parameters: (self, data, label=None, field_map=None)
docstring: Initial function.
          Parameters:
          data: NumPy 2D or pandas DataFrame of features data.
          label: one-dimensional array, it presents samples label.
          field_map: one-dimensional array, it presents the features'field respectively.
          This field_map like, [1, 2, 1, 3] means, the first and third features belong to field one, and the second belongs to field two, and so on.
          this parameter only useful for ffm model.
          Note: we only do roughly check, and do detail check in true work function.
docstring_summary: Initial function.
          Parameters:
          data: NumPy 2D or pandas DataFrame of features data.
          label: one-dimensional array, it presents samples label.
          field_map: one-dimensional array, it presents the features'field respectively.
          This field_map like, [1, 2, 1, 3] means, the first and third features belong to field one, and the second belongs to field two, and so on.
          this parameter only useful for ffm model.
          Note: we only do roughly check, and do detail check in true work function.
docstring_tokens: Initial function . Parameters : data : NumPy 2D or pandas DataFrame of features data . label : one - dimensional array it presents samples label . field_map : one - dimensional array it presents the features field respectively . This field_map like [ 1 2 1 3 ] means the first and third features belong to field one and the second belongs to field two and so on . this parameter only useful for ffm model . Note : we only do roughly check and do detail check in true work function .
function: def __init__(self, data, label=None, field_map=None):
          """
          Initial function.
          Parameters:
          data: NumPy 2D or pandas DataFrame of features data.
          label: one-dimensional array, it presents samples label.
          field_map: one-dimensional array, it presents the features'field respectively.
          This field_map like, [1, 2, 1, 3] means, the first and third features belong to field one, and the second belongs to field two, and so on.
          this parameter only useful for ffm model.
          Note: we only do roughly check, and do detail check in true work function.
          """
  
          self.__handle = ctypes.c_void_p()
          if (isinstance(data, ndarray) or isinstance(data, DataFrame)):
              self._init_from_npy2d(data, label, field_map)
          else:
              raise ValueError('Input data must be numpy.ndarray or pandas.DataFrame')
function_tokens: def __init__ ( self , data , label = None , field_map = None ) : self . __handle = ctypes . c_void_p ( ) if ( isinstance ( data , ndarray ) or isinstance ( data , DataFrame ) ) : self . _init_from_npy2d ( data , label , field_map ) else : raise ValueError ( 'Input data must be numpy.ndarray or pandas.DataFrame' )