# 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)
