| 127 | |
| 128 | |
| 129 | def test_sliced_backend(nx): |
| 130 | n = 100 |
| 131 | rng = np.random.RandomState(0) |
| 132 | |
| 133 | x = rng.randn(n, 2) |
| 134 | y = rng.randn(2 * n, 2) |
| 135 | |
| 136 | P = rng.randn(2, 20) |
| 137 | P = P / np.sqrt((P**2).sum(0, keepdims=True)) |
| 138 | |
| 139 | n_projections = 20 |
| 140 | |
| 141 | xb, yb, Pb = nx.from_numpy(x, y, P) |
| 142 | |
| 143 | val0 = ot.sliced_wasserstein_distance(x, y, projections=P) |
| 144 | |
| 145 | val = ot.sliced_wasserstein_distance(xb, yb, n_projections=n_projections, seed=0) |
| 146 | val2 = ot.sliced_wasserstein_distance(xb, yb, n_projections=n_projections, seed=0) |
| 147 | |
| 148 | assert val > 0 |
| 149 | assert val == val2 |
| 150 | |
| 151 | valb = nx.to_numpy(ot.sliced_wasserstein_distance(xb, yb, projections=Pb)) |
| 152 | |
| 153 | assert np.allclose(val0, valb) |
| 154 | |
| 155 | |
| 156 | def test_sliced_backend_type_devices(nx): |