def test_cmaes_stop_fitness_variance():
opt = CMAESOptimizer(n_samples_per_update=5)
opt.init(2)
params = np.empty(2)
it = 0
while not opt.is_behavior_learning_done():
opt.get_next_parameters(params)
opt.set_evaluation_feedback([0.0])
it += 1
assert_equal(it, 6)
def test_cmaes_stop_conditioning():
def objective(x):
return -1e10 * x[1] ** 2
opt = CMAESOptimizer(random_state=0)
opt.init(2)
params = np.empty(2)
it = 0
while not opt.is_behavior_learning_done():
opt.get_next_parameters(params)
opt.set_evaluation_feedback(objective(params))
it += 1
assert_less(it, 600)