refactors syn0 word vector lookup into method

gensim/models/keyedvectors.py

@@ -45,6 +45,17 @@ def save(self, *args, **kwargs):
         kwargs['ignore'] = kwargs.get('ignore', ['syn0norm'])
         super(KeyedVectors, self).save(*args, **kwargs)
 
+    def word_vec(self, word, use_norm=False):
+        if isinstance(word, ndarray):
+            return word
+        elif word in self.vocab:
+            if use_norm:
+                return self.syn0norm[self.vocab[word].index]
+            else:
+                return self.syn0[self.vocab[word].index]
+        else:
+            raise KeyError("word '%s' not in vocabulary" % word)
+
     def most_similar(self, positive=[], negative=[], topn=10, restrict_vocab=None, indexer=None):
         """
         Find the top-N most similar words. Positive words contribute positively towards the
@@ -87,13 +98,9 @@ def most_similar(self, positive=[], negative=[], topn=10, restrict_vocab=None, i
         # compute the weighted average of all words
         all_words, mean = set(), []
         for word, weight in positive + negative:
-            if isinstance(word, ndarray):
-                mean.append(weight * word)
-            elif word in self.vocab:
-                mean.append(weight * self.syn0norm[self.vocab[word].index])
+            mean.append(weight * self.word_vec(word))
+            if isinstance(word, string_types) and word in self.vocab:
                 all_words.add(self.vocab[word].index)
-            else:
-                raise KeyError("word '%s' not in vocabulary" % word)
         if not mean:
             raise ValueError("cannot compute similarity with no input")
         mean = matutils.unitvec(array(mean).mean(axis=0)).astype(REAL)
@@ -227,17 +234,8 @@ def most_similar_cosmul(self, positive=[], negative=[], topn=10):
 
         all_words = set()
 
-        def word_vec(word):
-            if isinstance(word, ndarray):
-                return word
-            elif word in self.vocab:
-                all_words.add(self.vocab[word].index)
-                return self.syn0norm[self.vocab[word].index]
-            else:
-                raise KeyError("word '%s' not in vocabulary" % word)
-
-        positive = [word_vec(word) for word in positive]
-        negative = [word_vec(word) for word in negative]
+        positive = [self.word_vec(word, use_norm=True) for word in positive]
+        negative = [self.word_vec(word, use_norm=True) for word in negative]
         if not positive:
             raise ValueError("cannot compute similarity with no input")
 
@@ -310,7 +308,7 @@ def doesnt_match(self, words):
         logger.debug("using words %s" % words)
         if not words:
             raise ValueError("cannot select a word from an empty list")
-        vectors = vstack(self.syn0norm[self.vocab[word].index] for word in words).astype(REAL)
+        vectors = vstack(self.word_vec(word) for word in words).astype(REAL)
         mean = matutils.unitvec(vectors.mean(axis=0)).astype(REAL)
         dists = dot(vectors, mean)
         return sorted(zip(dists, words))[0][1]
@@ -340,9 +338,9 @@ def __getitem__(self, words):
         """
         if isinstance(words, string_types):
             # allow calls like trained_model['office'], as a shorthand for trained_model[['office']]
-            return self.syn0[self.vocab[words].index]
+            return self.word_vec(words)
 
-        return vstack([self.syn0[self.vocab[word].index] for word in words])
+        return vstack([self.word_vec(word) for word in words])
 
     def __contains__(self, word):
         return word in self.vocab