def GA_minimize(function, guess):
result = GA(function,[guess, ], minimize=True) # set to minimize by default
start = timeit.default_timer()
mem = max(memory_usage((result.learn,(),)))
stop = timeit.default_timer()
print result.learn() #Comment this out for faster performance, i.e. if used purely for evaluation
exec_time = stop-start
print '{0} took {1} seconds'.format('Genetic Algorithm',exec_time)
print '{0} used {1} megabytes'.format('Genetic Algorithm',mem)
print
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()
def GA_minimize(function, guess):
"""
This function runs the genetic algorithm from PyBrain (http://pybrain.org/docs/api/optimization/optimization.html) on a function, provided with an initial guess.
| NOTE: Format is **crucial** here or this will not work (blame the authors of PyBrain, sorry):
| Function must take a **tuple** (**Even** if of only one element (in the form (a, )) and guess **MUST BE A LIST** (even if only with one element)
"""
result = GA(function, guess, minimize=True) # set to minimize by default
start = timeit.default_timer()
mem = max(memory_usage(-1,interval=.1))
print "The result is: ", result.learn()
stop = timeit.default_timer()
exec_time = stop-start
print '{0} took {1} seconds'.format('Genetic Algorithm',exec_time)
print '{0} used {1} megabytes'.format('Genetic Algorithm',mem)
print
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])
#with open('12nodeNN-20pop.pkl','wb') as output:
