def test_clone_population():
# for this test we don't require a real population of Individual objects
inds = np.array([1, 2, 3, 4, 5])
pop1 = pg.Population(individuals=inds)
pop2 = pop1.clone()
assert np.array_equal(pop1.individuals, pop2.individuals)
assert pop1.size == pop2.size
pop1.individuals = np.array([6, 7, 8])
pop1.size = 3
assert np.array_equal(pop1.individuals, pop2.individuals) is False
assert pop2.size == 5
def test_negative_tournament_selection():
np.random.seed(42)
individuals = [
pg.Individual(fitness=pg.Fitness(value=np.random.randint(10)))
for _ in range(10)
]
pop = pg.Population(individuals=np.array(individuals))
# [6, 3, 7, 4, 6, 9, 2, 6, 7, 4]
np.random.seed(42)
inds1 = [pg.negative_tournament_selection(pop, size=5) for _ in range(10)]
assert [inds1[i].fitness.value
for i in range(10)] == [6, 7, 6, 9, 9, 9, 7, 7, 7, 7]
np.random.seed(24)
inds2 = [pg.negative_tournament_selection(pop, size=5) for _ in range(10)]
assert [inds2[i].fitness.value
for i in range(10)] == [7, 6, 7, 7, 6, 6, 3, 4, 6, 7]
def test_random_selection():
np.random.seed(42)
individuals = [
pg.Individual(fitness=pg.Fitness(value=np.random.randint(10)))
for _ in range(10)
]
pop = pg.Population(individuals=np.array(individuals))
# [6, 3, 7, 4, 6, 9, 2, 6, 7, 4]
np.random.seed(42)
inds1 = [pg.random_selection(pop) for _ in range(10)]
assert [inds1[i].fitness.value
for i in range(10)] == [2, 4, 6, 6, 2, 4, 7, 2, 6, 6]
np.random.seed(24)
inds2 = [pg.random_selection(pop) for _ in range(10)]
assert [inds2[i].fitness.value
for i in range(10)] == [7, 4, 6, 6, 3, 3, 3, 6, 6, 4]
def test_make_population():
pop = pg.Population()
assert type(pop) is pg.Population
assert pop.individuals is None
assert pop.size == 0