Author: bugman
Date: Sun Oct 19 17:51:51 2008
New Revision: 7832
URL: http://svn.gna.org/viewcvs/relax?rev=7832&view=rev
Log:
Clean up of 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=7832&r1=7831&r2=7832&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 Sun
Oct 19 17:51:51 2008
@@ -453,39 +453,39 @@
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
+ def disassemble_result(self, param_vector=None, func=None, iter=None,
fc=None, gc=None, hc=None, warning=None, spin=None, sim_index=None,
model_type=None, scaling=None, scaling_matrix=None):
+ """Disassemble the optimisation results.
+
+ @keyword param_vector: The model-free parameter vector.
+ @type param_vector: numpy array
+ @keyword func: The optimised chi-squared value.
+ @type func: float
+ @keyword iter: The number of optimisation steps
required to find the minimum.
+ @type iter: int
+ @keyword fc: The function count.
+ @type fc: int
+ @keyword gc: The gradient count.
+ @type gc: int
+ @keyword hc: The Hessian count.
+ @type hc: int
+ @keyword warning: Any optimisation warnings.
+ @type warning: str or None
+ @keyword spin: The spin container.
+ @type spin: SpinContainer instance or None
+ @keyword sim_index: The Monte Carlo simulation index.
+ @type sim_index: int or None
+ @keyword model_type: The model-free model type, one of 'mf',
'local_tm', 'diff', or
+ 'all'.
+ @type model_type: str
+ @keyword scaling: If True, diagonal scaling is enabled
during optimisation to
+ allow the problem to be better
conditioned.
+ @type scaling: bool
+ @keyword scaling_matrix: The diagonal, square scaling matrix.
+ @type scaling_matrix: numpy diagonal matrix
+ """
+
+ # Alias the current data pipe.
+ cdp = pipes.get_pipe()
# Catch infinite chi-squared values.
if isInf(func):
@@ -500,7 +500,6 @@
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.
@@ -509,22 +508,22 @@
if model_type == 'mf' or model_type == 'local_tm':
# Chi-squared statistic.
- spin.chi2_sim[sim_index] = self.func
+ spin.chi2_sim[sim_index] = func
# Iterations.
- spin.iter_sim[sim_index] = self.iter_count
+ spin.iter_sim[sim_index] = iter
# Function evaluations.
- spin.f_count_sim[sim_index] = self.f_count
+ spin.f_count_sim[sim_index] = fc
# Gradient evaluations.
- spin.g_count_sim[sim_index] = self.g_count
+ spin.g_count_sim[sim_index] = gc
# Hessian evaluations.
- spin.h_count_sim[sim_index] = self.h_count
+ spin.h_count_sim[sim_index] = hc
# Warning.
- spin.warning_sim[sim_index] = self.warning
+ spin.warning_sim[sim_index] = warning
# Global minimisation statistics.
elif model_type == 'diff' or model_type == 'all':
@@ -532,16 +531,16 @@
cdp.chi2_sim[sim_index] = func
# Iterations.
- cdp.iter_sim[sim_index] = iter_count
+ cdp.iter_sim[sim_index] = iter
# Function evaluations.
- cdp.f_count_sim[sim_index] = f_count
+ cdp.f_count_sim[sim_index] = fc
# Gradient evaluations.
- cdp.g_count_sim[sim_index] = g_count
+ cdp.g_count_sim[sim_index] = gc
# Hessian evaluations.
- cdp.h_count_sim[sim_index] = h_count
+ cdp.h_count_sim[sim_index] = hc
# Warning.
cdp.warning_sim[sim_index] = warning
@@ -551,22 +550,22 @@
# Sequence specific minimisation statistics.
if model_type == 'mf' or model_type == 'local_tm':
# Chi-squared statistic.
- spin.chi2 = self.func
+ spin.chi2 = func
# Iterations.
- spin.iter = self.iter_count
+ spin.iter = iter
# Function evaluations.
- spin.f_count = self.f_count
+ spin.f_count = fc
# Gradient evaluations.
- spin.g_count = self.g_count
+ spin.g_count = gc
# Hessian evaluations.
- spin.h_count = self.h_count
+ spin.h_count = hc
# Warning.
- spin.warning = self.warning
+ spin.warning = warning
# Global minimisation statistics.
elif model_type == 'diff' or model_type == 'all':
@@ -574,16 +573,16 @@
cdp.chi2 = func
# Iterations.
- cdp.iter = iter_count
+ cdp.iter = iter
# Function evaluations.
- cdp.f_count = f_count
+ cdp.f_count = fc
# Gradient evaluations.
- cdp.g_count = g_count
+ cdp.g_count = gc
# Hessian evaluations.
- cdp.h_count = h_count
+ cdp.h_count = hc
# Warning.
cdp.warning = warning
r7832 - /branches/multi_processor_merge/specific_fns/model_free/mf_minimise.py