Split each cipher into its own file

[cipher-tools.git] / language_models.py

diff --git a/language_models.py b/language_models.py
index 0fa6e85dc7f3732e2c36a1c1bc4ead827005023e..a6a711f1562d8c70f091165fa15330a825a48559 100644 (file)
--- a/language_models.py
+++ b/language_models.py
@@ -7,51 +7,6 @@ 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'):
@@ -76,20 +31,20 @@ with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'words.txt')
 
 
 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):
@@ -144,12 +99,6 @@ def Pbigrams(letters):
     """
     return sum(P2l[p] for p in ngrams(letters, 2))
 
-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.