ch2/LDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ch2: 2.2.6 Linear Discriminant Analysis (LDA)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal-length</th>\n",
       "      <th>sepal-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal-length  sepal-width  petal-length  petal-width           Class\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n",
    "dataset = pd.read_csv(url, names=names)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Class'].unique() # array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# lbl_encoder object knows how to understand word labels.\n",
    "lbl_encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "dataset['Class']= lbl_encoder.fit_transform(dataset['Class'])\n",
    "dataset['Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal-length</th>\n",
       "      <th>sepal-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal-length  sepal-width  petal-length  petal-width  Class\n",
       "0             5.1          3.5           1.4          0.2      0\n",
       "1             4.9          3.0           1.4          0.2      0\n",
       "2             4.7          3.2           1.3          0.2      0\n",
       "3             4.6          3.1           1.5          0.2      0\n",
       "4             5.0          3.6           1.4          0.2      0\n",
       "..            ...          ...           ...          ...    ...\n",
       "145           6.7          3.0           5.2          2.3      2\n",
       "146           6.3          2.5           5.0          1.9      2\n",
       "147           6.5          3.0           5.2          2.0      2\n",
       "148           6.2          3.4           5.4          2.3      2\n",
       "149           5.9          3.0           5.1          1.8      2\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Preprocessing: assigns the first four columns of the dataset i.e. the feature set to X variable while the values in the fifth column (labels) are assigned to the y variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELLPR~1\\AppData\\Local\\Temp/ipykernel_7060/120526215.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  X = dataset.drop('Class', 1)\n"
     ]
    }
   ],
   "source": [
    "#X = dataset.iloc[:, 0:4].values\n",
    "#y = dataset.iloc[:, 4].values\n",
    "X = dataset.drop('Class', 1)\n",
    "y = dataset['Class']\n",
    "print (X.shape)\n",
    "print (y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "divide the data into training and test sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Scaling\n",
    "\n",
    "As was the case with PCA, we need to perform feature scaling for LDA too. Execute the following script to do so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape =  (120, 4)\n",
      "X_test shape =  (30, 4)\n",
      "y_train shape =  (120,)\n",
      "y_test shape =  (30,)\n"
     ]
    }
   ],
   "source": [
    "print (\"X_train shape = \", X_train.shape)\n",
    "print (\"X_test shape = \", X_test.shape)\n",
    "print (\"y_train shape = \", y_train.shape)\n",
    "print (\"y_test shape = \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying PCA for comparison:\n",
    "PCA depends only upon the feature set and not the label data. Therefore, PCA can be considered as an unsupervised machine learning technique.\n",
    "\n",
    "Performing PCA using Scikit-Learn is a two-step process:\n",
    "\n",
    "Initialize the PCA class by passing the number of components to the constructor.\n",
    "Call the fit and then transform methods by passing the feature set to these methods. The transform method returns the specified number of principal components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape =  (120, 4)\n",
      "X_test shape =  (30, 4)\n",
      "y_train shape =  (120,)\n",
      "y_test shape =  (30,)\n",
      "PCA_X_train shape =  (120, 3)\n",
      "PCA_X_test shape =  (30, 3)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=3)\n",
    "pca_X_train = pca.fit_transform(X_train)\n",
    "pca_X_test = pca.transform(X_test)\n",
    "\n",
    "print (\"X_train shape = \", X_train.shape)\n",
    "print (\"X_test shape = \", X_test.shape)\n",
    "print (\"y_train shape = \", y_train.shape)\n",
    "print (\"y_test shape = \", y_test.shape)\n",
    "\n",
    "print (\"PCA_X_train shape = \", pca_X_train.shape)\n",
    "print (\"PCA_X_test shape = \", pca_X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The LinearDiscriminantAnalysis class of the sklearn.discriminant_analysis library can be used to Perform LDA in Python. Like PCA, we have to pass the value for the n_components parameter of the LDA, which refers to the number of linear discriminates that we want to retrieve. In this case we set the n_components to 2, since the dataset has three classes , and need 2 linear discriminants, and LDA requires n_components not to be larger than min(n_features, n_classes - 1). Finally we execute the fit and transform methods to actually retrieve the linear discriminants.\n",
    "\n",
    "Notice, in case of LDA, the transform method takes two parameters: the X_train and the y_train. However in the case of PCA, the transform method only requires one parameter i.e. X_train. This reflects the fact that supervised LDA takes the output class labels into account while selecting the linear discriminants, while unsupervised PCA doesn't depend upon the output labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape =  (120, 4)\n",
      "X_test shape =  (30, 4)\n",
      "y_train shape =  (120,)\n",
      "y_test shape =  (30,)\n",
      "LDA_X_train shape =  (120, 2)\n",
      "LDA_X_test shape =  (30, 2)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "\n",
    "lda = LDA(n_components=2)\n",
    "lda.fit(X_train, y_train)\n",
    "LDA_X_train = lda.transform(X_train)\n",
    "LDA_X_test = lda.transform(X_test)\n",
    "\n",
    "print (\"X_train shape = \", X_train.shape)\n",
    "print (\"X_test shape = \", X_test.shape)\n",
    "print (\"y_train shape = \", y_train.shape)\n",
    "print (\"y_test shape = \", y_test.shape)\n",
    "\n",
    "print (\"LDA_X_train shape = \", LDA_X_train.shape)\n",
    "print (\"LDA_X_test shape = \", LDA_X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training and Making Predictions\n",
    "Since we want to compare the performance of LDA with one linear discriminant to the performance of PCA with one principal component, we will use the same Random Forest classifier that we used to evaluate performance of PCA-reduced algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "lda_classifier = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "\n",
    "lda_classifier.fit(LDA_X_train, y_train)\n",
    "lda_y_pred = lda.predict(X_test)\n",
    "\n",
    "pca_classifier = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "\n",
    "pca_classifier.fit(pca_X_train, y_train)\n",
    "pca_y_pred = pca_classifier.predict(pca_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the Performance\n",
    "As always, the last step is to evaluate performance of the algorithm with the help of a confusion matrix and find the accuracy of the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA Confusion Matrix\n",
      "[[11  0  0]\n",
      " [ 0 13  0]\n",
      " [ 0  0  6]]\n",
      "LDA Accuracy1.0\n",
      "PCA Confusion Matrix\n",
      "[[11  0  0]\n",
      " [ 0  8  5]\n",
      " [ 0  1  5]]\n",
      "Accuracy0.8\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "lda_cm = confusion_matrix(y_test, lda_y_pred)\n",
    "print('LDA Confusion Matrix') \n",
    "print(lda_cm)\n",
    "print('LDA Accuracy' + str(accuracy_score(y_test, lda_y_pred)))\n",
    "\n",
    "\n",
    "pca_cm = confusion_matrix(y_test, pca_y_pred)\n",
    "print('PCA Confusion Matrix') \n",
    "print(pca_cm)\n",
    "print('Accuracy' + str(accuracy_score(y_test, pca_y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compare plotting the projection spaces of both methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2wrfPDKxaBbz8sihtp08Xvk2FogSilvYiyLBhwPffZycn/uADef/PPxLFrFAoFH7xV0/P5QJiYwvXttMJ3Hqr+F7ZbFLL79lngR9/BHr2LFzbCkUJQ1mkIsSePeIjmrPCQ2YmcOIEMHt25ORSKBTFhAcfBKxW3+0GgyhAniW/gjB3rrSRliaWqYwMGawGDVLlZxQKL5QiFSE2bJD8ed6kpQF//hl+eRQKRTFjyBDgttvEMpXTApWcDDz1lOSZOnWqYG3PnCmDkTcuF7BuXcHaVChKKEqRihDVqwNEvttNJqnwoFAoFAHR6YAZM4C//wYqVcq9Ly0NOHkSeO653NtdrvxZlHR+Hg3M/vcpFKUU9YuIED16yNjnbZUyGoH774+ISApFqYSIahHRMiLaSUTbiej/Ii1TUFSpom15cjgkFQIgy3z33ivOl7GxQOfO4ozpjxEjtJcNY2LkXIWiGGO3A2fOiCtgKFCKVITQ6YAVK6SyQ2ysjG+1agE//STJiiPN2bPA2LFAv37A888DR49GWiKFoshwAHiCmZsCuBzAw0TULMIy5R9PrT0tPEt+ffqI82VmplilNmwArroKOHZM+7xbbwX695eByWgUpSouTqJhDCpGSVE8cbnkeVa+vDxvq1QBpk8vfLvqFxFBatUSd4MTJ8SPs3597eW+cHPwINCxo6wOZGQAv/8uVShWrADato20dApFaGHmEwBOuP9OIaKdAGoA2BFRwfKL1SqRdH/8kXvZzmwW8/a2bcDGjaJE5SQzE/jwQ+DNN33b1OmAr78GNm8Wp80KFcQfq2zZor0WhaIIeekl4P33s4O8MjOB//0v+/YuKEqRigKqVYu0BLl54gngwoXsnH6ZmfIaOVLGY4WipEJEdQG0BbA+wqIExxdfiL/AkSPy3uUCunWTlAU//aRtRcrKCry8BwDt28tLoSjmOBySYihnpDwg719+WSlSihCzeLF2YuS//xYLVWFT1CgU0QgRxQGYD+AxZk722jcSwEgAqB2NtewqV5YknKtWiUm5bVugVSvZ17y5fwfzVauA774r3FNEoSgGpKTI3EGLwrquKB8phQ/+koHq9co9QlEyISIjRImaxczfe+9n5unM3IGZOyQkJIRfwPxAJFaooUOzlSgAaNIEuPZa7QSeKSly/PLlYRNToYgEZcsC5cpp78v5cykISpFS+DBypO+YazJJLj6lSClKGkREAD4DsJOZJ0ZankLhdAKHD4uClJPvvgMeeED7HJsNGD4cmDJFQpkUihKITge8846vocBiAd5+u5BtF+70EhA6XAzZuxeYOFEcwI8fD337L70kvqtmM1CmjNxoHTuKX6pCUQK5AsAQANcQ0Vb368ZICxU0X30lYUhNmwIJCcA99wDp6bIvJgYYNUoi77RITASefhqoVw9YujRsIisU4WToUAlebd1aIve6d5cE/l27Fq5dYubCNUBUDUA1Zt5CRPEANgPoz8x+I146dOjAmzZtKlS/pZXXXgPGjcvOi8cMfPIJcPfdoe9r924J+GnYEGjTJvTtK0oXRLSZmTtEWo7CEpXj19KlwM035/akNZtl29y58j4jQ5LXaWUsz0n58pKXKlBaBYWilBFo/Cq0RYqZTzDzFvffKQA8ocOKELN1K/DWWzIeZmbKZDMjQyKci8Ii36SJ+KAqJUqhiHLGjfMNR0pPlyLD587J+9hY4IUX8q6I7nSqMjAKRRCE1EcqUOgwEY0kok1EtOmMWocvEHPm+KaCAcQJfOHC8MujUCiihP37tbfr9ZJYs1kzWdq74w7g449lluTP4VGVgVEogiJkv5ZAocNAMYl6iXL8rcIy+9+nUChKAQ6H9nabDVi9Gti5E/j8czEvX3EFsGuXJNzUKgNjMqkyMApFEIREkcordFgRGgYN0s7h5HIBffuGXx6FQhEFHDiQv7V9h0Nq7r30krwfOFAGDotFrFMWS3YZGJfLf9IdhSKEXLwIvP665H3t2RP4+edISxQ8hQ5mL1Ghw0XI7t3AN98ADRoAgwcXzHLeoQMwerRkZ83KEqu9Xi/vq1QpmFwuF7B2rYyvXbsC8fEFa0dRTGEGNm2S4oqdO0uthLxwOIC//pIkj1deqTK0Rpo9e+Q70Fr398bpzM4ZpdOJv8DGjdllYDp3Bh5/XOpBAcB11wGffgrUUG6vitCTkiIK1LFj4u8LyPNozBjgxRcjK1swhCJq70oAqwBsA+DJh/0cM//i75yojHopIlwuqdywalX2tpgYsbZ3KGD80rZt4kMaEyOTynr1Ct5O795AcrLk8rPbJZXMvfcWrD1FMSMxEbjhBsmhodOJdv7ii8Bzz/k/56+/pJJ1VpbcNMwSdt+vX57dqai9IiIxUVIeeJ5EedG2LbBli+/29HQp+HnmjChcgMzUqlcH9u2TJT+FIoS8954UEfZk6fAQGyvZxitWjIxcWgQavwptkWLm1QCioNRudPL887mVKEAmjt275x2F7I+WLeVVGBwOMaOeOpV7+6OPAu3aqUi9Eg8zcNNN8oDMWQ/ozTdlinjDDb7npKaK5u2d7HHwYDG51qpVtDIrtKlbV77LX37J/UQyGEQRymmpslpluq/F/PnyHXuUKED+TkqSaBZVRkYRYn7+2VeJAsRIsGGDDDfFARWaUcR8/LH2dpstslUZli/3jZYGZMydNi3s4ijCzY4dYsnwLqqYlgZMmqR9zoIF2lENTqdYpRSRY/ZsmQWVKycK1NVXi/WwVy+Z3nsy6z7/PHD77dpt7NkjipQ3NptkAVYoQkyNGtpuLg6HlI8sLqiCH0VMIGt7YQslFoYLF7S3O52qSkSpICnJf/j72bPa2y9c0I4Oy8rKzlWkiAwmk9S58K518eOPwMmTwIkTQOPG2lF6Hlq3FidJb4ujxVL4YmQKhQaPPirVi3JO6vV6oHZtWRkpLiiLVBETKIq4f/+wieFD9+7aBeGtVuCWW8IvjyLMtGvna40CxHpx663a51x3nfhFeWO1AjcWv4oqJYYlS6RYcc2aMqhs25Z7f9Wq4hcVSIkCJAt61aq5M5qbTPJU69Ur5GIrShZnzwL//KO90uGPDh1k1SY+Ptto2qIF8Pvv2kNNtKIUqSJmxgztif+jj/ove+UhKws4ckTbqnXqVOGMAJUr+yY5tlrF90q5QpQCzGZZwrNYsm3rFos8NB98UPucyy6TSIScD2SrVaIprrmmyEVWaDBnjjj6r1oloU8LFwJdugB//x18W0ajhEzdc4881cqWlWLGq1eLmSAnO3fKfdKzp/jVnT8fmutRFDvS0yXPa82aos8nJMgtkd84tu7dxT1v7ly5bbduLYbulswc9lf79u25NDF1KnNMjKTNJGLu0oU5NdX/8S4X89ixzHFxzBaLvJ57jtnpZN6yhblZM2nPZGLu2pU5MbHgsv35J/OgQcw33MD82WfMGRkFb0tRDNmwgXnIEObrrmP+4APmlJTAx7tczIsWMffvz3zjjcyzZzM7HPnqCsAmjsB4E+pX1IxfTidzlSqefLy5Xz17Fr798+eZL1zw3f7bbzIo6fXSV2wsc9WqzMePF75PRbFj6FC5BXLeflYr86xZgc9LS2Pu00fOLVtW/n/gAbmtPaSnM584ke8hpkgJNH6V7oEoDKxZI2NOzpssNpa5Xz//53z0ke85VivzCy8wlymTe7tez1yrFrPdHrZLUigKhFKkQszZs9kzNO9X+fIFb3fXLuaOHZmNRnldcQXz/v2yz+lkrl7dtz+jkfnBB0NzXYpiQ2qq/1uwZcvA5957r68CZrEwT5jAnJXFPHo0s9ksx1SqxPzFF4Hb27tX5oXp6SG7vFwEGr/U0l4R89ZbvuGdGRmyBnzihPY5b77pu86clgZMmOCbbNgTnfzrryETWaFQFAfi4/1n9nW5gGXLgq8dlZoqJWQ2bRInSrtdlvuuuEJCeo8c0Y5UsduBRYuCvwZFsebiRf+3oHdqnZzY7RJo6u22YrNJbqmbbxbfqfR0OebsWeDhh7Wznh8/Lr5WrVqJG2flysDMmQW/poKgFKkiZv9+7bHMZPIftefvBvTcVN7Y7cChQwWXUaFQFENMJuD++8XfzZuLF6X8y+jR2ueeOSNPqgkTJBWGh3nzZJDJOWi5XDKTW7BAlLeceaZyUq5cgS9FUTypWlW7GoZOJ0UP/JGR4b885LFjwG+/+QZD2WzAa6/l3sYscRBbt8rzMTlZgk4feghYvz6oSykUSpEqYq66StvZ3G4X310tWrTQ3l6liraDul4PdOpUcBkVCkUxZfx4YNgwyWDoTVqaFCr2juJbtAioUwd48kng2WdlOv/44/JUOnhQO1NwerrkHatQQXJU5YzsAyRQwZ/Spiix6HTA5Mm5g5b0eolBefNN/+fFx/uvyBHIiJqYmPv9tm1SatJbt09P958OryhQilQR88wzclPlNH96kgv7q2s3caLvJNNslglkjRq5KzWYzRKk07Fj6GVXKBRRjtEIfPSRDChaayxZWZLx3ENqqmSiT0+XKb7dLn9Pnw6sXClZ7bVma7Gx2Yl9vv5ack5ZrRLZFxMDDB0K3Hdf0VyjIqoZNEhcS3r1Aho1kqDPv/8GmjQJfN60aaKAeQJCvXVzb4h8n3OnT/sGlAKijIUzT6NSpEIEs9Spq11bbo7u3YHNm2Xit2mT3GxVqkh6gWnTsguwa9GjB7B0KXDttbLee9VVMhYOGACsWyepE2rWlMoQzz8P/PRT8cq5oXDjcABjx4p93JOLadeuSEtV6iCiz4noNBH9F2lZCkz58tq18IzG3Okq/vhD+8ljswFffgn06SMDS04LV2ys1PLzpLioVEkKHa9ZA8yaJSaBjz5Sg1Appls3Uab27BEjaIMGeZ9z9dVSBmbIEFlRGT7cf45gQIwGb7yRe1v79r5+w55jb7opuGsoFP680IvyFTVRLyHkmWe0I+22bYu0ZIqoZejQ3DcNkYRlHj4cacmKBERp1B6AbgDaAfgvP8dH5fh17JiEOHmHTpnNzCdPZh/33XfM8fHaYVbDhskxFy8yP/44c7VqEqE3ZkzgfC1hYOuJrTx903T+Zc8vbHeqEOWSSvv2Mgx635q1a0vqHy3GjpVnrefYmBjmunWZk5JCK1ug8Ys42KiOEBB11dMLSWqqWI68o/N0Oklu+c03kZFLEcUcPy7TNu/oAZNJwlMmToyMXEVIoOrpkYaI6gL4iZn9eChmE7Xj1/z5sq6i14t1yOGQZbgBA7KPSU4GqlXzDQu2WqWczHXXhVXkvHC4HLht3m3448AfAAA96VEuthxWDl+JuuXqRlY4RcjZuVOc1DMzxVUvLk6Sc65dK6vI/vj5Z+CDDySGol8/4P/+T4y0oSTQ+KVq7YWAxERtk6TLJct7CoUPO3bI8om3IpWVFd5wE0XJ4dZbgeuvBxYvFkWqZ09fR8wyZYAvvhAHdZdLfKRiY4E77xRfgihj8vrJ+GP/H7A5shW/NHsabv/udqy/T/1OShpNm8rzdO5cWTHu2FGCT/Pyn7rppjAv5XmhFKkQULOm9jotkdwYCoUPDRrItMsbg8F/2KYiYhDRSAAjAaB27doRliYA8fH+ayV6GDQI6NpVTOXnz4sVNC5OPISjrFLs9M3TcylRAOBiF/45+Q9Opp5E1biqEZJMUVhcLuDPP0VhatNGlCYiuYXvvz/S0gWHUqRCQLlyYlGfNSu3xdxslnp2CoUP9erJMsqSJbmtUjExwBNPRE4uhSbMPB3AdECW9iIsTuHxFEbr2VNix7OyRInv00fq9/nLshhmMpwaifMA6EiHTIfGRERRLDh1Sm6/48dFoSKSLBy//qqdFi0jQ9IsfPmlrFyPGAE88EDelqpwER2/lhLAhx+KCdITEFOuHPDJJ0DnzsD27fK/2SwFHd99V45hlklht24SffDOO9opXPLL6tWyPty6NfDYY5LYTBHFzJsnGnhsrNw4LVvKskzjxpGWTFHScbmA/v0lcWdqqihSNps4m8yalX1cZqb4Xn34oWQ9DDO3N78dMXrfHFnV4quhdtkotgwqAjJihFiiUlPltktLE48G74SbgNyqnTtLhPp//wH//CNphfr1Cz5xf1EREmdzIvocQB8Ap4u1s2YhmDFDfIQ9FimTSdwR5s0Tw4PLlfv4wYPFGW7mzGzlyWyWPBwbNmjn1wvErFnAyJHZ/RuNYiItlpW0SxtOZ7avSgkmWp3NiWgOgB4AKgE4BeBlZv7M3/ElYvzaskVytKSm+u7r1g1YsUL8+Lp3F2XKbhcr1Y03igOLVgoFNxmODEzdNBVf//s1YgwxGNV+FO5udTd0FPy8/WLGRVz+2eU4mnwUqVmpiNXHwqA34I8hf+DympcH3Z4i8qSni+O4d+ZyQFIEnTwpFqvff5fn2PvvyzPRG4NB0g01bgw89VR2do6iItD4FSpFqhuAVABflkZFKitLovYuXsy93WgUZen0ae3zTCZf3yqrVRJvDhmS//4dDunfuwSWwSB58j79NP9tKRRFRbQqUsFSIsavjRvFuTwlxXffFVcAq1ZJ6YW9e3NP+y0WebL5cWJxuBy48vMrse3Utku+TVajFf0v64+vb/m6QKJmOjLx7Y5vseLQCtQvVx/D2w5XvlHFmNRUeS5qlYjxPDPPnpV5pdOp7UrqjcUiNfpGjgy9vB4CjV8hWdpj5pUAzoeireLI3r3a5afsdv9KFKCdvy4tTeoMBcPBg9rO7g6HuOAoFApFLtq10zZ7Wyyy3Lx3r6SG9p5o22ySUdgPi3YvwvYz232i7L7f+T3+O52/fKdZziy88OcLqPROJZjHmtF/bn90rN4Rn/T9BM9e9axSooo5cXFy+2k9/zzPTJdLbrX8KFGAHPvkk/k/PtQoH6kQULGitpkSCJypVcuf02iUMjDBUKGC/wKQlSsH15ZCoShmHDsmGcsPHsz/OXo98O23YgL3LCnHxYk1avhwGdD8ZSr3N9gBWHpwKVKzNJYLAaw6tCpfot01/y5MXDsR59LPIcORgd/3/47On3bG8ZTj+TpfEf189pm4vngcy0OVFD9ShSHCpkgR0Ugi2kREm86cOROubsNC1apS1sW7QoPVKr5QWpQpIyZM7xvIaAzePFmxoqSP8Z5gWq3A008H15ZCoSgmOBySD6pBA2DgQKBZM0mm451s0x89eojH71tvSfHiH37Idkxp2lQ7A6LZDNx9t98mq8dX13QON+gMqBJXJU+RDl44iJ/2/oR0R3Z2YwYjw5GByesn5+uyFNFPixbA/v1SIWvIkIAud/nGbpdgrkgQNkWKmaczcwdm7pAQ5qvNygJmz5aamq+9lr9ihhcvSrjlvffK/0lJgY+fM0dq4sXGZmvar7wi4Zo5EwsDMvHbtAlYtkxq8RkMMnbFxYkfZ8OG/vtxucRFoWVLMY96Amy++kr8QnP2/9xzklk9XJxMPYlxq8ZhxIIRmLl1JtLt6XmftHu3hGCMGiVFAz1e+WlpUkh1xAipcH/2bNEKr1AUN955R6xKmZkyYGVkSGKexx7LfxuVK0sa6DfflKgYz8xOp5NBzdti1aIF8Mgjfpsb2noo9Drfp2KMIQY3Nco7Y+KOMzs0FbFMZyY2Ht+Yv2tShJV//5Xn6ptvyopwfqlYEfjf/2SYD7Ryk5OYGHl5x+WYTPL8rV49//2HFH+1Y4J9AaiLKKxVlZLC3LIlc1xcdh0ei4X5zz/9n3PgAHNCQnYZNIuFuVIl2Z4Xhw4xr18v/TIzp6Uxt2uXXQvIaJT2Fi9m/ugjKYWl12fX5rvtNmanU7ttp5O5cWPfOkQ33JB9zOHD0n9ycv4/o1Cw7sg6jnszjmNfj2W8AraOtXLDSQ35vO28/5O+/FI+AINBLiQujrlXL6kbVrNm9odmNjOXLcv8779hux5F6EGU1toL9hU1tfaqVtWumRcby+xwZB+3bZsMNt99x5yREVwfJ04wv/MO8+jRzN9/z2zPu87dkv1LOOGdBI57M46tY63c4IMGvO1U/oqO7j67m81vmBmvINfL+JqR//fb/4KTXVHkPPdc9jPMaJS/J08Ovp0RI7RLRXpeBgNzgwbM777LfOoU8/vvy+OhTBm53a+5hvl8gEdNKAg0foVKiZoD4AQAO4CjAEYEOj6cA9Frr8kH7f3FVKvmX2G58UZmnS738Todc+/ewff/zjvaN0jFiqLUeW+3Wpl//lm7rQ8/9H+jrVsXvGyhwuVycaNJjXwGP9PrJn7i9ye0T0pO9q3y7PkAunfPVq5yvjp0COt1KUKLUqRCjNbvB5CnWnq6DHB33y0DkNksxYorVQpLJXWH08F/n/ibt5/ezi6XK6hzr//qeo55PSbXWBL3ZhwfvHCwaIRVFIi//9a+BWNjmY8cCa6t9HTm22+XZ6LH6KH1aPj99+xz0tKYN2wIX433QONXqKL2BjNzNWY2MnNNDpCDJdzMnetbzgyQ2p27d2ufs3ixb94nl0u2c5DZImbP9i1mDMjKlda6cFqa5J7S4vPP/fczaVJwcoWS4ynHcST5iM/2LGcW5m33czErV2rbc9PSgDVrtL3nt27VDtdWKEoj3btre+k2bixrH19/LX5P6enySkmRJfKizGTIDBw8CP3hI2hTtQ2aJTQDBelJ/MPtP+Ce1vcg1hALHenQoVoHLB+6XBUpjjK++0772UoELFoUXFuxsfKsPnJEou8sFt9j0tKkrrYHi0XKykRDnsQSH7XnL8ehy+V/n7+08yZT8NEFWunuARlvtNrS6bRvIiBwvkarNTi5QkmMIcZjmfQh1uBH6NhY/4O5P89DovwvpisUJZ2JEyXrrvdAcuiQTEamTdMulXDqlCTbDDV//y1KXPPm4vjetKmkog4Si9GC6X2nI+25NGQ8n4GNIzeiffX2oZdXUSj0eu3Ic6KCVxhKSJD0Zf76867BHS2UeEXqgQd8lQwiceiuV0/7nDvv9I3AM5n8R+AF4sEHtfuvU0dbkYqNlehjLZ57zn8/zz8fvGyhopKlEjpU7wA95VaALEYLRrUfpX1St27aGqvFAtxyi6/WaDQCN9zgXzNVKEobl10mgRreEw+bTaJM/CXV0em0E88VhosXxUK2b1+2BWzXLtlWwLpXOtLBqI+SYmoKHwYN0h7CPdWHCkqjRv4DT++5p+DtFiUlXpEaMUK+VLNZntHx8ZKu4Pvv/Z8zcSLQqpUEqVgsogi1bCnbA2G3i0lz2jRg2zbZdtddEpkcEyPKmNksgTILF8orLk5kslrlmBdfFHOlFjfeCNxxh+/2l18WxSySzLl1DmqWqYl4UzwsRgssRguuq3cdRncerX2C0Sh1vcqWzf4AYmMljOOzz8Bdu8JpjoU91gSHxQxu0ECSj4Sa8+eBxx+XX+jy5aFvX6EoShYu1F4GT0kBrr5ae+IRGysDXKiw2SRkSmvZPSsr8GCrKLY0by7PnthYeXaZzfL3tGlS6qUgXLwI3Hqr9j4iqWEbjZT4dRKdTlwFduwQa3f16pJzKdAKUZkyUttnzRpg506xUHftGnhZb+9eMbKkpUmWc2YpYvz119lZzz0rWS6XvK6+WuoK/fyzjEXXX593+OacOcALL0gKhNhYYMwYKeQeaWqVrYX9o/dj6cGlOHzxMDpW74jWVVsHPunyy4ETJ4BffpFf0HXXAbVrI92ejt5DnHA2AVqeYByuCOxt6cBKC6OAv09tPvtMcmJ4+OoroG1byU1RUNu0QhFOAg1KAwdKJfP//pO6HDExYr2aMyc0iXs8PPyw/6XCjAzgeGQSaa5IXIHxa8fj6MWj6NmgJ57o8kS+clkp8s+YMWKZWrhQ5sYDBgDVqhW8vT59ZGVaC6tVDJ7BJqwOByGptRcsJaJWlRetWsl4lfPjtFjEwj5/fm7rtmdpcffu0GV0LUk8s+QZfLD+A2Q4sj0ZDToDrq9/PX6+6+fQdJKRIV+Q1v3/6qvASy+Fph/FJVStvSLgs8+A0aN910Jq1AAOH5YZ208/Sa2oGjWk+GYok+1kZopV2d8yotUqM8Xu3bH5+GZsObEF9cvXx9X1ri5QEWMPTpcTe87tQdnYsqgeL9fjKS0zbdM0JGclQ0c6uFiihkx6E8rGlMW/D/6rSsxEiM8/l3xTJ06IcWL8eJk7e9ixQ1Zj/C3rmUxAYmLhFLXCEGj8KvEWqXCQmCiasvcz2WaTyAbvG4NZqjrs3u3fsa4088XWL3IpUYAUQ/3jwB9It6fDbAyBn9Qnn/h3dp86VSlSiuLB0KHAggWSiDMzM9vq9P33YlXV6cS3oTBOK4HIyPANcc5Jp07I7NoZfb7qiTVH1gAQ36dqcdWwcvjKAik1C3YtwH2L7kOGPQMOlwMdanTAdwO/w+jfRmPR7kWXsqJ7lChAlKykjCSMWzUOH/T+IOg+FYVj0iRJnu95Fv7zD3DzzbIY0aOHbDt40H+gFyBxDJFSovJCKVIhIDPTv2VJq5gxIONbpAosRjt2p3YtLwbDyX4+0GAJ5AAboJaYQhFVGAyiSK1dKylFKlcWM3iZMuHpv0wZoHZtqffhTfPmwG+/YezqN/DX4b9ylX05eOEghv04DL/dHVyF9n9P/Ys7v78TNnv27HTd0XW4eubVOJh00GcClhO7y47f9/8eVH+KwuN0SpUPb4NCeroEUK0R/RqtW2unU/AQzZXllCNICGjcWAoHexMbK9q2lr+nxSLVFhS+DLhsAIy63FMTAqFd1XaIM8WFppOcvlHehLOujkJRWIjEifOZZyTkd8kSiXDt3l08f0Mdoefd97RpMqB5/K6MRlGwvv8eMJnw2d+f5VKiAMDBDvx58E+kZQUX0Tdp/SRkOnLPQB0uBw5eOAiDLm+7gFrWCz8XLvhfrsvpWlezprj1+aMob+PCUiwVKbtdnMG9fZICkZIikzbvAukul/gwzZyZ/1qf3hBJ4k1P5B0gfzduLP7LzZtLdB4g+y0WOT6U/p7hxOlyYvPxzdh6cmsu83moGHfdOFSLrwarUfJGWIwWlI0ti8/7uTOSOp3Ali2St8Z7WeHIEZniXLgQuJNKlSRC0JsKFYD33pO/L1yQtryLM7pc0veWLT79H00+ijVH1uB8+vn8Xi6wZw+wbp125laFIhgeflgiUBcvFgvV448D117r3zQeCq69Vu7fu+4SJ5eHHpLBuXFjALKs5g+7Kzjrb2JSoqZV2qg3+ihY3liMFjzZ9cmg+lMUnnLlfNMJeWjQQP5fu1ai9Xbt0s4VZTBIHtmoxV/K86J8FabEwsKFzOXKSY0dq5W5YUPmnTsDn/PWW9nl2sxm5quuYj53jvnHH3NXIiFifuWVgsl17hzz5ZdLeyaT1B164w3Zl5UlZa5GjZJtwabPjyaWHVzGCe8kcPyb8Rz3ZhzXnFiTNx3bFPJ+0rLS+PMtn/OoRaP4vbXv8TnbOdmxahVzlSpSRyAujrl6damPk5rKfNNNUp+gbFn5/+mnmfMqT7F0qdwQzZvLl2+3yzlPPpm7rT59pI+1a6W+kKf/qlWZV6/mtKw07jenH8e+Ectlx5XlmNdj+H+//S9weYyjR5nbtpU6C54b+tNPQ/URRh1QJWKKlj17tOthxcUx//BDxMS6f+H9bHzN6FNCqs3UNnme+93277j9tPZcY0INvvO7O/npxU9r1uKLfT2Wb55986Van97lZSxjLTz+r/E+7btcLv735L+8/uh6znJkFcXlK1iee97lZMxm5l9+kZKrFos8fwF5fhLJ/55atzVqSNnHSBJo/CpWUXv79sk6ak7LEZHkrDhyRDulwcKFkkgz5zkmk1jCV6zQtmitWgVceWVwsl1/vaQhyuleY7UCs2ZFuSYdBKdST6HBpAZIs+c2x5eNKYtjjx+D1VTE6dXPnQPq1pVQ7pyUKSNLGQsX5nY8s1qBDz6QZGLBMG0a8MQTuf2oYmPli/zlF998OfHxeOTjvvgs8ftcPhoWowXv9nwXD3V8yLcPZrmZd+zIbS2wWGRppkuX4GQuBqiovSLm00+B//s/bdP6qFESRBEBzqSdQcdPOuKs7SzS7GkwG8ww6o1YOWxlwBQp49eMx8vLX77kD6UnPawmKyxGC86nn79k6bIarXigwwMY3Xk02kxtgwsZYo3WkQ7NE5pj8o2T0b5aex+3gB1ndqDvnL44lXoKOtJBr9Pjy/5fom+TvkX0SZRemIEJE4Bx48TQX7u2RO316wdUrOg7pHp8jj3P9Mcfl3MjGeUeaPwqVkt7n3zi6wfMLM+7JUu0zxk/3ndcycoSZcmfDhkog7gWJ0+KFd1btrQ04N13g2srmpm1bZamWd3JTvy468eiF+Cbb7QjhJxOqSnm7b2fliY3QLBMmODrjJ6RIWvAGksk7HSCvpnn4+hqs9swYe0E7T7++w84cMC3vfR0SRKmCCtE1IuIdhPRPiJ6JtLyFIgKFbT9BUymgmdIBGRge/VVacNqlQR5e/fm+/QEawJ2PLwDH/T6ACPajsCrPV7Fvkf3BVSibHYbXln+Si6ncic7Ycuy4dp612J0p9FoWKEhOlbviE/6foJ3e76LUYtGISUr+4nsYhf2nt+LxfsW+yhRdqcd18y8BgcvHESaPQ0pWSlIykjCHfPvwIELB4L4cBT5gUhq6J09K8N0YiJw002S/kArj6vHbmW3y2vKFODbb8Mudr4pVlF7J05oB1Qx+/foP3VKe3sgzdbfOf44d078K7Wi8E6fDq6taOZU6inNqBi7044ztjCEVJw+re1HlJHh/ws9dy74fs778W9i1g4rSU9HxTTtOYlfX6nTp7VNqMwRS2BYWiEiPYAPAfQEcBTARiJayMxFUJCuCLnxRu34cb0eGDYs/+3YbJKfZcMGyQG1caP4CXru/Z9/lkSfO3dKmYh8YDFaMKLdCIxolz/r8L7z+zTzTDnYgY3HNmL3o7vx7vXZs1Sb3YalB5fC4cqd5T3DkYEvtn6BsdeOzbX9jwN/IN2RDkbu2bTdacenWz7Fm9e+mS85FcFBlH2LPv44sH59/s5LS5P57aBBRSdbYShWFqlevbKdtnPicPhfiuvVS3ts8TiFa3HTTcHJ1bix9kTQaJT+SwrX1LtGM2pOr9Oje53uRS9A9+7a1ZljY4Hy5X2363SSPj5YevTQzmxesaJ25WiLBTubJ/hsJhC61emm3UeHDtqat9ks6X0V4aQTgH3MfICZswDMBRB9C/LMwL//AsuWaU/jY2OBpUsl/CkuTpa8y5QB5s71X1g0J06nLGlXqiQJ7u6+W7Kg79uXewLBLBOaDz/MPi/EKUOqxVXz66Rep5xvPSxvBSonmU7f39nptNOagTJ2lx3HU9REpqhxuYAZM4JLAXT2bJGJU2iKlSJ1222itORMJ2C1SiS7v3Hi2WflGZszasBikTFAKxmm1Qq88Yb87XLJeKW1mnT+PJCcLH8bjZJwzGLJ3m8ySbTCs88GdYlRTc8GPdGxekdYjNkXajVa0bdxX7St1rboBejRA7jiitwftNUK9OwpGZ4tlmzLlCcE+828Z5b2DBvSLuQwHY4bJ6EjHg2cSNr+/HOJUMrZv8UC6t4d9z76BSxGy6VZtFFnRHxMPN657h3tTsuWlTS/ORXD2FhZPnnggXx8GIoQUgPAkRzvj7q3XYKIRhLRJiLadCYSCW2OHJGCn127Sh2OKlW0l4DbtJEaG8uWieXozBnJfJgfXntN/KjS0wMn2QTkCbh6NTBkiPweYmPFr89TZLSQJFgTcFOjmxBryD1xsRgtePZK30G1TEwZtKzS0me7UWfEgMsG+GzvVqebpvIVZ4pD74a9CyG5Ij84HMGlMzAaxeAatfjzQi/KV2GiXmw25okTmTt2ZL76auZ58/IOzJo9W4KiAGadjvm22ySSzulkHj1aArOsVub+/ZkvXJD2Jk9mrlhRovAqVGCeNEm2L1ggQTCeVdyqVZl37JB+Vq1i7tdPArGefpr55MkCX2bUkunI5I83fsyXf3o5X/n5lTxz60x2upzhE2D3bubGjbO/gGbNmA8elH2bNjEPGiRfwOjRzIcPB2wq5dwJXtWjPmfowVk68O7qMfzvD9Nk56FDzI88Im3dfjvz5s3MzHz0t2/5YAUdOwF2ApxYXsdHfpnLzMxbjm/hO769g9tObcsP/fQQJ15IzPt6fvuNuVcv5vbtmV99VW7AEgqiNGoPwEAAn+Z4PwTAZH/Hhz1qz+VibtGCWa/PHfZksTD/+Wdo+nA6mePjfSP+/L2MRhn8YmJyby9TJmThVamZqXzHt3dwzOsxbBlr4QpvV+Av//nS7/H/nvyXy44reymqzzrWyrXfq82nUk9pHv/QTw+xdaz1UnSf+Q0zd5jeQUXvFSH79jHPncv811/MrVrl71aLiWGuXJn5+PHIyh5o/Cp2ilSwrFsnYZbeYZfDh/s/Z/p031BNi4X5tdeyQzRzvkwmiZpXFDFpaZL6QKfL/vD1euaaNZkzM4NubnOz8mwz5P4yU0zgxE1LNY/POHeaj8eB7ZR9vJ3AJ63g9DMRjs0tBkSxItUFwO853j8L4Fl/x4ddkdq2zXdA8rz69QtNHzZb7t9VXi+z2XdgBST9wquvhkYmN0npSXzg/AG2O/MeZM+mneUJaybwyIUj+bMtn3FaVprfY10uF8/7bx5fM+Ma7jS9E7+/9n22ZdlCKbrCjcPBPGSI3B7x8WKMqFtXO1uH5/a66ipJKfTCC8ynT0f6Ckq5ItWnj/YXFRsruZ+0qFFD+xxPXgut14QJYbuk0suXX+Y2B3pe8fHM334bVFP71/7CaQbfLzJTB15+cyvNc/66vxcnm3zPSTaCVw+/NhRXWKKJYkXKAOAAgHoATAD+AdDc3/FhV6RWrMg2qXu/unQJTR8ulzzZ8lKg9HrmNm1kVunPgnXrraGRSVFimDLFdy6g1zOXLy8WJyJ5Hx8v7198MdIS+xJo/AqJj1Q0hw7v3q293WTyTVjt4cQJ7e2B1nS3bg1KLEVB2LvXN4cUIFFG+/YF1dS5bRtg1wiaM7mA+L2HNc/JOrAXZg2fWqsdsB/UqDWmKBYwswPAIwB+B7ATwDxm3h5ZqXLQrp04lXhjNocuSd3SpXk7rVStKr+/v/8WPy2tbOlmM9C5c2hkUpQYpkzxTUPkdEpOqcxMUa0MBuD224HDh8VdrzhRaEUqR+hwbwDNAAwmomaFbTdUdOigHYBlt/t3UPekrfdGK2DMQ/fuwcumCJLWrbXDNs1moFWroJqq3uV6xGg8mzL0wMU2TTXPsbTugHSNCNA0E2Bu2S6o/hXRBTP/wsyNmbkBM4/N+4wwEhcnCelyBlOYzUD16sCDDwbXltMpjus5o/4mTZLw4rzSbpw+nR212qKFhErnjGLV6WSQDDYBrqLEozX/9SYzU1IFlitX5OKEnFBYpKI6dPjFF32LBlssUmZNq6YPIGOW9zlms4w3WkpZmTJSK1RRxNx8szw8coZgmkyiEd9wQ1BN1WjRBZu61EFaDsXISUC6idD8de0M0O3HfoGT8YTMHKkuMvXA6Tig/Vszg+pfoQiKhx4CfvtNCpJdcYUkyNyyRQaf/DJvHlCtGtCkCZCQINP/hg0lG3p+avF554xasAB45BFJBGqxiHVswwbtCu6KUk3//tppiLxhlqDTYoe/Nb/8vgDcBt+Ilykax40EsAnAptq1a+e5Hrl0KXPXrsyVKjF368a8cqVsP32a+YEHJGCkbl3mceMkAi8QW7YwX3ONRObVqcP84Yd5R/q99Vb2mq7Fwvzmm7J92bLcrgGeGkAuF/OsWRJck5AgEYCeaL5Qct52nkf/Mpqrja/GtSfW5peXvcwZ9oyA5+w7t48HfTuIE95J4MumXMafbv6UXS4X2512fvevd7ne+/W4yrtV+P6F9/OJlMBO00knD/GyAe34RBkdHyun52VDrmLbRbez2fz5zK1by5d2443M//zDzMwHt63iOx+vw5WfJm78uJE/njCYXc4CRvqdO8c8cqQUXCxfnvnhh5mTkmTfrbdmRwMYjZe+tENJh3jI90O48ruVudGkRjxlwxR2upycaUvhH69K4JNWcIoRvLqujjd/O0na2rSJ+frr5VratZMij8yctPsfXt6mPJ+PBV+IAa9oXZ4v7Pg7sMxHjzIPGyaO8g0aMH/wgURJuVzMH30kUYiVKzPfdRdzYmLBPpdAHD/OPGKE9F+/vjj0ORzS/yefMDdpIjftHXcw798f+v7dIEp9pIJ9RW2tvUCsXq3tpJJf53JAwpsL+rvVYPfZ3TxhzQT+cMOHfDKl8CHO205t49u/vZ0bT27MA+YO4D8P/MlbT2zl5IzkEEirKAynT8uzMq9bLiYmeziPNgKNX4WutUdEAwHcwMz3ud8PAdCJmR/1d05etap++kkmSznXVM1mmVA9/HDuDOdms9S5+/HHQl1GLn7/XVwAcibRtliA2bPFknXsWLY7gdksy3rdukn+KY/MRGKR37wZaNQoNHJlOjLR8uOWOHTx0KVkdWaDGV1qdcGSIUtAGtm9jyYfRcuPWyI5M/lSAjqr0YqHOz6MQxcPYdGeRZfKMBh0BlS2VsaOh3agbGxZn7bsGTYcqlceNc9kIdY9gU03AHvqxqPV/70JGjMm9wdgseDMvC9w2arbkWRiuNzWPEsWcL+rDd4f93doPhhA8uusXeuz+fRrY9A85jNcyLhwqbyNxWjBPa3ugXPlMswy7obNbeDSO4HyWYQd3ech4bahuW9Ai0UW+oM1PZ47BzRrJonHPH4uFovc4Far5Kby9KPTSdKz//7Ld8boPElKApo2lWx2Ofvv31+sEx9/nLv/MmWk/xo1/LVYYFStvQjSp4/klSoMcXFSW6tNm0KL8+KfL2L82vHiqOvOvTaj/wwMal6w1NUbjm3A1TOvRoYjI1eiTYvRAhe7MLrTaLx13VuaY6Si6ElNlTyxFy/6P8ZslmHxiy/CJ1cwFHWtvaMAauV4XxNAoVLDPvaYr2NaerrkKTx3LncS3fR0YPFiYHsIXUMff9y3EonNJu4Ip0/n9slMT5fix6+9lltmZnn/+uuhk2v+zvk4kXoiV8bfdEc61h9djw3HNmie8+5f7yItKy3X4JJmT8MH6z/Agl0LctWycrgcSMpIwoytMzTb2vTxi6hyPluJAgCzA2hwKAWuMU9pfgCn/zcSqYZsJQoAbCZgmmErzhzeGdwH4I/0dE0lCgCmrHgXKVkpuWoE2uw2/LL6c3wZk61EAYBTD6QaGB9PH+l7A9pswNNP528JJCdTp0rm1pzOwjabaOWffJK7H5dLRpxJk4LrIxCffSajl3f/8+cDkyf79m+zARMnhq5/RXRwIAT145hzL6sXkI3HNmLiuonIcGQg05mJdEc60h3pGPbjMFxIv5CjO8aEtRNQZXwV6F/To9XHrfDnwT8123zst8dgs9t8spXb7DZkODIwZeMUTN4wudCyKwrG0KH+lSizWVzt7rwzYnW1C00oFKmNABoRUT0iMgG4A8DCgjbmdAL7/QRAHT/uW0sWkPIsoZwg7tqlvf3ECe3+PYZJb5xOYM2a0Mm15sgapGb5eu052YmNxzdqnrPq8CrYXb6hZnrSa87ObHYblicu12wrc+0qxGsE9hidALK0iyBWOXoRWRrRcTEO4L/Nv2r2EzSrV/vdtaKWS7NERK2LpO1sbgSWl73guwMQJSfYOgUrV2rX59PrtesKZWaKZh4qVq7Urk+o12vX+svKknMUJYtu3bS/b39oHVulilg3C8nsbbM1a3bqdXr8vDfbavbSspfw0rKXLpVz2XZ6G/rO6Yu1R3wnTZuOB34A2Ow2jF9TgALmipAQyBj6/vsyrH76aeDSbdFMoRUpDnHosF7v31cxLk77gyYC6tYtaI++JPiWTQMgKzFapdYMBv8VFfxFABaERhUa5SrP4sGoM6Juubra51RsBIKvwuRgh6YiZdKb0KRSE822dA0a5nLO9pBlgF+TeVJZE3Qan02WHqhdr43mOUHT0rc0hIfLzsqSpTfnzQyHhh5jcAJNUvzMunW64ENKGjfWfii5XNrWLb1enIFDRZMm/r08tULqdbrQrUUroocnnhBrUs7fqdmsfW+88QYwcGC2qSA+XupM/vhj4Grv+cTFLmi6lDAuWZQyHBl4b917uSzmgChELy1/yefUCua8HdzPpReggLkiJGgNNR4aNw4cEV8cCEkeKQ5x6PCYMbnLmQHy/rnnfH/3BoMEcnXzUxu2IDz3nHb/zzzj279eD1SuLME0WtGBzz0XOrmGtB4Coy63AHrSo7y5PHo11K6O/HTXp2E25hYsRh+D6+pdh4YVGvq0Z9KZ8GAH7ZDq1o+9hSwDIade5CAgLVYHHjpU8wPQP/Z4rqVAQKxRV6RWQIM21/i/2GCoWlUKrWrwWJmeMOlzK0YmvQmVWnZGm9R4mLx+4CYn8NhVT2nfAA88EPyUafRo33NMJknl0LWr776YGFlbDhUPPui7HGM0ioLVo4dv/7GxwFNPha7/0syRI+ID9+23vkvF4SQxUYp3E2UrQnFxso5y4YJE7TVuLM6ea9YAzz8vS8/r1gHjx8s1HD0acMISDLe3uN1nTAJkcndjIymodiLFTzI/ADtO7/DZ9niXxzUnmTnpWL1jkJIqCotntaatn1KsRmPJSB0UlUWLn3pK3FHi4uTZHB8vaQzGjJG8cZddJuO/ySTPguXLQzJRusSjj4oCFB+f3f+zz8r4snw50Ly59G0yAVddJSshX3wBDB4szyFP7dkvvgjtTVLBXAErhq1Ay8otYdKbYNQZ0bVWV6wevlrT6gIA7au3xxf9vkDZmLLQkQ460qF7ne74ZuA3WDJkCdpUbQNy/0uwJGDBHQs0q6sDQNkqtXHutx+wp5YZmXoJ/d/RIB72Fcug/+hjccQ2m+VVqRLw4Yeo9/SbmN/iddRK1SPWIUrUzWk1Mf/5fwJf7IkT8qF37y6h33v2AJAZ69z/5qL3rN64adZN+G7HdzKLPXjQV5m6/HI0nbUYc2+di/Kx5S9dZ+canbFw8EL8NOYf9E6tCpNbrnopBixs9w6aPPaG+AmVL599PaNGAW+/HfR3hkaNJHqifv3sm/aGG4BffpEZft++si0mBqhdG/jhB8nREyrq1ZOw+YYNs/vv2VMcC7/7TpzOY2Lkpq1ZUx76/kY9Rf555RVRTkaPlrxK1aoBf/0VGVkGD872S/CYzp1OYOFCGUw/+ECWf++7TwoPe2jVSqJ7brtN2xRfQLrW6oqR7UbCYrRAT3qYdCbEGmLx8Y0fo5JFfsNV46qCoR0Ile5Ix02zbsKCXQsuWbae7Pok7m93P2INsT4KlY50sBqtmHiD8v0LF6mpwP33y/zTs2LjPZ8jkmdkSfD/L3TUXkHIb9RLVpasnSYk+FqCTp+W8b+sb3BZyPD0X6mS701w5ozI5L3SY7OJU12VKto5p0LFWdtZGHQGlIstF/C4ixkX0W56OxxPPo4Mp/glWIwWTOk9BYeTD4szuj3t0vbmCc2x+t7VPlYcb84f2Qud3oBy1b2ymqanS6RY5cq5fIDY5cKpxP8QV64y4irkEZG2fz/QsaN8mJmZ8ks0mcC//oqBJyfht32/XZLZarRiQNMB+GrAV3LuyZPAP/+IidJshs1uQ8dPOiIxKRE2uw0EgtloxlvXvoVHO0tgacq540i7eBZV6rYA5fzSHA75osuXL/yDhFluWovFN4FZaqq8qlQpulHF07/Z7Jt7KC1NEjQWZf8oRVF7K1cCvXv7WqHKlQNOnQqJw3a+OXtWIjC1spYT5XbutFgkAOLOO8Mi2taTW7Fw90KYDWYMaj7IZwL3wp8vaC7vebAarbiy9pU4mnwUx1KOoVP1Tnih2wuIj4nHOds5TFo/CTvP7kSH6h3wQrcX0CwhavJEl3i6dZOUYplu91QiGXrsdhlWiWRYf+stiYQvDgQav6JakVIUnjdWvoGxq8b6OHdajBY4XU4fR2yr0Yqpfabi7lZ3h1PM3Nx6q1hrvBzPbA3qoPK9Zy8pUR4sRgtWDV+FdtV8s4tPXj8Zzyx9xmcwNhvMOPnkSZSJCSKhoaJQlBpFauhQ4KuvfCNQypSRHC5BJo8tFCdPigNppm/AhSZ164p1NwpgZoxfOx7v/PUOztnEv8mflcqDkYzYNHITWlUNrtKBInRs3SpJ77UCs7yxWIBly4BOnYpcrEJT1OkPFFHMgl0LNCNkmBl6na+3dZo9DT/u+jEMkgVg6VJN7/2YQ0dgSPH9dWY5s7DkwBLNpn7Y9YPmjNaoN2Ld0XWFl1Wh8MZm0w7jBbQjOIuSqlVlWTm/HDlSdLIECRHhqa5P4cxTZ3B/u/vzVKIAwM52dJ/RHRczLl6K9lOEl1278r8ak5EhRtDijlKkSjgenwNvXOzSjObTkx6VrZWLWqzA+Kndw6QDx/o6e8foY1A+trzmOQnWBM3rdLErX5E+CkXQ3HGHdhiS3S5OneFm1iyxhnmCQeLi/KdCqKPtH+mDzSaJW8+FJxKuSlwVn8AYfyRlJqHiOxVR+73aqDGxBr7d/m0RS6fISdOm+autB8h8OSmpSMUJC0qRKuH83+X/B6sx96CuJz2aVGyC+BhfhcWkN+GBDg+ESzxtRo/2jZqLiYH9ln5wGH2taESEgc0Hajb1aKdHfSKEdKRDVWtVtK/WPmQiKxSX6N9fouQ8BbYNBlFiPvqoaJ06/dG2rSTkfPNN+W198om8tCJTx43Lu7233xbH1a5dxf9q8OAit7QNazPMb0CNFk4Wt4WTqScxbMEwrEgsfG62vw7/hQd/ehAjF43EsoPLtFM4KLBihX+DrDdWq2TaKO4oH6lSwLjV4/Daitdg0pvgdDlRp2wd/Hb3b0jOTMbVM6/G+fTzl5b6pt44Ffe2vzeyAjudEuk0e7bYiF0uKdS6aBH+OLUWA2f3B2eJz4chxozv7/4J3ev6D4+cvH4yxiwZA6PeCBe7UNVaFb/d/RsaVAhhkq9wsmeP+No4ncAttxQuLP3MGfmcT58GrrlGXkXkcF5qfKQAuWcXL5YozPLlgWHDJEKuIHgi7BYtknxOI0YUvK2czJkj4clHjoglatw4YFAeJVrmzpX+vet33XGHpEkoQn7Y+QOG/jgURARmRmpWar6W+wCgZ/2eWDxkcYH7fnbJs5i0YRLS7ZLc1mK04O5Wd2Nqn2KairsI6dBBSqPlRUyMBAp/+aXEXzRp4ncxIipQzuYKXEi/gI3HNyLBkiApD4jw+G+P47317+U6rn75+tj7yF7oijLkMC/On5cw7KNHZcA2m2V2v2YNMHIkslYuw9qaAAHocgQw9r5JUgwEIDkzGeuPrkd5c3m0r9a++Nbceu89ycNht2eX7HjySalRFCzLlkn6BadTLApxcZcU1nyVag+SUqVIhQq7XZzTN2wQ712DQb6b6dOBuyMQENK6NfDvv77bY2MlQrCIMytmOjKx7ug6xBpikZiUiMHzB+dLmapXrh6m9ZmGU2mncEWtK1CvfL08z/Gw++xutJnWRjNgZ8WwFehQvdjf0iGlc2e5Xf3hSWeW0w1Wr5eh7JlnJNVRNA7PSpFS+HA8+ThqvKddmPbVHq/ipe6+2YPDxoMPSo24nEUVdTqZsuz0U59v2bLI+J+Ek0OHxBLhvYxiNgPr1wdnmXI4xBHZ28fFYpFafyNGFF5eL5QiVQC++kp+D94hUBaLWBHDnRK6ShXp1xuzGdi7t0iKXQfiow0f4dFfH4UL/p3KddAhxhADg84ABsPhcmB4m+H48MYP8zWhem/te3hm6TO5apx62n2h2wt49epXC30dJYmvvgLuuadg51osUipm8ODQyhQKVNSewodABTw//7toTfR5Mm9ebiUKkOmLvyKIgBRsKuks9FPCMisL+P774NraskU7t5DNBsyYEbRoiiJizhztOHKDIWCNySLjyiu1Q7Li4yXpaJh5qNND2PHwDgxpNQRNKjbBZRUvQ4w+d0AKg5HhyEBKVgpSs1KR4cjAl/98ibn/zc1XHxajRdM/y6A3wGoq5rVNioDBg7PdA4PFZitY3uNIoxSpUkogx009aRShCydaxXyL4pzihl6vbfMmCv769Xr/HqGl4bN0Q0QDiWg7EbmIKPqsZYEsTt4lmYqSlBTxIh46VGTKeY9YLJIdPULuAE0qNcGXA77Erkd2YcfDO/DpzZ+iWUIzVDRXRI+6PRBjiPFZ/kuzp+GjTR/lq/1bmt6i6ViuJz1ub357SK6hJGEwSDUtrcDQ/CzZaRk8ox2lSJVS/u/y//O776GOD4VREg3uusu3BpxeD7Rp4/+cp58uUpGigv79tZUfgyH40Je2bbU9O61WKRVSevgPwC0AVkZaEE1GjdJWpmJjxZ8tHEyaJEt6N98s5oaqVeVebNBAyg39/LM4m3tz8aJkcg+j+wgR4e5Wd2P7Q9tx9umzmHj9RL9pE1Iz8xejn2BNwOxbZ8NitCDeFI94UzzMBjOm953ut5xWaeeJJyRANacyZTaLO2cgdDoJeC1uKEUqwmQ5s/Dr3l8xf8d8nE8/X2T9HEs+hnnb52HZwWVwupyoZKmE169+3ee4tlXa4n+XS87+X2c8j9f+rw2+m/QgXIHKd7thZqw5sgbf/PcN9p/fX3BhX39dChpaLKJAWSyybLBggUSpeTNkiHg4lnSqV5cQ+thYGZU8hR3ffFP8x4JBp5Ps8WXKiB3eZJLPuU+fsJUIiQaYeScz7460HH657jopKhwTI99TfLxEAf7yS3gsh8uWSc3L9HQgOVnWXg4ckMjRffskMtHbN/H0aaBXLykTVaeO3Jtr1hRaFGbGisQVeHLxk3h1+av5GmNaVmkJo95XkTIbzLi9Rf6tSf0v64+TT5zEZzd/hul9p+PY48ciW/0hyqlaFfj7bzFg1qol87ZPP5Xg1UCGy/h4Gf6LG8rZPIKsO7oON866EU52gplhd9kxoecEPNQpdBYhZsbTS57GlPVTLg0o5WLLYek9S9GoYiPsP78fry5/FclZybiv7X3o06QPks8cRe9na+Pvqgy9C3AR0OAC8NMja1C7WRfNfk6mnsS1X16LwxcPg0Cwu+y4reltmNF/hmYG9YBkZYmF5bffsm3BzZsDS5bIwH7nnRJpBsiU57vvgJtuKuhHVPw4flzC6p1OoF+//CdR1CIlRfyrzp6VB2L7osutFc3O5kS0HMCTzJznwBSR8evIEbn3y5WTKD5vi21R0b+/TGC8SDfpMfDJWqh+eU88f9Xz2ZYZZil2vHt3bj/HuDhgxw55qhYAZsbg+YPx056fYLPbYNAZYNAZMK3vNAxpNSTguT/v+RmDvhsEu9MOu8sOq9GK+uXrY+2ItcrHKcwsWwYMGCDGSm+qVJGYmcIMZ0WJitqLQjIcGag2vhqSMpNybbcYLPhrxF9oU7VNSPr5cdePuPv7u3PVpyMQGldsjJ0P79SMWrlraDy+r5WKjBwTOZMD6HZUjz++0LZMXfvltVh5aCUcruz9FqMFb1/3Nh7p9EhwQr/2mlSzTE/PIYBJZufLluXeDoglJTFRkgQqopZIKVJEtASAVqXs55l5gfuY5QigSBHRSAAjAaB27drtDx06VETSRhlduwJr1/psTooBbh4MrK1nQJwpDltGbZGUAmvXAtdf75va2pOmY+zYAonx056fcMd3d/jU2cxvzcz95/dj+ubpOJJ8BL0a9sLtzW9HjCFMyqjiEklJsrjgHXgcGyuGz5ciGCyeFypqLwpZvH+xZshuhjMjpFFzUzZM8Rl8GIyjyUex48wOzXN+r5JbiQKALAOwsqYTGalJPsefs53D6sOrcylRAGCz2zBlw5TghZ42zVdZysoCfv9dswYfAGD+/OD7UZQKmPk6Zm6h8fI1tfhvYzozd2DmDgmlSWHv21eecl4YncCWaoCDHUjJSsEry1+RHYmJ2h7FWVmBo241cLgclxJgzvlvjs84BkjQjFadTWbGWdtZpGaJQtegQgO83fNtzL51Nu5pfY9SoiJEuXLACy/kdvszmYAKFYBHgpxvRxNKkYoQqVmpmpEgLnYhKSMpZP0kZyZrbtfr9JcGGW+8lSgPTh00Fal0R7rfSL+0rHyUAPcmZ9bknDD7pkUAZFtKSvD9KBSKwDz8sPjmuSMEXQDSjMDT1wNpbl3EyU4sS1wmb9q3lxxl3lgs+c7zZrPbMHLRSMSPi0f8uHi0+KgFktKTNGtmEpGPM/nqw6vReEpj1JhYAxXfqYh+c/sVyv/0nO0cxq8Zj2E/DsOUDVP8jqmK/PH881JMoXt3oFkzcQHculWUqeJKoRSpqA8djmKuqXcN7C5fpcBqtOLWpreGrJ9BzQfBbPANkyYQ2lZrq3lOxxM66LwNPww0PQOUq1rX5/ga8TVQJa6Kz3ajzoj+TfsHL/SNN2o70jZooDk7hsEA9O4dfD+KUg8RDSCiowC6APiZiH6PtExRRZky4jX88suwX94JC5rp0Psu4KNOuQ+rFu/OIdW4sVixctbxMxjEQX7o0Hx1eft3t+Orf79ChiMDTnZi+5nt+DPxT00rUoY9A1fVvurS+wMXDqDX172w7/w+ZDmzLgXz9Pq6V9CXDgC7zu5Co8mN8NKylzDzn5kYs2QMGk9ujCMXjxSoPYVw883A8uXA9u3AO+8Uf6+Mwlqkojt0OIqpGlcVr/V4DRaj5dJMy2q04qraV6FP4z4h6+fBDg+iYYWGlwoXG8gAs8GMT2/+FCa9CYBE9O0+uxsuFu1p3IAPUS4DMLvzNcbYgbgs4PUWozX7ICLM7D8TVqP1UpsWowVV46ripW4FWPR++22pKebJk+OJWJo9W8Ksc9qFrVbJwt2iRfD9KIRTp2TZJR+RmSUNZv6BmWsycwwzV2HmGyItU9RRpgwwZgyMa9djziu3YkOD3AqNxWjBM1c8k71h1izglVeA+vUlfOvee6X4WpnAfkwAkJiUiCUHlviUY7E77WhSsQl0lPuRRUToO7fvpbHro40f+WQgt7vs2HFmB7ae3Jr/a3YzctFIJGUkId0hS4w2uw1nbWfx+OLHg25LUXLJfzltDZh5J4DiW7cswjx1xVPoVqcbPvv7MyRnJmNgs4Hof1n/4KPcAmA1WbHh/g2Ys20Oft77M6rHV8cDHR5As4RmOJp8FLfNuw3/nPoHetIjPiYeM/vPRM8+o/DAqdXY+ccsnLEAFdKBWu2vRp8HJvjtp1udbvjvof/w8caPse/CPvSo0wPD2gxDfEwBqlDWrCkP9s8+E+fVpk0lw1vNmhJDO2gQ8PXX4otxzz3AtdcW4hMqxZw/L3mBVqyQ+m1GIzBlSqlKf6AIjs/7fY57frgHv+z9BSa9CS524dUer2JA0wHZBxkMwFNPyStIDlw4gBh9jI8i5WQnnOxEjD7mklIDAJnOTGw9uRWL9y9Gr4a9sPPsTk1Lv570SExKDCqIx+60468jf/kk83SyE7/u/TW4C1OUaAqlSCkKT+eandG5ZtHmQIo1xGJ42+EY3nb4pW3MjGtmXoMDFw7AyZJKIM2ehgHfDMBTXZ/C+6d/gK1ZdhsW+3pYlj6Hd3q+47efuuXq4u2eIcrvX768dvY2Ign/vkEZDgpN//7AunXiY5aZKdvuv18sCZdfHlHRFNFJnCkO39/+PU6nncbJ1JNoVKERzEZf1wGny4lZ22ZhxtYZ0JEO97a9F3e0uMPHouRN00pNkenM9Nlu0ptQIbYC9vAen32pWalYkbgCvRr2Qrfa3bDs4LJcyhYAZLmy0LpK66CuVUc66El/ydrlLY9C4SHPpT0iWkJE/2m8+gXTERGNJKJNRLTpzJkzBZdYERL+OvIXTqSeuKREebA77Zi4diJs9twO3za7DR9t/EhzUFEUQ/bvBzZt8nXeT08HJvi3PCqKGXPnim+hySQFrzVyQhWEytbKaFWllaYSxcy4Zd4teOjnh7AscRmWHlyKkYtGYvD8vCvRVouvhsEtBsNiyPaxIhBiDbG4temtmgqM2WBG9fjqAID729+P+Jh4GCjbRmAxWnBb09skPUMQ6HV63NL0Fph0ufuMNcRiaOv8+XspSgd5KlKhCB12t1M6w4ejlOMpxzWjYOwuu2aYMSC5rzIdvrNFRTHkxAlZyvOGGSgtOZJKOl9+Kf6DBw6Iwrx7tyzb/vhjkXa7+vBqLD2wNNc4kmZPw897fsbGYxvzPP+Tvp/ghe4voHpcdViNVvRu2Bvr71uP+9rfp1nuxaAz4M6WshxdwVwBW0Zuwd2t7kYlSyXUK1cPr/Z4FTP6zyjQtXx000domtAUcaY4WI1WWI1WdKzeEWOvLVg+LEXJRC3tlVI61eik6UtgMVpQLa4a9l/wLb9Qp2wdzRmoohjSsqXk9vHGZAKuuSb88ihCz3PP+aYSsdmAZ56RZd0i4s+Df/pYtAHxZ/px149YlrgMZ9LO4Lr616Fng54+y316nR7PXvksnr3yWZ82lg1dhlvm3YKTqSdBIJSLLYd5A+ehoqXipWNqlKmBL/p/EZJrqWCugL9H/Y2/jvyFPef2oFWVVuhQXQWoK3JTKEWKiAYAmAwgARI6vFVFvRQP6pari3ta3YNZ22ZdmjnG6GNQNa4qPr/5c/Se3RsZjoxLS3kWowWTb5wcSZEVoaRsWUkl/M47QJrbcmA0yvbHVURSscfpBI4d09534ECRdl3RUhGxhlgfPyUDGTB+7XgQCJnOTEzdNBVdanXBz3f+rFkPT4vWVVtj36P7sOfcHjhcDjRLaFbkwU5EhCtrX4kra19ZpP0oii+FSn9QKkOHDx6Uum5Go+RKGTFCu3BQDtYeWYuO0ztC/5oeFd6ugJeXvQynyxnwnGBxsQtvr34bCe8mQP+aHq0/bo3licsBABuPbUTnTztD/5oe5d8qjxf+fAF2px0fXzMBk891QtuTOjQ8B/wvsSo2XT0H3ep2w7oR63DLZbegXrl66NWwF5YMWYIbG90YUpkDseirF9HkCRP0LxOqP63HxxMGg/1lNS8omZlSprxMGYk06tYN2LYttH1EMy+9BMycKY7l9esDI0cC//wjxWYVxRu9XoqXaVG7dpF27c+pPMOZgSxn1iVn8lR7KtYcWYOv//06qPaJCE0qNUHzys1VxLgiKlC19oLh4kWgUSPg3LnsUiUmkyyTbNyoWRph++nt6PRpp1ymbovRgiGthmBqn6khE23MH2MwZeOU3P0YLJjRfwaGLxiey1/BYrBgYPOBmDHpsFRl90RsEYlFYvfuiD5Mf/9mLG7Z9gJsOSaplizgjfh++N8zP4auo379gD/+yF2OJj5essQVsLiqwj/RXLQ4GIrN+DVtmlgXcy7vWSySVuSOO4q062UHl2HgtwMv5XTS6/TIcmZpLvn1qNsDy4YuK1J5FIrComrthYqvvpJlkJyWkawsUTz++kvzlHGrx/nkRLHZbZj5z0ycs50LiVhpWWmYvGGyb6Sdw4bHf3/ct3+HDf8tmQ3X+nXZShQgjsYZGcD06SGRq6A8t25sLiUKAGwm4PWLC+G0a/j1FIQDB4DFi31r+mVmApMmhaYPhSKSjBoFTJwoSTEBycM2dWqRK1EAcHW9q3HyyZP4/e7fsXjIYvxy5y9+y0hpOZDn5KztLJ5c/CQaTW6EjtM74ut/v9Ysr6VQRArlbB4MW7dq14FzuYCdO4ErfdfQ/z75t2bKgBh9DA5cOJDLSbKgHEs55jeJ56m0Uz4pDgCg5XkDXES+mnRGhmQhjiB7zema29MMjOSzx1C+WnBhzJrs2iUZ073LkGdlRfz6FYqQMWqUvJxO7bJLRYhBZ0CXWl0ASF4pq9GKlKzcNTGtRivub3e/3zYuZlxEu2ntcCrt1CXr1gM/PYDNxzfjvV7vBez/VOopvLjsRSzcvRAWowUPdngQ/+vyPxh06rGnCC3KIhUMrVvnriHlQaeT7NsatKnSRtNfINOZifrl64dErBrxNfz6XFWxVtGcCW6r4IDOpTGri40F2rULiVwFpVG6dmSgxUEoU6lGaDq57LLc1jgPJpMUXlUoShJhVqJ8utfpsWDwApSNKYt4Uzxi9DEwG8wY1HwQbmt2m9/zPtz4IU6knMhV9iXNnoaPN32MEykn/J6XnJmMdtPb4YutX+BU2ikcTDqIV5a/gr5z+mLNkTWq8HABcTp9554KpUgFxz33SG03XY6PzWQCmjQBrrhC85TnrnoOsYbchXYtRgvuaXVPSKxRgJSBeaTTI7AYcyt5FoMFE2+Y6Nu/wYLm1w2G7vLLxSrjgUgUqVGjQiJXQRnb+TlYvDIzWLKAl8reDL0xRBmF69cHrr8+u56fh5gYYLR2TUGFQlFwOtXohGOPH8PUPlPxbs93seH+Dfi83+d+HcYvpF/AGyvfgIN9a0DGGmKx+YR/y/HMrTORlJEEhyv7XJvDht/2/YYbvr4BVcdXxbhV4y7tS85MxqpDq7D33N5CXGHJJTMTePRRcSGNiwOaNQNWqgq7l1CKVDCULYuU1Uvx8r310eRRoPVDhI9HtYPzj8WajuYA0LxycywZsgQdqnWAjnQoH1seT3Z5Eh/e9GFIRXvrurdwZa0rLyXZNOlMeOOaNzCw+UD8OfRPdKrRCTrSoVxMOTx2+WP4tO+nwKJFEnVotcqM9ZprpLZdhKO2et3xAuY0eQ6Nk43QuYBqaTqMr3AHHnv6+9B29M03UsMvPl6U427dgNWrlaO5QlFEWE1W3NnyTjza+VG0qBy40Pjbf72tWS4GkAoMnmzmWqw8tFLTsR2QkjLpjnSMXTUWP+76EW+tfgtVx1dF3zl90Xpqa3T5rAvOpKnqGzkZMkRiFNLTxSq1cyfQuzfw33+Rliw6UFF7QZDlzEK7ae2w/8L+Sw7cFqMFfRr1wTcDv4mobPf8cA+++vcrn+3Lhi5Dj7o9wi+QQuGFitpTBEPDSQ01EwMDQJMKTbDzkZ1+rVlPLn4SE9bmXeqoaaWmOHzxcK6oZqPOiMtrXo6Vw5XJBQCOHxcDvrcnhF4vifK//DIycoUbFbUXIr7b8R0OXTyUKwrOZrdh0Z5F2H56e8TkSs1K1VSiAGDEghFhlkahUCgKj9Vo1dxOIMwdODdgDqlYfazffTlJTEr0KYlld9mx8fhGHLl4JP/ClmAOHBCPD2+cTskUo1CKVFAsT1yO1KxUn+1EhLVH10ZAImHtEf99H7qo6qYpFIrix8OdHvbx+9STHp1qdEKbqm0CnrvxRN41/Yw6o4//aM5959JDk56muNOkiXZcjsEAdOoUfnmiEaVIBUGdsnUQo4/x2a4nfcD1+qKmYYWGfvd5D0QKhUJRHLiv3X0Y2GwgYg2xiDfFI94Uj7rl6uLbgd/meW7dsnX95q0CAJPeBIvRgi41u2jmsSIiNK2kHYld2khIAIYN8w1Yj40FnnoqIiJFHUqRCoLhbYf75CDRkQ5lYsrg+gbXR0gqoF75eqgZX1Nz32OXPxZeYRSKYgQRvUtEu4joXyL6gYjKRVomhaAjHWb0n4FtD27D1D5TsWjwIux5dA9qlc07GOTRzo/6THr10CPeFI+G5RuCmeFkJ1YdXgWHy3FpXCcQLEYLJvWahBiD76S5tDJlCvDyy0D16qJQ9ewpRTHqhyaDT7GnRDmb794tiXsPHwZ69QLuvts3ur2wrDq0Cnd9fxfOpZ+Di124rOJl+G7Qd2hQoUFoOwqS06mn0WpqK5xKO3Vp24AmA/D9HSGOdAsTF9Iv4PO/P8dfR/5C84TmeKDDA6hRJkQ5pBQRIRqdzYnoegB/MrODiN4GAGYeE+gc5WxePFi4eyHuW3gfbHYbnC4nOtTogKk3TfUp2QVI8tAWCS1Qv0J9PNHlCXSt1TVCUiuilUDjV4lJ8frTT8CgQYDdDjgcwO+/S3WE9eulJm2ouKrOVTj02CHsO78PMYYY1C5btAVA88u209uQkpUCAxngYAfMBjO2ntqK8+nnUcFcIdLiBcXR5KNoP709UjJTkO5Ixy97f8H769/HsqHL0KF6VD2HFcUcZl6c4+06AP6zQyqKFTc3uRknnjiBvef3okxMGVSPr46PN36seaxBZ8C9be/Fo50fDbOUipJAiVjacziAoUMlx4XDnX8tLQ1ITCyasmlEhEYVG0WNEuViF+758R7Y7LZLyevSHek4lnIsV9K54sKYP8bgnO0c0h1SKibTmYnUrFTct/C+CEumKOHcC+DXSAuhCB16nR6XVbrskg/rxcyLubKke8hyZuFi5sVwi6coIZQIRWr7drFEeZORAcybF355ws3BCweRlJHksz3LmYX5O+eHX6BC8su+XzTrA+44s0OVdlAEDREtIaL/NF79chzzPAAHgFl+2hhJRJuIaNOZMypZY3Hl+gbXw6T3rY4Qa4jFDQ1uiIBEipJAiVjai4vLtkR5Ex8fXlkigdVk9VtrL84UF2ZpCo/FYEESkny2E5HmIKhQBIKZrwu0n4iGAugD4Fr24zTKzNMBTAfERyrkQirCQrtq7TC4xWDM/W/upfxRVqMVtzS9BR1rdIywdIriSomwSDVoADRqlLsEHiCVTx4tBUveVeOqon319j7hvhajBY90eiRCUhWcke1HwmzIHSVg0pnQt3Ffv3lfFIqCQES9AIwBcDMza9cUUQjMwLp1wMyZwMaN8r4Y8knfT/DNbd/gtma34damt2LOrXMws//MSIulKMaUmKi9xETg6quBc+4canY7cN994iMVIAFuieFY8jFc++W1OJ5yHIBk5x3cYjA+vflT6ChM+nJGBrBgAXDkCNCxo9Suy+PDv5B+Ad/v/B7Jmcm4vsH1aF65ObKcWRj07SAs3r8YBp1BoiMrXYY/hvyB8uby4bkWRciJ0qi9fQBiAHiyL65j5gcCnVMqo/aSkyXmPWcq6zZtgN9+kyUBhaKEE2j8KpQiRUTvAugLIAvAfgDDmTkpr/OKaiByuYBVq4CTJ4EuXYDa0eELHjaYGWuOrMHR5KPoVKMT6pWvF77O9+4FrrwSsNkkDa7JBLRrByxerF1fAMDSA0vRb664qdhdduhJj2FthuHDGz8EEWHX2V345+Q/qF++PjpU7xCwJIQi+olGRaoglEpF6t57gVmzgKwcjtoxMbL9o48iJ5dCESaKUpEKOgcLUEoHopJOx47A5s25zf1mM/Dcc8ALL/gcnunIRJXxVXwiZaxGK74b9B16NexV1BIrwoxSpIopzJKFMSPDd19cHJCSEn6ZFIowU2RFi5l5MTN73LzXAdBOr60o2Zw6BWzb5uszkZ4OfP655ikrDq0Aw1eJT7On4YutXxSFlAqFoqBk+aYMCLhdoShFhNJ5JmAOFhU+XIJxuYLe5y/KEAAcLj8hmAqFIvwQAT16+Po76nTiN6VQlHLyVKRCkYMFkPBhZu7AzB0SEhJCI70iOqhWTcImvYmJkTo9GnSv211TmbIarRjSakioJVQoFIXh44+BcuWya25ZLECFCsDkyREVS6GIBvLMIxWKHCyKUsDs2RKll5UlDudxcZKX4plnNA+3GC34+pavcef8O+FiF7KcWbAYLejTuA9ubnJzmIVXKBQBadwY2LdPluq3bpVAkuHDgfIqilahKFRCzhw5WLqrHCylnJYtJQfF3LnAoUNA587ATTcBBv+3WP/L+mPvo3sx5785SMpIQu+GvdG1VlcVnadQRCMVKgBPPhlpKRSKqKOwmc2nQHKw/OF++OWZg0VRgilbFhg1KqhTapSpgSe7qsFZoVAoFMWTQilSzNwwVIIoFAqFQqFQFDdKRIkYhUKhUCgUikigFCmFQqFQKBSKAqIUqRBjs9twIuUEXBwgt5JCoVAoihRmxum000jOTI60KIoSjlKkQkSGIwMjFo5AhbcroP6k+qg+oTrmbZ8XabEUCoWi1LH2yFpc9uFlqP1ebSS8m4DeX/fG6bTTkRZLUUJRilSIGLFwBGZvm41MZyYyHBk4lXYKwxcMx4rEFZEWTaFQKEoNRy4eQc+vemLPuT3IdGYiy5mFpQeX4tovr4VKdagoCpQiFQLO2c5h/o75yHDkLupps9vw5qo3IySVQqFQlD6mbZ4Gu8uea5vdZUdiUiLWHV0XIakUJRmlSIWAE6knYNKbNPftv7A/zNIoFApF6WX32d3IcvoWUyYQEpMSwy+QosSjFKkQUL98fTjZt26cnvToWqtrBCRSKBSK0km3Ot1gMVp8tttddrSr1i4CEilKOkqRCgEWowUvXPVCrh8vgWAxWvBitxcjKJlCoVCULoa2GYryseVh1BkvbbMYpI5nk0pNIiiZoqSiFKkQ8cyVz+CTvp+gReUWqGSphP6X9cf6+9ajUcVGkRZNoVAoSg1lYspg88jNGN5mOKpYq6BeuXp4pccrmHPrnEiLpiihUCSiGDp06MCbNm0Ke78KhSJyENFmZu4QaTlyQkSvA+gHwAXgNIBhzHw80Dlq/FIoSh+Bxi9lkVIoFKWZd5m5FTO3AfATgJciLI9CoShmKEVKoVCUWpg5Z9prKwCVaEihUASFIdICKBQKRSQhorEA7gFwEcDVfo4ZCWAkANSuXTt8wikUiqgnIj5SRHQGwKEcmyoBOBt2QYqeknpdQMm9NnVdRUcdZk4Id6dEtARAVY1dzzPzghzHPQsglplfzqM97/GrpBIN90y4KY3XDJTO6w72mv2OXxFRpHyEINoUbU6ooaCkXhdQcq9NXVfphYjqAPiZmVtEWpZooDTeM6XxmoHSed2hvGblI6VQKEotRJQzP8nNAHZFShaFQlE8UT5SCoWiNPMWETWBpD84BOCBCMujUCiKGdGiSE2PtABFREm9LqDkXpu6rlIEM98aaRmimNJ4z5TGawZK53WH7JqjwkdKoVAoFAqFojiifKQUCoVCoVAoCkjUKFJE9C4R7SKif4noByIqF2mZQgERDSSi7UTkIqJiHxVBRL2IaDcR7SOiZyItT6ggos+J6DQR/RdpWUIJEdUiomVEtNN9H/5fpGVSFB+I6BUiOkZEW92vGyMtU1FRUse2QBBRIhFtc3+3Jbbukdb4TkQViOgPItrr/r98QduPGkUKwB8AWjBzKwB7ADwbYXlCxX8AbgGwMtKCFBYi0gP4EEBvAM0ADCaiZpGVKmTMANAr0kIUAQ4ATzBzUwCXA3i4BH1nivDwHjO3cb9+ibQwRUEJH9vy4mr3d1vsJ/oBmAHf8f0ZAEuZuRGApe73BSJqFClmXszMDvfbdQBqRlKeUMHMO5l5d6TlCBGdAOxj5gPMnAVgLqTga7GHmVcCOB9pOUINM59g5i3uv1MA7ARQI7JSKRRRR4kd2xR+x/d+AGa6/54JoH9B248aRcqLewH8GmkhFD7UAHAkx/ujUA/lYgMR1QXQFsD6CIuiKF484na5+Lwwyx9RTmkd2xjAYiLa7C6DVJqowswnAJlwAqhc0IbCmv4gP6UaiOh5yHLErHDKVhjyW4KiBEAa21TYZzGAiOIAzAfwmFehXkUpJ9D4BeBjAK9DfuevA5gAmeiWNErr2HYFMx8nosoA/iCiXW7rjSIIwqpIMfN1gfYT0VAAfQBcy8UoL0Ne11WCOAqgVo73NQEcj5AsinxCREaIEjWLmb+PtDyK6CK/4xcRfQLgpyIWJ1KUyrGNmY+7/z9NRD9AljhLiyJ1ioiqMfMJIqoG4HRBG4qapT0i6gVgDICbmdkWaXkUmmwE0IiI6hGRCcAdABZGWCZFAIiIAHwGYCczT4y0PIrihfsB42EAJHimJFLqxjYishJRvOdvANej5H6/WiwEMNT991AABV49ipqEnES0D0AMgHPuTeuYudiXayCiAQAmA0gAkARgKzPfEFGhCoE7/Pl9AHoAnzPz2MhKFBqIaA6AHpCK4KcAvMzMn0VUqBBARFcCWAVgG6QMCgA8V1KjrxShhYi+AtAGssyVCGCUx6+kpFFSxzZ/EFF9AD+43xoAzC6p16w1vgP4EcA8ALUBHAYwkJkLFHAUNYqUQqFQKBQKRXEjapb2FAqFQqFQKIobSpFSKBQKhUKhKCBKkVIoFAqFQqEoIEqRUigUCoVCoSggSpFSKBQKhUKhKCBKkVIoFAqFQqEoIEqRUigUCoVCoSggSpFSKBQKhUKhKCD/D0si/VuizKOfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])\n",
    "label_color = [colors[l] for l in y_train]\n",
    "\n",
    "\n",
    "# Comparing before and after PCA, for the first two features only\n",
    "fig = plt.figure( figsize=(10,3))\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(X_train[:,0],X_train[:,1], marker='o', c=label_color)\n",
    "plt.title(\"The Original Data\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(pca_X_train[:,0],pca_X_train[:,1], marker='o', c=label_color)\n",
    "plt.title(\"The PCA Projected Data\")\n",
    "plt.show()\n",
    "\n",
    "# Comparing before and after LDA, for the first two features only\n",
    "fig = plt.figure( figsize=(10,3))\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(X_train[:,0],X_train[:,1], marker='o', c=label_color)\n",
    "plt.title(\"The Original Data\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(LDA_X_train[:,0],LDA_X_train[:,1], marker='o', c=label_color)\n",
    "plt.title(\"The LDA Projected Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}