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| author | Rémi Flamary <remi.flamary@gmail.com> | 2018-06-11 12:01:27 +0200 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-06-11 12:01:27 +0200 |
| commit | ef17fcd2d5e85b986ff21d8039483bdaf03e37da (patch) | |
| tree | 25cf5915a26273b3e0726494ed4a1405ce1e2786 /test/test_optim.py | |
| parent | 8046b8c424d7b80f520e212e2bf8de41cb624aab (diff) | |
| parent | 530dc93a60e9b81fb8d1b44680deea77dacf660b (diff) | |
Merge branch 'master' into smooth_ot
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))) |