'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)