break;
if(prevgen!=ea.generation()):
prevgen=ea.generation()
f=file(filename,'a+')
f.write('%1.1f %1.6f %s\n'%((prevgen-1),ea.getBestInd().getFitness().getValue(),' '.join(map(str, ea.getBestInd().getGenome().getVector()))))
f.close()
ind = ea.getCurrentInd();
indNo = ea.currentOne();
val = ind.getGenome().getVector();
net.reset()
time.sleep(2)
net.run(t,dt)
#name = base+"g"+str(ea.generation())+"_i"+str(ea.currentOne()) # set the name (for file storing)
#prosp = evalConfiguration(val[0],val[1],val[2],QLambda.DEF_IMPORTANCE,t,dt,name,indNo) # evals
prosp = rl.getOrigin(QLambda.topicProsperity).getValues().getValues(); # read the prosperity
print '----------------- exp named: '+name+' done, the value is '+str(prosp[0])
ind.getFitness().setValue(prosp[0]); # set the fitness
ea.nextIndividual(); # continue
print 'done'
# Should be something about 2-3000
#fitness = ea.getBestInd().getFitness().getValue();
#print "==== The result is: "+ea.getBestInd().toString()
f.close()
ind = ea.getCurrentInd()
indNo = ea.currentOne()
val = ind.getGenome().getVector()
name = base + "g" + str(ea.generation()) + "_i" + str(
ea.currentOne()) # set the name (for file storing)
prosp = evalConfiguration(val[0], val[1], val[2], QLambda.DEF_IMPORTANCE,
t, dt, name, indNo) # eval
print '----------------- exp named: ' + name + ' done, the value is ' + str(
prosp[0])
ind.getFitness().setValue(prosp[0])
# set the fitness
ea.nextIndividual()
# continue
# Should be something about 2-3000
fitness = ea.getBestInd().getFitness().getValue()
print "==== The result is: " + ea.getBestInd().toString()
# run the experiment several times, plot average in the matlab
#for j in range(len(vals)):
# print 'xxxxxxx testing the value: '+str(vals[j])
# for i in range(runs):
# name = base + '_%d'%i;
# print '----------------- starting experiment named: '+name
# prosp = evalConfiguration(QLambda.DEF_ALPHA,QLambda.DEF_GAMMA,QLambda.DEF_LAMBDA,QLambda.DEF_IMPORTANCE,t,dt,name+"_"+str(vals[j]))
# print '----------------- exp named: '+name+' done, the value is '+str(prosp[0])
prevgen=0
filename = 'prosperities.txt'
# now run the EA, store the course of the prosperity (per time step) only for the first one (best one)
while(ea.wantsEval()):
if(prevgen!=ea.generation()):
prevgen=ea.generation()
f=file(filename,'a+')
f.write('%1.1f %1.6f %s\n'%((prevgen-1),ea.getBestInd().getFitness().getValue(),' '.join(map(str, ea.getBestInd().getGenome().getVector()))))
f.close()
ind = ea.getCurrentInd();
indNo = ea.currentOne();
val = ind.getGenome().getVector();
name = base+"g"+str(ea.generation())+"_i"+str(ea.currentOne()) # set the name (for file storing)
prosp = evalConfiguration(val[0],val[1],val[2],QLambda.DEF_IMPORTANCE,t,dt,name,indNo) # eval
print '----------------- exp named: '+name+' done, the value is '+str(prosp[0])
ind.getFitness().setValue(prosp[0]); # set the fitness
ea.nextIndividual(); # continue
# Should be something about 2-3000
fitness = ea.getBestInd().getFitness().getValue();
print "==== The result is: "+ea.getBestInd().toString()
