parameters: (self, raw_documents, y=None)
return_statement: return self._tfidf.transform(X, copy=False)
docstring: Learn vocabulary and idf, return term-document matrix.
  
          This is equivalent to fit followed by transform, but more efficiently
          implemented.
  
          Parameters
          ----------
          raw_documents : iterable
              an iterable which yields either str, unicode or file objects
  
          Returns
          -------
          X : sparse matrix, [n_samples, n_features]
              Tf-idf-weighted document-term matrix.
docstring_summary: Learn vocabulary and idf, return term-document matrix.
docstring_tokens: Learn vocabulary and idf return term - document matrix .
function: def fit_transform(self, raw_documents, y=None):
          """Learn vocabulary and idf, return term-document matrix.
  
          This is equivalent to fit followed by transform, but more efficiently
          implemented.
  
          Parameters
          ----------
          raw_documents : iterable
              an iterable which yields either str, unicode or file objects
  
          Returns
          -------
          X : sparse matrix, [n_samples, n_features]
              Tf-idf-weighted document-term matrix.
          """
          X = super(TfidfVectorizer, self).fit_transform(raw_documents)
          self._tfidf.fit(X)
          # X is already a transformed view of raw_documents so
          # we set copy to False
          return self._tfidf.transform(X, copy=False)
function_tokens: def fit_transform ( self , raw_documents , y = None ) : X = super ( TfidfVectorizer , self ) . fit_transform ( raw_documents ) self . _tfidf . fit ( X ) # X is already a transformed view of raw_documents so # we set copy to False return self . _tfidf . transform ( X , copy = False )