(self, flag)
Enable/disable automated output scaling. If switched off, the output transform becomes the identity transform.
        If enabled, data will be automatically scaled to zero mean and unit variance. When the output normalization is
        switched on or off, the data in the model is automatically adapted.
        
        :param flag: boolean, turn output scaling on or off
Enable/disable automated output scaling. If switched off, the output transform becomes the identity transform.
        If enabled, data will be automatically scaled to zero mean and unit variance. When the output normalization is
        switched on or off, the data in the model is automatically adapted.
        
        :param flag: boolean, turn output scaling on or off
Enable / disable automated output scaling . If switched off the output transform becomes the identity transform . If enabled data will be automatically scaled to zero mean and unit variance . When the output normalization is switched on or off the data in the model is automatically adapted . : param flag : boolean turn output scaling on or off
def normalize_output(self, flag):
        """
        Enable/disable automated output scaling. If switched off, the output transform becomes the identity transform.
        If enabled, data will be automatically scaled to zero mean and unit variance. When the output normalization is
        switched on or off, the data in the model is automatically adapted.
        
        :param flag: boolean, turn output scaling on or off
        """

        self._normalize_Y = flag
        if not flag:
            # Output normalization turned off. Reset transform to identity
            self.output_transform = LinearTransform(np.ones(self.Y.value.shape[1]), np.zeros(self.Y.value.shape[1]))
        else:
            # Output normalization enabled. Trigger scaling.
            self.Y = self.Y.value
def normalize_output ( self , flag ) : self . _normalize_Y = flag if not flag : # Output normalization turned off. Reset transform to identity self . output_transform = LinearTransform ( np . ones ( self . Y . value . shape [ 1 ] ) , np . zeros ( self . Y . value . shape [ 1 ] ) ) else : # Output normalization enabled. Trigger scaling. self . Y = self . Y . value