Author: tlinnet
Date: Tue Sep 2 14:03:38 2014
New Revision: 25544
URL: http://svn.gna.org/viewcvs/relax?rev=25544&view=rev
Log:
Initial try to implement using BFGS, when minimising in bootstrap in
systemtest: test_task_model_par_est_tsmfk01.
Results:
50 boot
Simplex, with constraints
param: r2a_err, with err: 0.48131663, compared to MC: 0.49354342, with 50
boot 0.45852174
param: dw_err, with err: 0.30874093, compared to MC: 0.32813703, with 50 boot
0.34714120
param: k_AB_err, with err: 0.64606994, compared to MC: 0.65384783, with 50
boot 0.68158598
Simplex, without constraints
param: r2a_err, with err: 0.48131663, compared to MC: 0.49354342, with 50
boot 0.50583526
param: dw_err, with err: 0.30874093, compared to MC: 0.32813703, with 50 boot
0.30275995
param: k_AB_err, with err: 0.64606994, compared to MC: 0.65384783, with 50
boot 0.64242987
BFGS, with constraints
Cannot compute! Extremely show
BFGS, without constraints
param: r2a_err, with err: 0.48131663, compared to MC: 0.49354342, with 50
boot 0.44000111
param: dw_err, with err: 0.30874093, compared to MC: 0.32813703, with 50 boot
0.00554190
param: k_AB_err, with err: 0.64606994, compared to MC: 0.65384783, with 50
boot 0.69600469
Is there something wrong with scaling again?
task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR
estimation from Jacobian and Co-variance matrix of dispersion models.
Modified:
branches/est_par_error/test_suite/system_tests/relax_disp.py
Modified: branches/est_par_error/test_suite/system_tests/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/branches/est_par_error/test_suite/system_tests/relax_disp.py?rev=25544&r1=25543&r2=25544&view=diff
==============================================================================
--- branches/est_par_error/test_suite/system_tests/relax_disp.py
(original)
+++ branches/est_par_error/test_suite/system_tests/relax_disp.py Tue
Sep 2 14:03:38 2014
@@ -23,7 +23,7 @@
# Python module imports.
from os import F_OK, access, getcwd, path, sep
-from numpy import array, asarray, exp, median, inf, log, save, std, sum,
zeros
+from numpy import array, asarray, dot, exp, median, inf, log, save, std,
sum, zeros
from minfx.generic import generic_minimise
from random import gauss
import re, math
@@ -7393,9 +7393,11 @@
# Generate spin string.
spin_string = generate_spin_string(spin=cur_spin,
mol_name=mol_name, res_num=resi, res_name=resn)
+ # Get the model.
+ model = cur_spin.model
+
# Get the param_vector
- param_vector = assemble_param_vector(spins=[cur_spin])
- model = cur_spin.model
+ x0 = assemble_param_vector(spins=[cur_spin])
param_key_list = []
for exp_type, frq, offset, ei, mi, oi, in
loop_exp_frq_offset(return_indices=True):
@@ -7470,14 +7472,16 @@
sim_boot = 50
# Set algorithm.
- #min_algor = 'simplex'
+ min_algor = 'simplex'
min_algor = 'BFGS'
constraints = True
if constraints:
min_options = ('%s'%(min_algor),)
min_algor = 'Log barrier'
#min_algor = 'Method of Multipliers'
+ # OBS! This is a list per spin, so extract.
scaling_matrix = assemble_scaling_matrix(scaling=True)
+ scaling_matrix = scaling_matrix[0]
# Collect spins
all_spin_ids = []
@@ -7487,10 +7491,12 @@
spins = spin_ids_to_containers(all_spin_ids[:1])
# Get constraints
- A, b = linear_constraints(spins=spins,
scaling_matrix=scaling_matrix[0])
+ A, b = linear_constraints(spins=spins,
scaling_matrix=scaling_matrix)
+
else:
min_options = ()
A, b = None, None
+ scaling_matrix = None
# Number of spectrometer fields.
fields = [None]
@@ -7532,9 +7538,13 @@
values_err[ei][si][mi][oi][j] = deepcopy(r2_error)
# Init the Dispersion clas with data, so we can call
functions in it.
- tfunc = Dispersion(model=model, num_params=model_param_num,
num_spins=num_spins, num_frq=field_count, exp_types=exp_types,
values=values_err, errors=errors, missing=missing, frqs=frqs, frqs_H=frqs_H,
cpmg_frqs=cpmg_frqs, spin_lock_nu1=spin_lock_nu1,
chemical_shifts=chemical_shifts, offset=offsets, tilt_angles=tilt_angles,
r1=r1, relax_times=relax_times, r1_fit=r1_fit)
- results = generic_minimise(func=tfunc.func,
dfunc=tfunc.dfunc, args=(), x0=param_vector, min_algor=min_algor,
min_options=min_options, A=A, b=b, full_output=True, print_flag=0)
+ tfunc = Dispersion(model=model, num_params=model_param_num,
num_spins=num_spins, num_frq=field_count, exp_types=exp_types,
values=values_err, errors=errors, missing=missing, frqs=frqs, frqs_H=frqs_H,
cpmg_frqs=cpmg_frqs, spin_lock_nu1=spin_lock_nu1,
chemical_shifts=chemical_shifts, offset=offsets, tilt_angles=tilt_angles,
r1=r1, relax_times=relax_times, scaling_matrix=scaling_matrix, r1_fit=r1_fit)
+ results = generic_minimise(func=tfunc.func,
dfunc=tfunc.dfunc, args=(), x0=x0, min_algor=min_algor,
min_options=min_options, A=A, b=b, full_output=True, print_flag=1)
param_vector, chi2, iter_count, f_count, g_count, h_count,
warning = results
+
+ # Scaling.
+ if scaling_matrix != None:
+ param_vector = dot(scaling_matrix, param_vector)
# Get the parameters fitted in the model.
params_err = my_dic[spin_id]['params_err']
r25544 - /branches/est_par_error/test_suite/system_tests/relax_disp.py