def multiple_chromosome_n_point_crossover(random, mom, dad, args):
children = MultipleChromosomeGenome(keys=mom.keys)
for key in children.keys:
children[key] = set_n_point_crossover(random, [mom[key], dad[key]],
args)
return children
def multiple_chromosome_set_generator(random, args):
"""
Generates a candidate in with a genome containing multiple chromosomes.
Parameters
----------
random : Random
args : dict
this dictionary contains an extra key: "keys", which will be the names of the chromosomes.
for each argument on <unique_set>, this dictionary must contain the same fields, starting with
the values in "keys".
Returns
-------
MultipleChromosomeGenome
"""
keys = args.get('keys')
candidate = MultipleChromosomeGenome(keys=keys)
for key in keys:
key_args = {
'representation': args.get("%s_representation" % key),
'max_size': args.get("%s_max_size" % key),
'variable_size': args.get('variable_size')
}
candidate[key] = set_generator(random, key_args)
return candidate
def test_two_chromosomes(self):
genome = MultipleChromosomeGenome(["A", "B"])
self.assertIsInstance(genome["A"], list)
self.assertIsInstance(genome["B"], list)
genome["A"] = [1, 2, 3, 4]
genome["B"] = ["A", "B", "C"]
self.assertEqual(genome["A"], OrderedSet([1, 2, 3, 4]))
self.assertEqual(genome["B"], OrderedSet(["A", "B", "C"]))
del genome["A"]
self.assertRaises(KeyError, genome.__getitem__, "A")
def test_multiple_chromosome_set_mutation(self):
genome = MultipleChromosomeGenome(["A", "B"])
genome["A"] = [1, 2, 3, 4]
genome["B"] = [1, 5, 7, 10]
representation = list(range(10))
args = {
"A_representation": representation,
"B_representation": representation,
"A_mutation_rate": 1,
"B_mutation_rate": 1
}
new_individuals = multiple_chromosome_set_mutation(Random(SEED), [genome], args)
self.assertEqual(new_individuals[0]["A"], OrderedSet([0, 6, 7, 8]))
self.assertEqual(new_individuals[0]["B"], OrderedSet([0, 6, 8, 9]))
def multiple_chromosome_set_mutation(random, individual, args):
"""
Mutates a given set based on the entries available on the representation.
Parameters
----------
random: Random
individual: MultipleChromosomeGenome
with unique integers in each chromosome
args: dict
must contain the representation of each chromosome
Returns
-------
MultipleChromosomeGenome
A mutated individual
"""
new_individual = MultipleChromosomeGenome(keys=individual.keys)
for key in individual.keys:
representation = args.get('%s_representation' % key)
indices = range(len(representation))
indices = _subtract(
indices, individual[key]
) # remove indices already present in the individual
mutation_rate = args.get('%s_mutation_rate' % key, .1)
for value in individual[key]:
if random.random() < mutation_rate and len(indices) > 0:
index = random.sample(indices, 1)[0]
indices.remove(index)
new_individual[key].append(index)
else:
new_individual[key].append(value)
assert len(new_individual[key]) == len(individual[key])
new_individual[key] = sorted(new_individual[key])
return new_individual
def test_multiple_chromosome_set_indel(self):
genome = MultipleChromosomeGenome(["A", "B"])
genome["A"] = [1, 2, 3, 4]
genome["B"] = [1, 5, 7, 10]
representation = list(range(10))
args = {
"A_representation": representation,
"B_representation": representation,
"A_indel_rate": 1,
"B_indel_rate": 1
}
random = Random(SEED)
new_individuals = multiple_chromosome_set_indel(random, [genome for _ in range(5)], args)
self.assertEqual(new_individuals[0]["A"], OrderedSet([1, 2, 3, 4, 7]))
self.assertEqual(new_individuals[0]["B"], OrderedSet([1, 5, 10]))
self.assertEqual(new_individuals[1]["A"], OrderedSet([2, 3, 4]))
self.assertEqual(new_individuals[1]["B"], OrderedSet([1, 5, 7, 8, 10]))
self.assertEqual(new_individuals[2]["A"], OrderedSet([1, 2, 3, 4, 6]))
self.assertEqual(new_individuals[2]["B"], OrderedSet([1, 5, 7]))
self.assertEqual(new_individuals[3]["A"], OrderedSet([1, 2, 3, 4, 8]))
self.assertEqual(new_individuals[3]["B"], OrderedSet([0, 1, 5, 7, 10]))
self.assertEqual(new_individuals[4]["A"], OrderedSet([1, 2, 3, 4, 7]))
self.assertEqual(new_individuals[4]["B"], OrderedSet([1, 5, 7, 8, 10]))