def _cov_eigen(self, X):
        v, W = linalg.eigh(self.cov(X.T))
        # The resulting components are in *ascending* order of eigenvalue, and
        # W contains eigenvectors in its *columns*, so we simply reverse both.
        return v[::-1], W[:, ::-1]
Perform direct computation of covariance matrix eigen{values,vectors}.
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/pca.py/CovEigPCA._cov_eigen