def subtree_mutate(ind_tree):
"""
Creates a list of all nodes and picks one node at random to mutate.
Because we have a list of all nodes, we can (but currently don't)
choose what kind of nodes to mutate on. Handy.
:param ind_tree: The full tree of an individual.
:return: The full mutated tree and the associated genome.
"""
# Find the list of nodes we can mutate from.
targets = ind_tree.get_target_nodes(
[], target=params['BNF_GRAMMAR'].non_terminals)
# Pick a node.
new_tree = choice(targets)
# Set the depth limits for the new subtree.
if params['MAX_TREE_DEPTH']:
# Set the limit to the tree depth.
max_depth = params['MAX_TREE_DEPTH'] - new_tree.depth
else:
# There is no limit to tree depth.
max_depth = None
# Mutate a new subtree.
generate_tree(new_tree, [], [], "random", 0, 0, 0, max_depth)
return ind_tree
def subtree_mutate(ind_tree):
"""
Creates a list of all nodes and picks one node at random to mutate.
Because we have a list of all nodes, we can (but currently don't)
choose what kind of nodes to mutate on. Handy.
:param ind_tree: The full tree of an individual.
:return: The full mutated tree and the associated genome.
"""
# Find the list of nodes we can mutate from.
targets = ind_tree.get_target_nodes([], target=params[
'BNF_GRAMMAR'].non_terminals)
# Pick a node.
new_tree = choice(targets)
# Set the depth limits for the new subtree.
if params['MAX_TREE_DEPTH']:
# Set the limit to the tree depth.
max_depth = params['MAX_TREE_DEPTH'] - new_tree.depth
else:
# There is no limit to tree depth.
max_depth = None
# Mutate a new subtree.
generate_tree(new_tree, [], [], "random", 0, 0, 0, max_depth)
return ind_tree
def generate_ind_tree(max_depth, method):
"""
Generate an individual using a given subtree initialisation method.
:param max_depth: The maximum depth for the initialised subtree.
:param method: The method of subtree initialisation required.
:return: A fully built individual.
"""
# Initialise an instance of the tree class
ind_tree = Tree(str(params['BNF_GRAMMAR'].start_rule["symbol"]), None)
# Generate a tree
genome, output, nodes, _, depth = generate_tree(ind_tree, [], [], method,
0, 0, 0, max_depth)
# Get remaining individual information
phenotype, invalid, used_cod = "".join(output), False, len(genome)
if params['BNF_GRAMMAR'].python_mode:
# Grammar contains python code
phenotype = python_filter(phenotype)
# Initialise individual
ind = individual.Individual(genome, ind_tree, map_ind=False)
# import pdb; pdb.set_trace()
# Set individual parameters
ind.phenotype, ind.nodes = phenotype, nodes
ind.depth, ind.used_codons, ind.invalid = depth, used_cod, invalid
# Generate random tail for genome.
ind.genome = genome + [
randint(0, params['CODON_SIZE'])
for _ in range(int(ind.used_codons / 2))
]
return ind
def generate_ind_tree(max_depth, method):
"""
Generate an individual using a given subtree initialisation method.
:param max_depth: The maximum depth for the initialised subtree.
:param method: The method of subtree initialisation required.
:return: A fully built individual.
"""
# Initialise an instance of the tree class
ind_tree = Tree(str(params['BNF_GRAMMAR'].start_rule["symbol"]), None)
# Generate a tree
genome, output, nodes, _, depth = generate_tree(ind_tree, [], [], method,
0, 0, 0, max_depth)
# Get remaining individual information
phenotype, invalid, used_cod = "".join(output), False, len(genome)
if params['BNF_GRAMMAR'].python_mode:
# Grammar contains python code
phenotype = python_filter(phenotype)
# Initialise individual
ind = individual.Individual(genome, ind_tree, map_ind=False)
# Set individual parameters
ind.phenotype, ind.nodes = phenotype, nodes
ind.depth, ind.used_codons, ind.invalid = depth, used_cod, invalid
# Generate random tail for genome.
ind.genome = genome + [randint(0, params['CODON_SIZE']) for
_ in range(int(ind.used_codons / 2))]
return ind
