diff options
Diffstat (limited to 'test/test_bregman.py')
| -rw-r--r-- | test/test_bregman.py | 42 |
1 files changed, 33 insertions, 9 deletions
diff --git a/test/test_bregman.py b/test/test_bregman.py index f70df10..6aa4e08 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -57,6 +57,9 @@ def test_sinkhorn_empty(): np.testing.assert_allclose(u, G.sum(1), atol=1e-05) np.testing.assert_allclose(u, G.sum(0), atol=1e-05) + # test empty weights greenkhorn + ot.sinkhorn([], [], M, 1, method='greenkhorn', stopThr=1e-10, log=True) + def test_sinkhorn_variants(): # test sinkhorn @@ -106,7 +109,6 @@ def test_sinkhorn_variants_log(): @pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_barycenter(method): - n_bins = 100 # nb bins # Gaussian distributions @@ -125,7 +127,7 @@ def test_barycenter(method): # wasserstein reg = 1e-2 - bary_wass = ot.bregman.barycenter(A, M, reg, weights, method=method) + bary_wass, log = ot.bregman.barycenter(A, M, reg, weights, method=method, log=True) np.testing.assert_allclose(1, np.sum(bary_wass)) @@ -133,7 +135,6 @@ def test_barycenter(method): def test_barycenter_stabilization(): - n_bins = 100 # nb bins # Gaussian distributions @@ -154,14 +155,13 @@ def test_barycenter_stabilization(): reg = 1e-2 bar_stable = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn_stabilized", - stopThr=1e-8) + stopThr=1e-8, verbose=True) bar = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn", - stopThr=1e-8) + stopThr=1e-8, verbose=True) np.testing.assert_allclose(bar, bar_stable) def test_wasserstein_bary_2d(): - size = 100 # size of a square image a1 = np.random.randn(size, size) a1 += a1.min() @@ -185,7 +185,6 @@ def test_wasserstein_bary_2d(): def test_unmix(): - n_bins = 50 # nb bins # Gaussian distributions @@ -207,7 +206,7 @@ def test_unmix(): # wasserstein reg = 1e-3 - um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,) + um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, ) np.testing.assert_allclose(1, np.sum(um), rtol=1e-03, atol=1e-03) np.testing.assert_allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03) @@ -256,7 +255,7 @@ def test_empirical_sinkhorn(): def test_empirical_sinkhorn_divergence(): - #Test sinkhorn divergence + # Test sinkhorn divergence n = 10 a = ot.unif(n) b = ot.unif(n) @@ -337,3 +336,28 @@ def test_implemented_methods(): ot.bregman.sinkhorn(a, b, M, epsilon, method=method) with pytest.raises(ValueError): ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + + +def test_screenkhorn(): + # test screenkhorn + rng = np.random.RandomState(0) + n = 100 + a = ot.unif(n) + b = ot.unif(n) + + x = rng.randn(n, 2) + M = ot.dist(x, x) + # sinkhorn + G_sink = ot.sinkhorn(a, b, M, 1e-03) + # screenkhorn + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) + # check marginals + np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02) + np.testing.assert_allclose(G_sink.sum(1), G_screen.sum(1), atol=1e-02) + + +def test_convolutional_barycenter_non_square(): + # test for image with height not equal width + A = np.ones((2, 2, 3)) / (2 * 3) + b = ot.bregman.convolutional_barycenter2d(A, 1e-03) + np.testing.assert_allclose(np.ones((2, 3)) / (2 * 3), b, atol=1e-02) |