sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as  pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk \n",
    "import re\n",
    "from sklearn.model_selection import train_test_split \n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from langdetect import detect\n",
    "\n",
    "nltk.download('all')\n",
    "\n",
    "DATASET_ENCODING = \"ISO-8859-1\" #To HANDLE DIFFERENT EMOJIS AND SYMBOLS \n",
    "DATASET_COLUMNS  = [\"sentiment\", \"ids\", \"date\", \"flag\", \"user\", \"text\"] #THE DATA SET I FOUND ON KAGGLE HADNT BEEN INDEXED(structured) PROPERLY \n",
    "data=pd.read_csv(\"training.1600000.processed.noemoticon.csv\", encoding=DATASET_ENCODING,names=DATASET_COLUMNS)\n",
    "train_data, test_data = train_test_split(data, test_size=0.30, random_state=42)\n",
    "train_data, val_data = train_test_split(train_data, test_size=0.15, random_state=42)\n",
    "# print(len(train_data))\n",
    "# print(len(test_data))\n",
    "# print(len(val_data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         sentiment         ids                          date      flag  \\\n",
      "487661           0  2182218195  Mon Jun 15 12:52:48 PDT 2009  NO_QUERY   \n",
      "476792           0  2177985387  Mon Jun 15 07:01:16 PDT 2009  NO_QUERY   \n",
      "59496            0  1686162333  Sun May 03 04:05:23 PDT 2009  NO_QUERY   \n",
      "910593           4  1751644678  Sat May 09 19:56:12 PDT 2009  NO_QUERY   \n",
      "1312434          4  2013655370  Tue Jun 02 23:13:20 PDT 2009  NO_QUERY   \n",
      "\n",
      "                    user                                               text  \\\n",
      "487661        LegalAdmin  Since I can't modify anything on Twitter til I...   \n",
      "476792         _Anniken_  Going to the stable soon... last jumping lesso...   \n",
      "59496           nitynola  @Giggles__Ox yeh they have been on, they showe...   \n",
      "910593   blackwhiteworld  @goodlemax can you send me two coldplay songs?...   \n",
      "1312434     enamoredsoul    @krist0ph3r does that satisfy as an answer!?!?    \n",
      "\n",
      "                                              cleaned_text  \n",
      "487661   since cant modify anything twitter til change ...  \n",
      "476792   going stable soon last jumping lesson actually...  \n",
      "59496    yeh showed clip toms acting skills hollyoaks a...  \n",
      "910593         send two coldplay songs clocks viva la vida  \n",
      "1312434                                     satisfy answer  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from langdetect import detect\n",
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    if not text:\n",
    "        return \"\"  \n",
    "    # Applying regex to remove URLs, hashtags, and mentions\n",
    "    text = re.sub(r\"http\\S+|www\\S+|https\\S+\", \"\", text)  \n",
    "    text = re.sub(r\"#\\S+\", \"\", text)  \n",
    "    text = re.sub(r\"@\\S+\", \"\", text)  \n",
    "\n",
    "    # Replacing emoticons \n",
    "    emoticons = {\":)\": \"positive\", \":D\": \"positive\", \":(\": \"negative\", \":/\": \"negative\"}\n",
    "    for emoticon, sentiment in emoticons.items():\n",
    "        text = text.replace(emoticon, sentiment)\n",
    "\n",
    "    # Removing punctuation and digits\n",
    "    text = re.sub(r\"[^\\w\\s]\", \"\", text) \n",
    "    text = re.sub(r\"\\d+\", \"\", text)  \n",
    "\n",
    "    # Tokenize and removing stopwords\n",
    "    stop_words = set(stopwords.words(\"english\"))\n",
    "    tokens = word_tokenize(text.lower())\n",
    "    filtered_tokens = [word for word in tokens if word not in stop_words]\n",
    "    \n",
    "    return \" \".join(filtered_tokens)\n",
    "\n",
    "# Check if text is in English using langdetect\n",
    "def is_english(text):\n",
    "    try:\n",
    "        return detect(text) == 'en'\n",
    "    except:\n",
    "        return False\n",
    "\n",
    "\n",
    "train_data[\"cleaned_text\"] = train_data[\"text\"].apply(preprocess_text)\n",
    "val_data[\"cleaned_text\"] = val_data[\"text\"].apply(preprocess_text)\n",
    "test_data[\"cleaned_text\"] = test_data[\"text\"].apply(preprocess_text)\n",
    "\n",
    "\n",
    "print(train_data.head(5))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Naive Bayes\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "nb_unigram = MultinomialNB()\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(train_data[\"cleaned_text\"])\n",
    "vectorizer.get_feature_names_out() #unigram \n",
    "\n",
    "vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2))\n",
    "X2 = vectorizer2.fit_transform(train_data[\"cleaned_text\"]) #bigram\n",
    "vectorizer2.get_feature_names_out()\n",
    "\n",
    "X_train_unigram, X_val_unigram, y_train_unigram, y_val_unigram = train_test_split(\n",
    "    X, train_data[\"sentiment\"], test_size=0.2, random_state=42)\n",
    "\n",
    "X_train_bigram, X_val_bigram, y_train_bigram, y_val_bigram = train_test_split(\n",
    "    X2, train_data[\"sentiment\"], test_size=0.2, random_state=42)\n",
    "\n",
    "nb_unigram.fit(X_train_unigram, y_train_unigram)\n",
    "y_pred_unigram = nb_unigram.predict(X_val_unigram)\n",
    "accuracy_unigram = accuracy_score(y_val_unigram, y_pred_unigram)\n",
    "\n",
    "nb_bigram = MultinomialNB()\n",
    "nb_bigram.fit(X_train_bigram, y_train_bigram)\n",
    "y_pred_bigram = nb_bigram.predict(X_val_bigram)\n",
    "accuracy_bigram = accuracy_score(y_val_bigram, y_pred_bigram)\n",
    "\n",
    "vectorizer3 = CountVectorizer(analyzer='word', ngram_range=(3, 3))\n",
    "X3 = vectorizer3.fit_transform(train_data[\"cleaned_text\"])\n",
    "X_train_trigram, X_val_trigram, y_train_trigram, y_val_trigram = train_test_split(\n",
    "    X3, train_data[\"sentiment\"], test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "nb_trigram = MultinomialNB()\n",
    "nb_trigram.fit(X_train_trigram, y_train_trigram)\n",
    "y_pred_trigram = nb_trigram.predict(X_val_trigram)\n",
    "\n",
    "accuracy_trigram = accuracy_score(y_val_trigram, y_pred_trigram)\n",
    "\n",
    "models = [\"Naive Bayes (Unigram)\", \"Naive Bayes (Bigram)\", \"Naive Bayes (Trigram)\"]\n",
    "scores = [accuracy_unigram, accuracy_bigram, accuracy_trigram]\n",
    "\n",
    "plt.bar(models, scores, color=[\"blue\", \"green\", \"red\"])\n",
    "plt.xlabel(\"Models\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Comparison of Unigram, Bigram, and Trigram Model Accuracies\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating for n-gram range: (1, 1)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'train_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 16\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEvaluating for n-gram range: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mngram_range\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     15\u001b[0m tfidf_vect \u001b[38;5;241m=\u001b[39m TfidfVectorizer(ngram_range\u001b[38;5;241m=\u001b[39mngram_range)\n\u001b[1;32m---> 16\u001b[0m X_tfidf \u001b[38;5;241m=\u001b[39m tfidf_vect\u001b[38;5;241m.\u001b[39mfit_transform(\u001b[43mtrain_data\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcleaned_text\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     19\u001b[0m X_train, X_val, y_train, y_val \u001b[38;5;241m=\u001b[39m train_test_split(\n\u001b[0;32m     20\u001b[0m     X_tfidf, train_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msentiment\u001b[39m\u001b[38;5;124m\"\u001b[39m], test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m\n\u001b[0;32m     21\u001b[0m )\n\u001b[0;32m     23\u001b[0m svm_model \u001b[38;5;241m=\u001b[39m SVC(kernel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m)  \n",
      "\u001b[1;31mNameError\u001b[0m: name 'train_data' is not defined"
     ]
    }
   ],
   "source": [
    "#USING SVM\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ngram_ranges = [(1, 1), (2, 2), (3, 3)]\n",
    "accuracies = []\n",
    "\n",
    "for ngram_range in ngram_ranges:\n",
    "    print(f\"Evaluating for n-gram range: {ngram_range}\")\n",
    "    \n",
    "    \n",
    "    tfidf_vect = TfidfVectorizer(ngram_range=ngram_range)\n",
    "    X_tfidf = tfidf_vect.fit_transform(train_data[\"cleaned_text\"])\n",
    "    \n",
    "   \n",
    "    X_train, X_val, y_train, y_val = train_test_split(\n",
    "        X_tfidf, train_data[\"sentiment\"], test_size=0.2, random_state=42\n",
    "    )\n",
    "    \n",
    "    svm_model = SVC(kernel='linear', verbose=False,max_iter=1000)  \n",
    "    svm_model.fit(X_train, y_train)\n",
    "    \n",
    "   \n",
    "    y_pred = svm_model.predict(X_val)\n",
    "    svm_accuracy = accuracy_score(y_val, y_pred,max_iter=1000)\n",
    "    accuracies.append(svm_accuracy)\n",
    "    \n",
    "    \n",
    "    print(f\"SVM Model Accuracy ({ngram_range[0]}-gram to {ngram_range[1]}-gram):\", svm_accuracy)\n",
    "    \n",
    "   \n",
    "    conf_matrix = confusion_matrix(y_val, y_pred)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=svm_model.classes_)\n",
    "    disp.plot(cmap=\"Blues\", values_format=\"d\")\n",
    "    plt.title(f\"Confusion Matrix for SVM Model ({ngram_range[0]}-gram to {ngram_range[1]}-gram)\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plt.bar([\"Unigram\", \"Bigram\", \"Trigram\"], accuracies, color=[\"blue\", \"green\", \"red\"])\n",
    "plt.ylim(0, 1) \n",
    "plt.title(\"SVM Model Accuracy Comparison (Unigram, Bigram, Trigram)\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"N-gram Type\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Arnav Kaul\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:304: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Model Accuracy (1-gram to 1-gram):\n",
      "  Train Accuracy: 0.5619327731092437\n",
      "  Test Accuracy: 0.5649041666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Arnav Kaul\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:304: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Model Accuracy (2-gram to 2-gram):\n",
      "  Train Accuracy: 0.5016964285714286\n",
      "  Test Accuracy: 0.5073291666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Arnav Kaul\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:304: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Model Accuracy (3-gram to 3-gram):\n",
      "  Train Accuracy: 0.49813025210084033\n",
      "  Test Accuracy: 0.5026583333333333\n"
     ]
    },
    {
     "data": {
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bNm2S9M/9ZNu2bbObhrt9+/aqV6+eunXrJn9/f3Xo0EELFy68LyHqwIEDWrVqlXx9fe1eISEhkv43Gcibb76p0qVL67nnnlOhQoXUtWtX230ed+v8+fNasWKFgoODdfDgQdurXr162rp1q37//XdJ0unTp5WUlHTHy0alf46lWZ+sKliwoFxcXNK17969W23atJG3t7e8vLzk6+trm3Aj7Tt06NAhSTKtqUmTJgoMDLSFxdTUVM2fP1+tWrUynV0wJSVFMTExatSokQ4fPmw7hrVr19bJkycVFxdn65uV4/Pvn5ujR49KksqUKZOub9myZW2fW61WjR8/XitXrpS/v78aNGigDz/8UImJibb+wcHBevHFFzVy5Ej5+PioVatWioqK0vXr1zNVWxpvb2/169dPy5cvz/AesZSUFCUmJtq9kpOTderUKV29ejXDWQdvNxNhRucR6Z/wV6dOHbm6uuqJJ56Qr6+vPv/88wzPI2l/GLm1fkkqXLhwhu1p55NixYopMjJSM2bMkI+Pj0JDQzVt2jTubwLuM4ITgCxzcXFRrVq1NGbMGH3++ee6ceOGFi1aZNfn5ZdfVlJSkr777jslJyfrm2++sd3DcCedOnXSggULNG/ePLVv3/6+zNr179Em6Z+b7iMjI9WgQQN9/fXX+uGHHxQbG6sKFSpkKpzcei9XVrVs2VJubm5auHChJGnhwoVycnKyTayQVvP69eu1evVqvfLKK9q1a5fat2+vJk2amE5AkVWpqalq0qSJYmNjM3yljXj4+fkpPj5ey5cv1/PPP6+1a9fqueeeu6fZ7xYtWqTr169rwoQJKlWqlO2V9tyv201wcC9uN/J0u+Oa0ffn/PnzCg4O1s6dOzVq1Ch9++23io2Ntd37ldWA6+zsrE6dOumbb77RtWvXtHbtWv3111+ZmvlwzZo1OnHihGJiYuyOYdofLe72GGa035nVr18//f777xo7dqxcXV01dOhQlStXzhZuLBaLFi9erE2bNqlXr146fvy4unbtqho1aujSpUtZ2lbfvn2VN2/eDEedjh07psDAQLvXxo0b72qfMjoe//d//6fnn39erq6u+uyzz7RixQrFxsaqU6dOdiPKaW533rhd+63rmDBhgnbt2qV3331XV69eVZ8+fVShQgX9+eefd7U/AMwxOQSAe5J2mdSJEyfs2p9//nl5enpq3rx5yp07t86dO3fHy/TSdOrUScOGDdOJEyfSPW/oflq8eLEaNWqkmTNn2rWfP3/+riYQyAp3d3e1aNFCixYt0sSJE7VgwQLVr18/3YQUTk5Oaty4sRo3bqyJEydqzJgxeu+997R27VrbaFB2KFGihC5dupSpdbq4uKhly5Zq2bKlUlNT9eabb+qLL77Q0KFDVbJkySxfDhcdHa2KFStmOG32F198oXnz5mnkyJHy9fWVl5eXfvvtN9N9MeuTL18+Sf/8t75V2ohJZqxbt05///23lixZogYNGtjaDx8+nK4eSfrtt99Mj29YWJgmTJigb7/9VitXrpSvr69CQ0NNa4mOjpafn5+mTZuW7rMlS5Zo6dKlmj59uvLkyZOp43M7RYsWlSTt378/3cjw/v37bZ+nKVGihPr376/+/fvrwIEDqlq1qiZMmKCvv/7a1qdOnTqqU6eOPvjgA82bN0+dO3dWTEyMunXrlum60kadRowYkS7EBwQEpLu0tEqVKvLy8pKrq6sOHjyYbn0Ztd3ON998I1dXV/3www+yWq229qioqEyvIysqVaqkSpUqaciQIdq4caPq1aun6dOn3/Y5egDuDSNOADJl7dq1Gf7FdMWKFZLSX66TJ08etWnTRitWrNDnn38ud3d3tWrVynQ7JUqU0OTJkzV27Fg9+eST2VN8Jjg7O6fbv0WLFmVq2ufs0L59e/3111+aMWOGdu7caXeZniSdPXs23TJVq1aVJLvLmfbt26eEhIR7qqVdu3batGmTfvjhh3SfnT9/Xjdv3pT0z+xyt3JycrI9SyutJnd3d9tyZo4dO6b169erXbt2atu2bbpXRESEDh48qF9++UVOTk5q3bq1vv32W23dujXdutL+W7744ovauXOnli5dets+aWEm7d4t6Z/Rpi+//NK05jRpIwS3foeSk5P12Wef2fWrXr26ihUrpsmTJ6c7Jv/+/qU9m2zGjBn65ptv1KFDB+XKdee/d169elVLlixRixYtMjyGvXr10sWLF233JWbm+NxOzZo15efnp+nTp9t9B1euXKm9e/faZpC7cuWK7ZLdNCVKlJCnp6dtuXPnzqXbXkbf78xKu4csbYbONK6urgoJCbF75cuXT87OzgoJCdGyZcv0119/2fofPHhQK1euzPR2nZ2dZbFY7EYrjxw5omXLlmV5H+4kKSnJ9nOYplKlSnJycrqr4wUgcxhxApApvXv31pUrV9SmTRuVLVtWycnJ2rhxoxYsWKCgoKAMb5Z/+eWX9f/+3//TDz/8oM6dO9t+iTbTt2/f7C7fVIsWLTRq1ChFREToqaee0q+//qro6OjbPmMpuzVr1kyenp4aMGCAnJ2d000AMGrUKK1fv17NmzdX0aJFderUKX322WcqVKiQnn76aVu/cuXKKTg4OFPPSrqdt99+W8uXL1eLFi3UpUsX1ahRQ5cvX9avv/6qxYsX68iRI/Lx8VG3bt109uxZPfPMMypUqJCOHj2qqVOnqmrVqrYpx6tWrSpnZ2eNHz9eFy5ckNVq1TPPPGP37K808+bNk2EYev755297jHLlyqXo6GjVrl1bY8aM0Y8//qjg4GC9/vrrKleunE6cOKFFixbpp59+Ut68efX2229r8eLFeumll2yXfp09e1bLly/X9OnTVaVKFVWoUEF16tTR4MGDdfbsWT3xxBOKiYlJ94vpnTz11FPKly+fwsPD1adPH1ksFs2dOzddGHByctLnn3+uli1bqmrVqoqIiFBgYKD27dun3bt3pwurYWFhGjBggCRl6jK95cuX6+LFi7c9hnXq1JGvr6+io6PVvn37TB2f28mdO7fGjx+viIgIBQcHq2PHjrbpyIOCgvTWW29Jkn7//Xc1btxY7dq1U/ny5ZUrVy4tXbpUJ0+eVIcOHSRJc+bM0WeffaY2bdqoRIkSunjxor766it5eXmpWbNmpvv9b97e3urbt2+WJokYMWKEfvzxR9WrV089evRQSkqKPv30U1WsWFHx8fGZWkfz5s01ceJENW3aVJ06ddKpU6c0bdo0lSxZUrt27cryftzOmjVr1KtXL7300ksqXbq0bt68qblz52Z47gCQjRwylx+Ah87KlSuNrl27GmXLljU8PDwMFxcXo2TJkkbv3r2NkydPZrjMzZs3jcDAQEOSsWLFigz73Dod+Z0oG6cjz2gK32vXrhn9+/c3AgMDjTx58hj16tUzNm3aZAQHB9tN1X276cjd3d1vu2+Z1blzZ0OSERISku6zuLg4o1WrVkaBAgUMFxcXo0CBAkbHjh3TTRkuya7ezPj3dOSGYRgXL140Bg8ebJQsWdJwcXExfHx8jKeeesr4+OOPbVPQL1682Hj22WcNPz8/w8XFxShSpIjRvXt348SJE3br+uqrr4zixYsbzs7Od5yavFKlSkaRIkXuWGvDhg0NPz8/48aNG4ZhGMbRo0eNsLAww9fX17BarUbx4sWNnj17GtevX7ct8/fffxu9evUyChYsaLi4uBiFChUywsPDjTNnztj6HDp0yAgJCTGsVqvh7+9vvPvuu0ZsbGyG05GnTX39bxs2bDDq1Klj5MmTxyhQoIBtyv6M9vmnn34ymjRpYnh6ehru7u5G5cqVjalTp6Zb54kTJwxnZ2ejdOnSdzwuaVq2bGm4uroaly9fvm2fLl26GLlz57btv9nxudPPjWEYxoIFC4xq1aoZVqvVeOKJJ4zOnTsbf/75p+3zM2fOGD179jTKli1ruLu7G97e3kbt2rWNhQsX2vps377d6Nixo1GkSBHDarUafn5+RosWLYytW7ea7vPt/pucO3fO8Pb2zvR05Ibxz89ZtWrVDBcXF6NEiRLGjBkzjP79+xuurq52/e50Ppo5c6ZRqlQpw2q1GmXLljWioqIyPBdktI7bTZ/+7/8Gf/zxh9G1a1ejRIkShqurq/HEE08YjRo1MlavXp2p/QRwdyyGYTIWDwAAHOLMmTMKDAzUsGHDNHToUEeX81hq3bq1du/erQMHDji6FAAOxj1OAADkULNnz1ZKSopeeeUVR5fyWLh69ard+wMHDmjFihVq2LChYwoCkKMw4gQAQA6zZs0a7dmzR0OHDlWjRo20ZMkSR5f0WAgMDFSXLl1UvHhxHT16VJ9//rmuX7+uHTt2qFSpUo4uD4CDEZwAAMhhGjZsaJte+uuvv1bBggUdXdJjISIiQmvXrlViYqKsVqvq1q2rMWPGqHr16o4uDUAO4NDgtH79en300Ufatm2bTpw4oaVLl6p169Z3XGbdunWKjIzU7t27VbhwYQ0ZMkRdunR5IPUCAAAAeDw59B6ny5cvq0qVKhk+pC8jhw8fVvPmzdWoUSPFx8erX79+6tatW4bPGgEAAACA7JJjLtWzWCymI04DBw7U999/b/eU8w4dOuj8+fNatWrVA6gSAAAAwOPooXoA7qZNmxQSEmLXFhoaqn79+t12mevXr9s9RTs1NVVnz55V/vz5ZbFY7lepAAAAAHI4wzB08eJFFShQQE5Od74Y76EKTomJifL397dr8/f3V1JSkq5evao8efKkW2bs2LFZenI4AAAAgMfLsWPHVKhQoTv2eaiC090YPHiwIiMjbe8vXLigIkWK6NixY/Ly8nJgZQAAAAAcKSkpSYULF5anp6dp34cqOAUEBOjkyZN2bSdPnpSXl1eGo02SZLVaZbVa07V7eXkRnAAAAABk6hYeh86ql1V169ZVXFycXVtsbKzq1q3roIoAAAAAPA4cGpwuXbqk+Ph4xcfHS/pnuvH4+HglJCRI+ucyu7CwMFv/N954Q3/88Yfeeecd7du3T5999pkWLlyot956yxHlAwAAAHhMODQ4bd26VdWqVVO1atUkSZGRkapWrZqGDRsmSTpx4oQtRElSsWLF9P333ys2NlZVqlTRhAkTNGPGDIWGhjqkfgAAAACPhxzzHKcHJSkpSd7e3rpw4QL3OAEAAOQwhmHo5s2bSklJcXQpeETkzp1bzs7OGX6WlWzwUE0OAQAAgEdXcnKyTpw4oStXrji6FDxCLBaLChUqJA8Pj3taD8EJAAAADpeamqrDhw/L2dlZBQoUkIuLS6ZmOgPuxDAMnT59Wn/++adKlSp125GnzCA4AQAAwOGSk5OVmpqqwoULy83NzdHl4BHi6+urI0eO6MaNG/cUnB6q6cgBAADwaHNy4tdTZK/sGrnkmwkAAAAAJghOAAAAAGCCe5wAAACQo43bceaBbWtQNZ8Htq3bCQoKUr9+/dSvXz9Hl4JbMOIEAAAA3AWLxXLH14gRI+5qvVu2bNHrr7+eLTXOnz9fzs7O6tmzZ7as73FGcAIAAADuwokTJ2yvyZMny8vLy65twIABtr5pD/bNDF9f32ybWXDmzJl65513NH/+fF27di1b1nm3kpOTHbr9e0VwAgAAAO5CQECA7eXt7S2LxWJ7v2/fPnl6emrlypWqUaOGrFarfvrpJx06dEitWrWSv7+/PDw8VKtWLa1evdpuvUFBQZo8ebLtvcVi0YwZM9SmTRu5ubmpVKlSWr58uWl9hw8f1saNGzVo0CCVLl1aS5YsSddn1qxZqlChgqxWqwIDA9WrVy/bZ+fPn1f37t3l7+8vV1dXVaxYUd99950kacSIEapatarduiZPnqygoCDb+y5duqh169b64IMPVKBAAZUpU0aSNHfuXNWsWVOenp4KCAhQp06ddOrUKbt17d69Wy1atJCXl5c8PT1Vv359HTp0SOvXr1fu3LmVmJho179fv36qX7++6TG5FwQnAAAA4D4ZNGiQxo0bp71796py5cq6dOmSmjVrpri4OO3YsUNNmzZVy5YtlZCQcMf1jBw5Uu3atdOuXbvUrFkzde7cWWfPnr3jMlFRUWrevLm8vb318ssva+bMmXaff/755+rZs6def/11/frrr1q+fLlKliwp6Z8HEj/33HPasGGDvv76a+3Zs0fjxo3L8nOQ4uLitH//fsXGxtpC140bNzR69Gjt3LlTy5Yt05EjR9SlSxfbMsePH1eDBg1ktVq1Zs0abdu2TV27dtXNmzfVoEEDFS9eXHPnzrX1v3HjhqKjo9W1a9cs1ZZVTA4BAAAA3CejRo1SkyZNbO+feOIJValSxfZ+9OjRWrp0qZYvX2432vNvXbp0UceOHSVJY8aM0ZQpU7R582Y1bdo0w/6pqamaPXu2pk6dKknq0KGD+vfvr8OHD6tYsWKSpPfff1/9+/dX3759bcvVqlVLkrR69Wpt3rxZe/fuVenSpSVJxYsXz/L+u7u7a8aMGXJxcbG13RpwihcvrilTpqhWrVq6dOmSPDw8NG3aNHl7eysmJka5c+eWJFsNkvTqq68qKipKb7/9tiTp22+/1bVr19SuXbss15cVjDgBAAAA90nNmjXt3l+6dEkDBgxQuXLllDdvXnl4eGjv3r2mI06VK1e2/dvd3V1eXl7pLm+7VWxsrC5fvqxmzZpJknx8fNSkSRPNmjVLknTq1Cn99ddfaty4cYbLx8fHq1ChQnaB5W5UqlTJLjRJ0rZt29SyZUsVKVJEnp6eCg4OliTbMYiPj1f9+vVtoenfunTpooMHD+rnn3+WJM2ePVvt2rWTu7v7PdVqhhEnAAAA4D759y/zAwYMUGxsrD7++GOVLFlSefLkUdu2bU0nTvh3iLBYLEpNTb1t/5kzZ+rs2bPKkyePrS01NVW7du3SyJEj7dozYva5k5OTDMOwa7tx40a6fv/e/8uXLys0NFShoaGKjo6Wr6+vEhISFBoaajsGZtv28/NTy5YtFRUVpWLFimnlypVat27dHZfJDgQnAAAA4AHZsGGDunTpojZt2kj6ZwTqyJEj2bqNv//+W//5z38UExOjChUq2NpTUlL09NNP68cff1TTpk0VFBSkuLg4NWrUKN06KleurD///FO///57hqNOvr6+SkxMlGEYslgskv4ZKTKzb98+/f333xo3bpwKFy4sSdq6dWu6bc+ZM0c3bty47ahTt27d1LFjRxUqVEglSpRQvXr1TLd9r7hUDwAAAHhASpUqpSVLlig+Pl47d+5Up06d7jhydDfmzp2r/Pnzq127dqpYsaLtVaVKFTVr1sw2ScSIESM0YcIETZkyRQcOHND27dtt90QFBwerQYMGevHFFxUbG6vDhw9r5cqVWrVqlSSpYcOGOn36tD788EMdOnRI06ZN08qVK01rK1KkiFxcXDR16lT98ccfWr58uUaPHm3Xp1evXkpKSlKHDh20detWHThwQHPnztX+/fttfUJDQ+Xl5aX3339fERER2XXo7ogRJwAAAORog6r5OLqEbDNx4kR17dpVTz31lHx8fDRw4EAlJSVl6zZmzZqlNm3a2EaCbvXiiy/qlVde0ZkzZxQeHq5r165p0qRJGjBggHx8fNS2bVtb32+++UYDBgxQx44ddfnyZZUsWVLjxo2TJJUrV06fffaZxowZo9GjR+vFF1/UgAED9OWXX96xNl9fX82ePVvvvvuupkyZourVq+vjjz/W888/b+uTP39+rVmzRm+//baCg4Pl7OysqlWr2o0qOTk5qUuXLhozZozCwsLu9ZBlisX498WJj7ikpCR5e3vrwoUL8vLycnQ5AAAAkHTt2jXbjG+urq6OLgcPgVdffVWnT582fabVnb5bWckGjDgBAAAAeGhcuHBBv/76q+bNm5epBwFnF4ITAAAAgIdGq1attHnzZr3xxht2z8i63whOAAAAAB4aD2Lq8Ywwqx4AAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJpiMHAABAzjbP8uC21cl4cNvCQ4URJwAAAOAuWCyWO75GjBhxT+tetmxZpvt3795dzs7OWrRo0V1vE3fGiBMAAABwF06cOGH794IFCzRs2DDt37/f1ubh4fFA6rhy5YpiYmL0zjvvaNasWXrppZceyHZvJzk5WS4uLg6t4X5gxAkAAAC4CwEBAbaXt7e3LBaLXVtMTIzKlSsnV1dXlS1bVp999plt2eTkZPXq1UuBgYFydXVV0aJFNXbsWElSUFCQJKlNmzayWCy297ezaNEilS9fXoMGDdL69et17Ngxu8+vX7+ugQMHqnDhwrJarSpZsqRmzpxp+3z37t1q0aKFvLy85Onpqfr16+vQoUOSpIYNG6pfv35262vdurW6dOliex8UFKTRo0crLCxMXl5eev311yVJAwcOVOnSpeXm5qbixYtr6NChunHjht26vv32W9WqVUuurq7y8fFRmzZtJEmjRo1SxYoV0+1r1apVNXTo0Dsej/uF4AQAAABks+joaA0bNkwffPCB9u7dqzFjxmjo0KGaM2eOJGnKlClavny5Fi5cqP379ys6OtoWkLZs2SJJioqK0okTJ2zvb2fmzJl6+eWX5e3treeee06zZ8+2+zwsLEzz58/XlClTtHfvXn3xxRe20bDjx4+rQYMGslqtWrNmjbZt26auXbvq5s2bWdrfjz/+WFWqVNGOHTtswcbT01OzZ8/Wnj179Mknn+irr77SpEmTbMt8//33atOmjZo1a6YdO3YoLi5OTz75pCSpa9eu2rt3r92+79ixQ7t27VJERESWassuXKoHAAAAZLPhw4drwoQJeuGFFyRJxYoV0549e/TFF18oPDxcCQkJKlWqlJ5++mlZLBYVLVrUtqyvr68kKW/evAoICLjjdg4cOKCff/5ZS5YskSS9/PLLioyM1JAhQ2SxWPT7779r4cKFio2NVUhIiCSpePHituWnTZsmb29vxcTEKHfu3JKk0qVLZ3l/n3nmGfXv39+ubciQIbZ/BwUFacCAAbZLCiXpgw8+UIcOHTRy5EhbvypVqkiSChUqpNDQUEVFRalWrVqS/gmSwcHBdvU/SIw4AQAAANno8uXLOnTokF599VV5eHjYXu+//77tErguXbooPj5eZcqUUZ8+ffTjjz/e1bZmzZql0NBQ+fj4SJKaNWumCxcuaM2aNZKk+Ph4OTs7Kzg4OMPl4+PjVb9+fVtouls1a9ZM17ZgwQLVq1dPAQEB8vDw0JAhQ5SQkGC37caNG992na+99prmz5+va9euKTk5WfPmzVPXrl3vqc57wYgTAAAAkI0uXbokSfrqq69Uu3Ztu8+cnZ0lSdWrV9fhw4e1cuVKrV69Wu3atVNISIgWL16c6e2kpKRozpw5SkxMVK5cuezaZ82apcaNGytPnjx3XIfZ505OTjIM+yna/32fkiS5u7vbvd+0aZM6d+6skSNHKjQ01DaqNWHChExvu2XLlrJarVq6dKlcXFx048YNtW3b9o7L3E8EJwAAACAb+fv7q0CBAvrjjz/UuXPn2/bz8vJS+/bt1b59e7Vt21ZNmzbV2bNn9cQTTyh37txKSUm543ZWrFihixcvaseOHbZAJkm//fabIiIidP78eVWqVEmpqan673//a7tU71aVK1fWnDlzdOPGjQxHnXx9fe1mD0xJSdFvv/2mRo0a3bG2jRs3qmjRonrvvfdsbUePHk237bi4uNves5QrVy6Fh4crKipKLi4u6tChg2nYup8ITgAAAEA2GzlypPr06SNvb281bdpU169f19atW3Xu3DlFRkZq4sSJCgwMVLVq1eTk5KRFixYpICBAefPmlfTPPUFxcXGqV6+erFar8uXLl24bM2fOVPPmzW33BaUpX7683nrrLUVHR6tnz54KDw9X165dNWXKFFWpUkVHjx7VqVOn1K5dO/Xq1UtTp05Vhw4dNHjwYHl7e+vnn3/Wk08+qTJlyuiZZ55RZGSkvv/+e5UoUUITJ07U+fPnTfe/VKlSSkhIUExMjGrVqqXvv/9eS5cuteszfPhwNW7cWCVKlFCHDh108+ZNrVixQgMHDrT16datm8qVKydJ2rBhQxb/K2Qz4zFz4cIFQ5Jx4cIFR5cCAACA/9/Vq1eNPXv2GFevXnV0KXclKirK8Pb2tmuLjo42qlatari4uBj58uUzGjRoYCxZssQwDMP48ssvjapVqxru7u6Gl5eX0bhxY2P79u22ZZcvX26ULFnSyJUrl1G0aNF020tMTDRy5cplLFy4MMN6evToYVSrVs0wjH+O7VtvvWUEBgYaLi4uRsmSJY1Zs2bZ+u7cudN49tlnDTc3N8PT09OoX7++cejQIcMwDCM5Odno0aOH8cQTTxh+fn7G2LFjjVatWhnh4eG25YsWLWpMmjQpXQ1vv/22kT9/fsPDw8No3769MWnSpHTH6JtvvrEdIx8fH+OFF15It5769esbFSpUyHA/M+NO362sZAOLYfzrosVHXFJSkry9vXXhwgV5eXk5uhwAAABIunbtmg4fPqxixYrJ1dXV0eUghzAMQ6VKldKbb76pyMjIu1rHnb5bWckGXKoHAAAAIMc5ffq0YmJilJiY6LBnN92K4AQAAAAgx/Hz85OPj4++/PLLDO/xetAITgAAAABynJx2RxEPwAUAAAAAEwQnAAAA5Bg5bZQBD7/s+k4RnAAAAOBwaQ9fvXLlioMrwaMmOTlZkuweEnw3uMcJAAAADufs7Ky8efPq1KlTkiQ3NzdZLBYHV4WHXWpqqk6fPi03NzflynVv0YfgBAAAgBwhICBAkmzhCcgOTk5OKlKkyD0HcYITAAAAcgSLxaLAwED5+fnpxo0bji4HjwgXFxc5Od37HUoEJwAAAOQozs7O93w/CpDdmBwCAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADAhMOD07Rp0xQUFCRXV1fVrl1bmzdvvmP/yZMnq0yZMsqTJ48KFy6st956S9euXXtA1QIAAAB4HDk0OC1YsECRkZEaPny4tm/fripVqig0NFSnTp3KsP+8efM0aNAgDR8+XHv37tXMmTO1YMECvfvuuw+4cgAAAACPE4cGp4kTJ+q1115TRESEypcvr+nTp8vNzU2zZs3KsP/GjRtVr149derUSUFBQXr22WfVsWNH01EqAAAAALgXDgtOycnJ2rZtm0JCQv5XjJOTQkJCtGnTpgyXeeqpp7Rt2zZbUPrjjz+0YsUKNWvW7LbbuX79upKSkuxeAAAAAJAVuRy14TNnziglJUX+/v527f7+/tq3b1+Gy3Tq1ElnzpzR008/LcMwdPPmTb3xxht3vFRv7NixGjlyZLbWDgAAAODx4vDJIbJi3bp1GjNmjD777DNt375dS5Ys0ffff6/Ro0ffdpnBgwfrwoULttexY8ceYMUAAAAAHgUOG3Hy8fGRs7OzTp48add+8uRJBQQEZLjM0KFD9corr6hbt26SpEqVKuny5ct6/fXX9d5778nJKX0OtFqtslqt2b8DAAAAAB4bDhtxcnFxUY0aNRQXF2drS01NVVxcnOrWrZvhMleuXEkXjpydnSVJhmHcv2IBAAAAPNYcNuIkSZGRkQoPD1fNmjX15JNPavLkybp8+bIiIiIkSWFhYSpYsKDGjh0rSWrZsqUmTpyoatWqqXbt2jp48KCGDh2qli1b2gIUAAAAAGQ3hwan9u3b6/Tp0xo2bJgSExNVtWpVrVq1yjZhREJCgt0I05AhQ2SxWDRkyBAdP35cvr6+atmypT744ANH7QIAAACAx4DFeMyucUtKSpK3t7cuXLggLy8vR5cDAAAAwEGykg0eqln1AAAAAMARCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYMLhwWnatGkKCgqSq6urateurc2bN9+x//nz59WzZ08FBgbKarWqdOnSWrFixQOqFgAAAMDjKJcjN75gwQJFRkZq+vTpql27tiZPnqzQ0FDt379ffn5+6fonJyerSZMm8vPz0+LFi1WwYEEdPXpUefPmffDFAwAAAHhsWAzDMBy18dq1a6tWrVr69NNPJUmpqakqXLiwevfurUGDBqXrP336dH300Ufat2+fcufOfVfbTEpKkre3ty5cuCAvL697qh8AAADAwysr2cBhl+olJydr27ZtCgkJ+V8xTk4KCQnRpk2bMlxm+fLlqlu3rnr27Cl/f39VrFhRY8aMUUpKym23c/36dSUlJdm9AAAAACArHBaczpw5o5SUFPn7+9u1+/v7KzExMcNl/vjjDy1evFgpKSlasWKFhg4dqgkTJuj999+/7XbGjh0rb29v26tw4cLZuh8AAAAAHn0OnxwiK1JTU+Xn56cvv/xSNWrUUPv27fXee+9p+vTpt11m8ODBunDhgu117NixB1gxAAAAgEeBwyaH8PHxkbOzs06ePGnXfvLkSQUEBGS4TGBgoHLnzi1nZ2dbW7ly5ZSYmKjk5GS5uLikW8ZqtcpqtWZv8QAAAAAeKw4bcXJxcVGNGjUUFxdna0tNTVVcXJzq1q2b4TL16tXTwYMHlZqaamv7/fffFRgYmGFoAgAAAIDs4NBL9SIjI/XVV19pzpw52rt3r3r06KHLly8rIiJCkhQWFqbBgwfb+vfo0UNnz55V37599fvvv+v777/XmDFj1LNnT0ftAgAAAIDHgEOf49S+fXudPn1aw4YNU2JioqpWrapVq1bZJoxISEiQk9P/sl3hwoX1ww8/6K233lLlypVVsGBB9e3bVwMHDnTULgAAAAB4DDj0OU6OwHOcAAAAAEgPyXOcAAAAAOBhQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwkcvRBeARNs/i6AqQFZ0MR1cAAACQY2U5OAUFBalr167q0qWLihQpcj9qeuyM23HG0SXcF4McXQAAAACQTbJ8qV6/fv20ZMkSFS9eXE2aNFFMTIyuX79+P2oDAAAAgBzhroJTfHy8Nm/erHLlyql3794KDAxUr169tH379vtRIwAAAAA41F1PDlG9enVNmTJFf/31l4YPH64ZM2aoVq1aqlq1qmbNmiXD4H4JAAAAAI+Gu54c4saNG1q6dKmioqIUGxurOnXq6NVXX9Wff/6pd999V6tXr9a8efOys1YAAAAAcIgsB6ft27crKipK8+fPl5OTk8LCwjRp0iSVLVvW1qdNmzaqVatWthYKAAAAAI6S5eBUq1YtNWnSRJ9//rlat26t3Llzp+tTrFgxdejQIVsKBAAAAABHy3Jw+uOPP1S0aNE79nF3d1dUVNRdFwUAAAAAOUmWJ4c4deqUfvnll3Ttv/zyi7Zu3ZotRQEAAABATpLl4NSzZ08dO3YsXfvx48fVs2fPbCkKAAAAAHKSLAenPXv2qHr16unaq1Wrpj179mRLUQAAAACQk2Q5OFmtVp08eTJd+4kTJ5Qr113Pbg4AAAAAOVaWg9Ozzz6rwYMH68KFC7a28+fP691331WTJk2ytTgAAAAAyAmyPET08ccfq0GDBipatKiqVasmSYqPj5e/v7/mzp2b7QUCAAAAgKNlOTgVLFhQu3btUnR0tHbu3Kk8efIoIiJCHTt2zPCZTgAAAADwsLurm5Lc3d31+uuvZ3ctAAAAAJAj3fVsDnv27FFCQoKSk5Pt2p9//vl7LgoAAAAAcpIsB6c//vhDbdq00a+//iqLxSLDMCRJFotFkpSSkpK9FQIAAACAg2V5Vr2+ffuqWLFiOnXqlNzc3LR7926tX79eNWvW1Lp16+5DiQAAAADgWFkecdq0aZPWrFkjHx8fOTk5ycnJSU8//bTGjh2rPn36aMeOHfejTgAAAABwmCyPOKWkpMjT01OS5OPjo7/++kuSVLRoUe3fvz97qwMAAACAHCDLI04VK1bUzp07VaxYMdWuXVsffvihXFxc9OWXX6p48eL3o0YAAAAAcKgsB6chQ4bo8uXLkqRRo0apRYsWql+/vvLnz68FCxZke4EAAAAA4GhZDk6hoaG2f5csWVL79u3T2bNnlS9fPtvMegAAAADwKMnSPU43btxQrly59Ntvv9m1P/HEE4QmAAAAAI+sLI045c6dW0WKFOFZTQCA+2cef4h7qHQyHF0BADwQWZ5V77333tO7776rs2fP3o96AAAAACDHyfI9Tp9++qkOHjyoAgUKqGjRonJ3d7f7fPv27dlWHAAAAJApjFY/XB7C0eosB6fWrVvfhzIAAFkxbscZR5dw3wxydAEAAGQgy8Fp+PDh96MOAAAAAMixshycAAAA8HBitBq4e1kOTk5OTnecepwZ9wAAAAA8arIcnJYuXWr3/saNG9qxY4fmzJmjkSNHZlthAAAAAJBTZDk4tWrVKl1b27ZtVaFCBS1YsECvvvpqthQGAAAAADlFlp/jdDt16tRRXFxcdq0OAAAAAHKMbAlOV69e1ZQpU1SwYMHsWB0AAAAA5ChZvlQvX758dpNDGIahixcvys3NTV9//XW2FgcAAAAAOUGWg9OkSZPsgpOTk5N8fX1Vu3Zt5cuXL1uLAwAAAICcIMvBqUuXLvehDAAAAADIubJ8j1NUVJQWLVqUrn3RokWaM2dOthQFAAAAADlJloPT2LFj5ePjk67dz89PY8aMyZaiAAAAACAnyXJwSkhIULFixdK1Fy1aVAkJCdlSFAAAAADkJFkOTn5+ftq1a1e69p07dyp//vzZUhQAAAAA5CRZDk4dO3ZUnz59tHbtWqWkpCglJUVr1qxR37591aFDh/tRIwAAAAA4VJZn1Rs9erSOHDmixo0bK1eufxZPTU1VWFgY9zgBAAAAeCRlOTi5uLhowYIFev/99xUfH688efKoUqVKKlq06P2oDwAAAAAcLsvBKU2pUqVUqlSp7KwFAAAAAHKkLN/j9OKLL2r8+PHp2j/88EO99NJL2VIUAAAAAOQkWQ5O69evV7NmzdK1P/fcc1q/fn22FAUAAAAAOUmWg9OlS5fk4uKSrj137txKSkrKlqIAAAAAICfJcnCqVKmSFixYkK49JiZG5cuXz5aiAAAAACAnyfLkEEOHDtULL7ygQ4cO6ZlnnpEkxcXFad68eVq8eHG2FwgAAAAAjpbl4NSyZUstW7ZMY8aM0eLFi5UnTx5VqVJFa9as0RNPPHE/agQAAAAAh7qr6cibN2+u5s2bS5KSkpI0f/58DRgwQNu2bVNKSkq2FggAAAAAjpble5zSrF+/XuHh4SpQoIAmTJigZ555Rj///HN21gYAAAAAOUKWRpwSExM1e/ZszZw5U0lJSWrXrp2uX7+uZcuWMTEEAAAAgEdWpkecWrZsqTJlymjXrl2aPHmy/vrrL02dOvV+1gYAAAAAOUKmR5xWrlypPn36qEePHipVqtT9rAkAAAAAcpRMjzj99NNPunjxomrUqKHatWvr008/1ZkzZ+5nbQAAAACQI2Q6ONWpU0dfffWVTpw4oe7duysmJkYFChRQamqqYmNjdfHixftZJwAAAAA4TJZn1XN3d1fXrl31008/6ddff1X//v01btw4+fn56fnnn78fNQIAAACAQ931dOSSVKZMGX344Yf6888/NX/+/OyqCQAAAABylHsKTmmcnZ3VunVrLV++PDtWBwAAAAA5SrYEJwAAAAB4lBGcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMBEjghO06ZNU1BQkFxdXVW7dm1t3rw5U8vFxMTIYrGodevW97dAAAAAAI81hwenBQsWKDIyUsOHD9f27dtVpUoVhYaG6tSpU3dc7siRIxowYIDq16//gCoFAAAA8LhyeHCaOHGiXnvtNUVERKh8+fKaPn263NzcNGvWrNsuk5KSos6dO2vkyJEqXrz4A6wWAAAAwOPIocEpOTlZ27ZtU0hIiK3NyclJISEh2rRp022XGzVqlPz8/PTqq6+abuP69etKSkqyewEAAABAVjg0OJ05c0YpKSny9/e3a/f391diYmKGy/z000+aOXOmvvrqq0xtY+zYsfL29ra9ChcufM91AwAAAHi8OPxSvay4ePGiXnnlFX311Vfy8fHJ1DKDBw/WhQsXbK9jx47d5yoBAAAAPGpyOXLjPj4+cnZ21smTJ+3aT548qYCAgHT9Dx06pCNHjqhly5a2ttTUVElSrly5tH//fpUoUcJuGavVKqvVeh+qBwAAAPC4cOiIk4uLi2rUqKG4uDhbW2pqquLi4lS3bt10/cuWLatff/1V8fHxttfzzz+vRo0aKT4+nsvwAAAAANwXDh1xkqTIyEiFh4erZs2aevLJJzV58mRdvnxZERERkqSwsDAVLFhQY8eOlaurqypWrGi3fN68eSUpXTsAAAAAZBeHB6f27dvr9OnTGjZsmBITE1W1alWtWrXKNmFEQkKCnJweqluxAAAAADxiHB6cJKlXr17q1atXhp+tW7fujsvOnj07+wsCAAAAgFswlAMAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJnJEcJo2bZqCgoLk6uqq2rVra/Pmzbft+9VXX6l+/frKly+f8uXLp5CQkDv2BwAAAIB75fDgtGDBAkVGRmr48OHavn27qlSpotDQUJ06dSrD/uvWrVPHjh21du1abdq0SYULF9azzz6r48ePP+DKAQAAADwuHB6cJk6cqNdee00REREqX768pk+fLjc3N82aNSvD/tHR0XrzzTdVtWpVlS1bVjNmzFBqaqri4uIecOUAAAAAHhcODU7Jycnatm2bQkJCbG1OTk4KCQnRpk2bMrWOK1eu6MaNG3riiScy/Pz69etKSkqyewEAAABAVjg0OJ05c0YpKSny9/e3a/f391diYmKm1jFw4EAVKFDALnzdauzYsfL29ra9ChcufM91AwAAAHi8OPxSvXsxbtw4xcTEaOnSpXJ1dc2wz+DBg3XhwgXb69ixYw+4SgAAAAAPu1yO3LiPj4+cnZ118uRJu/aTJ08qICDgjst+/PHHGjdunFavXq3KlSvftp/VapXVas2WegEAAAA8nhw64uTi4qIaNWrYTeyQNtFD3bp1b7vchx9+qNGjR2vVqlWqWbPmgygVAAAAwGPMoSNOkhQZGanw8HDVrFlTTz75pCZPnqzLly8rIiJCkhQWFqaCBQtq7NixkqTx48dr2LBhmjdvnoKCgmz3Qnl4eMjDw8Nh+wEAAADg0eXw4NS+fXudPn1aw4YNU2JioqpWrapVq1bZJoxISEiQk9P/BsY+//xzJScnq23btnbrGT58uEaMGPEgSwcAAADwmHB4cJKkXr16qVevXhl+tm7dOrv3R44cuf8FAQAAAMAtHupZ9QAAAADgQSA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAICJHBGcpk2bpqCgILm6uqp27dravHnzHfsvWrRIZcuWlaurqypVqqQVK1Y8oEoBAAAAPI4cHpwWLFigyMhIDR8+XNu3b1eVKlUUGhqqU6dOZdh/48aN6tixo1599VXt2LFDrVu3VuvWrfXbb7894MoBAAAAPC4cHpwmTpyo1157TRERESpfvrymT58uNzc3zZo1K8P+n3zyiZo2baq3335b5cqV0+jRo1W9enV9+umnD7hyAAAAAI+LXI7ceHJysrZt26bBgwfb2pycnBQSEqJNmzZluMymTZsUGRlp1xYaGqply5Zl2P/69eu6fv267f2FCxckSUlJSfdYffa5dumio0u4L5KuOLoCZEkO+pmAuUf1vCFx7njocO54qHDuQI6RQ84daZnAMAzTvg4NTmfOnFFKSor8/f3t2v39/bVv374Ml0lMTMywf2JiYob9x44dq5EjR6ZrL1y48F1WjcxKf9SRo73m7egKAEmcOx46nDuQQ3DueMjksHPHxYsX5e1955ocGpwehMGDB9uNUKWmpurs2bPKnz+/LBaLAyt7tCUlJalw4cI6duyYvLy8HF0OgIcE5w4Ad4NzB+6WYRi6ePGiChQoYNrXocHJx8dHzs7OOnnypF37yZMnFRAQkOEyAQEBWepvtVpltVrt2vLmzXv3RSNLvLy8OIEByDLOHQDuBucO3A2zkaY0Dp0cwsXFRTVq1FBcXJytLTU1VXFxcapbt26Gy9StW9euvyTFxsbetj8AAAAA3CuHX6oXGRmp8PBw1axZU08++aQmT56sy5cvKyIiQpIUFhamggULauzYsZKkvn37Kjg4WBMmTFDz5s0VExOjrVu36ssvv3TkbgAAAAB4hDk8OLVv316nT5/WsGHDlJiYqKpVq2rVqlW2CSASEhLk5PS/gbGnnnpK8+bN05AhQ/Tuu++qVKlSWrZsmSpWrOioXUAGrFarhg8fnu4ySQC4E84dAO4G5w48CBYjM3PvAQAAAMBjzOEPwAUAAACAnI7gBAAAAAAmCE4AAAAAYILghCwJCgrS5MmTHV0GgBzsyJEjslgsio+Pd3QpAB5CI0aMUNWqVR1dBpAOwekx0bBhQ/Xr1y9d++zZs7P0QOAtW7bo9ddfz77CADx0unTpIovFYnvlz59fTZs21a5duyRJhQsX1okTJ5jtFIDduSKj14gRI9ItM2DAgHTP7ARyAoITssTX11dubm73tI4bN25kUzUAHKVp06Y6ceKETpw4obi4OOXKlUstWrSQJDk7OysgIEC5ct39Ey9SUlKUmpqaXeUCcJC088SJEyc0efJkeXl52bUNGDDA1tcwDN28eVMeHh7Knz//PW03OTn5XksH0iE4waZLly5q3bq1Pv74YwUGBip//vzq2bOnXdD596V6+/bt09NPPy1XV1eVL19eq1evlsVi0bJlyyT975KdBQsWKDg4WK6uroqOjtbff/+tjh07qmDBgnJzc1OlSpU0f/58u3oaNmyo3r17q1+/fsqXL5/8/f311Vdf2R6Q7OnpqZIlS2rlypUP4vAAuIXValVAQIACAgJUtWpVDRo0SMeOHdPp06czvFRv+fLlKlWqlFxdXdWoUSPNmTNHFotF58+fl/S/0e/ly5erfPnyslqtSkhI0JYtW9SkSRP5+PjI29tbwcHB2r59u10tFotFX3zxhVq0aCE3NzeVK1dOmzZt0sGDB9WwYUO5u7vrqaee0qFDhx7gEQIgyXaeCAgIkLe3tywWi+39vn375OnpqZUrV6pGjRqyWq366aef0l2qd/PmTfXp00d58+ZV/vz5NXDgQIWHh6t169a2Pg0bNlSvXr3Ur18/+fj4KDQ0VJI0ceJEVapUSe7u7ipcuLDefPNNXbp0ybZc2rnnu+++U5kyZeTm5qa2bdvqypUrmjNnjoKCgpQvXz716dNHKSkpD+qwIYciOMHO2rVrdejQIa1du1Zz5szR7NmzNXv27Az7pqSkqHXr1nJzc9Mvv/yiL7/8Uu+9916GfQcNGqS+fftq7969Cg0N1bVr11SjRg19//33+u233/T666/rlVde0ebNm+2WmzNnjnx8fLR582b17t1bPXr00EsvvaSnnnpK27dv17PPPqtXXnlFV65cye5DASCTLl26pK+//lolS5bM8K/Ehw8fVtu2bdW6dWvt3LlT3bt3z/BcceXKFY0fP14zZszQ7t275efnp4sXLyo8PFw//fSTfv75Z5UqVUrNmjXTxYsX7ZYdPXq0wsLCFB8fr7Jly6pTp07q3r27Bg8erK1bt8owDPXq1eu+HQMAd2/QoEEaN26c9u7dq8qVK6f7fPz48YqOjlZUVJQ2bNigpKQk2x9obzVnzhy5uLhow4YNmj59uiTJyclJU6ZM0e7duzVnzhytWbNG77zzjt1yV65c0ZQpUxQTE6NVq1Zp3bp1atOmjVasWKEVK1Zo7ty5+uKLL7R48eL7sv94iBh4LAQHBxt9+/ZN1x4VFWV4e3sbhmEY4eHhRtGiRY2bN2/aPn/ppZeM9u3b294XLVrUmDRpkmEYhrFy5UojV65cxokTJ2yfx8bGGpKMpUuXGoZhGIcPHzYkGZMnTzatsXnz5kb//v3tan766adt72/evGm4u7sbr7zyiq3txIkThiRj06ZNpusHkD3Cw8MNZ2dnw93d3XB3dzckGYGBgca2bdsMw/jfz/2OHTsMwzCMgQMHGhUrVrRbx3vvvWdIMs6dO2cYxj/nIklGfHz8HbedkpJieHp6Gt9++62tTZIxZMgQ2/tNmzYZkoyZM2fa2ubPn2+4urrey24DuEe3/s5hGIaxdu1aQ5KxbNkyu37Dhw83qlSpYnvv7+9vfPTRR7b3N2/eNIoUKWK0atXK1hYcHGxUq1bNtIZFixYZ+fPnt6tJknHw4EFbW/fu3Q03Nzfj4sWLtrbQ0FCje/fumdlNPMIYcYKdChUqyNnZ2fY+MDBQp06dyrDv/v37VbhwYQUEBNjannzyyQz71qxZ0+59SkqKRo8erUqVKumJJ56Qh4eHfvjhByUkJNj1u/UvT87OzsqfP78qVapka/P395ek29YI4P5o1KiR4uPjFR8fr82bNys0NFTPPfecjh49mq7v/v37VatWLbu2jM4VLi4u6f7afPLkSb322msqVaqUvL295eXlpUuXLt3xXJF2Xvj3ueLatWtKSkrK+s4CuK/+/TvCrS5cuKCTJ0/anTOcnZ1Vo0aNdH0zalu9erUaN26sggULytPTU6+88or+/vtvuytV3NzcVKJECdt7f39/BQUFycPDw66N3zVAcHpMeHl56cKFC+naz58/L29vb9v73Llz231usViy5QZtd3d3u/cfffSRPvnkEw0cOFBr165VfHy8QkND093MmVE9t7ZZLBZJ4iZy4AFzd3dXyZIlVbJkSdWqVUszZszQ5cuX9dVXX931OvPkyWP7mU4THh6u+Ph4ffLJJ9q4caPi4+OVP3/+O54r0tbBuQJ4OPz7d4TsWs+RI0fUokULVa5cWd988422bdumadOmSbKfPMLsd420Ns4fIDg9JsqUKZPuhmpJ2r59u0qXLn3X6zx27JhOnjxpa9uyZUumlt2wYYNatWqll19+WVWqVFHx4sX1+++/31UdABzPYrHIyclJV69eTfdZmTJltHXrVru2rJwr+vTpo2bNmqlChQqyWq06c+ZMttQMIOfz9vaWv7+/3TkjJSUlw99p/m3btm1KTU3VhAkTVKdOHZUuXVp//fXX/SwXjziC02OiR48e+v3339WnTx/t2rVL+/fv18SJEzV//nz179//rtbZpEkTlShRQuHh4dq1a5c2bNigIUOGSFK6vxr/W6lSpRQbG6uNGzdq79696t69u10AA5CzXb9+XYmJiUpMTNTevXvVu3dvXbp0SS1btkzXt3v37tq3b58GDhyo33//XQsXLrRNOpOZc8XcuXO1d+9e/fLLL+rcubPy5MlzP3YJQA7Vu3dvjR07Vv/5z3+0f/9+9e3bV+fOnTM9f5QsWVI3btzQ1KlT9ccff2ju3Lm2SSOAu0FwekwUL15c69ev1759+xQSEqLatWtr4cKFWrRokZo2bXpX63R2dtayZct06dIl1apVS926dbPNlOXq6nrHZYcMGaLq1asrNDRUDRs2VEBAgN20ogBytlWrVikwMFCBgYGqXbu2tmzZokWLFqlhw4bp+hYrVkyLFy/WkiVLVLlyZX3++ee2c4XVar3jdmbOnKlz586pevXqeuWVV9SnTx/5+fndj10CkEMNHDhQHTt2VFhYmOrWrSsPDw+Fhoaa/q5RpUoVTZw4UePHj1fFihUVHR2tsWPHPqCq8SiyGIZhOLoIPDo2bNigp59+WgcPHrS70RIAbvXBBx9o+vTpOnbsmKNLAfCQSU1NVbly5dSuXTuNHj3a0eXgMXL3j3UHJC1dulQeHh4qVaqUDh48qL59+6pevXqEJgB2PvvsM9WqVUv58+fXhg0b9NFHH/FcJQCZcvToUf34448KDg7W9evX9emnn+rw4cPq1KmTo0vDY4bghHty8eJFDRw4UAkJCfLx8VFISIgmTJjg6LIA5DAHDhzQ+++/r7Nnz6pIkSLq37+/Bg8e7OiyADwEnJycNHv2bA0YMECGYahixYpavXq1ypUr5+jS8JjhUj0AAAAAMMHkEAAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgCAbNWlSxdZLBaNGzfOrn3ZsmWyWCwOqip7pO3b7V5BQUGOLhEAcJ8QnAAA2c7V1VXjx4/XuXPn7vu2kpOT7/s20nzyySc6ceKE7SVJUVFRtvdbtmx5YLUAAB4sghMAINuFhIQoICBAY8eOzfKy77//vvz8/OTp6alu3bpp0KBBqlq1qu3zLl26qHXr1vrggw9UoEABlSlTRpI0d+5c1axZU56engoICFCnTp106tQp23Lr1q2TxWLRDz/8oGrVqilPnjx65plndOrUKa1cuVLlypWTl5eXOnXqpCtXrmRYm7e3twICAmwvScqbN68CAgL07rvvKiIiwq7/jRs35Ofnp5kzZ0qSGjZsqF69eqlXr17y9vaWj4+Phg4dqlufRX/9+nUNGDBABQsWlLu7u2rXrq1169Zl+TgCALIXwQkAkO2cnZ01ZswYTZ06VX/++Weml4uOjtYHH3yg8ePHa9u2bSpSpIg+//zzdP3i4uK0f/9+xcbG6rvvvpP0T0gZPXq0du7cqWXLlunIkSPq0qVLumVHjBihTz/9VBs3btSxY8fUrl07TZ48WfPmzdP333+vH3/8UVOnTs3yPnfr1k2rVq2yjURJ0nfffacrV66offv2trY5c+YoV65c2rx5sz755BNNnDhRM2bMsH3eq1cvbdq0STExMdq1a5deeuklNW3aVAcOHMhyTQCAbGQAAJCNwsPDjVatWhmGYRh16tQxunbtahiGYSxdutQw+7+d2rVrGz179rRrq1evnlGlShW79fv7+xvXr1+/47q2bNliSDIuXrxoGIZhrF271pBkrF692tZn7NixhiTj0KFDtrbu3bsboaGhpvtpGIYhyVi6dKntffny5Y3x48fb3rds2dLo0qWL7X1wcLBRrlw5IzU11dY2cOBAo1y5coZhGMbRo0cNZ2dn4/jx43bbady4sTF48OBM1QQAuD8YcQIA3Dfjx4/XnDlztHfv3nSfeXh42F5vvPGGJGn//v168skn7fr9+70kVapUSS4uLnZt27ZtU8uWLVWkSBF5enoqODhYkpSQkGDXr3LlyrZ/+/v7y83NTcWLF7dru/USv6zo1q2boqKiJEknT57UypUr1bVrV7s+derUsZsko27dujpw4IBSUlL066+/KiUlRaVLl7Y7Pv/973916NChu6oJAJA9cjm6AADAo6tBgwYKDQ3V4MGD0102Fx8fb/u3l5dXltbr7u5u9/7y5csKDQ1VaGiooqOj5evrq4SEBIWGhqabPCJ37ty2f1ssFrv3aW2pqalZqidNWFiYBg0apE2bNmnjxo0qVqyY6tevn+nlL126JGdnZ23btk3Ozs52n3l4eNxVTQCA7EFwAgDcV+PGjVPVqlVtkzikKVmyZLq+ZcqU0ZYtWxQWFmZry8xMdfv27dPff/+tcePGqXDhwpKkrVu33mPlWZc/f361bt1aUVFR2rRpU7rJIiTpl19+sXv/888/q1SpUnJ2dla1atWUkpKiU6dOZSlwAQDuP4ITAOC+qlSpkjp37qwpU6aY9u3du7dee+011axZU0899ZQWLFigXbt22V1Kl5EiRYrIxcVFU6dO1RtvvKHffvtNo0ePzq5dyJJu3bqpRYsWSklJUXh4eLrPExISFBkZqe7du2v79u2aOnWqJkyYIEkqXbq0OnfurLCwME2YMEHVqlXT6dOnFRcXp8qVK6t58+YPencAAP8/7nECANx3o0aNytTlb507d9bgwYM1YMAAVa9eXYcPH1aXLl3k6up6x+V8fX01e/ZsLVq0SOXLl9e4ceP08ccfZ1f5WRISEqLAwECFhoaqQIEC6T4PCwvT1atX9eSTT6pnz57q27evXn/9ddvnUVFRCgsLU//+/VWmTBm1bt1aW7ZsUZEiRR7kbgAA/sViGLc8PAIAgBymSZMmCggI0Ny5cx1dSqZcunRJBQsWVFRUlF544QW7zxo2bKiqVatq8uTJjikOAHDXuFQPAJBjXLlyRdOnT1doaKicnZ01f/58rV69WrGxsY4uzVRqaqrOnDmjCRMmKG/evHr++ecdXRIAIBsRnAAAOYbFYtGKFSv0wQcf6Nq1aypTpoy++eYbhYSEOLo0UwkJCSpWrJgKFSqk2bNnK1cu/i8WAB4lXKoHAAAAACaYHAIAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMDE/wcE+SPNwOYIDgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution Time: 294.7050874233246 seconds\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "ngram_ranges = [(1, 1), (2, 2), (3, 3)]\n",
    "train_accuracies = []\n",
    "test_accuracies = []\n",
    "\n",
    "for ngram_range in ngram_ranges:\n",
    "    \n",
    "    tfidf_vect = TfidfVectorizer(ngram_range=ngram_range)\n",
    "    X_tfidf = tfidf_vect.fit_transform(train_data[\"cleaned_text\"])\n",
    "    \n",
    "   \n",
    "    X_train, X_val, y_train, y_val = train_test_split(\n",
    "        X_tfidf, train_data[\"sentiment\"], test_size=0.2, random_state=42\n",
    "    )\n",
    "    \n",
    " \n",
    "    svm_model = SVC(kernel='linear', verbose=False, max_iter=1000)\n",
    "    svm_model.fit(X_train, y_train)\n",
    "    \n",
    "   \n",
    "    y_val_pred = svm_model.predict(X_val)\n",
    "    val_accuracy = accuracy_score(y_val, y_val_pred)\n",
    "    train_accuracies.append(val_accuracy)\n",
    "    \n",
    "\n",
    "    X_test_tfidf = tfidf_vect.transform(test_data[\"cleaned_text\"])\n",
    "    y_test = test_data[\"sentiment\"]\n",
    "    y_test_pred = svm_model.predict(X_test_tfidf)\n",
    "    test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "    test_accuracies.append(test_accuracy)\n",
    "    \n",
    "  \n",
    "    print(f\"SVM Model Accuracy ({ngram_range[0]}-gram to {ngram_range[1]}-gram):\")\n",
    "    print(f\"  Train Accuracy: {val_accuracy}\")\n",
    "    print(f\"  Test Accuracy: {test_accuracy}\")\n",
    "\n",
    "\n",
    "labels = [\"Unigram\", \"Bigram\", \"Trigram\"]\n",
    "x = range(len(labels))\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(x, train_accuracies, width=0.4, label=\"Train Accuracy\", align=\"center\", color=\"skyblue\")\n",
    "plt.bar(x, test_accuracies, width=0.4, label=\"Test Accuracy\", align=\"edge\", color=\"orange\")\n",
    "plt.xticks(x, labels)\n",
    "plt.ylim(0, 1)  \n",
    "plt.title(\"SVM Train vs. Test Accuracy Across N-grams\")\n",
    "plt.xlabel(\"N-gram Type\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  }
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