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import torch
import numpy as np
from botorch.test_functions import Ackley
device = torch.device("cpu")
dtype = torch.double
#
#
# Ackley2D: 2D objective, 2 constraints
#
# Reference:
# Eriksson D, Poloczek M (2021) Scalable con-
# strained bayesian optimization. In: Interna-
# tional Conference on Artificial Intelligence and
# Statistics, PMLR, pp 730–738
#
#
def Ackley2D(individuals):
assert torch.is_tensor(individuals) and individuals.size(1) == 2, "Input must be an n-by-2 PyTorch tensor."
#############################################################################
#############################################################################
# Set function here:
dimm = 2
fun = Ackley(dim=dimm, negate=True).to(dtype=dtype, device=device)
fun.bounds[0, :].fill_(-5)
fun.bounds[1, :].fill_(10)
dim = fun.dim
lb, ub = fun.bounds
#############################################################################
#############################################################################
n = individuals.size(0)
fx = fun(individuals)
fx = fx.reshape((n, 1))
#############################################################################
## Constraints
gx1 = torch.sum(individuals,1) # sigma(x) <= 0
gx1 = gx1.reshape((n, 1))
gx2 = torch.norm(individuals, p=2, dim=1)-5 # norm_2(x) -3 <= 0
gx2 = gx2.reshape((n, 1))
gx = torch.cat((gx1, gx2), 1)
#############################################################################
return gx, fx
def Ackley2D_Scaling(X):
assert torch.is_tensor(X) and X.size(1) == 2, "Input must be an n-by-2 PyTorch tensor."
X_scaled = X*15-5
return X_scaled
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