Merge pull request #108 from kilianFatras/master

[MRG] Fix log and nbiter bug in gromov_wasserstein and gromov_wasserstein2

2 files changed, 37 insertions, 0 deletions

diff --git a/test/test_gromov.py b/test/test_gromov.py
index 70fa83f..43da9fc 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -44,10 +44,14 @@ def test_gromov():
 

     gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True)

 

+    gw_val = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=False)

+

     G = log['T']

 

     np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1)

 

+    np.testing.assert_allclose(gw, gw_val, atol=1e-1, rtol=1e-1)  # cf log=False

+

     # check constratints

     np.testing.assert_allclose(

         p, G.sum(1), atol=1e-04)  # cf convergence gromov

diff --git a/test/test_optim.py b/test/test_optim.py
index ae31e1f..aade36e 100644
--- a/test/test_optim.py
+++ b/test/test_optim.py
@@ -37,6 +37,39 @@ def test_conditional_gradient():
     np.testing.assert_allclose(b, G.sum(0))
 
 
+def test_conditional_gradient2():
+    n = 4000  # nb samples
+
+    mu_s = np.array([0, 0])
+    cov_s = np.array([[1, 0], [0, 1]])
+
+    mu_t = np.array([4, 4])
+    cov_t = np.array([[1, -.8], [-.8, 1]])
+
+    xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
+    xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
+
+    a, b = np.ones((n,)) / n, np.ones((n,)) / n
+
+    # loss matrix
+    M = ot.dist(xs, xt)
+    M /= M.max()
+
+    def f(G):
+        return 0.5 * np.sum(G**2)
+
+    def df(G):
+        return G
+
+    reg = 1e-1
+
+    G, log = ot.optim.cg(a, b, M, reg, f, df, numItermaxEmd=200000,
+                         verbose=True, log=True)
+
+    np.testing.assert_allclose(a, G.sum(1))
+    np.testing.assert_allclose(b, G.sum(0))
+
+
 def test_generalized_conditional_gradient():
 
     n_bins = 100  # nb bins