1 files changed, 1 insertions, 56 deletions
diff --git a/test/test_ot.py b/test/test_ot.py
index 4dfc510..5bfde1d 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -8,11 +8,11 @@ import warnings
 
 import numpy as np
 import pytest
-from scipy.stats import wasserstein_distance
 
 import ot
 from ot.datasets import make_1D_gauss as gauss
 from ot.backend import torch
+from scipy.stats import wasserstein_distance
 
 
 def test_emd_dimension_and_mass_mismatch():
@@ -165,61 +165,6 @@ def test_emd_1d_emd2_1d():
         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,)), w_u, w_v)
-    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
-    n = 20
-    m = 30
-    rng = np.random.RandomState(0)
-    u = rng.randn(n, 1)
-    v = rng.randn(m, 1)
-
-    M = ot.dist(u, v, metric='sqeuclidean')
-
-    G, log = ot.emd([], [], M, log=True)
-    wass = log["cost"]
-
-    wass1d = ot.wasserstein_1d(u, v, [], [], p=2.)
-
-    # check loss is similar
-    np.testing.assert_allclose(np.sqrt(wass), wass1d)
-
-
 def test_emd_empty():
     # test emd and emd2 for simple identity
     n = 100