Lab1_Text_dataV8.ipynb

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    "# Lab Assignment One: Exploring Text Data \n",
    "\n",
    "\n",
    "LJ Brown, Joe McGuire, Henry Warren, Mike Lefebvre, Xingming Qu\n",
    "\n",
    "\n",
    "## 1. Business Understanding\n",
    "\n",
    "We analyzed the content of a subset of news articles, publicly provided by the BBC, that were catagorized into one of 5 classes: athletics, cricket, football, rugby, tennis. Third parties might be interested in our results because it could enable easy categorization, analysis, and prioritization of a large set of aggregated articles. It could also be useful as a moderation tool for any site where articles and content are user submitted, to ensure that the user has properly tagged or categorized the article they submitted.  To be worthwhile, our prediction model would need to perform at a level of at least 95%+ accuracy in order for widespread adoption to be possible, as we believe the general public would not tolerate a high degree of error in a tool which can be so ideologically sensitive, and that might seem trivial to a non-technical observer.\n",
    "\n",
    "The data analyzed may look largely similar to the data used in class, i.e., article summaries from the New York Times; however, for each BBC article, we will be assessing the entire corpus of each article's text content. The dataset can be downloaded in a pre-processed format, but for the sake of the exercise we chose to download all the articles in raw text format. The only two features that are usable without any other preprocessing of the dataset are the bodies of the articles and their respective labels. When processing articles, we found that almost all words  were proper English words, so it was unnecessary to use a dictionary to filter words out based on language. In order to get the most impactful and predictive value from the words, we also decided to stem all of the words, and remove the ‘stop’ words (e.g., ‘a, I, are,’ etc), that are so frequently used in the English language that they would not be useful in our bag of words model. \n"
   ]
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    "## 2. Data Encoding\n",
    "\n",
    "Dataset: BBCSport\n",
    "\n",
    "All rights, including copyright, in the content of the original articles are owned by the BBC.\n",
    "\n",
    "Consists of 737 documents from the BBC Sport website corresponding to sports news articles in five topical areas from 2004-2005.\n",
    "Class Labels: 5 (athletics, cricket, football, rugby, tennis)\n",
    "\n",
    "Reference:http://mlg.ucd.ie/datasets/bbc.html\n",
    "\n",
    "### 2.1 Read document data\n",
    "Read in document data as strings using python. Read the data as raw text documents\n",
    "\n",
    "***readtext function could be merged with the cell below it\n"
   ]
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   "cell_type": "code",
   "execution_count": 103,
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   "source": [
    "# read txt file\n",
    "def readtxt(filename):    \n",
    "    with open(filename, 'r',encoding='gb18030', errors='ignore') as file_to_read:\n",
    "        content = file_to_read.read()\n",
    "#     print(content)\n",
    "    return content"
   ]
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  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "Document catagories are  ['football', 'cricket', 'athletics', 'rugby', 'tennis']\n",
      "now reading football docs...\n",
      "now reading cricket docs...\n",
      "now reading athletics docs...\n",
      "now reading rugby docs...\n",
      "now reading tennis docs...\n",
      "==============================\n",
      "There are 265 football documents\n",
      "There are 124 cricket documents\n",
      "There are 101 athletics documents\n",
      "There are 147 rugby documents\n",
      "There are 100 tennis documents\n"
     ]
    }
   ],
   "source": [
    "#read all the documents \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os,shutil\n",
    "########################################Change your folder first#############################\n",
    "folder='bbcsport' \n",
    "folders=os.listdir(folder)\n",
    "print('Document catagories are ',folders) \n",
    "\n",
    "summary_text=[]\n",
    "for item in folders:  # find each catagory's dir\n",
    "    each_catagory=[]\n",
    "    print('now reading',item,'docs...')\n",
    "    path=os.path.join(folder,item)\n",
    "    docs=os.listdir(path)\n",
    "    for doc in docs:  #find each doc in a folder\n",
    "        doc_dir= os.path.join(path, doc)\n",
    "        each_catagory.append(readtxt(doc_dir))\n",
    "    summary_text.append(each_catagory)\n",
    "print('==============================')    \n",
    "it=0\n",
    "for c in summary_text:\n",
    "    print('There are',len(c),folders[it],'documents')\n",
    "    it+=1\n",
    "    \n",
    "##summary_text contains the whole corpus, first index is catagories and second index is the number of document"
   ]
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    "### 2.2 Verify data quality: \n",
    "The following functions are designed to remove words from the vocabulary that are not relevant, or to find ones that should not be included. \n",
    "\n",
    "Stopwords need to be removed, because stop words like 'the','a','an' are so common in language and usually have no specific meaning.\n",
    "We also need to remove redundant words that have different capitalization. Eg. APPLE, apple\n",
    "\n",
    "***removeStopWords() could be merged with the cell below"
   ]
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   "source": [
    "from nltk.corpus import stopwords\n",
    "stoplist = stopwords.words('english')\n",
    "\n",
    "# remove stop words from a document. Return a list of words that contain no stop words.\n",
    "def removeStopWords(text):\n",
    "    cleanwordlist = [word for word in text.lower().split() if word not in stoplist]\n",
    "    return cleanwordlist\n"
   ]
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   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
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     "text": [
      "Now cleaning football documents. Total number 265\n",
      "Now cleaning cricket documents. Total number 124\n",
      "Now cleaning athletics documents. Total number 101\n",
      "Now cleaning rugby documents. Total number 147\n",
      "Now cleaning tennis documents. Total number 100\n",
      "Finished\n"
     ]
    }
   ],
   "source": [
    "it=0\n",
    "cleanWordLlist=[]\n",
    "for c in summary_text: # traverse each catagories\n",
    "    print('Now cleaning',folders[it],'documents.','Total number',len(c))\n",
    "    it+=1\n",
    "    each_catagory_clean=[]\n",
    "    for doc in c: #traverse each document\n",
    "        cleaned_words=removeStopWords(doc) #we get a list of words\n",
    "        cleaned_string=\"\"   # we need to concatenate all these words\n",
    "        for word in cleaned_words:\n",
    "            cleaned_string=cleaned_string+word+' '\n",
    "        \n",
    "        each_catagory_clean.append(cleaned_string)\n",
    "        \n",
    "    cleanWordLlist.append(each_catagory_clean)  \n",
    "print('Finished')"
   ]
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      "van nistelrooy hungry return manchester united striker ruud van nistelrooy said \"hungry play\" returned training tuesday. dutch striker, 28, closing comeback nearly three months achilles injury. \"i can't wait get back training meet team-mates again,\" told daily mirror newspaper. \"i'm ahead schedule i've made good progress physio holland. away united long time, i'm fit again.\" united boss sir alex ferguson hinted return van nistelrooy fa cup fifth round tie everton saturday. said: \"there chance could involved everton we'll see comes training.\" united lose 14 games van nistelrooy involved injury - although form remained good since sidelined. 2-0 derby win manchester city sunday made 13 wins 15 league games. however, van nistelrooy still club's top scorer season 12 goals, despite since 3-0 win west brom 27 november. united also hit injuries alan smith louis saha van nistelrooy's absence, meaning wayne rooney sometimes play lone role front. teenager responded six goals nine games, including first goal city sunday. \n",
      "----------------------------------------------------------------------------------------\n",
      "Van Nistelrooy hungry for return\n",
      "\n",
      "Manchester United striker Ruud van Nistelrooy said he was \"hungry to play\" as he returned to training on Tuesday.\n",
      "\n",
      "The Dutch striker, 28, is closing in on a comeback after nearly three months out with an Achilles injury. \"I can't wait to get back training and meet up with my team-mates again,\" he told the Daily Mirror newspaper. \"I'm ahead of schedule and I've made good progress with my physio in Holland. I have been away from United for a long time, but I'm fit again.\" United boss Sir Alex Ferguson hinted at a return for Van Nistelrooy in the FA Cup fifth round tie at Everton on Saturday. He said: \"There is a chance he could be involved at Everton but we'll just have to see how he comes through training.\" United did not lose any of the 14 games in which Van Nistelrooy was involved before his injury - although their form has remained good since he was sidelined. Their 2-0 derby win at Manchester City on Sunday made it 13 wins in 15 league games.\n",
      "\n",
      "However, Van Nistelrooy is still the club's top scorer this season with 12 goals, despite being out since the 3-0 win against West Brom on 27 November. United have also been hit by injuries to both Alan Smith and Louis Saha during Van Nistelrooy's absence, meaning Wayne Rooney has sometimes had to play in a lone role up front. The teenager has responded with six goals in nine games, including the first goal against City on Sunday.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# compare cleaned doc and non-cleaned doc\n",
    "#***Thisis meant to highlight the value of removing stopwords\n",
    "print(cleanWordLlist[0][0]) \n",
    "print('----------------------------------------------------------------------------------------')\n",
    "print(summary_text[0][0])     \n",
    "# print(cleanWordLlist[0])"
   ]
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    "### 2.3 Convert the data from raw text into a sparse encoded bag-of-words representation. \n",
    "\n",
    "Explain any parameters selected to convert to bag-of-words. \n",
    "\n",
    "max_df : \n",
    "    When building the vocabulary, ignore terms that have a document\n",
    "    frequency strictly higher than the given threshold (corpus-specific\n",
    "    stop words).  \n",
    "   We set this parameter to 0.8. Although we have already removed stop words, there are also some common words in the news,\n",
    "   for example 'said'. Reporters comments might always start as ... said ......\n",
    "   Therefore, other words similar to 'said' will be removed\n",
    "   \n",
    "min_df : \n",
    "    When building the vocabulary, ignore terms that have a document\n",
    "    frequency lower than the given threshold. This value is also\n",
    "    called cut-off in the literature.\n",
    " We set this parameter to 0.03, which makes each word at least appear 4 times. We reduced the number of rare words that unexpectedly occurred. Also some common names like Jimmy, Matt have been removed. "
   ]
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     "text": [
      "Now encoded bag-of-words football\n",
      "There are 265 document in football  And 1122 unique words (265, 1122)  \n",
      "\n",
      "Now encoded bag-of-words cricket\n",
      "There are 124 document in cricket  And 1395 unique words (124, 1395)  \n",
      "\n",
      "Now encoded bag-of-words athletics\n",
      "There are 101 document in athletics  And 932 unique words (101, 932)  \n",
      "\n",
      "Now encoded bag-of-words rugby\n",
      "There are 147 document in rugby  And 1218 unique words (147, 1218)  \n",
      "\n",
      "Now encoded bag-of-words tennis\n",
      "There are 100 document in tennis  And 1088 unique words (100, 1088)  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "##########............turns out CountVectorizer() have a parameter stop_words, which can automatically remove stop words........\n",
    "count_vect = CountVectorizer(max_df=0.8,min_df=0.03) # an object capable of counting words in a document!\n",
    "\n",
    "# count_vect.fit(summary_text)\n",
    "# count_vect.transform(summary_text)\n",
    "it=0\n",
    "all_bag_words=[]\n",
    "all_df=[]\n",
    "for each_catgory in cleanWordLlist: #encoded bag-of-words..  And create a pandas API out of this at the same time\n",
    "    print('Now encoded bag-of-words',folders[it])\n",
    "    bag_words = count_vect.fit_transform(each_catgory)\n",
    "    all_bag_words.append(bag_words)\n",
    "    \n",
    "    df = pd.DataFrame(data=bag_words.toarray(),columns=count_vect.get_feature_names()) #store as df\n",
    "    all_df.append(df)\n",
    "    print('There are',df.shape[0],'document in',folders[it], ' And',df.shape[1],'unique words', df.shape,' \\n') # print the shape of each category's df\n",
    "    it+=1\n"
   ]
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     "text": [
      "(265, 1122)\n",
      "=========\n",
      "  (0, 524)\t1\n",
      "  (0, 521)\t1\n",
      "  (0, 20)\t1\n",
      "  (0, 358)\t1\n",
      "  (0, 47)\t1\n",
      "  (0, 947)\t1\n",
      "  (0, 112)\t1\n",
      "  (0, 851)\t1\n",
      "  (0, 9)\t1\n",
      "  (0, 691)\t1\n",
      "  (0, 24)\t1\n",
      "  (0, 382)\t1\n",
      "  (0, 1)\t1\n",
      "  (0, 3)\t1\n",
      "  (0, 261)\t1\n",
      "  (0, 471)\t4\n",
      "  (0, 282)\t1\n",
      "  (0, 260)\t1\n",
      "  (0, 369)\t1\n",
      "  (0, 386)\t1\n",
      "  (0, 730)\t1\n",
      "  (0, 979)\t1\n",
      "  (0, 65)\t1\n",
      "  (0, 132)\t1\n",
      "  (0, 1038)\t1\n",
      "  :\t:\n",
      "  (0, 1014)\t2\n",
      "  (0, 532)\t1\n",
      "  (0, 298)\t1\n",
      "  (0, 849)\t2\n",
      "  (0, 294)\t1\n",
      "  (0, 907)\t2\n",
      "  (0, 720)\t1\n",
      "  (0, 573)\t2\n",
      "  (0, 304)\t2\n",
      "  (0, 840)\t1\n",
      "  (0, 67)\t2\n",
      "  (0, 668)\t1\n",
      "  (0, 1080)\t2\n",
      "  (0, 35)\t1\n",
      "  (0, 951)\t1\n",
      "  (0, 1031)\t3\n",
      "  (0, 674)\t2\n",
      "  (0, 99)\t1\n",
      "  (0, 804)\t2\n",
      "  (0, 383)\t4\n",
      "  (0, 742)\t1\n",
      "  (0, 73)\t1\n",
      "  (0, 270)\t2\n",
      "  (0, 697)\t2\n",
      "  (0, 51)\t4\n"
     ]
    }
   ],
   "source": [
    "# Let's print athletics bag_words as example\n",
    "print(all_bag_words[0].shape) # this is a sparse matrix\n",
    "print('=========')\n",
    "print(bag_words[0])"
   ]
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      "1088\n",
      "{'agassi': 51, 'past': 697, 'dent': 270, 'andre': 73, 'put': 742, 'fourth': 383, 'round': 804, 'australian': 99, 'open': 674, 'victory': 1031, 'taylor': 951, '34': 35, 'year': 1080, 'old': 668, 'american': 67, 'seeded': 840, 'eighth': 304, 'made': 573, 'poor': 720, 'start': 907, 'dropping': 294, 'serve': 849, 'early': 298, 'later': 532, 'two': 1014, 'chances': 176, 'set': 853, 'secured': 837, 'lead': 534, 'still': 915, 'failed': 338, 'take': 944, 'control': 224, 'players': 714, 'forced': 376, 'breaks': 146, 'tie': 984, 'break': 144, 'win': 1060, 'seed': 839, 'survived': 935, 'injury': 491, 'scare': 827, 'battled': 113, 'mario': 585, 'ancic': 71, 'russian': 813, 'turned': 1012, 'right': 794, 'ankle': 77, 'third': 975, 'game': 395, 'called': 155, 'treatment': 1003, 'immediately': 477, 'showed': 869, 'problem': 733, 'court': 233, 'hours': 469, 'minutes': 614, 'wimbledon': 1059, 'semi': 843, 'finalist': 359, '2004': 22, 'looked': 559, 'push': 741, 'safin': 814, 'way': 1043, 'took': 997, 'second': 835, 'croatian': 236, 'trying': 1009, 'keep': 518, 'tournament': 1001, 'hit': 455, 'head': 440, 'one': 673, 'try': 1008, 'stay': 912, 'go': 409, 'like': 544, 'might': 610, 'never': 649, 'come': 201, 'back': 104, 'he': 439, 'tough': 999, 'opponent': 678, 'little': 550, 'bit': 135, 'enough': 314, 'added': 44, 'worried': 1074, 'lot': 568, 'problems': 734, 'easy': 300, 'retire': 786, 'match': 590, 'top': 998, 'defending': 264, 'champion': 171, 'leading': 535, 'pulled': 740, 'federer': 344, 'form': 377, '19': 12, 'unforced': 1018, 'errors': 318, 'winners': 1062, 'world': 1073, 'number': 662, 'play': 711, 'next': 651, 'former': 378, 'junior': 513, 'beat': 116, 'tommy': 996, 'robredo': 799, 'admitted': 46, 'pressure': 729, 'winning': 1063, 'streak': 918, 'career': 164, 'best': 131, '24': 27, 'they': 971, 'used': 1026, 'simple': 872, 'feeling': 346, 'could': 229, 'always': 64, 'want': 1038, 'better': 132, 'am': 65, 'thought': 979, 'pretty': 730, 'french': 386, 'five': 369, 'defeat': 260, 'dominik': 282, 'hrbaty': 471, 'defeated': 261, '10th': 3, '10': 1, 'four': 382, '21': 24, 'pair': 691, '16': 9, 'service': 851, 'battle': 112, 'taking': 947, 'advantage': 47, 'final': 358, '2002': 20, 'kevin': 521, 'kim': 524, 'tennis': 956, 'coach': 199, 'approach': 85, 'roger': 802, 'new': 650, 'full': 391, 'time': 988, 'say': 824, 'reports': 782, 'melbourne': 605, 'troubled': 1007, 'hip': 453, 'again': 50, 'however': 470, 'happy': 435, 'work': 1070, 'swiss': 940, 'star': 906, 'basis': 110, 'month': 626, 'defence': 262, 'crown': 239, 'without': 1067, 'since': 874, 'peter': 704, 'lundgren': 572, '2003': 21, 'davis': 247, 'cup': 241, 'player': 713, 'australia': 98, 'reached': 766, 'us': 1023, 'finals': 361, 'doubles': 286, 'titles': 992, 'john': 509, 'also': 62, 'ivan': 500, 'grand': 417, 'slam': 880, 'worked': 1071, 'lleyton': 556, 'hewitt': 450, 'claim': 185, 'wanted': 1039, 'confirmed': 216, 'would': 1076, '2005': 23, 'named': 642, 'saturday': 820, 'add': 43, 'bbc': 115, 'sports': 901, 'association': 93, 'already': 61, 'henman': 447, 'makes': 577, 'tim': 987, 'campaign': 157, 'argentine': 87, 'david': 246, 'nalbandian': 640, 'kooyong': 528, 'classic': 190, 'exhibition': 329, 'wednesday': 1046, 'british': 148, 'warm': 1041, 'up': 1021, 'event': 320, 'friday': 388, 'gaston': 397, 'gaudio': 398, 'saw': 823, 'olympic': 670, 'gold': 413, 'nicolas': 653, 'massu': 589, 'andy': 75, 'roddick': 801, 'ljubicic': 554, 'paradorn': 692, 'srichaphan': 903, 'impressive': 480, 'serving': 852, 'briton': 149, 'games': 396, 'six': 878, 'matches': 592, 'it': 497, 'great': 418, 'looking': 560, 'for': 374, 'told': 995, 'website': 1045, 'over': 686, 'years': 1081, 've': 1027, 'found': 381, 'difficult': 275, 'returns': 790, 'well': 1050, 'sometimes': 890, 'get': 403, 'result': 785, 'beating': 118, 'somebody': 887, 'good': 415, 'confidence': 214, 'beginning': 124, 'revealed': 791, 'suffered': 926, 'off': 665, 'season': 833, 'end': 309, 'couple': 231, 'weeks': 1049, 'anything': 83, 'when': 1053, 'started': 908, 'really': 768, 'much': 632, 'enjoyed': 313, 'think': 974, 'dropped': 293, 'tight': 986, 'first': 366, 'content': 219, 'getting': 405, 'surface': 932, 'the': 967, 'doha': 280, 'served': 850, 'all': 58, 'that': 966, 'day': 249, 'hopefully': 461, 'delighted': 267, 'months': 627, 'felt': 351, 'played': 712, 'and': 72, 'maybe': 597, 'things': 973, 'sharapova': 859, 'overcomes': 689, 'molik': 620, 'maria': 584, 'recovered': 773, 'losing': 565, 'opening': 675, 'qatar': 744, 'alicia': 57, 'title': 991, 'amelie': 66, 'mauresmo': 594, 'broke': 150, 'went': 1051, 'healthy': 441, 'fifth': 354, 'decider': 256, 'although': 63, 'stayed': 913, 'held': 443, 'finished': 365, 'ace': 41, 'point': 718, 'key': 522, 'positive': 722, 'mind': 613, 'in': 482, 'playing': 715, 'decided': 255, 'ball': 107, 'rest': 784, 'schedule': 828, 'need': 644, 'see': 838, 'moya': 631, 'emotional': 308, 'carlos': 165, 'spain': 893, 'usa': 1024, 'challenge': 170, 'seville': 858, 'missing': 618, '2000': 18, 'give': 407, 'hosts': 467, 'unassailable': 1017, 'many': 580, 'energy': 312, 'focused': 370, 'today': 994, 'what': 1052, 'lived': 552, 'live': 551, 'came': 156, 'ago': 53, 'dream': 291, 'nervous': 647, 'outset': 685, 'some': 886, 'people': 701, 'helps': 445, 'reach': 765, 'goals': 410, 'incredible': 485, 'something': 889, 'spanish': 895, 'captain': 161, 'jordi': 510, 'arrese': 89, 'opportunity': 680, 'let': 540, 'down': 288, 'lost': 567, 'three': 980, 'times': 989, 'him': 452, 'waiting': 1037, 'position': 721, 'remarkable': 781, 'performance': 703, 'rafael': 756, 'nadal': 638, 'singles': 875, 'aged': 52, '18': 11, 'days': 250, 'became': 119, 'youngest': 1086, 'finish': 364, 'afterwards': 49, 'mcenroe': 598, 'wants': 1040, 'team': 952, 'clay': 191, 'help': 444, 'guys': 423, 'even': 319, 'slow': 884, 'hard': 436, 'courts': 234, 'obviously': 663, 'we': 1044, 're': 764, 'going': 412, 'but': 152, 'certainly': 169, 'events': 321, 'stuff': 922, 'left': 539, 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30, 'september': 845, 'cannot': 159, 'turn': 1011, 'around': 88, 'consecutive': 217, 'earn': 299, 'tired': 990, 'tiebreak': 985, 'net': 648, 'score': 831, 'volley': 1035, '40': 36, 'hold': 457, 'making': 578, 'previous': 731, '30': 32, 'upset': 1022, 'support': 930, 'clinching': 196, 'defend': 263, 'plays': 716, 'comfortable': 204, 'takes': 946, 'seize': 842, 'comes': 203, 'tested': 961, 'clinch': 195, 'wide': 1056, 'far': 340, 'lucky': 571, 'middle': 609, 'umpire': 1015, 'call': 154, 'short': 865, 'cross': 237, 'clash': 189, 'claims': 188, 'race': 752, 'ruled': 808, 'future': 393, 'needs': 646, 'relief': 777, 'longer': 558, 'shocked': 863, 'whether': 1054, 'to': 993, 'after': 48, 'johansson': 508, 'handle': 429, 'happier': 434, 'sydney': 942, 'nadia': 639, 'petrova': 705, 'runner': 810, 'ferrero': 353, 'juan': 512, 'injuries': 490, 'confident': 215, 'mentally': 608, 'rainer': 758, 'schuettler': 830, 'joachim': 506, 'difference': 273, 'rib': 793, 'physically': 707, 'december': 253, 'fell': 349, 'among': 68, 'hand': 427, 'history': 454, 'fellow': 350, 'rubbers': 807, 'retirement': 788, 'quality': 746, 'dubai': 295, 'moved': 630, 'russia': 812, 'bovina': 143, 'progressed': 737, 'china': 182, 'hurt': 473, 'conchita': 213, 'martinez': 588, 'ousted': 683, 'nathalie': 643, 'dechy': 254, 'france': 384, 'patty': 699, 'schnyder': 829, 'switzerland': 941, 'li': 542, 'na': 637, 'sania': 817, 'mirza': 616, 'india': 486, 'jankovic': 502, 'serbia': 846, 'montenegro': 625, 'tuesday': 1010, 'so': 885, 'younger': 1085, 'sister': 876, 'bye': 153, 'fast': 342, 'shock': 862, 'kiefer': 523, 'championships': 174, 'german': 401, 'striking': 919, 'momentum': 622, 'breaking': 145, 'feliciano': 348, 'lopez': 563, 'thailand': 963, 'fabrice': 334, 'santoro': 818, 'sixth': 879, 'nikolay': 655, 'davydenko': 248, 'mikhail': 611, 'youzhny': 1087, 'course': 232, 'progress': 736, 'seal': 832, 'frustrating': 390, 'according': 40, 'yet': 1082, 'her': 448, 'did': 272, 'conceded': 212, 'fact': 337, 'improve': 481, 'received': 770, 'worse': 1075, 'announcement': 80, 'though': 978, 'places': 710, 'debut': 252, 'step': 914, 'move': 629, 'competing': 209, 'talented': 948, 'job': 507, 'saved': 822, 'fighting': 356, 'racket': 755, 'thanks': 965, 'secure': 836, 'mates': 593, 'body': 137, 'quick': 748, 'continued': 222, 'indian': 487, 'hyderabad': 474, 'needed': 645, 'quickly': 749, 'comeback': 202, 'farina': 341, 'elia': 307, '22': 25, 'she': 860, 'hang': 430, 'meet': 602, 'likhovtseva': 545, 'series': 848, 'appearance': 84, 'sebastien': 834, 'completed': 210, 'albert': 55, 'knee': 525, 'picked': 708, 'use': 1025, 'tournaments': 1002, 'lee': 538, 'practice': 727, 'available': 101, 'experience': 331, 'championship': 173, 'successes': 924, 'deep': 259, 'cool': 227, 'rhythm': 792, 'explain': 332, 'badly': 106, 'balls': 108, 'trouble': 1006, 'boost': 140, 'frenchman': 387, 'horrible': 465, 'extremely': 333, 'falls': 339, 'adelaide': 45, 'hardcourt': 437, 'ignacio': 476, 'chela': 179, 'olivier': 669, 'rochus': 800, 'swede': 937, 'ginepri': 406, 'robby': 798, '13': 6, 'taik': 943, 'czech': 243, 'republic': 783, 'qualifier': 745, 'kenneth': 519, 'carlsen': 166, 'criticised': 235, 'issue': 496, 'equal': 317, 'disputed': 278, 'club': 198, 'definitely': 265, 'aside': 91, 'free': 385, 'spectators': 897, 'everyone': 325, 'martina': 587, 'volvo': 1036, 'pattaya': 698, 'surgery': 933, 'october': 664, 'champions': 172, 'nice': 652, 'lisa': 548, 'raymond': 763, 'karol': 517, 'antwerp': 82, 'considered': 218, 'paris': 693, 'recovery': 774, 'then': 969, 'due': 296, 'stages': 905, 'withdraw': 1065, 'tried': 1004, '1998': 15, 'means': 600, 'ivo': 501, 'minar': 612, 'quite': 751, '1994': 14, 'samantha': 815, 'stosur': 916, 'nicole': 654, 'at': 94, 'pull': 739, '20': 17, 'angry': 76, 'tear': 953, 'chennai': 180, 'efforts': 302, 'hoping': 463, 'lives': 553, '32': 34, 'remaining': 779, 'life': 543, 'professional': 735, '11': 4, 'belgium': 128, 'powered': 725, 'anna': 78, '23': 26, 'including': 484, 'wrong': 1078, 'talk': 949, 'thank': 964, 'successful': 925, '1999': 16, 'blow': 136, 'justin': 515, 'feels': 347, 'november': 660}\n"
     ]
    }
   ],
   "source": [
    "# print tennis document dictionary as example\n",
    "print(len(count_vect.vocabulary_))\n",
    "print(count_vect.vocabulary_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>265 rows × 1122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     000  10  11  12  13  14  15  16  17  18  ...    would  wright  wrong  \\\n",
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       "\n",
       "     yard  yards  year  years  yet  you  young  \n",
       "0       0      0     0      0    0    0      0  \n",
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       "\n",
       "[265 rows x 1122 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's still use athletics as example. First print its df\n",
    "all_df[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "league     237\n",
       "players    240\n",
       "united     246\n",
       "arsenal    247\n",
       "we         261\n",
       "club       274\n",
       "would      287\n",
       "game       297\n",
       "chelsea    305\n",
       "said       475\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 most common words in our athletics data\n",
    "all_df[0].sum().sort_values()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deny          8\n",
       "fierce        8\n",
       "afterwards    8\n",
       "myself        8\n",
       "sol           8\n",
       "using         8\n",
       "effect        8\n",
       "spoke         8\n",
       "spoken        8\n",
       "alone         8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 least common words in our athletics data\n",
    "all_df[0].sum().sort_values()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Convert the data into a sparse encoded tf-idf representation\n",
    "\n",
    "Explain any parameters selected to convert to tf-idf representation. \n",
    "\n",
    "Because using tf-idf representation to define the importance of word makes more sence than just using frequency \n",
    "We can expand the interval a little bit\n",
    "\n",
    "max_df : \n",
    "   We set this parameter to 0.85. Although we have already remove stop words, there are also some common words in the sport news,\n",
    "   for example 'said'. Reporters comments might always start as ... said ......\n",
    "   Therefore, other words similar to 'said' will be removed\n",
    "   \n",
    "min_df : \n",
    " We set this parameter to 0.02. We reduced the number of rare words that unexpectedly occurred. Also some common names like Jimmy, Matt have been removed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now encoded tf-idf representation football\n",
      "There are 265 document in football  And 1470 unique words (265, 1470)  \n",
      "\n",
      "Now encoded tf-idf representation cricket\n",
      "There are 124 document in cricket  And 1809 unique words (124, 1809)  \n",
      "\n",
      "Now encoded tf-idf representation athletics\n",
      "There are 101 document in athletics  And 1240 unique words (101, 1240)  \n",
      "\n",
      "Now encoded tf-idf representation rugby\n",
      "There are 147 document in rugby  And 1866 unique words (147, 1866)  \n",
      "\n",
      "Now encoded tf-idf representation tennis\n",
      "There are 100 document in tennis  And 1644 unique words (100, 1644)  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf_vect = TfidfVectorizer(max_df=0.85,min_df=0.02) # an object capable of counting words in a document!\n",
    "\n",
    "it=0\n",
    "all_tfidf_mat=[]\n",
    "all_tfidf_df=[]\n",
    "for each_catgory in cleanWordLlist: #encoded  tf-idf representation.  And create a pandas API out of this at the same time\n",
    "    print('Now encoded tf-idf representation',folders[it])\n",
    "    tfidf_mat = tfidf_vect.fit_transform(each_catgory)\n",
    "    all_tfidf_mat.append(tfidf_mat)\n",
    "    \n",
    "    df = pd.DataFrame(data=tfidf_mat.toarray(),columns=tfidf_vect.get_feature_names()) #store as df\n",
    "    all_tfidf_df.append(df)\n",
    "    print('There are',df.shape[0],'document in',folders[it], ' And',df.shape[1],'unique words', df.shape,' \\n') # print the shape of each category's df\n",
    "    it+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067594</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035730</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.063995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.089072</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093112</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035144</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032529</td>\n",
       "      <td>0.045705</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143897</td>\n",
       "      <td>0.154190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.072955</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066865</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062174</td>\n",
       "      <td>0.043678</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037697</td>\n",
       "      <td>0.052965</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035284</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.109712</td>\n",
       "      <td>0.048018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029865</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053489</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103741</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.082614</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.119520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.077523</td>\n",
       "      <td>0.067861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042205</td>\n",
       "      <td>0.059300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039545</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.187430</td>\n",
       "      <td>0.082034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071685</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057363</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.102739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040691</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047104</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039358</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041619</td>\n",
       "      <td>0.041619</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.180636</td>\n",
       "      <td>0.038857</td>\n",
       "      <td>0.052263</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088942</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.085816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085816</td>\n",
       "      <td>0.023053</td>\n",
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       "      <td>0.056228</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050318</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032529</td>\n",
       "      <td>0.045705</td>\n",
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       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
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       "      <td>...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>265 rows × 1470 columns</p>\n",
       "</div>"
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       "10   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.089072   \n",
       "11   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "12   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "13   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "14   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "15   0.000000  0.000000  0.000000    ...     0.093112  0.000000  0.035144   \n",
       "16   0.000000  0.000000  0.000000    ...     0.000000  0.032529  0.045705   \n",
       "17   0.000000  0.000000  0.141704    ...     0.000000  0.072955  0.000000   \n",
       "18   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "19   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "20   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "21   0.000000  0.000000  0.000000    ...     0.000000  0.062174  0.043678   \n",
       "22   0.000000  0.000000  0.000000    ...     0.000000  0.037697  0.052965   \n",
       "23   0.000000  0.000000  0.000000    ...     0.000000  0.035284  0.000000   \n",
       "24   0.000000  0.000000  0.000000    ...     0.000000  0.029865  0.000000   \n",
       "25   0.000000  0.082614  0.000000    ...     0.000000  0.000000  0.119520   \n",
       "26   0.000000  0.000000  0.081978    ...     0.000000  0.042205  0.059300   \n",
       "27   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "28   0.000000  0.000000  0.000000    ...     0.000000  0.027852  0.000000   \n",
       "29   0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.071685   \n",
       "..        ...       ...       ...    ...          ...       ...       ...   \n",
       "235  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "236  0.000000  0.000000  0.000000    ...     0.000000  0.057363  0.000000   \n",
       "237  0.000000  0.000000  0.000000    ...     0.000000  0.041708  0.000000   \n",
       "238  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "239  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "240  0.000000  0.049156  0.000000    ...     0.094208  0.000000  0.000000   \n",
       "241  0.000000  0.041619  0.041619    ...     0.000000  0.000000  0.180636   \n",
       "242  0.000000  0.000000  0.000000    ...     0.000000  0.090609  0.000000   \n",
       "243  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "244  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "245  0.000000  0.000000  0.000000    ...     0.085816  0.023053  0.000000   \n",
       "246  0.000000  0.000000  0.000000    ...     0.000000  0.050318  0.000000   \n",
       "247  0.000000  0.000000  0.000000    ...     0.000000  0.032529  0.045705   \n",
       "248  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "249  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.065321   \n",
       "250  0.000000  0.000000  0.000000    ...     0.000000  0.035401  0.000000   \n",
       "251  0.000000  0.095655  0.000000    ...     0.000000  0.098493  0.000000   \n",
       "252  0.000000  0.000000  0.000000    ...     0.000000  0.026995  0.000000   \n",
       "253  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "254  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "255  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "256  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "257  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.078840   \n",
       "258  0.000000  0.092913  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "259  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "260  0.000000  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "261  0.000000  0.000000  0.000000    ...     0.000000  0.032417  0.045547   \n",
       "262  0.000000  0.096633  0.000000    ...     0.000000  0.000000  0.139801   \n",
       "263  0.043467  0.000000  0.000000    ...     0.000000  0.000000  0.000000   \n",
       "264  0.000000  0.000000  0.000000    ...     0.000000  0.021782  0.000000   \n",
       "\n",
       "          yet  yorkshire       you     young  youth  zidane  zinedine  \n",
       "0    0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "1    0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "2    0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "3    0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "4    0.000000   0.000000  0.044140  0.000000    0.0     0.0       0.0  \n",
       "5    0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "6    0.000000   0.000000  0.067594  0.000000    0.0     0.0       0.0  \n",
       "7    0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "8    0.000000   0.000000  0.063995  0.000000    0.0     0.0       0.0  \n",
       "9    0.000000   0.000000  0.064480  0.000000    0.0     0.0       0.0  \n",
       "10   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "11   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "12   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "13   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "14   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "15   0.000000   0.000000  0.000000  0.045359    0.0     0.0       0.0  \n",
       "16   0.000000   0.000000  0.000000  0.058990    0.0     0.0       0.0  \n",
       "17   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "18   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "19   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "20   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "21   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "22   0.000000   0.000000  0.000000  0.068361    0.0     0.0       0.0  \n",
       "23   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "24   0.000000   0.000000  0.053489  0.000000    0.0     0.0       0.0  \n",
       "25   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "26   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "27   0.000000   0.000000  0.000000  0.039545    0.0     0.0       0.0  \n",
       "28   0.000000   0.000000  0.049885  0.000000    0.0     0.0       0.0  \n",
       "29   0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "..        ...        ...       ...       ...    ...     ...       ...  \n",
       "235  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "236  0.000000   0.000000  0.102739  0.000000    0.0     0.0       0.0  \n",
       "237  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "238  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "239  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "240  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "241  0.038857   0.052263  0.000000  0.000000    0.0     0.0       0.0  \n",
       "242  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "243  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "244  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "245  0.000000   0.056228  0.000000  0.000000    0.0     0.0       0.0  \n",
       "246  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "247  0.000000   0.000000  0.000000  0.058990    0.0     0.0       0.0  \n",
       "248  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "249  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "250  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "251  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "252  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "253  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "254  0.042758   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "255  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "256  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "257  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "258  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "259  0.000000   0.052087  0.000000  0.000000    0.0     0.0       0.0  \n",
       "260  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "261  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "262  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "263  0.000000   0.047088  0.000000  0.000000    0.0     0.0       0.0  \n",
       "264  0.000000   0.000000  0.000000  0.000000    0.0     0.0       0.0  \n",
       "\n",
       "[265 rows x 1470 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's still use athletics as example. First print its df\n",
    "all_tfidf_df[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "it          8.004725\n",
       "league      8.086000\n",
       "arsenal     8.680101\n",
       "would       8.683293\n",
       "game        8.876416\n",
       "we          9.170290\n",
       "club        9.519287\n",
       "united      9.541094\n",
       "chelsea    11.372855\n",
       "said       12.486054\n",
       "dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 most common words in our athletics data\n",
    "all_tfidf_df[0].sum().sort_values()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "successful     0.287440\n",
       "ricardo        0.303306\n",
       "yorkshire      0.317105\n",
       "brilliantly    0.351871\n",
       "by             0.356581\n",
       "dream          0.370856\n",
       "figure         0.372444\n",
       "perfectly      0.373323\n",
       "path           0.376177\n",
       "broke          0.379614\n",
       "dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 least common words in our athletics data\n",
    "all_tfidf_df[0].sum().sort_values()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Visualize statistical summaries of the text data -----word frequencies, most relevant words, document lengths, vocabulary size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 Visualize the word frequencies and most relevant words of the whole dataset\n",
    "\n",
    "***To assess the validity of our models, we present the following visuals to assess: word frequencies, most relevant words, document lengths, and vocabulary size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "737"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## collect all the words from the dataset (after removed stop words)\n",
    "corpus=[]\n",
    "for each_catagory in cleanWordLlist:\n",
    "    for each_doc in each_catagory:\n",
    "        corpus.append(each_doc)\n",
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The corpus has 4727 unique words\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vect = CountVectorizer(max_df=0.5,min_df=0.005) # an object capable of counting words in a document!\n",
    "\n",
    "#encoded bag-of-words..  And create a pandas API out of this at the same time\n",
    "corpus_bag_words = count_vect.fit_transform(corpus)\n",
    "corpus_df = pd.DataFrame(data=corpus_bag_words.toarray(),columns=count_vect.get_feature_names()) #store as df\n",
    "print('The corpus has',corpus_bag_words.shape[1],'unique words') # this is a sparse matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We manually adjusted max_df=0.5,min_df=0.005 to remove words like 'said'and makes each word at least appear 4 times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "last       549\n",
       "win        551\n",
       "time       561\n",
       "it         574\n",
       "would      581\n",
       "we         589\n",
       "two        605\n",
       "year       647\n",
       "game       681\n",
       "england    749\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 most common words in our data\n",
    "corpus_df.sum().sort_values()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuming      4\n",
       "features    4\n",
       "favours     4\n",
       "favoured    4\n",
       "farewell    4\n",
       "familiar    4\n",
       "falls       4\n",
       "fairness    4\n",
       "awful       4\n",
       "fading      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 least common words in our  data\n",
    "corpus_df.sum().sort_values()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare with the previous condition that we made bag-of-words by each category:\n",
    "\n",
    "There are 101 document in athletics  And 932 unique words (101, 932)  \n",
    "There are 124 document in cricket  And 1395 unique words (124, 1395)  \n",
    "There are 265 document in football  And 1122 unique words (265, 1122)  \n",
    "There are 147 document in rugby  And 1218 unique words (147, 1218)  \n",
    "There are 100 document in tennis  And 1088 unique words (100, 1088)  \n",
    "\n",
    "So for these sports news, there are about 5000 words that often appear and make sense.\n",
    "\n",
    "Then let's see the tf-idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The corpus has 4729 unique words\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf_vect = TfidfVectorizer(max_df=0.75,min_df=0.005) # an object capable of counting words in a document!\n",
    "\n",
    "#encoded  tf-idf representation.  And create a pandas API out of this at the same time\n",
    "corpus_tfidf_mat = tfidf_vect.fit_transform(corpus)\n",
    "corpus_tfidf_df = pd.DataFrame(data=corpus_tfidf_mat.toarray(),columns=tfidf_vect.get_feature_names()) #store as df\n",
    "print('The corpus has',corpus_tfidf_mat.shape[1],'unique words') # this is a sparse matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one        14.687249\n",
       "win        14.933463\n",
       "would      15.358223\n",
       "world      15.441429\n",
       "it         15.587404\n",
       "year       16.578932\n",
       "game       16.891177\n",
       "we         17.423121\n",
       "first      17.491276\n",
       "england    21.890783\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 most common words in our data\n",
    "corpus_tfidf_df.sum().sort_values()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "拢100m            0.155625\n",
       "ideally          0.164691\n",
       "prompted         0.168004\n",
       "scamper          0.168593\n",
       "brutal           0.171588\n",
       "cheaply          0.172368\n",
       "smash            0.172662\n",
       "causes           0.174276\n",
       "possibilities    0.174310\n",
       "ante             0.176010\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out 10 least common words in our data\n",
    "corpus_tfidf_df.sum().sort_values()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this python magics will allow plot to be embedded into the notebook\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter('ignore', DeprecationWarning)\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Top20words=corpus_df.sum().sort_values()[-20:]\n",
    "Top20relvantwords = corpus_tfidf_df.max().sort_values()[-20:]\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "fig = plt.figure(figsize=(14,10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "Top20words.plot(kind='barh')\n",
    "plt.title('Word Frequency of BBC Datasets')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "Top20relvantwords.plot(kind='barh')\n",
    "\n",
    "#plt.subplot(1,3,3)\n",
    "#df_imputed.Age.plot.hist(bins=20, )\n",
    "#df_imputed.Age.plot.kde(bw_method=0.1, secondary_y=True)\n",
    "plt.title('Most Relevant Words of cricket data (tf-idf)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 Visualize document lengths and vocabulary size,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Len\n",
       "0  1025\n",
       "1  1182\n",
       "2   991\n",
       "3  1484\n",
       "4  1869"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###### Visualize document lengths \n",
    "\n",
    "# get the lengths of all the document\n",
    "all_doc_length=[]\n",
    "for each_catagory in cleanWordLlist:\n",
    "    for each_doc in each_catagory:\n",
    "        all_doc_length.append(len(each_doc))\n",
    "length_df=pd.DataFrame()\n",
    "length_df['Len']=all_doc_length\n",
    "length_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:\n",
      "\n",
      "The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a126b24e0>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True) # one of the many color mappings\n",
    "sns.distplot(length_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = length_df.boxplot(column='Len')\n",
    "#violinplot\n",
    "# f, ax = plt.subplots(figsize=(4, 4))\n",
    "# sns.violinplot(y=\"Len\", data=length_df, \n",
    "#                split=True, orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S0LdvX+OxRo0a9VznJ4QQQgipTL3vgPNPqnEDavR40q0nKt1my5Yt+PXXX+Hq6vpcx758+TLkcjm8vb2rG48QQgghxKSoZdLE5syZg8TERIwZMwZr167FmDFj4OvriwEDBiAsLAwAkJ2dXWZ9UlIStm3bhp9//hn+/v74888/AQDnz5/HoEGD0KtXL5w6dQpASQvkoEGD0L9/f/Tv379UayYhhBBCiClRMWli3333HZydnREQEIDk5GR4eHjg/PnzmD9/PqZPnw4A+Oabb8qsb968OcaOHYvJkycjKCgIvXr1AgAkJycjMDAQu3fvxty5c6FSqaBUKrFv3z788ccf2LBhAxYsWMDnJRNCCCGkAWlwt7n5dO3aNWzevBkA4OPjg+zsbOTl5VW4vjxvvfUWBAIBWrZsCVdXV0RHR8PV1RXz5s1DWFgYBAIB4uLizHZNhBBCCGnYqJg0o/KG9OQ4rsL15fnneo7jsH79ejg6OiIoKAgGgwEuLi41E5gQQgghpBJ0m9uMevfujQMHDgAo6Vxjb28PKyurCtcrFAoUFBSUOsbhw4dhMBgQHx+PxMREtG7dGvn5+XBycoJAIMC+ffug1+vNfm2EEEIIaZioZdKM5s6di5kzZ8LX1xcymQw//fTTM9e/9tprmDBhAk6ePImvv/4aANC6dWu89dZbSE9Px7fffgupVIrx48dj/PjxOHLkCPr06QOZTMbbNRJCCCGkYan30ynev38fEomE7xh1kkajQdOmTfmOQQghhJBajG5zE0IIIYSQaqNikhBCCCGEVBsVk4QQQgghpNqomCSEkAbsxx9/RIcOHTBq1Kjn2u/cuXO4cuWKcXncuHHYv39/lfdPSEhAp06djMd68803n+v8hJDag3pzE0JIA7Z27VqcOHECLVq0eK79zp07B4VCgX/9618mSkYIqSuoZZIQQhqoKVOmIC4uDoMHD8bKlSsxZMgQeHp6olevXggNDQUAZGVllVmfkJCA9evXY9WqVejcuTMuXrwIADhz5gx8fHzQtm1bHD16FEBJC6SPjw+6du2Krl27lmrNJITUD1RM1gInT57Ejz/+WOHrSUlJ6Nu3b5WPl5SUZBwEnRBCKrJ+/Xo0adIEQUFBSEhIQJcuXRAaGorly5djzJgxAIDFixeXWe/m5oYpU6Zg1qxZuH37Nnx8fACUFI7nz5/HsWPHMGXKFKhUKjRq1AinT5/GrVu3sGfPHsyYMYPPSyaEmECDu839xta/a/R4x8d5vND+Op0Or7/+Ol5//fUaSgQkJycjICAAw4YNq7FjElJfML0e+qx06DPSoM9IgyEvB4aiArDiIhiKi8CKC2EoevInDHpAJAInFAJCETihCBAKS/4UiyCQW0FoYw+BrR2EtvYQ2Dz+0x4Ca5sKp0atbS5dumT8T2i/fv2QmZmJ3NzcCteX591334VAIECbNm3QsmVLREREoEWLFpg+fTpu374NoVCIqKgos10TIcQ8GlwxyYc9e/Zg7dq14DgO7u7uEAqFsLW1xZ07d+Dh4QF3d3fcvn0bK1asQFpaGubOnYvExEQAwDfffANnZ2fjsRISEjBhwgSsXLkSnp6e+Oqrr3DlyhWo1WpMmDABY8eOxdKlSxEVFQV/f3+MGDECU6ZM4evSCeGNLu0htLER0CbFQZdyD7qH96B7eB/6zIySAtEcRCKInJtC3MwNomZuJX+6uEHs0gICucI8GaqovPkrOI6rcH15/rme4zisWrUKTk5OCAkJgcFggFQqrZnAhJBag4pJE4uIiMAPP/yAo0ePwsHBAdnZ2fjiiy8QFxeH/fv3QygU4rfffjNuv2DBAvzrX//Ctm3boNfrUVhYiJycHABATEwMPvzwQ6xevRoeHh7Yvn07rK2tcerUKajVarz55pvw8/PDwoULsXbtWuzatYuvyybEbJjBAN29RGjiIqGNjYQmNhLa+EgY8spvPTMrnQ66e4nQ3UsEcL7USwJbB4jdWsGivQckHTxh0d4TAoUVPzkB9O3bF7t27cKiRYtw7tw5KJVKWFtbV7jeysoKeXl5pY6xb98+jB07FvHx8YiLi0O7du2Qm5uLZs2aQSAQGH+vEULqFyomTezSpUsYNGgQHBwcAAB2dnYAgEGDBkEoFJa7/Zo1awAAQqEQ1tbWyMnJQUZGBsaMGYPNmzejffv2AEp6U4aHh+PIkSMAgPz8fMTFxdH0kaReY4xBmxAD9e1rUN2+DnXYbbDiIr5jPTdDTibUtzOhvn29ZAXHQdS8BSzae8Gigyck7p4QN3U1W54lS5Zg/Pjx8PT0hEwmw7Zt2565ftCgQXjnnXcQGBiIn376CQDQrl07+Pr6IjU1FevXr4dUKsW0adMwbNgw7Nu3D/7+/pDL5Wa7JkKIeVAxaWKMsXJvCT3vL1Rra2s0adIE169fNxaTjDEsX74c/fr1K7Xt5cuXqx+YkFpIl5YC1V/XoL59HarQGzDkZPEdqeYxBl1iHHSJcSj8/SAAQOjoDEtvH0i9fSD16g5OXPP/UUxISDD+PTAwsMzr9vb25a5v27atscc3AGMnnH9q06ZNqe2+/vprAICbmxvu3LkDAPDz84Ofn1914hNCagEqJk3Mx8cH48aNw+TJk2Fvb4/s7OxKt9+6dSsmT54MvV6PoqKSFheJRIJt27ZhxIgRkMvlGDZsGPz9/bF161b4+PhALBYjNjYWzs7OUCgUKCgoMMflEWIy2nsJKLpwGkUXT0OXFMd3HF7o0x+i4Ng+FBzbB85SBmnnnrDs2RdS75cgtLHjOx4hhACgYtLk2rdvj48//hhDhgyBQCCAh8eze38vXboUc+bMwe7duyEQCPDNN9/AyckJQElr5q5duzB8+HDIZDKMHj0aycnJePnll8EYg4ODA7Zt2wZ3d3eIRCL4+flh5MiR1AGH1Bm6lHsounAKRRdPQxsfzXecWoUVF6H4ahCKrwYBAgEk7T0g7zcQMt9XIZDVrs48hJCGhWPlddWrR+7fv0/PEFaTRqNB06ZN+Y5B6jl9bg4K/ziKovO/Qxtzl+84dQ5nYQHL3v6QvzwIFl49wAlo+GBCiHlRMUkqRMUkMSV1VBgKju5F8cUzYBo133HqBaGjM+T9B0L+8iCIGjfjOw4hpIGgYpJUiIpJUtOYRo2iC6dRcGwvNFHhfMepvzgO0q69YfX2KEi79OQ7DSGknqNiklSIiklSU/RZGcg//BsKfz8EQ14O33EaFHGLNrB6exRkfq+XzNpDCCE1jIpJUiEqJsmL0mdnIm//NhSeOACmplvZfBI6OsNq6GjIXxsCgQXNQkMIqTlUTJIKUTFJqkufk4X8/dtRcHw/mFrFdxzyFIGNHayHj4XizRHgxGK+4xBC6gEqJkmFqJgkz0ufm11SRB7bR0VkLSd0agKbMdMg832twrm2CSGkKmgMCR798MMPxr8nJSWhb9++5W730UcfGadMrMhvv/2Ghw8fGpdnzZqFyMjImglKSCWYVou8PZuRMmEw8gN2UCFZB+hTHyDr24VI/XgMVCE3+I5DCKnDGtzT2AE702v0eENHO1Z73x9++AEff/xxjeT47bff0L59ezg7OwMAVq1aVSPHJaQyxTcuI+fnldA9SOI7CqkGbcxdpM+fAmn3PrCdMANi11Z8RyKE1DENrpjky5gxY/DgwQOo1WpMmjQJiYmJUKlU8Pf3R7t27TB//nzo9XrMnj0bwcHBcHZ2xvbt22FpaVnqOCEhIfjiiy9QWFgIe3t7/PTTT7h+/Tpu376NqVOnQiqV4vjx43jvvfewZMkSdO7cGWfPnsWyZcug1+vh4OCAAwcO4MqVK1iwYAEAgOM4HD58GAoFzaJBqk6Xcg/Zv6yE6vpFvqOQGqC6cRkPb/0JxaB3YTNmGgRSy8p3IoQQUDFpNqtXr4adnR2Ki4vx2muv4dChQ9i0aROCgoIAlNzmjouLw/r16/H9999j4sSJOHr0KIYPH248hlarxeeff47t27dDqVTi0KFDWL58OVavXo1NmzYZi8enZWRkYPbs2QgMDISrq6txbvC1a9dixYoV6NmzJwoKCiCVUu9OUjUGtQr5e7cgP2AnDTZe3xj0KAj8FcV/nof99PmQdu3FdyJCSB1AxaSZbNiwAcePHwdQ0ikoLi6uzDbNmzc3zt3t6emJ5OTkUq/HxMQgIiLCWGAaDAY0atTomee9efMmevXqBVdXVwCAnZ0dAMDb2xuLFy/GsGHDMHDgQGqVJFWivvMXMr9fAn3qfb6jEBPSpz5A+qLpkPV7A7aTZkNobct3JEJILUbFpBlcvnwZFy5cwPHjxyGTyTBkyBCoyxlzz8LCwvh3oVAIlap0JwbGGNq1a4cTJ05U+dyMsXJ7as6YMQMvv/wyzpw5gwEDBmD//v1o06bNc1wVaUiYVovcHWuRf3AXYDDwHYeYSdHZ41Dd+hO2k2ZD7vc633EIIbUU9eY2g7y8PNja2kImkyE6Oho3b94EAIjFYmi12iofp3Xr1sjMzERwcDCAktveERERAACFQoGCgoIy+3Tv3h1Xr15FYmIiABhvc8fHx8Pd3R0zZsyAl5cXoqOjX+gaSf2liY9G6qwxyD+wgwrJBsiQk4Wsbxci/b+zoafZiwgh5aBi0gz69esHnU4HX19frFixAt26dQMAvP/++/Dz88OUKVOqdByJRIJNmzbhq6++gp+fH/r162csLEeMGIG5c+fC398fxcXFxn2USiVWrlyJ8ePHw8/PD5MmTQIA/PLLL+jbty/8/PxgaWmJ/v371/BVk7qOGQzI278NqR+PgTae/rPR0KmuXUDqjFFQh9/mOwohpJahQctJhWjQ8oZLl/4QWd8ugjrsL76jkNpGKITN6KmwGj6WBjsnhACglklCyD+oQoKROvN9KiRJ+fR65G5bg4zFM6HPzeY7DSGkFqBikhBilLdvK9IXToeBigRSCdXNK0j96N9Q3bnFdxRCCM+omCSEGOnuJwIGPd8xSB2hz0xH+vypKDgRwHcUQgiPqJgkpIFijGHz1QREpOYb19lNXwALj678hSJ1j16P7DXLkbN5Ner5I/iEkApQMUlIA6TS6vH54TtYdykOnwSEIr2gZNxTTiSCw/xvIGriwnNCUtfkH9iBzK8/g0GtqnxjQki9QsUkIQ1MeoEaH+y+iT+i0gEAaQVqzA4IhUpbcntbaG0L5eJV4ORWfMYkdVDx5bNI/3wK9DlZfEchhJgRFZN1wOXLlzFq1Ci+Y5B6IDGrCB/suomotNID3Eek5mPRsXDjbUpxMzco5/8fIBTyEZPUYZrIO0idPR7apHi+oxBCzKTBTae4cuXKGj3eJ5988lzbM8bAGINAQHU8Ma+7D/Mw80AIsovKn3XpXHQ61lyIxUe+rQEA0s7esJs2D9k/LTNnTFIP6FPvI+3TiXBc9j9IWrXnOw4hxMSoojGDpKQk9OnTB59++in69+8PZ2dn42tHjhzBRx99BKBkisMBAwbg1VdfxYoVK+Dm5mbcLj8/H2PHjsVLL72EOXPmwGAwYNeuXVi0aJFxmx07dpRaJuSx4MQsTN3zV4WF5GPbryfhyJ0U47Li9behGPJvU8cj9ZAhPxfp86dBExPBdxRCiIlRMWkmMTExePfdd3H27FnIZLJyt1m4cCEmTZqEU6dOlSo4AeCvv/7Cl19+ifPnzyMhIQHHjh3DkCFDcPLkSeP83r/++ivee+89k18LqVvORqbh4wOhKNRUbcifr09F4Fbyk3EmbT/4GFJvH1PFI/WYoSAPafOnQhMdzncUQogJUTFpJi4uLujevfszt7lx4wYGDx4MABg2bFip17p06QI3NzcIhUIMHToU165dg1wuh4+PD06dOoXo6GjodDq4u7ub7BpI3XP47wf4/MgdaPSGKu+j1TN8GngH97KLAACcQACHT5dB3KKNqWKSeowV5iNtwTSoI+/wHYUQYiJUTJrJ062RT89nq1JVbRiNf86B+3h51KhR2LNnD3799VeMHDmyBpKS+uL3uw+x7PcIGKox9F9usRYfB4QiX1XS6i2wlEG5eBUEdg41nJI0BKywAOmLpkMdQQUlIfURFZM8cHR0RFRUFAwGA44fP25c361bNxw9ehQAcPDgwVL7/PXXX0hMTITBYMChQ4fg7e0DaKsIAAAgAElEQVRt3Of+/fsICAjA0KFDzXcRpFY7F52OxcfvVquQfCwxqwifBd6BzlDSqilydIZy0UpwFhY1lJI0JCUF5X/oljch9RAVkzxYtGgRRo0ahaFDh8LJycm4funSpVi/fj1ee+01pKamwtra2vha9+7dsXTpUvTt2xfNmzfHwIEDja+99dZb8Pb2hq2trVmvg9ROV+MzMf/IHehfpJJ8JDgpG9+eiTIuW7TrBPtZXwL/aCknpCpYUSHSl8yC7uF9vqMQQmoQx+r5/Ff379+HRCLhO0aVFBUVwdLSEhzH4eDBgzh48CC2b99e6X6jRo3C5MmT0bdv3xrNo9Fo0LRp0xo9JjGtW8nZmLE/BGpd1Z+RrIpZ/q3x7+7Njcu5v25E3s71NXoO0nCImrmh0XebILSy4TsKIaQGUMtkLRIaGgp/f3/4+vpiy5YtWLJkyTO3z83NRa9evSCVSmu8kCR1T1hKHmYHhNZ4IQkAq8/F4GJshnHZ5r2JkPkNqPHzkIZBdy8BmV/NAdNq+I5CCKkB1DJJKkQtk3XHwzwVxu64gawi0/3jLBMLsfHf3dCmkQIAwLQapH0+BZq7oSY7J6nfLPu+AodPl5fpYEgIqVuoZZKQOq5Yo8fsgFCTFpIAUKTVY/bBEGQUqAEAnFgC5aKVEDrRfzhI9RRfOI3cLT/yHYMQ8oKomCSkDmOMYdGxMESnF1S+cQ14mKfGnEN/Q60rGQBdaGMHxyWrwMnkZjk/qX/yD+xAwe+H+I5BCHkBVEwSUoetuRCL8zEZlW9Yg8JS8rDk+F08fkJG3LwlHOatAARCs+Yg9UfO+m+hiYuqfENCSK1ExSQhddTROynYfj2Jl3OfiUzDz5fjjcuW3XrD9sPZvGQhdR/TqJH59TwYiszTwk4IqVlUTJpYbm4uNm/eXOPHvX37NubPn1/jxyV1Q/jDPCw/FcFrhk1XE3Ay/KFx2WrQCCjeHM5fIFKn6R4kIWv1Ur5jEEKqocH15ra7O6dGj5/d4btnvp6UlITRo0fjwoULNXpec6De3LVToUaH0duCcS+nmO8okAgFWDuiC7yalowXyPR6ZHw5C6qbV3hOVjuo9AYM/zMSGgODjjG84WyHT9o2AWMM30Y9wLGUbAg5DqNdHTHBrVGZ/ffdy8RPMSkAgI9aN8bwZg5Q6w2YeDMWKSoNxrg6YoxryX6f/Z2I95s7opONrMxx6hLbyXNgNZimhiWkLhHxHaC+W7p0KRISEozjRyqVShw+fBhqtRpvvPEGPvvsMyQlJeG9995Dz549ERwcDGdnZ2zfvh2WlpYYMmQIunbtisuXLyM3Nxc//PADevXqhcuXL2Pt2rXYtWsXrly5ggULFgAombP78OHDUCgUPF85MZUVpyJrRSEJABq9AXMPhWLLqO5oamsJTiiEw7zlSJ0zAbrEOL7j8c5CwOG3nm0hFwmhNTAMuxoBf0drxBSo8EClQZBvRwg4DhlqbZl9czQ6/BD9AMf6dAA4YOClu3jFyQbXswrgYSPDth6t8caluxjj2gjheUVgjNX5QhIAcjathqRdJ1i068R3FEJIFdFtbhNbuHAh3NzcEBQUBF9fX8THx+P3339HUFAQQkNDcfXqVQBAXFwcxo8fj4sXL8LGxsY4RzcA6HQ6/P7771i6dCm+/fbbMudYu3YtVqxYgaCgIBw+fBhSqdRs10fM6+idFJy8m8p3jFKyi7SYfTAUBWodAEAgU8Bx8Q8Q2NjxnIx/HMdBLirpmKRjJa2THMdhR1I6Pm7dGIJH4ysqLcRl9j2fkQcfpTVsJSLYikXwUVrjfHoexBwHld4A3VM3lb6LeoDZbZuY56JMTadF5orP6flJQuoQKibN6Ny5czh37hz69euH/v37Izo6GnFxJa03zZs3h4eHBwDA09MTycnJxv0ez8P9z/WPeXt7Y/HixdiwYQPy8vIgElGDc32UmFVUap7s2iQuo7DUfOAipyZQLvwOENOEAXrG8PrFcHQ5E4KXlNboYitHYpEaR1KyMfDSXYwJjkZ8oarMfg9VGjSxfPL+NZZK8FClgY/SGulqLd66EoEpLZ1xKjUHHjYyOEvrz3utT0tBzoZVfMcghFQRFZNmxBjDjBkzEBQUhKCgIFy/fh2jRo0CAFhYWBi3EwqF0Ol0xuXHrwmFQuj1+jLHnTFjBr7//nsUFxdjwIABiI6ONvGVEHPT6g1YeDQMRdqyn39tcTU+C9+fffLds3D3gv3MRTwmqh2EHIeTPu641s8DITmFiMwvhsbAYCEQ4NhLHfCeixJzQhPL7Ffew+wcOIgEHH7q0hInXnLHwMZ22JyQhg9bOOG/4cmYfCsWp1JzTH9RZlB4KhDFN+jZW0LqAiomTUyhUKCgoOR2jb+/P3799VfjckpKCtLT01/4HPHx8XB3d8eMGTPg5eVFxWQ9tPFKPCJS8/mOUam9f93D3lv3jMty/wGwHjmRx0S1h41YhF4OVjiXnovGUjEGONsCAF53skVEflGZ7RtLJXhQ/GRWoxSVBk7S0rfDtyemYVhTB9zMLoRYwGFtl5bGDjv1QfZPy+h2NyF1ABWTJmZvbw9vb2/07dsX58+fx9ChQzFw4ED4+vpiwoQJxsLyRfzyyy/o27cv/Pz8YGlpif79+9dAclJbxKQX8DaeZHV8fzYaV+MzjcvWoyfDsu8rPCbiT6Zai1xtyV0Gld6ASxn5aCWX4lUnW1zJLPnPwZ9ZBWghL/ucs6/SGhcz8pCj1SFHq8PFjDz4Kq2Nr+dodfgjLRfvNLWHymCAgOPAAVAb6s8AHfqMVORsWcN3DEJIJRrc0ECk6mhoIP4ZGMPEXTfxd0oe31Gei1wixKZR3dBKWTKqANOokTZvMjSRd3hOZl5384owOzQBelbyWb7Z2A4ft2mCXK0OM2/H436xBnKREMs7NYe7tQwhOYXYlZSObzzdAAB7kjOwJrZkLM+PWjnjXRel8dhfhifjNSdb9HKwgkpvwAc3Y/BQpcXo5o4YX84wQ3UWx6HRNxth4e7FdxJCSAWomCQVomKSf3tv3cO3f9TOTjeVaWIjxdbR3WEnK/n502dnInXWWOjTH1ayJyGliVxbwvnH3eCocyEhtRLd5iaklkovUGPtxVi+Y1Tbg1wV5h76GxqdAQAgtHOAcvEqcJZ1fyxEYl66xDgUHPmN7xiEkApQMUlILfXNmSgUampv7+2qCLmfi6W/3zUuS1q0gcOnywAB/eohzyfvt80w5Netxz0IaSjoNzohtdCV+Eyci37xnv61wYnwVGy6Gm9ctvT2ge2EmTwmInWRoSAPuXs28R2DEFKOel9MisViaLVlpyojz6bVaiEWl52Vg5iegTGsOV93b2+X5+dL8Tgd8WTmHqu3R0E+YCiPiUhdVHBkL3QP7/MdgxDyD/W+Aw5jDOnp6VRQPiexWAxHR0dwj6Z7I+Zz7E4Klpy4W/mGdYyFSICfR3ZFx8Ylw9swvQ7pi2ZAHXKd52SkLrH0eQXKeV/zHYMQ8pR6X0wSUpdodAYM23QVD/PUfEcxCQe5BFtHd4ezdcm4ioaCfKR+Mg66e2VngCGkIo1WboVF+058xyCEPFLvb3MTUpfsuXWv3haSAJBZqMHsgFAUaUoG8hYorOC4ZDUE1jY8JyN1Sc7mH/iOQAh5ChWThNQSeSottl5L4DuGyUWnF2DB0TAYHt0UETVuBocF3wIiekaXVI0m7DZUITf4jkEIeYSKSUJqie3Xk5Cn0vEdwywuxWZidVCMcVnaqSvsp8/nMRGpa/L3b+M7AiHkESomCakFCtQ67P/rHt8xzGr3zWQEhDzpmSt/ZRCs3hnLYyJSl6huXYUmPprvGIQQUDFJGpjDhw9jxYoVFb6ekJCATp2q/mB/QkICdu/e/cK5Am7fr/MDlFfHN2eicD0xy7hsM246LHv785iI1CX5+7fzHYEQAiomSQOi0+kwePBgzJs3r8aOWRPFpFZvwG+3kmsoUd2iNzDMC7yDhKxCAADHcbCf8xXErTvwnIzUBUUXT0GXlsJ3DEIaPComSb2yfft2eHp6wsvLC++//z7GjRuH2bNnw9/fH5999hm2bt2K6dOnAwBSU1Px9ttvw8vLC15eXrhy5UqpY8XFxaFLly4IDg6GXq/H3Llz0aNHD3h6euLnn38GAMybNw8XL15E586dsWrVqmplPnk3FekFmhe78DosX63D7AOhyCkuGQtWIJXC8YvvIXRoxHMyUuvp9cg/uIvvFIQ0eFRMknojLCwMy5Ytw9mzZxESEoLVq1cDAKKionDmzBmsXLmy1PYzZsyAr68vQkJCcOvWLXTs2NH4WmRkJIYNG4YtW7agR48e2LRpE2xsbBAcHIzg4GBs2LAB8fHxWLFiBXx8fHD79m3MmjXruTMzxrAzOOnFLrweSM4pxqeH/oZWbwAACB0coVz8PTipJc/JSG1X+Psh6PNz+Y5BSINGxSSpN86ePYt33nkHSqUSAGBvbw8AGD58OIRCYbnbT506FQAgFAphY1My1mF6ejreeust7Ny5E507dwYAnDp1Ctu3b0fnzp3Rs2dPZGZmIjr6xR/+vxyXibiMwhc+Tn3w170cLD8VYVyWtGoP+zlfATQLE3kGplah6OxxvmMQ0qBRMUnqDcZYudM/yuXy5zqOjY0NXFxccPny5VLH/umnn3D79m3cvn0b8fHxePXVV184855bDasHd2WO3nmIbdeezIYj6+0Hm7H/4TERqQsKzxzhOwIhDRoVk6Te6N+/P/bu3YvMzEwAQFZWVqXbr1u3DgCg1+uRl5cHAJBIJDh06BC2b99u7Fzz2muvYd26dcY53qOiolBYWAgrKyvk5+dXK++D3GJcS3h2xobofxdiERSVbly2Hj4O8lcG8ZiI1HbauChoYiP5jkFIg0XFJKk3OnbsiAULFsDX1xdeXl6YPXv2M7dfvXo1goKC4OHhgW7duiEsLMz4mlwux9GjR7Fq1SoEBgZi4sSJcHd3R9euXdGpUydMnjwZOp0Onp6eEIlE8PLyeu4OOIF/p4BV60rrNwbgi+NhiEh9UqTbTV8AC4+u/IUitR61ThLCH44xRv+eEWJmBsYw6OcrSMuvv/Nwv6hGCgtsGd0djawsAAD6/FykzR4H3YOGOYwSeTaBtQ2abD8JTkzTchJibtQySQgPghOzqZCsRFqBGp8cDIVKWzKYu9DKBsrFqyBQWPOcjNRGhrxcFF+/yHcMQhokKiYJ4cGxMBpouSoiUvOx6Fg4Ht9AETdzg8P8/wPK6Z1PCN3qJoQfVEwSYmbFGj3ORWfwHaPOOBedjjUXYo3LUq8esJtWc7MYkfpDdesqDIUFfMcgpMGhYpIQM7scn4libcObh/tFbL+ehCN3nrTmKl5/G4q3R/GYiNRKOh1Ut67ynYKQBoeKSULM7GIMtUpWx9enInArOdu4bDthJqTePjwmIrURPTdJiPlRMUmIGekNDJfjM/mOUSdp9QyfBt5BcnYRAIATCODw6TKIW7TlORmpTVQ3roAZDHzHIKRBoWKSEDMKuZ+D3GIt3zHqrNxiLWYFhCJfVfIeCixlUC7+HgI7B56TkdrCkJcDTcTffMcgpEGhYpIQM7oYS62SLyoxqwifBd6B7lHrk8jRGcpFK8FZWPCcjNQWxcGX+I5ASINCxSQhZnSBnpesEcFJ2fj2TJRx2aJdJ9jP+hIoZ2520vCorl3gOwIhDQoVk4SYSVJ2EZIePe9HXlxAyAPsvpFkXJb5vAzr0ZN5TERqC21iLHQZaXzHIKTBEPEdgJCG4kZiduUbmUj83m+Re/dPiBS26PTJJuP61MsHkXb5EDihEDbte8JlYNli7OGF/cgIPg6Ag8y5Bdze/RQCsQRxu5ej+GEcbDr0QrMBEwEAD87sgGXjlrDr2Mcs17X6XAxc7GTwaaUEANiMnAjdvUQUBZ0wy/lJ7aWJCIXopZf5jkFIg0Atk4SYScj9XN7Orez+Gtp88HWpdXkxfyEn7Ao6zt6ATp9shrPvu2X20+SmI+3yQbjPWIdOn2wCYwZkhZxFUUrJIOIdZ29EQfzf0BUXQJOXicLkCLMVkgBgYMDCI2GITnsyULX9zEWQuHuZLQOpnTQRd/iOQEiDQcUkIWYScj+Ht3NbtfSESFZ6Tuv0P4+gsf9ICEQSAIBYYVfuvsygh0GrBtPrYdCoILZWghOIYNCpwQwGML0OnECIB6e2oumr40x9KWUUafWYfTAEGQUlc51zYgmUC7+D0Kmp2bOQ2kMdEcp3BEIaDComCTGDjAI17ueq+I5Riir9HvLj/8bdn/6DiHWzUJgcUWYbiY0jnH2HI3T5ewhZOhxCqQI2bbvD0skVEttGCF89BXaevlBn3gcYg6xpGx6uBHiYp8acQ39DrSuZWUhoYwfHJavAyRW85CH808ZGgul0fMcgpEGgYpIQMwjl8RZ3RZhBD31xAdpPX4NmAycjdudXYIyV2kZXlI+csCvwmLcLngv3wqAtRuat0wCA5oP/g46zfoGz77u4//sWNHl1HB78sQuxO/+L9GvHzH49YSl5WHL8rvEaxM1bQjlvBSAQmj0L4R/TqKGJLfsfJEJIzaNikhAz4PN5yYpIbBxh2+klcBwHRfP24DgOusLSOfNibsHC3hlihS0EQhFsO/mgIDG81DbZYZchb9YOBo0KqofxaDX6C2TeOg29xvwtsWci0/Dz5XjjsrRrL9hOnmP2HKR20ETSc5OEmAMVk4SYQeiD2ldM2nbsg/yYvwAAqvRkGPQ6iOQ2pbaR2DZCQdJd6DUqMMaQH3ML0kbNja8b9DqkXQqAk++7MGjVT8Z5ZAxMz88txk1XE3Ai/KFx2erN4VC8WbZzEan/NHfpuUlCzIGGBiLExAyMITq9oPINTShu11Lkx4VAV5iLkGUj0OSVsVD2eB0J+77FnZUfQCAUocWIz8BxHDS5GUjYvxJtP/gaiuYdYOfRF3dXTwEEQsiatoZjz4HG46ZfCYRDt1chlEhh2bglwBjCvp8Im/beEFny97zi0pMRaGJjCa+mJcWx7YefQJdyD6qbV3jLRMxPmxTHdwRCGgSO/fMhKUJIjbqfU4whG67yHaPBsZOJsWVUdzS1tQQAGIoKkDpnAnSJVGA0FJyFBZoeuASOZkYixKToNjchJpaQVch3hAYpu0iL2QdDUaAuud0ukCnguPgHCGzteU5GzIWp1dBnpPIdg5B6j4pJQkwsPpOmUORLXEYh5h+5A72h5AaMyKkJlAu/A8QSnpMRc9HdT6p8I0LIC6FikhATi8+klkk+XY3Pwvdno43LFh08Yf/xFzwmIuake0DFJCGmRsUkISZGxST/9v51D3tv3TMuy/1eh/XIiTwmIuaipZZJQkyOiklCTCyBbnPXCt+fjcbV+EzjsvXoybDs+wqPiYg56B4k8x2BkHqPiklCTKhQo0O+mqZ0qw30jOHzw3cQm1EyTBPHcXCYtQSSdp14TkZMSZdyr/KNCCEvhIpJQkwos1DDdwTylEKNHrMDQpFdVPK5cBILKBethNDRmedkxFQMeTl8RyCk3qNikhATyqJistZ5kKvCnIN/Q6MzAACEdg5QLvkBnKWc52TEFAwFeXxHIKTeo2KSEBOilsnaKfRBLr46ede4LHFrDYdPlwEC+pVY7+j1MBTyOwMVIfUd/eYkxISomKy9Tt5NxcYr8cZlS++XYDthJo+JiKnQrW5CTIuKSUJMKLOIisna7JfL8Tgd8WSGFKu3R0E+YCiPiYgp0K1uQkyLiklCTIiemazdGIAvT9xFWMqTYsNu6qew6OzNXyhS4/R5uXxHIKReo2KSEBMqoGGBaj21zoBPDobiYZ4KAMAJRVB+/n8QNXPjNxipMYZ8KiYJMSUqJgkxIa3ewHcEUgWZhRrMDghFkaak+BcorOC45AcIrG14TkZqAlMV8x2BkHqNiklCTEirZ3xHIFUUnV6ABUfDYGAln5mocTM4LPgWEIl5TkZemEHPdwJC6jUqJgkxIWqZrFsuxWZidVCMcVnaqSvsP1rAYyJSE5ieiklCTEnEdwBC6jOdgYrJumb3zWS4Osgw1KspAED+8pvQPUhC/tF9PCcj1cUJhXxHIKRe4xhjdB+OEBMZv/MG7qTQsCR1jVDA4cd3vODtas93FEIIqfWoZZIQE6Lb3HWHk5UFWikVaKWUo5VSDkeFBd+RCCGkTqBikhDSoNhaih8VjI8KR8eSPxUWlf86ZAWJAKP/INRJEltwFnZ8pyCkXqJikhATshTTs1p8kUuEaOkgR0vHpwpHpQIOckm52xcX65CVoUZ2hhpZmSpwAg4+/RqX2oZlh8EQv8cc8UkN45q/BaEbzW5EiClQMUmICUmpmDQ5C5EArvay0q2NSjka21iWu71Goy8pGjPVyHpUOGZnqlFcVLrH71sj3AAADx48QFRUFPz8/CBweQNMkwN2/3dTXxapYRxHP4uEmAoVk4SYELVM1hwhx8HFzrJU0djSUQ4XWxmEAq7M9jqdATlZjwvGJ8VjQb620nO172QL5yYyMMZw6NAhJCUlQaFQoHv37hC0fA8GTQ5Y+jVTXCYxFSomCTEZKiYJMSEqJp8fB6CxjRQt/9HS6GYvh0RUdmhcg4EhO+vJ7enHxWNejgbVGatCKhWi50uNAADBwcFISkoCAAQEBEChUKB9+/YQtPsQBm0+WE74i1wqMScB/SwSYipUTBJiQlRMPpu9TIJWSjlaOz4pGlsq5ZBJyv5qYowhP1eDrMynWxtVyMnSQF+DMw15v9QIUksRCgsLcfLkSeN6g8GA3bt3Y9KkSXBxcYHAfQb0IcuBwqQaOzcxIWqZJMRkqJgkxIQsJfQPGABYWYgetTSWbm20lZXfGaaoUIesDFWp29PZWWpoNabtSd2osSXad7IFAJw4cQJFRUWlXtdoNNi6dSumTp0KpVIJoccn0P/1FaDOMGkuUgM4+ueOEFOhny5CTEhh0bCKSalYgBb28ie3qB1LikYnK2m526tV+pKC8VHh+Lh4VBWbf/o7jgN8+jUGx3FITEzEzZs3y92usLAQmzdvxtSpU2FlZQuhx1zoQ5YC2nwzJybPRWLDdwJC6i0qJgkxofo68LVIwKG5sQf1k9bGpraWEHDld4bJfnRr+vFzjdmZahQW6HhIX76OXvZQNpLCYDDg0KFDeNbkYFlZWdiyZQsmT54MC5kzhB1nQR+6AjBozJiYPA/OgmYzIsRUqJgkxIQqapGrKwQc0MTGslTR2FIph5u9DCJh+Z1hsjJL357OylQjP7d6nWHMRSYToce/HAEAV65cQUpKSqX7PHjwADt27MC4ceMgsm4FQYf/wBD+I8DM36pKqoAGLCfEZKiYJMSEnKzqTstkI4XFk+caH3WIaekgL3esTMYYcnM0jwrGR8Vjhho5ORoYarAzjLn08nWCxEKIvLw8nD59usr7xcTEYN++fRg5ciQEDp2BNuNgiNpkwqSkWjghIKbb3ISYChWThJhQbWyZtLEUo6WD/Elr46PC0VoqLnf7wgKtsYUxK6NkgO/sLDV02rpXNJaniYsMbdqXFBrHjh2DWq1+rv1DQkJgZWWFN998EwLnvoA6G4bEAFNEJdUlsQNXzuMXhJCaQcUkISZkKRHCykKEfLX5nw2UiYVooZSXea5RWcFznCqVvqSV0Tg7TEnhqFbX37moBQLgpUdTJsbExCAkJKRax7l06RKsra3Rt29fCFzfKpklJ+VsTUYlL4JucRNiUlRMEmJiTlYWJi0mxUIObsYe1E+KxiY20nJbY7RaQ5nb01mZahQV1p7OMObi2c0BdvYW0Ol0CAwMfKFjnThxAtbW1ujcuTMErd+HQZMLlll+j3BiXtT5hhDTomKSEBNztpEiJqPwhY8j5Dg0tbUsc3vaxc4SIkHZzjB6PUNOlqrU7emsDDXy8yqfTrAhUFiL0bVnSaebixcvIj09/YWOxxjDvn37IJfL0aZNGwg6TIE+9FsgL6om4pIXQcUkISZFxSQhJtbCXo5LsZnPtY+ztUWpwb1bKRVwc5DBQlR+Z5icbPVTt6dLht/Jy9HAUH/vUL+wf/k6QSwWIDs7G2fP1swtab1ej507d+LDDz9E06ZNIez0MfS3lwFF92vk+KR6qGWSENOiYpIQE2uplFf4mr1MjJb/KBpbKuVQWJT/o5mfp0X2U/NPZ2eqkZOlhk5XPzrDmEvzFgq0aG0NADh8+DC02pprrVWr1diyZQumTZsGe3t7CD3mlMySo8mqsXOQ5yShZyYJMSUqJgkxsVZKOeQS4ZNZYZ4qHO3l5U8nWFysK2lpzHhqkO8sNTT1uDOMuYhEHPr4OwMAwsPDcffu3Ro/R0FBATZv3owpU6ZAoXhUUIYsA3Qv/rgDeX7UMkmIaXHsWdM8EEJeGGOswmFJNBp9mdvT2ZlqFBfRwNem0uNfjuja0xFarRbff/89srOzTXYuFxcXTJo0CRKJBCw3Cvq/vwEM9MyqeXEQ9v4fOHHFdwgIIS+GWiYJMTGO46DTGZCTpS51ezorQ42CfCoszMnGTgKvbg4AgLNnz5q0kASA5ORk7Nq1C2PGjIHQpi0E7afAEL4GAP0f3mwsnamQJMTEqJgkxAyOH0xCyr0ivmM0eC/5O0MoEiA9PR0XLlwwyzkjIyMREBCA4cOHQ6DsDrQeA0PMNrOcmwCcdWu+IxBS75UdT4QQUuOUjWrfTDgNTcu21mjmqgAABAYGQq8336MEN2/exMmTJwEAgib9wDUfbLZzN3RUTBJielRMEmIGjo0s+Y7QoIklAvzL1wlAyfSHMTExZs9w7tw5XL16FQAgdBsGzrmv2TM0RFRMEmJ6dJubEDNQOlHLJJ+693KEXCGGWq3GsWPHeMtx+PBhKBQKeHh4QNBmPAyaPLCs2yY/78QVl3H86n00spPi9taSVtHFm/7C4UvJEAg4NLKVYtPnfdBEKSuzb1JqASZ/cxX30orAccDh/+sPt8YKvP/VRVyON8QAACAASURBVNyJy8bA3s2w9MOuAIBl20Lh0coWg19qbvJrqhKhJSBrwncKQuo9apkkxAxs7SQQi+nHjQ/2Sgt06lIyNMypU6eQl5fHWxbGGPbs2YO4uDhwnACCDtMAq1YmP+/YAa1x9Nv+pdZ9MrIj/toyGDc3DcIbvZth6bbQcvcdv/wyPhnZEX/veAtX1r+BRnZShMaWdFz6a8tgXApNQ26BBimZRQi+m1F7CkkAnFVLcBz93BFiavRTRogZcBxHz03yxKdfYwgEHB48eGC8zcwnnU6H7du34+HDh+CEFhB2mg1YNjbpOX28nGBvZVFqnfVTY5wWqXQob/Cq8IQc6PQGvNyjpHVPIRNDJhVBLOSgUuthMDBodAYIBRyWbLqNxRO8THkZz49ucRNiFlRMEmImTZvT8CTm1tbdBs5NZWCMITAwEIZaMr+kSqXC5s2bkZOTA06sgNDjE0Bia/Ycizb8hRbv7MevZ+Kx5IPOZV6PTs6DrUKC4QvPofsHR/DZuhvQ6w3o4GYLFyc5ekw6iuH+roi5nw8GoEtbB7Nfw7PQ85KEmAcVk4SYiWtLBd8RGhQLqRC9+pZ0urlx4wYSExN5TlRaXl4eNm/ejKKiInBSRwg7fVLyjJ8ZfTWpC+L3v4P3Xm6BtQERZV7X6Rkuhabh/6Z1w58/D0T8gwJsOxkLAPj+ox64uWkQZo3oiCWbbmPJhM74ekco3lt8HhuPRJn1OsrHgTPDIwSEEComCTEbZSNLyBXU581cvPs0gqWlCEVFRcZheWqbtLQ0bNu2DVqtFpyiOQTuMwDO/N+RkS+3wMELSWXWN3WUoXMbe7RsYgWRSIDBL7n8f3v3HVd1vT9w/PX9nsNh7ymKEwcgww3iwJXlyHFdbdP0VmqWZWpdb/lraVsLrW6iZmallrnLhSMHLsyF4t4CIgIyD+f8/uByrgQiIHBA38/Hw8dDzne9v+d8z/e8v5/JgROF5xhfsf08rZq5citLz5HTKSye1plFf5wmI0tfVeEXTwYrF6LKSDIpRBWq28De3CE8EDy8rPALzK82XrduHbduVd85sc+dO8fixYsxGAyozv6oTUdDsS0YK1b8xf91RFr55wWa1nUosk6bZq7cSMshMSULgM37r+JX39G0PFdv4Iulcbw6LCC/3eV/wzYYjeTkmrdJgeLY1KzHF+JBIsUkQlSheg3tOHaocqfwe9ApCnToWgtFUTh//jx79uwxd0h3dfToUX777TcGDBiA6tEOclIwnP6hwvb/5LStbIm9RtLNLOoPWsq/nw1m3a5LnLiQiqJAPU87Il8NBWBvXBLfrDjBN6+3R6NR+fCFVjz0yh8YjdCyqSvP9Wls2u/sX+N46uFG2FhpCWrkjNEIIcNX8EhobZzsdXcKp0oo7m3MenwhHiSK0WiUSWKFqCJ6vYEFc46j18vXrrIEBDvToWstDAYDkZGRXLp0ydwhlVr37t3p3r07AHmnf8R4ca2ZI6qhLOzRhM6SYYGEqCLyTROiCmm1Kt4+0o6rsljbaGgT7gHAzp07a1QiCbBhwwZiYmIAUBsMRfFob+aIaibFrbUkkkJUIanmFqKK1Wtoz/kz6eYOo5DvfvwXh45uwd7OhX+//hsAK9bO4uDhzSiKgr2dK8889h5Ojh6Ftjsev5slv80w/X014QzPPfUxIYHdmPv961y+Ek+gf2f6934ZgNV/zKG2d1NCmnetlPMI7eiJpaWGtLQ01q9fXynHqGzLly/Hzs4Of39/1CYjMeSmYrxx2Nxh1SiKeztzhyDEA0Ue3YSoYtVxiKCwNv0ZN/rrQq/16DKCqRN/5V+v/UKgf2dW/zGnyHZNG7fjX6/9wr9e+4VXXohCZ2GFf9P2XLx8HICpE3/l5Ol9ZGamcTM1kbPnD1VaIlmrjg1N/PM73axevZqsrKxKOU5lMxgMLF68mHPnzqGoWlT/cWBX39xh1Rw6J+l8I0QVk2RSiCpma2dR7WbDadyoNTY2joVes7b6X9Kbk5OJopTcw3j/X38Q4NcRnc4ajUZLbm42BoMBfV4uiqqyYu0X9H14XKXEr6r5M90AnDp1itjYyp/vujLl5uayYMECEhISUDRW+WNQWnncfUOB4tZGqriFqGLyjRPCDOo2qH6lk8VZvmYmU/6vGzH7V9H34bElrrv3wFratOgFQC3PRrg4efH+p4NoFfIwiUn5YxjWreNXKXEGtnTF2dWSvLw8fvvtt0o5RlXLyMggKiqK1NRUFJ0DmsDXwKLo8D2iMNW9rblDEOKBI8mkEGbQuJnj3VeqBvr3Gs8H/95I25Z9iN5+56FqbqYmculKPAHNwk2vDRkwhX+99gs9Iob/t1RyLGvWf803CyawbeeSCovRzl5Lq1B3ALZt20ZCQkKF7dvcUlJSiIqKIisrC8XaM38eb9Xy7hs+qHQu4ND47usJISqUJJNCmIGTi2WNmqu7TcveHPjrzh1a9sauIySwGxqNRZFlsYc3Uc+nOdk5mVy+Gs/oZz5l976V5ORkVkhsYZ29sLBQSUlJYdOmTRWyz+rk6tWrfPfdd+j1ehT7BvltKBWNucOqlhT3tndtjiGEqHiSTAphJs1DXMwdQomuJf5vLuu/jmzG06PBHdfdu3+NqYr7dnl5uWzeupCHujxLbk4myn9ndjEa89tS3iuf+nY0bJxf9btixQpycnLueZ/V0enTp/npp5/yZ8lxCURtMpKqmCWnppEqbiHMQ4YGEsJM6jW0w87BgvTUe0+q7tW3C1/jxMk9pN9KYfK0rvTtOYbDx7ZyLfEsiqLi4lyLxwe9BcC5C4fZuuNnnhr6fwAkJV8iOeUqjRsVnXEkevtiQtv0Q6ezprZ3U4wY+b8P+9PcryM21vfW/k+jUQjv4gVAXFwcR48evaf9VXeHDh3C3t6eRx99FNUzPH+WnDM/mzus6sPaE8WhkbmjEOKBJDPgCGFGsXuS2L39/mnjV5VahbrTOsyd3NxcPvvsM5KTk80dUpV4+OGHiYiIACDv5PcYL9fM8TQrmur7FKp3d3OHIcQDSaq5hTCjZs2d0GikurKsHJx0tGjjCsDmzZsfmEQS4Pfff2ffvn0AqI0eR5GqXdDaonh2NHcUQjywJJkUwoysrLU0airDvZRVhy5eaLQqSUlJbN261dzhVCmj0ciyZcs4fvw4iqKiNh2N4tjM3GGZlVIrAkUjvdyFMBdJJoUws+reEae6aeBrj0/9/HE6f/vtN/R6vZkjqnoGg4FFixZx8eJFFNUCNWA82PqYOyzzUDRSvS2EmUkyKYSZuXta41HL2txh1AgWFirtI/I73fz111/Ex8ebOSLzycnJYf78+SQlJaFobfJnybF0M3dYVU5xa4tiWboHspSUFGbPnl3hMezdu5eXXnqpwvcrRE0hyaQQ1UDzYCmdLI1WoW7Y2VuQnZ3NqlWrzB2O2aWnpxMVFUVaWhqKpTOawFdBW3PGL713CmrdPqVeu7KSydatWzNr1qwK368QNYUkk0JUAw2bOGBjIyN1lcTZ1ZLAlvmdbtavX09qaqqZI6oekpOTmT9/PtnZ2Sg23v+dJUdn7rCqhOLaEsW2TqnXnzx5MqdOnSIkJISJEyfy0Ucf0aZNG4KCgnjrrfyhr86ePYufnx+jRo0iICCAhx56iMzM/AH2IyIimDRpEm3btqVJkyZs27YNgOjoaPr0yU9qt2zZQkhICCEhIbRo0YK0tLQKPmshqh9JJoWoBjQahRbtHrwqyrLo2NULVVW4evUqO3bsMHc41cqlS5f4/vvvycvLQ3HwRfV7kQfh9q7WfbRM60+fPp1GjRoRGxtLjx49iI+PJyYmhtjYWPbt22fqzBUfH8+YMWM4cuQITk5OLFu2zLQPvV5PTEwMn3/+OdOmTStyjI8//pjIyEhiY2PZtm0b1tbShEXc/+7/u40QNYR/kDNOzg9GiVJZNfFzpFYdW4xGI8uXL8dgMJg7pGonPj6epUuXYjQaUV1boDZ+xtwhVSrFJRjFvn65t//jjz/4448/aNGiBS1btiQuLs7UBrdBgwaEhIQA0KpVK86ePWvabuDAgcW+XiA8PJwJEyYwa9YsUlJS0GqlxkHc/ySZFKKaUFWFdh09zR1GtaOzVAntlP++7Nu3r9gfcJHvwIEDrF27FgC1VgRqvf5mjqjyqHX73dP2RqORKVOmEBsbS2xsLCdPnmTkyJEAWFr+b5ghjUZTaMSAgmV/f73A5MmT+fbbb8nMzCQ0NJS4uLh7ilOImkCSSSGqkfqN7PGuY2PuMKqVtuEeWNtoycjIMCVK4s62bt3K9u3bAVDrDUCp1cXMEVU8xa1NuaZOtLe3N7Vh7NmzJ1FRUaSnpwP5TQUSEu59NqpTp04RGBjIpEmTaN26tSST4oEgyaQQ1UxBKZwAd08r/IOcgfyZX27dumXmiGqG1atXc/DgQQBU36dRXFuaOaIKpLFCbfR4uTZ1dXUlPDyc5s2bs379eh5//HHCwsIIDAxk0KBBFdJZ5vPPP6d58+YEBwdjbW3NI488cs/7FKK6k7m5haiGNq27RPyxm+YOw6wUBQYMa4C7lzUXLlxg9uzZyO2q9DQaDc8++yy+vr4Y83LIO/QhpNb8cTnVBkNRfXqZOwwhxG2kZFKIaqhtuAda7YM9Z7dfoDPuXtYYDAaWL18uiWQZ5eXlsXDhQi5fvoyi0aEJeAVsvM0d1r2xqY1Sp6e5oxBC/I0kk0JUQ3b2FqYxFR9E1jYa2oZ7ALB7924uXbpk5ohqpuzsbObNm0dycjKKhS2a5q+BztncYZWbxvdpFEVj7jCEEH8jyaQQ1VSLNm5Y2zyYP5ztOnhiaaUhPT2d33//3dzh1GhpaWlERUVx69YtFCvX/86SU/M6eSke7VGcmpk7DCFEMSSZFKKastCptA7zMHcYVc6rtg1NA5yA/I4kWVlZZo6o5ktKSmLBggXk5OSg2Pqg8R8PioW5wyo9jQ1qw2HmjkIIcQeSTApRjTVr7oRHrQdnBg1VzZ/pBuD06dMcOHDAzBHdP86fP88PP/yQP0uOUzPUZv8Eaka7XLX+QBSdo7nDEELcgSSTQlRjqqrQpaf3A9MZp3kLF1zcrMjLy+O3334zdzj3nbi4OH799VcAVPc2qI2eMHNEpWBXD8W7m7mjEEKUQJJJIao5J2dL2nW4/8eetLXT0jo0v1p/+/btXLt2zcwR3Z/27t3LH3/8AYBauweKTx8zR1QS5b+dbuSnSojqTL6hQtQAASHOePvUvE4TZdG+sxcWOpWbN2+yceNGc4dzX9u0aRO7du0CQNNgMIpnBzNHVDzFpzeKg6+5wxBC3IXMQC9EDaAoChEP1WbpwlPk5BjMHU6Fq1PPloZNHABYsWIFOTk5VXbsNWvWcOrUKWxsbExzMyckJPD777+Tk5ODo6Mjffv2LTRfc4G9e/dy8OBBjEYjwcHBtGnTBoDo6GhOnz6Nh4cHffrkl/wdPnyYrKwsWrduXWXnVpLffvsNOzs7mjdvjtpkBIacVIw3/jJ3WCaKYzPU+v8wdxhCiFKQkkkhagh7Bws6dqtl7jAqnEaj0KFr/nkdP36cI0eOVOnxAwMDGTx4cKHX1q5dS+fOnRk5ciRNmjRh9+7dRbZLTEzk4MGDPP3004wYMYJTp06RnJxMdnY2ly5dYsSIERiNRhITE8nNzeXw4cO0aNGiqk7rroxGIz/++CNnz55FUTSo/mPBvqG5w8qnc0T1e0Gqt4WoIeSbKh5Y77//vun/Z8+epXnz5sWuN3z4cJYuXVrivubPn8/ly5dNfz/33HMcPXq0YgK9jW8zR9OwOfeL4NauODrpyM3NZcWKFVV+fB8fH6ytC/eYT05OxsfHB4D69etz4sSJIttdv34db29vLCwsUFUVHx8f4uPzpyvMy8vDaDSi1+tRVZWYmBhatWqFRlO9xg3V6/UsWLCAa9euoWgs0TSfANbmbp+romn2Ioru/rrOhbifSTIpHli3J5P36u/J5Lfffou/v3+F7f924V28cHYpWuVaEzk4WtCirRuQXzV8/fp1M0eUz83NjZMnTwL5PaDT0tKKXefChQtkZmaSm5vL6dOnSU1NxdLSkqZNmzJ//nwcHR2xtLTkypUrNG7cuKpPo1QyMzOJiori5s2bKBb2aJpPBAvzDcOj1h8og5MLUcNIMikeCP3796dVq1YEBATwzTffMHnyZDIzMwkJCeGJJ/KHR8nLy2PUqFEEBATw0EMPkZmZWWQ/+/bto3PnzrRq1YqePXty5coVli5dyt69e3niiScICQkhMzOTiIgI9u7dC8C6deto2bIlwcHBdOuWP8TJli1bCAkJISQkhBYtWhSbrNyJhYVK996174vhgsK7eKHVqiQlJbFlyxZzh2PSq1cv9u/fz/z588nJyUFVi94q3dzcaNeuHT/99BM///wzHh4epvXatWvHs88+S9euXdm2bRsdO3bk4MGDLF++nB07dlT16dzVzZs3iYqKIjMzE8XaPX+WHI1VlcehuARX897lQojiSDIpHghRUVHs27ePvXv3MmvWLCZOnIi1tTWxsbEsWrQIgPj4eMaMGcORI0dwcnJi2bJlhfaRm5vLuHHjWLp0Kfv27WPEiBG8+eabDBo0iNatW7No0SJiY2MLVZkmJiYyatQoli1bxsGDB1myZAkAH3/8MZGRkcTGxrJt27Yi1ax34+JmRfsIr3t8V8yrvq89dRvYA/mdbvR6vZkj+h9XV1eGDh3K8OHD8ff3x9m5+Pmsg4ODGT58OE888QRWVlZF1isY3sjZ2ZnDhw/Tv39/EhMTSU5OrvRzKKtr166xYMECcnNzUezqofq/BFU5D7alG2rT0ShKzX9IEuJBI8mkeCDMmjWL4OBgQkNDuXDhgqlt2+0aNGhASEgIAK1ateLs2bOFlh8/fpzDhw/To0cPQkJCePfdd7l48WKJx921axedOnWiQYMGALi4uAAQHh7OhAkTmDVrFikpKWi1ZR9YwS/QmaCWLmXerjrQWiiE/zcZPnToULFtEs3p1q1bQH4nlR07dpiuizutl5qayokTJ4o0bdi2bRsdOnTAYDBgNBqB/J751Slxvt3Zs2f58ccfMRgMqM4BqE1HUSWz5ChaNP5jUCzsKv9YQogKJ0MDiftedHQ0GzZsYOfOndjY2BAREVHsfM+3D/2i0WiKVHMbjUYCAgLYuXNnqY9tNBqLLWmZPHkyvXv3Zs2aNYSGhrJhwwaaNSt7O7HQTp7cStdz6kRqmbc1p1bt3LGztyA7O5tVq1aZNZYVK1Zw/vx5MjMziYyMpEOHDuTm5rJ//34AmjRpQmBgIABpaWmsW7fO1Pt7+fLlZGZmoqoqPXr0wMrqf1XDJ06cwMvLC3v7/NJXb29v5s6di4eHBx4e1XfO9SNHjrBixQr69++P6hEGOSkYTv9YqcdUGz2GUl16kgshykySSXHfu3nzJs7OztjY2BAXF2carNnCwoLc3FwsLCxKtZ+mTZuSmJjIzp07CQsLIzc3lxMnThAQEIC9vX2x7R7DwsIYM2YMZ86coUGDBiQnJ+Pi4sKpU6cIDAwkMDCQnTt3EhcXV65kUlHyp1vMzNBz+WJGmbc3B2cXSwJbugKwceNGbt68adZ4Hn300WJfL248SHt7+0LDCBW0ty1OkyZNaNKkienvrl273kOUVWvXrl3Y29vTrVs31DqPYMxOwXhpXaUcS/HqjOrdvVL2LYSoGlLNLe57Dz/8MHq9nqCgIKZOnUpoaCgAo0ePJigoqMSE4HY6nY6lS5cyadIkgoODCQkJMXWmGD58OM8//7ypA04Bd3d3vvnmGwYOHEhwcDBDhw4F4PPPP6d58+YEBwdjbW3NI488Uu7z02hVHnrUBxfXmtHDu0M3LzQahWvXrrF9+3ZzhyPuYP369ezZswcAteEwFPfQCj+G4h6K2nh4he9XCFG1FGNBQx4hRI2WnpbL8h/PcCu9erbHA2jczJGuj9QG4Ouvv+bMmTNmjkiURFVVnn76aZo1a4bRoMdw+FOMKRUzqLzi2gLVfxxKVXbyEUJUCimZFOI+YWdvQa8BddFZVs+vtc5SJbRT/oDY+/btk0SyBjAYDCxatIjz58+jqFpU/3FgV++e96s4+aP6jZFEUoj7RPX81RFClIuLmxU9+/qgaqrf8Cpt2ntgY6slMzOTtWvXmjscUUq5ubksWLCAxMREFK11/iw5Vu7l36GDL2rAyyhq6doqCyGqP0kmhbjPePvY0qWnt7nDKMTNw4qA4PwxGH///XfS09PNHJEoi1u3bhEVFUVqaiqKzglN4GtgYV/2HdnVQ9P8VRRNzWjfK4QoHUkmhbgP+TZ1pH1nc8+xnE9RoGO3WiiKwsWLF9m9e7e5QxLlcOPGDebNm0dWVhaKtReagFdALUNSaOONJnAiitam8oIUQpiFJJNC3KcCW7rSuUctzD2hSLPmTnh4WWMwGFi+fDnS56/munLlCgsXLkSv16M4NEL1H1O6WXKs3NEEvo5SntJMIUS1J8mkEPexZs2deaivj9nm8bay1tC2Q34JaUxMzF1nDBLV36lTp1iyZAlGoxHVJRi18bMlb6BzQRM4CcWy+CkphRA1nySTQtzn6jeyp9fAeliaoZd3u46eWFlpSE9P5/fff6/y44vKcfDgQdPMRapXR9T6g4pf0aYOmhZTUazvocOOEKLak2RSiAdArdo2PDqkPrZ2VTfplZe3NU39HQFYs2ZNkekpRc32559/smXLFgDUun1RvLsVWq44+qEJeRPFsmbOHy+EKD1JJoV4QLi4WdFvaAOcXHSVfixVhQ7/7XRz5swZDhw4UOnHFFVv3bp1ps9WbfQkilv+FJSKexhq4GvS2UaIB4Qkk0I8QOwdLOg3pD4eXlaVepyAEBdc3azIy8uTTjf3MaPRyNKlS4mPj0dRVNRmz6P6Dkdt9k8UtepKwYUQ5iXJpBAPGCtrLX0G1cenvm2l7N/GVkvrsPw2cn/++SfXrl2rlOOI6iEvL4+FCxdy6dIlFNUC1bsLirmHEBBCVClJJoV4AFlYqPR8tC5NA5wqfN9hnT3R6TTcvHmTDRs2VPj+RfVTu3ZtnJwq/loSQtQMUg8hxANKo1GIeMgbbx8btm+6Sm6O4Z73WaeuLb5N8zvdrFq1ipycnHvep6je2rRpQ//+/dFoZJ5tIR5UkkwK8YBr4ueEZy0bNq65SOK1rHLvR9UohHf1AuDEiRMcOnSookIU1ZCqqvTq1YsOHTqYOxQhhJlJMimEwNFJR7+hDYj58xp/7Usu1z6CW7ni5GyJXq9nxYoVFRyhqE5cXFwYNmwYdevWNXcoQohqQJJJIQSQX+0d1smL2j62RP9xmcyMvFJva+9gQct2bgBER0eTlJRUWWEKMwsJCaF///5YWVXuiABCiJpDOuAIIQqp28CeQU82onbd0vf2Du/ihVarcv36daKjoysvOGE2Op2OwYMHM2zYMEkkhRCFSMmkEKIIG1stvQfW5eDe6+zZkYChhL459RvZU6+hPQArV65Er9dXUZSiqtSpU4dhw4bh5uZm7lCEENWQJJNCiGIpikJIGzdq1bEh+vfLpNwo2jNbq1VoH+EJwOHDh4mLi6vqMEUlUhSFjh070rNnT+mtLYS4I0kmhRAl8qxlw6CnGvHXvuvsj0lEn/u/2WxatnPH3kFHTk4Oq1atMmOUoqLVrl2bAQMGUKdOHXOHIoSo5iSZFELclUaj0KKtG439HNkRfZUzJ9NwctER1MoVgI0bN5KSkmLmKEVFsLKyomfPnrRr1w5VlWb1Qoi7k2RSCFFqdvYWPNTXhwvn0rHQqmg0CgkJCWzfvt3coYkKEBISQu/evbG3tzd3KEKIGkSSSSFEmfnUszP9PyYmhry80g8jJKofd3d3+vXrh6+vr7lDEULUQIrRaDTefTUhhLizQ4cOsW7dOq5fv27uUEQZ2Nra0rlzZ9q3b49WK2ULQojykWRSCFEh9Ho9O3fuZPPmzWRkZJg7HFECa2trOnXqRPv27bG0tDR3OEKIGk6SSSFEhcrJyWH37t1s27aN1NRUc4cjbmNlZUWHDh3o0KGDDDwuhKgwkkwKISqFXq9n3759bNmyheTk8s33LSqGTqcjPDycjh07YmNjY+5whBD3GUkmhRCVymAwcPDgQaKjo7l27Zq5w3mgODg40LZtW8LCwrC1Lf30mEIIURaSTAohqoTRaOTo0aPs3LmTU6dOIbeeytOwYUPCwsLw9/eXmWuEEJVOkkkhRJVLTk5m37597N27l5s3b5o7nPuCpaUlLVu2JDQ0FE9PT3OHI4R4gEgyKYQwG4PBwMmTJ9mzZw9Hjx6V8SrLwcfHh5YtW9KyZUvpmS2EMAtJJoUQ1cKtW7c4cOAABw8e5OLFi1INfgeKouDj40NgYCDNmzfH2dnZ3CEJIR5wkkwKIaqdtLQ04uLiOHbsGCdPniQnJ8fcIZmVoijUrVvXlEA6OTmZOyQhhDCRZFIIUa3p9XpOnTrFsWPHOHbs2APTxtLe3p6GDRvSsGFD/Pz8cHBwMHdIQghRLEkmhRA1yrVr1zh37hznz5/n/PnzJCYm3hdV4o6OjjRo0MCUQLq5uZk7JCGEKBVJJoUQNVpWVpYpsbxw4QLnz58nMzPT3GGVyN7eHk9PTzw8PKhVqxYNGzbE1dXV3GEJIUS5SDIphLjvpKamcv369SL/kpKSyM7OrpIYVFXFzs4Od3d3PD09Tcmjp6enzEIjhLivSDIphHigpKenc+PGDTIyMsjMzCQrK4vMzMwi/8/NzUVVVRRFQaPRoCiK6W9VVVFVFZ1Oh62tLTY2Ntja2mJra4uDgwMODg7Y2dmhqqq5T1cIISqdJJNCCCGEEKLc5LFZCCGEEEKUmySTQgghhBCi3CSZFEIIIYQQ5SbJpBBCCCGEKDdJJoUQopqJjo6mT58+5g5DCCFKRZJJIYSoREajEYPBYO4whBCi0kgyKYQQFezsNwvJ5wAAIABJREFU2bP4+fnx4osv0rJlSzQajWnZ0qVLGT58OACnTp0iNDSUNm3a8O9//xs7OzvTeqmpqQwYMAB/f3+ef/55DAYDc+fO5ZVXXjGt85///IcJEyZU2XkJIURxJJkUQohKcPz4cZ5++mkOHDiAra1tseuMHz+e8ePHs2fPHry9vQsti4mJ4ZNPPuHQoUOcOnWKX375hWHDhrFixQpyc3MBmDdvHs8++2yln4sQQpREkkkhhKgE9erVIzQ0tMR1du7cyeDBgwF4/PHHCy1r27YtDRs2RKPR8Nhjj7F9+3ZsbW3p2rUrq1atIi4ujtzcXAIDAyvtHIQQojS05g5ACCHuR7eXRiqKYvp/VlZWqba/fZvb/37uued4//33adasmZRKCiGqBSmZFEKISubp6cmxY8cwGAz8+uuvptdDQ0NZtmwZAD/++GOhbWJiYjhz5gwGg4GffvqJDh06ANCuXTsuXLjADz/8wGOPPVZ1JyGEEHcgyaQQQlSy6dOn06dPH7p27UqtWrVMr3/++ed8+umntG3blitXruDo6GhaFhYWxuTJk2nevDkNGjRgwIABpmVDhgwhPDwcZ2fnKj0PIYQojmI0Go3mDkIIIR5EGRkZWFtboygKP/74I4sXL+a3336763Z9+vThlVdeoVu3blUQpRBClEzaTAohhJns27ePsWPHYjQacXJyIioqqsT1U1JSaNu2LcHBwZJICiGqDSmZFEIIIYQQ5SZtJoUQQgghRLlJMimEEEIIIcpNkkkhhBBCCFFukkwKIYQQQohyk2RSCCGEEEKUmySTQgghhBCi3CSZFEIIIYQQ5SbJpBBCCCGEKDdJJoUQQgghRLlJMimEEEIIIcpNkkkhhBBCCFFukkwKIYQQQohyk2RSCCGEEEKUm1mSyejoaKZOnWqOQ993hgwZwtWrV80dxn1pzJgx/PXXX+YOo0JdvnyZ119/naeffpo1a9ZU6L7v5Xt95MgRnn/+edPf9+N7/80337B06dK7rvf222+zcePGKoioZinpXnevvykTJkzgyJEj5d7+QSK/Offml19+4auvvjJ3GBVOe7cVxowZQ0pKCqr6v7wzIiKCkSNHVmpgFWHMmDH885//JCgoyNyhFPH2228zePBgAgICzB1KtfHzzz9z9epVXnrpJXOHct/67bff8Pf358MPP7yn/SQkJDB27FgWL16MRqOpoOhKJzo6mrVr13L16lWsra3p0KEDjz32WKE4/vzzT5YuXUpSUhJOTk68+OKL+Pn5meK2tLQ0rduvXz8GDRpU6XGPHj26UvcfHR3Nxo0beeedd+64zuHDh1m2bBmnT5/Gzs6OyMhI07KbN28yb948jh07RlZWFnXr1uXpp5+mcePGRfYze/ZsoqOjmTVrFl5eXhV+Lm+//TYdO3akW7duFb7vyMhIXF1dGTZsmOm1Tz/9tEKPUdL7DPnfnzlz5hAfH4+bmxsjRoww/U6dP3+ehQsXcvr0adLS0vj5558rNLbqQq/XM3PmTE6fPk1iYiJvvfVWsb+Her2e1157jaysLFMSduzYMd5///1C62VnZzNhwgRCQ0PLHEtV5goDBw4s13Y7duxgzZo1nD17Fl9fX95+++1Cy8+ePcucOXO4dOkStWvX5oUXXqB+/foAGI1GFi1axKZNmwDo2rUrTzzxBIqi3MupFHLXZBJg0qRJ1TIhq67y8vKq/AdWVF/V6XpISkqiffv25g7jnuTk5DB8+HAaN25MamoqM2bMwM7Ojv79+wPw119/sWjRIl5++WV8fX1JSUkpso/58+dX6WdiMBgKPZCbi5WVFV26dCE8PJxff/210LKsrCx8fX155plncHR0ZNOmTUyfPp3IyEisrKxM68XFxXHt2rWqDr1GKel9Bpg5cyZNmjRhypQp7N+/n08//ZRZs2bh4OCAVqslLCyMhx56iI8++qhC46pO9yKAZs2a0bt37xKT+RUrVuDo6EhWVpbpNT8/PxYuXGj6+8iRI8yYMYOQkJBKjdec7Ozs6NWrF5cvX+bw4cOFlun1ej788EN69epFz549Wb9+PR9++CGzZs1Cq9WyYcMG9uzZw0cffYSiKLzzzjt4eHjw0EMPVVh8pUom7+Tq1at8/fXXnDt3DkVRCA4OZuTIkdja2gL5P1zz58/n2LFjGI1GwsPDC5Vofvfdd2zevBkbGxuee+45WrRoUexx7rSfko7/xRdfkJSUxIwZM1BVlUGDBtGvXz9OnDjBd999x8WLF3F3d2f48OGmp6GEhAQiIyM5c+YMjRs3platWmRkZJhKyvbu3csPP/xAcnIy9evX57nnnqNOnTpA/pNNjx492L59O5cvX2bYsGGcOHGC1157zXQeUVFRqKrK8OHDC53fyZMn+fbbb7ly5Qo6nY4OHTrwzDPPFPterFixglWrVqEoCkOHDi20LCMjg6ioKA4cOIClpSXdunVjwIABph+xDRs2sHr1aq5fv46rqyvjxo2jYcOGDBkypFAJw+1P7keOHOGLL77gkUceYeXKlaiqynPPPYdWq2XBggWkpqbSt29f09OWwWBgxYoVbNy4kVu3btG8eXNGjx6NnZ2dqVToxRdf5KeffiInJ4fevXszcOBAYmNjTTfdPXv24OXlVeyNdMyYMfTs2ZOtW7eSmJhISEgIY8aMQafTFVsyc/u5RUZGYmlpSUJCAseOHaN+/fq8+uqrLF++nC1btuDo6Mj48eNp0KCBaftTp04xb948UlJSaNOmDc899xw6nQ6Affv28eOPP5KYmEidOnUYNWoU9erVK/Z6WLhwodlv4tOmTePo0aPExcUxf/58ZsyYgZOT0x2vGYPBwK+//srGjRvJyckhJCSEESNGYGNjw1tvvQVgupZvr2KMiopiy5YtODs7M3LkSAIDAwHYvHkzK1as4Pr16zg4ONCvXz969OhR5vO4/Qbo4uJCx44dC91cf/75ZwYNGkSTJk1M61SWuLg4vv/+ey5evIi1tTVDhw4lIiKCyMhIdDodSUlJHD16lIkTJ7Jt27ZCJWJ79uzh559/JiEhAQcHB0aOHFnkx/DGjRu89957dOrUiUcffZSMjAwWLFjAgQMHUBSFLl26MGTIEC5fvsx//vMf9Ho9Tz31FBqNhvnz5xeJ19fXF19f32KbEHh6etKnTx/T3927d2fhwoVcvnyZhg0bAvnJSFRUFGPGjGHixInlft/S09P58ssviY+Px2Aw0LRpU0aNGoWrqyuLFy/m2LFjxMfHM3/+/EI1YX/99Rfvv/8+aWlppt+B4kpXLl26RFRUFKdPn8bBwYGhQ4fSvn17NmzYwPbt2wFYvXo1AQEBTJ48uVDJlMFgYPny5WzevJmbN29Sq1YtJk6ciKurKwsWLGD79u3k5ubi7u7OSy+9RN26dcv0Pl++fJkzZ87wr3/9C51OR2hoKGvWrGHXrl089NBDeHt74+3tXSFVyQX374cffpjVq1cTFBREYGBgiffJtLQ0IiMjOXbsGN7e3gQHB3PkyJFC6+/fv581a9aQmZlJREQETzzxBHl5eYwePZpp06aZ3pObN2/y4osvMmfOHBwcHArFptVq6d27N8AdH7QSEhLYtm0bTz/9NF9//fUdz3PLli2EhoYWeugprfLkCm+//TbNmjXjyJEjnDt3jiZNmvDSSy/h4OBQ4u8cFK6By8nJ4auvviI2NhaDwUCtWrWYNGkSTk5OReIsKNArrgnMkSNHyMvLo3fv3iiKQq9evVi5ciWHDx8mJCSELVu20LdvX1xdXQHo27cvGzdurD7JJMCAAQPw8/MjMzOTTz75hCVLljB8+HAMBgMzZswgICCAyMhIVFXl9OnTpu1OnjxJ586dmTt3Lhs2bOCrr77iq6++KnJjuNt+7nT8cePGERcXV6joOjk5menTpzN27FhCQkI4fPgwn3zyCZ9//jkODg7MnDmTpk2bMnXqVE6ePMkHH3xAq1atgPwbwMyZM5k4cSL+/v6sXr2aGTNm8Nlnn6HV5r+Nf/75J5MnT8bBwYFbt26xZMkSbt26ha2tLXl5eezYsYM33ngDoFAR9bx58+jVqxedOnUiKyuL8+fPF/tex8bGsnLlSqZOnYqHh0eRL1dUVBQZGRl8+eWXpKWl8d577+Hs7EzXrl3ZuXMnS5YsYeLEiTRq1Ihr166VOrlJSUkhNzeXr776iujoaL7++muCgoKYPn06SUlJTJ48mfDwcDw9PVm7di179uzh7bffxsHBgXnz5vHtt9/y8ssvm/YXFxfHzJkzuXz5Mm+88QZt27YlJCSEAQMGlKqae+fOnbzxxhvodDqmTp1KdHR0qb8UO3fu5M0336ROnTp88MEHvPnmmwwZMoSnn36an3/+me+++86UKAFs376dN998EysrK2bMmMEvv/zCsGHDOH36NHPmzGHSpEk0atSIrVu38uGHH/L5559jYWEBFL4ezJ1IArz11ltFqg+//PLLO14z0dHRREdH89Zbb+Ho6MiXX37J3LlzGTduHNOmTWPs2LGFSvguX75MfHw87dq1Y+7cucTExPDxxx8TGRmJnZ0djo6OTJo0CU9PT1M1VaNGjUyJyp3ExcUxffr0YpMjgKNHj+Lj4wPk3y9OnTpF69atGTduHLm5ubRp04annnrK9BAA8OKLL6IoCkFBQTz55JNFfuhKIykpiffff5/Ro0cTGhpKZmYm169fNy3fvn07U6ZMYdKkSej1erZt22ZadvLkSb788kteffVVmjdvTkpKCpmZmYX2n5CQwHvvvUffvn3p3r07kP95OTk5MWvWLLKzs5k+fTqurq706NGDUaNGFUkStm/fzvLly/n444/LfH5nz55Fr9cXqsZevXo1fn5+poem8jIajURERPDKK69gMBiYM2cOc+fO5fXXX+exxx7j+PHjxVZz79+/nw8++IDMzEwmTZpE69atiyTgWVlZvPvuuwwZMoQ33niDc+fO8d577+Hj40P37t05fvx4kWru261atYo///yTKVOmUKtWLc6dO4elpSUHDx7k2LFjzJw5ExsbGy5dumQqOCnL+3zx4kU8PT2xtrY2vVavXj0uXrxY1rexVFJSUkhPT2f27NkYjUZ27NhR4vpz587FysqKb775hsTERN577z3c3NwKrbNnzx6mT59OVlYW77zzDt7e3nTr1o3w8HC2bt3Kk08+CeTfAwMDA03fr+HDhzN58mSaNWtWqtijoqJ47LHHCn13/y47O5tdu3YxadKkUu3z78qTKxSc25QpU3Bzc+P9999n5cqVPPHEE6b9Fvc7V1D4VGDLli1kZGQwZ84cLCwsOHv2rOlcly9fTlxcHJMnT77rOVy4cIF69eoVyp/q1avHhQsXCAkJMS3/+7KKVKp6l48++ojhw4eb/m3YsAEALy8vgoKCsLCwwMHBgd69e3P06FEg/2aZnJzMU089hZWVFTqdrtAF5ObmRvfu3VFVlc6dO3Pjxg1u3rxZ5Ngl7aek4xdn69attGjRgpYtW6KqKkFBQTRq1Ij9+/eTlJTEqVOnGDp0KFqtlmbNmpkSSchvr9CiRQuCgoLQarX07duXnJwcjh8/blrnkUcewc3NDZ1Oh7OzM35+fuzcuRPITwTt7e2L/eHUarVcvXqV1NRUrKysTCUqf7djxw4iIiKoW7cuVlZWDB482LTMYDCwY8cOHn/8caytrfHw8KBPnz5s3boVgE2bNtGvXz98fX1RFAUvLy/c3d3v+F7dTqPRMHDgQLRaLeHh4aSlpdGrVy+sra3x8fGhTp06nDt3Dsgv/Rw2bBiurq5YWFgwePBgdu/eTV5enml/gwcPRqfTUb9+ferVq2fatrQeeeQRXFxcsLOzo1WrVpw9e7bU27Zp04aGDRui0+lo27YtOp2Ozp07o6oq7du358yZM4XW79mzJ25ubtjZ2TFgwAD+/PNPIP/psHv37jRu3BhVVYmIiECr1RIfH18ozoLroTq62zWzfft2+vTpg6enJ1ZWVjz++OPs2LGj0Gf5d46OjvTu3RutVkv79u3x9vZm//79ALRs2RIvLy8URcHf35+goCDi4uLuGmezZs3umEhu3ryZ06dP07dvXyD/hzMvL49du3bxf//3f3z44YecPXuWZcuWAeDg4MAHH3zA7NmzmT59OpmZmXzxxRdledtMtm3bRmBgIB06dECr1WJvb29qowT511qzZs1QVbXINbBp0ya6dOlCUFAQqqri4uJC7dq1TcsvXrzItGnTGDx4sCmRTElJITY2luHDh2NlZWV6r0tKDjp06FCuRDIjI4MvvviCQYMGYWNjA+Qnz+vXry9SI1Ie9vb2hIaGYmlpibW1NQMHDuTYsWN33a5///7Y2tri5uZGQEBAsd/9/fv34+7uTpcuXdBoNDRs2JB27dqxa9euUsW2ceNGhg0bhre3N4qiUL9+fezt7dFqtWRlZXHp0iWMRiN16tTB2dkZKNv7nJWVZXpPC9jY2BR5mKgoiqIwZMgQLCws7novMhgM7N69myFDhmBpaUmdOnXo3LlzkfX69euHnZ0dbm5u9OrVy3Rf7Ny5M3/++ScGgwHI/83t1KmTabv58+eXOpGMiYkhLy+Ptm3blrje7t27sbe3x9/fv1T7LY2ScoUCEREReHt7o9PpCAsLK3ItluZ3TqPRkJ6eztWrV1FVlYYNG5qujf79+5cqkYS7X1N/X25jY0NWVhZGo7FU+y+NUpVMTpw4sdg2k39vsG0wGLCzswPybzzu7u53LJG5vRi3oDH87W0iCpS0n5KOX5ykpCR27drFvn37TK/l5eUREBBAcnIydnZ2hRrmu7m5kZSUBORXN92efKmqipubG8nJyYXWv13nzp35448/6N69O9u2bSv0pbrd888/z08//cQrr7yCh4cHgwYNKpTIFrhx40ahZPT2eFJTU9Hr9YVicHd3N8WXlJSEp6fnHd+bktjb25uqIQpuRo6OjqblOp3O9NklJiby8ccfF3pCUlW10IPC3z/74j73kty+vU6nK/QZlHXbO51HgZLezy1btrBu3TrTcr1eX+L1UJJvvvnGVHJVUNpe0MDc3d2dTz/9lAkTJpCYmAjAG2+8wbFjx0xNAzp27FjmDh53u2b+fs27ubmRl5dX7ENfARcXl0Kf/e37O3DgAEuXLuXy5csYjUays7OLrSIsrZiYGH744QemTp1qKi0ouD4ffvhh0w997969+eWXX3jsscewsrKiUaNGQP61MHLkSEaPHk1GRkaRm/HdXL9+vcTvVEGV0p22vVOzHshP5L28vAp1JkhKSjJVJRYwGo0lHqc8cnJymDFjBo0bN2bAgAGm1+fPn18oubwX2dnZLFiwgNjYWG7dugVAZmbmXduWlubekZiYSHx8fKHmRHl5eXe8//7dnT7X5s2b07NnT+bOnUtSUhJt27blqaeeKvP7YWVlRUZGRqHXMjMzC5VUViQHB4dSP9CmpqaSl5dX6Joq7vq6/TV3d3du3LgBQOPGjbG0tOTo0aM4Oztz9epVWrduXeaYs7Ky+P7775kyZcpd192yZQudO3eu0M4kJeUKBe52LZbmWu3UqRPXr1/n888/JyMjg44dOzJs2DBTbWdpWVlZFXkYycjIMF1Tf1+emZmJlZVV1XfAuZMffvgBgI8//hh7e3tiYmKIiooC/peI3WuD35L2U9Lxi+Pq6krHjh0LDUFSIDExkfT0dLKzs00JZUEiCeDs7Fyo+tloNJKUlFRie6w2bdrw7bffcv78efbt22cq+v+7WrVq8fLLL2MwGIiJieHTTz81VTXcztnZuVA12u3xFVSlJiUlmYrSb4/Pzc3tjo3mLS0tyc7ONv2dkpJS7h8oV1dXXnjhhWKfPhMSEkrc9l4vbEtLS3Jyckx/F9fxoqxuf49vfz9dXV0ZOHBguXvm/d3o0aOLJIO3NzCHoj1O/fz87un4d7tmnJ2dTclrwTKNRoOjo+MdE/jk5GSMRqPps0xKSqJ169bk5ubyySefMHbsWFq3bo1Wq72nHuWxsbF8/fXXTJkypVBCamdnh6ura4XeJO/E1dWVkydP3nF5STG4urqW2CZu8ODBxMbGMmvWLF5++WVUVcXV1RWtVsvcuXMrrdlEbm4uH330ES4uLkWux8OHD3P8+HG+//5702tvvvkmzz77LB06dCjTcVauXMnly5d5//33cXJy4uzZs7z++uumkpJ7+fxcXV3x9/e/41BBd9u3q6sr165dK/ZBp1evXvTq1YubN2/y2WefsWLFijtWl99JnTp1SEhIKJRAnjt3jvDw8DLtp7T+fr4l3ScL7gnXr1/H29sboNBvToHr16+bmpYkJSWZHtwgvxBl27ZtODk5ERoaWq6amatXr5KYmMi///1vIP9BPSMjg1GjRvHee+/h4eFhOvaRI0cYNWpUmY9RkpJyhYqk1WoZPHgwgwcPJiEhgQ8++ABvb2+6du1apv34+PiwatWqQvfe8+fP8/DDD5uWF/QCh/wmLAWfX0W5p+6FBdmtra0tycnJrFy50rTM19cXZ2dnFi1aRFZWFjk5OaWq0vq7kvZT0vEh/8ng9gSmY8eO7Nu3z9TYNScnhyNHjnD9+nXc3d1p1KgRS5YsQa/Xc+LEiUJPJe3bt+fAgQMcOnQIvV7PypUrsbCwoGnTpneMXafT0a5dO2bNmoWvr+8dS6q2bt1KamoqqqqannKLezoPCwsjOjqaixcvkp2dzZIlS0zLVFUlLCyMxYsXk5mZSWJiIqtWraJjx45A/lAAK1eu5PTp0xiNRtOXFaB+/fps374dg8FAbGxsiU0F7qZHjx6mTimQ/6S7Z8+eUm3r6OhIYmKiqYqkrAragZw9e5acnJwKGVLj999/5/r166Snp/Prr78SFhYGQLdu3Vi/fj3x8fEYjUaysrLYv39/pVVVVYa7XTPh4eGsXr2ahIQEsrKyWLx4MWFhYWg0GhwcHFAUpcgDys2bN1m7di16vZ6dO3dy6dIlWrRogV6vJzc31/RjdeDAgXKPI3n48GFmzZrFq6++aro53i4iIoJ169Zx8+ZN0tPTWbNmDS1btgQgPj6ey5cvYzAYSEtLY968eQQEBJSrtK1jx44cOnTIVPWflpZW6iYXBW1SDx06hMFgIDk5mUuXLpmWazQaJkyYQHZ2Nl988QUGgwFnZ2eCg4P57rvvyMjIwGAwcPXqVdP31cnJieTkZPR6/R2PW3Dfy8vLw2g0kpOTY1pfr9fzySefYGFhwdixY4vcg2bOnMlHH31k+gf5I33crRqyOFlZWeh0OmxsbEhPTy90L4P8e0F5e4y3atWKK1eusHXrVvR6PXq9npMnT5raJN5t3926deOnn37iypUrGI1Gzp07R1paGidPniQ+Ph69Xo+lpSUWFhZ3LEUt6X329vamfv36LFmyhJycHGJiYjh37pypFPrv6+fk5JCbm1uu96I4Jd0nVVWlbdu2LFmyhOzsbC5dusSWLVuK7GPFihWkp6eTlJTEmjVrCo0Q0alTJ2JiYkqsjSuQm5trSmz1ej05OTkYjUZ8fHyYM2eO6Vp7/vnncXJy4qOPPir0O7p161aaNm16z8NTlSVXqEiHDx/m/PnzGAwGbGxs0Gq15bqmAgICUFWVtWvXkpuba6oxa968OZD/maxevZrk5GSSk5NZtWpVsc0X7kWpSiYLejkVCAoKYuLEiQwePJgvv/ySZ555Bi8vL1PAkH9RTpo0iaioKFNj9/Dw8FK3lyhQ0n5KOj7ktzmIiori+++/Z+DAgTz66KO8/vrrfP/998ycORNVVfH19TU91YwbN47Zs2czYsQIfH19ad++vSmx8fb2Zty4cURFRZl6c0+aNOmuxdERERFs2rSJF1544Y7rxMbG8t1335GdnY27uzvjx48v9mmuRYsW9O7dm2nTpqGqKkOHDjX1TAQYMWIEUVFRjB07Fp1OR7du3ejSpQuQn4impaUxc+ZMkpOT8fDwYOzYsaZeapGRkfz++++0adOGNm3alP4D+ptevXoB8O6773Ljxg0cHR0JCwsr1T7DwsLYtm0bI0eOxMPDgxkzZpTp2N7e3gwaNIh33nkHnU7HY489ZmrfW14dOnQwnUvr1q35xz/+AUCjRo345z//SVRUlKkXfrNmzfDz87un41W1kq6ZLl26cOPGDd566y1ycnIIDg5mxIgRQH7pxsCBA5k6dSp5eXmmjmWNGzfmypUrjBw5EicnJyZMmIC9vT0Azz77LJ999hm5ubm0atWq1NVfBZ11Ckpqly1bRkZGBh988IFpHT8/P1MM//jHP0hLS2P8+PFYWFgQFhZmKsG9du0aixcvJjU1FWtra4KCghg/fny53js3NzemTJnCwoUL+frrr7GxsWHo0KGF2k3eia+vLy+++CILFiwgISEBR0dHRo4cWajdpFar5dVXX2XGjBnMmTOHF154gbFjx7Jo0SImTJhAZmYmnp6e9OvXD8j/4SgYVUBVVebOncu2bdv49ddfTaXax44dY9q0aaZjPPnkk/j7+/P2229z4sQJ9u/fj06nK1RF/MYbb+Dn51eoSUiBslSh3q5Xr17MmjWLkSNH4uLiQp8+fQo9dPbq1YvIyEjWr19Px44dTdddaVhbW/Ovf/2LBQsWsGDBAoxGI/Xq1TONkNG1a1c+/fRThg8fjr+/P6+//nqh7fv06UNubi7vvvsuaWlp1K5dm9dee43MzEwWLFjAtWvX0Ol0BAcH8+ijjwKU6X0GGD9+PLNnz+bZZ5/Fzc2NCRMmmJpqJCYmMnbs2ELburu7Fxmrsrzudp8cOXIkkZGRjB49Gm9vb8LDwwt1egVo3bo1kydPJiMjg4iIiEIlaa6urjRs2JCrV68WuR8+9dRTpusJ4OWXXzYVPLz33ntAficzDw+PQtXEdnZ2KIpSpJfz1q1bTZ/BvShrrlBRUlJS+M9//kNycjJWVlaEhYWZHuZ/+eUX4uLiTPe1rVu3Mnv2bNO2Tz75JJ2xA191AAABUUlEQVQ7d2bMmDFotVomTpzIV199xaJFi6hTpw4TJ0405Sc9evQgISGBV199Fch/YCrPSBolUYwV2QLzPvPZZ59Ru3ZthgwZUu59JCUl8fLLL/PNN99USFsjIYQQoqp8//33pKSkFEpw72b27Nm4uLiUuQmAqLnMP4puNXLy5EmuXr1qqu7du3fvPZXSGQwGVq1aRfv27SWRFEIIUe1dunSJc+fOYTQaOXnyJJs3by5TU4aEhARiYmLK3O5P1Gz3PM7k/SQlJYVPPvmEtLQ0XF1dee655woNYF0WWVlZjBo1Cnd3d1MxtRBCCFGdZWZmMnPmTFMzpT59+pS6UOXHH39k9erVDBgwwNRJRjwYpJpbCCGEEEKUm1RzCyGEEEKIcpNkUgghhBBClJskk0IIIYQQotwkmRRCCCGEEOUmyaQQQgghhCg3SSaFEEIIIUS5/T+fMm802+CDpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "########## Visualize vocabulary size and the percentage of each category\n",
    "\n",
    "# Next let's see vocabulary size of each catagory\n",
    "vo_size=[each_catagory.shape[1] for each_catagory in all_tfidf_df]\n",
    "fig = plt.figure()\n",
    "label=folders\n",
    "\n",
    "plt.bar(label,vo_size,0.4,color=\"green\")\n",
    "plt.xlabel(\"catagory\")\n",
    "plt.ylabel(\"vocabulary size\")\n",
    "plt.title(\"vocabulary size of each catagory\")\n",
    "\n",
    "\n",
    "#Let us see what percentage of each category\n",
    "# Draw a pie chart\n",
    "\n",
    "values=[len(c) for c in summary_text]\n",
    "# plt.subplot(2,2,1)\n",
    "plt.figure(figsize=(5,5))\n",
    "explode=[0.02 for x in summary_text]# Distance to center radius\n",
    "\n",
    "plt.pie(values,explode=explode,labels=label,autopct='%1.1f%%')# draw chart\n",
    "plt.title('percentage of each category')#Title\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(-0.5, 1))\n",
    "Xstring=\"Each category's document number  ----  \"\n",
    "it=0\n",
    "for c in summary_text:\n",
    "    Xstring=Xstring+folders[it]+':'+str(len(c))+'   '\n",
    "    it+=1    \n",
    "plt.xlabel(Xstring)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a little analysis of the above figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Choose visualizations that you think summarize your data best. Explain all visualizations. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 For at least three target classes, visualize the most common relevant words and word frequencies. Are there any prevalent differences between your target classes? Is there separation in the visualizations that could be used to help classify the classes? If you have many target classes, choose a representative subset of classes to perform this visualization. \n",
    "Word clouds can be powerful visualizations for this task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make word clouds \n",
    "## Firstneed to install \n",
    "# pip install wordcloud\n",
    "# pip install jieba\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud, STOPWORDS \n",
    "## concatenate all the document to a long string\n",
    "\n",
    "doc_string_arr=[]\n",
    "for each_catagory in cleanWordLlist:\n",
    "    each_catagory_string=\"\"\n",
    "    for each_doc in each_catagory:\n",
    "        each_catagory_string=each_catagory_string+each_doc+' '\n",
    "    doc_string_arr.append(each_catagory_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input is a long string and generate wordcloud\n",
    "new_stopwords={'it','to', 'of','said','he','we','also','one','two','three','the','six','i\\'m'}\n",
    "def generate_wordcloud(text): # optionally add: stopwords=STOPWORDS and change the arg below\n",
    "    wordcloud = WordCloud(background_color ='white',\n",
    "                          width = 1400, height = 700,\n",
    "                          relative_scaling = 1.0,\n",
    "                          max_words=50,\n",
    "                          stopwords =new_stopwords  # set or space-separated string\n",
    "                          ).generate(text)\n",
    "    plt.figure(figsize = (11, 11), facecolor = None)\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the word cloud for football\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the word cloud for cricket\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the word cloud for athletics\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the word cloud for rugby\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the word cloud for tennis\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### ['athletics', 'cricket', 'football', 'rugby', 'tennis']\n",
    "for i in range(5):\n",
    "    print('This is the word cloud for', folders[i] )\n",
    "    generate_wordcloud(doc_string_arr[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we need to answer these questions later\n",
    "\n",
    "Are there any prevalent differences between your target classes? Is there separation in the visualizations that could be used to help classify the classes? If you have many target classes, choose a representative subset of classes to perform this visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there separation in the visualizations that could be used to help classify the classes?\n",
    "\n",
    "I think separation in the visualizations could not be useful for classification.\n",
    "\n",
    "#### So let's perform Dimension Reduction to have a better visualization of the 5 catagory\n",
    "we can use powerful T-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "737\n"
     ]
    }
   ],
   "source": [
    "## make lable list\n",
    "label_length=[len(c) for c in summary_text]\n",
    "# print(label_length)\n",
    "label_list=[]\n",
    "category=0\n",
    "for i in label_length:\n",
    "    for j in range(i):\n",
    "        label_list.append(category)\n",
    "    category+=1\n",
    "print(len(label_list))\n",
    "\n",
    "# print(y.shape)\n",
    "y=np.array(label_list)\n",
    "# print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.2 s, sys: 1.1 s, total: 18.3 s\n",
      "Wall time: 17 s\n",
      "Org data dimension is 4727. Embedded data dimension is 2\n"
     ]
    }
   ],
   "source": [
    "# perform T-SNE\n",
    "from sklearn import manifold, datasets\n",
    "tsne = manifold.TSNE(n_components=2)\n",
    "X = corpus_df\n",
    "y=np.array(label_list)\n",
    "# target_names = folders\n",
    "%time X_tsne = tsne.fit_transform(X)\n",
    "print(\"Org data dimension is {}. Embedded data dimension is {}\".format(X.shape[-1], X_tsne.shape[-1]))\n",
    "# ax = scatter(X_tsne[:,0], X_tsne[:,1], c=y, s=(y+2)*10, cmap=cmap)\n",
    "# X_tsne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:164: UserWarning:\n",
      "\n",
      "Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
      "\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:181: UserWarning:\n",
      "\n",
      "Numerical issues were encountered when scaling the data and might not be solved. The standard deviation of the data is probably very close to 0. \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import scatter\n",
    "# print(X_tsne.shape)\n",
    "x_min, x_max = X_tsne.min(0), X_tsne.max(0)\n",
    "from sklearn import preprocessing\n",
    "X_norm=preprocessing.scale(X_tsne)\n",
    "X_norm = (X_tsne - x_min) / (x_max - x_min)  \n",
    "# print(X_norm)\n",
    "plt.figure(figsize=(16, 16))\n",
    "# fig = plt.figure(figsize=(14,10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "for i in range(X_norm.shape[0]):\n",
    "    plt.text(X_norm[i, 0], X_norm[i, 1], str(y[i]), color=plt.cm.Set1(y[i]), \n",
    "             fontdict={'weight': 'bold', 'size': 9})\n",
    "\n",
    "\n",
    "# ax = scatter(X_norm[:,0], X_norm[:,1], c=y, s=(y+2)*10, cmap=cmap)\n",
    "plt.subplot(2,2,2)\n",
    "# plt.legend(loc='upper left', bbox_to_anchor=(-0.5, 1))\n",
    "ax = scatter(X_tsne[:,0], X_tsne[:,1], c=y )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0: 'athletics', 1: 'cricket', 2: 'football', 3: 'rugby', 4:'tennis'\n",
    "\n",
    "#### After Dimension Reduction, we can see from the above image that news with same category tend to group together.\n",
    "There for we can perform machine learning algorithms like SVM, KNN to predict the exsit news and new news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4 Exceptional Work\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  No matter what we are going to do next,  just implement stemming of the words to reduce redundancy in representing the words first..... and we can take a few points .......\n",
    "############# new add on 9/3 evening "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "##There are more stemming algorithms, but Porter (PorterStemer) is the most popular.\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "van nistelrooy hungri return manchest unit striker ruud van nistelrooy said `` hungri play '' return train tuesday . dutch striker , 28 , close comeback nearli three month achil injuri . `` i ca n't wait get back train meet team-mat again , '' told daili mirror newspap . `` i 'm ahead schedul i 've made good progress physio holland . away unit long time , i 'm fit again . '' unit boss sir alex ferguson hint return van nistelrooy fa cup fifth round tie everton saturday . said : `` there chanc could involv everton we 'll see come train . '' unit lose 14 game van nistelrooy involv injuri - although form remain good sinc sidelin . 2-0 derbi win manchest citi sunday made 13 win 15 leagu game . howev , van nistelrooy still club 's top scorer season 12 goal , despit sinc 3-0 win west brom 27 novemb . unit also hit injuri alan smith loui saha van nistelrooy 's absenc , mean wayn rooney sometim play lone role front . teenag respond six goal nine game , includ first goal citi sunday . \n",
      "hungry--->hungri\n",
      "manchester--->manchest\n",
      "united--->unit\n",
      "hungry--->hungri\n",
      "returned--->return\n",
      "training--->train\n",
      "closing--->close\n",
      "nearly--->nearli\n",
      "months--->month\n",
      "achilles--->achil\n",
      "injury--->injuri\n",
      "training--->train\n",
      "team-mates--->team-mat\n",
      "daily--->daili\n",
      "newspaper--->newspap\n",
      "schedule--->schedul\n",
      "united--->unit\n",
      "united--->unit\n",
      "hinted--->hint\n",
      "chance--->chanc\n",
      "involved--->involv\n",
      "comes--->come\n",
      "training--->train\n",
      "united--->unit\n",
      "games--->game\n",
      "involved--->involv\n",
      "injury--->injuri\n",
      "remained--->remain\n",
      "since--->sinc\n",
      "sidelined--->sidelin\n",
      "derby--->derbi\n",
      "manchester--->manchest\n",
      "city--->citi\n",
      "wins--->win\n",
      "league--->leagu\n",
      "games--->game\n",
      "however--->howev\n",
      "goals--->goal\n",
      "despite--->despit\n",
      "since--->sinc\n",
      "november--->novemb\n",
      "united--->unit\n",
      "injuries--->injuri\n",
      "louis--->loui\n",
      "absence--->absenc\n",
      "meaning--->mean\n",
      "wayne--->wayn\n",
      "sometimes--->sometim\n",
      "teenager--->teenag\n",
      "responded--->respond\n",
      "goals--->goal\n",
      "games--->game\n",
      "including--->includ\n",
      "city--->citi\n"
     ]
    }
   ],
   "source": [
    "def stemEachDoc(doc):\n",
    "    \n",
    "    words = word_tokenize(doc)\n",
    "    after_stemming_string=''\n",
    "    for w in words:\n",
    "        after_stemming_string=after_stemming_string+ps.stem(w)+' '\n",
    "    return after_stemming_string\n",
    "\n",
    "##test\n",
    "print(stemEachDoc(cleanWordLlist[0][0]))\n",
    "\n",
    "### Let's see what words have been stemmed\n",
    "sentence =cleanWordLlist[0][0]\n",
    "words = word_tokenize(sentence)\n",
    "for word in words:\n",
    "    if word!=ps.stem(word):\n",
    "        print(word + \"--->\" + ps.stem(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually there are some mistake like hurdles--->hurdl\n",
    "title--->titl favourite--->favourit trailing--->trail preparing--->prepar\n",
    "##### TO fix this, let's try Lemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "van nistelrooy hungry return manchester united striker ruud van nistelrooy said `` hungry play '' returned training tuesday . dutch striker , 28 , closing comeback nearly three month achilles injury . `` i ca n't wait get back training meet team-mates again , '' told daily mirror newspaper . `` i 'm ahead schedule i 've made good progress physio holland . away united long time , i 'm fit again . '' united bos sir alex ferguson hinted return van nistelrooy fa cup fifth round tie everton saturday . said : `` there chance could involved everton we 'll see come training . '' united lose 14 game van nistelrooy involved injury - although form remained good since sidelined . 2-0 derby win manchester city sunday made 13 win 15 league game . however , van nistelrooy still club 's top scorer season 12 goal , despite since 3-0 win west brom 27 november . united also hit injury alan smith louis saha van nistelrooy 's absence , meaning wayne rooney sometimes play lone role front . teenager responded six goal nine game , including first goal city sunday . \n",
      "months--->month\n",
      "boss--->bos\n",
      "comes--->come\n",
      "games--->game\n",
      "wins--->win\n",
      "games--->game\n",
      "goals--->goal\n",
      "injuries--->injury\n",
      "goals--->goal\n",
      "games--->game\n"
     ]
    }
   ],
   "source": [
    "le = WordNetLemmatizer()\n",
    "def LemmatizEachDoc(doc):\n",
    "    \n",
    "    words = word_tokenize(doc)\n",
    "    after_stemming_string=''\n",
    "    for w in words:\n",
    "        after_stemming_string=after_stemming_string+le.lemmatize(w)+' '\n",
    "    return after_stemming_string\n",
    "\n",
    "##test\n",
    "print(LemmatizEachDoc(cleanWordLlist[0][0]))\n",
    "\n",
    "### Let's see what words have been lemmatized\n",
    "sentence =cleanWordLlist[0][0]\n",
    "words = word_tokenize(sentence)\n",
    "for word in words:\n",
    "    if word!=le.lemmatize(word):\n",
    "        print(word + \"--->\" + le.lemmatize(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although some words(like hunting, owns) have not been lemmatize, we don't want to mistakely stem words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now let's lemmatize the whole corpus\n",
    "lemmatized_corpus=[]\n",
    "for each_catagory in cleanWordLlist:\n",
    "    for each_doc in each_catagory:\n",
    "        lemmatized_corpus.append(LemmatizEachDoc(each_doc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "van nistelrooy hungry return manchester united striker ruud van nistelrooy said `` hungry play '' returned training tuesday . dutch striker , 28 , closing comeback nearly three month achilles injury . `` i ca n't wait get back training meet team-mates again , '' told daily mirror newspaper . `` i 'm ahead schedule i 've made good progress physio holland . away united long time , i 'm fit again . '' united bos sir alex ferguson hinted return van nistelrooy fa cup fifth round tie everton saturday . said : `` there chance could involved everton we 'll see come training . '' united lose 14 game van nistelrooy involved injury - although form remained good since sidelined . 2-0 derby win manchester city sunday made 13 win 15 league game . however , van nistelrooy still club 's top scorer season 12 goal , despite since 3-0 win west brom 27 november . united also hit injury alan smith louis saha van nistelrooy 's absence , meaning wayne rooney sometimes play lone role front . teenager responded six goal nine game , including first goal city sunday . \n"
     ]
    }
   ],
   "source": [
    "print(lemmatized_corpus[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.0 Attempting bigram analysis (9/3/2018 ...needs more work...currently incomplete)\n",
    "note: do we need to tokenize words? If not tokenized, the results seemed wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('norwich', 'union'),\n",
       " ('grand', 'prix'),\n",
       " ('jason', 'gardener'),\n",
       " ('katerina', 'thanou'),\n",
       " ('cross', 'country'),\n",
       " ('new', 'york'),\n",
       " ('60m', 'hurdles'),\n",
       " ('bbc', 'sport'),\n",
       " ('kostas', 'kenteris'),\n",
       " ('record', 'holder')]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inspired by: http://www.nltk.org/howto/collocations.html\n",
    "\n",
    "import nltk\n",
    "from nltk.collocations import *\n",
    "\n",
    "bigram_measures = nltk.collocations.BigramAssocMeasures()\n",
    "\n",
    "rawCleanText = ''.join(cleanWordLlist[2])\n",
    "tokens = nltk.wordpunct_tokenize(rawCleanText)\n",
    "\n",
    "finder = BigramCollocationFinder.from_words(tokens)\n",
    "\n",
    "finder.apply_freq_filter(15) ##*** what frequency level should we set??\n",
    "finder.nbest(bigram_measures.pmi, 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('\"', 'i'),\n",
       " ('\"', 'it'),\n",
       " ('\"', 'the'),\n",
       " ('\"', 'we'),\n",
       " (\"'\", 's'),\n",
       " (',\"', 'said'),\n",
       " ('.', '\"'),\n",
       " (':', '\"'),\n",
       " ('i', \"'\"),\n",
       " ('said', ':')]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspired by: http://www.nltk.org/howto/collocations.html\n",
    "\n",
    "#bigram_measures = nltk.collocations.BigramAssocMeasures()\n",
    "\n",
    "rawCleanText = ''.join(cleanWordLlist[0]) #only first element being analyzed\n",
    "tokens = nltk.wordpunct_tokenize(rawCleanText)\n",
    "finder = BigramCollocationFinder.from_words(tokens)\n",
    "scored = finder.score_ngrams(bigram_measures.raw_freq)\n",
    "sorted(finder.nbest(bigram_measures.raw_freq,10))  #consider adding filter?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1.... sentiment analysis attempt\n",
    "\n",
    "Sentiment analysis can highlight the overall positive or negative sentiment of a corpus of data. By analyzing sentiment by category, we can assess which sport may illicit emotion in the author, which in turn, may be influential on the reader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Athletics {'positive': 4605, 'neutral': 61206, 'negative': 2204, 'positive_pct': 6.770565316474307, 'neutral_pct': 89.9889730206572, 'negative_pct': 3.2404616628684852}\n",
      "Tennis {'positive': 1738, 'neutral': 35168, 'negative': 1043, 'positive_pct': 4.579830825581702, 'neutral_pct': 92.67174365595932, 'negative_pct': 2.7484255184589848}\n",
      "Football {'positive': 1410, 'neutral': 21653, 'negative': 802, 'positive_pct': 5.90823381521056, 'neutral_pct': 90.73119631259166, 'negative_pct': 3.3605698721977793}\n",
      "Rugby {'positive': 2431, 'neutral': 39403, 'negative': 1472, 'positive_pct': 5.613540848843116, 'neutral_pct': 90.98739204729137, 'negative_pct': 3.399067103865515}\n",
      "Cricket {'positive': 1675, 'neutral': 20557, 'negative': 820, 'positive_pct': 7.266180808606629, 'neutral_pct': 89.17664410897103, 'negative_pct': 3.5571750824223494}\n"
     ]
    }
   ],
   "source": [
    "#Reference: https://medium.com/@sharonwoo/sentiment-analysis-with-nltk-422e0f794b8\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "positives=[]\n",
    "neutrals=[]\n",
    "negatives=[]\n",
    "\n",
    "def doSentiments(tokens):    #this function from the reference above\n",
    "    sid = SentimentIntensityAnalyzer()\n",
    "    summary = {\"positive\":0,\"neutral\":0,\"negative\":0}\n",
    "    total = 0\n",
    "    \n",
    "    for x in tokens: \n",
    "        ss = sid.polarity_scores(x)\n",
    "        if ss[\"compound\"] == 0.0: \n",
    "            summary[\"neutral\"] +=1\n",
    "        elif ss[\"compound\"] > 0.0:\n",
    "            summary[\"positive\"] +=1\n",
    "        else:\n",
    "            summary[\"negative\"] +=1\n",
    "        \n",
    "        total += 1\n",
    "    \n",
    "    summary[\"positive_pct\"] = summary[\"positive\"]/total*100\n",
    "    summary[\"neutral_pct\"] = summary[\"neutral\"]/total*100\n",
    "    summary[\"negative_pct\"] = summary[\"negative\"]/total*100\n",
    "    \n",
    "    positives.append(summary[\"positive_pct\"])\n",
    "    neutrals.append(summary[\"neutral_pct\"])\n",
    "    negatives.append(summary[\"negative_pct\"])\n",
    "        \n",
    "    print(summary)\n",
    "\n",
    "categories = ['Athletics', 'Tennis', 'Football', 'Rugby', 'Cricket']\n",
    "k = 0 \n",
    "for words in cleanWordLlist:\n",
    "    message = ''.join(words)\n",
    "    tokens = nltk.wordpunct_tokenize(message)\n",
    "    \n",
    "    print(categories[k], end=\" \")\n",
    "    k += 1\n",
    "    doSentiments(tokens)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LFy/GsmXL0LhxY5iYmCA8PBz79++Xme/dm9EikahI2WVtiVXScoVf8uXLlyMwMLDI9Bo1apQYY+HyhbGWFqNUKsX06dMxePDgItPevpGtyOdobW0NkUiErKysItPkfSfeHa+jo1NkGXnj1NlcXktLSyYZFcb39jrf3RZF9wN5n9fbZUulUgwePBgzZswosqyVlZUQX1m+F4Xe/RxzcnLw4YcfonXr1ti4caPwmXt5eSn0Y7hQ+/btkZaWhkOHDuH48eMYNGgQvL29ceTIEWH7fv/9d7i5uRVZ9u2GMyXt08p6d9+RN+7t8hU5BhcXZ+HyisjKypL54VKcMrWxLUxS6enpsLW1RY0aNXDo0CG587q4uEBPTw8nTpyQGR8TEwMvL68S19O0aVPcvHkTpqamcHFxkflzcHAQ5jM2NsbHH3+MyMhIXLhwAYmJiUXW966zZ8/KDJ85c0bYLgAwNTVFv379sG7dOqxbtw49e/YssfVV4S/wM2fOCOMkEgkuXrxYYhwAhHqIiYmRGX/y5ElhWuGHef/+fWF6enq63CbIpW3bu2JiYtChQweMHDkSjRo1gouLS5l+vXp6eiI2NlZm3LvD73JxcYGurq5MvQGy22BnZwcnJyckJSUV2Q9cXFyKnL0Ux8zMDI6OjqXG2LRpUyQkJMhdV2lnVO/S0dFB/fr1i5wpe3l5yf1OGBgYoE6dOkqtozS6urooKCgoMg5AkfFl4enpifPnz8uU9e7nWVZNmzZFfHw86tatW+SzsLCwAADY2toiPT1dZv1xcXEy5SizvYmJicjIyMDChQsRGBgIDw8PPHnyROFnhd5maWmJ/v37Y+3atdi/fz9OnDiBa9euwcvLC/r6+rh586bc/eztHwilkff5qoqix2BFYgTk1/+rV6+QkpIi92rUu0o9w/L390f//v3RtGlT2NjYIDk5GTNnzoS5ubnwa3fu3LkYN24c7Ozs0KtXL0ilUhw7dgz9+vWDtbU1Jk6ciDlz5sDGxgY+Pj74/fff8ddff+Hw4cMlrnvgwIEIDw9Hp06dsHDhQri5ueHRo0c4evQoPDw80L17dyxduhQODg7w8fGBoaEhtm3bBm1tbbm/Wt62b98+rFy5Eu3bt8fBgwexfft2/PbbbzLzjBkzRri0ceTIkRLLc3V1RZcuXTBhwgSsXbsWNjY2CAsLw7Nnz0r9tVm3bl307t0b48ePx9q1a+Hs7Iw1a9bg6tWr+PXXXwG8aW7cqlUrfPfdd3B3d4dEIsGsWbNkLtEps21vq1evHrZu3Ypjx47B0dERP/30E/755x/hgKCoKVOmoHfv3vD19UXHjh1x6tQpbN26tcRljIyMMGbMGMyePRt2dnZwc3PDli1bkJiYKPOLa+HChRg5ciTMzc3RvXt36OjoIDExEVFRUVi7dq1SMc6ZMwfu7u7w8/PDnj17EB0dLTPPt99+iw8//BCTJ0/G0KFDYWJighs3buD333/HypUrlW723bFjxyLJ6auvvkKXLl0QGhqKHj164PLly/jmm28wZcoUlTerrl27No4dO4aUlBSYmZnBzMxMuPT7999/w8vLC3p6ekp/3oXGjx+P8PBwjBs3DpMnT8bDhw8xa9YsAGU/4y40c+ZM+Pr6YtCgQZg0aRJsbGyQmpqK3bt3Y9KkSahTpw4CAwORk5ODOXPmYOTIkYiLi8OqVauK1AEA7NmzB61bt4aBgUGxPz6cnZ2hp6eHFStWYMqUKUhNTcWMGTOU3pZZs2ahSZMm8PLygpaWFn755RcYGxujZs2aMDY2xsyZMzFz5kwAQEhICCQSCa5cuYJLly5hyZIlCq9H3ucr70yqLBQ5BiuipP3t1KlT0NPTg7+/f+kFlXaTa/HixdS6dWuysbEhPT09cnJyooEDB1JCQoLMfD///DM1aNCAdHV1ydLSkjp27Ci0EFG0Wbu8G5iZmZk0duxYYVkHBwfq3r07xcXFERHRDz/8QI0bNyYTExMyMjKipk2b0u7du0vcJgAUHh5O3bp1IwMDA7K3t6fvvvtO7rw+Pj7k5uZWWjUJsfbs2ZMMDAzIxsaG5syZQ7169aLOnTsL87x7Q7xQdna20KxdV1e3SLN2IqKkpCRq27YtGRoakouLC+3cuVNuowtFt63Q06dPqXfv3mRiYkKWlpY0fvx4mj17tkzT08Jm3G+TdyM7IiKCHBwcSF9fn4KCgoRWbIo0azcxMSEzMzMaN24cTZo0qUjjgl27dpGfnx8ZGBiQiYkJNWzYkObNmydMl1e38+fPl9mOgoIC+uqrr8jKyooMDQ2pZ8+ecpu1x8TEUFBQEBkbG5OhoSG5u7vTpEmThJvP8uqjOCkpKSQWiyktLU1m/ObNm8nd3V3Yr2fOnFmkWfu72yPvBnv79u1p4MCBxS6XkpJCbdq0ISMjI5kb31u2bKFatWqRWCwutVn72+TdQD98+DB5eXmRrq4ueXt7C40u3m0i/TZFGksQEcXHx1PXrl3J3Nyc9PX1qW7dujR69GiZlssbNmyg2rVrk76+PnXo0IG2bdtWpJxJkyaRra0tiUQimWbt8r6Pv//+O7m4uJCenh75+PjQ8ePHSVtbmzZt2iTMU9wxq9C3335LXl5eZGRkRKamptS2bdsijabWr18vPKphbm5Ovr6+Mq3lnJ2daf78+TLLjBw5kvz9/YXh4j7fd+OTF++720REpKenJ9OqtbRjcGGji3e/4++WLW9/I3rzGNEnn3wivxLfIfr/DWFySCQSODs744svvsCUKVOUXr6goADu7u7o2rUrwsLC1BBhUSKRCFu3blXsITwN1q5dO1hYWGDnzp2VHYpKjBw5EiYmJoiIiKjsUCpETEwM/P39ER8fD29v78oOh2moO3fuoEGDBrh06RJq1apV6vzcaZgcUqkU6enpWLt2LV68eKFwv2wxMTFIT09Ho0aN8Pz5c4SHhyM1NVVutynsf65cuYK4uDi0aNEC+fn5wuXJAwcOVHZoKrN48WJs3LgRUqm0Qrpnqmhr1qxBw4YN4eDggGvXrmHy5Mlo3rw5JytWotTUVKxbt06hZAVwwpIrLS0NtWvXxgcffIBNmzbBzMxMoeUKCgqwYMECJCcnCzfbjx07xl/aUohEIqxZswYTJ06EVCqFu7s7du3aJTRbrw5sbW3ltnSrLm7fvo3Fixfj0aNHsLe3R0hIiFL3Ydj7qU2bNkrNz5cEGWOMVQnV79oEY4yxaokTFmOMsSqhyt/DevtBWnWytrZGZmZmhayrKuF6kY/rRT6uF/kqul6UeehXk/AZFmOMsSqBExZjjLEqgRMWY4yxKqHK38Ni7yciQm5uLqRSabn7q1OHR48eIS8vr7LDkIuIoKWlBX19fY2sO8aKwwmLVUm5ubnQ0dGBWKyZu7BYLFaqx+2KJpFIkJubq3RHvoxVJr4kyKokqVSqscmqKhCLxWp9hxZj6sAJi1VJfCmr/LgOWVXDCYsxxliVwNdUWLXg6KjaByHv3Sv9gXRHR0d88sknmDt3LgDghx9+wMuXL8v0KpqSREZGYuLEicJw165dsWfPHpWug7GqgBMWY2Wkp6eHqKgofPbZZ7C0tFTbelasWCGTsDhZab7r1x2VnF+58t3c7im3QDXBlwQZKyNtbW0MHDgQP/74Y5FpmZmZGD16NDp27IiOHTvi/PnzAIDHjx+jX79+aN++Pb788kv4+voiKysLADBixAh06NABgYGB+PnnnwEAixYtQm5uLkJCQvDpp58CAFxdXQEAY8eOxZEjR4R1fv7559i/fz8KCgowf/58dOzYEcHBwdi6data64GxisIJi7FyGDZsGHbt2oVnz57JjJ89ezZGjx6NAwcOYN26dZg6dSoAYNmyZWjVqhUOHTqEjz76CPfu/e+XclhYGA4ePIgDBw5g48aNyMrKwsyZM6Gvr4/Dhw9j5cqVMuvo1q2bcLaVn5+PU6dOoV27dti2bRtMTExw4MAB7N+/H7/++ivS0tLUXBOMqR9fEmSsHExMTNCrVy9s2LBB5pmmmJgYJCUlCcMvXrzAixcvcO7cOWzYsAEAEBgYCHNzc2GejRs3IioqCsCbTp1v3bpV4qXGwMBAzJkzB3l5eTh+/Dj8/PxgYGCAEydOIDExEfv37wcAPH/+HLdu3ULNmjVVuu2MVTROWIyV06hRo9ChQwf07dtXGEdE2LNnT5EHc4t7X+rp06dx8uRJ7N27FwYGBujVq1epPWXo6+ujRYsWOHHiBPbs2YNu3boJ0xYsWICAgICybxRjGogvCTJWThYWFujSpQu2bdsmjPP398fmzZuF4atXrwIAfH19sXfvXgDAiRMn8PTpUwBvzoLMzMxgYGCA5ORkxMXFCcvq6Ojg9evXctfdrVs3bN++Hf/884+QoPz9/fHTTz8Jy6SkpCAnJ0dl28tYZeEzLFYtKNIMXZ3GjBmDTZs2CcMLFy7E9OnTERwcDIlEgubNm2PJkiX44osvMH78eOzZswd+fn6ws7ODkZERAgICsHXrVgQHB6NOnTpo3LixUNbAgQMRHBwMb2/vIvex/P39MWnSJHz44YfQ1dUFAAwYMAB37txBhw4dQESwtLTExo0bK6YiGFMjERV3jaKK4Bc4Vq7KqpecnBwYGhpW+HoVJRaLIZFIiozPy8uDtrY2xGIxLly4gK+++gqHDx+uhAgrpw7fl++Rss3alVXeZu1V9QWOfIbFWAW6d+8exo4dC6lUCl1dXSxdurSyQ2KsyuCExVgFqlOnDv7+++/KDoOxKokbXTDGGKsSOGExxhirEir8kuDq1asRFxcHMzMzhIWFAXjzUGV4eDgyMjJgY2ODyZMnw9jYuKJDY4wxpsEq/AwrICAAM2fOlBm3e/dueHt7IzIyEt7e3ti9e3dFh8UYY0zDVfgZlqenJ9LT02XGnT9/Ht988w2AN8+VfPPNNxg0aFBFh8aqMFU3I1ak2bCTkxPc3d1RUFAAFxcXLF++XOlXzk+dOhWffPIJ3Nzc+DUijJVCI1oJZmdnw8LCAsCbXgPe7Uj0bdHR0YiOjgYAhIaGwtraukzrPH1aT6n5le3+v2XLkrvVqS7EYnGZP4PyePToEcRi9e2+ipStr6+PY8eOAQDGjRuHX375BWPHjlWqjIiICOH/FStW4IsvvhCGDxw4oEzIStPT06vwz66y9peKpuzxQlnvQx3KoxEJSxnBwcEIDg4WhjX1IURNjUvVKutB0MIHcNVF3kO/Jc3XrFkzJCYmQiKRYO3atdi+fTsAoH///hg9ejRycnIwZswYPHjwAFKpFJMmTUK3bt3Qq1cvzJkzB/v370dubi4CAwNRr149rFy5Eq6urrhx4wbGjh2L3r17IygoCMCb14iEhISgQ4cOWLRoEc6cOYP8/HwMHToUgwcPVngb8/LyKvyze18eHFa38tYhPzhcDmZmZnjy5AksLCzw5MkTmJqaVnZIjClMIpHg2LFjCAgIQHx8PHbs2IGoqChIJBJ07twZLVq0wO3bt2Fvby+8m+rdqwgzZ87Epk2b5PZ6UfgakaCgIOE1IosXL5Z5jUheXh66d+8Of39/7pWdVVsa0ay9adOmOHHiBIA3HYI2a9askiNirHSFL1b86KOP4OjoiP79++PcuXPo0KEDjIyMYGRkhI8++gj//PMP3N3dcfLkSSxcuBD//POPUj/KAgMDERsbi7y8PBw7dkzmNSJ//PEHQkJC0LlzZzx58gS3bt1S4xYzVrkq/AwrIiIC165dw/PnzzF27Fj06dMH3bt3R3h4OI4ePQpra2uZ6/iMaarCFyu+rbiuOevWrYuoqCgcPXoUixcvhr+/PyZPnqzwevg1IoxVQsL6/PPP5Y7/+uuvKzgSxlTPz88PkydPxqRJkyCRSHDw4EFERkbi4cOHMDc3R8+ePWFkZIQdO3YUWbbwNSI6OjpFpnXr1g3btm3Dv//+i/DwcAD/e41Iq1atoKOjg5SUFHzwwQca3SkwY+WhVMK6efMm4uLicPv2baGnZ2dnZzRq1Ah169ZVV4yMlaq8vVerire3N3r37o0OHToAeNPoon79+jh+/DgWLFgAkUgEHR0dLF68uMiy/BoRxkqm0OtF/v33X2zbtg2vXr2Cp6cnHB0dYWBggFevXuHevXu4du0a9PX10b9q2MLkAAAgAElEQVR/f/j4+FRE3IKyvl5E07v/ryr49SLyFfd6EU3CrxdRH00/vlTrVoKHDx/GqFGj4OLiUuw8ycnJ+Ouvvyo8YTHGGHs/KJSwpk6dWuo8Li4umDJlSrkDYowxxuQpV6OLf//9F7dv34adnR18fX0hEolUFRdjJariL8rWCFyHrKop83NY27dvx969e/HixQscOHCgyE1ixtRJS0tL4+8RaTKJRAItLY14DJMxhSl8hnX+/HmZB3oTExOFDmslEglGjx6t8uAYK46+vj5yc3ORl5enkWf2enp6yMvTzP4kiQhaWlrQ19ev7FAYU4rCCSsuLg5Hjx7F8OHDYWtrixo1auDHH39E3bp1kZCQUGKDDMZUTSQSKd0zekV6X1rDMVaRFE5YY8aMwfXr17F8+XI0atQIAwcOxMmTJ3Hr1i3UqlVLpkNaxqobZZspK9tb9/vyGARj5aHURWw3NzfMnz8fBgYGmDdvHuzs7DBq1Ch07dpVo5+JYYwxVvUpfIZFRDh37hwePXoEJycnfPnll9i8eTOOHDmCYcOGwdLSstzBHDhwAEeOHAERISgoCJ06dSp3mYwxxqoHhRPWypUrkZ6eDg8PD/z555/w9PTEF198gcuXLyM0NBRt2rRBly5dyhxIWloajhw5gkWLFkEsFmPRokVo3LgxPvjggzKXyRhjrPpQqtHFunXrIBaLkZ+fj1mzZgldMXl6emL37t3lCuTevXtwdXWFnt6bNwF7eHjg3LlzMj1TM8YYe38pnLBcXFywY8cO1K9fH/Hx8XB1dRWm6erqok+fPuUKxMnJCb/99hueP38OXV1dXLp0iTvUZUzDcWMUVpEU6vwWeNNRZnR0NNLT0+Hk5IR27drJfQ1CeRw9ehSHDh2Cvr4+HB0doauri2HDhsnMEx0djejoaABAaGgo8vPzy7Su06f1yhtuiVq21MxncFStKnTyqgq8v8jH9SKfptdLYY//VY3CCaui/frrr7CyskL79u1LnI97a69c78vzRry/yMf1Ip+m10tV7a1doWbtW7ZswdOnT0uc5+nTp9iyZUu5gsnOzgYAZGZm4ty5c2jVqlW5ymOMMVZ9KHQPy8HBAV999RVq1KgBDw8PODg4CO/DevDgAa5du4b79++jR48e5QomLCwMz58/h1gsxsiRI2FsbFyu8hhjjFUfCiWskJAQBAYG4sKFC7h06RLOnz+PnJwcGBkZoWbNmggJCUGTJk2gra1drmC+/fbbci3PGGOs+lK4laBYLIafnx/8/PzUGQ9jjDEmF79fgDHGWJXACYsxxliVwAmLMcZYlcAJizHGWJWgcKOLt929exdnz57F06dPMWrUKNy7dw8SiQTOzs6qjo8xxhgDUIYzrDNnzuCbb75BVlYWTp48CQDIzc3FTz/9pPLgGGOMsUJKn2Ht2LEDs2fPRq1atXDmzBkAgLOzM1JTU1UdG2OMMSZQ+gwrOzu7yKU/kUgEkUiksqAYY4yxdymdsOrUqYOYmBiZcbGxsXBxcVFZUIwxxti7lL4kOHz4cCxYsABHjx5FXl4eFi5ciPv372P27NnqiI8xxhgDUIaE5ejoiIiICFy8eBFNmjSBlZUVmjRpAn19/XIHs2/fPhw9ehQikQhOTk4YP358lX1vC2OMMdUqU7N2PT09tGzZUqWBZGVlISoqCuHh4dDV1cWyZctw+vRpBAQEqHQ9jDHGqialE9bXX38tt4GFWCyGlZUVfH190bRp0zIFI5VKkZ+fD21tbeTn58PCwqJM5TDGGKt+lE5Ynp6eOHHiBPz9/YW3zcbExKB169YgIqxZswZdu3ZFt27dlCrX0tISXbp0wbhx46Crq4uGDRuiYcOGReaLjo5GdHQ0ACA0NBTW1tbKbgIA4Pr1Mi2msLLGVdWIxeL3Ylt5f5GP60U+rhf1UDphxcfHY9asWahRo4Ywrk2bNli1ahUWLVqE5s2bIyIiQumE9eLFC5w/fx6rVq2CoaEhli1bhpiYGLRt21ZmvuDgYAQHBwvDmvp6dk2NS9UKf7Sw8uE6lI/rRb7y1ouDg4OKIqlYSjdrv3fvHuzs7GTG2djY4P79+wAAFxcX4VX3yrhy5QpsbW1hamoKsViM5s2b47q6f6YwxhirMpROWB4eHli9ejUePnyI/Px8PHz4ED/88APc3d0BAGlpaWW692RtbY0bN24gLy8PRIQrV67A0dFR6XIYY4xVT0pfEvz000+xfv16TJ48GVKpFNra2vD19cX48ePfFCgWY9KkSUoH4urqCj8/P0yfPh3a2tqoVauWzKU/xhhj7zelE5axsTE+//xzSKVSPHv2DKamptDS+t+JWnmujfbp0wd9+vQp8/KMMcaqrzI9hwUAeXl5yM/PR0ZGhjDu3XtbjDHGmKoonbDu3r2LyMhI3L59u8i07du3qyQoxhhj7F1KN7pYv349vLy8sHHjRhgaGmLTpk0ICQnBhAkT1BEfY4wxBqAMCev27dsYOHAgjIyMQEQwNDTEoEGD+OyKMcaYWimdsHR0dFBQUAAAMDExQWZmJogIL168UHlwjDHGWCGl72G5u7vjzJkzCAgIgJ+fHxYtWgQdHR14eXmpIz7GGGMMQBkS1hdffCH8379/fzg5OSE3Nxf+/v4qDYwxxhh7m9IJa8+ePejatSsAQEtLS+jrb9++fejcubNqo2MV7vp15XoXUbb3LDe3e8otwBhj/0/pe1g7d+5UajxjjDGmCgqfYV29ehXAm3dWFf5f6NGjRzAwMFBtZIwxxthbFE5Ya9asAQDk5+cL/wOASCSCubk5RowYUa5A7t+/j/DwcGE4PT0dffr0QadOncpVLmOMsepB4YS1atUqAMDKlSvx6aefqjwQBwcHLF26FMCbs7gxY8bA19dX5ethjDFWNZWpt/ZCUqlUZtrbneCWx5UrV2Bvbw8bGxuVlMcYY6zqUzph3bx5Exs2bEBaWhry8/Nlpqmqt4vY2Fi0atVK7rTo6GhER0cDAEJDQ8v8qmh+hbV8XC/ycb3Ix/UiH9eLeiidsFatWoUmTZpg3Lhx0NPTU3lAEokEFy9exIABA+RODw4OlnlPlqa+QltT46psXC/ycb3Ix/UiX3nrpTyvgapMSieszMxM9O/fHyKRSB3x4NKlS6hduzbMzc3VUj5jjLGqSembTs2aNcO///6rjlgAlHw5kDHG2PtL6TOs169f4/vvv4e7u3uRs6Dyth7My8tDfHw8Pvnkk3KVwxhjrPpROmHVqFEDNWrUUEcs0NPTw8aNG9VSNmOMsapN6YTVu3dvdcTBGGOMlUjphAUA8fHxiI2NRXZ2NmbMmIGUlBS8evUK9evXV3V8jDHGGIAyNLqIiorCunXr8MEHHyAxMREAoKuri99++03lwTHGGGOFlE5YBw4cwJw5c9C9e3ehZwtHR0fcv39f5cExxhhjhZROWK9evSrylLVEIoFYXKari4wxxphClE5YHh4e2L17t8y4qKgoeHl5qSwoxhhj7F1KJ6wRI0bg3LlzmDBhAnJzczFp0iScPXsWQ4cOVUd8jDHGGIAytBK0sLDA4sWLkZKSgoyMDFhZWcHFxUVlPbUzxhhj8iidsFJTU2FsbAwXFxe4uLgAeNO/4IsXL1CrVi1Vx8cYY4wBKMMlwRUrVqCgoEBmnEQiwcqVK1UWFGOMMfauMvXWbmdnJzPO3t4eGRkZ5Q7m5cuX+OGHH3Dnzh2IRCKMGzcObm5u5S6XMcZY1ad0wrK0tMTNmzdRp04dYdzNmzdhYWFR7mA2bdoEHx8fTJkyBRKJBHl5eeUukzHGWPWgdMLq1KkTli5diq5du8LOzg6PHj3C3r170aNHj3IFkpOTg8TEREyYMOFNYGIxP9vFGGNMoHRGCA4OhpGREY4ePYrHjx/DysoKQ4YMgZ+fX7kCSU9Ph6mpKVavXo3bt2+jTp06GDZsGPT19WXmi46ORnR0NAAgNDS0zK+K5ldYy8f1Ih/Xi3xcL/JxvaiHUglLKpXi999/R48ePdCiRQuVBlJQUIBbt25hxIgRcHV1xaZNm7B7927069dPZr7g4GAEBwcLw5r6Cm1Njauycb3Ix/UiH9eLfOWtFwcHBxVFUrGUaiWopaWFQ4cOQVtbW+WBWFlZwcrKCq6urgAAPz8/3Lp1S+XrYYwxVjUp3azd398fhw8fVnkg5ubmsLKyEjrRvXLlitpeFMkYY6zqUfoeVnJyMg4ePIg9e/bAysoKIpFImDZv3rxyBTNixAhERkZCIpHA1tYW48ePL1d5jDHGqg+lE1ZQUBCCgoLUEQtq1aqF0NBQtZTNGGOsalM6YQUEBKghDMYYY6xkSicsIsKRI0cQGxuL58+f4/vvv8e1a9fw9OlTtGzZUh0xMsYYY8o3uti+fTuOHTuG4OBgoWmllZUV/vrrL5UHxxhjjBVSOmGdOHEC06dPR6tWrYQGF7a2tkhPT1d5cIwxxlghpROWVCot0vtEbm5ukXGMMcaYKimdsBo1aoSffvoJr1+/BvDmntb27dvRpEkTlQfHGGOMFVI6YQ0ZMgRZWVkYNmwYcnJyMGTIEGRkZGDgwIHqiI8xxhgDUIZWgoaGhvjyyy+RnZ2NjIwMWFtbw9zcXB2xMcYYYwKFE1ZeXh527tyJO3fuoHbt2vj444/h4uKiztgYY4wxgcKXBDds2ICLFy/C0dER//zzD7Zu3arOuBhjjDEZCp9hXb58GUuWLIGFhQU6dOiAuXPnYsSIESoNZsKECdDX14eWlha0tbW5mybGGGMCpS4JWlhYAHjz8rCcnBy1BDR37lyYmpqqpWzGGGNVl8IJq6CgAFevXhWGpVKpzDAA1K9fX3WRMcYYY29ROGGZmZlhzZo1wrCxsbHMsEgkwsqVK8sd0MKFCwEAISEhMm8WLhQdHY3o6GgAQGhoaJlfFc2vsJaP60U+rhf5uF7k43pRDxERUWUHUSgrKwuWlpbIzs7GggULMHz4cHh6epa4TOELH5V1/bpjmZZTlJvbPbWWry5cL/JxvcjH9SKfpteLg4ODiiKpWEo/OKxOlpaWAN6czTVr1gzJycmVHBFjjDFNoTEJKzc3F69evRL+j4+PR82aNSs5KsYYY5pC6Z4u1CU7Oxvff/89gDcNPFq3bg0fH59Kjooxxpim0JiEZWdnh6VLl1Z2GIwxxjSU0pcEv/zyS7njZ8yYUe5gGGOMseIonbAePnxYZBwR4dGjRyoJiDHGGJNH4UuChc9YSSSSIs9bZWRkwMnJSbWRMcYYY29ROGHZ2dnJ/V8kEqFevXpo0aKFaiNjjDHG3qJwwurduzcAwNXVlVvvMcYYq3BKtxL08fHB/fv3kZqaitzcXJlp7dq1U1lgjDHG2NuUTlh//vkndu7cCWdnZ+jp6clM44TFGGNMXZROWAcOHMCiRYvg7OysjngYY4wxuZRu1q6rqwtHR/V27MgYY4y9S+mE1bdvX2zcuBFPnjyBVCqV+WOMMcbURelLgqtXrwYAHDlypMi07du3lzsgqVSKGTNmwNLSknvPYIwxJlA6YaniJY0lOXDgABwdHYWe2xljjDGgDJcEbWxsYGNjAysrK4jFYmHYxsam3ME8fvwYcXFxCAoKKndZjDHGqhelz7BevnyJ9evX4+zZsxCLxdi6dSsuXLiA5ORk9OvXr1zBbN68GYMGDSrx7Co6OhrR0dEAgNDQ0DK/KppfYS0f14t8XC/ycb3Ix/WiHkonrHXr1sHIyAirV6/GF198AQBwc3PDTz/9VK6EdfHiRZiZmaFOnTpISEgodr7g4GAEBwcLw5mZmWVepzppalyVjetFPq4X+bhe5CtvvTg4OKgokoqldMK6cuUK1q5dC7H4f4uampoiOzu7XIEkJSXhwoULuHTpEvLz8/Hq1StERkZi4sSJ5SqXMcZY9aB0wjI0NMTz589hYWEhjMvMzJQZLosBAwZgwIABAICEhATs3buXkxVjjDGB0o0ugoKCEBYWhqtXr4KIcP36daxatQohISHqiI8xxhgDUIYzrG7dukFHRwcbNmxAQUEB1qxZg+DgYHTs2FFlQXl5ecHLy0tl5THGGKv6lE5YIpEInTp1QqdOndQRD2OMMSaX0gkLANLT05GWllbk9SKtW7dWSVCMMcbYu5ROWLt27cIff/wBJycn6OrqCuNFIhEnLMYYY2qjdMLat28flixZgho1aqgjHsYYq/ICA0mt5d+7d1+t5WsqpVsJGhsbq6QbJsYYY0wZSp9hDRs2DGvXrkWnTp1gZmYmM+197S6EMcaY+imdsCQSCeLj4xEbG1tkmipeL8IYqzr40herSEonrPXr16N///5o1aqVTKMLxqozPjAzVvmUTlhSqRSBgYHQ0lL69hdjjDFWZkonrC5dumD37t34+OOPIRKJ1BFTheBfzIwxVrUonbCioqLw9OlT7Nq1C8bGxjLT1qxZU+ZA8vPzMXfuXEgkEhQUFMDPzw99+vQpc3mMMcaqF6UT1meffaaOOKCjo4O5c+dCX18fEokEX3/9NXx8fODm5qaW9THGGKtalE5Ynp6e6ogDIpEI+vr6AICCggIUFBRU6UuOjDHGVEuhhPXnn3+iR48eAEpuut63b99yBSOVSjF9+nQ8fPgQ7du3h6ura5F5oqOjER0dDQAIDQ3V2Ge/NDWu0vCrvSsH14t8XC/yva/1olDCevz4sdz/VU1LSwtLly7Fy5cv8f333yMtLQ01a9aUmSc4OBjBwcHCcNlfFa3eV0Tzq73lq7r1wvuLfFwv8ml2vTg4qDc+dVEoYY0ePVr4f/z48WoLppCRkRE8PT1x+fLlIgmLMcbY+0nph6mGDx8ud/yoUaPKFcizZ8/w8uVLAG9aDF65cgWOjo7lKpMxxlj1oXSji4KCgiLjJBIJpFJpuQJ58uQJVq1aBalUCiJCixYt0KRJk3KVyRhjrPpQOGF9/fXXEIlEeP36NebOnSsz7fHjx+Vufu7s7IzvvvuuXGWw8uMHqhljmkrhhNWuXTsAQHJyMgIDA4XxIpEIZmZmqF+/vuqjY4wxxv6fwgkrICAAAODq6sr3lhhjjFU4pe9hOTo64t9//0Vqaipyc3NlppX3OSzGGGOsOEonrA0bNuDMmTPw8vKCnp6eOmJijDHGilA6YcXGxuK77757b5+0ZowxVjmUfg7LxMQERkZG6oiFMcYYK5bSZ1idO3dGZGQkPv74Y5iZmclMs7OzU1lgjDHG2NuUTljr168HAMTFxRWZVlLHuIwxxlh5KJ2wOCkxxhirDErfwyqUmZmJ6+p+FwVjjDH2/5Q+w8rMzMTy5cuRmpoKANi6dSvOnj2Ly5cvY+zYsWUOJDMzE6tWrcLTp08hEokQHByMjh07lrk8xhhj1YvSZ1g//vgjGjVqhC1btkAsfpPvGjRogPj4+HIFoq2tjcGDByM8PBwLFy7EoUOHcPfu3XKVyRhjrPpQOmElJyeje/fu0NL636KGhobIyckpVyAWFhaoU6cOAMDAwACOjo7IysoqV5mMMcaqD6UTlpmZGR4+fCgz7u7duyp9kDg9PR23bt2Ci4uLyspkjDFWtSl9D6tLly5YsmQJunfvDqlUilOnTmHXrl3o3r27SgLKzc1FWFgYhg0bBkNDwyLTo6OjER0dDQAIDQ3V2B43NDWuysb1Ih/Xi3xcL/K9r/UiIiKlX4B07tw5HDlyBBkZGbCyskJISAh8fX3LHYxEIsGSJUvQsGFDdO7cWaFl7t8v2/uVHB0dyrScoqrqe5+4XuTjepGP60U+Ta8XBwf1xqcuSp9hAYCvr69KEtTbiAg//PADHB0dFU5WjDHG3h8KJ6ybN29CLBajZs2aAIBnz55h8+bNuHPnDlxdXTFkyBDo6+uXOZCkpCTExMSgZs2amDZtGgCgf//+aNy4cZnLZIwxVn0onLA2b96MXr16CQnrhx9+wJMnTxAUFITY2Fj8/PPPGDVqVJkDcXd3x44dO8q8PGOMsepN4VaC9+7dg4eHBwDg5cuXuHTpEj777DN06NABkyZNwsWLF9UWJGOMMaZwwiooKBAeFL5x4wbMzc2FG3fW1tZ4+fKleiJkjDHGoETCcnJywpkzZwC8eYmjt7e3MC0rK0tuE3TGGGNMVRROWAMHDsS6deswfPhwxMXFyTx3dfr0adSrV08tATLGGGOAEo0u3N3dsXr1ajx48AAffPABDAwMhGmNGzdGy5Yt1RIgY4wxBij5HJaBgYHQ39/bqupDaIwxxqqOMr8PizHGGKtInLAYY4xVCZywGGOMVQmcsBhjjFUJnLAYY4xVCWXqrV0dVq9ejbi4OJiZmSEsLKyyw2GMMaZhNOYMKyAgADNnzqzsMBhjjGkojUlYnp6eMDY2ruwwGGOMaSiNuSSoqOjoaERHRwMAQkNDNfZV0ZoaV2XjepGP60U+rhf53td6qXIJKzg4GMHBwcJwZmZmGUtSb+8cZY+rsnG9yMf1Ih/Xi3yaXS9VtXcijbkkyBhjjJWEExZjjLEqQWMuCUZERODatWt4/vw5xo4diz59+qBdu3aVHRZjjDENoTEJ6/PPP6/sEBhjjGkwviTIGGOsSuCExRhjrErghMUYY6xK4ITFGGOsSuCExRhjrErghMUYY6xK4ITFGGOsSuCExRhjrErghMUYY6xK4ITFGGOsStCYrpkA4PLly9i0aROkUimCgoLQvXv3yg6JMcaYhtCYMyypVIoNGzZg5syZCA8PR2xsLO7evVvZYTHGGNMQGpOwkpOTYW9vDzs7O4jFYrRs2RLnz5+v7LAYY4xpCI25JJiVlQUrKyth2MrKCjdu3CgyX3R0NKKjowEAoaGhZX5zJlHZ4lRc1XyjJ9eLfFwv8nG9yMf1oh4ac4ZFcj5hkUhUZFxwcDBCQ0MRGhpaEWEJZsyYUaHrqyq4XuTjepGP60U+rhfFaEzCsrKywuPHj4Xhx48fw8LCohIjYowxpkk0JmHVrVsXDx48QHp6OiQSCU6fPo2mTZtWdliMMcY0hMbcw9LW1saIESOwcOFCSKVSBAYGwsnJqbLDEgQHB1d2CBqJ60U+rhf5uF7k43pRjIjk3TxijDHGNIzGXBJkjDHGSsIJizHGWJVQbRPWuXPn0KdPH9y7dw8AkJ6ejlOnTgnTjx8/jg0bNshddsKECXj27FmJ5f/5558yw7Nnzy5nxJXj+fPnmDZtGqZNm4bRo0djzJgxwrBEIlHJOjIzMxEeHq6SstSpb9++wrZPmzYN6enpSpeRmpqKuLg4YXjHjh3Ys2ePUmUMHjwYwJt9dsqUKUrHoG6F9TRlyhSEhobi5cuXZS6rLPWjiZ4+fYqIiAh89tlnmDx5MhYvXoz79+8Xma+048Q333yDlJQUhde7f/9+5OXlKR1vVaUxjS5U7dSpU3B3d0dsbCz69OmDjIwMnDp1Cq1bt1ZJ+bt27UKPHj2E4QULFqik3IpmYmKCpUuXAnhz8NDX10fXrl1Vug5ra2tMnjxZpWWqg66urlAXZZWamoqUlBQ0btxYRVFpnrfraeXKlTh06JDMd+F9Q0RYunQp/P398fnnnwN4sx9kZ2cLHRtIpVJoaWmp/Dhx4MABtGnTBnp6eiotV1NVy4SVm5uLpKQkzJ07F9999x369OmDX3/9FXfv3sW0adPg7+8PY2NjPHnyBAsXLsSjR4/g6+uLQYMGFSkrJiYGUVFRkEgkcHV1xahRo7Bt2zbk5+dj2rRpcHJywsSJEzF48GBs3boVAPDXX38hJiYGWlpa8PHxwcCBA3HgwAEcPnwY2traqFGjhrBja7Ljx4/j0KFDkEgkqFevHkaMGAEiwsiRIxESEoLLly9DV1cXX375JczMzBAZGQkTExOkpKTg6dOnGDJkCHx9ffHw4UOEhYVh6dKlSEtLw5o1ayCRSEBEmDZtGuzs7Cp7U4uVn5+P9evXIyUlBdra2hgyZAjq168vd7y7uzu2b9+O/Px8/Pfff/j4448BALdv38a8efPw+PFjdO3aFcHBwcjNzcV3332Hly9fQiKRoF+/fmjWrFklb63y3NzckJaWBgBISEjA3r17hYdgN2zYgLp16yIgIABxcXH46aefYGJigtq1ayM9PV2YT179rFixAn5+fkKdREZGomXLlhr5qEtCQgLEYjE+/PBDYVytWrWQkJCAefPmwdzcHKmpqQgPDy/1OFFIKpVi9erVsLa2Rr9+/fDvv/9ix44dkEgksLOzw/jx43H06FFkZWVh3rx5MDU1xdy5cyt82ytatUxY586dg4+PDxwcHGBsbIybN29iwIABMl+m48ePIzU1Fd999x3EYjE+//xzdOjQAdbW1kI5d+/exenTpzF//nyIxWKsX78eJ0+exMCBA3Hw4EG5v8YvXbqE8+fPY9GiRdDT08OLFy8AvNk5V65cCR0dnXJdQqkoaWlpOHfuHBYsWABtbW2sXbsWp0+fRosWLZCTkwNPT08MHDgQW7ZswbFjx4Se9bOzszF//nykpaUhPDwcvr6+MuUeOnQIXbp0QcuWLfH69Wu5PZxUlsIfIQBga2uLadOm4dChQwCAsLAw3Lt3DwsWLMDy5cuLHd+3b1+kpKRg5MiRAN6ctaalpWHhwoXIzc3F9OnT0bhxY5iZmWHq1KkwNDTEs2fPMGvWLDRt2lRu7y6aSiqV4urVq2jXrl2J8+Xn52PdunWYN28ebG1tERERITNdXv0EBQVh3759aNasGXJycpCUlIQJEyaoc3PKLC0tDbVr15Y7LTk5GWFhYbC1tZUZX9xxAgAKCgoQGRmJmjVrokePHnj27Bn+/PNPzJkzB/r6+ti9ezf27duHXr16Yf/+/Zg7dy5MTU3Vuo2aolomrNjYWHTq1AkA0LJlS8TGxsq9RFO/fn0YGhoCAGrUqIHMzEyZhHX16lXcunULX331FYA3X7zSdowrV64gICBAOEU3NjYGANSsWRORkZFo1qxZkYO4Jrpy5QpSUlKEBJ+fny/09airq4tGjRoBAGIiNBMAAAodSURBVOrUqYPExERhOV9fX4hEIjg7OyMrK6tIufXq1cPOnTuRkZGB5s2bw97evgK2RjHyLgn+999/+OijjwAAjo6OsLGxwYMHD4odL0/Tpk2hq6sLXV1deHl5ITk5GY0bN8a2bduQmJgIkUiErKwsZGdnw9zcXL0bqQKFiT0jIwN16tRBgwYNSpz//v37sLW1FQ7arVu3FvoDBeTXj6+vLzZs2IDs7Gz8888/aN68ObS1tdW6Xerg4uJSJFkBxR8nAGDdunVo0aKFcJn1xo0buHv3LubMmQMAkEgkcHNzq4DoNU+1S1jPnz/H1atXcefOHYhEIkilUgAQDrBv09HREf7X0tJCQUGBzHQigr+/PwYMGKDw+olI7q/kr776CteuXcOFCxewc+dOLFu2TKO/gESEwMBA9OvXT2Z8QUEBxOL/7TZaWlpCHQOQmSbv7Klt27Zwc3NDXFwc5s+fjwkTJsDT01MNW6AaxZ0BKnNm+O7+IBKJcOrUKTx79gyhoaEQi8WYMGEC8vPzyxVrRSlM7Dk5OQgNDcXBgwfRsWNHaGtry9TL69evAZReV/LqBwDatGmDkydP4vTp0xg3bpyKt0J1nJyc8M8//8idVty9peKOE8Cby6wJCQno3LkzdHV1QUTw9vauErcR1K3atRI8e/Ys/P39sXr1aqxatQpr1qyBra0ttLS08OrVK6XK8vb2xtmzZ5GdnQ0AePHiBTIyMgC8OTDLa0XXsGFDHDt2TGi58+LFC0ilUmRmZqJ+/foYNGgQcnJykJubW84tVa8GDRrgzJkzQmvJ58+fIzMzs9zlPnr0CPb29ujYsSMaN24s3P/QVJ6enjh58iSAN2cKmZmZcHBwKHa8vr5+kf3s/PnzyM/Px/Pnz5GQkIC6desiJycHZmZmEIvFuHr1qrBfVSWGhoYYPnw49u7dC4lEAmtra9y9exevX79GTk4Orly5AuDNGWh6errQ6vL06dMy5cirHwAICAjAgQMHAECjer15V/369fH69WuZs8bk5GRcu3at2GXkHScKtWvXDo0aNcKyZctQUFAANzc3JCUl4eHDhwCAvLw8oQWivr6+xh9LVKnanWHFxsYWeVNx8+bNERsbC21tbZlGF6WpUaMG+vXrhwULFoCIoK2tjZEjR8LGxgZBQUGYNm0aateujYkTJwrL+Pj4IDU1FTNmzIBYLEajRo3Qp08frFixAjk5OQCATp06wcjISLUbrmI1a9ZE7969MX/+fGHbR48eXe4OiU+dOiV8FpaWlkXO4DTNhx9+iHXr1mHKlCnQ1tbG+PHjoaOjU+z4+vXr46+//sK0adOERhcuLi4IDQ1FZmYmevbsCUtLS7Ru3RpLlizBjBkzUKtWLTg6OlbylpZN7dq14ezsjNOnT6Nt27Zo0aIFpk6dig8++EC4r6Orq4uRI0di0aJFMDExgYuLi0wZ8uoHAMzNzeHo6KjxjVFEIhGmTp2KzZs346+//oKOjg5sbGxKjFveceLtKzmdO3dGTk4OVqxYgYkTJ2LChAlYvny5cNbar18/ODg4IDg4GIsWLYKFhcV70eiCu2ZijKldbm4u9PX1QUTYsGED7O3t0blz5xKXycvLw9SpU7FkyRLhXjN7v1W7MyzGmOaJjo7GiRMnIJFIULt2bYSEhJQ4f3x8PNasWYPOnTtzsmICPsNijDFWJVS7RheMMcaqJ05YjDHGqgROWIwxxqoETliMMcaqBG4lyN47p06dwr59+3Dv3j0YGBigVq1a6NGjB9zd3Utcrk+fPoiMjNSo7qQYe59wwmLvlX379mH37t0YPXo0GjZsCLFYjMuXL+P8+fOlJqzKUlBQoNHdeDFWUbhZO/u/9u4opKk2DOD43zkPcyhhQhEE2qolaKJXS1FStIu6SyqjoMCYMm8MvKoLg7rpzosMJJRABA26iZqIKZa7kaQMkkWbCwOniTmLjZzbOb3fxUeHhp/fRVhf+/b87rb3nGeHl8ED7/ue58kYX79+pa2tjfb2dqqqqraMz8/Pc//+fcLhMJqm4XK5uHz5MlarlRs3bvD27VuzNpzH46G6upqXL18yPDzM6uoq+/fvx+12U1RUBMD79+/p7e3l48ePVFRUkJWVxb59+8zqHuPj4zx69IhYLEZJSQlut9us8nDu3DlaWloYGRnBMAwqKyvRNI1Lly6Zz3v79m2OHj1qFnoW4n9PCZEhZmdnVXNzs9J1/R/HQ6GQevfundJ1Xa2srKirV6+qJ0+emONnz55Vy8vLKddfuXJFBQIBZRiGmpycVO3t7SqRSKhkMqk8Ho/yer0qmUyq6elpdf78eTU0NKSUUurNmzeqpaVFhUIhlUgkVH9/v+rq6kr5rZs3b6poNKo2NzdVMBhUra2tyjAMpZRSX758URcvXlTr6+u/YqqE+CPJoQuRMaLRKPn5+dsurzkcDpxOJ9nZ2ezZs4fGxsZ/LWA6MTFBY2Mjhw8fxmKxUFdXh9VqJRgMEggEMAyDkydPYrVacblcKTX0fD4f9fX1OBwOcnJyuHDhAoFAwCwQC3D69Gny8vLQNI1Dhw5ht9uZm5sD/i4gW1pamhbtSITYKbKHJTJGfn4+0Wh02z2hpaUlBgYGCIVCJBIJDMPA4XBsG+/Tp088f/6c0dFR8ztd14lEImRlZbF79+6UFhLf+4kBrK+vpzT9s9ls5OXlEYlEzP5JP14PcPz4caampigvL8fn85n9uITIFJKwRMZwOp3k5OQwMzPDsWPHtoz39fVRXFxMR0cHubm5eL1epqent41XWFhIU1OT2WjvR36/n0gkktL3aG1tzTxhWFBQkNKuJR6PE4vFzD0s2Nonqra2ls7OThYWFlhcXEyLRqBC7CRZEhQZw26309zcTH9/Py9evGBzcxNd15mdnWVwcJCNjQ3sdjs2m41wOMzY2FjK/bt27WJlZcX83NDQwNOnTwkGgyiliMfjvHr1io2NDZxOJxaLhdHRUQzDYGZmhvn5efPempoaJicnWVhYIJlMMjQ0tG132u8KCws5ePAgPT09uFwuNE3b+UkS4g8mpwRFxvH5fHi9XsLhMDabDYfDQVNTE4ZhcO/ePdbW1jhw4AClpaXMzc1x69YtAMbGxnj48CGJRILW1laqq6t5/fo1Dx48YHl5GU3TKCkpwePxkJubSygUMk8JVlZW8u3bN4qLizlz5owZ7/Hjx8RiMY4cOYLb7TaXAbd752tqaoqenh66urooKyv7vRMnxH9MEpYQv8n169c5ceIE9fX1Px3D7/dz584d7t69i8UiCyQis8g/XohfxO/38/nzZwzD4NmzZ3z48IGKioqfjqfrOiMjIzQ0NEiyEhlJDl0I8YssLS3R3d1NPB5n7969dHZ2UlBQ8FOxFhcXuXbtGkVFRZw6dWqHn1SI9CBLgkIIIdKCrCsIIYRIC5KwhBBCpAVJWEIIIdKCJCwhhBBpQRKWEEKItPAXwHDGMbQSQzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reference: https://matplotlib.org/examples/pylab_examples/bar_stacked.html\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 5\n",
    "ind = np.arange(N)    # the x locations for the groups\n",
    "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "p1 = plt.bar(ind, negatives, width, color='b')\n",
    "p2 = plt.bar(ind, positives, width,\n",
    "             bottom=negatives, color='y')\n",
    "\n",
    "plt.title('Scores by group and gender (omitting neutral sentiment)')\n",
    "plt.xlabel('Category')\n",
    "plt.xticks(ind, categories)\n",
    "plt.ylabel('Sentiment Percentage (%)')\n",
    "plt.yticks(np.arange(0,11,step=1))\n",
    "plt.legend((p1[0], p2[0]), ('Negative', 'Positive'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vizulazation of Bigrams and Connected Terms\n",
    "### We use graphs and a \"co-occurence matrix\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the co-occurence matrix\n",
    "\n",
    "1. Build a co-occurrence matrix from a corpus which represents how frequently word pairs occur together.\n",
    "\n",
    "Note: bigrams are weighted by there distance apart: \n",
    "    (1/log(1+d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'van nistelrooy hungry return manchester united striker ruud van nistelrooy said \"hungry play\" return'"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# To build a cooccurence matrix we first join the entire corpus into one single string\n",
    "#\n",
    "\n",
    "the_whole_string=''.join(corpus)\n",
    "the_whole_string[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 59s, sys: 2.72 s, total: 3min 1s\n",
      "Wall time: 3min 2s\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Build Co-occurence Matrix, A\n",
    "#\n",
    "\n",
    "import itertools\n",
    "\n",
    "# external\n",
    "import numpy as np\n",
    "import nltk\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.util import ngrams\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "\n",
    "def process_text(text):\n",
    "\t\"\"\" return list of lowercase alphabetic words from text \"\"\"\n",
    "\ttokenizer = RegexpTokenizer(r'\\w+')\n",
    "\treturn tokenizer.tokenize(text.lower())\n",
    "\n",
    "def ngram_tupples(corpus, n):\n",
    "\t\"\"\" Create ngram tupples by sentence. Where n is the distance between words in a sentence. \"\"\"\n",
    "\tsentences = sent_tokenize(corpus)\n",
    "\n",
    "\tpairs = []\n",
    "\tfor s in sentences:\n",
    "\t\tunique_tokens = process_text(s)\n",
    "\t\tpairs.extend(ngrams(unique_tokens,n))\n",
    "\n",
    "\treturn pairs\n",
    "\n",
    "def get_unique_words(corpus):\n",
    "\treturn list(set(process_text(corpus)))\n",
    "\n",
    "def w2id_id2w_maps(unique_words):\n",
    "\t\"\"\" return both dictonaries for mapping between words and ids \"\"\"\n",
    "\tid2w = {i:w for i,w in enumerate(unique_words)}\n",
    "\tw2id = {w:i for i,w in id2w.items()}\n",
    "\treturn w2id, id2w\n",
    "\n",
    "def ngram_inc_amt(n):\n",
    "\t\"\"\" return float for increment weight of pair occurence n distance appart. \\nWeight increment ~ 1/n \"\"\"\n",
    "\treturn 1/float(n**2)\n",
    "\n",
    "def words2ids(words, w2id):\n",
    "\t\"\"\" return list of ids inplace of list of words using w2id dictionary \"\"\"\n",
    "\treturn [w2id[w] for w in words]\n",
    "\n",
    "def cooccurence_pair_of_distance(sentence_list, d):\n",
    "    \"\"\" return list of unique coocurence pairs of distace d \"\"\"\n",
    "\n",
    "    all_ngrams = ngrams(sentence_list,d)\n",
    "\n",
    "    all_pairs = []\n",
    "    for t in all_ngrams:\n",
    "        if len(t) > 1:\n",
    "            all_pairs.extend(list(itertools.combinations(t, 2)))\n",
    "\n",
    "    return list(set(all_pairs))\n",
    "\n",
    "def break_corpus(corpus):\n",
    "    \"\"\" Build Cooccurence Matrix. Return A, n, w2id, id2w \"\"\"\n",
    "\n",
    "    unique_words = get_unique_words(corpus)\n",
    "    n = len(unique_words)\n",
    "    w2id, id2w = w2id_id2w_maps(unique_words)\n",
    "\n",
    "    #create empty cooccurence matrix (initilize to all 1's)\n",
    "    A = np.ones([n,n],np.float32)\n",
    "\n",
    "    #compute cooccurence matrix\n",
    "    sentences = sent_tokenize(corpus)\n",
    "    for s in sentences:\n",
    "        s = process_text(s)\n",
    "        max_distance = len(s) + 1\n",
    "        s = [w2id[w] for w in s]\t#convert words to ids\n",
    "\n",
    "        for d in range(2,max_distance):\n",
    "            pairs = cooccurence_pair_of_distance(s, d)\n",
    "\n",
    "            #update cooccurence matrix for each pair\n",
    "            for p in pairs:\n",
    "                A[p[0],p[1]] += ngram_inc_amt(d)\n",
    "                A[p[1],p[0]] += ngram_inc_amt(d)\n",
    "\n",
    "    return A, n, w2id, id2w\n",
    "\n",
    "%time A, n, w2id, id2w = break_corpus(the_whole_string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\"\n",
    "\tNormalize and clean text methods\n",
    "\"\"\"\n",
    "import logging\n",
    "import string\n",
    "import math\n",
    "\n",
    "import nltk\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk import FreqDist\n",
    "from nltk.tag import pos_tag\n",
    "\n",
    "# logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "def is_stopword(word):\n",
    "\t\"\"\" Return True of word is in stop word list \"\"\"\n",
    "\tstop_words = nltk.corpus.stopwords.words('english')\n",
    "\treturn word in stop_words\n",
    "\n",
    "def is_punctuation(word):\n",
    "\treturn len(word) == 1 and word in string.punctuation\n",
    "\n",
    "def is_number(word):\n",
    "\ttry:\n",
    "\t\tfloat(word)\n",
    "\t\treturn True\n",
    "\texcept ValueError:\n",
    "\t\tlogger.debug('ValueError is_number')\n",
    " \n",
    "\ttry:\n",
    "\t\timport unicodedata\n",
    "\t\tunicodedata.numeric(word)\n",
    "\t\treturn True\n",
    "\texcept (TypeError, ValueError):\n",
    "\t\tlogger.debug('ValueError is_number')\n",
    "\t \n",
    "\treturn False\n",
    "\n",
    "def is_shorter(word,n=3):\n",
    "\tif len(word) < n:\n",
    "\t\treturn True\n",
    "\treturn False\n",
    "\n",
    "def stem(word):\n",
    "\tps = PorterStemmer()\n",
    "\treturn ps.stem(word)\n",
    "\n",
    "def clean_word(raw_word):\n",
    "\t\"\"\" Takes string converts to lower case, stems \n",
    "\tand returns empty string if word is stop word, \n",
    "\tpunctation or is less than 3 characters long \"\"\"\n",
    "\n",
    "\traw_word = raw_word.lower()\n",
    "\tif is_stopword(raw_word) or is_punctuation(raw_word) or is_shorter(raw_word) or is_number(raw_word):\n",
    "\t\tword = \"\"\n",
    "\telse:\n",
    "\t\tword = stem(raw_word)\n",
    "\treturn word\n",
    "\n",
    "def remove_short_and_stopwords(token_list):\n",
    "\tfiltered_token_list = []\n",
    "\tfor t in token_list:\n",
    "\t\tif is_stopword(t) or is_punctuation(t) or is_shorter(t) or is_number(t):pass\n",
    "\t\telse: filtered_token_list.append(t)\n",
    "\treturn filtered_token_list\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding related terms \n",
    "Now that we have built a co-occurence matrix we can use it to find related terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 most related terms to 'win':\n",
      "['first', 'cup', 'game', 'england', 'final']\n"
     ]
    }
   ],
   "source": [
    "def rank_most_related_words(A, w2id, id2w, w1):\n",
    "    col = A[:,w2id[w1]].tolist()\n",
    "\n",
    "    list_of_index_value_tuples = list(zip(list(range(len(col))),col))\n",
    "    #(colindex,value)\n",
    "    sorted_indexs_value_tuples = sorted(list_of_index_value_tuples, key=lambda x: x[1])\n",
    "    sorted_indexs_value_tuples.reverse()\n",
    "    indexs, values = zip(*sorted_indexs_value_tuples)\n",
    "    ranked_words = [id2w[i] for i in indexs]\n",
    "    return ranked_words\n",
    "\n",
    "# rank_most_related_words(A,w2id, id2w, 'aa')\n",
    "\n",
    "def related_words_list_filtered_decending(A, w2id, id2w, w1):\n",
    "    raw_related_words = rank_most_related_words(A, w2id, id2w, w1)\n",
    "    filtered_related_words = remove_short_and_stopwords(raw_related_words)\n",
    "    return filtered_related_words\n",
    "\n",
    "k = 5\n",
    "query = 'win'\n",
    "top_k_most_related_terms = related_words_list_filtered_decending(A,w2id, id2w, query)[:k]\n",
    "\n",
    "print(\"Top %s most related terms to \\'%s\\':\" % (k, query))\n",
    "print(top_k_most_related_terms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Embedding Methods\n",
    "\n",
    "Bellow we test some diffrent techniques to map unique words found in a corpus to vectors in a vector space. The idea/hope is that some relationships between words found in the corpus will be preserved through this mapping and will manifest as characteristics of the word vectors. [More Information On Vector Representations Of Words](https://www.tensorflow.org/tutorials/word2vec)\n",
    "\n",
    "1. Build a co-occurrence matrix from a corpus which represents how frequently word pairs occur together.\n",
    "\n",
    "1. Search for word vectors with the soft constraint that given a word vector pair, their inner product will yield a value close to the two values in the co-occurrence matrix associated with those two words.\n",
    "\n",
    "Methods implimented for decomposing the cooccurence matrix into word vectors:\n",
    "\n",
    "* Stochastic Gradient Descent, which draws heavily on the implementations by \"Word2vec\" and \"GloVe\"\n",
    "\n",
    "* Methods usgin Eigen Decomposition and Singular Value Decomposition.\n",
    "\n",
    "Decompose Co-occurence Matrix, A, into matrix of word vectors, W, so that\n",
    "A = WW.t\n",
    "\n",
    "More about this at this [link](https://github.com/browlm13/Word-Embedding/blob/master/word_embedding_mock_1_v4.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#internal\n",
    "import random\n",
    "\n",
    "#external\n",
    "import numpy as np\n",
    "\n",
    "\"\"\"\n",
    "Matrix Decomposition\n",
    "\tMethod 1) Singular Value Decomposition of Forced Square Symmetric Positive Definite Matrix\n",
    "\tMethod 2) Eigen Decomposition of Forced Square Symmetric Positive Matrix\n",
    "\tMethod 3) Stocastic Gradient Decent Decomposition\n",
    "\"\"\"\n",
    "\n",
    "# check symmetry of matrix up to some tolerance\n",
    "def check_symmetric(a, tol=1e-8):\n",
    "    return np.allclose(a, a.T, atol=tol)\n",
    "\n",
    "#force square symetric matrix to symetric positive definite matrix\n",
    "def to_positive_definite(S):\n",
    "    \"\"\" Take absolute value of S and update diagnol entries to make a diagonally dominant matrix with diagonal entries greater than 0. \"\"\"\n",
    "    # take absolute value of S\n",
    "    S = np.absolute(S)\n",
    "\n",
    "    # Sum rows in S\n",
    "    new_diagonals = S.sum(axis=1)\n",
    "\n",
    "    #replace diagnols in S\n",
    "    np.fill_diagonal(S, new_diagonals)\n",
    "\n",
    "    return S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "####################################################################################\n",
    "# Singular Value Decomposition of Forced Square Symmetric Positive Definite Matrix #\n",
    "####################################################################################\n",
    "1.) Force Cooccurence Matrix A to Positive Definite Matrix\n",
    "    \" A diagonally dominant(by rows) symetric matrix with diagonal elements all greater than zero is positive definite.\"\n",
    "    Take symmetric matrix and make diagonally dominant with diagnonal entries greater than 0\n",
    "2.) Square Symmetric Positive Definite Matrix Decomposition\n",
    "    \" If A is positive definite, then A = QLQt = UDV (where U=V=Q and L=D) can be written as A = WWt where W = Qsqrt(L) \"\n",
    "    -SVD: A=UDV, W = Vsqrt(diagnol(D))\n",
    "    Find V and D from singular value decomposition of A\n",
    "    return W = Vsqrt(D)\n",
    "overview of code:\n",
    "    #\n",
    "    # force symmetric matrix to positive definite matrix\n",
    "    #\n",
    "    # take absolute value of A\n",
    "    A = np.absolute(A)\n",
    "    # Sum rows in a\n",
    "    new_diagonals = A.sum(axis=1)\n",
    "    #replace diagnols in A\n",
    "    np.fill_diagonal(A, new_diagonals)\n",
    "    #\n",
    "    # decompose positive definite matrix\n",
    "    #\n",
    "    # singular value decomposition\n",
    "    U, D, V = np.linalg.svd(A, full_matrices=False)\n",
    "    #\n",
    "    # compute W from V and D of singular value decomposition\n",
    "    #\n",
    "    # Create matrix W = Vtsqrt(diagnol(D)) #why Vt?\n",
    "    W = np.dot(np.transpose(V), np.sqrt(np.diag(D)))\n",
    "    #A = WWt\n",
    "\"\"\"\n",
    "\n",
    "def svd_spd_decomposition(P):\n",
    "    \"\"\" return M such that P = MMt, where matrix parameter P is SPD \"\"\"\n",
    "    # Assert Matrix P is symetric\n",
    "    assert check_symmetric(P)\n",
    "\n",
    "    # singular value decomposition\n",
    "    U, D, V = np.linalg.svd(P, full_matrices=False)\n",
    "\n",
    "    # Create matrix W = Vtsqrt(diagnol(D)) #why Vt?\n",
    "    M = np.dot(np.transpose(V), np.sqrt(np.diag(D)))\n",
    "\n",
    "    return M\n",
    "\n",
    "def spd_decomposition(S):\n",
    "\t\"\"\" Force Cooccurence Matrix A to Positive Definite Matrix and decompose into W such that A = WWt. \"\"\"\n",
    "\tP = to_positive_definite(S)\n",
    "\tM = svd_spd_decomposition(P)\n",
    "\treturn M\n",
    "\n",
    "#\n",
    "# Perform Symmetric Positive Definite Decomposition\n",
    "#\n",
    "\n",
    "%time W = spd_decomposition(A)\n",
    "\n",
    "print(\"\\n\\n\\nSymmetric Positive Definite Decomposition:\\n\")\n",
    "print(\"\\n\\nA:\")\n",
    "print (A)\n",
    "print(\"\\n\\nA2 (modified into SPD):\\n\")\n",
    "print(to_positive_definite(A))\n",
    "print(\"\\nWWt:\\n\")\n",
    "print(np.dot(W, np.transpose(W)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "##################################################################\n",
    "# Eigen Decomposition of Forced Square Symmetric Positive Matrix #\n",
    "##################################################################\n",
    "1.) Force Cooccurence Matrix A to Positive Definite Matrix\n",
    "\t\" A diagonally dominant(by rows) symetric matrix with diagonal elements all greater than zero is positive definite.\"\n",
    "\tTake symmetric matrix and make diagonally dominant with diagnonal entries greater than 0\n",
    "2.) Square Symmetric Positive Definite Matrix Decomposition\n",
    "\t\" If A is positive definite, then A = QLQt = UDV (where U=V=Q and L=D) can be written as A = WWt where W = Qsqrt(L) \"\n",
    "\t-EVD: A=QLQt, W = Qsqrt(diagnol(L))\n",
    "\tFind Q and L from eigen decomposition of A\n",
    "\treturn W = Qsqrt(L)\n",
    "overview of code:\n",
    "\t#\n",
    "\t# force symmetric matrix to positive definite matrix\n",
    "\t#\n",
    "\t# take absolute value of A\n",
    "\tA = np.absolute(A)\n",
    "\t# Sum rows in a\n",
    "\tnew_diagonals = A.sum(axis=1)\n",
    "\t#replace diagnols in A\n",
    "\tnp.fill_diagonal(A, new_diagonals)\n",
    "\t#\n",
    "\t# decompose positive definite matrix\n",
    "\t#\n",
    "\t# singular value decomposition\n",
    "\tL, Q = np.linalg.eig(A)\n",
    "\t#\n",
    "\t# compute W from Q and L of singular value decomposition\n",
    "\t#\n",
    "\t# Create matrix W = Vtsqrt(diagnol(D)) #why Vt?\n",
    "\tW = np.dot(np.transpose(Q), np.sqrt(np.diag(L)))\n",
    "\t#A = WWt\n",
    "\"\"\"\n",
    "\n",
    "#\n",
    "# Perform Eigen Decomposition of Forced Square Symmetric Positive Matrix\n",
    "#\n",
    "\n",
    "def evd_spd_decomposition(P):\n",
    "\t\"\"\" return M such that P = MMt, where matrix parameter P is SPD \"\"\"\n",
    "\t\n",
    "\t# Assert Matrix P is symetric\n",
    "\tassert check_symmetric(P)\t\n",
    "\n",
    "\t# singular value decomposition\n",
    "\tL, Q = np.linalg.eig(P)\n",
    "\n",
    "\t#if L and Q returned in incorrect order\n",
    "\t#L = np.sort(L)\n",
    "\t#Q = Q[:, L.argsort()]\n",
    "\n",
    "\t# Create matrix W = Vtsqrt(diagnol(D))\n",
    "\tM = np.dot(Q, np.sqrt(np.diag(L)))\n",
    "\n",
    "\treturn M\n",
    "\n",
    "P = to_positive_definite(A)\n",
    "%time W = evd_spd_decomposition(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Stocastic Gradient Decent Decomposition\n",
    "#\n",
    "\n",
    "def total_squared_error():\n",
    "\ttotal_error = 0\n",
    "\tfor i in range(0,n):\n",
    "\t\twi = np.transpose(W[:,i])\n",
    "\t\tfor j in range(0,n):\n",
    "\t\t\tif i != j:\n",
    "\t\t\t\twj = W[:,j]\n",
    "\t\t\t\ttotal_error += (np.dot(wi, wj) - A[i,j])**2\n",
    "\treturn total_error\n",
    "\n",
    "def mean_squared_error(A, B):\n",
    "\treturn ((A - B) ** 2).mean(axis=None)\n",
    "\n",
    "def djdx(x):\n",
    "\twi = np.transpose(W[:,x])\n",
    "\ttotal_error = 0\n",
    "\tfor i in range(0,n):\n",
    "\t\tif i != x:\n",
    "\t\t\twj = W[:,i]\n",
    "\t\t\ttotal_error += np.dot(wi, wj) - A[x,i]\n",
    "\treturn np.multiply(W[:,x],total_error)\n",
    "\n",
    "def dot_improvement():\n",
    "\tfor i in range(0,n):\n",
    "\t\twi = np.transpose(W[:,i])\n",
    "\t\tfor j in range(0,n):\n",
    "\t\t\tif i != j:\n",
    "\t\t\t\twj = W[:,j]\n",
    "\t\t\t\tprint (\"A[%d,%d] = %f\" % (i,j,A[i,j]))\n",
    "\t\t\t\tprint (\"w[%d] dot w[%d] = %f\" % (i,j, np.dot(wi, wj)))\n",
    "\n",
    "iterations = 10000\n",
    "learning_rate = 0.001\n",
    "starting_error = total_squared_error()\n",
    "\n",
    "print(\"\\n\\n\\nStocastic Gradient Decent Decomposition Training:\\n\")\n",
    "\n",
    "#dot_improvement()\n",
    "print (\"starting mean squared error: %f\" % mean_squared_error(A, W))\n",
    "\n",
    "for i in range(iterations):\n",
    "\n",
    "\tx = random.randrange(0, n)\n",
    "\tW[:,x] = W[:,x] - learning_rate * djdx(x)\n",
    "\n",
    "\n",
    "print(\"\\n\\n\\nFinished training\\n\\n\\n\")\n",
    "#dot_improvement()\n",
    "ending_error = total_squared_error()\n",
    "print (\"\\nfinal mean squared error: %f\" % mean_squared_error(A, W))\n",
    "print (\"error improvment = %f\" % (starting_error - ending_error))\n",
    "\n",
    "\n",
    "print(\"\\n\\n\\nStocastic Gradient Decent Decomposition Results:\\n\")\n",
    "\n",
    "print(\"\\n\\nA:\")\n",
    "print (A)\n",
    "print(\"\\nWtW:\\n\")\n",
    "print(np.dot(W, np.transpose(W)))\n",
    "\n",
    "print(\"\\nMean Squared Error: %f\\n\" % mean_squared_error(W,A))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphing Bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def get_related_terms_graph_edges(selected_terms, connections_per_term, A, w2id, id2w):\n",
    "    # list of lists where the first index is a corpus term and the trailing indexs are its ranked related words\n",
    "    list_of_related_word_lists = []\n",
    "    for t in selected_terms:\n",
    "        print('\\nterm: %s' % t)\n",
    "        related_terms = related_words_list_filtered_decending(A, w2id, id2w, t)\n",
    "        print(\"\\t%s most related words:\" % connections_per_term)\n",
    "        print(\"\\t\\t%s\" % related_terms[:connections_per_term])\n",
    "\n",
    "        list_of_related_word_lists.append(related_terms[:connections_per_term])\n",
    "\n",
    "    np_related_words = np.array(list_of_related_word_lists)\n",
    "\n",
    "    related_words_network = [[term,related_terms] for term,related_terms in zip(selected_terms, np_related_words.tolist())]\n",
    "\n",
    "    edges_related_words_network = []\n",
    "    for term,related_terms in related_words_network:\n",
    "        edges_cur_related_word = [(term, r) for r in related_terms]\n",
    "        edges_related_words_network += edges_cur_related_word\n",
    "\n",
    "    return edges_related_words_network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_graph(terms, terms_graph_edges, sample_size=None, bounciness_coeff=0.3, zoom=50, size=(8,8), text_size=15):\n",
    "    \n",
    "    if sample_size==None:\n",
    "        sample_size=len(terms)\n",
    "\n",
    "    G=nx.Graph()#  G is an empty Graph\n",
    "\n",
    "    random_terms_sample = list(random.sample(terms, sample_size))\n",
    "    Nodes= random_terms_sample\n",
    "    G.add_nodes_from(Nodes)\n",
    "    Edges= [e for e in terms_graph_edges if e[0] in random_terms_sample]\n",
    "    G.add_edges_from(Edges)\n",
    "\n",
    "    pos = nx.spring_layout(G,k=bounciness_coeff, iterations=zoom)\n",
    "\n",
    "    from matplotlib.pyplot import figure\n",
    "    figure(num=None, figsize=size, dpi=100, facecolor='w', edgecolor='k')\n",
    "    nx.draw(G, node_color='w',edge_color='c',font_size=text_size,with_labels=True, pos=pos)\n",
    "    plt.show()\n",
    "    print(list(random.sample(terms, sample_size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Graphing the connections of the terms: 'football','tennis','rugby','athletics','cricket'\n",
    "# These are the article category tags and all involve athletics\n",
    "#\n",
    "\n",
    "m = 3 # number of connections per term\n",
    "graph_1_corpus_terms =['football','tennis','rugby','athletics','cricket']\n",
    "\n",
    "%time graph_1_edges = get_related_terms_graph_edges(graph_1_corpus_terms, m, A, w2id, id2w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_graph(graph_1_corpus_terms, graph_1_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# graphing randomly selected term connections\n",
    "#\n",
    "\n",
    "import random\n",
    "\n",
    "n = 30 # sample size\n",
    "m = 3 # number of connections per term\n",
    "random_indexs = random.sample(range(0, len(id2w)-1), n)\n",
    "graph_2_corpus_terms = [id2w[i] for i in random_indexs]\n",
    "\n",
    "\n",
    "%time graph_2_edges = get_related_terms_graph_edges(graph_2_corpus_terms, m, A, w2id, id2w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# visulizing randomly sampled term connections\n",
    "draw_graph(graph_2_corpus_terms, graph_2_edges, bounciness_coeff=0.5, zoom=150, size=(20,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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