Assignment1_NLP_16110007

Assignment1_NLP_16110007.ipynb

{
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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ASSIGNMENT 1\n",
    "NLP\n",
    "Akhilesh Ravi\n",
    "16110007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('tweets-dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@BubblyDentist @MeetUunngLee nahi nahi, mere s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bohot hi badiya ji aap sunao?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parvez Musharraf is Digvijay Singh of Pakistan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aman ki maa ki... Asha https://twitter.com/ash...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakistan can wait more more and more .  .  .  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@sagarcasm Jai Mahesh !!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"Kaam ho jayega, thoda kharcha paani lagega\" \\...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Sentence\n",
       "0  @BubblyDentist @MeetUunngLee nahi nahi, mere s...\n",
       "1  going to the grammys first entertainment law b...\n",
       "2                      bohot hi badiya ji aap sunao?\n",
       "3  Parvez Musharraf is Digvijay Singh of Pakistan...\n",
       "4  guddu ko bass john cena k sticker ki padii hai...\n",
       "5  Aman ki maa ki... Asha https://twitter.com/ash...\n",
       "6  pakistan can wait more more and more .  .  .  ...\n",
       "7                           @sagarcasm Jai Mahesh !!\n",
       "8  RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...\n",
       "9  \"Kaam ho jayega, thoda kharcha paani lagega\" \\..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s = re.sub('[^0-9a-zA-Z]+', '*', s)\n",
    "# https://stackoverflow.com/questions/12985456/replace-all-non-alphanumeric-characters-in-a-string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanText(raw_text):\n",
    "    '''\n",
    "    Convert a raw review to a cleaned review\n",
    "    '''\n",
    "    text = BeautifulSoup(raw_text, 'html').get_text()  #remove html\n",
    "    words = text.split()\n",
    "    words = [w for w in words if '@' not in w and '#' not in w] # remove the @-words and #-words\n",
    "    text = ' '.join(words)\n",
    "    letters_only = re.sub('[^a-zA-Z]+', ' ', text)  # remove non-character\n",
    "    \n",
    "    return( letters_only.lower())\n",
    "\n",
    "vclean = np.vectorize(cleanText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Sentence</th>\n",
       "      <th>Cleaned sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@BubblyDentist @MeetUunngLee nahi nahi, mere s...</td>\n",
       "      <td>nahi nahi mere saath jaakar pachtaogi ye uunng...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bohot hi badiya ji aap sunao?</td>\n",
       "      <td>bohot hi badiya ji aap sunao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parvez Musharraf is Digvijay Singh of Pakistan...</td>\n",
       "      <td>parvez musharraf is digvijay singh of pakistan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aman ki maa ki... Asha https://twitter.com/ash...</td>\n",
       "      <td>aman ki maa ki asha https twitter com ashabhos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakistan can wait more more and more .  .  .  ...</td>\n",
       "      <td>pakistan can wait more more and more aakhir pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@sagarcasm Jai Mahesh !!</td>\n",
       "      <td>jai mahesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...</td>\n",
       "      <td>rt aap najafgarh rt aapinnews when ddca lowere...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"Kaam ho jayega, thoda kharcha paani lagega\" \\...</td>\n",
       "      <td>kaam ho jayega thoda kharcha paani lagega sir...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Sentence  \\\n",
       "0  @BubblyDentist @MeetUunngLee nahi nahi, mere s...   \n",
       "1  going to the grammys first entertainment law b...   \n",
       "2                      bohot hi badiya ji aap sunao?   \n",
       "3  Parvez Musharraf is Digvijay Singh of Pakistan...   \n",
       "4  guddu ko bass john cena k sticker ki padii hai...   \n",
       "5  Aman ki maa ki... Asha https://twitter.com/ash...   \n",
       "6  pakistan can wait more more and more .  .  .  ...   \n",
       "7                           @sagarcasm Jai Mahesh !!   \n",
       "8  RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...   \n",
       "9  \"Kaam ho jayega, thoda kharcha paani lagega\" \\...   \n",
       "\n",
       "                                    Cleaned sentence  \n",
       "0  nahi nahi mere saath jaakar pachtaogi ye uunng...  \n",
       "1  going to the grammys first entertainment law b...  \n",
       "2                      bohot hi badiya ji aap sunao   \n",
       "3  parvez musharraf is digvijay singh of pakistan...  \n",
       "4  guddu ko bass john cena k sticker ki padii hai...  \n",
       "5  aman ki maa ki asha https twitter com ashabhos...  \n",
       "6  pakistan can wait more more and more aakhir pa...  \n",
       "7                                        jai mahesh   \n",
       "8  rt aap najafgarh rt aapinnews when ddca lowere...  \n",
       "9   kaam ho jayega thoda kharcha paani lagega sir...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cleaned sentence'] = vclean(data['Sentence'])\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    nahi nahi mere saath jaakar pachtaogi ye uunng...\n",
       "1    going to the grammys first entertainment law b...\n",
       "2                        bohot hi badiya ji aap sunao \n",
       "3    parvez musharraf is digvijay singh of pakistan...\n",
       "4    guddu ko bass john cena k sticker ki padii hai...\n",
       "5    aman ki maa ki asha https twitter com ashabhos...\n",
       "6    pakistan can wait more more and more aakhir pa...\n",
       "7                                          jai mahesh \n",
       "8    rt aap najafgarh rt aapinnews when ddca lowere...\n",
       "9     kaam ho jayega thoda kharcha paani lagega sir...\n",
       "Name: Cleaned sentence, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]['Cleaned sentence']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(s):\n",
    "    return tuple(s.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tokens: 303161\n",
      "Number of word types: 32707\n"
     ]
    }
   ],
   "source": [
    "tokens = []\n",
    "for i in range(len(data)):\n",
    "    tokens.extend( tokenize( data.iloc[i]['Cleaned sentence']) )\n",
    "sorted_tokens = sorted(tokens)\n",
    "word_types = list(set(tokens))\n",
    "print('Number of tokens:', len(tokens))\n",
    "print('Number of word types:', len(word_types))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TTR: 0.10788656852299604\n"
     ]
    }
   ],
   "source": [
    "print('TTR:', len(word_types)/len(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Zipf's Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_count = {}\n",
    "for s in sorted_tokens:\n",
    "    if s in token_count:\n",
    "        token_count[s] += 1\n",
    "    else:\n",
    "        token_count[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 50 Word types in decreasing order of number of occurences:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('hai', 10030),\n",
       " ('to', 4154),\n",
       " ('ki', 3224),\n",
       " ('ke', 3170),\n",
       " ('nahi', 3169),\n",
       " ('bhi', 2929),\n",
       " ('the', 2866),\n",
       " ('se', 2601),\n",
       " ('ho', 2365),\n",
       " ('ka', 2310),\n",
       " ('bhai', 2266),\n",
       " ('ko', 2208),\n",
       " ('me', 1955),\n",
       " ('ye', 1869),\n",
       " ('kya', 1815),\n",
       " ('hi', 1801),\n",
       " ('aur', 1797),\n",
       " ('twitter', 1760),\n",
       " ('com', 1724),\n",
       " ('kar', 1681),\n",
       " ('i', 1509),\n",
       " ('in', 1387),\n",
       " ('t', 1319),\n",
       " ('https', 1310),\n",
       " ('is', 1296),\n",
       " ('mein', 1276),\n",
       " ('a', 1202),\n",
       " ('ek', 1165),\n",
       " ('and', 1126),\n",
       " ('status', 1108),\n",
       " ('of', 1074),\n",
       " ('on', 1071),\n",
       " ('na', 1026),\n",
       " ('s', 1009),\n",
       " ('ab', 969),\n",
       " ('toh', 963),\n",
       " ('rt', 944),\n",
       " ('tha', 937),\n",
       " ('http', 905),\n",
       " ('for', 885),\n",
       " ('you', 885),\n",
       " ('aaj', 873),\n",
       " ('co', 872),\n",
       " ('raha', 868),\n",
       " ('par', 826),\n",
       " ('ne', 824),\n",
       " ('aap', 820),\n",
       " ('hain', 816),\n",
       " ('koi', 802),\n",
       " ('kuch', 801),\n",
       " ('liye', 780),\n",
       " ('k', 754),\n",
       " ('tu', 748),\n",
       " ('ji', 747),\n",
       " ('it', 704),\n",
       " ('p', 702),\n",
       " ('sir', 691),\n",
       " ('d', 690),\n",
       " ('do', 684),\n",
       " ('pe', 667),\n",
       " ('main', 665),\n",
       " ('mujhe', 643),\n",
       " ('gaya', 631),\n",
       " ('rahe', 621),\n",
       " ('h', 619),\n",
       " ('baat', 600),\n",
       " ('be', 598),\n",
       " ('sab', 586),\n",
       " ('with', 570),\n",
       " ('at', 566),\n",
       " ('he', 557),\n",
       " ('aa', 554),\n",
       " ('de', 536),\n",
       " ('url', 532),\n",
       " ('jo', 531),\n",
       " ('yaar', 530),\n",
       " ('kiya', 508),\n",
       " ('hum', 499),\n",
       " ('hota', 487),\n",
       " ('le', 479),\n",
       " ('tum', 475),\n",
       " ('mere', 468),\n",
       " ('this', 454),\n",
       " ('diya', 452),\n",
       " ('modi', 449),\n",
       " ('log', 448),\n",
       " ('ya', 425),\n",
       " ('my', 424),\n",
       " ('tomorrow', 424),\n",
       " ('that', 419),\n",
       " ('gaye', 415),\n",
       " ('bas', 413),\n",
       " ('din', 412),\n",
       " ('hu', 410),\n",
       " ('kabhi', 410),\n",
       " ('abhi', 405),\n",
       " ('u', 394),\n",
       " ('india', 388),\n",
       " ('day', 387),\n",
       " ('time', 385)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_token_count = list(token_count.items())\n",
    "sorted_token_count.sort(key=lambda x:x[1], reverse=True)\n",
    "print('Top 50 Word types in decreasing order of number of occurences:\\n')\n",
    "sorted_token_count[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Zipf's Law\")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "length = [len(item[0]) for item in sorted_token_count]\n",
    "frequency = [item[1] for item in sorted_token_count]\n",
    "plt.scatter(length, frequency)\n",
    "\n",
    "plt.xlabel('f - Frequency of token')\n",
    "plt.ylabel('|r - rank of token')\n",
    "plt.title('Zipf\\'s Law')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice\n",
    "from nltk.corpus import wordnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_meanings(word):\n",
    "    synonyms = []\n",
    "    antonyms = []\n",
    "\n",
    "    for syn in wordnet.synsets(word):\n",
    "        for l in syn.lemmas():\n",
    "            synonyms.append(l.name())\n",
    "            if l.antonyms():\n",
    "                 antonyms.append(l.antonyms()[0].name())\n",
    "\n",
    "    return len(list(set(synonyms)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('twitter', 1760),\n",
       " ('hi', 1801),\n",
       " ('he', 557),\n",
       " ('at', 566),\n",
       " ('sir', 691),\n",
       " ('me', 1955),\n",
       " ('log', 448),\n",
       " ('of', 1074),\n",
       " ('india', 388),\n",
       " ('time', 385)]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choose = sorted_token_count[:100]\n",
    "l1 = ['twitter', 'hi', 'he', 'at', 'sir', 'me','log', 'of', 'india','time']\n",
    "lst = []\n",
    "for k in l1:\n",
    "    item = (k, token_count[k])\n",
    "    lst.append(item)\n",
    "# lst.sort(key=lambda x:x[1], reverse=True)\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_meanings('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 9, 4, 4, 2, 3, 3, 0, 3, 9]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meanings = {'to':4,'the':4 ,'raha':2, 'diya':3, 'my':2, 'for':3, 'com':3, 'do':2, 'india':1,'time':1}\n",
    "# m = [meanings[lst[i][0]] for i in range(10)]\n",
    "m = [num_meanings(lst[i][0]) for i in range(10)]\n",
    "f = [lst[i][1] for i in range(10)]    \n",
    "plt.scatter(m, f)\n",
    "plt.xlabel('m - Number of meanings')\n",
    "plt.ylabel('f - Frequency of the token')\n",
    "plt.title('Zipf\\'s Law')\n",
    "m\n",
    "# lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Heaps' Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "vsize = 0\n",
    "num_tokens = 0\n",
    "unique_tokens = []\n",
    "V = []\n",
    "N = []\n",
    "\n",
    "for i in range(len(tokens)):\n",
    "    s = tokens[i]\n",
    "    if s not in unique_tokens:\n",
    "        unique_tokens.append(s)\n",
    "        vsize += 1\n",
    "    V.append(vsize)\n",
    "    N.append(i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = np.array(N)\n",
    "V = np.array(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Heaps' Law\")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(N, V)\n",
    "plt.xlabel('N - Number of tokens')\n",
    "plt.ylabel('|V| - Size of vocabulary')\n",
    "plt.title('Heaps\\' Law')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 610\n",
      "10000 3597\n",
      "20000 5920\n",
      "30000 7753\n",
      "50000 10806\n",
      "100000 16675\n"
     ]
    }
   ],
   "source": [
    "for i in [1000, 10000, 20000, 30000, 50000, 100000]:\n",
    "    print(i, V[i-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3188109468386284\n",
      "0.6824809994294719\n",
      "1.3976147883124628\n",
      "0.655343979207257\n",
      "1.5589214096765935\n",
      "0.6405478197637083\n"
     ]
    }
   ],
   "source": [
    "print(8385/6358)\n",
    "print(log(1.3188, 3/2))\n",
    "print(11719/8385)\n",
    "print(log(1.397614, 5/3))\n",
    "print(18269/11719)\n",
    "print(log(1.558921, 10/5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.340940789558791"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11719 / 50000 ** 0.65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Heaps' Law\")"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(N, V)\n",
    "\n",
    "k = 10.34\n",
    "beta = 0.64\n",
    "plt.plot(N, k * (N**beta))\n",
    "plt.xlabel('N - Number of tokens')\n",
    "plt.ylabel('|V| - Size of vocabulary')\n",
    "plt.title('Heaps\\' Law')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('tweets-dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <t{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ASSIGNMENT 1\n",
    "NLP\n",
    "Akhilesh Ravi\n",
    "16110007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('tweets-dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@BubblyDentist @MeetUunngLee nahi nahi, mere s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bohot hi badiya ji aap sunao?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parvez Musharraf is Digvijay Singh of Pakistan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aman ki maa ki... Asha https://twitter.com/ash...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakistan can wait more more and more .  .  .  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@sagarcasm Jai Mahesh !!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"Kaam ho jayega, thoda kharcha paani lagega\" \\...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Sentence\n",
       "0  @BubblyDentist @MeetUunngLee nahi nahi, mere s...\n",
       "1  going to the grammys first entertainment law b...\n",
       "2                      bohot hi badiya ji aap sunao?\n",
       "3  Parvez Musharraf is Digvijay Singh of Pakistan...\n",
       "4  guddu ko bass john cena k sticker ki padii hai...\n",
       "5  Aman ki maa ki... Asha https://twitter.com/ash...\n",
       "6  pakistan can wait more more and more .  .  .  ...\n",
       "7                           @sagarcasm Jai Mahesh !!\n",
       "8  RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...\n",
       "9  \"Kaam ho jayega, thoda kharcha paani lagega\" \\..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s = re.sub('[^0-9a-zA-Z]+', '*', s)\n",
    "# https://stackoverflow.com/questions/12985456/replace-all-non-alphanumeric-characters-in-a-string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanText(raw_text):\n",
    "    '''\n",
    "    Convert a raw review to a cleaned review\n",
    "    '''\n",
    "    text = BeautifulSoup(raw_text, 'html').get_text()  #remove html\n",
    "    words = text.split()\n",
    "    words = [w for w in words if '@' not in w and '#' not in w] # remove the @-words and #-words\n",
    "    text = ' '.join(words)\n",
    "    letters_only = re.sub('[^a-zA-Z]+', ' ', text)  # remove non-character\n",
    "    \n",
    "    return( letters_only.lower())\n",
    "\n",
    "vclean = np.vectorize(cleanText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Sentence</th>\n",
       "      <th>Cleaned sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@BubblyDentist @MeetUunngLee nahi nahi, mere s...</td>\n",
       "      <td>nahi nahi mere saath jaakar pachtaogi ye uunng...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bohot hi badiya ji aap sunao?</td>\n",
       "      <td>bohot hi badiya ji aap sunao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parvez Musharraf is Digvijay Singh of Pakistan...</td>\n",
       "      <td>parvez musharraf is digvijay singh of pakistan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aman ki maa ki... Asha https://twitter.com/ash...</td>\n",
       "      <td>aman ki maa ki asha https twitter com ashabhos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakistan can wait more more and more .  .  .  ...</td>\n",
       "      <td>pakistan can wait more more and more aakhir pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@sagarcasm Jai Mahesh !!</td>\n",
       "      <td>jai mahesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...</td>\n",
       "      <td>rt aap najafgarh rt aapinnews when ddca lowere...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"Kaam ho jayega, thoda kharcha paani lagega\" \\...</td>\n",
       "      <td>kaam ho jayega thoda kharcha paani lagega sir...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Sentence  \\\n",
       "0  @BubblyDentist @MeetUunngLee nahi nahi, mere s...   \n",
       "1  going to the grammys first entertainment law b...   \n",
       "2                      bohot hi badiya ji aap sunao?   \n",
       "3  Parvez Musharraf is Digvijay Singh of Pakistan...   \n",
       "4  guddu ko bass john cena k sticker ki padii hai...   \n",
       "5  Aman ki maa ki... Asha https://twitter.com/ash...   \n",
       "6  pakistan can wait more more and more .  .  .  ...   \n",
       "7                           @sagarcasm Jai Mahesh !!   \n",
       "8  RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...   \n",
       "9  \"Kaam ho jayega, thoda kharcha paani lagega\" \\...   \n",
       "\n",
       "                                    Cleaned sentence  \n",
       "0  nahi nahi mere saath jaakar pachtaogi ye uunng...  \n",
       "1  going to the grammys first entertainment law b...  \n",
       "2                      bohot hi badiya ji aap sunao   \n",
       "3  parvez musharraf is digvijay singh of pakistan...  \n",
       "4  guddu ko bass john cena k sticker ki padii hai...  \n",
       "5  aman ki maa ki asha https twitter com ashabhos...  \n",
       "6  pakistan can wait more more and more aakhir pa...  \n",
       "7                                        jai mahesh   \n",
       "8  rt aap najafgarh rt aapinnews when ddca lowere...  \n",
       "9   kaam ho jayega thoda kharcha paani lagega sir...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cleaned sentence'] = vclean(data['Sentence'])\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    nahi nahi mere saath jaakar pachtaogi ye uunng...\n",
       "1    going to the grammys first entertainment law b...\n",
       "2                        bohot hi badiya ji aap sunao \n",
       "3    parvez musharraf is digvijay singh of pakistan...\n",
       "4    guddu ko bass john cena k sticker ki padii hai...\n",
       "5    aman ki maa ki asha https twitter com ashabhos...\n",
       "6    pakistan can wait more more and more aakhir pa...\n",
       "7                                          jai mahesh \n",
       "8    rt aap najafgarh rt aapinnews when ddca lowere...\n",
       "9     kaam ho jayega thoda kharcha paani lagega sir...\n",
       "Name: Cleaned sentence, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]['Cleaned sentence']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(s):\n",
    "    return tuple(s.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tokens: 303161\n",
      "Number of word types: 32707\n"
     ]
    }
   ],
   "source": [
    "tokens = []\n",
    "for i in range(len(data)):\n",
    "    tokens.extend( tokenize( data.iloc[i]['Cleaned sentence']) )\n",
    "sorted_tokens = sorted(tokens)\n",
    "word_types = list(set(tokens))\n",
    "print('Number of tokens:', len(tokens))\n",
    "print('Number of word types:', len(word_types))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TTR: 0.10788656852299604\n"
     ]
    }
   ],
   "source": [
    "print('TTR:', len(word_types)/len(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Zipf's Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_count = {}\n",
    "for s in sorted_tokens:\n",
    "    if s in token_count:\n",
    "        token_count[s] += 1\n",
    "    else:\n",
    "        token_count[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 50 Word types in decreasing order of number of occurences:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('hai', 10030),\n",
       " ('to', 4154),\n",
       " ('ki', 3224),\n",
       " ('ke', 3170),\n",
       " ('nahi', 3169),\n",
       " ('bhi', 2929),\n",
       " ('the', 2866),\n",
       " ('se', 2601),\n",
       " ('ho', 2365),\n",
       " ('ka', 2310),\n",
       " ('bhai', 2266),\n",
       " ('ko', 2208),\n",
       " ('me', 1955),\n",
       " ('ye', 1869),\n",
       " ('kya', 1815),\n",
       " ('hi', 1801),\n",
       " ('aur', 1797),\n",
       " ('twitter', 1760),\n",
       " ('com', 1724),\n",
       " ('kar', 1681),\n",
       " ('i', 1509),\n",
       " ('in', 1387),\n",
       " ('t', 1319),\n",
       " ('https', 1310),\n",
       " ('is', 1296),\n",
       " ('mein', 1276),\n",
       " ('a', 1202),\n",
       " ('ek', 1165),\n",
       " ('and', 1126),\n",
       " ('status', 1108),\n",
       " ('of', 1074),\n",
       " ('on', 1071),\n",
       " ('na', 1026),\n",
       " ('s', 1009),\n",
       " ('ab', 969),\n",
       " ('toh', 963),\n",
       " ('rt', 944),\n",
       " ('tha', 937),\n",
       " ('http', 905),\n",
       " ('for', 885),\n",
       " ('you', 885),\n",
       " ('aaj', 873),\n",
       " ('co', 872),\n",
       " ('raha', 868),\n",
       " ('par', 826),\n",
       " ('ne', 824),\n",
       " ('aap', 820),\n",
       " ('hain', 816),\n",
       " ('koi', 802),\n",
       " ('kuch', 801)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_token_count = list(token_count.items())\n",
    "sorted_token_count.sort(key=lambda x:x[1], reverse=True)\n",
    "print('Top 50 Word types in decreasing order of number of occurences:\\n')\n",
    "sorted_token_count[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Zipf's Law\")"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "length = [len(item[0]) for item in sorted_token_count]\n",
    "frequency = [item[1] for item in sorted_token_count]\n",
    "plt.scatter(length, frequency)\n",
    "\n",
    "plt.xlabel('f - Frequency of token')\n",
    "plt.ylabel('|r - rank of token')\n",
    "plt.title('Zipf\\'s Law')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('to', 4154),\n",
       " ('the', 2866),\n",
       " ('com', 1724),\n",
       " ('for', 885),\n",
       " ('raha', 868),\n",
       " ('do', 684),\n",
       " ('diya', 452),\n",
       " ('my', 424),\n",
       " ('india', 388),\n",
       " ('time', 385)]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choose = sorted_token_count[:100]\n",
    "l1 = ['to', 'the', 'raha', 'diya', 'my', 'for','com', 'do', 'india','time']\n",
    "lst = []\n",
    "for k in l1:\n",
    "    item = (k, token_count[k])\n",
    "    lst.append(item)\n",
    "lst.sort(key=lambda x:x[1], reverse=True)\n",
    "lst\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Zipf's Law\")"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meanings = {'to':4,'the':4 ,'raha':2, 'diya':3, 'my':2, 'for':3, 'com':3, 'do':2, 'india':1,'time':1}\n",
    "m = [meanings[lst[i][0]] for i in range(10)]\n",
    "f = [lst[i][1] for i in range(10)]    \n",
    "plt.scatter(m, f)\n",
    "plt.xlabel('m - Number of meanings')\n",
    "plt.ylabel('f - Frequency of the token')\n",
    "plt.title('Zipf\\'s Law')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Heaps' Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "vsize = 0\n",
    "num_tokens = 0\n",
    "unique_tokens = []\n",
    "V = []\n",
    "N = []\n",
    "\n",
    "for i in range(len(tokens)):\n",
    "    s = tokens[i]\n",
    "    if s not in unique_tokens:\n",
    "        unique_tokens.append(s)\n",
    "        vsize += 1\n",
    "    V.append(vsize)\n",
    "    N.append(i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = np.array(N)\n",
    "V = np.array(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Heaps' Law\")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(N, V)\n",
    "plt.xlabel('N - Number of tokens')\n",
    "plt.ylabel('|V| - Size of vocabulary')\n",
    "plt.title('Heaps\\' Law')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 610\n",
      "10000 3597\n",
      "20000 5920\n",
      "30000 7753\n",
      "50000 10806\n",
      "100000 16675\n"
     ]
    }
   ],
   "source": [
    "for i in [1000, 10000, 20000, 30000, 50000, 100000]:\n",
    "    print(i, V[i-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3188109468386284\n",
      "0.6824809994294719\n",
      "1.3976147883124628\n",
      "0.655343979207257\n",
      "1.5589214096765935\n",
      "0.6405478197637083\n"
     ]
    }
   ],
   "source": [
    "print(8385/6358)\n",
    "print(log(1.3188, 3/2))\n",
    "print(11719/8385)\n",
    "print(log(1.397614, 5/3))\n",
    "print(18269/11719)\n",
    "print(log(1.558921, 10/5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.340940789558791"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11719 / 50000 ** 0.65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Heaps' Law\")"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(N, V)\n",
    "\n",
    "k = 10.34\n",
    "beta = 0.64\n",
    "plt.plot(N, k * (N**beta))\n",
    "plt.xlabel('N - Number of tokens')\n",
    "plt.ylabel('|V| - Size of vocabulary')\n",
    "plt.title('Heaps\\' Law')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
h>0</th>\n",
       "      <td>@BubblyDentist @MeetUunngLee nahi nahi, mere s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bohot hi badiya ji aap sunao?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parvez Musharraf is Digvijay Singh of Pakistan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aman ki maa ki... Asha https://twitter.com/ash...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakistan can wait more more and more .  .  .  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@sagarcasm Jai Mahesh !!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"Kaam ho jayega, thoda kharcha paani lagega\" \\...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Sentence\n",
       "0  @BubblyDentist @MeetUunngLee nahi nahi, mere s...\n",
       "1  going to the grammys first entertainment law b...\n",
       "2                      bohot hi badiya ji aap sunao?\n",
       "3  Parvez Musharraf is Digvijay Singh of Pakistan...\n",
       "4  guddu ko bass john cena k sticker ki padii hai...\n",
       "5  Aman ki maa ki... Asha https://twitter.com/ash...\n",
       "6  pakistan can wait more more and more .  .  .  ...\n",
       "7                           @sagarcasm Jai Mahesh !!\n",
       "8  RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...\n",
       "9  \"Kaam ho jayega, thoda kharcha paani lagega\" \\..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s = re.sub('[^0-9a-zA-Z]+', '*', s)\n",
    "# https://stackoverflow.com/questions/12985456/replace-all-non-alphanumeric-characters-in-a-string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanText(raw_text):\n",
    "    '''\n",
    "    Convert a raw review to a cleaned review\n",
    "    '''\n",
    "    text = BeautifulSoup(raw_text, 'html').get_text()  #remove html\n",
    "    words = text.split()\n",
    "    words = [w for w in words if '@' not in w and '#' not in w] # remove the @-words and #-words\n",
    "    text = ' '.join(words)\n",
    "    letters_only = re.sub('[^a-zA-Z]+', ' ', text)  # remove non-character\n",
    "    \n",
    "    return( letters_only.lower())\n",
    "\n",
    "vclean = np.vectorize(cleanText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Sentence</th>\n",
       "      <th>Cleaned sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@BubblyDentist @MeetUunngLee nahi nahi, mere s...</td>\n",
       "      <td>nahi nahi mere saath jaakar pachtaogi ye uunng...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "      <td>going to the grammys first entertainment law b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bohot hi badiya ji aap sunao?</td>\n",
       "      <td>bohot hi badiya ji aap sunao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parvez Musharraf is Digvijay Singh of Pakistan...</td>\n",
       "      <td>parvez musharraf is digvijay singh of pakistan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "      <td>guddu ko bass john cena k sticker ki padii hai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aman ki maa ki... Asha https://twitter.com/ash...</td>\n",
       "      <td>aman ki maa ki asha https twitter com ashabhos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakistan can wait more more and more .  .  .  ...</td>\n",
       "      <td>pakistan can wait more more and more aakhir pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@sagarcasm Jai Mahesh !!</td>\n",
       "      <td>jai mahesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...</td>\n",
       "      <td>rt aap najafgarh rt aapinnews when ddca lowere...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"Kaam ho jayega, thoda kharcha paani lagega\" \\...</td>\n",
       "      <td>kaam ho jayega thoda kharcha paani lagega sir...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Sentence  \\\n",
       "0  @BubblyDentist @MeetUunngLee nahi nahi, mere s...   \n",
       "1  going to the grammys first entertainment law b...   \n",
       "2                      bohot hi badiya ji aap sunao?   \n",
       "3  Parvez Musharraf is Digvijay Singh of Pakistan...   \n",
       "4  guddu ko bass john cena k sticker ki padii hai...   \n",
       "5  Aman ki maa ki... Asha https://twitter.com/ash...   \n",
       "6  pakistan can wait more more and more .  .  .  ...   \n",
       "7                           @sagarcasm Jai Mahesh !!   \n",
       "8  RT AAP_Najafgarh \" RT AAPInNews : When DDCA lo...   \n",
       "9  \"Kaam ho jayega, thoda kharcha paani lagega\" \\...   \n",
       "\n",
       "                                    Cleaned sentence  \n",
       "0  nahi nahi mere saath jaakar pachtaogi ye uunng...  \n",
       "1  going to the grammys first entertainment law b...  \n",
       "2                      bohot hi badiya ji aap sunao   \n",
       "3  parvez musharraf is digvijay singh of pakistan...  \n",
       "4  guddu ko bass john cena k sticker ki padii hai...  \n",
       "5  aman ki maa ki asha https twitter com ashabhos...  \n",
       "6  pakistan can wait more more and more aakhir pa...  \n",
       "7                                        jai mahesh   \n",
       "8  rt aap najafgarh rt aapinnews when ddca lowere...  \n",
       "9   kaam ho jayega thoda kharcha paani lagega sir...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cleaned sentence'] = vclean(data['Sentence'])\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    nahi nahi mere saath jaakar pachtaogi ye uunng...\n",
       "1    going to the grammys first entertainment law b...\n",
       "2                        bohot hi badiya ji aap sunao \n",
       "3    parvez musharraf is digvijay singh of pakistan...\n",
       "4    guddu ko bass john cena k sticker ki padii hai...\n",
       "5    aman ki maa ki asha https twitter com ashabhos...\n",
       "6    pakistan can wait more more and more aakhir pa...\n",
       "7                                          jai mahesh \n",
       "8    rt aap najafgarh rt aapinnews when ddca lowere...\n",
       "9     kaam ho jayega thoda kharcha paani lagega sir...\n",
       "Name: Cleaned sentence, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]['Cleaned sentence']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(s):\n",
    "    return tuple(s.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tokens: 303161\n",
      "Number of word types: 32707\n"
     ]
    }
   ],
   "source": [
    "tokens = []\n",
    "for i in range(len(data)):\n",
    "    tokens.extend( tokenize( data.iloc[i]['Cleaned sentence']) )\n",
    "sorted_tokens = sorted(tokens)\n",
    "word_types = list(set(tokens))\n",
    "print('Number of tokens:', len(tokens))\n",
    "print('Number of word types:', len(word_types))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TTR: 0.10788656852299604\n"
     ]
    }
   ],
   "source": [
    "print('TTR:', len(word_types)/len(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Zipf's Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_count = {}\n",
    "for s in sorted_tokens:\n",
    "    if s in token_count:\n",
    "        token_count[s] += 1\n",
    "    else:\n",
    "        token_count[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 50 Word types in decreasing order of number of occurences:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('hai', 10030),\n",
       " ('to', 4154),\n",
       " ('ki', 3224),\n",
       " ('ke', 3170),\n",
       " ('nahi', 3169),\n",
       " ('bhi', 2929),\n",
       " ('the', 2866),\n",
       " ('se', 2601),\n",
       " ('ho', 2365),\n",
       " ('ka', 2310),\n",
       " ('bhai', 2266),\n",
       " ('ko', 2208),\n",
       " ('me', 1955),\n",
       " ('ye', 1869),\n",
       " ('kya', 1815),\n",
       " ('hi', 1801),\n",
       " ('aur', 1797),\n",
       " ('twitter', 1760),\n",
       " ('com', 1724),\n",
       " ('kar', 1681),\n",
       " ('i', 1509),\n",
       " ('in', 1387),\n",
       " ('t', 1319),\n",
       " ('https', 1310),\n",
       " ('is', 1296),\n",
       " ('mein', 1276),\n",
       " ('a', 1202),\n",
       " ('ek', 1165),\n",
       " ('and', 1126),\n",
       " ('status', 1108),\n",
       " ('of', 1074),\n",
       " ('on', 1071),\n",
       " ('na', 1026),\n",
       " ('s', 1009),\n",
       " ('ab', 969),\n",
       " ('toh', 963),\n",
       " ('rt', 944),\n",
       " ('tha', 937),\n",
       " ('http', 905),\n",
       " ('for', 885),\n",
       " ('you', 885),\n",
       " ('aaj', 873),\n",
       " ('co', 872),\n",
       " ('raha', 868),\n",
       " ('par', 826),\n",
       " ('ne', 824),\n",
       " ('aap', 820),\n",
       " ('hain', 816),\n",
       " ('koi', 802),\n",
       " ('kuch', 801)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_token_count = list(token_count.items())\n",
    "sorted_token_count.sort(key=lambda x:x[1], reverse=True)\n",
    "print('Top 50 Word types in decreasing order of number of occurences:\\n')\n",
    "sorted_token_count[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Zipf's Law\")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "length = [len(item[0]) for item in sorted_token_count]\n",
    "frequency = [item[1] for item in sorted_token_count]\n",
    "plt.scatter(length, frequency)\n",
    "\n",
    "plt.xlabel('f - Frequency of token')\n",
    "plt.ylabel('|r - rank of token')\n",
    "plt.title('Zipf\\'s Law')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('to', 4154),\n",
       " ('bhi', 2929),\n",
       " ('the', 2866),\n",
       " ('https', 1310),\n",
       " ('mein', 1276),\n",
       " ('ek', 1165),\n",
       " ('status', 1108),\n",
       " ('on', 1071),\n",
       " ('http', 905),\n",
       " ('co', 872),\n",
       " ('aap', 820),\n",
       " ('log', 448)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choose = sorted_token_count[:100]\n",
    "lst = []\n",
    "for i in range(10):\n",
    "    item = choice(choose)\n",
    "    choose.remove(item)\n",
    "    lst.append(item)\n",
    "lst.sort(key=lambda x:x[1], reverse=True)\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanings = {'to':3, 'bhi':2, 'the':3, 'https':1, 'mein':}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Heaps' Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "vsize = 0\n",
    "num_tokens = 0\n",
    "unique_tokens = []\n",
    "V = []\n",
    "N = []\n",
    "\n",
    "for i in range(len(tokens)):\n",
    "    s = tokens[i]\n",
    "    if s not in unique_tokens:\n",
    "        unique_tokens.append(s)\n",
    "        vsize += 1\n",
    "    V.append(vsize)\n",
    "    N.append(i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = np.array(N)\n",
    "V = np.array(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Heaps' Law\")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(N, V)\n",
    "plt.xlabel('N - Number of tokens')\n",
    "plt.ylabel('|V| - Size of vocabulary')\n",
    "plt.title('Heaps\\' Law')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 610\n",
      "10000 3597\n",
      "20000 5920\n",
      "30000 7753\n",
      "50000 10806\n",
      "100000 16675\n"
     ]
    }
   ],
   "source": [
    "for i in [1000, 10000, 20000, 30000, 50000, 100000]:\n",
    "    print(i, V[i-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3188109468386284\n",
      "0.6824809994294719\n",
      "1.3976147883124628\n",
      "0.655343979207257\n",
      "1.5589214096765935\n",
      "0.6405478197637083\n"
     ]
    }
   ],
   "source": [
    "print(8385/6358)\n",
    "print(log(1.3188, 3/2))\n",
    "print(11719/8385)\n",
    "print(log(1.397614, 5/3))\n",
    "print(18269/11719)\n",
    "print(log(1.558921, 10/5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.340940789558791"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11719 / 50000 ** 0.65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Heaps' Law\")"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xL4hIvIisE5EePuWXu7J4EenvU95aROa78rEiYpMtG1OCMrNz+XTKPMa/dg89p11K35yxHGxyFnn3zaDRwxOJsCRSIfjbRvI4cCewF/gYeFZVc0QkAtgAPFfIYbnA06q6WERqAItEZKrb9o6qvnnYNToAfYCTgSbANBFp5zZ/AFwKJOJVsU1Q1dXAG+5cY0RkCHAvMNjfmzfGHJu9GVn8NGcBleYP4hadTozkkdz6Cqr1fIHjGp0c7vBMCfO3jaQOcJ2qbvUtVNV8EbmysANUdSew0y2ni8gaoOlRrtEbGKOqWcBmEYkHurht8aq6CUBExgC93fkuAm51+4wABmKJxJiQyczO5aufZ1N1wfvcIrOJEEhtdz31L+9P/Xptwh2eCZMiE4mIRAJ9VHVgYdtVdY0f52gFnA7MB7oDj4jInUAc3lNLCl6SmedzWCJ/JJ6Ew8q7AvWAVFXNLWR/Y0wQ7c3IYsLUGTRePojb838lLzKazJPvoPYlz1C/tr2FXtEVmUhUNc+1T7RQ1W3FvYCIVAe+Bp5Q1f0iMhh4BVD38y3gnuKet5gx9AP6AbRo0SKUlzKmXIlPSufbKb/QYcMQ7pLfyY6oRNIp99H48meJqdEo3OGZUqI4VVurRGQBcKCgUFWvPtpBIhKNl0RGquo37pjdPtuHAT+61e2A7z9tmrkyjlC+D6gtIlHuqcR3/z9R1aHAUIDY2FjrtmzMUagqi7elMmn6L3TcPJSnI+aTE1WZ1I4PUfeSp2lcrV64QzSljL+J5KXinlhEBPgEWKOqb/uUN3btJwDXAivd8gRglIi8jdfY3hZYAAjQVkRa4yWKPsCtqqoiMgO4ARgD9AW+L26cxhhPfr4yf3MyE36eSvcdw/lrxAJyo6twqPNjVD3vcSpZAjFH4O97JLOO4dzdgTuAFSKy1JX9FbhFRDrhVW1tAR5w11jl3k1Zjdfj62FVzQMQkUeAKUAkMFxVV7nzPQ+MEZFXgSV4icsYUwyHcvIYF5fA1Bkz6HNwFK9FLiA7phq5XZ8k5pxHialaN9whmlJO/HlBXUS6Af8BTgJi8P6gH1DVmqENL/hiY2M1Li4u3GEYE3ZZuXmMnr+NaTOmc1vWGHpGLiQnqjp0fYDo7o+AJRDjQ0QWqWpsYdv8rdoahFel9BUQi/dOSbujHmGMKZWycvP4etF2pv7yM30yR/NlZBy5lavD2c8T3e1BqGJDuZvi8XvoTVWNF5FIV930qYgsAV4IXWjGmGDKycvn+6U7mDx1MjcdGMWnkYvIqVwDPft5oiyBmAD4m0gy3fAjS92QKDspxvAqxpjwSTuYw+e/bWHBvFnceWgUH0cuIqdyTbT7C0R3/QtUqR3uEE0Z528iuQOvXeQR4Em87rjXhyooY0zgsnLz+GDGRubMncN9eWN4NHIBOZVrkH/2C0Sf9SBUrhXuEE054W+vrYKhUQ4CL4cuHGNMoDKychk9fxtT5vzKbYfG8ETkb2jlqnDWs0Sf9bBVYZmgO2oiEZEVeN10C6WqpwU9ImPMMYlPyuDz37fwW9xi+ul4xkbNgZgYIro9Bmc/DvYeiAmRop5ICh2Q0RhTeixNSOW9aetZs24tj0V/x4DImUhkFBGd/wLnPAnVG4Y7RFPOHTWRHD7arzGmdFBVZq7bw0ezN7Jx0yaeqPwDw6pMJ1IUOeMuOO8ZqNkk3GGaCsLf+UjS+aOKKwaIpoy+kGhMWZaXr8xcl8T70zeQkJjAU1Un0qfqz0RqDtLpVjjvWajTMtxhmgrG38b2GgXLbgyt3kC3UAVljPmz/Hxl8qpdvPXzOvbs2c1T1X7mtmoTico7iJx2M5z/HNh8ICZM/H4hsYCbcvc7ERkA9C9qf2PMsVNV5mzYy1tT17M+YRdP1ZzBXTW+IzonHU6+Fi54ARqcGO4wTQXnb9XWdT6rEXjDpBwKSUTGGFSVufH7+PeUtaxO3Md9Vecwpta3VMnaC+0uh4tehONODXeYxgD+P5Fc5bOcizdqb++gR2NMBZeXr/yyNokhszayeOs+7qy2kC/rfk2NzERodDZcMhJaWK2yKV38bSO5O9SBGFORFbSB/HvKOjbvzeC66qsYVn88dTPWQ41T4dp34YRLQCTcoRrzP/yt2joeeA+vgV2B34EnVXVTCGMzpkJYsi2F1yauZcGWZK6pu43xTcZRL3kxRLeG6z+Bk6+DCBvazpRe/lZtjQI+wJvRELwh5UcDXUMRlDHlXX6+MnvDHobP3cKcDXvoWmUHvzafQLM9syHiOLjibTjjToiMDneoxhTJ30RSVVW/8Fn/UkSeDUVAxpR3v23cy6s/rmH1zv2cXj2FSc0mcOKen5H0mnDJQOjyAMRUDXeYxvitqLG2CqZImyQi/fHmRlfgZmBiiGMzplzZsvcA/5qylokrdnFq7SymnTiZNglfIynR3lAm3R+zARVNmVTUE8kivMRR0ML3gM82xSa2MqZI63en8+nczYyLS6RWZBaj2v7GWbtGIglZcEZf72XCGseFO0xjjllRY221LqlAjClv1u7az3vTNjBp5S6qRClvHb+Uq1JGEJmQBCdd7VVj2dvophzw+812ETkF6ABULihT1c9DEZQxZdnqHfv558Q1/Bq/l2oxEbzTcQdX7/mIyMQN0Lwb9BkJzbuEO0xjgsbf7r8DgAvwEslEoCfwK3DURCIizd0+jfCqwoaq6nuu7WUs0Arv5cabVDXFjeP1HtALyATuUtXF7lx9gRfdqV9V1RGu/EzgM6CKi+1xN4yLMSUqPimDf09Zy5RVu6lVJZo3z8qm954hRK+bB/VOgJtHQvsr7F0QU+74+0RyA9ARWKKqd4tII+BLP47LBZ5W1cUiUgNYJCJTgbuA6ar6umvE7w88j5eg2rpPV2Aw0NUlngF4Q7OoO88EVU1x+9wPzMdLJJcDk/y8L2MCdiArl3enrWf43C1UjorgpbOrcPuBT6m0ZAJUa2BdeU25528iOaiq+SKSKyI1gSS8eduPSlV3AjvdcrqIrAGa4g2vcoHbbQQwEy+R9AY+d08U80Sktog0dvtOVdVkAJeMLheRmUBNVZ3nyj8HrsESiSkBOXn5jF6wjfenx7M3I4u7O9XguaoTqLL0My9pnP88nP0oVKpR5LmMKcv8TSRxIlIbGIbXkysD7+12v4lIK+B0vCeHRi7JAOzCq/oCL8kk+ByW6MqOVp5YSPnh1+4H9ANo0aJFccI2plDrdqXz3NfLWZaQytktqvJ9xyU0XfkRZGd4Tx8XvGA9sUyF4e9YWw+5xSEiMhnvKWC5vxcRkerA18ATqrpffOqIVVVFJKRtGqo6FBgKEBsba+0n5phlZufyyo+rGbswgWqVIvmq+3ZiN7yHLEqEE3vBxQOgYftwh2lMifK3sf1a4BdVTVPVLa7K6RpV/c6PY6PxkshIVf3GFe8WkcaqutNVXSW58u38ucqsmSvbzh9VYQXlM115s0L2NybofovfywvfriAhOZO/npbBXfuHErUoDo47Da4dAq3PDXeIxoSFvyPBDVDVtIIVVU3Fa/w+KtcL6xNgjaq+7bNpAtDXLfcFvvcpv1M83YA0VwU2BbhMROqISB3gMmCK27ZfRLq5a93pcy5jgmJ76kEeG72EWz+eT/28JOa1G8196/oRlZ4AvT+AfjMtiZgKzd82ksISjj/HdgfuAFaIyFJX9lfgdWCciNwLbAVuctsm4nX9jcfr/ns3gKomi8grwEK3398LGt6Bh/ij++8krKHdBEn6oRw+m7uFD2bGU1kPMbrNLLrtHo1sx5sbvfsTUKl6uMM0JuzEn1cuRGQ4kIo3AjDAw0BdVb0rdKGFRmxsrMbFxYU7DFOK5ecrIxds492p60k5cIgBzZZye+bnRGYmwSk3eG+k1y6y06Ix5YqILFLV2MK2+ftE8ijwEt5LhABT8ZKJMeXKqh1p/N/3q1i0NYW7miTwXK0RVN27Gpp1hltGQfPO4Q7RmFLH315bB4D+7qVCVdWM0IZlTMlKP5TDsDmbGfTLBtpX2suvLb+j2e5foGYzb3KpU663N9KNOQJ/e22dijfUSV23vhfoq6orQxibMSGnqnwVl8g/J60hOzOdj5pM45K08UhyNFz0Ipz1CERXCXeYxpRq/lZtfQQ8paozAETkArz3Ms4OUVzGhNzyxFTemLyWufF7eaLhMh6qPIKY5N3Q8RavHcReKDTGL/4mkmoFSQRAVWeKSLUQxWRMSKVl5vDBzHg+nrOJblUSmddoFMelLYXGneCWL21kXmOKyd9EsklEXgIKptu9HdgUmpCMCY1DOXkMn7uZYbM3oZnJjGoyka4pPyA5deHq/0Cn2yHC31erjDEF/E0k9wAvAwVvps92ZcaUeqrKnA17+b/vV5KwL53/a7yA26I/Jyolw5sf/YL+UKV2uMM0pszyN5G0VtXHQhqJMSGwcnsar01aw9z4fVxZaxM/Nh5J9ZQ10Po86PkvaHhSuEM0pszzN5G8JSLHAeOBsdZby5R2iSmZvDF5HT8s28GJVfYzrcW3nJA0BSo3h5s+96a6te68xgSFv++RXOgSyU3AR25OkrGq+mpIozOmmPLylVHzt/LapLVEaA6fnTif83d8giTnw/n9ofvjEFM13GEaU674PWe7qu4C3heRGcBzwP8BlkhMqbFxTwZPj1vG0oRU7mm+i/75Q4nZuhba9YSer0OdVuEO0Zhyyd8XEk8CbgauB/bhDZXydAjjMsZvGVm5fDgjnk9+3cxxURnMOOFHWid+B7WaQ5/R0L5XuEM0plzz94lkODAG6KGqO0IYjzHFMmv9Hv76zQp2ph3gH80XcXPap0TsOADnPAXnPQMx9rqTMaHmbxvJWaEOxJjiSEjOZOCEVUxfm8RldXYxpekIqictg1bnwhVvQYMTwx2iMRWG320kxpQGuXn5vP9LPB/N2kgNyWTC8ZM5ded4JKI+XPcxnHqD9cYypoRZIjFlxqKtyTw9bhlb9h1gYOu13JE2hMid+6Dz/XDhX+2lQmPC5KiJRES+UNU7RORxVX2vpIIyxte+jCzemLyWcXGJdKyeyqJWo6m3cw40OQNu/wqanB7uEI2p0Ip6IjlTRJoA94jI58Cf6gx8prs1JuiycvMYNnsTQ2ZtIjsnm0/azueiXZ8gyRHeW+md74OIyHCHaUyFV1QiGQJMB44HFvHnRKKu3Jig27Qng2fHL2fR1hTuPT6NZ7M/oHLCSu+dkCvehFrNwh2iMcY5aiJR1ffxXkIcrKoPllBMpgLLz1fem76BIbM2Uisqm6kdptN28xdQrYENbWJMKeVv998HRaQjcK4rmq2qy0MXlqmIEpIz+dt3K5m9fg/PttnGA+mDiNqUCGfe7U00ZY3pxpRKfk2+ICKPASOBhu4zUkQe9eO44SKSJCIrfcoGish2EVnqPr18tr0gIvEisk5EeviUX+7K4kWkv095axGZ78rHikiMf7dtSpP8fOXtqeu55O1ZbNq8iV9afcHD2/sTFVMN7p4MV71rScSYUkxUteidRJYDZ6nqAbdeDfhdVU8r4rjzgAzgc1U9xZUNBDJU9c3D9u0AjAa6AE2AaUA7t3k9cCmQCCwEblHV1SIyDvhGVceIyBBgmaoOPlpMsbGxGhcXV+Q9m5KxYXc6L3yzgrityQxosYK+aUOIyM2Ec5+Bc56AqErhDtEYA4jIIlWNLWybv++RCJDns57HYT24CqOqs0WklZ/X6A2MUdUsYLOIxOMlFYB4Vd0EICJjgN4isga4CLjV7TMCGAgcNZGY0iEvX/l07mb+NWUdLaNS+b3FaBonzYLmXb3ZCu3NdGPKDH8TyafAfBH51q1fA3wSwHUfEZE7gTjgaVVNAZoC83z2SXRlAAmHlXcF6gGpqppbyP5/IiL9gH4ALVq0CCBsEwxrdu6n/zcrWJaQwoCmi+mbPpSI5Fzo8Rp0fcC69BpTxvjb2P62iMwEznFFd6vqkmO85mDgFbzuw68AbxHiaXtVdSgwFLyqrVBeyxxZTl4+b/68jo/nbKZdpRTmN/+SRnt+g5bdvaeQem3CHaIx5hgUZz6SxcDiQC+oqrsLlkVkGPCjW90ONPfZtZkr4wjl+4DaIhLlnkp89zelzJa9B3ju6+Us3LyXt45fwrV7hiCpQPwxtpUAABkjSURBVK83IfZeiPCr34cxphQq8bG2RKSxqu50q9cCBT26JgCjRORtvMb2tsACvLaYtiLSGi9R9AFuVVV1k2zdgDfEfV/g+5K7E+OPQzl5fDgjnsGzNnJC1F7mN/2ChjsWQOvz4er3bbIpY8qBkCYSERkNXADUF5FEYABwgYh0wqva2gI8AKCqq1wvrNVALvCwqua58zwCTAEigeGquspd4nlgjIi8CiwhsHYbE2Qrt6fxwBeL2JF6gH+3XMD1+4Yh6VFw5btw5l32YqEx5YRf3X8BRKQl0FZVp4lIFSBKVdNDGl0IWPff0FNVPvl1M/+avI6Tqu3n0zqfUXf3b9DmYrjqPajdvOiTGGNKlYC7/4rI/Xi9nuoCbfDaI4YAFwcrSFM+7D+Uw/PjlzNp5U7+2mwF96V/6PXIuvId7w11ewoxptzxt2rrYbx3OuYDqOoGEWkYsqhMmbQ8MZVHRi0hI2UX05uPp82ead57IdcMth5ZxpRj/iaSLFXNFvevSRGJwmvjMIbcvHw+nLmRd6et5+oqy/l3rY+J3pfmjY919mP2Xogx5Zy/iWSWiPwVqCIilwIPAT+ELixTVsQnpdP/6xWs2bqDLxp+Q/f9P0GtU+Da7+G4U8IdnjGmBPibSPoD9wIr8HpZTVTVYSGLypR62bn5DJ65kUEzNnB29AYW1B1K1fQdcM6TcMELNkaWMRWIv4nkNrxxsP6bPETkSlX98SjHmHJq3qZ9vPLjatbuSOE/TabSM+VLpFIL6DMJWnQLd3jGmBLm7+vE/wHmiMhJPmV/D0E8phTLys3jtUlr6DN0HjHpCcQ1eYteyZ8jp/WBv/xqScSYCsrfJ5LNeFVb40VkoKp+hR+j/5ryY0fqQf7y5SKWJ6bxetu13Jz0DnJA4PpP4NQbwh2eMSaM/E0kqqqLReR8YLSIdMV7y9yUc6rK1NW76f/NCqJy0pnb7juabvve69Z73TCo0zLcIRpjwszfRLITQFX3upkL3wCsS045l5Wbx9++Xcn4RYn0qrudd6sNIiYhAc7vD+c9C5ElPlSbMaYU8ncY+St8lvOBZ93HlFO70g7x4MhFLN2WzBftfuOcxKFIjcZw10RoeVa4wzPGlCJHTSQi8q6qPiEiP1DIC4iqenXIIjNhoap8FZfIPyetoUpOCgtajKDBtrlw8rXeYIs2d7ox5jBFPZF84X6+edS9TLmw/1AOr/ywmq8WJXJH4+0MyH6TqH2pNlqvMeaojppIVHWR+zkLQESi8dpGtqtqUujDMyVlw+507v88jq37MvjshLmcv30oUqcl3D4eGp8W7vCMMaXYUd8jEZEhInKyW64FLAM+B5aIyC0lEJ8pAbPX7+GGIb8TeSiZJW2GcUHiYKTD1dBvliURY0yRinoh8VyfSaTuBtar6qnAmcBzIY3MhFxevvL6pLXcOXwBF1TZyJQqf6P2rt/hirfghk+hcs1wh2iMKQOKaiPJ9lm+FPgKQFV3idWXl2lpB3N4YswSZqxL4oPWv9Fr1xCkdnO4dSo06RTu8IwxZUhRiSRVRK7Emyu9O97b7QXDyFcJcWwmRBJTMuk7fAF7k5OZ2Wo0rXb+DCddBb0/gMq1wh2eMaaMKSqRPAC8DxwHPKGqu1z5xcBPoQzMhMbMdUk8PW4Zx+Vt57f6g6i2eyNc8jJ0f9x6ZRljjklRvbbWA5cXUj4FmBKqoEzwqSqf/baFV35czR111/B/Oe8SmR0Nt38DbS4Md3jGmDLM39F/j4mIDBeRJBFZ6VNWV0SmisgG97OOKxcReV9E4kVkuYic4XNMX7f/BhHp61N+poiscMe8L9ZwU6icvHyeHreMv/+wkncaTuLlA68QWe946DfTkogxJmAhTSTAZ/zvE01/YLqqtgWmu3WAnkBb9+kHDAYv8QADgK5488YPKEg+bp/7fY77n6enii7tYA73jYhj2pJ1TG88mN5pX0DHW+GeKTbgojEmKEKaSFR1NpB8WHFvYIRbHgFc41P+uXrmAbVFpDHQA5iqqsmqmgJMBS5322qq6jxVVbz3W67B/FdCciY3DP6N7fHL+bXuPzg+bT70ehOu+RCira+EMSY4ip1IRCTQWREbqepOt7wLaOSWmwIJPvslurKjlScWUm6ARVuT6fX+HI7fP5/J1QZSkwPQ90focr81qhtjgupYnkiC9sfaPUn8z2CQwSYi/UQkTkTi9uzZE+rLhd3I+VvpM/R37on+mSHyOlF1WsD9v9iovcaYkDiWRLIkwGvudtVSuJ8FY3ZtB5r77NfMlR2tvFkh5f9DVYeqaqyqxjZo0CDA8EsvVeXNKesY8O1SPqozkidzPkba9YB7rT3EGBM6xU4kqnpPgNecABT0vOoLfO9TfqfrvdUNSHNVYFOAy0SkjmtkvwyY4rbtF5FurrfWnT7nqnBUldcnr+XLGUuYXO8dLsr4Cc55Em4eCZVqhDs8Y0w5FtIp7kRkNHABUF9EEvF6X70OjBORe4GtwE1u94lALyAeyMQb2wtVTRaRV4CFbr+/q2pBA/5DeD3DqgCT3KfCUVXembqen2fPZVrNd6h3KAmu/Qg69gl3aMaYCkC8ZoqKIzY2VuPi4sIdRtDk53tPInFzJvNF1XeoWika6TMKWnQNd2jGmHJERBapamxh22zS7TIsL1958buVpMSNZ2zlD4mq1Ry5fTzUPT7coRljKpCiptq908/zLFXV5UGIx/gpNy+f/t+soObSYfwz5kto2hm5ZQxUqxfu0IwxFUxRTySt/TzPlgDjMMVwICuXJ0Yvolv8O9wbPQnaXwnXf2wvGRpjwqKoRPJPVc0pkUiMX5IPZHPXsDk8sO91rohaAF0fhB7/gIjIcIdmjKmgikok20VkAjAa+EUrWst8KZOYkskDH8/kxfRXOStyFfT4J5z1cLjDMsZUcEW9R3ISXrfbF4EEEXnPveNhStjyxFT6DprC6xkv0i1yLVw71JKIMaZUOGoiUdV9qvqRql6IN/LuJuAdEdkoIv8okQgNi7el8OTHk/g4/yVOjkpE+oyEjjeHOyxjjAGK8Wa7qu4APsEbuj0duC9UQZk/rEhM4+XPfuBLeYmW0SlE3P41nNgz3GEZY8x/FZlIRKSyiNwoIt/gvXV+Ed4cIk1CHVxFF5+UwcBhYxme/xINK+URcdeP0PrccIdljDF/UtR7JKOAS4BZwEjgVlU9VBKBVXRb9h7g1Y/H8om8TPVqNYi8awI0aBfusIwx5n8U1WtrMvCAqqaXRDDGs3XfAV4aMpJBuQOpUq0WUff+ZG+rG2NKraISyUygYNTdo0lV1f3BCali25F6kFeHjuKD3IFUqV6b6Ht+grr+vhdqjDElr6hEMqKI7eBNTPUZ3lS3JgD7MrJ4efDnvHVoIJVq1CX63ok2j4gxptQ7aiJx3X5NCTiQlcvfP/qSNw8NIKZGfSrdNxFqtwh3WMYYUyQb/bcUyMjK5dXh43l5/0tEVa9HpfsnQ61mRR9ojDGlwLFMtWuCKC9fee3LH3l613NUqlyNKvf+aEnEGFOm2BNJmA36dgYPbnuaGjERVL73B2tYN8aUOZZIwmji70u5atmD1I86SOW7J0LD9uEOyRhjis0SSZgsWLeVlpP60iQyhag7v4MmncIdkjHGHBNrIwmDTbtSyBl9B+0jtpF3/QiiWp0d7pCMMeaYWSIpYbm5eWz89D66s4y0i/9NtVNsAEZjTNkWtkQiIltEZIWILBWROFdWV0SmisgG97OOKxcReV9E4kVkuYic4XOevm7/DSLSN1z3469ZHz/DpVnT2HDSQ9Q91wZQNsaUfeF+IrlQVTupaqxb7w9MV9W2wHS3DtATaOs+/fCGskdE6gIDgK5486UM8GM4l7CZNe49Lt41nGX1rqDtTf8MdzjGGBMU4U4kh+vNH8OyjACu8Sn/XD3zgNoi0hjoAUxV1WRVTQGmApeXdND+WDJ3Ct1W/Z01lU/nlL98BiLhDskYY4IinIlEgZ9FZJGI9HNljVR1p1veBTRyy02BBJ9jE13Zkcr/RET6iUiciMTt2bMnmPfgl12Jm2j2cz/2RdSn1V++IjI6psRjMMaYUAln999zVHW7iDQEporIWt+NqqoiosG4kKoOBYYCxMbGBuWc/srPyiTjs5tozCHSbvmWKrUblOTljTEm5ML2RKKq293PJOBbvDaO3a7KCvczye2+HWjuc3gzV3ak8tJBlc2f3ccJuRtY2vlfNGl3RtHHGGNMGROWRCIi1USkRsEycBmwEpgAFPS86gt875YnAHe63lvdgDRXBTYFuExECuZMucyVlQp7Zw2lzc6f+LrmHZx9xZ3hDscYY0IiXFVbjYBvxWtwjgJGqepkEVkIjBORe4GtwE1u/4lAL7w54zOBuwFUNVlEXgEWuv3+rqrJJXcbR5a/Yzk1Z/6NuXoaZ939BmKN68aYciosiURVNwEdCynfB1xcSLkCDx/hXMOB4cGOMSBZ6aR9cRvZWo2dF79H9zrVwh2RMcaETGnr/lsu7P/6MWpmJjC6+QCuP/f0cIdjjDEhZYkkyHTlN9Rc/w1DI27kjj63WZWWMabcs9F/gyl9N9nfP8ma/OOp2+sF6lWvFO6IjDEm5OyJJFhUOfTto2jOAYbVe54bu9gEVcaYisESSbAsH0vlTVN4J78PT996JRERVqVljKkYrGorGDKTyZ74Aivy21Lnosc5vkH1cEdkjDElxp5IgiB36stEZqUypPrD3Htum3CHY4wxJcoSSaAS44hcMoLPcntw13VXER1p/0mNMRWL/dULhCrZPz5LktZmdbuH6X5C/XBHZIwxJc4SSSDW/EDMrsW8m3cjT10dW/T+xhhTDllj+7HKyyX754Fs1aZU6Xw7TWtXCXdExhgTFvZEcqyWjSYmdSP/4RYevKh9uKMxxpiwsSeSY5GfT9ast9mQ34pW3W+kQQ17g90YU3HZE8mxWPcTldI2MZze3HPu8eGOxhhjwsqeSI5B9ux32aUNqdbpWmpXtfnXjTEVmz2RFNfu1cTsjGNE3mXcf/6J4Y7GGGPCzp5Iiilv0efkE8We1tfSol7VcIdjjDFhZ4mkOPJyyV06hml5Z3DdOaeFOxpjjCkVrGqrOBLmUyk7hd+rnM95bRuEOxpjjCkV7ImkGDJX/kiURlL31MttmHhjjHEskRRD5rpfWJ9/Ild1sUZ2Y4wpUC6qtkTkchFZJyLxItI/JBfJOUjt9A1sq3oybRvVCMkljDGmLCrziUREIoEPgJ5AB+AWEekQ7Osc3LmGKPKIaX56sE9tjDFlWplPJEAXIF5VN6lqNjAG6B3si2zZtAGAFq3bBfvUxhhTppWHRNIUSPBZT3Rl/yUi/UQkTkTi9uzZc0wX0WoNmFu9Bye0DfrDjjHGlGkVorFdVYcCQwFiY2P1WM7RofNF0PmioMZljDHlQXl4ItkONPdZb+bKjDHGlIDykEgWAm1FpLWIxAB9gAlhjskYYyqMMl+1paq5IvIIMAWIBIar6qowh2WMMRVGmU8kAKo6EZgY7jiMMaYiKg9VW8YYY8LIEokxxpiAWCIxxhgTEEskxhhjAiKqx/R+XpklInuArcd4eH1gbxDDCafyci92H6WL3UfpE6x7aamqhU7EVOESSSBEJE5VY8MdRzCUl3ux+yhd7D5Kn5K4F6vaMsYYExBLJMYYYwJiiaR4hoY7gCAqL/di91G62H2UPiG/F2sjMcYYExB7IjHGGBMQSyTGGGMCYonETyJyuYisE5F4Eekf7ngKiMgWEVkhIktFJM6V1RWRqSKywf2s48pFRN5397BcRM7wOU9ft/8GEenrU36mO3+8O1aCFPdwEUkSkZU+ZSGP+0jXCPJ9DBSR7e47WSoivXy2veBiWiciPXzKC/39ctMjzHflY91UCYhIJbce77a3CvA+movIDBFZLSKrRORxV16mvpOj3EdZ/E4qi8gCEVnm7uXlY71+sO7xiFTVPkV88Ian3wgcD8QAy4AO4Y7LxbYFqH9Y2b+A/m65P/CGW+4FTAIE6AbMd+V1gU3uZx23XMdtW+D2FXdszyDFfR5wBrCyJOM+0jWCfB8DgWcK2beD+92pBLR2v1ORR/v9AsYBfdzyEOBBt/wQMMQt9wHGBngfjYEz3HINYL2Lt0x9J0e5j7L4nQhQ3S1HA/Pdf79iXT+Y93jEWIPxR6G8f4CzgCk+6y8AL4Q7LhfLFv43kawDGrvlxsA6t/wRcMvh+wG3AB/5lH/kyhoDa33K/7RfEGJvxZ//AIc87iNdI8j3MZDC/2j96fcGbw6ds470++X+kOwFog7/PSw41i1Huf0kiN/N98ClZfU7KeQ+yvR3AlQFFgNdi3v9YN7jkT5WteWfpkCCz3qiKysNFPhZRBaJSD9X1khVd7rlXUAjt3yk+zhaeWIh5aFSEnEf6RrB9oir8hnuU1VT3PuoB6Sqau5h5X86l9ue5vYPmKsSOR3vX8Bl9js57D6gDH4nIhIpIkuBJGAq3hNEca8fzHsslCWSsu8cVT0D6Ak8LCLn+W5U758UZa6Pd0nEHcJrDAbaAJ2AncBbIbhGSIhIdeBr4AlV3e+7rSx9J4XcR5n8TlQ1T1U7Ac2ALkD7MIdUKEsk/tkONPdZb+bKwk5Vt7ufScC3eL9su0WkMYD7meR2P9J9HK28WSHloVIScR/pGkGjqrvdH4B8YBjed3Is97EPqC0iUYeV/+lcbnstt/8xE5FovD++I1X1G1dc5r6Twu6jrH4nBVQ1FZiBV81U3OsH8x4LZYnEPwuBtq4nQwxeQ9aEMMeEiFQTkRoFy8BlwEq82Ap6y/TFqyfGld/petx0A9JclcIU4DIRqeMe+S/DqxPdCewXkW6uh82dPucKhZKI+0jXCJqCP4rOtXjfScG1+7jeNa2BtngN0IX+frl/nc8AbigkXt/7uAH4xe1/rDEL8AmwRlXf9tlUpr6TI91HGf1OGohIbbdcBa+tZ80xXD+Y91i4YDVqlfcPXi+V9Xh1lH8LdzwupuPxelosA1YVxIVXxzkd2ABMA+q6cgE+cPewAoj1Odc9QLz73O1THov3P91GYBDBazwcjVfFkINXB3tvScR9pGsE+T6+cHEud/8TN/bZ/28upnX49IA70u+X+44XuPv7Cqjkyiu79Xi3/fgA7+McvCql5cBS9+lV1r6To9xHWfxOTgOWuJhXAv93rNcP1j0e6WNDpBhjjAmIVW0ZY4wJiCUSY4wxAbFEYowxJiCWSIwxxgTEEokxxpiAWCIxZZaIqIi85bP+jIgMLOY5PhNvVNhKbr2+iGwJUnwXiMiPwThXEddp4EZqXSIi5x627QkRqerHOTJCF6Ep7yyRmLIsC7hOROoHeJ48vHcfShURifRz14uBFap6uqrOOWzbE3gD/hkTMpZITFmWizcf9ZMBnudd4EmfISGA/32iEJFBInKXW94iIq+JmwdGRM4QkSkislFE/uJzmpoi8pN4cz4MEZEId/xlIvK7iCwWka/c2FAF531DRBYDNx4WTysR+UW8gQeni0gLEemENwx7bxdLFZ/9HwOaADNEZIYru0W8OUFWisgbh/+HcE9kv4vIFW79WRFZ6K75sk8ca0RkmHjzZPxccF0ReUy8uUCWi8iYY/s6TFljicSUdR8At4lIrQDOsQ34FbijuMepN6DeHOAzvCElugEv++zTBXgUb06INvzxBPUicIl6A27GAU/5HLNPVc9Q1cP/EP8HGKGqpwEjgfdVdSnwf3hzT3RS1YMFO6vq+8AO4EJVvVBEmgBvABfhDV7YWUSuKdhfRBoBP+G9Qf2TiFyGN5xGF7f/mfLHoKBtgQ9U9WQgFbjelfcHTncx+iZUU45FFb2LMaWXqu4Xkc+Bx4CDRe1/FK/hjSf0UzGOKRhvbQXeBETpQLqIZBWMkQQsUNVNACIyGm8Ij0N4iWWuNzQUMcDvPucde4TrnQVc55a/wHsSKY7OwExV3ePiGYk3Mdd3eBMnTQceVtVZbv/L3GeJW6+Ol0C2AZtdEgNYhDcnC3jDeYwUke/ceU0FYInElAfv4k3682lhG0VkCt4cF3Gqel9h+6jqBvHmfbjJpziXPz+1Vz7ssCz3M99nuWC94P+tw8cgUrxxqqaq6i2F3g0cOEJ5KOXiJYQeQEEiEeA1Vf3Id0fx5vnwvd88oKBK7Qq85HQV8DcROVX/mNfClFNWtWXKPFVNxpsa9N4jbO/hqn0KTSI+/gE847O+FejgRk2tjdeoXVxd3OiqEcDNeFVo84DuInIC/HcU53Z+nOs3vBFaAW7Dq1IrSjrelLPgDcJ3vmsHicSbpbAgaSheh4P2IvK8K5sC3OPTftNURBoe6ULuHpur6gzgebxhzKv7EaMp4+yJxJQXbwGPBHICVV3lGrnPcOsJIjIOb+TVzfxRxVMcC/FGuj0Bb2jub1U13zXajy7odozXZrK+iHM9CnwqIs8Ce4C7/bj+UGCyiOxw7ST9XRwC/KSq/x0eXFXzROQWYIKIpKvqhyJyEvC7q4LLAG7HewIpTCTwpWuvErw2nFQ/YjRlnI3+a4wxJiBWtWWMMSYglkiMMcYExBKJMcaYgFgiMcYYExBLJMYYYwJiicQYY0xALJEYY4wJyP8D0Ts/I8W7IIQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(N, V)\n",
    "\n",
    "k = 10.34\n",
    "beta = 0.64\n",
    "plt.plot(N, k * (N**beta))\n",
    "plt.xlabel('N - Number of tokens')\n",
    "plt.ylabel('|V| - Size of vocabulary')\n",
    "plt.title('Heaps\\' Law')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}