| 75 | |
| 76 | |
| 77 | def test_not_implemented_method(): |
| 78 | # test sinkhorn |
| 79 | w = 10 |
| 80 | n = w**2 |
| 81 | rng = np.random.RandomState(42) |
| 82 | A_img = rng.rand(2, w, w) |
| 83 | A_flat = A_img.reshape(n, 2) |
| 84 | a1, a2 = A_flat.T |
| 85 | M_flat = ot.utils.dist0(n) |
| 86 | not_implemented = "new_method" |
| 87 | reg = 0.01 |
| 88 | with pytest.raises(ValueError): |
| 89 | ot.sinkhorn(a1, a2, M_flat, reg, method=not_implemented) |
| 90 | with pytest.raises(ValueError): |
| 91 | ot.sinkhorn2(a1, a2, M_flat, reg, method=not_implemented) |
| 92 | with pytest.raises(ValueError): |
| 93 | ot.barycenter(A_flat, M_flat, reg, method=not_implemented) |
| 94 | with pytest.raises(ValueError): |
| 95 | ot.bregman.barycenter_debiased(A_flat, M_flat, reg, method=not_implemented) |
| 96 | with pytest.raises(ValueError): |
| 97 | ot.bregman.convolutional_barycenter2d(A_img, reg, method=not_implemented) |
| 98 | with pytest.raises(ValueError): |
| 99 | ot.bregman.convolutional_barycenter2d_debiased( |
| 100 | A_img, reg, method=not_implemented |
| 101 | ) |
| 102 | |
| 103 | |
| 104 | @pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) |