def sample_banana2(N, D, target):
momentum = IsotropicZeroMeanGaussian(D=D, sigma=1.0)
num_steps = 1000
step_size = 0.2
hmc = HMCBase(target, momentum, num_steps, num_steps, step_size, \
step_size, adaptation_schedule=None)
start_samples = sample_banana(N, D)
# simulate trajectory from starting point, note _proposal_trajectory is a "hidden" method
Qs_total = []
for i in xrange(N):
current = start_samples[i]
current_log_pdf = target.log_pdf(current)
Qs, acc_probs, log_pdf_q = hmc._proposal_trajectory(
current, current_log_pdf)
Qs_total.append(Qs[-1])
return np.asarray(Qs_total)
momentum = IsotropicZeroMeanGaussian(D=D, sigma=1.0)
hmc = HMCBase(target, momentum, num_steps, num_steps, step_size, \
step_size, adaptation_schedule=None)
# 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 = hmc._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
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
e_x1 = expectation(Qs[1:, 0])
mean_x1_total.append(e_x1)