# kmc sampler instance
kmc = KMCStatic(surrogate, target, momentum, num_steps, num_steps,
step_size, step_size)
# simulate trajectory from starting point, note _proposal_trajectory is a "hidden" method
Qs_total = []
acc_probs_total = []
accor_total = []
ksd_total = []
ess_total = []
mean_x1_total = []
np.random.seed(seed + 1)
for i in xrange(M):
current = start_samples[i]
current_log_pdf = target.log_pdf(current)
Qs, acc_probs, log_pdf_q = kmc._proposal_trajectory(
current, current_log_pdf)
# compute auto correlation on first dim
accor = autocorr(Qs[:, 0])
accor_total.append(accor)
Qs_total.append(Qs)
# compute min ESS
T_ess = 1800
ess = RCodaTools.ess_coda_vec(Qs[T_ess + 1:])
ess = np.minimum(ess, Qs[T_ess + 1:].shape[0])
min_ess = np.min(ess)
ess_total.append(min_ess)
# compute acceptance prob
acc_probs_total.append(acc_probs)
# compute E[x1] estimates for different time t
