should pass tests now

Author: rflamary

ot/gromov.py

@@ -307,6 +307,8 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs):
         Print information along iterations
     log : bool, optional
         record log if True
+    **kwargs : dict
+        parameters can be directly pased to the ot.optim.cg solver
 
     Returns
     -------

test/test_gromov.py

@@ -28,7 +28,36 @@ def test_gromov():
     C1 /= C1.max()
     C2 /= C2.max()
 
-    G = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)
+    G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss')
+
+    # check constratints
+    np.testing.assert_allclose(
+        p, G.sum(1), atol=1e-04)  # cf convergence gromov
+    np.testing.assert_allclose(
+        q, G.sum(0), atol=1e-04)  # cf convergence gromov
+
+
+def test_entropic_gromov():
+    n_samples = 50  # nb samples
+
+    mu_s = np.array([0, 0])
+    cov_s = np.array([[1, 0], [0, 1]])
+
+    xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
+
+    xt = xs[::-1].copy()
+
+    p = ot.unif(n_samples)
+    q = ot.unif(n_samples)
+
+    C1 = ot.dist(xs, xs)
+    C2 = ot.dist(xt, xt)
+
+    C1 /= C1.max()
+    C2 /= C2.max()
+
+    G = ot.gromov.entropic_gromov_wasserstein(
+        C1, C2, p, q, 'square_loss', epsilon=5e-4)
 
     # check constratints
     np.testing.assert_allclose(

test/test_plot.py

@@ -12,6 +12,7 @@
 def test_plot1D_mat():
 
     import ot
+    import ot.plot
 
     n_bins = 100  # nb bins
 
@@ -32,6 +33,7 @@ def test_plot1D_mat():
 def test_plot2D_samples_mat():
 
     import ot
+    import ot.plot
 
     n_bins = 50  # nb samples