+"""Language-specific functions, including models of languages based on data of
+its use.
+"""
+
import string
-import norms
import random
+import norms
import collections
import unicodedata
import itertools
return ''.join([c for c in text if c in string.ascii_letters])
def unaccent(text):
- """Remove all accents from letters.
+ """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.
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')
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
+ """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)
+ """Generate a random letter based on English letter counts
+ """
+ return weighted_choice(normalised_english_counts)
def ngrams(text, n):
return sum(P2l[p] for p in ngrams(letters, 2))
def Ptrigrams(letters):
- """The Naive Bayes log probability of the bigrams formed from a sequence
+ """The Naive Bayes log probability of the trigrams formed from a sequence
of letters.
"""
return sum(P3l[p] for p in ngrams(letters, 3))