(self, X, Y)
return num_outputs_sum
Update the training data of the contained models

        Sets the _needs_setup attribute to True so the contained models are optimized and :meth:`setup` is run again
        right before evaluating the :class:`Acquisition` function.

        Let Q be the the sum of the output dimensions of all contained models, Y should have a minimum of
        Q columns. Only the first Q columns of Y are used while returning the scalar Q

        :param X: input data N x D
        :param Y: output data N x R (R >= Q)
        :return: Q (sum of output dimensions of contained models)
Update the training data of the contained models
Update the training data of the contained models
def set_data(self, X, Y):
        """
        Update the training data of the contained models

        Sets the _needs_setup attribute to True so the contained models are optimized and :meth:`setup` is run again
        right before evaluating the :class:`Acquisition` function.

        Let Q be the the sum of the output dimensions of all contained models, Y should have a minimum of
        Q columns. Only the first Q columns of Y are used while returning the scalar Q

        :param X: input data N x D
        :param Y: output data N x R (R >= Q)
        :return: Q (sum of output dimensions of contained models)
        """
        num_outputs_sum = 0
        for model in self.models:
            num_outputs = model.Y.shape[1]
            Ypart = Y[:, num_outputs_sum:num_outputs_sum + num_outputs]
            num_outputs_sum += num_outputs

            model.X = X
            model.Y = Ypart

        self.highest_parent._needs_setup = True
        return num_outputs_sum
def set_data ( self , X , Y ) : num_outputs_sum = 0 for model in self . models : num_outputs = model . Y . shape [ 1 ] Ypart = Y [ : , num_outputs_sum : num_outputs_sum + num_outputs ] num_outputs_sum += num_outputs model . X = X model . Y = Ypart self . highest_parent . _needs_setup = True return num_outputs_sum