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 numpy_sugar.linalg import ddot, sum2diag

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

        s = self._cov["scale"]
        d = self._cov["delta"]
        Q = self._cov["QS"][0][0]
        S = self._cov["QS"][1]

        ddot(self.A * self._site.tau, Q, left=True, out=self._NxR)
        B = dot(Q.T, self._NxR, out=self._RxR)
        B *= 1 - d
        sum2diag(B, 1.0 / S / s, out=B)
        self._L_cache = _cho_factor(B)
        return self._L_cache
def L(self):
        r
        from numpy_sugar.linalg import ddot, sum2diag

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

        s = self._cov["scale"]
        d = self._cov["delta"]
        Q = self._cov["QS"][0][0]
        S = self._cov["QS"][1]

        ddot(self.A * self._site.tau, Q, left=True, out=self._NxR)
        B = dot(Q.T, self._NxR, out=self._RxR)
        B *= 1 - d
        sum2diag(B, 1.0 / S / s, out=B)
        self._L_cache = _cho_factor(B)
        return self._L_cache
def L ( self ) : from numpy_sugar . linalg import ddot , sum2diag if self . _L_cache is not None : return self . _L_cache s = self . _cov [ "scale" ] d = self . _cov [ "delta" ] Q = self . _cov [ "QS" ] [ 0 ] [ 0 ] S = self . _cov [ "QS" ] [ 1 ] ddot ( self . A * self . _site . tau , Q , left = True , out = self . _NxR ) B = dot ( Q . T , self . _NxR , out = self . _RxR ) B *= 1 - d sum2diag ( B , 1.0 / S / s , out = B ) self . _L_cache = _cho_factor ( B ) 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 .