import numpy as np
import pygame
import random
import json
import matplotlib.pyplot as plt
from tqdm import tqdm
from pygame.locals import *
def relu_l(array):
return np.minimum(np.maximum(array, 0), 1)
class NetWork:
def __init__(self):
self.input = np.array([0 for _ in range(8)]) # 输入层 8个神经元
self.l2 = np.array([0 for _ in range(4)]) # 隐含层1 4个神经元
self.l3 = np.array([0 for _ in range(4)]) # 隐含层2 4个神经元
self.output = np.array([0 for _ in range(4)]) # 输出层 4个神经元
self.chromosome = np.random.randn(100)
self.rebuild_from_chromosome()
def run(self, input):
# 输入格式：
# ↑ ↗ → ↘ ↓ ↙ ← ↖
self.input = input
self.l2 = relu_l(np.dot(self.w_in_to_2, self.input) + self.b_in_to_2)
self.l3 = relu_l(np.dot(self.w_2_to_3, self.l2) + self.b_2_to_3)
self.output = relu_l(np.dot(self.w_3_to_out, self.l3) + self.b_3_to_out)
# 输出格式：
# ↑ ← ↓ →
return self.output
def rebuild_from_chromosome(self):
self.w_in_to_2 = self.chromosome[ :32 ].reshape(4, 8)
self.w_2_to_3 = self.chromosome[32 :32 + 16 ].reshape(4, 4)
self.w_3_to_out = self.chromosome[32 + 16 :32 + 16 + 16 ].reshape(4, 4)
self.b_in_to_2 = self.chromosome[32 + 16 + 16 :32 + 16 + 16 + 4 ]
self.b_2_to_3 = self.chromosome[32 + 16 + 16 + 4 :32 + 16 + 16 + 4 + 4 ]
self.b_3_to_out = self.chromosome[32 + 16 + 16 + 4 + 4:32 + 16 + 16 + 4 + 4 + 4]
@classmethod
def crossover(cls, a, b):
crosspoint = random.randint(0, 99)
c_chromosome = np.array([*a.chromosome[:crosspoint], *b.chromosome[crosspoint:]])
c = cls()
c.chromosome = c_chromosome
c.rebuild_from_chromosome()
return c
class Player:
def __init__(self, action_from, pos, color):
self.action_from = action_from
self.x, self.y = pos
self.direction = [0, 0]
self.fitness = 0
self.color = color
def update(self, screen, food_pos, show):
food_x, food_y = food_pos
inputs = np.array(
[
int((food_x == self.x) and (food_y < self.y)),
int((food_x > self.x) and (food_y < self.y)),
int((food_x > self.x) and (food_y == self.y)),
int((food_x > self.x) and (food_y > self.y)),
int((food_x == self.x) and (food_y > self.y)),
int((food_x < self.x) and (food_y > self.y)),
int((food_x < self.x) and (food_y == self.y)),
int((food_x < self.x) and (food_y < self.y))
]
)
action = self.action_from.run(inputs)
self.direction = [0, 0]
self.direction[0] += action[3] * 2 # x+
self.direction[0] -= action[1] * 2 # x-
self.direction[1] += action[2] * 2 # y+
self.direction[1] -= action[0] * 2 # y-
self.x += self.direction[0]
self.y += self.direction[1]
if self.x > 400:
self.x = 400
if self.x < 0:
self.x = 0
if self.y > 400:
self.y = 400
if self.y < 0:
self.y = 0
distance = np.sqrt(
abs((food_x - self.x) ** 2) + \
abs((food_y - self.y) ** 2))
self.fitness = 566 - distance
if show:
rect = pygame.Rect(self.x - 5, self.y - 5, 10, 10)
pygame.draw.rect(screen, self.color, rect)
def __gt__(self, b):
return self.fitness > b.fitness
def __int__(self):
return self.fitness
def main1(gen_end, show):
if show:
pygame.init()
screen = pygame.display.set_mode((400, 400))
players = [Player(NetWork(), [200, 200],
(
round(_ * 0.51), 0, round((500 - _) * 0.51)
)) for _ in range(500)]
font = pygame.font.SysFont(None, 50)
else:
screen = None
players = [Player(NetWork(), [200, 200], (0, 0, 0)) for _ in range(500)]
highest_fitnesses = []
average_fitnesses = []
for gen in tqdm(range(1, gen_end + 1, 1)):
foodpos = [random.randint(0, 400), random.randint(0, 400)]
for _ in range(200):
if show:
screen.fill(0)
for event in pygame.event.get():
if event.type == QUIT:
return 0
for player in players:
player.update(screen, foodpos, show)
if show:
foodrect = pygame.Rect(foodpos[0] - 5, foodpos[1] - 5, 10, 10)
pygame.draw.rect(screen, (0, 255, 0), foodrect)
text = font.render("GEN {}".format(gen), True, (255, 255, 255))
screen.blit(text, (0, 0))
pygame.display.update()
# 排序并算出最高适应度和平均适应度
players.sort()
highest_fitness = players[-1].fitness
highest_fitnesses.append(highest_fitness)
sum_fitness = 0
for player in players:
sum_fitness += player.fitness
average_fitness = sum_fitness / len(players)
average_fitnesses.append(average_fitness)
# 选择（选择离目标最近的50个）
players = players[-50:]
# 交叉（随机选择450组，单点交叉后放入队列末端）
for _ in range(450):
p1 = random.choice(players)
p2 = random.choice(players)
n3 = NetWork.crossover(p1.action_from, p2.action_from)
p3 = Player(n3, [200, 200], (0, 0, 0))
players.append(p3)
# 变异（每个player有一定几率改变染色体中任意五个数）
for i in range(len(players)):
if random.randint(1, 10) == 1 and i > 0:
for _ in range(5):
mutationpoint = random.randint(0, 99)
players[i].action_from.chromosome[mutationpoint] = np.random.rand()
# 将所有玩家放回起点，重置适应值，上色
for i in range(len(players)):
players[i].x = 200
players[i].y = 200
players[i].fitness = 0
if show:
players[i].color = (
round(i * 0.51), 0, round((500 - i) * 0.51)
)
# 演化结束，将适应度最高的玩家的染色体放入res.json
players.sort()
res = {
'gen': gen_end,
'chromosome': list(players[-1].action_from.chromosome)
}
res2 = {
'gen_end': gen_end,
'highest_fitnesses': highest_fitnesses,
'average_fitnesses': average_fitnesses
}
with open("res.json", "w") as f_obj:
json.dump(res, f_obj)
with open("fitnesses.json", 'w') as f_obj:
json.dump(res2, f_obj)
pygame.quit()
def main2():
with open("fitnesses.json", "r") as f_obj:
res = json.load(f_obj)
#fig = plt.Figure()
x = list(range(1, res['gen_end'] + 1))
y = res['highest_fitnesses']
y2 = res['average_fitnesses']
highest, = plt.plot(x, y, label="highest fitness")
average, = plt.plot(x, y2, label="average fitness")
plt.xlabel("Generation")
plt.ylabel("Fitness")
plt.legend(handles=[highest, average], loc='best')
plt.show()
def main3():
pygame.init()
with open("res.json", "r") as f_obj:
res = json.load(f_obj)
gen = res['gen']
net = NetWork()
net.chromosome = np.array(res["chromosome"])
net.rebuild_from_chromosome()
player = Player(net, (200, 200), (255, 255, 255))
foodpos = [random.randint(0, 400), random.randint(0, 400)]
font = pygame.font.SysFont(None, 50)
screen = pygame.display.set_mode((400, 400)
