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 = 21
lower = -1.
upper = 1.
kappa = 1
m_center = m_gbest * 0
pdf_m, n_bin, x_bin = pp.marginal_law(m_grid,
f_grid,
m_center,
ndata,
n_inter=n_inter,
lower=lower,
upper=upper,
kappa=kappa,
rms=True)
# ---> save m_grid, f_grid, r_grid_error, m_gbest,
# f_best, delta_m , xbin, n_bin, pdf_m, m_weight, m_pow
# kappa, lower, upper
if save_netcdf:
if irun == 0:
if os.path.isfile(outfile):
os.remove(outfile)
print "remove ", outfile
nc = Dataset(outfile, "w", format='NETCDF4')
# dimensions: name, size
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))
for irun in range(len(vec_run)):
pdf_error[irun, :] = np.linalg.norm(pdf_m[irun, :] - pdf_m[-1, :], 2)
plt.figure()
# ---> plot selection
for iparam in range(nparam):
plt.plot(vec_run, pdf_error[:, iparam])
plt.xlabel('number of runs')
rms=False,
log=False)
print "STD-difference between log and physical space :", np.max(
np.abs(stdpow_weight - std_weight))
# ---- marginal laws using kappa damping coefficient
n_inter = 21
lower = -1.
upper = 1.
kappa = ndata
pdf_m, n_bin, x_bin = pp.marginal_law(m_grid,
f_grid,
m_gbest,
ndata,
n_inter=n_inter,
lower=lower,
upper=upper,
kappa=kappa,
rms=False)
# ---> save m_grid, f_grid, r_grid_error, m_gbest,
# f_best, delta_m , xbin, n_bin, pdf_m, m_weight, m_pow
# kappa, lower, upper
if save_netcdf:
if os.path.isfile(outfile):
os.remove(outfile)
print "remove ", outfile
nc = Dataset(outfile, "w", format='NETCDF4')
# dimensions: name, size
nc.createDimension('popsize', f_grid.shape[0])