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"""Tests for module utils for timing and parallel computation """
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import ot
import numpy as np
def test_parmap():
n = 100
def f(i):
return 1.0 * i * i
a = np.arange(n)
l1 = list(map(f, a))
l2 = list(ot.utils.parmap(f, a))
np.testing.assert_allclose(l1, l2)
def test_tic_toc():
import time
ot.tic()
time.sleep(0.5)
t = ot.toc()
t2 = ot.toq()
# test timing
np.testing.assert_allclose(0.5, t, rtol=1e-2, atol=1e-2)
# test toc vs toq
np.testing.assert_allclose(t, t2, rtol=1e-2, atol=1e-2)
def test_kernel():
n = 100
x = np.random.randn(n, 2)
K = ot.utils.kernel(x, x)
# gaussian kernel has ones on the diagonal
np.testing.assert_allclose(np.diag(K), np.ones(n))
def test_unif():
n = 100
u = ot.unif(n)
np.testing.assert_allclose(1, np.sum(u))
def test_dist():
n = 100
x = np.random.randn(n, 2)
D = np.zeros((n, n))
for i in range(n):
for j in range(n):
D[i, j] = np.sum(np.square(x[i, :] - x[j, :]))
D2 = ot.dist(x, x)
D3 = ot.dist(x)
# dist shoul return squared euclidean
np.testing.assert_allclose(D, D2)
np.testing.assert_allclose(D, D3)
def test_dist0():
n = 100
M = ot.utils.dist0(n, method='lin_square')
# dist0 default to linear sampling with quadratic loss
np.testing.assert_allclose(M[0, -1], (n - 1) * (n - 1))
def test_dots():
n1, n2, n3, n4 = 100, 50, 200, 100
A = np.random.randn(n1, n2)
B = np.random.randn(n2, n3)
C = np.random.randn(n3, n4)
X1 = ot.utils.dots(A, B, C)
X2 = A.dot(B.dot(C))
np.testing.assert_allclose(X1, X2)
def test_clean_zeros():
n = 100
nz = 50
nz2 = 20
u1 = ot.unif(n)
u1[:nz] = 0
u1 = u1 / u1.sum()
u2 = ot.unif(n)
u2[:nz2] = 0
u2 = u2 / u2.sum()
M = ot.utils.dist0(n)
a, b, M2 = ot.utils.clean_zeros(u1, u2, M)
assert len(a) == n - nz
assert len(b) == n - nz2
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