Author: bugman
Date: Thu Oct 16 17:39:20 2008
New Revision: 7769
URL: http://svn.gna.org/viewcvs/relax?rev=7769&view=rev
Log:
Fixed the indentation in the disassemble_result() method.
Modified:
branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py
Modified:
branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py
URL:
http://svn.gna.org/viewcvs/relax/branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py?rev=7769&r1=7768&r2=7769&view=diff
==============================================================================
--- branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py
(original)
+++ branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py Thu
Oct 16 17:39:20 2008
@@ -1023,138 +1023,137 @@
def disassemble_result(self, param_vector, func, iter, fc, gc, hc,
warning, spin, sim_index, model_type, scaling, scaling_matrix):
"""Disassemble the optimisation results."""
- #print '***', param_vector, func, iter, fc, gc, hc, warning,
spin, sim_index, model_type, scaling
- #self.write_columnar_line(file=sys.stdout)
- #self.param_vector=param_vector
-
-# print 'disassembel result'
-# print 'param_vector', param_vector
-# print 'func', func
-# print 'iter', iter
-# print 'fc', fc
-# print 'gc', gc
-# print 'hc', hc
-# print 'warning', warning
-# print 'run', run
-# print 'index', index
-# print 'sim_index', sim_index
-# print ' model_type ', model_type
-# print 'scaling', scaling
-# print 'scaling_matrix', scaling_matrix
- #FIXME this is a fix for old code
-# self.iter_count = iter
-# self.f_count = fc
-# self.g_count = gc
-# self.h_count = hc
-# self.run=run
-
- self.func=func
- self.warning=warning
- self.iter_count = self.iter_count + iter
- self.f_count = self.f_count + fc
- self.g_count = self.g_count + gc
- self.h_count = self.h_count + hc
-
- # Catch infinite chi-squared values.
- if isInf(func):
- raise RelaxInfError, 'chi-squared'
-
- # Catch chi-squared values of NaN.
- if isNaN(func):
- raise RelaxNaNError, 'chi-squared'
-
- # Scaling.
- if scaling:
- param_vector = dot(scaling_matrix, param_vector)
-
- # Disassemble the parameter vector.
- # FIXME pass param_vector
- self.disassemble_param_vector(model_type,
param_vector=param_vector, spin=spin, sim_index=sim_index)
-
- # Monte Carlo minimisation statistics.
- if sim_index != None:
- # Sequence specific minimisation statistics.
- if model_type == 'mf' or model_type == 'local_tm':
-
- # Chi-squared statistic.
- spin.chi2_sim[sim_index] = self.func
-
- # Iterations.
- spin.iter_sim[sim_index] = self.iter_count
-
- # Function evaluations.
- spin.f_count_sim[sim_index] = self.f_count
-
- # Gradient evaluations.
- spin.g_count_sim[sim_index] = self.g_count
-
- # Hessian evaluations.
- spin.h_count_sim[sim_index] = self.h_count
-
- # Warning.
- spin.warning_sim[sim_index] = self.warning
-
- # Global minimisation statistics.
- elif model_type == 'diff' or model_type == 'all':
- # Chi-squared statistic.
- cdp.chi2_sim[sim_index] = func
-
- # Iterations.
- cdp.iter_sim[sim_index] = iter_count
-
- # Function evaluations.
- cdp.f_count_sim[sim_index] = f_count
-
- # Gradient evaluations.
- cdp.g_count_sim[sim_index] = g_count
-
- # Hessian evaluations.
- cdp.h_count_sim[sim_index] = h_count
-
- # Warning.
- cdp.warning_sim[sim_index] = warning
-
- # Normal statistics.
- else:
- # Sequence specific minimisation statistics.
- if model_type == 'mf' or model_type == 'local_tm':
- # Chi-squared statistic.
- spin.chi2 = self.func
-
- # Iterations.
- spin.iter = self.iter_count
-
- # Function evaluations.
- spin.f_count = self.f_count
-
- # Gradient evaluations.
- spin.g_count = self.g_count
-
- # Hessian evaluations.
- spin.h_count = self.h_count
-
- # Warning.
- spin.warning = self.warning
-
- # Global minimisation statistics.
- elif model_type == 'diff' or model_type == 'all':
- # Chi-squared statistic.
- cdp.chi2 = func
-
- # Iterations.
- cdp.iter = iter_count
-
- # Function evaluations.
- cdp.f_count = f_count
-
- # Gradient evaluations.
- cdp.g_count = g_count
-
- # Hessian evaluations.
- cdp.h_count = h_count
-
- # Warning.
- cdp.warning = warning
+ #print '***', param_vector, func, iter, fc, gc, hc, warning, spin,
sim_index, model_type, scaling
+ #self.write_columnar_line(file=sys.stdout)
+ #self.param_vector=param_vector
+
+ #print 'disassembel result'
+ #print 'param_vector', param_vector
+ #print 'func', func
+ #print 'iter', iter
+ #print 'fc', fc
+ #print 'gc', gc
+ #print 'hc', hc
+ #print 'warning', warning
+ #print 'spin', spin
+ #print 'sim_index', sim_index
+ #print ' model_type ', model_type
+ #print 'scaling', scaling
+ #print 'scaling_matrix', scaling_matrix
+ #FIXME this is a fix for old code
+ #self.iter_count = iter
+ #self.f_count = fc
+ #self.g_count = gc
+ #self.h_count = hc
+ #self.run=run
+
+ self.func=func
+ self.warning=warning
+ self.iter_count = self.iter_count + iter
+ self.f_count = self.f_count + fc
+ self.g_count = self.g_count + gc
+ self.h_count = self.h_count + hc
+
+ # Catch infinite chi-squared values.
+ if isInf(func):
+ raise RelaxInfError, 'chi-squared'
+
+ # Catch chi-squared values of NaN.
+ if isNaN(func):
+ raise RelaxNaNError, 'chi-squared'
+
+ # Scaling.
+ if scaling:
+ param_vector = dot(scaling_matrix, param_vector)
+
+ # Disassemble the parameter vector.
+ # FIXME pass param_vector
+ self.disassemble_param_vector(model_type, param_vector=param_vector,
spin=spin, sim_index=sim_index)
+
+ # Monte Carlo minimisation statistics.
+ if sim_index != None:
+ # Sequence specific minimisation statistics.
+ if model_type == 'mf' or model_type == 'local_tm':
+
+ # Chi-squared statistic.
+ spin.chi2_sim[sim_index] = self.func
+
+ # Iterations.
+ spin.iter_sim[sim_index] = self.iter_count
+
+ # Function evaluations.
+ spin.f_count_sim[sim_index] = self.f_count
+
+ # Gradient evaluations.
+ spin.g_count_sim[sim_index] = self.g_count
+
+ # Hessian evaluations.
+ spin.h_count_sim[sim_index] = self.h_count
+
+ # Warning.
+ spin.warning_sim[sim_index] = self.warning
+
+ # Global minimisation statistics.
+ elif model_type == 'diff' or model_type == 'all':
+ # Chi-squared statistic.
+ cdp.chi2_sim[sim_index] = func
+
+ # Iterations.
+ cdp.iter_sim[sim_index] = iter_count
+
+ # Function evaluations.
+ cdp.f_count_sim[sim_index] = f_count
+
+ # Gradient evaluations.
+ cdp.g_count_sim[sim_index] = g_count
+
+ # Hessian evaluations.
+ cdp.h_count_sim[sim_index] = h_count
+
+ # Warning.
+ cdp.warning_sim[sim_index] = warning
+
+ # Normal statistics.
+ else:
+ # Sequence specific minimisation statistics.
+ if model_type == 'mf' or model_type == 'local_tm':
+ # Chi-squared statistic.
+ spin.chi2 = self.func
+
+ # Iterations.
+ spin.iter = self.iter_count
+
+ # Function evaluations.
+ spin.f_count = self.f_count
+
+ # Gradient evaluations.
+ spin.g_count = self.g_count
+
+ # Hessian evaluations.
+ spin.h_count = self.h_count
+
+ # Warning.
+ spin.warning = self.warning
+
+ # Global minimisation statistics.
+ elif model_type == 'diff' or model_type == 'all':
+ # Chi-squared statistic.
+ cdp.chi2 = func
+
+ # Iterations.
+ cdp.iter = iter_count
+
+ # Function evaluations.
+ cdp.f_count = f_count
+
+ # Gradient evaluations.
+ cdp.g_count = g_count
+
+ # Hessian evaluations.
+ cdp.h_count = h_count
+
+ # Warning.
+ cdp.warning = warning
def minimise_data_setup(self, model_type, min_algor, num_data_sets,
min_options, spin=None, sim_index=None):
r7769 - /branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py