def test_rosen_cpp_parallel():
popsize = 8
dim = 2
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 10000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = cmaescpp.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize,
workers=popsize)
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
assert (almost_equal(ret.x, wrapper.get_best_x())) # wrong best X returned
assert (almost_equal(ret.fun, wrapper.get_best_y(),
eps=1E-1)) # wrong best y returned
def cma_cpp(self, fun, guess, bounds, sdevs, rg):
"""CMA_ES C++ implementation."""
ret = cmaescpp.minimize(fun, bounds, guess,
input_sigma=sdevs, max_evaluations=self.store.eval_num(),
popsize=self.popsize, stop_fittness = self.stop_fittness,
rg=rg, runid=self.store.get_count_runs())
return ret.x, ret.fun, ret.nfev
def test_rosen_cpp():
if not sys.platform.startswith('linux'):
return
popsize = 31
dim = 5
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 100000
limit = 0.00001
for i in range(5):
wrapper = Wrapper(testfun.func, dim)
ret = cmaescpp.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize > ret.nfev) # too much function calls
assert (ret.nfev == wrapper.get_count()
) # wrong number of function calls returned
assert (almost_equal(ret.x, wrapper.get_best_x())) # wrong best X returned
assert (ret.fun == wrapper.get_best_y()) # wrong best y returned
def test_cma_cpp(problem, num):
best = math.inf
t0 = time.perf_counter()
for i in range(num):
ret = cmaescpp.minimize(problem.fun, bounds=problem.bounds)
best = min(ret.fun, best)
print("{0}: time = {1:.1f} best = {2:.1f} f(xmin) = {3:.1f}".format(
i + 1, dtime(t0), best, ret.fun))
def test_cma_cpp(problem, num):
best = math.inf
t0 = time.perf_counter();
for i in range(num):
ret = cmaescpp.minimize(problem.fun, max_evaluations = 100000, bounds = problem.bounds)
if best > ret.fun or i % 100 == 99:
print("{0}: time = {1:.1f} best = {2:.1f} f(xmin) = {3:.1f}"
.format(i+1, dtime(t0), best, ret.fun))
best = min(ret.fun, best)
def minimize(self,
fun,
bounds,
guess=None,
sdevs=0.3,
rg=Generator(MT19937()),
store=None,
workers=None):
ret = cmaescpp.minimize(
fun,
bounds,
self.guess if not self.guess is None else guess,
input_sigma=self.sdevs if not self.sdevs is None else sdevs,
max_evaluations=self.max_eval_num(store),
popsize=self.popsize,
stop_fitness=self.stop_fitness,
rg=rg,
runid=self.get_count_runs(store),
update_gap=self.update_gap,
workers=self.workers if workers is None else workers)
return ret.x, ret.fun, ret.nfev