population_size = 50
num_iter = population_size * num_iter_per_particle
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
start = np.zeros(D)
sampler = get_AdaptiveIndependentMetropolis_instance(D, target_log_pdf)
start_time = time.time()
samples, log_target_densities, times = mini_pmc(sampler, start, num_iter, population_size)
time_taken = time.time() - start_time
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 "ESS: %.3f" % sampler.get_current_ess()
if False:
import matplotlib.pyplot as plt
bananicity = 0.03
V = 100
Z = sample_banana(700, D, bananicity, V)
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 = [
get_AdaptiveMetropolis_instance(D, target_log_pdf),
# the below two don't yet support weights in the update method
# get_Kameleon_instance(D, target_log_pdf),
# get_OracleKameleon_instance(D, target_log_pdf),
get_StaticLangevin_instance(D, target_log_pdf, target_grad),
get_StaticMetropolis_instance(D, target_log_pdf),
get_AdaptiveLangevin_instance(D, target_log_pdf, target_grad),
get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf,
target_grad),
get_KernelAdaptiveLangevin_instance(D, target_log_pdf, target_grad),
]
for sampler in samplers:
start = np.zeros(D)
num_iter = 1000
samples, log_target_densities, times = mini_pmc(
sampler, start, num_iter, 10)
visualize_scatter_2d(samples)
plt.suptitle("%s" % \
(sampler.__class__.__name__,))
plt.show()