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| author | Nicolas Courty <ncourty@irisa.fr> | 2018-06-10 22:59:27 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-06-10 22:59:27 +0200 |
| commit | 530dc93a60e9b81fb8d1b44680deea77dacf660b (patch) | |
| tree | a2ae2e21bf7fac9fe2fa368782d7df5ea9974342 /test/test_optim.py | |
| parent | 90efa5a8b189214d1aeb81920b2bb04ce0c261ca (diff) | |
| parent | 2b375f263ef88100b0321c8ef1b3605dfbb95b3d (diff) | |
Merge pull request #49 from rflamary/dataset_fun
Dataset functions + test/notebooks update
Diffstat (limited to 'test/test_optim.py')
| -rw-r--r-- | test/test_optim.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/test/test_optim.py b/test/test_optim.py index 69496a5..dfefe59 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -16,8 +16,8 @@ def test_conditional_gradient(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) @@ -45,8 +45,8 @@ def test_generalized_conditional_gradient(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) |