fix bug numItermax emd in cg

2 files changed, 37 insertions, 2 deletions

diff --git a/ot/optim.py b/ot/optim.py
index 0abd9e9..4012e0d 100644
--- a/ot/optim.py
+++ b/ot/optim.py
@@ -134,7 +134,7 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val,
     return alpha, fc, f_val
 
 
-def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
+def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000,
        stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False, **kwargs):
     """
     Solve the general regularized OT problem with conditional gradient
@@ -172,6 +172,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
         initial guess (default is indep joint density)
     numItermax : int, optional
         Max number of iterations
+    numItermaxEmd : int, optional
+        Max number of iterations for emd
     stopThr : float, optional
         Stop threshol on the relative variation (>0)
     stopThr2 : float, optional
@@ -238,7 +240,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
         Mi += Mi.min()
 
         # solve linear program
-        Gc = emd(a, b, Mi)
+        Gc = emd(a, b, Mi, numItermax=numItermaxEmd)
 
         deltaG = Gc - G
 
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