(self, y)
Set the output data. In case normalize_Y=True, the appropriate output transform is updated. It is then
        applied on the data before setting the data of the wrapped model.
Set the output data. In case normalize_Y=True, the appropriate output transform is updated. It is then
        applied on the data before setting the data of the wrapped model.
Set the output data . In case normalize_Y = True the appropriate output transform is updated . It is then applied on the data before setting the data of the wrapped model .
def Y(self, y):
        """
        Set the output data. In case normalize_Y=True, the appropriate output transform is updated. It is then
        applied on the data before setting the data of the wrapped model.
        """
        value = y.value if isinstance(y, DataHolder) else y
        if self.normalize_output:
            self.output_transform.assign(~LinearTransform(value.std(axis=0), value.mean(axis=0)))
        self.wrapped.Y = self.output_transform.forward(value)
def Y ( self , y ) : value = y . value if isinstance ( y , DataHolder ) else y if self . normalize_output : self . output_transform . assign ( ~ LinearTransform ( value . std ( axis = 0 ) , value . mean ( axis = 0 ) ) ) self . wrapped . Y = self . output_transform . forward ( value )