code: def fit_transform(self, raw_documents, y=None):
          
          documents = super(CountVectorizer, self).fit_transform(
              raw_documents=raw_documents, y=y)
          self.n = len(raw_documents)
          m = (self.transform(raw_documents) > 0).astype(int)
          m = m.sum(axis=0).A1
          self.period_ = m
          self.df_ = m / self.n
          return documents
docs: Learn the vocabulary dictionary and 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 : array, [n_samples, n_features]
              Document-term matrix.