{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Deep Neural Networks - Programming Assignment\n",
        "## Comparing Linear Models and Multi-Layer Perceptrons\n",
        "\n",
        "**Student Name:** Chingepally Pavan Kumar  \n",
        "**Student ID:** 2025aa05003    \n",
        "**Date:** 26-11-2025\n",
        "\n",
        "---\n",
        "\n",
        "## ⚠️ IMPORTANT INSTRUCTIONS\n",
        "\n",
        "1. **Complete ALL sections** marked with `TODO`\n",
        "2. **DO NOT modify** the `get_assignment_results()` function structure\n",
        "3. **Fill in all values accurately** - these will be auto-verified\n",
        "4. **After submission**, you'll receive a verification quiz based on YOUR results\n",
        "5. **Run all cells** before submitting (Kernel → Restart & Run All)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✓ Libraries imported successfully\n"
          ]
        }
      ],
      "source": [
        "# Import required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score, roc_auc_score, precision_score, recall_score, f1_score, roc_curve, matthews_corrcoef,classification_report\n",
        "import time\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import seaborn as sns\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import pickle\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Set random seed for reproducibility\n",
        "np.random.seed(42)\n",
        "print('✓ Libraries imported successfully')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Section 1: Dataset Selection and Loading\n",
        "\n",
        "**Requirements:**\n",
        "- ≥500 samples\n",
        "- ≥5 features\n",
        "- Public dataset (UCI/Kaggle)\n",
        "- Regression OR Classification problem"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      baseline value  accelerations  fetal_movement  uterine_contractions  light_decelerations  severe_decelerations  prolongued_decelerations  abnormal_short_term_variability  \\\n",
            "0                120          0.000           0.000                 0.000                0.000                   0.0                       0.0                               73   \n",
            "1                132          0.006           0.000                 0.006                0.003                   0.0                       0.0                               17   \n",
            "2                133          0.003           0.000                 0.008                0.003                   0.0                       0.0                               16   \n",
            "3                134          0.003           0.000                 0.008                0.003                   0.0                       0.0                               16   \n",
            "4                132          0.007           0.000                 0.008                0.000                   0.0                       0.0                               16   \n",
            "...              ...            ...             ...                   ...                  ...                   ...                       ...                              ...   \n",
            "2121             140          0.000           0.000                 0.007                0.000                   0.0                       0.0                               79   \n",
            "2122             140          0.001           0.000                 0.007                0.000                   0.0                       0.0                               78   \n",
            "2123             140          0.001           0.000                 0.007                0.000                   0.0                       0.0                               79   \n",
            "2124             140          0.001           0.000                 0.006                0.000                   0.0                       0.0                               78   \n",
            "2125             142          0.002           0.002                 0.008                0.000                   0.0                       0.0                               74   \n",
            "\n",
            "      mean_value_of_short_term_variability  percentage_of_time_with_abnormal_long_term_variability  mean_value_of_long_term_variability  histogram_width  histogram_min  histogram_max  \\\n",
            "0                                      0.5                                                 43                                       2.4               64             62            126   \n",
            "1                                      2.1                                                  0                                      10.4              130             68            198   \n",
            "2                                      2.1                                                  0                                      13.4              130             68            198   \n",
            "3                                      2.4                                                  0                                      23.0              117             53            170   \n",
            "4                                      2.4                                                  0                                      19.9              117             53            170   \n",
            "...                                    ...                                                ...                                       ...              ...            ...            ...   \n",
            "2121                                   0.2                                                 25                                       7.2               40            137            177   \n",
            "2122                                   0.4                                                 22                                       7.1               66            103            169   \n",
            "2123                                   0.4                                                 20                                       6.1               67            103            170   \n",
            "2124                                   0.4                                                 27                                       7.0               66            103            169   \n",
            "2125                                   0.4                                                 36                                       5.0               42            117            159   \n",
            "\n",
            "      histogram_number_of_peaks  histogram_number_of_zeroes  histogram_mode  histogram_mean  histogram_median  histogram_variance  histogram_tendency  fetal_health  \n",
            "0                             2                           0             120             137               121                  73                   1             2  \n",
            "1                             6                           1             141             136               140                  12                   0             1  \n",
            "2                             5                           1             141             135               138                  13                   0             1  \n",
            "3                            11                           0             137             134               137                  13                   1             1  \n",
            "4                             9                           0             137             136               138                  11                   1             1  \n",
            "...                         ...                         ...             ...             ...               ...                 ...                 ...           ...  \n",
            "2121                          4                           0             153             150               152                   2                   0             2  \n",
            "2122                          6                           0             152             148               151                   3                   1             2  \n",
            "2123                          5                           0             153             148               152                   4                   1             2  \n",
            "2124                          6                           0             152             147               151                   4                   1             2  \n",
            "2125                          2                           1             145             143               145                   1                   0             1  \n",
            "\n",
            "[2126 rows x 22 columns]\n",
            "Dataset: Student_Performance\n",
            "Source: Kaggle\n",
            "Samples: 10000, Features: 5\n",
            "Problem Type: regression\n",
            "Primary Metric: r2\n",
            "Index(['baseline value', 'accelerations', 'fetal_movement', 'uterine_contractions', 'light_decelerations', 'severe_decelerations', 'prolongued_decelerations', 'abnormal_short_term_variability',\n",
            "       'mean_value_of_short_term_variability', 'percentage_of_time_with_abnormal_long_term_variability', 'mean_value_of_long_term_variability', 'histogram_width', 'histogram_min', 'histogram_max',\n",
            "       'histogram_number_of_peaks', 'histogram_number_of_zeroes', 'histogram_mode', 'histogram_mean', 'histogram_median', 'histogram_variance', 'histogram_tendency', 'fetal_health'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "pd.set_option('display.max_columns', None)     # show all columns\n",
        "pd.set_option('display.width', 200)\n",
        "\n",
        "# TODO: Load your dataset\n",
        "data_set = pd.read_csv(\"fetal_health.csv\")\n",
        "print(data_set)\n",
        "\n",
        "# Dataset information (TODO: Fill these)\n",
        "dataset_name = \"Student_Performance\"  # e.g., \"Breast Cancer Wisconsin\"\n",
        "dataset_source = \"Kaggle\"  # e.g., \"UCI ML Repository\"\n",
        "n_samples = 10000      # Total number of rows\n",
        "n_features = 5     # Number of features (excluding target)\n",
        "problem_type = \"regression\"  # \"regression\" or \"binary_classification\" or \"multiclass_classification\"\n",
        "\n",
        "# Problem statement (TODO: Write 2-3 sentences)\n",
        "problem_statement = \"\"\"\n",
        "This dataset provides details about students like Hours they Studied, Previous Scores, Extracurricular Activities,Sleep Hours,\n",
        "Sample Question Papers Practiced. I'm using **Performance Index** as the primary metric because it\n",
        "determines the performance of each student.\n",
        "\"\"\"\n",
        "\n",
        "# Primary evaluation metric (TODO: Fill this)\n",
        "primary_metric = \"r2\"  # e.g., \"recall\", \"accuracy\", \"rmse\", \"r2\"\n",
        "\n",
        "# Metric justification (TODO: Write 2-3 sentences)\n",
        "metric_justification = \"\"\"\n",
        "it provides an intuitive and comprehensive measurement of how well the model predicts the target variable. \n",
        "Unlike MSE, RMSE, or MAE, which measure error magnitude only, R² explains how much of the total variance in \n",
        "the dependent variable is captured by the model.\n",
        "\"\"\"\n",
        "\n",
        "print(f\"Dataset: {dataset_name}\")\n",
        "print(f\"Source: {dataset_source}\")\n",
        "print(f\"Samples: {n_samples}, Features: {n_features}\")\n",
        "print(f\"Problem Type: {problem_type}\")\n",
        "print(f\"Primary Metric: {primary_metric}\")\n",
        "\n",
        "print(data_set.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Section 2: Data Preprocessing\n",
        "\n",
        "Preprocess your data:\n",
        "1. Handle missing values\n",
        "2. Encode categorical variables\n",
        "3. Split into train/test sets\n",
        "4. Scale features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      baseline value  accelerations  fetal_movement  uterine_contractions  light_decelerations  severe_decelerations  prolongued_decelerations  abnormal_short_term_variability  \\\n",
            "1233             125          0.000             0.0                 0.008                0.000                   0.0                       0.0                               32   \n",
            "480              140          0.000             0.0                 0.001                0.000                   0.0                       0.0                               60   \n",
            "1111             122          0.000             0.0                 0.000                0.001                   0.0                       0.0                               20   \n",
            "1303             137          0.005             0.0                 0.005                0.002                   0.0                       0.0                               36   \n",
            "861              142          0.003             0.0                 0.004                0.000                   0.0                       0.0                               46   \n",
            "\n",
            "      mean_value_of_short_term_variability  percentage_of_time_with_abnormal_long_term_variability  mean_value_of_long_term_variability  histogram_width  histogram_min  histogram_max  \\\n",
            "1233                                   1.1                                                  3                                      13.0               51             96            147   \n",
            "480                                    0.8                                                 32                                      11.2              120             79            199   \n",
            "1111                                   1.8                                                  0                                      13.8               39            103            142   \n",
            "1303                                   0.9                                                  0                                       5.0               63            115            178   \n",
            "861                                    0.7                                                 27                                       5.9               26            133            159   \n",
            "\n",
            "      histogram_number_of_peaks  histogram_number_of_zeroes  histogram_mode  histogram_mean  histogram_median  histogram_variance  histogram_tendency  \n",
            "1233                          4                           0             126             125               127                   2                   0  \n",
            "480                           9                           0             141             141               142                   3                   0  \n",
            "1111                          1                           0             120             119               121                   3                   0  \n",
            "1303                          4                           0             148             148               149                   9                   0  \n",
            "861                           1                           0             150             148               150                   1                   0  \n",
            "      fetal_health\n",
            "1233             1\n",
            "480              2\n",
            "1111             1\n",
            "1303             1\n",
            "861              1\n",
            "      baseline value  accelerations  fetal_movement  uterine_contractions  light_decelerations  severe_decelerations  prolongued_decelerations  abnormal_short_term_variability  \\\n",
            "282              133          0.002           0.010                 0.003                0.002                   0.0                     0.000                               46   \n",
            "1999             125          0.000           0.001                 0.009                0.008                   0.0                     0.000                               62   \n",
            "1709             131          0.004           0.003                 0.004                0.005                   0.0                     0.001                               60   \n",
            "988              131          0.011           0.000                 0.005                0.000                   0.0                     0.000                               29   \n",
            "2018             125          0.000           0.000                 0.008                0.007                   0.0                     0.001                               64   \n",
            "\n",
            "      mean_value_of_short_term_variability  percentage_of_time_with_abnormal_long_term_variability  mean_value_of_long_term_variability  histogram_width  histogram_min  histogram_max  \\\n",
            "282                                    1.1                                                  0                                      15.4               69             95            164   \n",
            "1999                                   1.7                                                  0                                       1.1               72             68            140   \n",
            "1709                                   2.1                                                  0                                       0.9               90             78            168   \n",
            "988                                    1.3                                                  0                                       4.5               89             82            171   \n",
            "2018                                   1.3                                                  0                                       2.6               77             78            155   \n",
            "\n",
            "      histogram_number_of_peaks  histogram_number_of_zeroes  histogram_mode  histogram_mean  histogram_median  histogram_variance  histogram_tendency  \n",
            "282                           5                           0             139             135               138                   9                   0  \n",
            "1999                          5                           0             130             116               125                  29                   1  \n",
            "1709                          8                           0             133             127               132                  21                   0  \n",
            "988                           8                           0             143             145               145                   9                   1  \n",
            "2018                          4                           0             114             111               114                   7                   0  \n",
            "      fetal_health\n",
            "282              1\n",
            "1999             1\n",
            "1709             1\n",
            "988              1\n",
            "2018             1\n"
          ]
        }
      ],
      "source": [
        "X = data_set.iloc[:,:-1]\n",
        "y = data_set[[\"fetal_health\"]]\n",
        "\n",
        "X_Train,X_Test,y_Train,y_Test = train_test_split(X,y,random_state=42,test_size=0.2)\n",
        "\n",
        "print(X_Train.head(5))\n",
        "print(y_Train.head(5))\n",
        "print(X_Test.head(5))\n",
        "print(y_Test.head(5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "baseline value                                            0\n",
            "accelerations                                             0\n",
            "fetal_movement                                            0\n",
            "uterine_contractions                                      0\n",
            "light_decelerations                                       0\n",
            "severe_decelerations                                      0\n",
            "prolongued_decelerations                                  0\n",
            "abnormal_short_term_variability                           0\n",
            "mean_value_of_short_term_variability                      0\n",
            "percentage_of_time_with_abnormal_long_term_variability    0\n",
            "mean_value_of_long_term_variability                       0\n",
            "histogram_width                                           0\n",
            "histogram_min                                             0\n",
            "histogram_max                                             0\n",
            "histogram_number_of_peaks                                 0\n",
            "histogram_number_of_zeroes                                0\n",
            "histogram_mode                                            0\n",
            "histogram_mean                                            0\n",
            "histogram_median                                          0\n",
            "histogram_variance                                        0\n",
            "histogram_tendency                                        0\n",
            "fetal_health                                              0\n",
            "dtype: int64\n",
            "\n",
            "\n",
            "No Null Values\n"
          ]
        }
      ],
      "source": [
        "print(data_set.isna().sum())\n",
        "print(\"\\n\")\n",
        "print(\"No Null Values\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {},
      "outputs": [],
      "source": [
        "def find_outliers(data, name):\n",
        "    data = np.array(data)\n",
        "    data = data[~np.isnan(data)]   # remove NaNs\n",
        "\n",
        "    Q1 = np.percentile(data, 25)\n",
        "    Q3 = np.percentile(data, 75)\n",
        "    IQR = Q3 - Q1\n",
        "\n",
        "    lower = Q1 - 1.5 * IQR\n",
        "    upper = Q3 + 1.5 * IQR\n",
        "\n",
        "    outliers = data[(data < lower) | (data > upper)]\n",
        "\n",
        "    print(f\"\\n====== {name.upper()} ======\")\n",
        "    print(\"Q1:\", Q1)\n",
        "    print(\"Q3:\", Q3)\n",
        "    print(\"IQR:\", IQR)\n",
        "    print(\"Lower Bound:\", lower)\n",
        "    print(\"Upper Bound:\", upper)\n",
        "    print(\"Outliers:\", outliers)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "====== ABNORMAL_SHORT_TERM_VARIABILITY ======\n",
            "Q1: 32.0\n",
            "Q3: 61.0\n",
            "IQR: 29.0\n",
            "Lower Bound: -11.5\n",
            "Upper Bound: 104.5\n",
            "Outliers: []\n",
            "\n",
            "====== MEAN_VALUE_OF_LONG_TERM_VARIABILITY ======\n",
            "Q1: 4.6\n",
            "Q3: 10.925\n",
            "IQR: 6.325000000000001\n",
            "Lower Bound: -4.887500000000001\n",
            "Upper Bound: 20.4125\n",
            "Outliers: [25.4 24.7 34.7 20.5 36.9 26.3 21.4 25.6 29.1 22.7 25.3 33.5 27.3 25.8\n",
            " 23.1 23.6 29.6 21.7 23.4 25.8 27.6 29.3 21.1 26.1 20.8 26.3 23.  25.1\n",
            " 27.6 29.  21.1 25.9 41.8 21.3 21.7 25.2 21.  21.3 50.7 21.3 28.9 29.5\n",
            " 21.5 22.1 24.2 21.3 27.  28.  22.5 23.3 35.7 24.1 21.7 40.8 20.5 24.9]\n",
            "\n",
            "====== HISTOGRAM_WIDTH ======\n",
            "Q1: 36.0\n",
            "Q3: 101.0\n",
            "IQR: 65.0\n",
            "Lower Bound: -61.5\n",
            "Upper Bound: 198.5\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MIN ======\n",
            "Q1: 66.0\n",
            "Q3: 120.0\n",
            "IQR: 54.0\n",
            "Lower Bound: -15.0\n",
            "Upper Bound: 201.0\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MAX ======\n",
            "Q1: 152.0\n",
            "Q3: 174.0\n",
            "IQR: 22.0\n",
            "Lower Bound: 119.0\n",
            "Upper Bound: 207.0\n",
            "Outliers: [238 211 213 238 230 211 228 238 238 211 228 230 238 228]\n",
            "\n",
            "====== HISTOGRAM_MODE ======\n",
            "Q1: 129.0\n",
            "Q3: 148.0\n",
            "IQR: 19.0\n",
            "Lower Bound: 100.5\n",
            "Upper Bound: 176.5\n",
            "Outliers: [ 91  86  67  86  89 180  60  99  86  88 186  71  67  86  99  88  95 186\n",
            "  60  75  99  91  86  60 180  76  90 180  88 186  98  86  88  67  99  77\n",
            "  86 186 186  91  75  75  86  88  60  91  69  91  67  75  93  75  86 186\n",
            " 179  67  91  75  89  88]\n",
            "\n",
            "====== HISTOGRAM_MEAN ======\n",
            "Q1: 125.0\n",
            "Q3: 145.0\n",
            "IQR: 20.0\n",
            "Lower Bound: 95.0\n",
            "Upper Bound: 175.0\n",
            "Outliers: [ 94  85  76  83  80  93 178  85  84  82  88  90  80  88  78  89  84 180\n",
            "  83  93  91  92  84  93  75  85  82  92  73  94  87  79  91  89  83 182\n",
            "  81  87  86  85]\n",
            "\n",
            "====== HISTOGRAM_MEDIAN ======\n",
            "Q1: 128.0\n",
            "Q3: 148.0\n",
            "IQR: 20.0\n",
            "Lower Bound: 98.0\n",
            "Upper Bound: 178.0\n",
            "Outliers: [ 97  79 180  92  78  94  91 183  90  92  94  86  93  95  95  82  79  95\n",
            " 186  87  77  97]\n",
            "\n",
            "====== HISTOGRAM_VARIANCE ======\n",
            "Q1: 2.0\n",
            "Q3: 24.0\n",
            "IQR: 22.0\n",
            "Lower Bound: -31.0\n",
            "Upper Bound: 57.0\n",
            "Outliers: [ 62  64 147 113  68  92  71 110  79 148  95  83  75  59  83 241  71  74\n",
            "  70  76  79  84  69 119  79  90 101  62  87  61  83  75  72  94 215  72\n",
            " 109  69  61  88  61  66 129  72  73  75  76  95  70  97 157  75  72 269\n",
            " 104  78  94  76  96  65  78  61  92  80 250 134  73  79 100 170  95  65\n",
            "  63 128  59 121 114 108  64 103 182  59  71  98  84  73  72  74 177 103\n",
            "  64 108 106  80  73  76  59  75  87  86  76 136 144 148 109  94 117  72\n",
            " 190 101 137  72  65 126  64 113  61  70  95  68  59  80  63  70 128 114\n",
            "  59  59  70  83 108 148 100  73  75  82 115  77  69  76  89  61  77  86\n",
            " 195  90 127  71  91  59]\n",
            "\n",
            "====== BASELINE VALUE ======\n",
            "Q1: 127.0\n",
            "Q3: 141.0\n",
            "IQR: 14.0\n",
            "Lower Bound: 106.0\n",
            "Upper Bound: 162.0\n",
            "Outliers: []\n",
            "\n",
            "====== ACCELERATIONS ======\n",
            "Q1: 0.0\n",
            "Q3: 0.005\n",
            "IQR: 0.005\n",
            "Lower Bound: -0.0075\n",
            "Upper Bound: 0.0125\n",
            "Outliers: [0.013 0.013 0.013 0.019 0.016 0.017 0.017 0.013 0.014 0.013 0.016 0.014]\n",
            "\n",
            "====== FETAL_MOVEMENT ======\n",
            "Q1: 0.0\n",
            "Q3: 0.003\n",
            "IQR: 0.003\n",
            "Lower Bound: -0.0045000000000000005\n",
            "Upper Bound: 0.007500000000000001\n",
            "Outliers: [0.01  0.012 0.049 0.019 0.054 0.02  0.013 0.016 0.008 0.008 0.099 0.085\n",
            " 0.009 0.071 0.012 0.009 0.01  0.009 0.049 0.048 0.009 0.008 0.036 0.011\n",
            " 0.014 0.47  0.025 0.033 0.017 0.477 0.027 0.01  0.01  0.015 0.016 0.01\n",
            " 0.022 0.222 0.041 0.01  0.009 0.011 0.052 0.011 0.051 0.03  0.008 0.009\n",
            " 0.013 0.054 0.011 0.03  0.05  0.34  0.035 0.048 0.009 0.013 0.019 0.089\n",
            " 0.06  0.015 0.021]\n",
            "\n",
            "====== UTERINE_CONTRACTIONS ======\n",
            "Q1: 0.002\n",
            "Q3: 0.006\n",
            "IQR: 0.004\n",
            "Lower Bound: -0.004\n",
            "Upper Bound: 0.012\n",
            "Outliers: [0.013 0.014]\n",
            "\n",
            "====== LIGHT_DECELERATIONS ======\n",
            "Q1: 0.0\n",
            "Q3: 0.003\n",
            "IQR: 0.003\n",
            "Lower Bound: -0.0045000000000000005\n",
            "Upper Bound: 0.007500000000000001\n",
            "Outliers: [0.008 0.009 0.008 0.012 0.012 0.013 0.008 0.008 0.008 0.012 0.01  0.008\n",
            " 0.013 0.011 0.008 0.009 0.011 0.008 0.012 0.008 0.008 0.015 0.014 0.008\n",
            " 0.008 0.009 0.014 0.008 0.008 0.01  0.008]\n",
            "\n",
            "====== SEVERE_DECELERATIONS ======\n",
            "Q1: 0.0\n",
            "Q3: 0.0\n",
            "IQR: 0.0\n",
            "Lower Bound: 0.0\n",
            "Upper Bound: 0.0\n",
            "Outliers: []\n",
            "\n",
            "====== PROLONGUED_DECELERATIONS ======\n",
            "Q1: 0.0\n",
            "Q3: 0.0\n",
            "IQR: 0.0\n",
            "Lower Bound: 0.0\n",
            "Upper Bound: 0.0\n",
            "Outliers: [0.001 0.001 0.003 0.003 0.001 0.002 0.002 0.002 0.001 0.002 0.002 0.002\n",
            " 0.001 0.001 0.001 0.002 0.001 0.002 0.001 0.001 0.001 0.002 0.001 0.002\n",
            " 0.004 0.001 0.001 0.001 0.002 0.002 0.003 0.002 0.001 0.001 0.002 0.003]\n",
            "\n",
            "====== ABNORMAL_SHORT_TERM_VARIABILITY ======\n",
            "Q1: 33.0\n",
            "Q3: 61.0\n",
            "IQR: 28.0\n",
            "Lower Bound: -9.0\n",
            "Upper Bound: 103.0\n",
            "Outliers: []\n",
            "\n",
            "====== MEAN_VALUE_OF_SHORT_TERM_VARIABILITY ======\n",
            "Q1: 0.7\n",
            "Q3: 1.7\n",
            "IQR: 1.0\n",
            "Lower Bound: -0.8\n",
            "Upper Bound: 3.2\n",
            "Outliers: [3.6 4.  4.8 6.  3.9 4.1 3.4 3.5 3.4 4.2 3.4 3.8 4.8 4.5 3.4 3.4 4.  4.9]\n",
            "\n",
            "====== PERCENTAGE_OF_TIME_WITH_ABNORMAL_LONG_TERM_VARIABILITY ======\n",
            "Q1: 0.0\n",
            "Q3: 11.75\n",
            "IQR: 11.75\n",
            "Lower Bound: -17.625\n",
            "Upper Bound: 29.375\n",
            "Outliers: [84 90 71 84 54 58 45 68 44 33 38 61 35 48 39 64 71 42 50 34 37 91 44 43\n",
            " 75 32 43 56 31 50 53 40 49 31 44 65 44 59 40 57 37 30 58 35 35 54 32 58\n",
            " 56 32 48 37 61 56 39 53 84 55 38 69 45 46 32 72 60 46 64 30]\n",
            "\n",
            "====== MEAN_VALUE_OF_LONG_TERM_VARIABILITY ======\n",
            "Q1: 4.6\n",
            "Q3: 10.3\n",
            "IQR: 5.700000000000001\n",
            "Lower Bound: -3.950000000000001\n",
            "Upper Bound: 18.85\n",
            "Outliers: [19.3 18.9 27.3 22.2 19.7 20.  21.9 28.4 19.1 19.3 23.8 21.4 26.1 27.4\n",
            " 20.8 23.4 23.1 22.3 22.3 21.5]\n",
            "\n",
            "====== HISTOGRAM_WIDTH ======\n",
            "Q1: 39.0\n",
            "Q3: 98.0\n",
            "IQR: 59.0\n",
            "Lower Bound: -49.5\n",
            "Upper Bound: 186.5\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MIN ======\n",
            "Q1: 68.25\n",
            "Q3: 120.0\n",
            "IQR: 51.75\n",
            "Lower Bound: -9.375\n",
            "Upper Bound: 197.625\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MAX ======\n",
            "Q1: 152.0\n",
            "Q3: 174.0\n",
            "IQR: 22.0\n",
            "Lower Bound: 119.0\n",
            "Upper Bound: 207.0\n",
            "Outliers: [210 210 211 230 238 210 228 211 210 228]\n",
            "\n",
            "====== HISTOGRAM_NUMBER_OF_PEAKS ======\n",
            "Q1: 2.0\n",
            "Q3: 6.0\n",
            "IQR: 4.0\n",
            "Lower Bound: -4.0\n",
            "Upper Bound: 12.0\n",
            "Outliers: [14 14 13]\n",
            "\n",
            "====== HISTOGRAM_NUMBER_OF_ZEROES ======\n",
            "Q1: 0.0\n",
            "Q3: 1.0\n",
            "IQR: 1.0\n",
            "Lower Bound: -1.5\n",
            "Upper Bound: 2.5\n",
            "Outliers: [3 3 8 3 3]\n",
            "\n",
            "====== HISTOGRAM_MODE ======\n",
            "Q1: 129.0\n",
            "Q3: 148.0\n",
            "IQR: 19.0\n",
            "Lower Bound: 100.5\n",
            "Upper Bound: 176.5\n",
            "Outliers: [ 93 100  60  86  86  60  99  91  89  97 100 187 180]\n",
            "\n",
            "====== HISTOGRAM_MEAN ======\n",
            "Q1: 125.0\n",
            "Q3: 146.0\n",
            "IQR: 21.0\n",
            "Lower Bound: 93.5\n",
            "Upper Bound: 177.5\n",
            "Outliers: [85 90 88 83]\n",
            "\n",
            "====== HISTOGRAM_MEDIAN ======\n",
            "Q1: 130.0\n",
            "Q3: 148.0\n",
            "IQR: 18.0\n",
            "Lower Bound: 103.0\n",
            "Upper Bound: 175.0\n",
            "Outliers: [102 101 101  95 176]\n",
            "\n",
            "====== HISTOGRAM_VARIANCE ======\n",
            "Q1: 2.0\n",
            "Q3: 25.0\n",
            "IQR: 23.0\n",
            "Lower Bound: -32.5\n",
            "Upper Bound: 59.5\n",
            "Outliers: [ 90  61 243 103 137  89  72  97  89  79  68 103  85  75  79  64 254 116\n",
            "  94  65  72  98  60  85 103  70  80  62 137 138  60  66  74  70]\n",
            "\n",
            "====== HISTOGRAM_TENDENCY ======\n",
            "Q1: 0.0\n",
            "Q3: 1.0\n",
            "IQR: 1.0\n",
            "Lower Bound: -1.5\n",
            "Upper Bound: 2.5\n",
            "Outliers: []\n"
          ]
        }
      ],
      "source": [
        "# List of columns I want to analyze in train_data_set\n",
        "iqr_columns = [\n",
        "    \"abnormal_short_term_variability\",\n",
        "    \"mean_value_of_long_term_variability\",\n",
        "    \"histogram_width\",\n",
        "    \"histogram_min\",\n",
        "    \"histogram_max\",\n",
        "    \"histogram_mode\",\n",
        "    \"histogram_mean\",\n",
        "    \"histogram_median\",\n",
        "    \"histogram_variance\"\n",
        "]\n",
        "\n",
        "# Loop through each column and compute outliers\n",
        "for col in iqr_columns:\n",
        "    find_outliers(X_Train[col], col)\n",
        "\n",
        "# List of columns I want to analyze in test_data_set\n",
        "\n",
        "for col in X_Test.columns:\n",
        "    find_outliers(X_Test[col], col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {},
      "outputs": [],
      "source": [
        "def remove_outliers(df, columns, thresh=3.5):\n",
        "    clean = df.copy()\n",
        "\n",
        "    for col in columns:\n",
        "        x = clean[col]\n",
        "        median = np.nanmedian(x)\n",
        "        mad = np.nanmedian(np.abs(x - median))\n",
        "\n",
        "        if mad == 0:\n",
        "            continue\n",
        "\n",
        "        modified_z = 0.6745 * (x - median) / mad\n",
        "        clean = clean[np.abs(modified_z) <= thresh]\n",
        "\n",
        "    return clean\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Box plots for train_data_set before remving outliers\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1400x1400 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"Box plots for train_data_set before remving outliers\")\n",
        "\n",
        "plt.figure(figsize=(14, 14))\n",
        "\n",
        "for i, col in enumerate(iqr_columns):\n",
        "        plt.subplot(5, 5, i + 1)     # 3 rows, 3 columns\n",
        "        plt.boxplot(X_Train[col])\n",
        "        plt.title(col)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Box plots for test_data_set before removing outliers\n"
          ]
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABW4AAAI4CAYAAAD6RHB6AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAxuRJREFUeJzs3QeUFGX2//875CELSlqiCDIqgqBLFEkLAiJxFQVEQcAALsGECyiIIkhSJKjrgiwgRlBQUQQBlaDgYkCiC4KSdkUyDGn+5/P8v9W/7pmeYWaYmQ7zfp3Tp6erarqrq7ufqrp1n/vEJCQkJBgAAAAAAAAAIGzkCPUKAAAAAAAAAAACEbgFAAAAAAAAgDBD4BYAAAAAAAAAwgyBWwAAAAAAAAAIMwRuAQAAAAAAACDMELgFAAAAAAAAgDBD4BYAAAAAAAAAwgyBWwAAAAAAAAAIMwRuAQAAAAAAACDMhF3gdubMmRYTE2Pr1q2z7Gjnzp3u/Ws7ZKSnnnrKPe///ve/DH1eROZ3JS3fh4oVK9rdd9/te7x8+XL3v7r3aL6WQ8ZJvN3D3bFjx+zee++1UqVKue/HgAEDUvV/jRs3djdEn/R+tl7bNm7cuFS3ZWlts3Bx+4ZIa5+y674/LftmLVuwYMEMWEMg9WhzIkuwfS4QarQjyA7CLnCL6PDss8/aggUL0vx/J06ccI0vJ9iRjc8xe/7mFUi4//777V//+pd1797dotHcuXNt0qRJoV4NXCQ+x6z30Ucfuf07Qod9M7IT2hwAtCOIFrlCvQKI3iBO586drX379mk+qRgxYoT7myy8lFWoUMFOnjxpuXPntsy0ZcsWy5Ej5Ws8r776qp0/f973mM8x+1m2bJnVrVvXnnzySYv2gN+PP/6Y6ozi7OzTTz/N9NcYOnSoPf744yku06hRI9dW5smTxzeNzzHz9wvBgihTpkwheBvCfT/7ZkQq2pzwkJp9LhCuaEcQyci4vYBTp04FBKSQvISEBHcyEY6OHz9u0eLs2bN2+vRp11UpX758ljNnzkx9vbx5817wBFHztRyyrwMHDljRokVDvRoRSfsY7WuihYJDokCpf7A0M+TKlcu1gylRgFHLpDXQiIvbL4SbaDkOyKp9PxBOaHPCQ2r2uUC4oh1BJMuys5hffvnFHnjgAbvyyistNjbWihcvbn/9619dra7kTvz69u3rlitcuLDddddd9scffwQso7pdt9xyi3355Zf25z//2e1ILr/8cps1a1aS5/vPf/7jXq9YsWKWP39+lxn24YcfBizj1cGbN2+eu6L4pz/9yS175MgRX+2vXbt2udfU35qv7BH54YcfrGnTplagQAGXDaFsHn8HDx60hx9+2KpXr+7+V++pVatW9t1332XA1jWbPHmyXX311W59L7nkErv++uuTrIMcOnTIvRcFWIoUKWL33HOP7yTbPzD49NNPW+XKlV0Dp+38xBNPWHx8fNDt/8knn7jX0+f68ssvu22oE6TXX3/d/a1bamrh6btw2WWXub+Vdev9r3/Xys2bN7tMXn2O+rz1uh988EHQOskrVqxw37kSJUpY2bJlfVm811xzjX3//fd20003ue11xRVX2DvvvOPm63/q1Knj3ou+q5999lmqP4MzZ8649dI2TUzfIa2vvgOiwOvw4cOtdu3a7nPQ9+bGG2+0zz//PNlaj+qe7X0mP/30U9A6d3pf2tb6Hej1VG+0Z8+e9vvvvwddZ9UxvO2229z3Ub+1v/3tb0kCSKmpZehfRy+lz7Fdu3bub5XR6Natm3vvWnbYsGH2zDPPuMBKy5Yt3fpo3cePHx/wOvoOKqNTn5m2Q7ly5ezRRx9N8t2cMWOG+z3qs9dyV111lU2bNi3JeqelDUmJvlNNmjQJGpBTO6HvrEefZf369d321vdM3wHv+5eeumLe9z1xW/rxxx+775S+W4UKFbI2bdrYxo0bLT0B2V69elnJkiXd9qlRo4b7bSduN3fs2OHaVO/zTq5tz4jXTPzbeOWVV3y/jRtuuMG++eabJM/59ttvu++Bnk+f1/z589Ncm1nth96j9mfe+/T//9R+P/V//fr1szlz5rh2W8suXrzY91nq+/jQQw+534baau0L1Wao/da+UG28bnpuXTBLLb2m9j+J23y544473G/u3Llz7vH777/vvjNlypRx66ftq/2CN99/m2h7rl+/3mW2qk3V/sKb599zIrXtnr+JEye6fap+K2qzle2c1np7iWvcJvc5qk6z1kntYGK//vqrC5SNHj3asoMLHSsk3i9o/6f2vkqVKu43pvatYcOGtmTJEjdfy3rHS9429//cdMwwePBg95vR9037X/22E3+/dXFYv41LL73UtWu33nqr/fbbb0mOFbzvhfaVd955p/u9aH3Ssp/0nmPr1q1J9ldar927d7t9WnL7qwsZNGiQ207+77F///7uNV988UXftP3797tp3j4suRq32q/qt+jfxqX1GEu0PdVbSm2FltdxS+LfPZDRaHMyv83xP+7V/tA7d9O5qbd/fO+999xjtSPaV//73/++4D7XO6bx2iC14Tq20XENkJVoRyKjHUnNcZiO96pVq+Zu/omBBw8etNKlS7tz6ex2bJJlpRJ0Ir1q1Srr0qWLC6LpAFIHoTqB0oG1Tvb8aQegEwZ9+ZTWrmV1kuWdgHm2b9/ugiI6ye/Ro4f985//dF8EfUm00/AOevXh6qRDB/w6UFYQQAf8Cph06NAh4LV1cqosIR2s6oTbyxjSl0PBVp2cjh071p10az11ovf3v//dunbtah07drTp06e7k+t69epZpUqVfIFj7dAUPNY0rZOCnDoR1fvXyXF6qZu63pe2gxd40w9i7dq17oTFn4J0en2dfH777bf2j3/8wwW3xowZ41tGAwxp++j5dCKl59HymzZtSnIioM9GJ/wKLPTu3dudbKm+pZ5DgbA+ffq45XTSfyFqVPQ5q0amPhNtS7n22mvdvYJODRo0cIEwddPRdn/rrbfcCca7776b5HNU0FbPqUCBf6aNLgCosdF3UZ+HXlN/6/NU9+f77rvPbbfnn3/ebQM1cDpBvBBlH2kd1Fjps/XPNNNnr++SXscL5Grba9tpux09etRee+01F7T8+uuvrWbNmkkCkfpctT11QKQAcbBMcJ0k67umk2w1gtpmCmrpfs2aNUkOtvR9UOOrz1fzdbKo7ZPWwGVqP0dtAwXa9T6aN29uzz33nAugjBo1ygXp1PDru6LPVJ+HfoMKxOk3p/er36yCWvr/uLg4d8FEgR3tnPxrKuv19fvX8soOWLhwofs+6DkefPDBgPVNTRtyIbfffrtrq/bt2+e2u0frumfPHt/nLi+88IJbL7UXCmTpQpG+h4sWLXKBsoyg36Dei75P+m2r7dM2UeBCO8/UBiu1o1QbrW2ktk5thwKg2j46OFJ7o89Brzdw4EDXtqvNEC9AkFapeU1/ukCl34/aIH2/1TbrO6ffgZcRqO+YPiMdROi7ru+4Pm+1JWmhdv7w4cMuiKfvnXiD+aTl++mVllD7pfeoIJQ+kw0bNviCN/oeKbii36V+w9ofah9avnx5V4pG3c7VRukkSfub1NA2UPBM20PfOY++H/qNaBt7WXwKCum9Kbike62v2lK1XXpdfzrQ0r5R33MdJOq3HExa2z21Q1pGv1m1f/rt6IKMtmtyr3Exn6NuarPefPNNmzBhQkBG4xtvvOEOePW7zQ5Sc6zgT+2flvX2/fqsNcis/vcvf/mL+32qLdQ+Su2FP21X/XYUwNfvUt8DXRB+5JFHXBDR+4xE31H9blRDWxfgdbE1pXZT33MFk/Wb8QKkad1P6nej37P//kr7YO3n9X3UNkm8v0oNXbTQe9Pr6ncsX3zxhbuAqXsd13nTJKXnVVmSTp06uYtT+hz0m9T78y5ap+YYyzvO1e9RF7AVONfFa53Uab+s/wMyC21O5rc5Hh1f6TxH7bL22fqtt23b1p2/6sKrjpdFbYk+l9R0L9exj85/9L86Z9L5hNokJTzpvBvICrQjkdGOpOY4TDEBxaMU+9Fx+4QJE9z/6pxAx/A6T8l2PY8SssiJEyeSTFu9erWOohNmzZrlmzZjxgw3rXbt2gmnT5/2TR87dqyb/v777/umVahQwU1buXKlb9qBAwcS8ubNmzB48GDftAEDBrjlvvjiC9+0o0ePJlSqVCmhYsWKCefOnXPTPv/8c7fc5ZdfnmR9e/To4eY9++yzvml//PFHQmxsbEJMTEzCvHnzfNM3b97sln3yySd9006dOuV7Hc+OHTvcuo4cOTJgmv5X2yG12rVrl3D11VenuIzWRc/bs2fPgOkdOnRIKF68uO/xhg0b3HL33ntvwHIPP/ywm75s2bIk23/x4sVJXq9AgQJum6XVf//73yTbztOsWbOE6tWru23pOX/+fEL9+vUTqlSpkuQ71LBhw4SzZ88GPMdNN93k5s2dOzfJ55UjR46ENWvW+KZ/8sknaf4svP9ZuHBhwPTWrVu775VH6xUfHx+wjL5PJUuWDPiMvO9D4cKF3XfbX7DvSrDf2RtvvJHkd+J9H2699daAZR944AE3/bvvvgv4nP0/S+93onuP5mu5C32O3uvmz5/f93vQttD71vTOnTsn+X15r/2vf/3LfUb+v2OZPn26+9+vvvoqxe3QsmXLgM8gLW3IhWzZssU9z+TJk5Nsz4IFCwasT+J1Uzt3zTXXJDRt2jTJuvlvd2/bJeZ93/V98Nq2okWLJvTu3TtguX379iUUKVIkyfSUTJo0yT337NmzA9a3Xr167n0dOXIkYH3btGmTkFb6TeqW1tf0vv9qvw4ePOhbVvuIxL9BtRtly5Z128azfPlyt5z/9zY19B6D/U9avp9ee7Nx48agn6W+q2rbPHrv2s/cd999vmn63eg9+W+7C9Fz/ulPf0ro1KlTwPS33norye8g2G+ob9++7rfr3wZ7bare54U+27S2e/r9//rrr77pa9euddMHDhyY4u8iNW1Wcp+j14Z//PHHAdOvvfbaNG3rSJXaY4XE27hGjRoX/P0/+OCDQduwBQsWuOmjRo0KmK79gb7327dvd4/Xr1/vltMxnb+77747yf7Gex933HFHktdL636yT58+SX53Wq/nnnsu2f1Vamhfo+efOnWqe3zo0CHXLvz1r391vwnPQw89lFCsWDFfmxBs31+zZs2E0qVLu+fwfPrpp0nauJSOsbzjXP9jUrnuuuvcMTmQGWhzsq7N8T/uXbVqVZL9np7vl19+8U1/+eWXk+w7g+1z9ThPnjy+tlp0HhHsuBjIDLQjkdWOpPY4TIYMGeKOjVauXJnw9ttvu2V0rpgdZVmpBEXN/bvUKRtA3UmVRaSMjMSUseRfP01X+pU5pywjf8ouUNaCf0aBsj4Vxffof5QB4nWTE2XW6DWU+auMV3/KVPNfX3/KJvFo3fVayvzUlQSPpmme/zooS9K7yqCMBr1/rYOWDfb+00KvpcyhYN2DE1M2qT9tO62LsmPE277KsvLnZdElLi+hjBxlZ2Q2pcUr40vbWRlY6uKvm9Zdr79t2zaXmeNPGV3BrsRou/tnQHqfl65KKcvE4/3t/zleiK5gKXtOGVseZffpypKufHm0Xl5GrjL19P5UokLdDYJ9H3TVOjUZjP7fW2WoaRspK0mCPW/i7FNl+kni31lGU4af1z1a28LLaPbP5PN+X972V9alPiN1mfA+f920zcW/u7X/dtBVOS2n7HY9lx6ntQ25kKpVq7osMf/PXb9zZfTr6qP/+vj/re+G1kevf7HtgEffNWWmKqvRfztpO+s7nVK39MT0PdCVUD2XR+2yMsHUrVzZbhktra+p35W6QXu8z9L7/JTlpwxNZaV62bGi74MycDNKWr6f3uvruxeMsg79s/70uencSNM9+jzVXqTle6rnVAaitrG2pUffW2Uf++8j/b+nXpurbavfrkrW+NP+LViJmMTS2u4p894/K1r7cW2LzGyf1BNAPWCUheBReQb1YlFGQXZxoWOFxNReK1NC++K00uep74aXYep/3KHvvcq+iNft1sviSLzfSs37SM9+0v+4z/vdJf49Jt5fpYb2NWovVq5c6R5/9dVX7vmVaaxeWd62VMatfpvJlQTZu3evy9bXsau6RHqU6ZxcG5PWzz4t7wtID9qczG9zPGoX1Cs08fmOjlfUqyfx9NS8hvad/r0rlcmvrti0HchKtCOR0Y6k5ThMPbrUA7ZHjx7u+E/nT4mPF7OLLAvcqvurull69csU3NJBqwIMiQMpoq5t/nTCrXoWiesm+n8xPDqJ96+HqxIL+lImppNsb74/r7xBYqrBkTh4poNkdUVLfECt6f7roJNUdYnT+/J//zoZDPb+0+Kxxx5z20cntXp+BeN0AhBM4u3lBTy8ddW2UIBZQXV/CqTox53abZXRlI6vxkb1WbTd/G/eKPaqi5madUvu89J3M/E0SVxbOSW6uKAgq+pDenUt1XVIFyv8A7ei9H8d2Hj1APVeFBgP9n1I7XZWIERdyb2yA3pO739T8zvTQZc+/4upT5oa6nLrBUb021DQXTuVxF34/X9HOolVYCDx56+gaeLPX99/HUTqooq+t1rOq7uZeDukpg1JDX2+el3vAoLKumidEn/uKomgnZM+d3VZ8bqvXmw74PFO9rXjTLyt1J028e8kJfq96zuSuItccm1nRkjra6amTZPEbVpy09IrLd/PC/2mE78nry0K1kal53uq/bFXG1wBXAXOFND1bxf1XtSdWq+hky+9Fy9wmfi7quBqagchS0u7l7h9Em3PzGyf9L1TOQSVtvBquqqt0vr6l5eIdhf6XSU2cuRIdzynz0cXRBR81PFNaug3qmB54pJEiX/z3vFJ4t9OSr/jYL+ztO4ng/0e9X3QcdzF/h4VFPVKIeheJ1a6ad+gxwqUaywE/4uLiXnbJ9jvJdixb0qCHeemZ38IpBVtTta0Ock9v1zMeVBGHUsDF4N2JDLakbQch+n8QmUMd+zY4RJJVD7yQmNbRKssq3GrjAhtaNUQVXReH6I2ujIfg9XqTK3kalukZcCWxJLLtk3utVKzDqqvpqCjCi+rhq4OynUCou1xMe/fO7lR3RAFhJSRonqvU6dOdYFy1UhM67pKan8QyW2rjOZtI9VhSS7DN/HJW2Z8jqmh77TqxyhLSBljqsenrBoNsOSZPXu2q9Wn+TrBVSDTG/jm559/Tvd2Vkay6mDqOZUBqoC+tt3NN9+cqu9ZVjWECm4rGKLvqYKdCiQlN0qtt/21/goIeDVuEvN2FNp+zZo1c9tcy2q6Gn0Fp3TxJPF2yKjPXQGxIUOGuMxL/a71uaud07b36ERctRxVR0jvXRejlE2qtjHYYIKp+WwSF2b33p/qSPrX2/W/uBBNMmMfkB6p/X6m5jedljYqre9TFw10gUTfT9WmUm1b/f78LzAoAKcr2grYKiCnCzr6feoquC4UJv4NpbZ9Smu7FyrKzlb2v4K3yvzWb1N10f2zGaNdWn9XatP0GeqipS4QqSau2lvVOvPP+shqwb6bad1PBtsWGdXuKJNW4xQoE0X7BwVo1dZruh4roK11Silwm5GyXb04hA3anJS3RUYe62TGeVC4HIshe6MdiYx2JK3HYRr3wMvOVaJMViUOhpssO4NXl2GlOPuPXKeNrxPEYPSh+I/SrqwgdQdr3bp1ml9bI1IrsJmY191T87Pi/ev9aCAWf3r/ia98pIcyC3XirZsGPNKgE88884wLJCUXEAtG20I/GG1/L9tF1G1P65rabZXeAGBy/6dRB0VBLmVShjOdwCogp+7HOvlSiQcV1U78fdB7Ujau/3v2sofTQ1eyli5d6oL1Ctp7Uuq6mrjxU2azPv/UDl6V3s9f31NlmSpopAC3stAvdMKo4JEyjxSUTen59ZzeIGj+VwPTUiIgPbQdlfWuz10DTumzVYBK782jiyr6PWoH5D9dgdsL8TLe9DtUFrEncQaq11VNQbGL/a3o966sOX0n/DNgM7PtzOjX9JbXdzuxYNMuJLnvXmq/n+FAB0wa6EvZfPq+6vfudVHyssXVLV7fYf/BCnS1+2Kktd0L1nZpoLeLbZ8kpc9IA0Vdd9117uKSemhocJXJkydf9GtGO12QVskM3XTMpu+Ourh5gdvktrl+oxoES5kU/lm3iX/z3vGJvof+2aVp+R2nZz+ZmbyArErcqNyVBl4VbTvtIxW41fGdBstMjrd9gr2HxMe+4d42AWlBmwPgYtGOZK20HofpnFBJJPfcc48rC6VjSpXAy07JFFleKkFBmcTRfJ0IJc4W82hkOXUv9+gAVrXwNHJ1WinYqxGrV69e7Zt2/Phx9xo6AUxPDbCMeP/KzEtclzU9dILtT9mFek96Pf9tmBpeYHzSpEkB070sstSOeq8TjeSC8inJnz+/u0/8vwpCaaR5ZbIqgJ/Yf//7XwsXCjZ17tzZBRCV9ajvbeLu8l6Q0v87sXbt2oDvaFoFe85gn6U/jTDvzwtOpOd3lprP0aNaNeourawsBTNTc+VMwSb9XpSdlJgyBvWbTm47qNtFaoKjF0ufs0bDVJcO1esJ9rnrxNm/3VO3b2X2XYgXkPXqIYres7qe+1NGujIlleUf7Peflt+K2oN9+/YF1O7V91nfE10dVVZmRsvo11TgQ4G4WbNmBdR1Va1c7fjTSm1bsO7Uqf1+hgN9L3VxQ98d9dLwr9Ge3G9IFwSVJX4x0tru6Xfhv4/UflzLX2z7lNLn6OnevbvLHFX7qZIOGfGa0SzxcYh+q+oF45UM8rZ5sP2CfvNqE1966aWA6crYVXvpbXuvt03i72Fagurp2U9mJu37VGpE71XttUZP9gK6ymDWxQ5dVEmpp4QuFCtjRb9n/++0gsGJx3C40L4ZiBS0OQBoR0Jz7HIx0nIcpuMi9dTTudwLL7xgM2fOdMmEAwcOtOwoyzJu1c1QQSxFxxVU1ImaMix0QhSMThKVuaQTSmUM6EBd2YvqZpxWymB444033MG/ihnryooOcJW1oaBR4lqKmfX+vasF9evXdwEDZfN4maQXo0WLFq5LtA74VStk06ZN7gRIQdbENeMuRN35lRmtoLbXXVYny9peyh70z4JOibJD9Pkq4Ksfm05O/Af+Sqlbo74fCtqoVp4+KwVddFOQUd8BdUfWwGPadvrx6rukwdmU7RZOgRGdTCqTTOvrn73sfR+UdaYakvqc9F1Ul1K9d//gUlooWKcsnbFjx7qGTieDCjyklCWnefpNqWuCtqO6Mqv7tH9Zh/RI7nNM3B1ZpS+8k9Tdu3en+JwKpKh7twrPK3tW33ed7CsrS9OVxaragPo96OKFBgXr27ev254Kpin4Hyzon5HUXuk96ab3nDjjVZ+1fhPa3trOqnuq77UCHBeqB6n3pQxiFZVX1xLt+BQgVl0gZQT6fw90oUvbq1atWq50h7eMaolquyUOkCRHAzjqYol2muvXr3cXuhRIUHkL7WDT2r6E6jUVxG7Xrp1772qDdbVX20DfybT+3tS26XutARxvuOEGF6DSdy21389woO+FvnPqCaDAWuILDNpHKcNb+wLtMxU80/77Yrs8prXd0zqqzdfgpFpPL4j66KOP2sVK7nP06Pep15k/f757ff/BUpGUPkNdXNV2Vdu3bt0697tV7wP/bS76TikIqzZM7ZO2u44t9H3UhSztf7TvUtkFlZ3xLlrp/1VmR98DBW0U0NQFGGVhpzabND37ycym/d+8efPcsYLXs0K/UQW69d70XbwQlRvRb0q/F5XkUv04HYPoIqn/byulYywgktDmAKAdCd2xS3ql5Ths1KhRLstWGbqFChVySV/K0h06dKhLkktPT/xIlmWBW0XJdZCuYKVKJOikVoG95OqV6qRay+rD0YeqOnMvvvhiurp5KZipOhqqzacDWb2+PnhlRKY2g/RiaWAkZVypVp4OmHVQriCK1y3uYig4pW2lgJAO0NW1UydG+lKnh7IgFRTVVQ2dtCoorJILaenGr3VRAEbroGwzBQBSE7j1Xl81kXU1RQF8va5OKnSQppNBpdZr3XTipmCcurT6p9qHAwU+VNNSwcjEQRFRUEpZhQpQKaCj96agqbKw1U05vfT90rZTMFBBFgX7VIpAwfNg9F3UttP3UBk9OslWbceMEOxz9KcBgPSb1El5asqF6AKLMvCUmaTsSX03lT2k76oKnHuDQGkwFgUM9N1TAFXfXwVeFLzUCW1m0m9Pn72CjOrKkTjYowHDVC7lueeecwEJXdAYM2aMC1ZcKHCr59J71oiaqpet96Xn0Im+gpH+dKKvz1yvo89TQS/tGBUgSLxsSnSSr++jvh+6eKOu9dq+yl7WdzgzZMZrKjCki3fqtq3nVTdrtSF6fg3ClRba/jqI0Prou6huynr+1H4/w4XaJZXTUXBU+yN/Co6qZvrgwYPd70jfMQ1Mpoupye2zUyOt7Z4u7mi7KlCnixwqRaJjA2UYXqzkPkf/4wa1n6qNraA8UqZjDpWn0cG32httTx1w6yKTRyWctE9QkFKfu/ZRCtzqM9b/al+kfZI+E12wUdul76A//bbU9un3rN+YLo7pf9RGpLYsVFr3k5nNC9wq6OrR/ljjQeg4OTX1bXUxUL8j/V51vKb9qrajgt+Jf1vJHWMBkYQ2BwDtSOiOXS5Gao7DNK6GEm8Um/BPHHz88cfdsY2S+HQO518+MNrFJFA1HEAIqJSAAjA6WVcgEshq6l6sgL66FAOJKTNYvWPSUwsZWUcBeF3AVTBYFwQBgDYHQDjj2AVhW+MWAPwp41FdyclmQ2ZTrw3VyfWnLDSVV1H3biAxlVVRrxjap/CiHjyJKSNbWbv+A+kBAG0OgHDAsQsiqlQC0k5d2FSnLCWqGayuxeFOAboLDYqkGoO6haNIX/9wsmzZMjdgirppq25yRowQn5nUtTsl+v1F2siWWdG26PeS3OCTojrEqq+YFTTAlbpUq7u/uuGo7qxqq6rLtWrSiraHtktyVOpH2bnhKtLXP1yoxpZKnag7ucqTqBQRwodqoqn2tbrNqZyAutbpptJMKk8ULsKp/QOQfrQ5AC4W7QgyhEolIDx9/vnnGgkmxduMGTMSIsGOHTsu+F6efPLJhHAV6esfTm666aaE3LlzJzRu3Djh119/TQh3F/rce/TokRBpsqJtqVChQorPr+9BVjl06FDCbbfdlvCnP/0pIU+ePAmXXHJJQufOnRO2b9/uW0brk9L66v2Es0hf/3Ch7722V/ny5RPefvvtUK8OEvn0008TGjRo4H7D2o9Urlw54amnnko4c+ZMWG2rcGr/AKQfbQ6Ai0U7goxAjdswppHPlVmSEo0YnBEDtWQ2DQj35ZdfpriMBvHRLRxF+voj/TQ4TEqUwalBliJJVrQtyloM1jXIowGvvFHmw4G2h7ZLcpR9rEE1w1Wkrz8QTSKt/QMQ2WhzANCORDcCtwAAAAAAAAAQZhicDAAAAAAAAADCTEQOTnb+/Hnbs2ePFSpUyGJiYkK9OkC2pPKvR48edaUCNKJ3JKNNAUKPNgUAbUpwHKcAocdxCoBQtSkRGbhV0DacRg8GsrPdu3db2bJlLZLRpgDhgzYFAG1KII5TgPDBcQqArG5TIjJwq0xb7w0WLlw41KsDZEtHjhxxF1C832Mko00BQo82BQBtSnAcpwChx3EKgFC1KREZuPXKIyhoS+AWCK1oKFdCmwKED9oUALQpwdtFzn2AyD5OGT16tL333nu2efNmi42Ntfr169uYMWPsyiuvDNqNunXr1rZ48WKbP3++tW/f3jdv165ddv/999vnn39uBQsWtB49erjnzpUrdeEd2hQgstqUyC5MCQAAAAAAEOZWrFhhDz74oK1Zs8aWLFliZ86csRYtWtjx48eTLDtp0qSgAZ1z585ZmzZt7PTp07Zq1Sp7/fXXbebMmTZ8+PAsehcAslpEZtwCAAAAAABECmXP+lPAtUSJErZ+/Xpr1KiRb/qGDRts/Pjxtm7dOitdunTA/3z66af2008/2WeffWYlS5a0mjVr2tNPP22PPfaYPfXUU5YnT54sez8AsgYZtwCAiKbMg+XLl9sbb7zh7vUYAAAACGeHDx9298WKFfNNO3HihN155502ZcoUK1WqVJL/Wb16tVWvXt0FbT0tW7Z09TI3btwY9HXi4+PdfP8bIhfnPtkPgVsAQMRSnbArrrjCmjRp4g5yda/Hmg4AAACEo/Pnz9uAAQOsQYMGds011/imDxw40NW+bdeuXdD/27dvX0DQVrzHmheM6t8WKVLEd9OASIhMnPtkTwRuAQARe+DSuXNnl3Wg7IOjR4/6shA0neAtAAAAwpFq3f744482b94837QPPvjAli1b5urbZqQhQ4a47F7vtnv37gx9fmQNzn2yLwK3AICI7CI0ePBgu+WWW2zBggVWt25dN6qu7vVY0x9++GHKJgAAACCs9OvXzxYtWmSff/65lS1b1jddQduff/7ZihYtarly5XI36dSpkzVu3Nj9rfIJ+/fvD3g+73Gw0gqSN29eK1y4cMANkYVzn+yNwcmQKVSbZ/PmzcnOP3nypO3cudMqVqxosbGxKT5XtWrVLH/+/JmwlgAi1RdffOHaENW1zZEj8BqkHiuzQN3MtJx3oAsAHo5TAGQk2hSkRkJCgvXv39/mz5/vxmWoVKlSwPzHH3/c7r333oBp6kk2ceJEa9u2rXtcr149e+aZZ+zAgQNuYDNZsmSJC8ZeddVVfBBRinOf7I3ALTKFgra1a9fOkOfSKJu1atXKkOcCEB327t3r7v1rgvnzpnvLAYA/jlMAZCTaFKS2PMLcuXPt/ffft0KFCvlq0qrurJKZlDEbLGu2fPnyviBvixYtXIC2e/fuNnbsWPccQ4cOdc+tzFpEJ859sjcCt8gUypJVwDU5mzZtsm7dutns2bMtLi7ugs8FAP5Kly7t7lUbTOUREtN0/+UAgOMUAJmFcx+kxrRp09x94t5gM2bMsLvvvjtVz5EzZ05XZuH+++932bcFChSwHj162MiRI/kQohjnPtkbgVtkCpU2SE2WrIK2ZNMCSKsbb7zRlVp59tln7d1337WvvvrKXYnWQY1G59XoucpM0HIAwHEKgMzEuQ9SWyohI/6nQoUK9tFHH7HRs+m5j8bz8C8Vd/78ec59ohyBWwBAxFG2wfjx491gDepeprrZHnU102MFdLUcAAAAAET6uU/nzp2tffv2bjwPlYZTL0MlrCgL+5133uHcJ0oRuAUARLRTp06l+BgAAAAAIlnHjh1dcHbw4MFuEGaPehlquuYjOhG4BQBEnHPnzrnaXtK6dWt38zJt1XXsww8/dPPbtWvHlWcAAAAAEU/BWZ3ffPHFF74ycSqjQC/D6EbgFgAQcZYvX24HDhywhg0b2gcffBBQ5+m+++6zRo0aubq3Wq5Zs2YhXVcAAAAAyAgK0iYe4A7R7f+d6QIAECEUkJURI0YEBG1Fj5966qmA5QAAAAAAiDQEbgEAAAAAAAAgzBC4BQBEHK970JNPPmnnz58PmKfHysT1Xw4AAAAAomGsD/UqfOONN9y9HiO6EbgFAEQcBWQvu+wy+/LLL12B/tWrV9vRo0fdvR5reokSJQjcAgAAAIgK7733nl1xxRXWpEkTu/POO929Hms6oheBWwBARBblnz59uvt76dKlVr9+fStcuLC7X7ZsmZs+bdo0RlgFAAAAEPEUnO3cubNVr149IGlFjzWd4G30yvDArdK0hw0bZpUqVbLY2FirXLmyPf3005aQkOBbRn8PHz7cSpcu7ZZp3ry5bdu2LaNXBQAQxTp27GiPPPKInT59OmB6fHy8m675AAAAABDJFGcbPHiw3XLLLbZgwQKrW7euFSxY0N3rsaY//PDDlE2IUrky+gnHjBnjspxef/11u/rqq23dunV2zz33WJEiReyhhx5yy4wdO9ZefPFFt4wCvAr0tmzZ0n766SfLly9fRq8SACAK6aryuHHjrE2bNtaqVSt3IfDkyZP28ccfu+k6kCF4CwAAACCSffHFF7Zz505X1/bs2bM2depU+/nnn12i5AMPPGBDhgxxPQ+1HGN8RJ8MD9yuWrXK1RfUibRUrFjRfbm+/vprX7btpEmTbOjQoW45mTVrlpUsWdJdKejSpUtGrxIAIMqvOufI8f86kNx3333Wvn17d9VZ+xmVVQAAAACASLR37153P2/ePLvxxhtd8NajnoYPPvhgwHKILhleKkFRftUb3Lp1q3v83XffuUFilA0lO3bssH379rnyCB5l49apU8fV5whG3V6PHDkScAMAZF/eVecnnngiIGgreqyrztrfaDkAAAAAiFQqMyovvPCCFS9e3F599VUXpNW9Hmu6/3KILhmecfv444+7wGq1atVclpOyop555hnr2rWrm6+grSjD1p8ee/MSGz16tI0YMSKjVxUAEKG8q8nXXHNN0PnedK46AwAAAIhkSnSUPHny2K5du9y93HvvvXbXXXdZoUKF3Lgf3nKILhmecfvWW2/ZnDlzbO7cufbtt9+6OraqNaj79FLm1OHDh3233bt3Z+g6AwAii3c1+ccffww635vOVWcAAAAAkezll1929wrOdu7c2fVWP3r0qLvXY2+wZm85RJcMD9yqvoayblWrtnr16ta9e3cbOHCgy5qVUqVKufv9+/cH/J8ee/MSy5s3rxUuXDjgBgDIvlTbSTXUn332WTt//nzAPD3WPkeDX2o5AAAAAIhUGohM/vGPf9gPP/zgSpQqLqZ7JayoZIL/coguGR64PXHiRJJ6gyqZ4J1Y60RaAVrVwfWotMLatWutXr16Gb06AIAopP3K+PHjbdGiRW4gMv+rznqs6ertwcBkAAAAACJZ5cqV3X1CQoJt377dPv/8c9fLXffbtm3zxdu85RBdMjxw27ZtW1fT9sMPP3QDx8yfP98mTJhgHTp0cPNjYmJswIABNmrUKPvggw/c1QLV5ChTpow72QYAf8qcvOGGG1zdnhIlSrh2YsuWLQHLNG7c2LUt/rf77ruPDRnlOnbsaO+8807Qq86arvkAAAAAEMkeeOABy5Urlw0dOtQFb3X+e8cdd7h7PR4+fLibr+UQfTI8cDt58mRXY0NfmLi4OHv44Yetb9++9vTTT/uWefTRR61///7Wp08fF5A5duyYLV682PLly5fRqwMgwq1YscIefPBBW7NmjS1ZssTOnDljLVq0sOPHjwcs17t3bzcQlXcbO3ZsyNYZWUfB2WBXnQnaAgAywsqVK11iipJMdGF4wYIFyS6ri8ZaZtKkSQHTDx486AZq1gXGokWLWq9evdz5DwAAqaHByFSCVCVGy5Yta6+88ort2bPH3euxpmu+N2gZokuujH5CZcXpYCXxAYs/HdCMHDnS3QAgJbqo42/mzJku83b9+vXWqFEj3/T8+fMnWycb0U3lEHS1GQCAjKYLxTVq1LCePXumeFFQvQx1kVkB3sQUtNVFZe8C9D333OMSWHTBEQCA1PASkyZOnOiSIz3KtNVYUyQuRa8MD9wCQGY6fPiwuy9WrFjA9Dlz5tjs2bNd8FaZMcOGDXPB3GDi4+Pdzb/ONgAAQGKtWrVyt5T89ttvrjfhJ598Ym3atAmYt2nTJncR+ptvvrHrr7/e10OxdevWrhZ7sEAvAADBKDirsqNTp051A5Gppq16u5NpG90I3AKIGCq6rhrZDRo0sGuuucY3/c4777QKFSq4k5/vv//eHnvsMVcH97333ku2bu6IESOycM0BAEC0Hpt0797dZTtdffXVSeZr0EyVR/CCttK8eXM3mLMGZ/bGAfHHBWYAQHIUpNU5MbIPArcAIoZq3WrgqS+//DJgurobeqpXr26lS5e2Zs2a+a5CJjZkyBAbNGhQQMZtuXLlMnntAQBAtBkzZozrpvrQQw8Fnb9v3z5X4smfllfPIc0LhgvMAAAg0wYnA4DM0K9fP1u0aJEbfEoF2FNSp04dd69Bq4LJmzevGyDE/wYAAJAWqrf/wgsvuPr7GsMjo+gCs0pDebfdu3fzwQAAkE0RuAUQ1hISElzQVoN+LFu2zCpVqnTB/9mwYYO7V+Ytot+5c+ds+fLl9sYbb7h7PUb2daER4O+++2433f928803ByzDCPAAUuOLL76wAwcOWPny5V0WrW6//PKLDR482CpWrOiWUe19LePv7Nmzrp1JblBVLjADAAAPpRIAhH15BI26/P7771uhQoV83QqLFClisbGxrhyC5muQj+LFi7satwMHDrRGjRrZtddeG+rVRyZTHWOdIO/cudM3TSfL48ePT3H0b2TvEeAVqJ0xY0ZAkMQfI8ADSA3VtlW9Wn8tW7Z00++55x73uF69enbo0CGXnVu7dm03TReiVRvX6yEEAACQHDJuAYS1adOmuW6CjRs3dhm03u3NN9/0FWf/7LPPrEWLFlatWjUXxOvUqZMtXLgw1KuOLAjadu7c2dU11uAvR48edfd6rOnJDU6H6KbR3zXabrABf/wDtcp0826XXHJJkhHg//GPf7igSsOGDd0I8PPmzbM9e/Zk0bsAEC6OHTvmevJ4vXl27Njh/t61a5e7YKzBUv1vuXPndu3KlVde6ZaPi4tzF4t69+5tX3/9tX311VeuJ1GXLl1czwAA2YfqV99www0uGUW1r9u3b+8GVPYoE79///6u/VCCirL5VT9b50L+1P60adPG8ufP755HgyMqkx9AdCLjFkDYl0pIiQYVW7FiRZatD8KDyiEoSH/LLbfYu+++606EFaxXUF+PFbx/+OGHrV27dpYzZ85Qry7CjEpq6ERHAdumTZu6QK8CMMII8AD8rVu3zpo0aeJ77A1u2qNHD1fbNjXmzJnjgrUaODVHjhxuH/Xiiy+yoYFsRucs6k2o4K0CrU888YRLPvnpp5+sQIEC7gKxbuPGjbOrrrrKlV6577773LR33nnHdwysoK0uEK1atcr27t1rd911l7to9Oyzz4b6LQLIBARuAQARWVdQ5RH69u1rVatWTVIqoU+fPi6Qq+WUrQ14lPmmEgqql61SKzppUpauArYK8jMCPAB/2odc6CKyP//9kadYsWKurBOA7E09evzp4o8uJKuUisq8KWtfCQieypUr2zPPPGPdunVzgV7V0f70009doFc9DkuWLGk1a9a0p59+2h577DF76qmnXG9EANGFwC0AIOIou0AUdFPWgbqIqUvZyZMn7eOPP7a///3vAcsBHnVP9qishmph68RIWbjKhkvvCPBeFp4cOXLE9QYAAABIjlcCQRd3UlqmcOHCLmgrXlkwBW39a2vff//9tnHjRrvuuuuSPEd8fLy7+R+nAIgcBG4BABFH2QmiGmA//vijLVq0KCDjVtM3b97sWw5IzuWXX26XXnqpbd++3QVu0zsCfOIBzgAAAJKjAQoHDBhgDRo0cJm2wfzvf/9z2bTqSeZRzyD/oK14j71BnIPV1h0xYgQfBhChGJwMABCxFJzVwa7/4GR6rOlAavz666/2+++/u/rIiUeA9zACPAAAyEiqdavkAw1+GoyyYtWrTLVuVQLhYqhnkDJ3vdvu3bsv6vkAZC0ybgEAESdxRoHqD3q3lJZD9hgBXtmzHm8EeHVD1E0ZJxoYSNmzqnH76KOP2hVXXOG6GSYeAX769Ol25swZRoAHAAAZRoMVqrfYypUrrWzZsknmKxlBxyKFChWy+fPnu4HHPDp++frrrwOW379/v29eMPQMAiIbGbcAgIjz3//+192rnpeyFerXr+/qf+le9b00Aq//csheI8CrvptX4021Z/X38OHD3eBj33//vd16661uULtevXpZ7dq13SB2/qUONAJ8tWrVXOmE1q1bW8OGDe2VV14J4bsCAACRTgkGCtoqGKvePBooNVimbYsWLdwgYx988IHly5cvYL56Bv3www8BZZ2WLFnijoOVnQsg+pBxCwCIOJdddplv9O6tW7faV1995QYiU3d31Qpr165dwHLIPi40Avwnn3xywedgBHgAAJAZ5RHmzp1r77//vsum9XqGFSlSxA2y6wVtT5w4YbNnz3aPvYHEdEyrC9CarwBt9+7dbezYse45hg4d6p6bevtAdCJwCwCIOH/605/c/ccff2wdOnSwypUr26lTp1xWwrhx49x0/+UAAACAUJo2bZrvIrO/GTNm2N13323ffvutrV271k1TGSd/Kv2kAXgVvFWZBfU6U/ZtgQIFrEePHjZy5MgsfCcAshKBWwBAxLnxxhvdwevJkyftww8/TDJfo+vmz5/fLQcAAACEWko9glLTa8hToUIF++ijjzJwzQCEM2rcAgAijrINatSo4RuMITFNv/baa91yAAAAAABEIgK3AICIc/r0aVu4cKH7O3E9L28QB83XcgAAAAAARCICtwCAiDN58mQ7f/686ypWqlSpJGUSNF3ztRwAAAAAAJGIwC0AIOJ8+eWX7v6XX35xJRFWr15tR48edfd6rOn+ywEAAAAAEGkYnAwAEHE0gq5cffXV9tZbb9n06dNtzpw5VrlyZff4+uuvt40bN/qWAwAAAIBIp1JwU6dOtZ9//tmd+zzwwAOWJ0+eUK8WMhGBWwBAxKlZs6YL1G7bts0KFixo586d8817+OGHfYOSaTkAAAAAiHSPPvqoTZw40c6ePeub9sgjj9jAgQNt7NixIV03ZB5KJQAAIk7p0qV9V5xVy7Zbt27273//293rsTcombccAAAAAERy0Pb555+3Sy65xGrUqGHVqlVz93qs6ZqP6ETGLQAg4lx22WW+vxMSEmz27NnultJyAAAAABBplJSiTNtcuXLZf//7X3fzp+maP2rUKMomRCEybgEAEeeHH35w9xUqVLBy5coFzCtfvryb7r8cAAAAAEQi1bRVeQTdYmJirHv37vbdd9+5ez325mk5RB8ybgEAEWfnzp3u/pdffrE2bdpYhw4d7NSpU5YvXz5XqP/DDz8MWA4AAAAAItGmTZvcvYK0J06ccOc8MmvWLHvllVcsf/78rheitxyiC4FbAEDE0Qiq0rJlS/vkk08CCvSrq9Bf/vIXW7JkiW85AAAAAIhEq1atcvd16tTxBW09enzDDTfY119/7VsO0YXALQAg4jzwwAM2ePBgF7Rt3bq1y7qNjY21kydPumzbjz76yHLkyOGWAwAAAIBIt23bNpewokQVjx5v3749pOuFzEWNWwBAxMmZM6cVLFjQ/b1+/Xp38KLsW93rsWi+lgMAAACASHXFFVe4+99//93Kli3ryiPs2bPH3evxwYMHA5ZDdCHjFgAQcb744gs7cuSIde3a1d58803r27evb56Ct3feeafNnTvXLde4ceOQrisAAAAApNe//vUvK1SokPv7v//9b8C5j3+iipZD9CHjFgAQcfbu3evup0+f7gK4Dz74oLVo0cLd67Gm+y8HAAAAAJFIPQlVx1Y0CFmtWrXstttuc/fnz5930zXf65GI6ELgFkBYGz16tNsJ6QpjiRIlrH379rZly5aAZU6dOuUCdsWLF3c7q06dOtn+/ftDts7IfKVLl3b3L730kl111VU2ZcoU+/TTT929Hmu6/3IAAAAAEKk0+JjOixW4/fbbb+2tt95y93rsDU6G6ETgFkBYW7FihQvKrlmzxpYsWWJnzpxxmZXHjx/3LTNw4EBbuHChvf3222551fvp2LFjSNcbmevGG290gfwhQ4bY1Vdf7QK2//znP929Hj/xxBNuvpYDAAAAgEin4OzRo0ddMlP16tXdvR4TtI1u1LgFENYWL14c8HjmzJkuIKcBqBo1amSHDx+21157zdUzbdq0qVtmxowZFhcX54K9devWDdGaI7Pp6rIsW7bMPvzwQ9/02NhYNj4AAACAqKMepvPnzw/1aiALkXELIKIoUCvFihVz9wrgKgu3efPmvmWqVatm5cuXt9WrVwd9jvj4eFcH1f+GyKJBx1SYP5iYmBh3f+DAAbccAAAAAACRiMAtgIihwusDBgywBg0a2DXXXOOm7du3z/LkyWNFixYNWLZkyZJuXnJ1c4sUKeK7lStXLkvWHxnnt99+c/etWrVyAVz/wckUsNV0/+UAAAAAAIg0BG4BRAwF5X788UebN2/eRT2P6qIqc9e77d69O8PWEVnDy7ZVMF9Be//ByfT43LlzAcsBAJAeK1eutLZt21qZMmVcj44FCxb45qnHz2OPPebqDBYoUMAtc9ddd7la+/4OHjxoXbt2tcKFC7t9VK9evezYsWN8IAAA4III3AKICP369bNFixbZ559/bmXLlvVNL1WqlJ0+fdoOHToUsPz+/fvdvGDy5s3rTp78b4gsl112mbv/5JNPXNmMhx9+2KZOneru9VhBXP/lAABIDw2GWqNGDXdhMLETJ064Eb2HDRvm7t977z3bsmWL3XrrrQHLKWi7ceNGN8iqjmUUDO7Tpw8fCAAAuCAGJwMQ9gNQ9e/f3xVgX758uVWqVClgfu3atS137ty2dOlS69Spk5umk6Zdu3ZZvXr1QrTWyGz+AVnVKB43bpzvcb58+YIuBwBAWqn0jld+JzGVW1Iw1t9LL71kf/7zn91xiOrtb9q0yQ20+s0339j111/vlpk8ebK1bt3a7buUpQsAAJAcArcAwr48wty5c+3999+3QoUK+erW6mQpNjbW3avL4aBBg1ympbJnFehV0LZu3bqhXn1kkh9++MH396lTp5IMPue/nGrfAgCQFVSCSSUVvNr7GihVf3tBW9GAqjly5LC1a9dahw4dkjyH9mP++zIGUQ29bdu22dGjR9P9/wrg+9+nl46Fq1SpclHPAQCILARuAYS1adOmufvGjRsHTJ8xY4bdfffd7u+JEye6EyBl3OpEp2XLlq7bPKLXf/7zn4CsWtUUvPzyy930WbNm+Wrb+i8HAEBm0oVE1by94447fGWYdMG5RIkSAcvlypXLXWxOaRDVESNG8GGFUdC2atWqGfJc3bp1u+jn2Lp1K8FbAMhGCNwCCPtSCReirvGqPRes/hyikwYlE9Ux1uc/fvx43zyV08iZM6c7IfaWAwAgM2mgsttuu80dt3gXnS9mEFX1JPLPuC1XrlwGrCXSw8u0nT17tsXFxaXrOU6ePGk7d+60ihUruh5j6aFsXQV+LybzF6GlizKqhb1582b3Pahfv76NGTPGrrzyyoALQIMHD3aDMfsnpJQsWdK3jEqx3H///W7sj4IFC1qPHj3cc+uiEIDokym/7N9++81dbf74449d0f4rrrjCZcd5XYR0QPPkk0/aq6++6gYUatCggTvAodsHACA1vC6oOqDViZC6ou7du9dKly7tymR4A9N5ywEAkNlB219++cWWLVsWMOip9kcHDhwIWP7s2bN28ODBFAdR1Q3hRUHbWrVqpfv/dc6L7G3FihWuDNwNN9zg2oEnnnjClfT66aefrECBAm6ZgQMH2ocffmhvv/22KwmnAZo7duxoX331lZt/7tw5a9OmjWs/Vq1a5Y5/1fNMY348++yzIX6HACIicPvHH3+4nVKTJk1c4FZdWNW95JJLLvEtM3bsWHvxxRft9ddfd5lRGolVV5LUYPkPKoPwRZ0nAKHkZRRon1OhQgUbOXKk3XLLLW607i5duriLgv7LAQCQmUFbHRsr+6148eIB83UxUfuk9evXuwFVRcFd9QipU6cOHwqQjWigQn8zZ850pVTUPjRq1MjVyH7ttdfc+B5NmzZ1yygBThcN1qxZ48bv+PTTT13c5LPPPnNZuDVr1rSnn37aJc499dRTlidPnhC9OwCZJcPPaJXqr648amA8/qPAK9t20qRJNnToUGvXrp2bpnqEanQWLFjgTrgR3qjzBCDUVPN41KhR9qc//cmVROjbt69vnoK1mq7eH4lrIwMAkBbHjh2z7du3+x7v2LHDNmzY4GrUqpdH586d7dtvv3UXDpUJ59Wt1XwFUBRwufnmm6137942ffp0F+hVBp3OecqUKcOHAWRjCtR67YUogKs2QgMYeqpVq2bly5d3vcsUuNV99erVA0onKAlOpRM2btxo1113XZLXYcDDyKOe6yqpkRHlV/Qdyp8/fyasJSI2cPvBBx+4huOvf/2r6wqgk+cHHnjAHax4Bzs6oPFvjNQFQFec1QgFC9zS0IQX6jwBCDUFZNWjQ8HZ1q1bu5I8qgmmXhs6wf7oo49cBgOBWwDAxVi3bp3rSejxas+qpqSy23TuI8p686fsW28fNGfOHBesbdasmW8wVfU+BJB9Ket+wIABrrfyNddc46YpTqILPolLfSlI610U0r1/0Nab780LhgEPI4+Ctl4vjYulCwIXU+YFURi41QjeqlergxrVbPnmm2/soYcecg2QDnC8xiRYY0NDE1mo8wQgVDT4mDKXdPKrk2MFaj3eFWXti7QcAADppeBrSgOlpmYQVWXTqeszAHhU6/bHH3+0L7/8MtM3CgMeRh5lySrgeqHBClMzaKKeC5EtV2ZcOdIgZF5hbKXqq0HSCbYCt+lBQwMASEwDNbz77rvuQqEGhPEo03b8+PFuPgAAABBOlIGv8iorV660smXL+qZrwLHTp0+7utj+Wbf79+/3DWao+6+//jrg+TTfmxcMAx5GHiWipCZL9mKT6ZBNA7eq9XTVVVcl+TLp5Nq/MVHjomU9epy4i5GHhgYAsrfk6jyprpNG3dWADZqvK8qq/6VMW9UcDIY6TwAAAMhqytDv37+/zZ8/35YvXx4wFpCoa3zu3Llt6dKlrleZbNmyxXbt2uUGOhTdP/PMM3bgwAGXrCBLliyxwoULJ4nDAIgOGR64VY0WNS7+tm7d6kb9FjVOCt6qMfICtUeOHLG1a9e6gtoAACRGnScAAABEenkElU15//33rVChQr5SkRrzRwNM6b5Xr16uN5lKrCgYq0CvgrVKTJAWLVq4AG337t1t7Nix7jk08LueWwlvAKJPhgduBw4caPXr13elEm677TaXxv/KK6+4m8TExLgi3BoNvEqVKi6QO2zYMDeqavv27TN6dQAAUYA6TwAAAIhkGn9BEg+eO2PGDLv77rvd3xMnTvQNYqhB2jXw+9SpU33LqleZyiwo6U0B3QIFCriSlCNHjszidwMgYgO3N9xwg0v9V11aNR4KzE6aNMm6du3qW+bRRx+148ePW58+fVz9loYNG9rixYvdaOAAACRGnScAAABEstQMZqiYyJQpU9wtOerN7D8wL4DoluGBW7nlllvcLTnKulVQl6tCAAAAAAAAAJBUjiDTAAAAAAAAAAAhROAWAAAAAAAAAMIMgVsAAAAAAAAACDMEbgEAAAAAAAAgzBC4BQAAAAAAAIAwQ+AWAAAAAAAAAMIMgVsAAAAAAAAACDMEbgEAAAAAAAAgzBC4BQAAAAAAAIAwQ+AWAABEjZUrV1rbtm2tTJkyFhMTYwsWLAiYn5CQYMOHD7fSpUtbbGysNW/e3LZt2xawzMGDB61r165WuHBhK1q0qPXq1cuOHTuWxe8EAAAAQHZH4BYAAESN48ePW40aNWzKlClB548dO9ZefPFFmz59uq1du9YKFChgLVu2tFOnTvmWUdB248aNtmTJElu0aJELBvfp0ycL3wUAAAAAmOViIwAAgGjRqlUrdwtG2baTJk2yoUOHWrt27dy0WbNmWcmSJV1mbpcuXWzTpk22ePFi++abb+z66693y0yePNlat25t48aNc5m8AAAAAJAVyLgFAADZwo4dO2zfvn2uPIKnSJEiVqdOHVu9erV7rHuVR/CCtqLlc+TI4TJ0g4mPj7cjR44E3AAAAADgYhG4BQAA2YKCtqIMW3967M3TfYkSJQLm58qVy4oVK+ZbJrHRo0e7ALB3K1euXKa9BwAAAADZB6USAIQ11ZZ8/vnnbf369bZ3716bP3++tW/f3jf/7rvvttdffz3gf1SvUl2dASArDBkyxAYNGuR7rIxbgrehpQHnjh49mu7/V8kM//v0KlSokFWpUuWingMAAADZF4FbABEx0FDPnj2tY8eOQZe5+eabbcaMGb7HefPmzcI1BBApSpUq5e73799vpUuX9k3X45o1a/qWOXDgQMD/nT171g4ePOj7/8TU5tDuhFfQtmrVqhnyXN26dbvo59i6dSvBWwAAAKQLgVsAETvQkEcBk+QCKgDgqVSpkmsrli5d6gvUKjtWtWvvv/9+97hevXp26NAhl+Vfu3ZtN23ZsmV2/vx5VwsX4c/LtJ09e7bFxcWl6zlOnjxpO3futIoVK1psbGy6nkPZugr8XkzmLwAAALI3ArcAIt7y5ctdTcpLLrnEmjZtaqNGjbLixYsnu7wGEtLNw0BCQPQ4duyYbd++PWBAsg0bNrgateXLl7cBAwa4NkLd1xXIHTZsmJUpU8ZXgkWBPmXx9+7d26ZPn25nzpyxfv36WZcuXdxyiBz6LGvVqpXu/2/QoEGGrg8AAACQVgRuAUQ0BVhUQkEBmJ9//tmeeOIJl6GrkeFz5syZ7EBCI0aMyPJ1BZD51q1bZ02aNPE99mrP9ujRw2bOnGmPPvqoK8HSp08fl1nbsGFDVxM7X758vv+ZM2eOC9Y2a9bMcuTIYZ06dbIXX3yRjw8AAABAliJwi3QpVTDGYg9tNduTI2RbUK+v9UD2piw4T/Xq1e3aa6+1ypUruyxcBV2CYSAhIHo1btzYEhISkp0fExNjI0eOdLfkKDt37ty5mbSGAAAAAJA6BG6RLn1r57G4lX3NVoZuA8b933oA/i6//HK79NJLXVfp5AK3DCQEAAAAAADCHYFbpMvL60/b7cNnWly1aiHbgps2b7aXx99pt4ZsDRCOfv31V/v9998DRowHAAAAAACINARukS77jiXYyaJVzcr8/6Nyh8LJfefdeiD7DjSkm2rVqv6kRopXjVvVr7ziiiusZcuWIV1vAAAQ+VauXGnPP/+8rV+/3vbu3Wvz58/3DWYoKs3y5JNP2quvvurqZmtQu2nTprkBED0HDx60/v3728KFC311s1944QUrWLBgiN4VAACIFKErUAoAqRxo6LrrrnM3b6Ah/T18+HA3+Nj3339vt956q1WtWtV69epltWvXti+++MKVQwAAALgYGsywRo0aNmXKlKDzx44d6wYvnD59uq1du9YKFCjgLh6fOnXKt0zXrl1t48aNtmTJElu0aJELBmuARAAAgAsh4xZARA809Mknn2Tp+gAAgOyjVatW7haMjk8mTZpkQ4cOtXbt2rlps2bNspIlS9qCBQvcAKqbNm2yxYsX2zfffGPXX3+9W2by5MnWunVrGzdunJUpUyZL3w8AAIgsZNwCAAAAQBqpfNO+ffusefPmvmlFihSxOnXq2OrVq91j3RctWtQXtBUtr5IJytANJj4+3o4cORJwAxAdlHHftm1bd9EmJibGXeRJXCauX79+VrZsWYuNjbWrrrrKZfT7U0b/gw8+aMWLF3clV1R+Zf/+/Vn8TgBkFQK3AAAAAJBGCtqKMmz96bE3T/clSpQImJ8rVy5Xp99bJrHRo0e7ALB3K1euHJ8NkE3Kr6gsnLL0Z8+e7TL2BwwY4AK5H3zwgW+ZgQMHuprZb7/9tq1YscL27NljHTt2zMJ3ASArEbgFAAAAgDAxZMgQO3z4sO+2e/fuUK8SgAyi0iujRo2yDh06BJ2/atUq69GjhysXV7FiRVcPW4Her7/+2s1Xm/Daa6/ZhAkTrGnTpm58jxkzZrj/W7NmDZ8TEIUI3AIAAABAGpUqVcrdJ+6irMfePN0fOHAgYP7Zs2ft4MGDvmUS0wCrhQsXDrgByB7q16/vsmt/++03V0f7888/t61bt1qLFi3c/PXr19uZM2cCSrRUq1bNypcv7yvRkhjlV4DIRuAWAAAAANKoUqVKLvi6dOlS3zTVo1Xt2nr16rnHuj906JALtniWLVtm58+fd7VwAcCfBi9UXVvVuM2TJ4/dfPPNrqxCo0aN3HyVWNF01c5OrkRLYpRfASJbrlCvAAAAAACEIw0UtH379oAByTZs2OBq1CrDTfUn1e25SpUqLpA7bNgwN+hQ+/bt3fJxcXEu8NK7d283wJAy5VSvskuXLm45AEgcuFXJA2XdVqhQwQ1mpoHI1F74Z9mmtfyKauf6X2CidjYQOQjcAgAAAEAQ69atsyZNmvgee8EP1aCcOXOmPfroo26wIdWhVGZtw4YN3cBC+fLl8/3PnDlzXLC2WbNmliNHDjcC/Isvvsj2BhDg5MmT9sQTT9j8+fOtTZs2btq1117rLhaNGzfOBW6V5X/69GnX3vhn3fqXaAlWfkU3AJGJwC0AAAAABKEBglRnMjkxMTE2cuRId0uOsnPnzp3L9gWQImXk66YLPP5y5szpyquIBiPLnTu3K9Gii0CyZcsW27Vrl69EC4DoQuAWAAAAAIBklCoYY7GHtprtCd0QMXp9rQeiu/zKTTfdZI888ojFxsa6UgkrVqywWbNm2YQJE9zyRYoUsV69ernsf/2PBi/s37+/C9rWrVs3hO8MQGYhcAsAAAAAQDL61s5jcSv7mq0M3SaK+7/1QHSXX5k3b56rSdu1a1c7ePCgC94+88wzdt999/n+Z+LEib6yK/Hx8dayZUubOnVqSN4PgMxH4BYAAAAAgGS8vP603T58psVVqxaybbRp82Z7efyddmvI1gBZUX5FdWpnzJiR4nOohvaUKVPcDUD0I3ALAAAAAEAy9h1LsJNFq5qVqRmybXRy33m3HgCA7CV0RXoAAAAAAAAAAEERuAUAAAAAAACAMEOpBAAAAAAAACALbNu2zY4ePZru/9+0aVPAfXoVKlTIqlSpclHPgcxH4BYAAAAAAADIgqBt1apVM+S5unXrdtHPsXXrVoK3YY7ALQAAAAAAAJDJvEzb2bNnW1xcXLqe4+TJk7Zz506rWLGixcbGpus5lK2rwO/FZP4iaxC4BQAAAAAAALKIgra1atVK9/83aNAgQ9cH4YvByQAAAAAAAAAguwVun3vuOYuJibEBAwb4pp06dcoefPBBK168uBUsWNA6depk+/fvz+xVAQAAAAAAAICIkKmB22+++cZefvllu/baawOmDxw40BYuXGhvv/22rVixwvbs2WMdO3bMzFUBAAAAAAAAgIiRaYHbY8eOWdeuXe3VV1+1Sy65xDf98OHD9tprr9mECROsadOmVrt2bZsxY4atWrXK1qxZk1mrAwAAAAAAAAARI9MCtyqF0KZNG2vevHnA9PXr19uZM2cCplerVs3Kly9vq1evDvpc8fHxduTIkYAbAAAAAAAAAESrXJnxpPPmzbNvv/3WlUpIbN++fZYnTx4rWrRowPSSJUu6ecGMHj3aRowYkRmrCgAAAAAAAADRn3G7e/du+9vf/mZz5syxfPnyZchzDhkyxJVY8G56DQDZw8qVK61t27ZWpkwZN9DhggULAuYnJCTY8OHDrXTp0hYbG+uy+bdt2xay9QUAAAAAAAjLjFuVQjhw4IDVqlXLN+3cuXMu+PLSSy/ZJ598YqdPn7ZDhw4FZN3u37/fSpUqFfQ58+bN624Asp/jx49bjRo1rGfPnkEHMRw7dqy9+OKL9vrrr1ulSpVs2LBh1rJlS/vpp58y7OIRMp+C7UePHk33/2/atCngPr0KFSpkVapUuajnAAAAAAAgLAO3zZo1sx9++CFg2j333OPq2D722GNWrlw5y507ty1dutQ6derk5m/ZssV27dpl9erVy+jVARDhWrVq5W7BKNt20qRJNnToUGvXrp2bNmvWLFd6RZm5Xbp0yeK1RXqDtlWrVs2QjdetW7eLfo6tW7cSvAUiXKmCMRZ7aKvZnkwbzuGC9PpaDwAAACBsArfKVrrmmmsCphUoUMCKFy/um96rVy8bNGiQFStWzAoXLmz9+/d3Qdu6detm9OoAiGI7duxwtbH9BzssUqSI1alTxw12mFzgVgMe6uZhwMPQ8jJtZ8+ebXFxcel6jpMnT9rOnTutYsWKrmRGeihbV4Hfi8n8BRAe+tbOY3Er+5qtDN06xP3fegAAAABhNTjZhUycONFy5MjhMm4VPFG35qlTp4ZiVQBEMG9AQ2XYpnawQ2HAw/CkoK1/mZ20atCgQYauD4DI9fL603b78JkWV61ayNZh0+bN9vL4O+3WkK0BAAAAIl2WBG6XL18e8Fh1J6dMmeJuAJDVNOChsv79M25VxgUAEB32HUuwk0WrmpWpGbJ1OLnvvFsPAAAAIL1CV/gLAC6SN6ChBjf0l9Jgh6LBDlWmxf8GAAAAAAAQTgjcAohYlSpVcgFaDXbonz27du1aBjsEAAAAAAARLSQ1bgEgtY4dO2bbt28PGJBsw4YNbnDD8uXL24ABA2zUqFFWpUoVF8gdNmyYlSlTxtq3b89GBgAAAAAAEYvALYCwtm7dOmvSpInvsVebtkePHjZz5kx79NFH7fjx49anTx87dOiQNWzY0BYvXuxqaQMAAAAAAEQqArcAwlrjxo0tISH5wV1iYmJs5MiR7gYAAAAAQDgrVTDGYg9tNdsTuuqlen2tB8IfgVsAAAAAAAAgC/StncfiVvY1Wxm6zR33f+uB8EfgFgAAAADS4dy5c/bUU0/Z7Nmzbd++fa7O/t13321Dhw51vYJEPYeefPJJe/XVV11ZpwYNGti0adNcfX4A2cvKlSvt+eeft/Xr19vevXtt/vz5Scbm2LRpkz322GO2YsUKO3v2rF111VX27rvvuvE95NSpUzZ48GCbN2+excfHW8uWLW3q1KlWsmTJEL0rpNXL60/b7cNnWly1aiHbeJs2b7aXx99pt4ZsDZBaBG4BAAAAIB3GjBnjgrCvv/66XX311a42/z333GNFihSxhx56yC0zduxYe/HFF90y3kCqCrT89NNP1OQHshmNzVGjRg3r2bOndezYMcn8n3/+2Y3Z0atXLxsxYoQVLlzYNm7cGNBWDBw40D788EN7++23XVvTr18/91xfffVVFr8bpNe+Ywl2smhVszI1Q7YRT+4779YD4Y/ALQAAAACkw6pVq6xdu3bWpk0b97hixYr2xhtv2Ndff+3Ltp00aZLLwNVyMmvWLJcZt2DBAuvSpQvbHchGWrVq5W7J+fvf/26tW7d2F3w8lStX9v19+PBhe+2112zu3LnWtGlTN23GjBkWFxdna9assbp162byOwCQ1UJXCRkAAAAAIlj9+vVt6dKltnXrVvf4u+++sy+//NIXmNmxY4crodC8eXPf/yhDrk6dOrZ69eqgz6muz0eOHAm4AYh+58+fd5m0VatWdVn5JUqUcG2FLvJ4VGLhzJkzAW1KtWrVXBkF2hQgOhG4BQAAAIB0ePzxx13WrAInuXPntuuuu84GDBhgXbt2dfMVtJXEtSf12JuX2OjRo11w17uVK1eOzwbIBg4cOGDHjh2z5557zm6++Wb79NNPrUOHDq4MgurditqNPHnyWNGiRQP+lzYFiF6USkCanThxwt1/++236d56J0+etJ07d7ruZLGxsel6DhVtBwAAAELlrbfesjlz5rhuy6pxu2HDBhe41SBlPXr0SNdzDhkyxAYNGuR7rIxbgrdA9si4FZVVUR1bqVmzpivJMn36dLvpppvS9by0KUBkI3CLNNu8ebO77927d1hsvUKFCoV6FQAAAJANPfLII76sW6levbr98ssvLmtWgdtSpUq56fv377fSpUv7/k+PFZAJJm/evO4GIHu59NJLLVeuXHbVVVcFTFf9WpVgEbUpp0+ftkOHDgVk3apN8dqbxGhTgMhG4BZp1r59e3evLmH58+dPd7Zst27dbPbs2W5HdDFB2ypVqqT7/wEA2ctTTz3lRmn2d+WVV/ouSp46dcoGDx5s8+bNc3UmVWNu6tSpSbo5A4DXEy1HjsDqczlz5vRlzlWqVMkFU1QH1wvUKoN27dq1dv/997MRAfioBMINN9xgW7ZsCdgqqqFdoUIF93ft2rVdWRa1KZ06dXLTtPyuXbusXr16bE0gChG4RbquBN57770ZsuUUtK1VqxafAgAgy6g782effeZ7rOwWj7omamCQt99+29WW7Nevn6st99VXX/EJAUiibdu29swzz7iBgdS2/Pvf/7YJEyZYz5493fyYmBhXOmHUqFEu2UCB3GHDhrlSCl4yBIDsQzVst2/f7nusAQxVYqVYsWKuHVEW/+23326NGjWyJk2a2OLFi23hwoW2fPlyt7yOTXr16uXKqeh/ChcubP3793dB27p164bwnQHILARuAQBAtqJAbbDuhIcPH7bXXnvN1aps2rSpmzZjxgx3kXHNmjWcEAFIYvLkyS4Q+8ADD7iBhRSQ7du3rw0fPty3zKOPPmrHjx+3Pn36uO7NDRs2dMGYfPnysUWBbGbdunUuIOvx6lmrtMrMmTPdYGSqZ6tyKw899JDrFfTuu++6dsMzceJEl+mvjFv/3kEAohOBWwAAkK1s27bNBVcUNFGGik6OlOWyfv16O3PmjDVv3ty3rMoCad7q1auTDdzqpEk3j7pBA8geVLZr0qRJ7pYcZd2OHDnS3QBkb40bN7aEhIQUl1HGvpe1H4yOX6ZMmeJuAKIfgVsAQMiVKhhjsYe2mu0JrBOYlfT6Wg9Etzp16riMFmWw7N2719W7vfHGG+3HH3+0ffv2ufpy/oN9iOrbal5yFPhNXDcXAAAAAC4WgVsAQMj1rZ3H4lb2NVsZunWI+7/1QHRr1aqV7+9rr73WBXI14Mdbb71lsbGx6XrOIUOG+Lo6ehm35cqVy5D1BQAAAJB9EbgFAITcy+tP2+3DZ1pctWohW4dNmzfby+PvtFtDtgYIBWXXVq1a1Q0U8pe//MVOnz7talD6Z93u378/aE1cT968ed0NAAAAADISgVsAQMjtO5ZgJ4tWNStTM2TrcHLfebceyF40uvPPP/9s3bt3t9q1a1vu3Llt6dKlbsAP2bJli+3atcvVwgUAAACArETgFgAAZBsPP/ywtW3b1pVH2LNnjz355JOWM2dOu+OOO6xIkSLWq1cvV/agWLFiVrhwYevfv78L2iY3MBkAAAAAZBYCtwAAINv49ddfXZD2999/t8suu8waNmxoa9ascX/LxIkTLUeOHC7jNj4+3lq2bGlTp04N9WoDAAAAyIYI3AIAgGxj3rx5Kc7Ply+fTZkyxd0AAAAAIJQI3AIAAAAAAACZ7MSJE+7+22+/TfdznDx50nbu3GkVK1a02NjYdD3Hpk2b0v36yFoEbgEAAAAAAIBMtnnzZnffu3fvsNjWhQoVCvUq4AII3AIAAAAAAACZrH379u6+WrVqlj9//nRny3br1s1mz55tcXFxFxW0rVKlSrr/H1mDwC0AAAAAAACQyS699FK79957M+S5FLStVatWhjwXwleOUK8AAAAAAAAAACAQgVsAEe+pp56ymJiYgJu6ngAAAAAAAEQqSiUAiApXX321ffbZZ77HuXLRvAFAdsRozQAAAIgWRDYARAUFakuVKhXq1QAAhBijNQMAACBaELgFEBW2bdtmZcqUsXz58lm9evVs9OjRVr58+aDLxsfHu5vnyJEjWbimAIDMxGjNADISWfwAgFAicAsg4tWpU8dmzpxpV155pe3du9dGjBhhN954o/34449WqFChJMsrqKtlAADRh9GaAWQksvgBAKFE4BZAxGvVqpXv72uvvdYFcitUqGBvvfWW9erVK8nyQ4YMsUGDBgVk3JYrVy7L1hcAAACRgSx+AEAoEbgFEHWKFi1qVatWte3btwednzdvXndDeKALIgAACFdk8QMAQonALYCoc+zYMfv555+te/fuoV4VpAJdEAEAAAAASIrALYCI9/DDD1vbtm1deYQ9e/bYk08+aTlz5rQ77rgj1KuGVKALIgAAAAAASRG4BRDxfv31Vxek/f333+2yyy6zhg0b2po1a9zfCH90QQQAAAAAICkCtwAi3rx580K9CgAAAAAAABkqR8Y+HQAAAAAAAADgYhG4BQAAAAAAAIAwQ+AWAAAAAAAAAKI9cDt69Gi74YYbrFChQlaiRAk3WviWLVsCljl16pQ9+OCDVrx4cStYsKB16tTJ9u/fn9GrAgAAAAAAEBZWrlxpbdu2tTJlylhMTIwtWLAg2WXvu+8+t8ykSZMCph88eNC6du1qhQsXtqJFi1qvXr3s2LFjWbD2AKIicLtixQoXlNWI7kuWLLEzZ85YixYt7Pjx475lBg4caAsXLrS3337bLb9nzx7r2LFjRq8KAAAAAABAWFBcpEaNGjZlypQUl5s/f76LqSjAm5iCths3bnTxlkWLFrlgcJ8+fTJxrQFEVeB28eLFdvfdd9vVV1/tGqSZM2farl27bP369W7+4cOH7bXXXrMJEyZY06ZNrXbt2jZjxgxbtWqVa5gAAAAAIFL89ttv1q1bN9ebMDY21qpXr27r1q3zzU9ISLDhw4db6dKl3fzmzZvbtm3bQrrOAEKjVatWNmrUKOvQoUOKbUr//v1tzpw5ljt37oB5mzZtcjGXf/zjH1anTh1r2LChTZ482ebNm+cS4gBEn0yvcatArRQrVszdK4CrLFwdsHiqVatm5cuXt9WrVwd9jvj4eDty5EjADQAAAABC6Y8//rAGDRq44MrHH39sP/30k40fP94uueQS3zJjx461F1980aZPn25r1661AgUKWMuWLV35OADwd/78eevevbs98sgjLhkuMcVMVB7h+uuv901TbCVHjhyufQmGeAoQ2XJldqMzYMAAdzBzzTXXuGn79u2zPHnyuMbGX8mSJd285OrmjhgxIjNXFQAAAADSZMyYMVauXDnXg9BTqVKlgGxb1accOnSotWvXzk2bNWuWO/dRbcsuXbqwxQEEtCm5cuWyhx56KOhWUcxEYwn50/JKlCOeAkSnTM24Va3bH3/80aXtX4whQ4a4zF3vtnv37gxbRwAAAABIjw8++MBlvv31r391wZTrrrvOXn31Vd/8HTt2uGCKf2/DIkWKuC7O9DYE4E+9k1944QVXblKDkmUU4ilAZMu0wG2/fv1coezPP//cypYt65teqlQpO336tB06dChg+f3797t5weTNm9eNmOh/AwAAAIBQ+s9//mPTpk2zKlWq2CeffGL333+/y5R7/fXX3XwvA04ZtmnpbajgrndTRi+A6PfFF1/YgQMHXBlJZdHq9ssvv9jgwYOtYsWKbhnFTLSMv7Nnz9rBgweJpwBRKsMDt+oOpKCtRkFctmxZQFch0WBkqgG1dOlS37QtW7a4Aczq1auX0asDAAAAAJlWGq5WrVr27LPPumxbjezeu3dvV882vciOA7In1bb9/vvvbcOGDb5bmTJlXL1bXRgSxUyUBOcN/i6Ku6gtUiY/gOiTKzPKI8ydO9fef/99K1SokO9Ksq4WaxRV3ffq1csGDRrk6rAoe1YjJqoBqlu3bkavDgAAAABkitKlS9tVV10VMC0uLs7effdd97fXo1C9C7WsR49r1qyZbG9D3QBEn2PHjtn27dsDyqkoQKvYiDJtixcvHrC8kt7Ujlx55ZW+9uXmm2/2XSDSwO9KnFO9bAV5AUSfDM+4VVch1aFt3LixOzjxbm+++aZvmYkTJ9ott9xinTp1skaNGrmG6L333svoVQEAAACATKNBmNV70N/WrVutQoUK7m/1PtS5jn9vwyNHjrjR3+ltCGQ/69atc9n5uokS2vT38OHDU/0cc+bMsWrVqlmzZs2sdevW1rBhQ3vllVcyca0BRFXGrUolXEi+fPlsypQp7gYAAAAAkWjgwIFWv359Vyrhtttus6+//toFULwgigYYGjBggI0aNcrVwVUgd9iwYS4zrn379qFefQBZTAluqYmZeHbu3JlkmrJz1csZQPaQ4YFbAAAAAMgObrjhBje2h+rSjhw50gVmJ02aZF27dvUt8+ijj9rx48dd/VvVplR23OLFi10yCwAAQEoI3AIAAABAOqkEnG7JUdatgrq6AQAAhLTGLQAAAAAAAADg4hC4BQAAAAAAAIAwQ+AWAAAAAAAAAMIMgVsAAAAAAAAACDMEbgEAAAAAAAAgzBC4BQAAAAAAAIAwQ+AWAAAAAAAAAMIMgVsAAAAAAAAACDMEbgFEjSlTpljFihUtX758VqdOHfv6669DvUoAAAAAAADpQuAWQFR48803bdCgQfbkk0/at99+azVq1LCWLVvagQMHQr1qAAAAAAAAaUbgFkBUmDBhgvXu3dvuueceu+qqq2z69OmWP39+++c//xnqVQMAAAAAAEgzArcAIt7p06dt/fr11rx5c9+0HDlyuMerV68O6boBAAAAAACkR650/RdwASdOnLDNmzcnO3/Tpk0B9ympVq2ay5wEkvO///3Pzp07ZyVLlgyYrsfBvofx8fHu5jly5AgbN8zRpgCgTQEQrjhOAUCbgsxC4BaZQsGy2rVrX3C5bt26XXAZZVLWqlUrg9YMMBs9erSNGDGCTRFBaFMA0KYACFccpwCgTUFmIXCLTKEsWQVck3Py5EnbuXOnVaxY0WJjYy/4XEBKLr30UsuZM6ft378/YLoelypVKsnyQ4YMcQOZ+WfclitXjo0cxmhTANCmAAhXHKcAoE1BZiFwi0yh0gYXypJt0KABWx8ZIk+ePC7De+nSpda+fXs37fz58+5xv379kiyfN29ed0PkoE0BQJsCIFxxnAKANgWZhcAtgKigDNoePXrY9ddfb3/+859t0qRJdvz4cbvnnntCvWoAAAAAAABpliPt/wIA4ef222+3cePG2fDhw61mzZq2YcMGW7x4cZIBywAgtaZMmeJK+uTLl8/q1KljX3/9NRsPAAAAQJYhcAsgaqgswi+//GLx8fG2du1aF2gBgPR48803XSb/k08+ad9++63VqFHDWrZsaQcOHGCDAgAAAMgSBG4BAAASmTBhgvXu3duVW7nqqqts+vTprobhP//5T7YVAAAAgCxB4BYAAMDP6dOnbf369da8efP/d8CUI4d7vHr16iTbSln+R44cCbgBAAAAwMUicAsAAODnf//7n507dy5JjWw93rdvX5JtNXr0aCtSpIjvVq5cObYnAAAAgIuWyyJQQkKCuyejBQgd7/fn/R4jGW0KEHqR3KYMGTLE1cP1HD582MqXL89xChBCkdymJMZxChB6tCkAQtWmRGTg9ujRo+6ejBYgPH6PyjCLZLQpQPgIhzbl0ksvtZw5c9r+/fsDputxqVKlkiyfN29ed0t8IMZxChB64dCmXCyOU4DwQZsCIKvblJiECLwMff78eduzZ48VKlTIYmJiQr06SAed1OqEdvfu3Va4cGG2YQRS06FGpkyZMq72YySjTYl8tCmRL9zalDp16tif//xnmzx5sq+dUBZtv3797PHHH0/xf2lTIh9tSuQLtzblYtCmRD7alMhHm4JwQpuSvdqUiAzcIjoaGl1VUHdSArcAaFMQbt58803r0aOHvfzyyy6AO2nSJHvrrbds8+bNSWrfIvpwnAKANgVAuOI4JXuJyFIJAAAAmen222+3//73vzZ8+HA3IFnNmjVt8eLFBG0BAAAAZBkCtwAAAEGoLIJuAAAAABAKkV3wCRFLg7g8+eSTAYO5AABtCoBwwHEKANoUAOGK45TshRq3AAAAAAAAABBmyLgFAAAAAAAAgDBD4BYAAAAAAAAAwgyBWwAAAAAAAAAIMwRuAQAAAAAAACDMELhFllq5cqW1bdvWypQpYzExMbZgwQI+AQC0KQDCAscpAGhTAIQrjlOyJwK3yFLHjx+3GjVq2JQpU9jyAGhTAIQVjlMA0KYACFccp2RPuUK9AsheWrVq5W4AQJsCINxwnAKANgVAuOI4JXsi4xYAAAAAAAAAwgyBWwAAAAAAAAAIMwRuAQAAAAAAACDMELgFAAAAAAAAgDBD4BYAAAAAAAAAwkyuUK8Aspdjx47Z9u3bfY937NhhGzZssGLFiln58uVDum4AIg9tCgDaFADhiuMUALQpuFgxCQkJCRf9LEAqLV++3Jo0aZJkeo8ePWzmzJlsRwBpQpsCICPRpgCgTQEQrjhOyZ4I3AIAAAAAAABAmKHGLQAAAAAAAACEGQK3AAAAAAAAABBmCNwCAAAAAAAAQJghcAsAAAAAAAAAYYbALQAAAAAAAACEGQK3AAAAAAAAABBmCNwCAAAAAAAAQJghcAsAAAAAAAAAYYbALQAAAAAAAACEGQK3AAAAAAAAABBmCNwCAAAAAAAAQJghcAsAAAAAAAAAYYbALQAAAAAAAACEGQK3AAAAAAAAABBmCNwCAAAAAAAAQJghcAsAAAAAAAAAYYbALQAAAAAAAACEGQK3Weipp56ymJgY+9///pfichUrVrS77747y9YLmft5A5mFNgUAbQaSM3PmTHccsnPnTt+0xo0buxvAcQf8cf4JAOGLwG2U+Oijj1wQBwBoUwBwHAIgknAuAwBAcLmSmY4Q2rJli+XIkSPNBztTpkwheAuANgUAxyEI6tNPP2XLINNxLpM9PjMAQNagdQ5DefPmtdy5c1skOX78eKhXAUAyaFMApAVtRvTKkyePuwGZiTYkMiQkJNjJkycj9jMDgOyCwG0IHDp0yNWwLVq0qBUpUsTuueceO3HiRLI1hs6cOWMjRoywKlWqWL58+ax48eLWsGFDW7JkiZuvZZVtK6pl5t38g6qDBw+2cuXKuZ3ylVdeaePGjXM7a3/acT/00EN26aWXWqFChezWW2+13377zT2XfxkGr67mTz/9ZHfeeaddcsklbn3k+++/d+tz+eWXu3UtVaqU9ezZ037//feA1/KeY+vWrdatWze3HS677DIbNmyYW6/du3dbu3btrHDhwu45xo8fn+btrO14yy232PLly+3666+32NhYq169unss7733nnus9axdu7b9+9//TvIcy5YtsxtvvNEKFCjgPi+t06ZNm5Is9+WXX9oNN9zgnqty5cr28ssvJ7tes2fPdq+n9SlWrJh16dLFvV8gvWhTIqdN2bx5s3Xu3Nn99rWcnueDDz4IWObgwYP28MMPu+cqWLCgW+dWrVrZd999F7CcXlfv+a233rJnnnnGypYt656zWbNmtn379jS/P2QftBnR1WbIxo0brWnTpu411BaMGjXKzp8/n2S5xDVuT58+bcOHD3evr22g4x0d93z++ecB/6c6udpeOn585ZVX3LGOjil17PPNN9+kedsgstGGZH4borZD51PB1KtXz7UFnhkzZrjff4kSJdzv8qqrrrJp06Yl2yZ98sknvjbJO2dJfP6ZWccia9eutdatW7vzR7U31157rb3wwgvpavcAILugVEII3HbbbVapUiUbPXq0ffvtt/aPf/zD7WjHjBkTdHkdGGjZe++91/785z/bkSNHbN26de5///KXv1jfvn1tz549LpD7r3/9K+B/deCgAKwOwHv16mU1a9Z0O+tHHnnEBWUnTpzoW1Y7a+10u3fvbnXr1rUVK1ZYmzZtkn0ff/3rX10w+dlnn/UFgbUO//nPf1wwWgcpOpHQAb7u16xZk2Swrttvv93i4uLsueeesw8//NCdaGgnrYMIHYBom8yZM8cdOOjkoFGjRmna1jpgUHBZ20gHVTrhaNu2rU2fPt2eeOIJe+CBB9xy2r76XPy7CX322WfuAEUHTfoMFNiePHmyNWjQwG17HeDIDz/8YC1atHAHa1ru7Nmz9uSTT1rJkiWTrI8OaHRAp9fS5/nf//7XPafel07YFBwG0oo2JTLaFLWDaj/+9Kc/2eOPP+5OWNTmtm/f3t59913r0KGDW05t6IIFC1wbq33F/v373frfdNNN7oJZmTJlAtZJ71Wvofd0+PBhGzt2rHXt2tWdHAG0GdHfZuzbt8+aNGnijj+85fSeFZS5EB1T6jj0jjvusN69e9vRo0fttddes5YtW9rXX3/tjhv9zZ071y2j96NtqfamY8eOrt0iWy/74Lgj89sQPe9dd93lLozo/zy//PKLa8uef/553zQFaa+++mp3zpcrVy5buHCha1t08ebBBx8MeF61Mfq96zes37wSeoLJjGMRnScqcFy6dGn729/+5s4VlRCzaNEi9zgt7R4AZCsJyDJPPvmkopsJPXv2DJjeoUOHhOLFi/seV6hQIaFHjx6+xzVq1Eho06ZNis/94IMPuudObMGCBW76qFGjAqZ37tw5ISYmJmH79u3u8fr1691yAwYMCFju7rvvdtO17onfxx133JHk9U6cOJFk2htvvOGWX7lyZZLn6NOnj2/a2bNnE8qWLevW67nnnvNN/+OPPxJiY2MDtklqaDvqNVatWuWb9sknn7hper5ffvnFN/3ll1920z///HPftJo1ayaUKFEi4ffff/dN++677xJy5MiRcNddd/mmtW/fPiFfvnwBz/fTTz8l5MyZM+Az2blzp5v2zDPPBKznDz/8kJArV64k04ELoU2JrDalWbNmCdWrV084deqUb9r58+cT6tevn1ClShXfNM0/d+5cwGvv2LEjIW/evAkjR470TdNz6zXi4uIS4uPjfdNfeOEFN11tC0CbEf1tho7d9L9r1671TTtw4EBCkSJF3HS1H56bbrrJ3fzfs3/74b3fkiVLBhyv6jn0XDpePXjwoG/6+++/76YvXLgwTdsGkYnjjqxrQw4fPuz2+4MHDw6YPnbsWPf8/u1HsPOvli1bJlx++eVB26TFixcnWT7x+WdGH4to21SqVMm9jraHP7VraW33ACA7oVRCCNx3330Bj9UlTaUElPUQjLIwdfVx27ZtaX4tDVqWM2dOVwLBn0onKEv2448/do8XL17s7r3MD0///v1T/T7EP7vj1KlT9r///c9l74qyVBNT1qlH66muMFovZQf7v39dDdaV37RSVyF1J/LUqVPH3esKePny5ZNM915j7969tmHDBpeFrKvmHnXnUZaztqucO3fOZTDrKrD/8+nKu7JV/KlLpK58K0tB28W76WqzMpcTd0sEUos2JfzbFHU5VOkV/f6Vreb9/tX2q61Q+65eEKJujl7GndoYLaNuilrnYO2oejj416zUPsX/tYHEaDOiq83QMYmOtdQry6NeQMp2uxC9Z6/90DGKXleZu9oOwdobZQGqi7OH9iZ7og3J/DbEK02gbFP/8nZvvvmm+737tx/+51/KdlVbocxYvZ4e+1P2bOJzlGAy+lhEPQt37NhhAwYMSNLD0OsJkZZ2DwCyEwK3IeC/oxXvAPiPP/4IuvzIkSNdLamqVau6OkMqc6Basqmh7jTqyqKatf4UWPTme/faOWtn7u+KK65I9rkTL+vtcNXVRWUCdBChEwdvucQHDsG2hepDqZaR6uwmnp7c9klJsOcX1fsNNt17DW+7BOs+pG2ngwjVDlapA5VQUOA1scT/q4MNHXhpWW0X/5u6CR04cCDN7w8I9j2nTQm/NkXdpfX7V6mUxL9/lVYRrw1Q8ERlbNRW6MRJ667l1O6nph290OcP0GZEV5uhY5bUHIck5/XXX3cXpr1xFPQa6vJNe4PUfq857sicNkQXSlQrd/Xq1e7xzz//bOvXr3fT/X311VfWvHlz35gc+g2rFIsEC9ymRkYfi2jd5Zprrkn2NdPS7gFAdkKN2xDQ1dhgEg8W5lEtJO3s3n//ffv0009dLTLtSFUfzf8qb1YLVjtNV0hXrVrlgsuqi6Yrs9rx33zzzUEHyQi2LdK6fVKS3HNl5Guklt6/rigryznY62tbAelBmxL+bYrX/qn2W3KZLt6FMtUN10mLBnZ8+umnXda/LqwpSyW17aj/awMX+53hOCS824yLoQFT1btIPYd07KYxF7RuqrnrBVrSst7IHmhDUt4WGfU7UT3s/Pnzu6zb+vXru3sdD6jurEe/Uw0EVq1aNZswYYK7KKTMV2Xi63wx8XFDampfh+pYJKvaPQCINARuI4R2luqCotuxY8fcSZQGwvICt4kH2/BUqFDBDbKl7ib+WbcardOb791rZ6kuLP5ZG2kZmVxXVJcuXWojRoxwIxR70lPiIdS87aIC/olp2+mqs65q64q6DoCCvcfE/6sRmHXwoivdyp4GQok2JWt5I0Nr8B5lxaTknXfecQMNaYAgf+p5kTiDB8gqtBnh22bomCU1xyHJtTd6LZVz8j+W9LLbgIxCG5J2OtfQYF5vv/22C8qqTIJKEPgPDKaByOLj4+2DDz4IyHq92BJsGX0sovMg+fHHH5Nt09LS7gFAdkKphAiguj6JMzN1tVE7af8du7cz9de6dWtXl+ill14KmK4rsDpAV+0k8a5qTp06NWC5yZMnp3o9vSutia+sTpo0ySKNRjtVxrC6D/pvUx1sKOtZ29V7z9p2GnV1165dvuVU+kC1b/1p1GUtr8B24m2kx4k/ZyCz0KZkPWWxNW7c2I3IrBraiansikftROI2Qidt1HVDqNBmhHeboWMSjTL/9ddfB8zXSPbpOXbTKPBe12wgI9CGpJ/KIuzZs8f1uPzuu++SlEkI9htWKYMZM2ZcxKtm/LFIrVq1XPKKzgsTn696r5OWdg8AshMybiOABrbQTqx27druavW6devcVdB+/fr5ltE80SBkCiRqZ9ulSxfXxUZXS//+97/bzp07rUaNGi7wqLIL6uriXf3U/3fq1MntTHVwpaL3K1assK1bt6aY0Zu4iL4ygceOHWtnzpyxP/3pT+61lMUbiZ5//nkX2NagIhpgQLVsFchWjSplO3sUiNXgbroCrsHdNKiHlrv66qsDahFrW48aNcqGDBniPgt1S1QWtLbP/PnzrU+fPq5rEJDZaFNCY8qUKdawYUNXq7x3794us2T//v0uQPLrr7+6EzJRdo1qm6uHhbpG/vDDDy4A42WiAFmNNiO824xHH33U/vWvf7myVBpnQBfzX3nlFZeJe6ExEdTeKNu2Q4cO1qZNG3dMolJc+szVwwvICLQh6acLMzpf0DmCzu90vuavRYsWrjSCzvn69u3rfrevvvqqC4IGC36mVkYfi6jMwrRp09x6KjlGz6tEGfVk1CDcXsJLats9AMhOCNxGAAVj1f1FQVBl2epAXAFA1SLzz+bs37+/zZs3z9Ur05VLBW61k9T/qnSButfo6mvFihVdUHLw4MEBrzNr1iwrVaqUvfHGGy6QqC4q+h8NbqGSAKkxd+5ctx7a6WoddDChmq7+XXoihd6/ArLqLqjtp247GqF1zJgxAYX9NaCHDjYGDRrklitbtqwL5upgKfEJ0+OPP+7KJCjjWcuIalFpO916661Z/h6RPdGmhO7EVRfe9NufOXOmu0imE6vrrrsuoLyMBhTR4IdqT9UGK0tFAwWp/QBCgTYjvNsMBT/ULVrHX88995wbYOy+++5zx17+I9sHo/q2+/btcxluOpbRa+o4Upl1y5cvz4J3ieyANiT9dA6mcwQFTXVuojbAn87TlNAzdOhQF9zVudz999/vBvRSfdr0yoxjESUXqa1SmzZ+/HhXpk+JLQrQprXdA4DsJCaB0QSQgg0bNrgdpQ7iu3btyrYCcFFoUwDQZgDIKhx3AAAiHTVu4aNSAImpdIKydlUCAQDSgjYFAG0GgKzCcQcAIBpRKgE+qk27fv16VxM3V65crsSBbqq9qu784UKF6TXgWnJU50m1gAGEFm0KANoMABx3BOJcBgCQFpRKgM+SJUtcPaGffvrJFbYvX768de/e3Q1spkBuuFCN3l9++SXZ+apDS102IPRoUwDQZgDguCMQ5zIAgLQgcIuI89VXXwXtCuW55JJLrHbt2lm6TgAiF21KdFm5cqUbgFM9SDRIpAbbbN++vW++LkxqcJUFCxa4QU802KQGztFgTp5Tp065ATw14KcGBdWAKlOnTrWSJUuG6F0hnNBmAKANAQBkFQK3AAAgaqjEjwJruoDXsWPHJIFblf9ZtmyZ/eMf/3BZT59++qk98MAD9t5777mRu0Ujcmv0bI1oXaRIEevXr5+r967nBQAAAICsQuAWAABEpZiYmCSB22uuucZuv/12GzZsmG+agrytWrWyUaNG2eHDh+2yyy6zuXPnWufOnd38zZs3W1xcnK1evdrq1q0bkvcCAAAAIPsJn8KlaXD+/Hnbs2ePFSpUyJ2UAch6CQkJdvToUStTpozLRItktClA9mlT6tevbx988IH17NnTvZZqom/dutUmTpzo5qvEwpkzZ6x58+a+/6lWrZqr+55c4FblFHTzb1MOHjxoxYsX5zgFCBGOUwDQpgCIBhEZuFXQtly5cqFeDQBmtnv3bitbtmxEbwvaFCD7tCmTJ0925RL0Ghp4U0HiV1991Ro1auTm79u3z/LkyWNFixYN+D/Vt9W8YEaPHu0G9wQQfjhOAUCbAiCSRWTgVpm23oFY4cKFQ706QLZ05MgRdwHF+z1GMtoUIPu0KQrcrlmzxmXdVqhQwQ1m9uCDD7rsW/8s27QYMmSIDRo0yPdY5RaUoctxChA6HKcAoE0BEA0iMnDrlUdQ0JbALRBa0VCuhDYFyB5tysmTJ+2JJ55wdW/btGnjpl177bW2YcMGGzdunAvclipVyk6fPm2HDh0KyLrdv3+/mxdM3rx53S0xjlOA0OM4BQBtCoBIFtmFKQEAAFJJtWt1S1xDN2fOnK4urTdQWe7cuW3p0qW++Vu2bLFdu3ZZvXr12NYAAAAAskxEZtwCAAAEc+zYMdu+fbvv8Y4dO1xGbbFixVz5gptuuskeeeQRi42NdaUSVqxYYbNmzbIJEya45YsUKWK9evVypQ/0P8qa7d+/vwvaBhuYDAAAAAAyC4FbZLlz587ZF198YXv37rXSpUvbjTfe6LKdAAC4WOvWrbMmTZr4Hnu1Z3v06GEzZ860efPmuZq0Xbt2tYMHD7rg7TPPPGP33Xef738mTpzosnI7depk8fHx1rJlS5s6dSofTjbBcQoA2hQAQLggcIss9d5779ngwYNt586dvmkVK1a08ePHW8eOHfk0AAAXpXHjxpaQkJDsfNWpnTFjRorPkS9fPpsyZYq7IXvhOAUAbQoAIJxQ4xZZejLUuXNnq169uq1evdqOHj3q7vVY0zUfAAAgFDhOAUCbAgAINzEJKaWlhKkjR464GnSHDx92tecQGd0Or7jiChekXbBgQcDAMBoQpn379vbjjz/atm3bKJsQIaLpdxhN7wWIVNH0O4ym95JdcJwSfaLpdxhN7yW7oE2JPvwOAYQKpRKQJVTTVuUR3njjjSSjeeux6g3Wr1/fLadurgDg78SJE7Z58+ZkN8rJkyddG6PSKxp0KiXVqlWz/Pnzs4EBcJyCFI0ePdplYmv/o32LjlXHjBljV155pW8ZHbdqkEN/ffv2tenTp/se79q1y+6//377/PPPrWDBgq7mtp47Vy5OxaIV5z4AgIzC0QKyhAYik2uuuSbofG+6txwA+NNJc+3atTNko6xfv95q1arFBgbAcQpSpIDsgw8+aDfccIOdPXvWnnjiCWvRooX99NNPVqBAAd9yvXv3tpEjR/oe+18cVOZlmzZtXH3tVatWuWPdu+66y3Lnzm3PPvssn0CU4twHAJBRCNwiS5QuXdrdqxxC3bp1k8zXdP/lACBxlqwCrsnZtGmTdevWzWbPnm1xcXEXfC4A4DgFF7J48eKAxzNnzrQSJUq4/VGjRo0CArUKzAbz6aefukDvZ599ZiVLlrSaNWva008/bY899pg99dRTlidPHj6IKMS5DwAgoxC4RZa48cYbXRdmZRYEq3Gr7mKVKlVyywFAYjopTk2WrIK2ZNMC4DgFmUE1ZqVYsWIB0+fMmeMuHCp427ZtWxs2bJgv69YbiFdBW0/Lli1d6YSNGzfaddddl+R14uPj3c2/tiYiC+c+AICMQuAWWSJnzpw2fvx469y5sxuITDVtVR5BmbYK2i5atMjeeecdBiYDAABZjuMUXIgSDQYMGGANGjQIKP115513WoUKFaxMmTL2/fffu0zaLVu2uNq4sm/fvoCgrXiPNS8YHRuPGDGCDyVK2pR27drZzTff7Ookqya/Mrk//PBDzn0AAKlC4BZZpmPHju4AZfDgwW5wB48ybTVd8wEAAEKB4xSkRLVulXDw5ZdfBkzv06eP729l1qqLfLNmzeznn3+2ypUrp2ujKsFh0KBBARm35cqV4wOKwDbl4YcftokTJ7okFY8GpdN0zn0AAKlB4BZZSgcouuqskVZVtF8Ht+pKpKvSAAAAocRxCoLp16+fC7ytXLnSypYtm+JGqlOnjrvfvn27C9yqfMLXX38dsMz+/fvdfXJ1cfPmzetuiGzKuh43bpwbnK5Vq1a+jNuPP/7YTde4HwRvAQAXQuAWAAAA+D+6mNy4cWO2BywhIcH69+9v8+fPt+XLl7teYheyYcOGgMGp6tWrZ88884wdOHDADWwmS5YsscKFC9tVV13FVo5S586dc70Mb7nlliTje9x3332udJyybpXQQgILACAl/28Pkkq60qyi+6rjFBMT43ZE/o4dO+auSutqtK4q6oBk+vTpAcucOnXKdTcqXry4FSxY0Dp16uS78ozov/J8xRVXWJMmTVxNMN3rsVcHDAAAAAgHOl/RoGNz5861QoUKuZq0uilrUlQO4emnn7b169fbzp077YMPPrC77rrLGjVqZNdee61bpkWLFu58qHv37vbdd9/ZJ598YkOHDnXPTVZt9FLvQn0nnnjiiYCgreixymHs2LHDLQcAQIYGbo8fP241atSwKVOmBJ2vekwquK6DnE2bNrki/grk6kDGM3DgQFu4cKG9/fbbtmLFCtuzZw/dRLIBBWdVoF/1vzTC7tGjR30j7Wo6wVsAAACEi2nTptnhw4ddBrYyaL3bm2++6ebnyZPHPvvsMxecrVatmsuwVEKKznM8yqZUmQXdK/u2W7duLrg7cuTIEL4zZDaVhBP/gez8edO95QAAyLBSCarPo1tyVq1aZT169PB1MVPB/pdfftnVdrr11lvdwc9rr73mrlw3bdrULTNjxgyLi4uzNWvWuFo/yD7dhfR56zHdhQAAABBupRJSogHDlIRyIRUqVLCPPvooA9cM4c4rlaEB7YKd32q6/3IAAGRYxu2F1K9f32XX/vbbb+5g5/PPP7etW7e6K9GirkRnzpyx5s2b+/5HV6jLly/vsi+DiY+Pd6Op+t8QWeguBAAAACA70ODLFStWtGeffdbOnz8fME+PR48e7WomazkAALI0cDt58mRXx0k1btV96Oabb3ZlFVTrSVQXStOLFi0a8H8lS5Z084LRjq1IkSK+m65uI7LQXQgAAABAdqDSGOPHj3dlMtSz0L9MnB5r+rhx4xiYDAAQmsCtSh4o61bZtdphqfi+6j+ll4q3q8SCd9u9e3eGrjOytrtQMHQXAgAAABAtOnbsaO+884798MMPrldq4cKF3b3OezRd8wEAyPAatynRCKsaOXP+/PnWpk0bN00jqm7YsMFdUVR5hFKlStnp06ft0KFDAVm3+/fvd/OC0YirjLoaPd2F/GvcCt2FAABAVjpx4oRt3rw5xWNajQivY5fY2NgUn0slv/Lnz58Jawkg0ik4265dO1c2Tj0Qlcyi8yJl5AIAkOWBW9Wu1c0/KCfaMXm1fWrXrm25c+e2pUuXulFXZcuWLbZr1y430iqiu7tQ586dXfcgZVFrNFVdcVYpDHUX0pVnDmIAAEBmU9BWx6QZQT3MatWqlSHPBSD66PzGG7gbAIBMD9weO3bMtm/f7nu8Y8cOl1FbrFgxN8DYTTfdZI888ojLTtAIqhppddasWTZhwgS3vGrU9urVywYNGuT+R11G+vfv74K2wUbcRPR1Fxo8eLDrJuRRYX66CwEAgKyiLFkFXJOzadMm69atm82ePdvi4uIu+FwAAABAWARu161bZ02aNPE9VgBWevToYTNnzrR58+a5bMquXbvawYMHXfD2mWeesfvuu8/3PxMnTnRZucq4jY+Pt5YtW9rUqVMz6j0hjNFdCAAAhJpKG6QmS1ZBW7JpAQAAEDGBW3XzSEhISHa+6tTOmDEjxefIly+fTZkyxd2Q/dBdCAAAAAAAAEhZYDFaAAAAAAAAAEDIEbgFAAAAAAAAgDBD4BYAAAAAAAAAwgyBWwAhtXLlSmvbtq2VKVPGYmJibMGCBUFH97711lutSJEiVqBAAbvhhhts165dvvmnTp2yBx980IoXL24FCxZ0Ax/u378/i98JAAAAAABAxiFwCyCkjh8/bjVq1Eh2sMKff/7ZGjZsaNWqVbPly5fb999/b8OGDXODHHoGDhxoCxcutLfffttWrFhhe/bssY4dO2bhuwAAAAAAAMhYuTL4+QAgTVq1auVuyfn73/9urVu3trFjx/qmVa5c2ff34cOH7bXXXrO5c+da06ZN3bQZM2ZYXFycrVmzxurWrcsnAgAAAAAAIg4ZtwDC1vnz5+3DDz+0qlWrWsuWLa1EiRJWp06dgHIK69evtzNnzljz5s1905SdW758eVu9enXQ542Pj7cjR44E3AAAAAAAAMIJGbfIFCdOnLDNmzcnO//kyZO2c+dOq1ixosXGxqb4XArC5c+fPxPWEuHuwIEDduzYMXvuueds1KhRNmbMGFu8eLErg/D555/bTTfdZPv27bM8efJY0aJFA/63ZMmSbl4wo0ePthEjRmTRuwAAAAAAAEg7ArfIFAra1q5dO0OeSxmVtWrVypDnQuRl3Eq7du1cHVupWbOmrVq1yqZPn+4Ct+kxZMgQGzRokO+xMm7LlSuXQWsNAAAAAABw8QjcIlMoS1YB1+Rs2rTJunXrZrNnz3a1SC/0XMieLr30UsuVK5ddddVVAdP1nfnyyy/d36VKlbLTp0/boUOHArJu9+/f7+YFkzdvXncDAAAAAAAIVwRukSlU2iA1WbIKwJFNi+SoBMINN9xgW7ZsCZi+detWq1Chgvtbmd25c+e2pUuXWqdOndw0Lb9r1y6rV68eGxcAAAAAAEQkArcAQko1bLdv3+57vGPHDtuwYYMVK1bMDTD2yCOP2O23326NGjWyJk2auBq3CxcutOXLl7vlixQpYr169XKlD/Q/hQsXtv79+7ugbd26dUP4zgAAAAAAANKPwC2AkFq3bp0LyHq82rM9evSwmTNnWocOHVw9Ww0o9tBDD9mVV15p7777rjVs2ND3PxMnTrQcOXK4jNv4+Hhr2bKlTZ06NSTvBwAAAAAAICMQuAUQUo0bN7aEhIQUl+nZs6e7JSdfvnw2ZcoUdwMAAAAAAIgGOUK9AgAAAAAAAACAQARuAQAAAAAAACDMELgFAAAAAAAAgDBD4BYAAESNlStXWtu2ba1MmTIWExNjCxYsSLLMpk2b7NZbb7UiRYpYgQIF7IYbbrBdu3b55p86dcoefPBBK168uBUsWNANfLh///4sficAAAAAsjsCtwAAIGocP37catSokexghT///LM1bNjQqlWrZsuXL7fvv//ehg0b5gY59AwcONAWLlxob7/9tq1YscL27NljHTt2zMJ3AQAAAABmudgIAAAgWrRq1crdkvP3v//dWrdubWPHjvVNq1y5su/vw4cP22uvvWZz5861pk2bumkzZsywuLg4W7NmjdWtWzeT3wEAAAAApDPjli6IAAAgEp0/f94+/PBDq1q1qrVs2dJKlChhderUCSinsH79ejtz5ow1b97cN03ZueXLl7fVq1cHfd74+Hg7cuRIwA1A5Bs9erQrpVKoUCHXXrRv3962bNkSsExqSquoFEubNm0sf/787nkeeeQRO3v2bBa/GwAAkC0Ct3RBBAAAkejAgQN27Ngxe+655+zmm2+2Tz/91Dp06ODKIKgkguzbt8/y5MljRYsWDfjfkiVLunnJBXdUL9e7lStXLkveD4DMpXZBQVll2y9ZssRd1GnRooU7H0ptaZVz5865oO3p06dt1apV9vrrr9vMmTNt+PDhfHwAACDjSyXQBREAAERqxq20a9fOBVukZs2aLpgyffp0u+mmm9L1vEOGDLFBgwb5HivjluAtEPkWL14c8FgBV2XMKjO/UaNGqSqtogtEP/30k3322WfuApDanKefftoee+wxe+qpp9yFIgAAgCwZnIwuiAAAIFxdeumllitXLrvqqqsCpivIoq7MUqpUKZcZd+jQoYBl1PVZ84LJmzevFS5cOOAGIPooUCvFihVLdWkV3VevXt0FbT0q1aILPBs3bgz6OpRfAQAAmRK4pQsiAAAIV8psU73KxDUqt27dahUqVHB/165d23Lnzm1Lly71zdfyCuzWq1cvy9cZQHhQgsqAAQOsQYMGds0116S6tIru/YO23nxvXjCUXwEAAOkulZASuiACAIBQUg3b7du3+x7v2LHDNmzY4DLklAWnQYFuv/121825SZMmriu06lMuX77cLa8atb169XKlD/Q/yp7t37+/C9qq2zOA7Em1bn/88Uf78ssvM/21KL8CAAAyJXCbUhdE7yDHvwui/9XpC3VB1A0AACAl69atcwFZj1d7tkePHq4+pQYjUz1bZbQ99NBDduWVV9q7775rDRs29P3PxIkTLUeOHG50eHVZVrfmqVOnsuGBbKpfv362aNEiW7lypZUtW9Y3PTXnNbr/+uuvA55P8715wXDuAwAAMqVUAl0QAQBAKDVu3NgSEhKS3BS09fTs2dO2bdtmJ0+edNm4GqzMX758+WzKlCl28OBBN3r8e++9l2yABUD0UtuhoO38+fNt2bJlVqlSpYD5qSmtovsffvjBlZTzLFmyxGXzJ052AQAAuOiMW7ogAgAAAMgO5RHmzp1r77//vhUqVMhXk1YlVWJjY1NVWqVFixYuQNu9e3cbO3ase46hQ4e656ZHIQAAyPDALV0QAQAAAES7adOm+TL5/c2YMcPuvvvuVJVWyZkzpyuzcP/997uAboECBVzplpEjR2bxuwEAANkicOt1QUyJuiDqlhyvC6JuAAAAABBuLnTOk9rzmgoVKthHH32UwWsHAACygwytcQsAAAAAAAAAuHgEbgEAAAAAAAAgzBC4BQAAAAAAAIAwQ+AWAAAAAAAAAMIMgVsAAAAAAAAACDMEbgEAAAAAAAAgzBC4BQAAAAAAAIAwkyvUKwAAwLZt2+zo0aPp3hCbNm0KuE+vQoUKWZUqVfhAAAAAAAAhR+AWABDyoG3VqlUz5Lm6det20c+xdetWgrcAAAAAgJAjcAsACCkv03b27NkWFxeXruc4efKk7dy50ypWrGixsbHpeg5l6yrwezGZvwAAAAAAZBQCtwCAsKCgba1atdL9/w0aNMjQ9QEAAAAAIJQI3CJdqEcJAAAAAAAAZB4Ct0gz6lECAAAAAAAAmYvALdKMepQAAAAAAABA5iJwi3SjHiUAAAAAAACQOXJk0vMCAAAAAAAAANKJwC0AAAAAAAAAhBkCtwBCauXKlda2bVsrU6aMxcTE2IIFC5Jd9r777nPLTJo0KWD6wYMHrWvXrla4cGErWrSo9erVy44dO5YFaw8AAAAAAJA5CNwCCKnjx49bjRo1bMqUKSkuN3/+fFuzZo0L8CamoO3GjRttyZIltmjRIhcM7tOnTyauNQAAAAAAQOZicDIAIdWqVSt3S8lvv/1m/fv3t08++cTatGkTMG/Tpk22ePFi++abb+z666930yZPnmytW7e2cePGBQ30AgCi27Zt2+zo0aPp/n/tW/zv06tQoUJWpUqVi3oOAAAAZF8EbgGEtfPnz1v37t3tkUcesauvvjrJ/NWrV7vyCF7QVpo3b245cuSwtWvXWocOHZL8T3x8vLt5jhw5konvAACQ1UHbqlWrZshzdevW7aKfY+vWrQRvAQAAkDWBW3VBfv755239+vW2d+9e1325ffv2ydajfPnll23ixIk2YMCAgHqUyp5buHChC6506tTJXnjhBStYsGD63gWAqDVmzBjLlSuXPfTQQ0Hn79u3z0qUKBEwTcsXK1bMzQtm9OjRNmLEiExZXwBAaHmZtrNnz7a4uLh0PcfJkydt586dVrFiRYuNjU3XcyhbV4Hfi8n8BQAAQPaWK731KHv27GkdO3ZMdz1KBX1Vj/LMmTN2zz33uHqUc+fOTfs7ABC1dIFIF3W+/fZbNyhZRhkyZIgNGjQoIOO2XLlyGfb8AIDQU9C2Vq1a6f7/Bg0aZOj6AAAAAJkeuKUeJYCs8sUXX9iBAwesfPnyvmnnzp2zwYMH26RJk1w2VKlSpdwy/s6ePesy+zUvmLx587obAAAAAABAtqlxSz3K7KFUwRiLPbTVbE+OkK2DXl/rgeil2raqV+uvZcuWbroy9aVevXp26NAhl51bu3ZtN23ZsmWuLapTp05I1hsAAAAAACDsArfUo8we+tbOY3Er+5qtDN06xP3feiCyHTt2zLZv3+57vGPHDtuwYYOrUatM2+LFiwcsnzt3bpdJe+WVV/q6wt58883Wu3dvmz59uiu/0q9fP+vSpUvQUi0AAAAAAADZLnBLPcrs4+X1p+324TMtrlq1kK3Dps2b7eXxd9qtIVsDZIR169ZZkyZNfI+92rM9evSwmTNnpuo55syZ44K1zZo18w14+OKLL/IBAQAAAACAiJWhgVvqUWYf+44l2MmiVc3K1AzZOpzcd96tByJb48aNLSEh9Z+j6tompuxcBjcEAAAAAADRJEMDt9SjBAAAAAAAAIAQBG6pRwkAyGgMeAgACEcrV660559/3pWE27t3r82fP9/at2/vm3/33Xfb66+/nmQg1cWLF/seHzx40Pr3728LFy70lXR64YUXrGDBgln6XgAAQDYI3FKPEgCQ0RjwEAAQjo4fP241atSwnj17WseOHYMuo0FSZ8yY4XucN2/egPldu3Z1Qd8lS5a4QVTvuece69OnD2WeAABAxgduqUcJAMhoDHgIAAhHrVq1creUKFBbqlSpoPM2bdrksm+/+eYbu/766920yZMnW+vWrW3cuHFWpkyZTFlvAAAQHTK0xi0AAOnBgIcAgEi1fPlyK1GihF1yySXWtGlTGzVqlBUvXtzNW716tRUtWtQXtJXmzZu7kglr1661Dh06JHm++Ph4d/McOXIki94JAAAINzlCvQIAAAAAEIlUJmHWrFm2dOlSGzNmjK1YscJl6J47d87N37dvnwvq+suVK5cVK1bMzQtm9OjRVqRIEd+tXLlyWfJeAABA+CHjFgAAAADSoUuXLr6/q1evbtdee61VrlzZZeE2a9YsXdt0yJAhNmjQoICMW4K3AABkT2TcAgAAAEAGuPzyy+3SSy+17du3u8eqfXvgwIGAZc6ePWsHDx5Mti6uauYWLlw44AYAALInMm4BAEDUWLlypT3//PO2fv16N4r7/PnzrX379kGXve++++zll1+2iRMn2oABA3zTFVDp37+/LVy40NWh7NSpk73wwgtWsGDBLHwnuBilCsZY7KGtZntCl6Og19d6IHv59ddf7ffff7fSpUu7x/Xq1bNDhw65Nql27dpu2rJly+z8+fNWp06dEK8tAAAIdwRuAQBA1Dh+/LjVqFHDevbsaR07dkx2OQV016xZE3RE965du7qg75IlS+zMmTN2zz33WJ8+fWzu3LmZvPbIKH1r57G4lX3NVoZum8b933ogsh07dsyXPSs7duywDRs2uBq1uo0YMcJd3FH27M8//2yPPvqoXXHFFdayZUu3fFxcnKuD27t3b5s+fbprU/r16+dKLARrfxB9VO/4iy++cPsVBfRvvPFGy5kzZ6hXCwAQIQjcAgCAqKFBgXRLyW+//eYyaj/55BNr06ZNwLxNmzbZ4sWL7ZtvvvGNAj958mRr3bq1jRs3jkBLhHh5/Wm7ffhMi6tWLWTrsGnzZnt5/J12a8jWABlh3bp11qRJE99jr/Zsjx49bNq0afb999/b66+/7rJqFYht0aKFPf30067cgWfOnDkuWKuat14W/4svvsgHlA2899577jvzyy+/+KZVqFDBJkyYkOLFRQAAPARuAQBAtqHuyd27d7dHHnnErr766iTzV69ebUWLFvUFbaV58+Yu2LJ27Vrr0KFDkv+Jj493N/+BhBBa+44l2MmiVc3K1AzZOpzcd96tByJb48aNLSEh+c9RF4AuRJm5ZOxnz6CtgvQxMYElU3bt2uWmv/vuuwRvAQAXxOBkAAAg2xgzZozlypXLHnrooaDz9+3bZyVKlAiYpuUVeNG8YEaPHm1FihTx3Rj9HQCyN5VHUMkeueyyy+zVV191pRJ0r8ei+VoOAICUkHGLNDtx4oS7//bbb9O99U6ePGk7d+60ihUrWmxsbLqeQ91ZAQBILQ0OpEHGtP9KnAF1MYYMGeLrPu1l3BK8BYDsSwPQHT582C655BJXnkcXAOXee++1u+++210g/OOPP9xyf/nLX0K9ugCAMEbgFmm2efNmd69BFsJBoUKFQr0KAIAIoMFhDhw4YOXLl/dNU7bT4MGDbdKkSe6CogYY0jL+zp49awcPHnTzglEtS/96lgCA7O1f//qXux85cqQvaOvR46eeesr+9re/ueUI3AIAUkLgFmnWvn17d1+tWjXLnz9/urNlu3XrZrNnz3aj7V5M0LZKlSrp/n8AQPah2raqV+tPI79r+j333OMe16tXzw0ypOzc2rVru2nKiFJt3Dp16oRkvQEAkeXo0aPuvlKlSkHnq9eh/3IAACSHwC3S7NJLL3XdfDKCgra1atXiUwAAZIhjx47Z9u3bfY937NhhGzZscDVqlWlbvHjxgOVz587tMmmvvPJK337p5ptvdr1Kpk+fbmfOnHGjwXfp0sWNGA8AwIXceOONtmDBAvv73/9urVq1cgNcenQhcNiwYb7lAABICYOTAQCAqLFu3Tq77rrr3E1Ue1Z/Dx8+PNXPMWfOHNerpFmzZta6dWtr2LChvfLKK5m41gCAaKILfgrWfvfdd9auXTtbvXq1y67VvR5///33br6WAwAgJWTcAgCAqNG4cWNLSEhI9fKqa5uYsnPnzp2bwWsGAMgu8uTJ4+qnP//88/bRRx/ZokWLfPNy5szp7jVfywEAkBICtwAAAAAAZKCxY8e6+wkTJiSZ98gjj/jmAwCQEgK3AAAAAABkMAVnR40aZVOnTrWff/7ZKleubA888ACZtgCAVCNwCwAAAABAJlA5hAEDBrBtAQDpwuBkAAAAAAAAABBmCNwCAAAAAAAAQKQHbleuXGlt27a1MmXKWExMjC1YsMA378yZM/bYY49Z9erVrUCBAm6Zu+66y/bs2RPwHAcPHrSuXbta4cKFrWjRotarVy87duxYxrwjAAAAAAAAAMhugdvjx49bjRo1bMqUKUnmnThxwr799lsbNmyYu3/vvfdsy5YtduuttwYsp6Dtxo0bbcmSJbZo0SIXDO7Tp8/FvRMAAAAAAAAAyK6Dk7Vq1crdgilSpIgLxvp76aWX7M9//rPt2rXLypcvb5s2bbLFixfbN998Y9dff71bZvLkyda6dWsbN26cy9IFAGQfuugnuuCXXidPnrSdO3daxYoVLTY2Nl3Pof0TAAAAAAARG7hNq8OHD7uSCiqJIKtXr3Z/e0Fbad68ueXIkcPWrl1rHTp0SPIc8fHx7uY5cuRIZq82ACCLbN682d337t07LLZ5oUKFQr0KAAAgSpw7d86++OIL27t3r5UuXdpuvPFGy5kzZ6hXCwAQITI1cHvq1ClX8/aOO+5w9Wxl3759VqJEicCVyJXLihUr5uYFM3r0aBsxYkRmrioAIETat2/v7qtVq2b58+dPd7Zst27dbPbs2RYXF3dRQdsqVaqk+/8BAAA8Kh04ePBg1yvIo95B48ePt44dO7KhAAChC9xqoLLbbrvNEhISbNq0aRf1XEOGDLFBgwYFZNyWK1cuA9YSABBql156qd17770Z8lwK2taqVStDngsAAOBigradO3e2W265xd544w275ppr7Mcff7Rnn33WTX/nnXcI3gIAQhO49YK2v/zyiy1btsyXbSulSpWyAwcOBCx/9uxZO3jwoJsXTN68ed0NAAAAAIBwL4+gTFsFbRcsWODKAkrdunXdY/U2evjhh61du3aUTQAApOj/34NkQtB227Zt9tlnn1nx4sUD5terV88OHTpk69ev901TcPf8+fNWp06djF4dAAAAAACyjGraqjzCE0884QvaevRYPUp37NjhlgMAIEMzbo8dO2bbt2/3PdYOZ8OGDa5GrYqtq9uHRgZftGiRu9Lo1a3V/Dx58rhurDfffLMbhGb69Oku0NuvXz/r0qWLlSlTJq2rAwAAAABA2NBAZKLyCMF4073lAADIsIzbdevW2XXXXeduotqz+nv48OH222+/2QcffGC//vqr1axZ0wVyvduqVat8zzFnzhw3CE2zZs2sdevW1rBhQ3vllVfSuioAAAAAAIQVnf+KatoG4033lgMAIMMybhs3buwGHEtOSvM8yr6dO3duWl8aAAAAAICwduONN1rFihXdQGT+NW5FJQJHjx5tlSpVcssBAJCSDK9xCwBpsXLlSmvbtq0rlRITE+MObj0qpfLYY49Z9erVrUCBAm6Zu+66y/bs2RPwHBrcsGvXrm4gxKJFi1qvXr1cWRcAAAAgq+XMmdPGjx/vygdqILLVq1fb0aNH3b0ea/q4ceMYmAwAkPEZtwCQkY4fP241atSwnj17WseOHQPmnThxwtXMHjZsmFvmjz/+sL/97W926623urItHgVtVSNsyZIlLth7zz33WJ8+fcjsB4BsSPsO0f4jvU6ePOkGFlLGXGxsbLqeY9OmTel+fQCRT8e177zzjg0ePNjq16/vm65MW01PfNwLAEAwBG4BhFSrVq3cLZgiRYq4YKy/l156yf785z/brl27rHz58u7EePHixfbNN9/Y9ddf75aZPHmyq5+tTAYGPQSA7GXz5s3uXgPhhoNChQqFehUAhIiCsy1atLDu3bvbzz//bJUrV7Z//etfVrBgQT4TAECqELgFEFEOHz7sSiqoJIKoy5n+9oK20rx5c1dLbO3atdahQ4ckzxEfH+9uniNHjmTR2gMAMpu6IYsGws2fP3+6nkMXBbt162azZ8+2uLi4iwraVqlSJd3/DyDy26P333/f9/iHH35w7UK7du0CyoMBAJAcArcAIsapU6dczds77rjD1bOVffv2WYkSJQKWy5UrlxsEUfOC0YAQI0aMyJJ1BgBkrUsvvdTuvffeDHkuBW1r1aqVIc8FIHsGbfPkyWODBg1y7dI//vEPmzBhgpuu+QRvAQAXwuBkACKCatfedtttlpCQYNOmTbuo5xoyZIjL3PVuu3fvzrD1BAAA2WMQVdFxyfDhw6106dKuHrJ6/Wzbti1gGQZRzX5UJ9sL2mpQMiUNqEyC7vVY0zVfywEAkBICtwAiJmj7yy+/uJq3XratlCpVyg4cOBCw/NmzZ91JkuYFkzdvXvcc/jcAAIDkBlGdMmVK0I0zduxYe/HFF2369OmuRFOBAgWsZcuWrpeQ/yCqGzdudMcwixYtcsFgDaKK6PXII4+4e2Xa5syZ05YvX25vvPGGu9fjAQMGBCwHAEByKJUAICKCtspe+fzzz6148eIB8+vVq2eHDh2y9evXW+3atd20ZcuW2fnz561OnTohWmsAABDtg6gq23bSpEk2dOhQV7NUZs2aZSVLlnSZuV26dGEQ1WzKy7ouW7asXXHFFbZz507fvIoVK9rgwYMDlgMAIDlk3AIIqWPHjtmGDRvcTXbs2OH+3rVrlwvadu7c2datW2dz5syxc+fOubq1up0+fdpXf/Dmm2/+/9q7GzCf6vTx4/cgMwgtxZiYRmLNeszYDMZmohU1azzUn2WbIuPKqoZQWqNIbPLUYJKtTTZTW5SVWrtdVB6SQpSiEPKQcZU8zTDF+F/3/fud7+98x4yH8Z3vfL/zfb+u61xnPud8jDNj5jjnPve5b+se/sknn8iaNWtk6NChdrOkrzUCAACUBL1m0WsSLY/gqF69uj041uapF9NEtTDaQFUbp7oXBBenKaFek2ZnZ3vt0/EDDzzgNQ8AgKIQuAVQqjQoe+ONN9rivFKmH2u9uP3798uSJUtk37590rJlS6sf5ywfffSR53NoUFe7h3fq1Em6desmCQkJMnfu3FL8qgAAQFnnNEHVDFs3HTv7ittEVQPAzlKvXr0S+xpQMv761796Pr755put1Mbf//53W+u4sHkAABSGUgkASlXHjh3tVcOinG+fQ29+srKyfHxkAAAA/qdNVPVBtkMzbgneBhd3NvWyZctsKWqeJh4AAFAUMm4BAAAA4BI5TVALexXe2UcT1dCkTch8OQ8AELoI3AIAAADAJapfv74FZpcvX+6VHatZlNo8tWATVQdNVMs+bZKr4uPj5ejRo5KcnCzNmjWztY51u3seAABFoVQCAAAAABTRRHXHjh2esdNEVcs0RUdHS1pamkyYMMGaTGkgNz093ZqjaoCuYBPVOXPmWONVmqiWfTVr1rT1999/L02bNpW9e/fa+IsvvrCxNqdzzwMAoCgEbgEAAACgiCaqiYmJnrFTezYlJUXmzZsno0aNkpycHElNTbXMWm2QqvVMIyIivJqoarBWa5lqwK5Xr16SkZHB97sMcxrW7dmz55x9ThDXPQ8AgKIQuAUAAACAYjRRDQsLk/Hjx9tSFJqohh6nxrGv5gEAQheBW5SI3Nxc2bZtW5H7t27d6rU+n8aNG0vlypV9enwAAAAAUBJ+/vlnW5cvX96Cs/v37/fsq1u3rpVQOHPmjGceAABFIXCLEqFB27i4uAvO69+//wXnaDOHVq1a+ejIAAAAAKDkZGVl2VqDswcOHPDap0FcJ4tb53Xt2pV/CgBAkQjcokRolqy7e25BJ0+elN27d0tMTIxUqlTpgp8LAAAAAIKlqZ27nIa73IbWOdaAbsF5AAAUhsAtSoSWNrhQlmz79u357gMAAAAoU+Lj42Xx4sVWKuH48eOybt06K49Qp04dadOmjVStWtWCtzoPAIDzKXfevUAJ0FpOM2bMkAceeMDW1HYCAAAAUFZowFZpcLZ3797y5Zdf2huHutaxk3HrzAMAwGeB25UrV0pSUpJERUXZax/6JNFNXwMZO3asPU3UV+A7d+4s27dv95pz+PBh6devn1SrVk2uuuoqGThwIK+JhIhRo0ZJlSpVZNiwYTJr1ixb61i3AwAAAECw27Nnj+fjd999V4YOHWr3vLrWcWHzAADwSeA2JydHWrRoIbNnzy50/+TJkyUjI0PmzJljr4RoUK5Lly5y6tQpzxwN2urTxvfee0+WLl1qweDU1NRLPRQEGQ3OPvPMM1KzZk0ZMWKEZGZm2lrHup3gLQDgcp3vAfMvv/wijzzyiDRr1syuT3TO3XfffU7jGB4wAwAuR4MGDXw6DwAQui65xq12vSyq86Vm2+qr72PGjJHu3bvbtvnz50vt2rXtxqlPnz6ydetWWbZsmXz66afSunVrmzNz5kzp1q2bTJkyxW6iUPZoOYTp06dL9erVJSIiwv6tHdddd51t1/0TJkyQihUrluqxAgCCl/OAecCAAdKzZ0+vfbm5ubJx40ZJT0+3OT/99JM89NBD8oc//EHWr1/v9YBZaxHqA2YN9t577732gNnpEg4AwPkMHjzY3ixUV199tdxyyy32wFD/j1qxYoX88MMPnnkAAPitxu2uXbvk4MGDVh7BoQE5LcC+du1aG+tayyM4QVul87W7pmboFiYvL0+OHTvmtSC4aHbt6dOn5ejRo9K8eXP7OdBC/brWsW7X/ToPAIDi0ofL+hCwR48e5+zTaxINxt51113y61//2prCaNmeDRs2yHfffWdznAfML7zwgl2/JCQk2APm11577ZzMXAAACrN69WrPx3qfW7duXYmLi7O1jgubBwBAiQduNWirNMPWTcfOPl3XqlXLa3+FChWkRo0anjkFTZo0yW62nKVevXq+PGz4gVPn+NZbb5VFixZZ6Yy3337b1jrW7e55AAD4gz441JIK+lBZ8YAZAHC5/vGPf9haS/McOnRIpk2bZvVtda3jJk2aeM0DAMBnpRJKw+jRo2X48OGesWbcErwNLnpTrK688kpp1KiR7N6927MvJibGXll1zwMAoKTpw0Otedu3b19rmHo5D5jHjRvHPxgAwOibheqLL76wht0nT570fGd0rP1e3PMAAPBLxm1kZKSts7Ozvbbr2Nmna33K6KavyGsjEGdOQeHh4XZD5V4QXPR1U/XWW2/ZE2Z3qQQd/+tf//KaBwBASdLatVoyQevzP/fcc5f9gFkzd51l7969PjtOAEDwadeunedjd9C24Ng9DwCAEg/c1q9f34Kvy5cv98qO1dq1bdu2tbGujxw5YvXkHFqgPT8/n6BdGeZuOqcNYD7//HP72dC1uyEMzekAAP4K2u7Zs8dq3rofCPOAGQBwuZo2ber11kanTp2kf//+ttZxYfMAAPBJqYQTJ07Ijh07vBqSbdq0yV4hjI6OlrS0NGsK0rBhQwvkaudmDcYlJyfb/NjYWLnttttk0KBBMmfOHLt50no/ffr0IWgXAq699lp71dTdQVUvXnT7/v37S/XYAAChE7TVmurvv/++1KxZ02u/+wGzNpJRPGAGAFyKVatWeb1d6k5sKjhPm2oCAOCzwK1mRyYmJnrGTu3ZlJQUmTdvnowaNUpycnIkNTXVbny0G7N2Z46IiPD8mQULFliwVp84alfNXr16SUZGxqUeCoKIUx5Dg7O33367NGjQwGoL6s/Fzp075Z133vGaBwBAcZzvAXOdOnWkd+/esnHjRlm6dKmcOXPGU7dW91esWJEHzACAy+Z+o9AX8wAAoeuSA7cdO3a0enBF0eZS48ePt6UoenOUlZV1qX81gpjeLDsNXJ5//nlPoFZpZvbEiRPlscce88wDAKA4zveA+YknnpAlS5bYuGXLll5/TrNv9RpH8YAZAHA5tEeLKl++vJXgcb9Z6LyBqA8PnXkAAPgscAsUR4cOHSQmJkYWLVpk9Yzd9KLlzTfftACuzgMAoLgu9ID5fPscPGAGAFwOfavUuc85cOCA1z4dO/8XOfMAACgK/1PAL/Rp85133mmZUHl5eTJ37ly7aNG1jnW7vr6q8wAAAAAgWLkbLhd8YOge05gZAHAhBG7hF/q0+Y033pDWrVtLpUqVrAayXqjounLlyrZ94cKFNg8AAAAAgpWWD/TlPABA6CJwC7/Qjqm7d++2RnQFnzpr6YSePXtaAxl3B1YAAAAACDbVqlXz6TwAQOgicAu/+P77722tDciaN28ua9eulePHj9tax3/5y1+85gEAAABAMNq3b59P5wEAQheBW/hFrVq1bN2+fXtrUHbq1Cl5++23ba1j3e6eBwAAAADBqGAz5sudBwAIXRVK+wAQWn744Qdp1KiRlU1wxMTESERERKkeFwAAAAD4WoUKFeR3v/ud1KlTx94uXLlypZw+fZpvNADgohC4hV8cOnTI1tu2bZPatWvL3Llz5Y477pClS5dKenq6J5DrzAMAAACAYOTu6aGB2xUrVnjG2qjZCdwW7P0BAEBBBG7hF04JhNjYWMnNzZXU1FSvjNvGjRtbUJdSCQAAAACC/S1DR1hY2EXNAwCgMARu4Vc1a9aUzZs3y5o1a+xVIX1lSOvbJiYm8i8BAAAAIOhVqVKlyDq27ixb9zwAAApD4BZ+4ZRAWL16tfTo0UNuuOEGOXnypL0qNGXKFAvkuucBAAAAQDDQNwr17UHH9ddfX+Rcd+BW523cuNFrv76JWLly5RI6UgBAsCFwC7/QzFrVoUMHeeedd87Zr9tXrVrlmQcAAAAAwUCDtnFxcYXuy8vLK3I8ffp0W9w2bNggrVq1KqEjBQAEGwK38AsNzFavXt2Cs9dcc42kpKTYE+Zvv/1WXn75Zduu+3UeAAAAAAQLzZLVgKvbs88+K/Pnz7cat+4s23Llyln5hLvvvlseeuihQj8XAACOcp6PgBJ05swZOX78uH3cpk0b6dmzp/Tv39/WOla6X+chtKxcuVKSkpIkKirKLmwXL17stV8vdMeOHWvZ2Fpao3PnzrJ9+3avOYcPH5Z+/fpJtWrV5KqrrpKBAwfKiRMn/PyVAACAUPPEE0/Y9Yt7cQfeTp06JX/+85+tz8OVV14pvXr1kuzs7FI9ZvieljbQLFn3oskpI0eOlPLly3vN1cCtbtf9Bf+MLpRJAAC4EbiFX2RmZtqT5fvvv1+2bNki7dq1syCbrr/88ksZPHiw7dd5CC05OTnSokULmT17dqH7J0+eLBkZGTJnzhxZt26dNXHo0qWL3Qg5NGirP0fvvfeeLF261ILBqampfvwqAABAqGrSpIk13XUW7engGDZsmLz99tvyxhtvyIcffigHDhywxAWEBr2O1Wvd4cOH21jXOtbtAABcDEolwC927txpa82cnDlzppVG0AtbzaLU8giaefD888975iF0dO3a1ZbCaLbtjBkzZMyYMdK9e3fbpq+c1a5d2zJz+/TpI1u3bpVly5bJp59+Kq1bt7Y5+jPWrVs3a3ynmbwAAAAlpUKFChIZGXnO9qNHj8qLL74oWVlZcsstt9i2l156SWJjY+Xjjz+W+Ph4/lFCQMWKFS3JYNq0abbWMQAAF4uMW/hFgwYNbK3ZkPq6UMeOHaVv37621rFud88D1K5du+TgwYNWHsGhtZC1vMbatWttrGstj+AEbZXO19fQNEO3MNoU4tixY14LAABAcWgJJ31QrP0bNDD33Xff2XatefrLL794XcdoGYXo6GjPdQzXKQAA4HwI3MIvhgwZYtkImjl5+vRpr3061kxc3a/zAIcGbZVm2Lrp2Nmn61q1annt15+lGjVqeOYUNGnSJAsAO0u9evX4pgMAgEumD5PnzZtnb/8899xz9tBZ3ybT3g16HaLZlfqAuajrGK5TAADA+RC4hV/oRavW+NKSCHXr1pW5c+dajS9d61i3635eHYI/jB492l5fdJa9e/fyjQcAAJdMyz3deeed0rx5c6vB/+6778qRI0fk9ddf5zoFAABcNmrcwm+cIvzTp0+3ZmSeH8IKFayzKkX6UZBTL04D+1oP2aHjli1beuYcOnTonCzuw4cPF1pvToWHh9sCAADgS5pd26hRI9mxY4fceuut8vPPP1sg1511q9cxRV2jcJ0CAADcyLhFqXRW1eDt0KFDbU1nVRSlfv36dmOzfPlyzzatR6u1a9u2bWtjXesNkdaRc6xYsULy8/Pt9UUAAAB/OXHihDXb1QfOcXFxcsUVV3hdx3z99ddWA9e5jgEAAPBr4PbMmTOSnp5uAZdKlSpZs6knn3zSusM79GOtaaoXNDpHC/ZrUX+EBi2HkJaWJjNnzrQ15RFCm97gbNq0yRalteH0Y72pCQsLs5+RCRMmyJIlS+SLL76Qu+++2xqAJCcn23ztzHzbbbfJoEGD5JNPPpE1a9bYQ4E+ffrYPAAAgJIyYsQI+fDDD2X37t3y0UcfSY8ePazxrjbh1Tr6AwcOlOHDh8v7779vD5nvvfdeC9rGx8fzjwIAAPxfKuHpp5+2wvwvv/yyNGnSRNavX28XKHrh8uCDD3qyLjMyMmyOBng10Ks1ob766iuJiIjw9SEBCGB6jkhMTPSM9eZGpaSkWLOPUaNGWVZ2amqqZdYmJCRYAxD3uWLBggUWrO3UqZOUK1dOevXqZecYAACAkrRv3z4L0v74449yzTXX2HXKxx9/bB8rfbvMuTbJy8uze57MzEz+UQAAQOkEbvVJc/fu3eX222+3cUxMjLz66quWCedk286YMUPGjBlj89T8+fOtu+rixYstSw5A6OjYsaNXRn5BmnU7fvx4W4pSo0YNycrKKqEjBAAAKNxrr7123m+NPmiePXu2LQAAAKVeKqFdu3ZWx+mbb76x8ebNm2X16tXWcdV5DfrgwYNWHsGh2bhai3Lt2rWFfk59Oq11Ld0LAAAAAAAAAJRVPs+4ffTRRy2w2rhxY6vvpDVvn3rqKenXr5/t16Ct0gxbNx07+wqaNGmSjBs3zteHCgAAAAAAAAChkXH7+uuvW71JfW1548aNVsd2ypQpti6u0aNHy9GjRz3L3r17fXrMAAAAAAAAAFCmM25HjhxpWbdOrdpmzZrJnj17LGtWmw1FRkba9uzsbKlTp47nz+m4ZcuWhX7O8PBwWwAAAAAAAAAgFPg84zY3N9c6p7ppyYT8/Hz7uH79+ha81Tq4Di2tsG7dOmnbtq2vDwcAAAAAAAAAgo7PM26TkpKspm10dLQ0adJEPvvsM5k2bZoMGDDA0yE+LS1NJkyYIA0bNrRAbnp6ukRFRUlycrKvDwcAAAAAAAAAgo7PA7czZ860QOyQIUPk0KFDFpAdPHiwjB071jNn1KhRkpOTI6mpqXLkyBFJSEiQZcuWSUREhK8PBwBQBujbHNu2bSty/9atW73W56PNMytXruzT4wMQXDinAAAAICQDt1WrVpUZM2bYUhTNuh0/frwtAABciAZt4+LiLjivf//+F5yzYcMGadWqFd90IIRxTgEAAEBIBm4BAPA1zZLVgGtRTp48Kbt375aYmBipVKnSBT8XgNDGOQUAAADBgMAtACDgaWmDC2XJtm/f3m/HAyC4cU4BAABAMCBwCwAAAPyvM2fOyKpVq+T777+XOnXqSIcOHaR8+fJ8fwAAAOB3BG4BAEGNIAsAX3nzzTfl4YcfttIrDi3BMnXqVOnZsyffaCBEbd++XY4fP17sP38pTVQv1E+mYcOGl/U5AADBhcAtACBoEWQB4MvzSe/evaVbt27SvXt3q52tNbN37Nhh2xcuXEjwFgjRoG2jRo188rkuponqhXzzzTcEbwEghBC4BQAEdZDljjvukFdffVWaNm0qW7ZskYkTJxJkAXDJmfuaaXv99dfLf/7zH3nnnXc8+ypUqGDbR4wYYQFdyiYAocXJtH3llVckNja2WJ/jUpqoFkWzdTXwezmZvwCA4EPgFgAQtEEWDdouXrxYypUrZ9vj4+NtnJycTJAFwEXTmrZOeYTatWvLhAkT7PyydOlSGTNmjOzcudMzr2PHjnxngRCkQdsLNUo9H5qoAgCK43/udAEACMIgy2OPPeYJ2jp0PHr0aNm1a5fNA4AL2bt3r61r1aol+/btk/vuu08iIyNtrWPd7p4HAAAA+AOBWwBA0NFu70rLI2j27QcffGDlEnStY93unofQsXLlSklKSpKoqCgJCwuzDGy3s2fPytixY6VOnTr2umrnzp2tfqHb4cOHpV+/flKtWjW56qqrZODAgXLixAk/fyXwp3Xr1tl6wIABVhrBTcf33HOP1zwAAADAHwjcAgCCjgbd1KxZs+SGG26QxMRE+eMf/2hrHet29zyEjpycHGnRooXMnj270P2TJ0+WjIwMmTNnjgXhqlSpIl26dJFTp0555mjQ9ssvv5T33nvPXpXXYHBqaqofvwr4mwb01YYNGyQ/P99rn44/++wzr3kAAACAPxC4BQAEnQ4dOsg111xjJRE0u3bt2rXWrEPXOtYSCvpqs85DaOnatavVJ+3Ro8c5+zToNmPGDKtZqk2mmjdvLvPnz5cDBw54MnO1+cuyZcvkhRdekDZt2khCQoLMnDlTXnvtNZuHsqlhw4a21mC91sh2n1N0rNvd8wAAAAB/IHALAAhK+hq8OyDnLEBRtO7xwYMHrTyCo3r16hag1QCd0rWWR2jdurVnjs7X2slFvSafl5cnx44d81oQXIYMGWIlEfTnYfPmzdKuXTsrlaHrzz//3Lbrfp0HAAAA+AuBWwBA0NGmY4cOHZJJkybJli1bvIIs+or7xIkTbT/NyeCmQVtVu3Ztr+06dvbp2mlE5dCAXY0aNTxzCtKfQw3sOUu9evX4xgeZihUryrBhw+To0aMWiB8+fLiVXNG1ltHQ7bpf5wEAAAD+4t19AQCAIOA0HRs6dKiMHDnSArS6TWvaanmE3NxcK5dAczL4g5bs0ACfQzNuCd4GH61/rKZPny7Tpk3zCtzrecbZDwAAAPgLgVsAQNBxmo5ptm18fLx07NjRa79ud88DVGRkpK2zs7O9fjZ03LJlS88czdZ2O336tBw+fNjz5wsKDw+3BcFPg7NaIzkzM1N27twpDRo0sPIIZNoCAACgNBC4BQAEHc2qjYmJsZIIixYtkjVr1ngybtu3b2+vrtevX5/mZPCiPxMafF2+fLknUKvZsVq79v7777dx27Zt5ciRI7JhwwaJi4uzbStWrJD8/HyrhYuyT4O0aWlppX0YAAAAAIFbAEDwKV++vEydOlV69+5tNUVPnjzp2VepUiWrSblw4UKbh9By4sQJ2bFjh1dDsk2bNlmN2ujoaAvIaUZlw4YNLZCbnp4uUVFRkpycbPNjY2Pltttuk0GDBsmcOXPkl19+sZIcffr0sXkAAAAA4C9k3AIAgtbZs2fP2RYWFlbodoSG9evXS2Jiomfs1J5NSUmRefPmyahRoyQnJ0dSU1MtszYhIUGWLVsmERERnj+zYMECC9Z26tRJypUrJ7169ZKMjIxS+XoAAKUv8sowqXTkG5EDpdfbW/9+PQ4AQGghcAsACDpnzpyRhx9+WJKSkgotlaCBthEjRkj37t3Jug0xWu/4fIF7DeyPHz/elqJodm5WVlYJHSEAINgMjqsosSsHi6wsvWOI/d/jAACEFgK38Luff/6Zph8ALsuqVatk9+7d8uqrr1pGpJuOR48eLe3atbN5BRuXAcCFHgzpucN5GKQ1tSm7AoS25zf8LP9v7DyJbdy41I5h67Zt8vzUP8ofSu0IAABlJnC7f/9+eeSRR+Tf//635Obmyg033CAvvfSStG7d2vZrJszjjz8uf/vb3+w1Rc2Oeu6556zeHMo2fUV1+vTp1qHbMXLkSBk2bJh1cgaAi6EBFaVd3/v27WtBXIc2LdMapu55AHAx3nzzTcvmL3hO0ZraPXv25JuIyzJ79mx55pln5ODBg9KiRQuZOXOm3HTTTXxXg8DBE2fl5FWNRKL+p7FlaTh5MN+OAwAQWnxepOenn36yQOwVV1xhgduvvvrKLnZ/9atfeeZogE5rxWnTD+3kXKVKFenSpYs1k0HZDtrqxWrNmjUtaK8BFV3rWLfrfgC4GJoFp/70pz9Js2bNZO3atXL8+HFb61i3u+cBwMUEbbXhYWHnFN2u+4Hi+uc//2k1tzV5ZePGjRa41fufQ4cO8U0FAABFCjvr4w4ujz76qNUa1FfMCqN/nXZl1mwGrT+ojh49KrVr17amIdq1+UKOHTtmXcT1z1WrVs2Xh48SLI+gAXoN0u7bt08qVPi/ZG/Nvq1bt678+OOP1jCmYkVqNwWDsvR7WJa+llDBOaXsKUu/h2Xpawml8gj6hpgGaRcvXuxVgiU/P1+Sk5Nly5Ytsn37dsomBIlA+z1s06aN/Pa3v5VZs2Z5fq7q1asnDzzwgN0/BdPXEmpWr15tJVM04aRVq1bF+hwnT560TH7N4K9UqVKxPsfWrVulf//+smHDhmIfB4qP30MAZaZUwpIlS+zp8Z133ikffvihXHvttTJkyBAZNGiQ7d+1a5e9HtS5c2fPn9ELEb2Y0ayGwgK3eXl5trhPmggumZmZFqDV15fdQVulY20SM3jwYJuXlpZWascJIDh89NFHdk7Jzs6215e1pm3Tpk0tsDJp0iTb7syjxi2AC6FuNkr6YaMG2/T/Koc+HND7Ib3/KYh7n8Cybds2Wzv3s6WtatWqpX0IAIBgDtx+++23Vq9WXwV67LHH5NNPP5UHH3zQsihTUlIsaKs0w9ZNx86+gvQmfNy4cb4+VPiR1qFUd9xxR6H7ne3OPAA4H6d27SuvvCJjxoyxRmSO+vXr23bNSqHGLYCL4Zwr9AFQYZztnFNQHD/88INldRd2/+MEBd249wksmnGvGjduLJUrVz5vNqwv6DVMbGxskUFb+sIAQGjxeeBWX/vRJmQTJ0608Y033mgZUFrPVgO3xaFPpzUQ7M641VeLEDwaNGhg66VLl8p99913zn7d7p4HAOfj1K7Vc8aOHTvO6QD/ySefeM0DgIs5p+g1a3x8/Dn7dTvnFPgL9z6B5eqrry70/sVNg7qaVe2LUgnnCxADAEJPhZK48P3Nb37jtU2fGC5atMg+joyMtLW+xuq+odZxy5aFd+kMDw+3BcFLy2WMHDnSMuPuueeec2rcjh071rbpPAC4EA3O6s2PPiTUepTucgj6AFGzlTTzVucBwKWeUwrWuOWcgssN/JUvX95TxsehY+feyI17n+CjgdYL1Z3VBt4AAFyq/7sq9RH9D+nrr7/22vbNN9/IddddZx/rjbReoCxfvtwrg3bdunXStm1bXx8OAoSWyhg2bJhdoGojsrlz58qBAwdsrWPdrvtpTIaC9NXC9PR0O3dohoJmWD755JPW6NChH2vwXx8G6RytGacNZFB26Q3w1KlTLVtfX2F0d4DXsW6fMmUKTYQAcE5BqdPr27i4OK/7H30goGPufwAAgF8zbjX4prUGNWPhrrvustdVNTiniwoLC7PmU9qkSuvzaDBGgzJRUVGe+kEomyZPnmzr6dOnWyMyh2baajausx9we/rpp61u9ssvvyxNmjSR9evXy7333mtNDbV+tvOzlZGRYXOcc4o2Sfzqq68kIiKCb2gZpU3JFi5cKA8//PA5NW51u+4HAM4pCARa9k3LxmlJuZtuuklmzJghOTk5dk0DAABQlLCz7rQ1H9FMJ63NpBlvegOtFyruLpz6Vz7++OMWzD1y5IgkJCRIZmamNGrU6KI+v2boatDm6NGjUq1aNV8fPvzQWVf/vbURmWZPankEMm2Dj79+D7VxnTbvePHFFz3bevXqZZm12rxBzyf64EeDdyNGjLD9ekz6Z+bNmyd9+vQJmK8FJZeVXbDGrWbkIriUpd/DsvS1hCLOKWVDIP4ezpo1S5555hlryKwl4vShc5s2bYLyawFCDb+HAMpU4LakcdIEQuf3ULP39SHPf//7X3u4s3nzZvn9738v06ZNk379+sm3335rDwA+++wzrzrZN998s42fffbZcz5nXl6eLQUbHnJDBJSesvR/e1n6WoBgVZZ+D8vS1wIEK34PAZSZUgkA4EuPPvqoXShph13NotRMqKeeesqCtkqzVpRm2Lrp2NlXkDaZGTduHP9QAAAAAAAgdJqTAYAvvf7667JgwQLJysqSjRs3Wh1bbTql6+LSUi6ateIse/fu9ekxAwAAAAAAhGTGrVPdQbPwAJQO5/evpKutaOM6zbp1atU2a9ZM9uzZY1mz2uQjMjLStmdnZ1t9U4eO3aUT3MLDw21xcE4BQuec4g+cU4DSxzkFAOcUAGVBUAZujx8/bmutSQmg9H8fte5aScnNzZVy5bxfDtCSCfn5+faxNkDU4O3y5cs9gVq9WVu3bp3cf//9F/V3cE4BQuec4g+cU4DAwTkFAOcUAMEsKAO32kFeX22uWrWqhIWFlfbhoBicZlD670iTheDNKNObIf19LElJSUlW0zY6OlqaNGliTci0MdmAAQNsv54D0tLSZMKECdKwYUML5Kanp9txJScnX9TfwTkl+HFOCX7+Oqf4A+eU4Mc5JfhxTkEg4ZwS/MrSOQVAcAk7WxbeSUTQoSsnLpZeIGkg9q233pJDhw7ZxVLfvn1l7NixUrFiRZujp7HHH39c5s6dK0eOHJGEhATJzMyURo0a8Y0OEZxTAHBOARCouE4BABQXgVuUCi5eAHBOARCouE4BwDkFABAIvAtHAgAAAAAAAABKHYFblIrw8HB7tV3XAMA5BUAg4ToFAOcUAEAgoFQCAAAAAAAAAAQYMm4BAAAAAAAAIMAQuAUAAAAAAACAAEPgFgAAAAAAAAACDIFbAAAAAAAAAAgwBG7hVytXrpSkpCSJioqSsLAwWbx4Mf8CADinAAgIXKcA4JwCAAgkBG7hVzk5OdKiRQuZPXs233kAnFMABBSuUwBwTgEABJIKpX0ACC1du3a1BQA4pwAINFynAOCcAgAIJGTcAgAAAAAAAECAIXALAAAAAAAAAAGGwC0AAAAAAAAABBgCtwAAAAAAAAAQYAjcAgAAAAAAAECAqVDaB4DQcuLECdmxY4dnvGvXLtm0aZPUqFFDoqOjS/XYAAQfzikAOKcACFRcpwAALlfY2bNnz172ZwEu0gcffCCJiYnnbE9JSZF58+bxfQRwSTinAPAlzikAOKcAAAIJgVsAAAAAAAAACDDUuAUAAAAAAACAAEPgFgAAAAAAAAACDIFbAAAAAAAAAAgwBG4BAAAAAAAAIMAQuAUAAAAAAACAAEPgFgAAAAAAAAACDIFbAAAAAAAAAAgwBG4BAAAAAAAAIMAQuAUAAAAAAACAAEPgFgAAAAAAAAACDIFbAAAAAAAAAAgwBG4BAAAAAAAAQALL/wf9IN1VC1dEIQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1400x1400 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"Box plots for test_data_set before removing outliers\")\n",
        "\n",
        "plt.figure(figsize=(14, 14))\n",
        "\n",
        "for i, col in enumerate(iqr_columns):\n",
        "        plt.subplot(5, 5, i + 1)     # 3 rows, 3 columns\n",
        "        plt.boxplot(X_Test[col])\n",
        "        plt.title(col)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {},
      "outputs": [],
      "source": [
        "clean_dataset_train = remove_outliers(X_Train, iqr_columns)\n",
        "clean_dataset_test = remove_outliers(X_Test, iqr_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Box plots for train_data_set after removing outliers\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1400x1400 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"Box plots for train_data_set after removing outliers\")\n",
        "\n",
        "plt.figure(figsize=(14, 14))\n",
        "\n",
        "for i, col in enumerate(iqr_columns):\n",
        "        plt.subplot(5, 5, i + 1)     # 3 rows, 3 columns\n",
        "        plt.boxplot(clean_dataset_train[col])\n",
        "        plt.title(col)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Box plots for test_data_set after removing outliers\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1400x1400 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"Box plots for test_data_set after removing outliers\")\n",
        "\n",
        "plt.figure(figsize=(14, 14))\n",
        "\n",
        "for i, col in enumerate(iqr_columns):\n",
        "        plt.subplot(5, 5, i + 1)     # 3 rows, 3 columns\n",
        "        plt.boxplot(clean_dataset_test[col])\n",
        "        plt.title(col)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "====== ABNORMAL_SHORT_TERM_VARIABILITY ======\n",
            "Q1: 33.0\n",
            "Q3: 61.0\n",
            "IQR: 28.0\n",
            "Lower Bound: -9.0\n",
            "Upper Bound: 103.0\n",
            "Outliers: []\n",
            "\n",
            "====== MEAN_VALUE_OF_LONG_TERM_VARIABILITY ======\n",
            "Q1: 5.2\n",
            "Q3: 11.0\n",
            "IQR: 5.8\n",
            "Lower Bound: -3.499999999999999\n",
            "Upper Bound: 19.7\n",
            "Outliers: [19.9 21.7 23.4 19.8 20.8 23.  21.1 21.3 21.7 20.4 21.3 21.3 22.1 19.9\n",
            " 21.3 23.3 19.8 24.1 21.7 20.5]\n",
            "\n",
            "====== HISTOGRAM_WIDTH ======\n",
            "Q1: 31.0\n",
            "Q3: 84.0\n",
            "IQR: 53.0\n",
            "Lower Bound: -48.5\n",
            "Upper Bound: 163.5\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MIN ======\n",
            "Q1: 74.0\n",
            "Q3: 124.0\n",
            "IQR: 50.0\n",
            "Lower Bound: -1.0\n",
            "Upper Bound: 199.0\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MAX ======\n",
            "Q1: 150.0\n",
            "Q3: 170.0\n",
            "IQR: 20.0\n",
            "Lower Bound: 120.0\n",
            "Upper Bound: 200.0\n",
            "Outliers: [205]\n",
            "\n",
            "====== HISTOGRAM_MODE ======\n",
            "Q1: 129.0\n",
            "Q3: 148.0\n",
            "IQR: 19.0\n",
            "Lower Bound: 100.5\n",
            "Upper Bound: 176.5\n",
            "Outliers: [186 180 180 186 179]\n",
            "\n",
            "====== HISTOGRAM_MEAN ======\n",
            "Q1: 128.0\n",
            "Q3: 146.0\n",
            "IQR: 18.0\n",
            "Lower Bound: 101.0\n",
            "Upper Bound: 173.0\n",
            "Outliers: [ 85 178  88  97 175 180  91  85  92  94  89  86]\n",
            "\n",
            "====== HISTOGRAM_MEDIAN ======\n",
            "Q1: 130.0\n",
            "Q3: 148.0\n",
            "IQR: 18.0\n",
            "Lower Bound: 103.0\n",
            "Upper Bound: 175.0\n",
            "Outliers: [180 176 177 178 183]\n",
            "\n",
            "====== HISTOGRAM_VARIANCE ======\n",
            "Q1: 1.0\n",
            "Q3: 11.0\n",
            "IQR: 10.0\n",
            "Lower Bound: -14.0\n",
            "Upper Bound: 26.0\n",
            "Outliers: [31 27 31 30 30 31 29 29 29 30 31 28 31 29 28 31 29 31 30 30 31 28 31 30\n",
            " 29 27 27 30 28 29 28 30 27 31 31 29 29 27 27 28 31 28 30 30 28 30 27 27\n",
            " 27 30 27 31]\n",
            "\n",
            "====== ABNORMAL_SHORT_TERM_VARIABILITY ======\n",
            "Q1: 35.5\n",
            "Q3: 62.0\n",
            "IQR: 26.5\n",
            "Lower Bound: -4.25\n",
            "Upper Bound: 101.75\n",
            "Outliers: []\n",
            "\n",
            "====== MEAN_VALUE_OF_LONG_TERM_VARIABILITY ======\n",
            "Q1: 5.1\n",
            "Q3: 10.5\n",
            "IQR: 5.4\n",
            "Lower Bound: -3.0000000000000018\n",
            "Upper Bound: 18.6\n",
            "Outliers: [19.3 18.9 19.7 19.1 19.3 21.4 21.5]\n",
            "\n",
            "====== HISTOGRAM_WIDTH ======\n",
            "Q1: 33.0\n",
            "Q3: 81.5\n",
            "IQR: 48.5\n",
            "Lower Bound: -39.75\n",
            "Upper Bound: 154.25\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MIN ======\n",
            "Q1: 80.0\n",
            "Q3: 123.5\n",
            "IQR: 43.5\n",
            "Lower Bound: 14.75\n",
            "Upper Bound: 188.75\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MAX ======\n",
            "Q1: 150.0\n",
            "Q3: 171.0\n",
            "IQR: 21.0\n",
            "Lower Bound: 118.5\n",
            "Upper Bound: 202.5\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MODE ======\n",
            "Q1: 131.0\n",
            "Q3: 148.0\n",
            "IQR: 17.0\n",
            "Lower Bound: 105.5\n",
            "Upper Bound: 173.5\n",
            "Outliers: [105 180]\n",
            "\n",
            "====== HISTOGRAM_MEAN ======\n",
            "Q1: 128.0\n",
            "Q3: 147.0\n",
            "IQR: 19.0\n",
            "Lower Bound: 99.5\n",
            "Upper Bound: 175.5\n",
            "Outliers: []\n",
            "\n",
            "====== HISTOGRAM_MEDIAN ======\n",
            "Q1: 132.0\n",
            "Q3: 149.0\n",
            "IQR: 17.0\n",
            "Lower Bound: 106.5\n",
            "Upper Bound: 174.5\n",
            "Outliers: [101 176]\n",
            "\n",
            "====== HISTOGRAM_VARIANCE ======\n",
            "Q1: 1.0\n",
            "Q3: 11.0\n",
            "IQR: 10.0\n",
            "Lower Bound: -14.0\n",
            "Upper Bound: 26.0\n",
            "Outliers: [29 30 31 28 27 27 28 27 30 28 29 31 29]\n"
          ]
        }
      ],
      "source": [
        "# List of columns I want to analyze in train_data_set\n",
        "\n",
        "# Loop through each column and compute outliers\n",
        "for col in iqr_columns:\n",
        "    find_outliers(clean_dataset_train[col], col)\n",
        "\n",
        "# List of columns I want to analyze in test_data_set\n",
        "\n",
        "for col in iqr_columns:\n",
        "    find_outliers(clean_dataset_test[col], col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train samples: 1700\n",
            "Test samples: 426\n",
            "Split ratio: 80.0%\n"
          ]
        }
      ],
      "source": [
        "# Fill these after preprocessing\n",
        "train_samples = 1700       # Number of training samples\n",
        "test_samples = 426         # Number of test samples\n",
        "train_test_ratio = 0.8  # e.g., 0.8 for 80-20 split\n",
        "\n",
        "print(f\"Train samples: {train_samples}\")\n",
        "print(f\"Test samples: {test_samples}\")\n",
        "print(f\"Split ratio: {train_test_ratio:.1%}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Step 2: Machine Learning Classifi cation models and Evaluation metrics\n",
        "Implement the following classifi cation models using the dataset chosen above. All the 6 ML models have to be implemented on the same dataset.\n",
        "1. Logistic Regression\n",
        "2. Decision Tree Classifi er\n",
        "3. K-Nearest Neighbor Classifi er\n",
        "4. Naive Bayes Classifi er - Gaussian or Multinomial\n",
        "5. Ensemble Model - Random Forest\n",
        "6. Ensemble Model - XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training LR model...\n",
            "✓ LR training completed in 0.03s\n"
          ]
        }
      ],
      "source": [
        "LR_model = LogisticRegression()\n",
        "\n",
        "# Train Base Model\n",
        "print(\"Training LR model...\")\n",
        "LR_start_time = time.time()\n",
        "\n",
        "y = y_Train.loc[clean_dataset_train.index]\n",
        "\n",
        "LR_model.fit(clean_dataset_train, y)\n",
        "\n",
        "LR_pred = LR_model.predict(clean_dataset_test)\n",
        "\n",
        "LR_training_time = time.time() - LR_start_time\n",
        "print(f\"✓ LR training completed in {LR_training_time:.2f}s\")\n",
        "\n",
        "LR_prob = LR_model.predict_proba(clean_dataset_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Section 5: Evaluation and Metrics\n",
        "\n",
        "Calculate appropriate metrics for your problem type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {},
      "outputs": [],
      "source": [
        "cm1 = confusion_matrix(y_Test.loc[clean_dataset_test.index], LR_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.heatmap(cm1, \n",
        "            annot=True, \n",
        "            fmt='d', \n",
        "            cmap='Greens',\n",
        "            xticklabels=['normal','suspect','pathological'], \n",
        "            yticklabels=['normal','suspect','pathological'],\n",
        "            )\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for Logistic Regression')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report for Logistic Regression:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.88      0.94      0.91       256\n",
            "     Suspect       0.62      0.48      0.54        60\n",
            "Pathological       0.58      0.47      0.52        15\n",
            "\n",
            "    accuracy                           0.83       331\n",
            "   macro avg       0.69      0.63      0.66       331\n",
            "weighted avg       0.82      0.83      0.82       331\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_test = y_Test.loc[clean_dataset_test.index]\n",
        "\n",
        "print(\"Classification Report for Logistic Regression:\")\n",
        "print(classification_report(y_test, LR_pred, target_names=[\"Normal\",\"Suspect\",\"Pathological\"]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {},
      "outputs": [],
      "source": [
        "def calculate_metrics(y_true, y_pred,y_prob):\n",
        "\n",
        "    metrics = {}\n",
        "\n",
        "    acc = accuracy_score(y_true, y_pred)\n",
        "    print(\"Accuracy:\", acc)\n",
        "\n",
        "    auc = roc_auc_score(y_true, y_prob, multi_class=\"ovr\")\n",
        "    print(\"ROC AUC:\", auc)\n",
        "\n",
        "    precision = precision_score(y_true, y_pred, average=\"weighted\")\n",
        "    print(\"Precision:\", precision)\n",
        "\n",
        "    recall = recall_score(y_true, y_pred, average=\"weighted\")\n",
        "    print(\"Recall:\", recall)\n",
        "\n",
        "    f1score = f1_score(y_true, y_pred, average=\"weighted\")\n",
        "    print(\"F1 Score:\", f1score)\n",
        "\n",
        "    matthews_corcoef = matthews_corrcoef(y_true, y_pred)\n",
        "    print(\"Matthews Correlation Coefficient:\", matthews_corcoef)\n",
        "\n",
        "    metrics['accuracy_score'] = acc\n",
        "    metrics['roc_auc'] = auc\n",
        "    metrics['precision'] = precision\n",
        "    metrics['recall'] = recall\n",
        "    metrics['f1_score'] = f1score\n",
        "    metrics['matthews_correlation'] = matthews_corcoef\n",
        "\n",
        "    return metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Logistic Regression:\n",
            "Accuracy: 0.8338368580060423\n",
            "ROC AUC: 0.9173739103589611\n",
            "Precision: 0.8207058294166801\n",
            "Recall: 0.8338368580060423\n",
            "F1 Score: 0.8248592597916369\n",
            "Matthews Correlation Coefficient: 0.5123416314446448\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Logistic Regression:\")\n",
        "LR_metrics = calculate_metrics(y_test, LR_pred,LR_prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {},
      "outputs": [],
      "source": [
        "DecisionTree_model = DecisionTreeClassifier(\n",
        "    criterion='entropy',\n",
        "    max_depth=10, \n",
        "    random_state=42,\n",
        "    min_samples_split=50,\n",
        "    )\n",
        "\n",
        "\n",
        "DecisionTree_model.fit(clean_dataset_train, y)\n",
        "\n",
        "DT_Pred = DecisionTree_model.predict(clean_dataset_test)\n",
        "DT_Prob = DecisionTree_model.predict_proba(clean_dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm2 = confusion_matrix(y_test, DT_Pred)\n",
        "\n",
        "sns.heatmap(cm2, \n",
        "            annot=True, \n",
        "            fmt='d', \n",
        "            cmap='Greens',\n",
        "            xticklabels=['normal','suspect','pathological'], \n",
        "            yticklabels=['normal','suspect','pathological'],\n",
        "            )\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report for Decision Tree Regression:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.95      0.97      0.96       256\n",
            "     Suspect       0.89      0.78      0.83        60\n",
            "Pathological       0.93      0.93      0.93        15\n",
            "\n",
            "    accuracy                           0.94       331\n",
            "   macro avg       0.92      0.90      0.91       331\n",
            "weighted avg       0.94      0.94      0.94       331\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report for Decision Tree Regression:\")\n",
        "print(classification_report(y_test, DT_Pred, target_names=[\"Normal\",\"Suspect\",\"Pathological\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Decision Tree Classifer:\n",
            "Accuracy: 0.9365558912386707\n",
            "ROC AUC: 0.9558624885497272\n",
            "Precision: 0.9352875249062109\n",
            "Recall: 0.9365558912386707\n",
            "F1 Score: 0.9352058094316876\n",
            "Matthews Correlation Coefficient: 0.8225695089325761\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Decision Tree Classifer:\")\n",
        "DT_metrics = calculate_metrics(y_test, DT_Pred,DT_Prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {},
      "outputs": [],
      "source": [
        "KNN_model = KNeighborsClassifier(\n",
        "    n_neighbors=7\n",
        ")\n",
        "\n",
        "KNN_model.fit(clean_dataset_train, y)\n",
        "KNN_Pred = KNN_model.predict(clean_dataset_test)\n",
        "KNN_Prob = KNN_model.predict_proba(clean_dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm3 = confusion_matrix(y_test, KNN_Pred)\n",
        "\n",
        "sns.heatmap(cm3, \n",
        "            annot=True, \n",
        "            fmt='d', \n",
        "            cmap='Greens',\n",
        "            xticklabels=['normal','suspect','pathological'], \n",
        "            yticklabels=['normal','suspect','pathological'],\n",
        "            )\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report for KNN Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.90      0.96      0.93       256\n",
            "     Suspect       0.72      0.57      0.64        60\n",
            "Pathological       0.89      0.53      0.67        15\n",
            "\n",
            "    accuracy                           0.87       331\n",
            "   macro avg       0.84      0.69      0.74       331\n",
            "weighted avg       0.87      0.87      0.86       331\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report for KNN Classifier:\")\n",
        "print(classification_report(y_test, KNN_Pred, target_names=[\"Normal\",\"Suspect\",\"Pathological\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for KNN Classifier:\n",
            "Accuracy: 0.8731117824773413\n",
            "ROC AUC: 0.9061595720387583\n",
            "Precision: 0.8660789550061261\n",
            "Recall: 0.8731117824773413\n",
            "F1 Score: 0.8649329295777448\n",
            "Matthews Correlation Coefficient: 0.6253315171811878\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for KNN Classifier:\")\n",
        "KNN_metrics = calculate_metrics(y_test, KNN_Pred,KNN_Prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "metadata": {},
      "outputs": [],
      "source": [
        "GaussianNB_model = GaussianNB()\n",
        "GaussianNB_model.fit(clean_dataset_train, y)\n",
        "GaussianNB_Pred = GaussianNB_model.predict(clean_dataset_test)\n",
        "GaussianNB_Prob = GaussianNB_model.predict_proba(clean_dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 187,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm4 = confusion_matrix(y_test, GaussianNB_Pred)\n",
        "\n",
        "sns.heatmap(cm4, \n",
        "            annot=True, \n",
        "            fmt='d', \n",
        "            cmap='Greens',\n",
        "            xticklabels=['normal','suspect','pathological'], \n",
        "            yticklabels=['normal','suspect','pathological'],\n",
        "            )\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for Gaussian Naive Bayes Classifier')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 188,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report for Gaussian Naive Bayes Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.96      0.79      0.86       256\n",
            "     Suspect       0.48      0.85      0.61        60\n",
            "Pathological       0.47      0.47      0.47        15\n",
            "\n",
            "    accuracy                           0.78       331\n",
            "   macro avg       0.64      0.70      0.65       331\n",
            "weighted avg       0.85      0.78      0.80       331\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report for Gaussian Naive Bayes Classifier:\")\n",
        "print(classification_report(y_test, GaussianNB_Pred, target_names=[\"Normal\",\"Suspect\",\"Pathological\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 189,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Gaussian Naive Bayes Classifier:\n",
            "Accuracy: 0.7824773413897281\n",
            "ROC AUC: 0.9003644815621414\n",
            "Precision: 0.8513567606613913\n",
            "Recall: 0.7824773413897281\n",
            "F1 Score: 0.800491950510829\n",
            "Matthews Correlation Coefficient: 0.547978896191767\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Gaussian Naive Bayes Classifier:\")\n",
        "GaussianNB_metrics = calculate_metrics(y_test, GaussianNB_Pred,GaussianNB_Prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 190,
      "metadata": {},
      "outputs": [],
      "source": [
        "RandomForestClassifier_model = RandomForestClassifier(\n",
        "    n_estimators=100,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "RandomForestClassifier_model.fit(clean_dataset_train, y)\n",
        "RF_Pred = RandomForestClassifier_model.predict(clean_dataset_test)\n",
        "RF_Prob = RandomForestClassifier_model.predict_proba(clean_dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 191,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm5 = confusion_matrix(y_test, RF_Pred)\n",
        "\n",
        "sns.heatmap(cm5, \n",
        "            annot=True, \n",
        "            fmt='d', \n",
        "            cmap='Greens',\n",
        "            xticklabels=['normal','suspect','pathological'], \n",
        "            yticklabels=['normal','suspect','pathological'],\n",
        "            )\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for Random Forest Classifier')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 192,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report for Random Forest Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.96      0.98      0.97       256\n",
            "     Suspect       0.89      0.82      0.85        60\n",
            "Pathological       0.93      0.87      0.90        15\n",
            "\n",
            "    accuracy                           0.95       331\n",
            "   macro avg       0.93      0.89      0.91       331\n",
            "weighted avg       0.94      0.95      0.94       331\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report for Random Forest Classifier:\")\n",
        "print(classification_report(y_test, RF_Pred, target_names=[\"Normal\",\"Suspect\",\"Pathological\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 193,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Random Forest Classifier:\n",
            "Accuracy: 0.945619335347432\n",
            "ROC AUC: 0.9855276029421688\n",
            "Precision: 0.9445166926890135\n",
            "Recall: 0.945619335347432\n",
            "F1 Score: 0.9446265097804887\n",
            "Matthews Correlation Coefficient: 0.8482611174070995\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Random Forest Classifier:\")\n",
        "RF_metrics = calculate_metrics(y_test, RF_Pred,RF_Prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "metadata": {},
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'XGBClassifier' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
            "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[194]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m XGBClassifier_model = \u001b[43mXGBClassifier\u001b[49m(\n\u001b[32m      2\u001b[39m     objective=\u001b[33m'\u001b[39m\u001b[33mmulti:softprob\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m      3\u001b[39m     num_class=\u001b[32m3\u001b[39m,\n\u001b[32m      4\u001b[39m     eval_metric=\u001b[33m'\u001b[39m\u001b[33mmlogloss\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m      5\u001b[39m     )\n\u001b[32m      7\u001b[39m XGBClassifier_model.fit(clean_dataset_train, y-\u001b[32m1\u001b[39m)\n\u001b[32m      8\u001b[39m XGB_Pred = XGBClassifier_model.predict(clean_dataset_test)\n",
            "\u001b[31mNameError\u001b[39m: name 'XGBClassifier' is not defined"
          ]
        }
      ],
      "source": [
        "XGBClassifier_model = XGBClassifier(\n",
        "    objective='multi:softprob',\n",
        "    num_class=3,\n",
        "    eval_metric='mlogloss'\n",
        "    )\n",
        "\n",
        "XGBClassifier_model.fit(clean_dataset_train, y-1)\n",
        "XGB_Pred = XGBClassifier_model.predict(clean_dataset_test)\n",
        "XGB_Prob = XGBClassifier_model.predict_proba(clean_dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm6 = confusion_matrix(y_test, XGB_Pred+1)\n",
        "\n",
        "sns.heatmap(cm6, \n",
        "            annot=True, \n",
        "            fmt='d', \n",
        "            cmap='Greens',\n",
        "            xticklabels=['normal','suspect','pathological'], \n",
        "            yticklabels=['normal','suspect','pathological'],\n",
        "            )\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for XGBoost Classifier')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report for XGBoost Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.97      0.98      0.98       256\n",
            "     Suspect       0.92      0.90      0.91        60\n",
            "Pathological       1.00      0.93      0.97        15\n",
            "\n",
            "    accuracy                           0.96       331\n",
            "   macro avg       0.96      0.94      0.95       331\n",
            "weighted avg       0.96      0.96      0.96       331\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report for XGBoost Classifier:\")\n",
        "print(classification_report(y_test, XGB_Pred+1, target_names=[\"Normal\",\"Suspect\",\"Pathological\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for XGBoost Classifier:\n",
            "Accuracy: 0.9637462235649547\n",
            "ROC AUC: 0.9883931865525143\n",
            "Precision: 0.96365413233589\n",
            "Recall: 0.9637462235649547\n",
            "F1 Score: 0.9636250345522557\n",
            "Matthews Correlation Coefficient: 0.9002311815096503\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for XGBoost Classifier:\")\n",
        "XGB_metrics = calculate_metrics(y_test, XGB_Pred+1, XGB_Prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'XGBClassifier_model' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
            "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[156]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      4\u001b[39m pickle.dump(GaussianNB_model, \u001b[38;5;28mopen\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mgaussian_nb_model.pkl\u001b[39m\u001b[33m\"\u001b[39m,\u001b[33m\"\u001b[39m\u001b[33mwb\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m      5\u001b[39m pickle.dump(RandomForestClassifier_model, \u001b[38;5;28mopen\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mrandom_forest_model.pkl\u001b[39m\u001b[33m\"\u001b[39m,\u001b[33m\"\u001b[39m\u001b[33mwb\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m pickle.dump(\u001b[43mXGBClassifier_model\u001b[49m, \u001b[38;5;28mopen\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mxgboost_model.pkl\u001b[39m\u001b[33m\"\u001b[39m,\u001b[33m\"\u001b[39m\u001b[33mwb\u001b[39m\u001b[33m\"\u001b[39m))\n",
            "\u001b[31mNameError\u001b[39m: name 'XGBClassifier_model' is not defined"
          ]
        }
      ],
      "source": [
        "pickle.dump(LR_model, open(\"LR_model.pkl\",\"wb\"))\n",
        "pickle.dump(DecisionTree_model, open(\"decision_tree_model.pkl\",\"wb\"))\n",
        "pickle.dump(KNN_model, open(\"knn_model.pkl\",\"wb\"))\n",
        "pickle.dump(GaussianNB_model, open(\"gaussian_nb_model.pkl\",\"wb\"))\n",
        "pickle.dump(RandomForestClassifier_model, open(\"random_forest_model.pkl\",\"wb\"))\n",
        "pickle.dump(XGBClassifier_model, open(\"xgboost_model.pkl\",\"wb\"))\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}