def test_rosen_ask_tell():
popsize = 31
dim = 5
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 100000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
es = cmaes.Cmaes(testfun.bounds, popsize=popsize, input_sigma=sdevs)
iters = max_eval // popsize
for j in range(iters):
xs = es.ask()
ys = [wrapper.eval(x) for x in xs]
stop = es.tell(ys)
if stop != 0:
break
ret = OptimizeResult(x=es.best_x,
fun=es.best_value,
nfev=wrapper.get_count(),
nit=es.iterations,
status=es.stop)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval / popsize + 2 > ret.nit) # too much iterations
def test_ask_tell(problem, num):
best = math.inf
t0 = time.perf_counter();
for i in range(num):
es = cmaes.Cmaes(bounds = problem.bounds)
iters = 3000
for _ in range(iters):
xs = es.ask()
ys = [problem.fun(x) for x in xs]
stop = es.tell(ys)
if stop != 0:
break
best = min(es.best_value, best)
print("{0}: time = {1:.1f} best = {2:.1f} f(xmin) = {3:.1f}"
.format(i+1, dtime(t0), best, es.best_value))
def minimize(self,
fun,
bounds,
guess=None,
sdevs=0.3,
rg=Generator(MT19937()),
store=None):
es = cmaes.Cmaes(bounds,
popsize=self.popsize,
input_sigma=sdevs,
rg=rg)
iters = self.max_eval_num(store) // self.popsize
evals = 0
for j in range(iters):
xs = es.ask()
ys = [fun(x) for x in xs]
evals += len(xs)
stop = es.tell(ys)
if stop != 0:
break
return es.best_x, es.best_value, evals
