r24696 - /branches/zooming_grid_search/user_functions/minimisation.py -- July 24, 2014 - 10:17

Author: bugman
Date: Thu Jul 24 10:17:54 2014
New Revision: 24696

URL: http://svn.gna.org/viewcvs/relax?rev=24696&view=rev
Log:
Large expansion of the minimise.grid_search user function documentation.

The previous documentation was essentially non-existent.


Modified:
    branches/zooming_grid_search/user_functions/minimisation.py

Modified: branches/zooming_grid_search/user_functions/minimisation.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/zooming_grid_search/user_functions/minimisation.py?rev=24696&r1=24695&r2=24696&view=diff
==============================================================================
--- branches/zooming_grid_search/user_functions/minimisation.py (original)
+++ branches/zooming_grid_search/user_functions/minimisation.py Thu Jul 24 
10:17:54 2014
@@ -319,8 +319,8 @@
 
 # The minimise.grid_search user function.
 uf = uf_info.add_uf('minimise.grid_search')
-uf.title = "Perform a grid search."
-uf.title_short = "Grid search."
+uf.title = "Perform a grid search to find an initial non-biased parameter 
set for optimisation."
+uf.title_short = "Grid search optimisation."
 uf.display = True
 uf.add_keyarg(
     name = "lower",
@@ -367,11 +367,14 @@
 )
 # Description.
 uf.desc.append(Desc_container())
-uf.desc[-1].add_paragraph("This will perform a grid search across the 
parameter space.")
+uf.desc[-1].add_paragraph("The optimisation of a mathematical model normally 
consists of two parts - a coarse grid search across the parameter space to 
find an initial set of parameter values followed by the use of a high 
precision optimisation algorithm to exactly find the local or global 
solution.  The grid search is an essential tool as it allows a non-biased 
initial optimisation position to be found.  It avoids the statistical bias 
and preconditioning introduced by using a self chosen initial parameter set.  
The high computational cost of the grid search is almost always favourable to 
the statistical bias of a user defined starting position.")
+uf.desc[-1].add_paragraph("The region of the parameter space that the grid 
search covers is defined by the lower and upper grid bounds.  These will 
generally default to the entire parameter space except for when the parameter 
is non-bounded, for example a 3D position in the PDB space.  This user 
function will print out the grid bounds used and, if the default bounds are 
deemed to be insufficient, then the lower, upper or both bounds can supplied. 
 This only works if all active models have the same parameters.  The 
coarseness or fineness of the grid is defined by the number of increments to 
search across between the bounds.  For an alternative to using large numbers 
of increments, see the zooming grid search.")
+uf.desc[-1].add_paragraph("It is possible to decrease the dimensionality of 
the grid search, and hence drop the computational cost by orders of 
magnitude, if certain parameter values are know a priori.  For example if the 
values are determined via a different experiment.  Such parameters can be set 
with the value setting user function.  Then, when the skip preset flag is 
set, these parameters will be skipped in the grid search.  This feature 
should not be abused and statistical bias should be avoided at all cost.")
 uf.backend = minimise.grid_search
 uf.menu_text = "&grid_search"
 uf.gui_icon = "relax.grid_search"
-uf.wizard_size = (800, 500)
+uf.wizard_height_desc = 370
+uf.wizard_size = (1000, 700)
 uf.wizard_image = WIZARD_IMAGE_PATH + 'minimise.png'