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Pytest with 85% coverage
Pytest with 89% coverage
| before_script: # configure a headless display to test plot generation | ||
| - "export DISPLAY=:99.0" | ||
| - "sh -e /etc/init.d/xvfb start" | ||
| - sleep 3 # give xvfb some time to start |
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| pytest : FORCE | ||
| python -m py.test -v test/ | ||
| python -m py.test -v test/ --cov=ot |
| reg = 1e-3 | ||
| bary_wass = ot.bregman.barycenter(A, M, reg, weights) | ||
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| assert np.allclose(1, np.sum(bary_wass)) |
| def test_sinkhorn_empty(): | ||
| # test sinkhorn | ||
| n = 100 | ||
| np.random.seed(0) |
| G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, verbose=True, log=True) | ||
| # check constratints | ||
| assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn | ||
| assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn |
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| # Gaussian distributions | ||
| a1 = ot.datasets.get_1D_gauss(n, m=30, s=10) # m= mean, s= std | ||
| a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) |
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| def test_unmix(): | ||
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| n = 50 # nb bins |
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| import ot | ||
| import numpy as np |
| # import pytest | ||
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| def test_OTDA(): |
| n = 150 # nb bins | ||
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| xs, ys = ot.datasets.get_data_classif('3gauss', n) | ||
| xt, yt = ot.datasets.get_data_classif('3gauss2', n) |
| import pytest | ||
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| try: # test if cudamat installed | ||
| import ot.dr |
| def test_otda(): | ||
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| n_samples = 150 # nb samples | ||
| np.random.seed(0) |
| def test_conditional_gradient(): | ||
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| n_bins = 100 # nb bins | ||
| np.random.seed(0) |
| l2 = ot.utils.parmap(f, a) | ||
| l2 = list(ot.utils.parmap(f, a)) | ||
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| assert np.allclose(l1, l2) |
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| # dist shoul return squared euclidean | ||
| assert np.allclose(D, D2) | ||
| assert np.allclose(D, D3) |
| M = ot.utils.dist0(n, method='lin_square') | ||
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| # dist0 default to linear sampling with quadratic loss | ||
| assert np.allclose(M[0, -1], (n - 1) * (n - 1)) |
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