'domain': (1, 4),
'dimensionality': 1
},
{
'name': 'x37',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
{
'name': 'x38',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
{
'name': 'x39',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
]
for i in range(10):
dim = len(domain)
f = GaussianMixtureFunction(dim=dim, mean_1=2, mean_2=3)
X = np.array([np.full(dim, 1)])
method = BayesianOptimizationExt(f=f, domain=domain, maximize=True, X=X)
method.run_optimization(max_iter=500)
from bayopt.objective_examples.experiments import BraininFunction
from bayopt.methods.bo import BayesianOptimizationExt
domain = [{'name': 'x0', 'type': 'continuous', 'domain': (-5, 15), 'dimensionality': 1},
{'name': 'x1', 'type': 'continuous', 'domain': (-5, 15), 'dimensionality': 1},
{'name': 'x2', 'type': 'continuous', 'domain': (-5, 15), 'dimensionality': 1},
{'name': 'x3', 'type': 'continuous', 'domain': (-5, 15), 'dimensionality': 1},
{'name': 'x4', 'type': 'continuous', 'domain': (-5, 15), 'dimensionality': 1},
]
for i in range(1):
dim = len(domain)
f = BraininFunction(1, 3)
method = BayesianOptimizationExt(f=f, domain=domain, maximize=False, ard=True)
method.run_optimization(max_iter=250, eps=0)