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import torch
import numpy as np
from botorch.test_functions.synthetic import Rosenbrock, Levy, DixonPrice
device = torch.device("cpu")
dtype = torch.double
def RosenbrockND2(individuals):
# assert torch.is_tensor(individuals) and individuals.size(1) == 10, "Input must be an n-by-10 PyTorch tensor."
#############################################################################
#############################################################################
# Set function here:
dimm = individuals.shape[1]
Rosenbrockfun = Rosenbrock(dim=dimm, negate=True)
Rosenbrockfun.bounds[0, :].fill_(-3.0)
Rosenbrockfun.bounds[1, :].fill_(5.0)
fx = Rosenbrockfun(individuals)
fx = fx.reshape(individuals.shape[0],1)
Levyfun = Levy(dim=dimm, negate=False)
Levyfun.bounds[0, :].fill_(-3.0)
Levyfun.bounds[1, :].fill_(5.0)
DixonPricefun = DixonPrice(dim=dimm, negate=False)
DixonPricefun.bounds[0, :].fill_(-3.0)
DixonPricefun.bounds[1, :].fill_(5.0)
G1 = Levyfun(individuals) -1e3
G2 = DixonPricefun(individuals) -4e7
gx = torch.cat((G1.reshape(individuals.shape[0],1), G2.reshape(individuals.shape[0],1)), 1)
return gx, fx
def RosenbrockND2_Scaling(X):
# assert torch.is_tensor(X) and X.size(1) == 10, "Input must be an n-by-10 PyTorch tensor."
X_scaled = X*8-3
return X_scaled
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