LDA模型中 阿尔法和贝塔如果不设置有默认值吗

想问一下编辑LDA代码中需要设置阿尔法和贝塔的参数吗，如果需要的话应该写什么代码，不需要的话他们的默认值是多少呢

• 你可以看下这个问题的回答https://ask.csdn.net/questions/7412132
• 我还给你找了一篇非常好的博客，你可以看看是否有帮助，链接：主题模型LDA的实现
• 除此之外, 这篇博客: 《学术小白的学习之路 09》基于困惑度和余弦相似度确定LDA最优主题数中的 1.2、定义困惑度函数 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  

• import math
  def perplexity(ldamodel, testset, dictionary, size_dictionary, num_topics):
      print('the info of this ldamodel: \n')
      print('num of topics: %s' % num_topics)
      prep = 0.0
      prob_doc_sum = 0.0
      topic_word_list = [] 
      for topic_id in range(num_topics):
          topic_word = ldamodel.show_topic(topic_id, size_dictionary)
          dic = {}
          for word, probability in topic_word:
              dic[word] = probability
          topic_word_list.append(dic)  
      doc_topics_ist = []  
      for doc in testset:
          doc_topics_ist.append(ldamodel.get_document_topics(doc, minimum_probability=0))
      testset_word_num = 0
      for i in range(len(testset)):
          prob_doc = 0.0  # the probablity of the doc
          doc = testset[i]
          doc_word_num = 0  
          for word_id, num in dict(doc).items():
              prob_word = 0.0  
              doc_word_num += num
              word = dictionary[word_id]
              for topic_id in range(num_topics):
                  # cal p(w) : p(w) = sumz(p(z)*p(w|z))
                  prob_topic = doc_topics_ist[i][topic_id][1]
                  prob_topic_word = topic_word_list[topic_id][word]
                  prob_word += prob_topic * prob_topic_word
              prob_doc += math.log(prob_word)  # p(d) = sum(log(p(w)))
          prob_doc_sum += prob_doc
          testset_word_num += doc_word_num
      prep = math.exp(-prob_doc_sum / testset_word_num)  # perplexity = exp(-sum(p(d)/sum(Nd))
      print("模型困惑度的值为 : %s" % prep)
      return prep