通过sklearn使用tf |
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Demo1
TfidfTransformer + CountVectorizer = TfidfVectorizer from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer corpus = [ 'This This is the first document.', 'This This is the second second document.', 'And the third one.', 'Is this the first document?', ] tfidf_model = TfidfVectorizer() tfidf_matrix = tfidf_model.fit_transform(corpus) word_dict = tfidf_model.get_feature_names() print(word_dict) print(tfidf_matrix)['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'] (0, 1) 0.3493402123185688 (0, 2) 0.431504661587479 (0, 6) 0.2856085141790751 (0, 3) 0.3493402123185688 (0, 8) 0.6986804246371376 (1, 5) 0.7717016211057586 (1, 1) 0.24628357422338598 (1, 6) 0.20135295972313796 (1, 3) 0.24628357422338598 (1, 8) 0.49256714844677196 (2, 4) 0.5528053199908667 (2, 7) 0.5528053199908667 (2, 0) 0.5528053199908667 (2, 6) 0.2884767487500274 (3, 1) 0.4387767428592343 (3, 2) 0.5419765697264572 (3, 6) 0.35872873824808993 (3, 3) 0.4387767428592343 (3, 8) 0.4387767428592343 参数设置关于参数： input：string{'filename', 'file', 'content'} |
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