t=2
pMut = 0.3
pCross = 0.9;
popsize = 50;
maxgen = 100;
if config == 3:
t=2
pMut = 0.3
pCross = 0.9;
popsize = 30;
maxgen = 100;
simName = util.randomString(20);
net=nef.Network('Vivae - Turning controller for agent')
net.add_to_nengo()
ea = EA(maxgen,popsize,minw,maxw,genomeLen);
ea.setProbabilities(pMut,pCross);
ea.initPop();
simulator = buildExperiment(net,wa,wb,wc,visibility=False);
vivae = simulator.getControls();
expNo = round(1000000*random.random(),0); # generate some number for text file data
print expNo
f = open('data/tmp/ea_%d.txt'%expNo, 'w');
sx = Saver('ea_%d_agents.txt'%expNo); # saves best agent from actual generation during the evolution into a file
# evolution insert here
while ea.wantsEval():
print 'Gen: '+repr(ea.generation())+'/'+repr(maxgen)+' actual ind is ' +repr(ea.actualOne())+'/'+repr(popsize)+' best so far: '+repr(ea.getBestFitness());
ind = ea.getInd();
if config == 1: # this works pretty well (approximates sum)
pMut = 0.1
pCross = 0.9
popsize = 3
maxgen = 1
if config == 2:
pMut = 0.1
pCross = 0.9
popsize = 3
maxgen = 2
# simName = util.randomString(20);
simName = "".join(random.choice(string.ascii_uppercase) for i in range(20))
net = nef.Network("Vivae - Turning controller for agent")
net.add_to_nengo()
ea = EA(maxgen, popsize, minw, maxw, genomeLen)
ea.setProbabilities(pMut, pCross)
ea.initPop()
simulator = buildExperiment(net, wa, wb, wc)
print "starting build"
expNo = round(1000000 * random.random(), 0)
# generate some number for text file data
print expNo
f = open("data/ea_%d.txt" % expNo, "w")
# evolution insert here
while ea.wantsEval():
print "Gen: " + repr(ea.generation()) + "/" + repr(maxgen) + " actual ind is " + repr(ea.actualOne()) + "/" + repr(
popsize
) + " best so far: " + repr(ea.getBestFitness())
if config == 2:
t = 2
pMut = 0.3
pCross = 0.9
popsize = 50
maxgen = 100
if config == 3:
t = 2
pMut = 0.4
pCross = 0.9
popsize = 20
maxgen = 100
simName = util.randomString(20)
net = nef.Network('Vivae - Turning controller for agent')
net.add_to_nengo()
ea = EA(maxgen, popsize, minw, maxw, genomeLen)
ea.setProbabilities(pMut, pCross)
ea.initPop()
simulator = buildExperiment(net, wa, wb, wc, visibility=False)
vivae = simulator.getControls()
expNo = round(1000000 * random.random(), 0)
# generate some number for text file data
print expNo
f = open('data/tmp/ea_%d.txt' % expNo, 'w')
sx = Saver('ea_%d_agents.txt' % expNo)
# saves best agent from actual generation during the evolution into a file
# evolution insert here
while ea.wantsEval():
print 'Gen: ' + repr(
ea.generation()) + '/' + repr(maxgen) + ' actual ind is ' + repr(
genomeLen = 29;
# which setup to use?
config=1
if config == 1: # this works pretty well (approximates sum)
pMut = 0.1
pCross = 0.9;
popsize = 3;
maxgen = 1;
if config == 2:
pMut = 0.1
pCross = 0.9;
popsize = 50;
maxgen = 150;
ea = EA(maxgen,popsize,minw,maxw,genomeLen);
ea.setProbabilities(pMut,pCross);
ea.initPop();
print 'starting build'
#expNo = round(1000000*random.random(),0); # generate some number for text file data
#print expNo
#f = open('data/ea_%d.txt'%expNo, 'w');
# load the best one found
#ind = ea.getIndNo(ea.getBest());
#net = buildExperiment(ind);
#print 'best fitness is:'
#print ind.getFitness().get();
#print 'build done writing matrix to file named: \n\n'
