benchmark_samples = benchmark_samples[np.arange(0, len(benchmark_samples), step=100)]
return benchmark_samples
if __name__ == "__main__":
benchmark_samples = get_benchmark_samples_mcmc()
true_mean = np.mean(benchmark_samples, axis=0)
true_var = np.var(benchmark_samples, axis=0)
print("%d benchmark samples" % len(benchmark_samples))
print "mean:", repr(true_mean)
print "var:", repr(true_var)
print "np.mean(var): %.3f" % np.mean(true_var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(true_mean)
print "median heuristic sigma: %.3f" % (1. / gamma_median_heuristic(benchmark_samples))
visualise_pairwise_marginals(benchmark_samples)
plt.show()
m = 1000
lmbda = 1.
res = 10
log2_sigmas = np.linspace(-4, 10, res)
log2_sigmas = np.log2([0.5, 0.7, 0.9, 1.1, 1.3, 1.5])
print "log2_sigmas:", log2_sigmas
print "sigmas:", 2 ** log2_sigmas
Js_mean = np.zeros(res)
Js_var = np.zeros(res)
for i, log2_sigma in enumerate(log2_sigmas):
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
visualise_pairwise_marginals(samples)
plt.show()
if __name__ == "__main__":
benchmark_samples = get_benchmark_samples_mcmc()
true_mean = np.mean(benchmark_samples, axis=0)
true_var = np.var(benchmark_samples, axis=0)
print("%d benchmark samples" % len(benchmark_samples))
print "mean:", repr(true_mean)
print "var:", repr(true_var)
print "np.mean(var): %.3f" % np.mean(true_var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(true_mean)
print "median heuristic sigma: %.3f" % (
1. / gamma_median_heuristic(benchmark_samples))
visualise_pairwise_marginals(benchmark_samples)
plt.show()
m = 1000
lmbda = 1.
res = 10
log2_sigmas = np.linspace(-4, 10, res)
log2_sigmas = np.log2([0.5, 0.7, 0.9, 1.1, 1.3, 1.5])
print "log2_sigmas:", log2_sigmas
print "sigmas:", 2**log2_sigmas
Js_mean = np.zeros(res)
Js_var = np.zeros(res)
for i, log2_sigma in enumerate(log2_sigmas):
