diff options
-rw-r--r-- | ot/lp/__init__.py | 2 | ||||
-rw-r--r-- | test/test_ot.py | 38 |
2 files changed, 39 insertions, 1 deletions
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index cdd505d..4c968ca 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -656,7 +656,7 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, perm_a = np.argsort(x_a_1d) perm_b = np.argsort(x_b_1d) - G_sorted, indices, cost = emd_1d_sorted(a, b, + G_sorted, indices, cost = emd_1d_sorted(a[perm_a.flatten()], b[perm_b.flatten()], x_a_1d[perm_a], x_b_1d[perm_b], metric=metric, p=p) G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])), diff --git a/test/test_ot.py b/test/test_ot.py index 47df946..7afdae3 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -91,6 +91,44 @@ def test_emd_1d_emd2_1d(): with pytest.raises(AssertionError): ot.emd_1d(u, v, [], []) +def test_emd_1d_emd2_1d_with_weights(): + + # test emd1d gives similar results as emd + n = 20 + m = 30 + rng = np.random.RandomState(0) + u = rng.randn(n, 1) + v = rng.randn(m, 1) + + w_u = rng.uniform(0., 1., n) + w_u = w_u / w_u.sum() + + w_v = rng.uniform(0., 1., m) + w_v = w_v / w_v.sum() + + M = ot.dist(u, v, metric='sqeuclidean') + + G, log = ot.emd(w_u, w_v, M, log=True) + wass = log["cost"] + G_1d, log = ot.emd_1d(u, v, w_u, w_v, metric='sqeuclidean', log=True) + wass1d = log["cost"] + wass1d_emd2 = ot.emd2_1d(u, v, w_u, w_v, metric='sqeuclidean', log=False) + wass1d_euc = ot.emd2_1d(u, v, w_u, w_v, metric='euclidean', log=False) + + # check loss is similar + np.testing.assert_allclose(wass, wass1d) + np.testing.assert_allclose(wass, wass1d_emd2) + + # check loss is similar to scipy's implementation for Euclidean metric + wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,))) + np.testing.assert_allclose(wass_sp, wass1d_euc) + + # check constraints + np.testing.assert_allclose(w_u, G.sum(1)) + np.testing.assert_allclose(w_v, G.sum(0)) + + + def test_wass_1d(): # test emd1d gives similar results as emd |