def genlda(textlist,n):
ticks = str(time.time()).replace('.','')[-6:-1]
nn=str(n)
dictionary = corpora.Dictionary(textlist)
corpus = [ dictionary.doc2bow(text) for text in textlist ]
#tfidf = models.TfidfModel(corpus)
#corpus_tfidf = tfidf[corpus]
#print(list(corpus_tfidf))#输出词的tfidf
#print(list(corpus))#输出文本向量空间
#########Run the LDA model for XX topics ###############################
lda =LdaMulticore(corpus=corpus, id2word=dictionary, num_topics=n,passes=100,workers=3)
doc_topic = [a for a in lda[corpus]]
####### write the topics in file topics_result.txt ##############
topics_r = lda.print_topics(num_topics = n, num_words =20)
topic_name = codecs.open('词汇矩阵主题个数'+nn+'时间'+ticks+'.txt','w')
for v in topics_r:
topic_name.write(str(v)+'\n')
lda.save('模型主题个数'+nn+'时间'+ticks)
print('主题数',nn,ticks,lda.log_perplexity(corpus))
f=open('每篇分类主题个数'+nn+'时间'+ticks+'.txt','a+')
k=0
for i in lda.get_document_topics(corpus)[:]:
listj=[]
for j in i:
listj.append(j[1])
bz=listj.index(max(listj))
print(k,i[bz][0],i[bz][1],listj,listj.index(max(listj)),file=f)
k=k+1
