def covariance(self):
        r"""Covariance of the prior.

        Returns
        -------
        :class:`numpy.ndarray`
            :math:`v_0 \mathrm K + v_1 \mathrm I`.
        """
        from numpy_sugar.linalg import ddot, sum2diag

        Q0 = self._QS[0][0]
        S0 = self._QS[1]
        return sum2diag(dot(ddot(Q0, self.v0 * S0), Q0.T), self.v1)
def covariance(self):
        r"""Covariance of the prior.

        Returns
        -------
        :class:`numpy.ndarray`
            :math:`v_0 \mathrm K + v_1 \mathrm I`.
        """
        from numpy_sugar.linalg import ddot, sum2diag

        Q0 = self._QS[0][0]
        S0 = self._QS[1]
        return sum2diag(dot(ddot(Q0, self.v0 * S0), Q0.T), self.v1)
def covariance ( self ) : from numpy_sugar . linalg import ddot , sum2diag Q0 = self . _QS [ 0 ] [ 0 ] S0 = self . _QS [ 1 ] return sum2diag ( dot ( ddot ( Q0 , self . v0 * S0 ) , Q0 . T ) , self . v1 )
r"""Covariance of the prior.

        Returns
        -------
        :class:`numpy.ndarray`
            :math:`v_0 \mathrm K + v_1 \mathrm I`.
r Covariance of the prior .