+import string
+import norms
+import random
+import collections
import unicodedata
+import itertools
+from math import log10
+
+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.
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(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('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