(self, model, domain=None, normalize_Y=False)
:param model: model to be wrapped
        :param domain: (default: None) if supplied, the input transform is configured from the supplied domain to
            :class:`.UnitCube`. If None, the input transform defaults to the identity transform.
        :param normalize_Y: (default: False) enable automatic scaling of output values to zero mean and unit
         variance.
:param model: model to be wrapped
        :param domain: (default: None) if supplied, the input transform is configured from the supplied domain to
            :class:`.UnitCube`. If None, the input transform defaults to the identity transform.
        :param normalize_Y: (default: False) enable automatic scaling of output values to zero mean and unit
         variance.
: param model : model to be wrapped : param domain : ( default : None ) if supplied the input transform is configured from the supplied domain to : class : . UnitCube . If None the input transform defaults to the identity transform . : param normalize_Y : ( default : False ) enable automatic scaling of output values to zero mean and unit variance .
def __init__(self, model, domain=None, normalize_Y=False):
        """
        :param model: model to be wrapped
        :param domain: (default: None) if supplied, the input transform is configured from the supplied domain to
            :class:`.UnitCube`. If None, the input transform defaults to the identity transform.
        :param normalize_Y: (default: False) enable automatic scaling of output values to zero mean and unit
         variance.
        """
        # model sanity checks, slightly stronger conditions than the wrapper
        super(DataScaler, self).__init__(model)

        # Initial configuration of the datascaler
        n_inputs = model.X.shape[1]
        n_outputs = model.Y.shape[1]
        self._input_transform = (domain or UnitCube(n_inputs)) >> UnitCube(n_inputs)
        self._normalize_Y = normalize_Y
        self._output_transform = LinearTransform(np.ones(n_outputs), np.zeros(n_outputs))

        self.X = model.X.value
        self.Y = model.Y.value
def __init__ ( self , model , domain = None , normalize_Y = False ) : # model sanity checks, slightly stronger conditions than the wrapper super ( DataScaler , self ) . __init__ ( model ) # Initial configuration of the datascaler n_inputs = model . X . shape [ 1 ] n_outputs = model . Y . shape [ 1 ] self . _input_transform = ( domain or UnitCube ( n_inputs ) ) >> UnitCube ( n_inputs ) self . _normalize_Y = normalize_Y self . _output_transform = LinearTransform ( np . ones ( n_outputs ) , np . zeros ( n_outputs ) ) self . X = model . X . value self . Y = model . Y . value