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(); # get wa,b,c from ind genome wa = ind.m.getRange(0,16); wb = ind.m.getRange(16,25); wc = ind.m.getRange(25,29); #simName = util.randomString(20); fitness = evalInd(net,simulator,ind); ind.getFitness().set(fitness); print 'Ind: '+repr(ea.actualOne())+' AVG Speed is: '+repr(fitness) +' fitness is: '+repr(ind.getFitness().get()); # evaluated the last individual in the generatio? write stats if (ea.actualOne() == (popsize-1)):
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()) ind = ea.getInd() # get wa,b,c from ind genome wa = ind.m.getRange(0, 16) wb = ind.m.getRange(16, 25) wc = ind.m.getRange(25, 29) # simName = util.randomString(20); fitness = evalInd(net, simulator, ind) ind.getFitness().set(fitness) print "Ind: " + repr(ea.actualOne()) + " AVG Speed is: " + repr(fitness) + " fitness is: " + repr( ind.getFitness().get()
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() # get wa,b,c from ind genome wa = ind.m.getRange(0, 16) wb = ind.m.getRange(16, 25) wc = ind.m.getRange(25, 29) #simName = util.randomString(20); fitness = evalInd(net, simulator, ind) ind.getFitness().set(fitness) print 'Ind: ' + repr(ea.actualOne()) + ' AVG Speed is: ' + repr( fitness) + ' fitness is: ' + repr(ind.getFitness().get())