Topic Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import string\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import sklearn\n",
    "import sys\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from gensim.models import ldamodel\n",
    "import gensim.corpora\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.preprocessing import normalize\n",
    "import pickle\n",
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import Dictionary\n",
    "import pyLDAvis.gensim\n",
    "import warnings\n",
    "from itertools import chain\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "lmtzr = WordNetLemmatizer()\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import string\n",
    "import re\n",
    "from tqdm._tqdm_notebook import tqdm_notebook,tnrange,tqdm\n",
    "from collections import Counter,OrderedDict\n",
    "from gensim import models,corpora\n",
    "from gensim.summarization import summarize,keywords\n",
    "import warnings\n",
    "import pyLDAvis.gensim\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pyLDAvis.gensim\n",
    "import gensim.models.phrases as gen\n",
    "from gensim.models.coherencemodel import CoherenceModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "337"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('/Users/ALEX/Desktop/Projects/articles.csv')\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>author</th>\n",
       "      <th>claps</th>\n",
       "      <th>reading_time</th>\n",
       "      <th>link</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Justin Lee</td>\n",
       "      <td>8.3K</td>\n",
       "      <td>11</td>\n",
       "      <td>https://medium.com/swlh/chatbots-were-the-next...</td>\n",
       "      <td>Chatbots were the next big thing: what happene...</td>\n",
       "      <td>Oh, how the headlines blared:\\nChatbots were T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Conor Dewey</td>\n",
       "      <td>1.4K</td>\n",
       "      <td>7</td>\n",
       "      <td>https://towardsdatascience.com/python-for-data...</td>\n",
       "      <td>Python for Data Science: 8 Concepts You May Ha...</td>\n",
       "      <td>If you’ve ever found yourself looking up the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>William Koehrsen</td>\n",
       "      <td>2.8K</td>\n",
       "      <td>11</td>\n",
       "      <td>https://towardsdatascience.com/automated-featu...</td>\n",
       "      <td>Automated Feature Engineering in Python – Towa...</td>\n",
       "      <td>Machine learning is increasingly moving from h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gant Laborde</td>\n",
       "      <td>1.3K</td>\n",
       "      <td>7</td>\n",
       "      <td>https://medium.freecodecamp.org/machine-learni...</td>\n",
       "      <td>Machine Learning: how to go from Zero to Hero ...</td>\n",
       "      <td>If your understanding of A.I. and Machine Lear...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emmanuel Ameisen</td>\n",
       "      <td>935</td>\n",
       "      <td>11</td>\n",
       "      <td>https://blog.insightdatascience.com/reinforcem...</td>\n",
       "      <td>Reinforcement Learning from scratch – Insight ...</td>\n",
       "      <td>Want to learn about applied Artificial Intelli...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             author claps  reading_time  \\\n",
       "0        Justin Lee  8.3K            11   \n",
       "1       Conor Dewey  1.4K             7   \n",
       "2  William Koehrsen  2.8K            11   \n",
       "3      Gant Laborde  1.3K             7   \n",
       "4  Emmanuel Ameisen   935            11   \n",
       "\n",
       "                                                link  \\\n",
       "0  https://medium.com/swlh/chatbots-were-the-next...   \n",
       "1  https://towardsdatascience.com/python-for-data...   \n",
       "2  https://towardsdatascience.com/automated-featu...   \n",
       "3  https://medium.freecodecamp.org/machine-learni...   \n",
       "4  https://blog.insightdatascience.com/reinforcem...   \n",
       "\n",
       "                                               title  \\\n",
       "0  Chatbots were the next big thing: what happene...   \n",
       "1  Python for Data Science: 8 Concepts You May Ha...   \n",
       "2  Automated Feature Engineering in Python – Towa...   \n",
       "3  Machine Learning: how to go from Zero to Hero ...   \n",
       "4  Reinforcement Learning from scratch – Insight ...   \n",
       "\n",
       "                                                text  \n",
       "0  Oh, how the headlines blared:\\nChatbots were T...  \n",
       "1  If you’ve ever found yourself looking up the s...  \n",
       "2  Machine learning is increasingly moving from h...  \n",
       "3  If your understanding of A.I. and Machine Lear...  \n",
       "4  Want to learn about applied Artificial Intelli...  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['length_text'] = data['text'].str.len()\n",
    "sns.distplot(data['length_text'], color=\"r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['length_title'] = data['title'].str.len()\n",
    "sns.distplot(data['length_title'], color=\"r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = [x for x in data['title']]\n",
    "docs = [x for x in data['text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/anusha/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "We use NLTK’s Wordnet to find the meanings of words, synonyms, antonyms, and more. In addition, we use WordNetLemmatizer to get the root word. Filter out stop words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to prepare the text for topic modelling\n",
    "def words(text):\n",
    "    regex = re.compile('[' + re.escape(string.punctuation) + '0-9\\\\r\\\\t\\\\n]')\n",
    "    text = regex.sub(\" \", text.lower())\n",
    "    words = text.split(\" \")\n",
    "    words = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in words]\n",
    "    words = [re.sub('\\s+', ' ', sent) for sent in words]\n",
    "    words = [re.sub(\"\\'\", \"\", sent) for sent in words]\n",
    "    words = [w for w in words if not len(w) < 2]\n",
    "    words = [w for w in words if w not in stop_words]\n",
    "    words = [lmtzr.lemmatize(w) for w in words]\n",
    "    \n",
    "    return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /Users/anusha/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "nltk.download('wordnet')\n",
    "docs = [words(x) for x in data['text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique words in initital documents: 18975\n",
      "Number of unique words after removing rare and common words: 2720\n"
     ]
    }
   ],
   "source": [
    " # Create a dictionary representation of the documents.\n",
    "dictionary = Dictionary(docs)\n",
    "print('Number of unique words in initital documents:', len(dictionary))\n",
    "\n",
    "# Filter out words that occur less than 10 documents, or more than 20% of the documents.\n",
    "dictionary.filter_extremes(no_below=10, no_above=0.2)\n",
    "print('Number of unique words after removing rare and common words:', len(dictionary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word 47 (\"close\") appears 1 time.\n",
      "Word 51 (\"command\") appears 1 time.\n",
      "Word 65 (\"cool\") appears 1 time.\n",
      "Word 121 (\"gate\") appears 1 time.\n",
      "Word 189 (\"multi\") appears 1 time.\n",
      "Word 213 (\"predicted\") appears 1 time.\n",
      "Word 247 (\"scene\") appears 1 time.\n",
      "Word 281 (\"terminal\") appears 1 time.\n",
      "Word 321 (\"column\") appears 6 time.\n",
      "Word 339 (\"excellent\") appears 1 time.\n",
      "Word 370 (\"matrix\") appears 1 time.\n",
      "Word 431 (\"www\") appears 1 time.\n",
      "Word 434 (\"additional\") appears 1 time.\n",
      "Word 455 (\"collection\") appears 3 time.\n",
      "Word 489 (\"github\") appears 1 time.\n",
      "Word 503 (\"irrelevant\") appears 1 time.\n",
      "Word 506 (\"learning”\") appears 1 time.\n",
      "Word 535 (\"ready\") appears 1 time.\n",
      "Word 538 (\"relationship\") appears 3 time.\n",
      "Word 571 (\"“deep\") appears 1 time.\n",
      "Word 579 (\"andrew\") appears 1 time.\n",
      "Word 617 (\"guess\") appears 1 time.\n",
      "Word 652 (\"neuron\") appears 4 time.\n",
      "Word 736 (\"comprehensive\") appears 1 time.\n",
      "Word 767 (\"enables\") appears 1 time.\n",
      "Word 774 (\"explanation\") appears 1 time.\n",
      "Word 777 (\"faced\") appears 1 time.\n",
      "Word 857 (\"studied\") appears 1 time.\n",
      "Word 906 (\"combination\") appears 3 time.\n",
      "Word 931 (\"diagram\") appears 2 time.\n",
      "Word 932 (\"direct\") appears 3 time.\n",
      "Word 938 (\"earlier\") appears 1 time.\n",
      "Word 950 (\"feed\") appears 1 time.\n",
      "Word 987 (\"multiplication\") appears 1 time.\n",
      "Word 991 (\"notice\") appears 1 time.\n",
      "Word 998 (\"pixel\") appears 2 time.\n",
      "Word 1052 (\"apple\") appears 2 time.\n",
      "Word 1103 (\"trend\") appears 2 time.\n",
      "Word 1107 (\"visually\") appears 1 time.\n",
      "Word 1167 (\"correlation\") appears 2 time.\n",
      "Word 1170 (\"economics\") appears 1 time.\n",
      "Word 1227 (\"connection\") appears 2 time.\n",
      "Word 1296 (\"noticed\") appears 1 time.\n",
      "Word 1353 (\"third\") appears 1 time.\n",
      "Word 1419 (\"discover\") appears 1 time.\n",
      "Word 1461 (\"university\") appears 1 time.\n",
      "Word 1487 (\"considered\") appears 2 time.\n",
      "Word 1559 (\"oxford\") appears 1 time.\n",
      "Word 1597 (\"super\") appears 1 time.\n",
      "Word 1612 (\"what’s\") appears 1 time.\n",
      "Word 1624 (\"backwards\") appears 1 time.\n",
      "Word 1650 (\"handle\") appears 1 time.\n",
      "Word 1666 (\"ok\") appears 2 time.\n",
      "Word 1676 (\"situation\") appears 1 time.\n",
      "Word 1738 (\"smarter\") appears 1 time.\n",
      "Word 1803 (\"trick\") appears 1 time.\n",
      "Word 1976 (\"editor\") appears 1 time.\n",
      "Word 2099 (\"hidden\") appears 1 time.\n",
      "Word 2190 (\"latest\") appears 1 time.\n",
      "Word 2304 (\"exhibit\") appears 1 time.\n",
      "Word 2359 (\"io\") appears 1 time.\n",
      "Word 2404 (\"adjusting\") appears 2 time.\n",
      "Word 2507 (\"calculates\") appears 1 time.\n",
      "Word 2520 (\"invention\") appears 1 time.\n",
      "Word 2528 (\"submit\") appears 1 time.\n",
      "Word 2533 (\"wild\") appears 1 time.\n",
      "Word 2606 (\"politics\") appears 1 time.\n",
      "Word 2618 (\"permission\") appears 1 time.\n",
      "Word 2671 (\"py\") appears 1 time.\n",
      "Word 2673 (\"behaviour\") appears 2 time.\n"
     ]
    }
   ],
   "source": [
    "corpus = [dictionary.doc2bow(doc) for doc in docs]\n",
    "#print(len(corpus))\n",
    "#corpus[336]\n",
    "bow_doc_300 = corpus[300]\n",
    "\n",
    "for i in range(len(bow_doc_300)):\n",
    "    print(\"Word {} (\\\"{}\\\") appears {} time.\".format(bow_doc_300[i][0], \n",
    "                                                     dictionary[bow_doc_300[i][0]], \n",
    "                                                     bow_doc_300[i][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling using LDA\n",
    "LDA :: Latent Dirichlet Allocation is a probabilistic model. It is a unsupervised machine learning technique. And to obtain cluster assignments, it uses two probability values: P( word | topics) and P( topics | documents).\n",
    "\n",
    "pyLDAvis is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lda_topics(model, num_topics):\n",
    "    word_dict = {};\n",
    "    for i in range(num_topics):\n",
    "        words = model.show_topic(i, topn = 20);\n",
    "        word_dict['Topic # ' + '{:02d}'.format(i+1)] = [i[0] for i in words];\n",
    "    return pd.DataFrame(word_dict);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>Topic # 01</th>\n",
       "      <th>Topic # 02</th>\n",
       "      <th>Topic # 03</th>\n",
       "      <th>Topic # 04</th>\n",
       "      <th>Topic # 05</th>\n",
       "      <th>Topic # 06</th>\n",
       "      <th>Topic # 07</th>\n",
       "      <th>Topic # 08</th>\n",
       "      <th>Topic # 09</th>\n",
       "      <th>Topic # 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neuron</td>\n",
       "      <td>star</td>\n",
       "      <td>cpu</td>\n",
       "      <td>de</td>\n",
       "      <td>cluster</td>\n",
       "      <td>bot</td>\n",
       "      <td>car</td>\n",
       "      <td>table</td>\n",
       "      <td>pixel</td>\n",
       "      <td>cnn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>activation</td>\n",
       "      <td>review</td>\n",
       "      <td>house</td>\n",
       "      <td>member</td>\n",
       "      <td>agent</td>\n",
       "      <td>conversation</td>\n",
       "      <td>music</td>\n",
       "      <td>reward</td>\n",
       "      <td>woman</td>\n",
       "      <td>sheet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>matrix</td>\n",
       "      <td>rating</td>\n",
       "      <td>gtx</td>\n",
       "      <td>sound</td>\n",
       "      <td>policy</td>\n",
       "      <td>behavior</td>\n",
       "      <td>graph</td>\n",
       "      <td>startup</td>\n",
       "      <td>men</td>\n",
       "      <td>box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zero</td>\n",
       "      <td>average</td>\n",
       "      <td>sentence</td>\n",
       "      <td>title</td>\n",
       "      <td>host</td>\n",
       "      <td>social</td>\n",
       "      <td>batch</td>\n",
       "      <td>recognize</td>\n",
       "      <td>convolution</td>\n",
       "      <td>region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>player</td>\n",
       "      <td>weighted</td>\n",
       "      <td>gpu</td>\n",
       "      <td>speech</td>\n",
       "      <td>response</td>\n",
       "      <td>simulation</td>\n",
       "      <td>vehicle</td>\n",
       "      <td>array</td>\n",
       "      <td>kernel</td>\n",
       "      <td>pixel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>policy</td>\n",
       "      <td>university</td>\n",
       "      <td>sequence</td>\n",
       "      <td>et</td>\n",
       "      <td>lstm</td>\n",
       "      <td>apps</td>\n",
       "      <td>track</td>\n",
       "      <td>tutorial</td>\n",
       "      <td>facial</td>\n",
       "      <td>bounding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>man</td>\n",
       "      <td>interview</td>\n",
       "      <td>app</td>\n",
       "      <td>en</td>\n",
       "      <td>noise</td>\n",
       "      <td>interface</td>\n",
       "      <td>driving</td>\n",
       "      <td>translation</td>\n",
       "      <td>channel</td>\n",
       "      <td>mask</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sigmoid</td>\n",
       "      <td>engineering</td>\n",
       "      <td>card</td>\n",
       "      <td>watson</td>\n",
       "      <td>trust</td>\n",
       "      <td>cognitive</td>\n",
       "      <td>placeholder</td>\n",
       "      <td>phrase</td>\n",
       "      <td>filter</td>\n",
       "      <td>proposal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>derivative</td>\n",
       "      <td>programming</td>\n",
       "      <td>gpus</td>\n",
       "      <td>feed</td>\n",
       "      <td>reinforcement</td>\n",
       "      <td>response</td>\n",
       "      <td>song</td>\n",
       "      <td>distance</td>\n",
       "      <td>edge</td>\n",
       "      <td>segmentation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>alphago</td>\n",
       "      <td>assignment</td>\n",
       "      <td>letter</td>\n",
       "      <td>netflix</td>\n",
       "      <td>unit</td>\n",
       "      <td>chatbots</td>\n",
       "      <td>computation</td>\n",
       "      <td>convolution</td>\n",
       "      <td>orientation</td>\n",
       "      <td>www</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>backpropagation</td>\n",
       "      <td>github</td>\n",
       "      <td>market</td>\n",
       "      <td>wave</td>\n",
       "      <td>rl</td>\n",
       "      <td>political</td>\n",
       "      <td>sort</td>\n",
       "      <td>cloud</td>\n",
       "      <td>“do</td>\n",
       "      <td>numpy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>descent</td>\n",
       "      <td>option</td>\n",
       "      <td>translation</td>\n",
       "      <td>recommendation</td>\n",
       "      <td>sequence</td>\n",
       "      <td>participant</td>\n",
       "      <td>matrix</td>\n",
       "      <td>api</td>\n",
       "      <td>page</td>\n",
       "      <td>cnns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>relu</td>\n",
       "      <td>estimated</td>\n",
       "      <td>ti</td>\n",
       "      <td>audio</td>\n",
       "      <td>worker</td>\n",
       "      <td>interaction</td>\n",
       "      <td>stock</td>\n",
       "      <td>expected</td>\n",
       "      <td>matrix</td>\n",
       "      <td>org</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tree</td>\n",
       "      <td>skill</td>\n",
       "      <td>price</td>\n",
       "      <td>recipe</td>\n",
       "      <td>supervised</td>\n",
       "      <td>intelligent</td>\n",
       "      <td>representation</td>\n",
       "      <td>device</td>\n",
       "      <td>classifier</td>\n",
       "      <td>map</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>distribution</td>\n",
       "      <td>coursera</td>\n",
       "      <td>character</td>\n",
       "      <td>personalization</td>\n",
       "      <td>clustering</td>\n",
       "      <td>control</td>\n",
       "      <td>minute</td>\n",
       "      <td>block</td>\n",
       "      <td>opposite</td>\n",
       "      <td>insight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>regression</td>\n",
       "      <td>cover</td>\n",
       "      <td>box</td>\n",
       "      <td>assistant</td>\n",
       "      <td>transaction</td>\n",
       "      <td>turing</td>\n",
       "      <td>index</td>\n",
       "      <td>agent</td>\n",
       "      <td>age</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>board</td>\n",
       "      <td>timeline</td>\n",
       "      <td>rnn</td>\n",
       "      <td>offline</td>\n",
       "      <td>reward</td>\n",
       "      <td>digital</td>\n",
       "      <td>bias</td>\n",
       "      <td>map</td>\n",
       "      <td>wear</td>\n",
       "      <td>wikipedia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>outcome</td>\n",
       "      <td>taught</td>\n",
       "      <td>dl</td>\n",
       "      <td>hypothesis</td>\n",
       "      <td>recall</td>\n",
       "      <td>automated</td>\n",
       "      <td>recommendation</td>\n",
       "      <td>max</td>\n",
       "      <td>straight</td>\n",
       "      <td>scipy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>representation</td>\n",
       "      <td>andrew</td>\n",
       "      <td>aws</td>\n",
       "      <td>message</td>\n",
       "      <td>score</td>\n",
       "      <td>asked</td>\n",
       "      <td>generator</td>\n",
       "      <td>tweet</td>\n",
       "      <td>measurement</td>\n",
       "      <td>scikit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>semantic</td>\n",
       "      <td>paid</td>\n",
       "      <td>recurrent</td>\n",
       "      <td>chunk</td>\n",
       "      <td>github</td>\n",
       "      <td>customer</td>\n",
       "      <td>programming</td>\n",
       "      <td>grid</td>\n",
       "      <td>eye</td>\n",
       "      <td>panda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Topic # 01   Topic # 02   Topic # 03       Topic # 04     Topic # 05  \\\n",
       "0            neuron         star          cpu               de        cluster   \n",
       "1        activation       review        house           member          agent   \n",
       "2            matrix       rating          gtx            sound         policy   \n",
       "3              zero      average     sentence            title           host   \n",
       "4            player     weighted          gpu           speech       response   \n",
       "5            policy   university     sequence               et           lstm   \n",
       "6               man    interview          app               en          noise   \n",
       "7           sigmoid  engineering         card           watson          trust   \n",
       "8        derivative  programming         gpus             feed  reinforcement   \n",
       "9           alphago   assignment       letter          netflix           unit   \n",
       "10  backpropagation       github       market             wave             rl   \n",
       "11          descent       option  translation   recommendation       sequence   \n",
       "12             relu    estimated           ti            audio         worker   \n",
       "13             tree        skill        price           recipe     supervised   \n",
       "14     distribution     coursera    character  personalization     clustering   \n",
       "15       regression        cover          box        assistant    transaction   \n",
       "16            board     timeline          rnn          offline         reward   \n",
       "17          outcome       taught           dl       hypothesis         recall   \n",
       "18   representation       andrew          aws          message          score   \n",
       "19         semantic         paid    recurrent            chunk         github   \n",
       "\n",
       "      Topic # 06      Topic # 07   Topic # 08   Topic # 09    Topic # 10  \n",
       "0            bot             car        table        pixel           cnn  \n",
       "1   conversation           music       reward        woman         sheet  \n",
       "2       behavior           graph      startup          men           box  \n",
       "3         social           batch    recognize  convolution        region  \n",
       "4     simulation         vehicle        array       kernel         pixel  \n",
       "5           apps           track     tutorial       facial      bounding  \n",
       "6      interface         driving  translation      channel          mask  \n",
       "7      cognitive     placeholder       phrase       filter      proposal  \n",
       "8       response            song     distance         edge  segmentation  \n",
       "9       chatbots     computation  convolution  orientation           www  \n",
       "10     political            sort        cloud          “do         numpy  \n",
       "11   participant          matrix          api         page          cnns  \n",
       "12   interaction           stock     expected       matrix           org  \n",
       "13   intelligent  representation       device   classifier           map  \n",
       "14       control          minute        block     opposite       insight  \n",
       "15        turing           index        agent          age            en  \n",
       "16       digital            bias          map         wear     wikipedia  \n",
       "17     automated  recommendation          max     straight         scipy  \n",
       "18         asked       generator        tweet  measurement        scikit  \n",
       "19      customer     programming         grid          eye         panda  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_lda_topics(lda_model, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=10, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=500,\n",
    "                                           passes=20,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el21641112276277432697244455\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el21641112276277432697244455_data = {\"mdsDat\": {\"x\": [0.08354724351964586, 0.0011095394469031706, -0.003215127426502155, 0.07679783861113189, 0.042512055631010466, 0.06173591860467902, 0.028200390022526386, 0.06931311995092795, -0.3577252648950901, -0.002275713465232747], \"y\": [-0.027386273866716236, 0.12142346247539924, 0.050100688866953964, -0.1454480218592333, -0.01522815685204614, 0.09320534461464725, 0.10835747858872181, 0.03874820156487336, -0.0059840715828736735, -0.21778865194972608], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [14.124917984008789, 13.30905818939209, 12.913647651672363, 12.042168617248535, 11.228286743164062, 10.069934844970703, 8.781598091125488, 8.402618408203125, 5.084514141082764, 4.043257236480713]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\"], \"Freq\": [439.0, 315.0, 228.0, 225.0, 188.0, 252.0, 159.0, 266.0, 195.0, 380.0, 229.0, 299.0, 299.0, 182.0, 166.0, 170.0, 217.0, 151.0, 195.0, 210.0, 156.0, 160.0, 139.0, 176.0, 134.0, 211.0, 298.0, 131.0, 194.0, 115.0, 126.32455444335938, 120.99132537841797, 21.33541488647461, 48.042938232421875, 10.728221893310547, 28.024330139160156, 136.6973419189453, 16.325265884399414, 51.273681640625, 82.2645492553711, 16.716733932495117, 25.10590934753418, 80.84051513671875, 62.89399719238281, 31.33316993713379, 53.9773063659668, 9.661550521850586, 18.910385131835938, 48.22140121459961, 24.113441467285156, 13.041604995727539, 98.76690673828125, 22.930618286132812, 17.542409896850586, 10.522476196289062, 32.7227897644043, 13.225116729736328, 21.2480525970459, 16.059431076049805, 95.16544342041016, 125.45637512207031, 43.111122131347656, 56.14888381958008, 47.83103942871094, 105.6833267211914, 89.89002227783203, 37.44011688232422, 203.5780792236328, 76.18976593017578, 144.95852661132812, 44.86896514892578, 32.4587516784668, 117.35877227783203, 44.69968795776367, 46.33403396606445, 51.250240325927734, 50.706905364990234, 49.93502426147461, 55.975196838378906, 48.42884826660156, 68.41548919677734, 48.59833526611328, 46.82578659057617, 48.735626220703125, 47.95716094970703, 73.09989929199219, 153.40818786621094, 37.55394744873047, 44.05335235595703, 50.7115592956543, 34.910400390625, 35.12562942504883, 48.928794860839844, 23.077272415161133, 19.078454971313477, 41.02074432373047, 13.353598594665527, 9.363524436950684, 24.242774963378906, 23.23143768310547, 13.103412628173828, 22.203723907470703, 12.804786682128906, 9.604862213134766, 17.64410972595215, 63.934818267822266, 40.7492790222168, 14.638351440429688, 25.593326568603516, 8.758601188659668, 11.67724895477295, 45.22221755981445, 23.87270164489746, 37.93711471557617, 34.465065002441406, 39.277034759521484, 120.08991241455078, 37.34077072143555, 67.54853057861328, 28.62920570373535, 59.7949104309082, 65.34632110595703, 70.60398864746094, 36.056480407714844, 90.21224212646484, 71.74117279052734, 84.2284927368164, 37.33418655395508, 36.246341705322266, 40.10905075073242, 60.66822052001953, 45.12099075317383, 57.33480453491211, 48.67721176147461, 55.00957489013672, 45.496376037597656, 44.96697998046875, 43.37030029296875, 41.170677185058594, 40.74404525756836, 39.96684646606445, 57.926536560058594, 22.642057418823242, 56.15843200683594, 45.43729782104492, 113.35729217529297, 14.94223403930664, 9.109130859375, 31.268882751464844, 25.247966766357422, 25.655048370361328, 29.979385375976562, 25.090335845947266, 98.74929809570312, 80.44048309326172, 16.224306106567383, 26.98826789855957, 30.583642959594727, 28.484235763549805, 29.774518966674805, 8.909778594970703, 23.655248641967773, 22.113969802856445, 13.265299797058105, 13.78653335571289, 17.509307861328125, 16.50267791748047, 25.078693389892578, 11.480355262756348, 9.905155181884766, 11.315119743347168, 24.121728897094727, 51.54244613647461, 29.434425354003906, 41.44715118408203, 29.973472595214844, 65.66470336914062, 84.48075103759766, 46.5855827331543, 66.38008117675781, 57.13749313354492, 34.962379455566406, 63.39472198486328, 41.23667526245117, 65.08519744873047, 64.0570068359375, 48.18230056762695, 52.471153259277344, 48.03010940551758, 44.23056411743164, 43.52082061767578, 36.497257232666016, 36.261173248291016, 45.21490478515625, 44.847679138183594, 38.18070983886719, 39.70753479003906, 51.19257736206055, 43.311195373535156, 39.93407440185547, 39.2849235534668, 42.99800109863281, 40.72955322265625, 55.49207305908203, 43.85848617553711, 50.33779525756836, 135.20252990722656, 126.6611328125, 160.9584503173828, 107.59754180908203, 98.34579467773438, 70.15030670166016, 10.646644592285156, 30.041419982910156, 117.09520721435547, 26.06840705871582, 117.72373962402344, 37.765228271484375, 12.711225509643555, 57.27207565307617, 31.28013038635254, 41.78318786621094, 26.871082305908203, 38.08637619018555, 48.29825973510742, 129.60008239746094, 167.0457000732422, 71.81216430664062, 26.66115379333496, 12.16331958770752, 20.985960006713867, 21.50640869140625, 38.57655334472656, 111.39059448242188, 144.15896606445312, 77.98245239257812, 68.53560638427734, 94.6056137084961, 78.67428588867188, 118.7377700805664, 111.71783447265625, 50.46004104614258, 45.041378021240234, 56.030059814453125, 49.12074279785156, 63.75007629394531, 51.205875396728516, 74.74174499511719, 56.5030403137207, 53.21249771118164, 53.48545455932617, 16.168819427490234, 39.09208297729492, 21.393592834472656, 31.613643646240234, 55.240150451660156, 11.464068412780762, 66.76215362548828, 79.8152084350586, 10.749463081359863, 46.78737258911133, 39.869632720947266, 28.89487648010254, 16.056730270385742, 73.4921875, 40.97948455810547, 63.834712982177734, 44.8550910949707, 8.15114974975586, 8.184770584106445, 51.41979217529297, 44.30488586425781, 9.865883827209473, 7.764006614685059, 22.708402633666992, 12.174808502197266, 24.851768493652344, 11.420490264892578, 20.6905574798584, 67.47187042236328, 137.6147918701172, 78.91873931884766, 50.58391189575195, 24.764238357543945, 93.64270782470703, 28.632516860961914, 56.34349822998047, 137.10552978515625, 29.726566314697266, 60.55009841918945, 60.11190414428711, 53.668479919433594, 38.831573486328125, 39.41710662841797, 31.31045913696289, 35.85019302368164, 45.35950469970703, 36.24239730834961, 34.79359436035156, 37.007110595703125, 35.93174362182617, 24.09342384338379, 13.859607696533203, 22.124340057373047, 22.620792388916016, 9.90732479095459, 18.76787757873535, 26.827556610107422, 11.36180305480957, 137.6657257080078, 15.815911293029785, 49.461181640625, 105.2244644165039, 14.5048246383667, 159.81036376953125, 9.521846771240234, 8.653396606445312, 42.05146408081055, 15.506625175476074, 26.335174560546875, 25.94877815246582, 20.702041625976562, 20.62152671813965, 10.23867416381836, 36.962852478027344, 9.308390617370605, 16.391685485839844, 21.331233978271484, 15.542844772338867, 77.16395568847656, 91.34323120117188, 146.517333984375, 147.18894958496094, 87.50233459472656, 81.54418182373047, 108.732666015625, 85.695068359375, 56.862369537353516, 53.87161636352539, 36.47379684448242, 43.653709411621094, 39.55654525756836, 39.12103271484375, 50.36069869995117, 34.86363220214844, 26.938779830932617, 44.47960662841797, 48.3316535949707, 39.154842376708984, 50.00697326660156, 46.39552688598633, 49.313785552978516, 45.18529510498047, 45.47213363647461, 38.645790100097656, 37.65665817260742, 59.354122161865234, 46.7745246887207, 114.32247924804688, 128.1501922607422, 33.997276306152344, 75.62738800048828, 70.65970611572266, 44.32687759399414, 37.51161193847656, 25.5928955078125, 72.91958618164062, 53.73992156982422, 19.3254451751709, 25.478118896484375, 49.476985931396484, 40.71086120605469, 45.12527084350586, 28.410587310791016, 15.96619987487793, 8.295379638671875, 10.608522415161133, 6.875432968139648, 43.9865837097168, 16.512432098388672, 17.549129486083984, 23.62010955810547, 13.836057662963867, 12.019506454467773, 7.747072219848633, 38.77120590209961, 169.49217224121094, 37.013031005859375, 32.751102447509766, 46.31716537475586, 24.67888069152832, 22.636316299438477, 34.847755432128906, 28.008586883544922, 52.693546295166016, 43.730186462402344, 49.608619689941406, 32.559913635253906, 33.41273498535156, 35.55069351196289, 36.08913040161133, 33.23374938964844, 39.53564453125, 36.20907211303711, 33.079505920410156, 82.1329574584961, 394.0389099121094, 59.67280960083008, 144.48953247070312, 13.256309509277344, 87.71983337402344, 36.618797302246094, 34.61255645751953, 63.62591552734375, 9.257856369018555, 55.15433883666992, 16.945026397705078, 26.58650779724121, 98.17598724365234, 47.877288818359375, 19.925376892089844, 120.6055908203125, 26.98151969909668, 21.746124267578125, 24.280115127563477, 36.79036331176758, 25.051990509033203, 19.9578800201416, 54.70011520385742, 20.367660522460938, 7.392475605010986, 9.476812362670898, 8.762712478637695, 8.73940372467041, 11.095229148864746, 70.12892150878906, 17.4905948638916, 29.009510040283203, 112.25618743896484, 154.46380615234375, 60.24589920043945, 45.66480255126953, 45.598487854003906, 73.88243865966797, 84.9152603149414, 35.038658142089844, 43.857425689697266, 39.67066955566406, 39.93063735961914, 42.4182014465332, 33.01360321044922, 31.032461166381836, 29.42940330505371, 296.85443115234375, 208.22120666503906, 58.62960433959961, 29.607677459716797, 185.53811645507812, 54.63981246948242, 126.36125946044922, 149.02537536621094, 14.290308952331543, 38.830692291259766, 13.169388771057129, 12.697437286376953, 32.67108917236328, 29.769845962524414, 62.28737258911133, 52.15739059448242, 29.47855567932129, 12.143129348754883, 75.60247039794922, 40.2333984375, 32.48269271850586, 45.8683967590332, 33.97804260253906, 19.658973693847656, 109.84491729736328, 59.361751556396484, 34.3839225769043, 11.221085548400879, 22.8782901763916, 30.581451416015625, 23.795583724975586, 138.50869750976562, 49.74675750732422, 37.52996826171875, 55.537681579589844, 62.537330627441406, 57.22416305541992, 40.964317321777344, 61.51329040527344, 43.75419998168945, 38.7742805480957, 36.06389617919922, 35.28829574584961, 33.98426818847656, 41.62037658691406, 63.9876594543457, 28.954805374145508, 9.809109687805176, 51.502662658691406, 29.137130737304688, 7.790921688079834, 44.52956771850586, 15.854820251464844, 9.435169219970703, 9.269360542297363, 12.80752944946289, 9.370068550109863, 29.61180877685547, 44.039222717285156, 6.814145088195801, 91.35037231445312, 10.082984924316406, 12.242537498474121, 5.659773349761963, 10.788360595703125, 11.640507698059082, 16.831987380981445, 5.868072032928467, 9.451899528503418, 5.204896450042725, 5.589221477508545, 5.604738235473633, 37.04674530029297, 5.867416858673096, 8.943288803100586, 31.60398292541504, 30.869556427001953, 30.14246368408203, 19.10338592529297, 35.763916015625, 45.02862548828125, 70.90096282958984, 26.947980880737305, 50.90397644042969, 42.154727935791016, 44.25696563720703, 18.06661605834961, 17.49835205078125, 24.86111068725586, 25.292844772338867, 19.387928009033203, 18.76067352294922, 20.74376678466797, 20.684429168701172, 22.143503189086914, 19.89954376220703, 20.606185913085938, 18.815109252929688], \"Term\": [\"cnn\", \"de\", \"star\", \"review\", \"rating\", \"average\", \"weighted\", \"matrix\", \"house\", \"neuron\", \"policy\", \"bot\", \"box\", \"university\", \"region\", \"cpu\", \"table\", \"member\", \"agent\", \"sequence\", \"reward\", \"sheet\", \"gtx\", \"player\", \"cluster\", \"app\", \"pixel\", \"letter\", \"price\", \"et\", \"men\", \"kernel\", \"feedforward\", \"wear\", \"detected\", \"relate\", \"woman\", \"calculating\", \"placeholder\", \"facial\", \"anomaly\", \"stride\", \"filter\", \"orientation\", \"synapsis\", \"\\u201cdo\", \"suffers\", \"learnt\", \"augmentation\", \"dropout\", \"answered\", \"channel\", \"formula\", \"deviation\", \"invention\", \"initialization\", \"floating\", \"pretrained\", \"forecasting\", \"bias\", \"convolution\", \"curve\", \"opposite\", \"red\", \"batch\", \"graph\", \"angle\", \"neuron\", \"edge\", \"pixel\", \"distance\", \"hypothesis\", \"matrix\", \"straight\", \"index\", \"api\", \"hidden\", \"grid\", \"operation\", \"dimension\", \"bot\", \"label\", \"column\", \"price\", \"activation\", \"vehicle\", \"sheet\", \"economy\", \"fiction\", \"discipline\", \"scipy\", \"wiki\", \"ii\", \"patient\", \"ubiquitous\", \"novel\", \"organized\", \"death\", \"city\", \"resolution\", \"historically\", \"emerge\", \"augmented\", \"technologist\", \"\\u201cai\", \"worker\", \"wikipedia\", \"electrical\", \"viewed\", \"thereby\", \"construction\", \"org\", \"dream\", \"humanity\", \"panda\", \"automation\", \"car\", \"medical\", \"driving\", \"scientific\", \"numpy\", \"title\", \"www\", \"body\", \"engineering\", \"control\", \"social\", \"society\", \"generator\", \"develop\", \"art\", \"progress\", \"music\", \"public\", \"industry\", \"decade\", \"generation\", \"generated\", \"skill\", \"analysis\", \"drive\", \"song\", \"demo\", \"enterprise\", \"landscape\", \"startup\", \"pocket\", \"inspire\", \"talent\", \"parent\", \"corporate\", \"filtering\", \"ecosystem\", \"customer\", \"assistant\", \"tough\", \"chip\", \"designer\", \"provider\", \"giant\", \"company\\u2019s\", \"crazy\", \"rush\", \"anymore\", \"hadoop\", \"china\", \"baidu\", \"collaborative\", \"arm\", \"java\", \"volume\", \"leader\", \"offer\", \"gold\", \"client\", \"bank\", \"voice\", \"interview\", \"innovation\", \"music\", \"phone\", \"marketing\", \"industry\", \"analytics\", \"speech\", \"device\", \"cloud\", \"recommendation\", \"apps\", \"mobile\", \"track\", \"child\", \"watch\", \"intelligent\", \"home\", \"billion\", \"database\", \"app\", \"website\", \"developer\", \"tech\", \"market\", \"conversation\", \"gb\", \"lstms\", \"cuda\", \"gtx\", \"letter\", \"cpu\", \"gpus\", \"ti\", \"mario\", \"painting\", \"karpathy\", \"lstm\", \"andrej\", \"card\", \"intel\", \"inefficient\", \"processor\", \"ubuntu\", \"desktop\", \"linux\", \"installed\", \"nvidia\", \"rnn\", \"sequence\", \"aws\", \"installation\", \"plausible\", \"handwritten\", \"brother\", \"encoding\", \"gpu\", \"sentence\", \"dl\", \"super\", \"character\", \"recurrent\", \"translation\", \"market\", \"install\", \"module\", \"english\", \"chunk\", \"recognize\", \"translate\", \"box\", \"unit\", \"feed\", \"price\", \"sparse\", \"backward\", \"nervous\", \"tanh\", \"host\", \"cue\", \"relu\", \"sigmoid\", \"hint\", \"tweet\", \"trust\", \"linearity\", \"template\", \"backpropagation\", \"binary\", \"c\", \"chatbot\", \"teacher\", \"buzzword\", \"entropy\", \"tf\", \"virtually\", \"inevitably\", \"dead\", \"christmas\", \"threshold\", \"odds\", \"trip\", \"derivative\", \"activation\", \"page\", \"phrase\", \"request\", \"zero\", \"format\", \"distribution\", \"neuron\", \"recall\", \"interview\", \"conversation\", \"response\", \"chatbots\", \"what\\u2019s\", \"profile\", \"outcome\", \"translation\", \"measure\", \"nature\", \"programming\", \"github\", \"ball\", \"stability\", \"discounted\", \"hole\", \"donating\", \"juliani\", \"obtain\", \"consist\", \"reward\", \"unity\", \"utility\", \"simulation\", \"introducing\", \"agent\", \"uniform\", \"doable\", \"openai\", \"infinitely\", \"atari\", \"discrete\", \"neuroscience\", \"biological\", \"appreciated\", \"greater\", \"acquire\", \"immediate\", \"realistic\", \"construct\", \"rl\", \"estimate\", \"table\", \"policy\", \"tutorial\", \"reinforcement\", \"sound\", \"behavior\", \"define\", \"audio\", \"frequency\", \"definition\", \"appropriate\", \"exploration\", \"expected\", \"possibility\", \"updating\", \"noise\", \"situation\", \"wave\", \"supervised\", \"proposal\", \"regression\", \"block\", \"cognitive\", \"return\", \"insight\", \"personality\", \"personalization\", \"cluster\", \"member\", \"emotion\", \"participant\", \"political\", \"emotional\", \"offline\", \"ranking\", \"message\", \"measurement\", \"president\", \"candidate\", \"netflix\", \"messaging\", \"chat\", \"dark\", \"landmark\", \"measuring\", \"stated\", \"wired\", \"fake\", \"international\", \"empty\", \"variant\", \"arrow\", \"trait\", \"uncanny\", \"communication\", \"bot\", \"turing\", \"similarity\", \"eye\", \"\\u201ci\", \"personalized\", \"clustering\", \"opinion\", \"response\", \"instance\", \"conversation\", \"automated\", \"title\", \"recommendation\", \"relationship\", \"interface\", \"pixel\", \"rating\", \"interaction\", \"mask\", \"cnn\", \"segmentation\", \"region\", \"submission\", \"bounding\", \"chess\", \"winning\", \"alphago\", \"aligned\", \"watson\", \"straightforward\", \"carlo\", \"man\", \"recipe\", \"roll\", \"player\", \"ingredient\", \"monte\", \"behaviour\", \"imagenet\", \"essay\", \"native\", \"board\", \"blood\", \"genius\", \"proven\", \"texture\", \"experimenting\", \"champion\", \"proposal\", \"proceeding\", \"win\", \"app\", \"box\", \"map\", \"cnns\", \"playing\", \"policy\", \"pixel\", \"competition\", \"tree\", \"strategy\", \"expert\", \"insight\", \"kera\", \"shown\", \"relatively\", \"de\", \"star\", \"timeline\", \"udacity\\u2019s\", \"review\", \"coursera\", \"weighted\", \"rating\", \"seven\", \"exercise\", \"apache\", \"octave\", \"varies\", \"discount\", \"estimated\", \"taught\", \"se\", \"introductory\", \"et\", \"six\", \"graduate\", \"paid\", \"purchase\", \"collective\", \"university\", \"en\", \"udacity\", \"azure\", \"algebra\", \"eight\", \"noted\", \"average\", \"assignment\", \"introduction\", \"cover\", \"github\", \"option\", \"scikit\", \"programming\", \"student\", \"microsoft\", \"ten\", \"guide\", \"depending\", \"latent\", \"semantic\", \"decomposition\", \"deeplearning\", \"notebook\", \"hinton\", \"jargon\", \"andrew\", \"circle\", \"nonetheless\", \"sold\", \"rank\", \"ng\\u2019s\", \"jupyter\", \"document\", \"presence\", \"house\", \"syntax\", \"applies\", \"iteratively\", \"lowest\", \"bedroom\", \"relative\", \"osx\", \"neighborhood\", \"topology\", \"suitable\", \"pressing\", \"pooling\", \"semantics\", \"principal\", \"internal\", \"math\", \"teach\", \"graphic\", \"drive\", \"analysis\", \"matrix\", \"max\", \"price\", \"relationship\", \"representation\", \"hierarchical\", \"represented\", \"cnns\", \"dl\", \"equation\", \"click\", \"component\", \"recurrent\", \"programming\", \"square\", \"cnn\", \"car\"], \"Total\": [439.0, 315.0, 228.0, 225.0, 188.0, 252.0, 159.0, 266.0, 195.0, 380.0, 229.0, 299.0, 299.0, 182.0, 166.0, 170.0, 217.0, 151.0, 195.0, 210.0, 156.0, 160.0, 139.0, 176.0, 134.0, 211.0, 298.0, 131.0, 194.0, 115.0, 130.71580505371094, 125.83680725097656, 22.257410049438477, 50.33224868774414, 11.614367485046387, 31.017072677612305, 152.15353393554688, 18.429418563842773, 58.1130485534668, 95.14413452148438, 19.365859985351562, 29.15394401550293, 94.2739486694336, 73.61144256591797, 36.80618667602539, 63.988258361816406, 11.617755889892578, 23.263790130615234, 59.98311233520508, 30.14327049255371, 16.514911651611328, 125.08914947509766, 29.228404998779297, 22.370141983032227, 13.564766883850098, 42.67087936401367, 17.439035415649414, 28.098976135253906, 21.319904327392578, 127.10041046142578, 179.2672576904297, 59.147254943847656, 78.55335998535156, 66.77220916748047, 160.99705505371094, 135.77371215820312, 52.24306869506836, 380.0446472167969, 131.88555908203125, 298.3820495605469, 68.13480377197266, 45.63022232055664, 266.8935546875, 73.91934204101562, 81.95845794677734, 97.29620361328125, 101.97046661376953, 102.16262817382812, 129.63864135742188, 102.12615203857422, 299.6387634277344, 109.12956237792969, 107.44749450683594, 194.7273712158203, 215.42213439941406, 76.21292877197266, 160.02969360351562, 41.024688720703125, 48.778072357177734, 57.3936882019043, 39.8197021484375, 40.09825897216797, 59.48402786254883, 28.317039489746094, 23.53404426574707, 50.74974060058594, 16.602638244628906, 11.694198608398438, 30.279190063476562, 29.307905197143555, 16.672222137451172, 28.325748443603516, 16.631465911865234, 12.685318946838379, 23.37847137451172, 84.9953384399414, 54.23359680175781, 19.51582145690918, 34.20570373535156, 11.710244178771973, 15.626473426818848, 60.53644561767578, 32.1998405456543, 51.90948486328125, 47.1870002746582, 55.05705642700195, 179.289794921875, 52.962284088134766, 100.58396911621094, 40.051204681396484, 91.74857330322266, 108.07762908935547, 124.42924499511719, 55.59811019897461, 170.67184448242188, 130.29867553710938, 170.29429626464844, 58.0118408203125, 56.520328521728516, 66.5555419921875, 124.87186431884766, 80.01915740966797, 126.00367736816406, 107.4356689453125, 148.93128967285156, 98.59602355957031, 115.1501235961914, 112.28858184814453, 110.04558563232422, 147.93096923828125, 110.23140716552734, 62.26165771484375, 26.06121253967285, 65.17220306396484, 53.10453796386719, 133.1639404296875, 18.062137603759766, 11.012650489807129, 38.07269287109375, 31.07000732421875, 32.06350326538086, 38.04552459716797, 32.09748458862305, 127.02314758300781, 103.93607330322266, 20.990629196166992, 35.013545989990234, 40.05698013305664, 37.95598220825195, 39.88578796386719, 11.981722831726074, 31.86039924621582, 29.919189453125, 17.985578536987305, 18.761016845703125, 23.9063777923584, 22.75348472595215, 34.82845687866211, 16.028545379638672, 13.928290367126465, 15.942289352416992, 34.01431655883789, 75.88742065429688, 42.80421447753906, 61.75721740722656, 44.838829040527344, 108.52132415771484, 154.9962921142578, 80.66068267822266, 126.00367736816406, 119.81424713134766, 61.45219421386719, 148.93128967285156, 78.6389389038086, 162.41322326660156, 169.72007751464844, 109.38590240478516, 130.50436401367188, 111.2459945678711, 95.7039566040039, 97.08084106445312, 67.41600799560547, 67.45183563232422, 113.43928527832031, 113.27656555175781, 79.4442138671875, 90.67743682861328, 211.90480041503906, 128.7036590576172, 100.44261169433594, 94.08320617675781, 217.80621337890625, 177.90895080566406, 56.461997985839844, 44.77761459350586, 51.6046142578125, 139.18698120117188, 131.4065704345703, 170.31260681152344, 114.83524322509766, 105.09548950195312, 76.09691619873047, 11.676703453063965, 33.099849700927734, 129.51368713378906, 29.20322036743164, 133.79379272460938, 42.9722785949707, 14.585761070251465, 66.29804229736328, 36.97534942626953, 49.88505172729492, 32.19429397583008, 46.77985763549805, 59.42839050292969, 159.55831909179688, 210.53875732421875, 91.55866241455078, 34.060813903808594, 15.592836380004883, 27.25092124938965, 28.194292068481445, 50.6881103515625, 147.9685516357422, 194.75218200683594, 106.79962921142578, 99.33049774169922, 145.16180419921875, 121.46592712402344, 198.6059112548828, 217.80621337890625, 76.07070922851562, 64.29102325439453, 92.26163482666016, 75.87734985351562, 131.0049591064453, 88.9516372680664, 299.5947265625, 141.6959991455078, 156.1481170654297, 194.7273712158203, 17.582901000976562, 43.965267181396484, 24.401992797851562, 36.124916076660156, 63.585201263427734, 13.62681770324707, 81.96085357666016, 100.49842834472656, 13.648406028747559, 59.774131774902344, 51.873470306396484, 38.015682220458984, 21.26554298400879, 97.61817932128906, 54.684425354003906, 87.24506378173828, 61.61137771606445, 11.65964126586914, 11.80215072631836, 74.338134765625, 64.2463607788086, 14.648006439208984, 11.685795783996582, 34.242191314697266, 18.534164428710938, 38.094749450683594, 17.56458282470703, 32.23409652709961, 105.31019592285156, 215.42213439941406, 126.31729888916016, 80.99946594238281, 40.28227615356445, 183.291015625, 48.68103790283203, 106.668701171875, 380.0446472167969, 51.79873275756836, 154.9962921142578, 177.90895080566406, 170.71337890625, 94.88322448730469, 104.67263793945312, 65.84153747558594, 98.83434295654297, 198.6059112548828, 106.73625946044922, 106.08818817138672, 192.0177459716797, 162.46951293945312, 25.51813507080078, 14.740964889526367, 23.591327667236328, 24.51673126220703, 10.80709171295166, 20.585065841674805, 29.50447654724121, 12.765206336975098, 156.12240600585938, 18.562530517578125, 59.01319122314453, 125.6484375, 17.66437339782715, 195.46456909179688, 11.726799964904785, 10.790502548217773, 53.02602005004883, 19.60440444946289, 34.30585861206055, 34.37418746948242, 27.444311141967773, 27.401348114013672, 13.685844421386719, 50.19364547729492, 12.746040344238281, 22.514598846435547, 29.423707962036133, 21.6053524017334, 108.69695281982422, 129.39349365234375, 217.43777465820312, 229.8066864013672, 135.3255157470703, 128.1671905517578, 186.79971313476562, 142.73910522460938, 90.09774780273438, 87.83855438232422, 55.05167770385742, 69.34867858886719, 61.93806076049805, 65.43225860595703, 93.03927612304688, 57.13071823120117, 40.31120681762695, 86.43647003173828, 104.64054107666016, 73.15632629394531, 128.3908233642578, 117.74325561523438, 143.74322509765625, 118.16167449951172, 124.14985656738281, 92.85905456542969, 149.05624389648438, 64.58905792236328, 52.724239349365234, 134.30184936523438, 151.9461669921875, 40.66465759277344, 91.28252410888672, 85.38662719726562, 56.5601806640625, 48.69857406616211, 33.826045989990234, 97.2281723022461, 74.2501449584961, 26.913663864135742, 36.647090911865234, 71.69600677490234, 59.33668899536133, 67.4370346069336, 43.43296813964844, 24.639249801635742, 12.873306274414062, 16.749040603637695, 10.861212730407715, 70.08641052246094, 26.653236389160156, 28.595115661621094, 38.519832611083984, 22.659400939941406, 19.709699630737305, 12.803174018859863, 64.58951568603516, 299.6387634277344, 67.12348175048828, 61.1304817199707, 99.10858154296875, 49.007354736328125, 44.75455093383789, 82.32842254638672, 61.108245849609375, 170.71337890625, 130.26870727539062, 177.90895080566406, 84.23268127441406, 108.07762908935547, 130.50436401367188, 158.69699096679688, 114.06436157226562, 298.3820495605469, 188.2340850830078, 119.27388763427734, 83.10816955566406, 439.84429931640625, 66.70293426513672, 166.3784942626953, 15.479032516479492, 103.44459533691406, 45.7426643371582, 43.80038833618164, 86.30043029785156, 12.587958335876465, 75.10738372802734, 23.29583740234375, 36.8965950012207, 139.75816345214844, 69.05543518066406, 29.12025260925293, 176.3949737548828, 39.75480651855469, 32.063804626464844, 35.90005111694336, 55.29698944091797, 37.7317008972168, 30.21373748779297, 85.64877319335938, 31.99787139892578, 11.737396240234375, 15.577301025390625, 14.505105018615723, 14.466899871826172, 18.45528793334961, 117.74325561523438, 29.10419273376465, 49.88064193725586, 211.90480041503906, 299.5947265625, 129.01268005371094, 96.7509765625, 97.25120544433594, 229.8066864013672, 298.3820495605469, 77.76272583007812, 115.15129852294922, 112.63289642333984, 122.52336883544922, 149.05624389648438, 108.72478485107422, 104.15843963623047, 73.711669921875, 315.40753173828125, 228.86422729492188, 65.29268646240234, 34.56208038330078, 225.00180053710938, 67.36164855957031, 159.22804260253906, 188.2340850830078, 18.868810653686523, 52.58283996582031, 17.974523544311523, 17.883087158203125, 46.14445114135742, 42.505340576171875, 89.74333190917969, 75.3482437133789, 43.30839920043945, 17.895666122436523, 115.01866912841797, 61.47138214111328, 50.18857955932617, 72.08526611328125, 54.17751693725586, 32.28063201904297, 182.6707000732422, 99.41706848144531, 57.748504638671875, 19.01488494873047, 38.81336212158203, 52.08256912231445, 40.87776184082031, 252.63258361816406, 90.05150604248047, 74.12081146240234, 130.841552734375, 162.46951293945312, 149.1470489501953, 91.51105499267578, 192.0177459716797, 104.50949096679688, 89.4386215209961, 85.33280944824219, 102.49589538574219, 80.42557525634766, 50.179019927978516, 77.3391342163086, 37.67597579956055, 15.464630126953125, 86.71893310546875, 49.51329040527344, 13.552352905273438, 81.2746810913086, 29.011180877685547, 17.484752655029297, 17.381031036376953, 25.160276412963867, 18.521953582763672, 59.82355499267578, 91.69815826416016, 14.520742416381836, 195.07879638671875, 21.650440216064453, 27.140369415283203, 12.573400497436523, 24.25316619873047, 26.283828735351562, 38.90825653076172, 13.603294372558594, 22.358192443847656, 12.550125122070312, 13.527190208435059, 13.663455963134766, 90.84918212890625, 14.608694076538086, 22.422595977783203, 81.56116485595703, 82.38980102539062, 82.98115539550781, 53.465293884277344, 110.23140716552734, 147.93096923828125, 266.8935546875, 83.3425521850586, 194.7273712158203, 158.69699096679688, 176.50439453125, 50.66725158691406, 52.58318328857422, 96.7509765625, 106.79962921142578, 73.97834777832031, 82.11122131347656, 114.1574478149414, 121.46592712402344, 192.0177459716797, 119.70750427246094, 439.84429931640625, 179.289794921875], \"loglift\": [30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 1.9230999946594238, 1.9179999828338623, 1.9148999452590942, 1.9106999635696411, 1.8779000043869019, 1.8558000326156616, 1.850100040435791, 1.8359999656677246, 1.8320000171661377, 1.8118000030517578, 1.8100999593734741, 1.8077000379562378, 1.8035000562667847, 1.7999000549316406, 1.7962000370025635, 1.7870999574661255, 1.7727999687194824, 1.75, 1.7389999628067017, 1.7339999675750732, 1.7210999727249146, 1.7209999561309814, 1.7145999670028687, 1.7141000032424927, 1.7032999992370605, 1.6917999982833862, 1.6806000471115112, 1.6778000593185425, 1.6739000082015991, 1.6678999662399292, 1.6002999544143677, 1.6410000324249268, 1.621500015258789, 1.6236000061035156, 1.5362999439239502, 1.544800043106079, 1.6240999698638916, 1.3329999446868896, 1.4084999561309814, 1.2352999448776245, 1.5394999980926514, 1.6166000366210938, 1.135599970817566, 1.454200029373169, 1.3868999481201172, 1.3162000179290771, 1.2585999965667725, 1.2414000034332275, 1.117400050163269, 1.2110999822616577, 0.48030000925064087, 1.148300051689148, 1.1267000436782837, 0.5720000267028809, 0.45489999651908875, 1.975000023841858, 1.9744999408721924, 1.9283000230789185, 1.9148000478744507, 1.892899990081787, 1.88510000705719, 1.8842999935150146, 1.8214000463485718, 1.8121000528335571, 1.8068000078201294, 1.8039000034332275, 1.7989000082015991, 1.7944999933242798, 1.7943999767303467, 1.7843999862670898, 1.7759000062942505, 1.7732000350952148, 1.7552000284194946, 1.7384999990463257, 1.7352999448776245, 1.7319999933242798, 1.7309000492095947, 1.729099988937378, 1.726699948310852, 1.7263000011444092, 1.7253999710083008, 1.725100040435791, 1.7174999713897705, 1.7031999826431274, 1.7026000022888184, 1.6790000200271606, 1.6160000562667847, 1.667199969291687, 1.6186000108718872, 1.680999994277954, 1.5886000394821167, 1.5135999917984009, 1.4500999450683594, 1.5836999416351318, 1.37909996509552, 1.4199999570846558, 1.3127000331878662, 1.5759999752044678, 1.5724999904632568, 1.5103000402450562, 1.2948999404907227, 1.4437999725341797, 1.2293000221252441, 1.225000023841858, 1.0206999778747559, 1.243299961090088, 1.0764000415802002, 1.0654000043869019, 1.0335999727249146, 0.7272999882698059, 1.0022000074386597, 1.9746999740600586, 1.9062000513076782, 1.8980000019073486, 1.8910000324249268, 1.8858000040054321, 1.857300043106079, 1.857100009918213, 1.850000023841858, 1.839400053024292, 1.8238999843597412, 1.8085999488830566, 1.8006000518798828, 1.7950999736785889, 1.7905999422073364, 1.7892999649047852, 1.7865999937057495, 1.7769999504089355, 1.7597999572753906, 1.7545000314712524, 1.7506999969482422, 1.7490999698638916, 1.7446000576019287, 1.7424999475479126, 1.738800048828125, 1.7354999780654907, 1.7257000207901, 1.718500018119812, 1.7131999731063843, 1.7059999704360962, 1.7039999961853027, 1.7031999826431274, 1.659999966621399, 1.6723999977111816, 1.6481000185012817, 1.6440999507904053, 1.5444999933242798, 1.440000057220459, 1.4979000091552734, 1.406000018119812, 1.306399941444397, 1.4829000234603882, 1.1928000450134277, 1.4012999534606934, 1.1324000358581543, 1.0724999904632568, 1.2269999980926514, 1.135699987411499, 1.2070000171661377, 1.274999976158142, 1.2446000576019287, 1.4332000017166138, 1.426200032234192, 1.1269999742507935, 1.120300054550171, 1.3142000436782837, 1.2210999727249146, 0.6262999773025513, 0.9577999711036682, 1.124500036239624, 1.1734999418258667, 0.4244000017642975, 0.5726000070571899, 2.099400043487549, 2.0959999561309814, 2.091900110244751, 2.0876998901367188, 2.0799999237060547, 2.06030011177063, 2.0517001152038574, 2.0504000186920166, 2.035399913787842, 2.024399995803833, 2.0197999477386475, 2.0160000324249268, 2.003200054168701, 1.988800048828125, 1.9875999689102173, 1.979200005531311, 1.9703999757766724, 1.9494999647140503, 1.9394999742507935, 1.9359999895095825, 1.9112000465393066, 1.9093999862670898, 1.9088000059127808, 1.8854000568389893, 1.8738000392913818, 1.8717999458312988, 1.868399977684021, 1.8554999828338623, 1.8459999561309814, 1.8437000513076782, 1.832800030708313, 1.8158999681472778, 1.802299976348877, 1.7457000017166138, 1.688599944114685, 1.6823999881744385, 1.6023999452590942, 1.4491000175476074, 1.7063000202178955, 1.7609000205993652, 1.6180000305175781, 1.6819000244140625, 1.3964999914169312, 1.5644999742507935, 0.7283999919891357, 1.1973999738693237, 1.0401999950408936, 0.8245999813079834, 2.1029000282287598, 2.0692999362945557, 2.0552000999450684, 2.053299903869629, 2.0460000038146973, 2.0139000415802, 1.981600046157837, 1.9563000202178955, 1.9479999542236328, 1.9417999982833862, 1.9234999418258667, 1.9124000072479248, 1.9057999849319458, 1.9027999639511108, 1.8982000350952148, 1.8743000030517578, 1.8693000078201294, 1.8287999629974365, 1.8207000494003296, 1.8180999755859375, 1.8150999546051025, 1.7914999723434448, 1.777899980545044, 1.7760000228881836, 1.7664999961853027, 1.759600043296814, 1.7562999725341797, 1.743399977684021, 1.7415000200271606, 1.7386000156402588, 1.7164000272750854, 1.71589994430542, 1.7001999616622925, 1.5151000022888184, 1.656000018119812, 1.5484999418258667, 1.167199969291687, 1.6313999891281128, 1.2467999458312988, 1.101699948310852, 1.0296000242233276, 1.2933000326156616, 1.2101000547409058, 1.4434000253677368, 1.1726000308990479, 0.7099999785423279, 1.106600046157837, 1.0719000101089478, 0.5403000116348267, 0.6779000163078308, 2.2381999492645264, 2.2339999675750732, 2.2314000129699707, 2.215100049972534, 2.208699941635132, 2.203200101852417, 2.200500011444092, 2.1791000366210938, 2.169800043106079, 2.135499954223633, 2.11899995803833, 2.1182000637054443, 2.0985000133514404, 2.0941998958587646, 2.0873000621795654, 2.074899911880493, 2.063699960708618, 2.0611000061035156, 2.0311999320983887, 2.014400005340576, 2.013700008392334, 2.011399984359741, 2.0053999423980713, 1.9895999431610107, 1.9812999963760376, 1.9781999588012695, 1.9739999771118164, 1.9663000106811523, 1.9529999494552612, 1.9473999738693237, 1.9007999897003174, 1.850100040435791, 1.8595999479293823, 1.843400001525879, 1.7545000314712524, 1.7854000329971313, 1.8353999853134155, 1.8066999912261963, 1.8839000463485718, 1.832800030708313, 1.8472000360488892, 1.7812999486923218, 1.6818000078201294, 1.8016999959945679, 1.8926000595092773, 1.6311999559402466, 1.5232000350952148, 1.6705000400543213, 1.3526999950408936, 1.364300012588501, 1.2258000373840332, 1.3343000411987305, 1.291200041770935, 1.4190000295639038, 0.9197999835014343, 2.3480000495910645, 2.3127999305725098, 2.271399974822998, 2.262200117111206, 2.2534000873565674, 2.2444000244140625, 2.2432000637054443, 2.188800096511841, 2.1714999675750732, 2.153599977493286, 2.1447999477386475, 2.1092000007629395, 2.101300001144409, 2.069000005722046, 2.0615999698638916, 2.055799961090088, 2.0308001041412354, 2.0081000328063965, 1.9986000061035156, 1.9931000471115112, 1.9758000373840332, 1.9752999544143677, 1.9666999578475952, 1.9536999464035034, 1.9443000555038452, 1.9434000253677368, 1.9392000436782837, 1.9378999471664429, 1.9300999641418457, 1.9220999479293823, 1.8626999855041504, 1.8372000455856323, 1.80840003490448, 1.6718000173568726, 1.746500015258789, 1.7509000301361084, 1.5728000402450562, 1.652400016784668, 1.2569999694824219, 1.340999960899353, 1.155400037765503, 1.4819999933242798, 1.2585999965667725, 1.132099986076355, 0.9514999985694885, 1.1993000507354736, 0.41130000352859497, 0.7840999960899353, 1.149999976158142, 2.4647998809814453, 2.3666999340057373, 2.365299940109253, 2.335599899291992, 2.3215999603271484, 2.3117001056671143, 2.254199981689453, 2.2411999702453613, 2.171799898147583, 2.1693999767303467, 2.167799949645996, 2.1582999229431152, 2.148900032043457, 2.123500108718872, 2.1103999614715576, 2.0971999168395996, 2.096400022506714, 2.0889999866485596, 2.0882999897003174, 2.0855000019073486, 2.0690999031066895, 2.0671000480651855, 2.062000036239624, 2.0281999111175537, 2.024899959564209, 2.0143001079559326, 1.979699969291687, 1.972599983215332, 1.972599983215332, 1.9678000211715698, 1.9585000276565552, 1.9673999547958374, 1.9345999956130981, 1.8413000106811523, 1.8142000436782837, 1.7151999473571777, 1.7258000373840332, 1.7192000150680542, 1.3418999910354614, 1.2199000120162964, 1.6793999671936035, 1.511299967765808, 1.4330999851226807, 1.3554999828338623, 1.2199000120162964, 1.2847000360488892, 1.2656999826431274, 1.558500051498413, 2.918299913406372, 2.884399890899658, 2.871299982070923, 2.824199914932251, 2.786099910736084, 2.769700050354004, 2.74780011177063, 2.7453999519348145, 2.7009999752044678, 2.675800085067749, 2.6679000854492188, 2.6364998817443848, 2.633699893951416, 2.62280011177063, 2.613800048828125, 2.6110999584198, 2.5943000316619873, 2.591200113296509, 2.5594000816345215, 2.5550999641418457, 2.5439000129699707, 2.526900053024292, 2.512399911880493, 2.4830000400543213, 2.470400094985962, 2.4632999897003174, 2.4605000019073486, 2.4514999389648438, 2.4504001140594482, 2.446500062942505, 2.4379000663757324, 2.378000020980835, 2.385499954223633, 2.2983999252319336, 2.121999979019165, 2.024199962615967, 2.0209999084472656, 2.1751999855041504, 1.8406000137329102, 2.108299970626831, 2.143199920654297, 2.1177000999450684, 1.9127000570297241, 2.117500066757202, 3.0211000442504883, 3.0185999870300293, 2.9447999000549316, 2.7528998851776123, 2.6870999336242676, 2.6779000759124756, 2.6545000076293945, 2.6064000129699707, 2.6038999557495117, 2.591200113296509, 2.5794999599456787, 2.532900094985962, 2.526700019836426, 2.5048999786376953, 2.4746999740600586, 2.4514999389648438, 2.449399948120117, 2.4439001083374023, 2.4119999408721924, 2.409899950027466, 2.3980000019073486, 2.393699884414673, 2.3701999187469482, 2.367300033569336, 2.347100019454956, 2.328000068664551, 2.3243000507354736, 2.316999912261963, 2.3111000061035156, 2.2959001064300537, 2.2890000343322754, 2.259999990463257, 2.2263998985290527, 2.1953999996185303, 2.178999900817871, 2.0824999809265137, 2.018699884414673, 1.882599949836731, 2.0790998935699463, 1.8665000200271606, 1.8825000524520874, 1.8248000144958496, 2.1768999099731445, 2.107800006866455, 1.8493000268936157, 1.767699956893921, 1.86899995803833, 1.7317999601364136, 1.5027999877929688, 1.4378999471664429, 1.0480999946594238, 1.4138000011444092, 0.14730000495910645, 0.9538000226020813], \"logprob\": [30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, -4.857800006866455, -4.900899887084961, -6.636300086975098, -5.82450008392334, -7.323800086975098, -6.36359977722168, -4.778900146484375, -6.903900146484375, -5.759500026702881, -5.2866997718811035, -6.880199909210205, -6.473499774932861, -5.304200172424316, -5.555200099945068, -6.251999855041504, -5.708099842071533, -7.428500175476074, -6.756899833679199, -5.820799827575684, -6.513899803161621, -7.128499984741211, -5.103899955749512, -6.564199924468994, -6.831999778747559, -7.343100070953369, -6.208600044250488, -7.114500045776367, -6.640399932861328, -6.920300006866455, -5.140999794006348, -4.864699840545654, -5.932799816131592, -5.668600082397461, -5.828999996185303, -5.036200046539307, -5.197999954223633, -6.07390022277832, -4.3805999755859375, -5.363399982452393, -4.720200061798096, -5.892899990081787, -6.216700077056885, -4.931399822235107, -5.896699905395508, -5.860799789428711, -5.759900093078613, -5.770599842071533, -5.785900115966797, -5.6717000007629395, -5.816500186920166, -5.4710001945495605, -5.813000202178955, -5.850200176239014, -5.810200214385986, -5.826300144195557, -5.345300197601318, -4.604000091552734, -6.01140022277832, -5.8516998291015625, -5.710999965667725, -6.084400177001953, -6.078199863433838, -5.746799945831299, -6.498300075531006, -6.688600063323975, -5.923099994659424, -7.045300006866455, -7.400300025939941, -6.448999881744385, -6.491600036621094, -7.064300060272217, -6.536900043487549, -7.087299823760986, -7.374899864196777, -6.76669979095459, -5.479300022125244, -5.929699897766113, -6.953499794006348, -6.394800186157227, -7.467100143432617, -7.179500102996826, -5.825500011444092, -6.464399814605713, -6.001200199127197, -6.0971999168396, -5.9664998054504395, -4.848899841308594, -6.017000198364258, -5.424300193786621, -6.282700061798096, -5.546199798583984, -5.457399845123291, -5.380000114440918, -6.052000045776367, -5.135000228881836, -5.364099979400635, -5.20359992980957, -6.017199993133545, -6.046800136566162, -5.945499897003174, -5.531700134277344, -5.8277997970581055, -5.588200092315674, -5.7519001960754395, -5.6296000480651855, -5.819499969482422, -5.831200122833252, -5.867400169372559, -5.919400215148926, -5.929800033569336, -5.949100017547607, -5.547800064086914, -6.487199783325195, -5.578800201416016, -5.790599822998047, -4.876399993896484, -6.9028000831604, -7.39769983291626, -6.164400100708008, -6.378200054168701, -6.362199783325195, -6.206500053405762, -6.384500026702881, -5.014400005340576, -5.2195000648498535, -6.820499897003174, -6.311600208282471, -6.186500072479248, -6.257599830627441, -6.2133002281188965, -7.4197998046875, -6.443399906158447, -6.510799884796143, -7.0218000411987305, -6.98330020904541, -6.744200229644775, -6.803500175476074, -6.385000228881836, -7.166299819946289, -7.313899993896484, -7.180799961090088, -6.423900127410889, -5.664599895477295, -6.224800109863281, -5.882599830627441, -6.206699848175049, -5.422399997711182, -5.170499801635742, -5.765699863433838, -5.411600112915039, -5.561500072479248, -6.052700042724609, -5.457600116729736, -5.887599945068359, -5.431300163269043, -5.447199821472168, -5.73199987411499, -5.646699905395508, -5.735099792480469, -5.817599773406982, -5.833700180053711, -6.009699821472168, -6.016200065612793, -5.795499801635742, -5.803699970245361, -5.964600086212158, -5.9253997802734375, -5.67140007019043, -5.838600158691406, -5.9197001457214355, -5.936100006103516, -5.845799922943115, -5.900000095367432, -5.520899772644043, -5.756100177764893, -5.618299961090088, -4.630300045013428, -4.6956000328063965, -4.455999851226807, -4.858699798583984, -4.948599815368652, -5.286499977111816, -7.171899795532227, -6.134500026702881, -4.774099826812744, -6.276400089263916, -4.768799781799316, -5.905700206756592, -6.99459981918335, -5.489299774169922, -6.094099998474121, -5.804599761962891, -6.246099948883057, -5.897200107574463, -5.6596999168396, -4.672699928283691, -4.418799877166748, -5.2631001472473145, -6.253900051116943, -7.038700103759766, -6.493299961090088, -6.468800067901611, -5.884500026702881, -4.824100017547607, -4.566199779510498, -5.180600166320801, -5.309800148010254, -4.987400054931641, -5.171800136566162, -4.760200023651123, -4.821100234985352, -5.615900039672852, -5.729499816894531, -5.511199951171875, -5.6427998542785645, -5.3821001052856445, -5.60129976272583, -5.223100185394287, -5.502799987792969, -5.56279993057251, -5.557700157165527, -6.684000015258789, -5.801199913024902, -6.4039998054504395, -6.013500213623047, -5.455399990081787, -7.027900218963623, -5.265999794006348, -5.087399959564209, -7.092299938201904, -5.621500015258789, -5.781499862670898, -6.103499889373779, -6.690999984741211, -5.169899940490723, -5.7540998458862305, -5.310800075531006, -5.663700103759766, -7.36899995803833, -7.3649001121521, -5.527100086212158, -5.676000118255615, -7.177999973297119, -7.417600154876709, -6.344399929046631, -6.967800140380859, -6.254199981689453, -7.031700134277344, -6.437399864196777, -5.25540018081665, -4.542699813842773, -5.098700046539307, -5.543499946594238, -6.257699966430664, -4.927599906921387, -6.112599849700928, -5.435699939727783, -4.54640007019043, -6.075099945068359, -5.363699913024902, -5.3709001541137695, -5.484300136566162, -5.8078999519348145, -5.792900085449219, -6.023200035095215, -5.887800216674805, -5.652500152587891, -5.8769001960754395, -5.917699813842773, -5.855999946594238, -5.885499954223633, -6.176300048828125, -6.729300022125244, -6.261600017547607, -6.2393999099731445, -7.065000057220459, -6.42609977722168, -6.06879997253418, -6.927999973297119, -4.4334001541137695, -6.5971999168396, -5.457099914550781, -4.702099800109863, -6.683800220489502, -4.284299850463867, -7.104700088500977, -7.200300216674805, -5.6194000244140625, -6.617000102996826, -6.087299823760986, -6.102099895477295, -6.328000068664551, -6.331900119781494, -7.032100200653076, -5.748300075531006, -7.127299785614014, -6.561500072479248, -6.298099994659424, -6.61460018157959, -5.01230001449585, -4.843599796295166, -4.371099948883057, -4.366499900817871, -4.886600017547607, -4.957099914550781, -4.669400215148926, -4.907400131225586, -5.317599773406982, -5.371600151062012, -5.761600017547607, -5.581999778747559, -5.680500030517578, -5.6915998458862305, -5.439000129699707, -5.80679988861084, -6.064700126647949, -5.563199996948242, -5.480199813842773, -5.690700054168701, -5.446100234985352, -5.520999908447266, -5.460000038146973, -5.547500133514404, -5.541100025177002, -5.703800201416016, -5.729700088500977, -5.137800216674805, -5.375999927520752, -4.4822998046875, -4.368100166320801, -5.695099830627441, -4.895500183105469, -4.963500022888184, -5.429800033569336, -5.596700191497803, -5.979000091552734, -4.932000160217285, -5.237199783325195, -6.259900093078613, -5.983500003814697, -5.319799900054932, -5.514900207519531, -5.411900043487549, -5.874599933624268, -6.450900077819824, -7.105599880218506, -6.8597002029418945, -7.293399810791016, -5.4375, -6.417200088500977, -6.356299877166748, -6.059199810028076, -6.594099998474121, -6.734799861907959, -7.173999786376953, -5.563700199127197, -4.088500022888184, -5.610099792480469, -5.732399940490723, -5.385799884796143, -6.015399932861328, -6.101799964904785, -5.670400142669678, -5.888800144195557, -5.256899833679199, -5.443299770355225, -5.317200183868408, -5.73829984664917, -5.712399959564209, -5.650400161743164, -5.63539981842041, -5.717800140380859, -5.544099807739258, -5.631999969482422, -5.722400188446045, -4.768899917602539, -3.2007999420166016, -5.088399887084961, -4.203999996185303, -6.592800140380859, -4.703100204467773, -5.576700210571289, -5.632999897003174, -5.024199962615967, -6.9517998695373535, -5.167099952697754, -6.347300052642822, -5.8968000411987305, -4.5904998779296875, -5.308599948883057, -6.185200214385986, -4.384699821472168, -5.8821001052856445, -6.097799777984619, -5.987599849700928, -5.572000026702881, -5.956299781799316, -6.183599948883057, -5.1753997802734375, -6.163300037384033, -7.176799774169922, -6.928400039672852, -7.006700038909912, -7.009399890899658, -6.770699977874756, -4.9268999099731445, -6.3155999183654785, -5.809599876403809, -4.456399917602539, -4.13730001449585, -5.078800201416016, -5.355899810791016, -5.357399940490723, -4.874800205230713, -4.735599994659424, -5.620800018310547, -5.396299839019775, -5.496600151062012, -5.490099906921387, -5.429699897766113, -5.680300235748291, -5.742199897766113, -5.795199871063232, -2.981600046157837, -3.3362998962402344, -4.603600025177002, -5.286900043487549, -3.4516000747680664, -4.674099922180176, -3.835700035095215, -3.670799970626831, -6.0152997970581055, -5.015699863433838, -6.0970001220703125, -6.133500099182129, -5.188399791717529, -5.281400203704834, -4.543099880218506, -4.720600128173828, -5.291200160980225, -6.178100109100342, -4.349400043487549, -4.980199813842773, -5.194200038909912, -4.849100112915039, -5.149199962615967, -5.696400165557861, -3.975800037384033, -4.59119987487793, -5.13730001449585, -6.2571001052856445, -5.5447001457214355, -5.254499912261963, -5.50540018081665, -3.74399995803833, -4.767899990081787, -5.049799919128418, -4.657800197601318, -4.539100170135498, -4.627900123596191, -4.962200164794922, -4.555600166320801, -4.896299839019775, -5.017099857330322, -5.089600086212158, -5.111299991607666, -5.14900016784668, -4.717199802398682, -4.287099838256836, -5.079999923706055, -6.162399768829346, -4.5040998458862305, -5.073699951171875, -6.3927998542785645, -4.649600028991699, -5.682300090789795, -6.201300144195557, -6.218999862670898, -5.895699977874756, -6.208199977874756, -5.057600021362305, -4.660699844360352, -6.526700019836426, -3.930999994277954, -6.134900093078613, -5.940800189971924, -6.712399959564209, -6.067299842834473, -5.991199970245361, -5.622499942779541, -6.676199913024902, -6.19950008392334, -6.79610013961792, -6.724899768829346, -6.722099781036377, -4.833600044250488, -6.676300048828125, -6.254799842834473, -4.992499828338623, -5.015999794006348, -5.03980016708374, -5.4959001541137695, -4.868800163269043, -4.638400077819824, -4.184500217437744, -5.151800155639648, -4.5157999992370605, -4.704400062561035, -4.655700206756592, -5.551700115203857, -5.583600044250488, -5.232399940490723, -5.215199947357178, -5.481100082397461, -5.513999938964844, -5.41349983215332, -5.416399955749512, -5.348199844360352, -5.454999923706055, -5.420100212097168, -5.511099815368652]}, \"token.table\": {\"Topic\": [5, 6, 1, 4, 5, 6, 8, 10, 2, 3, 5, 6, 7, 8, 9, 10, 5, 6, 9, 10, 2, 8, 2, 5, 6, 8, 1, 2, 3, 4, 5, 6, 7, 9, 10, 3, 5, 9, 2, 4, 1, 2, 3, 5, 6, 8, 9, 10, 1, 6, 10, 1, 2, 1, 3, 2, 3, 6, 3, 9, 1, 2, 3, 4, 5, 8, 9, 1, 2, 3, 5, 8, 10, 1, 3, 5, 10, 2, 6, 9, 3, 5, 6, 7, 9, 1, 2, 3, 5, 8, 9, 3, 7, 4, 7, 10, 1, 2, 3, 4, 5, 6, 8, 10, 2, 3, 5, 9, 10, 3, 4, 5, 6, 7, 9, 4, 6, 8, 3, 4, 6, 9, 1, 8, 10, 1, 2, 7, 1, 2, 3, 5, 7, 8, 10, 2, 3, 4, 1, 3, 4, 5, 6, 7, 8, 9, 10, 3, 4, 7, 9, 2, 3, 9, 1, 5, 6, 8, 10, 4, 5, 2, 3, 5, 9, 6, 1, 2, 3, 7, 1, 2, 3, 4, 5, 6, 8, 10, 1, 3, 4, 10, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 8, 1, 2, 3, 5, 6, 7, 8, 1, 2, 3, 5, 7, 1, 4, 5, 7, 8, 1, 6, 9, 1, 2, 4, 5, 6, 10, 2, 8, 2, 3, 5, 6, 7, 8, 1, 2, 3, 5, 6, 9, 1, 2, 3, 4, 5, 6, 7, 8, 1, 8, 1, 2, 3, 4, 5, 6, 8, 10, 4, 6, 8, 3, 5, 3, 5, 8, 1, 3, 2, 3, 4, 7, 8, 1, 2, 3, 4, 6, 9, 10, 2, 3, 4, 7, 6, 8, 2, 6, 8, 1, 2, 4, 5, 7, 10, 1, 2, 4, 5, 6, 7, 8, 1, 3, 4, 5, 7, 1, 2, 5, 7, 1, 2, 3, 5, 3, 8, 1, 2, 3, 4, 5, 1, 3, 2, 3, 4, 7, 2, 5, 2, 4, 6, 8, 9, 10, 1, 4, 7, 8, 9, 10, 2, 3, 10, 1, 3, 4, 5, 7, 9, 10, 3, 4, 6, 10, 1, 2, 3, 4, 7, 8, 9, 10, 2, 3, 4, 7, 2, 3, 5, 7, 9, 10, 1, 4, 6, 8, 10, 1, 4, 5, 6, 8, 10, 1, 2, 3, 4, 5, 6, 7, 9, 10, 1, 3, 5, 9, 10, 2, 3, 7, 9, 1, 3, 4, 6, 10, 1, 2, 3, 4, 5, 6, 7, 9, 3, 5, 1, 3, 4, 5, 7, 8, 9, 2, 3, 4, 5, 6, 7, 8, 10, 2, 6, 2, 5, 6, 7, 2, 5, 7, 1, 2, 3, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 10, 1, 4, 5, 10, 2, 3, 5, 7, 3, 9, 10, 1, 2, 3, 4, 5, 6, 8, 9, 10, 2, 4, 8, 10, 3, 8, 10, 4, 5, 9, 1, 2, 3, 5, 10, 1, 2, 3, 5, 6, 7, 10, 1, 4, 7, 1, 2, 3, 4, 5, 6, 7, 9, 1, 3, 5, 8, 9, 3, 4, 5, 8, 1, 2, 1, 2, 3, 5, 6, 8, 9, 10, 1, 6, 7, 10, 1, 5, 10, 1, 3, 4, 5, 6, 7, 9, 1, 3, 4, 6, 8, 9, 10, 3, 4, 1, 3, 4, 5, 6, 7, 9, 1, 2, 3, 4, 5, 6, 10, 2, 3, 4, 7, 3, 4, 1, 1, 2, 3, 5, 7, 8, 1, 2, 3, 4, 5, 8, 10, 1, 7, 1, 2, 3, 4, 7, 8, 1, 3, 4, 5, 6, 7, 8, 10, 2, 5, 9, 3, 6, 9, 6, 3, 5, 6, 1, 2, 4, 7, 8, 9, 2, 4, 5, 6, 7, 9, 4, 5, 10, 5, 6, 2, 3, 4, 6, 7, 9, 10, 6, 2, 4, 5, 2, 3, 4, 7, 10, 1, 2, 3, 5, 6, 10, 1, 3, 4, 10, 2, 4, 7, 1, 3, 7, 1, 2, 3, 4, 7, 8, 9, 10, 1, 5, 7, 8, 9, 1, 2, 5, 2, 3, 4, 2, 7, 9, 2, 6, 7, 1, 3, 5, 7, 8, 10, 2, 6, 9, 4, 5, 6, 2, 3, 5, 6, 7, 8, 9, 10, 3, 4, 5, 6, 7, 8, 2, 3, 1, 2, 5, 6, 1, 5, 6, 7, 8, 10, 1, 5, 8, 1, 2, 4, 6, 7, 8, 10, 1, 3, 4, 6, 9, 4, 5, 6, 8, 9, 1, 2, 4, 6, 9, 2, 3, 5, 6, 7, 8, 9, 2, 7, 8, 9, 1, 2, 3, 4, 5, 7, 8, 9, 10, 2, 5, 6, 8, 9, 1, 3, 4, 5, 7, 8, 9, 10, 1, 3, 4, 7, 9, 1, 4, 7, 1, 2, 3, 4, 5, 6, 7, 8, 10, 1, 2, 3, 4, 5, 9, 1, 3, 6, 3, 5, 6, 7, 1, 4, 5, 1, 2, 5, 1, 5, 6, 10, 1, 3, 1, 3, 4, 6, 7, 10, 4, 1, 2, 4, 7, 8, 1, 2, 3, 4, 5, 6, 8, 2, 4, 8, 3, 8, 2, 3, 4, 8, 10, 1, 2, 3, 4, 5, 6, 8, 9, 10, 3, 4, 6, 8, 1, 3, 4, 7, 8, 10, 4, 8, 1, 2, 3, 7, 9, 1, 2, 3, 4, 5, 6, 7, 10, 3, 4, 7, 10, 2, 3, 5, 6, 7, 9, 1, 2, 4, 5, 6, 7, 4, 8, 2, 4, 5, 6, 8, 9, 10, 3, 9, 4, 6, 10, 1, 2, 5, 7, 10, 3, 4, 5, 6, 7, 10, 5, 8, 2, 3, 5, 8, 10, 2, 3, 6, 9, 1, 2, 3, 4, 6, 7, 8, 9, 10, 2, 3, 4, 5, 1, 2, 3, 4, 7, 8, 9, 10, 1, 2, 3, 6, 1, 2, 6, 7, 2, 6, 10, 3, 4, 8, 6, 8, 1, 3, 4, 5, 7, 1, 2, 3, 4, 6, 8, 9, 10, 4, 5, 2, 5, 9, 1, 6, 2, 3, 6, 8, 10, 1, 5, 1, 2, 3, 6, 7, 8, 2, 3, 4, 6, 7, 8, 9, 10, 3, 6, 1, 2, 4, 7, 1, 3, 4, 2, 4, 7, 9, 1, 2, 4, 5, 6, 7, 8, 10, 3, 4, 2, 3, 4, 5, 6, 8, 9, 1, 2, 3, 4, 5, 6, 7, 9, 1, 2, 3, 4, 5, 6, 7, 9, 1, 3, 4, 5, 6, 7, 10, 2, 5, 7, 8, 2, 3, 5, 7, 10, 6, 7, 8, 1, 3, 4, 9, 10, 1, 7, 9, 1, 4, 6, 1, 2, 6, 10, 2, 3, 4, 10, 1, 3, 8, 6, 9, 4, 9, 10, 2, 4, 1, 2, 4, 8, 10, 1, 7, 10, 1, 2, 3, 4, 5, 6, 8, 10, 1, 7, 1, 2, 3, 4, 2, 3, 5, 10, 3, 5, 6, 7, 1, 5, 6, 4, 5, 6, 8, 1, 5, 10, 3, 4, 5, 7, 4, 5, 6, 10, 2, 4, 5, 4, 1, 2, 4, 5, 7, 8, 1, 3, 4, 5, 6, 8, 3, 4, 2, 3, 4, 5, 7, 8, 10, 1, 2, 3, 5, 8, 10, 8, 1, 3, 5, 6, 8, 9, 10, 1, 3, 4, 5, 7, 10, 1, 4, 5, 8, 10, 1, 2, 4, 5, 6, 7, 2, 4, 5, 6, 7, 6, 7, 1, 2, 3, 6, 1, 2, 3, 7, 1, 2, 7, 1, 3, 4, 5, 7, 8, 1, 2, 5, 6, 7, 1, 2, 3, 5, 8, 9, 1, 3, 4, 5, 7, 8, 2, 4, 5, 8, 6, 8, 2, 3, 5, 3, 5, 8, 1, 2, 4, 5, 6, 7, 8, 9, 10, 4, 7, 10, 5, 8, 1, 2, 3, 7, 1, 3, 4, 5, 6, 8, 10, 1, 5, 6, 2, 3, 5, 10, 1, 2, 3, 5, 6, 7, 10, 1, 3, 10, 1, 4, 9, 10, 1, 2, 3, 5, 7, 9, 2, 4, 5, 1, 2, 3, 5, 10, 4, 7, 8, 6, 7, 2, 9, 4, 5, 2, 3, 4, 5, 6, 7, 3, 7, 8, 2, 5, 6, 1, 2, 3, 4, 5, 6, 10, 1, 2, 4, 6, 7, 1, 2, 5, 6, 7, 8, 10, 1, 2, 3, 4, 5, 8, 9, 10, 2, 5, 7, 10, 2, 5, 7, 1, 4, 7, 10, 4, 7, 10, 2, 3, 4, 5, 6, 8, 2, 3, 4, 5, 7, 8, 9, 2, 3, 5, 7, 9, 4, 2, 3, 5, 2, 3, 7, 1, 2, 4, 5, 6, 7, 1, 2, 6, 1, 7, 2, 3, 7, 2, 3, 5, 7, 1, 2, 3, 4, 5, 6, 7, 8, 2, 4, 5, 10, 1, 2, 3, 4, 5, 6, 7, 8, 10, 1, 2, 4, 6, 2, 3, 4, 6, 7, 8, 1, 2, 4, 5, 6, 8, 10, 3, 7, 2, 5, 6, 7, 8, 1, 2, 3, 7, 1, 4, 5, 8, 10, 1, 2, 3, 4, 6, 6, 8, 10, 1, 3, 7, 2, 3, 10, 1, 4, 1, 2, 3, 4, 5, 6, 7, 10, 2, 6, 7, 10, 2, 4, 5, 8, 1, 2, 3, 4, 7, 1, 3, 4, 5, 7, 2, 3, 4, 5, 6, 8, 9, 10, 1, 2, 3, 5, 7, 6, 8, 1, 5, 7, 8, 2, 3, 4, 1, 2, 3, 5, 6, 7, 9, 2, 3, 7, 9, 2, 4, 5, 7, 9, 10, 3, 7, 8, 3, 7, 9, 2, 4, 6, 1, 2, 3, 4, 5, 7, 2, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 10, 2, 3, 4, 5, 7, 8, 9, 1, 2, 4, 5, 6, 10, 1, 6, 8, 10, 1, 4, 5, 6, 7, 8, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 3, 5, 6, 8, 9, 1, 7, 1, 2, 3, 4, 5, 6, 7, 10, 1, 3, 4, 5, 8, 10, 2, 3, 4, 5, 6, 7, 8, 9, 1, 5, 1, 2, 3, 4, 5, 6, 7, 8, 10, 1, 2, 3, 5, 6, 8, 10, 1, 2, 3, 5, 2, 6, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 10, 2, 3, 4, 5, 7, 8, 9, 10, 5, 6, 8, 2, 5, 6, 8, 2, 4, 5, 10, 2, 5, 7, 8, 3, 5, 10, 1, 2, 6, 7, 1, 2, 3, 5, 9, 10, 2, 9, 1, 2, 4, 5, 9, 1, 8, 4, 7, 10, 3, 4, 5, 10, 1, 3, 4, 5, 6, 8, 10, 2, 4, 5, 6, 3, 9, 2, 10, 1, 2, 3, 4, 5, 6, 7, 8, 1, 4, 5, 2, 3, 4, 5, 6, 7, 9, 10, 6, 7, 8, 1, 2, 4, 6, 7, 8, 10, 1, 3, 9, 2, 3, 5, 6, 7, 9, 10, 1, 2, 3, 5, 6, 7, 9, 2, 3, 5, 6, 7, 1, 2, 5, 10, 1, 3, 8, 1, 2, 3, 4, 5, 6, 1, 5, 1, 2, 3, 4, 5, 6, 7, 1, 2, 4, 5, 6, 7, 8, 10, 6, 3, 7, 9, 1, 3, 6, 8, 10, 5, 6, 7, 10, 1, 2, 3, 4, 5, 8, 2, 6, 7, 8, 1, 2, 3, 4, 6, 7, 8, 1, 3, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 8, 1, 2, 1, 2, 6, 8, 10, 1, 2, 4, 5, 7, 8, 3, 5, 6, 7, 8, 9, 10, 1, 6, 8, 3, 10, 3, 4, 6, 7, 8, 3, 5, 7, 1, 5, 2, 5, 7, 8, 9, 1, 2, 3, 4, 6, 7, 8, 9, 10, 5, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 5, 5, 9, 1, 2, 3, 4, 5, 7, 9, 10, 1, 8, 1, 2, 5, 9, 2, 5, 1, 5, 7, 4, 8, 2, 3, 6, 9, 2, 3, 4, 6, 7, 8, 1, 10, 2, 3, 5, 1, 2, 3, 4, 5, 7, 1, 4, 7, 3, 4, 5, 6, 7, 2, 3, 4, 5, 7, 10, 1, 3, 5, 6, 7, 8, 9, 2, 5, 6, 8, 2, 5, 7, 1, 2, 4, 5, 7, 1, 2, 3, 4, 5, 6, 8, 3, 5, 7, 8, 2, 3, 4, 5, 8, 1, 3, 5, 8, 9, 1, 5, 9, 2, 7, 8, 6, 9, 1, 3, 4, 5, 6, 7, 8, 9, 10, 6, 9, 1, 2, 3, 6, 7, 8, 9, 10, 4, 5, 6, 7, 2, 3, 6, 4, 5, 6, 7, 8, 1, 6, 7, 9, 2, 3, 2, 5, 7, 1, 2, 5, 7, 2, 3, 4, 5, 6, 7, 9, 3, 4, 1, 2, 3, 4, 6, 7, 10, 3, 7, 8, 1, 2, 3, 6, 7, 10, 1, 2, 1, 2, 3, 4, 5, 7, 8, 9, 10, 1, 5, 6, 7, 9, 10, 1, 2, 3, 4, 5, 6, 8, 2, 7, 2, 4, 9, 10, 3, 5, 7, 8, 3, 7, 8, 5, 7, 1, 2, 4, 5, 7, 2, 3, 5, 7, 9, 10, 1, 2, 3, 5, 7, 8, 9, 10, 1, 2, 3, 5, 6, 8, 10, 1, 2, 1, 3, 8, 9, 10, 4, 5, 7, 9, 10], \"Freq\": [0.1569114774465561, 0.706101655960083, 0.22281832993030548, 0.03713638707995415, 0.640602707862854, 0.051062535494565964, 0.013926145620644093, 0.03249434009194374, 0.05627618357539177, 0.03581211715936661, 0.0051160166040062904, 0.8185626864433289, 0.015348049812018871, 0.03581211715936661, 0.0051160166040062904, 0.030696099624037743, 0.20611459016799927, 0.10305729508399963, 0.5925794243812561, 0.10305729508399963, 0.1588820070028305, 0.7149689793586731, 0.10428684949874878, 0.06952456384897232, 0.08111199736595154, 0.7415953874588013, 0.07435900717973709, 0.2771562933921814, 0.15547792613506317, 0.02703963965177536, 0.02027972973883152, 0.06083918735384941, 0.02703963965177536, 0.0675990954041481, 0.30419594049453735, 0.5213702321052551, 0.05086538568139076, 0.4196394383907318, 0.06848559528589249, 0.8903127908706665, 0.0738237276673317, 0.04921581968665123, 0.11073559522628784, 0.0738237276673317, 0.03691186383366585, 0.04921581968665123, 0.06151977553963661, 0.5536779761314392, 0.7082279324531555, 0.05742388591170311, 0.21055424213409424, 0.8778334856033325, 0.10327452421188354, 0.7871673703193665, 0.12110267579555511, 0.05560010299086571, 0.7228013277053833, 0.16680030524730682, 0.22253718972206116, 0.7232458591461182, 0.5241725444793701, 0.09250103682279587, 0.11305682361125946, 0.11305682361125946, 0.010277893394231796, 0.14389050006866455, 0.010277893394231796, 0.16988760232925415, 0.023595500737428665, 0.24067410826683044, 0.014157300814986229, 0.52853924036026, 0.023595500737428665, 0.18422740697860718, 0.036845482885837555, 0.29476386308670044, 0.44214579463005066, 0.14613640308380127, 0.7306820154190063, 0.07306820154190063, 0.16145162284374237, 0.08072581142187119, 0.6458064913749695, 0.04843548312783241, 0.06458064913749695, 0.3685525953769684, 0.08090178668498993, 0.4314762055873871, 0.01797817461192608, 0.08989087492227554, 0.00898908730596304, 0.6862756013870239, 0.24955476820468903, 0.22065897285938263, 0.6178451180458069, 0.13239538669586182, 0.08008208870887756, 0.4885007441043854, 0.04804925620555878, 0.04804925620555878, 0.016016418114304543, 0.15215596556663513, 0.06406567245721817, 0.11211492866277695, 0.011104756034910679, 0.05552377924323082, 0.17767609655857086, 0.5552377700805664, 0.19988560676574707, 0.7697038650512695, 0.028863895684480667, 0.09621298313140869, 0.019242597743868828, 0.048106491565704346, 0.028863895684480667, 0.0874486193060875, 0.7578880190849304, 0.11659815907478333, 0.28461307287216187, 0.04553809016942978, 0.6147642135620117, 0.04553809016942978, 0.8002252578735352, 0.16671359539031982, 0.016671359539031982, 0.06012699007987976, 0.7816509008407593, 0.12025398015975952, 0.04748750478029251, 0.17807814478874207, 0.2730531692504883, 0.03561563044786453, 0.39177194237709045, 0.023743752390146255, 0.03561563044786453, 0.7083560824394226, 0.23611868917942047, 0.018162976950407028, 0.13062447309494019, 0.011874952353537083, 0.019791588187217712, 0.03166653960943222, 0.08312466740608215, 0.09499961882829666, 0.03166653960943222, 0.5502061247825623, 0.039583176374435425, 0.12014155089855194, 0.7863810658454895, 0.010921959765255451, 0.07645371556282043, 0.210361510515213, 0.15777114033699036, 0.5784941911697388, 0.10243993252515793, 0.7478115558624268, 0.08195194602012634, 0.020487986505031586, 0.03073197975754738, 0.0909809097647667, 0.8870638608932495, 0.04394931346178055, 0.7471383213996887, 0.04394931346178055, 0.1757972538471222, 0.9405075907707214, 0.13381259143352509, 0.13381259143352509, 0.6690629720687866, 0.06690629571676254, 0.6583971381187439, 0.031056469306349754, 0.031056469306349754, 0.12422587722539902, 0.006211293861269951, 0.024845175445079803, 0.09316940605640411, 0.031056469306349754, 0.07609241455793381, 0.19023104012012482, 0.30436965823173523, 0.45655450224876404, 0.05604631081223488, 0.06305209547281265, 0.00700578885152936, 0.03502894192934036, 0.01401157770305872, 0.6024978160858154, 0.21717944741249084, 0.1949857920408249, 0.02785511314868927, 0.08356533944606781, 0.6685227155685425, 0.7474405169487, 0.05507456511259079, 0.03933897614479065, 0.02360338531434536, 0.03147118166089058, 0.007867795415222645, 0.09441354125738144, 0.0755246952176094, 0.27692389488220215, 0.4783230721950531, 0.012587449513375759, 0.1510493904352188, 0.03657348453998566, 0.01828674226999283, 0.7497564554214478, 0.054860226809978485, 0.1280072033405304, 0.18247278034687042, 0.7663856744766235, 0.036494553089141846, 0.1861855834722519, 0.1354077011346817, 0.1861855834722519, 0.08462981134653091, 0.3808341324329376, 0.025388943031430244, 0.34377285838127136, 0.6250416040420532, 0.10508031398057938, 0.0583779513835907, 0.0583779513835907, 0.08172913640737534, 0.0583779513835907, 0.6421574950218201, 0.10791733860969543, 0.6475040316581726, 0.03597244620323181, 0.07194489240646362, 0.08993111550807953, 0.03597244620323181, 0.22693993151187897, 0.03337351977825165, 0.013349407352507114, 0.05006027966737747, 0.09010849893093109, 0.013349407352507114, 0.5640124678611755, 0.006674703676253557, 0.14500515162944794, 0.8506969213485718, 0.11014880239963531, 0.0033378424122929573, 0.020027054473757744, 0.25033819675445557, 0.023364897817373276, 0.020027054473757744, 0.5140277147293091, 0.05340547859668732, 0.7802997827529907, 0.035468172281980515, 0.17734086513519287, 0.25419095158576965, 0.6778425574302673, 0.14900556206703186, 0.7335658669471741, 0.1146196648478508, 0.8681771159172058, 0.05426106974482536, 0.05457459017634392, 0.05457459017634392, 0.10914918035268784, 0.6821823716163635, 0.08186188340187073, 0.11155124753713608, 0.6693074703216553, 0.04462049901485443, 0.022310249507427216, 0.02788781188428402, 0.01673268713057041, 0.10597368329763412, 0.007474188227206469, 0.07474188506603241, 0.8819541931152344, 0.029896752908825874, 0.24392494559288025, 0.7317748665809631, 0.1625550389289856, 0.21674004197120667, 0.5960351228713989, 0.7914355397224426, 0.09593158215284348, 0.05596008896827698, 0.007994298823177814, 0.03197719529271126, 0.007994298823177814, 0.11022183299064636, 0.06888864189386368, 0.6544421315193176, 0.06199977919459343, 0.07577750831842422, 0.020666593685746193, 0.013777729123830795, 0.01482864748686552, 0.08897188305854797, 0.08897188305854797, 0.11862917989492416, 0.667289137840271, 0.08115383982658386, 0.09738460928201675, 0.7303845882415771, 0.08115383982658386, 0.33725666999816895, 0.09485343843698502, 0.1475497931241989, 0.411031574010849, 0.1748914271593094, 0.8088728785514832, 0.04449981823563576, 0.029666544869542122, 0.5339977741241455, 0.26699888706207275, 0.11866617947816849, 0.2509790360927582, 0.7529371380805969, 0.08568112552165985, 0.7711301445960999, 0.05712075158953667, 0.05712075158953667, 0.26977208256721497, 0.6474530100822449, 0.013179163448512554, 0.645779013633728, 0.23722493648529053, 0.013179163448512554, 0.05271665379405022, 0.03953748941421509, 0.13787788152694702, 0.034469470381736755, 0.13787788152694702, 0.10340840369462967, 0.034469470381736755, 0.5515115261077881, 0.7926235795021057, 0.06605196744203568, 0.09907794743776321, 0.0974288284778595, 0.2313934713602066, 0.15832184255123138, 0.07307162135839462, 0.12178603559732437, 0.07307162135839462, 0.2313934713602066, 0.66389000415802, 0.21050171554088593, 0.08096219599246979, 0.032384879887104034, 0.10970334708690643, 0.04570972919464111, 0.43881338834762573, 0.15541307628154755, 0.07313556969165802, 0.08227751404047012, 0.05485167354345322, 0.03656778484582901, 0.04467548429965973, 0.0670132264494896, 0.02978365495800972, 0.8488341569900513, 0.04858589544892311, 0.08502531796693802, 0.04858589544892311, 0.4251265823841095, 0.3522477447986603, 0.04858589544892311, 0.03637650981545448, 0.00454706372693181, 0.01591472327709198, 0.8957715034484863, 0.04774416610598564, 0.07235068827867508, 0.051679063588380814, 0.10335812717676163, 0.031007438898086548, 0.4754473865032196, 0.25839531421661377, 0.016109563410282135, 0.19331476092338562, 0.16915041208267212, 0.016109563410282135, 0.08860260248184204, 0.36246517300605774, 0.10471216589212418, 0.008054781705141068, 0.024164345115423203, 0.028712153434753418, 0.7178038358688354, 0.11484861373901367, 0.028712153434753418, 0.08613646030426025, 0.21684829890727997, 0.0929349809885025, 0.061956655234098434, 0.6195665597915649, 0.4374229609966278, 0.17683055996894836, 0.2140580415725708, 0.1023755818605423, 0.06514810025691986, 0.015482388436794281, 0.0774119421839714, 0.20127105712890625, 0.030964776873588562, 0.030964776873588562, 0.04644716531038284, 0.6038131713867188, 0.015482388436794281, 0.7511440515518188, 0.16692090034484863, 0.07715778797864914, 0.0900174230337143, 0.21861372888088226, 0.012859631329774857, 0.0900174230337143, 0.4500871002674103, 0.06429816037416458, 0.07007864862680435, 0.05255898833274841, 0.3416334390640259, 0.13139747083187103, 0.06131881847977638, 0.07883848249912262, 0.07883848249912262, 0.18395645916461945, 0.07833794504404068, 0.8617173433303833, 0.09256965667009354, 0.09256965667009354, 0.7405572533607483, 0.04628482833504677, 0.7679275870323181, 0.12798793613910675, 0.06399396806955338, 0.10744544863700867, 0.5525766015052795, 0.09209609776735306, 0.20721623301506042, 0.023024024441838264, 0.015349349938333035, 0.04496682062745094, 0.028104262426495552, 0.2304549664258957, 0.03934596851468086, 0.3372511565685272, 0.01686255820095539, 0.2810426354408264, 0.011241705156862736, 0.011241705156862736, 0.6972829103469849, 0.25660011172294617, 0.005578263662755489, 0.03346958011388779, 0.031188108026981354, 0.810890793800354, 0.09356432408094406, 0.06237621605396271, 0.029690485447645187, 0.8164883255958557, 0.14845243096351624, 0.10699965059757233, 0.08407115191221237, 0.08407115191221237, 0.04585698992013931, 0.03821415826678276, 0.04585698992013931, 0.15285663306713104, 0.4279986023902893, 0.015285664238035679, 0.011743111535906792, 0.9453204870223999, 0.017614668235182762, 0.017614668235182762, 0.7532862424850464, 0.0941607803106308, 0.12554770708084106, 0.9689056277275085, 0.8072317838668823, 0.07338470220565796, 0.7269991040229797, 0.03381391242146492, 0.01690695621073246, 0.1690695583820343, 0.05072086676955223, 0.03149032220244408, 0.05510806664824486, 0.779385507106781, 0.06298064440488815, 0.03149032220244408, 0.023617742583155632, 0.01574516110122204, 0.13814391195774078, 0.18419188261032104, 0.6446715593338013, 0.03308430686593056, 0.19850583374500275, 0.4411240816116333, 0.06616861373186111, 0.03308430686593056, 0.03308430686593056, 0.08822481334209442, 0.09925291687250137, 0.03487551584839821, 0.012682005763053894, 0.0031705014407634735, 0.006341002881526947, 0.9416388869285583, 0.11681495606899261, 0.08761121332645416, 0.6716859936714172, 0.11681495606899261, 0.17102497816085815, 0.7696123719215393, 0.07099677622318268, 0.4564078450202942, 0.1014239713549614, 0.08113917708396912, 0.1318511664867401, 0.07099677622318268, 0.0507119856774807, 0.04056958854198456, 0.07962633669376373, 0.07962633669376373, 0.053084224462509155, 0.769721269607544, 0.06466368585824966, 0.25865474343299866, 0.6466368436813354, 0.14428773522377014, 0.06659433990716934, 0.08879245072603226, 0.011099056340754032, 0.6326462030410767, 0.022198112681508064, 0.04439622536301613, 0.08651931583881378, 0.057679541409015656, 0.057679541409015656, 0.6344749331474304, 0.028839770704507828, 0.057679541409015656, 0.07209942489862442, 0.8825376033782959, 0.07674240320920944, 0.11190470308065414, 0.0746031329035759, 0.03730156645178795, 0.23624326288700104, 0.08703698962926865, 0.03730156645178795, 0.4227510988712311, 0.15193210542201996, 0.009495756588876247, 0.018991513177752495, 0.0854618102312088, 0.6362156867980957, 0.018991513177752495, 0.06647029519081116, 0.049928877502679825, 0.7738975882530212, 0.049928877502679825, 0.12482219189405441, 0.14032259583473206, 0.8419355750083923, 0.9471027851104736, 0.06010017916560173, 0.6010017991065979, 0.07512522488832474, 0.15025044977664948, 0.07512522488832474, 0.030050089582800865, 0.09955933690071106, 0.06969153881072998, 0.39823734760284424, 0.08960340172052383, 0.10951527208089828, 0.21903054416179657, 0.0199118684977293, 0.804643988609314, 0.1788097769021988, 0.1708695888519287, 0.19443780183792114, 0.37709149718284607, 0.053028494119644165, 0.09427287429571152, 0.10605698823928833, 0.47000694274902344, 0.05875086784362793, 0.06854268163442612, 0.06854268163442612, 0.10770992189645767, 0.029375433921813965, 0.04895905777812004, 0.13708536326885223, 0.8885994553565979, 0.03484703600406647, 0.06969407200813293, 0.047052912414073944, 0.23526455461978912, 0.7057936787605286, 0.932546079158783, 0.029091596603393555, 0.20364117622375488, 0.7563815116882324, 0.6604554057121277, 0.014676786959171295, 0.05870714783668518, 0.22015181183815002, 0.02935357391834259, 0.014676786959171295, 0.06562374532222748, 0.06562374532222748, 0.5249899625778198, 0.25312018394470215, 0.056248925626277924, 0.018749643117189407, 0.7303395867347717, 0.028089985251426697, 0.23408320546150208, 0.09267409145832062, 0.8340668082237244, 0.043621376156806946, 0.08724275231361389, 0.06543206423521042, 0.05452672392129898, 0.22901223599910736, 0.021810688078403473, 0.4798351526260376, 0.9253183007240295, 0.7453452944755554, 0.1242242157459259, 0.09316816180944443, 0.36287298798561096, 0.1179337203502655, 0.14514920115470886, 0.04535912349820137, 0.32658568024635315, 0.059651654213666916, 0.6760520935058594, 0.059651654213666916, 0.019883884117007256, 0.07953553646802902, 0.09941942244768143, 0.7961975932121277, 0.1326996088027954, 0.03317490220069885, 0.03317490220069885, 0.9262714982032776, 0.024375565350055695, 0.04875113070011139, 0.031155088916420937, 0.7788772583007812, 0.15577544271945953, 0.5762571692466736, 0.05307631939649582, 0.022746993228793144, 0.18197594583034515, 0.06065864861011505, 0.030329324305057526, 0.037911657243967056, 0.030329324305057526, 0.1536022573709488, 0.0384005643427372, 0.1152016893029213, 0.09600140899419785, 0.5952087044715881, 0.051240477710962296, 0.7686071395874023, 0.15372143685817719, 0.7766785025596619, 0.035303570330142975, 0.1412142813205719, 0.12295689433813095, 0.8361068964004517, 0.02459137886762619, 0.0884014144539833, 0.12376198172569275, 0.7779324650764465, 0.13988402485847473, 0.03497100621461868, 0.06994201242923737, 0.6294781565666199, 0.03497100621461868, 0.06994201242923737, 0.37216949462890625, 0.020117269828915596, 0.5934594869613647, 0.7694112062454224, 0.13809944689273834, 0.07891397178173065, 0.5273277759552002, 0.15819832682609558, 0.04687357693910599, 0.03515518456697464, 0.11718394607305527, 0.01757759228348732, 0.029295986518263817, 0.0644511729478836, 0.1842586100101471, 0.6069695353507996, 0.032516222447156906, 0.04335496574640274, 0.08670993149280548, 0.04335496574640274, 0.12275172024965286, 0.8592620491981506, 0.04035613685846329, 0.05380818247795105, 0.6860543489456177, 0.20178069174289703, 0.20276203751564026, 0.1351746916770935, 0.3514541983604431, 0.013517468236386776, 0.027034936472773552, 0.25683191418647766, 0.2120233029127121, 0.07950873672962189, 0.6625728607177734, 0.023185091093182564, 0.00772836385294795, 0.1622956395149231, 0.7032811045646667, 0.023185091093182564, 0.0154567277058959, 0.054098546504974365, 0.01114288903772831, 0.08914311230182648, 0.10028599947690964, 0.10028599947690964, 0.6908591389656067, 0.0695539265871048, 0.14780209958553314, 0.0086942408233881, 0.11302512884140015, 0.6607623100280762, 0.07607044279575348, 0.05705283209681511, 0.09508805721998215, 0.01901761069893837, 0.7416868209838867, 0.08598519116640091, 0.053740743547677994, 0.010748148895800114, 0.5374074578285217, 0.08598519116640091, 0.17197038233280182, 0.042992595583200455, 0.13824662566184998, 0.06912331283092499, 0.6221097707748413, 0.13824662566184998, 0.04897024855017662, 0.14691075682640076, 0.07345537841320038, 0.06529366970062256, 0.2040427029132843, 0.0408085398375988, 0.3264683187007904, 0.04897024855017662, 0.0408085398375988, 0.2445276975631714, 0.045848943293094635, 0.5960362553596497, 0.030565962195396423, 0.07641490548849106, 0.15134915709495544, 0.09080949425697327, 0.08071954548358917, 0.010089943185448647, 0.46413740515708923, 0.03026983141899109, 0.07062960416078568, 0.10089943557977676, 0.8618502616882324, 0.06306221336126328, 0.03153110668063164, 0.03153110668063164, 0.010510369203984737, 0.042804304510354996, 0.3138982355594635, 0.627796471118927, 0.10246681421995163, 0.1280835121870041, 0.04482923075556755, 0.3394213318824768, 0.051233407109975815, 0.18572109937667847, 0.019212527200579643, 0.0896584615111351, 0.04482923075556755, 0.9435060024261475, 0.9020446538925171, 0.020501015707850456, 0.020501015707850456, 0.020501015707850456, 0.020501015707850456, 0.8591981530189514, 0.09546645730733871, 0.03182215243577957, 0.7885290384292603, 0.1314215064048767, 0.026284299790859222, 0.052568599581718445, 0.7454540729522705, 0.05734262242913246, 0.11468524485826492, 0.7504724264144897, 0.09380905330181122, 0.09380905330181122, 0.24650254845619202, 0.5957145094871521, 0.061625637114048004, 0.08216751366853714, 0.7869057655334473, 0.17106646299362183, 0.018164750188589096, 0.05449425056576729, 0.09082375466823578, 0.6539310216903687, 0.10898850113153458, 0.05449425056576729, 0.97410649061203, 0.07124499976634979, 0.382941871881485, 0.25826311111450195, 0.14248999953269958, 0.1335843801498413, 0.12158041447401047, 0.39079418778419495, 0.3126353621482849, 0.060790207237005234, 0.008684315718710423, 0.052105892449617386, 0.04342157766222954, 0.6369389891624451, 0.24769848585128784, 0.10615649819374084, 0.34079107642173767, 0.5963844060897827, 0.025071587413549423, 0.7521476149559021, 0.10028634965419769, 0.050143174827098846, 0.07521475851535797, 0.11694502085447311, 0.09848001599311829, 0.006155000999569893, 0.09848001599311829, 0.22158002853393555, 0.02462000399827957, 0.04924000799655914, 0.3877650499343872, 0.006155000999569893, 0.6775034070014954, 0.16353529691696167, 0.023362185806035995, 0.11681092530488968, 0.04730734974145889, 0.020274577662348747, 0.7501593828201294, 0.040549155324697495, 0.08785650879144669, 0.04730734974145889, 0.9404778480529785, 0.052248768508434296, 0.09962426126003265, 0.09962426126003265, 0.09962426126003265, 0.03984970226883888, 0.6375952363014221, 0.6628676652908325, 0.05892156809568405, 0.03682598099112511, 0.03682598099112511, 0.05155637115240097, 0.022095587104558945, 0.0073651960119605064, 0.13257353007793427, 0.0561111681163311, 0.5237042307853699, 0.037407442927360535, 0.35537073016166687, 0.059768520295619965, 0.03984568268060684, 0.03984568268060684, 0.7371451258659363, 0.0996142029762268, 0.01992284134030342, 0.4894157648086548, 0.029364945366978645, 0.2447078824043274, 0.019576629623770714, 0.20555461943149567, 0.009788314811885357, 0.9699183106422424, 0.02155373990535736, 0.08780839294195175, 0.1756167858839035, 0.23415571451187134, 0.03902595117688179, 0.08780839294195175, 0.34147709608078003, 0.019512975588440895, 0.7462282180786133, 0.21320806443691254, 0.7706161737442017, 0.11008802056312561, 0.11008802056312561, 0.5001448392868042, 0.06864732503890991, 0.3236231207847595, 0.009806760586798191, 0.08826085180044174, 0.03947322815656662, 0.27631258964538574, 0.11841968446969986, 0.05920984223484993, 0.11841968446969986, 0.3552590608596802, 0.8059549331665039, 0.14653725922107697, 0.020196598023176193, 0.14137618243694305, 0.14137618243694305, 0.10098298639059067, 0.5857013463973999, 0.7797400951385498, 0.17994001507759094, 0.9381348490715027, 0.040788471698760986, 0.008827950805425644, 0.18538697063922882, 0.39725780487060547, 0.1412472128868103, 0.008827950805425644, 0.035311803221702576, 0.10593541711568832, 0.017655901610851288, 0.10593541711568832, 0.03145385906100273, 0.03145385906100273, 0.047180790454149246, 0.8649811148643494, 0.12815333902835846, 0.005126133561134338, 0.05638747289776802, 0.24605442583560944, 0.07176586985588074, 0.020504534244537354, 0.005126133561134338, 0.466478168964386, 0.038528602570295334, 0.7320435047149658, 0.09632150828838348, 0.1155858114361763, 0.7012895941734314, 0.15340709686279297, 0.1095765009522438, 0.02191529981791973, 0.8237505555152893, 0.06724493950605392, 0.10086741298437119, 0.036168333142995834, 0.2712624967098236, 0.6691141724586487, 0.7106500267982483, 0.2220781296491623, 0.561259925365448, 0.1342143416404724, 0.09761042892932892, 0.15861694514751434, 0.036603908985853195, 0.05371604487299919, 0.3692978024482727, 0.4230138659477234, 0.026858022436499596, 0.04028703272342682, 0.02014351636171341, 0.04028703272342682, 0.02014351636171341, 0.8912801742553711, 0.06856001168489456, 0.2567219138145447, 0.6845918297767639, 0.08557397872209549, 0.15302683413028717, 0.8161431550979614, 0.025154192000627518, 0.07546257227659225, 0.050308384001255035, 0.6791631579399109, 0.1509251445531845, 0.7733611464500427, 0.210916668176651, 0.024795228615403175, 0.16116897761821747, 0.5826878547668457, 0.04959045723080635, 0.12397614121437073, 0.061988070607185364, 0.12746866047382355, 0.21468406915664673, 0.02683550864458084, 0.2549373209476471, 0.04696213826537132, 0.28177282214164734, 0.02683550864458084, 0.02683550864458084, 0.8172419667243958, 0.09080465883016586, 0.18403929471969604, 0.013145663775503635, 0.6572831869125366, 0.1314566433429718, 0.17615550756454468, 0.029359251260757446, 0.7926998138427734, 0.06413016468286514, 0.8123154044151306, 0.08550688624382019, 0.021376721560955048, 0.09979373216629028, 0.09979373216629028, 0.21494033932685852, 0.007676440756767988, 0.046058643609285355, 0.33776339888572693, 0.16888169944286346, 0.023029321804642677, 0.09308326244354248, 0.8842909932136536, 0.3437962532043457, 0.39668798446655273, 0.026445865631103516, 0.061707016080617905, 0.10578346252441406, 0.017630577087402344, 0.04407644271850586, 0.0586884543299675, 0.2431378811597824, 0.21798568964004517, 0.025152195245027542, 0.050304390490055084, 0.07545658200979233, 0.27667415142059326, 0.050304390490055084, 0.22794148325920105, 0.23670846223831177, 0.14027167856693268, 0.052601881325244904, 0.017533959820866585, 0.026300940662622452, 0.2893103361129761, 0.017533959820866585, 0.15938958525657654, 0.06130368635058403, 0.22069326043128967, 0.09808589518070221, 0.06130368635058403, 0.012260736897587776, 0.39234358072280884, 0.15007558465003967, 0.15007558465003967, 0.6378212571144104, 0.07503779232501984, 0.012903534807264805, 0.5419484376907349, 0.3935577869415283, 0.02580706961452961, 0.019355300813913345, 0.8491668105125427, 0.05661112070083618, 0.05661112070083618, 0.28332123160362244, 0.08094892650842667, 0.06745743751525879, 0.5126765370368958, 0.05396594852209091, 0.22351780533790588, 0.05587945133447647, 0.6705533862113953, 0.8109243512153625, 0.07372039556503296, 0.14744079113006592, 0.3181319236755371, 0.07953298091888428, 0.15906596183776855, 0.47719788551330566, 0.07378792762756348, 0.07378792762756348, 0.22136376798152924, 0.5903034210205078, 0.07179632037878036, 0.7179632186889648, 0.14359264075756073, 0.9229992628097534, 0.048578906804323196, 0.4178955852985382, 0.0668632984161377, 0.5014747381210327, 0.06042323634028435, 0.9063485264778137, 0.12876549363136292, 0.3035186529159546, 0.19314822554588318, 0.3035186529159546, 0.05518520995974541, 0.9615628719329834, 0.0158936008810997, 0.0158936008810997, 0.4490075707435608, 0.009163419716060162, 0.09163419902324677, 0.036653678864240646, 0.1282878816127777, 0.14661471545696259, 0.11912445724010468, 0.02749026007950306, 0.3246852159500122, 0.6493704319000244, 0.018830781802535057, 0.05649234727025032, 0.8473852276802063, 0.05649234727025032, 0.11957188695669174, 0.019928647205233574, 0.019928647205233574, 0.8370031714439392, 0.705585241317749, 0.05879877135157585, 0.1175975427031517, 0.1175975427031517, 0.8167198896408081, 0.08597051352262497, 0.042985256761312485, 0.9664661288261414, 0.007609969470649958, 0.007609969470649958, 0.02282990887761116, 0.13152466714382172, 0.7628430724143982, 0.07891479879617691, 0.031061405315995216, 0.8386579155921936, 0.06212281063199043, 0.06212281063199043, 0.1649269163608551, 0.24739037454128265, 0.08246345818042755, 0.45354902744293213, 0.0077211917378008366, 0.9033794403076172, 0.08493310958147049, 0.9826338291168213, 0.035776086151599884, 0.057241737842559814, 0.02146565169095993, 0.0930178239941597, 0.07870738953351974, 0.7012112736701965, 0.0387558788061142, 0.17052587866783142, 0.08526293933391571, 0.06976058334112167, 0.1550235152244568, 0.46507057547569275, 0.06570568680763245, 0.9198796153068542, 0.17446701228618622, 0.19742320477962494, 0.5142185688018799, 0.045912373811006546, 0.03672989830374718, 0.009182474575936794, 0.01836494915187359, 0.16272811591625214, 0.11390968412160873, 0.5695484280586243, 0.08136405795812607, 0.03254562243819237, 0.03254562243819237, 0.9866659641265869, 0.04854969680309296, 0.07282454520463943, 0.32771047949790955, 0.03641227260231972, 0.08496197313070297, 0.060687124729156494, 0.3762601613998413, 0.4383770227432251, 0.05994899198412895, 0.10865755379199982, 0.11240436136722565, 0.01124043669551611, 0.2660236656665802, 0.14398406445980072, 0.19197876751422882, 0.2279747873544693, 0.10798805207014084, 0.3239641487598419, 0.20611552894115448, 0.04684443771839142, 0.037475548684597015, 0.33727994561195374, 0.2154844105243683, 0.14990219473838806, 0.013467987067997456, 0.18855181336402893, 0.04040396213531494, 0.02693597413599491, 0.727271318435669, 0.3107204735279083, 0.6214409470558167, 0.05664408206939697, 0.6986103653907776, 0.1510508805513382, 0.05664408206939697, 0.0460689477622509, 0.07897534221410751, 0.026325112208724022, 0.8424035906791687, 0.9639232158660889, 0.007650184445083141, 0.02295055240392685, 0.020570170134305954, 0.051425423473119736, 0.04114034026861191, 0.1131359338760376, 0.7508112192153931, 0.020570170134305954, 0.13482382893562317, 0.016852978616952896, 0.08426489681005478, 0.06741191446781158, 0.6909721493721008, 0.04472341015934944, 0.06708510965108871, 0.2907021641731262, 0.06708510965108871, 0.08944682031869888, 0.43605324625968933, 0.19852888584136963, 0.45975109934806824, 0.05224444344639778, 0.020897777751088142, 0.03134666755795479, 0.21942666172981262, 0.21775978803634644, 0.6999421715736389, 0.046662814915180206, 0.015554270707070827, 0.31187814474105835, 0.6861319541931152, 0.45236775279045105, 0.5237942337989807, 0.007936276495456696, 0.1654876321554184, 0.1323901116847992, 0.6619505286216736, 0.08483508229255676, 0.24507911503314972, 0.018852239474654198, 0.3299142122268677, 0.08483508229255676, 0.009426119737327099, 0.16024404764175415, 0.02827836014330387, 0.02827836014330387, 0.3578106760978699, 0.17890533804893494, 0.4025370180606842, 0.8605854511260986, 0.0819605216383934, 0.027895556762814522, 0.06973889470100403, 0.2092166692018509, 0.6834411025047302, 0.5367790460586548, 0.010525079444050789, 0.01578761823475361, 0.36048397421836853, 0.03420650586485863, 0.007893809117376804, 0.03420650586485863, 0.14574970304965973, 0.07287485152482986, 0.7651858925819397, 0.10797996819019318, 0.21595993638038635, 0.10797996819019318, 0.4859098792076111, 0.1619686633348465, 0.05784595385193825, 0.08098433166742325, 0.0694151446223259, 0.5090444087982178, 0.0925535261631012, 0.0231383815407753, 0.22877074778079987, 0.1715780645608902, 0.5147342085838318, 0.023063015192747116, 0.2998192012310028, 0.06918904185295105, 0.5996384024620056, 0.02446317858994007, 0.09785271435976028, 0.04892635717988014, 0.1223158910870552, 0.09785271435976028, 0.5871163010597229, 0.8078858852386475, 0.09852267056703568, 0.07881813496351242, 0.03269805759191513, 0.6539611220359802, 0.04359740763902664, 0.16349028050899506, 0.0980941653251648, 0.8076947927474976, 0.06730789691209793, 0.1009618490934372, 0.9151153564453125, 0.0338931605219841, 0.22367502748966217, 0.726943850517273, 0.28466373682022095, 0.6262602210044861, 0.052709657698869705, 0.6852255463600159, 0.10541931539773941, 0.039532244205474854, 0.013177414424717426, 0.09224189817905426, 0.12320689111948013, 0.780310332775116, 0.08213792741298676, 0.11315199732780457, 0.07543466240167618, 0.7920639514923096, 0.4319699704647064, 0.1079924926161766, 0.10027874261140823, 0.09256499260663986, 0.16970248520374298, 0.03856874629855156, 0.061709996312856674, 0.08182201534509659, 0.16364403069019318, 0.06545761227607727, 0.21273724734783173, 0.4582033157348633, 0.7128912210464478, 0.03819059953093529, 0.02546039968729019, 0.02546039968729019, 0.06365100294351578, 0.02546039968729019, 0.08911140263080597, 0.006704792380332947, 0.10727667808532715, 0.04693354666233063, 0.18102939426898956, 0.09386709332466125, 0.16091501712799072, 0.38217318058013916, 0.02011437714099884, 0.7433538436889648, 0.14867077767848969, 0.0330379493534565, 0.0495569221675396, 0.7830080986022949, 0.060231391340494156, 0.060231391340494156, 0.8558452129364014, 0.06792422384023666, 0.027169689536094666, 0.040754534304142, 0.3675580322742462, 0.14702321588993073, 0.441069632768631, 0.09106146544218063, 0.12141528725624084, 0.03035382181406021, 0.36424586176872253, 0.12141528725624084, 0.26306644082069397, 0.07124914973974228, 0.07124914973974228, 0.015833145007491112, 0.6254091858863831, 0.11874858289957047, 0.05541600286960602, 0.031666290014982224, 0.1664695292711258, 0.06936230510473251, 0.013872460462152958, 0.09710722416639328, 0.63813316822052, 0.9420467019081116, 0.7205374240875244, 0.2331150472164154, 0.02119227685034275, 0.06437075883150101, 0.8046345114707947, 0.06437075883150101, 0.04381999745965004, 0.01095499936491251, 0.04381999745965004, 0.032864999026060104, 0.032864999026060104, 0.8325799703598022, 0.03531442582607269, 0.8122317790985107, 0.10594327747821808, 0.06192999333143234, 0.9134674072265625, 0.05689982697367668, 0.037933215498924255, 0.8914306163787842, 0.17875276505947113, 0.29047325253486633, 0.02234409563243389, 0.5139142274856567, 0.09180878102779388, 0.07511627674102783, 0.4757364094257355, 0.11684753745794296, 0.0083462530747056, 0.08346252888441086, 0.05842376872897148, 0.10015503317117691, 0.2716067433357239, 0.024691520258784294, 0.6296337842941284, 0.07407455891370773, 0.4859541654586792, 0.010054224170744419, 0.016757039353251457, 0.05027111992239952, 0.0033514080569148064, 0.0033514080569148064, 0.13405631482601166, 0.2848696708679199, 0.013405632227659225, 0.8775997757911682, 0.10324703902006149, 0.7695841789245605, 0.12826402485370636, 0.05669095739722252, 0.1473964899778366, 0.03401457518339157, 0.05669095739722252, 0.022676382213830948, 0.685960590839386, 0.04113059490919113, 0.13367444276809692, 0.051413245499134064, 0.0616958923637867, 0.22621826827526093, 0.4730018377304077, 0.020565297454595566, 0.8304665088653564, 0.11072886735200882, 0.017405934631824493, 0.008702967315912247, 0.6396680474281311, 0.008702967315912247, 0.32200977206230164, 0.04684574156999588, 0.08198004961013794, 0.03513430804014206, 0.831511914730072, 0.18712331354618073, 0.1981305629014969, 0.011007254011929035, 0.18712331354618073, 0.40726837515830994, 0.0875185951590538, 0.01750371791422367, 0.19254089891910553, 0.0875185951590538, 0.6126301288604736, 0.20660100877285004, 0.27546802163124084, 0.4820690155029297, 0.03715584799647331, 0.22293508052825928, 0.7059611082077026, 0.2927516996860504, 0.2195637822151184, 0.4391275644302368, 0.7473582029342651, 0.21353091299533844, 0.2516338527202606, 0.010270769707858562, 0.10270769894123077, 0.2721754014492035, 0.03594769537448883, 0.020541539415717125, 0.04621846228837967, 0.26190462708473206, 0.1783914715051651, 0.1783914715051651, 0.1783914715051651, 0.4013808071613312, 0.13743723928928375, 0.06871861964464188, 0.17179656028747559, 0.584108293056488, 0.015083401463925838, 0.04525020346045494, 0.030166802927851677, 0.8597539067268372, 0.030166802927851677, 0.13669182360172272, 0.045563943684101105, 0.03037596121430397, 0.4708274006843567, 0.31894758343696594, 0.18227481842041016, 0.06770207732915878, 0.05728637054562569, 0.19269052147865295, 0.005207851994782686, 0.05728637054562569, 0.3228868246078491, 0.11457274109125137, 0.24994014203548431, 0.5623653531074524, 0.06248503550887108, 0.08747905492782593, 0.0124970069155097, 0.39068055152893066, 0.5945138931274414, 0.06419597566127777, 0.06419597566127777, 0.2567839026451111, 0.5777637362480164, 0.18442416191101074, 0.737696647644043, 0.05269261449575424, 0.04653947800397873, 0.4560868740081787, 0.18615791201591492, 0.00930789578706026, 0.01861579157412052, 0.2606210708618164, 0.01861579157412052, 0.14766272902488708, 0.14766272902488708, 0.07383136451244354, 0.6275665760040283, 0.15898077189922333, 0.07949038594961166, 0.03974519297480583, 0.1192355751991272, 0.07949038594961166, 0.5166875123977661, 0.14781509339809418, 0.7686384916305542, 0.05912603437900543, 0.010625068098306656, 0.1912512332201004, 0.7915675640106201, 0.10195859521627426, 0.13594479858875275, 0.7137101888656616, 0.01930549181997776, 0.11583294719457626, 0.01930549181997776, 0.2123604118824005, 0.5791647434234619, 0.05791647359728813, 0.014481119811534882, 0.057924479246139526, 0.014481119811534882, 0.043443359434604645, 0.17377343773841858, 0.6950937509536743, 0.007633298635482788, 0.07633298635482788, 0.04579979181289673, 0.48853111267089844, 0.06869968771934509, 0.05343309044837952, 0.13739937543869019, 0.06869968771934509, 0.05343309044837952, 0.09961352497339249, 0.39845409989356995, 0.05363805219531059, 0.05363805219531059, 0.27585285902023315, 0.030650315806269646, 0.09195094555616379, 0.06586208939552307, 0.04116380587220192, 0.6503881812095642, 0.04116380587220192, 0.032931044697761536, 0.17288799583911896, 0.7188619375228882, 0.05990516021847725, 0.07488144934177399, 0.13478662073612213, 0.03606235235929489, 0.012020784430205822, 0.04808313772082329, 0.006010392215102911, 0.006010392215102911, 0.8654964566230774, 0.024041568860411644, 0.055654797703027725, 0.03478424623608589, 0.03478424623608589, 0.03478424623608589, 0.1321801394224167, 0.34088563919067383, 0.03478424623608589, 0.11826644092798233, 0.11826644092798233, 0.10435274243354797, 0.00780230900272727, 0.046813853085041046, 0.08582539856433868, 0.639789342880249, 0.18725541234016418, 0.03901154547929764, 0.9027286171913147, 0.06448061764240265, 0.11972501873970032, 0.13232764601707458, 0.12602633237838745, 0.04410921782255173, 0.05671184882521629, 0.018903950229287148, 0.22684739530086517, 0.26465529203414917, 0.10280594229698181, 0.15420891344547272, 0.025701485574245453, 0.15420891344547272, 0.12850743532180786, 0.4369252622127533, 0.09496460855007172, 0.01356637291610241, 0.17636284232139587, 0.10853098332881927, 0.10853098332881927, 0.04069911688566208, 0.3934248089790344, 0.0678318664431572, 0.17081324756145477, 0.8174633979797363, 0.07931813597679138, 0.028327906504273415, 0.045324649661779404, 0.2719478905200958, 0.06798697263002396, 0.1699674427509308, 0.07365255802869797, 0.011331162415444851, 0.24928557872772217, 0.11410491913557053, 0.095087431371212, 0.05705245956778526, 0.07606995105743408, 0.2852623164653778, 0.03803497552871704, 0.32329729199409485, 0.07447443902492523, 0.07447443902492523, 0.2234233170747757, 0.6206203699111938, 0.784771203994751, 0.1706024408340454, 0.1230132058262825, 0.0058577717281877995, 0.02928885817527771, 0.03514663130044937, 0.31631967425346375, 0.09372434765100479, 0.3104619085788727, 0.041004400700330734, 0.023431086912751198, 0.017573315650224686, 0.09692107886075974, 0.11845909804105759, 0.010769008658826351, 0.11845909804105759, 0.021538017317652702, 0.4199913442134857, 0.05384504422545433, 0.043076034635305405, 0.10769008845090866, 0.004444408696144819, 0.04444408789277077, 0.022222043946385384, 0.02666645310819149, 0.04444408789277077, 0.022222043946385384, 0.8266600370407104, 0.008888817392289639, 0.025620922446250916, 0.8839218020439148, 0.08326799422502518, 0.009199890308082104, 0.05519933998584747, 0.708391547203064, 0.2207973599433899, 0.012534601613879204, 0.8147491216659546, 0.08774220943450928, 0.0814749151468277, 0.06868072599172592, 0.1717018038034439, 0.06868072599172592, 0.6868072152137756, 0.7353140115737915, 0.13369345664978027, 0.1002700924873352, 0.0998721495270729, 0.7240731120109558, 0.0998721495270729, 0.04993607476353645, 0.01092764176428318, 0.4261780083179474, 0.01092764176428318, 0.03278292343020439, 0.44803330302238464, 0.06556584686040878, 0.8789618611335754, 0.10045278817415237, 0.11545104533433914, 0.09236083924770355, 0.09236083924770355, 0.023090209811925888, 0.6696160435676575, 0.08995106816291809, 0.8995106816291809, 0.03879019245505333, 0.11637058109045029, 0.8275241255760193, 0.136904776096344, 0.205357164144516, 0.205357164144516, 0.410714328289032, 0.015404191799461842, 0.015404191799461842, 0.7394012212753296, 0.1386377215385437, 0.020538922399282455, 0.010269461199641228, 0.05648203566670418, 0.03324803337454796, 0.7932031154632568, 0.08549495041370392, 0.08074522763490677, 0.21199004352092743, 0.7419651746749878, 0.9560725688934326, 0.03749304264783859, 0.355228066444397, 0.02880227565765381, 0.03840303421020508, 0.02880227565765381, 0.0960075855255127, 0.06720530986785889, 0.08640682697296143, 0.29762351512908936, 0.15920646488666534, 0.039801616221666336, 0.7960323691368103, 0.016358451917767525, 0.08179225772619247, 0.03271690383553505, 0.16358451545238495, 0.016358451917767525, 0.539828896522522, 0.03271690383553505, 0.11450915783643723, 0.8356649875640869, 0.03183485567569733, 0.1273394227027893, 0.13379135727882385, 0.1815739870071411, 0.028669577091932297, 0.45871323347091675, 0.057339154183864594, 0.08600872755050659, 0.03822610154747963, 0.2114805281162262, 0.13014185428619385, 0.6507092714309692, 0.3725728690624237, 0.3362242877483368, 0.0908714309334755, 0.054522860795259476, 0.01817428693175316, 0.08178428560495377, 0.04543571546673775, 0.24663186073303223, 0.49326372146606445, 0.07633843272924423, 0.01761656254529953, 0.0293609369546175, 0.13506031036376953, 0.005872187204658985, 0.6378008127212524, 0.17237859964370728, 0.017237860709428787, 0.08618929982185364, 0.06895144283771515, 0.28766992688179016, 0.05753398686647415, 0.05753398686647415, 0.5178058743476868, 0.016061250120401382, 0.9315524697303772, 0.04818374663591385, 0.04282661899924278, 0.08565323799848557, 0.10706654191017151, 0.09635989367961884, 0.08565323799848557, 0.5835126638412476, 0.05687343701720238, 0.909974992275238, 0.0123142683878541, 0.0246285367757082, 0.40021371841430664, 0.2770710289478302, 0.00615713419392705, 0.20934255421161652, 0.06772847473621368, 0.22554977238178253, 0.041768476366996765, 0.1253054291009903, 0.08353695273399353, 0.1587202101945877, 0.08353695273399353, 0.11695173382759094, 0.16707390546798706, 0.9497343301773071, 0.07427984476089478, 0.008738805539906025, 0.908835768699646, 0.06758586317300797, 0.8485780954360962, 0.022528622299432755, 0.022528622299432755, 0.03003816120326519, 0.05970491096377373, 0.05970491096377373, 0.6567540168762207, 0.17911474406719208, 0.6087716817855835, 0.013528258539736271, 0.09469781070947647, 0.17586736381053925, 0.027056517079472542, 0.06764129549264908, 0.042926125228405, 0.17170450091362, 0.042926125228405, 0.7297440767288208, 0.14205440878868103, 0.1242976114153862, 0.22196002304553986, 0.026635201647877693, 0.08878400921821594, 0.03551360219717026, 0.35513603687286377, 0.857516884803772, 0.10290203243494034, 0.028705526143312454, 0.05741105228662491, 0.028705526143312454, 0.1818016767501831, 0.15309613943099976, 0.07654806971549988, 0.028705526143312454, 0.4210143983364105, 0.028705526143312454, 0.0646035224199295, 0.8398457765579224, 0.8607514500617981, 0.08607514202594757, 0.14785036444664001, 0.07392518222332001, 0.07392518222332001, 0.22177554666996002, 0.44355109333992004, 0.040269605815410614, 0.07047180831432343, 0.6946507096290588, 0.020134802907705307, 0.06040440872311592, 0.12080881744623184, 0.023366155102849007, 0.17914052307605743, 0.38943594694137573, 0.07788718491792679, 0.1246194988489151, 0.13240821659564972, 0.07009846717119217, 0.8422496914863586, 0.054338689893484116, 0.054338689893484116, 0.5080727934837341, 0.46188437938690186, 0.14716854691505432, 0.013797050341963768, 0.6760554909706116, 0.1517675518989563, 0.009198034182190895, 0.8142318725585938, 0.13132771849632263, 0.026265544816851616, 0.11072690039873123, 0.8858152031898499, 0.10617367923259735, 0.09290196746587753, 0.026543419808149338, 0.07963025569915771, 0.6901289224624634, 0.048203714191913605, 0.048203714191913605, 0.3133241534233093, 0.024101857095956802, 0.024101857095956802, 0.060254644602537155, 0.03615278750658035, 0.08435650169849396, 0.36152786016464233, 0.6861274838447571, 0.17153187096118927, 0.08576593548059464, 0.05314444750547409, 0.12754666805267334, 0.41452670097351074, 0.07440222799777985, 0.010628889314830303, 0.010628889314830303, 0.06377333402633667, 0.08503111451864243, 0.15943333506584167, 0.7883128523826599, 0.15766257047653198, 0.7523908615112305, 0.18809771537780762, 0.07031293213367462, 0.1523446887731552, 0.08203175663948059, 0.07031293213367462, 0.03515646606683731, 0.12890703976154327, 0.42187759280204773, 0.03515646606683731, 0.3447062373161316, 0.620471179485321, 0.23347626626491547, 0.031130168586969376, 0.6848636865615845, 0.031130168586969376, 0.7685578465461731, 0.1707906275987625, 0.21000269055366516, 0.6562584042549133, 0.10500134527683258, 0.9324852824211121, 0.057090938091278076, 0.04594695195555687, 0.015315650962293148, 0.030631301924586296, 0.9036234021186829, 0.6014195680618286, 0.018505217507481575, 0.03701043501496315, 0.009252608753740788, 0.3053360879421234, 0.018505217507481575, 0.5577633380889893, 0.3984023928642273, 0.04764030501246452, 0.7622448801994324, 0.14292091131210327, 0.16481110453605652, 0.09270624071359634, 0.45323053002357483, 0.10300693660974503, 0.061804160475730896, 0.12360832095146179, 0.2029457688331604, 0.1522093266248703, 0.6088373064994812, 0.06745238602161407, 0.5733452439308167, 0.2922936677932739, 0.03372619301080704, 0.022484127432107925, 0.0201403871178627, 0.11580722779035568, 0.5991765260696411, 0.22657935321331024, 0.015105290338397026, 0.015105290338397026, 0.13026340305805206, 0.052105359733104706, 0.09552649408578873, 0.06947381794452667, 0.23447412252426147, 0.3821059763431549, 0.04342113435268402, 0.031023049727082253, 0.6514840722084045, 0.1861383020877838, 0.0930691510438919, 0.13494373857975006, 0.7711070775985718, 0.09638838469982147, 0.014897916465997696, 0.07448957860469818, 0.029795832931995392, 0.3128562271595001, 0.5512229204177856, 0.11084384471178055, 0.08867507427930832, 0.02955835685133934, 0.02955835685133934, 0.0738958939909935, 0.6502838730812073, 0.02216876856982708, 0.016729645431041718, 0.7862933278083801, 0.13383716344833374, 0.05018893629312515, 0.8073410391807556, 0.16996653378009796, 0.838396430015564, 0.05409009009599686, 0.08113513886928558, 0.034632932394742966, 0.0519493967294693, 0.19048112630844116, 0.12121526151895523, 0.5887598395347595, 0.028933443129062653, 0.08680032938718796, 0.8680033087730408, 0.078105628490448, 0.624845027923584, 0.2343169003725052, 0.8527475595474243, 0.08527475595474243, 0.1623193323612213, 0.14114724099636078, 0.4022696614265442, 0.17643405497074127, 0.007057362236082554, 0.028229448944330215, 0.014114724472165108, 0.007057362236082554, 0.06351625919342041, 0.861951470375061, 0.10774393379688263, 0.0875893086194992, 0.12043529748916626, 0.0109486635774374, 0.0054743317887187, 0.08211497217416763, 0.06021764874458313, 0.6021764874458313, 0.03832032158970833, 0.09922798722982407, 0.09922798722982407, 0.6697889566421509, 0.12403498589992523, 0.08472681790590286, 0.05083608999848366, 0.830322802066803, 0.1557639241218567, 0.07788196206092834, 0.07788196206092834, 0.6230556964874268, 0.0519213043153286, 0.10835538804531097, 0.13002647459506989, 0.021671077236533165, 0.7151455879211426, 0.9578427076339722, 0.026242267340421677, 0.760107159614563, 0.17540934681892395, 0.058469779789447784, 0.1365373581647873, 0.06826867908239365, 0.6826867461204529, 0.06826867908239365, 0.09214778989553452, 0.6081753969192505, 0.027644336223602295, 0.11057734489440918, 0.09214778989553452, 0.05528867244720459, 0.018429556861519814, 0.6899887323379517, 0.25090500712394714, 0.029650786891579628, 0.014825393445789814, 0.5337141752243042, 0.16307932138442993, 0.014825393445789814, 0.10377775132656097, 0.13342854380607605, 0.15977124869823456, 0.09319989383220673, 0.7322848439216614, 0.013669357635080814, 0.05467743054032326, 0.23237907886505127, 0.5331049561500549, 0.10935486108064651, 0.04100807383656502, 0.9536629319190979, 0.019867977127432823, 0.18647488951683044, 0.08546765893697739, 0.3341008424758911, 0.01553957350552082, 0.132086381316185, 0.09323744475841522, 0.05438850820064545, 0.06215829402208328, 0.03107914701104164, 0.10676511377096176, 0.050242405384778976, 0.01884090155363083, 0.012560601346194744, 0.791317880153656, 0.01884090155363083, 0.07642876356840134, 0.08598235249519348, 0.1337503343820572, 0.17196470499038696, 0.37259021401405334, 0.009553595446050167, 0.14330393075942993, 0.8728558421134949, 0.0997549518942833, 0.7559889554977417, 0.03687750920653343, 0.12907128036022186, 0.0553162656724453, 0.20047856867313385, 0.10023928433656693, 0.10023928433656693, 0.5813878774642944, 0.13698509335517883, 0.045661695301532745, 0.7990796566009521, 0.27621224522590637, 0.6444952487945557, 0.9004063010215759, 0.01971692591905594, 0.01971692591905594, 0.013144617900252342, 0.03943385183811188, 0.752982497215271, 0.03529605269432068, 0.10588815808296204, 0.0588267557322979, 0.02353070303797722, 0.02353070303797722, 0.12055043876171112, 0.5706053972244263, 0.10447704792022705, 0.008036695420742035, 0.12858712673187256, 0.03214678168296814, 0.008036695420742035, 0.024110088124871254, 0.17458575963974, 0.13639512658119202, 0.005455804988741875, 0.5128456354141235, 0.08729287981987, 0.016367414966225624, 0.0654696598649025, 0.21387198567390442, 0.769939124584198, 0.8439048528671265, 0.06251146644353867, 0.03125573322176933, 0.015627866610884666, 0.03125573322176933, 0.2856714129447937, 0.06121530011296272, 0.5101274847984314, 0.08162040263414383, 0.06121530011296272], \"Term\": [\"acquire\", \"acquire\", \"activation\", \"activation\", \"activation\", \"activation\", \"activation\", \"activation\", \"agent\", \"agent\", \"agent\", \"agent\", \"agent\", \"agent\", \"agent\", \"agent\", \"algebra\", \"algebra\", \"algebra\", \"algebra\", \"aligned\", \"aligned\", \"alphago\", \"alphago\", \"alphago\", \"alphago\", \"analysis\", \"analysis\", \"analysis\", \"analysis\", \"analysis\", \"analysis\", \"analysis\", \"analysis\", \"analysis\", \"analytics\", \"analytics\", \"analytics\", \"andrej\", \"andrej\", \"andrew\", \"andrew\", \"andrew\", \"andrew\", \"andrew\", \"andrew\", \"andrew\", \"andrew\", \"angle\", \"angle\", \"angle\", \"anomaly\", \"anomaly\", \"answered\", \"answered\", \"anymore\", \"anymore\", \"anymore\", \"apache\", \"apache\", \"api\", \"api\", \"api\", \"api\", \"api\", \"api\", \"api\", \"app\", \"app\", \"app\", \"app\", \"app\", \"app\", \"applies\", \"applies\", \"applies\", \"applies\", \"appreciated\", \"appreciated\", \"appreciated\", \"appropriate\", \"appropriate\", \"appropriate\", \"appropriate\", \"appropriate\", \"apps\", \"apps\", \"apps\", \"apps\", \"apps\", \"apps\", \"arm\", \"arm\", \"arrow\", \"arrow\", \"arrow\", \"art\", \"art\", \"art\", \"art\", \"art\", \"art\", \"art\", \"art\", \"assignment\", \"assignment\", \"assignment\", \"assignment\", \"assignment\", \"assistant\", \"assistant\", \"assistant\", \"assistant\", \"assistant\", \"assistant\", \"atari\", \"atari\", \"atari\", \"audio\", \"audio\", \"audio\", \"audio\", \"augmentation\", \"augmentation\", \"augmentation\", \"augmented\", \"augmented\", \"augmented\", \"automated\", \"automated\", \"automated\", \"automated\", \"automated\", \"automated\", \"automated\", \"automation\", \"automation\", \"automation\", \"average\", \"average\", \"average\", \"average\", \"average\", \"average\", \"average\", \"average\", \"average\", \"aws\", \"aws\", \"aws\", \"aws\", \"azure\", \"azure\", \"azure\", \"backpropagation\", \"backpropagation\", \"backpropagation\", \"backpropagation\", \"backpropagation\", \"backward\", \"backward\", \"baidu\", \"baidu\", \"baidu\", \"baidu\", \"ball\", \"bank\", \"bank\", \"bank\", \"bank\", \"batch\", \"batch\", \"batch\", \"batch\", \"batch\", \"batch\", \"batch\", \"batch\", \"bedroom\", \"bedroom\", \"bedroom\", \"bedroom\", \"behavior\", \"behavior\", \"behavior\", \"behavior\", \"behavior\", \"behavior\", \"behavior\", \"behaviour\", \"behaviour\", \"behaviour\", \"behaviour\", \"bias\", \"bias\", \"bias\", \"bias\", \"bias\", \"bias\", \"bias\", \"billion\", \"billion\", \"billion\", \"billion\", \"billion\", \"binary\", \"binary\", \"binary\", \"binary\", \"binary\", \"biological\", \"biological\", \"biological\", \"block\", \"block\", \"block\", \"block\", \"block\", \"block\", \"blood\", \"blood\", \"board\", \"board\", \"board\", \"board\", \"board\", \"board\", \"body\", \"body\", \"body\", \"body\", \"body\", \"body\", \"bot\", \"bot\", \"bot\", \"bot\", \"bot\", \"bot\", \"bot\", \"bot\", \"bounding\", \"bounding\", \"box\", \"box\", \"box\", \"box\", \"box\", \"box\", \"box\", \"box\", \"brother\", \"brother\", \"brother\", \"buzzword\", \"buzzword\", \"c\", \"c\", \"c\", \"calculating\", \"calculating\", \"candidate\", \"candidate\", \"candidate\", \"candidate\", \"candidate\", \"car\", \"car\", \"car\", \"car\", \"car\", \"car\", \"car\", \"card\", \"card\", \"card\", \"card\", \"carlo\", \"carlo\", \"champion\", \"champion\", \"champion\", \"channel\", \"channel\", \"channel\", \"channel\", \"channel\", \"channel\", \"character\", \"character\", \"character\", \"character\", \"character\", \"character\", \"character\", \"chat\", \"chat\", \"chat\", \"chat\", \"chat\", \"chatbot\", \"chatbot\", \"chatbot\", \"chatbot\", \"chatbots\", \"chatbots\", \"chatbots\", \"chatbots\", \"chess\", \"chess\", \"child\", \"child\", \"child\", \"child\", \"child\", \"china\", \"china\", \"chip\", \"chip\", \"chip\", \"chip\", \"christmas\", \"christmas\", \"chunk\", \"chunk\", \"chunk\", \"chunk\", \"chunk\", \"chunk\", \"circle\", \"circle\", \"circle\", \"circle\", \"circle\", \"circle\", \"city\", \"city\", \"city\", \"click\", \"click\", \"click\", \"click\", \"click\", \"click\", \"click\", \"client\", \"client\", \"client\", \"client\", \"cloud\", \"cloud\", \"cloud\", \"cloud\", \"cloud\", \"cloud\", \"cloud\", \"cloud\", \"cluster\", \"cluster\", \"cluster\", \"cluster\", \"clustering\", \"clustering\", \"clustering\", \"clustering\", \"clustering\", \"clustering\", \"cnn\", \"cnn\", \"cnn\", \"cnn\", \"cnn\", \"cnns\", \"cnns\", \"cnns\", \"cnns\", \"cnns\", \"cnns\", \"cognitive\", \"cognitive\", \"cognitive\", \"cognitive\", \"cognitive\", \"cognitive\", \"cognitive\", \"cognitive\", \"cognitive\", \"collaborative\", \"collaborative\", \"collaborative\", \"collaborative\", \"collaborative\", \"collective\", \"collective\", \"collective\", \"collective\", \"column\", \"column\", \"column\", \"column\", \"column\", \"communication\", \"communication\", \"communication\", \"communication\", \"communication\", \"communication\", \"communication\", \"communication\", \"company\\u2019s\", \"company\\u2019s\", \"competition\", \"competition\", \"competition\", \"competition\", \"competition\", \"competition\", \"competition\", \"component\", \"component\", \"component\", \"component\", \"component\", \"component\", \"component\", \"component\", \"consist\", \"consist\", \"construct\", \"construct\", \"construct\", \"construct\", \"construction\", \"construction\", \"construction\", \"control\", \"control\", \"control\", \"control\", \"control\", \"control\", \"conversation\", \"conversation\", \"conversation\", \"conversation\", \"conversation\", \"conversation\", \"conversation\", \"conversation\", \"conversation\", \"convolution\", \"convolution\", \"convolution\", \"convolution\", \"corporate\", \"corporate\", \"corporate\", \"corporate\", \"coursera\", \"coursera\", \"coursera\", \"cover\", \"cover\", \"cover\", \"cover\", \"cover\", \"cover\", \"cover\", \"cover\", \"cover\", \"cpu\", \"cpu\", \"cpu\", \"cpu\", \"crazy\", \"crazy\", \"crazy\", \"cuda\", \"cue\", \"cue\", \"curve\", \"curve\", \"curve\", \"curve\", \"curve\", \"customer\", \"customer\", \"customer\", \"customer\", \"customer\", \"customer\", \"customer\", \"dark\", \"dark\", \"dark\", \"database\", \"database\", \"database\", \"database\", \"database\", \"database\", \"database\", \"database\", \"de\", \"de\", \"de\", \"de\", \"de\", \"dead\", \"dead\", \"dead\", \"dead\", \"death\", \"death\", \"decade\", \"decade\", \"decade\", \"decade\", \"decade\", \"decade\", \"decade\", \"decade\", \"decomposition\", \"decomposition\", \"decomposition\", \"decomposition\", \"deeplearning\", \"deeplearning\", \"deeplearning\", \"define\", \"define\", \"define\", \"define\", \"define\", \"define\", \"define\", \"definition\", \"definition\", \"definition\", \"definition\", \"definition\", \"definition\", \"definition\", \"demo\", \"demo\", \"depending\", \"depending\", \"depending\", \"depending\", \"depending\", \"depending\", \"depending\", \"derivative\", \"derivative\", \"derivative\", \"derivative\", \"derivative\", \"derivative\", \"derivative\", \"designer\", \"designer\", \"designer\", \"designer\", \"desktop\", \"desktop\", \"detected\", \"develop\", \"develop\", \"develop\", \"develop\", \"develop\", \"develop\", \"developer\", \"developer\", \"developer\", \"developer\", \"developer\", \"developer\", \"developer\", \"deviation\", \"deviation\", \"device\", \"device\", \"device\", \"device\", \"device\", \"device\", \"dimension\", \"dimension\", \"dimension\", \"dimension\", \"dimension\", \"dimension\", \"dimension\", \"dimension\", \"discipline\", \"discipline\", \"discipline\", \"discount\", \"discount\", \"discount\", \"discounted\", \"discrete\", \"discrete\", \"discrete\", \"distance\", \"distance\", \"distance\", \"distance\", \"distance\", \"distance\", \"distribution\", \"distribution\", \"distribution\", \"distribution\", \"distribution\", \"distribution\", \"dl\", \"dl\", \"dl\", \"doable\", \"doable\", \"document\", \"document\", \"document\", \"document\", \"document\", \"document\", \"document\", \"donating\", \"dream\", \"dream\", \"dream\", \"drive\", \"drive\", \"drive\", \"drive\", \"drive\", \"driving\", \"driving\", \"driving\", \"driving\", \"driving\", \"driving\", \"dropout\", \"dropout\", \"dropout\", \"dropout\", \"economy\", \"economy\", \"economy\", \"ecosystem\", \"ecosystem\", \"ecosystem\", \"edge\", \"edge\", \"edge\", \"edge\", \"edge\", \"edge\", \"edge\", \"edge\", \"eight\", \"eight\", \"eight\", \"eight\", \"eight\", \"electrical\", \"electrical\", \"electrical\", \"emerge\", \"emerge\", \"emerge\", \"emotion\", \"emotion\", \"emotion\", \"emotional\", \"emotional\", \"emotional\", \"empty\", \"empty\", \"empty\", \"empty\", \"empty\", \"empty\", \"en\", \"en\", \"en\", \"encoding\", \"encoding\", \"encoding\", \"engineering\", \"engineering\", \"engineering\", \"engineering\", \"engineering\", \"engineering\", \"engineering\", \"engineering\", \"english\", \"english\", \"english\", \"english\", \"english\", \"english\", \"enterprise\", \"enterprise\", \"entropy\", \"entropy\", \"entropy\", \"entropy\", \"equation\", \"equation\", \"equation\", \"equation\", \"equation\", \"equation\", \"essay\", \"essay\", \"essay\", \"estimate\", \"estimate\", \"estimate\", \"estimate\", \"estimate\", \"estimate\", \"estimate\", \"estimated\", \"estimated\", \"estimated\", \"estimated\", \"estimated\", \"et\", \"et\", \"et\", \"et\", \"et\", \"exercise\", \"exercise\", \"exercise\", \"exercise\", \"exercise\", \"expected\", \"expected\", \"expected\", \"expected\", \"expected\", \"expected\", \"expected\", \"experimenting\", \"experimenting\", \"experimenting\", \"experimenting\", \"expert\", \"expert\", \"expert\", \"expert\", \"expert\", \"expert\", \"expert\", \"expert\", \"expert\", \"exploration\", \"exploration\", \"exploration\", \"exploration\", \"exploration\", \"eye\", \"eye\", \"eye\", \"eye\", \"eye\", \"eye\", \"eye\", \"eye\", \"facial\", \"facial\", \"facial\", \"facial\", \"facial\", \"fake\", \"fake\", \"fake\", \"feed\", \"feed\", \"feed\", \"feed\", \"feed\", \"feed\", \"feed\", \"feed\", \"feed\", \"feedforward\", \"fiction\", \"fiction\", \"fiction\", \"fiction\", \"fiction\", \"filter\", \"filter\", \"filter\", \"filtering\", \"filtering\", \"filtering\", \"filtering\", \"floating\", \"floating\", \"floating\", \"forecasting\", \"forecasting\", \"forecasting\", \"format\", \"format\", \"format\", \"format\", \"formula\", \"formula\", \"frequency\", \"frequency\", \"frequency\", \"frequency\", \"frequency\", \"frequency\", \"gb\", \"generated\", \"generated\", \"generated\", \"generated\", \"generated\", \"generation\", \"generation\", \"generation\", \"generation\", \"generation\", \"generation\", \"generation\", \"generator\", \"generator\", \"generator\", \"genius\", \"genius\", \"giant\", \"giant\", \"giant\", \"giant\", \"giant\", \"github\", \"github\", \"github\", \"github\", \"github\", \"github\", \"github\", \"github\", \"github\", \"gold\", \"gold\", \"gold\", \"gold\", \"gpu\", \"gpu\", \"gpu\", \"gpu\", \"gpu\", \"gpu\", \"gpus\", \"gpus\", \"graduate\", \"graduate\", \"graduate\", \"graduate\", \"graduate\", \"graph\", \"graph\", \"graph\", \"graph\", \"graph\", \"graph\", \"graph\", \"graph\", \"graphic\", \"graphic\", \"graphic\", \"graphic\", \"greater\", \"greater\", \"greater\", \"greater\", \"greater\", \"greater\", \"grid\", \"grid\", \"grid\", \"grid\", \"grid\", \"grid\", \"gtx\", \"gtx\", \"guide\", \"guide\", \"guide\", \"guide\", \"guide\", \"guide\", \"guide\", \"hadoop\", \"hadoop\", \"handwritten\", \"handwritten\", \"handwritten\", \"hidden\", \"hidden\", \"hidden\", \"hidden\", \"hidden\", \"hierarchical\", \"hierarchical\", \"hierarchical\", \"hierarchical\", \"hierarchical\", \"hierarchical\", \"hint\", \"hint\", \"hinton\", \"hinton\", \"hinton\", \"hinton\", \"hinton\", \"historically\", \"historically\", \"hole\", \"hole\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"host\", \"host\", \"host\", \"host\", \"house\", \"house\", \"house\", \"house\", \"house\", \"house\", \"house\", \"house\", \"humanity\", \"humanity\", \"humanity\", \"humanity\", \"hypothesis\", \"hypothesis\", \"hypothesis\", \"hypothesis\", \"ii\", \"ii\", \"ii\", \"imagenet\", \"imagenet\", \"imagenet\", \"immediate\", \"immediate\", \"index\", \"index\", \"index\", \"index\", \"index\", \"industry\", \"industry\", \"industry\", \"industry\", \"industry\", \"industry\", \"industry\", \"industry\", \"inefficient\", \"inefficient\", \"inevitably\", \"inevitably\", \"inevitably\", \"infinitely\", \"infinitely\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"initialization\", \"initialization\", \"innovation\", \"innovation\", \"innovation\", \"innovation\", \"innovation\", \"innovation\", \"insight\", \"insight\", \"insight\", \"insight\", \"insight\", \"insight\", \"insight\", \"insight\", \"inspire\", \"inspire\", \"install\", \"install\", \"install\", \"install\", \"installation\", \"installation\", \"installation\", \"installed\", \"installed\", \"installed\", \"installed\", \"instance\", \"instance\", \"instance\", \"instance\", \"instance\", \"instance\", \"instance\", \"instance\", \"intel\", \"intel\", \"intelligent\", \"intelligent\", \"intelligent\", \"intelligent\", \"intelligent\", \"intelligent\", \"intelligent\", \"interaction\", \"interaction\", \"interaction\", \"interaction\", \"interaction\", \"interaction\", \"interaction\", \"interaction\", \"interface\", \"interface\", \"interface\", \"interface\", \"interface\", \"interface\", \"interface\", \"interface\", \"internal\", \"internal\", \"internal\", \"internal\", \"internal\", \"internal\", \"internal\", \"international\", \"international\", \"international\", \"international\", \"interview\", \"interview\", \"interview\", \"interview\", \"interview\", \"introducing\", \"introducing\", \"introducing\", \"introduction\", \"introduction\", \"introduction\", \"introduction\", \"introduction\", \"introductory\", \"introductory\", \"introductory\", \"invention\", \"invention\", \"invention\", \"iteratively\", \"iteratively\", \"iteratively\", \"iteratively\", \"jargon\", \"jargon\", \"jargon\", \"jargon\", \"java\", \"java\", \"java\", \"juliani\", \"juliani\", \"jupyter\", \"jupyter\", \"jupyter\", \"karpathy\", \"karpathy\", \"kera\", \"kera\", \"kera\", \"kera\", \"kera\", \"kernel\", \"kernel\", \"kernel\", \"label\", \"label\", \"label\", \"label\", \"label\", \"label\", \"label\", \"label\", \"landmark\", \"landmark\", \"landscape\", \"landscape\", \"landscape\", \"landscape\", \"latent\", \"latent\", \"latent\", \"latent\", \"leader\", \"leader\", \"leader\", \"leader\", \"learnt\", \"learnt\", \"learnt\", \"letter\", \"letter\", \"letter\", \"letter\", \"linearity\", \"linearity\", \"linearity\", \"linux\", \"linux\", \"linux\", \"linux\", \"lowest\", \"lowest\", \"lowest\", \"lowest\", \"lstm\", \"lstm\", \"lstm\", \"lstms\", \"man\", \"man\", \"man\", \"man\", \"man\", \"man\", \"map\", \"map\", \"map\", \"map\", \"map\", \"map\", \"mario\", \"mario\", \"market\", \"market\", \"market\", \"market\", \"market\", \"market\", \"market\", \"marketing\", \"marketing\", \"marketing\", \"marketing\", \"marketing\", \"marketing\", \"mask\", \"math\", \"math\", \"math\", \"math\", \"math\", \"math\", \"math\", \"matrix\", \"matrix\", \"matrix\", \"matrix\", \"matrix\", \"matrix\", \"max\", \"max\", \"max\", \"max\", \"max\", \"measure\", \"measure\", \"measure\", \"measure\", \"measure\", \"measure\", \"measurement\", \"measurement\", \"measurement\", \"measurement\", \"measurement\", \"measuring\", \"measuring\", \"medical\", \"medical\", \"medical\", \"medical\", \"member\", \"member\", \"member\", \"member\", \"men\", \"men\", \"men\", \"message\", \"message\", \"message\", \"message\", \"message\", \"message\", \"messaging\", \"messaging\", \"messaging\", \"messaging\", \"messaging\", \"microsoft\", \"microsoft\", \"microsoft\", \"microsoft\", \"microsoft\", \"microsoft\", \"mobile\", \"mobile\", \"mobile\", \"mobile\", \"mobile\", \"mobile\", \"module\", \"module\", \"module\", \"module\", \"monte\", \"monte\", \"music\", \"music\", \"music\", \"native\", \"native\", \"native\", \"nature\", \"nature\", \"nature\", \"nature\", \"nature\", \"nature\", \"nature\", \"nature\", \"nature\", \"neighborhood\", \"neighborhood\", \"neighborhood\", \"nervous\", \"nervous\", \"netflix\", \"netflix\", \"netflix\", \"netflix\", \"neuron\", \"neuron\", \"neuron\", \"neuron\", \"neuron\", \"neuron\", \"neuron\", \"neuroscience\", \"neuroscience\", \"neuroscience\", \"ng\\u2019s\", \"ng\\u2019s\", \"ng\\u2019s\", \"ng\\u2019s\", \"noise\", \"noise\", \"noise\", \"noise\", \"noise\", \"noise\", \"noise\", \"nonetheless\", \"nonetheless\", \"nonetheless\", \"notebook\", \"notebook\", \"notebook\", \"notebook\", \"noted\", \"noted\", \"noted\", \"noted\", \"noted\", \"noted\", \"novel\", \"novel\", \"novel\", \"numpy\", \"numpy\", \"numpy\", \"numpy\", \"numpy\", \"nvidia\", \"nvidia\", \"nvidia\", \"obtain\", \"obtain\", \"octave\", \"octave\", \"odds\", \"odds\", \"offer\", \"offer\", \"offer\", \"offer\", \"offer\", \"offer\", \"offline\", \"offline\", \"offline\", \"openai\", \"openai\", \"openai\", \"operation\", \"operation\", \"operation\", \"operation\", \"operation\", \"operation\", \"operation\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opposite\", \"opposite\", \"opposite\", \"opposite\", \"opposite\", \"opposite\", \"opposite\", \"option\", \"option\", \"option\", \"option\", \"option\", \"option\", \"option\", \"option\", \"org\", \"org\", \"org\", \"org\", \"organized\", \"organized\", \"organized\", \"orientation\", \"orientation\", \"orientation\", \"orientation\", \"osx\", \"osx\", \"osx\", \"outcome\", \"outcome\", \"outcome\", \"outcome\", \"outcome\", \"outcome\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"paid\", \"paid\", \"paid\", \"paid\", \"paid\", \"painting\", \"panda\", \"panda\", \"panda\", \"parent\", \"parent\", \"parent\", \"participant\", \"participant\", \"participant\", \"participant\", \"participant\", \"participant\", \"patient\", \"patient\", \"patient\", \"personality\", \"personality\", \"personalization\", \"personalization\", \"personalization\", \"personalized\", \"personalized\", \"personalized\", \"personalized\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phrase\", \"phrase\", \"phrase\", \"phrase\", \"pixel\", \"pixel\", \"pixel\", \"pixel\", \"pixel\", \"pixel\", \"pixel\", \"pixel\", \"pixel\", \"placeholder\", \"placeholder\", \"plausible\", \"plausible\", \"player\", \"player\", \"player\", \"player\", \"player\", \"player\", \"playing\", \"playing\", \"playing\", \"playing\", \"playing\", \"playing\", \"playing\", \"pocket\", \"pocket\", \"policy\", \"policy\", \"policy\", \"policy\", \"policy\", \"political\", \"political\", \"political\", \"political\", \"pooling\", \"pooling\", \"pooling\", \"pooling\", \"pooling\", \"possibility\", \"possibility\", \"possibility\", \"possibility\", \"possibility\", \"presence\", \"presence\", \"presence\", \"president\", \"president\", \"president\", \"pressing\", \"pressing\", \"pressing\", \"pretrained\", \"pretrained\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"principal\", \"principal\", \"principal\", \"principal\", \"proceeding\", \"proceeding\", \"proceeding\", \"proceeding\", \"processor\", \"processor\", \"processor\", \"processor\", \"processor\", \"profile\", \"profile\", \"profile\", \"profile\", \"profile\", \"programming\", \"programming\", \"programming\", \"programming\", \"programming\", \"programming\", \"programming\", \"programming\", \"progress\", \"progress\", \"progress\", \"progress\", \"progress\", \"proposal\", \"proposal\", \"proven\", \"proven\", \"proven\", \"proven\", \"provider\", \"provider\", \"provider\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"purchase\", \"purchase\", \"purchase\", \"purchase\", \"rank\", \"rank\", \"rank\", \"rank\", \"rank\", \"rank\", \"ranking\", \"ranking\", \"ranking\", \"rating\", \"rating\", \"rating\", \"realistic\", \"realistic\", \"realistic\", \"recall\", \"recall\", \"recall\", \"recall\", \"recall\", \"recall\", \"recipe\", \"recipe\", \"recipe\", \"recipe\", \"recipe\", \"recipe\", \"recognize\", \"recognize\", \"recognize\", \"recognize\", \"recognize\", \"recognize\", \"recognize\", \"recognize\", \"recognize\", \"recommendation\", \"recommendation\", \"recommendation\", \"recommendation\", \"recommendation\", \"recommendation\", \"recommendation\", \"recurrent\", \"recurrent\", \"recurrent\", \"recurrent\", \"recurrent\", \"recurrent\", \"red\", \"red\", \"red\", \"red\", \"region\", \"region\", \"region\", \"region\", \"region\", \"region\", \"region\", \"regression\", \"regression\", \"regression\", \"regression\", \"regression\", \"regression\", \"regression\", \"regression\", \"regression\", \"regression\", \"reinforcement\", \"reinforcement\", \"reinforcement\", \"reinforcement\", \"reinforcement\", \"reinforcement\", \"relate\", \"relate\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relative\", \"relative\", \"relative\", \"relative\", \"relative\", \"relative\", \"relatively\", \"relatively\", \"relatively\", \"relatively\", \"relatively\", \"relatively\", \"relatively\", \"relatively\", \"relu\", \"relu\", \"representation\", \"representation\", \"representation\", \"representation\", \"representation\", \"representation\", \"representation\", \"representation\", \"representation\", \"represented\", \"represented\", \"represented\", \"represented\", \"represented\", \"represented\", \"represented\", \"request\", \"request\", \"request\", \"request\", \"resolution\", \"resolution\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"reward\", \"reward\", \"reward\", \"rl\", \"rl\", \"rl\", \"rl\", \"rnn\", \"rnn\", \"rnn\", \"rnn\", \"roll\", \"roll\", \"roll\", \"roll\", \"rush\", \"rush\", \"rush\", \"scientific\", \"scientific\", \"scientific\", \"scientific\", \"scikit\", \"scikit\", \"scikit\", \"scikit\", \"scikit\", \"scikit\", \"scipy\", \"scipy\", \"se\", \"se\", \"se\", \"se\", \"se\", \"segmentation\", \"segmentation\", \"semantic\", \"semantic\", \"semantic\", \"semantics\", \"semantics\", \"semantics\", \"semantics\", \"sentence\", \"sentence\", \"sentence\", \"sentence\", \"sentence\", \"sentence\", \"sentence\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"seven\", \"seven\", \"sheet\", \"sheet\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"sigmoid\", \"sigmoid\", \"sigmoid\", \"similarity\", \"similarity\", \"similarity\", \"similarity\", \"similarity\", \"similarity\", \"similarity\", \"similarity\", \"simulation\", \"simulation\", \"simulation\", \"situation\", \"situation\", \"situation\", \"situation\", \"situation\", \"situation\", \"situation\", \"six\", \"six\", \"six\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"society\", \"society\", \"society\", \"society\", \"society\", \"sold\", \"sold\", \"sold\", \"sold\", \"song\", \"song\", \"song\", \"sound\", \"sound\", \"sound\", \"sound\", \"sound\", \"sound\", \"sparse\", \"sparse\", \"speech\", \"speech\", \"speech\", \"speech\", \"speech\", \"speech\", \"speech\", \"square\", \"square\", \"square\", \"square\", \"square\", \"square\", \"square\", \"square\", \"stability\", \"star\", \"star\", \"star\", \"startup\", \"startup\", \"startup\", \"startup\", \"startup\", \"stated\", \"stated\", \"stated\", \"stated\", \"straight\", \"straight\", \"straight\", \"straight\", \"straight\", \"straight\", \"straightforward\", \"straightforward\", \"straightforward\", \"straightforward\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"stride\", \"stride\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"submission\", \"submission\", \"suffers\", \"suffers\", \"suitable\", \"suitable\", \"suitable\", \"suitable\", \"suitable\", \"super\", \"super\", \"super\", \"super\", \"super\", \"super\", \"supervised\", \"supervised\", \"supervised\", \"supervised\", \"supervised\", \"supervised\", \"supervised\", \"synapsis\", \"synapsis\", \"synapsis\", \"syntax\", \"syntax\", \"table\", \"table\", \"table\", \"table\", \"table\", \"talent\", \"talent\", \"talent\", \"tanh\", \"tanh\", \"taught\", \"taught\", \"taught\", \"taught\", \"taught\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teacher\", \"teacher\", \"teacher\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"technologist\", \"technologist\", \"template\", \"template\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"texture\", \"texture\", \"tf\", \"tf\", \"tf\", \"tf\", \"thereby\", \"thereby\", \"threshold\", \"threshold\", \"threshold\", \"ti\", \"ti\", \"timeline\", \"timeline\", \"timeline\", \"timeline\", \"title\", \"title\", \"title\", \"title\", \"title\", \"title\", \"topology\", \"topology\", \"tough\", \"tough\", \"tough\", \"track\", \"track\", \"track\", \"track\", \"track\", \"track\", \"trait\", \"trait\", \"trait\", \"translate\", \"translate\", \"translate\", \"translate\", \"translate\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"tree\", \"tree\", \"tree\", \"tree\", \"tree\", \"tree\", \"tree\", \"trip\", \"trip\", \"trip\", \"trip\", \"trust\", \"trust\", \"trust\", \"turing\", \"turing\", \"turing\", \"turing\", \"turing\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"ubiquitous\", \"ubiquitous\", \"ubuntu\", \"ubuntu\", \"ubuntu\", \"udacity\", \"udacity\", \"udacity\", \"udacity\", \"udacity\", \"udacity\\u2019s\", \"udacity\\u2019s\", \"udacity\\u2019s\", \"uncanny\", \"uncanny\", \"uncanny\", \"uniform\", \"uniform\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unity\", \"unity\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"updating\", \"updating\", \"updating\", \"updating\", \"utility\", \"utility\", \"utility\", \"variant\", \"variant\", \"variant\", \"variant\", \"variant\", \"varies\", \"varies\", \"varies\", \"varies\", \"vehicle\", \"vehicle\", \"viewed\", \"viewed\", \"viewed\", \"virtually\", \"virtually\", \"virtually\", \"virtually\", \"voice\", \"voice\", \"voice\", \"voice\", \"voice\", \"voice\", \"voice\", \"volume\", \"volume\", \"watch\", \"watch\", \"watch\", \"watch\", \"watch\", \"watch\", \"watch\", \"watson\", \"watson\", \"watson\", \"wave\", \"wave\", \"wave\", \"wave\", \"wave\", \"wave\", \"wear\", \"wear\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"weighted\", \"weighted\", \"weighted\", \"weighted\", \"weighted\", \"weighted\", \"what\\u2019s\", \"what\\u2019s\", \"what\\u2019s\", \"what\\u2019s\", \"what\\u2019s\", \"what\\u2019s\", \"what\\u2019s\", \"wiki\", \"wiki\", \"wikipedia\", \"wikipedia\", \"wikipedia\", \"wikipedia\", \"win\", \"win\", \"win\", \"win\", \"winning\", \"winning\", \"winning\", \"wired\", \"wired\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"worker\", \"worker\", \"worker\", \"worker\", \"worker\", \"worker\", \"www\", \"www\", \"www\", \"www\", \"www\", \"www\", \"www\", \"www\", \"zero\", \"zero\", \"zero\", \"zero\", \"zero\", \"zero\", \"zero\", \"\\u201cai\", \"\\u201cai\", \"\\u201cdo\", \"\\u201cdo\", \"\\u201cdo\", \"\\u201cdo\", \"\\u201cdo\", \"\\u201ci\", \"\\u201ci\", \"\\u201ci\", \"\\u201ci\", \"\\u201ci\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 3, 10, 2, 7, 5, 9, 1, 6, 8]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el21641112276277432697244455\", ldavis_el21641112276277432697244455_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el21641112276277432697244455\", ldavis_el21641112276277432697244455_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el21641112276277432697244455\", ldavis_el21641112276277432697244455_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "3      0.083547 -0.027386       1        1  14.124918\n",
       "2      0.001110  0.121423       2        1  13.309058\n",
       "9     -0.003215  0.050101       3        1  12.913648\n",
       "1      0.076798 -0.145448       4        1  12.042169\n",
       "6      0.042512 -0.015228       5        1  11.228287\n",
       "4      0.061736  0.093205       6        1  10.069935\n",
       "8      0.028200  0.108357       7        1   8.781598\n",
       "0      0.069313  0.038748       8        1   8.402618\n",
       "5     -0.357725 -0.005984       9        1   5.084514\n",
       "7     -0.002276 -0.217789      10        1   4.043257, topic_info=     Category        Freq            Term       Total  loglift  logprob\n",
       "term                                                                   \n",
       "734   Default  439.000000             cnn  439.000000  30.0000  30.0000\n",
       "601   Default  315.000000              de  315.000000  29.0000  29.0000\n",
       "1862  Default  228.000000            star  228.000000  28.0000  28.0000\n",
       "397   Default  225.000000          review  225.000000  27.0000  27.0000\n",
       "2025  Default  188.000000          rating  188.000000  26.0000  26.0000\n",
       "20    Default  252.000000         average  252.000000  25.0000  25.0000\n",
       "1041  Default  159.000000        weighted  159.000000  24.0000  24.0000\n",
       "370   Default  266.000000          matrix  266.000000  23.0000  23.0000\n",
       "1524  Default  195.000000           house  195.000000  22.0000  22.0000\n",
       "652   Default  380.000000          neuron  380.000000  21.0000  21.0000\n",
       "823   Default  229.000000          policy  229.000000  20.0000  20.0000\n",
       "28    Default  299.000000             bot  299.000000  19.0000  19.0000\n",
       "29    Default  299.000000             box  299.000000  18.0000  18.0000\n",
       "1461  Default  182.000000      university  182.000000  17.0000  17.0000\n",
       "2434  Default  166.000000          region  166.000000  16.0000  16.0000\n",
       "1413  Default  170.000000             cpu  170.000000  15.0000  15.0000\n",
       "420   Default  217.000000           table  217.000000  14.0000  14.0000\n",
       "2218  Default  151.000000          member  151.000000  13.0000  13.0000\n",
       "716   Default  195.000000           agent  195.000000  12.0000  12.0000\n",
       "846   Default  210.000000        sequence  210.000000  11.0000  11.0000\n",
       "840   Default  156.000000          reward  156.000000  10.0000  10.0000\n",
       "1454  Default  160.000000           sheet  160.000000   9.0000   9.0000\n",
       "1714  Default  139.000000             gtx  139.000000   8.0000   8.0000\n",
       "661   Default  176.000000          player  176.000000   7.0000   7.0000\n",
       "1062  Default  134.000000         cluster  134.000000   6.0000   6.0000\n",
       "10    Default  211.000000             app  211.000000   5.0000   5.0000\n",
       "998   Default  298.000000           pixel  298.000000   4.0000   4.0000\n",
       "1539  Default  131.000000          letter  131.000000   3.0000   3.0000\n",
       "1091  Default  194.000000           price  194.000000   2.0000   2.0000\n",
       "2534  Default  115.000000              et  115.000000   1.0000   1.0000\n",
       "...       ...         ...             ...         ...      ...      ...\n",
       "1665  Topic10    9.451900    neighborhood   22.358192   2.3471  -6.1995\n",
       "2645  Topic10    5.204896        topology   12.550125   2.3280  -6.7961\n",
       "2267  Topic10    5.589221        suitable   13.527190   2.3243  -6.7249\n",
       "2132  Topic10    5.604738        pressing   13.663456   2.3170  -6.7221\n",
       "999   Topic10   37.046745         pooling   90.849182   2.3111  -4.8336\n",
       "1195  Topic10    5.867417       semantics   14.608694   2.2959  -6.6763\n",
       "533   Topic10    8.943289       principal   22.422596   2.2890  -6.2548\n",
       "971   Topic10   31.603983        internal   81.561165   2.2600  -4.9925\n",
       "645   Topic10   30.869556            math   82.389801   2.2264  -5.0160\n",
       "695   Topic10   30.142464           teach   82.981155   2.1954  -5.0398\n",
       "1713  Topic10   19.103386         graphic   53.465294   2.1790  -5.4959\n",
       "1422  Topic10   35.763916           drive  110.231407   2.0825  -4.8688\n",
       "314   Topic10   45.028625        analysis  147.930969   2.0187  -4.6384\n",
       "370   Topic10   70.900963          matrix  266.893555   1.8826  -4.1845\n",
       "1722  Topic10   26.947981             max   83.342552   2.0791  -5.1518\n",
       "1091  Topic10   50.903976           price  194.727371   1.8665  -4.5158\n",
       "538   Topic10   42.154728    relationship  158.696991   1.8825  -4.7044\n",
       "838   Topic10   44.256966  representation  176.504395   1.8248  -4.6557\n",
       "2455  Topic10   18.066616    hierarchical   50.667252   2.1769  -5.5517\n",
       "1325  Topic10   17.498352     represented   52.583183   2.1078  -5.5836\n",
       "904   Topic10   24.861111            cnns   96.750977   1.8493  -5.2324\n",
       "2709  Topic10   25.292845              dl  106.799629   1.7677  -5.2152\n",
       "1642  Topic10   19.387928        equation   73.978348   1.8690  -5.4811\n",
       "46    Topic10   18.760674           click   82.111221   1.7318  -5.5140\n",
       "458   Topic10   20.743767       component  114.157448   1.5028  -5.4135\n",
       "1319  Topic10   20.684429       recurrent  121.465927   1.4379  -5.4164\n",
       "386   Topic10   22.143503     programming  192.017746   1.0481  -5.3482\n",
       "854   Topic10   19.899544          square  119.707504   1.4138  -5.4550\n",
       "734   Topic10   20.606186             cnn  439.844299   0.1473  -5.4201\n",
       "728   Topic10   18.815109             car  179.289795   0.9538  -5.5111\n",
       "\n",
       "[551 rows x 6 columns], token_table=      Topic      Freq        Term\n",
       "term                             \n",
       "2270      5  0.156911     acquire\n",
       "2270      6  0.706102     acquire\n",
       "711       1  0.222818  activation\n",
       "711       4  0.037136  activation\n",
       "711       5  0.640603  activation\n",
       "711       6  0.051063  activation\n",
       "711       8  0.013926  activation\n",
       "711      10  0.032494  activation\n",
       "716       2  0.056276       agent\n",
       "716       3  0.035812       agent\n",
       "716       5  0.005116       agent\n",
       "716       6  0.818563       agent\n",
       "716       7  0.015348       agent\n",
       "716       8  0.035812       agent\n",
       "716       9  0.005116       agent\n",
       "716      10  0.030696       agent\n",
       "1203      5  0.206115     algebra\n",
       "1203      6  0.103057     algebra\n",
       "1203      9  0.592579     algebra\n",
       "1203     10  0.103057     algebra\n",
       "2610      2  0.158882     aligned\n",
       "2610      8  0.714969     aligned\n",
       "717       2  0.104287     alphago\n",
       "717       5  0.069525     alphago\n",
       "717       6  0.081112     alphago\n",
       "717       8  0.741595     alphago\n",
       "314       1  0.074359    analysis\n",
       "314       2  0.277156    analysis\n",
       "314       3  0.155478    analysis\n",
       "314       4  0.027040    analysis\n",
       "...     ...       ...         ...\n",
       "1613      7  0.058827      worker\n",
       "1613      9  0.023531      worker\n",
       "1613     10  0.023531      worker\n",
       "431       1  0.120550         www\n",
       "431       2  0.570605         www\n",
       "431       3  0.104477         www\n",
       "431       5  0.008037         www\n",
       "431       7  0.128587         www\n",
       "431       8  0.032147         www\n",
       "431       9  0.008037         www\n",
       "431      10  0.024110         www\n",
       "1047      1  0.174586        zero\n",
       "1047      2  0.136395        zero\n",
       "1047      3  0.005456        zero\n",
       "1047      5  0.512846        zero\n",
       "1047      6  0.087293        zero\n",
       "1047      8  0.016367        zero\n",
       "1047     10  0.065470        zero\n",
       "1618      1  0.213872         “ai\n",
       "1618      2  0.769939         “ai\n",
       "1619      1  0.843905         “do\n",
       "1619      3  0.062511         “do\n",
       "1619      8  0.031256         “do\n",
       "1619      9  0.015628         “do\n",
       "1619     10  0.031256         “do\n",
       "1688      4  0.285671          “i\n",
       "1688      5  0.061215          “i\n",
       "1688      7  0.510127          “i\n",
       "1688      9  0.081620          “i\n",
       "1688     10  0.061215          “i\n",
       "\n",
       "[2204 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 3, 10, 2, 7, 5, 9, 1, 6, 8])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(model, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is the visual representation of the topic modelling and the below is the raw representation and we can specify #words with highest probability to display in each topic. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '0.019*\"neuron\" + 0.010*\"activation\" + 0.009*\"matrix\" + 0.008*\"zero\" + 0.008*\"player\" + 0.008*\"policy\"')\n",
      "(1, '0.017*\"star\" + 0.015*\"review\" + 0.012*\"rating\" + 0.011*\"average\" + 0.010*\"weighted\" + 0.010*\"university\"')\n",
      "(2, '0.010*\"cpu\" + 0.010*\"house\" + 0.009*\"gtx\" + 0.009*\"sentence\" + 0.009*\"gpu\" + 0.008*\"sequence\"')\n",
      "(3, '0.034*\"de\" + 0.014*\"member\" + 0.012*\"sound\" + 0.011*\"title\" + 0.009*\"speech\" + 0.009*\"et\"')\n",
      "(4, '0.014*\"cluster\" + 0.012*\"agent\" + 0.008*\"policy\" + 0.007*\"host\" + 0.007*\"response\" + 0.007*\"lstm\"')\n",
      "(5, '0.011*\"bot\" + 0.006*\"conversation\" + 0.005*\"behavior\" + 0.005*\"social\" + 0.005*\"simulation\" + 0.004*\"apps\"')\n",
      "(6, '0.011*\"car\" + 0.010*\"music\" + 0.008*\"graph\" + 0.008*\"batch\" + 0.007*\"vehicle\" + 0.006*\"track\"')\n",
      "(7, '0.023*\"table\" + 0.009*\"reward\" + 0.008*\"startup\" + 0.007*\"recognize\" + 0.007*\"array\" + 0.006*\"tutorial\"')\n",
      "(8, '0.020*\"pixel\" + 0.018*\"woman\" + 0.016*\"men\" + 0.015*\"convolution\" + 0.015*\"kernel\" + 0.011*\"facial\"')\n",
      "(9, '0.095*\"cnn\" + 0.035*\"sheet\" + 0.035*\"box\" + 0.034*\"region\" + 0.021*\"pixel\" + 0.020*\"bounding\"')\n"
     ]
    }
   ],
   "source": [
    "lda_model.save('model10.gensim')\n",
    "topics = lda_model.print_topics(num_words=6)\n",
    "for topic in topics:\n",
    "    print(topic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Performance evaluation by classifying sample document using LDA Bag of Words model\n",
    "for index, score in sorted(lda_model[corpus[300]], key=lambda tup: -1*tup[1]) :\n",
    "    print(\"\\nScore: {}\\t \\nTopic: {}\".format(score, lda_model.print_topic(index, 40)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling using NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_text = data[['text']]\n",
    "data_text = data_text.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "articles = [value[0] for value in data_text.iloc[0:].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "articles_sentences = [' '.join(text) for text in articles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain a Counts design matrix\n",
    "vectorizer = CountVectorizer(analyzer='word', max_features=1000);\n",
    "x_counts = vectorizer.fit_transform(articles_sentences);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a TfIdf transformer, and transfer the counts with the model.\n",
    "transformer = TfidfTransformer()\n",
    "x_tfidf = transformer.fit_transform(x_counts);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize the TfIdf values\n",
    "xtfidf_norm = normalize(x_tfidf, norm='l1', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NMF(alpha=0.0, beta_loss='frobenius', init='nndsvd', l1_ratio=0.0,\n",
       "  max_iter=200, n_components=10, random_state=None, shuffle=False,\n",
       "  solver='cd', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_topics = 10\n",
    "model = NMF(n_components=num_topics, init='nndsvd');\n",
    "model.fit(xtfidf_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get words from each topic \n",
    "def get_nmf_topics(model, n_top_words):\n",
    "    #the word ids obtained need to be reverse-mapped to the words so we can print the topic names.\n",
    "    feat_names = vectorizer.get_feature_names()\n",
    "    word_dict = {};\n",
    "    for i in range(num_topics):\n",
    "        words_ids = model.components_[i].argsort()[:-20 - 1:-1]\n",
    "        words = [feat_names[key] for key in words_ids]\n",
    "        words = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in words]\n",
    "        words = [re.sub('\\s+', ' ', sent) for sent in words]\n",
    "        words = [re.sub(\"\\'\", \"\", sent) for sent in words]\n",
    "        word_dict['Topic # ' + '{:02d}'.format(i+1)] = words;\n",
    "    return pd.DataFrame(word_dict);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>Topic # 01</th>\n",
       "      <th>Topic # 02</th>\n",
       "      <th>Topic # 03</th>\n",
       "      <th>Topic # 04</th>\n",
       "      <th>Topic # 05</th>\n",
       "      <th>Topic # 06</th>\n",
       "      <th>Topic # 07</th>\n",
       "      <th>Topic # 08</th>\n",
       "      <th>Topic # 09</th>\n",
       "      <th>Topic # 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>you</td>\n",
       "      <td>feature</td>\n",
       "      <td>policy</td>\n",
       "      <td>you</td>\n",
       "      <td>bot</td>\n",
       "      <td>you</td>\n",
       "      <td>you</td>\n",
       "      <td>you</td>\n",
       "      <td>you</td>\n",
       "      <td>you</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ml</td>\n",
       "      <td>features</td>\n",
       "      <td>learning</td>\n",
       "      <td>functions</td>\n",
       "      <td>the</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>learning</td>\n",
       "      <td>featuretools</td>\n",
       "      <td>learn</td>\n",
       "      <td>list</td>\n",
       "      <td>bots</td>\n",
       "      <td>happening</td>\n",
       "      <td>happening</td>\n",
       "      <td>happening</td>\n",
       "      <td>happening</td>\n",
       "      <td>happening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>machine</td>\n",
       "      <td>data</td>\n",
       "      <td>actions</td>\n",
       "      <td>function</td>\n",
       "      <td>it</td>\n",
       "      <td>hand</td>\n",
       "      <td>hand</td>\n",
       "      <td>hand</td>\n",
       "      <td>hand</td>\n",
       "      <td>hand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>course</td>\n",
       "      <td>primitives</td>\n",
       "      <td>deep</td>\n",
       "      <td>data</td>\n",
       "      <td>chatbots</td>\n",
       "      <td>guis</td>\n",
       "      <td>guis</td>\n",
       "      <td>guis</td>\n",
       "      <td>guis</td>\n",
       "      <td>guis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>even</td>\n",
       "      <td>table</td>\n",
       "      <td>value</td>\n",
       "      <td>if</td>\n",
       "      <td>and</td>\n",
       "      <td>growthbot</td>\n",
       "      <td>growthbot</td>\n",
       "      <td>growthbot</td>\n",
       "      <td>growthbot</td>\n",
       "      <td>growthbot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>get</td>\n",
       "      <td>tables</td>\n",
       "      <td>the</td>\n",
       "      <td>python</td>\n",
       "      <td>apps</td>\n",
       "      <td>growth</td>\n",
       "      <td>growth</td>\n",
       "      <td>growth</td>\n",
       "      <td>growth</td>\n",
       "      <td>growth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>if</td>\n",
       "      <td>the</td>\n",
       "      <td>this</td>\n",
       "      <td>numpy</td>\n",
       "      <td>re</td>\n",
       "      <td>group</td>\n",
       "      <td>group</td>\n",
       "      <td>group</td>\n",
       "      <td>group</td>\n",
       "      <td>group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>re</td>\n",
       "      <td>client</td>\n",
       "      <td>state</td>\n",
       "      <td>lambda</td>\n",
       "      <td>human</td>\n",
       "      <td>greedy</td>\n",
       "      <td>greedy</td>\n",
       "      <td>greedy</td>\n",
       "      <td>greedy</td>\n",
       "      <td>greedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>world</td>\n",
       "      <td>engineering</td>\n",
       "      <td>rl</td>\n",
       "      <td>one</td>\n",
       "      <td>way</td>\n",
       "      <td>great</td>\n",
       "      <td>great</td>\n",
       "      <td>great</td>\n",
       "      <td>great</td>\n",
       "      <td>great</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>new</td>\n",
       "      <td>entityset</td>\n",
       "      <td>function</td>\n",
       "      <td>syntax</td>\n",
       "      <td>we</td>\n",
       "      <td>grasp</td>\n",
       "      <td>grasp</td>\n",
       "      <td>grasp</td>\n",
       "      <td>grasp</td>\n",
       "      <td>grasp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>don</td>\n",
       "      <td>deep</td>\n",
       "      <td>in</td>\n",
       "      <td>pandas</td>\n",
       "      <td>that</td>\n",
       "      <td>graphical</td>\n",
       "      <td>graphical</td>\n",
       "      <td>graphical</td>\n",
       "      <td>graphical</td>\n",
       "      <td>graphical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>video</td>\n",
       "      <td>many</td>\n",
       "      <td>use</td>\n",
       "      <td>science</td>\n",
       "      <td>conversational</td>\n",
       "      <td>gradients</td>\n",
       "      <td>gradients</td>\n",
       "      <td>gradients</td>\n",
       "      <td>gradients</td>\n",
       "      <td>gradients</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cool</td>\n",
       "      <td>multiple</td>\n",
       "      <td>we</td>\n",
       "      <td>re</td>\n",
       "      <td>information</td>\n",
       "      <td>got</td>\n",
       "      <td>got</td>\n",
       "      <td>got</td>\n",
       "      <td>got</td>\n",
       "      <td>got</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>data</td>\n",
       "      <td>loans</td>\n",
       "      <td>action</td>\n",
       "      <td>dataframes</td>\n",
       "      <td>like</td>\n",
       "      <td>google</td>\n",
       "      <td>google</td>\n",
       "      <td>google</td>\n",
       "      <td>google</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>people</td>\n",
       "      <td>we</td>\n",
       "      <td>environment</td>\n",
       "      <td>columns</td>\n",
       "      <td>one</td>\n",
       "      <td>going</td>\n",
       "      <td>going</td>\n",
       "      <td>going</td>\n",
       "      <td>going</td>\n",
       "      <td>going</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>understand</td>\n",
       "      <td>use</td>\n",
       "      <td>estimates</td>\n",
       "      <td>column</td>\n",
       "      <td>nlp</td>\n",
       "      <td>ft</td>\n",
       "      <td>ft</td>\n",
       "      <td>ft</td>\n",
       "      <td>ft</td>\n",
       "      <td>ft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>this</td>\n",
       "      <td>process</td>\n",
       "      <td>us</td>\n",
       "      <td>resources</td>\n",
       "      <td>user</td>\n",
       "      <td>goal</td>\n",
       "      <td>goal</td>\n",
       "      <td>goal</td>\n",
       "      <td>goal</td>\n",
       "      <td>goal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>that</td>\n",
       "      <td>model</td>\n",
       "      <td>network</td>\n",
       "      <td>creating</td>\n",
       "      <td>but</td>\n",
       "      <td>go</td>\n",
       "      <td>go</td>\n",
       "      <td>go</td>\n",
       "      <td>go</td>\n",
       "      <td>go</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>it</td>\n",
       "      <td>dataframe</td>\n",
       "      <td>good</td>\n",
       "      <td>example</td>\n",
       "      <td>interfaces</td>\n",
       "      <td>gives</td>\n",
       "      <td>gives</td>\n",
       "      <td>gives</td>\n",
       "      <td>gives</td>\n",
       "      <td>gives</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic # 01    Topic # 02   Topic # 03  Topic # 04      Topic # 05  \\\n",
       "0          you       feature       policy         you             bot   \n",
       "1           ml      features     learning   functions             the   \n",
       "2     learning  featuretools        learn        list            bots   \n",
       "3      machine          data      actions    function              it   \n",
       "4       course    primitives         deep        data        chatbots   \n",
       "5         even         table        value          if             and   \n",
       "6          get        tables          the      python            apps   \n",
       "7           if           the         this       numpy              re   \n",
       "8           re        client        state      lambda           human   \n",
       "9        world   engineering           rl         one             way   \n",
       "10         new     entityset     function      syntax              we   \n",
       "11         don          deep           in      pandas            that   \n",
       "12       video          many          use     science  conversational   \n",
       "13        cool      multiple           we          re     information   \n",
       "14        data         loans       action  dataframes            like   \n",
       "15      people            we  environment     columns             one   \n",
       "16  understand           use    estimates      column             nlp   \n",
       "17        this       process           us   resources            user   \n",
       "18        that         model      network    creating             but   \n",
       "19          it     dataframe         good     example      interfaces   \n",
       "\n",
       "   Topic # 06 Topic # 07 Topic # 08 Topic # 09 Topic # 10  \n",
       "0         you        you        you        you        you  \n",
       "1        good       good       good       good       good  \n",
       "2   happening  happening  happening  happening  happening  \n",
       "3        hand       hand       hand       hand       hand  \n",
       "4        guis       guis       guis       guis       guis  \n",
       "5   growthbot  growthbot  growthbot  growthbot  growthbot  \n",
       "6      growth     growth     growth     growth     growth  \n",
       "7       group      group      group      group      group  \n",
       "8      greedy     greedy     greedy     greedy     greedy  \n",
       "9       great      great      great      great      great  \n",
       "10      grasp      grasp      grasp      grasp      grasp  \n",
       "11  graphical  graphical  graphical  graphical  graphical  \n",
       "12  gradients  gradients  gradients  gradients  gradients  \n",
       "13        got        got        got        got        got  \n",
       "14     google     google     google     google     google  \n",
       "15      going      going      going      going      going  \n",
       "16         ft         ft         ft         ft         ft  \n",
       "17       goal       goal       goal       goal       goal  \n",
       "18         go         go         go         go         go  \n",
       "19      gives      gives      gives      gives      gives  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_nmf_topics(model, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cosine Similarity between Documents\n",
    "Given a keyword, Document Recommender system can suggest you the best documents from the pool of documents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_list=list(chain.from_iterable(docs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cosine similarity\n",
    "def cosine_sim(text1, text2):\n",
    "    tfidf_score = TfidfVectorizer().fit_transform([text1, text2])\n",
    "    return ((tfidf_score * tfidf_score.T).A)[0, 1]\n",
    "\n",
    "# Most similar article\n",
    "def closest_doc_name(sentence, docs):\n",
    "    cos = []\n",
    "    for i in range(len(docs)):\n",
    "        cos.append(cosine_sim(', '.join(sentence.split(' ')),', '.join(docs[i])))\n",
    "    return [titles[x] for x in np.argsort(cos)[-10:][::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(tokenizer=words, stop_words=stop_words).fit(words_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['How Artificial Intelligence can improve online news',\n",
       " 'The 7 Best Data Science and Machine Learning Podcasts',\n",
       " 'The Rise of the Weaponized AI Propaganda Machine – Scout: Science Fiction + Journalism – Medium',\n",
       " 'The Rise of the Weaponized AI Propaganda Machine – Scout: Science Fiction + Journalism – Medium',\n",
       " 'AI is coming, and it will be boring – Denny Vrandečić – Medium',\n",
       " 'ИИ-психопат и ИИ-обманщик – Hey Machine Learning',\n",
       " 'Turn your Raspberry Pi into homemade Google Home – Becoming Human: Artificial Intelligence Magazine',\n",
       " 'What worries me about AI – François Chollet – Medium',\n",
       " 'Announcing Poncho the WeatherBot – Render-from-betaworks',\n",
       " 'Investing in Artificial Intelligence – Nathan Benaich – Medium']"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest_doc_name('news', docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Cosine Similarity as a means of recommending articles"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}