Sentences = ['We are using the Bag of Word model', 'Bag of Word model is used for extracting the features.'] vectorizer_count = CountVectorizer() features_text = vectorizer.fit_transform(Sentences).todense() pr
mailbag 安装: pip install mailbag 使用示例: import mailbag with mailbag.MailBag("example.eml") as mb: for msg in mb: print(msg.subject) for attachment in msg.attachments: print(attachment.filename) 说明: mailbag 用于处理和存储邮件附件,提供了方便的方法来访问邮件的附件和其他相关信息。 py附...
doc2bow()中的 bow 是 Bag-of-Words的缩写,代表词袋模型,该模型用来统计评论中的词频。 corpus变量与texts变量相对应。...corpus[0]中的第一个元组(0, 1)代表第一条评论中热好一词的出现的次数是1,第二个元组(1, 1)代表饭出现的次数是1。...# lsi[corpus] 是所有评论对应的向量 index = similarities...
Memoizing (Caching) the Return Values of Functions Recipe 18.6. Implementing a FIFO Container Recipe 18.7. Caching Objects with a FIFO Pruning Strategy Recipe 18.8. Implementing a Bag (Multiset) Collection Type Recipe 18.9. Simulating the Ternary Operator in Python Recipe 18.10. Computing Prime ...