linalg.svd(a, full_matrices=True, compute_uv=True, hermitian=False)
Examples
>>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6) >>> b = np.random.randn(2, 7, 8, 3) + 1j*np.random.randn(2, 7, 8, 3)
Reconstruction based on full SVD, 2D case:
>>> u, s, vh = np.linalg.svd(a, full_matrices=True) >>> u.shape, s.shape, vh.shape ((9, 9), (6,), (6, 6)) >>> np.allclose(a, np.dot(u[:, :6] * s, vh)) True >>> smat = np.zeros((9, 6), dtype=complex) >>> smat[:6, :6] = np.diag(s) >>> np.allclose(a, np.dot(u, np.dot(smat, vh))) True
参考：https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html