diff options
Diffstat (limited to 'numpy/linalg/tests/test_linalg.py')
| -rw-r--r-- | numpy/linalg/tests/test_linalg.py | 29 |
1 files changed, 24 insertions, 5 deletions
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 81347ac94..29e1f3480 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -837,10 +837,11 @@ class _TestNorm(object): array(c, dtype=self.dt)): _test(v) - def test_matrix(self): + def test_matrix_2x2(self): A = matrix([[1, 3], [5, 7]], dtype=self.dt) assert_almost_equal(norm(A), 84**0.5) assert_almost_equal(norm(A, 'fro'), 84**0.5) + assert_almost_equal(norm(A, 'nuc'), 10.0) assert_almost_equal(norm(A, inf), 12.0) assert_almost_equal(norm(A, -inf), 4.0) assert_almost_equal(norm(A, 1), 10.0) @@ -852,6 +853,22 @@ class _TestNorm(object): assert_raises(ValueError, norm, A, -3) assert_raises(ValueError, norm, A, 0) + def test_matrix_3x3(self): + # This test has been added because the 2x2 example + # happened to have equal nuclear norm and induced 1-norm. + # The 1/10 scaling factor accommodates the absolute tolerance + # used in assert_almost_equal. + A = (1/10) * np.array([[1, 2, 3], [6, 0, 5], [3, 2, 1]], dtype=self.dt) + assert_almost_equal(norm(A), (1/10) * 89**0.5) + assert_almost_equal(norm(A, 'fro'), (1/10) * 89**0.5) + assert_almost_equal(norm(A, 'nuc'), 1.3366836911774836) + assert_almost_equal(norm(A, inf), 1.1) + assert_almost_equal(norm(A, -inf), 0.6) + assert_almost_equal(norm(A, 1), 1.0) + assert_almost_equal(norm(A, -1), 0.4) + assert_almost_equal(norm(A, 2), 0.88722940323461277) + assert_almost_equal(norm(A, -2), 0.19456584790481812) + def test_axis(self): # Vector norms. # Compare the use of `axis` with computing the norm of each row @@ -866,7 +883,7 @@ class _TestNorm(object): # Matrix norms. B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4) - for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro']: + for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro', 'nuc']: assert_almost_equal(norm(A, ord=order), norm(A, ord=order, axis=(0, 1))) @@ -915,7 +932,7 @@ class _TestNorm(object): shape_err.format(found.shape, expected_shape, order, k)) # Matrix norms. - for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro']: + for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro', 'nuc']: for k in itertools.permutations(range(A.ndim), 2): expected = norm(A, ord=order, axis=k) found = norm(A, ord=order, axis=k, keepdims=True) @@ -935,10 +952,12 @@ class _TestNorm(object): B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4) # Using `axis=<integer>` or passing in a 1-D array implies vector - # norms are being computed, so also using `ord='fro'` raises a - # ValueError. + # norms are being computed, so also using `ord='fro'` + # or `ord='nuc'` raises a ValueError. assert_raises(ValueError, norm, A, 'fro', 0) + assert_raises(ValueError, norm, A, 'nuc', 0) assert_raises(ValueError, norm, [3, 4], 'fro', None) + assert_raises(ValueError, norm, [3, 4], 'nuc', None) # Similarly, norm should raise an exception when ord is any finite # number other than 1, 2, -1 or -2 when computing matrix norms. |