def L(self):
        r"""Cholesky decomposition of :math:`\mathrm B`.

        .. math::

            \mathrm B = \mathrm Q^{\intercal}\tilde{\mathrm{T}}\mathrm Q
                + \mathrm{S}^{-1}
        """
        from scipy.linalg import cho_factor
        from numpy_sugar.linalg import ddot, sum2diag

        if self._L_cache is not None:
            return self._L_cache

        Q = self._cov["QS"][0][0]
        S = self._cov["QS"][1]
        B = dot(Q.T, ddot(self._site.tau, Q, left=True))
        sum2diag(B, 1.0 / S, out=B)
        self._L_cache = cho_factor(B, lower=True)[0]
        return self._L_cache
def L(self):
        r
        from scipy.linalg import cho_factor
        from numpy_sugar.linalg import ddot, sum2diag

        if self._L_cache is not None:
            return self._L_cache

        Q = self._cov["QS"][0][0]
        S = self._cov["QS"][1]
        B = dot(Q.T, ddot(self._site.tau, Q, left=True))
        sum2diag(B, 1.0 / S, out=B)
        self._L_cache = cho_factor(B, lower=True)[0]
        return self._L_cache
def L ( self ) : from scipy . linalg import cho_factor from numpy_sugar . linalg import ddot , sum2diag if self . _L_cache is not None : return self . _L_cache Q = self . _cov [ "QS" ] [ 0 ] [ 0 ] S = self . _cov [ "QS" ] [ 1 ] B = dot ( Q . T , ddot ( self . _site . tau , Q , left = True ) ) sum2diag ( B , 1.0 / S , out = B ) self . _L_cache = cho_factor ( B , lower = True ) [ 0 ] return self . _L_cache
r"""Cholesky decomposition of :math:`\mathrm B`.

        .. math::

            \mathrm B = \mathrm Q^{\intercal}\tilde{\mathrm{T}}\mathrm Q
                + \mathrm{S}^{-1}
r Cholesky decomposition of : math : \ mathrm B .