pca.py 文件源码

def _eig_decomposition(self, A, largest=True):
        n_features = A.shape[0]

        if (largest):
            eig_range = (n_features - self.n_components, n_features - 1)
        else:
            eig_range = (0, self.n_components - 1)

        eigvals, eigvecs = spla.eigh(A, eigvals=eig_range)

        if (largest):
            eigvals = eigvals[::-1]
            eigvecs = sp.fliplr(eigvecs)

        return eigvals, eigvecs