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| author | Nicolas Courty <Nico@MacBook-Pro-de-Nicolas.local> | 2017-09-01 11:43:51 +0200 |
|---|---|---|
| committer | Nicolas Courty <Nico@MacBook-Pro-de-Nicolas.local> | 2017-09-01 11:43:51 +0200 |
| commit | 46fc12a298c49b715ac953cff391b18b54dab0f0 (patch) | |
| tree | fa40cb50af51004578b675840af6766bfd8cfe31 /examples/plot_gromov.py | |
| parent | 64a5d3c4e49688c13d236baf9ed23420070024d6 (diff) | |
solving conflicts :/
Diffstat (limited to 'examples/plot_gromov.py')
| -rw-r--r-- | examples/plot_gromov.py | 15 |
1 files changed, 0 insertions, 15 deletions
diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 99aaf81..92312ae 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -26,11 +26,7 @@ The Gromov-Wasserstein distance allows to compute distances with samples that do For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces.
"""
-<<<<<<< HEAD n_samples = 30 # nb samples
-======= -n = 30 # nb samples
->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
@@ -39,15 +35,9 @@ mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
-<<<<<<< HEAD xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
P = sp.linalg.sqrtm(cov_t)
xt = np.random.randn(n_samples, 3).dot(P) + mu_t
-======= -xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
-P = sp.linalg.sqrtm(cov_t)
-xt = np.random.randn(n, 3).dot(P) + mu_t
->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d """
@@ -85,13 +75,8 @@ Compute Gromov-Wasserstein plans and distance =============================================
"""
-<<<<<<< HEAD p = ot.unif(n_samples)
q = ot.unif(n_samples)
-======= -p = ot.unif(n)
-q = ot.unif(n)
->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)
gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)
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