In [79]:
cfd.plot()
Out[79]:
<AxesSubplot:xlabel='Samples', ylabel='Counts'>
import nltk
nltk.corpus.gutenberg.fileids()
['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']
emma = nltk.corpus.gutenberg.words('austen-emma.txt')
len(emma)
192427
tobe = nltk.corpus.gutenberg.words('shakespeare-hamlet.txt')
len(tobe)
37360
print(set(sorted(tobe)), end='')
{'gowne', 'health', 'indifferently', 'Englands', 'they', 'fixt', 'Lenton', 'Decayer', 'thirty', 'pat', 'sweet', 'drag', 'dispatch', 'sayes', 'England', 'Nettles', 'pocky', 'vnyoake', 'custome', 'Coffin', 'fought', 'Fennell', 'Nation', 'scapes', 'knauish', 'forty', 'Priam', 'paint', 'Calfe', 'Houres', 'sparke', 'extinct', 'Pluck', 'Foyle', 'shake', 'none', 'hill', 'speech', 'election', 'Sadnesse', 'spight', 'Policie', 'fie', 'intreaty', 'Se', 'marriage', 'vnuallued', 'imitated', 'tax', 'incorrect', 'escorted', 'Eterne', 'Lest', 'Commandment', 'Man', 'Italian', 'Extasie', 'wonted', 'lash', 'impon', 'wilde', 'attendant', 'Million', 'Vnction', 'answer', 'New', 'flaming', 'vse', 'ended', 'flash', 'her', 'debatement', 'thicker', 'Came', 'wondrous', 'indiscretion', 'Hearke', 'Wittemberge', 'sighes', 'Perpend', 'Clownes', 'Obserue', 'befalne', 'congregation', 'Each', 'Braine', 'thorny', 'heads', 'standing', 'Land', 'threats', 'or', 'Antiquity', 'note', 'cousend', 'imbark', 'Yoricks', 'thou', 'accuse', 'prolongs', 'adaies', 'makers', 'hearers', 'sure', 'personall', 'visit', 'breakes', 'Froward', 'knowledge', 'excellence', 'reade', 'except', 'winters', 'vngorg', 'Am', 'Magicke', 'Awake', 'Sheete', 'Proclaimes', 'Life', 'warrant', 'falshood', 'assistant', 'Conuersation', 'Rouse', 'Luxury', 'clothes', 'lost', 'Neither', 'plundge', 'radiant', 'wrong', 'Selfe', 'Mark', 'Maiesty', 'variable', 'Aduantage', 'Boyes', 'Soales', 'vnwholsome', 'loathsome', 'Welcome', 'Seales', 'seal', 'turneth', 'Within', 'plots', 'blow', 'Fellowship', 'Quiddits', 'foot', 'Rewards', 'earnest', 'iaw', 'dispatcht', 'hence', 'brother', 'ye', 'blasted', 'dil', 'quiet', 'and', 'infaith', 'Quite', 'publike', 'Obiect', 'Recoueries', 'lights', 'Blood', 'blesse', 'frighted', 'proofe', 'errors', 'extant', 'Quillets', 'Theame', 'apart', 'Armie', 'Theft', 'forgone', 'Sence', 'Arras', 'body', 'Fell', 'auoid', 'driues', 'ont', 'blast', 'deckt', 'recklesse', 'Motiue', 'Wooes', 'kept', 'presence', 'feeling', 'debt', 'Faulconers', 'Trumpets', 'sage', 'Arrowes', 'vpspring', 'Coronet', 'dealt', 'whine', 'strick', 'larger', 'Sonne', 'on', 'ran', 'withers', 'beetles', 'Yours', 'declines', 'adheres', 'naked', 'Weaknesse', 'Danish', 'woe', 'runs', 'baser', 'stops', 'Ghost', 'maine', 'your', 'Ice', 'vsuall', 'Conference', 'Start', 'there', 'canst', 'space', 'lacks', 'bought', 'rais', 'indued', 'euermore', 'Aboord', 'Russet', 'Foole', 'Orizons', 'somewhat', 'Oare', 'Iaw', 'Two', 'onely', 'Ducates', 'Fox', 'indirections', 'Bap', 'iudgements', 'shipped', 'affectation', 'made', 'Sect', 'Ominous', 'incens', 'obseruant', 'vsurpe', 'abhorred', 'deiect', 'rights', 'plaist', 'Duty', 'argall', 'learning', 'boades', 'beware', 'Ban', 'whom', 'did', 'vnpolluted', 'wish', 'proclaimes', 'Spirits', 'Puh', 'Flaxen', 'Formes', 'Geulles', 'vsurp', 'Sourse', 'Comicall', 'sometime', 'feather', 'valiant', 'wing', 'twelue', 'doubt', 'Safely', 'beene', 'buffets', 'honest', 'Abstracts', 'forthwith', 'Modestie', 'Denmarke', 'Husbandry', 'Orbed', 'vpon', 'Ambition', 'worke', 'liued', 'repulsed', 'Valour', 'sinnewes', 'ioyes', 'vertue', 'sending', 'quietly', 'Did', 'Burst', 'Helpe', 'Doe', 'Torrent', 'imagine', 'yeelding', 'bones', 'stoppes', 'Beer', 'retyrement', 'exercise', 'greeted', 'woer', 'diseas', 'bloudy', 'Elsonower', 'ayme', 'dire', 'nights', 'Annuall', 'double', 'thunders', 'Iephta', 'Brother', 'Counter', 'vncharge', 'lippes', 'Pelion', 'Virgin', 'dreame', 'robustious', 'Thornes', 'steals', 'generall', 'shreds', 'maintains', 'Station', 'Farwell', 'dread', 'been', 'solicited', 'wide', 'maist', 'weares', 'lead', 'noted', 'Politician', 'Medicine', 'Day', 'Choller', 'vnskilfull', 'Suites', 'Canker', 'Thought', 'homage', 'packing', 'thereunto', 'Sable', 'past', 'Succession', 'cry', 'clutch', 'remember', 'Enquire', 'happy', 'Infants', 'Weigh', 'perform', 'selleredge', 'kild', 'spring', 'Vice', 'shewne', 'Saint', 'carry', 'Beauer', 'wring', 'soules', 'Signet', 'Mirrour', 'distract', 'had', 'scuffling', 'Beast', 'Com', 'highest', 'yeare', 'Bisson', 'beget', 'step', 'Clo', 'fortune', 'coldly', 'palpable', 'importunate', 'Lay', 'begun', 'Willes', 'appurtenance', 'loued', 'blankes', 'hasty', 'Frend', 'Long', 'impious', 'vnwatch', 'vnbrac', 'Cauiarie', 'Indeede', 'Sprung', 'wart', 'buy', 'Repast', 'Sallery', 'great', 'day', 'reading', 'key', 'shoue', 'Roman', 'rebuke', 'gidge', 'Repentance', 'Charge', 'ours', 'behind', 'remain', 'opposition', 'Capons', 'Temperance', 'once', 'betime', 'leaues', 'fret', 'knauery', 'Purples', 'Walkes', 'stage', 'thence', 'recame', 'Fare', 'blowes', 'seru', 'withdrew', 'leuied', 'weaker', 'remembrance', 'hadst', 'two', 'Little', 'Very', 'from', 'Neuer', 'blew', 'venome', 'hop', 'vnhatch', 'tragedie', 'seekes', 'least', 'Complie', 'swallowed', 'Funerall', 'Willow', 'Romage', 'Swords', 'aske', 'meats', 'Deluer', 'flies', 'primall', 'sounding', 'Villany', 'aside', 'bowts', 'glassie', 'But', 'Promontory', 'behauior', 'behoues', 'affear', 'surrender', 'carriage', 'humour', 'heauen', 'Hill', 'stomacke', 'Takes', 'Harlot', 'Roaming', 'Musicke', 'Heathen', 'wants', 'collection', 'proue', 'Confession', 'Hold', 'Chaplesse', 'wrath', 'espials', 'Prouidence', 'art', 'Vntill', 'honour', 'Auspicious', 'bitter', 'horsse', 'rent', 'Wormes', 'Mercy', 'vpshot', 'Whereto', 'whiles', 'few', 'esteem', 'obseru', 'Excellent', 'Scornes', 'instances', 'Seale', 'Comment', 'seasons', 'Nay', 'Prima', 'winking', 'Gonzago', 'promis', 'stone', 'denies', 'Artire', 'might', 'find', 'others', 'answered', 'former', 'Idoll', 'Honor', 'desire', 'moued', 'Pox', 'breeder', 'Thus', 'commings', 'thousand', 'sourse', 'offend', 'slow', 'Moone', 'weakest', 'smear', 'while', 'Good', 'trace', 'cure', 'commend', 'share', 'Soule', 'shamefull', 'fond', 'distracted', 'liues', 'T', 'House', 'Teares', 'enmity', 'Ouer', 'Remembraunce', 'Worse', 'rank', 'Storie', 'Table', 'Fruite', 'twenty', 'suck', 'legs', 'choyce', 'deere', 'iocond', 'Soldiours', 'strength', 'Francisco', 'cried', 'adioyn', 'Forgiue', 'vnlimited', 'rose', 'brauery', 'long', 'purposes', 'morning', 'Indeed', 'bellowed', 'warm', 'Fran', 'Tanner', 'hedge', 'dutie', 'Stopes', 'Flourish', 'as', 'which', 'does', 'attractiue', 'Frailty', 'co', 'throwing', 'crowes', 'Marcellus', 'went', 'Exit', 'well', 'intreate', 'greet', 'Countenance', 'reserue', 'Napkin', 'Since', 'Euen', 'life', 'Actor', 'heart', 'hew', 'Canoniz', 'Drugges', 'thine', 'sized', 'Beware', 'strew', 'Together', 'ore', 'Crowners', 'fast', 'minutes', 'courtesie', 'workes', 'rotten', 'ignorant', 'peake', 'fortie', 'Scene', 'hore', 'seeme', 'rages', 'forgot', 'Dowrie', 'turne', 'nonce', 'naught', 'Lawyer', 'thrice', 'Parchment', 'dayes', 'Processe', 'middle', 'loe', 'reason', 'vnholy', 'Mother', 'Should', 'Millions', 'horses', 'stalkes', 'clay', 'Bias', 'Heres', 'blackest', 'giued', 'Turke', 'restrain', 'assay', 'heeles', 'Soueraignty', 'Poore', 'Angle', 'hell', 'It', 'shut', 'lonelinesse', 'Souldiers', 'Sit', 'wary', 'permanent', 'sustaine', 'question', 'That', 'growne', 'solidity', 'Watch', 'Foh', 'shalt', 'pester', 'Sauiours', 'Associates', 'Kites', 'smot', 'tormenting', 'hauing', 'To', 'weedy', 'pardon', 'brought', 'either', 'questionable', 'vnmatch', 'Poesie', 'parley', 'Hast', 'crie', 'faults', 'against', 'Arrow', 'senselesse', 'drabbing', 'Northerly', 'proper', 'vnseene', 'guilt', 'auoyd', 'witnesse', 'roughly', 'fiue', 'Golden', 'extremity', 'creatures', 'Ham', 'bee', 'Hazard', 'Full', 'ioyne', 'Till', 'Sonnes', 'waues', 'hent', 'My', 'conueyance', 'Dominions', 'Switzers', 'Traile', 'sauory', 'honying', 'confesse', 'blunt', 'vncurrant', 'wig', 'safety', 'Mouth', 'prosperously', 'Osr', 'Planets', 'fare', 'hang', 'importun', 'side', 'Mar', 'welcome', 'wel', 'Whilst', 'greefes', 'Bonds', 'parted', 'They', 'Lists', 'breadth', 'hath', 'ending', 'firme', 'iumpe', 'fretfull', 'ord', 'stay', 'held', 'Bodie', 'madnesse', 'Mad', 'seriuce', 'As', 'Gallant', 'father', 'frowningly', 'sleepe', 'grace', 'confound', 'faire', 'Babe', 'agreeing', 'pestilence', 'accent', 'seeke', 'twere', 'Grop', 'pompe', 'Here', 'bruite', 'Much', 'Natiue', 'eternall', 'Gentlewoman', 'Inobled', 'Owner', 'must', 'Oppression', 'wicked', 'effects', 'coniuring', 'top', 'seene', 'Violets', 'Assume', 'Delight', 'Paddocke', 'remembred', 'fool', 'Packet', 'Maiesticall', 'desires', 'Yea', 'Fight', 'Purse', 'Manet', 'hammers', 'lying', 'sword', 'Kin', 'lacke', 'keepe', 'Ah', 'reuerted', 'Lap', 'bone', 'Dawning', 'violent', 'stopping', 'Whereas', 'exchange', 'Vowes', 'offended', 'minute', 'nant', 'builde', 'therein', 'words', 'Sandal', 'checking', 'pate', 'study', 'friending', 'tryall', 'Eternity', 'Goose', 'Racke', 'Breuitie', 'alwayes', 'reueale', 'some', 'inward', 'temperately', 'accurst', 'pace', 'thereof', 'doth', 'caught', 'Becomes', 'horridly', 'shall', 'opposing', 'too', 'Buzze', 'told', 'wisedome', 'slightly', 'though', 'Peebles', 'rise', 'aloofe', 'iealousie', 'sweare', 'vbique', 'toyle', 'foredoes', 'Appeares', 'stickes', 'after', 'trippingly', 'Fortin', 'bene', 'Woodcocks', 'Eates', 'North', 'rises', 'spend', 'vnbaited', 'Or', 'path', 'Primus', 'Nunnery', 'interre', 'Chace', 'hide', 'Lapwing', 'bodykins', 'neyther', 'equall', 'mighty', 'meaning', 'pleasing', 'strook', 'haire', 'Seat', 'Gent', 'indeede', 'Spurnes', 'Flames', 'Had', 'Gumme', 'Monthes', 'peculiar', 'plentifull', 'load', 'Vniuersity', 'Owle', 'petty', 'Winds', 'Floud', 'Nimph', 'himselfe', 'yes', 'stuffe', 'drowne', 'Ere', 'Wherefore', 'saies', 'Offence', 'remoue', 'quoted', 'famous', 'Paradox', 'prophanely', 'confront', 'chance', 'n', 'praier', 'exterior', 'No', 'Hypocrite', 'shuffling', 'Loe', 'varnish', 'ioynt', 'sensible', 'Hillo', 'little', 'thicke', 'Flaggon', 'Cases', 'loses', 'talkes', 'compelled', 'Woodcocke', 'secricie', 'Bak', 'According', 'morrow', 'should', 'horson', 'here', 'neuer', 'Thoughts', 'sparrow', 'Dew', 'Song', 'Forme', 'Patientlie', 'defie', 'dismantled', 'how', 'massie', 'link', 'Axe', 'Mettle', 'hyes', 'Camell', 'cunning', 'Vanquisher', 'crimes', 'heyre', 'Stoopes', 'Siluer', 'sequell', 'beaten', 'stealers', 'knotty', 'humbly', 'cannot', 'props', 'spots', 'breaths', 'acquaint', 'knee', 'deerest', 'color', 'Lier', 'ship', 'Tribute', 'Fall', 'Pioner', 'Sweare', 'don', 'Before', 'sokes', 'Remorselesse', 'perswade', 'Loue', 'Dirt', 'Feathers', 'Ile', 'get', 'Article', 'fey', 'mount', 'Incest', 'reaks', 'greatnesse', 'operant', 'gouerne', 'Adieu', 'huge', 'coming', '?', 'Dungeons', 'posset', 'drossie', 'pitteous', 'proceeding', 'Capitall', 'Coarses', 'Sallets', 'Can', 'cheere', 'whoreson', 'paine', 'expell', 'Trappings', 'Offences', 'punish', 'blowne', 'prouoke', 'Porches', 'Robin', 'speechlesse', 'Richer', 'yon', 'defence', 'proclaim', 'Gyant', 'insert', 'Face', 'surge', 'necessaries', 'flat', 'Something', 'guilty', 'their', 'Norway', 'work', 'emulate', 'Wife', 'ease', 'document', 'twer', 'cut', 'Dutie', 'diet', 'vnfold', 'fellow', 'lungs', 'strife', 'single', 'adoption', 'forbeare', 'Cast', 'Resembles', 'further', 'windes', 'Adue', 'weepes', 'Maid', 'shooke', 'Easterne', 'diseases', 'bellow', 'lowd', 'propose', 'Moore', 'Guest', 'verie', 'Thine', 'discord', 'sate', 'Pole', 'incensed', 'Vlcerous', 'deed', 'trifle', 'Bakt', 'Mars', 'wax', 'honourd', 'Act', 'dallying', 'orderly', 'Quicknesse', 'Polon', 'Verie', 'blister', 'rac', 'Finding', 'goodnight', 'Sutes', 'lofty', 'Arm', 'Gentrie', 'flye', 'deede', 'Paris', 'makes', 'Girdle', 'vnsanctified', 'issue', 'carnall', 'doores', 'responsiue', 'Seas', 'Deliberate', 'essentially', 'Doue', 'Ignorance', 'Puzels', 'easily', 'doing', 'Milkie', 'rowsed', 'Doom', 'If', 'meere', 'thither', 'giuing', 'lightest', 'secure', 'Scoena', 'quite', 'tumbled', 'sleepes', 'chide', 'passe', 'Messengers', 'giues', 'delight', 'years', 'crafty', 'vnpregnant', 'strike', 'eares', 'substance', 'Iustice', 'breake', 'Bodkin', 'dreadfull', 'Tragedie', 'harping', 'sawes', 'Vertue', 'sounds', 'remisse', 'peece', 'Contents', 'Folded', 'yeeld', 'mens', 'Ophel', 'Carpenter', 'eaten', 're', 'Directly', 'was', 'hauiour', 'validitie', 'drinkes', 'deale', 'Shark', 'harrowes', 'Threatning', 'Was', 'despight', 'hearts', 'Last', 'red', 'Clamour', 'begins', 'Sister', 'stowed', 'counsell', 'prou', 'Guards', 'respect', 'Yong', 'In', 'Cap', 'Imployment', 'Things', 'heares', 'subiect', 'Enemies', 'whor', 'came', 'holds', 'Maiden', 'beckens', 'Shakespeare', 'euerie', 'First', 'allowed', 'by', 'cheefe', 'flie', 'hum', 'enuiously', 'surmise', 'Heart', 'Deuice', 'full', 'mortall', 'admirable', 'Spring', 'opprest', 'Pulse', 'allowance', 'desart', 'dozen', 'therfore', 'sayest', 'distraction', 'wit', 'argues', 'illume', 'Gracious', 'Hyrcanian', 'Riuer', 'Children', 'suppresse', 'Bakers', 'Heere', 'Saylors', 'Cat', 'pestilent', 'fancie', 'Angell', 'tune', '[', 'farwel', 'knocking', 'Dugge', 'Antike', 'nine', 'Angels', 'Osricke', 'Comerce', 'Captaines', 'she', 'prai', 'Cosin', 'muddied', 'Lye', 'inobled', 'Resort', 'Breathing', 'Law', 'Valentine', 'Mysterie', 'Precepts', 'fee', 'toward', 'teares', 'stubbornnesse', 'tale', 'Midnight', 'coynage', 'shrewdly', 'Hand', 'Wormwood', 'likely', 'defeate', 'bow', 'vnder', 'caution', 'amble', 'sliuer', 'pricke', 'merit', 'Lethe', 'Nature', 'pettie', 'Skill', 'flesh', 'Dread', 'rather', 'Rim', 'concernings', 'Smoothnesse', 'dispriz', 'doubtfull', 'What', 'vnequall', 'Nobility', 'pittious', 'conceited', 'streame', ';', 'thing', 'Mischeefe', 'France', 'Shipwright', 'desperate', 'euen', 'accesse', 'head', 'Forrest', 'accord', 'Beere', 'tongues', 'dotes', 'especiall', 'swearing', 'Baptista', 'Fee', 'erruption', 'soone', 'buzze', 'youth', 'sweaty', 'Iouerney', 'hermony', 'safe', 'guarded', 'wherefore', 'Rage', 'maruels', 'Horsebacke', 'Incenst', 'pleasures', 'spred', 'Holla', 'coniectures', 'fearing', 'whet', 'sickly', 'content', 'Obseruers', 'passing', 'Flats', 'Ministring', 'reeles', 'Volt', 'stalled', 'starling', 'burst', 'a', 'speed', 'declension', 'Calumny', 'Aspect', 'dreadfully', 'vnweeded', 'Fauour', 'vnwilling', 'Trophee', 'sends', 'showes', 'replication', 'Ambassadours', 'interim', 'vnderstand', 'remaines', 'Mouse', 'let', 'adue', 'dirty', 'heereafter', 'hollow', 'Sunne', 'Sicknesse', 'thanks', 'Seeme', 'ponderous', 'Lines', 'corner', 'eyes', 'wonderfull', 'old', 'Take', 'amisse', 'Scullion', 'cold', 'blurres', 'seiz', 'mistooke', 'like', 'Guard', 'neither', 'Purchases', 'actions', 'botch', 'Thunder', 'view', 'bend', 'halt', 'Reasons', 'Honesty', 'Audit', 'sets', 'murder', 'house', 'Belike', 'Resolutes', 'encompassement', 'Rew', 'Giues', 'deeds', 'bloody', 'thin', 'moues', 'behoue', 'elder', 'comfort', 'reuenge', 'counterfet', 'catch', 'Traitorous', 'betweene', 'Character', 'otherwise', 'recouer', 'tother', 'deceiu', 'Priest', 'Husbands', 'Admit', 'stroke', 'Machine', 'Region', 'Proclaime', 'towards', 'bloud', 'Ifaith', 'Vouchers', 'requite', 'grownd', 'Next', 'Sinners', 'wrought', 'losse', 'Boxe', 'veyled', 'forfeite', 'Conqueror', 'Haile', 'strutted', 'Sconce', 'ostentation', 'need', 'Labour', 'Fantasie', 'blason', 'followed', 'Giuen', 'Flagons', 'struck', 'friendship', 'necessary', 'Stage', 'ayrie', 'slay', 'Hoboyes', 'vnsatisfied', 'farre', 'foe', 'pure', 'opinions', 'ratified', 'Olde', 'Letters', 'From', 'appeares', 'annexment', 'Tenures', 'Reads', 'circumuent', 'Falne', 'myraculous', 'quickly', 'party', 'whole', 'Masters', 'define', 'Voyce', 'vnripe', 'twice', 'heate', 'important', 'good', 'Affection', 'begin', 'wittingly', 'locke', 'intruding', 'Leuies', 'behaued', 'Clowds', 'rude', 'Contumely', 'Borne', 'certaine', 'waxe', 'lighted', 'Whil', 'encorps', 'filme', 'Occasion', 'Requit', 'retaine', 'things', 'seuerall', 'churlish', 'temp', 'forth', 'vnkennell', 'barrell', 'Saw', 'Who', 'Euerlasting', 'foure', 'lay', 'Gauntlets', 'disclaiming', 'mee', 'nay', 'talk', 'Historicall', 'Adam', 'Nephewes', 'applaud', 'Birds', 'ha', 'gaged', 'walke', 'Throate', 'carriages', 'Starre', 'Brooch', 'craft', 'Polititian', 'pleasure', 'poysoning', 'Lesson', 'haunt', 'Pe', 'mugger', 'neere', 'abuse', 'thether', 'please', 'hoo', 'Cannoneer', 'chamber', 'Command', 'dismall', 'shee', 'Comedie', 'galls', 'bunghole', 'Remember', 'sicke', 'anothers', 'encrease', 'Complexion', 'encountred', 'about', 'Ambitious', 'Fie', 'the', 'man', 'Pastorall', 'Denmarkes', 'Danskers', 'Element', 'diligence', 'Galls', 'astonish', 'home', 'fencing', 'Aeneas', 'prouidence', 'lawful', 'modestie', 'stretcht', 'Reynoldo', 'Pol', 'Reuennew', 'particular', 'familiar', 'kill', 'Ayre', 'afarre', 'necke', 'weighing', 'Statists', 'indeed', 'prankes', 'Spheres', 'Dirge', 'Lunacies', 'eate', 'pressure', 'Nero', 'weare', 'spies', 'shapes', 'Which', 'rapsidie', 'Numbers', 'Marriages', 'money', 'very', 'Sleepes', 'Rossius', 'mirth', 'Offenders', 'wheaten', 'saw', 'Scena', 'Won', 'Voice', 'ere', 'prey', 'Barnardo', 'ake', 'Fathers', 'Drab', 'indeauour', 'remorse', 'tythe', 'Daughters', 'combatted', 'Maiestie', 'darke', 'with', 'die', 'yesternight', 'come', 'broken', 'dipt', 'water', 'Commencement', 'cloathes', 'before', 'compound', 'peepe', 'prouide', 'winch', 'dry', 'bloudie', 'fire', 'euery', 'helpfull', 'folly', 'hower', 'Quality', 'Aduancement', 'be', 'Sweets', 'tame', 'blunted', 'inquiry', 'gon', 'glow', 'Phrase', 'Springes', 'suffer', 'Whips', 'almost', 'Guil', 'serious', 'brow', 'Alarum', 'Yeomans', 'lines', 'bodie', 'Prepare', 'Fiends', 'wine', 'hoorded', 'becomes', 'Looser', 'poor', 'Vngartred', 'Madnesse', 'Fardles', 'Fore', 'mowes', 'heerein', 'scarft', 'cheerefully', 'Twere', 'forbid', 'beleeue', 'Ophe', 'Graue', 'Ambass', 'absolute', 'wold', 'beckons', 'Ratifiers', 'Cuplet', 'sighe', 'Baby', 'said', 'bosome', 'worser', 'obsequious', 'beautified', 'Brothers', 'thirtie', 'willing', 'Carue', 'gender', 'name', 'halfepeny', 'Vnpegge', 'worse', 'blessing', 'shoone', 'meete', 'limed', 'Partizan', 'pitious', 'occurrents', 'superfluous', 'Yes', 'vouch', 'tearmes', 'Replies', 'siz', 'case', 'downe', 'prepar', 'draw', 'calumnious', 'Pinch', 'dignity', 'regard', 'reaches', 'informe', 'expend', 'word', 'intreat', 'Theme', 'Princes', 'Collected', 'hourely', 'affection', 'nightly', 'Louer', 'brainish', 'swaggering', 'best', '&', 'do', 'manner', 'Creatures', 'Confines', 'answere', 'Both', 'seate', 'thinke', 'Vienna', 'nill', 'difference', 'Pluckes', 'Meane', 'Charme', 'married', 'Beasts', 'sixeteene', 'Armours', 'againe', 'wormes', 'practice', 'teeth', 'imperiall', 'round', 'Flesh', 'abroad', 'shame', 'without', 'plac', 'dream', 'comming', 'offends', 'prythee', 'extrauagant', 'Horat', 'Chronicles', 'Repugnant', 'Greetings', 'many', 'noble', 'Daughter', 'aye', 'turn', 'Harlots', 'Customary', 'assume', 'Naue', 'Eyes', 'Drum', 'Hoby', 'stirre', 'gets', 'Hecticke', 'Kingly', 'extent', 'womens', 'restore', 'neglected', 'weeds', 'Miching', 'griefes', 'arriued', 'Damnation', 'erring', 'exact', 'gall', 'Doubt', 'potently', 'Denmark', 'Gallowes', 'Cursing', 'ranck', 'stoupe', 'grew', 'Death', 'remainder', 'About', 'command', 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'choller', 'practises', 'shadowes', 'pale', 'false', 'hast', 'heaues', 'paire', 'soft', 'At', 'whipping', 'Sith', 'ready', 'Philosophy', 'fresh', 'Art', 'next', 'ennactors', 'mony', 'Word', 'is', 'playd', 'sauagenes', 'Seal', 'broad', 'finde', 'heard', 'Vengeance', 'richer', 'grunt', 'Cell', 'Countrey', 'Collection', 'Diadem', 'am', 'afternoone', 'Guts', 'wag', 'lowdly', 'greefe', 'Rascall', 'offences', 'wait', 'penetrable', 'Wherein', 'shoote', 'pluckes', 'distinguish', 'streets', 'Cannon', 'haste', 'swaies', 'hands', 'hard', 'deerely', 'hurt', 'excrements', 'gratis', 'delights', 'Postscript', 'potent', 'Winner', 'reignes', 'raw', 'Friend', 'Cum', 'brood', 'hundred', 'Hamlets', 'Marke', 'Actus', 'stood', 'Ditchers', 'account', 'With', 'Scul', 'haue', 'lest', 'serue', 'lock', 'died', 'matters', 'Pretty', 'oh', 'Exceedingly', 'peruse', 'Layes', 'speedie', 'Iudges', 'Follow', 'first', 'go', 'Shardes', 'Ladie', 'likeliehood', 'better', 'if', 'Woo', 'tongue', 'outHerod', 'squeezing', 'Currants', 'Rosin', 'Landlesse', 'purpos', 'Porpentine', 'awe', 'Too', 'Conscience', 'Attendants', 'chaste', 'moment', 'causes', 'enough', 'Iudicious', 'lodge', 'outward', 'pluck', 'voyce', 'prison', 'stale', 'safely', 'Colaterall', 'necessitie', 'Blush', 'vntimely', 'Month', 'Impotent', 'Put', 'rugged', 'knowe', 'Cut', 'Globe', 'hallow', 'conuey', 'exprest', 'seated', 'praise', 'thanke', 'Shepheards', 'each', 'staffe', 'anon', 'Enter', 'minds', 'foile', 'vs', 'brest', 'Wittenberg', 'Goodnight', 'know', 'selues', 'outragious', 'strong', 'Remembrances', 'sits', 'slight', 'patches', 'cracke', 'heare', 'lou', 'Qu', 'Beares', 'forgiuenesse', 'Nights', '1', 'Rheume', 'altogether', 'carefully', 'rare', 'lyest', 'pious', 'Wharfe', 'muddy', 'damn', 'slender', 'murther', 'Shall', 'Poem', 'Feare', 'skinnes', 'Vnto', 'helpe', 'Seruant', 'vndiscouered', 'wrist', 'seen', 'Faults', 'Against', 'secret', 'volly', 'ar', 'obseruation', 'pronounce', 'scarse', 'smelt', 'gauled', 'yesterday', 'world', 'Condolement', 'ordinate', 'Paconcies', 'Farme', 'sweete', 'trouble', 'Calmely', 'Some', 'reare', 'Murtherer', 'perturbed', 'feast', 'silence', 'suppliance', 'cruell', 'Wisedomes', 'may', 'paule', 'strooke', 'Farewell', 'Peace', 'ment', 'seuen', 'fancy', 'tremble', 'Vnmixt', 'actiuely', 'embracing', 'slaughters', 'since', 'furnish', 'yonger', 'imagination', 'Inheritor', 'iudgement', 'Occasions', 'Buyes', 'rot', 'panders', 'passes', 'Body', 'hurling', 'dead', 'kettle', 'Horse', 'sit', 'Faces', 'Origin', 'Bands', 'Shame', 'neerer', 'deny', 'Place', 'dull', 'darkest', 'goes', 'leaperous', 'such', 'Clouds', 'Dyes', 'releefe', 'broke', 'admit', 'Schollers', 'time', 'ayres', 'happen', 'rashly'}
#tobe.concordance("to be") => this one has an error
emma = nltk.Text(nltk.corpus.gutenberg.words('austen-emma.txt'))
emma.concordance('surprise')
Displaying 1 of 1 matches: that Emma could not but feel some surprise , and a little displeasure , on he
tobe = nltk.Text(nltk.corpus.gutenberg.words('shakespeare-hamlet.txt'))
tobe.concordance('be')
Displaying 25 of 192 matches: me with fear & wonder Barn . It would be spoke too Mar . Question it Horatio H he Sunday from the weeke , What might be toward , that this sweaty hast Doth m se of Voyce , Speake to me . If there be any good thing to be done , That may to me . If there be any good thing to be done , That may to thee do ease , and t our deere Brothers death The memory be greene : and that it vs befitted To b in greefe , and our whole Kingdome To be contracted in one brow of woe : Yet s e deere Brothers death , Our State to be disioynt , and out of Frame , Colleag st thou beg Laertes , That shall not be my Offer , not thy Asking ? The Head . Take thy faire houre Laertes , time be thine , And thy best graces spend it I Madam , it is common Queen . If it be ; Why seemes it so particular with th nschool ' d : For , what we know must be , and is as common As any the most vu till he that dyed to day , This must be so . We pray you throw to earth This ' tis a louing , and a faire Reply , Be as our selfe in Denmarke . Madam come State . And therefore must his choyce be circumscrib ' d Vnto the voyce and ye the Spring Too oft before the buttons be disclos ' d , And in the Morne and li agious blastments are most imminent . Be wary then , best safety lies in feare y vnproportion ' d thoughts his Act : Be thou familiar ; but by no meanes vulg t . Neither a borrower , nor a lender be ; For lone oft loses both it selfe an This aboue all ; to thine owne selfe be true : And it must follow , as the Ni e Night the Day , Thou canst not then be false to any man . Farewell : my Bles beene most free and bounteous . If it be so , as so tis put on me ; And that i e for fire . For this time Daughter , Be somewhat scanter of your Maiden prese larger tether may he walke , Then may be giuen you . In few , Ophelia , Doe no ls and Ministers of Grace defend vs : Be thou a Spirit of health , or Goblin d s from Heauen , or blasts from Hell , Be thy euents wicked or charitable , Tho
tobe.concordance('to' or 'To')
Displaying 25 of 685 matches: . ' Tis now strook twelue , get thee to bed Francisco Fran . For this releefe Stand : who ' s there ? Hor . Friends to this ground Mar . And Leige - men to to this ground Mar . And Leige - men to the Dane Fran . Giue you good night M , ha ' s this thing appear ' d againe to night Bar . I haue seene nothing Mar I haue intreated him along With vs , to watch the minutes of this Night , Tha He may approue our eyes , and speake to it Hor . Tush , tush , ' twill not ap ad Mar . Thou art a Scholler ; speake to it Horatio Barn . Lookes it not like not like the King ? Hor . As thou art to thy selfe , Such was the very Armour atch Hor . In what particular thought to work , I know not : But in the grosse , This boades some strange erruption to our State Mar . Good now sit downe , , Whose Image euen but now appear ' d to vs , Was ( as you know ) by Fortinbra on by a most emulate Pride ) Dar ' d to the Combate . In which , our Valiant is Lands Which he stood seiz ' d on , to the Conqueror : Against the which , a ed by our King : which had return ' d To the Inheritance of Fortinbras , Had h age of the Article designe , His fell to Hamlet . Now sir , young Fortinbras , esse Resolutes , For Foode and Diet , to some Enterprize That hath a stomacke oth well appeare vnto our State ) But to recouer of vs by strong hand And term any sound , or vse of Voyce , Speake to me . If there be any good thing to be ke to me . If there be any good thing to be done , That may to thee do ease , any good thing to be done , That may to thee do ease , and grace to me ; spea That may to thee do ease , and grace to me ; speak to me . If thou art priuy hee do ease , and grace to me ; speak to me . If thou art priuy to thy Countri me ; speak to me . If thou art priuy to thy Countries Fate ( Which happily fo We do it wrong , being so Maiesticall To offer it the shew of Violence , For i malicious Mockery Barn . It was about to speake , when the Cocke crew Hor . An
from nltk.corpus import gutenberg
gutenberg.fileids()
['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']
for fileid in gutenberg.fileids():
num_chars = len(gutenberg.raw(fileid))
num_words = len(gutenberg.words(fileid))
num_sents = len(gutenberg.sents(fileid))
num_vocab = len(set(w.lower() for w in gutenberg.words(fileid)))
print(round(num_chars/num_words), round(num_words/num_sents), round(num_words/num_vocab), fileid)
5 25 26 austen-emma.txt 5 26 17 austen-persuasion.txt 5 28 22 austen-sense.txt 4 34 79 bible-kjv.txt 5 19 5 blake-poems.txt 4 19 14 bryant-stories.txt 4 18 12 burgess-busterbrown.txt 4 20 13 carroll-alice.txt 5 20 12 chesterton-ball.txt 5 23 11 chesterton-brown.txt 5 18 11 chesterton-thursday.txt 4 21 25 edgeworth-parents.txt 5 26 15 melville-moby_dick.txt 5 52 11 milton-paradise.txt 4 12 9 shakespeare-caesar.txt 4 12 8 shakespeare-hamlet.txt 4 12 7 shakespeare-macbeth.txt 5 36 12 whitman-leaves.txt
macbeth_sent = gutenberg.sents('shakespeare-macbeth.txt')
macbeth_sent
[['[', 'The', 'Tragedie', 'of', 'Macbeth', 'by', 'William', 'Shakespeare', '1603', ']'], ['Actus', 'Primus', '.'], ...]
macbeth_sent[113]
['All', 'haile', 'Macbeth', ',', 'haile', 'to', 'thee', 'Thane', 'of', 'Cawdor']
longest_len = max(len(s) for s in macbeth_sent)
[s for s in macbeth_sent if len(s) == longest_len]
[['Doubtfull',
'it',
'stood',
',',
'As',
'two',
'spent',
'Swimmers',
',',
'that',
'doe',
'cling',
'together',
',',
'And',
'choake',
'their',
'Art',
':',
'The',
'mercilesse',
'Macdonwald',
'(',
'Worthie',
'to',
'be',
'a',
'Rebell',
',',
'for',
'to',
'that',
'The',
'multiplying',
'Villanies',
'of',
'Nature',
'Doe',
'swarme',
'vpon',
'him',
')',
'from',
'the',
'Westerne',
'Isles',
'Of',
'Kernes',
'and',
'Gallowgrosses',
'is',
'supply',
"'",
'd',
',',
'And',
'Fortune',
'on',
'his',
'damned',
'Quarry',
'smiling',
',',
'Shew',
"'",
'd',
'like',
'a',
'Rebells',
'Whore',
':',
'but',
'all',
"'",
's',
'too',
'weake',
':',
'For',
'braue',
'Macbeth',
'(',
'well',
'hee',
'deserues',
'that',
'Name',
')',
'Disdayning',
'Fortune',
',',
'with',
'his',
'brandisht',
'Steele',
',',
'Which',
'smoak',
"'",
'd',
'with',
'bloody',
'execution',
'(',
'Like',
'Valours',
'Minion',
')',
'caru',
"'",
'd',
'out',
'his',
'passage',
',',
'Till',
'hee',
'fac',
"'",
'd',
'the',
'Slaue',
':',
'Which',
'neu',
"'",
'r',
'shooke',
'hands',
',',
'nor',
'bad',
'farwell',
'to',
'him',
',',
'Till',
'he',
'vnseam',
"'",
'd',
'him',
'from',
'the',
'Naue',
'toth',
"'",
'Chops',
',',
'And',
'fix',
"'",
'd',
'his',
'Head',
'vpon',
'our',
'Battlements']]
from nltk.corpus import webtext
for fileid in webtext.fileids():
print(fileid, webtext.raw(fileid)[:65], '...')
firefox.txt Cookie Manager: "Don't allow sites that set removed cookies to se ... grail.txt SCENE 1: [wind] [clop clop clop] KING ARTHUR: Whoa there! [clop ... overheard.txt White guy: So, do you have any plans for this evening? Asian girl ... pirates.txt PIRATES OF THE CARRIBEAN: DEAD MAN'S CHEST, by Ted Elliott & Terr ... singles.txt 25 SEXY MALE, seeks attrac older single lady, for discreet encoun ... wine.txt Lovely delicate, fragrant Rhone wine. Polished leather and strawb ...
from nltk.corpus import nps_chat
chatroom = nps_chat.posts('10-19-20s_706posts.xml')
chatroom[123]
['i', 'do', "n't", 'want', 'hot', 'pics', 'of', 'a', 'female', ',', 'I', 'can', 'look', 'in', 'a', 'mirror', '.']
chatroom[:50]
[['now', 'im', 'left', 'with', 'this', 'gay', 'name'], [':P'], ['PART'], ['hey', 'everyone'], ['ah', 'well'], ['NICK', ':', 'U7'], ['U7', 'is', 'a', 'gay', 'name', '.'], ['.', 'ACTION', 'gives', 'U121', 'a', 'golf', 'clap', '.'], [':)'], ['JOIN'], ['hi', 'U59'], ['26', '/', 'm', '/', 'ky', 'women', 'that', 'are', 'nice', 'please', 'pm', 'me'], ['JOIN'], ['PART'], ['there', 'ya', 'go', 'U7'], ['do', "n't", 'golf', 'clap', 'me', '.'], ['fuck', 'you', 'U121', ':@'], ['whats', 'everyone', 'up', 'to', '?'], ['PART'], ['PART'], ['i', "'ll", 'thunder', 'clap', 'your', 'ass', '.'], ['PART'], ['and', 'i', 'dont', 'even', 'know', 'what', 'that', 'means', '.'], ['that', 'sounds', 'painful'], ['any', 'ladis', 'wanna', 'chat', '?', '29', 'm'], ['26', '/', 'm'], ['JOIN'], ['my', 'cousin', 'drew', 'a', 'messed', 'up', 'pic', 'on', 'my', 'cast'], ['PART'], ['24', '/', 'm'], ['boo', '.'], ['26', '/', 'm', 'and', 'sexy'], ['lol', 'U115'], ['boo', '.'], ['JOIN'], ['PART'], ['he', 'drew', 'a', 'girl', 'with', 'legs', 'spread'], ['boo', '.'], ['hope', 'he', 'didnt', 'draw', 'a', 'penis'], ['PART'], ['ewwwww', 'lol'], ['&', 'a', 'head', 'between', 'her', 'legs'], ['JOIN'], ['JOIN'], ['sounds', 'good', 'to', 'me', '.'], ['r', 'u', 'serious'], ['JOIN'], ['PART'], ['I', "'ll", 'take', 'one', ',', 'please', '.'], ['&', 'i', 'have', 'to', 'go', 'to', 'the', 'docs', 'tomorrow']]
from nltk.corpus import brown
brown.categories()
['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']
brown.words(categories)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) /tmp/ipykernel_6051/1524533781.py in <module> ----> 1 brown.words(categories) NameError: name 'categories' is not defined
brown.words(categories='news')
['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
brown.words(fileids=['cg22'])
['Does', 'our', 'society', 'have', 'a', 'runaway', ',', ...]
brown.words(categories=['news','editorial','reviews'])
['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
from nltk.corpus import brown
news_text = brown.words(categories='news')
fdist = nltk.FreqDist(w.lower() for w in news_text)
modals = ['can', 'could', 'might', 'must', 'will', 'would']
for m in modals:
print(m + ':', fdist[m], end=' / NICE! / ')
can: 94 / NICE! / could: 87 / NICE! / might: 38 / NICE! / must: 53 / NICE! / will: 389 / NICE! / would: 246 / NICE! /
cfd = nltk.ConditionalFreqDist(
(genre, word)
for genre in brown.categories()
for word in brown.words(categories=genre))
genres = ['news', 'religion', 'hobbies', 'science_fiction', 'romance', 'humor']
modals = ['can','could','may','might','must','will']
cfd.tabulate(conditions=genres, samples=modals)
can could may might must will
news 93 86 66 38 50 389
religion 82 59 78 12 54 71
hobbies 268 58 131 22 83 264
science_fiction 16 49 4 12 8 16
romance 74 193 11 51 45 43
humor 16 30 8 8 9 13
from nltk.corpus import reuters
print(reuters.fileids(), end='')
['test/14826', 'test/14828', 'test/14829', 'test/14832', 'test/14833', 'test/14839', 'test/14840', 'test/14841', 'test/14842', 'test/14843', 'test/14844', 'test/14849', 'test/14852', 'test/14854', 'test/14858', 'test/14859', 'test/14860', 'test/14861', 'test/14862', 'test/14863', 'test/14865', 'test/14867', 'test/14872', 'test/14873', 'test/14875', 'test/14876', 'test/14877', 'test/14881', 'test/14882', 'test/14885', 'test/14886', 'test/14888', 'test/14890', 'test/14891', 'test/14892', 'test/14899', 'test/14900', 'test/14903', 'test/14904', 'test/14907', 'test/14909', 'test/14911', 'test/14912', 'test/14913', 'test/14918', 'test/14919', 'test/14921', 'test/14922', 'test/14923', 'test/14926', 'test/14928', 'test/14930', 'test/14931', 'test/14932', 'test/14933', 'test/14934', 'test/14941', 'test/14943', 'test/14949', 'test/14951', 'test/14954', 'test/14957', 'test/14958', 'test/14959', 'test/14960', 'test/14962', 'test/14963', 'test/14964', 'test/14965', 'test/14967', 'test/14968', 'test/14969', 'test/14970', 'test/14971', 'test/14974', 'test/14975', 'test/14978', 'test/14981', 'test/14982', 'test/14983', 'test/14984', 'test/14985', 'test/14986', 'test/14987', 'test/14988', 'test/14993', 'test/14995', 'test/14998', 'test/15000', 'test/15001', 'test/15002', 'test/15004', 'test/15005', 'test/15006', 'test/15011', 'test/15012', 'test/15013', 'test/15016', 'test/15017', 'test/15020', 'test/15023', 'test/15024', 'test/15026', 'test/15027', 'test/15028', 'test/15029', 'test/15031', 'test/15032', 'test/15033', 'test/15037', 'test/15038', 'test/15043', 'test/15045', 'test/15046', 'test/15048', 'test/15049', 'test/15052', 'test/15053', 'test/15055', 'test/15056', 'test/15060', 'test/15061', 'test/15062', 'test/15063', 'test/15065', 'test/15067', 'test/15069', 'test/15070', 'test/15074', 'test/15077', 'test/15078', 'test/15079', 'test/15082', 'test/15090', 'test/15091', 'test/15092', 'test/15093', 'test/15094', 'test/15095', 'test/15096', 'test/15097', 'test/15103', 'test/15104', 'test/15106', 'test/15107', 'test/15109', 'test/15110', 'test/15111', 'test/15112', 'test/15118', 'test/15119', 'test/15120', 'test/15121', 'test/15122', 'test/15124', 'test/15126', 'test/15128', 'test/15129', 'test/15130', 'test/15132', 'test/15136', 'test/15138', 'test/15141', 'test/15144', 'test/15145', 'test/15146', 'test/15149', 'test/15152', 'test/15153', 'test/15154', 'test/15156', 'test/15157', 'test/15161', 'test/15162', 'test/15171', 'test/15172', 'test/15175', 'test/15179', 'test/15180', 'test/15185', 'test/15188', 'test/15189', 'test/15190', 'test/15193', 'test/15194', 'test/15197', 'test/15198', 'test/15200', 'test/15204', 'test/15205', 'test/15206', 'test/15207', 'test/15208', 'test/15210', 'test/15211', 'test/15212', 'test/15213', 'test/15217', 'test/15219', 'test/15220', 'test/15221', 'test/15222', 'test/15223', 'test/15226', 'test/15227', 'test/15230', 'test/15233', 'test/15234', 'test/15237', 'test/15238', 'test/15239', 'test/15240', 'test/15242', 'test/15243', 'test/15244', 'test/15246', 'test/15247', 'test/15250', 'test/15253', 'test/15254', 'test/15255', 'test/15258', 'test/15259', 'test/15262', 'test/15263', 'test/15264', 'test/15265', 'test/15270', 'test/15271', 'test/15273', 'test/15274', 'test/15276', 'test/15278', 'test/15280', 'test/15281', 'test/15283', 'test/15287', 'test/15290', 'test/15292', 'test/15294', 'test/15295', 'test/15296', 'test/15299', 'test/15300', 'test/15302', 'test/15303', 'test/15306', 'test/15307', 'test/15308', 'test/15309', 'test/15310', 'test/15311', 'test/15312', 'test/15313', 'test/15314', 'test/15315', 'test/15321', 'test/15322', 'test/15324', 'test/15325', 'test/15326', 'test/15327', 'test/15329', 'test/15335', 'test/15336', 'test/15337', 'test/15339', 'test/15341', 'test/15344', 'test/15345', 'test/15348', 'test/15349', 'test/15351', 'test/15352', 'test/15354', 'test/15356', 'test/15357', 'test/15359', 'test/15363', 'test/15364', 'test/15365', 'test/15366', 'test/15367', 'test/15368', 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'training/9939', 'training/9940', 'training/9941', 'training/9942', 'training/9943', 'training/9946', 'training/9947', 'training/995', 'training/9952', 'training/9953', 'training/9954', 'training/9955', 'training/9956', 'training/9957', 'training/9958', 'training/9959', 'training/9961', 'training/9963', 'training/9964', 'training/9965', 'training/9967', 'training/9970', 'training/9971', 'training/9972', 'training/9973', 'training/9974', 'training/9975', 'training/9976', 'training/9977', 'training/9978', 'training/998', 'training/9981', 'training/9982', 'training/9984', 'training/9985', 'training/9988', 'training/9989', 'training/999', 'training/9992', 'training/9993', 'training/9994', 'training/9995']
reuters.categories()
['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', 'dmk', 'earn', 'fuel', 'gas', 'gnp', 'gold', 'grain', 'groundnut', 'groundnut-oil', 'heat', 'hog', 'housing', 'income', 'instal-debt', 'interest', 'ipi', 'iron-steel', 'jet', 'jobs', 'l-cattle', 'lead', 'lei', 'lin-oil', 'livestock', 'lumber', 'meal-feed', 'money-fx', 'money-supply', 'naphtha', 'nat-gas', 'nickel', 'nkr', 'nzdlr', 'oat', 'oilseed', 'orange', 'palladium', 'palm-oil', 'palmkernel', 'pet-chem', 'platinum', 'potato', 'propane', 'rand', 'rape-oil', 'rapeseed', 'reserves', 'retail', 'rice', 'rubber', 'rye', 'ship', 'silver', 'sorghum', 'soy-meal', 'soy-oil', 'soybean', 'strategic-metal', 'sugar', 'sun-meal', 'sun-oil', 'sunseed', 'tea', 'tin', 'trade', 'veg-oil', 'wheat', 'wpi', 'yen', 'zinc']
from nltk.corpus import inaugural
inaugural.fileids()
['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', '1801-Jefferson.txt', '1805-Jefferson.txt', '1809-Madison.txt', '1813-Madison.txt', '1817-Monroe.txt', '1821-Monroe.txt', '1825-Adams.txt', '1829-Jackson.txt', '1833-Jackson.txt', '1837-VanBuren.txt', '1841-Harrison.txt', '1845-Polk.txt', '1849-Taylor.txt', '1853-Pierce.txt', '1857-Buchanan.txt', '1861-Lincoln.txt', '1865-Lincoln.txt', '1869-Grant.txt', '1873-Grant.txt', '1877-Hayes.txt', '1881-Garfield.txt', '1885-Cleveland.txt', '1889-Harrison.txt', '1893-Cleveland.txt', '1897-McKinley.txt', '1901-McKinley.txt', '1905-Roosevelt.txt', '1909-Taft.txt', '1913-Wilson.txt', '1917-Wilson.txt', '1921-Harding.txt', '1925-Coolidge.txt', '1929-Hoover.txt', '1933-Roosevelt.txt', '1937-Roosevelt.txt', '1941-Roosevelt.txt', '1945-Roosevelt.txt', '1949-Truman.txt', '1953-Eisenhower.txt', '1957-Eisenhower.txt', '1961-Kennedy.txt', '1965-Johnson.txt', '1969-Nixon.txt', '1973-Nixon.txt', '1977-Carter.txt', '1981-Reagan.txt', '1985-Reagan.txt', '1989-Bush.txt', '1993-Clinton.txt', '1997-Clinton.txt', '2001-Bush.txt', '2005-Bush.txt', '2009-Obama.txt', '2013-Obama.txt', '2017-Trump.txt']
cfd = nltk.ConditionalFreqDist(
(target, fileid[:4])
for fileid in inaugural.fileids()
for w in inaugural.words(fileid)
for target in ['america', 'citizen']
if w.lower().startswith(target))
cfd.plot()
<AxesSubplot:xlabel='Samples', ylabel='Counts'>
nltk.corpus.cess_esp.words()
--------------------------------------------------------------------------- LookupError Traceback (most recent call last) ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __load(self) 83 try: ---> 84 root = nltk.data.find(f"{self.subdir}/{zip_name}") 85 except LookupError: ~/.local/lib/python3.7/site-packages/nltk/data.py in find(resource_name, paths) 582 resource_not_found = f"\n{sep}\n{msg}\n{sep}\n" --> 583 raise LookupError(resource_not_found) 584 LookupError: ********************************************************************** Resource cess_esp not found. Please use the NLTK Downloader to obtain the resource: >>> import nltk >>> nltk.download('cess_esp') For more information see: https://www.nltk.org/data.html Attempted to load corpora/cess_esp.zip/cess_esp/ Searched in: - '/home/chae/nltk_data' - '/usr/nltk_data' - '/usr/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' ********************************************************************** During handling of the above exception, another exception occurred: LookupError Traceback (most recent call last) /tmp/ipykernel_6051/3091457612.py in <module> ----> 1 nltk.corpus.cess_esp.words() ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __getattr__(self, attr) 119 raise AttributeError("LazyCorpusLoader object has no attribute '__bases__'") 120 --> 121 self.__load() 122 # This looks circular, but its not, since __load() changes our 123 # __class__ to something new: ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __load(self) 84 root = nltk.data.find(f"{self.subdir}/{zip_name}") 85 except LookupError: ---> 86 raise e 87 88 # Load the corpus. ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __load(self) 79 else: 80 try: ---> 81 root = nltk.data.find(f"{self.subdir}/{self.__name}") 82 except LookupError as e: 83 try: ~/.local/lib/python3.7/site-packages/nltk/data.py in find(resource_name, paths) 581 sep = "*" * 70 582 resource_not_found = f"\n{sep}\n{msg}\n{sep}\n" --> 583 raise LookupError(resource_not_found) 584 585 LookupError: ********************************************************************** Resource cess_esp not found. Please use the NLTK Downloader to obtain the resource: >>> import nltk >>> nltk.download('cess_esp') For more information see: https://www.nltk.org/data.html Attempted to load corpora/cess_esp Searched in: - '/home/chae/nltk_data' - '/usr/nltk_data' - '/usr/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' **********************************************************************
nltk.corpus.floresta.words()
--------------------------------------------------------------------------- LookupError Traceback (most recent call last) ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __load(self) 83 try: ---> 84 root = nltk.data.find(f"{self.subdir}/{zip_name}") 85 except LookupError: ~/.local/lib/python3.7/site-packages/nltk/data.py in find(resource_name, paths) 582 resource_not_found = f"\n{sep}\n{msg}\n{sep}\n" --> 583 raise LookupError(resource_not_found) 584 LookupError: ********************************************************************** Resource floresta not found. Please use the NLTK Downloader to obtain the resource: >>> import nltk >>> nltk.download('floresta') For more information see: https://www.nltk.org/data.html Attempted to load corpora/floresta.zip/floresta/ Searched in: - '/home/chae/nltk_data' - '/usr/nltk_data' - '/usr/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' ********************************************************************** During handling of the above exception, another exception occurred: LookupError Traceback (most recent call last) /tmp/ipykernel_6051/4009314633.py in <module> ----> 1 nltk.corpus.floresta.words() ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __getattr__(self, attr) 119 raise AttributeError("LazyCorpusLoader object has no attribute '__bases__'") 120 --> 121 self.__load() 122 # This looks circular, but its not, since __load() changes our 123 # __class__ to something new: ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __load(self) 84 root = nltk.data.find(f"{self.subdir}/{zip_name}") 85 except LookupError: ---> 86 raise e 87 88 # Load the corpus. ~/.local/lib/python3.7/site-packages/nltk/corpus/util.py in __load(self) 79 else: 80 try: ---> 81 root = nltk.data.find(f"{self.subdir}/{self.__name}") 82 except LookupError as e: 83 try: ~/.local/lib/python3.7/site-packages/nltk/data.py in find(resource_name, paths) 581 sep = "*" * 70 582 resource_not_found = f"\n{sep}\n{msg}\n{sep}\n" --> 583 raise LookupError(resource_not_found) 584 585 LookupError: ********************************************************************** Resource floresta not found. Please use the NLTK Downloader to obtain the resource: >>> import nltk >>> nltk.download('floresta') For more information see: https://www.nltk.org/data.html Attempted to load corpora/floresta Searched in: - '/home/chae/nltk_data' - '/usr/nltk_data' - '/usr/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' **********************************************************************
from nltk.corpus import udhr
languages = ['Chickasaw', 'English', 'German_Deutsch']
cfd = nltk.ConditionalFreqDist(
(lang, len(word))
for lang in languages
for word in udhr.words(lang + '-Latin1'))
cfd.plot(cumulative=True)
<AxesSubplot:xlabel='Samples', ylabel='Cumulative Counts'>
raw = gutenberg.raw("burgess-busterbrown.txt")
raw[1:20]
'The Adventures of B'
words = gutenberg.words("burgess-busterbrown.txt")
print(words[1:20], end='')
['The', 'Adventures', 'of', 'Buster', 'Bear', 'by', 'Thornton', 'W', '.', 'Burgess', '1920', ']', 'I', 'BUSTER', 'BEAR', 'GOES', 'FISHING', 'Buster', 'Bear']
sents = gutenberg.sents("burgess-busterbrown.txt")
print(sents[1:20], end='')
[['I'], ['BUSTER', 'BEAR', 'GOES', 'FISHING'], ['Buster', 'Bear', 'yawned', 'as', 'he', 'lay', 'on', 'his', 'comfortable', 'bed', 'of', 'leaves', 'and', 'watched', 'the', 'first', 'early', 'morning', 'sunbeams', 'creeping', 'through', 'the', 'Green', 'Forest', 'to', 'chase', 'out', 'the', 'Black', 'Shadows', '.'], ['Once', 'more', 'he', 'yawned', ',', 'and', 'slowly', 'got', 'to', 'his', 'feet', 'and', 'shook', 'himself', '.'], ['Then', 'he', 'walked', 'over', 'to', 'a', 'big', 'pine', '-', 'tree', ',', 'stood', 'up', 'on', 'his', 'hind', 'legs', ',', 'reached', 'as', 'high', 'up', 'on', 'the', 'trunk', 'of', 'the', 'tree', 'as', 'he', 'could', ',', 'and', 'scratched', 'the', 'bark', 'with', 'his', 'great', 'claws', '.'], ['After', 'that', 'he', 'yawned', 'until', 'it', 'seemed', 'as', 'if', 'his', 'jaws', 'would', 'crack', ',', 'and', 'then', 'sat', 'down', 'to', 'think', 'what', 'he', 'wanted', 'for', 'breakfast', '.'], ['While', 'he', 'sat', 'there', ',', 'trying', 'to', 'make', 'up', 'his', 'mind', 'what', 'would', 'taste', 'best', ',', 'he', 'was', 'listening', 'to', 'the', 'sounds', 'that', 'told', 'of', 'the', 'waking', 'of', 'all', 'the', 'little', 'people', 'who', 'live', 'in', 'the', 'Green', 'Forest', '.'], ['He', 'heard', 'Sammy', 'Jay', 'way', 'off', 'in', 'the', 'distance', 'screaming', ',', '"', 'Thief', '!'], ['Thief', '!"'], ['and', 'grinned', '.'], ['"', 'I', 'wonder', ',"', 'thought', 'Buster', ',', '"', 'if', 'some', 'one', 'has', 'stolen', 'Sammy', "'", 's', 'breakfast', ',', 'or', 'if', 'he', 'has', 'stolen', 'the', 'breakfast', 'of', 'some', 'one', 'else', '.'], ['Probably', 'he', 'is', 'the', 'thief', 'himself', '."'], ['He', 'heard', 'Chatterer', 'the', 'Red', 'Squirrel', 'scolding', 'as', 'fast', 'as', 'he', 'could', 'make', 'his', 'tongue', 'go', 'and', 'working', 'himself', 'into', 'a', 'terrible', 'rage', '.'], ['"', 'Must', 'be', 'that', 'Chatterer', 'got', 'out', 'of', 'bed', 'the', 'wrong', 'way', 'this', 'morning', ',"', 'thought', 'he', '.'], ['He', 'heard', 'Blacky', 'the', 'Crow', 'cawing', 'at', 'the', 'top', 'of', 'his', 'lungs', ',', 'and', 'he', 'knew', 'by', 'the', 'sound', 'that', 'Blacky', 'was', 'getting', 'into', 'mischief', 'of', 'some', 'kind', '.'], ['He', 'heard', 'the', 'sweet', 'voices', 'of', 'happy', 'little', 'singers', ',', 'and', 'they', 'were', 'good', 'to', 'hear', '.'], ['But', 'most', 'of', 'all', 'he', 'listened', 'to', 'a', 'merry', ',', 'low', ',', 'silvery', 'laugh', 'that', 'never', 'stopped', 'but', 'went', 'on', 'and', 'on', ',', 'until', 'he', 'just', 'felt', 'as', 'if', 'he', 'must', 'laugh', 'too', '.'], ['It', 'was', 'the', 'voice', 'of', 'the', 'Laughing', 'Brook', '.'], ['And', 'as', 'Buster', 'listened', 'it', 'suddenly', 'came', 'to', 'him', 'just', 'what', 'he', 'wanted', 'for', 'breakfast', '.']]
from nltk.corpus import PlaintextCorpusReader
corpus_root = '/soupboat/~chae/K-Energy_script_0706.txt'
wordlists = PlaintextCorpusReader(corpus_root, '.*')
--------------------------------------------------------------------------- OSError Traceback (most recent call last) /tmp/ipykernel_6051/1559554002.py in <module> ----> 1 wordlists = PlaintextCorpusReader(corpus_root, '.*') ~/.local/lib/python3.7/site-packages/nltk/corpus/reader/plaintext.py in __init__(self, root, fileids, word_tokenizer, sent_tokenizer, para_block_reader, encoding) 60 corpus into paragraph blocks. 61 """ ---> 62 CorpusReader.__init__(self, root, fileids, encoding) 63 self._word_tokenizer = word_tokenizer 64 self._sent_tokenizer = sent_tokenizer ~/.local/lib/python3.7/site-packages/nltk/corpus/reader/api.py in __init__(self, root, fileids, encoding, tagset) 77 root = ZipFilePathPointer(zipfile, zipentry) 78 else: ---> 79 root = FileSystemPathPointer(root) 80 elif not isinstance(root, PathPointer): 81 raise TypeError("CorpusReader: expected a string or a PathPointer") ~/.local/lib/python3.7/site-packages/nltk/compat.py in _decorator(*args, **kwargs) 39 def _decorator(*args, **kwargs): 40 args = (args[0], add_py3_data(args[1])) + args[2:] ---> 41 return init_func(*args, **kwargs) 42 43 return wraps(init_func)(_decorator) ~/.local/lib/python3.7/site-packages/nltk/data.py in __init__(self, _path) 310 _path = os.path.abspath(_path) 311 if not os.path.exists(_path): --> 312 raise OSError("No such file or directory: %r" % _path) 313 self._path = _path 314 OSError: No such file or directory: '/soupboat/~chae/K-Energy_script_0706.txt'
from nltk.corpus import brown
cfd = nltk.ConditionalFreqDist(
(genre, word)
for genre in brown.categories()
for word in brown.words(categories=genre))
genre_word = [(genre, word)
for genre in ['news', 'romance']
for word in brown.words(categories=genre)]
len(genre_word)
170576
genre_word[:4]
[('news', 'The'), ('news', 'Fulton'), ('news', 'County'), ('news', 'Grand')]
# 여기에는 적혀있지 않지만, nltk 챕터 2에서 나온 수작업이 다음의 펑션을 이용해서 자동으로 마무리됨.
genre_word[-4:]
[('romance', 'afraid'),
('romance', 'not'),
('romance', "''"),
('romance', '.')]
cfd = nltk.ConditionalFreqDist(genre_word)
cfd
<ConditionalFreqDist with 2 conditions>
cfd.conditions()
['news', 'romance']
#hmmm mine looks different than the book; ['news', 'romance'] #[_conditions=cfd]
from nltk.corpus import inaugural
cfd = nltk.ConditionalFreqDist(
(target, fileid[:4])
for fileid in inaugural.fileids()
for w in inaugural.words(fileid)
for target in ['america', 'citizen']
if w.lower().startswith(target))
from nltk.corpus import udhr
languages = ['Chickasaw','English','German_Deutsch']
cfd = nltk.ConditionalFreqDist(
(lang, len(word))
for lang in languages
for word in udhr.words(lang + '-Latin1'))
cfd.tabulate(conditions=['English', 'German_Deutsch'],
samples=range(10), cumulative=True)
0 1 2 3 4 5 6 7 8 9
English 0 185 525 883 997 1166 1283 1440 1558 1638
German_Deutsch 0 171 263 614 717 894 1013 1110 1213 1275
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
cfd = nltk.ConditionalFreqDist(
(day, len(w))
for day in days
for w in brown.words())
cdf.tabulate(conditions=['romance', 'news'],
samples=days, cumulative=True)
Monday Tuesday Wednesday Thursday Friday Saturday Sunday romance 2 5 8 9 12 16 21 news 54 97 119 139 180 213 264
sent = ['In', 'the', 'beginning', 'Katya', 'created', 'the', 'heaven', 'and', 'the', 'earth','.']
list(nltk.bigrams(sent))
[('In', 'the'),
('the', 'beginning'),
('beginning', 'Katya'),
('Katya', 'created'),
('created', 'the'),
('the', 'heaven'),
('heaven', 'and'),
('and', 'the'),
('the', 'earth'),
('earth', '.')]
def generate_model(cfdist, word, num=15):
for i in range(num):
print(word, end=' ')
word = cfdist[word].max()
text = nltk.corpus.genesis.words('english-kjv.txt')
bigrams = nltk.bigrams(text)
cfd = nltk.ConditionalFreqDist(bigrams)
cfd['living']
FreqDist({'creature': 7, 'thing': 4, 'substance': 2, 'soul': 1, '.': 1, ',': 1})
generate_model(cfd, 'living')
living creature that he said , and the land of the land of the land
# need to continue from 3 More Python: Reusing Code