Got hillclimbing and simulated annealing searches working

[cipher-tools.git] / language_models.py

diff --git a/language_models.py b/language_models.py
index 0fa6e85dc7f3732e2c36a1c1bc4ead827005023e..da5d2d07fa2003a3bf95a4a6629c1eafd666382b 100644 (file)
--- a/language_models.py
+++ b/language_models.py
@@ -76,20 +76,20 @@ 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 +144,6 @@ 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.