def test_update_discrepancy(self):
space_1 = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
space_1 = (2.0 * space_1 - 1.0) / (2.0 * 6.0)
disc_init = qmc.discrepancy(space_1[:-1], iterative=True)
disc_iter = update_discrepancy(space_1[-1], space_1[:-1], disc_init)
assert_allclose(disc_iter, 0.0081, atol=1e-4)
# errors
with pytest.raises(ValueError,
match=r"Sample is not in unit "
r"hypercube"):
update_discrepancy(space_1[-1], space_1[:-1] + 1, disc_init)
with pytest.raises(ValueError, match=r"Sample is not a 2D array"):
update_discrepancy(space_1[-1], space_1[0], disc_init)
x_new = [1, 3]
with pytest.raises(ValueError,
match=r"x_new is not in unit "
r"hypercube"):
update_discrepancy(x_new, space_1[:-1], disc_init)
x_new = [[0.5, 0.5]]
with pytest.raises(ValueError, match=r"x_new is not a 1D array"):
update_discrepancy(x_new, space_1[:-1], disc_init)
def test_update_discrepancy(self):
# From Fang et al. Design and modeling for computer experiments, 2006
space_1 = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
space_1 = (2.0 * space_1 - 1.0) / (2.0 * 6.0)
disc_init = qmc.discrepancy(space_1[:-1], iterative=True)
disc_iter = update_discrepancy(space_1[-1], space_1[:-1], disc_init)
assert_allclose(disc_iter, 0.0081, atol=1e-4)
# n<d
rng = np.random.default_rng(241557431858162136881731220526394276199)
space_1 = rng.random((4, 10))
disc_ref = qmc.discrepancy(space_1)
disc_init = qmc.discrepancy(space_1[:-1], iterative=True)
disc_iter = update_discrepancy(space_1[-1], space_1[:-1], disc_init)
assert_allclose(disc_iter, disc_ref, atol=1e-4)
# errors
with pytest.raises(ValueError,
match=r"Sample is not in unit "
r"hypercube"):
update_discrepancy(space_1[-1], space_1[:-1] + 1, disc_init)
with pytest.raises(ValueError, match=r"Sample is not a 2D array"):
update_discrepancy(space_1[-1], space_1[0], disc_init)
x_new = [1, 3]
with pytest.raises(ValueError,
match=r"x_new is not in unit "
r"hypercube"):
update_discrepancy(x_new, space_1[:-1], disc_init)
x_new = [[0.5, 0.5]]
with pytest.raises(ValueError, match=r"x_new is not a 1D array"):
update_discrepancy(x_new, space_1[:-1], disc_init)
x_new = [0.3, 0.1, 0]
with pytest.raises(ValueError,
match=r"x_new and sample must be "
r"broadcastable"):
update_discrepancy(x_new, space_1[:-1], disc_init)