# append distributions to list sample_marg_PDF_list.append(sample_marg_PDF) sample_marg_PDF_list.append(sample_marg_PDF) f_marg_PDF_list.append(f_marg_PDF) f_marg_PDF_list.append(f_marg_PDF) # --------------------------------------------------------------------------- # MOVING PARTICLES # --------------------------------------------------------------------------- # initializing sampling method if sampling_method == 'mmh': sampler = mmh.ModifiedMetropolisHastings(sample_marg_PDF_list, f_marg_PDF_list, 'gaussian', Nb) elif sampling_method == 'cs': sampler = cs.CondSampling(sample_marg_PDF_list, 0.8, Nb) # initialization pf_list = [] theta_list = [] g_list = [] m_list = [] start_time = timer.time() for sim in range(0, n_simulations): pf_hat, theta_temp, g_temp, acc_rate, m_array = \ mp.mp_with_seed_selection(N, LSF, sampler, sample_marg_PDF_list, seed_selection_strategy) # transform samples back from u to x-space # for j in range(0, len(theta_temp)): # theta_temp[j] = transform_U2X(theta_temp[j])
# append distributions to list sample_marg_PDF_list.append(sample_marg_PDF) sample_marg_PDF_list.append(sample_marg_PDF) f_marg_PDF_list.append(f_marg_PDF) f_marg_PDF_list.append(f_marg_PDF) # --------------------------------------------------------------------------- # MOVING PARTICLES # --------------------------------------------------------------------------- # initializing sampling method if sampling_method == 'mmh': sampler = mmh.ModifiedMetropolisHastings(sample_marg_PDF_list, f_marg_PDF_list, 'gaussian', sigma_p, 0) elif sampling_method == 'cs': rho_k = np.sqrt(1 - sigma_p**2) sampler = cs.CondSampling(sample_marg_PDF_list, rho_k, 0) elif sampling_method == 'acs': sampler = acs.AdaptiveCondSampling(sample_marg_PDF_list, 0.1) ## apply subset-simulation # initialization pf_list = [] theta_list = [] g_list = [] start_time = timer.time() for sim in range(0, n_simulations): pf_hat, theta_temp, g_temp = \ sus.subsetsim(p0, N, LSF, sampler)