handling dm_mean

gensim/models/doc2vec.py

@@ -529,7 +529,7 @@ def repeat(self, word_count):
 
 class Doc2Vec(Word2Vec):
     """Class for training, using and evaluating neural networks described in http://arxiv.org/pdf/1405.4053v2.pdf"""
-    def __init__(self, documents=None,
+    def __init__(self, documents=None,dm_mean=None,
                  dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1,
                  docvecs=None, docvecs_mapfile=None, comment=None, trim_rule=None, **kwargs):
         """
@@ -600,9 +600,8 @@ def __init__(self, documents=None,
 
         """
 
-
         super(Doc2Vec, self).__init__(
-            sg=(1 + dm) % 2,
+            sg=(1 + dm) % 2, dm_mean=dm_mean,
             null_word=dm_concat, **kwargs)
 
         self.dbow_words = dbow_words

gensim/models/word2vec.py

@@ -469,11 +469,11 @@ def __init__(
         self.sorted_vocab = sorted_vocab
         self.batch_words = batch_words
 
-        if "dm_mean" in kwargs:
+        if "dm_mean" in kwargs and kwargs["dm_mean"] is not None:
             self.cbow_mean = int(kwargs["dm_mean"])
         else:
             self.cbow_mean = int(cbow_mean)
-            
+
         if sentences is not None:
             if isinstance(sentences, GeneratorType):
                 raise TypeError("You can't pass a generator as the sentences argument. Try an iterator.")

gensim/test/test_doc2vec.py

@@ -171,15 +171,15 @@ def model_sanity(self, model):
     def test_training(self):
         """Test doc2vec training."""
         corpus = DocsLeeCorpus()
-        model = doc2vec.Doc2Vec(size=100, min_count=2, iter=20, window=8, sample=0.01, workers=1)
+        model = doc2vec.Doc2Vec(size=100, min_count=2, iter=20, workers=1)
         model.build_vocab(corpus)
         self.assertEqual(model.docvecs.doctag_syn0.shape, (300, 100))
         model.train(corpus)
 
         self.model_sanity(model)
 
         # build vocab and train in one step; must be the same as above
-        model2 = doc2vec.Doc2Vec(corpus, size=100, min_count=2, iter=20, window=8, sample=0.01, workers=1)
+        model2 = doc2vec.Doc2Vec(corpus, size=100, min_count=2, iter=20, workers=1)
         self.models_equal(model, model2)
 
     def test_dbow_hs(self):