# ---------------------------------------------------------------------------
# initialization
pf_line_list = []
b_line_list = []
cov_line_list = []
legend_list = []
cov_at_pf_array_cs = np.zeros(n_sigma, float)
pf_mean_array_cs = np.zeros(n_sigma, float)
cov_at_pf_array_mmh = np.zeros(n_sigma, float)
pf_mean_array_mmh = np.zeros(n_sigma, float)
# conditional sampling
for i in range(0, n_sigma):
b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(
g_list_list_cs[i], n_initial_samples)
pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp)
b_line_list.append(b_line_mp)
pf_line_list.append(pf_line_mean_mp)
cov_line_list.append(pf_line_cov_mp)
legend_list.append(r'MP')
cov_at_pf_array_cs[i] = pf_line_cov_mp[0]
pf_mean_array_cs[i] = pf_line_mean_mp[0]
# modified metropolis hastings
for i in range(0, n_sigma):
b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(
g_list_list_mmh[i], n_initial_samples)
pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(
# ---------------------------------------------------------------------------
# POST-PROCESSING
# ---------------------------------------------------------------------------
b_line_analytical = np.linspace(0, 7, 100)
pf_line_analytical = analytical_CDF(b_line_analytical)
b_line_mcs, pf_line_mcs = uutil.get_pf_line_and_b_line_from_MCS(g_list_mcs)
b_line_sus, pf_line_list_sus = uutil.get_pf_line_and_b_line_from_SUS(
g_list_sus, p0, n_samples_per_level)
pf_line_mean_sus, pf_line_cov_sus = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_sus)
b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp, n_initial_samples)
pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp)
b_line_mp2, pf_line_list_mp2 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp_wSeedSel, n_initial_samples)
pf_line_mean_mp2, pf_line_cov_mp2 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp2)
# initialization
b_line_list = []
pf_line_list = []
cov_line_list = []
legend_list = []
b_line_list.append(b_line_analytical)
cov_at_pf_array_sss2 = np.zeros(len(nsamples_list), float)
pf_mean_array_sss2 = np.zeros(len(nsamples_list), float)
cov_at_pf_array_sss3 = np.zeros(len(nsamples_list), float)
pf_mean_array_sss3 = np.zeros(len(nsamples_list), float)
cov_at_pf_array_sss4 = np.zeros(len(nsamples_list), float)
pf_mean_array_sss4 = np.zeros(len(nsamples_list), float)
cov_at_pf_array_sss5 = np.zeros(len(nsamples_list), float)
pf_mean_array_sss5 = np.zeros(len(nsamples_list), float)
# seed selection stragegy 0
for i in range(0, len(nsamples_list)):
N = nsamples_list[i]
b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(
g_list_list_sss0[i], N)
pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp)
cov_at_pf_array_sss0[i] = pf_line_cov_mp[0]
pf_mean_array_sss0[i] = pf_line_mean_mp[0]
# seed selection strategy 1
for i in range(0, len(nsamples_list)):
N = nsamples_list[i]
b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(
g_list_list_sss1[i], N)
pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp)
cov_at_pf_array_sss1[i] = pf_line_cov_mp[0]
pf_mean_mp3, pf_cov_mp3 = uutil.get_mean_and_cov_pf_from_MP(
g_list_mp3, N_mp[2])
print('SUS: pf =', pf_mean_sus, '| cov =', pf_cov_sus)
print('MP1: pf =', pf_mean_mp1, '| cov =', pf_cov_mp1)
print('MP2: pf =', pf_mean_mp2, '| cov =', pf_cov_mp2)
print('MP3: pf =', pf_mean_mp3, '| cov =', pf_cov_mp3)
b_line_mcs, pf_line_mcs = uutil.get_pf_line_and_b_line_from_MCS(g_list_mcs)
b_line_sus, pf_line_list_sus = uutil.get_pf_line_and_b_line_from_SUS(
g_list_sus, 0.1, N_sus[0])
pf_line_mean_sus, pf_line_cov_sus = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_sus)
b_line_mp1, pf_line_list_mp1 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp1, N_mp[0])
pf_line_mean_mp1, pf_line_cov_mp1 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp1)
b_line_mp2, pf_line_list_mp2 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp2, N_mp[1])
pf_line_mean_mp2, pf_line_cov_mp2 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp2)
b_line_mp3, pf_line_list_mp3 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp3, N_mp[2])
pf_line_mean_mp3, pf_line_cov_mp3 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp3)
# ---------------------------------------------------------------------------
# PLOTS
pf_mean_array_sss4 = np.zeros(b_max, float)
cov_at_pf_array_sss5 = np.zeros(b_max, float)
pf_mean_array_sss5 = np.zeros(b_max, float)
# seed selection stragegy 0
# for i in range(0, b_max):
# b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(g_list_list_sss0[i], n_initial_samples)
# pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(pf_line_list_mp)
# cov_at_pf_array_sss0[i] = pf_line_cov_mp[0]
# pf_mean_array_sss0[i] = pf_line_mean_mp[0]
# seed selection strategy 1
for i in range(0, b_max):
b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(
g_list_list_sss1[i], n_initial_samples)
pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp)
cov_at_pf_array_sss1[i] = pf_line_cov_mp[0]
pf_mean_array_sss1[i] = pf_line_mean_mp[0]
# seed selection strategy 2
for i in range(0, b_max):
b_line_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(
g_list_list_sss2[i], n_initial_samples)
pf_line_mean_mp, pf_line_cov_mp = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp)
cov_at_pf_array_sss2[i] = pf_line_cov_mp[0]
pf_mean_array_sss2[i] = pf_line_mean_mp[0]
pf_mean_mp2, pf_cov_mp2 = uutil.get_mean_and_cov_pf_from_MP(g_list_mp2, 241)
pf_mean_mp3, pf_cov_mp3 = uutil.get_mean_and_cov_pf_from_MP(g_list_mp3, 121)
print('SUS: pf =', pf_mean_sus, '| cov =', pf_cov_sus)
print('MP1: pf =', pf_mean_mp1, '| cov =', pf_cov_mp1)
print('MP2: pf =', pf_mean_mp2, '| cov =', pf_cov_mp2)
print('MP3: pf =', pf_mean_mp3, '| cov =', pf_cov_mp3)
b_line_mcs, pf_line_mcs = uutil.get_pf_line_and_b_line_from_MCS(g_list_mcs)
b_line_sus, pf_line_list_sus = uutil.get_pf_line_and_b_line_from_SUS(
g_list_sus, 0.1, 5000)
pf_line_mean_sus, pf_line_cov_sus = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_sus)
b_line_mp1, pf_line_list_mp1 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp1, 478)
pf_line_mean_mp1, pf_line_cov_mp1 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp1)
b_line_mp2, pf_line_list_mp2 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp2, 241)
pf_line_mean_mp2, pf_line_cov_mp2 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp2)
b_line_mp3, pf_line_list_mp3 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp3, 121)
pf_line_mean_mp3, pf_line_cov_mp3 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp3)
# ---------------------------------------------------------------------------
# PLOTS
g_list_mp = np.load(direction + 'mp_example_1_d10_N100_Nsim2_b30_mmh_g_list.npy') theta_list_mp = np.load(direction + 'mp_example_1_d10_N100_Nsim2_b30_mmh_theta_list.npy') # --------------------------------------------------------------------------- # POST-PROCESSING # --------------------------------------------------------------------------- b_line_analytical = np.linspace(0,7,100) pf_line_analytical = analytical_CDF(b_line_analytical) b_line_mcs, pf_line_mcs = uutil.get_pf_line_and_b_line_from_MCS(g_list_mcs) b_line_list_sus, pf_line_sus = uutil.get_pf_line_and_b_line_from_SUS(g_list_sus, p0, n_samples_per_level) b_line_list_mp, pf_line_list_mp = uutil.get_pf_line_and_b_line_from_MP(g_list_mp, n_initial_samples) # initialization b_line_list = [] pf_line_list = [] legend_list = [] b_line_list.append(b_line_analytical) b_line_list.append(b_line_mcs) b_line_list.append(b_line_list_sus[0]) b_line_list.append(b_line_list_mp[0]) pf_line_list.append(pf_line_analytical) pf_line_list.append(pf_line_mcs) pf_line_list.append(pf_line_sus)
pf_mean_mp3, pf_cov_mp3 = uutil.get_mean_and_cov_pf_from_MP(g_list_mp3, 27)
print('SUS: pf =', pf_mean_sus, '| cov =', pf_cov_sus)
print('MP1: pf =', pf_mean_mp1, '| cov =', pf_cov_mp1)
print('MP2: pf =', pf_mean_mp2, '| cov =', pf_cov_mp2)
print('MP3: pf =', pf_mean_mp3, '| cov =', pf_cov_mp3)
b_line_analytical = np.linspace(0, 5, 100)
pf_line_analytical = analytical_CDF(b_line_analytical)
b_line_sus, pf_line_list_sus = uutil.get_pf_line_and_b_line_from_SUS(
g_list_sus, 0.1, 1000)
pf_line_mean_sus, pf_line_cov_sus = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_sus)
b_line_mp1, pf_line_list_mp1 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp1, 104)
pf_line_mean_mp1, pf_line_cov_mp1 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp1)
b_line_mp2, pf_line_list_mp2 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp2, 52)
pf_line_mean_mp2, pf_line_cov_mp2 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp2)
b_line_mp3, pf_line_list_mp3 = uutil.get_pf_line_and_b_line_from_MP(
g_list_mp3, 27)
pf_line_mean_mp3, pf_line_cov_mp3 = uutil.get_mean_and_cov_from_pf_lines(
pf_line_list_mp3)
# ---------------------------------------------------------------------------
# PLOTS