def test_evaluate(self, inst_mock):
""" Ensures the evaluation method works correctly. """
# Setup fake components
gene_one = Mock()
gene_one.get_information = [1, 1, 1]
gene_two = Mock()
gene_two.get_information = [0, 0, 0]
fake_equation = Mock() # Fake equation to evaluate it against
fake_equation.get_clause_evaluation = Mock(
return_value=[False, False, False]) # 0%
kgnm = KitanoGenome(genome_size=2)
kgnm._genes = []
assert kgnm.evaluate(fake_equation) == 0
def _reproduction(self, parents):
""" Reproduces the organism from the parent. """
genome_size = parents[0].get_expected_genome_size()
population = [] # New population
parent_a_genes = parents[0].get_genes(
) # Get the parents genes for crossover
parent_b_genes = parents[1].get_genes()
all_genes = []
all_genes.extend(parent_a_genes) # Generate a list of all genes
all_genes.extend(parent_b_genes)
while len(population) < 10:
# Make child by randomly selecting genes then pruning
child = KitanoGenome(genome_size)
child.clear_genes()
# Create a random permutation of the genes to be added
shuffle(all_genes)
# Add all genes to child then prune
for gene in all_genes:
child.add_gene(SubMatrixGene(gene.get_data()))
child.prune_genome()
population.append(child)
return population
def _initialise(self, no_vars):
""" Initialises the population of directly encoded genomes. """
self._repopulate([]) # Empty pop
while len(self.get_population()) < 10:
new_organism = KitanoGenome(no_vars)
self._add_organism(new_organism)
def test_instantiate_prune(self):
""" Ensures a random number of sub matrices are generated. """
kgnm = KitanoGenome(genome_size=10)
length = kgnm.get_genome_length()
grammar = ""
assert length == 10
grammar = ""
kgnm = KitanoGenome(genome_size=15) # Try an odd size
length = kgnm.get_genome_length()
assert length == 16
kgnm = KitanoGenome(genome_size=15) # Try a different size
kgnm._expected_genome_size = 6
kgnm.prune_genome()
length = kgnm.get_genome_length()
assert length == 6
kgnm = KitanoGenome(genome_size=10) # Try a different size
kgnm._expected_genome_size = 6
kgnm.prune_genome()
length = kgnm.get_genome_length()
assert length == 6
kgnm = KitanoGenome(9)
kgnm.clear_genes()
x1 = SubMatrixGene("aadc")
x2 = SubMatrixGene("aadc")
x3 = SubMatrixGene("c")
x4 = SubMatrixGene("cb")
x5 = SubMatrixGene("b")
x6 = SubMatrixGene("da")
kgnm._genes = [x1, x2, x3, x4, x5, x6]
kgnm.prune_genome()
length = kgnm.get_genome_length()
assert length == 10
def test_instantiate_1(self, sm_gene, prune_mock):
""" Ensures a random number of sub matrices are generated. """
kgnm = KitanoGenome(genome_size=10)
assert sm_gene.call_count == 10 # Generated 10 Genes?
assert len(kgnm._genes) == 10
assert prune_mock.call_count == 1
def test_init(self, inst_mock):
""" Ensures the init initialises the number of genes to be expected. """
bgnm = KitanoGenome(genome_size=500)
assert inst_mock.called