困惑度计算主题数量代码

以下是在Python中计算主题数量的困惑度的代码示例：
from gensim.models import CoherenceModel
from gensim.corpora import Dictionary
from gensim.models.ldamodel import LdaModel
from nltk.corpus import stopwords

# Load data
data = [["topic", "modeling", "is", "a", "useful", "technique"],
["it", "can", "help", "us", "to", "understand", "large", "text", "corpora"],
["there", "are", "many", "algorithms", "that", "can", "be", "used", "for", "topic", "modeling"]]

# Create dictionary and corpus
id2word = Dictionary(data)
texts = data
corpus = [id2word.doc2bow(text) for text in texts]

# Create list of stopwords
stop_words = stopwords.words('english')

# Build LDA model with different number of topics and compute coherence score
for num_topics in range(2, 11):
lda_model = LdaModel(corpus=corpus, id2word=id2word, num_topics=num_topics, passes=10, alpha='auto', eta='auto')
coherence_model_lda = CoherenceModel(model=lda_model, texts=texts, dictionary=id2word, coherence='c_v', stopwords=stop_words)
coherence_lda = coherence_model_lda.get_coherence()
print('Number of Topics =', num_topics, 'Coherence Score =', coherence_lda)
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在这个示例中，我们使用了Gensim库来创建字典和语料库，并使用NLTK库中的停用词列表。我们使用循环来计算不同主题数量下的困惑度，并选择困惑度最低的主题数量作为最终的主题数量。