print "number of models to filter: ", "{:e}".format(nruns * popsize * niter)
print''
threshold = np.max(filt_energy) # np.median(filt_energy)
m_near, f_near = pp.value_filter(filt_models, filt_energy, threshold)
# --- > regrid parameter space
delta_m = 1e-3
m_grid, f_grid, rgrid_error = pp.regrid(m_near, f_near, delta_m, center=True)
del m_near, f_near
print "number of particles in subgrid", "{:e}".format(m_grid.shape[0])
print "Error in energy minimum after subgrid :", np.min(f_grid) - f_gbest
print''
# ---> Xi2 weighted mean model, in log and physical space
m_weight = pp.weighted_mean(m_grid, f_grid, ndata, kappa=1, rms=True, log=True)
# ---> Xi2 weighted STD model, in log and physical space
std_weight = pp.weighted_std(m_weight, m_grid, f_grid, ndata, kappa=1, rms=True, log=True)
# ---- marginal laws using RMS
indx = indx + 1
pdf_m[indx, :, :], n_bin, x_bin = pp.marginal_law(m_grid, f_grid, logrhosynth, ndata,
n_inter=n_inter,lower=lower, upper=upper,
kappa=kappa, rms=True)
# ----------------------------------------------------------------------------
# Norme L2 Error
# | pdf(nruns, iparam) - pdf(irun, ipara)|_2
pdf_error = np.empty(shape=(len(vec_run), nparam))
# Error mcm exploration detected
np.max(np.abs(m_near - logrhosynth))
# --- > regrid parameter space
delta_m = 1e-3
m_grid, f_grid, rgrid_error = pp.regrid(m_near, f_near, delta_m, center=True)
del m_near, f_near
print "number of particles in subgrid", "{:e}".format(m_grid.shape[0])
print "Error in energy minimum after subgrid :", np.min(f_grid) - f_gbest
print''
print "max distance between m_grid and logrhosynth :"
print np.max(np.abs(m_grid - logrhosynth))
# ---> Xi2 weighted mean model, in log and physical space
m_weight = pp.weighted_mean(m_grid, f_grid, ndata, kappa=kappa, rms=rms,
log=True)
# ---> Xi2 weighted STD model, in log and physical space
std_weight = pp.weighted_std(m_weight, m_grid, f_grid, ndata, kappa=kappa,
rms=rms, log=True)
print''
# ---- marginal laws centered around solution
pdf_m, n_bin, x_bin = pp.marginal_law(m_grid, f_grid, logrhosynth, ndata,
n_inter=n_inter,lower=lower, upper=upper, kappa=kappa,
rms=rms)
print "check marginal law intervals and window for each parameter"
print "should be ", m_grid.shape[0]
print np.sum(n_bin, axis=1)
print "number of models to filter: ", "{:e}".format(nruns * popsize * niter)
print''
threshold =np.max(filt_energy)# np.median(filt_energy)
m_near, f_near = pp.value_filter(filt_models, filt_energy, threshold)
# --- > regrid parameter space
delta_m = 1e-3
m_grid, f_grid, rgrid_error = pp.regrid(m_near, f_near, delta_m, center=True)
del m_near, f_near
print "number of particles in subgrid", "{:e}".format(m_grid.shape[0])
print "Error in energy minimum after subgrid :", np.min(f_grid) - f_gbest
print''
# ---> Xi2 weighted mean model, in log and physical space
m_weight = pp.weighted_mean(m_grid, f_grid, ndata, kappa=1, rms=True, log=True)
mpow_weight = np.log10(pp.weighted_mean(m_grid, ndata, f_grid, kappa=1, rms=True, log=False))
print "Mean-difference between log and physical space :", np.max(np.abs(mpow_weight - m_weight))
print''
# ---> Xi2 weighted STD model, in log and physical space
std_weight = pp.weighted_std(m_weight, m_grid, f_grid, ndata, kappa=1, rms=True, log=True)
stdpow_weight = pp.weighted_std(10**mpow_weight, m_grid, f_grid, ndata, kappa=1, rms=True, log=False)
print "STD-difference between log and physical space :", np.max(np.abs(stdpow_weight - std_weight))
print''
# ---- marginal laws using kappa damping coefficient
n_inter = 40
lower = -2.
upper = 2.
kappa = 1
threshold = np.max(filt_energy) # np.median(filt_energy)
m_near, f_near = pp.value_filter(filt_models, filt_energy, threshold)
del filt_models, filt_energy
# --- > regrid parameter space
delta_m = 1e-3
m_grid, f_grid, rgrid_error = pp.regrid(m_near, f_near, delta_m, center=True)
del m_near, f_near
print "number of particles in subgrid", "{:e}".format(m_grid.shape[0])
print "Error in energy minimum after subgrid :", np.min(f_grid) - f_gbest
# ---> Xi2 weighted mean model, in log and physical space
m_weight = pp.weighted_mean(m_grid,
f_grid,
ndata,
kappa=1,
rms=False,
log=True)
mpow_weight = np.log10(
pp.weighted_mean(m_grid, ndata, f_grid, kappa=1, rms=False, log=False))
print "Mean-difference between log and physical space :", np.max(
np.abs(mpow_weight - m_weight))
# ---> Xi2 weighted STD model, in log and physical space
std_weight = pp.weighted_std(m_weight,
m_grid,
f_grid,
ndata,
kappa=1,
rms=False,
log=True)