def breed(parent_a, parent_b):
"""Performs sub-tree crossover on parent_a and parent_b returning the child tree."""
def crossover_recursive(receiver_index, donator_index):
if receiver_index < len(
child_pacman_cont.state_evaluator
) and donator_index < len(parent_pacman_cont.state_evaluator):
return
child_pacman_cont.state_evaluator[
receiver_index] = tree.TreeNode(
receiver_index, parent_pacman_cont.
state_evaluator[donator_index].value)
crossover_recursive(
child_pacman_cont.state_evaluator.get_left_child_index(
receiver_index),
parent_pacman_cont.state_evaluator.get_left_child_index(
donator_index))
crossover_recursive(
child_pacman_cont.state_evaluator.get_right_child_index(
receiver_index),
parent_pacman_cont.state_evaluator.get_right_child_index(
donator_index))
# Choose a random node (crossover point) from each state evaluator node list
crossover_node_a = parent_a.pacman_cont.state_evaluator[
random.choices([
n for n in parent_a.pacman_cont.state_evaluator if n.value
])[0].index]
crossover_node_b = parent_b.pacman_cont.state_evaluator[
random.choices([
n for n in parent_b.pacman_cont.state_evaluator if n.value
])[0].index]
child_pacman_cont = copy.copy(parent_a.pacman_cont)
parent_pacman_cont = parent_b.pacman_cont
# Extend the child's state evaluator if necessary
if len(child_pacman_cont.state_evaluator) < len(
parent_a.pacman_cont.state_evaluator):
child_pacman_cont.state_evaluator = child_pacman_cont.state_evaluator + [
tree.TreeNode(index, None) for index in range(
len(child_pacman_cont.state_evaluator),
len(parent_a.pacman_cont.state_evaluator))
]
# Perform sub-tree crossover
crossover_recursive(crossover_node_a.index, crossover_node_b.index)
# Finish generating the child
world = gpac_world_class.GPacWorld(self.config)
game_state = game_state_class.GameState(
world.pacman_coords, world.ghost_coords, world.pill_coords,
self.get_num_adj_walls(world, world.pacman_coords[0]))
pacman_cont = child_pacman_cont
ghosts_cont = parent_a.ghosts_cont
game_state.update_walls(world.wall_coords)
child = gpac_world_individual_class.GPacWorldIndividual(
world, game_state, pacman_cont, ghosts_cont)
return child
def crossover_recursive(receiver_index, donator_index):
# BONUS2
if receiver_index < len(child_pacman_conts[cont_index].state_evaluator) and donator_index < len(parent_pacman_cont.state_evaluator):
return
child_pacman_conts[cont_index].state_evaluator[receiver_index] = tree.TreeNode(receiver_index, parent_pacman_cont.state_evaluator[donator_index].value)
crossover_recursive(child_pacman_conts[cont_index].state_evaluator.get_left_child_index(receiver_index), parent_pacman_cont.state_evaluator.get_left_child_index(donator_index))
crossover_recursive(child_pacman_conts[cont_index].state_evaluator.get_right_child_index(receiver_index), parent_pacman_cont.state_evaluator.get_right_child_index(donator_index))
def __copy__(self):
"""Performs a deep copy of this object, except for the state evaluator."""
other = type(self)(self.config)
super(base_controller_class.BaseController, other).__init__()
other.config = self.config
other.max_fp_constant = float(self.config.settings['max fp constant'])
other.state_evaluator = tree_class.Tree(self.config)
other.state_evaluator.list[:] = [tree_class.TreeNode(node.index, node.value) if node else None for node in self.state_evaluator]
return other
def breed(parent_a, parent_b, unit_id):
"""Performs sub-tree crossover on parent_a and parent_b returning the child tree."""
def crossover_recursive(receiver_index, donator_index):
# BONUS1, BONUS2
if receiver_index < len(child_conts[cont_index].state_evaluator
) and donator_index < len(
parent_cont.state_evaluator):
return
child_conts[cont_index].state_evaluator[
receiver_index] = tree.TreeNode(
receiver_index,
parent_cont.state_evaluator[donator_index].value)
crossover_recursive(
child_conts[cont_index].state_evaluator.
get_left_child_index(receiver_index),
parent_cont.state_evaluator.get_left_child_index(
donator_index))
crossover_recursive(
child_conts[cont_index].state_evaluator.
get_right_child_index(receiver_index),
parent_cont.state_evaluator.get_right_child_index(
donator_index))
# BONUS1, BONUS2
if unit_id == self.PACMAN_ID:
if self.config.settings.getboolean(
'use single pacman controller'):
num_conts = 1
else:
num_conts = self.num_pacmen
else:
# Default to self.GHOST_ID
if self.config.settings.getboolean(
'use single ghost controller'):
num_conts = 1
else:
num_conts = self.num_ghosts
child_conts = [None] * num_conts
for cont_index in range(num_conts):
# Choose a random node (crossover point) from each state evaluator node list
crossover_node_a = parent_a.conts[cont_index].state_evaluator[
random.choice([
n for n in parent_a.conts[cont_index].state_evaluator
if n.value
]).index]
crossover_node_b = parent_b.conts[cont_index].state_evaluator[
random.choice([
n for n in parent_b.conts[cont_index].state_evaluator
if n.value
]).index]
child_conts[cont_index] = copy.copy(parent_a.conts[cont_index])
parent_cont = parent_b.conts[cont_index]
# Extend the child's state evaluator if necessary
if len(child_conts[cont_index].state_evaluator) < len(
parent_a.conts[cont_index].state_evaluator):
child_conts[cont_index].state_evaluator = child_conts[
cont_index].state_evaluator + [
tree.TreeNode(index, None) for index in range(
len(child_conts[cont_index].state_evaluator),
len(parent_a.conts[cont_index].state_evaluator)
)
]
# Perform sub-tree crossover
crossover_recursive(crossover_node_a.index,
crossover_node_b.index)
# Finish generating the child
child = cont_class.ControllerIndividual(child_conts)
return child
