def test_crowding_distance_assignment():
pareto = _tuples_to_NSGAMeta([(3, 5), (5, 3), (4, 4)])
three_five, five_three, four_four = pareto
crowding_distance_assignment(pareto)
assert three_five.distance == float('inf')
assert five_three.distance == float('inf')
assert four_four.distance == 2
def test_crowd_compare():
pareto = _tuples_to_NSGAMeta([(3, 5), (5, 3), (4, 4), (4.01, 3.99),
(4.5, 3.5)])
three_five, five_three, four_four, approx_four_four, half_half = pareto
fast_non_dominated_sort(pareto) # assigns rank
crowding_distance_assignment(pareto) # assigns distance
assert all([three_five.crowd_compare(other) == -1 for other in pareto[2:]])
assert all([five_three.crowd_compare(other) == -1 for other in pareto[2:]])
def test_crowding_distance_assignment_inf():
pareto = _tuples_to_NSGAMeta([(3, float('inf')), (5, 3), (4, 4)])
three_inf, five_three, four_four = pareto
crowding_distance_assignment(pareto)
assert three_inf.distance == float('inf')
assert five_three.distance == float('inf')
# In our implementation, we ignore 'axis' that contain inf values.
assert four_four.distance == 1