diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/linalg/linalg.py | 4 | ||||
| -rw-r--r-- | numpy/linalg/tests/test_linalg.py | 40 |
2 files changed, 24 insertions, 20 deletions
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index 4b3ae79f8..b7f21b1b7 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -100,7 +100,7 @@ def _raise_linalgerror_lstsq(err, flag): raise LinAlgError("SVD did not converge in Linear Least Squares") def _raise_linalgerror_qr(err, flag): - raise LinAlgError("Illegal argument found while performing " + raise LinAlgError("Incorrect argument found while performing " "QR factorization") def get_linalg_error_extobj(callback): @@ -785,7 +785,7 @@ def qr(a, mode='reduced'): Parameters ---------- a : array_like, shape (..., M, N) - A real or complex array with ``a.ndim >= 2``. + An array-like object with the dimensionality of at least 2. mode : {'reduced', 'complete', 'r', 'raw'}, optional If K = min(M, N), then diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 51f10f27c..2fbab8cba 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -1729,7 +1729,10 @@ class TestQR: assert_(q.shape[-2:] == (m, m)) assert_(r.shape[-2:] == (m, n)) assert_almost_equal(matmul(q, r), a) - assert_almost_equal(swapaxes(q, -1, -2).conj(), np.linalg.inv(q)) + I_mat = np.identity(q.shape[-1]) + 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) # mode == 'reduced' @@ -1741,7 +1744,10 @@ class TestQR: assert_(q1.shape[-2:] == (m, k)) assert_(r1.shape[-2:] == (k, n)) assert_almost_equal(matmul(q1, r1), a) - assert_almost_equal(swapaxes(q, -1, -2).conj(), np.linalg.inv(q)) + I_mat = np.identity(q1.shape[-1]) + 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), stack_I_mat) assert_almost_equal(np.triu(r1[..., :, :]), r1) # mode == 'r' @@ -1750,22 +1756,20 @@ class TestQR: assert_(isinstance(r2, a_type)) assert_almost_equal(r2, r1) - def test_stacked_inputs(self): - - normal = np.random.normal - sizes = [(3, 4), (4, 3), (4, 4), (3, 0), (0, 3)] - dts = [np.float32, np.float64, np.complex64] - for size in sizes: - for dt in dts: - a1, a2, a3, a4 = [normal(size=size), normal(size=size), - normal(size=size), normal(size=size)] - b1, b2, b3, b4 = [normal(size=size), normal(size=size), - normal(size=size), normal(size=size)] - A = np.asarray([[a1, a2], [a3, a4]], dtype=dt) - B = np.asarray([[b1, b2], [b3, b4]], dtype=dt) - self.check_qr_stacked(A) - self.check_qr_stacked(B) - self.check_qr_stacked(A + 1.j*B) + @pytest.mark.parametrize("size", [ + (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.csingle, np.cdouble]) + def test_stacked_inputs(self, outer_size, size, dt): + + A = np.random.normal(size=outer_size + size).astype(dt) + B = np.random.normal(size=outer_size + size).astype(dt) + self.check_qr_stacked(A) + self.check_qr_stacked(A + 1.j*B) class TestCholesky: |