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author | Rémi Flamary <remi.flamary@gmail.com> | 2018-09-24 10:33:41 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-09-24 10:33:41 +0200 |
commit | 414331cbec21f00333e9de36a8790666c373e93c (patch) | |
tree | 5e5fab72c66d2e885c2d98bfc9567b527dfdb62e /test | |
parent | 75fe96c183852971bb7be1da39af202b9f7d6e6c (diff) | |
parent | c9b99df8fffec1dcc6802ef43b6192774817c5fb (diff) |
Merge readme with master
Diffstat (limited to 'test')
-rw-r--r-- | test/test_bregman.py | 24 | ||||
-rw-r--r-- | test/test_stochastic.py | 8 |
2 files changed, 28 insertions, 4 deletions
diff --git a/test/test_bregman.py b/test/test_bregman.py index 52bbbd2..58afd7a 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -108,6 +108,30 @@ def test_bary(): ot.bregman.barycenter(A, M, reg, log=True, verbose=True) +def test_wassersteinbary(): + + size = 100 # size of a square image + a1 = np.random.randn(size, size) + a1 += a1.min() + a1 = a1 / np.sum(a1) + a2 = np.random.randn(size, size) + a2 += a2.min() + a2 = a2 / np.sum(a2) + # creating matrix A containing all distributions + A = np.zeros((2, 100, 100)) + A[0, :, :] = a1 + A[1, :, :] = a2 + + # wasserstein + reg = 1e-3 + bary_wass = ot.bregman.convolutional_barycenter2d(A, reg) + + np.testing.assert_allclose(1, np.sum(bary_wass)) + + # help in checking if log and verbose do not bug the function + ot.bregman.convolutional_barycenter2d(A, reg, log=True, verbose=True) + + def test_unmix(): n_bins = 50 # nb bins diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 0128317..f0f3fc8 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -32,7 +32,7 @@ def test_stochastic_sag(): # test sag n = 15 reg = 1 - numItermax = 300000 + numItermax = 30000 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -62,7 +62,7 @@ def test_stochastic_asgd(): # test asgd n = 15 reg = 1 - numItermax = 300000 + numItermax = 100000 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -92,7 +92,7 @@ def test_sag_asgd_sinkhorn(): # test all algorithms n = 15 reg = 1 - nb_iter = 300000 + nb_iter = 100000 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -167,7 +167,7 @@ def test_dual_sgd_sinkhorn(): # test all dual algorithms n = 10 reg = 1 - nb_iter = 150000 + nb_iter = 15000 batch_size = 10 rng = np.random.RandomState(0) |