def test_gutmann_branin_noisy_with_init(self):
"""Check solution of noisy braning with Gutmann, solver."""
bb = ti.TestNoisyBlackBox('branin', 0.1, 0.01)
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving branin with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='Gutmann',
global_search_method='solver',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
fast_objfun_rel_error=0.1,
fast_objfun_abs_error=0.01,
rand_seed=seed)
init_node_pos = [[0, 0], [-2, 2], [5, 10]]
init_node_val = [bb._function.evaluate(x) for x in init_node_pos]
alg = ra.OptAlgorithm(settings, bb, init_node_pos, init_node_val)
res = alg.optimize()
msg = ('Could not solve noisy branin with init and ' +
'Gutmann\'s algorithm')
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_gutmann_st_miqp3_noisy(self):
"""Check solution of noisy st_miqp3 with Gutmann, genetic."""
bb = ti.TestNoisyBlackBox('st_miqp3', 0.1, 0.01)
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving st_miqp3 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='Gutmann',
global_search_method='genetic',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
fast_objfun_rel_error=0.1,
fast_objfun_abs_error=0.01,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve st_miqp3 with Gutmann\'s algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_msrsm_parallel_prob03_noisy(self):
"""Check solution of noisy prob03 with MSRSM, sampling."""
bb = ti.TestNoisyBlackBox('prob03', 0.1, 0.01)
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving prob03 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='MSRSM',
global_search_method='sampling',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
num_cpus=4,
fast_objfun_rel_error=0.1,
fast_objfun_abs_error=0.01,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve prob03 with MSRSM algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
