def test_lhs_random_state(criterion):
n_dim = 2
n_samples = 20
lhs = Lhs()
h = lhs._lhs_normalized(n_dim, n_samples, 0)
h2 = lhs._lhs_normalized(n_dim, n_samples, 0)
assert_array_equal(h, h2)
lhs = Lhs(criterion=criterion, iterations=100)
h = lhs.generate([
(0., 1.),
] * n_dim, n_samples, random_state=0)
h2 = lhs.generate([
(0., 1.),
] * n_dim, n_samples, random_state=0)
assert_array_equal(h, h2)
def test_lhs_pdist():
n_dim = 2
n_samples = 20
lhs = Lhs()
h = lhs._lhs_normalized(n_dim, n_samples, 0)
d_classic = spatial.distance.pdist(np.array(h), 'euclidean')
lhs = Lhs(criterion="maximin", iterations=100)
h = lhs.generate([
(0., 1.),
] * n_dim, n_samples, random_state=0)
d = spatial.distance.pdist(np.array(h), 'euclidean')
assert np.min(d) > np.min(d_classic)