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Merge branch 'development' into solutions
[cipher-training.git]
/
language_models.py
diff --git
a/language_models.py
b/language_models.py
index 929746888d036fb54de3f1fbf228e296e0bcd027..59d858868dd5b67d5de9dd848fe26d6b5f1c6391 100644
(file)
--- a/
language_models.py
+++ b/
language_models.py
@@
-120,14
+120,22
@@
def log_probability_of_unknown_word(key, N):
return -log10(N * 10**((len(key) - 2) * 1.4))
Pw = Pdist(datafile('count_1w.txt'), log_probability_of_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)
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(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.
"""
def Pletters(letters):
"""The Naive Bayes log probability of a sequence of letters.
"""
@@
-139,15
+147,21
@@
def Pbigrams(letters):
"""
return sum(P2l[p] for p in ngrams(letters, 2))
"""
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_
distance
_score(text):
+def cosine_
similarity
_score(text):
"""Finds the dissimilarity of a text to English, using the cosine distance
of the frequency distribution.
"""Finds the dissimilarity of a text to English, using the cosine distance
of the frequency distribution.
- >>> cosine_
distance
_score('abcabc') # doctest: +ELLIPSIS
- 0.
370847405
...
+ >>> cosine_
similarity
_score('abcabc') # doctest: +ELLIPSIS
+ 0.
26228882
...
"""
"""
- return norms.cosine_
distance
(english_counts,
+ return norms.cosine_
similarity
(english_counts,
collections.Counter(sanitise(text)))
collections.Counter(sanitise(text)))