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"""Tests for module gromov """
# Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
#
# License: MIT License
import numpy as np
import ot
def test_gromov():
n = 50 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
xt = [xs[n - (i + 1)] for i in range(n)]
xt = np.array(xt)
p = ot.unif(n)
q = ot.unif(n)
C1 = ot.dist(xs, xs)
C2 = ot.dist(xt, xt)
C1 /= C1.max()
C2 /= C2.max()
G = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence gromov
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