def experiment1(self):
l = GA(self.fitnessFunction, self.myNetwork.params[self.indices])
l.minimize = False
l.verbose = True
l.maxLearningSteps = 500
params, fitness = l.learn()
self.myNetwork.params[self.indices] = params
self.metaInfo["numsteps"] = l.maxLearningSteps
self.metaInfo["fitness"] = fitness
# self.myNetwork._setParameters(self.originalWeights)
self.logNet()
popList.append(initPopulation)
for x in range(0,numberOfAgents):
weights = initPopulation[0]
NNetListParams.append(weights)
i = 0
while (i < 50):
for x in range(0,len(NNetListParams)):
for k in range(0,len(NNetListParams)):
NNetList[k]._setParameters(NNetListParams[k])
workingNN = x
ga = GA(evaluator,NNetListParams[x],maxEvaluations = 20,initRangeScaling = 2,elitism = False,populationSize = populationSize,initialPopulation = popList[x],mutationProb = 0.1)
ga.minimize = True
result = ga.learn()
NNetListParams[x] = result[0]
popList[x] = ga.currentpop
i += 1
print i
for x in range(0,len(NNetListParams)):
for k in range(0,len(NNetListParams)):
NNetList[k]._setParameters(NNetListParams[k])
