list3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model implementation\n",
    "from ml.models.clustering import KNN\n",
    "from ml.functions.distance import EuclideanDistance, MahalanobisDistance\n",
    "\n",
    "# model selection\n",
    "from ml.model_selection import GridSearchCV\n",
    "from ml.functions.metrics.classification import F1Score, BinaryAccuracy\n",
    "from ml.stats import ClassificationStats\n",
    "\n",
    "# data manipulation\n",
    "from ml.dataset import load_csv, split_train_test\n",
    "from ml.algorithms.normalization import MinMaxScaler\n",
    "\n",
    "# Utility\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "np.random.seed(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X, y = load_csv('./datasets/kc2.csv', remove_duplicates=True, remove_inconsistent_labels=True)\n",
    "X, y = load_csv('./datasets/kc2.csv')\n",
    "\n",
    "y = y.astype(int).squeeze()\n",
    "X_train, y_train, X_test, y_test = split_train_test(X, y, 0.8, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1 a,b - K-Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search with 10-fold cross-validation - hyperparameters K=[1, 5] and distance=['Euclidean', 'Mahalanobis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best KNN: {'k': 5, 'distance': <class 'ml.functions.distance.EuclideanDistance'>, 'data_scaler': <class 'ml.algorithms.normalization.MinMaxScaler'>}\n"
     ]
    }
   ],
   "source": [
    "grid = GridSearchCV(stats_generator=ClassificationStats)\n",
    "\n",
    "for k in [1, 5]:\n",
    "    for distance in [EuclideanDistance, MahalanobisDistance]:\n",
    "        grid.add(KNN, {\"k\": k, \"distance\": distance, \"data_scaler\": MinMaxScaler})\n",
    "\n",
    "grid.search(X_train, y_train, num_folds=10, score=F1Score(), score_minimize=False, shuffle=False)\n",
    "print(\"Best KNN:\", grid.best_model_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of the best model on the **test** dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8380952380952381\n",
      "F1-score: 0.6382978723404256\n"
     ]
    }
   ],
   "source": [
    "knnX = grid.best_model(**grid.best_model_params)\n",
    "knnX.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knnX.predict(X_test)\n",
    "\n",
    "print(\"Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of SKLearn's implementation on the **test** dataset with hyperparameters from grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK-Learn's KNN5 Accuracy: 0.8380952380952381\n",
      "SK-Learn's KNN5 F1-score: 0.6382978723404256\n"
     ]
    }
   ],
   "source": [
    "# Scale data with the same scaler we used during grid search\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Train SK-Learn's KNN with K=5 and euclidean distance\n",
    "knn5_sk = KNeighborsClassifier(n_neighbors=5, p=2, algorithm='brute')\n",
    "knn5_sk.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict data and report performance\n",
    "y_pred_sk = knn5_sk.predict(X_test_scaled)\n",
    "\n",
    "print(\"SK-Learn's KNN5 Accuracy:\", BinaryAccuracy().measure(y_test, y_pred_sk))\n",
    "print(\"SK-Learn's KNN5 F1-score:\", F1Score().measure(y_test, y_pred_sk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report K-fold mean +- standard deviation for: F1-score, Accuracy, Recall and Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1\n",
      "\tK=1, Euclidean: 0.34133069828722 +- 0.10887563909591992\n",
      "\tK=1, Mahalanobis: 0.31858695652173913 +- 0.08004368939803394\n",
      "\tK=5 Euclidean: 0.43449725369230013 +- 0.16319742186288475\n",
      "\tK=5 Mahalanobis: 0.4123443223443224 +- 0.23794664565852702\n",
      "RECALL\n",
      "\tK=1, Euclidean: 0.4544264069264069 +- 0.1447968274288643\n",
      "\tK=1, Mahalanobis: 0.39896103896103896 +- 0.11696168595456669\n",
      "\tK=5 Euclidean: 0.38106421356421355 +- 0.19753175312608093\n",
      "\tK=5 Mahalanobis: 0.319505772005772 +- 0.2164870098898964\n",
      "PRECISION\n",
      "\tK=1, Euclidean: 0.2858134757399463 +- 0.10523390258578474\n",
      "\tK=1, Mahalanobis: 0.280009435009435 +- 0.09212983430355487\n",
      "\tK=5 Euclidean: 0.5763095238095238 +- 0.1559900445250584\n",
      "\tK=5 Mahalanobis: 0.63 +- 0.26954694499392046\n",
      "BINARY_ACCURACY\n",
      "\tK=1, Euclidean: 0.6791521486643438 +- 0.0724222218749904\n",
      "\tK=1, Mahalanobis: 0.6883855981416958 +- 0.051951052033964114\n",
      "\tK=5 Euclidean: 0.8251451800232289 +- 0.061026166751576405\n",
      "\tK=5 Mahalanobis: 0.8396051103368178 +- 0.06396351737847175\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2160x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "candidates = [\"K=1, Euclidean\", \"K=1, Mahalanobis\", \"K=5 Euclidean\", \"K=5 Mahalanobis\"]\n",
    "num_candidates = len(grid.candidates)\n",
    "k=0\n",
    "plt.figure(figsize=(30, 4))\n",
    "plt.suptitle(\"KNN\", fontsize=20)\n",
    "plt.subplots_adjust(top=0.7)\n",
    "for metric in ['f1', 'recall', 'precision', 'binary_accuracy']:\n",
    "    means = []\n",
    "    std = []\n",
    "    \n",
    "    for i in range(num_candidates):\n",
    "        means.append(np.mean(grid.stats[i].stats[\"values\"][metric]))\n",
    "        std.append(np.std(grid.stats[i].stats[\"values\"][metric]))\n",
    "\n",
    "    plt.subplot(1, 4, k+1)\n",
    "    plt.errorbar(np.arange(num_candidates), means, std, fmt='ok', lw=3)\n",
    "    plt.errorbar(grid.index_of_best, means[grid.index_of_best], std[grid.index_of_best], ecolor='red', fmt='ok', lw=3)\n",
    "    \n",
    "    plt.title(metric.upper() + \" mean and std deviation\", fontsize=20)\n",
    "    plt.xticks(np.arange(num_candidates), labels=candidates, rotation=45)\n",
    "    plt.xlabel(\"K, distance\", fontsize=16)\n",
    "    plt.ylabel(metric, fontsize=16)\n",
    "\n",
    "    print(metric.upper())\n",
    "    for c in range(num_candidates):\n",
    "        print(f\"\\t{candidates[c]}:\", means[c], \"+-\", std[c])\n",
    "    \n",
    "    k+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1 a,b - Decision Trees\n",
    "### Grid search with 10-fold cross-validation - hyperparameters criterion=['Entropy', 'Gini']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Decision Tree: {'criterion': 'entropy'}\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "tree_grid = GridSearchCV(stats_generator=ClassificationStats)\n",
    "\n",
    "for criterion in ['entropy', 'gini']:\n",
    "    tree_grid.add(DecisionTreeClassifier, {\"criterion\": criterion})\n",
    "\n",
    "tree_grid.search(X_train_scaled, y_train, num_folds=10, score=F1Score(), score_minimize=False, shuffle=False)\n",
    "print(\"Best Decision Tree:\", tree_grid.best_model_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of the best model on the **test** dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8\n",
      "F1-score: 0.5882352941176471\n"
     ]
    }
   ],
   "source": [
    "tree = tree_grid.best_model(**tree_grid.best_model_params)\n",
    "tree.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = tree.predict(X_test_scaled)\n",
    "\n",
    "print(\"Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report K-fold mean +- standard deviation for: F1-score, Accuracy, Recall and Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1\n",
      "\tEntropy: 0.4202743824802647 +- 0.21943424362910868\n",
      "\tGini: 0.3731591937474291 +- 0.16500865425316255\n",
      "RECALL\n",
      "\tEntropy: 0.42832611832611833 +- 0.2544827351537479\n",
      "\tGini: 0.37581890331890333 +- 0.20008699847044517\n",
      "PRECISION\n",
      "\tEntropy: 0.44857142857142857 +- 0.23499457223432169\n",
      "\tGini: 0.3958333333333333 +- 0.16472138096170444\n",
      "BINARY_ACCURACY\n",
      "\tEntropy: 0.7940766550522648 +- 0.07173536373606715\n",
      "\tGini: 0.7722415795586527 +- 0.07237915950659944\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "candidates = [\"Entropy\", \"Gini\"]\n",
    "num_candidates = len(tree_grid.candidates)\n",
    "k=0\n",
    "plt.figure(figsize=(30, 4))\n",
    "plt.suptitle(\"Decision Trees\", fontsize=20)\n",
    "plt.subplots_adjust(top=0.7)\n",
    "for metric in ['f1', 'recall', 'precision', 'binary_accuracy']:\n",
    "    means = []\n",
    "    std = []\n",
    "    \n",
    "    for i in range(num_candidates):\n",
    "        means.append(np.mean(tree_grid.stats[i].stats[\"values\"][metric]))\n",
    "        std.append(np.std(tree_grid.stats[i].stats[\"values\"][metric]))\n",
    "\n",
    "    plt.subplot(1, 4, k+1)\n",
    "    plt.errorbar(np.arange(num_candidates), means, std, fmt='ok', lw=3)\n",
    "    plt.errorbar(tree_grid.index_of_best, means[tree_grid.index_of_best], std[tree_grid.index_of_best], ecolor='red', fmt='ok', lw=3)\n",
    "    \n",
    "    plt.title(metric.upper() + \" mean and std deviation\", fontsize=20)\n",
    "    plt.xticks(np.arange(num_candidates), labels=candidates, rotation=45)\n",
    "    plt.xlabel(\"Quality measure\", fontsize=16)\n",
    "    plt.ylabel(metric, fontsize=16)\n",
    "\n",
    "    print(metric.upper())\n",
    "    for c in range(num_candidates):\n",
    "        print(f\"\\t{candidates[c]}:\", means[c], \"+-\", std[c])\n",
    "\n",
    "    k+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "==================================================================\n",
    "==================================================================\n",
    "## EXTRA\n",
    "\n",
    "The dataset website reffered by the assignment document (https://www.openml.org/d/1063) refers to some papers which analyses this and other similar datasets.\n",
    "\n",
    "According to [1], the KC2 dataset suffers from data quality problems, such as:\n",
    "- Samples with implausible feature values\n",
    "- Identical samples\n",
    "- Inconsistent samples (samples that are identical but differ in their classification)\n",
    "- and more\n",
    "\n",
    "Still according to [1], removing problematic samples or features is arguable. For example, if duplicate samples exist, they might appear in both training and validation datasets, leading to an over-optimistic view of the predictive capability of a model (overfitting). However, if such duplicate cases naturaly occur (for example: similar profiles from different software), then accouting for duplicated cases can be treated as learning opportunities during training.\n",
    "\n",
    "Thus, removing problematic samples or features depend on research goal, and researchers in [1] provide cleaned versions of the KC2 datasets for use when removing problematic data is desirable.\n",
    "\n",
    "**So, for comparison we present results for a partially preprocessed KC2 dataset by:** \n",
    "- **Removing duplicate entries**\n",
    "- **Picking one class when identical samples map to different classes**\n",
    "\n",
    "[1] Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013) Data Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## E1 - Let's remove duplicates and see how it affects our models\n",
    "### **Spoiler: our models got worse at generalizing!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples in the original dataset: 522, Samples in the preprocessed dataset: 375\n"
     ]
    }
   ],
   "source": [
    "X, y = load_csv('./datasets/kc2.csv')\n",
    "original_size = X.shape[0]\n",
    "\n",
    "X, y = load_csv('./datasets/kc2.csv', remove_duplicates=True, remove_inconsistent_labels=False)\n",
    "preprocessed_size = X.shape[0]\n",
    "\n",
    "y = y.astype(int).squeeze()\n",
    "X_train, y_train, X_test, y_test = split_train_test(X, y, 0.8, shuffle=True)\n",
    "\n",
    "print(f\"Samples in the original dataset: {original_size}, Samples in the preprocessed dataset: {preprocessed_size}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our KNN implementation with K=5 and distance=Euclidean (from our previous grid search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our KNN5 - Accuracy: 0.7466666666666667\n",
      "Our KNN5 - F1-score: 0.45714285714285713\n"
     ]
    }
   ],
   "source": [
    "knn5 = KNN(k=5, distance=EuclideanDistance, data_scaler=MinMaxScaler)\n",
    "knn5.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knn5.predict(X_test)\n",
    "\n",
    "print(\"Our KNN5 - Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"Our KNN5 - F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SK-Learn's KNN with K=5 and distance=Euclidean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK-Learn's KNN5 - Accuracy: 0.7466666666666667\n",
      "SK-Learn's KNN5 - F1-score: 0.45714285714285713\n"
     ]
    }
   ],
   "source": [
    "# Scale data with the same scaler we used during grid search\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Train SK-Learn's KNN with K=5 and euclidean distance\n",
    "knn5_sk = KNeighborsClassifier(n_neighbors=5, p=2, algorithm='brute')\n",
    "knn5_sk.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict data and report performance\n",
    "y_pred_sk = knn5_sk.predict(X_test_scaled)\n",
    "\n",
    "print(\"SK-Learn's KNN5 - Accuracy:\", BinaryAccuracy().measure(y_test, y_pred_sk))\n",
    "print(\"SK-Learn's KNN5 - F1-score:\", F1Score().measure(y_test, y_pred_sk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### SK-Learn's Decision Tree with Entropy purity criterion (from our grid search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.64\n",
      "F1-score: 0.30769230769230765\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(criterion='entropy')\n",
    "tree.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = tree.predict(X_test_scaled)\n",
    "\n",
    "print(\"Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search with 10-fold cross-validation - hyperparameters K=[1, 5] and distance=['Euclidean', 'Mahalanobis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best KNN: {'k': 5, 'distance': <class 'ml.functions.distance.EuclideanDistance'>, 'data_scaler': <class 'ml.algorithms.normalization.MinMaxScaler'>}\n"
     ]
    }
   ],
   "source": [
    "grid = GridSearchCV(stats_generator=ClassificationStats)\n",
    "\n",
    "for k in [1, 5]:\n",
    "    for distance in [EuclideanDistance, MahalanobisDistance]:\n",
    "        grid.add(KNN, {\"k\": k, \"distance\": distance, \"data_scaler\": MinMaxScaler})\n",
    "\n",
    "grid.search(X_train, y_train, num_folds=10, score=F1Score(), score_minimize=False, shuffle=False)\n",
    "print(\"Best KNN:\", grid.best_model_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of the best model on the **test** dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7466666666666667\n",
      "F1-score: 0.45714285714285713\n"
     ]
    }
   ],
   "source": [
    "knnX = grid.best_model(**grid.best_model_params)\n",
    "knnX.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knnX.predict(X_test)\n",
    "\n",
    "print(\"Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of SKLearn's implementation on the **test** dataset with hyperparameters from grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK-Learn's KNN5 Accuracy: 0.7466666666666667\n",
      "SK-Learn's KNN5 F1-score: 0.45714285714285713\n"
     ]
    }
   ],
   "source": [
    "# Scale data with the same scaler we used during grid search\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Train SK-Learn's KNN with K=5 and euclidean distance\n",
    "knn5_sk = KNeighborsClassifier(n_neighbors=5, p=2, algorithm='brute')\n",
    "knn5_sk.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict data and report performance\n",
    "y_pred_sk = knn5_sk.predict(X_test_scaled)\n",
    "\n",
    "print(\"SK-Learn's KNN5 Accuracy:\", BinaryAccuracy().measure(y_test, y_pred_sk))\n",
    "print(\"SK-Learn's KNN5 F1-score:\", F1Score().measure(y_test, y_pred_sk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report K-fold mean +- standard deviation for: F1-score, Accuracy, Recall and Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1\n",
      "\tK=1, Euclidean: 0.5622126665604926 +- 0.0750643899316376\n",
      "\tK=1, Mahalanobis: 0.49496268351531514 +- 0.17357926645990962\n",
      "\tK=5 Euclidean: 0.610472730370092 +- 0.11643994678373618\n",
      "\tK=5 Mahalanobis: 0.5315856855562738 +- 0.1909262783958512\n",
      "RECALL\n",
      "\tK=1, Euclidean: 0.6183225108225108 +- 0.12505540319391834\n",
      "\tK=1, Mahalanobis: 0.4696103896103896 +- 0.1640648507670376\n",
      "\tK=5 Euclidean: 0.5943506493506493 +- 0.16112902016756184\n",
      "\tK=5 Mahalanobis: 0.43937229437229436 +- 0.19617907664141826\n",
      "PRECISION\n",
      "\tK=1, Euclidean: 0.5397005772005772 +- 0.1156708071612271\n",
      "\tK=1, Mahalanobis: 0.5558080808080808 +- 0.25651009996597135\n",
      "\tK=5 Euclidean: 0.6754184704184705 +- 0.18090339987257195\n",
      "\tK=5 Mahalanobis: 0.7702380952380953 +- 0.18853550042534406\n",
      "BINARY_ACCURACY\n",
      "\tK=1, Euclidean: 0.7233333333333334 +- 0.07895146188218011\n",
      "\tK=1, Mahalanobis: 0.7233333333333334 +- 0.11455226851626392\n",
      "\tK=5 Euclidean: 0.7833333333333334 +- 0.08198915917499232\n",
      "\tK=5 Mahalanobis: 0.7866666666666666 +- 0.08717797887081348\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "candidates = [\"K=1, Euclidean\", \"K=1, Mahalanobis\", \"K=5 Euclidean\", \"K=5 Mahalanobis\"]\n",
    "num_candidates = len(grid.candidates)\n",
    "k=0\n",
    "plt.figure(figsize=(30, 4))\n",
    "plt.suptitle(\"KNN\", fontsize=20)\n",
    "plt.subplots_adjust(top=0.7)\n",
    "for metric in ['f1', 'recall', 'precision', 'binary_accuracy']:\n",
    "    means = []\n",
    "    std = []\n",
    "    \n",
    "    for i in range(num_candidates):\n",
    "        means.append(np.mean(grid.stats[i].stats[\"values\"][metric]))\n",
    "        std.append(np.std(grid.stats[i].stats[\"values\"][metric]))\n",
    "\n",
    "    plt.subplot(1, 4, k+1)\n",
    "    plt.errorbar(np.arange(num_candidates), means, std, fmt='ok', lw=3)\n",
    "    plt.errorbar(grid.index_of_best, means[grid.index_of_best], std[grid.index_of_best], ecolor='red', fmt='ok', lw=3)\n",
    "    \n",
    "    plt.title(metric.upper() + \" mean and std deviation\", fontsize=20)\n",
    "    plt.xticks(np.arange(num_candidates), labels=candidates, rotation=45)\n",
    "    plt.xlabel(\"K, distance\", fontsize=16)\n",
    "    plt.ylabel(metric, fontsize=16)\n",
    "\n",
    "    print(metric.upper())\n",
    "    for c in range(num_candidates):\n",
    "        print(f\"\\t{candidates[c]}:\", means[c], \"+-\", std[c])\n",
    "    \n",
    "    k+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## E2 - Pick one class when identical samples map to different classes, remove only their duplicates\n",
    "### **Spoiler: now we got really good at labeling data from this dataset!!!!** (but we are arbitrarly saying that some labels are actually wrong)\n",
    "\n",
    "These inconsistencies really affect generalization capabilities of our models (as discussed in [1]) and whether they are correct or not is subject to business logic analysis, perhaps more features should be used so these inconsistencies woudln't occurr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples in the original dataset: 522, Samples in the preprocessed dataset: 375\n"
     ]
    }
   ],
   "source": [
    "X, y = load_csv('./datasets/kc2.csv')\n",
    "original_size = X.shape[0]\n",
    "\n",
    "X, y = load_csv('./datasets/kc2.csv', remove_duplicates=False, remove_inconsistent_labels=True)\n",
    "preprocessed_size = X.shape[0]\n",
    "\n",
    "y = y.astype(int).squeeze()\n",
    "X_train, y_train, X_test, y_test = split_train_test(X, y, 0.8, shuffle=True)\n",
    "\n",
    "print(f\"Samples in the original dataset: {original_size}, Samples in the preprocessed dataset: {preprocessed_size}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our KNN implementation with K=5 and distance=Euclidean (from our previous grid search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our KNN5 - Accuracy: 1.0\n",
      "Our KNN5 - F1-score: 1.0\n"
     ]
    }
   ],
   "source": [
    "knn5 = KNN(k=5, distance=EuclideanDistance, data_scaler=MinMaxScaler)\n",
    "knn5.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knn5.predict(X_test)\n",
    "\n",
    "print(\"Our KNN5 - Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"Our KNN5 - F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SK-Learn's KNN with K=5 and distance=Euclidean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK-Learn's KNN5 - Accuracy: 1.0\n",
      "SK-Learn's KNN5 - F1-score: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Scale data with the same scaler we used during grid search\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Train SK-Learn's KNN with K=5 and euclidean distance\n",
    "knn5_sk = KNeighborsClassifier(n_neighbors=5, p=2, algorithm='brute')\n",
    "knn5_sk.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict data and report performance\n",
    "y_pred_sk = knn5_sk.predict(X_test_scaled)\n",
    "\n",
    "print(\"SK-Learn's KNN5 - Accuracy:\", BinaryAccuracy().measure(y_test, y_pred_sk))\n",
    "print(\"SK-Learn's KNN5 - F1-score:\", F1Score().measure(y_test, y_pred_sk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### SK-Learn's Decision Tree with Entropy purity criterion (from our grid search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "F1-score: 1.0\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(criterion='entropy')\n",
    "tree.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = tree.predict(X_test_scaled)\n",
    "\n",
    "print(\"Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search with 10-fold cross-validation - hyperparameters K=[1, 5] and distance=['Euclidean', 'Mahalanobis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best KNN: {'k': 1, 'distance': <class 'ml.functions.distance.EuclideanDistance'>, 'data_scaler': <class 'ml.algorithms.normalization.MinMaxScaler'>}\n"
     ]
    }
   ],
   "source": [
    "grid = GridSearchCV(stats_generator=ClassificationStats)\n",
    "\n",
    "for k in [1, 5]:\n",
    "    for distance in [EuclideanDistance, MahalanobisDistance]:\n",
    "        grid.add(KNN, {\"k\": k, \"distance\": distance, \"data_scaler\": MinMaxScaler})\n",
    "\n",
    "grid.search(X_train, y_train, num_folds=10, score=F1Score(), score_minimize=False, shuffle=False)\n",
    "print(\"Best KNN:\", grid.best_model_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of the best model on the **test** dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "F1-score: 1.0\n"
     ]
    }
   ],
   "source": [
    "knnX = grid.best_model(**grid.best_model_params)\n",
    "knnX.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knnX.predict(X_test)\n",
    "\n",
    "print(\"Accuracy:\", BinaryAccuracy().measure(y_test, y_pred))\n",
    "print(\"F1-score:\", F1Score().measure(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of SKLearn's implementation on the **test** dataset with hyperparameters from grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK-Learn's KNN5 Accuracy: 1.0\n",
      "SK-Learn's KNN5 F1-score: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Scale data with the same scaler we used during grid search\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Train SK-Learn's KNN with K=5 and euclidean distance\n",
    "knn5_sk = KNeighborsClassifier(n_neighbors=5, p=2, algorithm='brute')\n",
    "knn5_sk.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict data and report performance\n",
    "y_pred_sk = knn5_sk.predict(X_test_scaled)\n",
    "\n",
    "print(\"SK-Learn's KNN5 Accuracy:\", BinaryAccuracy().measure(y_test, y_pred_sk))\n",
    "print(\"SK-Learn's KNN5 F1-score:\", F1Score().measure(y_test, y_pred_sk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report K-fold mean +- standard deviation for: F1-score, Accuracy, Recall and Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1\n",
      "\tK=1, Euclidean: 1.0 +- 0.0\n",
      "\tK=1, Mahalanobis: 0.891144046012467 +- 0.07297930102046682\n",
      "\tK=5 Euclidean: 1.0 +- 0.0\n",
      "\tK=5 Mahalanobis: 0.8923291740938801 +- 0.0902441651857916\n",
      "RECALL\n",
      "\tK=1, Euclidean: 1.0 +- 0.0\n",
      "\tK=1, Mahalanobis: 0.8808744033744034 +- 0.09805672948214382\n",
      "\tK=5 Euclidean: 1.0 +- 0.0\n",
      "\tK=5 Mahalanobis: 0.856940836940837 +- 0.15116655011841243\n",
      "PRECISION\n",
      "\tK=1, Euclidean: 1.0 +- 0.0\n",
      "\tK=1, Mahalanobis: 0.9093253968253968 +- 0.08293579592616068\n",
      "\tK=5 Euclidean: 1.0 +- 0.0\n",
      "\tK=5 Mahalanobis: 0.9542424242424243 +- 0.07423252871591554\n",
      "BINARY_ACCURACY\n",
      "\tK=1, Euclidean: 1.0 +- 0.0\n",
      "\tK=1, Mahalanobis: 0.9400000000000001 +- 0.038873012632301994\n",
      "\tK=5 Euclidean: 1.0 +- 0.0\n",
      "\tK=5 Mahalanobis: 0.9466666666666667 +- 0.04521553322083511\n"
     ]
    },
    {
     "data": {
      "image/png": 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mD/A9Lmn8PTQ7tlAKgq6ixPm7KOear2iStrathYzC/ZFeezDwsf9F1fzvN0yf2fhdN0z/KuV+TAIvbfF9/rTFNjeinEPfCTyg+o5X0fpctvZbavYbfl8173sN0z9dTf/fumnPr6ZdBmw8wu/0hdW6FrDumusHLdLOrX0ftDhm1/9uR2G5lt9XXZp5ND9fXULr88uTqnmf7eJ3clC13C9Zd99qB0rcuJOGmD3AOmu/43mU3gV+WC1/O+X49PAq3S6U48FtlOPVhcATW6yzrXtzVdqOxL4BPn9H4j7DiEst1rMF5Xrvd9U2/0k5V/o68NSG33Szx+y6dQ352nGAfA3r3IJSbnAh5Xr635TryaNY/7e+A7Aa+M0A2/9BlefHVe+n0+RamlJJ4QTKse+26nMvo/xXd2xIu3CA73Fm4++hxW/rm8Df6rZzMvDQJmlr25rOEMoGGh+2sFM73kZpyn825QD2HOBdwDZVX8tnUA5mCyjdtxxCuen3vPqVRMRTWHeD9VxKjZTtKAVjP42Il2TmOXWL7E05Gb2Q8se4m9KE/GWUJuN7ZeaVTfK7E+WG342UwsNtKN2nfScinp2ZF7b5uU+gtOD4JeUAtSWlcPJTwB7Aa1os91HguZQLnvMoB/7DKAf1Z9YnrLqu+TrlgvPrlBO8/6QEwN+2mU8i4qGUrm8eRAmO36TU2N25yudnKRcqCykHzwNZv+sFqulExHaUgLML5UTjp8BDKRc057Wbp2pdm1JuKu9BCZqLKF20fpAWLT8jYivKjeYnA78GTqXciHgu8LWIeGxmHlUlP5tyAvLqiHhfbtgtwYHV9j7eZF6j/6YUlP6cdd0T7UUJkDOr387qKu0nKb/bfVjXrUJbqj7Pj6RcLH2N8rt+HvBh4LkR8ZzcsCuGjSn/mYdRgteqavsnVPls1V2uNNruH8nCEfEA1h07B/3d5vo1R2vmVM8LM3NFRHyX0mXMf2XmT0aSv1F2AOUC73uU4+d/UE4Cp0fEkZRj408olSMeT7mxsEtEPCEz19RWEhE7UWLv9Cr9DykXNC8EfhgRb8rML9RtdzdgPqVSwPcpFxLTqvw8LyJelJk/bJLfrSg3lu6j3AzdFHg5cGpErMkmrT9aGMqxtF6ttupi4CLKTbJXAk+MiCfV/xYi4j8oF36bUM4lrgeeRPmeLmgzn7VasD+j1KQ+nxK3g3IecSDle7iREmugVFy5qNpOzdJqXUOOdwPkK4AzqzzcQInhm1Bq9z6+xTIbV/l/LuVm+NcoFwb7UrrbeRrVfy8zL4mIa4HnN+tONiL2pOzDb2bmikGyexil28GLKPtkEuWC5t2U39vTMvOfVdqFDHIO0kpEvAk4hXJR/A3KxdJMyu/tRdU5YbN1fI1yE/oHlAvh51NaGD0YeN0gn029qTYeXQ5xuV6IX/XupJzPbR4RD83Same49qHctLgV+OIAeboqIs6mHNtfT6lI0egkyk2aN1WtGK9rkmaozqAcg86h7IeXAQsi4n7gCZRj6/cox9ADKIV7K2loTRARr6dcb95LiRU3U64N30g5Djw9M+u70x3K8aneCynHqR9Q4vfulGPHHhGxe7bX8nAbyjXjzynx5TbK9dSLgHMi4rDMbLavhnrO/0lKZdI/V9/N/VXen0aJG626d1tPROxPidl3Ub7bW6rPsBslPte2e2yVnydWn++OavoddesalRhdrWtXynXxtpTv4wrKNfXZ1ftmywzlnsNplJtpr6XEpUaHVs8L28juKZSbmxdT9se2lN/NVyLi0Zn5wSrdHZTvcTblfKN+ny4daAMRsUn1mfahVAo6iXIz/WXA16vzpQ80WXS07o+MJ8+uni8bxrL/j3Lz/YSIWJyldUc7DgAeAnwhM++pxpv6DOV426xVczsat/3/KBWY3hkR36ecc55KOWbOGkJeW6m/zvpdRFwOPCcidsrMZS3SHl9/7dJMQ0wc7nJjrdV3N+bfSWZ+KyK+TDk3PToijqEcd7YGXttkO4PZg3KOfBGlstHjKYWCj4uIAyn3+P4AnE45PhwEnB8Ru2Tm3bWVDPHeHHQu9rUy5nF/OHGpmep674eUewS/oJy/raJUONmXct1/OSV2bgW8g/J/P7tuNVfUrWtI146D+CRDPLeIiFMpv9/llPvRd1AqiR4PPCsi9svMVZl5S0T8iPIfenxu2L3vQymVni/PzN8Nks+DKJW+LqT85mrdwNfOC2dkZq376rOr55bX861ExAurzxSU+wPLKL+rtwAHRul6+U9NFm27bKCpdktYfXTvwbpS33lNHrMHWXZJtexIWtjdSV3NAspNvKsppeL/APapmzeJcmBO4El10zeinKz/uz59Ne9hlIuCP7N+yfuDgS2a5OuJlEKOHzRMn173XR3TMO+51fRzhvD5H9Fk2iTKiX0CT2uYt7CafhMwreGzX1zN27Nu+ubV93c/MKNhXZ+o+yzT28jr26q072gy74HAA+rez2aAWh+UA3LS0GqNUkPn/tpvsc3v8ANV+m+yfu3+nSk1XprVkqh9j/+vYfpmlIC2puG39fkq/QY1BCkXngk8frDPTymgbFZz9fgq/Stb/D9mtvjstc8xvW7aM+p+Iw9p+I18t5r3gYb1LK39dhv244MpQfAORliTzoePzHWxpsn0vSnH+3tpqEHEuuPuHTSPUfOAN9el/68q/fJh5nGz6thxR+3/wLpaj19psUztPzS9jfUvGeh/3WYea8eYVbSOjysoF9X1y32JJrW8qzytAQ5umL4V5ST9HmD7uulbAts1ydeOlJu5v2+17ykXCpPrpu9efY6WLRibrGu4x9K7qDtWV/O+Vs17Rd20oFxgNvuu3lH3WQbdh6yrff2JJvM2oe4chEFamDOMeDdAvl5dpf8Fda0SKRfBN9C8Znjte/xMwz6c3Oy3Rak4ksARTbZfq2n8osE+P+Uif3KTdbyhSv/fLf4fs1t89trnmNmwjXur38hjGtKfXKVf0OR/k5QL3W3qpj+Qcj66mro47KO3HrX/cZPpz6YcD9dQV9t8gPQ9Eb/qftfzGqafVU2/gdI91tOAKYOsq/bbnlk37YPVtEVt5OWwKu2Pmnzny6vXL6vef6shzdJq+lBb2F1K1bq8mr4L5abK7ZQa4DvUzduKUqnttvrtUAok76v+vzs0bOdZ1X7+dsP04R6fVgHPapj3EZpcnwzw2TeloXZ3NX1LSk36FdSd1zd8v22d81Nu8mX1ndQf52pjIiZttrCjxK6kSQsLGs4pGLgXkVGL0dUy59HkGpdy47C2rtl104dzz+Fa6lqUN+zDFZQa8fW/xaafn+b3DjahFETf3+R3u4Qmx62G38PShmm12H1OQ54eXPf7+Y+66dPrvqdjGtY15Psjvfao+2zz6h7/S7nRvYZyfb1FwzIzaX4eVZv+1er9Z6v3b2/yfbZqYffDav4zqve1nkCWMnAPTbMbpj+AUnk7gfc0We5J1XqXV7+FBN4yCt/nDpTj37V1046o1n98Q9qHV9Pvp0kPGgNsY1jLDfR9NaSZV/tNNExfQpNjDzCVcn2UNLSo7NR3Urf85pTzgVWUgrEEvj7EddR+x0nra80VwNyGebXziHe0+M7bvTc35rFvkM8/5nGfIcalAfL6+Crtt5vMm0Rdi0BatDCrmz/ka8cB8jXkc4u67/FbTfbvvMbvi9ISL4ETm2y/1rr4bYN9fsr/s1mPTM+hnBee0uL/Ma/FZ699jtl102r37VcD/9WQ/r+r9Oe1+N+0VTbQcl+0s8N8dPdR96dv9lgyyLJLqnQjKbA7vsm8o6t5pzeZd2g179C6abWD18dabOsd1fznt5m3xZQT8fqLltqfeCnND9LLgL+Pwv54SrWdoxum1/6Ub2yyzOtouDFG6Ys6gdOapN+SEpiSoRXYzWkj7QYHobp5G1Nqr99F8y6map9xXpvf1XXVga3ZBUzt97Wwbtq2lGB5aYv1PbFa5qN102oB5RsNaR9SrevX7X7+Ftvcpkp/aov8z2yxXO27ml43rdaV0gb7iXIjYjVwY8P0pbT4D7Ou8PhxI/1d+/DBhhe981nXAngNdSdNdctMp3lsqn9cUZf+FdW0S4aZx9dUy3++blqt+5l7aN7VRe0/NL2N9S8Z6H/dZh5rx5gNChApNbgTuLjJvH1ouKFSd8z7Rott1WLr4W3mrdaNzrSG6Uk59j+oyTIXVfM3H+Hva7Bj6YeaLLMvDSfzlNZ6CVzUJP1kysVFW/uQdQV2H24j7cza/6PF/CHFu0G2VSvY3XeA39eSummTKBcSa7t/blhmq+o/fGbdtB2r/F7akHaTal2NNygH/PxNthmUCl8XtMj/7BbL1b6rmXXTat0LbbCfKDWPa12v1t+AXVIt8+wmyxxbzRvXXYH184Pm8egsynldUtcV2ADpeyZ+0foG4taUgpI1ddtcTalB/SHqKmPULVP7bc+sm1YruD6hjbzsX6W9pmF6UlcYSampnMB/1k1bWk0baoHds5rMu6Ca9/om875czdupbtonqmkvaLGtb1e/jw0qezZJO9jx6atNltm5mnfWYOtvY/vvrta1d8P02vfb1jk/664rNui6mnXH7KVt5qlWYPeoNtIupMV5FaMbo3es0t5I82v72u9rdt202nlR2/ccWFfh5q0NaWsF143Hm5afv8U2a13dvbZZ/gdYbmnj/qOca6yhofJKNa92Q/rUumnTa7+DFt/hqNwf6daDgY/fVwOvbrJM7b+xpMX0WoHdVMpx4u9U90UYoMCOUlCwGvhDw/RaxYwNugOs+y2dzbr4dTLlJm9SzsGbVuKgtLSrfdbvtfqOhvh91gptjqybVit0vIX1K4TtWaX9yxC3MazlGr6v2QOkmUfzeLukmr6wSnMs5Rj692r612leqDrm30nD9p7BunOdm2lybTvI8rXf8U+azNu7mvcnGo4H1e83gS/XTRvyvblB8jYqsW+Y3+uoxH2GEZcGyFOtwK6dbtan136/LeYP6dpxkG0N+dyCMgTD/dRVzqqbN7n6n/2qbtoDKPe7/9zkt/g7yvn7dnXTBvz8LT7Hb9nw3mot//NaLFP7rmbXTavdt99gP1HuQ/2JhvsrDLFsoNXDLjHHkcyMwVONiWbdCNxaPV/eZF6tyemOddOeUT3vFBHzmiyza/W8G6VmBQAR8QJKE9cZlK4sGn+z27HhoM9XZPPutm6uy8egImJbSun+8yk1QR/YkGSHFos2+75urp63rpv2lOr5osbEmXlnRFxB+91oLaZ0qXhSRDyX0pz8Z5SL8WxzHVC6wJpCCfB3Npm/hHVdgwwoIragNPW9OZsPmr2EDbvi2YNyQM8Wv5ONq+fdahMy8+cR8UdKk+etc92gvrOqdS1sM78PpFzEvYRSeLYF67pdgtb7eyhq+3yD7mAy848RsRzYOSK2bPj+78zM65usr9nvShqpxv9lAm/IzC8PsMyyzJw+dllaq9bNyNq8ZOaqiFgEvIdSoPfpDuSjHaMZO7dscUycWj3vVj8xIvaiHM+eQamduEnDcjtQbgbUuy4z72qyjfrjzN1N5q9nBMfS0YidqyPip5QuLttxEeV7f3/VhdY5lNjZ6jyiqWHGu4E8hXJD7qct1tXoUZSbB9cBR5VeUTZwD+vHzuUR8WNgv6qrl2uqWS+q1vWJHLwr6VpXnG+ijCm7O6XC0aS6JGMdO2+PiN9QbkI8hlLQUa/d35V6U+1/k5QL+58AX8rMrw6SvqaX4tcGqnPWl0bEdEprlxmUc+EnVI+3RMT+mXlpF7L3Hkqh3YmULo1GYiTxsNYNWC0e7hMRezRZ5sGU8/5H1dY5guPTqBw3IuKxlGvJvSldgm3WkKTZ9odyzt8yHlLiR9txjNKN80HALyPi65TupX6WmcuHsI4B8zSMGP3k6vmnLWLyEja8Vh7OPYfTKb0AHEppYV5Tu+Zd2E5mI2Iapbb9syhdkT+gIcmI4mHducYtmfmHJklqMfLJTeaNyv2RXlV/n6w6D30spSu9RVWXfXOHsc7bIuIEyj2WuZQCsoG8kXJ8WdgwfSHwUkrr5lbd5R1YPeqdT6mg0KqbxhMpw9Q8hHKcGZGImEQp9F1D+U8AkHXDD1C6CV080m11WbN7WV/OzNc3TuzGd5KZv4iIb1IqCX247t7WUA0Ud5sdD5pdhw753hx0JPa11IG4P5y41Mo1lN5yXlUNgfEdSuy+LDccImcwQ712HGxd0Oa5RTXExBMphXLvbHEdei/rX4feExFnUo6Lz6WKxxHxVMrx+9vZRvfjVVegsygFbU+k7KvJdUmG+j02M9B16KqIuJhSoPhkNry/MqLzSQvs1I5mBTer2pi3cd20bavnlw+yrc1rLyLiHZS+c2+nnLDcROmbO1nXd/6mTdZxR4t1r2L9g3VLVV/Nl1JqVfyKEqBXVOvYinIzstm2W22/9p3UHzy2rJ7/2mI9f2knrwCZuawac2YepebsQdWsmyPixMxs9wb2qOVpmOuq/U72qB6tbN7w/jRKbeqDKeMHQDkZu5/SpdqAqsB+AaVW1O8oNaxuY10/5sfQen8PRe07aTVOyZ8pF3hbsf5/644W6Zv9rqQRqV30Vhe8z6B0n/G5iFiWmUMae6SJ2m9/yDctImI3yhiff8jMSxpmL6TcXDyM3imwG83YuV/1aKU+dr6EUpv335TYeQOl9dwaSq2yfRh67IQ2jjMjPJY22/5Yx867IuLplFq2B1AuGAD+HhEnU1r9tTMWyGjGztr6VrTY9kCxc1cGLhhsjJ0LKb+rQyk3GWHdjYzT2spp2ccvodQ0/U6Vv9qYHO+kc7ETSuxcTzYf187YOU4MtbJir8avwWTmUkoX758HiIgdKa0sXkSp6fykQVZROy48vI3N1dLcOlCi6qbhWcDLIuKVmfn1Ntbdal2jGQ8HuzFdf5wb7vHpjsYJ1U0ZaPO4UcWWCyj3W35MuaF7F1X3YZSb8yONxS1jT5Xfdsbaq6X/VjVGy3soY968qfocl1Nalpzf5qp65Vqy7XsODRVYdsvM30fEgynX1Fdk5m8Hy2hE7EK5Z7A1pWLBeZTf9mrKjbxDGXk8HHYsZBTuj4wXmfkv4FcRcRCly8j/FxGfy8ybB1m0mU9Qxih6e0Sc1CpRREym/G/WUMYIrPdDym/1RRHxkMxs9rt9XWYurNazC6UA+ZWU+xpvbLbNzFwTEbXj2T1D+EytPJfSyurcXDfmU81CSuHUHNYVTtV+b9tGxGaZ+e82tzPc5aB8vzDwb7Y2r9X4cftm5pLqmmU3yj5+XUTcmJkfakjbqe+k0T0Nz8MxpLhbF+Oaxd227811KPYNZKzj/mheh66OiGdSeq97GevG7f1nRJxGib2DVpaty9dQrh0HWxe0f26xNaVy7lSGVkF1IeXezaGsq0Az1OvQ/6Xs1z9TGq3cwrr/zWzK/3ekRjv2tv2btsBOnVILCgdm5qA1UCJiI0rh01+Ap2TDQOwRMdY1wd5IKaw7NjPnNdn2O0ZhG7XvZPsW8x8ylJVl5u+BV1bf3RMp43y8DfhURPwrM7/U4TwNZ121ZT6Rme8ewra+wrqakadExJMpTcy/007NDMqJw56UJtavq59RDXo6lMAzkNrnewjlJnqjhzakk7qmuuD9UUS8iDLI9GkR8ejMXDmC1V5GOWneMSIelZl/HMKytdZ1j4mIVi2HHxcR/5GZPx9BHntJ7VjwjiFUvDieUptsRhUX1oqIz9N+rb/h6MSxdLRj53LgDVUNvd0pA0C/lXLxNInSFU5H81Stb5uI2LjJhddAsfPbmXlQk/mtfJtyEX1IRHyAcmH+PODKzGxsqbaBiJhBuSj+EaWrp1V18yYxeG30dtXHzqubzDd2aj09GL+GpCpAOJhSafGJEbFNZq4YYJFajeqZETF5kBbCz66ef9ZGVo6kHNc/EhHfbiP9WKr9v7ds0Rp8PR08PrVyFKWF1b6ZuaQhb7XvdaTqY8+NDdvYiNITTdst5DLz+8D3qwLvp1HGCH4L8L2IeHJdS+x289RMp64l27rnUOc01lVgeT+lxv5GtH/T8N2UGPq6zFxYPyMiXkWbPdQMoj4WNmMsrJOZd0TEtZTWEU9hXauGoazj3xFxFOV3UGtp18wLKWMkAixv0cIESqHehwfY3mrguoh4NaWg9w0RsXiIv+Xhql1nPXeA66z9I+LhmXlzZt4cETdRKhvvTSmkHtRwl6vUftvbDpBmu+r5jkHycT/w2+o84Rrg2Ij4fmb+pi5ZR76THjace3OdiH1NdSjuj/Z16O2UlrLviohHUq7T30QZJ3ErSu9B7eZrKNeOg60L2j+3qKX/TWY+hTZl6SntOuCAqsHMvyhj2/2dul73Wqkq1rydUkn4PzLznw3zX9VuXgbRtdjbV7Vp1NNqrSH+q83021EOUD9vUli3OeuapY6VR1bP32wyb7Rudv661foiYksGr03bVGauyszLM/N/KAc8KC0Sa2oX8c1K9P9AacX4pCoPjWYOIR//pBoYPiKadX3SbF2/otS+afd3UtvWzZSaPE+LiEcz9JoZtf39rSbzWu3vgb7HVmongDMbZ1QBekfgTy1aBEhdUdUs/gLl9/muEa7rHtbVPD16sPQRsWnd82sox4dTKa0mGh/nVosdNpI89pihxk4ox7NrmhTWTaK0UBxLwzmWDtVAsXMyw/yMWVydmZ9hXWvGF9claXnMH2a8G8ivKefozT5Ls3X9gXJj4ulVjeG2VP/HMyk3mZ5NGbB8KDcoa/t7cW7YfeaebNgdGIx+7NyKcr70b+D3jfM1sfVC/BqBe1nXlc9gLQ0vohyDHka5IdwqT4+l3MxaRYmlA8rSPdXJlEqMbxs8y2NqqPFwOMen0fRISm33JU3mjXk8pMSPYbUizsx/ZeYF1Q3aD1O61X5eXZKBjuOjGaNrx/7/rJZtNLPJtOGcN0E5b6lVYJlEuZZcRRs9tVSGc+9gNaz9XgZVnWvcQDnX2LVJkn2r5183mTdR1bocG8l9z69QfouvonRb3Ezt2uN7NL9GWVjNr1UQG1BmrmFdBfH/afc3MlwR8RBKoeNdNM//lyiVPGotCWsWVM9HVf+bgbZRHxOHu1ytMtlAlfdr8wateAZQVeT5b8pv5KN12+30d9KLhnNvrhOxb6Btw9jG/eHEpbZk5vVVI4t9KMNQ1BduDnb9NNRrx4EM6dyiagV4NfDYiNhmiNs6jdJl6isp3ctuRxkrrp0ebnahfObzmhTW7VjNbzTa16Ebse7/Meqx1wI7dcp3KCeYb42I5zdLEBHPiNL/LcDfKAVHT60K6GppNgY+xbqaM2NlafU8syGPT6bUNh0N36HUnH11VRuk3jzWNb0dVEQ8tUUBW63mR32N4n9Uz9MaE1cHxkWUMYfmNWxjBqW24VB8mXKc+Z/6E5aI2JlSG6Jx+3+rtj8jIj7YLAhGxCOq5RstrJ7fwLqaGd9rM59Lq+eZDdvahXVN0xu1/B4HULs5clRE1Maeql2onUj5rtppCSl12ocoNw/fGxEjHffpKEqtrFkR8bGI2ODkOSK2i4hPU7q5hdLlyLaULknekJlvbHxQ+vr/F/CKFsfDcSczL6N0rXRQRDS9CRsRj69qmNUsBXaNiIfVpQnKMX33scvt2m3D0I6lQ/Vz4Fpg74horKV5BO2PjUNEPDYimtWQHFLsrAwp3g2iNt7W/IhYO+5DdRF0VGPi6qL0M5Qafp9u8Z96aEQ02/8Lq+fXVo9VlDjcjqXV88yGbT2Y9ccBqjec2PlVSreqb6sqt9Q7HngQZbD4ezdYUup+/GoqIh5Yneu2qqX9Tko3U9dk5j9apAHWtso4nHJj7VNRukZu3N5ulK6pNgaOz8xlA62zznGUCgFz2bBb3U76LOU48ImIeFTjzIjYJCLqbyourZ5nNqQb6Pg0mpZSars/oWH7b2Bd98sjtbB6nlt/k6yKGx8ZyooiYu/qxlOjocbDUYvRVQv48ykFxkc05PdAmt9MHOo9h9q2ahVYdqAU7j8ROKe6Nm3H0up5ZsO2nkuLLg0Z/rVkAB+rv06OiO1Y1yPAoIXxE0FEvJjy27mf8rsclsxM4L2U732D/1VEPJzSfertwMubXaNUvU78lHID+dmN62ix3V9S7mU8hnJ+NpZeT6mwtahF/t9I6V4uKYWOtfPcT1AKxv4LOL2qRLWeiNg8Io6hfIeMcLlvU1qxHBARz2qyzOsolbhuoPlYXq2cCVwFPDsiZlbTOv2d9Jxh3ptbytjHvlaWVs8zG7Y9anF/mHGpqYjYubpGbrQ1pevO+i5Rb6f81ga6DoU2rx0HsbB6Hsq5xf9SKvec2uI3v3WU8eIbnU45d61dh9ZvfzBLq+f1Ck+r8oMv0LxHyeHE3bMpw2O9KkqXr/XeSfkt/CgzG8evGzG7xOxDEbGw7u1jquf/iYhaqfMXM3MoAWzEMvP+KP2In0vpauPnlAE2V1LGUtiDcgLzUGBllj65P03pkuKqiPgO5QCwL7ANZSDsfTfY0Og5nTJGwicjYl/gOsrYMC+k1MB75Ug3kJl3R8QcSj/LP4kywPefKbUWHgdcTGlK347XAG+KMpD3DZQD+iMoY1/cSxkLsOYXlO/9nRGxLev6NP5MljEmPkAZLPudVSHdTyn75ZWUpskHDOFjfpzSQuGlwK8j4lxKy8lXVJ+v2bqOoHzXxwGvqT7TXym1hnej/FZeBfypYbla117vpNyM+EybNTMAvkupmfzuiHg8pRbFNMr+/j7ND+gXUoLLRyLicZTvnNyw7/O1qmbfH6U0x/9dlLFB/kWptfo4ynf9sTbzLHVMZt4SEZ+j1Pb8f2xYcWGraD4Ydc3CLGP0kJl/rS6yzqZcqBwaEbVxSjeh/M9nUk5UX1wtX+uS5IsD5PGuiPgG5eLpEDY8MT8xIlr1A390w0nW+yNidou0n87MTtZefjWlBfGXIuLtwC8pN093BJ5AOXY8g1LRBcqF4ueA30QZtPx+YC9KYd13KXFhrAznWDokmZnVRd/5wDcj4lvVNp9EiV0/pNw4acd+lJtevwD+SPkOd6TUaFzD+sfjayn94h8cEfcDyygXTl+pbnwPJ9618n+UmHsAJVZ8hxLXXkYZX7fZDc/jKTcY30wZJ+WCKr8PpsTUvSg33Nfr0iwzfxYR11PG+9kY+O4QblBeSqldfFB1XvdTys3d51G+r2ZjZA12DrKBzFwaEe+k/Kd/HWWQ8tsoF8XPoLQw/O9my0o9EL9a2ZhyrntMRPyKcl10O+U6Zy9K1+7/ovynB5WZ50fELMrN+m9V6/wZpZXeYyk3yjamHNeOb2ed1XpXRMSHqWt50A2Z+YcoFVdOBa6OiB9SjtsbU2LLf1GOC7Vr3+Ecn0bTJynf+U+rY9adlNY5/0kZZ/ZlI91Adfz+DKX1Y+264n5KDLud1uOtNPNpSsutn1Fugt0HPJXSTfQy4Iy6tD+mXCd/oTrP+CdwR2Z+dpRjNJQuqn9BuSZ/DuVG+CMpLUU3OKcZ6j2Hhm2dRilc+0jd+3adDLwO+Ea1H26lnJ/tTykMaHbv4MeU2PutiDiHcnN2WWY2joFW70TKb/hA4MpquSnVeh4MfLTT93l6QcMx/IGUc95aq9APZGar8abakpkXVN91s0LgN1Baa3w1Bx6z7IuU//8cyv+jHUdTWp0cExGLMvO+wRYYqogI1hUqD3SddX1EXESJcc8Dvp+ZKyNif8oxbRbl/LM2fnZQ/qvPolSsOqJuXcNd7s7q+uwM4LwqDvyW8v3vSTkvvBOYlQN3Dd342TIijqbcT/pwROzV6e+khw313twnGePYN4BOxf0hxaUBPJFy/L+U0kvIrZRx4A6knNusrexa3Tv+JfBfEbGIcv6zmtKa8LcM79qxqeGcW2TmqRHxVErlsRuq6+CbKOe0O1Pua3+ZhnPaLN3IXkj5T6wCrsr1u6UdKJ9/iYgzKBXkroiI8yiNXvaj9LxyBRv2WjfY9Xyz7dxdnX9+A7iout90E+Uc6TmUa9k3tZPnIctMHz3+qH5AOdT0Azxmt7meeVX6mU3mzW61LkrASmBek3kPBk6g9DO7ktLU9zrKwfsQYKO6tBtR+oO/hnIC+xdKlwQ7UUrdE5hel356NW1hi8+zZIjf4+6Umqh/o1wwX04J3E230yxPbX4n+1GCyUrKAfA7lIvNlutrso6nUQYlvpJS+n8P5eLoy8DjmqTfnxJk7q77XdR/lw+hXBDfVq3rimqft/wcA+TtQZQaF7dQDpx/oAxovkur/UW56XEEpTbcnZRCx5soFzbvBLZtsa0v1n2ep7ZI0/S3S7mIW8S6gUqvptzY2ahKv6TJug6pvpt7aPifDvJ7OLja5/+svpOrKTdSN2uSdimwdKj/UR8+hvpo/A03mb895Vj4L2D7atp0Bo85reLIJpSL3HMoJ373Vf+Jqyg3jh5fpXtUtY6/ABsP8hn+o0p7Rd20pW3k70lV2iVtpH3xIHloeoyp5s2kdSyofZcLm8zbglKZ4nLKcfseyoXR9ykX/g9skocrqn31d8oF6ONbHTNocYyr5rU8lrVIP6Rj6UDHsUG+k6dSbvz9s3r8iFJ403J9TdaxGyU+XUaJd/dWv5ezKH3hN6bfgxKH7qQU6K23HYYR7wbI2yaUGzU31uVrPqUgoFVMCkoFnh9TzgXuq/Ly0+r38/AW2zqKdb/vl7ZI0/S3S7kQO7nK378pNyQ+TLmBuJQm8YsBzkEG+T08hzIeyO3Vd3I9pRBhqyZpl9DieMYA/1EfvfGo/S5GKz1dil916Wu/63l10yZV/4WPUypi3Eq5IfJPyg3IT9L8HHJJq3xV83ekFMpdxbrzzKWUwoc9B/kOl7eYtykl5tS+k41aradZXlvMW1j/32/xfTX77h9fLbusOg6soFxXfh54ZkPaIR2fBjs2MECsbJH+hZQuGv9JqWhzHuWmVdPtNMvTYN8J5bh/BOVm373V7+gkyk2rlutrsv5XUG74XUc5Nt9Vfa/zgalN0r+7bpvZ5LsccYyuW9cjKXH5Dsp/+BeUQoyW+4sh3HNoWO66ap3/ADYZym+Xcg56ASVG/ZMSe19M6/g5ufo93kj576/3+2q1/yjdh32g+mz31G3rVU3STmcU74/02oPmx+xVlOPyd4D9mixT2x9LWkz/aott7V6tO4GfVtMmUe5RJPCEQfI6pfoN3wc8uOG3tMFvuG65b1Zp3tZk3tJmv8Uhfof7Vev4dRtpX12l/U7D9ElUhc+UVuj/rv53f6Dcn9ngnHqEyz2Ocp/rT3XLXEvpbWKngX7rDHDsoVwPJKXApSvfSbu/iwGWrf2O5zWZN52BjwdNYxxDvDdHB2LfAJ+/I3GfYcSlJuvYscrbzyj3Oe6tfis/oIzB12yb36XEpzWN22EY144D5G1Y5xbVvv8e5R76fdXn+hWlt4vHtFjmENYdv9/TIk3T3261X+dTrgn/TRmr9CRKz0xLaBLfGOB6fqD9Vy33bco9g/so/4FTgIcN8B+ePpT/aOMjqgUkSZIkSZIkSZIkdYFj2EmSJEmSJEmSJEldZIGdJEmSJEmSJEmS1EUbdTsDkiRJkiRJkqSRiYh3Alu1kXRJZi4Z08xIkobMAjtJkiRJkiRJGv/eCezUZtolY5cNSdJwRGZ2Ow9jZrvttsvp06d3OxuSpEFcfvnlf8/Mqd3ORy8zpknS+GBMG5wxTZLGB2Pa4IxpktT7xlM86+sWdtOnT+eyyy7rdjYkSYOIiGXdzkOvM6ZJ0vhgTBucMU2Sxgdj2uCMaZLU+8ZTPJvU7QxIkiRJkiRJkiRJE5kFdpIkSZIkSZIkSVIXdbTALiJOjYi/RcTvWsyPiPh0RFwfEb+NiKfUzTs0Iq6rHod2LteSJEmSJEmSep33HiVJ41mnW9gtBPYfYP7zgF2rxxzgFICI2AY4BngasCdwTERsPZYZPfzww9loo42ICDbaaCMOP/zwsdycNOEtWrSI6dOnM2nSJKZPn86iRYu6nSVJkiRJkjS+LGQc3Hv0vqPUWd531HjR0QK7zLwYWDFAkgOB07O4BNgqIh4KPBc4PzNXZObtwPkMHHxH5PDDD+eUU05h9erVAKxevZpTTjnF4CmNkUWLFjFnzhyWLVtGZrJs2TLmzJlj8JQkSZIkSW0bD/ceve8odZb3HTWe9NoYdjsAN9e9X15NazV9TCxYsGBI0yWNzNy5c1m5cuV601auXMncuXO7lCNJkiRJktSHun7v0fuOUmd531HjyUbdzsBoi4g5lCbtTJs2bVjrqNVwaXe6pJG56aabhjRd6gURcSrwQuBvmfm4JvMD+BTwfGAlMDszf13NOxQ4qkr6ocw8rTO5liQNZN68eU1fS5I0nhjPxtZI7z1631HqLO87ajzptRZ2twAPr3u/YzWt1fQNZOaCzJyRmTOmTp06rExMnjx5SNMljUyrE9zhFrpLHbKQcTA2Ajg+gtRJjo0wvh177LFrH5IkjVfGswF1/d6j9x2lzvK+o8aTXiuwWwy8NoqnA3dm5p+Bc4HnRMTW1U3N51TTxsScOXOGNF3SyMyfP58pU6asN23KlCnMnz+/SzmSBjcexkYAx0eQOsmxESRJknpe1+89et9R6izvO2o86WiBXUT8H/AL4NERsTwi3hARb46IN1dJzgFuBK4HvgAcDpCZK4DjgUurx3HVtDFx8skn85a3vGVtzZbJkyfzlre8hZNPPnmsNilNaLNmzWLBggXstNNORAQ77bQTCxYsYNasWd3OmjQSXR8bARwfQeokx0aQJEnqrvFw79H7jlJned9R40lkZrfzMGZmzJiRl112WbezIUkaRERcnpkzup2PoYqI6cD3Woxh9z3ghMz8afX+x8B/AzOBzTLzQ9X0DwL3ZOaJTdZRPzbCU5ctWzacPLac18/nAFI3TJo0qen/KiJYs2ZNF3Kkoao/Zg73GDleY1oneZ0mSWNrNOJZtR5j2iCMaZLU+8ZTPOu1LjElSeoXXR8bARwfQeokx0aQJEmSJEnDZYGdJEljo+tjI4DjI0id5NgIkiRJkiRpuDbqdgYkSRqPqrERZgLbRcRy4BhgY4DM/BxlbITnU8ZGWAm8rpq3IiJqYyNAB8ZlhTJm3erVq5k8eTJz5sxxfARpDNTGQJg7dy433XQT06ZNY/78+Y6NIEmSJEmSBmWBnSRJw5CZrxpkfgJvbTHvVODUschXMyeffLIFdFKHzJo1ywI6SZIkSZI0ZHaJKUmSJEmSJEmSJHWRBXaSJEmSJEmSJElSF1lgJ0mSJEmSJEmSJHWRY9hJkiRJkiRJkiSpp8ybN6/p635lgZ0kaUQmWuCUJEmSJI2uiNgyM+/sdj4kSb3l2GOPXft6Itx3tMBOkjQiEy1wSp1kgbgkSZImiFsj4gzgc5l5abczI0lSN1hgJ0mS1KMsEJckSdIE8THg9cDsiPgtcArwtcy8u7vZkiSpcyZ1OwOSJEmSJEmSJq7MnAdMB14C3AqcDNwSEadExJO6lzNJkjrHAjtJkiRJkiRJXZWZazJzcWa+AHgE8GngAODyiPhlRMyOiE27m0tJksaOBXaSJEmSNIFFxKkR8beI+F2L+RERn46I6yPitxHxlLp5h0bEddXj0LHM5+GHH85GG21ERLDRRhtx+OGHj+XmpAlv0aJFTJ8+nUmTJjF9+nQWLVrU7SxpYrkLWAHcDQSwJfAl4PqI+M9uZkySpLFigZ0kSZIkTWwLgf0HmP88YNfqMYcyrhARsQ1wDPA0YE/gmIjYeiwyePjhh3PKKaewevVqAFavXs0pp5xioZ00RhYtWsScOXNYtmwZmcmyZcuYM2eOhXYacxGxV0ScDtwCHAtcADwxMx8D7A7cCHy+i1mUJGnMWGAnSZIkSRNYZl5MacXQyoHA6VlcAmwVEQ8Fngucn5krMvN24HwGLvgbtgULFgxpuqSRmTt3LitXrlxv2sqVK5k7d26XcqR+FxFvi4irgIuBpwDvA3bIzLdk5lUAmXktpaLIY7qXU0mSxs5G3c6AJEmSJKmn7QDcXPd+eTWt1fQNRMQcSus8pk2bNuQM1FrWtTtd0sjcdNNNQ5oujYITgbOBIzLzogHSXQcc15EcSZLUYbawkyRJkiSNqcxckJkzMnPG1KlTh7z85MmThzRd0si0KlgfToG71KZpmfnKQQrryMxbMvPYTmVKkqROssBOkiRJkjSQW4CH173fsZrWavqomzNnzpCmSxqZ+fPnM2XKlPWmTZkyhfnz53cpR5oAtoyIfZrNiIi9I2LXTmdIkqROs8BOkiRJkjSQxcBro3g6cGdm/hk4F3hORGwdEVsDz6mmjbqTTz6Zt7zlLWtb1E2ePJm3vOUtnHzyyWOxOWnCmzVrFgsWLGCnnXYiIthpp51YsGABs2bN6nbW1L8+CbyoxbwXAp/oXFYkSeoOx7CTJGmYImJ/4FPAZOCLmXlCw/ydgFOBqcAK4JDMXF7NWw1cVSW9KTMP6FjGJUmqExH/B8wEtouI5cAxwMYAmfk54Bzg+cD1wErgddW8FRFxPHBptarjMnPFWOXz5JNPtoBO6qBZs2ZZQKdOmgF8rsW8i4FDO5gXSZK6wgI7SZKGISImAycB+wHLgUsjYnFmXlOX7ETg9Mw8LSKeCXwEeE01757MfFIn8yxJUjOZ+apB5ifw1hbzTqVUTpEkaSS2AP7dYt79wJYdzIskSV1hgZ36zrx585q+lqRRtidwfWbeCBARZwAHAvUFdrsD765eXwic3ckMSpIk9Qqv0yQN4kbgWcB5TeY9E1jazkpG2AvKR4EXUIYQOh94R1VpRZKkjrDATn3n2GOPXfvaC0FJY2gH4Oa698uBpzWkuRI4iHLB+BJgi4jYNjP/AWwWEZcBq4ATMvPssc+yJElSd3idJmkQpwPHR8RNlIK2eyNiU+CNwDuBeYOtYCS9oETEfwB7AU+o0v0U2AdYMgqfTZKktkzqdgYkSepj7wX2iYjfUC72bgFWV/N2yswZwKuBT0bEIxoXjog5EXFZRFx22223dSzTkiRJktRhJwKLgc8A/4qIvwH/qt4vBv6njXWs7QUlM+8Dar2g1NsduKB6fWHd/AQ2AzYBNqWM5frXYX8aSZKGwRZ2kiQNzy3Aw+ve71hNWyszb6W0sCMiNgdempl3VPNuqZ5vjIglwJOBGxqWXwAsAJgxY4ZdsUiSJEnqS5m5GnhZ1eptP2Bb4O/AeZm5pM3VjKQXlF9ExIXAn4EAPpuZv2+2kYiYA8wBmDZtWptZkyRpcBbYSZI0PJcCu0bEzpSCuoMpreXWiojtgBWZuQY4kjJWAhGxNbCy6uZlO0rXKx/tZOYlSZIkqddk5gWsawE3Ft4LfDYiZgMXU/WCEhGPBHajVMQEOD8i/iszf9Ikj1aslCSNCQvsJEkahsxcFRFHAOdSBjQ/NTOvjojjgMsyczEwE/hIRCTlYvCt1eK7AZ+PiDWU7qlPaBhXQZIkSZI0NMPuBSUiDgMuycy7q3k/AJ4BbFBgp4mtfhxWx2SVNNossJMkaZgy8xzgnIZpR9e9Pgs4q8lyPwceP+YZlCRJkqRxoupq8i3AoynjyK0nMycPsoph94IC3AQcFhEfoXSJuQ/wyWF/GPWtY489du1rC+wkjbZJnd5gROwfEddGxPUR8f4m83eKiB9HxG8jYklE7Fg3b3VEXFE9Fnc255IkSZIkSZJGW0S8FvgMpdBtM+DLwFeBuyhjfR832DoycxVQ6wXl98CZtV5QIuKAKtlM4NqI+COwPTC/mn5WtZ2rKOPcXZmZ3x2dTydJUns62sIuIiYDJ1EGj10OXBoRixu6ATsROD0zT6sGmv0I8Jpq3j2Z+aRO5lmSJEmSJEnSmHon5R7g8cAbgZMz89fV+N9LgH+0s5IR9IKyGnjTMPMuSdKo6HQLuz2B6zPzxsy8DzgDOLAhze6sG1z2wibzJUmSJEmSJPWPXSnjfq+pHpsAZObtlFZw7+he1iRJ6oxOF9jtANxc9355Na3elVSDvwIvAbaIiG2r95tFxGURcUlEvHhMcypJkiRJkiSpE+4BJmVmAn8BdqmbdzfwsK7kSpKkDur4GHZteC+wT0T8hjLA6y3A6mreTpk5gzJg7Ccj4hGNC0fEnKpQ77LbbrutY5mWJEmSJEmSNCxXAY+sXv8E+EBEPCMi9gDmAX/oVsYkSeqUThfY3QI8vO79jtW0tTLz1sw8KDOfDMytpt1RPd9SPd9I6b/6yY0byMwFmTkjM2dMnTp1LD6DJEmSJEmSpNGzANi6ev1BYHPgp8AlwKOA93QpX5IkdcxGHd7epcCuEbEzpaDuYEprubUiYjtgRWauAY4ETq2mbw2szMx7qzR7AR/tZOYlSZIkSZIkja7M/Hrd6+sj4rHAM4ApwM8z8+9dy5wkSR3S0RZ2mbkKOAI4F/g9cGZmXh0Rx0XEAVWymcC1EfFHYHvKwLIAuwGXRcSVwIXACZl5TSfzL0mSJEmSJGn0RMQmEfGJqvtLADLzX5n5o8xcbGGdJGmi6HQLOzLzHOCchmlH170+CziryXI/Bx4/5hmUJEmSJEmS1BGZeV9EvAn4drfzIklSN3W8wE6SJEmSNPoi4sHAZo3TM/OmLmRHkqSh+A2lov7F3c6IJEndYoGdJEmSJI1TEfEg4FPAK4FNWySb3LkcSZI0LO8B/i8ilgHfz8zsdoYkSeo0C+wkSZIkafw6CXgp8CXgKuDe7mZHkqRh+QawJfAd4P6IuA2oL7TLzNypKzmTJKlDLLCTJEmSpPFrf+B9mXlStzMiSdII/Jj1C+gkSZpwLLCTJEmSpPHt2m5nQJKkkcjM2d3OgyRJ3Tap2xmQJEmSJA3bGcCLup0JSZIkSdLI2MJOkiRJksav84BPRsQWwDnAisYEmXlBx3MlSdIQRMRrB0uTmad3Ii+SJHWLBXaSJA1TROwPfAqYDHwxM09omL8TcCowlXID9ZDMXF7NOxQ4qkr6ocw8rWMZlyT1k+9UzzsDs+umJxDV8+QO50mSpKFa2GJ6/bh2FthJkvqaBXaSJA1DREwGTgL2A5YDl0bE4sy8pi7ZicDpmXlaRDwT+AjwmojYBjgGmEG5AL28Wvb2zn4KSVIf2LfbGZAkaRTs3GTatsALgVcDh3Q2O5IkdZ4FdpIkDc+ewPWZeSNARJwBHAjUF9jtDry7en0hcHb1+rnA+Zm5olr2fGB/4P/GPtuSpH6SmRd1Ow+SpN6xaNGiDd7PmjWrS7lpX2YuazJ5GfDriAjKddWrO5srSZI6a1K3MyBJ0ji1A3Bz3fvl1bR6VwIHVa9fAmwREdu2uSwRMSciLouIy2677bZRy7gkqf9ExDYR8YKIeE31vE238yRJ6qxFixYxZ86c9abNmTNng0K8cegnwAu6nQlJksaaBXaSJI2d9wL7RMRvgH2AW4DV7S6cmQsyc0Zmzpg6depY5VGSNM5FxIcoMWYxcBrwXeCWiDi+qxmTJHXU3LlzWbly5XrTVq5cydy5c7uUo1HzdODubmdCkqSxZpeYkiQNzy3Aw+ve71hNWyszb6VqYRcRmwMvzcw7IuIWYGbDskvGMrOSpP4UEe8EPgB8Cfgq8BfgIZSxfj4QEbdl5qcHWcf+wKeAycAXM/OEhvk7AacCU4EVwCGZubyatxq4qkp6U2YeMEofTZI0RDfddNOQpveSiDi6yeRNgMdRWtd9trM5kiSp8yywkyRpeC4Fdo2InSkFdQfTMKZCRGwHrMjMNcCRlJudAOcCH46Irav3z6nmS5I0VG8GPpWZ76qbdi1wUUTcDRwOtCywi4jJwEnAfpQumi+NiMWZWT8m64nA6Zl5WkQ8E/gI8Jpq3j2Z+aRR+zSSpGGbNm0ay5ZtOBTctGnTupCbIZvXZNq9lHHs5lNijyRJfc0uMSVJGobMXAUcQSl8+z1wZmZeHRHHRUStdcFM4NqI+COwPeVCk8xcARxPKfS7FDiumiZJ0lBNB77fYt73q/kD2RO4PjNvzMz7gDOAAxvS7A5cUL2+sMl8SVIPmD9/PlOmTFlv2pQpU5g/f36XctS+zJzU5PGAzHxMZs7LzHvbWU9E7B8R10bE9RHx/ibzd4qIH0fEbyNiSUTsWDdvWkScFxG/j4hrImL6KH5ESZIGZQs7SdKEEBGvHUr6zDy9jTTnAOc0TDu67vVZwFktlj2VdS3uJEkarn9Qugv7UZN5j63mD2QH4Oa698uBpzWkuZLSxfOngJcAW0TEtpn5D2CziLgMWAWckJlnN9tIRMwB5sC4aekhSePOrFmzADjkkEPWTluwYMHa6f1uFFqNnw7Mz8zzqyEN1nQw+5IkWWAnSZowFg4hbVIu1iRJ6nXfBo6PiH8A/5eZqyJiI+DlwHHAaaOwjfcCn42I2cDFlK6gV1fzdsrMWyJiF+CCiLgqM29oXEFmLgAWAMyYMSNHIU+SpCZmzZq1XoHdeCmsi4jXUWLKvCbz5gF/yszBYtraVuPVcrVW4/UFdrsD765eXwicXaXdHdgoM88HyMy7h/tZJEkaLrvElCRNFDsP4bFLl/IorbVo0aIB30tS5UjgCkrB3D0R8VfgHmARpWXcBwZZ/hbg4XXvd6ymrZWZt2bmQZn5ZGBuNe2O6vmW6vlGYAnw5BF9GknSRPUOWrcK/xvwzjbW0azV+A4NaWqtxqGu1TjwKOCOiPhWRPwmIj5WtdiTJKljbGEnSZoQMnPD0delHrVo0SLmzJmz3rTa+/FSS1pSZ2TmPyNib+AFwH8B2wArgIuAH2TmYK3ZLgV2jYidKQV1BwOvrk8QEdsBKzJzDaWA8NRq+tbAysy8t0qzF/DRUftwkqSJ5JHA1S3m/R54xChtp1Wr8Y0ocfTJwE3A14HZwJcaV2A3z5KksWILO0mSpB4zd+5cVq5cud60lStXMnfu3C7lSFIvy+J7mfnfmXlY9XxOG4V1ZOYq4AjgXMoN0TMz8+qIOC4iDqiSzQSujYg/AtsD86vpuwGXRcSVlG7FTmgYJ0iSpHatArZrMW9qm+sYSavx5cAVmXljFRvPBp7SbCOZuSAzZ2TmjKlT282aJEmDs4WdJGlCiIg/Ucama0dm5mjV4JSG7KabbhrSdEkaicw8BzinYdrRda/PAs5qstzPgcePeQYlSRPBr4A3A2c2mfdmSovwwQy71Xi17FYRMTUzbwOeCVw2nA8iSdJwWWAnSZooLqL9Ajupq6ZNm8ayZRv24mqXO5IAImI18IzM/FVErGHg+JaZ6XWfJKnXzQd+FBG/BL5IKXDbAXgjpaXbfoOtIDNXRUSt1fhk4NRaq3HgssxcTGk1/pGISEqXmG+tll0dEe8FfhwRAVwOfGGUP6MkSQPywk2SNCFk5uxu50Fq1/z585kzZ8563WJOmTKF+fPnD7CUpAnkOErXXbXXVkiRJI1rmXlRRLwM+CTw+bpZS4GXZuaSNtczrFbj1bzzgScMJd+SJI0mC+wkSZJ6zKxZswA45JBD1k5bsGDB2umSJrbMPLbu9bwuZkWSpFGTmd8BvhMRjwa2Bf6emX/scrYkSeoYC+wkSRNWRDwReDSwWeO8zDy98zmS1pk1a9Z6BXYW1klqV0RsA+wM/C4z7+12fiRJGorMvLbbeZAkqRsssJMkTTgRsRXwfeDptUnVc32XYhbYSZJ6XkQcBTwwM4+s3u8NfA94IHBLRDwrM6/rZh4lSRpMRHwC2C4zX9Nk3leAv2Tm+zqfM0mSOmdStzMgSVIXfJjSxcrelMK6lwDPBBYBNwJ7di9rkiQNySGU2FXzP8CVwIuBvwLHdyFPkiQN1QHAeS3mnUuJa5Ik9TVb2EmSJqLnAscCl1Tvl2fm5cCSiDgFeAfw2m5lTpKkIdgBuA4gIqZSKp08KzOXRMQmwKe7mTlJktq0A3BTi3nLq/mSJPW1UWlhFxHTIsIbm5Kk8eKhwI2ZuRr4N7BF3bxvAS/oSq4kSRq61cAm1eu9KXHtZ9X724BtupEpSZKG6HbgkS3mPRK4u4N5kSSpK0arS8w9gC+3kzAi9o+IayPi+oh4f5P5O0XEjyPitxGxJCJ2rJt3aERcVz0OHaW8S5Imnr8AW1WvlwHPqJvX6iJRktTHFi1aNOD7HnY1cEhEbA68HrgoM++v5j0c+FvXciZJUvt+BBwVEdvXT6zefwA4vyu5kiSpgzraJWZETAZOAvajNGe/NCIWZ+Y1dclOBE7PzNMi4pnAR4DXRMQ2wDHADCCBy6tlb+/kZ5Ak9YWfAk8Hvgd8BTgmIqYDq4BDgcXdy5okqdMWLVrEnDlz1ptWez9r1qxuZGkojgO+A8wC7qd0+1zzfODX3ciUJElD9EHgUuC6iPge67rBfCGl9fhRXcybJEkdMWCBXURc0OZ6praZbk/g+sy8sVr/GcCBQH2B3e7Au6vXFwJnV6+fC5yfmSuqZc8H9gf+r81tS5JUcyzwsOr1x4BtgVcCUyiFdW/rUr4kSV0wd+5cVq5cud60lStXMnfu3J4vsMvMcyNiN+ApwBWZeUPd7IuBK7uTM0mS2peZSyNiD0pFlP0o12h/B74NHJOZy7qZP0mSOmGwFnb7ADdXj4FMbnN7OzSsaznwtIY0VwIHAZ8CXgJsERHbtljWAWclSUNW3cy8oXp9P/Ce6jEkEbE/JV5NBr6YmSc0zJ8GnEbpfnMy8P7MPKdqzfd74Noq6SWZ+eZhfRhJ0ojddNNNQ5reazLzT8Cfmkz/fBeyI0nSsGTmUuC13c6HJEndMliB3fXALzJz9kCJIuJlwNdHKU/vBT4bEbMpNUJvoQyk3paImAPMAZg2bdooZUmS1E8iYmNgk8z8V5N5DwTuqxv/p9U62unm+SjgzMw8JSJ2B84BplfzbsjMJ434w0iSRmzatGksW7Zhxf1evZ6IiL2BX2fm3dXrAWXmxR3IliRJkiRpBAYrsLucMmbcYBKINtLdQhn4vGbHatq6FWXeSmlhRzVw+ksz846IuAWY2bDskg0ykrkAWAAwY8aMbCNPkqSJ54vAxsCrm8z7PHAf8PpB1tFON88JPKh6vSVw6wjyLEkaI/Pnz2fOnDnrdYs5ZcoU5s+f38VcDWgJZSzWX1WvW133RDWv3R5RJEnqmoh4MPAq4NHAZg2zMzPf0PlcSZLUOYMV2H0NWDlIGiiDwr6uzXS7RsTOlIK6g2m4WRoR2wErMnMNcCRwajXrXODDEbF19f451XxJkoZqX+B9LeYtpoxrN5h2unmeB5wXEW8DHgg8u27ezhHxG+Au4KjM/EnjBmw1LkmdURun7pBDDlk7bcGCBb08ft2+rKsgsm83MyJJ0miIiEcDv6Dcq3wgZfy6bSiVTm4H7uxe7iRJ3bBo0aIN3vfwNdqomDTI/NOBkwEi4tSqoG0DmXlTZp422MYycxVwBKXw7feUbsKujojjIuKAKtlM4NqI+COwPTC/WnYFcDyl0O9S4LhqmrRWsz+xpLEzjv9zDwb+1mLebZT4MxpeBSzMzB2B5wNfiYhJwJ+BaZn5ZODdwNci4kGNC2fmgsyckZkzpk6dOkpZkiQ103jh18sXgpl5UWbeXfd6wEe38ytJUhs+Rrnftz2lhfjzgAcAb6Q0JnhJ97ImSeq0RYsWMWfOnPWmzZkzZzzdexyWwQrsHghsWr2eDYz4bmFmnpOZj8rMR2RmrTDu6MxcXL0+KzN3rdK8MTPvrVv21Mx8ZPX48kjzov4yUf/EUreM8//c34DHt5j3eOAfbaxj0G6egTcAZwJk5i8o3bpsl5n3ZuY/qumXAzcAj2o795IkVSLiURGxT4t5e0fErp3OkyRJw7AHpdFA7T7gpMxclZmnAp8FPtmtjEmSOm/u3LnrDVkAsHLlSubOndulHHXGYAV2y4DDImJm9f7J1UVf08eY5lQaxET9E0vdMs7/c98DPhgRT6ifGBGPB+YC321jHWu7eY6ITSjdPC9uSHMT8Kxq3btRCuxui4ipETG5mr4LsCtw4wg+jyRp4vok8KIW814IfKJzWZEkadg2Z90QOXcC29XNu5RSoCdJmiBuuummIU3vF4ONYXcC8HngUMpg5Se3SOdg5uq6ifonlrplnP/njgb2Ay6PiEsp48/tAOwJ/Ak4arAVZOaqiKh18zwZOLXWzTNwWdVy/D3AFyLiXZQ4OTszs6rkclxE3A+sAd5sN8+SpGGaAXyuxbyLKddykiT1uqXAQ6rX1wIvB35YvX8hcEfnsyRJ6pZp06axbNmyptP72YAFdpl5akT8gNJN14XA2yljz0k9Z6L+iaVuGc//ucz8e0TsQRk/bj/gSZRBzecDn8jMtgY0z8xzgHMaph1d9/oaYK8my30T+OZw8y9JUp0tgH+3mHc/sGUH8yJJ0nCdT7k2+wbwv8AZEfGfwCrgMZRrNUnSBDF//nzmzJmzXu9eU6ZMYf78/g4Hg7WwIzP/DPw5Ik4Dvp+Zfxr7bElDN1H/xFK3jPf/XGbeQWlpd/QgSSVJ6mU3UrpfPq/JvGdSWixIktTrjgQ2BcjMMyPiHuCVwBTgU8AXupg3SVKHzZo1C4BDDjlk7bQFCxasnd6vBhvDbq3MfJ2Fdepls2bNYsGCBetNmwh/Yqlb+uE/FxHbRcQLI+LQiNimmrZZRLQdHyVJ6rLTgXdFxFsjYlOAiNg0It4KvBM4rZuZkwAWLVo04HtJysx7M/OuuvffzcxDMvOgzFyQmVmfPiJeGxFbdz6nkqROabzHOJ7uOQ6XNyTVVybin1jqpvH6n4viY5Sx6xYDpwLTq9nfAeZ2KWuSJA3ViZRY9hngXxHxN+Bf1fvFwP90MW8SixYtYs6cOetNmzNnjoV2koYtIiYDXwZ27nZeJEkaTRbYSZImoiOBI4DjgKcBUTfvu5RBzSVJ6nmZuTozXwY8m1J4dzbwUeCZmfnyzFzTzfxJc+fOXa8LdYCVK1cyd671oySNSAyeRJKk8cUCO0nSRPRG4LjM/DDw64Z51wOP6HyWJEkavsy8IDPfn5lzMvMDmbmk23mSAG666aYhTZekkYiI/SPi2oi4PiLe32T+ThHx44j4bUQsiYgdG+Y/KCKWR8RnO5drjRd28SxprFlgJ0maiHYALmkx7z7ggR3MiyRJI1J19XxARJwYEV+OiJ2q6ftExMO6nT9NbNOmTRvSdEkarqqrzJOA5wG7A6+KiN0bkp0InJ6ZT6D0uPKRhvnHAxePdV41/tjFs6ROsMBOkjQR3QI8rsW8JwJ/6mBeJEkatojYGvg5pSvMw4DXAttWsw8DNmhdIHXS/PnzmTJlynrTpkyZwvz587uUI0l9bE/g+sy8MTPvA84ADmxIsztwQfX6wvr5EfFUYHvgvA7kVeOMXTxL6gQL7CRJE9E3gKMjYq+6aRkRjwLeQ7mwkyRpPPgY8HBgL0pBXf2YPj8CntWNTEk1s2bNYsGCBetNW7BgAbNmzepSjiT1sR2Am+veL6+m1bsSOKh6/RJgi4jYNiImAR8H3jvYRiJiTkRcFhGX3XbbbaOQbY0HdvEsqRMssJMkTUTzgD9Qujq5rpr2DeCq6v0J3cmWJElDdiAwNzN/AWTDvJsohXkDGsl4PxFxaERcVz0OHemHUX9qLJyzsE7SKGiMee16L7BPRPwG2IfS+8pq4HDgnMxcPuiGMxdk5ozMnDF16tRhZkPjjV08S+oEC+wkSRNKRGwCfA04BphN6UbsR8ClwBxgv6r7FEmSxoPNKTcbm9mM9VvcbWAk4/1ExDaUePo0Sjdkx1RddEqSNNaaxbdbWL+iyo40xMjMvDUzD8rMJwNzq2l3AM8AjoiIpZS499qIsCKn1rKLZ0mdsFG3MyBJUidl5n0R8WzgU5n5FeAr3c6TJEkjcC3wHErlk0b7UFqPD2TteD8AEVEb7+eaujS7A++uXl9IGS8P4LnA+Zm5olr2fGB/4P+G/CkkSRNaRMwBFmXmvwZLm5mrad4I4VJg14jYmVJQdzDw6obtbAesyMw1wJHAqdU6Z9WlmQ3MyEzHgdVatdbhhxxyyNppdvEsabTZwk6SNBH9DHh6tzMhSdIoOBl4Z0TMBWp9Mm0VEa8DjqC0nhvIsMf7aXNZwPF+JEmDOgW4NSJOiognDGcFmbmKEvvOBX4PnJmZV0fEcRFxQJVsJnBtRPwR2B6weZTaZhfPksaaLewkSRPRe4CzI+JuSiuBP9MwBkJV41KSpJ6WmQsiYhfgWEp3lQDnA2uAj2bmolHYzHuBz1YtDi5m3Xg/Q8onsABgxowZwx13SJLUvx4BvIkybMGbI+JXwOeAr2fmv9tdSWaeA5zTMO3outdnAWcNso6FwMJ2tylJ0mixhZ0kaSK6inJB+ClgGXAfcH/dwzHsJEnjRtVlV+1G51HA4cCjM3NuG4uPZLyfQZeVJKkdmbk0M4+kxJWDgZWU7ipviYhPRMRuXc2gJEkdYAs7SdJEdBwNLeokSRpvImIT4C/A7MxcDHxxGKsZ9ng/lC7HPhwRW1fvn1PNlyRpWKpuLb8BfCMiHkmJbW8H3h4RP6W0Hv9+N/MoSdJYscBOkjThZOa8budBkqSRysz7ImIV0HZXYU3WsSoiauP9TAZOrY33A1xWFQTOBD4SEUnpEvOt1bIrIuJ4SqEfwHGZuWL4n0iSJIiILYDXUFqOPx74DaUQ70XA4oiYX9/NpSRJ/cIuMSVJGqaI2D8iro2I6yPi/U3mT4uICyPiNxHx24h4ft28I6vlro2I53Y255KkPnI28LKRrCAzz8nMR2XmIzJzfjXt6Kqwjsw8KzN3rdK8MTPvrVv21Mx8ZPX48kjyIUma2CJiRkR8AbgV+DhwBfCMzHxqZp6QmXsB86gqjkiS1G9sYSdJ0jBExGTgJGA/YDlwaUQszsxr6pIdBZyZmadExO6Uwc+nV68PBh4LPAz4UUQ8KjNXd/ZTSJL6wA+AT0fEWZTCuz/T0O1zZl7QhXxJktS2iPg18ETgT5QhDL7UotX2+cCxncybJEmdYoGdJEnDsydwfWbeCBARZwAHAvUFdgk8qHq9JaWmKFW6M6oWCn+KiOur9f2iExmXJPWVb1bPB1WPmgSiep7c6UxJkjREy4G5wA8zc6Dxxn8N7NyZLEmS1FkW2EmSNDw7ADfXvV8OPK0hzTzgvIh4G/BA4Nl1y17SsOwOjRuIiDnAHIBp06aNSqYlSX1n325nQJKkkYiITYAbgL8PUlhHZt4HLOtIxiRJ6jDHsJMkaey8CliYmTsCzwe+EhFtx97MXJCZMzJzxtSpU8csk5Kk8SszL8rMi4DfAPcDD66ef103T5KknlUVws0BHtDtvEiS1E0W2EmSNDy3AA+ve79jNa3eG4AzATLzF8BmwHZtLitJUlsi4mhKq++fAF+vnpdHxFFdzZgkSe27Anh8tzMhSVI3WWAnSdLwXArsGhE7V124HAwsbkhzE/AsgIjYjVJgd1uV7uCI2DQidgZ2BX7VsZxLkvpGRBxL6YL568B+lJud+1EqjBwbEfO6ljlJktr3HuC9EfHCiIhuZ0aSpG5wDDtJkoYhM1dFxBHAucBk4NTMvDoijgMuy8zFlIvOL0TEu4AEZldjMlwdEWcC1wCrgLdm5urufBJJ0jh3GPDxzHxf3bSrgQsi4k5KF2PzupExSZKG4BvAlsB3gPsj4jbKNVRNZuZOXcmZJEkdYoGdJEnDlJnnAOc0TDu67vU1wF4tlp0PzB/TDEqSJoItKZVHmvkh8JYO5kWSpOH6MesX0EmSNOFYYCdJkiRJ49cvgT2AHzWZt0c1X5KknpaZs7udB0mSuq3jBXYRsT/wKUr3YV/MzBMa5k8DTgO2qtK8PzPPiYjpwO+Ba6ukl2TmmzuVb0mSJEnqQW8Hvh0Rqyjdif0V2B54BfB64MCIWDt2eWau6UouJUkdc8wxx3Q7C5IkaRg6WmAXEZOBkyiDoC8HLo2IxVWXYTVHAWdm5ikRsTulq7Hp1bwbMvNJHcyyJEmSJPWy31bPJ1SPegFcVfc+sZcVSep78+bN63YWhi0ingg8GtiscV5mnt75HEmS1DmdvljbE7g+M28EiIgzgAOB+gK7BB5Uvd4SuLWjOZQkSeoR1o6W1IbjcMwfSdI4FxFbAd8Hnl6bVD3XxzgL7CRJfa3TBXY7ADfXvV8OPK0hzTzgvIh4G/BA4Nl183aOiN8AdwFHZeZPxjCvkiRJXTWea0dL6ozMnNftPEiSNAo+DGwL7A38BHgJcCele+dnAAd3L2uSJHXGpMGTdNyrgIWZuSPwfOAr1ZgLfwamZeaTgXcDX4uIBzUuHBFzIuKyiLjstttu62jGJUmSJEmSJA3ZcymFdpdU75dn5pLMfC3wI+AdXcuZJEkd0ukCu1uAh9e937GaVu8NwJkAmfkLSp/V22XmvZn5j2r65cANwKMaN5CZCzJzRmbOmDp16hh8BEmSJEmSJEmj6KHAjZm5Gvg3sEXdvG8BL+hKriRJ6qBOF9hdCuwaETtHxCaU5uyLG9LcBDwLICJ2oxTY3RYRUyNicjV9F2BX4MaO5VySJEmSJEnSWPgLsFX1ehmlG8yaR3Y8N5IkdUFHx7DLzFURcQRwLjAZODUzr46I44DLMnMx8B7gCxHxLsrAsrMzMyNib+C4iLgfWAO8OTNXdDL/kiRJkiRJkkbdT4GnA98DvgIcExHTgVXAoWxY4V+SpL7T0QI7gMw8BzinYdrRda+vAfZqstw3gW+OeQYlSZIkSZIkddKxwMOq1x8DtgVeCUyhFNa9rZ2VRMT+wKcoDQW+mJknNMzfCTgVmAqsAA7JzOUR8STgFOBBwGpgfmZ+fYSfSZKkIel4gZ0kSZIkSZIk1WTmDcAN1ev7KT1wvWco66iG0jkJ2A9YDlwaEYurxgE1JwKnZ+ZpEfFM4CPAa4CVwGsz87qIeBhweUScm5l3jPCjSZLUtk6PYSdJkiRJkiRJo21P4PrMvDEz7wPOAA5sSLM7cEH1+sLa/Mz8Y2ZeV72+FfgbpRWeJEkdYws7SZIkSZIkSV0VEbsArwCmAZs1zM7MfMMgq9gBuLnu/XLgaQ1prgQOonSb+RJgi4jYNjP/UZePPYFNqFr8NcnnHGAOwLRp0wbJkiRJ7bPATpIkSZIkSVLXRMSLgTMpvYH9Dbi3IUmO0qbeC3w2ImYDFwO3UMasq+XjocBXgEMzc02zFWTmAmABwIwZM0YrX5IkWWAnSZIkSZIkqauOB5YAszLztmGu4xbg4XXvd6ymrVV1d3kQQERsDry0Nk5dRDwI+D4wNzMvGWYeJEkaNsewkyRJkiRJktRNuwAnjqCwDuBSYNeI2DkiNgEOBhbXJ4iI7SKidj/0SODUavomwLeB0zPzrBHkQZKkYbPATpKkYYqI/SPi2oi4PiLe32T+JyLiiurxx4i4o27e6rp5ixuXlSRJkqQJ5A/AtiNZQWauAo4AzgV+D5yZmVdHxHERcUCVbCZwbUT8EdgemF9NfwWwNzC77jrtSSPJjyRJQ2WXmJIkDUNETAZOAvajDGZ+aUQszsxramky81116d8GPLluFfdk5pM6lF1JkiRJ6mX/D/hkRPwyM28c7koy8xzgnIZpR9e9PgvYoAVdZn4V+OpwtytJ0miwwE6SpOHZE7i+djEZEWcABwLXtEj/KuCYDuVNkiRJksaTeZQWdr+PiOuAFQ3zMzP36XiuJEnqILvElCRpeHYAbq57v7yatoGI2AnYGbigbvJmEXFZRFwSES9usdycKs1lt902kqEcJEmSJKmnrQauBX4O3Fa9r3+s6V7WJEnqDFvYSZI09g4GzsrM1XXTdsrMWyJiF+CCiLgqM2+oXygzFwALAGbMmJGdy64kaSKJiP2BTwGTgS9m5gkN86cBpwFbVWnen5nnRMR0yhhB11ZJL8nMN3cq35Kk/pGZM7udB0mSus0CO0mShucW4OF173espjVzMPDW+gmZeUv1fGNELKGMb3fDhotKkjR22hmTFTgKODMzT4mI3SljA02v5t3gmKySJEmSNHIW2EmSNDyXArtGxM6UgrqDgVc3JoqIxwBbA7+om7Y1sDIz742I7YC9gI92JNeSJK2vnTFZE3hQ9XpL4NaO5lCS1JciYm/g15l5d/V6QJl5cQeyJUlS11hgJ0nSMGTmqog4AjiX0j3YqZl5dUQcB1yWmYurpAcDZ2RmfZeWuwGfj4g1lPFkT2hoySBJUqc0G5P1aQ1p5gHnRcTbgAcCz66bt3NE/Aa4CzgqM38yhnmVJPWXJcDTgV9Vr2vXTFGXJqv3SbnukiSpb1lgJ0nSMGXmOZRuweqnHd3wfl6T5X4OPH5MMydJ0uh5FbAwMz8eEc8AvhIRjwP+DEzLzH9ExFOBsyPisZl5V+MKImIOMAdg2rRpncy7JKl37cu6Ft37djMjkiT1AgvsJEmSJGniamdM1jcA+wNk5i8iYjNgu8z8G3BvNf3yiLgBeBRwWeNGMnMBsABgxowZ2ThfkjTxZOZFja8j4kHA4ygtwG8BrsrMf3Ynh5IkddakbmdAkiRJktQ1a8dkjYhNKF05L25IcxPwLICI2A3YDLgtIqZGxORq+i7ArsCNHcu5JKmvRMTRlG6afwJ8vXpeHhFHdTVjkiR1iC3sJEmSJGmCanNM1vcAX4iId1HGEJqdmRkRewPHRcT9wBrgzZm5oksfRZI0jkXEscAHgS8CZwB/BbandMt8bERs1Gy4AUmS+okFdpIkSZI0gQ02JmtmXgPs1WS5bwLfHPMMSpImgsOAj2fm++qmXQ1cEBF3UsZBndeNjEmS1CkW2KnvHHPMMd3OgiRJkiRJktq3JaW1dzM/BN7SwbxIktQVFtip78ybN6/bWZAkSZIkSVL7fgnsAfyoybw9qvmSJPU1C+wkSZIkSZIkdVRETKp7+3bg2xGxCvgG68awewXweuDAzudQkqTOssBOkiRJkiRJUqetArLufQAnVA8apv8W72NKkvqcgU6SJEmSJElSpx3H+gV2kiRNaBbYSZIkSZIkSeqozJzX7TxIktRLJg2eRJIkSZIkSZIkSdJYscBOkiRJkiRJkiRJ6iIL7CRJkiRJkiRJkqQussBOkiRJkiRJkiRJ6qKOF9hFxP4RcW1EXB8R728yf1pEXBgRv4mI30bE8+vmHVktd21EPLezOZckSZIkSZIkSZJGX0cL7CJiMnAS8Dxgd+BVEbF7Q7KjgDMz88nAwcDJ1bK7V+8fC+wPnFytT5IkSZIkSdIE10ZDgZ0i4sdVI4ElEbFj3bxDI+K66nFoZ3MuSRJs1OHt7Qlcn5k3AkTEGcCBwDV1aRJ4UPV6S+DW6vWBwBmZeS/wp4i4vlrfLzqRcUlSc8ccc0y3s9A1EbE/8ClgMvDFzDyhYf4ngH2rt1OAB2fmVtW8QymVVAA+lJmndSTTkqSWJnJMk8aa/y9JY62uocB+wHLg0ohYnJn19x1PBE7PzNMi4pnAR4DXRMQ2wDHADMq9ycurZW/v7KdQrzOeSZ010f5znS6w2wG4ue79cuBpDWnmAedFxNuABwLPrlv2koZldxibbEqS2jVv3rxuZ6Er2rkYzMx31aV/G/Dk6rUXg5LUgyZqTJM6wf+XpA5op6HA7sC7q9cXAmdXr58LnJ+ZK6plz6f08PV/Y59tjSfGM6mzJtp/ruNj2LXhVcDCzNwReD7wlYhoO58RMSciLouIy2677bYxy6QkacJbezGYmfcBtYvBVl7Fuou9tReDVSFd7WJQkiRJkjQ8zRoKNFb2vxI4qHr9EmCLiNi2zWUB7z1KksZOpwvsbgEeXvd+x2pavTcAZwJk5i+AzYDt2lyWzFyQmTMyc8bUqVNHMeuSJK1nKBd0OwE7AxcMZVkvBCVJkiRpVL0X2CcifgPsQ7m3uHooK/DeoyRprHS6wO5SYNeI2DkiNgEOBhY3pLkJeBZAROxGKbC7rUp3cERsGhE7A7sCv+pYziVJGr6DgbMy0wtBSZIkSRobg1b2z8xbM/OgzHwyMLeadkc7y0qSNNY6WmCXmauAI4Bzgd8DZ2bm1RFxXEQcUCV7D3BYRFxJ6TpsdhZXU1reXQP8EHjrUG98SpI0ioZyQXcw64994MWgJEmSJI2uQRsKRMR2dUPvHAmcWr0+F3hORGwdEVsDz6mmSZLUMRt1eoOZeQ5wTsO0o+teXwPs1WLZ+cD8Mc2gJEntWXsxSClsOxh4dWOiiHgMsDXwi7rJ5wIfri4EoVwMHjm22ZUkSZKk/pWZqyKi1lBgMnBqraEAcFlmLgZmAh+JiAQuBt5aLbsiIo6nXOcBHJeZKzr+ISRJE1rHC+wkSeoHbV4MQinIOyMzs25ZLwYlSZIkaZS10VDgLOCsFsueyroWd5IkdVzU3T/sOxFxG7BshKvZDvj7KGRHneV+G7/cd+PTSPfbTpnpIG0DMKZNaO638cn9Nn4Z08bYKMQ0/1/jl/tufHK/jU+jsd+MaYMwpk1Y7rfxy303Pk2Ya7S+LrAbDRFxWWbO6HY+NDTut/HLfTc+ud/GB/fT+OR+G5/cb+OX+673uY/GL/fd+OR+G5/cb+OD+2l8cr+NX+678Wki7bdJgyeRJEmSJEmSJEmSNFYssJMkSZIkSZIkSZK6yAK7wS3odgY0LO638ct9Nz6538YH99P45H4bn9xv45f7rve5j8Yv99345H4bn9xv44P7aXxyv41f7rvxacLsN8ewkyRJkiRJkiRJkrrIFnaSJEmSJEmSJElSF1lgJ0mSJEmSJEmSJHWRBXbSKIqI6HYeJEkaDcY0SVI/MJ5JkvqFMU3qfxbYdUFETO52HjQ6GvdlOihkT/FEpn/U78uI2L6bedH6jGn9w5jWu4xn/cWY1puMZ/3DeNbbjGn9w3jWu4xp/cOY1tuMaf2jl2KaBXYdFhGTMnN1FM+NiAdHxCbdzpeGLiImV/tyUkR8LiL2r5vnAbvLIiJqJzIR8ZyImBkRD+p2vjQ8dfvycODdEbF1l7MkjGn9xJjWu4xn/ceY1nuMZ/3DeNbbjGn9xXjWm4xp/cOY1tuMaf2ll2LaRt3a8ERUd6AN4Bxge+Aq4OcR8dXM/Fd3c6ihqNuXFwA/z8wf1s3L6iRpTfdyOLHVHWjfALwbuAu4OCK+nZmXdDVzGpZqX84GXpqZt0fEAzLzni5na8IypvUXY1rvMp71J2Na7zCe9RfjWW8zpvUf41lvMab1F2NabzOm9Z9eiWm2sOugugPtx4BzMvMpwI+ARwGzI+KBXc2g2hIRB0TEw6q3+wArMvMDEbFPRMyPiG8AGDS7LyJeCLwoMx8LPA8I4ICIeFp3c6Z2RMTTI+KZVc3AjSn/txOA+yPi7cCiiDi6u7mcuIxp/cGYNj4Yz8Y/Y1rvMp71B+PZ+GFMG9+MZ73NmNYfjGnjhzFtfOvVmGaBXec9FTgCeEj1/uvApcAjgbeFzdR7WkRsBzwoM2+N0p/tr4EdIuJ3wEHAMmDTiHhzN/MpiIgtgKcD+0TEYzLzDuCzlJbFr46IGd3Mn9qyOfAH4GGZeT/wHeDjwBeqeQuBx4Vdr3STMW0cM6aND8azvmFM623Gs3HMeDZ+GNP6gvGs9xnTxjFj2vhhTOsLPRnT7BJzjNWao1evIzMvi4jXACdExJWZeWZVM+IBwH2ZeV9XM6yWqv33d+CrEXEE8Bjgo8B+wB6Z+eMq3V7AP7uX04mp2j9rB9/NzH9GxIeALYDjI+L9mXlDRJwMvIFykqMeVNuXmfmjiNgR+G5EzAfOBq4Gbqn274HAw4D7u5jdCcWY1j+Mab3LeNZfjGm9yXjWP4xnvc2Y1j+MZ73LmNY/jGm9zZjWP3o9pkXd70yjLOoGBwXmA2uAszLzN1GazH4I+J/M/L/6P33jAUDdFxGb1J/URMTOwPuAO4BTM/P6qmbFWcCtmfm67uR0Ymr4/7ydcjDdCngrsCnwAUptsqMz848RsVFmrupWfjWwxmNgFSDfDnwOODsz74+Iw6ppr87Mq7qU1QnFmNY/jGm9y3jWf4xpvcd41j+MZ73NmNZfjGe9yZjWP4xpvc2Y1l96PaZZYDfGqqD5HeBnlBaN76Ts6POq4PlF4MXpYJQ9K6pBXCNiU+CDwA3AdcBvgBOBvwHfBFZQ9u1H65frUrYnpIh4B/Bi4FDgQuAW4JmU/95HgK2Bw4BVnpz2noh4VGb+sXr9BuCxlJoti4HdgeOATwAXA3sB12XmH7qU3QnJmDb+GdPGB+PZ+GdM623Gs/HPeDZ+GNPGN+NZ7zOmjX/GtPHDmDa+jZeYZoHdGIuI2cAjKAMW1g6u/wm8NTO/GxFPyswrupdDDSRKn9BHAjsDJwNTgSuAF1D6s/06JXjeA5yQmX+tljNodlhEbEWpPXY08HpgBqWWy07AHsAmwOaZeVu38qjWImIKcB4lKF4IHAt8m9Lv/mOAtwC7Uv5vHwS+78lP5xnTxjdj2vhgPBv/jGm9z3g2vhnPxg9j2vhmPBsfjGnjmzFt/DCmjW/jKaZZYDfKoq7v6LppmwKfAm7OzPkRcSrwUuDpmfn7Ko0H2h4VEWcAzwC+lZnvqqY9BvgS8A7gdmCfzDy1e7mceJp14RARD6IcZD+Vmc+IiM0pNZGWZObzu5FPtS8idqMExocDr8/S9/52wKuB6Zn57og4CLg8M+0LvAOMaf3HmNZ7jGf9yZjWW4xn/cd41puMaf3HeNZ7jGn9x5jWm4xp/We8xLRJ3dpwP6qC3+qImBQRh0XEayJiu8y8F/g7cG+VdAXwkVrQBDBo9paqSwEAMvNg4EfAG6v+oqmaw14E7JSZN9SCZkREN/I70TT0Hf2aiHhbRLw6M++iDLx7TUTsALyI0iT9iC5mVwOo/89Ux8R3AVOA91TT/g78CpgWpQ/wb3kh2BnGtP5hTOtdxrP+YkzrTcaz/mE8623GtP5hPOtdxrT+YUzrbca0/jEeY5oFdqOk+iOvqX4EPwK2p/RnuzAiHgr8AdgtIq4CtsrME6rl3Ac9pqqttCaKx1UnRG8AvgX8PCKmVkn3ppTIr9WtprITUABExBzgTcDvgK9GxMsp3QQA/A/wUeDMzLyxK7nUgBpOgHaLiMdm6Uv6ecCOEfHJKumOwDTgQd3J6cRjTOsfxrSeZzzrE8a03mQ86x/Gs3HBmNYHjGe9y5jWP4xp44IxrQ+M15hml5ijICJ2zMzlVRB8LbBzZh4TERcCZ2XmSVH6ud0ceHxm/qBaboOmteoN1b48G9gMuBv4cbUfvwy8HDgVuDszP9C9XE48EbE7cH1m3ledwHwceD/wXErz5edl5qooTdK3BdZk5s3dy7HaERH/j1IraXNKP9LnUgZZ/j5l4N6LgI9n5tVdy+QEYkzrP8a03mM861/GtN5hPOs/xrPeZEzrT8az3mJM6z/GtN5kTOtP4y2mWctihCLiucAlEfH4qnn5DcAWEfEryuCEJ0XEjsDBmbm8LmhOMmj2tPmUA/RzgK8CD4+IwzLzdcD5wORa0LS2UmdUwfBtwIKI2CTLIK43Ah+j9M3+/CpoHg08JTOXGTR7X0Q8FjgA2AfYn3IMfRZwJ/AS4DLg+F4Jmv3OmNa3jGk9xHjWv4xpvcN41reMZz3GmNafjGe9xZjWt4xpPcaY1p/GY0zzDz8CVRPmcymDFX4pIh5N+SM/Cbg4M0+skn6qmrZW2nd0T6m6FKi9ngxsDdT6+j4H+C2wH0BmviQz31pbzn059qrv+W5KkLwP+Gi1z/4B7AUcnpn3R8RLgYOAW7uXWw2k/r9W2ZhSo2zrzPwrpQuIpwL7Z+Y1wCGZ+acOZ3NCMqb1D2Na7zKe9RdjWm8ynvUP41lvM6b1D+NZ7zKm9Q9jWm8zpvWPfohpFtgNUxU0V1e1HCYD/wZOpzSj/BClH9TvRsQPKE2Y31wt5+CgPabal7X+bB+QmauBHwLPj4inZ+a/gTOBLSNil7rl7FqgQ+q+530p/Ug/G/hwZn4G+CbwyYj4NvBe4DWZeX13cqqB1P9nImKb6v0VwM+BV0TEgzPzz5Sm6FsCZOb9XcvwBGJM6x/GtN5mPOsfxrTeZDzrH8az3mdM6w/Gs95lTOsfxrTeZ0zrD/0S0xzDbgSqIPhj4Argu8BzKLUhXgz8C9gV2CIzf1yln2StiN5S2yfVCdBCSv/Dn6MMJvp8SpPZ7wEzgX9k5pwuZXVCitIE/b7q9UGUAV0PpNSE+E/gjsz87yjdP2wH/C0zreXSgxqC5rspx8pNgVcCe1L+Y08ALgbeCDwnM6/rTm4nJmPa+GdM613Gs/5iTOttxrPxz3jW24xp/cN41vuMaeOfMa23GdP6Rz/FNAvshigiXg5cmZl/jIiNKLVbZmVmVoH048B/AHMy87d1y1krokdV++1zwN+A64BDgQXAr4BHAntTDtAfr6V3X469iHgSsBPw3erk5jXA9Mw8PiIeADwe+F/gSuBdtQCr3hYR+wPvo/zPPgg8HDgCuJcSTLcCzsnMP3YrjxOJMa3/GNN6j/GsfxnTeofxrP8Yz3qTMa0/Gc96izGt/xjTepMxrT/1Q0zbqNsZGE+qP+tK4Loog71eFRE7A/OAY6rg+SNK4HwZpf9hYL2mteo9bwFmAHtW3Q3cQSlp3wz4WmaeX0tobaWOmgz8kjLw7l2UftqPiYjzM/MS4FcRcSOwBbAN8JfuZVXtiIg9KP+tyzJzOfCmiPg4cArwtsxc2M38TTTGtL5lTOs9xrM+ZEzrHcazvmU8603GtD5jPOstxrS+ZUzrTca0PtMvMc0x7NoUEbOBp2Tm9ylNKd8dEbsDBwMHRsSHImIrSunt5zPz6K5lVgOKMrhr7fUmwJ+Be4B5UfqVXkyp6fIW4Gn1yxo0x15E7BYRbwOWAXcDn6X8z64G5gMfiYiXRMQsSi2J92WmQbMHVbXI6q0A/gg8KiJmAmTmeygnRf8bERs3WUZjwJjWP4xpvct41l+Mab3JeNY/jGe9zZjWP4xnvcuY1j+Mab3NmNY/+jWm2SVmGyJiKnA48DDg85Q/9OGUJpSLgL9T+iG+hfKdHlItZxPmHhPrD9r7VOCfmfmHiKj1Ab4U+HjVFPpJWQamVAdFxEsp/bL/ltJlwDOB1wAXAkuAp1BOUO8Hjncf9ab641/VHP2vwO2U4+X7gAdQmqAvqdI8ODP/1qXsTijGtP5hTOttxrP+YUzrTcaz/mE8633GtP5gPOtdxrT+YUzrfca0/tDPMc0Cu0HEusFBdwEOAh5NKXlfCrwD2BI4NTOvbrZcp/Or1mL9gV7PoZS47wr8KDM/XgXPl1L6lD6m7k/vCVAH1P9nIuL5wAuBaygnq3sDrwMuAL6SmfdHxEaZuaprGdaAav+biHg78GrgO8A+wNGU/fpu4CHA/2XmT/yfdYYxrX8Y03qX8az/GNN6j/GsfxjPepsxrb8Yz3qTMa1/GNN6mzGtv/RzTLNLzAFUtSLWVE0l/wWcBvwBeCswHfgUcCfwrojYrW65MGj2lqoUfU2UZumnU2pNHAdsC7w5Ik7I0mf0OcCN9X/g8fJnHs/q/zMR8XTgB8APKSeqbwIuAr5EOXk9uPpvGjR7UETsXJ3UZEQ8EzgQ2IvyX9scOBF4IuX4eRNwLfg/6wRjWv8wpvUu41l/Mab1JuNZ/zCe9TZjWv8wnvUuY1r/MKb1NmNa/5gIMc0Wdi3UldJOAr5PGazwgxHxCEoT5kcBJwE3A/tk5tldy6wGFBEvBN4DHJaZ10fE3sCvgO8CZ1KC6E8oJe7vrltu3JS894sofUi/HHh1Zi6PiIOAfSk1I74IPJ1yYnNLF7OpFiJiO+BYSjP0+ZSaLJOAZwGzKbWXPgo8m1Jz6ef+xzrDmNY/jGnjg/Fs/DOm9SbjWf8wno0fxrTxzXjWu4xp/cOYNn4Y08a3iRLTbGHXQt3O/Arw+8z8YDX9BuBsSrPmDwKb14JmVSNGvedK4HzKoKGPyMyLgZ2AP2fmFyh/8u8C69WcGI9/6PEsIv6DcjB9RWYuB8jMbwE/pgTM2Zn5E4NmT7sTOI9Sq+VdwF8y82ZgGvCRzLyb0uf+t4Hl/sc6x5jWV4xpPc541jeMaT3IeNZXjGfjgDGtLxjPepQxra8Y08YBY1pfmBAxbaNuZ6DXxPr92dYC4f9W7x+Ymf/KzBsi4qvAH6ofBeCBttfU9mVm3hwRpwGTgY9GxJHAP4CHRcRs4LXABfn/27v3eNvqef/jr3ftSil3hXLcip86Bz2O+/WXJLpw5LiTiEghd6nfryJHfv1cz/lxEHIJKUqhdEri1PnlTkJ0CFEI7eyd0/Vz/hhjZe7Val9q7TXH+K7X8/GYj/acc8y1vu25x3zNx2N8x3dUHdq/zhkuC2COv+dbAf9ZVRf3zy+pqqur6vgkS4EfTWOcWrWZ97K6Nb5PovsSujPw6iSHAwW8LskDgGcDO0x+dmrtsWntsGnDZc/aYtOGyZ61w54Nm01rhz0bLpvWDps2bDatHYutaZ5hN0v1a0cn2arfqS8DDkyyQVUtT7Jukv8HXFtVXwBnuAxRVlwHnH52xNuB7wP/RLcjHw5sAXzPaC68mb/nJA/sH/o+sH6Sx/bPX53k+UleVVWnzwRVwzK5z6Rbs/3K/rPxOOBuwEuq6k3Ap+i+vD5lzNEcG5vWBps2bPasHTZtuOxZG+zZ8Nm0NtizYbNpbbBpw2fT2rAYm+Y17OaQ5CXA44AD6c5CfCpwJ+AtdBcMvayq9pzeCLUyMzNc0q0DfhxwDnB7YF+6HXd/4N7A66vqZ7NfN40xL0b9+7MRcDbdcg+HAS+kO435SrqLgr6c7lT1H09pmFpNSfYBnkD3BegHVfWRJNvTrQ1+AXB4VV0zxSEuWjZt3Gza8Nmz9ti0YbJn42bPxsGmtcWeDZdNGzebNg42rS2LqWmeYcd1O/B1quo9dDvtvsB6wEfp1kh9GfCrmWg6w2WYJma4fAk4C/g83Q79OboZLm8BLqS7EOUKr1vgoS5KE/vbhtWtLbwz8D+BFwGfAD4DbAJsRXcRWKM5cEn2BJ4GvBi4K/CKJK+tqtOAE4BN6ZYe0AKwaW2xacNlz9pk04bDnrXFng2bTWuPPRsWm9YWmzZsNq09i61pi/4MuyTbVNW5/Z9fTXeh15lTzv8v3T+Cg2a2mXidsyIGLMnfAg8HPgicCnwc2A64BfBkuqUFrpreCBefJA8GzumXeHgY3ftzVFVdmORv6D5gT6mq1/bbu4+NQJKb081mOQF4Ft0XobfSfUH9TFUdnmSjqrp8isNcNGxam2zasNizdtm04bBnbbJnw2PT2mTPhsWmtcmmDY9Na9NibNqiPsMuyZHA/+r/vAWwIfCE/nRKqurVdGsNvz3JXSdeF3foYUmy3uT9qvoB8GHgpcAPq+oDwBeA+wEvm4mms5UWRr8+9FHA/+gf2oxuneHdkty5qn4JvITuYqEvA2ceDdHM/jK531TVcuAjwPrADsCzqup04CLgUUlu01I0h8ymtcOmDZc9a4dNGy571g57Nmw2rQ32bNhsWjts2rDZtDbYtM6SaQ9gWpK8F1i3qp7eP3Qp8C7g+XQ7M/1pld8CLqyqC2ZeW4v9tMSBSXKzqvqvJEuAdwA/BM6vqn9LchmwNMntgMcCL6+qz8y81vdy7UvyeOBQYI+q+laSTejWjl4GPB74xyQfoPvi+mG6pQQ0TPcEzquqSvJS4D7AN+mWgbiYbibZlkl2Bq4GXlhVf5zaaBcRm9YOmzZc9qw5Nm2A7Fk77Nmw2bSm2LOBsmntsGnDZtOaYtNYpEtiJjkQeA5w7+rWHX418IiqemKSe9Bd+PUZdEduv1tVe/Wvix+0w5LkxcAewGPoToe9A/AfwI7Au4HvAv8MbABcXlVP7l/ne7kAkmxNdwHew6vqiCSbA0cDB1bVV5LsAmwP/D3desO7VtVPpzdizaWf2bIE+BlwDN3MlnfRfQHaAtgYeB3dhbJ3Bjani+Z3pzDcRcemtcOmDZc9a4dNGy571g57Nmw2rQ32bNhsWjts2rDZtDbYtBUtugN26S48uTewJd3ap9vSXRj0+VX1s36bDYE7A383MyvCD9rhSvJJutOcT6uqA/oZL7vQXaz3TVV1epJNq+p3/fa+lwskyV2ANwNfpfvQPYRu/ej3TGxzC7pT1n9bVb+YykC1WpJsBnwF+Auwd1WdneR+wG7AbYGDq+r3STau7sK+WstsWnts2jDZs/bYtGGxZ+2xZ8Nl09piz4bHprXHpg2XTWuLTessugN2AEnWB54HPBp4MLB9VZ0/8fwjquprE/e9COXA9F+AaiaASY4AHgFsV1W/SXIzYCfgjcCeVXV2v53RXCAzf9dJ7gm8hm5f+3xV7T+xzU7A2VX1h2mNUys38T6uW1XXpFvm4dvAmVX1jH6b+wC7082GeU15YeUFZdPGz6YNmz1rh00bNns2fvZs+GxaG+zZ8Nm08bNpw2fT2mDTrm/RHbCb+EewHvBM4CHASVX1uf75zwG/qaq9pzlO3bCZHbj/85YzX3qSfITu9OZ/rKrl/YylravqW1Mc7qI082VzYn/7G+BA4CfAF6rqR0meBrwF2KGq/nOqA9acJr9oJrk73ZfVnyfZlG6d/WOq6pX989vQzVa6ZHojXnxs2vjZtGGzZ+2wacNmz8bPng2fTWuDPRs+mzZ+Nm34bFobbNrcFt0BO1ghnuvTXex1K7oLGO4G/K6q9pnqAHWDJj6Q16FbWmAZ8CfgXVX14yQfBu4EPLkmTo11hsvCSLJNVZ0767HJGS+vBc4Fbg7sSndB2B9NYahaA+nW298B2AQ4tqre3s94OQv4SvXr7Ws6bNp42bThsmftsmnDZc/Gy54Nm01rkz0bNps2XjZt2Gxam2zaitaZ9gCmod+JU1VXAh+iO/q+L93FQfeB60591sDMzJ4APgWcSvdB/AzgdUkeUFXPA64EXjLrdUZzLUuyI3BUkq0mH5/Y335Cd4HeBwJPolu/3WgOUJJ1J/78PODxVbUj3WflfkkO6Ge0PBx4YJLN+v1SU2DTxsumDZM9a4tNGw97Nl72bLhsWjvs2bjYtPGyacNl09ph01au+TPsVjbDYeII/HrAA6vqzP5x144emCTvB/4N+BzdRSZ3BY4Gjge+BGwG/D3whqr69ykNc9FK8gTg9XTrCJ851343sb9tDlxTVRdPZbBaqSSPAQ4Gdq6qpUkeB/yU7oLK2wOH0e17R9OtER4/LxeOTWuDTRsue9YWmzZc9qwN9mzYbFo77Nmw2bQ22LRhs2ntsGmr1uxsjiRbwl+Pss+1Tf/culV11UQ0F90/gqFLcjfgicDewKOq6qKqej/dxV7Pr6rDgPfSBfVOE69bNEfepyU9uovsXtNHczPgVUkOS7Jtkk1ghRkvvzaaw9TPcHk48FDgnUk2qaqTgUvo9rfXVdVZwCnAlsAt/bxcGDatHTZtmOxZe2zaMNmzdtiz4bJpbbFnw2XT2mHThsumtcWmrZ4mD9gl+Rjw2SS7wvXjmeT2/dF2qr+I6IwbmhWj6amqnwPHAHcF9k3y+P6pS4DH9UfmDwCOq6pPT7zO93Itqx7wMOBWSY4FPg4sAW4NvBy4D7ie9xj0n4dHACfRXRj7U308lwIXAq9Psh+wObBfVV06rbEuJjatLTZtmOxZe2za8Nizttiz4bJpbbFnw2TT2mLThsumtcWmrZ7mDtgl2R24L/A24AWz4rlOkpsDrwR27Ldv7u+gFUnumGRJf/dNwEeAc4DX9u/rN4FD6E6PvbyqDuxf5wyXBZRkvapaDjwYuB9wVlUdVlUvAi4Cdge/yAxZkvv0k5bWr6oLgU8A+wG/Bk5Mcgvg/cAFdKeov7qqLpjScBcVm9YOmzZ89qwNNm2Y7Fk77Nk42LTxs2fDZdPaYdPGwaaNn01bM0tWvcnofBI4l27t02uBFyWhqk7sT6FcnuQM4IAkXyxPkR2kJG+ju4Dru5OcRreO9IZ0O/SZwKvpToU+IsmnqmpZ/zrXAV9A6Zd2SLJxVS1Lcm/gmolZLecDW/fbXbOKH6cpSLI18F26iymf0c8U/BXw0qp6SpL3AUcBz66qg5JsVFWXT2/Ei45Na4BNGz571gabNmj2rAH2bBxs2vjZs8GzaQ2waeNg08bPpq25pmZ55K/rQn+rqi4DTqAL6d5Jtuu3eVB1a6O+mzYPWI5eP/voh8AZwKbAW4Cn0J3q/B66HfyDwGFJtp2IpuuAL6CZGCbZAjgyyWb9/ndtP7PsBXRffj5kNAdtKfAxYBnd/nYUcDVw9yR79DOWrgE+2s8i+8vURrrI2LQ22LThs2dNsWkDZM/aYM/GwaY1w54NlE1rg00bB5vWDJu2hpoJR/+hOXtd6KVJTgKWA69I8mHgOODsqjpmGuPUqlXVtUk+CqxLd0HXnwGbAL8DdgC2qapPJPlFVX1n4nWe+rxAZkXzWOBwYL0k96+qb/YzXp4A7FFV5051sLpB/efmr5McBBzIX2cJ3oMultsAVNU/JLmT+9jCsWntsGnDZs/aYdOGyZ61w54Nn01rgz0bLpvWDps2fDatDTbtxkkLfw8Tp8HOrCV9zuQHapIN6U69PLuqdp/OKLW6Zt7PJBsAewJ3Bn5QVUcluT/w86r6w+ztpzXexWZWNI+hi+Z3gM8Ar6mq05KsB2wwMwtJw5N+GYeJ/e1edOuy/wT4LN064JsD5zuDbGHZtLbYtOGyZ+2wacNkz9piz4bNprXBng2XTWuLTRs2m9YGm3bjjf6A3axovhR4MrATcNuq+lX/+P7A/arqaf191xseuImdeX3g+XRH3P8dOK6qrpzu6BafJDsAS6vq6/39LeiWfngT3ZfSY4BDqupEv8gMW5JtZs8+mtjf7gm8Fvgl8Omq+vFUBrmI2bQ22bThsGdtsWnDZc/aZM+Gxaa1w54Nm01rk00bFpvWDpt20436Gnazorkv8CRgF2Bv4JCJTQ83muPS78TpI/kh4AfA44H7T3dki9buwG0A+lkszwUOBb4HHE0fTXCJgCFLsiNwVJKtJh+f2N9+AhwGbA3s2n9x1QKxae2yaYNizxph04bLnrXLng2OTWuAPRs2m9YumzY4Nq0BNm1+jPIMuyT3qqrzJu7vB+wKPBHYi2694V2r6uqZ02j77TwCP0Are18mjsBvADy0qk5f4OEtekkCnAgcWVXH9o/dElgPOBk4tKqOn94ItTqSPAF4Pd3yAWfOtd9N7G93B/5SVRdNZbCLjE1ri00bLnvWDps2TPasLfZs2GxaG+zZcNm0tti0YbNpbbBp82d0Z9gleR7w5on729NdZPKJwAu5gWiCR+CHJsmW8Nej7HNt0z+3blVdMRPNG9pW8yvJo5Ps0+83fwCW9Y+vU1VLgU2Bvarq+CSj+yxZLNID3ghc00dzM+BVSQ5Lsm2STWCFGS8/M5oLw6a1w6YNlz1rh00bLnvWDns2bDatDfZs2GxaO2zasNm0Nti0+TfGf+zrAL+fuP8LulOW96I7LX3OaGpYknwM+GySXeH68Uxy+ySb98+t8D76BWjB/Bl4d5Jn052CfhlA9Us7VNUPq+rbk49peKoHPAy4VZJjgY8DS4BbAy8H7gPOBpwSm9YAmzZ49qwRNm3Q7FkD7Nko2LQG2LPBs2kNsGmjYNMaYNPm35JpD2B1JXl0VX0Z+B2wfObxqjo/yR36uzsazeFLsjtwX+BtwAuSUFUn9vFcB9gQeCXwU+BDcf3vqaiqbyR5EPAlug/YU5NcDvwRCHAVcDbdKevubwOWZL2qWp7kwXRfgo6qqsP6595Ct1b4mUZz4di0dti04bNnbbFpw2LP2mHPxsGmtcOeDY9Na4dNGweb1g6bNr9GccAuyV3pdtq9gPOAjSafr6qLgbf32xrN4fskcC5dGK8FXjQRz2uB5UnOAA5I8sX+/dUUVNU3kzwSOA24GjgQuCdw936Ts9zfhq3/TLwqycZVtSzJvYFrJma1nA9s7WfnwrFpzbFpI2DP2mDThsWeNceejYRNGz97Njw2rTk2bSRs2vjZtPk3igN2VXVBf8T9JOBHwNIkr6Sb8XIFcCFwK+Dkqlo2tYFqlWZ2YuBb/f0T6OK5d5JlVXV6kgdV1cnp1rcdxb/RllXVuUl2BM4A7ltVR017TFo9MzFMsgXwznRrg/924vkXAHsDuxvNhWPT2mHTxsWejZtNGx571g57Nj42bbzs2TDZtHbYtPGxaeNl09aOjOlMxCT3BU6hm+nyKmA74DbApcClVfWi6Y1OqzJxZH3247cBHgk8n25N2+Oq6hULPT6tXJL7A18HXlBVH5r2eLRys6J5LHA43VICd+hnMN0beCtwQFWdM82xLlY2bdxs2njZs/GxacNmz8bNno2bTRsXezZ8Nm3cbNq42bRxsWlrz6gO2AEk2Qb4KrBnVR0/x/NevHCAJt+XdGtJn1NV35l4fkPgu8DZVbX7dEapVUmyLXB5VZ037bHohs2K5jF00fwO8BngNVV1WpL1gA2cHThdNm2cbNr42bPxsGnjYM/GyZ61waaNgz0bD5s2TjatDTZtHGza2jW6A3YASR5Ad0HKt1bVWyceN5oDNCuaLwWeDOwE3LaqftU/vj9wv6p6Wn/fC75KayDJDsDSqvp6f38L4ATgTXRfSo8BDqmqE/2sHBabNi42TVr7bNo42bNxsWfS2mfPxsumjYtNk9Y+m7Zw1pn2AG6MqvoGsAtwt1mP+w9hYGZFc1/gSXTv3d7AIRObHm40pZtkd7qlOuhnsTwXOBT4HnA0fTTBz8qhsWnjYdOkBWPTRsiejYc9kxaMPRspmzYeNk1aMDZtgYz2wppVdRZwFjjDZYiS3KuqzpuI5n7ArsATgL2Ax/T3Z06jvbr/c4ymtGaSBLg1sDFAVV2V5F+A9YCTgUNnoqlhsmnDZtOkhWPTxs2eDZs9kxaOPRs/mzZsNk1aODZtYY3yDLvZjOawJHke8OaJ+9vTBfOJwAuBHYBdq+rqmTVvZ7b1vZRWX5JHJ9mn32/+ACzrH1+nqpYCmwJ7VdXxSZr4vF8M/BwcFpsmLQyb1h4/A4fFnkkLw561yc/BYbFp0sKwadMx2jPsNGjrAL+fuP8L4PHAPsDOwI5zRVPSGvsz8O4kS+lOQb8MYGa2WFX9cGZDZ5BJN5pNkxaGTZPWLnsmLQx7Jq19Nk1aGDZtCjxgp3mT5NFV9WXgd8Dymcer6vwkd+jvGk1pnlTVN5I8iO5i2LcGTk1yOfBHIMBVwNnAke5v0pqxadLCsmnS2mHPpIVlz6S1x6ZJC8umTYcH7DQvktyVbqfdCzgP2Gjy+aq6GHh7v63RlOZJVX0zySOB04CrgQOBewJ37zc5y/1NWjM2TZoOmybNL3smTYc9k+afTZOmw6YtvLh0r+ZLkgcAJwE/ApYCX6ab8XIFcCFwK+Dkqlo2rTFKrUpyX+AMYJ+qOmra45HGzqZJ02PTpPljz6TpsWfS/LJp0vTYtIXjGXaaN/1pstsDp9DNdDkB2A64DXApcGlVHTu9EUrtqqrvJXkM8PUkG1TVh6Y9JmnMbJo0PTZNmj/2TJoeeybNL5smTY9NWzieYad5l2Qb4KvAnlV1/BzPp/yHJ60VSbYFLq+q86Y9FqkFNk2aHpsmzR97Jk2PPZPml02TpsemrX0esNNa0Z+m/iXgrVX11onHjaYkaVRsmiSpBfZMktQKmyapVetMewBqU1V9A9gFuNusx42mJGlUbJokqQX2TJLUCpsmqVWeYacF4QwXSVIrbJokqQX2TJLUCpsmqRUesJMkSZIkSZIkSZKmyCUxJUmSJEmSJEmSpCnygJ0kSZIkSZIkSZI0RR6wkyRJkiRJkiRJkqbIA3aSJEmSJEmSJEnSFHnATgKS7JGkkmw56/EHJPljku8kud08/a5KcvDE/YOT1Br+jP2S7DYf45EktcWmSZJaYM8kSa2waZJWlwfspBuQ5KHAqcBPgUdX1SVr6VcdATxkDV+zH2A4JUmrxaZJklpgzyRJrbBpkuayZNoDkIYoyaOAzwPfA3aqqsvW1u+qqguBC9fWz5ckLW42TZLUAnsmSWqFTZN0QzzDTpolyQ7AScA3gB1vbDSTrJvk0CQXJbk8yVeSbDPHdtc7NT3Jy5P8KMlfkvwpyTeTPKl/7gLgLsCz+tPcK8mR/XNbJvlYkp/3r/1ZkvcmufWsn39kkguTbJvka/34fprkxXOM7279z7w4yRX9z3zXrG0eleS0JH9OsjzJl5L87Y35e5MkzR+bdr3x2TRJGiF7dr3x2TNJGimbdr3x2TRpggfspBXtDJwIfBXYuaqW34SfdTDwBuAo4B+AU4ATVvWiJM8C3gZ8EtgJeBZwLHCbfpMnARcDX6I7pf0hwJv65+4E/Iru1PUdgTcC2wNfnONX3QL4BPBx4Il0XxTem2S7ibHcDfg68EjgfwOPAw4Bbjexzc7AacAy4NnAM4FNgK8lufOq/n8lSWuNTbNpktQCe2bPJKkVNs2mSStXVd68LfobsAdQ/e18YIOb+PNuTReSf531+Ov633HwxGMHd7vidff/Bfj2Kn7+BcDHV2McS4CH979z24nHj+wf227isQ2APwDvn3jso/3/x51W8jvOB06b9dgtgEuAd077vfXmzZu3xXazaTbNmzdv3lq42TN75s2bN2+t3GyaTfPmbXVvnmEnregLwD2A/W/iz/k74ObAp2c9/qnVeO03gPsl+eckj0my0er+0iTrJ3lDkh8n+QtwFfC1/ul7zdr88qo6feZOVV0B/AT4m4ltHgt8vqp+cwO/byu6v6+jkiyZuQGXA/9BN0NGkjQdNs2mSVIL7Jk9k6RW2DSbJq2UB+ykFb0C+CBwUJLX3YSfc8f+v7+d9fjs+3P5KLA38CC608//mOSzSe66Gq99C93MmY/TnWb/QGC3/rmbzdr2T3O8/opZ292WlV+YdtP+vx+ki/TkbZf+9ZKk6bBpNk2SWmDP7JkktcKm2TRppZZMewDSwBSwF108DktyRVW980b8nIv6/24GnDvx+GarHEBVAe8D3tdftPWxdGtLH00X05V5OvDRqjp05oEkG6/BuGe7BNh8Jc//of/v/sCpczx/5U343ZKkm8amrcimSdI42bMV2TNJGi+btiKbJs3iATtplqq6NslzgfWBdyT5r6r61zX8Md8HlgNPBb488fjT13AsfwKOTvIg4EUTT10BbDjHSzaim2Uy6Xlr8jtnOQXYLckdq+qiOZ4/j25d622q6rCb8HskSWuBTVuBTZOkkbJnK7BnkjRiNm0FNk2axQN20hyq6pokz6SL53v6GS8fBkhyJPDcqspKXn9pkncAByT5M12AHgDsuarfneT9wJ/p1mL+HXBP4Dn9z5jxQ+ARSXYBLgYuqaoLgJOB5yY5h+6irLsBD12T//dZDgJ2As5K8k/9z9wceFxVPbuqKsk+wOeSrE+3dvYldDN6Hgr8sqrefhN+vyTpJrJp17FpkjRi9uw69kySRs6mXcemSbN4wE66AVV1dZKnAscDR/Tx/ATdRV1XZ03og4EALwD2Bc4GdmXFU9Xncibd7JTnALcEfkO3NvRBE9vsD3yALlQbAh8B9gBe2v/ON/fbfRF4BvD11Rjv9VTVBUkeDBxKt071xsCvgc9NbPPFJI8EDgCO6MdzMfD/6U6nlyRNmU2zaZLUAntmzySpFTbNpklzSbdsraTVleQ3wDur6v9MeyySJN0UNk2S1AJ7JklqhU2TFjcP2ElrIMlWdDM47lJVy6Y9HkmSbiybJklqgT2TJLXCpknygJ0kSZIkSZIkSZI0RetMewCSJEmSJEmSJEnSYuYBO0mSJEmSJEmSJGmKPGAnSZIkSZIkSZIkTZEH7CRJkiRJkiRJkqQp8oCdJEmSJEmSJEmSNEUesJMkSZIkSZIkSZKm6L8BZji2opvyC84AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2160x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "candidates = [\"K=1, Euclidean\", \"K=1, Mahalanobis\", \"K=5 Euclidean\", \"K=5 Mahalanobis\"]\n",
    "num_candidates = len(grid.candidates)\n",
    "k=0\n",
    "plt.figure(figsize=(30, 4))\n",
    "plt.suptitle(\"KNN\", fontsize=20)\n",
    "plt.subplots_adjust(top=0.7)\n",
    "for metric in ['f1', 'recall', 'precision', 'binary_accuracy']:\n",
    "    means = []\n",
    "    std = []\n",
    "    \n",
    "    for i in range(num_candidates):\n",
    "        means.append(np.mean(grid.stats[i].stats[\"values\"][metric]))\n",
    "        std.append(np.std(grid.stats[i].stats[\"values\"][metric]))\n",
    "\n",
    "    plt.subplot(1, 4, k+1)\n",
    "    plt.errorbar(np.arange(num_candidates), means, std, fmt='ok', lw=3)\n",
    "    plt.errorbar(grid.index_of_best, means[grid.index_of_best], std[grid.index_of_best], ecolor='red', fmt='ok', lw=3)\n",
    "    \n",
    "    plt.title(metric.upper() + \" mean and std deviation\", fontsize=20)\n",
    "    plt.xticks(np.arange(num_candidates), labels=candidates, rotation=45)\n",
    "    plt.xlabel(\"K, distance\", fontsize=16)\n",
    "    plt.ylabel(metric, fontsize=16)\n",
    "\n",
    "    print(metric.upper())\n",
    "    for c in range(num_candidates):\n",
    "        print(f\"\\t{candidates[c]}:\", means[c], \"+-\", std[c])\n",
    "    \n",
    "    k+=1"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ca48a1217c93acd9388dba04a449e77fab73d12f699c89515157fda392110c7b"
  },
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}