diff options
| -rw-r--r-- | test/test_optim.py | 22 |
1 files changed, 10 insertions, 12 deletions
diff --git a/test/test_optim.py b/test/test_optim.py index d5c4ad0..bc0b706 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -3,22 +3,20 @@ import numpy as np import ot -# import pytest - def test_conditional_gradient(): - n = 100 # nb bins + n_bins = 100 # nb bins np.random.seed(0) # bin positions - x = np.arange(n, dtype=np.float64) + x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + 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) # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) M /= M.max() def f(G): @@ -37,17 +35,17 @@ def test_conditional_gradient(): def test_generalized_conditional_gradient(): - n = 100 # nb bins + n_bins = 100 # nb bins np.random.seed(0) # bin positions - x = np.arange(n, dtype=np.float64) + x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + 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) # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) M /= M.max() def f(G): |