Fix ordering

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]])),

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