(self, newX, newY)
Update the underlying models of the acquisition function with new data.

        :param newX: samples, size N x D
        :param newY: values obtained by evaluating the objective and constraint functions, size N x R
Update the underlying models of the acquisition function with new data.
Update the underlying models of the acquisition function with new data .
def _update_model_data(self, newX, newY):
        """
        Update the underlying models of the acquisition function with new data.

        :param newX: samples, size N x D
        :param newY: values obtained by evaluating the objective and constraint functions, size N x R
        """
        assert self.acquisition.data[0].shape[1] == newX.shape[-1]
        assert self.acquisition.data[1].shape[1] == newY.shape[-1]
        assert newX.shape[0] == newY.shape[0]
        if newX.size == 0:
            return
        X = np.vstack((self.acquisition.data[0], newX))
        Y = np.vstack((self.acquisition.data[1], newY))
        self.acquisition.set_data(X, Y)
def _update_model_data ( self , newX , newY ) : assert self . acquisition . data [ 0 ] . shape [ 1 ] == newX . shape [ - 1 ] assert self . acquisition . data [ 1 ] . shape [ 1 ] == newY . shape [ - 1 ] assert newX . shape [ 0 ] == newY . shape [ 0 ] if newX . size == 0 : return X = np . vstack ( ( self . acquisition . data [ 0 ] , newX ) ) Y = np . vstack ( ( self . acquisition . data [ 1 ] , newY ) ) self . acquisition . set_data ( X , Y )