phonetilicious-final.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from topia.termextract import extract\n",
      "extractor = extract.TermExtractor()\t\n",
      "import pattern.en\n",
      "\n",
      "using_topia_tagger = True\n",
      "\n",
      "def extract_words(sentence):\n",
      "    # tokenized sentence. returns a list of [word, part of speech, word_singular]\n",
      "    if(using_topia_tagger):\n",
      "        tok = extractor.tagger(sentence)\n",
      "        if not tok:\n",
      "            return []\n",
      "        # hack for fixing the bug which sometimes makes extractor's first word of the senten lower case\n",
      "        first = sentence.strip()[:1] # first letter\n",
      "        if (first.upper() == first):\n",
      "            tok[0][0] = tok[0][0][:1].upper() + tok[0][0][1:]\n",
      "        else:\n",
      "            tok[0][0] = tok[0][0][:1] + tok[0][0][1:]\n",
      "    else:\n",
      "        # use nltk's tagger maxent_treebank_pos_tagger\n",
      "        text = word_tokenize(sentence)\n",
      "        tok = pos_tag(text)\n",
      "    \"\"\"\n",
      "    # pattern's tagger:\n",
      "    pattern.en.tag(text)\n",
      "    \"\"\"\n",
      "        \n",
      "    #if DEBUG:\n",
      "        #print tok \n",
      "        \n",
      "    # nothing to extract\n",
      "    if len(tok) == 0:\n",
      "        return []\n",
      "    \n",
      "    \n",
      "    formatted = map(lambda w: {\"word_original\": w[0], \"word_new\": w[0], \"ignore\": False, \"pos\": w[1], \"word_lower\": justlowerletters(w[0])}, tok)\n",
      "    \n",
      "\n",
      "    return formatted\n",
      "    #sentence = justlowerletters(sentence)\n",
      "    #return sentence.split(\" \")\n",
      "    \n",
      "# given string, returns ascii string, lowercase\n",
      "def justlowerletters(string):\n",
      "    string = string.encode('ascii','ignore')\n",
      "    #string = str(string)\n",
      "    return str.lower(string)\n",
      "    #return sub(r'\\s+',' ',sub(r'[^a-z\\s]', '', str.lower(string)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# given sentence, returns list of formatted words\n",
      "# TODO: consider using a tokenizer\n",
      "from nltk.corpus import stopwords \n",
      "stop = map(lambda string: string.encode('ascii','ignore'), stopwords.words('english'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nltk.corpus import wordnet as wn\n",
      "from nltk.corpus import cmudict as cmu\n",
      "from re import sub, split\n",
      "from random import randrange\n",
      "from numpy import *\n",
      "from nltk import word_tokenize, sent_tokenize, pos_tag    \n",
      "from random import shuffle\n",
      "import re\n",
      "\n",
      "DEBUG = True\n",
      "\n",
      "prondict = cmu.dict() \n",
      "Phonemes = [\"AA\", \"AE\", \"AH\", \"AO\", \"AW\", \"AY\", \"B\", \"CH\", \"D\", \"DH\", \"EH\", \"ER\", \"EY\", \"F\", \"G\", \"HH\", \"IH\", \"IY\", \"JH\", \"K\", \"L\", \"M\", \"N\", \"NG\", \"OW\", \"OY\", \"P\", \"R\", \"S\", \"SH\", \"T\", \"TH\", \"UH\", \"UW\", \"V\", \"W\", \"Y\", \"Z\", \"ZH\"]\n",
      "Consonants = [\"B\", \"CH\", \"D\", \"DH\", \"F\", \"G\", \"HH\", \"JH\", \"K\", \"L\", \"M\", \"N\", \"NG\", \"P\", \"R\", \"S\", \"SH\", \"T\", \"TH\", \"V\", \"W\", \"Y\", \"Z\", \"ZH\"]\n",
      "Vowels =  [\"AA\", \"AE\", \"AH\", \"AO\", \"AW\", \"AY\", \"EH\", \"ER\", \"EY\", \"IH\", \"IY\", \"OW\", \"OY\", \"UH\", \"UW\"]\n",
      "\n",
      "num_phonemes = len(Phonemes)\n",
      "max_synonyms = 100\n",
      "\n",
      "# TODO\n",
      "# consonance_only\n",
      "# assonance_only\n",
      "# look at first phoneme of each word\n",
      "# look at first phoneme of each syllable\n",
      "# look at first phoneme of stressed syllables\n",
      "# swap out synonyms in same part of speech only\n",
      "# only swap out Nouns, Adjectives, Verbs, Adverbs\n",
      "# ignore most common words\n",
      "# keep punctuation\n",
      "\n",
      "#alliteration\n",
      "\n",
      "# HEURISTIC. Efficient. Runs in n*m time. \n",
      "\n",
      "# given a sentence (list of Word Objects), returns phonetlicious score\n",
      "def phonetilicious_score(loWO, flatten=False, normalize=False):\n",
      "    if not loWO:\n",
      "        return 0\n",
      "    \n",
      "    phonevectors = map(lambda WO: WO[\"phonevector\"], loWO)\n",
      "    \n",
      "    if flatten:\n",
      "        # set each word's phoneme count to 0 or 1.\n",
      "        phonevectors = map(lambda v: map(lambda p: 0 if p == 0 else 1, v),  phonevectors)\n",
      "    \n",
      "    # sum up phonevectors for each word\n",
      "    vector_sum = reduce(lambda v,w: v + w, phonevectors)\n",
      "    \n",
      "    score = 0.0\n",
      "\n",
      "    phone_count = 0\n",
      "    for n in vector_sum:\n",
      "        if n > 0:\n",
      "            score += n * n * n  # recurring phones get cubed points\n",
      "            phone_count += n\n",
      "    \n",
      "    if normalize:\n",
      "        score /= phone_count\n",
      "        \n",
      "    # count repeated words\n",
      "    # no, this isn't the place to do this, because by now we've already maximized one example for each phoneme\n",
      "    # do this earlier\n",
      "    \n",
      "    words = map(lambda WO: WO[\"word_lower\"], loWO)\n",
      "    word_doubles = 0\n",
      "    for word in words:\n",
      "        extras = words.count(word) - 1\n",
      "        if extras > 0:\n",
      "            word_doubles += extras\n",
      "    score /= (0.2 * (word_doubles+1)) #severely penalized for extra words\n",
      "    \n",
      "    return score \n",
      "    \n",
      "\"\"\"\n",
      "Word Object looks like:\n",
      ",\n",
      "WO = {    'word_original': 'Truly',\n",
      "          'word_lower': 'truly'\n",
      "        'pos': 'RB',\n",
      "        'ignore': true,\n",
      "        'phonevector': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0])}\n",
      "\"\"\"\n",
      "# given a phone ph, and list of Word Objects, returns the Word Object with max occurance of ph\n",
      "# try to avoid words on blacklist (word_lower)\n",
      "def maxphone(ph, loWO, blacklist=[]):\n",
      "    if blacklist: \n",
      "        new_loWO = [WO for WO in loWO if WO[\"word_lower\"] not in blacklist]\n",
      "        if len(new_loWO) == 0:\n",
      "            new_loWO = loWO\n",
      "    else:\n",
      "        new_loWO = loWO\n",
      "    \n",
      "    result = max(new_loWO, key = lambda x: x['phonevector'][Phonemes.index(ph)])\n",
      "    \n",
      "    return result\n",
      "\n",
      "        \n",
      "\n",
      "# format new word with the capitalization of the old word\n",
      "def format_capitalization(new_word, old_word):\n",
      "    if old_word.capitalize() == old_word:\n",
      "        return new_word.capitalize()\n",
      "    elif old_word.upper() == old_word:\n",
      "        return new_word.upper()\n",
      "    elif old_word.lower() == old_word:\n",
      "        return new_word.lower()\n",
      "    else:\n",
      "        # if it's none of these, then it's something weirder\n",
      "        return new_word\n",
      "    \n",
      "    \n",
      "def pos_topia2wordnet(pos):\n",
      "    x = pos[:1]\n",
      "    if x == \"N\":\n",
      "        return \"n\"\n",
      "    elif x == \"V\":\n",
      "        return \"v\"\n",
      "    elif x == \"J\":\n",
      "        return \"a\"\n",
      "    elif x == \"R\":\n",
      "        return \"r\"\n",
      "    else:\n",
      "        return x.lower()\n",
      "    \n",
      "import random\n",
      "\n",
      "def algo(text, \n",
      "         alliteration_only = False, \n",
      "         number_of_examples = 4, \n",
      "         force_phoneme = [], \n",
      "         flatten = False, \n",
      "         split_by = \"sentences\", # \"sentences\" or \"punctuation\"\n",
      "         normalize = False, \n",
      "         try_to_avoid_dups = False, \n",
      "         word_additions = [],\n",
      "         swap_probability = 1.0,\n",
      "         hyper_profanity = False,\n",
      "         ignore_numbers = True,\n",
      "         debug = False):\n",
      "    \n",
      "    DEBUG = debug\n",
      "    \n",
      "    out = []\n",
      "    \n",
      "    if split_by == \"punctuation\":\n",
      "        sentences = re.split(r'( *[\\.\\?\\;\\,\\:\\\u2014\\n!][\\'\"\\)\\]]* *)', text)\n",
      "    elif split_by == \"lines\":\n",
      "        sentences = re.split(r'(\\n)', text)\n",
      "    else:\n",
      "        sentences = sent_tokenize(text)\n",
      "    \n",
      "    if hyper_profanity:\n",
      "        word_additions += PROFANITY\n",
      "    \n",
      "    if DEBUG:\n",
      "        print sentences\n",
      "        \n",
      "    for sentence in sentences: \n",
      "        \n",
      "        # should be tokenized with part of speech, and commonness\n",
      "        words = extract_words(sentence)\n",
      "        \n",
      "        if not words:\n",
      "            continue #empty sentence\n",
      "        \n",
      "        if DEBUG:\n",
      "            print words\n",
      "        # which words are we keeping?\n",
      "        \n",
      "        # just nouns, verbs, adjectives, adverbs\n",
      "        pos = [\"N\",\"V\",\"J\",\"R\"]\n",
      "        \n",
      "        for w in words:\n",
      "            w[\"ignore\"] = (w['word_lower'] in stop) or not (w['pos'][:1] in pos)\n",
      "            w[\"phonevector\"] = phonevector(phones(w[\"word_lower\"]), alliteration_only)\n",
      "        \n",
      "        #if DEBUG: \n",
      "            #print words\n",
      "        \n",
      "        \n",
      "        # for each word, find a list of related Word Objects\n",
      "        lolorWO = map(lambda w: \n",
      "                      [w] if w[\"ignore\"] else\n",
      "                      map(lambda rw: {\n",
      "                            \"word_lower\": rw, \n",
      "                            \"word_original\": w['word_original'],\n",
      "                            \"word_new\": format_capitalization(rw, w['word_original']),\n",
      "                            \"pos\": w['pos'],\n",
      "                            \"phonevector\": phonevector(phones(rw), alliteration_only)\n",
      "                        }, possible_words(w[\"word_lower\"],w['pos'], word_additions, swap_probability, ignore_numbers)),\n",
      "                    words) \n",
      "        \n",
      "        #if DEBUG: \n",
      "            #print lolorWO\n",
      "            \n",
      "        def generate_sentence_with_maxphone(p,lolorWO):\n",
      "            sentence = []\n",
      "            blacklist = []\n",
      "            for loWO in lolorWO:\n",
      "                mp = maxphone(p, loWO, blacklist)\n",
      "                if try_to_avoid_dups:\n",
      "                    blacklist.append(mp[\"word_lower\"])\n",
      "                sentence.append(mp)\n",
      "            return sentence\n",
      "            #sentence = map(lambda loWO: maxphone(p, loWO), lolorWO)\n",
      "            \n",
      "        \n",
      "        if len(force_phoneme) == 0:\n",
      "            phonemes_to_maximize = Phonemes\n",
      "        else:\n",
      "            phonemes_to_maximize = force_phoneme\n",
      "        loS = map(lambda p: generate_sentence_with_maxphone(p, lolorWO), phonemes_to_maximize)\n",
      "        \n",
      "        #if DEBUG:\n",
      "            #print loS\n",
      "        \n",
      "        \n",
      "        \n",
      "        \n",
      "        sentences_with_score = map(lambda s: {'sentence': sentencify(s, sentence), 'score': phonetilicious_score(s, flatten, normalize)}, loS)\n",
      "        \n",
      "        indexes = unique(sentences_with_score, return_index=True)[1]\n",
      "        sentences_unique = [sentences_with_score[index] for index in sorted(indexes)]\n",
      "        # unique array, still sorted by score. \n",
      "        # http://stackoverflow.com/questions/12926898/numpy-unique-without-sort\n",
      "        \n",
      "        best_sentences = sorted(sentences_unique, key=lambda s: s['score'], reverse=True)\n",
      "        \n",
      "        if DEBUG:\n",
      "            for s in best_sentences:\n",
      "                print s\n",
      "                \n",
      "        # trim down number of examples\n",
      "        best_sentences = best_sentences[:number_of_examples]\n",
      "                \n",
      "        if DEBUG:\n",
      "            print \"\\nBEST SENTENCE:\"\n",
      "            print best_sentences[0]\n",
      "            print \"\\n\"\n",
      "        \n",
      "        for s in best_sentences:\n",
      "            out.append(s['sentence'])\n",
      "        \n",
      "    return out\n",
      "\n",
      "# punctuation = (';', ':', ',', '.', '!', '?','...','\"',\"'\") \n",
      "\n",
      "# given a list of Word Objects, returns sentence\n",
      "def sentencify(loWO, original_sentence):\n",
      "    \n",
      "    \"\"\"\n",
      "    # remove unnecessary space around punctuation\n",
      "    i = 0\n",
      "    while i<len(words):\n",
      "        if i>0 and (words[i] in PUNCTUATION):\n",
      "            words[i-1] = words[i-1] + words[i]\n",
      "            words.pop(i)\n",
      "        else:\n",
      "            i+=1\n",
      "    \"\"\"\n",
      "\n",
      "    \n",
      "    # this is wrong, \n",
      "    # because if our replacements are [B->C, C->D] \n",
      "    # this will incorrectly go  A B C => A C C => A D C  \n",
      "    # instead of                A B C => A C D\n",
      "    x = 0\n",
      "    for WO in loWO:\n",
      "        original_sentence = original_sentence[:x] + original_sentence[x:].replace(WO['word_original'], WO['word_new'], 1)\n",
      "        x += len(WO['word_new'])\n",
      "    \n",
      "    words = original_sentence.split(\" \")\n",
      "        \n",
      "    # fix an; \"a egg\"  -> \"an egg\"\n",
      "    i = 0\n",
      "    for (i,word) in enumerate(words):\n",
      "        if (i+1) < len(words):\n",
      "            vowel_word = words[i+1][:1].lower() in [\"a\",\"e\",\"i\",\"o\",\"u\"]\n",
      "            if word.lower() == \"a\" and vowel_word:\n",
      "                words[i] = format_capitalization(\"an\",word)\n",
      "            elif word.lower() == \"an\" and not vowel_word:\n",
      "                words[i] = format_capitalization(\"a\",word)\n",
      "    \n",
      "    return \" \".join(words)\n",
      "\n",
      "from nltk.stem.wordnet import WordNetLemmatizer\n",
      "lemmatizer = WordNetLemmatizer()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#from pattern.en.wordlist import PROFANITY\n",
      "PROFANITY = []\n",
      "with open(\"profanity.txt\") as f:\n",
      "    for line in f:\n",
      "        PROFANITY.append(line.strip())\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# given a word, return list of related words\n",
      "# pos = part of speech\n",
      "# word_additions = extra words to choose from (ex: profanity)\n",
      "# addition_probably = probably that we add the word_additions to the new word list (0 to 1)\n",
      "def possible_words(word, pos, word_additions, swap_probability=1.0, ignore_numbers = True):\n",
      "    \n",
      "    if(swap_probability <= random.random()):\n",
      "        return [word]\n",
      "    \n",
      "    if ignore_numbers and (word.strip().isdigit()):\n",
      "        return [word]\n",
      "    \n",
      "    pos_wordnet = pos_topia2wordnet(pos)\n",
      "   \n",
      "    # synonyms\n",
      "    #lemma = lemmatizer.lemmatize(word, pos) \n",
      "    syn_sets = wn.synsets(word,pos_wordnet)\n",
      "\n",
      "    all_syn_sets = syn_sets\n",
      "    \n",
      "    #add hypernyms\n",
      "    for syn_set in syn_sets:\n",
      "        all_syn_sets += syn_set.hypernyms()\n",
      "        #all_syn_sets += syn_set.hyponyms()\n",
      "        \n",
      "    words = []\n",
      "\n",
      "    # TODO: pick synsets based on part of speech\n",
      "    for syn_set in all_syn_sets:\n",
      "        for w in syn_set.lemma_names():\n",
      "            if w not in words:\n",
      "                if w and (\"_\" not in w) and (w is not word):  # for now, ignore multi-word synonyms\n",
      "                    words.append(w)\n",
      "            elif len(words) >= max_synonyms:    \n",
      "                break\n",
      "        if len(words) >= max_synonyms:    \n",
      "            break\n",
      "    if not words:\n",
      "        words = [word]\n",
      "        \n",
      "    def format_word(w):\n",
      "        w = justlowerletters(w)\n",
      "        if pos[:1]==\"V\":\n",
      "            w = conjugate_verb(w, pos)\n",
      "        if pos[:1]==\"N\":\n",
      "            w = conjugate_noun(w, pos)\n",
      "        return w\n",
      "\n",
      "    \n",
      "    if word_additions:\n",
      "        # add extra words. (ex. profanity)\n",
      "        words = word_additions[:]\n",
      "    \n",
      "    # mix it up\n",
      "    shuffle(words)\n",
      "    \n",
      "    # conjugate, lowercase, etc\n",
      "    words = map(format_word, words)\n",
      "    return words\n",
      "    \n",
      "# given a word, return list of phones\n",
      "def phones(word):\n",
      "    if word in prondict:\n",
      "        return prondict[word][0]\n",
      "    else: # word is not in cmudict, return empty list\n",
      "        return []\n",
      "    \n",
      "# given a list of phones, return phone vector.\n",
      "def phonevector(lop, alliteration_only):\n",
      "    vector = [0]*num_phonemes\n",
      "\n",
      "    for p in lop:\n",
      "        p = p[:2] # ignore stress markers\n",
      "        vector[Phonemes.index(p)] += 1 #= 1 to flatten phonemes\n",
      "        if alliteration_only: \n",
      "            # only look at the first letter\n",
      "            break\n",
      "        \n",
      "    return array(vector)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pattern.en import conjugate, pluralize, singularize\n",
      "\n",
      "def conjugate_noun(noun, pos):\n",
      "    if pos==\"NNS\" or pos ==\"NNPS\":\n",
      "        return str(pluralize(noun))\n",
      "    elif pos==\"NN\" or pos ==\"NNP\":\n",
      "        return str(singularize(noun))\n",
      "    else:\n",
      "        return noun\n",
      "    \n",
      "# conjugate verb into new pos\n",
      "def conjugate_verb(verb, pos):\n",
      "    if pos[:1]==\"V\":\n",
      "        # verb which isn't be, do, have, or will/would/can/could\n",
      "        # we need to conjugate\n",
      "        if pos[2:3]==\"B\":\n",
      "            conj = conjugate(verb, tense = \"infinitive\")\n",
      "        elif pos[2:3]==\"D\":\n",
      "            conj = conjugate(verb, tense = \"past\")\n",
      "        elif pos[2:3]==\"G\":\n",
      "            conj = conjugate(verb, aspect = \"progressive\")\n",
      "        elif pos[2:3]==\"I\":\n",
      "            conj = conjugate(verb, tense = \"infinitive\")\n",
      "        elif pos[2:3]==\"N\":\n",
      "            conj = conjugate(verb, tense = \"past\", aspect=\"progressive\")\n",
      "        elif pos[2:3]==\"Z\":\n",
      "            conj = conjugate(verb, tense = \"present\", person = 3, number = \"singular\")\n",
      "        else:\n",
      "            conj = verb\n",
      "    return str(conj)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"taoteching.txt\",\"r\")\n",
      "tao = f.read()\n",
      "f.close()\n",
      "\n",
      "def tao_rmx(text):\n",
      "    rmx = algo(text,\n",
      "              alliteration_only = True, \n",
      "              number_of_examples = 1, \n",
      "              split_by = \"lines\",\n",
      "              force_phoneme = [], # Consonants, Vowels, [], [\"K\"]\n",
      "              word_additions = [],\n",
      "              swap_probability = 0.7,\n",
      "              hyper_profanity = True,\n",
      "              ignore_numbers = True,\n",
      "              flatten = False, \n",
      "              normalize = False, \n",
      "              try_to_avoid_dups = True,\n",
      "              debug = True)\n",
      "    linguistic_nectar = \"\\n\".join(rmx)\n",
      "    return linguistic_nectar\n",
      "\n",
      "\n",
      "body = tao_rmx(tao)\n",
      "\n",
      "f = open(\"taoteching-profanity-remixK.txt\",\"w+\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f.write(body)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pattern.en import conjugate, pluralize, singularize\n",
      "\n",
      "text = \"\"\"\n",
      "National Novel Generation Month - based on an idea I tweeted on a whim. This is the 2014 edition, see here for 2013.\n",
      "The Goal\n",
      "Spend the month of November writing code that generates a novel of 50k+ words \n",
      "\n",
      "The Rules\n",
      "\n",
      "The only rule is that you share at least one novel and also your source code at the end.\n",
      "\n",
      "The source code does not have to be licensed in a particular way, so long as you share it. The code itself does not need to be on GitHub, either. I'm just using this repo as a place to organize the community.\n",
      "\n",
      "The \"novel\" is defined however you want. It could be 50,000 repetitions of the word \"meow\". It could literally grab a random novel from Project Gutenberg. It doesn't matter, as long as it's 50k+ words.\n",
      "\n",
      "Please try to respect copyright. I'm not going to police it, as ultimately it's on your head if you want to just copy/paste a Stephen King novel or whatever, but the most useful/interesting implementations are going to be ones that don't engender lawsuits.\n",
      "\n",
      "This activity starts at 12:01am GMT on Nov 1st and ends at 12:01am GMT Dec 1st.\n",
      "\n",
      "How to Participate\n",
      "\n",
      "Open an issue on this repo and declare your intent to participate. You may continually update the issue as you work over the course of the month. Feel free to post dev diaries, sample output, etc.\n",
      "\n",
      "Also feel free to comment on other participants' issues.\n",
      "\n",
      "Resources\n",
      "\n",
      "There's an open issue where you can add resources (libraries, corpuses, APIs, techniques, etc).\n",
      "\n",
      "There are already a ton of resources on the old resources thread for the 2013 edition.\n",
      "\n",
      "You might want to check out corpora, a repository of public domain lists of things: animals, foods, names, occupations, countries, etc.\n",
      "\n",
      "That's It\n",
      "\n",
      "So yeah. Have fun with this!\n",
      "\n",
      "\"\"\"\n",
      "rmx = algo(text,\n",
      "      alliteration_only = True, \n",
      "      number_of_examples = 1, \n",
      "      split_by = \"sentence\",\n",
      "      force_phoneme = Consonants, #Consonants,#, Vowels, [], [\"K\"]\n",
      "      word_additions = [],\n",
      "      hyper_profanity = False,\n",
      "      flatten = False, \n",
      "      normalize = False, \n",
      "      try_to_avoid_dups = True,\n",
      "      debug = True)\n",
      "output = \" \".join(rmx)\n",
      "print output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['\\nNational Novel Generation Month - based on an idea I tweeted on a whim.', 'This is the 2014 edition, see here for 2013.', 'The Goal\\nSpend the month of November writing code that generates a novel of 50k+ words \\n\\nThe Rules\\n\\nThe only rule is that you share at least one novel and also your source code at the end.', 'The source code does not have to be licensed in a particular way, so long as you share it.', 'The code itself does not need to be on GitHub, either.', \"I'm just using this repo as a place to organize the community.\", 'The \"novel\" is defined however you want.', 'It could be 50,000 repetitions of the word \"meow\".', 'It could literally grab a random novel from Project Gutenberg.', \"It doesn't matter, as long as it's 50k+ words.\", 'Please try to respect copyright.', \"I'm not going to police it, as ultimately it's on your head if you want to just copy/paste a Stephen King novel or whatever, but the most useful/interesting implementations are going to be ones that don't engender lawsuits.\", 'This activity starts at 12:01am GMT on Nov 1st and ends at 12:01am GMT Dec 1st.', 'How to Participate\\n\\nOpen an issue on this repo and declare your intent to participate.', 'You may continually update the issue as you work over the course of the month.', 'Feel free to post dev diaries, sample output, etc.', \"Also feel free to comment on other participants' issues.\", \"Resources\\n\\nThere's an open issue where you can add resources (libraries, corpuses, APIs, techniques, etc).\", 'There are already a ton of resources on the old resources thread for the 2013 edition.', 'You might want to check out corpora, a repository of public domain lists of things: animals, foods, names, occupations, countries, etc.', \"That's It\\n\\nSo yeah.\", 'Have fun with this!\\n\\n']\n",
        "[{'ignore': False, 'word_original': 'National', 'word_lower': 'national', 'pos': 'NNP', 'word_new': 'National'}, {'ignore': False, 'word_original': 'Novel', 'word_lower': 'novel', 'pos': 'NN', 'word_new': 'Novel'}, {'ignore': False, 'word_original': 'Generation', 'word_lower': 'generation', 'pos': 'NNP', 'word_new': 'Generation'}, {'ignore': False, 'word_original': 'Month', 'word_lower': 'month', 'pos': 'NNP', 'word_new': 'Month'}, {'ignore': False, 'word_original': '-', 'word_lower': '-', 'pos': ':', 'word_new': '-'}, {'ignore': False, 'word_original': 'based', 'word_lower': 'based', 'pos': 'VBN', 'word_new': 'based'}, {'ignore': False, 'word_original': 'on', 'word_lower': 'on', 'pos': 'IN', 'word_new': 'on'}, {'ignore': False, 'word_original': 'an', 'word_lower': 'an', 'pos': 'DT', 'word_new': 'an'}, {'ignore': False, 'word_original': 'idea', 'word_lower': 'idea', 'pos': 'NN', 'word_new': 'idea'}, {'ignore': False, 'word_original': 'I', 'word_lower': 'i', 'pos': 'PRP', 'word_new': 'I'}, {'ignore': False, 'word_original': 'tweeted', 'word_lower': 'tweeted', 'pos': 'NN', 'word_new': 'tweeted'}, {'ignore': False, 'word_original': 'on', 'word_lower': 'on', 'pos': 'IN', 'word_new': 'on'}, {'ignore': False, 'word_original': 'a', 'word_lower': 'a', 'pos': 'DT', 'word_new': 'a'}, {'ignore': False, 'word_original': 'whim', 'word_lower': 'whim', 'pos': 'NN', 'word_new': 'whim'}, {'ignore': False, 'word_original': '.', 'word_lower': '.', 'pos': '.', 'word_new': '.'}]\n",
        "{'score': 379.99999999999994, 'sentence': '\\nCause Communication Quantity Entity - consumed on a content I tweeted on a caprice.'}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "{'score': 259.99999999999994, 'sentence': '\\nSomeone Artifact Sex Entity - situated on a strain I tweeted on a state.'}\n",
        "{'score': 229.99999999999997, 'sentence': '\\nBeing Book Breeding Entity - based on a belief I tweeted on a whim.'}\n",
        "{'score': 229.99999999999997, 'sentence': '\\nSomeone Fiction Phase Entity - found on a figuring I tweeted on a feeling.'}\n",
        "{'score': 229.99999999999997, 'sentence': '\\nPerson Product Procreation Period - had on a persuasion I tweeted on a whim.'}\n",
        "{'score': 159.99999999999997, 'sentence': '\\nSomeone Artifact Dealing Entity - determined on a design I tweeted on a desire.'}\n",
        "{'score': 159.99999999999997, 'sentence': '\\nMortal Artifact Multiplication Measure - had on a mind I tweeted on a whim.'}\n",
        "{'score': 128.33333333333331, 'sentence': '\\nNational Novel Genesi Entity - had on a knowledge I tweeted on a notion.'}\n",
        "{'score': 99.99999999999999, 'sentence': '\\nWhole Artifact Happening Entity - had on a content I tweeted on a whim.'}\n",
        "{'score': 99.99999999999999, 'sentence': '\\nSomeone Artifact Transaction Entity - taken on a tune I tweeted on a whim.'}\n",
        "{'score': 69.99999999999999, 'sentence': '\\nSomeone Artifact Group Entity - had on a goal I tweeted on a whim.'}\n",
        "{'score': 69.99999999999999, 'sentence': '\\nSomeone Writing Reproduction Entity - had on a reckoning I tweeted on a whim.'}\n",
        "{'score': 68.33333333333333, 'sentence': '\\nSomeone Artifact Genesi Entity - located on a line I tweeted on a whim.'}\n",
        "{'score': 59.99999999999999, 'sentence': '\\nSomeone Artifact Generation Entity - had on a content I tweeted on a whim.'}\n",
        "{'score': 58.33333333333332, 'sentence': '\\nSomeone Artifact Genesi Entity - had on a content I tweeted on a whim.'}\n",
        "{'score': 58.33333333333332, 'sentence': '\\nSomeone Artifact Genesi Entity - had on a thought I tweeted on a whim.'}\n",
        "{'score': 58.33333333333332, 'sentence': '\\nUnit Artifact Genesi Entity - had on a content I tweeted on a whim.'}\n",
        "{'score': 38.33333333333333, 'sentence': '\\nSomeone Volume Genesi Entity - had on a view I tweeted on a whim.'}\n",
        "\n",
        "BEST SENTENCE:\n",
        "{'score': 379.99999999999994, 'sentence': '\\nCause Communication Quantity Entity - consumed on a content I tweeted on a caprice.'}\n",
        "\n",
        "\n",
        "[{'ignore': False, 'word_original': 'This', 'word_lower': 'this', 'pos': 'DT', 'word_new': 'This'}, {'ignore': False, 'word_original': 'is', 'word_lower': 'is', 'pos': 'VBZ', 'word_new': 'is'}, {'ignore': False, 'word_original': 'the', 'word_lower': 'the', 'pos': 'DT', 'word_new': 'the'}, {'ignore': False, 'word_original': '2014', 'word_lower': '2014', 'pos': 'NN', 'word_new': '2014'}, {'ignore': False, 'word_original': 'edition', 'word_lower': 'edition', 'pos': 'NN', 'word_new': 'edition'}, {'ignore': False, 'word_original': ',', 'word_lower': ',', 'pos': ',', 'word_new': ','}, {'ignore': False, 'word_original': 'see', 'word_lower': 'see', 'pos': 'VB', 'word_new': 'see'}, {'ignore': False, 'word_original': 'here', 'word_lower': 'here', 'pos': 'RB', 'word_new': 'here'}, {'ignore': False, 'word_original': 'for', 'word_lower': 'for', 'pos': 'IN', 'word_new': 'for'}, {'ignore': False, 'word_original': '2013.', 'word_lower': '2013.', 'pos': 'NN', 'word_new': '2013.'}]\n",
        "{'score': 185.0, 'sentence': 'This is the 2014 form, figure here for 2013.'}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "{'score': 185.0, 'sentence': 'This is the 2014 whole, hear here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 grouping, gamble here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 content, call here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 number, notice here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 printing, play here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 sort, sight here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 type, trip here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 thought, think here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 version, visit here for 2013.'}\n",
        "{'score': 95.0, 'sentence': 'This is the 2014 work, wager here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, bet here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, change here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, date here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, call here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, journey here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, learn here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, move here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 version, reckon here for 2013.'}\n",
        "{'score': 65.0, 'sentence': 'This is the 2014 unit, call here for 2013.'}\n",
        "\n",
        "BEST SENTENCE:\n",
        "{'score': 185.0, 'sentence': 'This is the 2014 form, figure here for 2013.'}\n",
        "\n",
        "\n",
        "[{'ignore': False, 'word_original': 'The', 'word_lower': 'the', 'pos': 'DT', 'word_new': 'The'}, {'ignore': False, 'word_original': 'Goal', 'word_lower': 'goal', 'pos': 'NN', 'word_new': 'Goal'}, {'ignore': False, 'word_original': 'Spend', 'word_lower': 'spend', 'pos': 'VB', 'word_new': 'Spend'}, {'ignore': False, 'word_original': 'the', 'word_lower': 'the', 'pos': 'DT', 'word_new': 'the'}, {'ignore': False, 'word_original': 'month', 'word_lower': 'month', 'pos': 'NN', 'word_new': 'month'}, {'ignore': False, 'word_original': 'of', 'word_lower': 'of', 'pos': 'IN', 'word_new': 'of'}, {'ignore': False, 'word_original': 'November', 'word_lower': 'november', 'pos': 'NNP', 'word_new': 'November'}, {'ignore': False, 'word_original': 'writing', 'word_lower': 'writing', 'pos': 'VBG', 'word_new': 'writing'}, {'ignore': False, 'word_original': 'code', 'word_lower': 'code', 'pos': 'NN', 'word_new': 'code'}, {'ignore': False, 'word_original': 'that', 'word_lower': 'that', 'pos': 'IN', 'word_new': 'that'}, {'ignore': False, 'word_original': 'generates', 'word_lower': 'generates', 'pos': 'VBZ', 'word_new': 'generates'}, {'ignore': False, 'word_original': 'a', 'word_lower': 'a', 'pos': 'DT', 'word_new': 'a'}, {'ignore': False, 'word_original': 'novel', 'word_lower': 'novel', 'pos': 'NN', 'word_new': 'novel'}, {'ignore': False, 'word_original': 'of', 'word_lower': 'of', 'pos': 'IN', 'word_new': 'of'}, {'ignore': False, 'word_original': '50', 'word_lower': '50', 'pos': 'NN', 'word_new': '50'}, {'ignore': False, 'word_original': 'k', 'word_lower': 'k', 'pos': 'NN', 'word_new': 'k'}, {'ignore': False, 'word_original': '+', 'word_lower': '+', 'pos': 'SYM', 'word_new': '+'}, {'ignore': False, 'word_original': 'words', 'word_lower': 'words', 'pos': 'NNS', 'word_new': 'words'}, {'ignore': False, 'word_original': 'The', 'word_lower': 'the', 'pos': 'DT', 'word_new': 'The'}, {'ignore': False, 'word_original': 'Rules', 'word_lower': 'rules', 'pos': 'NNP', 'word_new': 'Rules'}, {'ignore': False, 'word_original': 'The', 'word_lower': 'the', 'pos': 'DT', 'word_new': 'The'}, {'ignore': False, 'word_original': 'only', 'word_lower': 'only', 'pos': 'RB', 'word_new': 'only'}, {'ignore': False, 'word_original': 'rule', 'word_lower': 'rule', 'pos': 'NN', 'word_new': 'rule'}, {'ignore': False, 'word_original': 'is', 'word_lower': 'is', 'pos': 'VBZ', 'word_new': 'is'}, {'ignore': False, 'word_original': 'that', 'word_lower': 'that', 'pos': 'IN', 'word_new': 'that'}, {'ignore': False, 'word_original': 'you', 'word_lower': 'you', 'pos': 'PRP', 'word_new': 'you'}, {'ignore': False, 'word_original': 'share', 'word_lower': 'share', 'pos': 'NN', 'word_new': 'share'}, {'ignore': False, 'word_original': 'at', 'word_lower': 'at', 'pos': 'IN', 'word_new': 'at'}, {'ignore': False, 'word_original': 'least', 'word_lower': 'least', 'pos': 'JJS', 'word_new': 'least'}, {'ignore': False, 'word_original': 'one', 'word_lower': 'one', 'pos': 'CD', 'word_new': 'one'}, {'ignore': False, 'word_original': 'novel', 'word_lower': 'novel', 'pos': 'NN', 'word_new': 'novel'}, {'ignore': False, 'word_original': 'and', 'word_lower': 'and', 'pos': 'CC', 'word_new': 'and'}, {'ignore': False, 'word_original': 'also', 'word_lower': 'also', 'pos': 'RB', 'word_new': 'also'}, {'ignore': False, 'word_original': 'your', 'word_lower': 'your', 'pos': 'PRP$', 'word_new': 'your'}, {'ignore': False, 'word_original': 'source', 'word_lower': 'source', 'pos': 'NN', 'word_new': 'source'}, {'ignore': False, 'word_original': 'code', 'word_lower': 'code', 'pos': 'NN', 'word_new': 'code'}, {'ignore': False, 'word_original': 'at', 'word_lower': 'at', 'pos': 'IN', 'word_new': 'at'}, {'ignore': False, 'word_original': 'the', 'word_lower': 'the', 'pos': 'DT', 'word_new': 'the'}, {'ignore': False, 'word_original': 'end', 'word_lower': 'end', 'pos': 'NN', 'word_new': 'end'}, {'ignore': False, 'word_original': '.', 'word_lower': '.', 'pos': '.', 'word_new': '.'}]\n",
        "{'score': 849.074074074074, 'sentence': 'The Cognition\\nConsume the quantity of Amount creating code that creates a communication of 50kilobyte+ components \\n\\nThe Concept\\n\\nThe only construct is that you contribution at least one creation and besides your cause codification at the conclusion.'}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "{'score': 602.4074074074074, 'sentence': 'The Part\\nPass the period of Amount publishing abstraction that produces a product of 50potassium+ paroles \\n\\nThe Pattern\\n\\nThe only principle is that you possession at least one production and besides your person writing at the place.'}\n",
        "{'score': 345.7407407407407, 'sentence': 'The Score\\nSpend the period of Amount saving abstraction that sires an unit of 50signaling+ scriptures \\n\\nThe Substance\\n\\nThe only state is that you stock at least one production and besides your seed writing at the someone.'}\n",
        "{'score': 291.2962962962963, 'sentence': 'The Destination\\nDeplete the period of Amount drawing abstraction that yields an unit of 50drug+ disputes \\n\\nThe Dominion\\n\\nThe only device is that you distribution at least one production and besides your document writing at the destruction.'}\n",
        "{'score': 279.0740740740741, 'sentence': 'The Act\\nDeplete the measure of Month marking abstraction that mothers an unit of 50matter+ messages \\n\\nThe Message\\n\\nThe only state is that you machine at least one production and besides your maker writing at the material.'}\n",
        "{'score': 235.74074074074073, 'sentence': 'The Region\\nDeplete the period of Amount writing abstraction that returns an unit of 50relation+ relations \\n\\nThe Rule\\n\\nThe only regulation is that you event at least one production and besides your root communication at the role.'}\n",
        "{'score': 202.4074074074074, 'sentence': 'The Location\\nDeplete the period of Amount lining abstraction that yields an unit of 50letter+ lyrics \\n\\nThe Law\\n\\nThe only state is that you event at least one production and likewise your germ writing at the last.'}\n",
        "{'score': 170.18518518518516, 'sentence': 'The Terminal\\nTransfer the period of Amount taping abstraction that yields an unit of 50green+ texts \\n\\nThe Concept\\n\\nThe only state is that you try at least one production and too your germ writing at the textile.'}\n",
        "{'score': 147.96296296296296, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that yields an unit of 50thou+ evidences \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your germ writing at the ending.'}\n",
        "{'score': 147.96296296296296, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that generates an unit of 50g+ evidences \\n\\nThe Generalization\\n\\nThe only generality is that you event at least one production and besides your germ writing at the jock.'}\n",
        "{'score': 144.62962962962962, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that begets a boogreen of 50k+ bibles \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your beginning writing at the being.'}\n",
        "{'score': 137.96296296296296, 'sentence': 'The Finish\\nDeplete the period of Amount taping abstraction that fathers a fiction of 50green+ evidences \\n\\nThe Formula\\n\\nThe only state is that you event at least one production and besides your facility writing at the fabric.'}\n",
        "{'score': 137.96296296296296, 'sentence': 'The Unit\\nDeplete the period of Amount taping abstraction that yields an entity of 50yard+ units \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your germ writing at the ending.'}\n",
        "{'score': 130.1851851851852, 'sentence': 'The Knowledge\\nDeplete the period of Nov taping abstraction that yields a novel of 50number+ news \\n\\nThe Normal\\n\\nThe only state is that you event at least one production and besides your germ writing at the ending.'}\n",
        "{'score': 125.74074074074073, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that yields an unit of 50green+ words \\n\\nThe Concept\\n\\nThe only state is that you wedge at least one production and besides your work writing at the ending.'}\n",
        "{'score': 121.29629629629629, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that yields an unit of 50thousand+ evidences \\n\\nThe Thought\\n\\nThe only state is that you event at least one production and besides your thing writing at the ending.'}\n",
        "{'score': 116.85185185185185, 'sentence': 'The Act\\nDeplete the period of Amount changing abstraction that yields an unit of 50green+ evidences \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your germ writing at the ending.'}\n",
        "{'score': 116.85185185185185, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that yields an unit of 50green+ evidences \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your germ writing at the ending.'}\n",
        "{'score': 115.74074074074073, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that yields an unit of 50green+ evidences \\n\\nThe Concept\\n\\nThe only state is that you share at least one production and besides your germ writing at the ending.'}\n",
        "{'score': 114.62962962962962, 'sentence': 'The Goal\\nGive the period of Amount taping abstraction that gets an unit of 50green+ evidences \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your germ writing at the ending.'}\n",
        "{'score': 113.5185185185185, 'sentence': 'The Whole\\nDeplete the period of Amount holding abstraction that yields an unit of 50green+ evidences \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your germ writing at the happening.'}\n",
        "{'score': 110.18518518518518, 'sentence': 'The Act\\nDeplete the period of Amount taping abstraction that yields a volume of 50green+ evidences \\n\\nThe Concept\\n\\nThe only state is that you event at least one production and besides your germ writing at the ending.'}\n",
        "\n",
        "BEST SENTENCE:\n",
        "{'score': 849.074074074074, 'sentence': 'The Cognition\\nConsume the quantity of Amount creating code that creates a communication of 50kilobyte+ components \\n\\nThe Concept\\n\\nThe only construct is that you contribution at least one creation and besides your cause codification at the conclusion.'}\n",
        "\n",
        "\n",
        "[{'ignore': False, 'word_original': 'The', 'word_lower': 'the', 'pos': 'DT', 'word_new': 'The'}, {'ignore': False, 'word_original': 'source', 'word_lower': 'source', 'pos': 'NN', 'word_new': 'source'}, {'ignore': False, 'word_original': 'code', 'word_lower': 'code', 'pos': 'NN', 'word_new': 'code'}, {'ignore': False, 'word_original': 'does', 'word_lower': 'does', 'pos': 'VBZ', 'word_new': 'does'}, {'ignore': False, 'word_original': 'not', 'word_lower': 'not', 'pos': 'RB', 'word_new': 'not'}, {'ignore': False, 'word_original': 'have', 'word_lower': 'have', 'pos': 'VBP', 'word_new': 'have'}, {'ignore': False, 'word_original': 'to', 'word_lower': 'to', 'pos': 'TO', 'word_new': 'to'}, {'ignore': False, 'word_original': 'be', 'word_lower': 'be', 'pos': 'VB', 'word_new': 'be'}, {'ignore': False, 'word_original': 'licensed', 'word_lower': 'licensed', 'pos': 'VBN', 'word_new': 'licensed'}, {'ignore': False, 'word_original': 'in', 'word_lower': 'in', 'pos': 'IN', 'word_new': 'in'}, {'ignore': False, 'word_original': 'a', 'word_lower': 'a', 'pos': 'DT', 'word_new': 'a'}, {'ignore': False, 'word_original': 'particular', 'word_lower': 'particular', 'pos': 'JJ', 'word_new': 'particular'}, {'ignore': False, 'word_original': 'way', 'word_lower': 'way', 'pos': 'NN', 'word_new': 'way'}, {'ignore': False, 'word_original': ',', 'word_lower': ',', 'pos': ',', 'word_new': ','}, {'ignore': False, 'word_original': 'so', 'word_lower': 'so', 'pos': 'RB', 'word_new': 'so'}, {'ignore': False, 'word_original': 'long', 'word_lower': 'long', 'pos': 'JJ', 'word_new': 'long'}, {'ignore': False, 'word_original': 'as', 'word_lower': 'as', 'pos': 'IN', 'word_new': 'as'}, {'ignore': False, 'word_original': 'you', 'word_lower': 'you', 'pos': 'PRP', 'word_new': 'you'}, {'ignore': False, 'word_original': 'share', 'word_lower': 'share', 'pos': 'NN', 'word_new': 'share'}, {'ignore': False, 'word_original': 'it', 'word_lower': 'it', 'pos': 'PRP', 'word_new': 'it'}, {'ignore': False, 'word_original': '.', 'word_lower': '.', 'pos': '.', 'word_new': '.'}]\n",
        "{'score': 1170.0, 'sentence': 'The somebody communication does not have to be certified in a special selection, so recollective as you certificate it.'}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
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        "{'score': 80.0, 'sentence': 'The communication itself does not demand to be on github, either.'}\n",
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        "{'score': 645.0, 'sentence': \"Besides be barren to inform on other beings' beginnings.\"}\n",
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        "{'score': 75.0, 'sentence': \"Besides sense liberal to note on other players' nets.\"}\n",
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        "{'score': 40.0, 'sentence': \"Besides sense liberal to inform on other players' occurrents.\"}\n",
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        "{'score': 40.0, 'sentence': \"Besides think liberal to inform on other players' occurrents.\"}\n",
        "{'score': 40.0, 'sentence': \"Besides sense liberal to verbalize on other players' occurrents.\"}\n",
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        "{'score': 144.23076923076923, 'sentence': \"Qualities\\n\\nThere's a candid commercialism where you can calculate powers (collections, capitals, entities, noeses, etc).\"}"
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        "{'score': 206.8181818181818, 'sentence': 'You might convey to curb out capital, a cause of public cognition contents of conceptions: creatures, solids, constituents, quantities, countries, etc.'}"
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        "{'score': 89.54545454545453, 'sentence': 'You might desire to decoration out part, a deposit of public domain databases of deeds: animals, solids, descents, amounts, districts, etc.'}\n",
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        "{'score': 44.848484848484844, 'sentence': 'You might lack to decoration out part, a location of public land listings of occurrents: animals, solids, lines, amounts, locations, etc.'}\n",
        "{'score': 43.030303030303024, 'sentence': 'You might bespeak to blemish out part, a being of public land listings of occurrents: beings, solids, bloods, businesses, locations, etc.'}\n",
        "{'score': 32.12121212121212, 'sentence': 'You might need to knowledge out part, a friend of public land listings of occurrents: animals, nutrients, names, amounts, nations, etc.'}\n",
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        "{'score': 23.030303030303028, 'sentence': 'You might want to work out part, a friend of public world listings of wonders: animals, solids, authorisations, amounts, locations, etc.'}\n",
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        "{'score': 17.424242424242422, 'sentence': 'You might convey to checkout out part, a chamber of public land listings of occurrents: animals, solids, authorisations, amounts, locations, etc.'}\n",
        "{'score': 16.666666666666664, 'sentence': 'You might convey to decoration out thing, a friend of public land listings of thoughts: animals, solids, authorisations, amounts, locations, etc.'}\n",
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        "\n",
        "\n",
        "[{'ignore': False, 'word_original': 'Have', 'word_lower': 'have', 'pos': 'VBP', 'word_new': 'Have'}, {'ignore': False, 'word_original': 'fun', 'word_lower': 'fun', 'pos': 'NN', 'word_new': 'fun'}, {'ignore': False, 'word_original': 'with', 'word_lower': 'with', 'pos': 'IN', 'word_new': 'with'}, {'ignore': False, 'word_original': 'this', 'word_lower': 'this', 'pos': 'DT', 'word_new': 'this'}, {'ignore': False, 'word_original': '!', 'word_lower': '!', 'pos': '.', 'word_new': '!'}]\n",
        "{'score': 50.0, 'sentence': 'Have humor with this!\\n\\n'}\n",
        "{'score': 50.0, 'sentence': 'Have wit with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have deed with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have fun with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have content with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have message with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have play with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have recreation with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have sport with this!\\n\\n'}\n",
        "{'score': 20.0, 'sentence': 'Have trait with this!\\n\\n'}\n",
        "\n",
        "BEST SENTENCE:\n",
        "{'score': 50.0, 'sentence': 'Have humor with this!\\n\\n'}\n",
        "\n",
        "\n",
        "\n",
        "Cause Communication Quantity Entity - consumed on a content I tweeted on a caprice. This is the 2014 form, figure here for 2013. The Cognition\n",
        "Consume the quantity of Amount creating code that creates a communication of 50kilobyte+ components \n",
        "\n",
        "The Concept\n",
        "\n",
        "The only construct is that you contribution at least one creation and besides your cause codification at the conclusion. The somebody communication does not have to be certified in a special selection, so recollective as you certificate it. The communication itself does not demand to be on github, either. I'm just having this repo as a home to handle the harmony. The \"book\" is been however you be. It could be 50,000 continuances of the component \"cry\". It could literally clutch a random communication from Cognition Gutenberg. It doesn't difficulty, as tenacious as it's 50drug+ disputes. Delight decide to deed document. I'm not comparing to constabulary it, as finally it's on your coil if you convey to just content/paste a Stephen Competition communication or whatever, but the most utilitarian/interesting enforcements are consorting to be conceptions that don't cause causes. This susceptibility startles at 12:01am GMT on Amount 1st and sections at 12:01am UT Space 1st. How to Participate\n",
        "\n",
        "Part a person on this repo and proclaim your purport to act. You may continually communicate the commercialism as you cognition over the course of the quantity. Knowing destitute to deed dev diaries, distribution dealing, etc. Besides be barren to inform on other beings' beginnings. Qualities\n",
        "\n",
        "There's a candid commercialism where you can calculate powers (collections, capitals, entities, noeses, etc). There are already a quantity of qualities on the honest-to-god relations cord for the 2013 creation. You might convey to curb out capital, a cause of public cognition contents of conceptions: creatures, solids, constituents, quantities, countries, etc. That's It\n",
        "\n",
        "So yeah. Have humor with this!\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def build_latex(book):\n",
      "    latex = ''\n",
      "    \n",
      "    for part in book[\"parts\"]:\n",
      "        latex+=ur'\\part*{' + part[\"title\"] + \"}\\n\\n\"\n",
      "        for chapter in part[\"chapters\"]:\n",
      "            latex+=ur'\\chapter*{' + chapter[\"title\"] + \"}\\n\\n\" + chapter[\"body\"] + \"\\n\\n\"\n",
      "    latex += ur'\\end{document}'\n",
      "    print latex\n",
      "    return latex\n",
      "\n",
      "from tex import latex2pdf \n",
      "\n",
      "\"\"\"\n",
      "f = open(\"alice-in-wonderland.txt\",\"r\")\n",
      "book_title = \"\"\n",
      "book_author = \"\"\n",
      "chapters = []\n",
      "inside_a_chapter = False\n",
      "for line in f:\n",
      "    line = line.strip()\n",
      "    if line[:27]==\"End of Project Gutenberg's \":\n",
      "        chapters.append(chapter)\n",
      "        break\n",
      "    elif line[0:8] == 'CHAPTER ':\n",
      "        if inside_a_chapter: \n",
      "            chapters.append(chapter)\n",
      "        chapter = {\"title\": line, \"body\": \"\"}\n",
      "    else:\n",
      "        chapter[\"body\"] += line\n",
      "\n",
      "alice_in_curiosity_country = {\"title\": \"Alice's Adventures in Curiosity Country\", \"chapters\": chapters}\n",
      "\"\"\"\n",
      "\n",
      "\n",
      "def alice_rmx(text):\n",
      "    rmx = algo(text,\n",
      "              alliteration_only = True, \n",
      "              number_of_examples = 1, \n",
      "              split_by = \"punctuation\",\n",
      "              force_phoneme = Consonants, # Consonants, Vowels, [], [\"K\"]\n",
      "              word_additions = [],\n",
      "              hyper_profanity = False,\n",
      "              flatten = False, \n",
      "              normalize = False, \n",
      "              try_to_avoid_dups = True,\n",
      "              debug = False)\n",
      "    linguistic_nectar = \"\".join(rmx)\n",
      "    return linguistic_nectar"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"gettysburg.txt\",\"r\")\n",
      "gettysburg_body = f.read()\n",
      "gettysburg_body = gettysburg_body.replace(\"\u2014\",\"--\")\n",
      "gettysburg_body = \"itinerary excellence patterns.\"\n",
      "f.close()\n",
      "\n",
      "\n",
      "def getty_rmx(text):\n",
      "    rmx = algo(text,\n",
      "              alliteration_only = True, \n",
      "              number_of_examples = 1, \n",
      "              split_by = \"sentences\",\n",
      "              force_phoneme = [], # Consonants, Vowels, [], [\"K\"]\n",
      "              word_additions = [],\n",
      "              hyper_profanity = False,\n",
      "              flatten = False, \n",
      "              normalize = False, \n",
      "              try_to_avoid_dups = True,\n",
      "              debug = True)\n",
      "    linguistic_nectar = \" \".join(rmx)\n",
      "    return linguistic_nectar\n",
      "\n",
      "gettysburg_address = {\n",
      "    \"title\": \"The Gettysburg Address\", \n",
      "    \"chapters\": [{\n",
      "        \"title\": getty_rmx(\"The Gettysburg Address\"), \n",
      "        \"body\" : getty_rmx(gettysburg_body)}\n",
      "    ]\n",
      "}\n",
      "\n",
      "print gettysburg_address['chapters'][0]['body']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "    \n",
      "    \n",
      "def censor(w):\n",
      "    x = w[0]\n",
      "    for i in range(len(w)-1):\n",
      "        x += random.choice([\"#\",\"@\",\"$\",\"%\",\"&\",\"*\",\"1\"])\n",
      "    x += w[-1]\n",
      "    return x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import inspect\n",
      "source = inspect.getsourcelines(alice_rmx)[0]\n",
      "code = \"\".join(map(lambda x: str(x), source))\n",
      "print code\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "book = {\"parts\": [\n",
      "       #alice_in_curiosity_country,\n",
      "        gettysburg_address\n",
      "]}\n",
      "latex = build_latex(book)\n",
      "f = open(\"book4.tex\",\"w\")\n",
      "f.write(latex)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from tex import latex2pdf \n",
      "pdf = latex2pdf(latex) \n",
      "f = open(\"book.pdf\", 'w+')\n",
      "f.write(pdf)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}