lda困惑度代码

以下是使用Python中gensim库计算LDA模型困惑度的示例代码： ```python from gensim.models.ldamodel import LdaModel from gensim.models.coherencemodel import CoherenceModel from gensim.corpora.dictionary import Dictionary # 加载[语料库](https://geek.csdn.net/educolumn/074f7fa36e7f212c39826682fe9c0168?spm=1055.2569.3001.10083)和词典 corpus = # 从文件或数据库加载[语料库](https://geek.csdn.net/educolumn/074f7fa36e7f212c39826682fe9c0168?spm=1055.2569.3001.10083) dictionary = Dictionary.load('dictionary.dict') # 训练LDA模型 lda_model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=10) # 计算困惑度 perplexity = lda_model.log_perplexity(corpus) print("Perplexity:", perplexity) # 计算一致性得分 coherence_model_lda = CoherenceModel(model=lda_model, texts=corpus, dictionary=dictionary, coherence='c_v') coherence_lda = coherence_model_lda.get_coherence() print("Coherence Score:", coherence_lda) 其中，`corpus`是一个由[文档](https://geek.csdn.net/edu/300981ef51993cfd737d329c71ba77f9?dp_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpZCI6NDQ0MDg2MiwiZXhwIjoxNzA3MzcxOTM4LCJpYXQiOjE3MDY3NjcxMzgsInVzZXJuYW1lIjoid2VpeGluXzY4NjQ1NjQ1In0.RrTYEnMNYPC7AQdoij4SBb0kKEgHoyvF-bZOG2eGQvc&spm=1055.2569.3001.10083)向量组成的[列表](https://geek.csdn.net/educolumn/ba94496e6cfa8630df5d047358ad9719?dp_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpZCI6NDQ0MDg2MiwiZXhwIjoxNzA3MzcxOTM4LCJpYXQiOjE3MDY3NjcxMzgsInVzZXJuYW1lIjoid2VpeGluXzY4NjQ1NjQ1In0.RrTYEnMNYPC7AQdoij4SBb0kKEgHoyvF-bZOG2eGQvc&spm=1055.2569.3001.10083)，每个[文档](https://geek.csdn.net/edu/300981ef51993cfd737d329c71ba77f9?dp_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpZCI6NDQ0MDg2MiwiZXhwIjoxNzA3MzcxOTM4LCJpYXQiOjE3MDY3NjcxMzgsInVzZXJuYW1lIjoid2VpeGluXzY4NjQ1NjQ1In0.RrTYEnMNYPC7AQdoij4SBb0kKEgHoyvF-bZOG2eGQvc&spm=1055.2569.3001.10083)向量由词