diff options
Diffstat (limited to 'numpy/linalg/tests')
| -rw-r--r-- | numpy/linalg/tests/test_linalg.py | 52 |
1 files changed, 49 insertions, 3 deletions
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index fdb243271..7ee7b8317 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -563,6 +563,7 @@ class _TestNorm(TestCase): self.assertRaises(ValueError, norm, A, 0) def test_axis(self): + # Vector norms. # Compare the use of `axis` with computing the norm of each row # or column separately. A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) @@ -572,10 +573,55 @@ class _TestNorm(TestCase): expected1 = [norm(A[k,:], ord=order) for k in range(A.shape[0])] assert_almost_equal(norm(A, ord=order, axis=1), expected1) - # Check bad case. Using `axis` implies vector norms are being - # computed, so also using `ord='fro'` raises a ValueError - # (just like `norm([1,2,3], ord='fro')` does). + # 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']: + assert_almost_equal(norm(A, ord=order), norm(A, ord=order, axis=(0,1))) + + n = norm(B, ord=order, axis=(1,2)) + expected = [norm(B[k], ord=order) for k in range(B.shape[0])] + print("shape is %r, axis=(1,2)" % (B.shape,)) + assert_almost_equal(n, expected) + + n = norm(B, ord=order, axis=(2,1)) + expected = [norm(B[k].T, ord=order) for k in range(B.shape[0])] + print("shape is %r, axis=(2,1)" % (B.shape,)) + assert_almost_equal(n, expected) + + n = norm(B, ord=order, axis=(0,2)) + expected = [norm(B[:,k,:], ord=order) for k in range(B.shape[1])] + print("shape is %r, axis=(0,2)" % (B.shape,)) + assert_almost_equal(n, expected) + + n = norm(B, ord=order, axis=(0,1)) + expected = [norm(B[:,:,k], ord=order) for k in range(B.shape[2])] + print("shape is %r, axis=(0,1)" % (B.shape,)) + assert_almost_equal(n, expected) + + def test_bad_args(self): + # Check that bad arguments raise the appropriate exceptions. + + A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) + 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. self.assertRaises(ValueError, norm, A, 'fro', 0) + self.assertRaises(ValueError, norm, [3, 4], 'fro', None) + + # Similarly, norm should raise an exception when ord is any finite + # number other than 1, 2, -1 or -2 when computing matrix norms. + for order in [0, 3]: + self.assertRaises(ValueError, norm, A, order, None) + self.assertRaises(ValueError, norm, A, order, (0,1)) + self.assertRaises(ValueError, norm, B, order, (1,2)) + + # Invalid axis + self.assertRaises(ValueError, norm, B, None, 3) + self.assertRaises(ValueError, norm, B, None, (2,3)) + self.assertRaises(ValueError, norm, B, None, (0,1,2)) class TestNormDouble(_TestNorm): |