def ipopcmaes(fitness_eval,domain,dim,maxevals):
# restart with increasing population size (IPOP)
bestever = cma.BestSolution()
(min,max)=domain
restevals = maxevals
popsize = 4+int(3*math.log(dim))
initial = np.random.rand(dim)*(max-min)+min
print "===================================="
print "Popsize: %d" %popsize
while restevals > 0:
# for lam in 10 * 2**np.arange(7): # 10, 20, 40, 80, ..., 10 * 2**6
initial = np.random.rand(dim)*(max-min)+min
es = cma.CMAEvolutionStrategy(initial,
(max-min)/2.0, # initial std sigma0
# bounds
{
'maxfevals': restevals,
'CMA_active': 1,
# 'verb_disp': 500,
# 'tolfun': 1e-12,
'popsize': popsize, 'verb_append': bestever.evalsall, 'bounds': [min,max]}) # pass options
#logger = logging.getLogger('ipopcmaes')
while not es.stop():
X = es.ask() # get list of new solutions
fit = [fitness_eval(x) for x in X] # evaluate each solution
es.tell(X, fit) # besides for termination only the ranking in fit is used
# display some output
#logger.add() # add a "data point" to the log, writing in files
es.disp() # uses option verb_disp with default 100
#print('termination:', es.stop())
#cma.pprint(es.best.__dict__)
bestever.update(es.best)
# show a plot
#logger.plot()
if bestever.f < 1e-8: # global optimum was hit
break
cma.pprint(es.result())
#cma.pprint(bestever.x)
#cma.pprint(bestever.f)
#print "NumEvals: %d" %es.result()[3]
if bestever.f < 1e-8: # global optimum was hit
break
else:
evals = es.result()[3]
restevals = restevals - evals
print "It rests %d evaluations" %restevals
popsize = 2*popsize
print "Popsize: %d" %popsize
print "-------------------------------"
print "===================================="
return (bestever.x,bestever.f)
import cma
import sys
# construct an object instance in 4-D, sigma0=1
es = cma.CMAEvolutionStrategy(4 * [1], 1, {'seed': 234})
id = 0
while not es.stop():
X = es.ask()
print X
es.tell(X, [cma.fcts.elli(x) for x in X])
es.disp() # by default sparse, see option verb_disp
cma.pprint(es.result())
while X_work:
pool = []
for ji in range(cma_jobs):
if len(X_work) == 0:
break
print "Launching #%d: " % id
ret_q = multiprocessing.Queue()
#eval(X_work[0], id, ret_q)
p = multiprocessing.Process(target=eval, args = (X_work[0], id , ret_q))
X_work = X_work[1:]
p.start()
pool.append( (id, p, ret_q) )
id += 1
for id_src, p, ret_q in pool:
p.join()
r = ret_q.get()
if isinstance(r, KeyboardInterrupt):
raise r
elif isinstance(r, Exception):
r = 0.0
print "got %f from %d" % (r, id_src)
tells.append(r)
print tells
es.tell(X, tells)
es.disp() # by default sparse, see option verb_disp
cma.pprint(es.result())
