6.14.7. scikits.learn.feature_extraction.text.Vectorizer¶
- Vectorizer(analyzer=WordNGramAnalyzer(max_n=1, min_n=1, charset='utf-8',
- stop_words=set(['all', 'six', 'less', 'being', 'indeed', 'over', 'move', 'anyway', 'four', 'not', 'own', 'through', 'yourselves', 'fify', 'where', 'mill', 'only', 'find', 'before', 'one', 'whose', 'system', 'how', 'somewhere', 'with', 'thick', 'show', 'had', 'enough', 'should', 'to', 'must', 'whom',...'amoungst', 'yours', 'their', 'rather', 'without', 'so', 'five', 'the', 'first', 'whereas', 'once']),
- preprocessor=RomanPreprocessor()), max_df=1.0, max_features=None, use_tf=True, use_idf=True)
Convert a collection of raw documents to a matrix
Equivalent to CountVectorizer followed by TfidfTransformer.
Methods
fit fit_transform transform - Vectorizer.fit_transform(raw_documents)¶
Learn the representation and return the vectors.
Parameters : raw_documents: iterable :
an iterable which yields either str, unicode or file objects
Returns : vectors: array, [n_samples, n_features] :
- Vectorizer.transform(raw_documents, copy=True)¶
Return the vectors.
Parameters : raw_documents: iterable :
an iterable which yields either str, unicode or file objects
Returns : vectors: array, [n_samples, n_features] :