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-rw-r--r--examples/plot_gromov.py15
1 files changed, 15 insertions, 0 deletions
diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py
index 92312ae..99aaf81 100644
--- a/examples/plot_gromov.py
+++ b/examples/plot_gromov.py
@@ -26,7 +26,11 @@ 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]])
@@ -35,9 +39,15 @@ 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
"""
@@ -75,8 +85,13 @@ 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)