{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ***Setup***"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"from nltk.corpus import wordnet as wn\n",
"\n",
"import numpy as np\n",
"\n",
"import pandas as pd\n",
"\n",
"import pickle\n",
"\n",
"from sklearn.feature_selection import SelectKBest, chi2\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.metrics import accuracy_score, classification_report\n",
"\n",
"from tabulate import tabulate"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warming up PyWSD (takes ~10 secs)... took 10.154893636703491 secs.\n"
]
}
],
"source": [
"from pywsd.lesk import simple_lesk"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"parent_dir = os.path.abspath('..')\n",
"sys.path.append(parent_dir)\n",
"from constants import CONSTANTS\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ***Load dataset***"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"train_df = pd.read_csv(CONSTANTS.AUGMENTED_TRAIN_SET_PATH)\n",
"test_df = pd.read_csv(CONSTANTS.AUGMENTED_TEST_SET_PATH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ***Load dep. parsing results***\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\TOSHIBA\\AppData\\Roaming\\Python\\Python39\\site-packages\\networkx\\utils\\backends.py:135: RuntimeWarning: networkx backend defined more than once: nx-loopback\n",
" backends.update(_get_backends(\"networkx.backends\"))\n"
]
}
],
"source": [
"with open(CONSTANTS.DEP_PARSED_TEXTS_OBJECT_PATH, 'rb') as f:\n",
" loaded_data = pickle.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ***Helper functions***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Get processed text by row id***"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n"
]
}
],
"source": [
"# Each sentence in the dataset has an id, and a document contain its stanza processing\n",
"def get_doc_by_id(target_id):\n",
" for obj in loaded_data:\n",
" if obj[\"id\"] == target_id:\n",
" return obj[\"processed_text\"]\n",
" return None # Return None if not found\n",
"print(get_doc_by_id(1).text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Get text tokens by row id***\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n",
"['My', 'skin', 'has', 'been', 'peeling', ',', 'especially', 'on', 'my', 'knees', ',', 'elbows', ',', 'and', 'scalp', '.', 'This', 'peeling', 'is', 'often', 'accompanied', 'by', 'a', 'burning', 'or', 'stinging', 'sensation', '.']\n"
]
}
],
"source": [
"def get_text_tokens(text_id):\n",
" processed_text = get_doc_by_id(text_id)\n",
" tokens = [word.text for sent in processed_text.sentences for word in sent.words]\n",
" return tokens\n",
"print(get_doc_by_id(1).text)\n",
"print(get_text_tokens(1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Get wordnet POS tag***"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n",
"[('skin', 'n'), ('peeling', 'v'), ('especially', 'r'), ('knees', 'n'), ('elbows', 'n'), ('scalp', 'n'), ('peeling', 'n'), ('often', 'r'), ('accompanied', 'v'), ('burning', 'n'), ('stinging', 'a'), ('sensation', 'n')]\n"
]
}
],
"source": [
"def get_wordnet_pos(text_id):\n",
" processed_text = get_doc_by_id(text_id)\n",
" pos_tags = [(word.text, word.upos) for sent in processed_text.sentences for word in sent.words]\n",
" pos_mapping = {\n",
" 'NOUN': 'n', # Noun\n",
" 'VERB': 'v', # Verb\n",
" 'ADJ': 'a', # Adjective\n",
" 'ADV': 'r' # Adverb\n",
" } \n",
" pos_tags = [(word, pos_mapping.get(pos, None)) for word, pos in pos_tags if pos in pos_mapping]\n",
" return pos_tags\n",
"print(get_doc_by_id(1).text)\n",
"print(get_wordnet_pos(1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Extract synsets from text***"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n",
"skin.n.01 skin.n.02 hide.n.02 skin.n.04 peel.n.02 skin.n.06 clamber.v.01 skin.v.02 bark.v.03 skin.v.04 hour_angle.n.02 have.v.01 have.v.02 experience.v.03 own.v.01 get.v.03 consume.v.02 have.v.07 hold.v.03 have.v.09 have.v.10 have.v.11 have.v.12 induce.v.02 accept.v.02 receive.v.01 suffer.v.02 have.v.17 give_birth.v.01 take.v.35 be.v.01 be.v.02 be.v.03 exist.v.01 be.v.05 equal.v.01 constitute.v.01 be.v.08 embody.v.02 be.v.10 be.v.11 be.v.12 cost.v.01 desquamation.n.01 skin.v.04 peel_off.v.04 undress.v.01 particularly.r.01 specially.r.01 on.a.01 on.a.02 along.r.01 on.r.02 on.r.03 knee.n.01 stifle.n.01 knee.n.03 elbow.n.01 elbow.n.02 elbow.n.03 elbow.n.04 elbow.n.05 elbow.v.01 elbow.v.02 scalp.n.01 scalp.v.01 scalp.v.02 desquamation.n.01 skin.v.04 peel_off.v.04 undress.v.01 be.v.01 be.v.02 be.v.03 exist.v.01 be.v.05 equal.v.01 constitute.v.01 be.v.08 embody.v.02 be.v.10 be.v.11 be.v.12 cost.v.01 frequently.r.01 much.r.05 often.r.03 attach_to.v.01 accompany.v.02 play_along.v.02 company.v.01 accompanied.a.01 accompanied.a.02 by.r.01 aside.r.06 angstrom.n.01 vitamin_a.n.01 deoxyadenosine_monophosphate.n.01 adenine.n.01 ampere.n.02 a.n.06 a.n.07 burning.n.01 burn.n.01 combustion.n.01 electrocution.n.01 burning.n.05 burning.n.06 burn.v.01 burn.v.02 burn.v.03 bite.v.02 burn.v.05 burn.v.06 burn.v.07 burn.v.08 burn.v.09 burn.v.10 cauterize.v.01 sunburn.v.01 cut.v.21 burn_off.v.01 burn.v.15 burning.s.01 oregon.n.01 operating_room.n.01 sting.n.01 bite.v.02 sting.v.02 stick.v.15 prick.v.02 sting.v.05 cutting.s.01 sensation.n.01 ace.n.03 sensation.n.03 sensation.n.04 sense.n.03\n"
]
}
],
"source": [
"def get_synsets(text_id):\n",
" words = get_text_tokens(text_id)\n",
" synsets = []\n",
" \n",
" for word in words:\n",
" syns = wn.synsets(word)\n",
" if syns:\n",
" synsets.extend([syn.name() for syn in syns])\n",
" \n",
" return \" \".join(synsets)\n",
"\n",
"print(get_doc_by_id(1).text)\n",
"print(get_synsets(1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Filter synsets by POS***\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n",
"skin.n.01 skin.n.02 hide.n.02 skin.n.04 peel.n.02 skin.n.06 skin.v.04 peel_off.v.04 undress.v.01 particularly.r.01 specially.r.01 knee.n.01 stifle.n.01 knee.n.03 elbow.n.01 elbow.n.02 elbow.n.03 elbow.n.04 elbow.n.05 scalp.n.01 desquamation.n.01 frequently.r.01 much.r.05 often.r.03 attach_to.v.01 accompany.v.02 play_along.v.02 company.v.01 burning.n.01 burn.n.01 combustion.n.01 electrocution.n.01 burning.n.05 burning.n.06 cutting.s.01 sensation.n.01 ace.n.03 sensation.n.03 sensation.n.04 sense.n.03\n"
]
}
],
"source": [
"def get_filtered_synsets(text_id):\n",
" words = get_text_tokens(text_id)\n",
" pos_tags = get_wordnet_pos(text_id)\n",
" synsets = []\n",
" for word, pos in pos_tags:\n",
" if pos: # Filter by WordNet-compatible POS\n",
" syns = wn.synsets(word, pos=pos)\n",
" if syns:\n",
" synsets.extend([syn.name() for syn in syns])\n",
" return \" \".join(synsets)\n",
"print(get_doc_by_id(1).text)\n",
"print(get_filtered_synsets(1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Filter synsets using WSD***\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n",
"skin.v.04 take.v.35 exist.v.01 skin.v.04 specially.r.01 on.r.03 stifle.n.01 elbow.v.02 scalp.n.01 skin.v.04 exist.v.01 often.r.03 play_along.v.02 by.r.01 vitamin_a.n.01 burning.n.06 oregon.n.01 sting.n.03 sense.n.03\n"
]
}
],
"source": [
"def get_wsd_synsets(text_id):\n",
" words = get_text_tokens(text_id)\n",
" text = get_doc_by_id(text_id).text\n",
" synsets = []\n",
" for word in words:\n",
" syn = simple_lesk(text, word) # Disambiguate based on context\n",
" if syn:\n",
" synsets.append(syn.name())\n",
" return \" \".join(synsets)\n",
"print(get_doc_by_id(1).text)\n",
"print(get_wsd_synsets(1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Features selection***\n",
"- Using `SelectKBest`"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def select_features(X_train, X_test, y_train, scorer, k_value):\n",
"\n",
" # Apply features selection\n",
" selector = SelectKBest(score_func=scorer, k=k_value)\n",
" X_train_selected = selector.fit_transform(X_train, y_train)\n",
" X_test_selected = selector.transform(X_test)\n",
" return X_train_selected, X_test_selected \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Plot train, and test accuracies vs number_of_features***\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def plot_accuracies(X_train, X_test, y_train, y_test, model, k_start=500, k_end=4500, step=250):\n",
" train_accuracies = []\n",
" test_accuracies = []\n",
" features_counts = []\n",
" for i in range(k_start, k_end + 1, step):\n",
" X_train_selected, X_test_selected = select_features(X_train, X_test, y_train, chi2, i)\n",
" model.fit(X_train_selected, y_train)\n",
" \n",
" # Training set acc\n",
" y_train_pred = model.predict(X_train_selected)\n",
" train_accuracy = accuracy_score(y_train, y_train_pred)\n",
" train_accuracies.append(train_accuracy)\n",
"\n",
" # Testing set acc\n",
" y_pred = model.predict(X_test_selected)\n",
" test_accuracy = accuracy_score(y_test, y_pred)\n",
" test_accuracies.append(test_accuracy)\n",
"\n",
" features_counts.append(i)\n",
"\n",
" # Plotting the accuracies\n",
" plt.figure(figsize=(10, 6))\n",
" plt.plot(features_counts, train_accuracies, label='Train Accuracy', marker='.')\n",
" plt.plot(features_counts, test_accuracies, label='Test Accuracy', marker='.')\n",
" plt.title('Train and Test Accuracy vs Number of Features')\n",
" plt.xlabel('Number of Features Selected')\n",
" plt.ylabel('Accuracy')\n",
" plt.legend()\n",
" plt.grid()\n",
"\n",
" # Finding closest points (3)\n",
" differences = np.abs(np.array(train_accuracies) - np.array(test_accuracies))\n",
" closest_indices = np.argsort(differences)[:3] # indices of the three smallest differences\n",
" colors = ['darkgreen', 'mediumseagreen', 'lightgreen']\n",
"\n",
" # Draw a rect\n",
" for i, idx in enumerate(closest_indices):\n",
" x = features_counts[idx] - 5\n",
" y_bottom = min(train_accuracies[idx], test_accuracies[idx])\n",
" y_top = max(train_accuracies[idx], test_accuracies[idx])\n",
" height = y_top - y_bottom\n",
"\n",
" plt.gca().add_patch(plt.Rectangle(\n",
" (x, y_bottom), 10, height,\n",
" color=colors[i], alpha=0.5\n",
" ))\n",
" \n",
" # Print the number of selected features for each closest point\n",
" print(f\"Closest Point {i+1}: Number of Features = {features_counts[idx]}, Train Accuracy = {train_accuracies[idx]}, Test Accuracy = {test_accuracies[idx]}\")\n",
"\n",
"\n",
" plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Evaluate model***\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def print_clf_report_as_table(report):\n",
" data = []\n",
" for key, value in report.items():\n",
" if key != 'accuracy' and key != 'macro avg' and key != 'weighted avg':\n",
" data.append([key, value['precision'], value['recall'], value['f1-score'], value['support']])\n",
"\n",
" data.append(['accuracy', '', '', report['accuracy'], ''])\n",
"\n",
" data.append(['macro avg', report['macro avg']['precision'], report['macro avg']['recall'], report['macro avg']['f1-score'], ''])\n",
"\n",
" data.append(['weighted avg', report['weighted avg']['precision'], report['weighted avg']['recall'], report['weighted avg']['f1-score'], ''])\n",
"\n",
" print(tabulate(data, headers=['Class', 'Precision', 'Recall', 'F1-score', 'Support'], tablefmt='psql'))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def evaluate_model(X_train, X_test, y_train, y_test, scorer, k_value, model):\n",
" X_train_selected, X_test_selected = select_features(X_train, X_test, y_train, scorer, k_value)\n",
" model.fit(X_train_selected, y_train)\n",
" \n",
" # Training set acc\n",
" y_train_pred = model.predict(X_train_selected)\n",
" train_accuracy = accuracy_score(y_train, y_train_pred)\n",
"\n",
" # Testing set acc\n",
" y_pred = model.predict(X_test_selected)\n",
" test_accuracy = accuracy_score(y_test, y_pred)\n",
"\n",
" print(f'Train Accuracy: {train_accuracy}')\n",
" print(f'Test Accuracy: {test_accuracy}')\n",
" print(f'Difference: {train_accuracy-test_accuracy}') \n",
" # Print classification report\n",
" report = classification_report(y_test, y_pred, output_dict=True)\n",
" print_clf_report_as_table(report)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ***Use synsets as Features***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ***Apply features extraction on the dataset***"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"train_df[\"features\"] = train_df[\"Id\"].apply(get_synsets)\n",
"test_df[\"features\"] = test_df[\"Id\"].apply(get_synsets)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ***Vectorize the features***\n",
"- **This will be done using `TF-IDF`**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train set shape after features extraction: (4320, 4549)\n",
"Test set shape after features extraction: (480, 4549)\n"
]
}
],
"source": [
"vectorizer = TfidfVectorizer()\n",
"X_train = vectorizer.fit_transform(train_df['features'])\n",
"X_test = vectorizer.transform(test_df['features'])\n",
"print(f\"Train set shape after features extraction: {X_train.shape}\")\n",
"print(f\"Test set shape after features extraction: {X_test.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"y_train = train_df[\"label\"]\n",
"y_test = test_df[\"label\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ***Select best number of features***"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closest Point 1: Number of Features = 500, Train Accuracy = 0.8923611111111112, Test Accuracy = 0.8458333333333333\n",
"Closest Point 2: Number of Features = 750, Train Accuracy = 0.9064814814814814, Test Accuracy = 0.8520833333333333\n",
"Closest Point 3: Number of Features = 1250, Train Accuracy = 0.9185185185185185, Test Accuracy = 0.8583333333333333\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = MultinomialNB()\n",
"plot_accuracies(X_train, X_test, y_train, y_test, model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ***Tune model's hyperparameters to reduce OF***\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closest Point 1: Number of Features = 500, Train Accuracy = 0.9423611111111111, Test Accuracy = 0.89375\n",
"Closest Point 2: Number of Features = 1250, Train Accuracy = 0.9652777777777778, Test Accuracy = 0.9104166666666667\n",
"Closest Point 3: Number of Features = 1000, Train Accuracy = 0.9618055555555556, Test Accuracy = 0.9041666666666667\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = MultinomialNB(alpha=0.1)\n",
"plot_accuracies(X_train, X_test, y_train, y_test, model)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closest Point 1: Number of Features = 500, Train Accuracy = 0.9423611111111111, Test Accuracy = 0.89375\n",
"Closest Point 2: Number of Features = 1250, Train Accuracy = 0.9652777777777778, Test Accuracy = 0.9104166666666667\n",
"Closest Point 3: Number of Features = 1000, Train Accuracy = 0.9618055555555556, Test Accuracy = 0.9041666666666667\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = MultinomialNB(alpha=0.01)\n",
"plot_accuracies(X_train, X_test, y_train, y_test, model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ***Evaluate best model***"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Accuracy: 0.9782407407407407\n",
"Test Accuracy: 0.9333333333333333\n",
"Difference: 0.044907407407407396\n",
"+---------------------------------+--------------------+--------------------+------------+-----------+\n",
"| Class | Precision | Recall | F1-score | Support |\n",
"|---------------------------------+--------------------+--------------------+------------+-----------|\n",
"| Acne | 1.0 | 0.9523809523809523 | 0.97561 | 21.0 |\n",
"| Arthritis | 0.9090909090909091 | 1.0 | 0.952381 | 20.0 |\n",
"| Bronchial Asthma | 0.9047619047619048 | 1.0 | 0.95 | 19.0 |\n",
"| Cervical spondylosis | 1.0 | 1.0 | 1 | 21.0 |\n",
"| Chicken pox | 0.6521739130434783 | 1.0 | 0.789474 | 15.0 |\n",
"| Common Cold | 0.9090909090909091 | 0.9523809523809523 | 0.930233 | 21.0 |\n",
"| Dengue | 0.9285714285714286 | 0.5909090909090909 | 0.722222 | 22.0 |\n",
"| Dimorphic Hemorrhoids | 0.95 | 1.0 | 0.974359 | 19.0 |\n",
"| Fungal infection | 1.0 | 0.9615384615384616 | 0.980392 | 26.0 |\n",
"| Hypertension | 1.0 | 0.8333333333333334 | 0.909091 | 18.0 |\n",
"| Impetigo | 0.9583333333333334 | 1.0 | 0.978723 | 23.0 |\n",
"| Jaundice | 1.0 | 1.0 | 1 | 22.0 |\n",
"| Malaria | 1.0 | 1.0 | 1 | 17.0 |\n",
"| Migraine | 1.0 | 0.9583333333333334 | 0.978723 | 24.0 |\n",
"| Pneumonia | 0.9565217391304348 | 1.0 | 0.977778 | 22.0 |\n",
"| Psoriasis | 0.8666666666666667 | 0.7647058823529411 | 0.8125 | 17.0 |\n",
"| Typhoid | 0.8947368421052632 | 0.9444444444444444 | 0.918919 | 18.0 |\n",
"| Varicose Veins | 0.9565217391304348 | 0.88 | 0.916667 | 25.0 |\n",
"| allergy | 0.8 | 0.8 | 0.8 | 15.0 |\n",
"| diabetes | 0.8947368421052632 | 1.0 | 0.944444 | 17.0 |\n",
"| drug reaction | 0.9285714285714286 | 0.8125 | 0.866667 | 16.0 |\n",
"| gastroesophageal reflux disease | 0.9130434782608695 | 1.0 | 0.954545 | 21.0 |\n",
"| peptic ulcer disease | 1.0 | 0.8888888888888888 | 0.941176 | 18.0 |\n",
"| urinary tract infection | 0.9583333333333334 | 1.0 | 0.978723 | 23.0 |\n",
"| accuracy | | | 0.933333 | |\n",
"| macro avg | 0.932548102799819 | 0.9308089724817665 | 0.927193 | |\n",
"| weighted avg | 0.9392592028490198 | 0.9333333333333333 | 0.932121 | |\n",
"+---------------------------------+--------------------+--------------------+------------+-----------+\n"
]
}
],
"source": [
"model = MultinomialNB(alpha=0.01)\n",
"evaluate_model(X_train, X_test, y_train, y_test, chi2, 1500, model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ***Use synsets as Features with filtering by POS***\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train set shape after features extraction: (4320, 3739)\n",
"Test set shape after features extraction: (480, 3739)\n"
]
}
],
"source": [
"train_df[\"features\"] = train_df[\"Id\"].apply(get_filtered_synsets)\n",
"test_df[\"features\"] = test_df[\"Id\"].apply(get_filtered_synsets)\n",
"\n",
"vectorizer = TfidfVectorizer()\n",
"X_train = vectorizer.fit_transform(train_df['features'])\n",
"X_test = vectorizer.transform(test_df['features'])\n",
"print(f\"Train set shape after features extraction: {X_train.shape}\")\n",
"print(f\"Test set shape after features extraction: {X_test.shape}\")\n",
"\n",
"y_train = train_df[\"label\"]\n",
"y_test = test_df[\"label\"]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closest Point 1: Number of Features = 500, Train Accuracy = 0.9604166666666667, Test Accuracy = 0.9083333333333333\n",
"Closest Point 2: Number of Features = 1500, Train Accuracy = 0.9810185185185185, Test Accuracy = 0.9270833333333334\n",
"Closest Point 3: Number of Features = 1000, Train Accuracy = 0.9766203703703704, Test Accuracy = 0.91875\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = MultinomialNB(alpha=0.01)\n",
"plot_accuracies(X_train, X_test, y_train, y_test, model, k_end=3500)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Accuracy: 0.9810185185185185\n",
"Test Accuracy: 0.9270833333333334\n",
"Difference: 0.05393518518518514\n",
"+---------------------------------+--------------------+--------------------+------------+-----------+\n",
"| Class | Precision | Recall | F1-score | Support |\n",
"|---------------------------------+--------------------+--------------------+------------+-----------|\n",
"| Acne | 1.0 | 0.9523809523809523 | 0.97561 | 21.0 |\n",
"| Arthritis | 0.9090909090909091 | 1.0 | 0.952381 | 20.0 |\n",
"| Bronchial Asthma | 1.0 | 1.0 | 1 | 19.0 |\n",
"| Cervical spondylosis | 1.0 | 0.9523809523809523 | 0.97561 | 21.0 |\n",
"| Chicken pox | 0.5909090909090909 | 0.8666666666666667 | 0.702703 | 15.0 |\n",
"| Common Cold | 0.8695652173913043 | 0.9523809523809523 | 0.909091 | 21.0 |\n",
"| Dengue | 0.9230769230769231 | 0.5454545454545454 | 0.685714 | 22.0 |\n",
"| Dimorphic Hemorrhoids | 0.95 | 1.0 | 0.974359 | 19.0 |\n",
"| Fungal infection | 1.0 | 0.9615384615384616 | 0.980392 | 26.0 |\n",
"| Hypertension | 1.0 | 0.8888888888888888 | 0.941176 | 18.0 |\n",
"| Impetigo | 0.92 | 1.0 | 0.958333 | 23.0 |\n",
"| Jaundice | 1.0 | 1.0 | 1 | 22.0 |\n",
"| Malaria | 1.0 | 1.0 | 1 | 17.0 |\n",
"| Migraine | 1.0 | 0.9583333333333334 | 0.978723 | 24.0 |\n",
"| Pneumonia | 0.9565217391304348 | 1.0 | 0.977778 | 22.0 |\n",
"| Psoriasis | 0.9285714285714286 | 0.7647058823529411 | 0.83871 | 17.0 |\n",
"| Typhoid | 0.85 | 0.9444444444444444 | 0.894737 | 18.0 |\n",
"| Varicose Veins | 0.9565217391304348 | 0.88 | 0.916667 | 25.0 |\n",
"| allergy | 0.8125 | 0.8666666666666667 | 0.83871 | 15.0 |\n",
"| diabetes | 0.8947368421052632 | 1.0 | 0.944444 | 17.0 |\n",
"| drug reaction | 0.9375 | 0.9375 | 0.9375 | 16.0 |\n",
"| gastroesophageal reflux disease | 0.9090909090909091 | 0.9523809523809523 | 0.930233 | 21.0 |\n",
"| peptic ulcer disease | 1.0 | 0.8333333333333334 | 0.909091 | 18.0 |\n",
"| urinary tract infection | 0.88 | 0.9565217391304348 | 0.916667 | 23.0 |\n",
"| accuracy | | | 0.927083 | |\n",
"| macro avg | 0.9286701999373624 | 0.9255657404722303 | 0.922443 | |\n",
"| weighted avg | 0.9345733080206406 | 0.9270833333333334 | 0.926302 | |\n",
"+---------------------------------+--------------------+--------------------+------------+-----------+\n"
]
}
],
"source": [
"model = MultinomialNB(alpha=0.01)\n",
"evaluate_model(X_train, X_test, y_train, y_test, chi2, 1500, model) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ***Use synsets as Features with filtering using lesk's algorithm for WSD***\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"train_df[\"features\"] = train_df[\"Id\"].apply(get_wsd_synsets)\n",
"test_df[\"features\"] = test_df[\"Id\"].apply(get_wsd_synsets)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train set shape after features extraction: (4320, 2573)\n",
"Test set shape after features extraction: (480, 2573)\n"
]
}
],
"source": [
"vectorizer = TfidfVectorizer()\n",
"X_train = vectorizer.fit_transform(train_df['features'])\n",
"X_test = vectorizer.transform(test_df['features'])\n",
"print(f\"Train set shape after features extraction: {X_train.shape}\")\n",
"print(f\"Test set shape after features extraction: {X_test.shape}\")\n",
"y_train = train_df[\"label\"]\n",
"y_test = test_df[\"label\"]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closest Point 1: Number of Features = 500, Train Accuracy = 0.9805555555555555, Test Accuracy = 0.95625\n",
"Closest Point 2: Number of Features = 2000, Train Accuracy = 0.9942129629629629, Test Accuracy = 0.9645833333333333\n",
"Closest Point 3: Number of Features = 1750, Train Accuracy = 0.9946759259259259, Test Accuracy = 0.9645833333333333\n"
]
},
{
"data": {
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H2bNnDwC1a9d2qeft7U3NmjUveB7ns337dgzDYPjw4YSHh7v85A13y5soYNSoUZw6dYqrr76a+vXr89xzz7Fx48ZLOv4PP/zA0aNHadKkCdu3b2f79u3s2rWL2267jU8//dQ5C+WOHTuA88/0lvehvKRngyss+fnuu+9o1qwZfn5+VKpUifDwcN555x2X13jHjh2YzeYCk7ez9e7dmzNnzvD1118DucPe1q5dy7/+9a8LxtejRw/OnDnjHAp4+vRp5s2bR7du3ZwJXqtWrejatSsjR44kLCyMTp06kZKSQnZ2dpHaIE9oaChPP/003377baH3XxXXudd/3gfwc4fT5pWf+/dnNpvz/R1cffXVAM77y7Zt20ZqaipVqlTJd52fPn3aeY3nOV/Ce7a8v82ChiXWrVvXuf5iNWnShNatW7v85J0P5Cao557PDz/84HI+J06c4KmnnqJq1ar4+/sTHh7uPD933Ud1bvtt27YNwzCoXbt2vnj/+OMPZ7yxsbEkJSXxwQcfEBYWRrt27Zg0aZLu9xIpBt3zJSJuV9AsZKdOnaJVq1aEhIQwatQo4uLi8PPz49dff+X5558v0tTyFoulwHLjnBv5z/Xyyy8zfPhw+vXrx+jRo6lUqRJms5mnn366wONe7HFKQl48zz77bKHf9OdNVd+yZUt27NjBN998ww8//MAHH3zA66+/zuTJk3nooYcu6vh5vVvdu3cvcP2PP/7IbbfddlH7LkxhvWDnTsSSp6Dr63//+x933303LVu25D//+Q+RkZF4e3uTkpJS6GQh51OvXj0aNmzItGnT6N27N9OmTcPHx6fQdjlbs2bNiImJ4fPPP6dXr17MmTOHM2fO0KNHD2cdk8nEl19+ycqVK5kzZw4LFiygX79+jB8/npUrVxbrQcV5936NHDmSiRMn5ltf3PYt7Povyb8Lh8ORrzf1bOHh4S7Llzqzobvl/d1OnTqViIiIfOu9vP75+NW9e3eWL1/Oc889R3x8PEFBQTgcDtq3b1+k98Hivp6Qv/0cDgcmk4nvv/++wNf17Otv/Pjx9O3b1/k+8+STTzJ27FhWrlzJVVdddcF4Ra50Sr5ExCOWLl3K8ePH+eqrr2jZsqWzfNeuXW4/9pdffsltt93Ghx9+6FJ+6tQp543sxVGjRg0g99vj22+/3VlutVrZtWsXDRo0uOhY83oMvL29i/TsoUqVKpGYmEhiYiKnT5+mZcuWJCcnO5Ov4gzvy8jI4JtvvqFHjx7ce++9+dY/+eSTTJ8+ndtuu805OcHvv/9eaJx55/L777+f97gVK1Ys8EGyxemlmDVrFn5+fixYsABfX19neUpKiku9uLg4HA4HmzdvJj4+/rz77N27N0lJSRw8eJAZM2bQoUMHl2Gv59O9e3feeOMN0tLSmDlzJjExMc6hZWdr1qwZzZo1Y8yYMcyYMYP777+fzz77rFjJc17vV3JysnMGv7PlxXzq1CmXIaKX2gtUGIfDwc6dO529XQB//vkngHPSibi4OBYtWkSLFi1KNLHK+9vcunWry99mXlne+pKW9/dQpUqV8/7dnjx5ksWLFzNy5EhGjBjhLC9o6HBhf7tnv55nK87rGRcXh2EYxMbGurxOhalfvz7169dn2LBhLF++nBYtWjB58mReeumlIh9T5EqlYYci4hF5366e/S15Tk4O//nPf0rl2Od+O//FF1+4TD1dHI0aNSI8PJzJkyeTk5PjLP/oo48KTCKKo0qVKtx66628++67HDx4MN/6s2c6O3tqasj9trpWrVouQ9fynudTlLi+/vprMjIyePzxx7n33nvz/dx1113MmjWL7OxsbrzxRmJjY5k4cWK+fee1dXh4OC1btmTKlCns3bu3wDqQ+0EwNTXVZcjkwYMHnUP+isJisWAymVy+/d+9ezezZ892qde5c2fMZjOjRo3K18tw7jVy3333YTKZeOqpp9i5c2exnsPWo0cPsrOz+fjjj5k/f36+HrOTJ0/mO15eMljcoYeA8967s2cTzJOXGCxbtsxZlpGRwccff1zs4xTV22+/7fzdMAzefvttvL29ueOOO4Dc5NRutzN69Oh829pstov+O2rUqBFVqlRh8uTJLu34/fff88cff7jMelmS2rVrR0hICC+//HKB9/Dl/d0W9D4IFNhjWdjfbkhICGFhYS6vJ1Cs99J77rkHi8XCyJEj88ViGIbzvSUtLQ2bzeayvn79+pjN5ou6TkWuROr5EhGPaN68ORUrVqRPnz48+eSTmEwmpk6dWipD+e666y5GjRpFYmIizZs357fffmP69OkXfX+Wt7c3L730EgMGDOD222+nR48e7Nq1i5SUlEu+5wtyb+q/+eabqV+/Pv3796dmzZocPnyYFStWsG/fPufzyerVq8ett95Kw4YNqVSpEr/88gtffvmly2QHDRs2BHJ7rdq1a4fFYqFnz54FHnf69OlUrlyZ5s2bF7j+7rvv5v3332fu3Lncc889vPPOO3Ts2JH4+HgSExOJjIxky5YtbNq0iQULFgDw5ptvcvPNN3PjjTfy8MMPExsby+7du5k7dy7r168HoGfPnjz//PN06dKFJ5980jlF99VXX13kCQg6dOjAhAkTaN++Pb169eLIkSNMmjSJWrVquSR1tWrV4oUXXmD06NHccsst3HPPPfj6+rJmzRqioqIYO3ass254eDjt27fniy++oEKFCsX64H7jjTc6j5Wdne0y5BDg448/5j//+Q9dunQhLi6O9PR03n//fUJCQkhISCjycfKEhoby1FNPFTjxRtu2balevToPPvggzz33HBaLhSlTphAeHp4vKS4Jfn5+zJ8/nz59+tC0aVO+//575s6dy9ChQ53DCVu1asWAAQMYO3Ys69evp23btnh7e7Nt2za++OIL3njjjQJ7Xy/E29ubV199lcTERFq1asV9993nnGo+JiaGZ555pqRPF8hNiN555x3+9a9/ceONN9KzZ09n+86dO5cWLVrw9ttvExISQsuWLRk3bhxWq5Xo6Gh++OGHAkcA5P3tvvDCC/Ts2RNvb286duxIYGAgDz30EK+88goPPfQQjRo1YtmyZc7exaKIi4vjpZdeYsiQIezevZvOnTsTHBzMrl27+Prrr3n44Yd59tlnWbJkCQMHDqRbt25cffXV2Gw2pk6disVioWvXriXWfiLlWqnPrygi5VZhU83nTYN9rp9//tlo1qyZ4e/vb0RFRRmDBg1yTmN97nTVBU01f/b02Xk4Z2rlgmRlZRn/93//Z0RGRhr+/v5GixYtjBUrVhitWrVymRY+b6r5c6dVLmxq5//85z9GbGys4evrazRq1MhYtmxZvn1eSEFTzRuGYezYscPo3bu3ERERYXh7exvR0dHGXXfdZXz55ZfOOi+99JLRpEkTo0KFCoa/v79Rt25dY8yYMUZOTo6zjs1mM5544gkjPDzcMJlMhU47f/jwYcPLy8v417/+VWismZmZRkBAgNGlSxdn2U8//WS0adPGCA4ONgIDA43rr7/eeOutt1y2+/33340uXboYFSpUMPz8/Iw6deoYw4cPd6nzww8/GNddd53h4+Nj1KlTx5g2bVqhU80//vjjBcb34YcfGrVr1zZ8fX2NunXrGikpKYVOsz5lyhTjhhtuMHx9fY2KFSsarVq1MhYuXJivXt4U8Q8//HCh7VKYF154wQCMWrVq5Vv366+/Gvfdd59RvXp1w9fX16hSpYpx1113Gb/88ssF91vY39jJkyeN0NDQAv9W1q5dazRt2tTw8fExqlevbkyYMKHQqeYLenxBQe1e0N9lnz59jMDAQGPHjh1G27ZtjYCAAKNq1arGiy++mG9qf8PIfTxDw4YNDX9/fyM4ONioX7++MWjQIOPAgQMXjOl8Zs6c6Xx9K1WqZNx///3Gvn37XOpczFTzF6r73//+12jXrp0RGhpq+Pn5GXFxcUbfvn1dXtd9+/Y5/x5CQ0ONbt26GQcOHCjwfWD06NFGdHS0YTabXV6rzMxM48EHHzRCQ0ON4OBgo3v37saRI0cKnWo+b4r/c82aNcu4+eabjcDAQCMwMNCoW7eu8fjjjxtbt241DMMwdu7cafTr18+Ii4sz/Pz8jEqVKhm33XabsWjRogu2mYjkMhlGKXzNLCIiUg588803dO7cmWXLljkfOSAiIlJUSr5ERESK6K677uKPP/5g+/btl/RsMhERuTLpni8REZEL+Oyzz9i4cSNz587ljTfeUOIlIiIXRT1fIiIiF2AymQgKCqJHjx5MnjzZ5TlNIiIiRaX/PURERC5A31OKiEhJ0HO+RERERERESoGSLxERERERkVKgYYcXyeFwcODAAYKDg3XjtYiIiIjIFcwwDNLT04mKisJsLrx/S8nXRTpw4ADVqlXzdBgiIiIiIlJG/PXXX1x11VWFrlfydZGCg4OB3AYOCQnxcDS5rFYrP/zwA23btsXb29vT4ZQ7al/3Uvu6j9rWvdS+7qX2dS+1r3upfd2rLLVvWloa1apVc+YIhVHydZHyhhqGhISUqeQrICCAkJAQj1+A5ZHa173Uvu6jtnUvta97qX3dS+3rXmpf9yqL7Xuh25E04YaIiIiIiEgpUPIlIiIiIiJSCpR8iYiIiIiIlALd8+VGhmFgs9mw2+2lcjyr1YqXlxdZWVmldswriafb12Kx4OXlpUcbiIiIiFymlHy5SU5ODgcPHiQzM7PUjmkYBhEREfz111/6gO4GZaF9AwICiIyMxMfHxyPHFxEREZGLp+TLDRwOB7t27cJisRAVFYWPj0+pfFh3OBycPn2aoKCg8z7cTS6OJ9vXMAxycnI4evQou3btonbt2nqNRURERC4zSr7cICcnB4fDQbVq1QgICCi14zocDnJycvDz89MHczfwdPv6+/vj7e3Nnj17nHGIiIiIyOVDn9DdSAmQlDRdUyIiIiKXL32SExERERERKQVKvkREREREREqBki9xq5iYGCZOnOjpMEREREREPE7JlwBgMpnO+5OcnHxR+12zZg0PP/xwicT46aefYrFYePzxx0tkfyIiIiIipUnJVxl3MPUMy3cc42DqGfce5+BB58/EiRMJCQlxKXv22WeddfMeHl0U4eHhJTbj44cffsigQYP49NNPycrKKpF9XqycnByPHl9ERERELj8eT74mTZpETEwMfn5+NG3alNWrVxda12q1MmrUKOLi4vDz86NBgwbMnz/fpU56ejpPP/00NWrUwN/fn+bNm7NmzRqXOn379s3Xs9O+fXu3nB/kJiuZObZi/0xdsZsWryyh1/uraPHKEqau2H3Bbc7k2F2WDcMoUowRERHOn9DQUEwmk3N5y5YtBAcH8/3339OwYUN8fX356aef2LFjB506daJq1aoEBQXRuHFjFi1a5LLfc4cdmkwmPvjgA7p06UJAQAC1a9fm22+/vWB8u3btYvny5QwePJirr76ar776Kl+dKVOmcO211+Lr60tkZCQDBw50rjt16hQDBgygatWq+Pn5cd111/Hdd98BkJycTHx8vMu+Jk6cSExMjHO5b9++dOnShddee42rrrqKOnXqADB16lQaNWpEcHAwERER9OrViyNHjrjsa9OmTdx1112EhIQQHBzMLbfcwo4dO1i2bBne3t4cOnTIpf7TTz/NLbfccsE2EREREZHLi0ef8zVz5kySkpKYPHkyTZs2ZeLEibRr146tW7dSpUqVfPWHDRvGtGnTeP/996lbty4LFiygS5cuLF++nBtuuAGAhx56iN9//52pU6cSFRXFtGnTaN26NZs3byY6Otq5r/bt25OSkuJc9vX1ddt5nrHaqTdiwSXtw2HA8G82MfybTcXabvOodgT4lMzLPHjwYF577TVq1qxJxYoV+euvv0hISGDMmDH4+vryySef0LFjR7Zu3Ur16tUL3c/IkSMZN24c//73v3nrrbe4//772bNnD5UqVSp0m5SUFDp06EBoaCgPPPAAH374Ib169XKuf+edd0hKSuKVV17hzjvvJDU1lZ9//hnIfT7XnXfeSXp6OtOmTSMuLo7NmzdjsViKdf5LlizB39+fBQsWOKd8t1qtjB49mjp16nDkyBGSkpLo27cv8+bNA2D//v20bNmSW2+9lSVLlhASEsLPP/+MzWajZcuW1KxZk6lTp/Lcc8859zd9+nTGjRtXrNhEREREpOzzaPI1YcIE+vfvT2JiIgCTJ09m7ty5TJkyhcGDB+erP3XqVF544QUSEhIAePTRR1m0aBHjx49n2rRpnDlzhlmzZvHNN9/QsmVLILdXY86cObzzzju89NJLzn35+voSERFRCmdZfowaNYo2bdo4lytVqkSDBg2cy6NHj+brr7/m22+/del1Olffvn257777AHj55Zd58803Wb16daG9jw6Hg48++oi33noLgJ49e/J///d/7Nq1i9jYWABeeukl/u///o+nnnrKuV3jxo0BWLRoEatXr+aPP/7g6quvBqBmzZrFPv/AwEDefPNNwsLCnMlXv379nOtr1qzJm2++SePGjTl9+jRBQUFMmjSJ0NBQPvvsM7y9vQGcMQA8+OCDpKSkOJOvOXPmkJWVRffu3Ysdn4iIiMiV5GBqFttSTRxMzaJ6mLenwykSjyVfOTk5rF27liFDhjjLzGYzrVu3ZsWKFQVuk52djZ+fn0uZv78/P/30EwA2mw273X7eOnmWLl1KlSpVqFixIrfffjsvvfQSlStXLjTe7OxssrOznctpaWlAbk+F1Wp1qWu1WjEMA4fDgcPhwNdi4vfkNhTHodQs2k78H46zRg2aTfDD07cQEepX4DaGYXA6/TRBwUGYTCYAfC0mHA5HsY6dV//cf2+88UaXfZ0+fZqRI0cyb948Dh48iM1m48yZM+zZs8elXl5b5Lnuuuucy/7+/oSEhHDo0KFC41ywYAEZGRm0b98eh8NBpUqVaN26NR9++CGjRo3iyJEjHDhwgNtuu63Afaxbt46rrrqKWrVqFbg+b2jmuTGfXWYYBtdddx0+Pj4u57N27VpGjhzJxo0bOXnypLN89+7d1KtXj3Xr1nHzzTdjsVgKPHbv3r0ZNmwYy5cvp1mzZqSkpNCtWzf8/f0LrO9wODAMA6vVWuyeu7Iu7+/o3L8nuXRqW/dS+7qX2te91L7upfYtHsMwsDkMcmwOcuyOs/7NX7Zky1GmrtyLgYX//LGMlzrVo1vDqzwWe1FfY48lX8eOHcNut1O1alWX8qpVq7Jly5YCt2nXrh0TJkygZcuWxMXFsXjxYr766ivsdjsAwcHB3HTTTYwePZprrrmGqlWr8umnn7JixQpq1arl3E/79u255557iI2NZceOHQwdOpQ777yTFStWFPqBduzYsYwcOTJf+Q8//JBvQgkvLy8iIiI4ffr0RU/MEOYLw9vXYvT87TiM3MRrePtahPk6sGVlFrqdv48Fe/Y/k3OkX8S8FFlZWRiG4UwwMzNzj+dwOJxlAM888wxLly5l9OjRxMbG4u/vT58+fTh9+rSznsPhICsry2U7m83mspx3jHPL8rz33nucOHGCwMBAZ5nD4WDDhg0kJSU5L/bC9mEymfLFfjar1ZovpvT0dJdtrFarc2hqeno6gDMhvP3225k8eTJhYWHs27ePrl27cvLkSdLS0vD29sZqtRZ6bD8/P9q3b897771HWFgY8+fPZ86cOYXWz8nJ4cyZMyxbtqzIk55cbhYuXOjpEMotta17qX3d41Q2HM0yceq7hVRw3x0CVzxdv+5R1q9fuwE2x98/RmH/mpx17MZZ5QXVOWedvQh1nPX+XjYwFfs8HAa8MHsT1r0bPdbOeZ+XL8Sjww6L64033qB///7UrVsXk8lEXFwciYmJTJkyxVln6tSp9OvXj+joaCwWCzfeeCP33Xcfa9euddbp2bOn8/f69etz/fXXExcXx9KlS7njjjsKPPaQIUNISkpyLqelpVGtWjXatm1LSEiIS92srCz++usvgoKC8vXCFUefW0Joe/1V7DmeSY3KAUSG+p+3vmEYpKenExwc7Oz5uhh+fn6YTCbneeUll8HBwS7n+ssvv5CYmOi89+r06dP89ddf+Pj4OOuZzWb8/Pxctsvr7cpjMpny1clz/Phx5s2bx4wZM7j22mud5Xa7nZYtW7Jy5Urat29PTEwMK1eupEOHDvn20bhxYw4cOMChQ4dchvzliY6O5ujRoy7ttmXLFsxmszMmb29vvLy8nO1gMpnYtm0bJ06c4LXXXqNatWrO7SB3iGJISAg33ngjn3zyCf7+/s5hh+caMGAA999/PzVr1iQuLo62bdsWWA9yry1/f39atmx5SddWWWS1Wlm4cCFt2rQptK3k4qht3Uvt6z5frN3HyG82O7+E9PQ32+WRrl/3Off6HdWxHnc3iDynR+ef3612o8DygsuM86z75998+zznX0fR5mXzGIvZhI/FhI+XGR+L2fmv1e5g3ynXHgYDE3HxzWgaW/gcAu5U2Bfn5/JY8hUWFobFYuHw4cMu5YcPHy70Xqzw8HBmz55NVlYWx48fJyoqisGDB7vcvxMXF8ePP/5IRkYGaWlpREZG0qNHj/Pe41OzZk3CwsLYvn17ocmXr69vgZNyeHt753uzstvtmEwmzGaz896gixVdMZDoioEXrsg/Q+Tyjn2x8rYt6N+z91u7dm2+/vpr7r77bkwmE8OHD8fhcOQ7/rnLBbVLYW01ffp0KleuTM+ePfMllAkJCaSkpJCQkEBycjKPPPIIVatWdU6u8fPPP/PEE09w22230bJlS7p168aECROoVasWW7Zscc5yefvtt/PEE0/w2muvce+99zJ//nzmz59PSEiIM6azj513PjExMfj4+DBp0iQeeeQRfv/9d8aMGeNyPk888QRvv/02vXr1YsiQIYSGhrJy5UqaNGninDHxzjvvJCQkhDFjxjBq1KjzvnZmsxmTyVTgdVdelOdz8zS1rXupffMzDINsm4PT2TZOZ9k4nW0j/e9/T2dbOZ1t/7vcyuksG+ln1TuRkcOWQ+nOfTkMGDp7M5+vPUDlQB+C/LwI8vUiyM+LYN+8370J8vUi+Kx1QX+vC/CxXNIXk+Wdrt/8HA6DTOs/16jz2nVew2dd12eXZ9k4kZnD9iOn/9mXAcO+3cywbzd78IzOz2TCmeD4np3seJldkx8vCz6Wv+ucp56vlxlvS8F1fAvdtxlfi8X5u8Vc8N/swdQztHhliUvyaDGZiKsa4rHruKjH9Vjy5ePjQ8OGDVm8eDGdO3cGcpOHxYsXn3eyBsjtmYmOjsZqtTJr1qwCJycIDAwkMDCQkydPsmDBgvPOHrdv3z6OHz9OZGTkJZ3TlWbChAn069eP5s2bExYWxvPPP1/krL+opkyZQpcuXQr8D7Nr167861//4tixY/Tp04esrCxef/11nn32WcLCwrj33nuddWfNmsWzzz7LfffdR0ZGBrVq1eKVV14B4JprruE///kPL7/8MqNHj6Zr1648++yzvPfee+eNLTw8nI8++oihQ4fy5ptvcuONN/Laa69x9913O+tUrlyZJUuW8Nxzz9GqVSssFgvx8fG0aNHCWcdsNtO3b19efvllevfufalNJiJyyRwOg4ycfz5IpmfbyDjr97M/fP7zgdT6d5ndmUydzrZhtZfsV+vr/zp1UduZTRDo+3ei5vdPsvZP4uZVwHrX5WBfbwJ9LXhZPP6kHjkPq91Bhkuif+61mz/ZPzeZOp1l43SOjSI+seeiXCgJcUmEzlr2thSSxFj+TozOWvY9z77PXWcxmy6bLygiQ/0Ze099hnz1m7Nn8eV7rrvgKLGywGQU9UFQbjBz5kz69OnDu+++S5MmTZg4cSKff/45W7ZsoWrVqvTu3Zvo6GjGjh0LwKpVq9i/fz/x8fHs37+f5ORkdu3axa+//kqFChWA3MkZDMOgTp06bN++neeeew4/Pz/+97//4e3t7ZwkomvXrkRERLBjxw4GDRpEeno6v/32W5GnnE9LSyM0NJTU1NQChx3mzcRXmkPD8u5ROrvHRkqOO9v3wQcf5OjRoxd85pmnrq3SYLVamTdvHgkJCfr2tYSpbd2rLLVvzt+9TBnn9DCd+4193nJGvgTqnw+hJS3I17U3KvisXqnAAnqrTluzGPbVFpcPvyYTDL+7NoFefvnP6dwPzmd9oLaX8Ngqf2+La8wFJGpnLwf65C17u5y/r5fZ4x92y8r1axgGWVYH6Wcl7mcnSBk5+a/T9LxE6pyybFvxJhq7EIvZRPDfr+O51+k/14C3y2uebXOQ9Pl6l+vXbIIfnmlF9UoBeFsun0SnLNt7LJ3P5/2X7gm3UT0s2KOxnC83OJtH7/nq0aMHR48eZcSIERw6dIj4+Hjmz5/vnIRj7969Lh9ys7KyGDZsGDt37iQoKIiEhASmTp3qTLwAUlNTGTJkCPv27aNSpUp07dqVMWPGON9QLBYLGzdu5OOPP+bUqVNERUXRtm1bRo8e7dZnfYkUJDU1ld9++40ZM2YU6WHTIlL2XOpUx4ZhcObvoU3pBSRIhfUoFfStfk4Jf+j0+vtDZ+4HTe9/kgnf/AlUvl6is+oF+nhhLmT4UGEyHBlszdrGtHl25zfbDyRY6NE0mkBz0Ybjw4U/1BfU63GhD/VnrHbOWO0cO519gaOfn5fZ5DI0MrjAtvUuNLELPCu5K2x41oVc6vVrdxjnSeQLSPzzkqlz67shSfbzNudet+e5TgtOoksmSc6x2V16ZsbeU59aVYJK9ByvdJGhftQONYgsZCbwssijPV+XM/V8XXnc0b633norq1evZsCAAbz++usXrK+eL7kYalv3+Wz1XoZ+nfvhymSCvs1juLF6xUJ7lNLPTqbO+tBZ0je9B/hY8t3zlP+D51kf6gvpffJkz0yGI4OfM38m45Q3O5akE3d7MIEVrLQIaFGs5Ksk5dgczte2KMPZCuqtcddwtkAfS6HDKQvrdVy96wTv/W8nxt/Xb+9mNbihekWXYaYFJaNnJ66ZOfYSPQ+Tib+HeBaWIBWejOYlonlJrHcZGB5alnpmyqOy9P/bZdHzJXKlW7p0qadDEJFC2B0GR9OzOZh6hkOpWRxKy+JQahYHU3P//etUJgfPmm3LMCDl592k/Lz7oo5nMZvyJUr5J5QorIfkn2/qA33K1z1JlULMmEMNKoSYubR+pkuXe3+MDxUDfS5pPwVN5JDxd8/mhYZTOhOjbBvpWVbnPXUZOXYycuwcvshWMgz4eMUePl6x56K297GY8yV6BSZILsmUd76EsLxNjHI59syIeyn5EhGRK062zc6RtGwOpeUlU2ecSdXB1CwOp2VxJD37ooZBXRMZTHSFgAJn3Cv8Q6k3ft6ev/9HSof5rEQbLu1DebbNXsBQ1X8St8J6sPafymT3sTP59ndNVBA1KgYV2BNa2PC8QF8Lvl4FPydVRFwp+RIRkXIlM8eW21OV10uVlvV371U2h9Jye7GOnc4p0r68zCaqhvgREepHxN//Robm/utjMfHItF/zTXU8pW/jy2LGLSkffL0s+AZZqBxUvPvWNx0+yl2vr3J5oK3JZPBaj3pcWzW8pMMUkb8p+RIRkcuCYRikZdn+TqrOuPRS/dNrdYa0rKLN1OfjZc5NpELyEip/IkP9qPr3cmSoH5WDfM87kcHlOtWxSNVQX25ufIaffvHHMEyYTAY3NzpD1VBNPibiTkq+RETE4xwOgxOZOS69VWcPBcy756qoN/cH+liIrODvTK4iQs/qtQrJLa8Q4H3Jw/x6NK7OTbEVdUO9XJbq1rRy7VU+eG01YatjYPexejokkXJPyZeIiLiVze7g6OnsAoYC5iZYh9KyOJyaTY69aNOkVwzwdvZO5fVW/ZNY5f4e7Fd6s17phnq5nAUHwFWhBvsC4FTJP95NRM6h5EtERC5a3sQVB88zFPBIelaRplI3mSAsyDffUMCIUF9nb1VEqB9+3rqxX0RELk9KvkREyrmLfYhqRrbNZXr1w2muCdah1CyOZ1zExBUuydU/SVaVYN8y8VweERERd1HyJQAXvO/hxRdfJDk5+aL3/fXXX9O5c+ci1R8wYAAffPABn332Gd26dbuoY4pIrplr9v49IYSF//yxjLH31Kd7o2qknbFxMC33nqrDZyVTB/++1+pQalaRJ67w9TKfk1D900uVV3ahiStERESuBEq+yrrU/XBiB1SKg9Botx3m4MGDzt9nzpzJiBEj2Lp1q7MsKCjIbcc+W2ZmJp999hmDBg1iypQpHk++cnJy8PG5tIdpinjK7mMZDP7qN4y/h/w5DHh+1m+8+M0msmxFu7+qtCauEBERuRIo+SoNhgHWzOJvt34GfD8IDAeYzHDnOIjvVXh9hyP3ODkWMP89dMc7IPdGiguIiIhw/h4aGorJZHIp++CDDxg/fjy7du0iJiaGJ598ksceewzITVCSkpKYNWsWJ0+epGrVqjzyyCMMGTKEmJgYALp06QJAjRo12L17d6FxfPHFF9SrV4/BgwcTFRXFX3/9RbVq1Zzrs7OzGTFiBDNmzODIkSNUq1aNIUOG8OCDDwKwadMmnn/+eZYtW4ZhGMTHx/PRRx8RFxfHrbfeSnx8PBMnTnTur3PnzlSoUIGPPvoIgJiYGB588EG2bdvG7Nmzueeee/joo494/vnn+frrr9m3bx8RERHcf//9jBgxAm/vf4ZwzZkzh1GjRvHbb78RFBTELbfcwtdff82oUaP4/PPP+f33313ONT4+no4dOzJ69OgLvj4iF2IYBn+dOMO6v06ybu8p1v11it/3n3ImXmfLS7wqBnjn3lMV4ptv4oq8KddLc+IKERGR8k7JV2mwZsLLUZe2D8MB857N/SmEGahwbuHQA+ATeEmHnj59OiNGjODtt9/mhhtuYN26dfTv35/AwED69OnDm2++ybfffsvnn39O9erV+euvv/jrr78AWLNmDVWqVCElJYX27dtjsZz/RvkPP/yQBx54gNDQUO68804++ugjhg8f7lzfu3dvVqxYwZtvvkmDBg3YtWsXx44dA2D//v20bNmSW2+9lSVLlhASEsLPP/+MzVa86Ztee+01RowYwYsvvugsCw4OZsqUKYSEhLBr1y4GDBhAcHAwgwYNAmDu3Ll06dKFF154gU8++YScnBzmzZsHQL9+/Rg5ciRr1qyhcePGAKxbt46NGzfy1VdfFSs2kTxpWVY2/pXK+rOSrRNFuP/KbILPHm7G9VdV0MQVIiIipUzJl1zQiy++yPjx47nnnnsAiI2NZfPmzbz77rv06dOHvXv3Urt2bW6++WZMJhM1atRwbhseHg5AhQoVXHrSCrJt2zZWrlzpTEgeeOABkpKSGDZsGCaTiT///JPPP/+chQsX0rp1awBq1qzp3H7SpEmEhoby2WefOXukrr766mKf7+23387//d//uZQNGzYMh8NBWloa1113Hdu2bXMOjwQYM2YMPXv2ZOTIkc5tGjRoAMBVV11Fu3btSElJcSZfKSkptGrVyiV+kcLYHQZ/Hk5n/V+nWLc3N9nafvR0vl4tH4uZelEh3FC9AvHVKnBj9Yr8vP0YQ7/+5yHAY++pT5PYyp45ERERkSuckq/S4B2Q2wNVHGkHYFKT3B6vPCYLPL4KQgruRXM4HKSlpxMSHIz57GGHlyAjI4MdO3bw4IMP0r9/f2e5zWYjNDQUgL59+9KmTRvq1KlD+/btueuuu2jbtm2xjzVlyhTatWtHWFgYAAkJCTz44IMsWbKEO+64g/Xr12OxWGjVqlWB269fv55bbrnFZSjgxWjUqFG+spkzZ/Lmm2+yfft2MjIysNlshISEuBz77PY5V//+/enXrx8TJkzAbDYzY8YMXn/99UuKU8qvI+lZrP+7N2vd3pP8ti+VjAIeLlytkj/x1SpyQ7UK3FC9AvWiQvD1cu3N6tmkOs1r6iHAIiIiZYGSr9JgMhV/6F9Ybej4Bsx5Ggx7buLVcWJueWEcDvC25x7LXDLTNZ8+fRqA999/n6ZNm7qsyxtCeOONN7Jr1y6+//57Fi1aRPfu3WndujVffvllkY9jt9v5+OOPOXToEF5eXi7lU6ZM4Y477sDf3/+8+7jQerPZjHFOV4HVas1XLzDQ9bVasWIF999/P8nJybRo0YKoqCg+//xzxo8fX+Rjd+zYEV9fX77++mt8fHywWq3ce++9591GrgxZVjubDqSxbu/Jv3u2TrH/1Jl89YJ8vbj+qlBuqF6BG6pVpEG1CoQH+xbpGHoIsIiISNmg5Kssu7E3xN0BJ3ZCpZpune2wMFWrViUqKoqdO3dy//33F1ovJCSEHj160KNHD+69917at2/PiRMnqFSpEt7e3tjt+b+1P9u8efNIT09n3bp1LveF/f777yQmJnLq1Cnq16+Pw+Hgxx9/dA47PNv111/Pxx9/jNVqLbD3Kzw83GVWR7vdzu+//85tt9123tiWL19OjRo1GDp0KGlpaYSEhLBnz558x168eDGJiYkF7sPLy4s+ffqQkpKCj48PPXv2vGDCJuWPYRjsPZHJur2nnEMINx9Mw2p3/VLAZIKrqwQ7hw/eUL0itaoEaap2ERGRy5ySr7IuNNojSdfZRo4cyZNPPkloaCjt27cnOzubX375hZMnT5KUlMSECROIjIzkhhtuwGw288UXXxAREUGFChWA3BkEFy9eTIsWLfD19aVixYr5jvHhhx/SoUMH531SeerVq8czzzzD9OnTefzxx+nTpw/9+vVzTrixZ88ejhw5Qvfu3Rk4cCBvvfUWPXv2ZMiQIYSGhrJy5UqaNGlCnTp1uP3220lKSmLu3LnExcUxYcIETp06dcHzr127Nnv37uWzzz7jmmuuYdmyZXz99dcudV588UXuuOMO4uLi6NmzJzabjXnz5vH888876zz00ENcc801APz888/FfBXkcpQ3Kca6vSdZ91duwlXQpBhhQT65wwerV+CGahWof1WoZhkUEREph5R8yQU99NBDBAQE8O9//5vnnnuOwMBA6tevz9NPPw3kzgQ4btw4tm3bhsVioXHjxsybN89539n48eNJSkri/fffJzo6Ot9U84cPH2bu3LnMmDEj37HNZjNdunThww8/5PHHH+edd95h6NChPPbYYxw/fpzq1aszdOhQACpXrsySJUt47rnnaNWqFRaLhfj4eFq0aAHkzjq4YcMGevfujZeXF88888wFe70A7r77bp555hmefPJJsrOzSUhIYPjw4S4Pnb711lv54osvGD16NK+88gohISG0bNnSZT+1a9emefPmnDhxIt8QTrn85U2KsW7vKecQwgtNinFD9dz7ta6q6K/nZImIiFwBTMa5N8FIkaSlpREaGkpqaqrLxAsAWVlZ7Nq1i9jYWPz8Su8ei7zZ+EJCQv6ZcENKzKW2r2EY1K5dm8cee4ykpKSLisFT11ZpsFqtzJs3j4SEhEueNKU0HEnPchk+uHFfKpmFTIpxQ7WKfw8fLHhSDHe73Nr2cqP2dY8MRwY/Z/6Mr8OXU/89RYXbKpBtzqZFQAsCzZf2CBWBY1lpvPvHd1TwCuSqnbCvJpyyZTDgmrsI8wu58A6kSPT+4F5lqX3PlxucTT1fIqXg6NGjfPbZZxw6dKjQ+8Kk7MqdFCPV+Tyt9eeZFKNBtdDcRKtaReKrVyAsqGiTYoiIiEj5p+RLpBRUqVKFsLAw3nvvvQLveZOy4+xJMfKGDxY2KUadqsHOHq34apoUQ0RERM5PyZdIKdDo3rIrLcvKhr+neF+vSTFERETEjZR8icgVw2Z38Ofh0877tNb9dYodhUyKcW10iHOad02KISIiIiVByZcbqbdDSpquqeI5kpbFOmev1oUnxch7rpYnJsUQERGR8k/JlxvkzbaSmZmpB+lKicrMzATw+Iw+ZVFxJ8XIm4FQk2KIiIhIaVHy5QYWi4UKFSpw5MgRAAICAkpluJLD4SAnJ4esrCxNNe8GnmxfwzDIzMzkyJEjVKhQAYvlyu6VMQyDPcczWffXSdb/nWz9UYRJMW6oXpG4cE2KISIiIp6h5MtNIiIiAJwJWGkwDIMzZ87g7697U9yhLLRvhQoVnNfWlST1jJWN+065zEB4MtOar965k2JcX60CQb56mxMREZGyQZ9K3MRkMhEZGUmVKlWwWvN/SHQHq9XKsmXLaNmypYaluYGn29fb27tc93gdTM1iW6qJfScyOWM3ufRqbT9yOl/9vEkx8p6npUkxREREpKxT8uVmFoul1D4wWywWbDYbfn5+Sr7cQO3rHoZhMP6HP5n03+0YWHh7808F1qteKcA5IcYN1StyTWSwJsUQERGRy4qSLxHxiJ1HT/PN+gN89es+/jqZf2KMRjEVaBYbxg3VK9CgmibFEBERkcufki8RKTWH07KYs+EA3244wMZ9qeet+39t6nJTXOVSikxERETE/ZR8iYhbpWVZmf/bIb7ZsJ/lO447H2hsMZu4pXYYra4OY/R3f+A4a6JCi8lETFiAZwIWERERcRMlXyJS4rKsdv675QjfrD/Akq1HyLE5nOsa1qhIp/goEupHOocSBvh4MeSr33AYYDbBy/dcR2SonpEnIiIi5YuSLxEpEXaHwcqdx5m9bj/zfz9EerbNua52lSA63xDN3Q2iqFYpf49Wj8bVuSm2Ip/P+y/dE26jelhwaYYuIiIiUiqUfInIRTMMg9/2pzJ73QHmbDzA0fRs57rIUD/ujo+iU4NorokMvuAU8JGhftQONYgM9XN32CIiIiIeoeRLRIpt17EMvlm/n2/XH2DnsQxneai/Nwn1I+kcH0XjmEqYzXrmloiIiEgeJV8iUiRH0rKYs/Eg36zf7zJToZ+3mdbXVKVzfDQtrw7Hx8vswShFREREyi4lXyJSqLQsK/N/P8S36w+wfMcx54yEFrOJm2uF0Sk+irbXRhDkq7cSERERkQvRJyYRcZFltbN0a+5MhYu3uM5UeGP1CnSKj6bD9ZF66LGIiIhIMSn5EhHnTIXfrN/P978fIj3rn5kKa1UJonN8FHc3iKZ6ZT17S0RERORiefzmjEmTJhETE4Ofnx9NmzZl9erVhda1Wq2MGjWKuLg4/Pz8aNCgAfPnz3epk56eztNPP02NGjXw9/enefPmrFmzxqWOYRiMGDGCyMhI/P39ad26Ndu2bXPL+YmUVYZh8Nu+VEZ/t5mbxi7m/g9W8fkv+0jPshEZ6seAljWZ++TNLHymJQNvr63ES0REROQSebTna+bMmSQlJTF58mSaNm3KxIkTadeuHVu3bqVKlSr56g8bNoxp06bx/vvvU7duXRYsWECXLl1Yvnw5N9xwAwAPPfQQv//+O1OnTiUqKopp06bRunVrNm/eTHR0NADjxo3jzTff5OOPPyY2Npbhw4fTrl07Nm/ejJ+fprmW8m3XsQy+XX+AbzbsZ+fRf2YqDPHzosP1kXSKj6aJZioUERERKXEeTb4mTJhA//79SUxMBGDy5MnMnTuXKVOmMHjw4Hz1p06dygsvvEBCQgIAjz76KIsWLWL8+PFMmzaNM2fOMGvWLL755htatmwJQHJyMnPmzOGdd97hpZdewjAMJk6cyLBhw+jUqRMAn3zyCVWrVmX27Nn07NmzlM5epPQcSc/iuw25MxVuOGumQl8vM63r5c1UGIavl8WDUYqIiIiUbx5LvnJycli7di1DhgxxlpnNZlq3bs2KFSsK3CY7Oztfz5S/vz8//fQTADabDbvdft46u3bt4tChQ7Ru3dq5PjQ0lKZNm7JixYpCk6/s7Gyys/95gGxaWhqQOxTSarUW9bTdKi+OshJPeXO5tW96lpUfNh9hzsZDrNh53DlTodkELeIq0/H6SNrUq/LPTIWGA6vVUfgO3exya9/LidrWvdS+7mFz2DBsBsbfb16GzcAwG9isNqxmtfWlstlsmB1g+vtt3+QAsyO3XNdyydH7g3uVpfYtagwmwzAMN8dSoAMHDhAdHc3y5cu56aabnOWDBg3ixx9/ZNWqVfm26dWrFxs2bGD27NnExcWxePFiOnXqhN1udyZGzZs3x8fHhxkzZlC1alU+/fRT+vTpQ61atdi6dSvLly+nRYsWHDhwgMjISOe+u3fvjslkYubMmQXGm5yczMiRI/OVz5gxg4AA3QsjZYPNAZtOmvj1mInfT5qwGf8MHYwJMmgY5iC+skGIjweDFBERESlnMjMz6dWrF6mpqYSEhBRa77Ka7fCNN96gf//+1K1bF5PJRFxcHImJiUyZMsVZZ+rUqfTr14/o6GgsFgs33ngj9913H2vXrr2kYw8ZMoSkpCTnclpaGtWqVaNt27bnbeDSZLVaWbhwIW3atMHb29vT4ZQ7ZbV97Q6D1btPMGfjIeZvOuwyU2HNsEDubhDJXddHUKNS2f6SoKy2b3mgtnUvta97ZDoyWXlmJb4OX1L/l0roLaFkm7Np5t+MAHPZfj+7HBzPTidl63xCvQKJ3g37YyDVlkFinfZU9g32dHjlht4f3KsstW/eqLgL8VjyFRYWhsVi4fDhwy7lhw8fJiIiosBtwsPDmT17NllZWRw/fpyoqCgGDx5MzZo1nXXi4uL48ccfycjIIC0tjcjISHr06OGsk7fvw4cPu/R8HT58mPj4+ELj9fX1xdc3/3ONvL29Pf5in6ssxlSelIX2NQyDTQfSmL1uP3M2HuBw2j9DYiNC/Lg7Poq7G0RxbVQIJtPlNXFGWWjf8kpt615q35Ll5fDCZDVhcuS+h5m8TJjMJry8vfA2q50vlZfdC4cZjL/nvTbM4DCDl5eXrmM30PuDe5WF9i3q8T2WfPn4+NCwYUMWL15M586dAXA4HCxevJiBAweed1s/Pz+io6OxWq3MmjWL7t2756sTGBhIYGAgJ0+eZMGCBYwbNw6A2NhYIiIiWLx4sTPZSktLY9WqVTz66KMleo4iJW33sQy+Oc9MhXc3iKZJbCUsmqlQREREpMzx6LDDpKQk+vTpQ6NGjWjSpAkTJ04kIyPDOfth7969iY6OZuzYsQCsWrWK/fv3Ex8fz/79+0lOTsbhcDBo0CDnPhcsWIBhGNSpU4ft27fz3HPPUbduXec+TSYTTz/9NC+99BK1a9d2TjUfFRXlTAJFyhLnTIUbDrDhr1POcl8vM62vqUqn+Cha1QnXTIUiIiIiZZxHk68ePXpw9OhRRowYwaFDh4iPj2f+/PlUrVoVgL1792I2//Mc6KysLIYNG8bOnTsJCgoiISGBqVOnUqFCBWed1NRUhgwZwr59+6hUqRJdu3ZlzJgxLl2BgwYNIiMjg4cffphTp05x8803M3/+fD3jS8qM9CwrCzYd5pv1+/l5+zHXmQprhdE5Ppq211Yl2E9DGEREREQuFx6fcGPgwIGFDjNcunSpy3KrVq3YvHnzeffXvXv3Aochns1kMjFq1ChGjRpVrFhF3CnbZmfp1qN8u/4Ai/44TLbtn2nf46tVoFN8FHddH0V4cP57D0VERESk7PN48iVyJXM4DFbuOs636w8w77eDpJ09U2F4IJ3jo+kUH0WNyoEejFJERERESoKSL5FSljdT4Tfr9zNnw0EOpWU511UN8eXuBlF0io++LGcqFBEREZHCKfkSKSV7jv89U+H6/ew4Z6bChPqR3B0fRdPYypqpUERERKScUvIl4kZH07OZu/EAs9cfYP1ZMxX6eJlpfU0VOsVHc6tmKhQRERG5Iij5Eilh6VlWfth0mNmFzFTYKT6adpqpUEREROSKo+RLpARk2+z8uPUo32w4wKLNrjMVNqhWgU4NorirQSRVgvU4AxEREZErlZIvkYvkcBis2nWCbzfsZ95vh0g9Y3WuqxkWSKe/ZyqMCdNMhSIiIiKi5EukyA6mZvHnKROBfx5l9Z5Uvl1/wGWmwirB/8xUeF20ZioUEREREVdKvkSKYPrKPQyb/TsGFvhjnbM82M+LhOsi6RQfRdOamqlQRERERAqn5EvkAnYePc0Ls3/PVz62S3263BiNn7dmKhQRERGRCzN7OgCRsuxUZg4PT/2lwHUxYYFKvERERESkyJR8iRTiUGoW3d9dwfYjGfnWWUwmYsICPBCViIiIiFyulHyJFGDXsQzunbycPw+fpkqwL8+0rk3e7VxmE7x8z3VEhvp7NkgRERERuazoni+Rc2w6kEqfKas5djqHGpUDmPZgU6pVCqBLfCSfz/sv3RNuo3pYsKfDFBEREZHLjJIvkbOs3nWCBz9aQ3q2jWsiQ/ikXxPCg30BiAz1o3aoQWSoHpQsIiIiIsWn5Evkb4v/OMxj038l2+agcUxFPujTmFB/b0+HJSIiIiLlhJIvEeCrX/fx3JcbsTsMbq9bhUm9bsTfRzMZioiIiEjJUfIlV7wpP+1i1HebAehyQzTj7r0eb4vmohERERGRkqXkS65YhmHw+sI/eXPJdgD6No9hxF31MOdNaygiIiIiUoKUfMkVyeEwePHbTUxduQeApDZX88TttTCZlHiJiIiIiHso+ZIrTo7Nwf99sYE5Gw5gMsGou6/lXzfFeDosERERESnnlHzJFSUzx8aj037lxz+P4mU2Mb57AzrFR3s6LBERERG5Aij5kivGqcwc+n20hl/3nsLP28zkBxpya50qng5LRERERK4QSr7kinA4LYveH65m6+F0Qvy8SElsTMMalTwdloiIiIhcQZR8Sbm3+1gG/5qyir9OnKFKsC+fPNiEuhEhng5LRERERK4wSr6kXNt8II3eU1Zz7HQ2NSoHMLVfU6pXDvB0WCIiIiJyBVLyJeXWmt0n6PfRGtKzbNSNCOaTB5tQJdjP02GJiIiIyBVKyZeUS0u2HOax6b+SZXXQqEZFPuzbmFB/b0+HJSIiIiJXMCVfUu7MXref//tiA3aHwW11wvnP/Q3x97F4OiwRERERucIp+ZJy5aOfd5E8ZzMAneOj+He3BnhbzB6OSkREREREyZeUE4ZhMHHRNt5YvA2Avs1jGHFXPcxmk4cjExERERHJpeRLLnsOh8HIOZv4eMUeAJ5pfTVP3lELk0mJl4iIiIiUHUq+5LJmtTv4v8838O2GAwCMvPta+jSP8WxQIiIiIiIFUPIll60zOXYenb6WpVuP4mU2Mb57AzrFR3s6LBERERGRAin5kstSaqaVfh+vYe2ek/h5m3nn/obcVreKp8MSERERESmUki+57BxJy6L3lNVsOZROiJ8XU/o2plFMJU+HJSIiIiJyXkq+5LKy93gmD3y4ir0nMgkP9uWTfk24JjLE02GJiIiIiFyQki+5bPxxMI3eU1ZzND2b6pUCmPpgE2pUDvR0WCIiIiIiRaLkSy4Lv+w+Qb+P1pCWZaNuRDCf9GtClRA/T4clIiIiIlJkSr6kzPvvliM8On0tWVYHDWtUZEqfxoQGeHs6LBERERGRYlHyJWXaN+v383+fb8DmMLi1Tjjv3N8Qfx+Lp8MSERERESk2s6cDmDRpEjExMfj5+dG0aVNWr15daF2r1cqoUaOIi4vDz8+PBg0aMH/+fJc6drud4cOHExsbi7+/P3FxcYwePRrDMJx1+vbti8lkcvlp3769285RLs4nK3bz9Mz12BwGneKjeL93IyVeIiIiInLZ8mjP18yZM0lKSmLy5Mk0bdqUiRMn0q5dO7Zu3UqVKvmf2TRs2DCmTZvG+++/T926dVmwYAFdunRh+fLl3HDDDQC8+uqrvPPOO3z88cdce+21/PLLLyQmJhIaGsqTTz7p3Ff79u1JSUlxLvv6+rr/hKVIDMPgzcXbeX3RnwD0vqkGyR2vxWw2eTgyEREREZGL59GerwkTJtC/f38SExOpV68ekydPJiAggClTphRYf+rUqQwdOpSEhARq1qzJo48+SkJCAuPHj3fWWb58OZ06daJDhw7ExMRw77330rZt23w9ar6+vkRERDh/Klas6NZzlaJxOAxGztnsTLyeuqM2I+9W4iUiIiIilz+P9Xzl5OSwdu1ahgwZ4iwzm820bt2aFStWFLhNdnY2fn6uM9z5+/vz008/OZebN2/Oe++9x59//snVV1/Nhg0b+Omnn5gwYYLLdkuXLqVKlSpUrFiR22+/nZdeeonKlSsXGm92djbZ2dnO5bS0NCB3KKTVai36ibtRXhxlJZ7istodDP5qE99uPAjAsIQ69LmpBjabzcOR5brc27esU/u6j9rWvdS+7mFz2DBsBoYj97YBw2ZgmA1sVhtWs9r6UtlsNswOMDlyl00OMDtyy3Utlxy9P7hXWWrfosZgMs6+GaoUHThwgOjoaJYvX85NN93kLB80aBA//vgjq1atyrdNr1692LBhA7NnzyYuLo7FixfTqVMn7Ha7MzFyOBwMHTqUcePGYbFYsNvtjBkzxiXJ++yzzwgICCA2NpYdO3YwdOhQgoKCWLFiBRZLwfcUJScnM3LkyHzlM2bMICAg4FKb44qXY4eUP81sPmXGbDK4P85Bo3CPXJoiIiIiIsWSmZlJr169SE1NJSQkpNB6l9Vsh2+88Qb9+/enbt26mEwm4uLiSExMdBmm+PnnnzN9+nRmzJjBtddey/r163n66aeJioqiT58+APTs2dNZv379+lx//fXExcWxdOlS7rjjjgKPPWTIEJKSkpzLaWlpVKtWjbZt2563gUuT1Wpl4cKFtGnTBm/vy2cq9rQzVgZMX8fmU6fw9TLzVs8G3FYn3NNh5XO5tu/lQu3rPmpb91L7ukemI5OVZ1bi6/Al9X+phN4SSrY5m2b+zQgw60vPS3U8O52UrfMJ9Qokejfsj4FUWwaJddpT2TfY0+GVG3p/cK+y1L55o+IuxGPJV1hYGBaLhcOHD7uUHz58mIiIiAK3CQ8PZ/bs2WRlZXH8+HGioqIYPHgwNWvWdNZ57rnnGDx4sDPBql+/Pnv27GHs2LHO5OtcNWvWJCwsjO3btxeafPn6+hY4KYe3t7fHX+xzlcWYCnMkPYveU35hy6F0gv28mNK3MY1jKnk6rPO6nNr3cqT2dR+1rXupfUuWl8MLk9WEyZF7z6/Jy4TJbMLL2wtvs9r5UnnZvXCYwfj77n/DDA4zeHl56Tp2A70/uFdZaN+iHt9jE274+PjQsGFDFi9e7CxzOBwsXrzYZRhiQfz8/IiOjsZmszFr1iw6derkXJeZmYnZ7HpaFosFh8NR6P727dvH8ePHiYyMvMizkYvx14lMuk1ewZZD6YQF+TLz4ZvKfOIlIiIiInKxPDrsMCkpiT59+tCoUSOaNGnCxIkTycjIIDExEYDevXsTHR3N2LFjAVi1ahX79+8nPj6e/fv3k5ycjMPhYNCgQc59duzYkTFjxlC9enWuvfZa1q1bx4QJE+jXrx8Ap0+fZuTIkXTt2pWIiAh27NjBoEGDqFWrFu3atSv9RrhCbTmURu8PV3MkPZurKvoz7cGmxIQFejosERERERG38Wjy1aNHD44ePcqIESM4dOgQ8fHxzJ8/n6pVqwKwd+9el16srKwshg0bxs6dOwkKCiIhIYGpU6dSoUIFZ5233nqL4cOH89hjj3HkyBGioqIYMGAAI0aMAHJ7wTZu3MjHH3/MqVOniIqKom3btowePVrP+iola/ecIDFlDWlZNupUDeaTB5tQNcTvwhuKiIiIiFzGPD7hxsCBAxk4cGCB65YuXeqy3KpVKzZv3nze/QUHBzNx4kQmTpxY4Hp/f38WLFhwMaFKCVi69QiPTFtLltXBjdUrkNK3CaEBGgMtIiIiIuWfx5MvuXJ8u+EASTPXY3MYtLo6nHceuJEAH12CIiIiInJl0CdfKRVTV+5hxDe/YxjQsUEU47s1wMfLY/O9iIiIiIiUOiVf4laGYfDWku1MWPgnAA80q87Iu6/DYjZ5ODIRERERkdKl5EvcxuEwGD13Myk/7wbgyTtq80zr2phMSrxERERE5Mqj5Evcwmp3MOjLjXy9bj8AL3asR2KLWA9HJSIiIiLiOUq+pMRlWe08Pv1XFm85gsVs4rVu19Plhqs8HZaIiIiIiEcp+ZISlXrGSv+Pf2H17hP4epn5z/03csc1VT0dloiIiIiIxyn5khJzND2b3lNW88fBNIJ9vfigTyOa1qzs6bBERERERMoEJV9SIv46kcm/PlzF7uOZhAX58HG/JlwbFerpsEREREREygwlX3LJth5Kp/eUVRxOy+aqiv5Me7ApMWGBng5LRERERKRMUfIll2TtnpP0+2gNqWesXF01iKkPNqVqiJ+nwxIRERERKXOUfMlF+/HPozwydS1nrHZuqF6BlL6NqRDg4+mwRERERETKJCVfclHmbDhA0ufrsdoNbqkdxrv/akiAjy4nEREREZHC6NOyFNu0lXsY/s3vGAZ0uD6S17vH4+Nl9nRYIiIiIiJlmpIvKTLDMJj03+289sOfANzftDqjOl2HxWzycGQiIiIiImWfki8pEofDYMy8P/jwp10APHF7LZLaXI3JpMRLRERERKQolHzJBVntDp6ftZGvft0PwPC76vHgzbEejkpERERE5PKi5EvOK8tqZ+CMX1n0xxEsZhPjul5P14ZXeTosEREREZHLjpIvKVRalpWHPv6F1btO4ONlZlKvG2lTr6qnwxIRERERuSwp+ZICHTudTZ8pq9l0II1gXy/e79OIZjUrezosEREREZHLlpIvyeevE5n0nrKaXccyCAvy4aPEJlwXHerpsERERERELmtKvsTFn4fT6f3hag6lZRFdwZ9pDzUlNizQ02GJiIiIiFz2lHyJ07q9J0n8aA2nMq3UrhLE1AebEhHq5+mwRERERETKBSVfAsD/th1lwNS1ZObYia9WgZS+jakY6OPpsEREREREyg0lX8LcjQd5euY6rHaDW2qHMfmBhgT66tIQERERESlJ+oR9hZuxai8vzP4Nw4AO9SOZ0KMBvl4WT4clIiIiIlLuKPm6QhmGwX+W7uDfC7YC0KtpdUZ3ug6L2eThyEREREREyiclX1cgh8Pg5Xl/8MFPuwB4/LY4nm1bB5NJiZeIiIiIiLso+brC2OwOnp/1G7N+3QfAsA7X8NAtNT0clYiIiIhI+afk6wqSZbXzxKfrWLj5MBaziVfuqU+3RtU8HZaIiIiIyBVBydcVIj3LykMf/8KqXSfw8TLz9n030PbaCE+HJSIiIiJyxVDydQU4djqbvimr+X1/GkG+XnzQpxHNalb2dFgiIiIiIlcUJV/l3L6TmfT+cDU7j2VQOdCHj/s14broUE+HJSIiIiJyxVHyVY5tO5zOvz5czaG0LKIr+DP1wSbUDA/ydFgiIiIiIlckJV/l1Pq/TtE3ZTWnMq3UqhLE1AebEBnq7+mwRERERESuWEq+yqGfdxznsRnrycyx06BaBT7q25iKgT6eDktERERE5Iqm5KscOZiaxfd/mVi0ai02B9xcK4x3/9WQQF+9zCIiIiIinqZP5eXEzDV7GTzrNwwsANSPDuHDvo3w9bJ4ODIREREREQEwezoAuXQHU8/8nXj9Y9OBNE5k5HgsJhERERERcaXkqxz4fd8Rl8QLwGHApv1HPBKPiIiIiIjkp+SrHKgSDORPv9Cs8iIiIiIiZYfHk69JkyYRExODn58fTZs2ZfXq1YXWtVqtjBo1iri4OPz8/GjQoAHz5893qWO32xk+fDixsbH4+/sTFxfH6NGjMYx/khPDMBgxYgSRkZH4+/vTunVrtm3b5rZzdLcqIT7UqfoLpr8TMBMGdaqupUqIZjgUERERESkrPJp8zZw5k6SkJF588UV+/fVXGjRoQLt27ThypODhcsOGDePdd9/lrbfeYvPmzTzyyCN06dKFdevWOeu8+uqrvPPOO7z99tv88ccfvPrqq4wbN4633nrLWWfcuHG8+eabTJ48mVWrVhEYGEi7du3Iyspy+zm7S2Tobjpct5KB9ex0uG4lkaG7PR2SiIiIiIicxaOzHU6YMIH+/fuTmJgIwOTJk5k7dy5Tpkxh8ODB+epPnTqVF154gYSEBAAeffRRFi1axPjx45k2bRoAy5cvp1OnTnTo0AGAmJgYPv30U2ePmmEYTJw4kWHDhtGpUycAPvnkE6pWrcrs2bPp2bNngbFmZ2eTnZ3tXE5LSwNye+OsVmtJNMdFs9vseJm8CPaxU9vX4Hi2nSyrF3ab3eOxlSd5bak2dQ+1r/uobd1L7eseNocNw2ZgOHJHdRg2A8NsYLPasJrV1pfKZrNhdoDJkbtscoDZkVuua7nk6P3BvcpS+xY1Bo8lXzk5Oaxdu5YhQ4Y4y8xmM61bt2bFihUFbpOdnY2fn59Lmb+/Pz/99JNzuXnz5rz33nv8+eefXH311WzYsIGffvqJCRMmALBr1y4OHTpE69atnduEhobStGlTVqxYUWjyNXbsWEaOHJmv/IcffiAgIKDoJ+4mHSt1dP7ezLcZ+MLan9d6MKLya+HChZ4OoVxT+7qP2ta91L7ukUXuqJTU/6UCsJSlHoymfKlHoPP36N0QTSCr9v3PcwGVY3p/cK+y0L6ZmZlFquex5OvYsWPY7XaqVq3qUl61alW2bNlS4Dbt2rVjwoQJtGzZkri4OBYvXsxXX32F3W531hk8eDBpaWnUrVsXi8WC3W5nzJgx3H///QAcOnTIeZxzj5u3riBDhgwhKSnJuZyWlka1atVo27YtISEhxTv5EnY49TDJc5IJDwynmW8zVmav5GjGUZI7JlM1tOqFdyBFYrVaWbhwIW3atMHb29vT4ZQ7al/3Udu6l9rXPTIdmaw8sxJfhy+p/0sl9JZQss3ZNPNvRoDZ8196Xu6OZ6eTsnU+oV6BRO+G/TGQassgsU57KvsGezq8ckPvD+5Vlto3b1TchVxWD1l+44036N+/P3Xr1sVkMhEXF0diYiJTpkxx1vn888+ZPn06M2bM4Nprr2X9+vU8/fTTREVF0adPn4s+tq+vL76+vvnKvb29Pf5iW7ws2AwbdnKTUDt2bIYNi5fF47GVR2XhNS/P1L7uo7Z1L7VvyfJyeGGymjA5TACYvEyYzCa8vL3wNqudL5WX3QuHGYy/7/43zOAwg5eXl65jN9D7g3uVhfYt6vE9NuFGWFgYFouFw4cPu5QfPnyYiIiIArcJDw9n9uzZZGRksGfPHrZs2UJQUBA1a9Z01nnuuecYPHgwPXv2pH79+vzrX//imWeeYezYsQDOfRfnuCIiIiIiIpfKY8mXj48PDRs2ZPHixc4yh8PB4sWLuemmm867rZ+fH9HR0dhsNmbNmuWcOANyx1uaza6nZbFYcDhy7yiNjY0lIiLC5bhpaWmsWrXqgscVERERERG5WB4ddpiUlESfPn1o1KgRTZo0YeLEiWRkZDhnP+zduzfR0dHOXqtVq1axf/9+4uPj2b9/P8nJyTgcDgYNGuTcZ8eOHRkzZgzVq1fn2muvZd26dUyYMIF+/foBYDKZePrpp3nppZeoXbs2sbGxDB8+nKioKDp37lzqbSAiIiIiIlcGjyZfPXr04OjRo4wYMYJDhw4RHx/P/PnznZNh7N2716UXKysri2HDhrFz506CgoJISEhg6tSpVKhQwVnnrbfeYvjw4Tz22GMcOXKEqKgoBgwYwIgRI5x1Bg0aREZGBg8//DCnTp3i5ptvZv78+flmUhQRERERESkpHp9wY+DAgQwcOLDAdUuXLnVZbtWqFZs3bz7v/oKDg5k4cSITJ04stI7JZGLUqFGMGjWquOGKiIiIiIhcFI/d8yUiIiIiInIlUfIlIiIiIiJSCpR8iYiIiIiIlAIlXyIiIiIiIqVAyZeIiIiIiEgpUPIlIiIiIiJSCpR8iYiIiIiIlAIlXyIiIiIiIqVAyZeIiIiIiEgpUPIlIiIiIiJSCpR8iYiIiIiIlAIlXyIiIiIiIqVAyZeIiIiIiEgpUPIlIiIiIiJSCpR8iYiIiIiIlAIlXyIiIiIiIqVAyZeIiIiIiEgpUPIlIiIiIiJSCpR8iYiIiIiIlAIlXyIiIiIiIqWg2MlXTEwMo0aNYu/eve6IR0REREREpFwqdvL19NNP89VXX1GzZk3atGnDZ599RnZ2tjtiExERERERKTcuKvlav349q1ev5pprruGJJ54gMjKSgQMH8uuvv7ojRhERERERkcveRd/zdeONN/Lmm29y4MABXnzxRT744AMaN25MfHw8U6ZMwTCMkoxTRERERETksuZ1sRtarVa+/vprUlJSWLhwIc2aNePBBx9k3759DB06lEWLFjFjxoySjFVEREREROSyVezk69dffyUlJYVPP/0Us9lM7969ef3116lbt66zTpcuXWjcuHGJBioiIiIiInI5K3by1bhxY9q0acM777xD586d8fb2zlcnNjaWnj17lkiAIiIiIiIi5UGxk6+dO3dSo0aN89YJDAwkJSXlooMSEREREREpb4o94caRI0dYtWpVvvJVq1bxyy+/lEhQIiIiIiIi5U2xk6/HH3+cv/76K1/5/v37efzxx0skKBERERERkfKm2MnX5s2bufHGG/OV33DDDWzevLlEghIRERERESlvip18+fr6cvjw4XzlBw8exMvromeuFxERERERKdeKnXy1bduWIUOGkJqa6iw7deoUQ4cOpU2bNiUanIiIiIiISHlR7K6q1157jZYtW1KjRg1uuOEGANavX0/VqlWZOnVqiQcoIiIiIiJSHhQ7+YqOjmbjxo1Mnz6dDRs24O/vT2JiIvfdd1+Bz/wSERERERGRi0i+IPc5Xg8//HBJxyIiIiIiIlJuXfQMGZs3b2bv3r3k5OS4lN99992XHJSIiIiIiEh5U+zka+fOnXTp0oXffvsNk8mEYRgAmEwmAOx2e8lGKCIiIiIiUg4Ue7bDp556itjYWI4cOUJAQACbNm1i2bJlNGrUiKVLl7ohRBERERERkctfsXu+VqxYwZIlSwgLC8NsNmM2m7n55psZO3YsTz75JOvWrXNHnCIiIiIiIpe1Yvd82e12goODAQgLC+PAgQMA1KhRg61bt15UEJMmTSImJgY/Pz+aNm3K6tWrC61rtVoZNWoUcXFx+Pn50aBBA+bPn+9SJyYmBpPJlO/n8ccfd9a59dZb861/5JFHLip+ERERERGRCyl2z9d1113Hhg0biI2NpWnTpowbNw4fHx/ee+89atasWewAZs6cSVJSEpMnT6Zp06ZMnDiRdu3asXXrVqpUqZKv/rBhw5g2bRrvv/8+devWZcGCBXTp0oXly5c7nzu2Zs0al3vPfv/9d9q0aUO3bt1c9tW/f39GjRrlXA4ICCh2/CIiIiIiIkVR7J6vYcOG4XA4ABg1ahS7du3illtuYd68ebz55pvFDmDChAn079+fxMRE6tWrx+TJkwkICGDKlCkF1p86dSpDhw4lISGBmjVr8uijj5KQkMD48eOddcLDw4mIiHD+fPfdd8TFxdGqVSuXfQUEBLjUCwkJKXb8IiIiIiIiRVHsnq927do5f69VqxZbtmzhxIkTVKxY0TnjYVHl5OSwdu1ahgwZ4iwzm820bt2aFStWFLhNdnY2fn5+LmX+/v789NNPhR5j2rRpJCUl5Ytv+vTpTJs2jYiICDp27Mjw4cML7f3Kzs4mOzvbuZyWlgbkDoO0Wq0XPlk3stvseJm8sGABwIIFL5MXdpvd47GVJ3ltqTZ1D7Wv+6ht3Uvt6x42hw3DZmA4cmdVNmwGhtnAZrVhNautL5XNZsPsAFPu9+mYHGB25JbrWi45en9wr7LUvkWNwWTkzRVfxJ36+/uzfv16rrvuuosOLs+BAweIjo5m+fLl3HTTTc7yQYMG8eOPP7Jq1ap82/Tq1YsNGzYwe/Zs4uLiWLx4MZ06dcJut7skR3k+//xzevXqxd69e4mKinKWv/fee9SoUYOoqCg2btzI888/T5MmTfjqq68KjDU5OZmRI0fmK58xY4aGK4qIiIiIXMEyMzPp1asXqamp5x1NV6yeL29vb6pXr+7RZ3m98cYb9O/fn7p162IymYiLiyMxMbHQYYoffvghd955p0viBfDwww87f69fvz6RkZHccccd7Nixg7i4uHz7GTJkCElJSc7ltLQ0qlWrRtu2bT0+XPFw6mGS5yQTHhhOM99mrMxeydGMoyR3TKZqaFWPxlaeWK1WFi5cSJs2bfD29vZ0OOWO2td91LbupfZ1j0xHJivPrMTX4Uvq/1IJvSWUbHM2zfybEWDWl56X6nh2Oilb5xPqFUj0btgfA6m2DBLrtKeyb7Cnwys39P7gXmWpffNGxV1IsYcdvvDCCwwdOpSpU6dSqVKlYgd2trCwMCwWC4cPH3YpP3z4MBEREQVuEx4ezuzZs8nKyuL48eNERUUxePDgAif72LNnD4sWLSq0N+tsTZs2BWD79u0FJl++vr74+vrmK/f29vb4i23xsmAzbNjJTYrt2LEZNixeFo/HVh6Vhde8PFP7uo/a1r3UviXLy+GFyWrC5Mi9ZcDkZcJkNuHl7YW3We18qbzsXjjMYPx9979hBocZvLy8dB27gd4f3KsstG9Rj1/s5Ovtt99m+/btREVFUaNGDQIDA13W//rrr0Xel4+PDw0bNmTx4sV07twZAIfDweLFixk4cOB5t/Xz8yM6Ohqr1cqsWbPo3r17vjopKSlUqVKFDh06XDCW9evXAxAZGVnk+EVERERERIqq2MlXXpJUUpKSkujTpw+NGjWiSZMmTJw4kYyMDBITEwHo3bs30dHRjB07FoBVq1axf/9+4uPj2b9/P8nJyTgcDgYNGuSyX4fDQUpKCn369MHLy/U0d+zYwYwZM0hISKBy5cps3LiRZ555hpYtW3L99deX6PmJiIiIiIjARSRfL774YokG0KNHD44ePcqIESM4dOgQ8fHxzJ8/n6pVc+9V2rt3L2bzPzPiZ2VlMWzYMHbu3ElQUBAJCQlMnTqVChUquOx30aJF7N27l379+uU7po+PD4sWLXImetWqVaNr164MGzasRM9NREREREQkT7GTL3cYOHBgocMMly5d6rLcqlUrNm/efMF9tm3blsImcqxWrRo//vhjseMUERERERG5WMVOvsxm83mf5+XJmRBFRERERETKqmInX19//bXLstVqZd26dXz88ccFPgdLRERERERELiL56tSpU76ye++9l2uvvZaZM2fy4IMPlkhgIiIiIiIi5Yn5wlWKplmzZixevLikdiciIiIiIlKulEjydebMGd58802io6NLYnciIiIiIiLlTrGHHVasWNFlwg3DMEhPTycgIIBp06aVaHAiIiIiIiLlRbGTr9dff90l+TKbzYSHh9O0aVMqVqxYosGJiIiIiIiUF8VOvvr27euGMERERERERMq3Yt/zlZKSwhdffJGv/IsvvuDjjz8ukaBERERERETKm2InX2PHjiUsLCxfeZUqVXj55ZdLJCgREREREZHyptjJ1969e4mNjc1XXqNGDfbu3VsiQYmIiIiIiJQ3xU6+qlSpwsaNG/OVb9iwgcqVK5dIUCIiIiIiIuVNsZOv++67jyeffJL//ve/2O127HY7S5Ys4amnnqJnz57uiFFEREREROSyV+zZDkePHs3u3bu544478PLK3dzhcNC7d2/d8yUiIiIiIlKIYidfPj4+zJw5k5deeon169fj7+9P/fr1qVGjhjviExERERERKReKnXzlqV27NrVr1y7JWERERERERMqtYt/z1bVrV1599dV85ePGjaNbt24lEpSIiIiIiEh5U+zka9myZSQkJOQrv/POO1m2bFmJBCUiIiIiIlLeFDv5On36ND4+PvnKvb29SUtLK5GgREREREREyptiJ1/169dn5syZ+co/++wz6tWrVyJBiYiIiIiIlDfFnnBj+PDh3HPPPezYsYPbb78dgMWLFzNjxgy+/PLLEg9QRERERESkPCh28tWxY0dmz57Nyy+/zJdffom/vz8NGjRgyZIlVKpUyR0xioiIiIiIXPYuaqr5Dh060KFDBwDS0tL49NNPefbZZ1m7di12u71EAxQRERERESkPin3PV55ly5bRp08foqKiGD9+PLfffjsrV64sydhERERERETKjWL1fB06dIiPPvqIDz/8kLS0NLp37052djazZ8/WZBsiIiIiIiLnUeSer44dO1KnTh02btzIxIkTOXDgAG+99ZY7YxMRERERESk3itzz9f333/Pkk0/y6KOPUrt2bXfGJCIiIiIiUu4Uuefrp59+Ij09nYYNG9K0aVPefvttjh075s7YREREREREyo0iJ1/NmjXj/fff5+DBgwwYMIDPPvuMqKgoHA4HCxcuJD093Z1xioiIiIiIXNaKPdthYGAg/fr146effuK3337j//7v/3jllVeoUqUKd999tztiFBERERERuexd9FTzAHXq1GHcuHHs27ePTz/9tKRiEhERERERKXcuKfnKY7FY6Ny5M99++21J7E5ERERERKTcKZHkS0RERERERM5PyZeIiIiIiEgpUPIlIiIiIiJSCpR8iYiIiIiIlAIlXyIiIiIiIqVAyZeIiIiIiEgpUPIlIiIiIiJSCpR8iYiIiIiIlAIlXyIiIiIiIqWgTCRfkyZNIiYmBj8/P5o2bcrq1asLrWu1Whk1ahRxcXH4+fnRoEED5s+f71InJiYGk8mU7+fxxx931snKyuLxxx+ncuXKBAUF0bVrVw4fPuy2cxQRERERkSubx5OvmTNnkpSUxIsvvsivv/5KgwYNaNeuHUeOHCmw/rBhw3j33Xd566232Lx5M4888ghdunRh3bp1zjpr1qzh4MGDzp+FCxcC0K1bN2edZ555hjlz5vDFF1/w448/cuDAAe655x73nqyIiIiIiFyxvDwdwIQJE+jfvz+JiYkATJ48mblz5zJlyhQGDx6cr/7UqVN54YUXSEhIAODRRx9l0aJFjB8/nmnTpgEQHh7uss0rr7xCXFwcrVq1AiA1NZUPP/yQGTNmcPvttwOQkpLCNddcw8qVK2nWrFm+42ZnZ5Odne1cTktLA3J74qxW66U2wyWx2+x4mbywYAHAggUvkxd2m93jsZUneW2pNnUPta/7qG3dS+3rHjaHDcNmYDgMgNzfzQY2qw2rWW19qWw2G2YHmBy5yyYHmB255bqWS47eH9yrLLVvUWMwGYZhuDmWQuXk5BAQEMCXX35J586dneV9+vTh1KlTfPPNN/m2qVy5MuPGjePBBx90lj3wwAP89NNP7N69u8BjREVFkZSUxNChQwFYsmQJd9xxBydPnqRChQrOujVq1ODpp5/mmWeeybef5ORkRo4cma98xowZBAQEFOOsRURERESkPMnMzKRXr16kpqYSEhJSaD2P9nwdO3YMu91O1apVXcqrVq3Kli1bCtymXbt2TJgwgZYtWxIXF8fixYv56quvsNvtBdafPXs2p06dom/fvs6yQ4cO4ePj45J45R330KFDBe5nyJAhJCUlOZfT0tKoVq0abdu2PW8Dl4bDqYdJnpNMeGA4zXybsTJ7JUczjpLcMZmqoVUvvAMpEqvVysKFC2nTpg3e3t6eDqfcUfu6j9rWvdS+7pHpyGTlmZX4OnxJ/V8qobeEkm3Oppl/MwLM+tLzUh3PTidl63xCvQKJ3g37YyDVlkFinfZU9g32dHjlht4f3KsstW/eqLgL8fiww+J644036N+/P3Xr1sVkMhEXF0diYiJTpkwpsP6HH37InXfeSVRU1CUd19fXF19f33zl3t7eHn+xLV4WbIYNO7kJqB07NsOGxcvi8djKo7Lwmpdnal/3Udu6l9q3ZHk5vDBZTZgcJgBMXiZMZhNe3l54m9XOl8rL7oXDDMbfd/8bZnCYwcvLS9exG+j9wb3KQvsW9fgenXAjLCwMi8WSb5bBw4cPExERUeA24eHhzJ49m4yMDPbs2cOWLVsICgqiZs2a+eru2bOHRYsW8dBDD7mUR0REkJOTw6lTp4p8XBERERERkUvh0eTLx8eHhg0bsnjxYmeZw+Fg8eLF3HTTTefd1s/Pj+joaGw2G7NmzaJTp0756qSkpFClShU6dOjgUt6wYUO8vb1djrt161b27t17weOKiIiIiIhcDI8PO0xKSqJPnz40atSIJk2aMHHiRDIyMpyzH/bu3Zvo6GjGjh0LwKpVq9i/fz/x8fHs37+f5ORkHA4HgwYNctmvw+EgJSWFPn364OXlepqhoaE8+OCDJCUlUalSJUJCQnjiiSe46aabCpzpUERERERE5FJ5PPnq0aMHR48eZcSIERw6dIj4+Hjmz5/vnIRj7969mM3/dNBlZWUxbNgwdu7cSVBQEAkJCUydOjXf5BmLFi1i79699OvXr8Djvv7665jNZrp27Up2djbt2rXjP//5j9vOU0RERERErmweT74ABg4cyMCBAwtct3TpUpflVq1asXnz5gvus23btpxvFn0/Pz8mTZrEpEmTihWriIiIiIjIxfDoPV8iIiIiIiJXCiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKlHyJiIiIiIiUAiVfIiIiIiIipUDJl4iIiIiISClQ8iUiIiIiIlIKPJ58TZo0iZiYGPz8/GjatCmrV68utK7VamXUqFHExcXh5+dHgwYNmD9/fr56+/fv54EHHqBy5cr4+/tTv359fvnlF+f6vn37YjKZXH7at2/vlvMTEREREREB8PLkwWfOnElSUhKTJ0+madOmTJw4kXbt2rF161aqVKmSr/6wYcOYNm0a77//PnXr1mXBggV06dKF5cuXc8MNNwBw8uRJWrRowW233cb3339PeHg427Zto2LFii77at++PSkpKc5lX19f956siIiIiIhc0TyafE2YMIH+/fuTmJgIwOTJk5k7dy5Tpkxh8ODB+epPnTqVF154gYSEBAAeffRRFi1axPjx45k2bRoAr776KtWqVXNJrGJjY/Pty9fXl4iICHecloiIiIiISD4eS75ycnJYu3YtQ4YMcZaZzWZat27NihUrCtwmOzsbPz8/lzJ/f39++ukn5/K3335Lu3bt6NatGz/++CPR0dE89thj9O/f32W7pUuXUqVKFSpWrMjtt9/OSy+9ROXKlQuNNzs7m+zsbOdyWloakDsU0mq1Fv3E3cBus+Nl8sKCBQALFrxMXthtdo/HVp7ktaXa1D3Uvu6jtnUvta972Bw2DJuB4TAAcn83G9isNqxmtfWlstlsmB1gcuQumxxgduSW61ouOXp/cK+y1L5FjcFkGIbh5lgKdODAAaKjo1m+fDk33XSTs3zQoEH8+OOPrFq1Kt82vXr1YsOGDcyePZu4uDgWL15Mp06dsNvtzsQoLzlLSkqiW7durFmzhqeeeorJkyfTp08fAD777DMCAgKIjY1lx44dDB06lKCgIFasWIHFYikw3uTkZEaOHJmvfMaMGQQEBFxye4iIiIiIyOUpMzOTXr16kZqaSkhISKH1Lqvk6+jRo/Tv3585c+ZgMpmIi4ujdevWTJkyhTNnzgDg4+NDo0aNWL58uXO7J598kjVr1hTao7Zz507i4uJYtGgRd9xxR4F1Cur5qlatGseOHTtvA5eGw6mHSZ6TTHhgOM18m7EyeyVHM46S3DGZqqFVPRpbeWK1Wlm4cCFt2rTB29vb0+GUO2pf91Hbupfa1z0yHZmsPLOSkNR0HP/bhvmW2qSFBtPMvxkBZn3peamOZ6eTsnU+UdZsam0/xPZaERzw9iWxTnsq+wZ7Orxyw3ZiD78u/IIb23TDq1INT4dT7pSl9k1LSyMsLOyCyZfHhh2GhYVhsVg4fPiwS/nhw4cLvRcrPDyc2bNnk5WVxfHjx4mKimLw4MHUrFnTWScyMpJ69eq5bHfNNdcwa9asQmOpWbMmYWFhbN++vdDky9fXt8BJOby9vT3+n63Fy4LNsGHHDoAdOzbDhsXL4vHYyqOy8JqXZ2pf91Hbupfat2R5Oby46tcl1PvhXUyGgbHDxOa2A/BqejPeZrXzpfKye1H/r59JWD8dMwY3bTcxL/5+vK69S9dxSfn1E7zmPEULw4GxYxymjm/Ajb09HVX5Ucbat6h/Nx5Lvnx8fGjYsCGLFy+mc+fOADgcDhYvXszAgQPPu62fnx/R0dFYrVZmzZpF9+7dnetatGjB1q1bXer/+eef1KhReDa8b98+jh8/TmRk5MWfkIiIiJQYU9oBZ+IFYDIM6i2YjH3rb+Ad6OHoLn8hOafpsGc5pr+XzRh0WD8N68md4BPk0djKBWsW7F7mbF+T4YBvn4CNX4C333k3lSIoqH3nPA1xd0BotCcjuyCPznaYlJREnz59aNSoEU2aNGHixIlkZGQ4Zz/s3bs30dHRjB07FoBVq1axf/9+4uPj2b9/P8nJyTgcDgYNGuTc5zPPPEPz5s15+eWX6d69O6tXr+a9997jvffeA+D06dOMHDmSrl27EhERwY4dOxg0aBC1atWiXbt2pd8IIiIiko/XvnXOxCuPCfDa/bNnAipnfAooMwE+e5YXsEZKzO5lno6g/DLscGKnkq/z6dGjB0ePHmXEiBEcOnSI+Ph45s+fT9Wqufcp7d27F7P5n+dAZ2VlMWzYMHbu3ElQUBAJCQlMnTqVChUqOOs0btyYr7/+miFDhjBq1ChiY2OZOHEi999/PwAWi4WNGzfy8ccfc+rUKaKiomjbti2jR4/Ws75ERETKgq3f4/Pdc/mKDZMJU5vR4F+xgI2kWM6cxPhhOCb+SXDVviXozEn4YTic1b6YzNBmlNq3JBTYvhaoVLPQTcoKjyZfAAMHDix0mOHSpUtdllu1asXmzZsvuM+77rqLu+66q8B1/v7+LFiwoNhxioiIiJvZsmHhi7Dqndzl0Ksw0g5gMhwYJgumjhN1z0wJMvmFYsx5GpNhV/u6g9rXvQpq3zLe6wVlIPkSERER4dh2+DIRDm3MXW72GLROxpZ6iFXff0rTO+/Du7JmiytRN/bGVqOV2tdd1L7udZm2r5IvERER8awNn8F3SWDNAP9K0PkdqNM+d11IFMeDr4GQKM/GWF6pfd1L7etel2H7KvkSERERz8g+DfOehQ2f5i7XuBm6vn9ZfZASESkOJV8iIiJS+g5ugC8S4cSO3IkIWg2Gls+C2eLpyERE3EbJl4iIiJQew4BV78LC4WDPgZBouOd9iGnh6chERNxOyZeIiIiUjswTMPsx+PP73OU6HaDT2xBQybNxiYiUEiVfIiIi4n67f4ZZD0H6AbD4QNsx0KQ/mEyejkxEpNQo+RIRERH3cdhh2b/hx1fBcEDlWnBvCkRe7+nIRERKnZIvERERcY/U/fDVw7Dnp9zlBr0g4d/gG+TZuEREPETJl4iIiJS8rfNh9qNw5gT4BEGHCdCgh6ejEhHxKCVfIiIiUnJs2bDwRVj1Tu5yZIPcYYaV4zwbl4hIGaDkS0RERErG8R3wRV84tDF3udlj0DoZvHw9GZWISJmh5EtEREQu3YaZMDcJck6DfyXo/A7Uae/pqEREyhQlXyIiInLxsk/DvGdhw6e5yzVuhq7vQ0iUZ+MSESmDlHyJiIjIxTm4Ab7sB8e3g8kMrQZDy2fBbPF0ZCIiZZKSLxERESkew4BV78LC4WDPgZBouOd9iGnh6chERMo0JV8iIiJSdJknYPZj8Of3uct1EqDTJAio5Nm4REQuA0q+REREpGh2/wyzHoL0A2DxgbYvQZOHwWTydGQiIpcFJV8iIiJyfg47LPs3/PgqGA6oXAvunZL7DC8RESkyJV8iIiJSuNT98NXDsOen3OUGvSDh3+Ab5Nm4REQuQ0q+REREpGBb58PsR+HMCfAOhLsmQIOeno5KROSypeRLREREXNmyYeGLsOqd3OWI6+HeFAir5dm4REQuc0q+RERE5B/Hd8AXfeHQxtzlZo9B62Tw8vVkVCIi5YKSLxEREcm1YSbMTYKc0+BfCTr/B+rc6emoRETKDSVfIiIiV7rs0zDvWdjwae5yjRa5D00OjfZsXCIi5YySLxERkSvZwQ3wZT84vh1MZmj1PLR8DswWT0cmIlLuKPkSERG5EhkGrH4PfhgG9hwIjoKuH0BMC09HJiJSbin5EhERudJknoBvHoet83KXr74z9/6ugEqejUtEpJxT8iUiInIl2f0zfNUf0vaDxQfajIamA8Bk8nRkIiLlnpIvERGRK4HDDsv+DT++CoYDKsVBtxSIbODpyERErhhKvkRERMq71P3w1cOw56fc5Qa9IOHf4Bvk2bhERK4wSr5ERETKs63zYfajcOYEeAfCXROgQU9PRyUickVS8iUiIlIe2bJh4Yuw6p3c5Yjr4d4UCKvl2bhERK5gSr5ERETKm+M74Iu+cGhj7nKzx6B1Mnj5ejIqEZErnpIvERGR8mTDTJibBDmnwb9S7hTyde70dFQiIoKSLxERkfIh+zTMexY2fJq7XKMF3PM+hEZ7Ni4REXFS8iUiInK5O7gBvuwHx7eDyQytnoeWz4HZ4unIRETkLEq+RERELleGAavfgx+GgT0HgqOg6wcQ08LTkYmISAGUfImIiFyOMk/AN4/D1nm5y1ffmXt/V0Alz8YlIiKFUvIlIiJyudn9M3zVH9L2g8UH2oyGpgPAZPJ0ZCIich5KvkRERC4XDjssew1+fAUMB1SKg24pENnA05GJiEgRmD0dwKRJk4iJicHPz4+mTZuyevXqQutarVZGjRpFXFwcfn5+NGjQgPnz5+ert3//fh544AEqV66Mv78/9evX55dffnGuNwyDESNGEBkZib+/P61bt2bbtm1uOT8REZESkXYAPr4blr6cm3g1uA8G/KjES0TkMuLR5GvmzJkkJSXx4osv8uuvv9KgQQPatWvHkSNHCqw/bNgw3n33Xd566y02b97MI488QpcuXVi3bp2zzsmTJ2nRogXe3t58//33bN68mfHjx1OxYkVnnXHjxvHmm28yefJkVq1aRWBgIO3atSMrK8vt5ywiIlJsW+fDOy1gz0/gHQidJ0OXyeAb7OnIRESkGDyafE2YMIH+/fuTmJhIvXr1mDx5MgEBAUyZMqXA+lOnTmXo0KEkJCRQs2ZNHn30URISEhg/fryzzquvvkq1atVISUmhSZMmxMbG0rZtW+Li4oDcXq+JEycybNgwOnXqxPXXX88nn3zCgQMHmD17dmmctoiISNHYsmH+EPi0B5w5ARHXw4BlEH+fpyMTEZGL4LF7vnJycli7di1DhgxxlpnNZlq3bs2KFSsK3Ob/27vzuKrq9A/gn3PZLqAXZJEdVFTcATUJFzAlwS2zRUf95ZLjaEpqlppNKlKpLS7VVFaT2qRlNaVZkzqJOyCNJi5pJIiasmgomwvr8/vjxNUrmxr3XsDP+/U6L7nf8z3nPufhC5zHc+73FBUVQavVGrTZ2tpi3759+tebN29GZGQkHn/8cezevRteXl6YOnUqJk2aBABIT09HVlYWIiIi9Ns4ODggJCQEiYmJ+Mtf/lLtexcVFelf5+fnA1BvhSwpKbnDo69bZaVlsFQsYQH1eS4WsIClYomy0jKzx9aYVOSSOTUO5td4mFvjMlp+L6XBcuMkKFlHAABl901Geb8FgKUNcA99Lzl+jYv5NS7m17jqU35vNwZFRMTIsVQpIyMDXl5eSEhIQGhoqL59zpw52L17N5KSkiptM3r0aBw+fBibNm2Cv78/4uLiMGzYMJSVlekLo4ribNasWXj88cfxv//9DzNmzMCqVaswbtw4JCQkoFevXsjIyICHh4d+3yNGjICiKPj888+rjDcmJgaLFi2q1P7pp5/Czs7uT+WCiIjoZt6X4hH428ewLL+OYgt7HPKbhCyHruYOi4iIqnH16lWMHj0aeXl50Ol01fZrULMdvvnmm5g0aRLatWsHRVHg7++PCRMmGNymWF5eju7du2Px4sUAgODgYBw7dkxffN2tefPmYdasWfrX+fn58PHxwYABA2pMsClk52Uj5tsYuNq74n6b+7G/aD8uXrmImKExcHNwM2tsjUlJSQl++OEHPPjgg7CysjJ3OI0O82s8zK1x1Wl+iwthsXUuNGfU/wgs9w2FMux9dNV51kGkDRPHr3Exv8bF/BpXfcpvxV1xtTFb8eXi4gILCwtkZ2cbtGdnZ8Pd3b3KbVxdXbFp0yZcv34dOTk58PT0xPPPP49WrVrp+3h4eKBDhw4G27Vv3x5fffUVAOj3nZ2dbXDlKzs7G0FBQdXGa2NjAxsbm0rtVlZWZv9mW1haoFRKUYYyAEAZylAqpbCwtDB7bI1RffieN2bMr/Ewt8b1p/ObeRj495NATiqgaIDwudCEzYZGY1F3QTZgHL/GxfwaF/NrXPUhv7f7/mabcMPa2hrdunVDXFycvq28vBxxcXEGtyFWRavVwsvLC6Wlpfjqq68wbNgw/bpevXohJSXFoP+vv/4KPz8/AEDLli3h7u5u8L75+flISkqq9X2JiIjqnAiQ9D7wzwi18GrqCYz7Fuj7PMDCi4ioUTHrbYezZs3CuHHj0L17d/To0QMrV67ElStXMGHCBADA2LFj4eXlhSVLlgAAkpKScP78eQQFBeH8+fOIiYlBeXk55syZo9/nM888g549e2Lx4sUYMWIEfvzxR3zwwQf44IMPAACKomDmzJl4+eWX0aZNG7Rs2RLz58+Hp6cnHn74YZPngIiI7mFXLwHfTANSvldft40Chr0L2DubNy4iIjIKsxZfI0eOxMWLF7FgwQJkZWUhKCgIW7duhZub+jmls2fPQqO5cXHu+vXrePHFF3Hq1Ck0adIEgwYNwieffAJHR0d9n/vuuw8bN27EvHnzEBsbi5YtW2LlypUYM2aMvs+cOXNw5coV/O1vf0Nubi569+6NrVu3VppJkYiIyGhOxwNfTwLyzwMW1sCDLwEhkwFFMXdkRERkJGafcCM6OhrR0dFVrtu1a5fB6/DwcBw/frzWfQ4ZMgRDhgypdr2iKIiNjUVsbOwdxUpERPSnlZcBe94Adi8FpBxw8gceWw14Bpk7MiIiMjKzF19ERET3jPwM4KtJwJk/nk/Z5S/A4DcAm6bmjYuIiEyCxRcREZEppGwFNj0FXLsEWNkDg5cBQaPMHRUREZkQiy8iIiJjKi0CtscA+99VX7t3AR5bA7i0NmtYRERkeiy+iIiIjCUnDfj3BPUZXgAQ8hTw4CLAsvJzI4mIqPFj8UVERGQMhz8H/jMLKC4EbJupU8i3G2TuqIiIyIxYfBEREdWlokLg+9nA4U/V1369gEc+BBy8zBsXERGZHYsvIiKiupJ5RL3NMCcVUDRA+FwgbDagsTB3ZEREVA+w+CIiIvoz8jPgkn8cmr0ngPhlQFkx0NQTePRDoEVvc0dHRET1CIsvIiKiu/XTv2D57Qz0knIg7Y+2tlHq57vsnc0aGhER1T8svoiIiO5G1lFg83QokJsaFfX5XSy8iIioChpzB0BERNSgFGQB2/4OfNgfMCi8oL6+lG6OqIiIqAHglS8iIqLbkXceiF8JHPwYKCuquo9iATi1MmlYRETUcLD4IiIiqsnlM8C+FUDyenUyDQDw7gGEzwHyMyDfPQNFyiCKBZShKzmlPBERVYvFFxERUVVy0oC9y4EjG4DyUrXNrzcQPhtoGQ4oCgCgtEVfJG35DCEDR8HK2c+MARMRUX3H4ouIiOhmF38F9r4BHP0SkHK1rVVfIGwO0KJX5f46T+Q0bQ/oPE0aJhERNTwsvoiIiAAg+2dgz+vAz5ugn0ijzQD1Ick+PcwZGRERNRIsvoiI6N6WkawWXb98d6MtYLB6e6FnsNnCIiKixofFFxER3ZvOHQB2vwac3PZHgwJ0GKZe6XLvZNbQiIiocWLxRURE95YzCWrRdWqn+lrRAJ0eA/o8CzRvZ97YiIioUWPxRUREjZ8IkL5Hvb3w9F61TbEAAkcBfWYBzv7mjY+IiO4JLL6IiKjxEgFS44A9rwG/JaltGisgeAzQ+xmgWQuzhkdERPcWFl9ERNT4iAApW9QrXRk/qW0WNkC3cUCvGYCDt3njIyKiexKLLyIiajzKy4ETm4E9bwDZR9U2S1ug+5NAr+lAU3fzxkdERPc0Fl9ERNTwlZcBP29Ui66LJ9Q26ybAfX8FQqOBJq7mjY+IiAgsvoiIqCErKwWOfgHsXQbkpKptNg5AyGTg/qcAOyfzxkdERHQTFl9ERNTwlBYDhz9Ti67cM2qb1hEInQb0+Btg62jO6IiIiKrE4ouIiBqOkuvAoU+A+DeBvN/UNjsXoGe0eouhTVPzxkdERFQDFl9ERFT/FV8FfvpYLboKMtW2Jm7qzIXdxgPW9mYNj4iI6Haw+GpEHEoK4VJ8HA5KIS6YOxgiorpQVAgc+AhIeBu4clFt03kBvWYCXZ8ArGzNGh4REdGdYPHVSIQVnsb4s8nQQBAKBWudgswdEhHR3bueB/z4AZD4LnDtktrm6Av0ngUEjQYsbcwbHxER0V1g8dUIaAqyMP7SIWgqXkMw/tIhXMw7Dzh6mDU2IqI7cu0ysH8VkPSeWoABgFMroM9zQJcRgIWVeeMjIiL6E1h8NQL2hVn6wquCBkDzdUOBFn2AFr0Av16ARyBPXIiofrqSAyT+A/jxQ6C4QG1zCQDCngM6PgJY8M8VERE1fPxr1gg08ewCUTRQpFzfJgCUkqvAyW3qAgBW9oBPjz+Ksd6AV1feukNE5lWQDSS8BRxYDZRcVdvcOqlFV/thgObW/1oiIiJquFh8NQYOXlCGvgn5diYUKYMoFlAGLwc8A4EzCcDpeOBMPHA9Fzi1U10AwFILeN8H+PVUr4x53wdY25n1UIjoHpGfoc5ceHAtUHpdbfMIBMLmAAGDWHQREVGjxOKrseg6FqV+4Uja8hlCBo6ClbOf2u4ZrD50tLwcuHjiRiF2Jl6dOez0XnUBAI2VejXM74/bFH1D+MwcIqpbuWeBfSuAQ+uAsmK1zfs+tehq8yCgKOaNj4iIyIhYfDUmOk/kNG0P6Dwrr9NoALeO6hLyN0AE+P3kjULsdDxQkAH8lqQu+5YDigXg0UUtxFr0BnzvB2ybmf64iKjhy0lTf68c3gCUl6ptfr2AsNlAq74suoiI6J7A4utepSiAa1t16T5BLcYun/6jGEsATu8Dcs8AGYfUJfEfABT1sxh+PdXPjfn2BJq4mvtIiKg+u/grsHcZcPQLoOJzqS3DgfA56n/qEBER3UNYfJFKUQCnluoS/H9qW965G4XYmQQg5ySQfVRdfnxf7eMScGM2Rb9egI5T2xMRgOzjwJ7XgZ83Qp0CCEDrB9Wiy6eHWUMjIiIyFxZfVD0Hb/W5Ol1GqK8LsoGzN03gceE48HuKuhxYrfZp1vLGbIp+PYFmfuaLn4hML/MwsPs14JfvbrQFDFZnL/Tqar64iIiI6gEWX3T7mroBHYerCwBcvaReETuTAJzZB2QdBS6nq8uhdWofB58bsym26K0+LJWf7SBqfM4dBPa8Bvy69Y8GBejwkPqZLvfOZg2NiIiovqgXc/m+8847aNGiBbRaLUJCQvDjjz9W27ekpASxsbHw9/eHVqtFYGAgtm7datAnJiYGiqIYLO3atTPo07dv30p9pkyZYpTja7TsnID2Q4CoxcDkPcDc08DoL4FeM9TZyzSWQN5vwJHPgW+nA293BZa1A76coD5I9cIJdRZGImq4ziQCnwwH/tlPLbwUDdD5cWDqfmDEv1h4ERER3cTsV74+//xzzJo1C6tWrUJISAhWrlyJyMhIpKSkoHnz5pX6v/jii1i3bh0+/PBDtGvXDtu2bcPw4cORkJCA4OBgfb+OHTti+/bt+teWlpUPddKkSYiNjdW/trPjM67+FK0D0HaAugBAUSFw7scbzxo7fwAozAJ+/lpdAMDOGfANVa+K+fVUJ/TQWJjvGIiodiLqIyp2v3bjURWKBRD4F6D3LMCltXnjIyIiqqfMXnwtX74ckyZNwoQJEwAAq1atwn/+8x+sXr0azz//fKX+n3zyCf7+979j0KBBAICnnnoK27dvx7Jly7Bu3Tp9P0tLS7i7u9f43nZ2drX2oT/Bpgng309dAKDkulqAVUzi8duPwNUc9bMhFZ8PsXFQp7Sv+NyYRxfAwsp8x0BEN4gAaXHA7teB3/arbRorIGg00PsZdcIeIiIiqpZZi6/i4mIcPHgQ8+bN07dpNBpEREQgMTGxym2Kioqg1WoN2mxtbbFv3z6DtpMnT8LT0xNarRahoaFYsmQJfH19DfqsX78e69atg7u7O4YOHYr58+dXe/WrqKgIRUVF+tf5+fkA1NsgS0pKbv+gjagijvoST2UWgFeIuvR8BigrhpJ5GMrZBChnE6H8th9KUR5wcpu6ABAre4hPD4hPKMSvJ8QjGLC0MUv09T+/DRvzazx/OrciUE5ugyZ+OTQZP6lNFjYoD/o/lIc+rU7Oo75BXYTb4HDsGhfza1zMr3Exv8ZVn/J7uzEoIiJGjqVaGRkZ8PLyQkJCAkJDQ/Xtc+bMwe7du5GUlFRpm9GjR+Pw4cPYtGkT/P39ERcXh2HDhqGsrExfHG3ZsgWFhYUICAhAZmYmFi1ahPPnz+PYsWNo2rQpAOCDDz6An58fPD09ceTIEcydOxc9evTA119/XWWsMTExWLRoUaX2Tz/9lLcr1hFFyuBw7QycC1PgXPgLnAt/hXXZFYM+ZYoVLtm3Rk6TAOQ0aYfL9v4o05inGCNq9KQcHnkH0TZrMxyvnQEAlCrWOO3yANLcBuG6FR+6TkREBABXr17F6NGjkZeXB51OV22/Bld8Xbx4EZMmTcK3334LRVHg7++PiIgIrF69GteuXavyfXJzc+Hn54fly5dj4sSJVfbZsWMH+vfvj9TUVPj7+1daX9WVLx8fH/z+++81JtiUSkpK8MMPP+DBBx+ElVUjuFVPyoELJ6A5m6heHfstEcqVi4ZdNFYQz64Q31CIb0+I932ATVOjhNPo8lvPML/Gc8e5LS+DcuIbWMSvgHLxBAD1KnR59ydRHjIVsOfD1W/GsWtczK9xMb/GxfwaV33Kb35+PlxcXGotvsx626GLiwssLCyQnZ1t0J6dnV3tZ7FcXV2xadMmXL9+HTk5OfD09MTzzz+PVq1aVfs+jo6OaNu2LVJTU6vtExISAgDVFl82Njawsal8hcXKysrs3+xb1ceY7pp3kLr0fEr9vMnvJ9Vp7f+YxEMpyIByLgk4lwQkrFQ/9O8ReOPBz773A7Z1+7/zjSq/9RDzazy15rasFDj2b2DPG+pD1QHARgeETIZy/1RY2DmB0+FUj2PXuJhf42J+jYv5Na76kN/bfX+zFl/W1tbo1q0b4uLi8PDDDwMAysvLERcXh+jo6Bq31Wq18PLyQklJCb766iuMGDGi2r6FhYVIS0vDE088UW2f5ORkAICHh8cdHweZiKIArm3VpfuTajF2Of3GbIpn4oHcM0DGT+qS8DYARZ1BsUWvG88bs3cx95EQ1S+lxcCRDcDe5erPFABoHYH7pwIhkwFbR3NGR0RE1GiYfbbDWbNmYdy4cejevTt69OiBlStX4sqVK/rZD8eOHQsvLy8sWbIEAJCUlITz588jKCgI58+fR0xMDMrLyzFnzhz9Pp977jkMHToUfn5+yMjIwMKFC2FhYYFRo0YBANLS0vDpp59i0KBBcHZ2xpEjR/DMM88gLCwMXbp0MX0S6O4oivrQZqdWQPD/qW15527MpngmHshJBbKPqkvSKrWPS8CNK2N+vQAdC266R5UWAYc+AfatVJ/JB6iPfwiNBu77K6CtH7dUExERNRZmL75GjhyJixcvYsGCBcjKykJQUBC2bt0KNzc3AMDZs2eh0dx4FvT169fx4osv4tSpU2jSpAkGDRqETz75BI6Ojvo+586dw6hRo5CTkwNXV1f07t0b+/fvh6ur+jkFa2trbN++XV/o+fj44NFHH8WLL75o0mMnI3DwBrqMUBcAKMhWi7AzCeq/F44Dv6eoy4HVah+nVn9cFeutFmWOvtXvn6gxKLkGHPwYiF8JFGSqbU3cgJ7Tge4TAGt7s4ZHRETUWJm9+AKA6Ojoam8z3LVrl8Hr8PBwHD9+vMb9bdiwocb1Pj4+2L179x3FSA1UUzeg0yPqAgBXcoCziX8UZPFA1lHg0il1OfTHc+IcfP64KtZTffizUyv1Klt+BlwKjgP5QYCzn9kOqdFifo2nIrc5bYC07eotuVcuqOt0XkCvmUDXJwArW7OGSURE1NjVi+KLyGTsnYH2Q9QFAK7nAWf3q4XY6Xgg45B6+9WRDeoCAE3cAZ0nLDMOoRcE8var6mfOWvU122E0Oqd2wfLAaubXGG7OberSG+0OvkCfZ4CgMWZ7dh4REdG9hsUX3du0DkDbSHUBgKJC4NyPf0zgkQCcPwAUZgGFWVD+2ESBAAc+UheqM8yv8Si3/IsBLwMhUwALzrxFRERkSiy+iG5m0wTw76cugPrZmAOrgW0vVO7r2l4t3ujPuZ4H/PEsKQPM759XXW49glh4ERERmQGLL6KaWNkCHR4G/vui+tDnCooF8H9fAQ5eZgut0cg7D6zsxPwaQ3W5dar+uYhERERkPJrauxDd4xy8gKFvQhT18bKiWABDV7IwqCvMr/Ewt0RERPUKr3wR3Y6uY1HqF46kLZ8hZOAoWHE2vrrF/BoPc0tERFRv8MoX0e3SeSKnaXtA52nuSBon5td4mFsiIqJ6gcUXERERERGRCbD4IiIiIiIiMgEWX0RERERERCbA4ouIiIiIiMgEWHwRERERERGZAIsvIiIiIiIiE2DxRUREREREZAIsvoiIiIiIiEyAxRcREREREZEJsPgiIiIiIiIyARZfREREREREJsDii4iIiIiIyARYfBEREREREZkAiy8iIiIiIiITYPFFRERERERkApbmDqChEhEAQH5+vpkjuaGkpARXr15Ffn4+rKyszB1Oo8P8GhfzazzMrXExv8bF/BoX82tczK9x1af8VtQEFTVCdVh83aWCggIAgI+Pj5kjISIiIiKi+qCgoAAODg7VrlektvKMqlReXo6MjAw0bdoUiqKYOxwAasXt4+OD3377DTqdztzhNDrMr3Exv8bD3BoX82tczK9xMb/GxfwaV33Kr4igoKAAnp6e0Giq/2QXr3zdJY1GA29vb3OHUSWdTmf2AdiYMb/GxfwaD3NrXMyvcTG/xsX8Ghfza1z1Jb81XfGqwAk3iIiIiIiITIDFFxERERERkQmw+GpEbGxssHDhQtjY2Jg7lEaJ+TUu5td4mFvjYn6Ni/k1LubXuJhf42qI+eWEG0RERERERCbAK19EREREREQmwOKLiIiIiIjIBFh8ERERERERmQCLLyIiIiIiIhNg8VXPxcTEQFEUg6Vdu3b69devX8e0adPg7OyMJk2a4NFHH0V2drbBPs6ePYvBgwfDzs4OzZs3x+zZs1FaWmrqQ6mXWrRoUSm/iqJg2rRpAIC+fftWWjdlyhSDfTC/N+zZswdDhw6Fp6cnFEXBpk2bDNaLCBYsWAAPDw/Y2toiIiICJ0+eNOhz6dIljBkzBjqdDo6Ojpg4cSIKCwsN+hw5cgR9+vSBVquFj48PXnvtNWMfmtnVlNuSkhLMnTsXnTt3hr29PTw9PTF27FhkZGQY7KOq8b506VKDPvdiboHax+748eMr5S4qKsqgD8du9WrLb1W/hxVFweuvv67vw/FbtSVLluC+++5D06ZN0bx5czz88MNISUkx6FNX5wq7du1C165dYWNjg9atW2Pt2rXGPjyzqy2/ly5dwtNPP42AgADY2trC19cX06dPR15ensF+qhrfGzZsMOjD/FY9fuvqXKze5FeoXlu4cKF07NhRMjMz9cvFixf166dMmSI+Pj4SFxcnBw4ckPvvv1969uypX19aWiqdOnWSiIgIOXTokHz//ffi4uIi8+bNM8fh1DsXLlwwyO0PP/wgAGTnzp0iIhIeHi6TJk0y6JOXl6ffnvk19P3338vf//53+frrrwWAbNy40WD90qVLxcHBQTZt2iSHDx+Whx56SFq2bCnXrl3T94mKipLAwEDZv3+/7N27V1q3bi2jRo3Sr8/LyxM3NzcZM2aMHDt2TD777DOxtbWV999/31SHaRY15TY3N1ciIiLk888/l19++UUSExOlR48e0q1bN4N9+Pn5SWxsrMF4Liws1K+/V3MrUvvYHTdunERFRRnk7tKlSwZ9OHarV1t+b85rZmamrF69WhRFkbS0NH0fjt+qRUZGypo1a+TYsWOSnJwsgwYNEl9fX4Pc1MW5wqlTp8TOzk5mzZolx48fl7ffflssLCxk69atJj1eU6stv0ePHpVHHnlENm/eLKmpqRIXFydt2rSRRx991GA/AGTNmjUG4/fmv33Mb/Xjty7OxepTfll81XMLFy6UwMDAKtfl5uaKlZWVfPnll/q2EydOCABJTEwUEfUPnkajkaysLH2f9957T3Q6nRQVFRk19oZoxowZ4u/vL+Xl5SKi/sDPmDGj2v7Mb/VuPcEqLy8Xd3d3ef311/Vtubm5YmNjI5999pmIiBw/flwAyP/+9z99ny1btoiiKHL+/HkREXn33XelWbNmBvmdO3euBAQEGPmI6o+qTl5v9eOPPwoAOXPmjL7Nz89PVqxYUe02zK2quuJr2LBh1W7DsXv7bmf8Dhs2TPr162fQxvF7ey5cuCAAZPfu3SJSd+cKc+bMkY4dOxq818iRIyUyMtLYh1Sv3JrfqnzxxRdibW0tJSUl+rbaxj3zq6oqv3VxLlaf8svbDhuAkydPwtPTE61atcKYMWNw9uxZAMDBgwdRUlKCiIgIfd927drB19cXiYmJAIDExER07twZbm5u+j6RkZHIz8/Hzz//bNoDqeeKi4uxbt06PPnkk1AURd++fv16uLi4oFOnTpg3bx6uXr2qX8f83r709HRkZWUZjFcHBweEhIQYjFdHR0d0795d3yciIgIajQZJSUn6PmFhYbC2ttb3iYyMREpKCi5fvmyio6n/8vLyoCgKHB0dDdqXLl0KZ2dnBAcH4/XXXze4LYO5rdmuXbvQvHlzBAQE4KmnnkJOTo5+Hcdu3cnOzsZ//vMfTJw4sdI6jt/aVdzu5uTkBKDuzhUSExMN9lHRp2If94pb81tdH51OB0tLS4P2adOmwcXFBT169MDq1ashNz1ql/lVVZffP3suVp/ya1l7FzKnkJAQrF27FgEBAcjMzMSiRYvQp08fHDt2DFlZWbC2tq50cuXm5oasrCwAQFZWlsFgrFhfsY5u2LRpE3JzczF+/Hh92+jRo+Hn5wdPT08cOXIEc+fORUpKCr7++msAzO+dqMhHVfm6ebw2b97cYL2lpSWcnJwM+rRs2bLSPirWNWvWzCjxNyTXr1/H3LlzMWrUKOh0On379OnT0bVrVzg5OSEhIQHz5s1DZmYmli9fDoC5rUlUVBQeeeQRtGzZEmlpaXjhhRcwcOBAJCYmwsLCgmO3Dn388cdo2rQpHnnkEYN2jt/alZeXY+bMmejVqxc6deoEAHV2rlBdn/z8fFy7dg22trbGOKR6par83ur333/HSy+9hL/97W8G7bGxsejXrx/s7Ozw3//+F1OnTkVhYSGmT58OgPkFqs9vXZyL1af8sviq5wYOHKj/ukuXLggJCYGfnx+++OKLe+IH0ZQ++ugjDBw4EJ6envq2m395du7cGR4eHujfvz/S0tLg7+9vjjCJalRSUoIRI0ZARPDee+8ZrJs1a5b+6y5dusDa2hqTJ0/GkiVLYGNjY+pQG5S//OUv+q87d+6MLl26wN/fH7t27UL//v3NGFnjs3r1aowZMwZardagneO3dtOmTcOxY8ewb98+c4fSKNWW3/z8fAwePBgdOnRATEyMwbr58+frvw4ODsaVK1fw+uuv64svqj6/je1cjLcdNjCOjo5o27YtUlNT4e7ujuLiYuTm5hr0yc7Ohru7OwDA3d290oxGFa8r+hBw5swZbN++HX/9619r7BcSEgIASE1NBcD83omKfFSVr5vH64ULFwzWl5aW4tKlSxzTt6Gi8Dpz5gx++OEHg6teVQkJCUFpaSlOnz4NgLm9E61atYKLi4vB7wKO3T9v7969SElJqfV3McDxe6vo6Gh899132LlzJ7y9vfXtdXWuUF0fnU53T/xncHX5rVBQUICoqCg0bdoUGzduhJWVVY37CwkJwblz51BUVASA+a0tvze7m3Ox+pRfFl8NTGFhIdLS0uDh4YFu3brBysoKcXFx+vUpKSk4e/YsQkNDAQChoaE4evSowUlBxUlZhw4dTB5/fbVmzRo0b94cgwcPrrFfcnIyAMDDwwMA83snWrZsCXd3d4Pxmp+fj6SkJIPxmpubi4MHD+r77NixA+Xl5fpftqGhodizZw9KSkr0fX744QcEBATcE7cVVaei8Dp58iS2b98OZ2fnWrdJTk6GRqPR3y7H3N6+c+fOIScnx+B3Acfun/fRRx+hW7duCAwMrLUvx69KRBAdHY2NGzdix44dlW69rKtzhdDQUIN9VPSp2EdjVVt+AfVv2YABA2BtbY3NmzdXumpbleTkZDRr1kx/1Zb5rT6/t7qbc7F6lV+TT/FBd+TZZ5+VXbt2SXp6usTHx0tERIS4uLjIhQsXRESdPtbX11d27NghBw4ckNDQUAkNDdVvXzH95oABAyQ5OVm2bt0qrq6u9+xU6FUpKysTX19fmTt3rkF7amqqxMbGyoEDByQ9PV2++eYbadWqlYSFhen7ML+GCgoK5NChQ3Lo0CEBIMuXL5dDhw7pZ9xbunSpODo6yjfffCNHjhyRYcOGVTnVfHBwsCQlJcm+ffukTZs2BtN15+bmipubmzzxxBNy7Ngx2bBhg9jZ2TX66aRrym1xcbE89NBD4u3tLcnJyQbT8VbM9JSQkCArVqyQ5ORkSUtLk3Xr1omrq6uMHTtW/x73am5Fas5vQUGBPPfcc5KYmCjp6emyfft26dq1q7Rp00auX7+u3wfHbvVq+90gok4Vb2dnJ++9916l7Tl+q/fUU0+Jg4OD7Nq1y+Bn/+rVq/o+dXGuUDFV9+zZs+XEiRPyzjvv3BNTodeW37y8PAkJCZHOnTtLamqqQZ/S0lIREdm8ebN8+OGHcvToUTl58qS8++67YmdnJwsWLNC/D/NbdX7r6lysPuWXxVc9N3LkSPHw8BBra2vx8vKSkSNHSmpqqn79tWvXZOrUqdKsWTOxs7OT4cOHS2ZmpsE+Tp8+LQMHDhRbW1txcXGRZ5991mD603vdtm3bBICkpKQYtJ89e1bCwsLEyclJbGxspHXr1jJ79myDZ0uIML8327lzpwCotIwbN05E1Onm58+fL25ubmJjYyP9+/evlPecnBwZNWqUNGnSRHQ6nUyYMEEKCgoM+hw+fFh69+4tNjY24uXlJUuXLjXVIZpNTblNT0+vch1uembdwYMHJSQkRBwcHESr1Ur79u1l8eLFBsWDyL2ZW5Ga83v16lUZMGCAuLq6ipWVlfj5+cmkSZMMpjUW4ditSW2/G0RE3n//fbG1tZXc3NxK23P8Vq+6n/01a9bo+9TVucLOnTslKChIrK2tpVWrVgbv0VjVlt/qxjYASU9PFxH1sRNBQUHSpEkTsbe3l8DAQFm1apWUlZUZvBfzWzm/dXkuVl/yq4jcNM8lERERERERGQU/80VERERERGQCLL6IiIiIiIhMgMUXERERERGRCbD4IiIiIiIiMgEWX0RERERERCbA4ouIiIiIiMgEWHwRERERERGZAIsvIiIiIiIiE2DxRUREd+T06dNQFAXJycnmDkXvl19+wf333w+tVougoCBzh9NorV27Fo6OjuYOo0r1OTYiogosvoiIGpjx48dDURQsXbrUoH3Tpk1QFMVMUZnXwoULYW9vj5SUFMTFxVXZpyJvty6pqal1EkN9P/m/evUq5s2bB39/f2i1Wri6uiI8PBzffPON2WKq7zkjIqprluYOgIiI7pxWq8Wrr76KyZMno1mzZuYOp04UFxfD2tr6rrZNS0vD4MGD4efnV2O/qKgorFmzxqDN1dX1rt7TmEpKSmBlZVWn+5wyZQqSkpLw9ttvo0OHDsjJyUFCQgJycnLq9H2IiKh6vPJFRNQARUREwN3dHUuWLKm2T0xMTKVb8FauXIkWLVroX48fPx4PP/wwFi9eDDc3Nzg6OiI2NhalpaWYPXs2nJyc4O3tXalgAdRb/Xr27AmtVotOnTph9+7dBuuPHTuGgQMHokmTJnBzc8MTTzyB33//Xb++b9++iI6OxsyZM+Hi4oLIyMgqj6O8vByxsbHw9vaGjY0NgoKCsHXrVv16RVFw8OBBxMbGQlEUxMTEVJsTGxsbuLu7GywWFhYAgG+++QZdu3aFVqtFq1atsGjRIpSWluq3Xb58OTp37gx7e3v4+Phg6tSpKCwsBADs2rULEyZMQF5env6KWkUciqJg06ZNBnE4Ojpi7dq1AG7cxvn5558jPDwcWq0W69evBwD885//RPv27aHVatGuXTu8++67+n0UFxcjOjoaHh4e0Gq18PPzq3E8bN68GS+88AIGDRqEFi1aoFu3bnj66afx5JNP6vsUFRXhueeeg5eXF+zt7RESEoJdu3ZVu8/byVtubi4mT54MNzc3/Vj57rvvaszZ7cSxdu1a+Pr6ws7ODsOHD2cRSUQNAosvIqIGyMLCAosXL8bbb7+Nc+fO/al97dixAxkZGdizZw+WL1+OhQsXYsiQIWjWrBmSkpIwZcoUTJ48udL7zJ49G88++ywOHTqE0NBQDB06VH8CnJubi379+iE4OBgHDhzA1q1bkZ2djREjRhjs4+OPP4a1tTXi4+OxatWqKuN78803sWzZMrzxxhs4cuQIIiMj8dBDD+HkyZMAgMzMTHTs2BHPPvssMjMz8dxzz91xDvbu3YuxY8dixowZOH78ON5//32sXbsWr7zyir6PRqPBW2+9hZ9//hkff/wxduzYgTlz5gAAevbsiZUrV0Kn0yEzM/Ou4nj++ecxY8YMnDhxApGRkVi/fj0WLFiAV155BSdOnMDixYsxf/58fPzxxwCAt956C5s3b8YXX3yBlJQUrF+/3qCwvpW7uzu+//57FBQUVNsnOjoaiYmJ2LBhA44cOYLHH38cUVFR+lzfad7Ky8sxcOBAxMfHY926dTh+/DiWLl0KCwuLGnNWWxxJSUmYOHEioqOjkZycjAceeAAvv/zyHeWbiMgshIiIGpRx48bJsGHDRETk/vvvlyeffFJERDZu3Cg3/1pfuHChBAYGGmy7YsUK8fPzM9iXn5+flJWV6dsCAgKkT58++telpaVib28vn332mYiIpKenCwBZunSpvk9JSYl4e3vLq6++KiIiL730kgwYMMDgvX/77TcBICkpKSIiEh4eLsHBwbUer6enp7zyyisGbffdd59MnTpV/zowMFAWLlxY437GjRsnFhYWYm9vr18ee+wxERHp37+/LF682KD/J598Ih4eHtXu78svvxRnZ2f96zVr1oiDg0OlfgBk48aNBm0ODg6yZs0aEbmRz5UrVxr08ff3l08//dSg7aWXXpLQ0FAREXn66aelX79+Ul5eXuNxV9i9e7d4e3uLlZWVdO/eXWbOnCn79u3Trz9z5oxYWFjI+fPnDbbr37+/zJs3r8pjrC1v27ZtE41Go/+e36qqnN1OHKNGjZJBgwYZrB85cmSV+Sciqk/4mS8iogbs1VdfRb9+/e7qak+Fjh07QqO5cSOEm5sbOnXqpH9tYWEBZ2dnXLhwwWC70NBQ/deWlpbo3r07Tpw4AQA4fPgwdu7ciSZNmlR6v7S0NLRt2xYA0K1btxpjy8/PR0ZGBnr16mXQ3qtXLxw+fPg2j/CGBx54AO+9957+tb29vT7e+Ph4gytdZWVluH79Oq5evQo7Ozts374dS5YswS+//IL8/HyUlpYarP+zunfvrv/6ypUrSEtLw8SJEzFp0iR9e2lpKRwcHACot4w++OCDCAgIQFRUFIYMGYIBAwZUu/+wsDCcOnUK+/fvR0JCAuLi4vDmm29i0aJFmD9/Po4ePYqysjL996ZCUVERnJ2dq9xnbXlLTk6Gt7d3pX3W5HbiOHHiBIYPH26wPjQ01OB2VCKi+ojFFxFRAxYWFobIyEjMmzcP48ePN1in0WggIgZtJSUllfZx68QOiqJU2VZeXn7bcRUWFmLo0KF49dVXK63z8PDQf11R/JiKvb09WrduXam9sLAQixYtwiOPPFJpnVarxenTpzFkyBA89dRTeOWVV+Dk5IR9+/Zh4sSJKC4urrH4UhTltr4PN+ei4rNkH374IUJCQgz6VXxGrWvXrkhPT8eWLVuwfft2jBgxAhEREfj3v/9dbSxWVlbo06cP+vTpg7lz5+Lll19GbGws5s6di8LCQlhYWODgwYP696hQVRFdEWdNebO1ta02lurcTRxERA0Fiy8iogZu6dKlCAoKQkBAgEG7q6srsrKyICL6Kejr8tlc+/fvR1hYGAD1iszBgwcRHR0NQC0MvvrqK7Ro0QKWlnf/p0an08HT0xPx8fEIDw/Xt8fHx6NHjx5/7gBu0rVrV6SkpFRZmAHAwYMHUV5ejmXLlumvEn7xxRcGfaytrVFWVlZpW1dXV2RmZupfnzx5ElevXq0xHjc3N3h6euLUqVMYM2ZMtf10Oh1GjhyJkSNH4rHHHkNUVBQuXboEJyenGvdfoUOHDvoreMHBwSgrK8OFCxfQp0+f29q+trx16dIF586dw6+//lrl1a+qcnY7cbRv3x5JSUkGbfv377+tmImIzInFFxFRA9e5c2eMGTMGb731lkF73759cfHiRbz22mt47LHHsHXrVmzZsgU6na5O3vedd95BmzZt0L59e6xYsQKXL1/Wz5w3bdo0fPjhhxg1ahTmzJkDJycnpKamYsOGDfjnP/9Z6YpGTWbPno2FCxfC398fQUFBWLNmDZKTk/UzAtaFBQsWYMiQIfD19cVjjz0GjUaDw4cP49ixY3j55ZfRunVrlJSU4O2338bQoUOrnCCkRYsWKCwsRFxcHAIDA2FnZwc7Ozv069cP//jHPxAaGoqysjLMnTv3tqaRX7RoEaZPnw4HBwdERUWhqKgIBw4cwOXLlzFr1iwsX74cHh4eCA4OhkajwZdffgl3d/dqn5vVt29fjBo1Ct27d4ezszOOHz+OF154AQ888AB0Oh10Oh3GjBmDsWPHYtmyZQgODsbFixcRFxeHLl26YPDgwXect/DwcISFheHRRx/F8uXL0bp1a/zyyy9QFAVRUVFV5qxt27a1xjF9+nT06tULb7zxBoYNG4Zt27bxlkMiahjM/JkzIiK6QzdPuFEhPT1drK2t5dZf6++99574+PiIvb29jB07Vl555ZVKE27cuq/w8HCZMWOGQZufn5+sWLFC/14A5NNPP5UePXqItbW1dOjQQXbs2GGwza+//irDhw8XR0dHsbW1lXbt2snMmTP1E0RU9T5VKSsrk5iYGPHy8hIrKysJDAyULVu2GPS53Qk3bj3Wm23dulV69uwptra2otPppEePHvLBBx/o1y9fvlw8PDzE1tZWIiMj5V//+pcAkMuXL+v7TJkyRZydnQWAPp7z58/LgAEDxN7eXtq0aSPff/99lRNuHDp0qFJM69evl6CgILG2tpZmzZpJWFiYfP311yIi8sEHH0hQUJDY29uLTqeT/v37y08//VTt8S1evFhCQ0PFyclJtFqttGrVSqZPny6///67vk9xcbEsWLBAWrRoIVZWVuLh4SHDhw+XI0eOiEjVE2TUlrecnByZMGGCODs7i1arlU6dOsl3331XY85qi0NE5KOPPhJvb2+xtbWVoUOHyhtvvMEJN4io3lNEbrkRnYiIiIiIiOocn/NFRERERERkAiy+iIiIiIiITIDFFxERERERkQmw+CIiIiIiIjIBFl9EREREREQmwOKLiIiIiIjIBFh8ERERERERmQCLLyIiIiIiIhNg8UVERERERGQCLL6IiIiIiIhMgMUXERERERGRCfw/rMymXVqH7NMAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = MultinomialNB(alpha=0.01)\n",
"plot_accuracies(X_train, X_test, y_train, y_test, model, k_end=2500)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Accuracy: 0.9960648148148148\n",
"Test Accuracy: 0.95625\n",
"Difference: 0.03981481481481475\n",
"+---------------------------------+--------------------+--------------------+------------+-----------+\n",
"| Class | Precision | Recall | F1-score | Support |\n",
"|---------------------------------+--------------------+--------------------+------------+-----------|\n",
"| Acne | 1.0 | 1.0 | 1 | 21.0 |\n",
"| Arthritis | 0.9523809523809523 | 1.0 | 0.97561 | 20.0 |\n",
"| Bronchial Asthma | 1.0 | 0.9473684210526315 | 0.972973 | 19.0 |\n",
"| Cervical spondylosis | 1.0 | 1.0 | 1 | 21.0 |\n",
"| Chicken pox | 0.6363636363636364 | 0.9333333333333333 | 0.756757 | 15.0 |\n",
"| Common Cold | 0.9545454545454546 | 1.0 | 0.976744 | 21.0 |\n",
"| Dengue | 1.0 | 0.6363636363636364 | 0.777778 | 22.0 |\n",
"| Dimorphic Hemorrhoids | 1.0 | 1.0 | 1 | 19.0 |\n",
"| Fungal infection | 1.0 | 0.9615384615384616 | 0.980392 | 26.0 |\n",
"| Hypertension | 1.0 | 0.9444444444444444 | 0.971429 | 18.0 |\n",
"| Impetigo | 0.8846153846153846 | 1.0 | 0.938776 | 23.0 |\n",
"| Jaundice | 1.0 | 1.0 | 1 | 22.0 |\n",
"| Malaria | 1.0 | 1.0 | 1 | 17.0 |\n",
"| Migraine | 1.0 | 1.0 | 1 | 24.0 |\n",
"| Pneumonia | 0.9565217391304348 | 1.0 | 0.977778 | 22.0 |\n",
"| Psoriasis | 0.9333333333333333 | 0.8235294117647058 | 0.875 | 17.0 |\n",
"| Typhoid | 0.9 | 1.0 | 0.947368 | 18.0 |\n",
"| Varicose Veins | 0.9565217391304348 | 0.88 | 0.916667 | 25.0 |\n",
"| allergy | 1.0 | 1.0 | 1 | 15.0 |\n",
"| diabetes | 1.0 | 1.0 | 1 | 17.0 |\n",
"| drug reaction | 0.9375 | 0.9375 | 0.9375 | 16.0 |\n",
"| gastroesophageal reflux disease | 0.9545454545454546 | 1.0 | 0.976744 | 21.0 |\n",
"| peptic ulcer disease | 1.0 | 0.8888888888888888 | 0.941176 | 18.0 |\n",
"| urinary tract infection | 0.9583333333333334 | 1.0 | 0.978723 | 23.0 |\n",
"| accuracy | | | 0.95625 | |\n",
"| macro avg | 0.9593608761407674 | 0.9563736082244209 | 0.954226 | |\n",
"| weighted avg | 0.962697899172084 | 0.95625 | 0.955957 | |\n",
"+---------------------------------+--------------------+--------------------+------------+-----------+\n"
]
}
],
"source": [
"model = MultinomialNB(alpha=0.01)\n",
"evaluate_model(X_train, X_test, y_train, y_test, chi2, 1750, model) "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "NLP",
"language": "python",
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},
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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