#exit()
#samples[:,:,hidden_in_sigma2_indices] = numpy.exp(samples[:,:,hidden_in_sigma2_indices])
posterior_mean_hidden_in_sigma2 = numpy.mean(samples[:,:,hidden_in_sigma2_indices].reshape(-1,len(hidden_in_sigma2_indices)),axis=0)
posterior_median_hidden_in_sigma2 = numpy.median(samples[:,:,hidden_in_sigma2_indices].reshape(-1,len(hidden_in_sigma2_indices)),axis=0)
print("diagnostics sigma2")
print(diagnostics_stan(samples[:,:,hidden_in_sigma2_indices]))
print("posterior mean sigma2 {}".format(posterior_mean_hidden_in_sigma2))
print("posterior median sigma2 {}".format(posterior_median_hidden_in_sigma2))
#print(mcmc_samples_beta["indices_dict"])
full_mcmc_tensor = get_params_mcmc_tensor(sampler=sampler1)
print(get_short_diagnostics(full_mcmc_tensor))
out = sampler1.get_diagnostics(permuted=False)
print("divergent")
processed_diag = process_diagnostics(out,name_list=["divergent"])
print(processed_diag.sum(axis=1))
#print(processed_diag.shape)
#processed_energy = process_diagnostics(out,name_list=["prop_H"])
print(energy_diagnostics(diagnostics_obj=out))
"gamma": 0.05,
"t_0": 10,
"kappa": 0.75,
"obj_fun": "accept_rate",
"par_type": "fast"
}
dim = len(v_fun(precision_type="torch.DoubleTensor").flattened_tensor)
adapt_cov_arguments = [adapt_cov_default_arguments(par_type="slow", dim=dim)]
dual_args_list = [ep_dual_metadata_argument]
other_arguments = other_default_arguments()
tune_settings_dict = tuning_settings(dual_args_list, [], adapt_cov_arguments,
other_arguments)
tune_dict = tuneinput_class(input_dict).singleton_tune_dict()
sampler_double = mcmc_sampler(tune_dict=tune_dict,
mcmc_settings_dict=mcmc_meta_double,
tune_settings_dict=tune_settings_dict)
sampler_double.start_sampling()
double_samples = sampler_double.get_samples(permuted=False)
print(double_samples[0, 100, 2])
print(double_samples[1, 100, 2])
short_diagnostics_double = get_short_diagnostics(double_samples)
print(short_diagnostics_double)
