ekernels.py 文件源码

def eKxz(self, Z, Xmu, Xcov):
        """
        Also known as phi_1: <K_{x, Z}>_{q(x)}.
        :param Z: MxD inducing inputs
        :param Xmu: X mean (NxD)
        :param Xcov: NxDxD
        :return: NxM
        """
        # use only active dimensions
        Xcov = self._slice_cov(Xcov)
        Z, Xmu = self._slice(Z, Xmu)
        D = tf.shape(Xmu)[1]
        if self.ARD:
            lengthscales = self.lengthscales
        else:
            lengthscales = tf.zeros((D,), dtype=settings.float_type) + self.lengthscales

        vec = tf.expand_dims(Xmu, 2) - tf.expand_dims(tf.transpose(Z), 0)  # NxDxM
        chols = tf.cholesky(tf.expand_dims(tf.matrix_diag(lengthscales ** 2), 0) + Xcov)
        Lvec = tf.matrix_triangular_solve(chols, vec)
        q = tf.reduce_sum(Lvec ** 2, [1])

        chol_diags = tf.matrix_diag_part(chols)  # N x D
        half_log_dets = tf.reduce_sum(tf.log(chol_diags), 1) - tf.reduce_sum(tf.log(lengthscales))  # N,

        return self.variance * tf.exp(-0.5 * q - tf.expand_dims(half_log_dets, 1))