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import torch
import numpy as np
#
# JLH1: 2D objective, 1 constraints
#
#
# Reference:
# Jetton C, Li C, Hoyle C (2023) Constrained
# bayesian optimization methods using regres-
# sion and classification gaussian processes as
# constraints. In: International Design Engi-
# neering Technical Conferences and Computers
# and Information in Engineering Conference,
# American Society of Mechanical Engineers, p
# V03BT03A033
#
#
def JLH1(individuals):
assert torch.is_tensor(individuals) and individuals.size(1) == 2, "Input must be an n-by-2 PyTorch tensor."
fx = []
gx = []
for x in individuals:
test_function = (- (x[0]-0.5)**2 - (x[1]-0.5)**2 )
fx.append(test_function)
gx.append( x[0] + x[1] - 0.75 )
fx = torch.tensor(fx)
fx = torch.reshape(fx, (len(fx),1))
gx = torch.tensor(gx)
gx = torch.reshape(gx, (len(gx),1))
return gx, fx
def JLH1_Scaling(X):
assert torch.is_tensor(X) and X.size(1) == 2, "Input must be an n-by-2 PyTorch tensor."
return X
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