if n_tot == 10000:
run_time_save = run_time
print("")
print("Runtime:" + str(round(run_time, 2)))
print(len(posteriors))
print("- log prob(theta_true)")
log_probs = []
for i in range(len(posteriors)):
posterior_samples = posteriors[i].sample_n(1000)
post_gauss_approx = func.fit_gaussian_dist(posterior_samples)
print(-post_gauss_approx.log_prob(theta_true))
log_probs.append(-post_gauss_approx.log_prob(theta_true))
# save samples
np.savetxt('data/abcsmc_posterior_' + str(nbr_sim[i]) + '_' + id_job +
'.csv',
posterior_samples.detach().numpy(),
delimiter=",")
# write new results
with open('results/abcsmc_' + id_job + '.txt', 'w') as f:
f.write('%.4f\n' % run_time_save)
for l in log_probs:
f.write('%.4f\n' % l)
run_time = end - start
print("")
print("Runtime:" + str(round(run_time, 2)))
log_probs = []
start = time.time()
torch.manual_seed(seed)
for i in range(nbr_rounds):
print(i)
posterior_sample = models_post[i].sample(1000, context=s_x_o)
posterior_sample = posterior_sample.reshape((1000, 4))
# log_probs.append(-models_post[i].log_prob(theta_true.reshape((1, 4)), context=s_x_o))
post_gauss_approx = func.fit_gaussian_dist(posterior_sample.detach()) # to get correct prob
log_probs.append(-post_gauss_approx.log_prob(theta_true))
if hp_tuning == 0 and lambda_val > 0:
np.savetxt('lambda_val/post_samples_snpla_' + str(i + 1) + "_" + id_job + '.csv',
posterior_sample.detach().numpy(), delimiter=",")
elif hp_tuning == 0:
np.savetxt('data/post_samples_snpla_' + str(i + 1) + "_" + id_job + '.csv',
posterior_sample.detach().numpy(), delimiter=",")
else:
np.savetxt('hp_tuning/post_samples_snpla_' + str(i + 1) + "_" + id_job + '.csv',