# Zuw,Xuw=base.pcn_unweighted_IS(post,N,beta,sigma_is,x0,1,1,Disp=0) #unweighted Is
# mean_uw[i,:]=np.mean(Xuw[Burn_in:,:,0],0)
"""
Weighted
"""
Zw, Xw, W_IS, _ = base.pcn_weighted_IS(post,
N,
beta_original,
sigma_is,
PRIOR,
x0,
Disp=1,
dim_q=7) #weighted IS
yy, ww = base.weight_samples(Xw, W_IS, N_temp=4)
xx = base.resample_IS(yy, ww, N)
mean_y[i, :] = np.mean(xx[Burn_in:, :, 0], 0)
"""
pCN
"""
# Zpcn,Xrwm=base.pcn(post,N*N_temp,sigma_is[0],x0[:,0],Disp=0) #random walk metropolis
# mean_r[i,:]=np.mean(Xrwm[N_temp*Burn_in:],0)
name_y = 'results/est_y_' + str(INDICATOR).zfill(2) + '.npy'
name_s = 'results/samp_w_' + str(INDICATOR).zfill(2) + '.npy'
np.save(name_y, mean_y)
Xsd = base.state_dependent_PT(post, N, beta, sigma_is, x0, Ns=1,
Disp=0) #full vanilla, reversible
Xptf = base.full_vanilla(post, N, beta, sigma_is, x0, Ns=1,
Disp=0) #full vanilla, reversible
Xuw = base.unweighted_IS(post, N, beta, sigma_is, x0, 1, 1,
Disp=0) #unweighted Is
Xw, W_IS, W_IS2 = base.weighted_IS(post,
N,
beta_original,
sigma_is,
x0,
Disp=0) #weighted IS
Xrwm = base.rwm(post, N * N_temp, sigma_rwm, x0[:, 0],
Disp=0) #random walk metropolis
yy, ww = base.weight_samples(Xw, W_IS)
xx = base.resample_IS(yy, ww, N)
mean_r[i, :] = np.mean(Xrwm[N_temp * Burn_in:], 0)
mean_pf[i, :] = np.mean(Xptf[Burn_in:, :, 0], 0)
mean_sd[i, :] = np.mean(Xsd[Burn_in:, :, 0], 0)
mean_uw[i, :] = np.mean(Xuw[Burn_in:, :, 0], 0)
mean_w[i, :] = np.average(yy[Burn_in * math.factorial(N_temp):, :, 0],
0,
weights=ww[Burn_in * math.factorial(N_temp):])
mean_y[i, :] = np.mean(xx[Burn_in:, :, 0], 0)
np.save('mean_r_elliptic_b.npy', mean_r)
np.save('mean_p_elliptic_b.npy', mean_p)