var_f2 = 3. # GP variance
len_f2 = 1. # GP lengthscale
prior1 = priors.Matern32(variance=var_f1, lengthscale=len_f1)
prior2 = priors.Matern32(variance=var_f2, lengthscale=len_f2)
prior = priors.Independent([prior1, prior2])
lik = likelihoods.HeteroscedasticNoise()
step_size = 5e-2
if method == 0:
inf_method = approx_inf.EEP(power=1, damping=0.5)
elif method == 1:
inf_method = approx_inf.EEP(power=0.5, damping=0.5)
elif method == 2:
inf_method = approx_inf.EKS(damping=0.5)
elif method == 3:
inf_method = approx_inf.UEP(power=1, damping=0.5)
elif method == 4:
inf_method = approx_inf.UEP(power=0.5, damping=0.5)
elif method == 5:
inf_method = approx_inf.UKS(damping=0.5)
elif method == 6:
inf_method = approx_inf.GHEP(power=1, damping=0.5)
elif method == 7:
inf_method = approx_inf.GHEP(power=0.5, damping=0.5)
elif method == 8:
inf_method = approx_inf.GHKS(damping=0.5)
train = np.setdiff1d(cvind, test)
# Set training and test data
X = inputs[train, :1]
R = inputs[train, 1:]
Y = Yall[train, :]
XT = inputs[test, :1]
RT = inputs[test, 1:]
YT = Yall[test, :]
if method == 0:
inf_method = approx_inf.EEP(power=1)
elif method == 1:
inf_method = approx_inf.EEP(power=0.5)
elif method == 2:
inf_method = approx_inf.EKS()
elif method == 3:
inf_method = approx_inf.UEP(power=1)
elif method == 4:
inf_method = approx_inf.UEP(power=0.5)
elif method == 5:
inf_method = approx_inf.UKS()
elif method == 6:
inf_method = approx_inf.GHEP(power=1)
elif method == 7:
inf_method = approx_inf.GHEP(power=0.5)
elif method == 8:
inf_method = approx_inf.GHKS()
