Using numpy.random.RandomState instead of numpy.random in LDA

CHANGELOG.md

@@ -5,6 +5,7 @@ Changes
 * topics, topn parameters changed to num_topics and num_words in show_topics() and print_topics()(@droudy, #747)
   - In hdpmodel and dtmmodel
   - NOT BACKWARDS COMPATIBLE!
+* Added random_state parameter to LdaState initializer and check_random_state() (@droudy, #113)
 
 0.13.1, 2016-06-22
 
@@ -36,7 +37,9 @@ Changes
 * Tests for wikidump (@jonmcoe, #723)
 * Mallet wrapper sparse format support (@RishabGoel, #664)
 * Doc2vec pre-processing script translated from bash to Python (@andrewjlm, #720)
-* Added random_state parameter to LdaState initializer and check_random_state() (@droudy, #113)
+
+
+0.12.4, 2016-01-29
 
 * Better internal handling of job batching in word2vec (#535)
   - up to 300% speed up when training on very short documents (~tweets)

gensim/models/hdpmodel.py

@@ -586,6 +586,7 @@ def show_topics(self, num_topics=10, num_words=10, log=False, formatted=True):
         if num_topics < 0:
             num_topics = len(self.data)
 
+
         num_topics = min(num_topics, len(self.data))
 
         for k in xrange(num_topics):

gensim/test/test_ldamodel.py

@@ -47,6 +47,7 @@ def testfile():
     # temporary data will be stored to this file
     return os.path.join(tempfile.gettempdir(), 'gensim_models.tst')
 
+
 def testRandomState():
     testcases = [numpy.random.seed(0), None, numpy.random.RandomState(0), 0]
     for testcase in testcases:
@@ -97,7 +98,7 @@ def testAlpha(self):
         expected_shape = (2,)
 
         # should not raise anything
-        self.class_(**kwargs)
+        self.class_(**kwarghs)
 
         kwargs['alpha'] = 'symmetric'
         model = self.class_(**kwargs)