMnMxc5HmM92Xm5mHo7bfLHJ9H2W3conlYNE4HlQ2ktQm88zN6IuB5VC+BujVAjDlJRhtiJXdqGelk0sVyJvl+/ave/l6jyzXvHbKOoq3w4ugvPnD37PuX1LGfbkctx9697hXGHlW1OmzkNhpUxZ8gyWq+++fNs7uBi0qGs/Z77VdDFoqWVfmodEKse+VrrcKfYOltOmQp2HQx35ZClf/wo99xLdXa7+ZK2T09DRaaKqqoozZ87w+uuvC84PHDiQ6OjohmnV6SgpKcHNze2uNNeHOKfiH4SjoyOhoaEEBQVhY2OYuHTlyhVyc3NZsGABPXr0IDw83GySdnh4OKdOnRKcO336tODvdu3aERsbS2hoqNlha2uatPX444+TmprKpk2b2LRpE0888YTx2uHDh5kyZQoPPvggrVq1wsfHh6SkJKvP4+lp+HHMyMgwnqs5abumrsaNG5vpur2UrkQiISoqinnz5nHu3DlsbW359ddfLd5z9uzZFBUVCY7Zs01L+snktvgGteBGnHDc8o3L0fiH/PnWvX9wJImXhXEmXj5Sb5wyuS3eAS1IuiIMm3QlmkbBlsP6BkeSdEX4oUmKO4J3UEtkMuGEt6tndqDVVBHRcUS9zyCT2+IV0ILkq0ItyVej8W1iWYtP40iSrwq13Lx6BK9Acy230ev15KTG1TthUSa3xdOvBSnXhPGnXovGu7EVPUGRpNayT7l2FM+AFlb1gMGDoNVY7029nU83a6fNlWgaWUkb38aRJNfKp5tXjuBdX9qk1Z0290s+5RTqjEdGnmHScniQqR9LJoVQfzk30qwv4dpQHuqnIDLMhs9/VpNXZGEoTZWerDyt8UjL1lBQrKVVmOnHWyaD8CZ2xN+sYzmiO6BneweK1DrOXa34S+IDKDweg0c/4TAdzwHdKTpzCb3GkI4Fx2Pw6CccqufRvzsFxyyPb88r1huP7AI9xWV6Qv1MVQOZFJr4SrmZZb2xdTNLJwgDEOYvDHMzS4eHi9DG01li1gA03vdW+U6Nr1W+462Xb++gSHP7a0fx8BeW75gD33P2jyUMfeJbPAPqHy4rk9vi5W+lTFnR4muhTCVfOYJXwN2Xb49GLUir9Zxp16PxDrSsxSswkrTrtezjj+Lp1wJpHd+9hiCT2+LhZ64nNT4a7yAr+RRoIZ/ij+Lpf/d6/knMnz8fZ2dnwWHJ65Cbm4tWqzVbYdPb25vMzMwG3WvRokWUlpYa56P+rxAbFf9wAgMDsbW15csvvyQxMZEtW7bw3nvvCWxmzJjBlStXmDVrFteuXePnn39m5cqVgMllOWvWLI4dO8YzzzxDTEwM8fHxbNmyheeee04QV5MmTejbty/Tp0/HxsaGsWPHGq+FhoayadMmYmJiOH/+PBMnTjQOxbKEQqGgS5cuLFiwgMuXL3Po0CHeeEPo8n3mmWfIz8/n4Ycf5uTJkyQmJrJ7926mTp2KVqvlxIkTfPjhh5w+fZrk5GQ2bdpETk4OzZs3t3hPOzs7nJycBEftXoHOAx7n3OENxBzZQG5GArvXf0hRfgbtehnW0N63aRG/ff+aIExmchyZyXFUV5ZSWpJPZnIcOenXjdc79nuUxMtHid6xjNyMBKJ3LONG3DE6969/RZYO/R7nYvQGLkZvIC8zgf0bPqQkP4M23Q16Dv22iO0/mPS06T6B4vx09m+cT15mgiHssY107DfVLO6LxzYQ2qY/CqXl4WK1adf7cS4d30Ds8Q3kZyZwcNOHlBRk0DrKoOXI74vYtdqkpXXUBIoL0jn463zyMxOIPb6B2OMbad/HpOX4jv+SFHeYotwUslPj2LN2DjlpV2hdYz13a7TpNYW4kxuIO7mR/KwEjvw2n5LCDFreWu/82PZF7F07y2jfousESgrSObplPvlZCcSd3EjcyY1E9jLpOfPHUlKuHaUoL4WC7ERiDq7g2unfaNq+7oZXuz6Pc+nYBi4dM+TTgdtpcyufjmxZxM4fa6RN91tps8mQT5eObeDS8Y2072vScuxW2hTeTpuf5pCTekWwf4RFLfdZPgHsP1vJoM72tAm1wddDyuQhDlRp9JyKMzXWHh3iwIge9sa/ZVLw95Th7ylDJgMXlQR/TxmeNSqn4/sr6NjclhXbSqms0uPkYJhsbFOPH37nUTUjeqvoEGGPv7ecmWNdqarWEx1j6l6fOc6V8YOcTHpkEORrQ5CvDXKZBFcnGUG+Nni7C+dhSCTQq70Dh8+WUccnEJmjA05twnFqEw6AQxN/nNqEYx/gC0Cz91+mzQqTl/bmsnUoghrR/JPXUYYH4z9lDAGPjyHxU9OqOUn/XYXHgCiC//Mkjs2CCf7Pk3j060rSl/WvNAdw9JKGPpFyWjSW4u0qYVwvG6o1EJNg8jA91NuGQR3lgjBh/lJ6tZHh6SyhVxsZoX5Sjl4yNRiPXNQQ6CWhd6QMdycJbUKkdAqXcSzWeqOyVY8pXDm5gSunNlKQlUD0lvmoCzOM+xmc2LGIfetM5TuiywTUBelE/z6fgqwErpzayJVTG2lTo3zHHPiOU7sW02vcB6jc/CgryaGsJIfqyron07ft/fitcmEoU4d+/RB1QQatbpWpo78vYneNMtUyyvCtOVSzTJ3YSLsa5fvEzv9y81aZykmN44+1c8hNu0LLespUqx6PcfX0Rq6e3khBdgLHthrSpXnn8QCc3Pkp+382pUvzzoZ0ObZ1AQXZCbfCbqJ1T5MWraaKvPQ48tLj0GmrKSvOJi89jqLcm3VqAWjdYwpXTtXIp99v6bmVTyd3LGL/+hp6buXTsRr5dPXURjM9uelx5KbHodNUU1qcRW4D9dwr7nbzu/o6OWtjacRJQ4Zlr127lnfeeYf169fj5fW/na8iDn/6h+Pp6cnKlSuZM2cOX3zxBe3atWPhwoWMGGGqADVp0oQNGzbwyiuv8Pnnn9O1a1fmzp3LU089ZaxQt27dmoMHDzJ37lx69OiBXq8nJCSE8ePHm93ziSee4I8//mD69OmCPSs+++wzpk6dSrdu3fDw8GDWrFkUFxfXqX/58uVMnTqVDh060KxZMz7++GMGDhxovN6oUSOOHj3KrFmzGDRoEJWVlQQFBTF48GCkUilOTk4cOnSIxYsXU1xcTFBQEIsWLWLIkCF/Ok1bdBxKubqAw1u/Rl2UjWejpkx4fhku7n4AqAtzKMrPEIT57r1Rxv9n3Iwl9uRWnN39eG7BPgACQtsxevqnHNi8mAO/fYGrZwCjp39W7x4VAOHth1JeWsCxHV9TWpyNh29TRj+9DOdbekqLciguMOlx8QhgzNPL2L9xPjGH1uDo7EXfcXONe1TcJj/rBmkJZxj7bO0lDq3TrN1QKkoLOL7ra8qKsnH3bcrIGctwcrulpVioxdk9gFEzlnHw1/lcOGzQ0nv0XMHeB5Xlxfyx/i3KinOwVajw9I9g7POr8Qmqf45HWORQKksLOb3nK+Mmb8OeWIrqlp6y4hzUBelGeyd3fx6YtpSjWxZw8ehPODp50X3kXOMeFWBYDebQpndRF2Yit7HHxasJ/SZ+TFjk0AalzYldX1N6K21GzRSmTUmttHnwVtqcv502Y8zTZu86U9p4+Ucw7oX60+Z+yyeAPScrsZFLGN9fgYO9hKQMLf/doBbsUeHqJBWMU3ZWSpn9mGmZ5v4d7enf0Z5rKRo+X2+Yp9Ez0vANe2mCcDnnH3eUcTzWundp6yE1tjYSpox0wVEhJSGligXLcwV7VLi7yATDM11VMj583vSjPKynimE9VVxOrOSDb03DOVqG2uHhKudgPRO0ndu3pOsfPxr/jlg4B4CUVZu48MRs7Hw9UdxqYACUJ6Vyavh0IhbNJuipSVSmZxP70gfGPSoACo6d49ykl2k270WazXuesoQUzk18qUF7VAAcPK/FRiZhZJQNCltIydHz/Y4qwR4VLo4SQT4lZ+tZu6+agR3kDGgvJ79Yz09/VBv3qABIzdXz455qBneU06+tnIISPb8f0xCTYL3VFRo5lMqyQs7s/YqyWxvODZm6FJVrjfJdWKN8u/kzZOpSjv2+gNhoQ/mOGjHXuPcBGDZp02mr2fPjC4J7te//DB0GCjvRatK03VAqygo4ucvwHXb3bcqIGmWqzEL5HjF9GYc3z+fCkTUonb3oNXquYI+KyvJi9v38FqXFOdgpVHj6RTDmufrLVEhrw3fv7B9f39r0M4zBU74xpUtJDqWFJi1Obv4MnvINx7Yt4PLxn3Bw8qLr8DnGPSpuh9n05Wjj3xcOL+fC4eX4NunIsOmr6tbTZigVZYWc/aNGPj2+VKCndj4Nvp1Pxwz51K1WPpUVZ7Pp8wdNeg4t58Kh5fgGd2T4DFOZ+Tu52zkVloY6WcLDwwOZTGbmlcjOzq53f7D169fzxBNP8Msvv9C/f/+70tsQJPo/u/amyD+aDz74gG+++YaUlJS/W8p9wY+H/m4FJib3hG8bvvnt/5Qn+8OSnfXb3SueGgyf/35/fLJeGC7hm//9Cn0NYuag+y+fnllY+HfLAOCr/7gwaXba3y3DyJr5fmyzqXsM/73igeqrvP7tXzc8625Z8KQ9n/52f5Tvl0dK+GrH363CwDNDYOGmv3Cy/13yn9FSFm2+P/LplVF/38Tu3efrWGCgAQxs0/D9QDp37kz79u35+uuvjeciIiIYOXKkxSFTYPBQTJ06lbVr1zJq1Ki70tpQRE/F/xO+/vprOnbsiLu7O0ePHuWTTz7508u8ioiIiIiIiIj8f+ZebmD38ssvM3nyZDp06EDXrl1ZtmwZycnJzJw5EzDMF01LS2PVKoNXae3atTz66KN8/vnndOnSxejlUCgUODtb35PlbhEbFf9PiI+P5/333yc/P5/AwEBeeeWVOsfuiYiIiIiIiIiIWOZe7lMxfvx48vLyePfdd8nIyKBly5Zs377duD9XRkaGYPn+pUuXotFoeOaZZ3jmmWeM5x977DHjnNr/BWKj4v8Jn332GZ999tnfLUNEREREREREROQOefrpp3n66actXqvdUDhw4MD/XpAFxEaFiIiIiIiIiIiIyB0gzkg2R2xUiIiIiIiIiIiIiNwBd7v53b8RsVEhIiIiIiIiIiIicgeIngpzxCVlRURERERERERERO6ALae19RvVwYgOsvqN/mGIngoREeCFz0v+bglGPn9BxWvflNdveA/4eKaCN1be3VrcfyXvT7Fl3urq+g3vAW8/YsO7a6zvBnwveWuSnLdX3R/pAjDvURtWHfy7VRh4tBesi75/+s4mdJPcN3tDLHjS/r7ZMwMM+2bcT/sffLzx/tgb4rUxUj5Yd3cV2L+SuRNk99UePX8X93JJ2X8K0r9bgIiIiIiIiIiIiIjIPxvRUyEiIiIiIiIiIiJyB4iTB8wRPRUid4xEImHz5s0Ntn/nnXeIjIz8n+kREREREREREbmX6JHc1fFvRPRU/MPIzs7mzTffZMeOHWRlZeHq6kqbNm1455136Nq16z3RkJGRgaur658O/8477zBv3jwAZDIZLi4uREREMHr0aJ566ins7Oz+Kql3zeDOtnRraYPCXsLNTC0b9leSmW99nK2Pm5ShXW3x95Lh7iRl08EKDsaYj3Xv3tqGvu1scXKUkJmnY9OhShLT6x4zO6CDnM7N5SjsIDlbx+bD1WQV1N1V0rKJlEEdbXB3lpBXpGfnyWpik0z6X59kh5vKvG8h+pKGzUesj9HvGymjQ1MpCltIzdXz+3Et2YV1a4kIktC/rRw3FeSXwJ6zGuKSTWH6RsroGymcuFZSruej9fXPFejVWkr7UCn2tpCWp2f7SS05RXWHaR4goU8bGa4qKCiBfee1XEkx6ekQJqVDUykujoa/s4v0HLqo43p63c/Zq5WUdqGSW1pgx6n6tYQHSOjTRoqrEgrUsC9Gx9VU033ah0noECbFRWn4O6cQDl2qXwtA7zZS2oeZ8mrbiQakTaCEvpEyY179cU6YNjXp0VJK/3Yyjl3WsvO09bJx+sAaju/6HnVRDp6Nwhgwfg6BYR2s2t+8epK9vywgJz0elYsXXQZNo32vh43XtZpqoncu5UL0ZkoKs3D3aULf0f8hpGXPuh8OOLnvJ47u+B51YQ6efqEMmTiHoKaWtZQUZrNr3Uek34wlP+smnftPZsjEOcJnO/gz54/+RnZaPACNGreg35iX8A9uXa+W2/RvJ6dTuAyFHaRk69kcXU12feW7sZQBHeS4O0nIK9az+7RGUL4BnBxgSCcbmgZIsZFDbpGejYeqScs1j9uteweCX3kC53YtsW/kxekxT5O15Y86Nbj16EjEwtdRRoRRmZ5NwqLvSF62TmDj8+BAmr7zAg4hgZQlJHP1rc/I+m1vg9Il9thPXDj4PWUlObh6h9J1+Bx8m1h/b9ITT3J86wIKsq7j4ORFm17TiOgywXg9PzOe03u+IDctFnVBOl2HzaZVj8capOXy8Z+4eHg55SU5uHiF0uWB2fjUoSUj8SQntn9EYfZ1HFRetOr5BM07TxDY3Li0m7N7vqA4Pxknt0DaD3yBxi0GNEgPQI+WEtqGSLC3gfR82HlaR25x3WGa+Ru+U7e/Nwcv6LiaZrrerbmEZv4S3J1Ao4XUXNh3Xkd+HVMPzx9ew+k/vqe0OAd3nzB6jZmDf4j1tEmNP8nBXxeQlxmPo7MXHfpNo013U/mOPbGJ3Wtmm4V7btEF5Db3Rx1BnFNhjuip+IcxZswYzp8/zw8//MC1a9fYsmULvXv3Jj8//55p8PHxueuKf4sWLYzbyu/fv59x48Yxf/58unXrRknJ/TFpul97W/q0tWXDgUo+XVdGSamepx9UYGdjPYytjeFH+/ejlRSVWq5gtQ2T82BPO3afquKTn8pISNcyc6QCV5X1novekXJ6tJaz+UgVX2yspKRMz5PD7OrUEugtZdIAW87Ga/nsl0rOxmt5ZIAtAV6m+3y5sZJ3fyg3Hst+rwTgQqL1Bk6PllK6RUjZelzDkq0aSsr1TBkox7aOLooATwnje8mJSdDy3y3VxCRomdBbjr+H8JmzCnQsWF9lPL7cXH+DIipCStdwKdtPafl2hwZ1OUzuV7cefw8JY3vIuHBDxzfbNFy4oWNsDxl+7iY9xWV69p7TsmyHhmU7NCRl6pnQS4ans/V4u0VI6NJcwo7TOr7bqUVdrueRvrJ6tMDY7lIu3NCxdLv2lhYpfu4mm5Iy+CNGx7c7tHy7Q8uNLD3je0rr1ALQvYWUrs2lbD+pZdl2Q9o8OqD+tBnXU8aFRB1LftdwIVHHQ71k+HmYv5+N3CW0D5OSmV/3r+vlU9vZs34+UUOfYtqbmwkIa8+6L56kKC/don1hbgrrv5xOQFh7pr25mW5DZrJ73QdcOWOaHXrwt8WcPbSeQQ+/yYx522nXcwIbljxLZvLlOrVcOrGdnT/Np+ewmcyc9ytBTTuw+tPpFFrRotFU4aByo+ewmXgHhFu0SbpyklZdHmDKrB+Y9sY6nN18+XHhExQXZNWp5Ta92sjo3krGb9HV/HdzFSXleqYNscW2rvLtJeHhfjaci9fy+cYqzsVrmdjPhgBPUz4pbOGpEXZodXpW7Kzis1+q2HZcQ3ml5Thljg4UX7hK7AvvNki3orE/HX9fRv6RMxzpOIrrH31Di8/m4vPgQKONS5dI2v70GWlrfuNw+5GkrfmNdmsX49Kp/gZXwvntHPt9Pm37zmT087/i07gDO5ZPR11gOa+K81PZuXwGPo07MPr5X2nbZwbRWz4g8aLpvdFUV+DkFkCnwa+gUHk26DkBEi9s58S2BUT2nsGoZzfh07g9u36YgbrQspaS/FR2/zATn8btGfXsJtr0ns7xrR9y49Juo01W8jn2r3uZ0LYjePC5zYS2HcG+tS+TnXK+QZq6hkvo3EzCrjM6VuzRoS7XM7GPtM7y7ecOo7tJuZSk57udOi4l6XkwSkojN5NNoJeEM9f1rNyj46cDOqQSmNhbio2VxYqunt3OgU3z6TTwKSa9thm/kPZsXvIkxfmW06YoL4Vfl07HL6Q9k17bTKcBMzmw8QPiY4Szv23tlUx//4jguF8aFGAY/nQ3x78RsVHxD6KwsJAjR47w0Ucf0adPH4KCgujUqROzZ8/mgQceAAxDk5YsWcKQIUNQKBQ0adKEX375RRBPWloa48ePx9XVFXd3d0aOHElSUpLAZvny5bRo0QI7Ozt8fX159tlnjddqD3+aNWsWTZs2xcHBgeDgYN58802qq+uuDMrlcnx8fGjUqBGtWrXiueee4+DBg1y6dImPPvrIaLd69Wo6dOiASqXCx8eHiRMnkp2dDYBeryc0NJSFCxcK4r506RJSqZSEhIQGp60lerW1YfepKi4kaMjI07F6TwU2NhLaN7P+S5+cpWPLkUrOXdOgsVIv793OluOx1RyPrSarQMevhyopUOuIamU93u6t5Ow7q+HSDR1ZBXrW76vGRg6RodaXpOvRSkZ8qo795zTkFOrZf07D9TQdPVqZfnFKK0BdbjqaB8nILdKRmG69x7lbhIyDF7RcTtaTXahn42EtNnJoE2z9c9ItQkpCuqGnP7cIDl3UkZChp1uEMIxOL9RTZqXyU5POzaUcvqTjSoqenCLYHG3Q06qJdT2dw6UkZOg5EqsjrxiOxOq4kamnc3NTmGtpeq6n68kvMfTW7zuvo0qDWUOodrw1tfx2TIeNHFo2rjtMYqaeo7F68orhaKzeoCXcupb9t7RYqujXpEtzKYcv6ohL1pNdCL8eNaRN6zrSpmuElMQMPYcvGXo8D1/SkZihp2tzYRhbOYzpIWPLcS3lVXX/Qp7Ys4LI7mNo22McHr4hDBw/FydXH84eXGvR/uzBdTi5+TJw/Fw8fENo22McbaJGc3zPcqPNxeO/ETVkJqGteuHqGUD73hMJjujOiRo2lojevZK2PcfQvtc4PBuFMGTiHJzcfDi1z7IWVw9/hk6aS2TUKOwVSos2Y2cspFPfifgGNsfTN5gRj7+HXq8j8fKxOrXcJqqlnP0xBi9DVoGenw/cKt8h1st3VEs519N0HDivJadIz4HzWq6n6YhqaSrfvdrIKSzVs+GQhtQcPQVqPQnpOvJLLOdXzq5DXHt7MZmb9zRId9D0CVQkZ3D5lQ9RX0kkZfkGUlZuIvjlqUabJs89Ru7eaBI+Xkbp1UQSPl5G7r7jNH6ufu/AhcMradZxDOGdxuHqHUK3EXNQOvtw+bjlvIo7vg6liy/dRszB1TuE8E7jaNZhNBcOmd4Jr4BWdHngNUIjH0Amr6PVVotLR36gafvRNOs4DhevELoMm4Ojsw9xJ9ZZtI87uQ5HF1+6DJuDi1cIzTqOo2n70Vw8bNISe3QVfqHdaNN7Oi5ewbTpPZ1GIV2IPbqqQZo6NZNwNFbP1VTIKYLfT+ixkUGLIOvfhU7NJNzIhOg4PXklhn+Tsgznb7PuoI4LN/TkFkN2IWw9qcPZUYKPm+U4z+5fQcsuY2jVbRzuPiH0HjMXlasPF45YzqcLR9bh5OpL7zFzcfcJoVW3cbToMpoz+4RlVyKR4OjkKThE7m/ERsU/CKVSiVKpZPPmzVRWWq9tvfnmm0aPxiOPPMLDDz9MXFwcAGVlZfTp0welUsmhQ4c4cuQISqWSwYMHU1VlWDp0yZIlPPPMM0yfPp2LFy+yZcsWQkNDrd5PpVKxcuVKLl++zOeff863337LZ599dsfPFx4ezpAhQ9i0aZPxXFVVFe+99x7nz59n8+bN3LhxgylTpgCGD87UqVNZsWKFIJ7ly5fTo0cPQkJC7ljDbdydJDg7SrmSbFoyVKuFhFQNTXz//NrSMikEeEm5mixscVy9qbUar5tKgpOjhGsppjBaHSSm6wjysV6EA72lxKcK73MtVWs1jEwK7cJknLpi3UvhqgSVg0Qw7Earg6RMPYFe1n/IAjylXK/VULmepiPQS6jFXSXhtYdseGWMDQ/1kuFquQ5nxEUJKoWEhAxT3FodJGXp66z8B3hKSMwQVqwS0vUEWAkjkRh+qG3kkGJh2EhNLTXj1ergZpZe0HtcG38PCQm1tWTo8bcSpqaW1BzrlXljXtVKm5tZeoG3ykyPp4SEdAtpU0vPA50Njdba6VgbraaKjORYmkR0F5wPjogiNeGcxTCpiTEER0QJ7Vv0ICPpElpN9a14q5Hb2Aps5Lb2pFw/a1WLRlNFRlIsoS2EcYe0iCLFipY/Q3VlOVqtBoVjPa4kbpVvBwnxqcJ8upGhI8jbevkO8pYKwgDEpwrDNA+SkpajY2I/G954xI7nH7SlY7O/bm18ly6R5Ow9KjiXs/swzu1bIpEbGjeuXSLJ3XtEYJO75zCuXdvWGbdWU0VuWiz+YcK88m8aRdZNy3mVlRyDf9Pa9t3JSY1Fp/3zSy5rNVXkpsfiV0uLX2gU2Va0ZCfH4Bdayz4sitw0k5bs5PP4hXYT6g2LIiu5/nfRxRGUCgmJmcLvTXK2wftpDT93YRiAxIy6v5e3PeIVFlYX12qqyEqJJShcWL4Dw6NIv2H5OTKSYggMF6ZN4/AeZCVfQlsjn6oqy/ju7T58+2ZPNi+dQXZK3V7Ie43oqTBHbFT8g5DL5axcuZIffvgBFxcXoqKimDNnDhcuXBDYjRs3jmnTptG0aVPee+89OnTowJdffgnAunXrkEqlfPfdd7Rq1YrmzZuzYsUKkpOTOXDgAADvv/8+r7zyCi+88AJNmzalY8eOvPjii1Z1vfHGG3Tr1o3GjRszfPhwXnnlFX7++ec/9Yzh4eECr8nUqVMZMmQIwcHBdOnShS+++IIdO3agVqsBePzxx7l69SonT54EoLq6mtWrVzN16lRL0TcYlaPhA1tSJiz5JWV647U/g6NCgkwqobhMWBkoKdejcrRcHFUOhvupy4Va1OV64zVr4UrKhOdKyrAapkUTGfZ2cOaq9b0XlArrWm5fsxzO4HkQhjGcv01Kjo4NRzT8sFvD5mgNKoWE6Q/YoKjD2620v6Wn1rL/pRXCuM3Dgbqi1jNU6M3CeLnA7PFy3nhYzrDOMtYf1JJrZT6C0v52PMLz6grTNWvhSi3prxXGywVef0jG3AkyHugk5edDdY+dvp0fpfWkuyU99aVNy8YSfN0k7D1b/zr+ZeoC9DotSid3wXlHJw/UxTkWw5QW5eLoJKwZKZ3c0ek0lKkLAAhu0Z0Te1aSn5WEXqcj8fJRrsX8gboo27qWkgJ0Oi2OtbQond1RF+XW+ywNZc+GT3Fy9Sa4Rbd6bW+na0mtMlVSrkflUHe4+sK4qSR0bi4jr0jP8h1VHI/TMqKbnHZhf81Pv523B5VZwnSrys5DamODrYdh3p2djweVWXkCm8qsPOx86u51rigzvDcKpTCvFEp3ykos51V5SY5Fe71OQ0VpQYOeybKWwltahO+kQuVOudqallwUqtpaPARaytW55nEqPSi38nw1cbz1fTD7dlTqcbSv41tsD6W1+iRLK03xWaJ/WynJOXqLc7HKSw355FDrWR1VHpSVWCnfxbk4qoTP7aAylO/yW+XbzSuYQZPmM3L6EoZM+RSZ3I71ix+mIDvJutB7jE4vuavj34g4UfsfxpgxY3jggQc4fPgwx44dY+fOnXz88cd89913xh782hO2u3btSkxMDABnzpzh+vXrqFQqgU1FRQUJCQlkZ2eTnp5Ov379Gqxpw4YNLF68mOvXr6NWq9FoNDg5Of2p59Pr9UgkpsJ27tw53nnnHWJiYsjPz0enM1RikpOTiYiIwNfXlwceeIDly5fTqVMntm7dSkVFBePGjbMYf2VlpZmXx87OjvbN5Izva/qqLt1yqyZWuzdBYuHcn8FSHDW6Lt57wqRlxfYqy0H+hBaJxHoPScdwGVeTdRTXaoi8Ock0RODHvRqLWiSSO0+W2mHi00x/ZRVCco6Gl8fY0DZESvRlU+V19njTZ+un/Za9Kn/mc20pTG4xfLNNg72thIhACaO6yVi5R2NsWLz+kKnXd+2BW1pqJURD0qYhvVa5xbB0uxZ7W2geKGVkVyk/7NEaGxatmkgY3sWkZ80+rSU5db4Ddem5fcrJAYZ0lLFqrwbNHe0NJkxhvV6PpK6ckpjbG04bzg8YP5ftq97gm7eGgESCq2cAbaJGc/7oJrOozKOuHTd1a7kDjmz/jksntjFl1ipsrIz/njfFdH7lzlvdv5bemzst3wjDSCSQlqtn12lDuU3P0+LtKqFzczln4/+iTS1ri7ydtjXPW7Jp4MOZ5VXNe1gOcGfn74TaUej1Fk7WNDcLYK7F7Pn0Zs8MBu/k0A6m8+sP/fmN+e7kvRrUXoKXC6zaW8/9LJbXutLc/LkN0RjO+zaJxLdJpPG6X5N2rPnkQWIOrabP2DcaqP5/y7/V23A3iI2KfyD29vYMGDCAAQMG8NZbbzFt2jTefvttY6PCErcLqk6no3379qxZs8bMxtPTE6n0znqwjh8/zoQJE5g3bx6DBg3C2dmZdevWsWjRojuK5zZxcXE0adIEgNLSUgYOHMjAgQNZvXo1np6eJCcnM2jQIONQLYBp06YxefJkPvvsM1asWMH48eNxcLDcxTd//nzjylO3efvttynzeoWbmaXGc3KZIb1UjhKKa3grVAqJmffiTigt16PV6XFylAKmj3TteBf/Ymr4yGWWbZT2ErOeypqUlJn3dBo8BuZhXJQSwvykrNptXtH4aovJHW1MF4VEEI+jvYTSOrRY6h13tDfvRa9JtQayCvS4Owl/fL7ZZvKk3E4bpb3QE+Jgb+4ZEeipuO3lED5D7TA6nWF1FNCTka+nkbuELuFStp4w5N3S7aZGjVGLQuitcLQz700002IhbWp7PExaICNfRyM3KZ3DpWw7adByNUVPWq4pbWTSGnpqPJejBc9IbT0qhTBtlPYSY141cpegVEiY8YDp50MmlRDkradTuJT3au0y7qB0RSKVoS4W9r6WleSZeSOMGp09KC0S9nKWluQjlcpROLoYbFRujHvmazTVlZSpC1G5eLF/00JcPPytPpuDyhWpVGbmlSgtzsPR2d1KqIZzdMf3HN66lEdfXY5PgPWdqr/YZCpnstvl20FYnpUW3seaqMtv55MJpUIYpqQMsguElcHsQj0tm/w1DajKrFwzj4Otpxu66mqq8goNNpm52PkI89nOy83Mw1EbewfDe1PbK1GhzsNBaTmvFCpPs17+cnUeEqkceweXBjyRNS0uSKQyC3Hnm3lGTFo8zLTX1mLwSgjf8wp1HvYW4oxP0/Ndnun9uF2+a38rHO0klFbU8S224AW19o0a2E5CUz8Jq/7QUWLlXVQ43sqn2uVbnYeDykr5dvKgtNZzl98q3/a3yndtJFIp3oGtKMxJsizkb0BsVJgjDn/6FxAREUFpqalCfPz4ccH148ePEx5uWLWkXbt2xMfH4+XlRWhoqOBwdnZGpVLRuHFj/vij7mUEb3P06FGCgoKYO3cuHTp0ICwsjJs3b/6p57hy5Qo7d+5kzJgxxr9zc3NZsGABPXr0IDw83DhJuyZDhw7F0dGRJUuWsGPHjjqHPs2ePZuioiLBMXv2bCqrDas23T4y83UUlepoFliz4gQh/nJuZNS99GtdaHWQkq2jWaBwXHOzQJkg3rxivfHIKtBTXKonLMAURiaF4EZSbmZa7z1KztIR5i+8T5i/zGKYjuEy1OVw5ab5tduTg/NLDBWSkjI9IY1MlRKZFBr7SEjOtv6FTcnREdJI+LkJbSQlOdu6fpkUPJ0lZo2gArXpyCkyDPkI9jXFLZVCY28JqVbmPhj06An2FVasgn0lVudLCHWZwlnWYroulUKQt4SUOuY+pObqCfYRagnxldQ5XwIMHYOyGklapRHmVU6RoWEZUiNtZLf11JFXqRbSJqSR6RkSM/R8taWab7ZqjEdaro6LiXq+2aox+6GVyW3xDWzBjcvCsfc34qLxD7E8rt4/OJIbcdFC+8tH8G3c0mxyrdzGDidXb3RaDVfO7qZppHUvq1xui2/jFiTECuNOvBxNgBUtDeXIju85+PsSHnnlW/yatKrTtmb5zi7QU1ymJ9RPmE9NfKXczLJePm5m6QRhAML8hWFuZunwcBHaeDpLKFT/NbWhwuMxePQTDvHyHNCdojOX0GsMjcuC4zF49BOOn/fo352CY3XPG5DJbfHwa0FavDCvUuOj8Q6ynFfegZGkmtkfxdO/BVJZwydlW9TSqAVp14Vxp1+PxsuKFq/ASNJr2afFH8XDz6TFK7CNWZxp16PxDjSPs0oj/N7kFhs6iJr4CL83gV6GJWCtkZYnDAMQ7GP+vRzUTkK4v4TV+3QUlWIVmdwW74AW3LwqLN/JV6Jp1MRy2vg2jiT5ivC5b145gndgS2RW8kmv15OTFndfTdbW6e/u+DciNir+QeTl5dG3b19Wr17NhQsXuHHjBr/88gsff/wxI0eONNr98ssvLF++nGvXrvH2229z8uRJ4+pNkyZNwsPDg5EjR3L48GFu3LjBwYMHeeGFF0hNTQUM+0gsWrSIL/6PvbOOjupa+/BzZhLiLsQJEYiSQLAIBJciRUqhcoG2lMotFapADfoVarSUyi3QFmlpgeLuToJLIIQACZAQd7eR749JZjLJzCSUSm7vedY6C2bPu/f57Xef2TnbFy/mxo0bnD9/Xr0moyl+fn6kpaWxZs0aUlJSWLx4MZs2bWoxLzKZjOzsbDIzM7l8+TJfffUVsbGxhIeH8/rrrwPg5eVFu3bt+Oqrr0hNTWXr1q188MEHzdKSSqVMnTqVWbNm4efnZ/C8DhMTE6ytrbUufdvjHrlQx+Ae7ejia4Srg4THhphSV6fkXLKm5/6xIaaMjNIsFpVKwN1RgrujBCMJ2Fiq/u9oo6nED5+vpXewMb2CjGhvJ2FsXxPsrCScuKx/IeHxyzIGdDUi2FtCezuBh/sbUyeDizc1DZGJ/Y0Z1tOoURw5/h4S+oUb4WQr0C/cCH93Cccua/ckC0D3zlLOXZe1qqKLuyontouUQC8BZ1uBcTFS6mRwKVXzMjM+RsrgbtJGcRT4uQn0CZHgaKPaltbXTdCa1jSsuxTv9gJ2lqrFy4/0N8LEGC7cNDzsfipJQZ8QCQGeAk42MCZSpefyLU28MVFSBoZrqrtT1xT4ugpEB0lwsFZtS+vjKnAqSRNnQLgELycBGwvVeoYBYRK82wta6TbTck1BTLCEzh4qLQ9GSqiTwZXbGsc+GClhgA4tUUGqfeGjggQ6ugicutZIS5gELyfUWvqHSejgLGilq4uTSQr6hKp842wLY6JVvklolIex0VIGdZVoxfF1E4gJluBordqW1sdVIL7eN7Uy1Y4wja9aGVTWqHaY0kWvwU9w8fh6Lh5fT35WCvvWzqekMItusao9+w9tXMjWH99Q23eLnURJQSb71i0gPyulPu4Geg/WdBhkpF7i2vm9FOWlk3bjLGsWT0OpVBA5dJpBn0QNmcr5o+s5f3QDeZkp7Pp1ASUFWfTor9Ky77eFbFz2placrLQkstKSqK2ppKKskKy0JHIzbqq/P77zew5uXMSYJz/E1tGdspI8ykryqKk28DbWiBNXZPQP1/y+J8TW/75TNL/vh/sZM7SHkVYcfw8JsWFSnGwEYsOk+LlLOHFF8/s+flmGl7NAv3ApDtYCYb4SegZIiU/UvW5KamGOdVgA1mGqTijzjh5YhwVg6ukKQOf/m0nYcs3ufHeWrsGsgxuBn76FZYAPHlPH4/nEeFI/1+zic/vrVTgOjsbntaex6OyDz2tP4zgwkttfrWzRL136TOXamfVcO7OBopwU4rYtoLw4i8D6cydO71rIobWasgrsPYnyokzity2gKCeFa2c2kHxmA136ap4b1aLrJPIzk1DI6qgozSE/M4mSfMOdYSExU7h+dgPXz26gODeFkzsWUF6SRUDPiQCc2fM5R35rpKXnJMqLMzm54yOKc1NUcc9tJLSPRktw1GQybsZx6cgyinNTuXRkGRk34wmOntyibwBOJyuJDhLo7A5ONjCql0CdHBLvaOqFUb0E+nURtOL4uKi2o3WwUv3r7aIKb2BYhECIt8DmeNUucxamqstIu49KTbf+T3Alfj1X4tdTkJ3C4Y3zKSvKokuMqpyOb13I7p80v+8uMZMoLcrkyMYFFGSnqOKe3EDEAI1v4nd9ze2kYxTnp5N7N4l9v8wm7+41ujQ6y0Kk7SFOf/ovwtLSkl69evHFF1+QkpJCXV0dnp6ePP3008yerTmMae7cuaxZs4bnn38eFxcXVq9eTVBQEADm5uYcPXqUN998k3HjxlFWVoa7uzsDBw5Ur4OYMmUK1dXVfPHFF7z22ms4Ojry0EMP6dT04IMP8sorr/DCCy9QU1PDiBEjeOedd3j//fcN5iUxMRFXV1ekUik2NjYEBQUxa9YsrcPvnJycWLFiBbNnz2bx4sV069aNzz77jNGjRzdL76mnnmL+/Pn3vUC7MQfO1WJsBA/1N8HcRHX43X82V1HT6N3fzkpAqdS8kNlYCLzxmIX688CIdgyMaMeNuzK+3qAaP75wQ4aFWQ1De5lgYy6QVaBgyZYqivRs8whw+KIMYyMY26dd/eFYCpZtr9HSYmslaE3LvpOj4Jf9tQztYcyQHkYUlCpZvb+2WS+1n4cEOysJZ661bo71sSsKjI0ERvc2wtRE1bO9Yq+M2kbvKbaWgnqOLKhGBtYdkTGomxEDu0opLIO1h2VavWPWFvBwrBHmJlBZrYqzZEcdxS28l524qsDICB7oKVUf8PbTAW09NhagbLQw7m6+kvXH5QwIk9I/TEJhOaw/Jiej0fQCS1OBsdESLM2gpk41FWv1QXmznVMaE3dVibFUyQM9VYfNZeTDzwflTbQI6vUBKi2w4biC/mES+neBwnLV54xGa1stTFUNo8ZafjmkMKgFVFvlGhnByF6qRfgZeUp+2m/YN+l5StYflTOgq5T+4RKKyuC3o3Kdh6W1lqAeD1BZUcTxHd9SXpKLk1snJs1Yio2DOwDlJXmUFGap7W0dPZk4Yyn71i3g3OHVWNo4M2TSHAIihqptZHU1HNmyiKK8dNqZmOMXGsvoJz/B1Nzweq6QXg9QWVHMka3fUFaSh7O7P4+9sgRbx0ZampxZ8d17Y9X/z7ydyOWT27F1cOOVzw4CcObgL8hldaz95iWteP0e/Df9x8xo0T9HLskxlgo8GG2MWTtVGfywq5baxr9vC0FrFCgtV8mvB+sY0t2IwRFGFJYq+eVAndao2N18JT/tq2NYDyMGdjWiqEzJtngZF1N0N4xtIkKIPPCT+nPQZ6q/K+mrNpLw1CxMXJ0wq29gAFTdvsuZUdMJWjiLDs89Rk1mLomvfEj2Js1ZDEXxF7jw2Ew6z32ZznNfpDIlnQuPvkLxae0NRnThG/YA1ZXFnD/wDZWledi7+DP8iSVY2anKqrIsT+ucCGt7D4Y9uYT4bR+RGP8LFtbORI2eg0+o5rmpLM1l45ea8kw4+iMJR3/E1acHo57R5L0pPl1UWi4c/Lb+ID5/hkz5Tq2lqiyP8mLNM2xl78GQKd9xaudHJJ38BXNrZ3qPnE3HEM0ZHu07dKX/xIWc2/cl5/d/hZW9JwMmLcTZM6xF3wDEX1NiZATDukvUh23+eljRvL5pVBdnFMCmOAWxXSTEhgoUlas+ZzY66iqifiH/vwZqtyK2nVJtNduUzt0eoLqiiFN7vqWiJBcH106MeXYp1vYq31SU5lFWpPGNjYMnY59ZypFNC7h0bDUWNs70Gz8H/3BNOdVUlbJ/zbtUlubRzswKZ48gJrz0My4dWn+g5J+N8h+62Pp+EJRKcVbYPwlBENi0aRNjxoz5u6X8pZw4cYJ+/fpx9+5d2rdvf8/xX/qybRy4B/DlS1a88Z2BydR/IZ88a8bbK/6gBZ1/AP83tR1zf/79W0P+kbz3uDHzVuvu8f2refcxI95b1Tb8AjB3sjGrjvzdKlRMjoU1cW3nz9ykKIG3lhlY1PIX8tHTpuww1r/2469mRF0yCze3jbJ6dYzAJxt+/2LoP5I3xkv4cM3vn3b7RzNnkpTv9rRs91fw7NCWbf4s7reOmxz7x+hoS4gjFSL/1dTU1JCens4777zDww8//LsaFCIiIiIiIiIi98I/dV3E/SCuqRD5r+bXX3+lc+fOlJSU8Mknn/zdckRERERERET+BxAPv2uOOFLxD+N/bTbb1KlTDW6lKyIiIiIiIiLyR/M/9rrVKsSRChERERERERERERGR+0IcqRARERERERERERG5B8Q1Fc0RGxUiIiIiIiIiIiIi94A4/ak54payIiIiIiIiIiIiIvfAkr0t2xjimSEt2/y3IY5UiIgAC9a1nT3AZz0sbTN6Zj0sZf7atqEFYPbEtqNn9kRpm9rHvq1oAZWeL7e1jf6ql0YJbebsA1Cdf/D5lrahZ+aDbc83beXcjBF1yRxJrPy7ZQAQG2zO9wf+bhUapg2EvZfaxvlFQ8La/W33FrvkmyM2KkRERERERERERETuAbFR0Rxx9ycRERERERERERERkftCbFS0caZOncqYMWP+bhkiIiIiIiIiIiL1KJT3d/0T+Z+Z/pSens7777/Prl27yM/Px9XVlTFjxvDuu+/i4ODwd8vj9u3bdOzYkQsXLhAeHq4O//LLL/+UA+0OHTrEvHnzuHTpEtXV1bi7uxMVFcUPP/yAkdH9PxaCILBp06b/+gZRTLBAuI+AqTFkFsLe8wrySw3H6ewOfUMk2FpCcTkcuaLgeobme09H6BUgwcUOrMwE1h+XcyPzv0tLn2CBcF+Nlj3nWqHFA2IbaTl8uYkWJ+jdWYKLvUZL4+//G/RcPfkLl4/9SFVZHrbOfvQeMQuXjt312melnubUzo8pzr2JuZUzoX2fIrDXJC2bW1f2cn7fYkoL07C29yJiyEt4Bw9uySVtSgvAlRO/cOHwD1SW5WHf3o/oB2fj5qNfT0bKaeK2fkRhzk0srJ0J7zeNkCiNnpTLezl/YAkl+Wko5DJsnDoQHvsEnSMebFFLYvwvJBxRabFr70fkqNm4GvBNZuppTm7/iKKcm5hbOxMWO42g3hotSafWceP8FgpzbgDg5B5Mj2Gv4OzZpTWuITHuFy410hM1ugU9KaeJb6QnPHYaQZHaeq6f09bTc9grOHu1rOeP9k1h9g3O7ltMfkYi5UWZRI6cRWifKS3qsI/pjs+rT2HTLQRTN2fOjn+enK2GFxbY9+lB0GdvYRnkT01mLikLvydt6RotG5exQ+j0/kuY+3pRmZJG8rtfkLNlf4t6AA7vWseeLSspKcrHzdOXiU++hn9QN522508e4Mju30i/nYysrg43Tx9GTXyW4K5RapvMtBS2rPmWtJQkCvKyePiJ1xg06rFWablwZDVn9v9AeUkejq7+DJgwGw8//eWUfv00hzZ8RH7WDSxtnOk5eBrhfR9Rf7/mi3+RfuN0s3g+wbGM//fSFvUc3bOGA1tXUFqch6uHL+OmvolfYIRO25KiPDat+pT01CTysu8QO/wxxk99s5ndoR0/cXzvOorys7CwtiW812BGP/oyxu1MWtTzV3D/72bCH6KjLfE/MVKRmppK9+7duX79Or/++is3b97ku+++48CBA0RGRlJYWPin3buuru6+4tvY2GBra/vHiKknMTGR4cOH06NHD44ePcrly5f56quvMDY2RqFoO4s9/256Bwj07CSw97yCFfsVVFQrmRQroZ2BNpe7A4yJlHDljpIf9iq4ckfJmEgJbvYaG2MjyC1Wsvd8633d5rR0Fth7TqPlkX4taxkbKeHyHSU/7FFw+Y6SsVFNtEjrtZy7t2ewrehJTdjJqR0fEd7vGca8sBEX7wj2rHyG8mLdrbSywrvsXfksLt4RjHlhI2H9pnNy+3xuXdFsKZKTdoFDa2bi13U0Y2dsxq/raA7+OpPc9Ev/NVoAblzcyfGtC4gY9CwTXtmEq093tn8/nbIi3XpKC+6y4/tncPXpzoRXNtFt4DMc3/IhKQl71DamZjZEDHyWcTPWMPHVLQT0GMfBtbNJSz5mUEvKpZ3Eb1tA1wHPMu7FTbh4d2fXj9Mp16el8C67f3wGF+/ujHtxE137P0Pc1g9JvazRkpV6Gt/wEYycvpIxz6/BwtaVnd8/RUVJTou+uXlxJ3H1esa/tAmXjt3Z+YMB3xTeZdePz+DSsTvjX1LpOdFET2bKafzCRzDqmZWM+fcaLG1d2dEKPX+Gb2R11Vjbe9Jz2KuYWTm16I8GpBbmlCYkk/jSvFbZm3l70GPbUgqPn+N4jzHc/Pg7gr+Yg8tYzRY7tr3D6frLF2Ss3sKxiAfJWL2Fbr8uwrZny42tM8f3sHb5pzww/ineWfgr/oFdWfx/L1CQl6XT/kbieQLDevPinK+Z8+lqOof04OsFL5GWek1tU1tTjVN7D8b+60WsbR1blU+Aa2d3cnD9AnoPe44pszbj4RfB+m+eprRQdzkV56ez4dvpePhFMGXWZnoPe5YDv31I8gVNOT04/SueW3BcfT3x9nYEiZTO3Ya1qOdc3G42rviYoeOe5s2Pf8M3MIL/zH+OwnzdvpHV1WJpbc+QcU/j3kH3wvwzx7az9ZdFDJ/wLHO+2MKjz87jfPwetv6yqGUH/UUolfd3/RP5n2hU/Pvf/6Zdu3bs3buX2NhYvLy8GD58OPv37ycjI4M5c+YA4O3tzQcffMCjjz6KpaUlbm5ufPXVV1pplZSUMH36dJydnbG2tmbAgAFcuqT5o/r+++8THh7Ojz/+iI+PDyYmJiiVSnbv3k1MTAy2trY4ODgwcuRIUlJS1PE6duwIQNeuXREEgX79+gHNpz/169ePF198kTfeeAN7e3tcXFx4//33tTReu3aNmJgYTE1NCQoKYv/+/QiCwObNmwHYt28frq6ufPLJJ4SEhODr68uwYcP4/vvvadeuHRUVFVhbW7N+/XqtdLdt24aFhQVlZWXU1tbywgsv4OrqiqmpKd7e3ixYsEDtR4CxY8ciCIL6c0MaERERmJqa4uPjw9y5c5HJZOrvBUFgyZIljBw5EnNzcwIDA4mPj+fmzZv069cPCwsLIiMjtXx36dIl+vfvj5WVFdbW1kRERHD27NkWnoqW6eEvEJek5HoG5JfC9tNKjKUQ5KW/d6G7v8CtHIi/pqSwTPXvnRzo0UkTJzUbjl5RtroXvq1p6dlJ4MRVJckZkFcC206ptAR30K+lR6d6LUlKCspU/97WoeXIFVW690Jb0XPl+Eo6RYyjc48J2Dr70nvkbCxsXEg6tUanfdJp1ctn75GzsXX2pXOPCXSKGMflYz+qbRJPrMLdL4qwftOxdfYhrN903Hx7k3hi1X+NFoBLR1YQ2HM8Qb0mYN/el5gHZ2Np68KV+F912ifGr8HSzpWYB2dj396XoF4TCOgxjotHNHrc/XrhEzoY+/a+2Dh6EdZnMg6uncm6dd6gloRjK+jcYzwBPSdg196XqNGzsbRx4epJ3VqSTqpeyqNGz8auvS8BPSfQufs4Eo5qtAx45DOCIx/F0S0QW2cf+o7/AKVSQcbN+BZ9c/nYCgJ6jCewl0pP9GiVb/TpuXpS5Zvoej2BvVR6LjXyzcBHPyM4SqXHztmHvg+1Ts+f4Rtnz1B6j3gDv/ARSI2MW/RHA3l7jnL9vUVkb97XKvsO0ydRnZbF1VfnU34tlfQf15O+YiM+M59U23ScMYX8/XGkfLKUiuRUUj5ZSv7Bk3jPaHnkZN+2n4kZOIY+g8fh6uHDxKdex87BhSN7ftNpP/Gp1xk2dire/sG0d+vA2Mdn4OzqRcLZI2obb/9gHpryCj1jhmFs3HrfnD24nNCo8XSJnoCDqy8DJszBytaFi0d1l9OlY2uwsnNlwIQ5OLj60iV6AqGR4zizX1NOZha2WNo4qa/b105g3M6UTq1oVBzavorIAeOIGjgeFw8fxk99EztHF47vXavT3sHZnYeeeItesaMxNbfUaXPr+iV8Onele8wIHJzdCQyLIiJ6OGmpV1vhob8GheL+rn8i//hGRWFhIXv27OH555/HzMxM6zsXFxcee+wx1q5dqx7G+vTTT+nSpQvnz59n1qxZvPLKK+zbp6rUlEolI0aMIDs7m507d3Lu3Dm6devGwIEDtUY7bt68ybp169iwYQMXL14EoKKigpkzZ3LmzBkOHDiARCJh7Nix6pGB06dVw4779+8nKyuLjRs36s3TypUrsbCw4NSpU3zyySfMmzdPrVGhUDBmzBjMzc05deoUS5cuVTeaGuc7KyuLo0eP6kzfwsKCSZMmsXz5cq3w5cuX89BDD2FlZcXixYvZunUr69atIzk5mZ9//lndeDhz5ozaPisrS/15z549PP7447z44otcvXqVJUuWsGLFCj788EOt+3zwwQdMnjyZixcvEhAQwKOPPsozzzzDrFmz1I2FF154QW3/2GOP4eHhwZkzZzh37hxvvfXWPVXQurC1AEszgVvZmu4EuQLS8sDDQIeSu4PArRztLojUHCXuDr9/mPO/RYu7gVmE7g4CqdlNtGQr8XC8v+HftqJHLqslPzMRd/9o7fv4RZN754LOOLlpF3H3a2LvH01+RiIKeV29zSXc/aK0bDz8o8lJ051mW9PSoCcvIxHPTtrpe3aKJue27rjZdy42s/fqHENeeiJyefPRX6VSyd0b8RTn3jI4pUouqyU/IxGPJr7x6BRNjh7f5KRdxKNTU/sY8u5qfNMUWV0VCrkME3MbvVoa9ORlJDZP31+/b3LuXGym37NzDPl3dfsGQFZbr8dMv56/yjd/Fra9w8nbf0IrLG/vMWwiQhDqp/Ta9Q4nf/9xLZv8fcewi+xqMG1ZXR1pKUkEhUVqhQeF9yblWssjdaD621xdVYm5peFnoiXkslqy0xLxDozRCvcOjCYjVXc5Zd66iHegdjl5B/Uh584Vvc/M5bgNBESMoJ2JuUE9Mlkd6alXCQjTrhsCukRxK/liC7nRj29AN9JTr3L75mUA8nPSuXrhGMHd+vzuNP9oxJGK5vzj11TcuHEDpVJJYGCgzu8DAwMpKioiLy8PgOjoaN566y0AOnXqxIkTJ/jiiy8YPHgwhw4d4vLly+Tm5mJioprT99lnn7F582bWr1/P9OnTAaitreWnn37CyUkz1Dt+/Hit+/7www84Oztz9epVQkJC1LYODg64uLgYzFOXLl147733APD39+frr7/mwIEDDB48mL1795KSksLhw4fV6Xz44YcMHqyZ9zxhwgT27NlDbGwsLi4u9O7dm4EDBzJ58mSsra0BmDZtGlFRUWRmZuLm5kZ+fj7bt29XN17S0tLw9/cnJiYGQRDo0KGDOv2GvNja2mrl5cMPP+Stt95iyhRVr5CPjw8ffPABb7zxhjo/AE888QQPP/wwAG+++SaRkZG88847DB06FICXXnqJJ554Qm2flpbG66+/TkBAgNon94uFqerfimrt8IpqJTYWAqC7RrA01RVHk94/Wov5X6ulLemprixGqZBjZqndyjOzcqDqRr7OOFVl+Zh10m75mFk6olTIqK4owtzamary/OZpWjpSVaY7zbamBaC6okilx0o7fXMrB9L1xK0sy8PcSvulyczKAUW9HgtrZwBqqspY+UEsClktgkRC33HvNWuMaGmprNdi2TSvDlTq0VJVloeZZUwz+8a+acrpXZ9jYdO+WSOsmZ4KPXqsWtDT1DeWzX3TmFMNevz16/mrfPNnYdLekZocbZ21uQVIjI1p52hHTXYeJi6O1OQUaNnU5BRg4mJ4WlZ5WREKhRxrW3utcGsbB0qLC/TE0mbf1p+ora6ie9T9nXhWVa4qJ4smvycLa0cqSvN0xqkozcfCWvu3a1H/e6oqL8LSRrucsm4nkJ95nWGPa3f46U5b5RsrG209VvfgG11ERA+nvLSQRe9MRgko5DJihkxkyJhpvztNkT+ff3yjoiUaRigEQdVDGRmp3RMRGRnJokWLADh37hzl5eXNFnZXVVVpTcfp0KGDVoMCICUlhXfeeYeTJ0+Sn5+vHqFIS0sjJCTknjR36aI9/9PV1ZXc3FwAkpOT8fT01HqZ79mzp5a9VCpl+fLl/N///R8HDx7k5MmTfPjhh3z88cecPn0aV1dXevbsSXBwMKtWreKtt97ip59+wsvLi759+wKqaVmDBw+mc+fODBs2jJEjRzJkiOHK8ty5c5w5c0ZrZEIul1NdXU1lZSXm5ubN8te+fXsAQkNDtcKqq6spLS3F2tqamTNnMm3aNH766ScGDRrEhAkT8PX11amhpqaGmpoarTATExOCvYwZFqHppV53XFU+TV9JBf3vqRqafP97+r5fHasZRPy7tbw2rpGWY7q16AszhPA7Bynamh7tRJqKUOoIbGzeLEJzMU2EKVGq66v/Gi060ldVvYbiNvmuQU6j8HYmFkycuYm6mkru3ojnxNaPsLb3wN2vl2EtzfKB4QdA33c6wi8e/p6UizsY+cwqjIxbuaC0aTrK5hq1zPX4TVecBj2jnm2dnj/TN386Tbt/GzQ0Dtdl09puYx3Pf2vyefrYLrat/Y7n3/qiWcPkd9NUi7Kl36IO7TrCARLi1uPo1glX79ZtNKBDDqqHuNXRm3Ej8Qx7Ni7j4Wlv4+0fSl52OhuWf8RuW0eGPfTs70/4D+SfuoPT/fCPn/7k5+eHIAhcvap7Ht61a9ews7PD0VH/PJKGH6pCocDV1ZWLFy9qXcnJybz++utqewsLi2ZpjBo1ioKCApYtW8apU6c4deoUoBrVuFeaTu0RBEHdSGm5YtHg7u7Ov/71L7755huuXr1KdXU13333nfr7adOmqadALV++nCeeeEKddrdu3bh16xYffPABVVVVPPzwwzz00EMG76dQKJg7d66W7y5fvsyNGzcwNdV0ETfOX8P9dIU15Pn9998nMTGRESNGcPDgQYKCgti0aZNODQsWLMDGxkbrWrBgATcylfy4T6G+qurbHZZNeq7NTQQqavTXJOXVYKE9yw5zHb3iLdGWtPywV6G+KvVosTAVqKhuQUsz/feupS3qATA1t0WQSJv12leVFzbr+W3AzMqxWQ9wVXkBgsQIU3NblY2lI1Vl2r2P1eUFmOpJs61pATC1sEOQSHWmb26lO665lZNOe4nECBMLW3WYIJFg49gBR/dAwvs9iW+XoZw/qH+nGlNz3Vqqywsw1+sbJx2+1PZNA5eO/MDFQ0t4YNr3OLi2fDJ0g290pa+/rAz4RoeeCweXMKIVev5s3/zZ1OTkNxtxaOdkj6KujtqCYpVNdj4mLtp/602c7ZuNcDTF0soOiURKaZF2z3tZSSHWNoYbCWeO72HlN/OY/uonBIX1bmVu9GNmqSqnilJtzZVlBZhb6X6P0TWKUVlWiERihJmlrVZ4XW0V187uoEuU4b/nmrTrfVOsyze/f2fN7Wu/pmffUUQNHI+bVyfCeg5k1CMvsnfzD21mQ5m/evrTt99+S8eOHTE1NSUiIoJjxwxvSnHkyBGtNayN3+/+LP7xjQoHBwcGDx7Mt99+S1VVldZ32dnZrF69mokTJ6pfVE+ePKllc/LkSfW0mm7dupGdnY2RkRF+fn5al6FGSUFBAUlJSbz99tsMHDhQPeWqMe3aqY6al8vl95XfgIAA0tLSyMnR7PLRsKbBEHZ2dri6ulJRUaEOe/zxx0lLS2Px4sUkJiaqpy01YG1tzcSJE1m2bBlr165lw4YN6rUlxsbGzfLSrVs3kpOTm/nOz88PieT+HsVOnTrxyiuvsHfvXsaNG9dsPUgDs2bNoqSkROuaNWsWtTIoKtdc+aVQXqXEu72mgSaRgJcT3DXw9yejQEnH9tqNuo7tBTIK7q0GaetaOro015JhYKRblxYfF4G7+fdes7Y1PQBSo3Y4ugWTcTNOKzzzZhzOHXTP13b2CieziX3GjRM4ugcjkRrX24Q1SzPjZhztvfTPAW9LWhr0OLkHk35dO+7d63G099Yd16VDOHeb2KdfP4GTZzBSqf71UkqUyGX6O2qkRu1wdA8m40YTLTfiaK/HN+29wrnbzP4ETh4a34DqBf78gf8w/MllOHmENk1Grx4n92Ad6ev3TfsOOvRcP4Gjh7ZvLh5W6XngqWU4ebas58/0zV9B8cmLOA7Unt7lNDiGknNXUNZvBlJ08iKOA7WnxzkOiqEo3vC6ICNjY7x8A7l6Sfv9IOnSSXwDwvTGO31sFyu+fo9pr8ynS/c/Zi2A1KgdLl7B3EnSXj9y51oc7j66y8mtYzh3rmmX0+2k47TvENLs95R8bhdyWS1BPUe3So+RkTGePkFcS9DeBCA5IZ6OncNblYYu6mqqmnWQChJp/dt42xgiUCqU93XdC2vXruXll19mzpw5XLhwgT59+jB8+HDS0tJ02t+6dYsHHniAPn36cOHCBWbPns2LL77Ihg0b/ois6+Uf36gA+Prrr6mpqWHo0KEcPXqU9PR0du/ezeDBg3F3d9eajnPixAk++eQTrl+/zjfffMNvv/3GSy+9BMCgQYOIjIxkzJgx7Nmzh9u3bxMXF8fbb79tcLchOzs7HBwcWLp0KTdv3uTgwYPMnDlTy8bZ2RkzMzN2795NTk4OJSUlvyuvgwcPxtfXlylTppCQkMCJEyfUC7UbfqBLlizhueeeU6+/SExM5M033yQxMZFRo0Zp6R43bhyvv/46Q4YMwcPDQ/3dF198wZo1a7h27RrXr1/nt99+w8XFRb39rbe3NwcOHCA7O1vdgHr33XdZtWqVemQhKSmJtWvX8vbbb/+uvIJq6tkLL7zA4cOHuXPnDidOnODMmTN619CYmJhgbW2tdTWsj2nKmRtKogIFOrmDozWM7CFQJ4eraZrKYGRPgdhQTcV39oaSju1V25zaW6n+9W4PZ65r4hgbgbOt6gKwtRRwtgVrA+vh2pKW09c1WpxsYFRPlZbEO5p0R/US6NdIy5nrSnxcVBocWqnFxqJlLW1JT0jMFK6f3cD1sxsozk3h5I4FlJdkEdBzouqeez7nyG+avdgDe06ivDiTkzs+ojg3RRX33EZC+2h2qwmOmkzGzTguHVlGcW4ql44sI+NmPMHRkw36pC1pAQiLnUrS6fUknd5AYU4Kx7csoKw4i5D68wzidy5k/68aPcGRkygryuTE1gUU5qSQdHoDSac3EB6r0XPuwBLSr5+gpCCdotxULh5ZzvWzW+gUYfhlqEufqVw7s55rZzZQlJNC3LYFlBdnEViv5fSuhRxa28g3vSdRXpRJ/LYFFOWkcO3MBpLPbKBLX42Wi4e/58yeRcRO+BAre3cqy/KoLMujrqai2f2bEtpnKtdON9KzVaWn4ayHU7sWcnCNRk9QvZ64RnqundlAWOz96/kzfKPaOCCJ/MwkFLI6KkpzyM9MoiT/jkEtUgtzrMMCsA5TdeqZd/TAOiwAU09XADr/30zCln+str+zdA1mHdwI/PQtLAN88Jg6Hs8nxpP6uWaHo9tfr8JxcDQ+rz2NRWcffF57GseBkdz+aqVBLQCDRz3O8QObOH5gM1l3U1n742cU5mcTO0TVo7/x58X8+KXmb9npY7v4cfG7TJgyE59OoZQU5VNSlE9lRZnaRlZXR/qtZNJvJSOT1VFcmEv6rWRys3S/KDbQfcATJMSt53LcegqyUji4fj6lRVmE9VGV09HNC9mx4g21fVifSZQWZnJw/QIKslK4HLeey3Eb6DHoyWZpJ8Stxz9sEGaWdi36pIH+IycTf2AD8Qc3kX03lQ0rPqYwP4uYwaq1kVt/WcSqr2drxbl7+xp3b1+jprqS8tJC7t6+RtZdzTTykIh+HN+3jnMndpGfe5drCXHsWPs1Id37IZFIW63tz+SvPPzu888/56mnnmLatGkEBgayaNEiPD09+c9//qPT/rvvvsPLy4tFixYRGBjItGnTePLJJ/nss8/+gJzr539iTYW/vz9nz57l/fffZ+LEiRQUFODi4sKYMWN47733sLfXDF+++uqrnDt3jrlz52JlZcXChQvVC4QFQWDnzp3MmTOHJ598kry8PFxcXOjbt6967r8uJBIJa9as4cUXXyQkJITOnTuzePFi9baxAEZGRixevJh58+bx7rvv0qdPHw4fPnzPeZVKpWzevJlp06bRo0cPfHx8+PTTTxk1apR6ilHPnj05fvw4zz77LJmZmVhaWhIcHMzmzZuJjY3VSu+pp57il19+4ckntSsfS0tLPv74Y27cuIFUKqVHjx7s3LlTPeKwcOFCZs6cybJly3B3d+f27dsMHTqU7du3M2/ePD755BOMjY0JCAhg2rTfv/BKKpVSUFDA5MmTycnJwdHRkXHjxjF37tzfnWYDJ68pMZLC0G4STNtBZgGsOaKgVrMDLtbmgtYBOBkFsPmkgtgQCX2DBYoqYHO8gsxGR6G42sFj/TWV4qBwlc8SbinYcUZ3TdPWtBhLYVjEPWqJVxAbKiE2pF5LXHMtjw/QaBncVaNl+2n9NXBb0ePT5QGqK4u5cPDb+oPD/Bky5Tus7NwB1aLW8mLNvu1W9h4MmfIdp3Z+RNLJXzC3dqb3yNl0DNGsTWrfoSv9Jy7k3L4vOb//K6zsPRkwaSHOnvp7R9uaFgD/8AeoqSjm7L5vqCjNw8HFn5FPLcHKXqWnsjRP6ywEawcPRkxbwomtH3H5xC9YWDsT8+AcfLsMVdvIaqs4unEe5cXZGBmbYuvckYGPfoJ/+AMGtfiGqXxz/sA3VJbmYe/iz/Anlqh9U1mWp3Weh7W9B8OeXEL8to9IjFdpiRo9B59QjZarJ39BIa9j/88vad2r26B/033wDIN6/MIfoKaymHP7G+l5spGe0uZ6hjfoiVPpiW6iJzFepWffT9p6Igb9m+5D9Ov5M3xTWZrLxi/Hqj8nHP2RhKM/4urTg1HP/KRXi01ECJEHNN8HfaZ6KU1ftZGEp2Zh4uqEWX0DA6Dq9l3OjJpO0MJZdHjuMWoyc0l85UOyN2nOWimKv8CFx2bSee7LdJ77IpUp6Vx49BWKTyfo1dFAj5ihVJSVsGPdUtXhd15+zJjzFQ7ObgCUFOVTmJ+ttj+6dwMKuYxfli3gl2UL1OGR/UfxxAzV2RvFRXl88KrmoMC9W1axd8sqOgVH8NoH3+vVEtD9Aaoqiojb+S0Vpbk4unZi/PNLsXFQlVN5aR5lRZrft62jJ+OfX8rBDQu4eHQ1ljbODJwwh85dh2qlW5hzi4yUc0yY8SP3QkTUMCrKitm94TtKi/Jw9fTjuVnfYu/U4Js8ipqcWfHxGxPU/09PvcrZ4zuxd3Jj7jeqszOGjp8OgsD2NV9RUpiLpbUdIRGxjHzkxXvS9mdyvzs46Vvj2bSjs7a2Vr2zZWOGDBlCXJz2CFQD8fHxzda5Dh06lB9++IG6urr73iFTH4Lyzziu+b8Ub29vXn75ZV5++eW/W8ofyokTJ4iJieHmzZt6FzDrY/Xq1bz00ktkZmaqp2j9E1mw7v6mnf2RzHpY2mb0zHpYyvy1bUMLwOyJbUfP7IlSPtnQNub2vjFe0ma0gErPl9vaxp+Wl0YJLNzcNrQAvDpG4PMtbUPPzAfbnm92GLe8FuWvYERdMkcSK/9uGQDEBpvzveHDxP9Spg2EvZfufT3on8GQsL/vveTj9fdX51ZdmdesA/S9995rdvZYZmYm7u7unDhxgqgozRS/+fPns3LlSpKTk5ul3alTJ6ZOncrs2ZoRori4OKKjo8nMzMTV1bVZnD+C/4mRiv81Nm3ahKWlJf7+/ty8eZOXXnqJ6Ojoe2pQVFZWcuvWLRYsWMAzzzzzj25QiIiIiIiIiIjcC4r73P5p1qxZzabC65uODTp2ZmthYx5d9rrC/0j+J9ZU/K9RVlbG888/T0BAAFOnTqVHjx5s2bLlntL45JNPCA8Pp3379syaNetPUioiIiIiIiIi8t/H/e7+1No1no6OjkilUrKzs7XCc3Nz9U69d3Fx0WlvZGTU7FiEPxJxpKIRt2/f/rsl/CFMnjyZyZNbXjxpiPfff7/ZEJyIiIiIiIiIiMhfdyp2u3btiIiIYN++fYwdq1mftG/fPh588EGdcSIjI9m2bZtW2N69e+nevfuftp4CxJEKERERERERERERkXtCoVTe13UvzJw5k++//54ff/yRpKQkXnnlFdLS0nj2WdVBgLNmzdLqTH722We5c+cOM2fOJCkpiR9//JEffviB11577Q/1QVPEkQoRERERERERERGRe0D5F+6N0bBz6bx588jKyiIkJISdO3fSoUMHALKysrTOrOjYsSM7d+7klVde4ZtvvsHNzY3Fixczfvz4P1Wn2KgQERERERERERERacM8//zzPP/88zq/W7FiRbOw2NhYzp8//yer0kbcUlZERERERERERETkHpj7c919xX/v8b/21Pm/AnGkQkQEmLda1rLRX8S7jxnx4Zq2cRbDnElS3lt1fxXnH8ncycZ88GvbKKt3HjFqU2dmtJWzTUB1vklbeobvdz/5P5I3H5Lwza6/W4WKfw+nzZ1v0pbOhmhLZ2asOvJ3q9AwORZ+PtY2+qMf7/PnbY/aEoq289NpM4iNChEREREREREREZF7QJzo0xyxUSEiIiIiIiIiIiJyD9zn2Xf/SMRGhYiIiIiIiIiIiMg9oBRbFc0QGxX/40ydOpWVK1eqP9vb29OjRw8++eQTunTp8jcqaxvEhkro5idg2g4yCmDXGTl5JYbjBHgK9A+TYGcJReVw8KKC5LuayifCX6C7vwRbS9XnvGI4ekXBzUzDFVSfEIGuvgKmxpBZCLvPKsgvNayls4cqDw1ajiQoSM7QfB8VKNDZQ8DBGmRyuJsPBy8pKCwznG6/MAkR/hLM2sHdfCU7TrXsl0AvgQHhUuytoLAMDlyQcy1dd577hEgY1E1K/FU5u8+2PHG1b4iEbr6actp9Vk5eC74J8BDo10Xjm0MJ2uUUHSQQ4CFp5BslBy4qKGjBN32CBcIbldOec60spxDVM1FcDocvK7jeqJw8naB3Zwku9mBlJrD+uFzre0PEBAuE+2j07D3fCj3uKp826DlypYkeR+gVIMHFTqPnRmbLWtrSM6xUKrlw8BuSz6yjtqoUJ88uRI56B7v2/gbj3b6yl/P7F1NamIa1vRfdBr+Ed/BgLZukk79w+fiPVJXlYevsR68Rs3Dx7q43zYTjqzl/8AcqSvOwd/Gn79jZuPvqt7978zTHNn9EYfYNLGyciRgwjdDoR9TfXz21kf2/zmoW7/lPEzAybn5ib2OunvyFy8c02nuPmIVLR/1aslJPc2rnxxTn3sTcypnQvk8R2GuSls2tK3s5v0/js4ghzX2mj8O71rFny0pKivJx8/Rl4pOv4R/UTaft+ZMHOLL7N9JvJyOrq8PN04dRE58luGuU2iYzLYUta74lLSWJgrwsHn7iNQaNeqxFHfYx3fF59SlsuoVg6ubM2fHPk7P1gOE4fXoQ9NlbWAb5U5OZS8rC70lbukbLxmXsEDq9/xLmvl5UpqSR/O4X5GzZ36Kes4dXc3LPD5SX5OHk5s/gibPx8tdfTneST7P/t4/Iy7yBla0zvYdOIyJW88zIZXXE7V5CQtxmyopzcHDpyIBxr+Eb0rdFLQBnD/1C/J4fKCvOw8nNj6GTZuPVSbeesuJc9q37mKw7iRTm3qHnwH8xdNLsZnZJ5/ZwePNiivLSsHPyov/Ylwno1rrnRuTvQTz8ToRhw4aRlZVFVlYWBw4cwMjIiJEjR+q1r6trOwt39fFHaIwKEugdKLDrrILvd8spr1Ly+AAp7Qw0xT0c4aEYCQm3FCzZKSfhloKH+khwd9DYlFXCgYsKlu2Ss2yXnFs5Sib2leBkoz/dyACBXp0F9pxTsHyfgvIqJY/2lxjU4u4A46IkXLmt5PvdCq7cVjI2WoKbvcbGy1ng3E0lK/Yp+OWwAokAj/aTYCzVn25MsITIQAk7T8tZulNGeRVMHmzUgl8EJvSVkpCq4D/bZCSkKng4Voq7Y/NFdm4OAhH+ErILW9cLFBUo0DtAYPc5BT/slVNRreSx/obLyd0BxkdLuHxbwdJdci7fVjA+WoJbo3LychY4c0PB8r1yVh+SIwjwaH+pQd/0DhDo2Vlg7zkFK/YrqKhW8ki/lstpbKSEy3eU/LBHweU7SsZGaZeTsRRyi5XsPXdvKwN7Bwj07CSw97xGz6TYlvWMiZRw5Y6SH/YquHJHyZjIJnqM6vWcb72etvQMA1w+9j2JJ1YQOeptRj+/DjNLR3Yvf4q6mgq9cXLTLnBo7Ux8u45mzIzN+HYdzaE1M8lNv6S2SU3YyamdHxEW+wwP/nsj7b0j2LvyGcqLdbe6rp/fydFNC+g++DkeeW0z7j4RbF3yNGVFuu1LCtLZunQ67j4RPPLaZroPepYjGz/k5qU9WnbtTC15at5xraulBkVqwk5O7fiI8H7PMOaFjbh4R7DHgPaywrvsXfksLt4RjHlhI2H9pnNy+3xuXdmrtslJu8ChNTPx6zqasTM249d1NAd/1faZPs4c38Pa5Z/ywPineGfhr/gHdmXx/71AQV6WTvsbiecJDOvNi3O+Zs6nq+kc0oOvF7xEWuo1tU1tTTVO7T0Y+68XsbZ1bFFDA1ILc0oTkkl8aV6r7M28PeixbSmFx89xvMcYbn78HcFfzMFl7BC1jW3vcLr+8gUZq7dwLOJBMlZvoduvi7DtabhD7+qZnexbu4DoB55j2jub8fSPYM3ipykp0F1OxfnprP1qOp7+EUx7ZzNRw59l75oPuXZO88wc2bKI80fXMvSRd3hm7k669Z3E+v+8QHba1Rbzmnh6J3vWLCDmgWd5+t1NeHXqzi9fTterRy6rxcLKnpgRz9LeI0Cnzd2UC2xYMpPQyNFMf28LoZGj2bDkFTJSW35u/iqUyvu7/omIjQoRTExMcHFxwcXFhfDwcN58803S09PJy8vj9u3bCILAunXr6NevH6ampvz8888oFArmzZuHh4cHJiYmhIeHs3v3bnWa48ePZ8aMGerPL7/8MoIgkJiYCIBMJsPKyoo9e1SV2vr16wkNDcXMzAwHBwcGDRpERYXmj/vy5csJDAzE1NSUgIAAvv32W/V3+jTeL70CJBy7ouBaupK8EtgSr8DYCEK89e820StAQmq2khOJSgpK4USiklvZSnoFaH5q1zOU3MxUUlim6rE/dElBrQydL9gN9OwscCJRSfJdyCuBbaeUGEshuIPhOLeyIS5JSUGZ6t/bOarwBtYcUZBwS0l+KeQWw/bTCmwsBFzs9SZL70AJxy4rSEpTklsMm07IMTaCLh31VyeRQRJSs5Qcu6LqmT52RUFqlpLIQO047YxgfB8pW0/KqaptXa3bs7OE44kKrt2tL6eT9eVkwDe9OteX01WVb05cVXIrR0mvzho9vx5W+SavFHKKYdspBbYWAq4GfNOzk8CJq0qSM1pfTj06CdzKgfj6coqvL6cenTRxUrPhyBWlVg99a+jhLxCXpOR6BuSXwvbTKj1BXvr1dPev13NN9YzGX1NyR4eeo1eUrR4tgbb1DCuVShJPrCKs3zN4Bw/Brn0n+j70EfK6alIubdcbLzFuFW6+UYTFTsfWyYew2Om4+fYmMW6V2ubKiZV0ihhH5x4TsHX2pfeI2VjYuHDt1BqdaV44vJzgXuMJiZyAvYsvfcfNwdLWhYTjv+q0v3JiDVa2rvQdNwd7F19CIicQ1Gsc5w/+2MRSwMLaSetqiSvHm2gfqdKepEd70uk1WNi60nvkbGydfencYwKdIsZx+ZhGS+KJVbj7RRHWbzq2zj6E9av32YlVOtNszL5tPxMzcAx9Bo/D1cOHiU+9jp2DC0f2/KbTfuJTrzNs7FS8/YNp79aBsY/PwNnVi4Szmq2TvP2DeWjKK/SMGYaxceu39Mzbc5Tr7y0ie/O+Vtl3mD6J6rQsrr46n/JrqaT/uJ70FRvxmfmk2qbjjCnk748j5ZOlVCSnkvLJUvIPnsR7xhSDaZ/at5zwmPF07TMBR1dfhkycg7WdC+eP6H5mzh9Zg7W9K0MmzsHR1ZeufSYQFj2Ok/s05XT55Baihz+LX2gsdk6eRPR7FJ+gGE7ta/pcNefkvhV0jRlP174TcHLzZeik2VjbuXD2sG49to4eDH1kDmFRYzA1s9STx1X4BEUR88AzOLr6EPPAM3QM6M2p/St12v8dKBTK+7r+iYiNChEtysvLWb16NX5+fjg4aLpt33zzTV588UWSkpIYOnQoX375JQsXLuSzzz4jISGBoUOHMnr0aG7cuAFAv379OHz4sDr+kSNHcHR05MgRVeV+5swZqquriY6OJisri0ceeYQnn3ySpKQkDh8+zLhx49Q7Kyxbtow5c+bw4YcfkpSUxPz583nnnXe0pm3p0ng/2FqqpnWkZml++HIF3MlR4umk/yXIw1EgJUu7skjJUuKhJ44gqF6qjI3gbp7uSsbWAizNBFKztbWk5apGRvTh7qAdByA1S4mHgcaLSf3f2Opa3d/bWYKVucDNLE0Ptdovzgb84iSQ0mR6V0pmc1+O6CXlxl2Flt8NYWtRX05NfHMnV7/PQVVOv9c3VXp801BOt5qWUx5aI1VN0VlO2Ya1tAZDelp6bm7lNNGTo8Td4ffraUvPMEBZ0V2qyvNx94tWh0mN2uHi3YPctAt64+WmXcLdP0orzN0/Wh1HLqulIDMRt0bpArj7RetMVy6rJfduIl4BMVrhXgHRZN3WrSPr9kW8ArTT7xDQh9z0K8jlmhHautpKls/tzw/v9WXr0mfIvWu4x1kuqyU/MxF3fx3a7+jWkpt2UcuHoPJHfkYiinotuWmXcPfT9pmHfzQ5BvwMIKurIy0liaCwSK3woPDepFxrXW+1QqGguqoSc0sDw8B/Era9w8nbf0IrLG/vMWwiQhCMVMNzdr3Dyd9/XMsmf98x7CK76k1XLqslKy2RjkHaz4xPUDR3U3T79G7qRXyCtMvJJ7gPWbevIJfV1adbh5FxOy0bo3ampN80fHiaXFZL1p1EfIK10/cN1q+nNejWHMPdmxd/d5p/NEql8r6ufyLimgoRtm/fjqWlqregoqICV1dXtm/fjkSiaXO+/PLLjBs3Tv35s88+480332TSJNXc2Y8//phDhw6xaNEivvnmG/r168dLL71Efn4+UqmUxMRE3nvvPQ4fPszzzz/P4cOHiYiIwNLSkuvXryOTyRg3bpz6yPnQ0FD1vT744AMWLlyovn/Hjh25evUqS5YsYcqUKXo13g+Wpqp/y6u1w8urVS9IhuJVNIlTUa1JrwFnW3hyiBQjKdTKYN1R/XPLLUw16WilW6PE2lwAdFdOlqZQUdM0jiY9XQzqKiEtT6l3fYSlmeplrqJKO7y8CvUaEX1ayqu1dZZXK7E003wO8RZwtRdYuqP15xs0xG9aThXVYPMHlFNjhnSVkJar3zd6y6laiU1L5aRDi6Fyag0G9Vj8tXra0jMMUFWWD4CZpXaLxtTSgQo9U30Aqsrzm8Uxs3RUp1dTWYxSIddh40BleX7z9CqKUCrkmFtptzrNrRypLM3TqaGyLB9zK8cm9g4oFDKqy4uwsHHGrr0Pgx9dgINrZ2qry7l4dBXrv3yER9/Ygq2Tt850q/Vpt3Kg6kZz7aDyo1knbe1mlo4oFTKqK4owt3Zu0Wf6KC8rQqGQY22rPeRkbeNAaXGBwbgN7Nv6E7XVVXSPGtKy8R+MSXtHanK081ibW4DE2Jh2jnbUZOdh4uJITY52XmpyCjBx0T+qVFmuemYsrbX9bmHtSLmeZ6aiJB+LYO0ysLRWPTOV5UVY2TrjExzDqX0r8PLvgZ2TF7euxXP94gGUSsP1cYMei2Z6HCgvMVzGhigvycfCpkmaNg568/h3oBTPqWiG2KgQoX///vznP/8BoLCwkG+//Zbhw4dz+vRptU337poFV6WlpWRmZhIdrd2LEB0dzaVLqh6kkJAQHBwcOHLkCMbGxoSFhTF69GgWL14MwOHDh4mNjQUgLCyMgQMHEhoaytChQxkyZAgPPfQQdnZ25OXlkZ6ezlNPPcXTTz+tvpdMJsPGRrv3qbFGfdTU1FBTo/2WYmJiQoi3ESN7Np76Ul+RNnnXEQR9rz8aWtMBkV8KS3bKMW0HgV4SHoyUsHKfXN2weH28Rsvao7+/5rqXzpChEQLOtrBqv/b9Zj+iqSZWH1T5pWmygtDyvXR93xBkbQ7De0hZtV+GrIXsvvmQZrL8r0d0l5OeoHv6vjHDIiQ42wqs2K/9B/a1cZpyWndMoTfde+2TEn7HoECwl8CwCE3Edcd16xH0v8NraPrc37ucNvUMp1zcxokt76s/D578H313a4Xzm3yvVCI0idM0CaVSiWDQiy2nache3etZH8fVOxxX73D1924du/HrZ2O5dPRnYse/bSDd5lJUBaBfS/N8aWtp9n9ASUv5a3yD5nFb8wM5fWwX29Z+x/NvfdGsYfKX0fThbdDdOFyXTase+ubPgMFnrKkf6+/RUA6DJ85h56q3+e7d4SAI2Dl5EhY9jksnNrZCC83KU6lsHnavNMuPUhXaVlD8Q0cb7gexUSGChYUFfn5+6s8RERHY2NiwbNkypk2bprZpSvNKRPOHQhAE+vbty+HDh2nXrh39+vUjJCQEuVzO5cuXiYuL4+WXXwZAKpWyb98+4uLi2Lt3L1999RVz5szh1KlTmJubA6opUL169dK6n1SqvRJTl8amLFiwgLlz52qFvffee5gGvs2SfM0Lo1F90pZm2r3gFibNe1sbU16NVu87qHpVm/akKxSqnWwAsgoVuNlL6BUgYcdp1cvQ93s0L0VSie50LEwEKqr1V2rlOnre9ekf0k2gk7vAqgMKypqMQny3XXOCdYMWSzPV6ETjPLbkFysz7bdZS1NBPeLh5iBgaSbwzAhNlSSVCHRor6RngIQPVsvUf2eX7m5UTo31NPZNK/Q0842OcgIYGiGp9428mW9+2Nu8nJr29FuYtlxOTXvezVt4znRxI1NJZqPF7fr0mJsIVNS0oKfJM2zegj910ZaeYa/AATh5aha/ymWquVFV5fmYWzurw6vLCzGz1D9XzczSkapy7Z7SqooCTOvjmJjbIkikVDbpha+u0J2umYWdTvvK8gLMrHTPCzO3cqSyrImG8kIkEiNMLWx1xhEkEtp7hVKcd1tv3kzrtTcdQagy4BMzK8dm2qvKCxAkRpiaq7SoRiW09VaXa3ymD0srOyQSKaVF2j35ZSWFWNsYbiScOb6Hld/M45nXPiEorLdB2z+Lmpz8ZiMO7ZzsUdTVUVtQrLLJzsfERbucTZztm41wNMbcUvXMlJc2eWbKCrCw1v3MWNg4UlGiXQYVZapnxqz+mbGwsmfCv79FVldDZXkxVrbOHNr4GbaOHgbzqdZTokuP4TI2hKWNY7M0K0oLsNSTR5G2gbimQqQZgiAgkUioqqrS+b21tTVubm4cP649FzQuLo7AwED154Z1FYcPH6Zfv34IgkCfPn347LPPqKqq0hrpEASB6Oho5s6dy4ULF2jXrh2bNm2iffv2uLu7k5qaip+fn9bVsWPHe87brFmzKCkp0bpmzZpFrUz1kt9w5ZVAWZUSH1dNw0kigQ7tBdL1rH0A1bajPi7ajS1fV0HveglN/jUvXqCtJb8UyquUdHTR1uLlrNo+Ux8ZBdpxAHxcBO7ma2sZ2k0gwEPg54MKSnRsfNOwoLywrN4vlUp8XTVipQ1+yTXglzxtXwL4uml8mZql5JutdXy3Xaa+MvIVXE5V8t12mVbHnVY5larKqalvOjgb9vnd/Nb5ZliEpN43cop1+KZV5eSk2uZWHxkFSjq2b1lLSzR9hhv0eLdvrqfF56aJno7tBTIK7k1PW3qGjU0ssHbooL5snf0ws3Qk42ac2kYuqyX79hmcvfTPZ3f2CiOzURyAjBtx6jhSo3Y4uAU3s8m8GaczXalRO5w9gklL1p57n5Ych6u3bh2u3uGkJWunn3btOM6eIUiluhceK5VK8jKSDC7Wlhq1w9EtWMsnau0ddGtx9grX4Y8TOLoHI6nX4uwV1izNjJtxtDfgZwAjY2O8fAO5eumkVnjSpZP4BoTpjXf62C5WfP0e016ZT5fufQze48+k+ORFHAdqryVxGhxDybkrKGWqjpqikxdxHKg94u84KIaieP1rEaRG7XD1CubWVe1n5lZSHB6+un3q4RPOrSTtMrh19Tiu3iFIjbSfGSNjE6zt2qOQy7h2fi+dwgcazKfUqB2uHYJJvaqdfupV/Xpag4dPOLeapXkCD7/w353mH424pqI5YqNChJqaGrKzs8nOziYpKYkZM2ZQXl7OqFGj9MZ5/fXX+fjjj1m7di3Jycm89dZbXLx4kZdeeklt069fPxITE7l8+TJ9+vRRh61evZpu3bphbW0NwKlTp5g/fz5nz54lLS2NjRs3kpeXp26gvP/++yxYsIAvv/yS69evc/nyZZYvX87nn39+z3k1MTHB2tpa6zIx0b3N4qlrCmKCJXT2EHCygQcjJdTJ4MptTWXwYKSEAeESrTi+rgJRQap986OCBDq6CJy6pum1HRAmwctJNeff2Rb6h0no4CxopduU08lKooMEOruDkw2M6iVQJ4fEO5o4o3oJ9OsiaMXxcVFt5elgpfrX20UV3sCwCIEQb4HN8aodqCxMVZeR9iCQFieTFPQJlRDgqZpqMiZaSp0MEm5p8jg2WsqgrhKtOL5uAjHBEhytVdvS+rgKxCep4tTKVDv3NL5qZVBZo9phyhCnkxXEBDUqp1715dTINw/2ljAgTKPn9HUFvi4CUYEq30QF1pdTsiYPw7tLCPUW2BQnp6aVvjl9XUlUoECnhnLqqaecQjXldOa6qpx615dT7wAB7/aq8AaMjVTPirOt6rONhcr31uaGfXPmhkaPozWM7KHSczVNk/bIngKxjfScvaGkY3uVDvtW6rG1bFlPW3qGBUEgOHoyCUeWcjtxH0U51zm2YTZSY1N8wzTbaR/57U3O7tHUM0GRk8m4GUfC0WUU56WScHQZmSnxBEdNVtuERE/h+rkNXD+7geLcFE7tWEB5SRYBPSfq1NK13xMknlxP4sn1FGancHTTfMqLsgiNVq1XO7FtIXt/fqNR+pMoK8rk6KYFFGanqOKe2kC3AZpdhU7t/po7SccoyU8n724SB36dTX7GNUIanWWhi5CYKVw/q9F+son2M3s+58hvb6rtA3tOorw4k5M7PqI4N0UV99xGQvtotARHqXx26cgyinNTuXRkGRk34wmOntzs/k0ZPOpxjh/YxPEDm8m6m8raHz+jMD+b2CEPAbDx58X8+KVmOtfpY7v4cfG7TJgyE59OoZQU5VNSlE9lhebQElldHem3kkm/lYxMVkdxYS7pt5LJzUozqEVqYY51WADWYaotUM07emAdFoCppysAnf9vJmHLP1bb31m6BrMObgR++haWAT54TB2P5xPjSf1cs5vS7a9X4Tg4Gp/Xnsaisw8+rz2N48BIbn9leIejXoOf4OLx9Vw8vp78rBT2rZ1PSWEW3WJVz8yhjQvZ+qPmmekWO4mSgkz2rVtAflZKfdwN9B6sKaeM1EtcO7+Xorx00m6cZc3iaSiVCiKHTjOoBaD34KlcOKZKMy8zhb1rFlBSmEVEP5WeAxsWsvmHN7XiZKclkZ2WRG1NJZVlhWSnJZGXeVP9fc9B/yLl6glO7FpGflYqJ3Yt41ZSPL0GGd4Z669E3P2pOeL0JxF2796Nq6uqYrSysiIgIIDffvuNfv36cfv2bZ1xXnzxRUpLS3n11VfJzc0lKCiIrVu34u+vOTgqJCQER0dHOnTooG5AxMbGIpfL1espQDXycfToURYtWkRpaSkdOnRg4cKFDB8+HIBp06Zhbm7Op59+yhtvvIGFhQWhoaHq6VN/FnFXlRhLlTzQU3XIW0Y+/HxQTq1mNhA2FoJWj8PdfNhwXEH/MAn9u0Bhuepz455qC1MYEyXF0gxq6iCnSMkvhxTNdrlpTPw1JUZGMKy7RH3A26+HFc21NJpelFEAm+IUxHaREBsqUFSu+pxZqIkT4a960f7XQO03sG2nVNt06uJ4ogIjIxjZS4qpCWTkKflpv6yJFlAqNS+H6XlK1h+VM6CrlP7hEorK4LejcjLusTdeF3FJSoykSoZ3ry+nAlh9WLucrM2bl9PGOAX9ukjoF6rqTd94QkFmo3LqXu+bKYO0q8ktJ+V6fXPymmqb1GERqnLKLFBteWpIS0YBbI5XEBsqITZEoKgCNjcpJ1c7eHyApowG1zfYEm4p2H5avw9PXlNiJIWh3e5Rz0kFsSES+gbX64lvruex/ho9g8I1enac0a2nLT3DAKF9piGrqyF+6zxqq0tx8ujCsCe+x9hEM42yoiQLQdA0Rtt36Eq/iQs5v+9Lzu//Cit7T/pPWoizp6bn3KfLA9RUFnPx0LdUluVh196fIZO/w9LOXaeOTt0eoLqyiNN7vqWiNBcH106MfmYp1vYq+8rSPMqKNOcy2Dh4Mnr6Uo5tXkDC8dVY2jgTO24OfmGaHe9qqko5uO5dKkrzMDGzwsk9iPEzfsalg+HzD3y6PEB1ZTEXDjbSPuU7rOq1V5XlUV6s0WJl78GQKd9xaudHJJ38BXNrZ3qPnE3HEM3C6PYdutJ/4kLONfLZgCY+00ePmKFUlJWwY91S1eF3Xn7MmPMVDs5uAJQU5VOYn622P7p3Awq5jF+WLeCXZQvU4ZH9R/HEDNX5EsVFeXzwquZwvr1bVrF3yyo6BUfw2gff69ViExFC5IGf1J+DPlMd1pa+aiMJT83CxNUJs/oGBkDV7bucGTWdoIWz6PDcY9Rk5pL4yodkb9Kc4VEUf4ELj82k89yX6Tz3RSpT0rnw6CsUn04w6JegHg9QWVHE8R3fUl6Si5NbJybNWIqNg6qcykvyKCnUlJOtoycTZyxl37oFnDusemaGTJpDQITmmZHV1XBkyyKK8tJpZ2KOX2gso5/8BFNza4NaAIJ7PkBVRTFHt32jPozvkZeWYNtIT2mTMyuWzRur/n/WnUSunNqOjYMbL358EABPv26Mm76Qw5u/5PDmxdg5eTJu+ue4+7T83PxV/EMHG+4LQflPHYMREbkH5q2WtWz0F/HuY0Z8uKb1OyD9mcyZJOW9VW3nsMO5k4354Ne2UVbvPGLE/LVto5xmT5SyYF3b0AIw62Fpm3qGP17fdrZpefMhCd/s+rtVqPj3cPhkQ9vxzRvjJRxJrPy7ZQAQG2zODuPOf7cMAEbUJbPqSMt2fxWTY+HnY23j1fHxPn/fwu2Xvixr2cgAX75k9QcpaTuIIxUiIiIiIiIiIiIi94C4+1NzxDUVIiIiIiIiIiIiIiL3hThSISIiIiIiIiIiInIPKP+hi63vB7FRISIiIiIiIiIiInIPiI2K5oiNChEREREREREREZF7QGxTNEdsVIiIiIiIiIiIiIjcA+JIRXPELWVFRERERERERERE7oFnPips2cgAS96y/4OUtB3EkQoREWhze/y3FT2zHpYy9+e2c07Fe48bt5kzRd59zIh3V9b+3TIAmDelXZs7T6StnH/wxnhJmzun4rONbUPPa+MkbeY8EVCdKfL9gb9bhYppA2kzZ0NMjqXNnJkBqnMz2tJZKyJtB7FRISIiIiIiIiIiInIPKMTpT80QGxUiIiIiIiIiIiIi94C4eqA54uF3Im2Cw4cPIwgCxcXFf7cUERERERERERGDKBXK+7r+iYgjFSItMnXqVFauXAmAVCrFzc2NESNGMH/+fOzs7P6Qe0RFRZGVlYWNjc0fkt4fRUywQLiPgKkxZBbC3vMK8ksNx+nsDn1DJNhaQnE5HLmi4HqG5ntPR+gVIMHFDqzMBNYfl3Mj879LS2wXCRF+EkzbQUaBkp2n5eSVGI4T6CnQP0yKnRUUlcHBS3KupWsq1u7+Erp3kmBrofqcW6Lk6GUFNzNbrnxjQyV08xPq9cCuMy3rCfAU6B8mwc4Sisrh4EUFyXc194rwF+jur/IdQF4xHL3Ssp7+YVIiOkkwawd385VsPyUnr9hwnCAvgQFdjbC3gsIyOHBBRlKa7jh9QiQMjjAi/qqcXWdang/fL0xChL9Gz45TrSgrL4EB4dJGerTLqkcnCd07a8oqr0TJ4UuGfXP15C9cPvYjVWV52Dr70XvELFw6dtdrn5V6mlM7P6Y49ybmVs6E9n2KwF6TtGxuXdnL+X2LKS1Mw9rei4ghL+EdPLhFn4Cql/HCwW9IPrOO2qpSnDy7EDnqHeza+xuMd/vKXs7v19yz2+Dm90w6+QuXj2vy2mvELFy89ef1avwvXKr3jZ2zH71HzsK1Bd+c3PExRfW+6RL7FEGNfFOYc4Nz+74iPyOR8uJMeo94i9CYKa3yC0CfEIGuvpq6ZvfZVtQ1HqrfYcPv6UiCguRGdU1UoEBnDwEHa5DJ4W4+HLykoLDMcLoXjqzmzP4fKC/Jw9HVnwETZuPhp9836ddPc2jDR+Rn3cDSxpmeg6cR3vcR9fdrvvgX6TdON4vnExzL+H8vNajl7OHVnNyj0uLk5s/gibPx8tev5U7yafb/9hF5mTewsnWm99BpRMRqtMhldcTtXkJC3GbKinNwcOnIgHGv4RvS16AO+5ju+Lz6FDbdQjB1c+bs+OfJ2Wp4MYp9nx4EffYWlkH+1GTmkrLwe9KWrtGycRk7hE7vv4S5rxeVKWkkv/sFOVv2G0y3gYTjqzl/8AcqSvOwd/Gn79jZuPvq983dm6c5tvkjCrNvYGHjTMSAaYRGP6LT9vr5HexeNROfkIGMnPZtq/T8FfxTGwb3gzhSIdIqhg0bRlZWFrdv3+b7779n27ZtPP/8839Y+u3atcPFxQVBEP6wNO+X3gECPTsJ7D2vYMV+BRXVSibFSmhnoCnu7gBjIiVcuaPkh70KrtxRMiZSglujTR6MjSC3WMne861frNmWtEQHSYgMkLDzjJxlu2SUV8G/BhoZ1OLhKPBQHykJtxR8t0NGwi0FD/WR4u6gKe/SSiX7L8hZukvG0l0ybmcrmRQrxamFdmZUkEDvQIFdZxV8v1tOeZWSxwdIW9ADD8VISLilYMlOeb0eCe4OGpuySjhwUcGyXXKW7ZJzK0fJxL4Sg3piQiREBknYcUrGkh0yyquUTBls2DeeTgITYo24lCLn2611XEqR83CsER6OzX8Lbg4C3TtJyS5sXXnFBEuIDJSw87ScpTtVZTW5BT0ejgIT+kpJSFXwn20yElIVPBwrxb2RnpJKJfvPy1m6Q8bSHTJuZSl5pL/+skpN2MmpHR8R3u8ZxrywERfvCPasfIbyYt0t2LLCu+xd+Swu3hGMeWEjYf2mc3L7fG5d2au2yUm7wKE1M/HrOpqxMzbj13U0B3+dSW76pVb55vKx70k8sYLIUW8z+vl1mFk6snv5U9TVVOiNk5t2gUNrZ+LbdTRjZmzGt+toDq3Rvmdqwk5O7fyIsNhnePDfG2nvHcFeA3lNSdhJ/I6P6Nr/GcbOUPlm9wr99qWFd9m9QuWbsTM2Et5/OvHbtH0jr63G2t6TnsNmYmbl2Cp/NBAZINCrs8CecwqW71NQXqXk0f4t1zXjoiRcua3k+90KrtxWMjZau67xchY4d1PJin0KfjmsQCLAo/0kGEv1p3vt7E4Orl9A72HPMWXWZjz8Ilj/zdOUFur2TXF+Ohu+nY6HXwRTZm2m97BnOfDbhyRf2KO2eXD6Vzy34Lj6euLt7QgSKZ27DTPol6tndrJv7QKiH3iOae9sxtM/gjWLn6akQL+WtV9Nx9M/gmnvbCZq+LPsXfMh185ptBzZsojzR9cy9JF3eGbuTrr1ncT6/7xAdtpVg1qkFuaUJiST+NI8g3YNmHl70GPbUgqPn+N4jzHc/Pg7gr+Yg8vYIWob297hdP3lCzJWb+FYxINkrN5Ct18XYduzS4vpXz+/k6ObFtB98HM88tpm3H0i2LrkacqKdPumpCCdrUun4+4TwSOvbab7oGc5svFDbl7a08y2tDCDY1s+xs1HfwPl70KhVN7X9U9EbFSItAoTExNcXFzw8PBgyJAhTJw4kb17NX/Eli9fTmBgIKampgQEBPDtt9q9CXFxcYSHh2Nqakr37t3ZvHkzgiBw8eJFQPf0pw0bNhAcHIyJiQne3t4sXLhQK01vb2/mz5/Pk08+iZWVFV5eXixdarin6V7o4S8Ql6Tkegbkl8L200qMpapeZX109xe4lQPx15QUlqn+vZMDPTpp4qRmw9ErSq0Rg/8mLb0CJRy7ouBaupK8EtgcJ8fYCEI76q9OegVISMlScjxRQUEpHE9UcCtbSa9ATZzrGUpuZqq0FpapejFrZeh8uW6admM9W+IVGBtBiLf+eL0CJKRmKzmRqKSgFE4kKlV6AvTrOVSvx92AnshAKUcvy0lKU5JbrGTjcZVvuvjo903vQAmpmUqOXVH1Bh+7oiA1S0nvIO047YzgoT5GbImXUdXKTad6B0o4dllRrwc2najXY6CsIoMkpGY11xPZuKzuKrmRoaSgDArKVI2vWpmqgaSLK8dX0iliHJ17TMDW2ZfeI2djYeNC0qk1Ou2TTq/BwtaV3iNnY+vsS+ceE+gUMY7Lx35U2ySeWIW7XxRh/aZj6+xDWL/puPn2JvHEqhb9olQqSTyxirB+z+AdPAS79p3o+9BHyOuqSbm0XW+8xLhVuPlGERY7HVsnH8Ji6+8Zp7nnlRNN8jpClddrevJ6+dhKOncfR0CPCdg5+xI5ajaWNi5cPanHN6fWYGnrSuSo2dg5+xJQ75uEoxrfOHmG0uuB1/ENG4FU2q5FfzSmZ2eBE4lKku9CXglsO6Wqa4I76H/ue3YWuJUNcUmqZyIuScntHFV4A2uOKEi4pSS/FHKLYftpBTYWAi4GdtU8e3A5oVHj6RI9AQdXXwZMmIOVrQsXj/6q0/7SsTVY2bkyYMIcHFx96RI9gdDIcZzZr/GNmYUtljZO6uv2tRMYtzOlUwuNilP7lhMeM56ufSbg6OrLkIlzsLZz4fwR3VrOH1mDtb0rQybOwdHVl659JhAWPY6T+zRaLp/cQvTwZ/ELjcXOyZOIfo/iExTDqUY2usjbc5Tr7y0ie/M+g3YNdJg+ieq0LK6+Op/ya6mk/7ie9BUb8Zn5pNqm44wp5O+PI+WTpVQkp5LyyVLyD57Ee0bLI1wXDi8nuNd4QiInYO/iS99xc7C0dSHhuG7fXDmxBitbV/qOm4O9iy8hkRMI6jWO8we1861QyNnz02v0Hj4DGwfPVuVV5O9FbFSI3DOpqans3r0bY2NjAJYtW8acOXP48MMPSUpKYv78+bzzzjvqKVNlZWWMGjWK0NBQzp8/zwcffMCbb75p8B7nzp3j4YcfZtKkSVy+fJn333+fd955hxUrVmjZLVy4kO7du3PhwgWef/55nnvuOa5du3bfebS1AEszgVvZmt4EuQLS8lS93PpwdxC4laPdA5Gao9Tqkf+v1mKpmiaVkqXpKZcr4HaO0uDLv6eTQGqWtpaUTCWeeuIIguolxtgI0vP19+g06GmctlwBd3KUel9wQdVQSWmqJ0uJh544jfXczdOtx84SrMwFrSlAcgXczjasxdNJws0s7ZGHm5kKvJy0q+cRvaRcz1A086M+1HqalNWdHCWezgZ84ySQkqmjrAz4JsRboJ0RpOvwjVxWS35mIu7+0Vrh7n7R5N65oDPN3LSLuPs1sfePJj8jEYW8rt7mEu5+Udra/aPJSdOdZmPKiu5SVZ6vdQ+pUTtcvHuQayB+btol3P217+nuH62OI5fVUpCZiFtT7X7ROtPV6xsD+chNu9jM3qNTNHmNfPN7aahrUpvWNbkt1zWN4wCkZhmuE0xUfz6o1tNAlstqyU5LxDswRivcOzCajFTdvsm8dRHvQG3feAf1IefOFeR6fHM5bgMBESNoZ2KuV6tcVktWWiIdg7S1+ARFczdFt5a7qRfxCdLW4hPch6zbV5DL6urTrcPIWLvRZ9TOlPSb5/Vq+T3Y9g4nb/8JrbC8vcewiQhBMFINQdn1Did//3Etm/x9x7CL7Gowbbmslty7iXgFaPvGKyCarNu6fZN1+yJeAdq+6RDQh9x07XI6vecbzCztCe49wXAG/ybENRXNEddUiLSK7du3Y2lpiVwup7q6GoDPP/8cgA8++ICFCxcybtw4ADp27MjVq1dZsmQJU6ZMYfXq1QiCwLJlyzA1NSUoKIiMjAyefvppvff7/PPPGThwIO+88w4AnTp14urVq3z66adMnTpVbffAAw+op2G9+eabfPHFFxw+fJiAgID7yq+Fqerfimrt8IpqJTYWAqC7QrA01RVHk95/uxZLU9VLQrmOdG0sDMWD8mptneXVSizNtO2cbeGpoUYYSaFWBmuPyMk3MP/f0rQhLe3w8mrU8/31xdPlG8smvnG2hSeHSNV61h3VP7fc0kzlm4oq7XxWVCuxtdD/cmVpBuVVTfRXoeWbEG8Jbg4CS7a3/owOjZ7maTesE9EZ7x7KatpwTVmtOax7rUZ1ZTFKhRwzS+23UjMrB6pu5OvUUFWWj1knB217S0eUChnVFUWYWztTVZ7fPE1LR6rKdKfZNP0G+8aYWjpQoWfaEdDiPWv05dXSgcry5roafGOuw15fPirL8vGw1PaNeRPf/F701jU1SqzNW6hraprGMVzXDOoqIS1PqXd9T1V5EUqFHAsr7bxaWDtSUZqnM05FaT4W1tq+tLByQKGQUVVehKWNtm+ybieQn3mdYY9/qF8oUFmvxdK6uZZyfVpK8rEI1tZiaa3SUllehJWtMz7BMZzatwIv/x7YOXlx61o81y8eQKn8Y88OMWnvSE2O9vNUm1uAxNiYdo521GTnYeLiSE1OgZZNTU4BJi5OBtOuqlD5xrxJOZlbOVKpxzeVZfmYN5mWZ15fTtXlRVjYOJOZeo7Ek+t59PXNrczlX4+4+1NzxEaFSKvo378///nPf6isrOT777/n+vXrzJgxg7y8PNLT03nqqae0GgkymUy96Do5OZkuXbpgaqr5C9OzZ0+D90tKSuLBBx/UCouOjmbRokXI5XKkUtVE3C5dNPM9BUHAxcWF3NxcvenW1NRQU6P918/ExIRgL2OGRWhe/NYdV/XuNq0yBP1/VzU0+f73jAu8OlbTS/13a5k1UVNN/HJI9x+735Ourjj5pfDdDhmm7QSCvATGRElZsU+m1bB462HNJOxfD9fraZpPoRWuacXfg/xSWLJTjmk7CPSS8GCkhJX75OqGxZxHjdW2qw/IdEnRG2aIxkVrbQ4P9JSycp8MmYGlFKEdBUb11vhm9UG5znsLQst51/V906CCUvhuu6asxkZLWb5Hpn8ReNMCVyp1BDY2bxah/otG4U3WYClR6lyXlXJxGye2vK/+PHjyf/TcVdksTV3KtKM0v2fTJJRKpY78GMKwjqb3U7/c/I41aa+P19Q1a4/+/kP57uX9amiEgLMtrNrfivvpyKvhtXfNnwld4QAJcetxdOuEq3fL6wZ0pt1Sueoppwb9gyfOYeeqt/nu3eEgCNg5eRIWPY5LJza2Us890LSAGrQ1Dtdl0+qCbfl3Yci+8TNcW13O3p9fZ+DEDzCzbLunTovnVDRHbFSItAoLCwv8/PwAWLx4Mf3792fu3Lm88MILgGoKVK9evbTiNLz46/oj0FILv7VxGqZgNSAIAgqF/j9UCxYsYO7cuVph7733HlZd3iGzUJO+tP7vbNMebXMTgYoa/drLq8GiSY+uuY5e8Zb4cZ8mD3+3lu92aHrHjaQaLY17181Nm/e2N9WiGuXQ6LUwFZrFUShUO8eAkqxCJW4OAr0DJGw/pfHHkp2aho1aj5n2aIWFieF8llfTrOfdwrT5iIdGD2QVKnCzl9ArQMKO0yo9/9mmGaqXSoV6LQLlVU3zaaCcqnRoMdOMMLg5CFiaCTw7UlNdSyUCHdor6RkgYV79iefJ6Uoy8mWNbKjXo102Fi08A+XVqilljcvK0lRoNuIhV1C/c4+SzAIlbo4CvQMlbDup/fszNbdFkEib9bxXlRdi1qTHvQEzK0cqm9kXIEiMMDW3VdlYOlJVpt0TWl1egKmONL0CB+DkqXlxlMtq69PM1+rZrzagSX3Pcu17VlVo7mlSn9em2qsrdKfb4JumoxiGfGOuyzcV2r65F77f07yuafpbsDARqKg2XNc0HeXT9xsc0k2gk7vAqgMKygzUGWaWdggSKRWl2nmtLCto1sutvqeOUYzKskIkEiPMLG21wutqq7h2dgcxI1/UL6Ie83ot5Tq0NB0ZUWuxcaSiRFtLRYMWC5UWCyt7Jvz7W2R1NVSWF2Nl68yhjZ9h6+jRoqZ7oSYnv9mIQzsnexR1ddQWFKtssvMxcdHOi4mzfbMRjqaYWdjpfOYrywv0bhKgeoab/I7KVb4xtbClMOsmpYUZbPv+OfX3SqXqOf1qZhD/mr0bW0cvg7r+Cv6pU5juB3FNhcjv4r333uOzzz5DLpfj7u5Oamoqfn5+WlfHjh0BCAgIICEhQWuE4OzZswbTDwoK4vhx7fmdcXFxdOrUSd1Y+T3MmjWLkpISrWvWrFnUylQvjw1XfimUVynxbq9p2Egk4OWk2gpRHxkFSjq2124MdWwvkFFwb5VPW9WSVwJlVUp8XDVVh0QC3u0F7hpY+5Cep8THVVuLj6tgcL1EA1KJdjzderR906G9oHN+fwN385X4uGin6+sq6F0v0YAgaF68APUi7sIyyCtWUlapxK+RFqkEvF0Ma0nPU+Drql0V+7lKSMtT/RFNzVLy9ZY6/rNNpr4y8hXq3Zka2tq1siZ6SqCsUqmVtrTBN7kGfKOjrHzdDOcBVP2OTcsKVGsVHN2CybgZpxWeeTMO5w6652s7e4WT2cQ+48YJHN2DkUiN623CmqWZcTOO9l7N0zQ2scDaoYP6snX2w8zSUSu+XFZL9u0zOOuIr9EVpkNXnDqO1KgdDm7BzWwyb8bpTFftmxuty4dKQ3jzfN84gVMj39wLuuqaji5N6hrnVtQ1TX5PPi7N64Sh3QQCPAR+PqigRP8mW0D9GhevYO4kaa8FuHMtDncf3b5x6xjOnWvavrmddJz2HUKQNvFN8rldyGW1BPUcbVhIvRZXr2BuXdXWcispDg9f3Vo8fMK5laSt5dbV47h6hyA10tZiZGyCtV17FHIZ187vpVP4wBY13QvFJy/iOFB7LZDT4BhKzl1BKVN1RBSdvIjjQO11Do6DYiiKN7xGSWrUDmePYNKStX2TlhyHq7du37h6h5OWrO2btGvHcfZUlZNdex8ee3Mbj76+WX35BA/Aw68Xj76+GStbl1bl+89GqVTe1/VPRGxUiPwu+vXrR3BwMPPnz+f9999nwYIFfPnll1y/fp3Lly+zfPly9ZqLRx99FIVCwfTp00lKSmLPnj189tlnQPNh/AZeffVVDhw4wAcffMD169dZuXIlX3/9Na+99tp96TYxMcHa2lrrMjEx0Wl75oaSqECBTu7gaA0jewjUyeFqo/MDRvYUiA3V5OHsDSUd26u2gLW3Uv3r3R7OXNfEMTZSzUd3tlV9trVUTQWw1r9OsE1pOZWkoE+IhABPAScbGBMppU4Gl29pejzHREkZGK6pXk5dU+DrKhAdJMHBWrUtrY+rwKkkTZwB4RK8nARsLFR6BoRJ8G4vaKWrU881BTHBEjp7qPQ8GCmhTgZXbmvy+WCkhAE69EQFqfbNjwoS6OgicOpaIz1hErycUOvpHyahg7OglW5T4pPk9OkiJdBLwNlWNSWoTgYJqZp0x8VIGdRN0zA+maTA100gJkSCo7VqW1ofN4GTV1VxamWqbX8bX7UyqKpRhRviZJKCPqGqsnK2hTENehr5dGy0lEFdJVpxfN0EYoLr9QSryiq+UVkN7CrBy1nAtt43A8NVZZWgp6xCYqZw/ewGrp/dQHFuCid3LKC8JIuAnhMBOLPnc478ptm8IbDnJMqLMzm54yOKc1NUcc9tJLSPZrea4KjJZNyM49KRZRTnpnLpyDIybsYTHD3ZoE9AVe8ER08m4chSbifuoyjnOsc2zEZqbIpv2Ei13ZHf3uTsns/Vn4MiVfdMOLqM4rxUEo4uIzMlnuAozT1Doqdw/Zwmr6ea5LUpoX2mkHx2A8lnN1CUm0L89gWUF2cR2Etlf3r35xxa18g3vSZRXpRJ/PaPKMpNqY+7kS59Nb5RLRhPoiAzCYW8jsrSXAoykyjJv9Oib04nK4kOEujsDk42MKqXqq5JvKN51kb1EujXRdCK4+Oi2o7WwUr1r7eLKryBYRECId4Cm+NVO4VZmKouIwN9RN0HPEFC3Houx62nICuFg+vnU1qURVgf1ZkcRzcvZMeKN9T2YX0mUVqYycH1CyjISuFy3Houx22gx6Anm6WdELce/7BBmFnategTgF6Dn+Di8fVcPL6e/KwU9q2dT0lhFt1iVVoObVzI1h81WrrFTqKkIJN96xaQn5VSH3cDvQdrtGSkXuLa+b0U5aWTduMsaxZPQ6lUEDl0mkEtUgtzrMMCsA5TrR007+iBdVgApp6uAHT+v5mELf9YbX9n6RrMOrgR+OlbWAb44DF1PJ5PjCf1c81uS7e/XoXj4Gh8Xnsai84++Lz2NI4DI7n91coWfdO13xMknlxP4sn1FGancHTTfMqLsgiNVvnmxLaF7P1Z45uQ6EmUFWVydNMCCrNTVHFPbaDbAJVvjIxNcHDtpHWZmFnTzsQCB9dOSI3ubUczkb8OcfqTyO9m5syZPPHEE9y8eZPvv/+eTz/9lDfeeAMLCwtCQ0N5+eWXAbC2tmbbtm0899xzhIeHExoayrvvvsujjz6qtc6iMd26dWPdunW8++67fPDBB7i6ujJv3jytRdp/NievKTGSwtBuqkPeMgtU2yLWNlora20uaPU4ZBTA5pMKYkMk9A0WKKqAzfEKMgs1cVzt4LH+mr+kg+pfdhNuKdhxRvdLYlvScuKqAiMj1Tz/hgPVfjog09JiYwFKpeal426+kvXH5QwIk9I/TEJhOaw/JtcaNbE0FRgbLcHSDGrqIKdIyeqD8ma7yjQl7qoSY6mSB3qqDnjLyIefD8qb6NH2zd182HBcQf8wCf27QGG56nNGo3WKFqaqxlFjPb8cUhjUc/yKAmOpwMheRpiaQEaeklX7mvpGW0t6npLfjsoY2NWIAeFSispg3RGZwZGf1nI8UVVWI3tJ1Xp+2m+4rNLzlKw/KmdAVyn9wyUUlcFvR+Vk5GuX1bgYCVZmqt17coqV/HRArndnKp8uD1BdWcyFg99SWZaHXXt/hkz5Dis7dwCqyvIoL85S21vZezBkynec2vkRSSd/wdzamd4jZ9MxRLOvfvsOXek/cSHn9n3J+f1fYWXvyYBJC3H2DGuVb0L7TENWV0P81nnUVpfi5NGFYU98j7GJZoV/RUkWgqBpcLXv0JV+ExdyvtE9+ze5p0+XB6ipLObioUZ5nfwdlvV5bYpvlweoqSjm/AGVvX17f4ZN1fimsiyPika+sbb3YNjU74jf8RFX630TOUrbN5VleWz8apz6c8KxH0k49iOuHXswcrrhLXfjrykxMoJh3SXqwyR/Paxo/gyjXddsilMQ20VCbKhAUbnqc+O6JsJf5cd/DdRuRWw7pdpqVhcB3R+gqqKIuJ3fUlGai6NrJ8Y/vxQbB5VvykvzKCvS+MbW0ZPxzy/l4IYFXDy6GksbZwZOmEPnrkO10i3MuUVGyjkmzDC8dWtjgno8QGVFEcd3fEt5SS5Obp2YNKORlpI8Sgq1tUycsZR96xZw7rBKy5BJcwiI0GiR1dVwZMsiivLSaWdijl9oLKOf/ARTc2uDWmwiQog88JNG22ezAUhftZGEp2Zh4uqEWX0DA6Dq9l3OjJpO0MJZdHjuMWoyc0l85UOyN2m2hS+Kv8CFx2bSee7LdJ77IpUp6Vx49BWKTye06JtO3R6gurKI03tU5eTg2onRzyzF2r7+GW5STjYOnoyevpRjmxeQcFzlm9hxc/ALG6rvFm0SpYGp1n8nRUVFvPjii2zduhWA0aNH89VXX2Fra6vTvq6ujrfffpudO3eSmpqKjY0NgwYN4qOPPsLNze2e7i0o/6ljMCJtmtWrV/PEE09QUlKCmZlZyxH+ZBas+2N327gfZj0sbTN6Zj0sZe7P97dN5R/Je48bM29163dA+jN59zEj3l3ZygMj/mTmTWnHe6vaTjnNnWzMJxvaxh/cN8ZL+Hh929AC8OZDEj7b2Db0vDZOwodr2kZdAzBnkpTvDR8M/ZcxbSCsOvJ3q1AxORZ2GHf+u2WoGVGXzDe7/m4VKv49/O+798TXWh75M8Tazzr8QUq0GT58OHfv3lWf2zV9+nS8vb3Ztm2bTvuSkhIeeughnn76acLCwigqKuLll19GJpO1OFW9KeJIhchfwqpVq/Dx8cHd3Z1Lly7x5ptv8vDDD7eJBoWIiIiIiIiIyL3QFvvkk5KS2L17NydPnlRvnrNs2TIiIyNJTk6mc+fmjVMbGxv27dM+SPGrr76iZ8+epKWl4eXV+kXxYqNC5C8hOzubd999l+zsbFxdXZkwYQIffmh4b3ARERERERERkbbI/e7+pG+Le33rPFtDfHw8NjY2Wrtx9u7dGxsbG+Li4nQ2KnRRUlKCIAh6p0zpQ1yoLfKX8MYbb3D79m2qq6u5desWX3zxBebmBlYDi4iIiIiIiIj8Q1mwYAE2NjZa14IFC+4rzezsbJydmx+A6ezsTHZ2dqvSqK6u5q233uLRRx/F2trw+p6miI0KERERERERERERkXtAqVDe16Vvi3tdvP/++wiCYPBqWP+ga1fNlg+NVFFXV8ekSZNQKBR8++239+wTcfqTiIiIiIiIiIiIyD2gUN7fhgv3MtXphRdeYNKkSQZtvL29SUhIICcnp9l3eXl5tG/f3mD8uro6Hn74YW7dusXBgwfveZQCxEaFiIiIiIiIiIiIyD3xV56o7ejoiKOj7hPKGxMZGUlJSQmnT5+mZ8+eAJw6dYqSkhKioqL0xtsJuKYAAI0wSURBVGtoUNy4cYNDhw7h4ODwu3SKW8qKiIiIiIiIiIiI3ANjnr9+X/E3f9vpD1KizfDhw8nMzGTJkiWAakvZDh06aG0pGxAQwIIFCxg7diwymYzx48dz/vx5tm/frjWiYW9vT7t2rT9sUBypEBGBNncWQ1vZO37OJGmbO/9APKeiOW3xnIq2tMf/isN/twoNU/vBws1toy/v1TEC3+35u1VoeHYo7L3UNn5TQ8La8fOxtlFOj/cR2sy5EKA6G6KtnJsxoi75b7t3W+2TX716NS+++CJDhqgOxBw9ejRff/21lk1ycjIlJSUA3L17V31QXnh4uJbdoUOH6NevX6vvLTYqRERERERERERERP4B2Nvb8/PPPxu0adwg8vb2/sMaSGKjQkREREREREREROQeUCjub6H2PxFxS1kRNUqlkunTp2Nvb48gCFy8eFFnWL9+/Xj55Zf/brkiIiIiIiIiIn8L97ul7D8RcaTiv5CW9hqeMmUKK1asuOd0d+/ezYoVKzh8+DA+Pj44OjrqDNu4cSPGxsYtpvf+++8zd+5cgza3bt3C29v7nrX+VcR2kRDhJ8G0HWQUKNl5Wk5eieE4gZ4C/cOk2FlBURkcvCTnWrqmAunuL6F7Jwm2FqrPuSVKjl5WcDPTcCXTJ0Sgq6+AqTFkFsLuswrySw1r6ewBsaES7CyhqByOJChIztB87+kEkQESXOzBykzgt2NyrmfoT6+BfmESIvwlmLWDu/lKdpxqhV+8BAaES7G3gsIyOHBB2y/aeZUwqJuU+Ktydp9tuTcoNlRCNz+hvpxg15mW9QR4CvQP0/jm4EUFyXc1eiL8Bbr7S7C1VH3OK4ajV1oup/5hUiI6aXyz/ZScvGLDcYK8BAZ0NWrkGxlJafp9MzjCiPircnadaXntTVspq7OHV3Nyzw+Ul+Th5ObP4Imz8fLvrtf+TvJp9v/2EXmZN7Cydab30GlExD6i/l4uqyNu9xIS4jZTVpyDg0tHBox7Dd+QvoYzB5w7vJpTezVaBj08G08DWtKun+ZAIy29hkyjW2Mt8jridy3hcrxGS7+xrdMCkBj/CwlHfqCyLA+79n5EjpqNa0f9ejJTT3Ny+0cU5dzE3NqZsNhpBPXWbDdZmH2Ds/sWk5+RSHlRJpEjZxHaZ0qrtFw6tpqzB36gojQPBxd/YsfPxsNXv5a7N05zZNNHFGTfwMLGme4DpxEWo/FN4qmN7F3dfO/9GQsTMDJueTvNo3vWcGDrCkqL83D18GXc1DfxC4zQaVtSlMemVZ+SnppEXvYdYoc/xvipbzazO7TjJ47vXUdRfhYW1raE9xrM6EdfxridYT1nD/1C/J4fKCvOw8nNj6GTZuPVSbdvyopz2bfuY7LuJFKYe4eeA//F0Emzm9klndvD4c2LKcpLw87Ji/5jXyag2+AW/ZJwfDXnD6rKyd7Fn75jZ+NuqJxunubY5o8orC+niAHTCI1+RKft9fM72L1qJj4hAxk5reWzCuxjuuPz6lPYdAvB1M2Zs+OfJ2frAcNx+vQg6LO3sAzypyYzl5SF35O2dI2WjcvYIXR6/yXMfb2oTEkj+d0vyNmyv0U9fxXK+9xS9p+IOFLxX0hWVpb6WrRoEdbW1lphX375pZZ9XV3rFnCmpKTg6upKVFQULi4uGBkZ6Qyzt7fHysqqxfRee+01LV0eHh7MmzdPK8zT0/N3+eCvIDpIQmSAhJ1n5CzbJaO8Cv410Ih2BpriHo4CD/WRknBLwXc7ZCTcUvBQHynuDpqGYGmlkv0X5CzdJWPpLhm3s5VMipXiZKM/3cgAgV6dBfacU7B8n4LyKiWP9pcY1OLuAOOiJFy5reT73Qqu3FYyNlqCm73Gpp0R5BQr2XOu9ZVjTLCEyEAJO0/LWbpT5ZfJg1v2y4S+UhJSFfxnm4yEVAUPx0pxd2zeQHZzEIjwl5Bd2LqenKgggd6BArvOKvh+t5zyKiWPD5C2oAceipGQcEvBkp3y+nKS4N5oF72ySjhwUcGyXXKW7ZJzK0fJxL4Sg+UUEyIhMkjCjlMyluyQUV6lZEoLvvF0EpgQa8SlFDnfbq3jUoqch2ON8NDjm+6dpGQXtq682kpZXT2zk31rFxD9wHNMe2cznv4RrFn8NCUFmTrti/PTWfvVdDz9I5j2zmaihj/L3jUfcu2cZlXxkS2LOH90LUMfeYdn5u6kW99JrP/PC2SnXW1Ry/51C4h64DmefHszHn4RrP3qaUoK9WtZ99V0PPwiePLtzUQOf5Z9az/k2nmNlqObF3Hh2FoGT3qHp9/fSde+k9j4XctaAFIu7SR+2wK6DniWcS9uwsW7O7t+nE55kW49pYV32f3jM7h4d2fci5vo2v8Z4rZ+SOpljR5ZXTXW9p70HPYqZlZOLWpoIPn8Tg5vXEDPIc/x2BubcfeNYPN/nqZUj29KCtLZtGQ67r4RPPbGZnoOfpbDGz7kxkXt1d/tTC2Z/n/Hta7WNCjOxe1m44qPGTruad78+Dd8AyP4z/znKMzP0mkvq6vF0tqeIeOexr2D7oXEZ45tZ+svixg+4VnmfLGFR5+dx/n4PWz9ZZFBLYmnd7JnzQJiHniWp9/dhFen7vzy5XS9z7BcVouFlT0xI56lvUeATpu7KRfYsGQmoZGjmf7eFkIjR7NhyStkpF4yqOX6+Z0c3bSA7oOf45HXNuPuE8HWJU9TpueZKSlIZ+vS6bj7RPDIa5vpPuhZjmz8kJuXmq/SLy3M4NiWj3Hz0d9AaYrUwpzShGQSX5rXKnszbw96bFtK4fFzHO8xhpsff0fwF3NwGTtEbWPbO5yuv3xBxuotHIt4kIzVW+j26yJse3Zpta4/G3Gkojlio+K/EBcXF/VlY2ODIAjqz9XV1dja2rJu3Tr69euHqakpP//8MwUFBTzyyCN4eHhgbm5OaGgov/76qzrNqVOnMmPGDNLS0hAEAW9vb51hQLPpTzU1Nbzxxht4enpiYmKCv78/P/zwA5aWllpapVIpVlZWuLi4sHfvXoKDg5HJtHfyGT9+PJMnTwZUIx3h4eEsWbIET09PzM3NmTBhAsXFxVpxli9fTmBgIKampgQEBPyuUyB10StQwrErCq6lK8krgc1xcoyNILSj/p9NrwAJKVlKjicqKCiF44kKbmUr6RWoiXM9Q8nNTCWFZape4IOXFNTK0PkS2UDPzgInEpUk34W8Eth2SomxFII7GI5zKxvikpQUlKn+vZ2jCm8gJQuOXFal21p6B0o4dllBUpqS3GLYdELlly4G/BIZJCE1S8mxK6rRlWNXFKRmKYkM1I7TzgjG95Gy9aScqtrWVbq9ArTLaUu8AmMjCPHW75teARJSs5WcSFRSUAonEpWqcgrQX06H6stJ18u1Op+BUo5eltf7RsnG4/W+8dHvm96BElIzm/umd1Bz3zzUx4gt8TKqWrlBTlspq1P7lhMeM56ufSbg6OrLkIlzsLZz4fyRX3Xanz+yBmt7V4ZMnIOjqy9d+0wgLHocJ/f9qLa5fHIL0cOfxS80FjsnTyL6PYpPUAynGtno4vT+5YRFjyc8RqVlcL2WC3q0XKjXMrheS3iMSsupvZr7XDm1hahhGi3dYh+lY1AMp1vQApBwbAWde4wnoOcE7Nr7EjV6NpY2Llw9qVtP0sk1WNq6EjV6NnbtfQnoOYHO3ceRcFRzL2fPUHqPeAO/8BFIjVoeVW7g/KHlhPQeT2jUBBxcfOk3fg5Wdi4kHNetJeH4GqztXOk3fg4OLr6ERk0guPc4zh3UzrcgCFhYO2ldreHQ9lVEDhhH1MDxuHj4MH7qm9g5unB871qd9g7O7jz0xFv0ih2NqbmlTptb1y/h07kr3WNG4ODsTmBYFBHRw0lLNdwAPLlvBV1jxtO17wSc3HwZOmk21nYunD2s2ze2jh4MfWQOYVFjMDXTreXUvlX4BEUR88AzOLr6EPPAM3QM6M2p/SsNarlweDnBvcYTEjkBexdf+o6bg6Wt/nK6cmINVrau9B03B3sXX0IiJxDUaxznm5STQiFnz0+v0Xv4DGwcWt/hl7fnKNffW0T25n2tsu8wfRLVaVlcfXU+5ddSSf9xPekrNuIz80m1TccZU8jfH0fKJ0upSE4l5ZOl5B88ifeM1o24/RWIjYrmiI2KfyhvvvkmL774IklJSQwdOpTq6moiIiLYvn07V65cYfr06fzrX//i1KlTAHz55ZfMmzcPDw8PsrKyOHPmjM4wXUyePJk1a9awePFikpKS+O6777C01F2JNjBhwgTkcrl6GzOA/Px8tm/fzhNPPKEOu3nzJuvWrWPbtm3s3r2bixcv8u9//1v9/bJly5gzZw4ffvghSUlJzJ8/n3feeYeVKw1Xyi1ha6maDpSSpekRlivgdo7S4Mu/p5NAapZ2ZZGSqcRTTxxBUDUMjI0gPV93JWNrAZZmAqnZmu/lCkjLVfW468PdQTsOQGqWYf0tYWcJVuYCN5v45U6OEk9n/el6OAmkZOrwi5N2nBG9pNy4q2jmQ300lFNje7UeJwN6HAVSmpZTlhIPPXEal9PdPN3a1L7J1NZyO9uwFk8niZY/AW5mKvBy0q6eR/SScj2j9b5pK2Ull9WSlZZIx6AYrXCfoGjuplzQGedu6kV8gqK17YP7kHX7CnJZXX26dRgZa++fbtTOlPSb5w1qydahpaMBLRmpF+nYREvHoD5k37mCXK7SItOj5W6Kfi0NevIzEvHw107fo1M0OXd068lJu4hHp6b2MeTdTUQh//3bCstlteSkJ9IhQNs3XgHRZN7SrSXr9kW8ArS1eAf0ISdN4xuA2ppKvn+vP8ve6cvmJc+Qm97yCI5MVkd66lUCwrQP7AroEsWt5IutzFVzfAO6kZ56lds3LwOQn5PO1QvHCO7WR28cuayWrDuJ+ARr59U3WP9z0xp0P+cx3L150aCW3LuJeOkop6zbrS+nDgF9yE3XLqfTe77BzNKe4N4T7jEn94Zt73Dy9p/QCsvbewybiBAEI9Uwql3vcPL3H9eyyd93DLvIrn+qNpH7Q1xT8Q/l5ZdfZty4cVphr732mvr/M2bMYPfu3fz222/06tULGxsbrKyskEqluLi4qO10hTXm+vXrrFu3jn379jFo0CAAfHx8WtRnZmbGo48+yvLly5kwQVWBrV69Gg8PD609kaurq1m5ciUeHh4AfPXVV4wYMYKFCxfi4uLCBx98wMKFC9V57dixI1evXmXJkiVMmfL7ezQsTVUvUOXV2uEV1WBjYSgelFdrv2SVVyuxNNO2c7aFp4YaYSSFWhmsPSInX888dwtTzb21tNQosTYXAN0vdZamUFHTNI4mvd+DpZnKLxVV2uHlVajXHujT0pJfQrwFXO0Flu5o/RkdlqYNaTXRU416zYq+eM38Wa1JrwFnW3hyiFRdTuuO6l/HovGNdj4rqpXYWuh/ibc0U/lPS38VTXwjwc1BYMn21p/R0VbKqrK8CKVCjqW19gmtFtaOlJfm6YxTUZKPRbB2i9nS2gGFQkZleRFWts74BMdwat8KvPx7YOfkxa1r8Vy/eAClUr+mBi0WTbVYOVKhR0t5aT4+VtpaLOq1VJUXYWnjjE9QDKf3r8CzXsvta/HcaEELQHWlSo+ZpbYeM0sHKsvydcapKsvDzDKmmb1SIaO6oghza2eD99RHVYVKi7lVc9/cKdNTTqX5eDfxjbmVtm/snX0Y+tgCHN06U1NdzoXDq1i76BEef3MLds7eevVUlBahUMixstHWY2XjQGlxwe/KI0BE9HDKSwtZ9M5klIBCLiNmyESGjJmmN47e58bagfIS3eXUGspL8rFokj8LGwe9vwvQX07mVo5U6olXWZaPuZ5yqi4vwsLGmczUcySeXM+jr2/+fZm5B0zaO1KTo+232twCJMbGtHO0oyY7DxMXR2pytMu5JqcAE5fWT+f7s1GIayqaITYq/qF07649H1Iul/PRRx+xdu1aMjIyqKmpoaamBgsLA29ereDixYtIpVJiY2PvOe7TTz9Njx49yMjIwN3dneXLlzN16lStheheXl7qBgWojqBXKBQkJycjlUpJT0/nqaee4umnn1bbyGQybGx0T3xvyHdjTExMCPWWMrKXVB32yyHdLwO/p49fV5z8UvhuhwzTdgJBXgJjoqSs2CdTNyxeH6/ppV579PdXXH/E1tOzH9FUE6sPqvzSNFlBaPleur5vCLI2h+E9pKzaL0PWQnbfelhTTr8elmsn1FiP4WRa5Zv8UliyU45pOwj0kvBgpISV++TqhsWcRzVTS1YfkOmSojfMEI2bitbm8EBPKSv3GfZNaEeBUb01vmkLZdXkzk3uoUQw9IsSmturglXhgyfOYeeqt/nu3eEgCNg5eRIWPY5LJzbeuxaUzcIMadE4qJGWn95m6XsaLV2ixpEQ1xotzTffUOq6pyE9LYXfCzr9bihdXb7U5Mm1YziuHcPV37t37MbqT8dy8ejP9H/o7XuVAyh/X0Vcz43EM+zZuIyHp72Nt38oednpbFj+EbttHRn20LMtaGnqm5Y3TmmJZr8BVeG3KmZTMYa16P49IQjUVpez9+fXGTjxA8ws7XXE/RNoWsk0aG8crsumDR0490+dwnQ/iI2KfyhNGwsLFy7kiy++YNGiRYSGhmJhYcHLL79Mbe39nV5qZmbWspEeunbtSlhYGKtWrWLo0KFcvnxZ6xh5XTRUmoIgqPeIXrZsGb169dKyk0qlzeICLFiwoNmOVO+99x4mAXO4m6/pBTaqj25pqt2LbG7avFe5MeXVDaMcmsrGwlRoFkehUO04BEqyCpW4OQj0DpCw/ZQqT9/v0bytSSUN6Wj3yFuYCFRU66/UynX0vFuYNO+hb4nvGvWON2hp2rtuoaPnv6kWKzNtv1iaCupedDcHAUszgWdGaKokqUSgQ3slPQMkfLBapv5bsmSnpsGnLiezpr5pWU/T0aOm/oXG5QRZhQrc7CX0CpCw47SqfP6zTTN1QCoV6rUIlFc1LX8D5VSlQ4sZzXzz7EjdvplXfxp8crqSjPy2UVaNMbe0Q5BIKS/V7pmsLCvAwlr3/D0LG0cqSrR7XSvKCpFIjDCzsFXZWNkz4d/fIqurobK8GCtbZw5t/AxbRw8dKWprqbgHLZbWzUcx1FosVVrMrex56HmVlqryYixtnTncghYAU3OVnqajEtXlBZg3Gb1owMzKiaom9lXlBQgSI0zNbQ3ezxBmFvVamvqmvKBZL3cDFtaOVDQZxaiq942phW4tgkRCe69QivNuG9RjYW2HRCJtNipRVlKItY1u37SG7Wu/pmffUUQNHA+Am1cnaqsr+XXpPIaMm45E0nxWuPoZLtH13Px+LZY2js3SrCgtwFLPswiNyqmseTmZ6SkncytHKpuWU7mmnAqzblJamMG2759Tf9+ws9FXM4P41+zd2Dp63VPeDFGTk99sxKGdkz2KujpqC4pVNtn5mLho58fE2b7ZCMffiVI8p6IZYqPif4Rjx47x4IMP8vjjjwOqQ1tu3LhBYGDgfaUbGhqKQqHgyJEj6ulP98K0adP44osvyMjIYNCgQc12g0pLSyMzMxM3NzcA4uPjkUgkdOrUifbt2+Pu7k5qaiqPPfZYq+43a9YsZs6cqRVmYmLCR79Bbbm2bVmVEh9XCdlFqopDIgHv9gL7L+ivSNLzlPi4Cpy8pgnzcRX0rpdojFSi6UkqaqKlvEpJRxeBnPqtSSUS8HKGgwY2CckoUMU5fV1zbx8Xgbut0NKYwjLtz2WVSnxdJeodiKQS6NBeYL+BHaTu1vslPkkT5usmkF6/PiE1S8k3W7Xng4+JkpJfAscT5VqdU019oyongewijW86tFBOd/OV+LgInLqmSdjXVdC7XqIBQdC8rENT3ygpq1Ti5yqod0OSSsDbRWCfAd+k5ynwdZUQf1Vj4+cqIS1P9Tk1S8nXW7R9MzZaSl6JkuNXFGrf1MraXlkBSI3a4eoVzK2rJwjoqtkq81bS/7d353E1pu8fwD/nlPZFShQ5lUZJhcbYh0r2IcsM2ZVlrGXLMoZkH2MLM9KQCmPPOt8hSiiMJUpUkhZL2ZIooc79+6NfR8cpY8x0Pydd79er19d5zjHP5/t00rnu576v+xwaNe1Ubob6ls2QEn9K7ljazWiYmNspLDxWraEOPYM6KC56i6TYcDRu0b3C/28qqmqo26AJ0hJjYP2RWepZNkPKdcUsdSV2UFFRzKJrUAfFxW+RdDUcjb+sOEtpHqN6TXA/5Rws7N7luZdyDua2LuX+nToNmiEjUT7PvZQY1K7fBGKVj1+UXV6WOmZNkJEcA6um77JkJp1DQ/vyr42JeTPcSZDPkpEUjToNFK9NKcYYHt9PhJFJow/mUVWtATNLWyTFn0fTlu/Onxx/HvZfOX/s/y0Fb1+/UhjRF4lV/n8EvPyffxVVNZhImuDOzXNy7V7v3DyHRs3K/z59jPqWzZB28xxadxlZ5r8Zg/pWzSr8OyqqajCu3wSZyTFo6FDm+5R8DpZ2H/g+3ZD/PmUmRcPYrOT7ZFDHEkNmyQ/onf9jLd68zkfHfnOhW7P86c+fKvfCNRj3lP8e1u7cHs+vJID9f/OWZxeuwahTO6T5v1sfaeTaHs/Of/oalv8a3alQRAu1qwkrKyucOHEC586dQ2JiIr7//ntkZ2f/6/+uubk5RowYAU9PTxw8eBBpaWmIiorCnj17PurvDxkyBPfv38dvv/0GT09Phec1NDQwYsQIxMXF4ezZs/Dy8sKAAQNkazwWLFiAZcuWwd/fH7du3cL169exdetWrF69utzzqaurQ09PT+5LXb381oZ/JUrxtZ0YNmYi1NYH+rRRwdsi4Hrauw9kfdqqoFOzdz9GfyVJ0dBEhHa2YhjqlbSltTQR4a/Ed3/HpZkYDWqLoK9dMmffpakY5nVEcv/d911MZmhnK4J1PaC2PtCrlQhvi4EbGe/+UevVSgQnB5Hc37GsW9KO1lC35H/N65YcL1VDFahTs+QLAGpqi1CnZskUl4pcSJTia/uS62JcE+jTruS6xJfJ37edClybi+X+TkNTEdo3EcNIr6TVackH15K/86YIeJQr//WmCCh4XdK16EP+SpKifRMxrOuXfJ/c2ojxtghISH/3/9OtjRgu5Xyf2tqKYKhX0pbWoq4IfyWV+T41FaNBbci+T85NxZAYi+T+u+87n1iMrx1U0LiBCMY1Rehbem3uvPvv9muvAlfHd3fSZNfG7v+vjZ0YlqYiXLhZ9towua83RcCr1yXHP0RZvletOnvgWvQ+XIvehydZqTixeyme52TBsWPJ3gqnwlbhcNBM2esdO7rj+dMHOLFnGZ5kpf7/392P1p3f/Rtx/04ckmLD8ezxXWSmXMaudaPBmBRtulY8Nx4AWrp6IC56H+JiSrKc3LMUeTlZaN6hJEvUgVU4svVdluYd3ZH39AFO/n+WuJh9iIvZj1ZdymRJi0Py/2e5m3IZu/1HA0yK1n+TBQAcvh6JpEv7kHRpP549TMW5I8vwMjcLjf9/34mLf67Cqd3v9lpo3NodL589wPkjy/DsYSqSLu1H8qX9cOjwLk9x0Rs8eZCIJw8SIS16i/y8h3jyIBHPn2R8MIujswcSzu9Dwvl9eJqdiqiwpXjxLAsO7UuyRB9ehWPb3l0bh/buyHv2AKfDluFpdmrJ372wH1+6vMty/s8NSE88i9wnd/HoXiJO/P4DHt9LgkP78vdIKMv5m+E4H7Ef5yMPIPveHewP/gk5T7LQvvMAAMDh39cidIP83g/30pNwLz0JrwsL8DIvB/fSk5B1L1X2vN2XTog+sQdXYv7Ek0f3kBR/Dn/s3gC7Fk4Qi8u/ww0ArTuPxNWzJe/Dxw9SEb5rGZ7nZOFLp5JrE7F/FQ5ukd8TIzszEdmZiXjzugAFL3KQnZmIxw9uy55v6ToMqTdjEPPnb3iSdQcxf/6GtMTzaOX64fWAzZ08cOPCPty4sA852ak4c2ApXj7Lgn27kiwxR1YhfPu775NdO3e8ePYAZw4sQ052asnf/Ws/HP//+6RaQx2GJo3kvtQ19aCmrg1Dk0ZQUVUrN0cpFW0t6DW1gV7Tkta5Whb1odfUBhpmJgAA68XT0HTrT7LXZwTugqbEFI1/ng0dG0vUH9kfZh79cWf1u25U6RtCYdS5HSxnjIG2tSUsZ4yBUac2SF//75qw/JcYk/6rr88R3amoJubNm4e0tDR07doVWlpaGDt2LPr06YPnz/9mF6yPsHHjRvzwww+YMGECnj59igYNGuCHHxQ3+SmPnp4e+vfvjz/++AN9+vRReN7Kygr9+vVDjx49kJOTgx49esi1jB09ejS0tLTw888/Y+bMmdDW1oa9vf1/suN3zE0pVFVL5rOXbhy2LaIIb8rM7tDXBhh790H+3hOGfdHFcGmqAuemYuS8BPadLcb9p/JTSfq2E0NHE3j9Fnj4jGFHZLFCp6ayzicxqKoC3VqIZRu87YySvpdFJJvPDJS85sA5KTo6iNHRXoRnL0seP8h593dMagHDXN79Iu3sWPLhMi5NiqN/lZ8n+kbJdfmmlQo01IH7jxm2nfzwdbn7mGHfmWK4NFeBczMxnr0A9p4pxv1/eNekPOduMtRQYejRsmSDt/tPgO2RxYrXpswQ+r0nwP5oKZybiuHsAOS8LHl8v8xMC22NkqKx7Pfp91PSD36fohOkqKEiwjetVGXXJvTE+9dGPsvdxwx7zxShU3NVuDRTwbMXwJ7TRf/4jlK5eZTke2X7VQ8U5D9D9B+/4uXzR6ht2gjukwOhb1gPAPDy+WM8z3m390BNIzMMnByIE3uW4UrUDujoG6OL+1zYfNlV9pqit69x+tBaPHt8F2rqWrCy74jeniugoaX3t1le5T9DTJksAybJZ8l7L8uAyYE4uWcZYk+XZOk8cC5sHN9lKX77GqcPr0Xu/2dpaN8RvT4iCwA0bNoDhQW5iI34BQX/v5FZd49N0DUoyVPw4jFe5r7bf0CvVn1089yE80eW48b536GtZ4y2vefC0v5dnoK8Rwjz7yt7HH8mCPFngmBi+RV6fb+twizWjj1QmP8Mfx3/FfnPH8HQpBH6jAuEXq2SLPl5j/Hi2btro29ohr7fB+L0gWWIO7sD2vrGcOo/F180e5fl9as8nNw1HwV5j6GmqQvj+rb4zns76kr+fr+BL9t2Q/6LXBzbH4C8Z49hYmaF8XN+Ra3aJXeunz97jGfv7Vnx08x3nYvu3rmJy9H/Q63apvD7pWRPhq79xwIiEY7uWo/nOY+go2cAuy874ptBXh/M0qRlD7zKz8WZI7/INk0c5L0JNcu+b97bs+K3he++B1kZN5Dw11HoG5rC66dIAICZlSP6jV2FqIP+iDq4Dga1zdBv7GrUs2z6wSyNHHugsOAZLh7/Ffl5Jd+n3t+/+z4VlPN96j02EGcPLkN8dMl7uGO/ubBq2rWiU/wj+l/aoU3Eu/eV7cqS3/93Q8MQP2oO1E1qQ/P/CwwAeJV+D5d6jYXtqjmQjB+C1w8e4cbUJcg+EC57zbPzV3F1yDRY+02BtZ8XClLv4urgqci9GP+fZCaVQ8SYEq16IdVS586d0bhxY6xbt07u+IIFC3Dw4EFcu3at0jP4bf/0Voz/Nd+hNbBk18d3QKpMc91V4BuqPNfGb3gNLHxvzr5Q5g9RxfyQf7cm6b+ycISa0n2fQk8LnaLE8I5AcJTQKd4Z6QSsOqgcv3an9xEhQHH/M8GM6wqExynHz1SXpmrYflY5vk9Dvxbhlz+FTvHOxO7AHzXK31yQt55vkwU7t/OAv/7V3z+1p9Xfv6iKoTsVRDA5OTkIDw9HZGQkNmzYIHQcQgghhJCPQgu1FVFRQQTj6OiIZ8+e4aeffoK1tXKMehBCCCGE/B1aqK2IigoimPT09A8+v2DBAixYsIBLFkIIIYSQj/W5Lrb+N6ioIIQQQggh5B+gOxWKqKUsIYQQQggh5F+hOxWEEEIIIYT8A7RQuxyMEPKvFRYWMl9fX1ZYWCh0FKXKwphy5VGmLIwpVx5lysKYcuWhLBVTpjzKlIUx5cqjTFkYU7485L9B+1QQ8h/Iy8uDvr4+nj9/Dj29v9/wqrpkUbY8ypRF2fIoUxZly0NZqkYeZcqibHmUKYsy5iH/DVpTQQghhBBCCPlXqKgghBBCCCGE/CtUVBBCCCGEEEL+FSoqCPkPqKurw9fXF+rq6kJHUaosgHLlUaYsgHLlUaYsgHLloSwVU6Y8ypQFUK48ypQFUL485L9BC7UJIYQQQggh/wrdqSCEEEIIIYT8K1RUEEIIIYQQQv4VKioIIYQQQggh/woVFYQQQgghhJB/hYoKQgghhBBCyL9CRQUhnyg3NxebN2/GnDlzkJOTAwCIjY3F/fv3BU5GCCGVJy0tTegIMrGxsbh+/brs8aFDh9CnTx/88MMPePPmDdcsRUVFCAkJQXZ2NtfzVhXbtm1Du3btYGpqioyMDADA2rVrcejQIYGTkf8KFRWEfIL4+Hg0atQIP/30E1auXInc3FwAwIEDBzBnzhxBMkVEROCHH37A6NGj4enpKfdFSFWUl5eHgwcPIjExUegognv16hUKCgpkjzMyMrB27VqEh4dzz2JlZQVnZ2ds374dhYWF3M9f1vfff49bt24BAO7cuQN3d3doaWlh7969mDlzJtcsqqqqGD9+PF6/fs31vFXBxo0bMW3aNPTo0QO5ubkoLi4GANSsWRNr164VNhz5z9A+FYR8AldXVzg6OmLFihXQ1dVFXFwcLC0tce7cOQwePBjp6elc8/j5+WHhwoVo0aIFTExMIBKJ5J4/cOBApWfo16/fR782LCysEpOULzc3FxcvXsSjR48glUrlnhs+fHilnpuuzccZMGAAOnTogEmTJuHVq1do2rQp0tPTwRjDrl270L9/f25ZQkJCYGRkhJ49ewIAZs6cicDAQNja2mLnzp2QSCTcsgBAly5d0K9fP4wbNw65ubmwsbFBjRo18OTJE6xevRrjx4/nliUhIQFBQUHYsWMHXr9+jYEDB2LUqFFo2bIltwyl9PX1ERsbi4YNG+Knn35CZGQkjh8/jpiYGLi7u+Pu3btc8zg7O2PKlClwc3Pjet6K3Lp1C1FRUeX+bM+fP59bDltbWyxduhR9+vSR+52ZkJAAJycnPHnyhFsWUnlUhQ5ASFV06dIlbNq0SeF4vXr1BLn1HRAQgODgYAwbNoz7uUvp6+sLdu6/c+TIEQwZMgT5+fnQ1dWVK7pEIlGlf3Aue20YYzhw4AD09fXRokULAMCVK1eQm5v7j4qP/4rQ16asM2fOYO7cuQBKCmHGGHJzcxESEoLFixdzLSqWLl2KjRs3AgDOnz+PDRs2YO3atTh69CimTp3KvfiLjY3FmjVrAAD79u1DnTp1cPXqVezfvx/z58/nWlTY2dlh9erVWLFiBY4cOYLg4GC0b98eX3zxBUaNGoVhw4ahdu3aXLIwxmQflk+ePIlvvvkGAGBmZibIB9UJEyZg2rRpuHv3Lr788ktoa2vLPe/g4MAty2+//Ybx48fDyMgIdevWVfjZ5llUpKWloXnz5grH1dXVkZ+fzy0HqWSMEPKPGRsbs9jYWMYYYzo6Oiw1NZUxxtjx48dZ/fr1ueepVasWu337NvfzVhVffPEF8/b2Zvn5+UJHYTNnzmSjR49mRUVFsmNFRUVs7NixbMaMGdzzKNO10dDQYJmZmYwxxoYNG8ZmzZrFGGMsIyODaWtrc82iqanJMjIyGGMl37Nhw4YxxhhLSEhgRkZGXLO8n+e7775jCxYsYIwxlpmZyTQ1NbnnKauwsJCtXr2aqaurM5FIxNTU1NiwYcPYgwcPKv3czs7ObPjw4Sw0NJTVqFGDpaSkMMYYi4qKYhKJpNLP/z6RSKTwJRaLZf/LU4MGDdjy5cu5nrMijRs3ZgcPHmSMyf/O9Pf3Z46OjkJGI/8hWlNByCdwc3PDwoUL8fbtWwAloz6ZmZmYPXs219HUUqNHj8bvv//O/bxVxf379+Hl5QUtLS2hoyAoKAgzZsyAioqK7JiKigqmTZuGoKAg7nmU6dqYmZnh/PnzyM/Px7Fjx9ClSxcAwLNnz6ChocE1i46ODp4+fQoACA8Ph6urKwBAQ0MDr1694poFKFnHcPDgQdy9exfHjx+XXZtHjx5BT0+Pex4AuHz5MiZMmAATExOsXr0aM2bMQGpqKiIjI3H//n0uU4DWrl2L2NhYTJo0CXPnzoWVlRWAkrs5bdu2rfTzvy8tLU3h686dO7L/5enZs2f47rvvuJ6zIj4+Ppg4cSJ2794NxhguXryIJUuW4IcffoCPj4/Q8ch/hKY/EfIJVq5ciR49esDY2BivXr1Cx44dkZ2djTZt2mDJkiXc8xQWFiIwMBAnT56Eg4MDatSoIff86tWruWfat28f9uzZg8zMTIUuLLGxsVyzdO3aFZcvX4alpSXX85anqKgIiYmJsLa2ljuemJioMOeZB2W6NlOmTMGQIUOgo6ODBg0awMnJCUDJtCh7e3uuWTp37ozRo0ejefPmuHXrlmxtxY0bN2Bubs41C1Ay/33w4MGYOnUqXFxc0KZNGwAlBU9500oq0+rVq7F161YkJyejR48eCA0NRY8ePSAWl4xTWlhYYNOmTbCxsan0LA4ODnLdn0r9/PPPcoU7L7zX2nzId999h/DwcIwbN07oKPDw8EBRURFmzpyJgoICDB48GPXq1YO/vz/c3d2Fjkf+I1RUEPIJ9PT0EB0djcjISMTGxkIqlcLR0VE2mslbfHw8mjVrBqBkEWVZ7y/a5mHdunWYO3cuRowYgUOHDsHDwwOpqam4dOkSJk6cyD1Pz5494ePjg5s3b8Le3l6h6Orduze3LB4eHvD09MTt27fRunVrAMCFCxewfPlyeHh4cMtRSpmuzYQJE9CyZUvcvXsXnTt3ln1ItbS0xOLFi7nlAIBffvkFP/74I+7evYv9+/fD0NAQQMn6l0GDBnHNAgDffvst2rdvj6ysLDRt2lR2vFOnTujbty/XLBs3boSnpyc8PDxQt27dcl/ToEEDbNmyhWuuly9fKhTm77+fedi2bRsCAgKQlpaG8+fPQyKRYO3atbCwsOC6gNvKygrz5s3DhQsXyv3Z9vLy4pYFAMaMGYMxY8bgyZMnkEqlMDY25np+Uvmo+xMhnyA9PV2Q0cqqwsbGBr6+vhg0aJBcp4/58+cjJycHGzZs4Jqn9MNpeUQikay9IQ9SqRQrV66Ev78/srKyAAAmJibw9vbG9OnTuY+uKtO1KfXmzRukpaWhYcOGUFWlsa+ybt++jdTUVHTo0AGamppgjAkycKAs0tLSMGnSJERFRcm1ty29Lrzfvxs3bsT8+fMxZcoULFmyBAkJCbC0tERwcDBCQkJw6tQpblksLCwqfE4kEnGfjgWUTNdLTk6GSCSCtbU1twX9hA8qKgj5BGKxGG3btsWwYcPw3XffoVatWkJHkrl37x5EIhHq1asnWAYtLS0kJiZCIpHA2NgYJ06cQNOmTZGSkoLWrVvL5qpXd3l5eQAg2Jx4ZVNQUIDJkycjJCQEQEk7TEtLS3h5ecHU1BSzZ8/mmufs2bPYtGkT7ty5g71796JevXrYtm0bLCws0L59e65Znj59igEDBuDUqVMQiURISUmBpaUlRo0ahZo1a2LVqlVc8wAl36/ypjfy7HBUum7C29sbderUUSiwOnbsyC0LQK1TK5KXl4eJEydi586dsrtJKioqGDhwIH755Rel7h5IPh4t1CbkE1y+fBlt2rTB4sWLYWpqCjc3N+zdu1ewTY+kUikWLlwIfX19SCQSNGjQADVr1sSiRYsEmadft25dWeEgkUhw4cIFACWjijSOUbKu4uTJk9i5c6fsQ9CDBw/w8uVLgZMJa86cOYiLi0NUVJTcwmxXV1fs3r2ba5b9+/eja9eu0NTURGxsrOxn+8WLF1i6dCnXLAAwdepU1KhRA5mZmXKL6gcOHIhjx45xzfL48WP07NkTurq6aNKkCZo3by73xVN8fDy2bt2KgQMHwsnJCR07dpT74k1ZW6cyxgT9t3f06NH466+/8McffyA3NxfPnz/H0aNHcfnyZYwZM0awXOQ/JlTbKUI+B1KplEVGRrLRo0czAwMDpqenxzw8PLjnmD17Nqtduzb79ddfWVxcHLt27Rr75ZdfWO3atdkPP/zAPc+oUaNkLS83btzINDU1maurK6tZsybz9PTknoexkhaT33zzDWvYsCGzsrJivXr1YmfOnOGeIz09ndnY2DAtLS2moqIia63o7e3Nvv/+e+55GFOea9OgQQN2/vx5xph828mUlBSmq6vLNUuzZs1YSEiIQparV6+yOnXqcM3CGGN16tRh165dU8hz584d7u12Bw8ezNq2bcsuXrzItLW1WXh4ONu2bRuztrZmR48e5ZrFycmJnThxgus5P0TZWqeGhIQwOzs7pq6uztTV1Zm9vT0LDQ3lnkNLS4udPXtW4fiZM2eYlpYW9zykclBRQch/5MqVK6xZs2bce5EzxpiJiQk7dOiQwvGDBw8yU1NT7nmKi4vZ27dvZY93797NJk+ezPz9/dnr16+559m2bRtTVVVlAwYMYP7+/mzt2rVswIABrEaNGmzHjh1cs7i5ubGhQ4ey169fy33oiIqKYlZWVlyzMKZc10ZTU1N2Pcpem2vXrjE9PT3uWdLS0hSypKamMnV1da5ZSjPcunVLIc/FixdZrVq1uGapW7cu++uvvxhjjOnq6rLk5GTGGGOHDh1i7dq145rl9u3bzNXVlQUHB7PLly+zuLg4uS/egoKCWL169diuXbuYtrY227lzJ1u8eLHszzytWrWKaWlpsZkzZ7JDhw6xgwcPMh8fH6alpcVWr17NNYuZmRmLj49XOB4XF8fq1avHNQupPFRUEPIvZGZmsp9++ok1bdqUicVi1q5dO/brr79yz6Guri77xV5WUlIS09DQ4J5H2djY2JT7S3TVqlXMxsaGaxZDQ0OWlJTEGJP/cJiWlibIJmbKdG06dOjA1q1bxxgruTZ37txhjDE2ceJE1rVrV65ZLC0tZSPgZb9PISEhrHHjxlyzMMZYjx492I8//ijLc+fOHVZcXMy+++471r9/f65ZdHV1ZQWXRCJh0dHRjLGSuya838Pnz59nFhYWSrHZXKnAwEDWoEEDWZ769euzzZs3c89hbm4uu9tWVnBwMDM3N+eaZdOmTczV1VVuQ8SsrCzWpUsXFhAQwDULqTzUVoOQTxAYGIgdO3YgJiYG1tbWGDJkCA4ePChYR6imTZtiw4YNWLdundzxDRs2yLWf5Ck3NxcXL17Eo0ePFNZ1DB8+nGuWO3fuoFevXgrHe/fujR9++IFrFqlUWm5Hmnv37kFXV5drFkC5rs2yZcvQrVs33Lx5E0VFRfD398eNGzdw/vx5nD59mmuW77//Ht7e3ggKCoJIJMKDBw9w/vx5zJgxA/Pnz+eaBSjZd8HJyQmXL1/GmzdvMHPmTNy4cQM5OTmIiYnhmsXa2hrJyckwNzdHs2bNsGnTJpibmyMgIAAmJiZcs3h6eqJ58+bYuXNnuQu1haAsrVOzsrLK3QCwbdu2ss5zvGzcuBG3b9+WrfkDgMzMTKirq+Px48fYtGmT7LW89zEi/x0qKgj5BIsWLYK7uzv8/f1l+0MIacWKFejZsydOnjyJNm3aQCQS4dy5c7h79y7+97//cc9z5MgRDBkyBPn5+dDV1ZX7RS8SibgXFWZmZoiIiJDttlsqIiICZmZmXLN07twZa9euRWBgIICS6/Hy5Uv4+vqiR48eXLMAynVt2rZti5iYGKxcuRINGzZEeHg4HB0dcf78ee6b382cORPPnz+Hs7MzCgsL0aFDB6irq2PGjBmYNGkS1yxASVeh+Ph4bNy4ESoqKsjPz0e/fv0wceJE7h/kp0yZIvtQ6uvri65du2LHjh1QU1NDcHAw1ywZGRk4fPiwwvtXKK9evQJjDFpaWjAyMkJGRgbWrl0LW1tb2S7ovFhZWWHPnj0KgwO7d+/GF198wTVLnz59uJ6PCINayhLyCZgS9oZ/8OABfvnlFyQlJYExBltbW0yYMAGmpqbcszRq1Ag9evTA0qVL5TrVCGXjxo2YMmUKPD090bZtW4hEIkRHRyM4OBj+/v74/vvvuWV58OABnJ2doaKigpSUFLRo0QIpKSkwMjLCmTNnuI9qKtO1UUYFBQW4efMmpFIpbG1toaOjI3QkpVNQUICkpCQ0aNAARkZGXM/dq1cvjBw5Ev379+d63op06dIF/fr1w7hx45Cbmwtra2uoqanhyZMnWL16NcaPH88ty/79+zFw4EC4urqiXbt2sp/tiIgI7Nmzh/umieTzR0UFIR8pPj4ednZ2EIvFiI+P/+BrefZpV0ba2tq4fv06LC0thY4ic+DAAaxatQqJiYkAgMaNG8PHx4frDrelXr16hV27duHKlSuy3diHDBkCTU1N7lkA5bk2KioqyMrKUiisnj59CmNjY64bmXl6esLf319hSlp+fj4mT56MoKAgblkA4MyZMx98vkOHDpySKJfAwEAsXrwYnp6egu8IDwBGRkY4ffo0mjRpgs2bN2P9+vW4evUq9u/fj/nz58t+xni5cuUK1qxZg8TERNlg0/Tp07m3/h05ciQ8PT2r7fu0uqCigpCPJBaLkZ2dDWNjY4jFYohEIrm+36WPee3iqsxFTr9+/eDu7o4BAwZwPW9VcObMGbRt21Zhp+iioiKcO3euWv/SLfszVtaDBw/QsGFDvHr1iluWigqcJ0+eoG7duigqKuKWBSh/5/Oyd0t5FVz5+fn46aefEBYWhvT0dIhEIlhYWODbb7/FjBkzuN+ZVLYd4bW0tGR3bQYMGIAmTZrA19cXd+/ehbW1NQoKCrjmURb9+/fHH3/8ATMzM3h4eGDEiBGCbtBKKgetqSDkI6WlpaF27dqyPwutWbNmsg9gzZo1UyhySgnxi7Vnz57w8fHBzZs3lWL0UJk4OzuX+2G1dP4+7++VMihtMCASibB582a5KUbFxcU4c+YMbGxsuGTJy8uTbRT24sULuU34iouL8b///U+QhbfPnj2Te/z27VtcvXoV8+bNw5IlS7hkePPmDTp27IiEhAR0794dvXr1AmMMiYmJWLJkCf7880+cOXNG4ee9MgmxueeHWFlZ4eDBg+jbty+OHz+OqVOnAgAePXoEPT29Sj9/Xl6e7Dx5eXkffC2PPKX279+Pp0+fYvv27QgODoavry9cXV0xatQouLm5cX3PkMpDdyoI+QTKMNqckZGBBg0aQCQSISMj44OvlUgklZ6nLGUYPaxVqxZu3boFIyMjGBgYfHANTE5OTqXnKSUWi/Hw4UNZgVrq1q1baNGixd9+EPgvKNu1sbCwAFDynq5fvz5UVFRkz6mpqcHc3BwLFy5Eq1atKj1L6V3IiohEIvj5+WHu3LmVnuVjnDlzBlOnTsWVK1cq/Vz+/v5YtmwZTp8+DWtra7nnkpKS4OTkhLlz52Ly5MmVnqU8hYWFckWgEPbt24fBgwejuLgYnTp1Qnh4OICSzmZnzpzBn3/+WannL3uHraL3Ms876hW5evUqgoKCZIMIQ4cOxYQJE7gvICf/LbpTQcgnUIbR5rKFQkZGxgeLHN5FhTKMHq5Zs0Y2H37NmjWCL6zv168fgJIPpSNHjoS6urrsueLiYsTHx5fb/rEyKNu1Kb3z5+zsjLCwMBgYGAiW5dSpU2CMwcXFBfv370etWrVkz6mpqUEikQjS/KAitWvXRnJyMpdzhYWFYd68eQoFBQDY2Nhg7ty52LdvH9eiori4GEuXLkVAQAAePnyIW7duwdLSEvPmzYO5uTlGjRrFLQsAfPvtt2jfvj2ysrLk2nl36tSJy8LoyMhI2Xv21KlTlX6+T5GVlYXw8HCEh4dDRUUFPXr0wI0bN2Bra4sVK1bI7u6QqofuVBDyCZRhtLksZVrgSsrn4eEBAAgJCcGAAQPkFmWXjsaPGTOGe/ccUr6MjAyYmZl98K4bT++vm2KMISsrC8uXL8fbt2+57FVRu3ZtREVFoUmTJuU+n5CQAGdnZzx+/LjSs5RauHAhQkJCsHDhQowZMwYJCQmwtLTEnj17sGbNGpw/f55bFgA4ceIE2rdvL1jTBWWTmZkJMzMzFBUV4fDhw9i6dSvCw8Ph4OCA0aNHY8iQIbIBjl27dmH8+PEKU/1I1UF3Kgj5B5RptLmsilrcPn36FNra2tzzAMDp06excuVKJCYmQiQSyToKff3119yzKEPRtXXrVgCAubk5ZsyYIdj35X3KcG3KunfvHg4fPozMzEy8efNG7rnVq1dzy1F6d6+goKDcLLybH1S0bqp169bcOlHl5ubC0NCwwucNDQ3x/PlzLllKhYaGIjAwEJ06dcK4ceNkxx0cHJCUlMQ1C1CyIPn169f48ssv0bFjRzg5OaFdu3aCtCKuqIGHSCSChoYGGjRoIPc7rDJYWFggKysLtra2kEqlGDRoEC5evFju/k5du3ZFzZo1KzUPqVxUVBDyD+jr6wMo+RCvq6urMNrcunVrjBkzhlseZS1ytm/fDg8PD/Tr1w9eXl5gjOHcuXPo1KkTgoODMXjwYK55Kroh+/r1a6ipqXHN4uvry/V8f0eZrk1ERAR69+4NCwsLJCcnw87ODunp6WCMwdHRkWuWx48fw8PDo8I58LyLrfebQ4jFYtSuXZvrGgKpVCq33uV9YrGY+3W5f/9+uRvfSaVSvH37lmsWoGRB/cWLF3H69GlERUXhl19+QWFhIRwdHeHk5ITly5dzy1JaiFakRo0aGDhwIDZt2lRp76PSf1/WrFmD77777oPnMTAwUIomKOTTUVFByD+gbKPNylbklFqyZInC3Fhvb2+sXr0aixYt4lZUKFNXobL27duHPXv2lDsCHhsbyyWDMl6bOXPmYPr06Vi4cCF0dXWxf/9+GBsbY8iQIejWrRvXLFOmTMGzZ89w4cIFODs748CBA3j48CEWL16MVatWcc0C8G+2UB7GGDp16qSwdqsU7za7ANCkSROcPXtW4frs3buX+14MQMmdvzZt2qBNmzaYPXs2EhISsHLlSuzYsQOXLl3iWlQcOHAAs2bNgo+PD1q2bAnGGC5duoRVq1bB19cXRUVFmD17Nn788UesXLmyUrMMGzasUv/7RDnQmgpCPgN+fn5KUeSUUldXx40bNxRGEG/fvg07OzsUFhZyyaFMXYVKrVu3DnPnzsWIESPw22+/wcPDA6mpqbh06RImTpzIrT2oMl4bXV1dXLt2DQ0bNoSBgQGio6PRpEkTxMXFwc3NDenp6dyymJiY4NChQ2jZsiX09PRw+fJlNGrUCIcPH8aKFSsQHR1d6RlKC7+P4eXlVYlJSvj5+X3U63jejTty5AiGDRuGOXPmYOHChfDz80NycjJCQ0Nx9OhRdO7cmVsWAEhMTJTdpTh9+jSKi4vRvn17ODk5oWPHjnKLtytby5YtsWjRInTt2lXu+PHjxzFv3jxcvHgRBw8exPTp05GamlopGcRiMRYvXvy30794vH9J5aOigpBPpAyjzcrKysoKPj4++P777+WOb9q0CStXrkRKSgrXPMrQVaiUjY0NfH19MWjQIOjq6iIuLg6WlpaYP38+cnJysGHDBq55lOna1K1bF5GRkbC1tUWTJk2wbNky9O7dG3FxcWjXrh1evnzJLYuenh7i4+Nhbm4Oc3Nz7NixA+3atUNaWhqaNGnCZROz0sLv74hEIty5c6eS0yiv48ePY+nSpXI71M+fPx9dunThnqV0WtqUKVPQu3fvChe186CpqYmrV68q3HFMSkpC8+bN8erVK6Snp8PW1rbS3s9isVhh0OJ91f39+zmh6U+EfIKyo82HDh1SGG0WgjIVOdOnT4eXlxeuXbuGtm3bQiQSITo6GsHBwfD39+eaBVCu1oqZmZmydS6ampp48eIFgJLpAa1bt+ZeVCjTtWndujViYmJga2uLnj17Yvr06bh+/TrCwsLQunVrrlmsra2RnJwMc3NzNGvWDJs2bYK5uTkCAgJgYmLCJQPNL/84Xbt2VRiNF4qXlxfOnDmDBQsW4ODBg3BycoKTkxO+/vpr7ou1bWxssHz5cgQGBsrWR719+xbLly+XFRr3799HnTp1KjXH5cuXBdkwkvBHRQUhn+DXX39FYGAgBg0ahJCQEMycOVNutJk3ZStyxo8fj7p162LVqlXYs2cPAKBx48bYvXs33NzcuOcBlKerUN26dfH06VNIJBJIJBJcuHABTZs2RVpaWoWLpiubslyb1atXy+5GLFiwAC9fvsTu3bthZWWFNWvWcMsBlKypyMrKAlAynadr167YsWMH1NTUEBwczDWLMmjevPlH72fCcxDD0tISly5dUuhKlZubC0dHR+4j4GvXrpWd/+zZszh9+jTmz5+P69evo1mzZrhw4QK3LL/88gt69+6N+vXrw8HBASKRCPHx8SguLsbRo0cBAHfu3MGECRMqLYPQe+AQvmj6EyGfQEtLC4mJiZBIJDA2NsaJEyfQtGlTpKSkoHXr1nj69CnXPMo2pUbZ/F1XocjISG5ZRo8eDTMzM/j6+iIgIADTpk1Du3btcPnyZfTr1w9btmzhlgVQrmujzAoKCpCUlIQGDRoItpeIkMXfx66nAPiuqRCLxcjOzlYYCX/48CEaNGiA169fc8tSVk5ODk6fPo1Tp04hKioKN27cQO3atZGdnc01x8uXL7F9+3bcunULjDHY2Nhg8ODBsr0hKltF3x/yeaI7FYR8AmUbbVa2KTXKRpm6CgUGBsp2HB83bhxq1aqF6Oho9OrVS67PPi/KdG0qEhYWhgULFlTYd58HLS0tODo6orCwECtXrsSMGTO4nl/odrvK1gr58OHDsj8fP35c1gkPKOleFhERAXNzc+65vL29ZUVErVq10KFDB4wdOxZOTk6ws7PjnkdHR0eQf1dK+fr6CrJHBxEII4T8Y6NGjWILFixgjDG2ceNGpqmpyVxdXVnNmjWZp6cn9zwWFhbsypUrjDHGWrRowQICAhhjjB0/fpwZGBhwyWBgYMAeP37MGGOsZs2azMDAoMIv3nR0dNjt27dl2RISEhhjjF27do1JJBLueZSJslybwMBA9u2337JBgwaxCxcuMMYYi4iIYM2aNWOampps7Nix3LI8fvyYHT16lB0/fpwVFRUxxhh78+YNW7t2LatTpw4zNDTklqXUV199xebNm8cYK/mepaamshcvXrDevXuzX3/9lXseoYlEIiYSiZhYLJb9ufRLTU2NNWrUiB05coR7rv79+7P169ez69evcz93eUJDQ1m7du2YiYkJS09PZ4wxtnr1anbw4EGuOYKCgtiePXsUju/Zs4cFBwdzzUIqD92pIOQTKNtos4uLC44cOQJHR0eMGjUKU6dOxb59+2RTanhYs2aN7Jb6mjVrlGourba2tmwahKmpKVJTU2VdWZ48eVLp5/8nI+y8d2oW+toAwMqVK/HDDz/AwcEBiYmJOHToEObOnYvVq1dj8uTJmDhxIrcpR+fOnUPPnj3x/PlziEQitGjRAlu3bkWfPn0glUrx448/wtPTk0uWshITE7Fz504AgKqqKl69egUdHR0sXLgQbm5uGD9+PLcsxcXFWLNmTYWNIXisKyv999fCwgKXLl0SbEra+/bt2yd0BJmNGzdi/vz5mDJlChYvXizbmNDAwABr167lur5t+fLlCAgIUDhubGyMsWPHYsSIEdyykEokdFVDSFXz9u1btmDBApaZmSl0FJni4mL29u1b2ePdu3ezyZMnM39/f/b69WsBkykHNzc3FhgYyBhjzMfHh1lZWbHFixczR0dH1qlTp0o/f0Ujqu9/icXiSs/yPqGvDWOM2djYsC1btjDGGDt16hQTiUSsU6dO7NmzZ1zOX5aLiwsbOHAgu379Ops6dSoTiUTMwsKChYSEMKlUyj1PqTp16rAbN24wxhiztbVlhw4dYoyV3FHS1tbmmmXevHnMxMSE/fzzz0xDQ4MtWrSIjRo1ihkaGjJ/f3+uWcojxPumrNu3b7NJkyaxTp06MVdXVzZ58mTZ3UCeGjduzA4cOMAYe3d3izHGrl+/zv1um7q6OktLS1M4npaWxjQ0NLhmIZWHigpCPoG2tna5/0AKQRmLHLFYzB4+fKhw/MmTJ4J8cE5NTWVxcXGMMcby8/PZ+PHjmb29Pevbt69sSkBlSk9P/+gv3oS+NowxpqmpyTIyMmSP1dTUZFOgeDM0NJRNAcvPz2disbjcaRu8KUPxV8rS0pIdPXqUMSY/fc7f358NGjSIa5bly5ezXbt2yR5/++23TCQSMVNTU3bt2jWuWRhj7NixY0xNTY21bNmSTZ06lU2ZMoW1bNmSqaurs/DwcK5ZNDQ0ZD/DZYuKW7ducf8gb2ZmJiuEyzp48CCrV68e1yyk8lBRQcgncHNzY1u3bhU6howyFTmMlYzMl1dU3L9/n/svs6KiIhYVFcVycnK4nrcqUJZr8/77pewHIGXIkpKSIkgWxhh79OgRY0w5ir9SWlpasiKwbt26svVcqampTE9Pj2sWCwsLFhMTwxhjLDw8nNWsWZMdP36cjRo1inXu3JlrFsYYa9asGZs1a5bC8VmzZrHmzZtzzdK4cWPZ2omyP1P+/v7cs/j4+DCJRMIiIyNZUVERKyoqYhEREUwikbDp06dzzUIqD62pIOQTdO/eHXPmzEFCQgK+/PJLaGtryz3fu3dvrnlcXV0RFRWFkSNHcj3v+9atWwegpDf55s2b5bp+FBcX48yZMwq7u1Y2FRUVdO3aFYmJiUqxazQAbNu2DQEBAUhLS8P58+chkUiwdu1aWFhYcJ3nrEzXpuz7paioCMHBwQrz5L28vCo9h0gkwosXL6ChoQHGGEQiEQoKCpCXlyf3Oj09vUrPAgD16tVD7969MWrUKFk3Li0tLfz6669czl+e+vXrIysrCw0aNICVlRXCw8Ph6OiIS5cuQV1dnWuWrKwsmJmZAQCOHj2KAQMGoEuXLjA3N0erVq24ZgFK1r6U7s1Tlqenp2wPC158fHwwceJEFBYWgjGGixcvYufOnVi6dCn31tWLFy9GRkYGOnXqBFXVko+eUqkUw4cPx9KlS7lmIZWHigpCPkHposjyesOLRCLZgjhelKXIKd2gjDGGgIAAqKioyJ5TU1OT7UjMm729Pe7cuQMLCwvu535f2cWTS5Yskb1XatasyX3xJKAc16ZBgwb47bffZI/r1q2Lbdu2yb1GJBJxKSoYY2jUqJHc4+bNm8s95vkzHhISgq1bt6JXr16oW7cuPDw8MHLkSDRs2JDL+cvTt29fREREoFWrVvD29sagQYOwZcsWZGZmYurUqVyzGBgY4O7duzAzM8OxY8ewePFiACXfJ97/DgNA7dq1ce3aNXzxxRdyx69du8Z9rwYPDw8UFRVh5syZKCgowODBg1GvXj2sX78eX3/9Ndcsampq2L17NxYtWoS4uDhoamrC3t4eEomEaw5SuWjzO0I+A2KxuMLnhChynJ2dERYWJvjod6nw8HDMmjULixYtKrfo4jXqDAC2trZYunQp+vTpI7dRYUJCApycnLh1XCqlTNdGGZw+ffqjXtexY8dKTiLv7t27CAoKQkhICDIyMtChQweMHj0a/fv3h4aGBtcs77tw4QLOnTsHKysr7ndpJ02ahKNHj+KLL77A1atXkZ6eDh0dHezevRs//fQT1929AWDhwoVYs2YNZs+ejbZt20IkEiE6Oho//fQTpk+fjh9//JFrnlJPnjyBVCpFcXExli5dis2bN+PVq1eCZCGfLyoqCPkEoaGhGDhwoMKt/jdv3mDXrl0YPny4QMlIecoWXWVb3fIedQZKNidMSkqCRCKRKypSUlLg4ODA/Re9Ml2bsgoLCwX/sKysIiIisHXrVhw4cABqamoYNGiQoNOhhPT27Vv4+/vj7t27GDlypOyu0tq1a6Gjo4PRo0dzzcMYw9q1a7Fq1So8ePAAQEmrZh8fH3h5eXFptZ2bm4uJEyciPDwcNWrUwOzZszFp0iT4+flh5cqVsLW1xbRp0zBo0KBKzTFt2jQsWrQI2tramDZt2gdfW9k7whM+qKgg5BOoqKggKytL4Xb206dPYWxsLNgHMWVy7949HD58uNw+9rx/gfzd6DPPUWdbW1ssW7YMbm5uckXFunXrEBISgitXrnDLAijXtSkdRQ0ICMDDhw9x69YtWFpaYt68eTA3N8eoUaO4ZakKP+P79+/H2LFjkZubW+l5Dh8+jO7du6NGjRpyu1mXh+fdivz8fIW7a0IpKirCjh070LVrV9StWxcvXrwAANn+PbxMmDABR44cwcCBA3Hs2DEkJiaia9euKCwshK+vL7efaWdnZxw4cAA1a9aEk5NThQWVSCRCZGQkl0ykctGaCkI+Qeko7vvu3bsHfX197nkWLlz4wefnz5/PKUmJiIgI9O7dGxYWFkhOToadnR3S09PBGIOjoyPXLAD/qSofUtHiyWXLlmHz5s3c8yjTtVmyZAlCQkKwYsUKjBkzRnbc3t4ea9as4VpUVDTe9vr1a6ipqXHL8b709HRs3boVISEhuHfvHpydnblclz59+iA7OxvGxsbo06dPha/jfXerTp06GDBgADw9PdG+fXtu5y2Pqqoqxo8fj8TERAD8i4lSf/zxB7Zu3QpXV1dMmDABVlZWaNSoEfeF4v7+/rLpk1FRUVzPTYRBRQUh/0Dz5s0hEokgEonkulgAJaOsaWlpsg4tPB04cEDu8du3b5GWlgZVVVU0bNiQe1ExZ84cTJ8+HQsXLoSuri72798PY2NjDBkyRJDrc+bMmQ8+36FDB05JKl486e/vD3d3d245SinTtQkNDUVgYCA6deoktzO9g4MDkpKSuGRQxg5mhYWF2Lt3L7Zu3YozZ86gXr16GDlyJDw8PGBubs4lQ+kO1u//WWg7d+5EcHAwOnXqBIlEAk9PTwwfPhympqaC5GnVqhWuXr0q6ALkBw8ewNbWFgBgaWkJDQ0N7tPAgJLfl6V3+ywtLXHp0iUYGhpyz0H4oaKCkH+gdITu2rVr6Nq1q9wHjtLuRv379+ee6+rVqwrH8vLyMHLkSPTt25d7nsTEROzcuRNAyejdq1evoKOjg4ULF8LNzU3WPYsXJycnhWNl7zTxnsoyZswYjBkzRrZ4snSKzf3791GvXj2uWZTp2ty/fx9WVlYKx6VSKd6+fcslg7J1MBs7diz27NmDwsJCuLm54Y8//kCXLl24zM2vCnr16oVevXrh6dOnCA0NRXBwMObNm4euXbvC09MTvXv3lhv8qWwTJkzA9OnTce/evXIbHzg4OFR6BqlUiho1asgeq6ioCDJFrGbNmkhLS4OxsTHS09OVqhgllYOKCkL+AV9fXwCAubk53N3dufdk/yf09PSwcOFCfPPNNxg2bBjXc2tra+P169cAShYppqamokmTJgDAvbsRADx79kzu8du3b3H16lXMmzcPS5Ys4Z6nVOk+DNnZ2ViyZIkgHVmU6do0adIEZ8+eVRjl3bt3r1xb18qUlpYGQHk6mF24cAF+fn4YNmwYatWqJWiWUl5eXrCyslJo8bthwwbcvn2b+zQbADA0NMTUqVMxdepUrF+/Hj4+Pvjf//4HIyMjjBs3DrNnz4aWllal5xg4cCCA8vdU4TU1jDGGkSNHyn4/FRYWYty4cQqFRVhYWKXm6N+/Pzp27AgTExOIRCK0aNFCrkgv686dO5WahfBBRQUhn8DFxQWPHz9G/fr1AQAXL17E77//DltbW4wdO1bgdO/k5ubi+fPn3M/bunVrxMTEwNbWFj179sT06dNx/fp1hIWFoXXr1tzzlLfOpXPnzlBXV8fUqVO5LI6uqCPLggULsHLlSjRp0gRBQUGVnuN9ynBtSvn6+mLYsGG4f/8+pFIpwsLCkJycjNDQUBw9epRbDgA4deoU1/NVJD4+Xu7xmzdvkJaWhoYNG3IdgS9r//795S7Wbtu2LZYvXy5IUZGdnY3Q0FBs3boVmZmZ+PbbbzFq1Cg8ePAAy5cvx4ULFxAeHl7pOUqLUiGNGDFC7vHQoUMFyREYGIh+/frh9u3b8PLywpgxYwRbZ0L4oO5PhHyCr7/+GmPHjsWwYcOQnZ2NRo0awc7ODrdu3YKXlxf3NQyl88BLMcaQlZWFbdu2oUOHDrKpSLzcuXMHL1++hIODAwoKCjBjxgxER0fDysoKa9asUZoNjxITE/HVV1/h5cuXlX4uZenI8rF4Xpuyjh8/jqVLl+LKlSuQSqVwdHTE/Pnz0aVLl0o/tzK3wCwoKMDkyZMREhICALLOWF5eXjA1NcXs2bO5ZdHQ0EBCQoLCVLXbt2/Dzs4OhYWF3LKEhYVh69atOH78OGxtbTF69GgMHToUNWvWlL3mxo0baN68uUIXusrw9OlT2bqBu3fv4rfffsOrV6/Qu3dv7hvOCS0+Ph52dnYQi8Xw8PDAunXrqKj4zFFRQcgnMDAwwIULF2BtbY1169Zh9+7diImJQXh4OMaNG8f9Vu77uyGLxWLUrl0bLi4umDNnTrX/h/z90d7Somv58uV4+/YtYmJiKj2DRCLBli1b4Orqijt37simjwgxqluWMlwbZVG2Baazs3OFrxOiBaa3tzdiYmKwdu1adOvWDfHx8bC0tMThw4fh6+tb7rqqymJnZ4dx48Zh0qRJcsfXr1+PjRs34ubNm9yy6Ovrw93dHaNHj8ZXX31V7mtevXqFFStWyKavVobr16+jV69euHv3Lr744gvs2rUL3bp1Q35+PsRiMfLz87Fv374Pds763JRty0wLtasHmv5EyCd4+/atbL7qyZMnZX3ZbWxskJWVxT2PMtxyr8jLly8VFujx3qW5WbNmEIlECm1CW7duzW3KkbJ0ZHmfMlybUpcuXYJUKkWrVq3kjv/1119QUVFBixYtKvX8Zac8Kcv0p1IHDx7E7t270bp1a7lF2ra2tkhNTeWaZdq0aZg0aRIeP34MFxcXACVtpFetWsW9SM7KyvrbtRKampqVWlAAwMyZM2Fvb4/t27dj+/bt+Oabb9CjRw9Zm+jJkydj+fLl1aqooIXa1Q8VFYR8giZNmiAgIAA9e/bEiRMnsGjRIgAlHxyFGolhjOHp06cQiUSCjwalpaVh0qRJiIqKkpsKIdQuze8XXaV3cnju2KwsHVnepwzXptTEiRMxc+ZMhaLi/v37+Omnn/DXX39xy/Lw4UPUqVOn3Ofi4+O5dPEp6/Hjxwob8QElm7/x7gTl6emJ169fY8mSJbJ/+8zNzbFx40YMHz6cSwapVAqpVCpXUDx8+BABAQHIz89H7969ue5bcenSJURGRsLBwQHNmjVDYGAgJkyYINuxfvLkyYKsJxMSLdSufmj6EyGfICoqCn379kVeXh5GjBghG9H94YcfkJSUVOldNcrKzs7GzJkzcfjwYdkOrnp6eujbty+WLVtW4QejytS2bVsAJVM26tSpo/ChR9nWD/AgFovRvXt32R2uI0eOwMXFhXtHFmWmo6Mjm9ZTVlpaGhwcHGTvbx6MjY2xefNmhd2hV65ciXnz5nHv0tWxY0d8++23mDx5MnR1dREfHw8LCwtMmjQJt2/fxrFjx7jmKfX48WNoamrKtdfmwcPDAzVq1EBgYCAA4MWLF2jSpAkKCwthYmKCmzdv4tChQ+jRoweXPGKxWLY5IFCy8V1cXJzsvfzw4UOYmpoqxU7sPB07dky2ULt036LyeHt7c05GKgPdqSDkEzg5OeHJkyfIy8uTazk5duxYLm0LS+Xl5aFt27Z4+fIlPDw8YGNjA8YYbt68iZ07dyI6OhqxsbHcf+HHx8fjypUrsLa25nre8kilUgQHByMsLAzp6ekQiUSwsLDAt99+i2HDhnEb5VWWjixlKcu1KaWuro6HDx8qFBVZWVncOx3NmjULAwcOxIgRI7BmzRrk5ORg2LBhuHHjBnbv3s01CwAsW7YM3bp1w82bN1FUVAR/f3/cuHED58+fx+nTp7nnKSoqQlRUFFJTUzF48GAAJXdq9fT0uPx7ExMTgw0bNsgeh4aGoqioCCkpKdDX18esWbPw888/cysqACj8vNBeIpBtdnrlyhV4e3tX+/V9nzu6U0FIFbZo0SKEhobi3LlzqF27ttxzjx49Qrt27eDh4YEffviBay5nZ2fMnTsXrq6uXM/7PsYYevXqhf/9739o2rSprOhKTEzE9evX0bt3bxw8eFDQjEJRxmvj7u6O7OxsHDp0SNbqNjc3F3369IGxsTH27NnDNU9cXByGDh2KwsJC5OTkyNaZCHH3DyhZDLxy5Uq5zlizZs2Cvb091xwZGRno1q0bMjMz8fr1a1knqilTpqCwsJDL5oDa2tpISEiQNano168f6tWrh/Xr1wMAbt68CScnJzx69KjSswB/fyfy9evXOHbsWLW7U0GqF7pTQchHcnR0REREBAwMDNC8efMPjkLFxsZyyfTHH3/ghx9+UCgogJLpG3PmzMFvv/3GvajYvHkzxo0bh/v378POzk5uLQHAZ1dZAAgODsaZM2cQERGh0M0nMjISffr0QWhoKLd54EDJfHR/f3+FEbv8/HxMnjyZ2+JoZbw2q1atQocOHSCRSGSb3V27dg116tTBtm3buOUoZWlpiSZNmmD//v0AgAEDBghWUACAvb29rKWskLy9vdGiRQvExcXJrd/q27cvt+YDGhoaclPQLly4gJ9//lnueZ7tkD/mTiTPnyVldOnSJezduxeZmZkK7X2r87TPzwndqSDkI/n5+cHHxwdaWlrw8/P74Gsru9NIqVq1auH8+fMVTjNKSkpC27ZtkZOTwyVPqQsXLmDw4MFIT0+XHSvtMMRzoXaXLl3g4uJSYQ//pUuX4vTp0zh+/DiXPIB8m8Wynjx5grp166KoqIhLDmW8NkBJcbVjxw7ExcVBU1MTDg4OGDRokEJhWtliYmIwdOhQGBoaYtu2bYiJicG0adPQrVs3bNq0iftO23l5eeUeF4lEUFdXh5qaGrcsRkZGiImJgbW1tdzagfT0dNja2qKgoKDSM7i4uKBVq1ZYtmwZzp49CycnJ9y7dw8mJiYAgBMnTmD8+PG4fft2pWchf2/Xrl0YPnw4unTpghMnTqBLly5ISUlBdnY2+vbti61btwodkfwXGCGkylJRUWHZ2dkVPp+VlcVUVFQ4JirRuHFj1q9fP3bhwgWWlpbG0tPT5b54qVOnDrt69WqFz8fGxrI6depwyfL8+XOWm5vLRCIRu337Nnv+/LnsKycnh4WEhDATExMuWRhTrmujjNTU1NisWbPYmzdvZMdu377N2rRpw+rVq8c9j0gkYmKxuMKvBg0asPnz57Pi4uJKz2JgYMBu3LjBGGNMR0eHpaamMsYYO3v2LDM2Nq708zPGWGRkJNPQ0GCWlpZMU1OTeXp6yj0/fvx4Nnz4cC5ZyN+zt7dnGzZsYIy9e89IpVI2ZswYNn/+fIHTkf8KTX8i5F948+YNHj16pNB/u0GDBlzOzxiTtSwsT3n7D/CQkZGBw4cPK+y4y1tOTs4Hp6vUqVMHz54945KlZs2aEIlEEIlEaNSokcLzIpHob++A/ZeU6dqUdevWLURFRZX7c8Vzp/rw8HCFLmUNGzZEdHQ0lixZwi1HqeDgYMydOxcjR45Ey5YtwRjDpUuXEBISgh9//BGPHz/GypUroa6uXunTHTt37oy1a9fKOi+JRCK8fPkSvr6+3BZGOzs748qVKzhx4gTq1q2L7777Tu75Zs2aoWXLllyykL+XmpqKnj17AihpyFDaCnnq1KlwcXHh+m8fqTw0/YmQT3Dr1i2MGjUK586dkzvOOE/vEYvF0NfXr3B9B2MMeXl53BcH9urVCyNHjkT//v25nvd9KioqyM7OLnfNCcC3zePp06fBGIOLiwv279+PWrVqyZ5TU1ODRCKBqalppecopUzXptRvv/2G8ePHw8jICHXr1pV7X4tEIi5rlXr06IGdO3fKFoovWbIEEydORM2aNQEAT58+xddff81112gA6NSpE77//nsMGDBA7viePXuwadMmREREYNu2bViyZAmSkpIqNcuDBw/g7OwMFRUVpKSkoEWLFkhJSYGRkRHOnDlT7n4apHozMzPD//73P9jb26Np06aYPXs2Bg0ahPPnz6Nbt254/vy50BHJf4DuVBDyCTw8PKCqqoqjR4/KNvYRgrLOQ+3VqxemTp2K69evw97eXmE+/Pu9/ysLYwwjR46UdWR53+vXr7nkAN7tzZGWloYGDRoI3m5Sma5NqcWLF2PJkiWYNWsW93OXOn78uNz/959++gmDBg2SFRVFRUVITk7mnuv8+fPldlVq3rw5zp8/DwBo3749MjMzKz2Lqakprl27hp07dyI2NhZSqRSjRo3CkCFDoKmpWennL+vw4cPlHheJRNDQ0ICVlZWsQxQRztdff40TJ07A3t4eAwYMgLe3NyIjI3HixAl06tRJ6HjkP0J3Kgj5BNra2rhy5QpsbGyEjvKP7Ny5E7179670nZz/bkoWr9FvDw+Pj3odz+Js69at0NHRUZiusXfvXhQUFCh0kaksynht9PT0cO3aNYV9KnhS1k3MGjVqhH79+mH58uVyx2fPno0DBw4gOTkZly9fhpubG+7fv881m5DEYnG50zzLNoZo3749Dh48yH1xPXknJycHhYWFMDU1hVQqxcqVKxEdHQ0rKyvMmzePvjefCSoqCPkEX331FdasWYP27dsLHeUfUYYPbcrs3r17MDU1/WBR9G9ZW1sjICBAoY3r6dOnMXbsWEFGwT8Gj2szatQofPXVVxg3blylnePvKGtRcfjwYXz33XewsbHBV199BZFIhEuXLiEpKQn79u3DN998g40bNyIlJQWrV6+u9DzJyclYv349EhMTIRKJYGNjg0mTJnEfaImIiMDcuXOxZMkS2RqKixcv4scff8S8efOgr6+P77//Hq1atcKWLVu4ZiMlioqKsGPHDnTt2hV169YVOg6pRFRUEPIJIiMj8eOPP2Lp0qXlTu/R09MTKNmHvf8BqTIUFRVBQ0MD165dg52dXaWdpzLwKLo0NDSQlJQEc3NzuePp6elo3LixXO99ZcLj2ixbtgyrV69Gz549y/258vLyqrRzl3p/rYmuri7i4+NlU2iEKiqAkgYIAQEBSE5OBmMMNjY2+P777xXeS5Vt3759GDRoEFq0aIE2bdoAKGkjfenSJfz+++8Kd+Eqk52dHQIDA9G2bVu54zExMRg7dixu3LiBkydPwtPTk8vUMFI+LS0tJCYmQiKRCB2FVCJaU0HIJyjdKfr9uaC8F2orI1VVVUgkkip5DXiMsRgbGyM+Pl7hg+D7G4kpGx7XJjAwEDo6Ojh9+jROnz4t95xIJOJSVLy/1qSwsBDjxo2T2xlZKBKJBMuWLRPs/KVmzpyJOXPmYOHChXLHfX19MWvWLK5FRWpqarmDOHp6erhz5w4A4IsvvsCTJ0+4ZSKKWrVqhatXr1JR8ZmjooKQT3Dq1CmhIyi1H3/8EXPmzMH27dvluhwRwN3dHV5eXtDV1UWHDh0AlEx98vb2hru7u8DphJWWliZ0BKXfGbmgoKDcHYl57VIPANnZ2eVeg6FDh8rtas3Dl19+CR8fH4SGhsruLj1+/BgzZ87EV199BQBISUlB/fr1ueYi8iZMmIDp06fj3r17+PLLLxXW9fF8/5LKQ0UFIZ/g/f71RN66detw+/ZtmJqaQiKRKPwC4dEaVFktXrwYGRkZ6NSpE1RVS/4JlkqlGD58OJYuXSpwOqKsHdUeP34MDw8P/Pnnn+U+z/POoJOTE86ePauwD010dDS+/vprbjkAYMuWLXBzc0P9+vVhZmYGkUiEzMxMWFpa4tChQwCAly9fYt68eVxzkRKenp5Yu3YtBg4cCEB+CmPZxfRV8c42UURFBSEfKT4+HnZ2dhCLxYiPj//ga6v7qEufPn2EjqC01NTUsHv3bixatAhxcXHQ1NSEvb09TQtAyQeQDwkKCuKURPlMmTIFz549w4ULF+Ds7IwDBw7g4cOHWLx4MVatWsU1S+/evTFr1ixcuXIFrVu3BlCypmLv3r3w8/OTa/Na2e2jra2tkZiYiOPHj+PWrVuytSadO3eWNRWgf4+EExISguXLlyvFXUhS+WihNiEfqWxXmIraGAJ8W6b+U3Z2dvjzzz9hZmYmdBSlRN2xKsbj2vTt21fu8du3b5GQkIDc3Fy4uLggLCys0s6t7ExMTHDo0CG0bNkSenp6uHz5Mho1aoTDhw9jxYoViI6O5pblYzuAKfO/hYSP97upkc8b3akg5COlpaXJ5uwq46hLbm4u9u3bh9TUVPj4+KBWrVqIjY1FnTp1UK9ePQBAQkIC10xXrlyRtZy0tbVF8+bNuZ7/n+IxxlJVR+N5XJsDBw4oHJNKpZgwYUK1L/Ty8/NlH8xq1aqFx48fo1GjRrC3t+c+nVAqlXI939+JiIhAREQEHj16pJBNWX+eqhOhN/ok/FBRQchHKjs9RdmmqsTHx8PV1RX6+vpIT0/HmDFjUKtWLRw4cAAZGRkIDQ3lmufRo0dwd3dHVFQUatasCcYYnj9/DmdnZ+zatUtWnCmbmzdvwtTUtFLP8ezZM7nH74/GKyse16Y8YrEYU6dOhZOTE2bOnMn9/MrC2toaycnJMDc3R7NmzbBp0yaYm5sjICAAJiYmXDL89ddfyMnJQffu3WXHQkND4evri/z8fPTp0wfr16+vcJf2yuDn54eFCxeiRYsWMDExoQ+wSqhRo0Z/+33JycnhlIZUJioqCPlE9+/fR0xMTLmjYzxaX5Y1bdo0jBw5EitWrICurq7sePfu3TF48GCuWQBg8uTJyMvLw40bN9C4cWMAJR9KR4wYAS8vL+zcubPSM/Tr1++jX1s6rYbHtDBlGI1X1mtTkdTUVBQVFQl2fmUwZcoUZGVlAShp3dq1a1fs2LEDampqCA4O5pJhwYIFcHJykhUV169fx6hRozBy5Eg0btwYP//8M0xNTbFgwQIueQAgICAAwcHBGDZsGLdzkn/Gz88P+vr6QscgHFBRQcgn2Lp1K8aNGwc1NTUYGhrKjcLw6qdf1qVLl7Bp0yaF4/Xq1UN2djbXLABw7NgxnDx5UlZQAICtrS1++eUXdOnShUuGqvRLjPdofNlrwxjDgQMHoK+vjxYtWgAombaWm5v7j4qP/8K0adPkHjPGkJWVhT/++EOh1Wt1UVBQAB8fHxw8eBBv375FeHg41q1bh/T0dCQlJaFBgwYwMjLikuXatWtYtGiR7PGuXbvQqlUr/PbbbwBKCk9fX1+uRcWbN28UNr4jysXd3Z3WVFQTVFQQ8gnmz5+P+fPnY86cOR+9aLEyaWhoIC8vT+F4cnKyIFONpFKpwm7IAFCjRg1u87GVtTVoRXiOxpe9NrNmzcKAAQMQEBAAFRUVACXtSSdMmMB9Z/irV6/KPRaLxahduzZWrVr1t2tRPle+vr4IDg7GkCFDoKmpid9//x3jx4/H3r174ejoyDXLs2fPUKdOHdnj06dPo1u3brLHX331Fe7evcs10+jRo/H7779Ty1glRdPRqhcqKgj5BAUFBXB3d1eKggIA3NzcsHDhQuzZswcAZL3aZ8+ejf79+3PP4+LiAm9vb+zcuVM2D//+/fuYOnWqwi7k1Y2yjcYHBQUhOjpaVlAAgIqKCqZNm4a2bdty3cyMNpVUFBYWhi1btsg2RhwyZAjatWuH4uJiue8ZD3Xq1EFaWhrMzMzw5s0bxMbGws/PT/b8ixcvyh1MqEyFhYUIDAzEyZMn4eDgoHD+1atXc81D5FGD0eqFigpCPsGoUaOwd+9ezJ49W+goAICVK1eiR48eMDY2xqtXr9CxY0dkZ2ejTZs2WLJkCfc8GzZsgJubG8zNzeU2pLK3t8f27du55wGAffv2Yc+ePeXuRsyze46yjcYXFRUhMTER1tbWcscTExMF6/Lz+PFjJCcnQyQSoVGjRkq7sJ+Hu3fvym0o17JlS6iqquLBgwfc17l069YNs2fPxk8//YSDBw9CS0tLLlt8fDwaNmzINVN8fDyaNWsGQLG7HY2SC0/ZOoWRykVFBSGfYNmyZfjmm29w7Ngx2NvbCz46pqenh+joaERGRiI2NhZSqRSOjo5wdXXlmqOUmZkZYmNjceLECSQlJYExBltbW8HyrFu3DnPnzsWIESNw6NAheHh4IDU1FZcuXcLEiRO5ZlG20XgPDw94enri9u3bchuZLV++HB4eHlyz5OfnY/LkyQgNDZV9GFFRUcHw4cOxfv16aGlpcc2jDIqLi6GmpiZ3TFVVVZCF64sXL0a/fv3QsWNH6OjoICQkRC5bUFAQtzVTpZTt54mQ6ow2vyPkEyxatAi+vr6wtrZGnTp1FBZqR0ZGcs2Tnp4Oc3Nzrud8X61atXDr1i0YGRnB09MT/v7+cp2ohGRjYwNfX18MGjQIurq6iIuLg6WlJebPn4+cnBxs2LBB6IiCkUqlWLlyJfz9/WXdhUxMTODt7Y3p06dznWLz/fff4+TJk9iwYQPatWsHAIiOjoaXlxc6d+6MjRs3csuiLMRiMbp37y7XpvXIkSNwcXGBtra27BjPjQGfP38OHR0dhfdGTk4OdHR0FIogQkj1QEUFIZ/AwMAAa9aswciRI4WOAqDkg0fbtm0xbNgwfPfdd6hVqxb3DDo6OoiPj4elpSVUVFSQnZ2tNNNWtLS0kJiYCIlEAmNjY5w4cQJNmzZFSkoKWrdujadPn1bq+Zs3b/7RUzF4b2RWVulif94LtEsZGRlh3759cHJykjt+6tQpDBgwAI8fPxYkl5A+9m5RVWtM8G/069cPwcHB0NPT+9sOZdV5F3ZCeKPpT4R8AnV1ddlIqjK4fPkydu7cicWLF8Pb2xtdu3bF0KFD0bt3b24bUbVp0wZ9+vTBl19+CcYYvLy8oKmpWe5ree9yW7duXTx9+hQSiQQSiQQXLlxA06ZNkZaWxmUhYZ8+fWR/LiwsxK+//gpbW1u0adMGQMl0oxs3bmDChAmVnqU8RUVFiIqKQmpqqmxfkwcPHkBPTw86OjrcchQUFMh1FyplbGyMgoICbjmUSXUqFj6Wvr6+rEjX09OjtROEKAm6U0HIJ1i2bBmysrKwbt06oaPIYYwhKioKv//+O/bv34/i4mL079+fy4f4hw8fYs2aNUhNTUVYWBi6du1aYUFT3gZwlWn06NGyHvoBAQGYNm0a2rVrh8uXL6Nfv37YsmUL1ywmJiZy/f6Bktahd+/e5V5wZWRkoFu3bsjMzMTr169x69YtWFpaYsqUKSgsLERAQAC3LJ06dYKhoSFCQ0OhoaEBAHj16hVGjBiBnJwcnDx5klsWQggh/wwVFYR8gr59+yIyMhKGhoZo0qSJwkJtZbjlHhsbi1GjRiE+Ph7FxcVcz21hYYHLly/D0NCQ63krIpVKIZVKoapacnN2z549iI6OhpWVlWwTQ1709fVx+fJlfPHFF3LHU1JS0KJFCzx//pxbFqDkLoquri62bNkCQ0ND2XqT06dPY/To0UhJSeGWJSEhAd26dUNhYSGaNm0KkUiEa9euQV1dHeHh4WjSpAm3LKRqcHFxQVhYGGrWrCl3PC8vD3369OG+vo2Q6oymPxHyCWrWrMl9t+GPcffuXezcuRO///47rl+/jjZt2giyCDktLY37OT9ELBbL7SkyYMAADBgwQJAsmpqaiI6OVigqoqOjZaPzPEVHRyMmJkahsJJIJLh//z7XLHZ2dkhJScH27dtlXcPc3d1lG78R8r6oqCiFFtFAyTTDs2fPCpCIkOqLigpCPoGyzXMODAzEjh07EBMTA2trawwZMgQHDx4UtCNUREQEIiIi8OjRI4Ve5byn+ABAbm4uLl68WG6e4cOHc8sxZcoUjB8/HleuXJFr4RoUFIT58+dzy1FKKpWWeyfr3r173Lt3PX36FIaGhhgzZgwyMzOxefNmJCcn4/Lly3L7IRASHx8v+/PNmzeRnZ0te1xcXIxjx46hXr16QkQjpNqi6U+EfAbMzMxkI7qlG0EJyc/PDwsXLkSLFi1gYmKisJCS95qKI0eOYMiQIcjPz4eurq5CC+CcnByuefbs2QN/f38kJiYCABo3bgxvb29B7p4MHDgQ+vr6CAwMhK6uLuLj41G7dm24ubmhQYMGXAro69evo1evXrh79y6++OIL7Nq1C926dUN+fj7EYjHy8/Oxb98+uQXvpHoTi8Wyn+PyPsZoampi/fr1gmwoSUh1RUUFIR9JmduCMsaUqgOKiYkJVqxYgWHDhgkdBQDQqFEj9OjRA0uXLq2WG6h9yIMHD+Ds7AwVFRXZuo6UlBQYGRnhzJkzMDY2rvQM3bt3h6qqKmbNmoXt27fj6NGj6NKlCzZv3gwAmDx5Mq5cuYILFy5UehZSNWRkZIAxBktLS1y8eFGufbWamhqMjY257rFCCKGigpCP5ufnJ/vz37UFXbZsWaXniY+Ph52dHcRisdxUgPI4ODhUep6yDA0NcfHiRTRs2JDreSuira2N69evw9LSUugoMleuXEFiYiJEIhFsbW3RvHlzwbK8evUKu3btwpUrV2S7sfNcx2BkZITIyEg4ODjg5cuX0NPTw8WLF9GiRQsAQFJSElq3bo3c3FwueQghhPxzVFQQ8gmUoS2oWCxGdnY2jI2NZVMByv44lz4WiUTcuz/NmjULOjo6mDdvHtfzVqRfv35wd3cXbHF2WY8ePYK7uzuioqJQs2ZNMMbw/PlzODs7Y9euXdw3DDxz5gzatm0r64xVqqioCOfOnUOHDh0qPUPZ9zIAuV3PgZJ2xaamptzfx6TquHnzJjIzMxUWbffu3VugRIRUP7RQm5BPsHfvXly+fFnh+NChQ9GiRQsuRUVaWprsA6iydVsqLCxEYGAgTp48CQcHB4WWu6tXr+aap2fPnvDx8cHNmzdhb2+vkIfnB4/JkycjLy8PN27cQOPGjQGUfCAaMWIEvLy8sHPnTm5ZAMDZ2RlZWVkK05xKCx1eH+Tfn76nTNP5iPK6c+cO+vbti+vXr8sNrJS+f6gQJYQfKioI+QTK0BZUIpHI/pyRkfHB0eayr+UhPj5etmA8ISGB67nLM2bMGADAwoULFZ7jfSfn2LFjOHnypKygAABbW1v88ssv6NKlC7ccpSpaj/P06VNoa2tzyzFy5EjZZomFhYUYN26c7PyvX7/mloNULd7e3rCwsMDJkydl6yuePn2K6dOnY+XKlULHI6RaoaKCkE9QUVvQLVu2wNfXl3seZRltLnXq1Cmu5/s777eQFZJUKlW4UwIANWrU4JqzdJ8VkUgk94EeKBndjY+PR9u2bblkGTFihNzjoUOHKryGZ9tfUnWcP38ekZGRqF27tmw/mvbt22PZsmXw8vLC1atXhY5ISLVBRQUhn2D27NmwtLSEv78/fv/9dwAlo82hoaEKdy94UJbR5o/ZEFAkEmH//v0c0ignFxcXeHt7Y+fOnTA1NQUA3L9/H1OnTkWnTp245dDX1wdQ8t7R1dWVW5StpqaG1q1by+7wVDZl2/eFVB3FxcXQ0dEBULLg/8GDB7C2toZEIkFycrLA6QipXqioIOQTld2VOTc3Fzt27MCyZcsQFxfH7c6AMo02A+8+qCqj06dPY+XKlbKOS40bN4aPjw/3TdU2bNgANzc3mJubw8zMDCKRCJmZmbC3t8f27du55Sj9IG9ubo4ZM2ZwLT4J+a/Y2dkhPj4elpaWaNWqFVasWAE1NTUEBgYqVbc3QqoD6v5EyL8QGRmJoKAghIWFQSKRoH///ujfvz+39qAeHh4AgJCQEAwYMEBhtNnc3BxjxoyBkZERlzzKavv27fDw8EC/fv3Qrl07MMZw7tw5HDhwAMHBwRg8eDD3TCdOnEBSUhIYY7C1tYWrqyv3DIRUdcePH0d+fj769euHO3fu4JtvvkFSUhIMDQ2xe/duuLi4CB2RkGqDigpC/qF79+4hODgYQUFByM/Px4ABAxAQEIC4uDjY2toKksnPz49Gmz+gcePGGDt2LKZOnSp3fPXq1fjtt99kO1tXtqKiImhoaODatWuws7Pjcs6PsW/fPuzZs6fclpy8N3Ik5N/KycmBgYEBdRAjhDOx0AEIqUp69OgBW1tb3Lx5E+vXr8eDBw+wfv16oWPB19eXCooPuHPnDnr16qVwvHfv3lzb8aqqqkIikShVm8t169bBw8MDxsbGuHr1Klq2bAlDQ0PcuXMH3bt3FzoeIRUqKiqCqqqqQoe5WrVqUUFBiABoTQUh/0B4eDi8vLwwfvx4QRZkfwiNNlfMzMwMERERsLKykjseEREBMzMzrll+/PFHzJkzB9u3b0etWrW4nrs8v/76KwIDAzFo0CCEhIRg5syZsLS0xPz585GTkyN0PEIqpIxFOiHVGd2pIOQfOHv2LF68eIEWLVqgVatW2LBhAx4/fix0LBpt/hvTp0+XFYPbtm3D9u3bMW7cOHh7e2PGjBlcs6xbtw5nz56FqakprK2t4ejoKPfFW2Zmpmwxv6amJl68eAEAGDZsGPeN+Aj5p0qLdCqACREe3akg5B9o06YN2rRpA39/f+zatQtBQUGYNm0apFIpTpw4ATMzM+jq6nLPRaPNHzZ+/HjUrVsXq1atwp49ewCUrLPYvXs33NzcuGbp06eP3M6/Qqtbty6ePn0KiUQCiUSCCxcuoGnTpkhLS1OajIRUZN26dbh9+zZMTU0hkUgUpoFW97u0hPBEC7UJ+ZeSk5OxZcsWbNu2Dbm5uejcuTMOHz7MNYOWlhYSExMhkUhgbGyMEydOoGnTpkhJSUHr1q3x9OlTrnmIooKCAvj4+ODgwYN4+/YtOnXqhPXr1wvemWv06NEwMzODr68vAgICMG3aNLRr1w6XL19Gv379sGXLFkHzEfIhfn5+H3xeiM1ICamuqKgg5D9SXFyMI0eOICgoiHtRYWlpiX379sHR0RFfffUVRo8eje+//x7h4eFwd3enuxVKwMfHB7/++iuGDBkCTU1N/P7773BycsLevXsFzSWVSiGVSqGqWnLjes+ePYiOjoaVlRXGjRsHNTU1QfMRQgipGqioIOQzQKPNimrVqoVbt27ByMjob9tL8ii6GjZsiCVLlsDd3R0AcPHiRbRr1w6FhYVQUVGp9PMT8rnKzc3Fvn37kJqaCh8fH9SqVQuxsbGoU6cO6tWrJ3Q8QqoNKioI+QzQaLOikJAQuLu7Q11dHcHBwR8sKkaMGFHpedTU1JCWlib3IUdTUxO3bt3i3oEqPj7+o1/r4OBQiUkI+Xfi4+Ph6uoKfX19pKenIzk5GZaWlpg3bx4yMjIQGhoqdERCqg0qKgip4oqKirBkyRJ4enpy/3BKPp6Kigqys7NRu3Zt2TFdXV3Ex8fDwsKCaxaxWPxRi8VFIhG16yRKzdXVFY6OjlixYgV0dXURFxcHS0tLnDt3DoMHD0Z6errQEQmpNqioIOQzoKOjg4SEBJibmwsdRSmpqKggKysLxsbGcsefPn0KY2NjLh+cxWIxunfvDnV1ddmxI0eOwMXFRa5jTVhYWKVnycjI+OjXSiSSSkxCyL+jr6+P2NhYNGzYUK6oyMjIgLW1NQoLC4WOSEi1QS1lCfkMuLq6IioqCiNHjhQ6ilKqaOzk9evX3KaGlTfFaujQoVzO/T4qFMjnQkNDA3l5eQrHk5OT5e4KEkIqHxUVhHwGunfvjjlz5iAhIQFffvmlQq/23r17C5RMWOvWrQNQMo1n8+bN0NHRkT1XXFyMM2fOwMbGhkuWrVu3cjnPp9i2bRsCAgKQlpaG8+fPQyKRYO3atbCwsOC+jwch/4SbmxsWLlwo239GJBIhMzMTs2fPRv/+/QVOR0j1QtOfCPkMiMXiCp+rzvPiS9cqZGRkoH79+nJdltTU1GBubo6FCxeiVatWQkUU3MaNGzF//nxMmTIFS5YsQUJCAiwtLREcHIyQkBCcOnVK6IiEVCgvLw89evTAjRs38OLFC5iamiI7Oxtt2rTB//73P4UBFkJI5aGighDy2XN2dkZYWBgMDAyEjqJ0bG1tsXTpUvTp00duTnpCQgKcnJzw5MkToSMS8rciIyMRGxsLqVQKR0dHuLq6Ch2JkGqHigpCPgOhoaEYOHCg3CJgAHjz5g127dqF4cOHC5SMKDtNTU0kJSVBIpHIFRUpKSlwcHDAq1evhI5ICCGkCqA1FYR8Bjw8PNCtWzeF7kYvXryAh4cHFRUA7t27h8OHDyMzMxNv3ryRe2716tUCpRKehYUFrl27prB4+88//4Stra1AqQj5eBEREVizZg0SExMhEolgY2ODKVOm0N0KQjijooKQzwBjrNzN3e7duwd9fX0BEimXiIgI9O7dGxYWFkhOToadnR3S09PBGIOjo6PQ8QTl4+ODiRMnorCwEIwxXLx4ETt37sSyZcuwefNmoeMR8kEbNmzA1KlT8e2338Lb2xsAcOHCBfTo0QOrV6/GpEmTBE5ISPVB058IqcKaN28OkUiEuLg4NGnSRLajNlDS3SgtLQ3dunWTdUaprlq2bIlu3bph4cKFsik+xsbGGDJkCLp164bx48cLHVFQv/32GxYvXoy7d+8CAOrVq4cFCxZg1KhRAicj5MPq1auHOXPmKBQPv/zyC5YsWYIHDx4IlIyQ6oeKCkKqMD8/P9n/Tp8+Xa5laml3o/79+3Pbi0FZ6erq4tq1a2jYsCEMDAwQHR2NJk2aIC4uDm5ubrTr7v978uQJpFKpbBrd/fv3Ua9ePYFTEVIxXV1dXL16FVZWVnLHU1JS0Lx5c7x8+VKgZIRUPzT9iZAqzNfXFwBgbm4Od3d3hYXapIS2tjZev34NADA1NUVqaiqaNGkCANTdqAwjIyMAQHZ2NpYsWYLNmzfTQm2i1Hr37o0DBw7Ax8dH7vihQ4fQq1cvgVIRUj1RUUHIZ8DFxQWPHz9G/fr1AQAXL17E77//DltbW4wdO1bgdMJr3bo1YmJiYGtri549e2L69Om4fv06wsLC0Lp1a6HjCSI3NxcTJ05EeHg4atSogdmzZ2PSpElYsGABVq5ciSZNmiAoKEjomIR8UOPGjbFkyRJERUWhTZs2AErWVMTExGD69OmyDTABwMvLS6iYhFQLNP2JkM/A119/jbFjx2LYsGHIzs5Go0aNYGdnh1u3bsHLywvz588XOqKg7ty5g5cvX8LBwQEFBQWYMWMGoqOjYWVlhTVr1ih0PqoOJkyYgCNHjmDgwIE4duwYEhMT0bVrVxQWFsLX1xcdO3YUOiIhf6t0g8u/IxKJcOfOnUpOQ0j1RkUFIZ8BAwMDXLhwAdbW1li3bh12796NmJgYhIeHY9y4cfTLlCiQSCTYsmULXF1dcefOHVhZWcHLywtr164VOhoh/9iTJ08gEolgaGgodBRCqi2x0AEIIf/e27dvZespTp48id69ewMAbGxskJWVJWQ0pfPy5Uvk5eXJfVVHDx48kO1DYWlpCQ0NDYwePVrgVIR8vNIpfEZGRqhTpw6MjY1hZGSESZMmITc3V+h4hFQ7tKaCkM9AkyZNEBAQgJ49e+LEiRNYtGgRgJIPjjRyB6SlpWHSpEmIiopCYWGh7Hjp/h7FxcUCphOGVCpFjRo1ZI9VVFSgra0tYCJCPl5OTg7atGmD+/fvY8iQIWjcuDEYY0hMTERwcDAiIiJw7tw5GBgYCB2VkGqDpj8R8hmIiopC3759kZeXhxEjRsgW2P7www9ISkpCWFiYwAmF1bZtWwCAt7c36tSpo7BRYHVcPyAWi9G9e3fZHa4jR47AxcVFobCo7u8dopymTJmCiIgInDx5EnXq1JF7Ljs7G126dEGnTp2wZs0agRISUv1QUUHIZ6K4uBh5eXlyI3Pp6enQ0tKS7TtQXeno6ODKlSuwtrYWOorS8PDw+KjXbd26tZKTEPLPmZubY9OmTejatWu5zx87dgzjxo2jPWgI4YiKCkLIZ8/Z2Rlz586Fq6ur0FEIIf8BdXV1pKamytpov+/evXuwsrKSm+5ICKlctKaCkCrK0dERERERMDAwQPPmzRWm9JQVGxvLMZny2bx5M8aNG4f79+/Dzs5Obi0BADg4OAiUTHienp7w9/eHrq6u3PH8/HxMnjyZ9qogSsnIyAjp6ekVFhVpaWm0nowQzuhOBSFVlJ+fH3x8fKClpQU/P78PvrZ05+3q6sKFCxg8eLDcVAiRSFStF2qXUlFRQVZWlsIUuSdPnqBu3booKioSKBkhFRs1ahRu376NEydOQE1NTe65169fo2vXrmjYsCG2bNkiUEJCqh8qKgghnz1bW1s0btwYM2fOLHehdnXc/C4vLw+MMRgYGCAlJQW1a9eWPVdcXIwjR45g9uzZePDggYApCSnfvXv30KJFC6irq2PixImwsbEBANy8eRO//vorXr9+jcuXL8PMzEzgpIRUH1RUEPIZefPmDR49egSpVCp3vEGDBgIlUg7a2tqIi4uDlZWV0FGUhlgs/uCUOZFIBD8/P8ydO5djKkI+XlpaGiZMmIDw8HCUfpQRiUTo3LkzNmzYQD/vhHBGayoI+QzcunULo0aNwrlz5+SO0/SeEi4uLlRUvOfUqVNgjMHFxQX79+9HrVq1ZM+pqalBIpHA1NRUwISEfJiFhQX+/PNPPHv2DCkpKQAAKysrufcyIYQfKioI+Qx4eHhAVVUVR48ehYmJyQdHoKujXr16YerUqbh+/Trs7e0VFmqX7kBenZTuzZGWloYGDRrQe4ZUWQYGBmjZsqXQMQip9mj6EyGfAW1tbVy5ckU2r5jIE4vFFT5X3e/kbN26FTo6Ovjuu+/kju/duxcFBQUYMWKEQMkIIYRUJRX/piWEVBm2trZ48uSJ0DGUllQqrfCrOhcUALB8+XIYGRkpHDc2NsbSpUsFSEQIIaQqoqKCkM/ATz/9hJkzZyIqKgpPnz5FXl6e3Fd1VlRUBFVVVSQkJAgdRSllZGTAwsJC4bhEIkFmZqYAiQghhFRFtKaCkM9A6U7RnTp1kjtOC7UBVVVVSCSSan0NPsTY2Bjx8fEwNzeXOx4XF0ebhxFCCPloVFQQ8hk4deqU0BGU2o8//og5c+Zg+/bt1BnmPe7u7vDy8oKuri46dOgAADh9+jS8vb3h7u4ucDpCCCFVBS3UJoR89po3b47bt2/j7du3kEgk0NbWlns+NjZWoGTCe/PmDYYNG4a9e/dCVbVknEkqlWL48OEICAhQ2K2YEEIIKQ8VFYRUUfHx8bCzs4NYLEZ8fPwHX+vg4MAplXLy8/P74PO+vr6ckiivW7duIS4uDpqamrC3t6+Wu4wTQgj5dFRUEFJFicViZGdnw9jYWLY7cnk/ztV9TQUhhBBCKh+tqSCkikpLS0Pt2rVlfyZ/78qVK0hMTIRIJIKtrS2aN28udCTBeXp6fvD5oKAgTkkIIYRUZVRUEFJFlZ2eQlNVPuzRo0dwd3dHVFQUatasCcYYnj9/DmdnZ+zatUtWnFVHz549k3v89u1bJCQkIDc3Fy4uLgKlIoQQUtVQUUHIZ+L+/fuIiYnBo0ePIJVK5Z7z8vISKJVymDx5MvLy8nDjxg00btwYAHDz5k2MGDECXl5e2Llzp8AJhXPgwAGFY1KpFBMmTIClpaUAiQghhFRFtKaCkM/A1q1bMW7cOKipqcHQ0BAikUj2nEgkwp07dwRMJzx9fX2cPHkSX331ldzxixcvokuXLsjNzRUmmBJLTk6Gk5MTsrKyhI5CCCGkCqA7FYR8BubPn4/58+djzpw5EIvFQsdROlKpFDVq1FA4XqNGDYW7OqREamoqioqKhI5BCCGkiqCigpDPQEFBAdzd3amgqICLiwu8vb2xc+dOmJqaAiiZLjZ16lSFXcirm2nTpsk9ZowhKysLf/zxB0aMGCFQKkIIIVUNTX8i5DMwc+ZM1KpVC7NnzxY6ilK6e/cu3NzckJCQADMzM4hEImRmZsLe3h6HDh1C/fr1hY4oGGdnZ7nHYrEYtWvXhouLCzw9PWUb4hFCCCEfQkUFIZ+B4uJifPPNN3j16hXs7e0VpvqsXr1aoGTK5cSJE0hKSgJjDLa2tnB1dRU6EiGEEPJZoCEoQj4DS5cuxfHjx2FtbQ0ACgu1q6NatWrh1q1bMDIygqenJ/z9/dG5c2d07txZ6GiEEELIZ4fuVBDyGTAwMMCaNWswcuRIoaMoDR0dHcTHx8PS0hIqKirIzs6u1vtRlNW8efOPLjZjY2MrOQ0hhJDPAd2pIOQzoK6ujnbt2gkdQ6m0adMGffr0wZdffgnGGLy8vKCpqVnua6vbrtF9+vSR/bmwsBC//vorbG1t0aZNGwDAhQsXcOPGDUyYMEGghIQQQqoaulNByGdg2bJlyMrKwrp164SOojQePnyINWvWIDU1FWFhYejatSvU1dXLfW15G8BVF6NHj4aJiQkWLVokd9zX1xd3796tdgUXIYSQT0NFBSGfgb59+yIyMhKGhoZo0qSJwkLtsLAwgZIpBwsLC1y+fBmGhoZCR1E6+vr6uHz5Mr744gu54ykpKWjRogWeP38uUDJCCCFVCU1/IuQzULNmTfTr10/oGEorLS1N6AhKS1NTE9HR0QpFRXR0NDQ0NARKRQghpKqhooKQz8DWrVuFjqD0IiIiEBERgUePHinsol2dp/hMmTIF48ePx5UrV9C6dWsAJWsqgoKCMH/+fIHTEUIIqSpo+hMh5LPn5+eHhQsXokWLFjAxMVHofFSd11QAwJ49e+Dv74/ExEQAQOPGjeHt7Y0BAwYInIwQQkhVQUUFIVUUtQX9eCYmJlixYgWGDRsmdBRCCCHks0TTnwipoqgt6Md78+YN2rZtK3QMpXblyhUkJiZCJBLB1tYWzZs3FzoSIYSQKoTuVBDyGaC2oB82a9Ys6OjoYN68eUJHUTqPHj2Cu7s7oqKiULNmTTDG8Pz5czg7O2PXrl20YSAhhJCPQkUFIZ8Bagv6Yd7e3ggNDYWDgwMcHBwUWu6uXr1aoGTCGzhwIFJTU7Ft2zY0btwYAHDz5k2MGDECVlZW2Llzp8AJCSGEVAU0/YmQzwC1Bf2w+Ph4NGvWDACQkJAgbBglc+zYMZw8eVJWUACAra0tfvnlF3Tp0kXAZIQQQqoSKioI+QxU1BZ0y5Yt8PX1FTid8E6dOiV0BKUllUoV7twAQI0aNRRa7xJCCCEVoelPhHwm3m8LamtrC29vb3zxxReyUfrq5mM2BBSJRNi/fz+HNMrJzc0Nubm52LlzJ0xNTQEA9+/fx5AhQ2BgYFDt2+0SQgj5OFRUEPIZys3NxY4dO7BlyxbExcWhuLhY6EiC8PDw+KjXVefNA+/evQs3NzckJCTAzMwMIpEImZmZsLe3x6FDh1C/fn2hIxJCCKkCqKgg5DMSGRmJoKAghIWFQSKRoH///ujfvz+1ByV/68SJE0hKSgJjDLa2tnB1dRU6EiGEkCqEigpCqrh79+4hODgYQUFByM/Px4ABAxAQEIC4uDjY2toKHY8osaKiImhoaODatWuws7MTOg4hhJAqTCx0AELIp+vRowdsbW1x8+ZNrF+/Hg8ePMD69euFjkWqCFVVVUgkkmo7PY4QQsh/h4oKQqqw8PBwjB49Gn5+fujZsydUVFSEjkSqmB9//BFz5sxBTk6O0FEIIYRUYdRSlpAq7OzZswgKCkKLFi1gY2ODYcOGYeDAgULHIlXIunXrcPv2bZiamkIikUBbW1vu+djYWIGSEUIIqUqoqCCkCmvTpg3atGkDf39/7Nq1C0FBQZg2bRqkUilOnDgBMzMz6OrqCh2TKLE+ffpAJBKBltcRQgj5N2ihNiGfmeTkZGzZsgXbtm1Dbm4uOnfujMOHDwsdiyiZgoIC+Pj44ODBg3j79i06deqE9evXw8jISOhohBBCqiAqKgj5TBUXF+PIkSMICgqiooIo8PHxwa+//oohQ4ZAU1MTv//+O5ycnLB3716hoxFCCKmCqKgghJBqqGHDhliyZAnc3d0BABcvXkS7du1QWFhIC/4JIYT8Y1RUEEJINaSmpoa0tDTUq1dPdkxTUxO3bt2CmZmZgMkIIYRURdRSlhBCqqHi4mKoqanJHVNVVUVRUZFAiQghhFRl1P2JEEKqIcYYRo4cCXV1ddmxwsJCjBs3Tq6tbFhYmBDxCCGEVDFUVBBCSDU0YsQIhWNDhw4VIAkhhJDPAa2pIIQQQgghhPwrtKaCEEIIIYQQ8q9QUUEIIYQQQgj5V6ioIIQQQgghhPwrVFQQQgghhBBC/hUqKgghhBBCCCH/ChUVhBBCCCGEkH+FigpCCCGEEELIv/J//2QJP41QPV8AAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 800x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "correlation_matrix = data.corr()\n",
+ "\n",
+ "plt.figure(figsize=(8, 4))\n",
+ "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', linewidths=0.5)\n",
+ "plt.title('Correlation Matrix Heatmap')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0c36206",
+ "metadata": {},
+ "source": [
+ "# Preprocessing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "435c0457",
+ "metadata": {},
+ "source": [
+ "## Remove Outliers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "ae17e493",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Remove outliers\n",
+ "\n",
+ "removed_outliers = remove_outliers(data, numeric_cols)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35862085",
+ "metadata": {},
+ "source": [
+ "## One Hot Encoding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "10caf523",
+ "metadata": {
+ "scrolled": true
+ },
+ "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>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " <th>Month_Dec</th>\n",
+ " <th>Month_Feb</th>\n",
+ " <th>Month_Jul</th>\n",
+ " <th>Month_June</th>\n",
+ " <th>Month_Mar</th>\n",
+ " <th>Month_May</th>\n",
+ " <th>Month_Nov</th>\n",
+ " <th>Month_Oct</th>\n",
+ " <th>Month_Sep</th>\n",
+ " <th>VisitorType_Other</th>\n",
+ " <th>VisitorType_Returning_Visitor</th>\n",
+ " <th>Weekend_True</th>\n",
+ " <th>Revenue_True</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.002899</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>64.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.10</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.002899</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>9</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2.666667</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>0.14</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>10</td>\n",
+ " <td>627.500000</td>\n",
+ " <td>0.020000</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "0 0 0.0 0 \n",
+ "1 0 0.0 0 \n",
+ "2 0 0.0 0 \n",
+ "3 0 0.0 0 \n",
+ "4 0 0.0 0 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "0 0.0 1 0.000000 \n",
+ "1 0.0 2 64.000000 \n",
+ "2 0.0 1 0.000000 \n",
+ "3 0.0 2 2.666667 \n",
+ "4 0.0 10 627.500000 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay OperatingSystems Browser \\\n",
+ "0 0.002899 0.20 0.0 0.0 1 2 \n",
+ "1 0.000000 0.10 0.0 0.0 2 2 \n",
+ "2 0.002899 0.20 0.0 0.0 4 2 \n",
+ "3 0.050000 0.14 0.0 0.0 3 2 \n",
+ "4 0.020000 0.05 0.0 0.0 3 2 \n",
+ "\n",
+ " Region TrafficType Month_Dec Month_Feb Month_Jul Month_June \\\n",
+ "0 1 1 0 1 0 0 \n",
+ "1 1 2 0 1 0 0 \n",
+ "2 9 3 0 1 0 0 \n",
+ "3 2 4 0 1 0 0 \n",
+ "4 1 4 0 1 0 0 \n",
+ "\n",
+ " Month_Mar Month_May Month_Nov Month_Oct Month_Sep VisitorType_Other \\\n",
+ "0 0 0 0 0 0 0 \n",
+ "1 0 0 0 0 0 0 \n",
+ "2 0 0 0 0 0 0 \n",
+ "3 0 0 0 0 0 0 \n",
+ "4 0 0 0 0 0 0 \n",
+ "\n",
+ " VisitorType_Returning_Visitor Weekend_True Revenue_True \n",
+ "0 1 0 0 \n",
+ "1 1 0 0 \n",
+ "2 1 0 0 \n",
+ "3 1 0 0 \n",
+ "4 1 1 0 "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#One hot encoding\n",
+ "\n",
+ "data_encoded = oneHotEncoding(removed_outliers, categorical_cols, True)\n",
+ "\n",
+ "data_encoded.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "706be55f",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(12205, 27)"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_encoded.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "4560bf4e",
+ "metadata": {
+ "scrolled": true
+ },
+ "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>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " <th>Month_Dec</th>\n",
+ " <th>Month_Feb</th>\n",
+ " <th>Month_Jul</th>\n",
+ " <th>Month_June</th>\n",
+ " <th>Month_Mar</th>\n",
+ " <th>Month_May</th>\n",
+ " <th>Month_Nov</th>\n",
+ " <th>Month_Oct</th>\n",
+ " <th>Month_Sep</th>\n",
+ " <th>VisitorType_Other</th>\n",
+ " <th>VisitorType_Returning_Visitor</th>\n",
+ " <th>Weekend_True</th>\n",
+ " <th>Revenue_True</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>6045</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>19</td>\n",
+ " <td>1138.793636</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.007018</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7750</th>\n",
+ " <td>3</td>\n",
+ " <td>273.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>11</td>\n",
+ " <td>385.740000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.007692</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>203</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>133.500000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.088889</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>8</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10452</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>27</td>\n",
+ " <td>630.750000</td>\n",
+ " <td>0.016</td>\n",
+ " <td>0.020000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1177</th>\n",
+ " <td>2</td>\n",
+ " <td>52.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>18</td>\n",
+ " <td>1262.000000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.009524</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "6045 0 0.0 0 \n",
+ "7750 3 273.0 0 \n",
+ "203 0 0.0 0 \n",
+ "10452 0 0.0 0 \n",
+ "1177 2 52.0 0 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "6045 0.0 19 1138.793636 \n",
+ "7750 0.0 11 385.740000 \n",
+ "203 0.0 3 133.500000 \n",
+ "10452 0.0 27 630.750000 \n",
+ "1177 0.0 18 1262.000000 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay OperatingSystems \\\n",
+ "6045 0.000 0.007018 0.0 0.0 2 \n",
+ "7750 0.000 0.007692 0.0 0.0 2 \n",
+ "203 0.000 0.088889 0.0 0.0 1 \n",
+ "10452 0.016 0.020000 0.0 0.0 2 \n",
+ "1177 0.000 0.009524 0.0 0.0 2 \n",
+ "\n",
+ " Browser Region TrafficType Month_Dec Month_Feb Month_Jul \\\n",
+ "6045 2 3 2 0 0 0 \n",
+ "7750 2 1 2 0 0 0 \n",
+ "203 2 1 8 0 0 0 \n",
+ "10452 2 3 1 0 0 0 \n",
+ "1177 2 1 2 0 0 0 \n",
+ "\n",
+ " Month_June Month_Mar Month_May Month_Nov Month_Oct Month_Sep \\\n",
+ "6045 0 0 0 0 0 0 \n",
+ "7750 0 0 0 0 1 0 \n",
+ "203 0 1 0 0 0 0 \n",
+ "10452 0 0 0 1 0 0 \n",
+ "1177 0 1 0 0 0 0 \n",
+ "\n",
+ " VisitorType_Other VisitorType_Returning_Visitor Weekend_True \\\n",
+ "6045 0 1 0 \n",
+ "7750 0 1 0 \n",
+ "203 0 1 0 \n",
+ "10452 0 1 1 \n",
+ "1177 0 0 0 \n",
+ "\n",
+ " Revenue_True \n",
+ "6045 0 \n",
+ "7750 1 \n",
+ "203 0 \n",
+ "10452 0 \n",
+ "1177 0 "
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Randomise the dataset\n",
+ "\n",
+ "data_sampled = data_encoded.sample(frac=1, random_state=42)\n",
+ "\n",
+ "#All features except the target are assigned to 'X'\n",
+ "X = data_sampled.drop(\"Revenue_True\", axis=1)\n",
+ "\n",
+ "#Target feature is assigned to 'Y'\n",
+ "Y = data_sampled[\"Revenue_True\"]\n",
+ "\n",
+ "data_sampled.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca7697d9",
+ "metadata": {},
+ "source": [
+ "# Dataset Rebalancing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1389cec9",
+ "metadata": {},
+ "source": [
+ "Undersampling and Oversampling sampling methods were used for modelling. Results for both methods will be compared to the result of modelling without any sampling method.\n",
+ "\n",
+ "Rebalancing is done on the training set only."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "ad90f3bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#split into train and test\n",
+ "\n",
+ "x, xtest, y, ytest = train_test_split(X, Y, test_size=0.2, random_state=None)\n",
+ "xtrain, xval, ytrain, yval = train_test_split(x, y, test_size=0.3, random_state=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "90c10eaf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2118, 26)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Undersampling\n",
+ "\n",
+ "undersampling = RandomUnderSampler(random_state=42)\n",
+ "\n",
+ "xtrain_undersampled, ytrain_undersampled = undersampling.fit_resample(xtrain, ytrain)\n",
+ "\n",
+ "xtrain_undersampled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "7f76dc3e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(11550, 26)"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Oversampling\n",
+ "\n",
+ "oversampling = RandomOverSampler(random_state=42)\n",
+ "\n",
+ "xtrain_oversampled, ytrain_oversampled = oversampling.fit_resample(xtrain, ytrain)\n",
+ "\n",
+ "xtrain_oversampled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "33486b2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled:\n",
+ " 0 1059\n",
+ "1 1059\n",
+ "Name: Revenue_True, dtype: int64\n",
+ "\n",
+ "Oversampled:\n",
+ " 0 5775\n",
+ "1 5775\n",
+ "Name: Revenue_True, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Target column is now balanced\n",
+ "\n",
+ "print(\"Undersampled:\\n\", ytrain_undersampled.value_counts())\n",
+ "\n",
+ "print(\"\\nOversampled:\\n\", ytrain_oversampled.value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35a04fc0",
+ "metadata": {},
+ "source": [
+ "# Dataset Scaling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "667b8bfb",
+ "metadata": {},
+ "source": [
+ "Both oversampled and undersampled datasets are scaled and PCA will be applied to them"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "42cfe508",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'\\nscaler_undersampled = StandardScaler()\\nxtrain_scaled_undersampled = scaler_undersampled.fit_transform(xtrain_undersampled)\\nxtest_scaled_undersampled = scaler_undersampled.transform(xtest)\\nxval_scaled_undersampled = scaler_undersampled.transform(xval)\\n\\n\\nxtrain_scaled_undersampled.shape'"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Scaling the data - undersampled dataset\n",
+ "'''\n",
+ "scaler_undersampled = StandardScaler()\n",
+ "xtrain_scaled_undersampled = scaler_undersampled.fit_transform(xtrain_undersampled)\n",
+ "xtest_scaled_undersampled = scaler_undersampled.transform(xtest)\n",
+ "xval_scaled_undersampled = scaler_undersampled.transform(xval)\n",
+ "\n",
+ "\n",
+ "xtrain_scaled_undersampled.shape'''"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "f515c607",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(11550, 26)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Scaling the data - oversampled dataset & undersampled\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "xtrain_oversampled[numeric_cols] = scaler.fit_transform(xtrain_oversampled[numeric_cols])\n",
+ "xtrain_undersampled[numeric_cols] = scaler.fit_transform(xtrain_undersampled[numeric_cols])\n",
+ "\n",
+ "\n",
+ "xtest[numeric_cols] = scaler.transform(xtest[numeric_cols])\n",
+ "xval[numeric_cols] = scaler.transform(xval[numeric_cols])\n",
+ "\n",
+ "\n",
+ "\n",
+ "xtrain_oversampled.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05988265",
+ "metadata": {},
+ "source": [
+ "# Dimensionality Reduction Using Principal Component Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "cfd07695",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#PCA - oversampled dataset\n",
+ "\n",
+ "# Initialize PCA with the desired number of components\n",
+ "pca_undersampled = PCA(n_components=24)\n",
+ "\n",
+ "# Fit PCA on the training data\n",
+ "pca_undersampled.fit(xtrain_undersampled)\n",
+ "\n",
+ "# Transform both the training and testing data\n",
+ "xtrain_pca_undersampled = pca_undersampled.transform(xtrain_undersampled)\n",
+ "xval_pca_undersampled = pca_undersampled.transform(xval)\n",
+ "xtest_pca_undersampled = pca_undersampled.transform(xtest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "98a3d312",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of xtrain_pca_undersampled: (11550, 24)\n",
+ "Shape of ytrain: (11550,)\n",
+ "Unique labels in ytrain: [0 1]\n",
+ "Number of unique labels: 2\n"
+ ]
+ }
+ ],
+ "source": [
+ "#PCA - oversampled dataset\n",
+ "\n",
+ "# Initialize PCA with desired number of components\n",
+ "pca = PCA(n_components=24)\n",
+ "\n",
+ "# Fit PCA on the training data\n",
+ "pca.fit(xtrain_oversampled)\n",
+ "\n",
+ "# Transform both training and testing data\n",
+ "xtrain_pca_oversampled = pca.transform(xtrain_oversampled)\n",
+ "xval_pca_oversampled = pca.transform(xval)\n",
+ "xtest_pca_oversampled = pca.transform(xtest)\n",
+ "\n",
+ "print(\"Shape of xtrain_pca_undersampled:\", xtrain_pca_oversampled.shape)\n",
+ "print(\"Shape of ytrain:\", ytrain_oversampled.shape)\n",
+ "\n",
+ "# Print unique labels and their counts\n",
+ "print(\"Unique labels in ytrain:\", np.unique(ytrain))\n",
+ "print(\"Number of unique labels:\", len(np.unique(ytrain)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "722202d4",
+ "metadata": {},
+ "source": [
+ "# Modelling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "626080bd",
+ "metadata": {},
+ "source": [
+ "## Decision Tree"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b0d35a9",
+ "metadata": {},
+ "source": [
+ "Modelling will be done on the dataset after scaling, and the dataset after PCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "5498f414",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DT_models = []\n",
+ "DT_name = []\n",
+ "x_val_list = []\n",
+ "x_test_list = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "1006a2d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccp_alpha = 0.005\n",
+ "txt = \"ccp_alpha: 0.005\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "5d824f45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - undersampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_oversampled = DecisionTreeClassifier()\n",
+ "DT_models.append(clf_oversampled)\n",
+ "DT_name.append(f\"Oversampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "# Fit the classifier to the training data\n",
+ "clf_oversampled.fit(xtrain_oversampled, ytrain_oversampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "9ec369bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - oversampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_undersampled = DecisionTreeClassifier()\n",
+ "\n",
+ "DT_models.append(clf_undersampled)\n",
+ "DT_name.append(f\"Undersampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "clf_undersampled.fit(xtrain_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "1064c58f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - oversampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_oversampled_pca = DecisionTreeClassifier()\n",
+ "\n",
+ "DT_models.append(clf_oversampled_pca)\n",
+ "DT_name.append(f\"Oversampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_oversampled)\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "3bf79310",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - undersampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_undersampled_pca = DecisionTreeClassifier()\n",
+ "\n",
+ "DT_models.append(clf_undersampled_pca)\n",
+ "DT_name.append(f\"Undersampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_undersampled)\n",
+ "x_test_list.append(xtest_pca_undersampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "clf_undersampled_pca.fit(xtrain_pca_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "0828f0bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cv_scores = cross_val_score(clf_undersampled, xtrain, ytrain, cv=10, scoring='accuracy')\n",
+ "y_pred = cross_val_predict(clf_undersampled, xtrain, ytrain, cv=10)\n",
+ "accuracy = accuracy_score(ytrain, y_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "dc6f68ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(models, names, x_list, Y, eval_type):\n",
+ " \n",
+ " print(f\"\\033[1mEvaluating {eval_type} data\\033[0m \\n\")\n",
+ " \n",
+ " fpr_list, tpr_list, roc_auc_list = [], [], []\n",
+ " \n",
+ " for model, name, X in zip(models, names, x_list):\n",
+ " print(f\"\\033[1mEvaluating {name}...\\033[0m\")\n",
+ " y_pred = model.predict(X)\n",
+ " accuracy = accuracy_score(Y, y_pred)\n",
+ " report = classification_report(Y, y_pred)\n",
+ " print(f\"{name} Accuracy:\", accuracy)\n",
+ " \n",
+ " \n",
+ " conf_matrix = confusion_matrix(Y, y_pred)\n",
+ " plt.figure(figsize=(3, 2))\n",
+ " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
+ " plt.title('Confusion Matrix')\n",
+ " plt.xlabel('Predicted')\n",
+ " plt.ylabel('Actual')\n",
+ " plt.show()\n",
+ " print(f\"{name} Classification report:\\n\", report)\n",
+ "\n",
+ " \n",
+ " y_prob = model.predict_proba(X)[:, 1]\n",
+ " fpr, tpr, _ = roc_curve(Y, y_prob)\n",
+ " fpr_list.append(fpr)\n",
+ " tpr_list.append(tpr)\n",
+ " \n",
+ " roc_auc = auc(fpr, tpr)\n",
+ " #print(f\"{name} AUC = {roc_auc}\")\n",
+ " roc_auc_list.append(roc_auc)\n",
+ " \n",
+ " cmap = plt.colormaps['tab10']\n",
+ " colors = cmap.colors[:len(names)]\n",
+ " plt.figure(figsize=(9,5))\n",
+ " for i, (fpr, tpr, roc_auc, name) in enumerate(zip(fpr_list, tpr_list, roc_auc_list, names)):\n",
+ " plt.plot(fpr, tpr, color=colors[i], lw=2, label=f'{name} (area = {roc_auc:.2f})')\n",
+ " plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
+ " plt.xlim([0.0, 1.0])\n",
+ " plt.ylim([0.0, 1.05])\n",
+ " plt.xlabel('False Positive Rate')\n",
+ " plt.ylabel('True Positive Rate')\n",
+ " plt.title('Receiver Operating Characteristic (ROC). MLP on Dataset 1')\n",
+ " plt.legend(loc='lower right')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "2bc9298e",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating validation data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Accuracy: 0.7808873720136519\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.87 0.87 0.87 2492\n",
+ " 1 0.27 0.28 0.28 438\n",
+ "\n",
+ " accuracy 0.78 2930\n",
+ " macro avg 0.57 0.57 0.57 2930\n",
+ "weighted avg 0.78 0.78 0.78 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Undersampled dataset(No PCA), ccp_alpha: 0.005 Accuracy: 0.6218430034129693\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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KKgjDdNcDw/I58DiAmfmLvS5nFVQQAu8BrwL9zWw+gCQf1sLltEK7T3gqsBT4j6T7JX2HiqdmnMtJxUVKueSytEFoZv8wszOA/YH/ED3C1lbSXyX1rqPyObdD8vE+YbV3Vcxsg5k9amYnEQ1m+l/q4H1C5zJRpNRLLtuhN+vD0zLDwuJczinIMWacyydFOd70TMWD0BWUXL/+S8WD0BWUYg9C5+LlzVHnYubN0Txzw/XX8srLE2ndug3PPBcNNP7LKy7jo4ULAVi3bh3NmjXjiWee44XnRzNi+APb9p07930ee/JZ9j/ggFjKXts+XbaUoTdcy6qVK0BiwCmncfqZPwLgycf+zjNPjKIoUcRRR/dkyM+vZM3q1Vx31WW8N/td+p50MldcfX3MZxDJxydmdukgHHDyqQw882yuu7bitufv/nDnts+//+0dNG3aFIAT+3+PE/t/D4B5c9/nskuHFEwAAiQSxVzyi6voekA3NmzYwHlnn8YRR36TlStWMOnlfzPisWeoX79+FKRA/Qb1ueCnl/DBgvl8sGBezKWvkI81Yf7dVKlFh/c4guYtWqRcZ2a8OH4sfU/s/5V1Y8e8QJ++J2a7eHWqZLfd6HpANwCaNGnCPh3347Ply/nHU49z9rnnU79+9B53q9ZtAGjUqDEHH3r4tvRcUVCPre3qpr09lTZt2rDPPvt+Zd34cWPo06+wgjDZ0iWLmffeHA7sfhAff/wh0//7NheccwZDLhjEnFkz4y5elYqklEtNSPpQ0kxJ70iaGtJaS5ogaV742yqkS9KfJc2XNEPSYRmXOdMdMyXpx3V9zEyMHfM8ffp9tRacMWM6DRs2onPnLjGUKvs2btzAdb+8jEuvvIYmTZuydetW1q5dw7ARoxjy8yv41TVXEEY6yUm18OzosWZ2iJn1CN+vAV4ys87AS+E7QF+ieVk6E730/tdMyxxHTXhzuhWSBkuaKmnqA/fH92RcaWkpL/1rAn369PvKuvFjXqBvgdaCpVu2cN0vL6N33xPpddwJALRt245vH3s8kujW/SCkIlavXhVzSdPbmZowjQHAiPB5BHByUvrDFnkTaCmpfSYHyErHjKQZ6VYB7dLtZ2bbnkv9ovSrE5PWlbfeeJ2OHfej3e7bj+JRVlbG+PFjeejhR2MqWfaYGb8eegP7dNyPM84+d1v6Mb2+w7Spkzn8iG/w8UcfUlq6hZYtW8VX0Gqku/yTNJioxio3LPzekhnwoiQD7gvr25nZ0rB+GRW/3z2BT5L2XRTSlrKDstU72g74LlD5v0wBr2fpmDvs6isvZ+qUyaxevYoTjuvJT4dcwqnfP41xY1Nf8709dQq7796eDnvtFUNps2vGO9MY98JovtapC4MGngrAhUMuo/+AU7j95l9x9ukDqFdcj+tvug2FmuX7/U9gw4b1lG7ZwqsT/80f7xlGx/06xXka28pWWfJ/8FU42swWS2oLTJD0XqU8LARorVI22veSHgAeNLOvTCYq6VEzO7O6POKsCXONT41WoaRpcZVty2kfrU35uzlsn+Y71CaVdBOwnmiM3V5mtjQ0NyeaWdcw+uBEMxsVtn+/fLsdOQ5k6ZrQzM5LFYBhXbUB6FymMr0mlNREUrPyz0Bv4F1gNDAobDYIeC58Hg2cE3pJjwTWZBKAsIvfrHeFZyf6YNoBz4bmbDHwqJmNkzQFeELSecBHRCMOAowB+gHzgY1Axr3+WWmO1gZvjlbw5miF6pqj7y5an/J3071D05y9Y+81oSso6TpmcpkHoSsoOf6EWkoehK6gFOVhFHoQuoKSh61RD0JXWPzNeudi5kHoXMzy8JLQg9AVFu+YcS5meRiDHoSusPjNeudilo8DPXkQuoKShzHoQegKi9+icC5meTgzmgehKyxeEzoXszyMQQ9CV1i8d9S5mPl9Qudi5k/MOBezfHx2NGcHesoVkganGKl5l+T/FtmRh3dV6tzg6jfZZfi/RRZ4EDoXMw9C52LmQVg9vwaq4P8WWeAdM87FzGtC52LmQZiGpD6S3g9zkl9T/R6FS9JwScslvRt3WQqRB2EKkhLAPUTzkncDBkrqFm+pYvUQ0CfuQhQqD8LUvg7MN7MPzOxL4DGiOcp3SWb2CrAy7nIUKg/C1NLNR+5crfMgdC5mHoSpLQb2SvreIaQ5V+s8CFObAnSW1FFSfeAMojnKnat1HoQpmFkpcDEwHpgDPGFms+ItVXwkjQLeALpKWhTmb3e1xJ+YcS5mXhM6FzMPQudi5kHoXMw8CJ2LmQehczHzIKxFkrZKekfSu5KelNR4J/J6SNIPwue/VfUAuaReko7K4BgfSirJtIyudngQ1q5NZnaImXUHvgQuSl4pKaMhJs3sfDObXcUmvYAdDkKXGzwIs+dVoFOopV6VNBqYLSkh6XeSpkiaIelCAEXuDu8w/gtoW56RpImSeoTPfSRNkzRd0kuS9iUK9l+EWvgYSbtJejocY4qkb4V920h6UdIsSX8D8m+QzgLkg/9mQajx+gLjQtJhQHczWyhpMLDGzI6Q1AB4TdKLwKFAV6L3F9sBs4HhlfLdDbgf6Bnyam1mKyXdC6w3s9+H7R4F/mhmkyTtTfTkzwHAjcAkM7tF0omAP/mSAzwIa1cjSe+Ez68CDxA1Eyeb2cKQ3hs4qPx6D2gBdAZ6AqPMbCuwRNK/U+R/JPBKeV5mlu4dv+OBbknzMjSX1DQc49Sw7wuSVmV2mq42eRDWrk1mdkhyQgiEDclJwCVmNr7Sdv1qsRxFwJFm9kWKsrgc49eEdW888FNJ9QAkdZHUBHgF+GG4ZmwPHJti3zeBnpI6hn1bh/R1QLOk7V4ELin/IumQ8PEV4MyQ1hdoVVsn5TLnQVj3/kZ0vTctDJx0H1GL5FlgXlj3MNFbC9sxs8+IhqJ/RtJ04PGw6p/AKeUdM8ClQI/Q8TObil7am4mCeBZRs/TjLJ2j2wH+FoVzMfOa0LmYeRA6FzMPQudi5kHoXMw8CJ2LmQehczHzIHQuZh6EzsXs/wHOFYMouyBh5QAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.90 0.63 0.74 2492\n",
+ " 1 0.22 0.60 0.32 438\n",
+ "\n",
+ " accuracy 0.62 2930\n",
+ " macro avg 0.56 0.61 0.53 2930\n",
+ "weighted avg 0.80 0.62 0.68 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Accuracy: 0.7750853242320819\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.87 0.87 0.87 2492\n",
+ " 1 0.25 0.25 0.25 438\n",
+ "\n",
+ " accuracy 0.78 2930\n",
+ " macro avg 0.56 0.56 0.56 2930\n",
+ "weighted avg 0.77 0.78 0.77 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Undersampled dataset(PCA), ccp_alpha: 0.005 Accuracy: 0.6023890784982935\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.61 0.72 2492\n",
+ " 1 0.20 0.57 0.30 438\n",
+ "\n",
+ " accuracy 0.60 2930\n",
+ " macro avg 0.55 0.59 0.51 2930\n",
+ "weighted avg 0.79 0.60 0.66 2930\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(DT_models, DT_name, x_val_list, yval, \"validation\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "10b082c7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating testing data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Accuracy: 0.7853338795575584\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.87 0.87 0.87 2083\n",
+ " 1 0.27 0.27 0.27 358\n",
+ "\n",
+ " accuracy 0.79 2441\n",
+ " macro avg 0.57 0.57 0.57 2441\n",
+ "weighted avg 0.78 0.79 0.79 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Undersampled dataset(No PCA), ccp_alpha: 0.005 Accuracy: 0.6206472757066775\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.63 0.74 2083\n",
+ " 1 0.21 0.55 0.30 358\n",
+ "\n",
+ " accuracy 0.62 2441\n",
+ " macro avg 0.55 0.59 0.52 2441\n",
+ "weighted avg 0.79 0.62 0.68 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Accuracy: 0.7849242113887751\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.87 0.87 2083\n",
+ " 1 0.27 0.28 0.28 358\n",
+ "\n",
+ " accuracy 0.78 2441\n",
+ " macro avg 0.57 0.58 0.58 2441\n",
+ "weighted avg 0.79 0.78 0.79 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Undersampled dataset(PCA), ccp_alpha: 0.005 Accuracy: 0.5870544858664482\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.59 0.71 2083\n",
+ " 1 0.20 0.58 0.29 358\n",
+ "\n",
+ " accuracy 0.59 2441\n",
+ " macro avg 0.54 0.58 0.50 2441\n",
+ "weighted avg 0.79 0.59 0.65 2441\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(DT_models, DT_name, x_test_list, ytest, \"testing\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cbf362dc",
+ "metadata": {},
+ "source": [
+ "### Using the oversampled dataset with PCA for hyperparameter tuning because of its high performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "fa5b7114-aee5-4e45-b0cb-676f2ee19102",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy Score on train data: 0.64627752534079\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a DecisionTreeClassifier with specified hyperparameters\n",
+ "clf_oversampled_pca = DecisionTreeClassifier(random_state=0, ccp_alpha=0.010)\n",
+ "\n",
+ "# Fit the classifier on the PCA-transformed oversampled training data\n",
+ "clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)\n",
+ "\n",
+ "# Evaluate the model on the PCA-transformed test data\n",
+ "y_pred_test_dt_pca = clf_oversampled_pca.predict(xtest_pca_oversampled)\n",
+ "\n",
+ "print('Accuracy Score on train data: ', accuracy_score(y_true=ytrain_oversampled, y_pred=clf_oversampled_pca.predict(xtrain_pca_oversampled)))\n",
+ "\n",
+ "DT_models.append(clf_oversampled_pca)\n",
+ "DT_name.append(\"Oversampled Dataset ccp_alpha: 0.010\")\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "plt.figure(figsize=(10,10))\n",
+ "plot_tree(clf_oversampled_pca, filled=True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "c21986e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy Score on train data: 0.64627752534079\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a DecisionTreeClassifier with specified hyperparameters\n",
+ "clf_oversampled_pca = DecisionTreeClassifier(random_state=0, ccp_alpha=0.010)\n",
+ "\n",
+ "# Fit the classifier on the PCA-transformed oversampled training data\n",
+ "clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled,)\n",
+ "\n",
+ "# Evaluate the model on the PCA-transformed test data\n",
+ "y_pred_test_dt_pca = clf_oversampled_pca.predict(xtest_pca_oversampled)\n",
+ "\n",
+ "print('Accuracy Score on train data: ', accuracy_score(y_true=ytrain_oversampled, y_pred=clf_oversampled_pca.predict(xtrain_pca_oversampled)))\n",
+ "\n",
+ "DT_models.append(clf_oversampled_pca)\n",
+ "DT_name.append(\"Oversampled Dataset (PCA) ccp_alpha: 0.010\")\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "plt.figure(figsize=(5,5))\n",
+ "plot_tree(clf_oversampled_pca, filled=True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "112aa85c-38a5-46b8-9d8d-3e685be6596a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating testing data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Accuracy: 0.7853338795575584\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.87 0.87 0.87 2083\n",
+ " 1 0.27 0.27 0.27 358\n",
+ "\n",
+ " accuracy 0.79 2441\n",
+ " macro avg 0.57 0.57 0.57 2441\n",
+ "weighted avg 0.78 0.79 0.79 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Undersampled dataset(No PCA), ccp_alpha: 0.005 Accuracy: 0.6206472757066775\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.63 0.74 2083\n",
+ " 1 0.21 0.55 0.30 358\n",
+ "\n",
+ " accuracy 0.62 2441\n",
+ " macro avg 0.55 0.59 0.52 2441\n",
+ "weighted avg 0.79 0.62 0.68 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Accuracy: 0.7849242113887751\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.87 0.87 2083\n",
+ " 1 0.27 0.28 0.28 358\n",
+ "\n",
+ " accuracy 0.78 2441\n",
+ " macro avg 0.57 0.58 0.58 2441\n",
+ "weighted avg 0.79 0.78 0.79 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Undersampled dataset(PCA), ccp_alpha: 0.005 Accuracy: 0.5870544858664482\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.59 0.71 2083\n",
+ " 1 0.20 0.58 0.29 358\n",
+ "\n",
+ " accuracy 0.59 2441\n",
+ " macro avg 0.54 0.58 0.50 2441\n",
+ "weighted avg 0.79 0.59 0.65 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled Dataset ccp_alpha: 0.010...\u001b[0m\n",
+ "Oversampled Dataset ccp_alpha: 0.010 Accuracy: 0.44285129045473165\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled Dataset ccp_alpha: 0.010 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.95 0.37 0.53 2083\n",
+ " 1 0.19 0.89 0.32 358\n",
+ "\n",
+ " accuracy 0.44 2441\n",
+ " macro avg 0.57 0.63 0.42 2441\n",
+ "weighted avg 0.84 0.44 0.50 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled Dataset (PCA) ccp_alpha: 0.010...\u001b[0m\n",
+ "Oversampled Dataset (PCA) ccp_alpha: 0.010 Accuracy: 0.44285129045473165\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled Dataset (PCA) ccp_alpha: 0.010 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.95 0.37 0.53 2083\n",
+ " 1 0.19 0.89 0.32 358\n",
+ "\n",
+ " accuracy 0.44 2441\n",
+ " macro avg 0.57 0.63 0.42 2441\n",
+ "weighted avg 0.84 0.44 0.50 2441\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(DT_models, DT_name, x_test_list, ytest, \"testing\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "bbb453fc-c18b-47e5-ba91-f1c6bf48c4a2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "clf = DecisionTreeClassifier(random_state=0)\n",
+ "path = clf.cost_complexity_pruning_path(xtrain, ytrain)\n",
+ "ccp_alphas, impurities = path.ccp_alphas, path.impurities\n",
+ "\n",
+ "clfs = []\n",
+ "for ccp_alpha in ccp_alphas:\n",
+ " clf_oversampled_pca = DecisionTreeClassifier(random_state=0, ccp_alpha=0.015)\n",
+ " clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)\n",
+ " clfs.append(clf_oversampled_pca)\n",
+ " \n",
+ "clfs = clfs[:-1]\n",
+ "ccp_alphas = ccp_alphas[:-1]\n",
+ "\n",
+ "# Calculate training and testing scores for each classifier\n",
+ "train_scores = [clf.score(xtrain_pca_oversampled, ytrain_oversampled) for clf in clfs]\n",
+ "test_scores = [clf.score(xtest_pca_oversampled, ytest) for clf in clfs]\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.set_xlabel(\"alpha\")\n",
+ "ax.set_ylabel(\"accuracy\")\n",
+ "ax.set_title(\"Accuracy vs alpha for training and testing sets\")\n",
+ "ax.plot(ccp_alphas, train_scores, marker='o', label=\"train\",\n",
+ " drawstyle=\"steps-post\")\n",
+ "ax.plot(ccp_alphas, test_scores, marker='o', label=\"test\",\n",
+ " drawstyle=\"steps-post\")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f4b8bf6",
+ "metadata": {},
+ "source": [
+ "## Support Vector Machine (SVM)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "cd2bb756",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SVM_models = []\n",
+ "SVM_name = []\n",
+ "x_val_list = []\n",
+ "x_test_list = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "08e4b27b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gamma=0.1\n",
+ "txt = \"gamma: 0.1\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "d00c0de6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - undersampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_oversampled = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "SVM_models.append(svm_oversampled)\n",
+ "SVM_name.append(f\"Oversampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_oversampled.fit(xtrain_oversampled, ytrain_oversampled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "83303917",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - oversampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_undersampled = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "\n",
+ "SVM_models.append(svm_undersampled)\n",
+ "SVM_name.append(f\"Undersampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_undersampled.fit(xtrain_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "0ec8bb49",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - oversampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_oversampled_pca = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "\n",
+ "SVM_models.append(svm_oversampled_pca)\n",
+ "SVM_name.append(f\"Oversampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_oversampled)\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "f77e3692",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - oversampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_undersampled_pca = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "\n",
+ "SVM_models.append(clf_undersampled_pca)\n",
+ "SVM_name.append(f\"Undersampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_undersampled)\n",
+ "x_test_list.append(xtest_pca_undersampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_undersampled_pca.fit(xtrain_pca_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "2d706ad2",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating SVM validation data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), gamma: 0.1...\u001b[0m\n",
+ "Oversampled dataset(No PCA), gamma: 0.1 Accuracy: 0.674061433447099\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.93 0.67 0.78 2492\n",
+ " 1 0.27 0.70 0.39 438\n",
+ "\n",
+ " accuracy 0.67 2930\n",
+ " macro avg 0.60 0.68 0.58 2930\n",
+ "weighted avg 0.83 0.67 0.72 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), gamma: 0.1...\u001b[0m\n",
+ "Undersampled dataset(No PCA), gamma: 0.1 Accuracy: 0.6293515358361774\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.94 0.61 0.74 2492\n",
+ " 1 0.25 0.77 0.38 438\n",
+ "\n",
+ " accuracy 0.63 2930\n",
+ " macro avg 0.60 0.69 0.56 2930\n",
+ "weighted avg 0.83 0.63 0.68 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), gamma: 0.1...\u001b[0m\n",
+ "Oversampled dataset(PCA), gamma: 0.1 Accuracy: 0.674061433447099\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.93 0.67 0.78 2492\n",
+ " 1 0.27 0.70 0.39 438\n",
+ "\n",
+ " accuracy 0.67 2930\n",
+ " macro avg 0.60 0.68 0.58 2930\n",
+ "weighted avg 0.83 0.67 0.72 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), gamma: 0.1...\u001b[0m\n",
+ "Undersampled dataset(PCA), gamma: 0.1 Accuracy: 0.6023890784982935\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.61 0.72 2492\n",
+ " 1 0.20 0.57 0.30 438\n",
+ "\n",
+ " accuracy 0.60 2930\n",
+ " macro avg 0.55 0.59 0.51 2930\n",
+ "weighted avg 0.79 0.60 0.66 2930\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(SVM_models, SVM_name, x_val_list, yval, \"SVM validation\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "baf45f6f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating SVM testing data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), gamma: 0.1...\u001b[0m\n",
+ "Oversampled dataset(No PCA), gamma: 0.1 Accuracy: 0.6812781646866038\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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WS9LDkhZJ+kDSccnG7EnoskqulHDaB73M7Fgz6xbODwOmm1kHYHo4D9CX4KZnHQjOKBuVbMyehC6r5EgJp/1wLlDyrM5xwHlx5U9aYA7QWFLLpGLen+icSzf7eYjCgFclvStpUFiWb2arwtergfzwdWtgedy2X5HkbV8O6IGZEbcOZ+abM2jaNI/n/x7caPx399/LmzPeoFatWrRp247b77ybhg0bAvDYmD8zaeLfyInlMHT4rZx8Svcow69WhQWrufvXv2D9uiKQOPu8C7jgkh8xdvTvmf3WG0g5NGnSlKEj7qRZ8xZs3ryJu0YOp2D1Knbu3MnFlw2g7znnR/0xyj1jJkyqQXFFj5hZ2VH+U8xshaQWwGuSFsQvNDOTtNcjIfaXwssE087XO/Z+/kV1e3feXOrWrcsvhw8tTcK3Z8/ihO+eSG5uLg/89v8AGHzLz/hi0SKG/WwIz0z4G4WFBVx79UCmvDSNWCyW6jBr5L6jRWvXULR2DR07d6F4yxauHXAxd9z3EM1b5FOvfn0AJk54hqWLv2DIsBE8/cQYtmzexLU3DmHD+nVccdE5TJw6g1q1aqU0zlaND6qwWXt9QVHC782pnfP2qU8q6dfAZoIL2Hua2aqwuznDzDqFl/bNMLPx4fqflay3L+8DB3h39Phu36Fho0Z7lJ108ink5gYdhKOPOZbCgtUAzHhjOn3O6sdBBx1EmzZtadu2PR99+EGNx5wqec2a07FzFwDq1qtHu0MPY+2agtIEBPh661YUdu2EKC4uxszYurWYBg0b1cgPUmWSPW1NUj1JDUpeA72Bj4ApwIBwtQHA38PXU4ArwlHSE4GNySQgHODd0cpMfn4iZ/btC0BBQQFHH3NM6bL8Q/IpLCiIKrSUWr1yBYs+X8CRRx0NwKOjHubVqVOoV78BD/zpMQDOv/BSfvnTm7ig36kUF29hxJ33k5MGB8r3YxAmH5gU/sjkAs+a2SuS5gLPSboKWEpwOR/AVOAsYBFQDAxMOuZkN0yWpKSDrUlj/jyKWG6Mfmf/IOpQatTW4mJGDBvMDYOHlraCV193M8+98A9OP7Mfk/46HoC5c2ZzRMdO/O2l13n0qb/x8P13sWXz5ihDB5IfmDGzL83smHA6ysx+E5YXmdlpZtbBzE43s3VhuZnZDWb2X2b2LTObl2zMUfx03VbeAkmDJM2TNO+xMdGdGff3Sc8z880Z3H3v/aXdr/z8fApWry5dp2B1AS3y88urIiPt2LGdEcMGc3qffvTodfpey0/v04+Zb/wDgJdfnEz3nqcjidZt29GyVWuWLV1c0yHvJQWHKFIuJd1RSeXtLIndQ7x7CUerHoGaGZhJZPZbM3li7KM8Nu5p6tSpU1r+/V6nMvxnt3D5gIEUFhawbNkSun7r6ChCTAkz4747R9L+0MO5qP+A0vKvli2lTbv2AMye+Trt2h8GQP4hLZk/718c/e3jWVe0luXLltCqdZtIYo+X7idrJ5KS0VFJBcCZwPqyi4C3zaxVZXXURBIO/ekQ5s19hw0b1tM0L4/rbriJsWMeYdv2bTRu1BiAbx1zDL8aeTsQdFEnT5pILBbj58N+wSndv5/qEIGaGR398L353HztAA4/ogNS0EG6+rqbmTplEsuXLSEnR+Qf0orBQ39F8xb5rF1TyL2330pR0RrMoP8VP+aMvuekPM7KRkfnLf5Pwu9Nt8Mapm16pioJHwMeN7O9HiYq6Vkz619ZHVG1hOnIH422W2VJOH9p4iQ8rn36JmFKuqNmdlUFyypNQOeSle77f4n4IQqXVTIwBz0JXXZJ91tZJOJJ6LKKPAmdi1YmHqLwJHRZJScDs9CT0GWVDOyNehK67OKHKJyLmCehcxHLwF1CT0KXXXxgxrmIZWAOehK67OIH652LmJ+25lzEMjAHPQlddvFDFM5FLA1u+LbPPAldVvGW0LmIZWAOehK67OKjo85FzI8TOhcxP2PGuYhl4rmjaftotHQhaVCC59gdkPx/kRoZeFSlxg2qfJUDhv8vUsCT0LmIeRI6FzFPwsr5PtBu/r9IAR+YcS5i3hI6FzFPwnJI6iPpM0mLJA2LOp4oSRorqVDSR1HHko08CROQFAP+CPQFugCXSuoSbVSRegLoE3UQ2cqTMLETgEVm9qWZbQP+ApwbcUyRMbOZwLqo48hWnoSJtQaWx81/FZY5V+08CZ2LmCdhYiuAtnHzbcIy56qdJ2Fic4EOkg6TdBBwCTAl4phclvIkTMDMdgA3AtOAT4HnzOzjaKOKjqTxwD+BTpK+knRV1DFlEz9jxrmIeUvoXMQ8CZ2LmCehcxHzJHQuYp6EzkXMk7AaSdop6T1JH0n6q6S6+1HXE5IuCF8/WtEJ5JJ6SjopifdYIqlZsjG66uFJWL22mtmxZtYV2Ab8JH6hpKRuMWlmV5vZJxWs0hPY5yR06cGTMHXeAo4IW6m3JE0BPpEUk/R/kuZK+kDStQAK/CG8hvEfQIuSiiTNkNQtfN1H0nxJ70uaLulQgmQfHLbC3SU1lzQxfI+5kk4Ot82T9KqkjyU9CmTeTTqzkN/8NwXCFq8v8EpYdBzQ1cwWSxoEbDSz70g6GJgt6VXg20AngusX84FPgLFl6m0OjAF6hHU1NbN1kkYDm83s/nC9Z4EHzGyWpHYEZ/4cCYwEZpnZ7ZL6AX7mSxrwJKxedSS9F75+C3iMoJv4jpktDst7A0eX7O8BjYAOQA9gvJntBFZKej1B/ScCM0vqMrPyrvE7HegS91yGhpLqh+/xw3DblyStT+5juurkSVi9tprZsfEFYSJsiS8CbjKzaWXWO6sa48gBTjSzrxPE4tKM7xPWvGnAdZJqAUjqKKkeMBO4ONxnbAn0SrDtHKCHpMPCbZuG5ZuABnHrvQrcVDIj6djw5Uygf1jWF2hSXR/KJc+TsOY9SrC/Nz+8cdKfCXokk4CF4bInCa5a2IOZrSG4Ff3zkt4HJoSLXgDOLxmYAW4GuoUDP5+we5T2NoIk/pigW7osRZ/R7QO/isK5iHlL6FzEPAmdi5gnoXMR8yR0LmKehM5FzJPQuYh5EjoXMU9C5yL2/1oSobrsG+DiAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.68 0.79 2083\n",
+ " 1 0.27 0.66 0.38 358\n",
+ "\n",
+ " accuracy 0.68 2441\n",
+ " macro avg 0.59 0.67 0.58 2441\n",
+ "weighted avg 0.83 0.68 0.73 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), gamma: 0.1...\u001b[0m\n",
+ "Undersampled dataset(No PCA), gamma: 0.1 Accuracy: 0.6329373207701762\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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IaEa0qxLAXmQ4M8Bxsk29HFtFcQnwM6JvIG9RKsL1RPtMOE6tow5qsML1hPcA90i63MzurUGbHCdjanvXMxVV+bK5XVLTkouwAeJl2TPJcTKnXkIpj9pMVUR4sZl9WXJhZmuJFjk6Tq0jIaU8ajNV+VifkKSwMWLJgsc9smuW42RGft1ztlYlEU4Enpb053B9CfB89kxynMzJSR8zRLNjhhC5tgCYA+yTNYscZxfI1Rkz2yW9TrSw8YdAS2BsxbkcJx5ySoRhu+tzw7GK4InKzHxhr1NrqYsirOg19n0ih07fNbMTw7dC32/eqdXsyrQ1SZ9ImitptqSZIay5pCmSFoS/zUK4JP1J0sLgf+aoTG2uSITfJ/KpMU3SQ5JOpXTWjOPUStL5mNkJTjGzbmZ2TLi+BphqZh2AqeEaIjcYHcIxhMgXTUakFaGZ/cPMBgCHANOIprC1lvSApJ6ZVug42SQL3wn7AiXe6EcBZyWFP2YRM4CmkvbNpIJKv6qY2UYzG21mZxI5s3mbGlhP6DiZkKfURxUxYLKkt5K8sRUk+ZhZBhSE82pzebhTK+vDbJmqeq1ynBonnY+ZIKpkN4d/CR7YkjnRzJZIag1MkfR+cqSZmaS0O5Vliu9P6OQUeWm6nlVxeWhmS8LfFZLGEXnUXl7icS10N1eE5NXm8rAOTvJxnPRk+k4oqbGkPUvOgZ5E7g3HE+2/Qvj7z3A+HrggjJJ2B9YldVt3Cm8JnZwiP/NBmAJgnKL8+cBoM5so6U3gGUmDgcVEE1YAJgB9iPag2ES0cUxmNmea0XFqI+m6o5VhZh8DR6QIX020QUz5cAOGZlRZOVyETk5R25ctpcJFmMTjox7l72OfRRIdOnTk5tt+y6033cDMmW+wZ5PI6dzNt43gkM6dY7a0+lm5Yhl33/Ybvly7GiR6nfkDvnfOefzuxl+y5LNPANj41QYaN9mTex55mg/fe5f7/3ALAGbGuYMu5fge8e+YVxdX1rsIA8uXL2f0k48xbvwEGjRowC+G/5SJE/4NwPArr+b0XmfEbGF2SSQSXDh0OAd37MymTRsZfvF5dDvmOK6+8Y6v0zxy/500btwEgAMOPJg//vlJEvn5rFm9kp9e2J9jT+hBIj/eR6outoQ+OppEcXExWzZvpqioiMLNm2nVevdxNN68RSsO7hi18I0aNabtAQeyemXphsxmxqvTptDjtOjHqH6Dhl8LbuvWrVBLHv5qmLZW47gIAwUFBQwcdCG9TjuF004+kT2bNOGEb54IwL1/uotzzj6T34+4PXrgcpzlS7/g4wUf0KlL16/D5s2ZRdPmzWnT9oCvwz6YP5ehA3/AFT/ux2XDfx17KwjRwEyqozZT4yKUlPFQbjZZv24d016cyoTJU5ky7WUKCwt57l//5IqfD+efz01k9NNjWbduHSMfzu3JQoWbNjHi+qu46PKraBS6ngAvvTCRk04t2yXv1OUw7h81ljsffIK/PTmSrVu21LS5O1AXfczE0RLelC4ieTfVRx6q2Yd9xoz/sl/btjRv3px69epx6mk9eeftt2nVqjWS2GOPPeh79vd59925NWpXTVJUtI0R11/Ft07rzQk9Skfli4uKeO3lFznplF4p832j3UE0aNiIxYsW1pSpaamLLWFW+g+S5qSLonQC7A4kTy3aXLTjxqTZZJ992zDnnXcoLCykQYMGvD7jNbp07crKlSto1ao1Zsa0qS/Qvn2HmjSrxjAz7r3jJtoecCBn9S+7G9jst16n7f7taNm69L9u2dIltGpVQCI/nxXLvmDJp4so2KdNTZu9A7X89S8l2erEFwC9gLXlwgX8N0t17hKHH34Ep/fsxYB+Z5NI5HNI586c068/l11yEWvXrsXM6HTIIfzm+rQNeZ3mvbmzmTb53xxwUAd+Org/AOdfPIxjup/Eyy9Ooke5ruh7c97mltF/JT8/HymPS39+LXs1bRaH6WVQLW/1UqHgybB6C5UeAf5qZjtsJipptJmdV1kZNd0S1mZ8a7RSOu3TqEKVzVq8PuVzc9QBe9VadWalJTSzwRXEVSpAx8mU2v7+l4r4x5Qdpxqpgxp0ETq5RW3/HJEKF6GTU9TFgRmfMePkFJn6mJH0DUnTJM2XNE/ST0P4jZKWBDeIsyX1Scrzq+Dy8ANJqT+iVgFvCZ2cIi/zD4VFwJVmNiussH9L0pQQd5eZ/SE5saQuwADgUKKNdF+Q1NHMdto3r7eETk4hpT4qw8yWmtmscL4BeI+Kvaf1BZ4ysy1mtohohf2xmdjsInRyiuqYtiapHXAk8HoIGha8bI8s8cBNNbo8dBE6OUU6ESbPSw7HkFT5JTUh2vDoZ2a2nsiz9sFANyKP9HdWt83+TujkFOleCavi8lBSPSIBPmlmfw/5lifFPwQ8Fy7d5aHjpCIvTymPylD0beMR4D0z+2NSeLJr+7OJ3CBC5PJwgKT6kg4k2pPijUxs9pbQySl2YRXFN4HzgbmSZoewa4FzJXUjcpH/CdFO1ZjZPEnPAPOJRlaHZjIyClmawF0d+ATuUnwCdymVTeBe8uXWlM/Nfk33qLVf8b0ldHIKn7bmODFTBzXoInRyC1/K5Dgxk2ZntFqNi9DJKbwldJyYqYMadBE6uYWPjjpOzNTFRb0uQiencL+jjhMzu7CoNzZq7bS12oKkIWEG/m6P/1tkhzr4VaXGSbnubDfF/y2ygIvQcWLGReg4MeMirBx/ByrF/y2ygA/MOE7MeEvoODHjIkyDpDOCZ+WFkq6J2544Ca7+Vkh6t/LUzs7iIkyBpARwP9Ab6ELkZ6RLvFbFyqPAGZUlcjLDRZiaY4GFZvaxmW0FniLyuLxbYmYvAWvitiNXcRGmptq8KztOZbgIHSdmXISpqTbvyo5TGS7C1LwJdJB0oKQ9iLbAGh+zTU6O4iJMgZkVAcOASURbZD1jZvPitSo+JI0BXgM6Sfpc0uC4bcolfMaM48SMt4SOEzMuQseJGReh48SMi9BxYsZF6Dgx4yKsRiQVS5ot6V1Jz0pqtAtlPSrpnHD+cEUTyCWdLOmEDOr4RFLLTG10qgcXYfVSaGbdzKwrsBW4NDlSUkYuJs3sIjObX0GSk4GdFqFTO3ARZo+XgfahlXpZ0nhgvqSEpN9LelPSHEmXQLRnuqT7whrGF4DWJQVJmi7pmHB+hqRZkt6RNFVSOyKx/zy0widJaiVpbKjjTUnfDHlbSJosaZ6kh4G656QzB3Hnv1kgtHi9gYkh6Cigq5ktkjQEWGdm/yOpPvCqpMnAkUAnovWLBUR7oY8sV24r4CGgRyiruZmtkfQg8JWZ/SGkGw3cZWavSNqfaOZPZ+AG4BUzu1nSdwCf+VILcBFWLw0lzQ7nLwOPEHUT3zCzRSG8J3B4yfsesDfQAegBjDGzYuALSS+mKL878FJJWWaWbo3faUCXpH0Z9pLUJNTx/ZD335LWZnabTnXiIqxeCs2sW3JAEMLG5CDgcjObVC5dn2q0Iw/obmabU9ji1DL8nbDmmQT8RFI9AEkdJTUGXgL6h3fGfYFTUuSdAfSQdGDI2zyEbwD2TEo3Gbi85EJSt3D6EnBeCOsNNKuum3Iyx0VY8zxM9L43KzhO+jNRj2QcsCDEPUa0aqEMZraSyBX93yW9Azwdov4FnF0yMANcARwTBn7mUzpKexORiOcRdUs/zdI9OjuBr6JwnJjxltBxYsZF6Dgx4yJ0nJhxETpOzLgIHSdmXISOEzMuQseJGReh48TM/wPZqVO1tFxJmwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.94 0.61 0.74 2083\n",
+ " 1 0.25 0.76 0.38 358\n",
+ "\n",
+ " accuracy 0.63 2441\n",
+ " macro avg 0.59 0.69 0.56 2441\n",
+ "weighted avg 0.84 0.63 0.69 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), gamma: 0.1...\u001b[0m\n",
+ "Oversampled dataset(PCA), gamma: 0.1 Accuracy: 0.6812781646866038\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.68 0.79 2083\n",
+ " 1 0.27 0.66 0.38 358\n",
+ "\n",
+ " accuracy 0.68 2441\n",
+ " macro avg 0.59 0.67 0.58 2441\n",
+ "weighted avg 0.83 0.68 0.73 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), gamma: 0.1...\u001b[0m\n",
+ "Undersampled dataset(PCA), gamma: 0.1 Accuracy: 0.5870544858664482\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 216x144 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.59 0.71 2083\n",
+ " 1 0.20 0.58 0.29 358\n",
+ "\n",
+ " accuracy 0.59 2441\n",
+ " macro avg 0.54 0.58 0.50 2441\n",
+ "weighted avg 0.79 0.59 0.65 2441\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(SVM_models, SVM_name, x_test_list, ytest, \"SVM testing\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5ce57cea",
+ "metadata": {},
+ "source": [
+ "## Multi Layer Perceptron"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "5c2ddfd6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MLP_models = []\n",
+ "MLP_name = []\n",
+ "x_val_list = []\n",
+ "x_test_list = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "f886f74d-b79a-4078-84af-08ee0c71529a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-1 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-1 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-1 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-1 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-1 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "MLPClassifier(random_state=42)"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "from sklearn.neural_network import MLPClassifier\n",
+ "\n",
+ "# Initialize the MLP classifier\n",
+ "mlp_oversampled = MLPClassifier(random_state=42)\n",
+ "MLP_models.append(mlp_oversampled)\n",
+ "MLP_name.append(f\"Oversampled dataset(No PCA)\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "mlp_oversampled.fit(xtrain_oversampled, ytrain_oversampled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "c6a16dad-b219-4c22-aee2-b04d15ae9240",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-2 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-2 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-2 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-2 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-2 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-2 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-2 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-2 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-2 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-2 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-2 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "MLPClassifier(random_state=42)"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "from sklearn.neural_network import MLPClassifier\n",
+ "\n",
+ "# Initialize the MLP classifier\n",
+ "mlp_undersampled = MLPClassifier(random_state=42)\n",
+ "\n",
+ "# Append the MLP model to the corresponding lists\n",
+ "MLP_models.append(mlp_undersampled)\n",
+ "MLP_name.append(f\"Undersampled dataset(No PCA)\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "mlp_undersampled.fit(xtrain_undersampled, ytrain_undersampled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "a6e13b8c-754f-4be4-a892-377abeff9d0b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-3 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-3 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-3 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-3 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-3 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-3 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-3 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-3 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-3 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-3 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-3 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "MLPClassifier(random_state=42)"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "# Initialize the MLP classifier\n",
+ "mlp_oversampled_pca = MLPClassifier(random_state=42)\n",
+ "\n",
+ "# Append the MLP model to the corresponding lists\n",
+ "MLP_models.append(mlp_oversampled_pca)\n",
+ "MLP_name.append(f\"Oversampled dataset(PCA)\")\n",
+ "x_val_list.append(xval_pca_oversampled)\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "mlp_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "0649fc68-ced7-4c59-aca5-68f9687435ee",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-4 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-4 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-4 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-4 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-4 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "MLPClassifier(random_state=42)"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Initialize the MLP classifier\n",
+ "mlp_undersampled_pca = MLPClassifier(random_state=42)\n",
+ "\n",
+ "# Append the MLP model to the corresponding lists\n",
+ "MLP_models.append(mlp_undersampled_pca)\n",
+ "MLP_name.append(f\"Undersampled dataset(PCA)\")\n",
+ "x_val_list.append(xval_pca_undersampled)\n",
+ "x_test_list.append(xtest_pca_undersampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "mlp_undersampled_pca.fit(xtrain_pca_undersampled, ytrain_undersampled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "bd04d1db-1748-4351-8620-10457f077d35",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating MLP validation data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA)...\u001b[0m\n",
+ "Oversampled dataset(No PCA) Accuracy: 0.7139931740614335\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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oqGDoYm4IJZ4SvlkWjTv3H6NbJxNs/G4UNNUF8A+JaepTblFKnhVh2byJsHzfDnO+Xwut1m3wOPtvaLTSAgCUiV7g3p0b8Br1NUzNLVBSXIQft4Vg3ZK5WLxup6Sf9p26wtF1EHT0DVHyrAiH92zHqu9mYFV4DJTe0R9pbs6bgfPz83H//n0YGxsDAOzt7cHn8xEXF4cRI0YAALKzs3H16lUEBwcDABwdHVFYWIhLly6hV69eAICLFy+isLBQkgjrg5KcnJ347TpO/Ha9xnqH982x++hFnE959S9axKHf4Dv8Q9hZmUmSXMSh3yTts7Kf4PuNPyN5/wK0M9FFxoM8xCWkIy4hXdIm8+98dG5ngAmf91H4JPfLgSjo6htg/KxFkjJ9QxPJnzU0W2He8lCpfcZOnosls75Cfm4OdA1erQa6eHwitf/wLyfhu2/GIi83GwbG7zXyWTQOea6uFhcX4/bt25LPGRkZSEtLg46ODnR0dLB48WIMHz4cxsbGyMzMxIIFC6Cnp4dPPnn1vQqFQvj6+mLOnDnQ1dWFjo4O5s6di+7du0tWWy0tLTFo0CBMmDABW7duBQBMnDgRQ4cOrffKKtDMSe7BgwfYvHkzEhISkJOTAx6PB0NDQzg5OWHy5MkwNTVtzvAaRULaXQx17o5dhxPx8HEh+va0gEU7A8z74UC17TXUVPGlV29kPMjDg5yCGvvVbqWOJ0XPGyvsd0baxXOwtuuNDYH+uHE1FW109dFvyHC4DBpW4z6lJcXg8XjQaNWq2nrRi1KcjzsKfUMT6OjVf5rU0sjziYfLly/D1dVV8rnyWp63tzc2b96MK1euYNeuXXj69CmMjY3h6uqKffv2QUtLS7JPSEgIVFRUMGLECJSWlsLNzQ2RkZFQfm2kvGfPHsyYMUOyCuvl5VXrvXnVabYkd+HCBXh4eMDU1BTu7u5wd3cHYwy5ubk4fPgwQkND8euvv+LDDz+stR+RSFRlmZtVlIPXQl/1PGflT9i0aDTunFgOsbgcFawCU5b8iIS0u1LtJn7eB8v9hqGVhgB/3c3BkCkbarzeZv6eHqZ84Yz/hBxqilNo0XJzHiL+2CEM+mQUPEf64O7Na9izdQ34fFV86Da4SvuyMhF+ityI3s4Doa4hneROHT2A/Ts2QPSiFMbvtce85aFQ4fOb6lTkTp7TVRcXFzDGaqw/fvx4nX2oqakhNDQUoaGhNbbR0dHB7t27GxRjpWZLcrNmzcL48eMREhJSY72fnx+Sk5Nr7ScoKAjff/+9VJmy4QfgG/eSW6zyNG2UC3p1b4/hM7cgK/sJPrLrhHX+I5GTV4TTF29I2kX/moxTF/+CkZ42/L7sj90rv0a/r9ZAVPZSqj9jfSGObJyKQydTERmT2NSn0+IwVgHzTpb4zHsqAKBdxy74+14G4o8drJLkXr58ic0r/wvGGL6cNq9KX46ug9DNthcKC/Lx68E92Bi0AAtXhUFVtXEuzjc2RX0LSbPdJ3f16lVMnjy5xvpJkybh6tWrdfbj7++PwsJCqU3F0F6eocqNmoCP76d7Yv7qQzh27iqu3nqILfvO4cCJ3+E3zk2qbVHxC9zJeozffr+D0XO3o4u5IT7uZyPVxlhfiNhtM3DxzwxMW7q3KU+lxWrdRg8mZuZSZSam7ZH/WHqV7uXLl9i0YgHyHj3EvGWhVUZxwKvrd0ZtzdDF2hbfLAhC9oN7+D3hTGOG36iUlHi1blzVbCM5Y2NjJCQk1HgBMTExUbISU5vqlr1b6lSVr6IMVb4KKt4Y5peXV9T5fzIeeFDl//ufy0RfiNiwmUhNz8LEgN21Th0UiYXV+8j5+55UWc7fWdDT//fxosoE9+jhfcwP2oRW2sJ69s4gFovlGG3TolctNbG5c+di8uTJSElJwYABA2BoaAgej4ecnBzExcVh+/btWLt2bXOF12Ca6qroaPrv/Vbt2+ri/c5tUVD0HPdzCnDu8i0E+g1D6QsxsrKfoI99J4wZ2gvz1xyStP9soD1OJaYjr6AYJgatMcenP0pFYhy/cA3AqxHc8e0zcT+7AP5rYqDf5t9RyKP8Z017wi2M+7BRWD53PH7eF4lefdxw9+Z1nIk9DJ/p/gCA8vKX2Bj4H9y7cwN+AatRUV6Bp0/yAQCttLShwucjN/tvXDofB2tbB2gJ26Ag/zF+ObALfFUBbD6o/60LLQ2XR2u14bFmHALs27cPISEhSElJkbx5QFlZGfb29pg9e7bk/hlZqdt+I88wZdLH3gInts+sUh51JAkTA3bDUFcLS6Z/jP6OXdFGWwNZ2U8QcSgB63fHA3iVwDYtGg1bS1O00dZAbv4zXPj9NgK3/Ypb9149+zfW0wFhS6p/E0NznXv8T8ua5bjVSbt0AQciNyHn4X3oG5pg4CejJKurjx89xLyvP6l2v/lBm2D5vj0K8h9jx/rlyLz9F0qKn0HYWgedrW3x8ShfGL/XrgnPpHaOnVrL1N5qwYla668Hutda/65q1iRXSSwWIy8vDwCgp6cH/luuYDVnklNULSnJKQpZk1y3hbUnuWvLuZnkWsTNwHw+v17X3wghDaeo09UWkeQIIY1PUV9/TkmOEAWhoIurlOQIURQ0XSWEcBolOUIIp9HNwIQQTqORHCGE0yjJEUI4TUFnq5TkCFEUNJIjhHAa3QxMCOE0mq4SQjiNpquEEE6jJFeLI0eO1LtDLy+vBgdDCGk8Sgo6X61Xkhs2bFi9OuPxeJKXXxJCWhYaydWioqKiseMghDQyZUpyhBAuU9DZasOSXElJCc6ePYusrCyUlZVJ1c2YMUMugRFC5EtZQbOczEkuNTUVgwcPxvPnz1FSUgIdHR3k5eVBQ0MDBgYGlOQIaaEU9ZqczLdAz5o1C56ennjy5AnU1dWRlJSEe/fuwd7eHqtWrWqMGAkhcqDE49W6cZXMSS4tLQ1z5syBsrIylJWVIRKJYGpqiuDgYCxYsKAxYiSEyIGSEq/WjatkTnJ8Pl/y8j1DQ0NkZWUBAIRCoeTPhJCWR1mJV+vGVTJfk7O1tcXly5fRuXNnuLq6YtGiRcjLy0NUVBS6d+/eGDESQuSAu2msdjKP5AIDAyW/kbp06VLo6upiypQpyM3NxbZt2+QeICFEPmgkV089e/aU/FlfXx/Hjh2Ta0CEkMZBv/FACOE0Li8u1EbmJGdubl7rvwh37959q4AIIY2Dy1PS2sic5Pz8/KQ+i8VipKamIjY2FvPmzZNXXIQQOVPMFNeAJDdz5sxqyzdu3IjLly+/dUCEkMahqCM5ub303cPDAwcPHpRXd4QQOaObgd/SgQMHoKOjI6/uCCFyJs/Hus6dOwdPT0+YmJiAx+Ph8OHDUvWMMSxevBgmJiZQV1eHi4sLrl27JtVGJBJh+vTp0NPTg6amJry8vPDgwQOpNgUFBRg3bhyEQiGEQiHGjRuHp0+fynbeMrXGq5uB7ezsJJutrS2MjY2xYMECeqyLkBZMniO5kpIS2NjYYMOGDdXWBwcHY82aNdiwYQOSk5NhZGSEAQMG4NmzZ5I2fn5+iImJQXR0NC5cuIDi4mIMHTpU6sW7o0ePRlpaGmJjYxEbG4u0tDSMGzdOplh5jDEmyw6LFy+WWl1VUlKCvr4+XFxc0LVrV5kO3lhevGzuCBSP+CW9WLWpaanJNkaZcfivWuvXD2vY318ej4eYmBjJG8QZYzAxMYGfnx/mz58P4NWozdDQECtXrsSkSZNQWFgIfX19REVFYeTIkQCAhw8fwtTUFMeOHcPAgQORnp4OKysrJCUlwcHBAQCQlJQER0dH/PXXX+jSpUu94pN54WHx4sWy7kIIaQHqGqyJRCKIRCKpMoFAAIFAINNxMjIykJOTA3d3d6l+nJ2dkZCQgEmTJiElJQVisViqjYmJCaytrZGQkICBAwciMTERQqFQkuAAoHfv3hAKhUhISKh3kpN5uqqsrIzc3Nwq5fn5+VBWVpa1O0JIE6nrsa6goCDJta/KLSgoSObj5OTkAHj1Ao/XGRoaSupycnKgqqqKNm3a1NrGwMCgSv8GBgaSNvUh80iuptmtSCSCqqqqrN0RQpqIch1DGn9/f8yePVuqTNZR3OvefGiAMVbno2VvtqmufX36eV29k9z69eslB92+fTtatWolqSsvL8e5c+dazDU5QkhVda2gNmRqWh0jIyMAr0ZilS/zAIDc3FzJ6M7IyAhlZWUoKCiQGs3l5ubCyclJ0ubRo0dV+n/8+HGVUWJt6p3kQkJCALzKolu2bJGamqqqqqJ9+/bYsmVLvQ9MCGlayk10K5y5uTmMjIwQFxcHW1tbAEBZWRnOnj2LlStXAgDs7e3B5/MRFxeHESNGAACys7Nx9epVBAcHAwAcHR1RWFiIS5cuoVevXgCAixcvorCwUJII66PeSS4jIwMA4OrqikOHDlWZSxNCWjZ5PvFQXFyM27dvSz5nZGQgLS0NOjo6MDMzg5+fHwIDA2FhYQELCwsEBgZCQ0MDo0ePBvDqJbu+vr6YM2cOdHV1oaOjg7lz56J79+7o378/AMDS0hKDBg3ChAkTsHXrVgDAxIkTMXTo0HovOgANuCZ3+vRpWXchhLQA8nyo4fLly3B1dZV8rryW5+3tjcjISHz77bcoLS3F1KlTUVBQAAcHB5w4cQJaWlqSfUJCQqCiooIRI0agtLQUbm5uiIyMlJol7tmzBzNmzJCswnp5edV4b15NZL5P7rPPPkPPnj3xn//8R6r8hx9+wKVLl/DTTz/JFEBjoPvkmh7dJ9f0ZL1PbunJ27XWf9e/09uE02LJfAvJ2bNnMWTIkCrlgwYNwrlz5+QSFCFE/pR5vFo3rpJ5ulpcXFztrSJ8Ph9FRUVyCYoQIn8cfga/VjKP5KytrbFv374q5dHR0bCyspJLUIQQ+aPfeKin7777DsOHD8edO3fQr18/AMCpU6fw448/4sCBA3IPkBAiH3XdDMxVMic5Ly8vHD58GIGBgThw4ADU1dVhY2OD+Ph4aGtrN0aMhBA5kPV1Slwh8+rqm54+fYo9e/YgPDwcf/zxh9RrUpoLra42PVpdbXqyrq6G/pZRa/30D83fJpwWq8ED2Pj4eIwdOxYmJibYsGEDBg8eTK8/J6QFo9XVenjw4AEiIyMRERGBkpISjBgxAmKxGAcPHqRFB0JaOA6vLdSq3iO5wYMHw8rKCtevX0doaCgePnyI0NDQxoyNECJHtLpahxMnTmDGjBmYMmUKLCwsGjMmQkgj4HIiq029R3Lnz5/Hs2fP0LNnTzg4OGDDhg14/PhxY8ZGCJEjpTo2rqr3uTk6OiIsLAzZ2dmYNGkSoqOj0bZtW1RUVCAuLk7qByoIIS2PPH+t613yVreQ3LhxA+Hh4YiKisLTp08xYMAAHDlyRJ7xNQjdQtL06BaSpifrLSR7Uh7UWj/G/r23CafFeqtRapcuXRAcHIwHDx5g79698oqJENIIeLzaN65665uBWyIayTU9Gsk1PVlHcvtS/661fqRt27cJp8WS+bEuQsi7icvX3WpDSY4QBSHLL1xxCSU5QhQElx/dqg0lOUIUhILeC0xJjhBFoQTFzHKU5AhRELTwQAjhNLomRwjhNAXNcZTkCFEUNF0lchcethWn4k4gI+MuBGpq6NHDFn6z56K9eQdJm5NxJ3Bg/z6kX7+Kp0+fYt+Bw+hqaSnVj6/POFxOviRVNtBjMIJXhTTJebxLdoRvw+lTccjMuAuBQA3v97DFdL85aN9e+tXeGXfvYP3a1fg9JRmsogIdOnbCih9CYGRsAgAoKyvD2tXBOB77C0QvRPjAoTf+s3ARDA2NmuO05IKmq0TuLidfwshRY9Cte3eUvyxH6PoQTJ7gi0NHfoGGhgYAoLT0OXrY2sJ94CB8H/DfGvsa/tkITP1mhuSzQE2t0eN/F/1+ORmfjxwNq27WKC8vx6bQtfhmsi9+OnQU6v985w/uZ2G8zxh4fTIck6Z8g1ZaWsi8eweqqgJJP6uDA3H+7BkErlwNobA11q4OxqzpUxC19wCUlZWb6/TeioLmOEpyjWnztnCpz0uWBcG1jyPSr1+Dfc8PAACeXsMAAH//XfsbItTU1KCnr98ocXJJ6OYwqc8BSwIxwPVDpKdfg539q+98Y+haOH3UFzNnzZO0e+89U8mfi589w/9iDmHJ8hVw6O0EAFgaGIwhA11xKSkRjh9+1ARnIn+KOpLj8rvyWpzif965py0UyrzvsV9+hvOHDvjEawhW/7ASJSXF8g6Pk4qL//nOtV995xUVFfjt/Fm0a9ce30wejwEuH8J7zEiciT8p2Sf9+jW8fClGb6cPJWX6Bgbo2MkCf/6R2rQnIEeK+j65Fp3k7t+/j6+//rrWNiKRCEVFRVKbSCRqogjrjzGGVcFBsLWzh4VFZ5n2HTzEEyt+WIPtkVGYOHkqTsYdx+yZ0xspUu5gjGHNqpXoYWuPTv9850+e5OP58+eIjNgOxw8/woYt2+Harz/mzZ6BlMuvrnvm5+eBz+dLEmMlHR1d5OXlNfl5yAuvjo2rWnSSe/LkCXbu3Flrm6CgIAiFQqnth5VBTRRh/QUtW4JbN29i5Q9rZN53+Ocj0NvRCRYWneExeAhWh6xHUmIC0q9fa4RIuSM4aClu37qB5StXScpYxas3izm79sOYcT7o0tUSPr4T8FFfFxz8aV+t/TGwd/ohd/pJwmZQ11uE7969W2cf/v7+mD17tlQZUxbU0Lp5BC1fijNn4hGxczcMjd5+dc7SqhtUVPi4d+8eLK26ySFC7gkOWoZzZ05jW0SU1Ipo6zatoayiAvMOHaXam5t3QFra7wAAXV09iMViFBUVSo3mCp48gY2NbdOcQCN4lxP022jWJDds2DDweDzU9t7Ouv7DCAQCCATSSa2lvDSTMYag5UsRfyoO4ZFRUhe338bt27fw8qUY+rQQUQVjDMFBy3Am/iS2hu9E2/ekX+nN56uiWzdr3MuU/jX5rHuZMP7n9pHKf0QuJiZgwEAPAEDe41zcuX0LM/zmNs2JNAIFzXHNm+SMjY2xceNGDBs2rNr6tLQ02NvbN21QchS49Hv8euwo1oZugqaGJvL++XWzVlpaUPvnFpDCp0+RnZ2Nx49zAQCZ//zl09PTg56+Pu5nZeGXo0fQp68zWrdpg7t37mD1DyvQ1dIKPWztmufEWrCVgUsQ++svWL12AzQ0NZGX98933urf73yc99fw/3YO7Ox7oucHDkj47QLOnzuDrdtfXRpppaWFjz/5FGtXB0PYujW0tYVYt+YHdLLojF69HZvt3N6Woia5Zn39uZeXF3r06IElS5ZUW//HH3/A1tYWFRWyvVq7pYzkbLp1qbZ8ybIgfPzJpwCA/8UcwqL/+ldpM3nqN5gybTpysrOx4D/zcPvWLTx/XgIjI2P0cXbG5CnfQNi6dWOGL5OW8vrznjaW1ZYHLAmE58efSD7/L+YgIiO2IffRI7Rrb46JU76Bi6ubpF4kEmHdmh9w/NejeCESoVev3pi/cBGMjIwb/RzqS9bXn6dkFtVab99e+23CabGaNcmdP38eJSUlGDRoULX1JSUluHz5MpydnWXqt6UkOUXSUpKcIpE1yf1eR5KzoyT37qAk1/QoyTU9WZNc6r3afxvZtp3W24TTYtETD4QoCEV9M3CLvk+OECJHcrobePHixeDxeFKb0Wu3RjHGsHjxYpiYmEBdXR0uLi64dk36nk6RSITp06dDT08Pmpqa8PLywoMHtT/a2FCU5AhREPJ8rKtbt27Izs6WbFeuXJHUBQcHY82aNdiwYQOSk5NhZGSEAQMG4Nmzf6fLfn5+iImJQXR0NC5cuIDi4mIMHToU5eXlcjvfSjRdJURByPMWEhUVFanRWyXGGNauXYuFCxfi009f3UGwc+dOGBoa4scff8SkSZNQWFiI8PBwREVFoX///gCA3bt3w9TUFCdPnsTAgQPlFyhoJEeIwuDV8T9ZngO/desWTExMYG5uji+++ELydFJGRgZycnLg7u4uaSsQCODs7IyEhAQAQEpKCsRisVQbExMTWFtbS9rIEyU5QhSEEq/2rbrnwIOCqj4H7uDggF27duH48eMICwtDTk4OnJyckJ+fj5ycHACAoaGh1D6GhoaSupycHKiqqqJNmzY1tpEnmq4SoiDqekSyuufA33xkEgA8PDwkf+7evTscHR3RsWNH7Ny5E7179672WIzV/XKD+rRpCBrJEaIgeLzaN4FAAG1tbamtuiT3Jk1NTXTv3h23bt2SXKd7c0SWm5srGd0ZGRmhrKwMBQUFNbaRJ0pyhCiIupJcQ4lEIqSnp8PY2Bjm5uYwMjJCXFycpL6srAxnz56Fk9Ortyzb29uDz+dLtcnOzsbVq1clbeSJpquEKAh5vf137ty58PT0hJmZGXJzc7Fs2TIUFRXB29sbPB4Pfn5+CAwMhIWFBSwsLBAYGAgNDQ2MHj0aACAUCuHr64s5c+ZAV1cXOjo6mDt3Lrp37y5ZbZUnSnKEKAh5Xe168OABRo0ahby8POjr66N3795ISkpCu3btAADffvstSktLMXXqVBQUFMDBwQEnTpyAlta/j42FhIRARUUFI0aMQGlpKdzc3BAZGdkoPxJEz64SuaBnV5uerM+u3n38otb6Dvrc/AU4GskRoiAU9dlVSnKEKApKcoQQLuPyzw7WhpIcIQqCpquEEI5TzCxHSY4QBUEjOUIIp9E1OUIItylmjqMkR4iioOkqIYTTGuM1Ru8CSnKEKAjFTHGU5AhRGLTwQAjhNAXNcZTkCFEUlOQIIZxG01VCCKcpZoqjJEeIwqBbSAghnEY3AxNCuI2SHCGEyxR14YGTP2TzrhKJRAgKCoK/v3+9ftSXvD36zrmPklwLUlRUBKFQiMLCQmhrazd3OAqBvnPuk+03zQgh5B1DSY4QwmmU5AghnEZJrgURCAQICAigC+BNiL5z7qOFB0IIp9FIjhDCaZTkCCGcRkmOEMJplOQIIZxGSa6F2LRpE8zNzaGmpgZ7e3ucP3++uUPitHPnzsHT0xMmJibg8Xg4fPhwc4dEGgkluRZg37598PPzw8KFC5Gamoo+ffrAw8MDWVlZzR0aZ5WUlMDGxgYbNmxo7lBII6NbSFoABwcH2NnZYfPmzZIyS0tLDBs2DEFBQc0YmWLg8XiIiYnBsGHDmjsU0ghoJNfMysrKkJKSAnd3d6lyd3d3JCQkNFNUhHAHJblmlpeXh/LychgaGkqVGxoaIicnp5miIoQ7KMm1EG++f58xprDv5CdEnijJNTM9PT0oKytXGbXl5uZWGd0RQmRHSa6Zqaqqwt7eHnFxcVLlcXFxcHJyaqaoCOEO+o2HFmD27NkYN24cevbsCUdHR2zbtg1ZWVmYPHlyc4fGWcXFxbh9+7bkc0ZGBtLS0qCjowMzM7NmjIzIG91C0kJs2rQJwcHByM7OhrW1NUJCQtC3b9/mDouzzpw5A1dX1yrl3t7eiIyMbPqASKOhJEcI4TS6JkcI4TRKcoQQTqMkRwjhNEpyhBBOoyRHCOE0SnKEEE6jJEcI4TRKcoQQTqMkR2S2ePFi9OjRQ/LZx8enWV44mZmZCR6Ph7S0tCY/Nnl3UJLjEB8fH/B4PPB4PPD5fHTo0AFz585FSUlJox533bp19X4UihITaWr0gD7HDBo0CDt27IBYLMb58+cxfvx4lJSUSL1aHQDEYjH4fL5cjikUCuXSDyGNgUZyHCMQCGBkZARTU1OMHj0aY8aMweHDhyVTzIiICHTo0AECgQCMMRQWFmLixIkwMDCAtrY2+vXrhz/++EOqzxUrVsDQ0BBaWlrw9fXFixcvpOrfnK5WVFRg5cqV6NSpEwQCAczMzLB8+XIAgLm5OQDA1tYWPB4PLi4ukv127NgBS0tLqKmpoWvXrti0aZPUcS5dugRbW1uoqamhZ8+eSE1NleM3R7iKRnIcp66uDrFYDAC4ffs29u/fj4MHD0JZWRkAMGTIEOjo6ODYsWMQCoXYunUr3NzccPPmTejo6GD//v0ICAjAxo0b0adPH0RFRWH9+vXo0KFDjcf09/dHWFgYQkJC8NFHHyE7Oxt//fUXgFeJqlevXjh58iS6desGVVVVAEBYWBgCAgKwYcMG2NraIjU1FRMmTICmpia8vb1RUlKCoUOHol+/fti9ezcyMjIwc+bMRv72CCcwwhne3t7s448/lny+ePEi09XVZSNGjGABAQGMz+ez3NxcSf2pU6eYtrY2e/HihVQ/HTt2ZFu3bmWMMebo6MgmT54sVe/g4MBsbGyqPW5RURETCAQsLCys2hgzMjIYAJaamipVbmpqyn788UepsqVLlzJHR0fGGGNbt25lOjo6rKSkRFK/efPmavsi5HU0XeWYo0ePolWrVlBTU4OjoyP69u2L0NBQAEC7du2gr68vaZuSkoLi4mLo6uqiVatWki0jIwN37twBAKSnp8PR0VHqGG9+fl16ejpEIhHc3NzqHfPjx49x//59+Pr6SsWxbNkyqThsbGygoaFRrzgIqUTTVY5xdXXF5s2bwefzYWJiIrW4oKmpKdW2oqICxsbGOHPmTJV+Wrdu3aDjq6ury7xPRUUFgFdTVgcHB6m6ymk1o9cekgaiJMcxmpqa6NSpU73a2tnZIScnByoqKmjfvn21bSwtLZGUlIQvv/xSUpaUlFRjnxYWFlBXV8epU6cwfvz4KvWV1+DKy8slZYaGhmjbti3u3r2LMWPGVNuvlZUVoqKiUFpaKkmktcVBSCWariqw/v37w9HREcOGDcPx48eRmZmJhIQE/Pe//8Xly5cBADNnzkRERAQiIiJw8+ZNBAQE4Nq1azX2qaamhvnz5+Pbb7/Frl27cOfOHSQlJSE8PBwAYGBgAHV1dcTGxuLRo0coLCwE8OoG46CgIKxbtw43b97ElStXsGPHDqxZswYAMHr0aCgpKcHX1xfXr1/HsWPHsGrVqkb+hggnNPdFQSI/by48vC4gIEBqsaBSUVERmz59OjMxMWF8Pp+ZmpqyMWPGsKysLEmb5cuXMz09PdaqVSvm7e3Nvv322xoXHhhjrLy8nC1btoy1a9eO8fl8ZmZmxgIDAyX1YWFhzNTUlCkpKTFnZ2dJ+Z49e1iPHj2Yqqoqa9OmDevbty87dOiQpD4xMZHZ2NgwVVVV1qNHD3bw4EFaeCB1ot94IIRwGk1XCSGcRkmOEMJplOQIIZxGSY4QwmmU5AghnEZJjhDCaZTkCCGcRkmOEMJplOQIIZxGSY4QwmmU5AghnPZ/13adoQWoRz0AAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.75 0.81 2455\n",
+ " 1 0.29 0.55 0.38 475\n",
+ "\n",
+ " accuracy 0.71 2930\n",
+ " macro avg 0.59 0.65 0.60 2930\n",
+ "weighted avg 0.80 0.71 0.74 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA)...\u001b[0m\n",
+ "Undersampled dataset(No PCA) Accuracy: 0.6412969283276451\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.93 0.62 0.74 2455\n",
+ " 1 0.28 0.74 0.40 475\n",
+ "\n",
+ " accuracy 0.64 2930\n",
+ " macro avg 0.60 0.68 0.57 2930\n",
+ "weighted avg 0.82 0.64 0.69 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA)...\u001b[0m\n",
+ "Oversampled dataset(PCA) Accuracy: 0.7225255972696246\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.90 0.75 0.82 2455\n",
+ " 1 0.31 0.59 0.41 475\n",
+ "\n",
+ " accuracy 0.72 2930\n",
+ " macro avg 0.61 0.67 0.61 2930\n",
+ "weighted avg 0.81 0.72 0.75 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA)...\u001b[0m\n",
+ "Undersampled dataset(PCA) Accuracy: 0.6351535836177474\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.93 0.61 0.74 2455\n",
+ " 1 0.27 0.75 0.40 475\n",
+ "\n",
+ " accuracy 0.64 2930\n",
+ " macro avg 0.60 0.68 0.57 2930\n",
+ "weighted avg 0.82 0.64 0.68 2930\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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/P7179zaZ72FJfsOQOnXqJDp16iQ93rRpkwAgvvjii3zvaUmPHj1EYGCgWbvhZ+/7778XWVlZIjU1VRw9elTUrVtX1K9f32Q40XfffScAiHXr1uX7XF5eXqJevXpFuia3PXv2WPy5LiwAYtSoUdL8hIkTJwoh9L8vTk5OIikpyeL3vijDkIz/lv/666/Cw8NDODs75zuHat26dRZ/rw1DtYz/1gIQXl5eJnMuoqKihFwuFwsXLsy3xvyGIRkY5p7cv3/f4nHD72zXrl3FCy+8ILUXZRiSVqsVmZmZonbt2mLcuHFSe2F+N7p16yaqVq1qNpdr9OjRwt7eXvp78SjDkIxxGFLFxj4mKpXatm0LW1tbODs7o3v37qhUqRJ+/vlnKBT6zrCrV6/iv//+k96112q10kfPnj0RGRmJS5cuAQB+//13BAUFoV69enk+36+//oqGDRuiadOmJvfq1q1bgSvw9OjRA97e3ibvsIeFheHevXsYOnSoyXMEBQXBx8fH5Dl69OgBADh48KDJfZ977jnY2toW7RuXB/FwOErudy27du0KLy8v6bGNjQ369++Pq1ev4s6dO8Va9w8//ID27dvDyckJCoUCtra2WL9+PcLDwx/ra5PJZOjTp49JW+PGjU2GORw8eFD6WTL26quvPtZz58fb2xutW7fOty6gaN8Xw8o6ue3btw/PPPMMXFxcYGNjA1tbW8ycORNxcXGIjo4GoO+Bys7OxqhRox7p6/n111/h6uqKPn36mPwcNG3aFN7e3ma/I40bNy7UEJbWrVvjr7/+wsiRIxEWFgaNRvNI9Rn8/vvvsLe3N/ndK6x79+5JvTWW9O/fH7a2tnB0dET79u2h0Wiwa9euR3pXXwhRpF4ESwy1Pu7qQoYhMt988w20Wi3Wr1+Pfv36FbjiUWEY/y3v3bs3vL298fvvv5v83clt3759UKlUUu+mgaG3eO/evSbtQUFBcHZ2lh57eXnB09OzwKFOhSEsDOVbt24dmjdvDnt7e+l3du/evYX+W6bVahESEoL69etDqVRCoVBAqVTiypUrJvco6HcjPT0de/fuxQsvvABHR0ezfwfT09Nx4sSJx/sGEIFLp1IptWnTJpw6dQr79u3DO++8g/DwcJMXdoYxwhMnToStra3Jx8iRIwEAsbGxAPRjTAvqbr1//z4uXLhgdi9nZ2cIIaR7WaJQKPDGG29g+/btUvfwV199hSpVqqBbt24mz/HLL7+YPUeDBg1M6jUwDLcoiGHoiWFIhCWGZQ+rVatm0u7t7W12rqHNMPygOOretm0b+vXrB19fX2zevBnHjx/HqVOnMHToUKSnpxfq68yLo6Mj7O3tTdrs7OxM7hsXF2fxxUl+L1iMVa9ePd/vryVubm5mbXZ2dkhLS5MeF/X7Yul7++effyI4OBgA8MUXX+Do0aM4deoUpk2bBgDS8xnmFTzq0IP79+8jISEBSqXS7GchKirqkX9+p0yZgqVLl+LEiRPo0aMH3Nzc0LVr1zyXJC1ITEwMfHx8Hmm8dVpamtnPkrGPPvoIp06dwsGDBzFt2jTcv38fffv2NRlHX5jfx5SUFMTGxkq/j4W5xhJDrcY/U4/KMPckJCQEZ8+exbBhwx77nkDO3/Jz587h3r17uHDhAtq3b5/vNXFxcfD29jYLU56enlAoFGZDowrzu/aobt68CTs7O1SuXBmAfv7Su+++izZt2uCnn37CiRMncOrUKXTv3r3Qzzd+/HjMmDEDffv2xS+//IKTJ0/i1KlTaNKkick9CvrdiIuLg1arxerVq81+J3v27AnA/O8z0aPgnAUqlerVqydNHgwKCkJ2dja+/PJL/Pjjj3j55Zfh7u4OQP/H1NLEUgDSuE8PDw/pXfK8uLu7w8HBwWySn/Hx/AwZMgRLliyR5kzs3LkTY8eOhY2Njck9GjdujAULFli8h4+Pj8njwr7r+Oyzz2Lq1KnYsWOH2TvnBob12J999lmT9qioKLNzDW2Gf4CLo+7NmzfD398f33//vcnx3JMVS4qbmxv+/PNPs3ZLX78l3bp1w+rVq3HixIkizVsoSFG/L5a+t9999x1sbW3x66+/mrzQzb0Gv2G89J07d8xCY2G4u7vDzc0NoaGhFo8bv7ObV62WKBQKjB8/HuPHj0dCQgL++OMPTJ06Fd26dcPt27fh6OhYpDo9PDxw5MgR6HS6IgcGd3d3aUy6JQEBAdLfpY4dO8LBwQHTp0/H6tWrMXHiRABAixYtUKlSJezcuRMLFy60+H3YuXMndDqd9PvYsmVLVK5cGT///HOe11hiqLWgv0+FUa1aNTzzzDOYM2cO6tatm+/cjqIw/lteWG5ubjh58qRZ70t0dDS0Wm2xfL2FcffuXZw5cwadOnWSerU3b96Mzp0749NPPzU5NykpqdD33bx5M958802EhISYtMfGxpr0UhX0u1GpUiXY2NjgjTfeyLPH0N/fv9B1EeWFPQtUJixevBiVKlXCzJkzodPpULduXdSuXRt//fUXWrZsafHD8OKlR48e2L9/vzQsyZLevXvj2rVrcHNzs3ivGjVq5FtfvXr10KZNG2zcuBFbt25FRkYGhgwZYvYc//zzD2rWrGnxOXK/6C6sli1bIjg4GOvXr8fRo0fNjh85cgQbNmxA9+7dTSY3A/rufOOVXLKzs/H999+jZs2a0jvQxVG3TCaDUqk0+Yc/KirK4qo/xfWOoLFOnTohKSkJv//+u0m7pZWfLBk3bhxUKhVGjhyJxMREs+NCCLMJwYVRlO9LfvdQKBQmwTQtLQ3ffPONyXnBwcGwsbExe5GTW17f/969eyMuLg7Z2dkWfw4M4fxxuLq64uWXX8aoUaMQHx8v9YjZ2dlJX1dBevTogfT09AJXuLIkMDAQ169fL/T5kydPRq1atbBo0SLpxaJSqcSkSZMQHh5usoqOQXR0NKZMmQIvLy9p4qutrS0++OAD/Pfff5g3b57F54qOjjb7/TbUmtdk76KaMGEC+vTpk+eKTE9K165dkZycbBZ4N23aJB0vaWlpaXjrrbeg1WpNVrWSyWTSz6PBhQsXcPz4cZO2/H5mLd1j165d+Q4ns/S74ejoiKCgIJw7dw6NGze2+HtpeNOnKL9DRLmxZ4HKhEqVKmHKlCmYPHkytm7dioEDB+Kzzz5Djx490K1bNwwePBi+vr6Ij49HeHg4zp49ix9++AGAfhfS33//HR07dsTUqVPRqFEjJCQkIDQ0FOPHj0dgYCDGjh2Ln376CR07dsS4cePQuHFj6HQ63Lp1C7t378aECRPQpk2bfGscOnQo3nnnHdy7dw/t2rUze/E0d+5c7NmzB+3atcOYMWNQt25dpKen48aNG/jtt9+wbt26Rx4ismnTJjzzzDMIDg7GmDFjpH9M9+3bh5UrVyIwMNDiiyd3d3d06dIFM2bMkFZD+u+//0xeRBdH3YZlNEeOHImXX34Zt2/fxrx581ClShWznbkbNWqEAwcO4JdffkGVKlXg7Oz82C9EBw0ahI8//hgDBw7E/PnzUatWLfz+++8ICwsDUPASgf7+/lKvUdOmTTF69Gg0a9YMgH41kg0bNkAIgRdeeKFIdRXl+5KXXr16Yfny5Xjttdfw9ttvIy4uDkuXLjV7MVKjRg1MnToV8+bNQ1paGl599VW4uLjg33//RWxsrLS0aaNGjbBt2zZ8+umnaNGiBeRyOVq2bIkBAwZgy5Yt6NmzJ95//320bt0atra2uHPnDvbv34/nn3++yF8/oF8ZqGHDhmjZsiU8PDxw8+ZNrFixAn5+ftIKYI0aNQIArFy5EoMGDYKtrS3q1q1r1psB6OehbNy4ESNGjMClS5cQFBQEnU6HkydPol69ehgwYECetXTu3BkbNmzA5cuXCzXfwtbWFiEhIejXrx9WrlyJ6dOnAwA++OAD/PXXX9J/+/fvDxcXF1y4cAFLlixBUlISfv31V5NleA0BY9asWfjzzz/x2muvoVq1akhMTMShQ4fw+eefY86cOSZDeE6cOAE3Nzfp+wPo/xYMHToUGzZsMFtNqyDBwcHSkLaCZGdn48cffzRrV6lU0nymR/Xmm29izZo1GDRoEG7cuIFGjRrhyJEjCAkJQc+ePfHMM8881v1zu3XrFk6cOAGdTofExEScO3cOGzZswM2bN7Fs2TKT70nv3r0xb948zJo1C506dcKlS5cwd+5c+Pv7mywl7ezsDD8/P/z888/o2rUrKleuDHd3d2lZ4a+++gqBgYFo3Lgxzpw5gyVLlpj9HS3M78bKlSvx9NNPo0OHDnj33XdRo0YNJCUl4erVq/jll1+wb98+AEDNmjXh4OCALVu2oF69enBycoKPj0++b/YcPHhQGr6YnZ2NmzdvSv/PO3XqlO+KVlTOWHFyNZGZvDbyEUKItLQ0Ub16dVG7dm2h1WqFEEL89ddfol+/fsLT01PY2toKb29v0aVLF7NVRW7fvi2GDh0qvL29ha2trfDx8RH9+vUzWeEiOTlZTJ8+XdStW1colUrh4uIiGjVqJMaNG2eyckfu1VwMEhMThYODQ74rscTExIgxY8YIf39/YWtrKypXrixatGghpk2bJu1oWpgVOixJTk4WISEhomnTpsLR0VE4OjqKxo0bi/nz51vcLRUPV0JZu3atqFmzprC1tRWBgYEWN3kqjroXLVokatSoIezs7ES9evXEF198IWbNmiVy/xk6f/68aN++vXB0dBQApJVu8loNydKqLJbue+vWLfHiiy8KJycn4ezsLF566SXx22+/CQDi559/zvd7a3Dt2jUxcuRIUatWLWFnZyccHBxE/fr1xfjx401Wi8lrU7ZBgwaZrTRU2O+L4f+XJRs2bBB169YVdnZ2IiAgQCxcuFCsX7/e4gpCmzZtEq1atRL29vbCyclJNGvWzGSFlPj4ePHyyy8LV1dXIZPJTOrIysoSS5cuFU2aNJGuDwwMFO+88464cuWKdJ6fn5/o1auXxVpz//4sW7ZMtGvXTri7uwulUimqV68uhg0bJm7cuGFy3ZQpU4SPj4+Qy+UmPwe5V0MSQv+3YubMmaJ27dpCqVQKNzc30aVLF3Hs2DGLNRkkJiYKJycns11789qUzaBNmzaiUqVKJhv26XQ6sWXLFtG5c2fh6uoqlEql8Pf3F++++67ZymLGfv75Z9GrVy/h4eEhFAqFqFSpkggKChLr1q0z2a1Zp9MJPz8/8d5775lcb7xzckHy+5kyyGs1JAAWPww/3/n9LS+MuLg4MWLECFGlShWhUCiEn5+fmDJlitkmj3l9DXn9nTZm+Jtl+LCxsRGVKlUSLVq0EGPHjrW4M3hGRoaYOHGi8PX1Ffb29qJ58+Zix44dFn+3//jjD9GsWTNhZ2cnAEj1PHjwQAwbNkx4enoKR0dH8fTTT4vDhw+b/SwX9ncjIiJCDB06VPj6+gpbW1vh4eEh2rVrJ+bPn29y3rfffisCAwOFra1toVZqMqzqZunD+O8wlX8yISxM9Seick8mk2HUqFH45JNPrF2K1YSEhGD69Om4desW1xwnAMB7772HvXv34uLFi4+9WlFJ2rt3L4KDg3Hx4kUEBgZauxwiKsc4DImIKgRDKAoMDERWVhb27duHVatWYeDAgQwKJJk+fTo2bdqEn376yWzpztJk/vz5GDp0KIMCEZU4hgUiqhAcHR3x8ccf48aNG8jIyED16tXxwQcfSOPMiQD9crpbtmzBgwcPrF1Knh48eIBOnTpJy0QTEZUkDkMiIiIiIiKLuHQqERERERFZxLBAREREREQWMSwQEREREZFFFW6Cs06nw7179+Ds7Fyql8UjIiIiIioKIQSSkpLg4+NT4IajhVXhwsK9e/dQrVo1a5dBRERERFQibt++XWzLgle4sODs7AxA/01Uq9VWroaIiIiIqHhoNBpUq1ZNer1bHCpcWDAMPVKr1QwLRERERFTuFOdQe05wJiIiIiIiixgWiIiIiIjIIoYFIiIiIiKyiGGBiIiIiIgsYlggIiIiIiKLGBaIiIiIiMgihgUiIiIiIrKIYYGIiIiIiCxiWCAiIiIiIosYFoiIiIiIyCKGBSIiIiIissiqYeHQoUPo06cPfHx8IJPJsGPHjgKvOXjwIFq0aAF7e3sEBARg3bp1JV8oEREREVEFZNWwkJKSgiZNmuCTTz4p1PkRERHo2bMnOnTogHPnzmHq1KkYM2YMfvrppxKulIiIiIio4lFY88l79OiBHj16FPr8devWoXr16lixYgUAoF69ejh9+jSWLl2Kl156qYSqJCIiIiIq3YQQSEvLKvb7WjUsFNXx48cRHBxs0tatWzesX78eWVlZsLW1NbsmIyMDGRkZ0mONRlPidRIRERFR6bLrQiSW77mElIzsx75XF90xvK39Fo5IK/Q1GbfkSPtHgXStDClyGcRjV5HjbJInPrvbGL7KmGK8q16ZCgtRUVHw8vIyafPy8oJWq0VsbCyqVKlids3ChQsxZ86cJ1UiERERERWDsBthWHN+DVKyUiyfkJUOZGgAkfOyW2f0ebNLAn2OCNhl6h9XAjCvGOu7BwCwL/T5lZL1/7UF4FpMNfydVhkrYxvjWKo3AOBiiqqY7pyjTIUFAJDJZCaPxcMfitztBlOmTMH48eOlxxqNBtWqVSu5AomIiIieAE1oKGJWrYYuJY8X0yUoPTsdKZkp0EFXuAuEMHlRXxgqGTD5EWozcEt6jItLWJyz/r/yR+xeiEhT44t7jXAwwfQ1bXU7DW5m5HHRIypTYcHb2xtRUVEmbdHR0VAoFHBzc7N4jZ2dHezs7J5EeUREREQlyjggaO/ft1odCgAuVnv2onvgVEI3lsn0H4WUoZTj167OuNLMHaObjkZwjeCCL8rl++//wZuvbYNOl5M0/P1dMXduEHr1qo7KlT8t8j3zU6bCwlNPPYVffvnFpG337t1o2bKlxfkKRERERGVRXr0GeQUERa5h2iay0pCeqUEyUOA4eV3hX/dKHvXd8cKQAXDUCdjn8Rw6yJAic0AG9G8My2SAs50C9rY2+tpUKniMGYN63buVXJFFFPSY13ftGgCVyhZJSZnw9nbCzJkdMWxYcyiVNiUyN9eqYSE5ORlXr16VHkdEROD8+fOoXLkyqlevjilTpuDu3bvYtGkTAGDEiBH45JNPMH78eAwfPhzHjx/H+vXr8e2331rrSyAiIiKy7OJ2YH8INOHJiDkD6AqxUE06gBQZ4JJc8LkPnIAMJfDL0zKcq5v3xFY5dIhWFP0ln6dWa7Fd93DlfQcdMOhBBjqm5EwYtsknbKTCAZ8pXsV+ebtC16Cys8GE4Lro2ch8XmpF8OBBGs6di0KXLv5Sm7u7I+bODUJGhhbvvdcGjo4l+4a5TIgiDiArRgcOHEBQkHm+GjRoEL766isMHjwYN27cwIEDB6RjBw8exLhx43Dx4kX4+Pjggw8+wIgRIwr9nBqNBi4uLkhMTIRarS6OL4OIiIjI1MXtwA+DAQDXfvNApubRX9AZxrcbpCmB7zvKcTLw0bbLctfmP8/AUQcMfpCBTqmmqwbl92K/or+oL24pKZlYteokFi8+Bp1O4Pr1MXBzcyzwupJ4nWvVsGANDAtERET02B72GiAjjy6ApHvSp1d+9oI2zQY6GZD4cLGawgz3ybI19Bo8wtigXARkyBBqyNNewbTOA/iivpTKzMzGF1+cwbx5h3D/fs4QtEmT2mHx4mcLvL4kXueWqTkLRERERFZjHBCMwkBeNLfsEfOPMzLTbSCHftjQu6PNX3rpsnJe1NnIZYDODkpNTyjSm+ob85nHzHf0y4fsbB22bPkbs2YdwI0bCVK7XC7Dm282wahRraxWG8MCERERkYWegjClDGsc5UgxvLEvsgEH6D9cfXKuldmY3a7ZfwLDjumH+xgGC6UpH56e/XAdIaNQwBf9FZMQAjt2/Ifp0/fj339N55289FI9zJsXhHr1PKxUnR7DAhEREZV7Fjf4Mt7UyzgIPBStyB0CLL9sahuuQ7/DOjhk5rTlXuP/jpt+nsGyTsseablMKp8+++wM3n13l0lbt241MX9+F7Rs6ZPHVU8WwwIRERGVKQXu7GtBdGq09LmlF/eW5awGZLw8qIDMZAlSt+T8p38u7eOEiBYe+LDtWAYFMvHaa40wffo+xMWl4amnqmLhwq7o1KmGtcsywbBAREREJe5RXuADMH33/6HoXOtzFv7Fv97j7+ybdziId3SVPk+3tcNvLZ/DS2++yuFFhH/+ica5c5F4440mUptabYdVq3pArbZDr161ISvCBm9PCsMCERERlQjjgGD8zn5+ivrCXy6ASoXYkyBPDkCycEA6lIW+xNXRVtr0S6ojj82/uj5GaVQ+XL/+ALNmHcCWLRdgZ6dA164B8PHJWQ/3tdcaWbG6gjEsEBERUbEyhISIxAiLxz0dPS22N/snDcN2PHis55Y55Lzrb+k9WsOOv4m2amxv2hun/ZoV+t6GScgd2EtAhXDvXhLmzz+EL744C+3DvS3S07VYseJEoZZBLS0YFoiIiOix5B5iZKkXQZbtIq3+0+Z2KoZd34rsf3QQWTkv6XVppi/vFQ7ZyIYcArIC3/1PVdjhm3rdcMS3CWp6qAq1shDf9aeSEB+fho8+OoLVq/9EWlrOvBc3NwdMmfI0Ro603jKoj4JhgYiIiB7JrguRCDn4HTTqDXmek53hgVan6+C1s+Fw1KYA+AHuskRkpgE5i4qa820fj2jfyuieuRTeavtC1aOys8FaLj9KVpKcnIkVK05gyZJj0GgypHYnJyXGj2+LCRPaQa22s2KFj4ZhgYiIiIps14VIvP/LV3CouhWA0VyDDEPvgAxCZw/oUuGefjDfe8mNhw7ZCjg00iK+mhu22L+Ota8054t/KhM2bDiHGTP2S4/t7GwwcmQrTJnyNDw8VFas7PHIhBD5r/dVzpTENthERERl1a4LkVi+5xJSMrILdb7W/jyaR/6EV44mwiFLJ7UXdoUhhUPO88jtFfCY9THUuSYFE5VFaWlZqF17NaKikjFkSFPMnNkJ1aq5PNEaSuJ1LnsWiIiIyrn8AkGUJh0A0FN+AuMVP0IlS8chlQ2+rmSHtFyjhJpdEuhxVKBqXP7PZxwIDOS2Ong0SoK6Wjrg7APYOQFB04AGDApUtggh8NNP4bh6NR4ffvi01O7gYIuvv+6LqlXVqFvX3YoVFi/2LBAREZUjloKBIRAYM4SDM05Z+LqSHTLkOb0E0QrT9xINQ4wshYREJwGVTsBeiJxAUL+y5eKkgND3kb42ImsSQmD37muYNm0fzpyJhEIhx6VLoxEQUMnapUnYs0BERESF6inIS2ePn3FffQyX5Nl4B0C0wuHhEcuTjT2zBV47pIN3vGl7ViWgRnOgXoAM0iKldi5A0FKGASp3jh27jalT9+LgwZtSm1arw5YtFzBjRicrVlbyGBaIiIjKmOV7LuFajPlOyD3lJzBemdNbYDyMqNklgT5HBGyzcl+Vs7SjXACQyQCZDHLIoVKqYG9jD21CDAAdIJdDWaMGPMaM4TwDqhD++isK06btw65dV0zamzb1RkhIF3TvXstKlT05DAtERERlhKFHISJWHxTkMsDTOWdZ0cmZP+GS4wPM9/Iwua5tuA7DdxZ21LF4+KEDkAgtEqUjyho1UPO3XY/3RRCVAVevxmPmzP349tt/TNrr1HHDvHlBePnl+pDLLW37V/4wLBAREZUBuy5EYtTWs+gpP4HPbPUTkRVyGTzsjNZtz4rCmEpe0sO24TqLQ4gSnQCVwgH2doUf0yxXqeAxZszjfhlEZcLevddNgkLVqmrMnt0JgwY1hUKR9/4g5REnOBMREZVShp6ENmmHMTRzK1SydFSRxed5vuaWPc78V1kaamRpOVPfFSs4hIioAFlZ2ahffy0SEtIxbVoHjBjREvb2pf89dk5wJiIiKm8ubgf2hwAZyUjLykZyhhaG9/GaC2AzoA8Icn0YuPaPB3RZD9/ZlNsAANIBpMgAl2TAO4+nUQYEcK4BUS5JSRlYvvw4YmJS8cknPaV2W1sbbNvWDzVquMLZueztulycGBaIiIieJKNwAABIuicdcgCQdcseMf845wQCAFegH1qkTbOxeEsFgNxbPyWqFXBzcJOGDzEkEOVIT9di7dpTWLjwCGJjUyGTAe++2xINGnhK5zRq5JXPHSoOhgUiIqInwRASYi9LTZpb9ogx6inIhhwirXCTJh+oc8KETuTskZBlp8Af3dzR6Y0paFsjuJiKJyoftFodNm48h7lzD+HOHY3ULpfLcPTobZOwQHoMC0RERCUpz5DgjEyNbb6XGgcCQB8K0pTA9x3lOBlofEz/ub+LP3b23YmgYiueqHzQ6QR++OEiZszYjytXcub9yGTAq682wpw5nVGrVh6bCVZwDAtERETFIffwIgBpWdnIupz4cFhRzpAGS8OJ4pxkgEw/V8FyIACMN07zdDR9B1Rlq8LopqOL4QshKl8OHLiBcePCcP58lEl77951sGBBFzRuzOFG+WFYICIiegSa0FDErFoNXUoKkJUGpD2weJ42Lf93K++4WQ4Gno6esDQgwhAKgjnEiKhQ7t7VmASFTp38EBLSFe3aVbNiVWUHwwIREVEuxkEgPSsbSRlaGC80bocMqFLTcl1lefKxsXhHV0CWCSFLR5qdziwkeDp6MgwQPSatVmeyF8KrrzbCRx8dhVJpg5CQrnj22QDIZBVjQ7XiwLBARESEnD0NUjKyseDnRfBJvA9A/w9lpQKuVThkS58nCCekQwlAP2lSJgNUrmr4TxqPW4HAxIMToR9OlPNixt/FnwGB6DFduhSLGTP2Iztb4Kef+kntcrkMu3e/AS8vFUPCI2BYICKiCksTGoqIJR8jJUEDdbbA7IftldL1q6ToAMgdABkEbKAzu15uq4OykYBdNR1S4YDPFK9iv7wdVHY2mBBcFz0bVQEAhN0Iw4zzaxBxMMLkeoYEosd361Yi5s49iK++Oo/sbH0X4KlTd9Gqla90jre3k7XKK/MYFoiIqMIx9CJM/Z++B0GZx3n26izU7BljfsDZB7BzAoKmAQ36AtAHgovn10CVdQAAsOw//QcARKdGm91iWadlDAlEjyE6OgULFx7G2rWnkZmZ07vn4eGIe/csbF9Oj4RhgYiIKgxDSKhy/hgmh4fBK/lhEJABMnsBGfRLKcpkMshtsuDRSP+CI8zRAWvc3JEilwF2asDWXn/dv6v1H7AcCCxhbwLR40lMTMeyZcfx8ccnkJycKbWr1XaYPLkd3n+/LZyc8noLgIqKYYGIiCqEXRci8e2SjZgcHobqyaYv7JXOefQgQB8UJnp55DRkafQf+ci9rCnAVYyIisPmzRfw/vuhiI/PWWDAwUGBMWPaYPLk9qhc2cGK1ZVPDAtERFTuHfjsWyi+WIdpyebv/ivVWn0PgkyOsMpVsMZRjhTDHEiZDNG5tjqwFAQMGAiISpZabScFBYVCjrffbo7p0zuiShVnK1dWfjEsEBFRuWK8qlEX3TEMu74VXsfMz1Oq9cOM1NXS9cOM3KshwkaYn2iE8wyInhydTuDBgzS4uTlKbX361EG7dtVQs2YlzJ7dGQEBBa1VRo+LYYGIiMqV5Xsu4VpMCsbc+xHP/3cYmRpbk+NmIaFSFUQobQGYBgXjHgT2GBA9OUII/PrrZUybtg/e3k7YvfsN6ZhMJsP+/YOgVBa8rwkVD4YFIiIqV+pdOoXJZ39F9eRoZMI0KHgEZcO9li3ClG5Y4yhHhMJ8zXVOQCayngMHbmDq1L04fvwOAODvv6Oxf38EgoL8pXMYFJ4shgUiIirzDDsupyZoMCo+1uy40tcDdwf3wgfOR5GSlWJx5SKGBCLrOX36HqZN24fdu6+ZtLdu7QtHR9s8rqIngWGBiIjKDONQkJShhXg4cqhyagIA83/UoioDv3aQ43xLW0SnbgYSze/JkEBkPeHhMZgxYz9++incpL1+fQ8sWNAFzz9fl7suWxnDAhERlQm7LkTCcfZH8E6IggJAXtMa45yBNCXwfUc5TgY+XMooV0+Cp6Mn5yEQWdlHHx3B1Kn7oNPlzBeqUcMVc+d2xmuvNYKNjTyfq+lJYVggIqJSwXgVo9xa3jiLPmd/hcfDTdR0AJQO2UiXyZD48AWFcUDwyAZk9mp4GjZPe4gBgaj0aNXKVwoKXl4qzJjREcOHt+CchFKGYYGIiKzGOCBEadLx9N2/8EZ4GBy1GQAAe2TCSZYGpJleZ6/OwtWXk003SwPgb++OZW2mMAwQlTIJCemIiUlB7dpuUluXLv54+eX6aNGiCt57rzVUKu66XBrJhBD5Lypdzmg0Gri4uCAxMRFqtdra5RARVRiW5hvodEIKBDIIiLSCxyZnuWTj+45y7GxoOumReyAQlT6pqVlYvfokPvroKAID3XH06FDOQShBJfE6lz0LRERUogwhIfP6dQDIc76BgOkLCIWD6XAkuZ0NPFoAA5srzZY8ZVAgKl0yM7Px5ZdnMW/eIURFJQMAjh+/g19/vYw+fepauToqCoYFIiIqEQc++xZi4+fwTogyOxZr7yL1KChgFArkNpDbAh4tAHXAw3HLdk5A0DSEqRww7vwa3NTcBIQOcpkcfmo/zkEgKkWys3XYuvVvzJp1ABERCVK7XC7Dm282QePGXtYrjh4JwwIRERUrTWgoIpZ8DK+7t8yO3XLyxDf1ukFdLQ1rlasA4OEuyq5IUdgCTp5m1wAA/l1ttjeCn9oPO/vuLPb6iajohBD4+edLmD59Hy5ejDE59tJL9TB3bhDq1/fI42oqzRgWiIioWOy6EIn9n23FsL1fIvc0RRu1Dg6NtGhW7Raay76Eh4gDoA8KJpOULWyWZolhbwQiKh3Gjg3FqlV/mrQFB9fEggVd0LKlj5WqouLAsEBERMUiau0sDDtw2KRNqc6CR6MkqKul5zQaLauxppKryfmejnn0LDzEpU+JSqfXXmskhYW2bati4cKu6Ny5hnWLomLBsEBEREVmvOTp4Ijv0PmvI2inMT3Ht308bOu4wEFRWWoLU8qwxlGOFBkAmQyxchkM6YGTlInKhn/+iUZKSibatKkqtbVpUxWTJrVDhw7V0bt3Ha54VI4wLBARUZEt33MJ12JSAACdzh+BSDI97tvdHurhnwEN+iLsRhjWnF+DlKwUs3kHhqDg7+LPoEBUyl2//gCzZx/A5s0X0LixF86efQdyeU4oWLz4WStWRyWFYYGIiIpk14VIKSh0vPcXYAgKMgGZswzpLzwP9ZRF0vlrzq9BRGKE2X0MQ44MQ4uIqHSKjEzC/PmH8MUXZ5GVpQMA/PXXffz447/o16+BlaujksawQEREhXNxO5JD56LhRQ3C/lZAaGUmm6gpXWSoeSIcYTfC8NyO55CSpQ8UsWmxAAC5TA53B3fOOyAqI+Lj07B48VGsWnUSaWlaqb1yZQdMmfI0+vSpY8Xq6ElhWCAiIssubgf2hwAZ+g2VkHQPulv2SD1W2eLpHm3tEXYjDBMPTrR4nEudEpUNycmZWLnyBJYsOYbExAypXaWyxfjxT2HChKfg4mJvxQrpSWJYICIiy/aHALGXTZpi/nE2eZzoBKQpgV87yHG+iSOicwUFDjUiKntGjfoNmzb9JT1WKm0wcmRLTJnSAZ6eKitWRtbAsEBERHoPexLSkhORnKFFZfEAKbfsEfOPGllZ+t2URXrOsKNlL8hxMlCec32W6XJIXN2IqGyaOPEpfPPNX5DJZBgypClmzuyE6tVdrF0WWQnDAhFRRWcYbvSwF8Hh4Yfmlj3u5jHk6I4bTIKC8f4InJNAVDYIIbBtWzgcHW3Ro0dtqb1RIy+sXt0DzzwTgLp13a1YIZUGDAtERBWdheFGkaIyNH+b7sMc93AEUpoS+L6jPigYdlJmMCAqO4QQ2LPnOqZO3YszZyJRu3ZlPPNMAGxtbaRzRo1qbcUKqTRhWCAiqoiMJi+LpCjIAGQLGa7dqoKofx0gz85ApRQdDH0HuYcc+bv4YxlDAlGZc/z4bUyZshcHD96U2q5cicevv17GCy/Us2JlVFoxLBARVRCa0FDELAlBdkIMZLpsAEC6TIYUuRd00M9FqJQMeCDN5DrDkCNPR08OMSIqoy5cuI/p0/fhl19MexGbNvVGSEgXdO9ey0qVUWnHsEBEVN5d3I7otbMQtzfLqFE/3EABIK9pi3HOQJadAge6uWNZpykMCERl0NWr8Zg16wC+/fZvCJHTXrt2ZcybF4RXXmlgsgszUW4MC0RE5dnF7cAPg5F0ygOArdQc52x+qgxyyGRAhlKOP7q5o9Mb+oAQ9OSqJaJiNmfOQWzd+rf0uGpVNWbN6oRBg5qYzFEgyotMCOOcWf5pNBq4uLggMTERarXa2uUQERWrXRcisXzPJaRk6IcZbbk6GvgnC5lJCkDo3z00nn/AoUVE5du1a/EIDFwDFxc7TJ3aASNHtoK9Pd8rLq9K4nUuf1qIiMqR5Xsu4VpMCgCgp/yEPihocnoUjJc85T4IROVHUlIGVqw4gYCASnj99cZSe82albF9e3907OgHtdrOihVSWcWwQERUxhn3JkQnpaOn/ATGK36E55143NXo90nQyYB7lU2XPGVQICr70tO1WLfuNEJCDiMmJhVVq6rx0kv1TXoPeveuY8UKqaxjWCAiKuOMexMAYLztw6BgtKHavcrA+Lf1f/INeyMQUdml1erw9dfnMWfOQdy+nbN7emRkEo4cuYVnngmwYnVUnjAsEBGVFQ/3RkhLTkRyhhaGKWebBQA74K9oBez/UiA6E8hKNt15+YeOCm6gRlQO6HQCP/74L2bM2I/Ll+NMjr36akPMnRuEWrUs77xO9CjkBZ9SstauXQt/f3/Y29ujRYsWOHz4cL7nb9myBU2aNIGjoyOqVKmCIUOGIC4uLt9riIjKul0XInHzp+lA7GU4pN+Hh4iDJ+LhiXhUkcVDdTsVfvsV8IrX75VgbNkLckS18cfOvjsZFIjKsLCwq2jZ8nP07/+jSVDo3bsOzp9/B1u3vsSgQMXOqmHh+++/x9ixYzFt2jScO3cOHTp0QI8ePXDr1i2L5x85cgRvvvkmhg0bhosXL+KHH37AqVOn8NZbbz3hyomInpwzv21E3R+7oGr2XQD6nZYjRWVEisq4fcsd536vYjLkCAAeOAFR7nJ82b8SotvU5LAjonJg27ZwnDsXJT3u2NEPR44MwS+/vIomTbytWBmVZ1ZdOrVNmzZo3rw5Pv30U6mtXr166Nu3LxYuXGh2/tKlS/Hpp5/i2rVrUtvq1auxePFi3L592+JzZGRkICMjQ3qs0WhQrVo1Lp1KRKVO2I0wrDm/BimpcRDpGgihAwDIoZPOaXYJ6HkEUGbq90SonKQzu8/m172xYMb+J1Y3EZUMIQRkspwN0+7e1aBWrdWoX98DISFdEBxc0+Q4UUksnWq1noXMzEycOXMGwcGmXeLBwcE4duyYxWvatWuHO3fu4LfffoMQAvfv38ePP/6IXr165fk8CxcuhIuLi/RRrVq1Yv06iIgeV9iNMDy34zlMPDgREYkRiM7SIMYGiFXIEauQI1qhkD66HQWqxANuyTqzoHDHTR8UOr0xxUpfCREVh8uX4zBgwI9YteqkSbuvrxp//vkWTp8ejm7dajEo0BNhtQnOsbGxyM7OhpeXl0m7l5cXoqKiLF7Trl07bNmyBf3790d6ejq0Wi2ee+45rF69Os/nmTJlCsaPHy89NvQsEBGVBmE3wjDx4ESzdk+tFgCggxzNLgn0PiJgnyWDy8P5CDoZkOisf7/HeMflBZyTQFRm3b6diLlzD2LjxvPIzhbYty8CQ4c2g7Nzzv4IjRp55XMHouJn9dWQcqfi3F1uxv7991+MGTMGM2fORLdu3RAZGYlJkyZhxIgRWL9+vcVr7OzsYGfHTUiIyPrCDs/Dmis/IMVoWFG0jenfO//MLIx8kIhaya74+UZbPHP3HpR3DfO4ckaN2vsHoMFvu6THQSVaORGVpJiYFCxceARr155CxsPd1w3Cw2PRurWvlSojsmJYcHd3h42NjVkvQnR0tFlvg8HChQvRvn17TJo0CQDQuHFjqFQqdOjQAfPnz0eVKlVKvG4ioqIwnocQnaUBbADA8hsiy+7HIDg1DZdv+uB2uDd6JpwwO0fh5QW5SgWPMWNKtnAiKnEaTQaWLTuG5ctPIDk5U2pXq+0weXI7vP9+Wzg5Ka1YIZEVw4JSqUSLFi2wZ88evPDCC1L7nj178Pzzz1u8JjU1FQqFack2NjYAACvO0yYikhiHA2RoEJ3HzDDPbP3fLJ0QcNABgx5koFGKA66KSnjwrxreiaZvpCgDAuAxZgzU3buV9JdARE/AJ5/8idmzDyAuLk1qc3BQYMyYNpg8uT0qV3awYnVEOaw6DGn8+PF444030LJlSzz11FP4/PPPcevWLYwYMQKAfr7B3bt3sWnTJgBAnz59MHz4cHz66afSMKSxY8eidevW8PHxseaXQkQViBQIslLMjkWnRuc8yBUUPLVaqHQCowMHIstlGJbvuYSbsSnQCWCKDPB0tofKzgYrbGY8vF4OZY0aDAlE5VBExAMpKCgUcgwf3hzTp3eEj4+zlSsjMmXVsNC/f3/ExcVh7ty5iIyMRMOGDfHbb7/Bz88PABAZGWmy58LgwYORlJSETz75BBMmTICrqyu6dOmCjz76yFpfAhFVQGvOr0FEYkSB5xkmKatgg9GpOgTLnICgaUCDvui67ACuxeSEjRc1/+Hdv/ZBl5ICbUI8AEDh4YGaRvMSiKhs0ukEtFodlEobqW3KlA5Yv/4c+vSpi9mzO6FmTW6mRqWTVfdZsIaSWH+WiCqWrj90RXRqNOQyOdwd3IHkaECnlY6rdAKjHyQg2LGaFA4Mdl2IxPI9lxDxsEeh472/MOTyHngnmK8CpwwIYFggKsOEENi16wqmTduHV16pj+nTO5ocj49P43AjKlYl8TrX6qshERGVJWE3wqShRu4KJ+y9rwHi7gBCB8jkgJM3YOcM9FpkMSQYehOevvsX3ggPQ/XkaLPn4CRmorLv4MEbmDp1H44d028ae+NGAkaObGUSDhgUqCxgWCAiKoI159dIn6tS4oDYyJyDbrWA0aekh4aAkJKRjShNutT+9N2/MO3UN2b35iRmorLvzJl7mDp1H3bvvmbSHhjojujoFAYEKnMYFoiICmA8oTk2LVZqH/0gIeck9zr6IUcP7boQiVFbz1q837Are0weMyQQlX3h4TGYMWM/fvop3KS9fn0PLFjQBc8/X5c7LlOZxLBARJQHQ0iwNJnZPzMLwakPlzx85WuTIUcAsHzPJZPH3mr9SkcTguui6nEBwwwH3xUrGBKIyrj33/8dn3xyCjpdzjTQGjVcMWdOZ7z+eiPY2OSxhjJRGcCwQET0UO4lUU2WQX3IUweotFk5vQpGQcF42FF0Us6wo7WvN0fPRlWgCQ1FzAdvITMmBoB+bgKDAlHZV6mSgxQUvLxUmDGjI4YPb2Gy+hFRWcWwQEQVXn49CAb+Lv4Y3XQ0gn96H0h6OE/hla+xK7sNli87YDYvAdDPTRh2ZQ+qHhe4AkB7/77JcblKVdxfChGVsISEdMjlMqjVdlLb+PFPYcuWvzFsWDO8915rqFTcdZnKD4YFIqrwLAUFT0dPAIBKp8PoB4kIjr0CXHsfSH64xKmzD9CgL5Yb7ZdgWOHIUZsBuVyGyqkJAAAtzBnmKRBR2ZCamoXVq0/io4+O4p13WmDhwmekY2q1Hf77bxSHG1G5xLBARBWWoUfhpuYmAEAuk8NP7afvQagRDFzcDvww2PLFdk4AgJSMbAD6/RKmWFjhyEDh5aV/jodLonL4EVHZkJmZjS+/PIt58w4hKioZALBy5UmMGdMGVark7LbMoEDlFcMCEVUYBc1J8FP7YWffnfqQ8EkrIPayyfEYmRuEEEiFAz5LeA77Q/aizn8nMedf8/0SGA6IyrbsbB22bv0bs2YdQEREgtQul8vQr18D6xVG9IQxLBBRhZHfvATDnAQAwP4Qs6Dwbub7+F3XBkDOcKOe2l1wT080uxdXOCIqu4QQ2LnzEqZN24eLF2NMjr30Uj3MnRuE+vU9rFQd0ZPHsEBEFYahR0Euk8PdwR0AoLJV5Qw7MsjQDzWATI6bMh8sSn/JJChY2lAN4H4JROVBnz7fYteuKyZtwcE1sWBBF7Rs6WOlqoish2GBiCqEsBth0rAjdwd37H1lr8Xzzod8AOcdWdBleUEHOaKFM17DbryG3bCxkaFSSoLJ+QovLw41IipHOneuIYWFtm2rYuHCrujcuYZ1iyKyIoYFIiq3jOcoGM9PUNlaXrJU88Vc2G3aiUzYSm3uMB9mZMDhRkRl2z//RMPDwxFeXk5S26hRrfDHH9cxalQr9O5dh7suU4XHsEBE5U5B+yZIcxOMaL6Yi7vLvjVpS3F0QAbsIJMBznYK2NvqN1hiTwJR2RYR8QCzZh3A5s0XMGpUK6xe3VM65uBgi9DQgVasjqh0YVggonIhr14EA09HT8vzEwDg4nbEfLEJMOpRONa5A4at+7yEqyaiJykyMgkLFhzG55+fQVaWDgDw2WdnMH78U/D3r2Tl6ohKJ4YFIirTCupFkHZeNgoImtBQxCwJgS4hFoAAdNnQpuf8OfytzVOoN2pOSZdORE/IgwdpWLz4KFauPIm0tJxtEitXdsCHH7Y3GYZERKYYFoioTMovJOTZi3BxO7A/BDGbk5GZYHyFjfRZvLMz6k1cgJ6NqpRY7UT0ZKSkZGLlypNYvPgoEhMzpHaVyhbjxz+FCROegouLvRUrJCr9GBaIqEyyFBQs9SIYaEJDETNnGnTpWmjT5QBkgExAYa9DNuQQkCFe4YxtrQdgGYMCUZknhEDHjl/h7NlIqU2ptMHIkS0xZUoHeHpaXuiAiEwxLBBRmZB79+XYtFgA+j0T/NR+eYYEIPfk5ZxeBKWrHJuffwMbE5pKbWtfb14i9RPRkyWTyTB8eHO8++4uyOUyDBnSFDNndkL16i7WLo2oTGFYIKIyIa8hR35qP+zsu9PiNZrQUMSsWo3M69dN2hUqGeRe/rjd9w1svO4sta99vTmHHxGVQUIIbNsWjhYtfFCjhqvUPnRoM/zzTzTee6816tZ1t16BRGUYwwIRlWqGHoWbmpsALO++bIkmNBR3x44za/ftbg/18FnYld0Go7aeldpreqgYFIjKGCEE9uy5jqlT9+LMmUgMHtwUGzc+Lx1XKm3wySc987kDERWEYYGISq2wG2GYeHCiSVt+PQlAzkpHmXdjTNqV6ix4tHOGesU5AMDyZQdMjk8Irls8RRPRE3H8+G1MnboPBw7ckNo2bfoL06d3QM2ala1XGFE5w7BARKWSpaBgmMBs0cXt+rkJoelmh3zbx0NdLR14ZSkAYNeFSFyLSZGOc/gRUdnx99/3MW3aPvzyy2WT9iZNvLBgQRcEBHC/BKLixLBARKXSmvNrpM/bhuvw3hkPOGRqAITgCkLML0iOhjZFmDQp1VnwaGULdb3qQNA0oEFfAMDyPZekczj8iKhsuHYtHjNnHsC33/4NYfSrXqtWZcybF4R+/RpALpdZr0CicophgYhKldxzFADgvTMesL19H9p8rstNPzdhoRQQAH2PwvI9lxARm9OrwOFHRKWfEAK9em3FpUtxUpuvrzNmzeqEwYObwtbWJp+riehxMCwQUalhaejRczfdYXs7Sv9ALofCwyPnYFYakJEE6HJihNxeAY9ZH0PdvZvJfXZdiDSZ0AywV4GorJDJZJg+vSPeeGM73NwcMHVqB7z7bks4ONhauzSico9hgYisynj/hOjUaKm9bbgOA4/awDMmSmpT1qiBmr/tyrn4k1ZA7F3TG77yNdCgcEGBvQpEpU9SUgZWrDiBl1+uj3r1ct4cePXVhoiPT8PgwU2hVttZsUKiioVhgYieqNybqxkHBEAfEvod1qFqHADoTI55jBljerOMZP1/ZXLArZbJvATDkKOUjGxEaUwnPXNCM1Hpk56uxbp1pxESchgxMan466/7+PHHftJxGxs5xoxpY8UKiSomhgUiemIsDTMyhAOHTP0eCpU0OrPrlAEB8BgzxnRo0cXtQNI9/edO3sDoUybXLN9zyWTFIwMGBaLSRavV4euvz2POnIO4fVsjtf/88yXcvp2IatW44zKRNTEsENETk3uFo9eOyOEdaxwOTIOCWUi4uB3YH6LvUTAEBQDJsMfzyw4gJSNbaotO0vcmyGWAp7M9VHY2mBBcl0GBqJTQ6QR++ulfzJix32TiMqAfcjRnTmcGBaJSgGGBiEpc2I0wHNq0CGPComCfqW9zSwJyhwOFlxcAQK5SWe5J+GGwxfsvy3oZ1xLMexEAwN9dhb0TOj/eF0BExUYIgbCwa5g6dS/OnYsyOdarV20sWNAFTZp4W6k6IsqNYYGIStya82vwXlgUfOMsH7c4zCi3/bn2VnD2AeycgKBp+P1nZwDpUi+CgaE3gYhKD51OYOLE3bh4MWeX9Q4dqiMkpCuefrq6FSsjIksYFoioRBh6E57ZHYvJGVpUejgXWScDdG4usLext9yDkJth6FHcVanpTJsVmPyvP1I02cDPOUOOPJ3tcWJq15L8sojoMdnYyDF/fhe88ML3aNbMGyEhXdGtW03IZNxQjag0eqSwoNVqceDAAVy7dg2vvfYanJ2dce/ePajVajg5ORV3jURUBh3atAgDt0aZtdv7B5guf5ofC0OPbsqr4qWDngDMhx2p7LgxE1FpcuVKHGbOPICJE59CixY+Uvvzz9fF77+/juDgmtx1maiUK3JYuHnzJrp3745bt24hIyMDzz77LJydnbF48WKkp6dj3bp1JVEnEZVyxj0Jdpk6DMy1qlGiWgFHFzf45l7+1BJDb0LsZZPmqzofLMt8yaTNW60fdsQhR0Slx507GsydexAbNpxDdrbAgwdpCA0dKB2XyWTo3r2WFSskosIqclh4//330bJlS/z1119wc3OT2l944QW89dZbxVocEZUdefUkAIDvihWol99QI4M8QgIAvJv5Pn7X5ayxbthUjasbEZUeMTEpWLToCNasOYUMo9XJzp6NRFRUMry9OfqAqKwpclg4cuQIjh49CqVSadLu5+eHu3fv5nEVEZVHmtBQxKxajVRNHAbGJpoce6CWI8vOBvK3BxY+KFhY7eiqzgfLtK+YBAXulUBUumg0GVi+/DiWLTuO5ORMqV2ttsOkSe3w/vtt4OzMXZeJyqIihwWdTofs7Gyz9jt37sDZ2blYiiKisiFm1WpkXr9u9oek0D0JQP5DjnKFBPYmEJUuWq0OK1eewMKFRxAXlya129srMGZMa3zwwdOoXNnBihUS0eMqclh49tlnsWLFCnz++ecA9OMOk5OTMWvWLPTs2bPYCySi0kuXop9krJMBD5yANCVgP2JIwUEhj83VDN7NfB9hog08ne3hDXBDNaJSysZGhu+/vygFBYVCjuHDm2P69I7w8eEbiETlgUwIIYpywb179xAUFAQbGxtcuXIFLVu2xJUrV+Du7o5Dhw7B09OzpGotFhqNBi4uLkhMTIRarbZ2OURl2pVOnaG9fx9xzsC7oxXwdPTE3lf2FnzhJ60szksw7k2o6cHN1IhKGyGE2RKnf/xxHcHB3+D11xtj9uxOqFmzspWqI6KSeJ1b5J4FHx8fnD9/Ht999x3OnDkDnU6HYcOG4fXXX4eDA7saico7TWgobixbiNTEeDgnaSE3OqayVeV/ce49E2RywMkbsHPClITn8G1yc8hlOcONiKh0EEJg164rmDZtH1av7oGOHf2kY127+uPy5fdQqxZDAlF5VOSehUOHDqFdu3ZQKExzhlarxbFjx9CxY8diLbC4sWeB6NGF3QiD/RuT4BmTZdJ+xw0Y/7YCyzotQ3CNYNOL8hty5F4HGH0KANA2ZC+iNOnwVnNjNaLS5ODBG5g6dR+OHbsNAGjfvhoOHx7CTdSISqFS0bMQFBSEyMhIs+FGiYmJCAoKsjj5mYjKJsNqR7qUFKRnp0OVkWiyE/MDJyDLToED3dyxrNMU06CQzzKoAPRBIWgadl2IxPI9l6RdmImodDh7NhJTp+5FWNg1k/aMjGw8eJDOictEFUSRw4Kl8YoAEBcXB5WqgCEIRFSmGFY7AvR/LNyMjsW62yLjm6UIrhGMIEsXWwoKzj6AnRMQNA1o0Be7LkRi1NazJqdwF2Yi6/rvv1jMmLEfP/74r0l7vXrumD+/C154IZC9CkQVSKHDwosvvghAv/rR4MGDYWeXs15ydnY2Lly4gHbt2hV/hURkNblXOzJQu3qh2YQpUFvqSch42PWQ/HCDNpkccKslBQQA+t6EZQdwLSbF5Pk4V4HIejIytBg5che++uov6HQ5I5T9/FwwZ05nDBzYGDY28rxvQETlUqHDgouLCwB9z4Kzs7PJZGalUom2bdti+PDhxV8hET1RYTfCcGjTIjyzOxaecfoJzA+cgFHvKeGn9sPopqPxdEoKsH86sGxszoUWlkAFoA8KD+clGCzfc8ksKHCjNSLrUiptcP16ghQUvLxUmD69I4YPbw47uyIPRCCicqLQv/0bN24EANSoUQMTJ07kkCOicurQpkUYuDXKpC1NCfip/bCz7848d1o24eyj/69hyNFDhvkJEbH6oCCXAf7u3GiNyBqSkjLg5KSUhhTJZDKEhHRBz55bMXlyO4wZ0wYqldLKVRKRtRV5NaSyjqshEeVv/9ON4B2rlR5HuSvwRzd3dHrj4QTm3HskGIIBYDIfwZKuuYYecS8FoicvNTULn3zyJxYtOoLNm19Ez561TY6npGQyJBCVUaViNSQA+PHHH/G///0Pt27dQmZmpsmxs2fP5nEVEZUFdpk66XPfFStQr3s3/QTmi9v1QcGwRwIAvPJ1nsHAWH49CkT0ZGRmZmP9+rOYN+8QIiP1c4umTduH7t1rQS7PmbDMoEBExoo8U2nVqlUYMmQIPD09ce7cObRu3Rpubm64fv06evToURI1EtETcmjTR6ik0YeFB2o51N275Rw0rG4kHoYJ9zqFDgqjtp7FtZgUGOZM+rvrexQ49Iio5GVn67B58wXUq7cGI0f+JgUFuVyGJk28kJKSWcAdiKgiK3LPwtq1a/H555/j1Vdfxddff43JkycjICAAM2fORHx8fEnUSERPQNiNMMg/+0p6nGW8hOnF7TlDj4xXNyqE5XsumTzmikdET4YQAjt3XsL06fvxzz/RJsdeeCEQ8+d3Qf36HlaqjojKiiKHhVu3bklLpDo4OCApKQkA8MYbb6Bt27b45JNPirdCIipxYTfC8OO68Rgfl9Mmf3ug5Y3VLKxuZEnuoUcAVzwielJSUjLRtesmnDx516T9mWcCEBLSBa1a+VqpMiIqa4ocFry9vREXFwc/Pz/4+fnhxIkTaNKkCSIiIlDB5koTlUlhN8Kw5vwapGTlvIiPTo3G8sM5cxWyvNXokPgD8IOF3ZcL6FEwhARLeygwKBA9GSqVEh4eOasWtmnji5CQrujSxd+KVRFRWVTksNClSxf88ssvaN68OYYNG4Zx48bhxx9/xOnTp6WN24iodAq7EYaJByeatbcN16GqUa9CjZo3gNh005Pc6+S70pFBXkGBQ4+ISs7ly3GoVauyyUTlBQu64ObNBMybF4TnnqvLXZeJ6JEUeelUnU4HnU4HhUKfM/73v//hyJEjqFWrFkaMGAGlsnSvosClU6kie27Hc4hIjJAed7vujF57k0yWSlWqs1CzZ0zORYUMCUDOZGaAeygQPQk3biRg1qwD2Lz5Ar799iX069fA5LgQgiGBqAIpide5xbrPwt27d+HrW7rHQTIsUEWSe8hRbFosdA9XM1rWaRlqjlyJzOvXTa7xbR8PdbWHvQoFLI1qGHKUkpENAIjS5PRGcA8FopITFZWMBQsO4bPPziArS/87XaeOGy5eHAmFosgLHRJROVFq9lnILSoqCgsWLMCXX36JtLS04rglET2mvIYcAYC/iz/a/qfDXUNQkMuhrOIGjxqXihQUDL0IlnDYEVHxe/AgDUuWHMPKlSeRmpoltVeqZI9hw5ohO1vHsEBExarQf1ESEhLw+uuvw8PDAz4+Pli1ahV0Oh1mzpyJgIAAnDhxAhs2bCjJWomokCwFBU9HT3g6esLfxR+jm45GzKrV0jFljRqo+UJaTlAoxB4KuZdE9Vbbw1ttj5oeKq56RFTMUlIysXDhYQQErMLChUekoKBS2WL69A64fv19TJ7cHnZ2xfIeIBGRpNB/VaZOnYpDhw5h0KBBCA0Nxbhx4xAaGor09HT8/vvv6NSpU0nWSURFsOb8GpPHyzotQ3CNYOmxJjQ0p1cBgMeLbYA7y3IuKMQeCoahRwCXRCUqSRpNBgIDP5E2UwMApdIG777bElOmPA0vLycrVkdE5V2hexZ27dqFjRs3YunSpdi5cyeEEKhTpw727dvHoEBUioTdCDOZxJw7KAAw7VXw9YDaOCgU0Kuw60Ikui47gOgkfS+Et9qeQYGoBKnVdujUqQYA/a7LQ4c2xeXLo7FiRXcGBSIqcYXuWbh37x7q168PAAgICIC9vT3eeuutEiuMiB6Nca+Cv4u/WVAAAF1KztKmHjVMhxPl16tgaZ6CyninZyJ6LEII7Np1Bd271zKZezB3bmfodAJz5nRGYKC79Qokogqn0D0LOp0Otra20mMbGxuoVKp8riAiazDebG1009H5nqtQyXLmKQAFTmrOPU+B+ycQFQ8hBPbsuYbWrb9Enz7fYtOmv0yO167thu+/f5lBgYieuEL3LAghMHjwYNjZ2QEA0tPTMWLECLPAsG3btiIVsHbtWixZsgSRkZFo0KABVqxYgQ4dOuR5fkZGBubOnYvNmzcjKioKVatWxbRp0zB06NAiPS9ReWK8RGpsWiwA/YTm3L0KmtBQxCwJgTb64T4Kupz9FQqz+pHxZmucp0BUPE6cuIOpU/di//4bUtvs2Qfw+uuNOGGZiKyu0H+FBg0aZPJ44MCBj/3k33//PcaOHYu1a9eiffv2+Oyzz9CjRw/8+++/qF69usVr+vXrh/v372P9+vWoVasWoqOjodVqLZ5LVFGsOb/GZJ4CAKhszXv+YlatRubdnA3X5Lb69dmLuvpRTQ8VgwLRY/r77/uYPn0/du407bFr3NgLCxZ0gVLJIX5EZH3FuilbUbVp0wbNmzfHp59+KrXVq1cPffv2xcKFC83ODw0NxYABA3D9+nVUrlz5kZ6Tm7JReWO8TKpcJoe7gztUtiqMbjoabf/TIWbVammOgjYmGtAJQCagdM6GRysF1PWcCtyhOfdcBfYqED26a9fiMWvWAWzd+jeM/wWuVasy5s7tjP79G0Iu567LRFR0pXZTtkeRmZmJM2fO4MMPPzRpDw4OxrFjxyxes3PnTrRs2RKLFy/GN998A5VKheeeew7z5s2Dg4ODxWsyMjKQkZEhPdZoNMX3RRCVAsYTmv3UftjZdyeAh8ujjh1n8RqlsxY136wEjD5VqOdgrwJR8YiLS0WjRp8iLS2nR9zX1xkzZ3bCkCFNYWvL3gQiKl2sFhZiY2ORnZ0NLy8vk3YvLy9ERUVZvOb69es4cuQI7O3tsX37dsTGxmLkyJGIj4/Pc0O4hQsXYs6cOcVeP1FpYFgmtW24Dv0O6+Ati8aVjzsDALT375ucq3DQ74sgt9XBo1ESELS0UM+Re64CJzQTPTo3N0e8/nojfPnlObi5OWDKlKcxcmQrODjYFnwxEZEVWH3mlExm2tUqhDBrM9DpdJDJZNiyZQtcXFwAAMuXL8fLL7+MNWvWWOxdmDJlCsaPHy891mg0qFatWjF+BURPlvFk5ujUaABAv8M6VI0DgERokWh2jW/7+CKtemSMvQpEjyY5OROffXYao0e3NpmoPGtWZ/j6qjF+/FNQq+2sWCERUcGsFhbc3d1hY2Nj1osQHR1t1ttgUKVKFfj6+kpBAdDPcRBC4M6dO6hdu7bZNXZ2dtIKTkTlgWEyc9twHT48rINDJlDJsLGrXA6Fh4d0rlylgkede1C7PlpQAEx3amavAlHB0tO1+Oyz01iw4DBiYlJha2uDMWPaSMerVlVj9uzO1iuQiKgICr3PQnFTKpVo0aIF9uzZY9K+Z88etGvXzuI17du3x71795CcnLPl/eXLlyGXy1G1atUSrZfI2sJuhOG5Hc/hpuYm2obrMH6HvjfBLQmQP5wkqazihtqvpKD2c/dR+7n7qNn1OtSVbuTcpIhBYdeFSERpuFMzUWFotTps2HAOdeqsxtixYYiJSQUAfPTRUWRlZRdwNRFR6fRIYeGbb75B+/bt4ePjg5s3bwIAVqxYgZ9//rlI9xk/fjy+/PJLbNiwAeHh4Rg3bhxu3bqFESNGANAPIXrzzTel81977TW4ublhyJAh+Pfff3Ho0CFMmjQJQ4cOzXOCM1F5YFjxKCIxAjqhn59gTFFZDWUlmX435tjLQNK9nA9R+OVRDXZdiETXZQdMVkDiTs1Elul0Aj/8cBENG67FsGE7cft2zkIa/fs3wP79gzhxmYjKrCIPQ/r0008xc+ZMjB07FgsWLEB2tv7dEldXV6xYsQLPP/98oe/Vv39/xMXFYe7cuYiMjETDhg3x22+/wc/PDwAQGRmJW7duSec7OTlhz549eO+999CyZUu4ubmhX79+mD9/flG/DKIyw3hpVABoG26Yn6Dn290eatf/zC909sn53O7h8qiFtHzPJZNJzQCHIBHlJoRAWNg1TJ26F+fOmQ6p7dmzNubPD0KzZuyNI6Kyrcj7LNSvXx8hISHo27cvnJ2d8ddffyEgIAD//PMPOnfujNjY2JKqtVhwnwUq7YwnMANAdGq0tNqRQ6Z+2JGBUp2Fmj1jTG/gXqfAfRMs2XUhEsv3XEJKRjaik9KhE4BcBvi7qzAhuC6HIBHlEhWVjBo1ViDDaF7P009XR0hIF3To4GfFyoiooioV+yxERESgWbNmZu12dnZISUmxcAUR5cdSODBmmJ9giUcjo+TwmCEhd08CoA8Keyd0LtL9iCoKb28njBzZCh9/fAJNm3ojJKQLunevleeKfkREZVGRw4K/vz/Onz8vDRUy+P3331G/fv1iK4yoojCsbmSJp6MnXjsSDSAnLCgcsvV7JbSyhTqgcs4Qo2IMCd5qe6jsbDj0iOihK1fisHTpMSxf3g0qlVJqnzLlabRtWxUvv1yfuy4TUblU5LAwadIkjBo1Cunp6RBC4M8//8S3336LhQsX4ssvvyyJGonKNUOPglwmh7uDOwBAZavC6Kaj0fY/He7G5uzC7Nv+AdTNqj1SODDILyTU9OCQIyJjd+5oMHfuQWzYcA7Z2QJ+fq6YOrWDdNzDQ4V+/RpYsUIiopJV5LAwZMgQaLVaTJ48GampqXjttdfg6+uLlStXYsCAASVRI1G5FXYjTBp25O7gjr2v7NUfuLgdmhUf4G5ozv4ISnWWPiiMPlXk5zGej2BYCtUYQwKRqdjYVCxceBhr1pwymZPw1Vfn8cEH7WFjY7WVx4mInqhH2pRt+PDhGD58OGJjY6HT6eDp6VncdRGVe7lXOVLZqgAAmi/mIuaLTcjU2Jqc79EoCQha+kjPxZ4EosLRaDLw8cfHsWzZcSQlZUrtarUdJk58CmPHtmVQIKIKpchhYc6cORg4cCBq1qwJd3f3kqiJqNzLHRQAYLRba2jGNnvYm2AaFHy720M9/LMi7ZNg6EkAgOgkfW+CXAZ4OufMR2BIINJLT9di7dpTCAk5jLi4NKnd3l6B995rjQ8+aA83N0crVkhEZB1FXjq1cePGuHjxIlq1aoWBAweif//+8PDwKKn6ih2XTiVrMqx8lHtC89qzWvgczzLrTVD6esBj0jSou3cr8N4FDTUC9D0JXN2IyFx0dAoCAlYiJSULAKBQyPHWW80wfXpH+Pry3woiKhtK4nVukftSL1y4gAsXLqBLly5Yvnw5fH190bNnT2zduhWpqanFUhRReWUpKCy7H2MxKPhOfA019x4qVFAAcoYa5Q4K3mp7eKvtpSFHRGTO01OF8eOfgkwGvP56I4SHj8Knn/ZmUCCiCq/IPQu5HT16FFu3bsUPP/yA9PR0aDSagi+yIvYskLUYDz2SCwG/LC1GJ2jQ9oYL7j6c1wwZoPQpfG+Cwa4LkRi19az+3hxqRJQnIQR+++0Kliw5hu3b+6NSJQfpWGJiOm7eTETjxl5WrJCI6NGVik3ZclOpVHBwcIBSqURSUlLBFxBVIMYbrhlvtuaXpcXm4w8QE+6Guw9y8rrSPwA1f9uV7z1zz0cAYNKbwI3UiCw7dOgmpk7di6NHbwMAliw5hpCQrtJxFxd7NG5sb63yiIhKpUcKCxEREdi6dSu2bNmCy5cvo2PHjpg9ezZeeeWV4q6PqMyyNInZYNLZFNw9VhmAaceex5gxBd43r5WNDDjUiMjU2bORmDZtH0JDr5q0Hz16G0II7rhMRJSPIoeFp556Cn/++ScaNWqEIUOGSPssEJFeXpOYPR09odLcx+i4WPiccUKm0TFlQAA8xowpcOjRrguRUlAwDDcy4LAjIlOXLsVixoz9+OGHf03aAwPdMX9+EF58sR6DAhFRAYocFoKCgvDll1+iQQPuWElkLK+QAADLOi1DcI1gYFk9aP4TuGs0mdl3xYoiTWI24HAjIstiYlLw4Yd/4Kuv/oJOl9N7V726C+bM6YyBAxtDoeBeCUREhVHksBASElISdRCVSXnNSTDwd/HH6KajEVwjGJrQUMT8AGQmVJaOKwMCCh0UjHsVAA43IsqLra0Ntm//TwoKnp4qTJ/eAW+/3QJ2do89VY+IqEIp1F/N8ePHY968eVCpVBg/fny+5y5fvrxYCiMqC/LqSTAOCQCgCQ3F3bHjzM4rzBwFwHS1I0C/XwKHGxHpabU6k54CV1d7fPjh0wgJOYzJk9vj/ffbQKVSWrFCIqKyq1Bh4dy5c8jKypI+J6roDD0KNzU3AQBymRzuDu5Q2apMQgJgOSgoK8ngMevjRxp+BLBXgQgAUlOzsGbNn1i9+k/8+edweHs7Scfee6813nqrOSpXdsjnDkREVJDH3mehrOE+C/So8hty5O/ij519d+qHGq1aDV1CLJCRBEBAm2vhIt/28VCP/wxo0LfA5zQskxoRmwLD0Ou1rzdnrwJVaFlZ2Vi//hzmzj2IyMhkAPpwsGpVDytXRkRkXaViB+ehQ4da3E8hJSUFQ4cOLZaiiEojw5AjS0FhkqY9rnXtiLtjxyHz+nVo4zXQpjx+UBi19SyuxeQEBQ4/ooosO1uHLVsuIDBwDd59d5cUFGQyfS9DBXvvi4joiShyz4KNjQ0iIyPh6elp0h4bGwtvb29otdpiLbC4sWeBHoXJ7stGQ44madrD57tDyLx+3ewahcPDTdPkNpDbAh5t7aEePqtIQcFYTQ8Vl0alCkkIgV9+uYxp0/bhn39Mw3rfvoGYPz8IDRp45nE1EVHFYdUdnDUaDYQQEEIgKSkJ9vY567tnZ2fjt99+MwsQROVB7s3V/NR+0pCjux+OM9kvAQCU6ix4tLKFup4LEDStUOEgt9xzFDj0iCqqiIgHeO21bThx4o5Je9eu/ggJ6YrWrbnPDxFRSSp0WHB1dYVMJoNMJkOdOnXMjstkMsyZM6dYiyOylrzmJ7QN1+G9Mxpc+bgztPfvm1yjVGfBo1FSoYcZWWI8R8GAQYEqMi8vJ9y4kSA9bt3aFyEhXdC1a4D1iiIiqkAKHRb2798PIQS6dOmCn376CZUrG60Vr1TCz88PPj4+JVIk0ZOW15Ko753xgO3t+8g92M63fTzU1dIB9zqFDgqGYJCSkS21RWnSTc7hHAWqaGJjU+Hu7ig9dnS0xYwZHbF27SnMn98Fzz9fl7suExE9QYUOC506dQIAREREoHr16vxjTeVaSpb+nf3cS6I6bAjRBwW5HApXJ8i1cfrehGoPX+QHTSvU/S3NScjNMEeBqCK4cSMBs2cfwP/+dxHh4aPg5+cqHXv77RZ4550WsLHhrstERE9aocLChQsX0LBhQ8jlciQmJuLvv//O89zGjRsXW3FET5ph+FFsWiwAwN3BHdsTOiFmw3fQZb4Pbar+PIWDDrWD/zO9+JWv8+xVyN2LkLsHwVudMwdIZWfDicxUYdy/n4wFCw5j3brTyMrSAQBmzz6IjRufl84x3nCNiIierEKFhaZNmyIqKgqenp5o2rQpZDKZxSXqZDIZsrOzLdyBqGwwDD9qG65Dv8M6OGdF467mW7Pz5DZZpg0FBIX8ehE4J4EqogcP0rB06TGsWHESqak5v0+VKtmjQQMPK1ZGRETGChUWIiIi4OHhIX1OVJ5IG6mlpODDtBjohA5u0lYiprMTFCrol0FtYQs4+wB2TgWueJR7ZSNDLwJ7EKgiSknJxKpVJ7F48TEkJOT0sDk62mLcuLaYOLEdXF3t87kDERE9SYUKC35+fhY/JyrrNKGhuDt2nPS4koVzFA7ZkNvq9Muhrgkv0v13XYjEtRiubEQEAP/8E41nntmE+/dzfieUShuMGNECU6d2gJeXkxWrIyIiS4o8EPTrr7/Grl27pMeTJ0+Gq6sr2rVrh5s3bxZrcUQlLWbVapPHcc76j0S1AsqAAPh2t0ft5++jZs8YqOsV/YWMca8CVzaiiq5OHTc4ONgCAORyGQYPborLl0dj5coeDApERKVUkcNCSEgIHBwcAADHjx/HJ598gsWLF8Pd3R3jxo0r4Gqi0kWXkvMO57IX5Bj1nhKLp9WG5n/LUPO3XTmrHAGFXunIIHevAlc2oopECIFz5yJN2pRKG8yd2xkvvVQP//zzLjZufN5k1SMiIip9Cr10qsHt27dRq1YtAMCOHTvw8ssv4+2330b79u3RuXPn4q6PqEQY5iloY2IA6HsTTgbK4f9wd2YAwMXtQNI9/efOPkXeP8E4KLBXgSqSP/64jqlT9+LMmUhcvDgSgYHu0rE33miCN95oYsXqiIioKIocFpycnBAXF4fq1atj9+7dUm+Cvb090tLSir1AouJgPIkZgNnuy2lK/X9HNx2d07g/JOdzu8IPkcgdFAD2KlDFcPLkHUydug/79uUshDFjxn788MMrVqyKiIgeR5HDwrPPPou33noLzZo1w+XLl9GrVy8AwMWLF1GjRo3iro/oseWexJzbHTfg+45y+Lv4I7hGsL7x4nYg9nLOSUUYgmTYS0EuA/zdVVzxiMq9f/6JxvTp+/Dzz6YrfzVq5Ik33+TeO0REZVmRw8KaNWswffp03L59Gz/99BPc3NwAAGfOnMGrr75a7AUSPQ5LQUHh5YX07HTEy1Kx5WmB44H69mWGXoWL24EfBudc4F6nSEOQDBuueTrbY++Ezo/3BRCVYtevP8CsWQewZcsFGG+9U7NmJcydG4QBAxpCLpdZr0AiInpsRQ4Lrq6u+OSTT8za58yZUywFERWn3KsdxUwbjKXORxGRGGfSLvUq5A4KQKF6FSzNU1DZ2Txy3USl3cmTd/D00xuh1eqkNh8fZ8yc2RFDhzaDrS1//omIyoMihwUASEhIwPr16xEeHg6ZTIZ69eph2LBhcHFxKe76iB6L8WpHvitW4IN0/Q7Nxvxd/HPmKhjPUwDy3ZnZGOcpUEXTsqUPateujPDwWFSu7IApU57GqFGtpKVRiYiofChyWDh9+jS6desGBwcHtG7dGkIIfPzxxwgJCcHu3bvRvHnzkqiT6LEovLxwPBCIOKgPCnKZHH5qP4xuOjrveQqFCAqGHoWI2JSH9+U8BSp/kpMzERp6FS+/XF9qs7GRY/HiZ3Hq1F2MH/8UXFy46zIRUXkkE8J4pGnBOnTogFq1auGLL76AQqHPGlqtFm+99RauX7+OQ4cOlUihxUWj0cDFxQWJiYlQq9XWLodKiGH1o4wbEZDpBB6o5XhnVM62Iv4u/jlLpAKW5ymMPlXg83RddsBsiVTOU6DyIiNDi88+O4MFCw4jOjoFZ868jebNGYKJiEqrknid+0g9C8ZBAQAUCgUmT56Mli1bFktRRI/KEBIyr18HABimVqbY6mC8B6HJEqmPOE8BsLzyEVFZp9Xq8M03f2H27IO4dStRap8xYz927XrNipUREdGTVuSwoFarcevWLQQGBpq03759G87OzsVWGFFR5A4JxgxLo3o6ekJlqzIfepQ7KBRynoIxrnxE5YEQAj/9FI4ZM/bjv/9iTY7169cAc+d2tk5hRERkNUUOC/3798ewYcOwdOlStGvXDjKZDEeOHMGkSZO4dCpZjaWgYAgJJwPlWNZpWU5AMHjEoGCYp5CSkY3opPTHL57IyoQQ2LMnZ9dlYz161ML8+V04/IiIqIIqclhYunQpZDIZ3nzzTWi1WgCAra0t3n33XSxatKjYCyQqiCY0VAoKOhlwr3JOSPB38ccy454EQB8S9oeYTmYGCh0URm09a9bOZVKpLNu//wa6ddts0vb009UREtIFHTr4WakqIiIqDYo8wdkgNTUV165dgxACtWrVgqOjY3HXViI4wbn8udazlxQW7rgB49/WZ+BC9yYABQYFS/soAIC32h4qOxuufkRlmhACrVp9gTNnItG0qTdCQrqge/dakMm4oRoRUVli1QnOqampmDRpEnbs2IGsrCw888wzWLVqFdzd3YulEKJHlaqJk36Qf+iokPZNMAsKgPk+Cu519JOZcwUF46FGAKRdmY2tfb05AwKVOVevxmP79nBMmtReapPJZPj44264dy8Jr7zSgLsuExGRpNBhYdasWfjqq6/w+uuvw97eHt9++y3effdd/PDDDyVZH1GeDm36COKLLXCPywIAxDkDUW1yLYlqrAj7KFjqRTCo6cF9FKjsuXtXg7lzD2L9+nPIzhZo27aqyRAjDjciIiJLCh0Wtm3bhvXr12PAgAEAgIEDB6J9+/bIzs6GjQ3Ha9OTE3YjDGvOr8F7n11B1bic9jRlriVRDSzNUXCvk2dQ2HUhUgoKcpl+pSMAHG5EZVJsbCoWLTqCNWtOIT1dK7UvXXqcAYGIiApU6LBw+/ZtdOjQQXrcunVrKBQK3Lt3D9WqVSuR4ogsWXN+DSISI+CQqX+skwGx7rawf3sgOhR2jkIe+yjknsDs785N1qhsSkrKwMcfn8DSpceQlJQptTs7KzFxYjuMG9fWitUREVFZUeiwkJ2dDaVSaXqxQiGtiET0pKRkpaBtuA5uSfrHOjcXdDp8Qh8MPmkFZCTnnJx0z/TiPOYoGCzfc8nkMTdZo7ImPV2LTz89hZCQI4iNTZXa7e0VGD26FT744Gm4u5eNBSmIiMj6Ch0WhBAYPHgw7OzspLb09HSMGDECKpVKatu2bVvxVkhkJOxGGALORGH8Dp3U5qh2039iaTlUY4VY8ch4ngInMFNZdOTILYwfv1t6bGMjw1tvNceMGR3h68sV4IiIqGgKHRYGDRpk1jZw4MBiLYYoL4Ydml1ib2G8RmdyzGPMGP0nhh4FmRxw8s45wc4p394ES8ui1vRQMShQmdS1qz86dfLDwYM38eqrDTF3bhBq1aps7bKIiKiMeuR9Fsoq7rNQdhgCgi4lBdr79y2e4zvxVajTf9EHheQoQOgAZx9gQnihn6frsgNmKx+xV4FKOyEEQkOvYufOS1i7tpfJnggXLtyHEAJNmnjncwciIipvrLrPAtGTUJiAEOcMZNvbotmgl6G+s8z8BDunQj2XoUchIjZn5SN/dy6LSqXf4cM3MXXqPhw5cgsA8PzzgejevZZ0vHFjL2uVRkRE5QzDApUqMatWS7sxG4tz1i+N+n1HOU4GyrFMozUPCs4+OUOO8pHXbsxc+YhKu3PnIjFt2j78/vtVk/bvvvvHJCwQEREVF4YFKlV0KQ9fwMvlSHSSI8lWKwUET0dPqJJisOx+NIJT00wvLGDysjFLQcGw0RpRaXT5chxmzNiP//3vokl7YKA75s8Pwosv1rNSZUREVN4xLFCpoQkNlYYeaSs7Y/jwFBh+RJd1WobglJScPRMMk5gLmLxsjMOOqKy5c0eD2bMP4KuvziM7O2d6WfXqLpg9uxPeeKMJFAq5FSskIqLyjmGBSo2YVaulz+NlOevD+2sFgn9633TPBLdawOhThb537s3WAA47otLv5s0ErF9/Tnrs6anCtGkd8M47LWBnxz/fRERU8h7pLalvvvkG7du3h4+PD27evAkAWLFiBX7++ediLY4qDk1oqMlchZ1dciYpj46LNd9crYB5Cbnl3myNw46oLGjfvjp69aoNFxc7zJ8fhGvXxmDMmDYMCkRE9MQUOSx8+umnGD9+PHr27ImEhARkZ2cDAFxdXbFixYriro8qCONehWgPW/xRUz9UyFOrRXBahn7ysrOPfgfmIsxPACxvtrZ3QmcOPaJSIy0tC0uXHkNw8DfQ6UxXs167theuX38f06Z1hJOT0koVEhFRRVXkt6dWr16NL774An379sWiRYuk9pYtW2LixInFWhxVDLl7Fb55Ohs6oc+xKp3Qz00owr4JhrkJKRn6IBulSZeOcbM1Kk2ysrKxYcM5zJ17CPfuJQEAfvrpX7zySgPpnOrVXaxVHhERUdHDQkREBJo1a2bWbmdnh5SUFAtXEOVNExqKu2PHSY/vuAEnA/VBwT8zC6MfJACO1Qp1r7yWRDXGoUdUGuh0At999w9mztyPa9ceSO0yGfDXX/dNwgIREZE1FTks+Pv74/z58/Dz8zNp//3331G/fv1iK4wqhhvLFsLW6PEPHeRSSAhOTdMPO7IwPyF37wFg2oNg4K22BwCo7Gy46hFZnRACv/56GdOm7cPff0ebHHv++bqYP78LGjb0tFJ1RERE5oocFiZNmoRRo0YhPT0dQgj8+eef+Pbbb7Fw4UJ8+eWXJVEjlVOHNn0Ej9s5L5iWvSBHdEA2dt6N1DfkMTfB0spGuRkmMDMcUGlx7lwkRo/+HceO3TZp79LFHyEhXdCmTVUrVUZERJS3IoeFIUOGQKvVYvLkyUhNTcVrr70GX19frFy5EgMGDCiJGqkc0oSGwiPkK+nxHTcgOiBbP+wI0Pco5AoKeQ0zMvQeAOxBoNLNOCi0auWDkJCueOaZACtWRERElD+ZEEIUfJplsbGx0Ol08PR89G7ztWvXYsmSJYiMjESDBg2wYsUKdOjQocDrjh49ik6dOqFhw4Y4f/58oZ9Po9HAxcUFiYmJUKvVj1w3PZ4Lz3aCrVGvQmxQMjp4aXJOsNCr0HXZAbOgsPb15gwGVCplZGjNljgdMOBH/P13NBYs6ILnn68LmUxmpeqIiKg8KonXuY+19ae7u/tjBYXvv/8eY8eOxbRp03Du3Dl06NABPXr0wK1bt/K9LjExEW+++Sa6du36yM9N1pWaGC99vrmPKDAoGC9/KpfphxkxKFBpdPNmAoYM+RktW34BrVZncmzdut64cGEE+vYNZFAgIqIyocg9C/7+/vn+I3fdaAnMgrRp0wbNmzfHp59+KrXVq1cPffv2xcKFC/O8bsCAAahduzZsbGywY8cO9iyUMWE3wqB6ZSzckoA4ZyD1lQf6ycxAnvMUjHsVanpw52Uqfe7fT8aCBYexbt1pZGXpQ8LGjc9j8OCm1i2MiIgqjJJ4nVvkOQtjx441eZyVlYVz584hNDQUkyZNKvR9MjMzcebMGXz44Ycm7cHBwTh27Fie123cuBHXrl3D5s2bMX/+/AKfJyMjAxkZGdJjjUaTz9lU0sJuhGHiwYkwxEOFEPkGBcM8hYjYnOFHXP6USpOEhHQsWXIUK1acRGpqltTu6mqPzMzsfK4kIiIq/YocFt5//32L7WvWrMHp06cLfZ/Y2FhkZ2fDy8vLpN3LywtRUVEWr7ly5Qo+/PBDHD58GApF4UpfuHAh5syZU+i6qGQd2rQIy8O0qJSsf6wy7FZrYUIzALMJzdxUjUqL1NQsrFp1Eh99dBQJCTnL9jo62uL999tg0qR2qFTJwYoVEhERPb4ih4W89OjRA1OmTMHGjRuLdF3uIU1CCIvDnLKzs/Haa69hzpw5qFOnTqHvP2XKFIwfP156rNFoUK1a4Tb5oscTdiMMa86vQUpWCpr9k4Zee5MwMFZrco6j4uE7r3nspWA8T8HfXcVeBSoVdu++hkGDdiAqKllqs7WV4513WmDatI7w9nayYnVERETFp9jCwo8//ojKlSsX+nx3d3fY2NiY9SJER0eb9TYAQFJSEk6fPo1z585h9OjRAACdTgchBBQKBXbv3o0uXbqYXWdnZwc7O7sifjX0uMJuhOHHdePx3mEdHDIBtyTzc5TqLHi0cwZeWZpnr4KBvzvnKVDpUaOGK2IMQVYuwxtvNMbs2Z1Ro4ardQsjIiIqZkUOC82aNTN5518IgaioKMTExGDt2rWFvo9SqUSLFi2wZ88evPDCC1L7nj178Pzzz5udr1ar8ffff5u0rV27Fvv27cOPP/4If3//on4pVILWnF+D9w7rUDXO/Fh0ZQF5kxR0GL3aYkgATHsVAM5TIOsRQuDevST4+uZMFKtTxw1DhjRFfHw65s0LQv36HlaskIiIqOQUOSz07dvX5LFcLoeHhwc6d+6MwMDAIt1r/PjxeOONN9CyZUs89dRT+Pzzz3Hr1i2MGDECgH4I0d27d7Fp0ybI5XI0bNjQ5HpPT0/Y29ubtZN1GA87qnk2WgoKQiZga6+D3FYHj0ZJqNesOhC0KM/dmTlPgUqLffsiMHXqXkRFJePSpdEm+yasW9cbNjaPtfo0ERFRqVeksKDValGjRg1069YN3t7ej/3k/fv3R1xcHObOnYvIyEg0bNgQv/32G/z8/AAAkZGRBe65QNZnCAkRiRFS24eHcuYm2DlrUbNnjH4Sc5DpkCNDOEjJ0M9diNLkTBQ1YK8CPWl//nkXU6fuxd69OT/Tn39+Bu+910Z6zKBAREQVQZH3WXB0dER4eLj0gr6s4T4Lxe+5Hc+ZBIW24TqM35GzGZVv+3iox39mcVnUUVvP5nnfmh76Cc3sVaAn5eLFaEyfvh87dvxn0t6woSeWLn0W3brVslJlREREBSsV+yy0adMG586dK7NhgYpfSpZ+yNBT/wGvH5HBMyYnKCjVWWZBwdJQIwDwVtsDAFR2NgwJ9ERFRDzArFkHsHnzBRi/fRIQUAlz53bGgAEN2ZNAREQVUpHDwsiRIzFhwgTcuXMHLVq0gEqlMjneuHHjYiuOSr+wG2GITo0GALx6UAvPeNPjHu2czXoULAWFta83Zzggq9i69W8MHrxD2nUZAKpUccLMmZ0wbFgz2NraWLE6IiIi6yp0WBg6dChWrFiB/v37AwDGjBkjHZPJZNL+CNnZ3LG0wri4HWtOzgIevpZyyBQAZIBMQOmshUejJKiHf2Z2mWF+gvHeCQwKZC1PP11dWuGtUiV7TJnyNEaNag1HR1srV0ZERGR9hQ4LX3/9NRYtWoSIiIiCT6YKIezwPEQ46MdstA3XwSVZ/4JL4ShDzTcrmU1mBvRDkAyTmD2d7bl3Aj1RKSmZ+O+/WLRo4SO1Va/ugkmT2kEmAyZObAcXF3srVkhERFS6FDosGOZBc65CxWa8PGq0QxYA8wnNcq8AYPQui9cbb7SmsuPwDnoyMjK0+PzzM1iw4DAA4Nq1MVCplNLx+fPNN3QkIiIioEgz9ow3Y6OKybBEqmGeAgD0O6wzOcfDaIhaboYhSACXRKWSl52tw1dfnUfdup9gzJhQ3L+fgvv3U7Bq1Ulrl0ZERFQmFGmCc506dQoMDPHx8fkep7Ir7EaYtESqXADu2VqodALeGTk/E74rVkDdvVuB9/JW23OeApUYIQS2bQvHjBn7ER4ea3LslVfq48UX61mpMiIiorKlSGFhzpw5cHFxKalaqJRbc36N9LlftsDO2/cAAFeUgdBCA4WXl8WgYLzxWnSS+aZrRMVFCIE9e65j6tS9OHMm0uRY9+61sGBBFzRvzpBKRERUWEUKCwMGDICnp2dJ1UKlnGE/BQAYHRcLzS17xPxbGVpNcr7XWVoqlfMVqCSsXv0n3n8/1KStfftqCAnpio4dOd+KiIioqAo9Z4HzFSo24/0UPHVAcGoaYv5xRmYCAJ1+zoI8154bgL5XwRAU5DL98CPDzsxExW3AgIZQqfRLnjZp4oVdu17D4cNDGBSIiIgeUZFXQ6KKyXgIkkqrXwVJl/Uwa8rlUNaoYXFis/HqR/7uKi6VSsXm2rV4/PtvDPr0yQmenp4qfPTRM3Bzc0S/fg0gl/NNDiIiosdR6LCg0+kKPonKJcPE5rbhOvQ7rIN3msAV4QVtun4okcLDAzV/M10q1TBPISI2Z/gRexOoONy9q8G8eYewfv05ODkpERHxPlxdc/ZGGDWqtRWrIyIiKl+KtHQqVTxhN8Iw8eBEAPolUqvGAYpUObRpNsDDzqbcw492XYjEqK1ncS0mBbqH59T0UHH1I3oscXGpmDRpN2rVWo3PPjsDrVaHhIR0rFx5wtqlERERlVtFmuBMFY/x8COHzIefyAQUlVwAWwfIVSqz4UfGQ48AcI4CPZakpAysWHECS5ceh0aTIbU7OSkxYcJTGDfuKStWR0REVL4xLFCejPdVaBuug1uSvl1RyQW1j+W9qZXxxmtrX2/OHgV6JOnpWqxbdxohIYcRE5MqtdvZ2WDUqFaYMqUD3N0drVghERFR+cewQBYZDz9qG67D+B05c1bkru55XrfrQiSiNPq9FLjxGj2O5cuPY9q0fdJjGxsZhg5thpkzO6FqVbUVKyMiIqo4OGeBLDIeftTvsOnkdkurHgE5cxUMuJcCPY6RI1uhUiX9xOVXX22I8PBR+PzzPgwKRERETxB7FsgiwwZsbcP1k5oNfFessLhLM2A+V4HzFKgwhBAIDb2K27c1ePvtFlK7q6s9vvzyOdSsWQlNmnhbsUIiIqKKi2GBzBg2YMs9/EjpijyDgvHmawDnKlDhHDlyC1On7sXhw7egUtni+efrwsvLSTr+4ov1rFgdERERcRgSmTEMQTIbftTC0tnmw4+4TCoV5Pz5KPTqtRUdOmzE4cO3AAApKVnYuPG8dQsjIiIiE+xZIACAJjQUMatWQ5eSgg/TYqATOlRKzjnu2z4e6oDKFq/l8CMqrMuX4zBz5n58//1Fk/a6dd0wf34X9iQQERGVMgwLBE1oKO6OHSc9rpTruFKdBXW1dMDOyaTd0i7NHH5Elty+nYi5cw9i48bzyM4WUnu1amrMnt0Zb77ZBAoFOzqJiIhKG4YFQsyq1SaPH6jl0Akd5ALwkmfBo9HDDRaCpknn5B56BHD4EeVty5a/8eWX56THHh6OmDatA0aMaAk7O/4ZIiIiKq34rzRBl5LTMxAzbTBG6TYDkMNTq8Xe2zH6A698jV3ZbbB82QGkZGRLeykYcJdmys9777XGihUnkJamxaRJ7fD++23g7Gxn7bKIiIioAAwLFZwmNBTa+/cBAAovLyxVhgIPc4BKpx8uclNeFf1/dkaU5qzFe3DoERmkpWVh7dpTSE7OxKxZnaV2lUqJbdv6o25dN7i5cddlIiKisoJhoYIzHoIkV6mQnHILeLiX2ugHCQCARekvIUpn2pPgrbaHys4GE4LrMigQsrKysXHjecydexB37yZBqbTB4MFN4efnKp3Trl016xVIREREj4RhoQLThIYi8/p16bHHmDEQd8fDMAQpILkS3tW+jd91bQAwIJA5nU7g++//wcyZB3D1arzUnpWVjT17ruOtt5pbsToiIiJ6XAwLFZhxr4IyIEC/4dp6/WMd5BhovwoAUJMBgXIRQmDXriuYNm0fLly4b3Ls+efrYt68IDRq5GWl6oiIiKi4MCxUYMYTm9f6d0XcsrcR656zfOWJqV2tURaVckeP3sLkyX/g2LHbJu1BQTUQEtIVbdtWtVJlREREVNwYFiogwwZs2hj9Skex9i742ScDDu7HpXPshcxa5VEpd+5clElQaNXKByEhXdG1qz9kMv7cEBERlSfcBakCilm1Wj9XQacDAKQq7KD02GNyTl+3PtYojUohIYTJ4+HDm8PPzwX163tg27Z+OHnyLTzzTACDAhERUTnEnoUKxnhSczZkSHR2gn/DaHjZyBH7MDsu02QjeNBCa5ZJpcDNmwmYM+cgbG3l+OyznPBoZ6fAvn2D4OfnAhsbvt9ARERUnjEsVDDGk5rvqdVYPjwZWXIg3kb/rrCnVotgmZO1yqNSIDo6BQsWHMK6dWeQmZkNuVyG8eOfQt267tI5AQGVrFghERERPSkMCxWM8aTm/3VNwl2lHMY/BiqZLRA0zQqVkbUlJqZjyZJjWLHiBFJSsqR2tdoO4eGxJmGBiIiIKgaGhQomPTsdCgBxzsDJwJwhJJ46QFXJH6ObjgZqBFuvQHriUlOz8Mknf2LRoiN48CBn8z1HR1u8/34bTJrUDpUqOVixQiIiIrIWhoWK4uJ2aL6YC0Ws6U7M/plZGJ3tiOAOM4EGfa1TG1nNd9/9g/HjwxAZmSy12drK8c47LTBtWkd4e3NIGhERUUXGsFBBaL6Yi7uhOUEhTQksux+D4F7rGBIqsPR0rRQUZDLgjTeaYPbsTvD355wEIiIi4tKpFUb08TSTx7+1l8O90UIGhQpECIHk5EyTtoEDG6NePXe88EIg/v77XXz9dV8GBSIiIpKwZ6Gc2nUhEsv3XEJKRjYGR3yHTok5a+Ave0GO07Wq4OMeQ6xYIT1J+/ZFYOrUvfDzc8X3378stSsUcvz553A4OSmtWB0RERGVVuxZKId2XYjEqK1ncS0mBf3+24xOh49Ix+646Sc2D6g93IoV0pPy55938eyz36Br1004efIu/ve/izh3LtLkHAYFIiIiygt7Fsqh5XsuSZ93vnTC5Nj3HeVY1mkZgrniUbl28WI0ZszYj+3b/zNpb9jQE2lpWitVRURERGUNw0I5Yhh6FBGbAoXzBXR78CMcEnOOL3tBjug2NRkUyrGIiAeYPfsgvvnmLwiR0x4QUAlz53bGgAENuesyERERFRrDQjmyfM8l3Ew/Dnv/PbCxi0Hf33LeQb7jBkS3qanfR4HKpcWLj2L69H3IytJJbVWqOGHGjI4YNqw5lEobK1ZHREREZRHDQjmx60IkbqYfh0PVrQCAtuE6VI3LOW7/Sgfs7Pu5laqjJyEgoJIUFCpVsseHHz6N0aNbw9HR1sqVERERUVnFsFBOhBz8TgoKANDvcM67y0pXoMNYBoXyJCUlEwkJ6fD1VUttL71UD506+aFDh+qYMKEdXF3trVghlRXZ2dnIysqydhlERFQItra2sLF5siMFGBbKgYUHv4dGvUF6nLtXwaMtXzSWF5mZ2fj88zOYP/8QWrTwwa5dr0nHZDIZ9u8fBJlMls8diPSEEIiKikJCQoK1SyEioiJwdXWFt7f3E/v3nmGhDAu7EYaPTqxETMZtqa1tuA7jdxj1KqizoB6+0BrlUTHKztZh8+YLmD37IG7cSAAA/PbbFRw5cgtPP11dOo9BgQrLEBQ8PT3h6OjInx0iolJOCIHU1FRER0cDAKpUqfJEnpdhoYwKuxGGiQcnmrW/tz8LQE73lMfbg7hLcxkmhMD27f9h+vR9CA+PNTn28sv14e3tZKXKqCzLzs6WgoKbm5u1yyEiokJycHAAAERHR8PT0/OJDEliWCij1pxfY/LYPzMLk86mwDYx58Wj74oVUHfv9qRLo2IghMAff1zH1Kn7cPr0PZNj3bvXwvz5QWjRwsdK1VFZZ5ij4OjoaOVKiIioqAx/u7OyshgWKG9xqUnS58vuxyA4NQ3Xzngg82GbMiCAQaEMGzPmd3zyySmTtnbtqmHhwq7o2NHPSlVRecOhR0REZc+T/tvN3ZnKoDO/bYRtWgwAwFOrRXBqGgBAl62UzvEYM8YqtVHx6NWrjvR5kyZe+PXXV3HkyBAGBSIiInqiGBbKmDO/bUSLP8fCBtmmB175GnDyBAAovLzYq1CGXLsWj4sXo03aunWricGDm+Lbb1/C2bPvoFevOnwXmIjydeDAAchkssde4apGjRpYsWJFka4ZPHgw+vbt+1jPa22XLl2Ct7c3kpKSCj65jPn111/RrFkz6HS6As/NzMxErVq1cPTo0SdQWcVSlP8PpQnDQhnjfnqpyWMhU+iDAicxlzn37iXh3Xd/RWDgGowc+RuEENIxmUyGjRufx4ABDSGXMyQQGbt9+zaGDRsGHx8fKJVK+Pn54f3330dcXFzBF1OpcOPGDchkMpw/f/6JPu9XX30FV1dXi8emTZuGUaNGwdnZGUBO+GrYsCGys03foHN1dcVXX331WLXIZDLpw9nZGS1btsS2bdtMztFoNJg2bRoCAwNhb28Pb29vPPPMM9i2bZvJvxkAsHXrVtjY2GDEiBFmz9W7d2/IZDJs3brV7Fhun3/+Ofz8/NC+ffvH+vpKs7///hudOnWCg4MDfH19MXfuXLPvpzHDz4Klj1OnTpmdHxcXh6pVq5qF96L8fyhNGBbKkF0XIqHMTjVpkzl7MiiUMXFxqZg8eQ9q1lyFdevOQKvV4dChm9i9+5q1SyMq9a5fv46WLVvi8uXL+Pbbb3H16lWsW7cOe/fuxVNPPYX4+PgSfX5uYFc+3blzBzt37sSQIUPMjl27dg2bNm0qkefduHEjIiMjcerUKTRp0gSvvPIKjh8/DgBISEhAu3btsGnTJkyZMgVnz57FoUOH0L9/f0yePBmJiYkm99qwYQMmT56M7777DqmpqWbPNWTIEPy/vTuP6yn7/wD+avt8+lTaV5UWaSNEJEZhEFliZiwjkiFj30ZjG5Wxf2kZjGVIdmISZqyRJXsqy1SiJFtJpNKqPu/fH/26XJ9PKUtZzvPxuI+He+65977vPbePe+45596VK1e+NaaVK1di1KhR73Vcn/LfSV5eHrp164aGDRsiJiYGK1euxPLlyxEYGFjlOu3bt0dGRgZvGjVqFExNTeHg4CCRf+TIkWjevLnUbdW0HD4lrLLwGblyaCOuKxehr6EBst8Y/Z535AjKHj+up8iYmnjxohQLFpyBufkKLFt2HsXFZQAAFRUB/Pxc4ORkXM8RMsynb/z48RAIBDh27BhcXFzQqFEj9OzZE8ePH8fDhw8xZ84cAMCsWbPQrl07ifWbN28OPz8/bj40NBQ2NjZQVFSEtbU1Vq9ezS2rfPq9e/dudOrUCYqKiti2bRvS09PRp08faGhoQFlZGU2bNsWhQ4cAVLyWduTIkTAzM4NIJIKVlRX++OMPXgyVXXYWLVoEPT09qKurY968eSgrK4OPjw80NTVhZGSEjRs3SsSya9cutG/fHoqKimjatClOnTpV7fk6f/48nJ2dIRKJYGxsjEmTJqGgoIBbnpWVhT59+kAkEsHMzAzbt29/axmUl5dj2rRpUFdXh5aWFn799VeJp7JHjhzBN998w+Xp3bs3UlNfPRAxMzMDANjb20NGRgadOnUCAMTExKBbt27Q1taGmpoaXFxcEBcXx9u2v78/GjVqBKFQiIYNG2LSa2P0SktL8euvv8LQ0BDKyspwdHTkztGpU6cwYsQI5Obmck+F/f39AQC7d+9GixYtYGRkJHG8EydOhJ+fH4qLi6s8J/fu3YO7uztUVFSgqqqKgQMH4nEN/k+u/LiWtbU11q5dC0VFRRw4cAAAMHv2bNy9exeXLl3C8OHDYWtrC0tLS3h7e+Pq1atQUXn19sO7d+/i/PnzmDlzJqytrfH3339L7Ktv3764fPky7ty5U2U8cXFxSElJQa9evXjpM2bMgKWlJZSUlGBubo65c+fyKgT+/v5o2bIlNm7cCHNzcwiFQhARcnNzMXr0aOjq6kJVVRVdunTBtWvXuPVSU1Ph7u4OPT09qKiooE2bNjh+/Phbz9v72L59O4qLi7Fp0yY0a9YM3333HWbPno3AwMAqWxcEAgH09fW5SUtLCwcOHMBPP/0k0UV4zZo1eP78OaZPl3y9PVCzcvjUsMrCZ2JNxCzEam3FdD0dpAkUIP7/i1NZQRl5R47g4ZSpXF5ZZeX6CpORori4DH/8cRHm5n9g7tyTyMsrAQAIhXKYNq0d7tyZBH//TlBVFdZzpAzzaXv27BmOHj2KcePGce8ar6Svrw8PDw+EhYWBiODh4YFLly7xblATEhJw48YNeHh4AADWr1+POXPmYOHChUhKSsKiRYswd+5cbN68mbftGTNmYNKkSUhKSoKrqyvGjx+PkpISnDlzBjdu3MDSpUu5GzexWAwjIyPs3r0biYmJ8PX1xezZs7F7927eNqOiovDo0SOcOXMGgYGB8Pf3R+/evaGhoYFLly5hzJgxGDNmDO7fv89bz8fHB7/88gvi4+PRvn179O3bt8ruVzdu3ICrqyu+++47XL9+HWFhYTh79iwmTJjA5fHy8sLdu3cRFRWFv//+G6tXr+Y++FSVgIAAbNy4ESEhITh79iyePXuGiIgIXp6CggJMmzYNMTExOHHiBGRlZdG/f3+ur/bly5cBAMePH0dGRgbX/SY/Px/Dhw9HdHQ0Ll68iCZNmsDNzY0bR/D3338jKCgI69atw+3bt7Fv3z7Y2dlx+x0xYgTOnTuHXbt24fr16xgwYAB69OiB27dvo3379ggODoaqqir3dLjyhu7MmTNSnxADwJQpU1BWVoZVq1ZJXU5E6NevH549e4bTp08jMjISqampGDRoULXn8U0KCgqQl5fHy5cvIRaLsWvXLnh4eKBhQ8nXZKuoqEBe/tULLTdu3IhevXpBTU0NQ4cORUhIiMQ6JiYm0NXVRXR0dJUxnDlzBpaWllBVVeWlN2jQAJs2bUJiYiL++OMPrF+/HkFBQbw8KSkp2L17N8LDw7nuZb169UJmZiYOHTqE2NhYtGrVCt9++y3XAvjixQu4ubnh+PHjiI+Ph6urK/r06YN79+5VGWN0dDRUVFSqnRYtWlTl+hcuXICLiwuEwlf/57q6uuLRo0e4e/duleu97sCBA8jOzoaXlxcvPTExEb///ju2bNkCWVnpt9g1KYdPDXt16ifs4PUMBEYmo6CkHOo6/+ChQIG33EzNDD55HfBw5lReOnsT0qfFy2sfwsISuHk5ORn89JM95s51hrGxWj1GxjB8fVaexZP8kjrfr04DIf6Z+M1b892+fRtEBBsbG6nLbWxskJOTgydPnqBZs2Zo3rw5duzYgblz5wKoeKLYpk0bWFpWvG1s/vz5CAgIwHfffQeg4ml3YmIi1q1bh+HDh3PbnTJlCpcHqHiK/P3333M3qebm5twyBQUFzJs3j5s3MzPD+fPnsXv3bgwcOJBL19TUxIoVKyArKwsrKyv873//Q2FhIWbPng2gomVkyZIlOHfuHAYPHsytN2HCBHz//fcAKp5gHjlyBCEhIfj1118lzseyZcswZMgQTJkyBQDQpEkTrFixAi4uLlizZg3u3buHw4cP4+LFi3B0dAQAhISEVHl+KwUHB2PWrFlcHGvXrsXRo0d5eSqXVQoJCYGuri4SExPRrFkz6OjoAAC0tLSgr6/P5evSpQtvvXXr1kFDQwOnT59G7969ce/ePa7fvoKCAho1aoS2bdsCqHhKvXPnTjx48IC7wZ4+fTqOHDmC0NBQLFq0CGpqapCRkeHtE6h4Mt+6dWupx6ukpAQ/Pz/Mnj0b3t7eUFPj/24fP34c169fR1paGoyNK1qIt27diqZNmyImJgZt2rSp9nwCQElJCZYtW4a8vDx8++23yM7ORk5ODqytrd+6rlgsxqZNm7iuLYMHD8a0adOQkpICCwsLXl5DQ8Nqb4jv3r0rtXLy22+/cf82NTXFL7/8grCwMN51V1paiq1bt3JlGxUVhRs3biArK4u7MV++fDn27duHv//+G6NHj0aLFi3QokULbhsLFixAREQEDhw4wKvUvs7BweGtY100NTWrXJaZmQlTU1Nemp6eHresstWrOiEhIXB1deXKG6gowx9//BHLli1Do0aNqm05eFs5fGpYZeETFhiZjNQnBZBvcB0FgoqWBFki6MlrYfo3c9DupliiosA+xPbpmTChLVdZGDy4GebN6wRLS/bVXObT8yS/BJl5VXe1+NRVdiGo7Bbg4eGBjRs3Yu7cuSAi7Ny5k7txfvLkCTdQ2tvbm9tGWVmZxM3gm0+cJ02ahLFjx+LYsWPo2rUrvv/+e17/5LVr12LDhg1IT09HUVERSktL0bJlS942mjZtynvyqKenh2bNmnHzcnJy0NLSknjK7+TkxP1bXl4eDg4OSEpKkno+YmNjkZKSwutaREQQi8VIS0vDrVu3uG1Usra2rnIAMADk5uYiIyNDahyvd+FITU3F3LlzcfHiRWRnZ3MtCvfu3eMd55uysrLg6+uLqKgoPH78GOXl5SgsLOSeNA8YMADBwcEwNzdHjx494Obmhj59+kBeXh5xcXEgIq4yWKmkpOStXyovKiqCoqJilctHjhyJwMBALF26VOKpdVJSEoyNjXk3jra2tlBXV0dSUlK1lYUff/wRcnJyKCoqgpqaGpYvX46ePXtyXZhq8ha8Y8eOoaCgAD179gQAaGtro3v37ti4caNErCKRSOp4hkpVnYe///4bwcHBSElJwYsXL1BWVibR+mBiYsJVFICK6+/FixcS576oqIhr8SsoKMC8efPw77//4tGjRygrK0NRUVG1LQsikUiiElRbb57XN387qvPgwQMcPXpUorVw1qxZsLGxwdChQ9+6jbeVw6eGVRY+UQevZyD1SQHaqIbjpuGrkfaNygn/eJ0GAKSO4/cpZBWF+kVEOHo0FaqqQrRv/+o/jW++aYTff++EPn2s0LKlfjVbYJj6pdOgfrrC1XS/FhYWkJGRQWJiotTXdN68eRMaGhrQ1tYGAAwZMgQzZ85EXFwcioqKcP/+fe4pfeXN6/r167mn6pXe/CKq8htdO0eNGgVXV1ccPHgQx44dw+LFixEQEICJEydi9+7dmDp1KgICAuDk5IQGDRpg2bJluHTpEm8bCgr8lmIZGRmpaTV5xWJVNzhisRg///wzr09/pUaNGiE5Obna9d9Hnz59YGxsjPXr16Nhw4YQi8Vo1qwZSktLq13Py8sLT548QXBwMExMTCAUCuHk5MStZ2xsjOTkZERGRuL48eMYN24cli1bhtOnT0MsFkNOTg6xsbESZfh6/35ptLW1kZOTU+VyeXl5LFiwAF5eXhJPvIlI6jmsKv11QUFB6Nq1K1RVVaGrq8ul6+joQENDo8qK4Os2btyIZ8+e8b7ILhaLER8fj/nz5/POxbNnz3g39G/S1tbGjRs3eGkXL17E4MGDMW/ePLi6ukJNTQ27du1CQEAAL9+bfydisRgGBgZSx9VUVkh9fHxw9OhRLF++HBYWFhCJRPjhhx+qvU6io6O5ilFVZs+ezbXSvUlfXx+ZmZm8tMpKeWULQ3VCQ0OhpaWFvn378tIrW1Iqx4tUVkC0tbUxZ84cXovj28rhU8MqC5+gg9czMPmfTVAyj8RN4RPesokvX/2nKn5tkBqrKNSvc+fuYfbsKJw5k47WrQ0QE+PN+09i7lyXeoyOYWqmJl2B6pOWlha6deuG1atXY+rUqbxxC5mZmdi+fTs8PT25vz0jIyM4Oztj+/btKCoqQteuXbmbAT09PRgaGuLOnTvcGIbaMDY25sYVzJo1C+vXr8fEiRMRHR2N9u3bY9y4cVze18dNvK+LFy/C2dkZQEUrSGxsbJXdNVq1aoWEhIQqn8La2NigrKwMV65c4bryJCcnV/udBjU1NRgYGEiNo1WrVgAqXhuZlJSEdevWoWPHjgCAs2fP8rYjEFR8RPTNV5JGR0dj9erVcHNzA1Dxmtzs7GxeHpFIhL59+6Jv374YP348rK2tcePGDdjb26O8vBxZWVncft8kEAgk9glUDLROTEys8riBilaNZcuW8W76gIpWhHv37uH+/ftc60JiYiJyc3Pf2qVLX19favnIyspi0KBB2Lp1K/z8/CS6BhUUFEAoFCI3Nxf79+/Hrl270LRpU265WCxGx44dcfjwYfTu3RsAUFxcjNTUVNjb21cZj729PdasWcOr6Jw7dw4mJibcywMAID09vdrjAiquv8zMTMjLy0t0+6kUHR0NLy8v9O/fH0DFGIa3dc95325ITk5OmD17NkpLS7nr8NixY2jYsGGVcVYiIoSGhsLT01Oich8eHo6ioiJuPiYmBj/99BOio6PRuHFjLr0m5fCpYZWFT1BgZDIEOpGQe6OiEFCkgO4dfQHw337EPsJWf65ezcScOVE4dOg2lxYbm4EjR1LQs2eTeoyMYb5Mq1atQvv27eHq6ooFCxbAzMwMCQkJ8PHxgaGhIRYuXMjL7+HhAX9/f5SWlkoMyPT398ekSZOgqqqKnj17oqSkBFeuXEFOTg6mTZtWZQxTpkxBz549YWlpiZycHERFRXE3hRYWFtiyZQuOHj0KMzMzbN26FTExMTXqB10Tf/75J5o0aQIbGxsEBQUhJycHP/30k9S8M2bMQLt27TB+/Hh4e3tDWVkZSUlJiIyMxMqVK2FlZYUePXrA29sbf/31F+Tl5TFlyhSJweNvmjx5MpYsWcLFERgYyKtgaGhoQEtLC3/99RcMDAxw7949zJw5k7cNXV1diEQiHDlyBEZGRlBUVISamhosLCywdetWODg4IC8vDz4+Prx4Nm3ahPLycjg6OkJJSQlbt26FSCSCiYkJtLS04OHhAU9PTwQEBMDe3h7Z2dmIioqCnZ0d3NzcYGpqihcvXuDEiRNo0aIFlJSUoKSkBFdXV4waNQrl5eUSrRKvW7JkCVxd+f/fdu3aFc2bN4eHhweCg4NRVlaGcePGwcXFpcpB0zWxaNEinDp1Co6Ojli4cCEcHBygoKCA6OhoLF68GDExMdi6dSu0tLQwYMAAiQG1vXv3RkhICFdZuHjxItdSU5XOnTujoKAACQkJXHcxCwsL3Lt3D7t27UKbNm1w8OBBiQHt0nTt2hVOTk7o168fli5dCisrKzx69AiHDh1Cv3794ODgAAsLC+zduxd9+vSBjIwM5s6d+9bWtPfthjRkyBDMmzcPXl5emD17Nm7fvo1FixbB19eXqyBdvnwZnp6eOHHiBAwNDbl1o6KikJaWhpEjR0ps9/UKAQCukmtjY8Pr2leTcvjk0FcmNzeXAFBubm59h1Ilx4XHyXZ9e2q2qRk1D21Kff6yoqMrbLjluYcPU6KVNTel9HSrx2i/TrduZdPgwX8T4M+bLC1XUljYf1ReLq7vEBmmSkVFRZSYmEhFRUX1Hco7uXv3Lnl5eZG+vj4pKCiQsbExTZw4kbKzsyXy5uTkkFAoJCUlJcrPz5dYvn37dmrZsiUJBALS0NAgZ2dn2rt3LxERpaWlEQCKj4/nrTNhwgRq3LgxCYVC0tHRoWHDhnH7Li4uJi8vL1JTUyN1dXUaO3YszZw5k1q0aMGtP3z4cHJ3d+dt08XFhSZPnsxLMzExoaCgIF4sO3bsIEdHRxIIBGRjY0MnTpzg8p88eZIAUE5ODpd2+fJl6tatG6moqJCysjI1b96cFi5cyC3PyMigXr16kVAopEaNGtGWLVt4+5Xm5cuXNHnyZFJVVSV1dXWaNm0aeXp68o4pMjKSbGxsSCgUUvPmzenUqVMEgCIiIrg869evJ2NjY5KVlSUXFxciIoqLiyMHBwcSCoXUpEkT2rNnDy+eiIgIcnR0JFVVVVJWVqZ27drR8ePHuW2WlpaSr68vmZqakoKCAunr61P//v3p+vXrXJ4xY8aQlpYWASA/Pz8iIiorKyNDQ0M6cuRIteeTiKh79+4EgEJDQ7m09PR06tu3LykrK1ODBg1owIABlJmZWeU5JCKJ8yHN8+fPaebMmdSkSRMSCASkp6dHXbt2pYiICBKLxWRnZ0fjxo2Tum54eDjJy8tzcYwePZp+/vnnavdHRDR48GCaOXMmL83Hx4e0tLRIRUWFBg0aREFBQaSmpsYt9/Pz413jlfLy8mjixInUsGFD7m/Vw8OD7t27R0QV13Xnzp1JJBKRsbExrVq1Surfwod2/fp16tixIwmFQtLX1yd/f38Si1/9v11Z9mlpabz1fvzxR2rfvn2N9lHV9VPTcqhOdb/hH+M+V4aomk/WfYHy8vKgpqaG3NxcicE5nwqHwACUaG0CAOiWleHE/UfIe26OJ7caQlxQIPE9BdYFqe48eJCH338/jY0b41Fe/upPx8hIFf7+Lhg+vCXk5dkbiZlPW3FxMdLS0mBmZlbtoE7m03H37l2YmZkhPj5eYrA082GsXr0a+/fvl3iz05fgyZMnsLa2xpUrV97aynXjxg107doVKSkp3NesmQ+jNuVQnep+wz/GfW6939WsXr2aO9jWrVtX+97ZvXv3olu3btDR0YGqqiqcnJy+uD/qo3ePchUFAFAWV9yQPrnRAKV37rCKQj2bMOEQ1q+P4yoK2tpKCApyxe3bEzFyZCtWUWAYhvlMjR49Gs7Oztw3Hb4kaWlp3P3W29jZ2eF///vfZ/Vqz89FbcrhU1KvdzZhYWGYMmUK5syZg/j4eHTs2BE9e/as8pVZZ86cQbdu3biPe3Tu3Bl9+vRBfHx8HUf+cRy9exTTT/O/+Dch5zkwYDPEZf9fVLKykNfTg8DcnFUU6oGfX8VAZVVVIX7/vRPu3JmEKVPaQVGRDf9hGIb5nMnLy2POnDlf5NP0tm3b1uojccOHD+d97I75MGpbDp+Keu2G5OjoiFatWmHNmjVcmo2NDfr164fFixfXaBtNmzbFoEGD4OvrK3V5SUkJSkpefWQoLy8PxsbGn1w3JGkVhYDHT9BdyRh5FvO5LzTL6+mhyelT9RDh16Wo6CXWrLkCKyst9OrFf2f31q3X4ObWBFpaSlWszTCfNtYNiWEY5vP11XRDKi0tRWxsLLp3785L7969O86fP1+jbYjFYuTn51f7iqzFixdDTU2Nm17/aMqnZOnFP3jzAY+foHthEfKEfbmKAgDIvvEeY+bDevmyHOvXx6JJk5X45Zdj8PGJRHk5/80Mw4a1YBUFhmEYhmG+CvVWWcjOzkZ5ebnEBzD09PQkPpZRlYCAABQUFGDgwIFV5pk1axZyc3O56f79++8V98fytPBVH8llj7PRXs4AGLAZTyIu8vLpSPm4DvP+xGLCrl3/oWnT1Rg9+l88fFhRHjdvZuPs2aq/JMkwDMMwDPMlq/eO1tI+uV2Tr0nu3LkT/v7+2L9/P++rh28SCoUQCuvnq6Q1tSZiFsRyzwFUvP3IhRpA9EvFOAxxQTCXj41R+PCICIcO3cacOVG4do0/eLxvXyvMn98ZzZu//YuODMMwDMMwX6J6qyxoa2tDTk5O6ie33/a57bCwMIwcORJ79uxB165dP2aYH19CBA5nRwCCii8BKosJImU1AOzDax/bmTPpmD37BM6d47c2depkikWLusDJ6dPsssYwDMMwDFNX6q0bkkAgQOvWrREZGclLj4yMRPv27atcb+fOnfDy8sKOHTvQq1evjx3mx3dyEQpkX7WkjCoWAp3nIO/IETZW4SNbteoyr6LQurUBjh4diqgoT1ZRYBiGYRiGQT13Q5o2bRqGDRsGBwcHODk54a+//sK9e/cwZswYABXjDR4+fIgtW7YAqKgoeHp64o8//kC7du24VgmRSAQ1NbV6O473UfQiFxBVfFpesUyEvhMvAwCe+PArQmyswoc3f35nhIcnwdJSCwsWdMZ339nUqAscwzAMwzDM16Jev7MwaNAgBAcH4/fff0fLli1x5swZHDp0CCYmJgCAjIwM3jcX1q1bh7KyMowfPx4GBgbcNHny5Po6hHeXEAGsagNB8RMuyT4ZSHXrhdsunVD62sdQ2FiF93PvXi5GjTqAbduu89KtrLRx+rQXbtwYi++/t2UVBYZh0KlTJ0yZMqW+w/ho/P393/sL0Hfv3oWMjAyuXr1aq/VMTU0RHBz8XvuubyEhIRJvcfxSTJ8+HZNq+GAyOTkZ+vr6X+QH7OpbbcqhrtT752bHjRuHu3fvoqSkBLGxsXB2duaWbdq0CadOneLmT506BSKSmDZt2lT3gb+vk4uA7FuQQ8VrOdsliTH1QP6rrzSLK9IF5uasovCOsrIKMHXqETRpshIhIfGYO/ckSkvLeXm++aYR++oyw3xGqrqZ37dvH6vwf2E2bdoEdXX1Ot+vl5cX+vXrJ5FeUlICX19fzJ07l0vz9/eHjIwM1yOi0tWrVyEjI/NeX0E+deoUZGRkuElHRwc9e/bEtWvXePlSUlIwYsQIGBkZQSgUwszMDD/++COuXLkisc3Ro0dDTk4Ou3btklj266+/IjQ0FGlpaW+Nbc6cORg/fvwX+QG7SuHh4bC1tYVQKIStrS0iIiKqzV95Lbw5Kb/WjfzNMq2cbt68yeWpTTnUFXaXVE+KXuQCAA4rKSFLXh4Do/nv8q/8SjPrflR7ubnF8PU9icaNVyA4+BJXQcjJKcKNG4/fsjbDMMy7Ky8vh1gsfntG5rMTHh4OFRUVdOzYkZeuqKiIkJAQ3Lp166PsNzk5GRkZGTh48CBycnLQo0cP5OZW3ENcuXIFrVu3xq1bt7Bu3TokJiYiIiIC1tbW+OWXX3jbKSwsRFhYGHx8fBASEiKxH11dXXTv3h1r166tNp4HDx7gwIEDGDFixHsdV2lp6Xut/zFduHABgwYNwrBhw3Dt2jUMGzYMAwcOxKVLl6pcZ/r06cjIyOBNtra2GDBggETeyjKtnJo0acItq2k51CVWWagn/8oR+hoa4Fc9bbRLEsPo6atlhsHBaHL6FBofOshaFWqhqOglli07B3PzFZg//wxevKj4IRKJ5DFzZgfcuTMZrVs3rOcoGYapC5XdbbZu3QpTU1Ooqalh8ODBvG4TBQUF8PT0hIqKCgwMDBAQECCxndLSUvz6668wNDSEsrIyHB0deS3elU+///33X+4pZHp6Ok6dOoW2bdtCWVkZ6urq6NChA9LT0wEAqampcHd3h56eHlRUVNCmTRscP36ct19TU1MsWLCAi8/ExAT79+/HkydP4O7uDhUVFdjZ2fGeHlfGsm/fPlhaWkJRURHdunV76/eFQkNDYWNjA0VFRVhbW2P16tW85ZcvX4a9vT0UFRXh4OCA+Pj4t57/rKws9OnTByKRCGZmZti+fbtEnsDAQNjZ2UFZWRnGxsYYN24cXrx4AaDiCeyIESOQm5vLPX319/cHAGzbtg0ODg5o0KAB9PX1MWTIEGRlZXHbzcnJgYeHB3R0dCASidCkSROEhoZyyx8+fIhBgwZBQ0MDWlpacHd351oA/P39sXnzZuzfv5/bb2V579q1C3379pU4DisrK3Tu3Bm//fZbtefk9OnTaNu2LYRCIQwMDDBz5kyUlZW99Vzq6upCX18fbdu2RUBAADIzM3Hx4kUQEby8vNCkSRNER0ejV69eaNy4MVq2bAk/Pz/s37+ft509e/bA1tYWs2bNwrlz56S2evTt2xc7d+6sNp7du3ejRYsWMDIy4tKePn2KH3/8EUZGRlBSUoKdnZ3Edjp16oQJEyZg2rRp0NbWRrdu3QAAiYmJcHNzg4qKCvT09DBs2DBkZ2dz6x05cgTffPMN1NXVoaWlhd69eyM1NfWt5+19BAcHo1u3bpg1axasra0xa9YsfPvtt9V2o1NRUYG+vj43PX78GImJiRg5cqRE3soyrZzk5OR4y2tSDnWp3r+z8FVKiMBWdTmk/f/rUl9vVWDdjt7N+vWx8Pc/jUePXt0IKCjIYvTo1pgzpyMMDL7cplKG+WDWuQAvst6e70NT0QV+Pv3BN5uamop9+/bh33//RU5ODgYOHIglS5Zg4cKFAAAfHx+cPHkSERER0NfXx+zZsxEbG8vr0z9ixAjcvXsXu3btQsOGDREREYEePXrgxo0b3NPAwsJCLF68GBs2bICWlhY0NTVhb28Pb29v7Ny5E6Wlpbh8+TLXTerFixdwc3PDggULoKioiM2bN6NPnz5ITk5Go0aNuH0HBQVh0aJFmDt3LoKCgjBs2DB06NABP/30E5YtW4YZM2bA09MTCQkJ3LYLCwuxcOFCbN68GQKBAOPGjcPgwYNx7tw5qedo/fr18PPzw6pVq2Bvb4/4+Hh4e3tDWVkZw4cPR0FBAXr37o0uXbpg27ZtSEtLq9E4QS8vL9y/fx9RUVEQCASYNGkS74YeAGRlZbFixQqYmpoiLS0N48aNw6+//orVq1ejffv2CA4Ohq+vL5KTkwFU3IwBFRW4+fPnw8rKCllZWZg6dSq8vLxw6NAhAMDcuXORmJiIw4cPQ1tbGykpKSgqKuLOT+fOndGxY0ecOXMG8vLyWLBgAXr06IHr169j+vTpSEpKQl5eHlfB0NTUBABER0fDw8ND6vEuWbIEbdq0QUxMDNq0aSOx/OHDh3Bzc4OXlxe2bNmCmzdvwtvbG4qKilwlqCZEIhEA4OXLl7h69SoSEhKwY8cOyMpKPvt9swtXSEgIhg4dCjU1Nbi5uSE0NBTz5s3j5Wnbti3u37+P9PR0bvzom86cOQMHBwdeWnFxMVq3bo0ZM2ZAVVUVBw8exLBhw2Bubg5HR0cu3+bNmzF27FicO3cORISMjAy4uLjA29sbgYGBKCoqwowZMzBw4EBERUUBqKjUT5s2DXZ2digoKICvry/69++Pq1evSj1uAFi0aBEWLVpU7bk8fPiwRCtRpQsXLmDq1Km8NFdX11qNudmwYQMsLS2l7sPe3h7FxcWwtbXFb7/9hs6dO/OW16Qc6hR9ZXJzcwkA5ebm1l8QKx2oywZrarapGXnPsKVEK2tuyj18pP7i+oyNGLGPAH8C/ElGxp88PSPozp1n9R0Ww3ySioqKKDExkYqKivgLllsT+anW/bTcusaxu7i40OTJkyXSIyIi6PX/0vz8/EhJSYny8vK4NB8fH3J0dCQiovz8fBIIBLRr1y5u+dOnT0kkEnHbT0lJIRkZGXr48CFvX99++y3NmjWLiIhCQ0MJAF29epW3HQB06tSpGh+Xra0trVy5kps3MTGhoUOHcvMZGRkEgObOnculXbhwgQBQRkYGL5aLFy9yeZKSkggAXbp0iTsvLVq04JYbGxvTjh07eLHMnz+fnJyciIho3bp1pKmpSQUFBdzyNWvWEACKj4+XeizJyclVxhEUFFTlOdi9ezdpaWlx86GhoaSmplZl/kqXL18mAJSfn09ERH369KERI0ZIzRsSEkJWVlYkFou5tJKSEhKJRHT06FEiIho+fDi5u7vz1svJySEAdObMGV766+dz8ODB1KVLFyIiio+PJwCUlpZGRESzZ8+W2O+ff/5JKioqVF5eLjXWkydPEgDKyckhIqLs7Gzq27cvNWjQgB4/fkxhYWEEgOLi4t56jm7dukUKCgr05MkTIqr4ezE2NpbYd+U9UnXXbosWLej3339/6z7d3Nzol19+4eZdXFyoZcuWvDxz586l7t2789Lu379PACg5OVnqdrOysggA3bhxo8p9P336lG7fvl3tVFhYWOX6CgoKtH37dl7a9u3bSSAQVLnO64qLi0lDQ4OWLl3KS7958yb99ddfFBsbS+fPn6exY8eSjIwMnT59mpfvbeVQ5W84fZz7XNayUNcSInC08D6yGugAAAadebWItSrUDBFBLCbIyb16ouDv3wk7dtyAm1sTzJ/fGU2bVv1Vb4ZhqqBST383H2m/pqamvAGYBgYG3NPt1NRUlJaWwsnJiVuuqakJKysrbj4uLg5EBEtLS952S0pKoKWlxc0LBAI0b96ctx0vLy+4urqiW7du6Nq1KwYOHAgDAwMAFU9K582bh3///RePHj1CWVkZioqKeG//A8DbZuXHSu3s7CTSsrKyoK+vDwCQl5fnPfW1traGuro6kpKS0LZtW972nzx5gvv372PkyJHw9vbm0svKyrjXkSclJaFFixZQUlLilr9+zqRJSkqqMo7XnTx5EosWLUJiYiLy8vJQVlaG4uJiFBQU8AaFvik+Ph7+/v64evUqnj17xo0RuXfvHmxtbTF27Fh8//33iIuLQ/fu3dGvXz/u+02xsbFISUmRGJhbXFxcbdeWypYJRUXFKvMsWLAANjY2OHbsGHR1+dd0UlISnJyceIPwO3TogBcvXuDBgwe8FqU3VXb3KSgoQJMmTbBnzx7o6uqCiACgRgP7Q0JC4OrqCm1tbQCAm5sbRo4ciePHj/Pe7lTZclFYWFjltoqKiiTOQ3l5OZYsWYKwsDA8fPgQJSUlKCkpkSjHN1skYmNjcfLkSa7V6HWpqamwtLREamoq5s6di4sXLyI7O5tX3s2aNZMao6amJtci9K7ePK9EVOOXKOzduxf5+fnw9PTkpVtZWfF+Y5ycnHD//n0sX76c94KfmpRDXWKVhbp2chH+1FBHuyQxBkaLoZ/zahEbzPx2J0+mYfbsKAwdaofx41/9x9eokRpSUibByEi1HqNjmM/cR+gK9KGpqqpygztf9/z5c6iq8v/+FRQUePMyMjLcjUbljVZ1xGIx5OTkEBsbK9Gn+PWbG5FIJHETERoaikmTJuHIkSMICwvDb7/9hsjISLRr1w4+Pj44evQoli9fDgsLC4hEIvzwww8SAz5fj79y+9LS3hxQLe2GRlpa5Xrr16/ndRUBwB1vTc7Tm2pyE5ueng43NzeMGTMG8+fPh6amJs6ePYuRI0fi5cuXVa5XUFCA7t27o3v37ti2bRt0dHRw7949uLq6cuevZ8+eSE9Px8GDB3H8+HF8++23GD9+PJYvXw6xWIzWrVtLHUOho6NT5X61tLQgIyODnJycKvM0btwY3t7emDlzpsQAYmk3mjW92Y+Ojoaqqip0dHR413hlJTYpKana1+GWl5djy5YtyMzMhLy8PC/9zVfBPnv2DED150JbW1viPAQEBCAoKAjBwcHcOJQpU6ZIXNNvVh7EYjH69OmDpUuXSuynsnLdp08fGBsbY/369WjYsCHEYjGaNWtW7QDp9+2GpK+vz33Lq1JWVhZXQX+bDRs2oHfv3lwlvjrt2rXDtm3beGk1KYe6xCoLda3kBSzvyWDkAf6PO2tVqF5MzEPMmROFyMg7AIC0tBx4ebWEsrKAy8MqCgzz5bO2tsbhw4cl0mNiYnhP7N7GwsICCgoKuHjxIvdUNycnB7du3YKLiwuAin7F5eXlyMrKqvKmojr29vawt7fHrFmz4OTkhB07dqBdu3aIjo6Gl5cX+vfvD6BiDMP7vGLzdWVlZbhy5QrXipCcnIznz5/D2tpaIq+enh4MDQ1x586dKvvi29raYuvWrSgqKuKedl68eLHaGGxsbKqMo9KVK1dQVlaGgIAArt/57t27edsRCAQoL+e/7vrmzZvIzs7GkiVLYGxszG3rTTo6OvDy8oKXlxc6duwIHx8fLF++HK1atUJYWBh0dXUlKpfV7VcgEMDW1haJiYnVfmfB19cXjRs3lng1qa2tLcLDw3mVhvPnz6NBgwYwNDSscnsAYGZmJvUVsi1btoStrS0CAgIwaNAgif77z58/h7q6Og4dOoT8/HzEx8fzKr03b96Eh4cHnj59yrWU/ffff1BQUEDTpk2rjMfe3h6JiYm8tOjoaLi7u2Po0KEAKioBt2/fho2NTbXH1qpVK4SHh8PU1JRXkan09OlTJCUlYd26ddzf4NmzZ6vdJgCMGTMGAwcOrDZPdefdyckJkZGRvHELx44d41qoqpOWloaTJ0/iwIEDb80LVLSUVVaMKtWkHOoSextSHco7cgQpuwkj37h+2CtSq5aU9ATff78bbdtu4CoKAKCtrYQHD/LqMTKGYerDuHHjkJqaivHjx+PatWu4desW/vzzT4SEhMDHx6fG21FRUcHIkSPh4+ODEydO4L///oOXlxfvhsvS0hIeHh7w9PTE3r17kZaWhpiYGCxdupQbTCtNWloaZs2ahQsXLiA9PR3Hjh3DrVu3uBsnCwsL7N27F1evXsW1a9cwZMiQD/a6VQUFBUycOBGXLl1CXFwcRowYgXbt2kl0Qark7++PxYsX448//sCtW7dw48YNhIaGIjAwEAAwZMgQyMrKYuTIkUhMTMShQ4ewfPnyamOwsrJCjx494O3tjUuXLiE2NhajRo3iKhtAxVP4srIyrFy5Enfu3MHWrVslXhVpamqKFy9e4MSJE8jOzkZhYSEaNWoEgUDArXfgwAHMnz+ft56vry/279+PlJQUJCQk4N9//+XOvYeHB7S1teHu7o7o6GikpaXh9OnTmDx5Mh48eMDt9/r160hOTkZ2djbX0uHq6vrWG1U9PT1MmzYNK1as4KWPGzcO9+/fx8SJE3Hz5k3s378ffn5+mDZtWpWDdN9GRkYGoaGhuHXrFpydnXHo0CHcuXMH169fx8KFC+Hu7g6gogtSr1690KJFCzRr1oybvv/+e+jo6PCeakdHR6Njx468snqTq6srLly4wKtQWVhYIDIyEufPn0dSUhJ+/vlniSfz0owfPx7Pnj3Djz/+iMuXL+POnTs4duwYfvrpJ5SXl3NvrPrrr7+QkpKCqKgoTJs27a3b1dTUhIWFRbVTdcc4efJkHDt2DEuXLsXNmzexdOlSHD9+nPeNl1WrVuHbb7+VWHfjxo0wMDBAz549JZYFBwdj3759uH37NhISEjBr1iyEh4djwoQJvHw1KYc69cFGP3wm6nOAc8w3DrzBzGxAc9XS0nJo+PAIkpWdxw1cBvzJzCyYtmy5SmVl0geEMQzzdtUNjvscXLlyhVxdXUlXV5dUVVXJwcGBdu7cycvz5kBeIqKgoCAyMTHh5vPz82no0KGkpKREenp69L///U9iAHVpaSn5+vqSqakpKSgokL6+PvXv35+uX79ORNIH4WZmZlK/fv3IwMCABAIBmZiYkK+vLzeYNC0tjTp37kwikYiMjY1p1apVEvs1MTGRGAwMgCIiIrj5tLQ03kDjyljCw8PJ3NycBAIBdenShe7evVvtedm+fTu1bNmSBAIBaWhokLOzM+3du5dbfuHCBWrRogUJBAJq2bIlhYeHVzvAmahiQHavXr1IKBRSo0aNaMuWLRLHFBgYSAYGBiQSicjV1ZW2bNnCG9BLRDRmzBjS0tIiAOTn50dERDt27CBTU1MSCoXk5OREBw4c4MUzf/58srGxIZFIRJqamuTu7k537tzhxebp6Una2tokFArJ3NycvL29ufuCrKws6tatG6moqBAAOnnyJBFVDNIWiUT0/Pnzas9nXl4eaWtr8wY4ExGdOnWK2rRpQwKBgPT19WnGjBn08uXLKs/hmwOcq5KcnEyenp7UsGFD7nr78ccfKS4ujjIzM0leXp52794tdd2JEyeSnZ0dN29paSnxt/SmsrIyMjQ0pCNHXt2/PH36lNzd3UlFRYV0dXXpt99+I09PT95A8apeTnDr1i3q378/qaurk0gkImtra5oyZQo3GDwyMpJsbGxIKBRS8+bN6dSpUxJ/Cx/Dnj17yMrKihQUFMja2prCw8N5y/38/Hi/J0RE5eXlZGRkRLNnz5a6zaVLl1Ljxo1JUVGRNDQ06JtvvqGDBw9K5HtbOdT1AGcZonfokPgZy8vLg5qaGnJzc6tsgvwo+z1yBA+nVDRniWWAR5rAkS66CJj/6fcRrms+Psfwxx+X8PLlqydt+voqmDvXGaNGtYJAIFfN2gzDvE1xcTHS0tJgZmZW7YBN5vOyadMmTJkyhdfdh/mwBg4cyHUt+9IcPHgQPj4+uH79utQuQa9bvXo19u/fj6NHj9ZRdF+PmpRDdb/hH+M+l41ZqAsJEXjsP4ebzdAElo3VxfRv5lSz0terpKScqyhoaChixowOmDChLW98AsMwDMPUtWXLltW4L/rnpqCgAKGhoW+tKADA6NGjkZOTg/z8fIk3SzHvpzblUFc+nUi+UFcXzUCDfeEoy5cHUDGo6WAHWRwbyloUAKCgoBSysjIQiV694WPOnI7YvTsBo0a1wvTp7aGuzp58MgzDMPXPxMQEEydOrO8wPoq3DQh+nby8PObMYQ88P4balENdYQOcP6K8I0cg3HIApXkKAFVUFB5oAbF27K09paXl+PPPy7CwWIkVKy7xlunpqSA9fQoWLOjCKgoMwzA15OXlxbogMQzzwbHKwkf0ZBn/Hb8PtIAwZ1moN/g03ptbH8rLxdiy5RqsrFZhwoTDyMx8gaVLz+H582JePqGQNXoxDMMwDMPUN3ZH9pHkHTmC0odPuPmA/rKIsZGHiaoJJrScUM2aXyYiwr59N/HbbyeRmPiEt+zbb81RUFDKWhEYhmEYhmE+Mayy8BG8/uYjAMjUBC5Zy0JXpI0D/b7MgVHVOX78DmbPPoGYmEe89O7dG2PRoi5o3bphPUXGMAzDMAzDVIdVFj6CJytW8ub/+ab6T7l/qYgI7u678M8/t3jpTk5GWLToW3TqZFo/gTEMwzAMwzA1wsYsfATiggLu3086v0Bk06/zuwAyMjJo1kyXm7ez08WBA4Nx7txPrKLAMAzDMAzzGWAtCx9Q3pEjeLJiJV5mPYEMAHlROZa3UuaWKysoV73yF+DOnRxoaYmgpvZq7IGPT3tERt7B1KntMHhwM8jKfp2tLAzDMAzDMJ8j1rLwAT1ZsRKld+5Ahio+KFYkANIEr74f8KUObM7IyMe4cQdhZbUKy5ef5y3T0BAhJsYbQ4bYsYoCwzDMR3Tq1CnIyMi89+tTTU1NERwcXKt1vLy80K9fv/fab31LTk6Gvr4+8vPz62X/06dPx6RJk2qUt75j/ZLVphy+Fqyy8IHkHTmC0jt3KmZkCALVlwhzfnV6zdTM0N20ez1F93E8e1aEmTOPo3HjFViz5grKysQICrqIx49f1HdoDMN8we7fv4+RI0eiYcOGEAgEMDExweTJk/H06dP6Do2pobt370JGRgZXr16t0/1u2rQJ6urqUpfNmTMH48eP575IXFn5qpx0dHTQs2dPXLt2jbdeSkoKRowYASMjIwiFQpiZmeHHH3/ElStXJPYxevRoyMnJYdeuXRLLfv31V4SGhiItLe2tx/FmrF+i8PBw2NraQigUwtbWFhEREdXm9/f355VX5aSs/KpXx5tlWjndvHmTy1ObcvhasMrCO8g7cgSpbr1w26VTxdTekf/2Iw0ZjP5ZAf82fdXL60tqVXjxohQLF56BufkfWLr0HIqKygAAKioC/PKLE5SUFN6yBYZhmHdz584dODg44NatW9i5cydSUlKwdu1anDhxAk5OTnj27NlH3f/Lly8/6vaZ+vHgwQMcOHAAI0aMkFiWnJyMjIwMHDx4EDk5OejRowdyc3MBAFeuXEHr1q1x69YtrFu3DomJiYiIiIC1tTV++eUX3nYKCwsRFhYGHx8fhISESOxHV1cX3bt3x9q1a9851tooLS19r/U/pgsXLmDQoEEYNmwYrl27hmHDhmHgwIG4dOlSletMnz4dGRkZvMnW1hYDBgyQyFtZppVTkyZNuGU1LYevCn1lcnNzCQDl5ua+8zZSerpRopV1ldPImbbUbFMzbuoT0ecDHkH9KS5+SX/8cZF0dZcR4M9NQuF8mjr1CGVlvajvEBmGqYGioiJKTEykoqKi+g6l1nr06EFGRkZUWFjIS8/IyCAlJSUaM2YMERHNnDmTHB0dJda3s7MjX19fbn7jxo1kbW1NQqGQrKys6M8//+SWpaWlEQAKCwsjFxcXEgqFtHHjRrp79y717t2b1NXVSUlJiWxtbengwYNERFRWVkY//fQTmZqakqKiIllaWlJwcDAvhuHDh5O7uzstXLiQdHV1SU1Njfz9/enly5c0ffp00tDQIENDQwoJCZGIZefOneTk5ERCoZBsbW3p5MmTXJ6TJ08SAMrJyeHSzp07Rx07diRFRUUyMjKiiRMn0osXr36rHz9+TL179yZFRUUyNTWlbdu2kYmJCQUFBVVZBmVlZTR16lRSU1MjTU1N8vHxIU9PT3J3d+fyHD58mDp06MDl6dWrF6WkpHDLAfAmFxcXIiK6fPkyde3albS0tEhVVZWcnZ0pNjaWt38/Pz8yNjYmgUBABgYGNHHiRG5ZSUkJ+fj4UMOGDUlJSYnatm3LnaPK8/P65OfnR0REAQEB5ODgwNuPtPN59uxZAkBHjhwhsVhMTZs2pdatW1N5ebnEeXp9PSKiTZs2Ubt27ej58+ckEokoLS1NYp1NmzaRsbFxFWeeqow1OzubBg8eTIaGhiQSiahZs2a0Y8cOXh4XFxcaP348TZ06lbS0tMjZ2ZmIiBISEqhnz56krKxMurq6NHToUHry5Am33tvK8mMYOHAg9ejRg5fm6upKgwcPrvE2rl69SgDozJkzXJq0MpWmJuVQn6r7Df8Q97lvYgOc3wH3tiNZWcirqwBFOQCAByI5bHeRxSUrWciWq0NFUR5aSg2+iFYFIoKj4wZcu/aYS5OTk8GIES3h6+sCY2O1eoyOYZgPYdC/g5BdlF3n+9UWaSOsd9hb8z179gxHjx7FwoULIRKJeMv09fXh4eGBsLAwrF69Gh4eHliyZAlSU1PRuHFjAEBCQgJu3LiBv//+GwCwfv16+Pn5YdWqVbC3t0d8fDy8vb2hrKyM4cOHc9ueMWMGAgICEBoaCqFQiNGjR6O0tBRnzpyBsrIyEhMToaKiAgAQi8UwMjLC7t27oa2tjfPnz2P06NEwMDDAwIEDuW1GRUXByMgIZ86cwblz5zBy5EhcuHABzs7OuHTpEsLCwjBmzBh069YNxsbG3Ho+Pj4IDg6Gra0tAgMD0bdvX6SlpUFLS0vifN24cQOurq6YP38+QkJC8OTJE0yYMAETJkxAaGgogIqxBvfv30dUVBQEAgEmTZqErKysasshICAAGzduREhICGxtbREQEICIiAh06dKFy1NQUIBp06bBzs4OBQUF8PX1Rf/+/XH16lXIysri8uXLaNu2LY4fP46mTZtCIBAAAPLz8zF8+HCsWLGC25ebmxtu376NBg0a4O+//0ZQUBB27dqFpk2bIjMzk9ctaMSIEbh79y527dqFhg0bIiIiAj169MCNGzfQvn17BAcHw9fXF8nJyQDAlduZM2fg4OBQ7XED4K67ly9f4urVq0hISMCOHTsgKyvZUePN7k4hISEYOnQo1NTU4ObmhtDQUMybN4+Xp23btrh//z7S09NhYmIiNQZpsRYXF6N169aYMWMGVFVVcfDgQQwbNgzm5uZwdHTk8m3evBljx47FuXPnQETIyMiAi4sLvL29ERgYiKKiIsyYMQMDBw5EVFQUgLeXpTSLFi3CokWLqj2Xhw8fRseOHaUuu3DhAqZOncpLc3V1rdVYmg0bNsDS0lLqPuzt7VFcXAxbW1v89ttv6Ny5M295Tcrhq/LBqh2fiQ9R47rl7EKJVtZ03ekbujvPlshPlchPlVw2NKVmm5pR85BvPmDEn45Fi85wrQmDBu2hmzefvH0lhmE+OVU9leqyuwuvVbSupi67u9Qo7osXLxIAioiIkLo8MDCQANDjx4+JiKh58+b0+++/c8tnzZpFbdq04eaNjY0lnr7Onz+fnJyciOjV0/w3Wwbs7OzI39+/RjETEY0bN46+//57bn748OFkYmLCexptZWVFHTt25ObLyspIWVmZdu7cyYtlyZIlXJ6XL1+SkZERLV26lIgkn5oOGzaMRo8ezYslOjqaZGVlqaioiJKTkwkAXbx4kVuelJREAKptWTAwMJAax+stC2/KysoiAHTjxg3e8cTHx1e5TuV5aNCgAf3zzz9EVPFU3dLSkkpLSyXypqSkkIyMDD18+JCX/u2339KsWbOIiCg0NJTU1NQk1m3RogXvWiGSPJ/Z2dnUt29fatCgAT1+/JjCwsIIAMXFxVV7DEREt27dIgUFBe6JfUREBBkbG0u0SFTeo5w6darKbUmLVRo3Nzf65ZdfuHkXFxdq2bIlL8/cuXOpe/fuvLT79+8TAEpOTpa63TfLUpqnT5/S7du3q53ebB18nYKCAm3fvp2Xtn37dhIIBFWu87ri4mLS0NDg/jYq3bx5k/766y+KjY2l8+fP09ixY0lGRoZOnz7Ny1eTcqhPrGXhE5d35AjKHlc8XRcUPYFReSaOKovwp4Y6nspVvO1HRfHzPq1EhGPHUmFvbwBd3VcDgyZNckRCwhP88osT7O0N6jFChmE+Bm2R9me9XyICUPGNFwDw8PDAxo0bMXfuXBARdu7ciSlTpgAAnjx5wg2U9vb25rZRVlYGNTV+S+mbT3EnTZqEsWPH4tixY+jatSu+//57NG/enFu+du1abNiwAenp6SgqKkJpaSlatmzJ20bTpk15T2X19PTQrFkzbl5OTg5aWloST/mdnJy4f8vLy8PBwQFJSUlSz0dsbCxSUlKwfft23jkSi8VIS0vDrVu3uG1Usra2rnIAMADk5uYiIyNDahyV5x8AUlNTMXfuXFy8eBHZ2dkQiyveEnjv3j3ecb4pKysLvr6+iIqKwuPHj1FeXo7CwkLcu3cPADBgwAAEBwfD3NwcPXr0gJubG/r06QN5eXnExcWBiGBpacnbZklJidSWl9cVFRVBUVFR6jIjIyMAFU/YmzRpgj179kBXV1fieqtOSEgIXF1doa1dca27ublh5MiROH78OLp3f/Xyk8qWi8LCwlrFWl5ejiVLliAsLAwPHz5ESUkJSkpKeIN7AclrOTY2FidPnuRaWF6XmpoKS0vLdypLTU1NaGpqVnkMNfHmeSWiGp1rANi7dy/y8/Ph6enJS7eysoKVlRU37+TkhPv372P58uVwdnbm0mtSDl+Tz/uuth68/nVmeYUyHFdWxHQ9HV4eLaXP9+0E58/fx6xZJ3DmTDqmTHFEUFAPbpmysgDbtn1Xj9ExDPMx1aQrUH2ysLCAjIwMEhMTpb6m8+bNm9DQ0OBuyIYMGYKZM2ciLi4ORUVFuH//PgYPHgwA3A3P+vXred00gIob9de9ecM1atQouLq64uDBgzh27BgWL16MgIAATJw4Ebt378bUqVMREBAAJycnNGjQAMuWLZMYmKmgwH8RhIyMjNS0yjirU9UNlFgsxs8//yz1NZCNGjXiuuLU9AasNvr06QNjY2OsX78eDRs2hFgsRrNmzd46qNbLywtPnjxBcHAwTExMIBQK4eTkxK1nbGyM5ORkREZG4vjx4xg3bhyWLVuG06dPQywWQ05ODrGxsRJlKO1m+HXa2trIycmRuiw6OhqqqqrQ0dGBqqoql15ZKUlKSpKoDL6uvLwcW7ZsQWZmJuTl5XnpISEhvMpC5QB9HR0die1UF2tAQACCgoIQHBwMOzs7KCsrY8qUKRLn+81rWSwWo0+fPli6dKnEfgwMKh4KvktZvm83JH19fWRmZvLSsrKyoKenV+02K23YsAG9e/eGvr7+W/O2a9cO27Zt46XVpBy+JqyyUEuvf51Zq9kLTNTgP60wUzP7LMcoXL/+GHPmROHff29xaatXX8Evv7SHkZFqNWsyDMPUDS0tLXTr1g2rV6/G1KlTeeMWMjMzsX37dnh6enI3v0ZGRnB2dsb27dtRVFSErl27cjcbenp6MDQ0xJ07d+Dh4VHrWIyNjTFmzBiMGTMGs2bNwvr16zFx4kRER0ejffv2GDduHJc3NTX1PY/8lYsXL3JPQMvKyhAbG4sJE6T/n9OqVSskJCTAwsJC6nIbGxuUlZXhypUraNu2LYCKt8RU950GNTU1GBgYSI2jVatWAICnT58iKSkJ69at424Gz549y9tO5RiF8vJyXnp0dDRWr14NNzc3ABWvyc3O5o+jEYlE6Nu3L/r27Yvx48fD2toaN27cgL29PcrLy5GVlVXlTahAIJDYJ1DRhz0xMVHqOmZmZlJbW1q2bMmN2Rg0aJBE//3nz59DXV0dhw4dQn5+PuLj43mVmJs3b8LDwwNPnz7lWj7+++8/KCgooGnTplJjqSrW6OhouLu7Y+jQoQAqKgG3b9+GjY1NldsBKq6R8PBwmJqa8ioylWpSltKMGTOGN0ZHGkNDwyqXOTk5ITIykjdu4dixY2jfvv1b952WloaTJ0/iwIEDb80LAPHx8VzFqFJNyuFrwioLtfWyCACQq0IY10EL2XJyqHipAhDgEvDZfUshJeUZfH1PYteu//BaCzKaNNHE/Pmd0bDh59tKwjDMl2fVqlVo3749XF1dsWDBApiZmSEhIQE+Pj4wNDTEwoULefk9PDzg7++P0tJSBAUF8Zb5+/tj0qRJUFVVRc+ePVFSUoIrV64gJycH06ZNqzKGKVOmoGfPnrC0tEROTg6ioqK4mzILCwts2bIFR48ehZmZGbZu3YqYmBiYmZl9kOP/888/0aRJE9jY2CAoKAg5OTn46aefpOadMWMG2rVrh/Hjx3MDt5OSkhAZGYmVK1fCysoKPXr0gLe3N/766y/Iy8tjypQpEoPH3zR58mQsWbKEiyMwMJBXwdDQ0ICWlhb++usvGBgY4N69e5g5cyZvG7q6uhCJRDhy5AiMjIygqKgINTU1WFhYYOvWrXBwcEBeXh58fHx48WzatAnl5eVwdHSEkpIStm7dCpFIBBMTE2hpacHDwwOenp4ICAiAvb09srOzERUVBTs7O7i5ucHU1BQvXrzAiRMn0KJFCygpKUFJSQmurq4YNWoUysvLJVolqiIjI4PQ0FB07doVzs7OmD17NqytrfHixQv8888/OHbsGE6fPo2QkBD06tULLVq04K3ftGlTTJkyBdu2bcPkyZMBVNz0d+zYsdoykBarhYUFwsPDcf78eWhoaCAwMBCZmZlvrSyMHz8e69evx48//ggfHx9oa2sjJSUFu3btwvr162tUltK8bzekyZMnw9nZGUuXLoW7uzv279+P48eP8yoqq1atQkREBE6cOMFbd+PGjTAwMEDPnj0lthscHAxTU1M0bdoUpaWl2LZtG8LDwxEeHs7LV5Ny+Kp8sNEPn4n3GfiRe/gw93rUaAfrz/r1qA8e5NLo0QdITm4e7zWoRkaBtH59LL18KfkaOIZhvgyf86tTiYju3r1LXl5epK+vTwoKCmRsbEwTJ06k7Oxsibw5OTkkFApJSUmJ8vPzJZZv376dWrZsSQKBgDQ0NMjZ2Zn27t1LRFUPwp0wYQI1btyYhEIh6ejo0LBhw7h9FxcXk5eXF6mpqZG6ujqNHTuWZs6cSS1atODWr3x16utcXFxo8uTJvLTXX2FaGcuOHTvI0dGRBAIB2djY0IkTJ7j80l4LefnyZerWrRupqKiQsrIyNW/enBYuXMgtz8jIoF69epFQKKRGjRrRli1b3vrq1JcvX9LkyZNJVVWV1NXVadq0aRKvTo2MjCQbGxsSCoXUvHlzOnXqlMTg9PXr15OxsTHJyspyr06Ni4sjBwcHEgqF1KRJE9qzZw8vnoiICHJ0dCRVVVVSVlamdu3a0fHjx7ltlpaWkq+vL5mampKCggLp6+tT//796fr161yeMWPGkJaWFu/VqWVlZWRoaEhHjhyp9nxKk5ycTJ6entSwYUMSCARkYmJCP/74I8XFxVFmZibJy8vT7t27pa47ceJEsrOz4+YtLS25Qe1VkRbr06dPyd3dnVRUVEhXV5d+++03iTKRdo0RVQy+7t+/P6mrq5NIJCJra2uaMmUKicViIqpZWX4Me/bsISsrK1JQUCBra2sKDw/nLffz8yMTExNeWnl5ORkZGdHs2bOlbnPp0qXUuHFjUlRUJA0NDfrmm2+41x6/riblUJ/qeoCzDNHrz5O/fHl5eVBTU0Nubi6v72FNXPvWFYKHFYOsHmgB07wVoKusA2UFZUxoOeGzaVUoLxfDwmIl7t59zqVpayth9uxvMHZsGyh+5gO0GYapXnFxMdLS0mBmZlbloE7m03L37l2YmZkhPj6+2v7xzLtbvXo19u/fj6NHj9bL/g8ePAgfHx9cv35dapeg19V3rF+y2pRDfanuN/x97nOr8mmehU/Ui+dPUdmoFuYsCx1FI5wYcKheY3oXcnKymDatHSZNOoIGDQSYPr09pk5thwYNhPUdGsMwDMPUi9GjRyMnJwf5+flo0KDuu+AWFBQgNDS0Rjeo9R3rl6w25fC1YGeihg5ez4C6TMUrtJ42AC5ZyyKg3ZT6DaoGiovLsGZNDH74wZb34bTRo1vj2bMijB/fFtraSvUYIcMwDMPUP3l5ecyZM6fe9v+2AcGvq+9Yv2S1KYevBass1MDB6xkYvyMOO/Cqx1ZAkcIn3e2orEyMTZuuYt6803jwIA+JiU+wfn1fbrlQKA8/v071FyDDMAxTY6ampvjKeg0zDPOJkP6dboYnMDIZ8g2uQ/z/r6KWJaB7R9/6DaoKYjEhLOw/2Nr+CW/vf/DgQR4AYPPma8jMfFHP0TEMwzAMwzCfE9ay8BYHr2fAKHknZiacgcb/32vLAkDTfvUYlSQiwuHDKZgzJwpXr/I/ZNK7tyUWLOgMff3qP0rDMAzDMAzDMK9jlYVqVHY/Wv/fORg9e5WupPDhv3b5PqKj0zF7dhTOnr3HS3d2NsGiRV3QoUOjeoqMYRiGYRiG+ZyxykI1KrsfiV5WfO1RLAOUq5bDdOSweo7slZcvyzF0aATu3cvl0lq1MsCiRV3QvXtj7kumDMMwDMMwDFNbbMxCFRafDkN+g1nonL8NWvkVafnKhObD1KA6am79BvcaBQU5+Pu7AACsrLSwZ88AXLniDVdXC1ZRYBiGYRiGYd4La1mQ4uD1DEQkB6NI+AIDo8VcupKcGOhcf68qu38/F7//fho+Ph1gaanFpQ8b1gIikQJ++MEW8vKs/scwDMMwDMN8GOzO8g1H7x7FnJhhKBHko12SGEZPXy0z9R5WLwObnzwpwNSpR2BhsRIbNsTDz+8Ub7m8vCwGD27GKgoMwzDvqVOnTpgyZUp9h/HR+Pv7v/cXoO/evQsZGRlcvXq1VuuZmpoiODj4vfZd30JCQtC9e/29Nr1NmzbYu3dvjfLWd6xfstqUw5eA3V2+5ujdo5h+ejrK5R9DLCPDa1UQmJvXefej3Nxi+PqehLn5CgQHX0JpacXYiSNHUvD0aWGdxsIwDPMpqOpmft++fazr5Rdm06ZNUFdXr/P9enl5oV+/fhLpJSUl8PX1xdy5r+4F/P39ISMjAxkZGcjJycHY2BijRo3CkydPeOuePHkSbm5u0NLSgpKSEmxtbfHLL7/g4cOHEvuxsrKCQCCQumzu3LmYOXMmxGKxxLK3xfqlISL4+/ujYcOGEIlE6NSpExISEqpdp1OnTlx5vT716tWLy/N6mVZO+vr6vO3UtBy+FKyy8P8OXs/AjBPLuPk3WxV0Jk2qs1iKil5i+fLzMDdfgfnzz+DFi1IAgEgkjxkzOiA1dRK0tNhXlxmGYT415eXlX80NxNcmPDwcKioq6NixIy+9adOmyMjIwL1797BmzRr8888/8PT05JavW7cOXbt2hb6+PsLDw5GYmIi1a9ciNzcXAQEBvG2dPXsWxcXFGDBgADZt2iQRQ69evZCbm4ujR4++U6y19fLly/da/2P63//+h8DAQKxatQoxMTHQ19dHt27dkJ+fX+U6e/fuRUZGBjf9999/kJOTw4ABA3j5Ksu0crpx4wZveU3L4UvBKgv/LzAyGS+piJufePLVH4jA3ByqPVw/egxlZWKsW3cFFhYr4eMTiWfPKuKRl5fFuHEOSE2dhCVLukJTU/TRY2EYhvmcVXa32bp1K0xNTaGmpobBgwfzbiQKCgrg6ekJFRUVGBgYSNy4AUBpaSl+/fVXGBoaQllZGY6Ojjh16hS3vPLp97///gtbW1sIhUKkp6fj1KlTaNu2LZSVlaGuro4OHTogPT0dAJCamgp3d3fo6elBRUUFbdq0wfHjx3n7NTU1xYIFC7j4TExMsH//fjx58gTu7u5QUVGBnZ0drly5IhHLvn37YGlpCUVFRXTr1g3379+v9lyFhobCxsYGioqKsLa2xurVq3nLL1++DHt7eygqKsLBwQHx8fFvPf9ZWVno06cPRCIRzMzMsH37dok8gYGBsLOzg7KyMoyNjTFu3Di8eFHxQaNTp05hxIgRyM3N5Z7u+vv7AwC2bdsGBwcHNGjQAPr6+hgyZAiysrK47ebk5MDDwwM6OjoQiURo0qQJQkNDueUPHz7EoEGDoKGhAS0tLbi7u+Pu3bsAKq6bzZs3Y//+/dx+K8t7165d6Nu3r8RxyMvLQ19fH4aGhujduzcmTZqEY8eOoaioCA8ePMCkSZMwadIkbNy4EZ06dYKpqSmcnZ2xYcMG+PryP/AaEhKCIUOGYNiwYdi4caPEV7vl5OTg5uaGnTt3Vnv+pcUaExODbt26QVtbG2pqanBxcUFcXBwvj4yMDNauXQt3d3coKytjwYIFAIB//vkHrVu3hqKiIszNzTFv3jyUlZXVqCw/BiJCcHAw5syZg++++w7NmjXD5s2bUVhYiB07dlS5nqamJvT19bkpMjISSkpKEpWFyjKtnHR0dHjLa1oOXwo2wPn/FZSUc//ullAOhVw5br4uWxWCgi7i0aOK/8xkZIChQ5vD378TzM016iwGhmG+Tmnf/4Cy7Ow636+8tjbMwv/+4NtNTU3Fvn378O+//yInJwcDBw7EkiVLsHDhQgCAj48PTp48iYiICOjr62P27NmIjY3l9ekfMWIE7t69i127dqFhw4aIiIhAjx49cOPGDTRp0gQAUFhYiMWLF2PDhg3Q0tKCpqYm7O3t4e3tjZ07d6K0tBSXL1/mukm9ePECbm5uWLBgARQVFbF582b06dMHycnJaNTo1XdxgoKCsGjRIsydOxdBQUEYNmwYOnTogJ9++gnLli3DjBkz4OnpiYSEBG7bhYWFWLhwITZv3gyBQIBx48Zh8ODBOHfunNRztH79evj5+WHVqlWwt7dHfHw8vL29oaysjOHDh6OgoAC9e/dGly5dsG3bNqSlpWHy5MlvPfdeXl64f/8+oqKiIBAIMGnSJN4NPQDIyspixYoVMDU1RVpaGsaNG4dff/0Vq1evRvv27REcHAxfX18kJycDAFRUKj4sWlpaivnz58PKygpZWVmYOnUqvLy8cOjQIQAVXUQSExNx+PBhaGtrIyUlBUVFRdz56dy5Mzp27IgzZ85AXl4eCxYsQI8ePXD9+nVMnz4dSUlJyMvL4yoYmpqaAIDo6Gh4eHi89dhFIhHEYjHKysqwZ88ersIpzevdrPLz87Fnzx5cunQJ1tbWKCgowKlTp9C5c2feOm3btsX//ve/amOQFmt+fj6GDx+OFStWAAACAgLg5uaG27dvo0GDBlw+Pz8/LF68GEFBQZCTk8PRo0cxdOhQrFixAh07dkRqaipGjx7N5QWqL8uq9OzZE9HR0dUeR1UVjrS0NGRmZvLGZAiFQri4uOD8+fP4+eefq91upZCQEAwePBjKysq89Nu3b6Nhw4YQCoVwdHTEokWLYG5uzstTk3L4YtBXJjc3lwBQbm4ul/bvtUfUeMEiarapGTXb1IyinKwp0apiSunpVqfx7d79HwH+1K/fLrpx43Gd7pthmK9DUVERJSYmUlFRES/9lrML99tXl9MtZ5cax+7i4kKTJ0+WSI+IiKDX/0vz8/MjJSUlysvL49J8fHzI0dGRiIjy8/NJIBDQrl27uOVPnz4lkUjEbT8lJYVkZGTo4cOHvH19++23NGvWLCIiCg0NJQB09epV3nYA0KlTp2p8XLa2trRy5Upu3sTEhIYOHcrNZ2RkEACaO3cul3bhwgUCQBkZGbxYLl68yOVJSkoiAHTp0iXuvLRo0YJbbmxsTDt27ODFMn/+fHJyciIionXr1pGmpiYVFBRwy9esWUMAKD4+XuqxJCcnVxlHUFBQledg9+7dpKWlxc2HhoaSmppalfkrXb58mQBQfn4+ERH16dOHRowYITVvSEgIWVlZkVgs5tJKSkpIJBLR0aNHiYho+PDh5O7uzlsvJyeHANCZM2d46W+ez6SkJLKwsKC2bdsSEdHYsWNJVVX1rcdARPTXX39Ry5YtufnJkyeTh4eHRL79+/eTrKwslZeXS91OVbG+qaysjBo0aED//PMPlwaApkyZwsvXsWNHWrRoES9t69atZGBgUOW23yxLaR48eEC3b9+udqrKuXPnCIDE36a3tzd179692v1WunTpEu9vo9KhQ4fo77//puvXr1NkZCS5uLiQnp4eZWdn8/K9rRw+pqp+w4mk3+e+r6++ZaHyK81K5pFcmqiUAFQ8pflYrQqnTt3Fb79F4c8/3dCixauBM99/b4u4uNGwtzf4KPtlGIapiry29he1X1NTU94TUwMDA+7pdmpqKkpLS+Hk5MQt19TUhJWVFTcfFxcHIoKlpSVvuyUlJdDSevX6aoFAgObNm/O24+XlBVdXV3Tr1g1du3bFwIEDYWBQ8bteUFCAefPm4d9//8WjR49QVlaGoqIi3Lt3j7ef17epp6cHALCzs5NIy8rK4gZgysvLw8HBgctjbW0NdXV1JCUloW3btrztP3nyBPfv38fIkSPh7e3NpZeVlUFNTQ0AkJSUhBYtWkBJ6dU4udfPmTRJSUlVxvG6kydPYtGiRUhMTEReXh7KyspQXFyMgoICiSe9r4uPj4e/vz+uXr2KZ8+ecWNE7t27B1tbW4wdOxbff/894uLi0L17d/Tr1w/t27cHAMTGxiIlJYV3XQBAcXExUlNTq9xnZcuEoqKixLIbN25ARUUF5eXlKCkpQadOnfDXX38BqOguU9OB9yEhIRg6dCg3P3ToUDg7O+P58+e8c1fZclFSUgKRSLJbclWxZmVlwdfXF1FRUXj8+DHKy8tRWFgocd29Xm5AxTmLiYnhWuSAirE5xcXFKCwshJKS0juVpaGhYY3OS3XePLe1Pd/NmjWT+Lvo2bMn9287Ozs4OTmhcePG2Lx5M6ZNm8Yte1s5fEm++spC5Vea5YSv3lygLK6oLMhrqn7wsQpXrjzCnDlROHas4kfpt99O4p9/fuSWy8rKsIoCwzD14mN0BfrQVFVVkZubK5H+/PlzqKqq8tIUFBR48zIyMtyNJb3RF1wasVgMOTk5xMbGQk5OjressksMUHHT8OYNSmhoKCZNmoQjR44gLCwMv/32GyIjI9GuXTv4+Pjg6NGjWL58OSwsLCASifDDDz+gtLS0yvgrty8t7c0B1dJulqSlVa63fv16ODo68pZVHm9NztObKtep7qYtPT0dbm5uGDNmDObPnw9NTU2cPXsWI0eOrHZQbUFBAbp3747u3btj27Zt0NHRwb179+Dq6sqdv549eyI9PR0HDx7E8ePH8e2332L8+PFYvnw5xGIxWrduLXUMxZv90l+npaUFGRkZ5OTkSCyzsrLCgQMHICcnx3VdqWRpaYnc3FxkZGRwlUVpEhMTcenSJcTExGDGjBlcenl5OXbu3ImxY8dyac+ePYOSklKVN6hVxerl5YUnT54gODgYJiYmEAqFcHJykrju3ry5F4vFmDdvHr777juJfSkqKr5zWb5PN6TKynFmZibvvGZlZXGV6OoUFhZi165d+P3339+aV1lZGXZ2drh9+zYv/W3l8CX56isLBSXlEOi8alXomyCGfOH/j/tW+HAXQFLSE8ydexLh4Um89Dt3cpCXVwJVVWEVazIMwzCVrK2tcfjwYYn0mJgYXqvA21hYWEBBQQEXL17kxgnk5OTg1q1bcHFxAQDY29ujvLwcWVlZ7/RWGXt7e9jb22PWrFlwcnLCjh070K5dO0RHR8PLywv9+/cHUHFDVDnA9n2VlZXhypUr3NPS5ORkPH/+HNbW1hJ59fT0YGhoiDt37lTZF9/W1hZbt25FUVERd1N08eLFamOwsbGpMo5KV65cQVlZGQICAiArW/F/7u7du3nbEQgEKC8v56XdvHkT2dnZWLJkCYyNjbltvUlHRwdeXl7w8vJCx44d4ePjg+XLl6NVq1YICwuDrq6uROWyuv0KBALY2toiMTFR4tsFAoEAFhYWUrf1ww8/YObMmfjf//6HoKAgieWVrQYhISFwdnbGn3/+yVu+detWhISE8CoL//33H1q1aiV1f9XFGh0djdWrV8PNzQ0AcP/+fWTXYIxSq1atkJycXOUx1qQspdmwYQPXClJbZmZm3ABle3t7ABVjWU6fPo2lS5e+df3du3ejpKSE15JTlZKSEiQlJUn8BrytHL4k7G1IAGRkSwBUvC516IFXT2hkq2kGran09OcYMWI/mjVbw6somJqqY/Pmfrh+fQyrKDAMw9TQuHHjkJqaivHjx+PatWu4desW/vzzT4SEhMDHx6fG21FRUcHIkSPh4+ODEydO4L///oOXlxd3swNUPBX28PCAp6cn9u7di7S0NMTExGDp0qXcYFpp0tLSMGvWLFy4cAHp6ek4duwYbt26BRsbGwAVFZW9e/fi6tWruHbtGoYMGfLBXreqoKCAiRMn4tKlS4iLi8OIESPQrl07ia4Wlfz9/bF48WL88ccfuHXrFm7cuIHQ0FAEBgYCAIYMGQJZWVmMHDkSiYmJOHToEJYvX15tDFZWVujRowe8vb1x6dIlxMbGYtSoUbwnsI0bN0ZZWRlWrlyJO3fuYOvWrVi7di1vO6ampnjx4gVOnDiB7OxsFBYWolGjRhAIBNx6Bw4cwPz583nr+fr6Yv/+/UhJSUFCQgL+/fdf7tx7eHhAW1sb7u7uiI6ORlpaGk6fPo3JkyfjwYMH3H6vX7+O5ORkZGdnc0/HXV1dcfbs2VqUBmBsbIygoCD88ccfGDlyJE6fPo309HScO3cOP//8M+bPn4+XL19i69at+PHHH9GsWTPeNGrUKMTGxuLatWvcNqOjo9/6sTVpsVpYWGDr1q1ISkrCpUuX4OHhUaOn4r6+vtiyZQv8/f2RkJCApKQkrrUMqFlZSmNoaAgLC4tqp6rIyMhgypQpWLRoESIiIri/XyUlJQwZMoTL5+npiVmzZkmsHxISgn79+vG6E1aaPn06Tp8+jbS0NFy6dAk//PAD8vLyMHz4cF6+mpTDF+ODjX74TLw+8OPfa4/IZMa/ZLu+vcTA5kQra8o9fOSd91NYWEoTJx4iBYXfCfDnJj29ZbRq1SUqKSn7gEfFMAxTc9UNjvscXLlyhVxdXUlXV5dUVVXJwcGBdu7cycvz5sBTIqKgoCAyMTHh5vPz82no0KGkpKREenp69L///U9iAHVpaSn5+vqSqakpKSgokL6+PvXv35+uX79ORNIH4WZmZlK/fv3IwMCABAIBmZiYkK+vLzcQMi0tjTp37kwikYiMjY1p1apVEvs1MTGRGAwMgCIiIrj5tLQ03kDjyljCw8PJ3NycBAIBdenShe7evVvtedm+fTu1bNmSBAIBaWhokLOzM+3du5dbfuHCBWrRogUJBAJq2bIlhYeHVzvAmahiQHavXr1IKBRSo0aNaMuWLRLHFBgYSAYGBiQSicjV1ZW2bNlCACgnJ4fLM2bMGNLS0iIA5OfnR0REO3bsIFNTUxIKheTk5EQHDhzgxTN//nyysbEhkUhEmpqa5O7uTnfu3OHF5unpSdra2iQUCsnc3Jy8vb25AaFZWVnUrVs3UlFRIQB08uRJIqoYvCwSiej58+fVnk9pIiMjydXVlTQ0NEhRUZGsra1p+vTp9OjRI/r7779JVlaWMjMzpa5rZ2dHEydOJKKKQcEKCgp0//79avcnLda4uDhycHAgoVBITZo0oT179kiUyZvXWKUjR45Q+/btSSQSkaqqKrVt25b++usvbnlNyvJDE4vF5OfnR/r6+iQUCsnZ2Zlu3LjBy+Pi4kLDhw/npVUOwD927JjU7Q4aNIgMDAxIQUGBGjZsSN999x0lJCTw8tS0HD6Wuh7gLEP0Dh0SP2N5eXlQU1NDbm4u+q+PQ+qTAihbLIKsQh7WrSyDxv93jzOcPuS9vtgsFhNat/4LV69mAgDU1RUxY0YHTJzYFsrKgg9xKAzDMO+kuLgYaWlpMDMzkzpgk/k8bdq0CVOmTOF192E+rIEDB3Jdy+qDj48PcnNzuQHU1anvWL9ktSmHj6G63/DX73Or6mpXW191N6SCknLIN7gOWYU8tEsScxUFeWXUuqJQUlLGm5eVlcGiRV2gpKSA2bO/QVraZMyc+Q2rKDAMwzDMZ2rZsmW8we11TVdXV6LbVVXqO9YvWW3K4Uvw1Q9wdsk5gMGHymD09FWarKBmr90CgNLScmzYEIcFC84gPHwgnJyMuWU9elggPX0KtLWVqtkCwzAMwzCfAxMTE0ycOLHe9l+bcTn1HeuXrDbl8CX4qlsWAGDw+TxeRQEAdEb+KD3za8rLxdi69RqsrVdh/PhDyMh4gdmzo3ivmZORkWEVBYZhGKZOeHl5sS5IDMN8cF99y0LFB9gAsQyg2OAldNooVNsFiYiwf38yfvstCgkJT3jLNDVFKCoqg5KSQhVrMwzDMAzDMMzn46uuLJQpXgVkKioLucpAU7cngLZllflPnLiD2bOjcPnyQ156t27mWLiwC9q0ef+vETIMw9SVr+z9FgzDMF+Euv7t/morC0f/y0ShykFuXhb/f+I7z5HIm5dXgu++C8OJE2m89HbtjLBoURd07mz2UWNlGIb5kCq/AlxYWPhVfH2UYRjmS1JYWAhA8iv1H8tXW1lYdfI2RHr53LyymIABm4Gm/STyNmggQEnJq685Nmumi4ULu6BPH8tqP2fPMAzzKZKTk4O6ujqysrIAAEpKSuy3jGEY5hNHRCgsLERWVhbU1dUhJydXJ/v9aisLlplzMOhQCfe6VEUZOa6i8OhRPgwMVLj/PGVkKl6D6uW1H7//3gmDBzeDnNxXPzacYZjPmL6+PgBwFQaGYRjm86Curs79hteFr/ajbBFtm8Aq91WNTGCoA6VtB7FgwRmsXx+HgweHoFu3xrx1y8rEkJdnlQSGYb4c5eXlePnyZX2HwTAMw9SAgoJCtS0KH+OjbF9ty4Lo//9vFMsQnmlqY6/WMKxrvAJFRRUfV5s9Owpdu5rzmuZZRYFhmC+NnJxcnTVlMwzDMJ+fer/7Xb16Nfe56tatWyM6Orra/KdPn0br1q2hqKgIc3NzrF279p32q/4CKBDLIyS3Kfok9EbwnkdcRUFZWQE9e1rg5UvxO22bYRiGYRiGYb4E9dqyEBYWhilTpmD16tXo0KED1q1bh549eyIxMRGNGjWSyJ+WlgY3Nzd4e3tj27ZtOHfuHMaNGwcdHR18//33tdr37pzG2JzTEk/LFQGUAAAEAjmMG+eAWbM6QldX+UMcIsMwDMMwDMN8tup1zIKjoyNatWqFNWvWcGk2Njbo168fFi9eLJF/xowZOHDgAJKSkri0MWPG4Nq1a7hw4UKN9lnZlwuYCUARACArK4MRI1rC19cFjRqpvdcxMQzDMAzDMEx9+KLGLJSWliI2NhYzZ87kpXfv3h3nz5+Xus6FCxfQvXt3XpqrqytCQkLw8uVLqe+bLSkpQUlJCTefm5tbuQQA0K+fNX77zRlNmmgBqDjJDMMwDMMwDPO5qbyP/ZBtAfVWWcjOzkZ5eTn09PR46Xp6esjMzJS6TmZmptT8ZWVlyM7OhoGBgcQ6ixcvxrx586RsLQgAsG9fxcQwDMMwDMMwX4KnT5/+f0+a91fvb0N680NARFTtx4Gk5ZeWXmnWrFmYNm0aN//8+XOYmJjg3r17H+wkMl+uvLw8GBsb4/79+x+sOY/5MrFrhakNdr0wNcWuFaY2cnNz0ahRI2hqan6wbdZbZUFbWxtycnISrQhZWVkSrQeV9PX1peaXl5eHlpaW1HWEQiGEQqFEupqaGvujY2pMVVWVXS9MjbBrhakNdr0wNcWuFaY2ZGU/3AtP6+3VqQKBAK1bt0ZkZCQvPTIyEu3bt5e6jpOTk0T+Y8eOwcHBQep4BYZhGIZhGIZh3l29fmdh2rRp2LBhAzZu3IikpCRMnToV9+7dw5gxYwBUdCHy9PTk8o8ZMwbp6emYNm0akpKSsHHjRoSEhGD69On1dQgMwzAMwzAM88Wq1zELgwYNwtOnT/H7778jIyMDzZo1w6FDh2BiYgIAyMjIwL1797j8ZmZmOHToEKZOnYo///wTDRs2xIoVK2r1jQWhUAg/Pz+pXZMY5k3semFqil0rTG2w64WpKXatMLXxMa6Xev3OAsMwDMMwDMMwn6567YbEMAzDMAzDMMyni1UWGIZhGIZhGIaRilUWGIZhGIZhGIaRilUWGIZhGIZhGIaR6ousLKxevRpmZmZQVFRE69atER0dXW3+06dPo3Xr1lBUVIS5uTnWrl1bR5Eyn4LaXC979+5Ft27doKOjA1VVVTg5OeHo0aN1GC1Tn2r721Lp3LlzkJeXR8uWLT9ugMwnpbbXS0lJCebMmQMTExMIhUI0btwYGzdurKNomfpU22tl+/btaNGiBZSUlGBgYIARI0bg6dOndRQtU1/OnDmDPn36oGHDhpCRkcG+ffveus4HucelL8yuXbtIQUGB1q9fT4mJiTR58mRSVlam9PR0qfnv3LlDSkpKNHnyZEpMTKT169eTgoIC/f3333UcOVMfanu9TJ48mZYuXUqXL1+mW7du0axZs0hBQYHi4uLqOHKmrtX2Wqn0/PlzMjc3p+7du1OLFi3qJlim3r3L9dK3b19ydHSkyMhISktLo0uXLtG5c+fqMGqmPtT2WomOjiZZWVn6448/6M6dOxQdHU1Nmzalfv361XHkTF07dOgQzZkzh8LDwwkARUREVJv/Q93jfnGVhbZt29KYMWN4adbW1jRz5kyp+X/99Veytrbmpf3888/Url27jxYj8+mo7fUija2tLc2bN+9Dh8Z8Yt71Whk0aBD99ttv5OfnxyoLX5HaXi+HDx8mNTU1evr0aV2Ex3xCanutLFu2jMzNzXlpK1asICMjo48WI/PpqUll4UPd435R3ZBKS0sRGxuL7t2789K7d++O8+fPS13nwoULEvldXV1x5coVvHz58qPFytS/d7le3iQWi5Gfnw9NTc2PESLziXjXayU0NBSpqanw8/P72CEyn5B3uV4OHDgABwcH/O9//4OhoSEsLS0xffp0FBUV1UXITD15l2ulffv2ePDgAQ4dOgQiwuPHj/H333+jV69edREy8xn5UPe49foF5w8tOzsb5eXl0NPT46Xr6ekhMzNT6jqZmZlS85eVlSE7OxsGBgYfLV6mfr3L9fKmgIAAFBQUYODAgR8jROYT8S7Xyu3btzFz5kxER0dDXv6L+qll3uJdrpc7d+7g7NmzUFRUREREBLKzszFu3Dg8e/aMjVv4gr3LtdK+fXts374dgwYNQnFxMcrKytC3b1+sXLmyLkJmPiMf6h73i2pZqCQjI8ObJyKJtLfll5bOfJlqe71U2rlzJ/z9/REWFgZdXd2PFR7zCanptVJeXo4hQ4Zg3rx5sLS0rKvwmE9MbX5bxGIxZGRksH37drRt2xZubm4IDAzEpk2bWOvCV6A210piYiImTZoEX19fxMbG4siRI0hLS8OYMWPqIlTmM/Mh7nG/qMdd2trakJOTk6iNZ2VlSdSsKunr60vNLy8vDy0trY8WK1P/3uV6qRQWFoaRI0diz5496Nq168cMk/kE1PZayc/Px5UrVxAfH48JEyYAqLgZJCLIy8vj2LFj6NKlS53EztS9d/ltMTAwgKGhIdTU1Lg0GxsbEBEePHiAJk2afNSYmfrxLtfK4sWL0aFDB/j4+AAAmjdvDmVlZXTs2BELFixgPSIYzoe6x/2iWhYEAgFat26NyMhIXnpkZCTat28vdR0nJyeJ/MeOHYODgwMUFBQ+WqxM/XuX6wWoaFHw8vLCjh07WB/Rr0RtrxVVVVXcuHEDV69e5aYxY8bAysoKV69ehaOjY12FztSDd/lt6dChAx49eoQXL15wabdu3YKsrCyMjIw+arxM/XmXa6WwsBCysvzbNzk5OQCvnhozDPAB73FrNRz6M1D5CrKQkBBKTEykKVOmkLKyMt29e5eIiGbOnEnDhg3j8le+Vmrq1KmUmJhIISEh7NWpX5HaXi87duwgeXl5+vPPPykjI4Obnj9/Xl+HwNSR2l4rb2JvQ/q61PZ6yc/PJyMjI/rhhx8oISGBTp8+TU2aNKFRo0bV1yEwdaS210poaCjJy8vT6tWrKTU1lc6ePUsODg7Utm3b+joEpo7k5+dTfHw8xcfHEwAKDAyk+Ph47jW7H+se94urLBAR/fnnn2RiYkICgYBatWpFp0+f5pYNHz6cXFxcePlPnTpF9vb2JBAIyNTUlNasWVPHETP1qTbXi4uLCwGQmIYPH173gTN1rra/La9jlYWvT22vl6SkJOratSuJRCIyMjKiadOmUWFhYR1HzdSH2l4rK1asIFtbWxKJRGRgYEAeHh704MGDOo6aqWsnT56s9h7kY93jyhCxNiuGYRiGYRiGYSR9UWMWGIZhGIZhGIb5cFhlgWEYhmEYhmEYqVhlgWEYhmEYhmEYqVhlgWEYhmEYhmEYqVhlgWEYhmEYhmEYqVhlgWEYhmEYhmEYqVhlgWEYhmEYhmEYqVhlgWEYhmEYhmEYqVhlgWEY5hO2adMmqKur13cY78zU1BTBwcHV5vH390fLli3rJB6GYRimdlhlgWEY5iPz8vKCjIyMxJSSklLfoWHTpk28mAwMDDBw4ECkpaV9kO3HxMRg9OjR3LyMjAz27dvHyzN9+nScOHHig+yvKm8ep56eHvr06YOEhIRab+dzrrwxDMPUFqssMAzD1IEePXogIyODN5mZmdV3WAAAVVVVZGRk4NGjR9ixYweuXr2Kvn37ory8/L23raOjAyUlpWrzqKioQEtL67339TavH+fBgwdRUFCAXr16obS09KPvm2EY5nPFKgsMwzB1QCgUQl9fnzfJyckhMDAQdnZ2UFZWhrGxMcaNG4cXL15UuZ1r166hc+fOaNCgAVRVVdG6dWtcuXKFW37+/Hk4OztDJBLB2NgYkyZNQkFBQbWxycjIQF9fHwYGBujcuTP8/Pzw33//cS0fa9asQePGjSEQCGBlZYWtW7fy1vf390ejRo0gFArRsGFDTJo0iVv2ejckU1NTAED//v0hIyPDzb/eDeno0aNQVFTE8+fPefuYNGkSXFxcPthxOjg4YOrUqUhPT0dycjKXp7ryOHXqFEaMGIHc3FyuhcLf3x8AUFpail9//RWGhoZQVlaGo6MjTp06VW08DMMwnwNWWWAYhqlHsrKyWLFiBf777z9s3rwZUVFR+PXXX6vM7+HhASMjI8TExCA2NhYzZ86EgoICAODGjRtwdXXFd999h+vXryMsLAxnz57FhAkTahWTSCQCALx8+RIRERGYPHkyfvnlF/z333/4+eefMWLECJw8eRIA8PfffyMoKAjr1q3D7du3sW/fPtjZ2UndbkxMDAAgNDQUGRkZ3PzrunbtCnV1dYSHh3Np5eXl2L17Nzw8PD7YcT5//hw7duwAAO78AdWXR/v27REcHMy1UGRkZGD69OkAgBEjRuDcuXPYtWsXrl+/jgEDBqBHjx64fft2jWNiGIb5JBHDMAzzUQ0fPpzk5ORIWVmZm3744QepeXfv3k1aWlrcfGhoKKmpqXHzDRo0oE2bNkldd9iwYTR69GheWnR0NMnKylJRUZHUdd7c/v3796ldu3ZkZGREJSUl1L59e/L29uatM2DAAHJzcyMiooCAALK0tKTS0lKp2zcxMaGgoCBuHgBFRETw8vj5+VGLFi24+UmTJlGXLl24+aNHj5JAIKBnz56913ECIGVlZVJSUiIABID69u0rNX+lt5UHEVFKSgrJyMjQw4cPeenffvstzZo1q9rtMwzDfOrk67eqwjAM83Xo3Lkz1qxZw80rKysDAE6ePIlFixYhMTEReXl5KCsrQ3FxMQoKCrg8r5s2bRpGjRqFrVu3omvXrhgwYAAaN24MAIiNjUVKSgq2b9/O5SciiMVipKWlwcbGRmpsubm5UFFRARGhsLAQrVq1wt69eyEQCJCUlMQboAwAHTp0wB9//AEAGDBgAIKDg2Fubo4ePXrAzc0Nffr0gbz8u//34uHhAScnJzx69AgNGzbE9u3b4ebmBg0Njfc6zgYNGiAuLg5lZWU4ffo0li1bhrVr1/Ly1LY8ACAuLg5EBEtLS156SUlJnYzFYBiG+ZhYZYFhGKYOKCsrw8LCgpeWnp4ONzc3jBkzBvPnz4empibOnj2LkSNH4uXLl1K34+/vjyFDhuDgwYM4fPgw/Pz8sGvXLvTv3x9isRg///wzb8xApUaNGlUZW+VNtKysLPT09CRuimVkZHjzRMSlGRsbIzk5GZGRkTh+/DjGjRuHZcuW4fTp07zuPbXRtm1bNG7cGLt27cLYsWMRERGB0NBQbvm7HqesrCxXBtbW1sjMzMSgQYNw5swZAO9WHpXxyMnJITY2FnJycrxlKioqtTp2hmGYTw2rLDAMw9STK1euoKysDAEBAZCVrRhCtnv37reuZ2lpCUtLS0ydOhU//vgjQkND0b9/f7Rq1QoJCQkSlZK3ef0m+k02NjY4e/YsPD09ubTz58/znt6LRCL07dsXffv2xfjx42FtbY0bN26gVatWEttTUFCo0VuWhgwZgu3bt8PIyAiysrLo1asXt+xdj/NNU6dORWBgICIiItC/f/8alYdAIJCI397eHuXl5cjKykLHjh3fKyaGYZhPDRvgzDAMU08aN26MsrIyrFy5Enfu3MHWrVslusW8rqioCBMmTMCpU6eQnp6Oc+fOISYmhrtxnzFjBi5cuIDx48fj6tWruH37Ng4cOICJEye+c4w+Pj7YtGkT1q5di9u3byMwMBB79+7lBvZu2rQJISEh+O+//7hjEIlEMDExkbo9U1NTwOzfBgAAAfJJREFUnDhxApmZmcjJyalyvx4eHoiLi8PChQvxww8/QFFRkVv2oY5TVVUVo0aNgp+fH4ioRuVhamqKFy9e4MSJE8jOzkZhYSEsLS3h4eEBT09P7N27F2lpaYiJicHSpUtx6NChWsXEMAzzyanPARMMwzBfg+HDh5O7u7vUZYGBgWRgYEAikYhcXV1py5YtBIBycnKIiD+gtqSkhAYPHkzGxsYkEAioYcOGNGHCBN6g3suXL1O3bt1IRUWFlJWVqXnz5rRw4cIqY5M2YPdNq1evJnNzc1JQUCBLS0vasmULtywiIoIcHR1JVVWVlJWVqV27dnT8+HFu+ZsDnA8cOEAWFhYkLy9PJiYmRCQ5wLlSmzZtCABFRUVJLPtQx5menk7y8vIUFhZGRG8vDyKiMWPGkJaWFgEgPz8/IiIqLS0lX19fMjU1JQUFBdLX16f+/fvT9evXq4yJYRjmcyBDRFS/1RWGYRiGYRiGYT5FrBsSwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBSscoCwzAMwzAMwzBS/R8RgBHKId7t0AAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 900x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(MLP_models, MLP_name, x_val_list, yval, \"MLP validation\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "f7bec527-4974-4a74-889b-c639b812fd63",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating MLP testing data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA)...\u001b[0m\n",
+ "Oversampled dataset(No PCA) Accuracy: 0.7070872593199509\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.90 0.73 0.81 2067\n",
+ " 1 0.28 0.57 0.38 374\n",
+ "\n",
+ " accuracy 0.71 2441\n",
+ " macro avg 0.59 0.65 0.59 2441\n",
+ "weighted avg 0.81 0.71 0.74 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA)...\u001b[0m\n",
+ "Undersampled dataset(No PCA) Accuracy: 0.6403113478082753\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.93 0.62 0.75 2067\n",
+ " 1 0.26 0.75 0.39 374\n",
+ "\n",
+ " accuracy 0.64 2441\n",
+ " macro avg 0.60 0.68 0.57 2441\n",
+ "weighted avg 0.83 0.64 0.69 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA)...\u001b[0m\n",
+ "Oversampled dataset(PCA) Accuracy: 0.7165096272019664\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.91 0.74 0.82 2067\n",
+ " 1 0.29 0.59 0.39 374\n",
+ "\n",
+ " accuracy 0.72 2441\n",
+ " macro avg 0.60 0.67 0.60 2441\n",
+ "weighted avg 0.81 0.72 0.75 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA)...\u001b[0m\n",
+ "Undersampled dataset(PCA) Accuracy: 0.6419500204834084\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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EMGgyXBV68X+tGzFxWBdaN3fHpa4t/SZ+ztf7fwXA1NSEWeN68cJzLfCsb09GZg5Rh39j5oJdJF1P1+7Ds74Dcyb2wc+nIRozU/YcPMukj/5Dyo3b2jatnqrPB2/2xreFOwUFCjv3xTHt39vIys575Odc0yzo25y6tc1LlO/+7TqrjvwBgKuthkGtXWnmVBuVChJv5TD/QAJpWfkA2FqYMtjXFW9XayxMTUjKyGXnyT85ciW9xH4fF8Y6XDXOs65CVpYaTv7+BxPnbC1RV8vCnFbN3Jiz/L/4DfyIAZOX09jdkf98OkanzTeLx6MoCkGjF9Lp1XmYm6nZNn+MdrjhUteWb5dO4OLV63QY+jEvjv+M5o2cWf7e0Ed2njXZjG/P8drWU9rtwz1F64PGXC5KUI61zZnVrTHX0nN4f/cF/vH1OXb8+if5BX+vMDX+OQ9cbDV8HBXPtK/PEXslnTc7NKCBnWW1nJM+qFSqMrfKOHDgAL169cLV1RWVSsXOnTt16hVFYdasWbi6umJpaUlgYCCnT5/WaZObm8uECRNwcHDAysqK4OBgEhMTddrcvHmToUOHYmtri62tLUOHDuXWrVuVilWSnJ7t/vkM7y7+hq+ifilRl5GZQ8+xi9i25wTnL6dw5GQCkz76D77N3XFzrgOAX6uGeLjaMyp8PacvXOP0hWuMDl9PG68GBD7bBICg9l7k/1VAWMRWzl9O4diZK4RFbKVPFx8aujk80vOtiW7nFpCe85d2a13PluSMXM7+mQlAfx8X4hIz2Hg8iYQb2aRk5nHijwwycv7S7qNx3Vp8/1sqF9PukJKZx46Tf5KVX/B4JzkTVZlbZWRlZdGyZcv7roE6d+5cPvnkExYtWkRsbCzOzs48//zz3L7992gkLCyMHTt2sHnzZn766ScyMzPp2bMnBQUF2jaDBg0iLi6OyMhIIiMjiYuLY+jQyv1jXq3D1cTERJYsWcLBgwdJTk5GpVLh5OSEv78/r732Gm5ubtUZ3iNhY21JYWEht25nA6AxN0VRFHLz/v6Dy8n7i4KCQvxbNeKHw+fQmJuSn1+gs7Zldm7RMMu/VSMuXU19tCdRg6lNVDzXsA7fnkkBQAX41Lfh61Mp/KNLQxrUseR6Zh5fnUrh6NW/h6LnUrLwa/AEJxIzuJNXQLsGT2BmouJMcmY1ncnD0+dwNSgoiKCgoFLrFEXh008/ZcaMGfTt2xeANWvW4OTkxMaNGxkzZgzp6emsWLGCdevW0aVLFwDWr1+Pm5sbe/fu5YUXXuDs2bNERkYSExND27ZtAVi+fDl+fn6cO3eOpk2bVijWauvJ/fTTTzRr1owdO3bQsmVLhg0bxpAhQ2jZsiU7d+6kRYsW/Pzzz+XuJzc3l4yMDJ1NKSwo93s1gcbclPffeJEt/z3K7awcAI6cTCArO48P33wRSwszalmYExHWG7XaBGcHGwD2HzmHk70NE4d1xsxUzRPWlrw3IRgA57q21XY+NdEzbrbUMldz4OINAGwsTLE0UxPs5cgvf9wmYu8lYq+mMzGwAc2crLTfm38gAbVKxRcDvFk7pCUj27nxyf54UjIf32ue5Q1XS/tbys3NrfRx4uPjSU5O1q56D6DRaAgICODgwYMAHDt2jPz8fJ02rq6ueHl5adscOnQIW1tbbYIDaNeuHba2tto2FVFtPbmJEycycuRI5s2bd9/6sLAwYmNjy9xPREQE7777rk6Z2ukZzFye1VusVcHU1IR1c17FRKXizYi/r9+l3sxk8FsrWPB2f8YNDKCwUGFr5DGOn7lCQWEhAGcvJTPqnXXMmdyX9yYEU1BYyOJN0SSnZlBYUFhdp1QjBTa2I+6PDG5mF/WMi0dlxxIz+O/Z6wBcvplNk7pWdGniwNk/s4CiIa2VuZoPdl/gdu5fPONmy5sBnrwbeZ6rt3Kq5VweVnlD0tL+lsLDw5k1a1aljpOcnAyAk5OTTrmTkxOXL1/WtjE3N6dOnTol2hR/Pzk5GUdHxxL7d3R01LapiGpLcqdOnWL9+vX3rR8zZgxLly4tdz/Tp09n0qRJOmWO7ac9dHxVydTUhA0fjcCjnj1Boxdqe3HF9sX8Rovgd7F/woq//iokPTOb+D2zufxHmrbNlsijbIk8iqOdNVnZuSgKvDGkEwl3tTF2DlZmeDtb80l0vLYsI7eAvwoV/rgnUf2RnkNTx6KenGNtc154qi5Tv/qNxPSidldu5tDUqTZdmzqw4rDuxfHHhUk5Sa60vyWNRvPAx7t3MkNRlHInOO5tU1r7iuznbtWW5FxcXDh48OB9x9WHDh3CxcWl3P1oNJoS/yFUNXhVouIE18i9Lt1GL+BGetZ926bdKqoLeKYJjna1+Sb6ZIk2xbeVDHuxHTl5+eyL+a1qAn8MBTxpT3rOX5xIzNCWFRQqXEq9g4uN7v8zLjYaUv93+4jGtOgqTiGKTptCRXms14IpLzGU9rf0IJydnYGintjdf8MpKSna3p2zszN5eXncvHlTpzeXkpKCv7+/ts2ff/5ZYv/Xr18v0UssS7Vdk5syZQqvvfYar7/+Ol999RUxMTEcPnyYr776itdff52xY8fy1ltvVVd4D8zK0pynm9Tj6Sb1AGhQz56nm9TDzbkOarUJG/81ktbN3Xl1xhrUJiqc7K1xsrfGzPTvxDw0uB3PejfAs74DA7o/w4a5I1i44QfOX07RtnmtfwdaPVWfJ90dGdOvA/Om9eOdhbtIz8x+5OdcE6mAgEZ2HLh0g0LdXMXXp1Pwa/AEnRrb4WRtTtemDrSub8uec0UTNtfSc0jKyGVkOzca2dfCsbY5PZrXxdvFWmdy4nFjYqIqc9MXT09PnJ2d2bNnj7YsLy+P6OhobQLz9fXFzMxMp01SUhKnTp3StvHz8yM9PZ0jR45o2xw+fJj09HRtm4qotp7cuHHjsLe3Z968eSxbtkw7baxWq/H19WXt2rX069evusJ7YK2be7D7ize1n+dOeQmAdbti+GDpd/QKfBqAI1um63yv68j5/HjsPABNGjjy3oRg7GxrcfnaDeau+J4F66N02rfx8uCfr/Wgdi1zziX8yesfbmLTt2VfvzQmXi7W1K1tzv7zN0rUHb2azorDiQR7ORHyTH2uZeQyLzqecylFPecCBebuu8iA1q5M7eSJxtSEP2/nseTnK8T9cbvE/h4XarX+EllmZiYXLlzQfo6PjycuLg47Ozvc3d0JCwtj9uzZNG7cmMaNGzN79mxq1arFoEGDALC1tWXEiBFMnjwZe3t77OzsmDJlCt7e3trZ1mbNmtGtWzdGjRrFsmXLABg9ejQ9e/as8MwqgEq5+z6EapKfn09qatG/og4ODpiZmT3U/ix9XtdHWKISek8cWd0hGJ1Nw1pVqn2LGbvLrD/9Ydcy6++2f/9+OnbsWKI8JCSE1atXoygK7777LsuWLePmzZu0bduWzz77DC8vL23bnJwcpk6dysaNG8nOzqZz584sXrxY59axGzdu8MYbb7Br1y4AgoODWbRoEU888USFY60RSU7fJMk9epLkHr3KJjnvmXvKrD/5/vMPEU3NJc+uCmEkjPXZVUlyQhiJx3lm+GFIkhPCSOhzBvVxIklOCCMhSU4IYdDkzcBCCIMmPTkhhEGTJCeEMGhGOlqVJCeEsZCenBDCoMnNwEIIgybDVSGEQZPhqhDCoEmSK0Pxa04qIjg4+IGDEUJUHRMjHa9WKMn17t27QjtTqVQ6ayYKIWoO6cmVobBQVoAS4nGnliQnhDBkRjpafbAkl5WVRXR0NFeuXCEvT3ex3TfeeEMvgQkh9EttpFmu0knuxIkTdO/enTt37pCVlYWdnR2pqanUqlULR0dHSXJC1FDGek2u0rdAT5w4kV69enHjxg0sLS2JiYnh8uXL+Pr68vHHH1dFjEIIPTBRqcrcDFWlk1xcXByTJ09GrVajVqvJzc3Fzc2NuXPn8vbbb1dFjEIIPXhU667WNJVOcmZmZtqX7zk5OXHlyhWgaB3F4p+FEDWP2kRV5maoKp3kfHx8OHr0KAAdO3bknXfeYcOGDYSFheHt7a33AIUQ+qEqZ6uoBg0aoFKpSmzjx48HIDQ0tERdu3btdPaRm5vLhAkTcHBwwMrKiuDgYBITE/VxmiVUOsnNnj0bFxcXAN5//33s7e0ZO3YsKSkpfP7553oPUAihH/rqycXGxpKUlKTd9uwpWs/1lVde0bbp1q2bTpvvvvtOZx9hYWHs2LGDzZs389NPP5GZmUnPnj2r5GGCSs+utmnTRvtz3bp1SwQvhKiZ9LXGQ926dXU+z5kzh0aNGhEQEKAt02g0ODs7l/r99PR0VqxYwbp16+jSpQsA69evx83Njb179/LCCy/oJc5ixvmCKSGMUHkTD7m5uWRkZOhsubm5Ze4zLy+P9evXM3z4cJ0kun//fhwdHWnSpAmjRo0iJSVFW3fs2DHy8/Pp2rWrtszV1RUvLy8OHjyo9/OudE/O09OzzH8RLl269FABCSGqRnlD0oiICN59912dsvDwcGbNmnXf7+zcuZNbt24RGhqqLQsKCuKVV17Bw8OD+Ph4Zs6cSadOnTh27BgajYbk5GTMzc2pU6eOzr6cnJxITk6u9HmVp9JJLiwsTOdzfn4+J06cIDIykqlTp+orLiGEnpU3WJ0+fTqTJk3SKdNoNGV+Z8WKFQQFBeHq6qot69+/v/ZnLy8v2rRpg4eHB99++y19+/a9774URamSZRMrneTefPPNUss/++wz7ayrEKLmKa8np9Foyk1qd7t8+TJ79+5l+/btZbZzcXHBw8OD8+fPA+Ds7ExeXh43b97U6c2lpKTg7+9f4eNXlN6uyQUFBbFt2zZ97U4IoWf6vhl41apVODo60qNHjzLbpaWlcfXqVe1dGb6+vpiZmWlnZQGSkpI4depUlSQ5vb2F5Msvv8TOzk5fuxNC6Jk+H90qLCxk1apVhISEYGr6dxrJzMxk1qxZvPTSS7i4uJCQkMDbb7+Ng4MDffr0AYoeHBgxYgSTJ0/G3t4eOzs7pkyZgre3t3a2VZ8qneR8fHx0xs2KopCcnMz169dZvHixXoMTQuiPPh/d2rt3L1euXGH48OE65Wq1mpMnT7J27Vpu3bqFi4sLHTt2ZMuWLVhbW2vbzZs3D1NTU/r160d2djadO3dm9erVqNVqvcVYTKUoilKZL8yaNUsnyZmYmFC3bl0CAwN56qmn9B7gg8j5q7ojMD5/3Myu7hCMTqO6lpVq/8bO38qsX9C7Zvz96lule3JlTScLIWouA348tUyVnnhQq9U6N/YVS0tLq5KuphBCP4z1Af1K9+TuN7rNzc3F3Nz8oQMSQlQNtZE+31ThJLdgwQKg6Pm3L774gtq1a2vrCgoKOHDgQI25JieEKMmQX4xZlgonuXnz5gFFPbmlS5fqDE3Nzc1p0KABS5cu1X+EQgi9UBtnjqt4kouPjweK3iG3ffv2Es+dCSFqNkO+7laWSl+T++GHH6oiDiFEFTPSHFf52dWXX36ZOXPmlCj/17/+pfPSPCFEzWKss6uVTnLR0dGlPqvWrVs3Dhw4oJeghBD6p1apytwMVaWHq5mZmaXeKmJmZkZGRoZeghJC6J8Bd9bKVOmenJeXF1u2bClRvnnzZpo3b66XoIQQ+mesw9VK9+RmzpzJSy+9xMWLF+nUqRMA+/btY+PGjXz55Zd6D1AIoR9yM3AFBQcHs3PnTmbPns2XX36JpaUlLVu2JCoqChsbm6qIUQihB3IzcCX06NFDO/lw69Yt7bqrv/zyS5UsKSaEeHjG2pN74NOOiopiyJAhuLq6smjRIrp37y6vPxeiBpPZ1QpITExk9erVrFy5kqysLPr160d+fj7btm2TSQchajgDnlsoU4V7ct27d6d58+acOXOGhQsXcu3aNRYuXFiVsQkh9EhmV8uxe/du3njjDcaOHUvjxo2rMiYhRBUw5ERWlgr35H788Udu375NmzZtaNu2LYsWLeL69etVGZsQQo9MytkMVYXPzc/Pj+XLl5OUlMSYMWPYvHkz9erVo7CwkD179nD79u2qjFMI8ZBMVKoyN0NV6YVs7nbu3DlWrFjBunXruHXrFs8//zy7du3SZ3wPRBayefRkIZtHr7IL2Ww4llhm/WDf+g8TTo31UL3Upk2bMnfuXBITE9m0aZO+YhJCVAGVquytoopX7Lt7c3Z21tYrisKsWbNwdXXF0tKSwMBATp8+rbOP3NxcJkyYgIODA1ZWVgQHB5OYWHYSflB6GYqr1Wp69+5dI3pxQojS6fM+uRYtWpCUlKTdTp48qa2bO3cun3zyCYsWLSI2NhZnZ2eef/55nUtaYWFh7Nixg82bN/PTTz+RmZlJz549q+Rhggd64kEI8fjR53U3U1NTnd5bMUVR+PTTT5kxYwZ9+/YFYM2aNTg5ObFx40bGjBlDenq69jJXly5dAFi/fj1ubm7s3buXF154QW9xgmFPqggh7nLvEPPeLTc3l4yMDJ0tNze31H2dP38eV1dXPD09GTBgAJcuXQKKlklITk6ma9eu2rYajYaAgAAOHjwIwLFjx8jPz9dp4+rqipeXl7aNPkmSE8JIlDdcjYiIwNbWVmeLiIgosZ+2bduydu1avv/+e5YvX05ycjL+/v6kpaWRnJwMgJOTk853nJyctHXJycmYm5uXWCfm7jb6JMNVIYxEefcCT58+nUmTJumUaTSaEu2CgoK0P3t7e+Pn50ejRo1Ys2YN7dq1A4p6jXdTFKVE2b0q0uZBSE9OCCNhgqrMTaPRYGNjo7OVluTuZWVlhbe3N+fPn9dep7u3R5aSkqLt3Tk7O5OXl8fNmzfv20afJMkJYSSq6mbg3Nxczp49i4uLC56enjg7O7Nnzx5tfV5eHtHR0fj7+wPg6+uLmZmZTpukpCROnTqlbaNPMlwVwkjo63VKU6ZMoVevXri7u5OSksIHH3xARkYGISEhqFQqwsLCmD17No0bN6Zx48bMnj2bWrVqMWjQIABsbW0ZMWIEkydPxt7eHjs7O6ZMmYK3t7d2tlWfJMkJYST0dbkrMTGRgQMHkpqaSt26dWnXrh0xMTF4eHgA8NZbb5Gdnc24ceO4efMmbdu2Zffu3VhbW2v3MW/ePExNTenXrx/Z2dl07tyZ1atXo1ar9RPkXR7qsa6aSh7revTksa5Hr7KPde05m1pm/fPNHB4mnBpLenJV7NjRWFavXMHZM6e4fv068xZ8RqfOf3fJFUVh6eJFbPvPFjIyMvB+uiXT//kOTz6p+zqrX+JOsHD+PE6e/BUzU1OaPtWMz5Yux8LC4lGfUo22Zd0KDkbvI/FyAuYaDc28WzJ8bBj13RvotLuScIlVS+ZzMu4YSmEh7p6NmP7eXBydXQC4kZbKisXziIuN4c6dLOq7N6D/0BE81/H5ajgr/TDkt/+WRZJcFcvOvkPTpk15sU9fJodNKFG/asVy1q1ZxXsfzsGjQQOWL1vCayNf5atvI7Gyqg0UJbhxY0YyfOQY/jFjJmZmZvz+22+YmMi80b1OnThGz779afJUCwoKClizfBEzJo5l2frtWFgW9XyS/rjK1HGv0rVnb4aMGEstq9pcvXwJ87tmEj9+fwZ3sjJ5Z86n2NjWYf+e/zInfBrz67nRqMlT1XV6D8VIc5wMVx+lli2a6vTkFEWhS2B7Bg8dxvCRo4GimahOHfx5c9IUXuk3AIAhA/vRzs+f198Iq67Qy1VTh6vpN28wsFcnPlq0Au9WvgDMCZ+G2tSUqTM/vO/3+j7vx/jJM+jcrae2rH/3AIaPC+OFnn2qPO6KqOxw9cDvN8qs79DE7mHCqbGkK1CN/khMJDX1On7/95y2zNzcHN82z/DLiRMApKWlcfLXX7Czt2fY4AF07ODP8JAhHD8miwZVRFZWJgDWNrYAFBYWEnvwR+q5efDPSWMZ2LMjYaOGcPBAlM73Wnj7cCDqe25npFNYWEj03kjy8/N42qfNIz8HfTHW98nV6CR39epVhg8fXmabyjxvV9Okpha9Wdne3l6n3N7egdTUoovEfyReBWDpZ4vo+/IrLF72Bc2aNWf0iFAuX054pPE+bhRFYfnCf9PiaR8aNHwSgFs3b5CdfYf/rF+Jb1t/Ppi3BP8OnfhwxmROnvj7H45/vPcRhX8V0L97AC92fJaF//qAf87+BJd6btV1Og9NVc5mqGp0krtx4wZr1qwps01pz9v966OSz9vVZKU/AlP0c2FhIQAv9+tP7z4v0axZc6b+420aeHqyc/u2Rx3qY2XxJxHEX/ydabPmaMsUpej32e65QPr0H0qjxk/Rb+hwnvXvwHc7v9S2W7v8M27fzmD2p8uY/8UG+vQfQsTMqcRfPP/Iz0NfZEnCalDe++eK32xQltKet1PU5T+KUhM4ONQF+N/9Ro7a8hs30rC3L5rOd6hb1KZho0Y63/Vs2IjkpGuPKNLHz5J5czj8czRzF63EwfHvR4VsbOugVpvi3kD39+nm4cnpk0WXCJL+uMrX2zazZO2XePyvB9iwcVNO/3KCb7ZvYcLUfz66E9Gjqngu9HFQrUmud+/eqFQqypr7KO8/jEajKfF8XU2deLhXvfr1cXCoS8zBn2nWrGjd2vy8PI4djeXNSVOK2tSrT11HRxLi43W+ezkhgefad3jkMdd0iqKwZN4cDh2IYs7CL3B2radTb2ZmRpNmzUm8mqBT/sfVyzg6Fd0+kpOTA4DqntlrE7UJyv961o8jI81x1TtcdXFxYdu2bRQWFpa6HT9+vDrD04s7WVn8dvYsv509CxRNNvx29ixJ166hUqkYPHQYK5YvY9/ePZw//zszZ0zHwsKC7j2KZvVUKhWhr45g04Z17Pk+kiuXL7NowackxF+iT9+Xq/PUaqTF/57ND7u/5a3wCCxrWXEjLZUbaank5uZo27w0MJQf931P5K5tXEu8wtfbNnP44AF69ukPgJtHA1zru7HwXx9w7sxJkv64yvZNazkRG4Nfh47VdWoPTV+vP3/cVOstJMHBwbRq1Yr33nuv1PpffvkFHx8f7XWpiqpJPbnYI4cZ+eqwEuXBL/bh/dlztDcDf7l1CxkZ6dqbgRs3bqLTfsXyz9myeQPp6ek0bfoUYZOm0Nq35sz01ZRbSLo/16rU8olvv8vz3V/Uft79zU62rl9BakoK9d09GDxiLH7t/05gf1y9zKqlCzjz6wmys+/gWs+dvgOH6dxSUt0qewvJsYSMMut9G9g8TDg1VrUmuR9//JGsrCy6detWan1WVhZHjx4lICCgUvutSUnOWNSUJGdMKpvkjpeT5FpLknt8SJJ79CTJPXqVTXInLpe9NrKPh3WZ9Y8reaxLCCNR3puBDZUkOSGMhSQ5IYQhM+RHt8oiSU4II2GkOU6SnBDGQmWk41VJckIYCZl4EEIYNHl2VQhh0Iw0x0mSE8JYGGuSq9HvkxNC6I++3gwcERHBM888g7W1NY6OjvTu3Ztz587ptAkNDUWlUuls7dq102mTm5vLhAkTcHBwwMrKiuDgYBITE/VyrneTJCeEkdDXm4Gjo6MZP348MTEx7Nmzh7/++ouuXbuSlZWl065bt24kJSVpt++++06nPiwsjB07drB582Z++uknMjMz6dmzJwUFBQ9zmiXIs6tCL+TZ1Uevss+uXrqeU2Z9w7oPtrzl9evXcXR0JDo6mg4dit5xGBoayq1bt9i5c2ep30lPT6du3bqsW7eO/v2LXnF17do13Nzc+O6773jhhRceKJbSSE9OCCNhoip7e9D1UtLT0wGws9Nd7Wv//v04OjrSpEkTRo0aRUpKirbu2LFj5Ofn07VrV22Zq6srXl5eHDx4UE9nXESSnBDGopzxamnrpURElL1eiqIoTJo0ieeeew4vLy9teVBQEBs2bCAqKop///vfxMbG0qlTJ23STE5OxtzcnDp16ujsz8nJieTkZD2etMyuCmE0yptcKG29lHuXFrjX66+/zq+//spPP/2kU148BAXw8vKiTZs2eHh48O2339K3b9/77q9oESf9TgNLkhPCSJT3xENp66WUZcKECezatYsDBw5Qv379Mtu6uLjg4eHB+fNFq505OzuTl5fHzZs3dXpzKSkp+Pv7VziGipDhqhBGQz/zq4qi8Prrr7N9+3aioqLw9PQs9ztpaWlcvXoVF5eixYJ8fX0xMzNjz5492jZJSUmcOnVK70lOenJCGAl9Pbs6fvx4Nm7cyFdffYW1tbX2GpqtrS2WlpZkZmYya9YsXnrpJVxcXEhISODtt9/GwcGBPn36aNuOGDGCyZMnY29vj52dHVOmTMHb25suXbroJ9D/kSQnhJHQ1/vklixZAkBgYKBO+apVqwgNDUWtVnPy5EnWrl3LrVu3cHFxoWPHjmzZsgVr679fsT5v3jxMTU3p168f2dnZdO7cmdWrV6NWq/USZzG5T07ohdwn9+hV9j655Iz8MuudbcweJpwaS3pyQhgJedWSEMKgyauWhBAGzThTnCQ5IYyGLGQjhDBoRprjJMkJYSwkyQkhDJoMV4UQBs04U5wkOSGMhtxCIoQwaHIzsBDCsEmSE0IYMmOdeDDIB/QfV7m5uURERDB9+vRKvbxQPDj5nRs+SXI1SEZGBra2tqSnp2NjY1Pd4RgF+Z0bPnkzsBDCoEmSE0IYNElyQgiDJkmuBtFoNISHh8sF8EdIfueGTyYehBAGTXpyQgiDJklOCGHQJMkJIQyaJDkhhEGTJFdDLF68GE9PTywsLPD19eXHH3+s7pAM2oEDB+jVqxeurq6oVCp27txZ3SGJKiJJrgbYsmULYWFhzJgxgxMnTtC+fXuCgoK4cuVKdYdmsLKysmjZsiWLFi2q7lBEFZNbSGqAtm3b0rp1a5YsWaIta9asGb179yYiIqIaIzMOKpWKHTt20Lt37+oORVQB6clVs7y8PI4dO0bXrl11yrt27crBgwerKSohDIckuWqWmppKQUEBTk5OOuVOTk4kJydXU1RCGA5JcjXEve/fVxTFaN/JL4Q+SZKrZg4ODqjV6hK9tpSUlBK9OyFE5UmSq2bm5ub4+vqyZ88enfI9e/bg7+9fTVEJYThkjYcaYNKkSQwdOpQ2bdrg5+fH559/zpUrV3jttdeqOzSDlZmZyYULF7Sf4+PjiYuLw87ODnd392qMTOib3EJSQyxevJi5c+eSlJSEl5cX8+bNo0OHDtUdlsHav38/HTt2LFEeEhLC6tWrH31AospIkhNCGDS5JieEMGiS5IQQBk2SnBDCoEmSE0IYNElyQgiDJklOCGHQJMkJIQyaJDkhhEGTJCcqbdasWbRq1Ur7OTQ0tFpeOJmQkIBKpSIuLu6RH1s8PiTJGZDQ0FBUKhUqlQozMzMaNmzIlClTyMrKqtLjzp8/v8KPQkliEo+aPKBvYLp168aqVavIz8/nxx9/ZOTIkWRlZem8Wh0gPz8fMzMzvRzT1tZWL/sRoipIT87AaDQanJ2dcXNzY9CgQQwePJidO3dqh5grV66kYcOGaDQaFEUhPT2d0aNH4+joiI2NDZ06deKXX37R2eecOXNwcnLC2tqaESNGkJOTo1N/73C1sLCQjz76iCeffBKNRoO7uzsffvghAJ6engD4+PigUqkIDAzUfm/VqlU0a9YMCwsLnnrqKRYvXqxznCNHjuDj44OFhQVt2rThxIkTevzNCUMlPTkDZ2lpSX5+PgAXLlxg69atbNu2DbVaDUCPHj2ws7Pju+++w9bWlmXLltG5c2d+//137Ozs2Lp1K+Hh4Xz22We0b9+edevWsWDBAho2bHjfY06fPp3ly5czb948nnvuOZKSkvjtt9+AokT17LPPsnfvXlq0aIG5uTkAy5cvJzw8nEWLFuHj48OJEycYNWoUVlZWhISEkJWVRc+ePenUqRPr168nPj6eN998s4p/e8IgKMJghISEKC+++KL28+HDhxV7e3ulX79+Snh4uGJmZqakpKRo6/ft26fY2NgoOTk5Ovtp1KiRsmzZMkVRFMXPz0957bXXdOrbtm2rtGzZstTjZmRkKBqNRlm+fHmpMcbHxyuAcuLECZ1yNzc3ZePGjTpl77//vuLn56coiqIsW7ZMsbOzU7KysrT1S5YsKXVfQtxNhqsG5ptvvqF27dpYWFjg5+dHhw4dWLhwIQAeHh7UrVtX2/bYsWNkZmZib29P7dq1tVt8fDwXL14E4OzZs/j5+ekc497Pdzt79iy5ubl07ty5wjFfv36dq1evMmLECJ04PvjgA504WrZsSa1atSoUhxDFZLhqYDp27MiSJUswMzPD1dVVZ3LByspKp21hYSEuLi7s37+/xH6eeOKJBzq+paVlpb9TWFgIFA1Z27Ztq1NXPKxW5LWH4gFJkjMwVlZWPPnkkxVq27p1a5KTkzE1NaVBgwaltmnWrBkxMTEMGzZMWxYTE3PffTZu3BhLS0v27dvHyJEjS9QXX4MrKCjQljk5OVGvXj0uXbrE4MGDS91v8+bNWbduHdnZ2dpEWlYcQhST4aoR69KlC35+fvTu3Zvvv/+ehIQEDh48yD//+U+OHj0KwJtvvsnKlStZuXIlv//+O+Hh4Zw+ffq++7SwsGDatGm89dZbrF27losXLxITE8OKFSsAcHR0xNLSksjISP7880/S09OBohuMIyIimD9/Pr///jsnT55k1apVfPLJJwAMGjQIExMTRowYwZkzZ/juu+/4+OOPq/g3JAxCdV8UFPpz78TD3cLDw3UmC4plZGQoEyZMUFxdXRUzMzPFzc1NGTx4sHLlyhVtmw8//FBxcHBQateurYSEhChvvfXWfSceFEVRCgoKlA8++EDx8PBQzMzMFHd3d2X27Nna+uXLlytubm6KiYmJEhAQoC3fsGGD0qpVK8Xc3FypU6eO0qFDB2X79u3a+kOHDiktW7ZUzM3NlVatWinbtm2TiQdRLlnjQQhh0GS4KoQwaJLkhBAGTZKcEMKgSZITQhg0SXJCCIMmSU4IYdAkyQkhDJokOSGEQZMkJ4QwaJLkhBAGTZKcEMKg/T9aufebDKLWqgAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.63 0.75 2067\n",
+ " 1 0.26 0.72 0.38 374\n",
+ "\n",
+ " accuracy 0.64 2441\n",
+ " macro avg 0.59 0.67 0.56 2441\n",
+ "weighted avg 0.82 0.64 0.69 2441\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(MLP_models, MLP_name, x_test_list, ytest, \"MLP testing\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99c5c469",
+ "metadata": {},
+ "source": [
+ "## Deep Reinforcement Learning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44854afd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a070cb47",
+ "metadata": {},
+ "source": [
+ "## ILP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "807fd99d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e4efbfeb",
+ "metadata": {},
+ "source": [
+ "# Cross Validation on Best Performing Datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "id": "7c9594de-ce70-4c61-80ef-75c0ac9883e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Decision Tree: 0.683330 (0.014182)\n",
+ "SVM: 0.749828 (0.008647)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MLP: 0.811432 (0.009344)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/user/HS400/ms04010/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 504x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Initialize the models\n",
+ "models = [\n",
+ " ('Decision Tree', DecisionTreeClassifier(ccp_alpha=0.002)),\n",
+ " ('SVM', svm.SVC(probability=True, random_state=42, gamma=0.1)),\n",
+ " ('MLP', MLPClassifier(random_state=42))\n",
+ "]\n",
+ "\n",
+ "# Lists to hold models, validation data, and test data\n",
+ "models_list = []\n",
+ "x_val_list = []\n",
+ "x_test_list = []\n",
+ "\n",
+ "# Lists to hold results and model names\n",
+ "results = []\n",
+ "names = []\n",
+ "\n",
+ "# Evaluate each model in turn\n",
+ "for name, model in models:\n",
+ " models_list.append(model)\n",
+ " x_test_list.append(xtest_pca_oversampled)\n",
+ " kfold = StratifiedKFold(n_splits=10, random_state=None)\n",
+ " cv_results = cross_val_score(model, xtrain_pca_oversampled, ytrain_oversampled, cv=kfold, scoring='accuracy')\n",
+ " results.append(cv_results)\n",
+ " names.append(name)\n",
+ " print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))\n",
+ "\n",
+ "# Compare Algorithms\n",
+ "plt.figure(figsize=(7, 7))\n",
+ "plt.boxplot(results, labels=names)\n",
+ "plt.title('Comparison of SVM, DecisionTree, and MLP Models on Dataset 1')\n",
+ "plt.xlabel('Model')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5de8b9a5",
+ "metadata": {},
+ "source": [
+ "# Evaluations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "31f0302e",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ffabfde",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "print(confusion_matrix(ytest, predictions_undersampled))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a4da96da",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "print(confusion_matrix(ytest, predictions_oversampled))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6915814c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(confusion_matrix(ytest, predictions))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}