Retain undocumented WordEmbeddingsKeyedVectors.most_similar(..topn=None)

gensim/models/keyedvectors.py

@@ -511,7 +511,7 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non
             Sequence of (word, similarity).
 
         """
-        if topn < 1:
+        if topn is not None and topn < 1:
             return []
 
         if positive is None:
@@ -553,6 +553,8 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non
 
         limited = self.vectors_norm if restrict_vocab is None else self.vectors_norm[:restrict_vocab]
         dists = dot(limited, mean)
+        if topn is None:
+            return dists
         best = matutils.argsort(dists, topn=topn + len(all_words), reverse=True)
         # ignore (don't return) words from the input
         result = [(self.index2word[sim], float(dists[sim])) for sim in best if sim not in all_words]

gensim/test/test_keyedvectors.py

@@ -103,6 +103,9 @@ def test_most_similar_topn(self):
         self.assertEqual(len(self.vectors.most_similar('war', topn=5)), 5)
         self.assertEqual(len(self.vectors.most_similar('war', topn=10)), 10)
 
+        predicted = self.vectors.most_similar('war', topn=None)
+        self.assertEqual(len(predicted), len(self.vectors.vocab))
+
     def test_relative_cosine_similarity(self):
         """Test relative_cosine_similarity returns expected results with an input of a word pair and topn"""
         wordnet_syn = [