def test_evaluate(self, inst_mock):
""" Ensures the evaluation method works correctly. """
fake_equation = Mock()
fake_equation.get_clause_evaluation = Mock(return_value=[False, False, False]) # 0%
bgnm = BinaryGenome(genome_size=2)
bgnm._genes = []
assert bgnm.evaluate(fake_equation) == 0
def _initialise(self, no_vars):
""" Initialises the population of directly encoded genomes. """
self._repopulate([]) # Empty pop
while len(self.get_population()) < 10:
new_organism = BinaryGenome(no_vars)
print(new_organism.get_genome_length())
self._add_organism(new_organism)
def test_instantiate(self, get_cnt_mock):
""" Ensures the gene count is returned appropriately. """
get_cnt_mock.return_value = 5
bgnm = BinaryGenome(genome_size=5) # Should call the function automatically.
assert len(bgnm._genes) == 5
get_cnt_mock.return_value = 7 # Try with another set of values.
bgnm = BinaryGenome(genome_size=7)
assert len(bgnm._genes) == 7
def _reproduction(self, parents):
""" Reproduces the organism from the parent. """
genome_size = len(parents[0].get_genes())
population = [] # New population
parent_a_genes = parents[0].get_genes(
) # Get the parents genes for crossover
parent_b_genes = parents[1].get_genes()
split_point = randint(0,
genome_size) # Choose a point to reproduce over
while len(population) <= 10:
new_gene_list = []
new_gene_list.extend(parent_a_genes[0:split_point])
new_gene_list.extend(parent_b_genes[split_point:genome_size])
# Generate new child
child = BinaryGenome(genome_size)
child.clear_genes()
for gene in new_gene_list:
child.add_gene(gene)
population.append(child)
return population
def test_init(self, inst_mock, set_cnt_mock):
""" Ensures the init initialises the number of genes to be expected. """
bgnm = BinaryGenome(genome_size=500)
assert inst_mock.called