#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)