def test_example(self):
method = SelectObjective(fill_in_strategy='random',
f=self.f,
domain=self.domain)
method.run_optimization(max_iter=10)
self.assertEqual(method.num_acquisitions, 10)
self.assertEqual(len(method.bernoulli_theta), 4)
self.assertTrue(True)
{'name': 'x42', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
{'name': 'x43', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
{'name': 'x44', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
{'name': 'x45', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
{'name': 'x46', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
{'name': 'x47', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
{'name': 'x48', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
{'name': 'x49', 'type': 'continuous', 'domain': (1, 4), 'dimensionality': 1},
]
for i in range(4):
dim = len(domain)
fill_in_strategy = 'random'
f = GaussianMixtureFunction(dim=dim, mean_1=2, mean_2=3)
method = SelectObjective(f=f, domain=domain, fill_in_strategy=fill_in_strategy, maximize=True)
method.run_optimization(max_iter=500, eps=0)
dim = len(domain)
fill_in_strategy = 'copy'
f = GaussianMixtureFunction(dim=dim, mean_1=2, mean_2=3)
method = SelectObjective(f=f, domain=domain, fill_in_strategy=fill_in_strategy, maximize=True)
method.run_optimization(max_iter=500, eps=0)
dim = len(domain)
fill_in_strategy = 'mix'
f = GaussianMixtureFunction(dim=dim, mean_1=2, mean_2=3)
method = SelectObjective(f=f, domain=domain, fill_in_strategy=fill_in_strategy, maximize=True, mix=0.5)
method.run_optimization(max_iter=500, eps=0)
'dimensionality': 1
},
{
'name': 'x2',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
{
'name': 'x3',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
{
'name': 'x4',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
]
dim = len(domain)
f = GaussianMixtureFunction(dim=dim, mean_1=2, mean_2=3)
method = SelectObjective(fill_in_strategy='random',
f=f,
domain=domain,
maximize=True)
method.run_optimization(max_iter=100)