+++ /dev/null
-"""Language-specific functions, including models of languages based on data of
-its use.
-"""
-
-import string
-import random
-import norms
-import collections
-import unicodedata
-import itertools
-from math import log10
-import os
-
-unaccent_specials = ''.maketrans({"’": "'"})
-
-def letters(text):
- """Remove all non-alphabetic characters from a text
- >>> letters('The Quick')
- 'TheQuick'
- >>> letters('The Quick BROWN fox jumped! over... the (9lazy) DOG')
- 'TheQuickBROWNfoxjumpedoverthelazyDOG'
- """
- return ''.join([c for c in text if c in string.ascii_letters])
-
-def unaccent(text):
- """Remove all accents from letters.
- It does this by converting the unicode string to decomposed compatability
- form, dropping all the combining accents, then re-encoding the bytes.
-
- >>> unaccent('hello')
- 'hello'
- >>> unaccent('HELLO')
- 'HELLO'
- >>> unaccent('héllo')
- 'hello'
- >>> unaccent('héllö')
- 'hello'
- >>> unaccent('HÉLLÖ')
- 'HELLO'
- """
- translated_text = text.translate(unaccent_specials)
- return unicodedata.normalize('NFKD', translated_text).\
- encode('ascii', 'ignore').\
- decode('utf-8')
-
-def sanitise(text):
- """Remove all non-alphabetic characters and convert the text to lowercase
-
- >>> sanitise('The Quick')
- 'thequick'
- >>> sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')
- 'thequickbrownfoxjumpedoverthelazydog'
- >>> sanitise('HÉLLÖ')
- 'hello'
- """
- # sanitised = [c.lower() for c in text if c in string.ascii_letters]
- # return ''.join(sanitised)
- return letters(unaccent(text)).lower()
-
-
-def datafile(name, sep='\t'):
- """Read key,value pairs from file.
- """
- with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), name), 'r') as f:
- for line in f:
- splits = line.split(sep)
- yield [splits[0], int(splits[1])]
-
-english_counts = collections.Counter(dict(datafile('count_1l.txt')))
-normalised_english_counts = norms.normalise(english_counts)
-
-english_bigram_counts = collections.Counter(dict(datafile('count_2l.txt')))
-normalised_english_bigram_counts = norms.normalise(english_bigram_counts)
-
-english_trigram_counts = collections.Counter(dict(datafile('count_3l.txt')))
-normalised_english_trigram_counts = norms.normalise(english_trigram_counts)
-
-with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'words.txt'), 'r') as f:
- keywords = [line.rstrip() for line in f]
-
-
-def weighted_choice(d):
- """Generate random item from a dictionary of item counts
- """
- target = random.uniform(0, sum(d.values()))
- cuml = 0.0
- for (l, p) in d.items():
- cuml += p
- if cuml > target:
- return l
- return None
-
-def random_english_letter():
- """Generate a random letter based on English letter counts
- """
- return weighted_choice(normalised_english_counts)
-
-
-def ngrams(text, n):
- """Returns all n-grams of a text
-
- >>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
- ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
- 'nf', 'fo', 'ox']
- >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
- ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
- 'rown', 'ownf', 'wnfo', 'nfox']
- """
- return [text[i:i+n] for i in range(len(text)-n+1)]
-
-
-class Pdist(dict):
- """A probability distribution estimated from counts in datafile.
- Values are stored and returned as log probabilities.
- """
- def __init__(self, data=[], estimate_of_missing=None):
- data1, data2 = itertools.tee(data)
- self.total = sum([d[1] for d in data1])
- for key, count in data2:
- self[key] = log10(count / self.total)
- self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
- def __missing__(self, key):
- return self.estimate_of_missing(key, self.total)
-
-def log_probability_of_unknown_word(key, N):
- """Estimate the probability of an unknown word.
- """
- return -log10(N * 10**((len(key) - 2) * 1.4))
-
-Pw = Pdist(datafile('count_1w.txt'), log_probability_of_unknown_word)
-Pw_wrong = Pdist(datafile('count_1w.txt'), lambda _k, N: log10(1/N))
-Pl = Pdist(datafile('count_1l.txt'), lambda _k, _N: 0)
-P2l = Pdist(datafile('count_2l.txt'), lambda _k, _N: 0)
-P3l = Pdist(datafile('count_3l.txt'), lambda _k, _N: 0)
-
-def Pwords(words):
- """The Naive Bayes log probability of a sequence of words.
- """
- return sum(Pw[w.lower()] for w in words)
-
-def Pwords_wrong(words):
- """The Naive Bayes log probability of a sequence of words.
- """
- return sum(Pw_wrong[w.lower()] for w in words)
-
-def Pletters(letters):
- """The Naive Bayes log probability of a sequence of letters.
- """
- return sum(Pl[l.lower()] for l in letters)
-
-def Pbigrams(letters):
- """The Naive Bayes log probability of the bigrams formed from a sequence
- of letters.
- """
- return sum(P2l[p] for p in ngrams(letters, 2))
-
-def Ptrigrams(letters):
- """The Naive Bayes log probability of the trigrams formed from a sequence
- of letters.
- """
- return sum(P3l[p] for p in ngrams(letters, 3))
-
-
-def cosine_similarity_score(text):
- """Finds the dissimilarity of a text to English, using the cosine distance
- of the frequency distribution.
-
- >>> cosine_similarity_score('abcabc') # doctest: +ELLIPSIS
- 0.26228882...
- """
- return norms.cosine_similarity(english_counts,
- collections.Counter(sanitise(text)))
-
-
-if __name__ == "__main__":
- import doctest
- doctest.testmod()
projects / cipher-training.git / blobdiff