14_Naive Baye.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f23ea664",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0d4db11d",
   "metadata": {},
   "source": [
    "## Problem Statement : \n",
    "\n",
    "### Text classification \n",
    "\n",
    "    weather a email is a spam or ham(not-spam)\n",
    "    \n",
    "    P[ y = spam | words in email ]    &   P[ y = ham | words in email ]\n",
    "    P[ y = 1 | w1,w2,w3,...,wk ]          P[ y = 0 | w1,w2,w3,...,wk ] \n",
    "    \n",
    "    \n",
    "    Data given is not always in a structures format\n",
    "    (especially in NLP problems.)\n",
    "    \n",
    "    Challenges in such problems : \n",
    "    Emails are of different length. (not given in tabular format)\n",
    "    \n",
    "    data pre-processing is required.\n",
    "    \n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b75a0fc",
   "metadata": {},
   "source": [
    "### Data : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90d5729b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"spam_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e163200d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type                                            message\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...    ...                                                ...\n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568   ham              Will Ì_ b going to esplanade fr home?\n",
       "5569   ham  Pity, * was in mood for that. So...any other s...\n",
       "5570   ham  The guy did some bitching but I acted like i'd...\n",
       "5571   ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d64fb7c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I'm gonna be home soon and i don't want to talk about this stuff anymore tonight, k? I've cried enough today.\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"message\"][10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4900c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"As per your request 'Melle Melle (Oru Minnaminunginte Nurungu Vettam)' has been set as your callertune for all Callers. Press *9 to copy your friends Callertune\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"message\"][7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6193ff9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Just forced myself to eat a slice. I'm really not hungry tho. This sucks. Mark is getting worried. He knows I'm sick when I turn down pizza. Lol\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"message\"][25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84c2dbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3242b2df",
   "metadata": {},
   "source": [
    "## Data Preprocessing : "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6480f5a",
   "metadata": {},
   "source": [
    "    reduce unique words by converting all the letters into lower case. \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4230b36c",
   "metadata": {},
   "source": [
    "## Tockenization : \n",
    "    Tokenization is used in natural language processing to split paragraphs and sentences into smaller units that can be more easily assigned meaning. \n",
    "\n",
    "\n",
    "    The first step of the NLP process is gathering the data (a sentence) and breaking it into understandable parts (words). Here’s an example of a string of data:  \n",
    "\n",
    "\n",
    "    “What restaurants are nearby?“\n",
    "\n",
    "\n",
    "    In order for this sentence to be understood by a machine, tokenization is performed on the string to break it into individual parts. With tokenization, we’d get something like this:  \n",
    "\n",
    "\n",
    "    ‘what’ ‘restaurants’ ‘are’ ‘nearby’  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6123158",
   "metadata": {},
   "source": [
    "https://www.tokenex.com/blog/ab-what-is-nlp-natural-language-processing-tokenization/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a136757",
   "metadata": {},
   "source": [
    "    remove punctiations.\n",
    "         . @ | ! / \\ , = etc. \n",
    "    stopword removal ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3db067e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Natural Language tool kit : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8bdb12e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "import re\n",
    "from nltk.corpus import stopwords \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "049e8811",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59603f12",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw = set(stopwords.words(\"english\"))\n",
    "# there are the words who have no sentiments \n",
    "sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94a62e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clean_email(email):\n",
    "    email = email.lower()\n",
    "    import re\n",
    "    pattern = \"[^a-z ]+\"\n",
    "    repl = \"\"\n",
    "    string = email\n",
    "    eml = re.sub(pattern, repl, string).strip()\n",
    "    eml = word_tokenize(eml)           # convert sentense into words list \n",
    "    \n",
    "    eml = [w for w in eml if w not in sw]\n",
    "    return \" \".join(eml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0bc1961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hi name sunny'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email = \"Hi !! ,.. my name IS SunnY :) -28-10-1991\"\n",
    "email = get_clean_email(email)\n",
    "email"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d4a8376",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"cleaned_message\"]  = df[\"message\"].apply(get_clean_email)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b13ffd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"type\"] = df[\"type\"].astype(\"category\").cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f50d2b0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>type</th>\n",
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       "      <th>cleaned_message</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>go jurong point crazy available bugis n great ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>free entry wkly comp win fa cup final tkts st ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>u dun say early hor u c already say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>nah dont think goes usf lives around though</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>1</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>nd time tried contact u u pound prize claim ea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>0</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "      <td>b going esplanade fr home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>0</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>pity mood soany suggestions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>0</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>guy bitching acted like id interested buying s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>0</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>rofl true name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type                                            message  \\\n",
       "0        0  Go until jurong point, crazy.. Available only ...   \n",
       "1        0                      Ok lar... Joking wif u oni...   \n",
       "2        1  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3        0  U dun say so early hor... U c already then say...   \n",
       "4        0  Nah I don't think he goes to usf, he lives aro...   \n",
       "...    ...                                                ...   \n",
       "5567     1  This is the 2nd time we have tried 2 contact u...   \n",
       "5568     0              Will Ì_ b going to esplanade fr home?   \n",
       "5569     0  Pity, * was in mood for that. So...any other s...   \n",
       "5570     0  The guy did some bitching but I acted like i'd...   \n",
       "5571     0                         Rofl. Its true to its name   \n",
       "\n",
       "                                        cleaned_message  \n",
       "0     go jurong point crazy available bugis n great ...  \n",
       "1                               ok lar joking wif u oni  \n",
       "2     free entry wkly comp win fa cup final tkts st ...  \n",
       "3                   u dun say early hor u c already say  \n",
       "4           nah dont think goes usf lives around though  \n",
       "...                                                 ...  \n",
       "5567  nd time tried contact u u pound prize claim ea...  \n",
       "5568                          b going esplanade fr home  \n",
       "5569                        pity mood soany suggestions  \n",
       "5570  guy bitching acted like id interested buying s...  \n",
       "5571                                     rofl true name  \n",
       "\n",
       "[5572 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41d50f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'XXXMobileMovieClub: To use your credit, click the WAP link in the next txt message or click here>> http://wap. xxxmobilemovieclub.com?n=QJKGIGHJJGCBL'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"message\"][15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fa238cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'xxxmobilemovieclub use credit click wap link next txt message click httpwap xxxmobilemovieclubcomnqjkgighjjgcbl'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cleaned_message\"][15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bf39915",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "19a0e3cd",
   "metadata": {},
   "source": [
    "## train test split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cc81dac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.865937\n",
       "1    0.134063\n",
       "Name: type, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"type\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "990827df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4825\n",
       "1     747\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"type\"].value_counts(normalize=False) # Imbalanced "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec670d73",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1cd2652a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f59171d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_X_train, df_X_test, y_train, y_test = train_test_split( df[\"cleaned_message\"],\n",
    "                                                     df[\"type\"],\n",
    "                                                    test_size=0.25,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df9b7ae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4281                                               u call\n",
       "585               tell u headache want use hour sick time\n",
       "4545    never try alone take weight tear comes ur hear...\n",
       "3034    rajipls favour pls convey birthday wishes nimy...\n",
       "2758                                         time im prob\n",
       "                              ...                        \n",
       "3772        came hostel going sleep plz call class hrishi\n",
       "5191                                 sorry ill call later\n",
       "5226                prabhaim sorydarealyfrm heart im sory\n",
       "5390                             nt joking seriously told\n",
       "860                                        work going min\n",
       "Name: cleaned_message, Length: 4179, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11f0ce62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4179,), (1393,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_train.shape,df_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3c1aecdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4179,), (1393,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dc6df31",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950aed35",
   "metadata": {},
   "source": [
    "## Bag of Words : \n",
    "    \n",
    "    \n",
    "    - one of the technique to convert text data to vector\n",
    "        text (email) ----> vector \n",
    "        \n",
    "    - Bag of words creates a vocabulary. \n",
    "    - word of all the unique words in the entire dataset .\n",
    "    \n",
    "    \n",
    "    - wherever a word appear, mark it as 1 else 0 for all the data points\n",
    "        \n",
    "    \n",
    "             a | an | apple | bag | dog | free | gift | many | people | price ...\n",
    "    email1:    0    0     0      0      0     1    1       0         0     1\n",
    "    email2:    1    0     0      1      0     0    1       0         0     0\n",
    "      .\n",
    "      .\n",
    "      .\n",
    "     \n",
    "     at the end of vecterization , we get a structured data .\n",
    "     a table of \n",
    "     X[n*d] (n : total data points , d: all the unique words)\n",
    "     \n",
    "     \n",
    "     \n",
    "        \n",
    "     \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45135f21",
   "metadata": {},
   "source": [
    "# Vectorization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd5bd9d",
   "metadata": {},
   "source": [
    "## BagOfWord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17e2e59a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# CountVectorizer : is a BagOfWord \n",
    "# to Learn the unique vocabulary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a444562",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "\n",
    "\n",
    "# CountVectorizer(\n",
    "#     *,\n",
    "#     input='content',\n",
    "#     encoding='utf-8',\n",
    "#     decode_error='strict',\n",
    "#     strip_accents=None,\n",
    "#     lowercase=True,\n",
    "#     preprocessor=None,\n",
    "#     tokenizer=None,\n",
    "#     stop_words=None,\n",
    "#     token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
    "#     ngram_range=(1, 1),\n",
    "#     analyzer='word',\n",
    "#     max_df=1.0,\n",
    "#     min_df=1,\n",
    "#     max_features=None,          if we set max_features = 5000\n",
    "                        # it will only select most frequent 5000 words.\n",
    "#     vocabulary=None,\n",
    "#     binary=False,\n",
    "#     dtype=<class 'numpy.int64'>,\n",
    "# )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4a80dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Learning the unique vocabulary\n",
    "vectorizer.fit(df_X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ea6e2a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = vectorizer.transform(df_X_train)\n",
    "X_test = vectorizer.transform(df_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf65278c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4179x7093 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 33926 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4b15a1f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29641647"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4179 * 7093 # unique words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12d2da3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "78e871f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1393x7093 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 10126 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c528f1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33926"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.nnz\n",
    "# Number of stored values, including explicit zeros.\n",
    "# number of non-zeros "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8c7a96d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0011445382910065692"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total numbers \n",
    "33926/(4179 * 7093) # % of total words ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "03df8ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if we want to see the whole vector of each data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f3ce3f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "385ed2d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4179, 7093)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.toarray().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "59fb1f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1393, 7093)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "18156511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4179, 7093)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c47005",
   "metadata": {},
   "source": [
    "## Naive Bayes : \n",
    "https://en.wikipedia.org/wiki/Naive_Bayes_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50344a1",
   "metadata": {},
   "source": [
    "- naive Bayes classifiers are a family of simple \"probabilistic classifiers\" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier).\n",
    "\n",
    "- They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels.\n",
    "\n",
    "- Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.\n",
    "- Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.\n",
    "\n",
    "\n",
    "- In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes.\n",
    "- All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18efcfc9",
   "metadata": {},
   "source": [
    "### at runtime what we need to find is :  \n",
    "     \n",
    "    argmax ( P[y=1|text] and P[y=0|text] )\n",
    "    \n",
    "    P[y=1|text]   !=   (1 - P[y=1|text])   (are not same)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c6a7ca",
   "metadata": {},
   "source": [
    "---\n",
    "### Bayes theorem: \n",
    "---\n",
    "    \n",
    "              P[B|A] * P[A]\n",
    "    P[A|B] =  --------------\n",
    "                   P[B]\n",
    "\n",
    "---\n",
    "                   \n",
    "               P[X|y=1] P[y=1]       \n",
    "    P[y=1|X] = -----------------\n",
    "                    P[X]\n",
    "                    \n",
    "                \n",
    "               P[X|y=0] P[y=0]       \n",
    "    P[y=0|X] = -----------------\n",
    "                    P[X]\n",
    "                    \n",
    "---                                 \n",
    "          \n",
    "    Posterior      likelyhood * prior\n",
    "    Proability  =  --------------------\n",
    "                  marginal probability\n",
    "                  \n",
    "--- \n",
    "\n",
    "    if P[y=1|x] = 0.85  and P[y=0|x] = 0.35 ,\n",
    "    then y^ = 1\n",
    "    since the probability of data belongs to class 1 is higher. \n",
    "    \n",
    "                  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4dd916",
   "metadata": {},
   "source": [
    "--- \n",
    "--- \n",
    "\n",
    "    # For Multiclass : \n",
    "    lets say : classes are : \n",
    "            SPAM , SOCIAL , PROMOTION and PRIMARY\n",
    "            \n",
    "    argmax (P[y = SPAM       |X],\n",
    "            P[y = SOCIAL     |X],\n",
    "            P[y = PROMOTION  |X],\n",
    "            P[y = PRIMARY    |X])\n",
    "            \n",
    "       calulate 4 posterior probabilities , and whichever is maximum\n",
    "       assign that class as y^"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b99ebe6",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d181b9",
   "metadata": {},
   "source": [
    "    \n",
    "    \n",
    "\n",
    "                        P[X|y=1] P[y=1]       \n",
    "            P[y=1|X] = -----------------      ∝    P[X|y=1] P[y=1]\n",
    "                            P[X]\n",
    "\n",
    "\n",
    "                       P[X|y=0] P[y=0]       \n",
    "            P[y=0|X] = -----------------      ∝    P[X|y=0] P[y=0]\n",
    "                            P[X]\n",
    "                            \n",
    "                            \n",
    "            P[y=1|X]   ∝    P[X|y=1] P[y=1]    \n",
    "            P[y=0|X]   ∝    P[X|y=0] P[y=0]\n",
    "            \n",
    "            \n",
    "                            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97fda81",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7b3d8f",
   "metadata": {},
   "source": [
    "####  Prior Probability\n",
    "    Most easiest thing to calculate is Prior Probability\n",
    "     \n",
    "     \n",
    "     P[y=1] and P[y=0] from the entire dataset\n",
    "     \n",
    "     \n",
    "                Number of train data points with y=1\n",
    "     P[y=1]  =  ------------------------------------\n",
    "                     total training points\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bbd017d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.866954\n",
       "1    0.133046\n",
       "Name: type, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fead310",
   "metadata": {},
   "source": [
    "#### Likelyhood - Conditional Probability \n",
    "\n",
    "           P[X|y=1]    and    P[X|y=0]\n",
    "           \n",
    "           P[ w1 w2 w3...wd  | y=1 ]       in howmany spam emails\n",
    "                                           we are getting these exact sequence\n",
    "                                           of words \n",
    "                                           \n",
    "                        and                   \n",
    "                           \n",
    "                           \n",
    "           P[ w1 w2 w3...wd  | y=0 ]       in howmany non-spam emails\n",
    "                                           we are getting these exact sequence\n",
    "                                           of words \n",
    "                                           \n",
    "           \n",
    "      since we are trying to find the exact sequence,\n",
    "      it's computationally very challenging and expensive \n",
    "      \n",
    "      thats why we take a NAIVE ASSUMPTION into account : \n",
    "      which is : \n",
    "      \n",
    "#### Naive Assumption : \n",
    "\n",
    "\n",
    "     Lets say : \n",
    "     X-test is : \"w5\" , \"w7\" , \"w10\"\n",
    "      array :   [00001010010000000000.......0...00..]\n",
    "      \n",
    "     getting exact same sequence in the training , is very very rare.\n",
    "     \n",
    "     so we have to take an assumption : \n",
    "             which is all the words are independent.\n",
    "             for example : \"Get a free subscription\"\n",
    "               then word \"Subscription\" is independent of word \"free\"\n",
    "     But still , all the words are conditioned on class label. \n",
    "     like , P[\"free\"|y=1] ,P[\"Subscription\"|y=1]\n",
    "     \n",
    "                    \n",
    "     P[ w5,w7,w10 | y=1 ] = P[w5 | y=1] * \n",
    "                            P[w7 | y=1,w5] *\n",
    "                            P[w10| y=1,w5,w7]\n",
    "                    rathen calculating above complex computation \n",
    "             use naive assumption \n",
    "             and find : \n",
    "             \n",
    "     \n",
    "     \n",
    "                            \n",
    "      \n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16333ec8",
   "metadata": {},
   "source": [
    "---\n",
    "    P[w1 w2 ... wd | y=1] = ( P[w1|y=1]*P[w2|y=1]* ... *P[wd|y=1] )* P[y=1]\n",
    "     \n",
    "                                d    \n",
    "                  P[X|y=1] =    π   P[wi|y=1]     * P[y=1]\n",
    "                               i=1\n",
    "                               \n",
    "                               \n",
    "---"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "586577eb",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3fb76b",
   "metadata": {},
   "source": [
    "                Ck is the class , given data belongs to \n",
    "                y^ is the predicted class\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12797d96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "064fe8bc",
   "metadata": {},
   "source": [
    "    lets say : \n",
    "    X_test = \"Lottery win 5000 USD\"\n",
    "     \n",
    "    P[x-test | y=1] = in how many mails we see word \"lottery\" \n",
    "                      ---------------------------------------\n",
    "                               out of all spam emails\n",
    "                               \n",
    "    most often , we will observe LOTTERY word in spam emails than in non-spam emails "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f235780",
   "metadata": {},
   "source": [
    "## Prior Probability : \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a1260a",
   "metadata": {},
   "source": [
    "## Conditional Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9864943b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets find the probability of word free occures in a all record. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fbc69500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['type'] == 1]['cleaned_message'].str.contains('free').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b4ae83de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "747"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['type'] == 1])  # total spam emails "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2c787399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26639892904953144"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "199/747  # 26% probability will be given to word \"free\" if the email is spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fdbfee5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['type'] == 0]['cleaned_message'].str.contains('free').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9316ae61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4825"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['type'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a731980c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013678756476683937"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "66/4825 # 1% probability when free word appears in non-spam "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49431684",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32935c5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f8617820",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets find the probability of word \"love\" occures in a all record. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8ae03869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['type'] == 0]['cleaned_message'].str.contains('love').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "040e5e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.044974093264248706"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "217/4825"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "01d985de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['type'] == 1]['cleaned_message'].str.contains('love').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8139c952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0107095046854083"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8/747"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d9e9b4a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'call': 830,\n",
       " 'tell': 6035,\n",
       " 'headache': 2608,\n",
       " 'want': 6654,\n",
       " 'use': 6497,\n",
       " 'hour': 2768,\n",
       " 'sick': 5416,\n",
       " 'time': 6180,\n",
       " 'never': 4019,\n",
       " 'try': 6343,\n",
       " 'alone': 176,\n",
       " 'take': 5966,\n",
       " 'weight': 6723,\n",
       " 'tear': 6020,\n",
       " 'comes': 1150,\n",
       " 'ur': 6482,\n",
       " 'heart': 2617,\n",
       " 'falls': 2003,\n",
       " 'eyes': 1979,\n",
       " 'always': 189,\n",
       " 'remember': 4967,\n",
       " 'stupid': 5827,\n",
       " 'friend': 2250,\n",
       " 'share': 5339,\n",
       " 'bslvyl': 759,\n",
       " 'rajipls': 4838,\n",
       " 'favour': 2038,\n",
       " 'pls': 4538,\n",
       " 'convey': 1237,\n",
       " 'birthday': 602,\n",
       " 'wishes': 6813,\n",
       " 'nimya': 4047,\n",
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       " 'hrs': 2786,\n",
       " 'meant': 3661,\n",
       " 'opposed': 4253,\n",
       " 'drunken': 1716,\n",
       " 'night': 4040,\n",
       " 'unless': 6447,\n",
       " 'book': 668,\n",
       " 'kinda': 3190,\n",
       " 'jokethet': 3095,\n",
       " 'really': 4891,\n",
       " 'looking': 3449,\n",
       " 'skinny': 5475,\n",
       " 'white': 6770,\n",
       " 'lineyou': 3383,\n",
       " 'theyre': 6118,\n",
       " 'casting': 920,\n",
       " 'look': 3446,\n",
       " 'hasnt': 2594,\n",
       " 'pattern': 4396,\n",
       " 'recently': 4912,\n",
       " 'crap': 1294,\n",
       " 'prakesh': 4647,\n",
       " 'said': 5151,\n",
       " 'bedroom': 531,\n",
       " 'minute': 3764,\n",
       " 'sed': 5254,\n",
       " 'sexy': 5317,\n",
       " 'mood': 3852,\n",
       " 'minuts': 3766,\n",
       " 'latr': 3286,\n",
       " 'caken': 821,\n",
       " 'speed': 5643,\n",
       " 'speedchat': 5644,\n",
       " 'swap': 5921,\n",
       " 'chatter': 990,\n",
       " 'chat': 987,\n",
       " 'poboxwwq': 4563,\n",
       " 'pmsg': 4551,\n",
       " 'rcd': 4867,\n",
       " 'hoping': 2751,\n",
       " 'langport': 3263,\n",
       " 'still': 5761,\n",
       " 'office': 4177,\n",
       " 'site': 5459,\n",
       " 'simulate': 5440,\n",
       " 'test': 6062,\n",
       " 'gives': 2386,\n",
       " 'tough': 6285,\n",
       " 'readiness': 4881,\n",
       " 'likely': 3366,\n",
       " 'mittelschmertz': 3790,\n",
       " 'google': 2441,\n",
       " 'paracetamol': 4354,\n",
       " 'worry': 6877,\n",
       " 'dunno': 1735,\n",
       " 'lei': 3327,\n",
       " 'shd': 5345,\n",
       " 'driving': 1704,\n",
       " 'sch': 5212,\n",
       " 'hr': 2784,\n",
       " 'oni': 4225,\n",
       " 'confuses': 1208,\n",
       " 'things': 6124,\n",
       " 'wrong': 6908,\n",
       " 'sort': 5604,\n",
       " 'invited': 2976,\n",
       " 'tho': 6140,\n",
       " 'nvm': 4146,\n",
       " 'wear': 6694,\n",
       " 'sport': 5680,\n",
       " 'shoes': 5376,\n",
       " 'anyway': 252,\n",
       " 'late': 3277,\n",
       " 'spending': 5651,\n",
       " 'brother': 748,\n",
       " 'next': 4028,\n",
       " 'ready': 4883,\n",
       " 'spoiled': 5670,\n",
       " 'escape': 1879,\n",
       " 'theatre': 6098,\n",
       " 'kavalan': 3153,\n",
       " 'minutes': 3765,\n",
       " 'consensus': 1216,\n",
       " 'cool': 1244,\n",
       " 'let': 3340,\n",
       " 'kicks': 3180,\n",
       " 'around': 309,\n",
       " 'waiti': 6629,\n",
       " 'min': 3744,\n",
       " 'typical': 6392,\n",
       " 'doctors': 1623,\n",
       " 'reminds': 4975,\n",
       " 'godid': 2404,\n",
       " 'little': 3402,\n",
       " 'lounge': 3475,\n",
       " 'liked': 3365,\n",
       " 'photos': 4472,\n",
       " 'pity': 4502,\n",
       " 'soany': 5553,\n",
       " 'suggestions': 5866,\n",
       " 'yet': 7030,\n",
       " 'id': 2853,\n",
       " 'easiest': 1756,\n",
       " 'barcelona': 472,\n",
       " 'ru': 5117,\n",
       " 'house': 2771,\n",
       " 'looks': 3450,\n",
       " 'scrumptious': 5233,\n",
       " 'eat': 1762,\n",
       " 'entertaining': 1852,\n",
       " 'getting': 2363,\n",
       " 'hugh': 2808,\n",
       " 'laurie': 3291,\n",
       " 'stick': 5759,\n",
       " 'especially': 1882,\n",
       " 'apart': 255,\n",
       " 'yesterday': 7029,\n",
       " 'happen': 2574,\n",
       " 'silent': 5430,\n",
       " 'tensed': 6049,\n",
       " 'prabhaim': 4639,\n",
       " 'sorydarealyfrm': 5611,\n",
       " 'sory': 5610,\n",
       " 'noe': 4064,\n",
       " 'la': 3239,\n",
       " 'wana': 6652,\n",
       " 'pei': 4419,\n",
       " 'bf': 576,\n",
       " 'rite': 5073,\n",
       " 'round': 5102,\n",
       " 'til': 6177,\n",
       " 'ish': 2999,\n",
       " 'apologetic': 259,\n",
       " 'fallen': 2001,\n",
       " 'actin': 50,\n",
       " 'spoilt': 5671,\n",
       " 'child': 1033,\n",
       " 'caught': 928,\n",
       " 'till': 6178,\n",
       " 'badly': 443,\n",
       " 'cheers': 1007,\n",
       " 'sir': 5451,\n",
       " 'receive': 4908,\n",
       " 'account': 37,\n",
       " 'delay': 1481,\n",
       " 'kkare': 3202,\n",
       " 'month': 3847,\n",
       " 'upto': 6479,\n",
       " 'calls': 852,\n",
       " 'standard': 5715,\n",
       " 'charge': 975,\n",
       " 'activate': 54,\n",
       " 'wirenet': 6809,\n",
       " 'stterms': 5809,\n",
       " 'pobox': 4557,\n",
       " 'uz': 6522,\n",
       " 'mobcudb': 3808,\n",
       " 'gim': 2375,\n",
       " 'caveboy': 932,\n",
       " 'garage': 2314,\n",
       " 'keys': 3171,\n",
       " 'arent': 293,\n",
       " 'bookshelf': 673,\n",
       " 'swhrt': 5932,\n",
       " 'deyhope': 1540,\n",
       " 'daylove': 1421,\n",
       " 'misstake': 3782,\n",
       " 'mad': 3549,\n",
       " 'found': 2206,\n",
       " 'diff': 1561,\n",
       " 'farm': 2019,\n",
       " 'shop': 5379,\n",
       " 'cheese': 1009,\n",
       " 'gon': 2420,\n",
       " 'rip': 5071,\n",
       " 'uterus': 6513,\n",
       " 'rimac': 5065,\n",
       " 'access': 25,\n",
       " 'somewhere': 5581,\n",
       " 'beneath': 561,\n",
       " 'pale': 4337,\n",
       " 'moon': 3853,\n",
       " 'light': 3361,\n",
       " 'someone': 5569,\n",
       " 'goodnite': 2434,\n",
       " 'meh': 3680,\n",
       " 'kkany': 3200,\n",
       " 'join': 3087,\n",
       " 'bus': 794,\n",
       " 'ericsson': 1868,\n",
       " 'oredi': 4269,\n",
       " 'lar': 3268,\n",
       " 'asked': 333,\n",
       " 'sen': 5281,\n",
       " 'chennai': 1012,\n",
       " 'search': 5238,\n",
       " 'login': 3428,\n",
       " 'voda': 6598,\n",
       " 'numbers': 4139,\n",
       " 'ending': 1829,\n",
       " 'award': 412,\n",
       " 'hava': 2600,\n",
       " 'match': 3626,\n",
       " 'quoting': 4821,\n",
       " 'rates': 4856,\n",
       " 'app': 263,\n",
       " 'posh': 4602,\n",
       " 'birds': 599,\n",
       " 'chaps': 972,\n",
       " 'user': 6500,\n",
       " 'trial': 6325,\n",
       " 'prods': 4724,\n",
       " 'champneys': 963,\n",
       " 'dob': 1616,\n",
       " 'asap': 326,\n",
       " 'ta': 5951,\n",
       " 'toopray': 6263,\n",
       " 'meremove': 3708,\n",
       " 'teeth': 6028,\n",
       " 'painful': 4332,\n",
       " 'maintaining': 3571,\n",
       " 'stuff': 5820,\n",
       " 'remb': 4966,\n",
       " 'sat': 5183,\n",
       " 'infernal': 2928,\n",
       " 'affairs': 95,\n",
       " 'mayb': 3649,\n",
       " 'worrying': 6878,\n",
       " 'appt': 279,\n",
       " 'shame': 5332,\n",
       " 'missed': 3777,\n",
       " 'quizzes': 4819,\n",
       " 'popcorn': 4594,\n",
       " 'hair': 2547,\n",
       " 'dad': 1373,\n",
       " 'boo': 667,\n",
       " 'hows': 2779,\n",
       " 'kerala': 3166,\n",
       " 'daysso': 1425,\n",
       " 'prepared': 4667,\n",
       " 'finalise': 2091,\n",
       " 'travel': 6312,\n",
       " 'visitneed': 6592,\n",
       " 'finish': 2103,\n",
       " 'works': 6869,\n",
       " 'complimentary': 1179,\n",
       " 'ibiza': 2843,\n",
       " 'await': 409,\n",
       " 'collection': 1135,\n",
       " 'sae': 5145,\n",
       " 'sk': 5469,\n",
       " 'wp': 6891,\n",
       " 'theyll': 6117,\n",
       " 'understand': 6426,\n",
       " 'wine': 6799,\n",
       " 'slurp': 5512,\n",
       " 'takes': 5969,\n",
       " 'atyour': 373,\n",
       " 'dead': 1432,\n",
       " 'midnight': 3735,\n",
       " 'earliest': 1749,\n",
       " 'ax': 419,\n",
       " 'youll': 7052,\n",
       " 'figure': 2075,\n",
       " 'trust': 6340,\n",
       " 'math': 3631,\n",
       " 'promise': 4737,\n",
       " 'secret': 5245,\n",
       " 'admirer': 74,\n",
       " 'ufind': 6395,\n",
       " 'rreveal': 5112,\n",
       " 'thinks': 6129,\n",
       " 'specialcall': 5634,\n",
       " 'cause': 929,\n",
       " 'yelling': 7013,\n",
       " 'scrappy': 5225,\n",
       " 'miss': 3775,\n",
       " 'lonely': 3441,\n",
       " 'disturbing': 1606,\n",
       " 'check': 997,\n",
       " 'listed': 3396,\n",
       " 'mrw': 3893,\n",
       " 'ninish': 4049,\n",
       " 'icky': 2851,\n",
       " 'american': 194,\n",
       " 'freek': 2226,\n",
       " 'callin': 841,\n",
       " 'eh': 1796,\n",
       " 'unfortunately': 6432,\n",
       " 'airport': 140,\n",
       " 'natalja': 3969,\n",
       " 'inviting': 2977,\n",
       " 'wwwsmsacunat': 6955,\n",
       " 'frnd': 2259,\n",
       " 'hex': 2661,\n",
       " 'explain': 1966,\n",
       " 'lifted': 3360,\n",
       " 'hopes': 2749,\n",
       " 'offer': 4172,\n",
       " 'approaches': 274,\n",
       " 'hurts': 2828,\n",
       " 'studying': 5818,\n",
       " 'anyways': 253,\n",
       " 'gr': 2460,\n",
       " 'weekend': 6716,\n",
       " 'wrkin': 6903,\n",
       " 'south': 5620,\n",
       " 'tampa': 5982,\n",
       " 'preferably': 4661,\n",
       " 'kegger': 3162,\n",
       " 'customer': 1355,\n",
       " 'tc': 6004,\n",
       " 'wwwtcbiz': 6958,\n",
       " 'pmin': 4549,\n",
       " 'polo': 4579,\n",
       " 'ltd': 3504,\n",
       " 'suite': 5867,\n",
       " 'london': 3439,\n",
       " 'wj': 6827,\n",
       " 'hl': 2693,\n",
       " ...}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vocabulary_ of all the words and count\n",
    "vectorizer.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e8b0ac0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b16ee916",
   "metadata": {},
   "source": [
    "### Learnable Parameters : \n",
    "\n",
    "    STEP 1\n",
    "    priors: \n",
    "                    P[y=1] and P[y=0]\n",
    "     \n",
    "    STEP 2\n",
    "    conditional probabilities: \n",
    "    \n",
    "             each word in a dictionary\n",
    "             \n",
    "             {\n",
    "             word1 : {\"0\": P[word1|y=0]  , \"1\": P[word1|y=1]},\n",
    "             word2 : {\"0\": P[word2|y=0]  , \"1\": P[word2|y=1]},\n",
    "             word3 : {\"0\": P[word3|y=0]  , \"1\": P[word3|y=1]},\n",
    "             .\n",
    "             .\n",
    "             .\n",
    "             word : {\"0\": P[word|y=0]  , \"1\": P[word|y=1]}\n",
    "             }\n",
    "         \n",
    "    STEP 3\n",
    "        posterior   ∝    Conditional    prior\n",
    "       probability      probability    probability\n",
    "        \n",
    "        P[y=1|X]   ∝    P[X|y=1]   *  P[y=1]    \n",
    "        P[y=0|X]   ∝    P[X|y=0]   *  P[y=0]"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "b0b0cc6a",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f92d057",
   "metadata": {},
   "source": [
    "## sklearn.naive_bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1ff66a3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "02fc595a",
   "metadata": {},
   "outputs": [],
   "source": [
    "BNB = BernoulliNB(alpha=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "edecc40b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BernoulliNB(alpha=0.001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BernoulliNB</label><div class=\"sk-toggleable__content\"><pre>BernoulliNB(alpha=0.001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BernoulliNB(alpha=0.001)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BNB.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ad5d0fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = BNB.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "556dbf4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9366391184573003, 0.9945750452079565)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred),f1_score(y_train, BNB.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7032a841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9945750452079565"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_train, BNB.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c11c8a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9985642498205313, 0.9834888729361091)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BNB.score(X_train,y_train),BNB.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b495b4ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "18f99eb3",
   "metadata": {},
   "source": [
    "## Space Complexity and Time Complexity \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70e4900",
   "metadata": {},
   "source": [
    "    space : \n",
    "        total number of words = d\n",
    "        total number of class = c\n",
    "        \n",
    "        O(d*c + c) ~ ~  O(d*c)\n",
    "        \n",
    "     \n",
    "    run-time complexity : \n",
    "            \n",
    "            O(d)\n",
    "        \n",
    "    train time complexity: \n",
    "        \n",
    "        O(ndc)   n - datapoints\n",
    "        \n",
    "        P[w|y= 0] : O(n+n+n+n....+d-times) * number of classes \n",
    "        =O(n*d * c)\n",
    "        \n",
    "        ~~O(nd)\n",
    "        \n",
    "    \n",
    "    \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "735d32ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
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Bmgsa9TZ5vA3bWstmvpo8rF43aZgq/TqzDd1aRne2tU0eFE38MyJotvP4p/oOaLyYtnlxWtKOtb1NQPokEUMqooaNVtokFaiuNbXVlRUi39QKTErB6N7KS1BS07svxEmqyUtlLtQaMhrpqbEwv4alGxRtnQ7yUG1Te1/sxDTESpU2NNTJX6PO27QWOz43DZMdMDj3QuXARKRZKodYi8J95l+VoXiz0ooUTE4Qu0lSV4bCAlOr1D8lPsK67voI9B2uDBLTKqtUWh2JFPMeK1XAYIC+RppuDtZuKdIs94kGc1c4dAOVZRDynhb7alEOiuWvcIthvdUvp+qgS1JahSXTJmDW6nzTRy4DxTB7lV200egp6YhVdl5I9AixN63UwzAwVjOs+rReabMVhREx1pfyQm6bjHTLtgXyK6qd27+tqCjZYK3KUzcLk8dbt2PI+MmYZdnkddhQ4ig1O5dvXBWui7ZWQLItCxPSliCnpE5e73BduHMVdwxJRtbcLGSKdOa+tq5fLJKniLwdCBjqKlC8Ow8bVu/AIWWopMySzu1xIe17In8mpyOlv9IeGIXYeKVdUlntQtWvYl03WcfOGDtCeblUagzoO8i8HctQVOFoqm6UyUjE4gVpIgdZhYdbt5EmzU9LxIgm8zLpYegbbcmfZcUVbldzGxWjqiJ5cymqpb81FSiSX25OxKxfikOxZJtyjNBXoGyn3Efkw0QMVWp60q5XCtKTVGs1UGybVKVdyN9d5mB5dch6fQMyhlnLE4m+rMj6PRuxTJkPJ1qOPwiMQIR2dAtP5Evd2DQkShUKGPSoPiDWcctaZO9R54ETLbwInIz1mxcj2SZLd8R+VdOU67ooRKvK57wa6xzKinNV8xNl0RTVPgwXeVJp1fJA3okZgegwpV3khuSpIswyqyv24PmMs+cuYlnzVMs6NxPp5nJGCL/S/pbwRJnm7L5yl2fyqTPcTBcunWtp54G5i5EVp8rV4S3ncfe3tXa+ul9ORqIl/YYgMWWWtdyp24Dig0q7G5xbRg/kwaT1yP5TMnR2apqNmpKN6vp61NefRdHzqrK3BR1W1bWjzFhdmaO0CdumY2iEOFBYmglYogxqE1HoqA/etvQH87AkbSj69uwrf4168qNPYclmd77CHYXE1ESlHcguUaZxoAi55v2ZKhKhuaYscQC3rn0epseo1z0CE5Yqg5zRVIfC1bMwYVB3RESNwDhx0jXr6bWqQkRNJP7H1iLDXP2jOInMWZCO0dd0x4Dkp5BzUB1ERCF9wWIkmnPK0Xys/dUEDBX7c2jaChQ6SKcdwuHBrjV9RWZTWiUigzoKm5wnAucy1cZIiLJJc6I7QWkV6kTA5cymc3gQc1HI2AysnWZeIrGsW+YjPb4vug+agKe2VHhg/d3j+vrpUbFtCdJjxLL3HYrRiZMx/Wlxwqiq5adlLqR9T+ZPWTgi3N2dtRUoVSWYJcnaZRn6mPXU21lOlcniRN4cPDRnk+aXTtAsU0TMdFVA0AbD45GutEoH0eoakQrKlWBDNxSJY5VK2OsKceio+Hu0DLmmPsDUeMvJo3a9RiBKU2OhTgRZSqtkZ4UIGewZjRHRzU9XDA3qsWNtpu2YR/KlWO+1v5yAAd0jMCB2nAigZuGpl+yX/M2NtrN/O2i/OmRTPqsYGlTH5fHRiHImOmz3vFOIOuUDzx4/n3F47qKt2SxxUFSLJ9EWHi/THO8rd3kmnzrB3XThyrmWzTxSYoc6t5/scmFb28y3Wc13kVGWCxhSfj9ke0fAbQ6W0RN5MH6og7xgEiICSfWFudb4/Xd+DCVLkBwzGfPFgUbaN7phych8dg2Wz7UGMa6Ikq7CKe11ew7JVymr9+WLeNYkZVJ7fOitGjmPxmLcr0QGlA7+IonET8vC8o2LkSEPt6N/CtbvO4TsucmaBFVdsEKcWCZjSYn1OkZI3DzsKtuF5VNUVynlA/VTGBebjhxxMkLeLgopG0txKCcLyeqTHRHMrhCBf/KiYs2VK+9kQPGiZAxNmS+CFDm3InpiJuatWo4sdXX8rXEh7ZMXCBmKEZarvXk4VCv21UElvJkUi+j+Q2G691OIQnHSXvd5qVyWS9p2stER2pgvpS/ai4Bnlgh25DtiA+ORMXc51i90WPITkdvcONcir3TJg5+ogcojC5Lx61Fleg/JTjMP6idGPKMMa2fOR7HSFb+wCBXlO7BmYaY4oDhz48wO9aNvO/NRXFuHsj3metNVj7xJRPRtXftErP/c3nqbmnlxymh26LeLk7bNyuFel4bs6ioUbRQnhNMSNSd3zYRHI+35Hag6e0KMn2W9uyO2yPwX8iwnEDJdIrJyDuHE14ew4/kU63TrcpC12bwFfUU1qvcorZKYKA8EpDZXpPaIeSitJic089RFe2KergpH9JTl2PH5WZwoXo8s1eNrxQuWI8/NR+o6zIG1yFxgya1YvKcCh95eg8Wz0zHC1ezqTNr3UP70CLEsI5RWSdZ7Z+0uh9TkTmlbytKUySKgkO+m2GWT5ufuwlk7yyM3b6Q1S+/SYw/OhZk6xI+3XoyqOFZsuTMRO6gvwiOjMVp51CKnXOS7o+ariLEYHW2dq3a9Sm0esazDCfWjzuOj4dJNukB1iFWNOk3h2Rp386Ue+c+nW75or5uajaqKIqx/PgsZ41ss+VvRtv3ankICVXPb6eRjo+2Qd+qqS5U2iTgeKncnOu58RqQ31aIWHnMywXlTmeZAu+ZTNTfShcvnWjbzyCs71DEXGW3mW3RUezYiEjCKlFYpIQ2Naudc3A55sBkXv/NzyYMfXfRo6zOtO9diw94OvOpaa35uXJKMjKnxlmcF68WB2T1RSJySrLTnoUgEPkXm932SkhGvvoWrOmhLJxlrX3fv6nuFOCm0mJaBNPOzvw31jm8JN1Sg2PyIT4g4uZi2HLu2zjN1S7aqTt5rylBsLlt7RyN5bi52vW4toOrECYe9ordIldH1ewuxQ2m/JFQfcNMX5GKNaoEzhyuPzQg6EeRZbDXduXNWbFyW0ibU5WKHKi0b9uZaH30UhU1GnHWeHcOAihLzYzQh0MVlYHlhHqx7PM+J920urTpxYmd56GWiSOe3WXKryK9KqzOcTfseyp/OajntRSN2ptIqrFgn9pfS7mmaMlms9/xX8rWPX522Bi3Rw1XvPixbizxn7gLX5GNWfAC6R0Sg+6DpTv1G/d5PXsUOHNpqak8eJS2p2DbmC04lO1Tvayar9p9Yr1HJlrvyUnrP3a3agjWFyLa8lyl+OTbWpZN63aARqm2Wh2xxYm7RUI1D6hM2jbbkywqUvaS0ChnT0hClPA9vqHf7YSCZW/u1A0QNm6y0STYgd6cqR9ZUi1NlezyQd84rfyVNFcjdrHpMZ/hoy0efO+58Jgqxk5RWybpcFKpO/Ko/t78lvKtMs68986mW6+nC9XMt7TywbAU2aN6lkt55UVo9Khbxc5VWoS5vB4ot6cOA4rdV787pMhA/TGkXW1Oneooir8LlnOJA+x6/6gpmYXT3CET07ItxS51L05f+sbfhGVg805x8y7AkYSgmP70BebsL5RfJVjw2Dn0fzHHuCo+reutU0Xo+5v9qPnIK8rDhNxOQ+Ev3v0sclZAiDrsmuavWiCLGJDnV9upALDIWZloyb9mi0Rj64FPYsE2s++585CybjnF9JyOnlZNSTQGzdD6mr85D/pYVmD5+AlRZVUUk/pUZGB0TjXGPiQObtK3F/DbkqJ5anRkrkqukGjkLxElE3wGYvCgH+fK4ecjZYi38Y+PNj8NFIUr1MmLdsnSMSByHcYlDEZ1gvcPW8fKQLrbFrAUrsOLpyRiRvMKanoYvFsOstw369reewgBLkPXoCuRsE+lwa+vVbYSMT8dyy9UykZZTJ2C6NM8F0zEhdYn1xD3uKUwe27EP4xhKViAjXuyHxOlYK6evQuSvy1Y9p5yJ2CFKq5cK763KPdvnY9bvRXrctgFP3ZuIWeYX3FvlStr3TP50VstpLxzJjy23Xi3emo7RYl+u2JIv78u8TSJNiRNwjzyyNzwd86daTynqlk0QeWYW5osD93zp3ZIrE7FYOakLF0GoNc2LfBY/DtOXKeWEtG8eHIARNo9ulW3JwNoSpePoBmS+Utj6wUr93s+iJcq7E2kYIT8qFoKhscrFmJ0bsMGcFmaKk1KlVTZwMrIsxxqxtA+PE8cakT+XiW0n8q6lRNNlInOii3dOhqcgU1UxTP5jo0X6mm+aduxoLHHwTlrb8qX2RGXJ76VpmNJlYrL9kt9Z7uzXjhA+PgXzLLuwDivulM4XTGXsuHjVPtTwQN5ZlIKhaU+J/SmOr3eK8sZSaQCQ8nS6NeDpsPOZcCSmzrOUTahbgXGxk/GUlEfFPEY/bH9LeFeZ5kB75lMN19OF6+daYj89ulh1ly8fs8YORbqyPuniOBTxWI5HAwGTECSmqdbtwBKk3DvdVIY/NgEpi6zvzsWLc6JEy+lIX0SpLhhhUZac7/PEOua1oVIEj+RBh8qQ/fu1yvllHQp/vwb5zmQwY6vcrerapk56zUevbOrhbvXjcNpqg+3RVhdpM32bqvmsdY/bVkOoajQfxtSuS4vrKdNWJ2lqbKq9tJCq1Xbw0TCl0VYLaMfn2uofrY3242yWagvFvrCum70m3vJhvBPqjwXaa2w+Alufp66aUzvevLmqKmtbqPZUU71iS8NaqJaxWfW0dhs739GwqTbZ0liq2LWpftGmWudWP8DX7KOYbcg3zThYtvOiv+23Rmwaex9mc54rVV23Yf3O7jKKEx/V+NZG86E1232i5kLaN3E1f7acPlqs+rXVtKet0tNuk7ReU12y29u+1Y/1LrdW/99amrcp+0pfUFexLRqbTwnYZ5PGpGa8al3Llmuqr5eaZh+alrR6rGleJrR4bFFp6WPQdtOnB/Jl1cZku7/TfmRcu8ytH78ULu7X5lrICy2W6y2XJ47zgIPjncK1vGOz7A4aux9gd/F8xr1zF0kLH8htYf+7XKa1YV85U6bZ5UY+td1ntvvfEZfShavnWrJWPiorfmupGrst29qO1tbNXvqVyjHbslRqLN9La5e8qzQt5UGH6+ved368o8KDsHjM23MCu55Pg+oRbZOBychclY2n2uVKuYiOf5+PNanqqwc6JM7JxqF9OzDPlZeoNaRa38z3fhRJKUi0W2tSCOKfLcKJwuVIG2a78lFInrkG2b9JFGO1YGAG1rytfm9BGJiC5YVlKH3RWk+SRdw8lJbnYrGmAgMT3bA0rNmXj3kjTXPUTc0Vy7YGmerqJhVRSVnILc5GiupRvvBJ87BjjrZSB91Y03iL77Ve/+hY87A+T7t9LOuprnZSEhKPrC3rkWG7L8xV7Lamfxqyy4uwZoZtxRYiXc1Yg6LybOut8o4SKPLXvkPIXWgnf+mikbaqFPnPxLacxrxBSCLmF6xBijof6UzvopVun6d6VKIFLqR9Ew/kT2c5kfakKj0rRBrKapYfdYieshi5z0+2ubvspv7JWFPQvFIIibSdli/LsF7tltJ8heNxF+ctx2TVPoudkS3GFeVhnDK2U58S0L73I4tT1f4zJNZm/4v8FmtnS0jHmp1SXlC9t6iIShXbr6KweZngJKlimPx9NulTzCXl+SKUbs20bi8zD+TLqGlrmpW3UanLsausFOunKj3c5eJ+7ShSHiiyKc9N5UAhdr2gfixOqy15J2VVrs15gsj7c3NRtFFbDbysw85nopC2sQi5do632bt3YY36sTgN7yrT7GrHfGrLpXTh6rmWLASxc3agzNH2nrsCmWPdfMe8FfK67VmDDNvPU4j8krGqCBWvN0+/IXEi/WzMaJZ2pfcp26p9jl+xSHvB+u5h/NxMJGtqCLQvQIqAlHYvYZDr/TYJcanqurawvHwbGI5wS33oHU/9EnBIWDhC7NRp7lCT2HZK/fJStX9ObTnpJTHLmUcr27tBjGt+d6a17WQZt+P2oVrdlsnom2a+QT4P0jWEeMv2cW6ZNPvC2e2ppt62IWJ7dfxmsEOdv9xcr0vOug4u5xE1V9K+ok350wVOpT11fmzP9KUqV1rdTi6Mq982HREpGwDpAsvrKZoTuA6hXlYPl/uW/ef0fmljvmzv8taVNNBhrNvM5bzYat4pxpKA0ZivdKW8XoXcqVFt3K/tuN0sZZnr8/CqMs2edsynzThbpqqWydW82lHb25bL27+987yHj1+m9XN+Ob0w+CFqO23wk4LsY7lIc+JqABF1AL04uUwSJ5clQPLGKuywfOuGyBtogx8sLILx2Uv15AIReZrff+eHiIg6ikH+ntLkUabAB3HLsZiBDxERdSAGP0RE1EEMKNv6FPKOSs/ur0FpQVbzd2GIiIjaER97o07JUFuGos/rla4IDI2Phc7Dj6wSkesMB/KRZxiBtLgOf8uHyEl6VOwutX67pe8IJA5pn5fSiajjMfghIiIiIiK/wMfeiIiIiIjILzD4ISIiIiIiv8Dgh4iIiIiI/AKDHyIiIiIi8gsMfoiIiIiIyC8w+CEiIiIiIr/A4IeIiIiIiPwCgx8iIiIiIvILDH6IiIiIiMgvMPghIiIiIiK/wOCHiIiIiIj8AoMfIiIiIiLyCwx+iIiIiIjILzD4ISIiIiIiv8Dgh4iIiIiI/AKDHyIiIiIi8gsMfoiIiIiIyC8w+CEiIiIiIr/A4IeIiIiIiPwCgx8iIiIiIvILDH6IiIiIiMgvMPghIiIiIiK/wOCHiIiIiIj8AoMfIiIiIiLyCwx+iIiIiIjILzD4ISIiIiIiv8Dgh4iIiIiI/AKDHyIiIiIi8gsMfoiIiIiIyC8w+CEiIiIiIr/A4IeIiIiIiPwCgx8iIr9Uh4LVi7Aou0zp9mcNOPjuFmwrrlW6W1dbvA1b3j0ofum9yrLF/l1dIPY0ERGZMfghIr/XVLcfW15ahqVLFmHpspewufCozUntQWxbsQzLbJptB5XB5DsMtTi47yj0SqdJDco/OYqDh2ps+jtyGhWlB3G0/HPPBxbfHsX+z2phUDqJiMizGPwQkV9rOrgFKzfsRtPgSZiWORtTfxaO08VbsPmD08oYwqkTqD0D6GLHYuzt1ibmamU4+Y76cux+txjVSqfJMEzJmoM5U29DuNKnZb0xfoYYf/YkDFT6eMz/ilHwYbmTQRgREbmKwQ8R+bXAoQlITZ+DqYkDoQsPR+SoKZh0QwhO/7cMlvDnQpP4LwS6YSMxcpS1GXi5aXAz5xqg1zdA+pWF0s9wUemWXDTg9NEy7N9XidoGzdiKJjQcq7Q7vKlBjwb59kAT9GIaZTXW0+WmhlpU7tuPsqOntfOzI+zyK8X/ynwOHMVpe7ccxHKalqMMR9Uj2FsniTS+bX/DaRw94MQySdP83jQP83pUHrPZloqmhjp5mvsP1aLhnNJTYXf7SNOuqhOBRRMa9XrNshvONaFJNLYcLYM0btM5m23h5HIbvnewfwwNqKwVqe7iBbFspuXT/L61bWhOT2I/6sX2CApU+hMRkUWAUVDaiYhIqHt3NdZ9PhAzZidBJ/WoegfLsutxx7NTESuP0YqyzVj0DnCPeny5nx4jp89GUl/R3VCGLX97B0ebQhAiTlKbDAaEDEvF7PuGQD5nbTiIbZu24WCDGB5sGh5+yzTMvCNSGiq/z/F+r0m44/sCvPNlEwL7jsWcR+Jw+v2XsOljPUJMExUnyTqMnTEDt/WWf6YivfOzDkcjxyKqbjfKvg9BoBi/CeGIe3Q2xkvLKLpq/r0F2z+ogT5QWs4mGM6IMUZNw+y7xXIYSrBpxU4EJj+LqaoNo//gJaz+5BpMzboH/UV37QfrsOXfdWgKMc3DEDgQ9zw+BbFhpvE1pO30HjDsOj0OHmoQ6yFmI+aJ3nGY9vh4REqX7PRi2/yjAAdPN5nWUwQhhsD+uGem2N7KNO1tnzuCduLdKtNwk3Blf5i2xf7L78Gz6cqKXKzF7k1bsPcrAwJ7iOWW5hEWiymP34OBYn9I03/n25HWNOLEcjfV7MWW7btR832gvNzSPm0KH4lpM5PEcGUZNLd8xDopaajVbWhOTwbztMXahQOnLw6zLiMREaHr/xOUdiIiggEH9xaiKmwEJtwgRwBAzccoPKLHmbrP8MG/CnGgsh4/9r4a114mzoLtEeN9dAS4bsyNUKag9DPg6hG3YFBP0Vn4T7z/dTSmzp2GCaNvRULcjYj+SSR6yJM8LU68N+PgZUmY+UQqxt0qhv/0DEp2/Adnho5CVA/x+/KPcOhwJb4dMAVPTpuIMcMjRdBUhreyDyBsXBYyHxyDW28bgxuv/ymuu0KciTfTgKP/KUVVrR59k2dgxv3jkHBbP3y7fz8++6EPEoZK0VIXhDScRbe4yZiSJKY3OgE3BVfhg71V6HZLLCJDeuPHqiKUftsLY8zbCnoU/+sDsVzjkXSdOPs+tBV/e0+PYelzMC05QSzTTehVXYh3qy9DwvVXKb9RkbZTRQ3OXT0RMx6dJNY9AWMGnsFn//4PjuJ6jOovVj7kIgw//gR3pYrhCbfi1lt/gh/2fYT/fP8T3DrY9OCave3T74YxGNPrS7EfwkVQMRsPjTHtC/O2+Kr7dZb1qMz7G979qjfGZzyJKeOleVyPn+gGYsAVpn0uTf/I2atx0y2DIMcfTix3l5AzOBt8CyY/OAFjpH06ohuqPtyLKtEv9ppwDLplDAaf/QylZ0TAMjcDE8zpp9VtKKWXHBzoMhJTZ4v0JLZJQlw/nNxfKkJc1TISEREfeyMi0ji0HXuOhSE2XnUrI6I3wi/vjf4xSZiWPgkxoZ9j96svYctnqseeXBQo3d5pqsdp85X+4HCEm89Qq0qw/1RvjLxrJMLNpXS/sRjV9zQqD6puDYTEIimpv+lOkSwIgWL8hm/rLI9LhV/eylss/W/DPUOUcboMRH+ddMPorKlbCBkah5H9rMFTSJ8rxYl0HY7Jd1BCEHdjfxEcHkSZ+TEsfRkqT4Vh0PXSPR8DSkoq0XTdWCT1Ny+ltG2jEVhVgUqlT3PhGDhyiHbd+4lT/KqjSkUAOsTeKoZbYk8dekeIuX1VrX1Xptn2cZKhBMWHDOh900TEmeOzLmL/928thGhluUOGIG5UJELMw4N1uFJMsu6rGqWHPU5swxP7cPBUIIaMSUJ/8zYJ7o9+jh7LJCLyY+YimIiIGsqweUclQkal4p4BSj9J/yTMzpyCsUMjEa4biNsemoGxVxtwtLjY7RfTeydOwm29TdVNr1y3DXtV7+zgu3pxynsaJa+pa5dbjd0npMBEFXAFdxXhh9owTLxvGHBgM5YuXY3N75ahzuZdGNc1oe5AATbLteEtxcrcckhvohh+VAbfGIshgTU4+KmpU3+gEqfDh2CkFPuIrVPfKKZwtEC1HqIR0zCcM8AaYrUmBLorxZo21lu3t74Ge7etw8rnFmHRMrFtjot+0qNppqEmzbaPk+rrxTqGof/gZs8Kuqj5cjfVlaEg+yUsW7oIS/+6FeUN2mCzOSe2Yd1pMZYO13i89gUios6HwQ8RkaThKN557R3UXX0Ppknvs7QoDAP7hTc/2XZFl0iMzZiLZ0RQNUoEQcXZq7Fyx1FloCQSCRkzMEPVzMycjWk/a/ntjbBhkzD7mbmYliTOhD9/B+v+ugklqorrXNOAsuyVWPehHtH3zMTcec9g7v8N19aI1mUYogcEouaQ9L0gPcoOn0b4oFjNOyZhwyZq1mPGjJmYPXsSYpThzmg4o9rSxwuweu1WVHRPwNQnn8Wzv52Lu+Vgy5OacMEc4LWBerkbyjZj5YYPoL9uImZmPYtn5k7F8F7KwFZ4YhsSERGDHyIi+eX2gte2oAwjMWVKrAhtbNipWevEN3ogOKTFOwsX1L+zVzuXEHj5QNw2aSZmiqCm4cA+yJ8O0vUWAcZpfH0qHOHh2ibMmVsZXUIQOTwJU0VgNaxLLfYdcDP60e9DSRUQO3EKRl4T5vDxsWHXD0Sg9OhbQyVqTvXGsJHm0EcH3eXipP/UaQTarEd4uOPpNVeDmq/Etro6Sg6qKksOQN9XBD53D0Fvt27ttKKPDuFdDDhW43bUqFAvtx77/lMD3DAJU0ZFIszB62LNObENr+ot0mwdjqljZyIisovBDxH5NynweWkT9jcNRNL98Qj7Xqpi2NTIVSWf24/Nz63Gln3m92iaREywGe+LoCDyhlHauyBmERHiZLQWFZ+YHnYyVO3Gug+073VUFmxGQaXlYSic1ouZ9ehpCrzEif1tA4CDb23G/m+Vt3cuGlBzyPbjqzZO78WWf+y3Puom1kWqITusZ2vvqjgQ0l0EdwZ8fUJZTkMt9r5/0PIIl8XQUYgJqcGBN8pR23sIYlRPi8X+bCTC63ZjU0GNpXrmpm8rUSk9puZQA2oOVMrVNeOiHpXbt6PsTDhGjh4iDw0JFqf8351ArbyeYn8cegclLb02o9YtRAQMp3HcXOubvaC0SywSbgiTv/f0jnkfnavD3h17W/moaUvLHYLuQWITfl0LvTRPsT9r/70TB7+Xfmcl1173fR1qpB2tLFur27BfLIaEN6Hyw3dgWlzTNtnr7DYhIvIjrOqaiPyaXK31vman87LwUTMw+24d9J9tw+Yd4qTfXItAlxDo4lMxLdHRy/QNKPvHS3jniPLIU/gwTJoYjj2bD6K/XLVyE+r2bcXW946KQCIQIV2aRIjRG3EPz8T4fqafyFUtv74Ve49Zw53Ay2OROsNBVcsSfSUKcrdjv1Q9s1zdcRPCrrsHMx6yczdLqepaU72zIE8X1n61UtXZxcodkC7hGHh3LALf3w3DndrqrWt2rMTm0gb0TpiNmT/ThoQNB7dh03b19gtD/8SpmBpv550auUpwA4YMM1grdxDzHXbfNEwapqyFuVrnM6bOkKtHYuzVNSg42t+yPexuH1kddr+0DnuVVep/j7Qe9qq61svB5zZVBRNh19yG1IfHytVW263qurXlPr4TL71agtNKABN+XRJiu+zE7nN3W+drOIgta7cp62auituJbXiqBJs370SNsk2kNDd2gFjXKus2ISIiBj9ERE4zfK+H4aIIVnqFWWvsaoH8oc2LIQjv5ejZrCb5Y6BNIgAKc/QYmKEBehHEBIY4+cibxJ3ftESenjj5d+lRNVtOrKtEDiKaMH7uNMR1EfMVJ/P2t7dpenBzHeV9iZb2jUL6eOkZJ7als8stfQBW+hhqDzE9h4++KdsqMAzhYeot1fo2dHq9iIj8FB97IyJyUkgv03sWzgQ+ksAwMX6LJ6HSSazq3Q17QsQJsBjHpRN8d37TEnl6bQl8JE6sq0WDVOEaEGyar/3tbZqeu+so70tnAgR5GZydjxPL3SXEtNwtvvOjbCtN4CNpfRs6vV5ERH6KwQ8REREREfkFPvZGRETe49uj2P+FAbqYYYj0pRsYvrrcRER+hsEPERERERH5BT72RkREREREfoHBDxERERER+QUGP0RERERE5BcY/BARERERkV9g8ENERERERH6BwQ8REREREfkFBj9EREREROQXGPwQEREREZFfYPBDRERERER+gcEPERERERH5BQY/RERERETkFxj8EBERERGRX2DwQ0REREREfoHBDxERERER+YUAo6C0E1E7OXzMgKrjBtQ3NIE5jojIvwUEABFhgRjQLwSDrwlR+hJRR2DwQ9TOig7+gAsXgct7dcX5JmOnDn4O1ZzFXTeHK11E5EhwUAB69eiqdJE/OieOB1KZecZwAaOH9VT6ElF742NvRO1IuuMjBT5h3bvg3PnOHfgQkfOCAwOUNvJXUhoYPrAHeoR0lY8VRNQxGPwQtSPpUTfpjg8RkVoQgx9SDO3fXT5WEFHHYPBD1I6kd3ykR92IiMy6dgmQGyKJdAdIOlYQUcdg8EPUjqTH3PioGxGp8a4P2eJxgqjjMPghIiLqQAx+iIguHQY/REREHYjBDxHRpcPgh4iIqIMEdg0AX/chIrp0GPwQERF1EN71ISK6tBj8EBERdRAGP0RElxaDHyIiog7Cj5sSEV1aDH6IiIg6AAMfIqJLj8EPERFRB+Ajb0RElx6DHyIiog7A4IeI6NJj8ENERNTOpOqtpWquiYjo0mLwQ0RE1M5414eIyDsw+CEiImpnDH6IiLwDgx8iIqJ2xuCHiMg7MPghIiJqR127BMgNERFdegFGQWknIg/L3nkaQ/t3V7o6RnXhKuytktoG47bpdyFK7tsxDtWcxV03hytdTvquBNmvFeM7pdOR6+6ZgzsGKB1EPiQkuAvCuvNaIzkmHSvSx/dWulxzvrER5y4oHSrBoaEI6qp0mF04j5NflOP496I9JAIDoqMQrh7nwkmUvpmDtz4VR5Cf3Y/7EqMQqgwy0+9ZiedO3Y+Fk6MQpPQj8iUMfojaUccHP59i8323YmWF1N4HmW98gYzh8oAO4Vbw89UWTBv6MN5ROh35/c7zmDNK6SDyIT17dEW3IN75IcfcD35OouB3M7HuiNKpkvSHPMy4UemQnNiD5/+4FiWngPC+ETj/zUk0donBI3+ej3uvlcIYMa15T+L9qKcxJykIH724BCXDl2NVaqTp9xJDOdbNfh8xf5mDONuoiMhH8FIUUWdS8SEKROAz6sFfYJQ4kP393x8rA7zY5WPxm+3vIU9pNj091tT/F6ss/aRmAu/6kI8K5vs+1G7Oo/EHICp9BTa/ulnTPDJMGUVy4TBeXbASpVc8gBWv/h0b176EzdmbMX/iYERdody/OVKAnON347FpIxDZLwZpjz+A87kFKFfdVap9dx3KJ6Qy8CGfxuCHqBOpLPkHKnEjbkt/DLdFA9/kfIjPlGFeK0SHmISxSFCam69T7hz1i7X0k5rrrgC+O7Ibe/YcwCmDGP5dDd7PXokXV23CPumZuW8qxTAxvKwOP8oTMDH9RjRH9Eofq++qCpC9SprGSmTvrMR3TcoAIg+Rvu0TwNiH2k096k+IALt7D4T2CtU06kfe9B/m4O1vYjB97gOI6qX07BqKEVPSEKMEMvqqw2gceQMGm3937WCMOFeF2m+U7sYSbN0Rgxl3q+4EEfkgBj9Encan2Pfmp8CV9+HG6BtxY6Lo9fVb+FR+BK5zOPLmnUiZOArZe0qw8oFBmJL5NP6woBq4TAys2iaGieF/3a15f8j0G9G8Wan0keix74U7MXrEvfj1AmkaT+PXqTEYPWkp9jUqoxB5AO/6UEe4RtdHabOnEeVF5UBCEsa08FTyjxfFf8GO3+I5nPsK9FNSEROi9CDyUQx+iDoL5ZE33HcLhog/UTGPif8/RcG/RUDUyayflYr1vRdi0559KNz9S9yg9HfWqTd/hUcW7gZ+/goKvziPr09/jcK/TgH2LMAf/j91kETUNqzimtrVN7WQ6rc5ebAAry7Nwsy5S/Bi7h7UShUaWFSh8qA4JgyMQpBU4cGRUpTuL0V5tfZu+OXhVwH/q4Wl7/d6nEIYwqRgR78H/yy6HWlS9PRNOQo2LcEflr6KgoPN76gTeTsGP0SdhOmRN+ChkcMRLP72HHkXHpL6b/9Q7t+ZnLpqGl5d/wzuiRmOmFgduin9nXMA/1yxRRzUH8VzS6Yh5grRKzAcMb/4JdJF6753i/GlPB5R20iPuzH4ofYVhODgYJz6sh6R49ORNjYCX+SuxK+e+AMKTiijSM6J5uQevJg5FU+9sA7rNq3BkqxH8fM/FuCk8k5P0NCRuOmLj/CR8ruTH+bjkxtvQUyv8yh/YxN6PJyKwefLse53K1He5wHM+HkkKv/6G6z7lLfLybcw+CHqFGpQ+ZF0h+cuRPUFfvj+O/wAHaKkR98q/oF9nejRN9nEZIxy94Xbryqwr1xqOYG98jtD5iYfX8aI3rvrcFIaTNRGQV0Z+FA7u+IO/DH773hpfhruGDkCCcm/xIp1TyPhYjlee6ME55XRJNXvf4LIZ/+OzS+/hJfWbsTflz2Aqz5dhxc/VO7ehCdg+swIvDlnKmZmPoontwRjxmN3IPx4AdYdSkbqLaFo/PhtFAx9DHOSByMy6g7MmhaDgi0fscwkn8Lgh6gz+N+HKPi31PIv/Pm+fhgzUmpuxZ8LpX6foqCk8z361naV+Gx3Ad63NCU41XssEsaGg4+0kyfwrg91CNtv+YTGYcxtwLmySvmROLOo9F/j3n5Kh+SnyUiKBso/rbQESX3GPI2Nm1/G8qWrsXnzH5HUtxElb7yJmGn3IVLMp+rwJ6bH55TxgwYOweAjtTildBP5AgY/RJ3A8Y/fwD7xd8j9T2PO03+yNo8/KL//U/lm53v0zW2h4TC9Gnw/ntnyHvK22TYzId0AImorBj90qQRJzz4bzstPuwFXoU+UOE6ctA1RRFnYV/w5XQ/Ng2tBqtrijmzFKydTce+N1ooQgm2DLSIfw+CHyOfVYF/Bh+LvjUiatgBTp//K2mQ9jnFXikGd8dE3W4HSIV4orsBhqSpsyXcl2LtTaTe7LB63SS/3YCU25tbIvYg8rYs4ukrVXBO1p8ZTJ7WBi+TCYXxSJP4OGwLT59H6YERCJM7t/QSHVd/swYVqVIrjQujgASIMskePPW98jNsfTVIuGAEREX1Qe1pVyYH+JGp7BcnvmRL5CgY/RL7ufx+iUHrkTa7i2tTL6haMSpMOW37w6Ft0HNKlVT25FE+MfRh/WPA4kscmYCfiTMMtwnFP1iu4R4z7TmYCUp5cgOx3tiF71dN4Iv5OvCi/D0TUNnzfh9rd+VK89uuZmP3c2yg/3ojzIrA5f+owCpY/h7e/D8e9DybA/Gpk5PhHcAfexnNr9uCk9IzbBT3Ks9fg7W8GI+3uwaaRbJz/dCs2IQ33X6f0ECKH3QIU7kKpHHE1ovS9XcCYeNifApF3YvBD5OOO738be6UWpYprW0OGS3W++cGjbyFj8X9/nYlRovVUxRa8uGo3+jy+D1teSGn+GNuAaXh5++v45fju2PPaUvz6/x7ErxesxP7I4ejTzXzbiMh9QYE8vFI7CxqBX66ej9u/34ols6fiodQUPPTEM3itog8eWLgaj6iCFoSKcZfOwZCKtZj5UApSUh/FH94F7v3DfCTJt8xtncT7W8qR/HCC9q5QdCrmJ36B5zNnYuYTT+D5g7fj6QcZ+pBvCTAKSjsReVj2ztMY2r+70tX5Hao5i7tubuEreh3BoMd3P4q/3cJxmRM1F/z4nR5yuBMoxne3BjkiG5f3DJQffSNyhnSsSB/fW+lyw/lGNJ6VWoIR2svxh0ol5xsbce6CGDNUea/Hngvn0SgKxtBQB9MyiPmda31eRN6IRTMRdS4hIoi5zLnAR9JNGldqGPiQh3TtGsDAhzqWUkmBM8FIkAh6LBUaONI1yHHgIwlxbl5E3ojFMxERkQcF830fIiKvxeCHiIjIg1jFNRGR92LwQ0RE5EEMfoiIvBeDHyIiIg+RAp8Axj5ERF6LwQ8REZGH8Ps+RETejcEPERGRh/CRNyIi78bgh4iIyAOkx90Y/BAReTcGP0RERB7AwIeIyPsx+CEiIvIABj9ERN6PwQ8REZEH8OOmRETej8EPERFRG3XpEoCuDH6IiLwegx8iIqI2CuYjb0REPoHBDxERURvxfR8iIt/A4IeoHUlV3/Jr70SdHz9uSm3B4wRRx2HwQ9SOIsICeUWYqJMLFIFPFx5NyU3nmozysYKIOgaLa6J2NKBfCL79/oLSRUSdES9wUFscqjkrHyuIqGMw+CFqR4OvCUFXkcsazl5EcFAAH20g6oQY/JA7pDs+B46ewRnDBflYQUQdI8AoKO1E1E4OHzOg6rgB9Q1NYI4jIvJv0oUw6VE36Y4PAx+ijsXgh4iIiIiI/AIfeyMiIiIiIr/A4IeIiIiIiPwCgx8iIiIiIvILDH6IiIiIiMgvMPghIiIiIiK/wOCHiIiIiIj8AoMfIiIiIiLyCwx+iIiIiIjILzD4ISIiIiIiv8Dgh4iIiIiI/AKDHyIiIiIi8gsBRkFpJ6J2sufLEvzn+Cc43nACzHJERP4tICAA/cL64uZ+NyHh2jilLxF1BAY/RO0s5+A/se+rMqWLiIjIatTVsUgb9oDSRUTtjcEPUTuS7vjkVW5Ht67BeOj6Sbihz/XoGtBVGUpERP7ogvECPjv5X/zjv9vw44VzSBkykXeAiDoIgx+idrSieC1qf/gKt/S7CV+f+Ubp2/lV1ddgdOTNShfRpXFZt1649rJIpYvI+0hB0PqyzYjseTWy4jOVvkTUnljhAVE7kt7xkXxztl7+S0QdJzS4h9JG5J0CAwLlv+ZjBRG1PwY/RO3IfGP1ovGi/JeIOk5YcKjSRuSdjOKf/JcP4RB1GAY/RETU6QR3DUa3rt2ULiIiIhMGP0RE1Onwrg8REdnD4IeIiDqd0CC+70NERM0x+CEiok6Hd36IiMgeBj9ERNSpdA/qjsAuplq0iIiI1Bj8UKf348VzckNE/iGMj7wREZED/MgpdSpHGqvw3x8O4/PGatQa6nBcND9e/FEe1q1LN/QL0SFSNINCo3B9z8G4LnSAPKy9zHlvnvx3QER/+a+/4EdO6VLqH34tegaHKV1E3u1vpa/Kf1feuUT+S0Tti8EP+bxzF8+huL4UJXpTc/7ieWVIy4K6BCEuPFY0NyE+YgSCuwQrQzyHwQ9RxwoQ/66/aoj8l8gXMPgh6lh87I18WkXD51jzv1exrOol7Pn2Y6cDH4k07p5v/yP/dk3Nq/K0iMi3hQb3YOBDREQOMfghn/X+6T1Y+7/XUHj630of9xV+82+srXkNO8U0ich3sZY3IiJqCYMf8kk7Tu3CpmP/QM2ZY0qftqs5ewyvimlK0yYi3xQWxOCHiIgcY/BDPmfn6Y+Qc3wbvmv6QenjOdI0pWlL8yAi3yJVby1Vc01EROQIgx/yKYcaPsdbdf9ql8DHTJr2m2Ie0ryIyHdI7/sQERG1hMEP+QypyuqCU4WoOVur9Gk//xPzkOZlriabiLwfH3kjIqLWMPghnyFVZ737myKlq/1J85LmSUS+gZUdEBFRaxj8kM8o0X+itHnKlfhp6JVKu32enyd1mB8Oo3DjW9i+8X0cab+nJMlLdOsajGDREBERtYQfOSWfUNnwBX5XuQRNxgtKn7a6EmMG/AZPRgBFNS9g+TdfK/21AgO64s9D5mFI2E+VPq7p2I+cnkH19n+jWq90Krpfcw2uGfpTRF4VpPRpf+5/5PQkyjaWwNGDjaE33YHEG528un/yI6xIfAEfIx6/Kvwtbuuj9KdO6YruEbi6Z1+li8h3ePQjpxfOo7HxHBAcitAQpZ895xvReFYaLRRBXZV+kgsnUfpmDt76FBj8s/txX2IUbEtc/Z6VeO7U/Vg4OQodd1Qh8hze+SGf8N+Gw54PfK64GsGiWA9rofSW5inN2zc04ZuirXhztbbZ8vQLeP6eTPxxUSm+Ucb0XnpUr9iEzQ6asuPioE5kRygfeSPC4ezHMfWRqZj6ernSx44Lh/HqL8U4YrxXDyr9ZCdRsOBJZNePwYzH7wB2PYNnttpcijKUY+vrwH13M/Ah38Xgh3zC543VSltbqQKfi99h//FF+H919u/6mHlu3h3nzr/8Bc/tkprlmPeX+zH0CuCb7S+jcM8ZZQxvdwNSXliIBZu0TcpNPMEl+1jZAfm9I6/iuTcjEBWldDtwOPs5vN0rCs1GO1KAnON347FpIxDZLwZpjz+A87kFKFddd6x9dx3KJ6QijtmNfBiDH/IJxw0nlLa2cD3wkXhm3h0ruFcPdO8pNb3QZ3Qy7nrA1P9QzWlTi9cLReTwGzDsZm0T1cf6Tkdj9WfYmy290yOa7I9wxIVVa6z+RHkf6C38K/8w9HYq9Wv80jpO4Z5aNHrqxiN5XI+g7ujaRf3sDpGfke7mLHsbuP9+jGnplowcIAH33jsGtm/I6asOo3HkDRhszkrXDsaIc1WoNT8y0FiCrTtiMOPuSKUHkW9i8ENezyj+HTecVLrc5V7gI2n7vC+18zhbb2ob2r+3qcWXnTmM7U88jmn3LMCqPymPxP3pBcwf81vkfdraY3HnUL1xAebcswgvK4/SbZj7Wzx25wvYa9nNjTiyToyTZB3n5SdmYc70rTjiKzfO/AxreSN/J9/NQRLmPHid48fRzAFS8hykRzcf68eL4r9gx5HT4dxXoJ+SipiW3iUi8gEMfsjrnbt4XjStnNR2S0LGtfGw/067+4GPpNV5e6FvDlfiyCdSU4o9f1qNnDeAXgkP4/YE3/kI5LnvG9H4g6oxBx49ohA1JBKJv38WKz/MxqaSTVgwe7AYcBh/f+kjnDKNZd8P5Shc8Rn0uAO/+vBNvPHfN7Ep9zf4+WMTcIuSePT/+huW/+Uz4N5ZeG6vGOezbDz3x9uB/2Tj9W3t/40pcl0oH3kjf6bczUl68pEWAxNLgPR/MXYDpMvDrwL+VyvKR8X3elGehiFMmqZ+D/5ZdDvSxoSLA0w5CjYtwR+WvoqCgzY17BD5AAY/1AkMxBODJmNSnwz86ad32ARAbQt8fFXxcy9gzUypeRlb36zE9/1H4Pb7h6CXMtz7FePl+9MxLU7VvG6ueCIYw379LJ5Ivwn9eocitGcEhqU/iLukQXuqcLylaq1/PIdG6a/0u25yH4QOuR0p6YOVk4Eq7HnlIzk4ynj6DkRFiF5dQxH1wAQkitYjH1S2HFxRh+sSEMA7P+S/LI+7/RYzhjm+a2N53G3uDIcBUtDQkbjpi4/wkfKk98kP8/HJjbcgptd5lL+xCT0eTsXg8+VY97uVKO/zAGb8PBKVf/0N1n0ql6pEPoPBD3m94C5Bomnp+x1Hse3Upzh5MQh9Lv+5KgDyTODT8ry9k7nCg8Vv/A6z/jIdd17zBd7J+j1e3lCF88o43i0C/aT3fEarmp7aA3vjl59h78ZXsGLGbzErbhH+Jfett94hsqf3DUj4uYhoTr+FpffMwqoV76PsS9WB+2QtjlRKLfU4uE15n0hu9uPUENG7qN56VZS8Au/6kP8SQckm6W7OvfhtunT32wGDCFiUAOmR65R+9oQnYPrMCLw5ZypmZj6KJ7cEY8ZjdyD8eAHWHUpG6i2haPz4bRQMfQxzkgcjMuoOzJoWg4ItH8HXHw4n/8Lgh7xegPjXL6Tlj7ScPLUavz+2XxMA3emhOz6tzdsbmSs86NV/AK4bfTPuWfwwEkT/6r8V4pD313ctDMHkPy/EgnWqJn2AMuwkyp6T3slZgFVbTyL8hvGYuukx+c5M60IR++warFx2H2J71IrgaQ2WJs3Cwuc+sbmjU4vq4k9QZmkOQ3+5FISFsnpXL8O7PuS3vtiKlfl69PnJeXy0aR3WrZeaXHxUJ4aVvy137/lSlPvbVqLgmz74qeEjZRzRvPGR/D218nype4/l22p9xjyNjZtfxvKlq7F58x+R1LcRJW+8iZhp9yGyK1B1+BNEDbRWcx00cAgGH6nlHXHyKQx+yCf0C2n944XaAGgqfuWhR92cmbfX6x6Ky+SWszhzVm7xWY0fvIWlr38G/V2zsOadZ5Ex+w7ccvNPcZUyvHWh6Jc8Dc8UvIFXckTQdG09Dr6+CNs/aAR6hCJcHicOP19jE3zJTXLz6mHpkuL3fchvBV2D+OQkjNAp3Q4EXR2PpOQRuMrZM76gUIT2Uj5+emQrXjmZintvtF72CWbFiuTjGPyQTxgU6twppzUAEh0eesfH2Xl7s7NFn+CA3KbDFc5HCV7pVNUn8t9bfhaDq8wH4fpvnbzy2Ijjn5sfcwtG+I3JmPgL0+Mi+jPngJ5DMGyS1CVVgc0HObxdUJdAdA9k1VPkp65NwIzpM2yaybhdCoZi7pW7E64FIsfYjiOaB2+HVGF1TLLUnSC3N6fHnjc+xu2PJlnepY2I6IPa06qHf/UnUdsrqFm12UTejMEP+YTrwwYjMMC5y02mAOh9vOOBwEeapzRvX1P8wgtYM1tppszFb3+9E7W4DCMXj8V1Pn6UumrQTfLdmY9XvILN0vs4q9dg4f3PodA02KpPJKLkmr0rsedV02Nt54vewB/vn4U5czeh8P3PUJafjddfkipSGIzrBki1G4Tilsdm4Rbxu4/n/w4L/5AtxitG4cZNWHX/AmyX3wcib8G7PkTt5/ynW7EJabhf9Z5Q5LBbgMJdKJWvITWi9L1dwJh4UYIS+Q4GP+QThoT9FHERI5Su1p08tRkvtzHwkUjzlObta745WIkjHyuNvgeum3g/Hn7tj3j4Tt//zk9own2Y+fANCD/9CbZL3+HJb0Tssr/hmUeVESwGIPYxMR7qUfb6h6g+DQTFjMfDjw7A+fy38PKTC7B07laU4QZMXPsbTJQqNJBcewd+9aroTgjGwX9uFeM9h5dXvIUjuihEdPO9as87M77vQ9ReTuL9LeVIfjhBeRRYEZ2K+Ylf4PnMmZj5xBN4/uDtePpBhj7kWwKMgtJO5NU++KYYy6teVro6xlMDnsDProhXulw357158t8BEf3lv/6iqr4GoyNvVrrax/kfGiGFIsE9W66EwDReEEJ7qm55XTiHxjOmeu9a+r15HlJ116G+84kkvzH4ikEI7trS3ifyfn8rfVX+u/LOJfJfr3DhPBoNQGiog/xlaETjuWCE9mL+I9/DOz/kM+IjRmDsFaOVrvYnzUuaJ3mnIBG0hLYS+EhM49k869dVHLSd+L15Hgx8vE9IYDcGPkTtReQth4GPJESUiwx8yEcx+CGf0a1LNyRdlYifdLf/aqYn9RfzkOYlzZOIvA+/70NERO5g8EM+ZWjYINyvuwuXBfZU+nieNO37xDykeRGRd+L7PkRE5A4GP+Rzxve+HWn9JrVLACRNU5q2NA8i8l4MfoiIyB0MfsgnTbhqHB655iH0736N0qft+ve4BtPENKVpE5H3Cg3qgS4BPHwREZHrePQgnzW+dwIy+/8CiVfcqvRxX2LvW5H5k1/gDjFNIvJu/L4PERG5i8EP+bTosEGY1f8RzB0wEwmX34ygLs7XPiONm3D5LfJvZ/3kEXlaROT9+MgbERG5i9/5oU7lSGMV/vvDYXzeWI1aQx2Oi+bHiz/Kw6Sa2/qF6BApmkGhUbi+52BcFzpAHtZe+J0fIs+SHne7/krzF2mJfJ9XfueHqBNj8EPUjhj8EHlWr2498ZPLPPeuH9GlxuCHqGPxsTciIvIZfOSNiIjagsEPERH5DH7clIiI2oLBDxER+YTgrkEICeymdBEREbmOwQ8REfkE3vUhIqK2YvBDREQ+ge/7EBFRWzH4ISIin8CPmxIRUVsx+CFqRwEBAfJf6dskROS+7oEhCOoSqHQRdQ4B4p/8VzlWEFH74xkZUTvqF9ZX/ntF9wj5LxG5h3d9qDNqMjbJf83HCiJqf/zIKVE72vNlCfIqt6Nb12A8dP0k3NDnenQN6KoMJSIif3TBeAGfnfwv/vHfbfjxwjmkDJmIhGvjlKFE1J4Y/BC1s5yD/8S+r8qULiIiIqtRV8cibdgDShcRtTcGP0QdQLoD9J/jn+B4wwkwyxER+TfpHR/pUbeb+93EOz5EHYzBDxERERER+QVWeEBERERERH6BwQ8REREREfkFBj9EREREROQXGPwQEREREZFfYPBDRERERER+gcEPERERERH5BQY/RERERETkFxj8EBERERGRX2DwQ0REREREfoHBDxERERER+QUGP0RERERE5AeA/x9xrxGDEN7jPwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "987fc10e",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4dc0968a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assumption is words are independent , \n",
    "# but are dependents on the label.\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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f8nClA5sS369oQSCPo28y36xKpJNYX89+dL1tvc883oBBI3wX5ADDCzXxecltn9PEIA5/zbb9zkyh1Kx0ZBEZQe+Le1Ej8kxiOnN7eFrQ+076ffAjLybWXZarxIPoct1WfXw96l8NipyYgRjXyDPrV8jtbj64nvq9eJfYXtJ0CP0vKnnFmObB2oz5Mm3ZokF0JdPDgcOX4+OZkr+byL/id8ur0Phif3oZxAgOK8tozYfW71a8aLs5ZzaMQct8UmUyVznJKy/kKB9qGTCHi+XoF/k/sZ2aTqszt+83ZFocHcm8HY18ZmzzxL5LbpP9rwYQySOQLWzfoabvYfPWp+jlLtQnlvXBw5YyGrvWH182S1kTeflaPtXkfOcVRfisKNNfNRpgSKSBUd6MNMuQBgvah8+xLdLEMHohHsYA9XA+JHtVFXUYCgyZXd9BtxyYTRhhub90aOXxHpTDIW/XDURSSx450462K4DnpWa4V8uBuZRUoWZ3vDdyYgSheC/R7acT3VGTun8ndCMrZut8I3LM3jpluEN3VFjHS3SuV0bjEyTN6KOveHQtw7hmpzXo/rAcNZt3/Xpdcprs80Z1u26ZuzrdxnZ94I0Gy3JoL8mxPxjV27drqd+xd5pHbw/OxMeVrOmRvbOnx9hr7ozjJTuRHn3vypFNw7ov03j2TmvWB96XkxgsaQa9rndSfmFf9jrdr8wv83d55pP3+/Sm5DCnSDNj2pTx7yjrfmBI5Ixc5sg30HSPSNvEb+TcHjv9Yg1yCPv1Bi3DdLZOTcOZQW+OZTM6l+67ZF3D4UOp7937mnVPxnlqevObU3IKKc/ls2zL8IDeXJ1pnHjnOjRsSX912Zp7B3SfZVqRJxOrPsfvolrkQ0t5tuZfNQ3T8rZYppRx3b87V5n06gMfyFFt8soLeZYP5ysDep9lOTSxXeWIc+w3tN1+sRaKObaj9sxAzjKxsH1Hs+4f9OkudfzdfclyMf5ms/U7Syfy8kju0prfvMb0zs3KsAydpiyTaZH24WnbIiPxGxvlNFu75xzfms98IgXsUmni/V4q/ZJlbd+Abpb0mSG91cgXG8VvzJXMitGXEusryv1cx1CiJcJAhu6wSX3gGbfu3lxpOTg4qsWwrfGuUx60M1YOKlxp00Kz7tBnLrTqzuT3mu5+olVvP9Kue3c6ktNoiR16NrZKhznNBo/evK9Jd9mCGs9x5fBjmU7TNVslIl5hnNGHDlgrKpW7vHr7gQa9Uh1/Y6s+rFSc0tNDTLe9WW/e49IdluEevfu6nMjwvqgImr8bH79VpEXrE25LGjqPqMGPrbJndCLdm4yK8AbrgVt7TgkOFjmQyS+fTOl9e1LfW9fDGgypB/ZMZr5nDRS0DQ2694hXb7Css1NvvRD/nck3veZyWLaZkT+N5RMVREvlyGJc796mTJPcjrbfEp21Ei4qPbJC76huEsvWLvJMnXXb7+mz5Gs1WEh02gaxfNWpsmB2lsBL5M/nUuusbffpQ+EpfTLo15sTlS5U6nUHxPyPdOvD5gxFEKAGnhV18eU72KS7k+vksgSa6rJpmjVfGRXI+Ja0p5VD9+xr1Vv3eazrLZY/VQqt+TffQGYm0Jra/ppb9x4f0IcG/Xq7WVZyV6zzygv5lg+RFpb9m6ikD8Rrn/rwQacynltvOmhsA69el9wnqUGpbTtu9urdg0P6QG+73rRZDK8W+80sgVnSgvYd6fs/Z+Ikiwiw1LRw7BT7P/tv208SpclvXlMiYDJ/U6s08077kdb4+ifHtZ0AyXMfXti2yGJKpG/iN+Y6JgnWfWWmQGZU98nlTeWvSREYy2lkfh//jsc6ztS4PnDcWP52vbN3WJ/8KD7YTp1/4oQj0e3GQIaWB9tBPdNO0brTjp8NT+zoJ99oUr5Tzw5ZKz6e76jnuNTvEpWDLGzL5zygHGQ+GNZbkxU60W1TzqTZpjOWu+E7Y9YD1PVu3aOMY1lG+3dKkGRND1GhDqR+1Rq82YIrYfRNvz6qXj0xDsSHlANxsuJosFX2dor1Sx7YbBVWrVUfStTv8q2o5RXISHnkE0sFVF0PtZKgLmdGtgqzuk1zfme7cmQ5w5/FiM9SEaxTt5W6zMZ3lkq4mFvArw+9a10Ra/pZ87U1kHHpzf2peY2+pGx/iKBZDlcrQ0bXekEOFsZfT82r4Y3UslmDAK8+pFSQ1QAxWZEV0oKs6lZ9wLq61t8VZUkNRq2BpybKQ+K7+QUylnTcYT2bPjU5JUrMXObIC3mXD9FpDXr3VdsOyrJvsJ2sUL9LVoity1P3umWN9Mn3514jw4L2HaJzPScCOUvF2BZgWa6Uqid50q+wWuUzL8OoCN7EsUMdPiOmVfbhar4sfB+ez7bIQp1XHvsOaz7JFMgoafu0vNr2wZDuleXZDKwSn+V2mxHLm3bVzrjKabnSJol9V2Ic+76J6HbhMzJUpGrQuNOJRMOU2oOVSDUW2Y9w4tbfSBCBs7IfdahzlSUf7oxGy1CebNq3C8FrsjcPDuem5Lyx2oX6rytNVZ4NIZzllm7tgB89T1SmphUiwQBSbRvVoXGb8qBlRa1YT9kvDJ4PZnnWwoGqB1O/WvLFejQqzRYPhsKWZxKc2xvgNB4wno4gdDGA/hNdGLge/850JZb94c2Hq+BIPoTvgGe3qGonREYQVp+RuANKNtejJbHuV/wIyPfQxC4F5EO0YjvscaMmVyNRlnwjtsru2lST0KKvdreobiScDSCYeaPkJTjSp2xTL5rVB21LRR6VvZlomxvgXitWJBZF+IrIR6e64L+gbrnJHA//i7y1Q1mrDTWyzyDycHI7it9T1m+V0gCD2h+dnpF9YuqLPalJRFpvmk2UuShi5ZWi9MYFR7K1uuRB90kfPCIyUVl+V9uP+q2pjViytR77k+NH0HNxYXftl2qVqYfzz3hR23AYvfJN66VaKW5fG2MavK/3oGmDtRley35jRx1q1qTSOLqmPLU//HZQPr9QCq0ilaD9z4rt/2IvRm6aawRtTX5rtKB9x7Zu+F/yQLM04hHCyIlULmjavEk+KG90MZQ/mMwtGA5lzi0ZZZyXwQnPLnHsEMNjETHv8/3oeXUAY/JbQ3A66xrMvQ/Pa1tkcWsy+Q4tbeVi5LASuL/WArPJgFebsKlhLxpdjegwkrHah6atpQh+t0181tB8qEksZxBdu/eiV3yv7e7EQMCP1mox7sUuNL00mOdzPkS3FwMZujuIg4VS/UuZCKFX9hoBzt6H4w92Jrral+VXC1SuqXMPI5Ll4fwaZ1VaBSh8LbWEwCY41spekwbHw7LXcC4kqqb5KBcVANlrmIhYDkLRd+RDtfeVo8q1BfWPt+Dwyfk1a1x6v1rVDmRd99vHibqnEg/BBuE/F1+v4EW/+d9I06atNbkropZ8I7aKw1qj1hybZJ8hgNACgrfYtJLuWyvhKKT+EhHBmfEA/qoyrHduEZXt/Wj5ttLkbwGs21HlhOu51Pr3DQbieWk2jIEz/eYwg2NNmewTFVw1snu51lLmyh7eK0riXGpQlXzHSoLtdx9z2Mq8+PyY7BUiIngvoNqbpmRzE7r2JOYg5n2qTVQCy7FKNlZw+yp1NdhUmZ4pLPuNM3tRpaZxWS0Oy69SRKX2SVEhTaSrqMT3vtCImgdWZW0cIpMF7TtcVen76ZshjCob6rBHXY8yVD05d27JKNO8TFGEzhxG48NiW5ZXocZdj73PiiA1zxfvzrkPz2tbZBGLJRs/qFlrDVznbaMXg99rhVsz8nCP2M4RaJu9GDjVCuf0IHqOjIigpgVN28X8rgTgNxvXqUPHS83wbG6A72ir+TMRsV8ZZiRDyxADGaJ7TOziYXgerkebfAeBtsGD5oOdaD8wVws4xcMhDsAe2R/sDYjKQRDD/bK2pDXB7SokWlimJnrRKIKX/aKCYZ7FrXCh6UA7ug81mV8vHqMC3IEGGcsEX9yCMqMFp4+vx95EC3LV7fGK0F3Fgbrjoxjr9cKjBlU3Bs2W3jwvjmS/8rBcratD96Ux+A94LJX88NkOUbH34PDF3GtU/PuOGEZe9KCqrs2s0BsnNSq3N6P1WDu8uVoHvF00kedkb/+EEt0tUOlWH4bencHU1JToZjAZaIdnnSjLJ9rQFdHgPdRsXjWKXB+VgZRyQm1dlVymPgRt750hWg4YyNDdbW3qwAC40X3dfC4sY2deQp+n8I1h2WewX1XJzVGh3KaEUdutWRFMqrdtbK3MeatRShhhtenX5Dssguja12Y2TWpwHRpG6OoAOsWBrNE5v4poJDwq+wx1Ba37kqmoR9Me2X9FrOPFEIblGdeML5mzs+QbsVWS9yrGRd5Vb0Rxo3IB61yyUrnacy7fqwhRDB5pNG8BMWi7/RgPDaP7iBdNWzOfh14QUQFu+UamyqoDngN+jJ3zpm6fsV9FPDCEmQzlzezeMF7Oly/b714QeVz2xk1a8rxWOY93+KQpReWudgxcF5W/kW54N6d+ceSFdvTfwdsoLfuNrd0Yz5S+Ztcav7UoobQSDUcGMD4zieHjXriTqzSCtlfUpoTtFn/fYRJlTb2+6f3eTIZ1iHd9uxa4Ra90ofmF5BrAdyGEsTc74Xu6EZsWsArz3hZ2abfBLaKVJSgtLRWd3Pnd7IXvWRG2bPOhOXGL5myuQFbkDFvT1LEPpmQfUL76Ljg5REWJgQwtD2tEJUX2GkavW6so87a2EjXJZ0UC6Hp9kc6izqaeB0B0EH2vKYf/fU5Uyt58aI96RFU4oR996kvKJgLwn5T9gmezM3vlTDnIRM/2oVNZpOaNcolupir0xnMITbtdyXu9p6J53jfwkfxvmA2h76Ry68fGmvwq9fO9/zvvfFIKz2755mkjTV/pk7czZXnJnN1aFzzKGdr+MwGl0hxG4LRyK8k2N5wLqF85NtTLPkMP+s4pOXQiLELbTEIIflv2Ck17GpLPLcWm8rv5sBDRt1rgeT4g+prQN5k4szuFmY/GMXCkAZW2905UblSemzrahf5Feomps9or+4RIHwbeTqVV7O0+9CXzvIam6sylcDg4ltwHRN8OYED2p4uZAXC8VJRAq25Ce6Af8RttDP139HkwrbImFTye60KPkhZZTYcwkriFrESDa087hk6n1gin7cGhYjH2HRlVwrlP9god3xXpKvsXW0QE+8mb4LY3oeGLyTUQ6yB752Fe2yITEdQlc+3Vhd0amVsMIydEIC7KSeuBxuQ+tXRNok85vsVmZBlwp166LE1GEltKQ3n5AiJBogVgIEPLQ4nYgasVx2f3o+VEP/pf7UJgIcdIcXhpOtQsK7TGbTE1qPpaC3pExTRwfhC9R/diS7ntJX556P/6FtQ+1YaOoy2of7Q2/vCkyQnf192WB0HnVFEP775UTdj47fpnO+K/vbVRVsAFrRnN27Odbe9H49Za7H9BTPdsPTZ5OlIHwY0+8Z1cIksgMIi2b7Sh92w/er5ZC/dTRkU1Dy/WoaqhRSxfG/Z+2Y396kPxzzYqZ+az0x6okn2GYfhf6cFIPtu5gHyiPvTff1qmorYf7ryuvDlQ/3Qq3+B0I7aIfNNxtAMtX9uCxuRLOTU076u3BFeFKt1ah9bkjCLo+HJVfPu/IPKmS9n+Fho0JR0OP78XXWfi+dntSTRpsHhCV7pkfhrD6PmQqPbFxcy37ovOVm8rFZXE9mQ6i7zp2oK9R3sxeF6UuzM9Ig3XY9M8bs0qEeUh9btBHN5Zi71GnhdpVbtTeblitSg7mxPBsgMOpcGMyNFGbHJvwRZ3FSofS11hsItd7ECTS4zjNtLW2F8EMPhdv9IwR3PmFxbeLhub4EvuN0RaPGbkmx70G2l8thcdT25BueVFrKLy+q0m1DxciS1PiuDSGE/sA3t6lWeqcp2EWYx9R0al8DzZnrpSIcpajUjzjlODZpr3nxDb8sFNc972lo9URV14qw37nxd50siPXxX7sXNy+HwUvC2y0VC1Q/ZeCGd4HjLxYuB41/iSeidAD7xy+JZvDuae1w0/fC+I0rKzA/uT5USUr8pN8kq0SHuZ3uER0W/0aO6059Yi1xN7p4VdlSZaEJ1omUhrctTsUi9/s35fSLO8RvOgLmXa9M7a5GcGaU1wZu7sL/nL1cSqxQfDthcA2rv091ao6aFVOCxN+KY6+3TWpk4tnfgNcZiXn9WmPNVmTbO/DDTthW+51t1oVtjWxGdim+XczsJc+UQ1ekRtUhi2d1zMZe58k7a959P8sjDea33JXqrTRHqntpeahuPH4+9+SOs0sY2SaWtNP2sTx7bmWpWmVO3TZZ2X0jm2eXV/SEmNOV+iaW2mNueyqeb63QwvuJ3qV5tnt47bekB5yWNie30k8ny2l97KzvVS6mWomS1W88vpZSBpzv2G0kS12L6p8p2pS3+BqtVC9x2iy1EespcB2VmaOc8kj3klXvyojic7h1iH5Gd12kXbh1ubC89m9JXEPkts97SXTNryVLYu5wt4E01aO9OOKeY+L/E+HM2tN+xJvZ/J2ly3YVzv3irnl8fLO4mWCq/I0LKh7erB0CHrQ6jGGajR0EJvNCiB6+Cw+YBjw4bEWbMEBzz7OuH/pjutJZpcWo/3Qb1fHlolGo6NYvCgq6DfSVrtQuu5MfQdqks7u+/Y6UNfKIDW6uy/XP/KMIb71fvdxSJtaEDnpUHbdCVwPz+Izp3qXDS4n/Fj7NIAWud64HVjM/oujdqmN56T6MPw8Ya0Zc+q1APvsSbL+PbnULIpJJ84dzQrt+050bg1n+tFCUa+CWCs34c6ewtaFXXw9Y8hMN/tbePY5U/bfsYZUG9vAEOvqLeepTj2dGLgGXfqqpHg2NmOoeAounfLAYvEsacH/l3yQxbmA+ObfQgkTpyva4A/lP5gucHIm77+dtSntUyWB+N3rw6j8wnrupv5+IlODF/1o2GdHCSV7mhNSyuj5aa+ET98X81wq+FKUR4vGeWxAZXWmYgJZVl/zrko235BjP3GhUkMGbf32ZezwoPmY360JM64V7di9GoffLuUZqWl5L7ikVxrtMB9xxyMMhAS286rNj9v0lC5S+wDjyzsyqepxI22s53W8myWszGMvtWq7CvmoZBtkYPTVS+3T7/Iywu6HSGzG350HBX72j1taE47poh93gt96DPKSiSA3hNGQyIO1In83rPblvo3RzAor2I5t21a+LYhmqcVRjQj+4mWh5hyq8rKUpTa7r9fKOMdBYmfL1ldipJ8HrC82Yv6B1K3+bRe0OH7Yuq38v6dfMzGEE28x6DQ9U9OazzYmfugmUyHeaZxcvoSMf3cx+fMlG1dcBrmkU9i59vgcB+O32axsR2jQfWh9MKk8s3caTt/YvvJlco7PYzbu8zno5ZquaIIPOvBlqNGM62t8L+kBASxCIJnetD+3YC8lUWD79KkqBCbH1LUPL2Yy6nmgXzy4bzSKrVNDCWlt/MdMoVQl3OO9VPTbR7bY6H7jjklt5OwkP1LVvMoZwUpYFukCaLDuQktxvNIe/owdbyusFuV55JI2znSda5tHDlVj/IG44joQff1gVSz3kS3GQMZonzYApm63smFt6BDSyReiYhFAujcU4/D5nsRuM3m7UoHNjlbzOdPPMfHMZB8t4o0PYj999XKl40aLQMOsVJDtADhE7VY/7jx7NJyDRLC6PGsx17j+cilCLaICsBby4jo7vK2z3wRXXllKogx3nPSxiBmfmZjyYeOB5/fj8NnQ4hEoyJYjCJybRAdj++XQQyg7W5GLYMYogVxbG9Gs7m7GkTXmWQTFstG7HwP2sxGXjS07vYwiKE7ioEMEd1VwiFbO1TVzRg4Nf9byu55jzSgY7cMAiODaPNUoVy+tby8shYtp+PPJjl2dmLwtbrUbWdEND9rPPAe8og4oRKVsUmEk7cBLg+T0Rg2GScsbK2eEd0JvLWMKB/GswAjY8lmZ8serIFzLXfgy1H4bBf634kf+Usr3Kjd7oS26PfA32NmIwicbEfvyUEMnI+/1d1U4ULd1nrUf70RDdUMYYgWTwyx2ZIleH5nsYjli4nl42GQ7jAGMkREREREVHR4axkRERERERUdBjJERERERFR0GMgQEREREVHRYSBDRERERERFh4EMEREREREVHQYyRERERERUdBjIEBERERFR0WEgQ0RERERERYeBDBERERERFR0GMkREREREVHQYyBDdK2IRBM+PIHRLfl5CsWhM9s3TbVxWuktMjKD/YgQLzHnLSAyRi70IXJMfiYgoDQMZyiCIri9vwZZE92pQDqcFiwyiRUnbrsty+FKbDqDt4XJscteg6v4aHL68+NW92LVetHjWY8WKFVhVtkr8X4+aoyOFVywXYVmDryr598tdIkfT3S2KwSN1qHeVY5W7A8FZObhoRdD7tVUodzViywu94tMiuNyllIkWDC7KjxYZ2/635a07nAjLbXmIihADGcoghui5AAKJbqFn1yllNoqwkrbR21XhemcEh2/Ifoyg7ewiV+0netG0uREdZ8NygCGMTQ9VokR+ytsiLGssquTfc9G76Cw9ZXSlB75vxyuBTU83wbnS7M0oFo0ianZ55ApRXkMX+9FztAM9Z+a4QhiLYORUFzrEuB0nBhGOyuFpRND1VLkI9GvQcUUOSqOhbl+r+Cuc9qLz/CLkYLEuqTIRvn37nuXEtv8NT8vhd8pyWx6iIsRAhpaH6CD2r1hhns1f8bVFOgNJKRtcaK2Q/XDBt80p+xdDDIHveNGb3GgOeEQlzLunATWVpXJYAZZ0WenuI/LfG+0i5BW0VjRsy5DnYiIgOd9jXjFcVVaGMqN7sj/nfiZ6rg1bHihDlasee59twd464wphOba8EBChiM1EP/Y+XI6ahv1oEeO2PF6L9ZWN6J2Q36uuxIMubV8bmjbKYRmUbG5E21ajL4LDJwfT53kXib61P77vF139Ke79iSh/DGRoebg1iZDspSWw2g3f1SmMjQxj7P1htD5S8HWSHEIInk1VPrRDfgy85kP7cT8akgFJAZZ0WemuEw2g/+V4/tOeqoXbll0ipxuxYpUISNx7bVcMs4tdPgzPlw8jkFanjiDw4hZ4XlRvmYyi/1A9em6I+e/uxEBgAJ27NTFqLxqf6oF1jmLcYy0i6PLA900Pcof5lfDs8sR7T/SgL3mV8u4zFeHen4jmh4EMzcmtmTc4CLH4LRlLcfn7lqggyN75StwyEivololc61Tg+sbi81/yO/HmO5+SUlRWu1C5Rn7OZFauc0E/HkNMuUWmpsIh+zJJ/P4ct3vls6wqmSZLnfQ5TctlmCv/JdJ4nuUons8zr2mu77JKpF2m5c71XSYLLAPzWf7ohUF0mX0a9m91mX0WIr0LE4b/hbb4FR7B9bQfo5OTGOtvReLXR17wwZ8ILG4Own/C6HGi5ZvN8Gz2oFl87zYGne1FQAlAYhe74BPjage8aMwjyHc8Vhf/HQyi/0J+QZhqXvlhAeY7v0ikwL3/AstQgmONDCUTv5fnosfXM//xLcz9ROYJ57s8Yoni4xe0QPOZpkD57hOlRLou4RLR3UgnSjOs+0TWMLKH0dW9PqoPH2nQK7XUMMfOdn34fTl6km263kk53GD9DoeG44MvderurW7dvUFLfad2O/26+ivppvTR4826p0KdTtMrd/n0vqtTchzhXb9eZ/nNGX2s16u7LevUqY9+YIyc67t0Uxc69eZtjuS45vjbmvXOC8r8E9TlEJ1vRA43hP16gzJPl0ijGfmVYSbUp/t2VeoirEyO49jm1f0hdawsbPO1bhshPJD226jw6M3HhvTJj+Q4GYweE9tuq0sXoUtqukSX2MZiG40PijTabvt9rVJvOJLh93Ms62RvnXX4R+N63zPu5O82vBEfd/hQanrAJ3KfYUYfek7JZ3v6xJJZTb3ZnPp+a7c+Lodn9dGkPnRM5D9L/jXyX7s+ZEviTNtP29Cg+/rHLNs5Ti0v8eWfutCu1yn5XNvs1QfC8bFzfZeQlnYfjOl+Je1ENUqvOzYaX5Zc32Uw7zKglEW1DBvL32db/szENj2QmK5ZH8gwu5l3R/WhwJDZdT6RmkfWfUuwXXcmxtFa9aHkSlvzj/OV0fjgEZ8cVqf7340P0meGdK8cL1XGJ3X/TmOYR+++LgfNaVT3JfYJu/vm2BemjA/60vJD35tiXys/W5ZVsJQXW7pkLkuqKX3YKAPqPtgs28Np5csuvv9wW44tamffTxVWhjKw738HRT4/4FH2X5rufsavj2XY109dH9A793lsy5q5rBvUdDPzwNSo3rkzUUY03XdJDFvA8phEOe07ZD02G/ttb2/29Ji6KtJwj22fLadJbC91X2Hf5tbvrPkoIX1/EE+n9DqDIPbhA/Z1EPsbz77OjOlKZMdAhjKwBR3ZOk1UHCw7pnkEMslKQJYuZyAzIw4WrszTmZ1b7wzJUS0HjDq9+enM02m7u3V/lt/U9g3YDswz+ugrHstB1dppukdUdiwHFPuBK1HJmREVlurUcG2331KJHhcVbJcynbVzid+Z4zBum69l29gCKHunPTOQ9aBoreTYOnMbj+mdmzN8p3SavYKWY1nVg6jzlQG9b7c1gDArB0LWypdaSUWT3mfJv2qlGLr7O3OEMR+IPL05SwAuurrXU9Pn3n4iaH16wBY0qeXFLfJrlnxW7dP932nI8l27qAanWCogO5v1ZiW/pTpNb/iO35IX1e+a37RXTRdaBrKXRfvyZzaqt2+U4+cReOaqsCeMv66kk63Mz3zPm/puhyyjyX2YVwl6UtsvkWYzgVYznZy2ExS5TYo8LueH1lQ+zmG8N0t+sHSLFMh8INJ/e/YyYN+P2eXcf4hOLfuFl6EMbPuWrJ0t74295s48XqLTGvQ+W4VeXbfm3gFbmRLjG6s2z+UxhQeylOF4Zz8RZhh/oynzSSej2yjylwyY5h/IzLE/EOnkt5ygGNf9ln24rdO8+kC2II5IYiBDGdiCDqOrcOlN++xnnsWB6rkhZWdpnS6vQEZekXHZzuaZV2mMTlSiM1U2TNe7dU/yNx163ZE+fSjQp3cfqBM7a1Eh61UOaxkPGA7dle1qQsbvrGdSjUqNusM2zgx6j3j1BksaOfXWC8rhxLYc8UDGFpCJymnigGISgYY6jWOnV28/0q63PqGcMZ+r0mebb2rbWM8yG2duuweH9IHedr3JqKTbl8Um0xUZbYPcdsfiS2Rc5TCXU2xXz75Wseyt8d9OTuPU24PmqHFZl9V2ENU02wEzdUY+e+VLqfiKrqlfraaKPJoM6NxznDU3Kpi2sqBsf0sFzpJPRWdc6TrYmnYFQw18MpZBkacrN2c7e53pO01vDaTynrUCIjtRrt3V1uXI+d02a7CwGGUg3hnlLX35LVcsM5kSlbnE+GknGtLlqrAnWMZJ7KcSLCdeZL665JNpoFTqJvv0BjlefB3G9e5t1nGMM/zdohy3H+nU/SNZ93LK8mQ++21hz2tmmWsW5c12NXRRAhmx3zroTH2nufWmg8b6eJWrQZmC35Sc+w/Red+USzKvMpRBhrynbRC/ta/JegwSnee48lvvi3xm5k1RzrY36632/a/onEese2A13TSxr0r0m91Gub+e7/IYAYB5dU92FXWi3Im0P9ik3EngsuxXZ0Q+tQaCxlWPVr11n3EFyHoybL6BzMyFVuVEkaa7nzD29+26N3klSqyfUk4Twb35ncg/3uMD+tCgX2830zaPE3REAgMZysBWidopKi/J239sO1DLrRfzCGSkXAfSrCyVCtvZyslJa6XGdsDQtosDiRwhrTKmfGdUUlI7Zuje7yVWNlExkZ2lgpfjO9tyGJUcyxnUtDNWtkDjgBo4GlcQEt/ZggE723xT2yZxu4scbqkITOmT7+dzIMm13Q2jIjAa1afUW8hmxDRKQJG8TceQdVntB1HRifTqVm8hlLJXvkR6H/ekvlNvL1Ov1tgq7HaWA7DorGc/p/SpZPBn3X7GGcYh9bvk9jO+y16WUN2cutXKXqlTvxMV6CblO/WqkjXt4ldK4utuWw7Ld2LrvaRUVi1XHRanDFjKm235C6qY2oOODObez0zpA/tS46TlZcuyy7PqM0N6q6w8Ng/GEye1T4kH14nP8crujMiD6VdNjCsK6ctj3W5zBXbDh9TtWKd3K/sSy22TixHIWPKh7XY59bs5A8y59h/zLUMZ2PKe8+BQatk+GNZblX2SfR8w+qZfH7VdwR0+pJSNRHAiWdNNdNWt+oB91ea5PNYAQE0P8Z1yPEvtV23l0X6cmZoSa5Myv0DGOg+P5Yq2+l3qhJPltxJXOKWpSesyEWXDh/1pbg9XwZF8L4MDnt3Nsl+IjCB8U/bfbms0iIOldBiNnv3oOhuKvx9B03K2CFSzqxFOOUJJ5SbUxHtN6neocFq+C9+SjaBGggicjfca6nbXipRJcKB2tzg8JZwNIJilRdHJc2K5GxLNTbvgO9ODhnXmBymEkROpiZs2b0o9aBqNofzBxNIFMRzKMpOcSqFViMOe1P9sLRpf7MXITeNxS/HdmsVoMcwJzy4nSkUeikXE+pzvR8+rAxiT3xqC0/N5vFOD9/UeNG3ItaXTObY3QVSW404MIiDfDRK+NJh8caZnp1vZnulCF3uUpnu98B1wKe/LKUXpatlr237aU/VwJ78rgbtuf/xdIYZID0bekf12nkbUJfJFRZUlT1q+0xyoVJr0DUSy5YkaNO4U28TsL0GV01IClO9Eem2wlABEEu9SWaQyYClvtuXvn5gjT9+aTLYKpq1cjLwaQzTXu2IsYvGHmEvcqH823gxA1+Ob0Ph4IzZ9vcPMH66XmuAuDaLrBfFZa4ZvjwiVr3SJcYwyr6Hh2ACGeuONCIy82gTfWwtpZFnsA/qV9DrQjEZlX1J6f7nsWxyRYACDsh876lCzJrFfEt2aclHqpW8HF9gi5SKVoQwclVXJfI7VLtR/PbnUIs+GEFZ2S87tDXAajY9MR0T5D6D/RBcGrse/M10R+UH2pvOg+6QPntSuNqN8l8ey/9njxqbZVNrHyiuT+4fgSCg+nq2sug/5rMeZ0tLC3/dlZ5lHHepcZan8EC1DebJcdyF4Ld5XqlWmtt0ZL2obDqP3YsRMx1JtEZaJ7gkMZKhg1gNiIFWxud0qGtH2kju5Iwyf7cJ+TxXKHqhC41GxXPm2tJRLqdgBy16LiRB6Za9hk8N6hNIcm2SfIYBQlmCvS2kdCWJODs22674ZwqhSNznske/AkF3Vk/3ym/kSFYEnu9CUaEFJBBq9LzSi5oFVWO9pQe87i/H2iihCZ0TA9nA5VpWLSrjbeC+HOGBlfRlgvmqwqXIeh7o1HjQckP3oweAFYx0jGDmXaDnJg7rHcoUxoiKj1sp3bkJVtsWwbb+0Ft3WOpSgJIKxsDLyvJSL4Fb2LpKsFeBFKgNWBS5/LJYMPmvWJquACyCCd6WCF52ekX1SbEZ5n4tDBPrxPuczgxg6KPZFRvk50YtQRIP7mQH4n3Mi9lYP2i+KoObZJnjE+MHz/niZ39mBjqc9cO/yoeOgMSCCrrPDC3hfjLX1QPeDjiWtCIavKVv/zF5UKfulsrJaHJZfLdhtLEPlIpBOUYJ2IfpOPw43VKH8vnJUubag/vEWHD6Z78t6a1A1j6boMy+Pbf/zcq2S7qJ7eC/Sjgq2sup+ONf+bZ4s8zDeq6Qsk+hqX5ZfKUo2N6FrT2JZxHqdakOjSxwnHqxFy6nQAsoC3UsYyFARK4HruSEEA+1o2KBUoozKxLNb4Hy8N3m2tjj0w/tC7pf0LYl1dei+NAb/AY/lKkT4bIcIPjw4fHE+V0sSYhh50YOqujYRFBlrpqFyezNaj7XDa77s704oQc02+dZ0oeecqDxGRzF8Wg7YVgf3fN5/Q7efqOglrvvMefUmLyWi0pXal6Rd0VKbiddEBS0ZKZTCfWgIkx9MYWpKdB9MYugVozwF0fNCFyKaF74njLPrEYSDsvIrKpOJOTkq5VqcCRbZPuveELt4GJ6H69EmKtfmXmyDB80HO9F+IN44Ns2XKL/HRzHW64VH3efeGESHCBqt72siyoyBDBUsEh6VfYY6ONbK3jtE2+yF/+okpkIDaN+ZqopHTnrRe1F+WGxrUxUow6jtLGDkXfXGKTcqc6SR62kvGmSNJnKyGd7Tym+J+ajntb3fmzGea8vY9e1SgrlClVai4cgAxmcmMXzcC3fyp0bQ9soCgqsrXWh+IflGDvguhDD2Zid8Tzdi02KcQJ+nks31aEnc6iAqj6F3guiTH623SGWiwfGw7DWcFgfibEdb2/YbvmGrpkbCGJa9xu9W2a5qLGuLWAbmLXnL6+JxbKiXfcLVsCXvW/Z9WyvTr9auLkVpaerWwsgpH1quAJ5DzcnboWK5amZiZvavZ6YT56WdKMl5iUV8qWSfwLvzLrV5cVQoW39rN8Yz7JPiXer9O/NyG8tQ+EbqlyDmGj+2BdG1T3mv0KFhhK4OoFNs08bkPZFLI/Py2PY/B4YwkzHdRfdGQzxL2MpqQOTrRWeZhxvd1zMsj+xaq+VoplJU7mrHwPUZTI50w7s5tf1GXmhHv+1KrnFrdd7vtaJ7AgMZmttH8r9hNoS+k8qF6401WSsow8Gx5EE5+nYAA7I/E01TzmydHsv7rGT48kjyFrLShzzwvjEE/474Z6NWYK9cLZq1LniUKwr9ZwLKMocROK1cyN/mhjPLsVXb7Yf/WDs6XkkcAiLo/YYPg8lbGirh3Cd7hY7v9i/+GdvpEEYSt5CVaHDtacfQ6db4Z8Pp8LznGQkNJ2/9wfYmNHwxceCfwtQS3jdQsnKuyowTdd+QT1hF+tB+LCArrHWof2zuilDlRuU5MXSg44T1KYBo4lkqMR9X8jY2Mav+AYwkX+IXw8ibfXK+gtYE1wbZXwwWqQwsiKg8Vcpee9AxX6XOmlSF7LQfA8kXWkYQOJPa99Vtq1HjhnSxEfQcEeNrrfDuToTGyq1ryn41Gaxs1dJ+M/Ju4hpQJXLX0R1wJvd9wnf7EFBeGBm+rp6AyuGCCMwT00VHEEg+CGOlVdaknoM514Wet+d77lyss5qPQva9zRKWoVnl1sHoIPpeU3LQPmc8b90MYTh5y54HTbtdyedYpkSlelHlszyCZf9ztAv9E7I/m7WVqFGfnXuhC4PqohvP2GR9uegwxhJlYFaU67NqcKWwzCOArtfzuZoSQ+hi4hayEmjVTWgP9CN19BHHu0QgMx1Ex1fLsaqsDKse2LLAOwXoriKiYyIbW4tJRpOTu4wmf+3N5tpbmLG2gGVOt9mtu9Oa/hSdrYUho6UV9XtRmdb9/X69/Y0xOUYGifefVNTpvt6B+Evv+n1KSzpKS1621mEsy53ruxwt6iSbFZZdollktalJIw0szY/a5pVqhWhUb1ffI6O2ThZstzSbqW1u0tvl+vYdN5o6dc7dTGXWdUw0/Ww0ldmp95kvDhzQu/cprfEsoNUh63bVdM9zfn2gv1v32t89oeaHHNsje4s5VtbxXHrTwQzvmHjf2kKW2eXbYp7tvT+pMiK2v/nCPrFsiVaBMmy/TM23utSW2+xl0FJecn1nK4PKd7nSLme6WloHtH63GGXAWt6yL39m47p/hxxX81lajYob1TtlU75Gl7WZd9lUeJytJazqJr2zv0/v3KM0kW5rKSqTROt41vVT0nqjTx82i22qJUgtbX3nWj8re2t6iWZ57Xkt9/YXyyGbQE5v6lttwcpo4U0txw697kB3fB+SaEI3r/JkpLc6D1Fej/j1vt52ve+qHGVeZSgDW94zljneJLzaZLTRKftU5eWmRqdtb9X9g3169zP296VYW/fK1XJi0nyWx2Q9XphNX4s0GzCPgWL/Ksqf/X1Fk2/YWsozm7A2mmyOv8zUeVAZX5R567iu9PJjdnPvD7r748cU/xGjaWjr+DNiPsZ2NbZnpznekD7wnWalpVClhTOx/Kn5im6OliXp3sFAhjJQK0oO3ZG284p3mV52NtXflHFco7nH1gPKS8XsB2xbc7zJLuvbrO1BU3pnedFirspTzopV9kq6cQDO/UJOe7O8gm1eanOqxk49tQPXlKaeRXVmrpfczbVTz7aOYp4OZXh659J9lyxrkEGONFKaprV3jgqlsqvmhxzbI2eFW3W10/bOhEzjTul9e9Rx7O+VyW1GVK48WdbN6NT3Jcy1/dLLUq5gJdd3tzeQWYwyYC1ThQYyokr3SiLoFsumNilrsqVVts4+H+Nlp1lfNpjH+y0+EJVfI28kgxWF0qyutrlBb0q+CyXD8quVaEvT69mIwCfbCwY1sS9P5lfbdswU1Judpjc859Xdyc+2CnnOdDI6634sG+u+L9U1vJHKG4WXoQzUvGdJD2tnzbO25p/VTuzDUvvPBQYyeS+PNMeLjGFvElukzkC2l8+anfreGev7ttTO9VyrklcK3x8kT9J9JPJOlrpFonO9lHqZrrX5cKNLBTl0b2MgQxkoB3+jbfypUb3TcobVoXsO9CnvllGJneUz1rNkxksWzfdcXGhN/UaGCspMqFtvsr1wM+fbrD+a1IeOxc8mWafJsHy5Kk85K1bWipD1O8OMPtbvs509E51xlah/LP3gY5uX9b0QtjOcypuWDVNX/brX9gK4+JUAn96X4V0qFjnWcepqn+4zryKovyu224YGvfNSPkeK3Gk0E+y0po/x4rPeMX1KbO9kBUnNDzmWNe9AxjigvmTNh5newWG9YmR/038eJof09gxp59jm1f0h69afutCZdkXTPJN6bDjDFS9rmlrLS67vbncgY1hYGbDml8IDGfXscfrLF21pla3LNJ/3h/V2y35PdGKd2i/MXSbGvxO/GpM1MA736V41L4jfzVjWcq5bFh+N630Z9sH+kNg3J9+Rk74dx98UAYtaMTbKab8RGojAKzEsU4Xc2A8faUi/emOc8T82pE9mPE7YiTx0vCn9Nw5a51ZYGcpAzXv7BvRJ+3bQKvWGY6l3KCV9YD8Garr7Gb8+NjWmd29NDFtgIFPI8iSI+fsPGC+0lOPLzth3G2UvfbopfazXm37MNOZzxJqGafttcVytM5dFuUqYZR88GWi3vRQ3Pr1nX6c+pBZ3sfx9hzLknYzrPaUPG1d1knd4ZJ433XtWGH9ExiGak/mQndFTUorSudr1nI7G3+eCEpTOObJVcj5CSZ7t21umWS2mWYKHgOeSWobC17kgybQV8tkW+YqJ303edrzY6xCD8d4bw23dPkpaZcpL4RO1WP+4fAhgTx+mjtcl738viJp2K9X3yKSbT/4uFretDFgE0eHcZD5Uv6BtmM089mWJdMi9fecuEyMvlqPmhYioN7diKOyDu5AkTebJArbFrFgm+U6nwstpan0KmqdKmX+u31jsMpTcXnOsc3K+c5Txhcp3eZLyTDdVfmmobNN5HGss88i5LmreybU8wpUObHK2IKj5MDrZmnpOi+5ZDGSI6N4iD/pT1/xo27EfvebztE74RobRWr3QKhHdCamA1IPu6wOp9yIVs1gAbY4tOCzyp/PIKEYPsMpG97jZMHofr0HjyQi054YQfsm94CCWih9bLSOie0r4VKP5grb1rkQQA2j7fGhmEFO0HNub0awZfYPoOpPvSwqXt/CpDjOIMYKz5h0MYujeFr3cg5Yvx4MYaM3o+SaDGIpjIENE95AYwlet773Wtrdj8IhncW9HottrjQfeQx6xMStRGZtEOHWXSpGKIhotRaUIzpyH2tDIF7TSPS58vgsd5yPme+P6RjrhWSO/oHseby0jontIFMETPQiY7+kpQfkjHng2OxjE3BViiM2WFPhcxzI3K9ZJ5NO7ap2I5uNmAL1XHfBs4/6arBjIEBERERFR0eGtZUREREREVHQYyBARERERUdFhIENEREREREWHgQwRERERERUdBjJERERERFR0GMgQEREREVHRYSBDRERERERFh4EMEREREREVHQYyRERERERUdBjIENHdLRZFbFb2070tQ16IXhtB4EoEMfmZiIiKBwMZouXsche2fHmL7FowGJHDaQ5RjBxtRFX5CqxYVYZVH1+B8of3ondCfp23CAa/mUh/0b0alMOpeGTPC+ET9aisrMEWZzkcX+8XW5vmFkRXcp+0BS1vMdWyY1oRLTUGMkTL2WwUgXMB2YURzffKQiyK0MVB9L7agZ4zAQRv5nO+OYbIlfg0XacG55hGjHuxF11HO9BxtAeDN6JyeLroW/tRvmIFar51u4KAGEZe9KDm2V6ElHpDZGUlKtfJDwWI3kykv+iiPG9fXHLlhQhGzqaCl8hJPwI35QfKIYZocp8UQHhaDqYMmFZES42BDNFdJvxWC7Y4ylDlqkXjN1qwt24LNj2wCusf70Eoy4E0dq0Hex9chXJnfJr9DbU5pgmj//EqlLsasf/ZFrQ8uxe1D1ai8VRYfq8KouelLkS0ZrTtdsphS+yGH74XRuQHQNvQAO/BZnjqnKiUw+gekTMvaHBtqxN/5Xe7G+FeKz8QEVFRYCBDdBcxrn7UfLUDgQx3MIRP7EXV1g4RWtjc7EfT5r3ouSE/K4xp3E/1itAlJXrmMOpPiCFaAzoHhzBwrEFUBiPobdif9hvRM11ouQh4DnnhWSMHLrFoKIhB2Q/UoeOsH+2HOjFw0I0SOZTuDXPlBceePoTDoxgKTiL8eiqoISKi4sBAhigvMUSjUbObz81FMXPa/KYsZFyrIHpe6EreKuN6bgDjU1OYCg+gtVoOvNiCtu+qYUkMgdea0Ssn0rb7MBSewvhIJxpkrS5y0oue84nliWDwVI/Z53y2Bc3b3PA87YNvqzFkEL3nld+OjaDrkBhX88K7yyEHLsCs3AZz3J4Rm56UfYZNcBR6lj0mt3PBDQTc7jxS2Hzmna8S6SE/2s1nWeZFLsdc21+VT14oWeeEe6OWd5Brrm+2ZZiWaZFn3kmm3UIbo8iVZwvMz4llWtK7KBPbcgHzmF/a5dqH5Ld/Scpzf7Rgi5BWRHc1nYiymNLHBzv15u2VuqjT60ZxMTutUm84MqRPfiRHS3jXr9clxtnp1yf1GX2s16t7KlLTapu9el9Yjm8zPujT6+zjvtmpu+VnoE73vytHziTYrjsT42o+fVgONmX7bmpAb04Mh0fvvi6HC+PHPXK46PYNiNQwDOs+Oayud9Icoov1HDogxzuUmutkb505zHN8XA6Zn5lQn+7bZd0G2oYG3dc/JuasmBzQvVvdunuDlhwv1c2RdoapUb37CbcyH4ded2xI9z+t/I6yfnG3MY98MKb3HWrQK7XUuJk634gc3zSlDx9rtvx+fNmG5fZMSWwvozO37Ufjet8zqfRoeCOxvQ0Zflekl2dfpz78vhxFof42YM2b1u/St9PUpW69eZtDGSe1/e3rkJRnXhg+pA5XlyuVzxPDpy51psqnKEOj8RFNUxdEHrAso6ZX7mrPmBZG2o0et6edMb5P77uadY1MadtI5Am/so3ieXY0Xi5yfWczdX1A79znseWt+DoMqZvdpKaNuh8QPhDfVae+03b7dbX0ZyrLjm1e3R/KtFSZ5Jl2WcqZW1k/x85OffQDY+Rc36XLe39kupNpRXRvYCBDlNGY3rk5dQDJ1Gm7+8QBUqEePEVlqflpl2X8ZFfdbqkEGcZ7GywHrMzdHJXxEV9qXPPgrRrVfckDtVNvD8rBF1pT02y0Ldf1biWIapWVvNSB2fu91AE1WSFMBDwzQ3qrMb+NohK4gOPu+JvNuiu5DOmd6+mB1MHfkv72bo60s1UqsnaWQOY25pF8l090yUDmg1G9fXuminy8s1ec1Eqy85UBvW+3Oq2m+y7JEef4XWgekb+sG32+gcyMyNO5tr/7tTE5pk2eeSG/QKZZ9w/aliO5XWf00Vc82cuu1qD7LUHpjJhnlm1udm69MyRHzcCSVjub9eaMeULTG77jz5JfNL35TWuwNPaaO8N4SifWoc9SdtS0USvnk9Y8Uy3SUwkGcpdll8i3c+0oCki7PMuZtrtb92f5TS158ialoP2R6U6lFdG9g4EMURZT4mBiVlC0St2zr1VvP9KqN21WK3BKQGDIWHly6K6t7rQznZaz5iJg8KjTmPNrFvOyneWfqzJ+yZca335FRlSEWzemfisRhFgrRrbgx1YZiM87FRBZD8pyPFnRH/9O/GpOcpypcX3geLtIw3a9s3c4/UpFJvZ0qfDozQdb087O170uqw4Zz8LH09+91asPWFZOZVxRslbMHdVNevO+Ot1lOfMrOtsVmduVRyxXx6pFxTo4qU+Fh3TfttQ07id8ZvoOmFfVRKXvoDM1jebWmw4a6e9VrvpZK7WWvKBptrzXrA/EI1RbWhlnw716+wHblaKNIvBVKmbzC2TG9W5l/Rw72/W+wJDedzy+DvZAzCLPvJBfICPSwrJtjEAvHmbOXGhNXekU47mfMPJAu+7dmcqjlgqxJU879LojffpQoE/vPlCnO8T0Db1Z18hkTSvZVbh0d7W1TOT8blu3Nd3eH9CbzfUT23J7s94qlr/1CfVKjljfI2pYnblybjkZYw/gwtZ879gp8ox9PhlO8FgUknZZy5lLjGsfnu076xXqgvdHpjuUVkT3EAYyRFmN6gO9o/qUWumeEQcmJSBIVGhMtoOntl0cbBI1mMk+vUn5Tj3YDR9SK1t1erdyUDMqyup3OQOZmSHdmxwXuuu5Pn1sckqfDA3oPtsZ9MQBdfQVpbKbdtuUtTIXPyMvKrLPyd96ekB8Ej4Q85UVPbNinPgsr/DMiApAg60iaFTGB3Ktizofo9O8+lCyYmyrTGut+pBygjJXpTkj23ZzHhxKVTw/GtM7t6a+S0+j25FHlEDRMlxQrqhpLynzsVS6clTIlEp2WiVZVLC67bc62Spznu8oy2L/TrmlcH6BjLUS2HpBDjZ8NKlPZrxty2quvJBfIBPvXM8N2AJwa6BlSQvLd4kgUFCvmiavckqTIjiVvdlY10fTPSJvxadJDzBT34lc+pJSziHKku2E/uibfn3Ukp7G1Q9lGsvVWmvaGPsS65Uz+xUDa1nWDgyJIQnqctuCfrtC0i5HOZv5ntcSpFnK4CWfEpiqV53nuz+6Q2lFdA/hw/5EWTnh2eVE6UogFglh5Hw/el4dwJj81hCczv4EZs2uRjhL5QfNgcqNsl/on0g8kh/EcL/SxNiBZjQq7zopvb9c9uWhxI3GV8QhUhp5uR5V5WUor6xF21tiEcTR2y42ne+7XcQymg/VlsD9tRaYc3m1CZsa9qLR1YgOYxWqfWjaWorgd9vEZw3Nh5pECgbRtXuv2ZiAtrsTAwF/vOGBi11oemkQ2d8+I9L7RCpdtKfq4V4tPxjLULc/1cJUpAcj78j+eYgGh9Ev+wE3mr/uRmKzYWUZypIfMrkdeUT8tvoTK1fJHkHpj3wwJftEfzCQaq1rRx1q1sgHho1uTblYaunbQZHSmWjwvt6Dpg3Wlbf8LurQuE1pxKGiFo07Zb8weD6YbHhifjRo22SvcHhPLfa/OojQLfFhpfjuNrWCZ9rWDf9LHmhiOydFggiclf0iLepcZak0jopyl9yWXQhek71rNIhgTzqMRs9+dJ0Nxd8PJQpozqyWpkakt8h7Zn8Jqpw1Zl+c+h3g2KB+F0bESEOFc3sDnEZ6TkcQuhhA/4kuDFyPf2e6EsvemENI5JMdbYg3cq2hodco42rTCday3LR5U6pBgWgM5Q8mlk3sC9WX/dgtIO3UclZSuUmkToqlDFY4Ld+FbyX2UIu0P7pdaUV0D2EgQ5RVFKEz4oD5cDlWlVehxl2Pvc8eRu8V+XVBysVBSPZaiAqC8nvuBx15t56UifOZQYwea0ClGrRU1KH9wiA6HpOfhfLV8blomtv8b/pgxlpZic0gVTV2pyqOG70Y/F4r3Jqo9JzqQe87EWibvRg41Qrn9CB6jojDdHULmraL2sGVAPwXjYnq0PFSMzybG+A72mr+TOTbgxjOFsncDGFUOU7XVNhaPVvrUCocEYyF539Qt7Zs5SywlbPbkUeM952kqnD9ZwYQNoPKKAKDfeYwg/P+MtknKmDXemWfcGYvqspEQJbsakU1cC412FSZnhMtv5vWCpgGx8Oy13AuBDVlC+dA4ws+kc/kxxuD6PpGLapEcF/VkLmJ8SXjqhJLYzMRQio1+rH3YTWNy1D7svxKVdGItpdEWZIfw2e7sN9ThbIHqtB4NIBIQS1w5W+uEyLRd/pxuKEK5feJtHVtQf3jLTh8Mr+THP0vtiVbPTTysOMB27xsZfmwx5pOVU+mTiPkdDvSrlQEoLLXYpH2R7ctrYjuIQxkiDKKvxG8qk4ceERF3aikVW5vRuuxdnjNpoaXq1I4n/Zj7F0RhBhNL4tu5nofvF8EJpNnWDWUl8dPQa4qVSKeiYj1CsmtiPL+GA1lyTOQYi5bfRhKzmMGk4F2eNYBwRNt6Ipo8B5qNs/6R66PIl4dUiq966pEWGPoQzDDu2uKx+3LI46dbfAlmtA+vRfrP74CK1aUYcuLsrKpNcN3u144ehuVVLdiKDiE9l3G6ysTjAC6BVucjeidkIOKRglczw0hKMpLwwal7EVEUPTsFjgft76z6XaIXTwMz8P1aDsVMq+gaRs8aD7YifYDykmOvAVx+EAHgtkvQi7A8ku7hVnKtCK6dzCQIcrkSheak28Ed8F3IYSxNzvhe7oRm3Ldw1CwEqP+mxR4VzkdtxArS1BaWmp25nn1a8MYTF4laIRrQ7xPe3BT6jajC2FrReBmGMOyF1oVHMpympLzkGfub/bC96yoWG/zoXmrHDab6ygt1jXbWdS1DhH6pAzfsFVRIsqyiQSsSlu4Aoj1SAkjku8muG15RFjtQsM3m1ApP6aI4GlXO4avdlpeOOqoiIeKpq3dGI8/D5mha43fJpgny+9iFOGbstcUUYJlYWtl5rPbhdLc8PaOYfL9MQwcqUtdGYn0wnsykf53gMijqdRwo/t6pvSNd8n3OEnGFUz/1UlMhQbQvjN1dt94Z1OveQXzdgmia1/iVieRiw8NI3R1AJ2HmtGYvN9qbtpuL5qT76pqQ/O3gqmru7ay7P3eTMY0Mrq+XXOX4zuSdou4P7qdaUV0L2AgQ5RBJDQsryQI25vQ8MXEQX0KU9kf7JgHB5w7ZK/hu30IKC9YC18flX35i10bQcjykrYwel/yISA/ac95UJOou2+sQX3ieBjpRN+5xCE1hpFzfeYZWoP2lDsV8GQkxj/Rjn5xEG890JisbJauSfTNyP9CbEZe+VFuV0vjhOuA7BUi/QMYSa6TmNebqWWD1pQMzObDEsyJNfCfUyop02GMqZVzxe3LI8KVDjR+rQchsaTtwRnMyKttUzOTGOv1wmVLR62yJrVO57rQ8/binPbVHvWIrZbQjz71BagTAfhPyn7Bs9mpxuiKYYwlrsTNhhE4m6oCppkIYiSxoddUwnOgD0Ovp8KHyFUReMr+225tJWqSz8EE0PX6SKpCmkP48kjyNqjShzzwvjEEf3IfEMHoAm6TLNjNEIaTJzg8aNrtSj5rMhXNMxNX+9D/Wjvav+VL5rmR55vRlfzdSjj3yV6h47v9875ycufSbpH2R7cxrYjuFQxkiDJIVcCFt9qw//leDJ7pQctX3dh/Tg5fFKVw72xNVfgiHdjirEfL0Q60PbkFNV8v7J5o4zYRt6jEVjlrsf+FDnQcbUHjwzVoPCkPs1oDukRQkroG4ULDocTzFxF0fN24xaQfvc/Xo+6FRDXdA9+uOc7b3/DDZ4y/swP7N6d+3XiwNl7tHETgYryaFx4R/UaP5kZVhTkogxK4G9pTVwuuHEbdV/eizUyXWtQlbqkSXM/Ww61eVCnUxjo0Kw+VDz5Zgy1PtplpV++sweEsz7vcvjwitowImuJnzScxemEY4WS8KR8Etr9dfGMTfPsSuSqIw49Vof7ZHvSfD4jAoRcdIm+Vf6238CCgoh7e5O+KUObrW8TvxvNZ/dbGVKMJWjOat6fSR3NsUoKaQezdJtL4yyJ/V65P5c00IgB/QQTd5etR/6JIW2PZz4u8KfJngtOl3nJ2uznRdKg5Of/gi6Lcfa0FPWeM5RxE79G92FJej171qtVEL9q216C8sh6HT4lyYKzTmV74z8jvxW/WWB5wW2JrNOXZn0G0faMNvWf70fPNWrifSpz6yK3uG6LivlqU2OpmJc+NoOX5HlkJL4XnSaUsn25EjXsvOuT6958QeefBTTgs9w9Z3dG0W5z90W1LK6J7iU5E6RIvdJTNYKqdo0J5b4DaHK+tyc9EE8dxk7p/Z+o7azO+47rf8vJBpdMcuiO5HHM1vzyqt+d8QaNLb35TbSI2Icf87e9nyCjRLKgzw4valPeZaG69YY8n+a4G6/sWMpvrRaGZ3iUyV5O7meR68WLW7X0b88hMoDVnOhid8Xbx9gtKI7RzvkRTs7zUNHszyDZz/m6mF/aJvKk0Sa12rudalWanU/OdfF1dngxd2osa0y1a88uW8qqa6yWNahO6tu2boUt7gapNrm2Uc/tZmi5Wv7M1K6x2Ig8nymqutLHkYVv+VpuknvOlv/b321gUmHa5ylnOMphj3YTC90d3Iq2I7i28IkOUSYkbbWc7UadeMZD36o++1arcXrMYHGg4Poy+Z1It8hjMe8HPD6FTvfUslxInvN+bxJD6HIHk2GbcVz6ITuUseYqY/2sB+G3zj69vAD27Mk2juOFHx9EIsKcNzZamRA0lcL3QF1+3SAC9JwYRFvOrOzaKnt1z/K7g2OVH6EInmjZblsxctqZjwwi93pC2rvNhPFQ+eMm2vY3lPDKM0dPxhgvS3MY8UrK5Bf2HkudpM4q804uWxxrRk7hta7ULrReM/GBrxc5Q4UHzMT9alKtneTN+99wY+g5lyGc7fegLBWxNyhqcaD6RIX1FPhh8qQnuDHlcVEwxGehEs9rEs2Tk574RP+oKamFuKYj8fXDYbOzC8gC6yQHPvk74v5m4Amo0tyu2x7FmeNKuRIpxD/Rh+Hjdbb7CVAL382K/oDxrYiyn+xk/xi4NoLXQRivWNqBNaQJ+8MmWZIMMZlm+6oc3bXsaz3mJfHOkPi0/pSyPtFvU/dGSpRXRvWWFEc3IfiJKEzPb7zeUrC5FifoeiaVg3Cpkzs54kH4elcyEWbHcyfeXFPBbyfmLqRINBcxlWkxj3LdeUopcszHeh2D+9EoxntICWr6S0wt5L9s8JOczx/qkLH0eCZ2ohfvxQUS0BnQeb0KqZeQowmf9OHw0dS99wxuT8O+0V+lSyyiWcmF5S6Xms7y2q7IceaevkMhjhnnmn9vBkkfnyAuFjHs7LLR8FkTdnoXkA2k5pJ1lGZZwf7TQtCK62zGQISJazqKD2F9Wiy6j/7kh6C/Zr/WE0PVYFfa/Hf9ktHTUnmg1joiI6C7GW8uIiJazWCz1YtKX27D/xAjC0SjMh/wnRtDzzf3wySDGaBWp8TEGMUREdG/gFRkiomXNePGmGzXJd9ZkZjxT1XM8/mJSIiKiewEDGSKiZS+K0JkedJ3qw+DpkeTzMNAq4d7mQcPOZtRvcyTfAUJERHQvYCBDRERERERFh8/IEBERERFR0WEgQ0RERERERYeBDBERERERFR0GMkREREREVHQYyBARERERUdFhIENEREREREWHgQwRERERERUdBjJERERERFR0GMgQEREREVHRYSBDRERERERFh4EMZReLIjYr+6XotREErkQQk5+JltRsFKGLAQRvFpjjYhEEz48gdEt+LmYZyiHRUuO+nu4k5j/KFwMZsoli5GgjqspXYMWqMqz6+AqUP7wXvRNA+EQ9KitrsMVZDsfX+xGRUyyNILq+vAVbZNfy1iLMLTKIlsX+zWJ2G9Mj+GpqPlu+3CW2bj7C6PlaJapcW7DpAQcaTyvLl2vZpwNoe7gcm9w1qLq/BocvF+OhMHs5vDOWoDzSsnV79/VLL/JWSzLvbvlyCwbvmuxrLZdbXs1vz7rc3W35b7FYjqPfHFzadJmOmCcRA8YJwcgyP4bqxeb9MX3guFdv2urSHWLxtQ1u3b3Hp/tHJuUINH8z+vAhl25kC0u3sV0f1Sd1/051eJ3uf1dOtiSGdZ+yDHW9i7B93/XrdYv9m8XsNqbH8KHUfACf2Lp5sC0fdvpFLpRyLfuILzWN0R3Ka27LSK5yeKcsQXmk22NqWO8+0q63z9W9OS4nuN37+qU32Vt3V61PirVcFt++LpPbk/+mgn16+7463V3tEPNw6K6tdXrzEb8+vIx3bZbjqHo8XEzvD+vtuyp1LZn+8U7b7NX7wnKcZaaIApkZfay3WXfZElftHHu69bEP5OhUuOvdukdJT21Dg+492Kx7Dg2J1Nf18eN1ycyt7e5bmkKUVHjFaerN5tzjM5CxWu6BjD6ud+/Q5DSa3vCGsny5lv2DIb21IvGdS/ddMnJvypz55E6boxzeGQxkipb9hEC2TqkE3959/dIr2kBmakBvTix3xorr3RjILHX+m9KHDrrTKuqpTtM9Lw2LsZafpQ5kZsSxp0FT5mHvNK8+tAzr2EVya1kMIy/Xwt3QhRE5JJPw2QCCd8M98XdINBTEoOwXO3t0nPWj/VAnBg66USKGOPb0IRwexVBwEuHX6yB2BMvKVCQk++ju4EDTG2GMB4cw+m4Y/p155rjVbviuTmFsZBhj7w+j9REj96Ys93wyVzkkWmrLfV9/z7g1iXvxqLaU+S/83UZseTGQ47asCAaPdSJwU368h5RUuNG402X2O3Z60d0rjj1PuFPpH+lAz9nld6NfUQQyscsd8D6vZLyKOrQPjmFyagpTU+MYPu6FW3PBd6YHDevkOHazMUSjUdHN416/xLS3+TbB2HyXd55i05Oyz7AJjrWyV1Gyzgn3Ri3vClVB6zBtjBud98N9kUhA9uXHsaZU9hWa1gvIS2nkb03LjwUwlznbdIk8W8DvJtOj0Pwut1ve89qqJXeMc6b7yhI4NrrhXFtgFb6kFJXVLlSukZ8VheaTRZPnNsmnHOaWyJ/zL0um+ZbHQvODKibnmVfjBon1LGQJ5zONZK5X9ulu1/46Pp/5b9um14YwFMjQ7ayUY8TNa1+fbZvL7XobkicPDmiJ/UKh+7oFrMe88setCArZW7m1VJW/0PnNO/8myqz8aDX/8pZP/kuWhbwbQxlB7wup00Ta7k4Mh416pOjCw/AfqhO5I16XrMtnv5trH1ng/jO5LvLzUsm9nR3wHBvEWHAc42+0o2lXA7zf6UbHDvm1ELihHp+WCXllZhkb17u3qZe2GnR/hvv0ZqYyXAj8aFIf7vXpTeY9kMpvVHh0b+9Y2qVD9fKzeevEB2O6/4DHfBYnPlzT3c/06eMfyQkymtGHnkvcDiO6PX1p81FvbcHWbrGGCjHPvkMNeqV6eU+r1BsO9WW8bS7Xpca8b+eZHNC9W926e4Oy3MkudRk+6++pty6Yy2DcBujVPcnbe5D7/srwgO7bqWwjza17+wf0zq2p6XPdyjJ6TCy7WH5LmildclrbLRa+kQKXM9O2kXlp7lt+1FsAWvXhmUl96Ij1txw72/Xh9+XoSep08TSfutSp1yWWWfNZnpuYCfXpPtv9rcatSb7+DMtoT4/BTPndn/l2zclh3X+oSXcpaWd0jm1e3X81vSza8+nohXa9Qc1vFXV6+wX7dDlum8h1a1mW7/LKJzNDeqvyfVN/WunVB/alvnd/x1J6M8p7m+RZDjOb0scHO/Xm7bZ7m419x5EhfTLnPstmPuVxAfnBNyIGpN2X7dDrjo1mvr1DjNu5T82nmTpbehVQdtOWbWpU70ymh6b7LsXHMxW0v851e94ctwh9NK4P2Ocj0sizr1Mfsm2KNGn7PTk8h+zHjoXvj8w8EZprjynmo6yr5bZSo06g5MXWC3KwaVSZzqm3B+NDrbeWieUu8Nie/3pkSJ8M+7rOSxlzdopIU2NflXlfILrksd6ep8YLnN+UPnys2XIMjO8z0m+tSqsfiTzZ90zqFi3LNlrQsTJX/jNM6aPHbcsstl/lLp/el2FfY2ErC60BOVwx9X76b2TcX9n3kYlnzHJ9lyZD+stynV4XsKWLpb5ne64yrZ6c/3ZOZy1vda/Pfcy73ZZ/IHO9W3cnEl50njwqDqYPRKXAstNP71wvjVoKlWVnt7NZb662jp/oXK/M8chtsF13Jsdv0vssGVIEOgdSv2WpCIkKRLZ5ml11sz5gq2Rnz9hz7QwUtsJt7QoMZMT4zU9neFDZ6KozPKwc9ue+J1N2aRUnhXW50rvktLb1dFQoOxu1y7icubeNS1Q8cu+g1QOOJuad5QCliW1syS/qdM26f9BnfU5MuX94XATIuZ4hcz09YA2ac253pbOlx8yg11pZTuvSn0uZaxvFO01vflPdrVoP0gsNZPLNJ6NHnKnh9hMRItDxJqdx693X5fAsCtomObdHrkBmTO/cnGmaVJf3febzKI8LzQ/ufeIAm3Ge9vwg5Ll8lvQqsOyqy9bcK4I6y7QNel9i1QveX9srneoWyZHXRQ7x786yvzA6zasP5Lpv3ZavFi+QWcj+SOSJkVx7TOtxEgeV9FCfGxGdUz0ei/yRWlevPiRnYQ1kHGL/m+i3dpmO7YWth5o+bnEs9GQuG2n7eRt7gyX2Lksgo4ljWt7z+2BUb9+ePV9pu/2W44Wahs5XBvQ+S55UAvwFHytz5b8sDaEkO7feGZKjZjLZpzco42u7u/XRXNtBym9/JfJCb7ZnTFzJoDppjvSH5hHTWFPKki5KfW/yjQZlu9vyZIHb2W68V/ltESD15Tyhdmcs+0DGcvUij4qDavQVmeErXHrTAaNlFm/qzJHZWYMM685OdmJa92b7Wc4cQYFJZJyNqfGtZ3XFjieZ0dX1sV15MqPyVr3VfuZRZF4102XL2IbsOwObjGeCjVY8jDPY4iApfzTr72WsgMWnt55BFDs7y0HUvs6aXrldBJB70s+aWw/6VvEz7fFW7BLjm63ZmctvnA2R0y5gOS2tqFTU6V6jlZ+DTbo7OV2GHZWFraJi/o7Il8ZO0XbWTXtOfahbnU7TNVu6JA/itgfEjbNfzQdb9eZt1mDNcjYlQ3poG8R0+9LPrHuOq7lO5G95oHJUN8XT4kCdNZ/aAgBr3jE6ke57xLZOu4LQmqx8pKXZAgOZvPNJrhMRooKRXF771VS7QrdJnuUwE2M/aS6XVmnuN9qPtOpNm9XfSZ2dzm6+5XEx8oPcFvar55Z9mvVqt7bdpw+Fp/TJoF9vTu5vK/U6c1/frQ+bMyy87KrLpmnWsplqOW4++2trfs43kJkJtCoVCZFPjw/oQ4N+vf0J42z4XAGBYCsPixfI5NgfWQIKkS92es2W0VrNZZbDM50wUliOx0pZm/meNzXc6JQ8YvlOCarmfWwveD1s29HsRDna7E7bp+Y6piWuyFimEWU7sa9yPzMg122+8xMBwUHlhI3IV00HjXKj1pGsJxIsaSjKhSXdRFA7MM/ylknW/GfZpzr0uiN9+lCgT+829zea3tCbc48s2FtEMzpNdz/RqQ+E1D2UVdb9VZYrZpm+sx7XjUBd/V5ss10ibx2wXcXa2KoPKycpLMuRyPeWkzv2NCh8O6tmxPEuFcS79NZA9jS6k5Z9IGPdASUKS57eHdL9AeNGJ4Vtp561oIqN6xE75cS3k280Kd/VZby9TTV+3JMaXz2Iq5WkbcrOWT1YiXl7v6dkebFzVr9rDaS+y5ixpewHo8ys658+ftbfs6Wptl3s2JMJ16c3Kd9ZKtJqpdD4Tq0s28665dzpm3JVEqR5Lqe1ImFttUPdNpYzg2lsB5ydncqtJ7adf67KvOhcz4mDmOUWCNvtjJZltO0w1d+2pYfz4FAqn34wrLcqwbiaVw2TAb8+9K6lZOUsq9a8U6d3K+Vn/HVruUvlb9u6LzCQicsjn4g1VSup6omI0VdSBwVrcGc3z20izFUOMxvVB3rF/krNFzNiXZVtmDt/CgsojwvLDy69uT81r9GXlAOvcRumHG6so3q7kXpLkZqH1Ftc5lN2rcsmuurWtCByfvvrXHkve163pOMO64msqckp6/EtE1t5yNjlfeywLafo5tofaQeyVeDmCK4td2OkroQlymAqyEzlrfHvuOUw690O1ryY77F9PuthS5/q5tStyh+J/KvuU9WrTFlYtoNtG8XNc36WgMBjPUGsfrdvIJVOljQUndagd9tu5VqcY2WO/Ge5UqXuG4TJydTxK5ccV3Wz3YadfX9lP6Ghfjep9+1RvlNPfNlOclnuNLJ/p+yH0/KDOE6rV4zTrnbNYzsnvS/2+VkDpOWlyF6IWYqy1PPZc1vrRsNm42GxGKITQQTO9qLr1LDlYarJ6WwPPdWgcadTzDFOe7ASIgCR+hGeo+EGx/YmiIpx3IlBBGRrauFLg8mXAXp2uuGQ/aGLPanGDLT9qN+aesStZGs99os9QFwEPReXdzsmNbsa4UwmnAOVG2W/0D+RSrjgSJ/ScogXzbsSqSGUlqFc9i6VfJfTsm32uLFpNv5QnvlgXnmlyClxwZGQsj5zeHgTKlfLfuMBu92impgQGUE4W4sp27rhf8kDbaX8bAph5ERqztpT9XAnf7sE7rr9SqsjPRh5R/bbOCqrkvkdq12o/3oqx+NsCGGlqGibG+A2HsCPRRG+EsDgqS74L1hKVo6HvTehSmmUwygrqbWPYOSOP0zoQP0TydKLnrcCiJp9YYyeTZZe1D2m5Nc0i7NN8ueEZ5fYX4l8EYuIeZ/vR8+rAxiT3xqCWfd1cQspjwvLD7Vo3JGal2NDokQZRL5LlgXxe0oBW6WUAbU/Oj0j+xaj7HrQfdIHT3Jjxd3O/XWpVpnKK2e8qG04jN6L8Teel2qleT+MvyTy2B81bd6UfJDZeMi4/MFkqmM4lDnVTRVVcCdXvBejN4z/YYQuxMug+6n9EAGa0IfgNeN/FKFg4tF4De6Hs5XPfI/ti7AenkbUJfZ1Kx1wumS/4Vo4S55bgDznFxHplHzkfUcdatYk1kt0a8pT6fHtoEiFTDR4X+9B04bkEcO0JMdK1RpNlMiEw2j07EeXODZFjYf9NS11/MplXQP8V4fRvjM9f0Te6UVbnch33xgUOS0bdX/lQJWaxpbvNDgeVo6h5yKZ01/k4sZtyrJUiN/YKfuFwfPBzGk1O4aep+rQdjH+Udvth/+gy7I/WMh2Dp5oQ5ecsXbAjx71eLDMFFkgox7U8jAbQeDV/ah9cBXKHJuwRRTy/c92KRmoAGKjF7QZ13jQcED2oweDF4yqkKiknUvsaNWKUETsgJWsKoZb5yU+PyZ7hUhoCXaAS6Zc7PBlr01sWnkL8dZKOO7oETnbctq2zcu1KCsrS3UP7xWHvoUrvV+tJgYQydaMuKsqPR/eDGFUWcSaCtsYax3JA4ixPmNzReFSuQjuUkSeU5cpEkDXU7VYv6oM651bROVqP1q+Pa+SlVZJDkTufO4u3dYgqvJS4kTEzREMnosPwrY6uCtkfyZLtE2yE5W4M+LA/nA5VpVXocZdj73PigrvFfl1HhZUHhcxP1jLgsoJ13OpiKJvUAaYs2EMnEmVQseaMtm3GGW3BlVp2/n27q9LNjeha09iDmLep9rQ6BLb+cFatJwSlTj5zR2Rx/7osEdJc9FVPZnvHlNs792yVwiFRdUyGsLwGeOThqqtbsTbWRPH+atGtTOMoPmdoREu5cRUTtmO7Yu2HgmlKFvqs3MW2ecXvtYr+4Qze1GlrFdZWa0IEeZSg02V9h3EbThWVjSi7aVUc8Dhs13Y76lC2QNVaDwqjpv5tl62xgXvG+OYeTfRUpnVyKsiDc4svGRZj6EplvRPa5nSCIBkr+FcCBlP7Yn9fdvJVHqXr3OknWya/3YW2/JS6njQ9NXl3fT/sg9ktAfUJiH7MRrKfVYxJYzex53Y8g0RuJhnchxw7fGi/bgvdaVkSZWgZltrssD1nBsWUfAohk/LAXNVhIiWq4leNIrK6n5RUTXPWlW40HSgHd2Hbk/Jui1KauBJVpp7EBiJIhocTh6I1aupd14MIy96UFXXht53jAObhsrtzWg91g7v1vgYS+q25YcSuJ/sQIPcLMEXt6BsxQqs+Ph67E3sV6vb0bQ9r/OyRcSBuuOjGOv1wqMeM24MoqOhCp4XRyx3GczFN2LeUm7t3mhIHquWjxJUOePXXAz910XevhFEn/mpHs6HHKiSZ64DF4KIqIHHzk2oWs41L5qnErieG0Iw0I6GDUqOjYTQ++wWOB/vje+D8lSy1oWGg30Yn5nE0BFrQGPW2WR/MQi+6EXH5UL2BLmUwv1sqmn2xofk4GVq+V+RecSdOjMqdHy3P6+MGn2rA42JaFVrgD9svG9GHNj33L4KSMnmerQkzgqdCSL0TmInDNTtrlWWwxaBXwjb1nES4QuyV9AqHcvwoFO4kpXKWpxbrleZbNvmwBBm7JWARLeAykAkPCr7DGKHWsi7Q9Y6sEn2GoZv2EpIJIxh2WusT5Ujv6UM30hNlTprFMXgkUb0JorWbj/GQ8PoPuJF09Z5lqybYVjW3n714o4QleavtSQvvfddCSF0JVl60TjXui7RNsnoSheaX0i8KtgF34UQxt7shO/pRmwqoE4/v/K4BPkhl3V1aPmGW35QOeA54MfYOW/qdoklK7t3Yn9dispd7Ri4PoPJkW54N6d+ceSFdvQvp5f32fK+93szmdNcdH27cqeM9qgHya19LYyR66PxfLnRgfJSEbC75NY+OYrwRDh5osHpUm7Hm69FXI/lxlGRChCxtRvjGdYp3rWKPUq+bs+x0qBt9sJ/dRJToQHLLWKRk170ylutsjPebSN7E0o0uA90wafc0oVbS/dOF0v6i6Of9U6jCCavy17D1srst/WKuq336cQWGkHb0x0IKgs9/+1cAk3UvWucm1Cz2Z3xfWzLyfIPZEpqUH9Iuc/wdCManx+0XkKMBHD4q+vReCp1OAmJg3vSnqbUizKnpzJfplsSTtR9Q97RGelD+7HESz3rUP+YtRg7q5VwTYw78HYqN8be7kNfslahoalavUqluDCKscR96NERBOZ5p8/t4thQL/sMPeg7p5RAcVBSK7dz06ApZ5/7Q7aK4wJUblSeXznahf4J2b8QH8n/htkQ+k4qF9031qCyoJcgOuFK3sYosk//AEaSzyPEMPKm8uyD1gTXBtlvN5t6tgDRQfS9lpwK2OeUt3GEEPy22WNq2tMAh7w/PjaVb8lS5iOE3vIrtxw4UVOZxyFuZYnY1c5HAflkYx2at8V7I/3taL8g02NnPdxzbp9F2iZ5iIjAIXkTwHaxr/tiInqZwlQBpxTnVx4XIz/kL/pWCzzPG7fnNqFvcib+IjvRzXw0joEjDcpzZ3FLUnaFxdhfDwfHkhWl6NsBDMj+dDGELiZuIRMVjOomtAf60Wp+NvQXdsv1kquEc5/sFfI9+ZiR+pzM6TEMhOSeYnuNGbBWbkiEOSMYGEw9EeZ5VKkzzNsirsc8aZoStIv1X6z5a5Xx9DOd60KPkn8XYqnKmyp8eSRZ/yt9yAPvG0PwJ1/YGMHoHLfphk81obJyC1pOBePP1iRMhzGmBhDr8nzmZh4sAboov33nlS07EYD/pOwXPJudWYK++Is72490wJc4YX6xDc3fTt0SNv/tHEb/4+uxqqwMq8pr0XVlcfLHkhHR2PJna5nB7BJNEVqaT0w1Ram2XmIMbzrWpw/0tutNtt9RW46xtsphe2+DreWXfJqwNL1vbQ3L7DK2PpJqwtTsEs3kWZotFJ2tyUp7SyJms38iXezNpebT+pH1t9LHz9qSSM5WomzNHaotT9lePiiqUvGmU+3rLLrMrUupjFZk1GnENj/i1/vENu+7KkeZ73Jm2jbitwcCQ/pQf7fu3enQnfbWQtLYW/uRzS0esTeTm39rRhbBdsu7DrTNTXqrvZlQ0Vnek5DWmlG8Cdn0ZsqdShOv45aXY6G6Se/sH9D9R5ps71qwlh9r3om3DmM0y2lfPmvZyLXu1u+07V69e0S2u5JzO+eRTxRT/WqLRvFu7rwozWebCHOVQzujNaDU+JrueU7kTSNf2t8dkC3vJMyrPC5GfrCto6VlomzTufTW3lF9fGpKF4FMqksrhIWX3ZzLljSf/bW96VexD9hsP4bJTtlWiSZQjfzT2S+WWyz7wHealSbC52jNcx7HruxpMP/9UXvvgLnsfceN/Yu6T8nFXl7jXbJlOmXdMrViljDvY3vB65E7fSzpmrEeYGUt22Jfsadd9/f79fY3xuQY852f8WJfdf9glPVuvc8oF4mmvW3LlzMNkxbjWGlbbjX/JVocq6jTfXI7DPX7lJa5creEZ21O2OgSzdunv4Iha+uwtn1Cru+y78szpb+9WWTR2d7/k217JpvfNzu1dbLCt7PpkrUFS7Up8+WoOAIZw1wvHzM64wVCiTfY2pqwS3XWlxGqlZJ57+xymrI2wSe69LeFSzmaBTS7tLe1CpkCJbPT9IbnvErzldYClslcFaisBTZnxTFXgCCqQerLlixd9u2UjbGTSh3cU12mg57RFbKcc24be9OGaWwHnCxd+sup8qw4CNnTMt6l/baaHppDd2RZP3uTjpamxdXO8hvZK65ZX0Salr9zrbvYQT+t7qCV73Nu5zzyiSqtfNlfcJtbwdtEmKscpkkLQFKdJa1z5J2E+ZTHheaHtHXMEchknZfSpb1xvcCym3PZVPPYX2cKjBPjth5QTr4lttVHogzY3gdi7+wvdk4zj2NX9jRYvP2RvUn3bKzlwejU96+JirPaxLDRZXi/00KO7YWtR+70saRrHoGMvQn1ZJesWC5gfplOEFs6a7PiOdNQteBjpW25k/nPfiIgvZvrxb9TlzptJ+kydZrufin/kxu5vsu5L58z/dPfEZV9e9rSRs2TBW5nU5EFMsXTatk6DzovTGLoWLP1gUeTA559nRi+OgDvI/JiYEUTOt/0Ks03ChV1aA8EMfpaoxxwO5TCs0t9yqcJnseyXLCUzQJ2PpFqlSNOg/sJY/38qVvkEtbUodW+npob3v5h+F+qL+D+1jvDscuP4f4My98bwNAr6q0ucyup9sJ/vAn2O5N6zRZtFsjYNqEx+A940p6x0jY0wNffjvoCGm+oO9aHTkvzj8Y9/n0YPt6Q9vv5MtIydKETTcr98yaRnk3HhhF6Pcdv7xD5a6QP6r330CrRcGwUg7YmHR17OjHwjDWPOna2Yyg4im6llaHMnGg+PYrRY9YHK0XlE30jGfJ3VqJcfbMLTWqai+2c+6aCuILyiaX1QWGPB+4C7hde0DbJV4kbbWc7IQ7QKWYZGsPoW63KLQxzm095XFh+KIxjTw/8u+SHLMJnO9C42YdA4m6IRS67SfPYX5fuaE1LK+N+fyPv+76aYW+90oXWS2PoO9SQll+T5fM55zxvs1xaZt4XaeBVm5Y1aajc5UPfkfq88r71NhyDS2lJrhJOW4MW2uZNCy9TisVaj3kpccF7qhtN6oPtBuOZINk7b6tF3jLqVMYtmfa8VeFB8zE/WjbPI2ctVXkT6d3Qm6MOaB4/6yxly670kWb0heRv2NNUMJbPqCMOPWc95i0JI/3PGWU7vdU0x06Rr0IBtFbnuxQibZ5vT9X3zu5Fy0mZQ+aznR9pQMfuxMgu+J7y5EzXO22FEc3I/qJitOeeOE6VrC5FiaUde8VsDFH5/oSS0jvT5n74RC3WPy4fWNnThylR2Oa89zIWRTS5gqUonWvB1fXMlR7LlvEA3iItv5IW4tdQOmfiFWhevz+Cwytq0CY/1b0+jr7djlQ+zmcbF8BSPgrM94lp59wO0yKPmvcYzzONk3l8IdsolW+wUqSh7RmJnPLajmH0eNZj79n4p6b+KXTvmN+d0wvZJvlZxDI0n99aaH6YUxSBZz3YcnQEqG6FX2mGFbEIgsb94t9NPIeowXdpEq2PmB9SlmrfUOj+el5ppeR14U4dz+Ylub7CIu/rbqs7uB5Lu/9Q89YiloslPBZb0mOe+zv1NxZ7+QqiplOhx7GCFLKd5bhLujyLo2gDmWVPZsypa3607dgvW/VxwjcyXECUTXcPayCDQ8PQDy7362X3MLPCMoXQyTbUfaM3Xjne6MPwSCtcLL53xpUObHK2mA0beI6PYyD5bhVpehD776tFvJkXN7qvD1mv2BER0V2nyF6IWTzCpxrNlw6tdyWCGEDb50MzgxiiZS6M3q8bLwxbj5pEEAMNzYeaGcTcSbOxZIuTg8/vx+GzIUSiIuAUXeTaIDoe3y+DGLG1djejlkEMEdFdj4HMkoghfFVpTlfQtrdj8IhnyZrzI6JFEgtjNPmGcIMGzyuDaL/rXrRYZNT7tiODaPNUoVy+pbq8shYtp+P3hDt2dmLwtdz3yhMR0d2Bt5YtiSiCJ3oQuGX0l6D8EQ88mx0MYu5pUYTOj6beYVS+Ce6HmCOWpWgQPd8NxN/bUVIO5zYP3BXcVsvCbASBk+3oPTmIgfOhVOMOFS7Uba1H/dcb0VDNEIaI6F7BQIaIiIiIiIoOby0jIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6DCQISIiIiKiosNAhoiIiIiIig4DGSIiIiIiKjoMZIiIiIiIqOgwkCEiIiIioqLDQIaIiIiIiIoOAxkiIiIiIio6DGSIiIiIiKjoMJAhIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6DCQISIiIiKiosNAhoiIiIiIig4DGSIiIiIiKjoMZIiIiIiIqOgwkCEiIiIioqLDQIaIiIiIiIoOAxkiIiIiIio6DGSIiIiIiKjoMJAhIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6DCQISIiIiKiosNAhoiIiIiIig4DGSIiIiIiKjoMZIioYLGfRRGNRjH9czkgIXgSL774Ks5Oys/T7+Dsn57Bxffk5zlFcPbVF/HqX0TkZyrIexdx5k/P4p1p+fkeEPS/iBdfPStyzhIoOP8SEdHtxECGiPIW/dEZvPryizj6H1/Fq6++im8dPYrv/sUNZK03j1/F5R+/g6sTUTmAFkcMN390GTd+Kj9Kt0KX8I5I7+s/kQOWUPTGZbzzXkx+uktlyr8/vYHLP7optgAREd1pDGSIKC/TwZP47p+9g5W/txP7DhzEwYPP4ekdG4DrI7iWLU75vV048O+ewZ4vlMoBtDiiuPr9sxj5e/lRWvOv9+GZf/c0dnxGDlhC4b85i/NX7/IANVP+/fsRnP3+VbEFiIjoTmMgQ0Rzi13E6cEJrNy4G/tqH8KaEmPgSpRWbcMTT+/GI9nilF/GMPvLWcTst6AJsVs3ELwUxI1bc53bnsV0NIro9Kz8nMHPp3Fz7DIuj93EdIafM26Fi98GJ37r3Wu4fOUG5pytMDsdwY0r8nft6yDmGf2Z/JHYLXO8a+9OiznMLbnukQzjK787O30T1y7Zf9dIjxuIiJr07IfyFr/Euvxcpvcv5eeEXOkjttF0VP6+HC9441b6byhiP7uG926J+X8Un7992+Rcvwzi46enn7ndbMtr3tZozwul90Mz0sXYtpeu4ab9e8s6RtO3VWL72a8w2fNvbBrXbooV/+Uv4uud+E1FxuUTZqcLTCcjH9h/Xw5Lbht1vWJGXrmGSIayZjLGNdMnQ5krIA9k2lbmuiXKgpR1WLY0yLVMc60bEd2zVuiC7Cciyig2cgJH/zKGLz69D5tzXVwxnpH58yge2fs0tpWLz5Nn8Wr3ZZR+5SB2O+OjGM8dnDlxBu/8bCVKSlZiNhbDyk9vxZ5/W4015jMy38WNB5/A0/9GM0e/+Rev4sTVUnxl3244V5uDLKbfOYMTf/YOoitLUPIxo9K5EtoXduGJf7VWjhH/zYmKbXhg4iyCPyvByllRQUUpqh9/GluN5bSLimU0njW5NWsuoxEgxFausy6Dsa7fK8HmfxnDSOAm8AmRTjEx/md24Ok/3gAz1rP75U2cP3EKb0fiv2us++ynNmDHnh3YYPldYMNnonhnbFqMJ373Q1GdW1ONPX+yFWv/1kjjCTlyXOmjMr3s6S/MmT7mNppAhecBTPxFEFFjuYz5/Vo1nmjeKgIEG7lNLVck1n8FBxvFBs5n/SymEfzTb+PPbyjpXLIBO5t34KFPpOeFxLa8/GtyfoLxjMyfxx7Chg+v4Z0PxTpC/Iao8K5x7cG+f21bx3+9BtcCNzCb2FZVO/HEZ6+K9EkNK310j5ifOl0i/8p5W1Zc5ImDIk/IT4aJgW/h5I8fxO5nviK+TbiG0//7aUz/ywPY4xIbNN988Oew/n7G8hWB8481BE9fxvTK1Xh45zP4yvr46HGzmPjrU3jrrybieWClyANi+6av51x5IPu2Wvffxf4hsBJfeU4sq3l6NIrz/+lVvD3txG5vIh1s2zNDGqys+Ar2/bETZhLktW5EdK/7lf+PIPuJiDK6eeUv8aMPP4Mt/+pBfEoOyyjyI/zgxzH81qbP48H7xOfpG/jh6E9E5f5L+JxZsb4lKi8ncVl3YtefNGH7v/oCHqv+bXxa24DfMn94Gjd+OIqf/vrv4/MVojozdhr/KTCNjbufwGNrjO9tJs/hP/2fo8Cm3Xjm39bisS88hs/9s3EMD13G1AMufKbMGCn+mzcmbqHc8wSe+KMteOyLn8ZPL1/Gjz74TTxWleGHS36J2D/9Nv5g5w5seewL+MIXfhsfXPoBfviz38YXPisjOWNdQzcQ+UhU1v63RvzBv3wMv7/yx3j78gQ+9rlH4UiLZGJ4540efC+iYdv/1oyd7vi6fxj8SwR+/Kv4fedaIxaSvzuBn//WdjzxuJi/WKcvVXyIH/31D3EDv4tHXS586Uufwv/4wY/NCvbTf/yleFolplXTP5/0MbfRDYz/r3LUNj2BHW4xv/Kf4oeXf4Sf3f8Yfvd+85dT7nsQn//SZ/Hhj0bx4cNP4EBTLb70e8bGzXP9VJND+P+e+wdUNvx77KkV6SzS+nOf/W2sXW2MacsLpviwn6z6jJynWOWrP8CPb87i01+Jb9svfPFL+J0Pf4ThyzfwyyqxHT5pTCbXMfoA6o18t1ms10djuBi8jB/eWoc/3r8HtWL7/fbPRvHDK/+AT33xcyhfkZgukX9X48HPfwmfnfkRRj/cgCcONKH2S2I8cylSSn8thrHhH+GfHviCzH/C+EUMXlmJ369/FGtXFpAPfgx8Rp1HxvJ1DdfCn8DmP3kaf/yvE3le9TGUTM/gV6vrsWvbl/CFGpFPPzGOv3p7HL/6eadYHjFKPnkgx7ZaeV8UY3/zY6z4F3KdY1fwV9//KT72sSh+6ZDLGnsHbwd+gk9v/gNznGv9/wl/Ed2A3U+LtBe/9dimT2F86C8w8c/F/H4jsUxzrRsR3et4axkRzWnKuEVE7C2MOs+CiArd5X9YiYe+9BUk66afWId1n5b9qukgTg5MQPPswbZM3wsTl68gWuLE9m3rkstW+vvb8MiaaQQvvSOHSOu+iK88JIOQj1VgnQbMxmbin9NocH7hIZQma94a1ohKVOwnYduzEaXY8AdfhCbHW71+rRgyi4w/Kyp3l0QldO0Xd6ZuxRPrvu1LD2Hle5cw8g9ymKkUFY+I+Sf20J/ejEdFGtwav1HQQ+aFpM+6mq8gkTz4zDrzVq3YP8nP+Sho/aRfMZZqFlP/K5GqK0UgkMgYBfhUBR5JLjywdrMIGH55CxPXrallrGMi36353XUilUvxyB9twzq5/db9zlqs/GUUkf8Z/zwva1xwPhDD1f+eSt+J/yuE2AOV2GgEt/NJpzmsrVF+K4OSqmo88ulUZF3ym/djNSJ4d1wOkHLmgVzbqrQS60pjCP9Yth0Xuo6b9z+KR9dGMfF3cnxjmFjPCuOKSuwiRsZmxX4glfZY7cRjD63Ejb+7JgfEzbVuRHRvYyBDRHMq+5RxO4xRjVmgf5wSFfG1cFTJz9noN3H2v/w5Jn5eAk3LXrE1A6zf0JRbeAxrUGpc3fmHd0VVbQGiE3j7zHfxrf/9Rbx49FWcN5rgNW6niX9buKkpTIvqo/YvbJdqfn21GCoqzzmb+BXpcL+Y7v+eKugh8yVNH7v5rN9vbMWOx9Yg8r1X8eK3voszfz2BaI5nc/JWouH+T4pY+B8LSS2hzFjWhSpBtQhCMR7CO+a6TOAdI3Cp3Bi/3XBB+SCzlcb9hznNInLlLE5++yhePvwyvtV3VSxDgYFqzm2loXJdCaJ/HzLz57Xxm1iz/iFs/BdrRfB9zSwzEzdFbltbAZEyMg1mcePsURztSHWn34mlnVyYe92I6F7GQIaI5qStWQP8bALXb8kBCzKL2Tke2o2OnsU7pVuxtSKGi2+eR87ZfiQqZLJ30bx3Fq92nUZo1WPY/e8O4uC/P4B/Y40G5mkWvyik8qiY/nCea7kU6ZNV4eu39l89gQMHnsau/4eoJF88iVdf/XPcWHAwM40Pbt9Kp6uqRAWu4dIlsRAT7+B6bC0qP6dWyOefDwo3jaD/W/ju96Oo/Mo+HGh9Dgf+nxsxn4scubbVut9Zh5W3biL8S9HdLMG6B0tR8jtrUTp5HaFfRhCaEMHc+sr4yKbVeHj7E3iiSen2PY2n//Bh+T0R0dwYyBDRnEqclVj7sVu4MmJ9yNz0cxGYyN45aWtEBSqCd2/Iz9mUPoJdu6pRXbsZ66Jv462RzG+qKf91UR17/11bxXcC7/0DsPK3HEh7UD1P1y5eQbRcBDH/JtFC2yL4TQ2lH4vh3QlrWBb7HxFEsQZrHXJARhOY+Enh67RU6ZPRQtbvE6Wo+MIO7PuTzdB+FoR619usruYu0Z9PkDMxgcgvV0L7F4u6hvn72AY8umE1boauYOJaGNPrNqI6kY8KTKdfqOubz7rbRS/h4jjg3L4LjzyweuG3h2bbVg9VYK0o2+/97XVMiMDNYdwO+hsPwlFyEzf+VmyPaCnW/o5MBDMNpjH5v1aitLTU2q1e8BIS0T2EgQwRza2kGjs96zB75SS+PXAt3nTxL2O4NXYW3/2PL+NElkAjjQgOHv30LK6dPWG2VmSKXsPZv3zHctWg9HecInASPasfwbbPr8HNwGlczDAL7YtfxLqPielPXY7f5vLLKK699RaCH5bikRrzJpZ5KfmEqEz94yRumleOZhEd+3NczBDDFeRjTmz+/VLcGjmFsxPxdZ+NvI1TP7iJks88BpflFPk0Jq5cQ9SYf8Z1WiWWEbh1Uy5UlgruUqWPcfuUMf/oTybEkgrGbxe0fpLIPyf/QqynXP7ZW1GRD1bjPrPhh1KU3SdS4oYIBox0+HkUwb4zuJwpq304geBYNB5Qi/z05wNBTP/aI3hsrlsY58FssetnEUwYy5El3Q3rPr8Ra967hLeuT+OhzyntmuWbTmVlIiVuIvTf47fHxcbP47t/NY9MWCLyikjV9yflbXaxm3hblLcCb7qbY1sJH6vEg+UiQPuh2D8kbiHDOlSsAyI/DCLySQceNB7iN8g0iPzVCZwdT5R8o5xdw80caUpEZMdWy4goL58o/xw+u/on+NHw23j77R/gBxeGcfnvforVG3di12Yt3tJS2QzGL47h2vUP8TvVD+JTllafzF/B2t/9bfzy+mX84Pt/hR/8QPzO5R/jw3/+WXyu8jfEt+ktVX1y3QP4+dW/wnD4n8H5ud+ytnz1iXJ8bt0v8eMfBvCXQ8Yy/RBjPxOV9B278QfJp4jTW7oymK1dwTosofTTn8RPLr+NH/yV+M0fvI0f/eNafOEzP8eNn5bi9z//oKi+CfYWpAzm+v4Uv6YOU3yq4nfx6//rCn4Q+Ev81fd/gLdHJ/Cx9duwe+dGfMpoJctg/u4n8C9+7ccYPPO9+Dr9Qwk2/OEe1P5OYp3W4Nd/PoaLly7G0/C9T8XXw75M+aRP2jYyRPCjeJNZyjBVCco/8ROM/vfLeFusxw+ufojPinT5rXzWTzH7i3/EtQt/hr849wMM/3AYP7jyD1hdvQuNm4za8Uqsvf/nGPuby7ho5LeRH+FnD+7A1tUif81WWFot+x+/VoVPiWCz/7+JPPXDMUR+dQN2/NtarE9mgQzrmGlb2YdlmK5EW4WfBH+IyxeM9PwRPvxM5m2NT5bjF+MBjEU/g8f+6HfxG8r655UPSn8LJZFR/PBvRLqIbTz87qew9Y8+g1s/+geU5li+NCvX4v6PxsSyyvImIvKVrkfxG38/gdnfkdNl/B1rHsi9rQwrsfofx/BXoVtY+/t/COcD8Ssra/5pAt8L/gQfe/AxbFdaCEymwfmAmQZGOfvhux/i/orPYe0/EyPks25EdM/je2SIqGDGS/9iv1yJkk+tRon9um5sGtGfr0Sp0UBALsZ4sVms/GQpzNZ2FyixTKtLF+H2GZPx4slpUXMVy7dYt5clGC82/FCse6bfNt8VMoutB/ag+mPGeGIRMqWzwUzDHN8rFj99hMR62LdhrvXLwHhRovGexIx5wXgxotFoQR75xPydX5bMnfcWLJ43ZleuznkrlPGOm7/4xA48t3ODHGKTRzot2jrJvLLQ7Z9zW82HTAPMkZZERJkwkCEiWk4yvNSSipDRYMR/vgZH4zP4yqI0FEFERHZ8RoaIiGixTLyNU396QgQxlxGr2Ip/zSCGiGjJ8IoMEdFy8tMbuPz/i0F7eAPWLvUdUrToZiNBnPvvEdzncKK6Slu82/iIiCgNAxkiIiIiIio6vLWMiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6DCQISIiIiKiosNAhoiIiIiIig4DGSIiIiIiKjoMZIiIiIiIqOgwkCEiIiIioqLDQIaIiIiIiIoOAxkiIiIiIio6DGSIiIiIiKjoMJAhIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOit0QfYT0SL6u3djGH8vhqnpWbCUERFRIVasAMpWr8T6T5fgsw+UyKFEpGIgQ7QEht/5AL/4JfBrn/oVfDSr39WBzNjEDBq3rpGfiIhosfxcHD+MfeyHsV+gZsN9cigRJfDWMqJFZlyJMYKY1as+hp9/dHcHMUREtHQ+sXIFNlZ8Ep8s+RXz2EJEVgxkiBaZcTuZcSWGiIhoMVStW2UeW4jIioEM0SIznokxbicjIiJaDMaVGePYQkRWDGSIFplxKxlvJyMiosXE4wpROgYyRERERERUdBjIEBERERFR0WEgQ0RERERERYeBDBERERERFR0GMkREREREVHQYyBARERERUdFhIENEREREREWHgQwRERERERUdBjJERERERFR0GMgQEREREVHRYSBDRERERERFh4EMEREREREVHQYyRERERERUdBjIEBERERFR0WEgQ3QXCAeO4WS30f03hOWwojEbRehiAIHzAQRvxuTAPMUiCIrpAudHELolhxEREdE9gYEMUdH7W7z9H5/Ht44Y3VMIXJGDi8FEP/ZWlqHKtQVb3Fuw6QEHtrw8gnzCmfDpvVi/qhybxHRb3DWour8ctd8K5jUtERERFT8GMkTFLvR9nA0Bj37t/4VH8T/xf/7138gvlrlYEId31aPnhgvNx4cxFhqAbzsQeL4OTacjcqQsrnSg8Ws9CFc0oXNkHJNy2sFveuaeloiIiO4KDGSIity1i3+Ka/gcvtj4JL5YCfyv3u/jR/K75Sx6rgdtFwHtOR/a97hQ+ZAHra/44EEEvS/5EZTjZTLyVjtGxP/mY51ornZAM6bt9cOriWm/0WN+R0RERHc3BjJERe1vcem//i1w/x/ic5Wfw+fcYtD7f4a/DcW/Xc5Cl7rM/42ba1Bi9gkVbtRtFf+vDCM0ER+ULoLw1fhVl/Ky5JTA6k1w7xD/I30YLqbb64iIiGheGMgQFTN5Wxn+8PN4SPxzPPyk+Pu3OPvXIrhZ1kQwcs3470T5/UowgjKUVxj/+xHOeodYKbR18b6pD6xPxMTMj0GE342an4mIiOjuxUCGqIjFbysD/viRjfiE+H/fI3+APzaGv/V9c/jy50D5GtlrKkVZuezNqgSbHmuGJvo6XmhBz/mQCIoC6PmGB/Un4mNMTvORfyIiorsdAxmiojWBaz8wrrz8ARyi8v/Bz/4RH4jqvcO4vSz0p7hUBLeXzVfp9nb0v+SGdrELe91VWF/ZiN6VLeh7yWl+X7LS/EdERER3MQYyRMXq77+Ps39t9Pw3/Ic//DS+9IjRfQH/IWAM+1ucvbjcby8z9CN8U/aajOdfjP8akDMYKYHruSFMfjCJsZFhjL0fxtArdSifiTcRUPWAcb2GiIiI7mYMZIiK1Ht/8wYuif8P/dGzeObZl1Ldn3zNfF7m2n9dzreXaah0xoONyffV51kmMXnd+F8Pp/mszBxWi9+pdqFyjfGcTRDDbxkDm+E0EoCIiIjuagxkiIrSBC6d/b74/zls2/MCdu/9Rqrz/gm23C++Wua3lzk37zefc+k6OYhwfBBwJQC/0eLYHjdqSuODEAuj/9l61D+rjCdEboQtL78Mn2pHu5hWe64O7sS0REREdNdiIENUjP7++wgYt5WZzS7HB6V8Ho82/Kb4v8xvL6tuQtduEcqcbkTj423oONqC+p0tCMIF31MeJGKR6LkO1B/tR//RWvRejA+LXTyMugfXo8qzH21HO9DSUIWahl5EtAZ0PeVONedMREREdy0GMkRF6L3Lb+Jto0c2u2z30Eaj7bLlf3tZ3WuD6NzpwMiJw2h5tgP902543/Sj9ZFUKFL6gEOENoJWB4ds4axkQx32H/AAZ7tw+NkWdJwKYdU2L/pG/KhbGx+HiIiI7m4rdEH2E9Ei8J+7hap1q+Snu9/YxAwat1raUC5YLBo1bxMrWV2aucWx6SiiKEXpavk5ISaGm/eXlaC0lNdhiOjuZRxbFrqvJbrb8IoMEd1xJaUiSBFd1maTRYCTFsQYSuLTMYghIiK69zCQISIiIiKiosNAhoiIiIiIig4DGSIiIiIiKjoMZIiIiIiIqOgwkCEiIiIioqLDQIaIiIiIiIoOAxkiIiIiIio6DGSIiIiIiKjoMJAhIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6DCQISIiIiKiosNAhmiRrVgR74iIiBYLjytE6RjIEC2ystUr8fGVPOIQEdHi+Pmsbh5biMiKgQzRIlv/6RL89Ge/kJ+IiIgWZmxixjy2EJEVAxmiRfbZB0rwK6JkTc/8Ep/4+AreDkBERPNiXIm5cuNDfBj7hXlsISKrFbog+4loEf3duzGMvxfD1PQsWMqIiKgQxkkw43Yy40oMgxiizBjIEBERERFR0eGtZUREREREVHQYyBARERERUdFhIENEREREREWHgQwRERERERUdBjJERERERFR0GMgQEREREVHRYSBDRERERERFh4EMEREREREVHQYyRERERERUdBjIEBERERFR0WEgQ0RERERERWeFLsh+IlpEf/duDOPvxTA1PQuWMiIiKsSKFUDZ6pVY/+kSfPaBEjmUiFQMZIiWwPA7H+AXvwR+7VO/go9m9bs+kBmbmEHj1jXyExERLYafi+OHsX/9MPYL1Gy4Tw4logTeWka0yIwrMUYQs3rVx/Dzj+7+IIaIiJbGJ1auwMaKT+KTJb9iHluIyIqBDNEiM24nM67EEBERLYaqdavMYwsRWTGQIVpkxjMxxu1kREREi8G4MmMcW4jIioEM0SIzbiXj7WRERLSYeFwhSsdAhoiIiIiIig4DGSIiIiIiKjoMZIiIiIiIqOgwkCEiIiIioqLDQIaIiIiIiIoOAxkiIiIiIio6DGSIiIiIiKjoMJAhIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6DCQISIiIiKiosNAhoiIiIiIig4DGSIiIiIiKjordEH2E9Ei8J+7hap1q+SnpTSBt7vfRFh+iivBms98Dg89/Hk4yuSg22BsYgaNW9fIT/NwK4SRq5OYQRmqXE5oJXJ4PmajCF0exeQMUPZgDZxrs0y8kHkQEd1hxrFlQftZorsQAxmiRXb7Apm/Qc9ntqBLfrL6TTzq7cWrf/J5fEIOWUoLCWTCp/diy9d6UgGZ5obvzABaq/OINCb6sXdrPXpuyM/Q4H6pHwPPuURIlxDDyMu1qHs+gIgcArjQ/KYfndsd8jMR0fLGQIYoHW8tIyp6f4gX/vw9fP+y0b2D//zyk/g9/E9c6vgm/jQkR1mmYpcPo9EIYqqb0T0yhrFBHzwIoG1HE/pvypGyiQVxeJcRxIig5PgwxkID8G0HAs/Xoel0KmSJvtViBjHY3Iq+0CTGR7rRXD2Crq824vDlmByLiIiIig0DGaK7wK9+6p/jPrNbh9+r//f4423G0L9F+OY/mt8vT1EEjrdhBBpaX2pHU3UlKre1ovMlDxDphe90UI6XWfRcD9ouAtpzPrTvcaHyIQ9aXzECoQh6X/IjPnUEgye7xF832l7zoe4hDY7qJnSe7BbjjaDtSL9ylYaIiIiKCQMZortOFB/8zPj/OTjW/nNzyPIUwvC3jf+NcLtSN4I5HqsTYQcQvBCyPf9jFboUv6mucXNN6jayCjfqtor/V4YRmjAGhBE+bfwvRdlq479UsQnujeL/6WGMRuODiIiIqLgwkCEqelGMB7+PSxdF94M30PPv6vEf/hr4vb0v4Y8q5SjL0c2wCGWEjeUoUx+HWVMOc7HPhHNcLYkgfM3470T5/erEZSivMP73I2xOrEEzAhuRRjMZ7yJLBDxERERUbBjIEBW974vgpRZ/8nXR7X0cXWdFzXzjk/ijLz+E++QYy9qD5SiXvaZSEYzI3rk5UG559rUUZZaJHdi0zSX+B9D2zcPovxJG+Eo/Dn/Vg5YrxvdBxPiYDBERUVFiIENU9FIP+7/1ZwP4T12v4I/wHRz6Wg1a/5yXG5zP+NG3x4HIW22od67HemcLRrf2oHuP8a0GrDRHIyIioiLDQIboLpB42P/TlV/Co1ufxAv/4RU8iv+Jsy+/AfMOrOXsdNj6LMzNMEaN/yLGUG8ay6wfYUvrZhGErxr/1QDFgbrj45iZGsdoYBTjH4yj7+lKrDKfjamBQ4xKRERExYeBDNHd6JOl8dvK3o/ig38yhyw/ayuxyQwiJjGlPnB/azIe2OxwihAkGw2VzngEMinWMWUSk9eN//Vwms/KpJSUOuDcLH7TeOj/VhCBM+L/xhpUrjW/JiIioiLDQIborhNDOPBnCBi9X/gsfutXzYHLkBPup4xgpAs9b6WuyQTPxZtObtpag9L4IMRu9KPla/VoUcZzbt5vXHdB18nB1BWdKwH4jWdf9rhRk5hYCN9Qr/nEMPLtdvSIPs836sRSEBERUTFawTf7Ey2u2/9m/9+Eo/ohrPmV+NB/uvl9/Ojvjb7Po/mNATRtnPsGrYWa95v9b/aj8dF69EZcaDpYj8rYMLqO9iNc7cPo+VY4zUWPYvCpMtSaTTX7MKy3wnh837iNrP/rTtSfjMC1pxX1lTEMf6cD/Tdc8F0KoPWR+HqHTzWipiGAsl2NaBI/GD7fi66zIrCxzIOIaHnjm/2J0jGQIVpktz+Qsfntz8P9hT/GHz3xdXzx07enlj7vQEaIXelC4879IgCJf9Y2e9FzvB2edfHPhuC3arDpmyPQdvgx3N+QuuVsOoiux+ux/7S84qK54f1ON9q3Kzel3RpBxzea0H4qJJtz1lC5qw3+15rhVK7aEBEtZwxkiNIxkCFaZLcvkFk+FhLIxMUQjRrtIJegtDRz8BWLRoHVpSjJ0MqY8Z05dZbvTdNRRGfF/5WlKFVfjklEVAQYyBCl4zMyRLQMGAGMCDCyBDGGEvF9tiDF+M6YPmsQYxBBjjkPBjFERER3BQYyRERERERUdBjIEBERERFR0WEgQ0RERERERYeBDBERERERFR0GMkREREREVHQYyBARERERUdFhIENEREREREWHgQwRERERERUdBjJERERERFR0GMgQEREREVHRYSBDRERERERFh4EMEREREREVHQYyRERERERUdBjIEC2yFSviHRER0WLhcYUoHQMZokVWtnolPr6SRxwiIlocP5/VzWMLEVkxkCFaZOs/XYKf/uwX8hMREdHCjE3MmMcWIrJiIEO0yD77QAl+RZSs6Zlf4hMfX8HbAYiIaF6MKzFXbnyID2O/MI8tRGS1QhdkPxEtor97N4bx92KYmp4FSxkRERXCOAlm3E5mXIlhEEOUGQMZIiIiIiIqOry1jIiIiIiIig4DGSIiIiIiKjoMZIiIiIiIqOgwkCEiIiIioqLDQIaIiIiIiIoOAxkiIiIiIio6DGSIiIiIiKjoMJAhIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6KzQBdlPRIvo796NYfy9GKamZ8FSRkREhVixAihbvRLrP12Czz5QIocSkYqBDNESGH7nA/zil8CvfepX8NGsftcHMmMTM2jcukZ+IiKixfBzcfww9q8fxn6Bmg33yaFElMBby4gWmXElxghiVq/6GH7+0d0fxBAR0dL4xMoV2FjxSXyy5FfMYwsRWTGQIVpkxu1kxpUYIiKixVC1bpV5bCEiKwYyRIvMeCbGuJ2MiIhoMRhXZoxjCxFZMZAhWmTGrWS8nYyIiBYTjytE6RjIEBERERFR0WEgQ0RERERERYeBDBERERERFR0GMkREREREVHQYyBARERERUdFhIENEREREREWHgQwRERERERUdBjJERERERFR0GMgQEREREVHRYSBDRERERERFh4EMEREREREVHQYyRERERERUdBjIEBERERFR0WEgQ0RERERERYeBDNHd4Nbf4OzJ53FoTy3+ZE8zvtX33/Dez+R3y14MkSsBBM4HMHItKocV4FYII2LawPkgIjE5zG42hrCcR+BiCNFs4xEREVHRYCBDVNREBb2vGdtrtqD1xWP4r3/9fVz66/+Ck8/VY/u2WvRcWeY19ukRHHY7UO7cgi3uLaipLMP6x/sRll/PJXx6L9bfX4UaMe0W9yaUO7bg8EXrOkff7kB95Sqsl/PY4qpC2ar12Hs637kQERHRcsRAhqiIvffnT+HJ5/4L3sM6uJ/tQ9/we/j+5Xfwn19+Er/36S/h0c+UyDGXowj6n6pD23nA89IAxkLD6H7ahfCJejS+HBQhWm6xy4fR+LUehKub0T0yhrFBHzwIoG1HE/pvypFESDT4nXb0ow6+3gEMBYbQd6QODjG852v70XNDjkZERERFZ4UuyH4iWgT+c7dQtW6V/LSE/un76HLXouf938S2V4Zw+Cvr5Be339jEDBq3rpGf8nSlA5ucLQhu68b4YJMILoSYCESMqyqRZgxMdcJTao6ZQRSDT5Wh9tsaWgNh+DbHA7bwiVqsf3wQzldGMfqM0xyG6YgImTRoq+MfjWn7Hy9D/QmgrncSfbs0OZyIaPkyji0F72eJ7nK8IkNUpD746z8TQYzo+cILeOoOBjHzFb46jKD4797hjgcxhpIauHcbPV0YfscckkUIw982/jfC7UpddXI8Vge3+B+8EErdnrZaDWIMMcSm432ONVkjJSIiIlrmGMgQFalw6DvxnkcewqfjfUUlMtFv/q/Uysz/cSUoK49fSQm9GzH/Z3QzLEIZYWM5ytS759aUo9L4fyYM+9SxaBTRSAiDLzfBexrQtnejaetyvvWOiIiIcmEgQ1Tk3L9dfFdjVOX3W6+KlGvJ6zNze7Ac5bLXVFpm/Zw0go6yMhEkVaH2+UE4nhnAcH9TPOghIiKiosRAhqjI/eT9HFcuSNLgPNKO9iNeNFU7MPKtWmxp6EJQ3mJGRERExYeBDFGR+vX7v2T+v/b3EXxg9hWn0bA1EAvfiN9yVrLS/Jfb6bC1qeabYYwa/zXjJjWVA54DXngPtJstnA0fdCJ8ej+avm08pUNERETFiIEMUZH69MN/gIeMnt43cWnKHFRUHA81mP/DkUnzf1wUU+ZHJzY9mKM1sbWV2GR+PYkp9R2atybjgc0OZ6oBgTQlcHnqzb7gpVDaszRERERUHBjIEBWryj/G7m1Gz3/Byy//F4TVN/n/7O/wX9uacfY9+XkZ0ly1qBP/g6/3YSRxi9etAPqN1sg2NsK9MT7ICG5Gju7FlsfVW8GccD9lRDJd6HkrdU0meM5vtoTWtLUG8Sdvwuj9ZhsCt8wPSUaLaaYSPuxPRERUrPgeGaJFdtveI2N4700c+rcN+K9/L/rv/ywe/Uz8KsatH38f4feBX9/Th4Hn/gCfMIcunXm9RwYxBF92Y9PzI9C2e9HyWAlCZ3vQcx5oeCMI/055RSbxvhnRa3nvy81+ND5aj96IC00H61EZG0bX0X6Eq30YPd8Kp4hRYm8fRtVjbSKcccCzrwFuRxkmgz3wnzKuxLjgGwmgtZrBDBEtf3yPDFE6BjJEi+y2BjKGn/0dzv4fz+O17v+G5AUYEdRs2/Ma/t97P4/75KClNL9AxhDG4Df3oulbAXmLlwN1x/rgf9qZesbl1iD2P1yLrogDrRfG4PtiKvCIXelC48796Jdv6Nc2e9FzvB0epSG36NsdaN7Xjt53UjeRaRsa0PLtLni/yPfIEFFxYCBDlI6BDNEiu+2BTMIvYvjg//4ns/dXP/XPl/wqjGr+gYw0HUV0VvwvKUVppgskMfF9rASlmb9ENBoT/7N9H2e8R8YYa67xiIiWIwYyROn4jAzR3eJXSnCfCGCM7nYGMYtitQhgSrMEMQYjwMn+ZXzaOYKTEnOcuccjIiKi4sBAhoiIiIiIig4DGSIiIiIiKjoMZIiIiIiIqOgwkCEiIiIioqLDQIaIiIiIiIoOAxkiIiIiIio6DGSIiIiIiKjoMJAhIiIiIqKiw0CGiIiIiIiKDgMZIiIiIiIqOgxkiIiIiIio6DCQISIiIiKiosNAhoiIiIiIig4DGSIiIiIiKjoMZIgW2YoV8Y6IiGix8LhClI6BDNEiK1u9Eh9fySMOEREtjp/P6uaxhYisGMgQLbL1ny7BT3/2C/mJiIhoYcYmZsxjCxFZrdAF2U9Ei2T4nQ8Q+7mO33Wswm+Ufpy3BBARUUGM2tk/RD/C/xWeQcknVqBmw33yGyJKYCBDtET+7v/f3v2HRn3eARz/ZKbdKf5INovJnKjZDxMZq0pLp+wHS5DNShmzbGOGjq0ONiz9Y+gG1eEfxa7QKWNtpQx0gw6FCcrazvqHTHGspqOlESYJjmICLmtK7RLaYA4bm9338nUzVZnaU+45Xy84vs/3ufv37uGd57nL6WKcGijG0MhYeUECgKuV/QEsO06W7cQsmmc3Bi5HyAAAAMnxHRkAACA5QgYAAEiOkAEAAJIjZAAAgOQIGQAAIDlCBgAASI6QAQAAkiNkAACA5AgZAAAgOUIGAABIjpABAACSUzdeko+BCjp5uhinBooxNDIW3mUAXIu6uojG6fXRMrcQi+YV8lngYkIGboBjJ96N8+9HfGzmlHhvbPyWCJme/tHoXDk7vwPgwzpXWj+yz9azxfOx4nMz8lngAkfLoMKynZgsYqZP/Uice+/WiBgAKu/2+rpY8ulpMa0wpby2AJMJGaiw7DhZthMDAJWweMHU8toCTCZkoMKy78Rkx8kAoBKynZlsbQEmEzJQYdlRMsfJAKgk6wpcSsgAAADJETIAAEByhAwAAJAcIQMAACRHyAAAAMkRMgAAQHKEDAAAkBwhAwAAJEfIAAAAyREyAABAcoQMAACQHCEDAAAkR8gAAADJETIAAEBy6sZL8jFQAbsPnYnFC6bmdzdSf/x15/PRl99lZsy/M1o+tSQ+3zIrn7l5evpHo3Pl7PzuWhVj8Pix6BmKmNq8LJa3NuTz12BkOIbHIgoNDVHIpwBqRba2XP9nLNQmOzKQrDfj5BOb4lcXPR59aHV8/+tzY+X3NsXBU8X8dVVupCsea18YzUs7oqO9I1a0NUbLg/snBdoVjQ1H75E9sX396miZ0RiNjeti/z/z5wCAmiZkIHnfiC1/Goijr56IPTt/Gw+tWhBvv/xkbH5gfRwcyF9StQZj//o18fMjEff+4kD09B6LnQ8vj77f3R+dj3fH/02xV3fE4vbO2PjMi1cXPgBAzRAyUAM+OnNWzJi5IFq//O1Y9+uXYtvaORFv7Y3NTz8f7+avqUrHd8fW3w9GrNoaTz9yb7S1Lo91T2yNzU0RXZt2xeHh/HVXcteGGBoaKj0OxOZ8CgC4NQgZqDmzov0HW+KL2XDfH+OVM+XJqtT392PRXbq2f7M9Fk5MRRRWRPsD2WBHHDtRnrmy+kI0NDSUHo1xM76VBABUDyEDtWj+krjzjmywN/qq+Dsjg/37y9e2psbydUIhGpuXlke9pwfLVwCADxIyUJOa4hN35cMENN8x+VfKmpv+uz8DAHBZQgZq0mCc6c+HAAA1SMhALXpnMP7Vmw2+Eh+/+NRWlXqtb/IRsr7XJ46cFerLFwCASwgZqEEDR56NP2SDtq9F6/zyVFVa2Lq2fO0bfKN8nTAcQ+XbpbHsM03lGQCADxIyUFOK8fZfHo3NP32uNJ4Tq378nWideKIqNS1fHWtK1+5n90XXyMRcnDkc+58pXZd0RvuSiaksbrp++cPoeHBHdF94HQBwS6sbL8nHQAXsPnQmFi+4GT8G/LfY9dmO2FEKloVfaI3ZU0oN8I+j0fdW9tycuHvDnnjqR/fE7eXX3ng9/aPRuXJ2fne1itH9eHss29QVTfdtiI1fKkTvwV2x60jE2r3dsftb+Y7M8e2xbOnG8k81r9nzRuz77sR891MdsfGFbDQafYe6oi+aou2ri6M5O5J237b488MTv34GkLpsbbn2z1iobXZkIHlvRt/LR+OVl0oRM+2euPv+LbHtudfiNzcxYq5fIZY+sjsO/KQ94oXtsfFnj5UiZmqsefLF2HUhYjKfbIsV5duF0Tbvf79wVhw+HIcPZY8sYjKD0XsknxsulmcAgNpkRwYq7ObtyFSX69uRucjIcAyPla6FhmgoTExNUiw9X8z+AeblngSobXZk4FJ2ZIDqMD37D/1XiJhMFjgiBgDICRkAACA5QgYAAEiOkAEAAJIjZAAAgOQIGQAAIDlCBgAASI6QAQAAkiNkAACA5AgZAAAgOUIGAABIjpABAACSI2QAAIDkCBkAACA5QgYqrK5u4gEAlWJdgUsJGaiwxun1cVu9FQeAyjg3Nl5eW4DJhAxUWMvcQvz7nfP5HQB8OD39o+W1BZhMyECFLZpXiCmld9bI6Ptx+211jgMAcF2ynZjjr5+Ns8Xz5bUFmKxuvCQfAxV08nQxTg0UY2hkLLzLALgW2R/BsuNk2U6MiIHLEzIAAEByHC0DAACSI2QAAIDkCBkAACA5QgYAAEiOkAEAAJIjZAAAgOQIGQAAIDlCBgAASI6QAQAAkiNkAACA5AgZAAAgMRH/AbrarKO9xwLjAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "7952d23e",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "52a18590",
   "metadata": {},
   "source": [
    "P[df] = 0.01\n",
    "P[smoke] = 0.10\n",
    "P[ smoke|df] = 0.90\n",
    "\n",
    "P[df|smoke ] = smoke|df * df / \n",
    "                smoke\n",
    "                \n",
    "             = 0.90 * 0.01  /\n",
    "                 0.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cfffc1a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09000000000000001"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.90*0.01)/(0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54c4d2f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e80441",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d5006ebd",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "05a84b1e",
   "metadata": {},
   "source": [
    "P[cancer|no cholestrol , blood pressure yes,  no exercises]  = \n",
    "   = \n",
    "    ( P[no- colestrol | cancer] * P[yes blood-perss | cancer] * P[no exe | cancer] ) * P[y=cancer]\n",
    "  = "
   ]
  },
  {
   "cell_type": "raw",
   "id": "5c310575",
   "metadata": {},
   "source": [
    "     cancer\n",
    "1         1\n",
    "1         1\n",
    "1         1\n",
    "0         0   \n",
    "0         1\n",
    "1         0\n",
    "\n",
    "3/4 * 1/4 * 3/4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "12a9c734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09375"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "3/4 * 1/4 * 3/4 *  4/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98b6f96d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91f2ff84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "340dcb1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15a1b407",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fae937ca",
   "metadata": {},
   "source": [
    "## Laplace Smoothing :    (handles outliers that are new words in test/query data )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e5684df",
   "metadata": {},
   "source": [
    "    at runtime :\n",
    "    \n",
    "    X-query : w2 w15 w25 w'\n",
    "    \n",
    "    w' is a new word , which is not present in current model vocabulary \n",
    "    \n",
    "    w' doent belongs to {w1 , w2 , w3, ... wd}\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    goal is to find P[y=1|x-query] and P[y=0|x-query]\n",
    "    \n",
    "                     (P[w2|y=1] * P[w15|y=1] * P[w25|y=1] * P[w'|y=1] ) * P[y=1]\n",
    "                                                            ********\n",
    "                                                              since w' is not present\n",
    "                                                              P[w'|y=1] is 0\n",
    "                                                       so , entire term P[y=1|X-query]\n",
    "                                                       and P[y=0|X-query] becomes 0\n",
    "                                                       \n",
    "                                                       \n",
    "       How can we tackle the new word :\n",
    "       \n",
    "       whenever we see a new word , we can ignore it . But it doent make sense. \n",
    "       \n",
    "       better way is : (quick hack) \n",
    "       \n",
    "       \n",
    "       find P[w'|y=1] = occurances when w' is present in entire data  + alfa\n",
    "                       ----------------------------------------------\n",
    "                                total data points + alfa * C\n",
    "                                \n",
    "                                C :distinct value w' can take.\n",
    "                                in Bernouli NB,  C = 2\n",
    "                                \n",
    "       alfa is a hyperparamet.\n",
    "       \n",
    "       We are assigning some weight to the new word.\n",
    "       makes algorithm more customizable.\n",
    "       \n",
    "\n",
    "       \n",
    "       \n",
    "       \n",
    "                                                \n",
    "                                                     \n",
    "                                                      \n",
    "                                                      \n",
    "                                                      \n",
    "                     \n",
    "                     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653bf783",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "## Bias variance tradeoff : \n",
    "\n",
    "    assume alfa  = 1 ( a small value)\n",
    "    \n",
    "    P[w'|y=1] =   n1'* alfa\n",
    "                 -----------\n",
    "                  n1 + 2*alfa\n",
    "             \n",
    "    P[w'|y=0] =   n0'* alfa\n",
    "                 -----------\n",
    "                  n0 + 2*alfa\n",
    "    \n",
    "    \n",
    "    P[w'|y=1] =   n1' * 1\n",
    "                  ----------\n",
    "                  n1 + 2*1\n",
    "                  \n",
    "              n1' = 0 , n1 = 100 \n",
    "              \n",
    "             \n",
    "              =  1\n",
    "                ----   atleast this term is not 0.   = 0.0098\n",
    "                102\n",
    "    \n",
    "            \n",
    "    P[w'|y=0] =     0 + 1            1\n",
    "                 -----------  =  ----------  = 0.005\n",
    "                  300 + 2*1         302\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    case : when alfa = 0   (not applying Laplace smoothing)\n",
    "            lets say for word \"free\"\n",
    "          n1 = 100\n",
    "          n1' = 10  (\"Free\")\n",
    "          \n",
    "          P[Free|y=1] = n1'\n",
    "                      /n1\n",
    "                    = 10 + 0 /\n",
    "                      100+ 0 \n",
    "                    = 0.10\n",
    "                    \n",
    "     case when alfa = 1\n",
    "           P[Free|y=1] = n1'\n",
    "                      /n1\n",
    "                    = 10 + 1 /\n",
    "                      100+ 2 \n",
    "                    = 11/102 ~~ 0.1\n",
    "                 even if we increase alfa value , still it will be close to 0.1\n",
    "                 \n",
    "      case when alfa is very large\n",
    "       alfa  = 10000\n",
    "        \n",
    "           P[Free|y=1] = 10 * 10000/\n",
    "                      100 + 20000\n",
    "                     ~~ 0.498\n",
    "                     \n",
    "           P[Free|y=0] = 5 * 10000/\n",
    "                  150 + 20000\n",
    "                 ~~ 0.5\n",
    "                 \n",
    "            so if we increase alfa to a very large number , \n",
    "            all the words probability will become close to 0.5\n",
    "            which will make no sense and word become useless. \n",
    "            makes model underfit. \n",
    "\n",
    "                all the conditional probabilities will be close to 0.5\n",
    "        \n",
    "        P[y=1|x] = P[wi|y=1]*P[y=1]  ~~ P[y=1] \n",
    "        P[y=0|x] = P[wi|y=0]*P[y=0]  ~~ P[y=0]\n",
    "         and we will only be relying on P[clsses]  (only comparison left to do)\n",
    "         \n",
    "         \n",
    "        \n",
    "        alfa increases : Underfit increases    [ applying too much smoothing]\n",
    "        alfa small value : Overfit increases   [not doing laplace smoothing ]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "         \n",
    "                   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "985c740b",
   "metadata": {},
   "source": [
    "### Imbalance impact : \n",
    "\n",
    "        P[y=1] = 0.30   30% spam emails\n",
    "        P[y=0] = 0.70\n",
    "        \n",
    "       \n",
    "        \n",
    "        \n",
    "       impact : \n",
    "       lets say X-query : \"Free lottery win\"\n",
    "       \n",
    "       P[wi|y=1] = 0.2\n",
    "       P[wi|y=0] = 0.1\n",
    "       \n",
    "       P[y=1|X-query] = 0.2*0.3 = 0.06\n",
    "       P[y=0|X-query] = 0.1*0.7 = 0.07   hence will conclude the x-query point belongs to non-spam\n",
    "                                                                       WRONG CONCLUSION \n",
    "                                                                       \n",
    "       please make , prior probability equal ~~= 0.5\n",
    "       \n",
    "       so we can take LIKELIHOOD probability only into account.\n",
    "       P[y=1|X-query] = 0.2 *0.5 = 0.1 \n",
    "       P[y=0|X-query]= 0.1 *0.5 = 0.05    and now we can see , X-query belongs to y= spam\n",
    "                                                                    CORRECT decision\n",
    "                                                                    \n",
    "                              DO NOT CONSIDER PRIOR PROBABILITY \n",
    "                              TAKE LIKELIHOOD  INTO CONSIDERATION \n",
    "       \n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ea877dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5163d287",
   "metadata": {},
   "source": [
    "#### more Vectorization techniques  [in deep learning - NLP ]\n",
    "1. Bag of Words\n",
    "2. IDF\n",
    "3. Word2Vec\n",
    "4. GloVe\n",
    "5. FastText"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0483b7eb",
   "metadata": {},
   "source": [
    "### Big issue with current algorithm : \n",
    "\n",
    "## Undeflow problem : \n",
    "\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "700c9213",
   "metadata": {},
   "source": [
    "    P[y=1|X] =  product(P[wi|y=1])* P[y=1]\n",
    "    P[y=0|X] =  product(P[wi|y=0])* P[y=0]\n",
    "      \n",
    "      are very very very very small number. ~~ 0.00000000...00016541\n",
    "      \n",
    "      \n",
    "     solution is to take a log transform \n",
    "     \n",
    "     log(P[y=1|X])  = ( log(P[w1|y=1]) + log(P[w2|y=1]) +  ... + log(P[wd|y=1]) ) + log(P[y=1])  \n",
    "     log(P[y=0|X])  = ( log(P[w1|y=0]) + log(P[w2|y=0]) +  ... + log(P[wd|y=0]) ) + log(P[y=0])\n",
    "     \n",
    "     \n",
    " ![image.png](attachment:image.png)    \n",
    "     \n",
    "     \n",
    "     \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50193898",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8eafb977",
   "metadata": {},
   "source": [
    "# Naive Bayes | in sklearn "
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
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"
    }
   },
   "cell_type": "markdown",
   "id": "8f9b37af",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdad3552",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de95ca8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "094fb660",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "db296f2a",
   "metadata": {},
   "source": [
    "## MultinomialNB\n",
    "\n",
    "    - in Multinomial NB , instead of taking 1 when word present in data and 0 when not present , \n",
    "         - we take frequency / occurances of word into account.\n",
    "         How many times the word wj present in the text. \n",
    "         We are adding more information to model. \n",
    "         \n",
    "             in BernouliNB \n",
    "             \n",
    "             if X-query : I was good good ... good. \n",
    "             \n",
    "            we find P[\"good\" |y=1 ]\n",
    "            in how many spam emails good is present.\n",
    "            \n",
    "            \n",
    "                                            in MultinomailNB we find \n",
    "                                            \n",
    "                                            P[\"good\" = 3 | y= 1]\n",
    "                                            In how many spam emails good is present 3 times. \n",
    "                                            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "30e67c03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.921832884097035, 0.9739910313901345)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import f1_score,accuracy_score\n",
    "\n",
    "MNB = MultinomialNB(alpha=0.8)\n",
    "\n",
    "MNB.fit(X_train,y_train)\n",
    "y_pred = MNB.predict(X_test)\n",
    "f1_score(y_test, y_pred),f1_score(y_train, MNB.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc94618a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "11c13d7f",
   "metadata": {},
   "source": [
    "    \n",
    "    in Naive Bayes: \n",
    "    \n",
    "    Classification : \n",
    "        \n",
    "         y is discrete : \n",
    "         \n",
    "         X : \n",
    "                 if features are categorical : \n",
    "                               only 2 unique values : BernauliNB\n",
    "                               multiple values  : MultinomialNB\n",
    "                               \n",
    "                 if features are Numerical : \n",
    "                             Gaussian NB.   - which assumes the features are normally distributed\n",
    "                             \n",
    "                             if features are not Normally distributed , we have to transform data into normal distribution.\n",
    "                             \n",
    "                             \n",
    "                             \n",
    "    XQuery  => age=41.5 , \n",
    "               weight = 75.3\n",
    "               \n",
    "               \n",
    "               P[ y=1 | age= 41.5 and weight = 75.3]\n",
    "               P[ y=1 | <41.5,75.3>]              \n",
    "                 \n",
    "                  = P[age = 41.5 |y=1]  : likelyhood of age=41.5 when data y=1\n",
    "![image.png](attachment:image.png)\n",
    "               \n",
    "                 "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "897f2b51",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4ed339",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "00f07060",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "83a32edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = [[1, 0, 1, 2, 0], [3, 0, 1, 3, 0], [0, 2, 1, 1, 3], [1, 2, 0, 0, 1], [0, 1, 1, 3, 0], [2, 0, 2, 3, 1], [3, 1, 2, 1, 3], [0, 0, 0, 1, 1], [0, 2, 1, 1, 3], [2, 2, 3, 1, 1], [3, 2, 3, 1, 1], [0, 2, 1, 2, 0], [0, 0, 0, 0, 2], [1, 0, 0, 0, 2], [3, 2, 1, 2, 2], [2, 3, 0, 0, 1], [2, 3, 3, 1, 3], [0, 1, 2, 0, 1], [1, 2, 0, 1, 3], [1, 3, 1, 2, 0], [1, 3, 2, 2, 0], [3, 0, 0, 1, 2], [3, 2, 1, 2, 0], [1, 3, 1, 2, 0], [3, 3, 2, 3, 1], [3, 0, 2, 1, 3], [0, 3, 1, 0, 0], [1, 0, 0, 0, 3], [0, 2, 0, 1, 0], [2, 1, 3, 1, 3], [3, 2, 1, 1, 1], [1, 0, 1, 1, 1], [3, 0, 0, 2, 3], [2, 2, 2, 1, 0], [3, 3, 2, 2, 1], [1, 2, 2, 3, 0], [0, 3, 1, 2, 0], [3, 1, 1, 3, 3], [1, 3, 2, 2, 0], [2, 3, 3, 3, 0], [2, 3, 2, 1, 3], [2, 0, 1, 2, 1], [0, 1, 2, 3, 2], [3, 1, 2, 0, 2], [2, 2, 3, 0, 1], [2, 1, 0, 3, 0], [0, 1, 1, 0, 0], [2, 3, 2, 2, 2], [3, 2, 1, 2, 3], [3, 2, 0, 0, 0]]\n",
    "y_train = [0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0]\n",
    "\n",
    "X_test = [[0, 2, 1, 1, 1], [2, 1, 1, 0, 0], [2, 0, 2, 2, 2], [2, 0, 3, 1, 0], [3, 3, 1, 2, 1], [3, 0, 1, 0, 2], [0, 3, 3, 3, 1], [3, 3, 0, 3, 2], [0, 3, 2, 2, 3], [2, 3, 3, 0, 0]]\n",
    "y_test = [0, 1, 1, 0, 0, 1, 0, 1, 0, 0]\n",
    "\n",
    "alphas = [0.1, 1, 100, 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5bafc65e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd'); }\n",
       "    "
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     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd'); }\n",
       "    "
      ],
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd'); }\n",
       "    "
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      ]
     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha:  0.1\n",
      "Score:  0.9\n",
      "\n",
      "Alpha:  1\n",
      "Score:  0.9\n",
      "\n",
      "Alpha:  100\n",
      "Score:  0.9\n",
      "\n",
      "Alpha:  1000\n",
      "Score:  0.6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "y_train = np.array(y_train).reshape(-1,1)\n",
    "y_test = np.array(y_test).reshape(-1,1)\n",
    "\n",
    "#Import Multinomial naive bayes from the appropraite library\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# Import accuracy_score from the appropraite library\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Define train_and_predict()\n",
    "def train_and_predict(a):\n",
    "    \n",
    "    \n",
    "    \n",
    "    # Instantiate the classifier: nb_classifier, and plug in alpha's value\n",
    "    nb_classifier = MultinomialNB(alpha=a)\n",
    "\n",
    "    # Fit to the training data\n",
    "    nb_classifier.fit(X_train,y_train)\n",
    "    \n",
    "    # Predict the labels: pred\n",
    "    y_pred = nb_classifier.predict(X_test)\n",
    "    \n",
    "    # Compute accuracy: score\n",
    "    score =   accuracy_score(y_test,y_pred)  \n",
    "\n",
    "    return score\n",
    "\n",
    "\n",
    "# Iterate over the alphas and print the corresponding score\n",
    "for i in alphas:\n",
    "    print('Alpha: ', i)\n",
    "    print('Score: ', train_and_predict(i))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc7693c8",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7877c352",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a82a82a",
   "metadata": {},
   "outputs": [],
   "source": []
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