def linkage(par):
st = time.time()
C = const()
f_red = h5py.File("spatial_reduced.hdf5", 'r')
f_link = h5py.File("responses.hdf5", 'r')
"""gather the calibration data"""
ns_cal_tot = np.sum(C['ns_cal'])
response_cal_tot = np.zeros(ns_cal_tot, dtype='float64')
reduced_cal_tot = np.zeros((ns_cal_tot, C['n_pc_tot']), dtype='float64')
c = 0
for ii in xrange(len(C['ns_cal'])):
c_ = c + C['ns_cal'][ii]
set_id = C['set_id_cal'][ii]
dset_name = "%s_%s" % (par, set_id)
response_cal_tot[c:c_] = f_link.get(dset_name)[...]
reduced_cal_tot[c:c_, :] = f_red.get('reduced_%s' % set_id)[...]
c = c_
"""gather the validation data"""
ns_val_tot = np.sum(C['ns_val'])
response_val_tot = np.zeros(ns_val_tot, dtype='float64')
reduced_val_tot = np.zeros((ns_val_tot, C['n_pc_tot']), dtype='float64')
c = 0
for ii in xrange(len(C['ns_val'])):
c_ = c + C['ns_val'][ii]
set_id = C['set_id_val'][ii]
dset_name = "%s_%s" % (par, set_id)
response_val_tot[c:c_] = f_link.get(dset_name)[...]
reduced_val_tot[c:c_, :] = f_red.get('reduced_%s' % set_id)[...]
c = c_
f_red.close()
f_link.close()
"""perform the regressions"""
n_ii = C['n_pc_tot']
n_jj = 3
coefmax = (n_ii + 1) * (n_jj + 2)
f_reg = h5py.File("regression_results.hdf5", 'a')
Rpred_cal_set = f_reg.create_dataset('Rpred_cal_%s' % par,
(n_ii * n_jj, ns_cal_tot),
dtype='float64')
Rpred_val_set = f_reg.create_dataset('Rpred_val_%s' % par,
(n_ii * n_jj, ns_val_tot),
dtype='float64')
f_reg.create_dataset('Rsim_cal_%s' % par, data=response_cal_tot)
f_reg.create_dataset('Rsim_val_%s' % par, data=response_val_tot)
order_set = f_reg.create_dataset('order_%s' % par, (n_ii * n_jj, 2),
dtype='int64')
meanerr_cal_set = f_reg.create_dataset('meanerr_cal_%s' % par,
(n_ii * n_jj, ),
dtype='float64')
meanerr_val_set = f_reg.create_dataset('meanerr_val_%s' % par,
(n_ii * n_jj, ),
dtype='float64')
maxerr_cal_set = f_reg.create_dataset('maxerr_cal_%s' % par,
(n_ii * n_jj, ),
dtype='float64')
maxerr_val_set = f_reg.create_dataset('maxerr_val_%s' % par,
(n_ii * n_jj, ),
dtype='float64')
coef_set = f_reg.create_dataset('coef_%s' % par, (n_ii * n_jj, coefmax),
dtype='float64')
c = 0
for ii in xrange(n_ii):
for jj in xrange(n_jj):
n_pc = ii + 1
n_poly = jj + 2
msg = "number of PCs: %s" % n_pc
rr.WP(msg, C['wrt_file'])
msg = "degree of polynomial: %s" % str(n_poly - 1)
rr.WP(msg, C['wrt_file'])
tmp = rr.regress(reduced_cal_tot, reduced_val_tot,
response_cal_tot, response_val_tot, n_pc, n_poly)
Rpred_cal = tmp[0]
Rpred_val = tmp[1]
meanerr_cal = tmp[2]
meanerr_val = tmp[3]
maxerr_cal = tmp[4]
maxerr_val = tmp[5]
coef = tmp[6]
meanerr_cal_set[c] = meanerr_cal
meanerr_val_set[c] = meanerr_val
maxerr_cal_set[c] = maxerr_cal
maxerr_val_set[c] = maxerr_val
Rpred_cal_set[c, :] = Rpred_cal
Rpred_val_set[c, :] = Rpred_val
order_set[c, :] = np.array([n_pc, n_poly])
coef_set[c, :len(coef)] = coef
c += 1
f_reg.close()
timeE = np.round(time.time() - st, 1)
msg = "regressions and cross-validations completed: %s s" % timeE
rr.WP(msg, C['wrt_file'])
def linkage(el, ns_cal_set, ns_val_set, set_id_cal_set, set_id_val_set,
resptyp, wrt_file):
st = time.time()
f_red = h5py.File("sve_reduced.hdf5", 'r')
f_link = h5py.File("linkage.hdf5", 'r')
"""gather the calibration data"""
ns_cal_tot = np.sum(ns_cal_set)
response_cal_tot = np.zeros(ns_cal_tot, dtype='float64')
reduced_cal_tot = np.zeros((ns_cal_tot, 20), dtype='complex128')
c = 0
for ii in xrange(len(set_id_cal_set)):
c_ = c + ns_cal_set[ii]
set_id = set_id_cal_set[ii]
response_cal_tot[c:c_] = f_link.get('%s_%s' % (resptyp, set_id))[...]
reduced_cal_tot[c:c_, :] = f_red.get('reduced_%s' % set_id)[...]
c = c_
"""gather the validation data"""
ns_val_tot = np.sum(ns_val_set)
response_val_tot = np.zeros(ns_val_tot, dtype='float64')
reduced_val_tot = np.zeros((ns_val_tot, 20), dtype='complex128')
c = 0
for ii in xrange(len(set_id_val_set)):
c_ = c + ns_val_set[ii]
set_id = set_id_val_set[ii]
response_val_tot[c:c_] = f_link.get('%s_%s' % (resptyp, set_id))[...]
reduced_val_tot[c:c_, :] = f_red.get('reduced_%s' % set_id)[...]
c = c_
f_red.close()
f_link.close()
"""perform the regressions"""
n_ii = 5
n_jj = 5
coefmax = (n_ii + 1) * (n_jj + 2)
f_reg = h5py.File("regression_results.hdf5", 'w')
order_set = f_reg.create_dataset('order', (n_ii * n_jj, 2), dtype='int64')
meanerr_set = f_reg.create_dataset('meanerr', (n_ii * n_jj, ),
dtype='float64')
maxerr_set = f_reg.create_dataset('maxerr', (n_ii * n_jj, ),
dtype='float64')
coef_set = f_reg.create_dataset('coef', (n_ii * n_jj, coefmax),
dtype='complex128')
resppred_set = f_reg.create_dataset('Rpred', (n_ii * n_jj, ns_val_tot),
dtype='float64')
f_reg.create_dataset('Rsim', data=response_val_tot)
c = 0
for ii in xrange(n_ii):
for jj in xrange(n_jj):
n_pc = ii + 1
n_poly = jj + 2
msg = "number of PCs: %s" % n_pc
rr.WP(msg, wrt_file)
msg = "degree of polynomial: %s" % str(n_poly - 1)
rr.WP(msg, wrt_file)
tmp = rr.regress(reduced_cal_tot, reduced_val_tot,
response_cal_tot, response_val_tot, n_pc, n_poly)
err_mean = tmp[0]
err_max = tmp[1]
coef = tmp[2]
response_val_tot_ = tmp[3]
order_set[c, :] = np.array([n_pc, n_poly])
meanerr_set[c] = err_mean
maxerr_set[c] = err_max
coef_set[c, :len(coef)] = coef
resppred_set[c, :ns_val_tot] = response_val_tot_
c += 1
f_reg.close()
timeE = np.round(time.time() - st, 1)
msg = "regressions and cross-validations completed: %s s" % timeE
rr.WP(msg, wrt_file)