]
sampler_is = get_StaticLangevin(
D, target_log_pdf,
target_grad) #get_AdaptiveLangevin(D, target_log_pdf, target_grad)
sampler_mh = get_StaticLangevin(
D, target_log_pdf, target_grad
) #get_AdaptiveLangevin(D, target_log_pdf, target_grad, prec=True, step_size=1.)
start = np.zeros(D)
num_iter = 100
samples, log_target_densities, unadj_samp, unadj_log_target, logw, unw_samptimes = mini_rb_pmc(
sampler_is, start, num_iter, pop_size, D, time_budget=100000)
mom_gen = np.mean((np.array([(samples**i).mean(0)
for i in range(1, max_moment)]) - moments)**2)
mcmc_samps = mini_mcmc(sampler_mh, start, num_iter, D)
#the weights we get back are not Rao-Blackwellized, which is what we do now.
#beware: this only works if the proposal is not adapted during sampling!!
#logw = logsumexp(np.array([sampler_is.proposal_log_pdf(i, unadj_samp) for i in unadj_samp]), 0)
res_idx = system_res(range(len(logw)), logw, resampled_size=10 * len(logw))
samples = unadj_samp[res_idx]
mom_unadj = np.mean(
(np.array([(unadj_samp**i).mean(0)
for i in range(1, max_moment)]) - moments)**2)
mom_w = np.mean((np.array(
[(unadj_samp**i * exp(logw - logsumexp(logw))[:, np.newaxis]).sum(0)
for i in range(1, max_moment)]) - moments)**2)
mom_mcmc = np.mean(
(np.array([(mcmc_samps[0]**i).mean(0)
mdl = SVoneSP500Model()
target_log_pdf = mdl.get_logpdf_closure()
# number of particles used for integrating out the latent variables
mdl.mdl_param.NX = mdl.mdl_param.NX * 1
D = mdl.dim
# from initial runs
initial_mean = np.array(
[0.19404095, -0.14017837, 0.35465807, -0.22049461, -4.53669311])
start = initial_mean
sampler = get_StaticMetropolis_instance(D, target_log_pdf)
samples, proposals, accepted, acc_prob, log_pdf, times, step_sizes = mini_mcmc(
sampler, start, num_iter, D)
logger.info("Storing results under %s" % result_fname)
store_samples(samples, result_fname)
mean = np.mean(samples, axis=0)
var = np.var(samples, axis=0)
print "mean:", repr(mean)
print "var:", repr(var)
print "np.mean(var): %.3f" % np.mean(var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(mean)
print "min ESS: %.3f" % min_ess(samples)
if False:
import matplotlib.pyplot as plt
visualise_trace_2d(samples, log_pdf, accepted, step_sizes)
num_iter = 20000
mdl = SVoneSP500Model()
target_log_pdf = mdl.get_logpdf_closure()
# number of particles used for integrating out the latent variables
mdl.mdl_param.NX = mdl.mdl_param.NX * 1
D = mdl.dim
# from initial runs
initial_mean = np.array([ 0.19404095, -0.14017837, 0.35465807, -0.22049461, -4.53669311])
start = initial_mean
sampler = get_StaticMetropolis_instance(D, target_log_pdf)
samples, proposals, accepted, acc_prob, log_pdf, times, step_sizes = mini_mcmc(sampler, start, num_iter, D)
logger.info("Storing results under %s" % result_fname)
store_samples(samples, result_fname)
mean = np.mean(samples, axis=0)
var = np.var(samples, axis=0)
print "mean:", repr(mean)
print "var:", repr(var)
print "np.mean(var): %.3f" % np.mean(var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(mean)
print "min ESS: %.3f" % min_ess(samples)
if False:
import matplotlib.pyplot as plt
visualise_trace_2d(samples, log_pdf, accepted, step_sizes)
target_log_pdf = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=False)
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
#
]
sampler_is = get_StaticLangevin(D, target_log_pdf, target_grad)#get_AdaptiveLangevin(D, target_log_pdf, target_grad)
sampler_mh = get_StaticLangevin(D, target_log_pdf, target_grad)#get_AdaptiveLangevin(D, target_log_pdf, target_grad, prec=True, step_size=1.)
start = np.zeros(D)
num_iter = 100
samples, log_target_densities, unadj_samp, unadj_log_target, logw, unw_samptimes = mini_rb_pmc(sampler_is, start, num_iter, pop_size, D, time_budget=100000)
mom_gen = np.mean((np.array([(samples**i).mean(0) for i in range(1, max_moment)]) - moments)**2)
mcmc_samps = mini_mcmc(sampler_mh, start, num_iter, D)
#the weights we get back are not Rao-Blackwellized, which is what we do now.
#beware: this only works if the proposal is not adapted during sampling!!
#logw = logsumexp(np.array([sampler_is.proposal_log_pdf(i, unadj_samp) for i in unadj_samp]), 0)
res_idx = system_res(range(len(logw)), logw, resampled_size=10*len(logw))
samples = unadj_samp[res_idx]
mom_unadj = np.mean((np.array([(unadj_samp**i).mean(0) for i in range(1, max_moment)]) - moments)**2)
mom_w = np.mean((np.array([(unadj_samp**i * exp(logw - logsumexp(logw))[:,np.newaxis]).sum(0) for i in range(1, max_moment)]) -moments)**2)
mom_mcmc = np.mean((np.array([(mcmc_samps[0]**i).mean(0) for i in range(1, max_moment)]) - moments)**2)
if False:
plt.scatter(samples.T[0], samples.T[1], c='r', marker='*', zorder=4, s=5)
# fig.suptitle("%s - importance resampled" % (sampler_is.__class__.__name__,))
plt.show()
