parameters: (self, X)
docstring: Transform feature->value dicts to array or sparse matrix.
  
          Named features not encountered during fit or fit_transform will be
          silently ignored.
  
          Parameters
          ----------
          X : Mapping or iterable over Mappings, length = n_samples
              Dict(s) or Mapping(s) from feature names (arbitrary Python
              objects) to feature values (strings or convertible to dtype).
  
          Returns
          -------
          Xa : {array, sparse matrix}
              Feature vectors; always 2-d.
docstring_summary: Transform feature->value dicts to array or sparse matrix.
docstring_tokens: Transform feature - > value dicts to array or sparse matrix .
function: def transform(self, X):
          """Transform feature->value dicts to array or sparse matrix.
  
          Named features not encountered during fit or fit_transform will be
          silently ignored.
  
          Parameters
          ----------
          X : Mapping or iterable over Mappings, length = n_samples
              Dict(s) or Mapping(s) from feature names (arbitrary Python
              objects) to feature values (strings or convertible to dtype).
  
          Returns
          -------
          Xa : {array, sparse matrix}
              Feature vectors; always 2-d.
          """
          if self.sparse:
              return self._transform(X, fitting=False)
  
          else:
              dtype = self.dtype
              vocab = self.vocabulary_
              X = _tosequence(X)
              Xa = np.zeros((len(X), len(vocab)), dtype=dtype)
  
              for i, x in enumerate(X):
                  for f, v in x.items():
                      if isinstance(v, str):
                          f = "%s%s%s" % (f, self.separator, v)
                          v = 1
                      try:
                          Xa[i, vocab[f]] = dtype(v)
                      except KeyError:
                          pass
  
              return Xa
function_tokens: def transform ( self , X ) : if self . sparse : return self . _transform ( X , fitting = False ) else : dtype = self . dtype vocab = self . vocabulary_ X = _tosequence ( X ) Xa = np . zeros ( ( len ( X ) , len ( vocab ) ) , dtype = dtype ) for i , x in enumerate ( X ) : for f , v in x . items ( ) : if isinstance ( v , str ) : f = "%s%s%s" % ( f , self . separator , v ) v = 1 try : Xa [ i , vocab [ f ] ] = dtype ( v ) except KeyError : pass return Xa