def setMachine(self, mac, ip=None, localApps=False):
mac = mac.strip()
if len(mac.split(':')) <> 6:
return False
try: ip = ip.strip()
except: pass
c = Cfg()
c.read(self.__configLTSPFile)
if not c.has_section(mac):
c.add_section(mac)
if ip is not None:
from rede import Rede
s = self.getServer()
masc = s[2]
rede = s[4]
del s
try:
i = Rede(ip, masc)
except:
return False
if i.getRedeAsStr() <> rede:
return False
c.set(mac, 'ip', ip)
c.set(mac, 'localapps', str(localApps))
c.write( open(self.__configLTSPFile,'w') )
del c
return True
def setRange(self, start, end):
s = self.getServer()
masc = s[2]
rede = s[4]
from rede import Rede
try:
i = Rede(start, masc)
f = Rede(end , masc)
except:
return False
if i.getRedeAsStr() <> rede:
return False
if f.getRedeAsStr() <> rede:
return False
section = 'range'
c = Cfg()
c.read(self.__configLTSPFile)
if not c.has_section( section ):
c.add_section( section )
c.set(section, 'start', start)
c.set(section, 'end' , end)
c.write( open(self.__configLTSPFile,'w') )
del c
return True
def setServer(self, ip, masc, iface):
from rede import Rede
r = Rede(ip, masc)
M = self.getMachines()
c = Cfg()
if not c.has_section('server'):
c.add_section('server')
c.set('server', 'ip', r.getIPAsStr())
c.set('server', 'masc', r.getMascAsStr())
c.set('server', 'rede', r.getRedeAsStr())
c.set('server', 'broadcast', r.getBroadcastAsStr())
c.set('server', 'iface', iface)
c.write( open(self.__configLTSPFile,'w') )
for i in M:
self.setMachine(i[0], i[1], i[2])
del c
def _create_neural_network(self, qtd_camadas=2):
self.rede = Rede(1 + (self.qtd_inimigos * 3))
[self.rede.add_camada(10) for i in range(qtd_camadas)]
self.rede.add_camada(3)
class Jogo:
def __init__(self, qtd_inimigos=3, width=WIDTH, height=HEIGHT):
pygame.init()
self.display = Display(width=WIDTH, height=HEIGHT)
self.clock = pygame.time.Clock()
self.jogador = BlocoPrincipal(self.display, width=width, height=height)
self.qtd_inimigos = qtd_inimigos
self.enemys = []
self.score = 0
def _check_event(self):
for event in pygame.event.get():
if event.type == pygame.QUIT:
return True
elif event.type == pygame.KEYDOWN:
self.jogador.move(event.key)
return False
def init(self, color_fill=GREY):
fechou = False
self._create_neural_network()
while not fechou:
self.display.fill(color_fill)
self._create_enemy()
self._enemys_fall()
self._check_fall_enemy()
fechou = self._check_colision() or fechou
fechou = self._check_event() or fechou
self.jogador.set_bloco()
self.display.write_score(self.score)
self._get_move_rna()
self.display.update()
self.clock.tick(60)
pygame.quit()
def _create_enemy(self, qtd=None, width=WIDTH, height=HEIGHT):
qtd = qtd or self.qtd_inimigos
while len(self.enemys) < qtd:
x = randint(0, WIDTH - WIDTH_BLOCO)
self.enemys.append(
BlocoInimigo(x=x,
speed=random() * 10,
display=self.display,
width=width,
height=height))
def _enemys_fall(self):
[self.enemys[i].fall() for i in range(len(self.enemys))]
def _check_fall_enemy(self):
for idx, enemy in enumerate(self.enemys):
if enemy.y >= (HEIGHT + HEIGHT_BLOCO):
del self.enemys[idx]
self.score += 1
self._create_enemy()
def _check_colision(self):
for enemy in self.enemys:
if enemy.y >= HEIGHT - HEIGHT_BLOCO:
x = enemy.x
x_high = x + (HEIGHT_BLOCO)
x_low = x - (HEIGHT_BLOCO)
if x_low <= self.jogador.x <= x_high:
return True
return False
def _create_neural_network(self, qtd_camadas=2):
self.rede = Rede(1 + (self.qtd_inimigos * 3))
[self.rede.add_camada(10) for i in range(qtd_camadas)]
self.rede.add_camada(3)
def _get_move_rna(self):
inputs = self._get_inputs()
print(inputs)
predicts = self.rede.predict(inputs)
max_predict = max(predicts)
if max_predict == predicts[0]:
self.jogador._move_left()
if max_predict == predicts[1]:
self.jogador._move_right()
if max_predict == predicts[2]:
pass
def _get_inputs(self):
inputs = []
inputs += [min_max_scaler(0, WIDTH, enemy.x) for enemy in self.enemys]
inputs += [min_max_scaler(0, HEIGHT, enemy.y) for enemy in self.enemys]
inputs += [min_max_scaler(0, 10, enemy.speed) for enemy in self.enemys]
inputs += [min_max_scaler(0, WIDTH, self.jogador.x)]
return inputs
def cria_redes(self):
qtd_inputs = 1 + (self.qtd_inimigos * 3)
rede = Rede(base)
rede.add_camada(5)
rede.add_camada(3)
base = rede.sum_shapes
lim_inf = [-2 for i in range(base)]
lim_sup = [2 for i in range(base)]
base = [10 for i in range(base)]
p1 = Populacao(10, base, lim_inf, lim_sup, prob_mutacao=1)
p1.gera_populacao_inicial()
np.array(rede.camadas[0]).reshape(rede.shapes[0])
aux = 0
ind = p1[0].real
ind2 = p1[1].real
camadas = []
camadas2 = []
for idx, prod in enumerate(rede.prod_shapes):
camadas.append(
np.array(ind[aux:aux + prod]).reshape(rede.shapes[idx]))
camadas2.append(
np.array(ind2[aux:aux + prod]).reshape(rede.shapes[idx]))
aux += prod
np.append(camadas[0][:, 1].reshape(-1, 1),
camadas2[0][:, 2].reshape(-1, 1),
axis=1)
novas_camadas = []
for i in range(len(camadas)):
camada1 = camadas[i]
camada2 = camadas2[i]
nova_camada = []
for j in range(camada1.shape[1]):
neuronio1 = np.array(camada1[:, j]).reshape(-1, 1)
neuronio2 = np.array(camada2[:, j]).reshape(-1, 1)
if random() < 0.5:
if len(nova_camada) == 0:
nova_camada = neuronio1
else:
nova_camada = np.append(nova_camada, neuronio1, axis=1)
else:
if len(nova_camada) == 0:
nova_camada = neuronio2
else:
nova_camada = np.append(nova_camada, neuronio2, axis=1)
novas_camadas.append(nova_camada)
print('''
Camada1:
{}
Camada2:
{}
Nova Camada:
{}
'''.format(camada1, camada2, nova_camada))
