{
'name': 'x27',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
{
'name': 'x28',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
{
'name': 'x29',
'type': 'continuous',
'domain': (1, 4),
'dimensionality': 1
},
]
dim = len(domain)
fill_in_strategy = 'random'
f = GaussianMixtureFunction(dim=dim, mean_1=2, mean_2=3)
X = np.array([np.full(dim, 1)])
method = Dropout(f=f,
domain=domain,
subspace_dim_size=5,
fill_in_strategy=fill_in_strategy,
maximize=True)
method.run_optimization(max_iter=200)
from bayopt.methods.dropout import Dropout
from bayopt.objective_examples.experiments import SchwefelsFunction
import numpy as np
domain = [{'name': 'x0', 'type': 'continuous', 'domain': (-1, 1), 'dimensionality': 1},
{'name': 'x1', 'type': 'continuous', 'domain': (-1, 1), 'dimensionality': 1},
{'name': 'x2', 'type': 'continuous', 'domain': (-1, 1), 'dimensionality': 1},
{'name': 'x3', 'type': 'continuous', 'domain': (-1, 1), 'dimensionality': 1},
{'name': 'x4', 'type': 'continuous', 'domain': (-1, 1), 'dimensionality': 1},
]
dim = len(domain)
fill_in_strategy = 'random'
f = SchwefelsFunction()
X = np.array([np.full(dim, 1)])
method = Dropout(f=f, domain=domain, subspace_dim_size=2, fill_in_strategy=fill_in_strategy, maximize=False,
X=X)
method.run_optimization(max_iter=300, eps=-1)
'type': 'continuous',
'domain': (-1, 1),
'dimensionality': 1
},
]
for i in range(5):
fill_in_strategy = 'random'
f = SchwefelsFunction()
method = Dropout(f=f,
domain=domain,
subspace_dim_size=5,
fill_in_strategy=fill_in_strategy,
maximize=False)
method.run_optimization(max_iter=500, eps=0)
fill_in_strategy = 'copy'
f = SchwefelsFunction()
method = Dropout(
f=f,
domain=domain,
subspace_dim_size=1,
fill_in_strategy=fill_in_strategy,
maximize=False,
)
method.run_optimization(max_iter=500, eps=0)
fill_in_strategy = 'mix'
f = SchwefelsFunction()
method = Dropout(f=f,