def transform(self, X, lenscale=None):
        """
        Apply the random 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.
        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, 2*nbases) where nbases is number of random bases to
            use, given in the constructor.
        """
        N, D = X.shape
        lenscale = self._check_dim(D, lenscale)[:, np.newaxis]

        WX = np.dot(X, self.W / lenscale)

        return np.hstack((np.cos(WX), np.sin(WX))) / np.sqrt(self.n)
def transform ( self , X , lenscale = None ) : N , D = X . shape lenscale = self . _check_dim ( D , lenscale ) [ : , np . newaxis ] WX = np . dot ( X , self . W / lenscale ) return np . hstack ( ( np . cos ( WX ) , np . sin ( WX ) ) ) / np . sqrt ( self . n )
Apply the random 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.
        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, 2*nbases) where nbases is number of random bases to
            use, given in the constructor.