r25549 - /branches/est_par_error/test_suite/system_tests/relax_disp.py -- September 02, 2014 - 15:29

Author: tlinnet
Date: Tue Sep  2 15:29:04 2014
New Revision: 25549

URL: http://svn.gna.org/viewcvs/relax?rev=25549&view=rev
Log:
Fix for scaling of parameters in bootstrap.

I cannot find the error, so a relax setup with BFGS will be setup.

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=25549&r1=25548&r2=25549&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 15:29:04 2014
@@ -24,6 +24,7 @@
 # Python module imports.
 from os import F_OK, access, getcwd, path, sep
 from numpy import array, asarray, dot, exp, median, inf, log, save, std, 
sum, zeros
+from numpy.linalg import inv
 from minfx.generic import generic_minimise
 from random import gauss
 import re, math
@@ -7469,12 +7470,14 @@
 
         if do_boot:
             # Number of simulations.
-            sim_boot = 50
+            sim_boot = 500
 
             # Set algorithm.
             min_algor = 'simplex'
             min_algor = 'BFGS'
-            constraints = True
+            constraints = False
+            opt_func_tol = 1e-25
+            opt_max_iterations = int(1e7)
             if constraints:
                 min_options = ('%s'%(min_algor),)
                 min_algor = 'Log barrier'
@@ -7483,6 +7486,9 @@
                 scaling_matrix = assemble_scaling_matrix(scaling=True)
                 scaling_matrix = scaling_matrix[0]
 
+                if len(scaling_matrix):
+                    x0 = dot(inv(scaling_matrix), x0)
+
                 # Collect spins
                 all_spin_ids = []
                 for cur_spin, mol_name, resi, resn, spin_id in 
spin_loop(full_info=True, return_id=True, skip_desel=True):
@@ -7539,7 +7545,7 @@
 
                 # 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, 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)
+                results = generic_minimise(func=tfunc.func, 
dfunc=tfunc.dfunc, args=(), x0=x0, min_algor=min_algor, 
min_options=min_options, func_tol=opt_func_tol, maxiter=opt_max_iterations, 
A=A, b=b, full_output=True, print_flag=0)
                 param_vector, chi2, iter_count, f_count, g_count, h_count, 
warning = results
 
                 # Scaling.