LDA(Latent Dirichlet allocation)模型是一種常用而用途廣泛地概率主題模型。其實現一般通過Variational inference和Gibbs Samping實現。作者在提出LDA模型時給出了其變分推理的C源碼（后續貼出C++改編的類），這里貼出基于Python的第三方模塊改寫的LDA類及實現。

#coding:utf-8import numpy as npimport ldaimport lda.datasetsimport jiebaimport codecsclass LDA_v20161130():  def __init__(self, topics=2):    self.n_topic = topics    self.corpus = None    self.vocab = None    self.ppCountMatrix = None    self.stop_words = [u'，', u'。', u'、', u'（', u'）', u'·', u'！', u' ', u'：', u'“', u'”', u'/n']    self.model = None  def loadCorpusFromFile(self, fn):    # 中文分詞    f = open(fn, 'r')    text = f.readlines()    text = r' '.join(text)    seg_generator = jieba.cut(text)    seg_list = [i for i in seg_generator if i not in self.stop_words]    seg_list = r' '.join(seg_list)    # 切割統計所有出現的詞納入詞典    seglist = seg_list.split(" ")    self.vocab = []    for word in seglist:      if (word != u' ' and word not in self.vocab):        self.vocab.append(word)    CountMatrix = []    f.seek(0, 0)    # 統計每個文檔中出現的詞頻    for line in f:      # 置零      count = np.zeros(len(self.vocab),dtype=np.int)      text = line.strip()      # 但還是要先分詞      seg_generator = jieba.cut(text)      seg_list = [i for i in seg_generator if i not in self.stop_words]      seg_list = r' '.join(seg_list)      seglist = seg_list.split(" ")      # 查詢詞典中的詞出現的詞頻      for word in seglist:        if word in self.vocab:          count[self.vocab.index(word)] += 1      CountMatrix.append(count)    f.close()    #self.ppCountMatrix = (len(CountMatrix), len(self.vocab))    self.ppCountMatrix = np.array(CountMatrix)    print "load corpus from %s success!"%fn  def setStopWords(self, word_list):    self.stop_words = word_list  def fitModel(self, n_iter = 1500, _alpha = 0.1, _eta = 0.01):    self.model = lda.LDA(n_topics=self.n_topic, n_iter=n_iter, alpha=_alpha, eta= _eta, random_state= 1)    self.model.fit(self.ppCountMatrix)  def printTopic_Word(self, n_top_word = 8):    for i, topic_dist in enumerate(self.model.topic_word_):      topic_words = np.array(self.vocab)[np.argsort(topic_dist)][:-(n_top_word + 1):-1]      print "Topic:",i,"/t",      for word in topic_words:        print word,      print  def printDoc_Topic(self):    for i in range(len(self.ppCountMatrix)):      print ("Doc %d:((top topic:%s) topic distribution:%s)"%(i, self.model.doc_topic_[i].argmax(),self.model.doc_topic_[i]))  def printVocabulary(self):    print "vocabulary:"    for word in self.vocab:      print word,    print  def saveVocabulary(self, fn):    f = codecs.open(fn, 'w', 'utf-8')    for word in self.vocab:      f.write("%s/n"%word)    f.close()  def saveTopic_Words(self, fn, n_top_word = -1):    if n_top_word==-1:      n_top_word = len(self.vocab)    f = codecs.open(fn, 'w', 'utf-8')    for i, topic_dist in enumerate(self.model.topic_word_):      topic_words = np.array(self.vocab)[np.argsort(topic_dist)][:-(n_top_word + 1):-1]      f.write( "Topic:%d/t"%i)      for word in topic_words:        f.write("%s "%word)      f.write("/n")    f.close()  def saveDoc_Topic(self, fn):    f = codecs.open(fn, 'w', 'utf-8')    for i in range(len(self.ppCountMatrix)):      f.write("Doc %d:((top topic:%s) topic distribution:%s)/n" % (i, self.model.doc_topic_[i].argmax(), self.model.doc_topic_[i]))    f.close()