Author: bugman
Date: Thu Oct 16 17:41:18 2008
New Revision: 7770
URL: http://svn.gna.org/viewcvs/relax?rev=7770&view=rev
Log:
Shifted the disassemble_result() method to its alphabetically sorted position.
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=7770&r1=7769&r2=7770&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 17:41:18 2008
@@ -450,6 +450,142 @@
spin.s2s_sim[sim_index] = 1e99
else:
spin.s2s_sim[sim_index] = spin.s2_sim[sim_index]
/ spin.s2f_sim[sim_index]
+
+
+ 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 '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 grid_search(self, lower=None, upper=None, inc=None,
constraints=True, verbosity=1, sim_index=None):
@@ -1020,142 +1156,6 @@
#self.relax.processor.run_queue()
- 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 '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):
"""Set up all the data required for minimisation.
r7770 - /branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py