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"""Tests for module regularization path"""
# Author: Haoran Wu <haoran.wu@univ-ubs.fr>
#
# License: MIT License
import numpy as np
import ot
def test_fully_relaxed_path():
n_source = 50 # nb source samples (gaussian)
n_target = 40 # nb target samples (gaussian)
mu = np.array([0, 0])
cov = np.array([[1, 0], [0, 2]])
rng = np.random.RandomState(0)
xs = ot.datasets.make_2D_samples_gauss(n_source, mu, cov, random_state=rng)
xt = ot.datasets.make_2D_samples_gauss(n_target, mu, cov, random_state=rng)
# source and target distributions
a = ot.utils.unif(n_source)
b = ot.utils.unif(n_target)
# loss matrix
M = ot.dist(xs, xt)
M /= M.max()
t, _, _ = ot.regpath.regularization_path(a, b, M, reg=1e-8, semi_relaxed=False)
G = t.reshape((n_source, n_target))
np.testing.assert_allclose(a, G.sum(1), atol=1e-05)
np.testing.assert_allclose(b, G.sum(0), atol=1e-05)
def test_semi_relaxed_path():
n_source = 50 # nb source samples (gaussian)
n_target = 40 # nb target samples (gaussian)
mu = np.array([0, 0])
cov = np.array([[1, 0], [0, 2]])
rng = np.random.RandomState(0)
xs = ot.datasets.make_2D_samples_gauss(n_source, mu, cov, random_state=rng)
xt = ot.datasets.make_2D_samples_gauss(n_target, mu, cov, random_state=rng)
# source and target distributions
a = ot.utils.unif(n_source)
b = ot.utils.unif(n_target)
# loss matrix
M = ot.dist(xs, xt)
M /= M.max()
t, _, _ = ot.regpath.regularization_path(a, b, M, reg=1e-8, semi_relaxed=True)
G = t.reshape((n_source, n_target))
np.testing.assert_allclose(a, G.sum(1), atol=1e-05)
np.testing.assert_allclose(b, G.sum(0), atol=1e-10)
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