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import torch
import numpy as np
#
#
# GKXWC1: 2D objective, 1 constraints
#
# Reference:
# Gardner JR, Kusner MJ, Xu ZE, et al (2014)
# Bayesian optimization with inequality con-
# straints. In: ICML, pp 937–945
#
#
def GKXWC1(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:
g = np.cos(x[0])*np.cos(x[1]) - np.sin(x[0])*np.sin(x[1]) -0.5
fx.append( - np.cos(2*x[0])*np.cos(x[1]) - np.sin(x[0]) )
gx.append( g )
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 GKXWC1_Scaling(X):
assert torch.is_tensor(X) and X.size(1) == 2, "Input must be an n-by-2 PyTorch tensor."
X_scaled = X*6;
return X_scaled
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