def predict(self, Xnew):
        """Prediction on the new data

        Parameters
        ----------
        Xnew : array_like, shape = (n_samples, n_features)
            The test data.

        Returns
        -------
        mean : array_like, shape = (n_samples, output.dim)
            Posterior mean at the location of Xnew

        var : array_like, shape = (n_samples, 1)
            Posterior variance at the location of Xnew
        """
        Xnew_warped = self.transform_data(Xnew, test_data=True)
        mean, var = super(InputWarpedGP, self).predict(Xnew_warped, kern=self.kernel, full_cov=False)
        return mean, var
def predict ( self , Xnew ) : Xnew_warped = self . transform_data ( Xnew , test_data = True ) mean , var = super ( InputWarpedGP , self ) . predict ( Xnew_warped , kern = self . kernel , full_cov = False ) return mean , var
Prediction on the new data

        Parameters
        ----------
        Xnew : array_like, shape = (n_samples, n_features)
            The test data.

        Returns
        -------
        mean : array_like, shape = (n_samples, output.dim)
            Posterior mean at the location of Xnew

        var : array_like, shape = (n_samples, 1)
            Posterior variance at the location of Xnew