(self)
| 1323 | _test(v) |
| 1324 | |
| 1325 | def test_axis(self): |
| 1326 | # Vector norms. |
| 1327 | # Compare the use of `axis` with computing the norm of each row |
| 1328 | # or column separately. |
| 1329 | A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) |
| 1330 | for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]: |
| 1331 | expected0 = [norm(A[:, k], ord=order) for k in range(A.shape[1])] |
| 1332 | assert_almost_equal(norm(A, ord=order, axis=0), expected0) |
| 1333 | expected1 = [norm(A[k, :], ord=order) for k in range(A.shape[0])] |
| 1334 | assert_almost_equal(norm(A, ord=order, axis=1), expected1) |
| 1335 | |
| 1336 | # Matrix norms. |
| 1337 | B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4) |
| 1338 | nd = B.ndim |
| 1339 | for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro']: |
| 1340 | for axis in itertools.combinations(range(-nd, nd), 2): |
| 1341 | row_axis, col_axis = axis |
| 1342 | if row_axis < 0: |
| 1343 | row_axis += nd |
| 1344 | if col_axis < 0: |
| 1345 | col_axis += nd |
| 1346 | if row_axis == col_axis: |
| 1347 | assert_raises(ValueError, norm, B, ord=order, axis=axis) |
| 1348 | else: |
| 1349 | n = norm(B, ord=order, axis=axis) |
| 1350 | |
| 1351 | # The logic using k_index only works for nd = 3. |
| 1352 | # This has to be changed if nd is increased. |
| 1353 | k_index = nd - (row_axis + col_axis) |
| 1354 | if row_axis < col_axis: |
| 1355 | expected = [norm(B[:].take(k, axis=k_index), ord=order) |
| 1356 | for k in range(B.shape[k_index])] |
| 1357 | else: |
| 1358 | expected = [norm(B[:].take(k, axis=k_index).T, ord=order) |
| 1359 | for k in range(B.shape[k_index])] |
| 1360 | assert_almost_equal(n, expected) |
| 1361 | |
| 1362 | def test_keepdims(self): |
| 1363 | A = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4) |
nothing calls this directly
no test coverage detected