ind_test = ind_split[fold] # np.sort(ind_shuffled[:N//10])
ind_train = np.concatenate(ind_split[np.arange(10) != fold])
x_train = x # [ind_train] # 90/10 train/test split
x_test = x # [ind_test]
y_train = y # [ind_train]
y_test = y # [ind_test]
N_batch = 5000
M = 5000
# z = np.linspace(701050, 737050, M)
z = np.linspace(x[0], x[-1], M)
prior_1 = priors.Matern52(variance=2., lengthscale=5.5e4)
prior_2 = priors.QuasiPeriodicMatern32(variance=1., lengthscale_periodic=2., period=365., lengthscale_matern=1.5e4)
prior_3 = priors.QuasiPeriodicMatern32(variance=1., lengthscale_periodic=2., period=7., lengthscale_matern=30*365.)
prior = priors.Sum([prior_1, prior_2, prior_3])
lik = likelihoods.Poisson()
if method == 0:
inf_method = approx_inf.EKS(damping=.5)
elif method == 1:
inf_method = approx_inf.UKS(damping=.5)
elif method == 2:
inf_method = approx_inf.GHKS(damping=.5)
elif method == 3:
inf_method = approx_inf.EP(power=1, intmethod='GH', damping=.5)
elif method == 4:
inf_method = approx_inf.EP(power=0.5, intmethod='GH', damping=.5)
elif method == 5:
inf_method = approx_inf.EP(power=0.01, intmethod='GH', damping=.5)
elif method == 6:
x_test = x[ind_test]
y_train = y[ind_train]
y_test = y[ind_test]
var_y = .1
var_f = 1. # GP variance
len_f = 1. # GP lengthscale
period = 1. # period of quasi-periodic component
len_p = 5. # lengthscale of quasi-periodic component
var_f_mat = 1.
len_f_mat = 1.
prior1 = priors.Matern32(variance=var_f_mat, lengthscale=len_f_mat)
prior2 = priors.QuasiPeriodicMatern12(variance=var_f, lengthscale_periodic=len_p,
period=period, lengthscale_matern=len_f)
prior = priors.Sum([prior1, prior2])
lik = likelihoods.Gaussian(variance=var_y)
if method == 0:
inf_method = approx_inf.EKS(damping=.1)
elif method == 1:
inf_method = approx_inf.UKS(damping=.1)
elif method == 2:
inf_method = approx_inf.GHKS(damping=.1)
elif method == 3:
inf_method = approx_inf.EP(power=1, intmethod='GH', damping=.1)
elif method == 4:
inf_method = approx_inf.EP(power=0.5, intmethod='GH', damping=.1)
elif method == 5:
inf_method = approx_inf.EP(power=0.01, intmethod='GH', damping=.1)