Apply the spectral mixture component basis to X.

        Parameters
        ----------
        X: ndarray
            (N, d) array of observations where N is the number of samples, and
            d is the dimensionality of X.
        mean: ndarray, optional
            array of shape (d,) frequency means (one for each dimension of X).
            If not input, this uses the value of the initial mean.
        lenscale: ndarray, optional
            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, 4*nbases) where nbases is number of random bases to
            use, given in the constructor (to nearest larger two power).

def transform(self, X, mean=None, lenscale=None):
        """
        Apply the spectral mixture component basis to X.

        Parameters
        ----------
        X: ndarray
            (N, d) array of observations where N is the number of samples, and
            d is the dimensionality of X.
        mean: ndarray, optional
            array of shape (d,) frequency means (one for each dimension of X).
            If not input, this uses the value of the initial mean.
        lenscale: ndarray, optional
            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, 4*nbases) where nbases is number of random bases to
            use, given in the constructor (to nearest larger two power).
        """
        mean = self._check_dim(X.shape[1], mean, paramind=0)
        lenscale = self._check_dim(X.shape[1], lenscale, paramind=1)

        VX = self._makeVX(X / lenscale)
        mX = X.dot(mean)[:, np.newaxis]
        Phi = np.hstack((np.cos(VX + mX), np.sin(VX + mX),
                         np.cos(VX - mX), np.sin(VX - mX))) / \
            np.sqrt(2 * self.n)

        return Phi