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import ot
import numpy as np
# import pytest
def test_doctest():
import doctest
# test lp solver
doctest.testmod(ot.lp, verbose=True)
# test bregman solver
doctest.testmod(ot.bregman, verbose=True)
def test_emd_emd2():
# test emd and emd2 for simple identity
n = 100
np.random.seed(0)
x = np.random.randn(n, 2)
u = ot.utils.unif(n)
M = ot.dist(x, x)
G = ot.emd(u, u, M)
# check G is identity
assert np.allclose(G, np.eye(n) / n)
w = ot.emd2(u, u, M)
# check loss=0
assert np.allclose(w, 0)
def test_emd2_multi():
from ot.datasets import get_1D_gauss as gauss
n = 1000 # nb bins
np.random.seed(0)
# bin positions
x = np.arange(n, dtype=np.float64)
# Gaussian distributions
a = gauss(n, m=20, s=5) # m= mean, s= std
ls = np.arange(20, 1000, 10)
nb = len(ls)
b = np.zeros((n, nb))
for i in range(nb):
b[:, i] = gauss(n, m=ls[i], s=10)
# loss matrix
M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
# M/=M.max()
print('Computing {} EMD '.format(nb))
# emd loss 1 proc
ot.tic()
emd1 = ot.emd2(a, b, M, 1)
ot.toc('1 proc : {} s')
# emd loss multipro proc
ot.tic()
emdn = ot.emd2(a, b, M)
ot.toc('multi proc : {} s')
assert np.allclose(emd1, emdn)
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_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)
# check constratints
assert np.allclose(G0, Gs, atol=1e-05)
assert np.allclose(G0, Ges, atol=1e-05) #
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