1 files changed, 110 insertions, 4 deletions
diff --git a/test/test_ot.py b/test/test_ot.py
index acd8718..ea6d9dc 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -4,12 +4,15 @@
 #
 # License: MIT License
 
+import warnings
+
 import numpy as np
+
 import ot
+from ot.datasets import get_1D_gauss as gauss
 
 
 def test_doctest():
-
     import doctest
 
     # test lp solver
@@ -66,9 +69,6 @@ def test_emd_empty():
 
 
 def test_emd2_multi():
-
-    from ot.datasets import get_1D_gauss as gauss
-
     n = 1000  # nb bins
 
     # bin positions
@@ -100,3 +100,109 @@ def test_emd2_multi():
     ot.toc('multi proc : {} s')
 
     np.testing.assert_allclose(emd1, emdn)
+
+    # emd loss multipro proc with log
+    ot.tic()
+    emdn = ot.emd2(a, b, M, log=True, return_matrix=True)
+    ot.toc('multi proc : {} s')
+
+    for i in range(len(emdn)):
+        emd = emdn[i]
+        log = emd[1]
+        cost = emd[0]
+        check_duality_gap(a, b[:, i], M, log['G'], log['u'], log['v'], cost)
+        emdn[i] = cost
+
+    emdn = np.array(emdn)
+    np.testing.assert_allclose(emd1, emdn)
+
+
+def test_warnings():
+    n = 100  # nb bins
+    m = 100  # nb bins
+
+    mean1 = 30
+    mean2 = 50
+
+    # bin positions
+    x = np.arange(n, dtype=np.float64)
+    y = np.arange(m, dtype=np.float64)
+
+    # Gaussian distributions
+    a = gauss(n, m=mean1, s=5)  # m= mean, s= std
+
+    b = gauss(m, m=mean2, s=10)
+
+    # loss matrix
+    M = ot.dist(x.reshape((-1, 1)), y.reshape((-1, 1))) ** (1. / 2)
+
+    print('Computing {} EMD '.format(1))
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        print('Computing {} EMD '.format(1))
+        ot.emd(a, b, M, numItermax=1)
+        assert "numItermax" in str(w[-1].message)
+        assert len(w) == 1
+        a[0] = 100
+        print('Computing {} EMD '.format(2))
+        ot.emd(a, b, M)
+        assert "infeasible" in str(w[-1].message)
+        assert len(w) == 2
+        a[0] = -1
+        print('Computing {} EMD '.format(2))
+        ot.emd(a, b, M)
+        assert "infeasible" in str(w[-1].message)
+        assert len(w) == 3
+
+
+def test_dual_variables():
+    n = 5000  # nb bins
+    m = 6000  # nb bins
+
+    mean1 = 1000
+    mean2 = 1100
+
+    # bin positions
+    x = np.arange(n, dtype=np.float64)
+    y = np.arange(m, dtype=np.float64)
+
+    # Gaussian distributions
+    a = gauss(n, m=mean1, s=5)  # m= mean, s= std
+
+    b = gauss(m, m=mean2, s=10)
+
+    # loss matrix
+    M = ot.dist(x.reshape((-1, 1)), y.reshape((-1, 1))) ** (1. / 2)
+
+    print('Computing {} EMD '.format(1))
+
+    # emd loss 1 proc
+    ot.tic()
+    G, log = ot.emd(a, b, M, log=True)
+    ot.toc('1 proc : {} s')
+
+    ot.tic()
+    G2 = ot.emd(b, a, np.ascontiguousarray(M.T))
+    ot.toc('1 proc : {} s')
+
+    cost1 = (G * M).sum()
+    # Check symmetry
+    np.testing.assert_array_almost_equal(cost1, (M * G2.T).sum())
+    # Check with closed-form solution for gaussians
+    np.testing.assert_almost_equal(cost1, np.abs(mean1 - mean2))
+
+    # Check that both cost computations are equivalent
+    np.testing.assert_almost_equal(cost1, log['cost'])
+    check_duality_gap(a, b, M, G, log['u'], log['v'], log['cost'])
+
+
+def check_duality_gap(a, b, M, G, u, v, cost):
+    cost_dual = np.vdot(a, u) + np.vdot(b, v)
+    # Check that dual and primal cost are equal
+    np.testing.assert_almost_equal(cost_dual, cost)
+
+    [ind1, ind2] = np.nonzero(G)
+
+    # Check that reduced cost is zero on transport arcs
+    np.testing.assert_array_almost_equal((M - u.reshape(-1, 1) - v.reshape(1, -1))[ind1, ind2],
+                                         np.zeros(ind1.size))