def learn_topics(texts, topicnum):
    print('Vectorizing...')
    CVzer = CountVectorizer(max_features=10000, lowercase=True)
    doc_vcnts = CVzer.fit_transform(texts)
    vocabulary = CVzer.get_feature_names()
    print('LDA')
    lda_model = LDA(topicnum, n_iter=4000, refresh=500)
    doc_topic = lda_model.fit_transform(doc_vcnts)
    topic_word = lda_model.topic_word_
    return (doc_topic, topic_word, vocabulary)
def learn_topics(texts, topicnum):
    print('Vectorizing...')
    CVzer = CountVectorizer(max_features=10000, lowercase=True)
    doc_vcnts = CVzer.fit_transform(texts)
    vocabulary = CVzer.get_feature_names()
    print('LDA')
    lda_model = LDA(topicnum, n_iter=4000, refresh=500)
    doc_topic = lda_model.fit_transform(doc_vcnts)
    topic_word = lda_model.topic_word_
    return (doc_topic, topic_word, vocabulary)