本文整理汇总了Python中brain.Brain.crossover方法的典型用法代码示例。如果您正苦于以下问题：Python Brain.crossover方法的具体用法？Python Brain.crossover怎么用？Python Brain.crossover使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类brain.Brain的用法示例。
在下文中一共展示了Brain.crossover方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from brain import Brain [as 别名]
# 或者: from brain.Brain import crossover [as 别名]
class Critter:
def __init__(self, pos):
"""
Initializes a critter creating his brain, setting
his energy and age using the settings functions
and assigning it the given position
Parameter pos is a tuple with the coordinates
of the critter in the world
"""
self.brain = Brain()
self.energy = Critter_s.start_energy()
self.age = Critter_s.start_age()
self.pos = pos
def process(self, world, partners = None):
"""
Moves this critter, makes this critter learn and
returns list of children from the available partners that this
critter decided to mate with
Parameter world is a grid (environment.Grid) of the world
Parameter partners is a list of critters available for mating
"""
sub = world.sub_grid(self.pos)
inputs = sub.values + [self.energy] # list composed by what the critter sees plus its energy
outputs = self.brain.process(inputs, 0) # list of four values indicating the movement
action = parse_move(outputs) # resulting movement vector obtained from the outputs
best = evaluate_move(sub, outputs) # evaluate the best possible move
self.brain.learn(inputs, best, 0) # make the critter learn in case he took the wrong direction
# assign new position
new_pos = [0, 0]
new_pos[0] = max(0, min(self.pos[0] + action[0], Environment_s.width -1))
new_pos[1] = max(0, min(self.pos[1] + action[1], Environment_s.height -1))
# decrease energy and increase age
if new_pos != self.pos:
self.energy -= Critter_s.move_cost
else:
self.energy -= Critter_s.stand_cost
self.age += 1
# move if the new position isn't occupied
if world.access_value(new_pos[0], new_pos[1]) != Environment_s.grid_crit:
self.pos = tuple(new_pos)
#ToDo decide if stay, push or kill
children = []
if partners != None and self.age >= Critter_s.min_mate_age and self.energy >= Critter_s.mate_cost: # if this critter is in condition to mate...
for partner in partners:
partner_constraints = partner.age >= Critter_s.min_mate_age2 and partner.energy >= Critter_s.mate_cost2
inputs = [partner.energy, partner.age, self.energy]
mate_decision = round(self.brain.process(inputs, 1)[0])
if partner_constraints and mate_decision: # ...the partner is in condition to mate,
children.append(self.crossover(partner)) # and this critter wants to, then they mate
return children
def crossover(self, partner):
"""
Returns the critters' child and decreases energy to
this critter and the partner
Parameter partner is the critter's partner
"""
self.energy -= Critter_s.mate_cost
partner.energy -= Critter_s.mate_cost2
child = Critter(Critter_s.randpos())
child.brain = self.brain.crossover(partner.brain)
return child
def copy(self):
"""
Returns a copy of this critter
"""
critter = Critter(self.pos)
critter.brain = self.brain.copy()
critter.energy = self.energy
critter.age = self.age
return critter
def eat(self, food = Environment_s.def_food):
"""
Increases energy depending on the food type
Parameter food is the food type, the constants are on the settings. Leave blank for deafult food
"""
self.energy += Critter_s.food_reward[food]
def collide(self):
"""
Decreases energy on collision
"""
self.energy -= Critter_s.coll_cost
# ToDo different kind of collisions
#.........这里部分代码省略.........