def eigh(self):
        """
        Eigen decomposition of K.

        Returns
        -------
        S : ndarray
            The eigenvalues in ascending order, each repeated according to its
            multiplicity.
        U : ndarray
            Normalized eigenvectors.
        """
        from numpy.linalg import svd

        if self._cache["eig"] is not None:
            return self._cache["eig"]

        U, S = svd(self.L)[:2]
        S *= S
        S += self._epsilon
        self._cache["eig"] = S, U

        return self._cache["eig"]
def eigh(self):
        """
        Eigen decomposition of K.

        Returns
        -------
        S : ndarray
            The eigenvalues in ascending order, each repeated according to its
            multiplicity.
        U : ndarray
            Normalized eigenvectors.
        """
        from numpy.linalg import svd

        if self._cache["eig"] is not None:
            return self._cache["eig"]

        U, S = svd(self.L)[:2]
        S *= S
        S += self._epsilon
        self._cache["eig"] = S, U

        return self._cache["eig"]
def eigh ( self ) : from numpy . linalg import svd if self . _cache [ "eig" ] is not None : return self . _cache [ "eig" ] U , S = svd ( self . L ) [ : 2 ] S *= S S += self . _epsilon self . _cache [ "eig" ] = S , U return self . _cache [ "eig" ]
Eigen decomposition of K.

        Returns
        -------
        S : ndarray
            The eigenvalues in ascending order, each repeated according to its
            multiplicity.
        U : ndarray
            Normalized eigenvectors.
Eigen decomposition of K .