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import numpy as np
import ot
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
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
u = ot.utils.unif(n)
M = ot.dist(x, x)
G = ot.emd(u, u, M)
# check G is identity
np.testing.assert_allclose(G, np.eye(n) / n)
# check constratints
np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn
np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn
w = ot.emd2(u, u, M)
# check loss=0
np.testing.assert_allclose(w, 0)
def test_emd_empty():
# test emd and emd2 for simple identity
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
u = ot.utils.unif(n)
M = ot.dist(x, x)
G = ot.emd([], [], M)
# check G is identity
np.testing.assert_allclose(G, np.eye(n) / n)
# check constratints
np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn
np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn
w = ot.emd2([], [], M)
# check loss=0
np.testing.assert_allclose(w, 0)
def test_emd2_multi():
from ot.datasets import get_1D_gauss as gauss
n = 1000 # nb bins
# 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, 20)
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')
np.testing.assert_allclose(emd1, emdn)
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