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import ot
import numpy as np
# import pytest
def test_sinkhorn():
# test sinkhorn
n = 100
np.random.seed(0)
x = np.random.randn(n, 2)
u = ot.utils.unif(n)
M = ot.dist(x, x)
G = ot.sinkhorn(u, u, M, 1, stopThr=1e-10)
# check constratints
assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn
assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn
def test_sinkhorn_empty():
# test sinkhorn
n = 100
np.random.seed(0)
x = np.random.randn(n, 2)
u = ot.utils.unif(n)
M = ot.dist(x, x)
G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, verbose=True, log=True)
# check constratints
assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn
assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn
G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10,
method='sinkhorn_stabilized', verbose=True, log=True)
# check constratints
assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn
assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn
G, log = ot.sinkhorn(
[], [], M, 1, stopThr=1e-10, method='sinkhorn_epsilon_scaling',
verbose=True, log=True)
# check constratints
assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn
assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn
def test_sinkhorn_variants():
# test sinkhorn
n = 100
np.random.seed(0)
x = np.random.randn(n, 2)
u = ot.utils.unif(n)
M = ot.dist(x, x)
G0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10)
Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10)
Ges = ot.sinkhorn(
u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10)
Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10)
# check values
assert np.allclose(G0, Gs, atol=1e-05)
assert np.allclose(G0, Ges, atol=1e-05)
assert np.allclose(G0, Gerr)
def test_bary():
n = 100 # nb bins
# Gaussian distributions
a1 = ot.datasets.get_1D_gauss(n, m=30, s=10) # m= mean, s= std
a2 = ot.datasets.get_1D_gauss(n, m=40, s=10)
# creating matrix A containing all distributions
A = np.vstack((a1, a2)).T
# loss matrix + normalization
M = ot.utils.dist0(n)
M /= M.max()
alpha = 0.5 # 0<=alpha<=1
weights = np.array([1 - alpha, alpha])
# wasserstein
reg = 1e-3
bary_wass = ot.bregman.barycenter(A, M, reg, weights)
assert np.allclose(1, np.sum(bary_wass))
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