26 lines
787 B
Python
26 lines
787 B
Python
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import torch
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from botorch.models import SingleTaskGP
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from botorch.fit import fit_gpytorch_mll
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from botorch.utils import standardize
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from gpytorch.mlls import ExactMarginalLogLikelihood
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from botorch.acquisition import UpperConfidenceBound
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from botorch.optim import optimize_acqf
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train_X = torch.rand(10, 2, dtype=torch.double)
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Y = 1 - torch.norm(train_X - 0.5, dim=-1, keepdim=True)
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Y = Y + 0.1 * torch.randn_like(Y) # add some noise
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train_Y = standardize(Y)
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gp = SingleTaskGP(train_X, train_Y)
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mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
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fit_gpytorch_mll(mll)
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UCB = UpperConfidenceBound(gp, beta=0.1)
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bounds = torch.stack([torch.zeros(2), torch.ones(2)])
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candidate, acq_value = optimize_acqf(
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UCB, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
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)
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print(candidate)
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