def process(self,pod,dt):
global best_brain
global best_fitness
# If we are trying to evolve and pod dies
if reap_pod(pod.state):
" here then time to replace the brain"
# save current brain and fitness in the pool
fitness=calc_fitness(pod.state)
if fitness > best_fitness:
best_brain=pod.brain
best_fitness=fitness
print fitness, " : ",pod.brain.params
# reset the pod and give it a new brain
world.init_pod(pod)
pod.brain=create_new()
return
# normal control stuff
control=pods.Control()
p=pod.brain.params
sensor=pod.sensors
state=pod.state
V=p[0]*sensor[0].val
if V>p[1]:
V=p[1]
cont=(V/(abs(state.vel+1e-6)+p[2])) - p[3]
if cont > 0:
control.up = cont
else:
control.down = abs(cont)
diff=(sensor[1].val+sensor[2].val)-(sensor[7].val+sensor[6].val)
turn_compensation=p[4]*sensor[0].val+p[5]
if diff>0.0:
control.left=abs((diff/p[6])**2/p[7])+turn_compensation
else:
control.right=abs((diff/p[6])**2/p[7])+turn_compensation
return control
def load(self,file):
self.list=[]
n=pickle.load(file)
print n
for i in range(n):
f=pickle.load(file)
net=loadBrain(file)
net.proof_count=0 # sorry we lost the proof count when we saved it
net.fitness=f
self.add(net)
print "RELOADED POOL"
for pod in pods:
# reset the pod and give it a new brain from the pool
world.init_pod(pod)
pod.ang += random()-0.5 # randomize the intial angle
pod.brain.brain=pool.create_new()
def process(self,sim):
# this is called just before each time step
# do admin tasks here
global pods
# output to a log file
if pool.reaping and log_file!=None and pool.touched:
log_file.write(str(sim.ticks) +','+ str(pool.best_fitness())+','+str(pool.average_fitness())+'\n')
pool.touched=False
keyinput = pygame.key.get_pressed()
# speed up/down display
if keyinput[pygame.K_KP_PLUS] or keyinput[pygame.K_EQUALS]:
sim.frameskipfactor = sim.frameskipfactor+1
print "skip factor" ,sim.frameskipfactor
if keyinput[pygame.K_MINUS]:
sim.frameskipfactor = max(1,sim.frameskipfactor-1)
print "skip factor" ,sim.frameskipfactor
# display the performance of the best pod in pool
if keyinput[pygame.K_b]:
if pool.reaping: # if reaping copy existing to allow restore
self.pods_copy=copy(pods)
pod=pods[0]
del pods[:]
pods.append(pod)
else:
pod=pods[0]
world.init_pod(pod)
pod.ang += random()-0.5 # randomize the intial angle
pod.control.brain=pool.create_best()
pool.reaping=False
# display the performance of the most proven pod
if keyinput[pygame.K_p]:
if pool.reaping: # if reaping copy existing to allow restore
self.pods_copy=copy(pods)
pod=pods[0]
del pods[:]
pods.append(pod)
else:
pod=pods[0]
world.init_pod(pod)
pod.ang += random()-0.5 # randomize the intial angle
pod.control.brain=pool.create_most_proven()
pool.reaping=False
# go back into evolution mode after looking at best pod
if not pool.reaping and keyinput[pg.K_r]:
del pods[:]
pods.extend(self.pods_copy)
pool.reaping=True
# save the pool to a file
if keyinput[pygame.K_s]:
file=open(POOL_FILE_NAME,"w")
pool.save(file)
file.close()
# reload the pool from a file
if keyinput[pygame.K_l]:
file=open(POOL_FILE_NAME,"r")
pool.load(file)
file.close()
if keyinput[pygame.K_d]:
for brain in pool.list:
print brain.fitness," : ",brain.params