Apply the RBF to X.

        Parameters
        ----------
        X: ndarray
            (N, d) array of observations where N is the number of samples, and
            d is the dimensionality of X.
        lenscale: scalar or ndarray, optional
            scalar or array of shape (d,) length scales (one for each dimension
            of X). If not input, this uses the value of the initial length
            scale.

        Returns
        -------
        ndarray:
            of shape (N, D) where D is number of RBF centres.

def transform(self, X, lenscale=None):
        """
        Apply the RBF to X.

        Parameters
        ----------
        X: ndarray
            (N, d) array of observations where N is the number of samples, and
            d is the dimensionality of X.
        lenscale: scalar or ndarray, optional
            scalar or array of shape (d,) length scales (one for each dimension
            of X). If not input, this uses the value of the initial length
            scale.

        Returns
        -------
        ndarray:
            of shape (N, D) where D is number of RBF centres.
        """
        N, d = X.shape
        lenscale = self._check_dim(d, lenscale)

        den = (2 * lenscale**2)
        return np.exp(- cdist(X / den, self.C / den, 'sqeuclidean'))