ML0101EN-Clas-K-Nearest-neighbors-CustCat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# K-Nearest Neighbors (SUPERVISED LEARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**K-Nearest Neighbors** is a supervised learning algorithm. Where the data is 'trained' with data points corresponding to their classification. To predict the class of a given data point, it takes into account the classes of the 'K' nearest data points and chooses the class in which the majority of the 'K' nearest data points belong to as the predicted class.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Here's an visualization of the K-Nearest Neighbors algorithm.\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/images/KNN_Diagram.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points), we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In this sense, it is important to consider the value of k. Hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the 'K' Nearest Neighbors (data points) when it predicts the classification of the test point.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ALGORITHM\n",
    "\n",
    "1. Pick a value for K.\n",
    "2. Calculate the distance of unknown case from all cases.\n",
    "3. Select the k-observation in the training data that the \"nearest\" to unknown data point.\n",
    "4. Predict the response of the unknown data point using the most popuplar response value from the k-nearest neigbour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split,cross_val_score, GridSearchCV\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"about_dataset\">\n",
    "    <h2>About the dataset</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset,  with predefined labels, we need to build a model to be used to predict class of a new or unknown case.\n",
    "\n",
    "The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns.\n",
    "\n",
    "The target field, called **custcat**, has four possible values that correspond to the four customer groups, as follows:\n",
    "1- Basic Service\n",
    "2- E-Service\n",
    "3- Plus Service\n",
    "4- Total Service\n",
    "\n",
    "Our objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  custcat  \n",
       "0       2        1  \n",
       "1       6        4  \n",
       "2       2        3  \n",
       "3       1        1  \n",
       "4       4        3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/teleCust1000t.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"visualization_analysis\">\n",
    "    <h2>Data Visualization and Analysis</h2> \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Let’s see how many of each class is in our data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "custcat\n",
       "3    281\n",
       "1    266\n",
       "4    236\n",
       "2    217\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['custcat'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### 281 Plus Service, 266 Basic-service, 236 Total Service, and 217 E-Service customers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can easily explore your data using visualization techniques:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x220764744c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kay=np.linspace(1,len(df['employ'].values),len(df['employ'].values))\n",
    "hay=np.linspace(1,len(df['custcat'].values),len(df['custcat'].values))\n",
    "plt.scatter(kay, df['employ'],color='b')\n",
    "plt.scatter(hay, df['custcat'],color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'custcat'}>]], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='custcat', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Feature set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's define feature sets, X:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
       "       'employ', 'retire', 'gender', 'reside', 'custcat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.,  13.,  44.,   1.,   9.,  64.,   4.,   5.,   0.,   0.,   2.],\n",
       "       [  3.,  11.,  33.,   1.,   7., 136.,   5.,   5.,   0.,   0.,   6.],\n",
       "       [  3.,  68.,  52.,   1.,  24., 116.,   1.,  29.,   0.,   1.,   2.],\n",
       "       [  2.,  33.,  33.,   0.,  12.,  33.,   2.,   0.,   0.,   1.,   1.],\n",
       "       [  2.,  23.,  30.,   1.,   9.,  30.,   1.,   2.,   0.,   0.,   4.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values  #.astype(float)\n",
    "X[0:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our labels?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "source": [
    "y = df['custcat'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization gives the data zero mean and unit variance, it is good practice, especially for algorithms such as KNN which is based on the distance of data points:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><b>WORKFLOW</b></p>\n",
    "<ul>\n",
    "    <li>The mean and standard deviation is calculated for each mean with the fit method</li>\n",
    "    <li>The mean of each feature is subtracted from every data point in that feature. This centers the data around zero, ensuring that the mean of each\n",
    "        feature becomes zero.</li>\n",
    "    <li>After subtracting the mean, each centered feature is divided by its corresponding standard deviation.This scales the data such that the\n",
    "        variance of each feature becomes one.</li>\n",
    "    <li>The output is a discrete normizalized version of the inpdendent data</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02696767, -1.055125  ,  0.18450456,  1.0100505 , -0.25303431,\n",
       "        -0.12650641,  1.0877526 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "        -0.23065004],\n",
       "       [ 1.19883553, -1.14880563, -0.69181243,  1.0100505 , -0.4514148 ,\n",
       "         0.54644972,  1.9062271 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "         2.55666158],\n",
       "       [ 1.19883553,  1.52109247,  0.82182601,  1.0100505 ,  1.23481934,\n",
       "         0.35951747, -1.36767088,  1.78752803, -0.22207644,  0.96655883,\n",
       "        -0.23065004],\n",
       "       [-0.02696767, -0.11831864, -0.69181243, -0.9900495 ,  0.04453642,\n",
       "        -0.41625141, -0.54919639, -1.09029981, -0.22207644,  0.96655883,\n",
       "        -0.92747794],\n",
       "       [-0.02696767, -0.58672182, -0.93080797,  1.0100505 , -0.25303431,\n",
       "        -0.44429125, -1.36767088, -0.89182893, -0.22207644, -1.03459817,\n",
       "         1.16300577]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit():The fit() method of the StandardScaler class computes the mean and standard deviation of each feature present in the dataset X.\n",
    "#transform(X.astype(float)):standardizes the data by subtracting the mean and dividing by the standard deviation for each feature.\n",
    "Xt = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n",
    "Xt[0:5]#features of the transform X data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A SCATTER PLOT WITH NORMALIZED DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['custcat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'normalized employ')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAGwCAYAAABRgJRuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACTsElEQVR4nO29eZwdRbn//znnkH0mQ+bMlsxMFgIKMUS2sARCFrheASUYuCgChit+FQIxLEZEUNAfi8qFsOaKiixiQGCiX7zyvUiWCQOBgIRAQmSLZJtklkzIBmHCnKnfH0XP9Jzppaq6qpczz/v16hfknO6qp556qk5Nd3/qSTHGGAiCIAiCIBJOOmoDCIIgCIIgdECLGoIgCIIgCgJa1BAEQRAEURDQooYgCIIgiIKAFjUEQRAEQRQEtKghCIIgCKIgoEUNQRAEQRAFwQFRGxAmnZ2d2Lp1K4qLi5FKpaI2hyAIgiAIARhj2LNnD0aMGIF02v1+TJ9a1GzduhW1tbVRm0EQBEEQhAKbN29GTU2N6/d9alFTXFwMgDtl6NChEVtDEARBEIQIu3fvRm1tbdfvuBt9alFjPXIaOnQoLWoIgiAIImH4vTpCLwoTBEEQBFEQ0KKGIAiCIIiCgBY1BEEQBEEUBLSoIQiCIAiiIKBFDUEQBEEQBQEtagiCIAiCKAhoUUMQBEEQREFAixqCIAiCIAoCWtQQBEEQBFEQ9KkdhY2RywENDUBjI9DaCpSXA1VV/LuWFqCiovuczk6gtJR/1tYGZLP8mrY2IJ0GJk8GMhmgqYl/ns3y78rLgepqYNIkoL4e+MMfgL17gZNOAubMAfr35/Xt3w/cey+vq7gY+PrXgbffBv71L4Ax4MADgVSK21BV1V3mihXAtm3A8OHdNji1r7m5p625HLBwIbBnD7920iSgshJ44w1eZnExcOGFwPTpvKz8cgBu044dwJYtwMiR/NypU/l39fX86OzseV5NDVBW1rMNDQ3A0qXApk3AiBHA7t3czkMOAb73Pf79ww8DGzYAgwcDEycCp5zSu66ODl5PczMwdGhv+y0/HXcccP/9wHvvcZ9OnAh8+CEwbBjw6qvcN5kMP2/EiG4/Atzedeu6+2XoUO4Xe7u8YmjoUGDNGuCjj3rGgL2vtm0DVq/mcTJ8OHD88dw+eyytWAFs3gy89BK/Zt8+4JhjnP0C8M+mTuXtcov7XA5Ytoz7YN++bl9b1zU1cd+2tvK+SqWA2lrn/nSqN3/s5ds3eXLvdn30EfdzezswejQwaxYwZUp33Fv+Xb6cxwdjPHZGjeLnZTK8H6wYz7dt8uTu+Nuwgbdp1KjuWM73l30cWW3Lj6/Jk53HjDX2rO+sOaWqivfBmjXchrFjgdmzed1WudksH5svvgh8/DHva3vddl/b7bGucxrT9fXA4sXAK69wHw0dCkyY4B5vVrxYc5v1X2vcMNY9Zles6B6TO3fyto8dCxx+OI+jl14Ctm4FioqAI47gfquu7p7DcjlgyRI+7jdu5H1/wQW8HHt7ZfrPPv7/+7+759oLL+SxYi+HMV6HFUvTp/O6rDl8zx4+X5aUAKtWAZ98wm087zxg7Vrgr3/ltsyYAXz/+z370u13pbmZl7VpU89Yz58fd+7sOcdNmeL922Lh9Buzbl3v2Mgfq2HBEsott9zCALC5c+cKX7Nr1y4GgO3atUufIXV1jNXUMMbD1/yRSvX+LJ1mbN48fqTT8mVmMj3/XVPD26WzfQMHMpbNip9fVMQP0fNV2i1Tl6z9YR/pNGMzZsj1VX6/i/olm+WxZjLunfozm+2OSys2nfpENBacxpKqbV5lifirqKh3W7LZ4DGXTsuNIy978o+BA+XK9os3nUdNDfe5qH1B+y+sQ6UvgxzWb4uF6G9MUVHPsaoB0d/vFGOMRbOcUufVV1/Fueeei6FDh2LatGm48847ha7bvXs3SkpKsGvXLj25nxYtAs45h3djIWHl1vjBD4D/+q/Cax+RbOrq+H/PPjtaOwiirzBvHv/vbbfJXVdXB8ycqcUE0d/vxC1q9u7di6OOOgoLFizATTfdhCOOOCKaRU0ux2/tbdkSrJw4Y91yJYg4UV3N/9vYGK0dBNFXsP7QlV0u1NTwx3AaHkWJ/n4n7kXhyy67DGeccQZOPfVU33Pb29uxe/fuHoc2GhoKe0ED0IKGiCeNjbSgIYgwsR4sybJlS/e7SyGRqBeFH3/8caxatQqvvvqq0Pm33norfvazn5kxZts2M+USBEEQRKEQ8m9lYu7UbN68GXPnzsWjjz6KgQMHCl1z7bXXYteuXV3H5s2b9Rk0fLi+sgiCIAiiEAn5tzIx79T85S9/wde+9jVkbM/mcrkcUqkU0uk02tvbe3znBL1TIwm9U0PEEXqnhiDChd6p0c8pp5yCNWvWYPXq1V3HMcccg/PPPx+rV6/2XdBoJ5MB7rqru7MLiVSKH1ddVZjtI5LN3XfzgyCIcPjBD/ghy113hb9fjVYhechMmTKF9qmx7yWga5+a2lrapyao/WEftE+NfCzQPjVy9uQfcd6npraW9qnRcajuU1NcTPvUqDB16tToJN12aEdh2lGYdhSmHYVpR2HaUZh2FDa2o3DB7lMTBGOLGoIgCIIgjFFw79QQBEEQBEF4QYsagiAIgiAKAlrUEARBEARRENCihiAIgiCIgoAWNQRBEARBFAS0qCEIgiAIoiCgRQ1BEARBEAUBLWoIgiAIgigIaFFDEARBEERBQIsagiAIgiAKAlrUEARBEARRENCihiAIgiCIgoAWNQRBEARBFAS0qCEIgiAIoiCgRQ1BEARBEAUBLWoIgiAIgigIDojaAEIvuRzQ0ABs2wYMHw5MngxkMlFbRRAEQRDmoUVNAbFoETB3LrBlS/dnNTXAXXcBM2dGZxdBEARBhAE9fioQFi0Czjmn54IGABob+eeLFkVjF0EQBEGEBS1qCoBcjt+hYaz3d9ZnV1zBzyMIgiCIQoUWNQVAQ0PvOzR2GAM2b+bnEQRBEEShQouaAmDbNr3nEQRBEEQSoUVNATB8uN7zCIIgCCKJ0KKmAJg8maucUinn71MpoLaWn0cQBEEQhQotagqATIbLtoHeCxvr33feSfvVEARBEIUNLWoKhJkzgaeeAqqre35eU8M/p31qCIIgiEKHNt8rIGbOBGbMoB2FCYIgiL4JLWoKjEwGmDo1aisIgiAIInzo8RNBEARBEAUB3akhCIIgEk1fS+Tb19orAy1qCIIgiMTS1xL59rX2ykKPnwiCIIhE0tcS+fa19qqQYswpDWJhsnv3bpSUlGDXrl0YOnRo1OYQBEEQiuRywOjR7nnvUil+B+ODDwrj0Uxfa28+or/fdKeGIAiCSBx9LZFvX2uvKrSoIQiCIBJHX0vk29faqwotagiCIIjE0dcS+fa19qpC6icicZCckSDCJY5jzkrk29jIH73kY71jUiiJfPtae1WhOzVEoli0iL8sN20a8M1v8v+OHk1v/ROEKeI65vpaIt++1l5VaFFDJAaSMxJEuMR9zPW1RL59rb0qkKSbSAR9Xc5IEGGTpDEXx8djJulr7QXEf7/pnRoiEcjIGSmhJ0EEJ0ljrq8l8u1r7ZWBHj8RiYDkjAQRLjTmiCRCd2qIREByxt4k/RZ0lPYn3XdhQGOOSCJ0p4ZIBJacMf+tf4tUCqit7TtyxrgqUkSJ0v6k+y4saMwRSYQWNUQiIDljN3FXpPgRpf1J912Y0Jgjkgipn4hEsWgRMHduzx+l2lo+ufYFOWOSFClORGl/0n0XFX19zBHxQPT3mxY1ROLoy+9D1NfzxyV+LFsWT3VElPYn3XdR0pfHHBEPSNJNFCx9Wc6YdEVKlPYn3XdR0pfHHJEs6J0agkgQSVekRGl/0n1HEIQ/tKghiASRdEVKlPYn3XcEQfhDixqCSBBJV6REaX/SfUcQhD+0qCGIhJH0pHZR2p903xEE4Q2pnwgioSRdkUI7ChMEIQpJuh2gRQ1BEARBJA+SdIdJLsc3wVi6FNiwgaevTaeBUaOAk04C1q3jn48ZA4wbx/9E3LSJ3/MuK+PHSy8Ba9YAu3YBVVXAMccA5eX8/6ur+Z+SuRywYAGwfn13WS++yG2w3m5cvrxn2RUVQFsbL6u6Gpg0ide/eDHwj38AQ4bwa+fMAfr353UsWQI8/DCwcSPfrWzWLGD6dF5+QwPffrW5GWht5XXZ2ztlCv+Tt6mJf5/N8nNXreLn2svLZJz/ZAa6/Wlvi+ULqw3W9yNH9qy3uZm3GQCGDgXeeKN33VYd9fX8/6dO5XWvWNFti91Xr74KfPIJr+uII4APP+S7kdXUAAceCLz+OvDWW7y+8eOBYcO4n0aMAHbv5v4ZOxY4/HBg+/bu8les4OdZvmpt5ban0902NTR022n5p6EB6Ozkde/YweOrpYUflg3ZLH9ZpLS0Zxzl+9fyH8A3aXn1VWDfPmDQIKCykvtt+vRu/9jttWKrqopf39LS03f19d12bt/O48CKudmzgZUre8aT5VOrv+3lVlTweHGK8dbW7utHjuT2Tp3a8/aLNU4tm0pLe15rxbIde1zbx1d+zDn5Y/9+4NFHu8fRBRfw8h99lPfX4MHA0Ufz861xms3y+HvuOR5jw4cDX/gC0K9f9+2k/BjP9+2JJ/I4a2gA/vUv/t3gwTwWJ03qfhvaGn/2WBgxAti5k9exbx9w1FHd7bH6Ir9+e3wddxxw//18jho7FvjOd4Df/a7737Nnd88z+TEoEmNW/2/YwGPbPudYMeJko+Wjf/yDf55K8THyxS8Cq1cDa9fy86qqgGOP7dav54+zTZu667Xbu3kzn8O3bgWKivgcMXx4b5tzOW7T4MH8O2uesMeTNYbstw/tsdvRwW2x+uiII/jvRlMT8PHHfExkMryfS0t5f3Z28muam/n88M1v8phqauLxtno18NFH/Pdq9mzeJmves88FNTXd89mYMbzcl14Ciou7y3SyP0xYQliwYAE7/PDDWXFxMSsuLmbHH388e+aZZ6TK2LVrFwPAdu3apc+wujrGslnG+HRl7igqYiydDl6OWxnpNGMzZvB6nL4fOFBvO4uKGJs3j7Gamp6fZ7PuNnx2dKYC+mHgQOc68n2jw99eRybjf04qpbdOAf9Kx07Qc00d2Swfn2GO06QcNTV8/IXtk0yGzzNu9cYhbmQOk/bW1PC4TWrsWvZrQvT3OzGPn/76178ik8ng4IMPBgA8/PDDuO222/D666/jC1/4glAZ2h8/LVoEnH128HIIKRgAF1UuQfRm3jzgttuitoKw4TaGaWzbSKX48iCpWJJCTW/g94l3akpLS3Hbbbfh4osvFjpf66Iml+O3IBsbg5VDEIRZ0ml+m5yIBbRw6UNoTKgm+vudSEl3LpfD448/jo8++ggnnHCC63nt7e3YvXt3j0Mb1rslBEHEG1rQxApa0PQhGOPvGzU0hFZlohY1a9asQVFREQYMGIBLLrkEf/7znzFu3DjX82+99VaUlJR0HbW1tfqMoQQxBEEQBOFPiL+XiVrUfP7zn8fq1avx8ssv49JLL8WsWbOwbt061/OvvfZa7Nq1q+vYvHmzPmMoQQxBEARB+BPi72Wi36k59dRTMXbsWNx///1C59M7NQTRB6F3amIFvVPTh6B3auRgjKG9vT2ayjMZ4O67o6m7j5PYVTgRDVdfHbUFxGewvP+6fU/APfNqUogooVpiFjU//vGP0dDQgA0bNmDNmjW47rrrUF9fj/PPPz86o2bOBOrq+AZRpikq4n9xBsWtjHQamDGD1+PEwIF621lczKW2NTU9P89m3W34jM6gYTtwoHMd+b7R4W8vRAa67olNwL+uSPijMxWDqSWb5ePzV78Kb5wmhdpaPv5C9kkqk8HWiTPQBud6A4/tsDE5R9TU8LhNauxGlFAtMY+fLr74YixZsgTbtm1DSUkJJkyYgGuuuQb/9m//JlyGsTQJtKOw0R2F//lRDZ5YUoZ3d1ehEdV4EZMws6wBN568FIcNju+OwhtfasQfnx+BodgNII31GIs3cTgqsR3bMBxzHp+EmZXJ3VH4lfVZLLy3Da0oxzbw3WYr0YImDMeLmITFNzTg5Fw97ShMOwr32lF40ZM5/OnSeoxvW4qR2ITNGIm1ZdNx7r2Te48J2lFYakfhjfvK8PyLGWxCLdpQilLsBNCJUuzAcDTjqJOHYtS1ydtRuE/sUyML5X5KLklLQJjL8d+xLVucv9f4qDkSCr19hHmSNqaTQCGPS8r9RBQUmUz3H09JoKHBfWIBem7fkKR2WRR6+wjzJG1MJwEalwl6p4YgkoTotgxJ3e6o0NtHEEmExiXdqSEII4huy5Ck7Y7sjwuam8WuSVL7CHXsr3wA/C5A/itNhHkKcd6Rhd6pIQgDWM+2Gxudc9Il7dn2okXA3Lk9b21b75k6kbT2EeosWgR897vd7+RaZLPAb34TuvilT1No846dPrFPDUHElUwGuOsu/v/5quyItm9QZtEi4Jxzej+r91rQAMlpH6HOokXA2Wf3XtAA/LOzz+bnEOFQSPOOKrSoIQhDzJzJt2moru75eUTbNyiRy/E7NF73c/MnyCS1j1AnlwO+/33/8+bOdV8AE/ophHknCPT4iSAMk2Tpan09MG2a/3nz5/NtLJLWPkId0dgA+PZHhaq2iStJnnecIEk3QcSEJEtXRVUSlZXAeeeZtYWIFzIKmkJW28SVJM87QaDHTwRBuEJqCsINmT6n+CDCghY1RGAsOedjj/H/0vPzwmHyZP4s3i0FVSrVves+0beYPLn3extO1NRQfBDhQYsaIhCLFnEJ4bRpPPXHtGn836R4KAxITUG4kckAd9/tf95dd1F8EOFBixpCGTepb2Mj/5wWNoVBX1dTEO7MnOmeRNpKkk7xQYQJqZ8IJQo5cRrhTKGpKQh90I7ChGkoS7cDtKjRh6ick6ScBEEQRFBoR2HCKJQ4jSAIgogbtKghlCCpL0EQBBE3aPM9QglL6uuXOM2UlJPe7yBU6Otx05fa79RWoO+0v69CixpCCUvqe845fAFjX9iYlvo6ZYyuqeH2kNKCcKOvx01far9TWy2Flj35ZqG2vy9DLwoTgXCaPGpr+YLGxERhycjzo9ZaSJHEmHCir8dNX2q/W1udKMT2FyqkfnKAFjVmCOuWNsnICRX6etz0pfb7tdWJQmp/IUPqJyI0rMRp551ndm+KhgbvyYoxYPNmfh5BWPT1uOlL7fdrqxOF1H6CFjVEgiAZOaFCX4+bvtT+IG0ohPYT9KIwkSBIRh4tcVPOiNrT1+MmLu336y8d8RWkDYXa/30O1ofYtWsXA8B27doVtSmEAh0djNXUMJZKMcZvGvc8UinGamv5eYRe6uq47+3+rqnhn8fdnr4eN3Fov19/6Yovv7b2xf4vFER/v+nxE5EYKGN0NMQtcamsPX09bqJuv19//fCH+uLLq61O9IX+73OEtMiKBXSnpjBw+quutja6uwaFjPWXb1z+yg1iT1+Pmyja79dfAGOZjP74cmprNsuPvtr/SUf095sk3UQiidv7HYVK3BKXBrWnr8dN2O0X7S8/VOKLdhQuLER/v+lFYSKRWDJywixxU84Etaevx03Y7dcVFyrluLW1L/d/X4DeqSEIwpW4KGdk6yElSzzQ1Q/Un4Qo9PgpJvT12+IqmPaZyfKDlh3WrXVrh1a/xKVh7ca6fz+vr7XV+XvaHTZe+MUPwPupszMe8dUXScpvj/Dvt+zLOrNmzWLLly9XetEnauL6onDc5LJJwLTPTJYftGzRlyB12ptK9ZbJWp+FFadO7XZ6sZTGTbzwi5958+IRX32RJP32iP5+Sy9qZs6cyQYMGMAOPvhgdvPNN7MtW7YoGxk2cVzUWAPeaXKmAe2MaZ+ZLD9o2W7Xu/3A64qhqJVDou2eNy8cewg5/OIn6vjqiyTtt8eo+qmtrQ2PPvooHnroIaxduxannnoqLr74YsyYMQP9+vVTvbtknLg9fupLieZ0YdpnJssPWnbUyfqiuk0t0+7aWhovcSWMHYUJMZL422Ps8VM+q1atYpdffjkbOHAgKysrY1dccQV79913gxZrhLjdqVm2TOwv7mXLorY0Ppj2mcnyg5Yten2hxZBsu5PcVoIIgyT+9oSyo/C2bdvw97//HX//+9+RyWRw+umn46233sK4ceMwf/78IEX3CeIml00Cpn1msvygZffVZH2ytie5rQQRBoX82yO9T82nn36Kp59+Gg8++CD+/ve/Y8KECbjyyitx/vnno7i4GADw+OOP49JLL8WVV16p3eBCIqg8tdBu14q0x7Sk12T5Qcvuq8n6ZG2PS1sLbXwSvUlqHxf01giyt4Cy2SwbNmwYmz17Nnv99dcdz9mxYwcbPXq0bNHGidvjpyCJ5pL01roIou0xnZzPZPlBy+6ryfpEttqPW1sLbXwSvUlyH8chyaksxtRPjzzyCNu3b5+yYVESt0UNY2py2aS9te6HbHtMS4xNlh+0bLfr3SamJMaDEyLqp7i0tdDGJ9GbQujjuGzVIIqxRY2dzZs3k6RbAzJyxrglGAyKantMS0BNlh+07L6arM9rn5q4tLXQxifRm0Lq4yRJ6Y1Jujs7O3HTTTfh9ttvx969ewEAxcXFuPrqq3HdddchnY5v5oW4SbrtiD6bjVuCwaAEaQ/tKNz3kvVZ7W5s5LsKl5cD1dXxaWuhjU+iN4XWx0l5L8hYQsvrrrsODzzwAH7xi1/gxBNPBGMML774Im688UZ88sknuPnmmwMZ3lcRTTRXaG+tB2mP6eR8JssPWnZfTdYX94SUhTY+id4UWh/HfUzJIr2oefjhh/G73/0OZ555ZtdnX/ziF1FdXY3Zs2fTosYwhfbWeqG1h+jbUDwXPtTH8Ub68dPAgQPx5ptv4nOf+1yPz9955x0cccQR2Ldvn1YDdRLnx0+ixC3BYFBMtycpt1bDhvziThDfyMZzWP1A/a0PU3NWkvsoDNuN7Sh87LHHsjlz5vT6/PLLL2fHHXecbHGhEtcXhWVJ2lvrfphqT5IllyYhv7ijwzei8RxWP1B/60f3nJXkPgrLdmPqp/r6ejZkyBB22GGHsW9/+9vs4osvZocddhgrKipizz//vLLBYVAoixrGkvXWugi621MIkksTkF/c0ekbkQSOYfQD9bc5dM1ZSe6jMG03mtBy69atuO+++/D222+DMYZx48Zh9uzZGDFihOqdpVAohMdPdpJ8u9IJXe1JYrK2MCC/uGPCN27xHFY/UH+bR4eKMal9FLbtor/fSouapFJoixrCmUKTXOqC/OJOmL4Jqy7q7/iT5D4K23atku4333xTuOIJEyYIn0sQJig0yaUuyC/uhOmbsOqi/o4/Se6juNoutKg54ogjkEql4HdTJ5VKIZfLaTGMIFRJquTS71Z20FvdSfVLGITpm7Dqov6OPyb6KKzXEuIaX0KPnzZu3Chc4KhRowIZZBJ6/NQ3SKLsfdEiYO7cns+na2qAu+4CZs70/16EJPolLML0TVh1UX/HH919pGOeECXs+DIm6U4yhaR+IrxJkuzdT0Ewb55eVU5S/BI2YfomrLqov+OPrj6KQkUVZnwZTWj59ttvs8suu4xNnz6dnXLKKeyyyy5jb7/9tpKhYUKLmr5FEmTvfsnxAMYyGffvVJLnJcEvURGmb8Kqi/o7/gTtoyiTbIYVX8Yk3U899RTOO+88HHPMMTjhhBMAAC+//DJeffVVLFy4EP/xH/+hfHvJNPT4qe8Rd9m7qILAD1mFQdz9EiVh+oZ2FCYsgvRR1CqqOO0oLJ376Yc//CGuvfZa/PznP+/x+Q033IBrrrkm1osaou8R92RtupQBsuXE3S9REqZvwqqL+jv+BOmjqJVIcYov6UVNU1MTvvWtb/X6/IILLsBtt92mxajEksvxJXN9Pf+3tVxtagJaW4HycqC6Gpg0iS9rly4FNmzgb1SNGgVMn84jI5cDFiwA3nuPf3fcccCIEd3LYYCfZ0WRvU6nzzs7gQMPBHbu5J9b/9/ZCezYATQ3AwMHAh99xKO+tBS4+mrg1FO7y1m8GPjHP4DBg7ktkyYBtbW8jQC3a/Nm4KWXgK1bgaFDgQsvBKZMAVas4G+TNTcDbW38/NJSoKKC/7u8HKiqAvbvBxYuBPbs4cv9448HPvywt9/sbZ08ufdnU6f2lg3l+8h+neWfHTuATZu6+2PKFF5OSwu3x95vmzYBI0f2PKeiomcfWf2/dWtPv3z968DbbwMbNmAiG4sDMBudyGAyGjAc27ANw9EA7le/z17EJExBPab9/g/AE3u5jUccAWzf3m3zihW8b1au5PalUtyOxkb+Jl9ZGfd/fmxu2sS/Ly3t9o39xm46za/ZvZv/217uyJHd8WzFh/VnnGVTYyMfF9lsdxyUlwNvvMG/HzIEmDAB2LWrO2aqqvgBdPsc6OnjoiJg/HjgzTeBjRt5zE6c2B0XW7dyXzAGHHII8L3v8X+7/Zm5fz8fj+vXA2PGAOPGAS++2HtcWfZZflyxwv9P11wOWLIE+MMfgL15/We1rampe+yk0z3HuJNfnXb7a2jo6e/WVl7mqlXdY/rYY4HXXuN+GTuWt7Ohgc9RjHX3986d3XPGxx8Dn3wCfO5zwG23AYMGeY+7SZOA++/vObdVVvbu8w8/7B6LtbU8RisquN32OcTyt9XW/fuBe+8Fnn+e23bUUd3zi3We5TfLH9b8Yu8jkX6x4s+at+zntbR0l11V5TwvWL8LVn9Y7Ro6FFizhs/HJ50EzJnDz8/vw7a2rv9+sbUcU1CNBkxGJ3rGWRq5rjnjsG0VwN8dfkfcEpHZYyy/3VZs2ttpfR/l7UDZ51qnnXYa+/3vf9/r89///vfsS1/6kmxxwtxyyy3smGOOYUVFRay8vJzNmDFD+j0eo+/U1NUxls16vxxhHem0+3cDB3p/bz+Kivjh8Hmn0+eyR79+zuXbj2zWu91Ob66pHk5+cSo/m+25H72TfSp2ifaL5NGBNNuFnn5uRZa1Iuv7WQd82uH1Qk4YbSwq6u1/WZvCPj5LXNPRwdjGr89juZSCvfltdEqGU1fnP76C+LWmhr9l7vfSlqajE2Cbjp7Bli1jrONJiflQV5/NmOEfv9ks63Szy+qjIP1i4kilhO3ZhBr2NdR1ffQ11LFN8On/bNY5TvzmdpE+0fhijbF3an7961/jpz/9Kc4991wcf/zxAPg7NU8++SR+9rOf9UiVcOaZZ2pbfH35y1/GN77xDUycOBEdHR247rrrsGbNGqxbtw5DhgwRKsPYOzWLFgFnnw0GIKWvVGWsDg3Dlri02ZF58/hfjzHHqb9kPout/5NKKgXGgGcHnol//+T/8o80lAkAeOqpbo3+2WcHLTVWWPH5CiZiIl5FCvGLzTDnxijoBACkcA6eAgA8hXMAMKSjMCY/5gNiLE1COi3mHtMb8bW2tqKiogLLly/HySefLHSNkUVNLgeMGgXW2FiwAyWxpNP8EQFBSNKJ7h8+bePa2rjj/ff5Y6ytW3WVHBvsPyY0H0ZDJ1LYgmqkAFRjSzQLGguNm9UYe1G4MyY/Ers+e85eWlrqek57ezva29u7/r3bevavk8+ec9IAjiExiVUieRj5IWCMv9u0YEFBLmgAWsjEgTQYRsIly2TYWDHf0BDam8SRLuJUYYzhqquuwkknnYTx48e7nnfrrbeipKSk66itrdVvTByTchAEEV/Wr4/aAoIIlxB/J6Xv1ADAK6+8gvr6erS0tPS6c3PHHXdoMcyLyy+/HG+++SZeeOEFz/OuvfZaXHXVVV3/3r17t/6FDSVOIQhChrFjo7aAIMIlxN9J6UXNLbfcguuvvx6f//znUVlZiVSq+4aj/f9NMWfOHDz99NN4/vnnUVNT43nugAEDMGDAALMGTZ6MT8qqMWA7PYKKHfRODaGIiXdqWCqF9rIa/N/sbMzI3oYBbVsLbs5I+js1BfHifSrFJeqAe2KmMG2pqemWh4eBrKyqoqKCPfjggwqCrGB0dnayyy67jI0YMYK9++67SmWYknR3PFnHcuCSRhGpm+h5qkenxjr8ytFZl/Zj3jxX+1RsjrqdTm2J2qZCPHJIsRxSbBFmaIvvzs/KtOS2X4PcnJGEw/LVy5gY27Z59Wec5zKp3xYr6dJniZk6/bZ9MHVoTgBlLPdTVVWV8qIiCJdeeikrKSlh9fX1bNu2bV3Hxx9/LFyGyX1qXppX12sPEbejA2b3qdmFol77nigd/fv77o/Qgiz7pCie+9TU1TE2E8794ru/i2y/BW1Xvp8d9ohooX1qQjk+ytaymahjqRRjv8A89imC71OzEbU99g8BeGwqj1MRv9bWhr5PzSLM6Gqb6Hyo5aitFdqnxmkM2fs96fvU5CddemlehPvUaE4AZWyfml/96lfYunUr7rzzTgP3jdxxe7T14IMP4qKLLhIqw3Tup0VP5vCnS+vxhbZ6AMDzn+3uWIUmVKAVrShHI6qxApPwtbIGPPadpUhv2qB9R+FFO6biyiuBsY31mIp6pNAJFB+Ic/5tJw47FEo7Cv/zvsVo+ds/sGP/YDRiBF7GJOwvr8U37pvMtyCI2Y7COWQwejSwZQvfUXMKuC8AoB5T8QIm8z74bj3S4DvDdrbtwKK7NmHfJylsxCjUYwo6kUElWrANw3v225beOwo//04FFtybw7i2hq7+LyvL4KpvbMWxOecdhTF2LDB7dveOoQ67eXY2bsPXrxiORduddxSeinp8b9AfMPNLe5E+kXYU1rGj8KL/m8HcuTx+DsB+XIYF+OKQ9Tjq7DH44nniOwrb+y5/p1cAyCCHc7JL8Md//wP+vmgvnvtkEt7AEajAdjSDt60KTahEM8YUt+HyOWmkp0/19qvCjsLvt4/AvS8di4N3vwaAYT3Gojk7Dj+Z0oDDBm7gvnLZUbhp4yf4f+9/Dpd8fBv2g+8oXFsL3Hl7DjNLe85NueMm4efV96N893sAUngZx6EJlZiAN3ASVuAjDMGG4gm4bvaHSG/Wv6PwotbJuPJKYExjA0agERVoRWdpOc6ZW42Tr4v/jsKv/LkRf3uoFet3Z1GGNmxHFgeXtOH0WeU49ms9d0XO5YDRo4GtW7p3FG5GBdLI4WQ0IAXgrbKp+GPjVGT6x39HYWP71HR2duKMM87Au+++i3HjxqFfv349vl+0aJGaxSEQRkLLXA645x7gyiv9zzWVXMyyQ3eCsSQlxauvl0/wpnJNPiZ8pMMuQh4dfSnad/PnJ3vOEL02DrGcpHnMiST5WifG9qmZM2cOli1bhmnTpiGbzYbycnCSyGR4KhMRTKrcTCQYi1PSMj9UErzpSApnwkdRJ6vrq+joS9E+EVV5x3XOEL02DrGcpHnMiST5OgqkFzWPPPII6urqcMYZZ5iwpyAQVa+RGtwcKn0Q136Lq12EP6J9IqryTnofUyyHR1/1tfTjp1GjRuHZZ5/FoYceasomY5h6/JR/O3DSJD5JuanpNO4cLWxT0m6xBsV6nuylaCwv57f97cl7va4R7Te77+2PoVX7wa8tuuOpr8eOTkT77v33vecMgL9WcuedvRNKR4FqjIQdy32ZQvO18O+37BvIv//979m5557LPvroI4X3l6PFhPqprq73S+NWclxL0Zb/IrtGlZuUTSbrjCOfKRqFBFj2BL1B+s3J9zr6IahdMvVQ7OhFtO9U4jWq9gSJkbBimSgsXxuTdB9xxBGsuLiYFRUVsfHjx7MjjzyyxxFndC9qrIBxUuClUs4qOc0qN2mbkhTEOvBbZDj5x+kakX5z872uflC1S6Z8ih0ziPZdXZ2YijaqPtEVI6ZjmeimUHxtTNL9s5/9zPP7G264Qaa4UNH5+Mm6tbfFJW+Y/bayk8rSBKI2JeV2oy7sitYrr+QKRCfs/gHkbq/7+d6tHpVHUSYeDVHsmEek78KKIxV0xwg95gyPQvC1MUl3ktG5qImjXC6ONsUJk/4RLTtoPaag2IkHcY4jihEiSkR/v5WydO/cuRO/+93vcO2112LHjh0AgFWrVqGxsVHN2gQSR7lcHG2KEyb9E9Y1pqDYiQdxjiOKESIJSEu633zzTZx66qkoKSnBhg0b8H/+z/9BaWkp/vznP2Pjxo145JFHTNgZO+Iol4ujTXHCpH/CusYUFDvxIM5xRDFCJAHpx0+nnnoqjjrqKPzqV79CcXEx3njjDRx00EFYsWIFvvnNb2LDhg2GTA2OiXdq4iSXi6NNTkT1fNekf0Qk5DrqMUVSYkcHcX6/IM5xJBIj1dXAQw8Z2yk/NMKKkTjHYtwwJukeOnQoe//99xljjBUVFbH169czxhjbsGEDGzBggGxxoWJK/RQnuVwcbcq3L0rJsEn/iEhy49IPTsQ9dnQQdfyJEOc48ooRoLdyK26+FSGsGElCLMYJY5LuiooKtmrVKsZYz0XNs88+y2pqahRMDY+w9qmJWi4XR5ssu+IgGTbpHz8JeRz6wYu4xo4O4hJ/IsQ5jpxsc5Ohx9G3XoQVI0mKxbhgTNL93e9+F62trXjiiSdQWlqKN998E5lMBmeddRZOPvnk0LN3yxDWjsJxuIUYN5viJhk26R/dOwqHTdxiRwdxiz8R4hxH+bZddFGyfOtEWDGSxFiMA8Yk3bt378bpp5+Ot956C3v27MGIESPQ1NSEE044Ac888wyGDBkS2HhThJGlm3CG5KBElFD8maNQfBtWOwrFX2FjLEv30KFD8cILL2Dp0qVYtWoVOjs7cdRRR+HUU08NZDBR2JAclIgSij9zFIpvw2pHofgrrkgvaiymT5+O6dOn67SFKGB0yUEL8dFIUHT5xK2cqH2uo36SI5sjzr6ViZ2w2hG3udCpHKB7F/bWVp78Nw6JVIUI4f2e2GDiRWFCjI4O/nKhm6IjleIvP3Z0uJdBaoHe6PKJV2LWqNVqOurXEX+EM3H1rWzshNWOOM2Fbi99u734HeV8a0z9lGRoURMtQSTDpBbojc7kgiKZocP2ue4+7wuS9aiIm29VYyesdsRhLpQd9/Z6ohgrtKhxgBY10aMiGbb+svEaZH3tr2xdPvErJyqfm+rzQpasR01cfBs0dsJqR5Rzoeq4j3K+NSbpTjKkfooHss+CSS3QG10+UUmgKFO+Kib7POp3hAqZOPhWR+zEdUfhuIx7kTp0o1X9tHv3buGKabFA+JHJyA0GUgv0RpdPwlJymCpXpX7Z+CPEiYNvdcROWO2Iai7UMW7jOt8KLWoOPPBApFIpoQJzuVwggwginzirK6JCl0/CUnKYKrcv9TkhRiHHTlzGva4yTCD0+Gn58uVd/79hwwb86Ec/wkUXXYQTTjgBAPDSSy/h4Ycfxq233opZs2aZszYg9PgpPli3XUUkg3FKthiH2+uWHTp8IpNAUaV8WexxceWVwPbt0fc50ZO4jAE34jBfmPKRyHgtLwfmz/eWYKuOeyC6sWcsoeX06dPZwoULe33+xz/+kU2ZMkW2uFChF4XjgVdeGzfJYBzUFXGTlOvyiUgCxTB87pfvKIo+J3oStzHgRpTzhWkf1dWJv9TrVa/suLf7sKDUT4MGDWLvvvtur8/feecdNmjQINniQoUWNdEjIiN0GzRRqiviKinX5RO3cpz2qTGlBBGdXEmtFA1xHQNuRDFfhOEjmUWNiIRdZp+aKMeeMfXT5z//eXzlK1/B7bff3uPzq6++Gv/zP/+Dd955R/q2UljQ46do8UvkZuF1ezOKW99xT0CX9B2FReJC5JY6YY64jwE3wpwvwvCR6BwqU29SdhQ2ltDymWeewdlnn42xY8fi+OOPBwC8/PLLWL9+Perq6nD66acHs9wgtKiJFlkZYVwk2iQpNwv5N/5QH/kTho+CSLGT3jeiv99p2YJPP/10vPvuuzjzzDOxY8cOtLW1YcaMGXj33XdjvaAhokdWAhgXySBJys1C/o0/1Ef+hOGjqK5NEkoJLWtra3HLLbfotqXgiWvCQBlbg5S1dq3cdSYkgyrtiqtENIzHQmHEpYx/7TZVVPDPW1rCHTdhj9c4zA9xHQNuRBEnqnEsY1MQ/8alb4yj8sLO888/z84//3x2wgknsC1btjDGGHvkkUdYQ0ODSnGhEeWLwnFNGChjq4pNooqW/JfbTGzDrdquOCbsC0NhEVZcdnS4v5ho9++TT3rHUhjjJmz1T1zURnEcA274zTkm41jER05xLGqTXx1x75sgGFM/PfXUU2zQoEHsO9/5DhswYABbv349Y4yx++67j5122mlq1oZEVIuauCYMlLFVxaY4JUwL2q44SMrzbTEVN2GrXETUHPPmianmTPZFFH6Jk9ooTmPAz8ao4sTPR25xLGOTTvVTkjC2qDniiCPYww8/zBhjrKioqGtR8/rrr7PKykoFU8MjikVNXBMGqtgqY5Nqu01IBnW1Kw4J+0wn9ww7eahInJSWiseSqXETN79E9dd3HMaAGzJzjkn/ufnoiSf0JaP0urMZx77RgdF9aj744APGWM9Fzfr169mAAQPkLQ2RKBY1y5bJ/7Dbj2XLQjNV2FYRm2Tb/eMf82tMTDI629XRwc9buNCcvV7obEsU5avWF/W4iatfwpwfLKIeA26oxJIp/zn5SFefipYzf358+kYHor/f0i8KDx8+HO+//z5Gjx7d4/MXXngBBx10UNBXfAqOuCYMDFKXyHmydo8fb05uqLNdUSfsM62wCFvlEnVCTN3lhe2XKBQtUY8BN1R8Ycp/Tj4KOxllZWX0gpMokJZ0f+9738PcuXOxcuVKpFIpbN26FX/84x/xgx/8ALNnzzZhY6KJa8LAIHWJnCdrt8l2Jk254YXptoTtq6gTYuouL2y/JCFmw0LFF0mcXyk2fFC5DfTjH/+YDRo0iKVSKZZKpdjAgQPZ9ddfr3RLKUyifKdGJb+G0/NVt1u/Om4J61Q4iD7fFikzaNtMKjfCvhUvEk9lZYy1t5sp39S7I37tqa4WG0Om36nxsqGmJjy/FIqiRScyc22U7yx62Vdaytjixf7zoV85bm2L66NDEYy9U2Px0UcfsVdffZWtXLmS7dmzR7WYUIlK/TRvnvyzXqc31sOQhetUOATJ8+TXZpXcRrqVG1HJbUUS0WWz6naErXIRbY9lg1cshaF+crMhiM9l6iskRYtuRGIpDupSv3nRbx7xm1tnzHC+Jg7bA6hibFHz8MMPs3Xr1vX6fN++fV2qqLgSxaJGVdY8b16wcnT/WKu+Re+1Z4RfmbolrbrbFaXctq5OTAERZGETpsrFb28Ry9debQ5D6eHldxN9H2e1UVzxi6Wo/Seyd5dILM2YIf4bEvV8pQNjCS3T6TSGDBmChx56CGeffXbX583NzRgxYgRyuZzGh2N6CTv3k0ryMYva2u4EZKrlBEmgZmJHYZnkaKaSw+loVxyS++VywKhR3Kde1NQAGzboTXBpiv37ub2trc7fp1I8bh56iO8OG8WOwlH0fRx2FE4acdh52s+++nrg3HOBHTucz/GKpf37gcGDeTluZDLAxx/z/0Y9X+lA+PdbdrWUSqXY7bffzgYNGsRuuOGGrs+bmppYOp2WLS5Uwr5To0vOnSRZuC7iLGmNg20yMZGU/o+DXwvBRiIZBIml+fPFrp0/v3BiVvT3W1r9BAAXXHABli5divvvvx/nnHMO9u3bp7b0KnB0ybmTJAvXRZwlrXGwTabspPR/HPyqq+6k+JyIjiCxtH692LXr1/e9mJXepyaVSgEAjj/+eKxcuRJnnnkmJk2ahF//+tfajUsyuRzQ3BysDEuSFxdZeJi3weMsW4yDbTJlNzcDjz0Wn1vvbsTBr4B3nIvW3dzMy/F6vCr7SFbWVlX6+uOuOCZyzWfsWLFrx45Vryfuj/Bckb0FlEqlWHNzc9e/P/roI3bWWWex4uJievz0GSpJHPNf3rJL8nTLwnW1yeSb83GWtMbBto4OLnP2i4FMJrw+C0oc/OoX5zJj0c3XXvODTP+YGJNJV8gEJexErqrx3t7ee2w7jf32drV6okoK6oUx9dONN97IPvroo16f//SnP2VTp06VLS5UwljUqKqd7AHmJufWUY7ONkUloY3DG/txsE02JuLiOy+i9KtonIvKcp1s1rHNgYytJtpfqETR/iDx7rdViJP6SaSeqJOCumF8n5okYnpRI5NQrbbWeX8ZN7lhRwffmEn0x0uXbDHqxHpxlrTGwTY3iXE6HV2fBSUKv8rGuejdWPt1ujakNDEmox7nURNl+4PE+7x5ve/YZDK9twQRrScuSUGd0Crpfvrpp3HaaaehX79+ePrpp13PS6VS+OpXv6rpwZh+TEu66+uBadP8z5s/H5gzp1uuLfL8VrTs/PKDIlrvsmXm8sHE+Rl/HGyz5KH19fzfBx4I/OAH/teZ7LOghO1XlTjP5YB77gGuvFLsOkB8DOfXZcfEmIzDOI+SqNsfJN737wcWLOAvBY8dC8yeDfTvr1aPzO+MRVgxIfr7LfSi8FlnnYWmpiZUVFTgrLPOcj0vlUrFep8a06gkGhNNDifzZrrORGZxeHM+rgn0gHjYlskAp5zCD4C/FCxCnNUOYftVJc4zGT7WdJYvco2JMRmHcR4lUbc/SLz37w9ccYWeeuKUFFQVoUVNZ2en4/8TPTGp3pC5Rqc6JC6KFEIc6jN5VH0WxZg3UWdfj5m+3n6LuCcFFUF6R+EkY/rxk8ndRk3sIOt1K9IuOb3ySmD7dv4k1Qlr92NAj0w1SmRu5bphYjdme1mAd/lWHDY2OveZVxyq2B6HR3BBUfWZzHWA2M7gfvOEaJ3vvw+sWOHfL0F3t40Ct5hTjUURn9p3s05qnPvh5wc7Ye/wrXVH4bvuukv4iDOmXxQ2nRdGRO2iQw4qK0mfN0+fTDVKZF66c0OnJNSprGy2d4w5la+iqlCxvZAkwKpKFBPKElH1k1udokludeUhChO3mAua2NfLp9bYK4Q490NE3SeSi023f7Sqn0aPHi10jBkzRovxpjC5qPGbrHRl8HVbOMmU7yVdFF3IyB5xmhSdkJFHuqFTEiqzNYDXNgCiqgoV2wtRAqyqRJH1tWqSV5E6582Tk6b7xVdc1IaMyW+ZIRuLbn9I6Cg7SfgtdkWS6er2j7GElknG1OMnkYSTQRILOtVnV7tMncoPmdusKkk2gxC329d2ZJLDeakKdD16VOkjr8cjfrfjVWyPQ1JPUwR5hCF6nakdhSdN4o9N/frl/fe9zwOA0lLgiSfE5xbThJXYN38n3YsuKsw498NtR2E/n9jR6R9jCS2TjKk7NUlKGBY0OWbQIw4+yEcmOZwbOmMgSB+p+FfF9iTFfF9CtF9EYz5O/RdFYl+K896o9IMO/4j+fkvnfgKALVu24Omnn8amTZuwf//+Ht/dcccdKkUmmqjlgDJEbUPU9TshkxzODZ0xEMRHJiWZ9vOSFPN9CVF/i8Z8nPovisS+FOe9ibvsW3pRs2TJEpx55pkYM2YM3nnnHYwfPx4bNmwAYwxHHXWUCRvjTS6Hw5obcB4aUYFWtKIc21CFNHKYguUYhQ1IIYVNqMXE+lJg7U5+XWkpOiuq8GZbNd4un4yq6gwmT8ohs8J2vy+XA5YvB/71L36P+pNPgJEjgaOO4htkNDcDq1YBGzcCgwbxe9iZDJcjlZXxMtragGy2678TV7bhmyhHI6rRAC6lmYJ6TEU9UujEhzgQpdgJoBOl2I7DsQ5DsRtvYgIewYXIoR+q0IRKNCOLNnQijXpMRQMm40SswAibH5pRjsOxBmPxLwApvIzjcFhjJXD7Gn7/e9s2vpAvLgYmTAB27QI6O4Gdn/kokwEmTuS2t7YCmzbx+5n29lk+2LQJnbUj8a+So9DCyjGq5VUMr8whfcBnZXz4IfdPVVW3XzdtAkaMwMWv7cR0NGMPhuIP+GaPNpahFTXYgi2owUmbyoA/VCDX0oZ3tmexb0sbBo0sx+enV6MiOxlA9/3VNHKYjAYMxzZsw3C8iEk4ESvwxTc3Ay+u5O0eM4a396WXuA++/nXg7bcx+fn3sQhbUYbtYADaUAoghcHYh1aUgSGDjRiFekxBJzKoRAu2YTgaMBnDh2eQ25/DmnvrcUBDPYqKgdpvTkYmDd7mDRu4D0eNAqZMQa4TOOjBBvwMQD2mYjmmovOzdvAY7o6Nic+XAh0VQGsrTvhHKx7CFmzGSNRjymdxtBwjsQlbUIPtKEMLqvC5LVXAEgBNTbwPhw0DXn21O9ujU980NHC/lJbyz0pLgb/8BXjtNe63Qw7hTt6+nY+J0aOBWbOA6dP59fnytUym9330pibktjZj82vNGPDWKhxQMgSlZ01G5vtzus9vbOTXrF4N7N3Lx1w2y7+fNAlYt46PTcb4joepFP/vjh383nxNDXLDSrF51XYMWPMPFH/cgsHDhyL9xQn8+spK4I03uERpyJDuMZBOd5e/YQOPk3HjgBdfBDo6ePlNTcC+fXwusHxXXo6Jz6/BPfgXUmDYhaGoweau+acNpchiB2qwBRUrRuItTO8at1ac5s8JVfcBq7dMxuFHZJDZ3uIswfvMp51bm/Cvla1oyWVRwZpx0J7VSH/8EXD88dxHf/0rsHs3b0tpKW/n2LHA4Yfz5xrWczh7HADA5MnIpTI48G9NmPvZ3NKIaryISZiMBkzDUozCBmSQQzna8BEGoxEjsBLHI4sPu84fXmEbo07P7Vas4H3e3Ay08bmtX+skXIm1OAkr8BGGYDUmoBQfYiQ2oBLb8RGG4HlMxvDsHCCX6flegPUs0ZIDWXX4PQu2+3X/fuCRR4C1a4GhQ4Hx49E5LItNWzP4V+1UpKdMxuTMCmRaHJ4RWfOcfcxPn87rzWsrOjt5XDU3A0VFPBY//LArjlFayr/ftAnTWlL4/zAKSzEdyzG1R7zAYR4BQpZ9y94CmjhxIvvJT37CGGOsqKiIrV+/nu3Zs4edeeaZbMGCBUq3lURZvnw5+8pXvsKGDx/OALA///nPUtdrf/wkKxNyOTahhv0C81hjJnhZMkcrsmwXirSU1QGPffn7wLE5VcO+hjoGMPY11LFN6NmXn8In+5yGozFTw/555jzWlhZ4i88jJr6GOvY11LFWqJcTydGvX+/8EOk0Y0XiMd6JlNT5ST/yx63QnOAkwYuB7b596yXv9MsO6Vc2UowNHOh9nl92WYXfE6V51yuHiuSxC0WO8WLNIzpTKRjL/VRUVMTef/99xhhjBx54IFu7di1jjLHVq1ezUaNGyVsqwTPPPMOuu+46VldXxyJf1ATNXGk7cgDr/OwwPRH0HIj66gzb9rgdOYDlkGK/wDyWQ4rlIvBPJ4L3aaemcpJ69LV257c1Se2Pk51KfrPLgxR/T6L2gVu7rc9noi509ZP046chQ4agvb0dADBixAisX78eX/jCFwAA27dv13UDyZHTTjsNp512mtE6hMjlgLlzefdpIA2AAUhpKU0cnfWFbXvcSAPoBMPVuAMAQzrv+zD8k0LwOLLKsP6/r9HX2pzf3iS1P062KtnCGH8sNHdu97/DqFcjbvVb88gfsnMxeMYM2B/Nm0Z6UXP88cfjxRdfxLhx43DGGWfg6quvxpo1a7Bo0SIcf/zxJmxUpr29vWsBBnBJmBYaGrRroqMOTiI4afD3UKJERxxRLBJESDAW/v4aIZECMLhtC/+9DDGRm/Si5o477sDevXsBADfeeCP27t2LP/3pTzj44IMxf/587QYG4dZbb8XPfvYz/QX3pVfdCYIgCEKVkH8vpRc1Bx10UNf/Dx48GAsWLNBqkE6uvfZaXHXVVV3/3r17N2pra4MXHLcMXgRBEAQRR0L+vVTap8Zi7969vbJ2m0gUqcqAAQMwYMAA/QVPnsxlbhpvG0bxTg2hl04AncggjU6koed9K1l0xFFffqeGKGxiF9tWVkhALItk0qip6d4CICTy32f05YMPPsAZZ5yBIUOGoKSkBMOGDcOwYcNw4IEHYtiwYSZsjB+ZDHDXXTwgNdCZ998kUmBDURredyncjqs++3f406YVP0H6Isn9yNDbfqfPZMsoZIL6K0pk7ey0XcPyxmfQNiv5zfr9uOsuftg/k6hXllD79667Qs8fIb2oOf/88/Hhhx/i97//PZYsWYKlS5di6dKlWLZsGZYuXWrCxi727t2L1atXY/Xq1QD4Amv16tXYtGmT0XodmTkTeOopvhINyBbU4leYh0YEL0uKbJZvtKSBTvlQKii2oBbn4Cn8CL/COXgKjajueUIIA9uKozZklcvYjizORh3OQR3ai9TLiYL96NcrDjuRxh7IxHgKKU1jIhGkevorlc2iY6BP+7NZfkQMS8nNOXyM8tjekjc+c4HVOSmkBg70PiV/Dqip4b8hM2d2/55UVztf64LKvKt1ri4qcv4NyWaBujrerpCRTmhZVFSE1157DZ///OdN2eRKfX09pk2b1uvzWbNm4aGHHvK93khCS6fMdPZda63dHGtr+a6M1m65th2F12Un44qrM2hr7d6FthkVXbsSj8a/UIlWDMIn2ICReB1HoQWVqEAzjsIqHHLARkw8eRDSFf47CqOtrWf2PO5YfnR2IldyIDa/uRN793RiWG47hu9Yh/Se3XyHyQsvBPr147uZNjejc3sb7r43jaf3iO0o/N6w43DHHyuRWadvR+Hc1mb8+YZVKG/fhE2f+aYV5TgOryKFHBgyeP/Aibj9+g+RqXLeURg7d6KzqRkt+4Zi3VHfRGl5P0yoaEK6tZnXa+2qadXZ0oZ/tmSx8J42bNzXvTtzZ96OwjPLGvCnO7chXW3bTXTzZmCl947CeP99YOtWdLZux+5dwJ4BpRg4MIXs4H1Il5dxv4wahdzkKfjmhRmkt3fvKMwff3XvBDxkEPD9pyaj3wHoEY+dI0fhm7+Zgu1twMngO7fadwJNpYCR1Tmsf6AemYb67h1+K/iOwpZfOmtHdpXjtKPwNlQBAKrQhAq0og3DcMqQV/GtC9x3e+5c3oB77u7Ehr2laEEVWlGKGfgLJuI1AAzv4RAMGACUd25H+tNP8AFG42HMQj2mI40cLsMCjMV6rMdY3IfZYMhgZnkDHp+/DekqszsK50oOxH0/34GSj/gu1G0oRRm24yj8AyPQgp0Yig8GT8CFCyYhPVx9R+HObU14/tl9eHH/UdiOcrSgqsd4S4GBDRmK2WduRjrNx0xuWCk2r94BtmkLUqNHonbWdGSmTO69yy2A3JJ6bP5DPfbuBTomxWNH4Xfqm7BvU2vXLt6ZyZP4OUuXcj/lcnyuGDwYuREjcNVjxyO1q3tHYfsYTSOHqekGVHT23PF7BBq7dksvKk5j8rWTkFqzFjWbVqBjwBA0VUzAMPYhBrRuQKple++4iWhH4VTTNpw3twKtbUAlWnr8fli72m/8bBdg+1xdiWaMKW7D5Zd2Ir2ze0fh3PgJuO+mD3vEcRY7UItNXWWtLZuOPzZO5U1QzbAsgbGEllOnTmXPPfec4vY50WIqoWVQdCSZjCKhWlSJzVRsKJR6ddmgy37V2PUq11TS1TDGSFhxEYf4izO6Yigp/tOd7DOO8WVs873f/e53uOSSS9DY2Ijx48ejX79+Pb6fMGGCbJF9Hh2KtyhU5nFIbBZVwrk4JLoLYoMu+1Xb53WdKZ+FMUbCios4xF+c0dXupPhPdzwlOb6kFzWtra1Yv349/vM//7Prs1QqBcYYUqkUcrloNx9LIjoUb6Jl5Odxc7oDqrvOoNd42Sxang4f2+1obha7Zt06flc2iJ+D2uDUdlW/5feFlUNPFq/6TSlAw1CWitah6jeA90GQvhcpX9ccIVKWzvosdPW17nnDlD+D2tnc3J1jFpCL4/p6vX0XGNlbQIcddhibOXMme/nll9kHH3zANmzY0OOIM3F9/NTRwfOYqaSSkkkY5pQvLT+nmim7VROb+dnsZ4OuhGpBc+AF8bOqDV5tV/GbU/3V1XL5H0X6Q8S2mhrzsaeC6JhQjQfRnIemxpvusnTPSRZB5lTT84YJfwZtr8q8ms2a6Ts3jCW0HDx4MHvvvfeUDYuSuC5qGOOBoDLwrHxoIuU7BahMGV7leg0m1TpEbXazIWjb/Oww1Vc6bBCpT8ZvYftAxDaTsRcEU3aJ9oHp8aarLFNzUr4NsnFret4w7c8gCznReTWK8WZsUfOVr3yFPfXUU8qGRUmcFzUdHXzl6xV0+X+V19aKBY+16vYKxCB/lfj99ShqZxCbnWxQqVfWDqd+0e1nFRtE2y7iN5H6RQ7Z/hCxzUTs6aCujt/F0hUPMn0QxngLWpZ1p83UnGQhemfLfui8UxSWP62yVNorO6/W1Hj/Vpm6Myr6+y0t6f7Nb36Dm266Cd/+9rdx+OGH93pR+Mwzz9T0YEw/RiTdmqivBxzU6r2YP58rS2WeX4qWvWyZet6xfCUi0K1kVHnOqmKziWfzMv3S1gbcdJP/ubJ+NhkbgL/fROt34/rrgVNOUesP2XcydMSeLpYsAU491f88kXiQiYE5c8IZb0HLEiHInGRhxceSJWLjc/FiHq9BiMKfVlmy7fWzK38M5nL64loG0d9v6ReFL7nkEgDAz3/+817f0YvC6oi+RV5ZCZx3npmyg7zJnsnoDWAVm3XbIGNHZSU/dJapYoNsbAD+fguqcBg3Tr1fRPrURL/roKVF7DwR/8rEgMoiTuccoVMRo6MsKz5EyxLtNy+i8Kd1nmx7/erMH1+PPSZfRphIL2rycz0RejCp4glTIaSLuNhswg5Zm6P2RdBy4xRXYaKz30zHQBS26qpPd1lhqlFN9n2QduhQJ0Y17qUWNR0dHRg4cCBWr16N8ePHm7KpT2LlyPTKaVZaym/92aV3OspOpSLJO9aDXK73ZpxBbNbxKMrydWkpsGOH+3l2O0z4WcUX+e0X2cxUtX4vrJjdv7+7/jg9IpKNE6fzAecyZPrNzw4TY9g+5jo7+YbZbW3Byxex1S+Ho4k5Kcx5UFddInOQWxxVVMiPW5GYjP3viezLOgcddBBbvXq14qs+0RLnF4UZE397XeVFNtMKoSDU1Tm/eGZJhWVt1iGjlHnhLps1r8SSVSr5Sb9V/BFEWeH1MrVJGahfm2TixOn8bLZ37NrLEFVxidihM7bcxpzbi5+qah0V9ZrJOSnMOoPWJTIH+cWR1ceyqjm/mIyi74ypn37/+9+z0047jbW1tSkbFxVxX9QwJh/IQcuOSh1it0lk0SBqsw4ZZVD5tCk/i6qBTMl+3eqfNy+44iIq2bWoLTIxIRMPKnYEjS2RMacjdlXVa6bnpDDrVK1LNN5E4shpDnVajMvGZNh9Z0z9dOSRR+L999/Hp59+ilGjRmHIkCE9vl+1apW2u0i6ibP6yY51W/jcc/1vOX7wgdytexMKIVVyOWDUKH4b04vqauDhh/0fV+RywOjRPP+kEyI+8yvDjfyyTfnZq1xZ21ViyK1+kZjVbYsqsnGiEhMi8QCoxWuQ2BIdc2VlwJ13due9TdqOwjrsiqoukXgrLQWeeKL7BV6/OKquBh56qOccCgSPyTD9aEz9dNZZZwWxixAgk+GH148DYzzhc0ODnPIjTkoRKxGyH42N3G4/ZU9Dg/dEIOIzvzJEyzblZ69yZW1XiSG3+kViVrctqsjGiUpMiMRDfb1avAaJLdExt307/yEM2hdxVa+FWadsXSLxtmNH95gTiaMtW5zn0KAxGaffEwvpRc0NN9xgwg4ijyQnFBNFxnYT0scgZZi6Poq645T8L45JJ3VJY4PaoQPdY47Qj6n4DGsOjRrpRY3Fa6+9hn/+859IpVIYN24cjjzySJ129XniLpvTgW4ZtA6fJVm+bFrOG0Y5cUo6aZ1XSNJYk1sPEHowFZ9x2DogDKTfqWlpacE3vvEN1NfX48ADDwRjDLt27cK0adPw+OOPo7y83JStgYnrOzVez9v9ZHN+7yCIPvOM6rm2yPN96/nx5MnesmTrWXQQn/mV4YaJ9x9kkbVd1B+i9qv6TsQW3ZmOZeJEpV06Ys3Ee0aiY66mBtiwQW+9Kv0X192iTSISb+Xl/DFR//764sjku5w6EP79ln0D+dxzz2VHH300W7duXddnb731FjvmmGPYN77xDdniQiWO6icv6ZwJSaCbVDTMbKv5dYuqMERkyTqkhrLyZRmlimm/itou4g8V++fNk1PWiNhiwo+ycSITEzpizbSs2a8NuqXNKv3npwSNaiuAMBBRP+mUWJtU3erCmKR76NCh7JVXXun1+cqVK1lJSYlscaESt0WNiHROtyTQSZYXVAKtww+ie2aoLiZUZK+iNjmVHaVfndovm/BSxX5ZKbyILSb9KBsnbvuAyGw5oMMOHbjFt33PJV31qPSfSCxF/SNrGpE/EHRIrGXl41FhTNJdXFyMhoYGHHHEET0+f/311zFlyhTs3r1b9q5SaMTp8ZOMrBTQKwm0yn7/fWDs2GASaF1Ytz6XLgXuuQfYs0fsOlOPfWRllU6PwaL0a5AdhVXsF/FXeTlPuFhVxf8dhkTfD5M7Cpu0Qwf5u3hPndo7loOWrypZF5XQR/k4xCSiPggqsQ4yz4WNscdPZ555Jjv55JNZY2Nj12dbtmxhU6ZMYWeddZZscaESpzs1y5aJ/RW7bJm5sufPN2eDKqK2m7YxSP+Y7NswULHfRJuT7se+jmr/qcwBhRYDsj5QbX+Sxpjo73dadrV07733Ys+ePRg9ejTGjh2Lgw8+GGPGjMGePXtwzz33qC/D+hgmpXOi16xfb84GVaKWJcuW53Re0mWRKvabaHPS/djXUe0/k3NeUpBtj+l5M0n+lZZ019bWYtWqVXjuuefw9ttvgzGGcePG4dRTTzVhX6KQufUnKolrbu6dwNKvHtGyd+4UO0+3fM/LfpOyZKvexkagtZU/DrHvmGq3q7lZvd6kyyJV7Fe5Rlccx9WPKsRpx++ghJldevjwvuk7+/kqj55E57nmZuCxxxLi13BuHMUDk4+fZN/w7+jg34u8oJX/lrtfPTJlex2pFH85rKMjPD/J2i5qo9fb/TU1zvmLvJIwetXr1wYTftWJiv0i/ZbNdl+jI47j7kdZolQhmkC1/554wnvsOZXx5JOF6TtT7RdRO7nNg1H51Zj6iTHGFi9ezK699lp28cUXs//8z//sccQZU4uaoG/4i77lP2+eeD2ysmRZ2036Sacs2ate1UNUDh22VFcnKvaLSoVlxkvS/ShKHFSIJlCVzsuMQ5l5MUmIKsBk2x90PozKr8YWNTfeeCNLp9Ps2GOPZTNmzGBnnXVWjyPOmFjU+K2o/f6aDLJi9qtHpuz8Q7d8T9ZPOmTJIvWq+F3UN1FIdXUia39Hh7cMPpXi5cmOl6T70Y+gc0jcEe0/2bFaW8vv6vQ13wVpv475MCq/GlvUVFVVsUceeUTZsCgxsajR8fZ4R4e4Ekm2no4O/tn114tdf/31/HzdwariJ8v2hQv5f9vbe/5bxEZVNVX+MX++XL1ubUja5Cpjvy5fe8VxUv3oRZIUKKqI9J+oH+xzVF/y3aOP8nno0UfV269zjIbtV9Hfb+kXhffv349JkybpfK0n0eh4ezyTASorzdhjZVEVtXPcODNZV1X85JQBVtY2XW/tV1b6Zwl3Io5ZbGWQsd9k4sWk+9GLQlSg5CPSfypzVF/3XZjJWYPUHybSku7vfOc7WLhwoQlbEokuhYbppIJRK0miqj9OyRoLnagSLyadqMdmXAhLdVdIhJmcNUj9YSK9o/DcuXPxyCOPYMKECZgwYQL69evX4/s77rhDq4E6MbGjsEjyMZHkcCLlZDJAZ6f796WlXHaXyfTerVUm6RmgLo10kxWqJl0LY2dgP1R31AySTBRIljxVNBledTX/f9Nx6GWj6A66uRywZAnw8MN8/A4eDEycCJxyipmdd73Gfm1tb59EleBRNsmpzoSo1q7U1lYMgHoyRxMS8LDHchjJWd3I7wvTsWdsR+GpU6e6HtOmTVN8WhYOptVPbm+Ui+ZT8VMKWG+5y7y5Lpv0LIis1O9aFSWEDpmmLvWTTN2itovmE4qzPFU2GZ7pOHSzUSbXUV0dY0VF7u0JK0eSdcyb5+/nMGJEpl9U+lBmrAZJ/GtCPh/VWFZVmOlUhIYRe0Yl3UnF9D41bsoPGQmcn1JAVtHkJJd2Kz+IrFRGri2ihNAtcfVTETjtU6Nat6x0XaUf44JoG/L72FQcutnoZ1++baLjS2d/qGQ4DzNGVKT4KjaK+sFvEeymkDMhn496LMsqBEXVpSLzYljzk7GElknGZEJLncn3/G6Litzq96rb7Rapqv2ybRdpn4lEhiI7Cos8QvGqW1cyUZW6wyZoMjzdcehm46hRvM+9sB4RA8DIkcDWrf5l26/T8dgi6GNSwFyMyCbgNTWX+NXr95jHxNyi0ncm+ilocla3pLf2efPKK/ncGVab7Bh7/JRkTN6pCVtaqDvpW5wSOEYp0wxat+5komG1W4UkJLGUGSfLlkWXTDHuUluZfgljLjEdDyZiNs5jWYSo5fPGJN2EM2FLC3UnfYtTAscoZZpB69adTDRIHaZJQhJLE3Xruk53GXEoL6i/45KYMYy4CXpt2CRFPk+LGk2ELS1UTfoWtLwXXwQ6OoC2tu7HN5YKQ7Z+t9ulUco0Rct0a7Po9WPHip3nxNatvZOcRoGJftJdpom6dV2nuwyd5eWPTdUx7oVTsl5Vuysq+KNjv0cvcYqbINdGkbwzMfJ5MzeK4onJx09hJ9+TSfwoUneQJJjV1fwlaZm2e6kPokxkKOoHt7f9RVIF1Nby3ZGDJB3Vrb5RwUQ/6S6zo4PHp58/a2r4uR0djI0YId4P1nVBiVMSWqexKTPGVZP1qvghleJ2iSqZoohZ3f1kQrklQtQJZkn95IDJRQ1jatJCE/Wp1q1D6ifSdhH1Qdi+1OVXGaWNDn9HvbAx0U+6y0yK+smr3W7jS/fY8BqbKmPcL7aDXqviizBjVnfsmFBuqdQfxbxMixoHTC9qGJOX1pmoL0jdspJxe1A7/cWUX79M8r6wfZnvB7+/8PP/MhFJFpfN+t+xymYZKy0V87uuOwVBMNFPust023IhLvvU2Ov1krrrHOv5iIxNkTHu1RaRceR2bb7kuKZG7K6o0/gIK2ZF5wER4pL4NKp5mSTdDpiUdNsJ+3mnvT4du4zmcsA993D5niyLF/P63NpeXw9Mm+ZfzrJlXAYcxbNjiyVLgFNP9T/PslW2bRZObayvF6vbqbwoCGt31kLcUdipXrd26x7rdkTj12+M57dFdC7xGxf5kuNcTm58OtlmImZV2+uH6vxigijmZdHfb3pR2ABhJ9/TXV+QBJstLd6JH2XfoI8ykWFLi9h5ssniRJI1itYtU69JTPSTibg+5RR+iJ7/pS/xI0y82m1yPIjGkd8YtyMzl4iMC/u/H3tMrVyv8oMSpL26zg9jPohzglnphJZE38CUEiQxb9BL2CCbLE7kvLDUFwRhYWpsJq3coPS19sYNevwUEVE+VhGxBVDf2TNo4s7ycl5v//4yrehZh8xjBvt1+be7x47VlyxOJmknILcTrttuvW67J3vVK7Ira1xiN2kE3fVVtj90JpQMspO335hXSRqrkszRL97ddtWVQcSu6mrgoYfkHh3u38/bI7Ojb5CxqjqPmoJ2FHYgjBeFRYhKkidjy7x53i/hOb2kJmq/iFJA1R+yL4SK+EHmbX9ZdYBXLMiqdrzKdCpbJflenGI3acj6TuR8v/hRTSipW90imhtJNpZE7RWNd6eXkVXnIb8XhWX7xe8lYScVWZDkxCrzqElI/eRAHBY1UUvyRGyRPVTefFcZpCJlqi4CvPrEKambbLI41aSdKqodHX3qZktcYjdpyPpONDb85NcqfWVK3SKSqFJ13PslAVYdEya3ERCtR8R+p4SxqmNVdR41DamfHIj68ZOpRI0mbPFi2DDguuuAqqrejzRkULmd6oZs4kL77VnRBJQyt6V1Ju0ExG4D60qK6GRLdTX//zjEbtJQSfjqd75ff3gh0lcmlGeisamaVNLJXh1jQiWJqkqdbo+P/MrKf1wf5HdGdR4NA3r85EDUd2qiTgimYotJG3X6QzZxoQkbomq7bJkmjyQl6AsL2b4Oqx/D7KtCSBRqIomqXz0q80RYCUXDHuuiv9+kfgqROEnygtYRZhI/kfNUE9BF1SdxS6ynizjYEDdk+zosH4bZV7oT8IZZhmxZOudWlTrDSE4se26Y0D41ISIryfPbfCr/lvD+/cCCBTwD9NixwOzZ7gqioLI/v+utN+eXLgU2bQJGjgSmT+/52ETVH06KHlUJtC6ZpKxSQDZxpsjjgDhIOUUSH4o+2pBV7qgouWRsVC1PNBmklZRx3Tqx84PilFAyH12PoXQn4A2zDNmydM2tuRzvI9k6g9hZEFtJhHTnKBZE/fhJZBt9rxQBXm/mz5vX+/tMhn/uZYvsC3QiW3G7vdwK9HzBVSZBmp+i54kn5BIXyvghk2HsySfl2+ulFJBJnOn0orKTikEkvlSOVIqXGyTRp91XIm2RUW6oKrlkbFQtTzTdhlP6Aa8jnfZOMCl6BFVfiSI73/iNOVP1uvWPbBLVoHNrkBQTIvW7pWmQTQAbJqR+ciDqRQ1j/gqAefPE39a33mSfMcO/TCdEpNVO9QV9cx7orUzwkmSK+MNSKYnW6+QHv/LdrlWpU8X/In0ho/SQiTF7PwRNJOh3jYxyQ7a9IvGrqzzRuJLte78y7P8WqV9VfSWLynyjQ2Ujq0TS0d4gc6vs/G9iXlK91iS0qHEg6kWNyF/S1l/EQSc6+5HJMNbe7myTmxxSVsZstU9klW+101rpe0kyRe8+WH+xPPmk2v4KTzzR+06XU/n5fxEF/atG5C95GZvc/JnvV9G7EU5SUdlEnyKxb13T3i6etE/lzpTXX906yxMtq7pabj+o/Lq9Ekyq/rVvMnGiqE1B68nvC9EEsflzQNAkqrJzq0wMetnW0aGe7NOynfapSQBRL2qiVKfMn+9uV0cHt23hQv5f++Tm9Lmu9tnfnnerS6XMjg7GFi9m7Prr+bF4sT7bVVQG+dfls3hxsL51Ktvy56OP8r5/9NHefejkc5E+F7VXxVfz54uXHWQ8BVV++JUnWtZ//Vewvgd4f7j1WUeHnE9lbFdVv1gxdv31ZuuxkOnXZ5+Vm/NE2yo6t8qMEy/bdPShyjxqEtHfb3pROESifFt8/Xr379ySk8kmLQuSoM2tLpUyZRMXytSjojLwO1cmeaVo2SJ953aO33WyiT7z/98Lrzh1K1uFoMoPv2tFy9qwQb1OC68EkyoJFk2rfKy4i0JN5Edbm3iyThFk51ZRWysrvV/Y1uFblXk0DtCiJkSifFt87FjzdZhI0GYq6ZvqdSoqA79zTSvRdGPSV6JxasJnQcrMv1Z3e2Xqlv0+/7ywVT5xqUdHXUHR5ZO+nPySdhQOEdFEZ4B38jdZ0mngf/8X2L5dLuGdSvI9kd0oAfEdKUV35wy6o61Kok3R9lZXAxs3uvvVL3GmF0GSf6rKdVUSCdbXA+eeC+zY4V5uTQ3wwAP8L2W38+zJAJuagCuv5HEt47faWvfdVP1iwAmnPhD10fvvq/e9riSyTrtX+/VVWRkvL0jcNTZ691++XUHk+SZ3ybWPI0u+L5Oo0l6GjE+CJg52SyKqe3sEXRTsjsL33XcfGz16NBswYAA76qij2PPPPy98bdTv1DAmp/bRlcOnqKjnv0US3qnKOWXVTzI+8ypPh1JCxO9OCQX92mp/uc7Nr26JM0UOFZltULmuSBy71SMap059bPkzyHhwUwOKxoBoH8j4SLZOWVWO6Lwj86K0ykujMi8ve9mlkvjSr05VRZdXe0TnTJmXp4P2uZeNurdH0ElBvij8+OOPs379+rHf/va3bN26dWzu3LlsyJAhbOPGjULXx2FRw5i32sfrHC91jtORTnv/OKh8JzKoRGTrOnzm5rsg+E0wbrJiP6WBJTn3ksk6qSJEJzqVyU5HOaYSCTodQRcz9sNP1i0ysYv4TmSsu53n5wsdCwq7Ukq1r4LGndeY1ikt163oEf1jK6js337Izp0iceU3P6m0ywQFuag59thj2SWXXNLjs0MPPZT96Ec/Ero+LosaxsRUJvnntLfzN9D95IlDhjD2q1+pS4X9gtlLCiizwaCqz7wUPTpob2esvFzOB+3tjJWVebdbRDIu2sey/WL3oU65rpe6Q9fWBKWlXJXiV155OWOPPOLfD4D/5mFO7VKJCy8fudX56KPe9YjYL9OuoH0lYotIHeXlPce0CWm5LkWPjM+Cyv6Dzp1Wm/3mFNk/mnVJ7kUpuEVNe3s7y2QybNGiRT0+//73v89OPvlkx2s++eQTtmvXrq5j8+bNQk6JM7qlsaqHmxQwTkk7VTEt7/YrU7dUOWjbTPpQ9BCNZ5m4l21j3Hynawzp6CsTcRfnuURHgk5Vv6u01+R2ImH5v+ASWm7fvh25XA6VefrEyspKNDU1OV5z6623oqSkpOuora0Nw1Sj6JbG6rYjTkk7VTEt7/YrU6e0WPZ72fNMXZ+PaDzLxH2QLQh0lhtVPTrLMRF3cZ5LdCToVLU7quSgUZStQuIk3alUqse/GWO9PrO49tprcdVVV3X9e/fu3UYXNjqSv/mVEaZU1As3O0TtW7eOqyycfOSVuFJGSSCiRnCqq7VVrA2q8m7RMoNe7xRLcZTRiiAazzJx72Sj1/gTbZNIosj8Ou3JUA88UOy6fHtU558wEkea3AaguRl47DF15ZEKKj7LT2yqc/sJXb8buuyJlHBuHAVH5fFTPibfqdH1hr5fGaLbX1vbzet8UdNevt87NaL1OqmJvBJXBlUS+L3pbx0qKRNEEmO6fZ+//b/sS7H5NrnF0hNPiCcRDYKORILWkc36x7M97kXeR3J6D8Rv/Mm0SSZ5plNfu73k79ZHQeYflXjz86VsPAQZU0H7QAWV+Haay2XKkEmLolKX1/zkdY2OxKMiFNw7NYzxF4UvvfTSHp8ddthhkb8orOMNfdEyZKSJXjJO0UGk0h4ZiaqTlN3v/CBKApU3/UV94CebdZNtq/Sx3/V+KisRO4Kic2sCr60O8mNItDyR2HHzbVDVi0o/u5UbdP5RscPLl7LxoDKmgo5VHQQdp/b2iV6vGrci/hdJCCxqlwkKclFjSbofeOABtm7dOnbFFVewIUOGsA0bNghdb2JRo+MNfV3J/oDeKeXdVvF+f5l5JcoTQXbvBdFEnrqUBKJv+ssmuBOROXt9L9KO/L/iZa63/Pfkk8H6VxTROBC9M+HlP1Hf5f9lKTuGRdvkNfZFk6H6xV/Q+SeI8km3rFzmGtHxa1Kh097uHbei9vgl07XaGzRu3XxpHz8qd+zCUkEV5KKGMb753qhRo1j//v3ZUUcdxZYvXy58rYlFjY439E0k+7OTL+MUTUjolShPBNnEdTKHLiWBqN9lfOAn4fX6XiYWglxvXaczeZ+IPxYv5oddli8b10GTAarGTn4CVtXxKFOnX/wFnX9Erz//fL5NxI9/HDy5oUrc2a9RUXaaUOgEUZjqUHmpXqcreXAYPrZTsAktZ8+ejdmzZ0dtRhc63tA3newvP3naY4+JleOVKE8E2cR1MuhSEohQWSnnB79kkl7fyyS0c7JJJh5lE5aq4lePaDxabQuaDFA1dvITsMomilSpE/COv6Dzj+j1Z5yhL9GjStzZrxGNFzsm5ocgClMdKi/V63QlD9Z9vS4SI+mOKzrUJGEn+ws72ZmJt+NVEwjqqMskQfsmiYnsok7iF/Z1Mtf6nUvxYu4aP4IoTFVUXjrjL8h5pq7XRYoxxqI2IixMJLRUSYSoUkZNDf/LwC/5nUhSNtH6giZ3sydD80pKaU/kqZK4UjRhoh0r0aJfXVbyxJYWd8mom5xSRWKrkixSJjlmfpu8ZO46ktf5yfNF+k40id/+/fw8N0m+PYnkihVyPnPyuV/yQcB9HAVJspi/ZcFFF4nb7nT9rFnudgRNFCuCUwzb+yc/ya7MWA9iv9842L8fGDzYfx7Jp6QEuOsu4MMP+XioqvLuQ0AtaSrgnrTSCdWErmHECFDACS2DYFr95PUWu5+80K8M66U80fOC2qzrJUArWaPbC2hB1U+yOXOsY8YMMdWB14tzVtvc2qwqsfVSIeQrcZzqmDFDrk1+MndVaayfPF8k15WMgkckZ5dbv3j5zM/nfr72Uuv5Xe+kdnHKS2XZ6ec7p+u9csSZVraIvARsxZ+s74PYLzIO6ur8k7DKxIlTH/qNQ1F1mMwWAzJqszBixKJgXxQOQtj71Mh2vtueFfnXi54nYrOOcqyyVGSX+ZO+lx+dkn6qyoXdfuDyJxmdh2gMeLXJSmgXpO1udvkl3JSZuHTZJ6LIEqmrtlZdxh/E536+cxuDToshL/muU8wGHS+qiR5FEbVHNY5U1XwiMmk/21MptQWPrqSpQcawm0LKab40oZh0Q/T3mx4/aUT09rfbbTrr9p/XYxrr9vnYsf7n+d0OFK0vaDleeN1e99pROEidQO9HEfa6rNvBqmWL1OvkU5E21db6978qXo/kZG4xB+0bQPy2uUhd5eXAxo3A5z6nZlNQn4uMe/uOwlOn9m63yFj1eqyo0ieqj6BF0BEjdkpL+QvEmUywHYVF/Qx4215dDbz3Hn/EKLpDeSoFjBgBtLfzR5pu5zjFks7HuFZ5uh6p64IePzlgOkt3WPLKIFJSnfbKlqNavok6veo2KQ/XUa/pZKVB+8pk30TlMx0+DyJ5DWtu0WmzjvaEbatOu0yO0yDzR5yTCXtRcAktk0BY8kpVabdqfbrK0Xm9yWR+YUgTg9RrOlmpFyI2hploMSyf6fC5yUSlfueFmTwxqnLDSMwpg8lxGmT+iIv02hS0qNFIWPLKuEm7o5ACmpSbhyFNDFKv6WSlXojYGOZWAGH5TIfPg/glrLlF13VRlBtGYk4ZTI7TIPNHXKTXpqB3ajQiKpV2e+4tI0lVlXbLyEEBdymhjIzYC7/n9l7PdlXkhxYi77aolu1HWRmwcCGPAfs7Q6LSYpH+VyGTATo7xWPKr2+Cvu/k9exfVFYd1Gc6fK5D8iq6dcT8+fydMKDnHAPIxbTdZkD/exS6x1htbTBbZWT61dXcP162l5UB3/kOcN99wJ49Ym2wv68j0s9u7xmKSLwnTQLuv797m5DZs923HIkL9E6NA6bfqWHM/634/DfiZeWKoon13NQTonJQJxvdyrDkuSqJ5/wkr16yStVkdzIqJF0KI5HDy4/5NssqWeznupUtIgUXlX6rqp9E+0Y095KTz2T95ne9iG91KERUY9I+x4hcn6/y0SXx19Uep2PePHVbVWT6qvOdSKzJ+MVJZq5iUybTrfKLKyTpdiCMRQ1jctlOZQPQHngykmxZOahTWX6SX5F9R/zstLdNRF7sNyFls/5SVzf8yvaSOar4wmqfqHRSJs6s673kmqI/dqLSb5U9hPxkxDKTtpPPZG3Kn+jd/OflW52SVxWf+i1S3JJmio5B3e2RTRIp2na3+lX/KFId4yKxqrpwZ0w927ZTvMcJknQ7YPrxE6BfqphPJgN8/DG/VahTAu4nJbTq9pP82iXSV14pJmd02rFVRmqe/0gN6H3rXfX2uV/ZXjJHv8eJXm3L3/XWaddfESmz221qp8eHojErI/2239ZvbgZuvtl7J1ivx5FB2pxfjv1Rg1f/WI81RHdcDkPyKmO/hdfjJKcdfAE92z3ItEdkh2MVvGTQqnO103x3xRXe8ycADBsGXHcdb2dbm/N2FXb7RPo5v2+D/P7Yf1/iBj1+ciCMOzVhyIHnz5erK0wJsCUXVPGD7LVxlyYGiYWw5PhB7ZSpNyxZsmhcJD3OgowxXWXr9o3J+VM1y7VImTJlyfpMpi90tMn6fYkbBZulO+6EIZezpIJxlABbNgWRaReKNNGkhFenj8KS14YlS47qvLAxuRVCVL4x6WvVLNciZcqUJVtv2H0R5ZYROqBFjWbCkMtZUkHRuj780Jwt+Vg2BZFpy0oTRW75798PLFjAB+yYMcC4cfyaDRv43yfpNN/9c/p0seRvMvapYD3qClp2czPfadXySy7X2w/PPKNup4x9fm2yaG7mdub3QRDJqtOuvaL2eNXrF3syyRpFsJfX3Cx+nUVFBfeBn73r1omVN3x4z7E1dizwve8BK1eKtTHfP6J9ooJqlmuRMnVkXXeLpbCl2rt28RjxeiQWxY7CwoR05ygWhPH4qaODv+BlSjWTyTDW3q63rlSKl+NXVibj/X02y22StS2V4i/KiV5rP19E8TBvXu+XIb0OXTlvgvSPn2pDpOz8NhcV6XkJUyYOLOrqGKuuDtZ+mbjIr9vpRfjSUv8cX07l2cv1U4CJJmsUQaQ8rzGWzcrb6zdmf/ADfxvc2uhUX3W1mbxrmQxjTz7Zs/4nnpCbF7xiraODx5NIXDvFk1csWXEvEqc6f3+clFWmlHAikPrJgbDUTyblwE5qjCB1OSVpc5OlirxVnz8IRG1wUz95SWRF1BlBlABRylbjKDsPEge62i8SF/nn6xxvfm3yU4ip9LOqD2X7VFbq7rcFgFcb/dSYsm0QPS/olgNeMS57jWgsicxj+apYHfOC7FxrGlrUOBDWooYx9z1h8v8KEf1LwWsfARWZp3WIrMYtuWFHh7/0O/+vF7+/upz+ghK1xS8renW1+l9ilm/c/koPGguik4rXnQK3soO02etQiQORvzJl2y8qne7okLs75Nbm/PpF2iTTB379HMSHAL87JzIORcvLZBhbuFC9jSJj1+mukputVt/73XmxbGhvDy7Fdror7dcHTvOciC+su+gycRrkN0G2fpF5Sge0qHEgzEUNY7yTly3jE8CyZd0D2vpMVJV02WXdj5z86rr+ermgXbxYzG7G5BUROhQUQW0JeuhSd6j2j4gNKjElc1x/vXoc6FSYuLXZbpsdU4k1TcWe7mSUJo/LLlNvo2h7Fi/m5z76KI/rRx/l/25vDzYn6BojOuY5nX2bX36QOUdH/boh9VMMyGT4C4n5WJ899phYOSee6L9vgFWX7BvwLS3uZeUj+xa+jrf2g9oSFF31qPaPiA12H4nGlAzjxvXsg7DVGE7luMVFGPWHneQxjuqr995Tu06mLS0twHnnOX8XZE7Qpe7RMc/p7Nv8soLMOTrqjwpKaBkhJt5ql30D3kTZqiomGcJKyqa7HtNtDSNRoEy/Rplk0FRizbCTPMYxAeEhh6hdJxMTpuYyXYkmdcxzOvs26viJS5zSjsKG8dt91CsBmcqunaK7ZIqWLbsjrD0Bpqn2iSYyHDECaGpy3/3WD6fdbe3125NRishWRX3oZoOInFJ3okArAZ5d5i7Tr0Awe/LjKb/9gPf4GjVKfXdat92dRXZg9tp12a0e+ziQTTzrVbbIbuHpdPeDBD/Ky/mdmtJSngBVFCuWJk/2Tw46cCBw9tl8d+Hp08WTUorEpUhyUr/krvadq0UT0aokzxVJcuk3j6rsbG6npsa7foAn8GxsNLsTMe0o7EDY79SISOBk1RwidYpIIkUVFyovm4kknVRpn0o+FJ3qJ6/6ZWSrMoclL5eRU5pQRQWJWxGlidf3VqJPkRfvnewUaZ+X4s+p3vzEtPnluH3vFateMeOWeFakj01JpIPEkkxCyKIiOdm7SFz6zQtW/4nYJ5OIVtVm1XnUb/4RaZ+VLNSvv3RtheEGvSjsQNjqJ1EJnK5EeDIyRb/EZUEkj/lt1NE+GXvyy9axT43oD3NQKbPMpOM1oelSP+iKW78fEV0/vG52OpXvtWAUTfKZf/gtZvL3CXJKZOgldXZKzhpk4R7F4bVg9DqC7Odj97PMIlv0D0S39gRJniuS5NKrfJF2Wtd7xZB9YSXiD1MLG0po6UBYj59kEzJa1wTZqVE2OZtToj7VspzwSjqpklQyaCLDIDsKqzzSA8wmNnWq0+nWdn09cO654o+7ZOuSeSQWNKFqUDvzdxTOf6QWJMmnhd9jp+pq4OGHeydFtWzw81N1NfDQQ93Xq9ppipISbsvKld5xZ38UVF8P/Pu/+5ftlew0H7e4lJnb7P5uahJLKumXiFbFZtlzrPNE5swtW/j1oomRDzrI/3GuTD/JQI+fHAjrTk0UyeB0JrczKTM02TZTkkJZf+iSMsetL1X9HJUkOUg8hJlYUbZO+/VxlHvLxP+yZXIS66BjXHWejHoOksXEtgth9pMTJOmOkCiSwemUCkdtl2oZYUttw7ZDpc6wklXqPF8XJhOKBkFnws64yGjtyCZ3lJFYB22vySSgquWbwMScGWY/BYEWNQFxuq1dViZ27dq17onD8utwu5VqqWpeekne9jAkgBUVwR+tySRvdEqEGBSTMnldDB/urMxSVTz41ZWPV+JGlcSLOnjmGZ6YMf9Rk52gSQRVcEvEKpNgUzbxZJhUVACrV4udu24d/9tepmwv/OaaMLdUUH2cBPRUvgHOjyt12CzTNhkpvMnEpL7ov0kUX3Q/fnJ7cUr25UKvpGBuqhfZl+zy7TO5Lbv9EFGo+CGTpM1EgjUZf+hID6DSl08+GU59AE9iaEckVYNXIk37Vuwm8htZcSiiZstPIqjbHmt7fae6q6u9XzSOoq9lY9FpvOs8VOZK+/mqiXafeMI/hu1zqqjyVUTRJ9p+pznLrZ1OaSv8zm1vF0vaKWOnDKR+ckDnoiZoojynwFFR3KjUIyrnNvUDoyrnFpGAmkqwJuoPp8RyJnxob6+KSifoYbVTVxu9ZKs6Dz8FjIiMVofvVMuMoq9FjrBschvfsmpTv37NjwNTsaWr/V5zlsy2C36ychN2ikKLGgd0LWp0JMpzCoL8ZJAm/hqTkVLrlgV7tVenPSpli+CXMM/yr71ekWusQ/av3EyGsccfj+av9kyGsY8/1lO3PTmgl7w6//PSUu+/np2Omhr/hIb2+FH5a1rEdyrXlZbq8Xcmw++26ZzDamrE/4oPesjOlU7zgd9cYk/eKuInK4Z1JaiUab8XMjJwr3NVfo90z8O0qHFA16ImDGWErjpuv50nhvNK/OdFR4eZJIn57TVhj+438E2qU+bP754UZfwdtG+uv57Hx7PP8v+//nrGLrlE7FrVpIZ+PnNLVpn/+eLFanXJxo9bYlqT48L0EcR/+fGjqyzVmFFVJtn7dfFi53lSZh4OW/UoOr+JJH/1OzdIu3TNw6R+MkgYyghddQwfDpxyivr1mQxQWanHFidk2yljj+5+MqlOqazsfgFQxt9Bk/ONG9cdH1/6Ev/vnDli16omNXTC7ie3ZJX5n6sm7xT1mWWTmz0mx4VpdI0NK9mpiUSqfgRNKKk7KWrYih/R+kTa6XduXJWETlBCSwXCUEaYSsYXVRk6yzaZKFN3vSavAYIn53OqS7RM1aSGonaYuAYQb59f+XFJ4KeC7mSjUSn+ZOoOQ/kUph+SUlfYsUE7CisQNFGeE/m7hQZJYmeVp2tH36AJ0dzw2gU4SCJQwH9XS7/yvXYjlUksp3LN/v3cJ36765aVAXfcAVx9tXzfeO1EvH8/MHiw98646TTwt78B558fbLdip7gHekpYAeckoiq76dbU8Ds1MgkI3XYjBtR2jU6n5RJBWjbp2nW5rAz4wx+4T++5B9izR62c8nJg40a+e3BjI3DFFf62pVJq81l+GdXVwAMP8DZ0dgK/+Q3Q1iY+xvLJTyAKdMfjrFniu+gC/nNTdTW3KUjSWV279sruUCxjs0rCYi9oR2EH4qx+Anq/fBjkZUS/3EuikjuTLwu72SMqh/TzpYr0069ulcRysioE0/4WUSaIJvwLaodfnHu9lOuVWNLtkO1HkbxRYSjc/PwURv1OR5DElkFsCZIwVHaOk8135KeuymblEnq6lRFUWST7uyCjBiT1U0iEtU+NSoDKTiZeh0jyNtHBLmOD0w+QqFLEnhROxF6RRaWs9FN0gMomlhO9xtQPZP4Pj6gCzikRqKzayOvQsTCSiU2RfWryx45fuW4LYZ2HiJ8shZhunwbZDyvI4aZ4k51vRcalaHJLvzjIL9drXndLgKlzseaF6u+C6G+eSkJmPyihpQMmElqKPipwo6wMGDBA/VFWeTlw++381qt1W140eZtfMkS/W+tlZcDChbztTrthOj0+8EoKB3gnBLQnVhN57OD0GEE1yaSOx3kij9S8bCsqAgYO9I41p3iw7+6r8ujRngj0jjv0PXb1S/4YhGwW+N73+KMelR2FRR8x2x87WDHe3Mz9DwClpfwRhsojQgs/P1mJCQH/x4YyWDH/zjvcFzL2qzxiA7i/nniiu7/yHwuJPAoqKwPuvNM9ua2FjsS9gPdjZ5EkkdbYtF45ELVH9fGO6d8FKx779xe3SQR6/OSAiYSWcUgopzNBno5rTftKVkqrUyYfl6SZSbcxynGhu61+9YThN9nEkDJH2NJ1HclZRfped7+obOWgIzmpqQSzYf0uiCL6+03qp4DEIYGZzgR5Oq7Vca4XsjJmnTL5uCTNDKusMMo1RVgyVL9zw/CbbGJIGUyV64aO5Kwi55rc8iHM5KSmEsyG9bugG9qnJiBxkHU2N/N9IvIf96xdK3b9unVc2aGShNCt/SaTK8rKmHXKTmUT1qmUGxSRhJOytuZy0SWmVCXfD27JAy1lU2cnf/xRVQU0NYnX8//+H390MHmy82O+MOaItWuDP0ZxI+i2AbK49ZvofGYvwyvudffLm2/yMSIz123dyh/xrlihnpxUth0yiVPtyMwBppILC6H/JlF8MfH4KUjiOx2J/PJf5AyyhXt+WV7KBq8tsL1enPRTS6TTYonVRF9ezGR4AkAdfSWbsE5nHInEilufBLXV1IuwmYyZF6NFt8VXUU/5xa6Tj4OmPDHlJ1E/WuMtDJWXbDoDp8Mraag97k0lLbX3m0rcBPGXyFj2S/ugqx90J7Uk9ZMDJhY1jKknvvNLnGf/d5yS2InIl3XU4VWnTD1O18n4WrRuXYoEv6RyMpLyoLaaUmTZlW46y3eTyUe1KLD3m+r1Jvyka9zoPnT1m4yKUqaOOM3D+f4SnV9EYk5HP+iWddOixgFTixrG5Fey9jsIbtdbsjin78LcH0JUEqwjCWcqxf/SEpFMi/pc5C6Ll6/tdaskzwsaR07SbxH/BLU1jKSqQe4CpdO970rm+8FUG2Ti2brb4XcHNf8vdl1+CtpHXnGnU+JvT2yq4+6WX59YdYkknc1m+XytkgRUp4/c/OWHqD/z764E7Yeg86EdknQ7YELSbcd6frtkCXDTTf7nL1vWM8+G6C63zc1cGh0m8+fzXDde72PU1wPTpumpb/FiXofITpf33CPmD7u/VXYUBsTbmN+3Moi8/yJyTlBbdfbnZZcBJ5zgvXu06Lix4xcnOtsQhPnzxWLUa5wF8ZO97IqK7rKs94gqKpy3hcgnP+4+/bQ7Z5gOrFgMo99k61q2jLf/1FPN2iWD6Dwj2sbFi3vmCtTVD0HmQwvR3296UVgjVjIwEwnW7N9FkTyushI47zzvc3S+8d7S4l8foJ7gUtTXXmWI1iWLSAI6ncn4dKhN/DjxRPf+lB03dvziJC6KLVEFkdc4C+Inp7JVFiO6koq6oTuhr8664hJLdnTb3tKidp2u+nVAixoDmE64GGXyuKDn6KxP9twwk3vGQRUX1Naw+9NEwsE49AOgL4mm6Dk6rwuzzDATZIaRENM0um3PPy9OiZVFoUWNJvJ3vqyp8U7+VV7Ov6+v99/10l5uLgcsXw4UF6snopNF1NbJk3m7g0pLnerLv+1tl5+L+vu443iZ+WXkJ0r0Sq4ZtG/zpe7ZrNhtfxWs/vBL3GhJm5187NdWP/LrCGKvjO3W5/v36x0rsgkZLTtnz+Y7PfvFzaRJ3f92i/nGRr5rrswu5qWlvLygMlu7Tdks8PrrfJfrTz5RL9PJxqBziV8/2ceoX5xbfThpEm97aal4Elcr+WZnJ5dv68JukzWnOSWDtfpaZGxZPlmypLsckfkuk+HtE5ljQiH46zvJwaT6KUiOEjfpW9gvBwax1W6zToVATY1zjhQnKbtfWaKSdb/kmqp9K9KfumWQMokbneSvQZQ3KuoHEZWNiO265doqh5vSxqttXolVdYgDgsRXWPOR3QdhqI1qahibMcO7H1VzYFnX6o5FEZuckgCrjGVrvnObQ9zmCFI/hYCJRY1KgkSnc3XJ6ILsU6Nqq5NPwl6M6Z787INVR9+6leV2je6FjV/iRi/5q+ii0k+JFMReWdvjcKgo9sLYC0YlvlT87JaU0m9xli+Fj/oPuxkz1PrFar9In4j4Lf97VXm2rD+terzGt6giMwikfnJAt/pJV0I0oGcSMUC+XHsiOKB3cr10mt+q/Na31JNvOtnq99hs82Zgzhxg165gdUaBzoSLMmWpJqrzwitxo2zivfzHf17JTFXtz3/kB/S+ra5z/NkZNgx4/HFeR1OTfxJWJ7yS+u3fz32qa4dtO8OGAddeC/ziF+6PSGTjS8XPZWV8/unf3/kxWkMDcO65YjbmcsGSBgdFZuyWlHA1Zm0tb+dBB/kn4BwxAnjkEef4zt+V3UpQK5LQF3BPtimaYNheTnU18NBDzo+37OXqGP9OUEJLB3TfqTGRqG7ZMnOJzUwmcAvTR33lMJWYUqV/wrBFFpOxpSMJqo7EjCqHaAJK0T41MR/JxF3S5hCV2DGdGDhIckpVO3Uj+vtNLwoHwIRMzWQSPpMJ3MKqsy8RpqxV13lhYtImHdLiMKTyTojKx033vdd1SY47P1Rix3Ri4CDJKYNeEza0qAmACZlaczNw+OFq1w4f7v2YQXdCwooKZzWRve44yiCTgpVoVOY2rl/SRoA/opw6VS7ZYjbLr/dTionYY78NXl8PLF0KbNjAb3GPGgVMn95tn9Mt+PJyYM0a4IUXxGxXwfK9aPI/J9yuNT0mdMrHZc7LxyupYZJl1H5Y86JMgspt24Cf/IT/v318OqFr6wOVcvLn/MmTeR8vWMAX02PHcqWf02PX0AjpzlEs0P34yVRCtOpq+bfla2r4Nt5uChadL9u5pTLIfwGwpoZvQR5GMjwTR1SJBJ36VuSFOxkVUDbbM9miXztFlGIi9tiVLV4vtGez+uNW5aiu5raoxIGbb0wmUsxmxRJQqmyxH6YP7Nvrd3SopSeI4igqko9ZpzQK1vj06g/R8vNT8qiU4zbnFxX1tj+T4WNXNwWnfrrpppvYCSecwAYNGsRKSkqUyjCpfor6x09GXSMSwE7/7/Rvv3LCSshnUv0Udd+KKs5U7PRKkiljn0wizSh9GWZcefWbyXlDNIGmbqm9Lh+4qXaijoUoDrc+ElFV5fvUqSzZcmQO3QubglvU/PSnP2V33HEHu+qqq2K1qGEsWtlhNtt9N0RXmV7JHWtq5CTj1l9cTneRRI50Wuyuldf+Hqrtt/o2yF+JuhKPeiWGC5J0rqaGXx+knfm2RZ1E0sShmpTQq99MzRs1Nf4JNFUSDaraK+sDNylwXR1jw4ZFHwu6DpEFojU+g453pz6QKUcl/jMZHoe6KLhFjcWDDz4Yu0UNYzxAli1j7PrrwxkQF17I2OLF3fXqKnf+/N6Bv2wZYwsX8v8uXqxW7rJlPcv6r//S64/Fi3vaLKoAmT+fsUcf7f6vZacd1TabOHSpGJzKDNpOHYqhsI8LLuBHFP0mG6syh24FlNN88OyzjP3qV4ydeGJwH9jnGK+Fluy8cdZZjH38cbhzs1+8XX+9XDvy/aZLkRfGOJ0/Xy6+vCD1E4D29na0t7d3/Xv37t3G6gqabE6W007rzqaqs87Kyp4vqOlKYLdtm9mknPZEbDJJLkUSdeYneYsSXSoGp+uDtjPMZIS6OP10/t9HHzVbj5tPZGJVBt0KKIv8+eBLX+L7oLz4onpdIslZLTZsEDvPoqYGGDQo3LnZi9NP5/ONzPyXb7cuRV4Y/hCNQ52kw68yPG699VaUlJR0HbW1tcbrDOttfXs9YSYe1JVMz1QiPNnyw07UGRRdKgan6+NSTpgMHx5u8kTZ71TRrYDSUYaOukTb5XR+HOJSZYzonDtN/W64IdtfOoj0Ts2NN96In/3sZ57nvPrqqzjmmGOUyr/22mtx1VVXdf179+7dxhY2dulpebn4Lo2MydeVn/hOJhGgF2VlwLPPAg88wMsrKwOqqrp3sbSS6ZWX8909RepyS2g2eTIv12+3zYEDgX79vBMSFhdzu197rae9fgnxvBII2nexragQs9WOtQMnoHfHaXsCO0tGn8vJJdmzU1bW7YMgcWT50kpIqGpPmNTWdktS/casV9I+P7JZnvDxuuuATZu6x1ZFBd/te9gwvhOtrl23q6uBceP8+8Da9deewNBrJ1ivBJt+c8LQocAxx/SWA7vtSJvNctn+hg09ZcKzZwM/+IHYDr/pNPCFL/C7IqLJN1XnZD+s+SCXA/74Ry53P/BAYOdO7+us8SmbVNetrE8/5f6wkiOLjlOV+M9keH+Fjr4nXvK0trayf/7zn57Hvn37elwTx3dqonhRWFeyMtFD9YVXL9WOyTfvRRMair5gLPuinD13TdC2yORgChpLQeLIdN4x3ce8eeJj18r/kwT1VtA+cJJhm0iwKZPs1S4TFp03ok5oah1uuZNEj6Ki3te6JZk0Zb9Xwk+3g9RPgsRtUWMqod7EiWLB5pesrLZWLSB1HW6BHReJpom+y1dPeZ0rslgqLtYr2feLpTgkEYzbESRTc1iHrh9xt+ziJnwqm+zVvrBxW1SpKtXi3i/5vjNVtv0QTZ7pdOhMZslYASa03LRpE3bs2IGnn34at912GxoaGgAABx98MIqKioTKiCKhZXk5cNttwPe/D8i8pyySRM0rWZn9FrFo8jMT1Nb2TpyXy/GdY2Ue58SVsjLgjjv4Y4T8XXZF4mP4cH4rurPT/ZxUiie9M+mv/FgymXQxn6Ii4IwzgL/+Ffj4Y/fzrGSBlZXdjybGjOH+Nx1L+Yk9ZZIBmqKsDPjud/ljlsmTgYsv1jfO7e01PX/IJIzMZHiM9O/PY3TBAuCdd3ji0eHDgYMPDiceZNCZGDessq0EyZMnq/W/iaS8BZfQctasWQxAr2OZhCYxqoSWJiSb9kNH4rgw7YuDTWH4P4ntjEqWrSpBDtvOuMnWTdtjeu5StSnO812hHDqSiepMgCn6+50Y9dNDDz0ExlivY6qoFtAAopI407I2HYnjTKJTkhhHokpcaIKoZNmqEuSw7YybbN20PVFIcv3wsiku/VIIbNumb7uIMCnofWpMIyqJMy1rC1suKotpOXfUuLUnie2MSpatKkEO2864ydZN2xOFJNcPL5vi0i+FQJhyf63ouzkUf8JOaGltTd3eLr8FvYiyQGSr8yi3rHezL2iCuri8COjnf5H4qK727+t0mp9n+kVhp1QHptUV9jEimuAwiviOyj9R2SPaLzoOmcSxflvvR90vbjbHwXcqsaU6vlTScPhRcI+f4kgmA9x1F///VKrnd9a/77yTv9R2991yZV91Ve8ynbjzTu8XsSwbRcoygZN9mYy8P+xcfXUgk3pg94uMj+z96+Z/kfi4+27e115cfXW3v2T7UeZ8e1u8bFet32+MiIwlp1hSiW+ZfneqX5d/VDBtj2i/uF0nU08q1R3/ItdfdRW3yY0o+yUfe/t026LiOxms2FIZXyJzo1H0raPiT5j71DglZaur89+rwJ5y3kta65b0TcbG/JW1zF8bfn99iNgn4g8v33hd67RnitNnXsk7vdor43+R+HCSqNr35nArx69fRNrn1RYZebesf93GiGiCQ1E7Ze2S6Wun64PeSfTb70dXf8mUL+OnujoxqXH+1gei+9SIIOIHa6sEt/O8+jGb9Zf4y7TPqm/gwN6fO+1T41e26t0hr2SibvYHmRtlKDhJtw50S7rt5EupvXblrK/nR2dn966S6TTPTzJ1qvMum42NXD6aLxtWsbGxkcuI29q66508mX+3dGnPXU/zdxTO301082Zg5UpediYDHHdc906tIvbZ/QF07zy8fDmX7KZSXP49fbqzb+y+LC3tttcqJ79PnD5zksPbdxS2t9evf/1873W9JVFdv77nLqpe5bj1i1f7ZGMpv87jjgPuvx947z3eP/Y+F/WvzM61MrHk1j4Zu2T72un6hgbnmHbaUTib7b0lgN3e/FiUsSd/Z2y//1fZUdjNp0uWAH/4A7B3L3DSSTyeV64MtqOwDPnlvfEGt7e4GLjwQj6nWJJop7ix+nHpUve5yGvOcGufVc+wYcCrr/KlwCGH8DZmMj3nQ+s3AfAeE167PVttqqrq3edNTeJzgZefgsyNooj+ftOihiAIgiCIWCP6+03v1BAEQRAEURDQooYgCIIgiIKAFjUEQRAEQRQEtKghCIIgCKIgoEUNQRAEQRAFAS1qCIIgCIIoCGhRQxAEQRBEQUCLGoIgCIIgCgJa1BAEQRAEURAcELUBYWJtnrx79+6ILSEIgiAIQhTrd9svCUKfWtTs2bMHAFBbWxuxJQRBEARByLJnzx6UlJS4ft+ncj91dnZi69atKC4uRkpjrvbdu3ejtrYWmzdvppxSBiE/hwP5OTzI1+FAfg4Hk35mjGHPnj0YMWIE0mn3N2f61J2adDqNmpoaY+UPHTqUBkwIkJ/DgfwcHuTrcCA/h4MpP3vdobGgF4UJgiAIgigIaFFDEARBEERBQIsaDQwYMAA33HADBgwYELUpBQ35ORzIz+FBvg4H8nM4xMHPfepFYYIgCIIgChe6U0MQBEEQREFAixqCIAiCIAoCWtQQBEEQBFEQ0KKGIAiCIIiCgBY1AVmwYAHGjBmDgQMH4uijj0ZDQ0PUJiWKW2+9FRMnTkRxcTEqKipw1lln4Z133ulxDmMMN954I0aMGIFBgwZh6tSpeOutt3qc097ejjlz5qCsrAxDhgzBmWeeiS1btoTZlERx6623IpVK4Yorruj6jPysh8bGRlxwwQXIZrMYPHgwjjjiCLz22mtd35Of9dDR0YHrr78eY8aMwaBBg3DQQQfh5z//OTo7O7vOIV/L8/zzz+OrX/0qRowYgVQqhb/85S89vtfl0w8//BAXXnghSkpKUFJSggsvvBA7d+4M3gBGKPP444+zfv36sd/+9rds3bp1bO7cuWzIkCFs48aNUZuWGP793/+dPfjgg2zt2rVs9erV7IwzzmAjR45ke/fu7TrnF7/4BSsuLmZ1dXVszZo17Otf/zobPnw42717d9c5l1xyCauurmbPPfccW7VqFZs2bRr74he/yDo6OqJoVqx55ZVX2OjRo9mECRPY3Llzuz4nPwdnx44dbNSoUeyiiy5iK1euZB988AFbvHgxe//997vOIT/r4aabbmLZbJb9z//8D/vggw/Yk08+yYqKitidd97ZdQ75Wp5nnnmGXXfddayuro4BYH/+8597fK/Lp1/+8pfZ+PHj2YoVK9iKFSvY+PHj2Ve+8pXA9tOiJgDHHnssu+SSS3p8duihh7If/ehHEVmUfFpaWhgAtnz5csYYY52dnayqqor94he/6Drnk08+YSUlJezXv/41Y4yxnTt3sn79+rHHH3+865zGxkaWTqfZ//7v/4bbgJizZ88edsghh7DnnnuOTZkypWtRQ37WwzXXXMNOOukk1+/Jz/o444wz2Le//e0en82cOZNdcMEFjDHytQ7yFzW6fLpu3ToGgL388std57z00ksMAHv77bcD2UyPnxTZv38/XnvtNXzpS1/q8fmXvvQlrFixIiKrks+uXbsAAKWlpQCADz74AE1NTT38PGDAAEyZMqXLz6+99ho+/fTTHueMGDEC48ePp77I47LLLsMZZ5yBU089tcfn5Gc9PP300zjmmGPwH//xH6ioqMCRRx6J3/72t13fk5/1cdJJJ2HJkiV49913AQBvvPEGXnjhBZx++ukAyNcm0OXTl156CSUlJTjuuOO6zjn++ONRUlIS2O99KqGlTrZv345cLofKysoen1dWVqKpqSkiq5INYwxXXXUVTjrpJIwfPx4Aunzp5OeNGzd2ndO/f38MGzas1znUF908/vjjWLVqFV599dVe35Gf9fCvf/0L//3f/42rrroKP/7xj/HKK6/g+9//PgYMGIBvfetb5GeNXHPNNdi1axcOPfRQZDIZ5HI53HzzzTjvvPMAUEybQJdPm5qaUFFR0av8ioqKwH6nRU1AUqlUj38zxnp9Rohx+eWX480338QLL7zQ6zsVP1NfdLN582bMnTsXf//73zFw4EDX88jPwejs7MQxxxyDW265BQBw5JFH4q233sJ///d/41vf+lbXeeTn4PzpT3/Co48+ioULF+ILX/gCVq9ejSuuuAIjRozArFmzus4jX+tHh0+dztfhd3r8pEhZWRkymUyvVWVLS0uvVSzhz5w5c/D0009j2bJlqKmp6fq8qqoKADz9XFVVhf379+PDDz90Paev89prr6GlpQVHH300DjjgABxwwAFYvnw57r77bhxwwAFdfiI/B2P48OEYN25cj88OO+wwbNq0CQDFs07mzZuHH/3oR/jGN76Bww8/HBdeeCGuvPJK3HrrrQDI1ybQ5dOqqio0Nzf3Kr+1tTWw32lRo0j//v1x9NFH47nnnuvx+XPPPYdJkyZFZFXyYIzh8ssvx6JFi7B06VKMGTOmx/djxoxBVVVVDz/v378fy5cv7/Lz0UcfjX79+vU4Z9u2bVi7di31xWeccsopWLNmDVavXt11HHPMMTj//POxevVqHHTQQeRnDZx44om9tiR49913MWrUKAAUzzr5+OOPkU73/AnLZDJdkm7ytX50+fSEE07Arl278Morr3Sds3LlSuzatSu43wO9ZtzHsSTdDzzwAFu3bh274oor2JAhQ9iGDRuiNi0xXHrppaykpITV19ezbdu2dR0ff/xx1zm/+MUvWElJCVu0aBFbs2YNO++88xwlhDU1NWzx4sVs1apVbPr06X1alimCXf3EGPlZB6+88go74IAD2M0338zee+899sc//pENHjyYPfroo13nkJ/1MGvWLFZdXd0l6V60aBErKytjP/zhD7vOIV/Ls2fPHvb666+z119/nQFgd9xxB3v99de7tirR5dMvf/nLbMKECeyll15iL730Ejv88MNJ0h0H7rvvPjZq1CjWv39/dtRRR3VJkQkxADgeDz74YNc5nZ2d7IYbbmBVVVVswIAB7OSTT2Zr1qzpUc6+ffvY5ZdfzkpLS9mgQYPYV77yFbZp06aQW5Ms8hc15Gc9/PWvf2Xjx49nAwYMYIceeij7zW9+0+N78rMedu/ezebOnctGjhzJBg4cyA466CB23XXXsfb29q5zyNfyLFu2zHFOnjVrFmNMn0/b2trY+eefz4qLi1lxcTE7//zz2YcffhjY/hRjjAW710MQBEEQBBE99E4NQRAEQRAFAS1qCIIgCIIoCGhRQxAEQRBEQUCLGoIgCIIgCgJa1BAEQRAEURDQooYgCIIgiIKAFjUEQRAEQRQEtKghCIIgCKIgoEUNQRAEQRAFAS1qCILo82zYsAGpVAqrV6+O2hSCIAJAixqCIAiCIAoCWtQQBBE5nZ2d+OUvf4mDDz4YAwYMwMiRI3HzzTcDANasWYPp06dj0KBByGaz+O53v4u9e/d2XTt16lRcccUVPco766yzcNFFF3X9e/To0bjlllvw7W9/G8XFxRg5ciR+85vfdH0/ZswYAMCRRx6JVCqFqVOnGmsrQRDmoEUNQRCRc+211+KXv/wlfvKTn2DdunVYuHAhKisr8fHHH+PLX/4yhg0bhldffRVPPvkkFi9ejMsvv1y6jttvvx3HHHMMXn/9dcyePRuXXnop3n77bQDAK6+8AgBYvHgxtm3bhkWLFmltH0EQ4XBA1AYQBNG32bNnD+666y7ce++9mDVrFgBg7NixOOmkk/Db3/4W+/btwyOPPIIhQ4YAAO6991589atfxS9/+UtUVlYK13P66adj9uzZAIBrrrkG8+fPR319PQ499FCUl5cDALLZLKqqqjS3kCCIsKA7NQRBRMo///lPtLe345RTTnH87otf/GLXggYATjzxRHR2duKdd96RqmfChAld/59KpVBVVYWWlhZ1wwmCiB20qCEIIlIGDRrk+h1jDKlUyvE76/N0Og3GWI/vPv30017n9+vXr9f1nZ2dsuYSBBFjaFFDEESkHHLIIRg0aBCWLFnS67tx48Zh9erV+Oijj7o+e/HFF5FOp/G5z30OAFBeXo5t27Z1fZ/L5bB27VopG/r37991LUEQyYUWNQRBRMrAgQNxzTXX4Ic//CEeeeQRrF+/Hi+//DIeeOABnH/++Rg4cCBmzZqFtWvXYtmyZZgzZw4uvPDCrvdppk+fjr/97W/429/+hrfffhuzZ8/Gzp07pWyoqKjAoEGD8L//+79obm7Grl27DLSUIAjT0KKGIIjI+clPfoKrr74aP/3pT3HYYYfh61//OlpaWjB48GA8++yz2LFjByZOnIhzzjkHp5xyCu69996ua7/97W9j1qxZ+Na3voUpU6ZgzJgxmDZtmlT9BxxwAO6++27cf//9GDFiBGbMmKG7iQRBhECK5T+MJgiCIAiCSCB0p4YgCIIgiIKAFjUEQRAEQRQEtKghCIIgCKIgoEUNQRAEQRAFAS1qCIIgCIIoCGhRQxAEQRBEQUCLGoIgCIIgCgJa1BAEQRAEURDQooYgCIIgiIKAFjUEQRAEQRQEtKghCIIgCKIg+P8BL1zlQyxU+RMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.linspace(1,len(Xt),len(Xt)), Xt[:,7],color='b')#employ feature\n",
    "plt.scatter(np.linspace(1,len(y),len(y)), y,color='r')\n",
    "plt.xlabel(\"count\")\n",
    "plt.ylabel(\"normalized employ\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Train Test Split\n",
    "\n",
    "Out of Sample Accuracy is the percentage of correct predictions that the model makes on data that the model has NOT been trained on. Doing a train and test on the same dataset will most likely have low out-of-sample accuracy, due to the likelihood of our model overfitting.\n",
    "\n",
    "It is important that our models have a high, out-of-sample accuracy, because the purpose of any model, of course, is to make correct predictions on unknown data. So how can we improve out-of-sample accuracy? One way is to use an evaluation approach called Train/Test Split.\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.\n",
    "\n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that has been used to train the model. It is more realistic for the real world problems.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (800, 11) (800,)\n",
      "Test set: (200, 11) (200,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( Xt, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "<div id=\"classification\">\n",
    "    <h2>Mulit-Class Classification</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h3>K nearest neighbor (KNN)</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Import library\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Classifier implementing the k-nearest neighbors vote.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Training\n",
    "\n",
    "Let's start the algorithm with k=4 for now:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "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: \"▸\";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: \"▾\";}#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>KNeighborsClassifier(n_neighbors=16)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=16)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=16)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 16\n",
    "#Train Model\n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Predicting\n",
    "\n",
    "We can use the model to make predictions on the test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "yhat = neigh.predict(X_test)#TESTING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "<h3> <b>Accuracy evaluation</b> </h3>\n",
    "\n",
    "In multilabel classification, **accuracy classification score** is a function that computes subset accuracy. This function is equal to the jaccard_score function. Essentially, it calculates how closely the actual labels and predicted labels are matched in the test set.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The accuracy_score function calculates the accuracy of a classification model, which is the ratio of the number of correct predictions to the total number of predictions.</p>\n",
    "$$Accuracy = \\frac{Number of corrected predictions}{Total number of prediction}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.45375\n",
      "Test set Accuracy:  0.36\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))#jaccard index\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JACCARD INDEX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$J(y,\\hat{y})=\\frac{|y\\cap\\hat{y}|}{|y\\cup\\hat{y}|}=\\frac{|y\\cap\\hat{y}|}{|y|+|\\hat{y}|-|y\\cap\\hat{y}|}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Average setting option appropriate for multi-class classification</p>\n",
    "<ul>\n",
    "<li><b>micro</b>: Computes metrics globally by counting the total true positives, false negatives, and false positives.</li>\n",
    "<li><b>macro</b>: Calculates metrics for each label and finds their unweighted mean. This does not take label imbalance into account.</li>\n",
    "<li><b>weighted</b>: Calculates metrics for each label and finds their average, weighted by support (the number of true instances for each label).</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.2871811740292793\n",
      "Test set Accuracy:  0.21777972350230412\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "print(\"Train set Accuracy: \",jaccard_score(y_train, neigh.predict(X_train), average='weighted'))\n",
    "print(\"Test set Accuracy: \",jaccard_score(y_test, yhat,average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='custcat', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.kdeplot(y_test,color=\"r\", label=\"Actual Value\")\n",
    "sns.kdeplot(yhat, color=\"b\", label=\"Fitted Values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.4426727900523772\n",
      "Test set Accuracy:  0.35661710356780907\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(\"Train set Accuracy: \",f1_score(y_train, neigh.predict(X_train), average='weighted'))\n",
    "print(\"Test set Accuracy: \",f1_score(y_test, yhat,average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F1-SCORE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><b>Precision</b>: Precision measures the accuracy of positive predictions. The ratio of correctly predicted positive observations to the total predicted positives.</p>\n",
    "$$Precision=\\frac{TP}{TP+FP}$$\n",
    "<p><b>Recall</b>: Recall measures the ability of the classifier to find all the positive samples.The ratio of correctly predicted positive observations to all the actual positives.</p>\n",
    "$$Recall=\\frac{TP}{TP+FN}$$\n",
    "<p><b>Support</b>: The number of actual occurrences of the class in the dataset.</p>\n",
    "<p><b>F1-score</b>: F1-score is the harmonic mean of precision and recall. It provides a balance between precision and recall.<b>It ranges from 0-1.</b></p>\n",
    "$$F1-score=2*\\frac{precision*Recall}{Recall+Precision}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "custcat\n",
       "3    54\n",
       "4    51\n",
       "1    51\n",
       "2    44\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts() #support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 3, 4], dtype=int64), array([64, 39, 59, 38], dtype=int64))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(yhat, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22,  7, 15,  7],\n",
       "       [ 7, 13, 11, 13],\n",
       "       [19,  8, 23,  4],\n",
       "       [16, 11, 10, 14]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p =confusion_matrix(y_test, yhat)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.34      0.43      0.38        51\n",
      "           2       0.33      0.30      0.31        44\n",
      "           3       0.39      0.43      0.41        54\n",
      "           4       0.37      0.27      0.31        51\n",
      "\n",
      "    accuracy                           0.36       200\n",
      "   macro avg       0.36      0.36      0.35       200\n",
      "weighted avg       0.36      0.36      0.36       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#with test set\n",
    "print(classification_report(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "sns.heatmap(p, annot=True, cmap='coolwarm', fmt='.2f')\n",
    "plt.title('Confusion Matrix For Testing')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.46      0.67      0.54       215\n",
      "           2       0.41      0.38      0.39       173\n",
      "           3       0.46      0.46      0.46       227\n",
      "           4       0.47      0.28      0.35       185\n",
      "\n",
      "    accuracy                           0.45       800\n",
      "   macro avg       0.45      0.44      0.44       800\n",
      "weighted avg       0.45      0.45      0.44       800\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#with training set\n",
    "print(classification_report(y_train, neigh.predict(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### What about other K?\n",
    "\n",
    "K in KNN, is the number of nearest neighbors to examine. It is supposed to be specified by the user. So, how can we choose right value for K?\n",
    "The general solution is to reserve a part of your data for testing the accuracy of the model. Then choose k =1, use the training part for modeling, and calculate the accuracy of prediction using all samples in your test set. Repeat this process, increasing the k, and see which k is the best for your model.\n",
    "\n",
    "We can calculate the accuracy of KNN for different values of k.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3  , 0.29 , 0.315, 0.32 , 0.315, 0.31 , 0.335, 0.325, 0.34 ,\n",
       "       0.33 , 0.315, 0.34 , 0.33 , 0.315, 0.34 , 0.36 , 0.355, 0.35 ,\n",
       "       0.345, 0.335])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 21\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Plot the model accuracy for a different number of neighbors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.fill_between(range(1,Ks),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\n",
    "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.36 with k= 16\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **PICKING THE BEST VALUE FOR K** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    " <li>A low value of k causes a highly complex model </li>\n",
    " <li>A low value of k captures anomaly/noise </li>\n",
    " <li>A large value of k captures an overgenerized value of data </li>\n",
    "</ul>"
   ]
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}