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 _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