diff options
Diffstat (limited to 'numpy/linalg/tests/test_linalg.py')
| -rw-r--r-- | numpy/linalg/tests/test_linalg.py | 57 |
1 files changed, 31 insertions, 26 deletions
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index b1dbd4c22..17ee40042 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -589,11 +589,12 @@ class TestEigvals(EigvalsCases): class EigCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): def do(self, a, b, tags): - evalues, evectors = linalg.eig(a) - assert_allclose(dot_generalized(a, evectors), - np.asarray(evectors) * np.asarray(evalues)[..., None, :], - rtol=get_rtol(evalues.dtype)) - assert_(consistent_subclass(evectors, a)) + res = linalg.eig(a) + eigenvalues, eigenvectors = res.eigenvalues, res.eigenvectors + assert_allclose(dot_generalized(a, eigenvectors), + np.asarray(eigenvectors) * np.asarray(eigenvalues)[..., None, :], + rtol=get_rtol(eigenvalues.dtype)) + assert_(consistent_subclass(eigenvectors, a)) class TestEig(EigCases): @@ -638,10 +639,11 @@ class SVDBaseTests: @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble]) def test_types(self, dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) - u, s, vh = linalg.svd(x) - assert_equal(u.dtype, dtype) - assert_equal(s.dtype, get_real_dtype(dtype)) - assert_equal(vh.dtype, dtype) + res = linalg.svd(x) + U, S, Vh = res.U, res.S, res.Vh + assert_equal(U.dtype, dtype) + assert_equal(S.dtype, get_real_dtype(dtype)) + assert_equal(Vh.dtype, dtype) s = linalg.svd(x, compute_uv=False, hermitian=self.hermitian) assert_equal(s.dtype, get_real_dtype(dtype)) @@ -844,7 +846,8 @@ class DetCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): def do(self, a, b, tags): d = linalg.det(a) - (s, ld) = linalg.slogdet(a) + res = linalg.slogdet(a) + s, ld = res.sign, res.logabsdet if asarray(a).dtype.type in (single, double): ad = asarray(a).astype(double) else: @@ -1144,7 +1147,8 @@ class TestEighCases(HermitianTestCase, HermitianGeneralizedTestCase): def do(self, a, b, tags): # note that eigenvalue arrays returned by eig must be sorted since # their order isn't guaranteed. - ev, evc = linalg.eigh(a) + res = linalg.eigh(a) + ev, evc = res.eigenvalues, res.eigenvectors evalues, evectors = linalg.eig(a) evalues.sort(axis=-1) assert_almost_equal(ev, evalues) @@ -1632,16 +1636,17 @@ class TestQR: k = min(m, n) # mode == 'complete' - q, r = linalg.qr(a, mode='complete') - assert_(q.dtype == a_dtype) - assert_(r.dtype == a_dtype) - assert_(isinstance(q, a_type)) - assert_(isinstance(r, a_type)) - assert_(q.shape == (m, m)) - assert_(r.shape == (m, n)) - assert_almost_equal(dot(q, r), a) - assert_almost_equal(dot(q.T.conj(), q), np.eye(m)) - assert_almost_equal(np.triu(r), r) + res = linalg.qr(a, mode='complete') + Q, R = res.Q, res.R + assert_(Q.dtype == a_dtype) + assert_(R.dtype == a_dtype) + assert_(isinstance(Q, a_type)) + assert_(isinstance(R, a_type)) + assert_(Q.shape == (m, m)) + assert_(R.shape == (m, n)) + assert_almost_equal(dot(Q, R), a) + assert_almost_equal(dot(Q.T.conj(), Q), np.eye(m)) + assert_almost_equal(np.triu(R), R) # mode == 'reduced' q1, r1 = linalg.qr(a, mode='reduced') @@ -1736,7 +1741,7 @@ class TestQR: assert_(r.shape[-2:] == (m, n)) assert_almost_equal(matmul(q, r), a) I_mat = np.identity(q.shape[-1]) - stack_I_mat = np.broadcast_to(I_mat, + stack_I_mat = np.broadcast_to(I_mat, q.shape[:-2] + (q.shape[-1],)*2) assert_almost_equal(matmul(swapaxes(q, -1, -2).conj(), q), stack_I_mat) assert_almost_equal(np.triu(r[..., :, :]), r) @@ -1751,9 +1756,9 @@ class TestQR: assert_(r1.shape[-2:] == (k, n)) assert_almost_equal(matmul(q1, r1), a) I_mat = np.identity(q1.shape[-1]) - stack_I_mat = np.broadcast_to(I_mat, + stack_I_mat = np.broadcast_to(I_mat, q1.shape[:-2] + (q1.shape[-1],)*2) - assert_almost_equal(matmul(swapaxes(q1, -1, -2).conj(), q1), + assert_almost_equal(matmul(swapaxes(q1, -1, -2).conj(), q1), stack_I_mat) assert_almost_equal(np.triu(r1[..., :, :]), r1) @@ -1764,12 +1769,12 @@ class TestQR: assert_almost_equal(r2, r1) @pytest.mark.parametrize("size", [ - (3, 4), (4, 3), (4, 4), + (3, 4), (4, 3), (4, 4), (3, 0), (0, 3)]) @pytest.mark.parametrize("outer_size", [ (2, 2), (2,), (2, 3, 4)]) @pytest.mark.parametrize("dt", [ - np.single, np.double, + np.single, np.double, np.csingle, np.cdouble]) def test_stacked_inputs(self, outer_size, size, dt): |