def test_offspring_returns_two_chromosomes_combining_parents():
parents = [Chromosome.random(), Chromosome.random()]
subject = Crossover(parents)
result = subject.offspring()
assert len(result) == 2
assert isinstance(result[0], Chromosome)
assert isinstance(result[1], Chromosome)
def test_init_assigns_chromosomes():
chromosomes = [
Chromosome(np.array(list("A" * 26))),
Chromosome(np.array(list("B" * 26))),
]
subject = Population(chromosomes)
assert subject.chromosomes == chromosomes
def test_mutated_generates_genes(random):
random.side_effect = [0.01, 0.01] + [1] * 24
subject = Chromosome(np.array(list(string.ascii_uppercase)))
result = subject.mutated()
expected = Chromosome(np.array(list("IJCDEFGHIJKLMNOPQRSTUVWXYZ")))
assert isinstance(result, Chromosome)
assert result == expected
def test_best_chromosomes_sorts_them_in_descending_order():
chromosomes = [
Chromosome(np.array(list("ABCXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list("ABXXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list(string.ascii_uppercase))),
]
subject = Population(chromosomes)
expected = [chromosomes[2], chromosomes[0], chromosomes[1]]
assert subject.best_chromosomes() == expected
def test_parents_probabilities_returns_values_proportional_to_their_fitness():
chromosomes = [
Chromosome(np.array(list("ABCXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list("ABXXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list(string.ascii_uppercase))),
]
subject = Population(chromosomes)
result = subject.parents_probabilities()
expected = [0.12121212, 0.09090909, 0.78787879]
np.testing.assert_allclose(result, expected)
def test_random_parents_returns_two_random_chromosomes():
chromosomes = [
Chromosome(np.array(list("A" * 26))),
Chromosome(np.array(list("B" * 26))),
Chromosome(np.array(list("C" * 26))),
]
subject = Population(chromosomes)
random_parents = subject.random_parents()
assert len(random_parents) == 2
assert random_parents[0] == chromosomes[0]
assert random_parents[1] == chromosomes[0]
def test_offspring_returns_chromosomes_combining_parents(monkeypatch):
parents = [
Chromosome(np.array(list("XXXXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list("YYYYYYYYYYYYYYYYYYYYYYYYYY"))),
]
subject = Crossover(parents)
monkeypatch.setattr(subject, "pivot", 3)
result = subject.offspring()
assert result[0] == Chromosome(np.array(
list("XXXYYYYYYYYYYYYYYYYYYYYYYY")))
assert result[1] == Chromosome(np.array(
list("YYYXXXXXXXXXXXXXXXXXXXXXXX")))
def test_call_returns_parents_if_must_not_crossover(monkeypatch):
parents = [Chromosome.random(), Chromosome.random()]
subject = Crossover(parents)
monkeypatch.setattr(subject, "will_crossover_happen", lambda: False)
assert subject() == parents
def test_will_crossover_happen_returns_false_if_random_is_greater_than_probability(
random):
parents = [Chromosome.random(), Chromosome.random()]
subject = Crossover(parents, probability=0.8)
random.return_value = 0.9
assert not subject.will_crossover_happen()
def test_call_returns_crossed_over_if_must_crossover(monkeypatch):
parents = [Chromosome.random(), Chromosome.random()]
subject = Crossover(parents)
monkeypatch.setattr(subject, "will_crossover_happen", lambda: True)
assert subject() == subject.offspring()
def test_fitness_returns_number_of_correct_genes():
subject = Chromosome(np.array(list("ABXXXXXXXXXXXXXXXXXXXXXXXX")))
assert subject.fitness() == 3
def test_fitness_returns_chromosome_length_for_ideal_result():
subject = Chromosome(np.array(list(string.ascii_uppercase)))
assert subject.fitness() == 26
def test_random_generates_genes():
subject = Chromosome.random()
assert isinstance(subject, Chromosome)
assert np.array_equal(subject.genes, GENES_FIXTURE)
def test_init_assigns_genes():
subject = Chromosome(GENES_FIXTURE)
assert np.array_equal(subject.genes, GENES_FIXTURE)
def first_generation(self):
chromosomes = []
for _ in range(self.SIZE):
chromosomes.append(Chromosome.random())
return Population(chromosomes)
def offspring(self):
a = self.parents[0]
b = self.parents[1]
genes_1 = np.concatenate([a.genes[:self.pivot], b.genes[self.pivot:]])
genes_2 = np.concatenate([b.genes[:self.pivot], a.genes[self.pivot:]])
return [Chromosome(genes_1), Chromosome(genes_2)]