view util/SMALL_learn.m @ 195:d50f5bdbe14c luisf_dev

- Added SMALL_DL_test: simple DL showcase
- Added dico_decorr_symmetric: improved version of INK-SVD decorrelation step
- Debugged SMALL_learn, SMALLBoxInit and SMALL_two_step_DL

function DL = SMALL_learn(Problem,DL)
%% SMALL Dictionary Learning   
%   
%   Function gets as input Problem and Dictionary Learning (DL) structures 
%   In Problem structure field b with the training set needs to be defined
%   In DL fields with name of the toolbox and solver, and parameters file 
%   for particular dictionary learning technique needs to be present.
%
%   Outputs are Learned dictionary and time spent as a part of DL structure

%
%   Centre for Digital Music, Queen Mary, University of London.
%   This file copyright 2009 Ivan Damnjanovic.
%
%   This program is free software; you can redistribute it and/or
%   modify it under the terms of the GNU General Public License as
%   published by the Free Software Foundation; either version 2 of the
%   License, or (at your option) any later version.  See the file
%   COPYING included with this distribution for more information.
%%
global SMALL_path
if (DL.profile)
  fprintf('\nStarting Dictionary Learning %s... \n', DL.name);
end
  start=cputime;
  tStart=tic;
    
  % configuration file
  run([SMALL_path 'config/SMALL_learn_config.m']);
    
%%
%   Dictionary Learning time
tElapsed=toc(tStart);
DL.time = cputime - start;
if (DL.profile)
  fprintf('\n%s finished task in %2f seconds (cpu time). \n', DL.name, DL.time);
  fprintf('\n%s finished task in %2f seconds (tic-toc time). \n', DL.name, tElapsed);
end
DL.time=tElapsed;
%   If dictionary is given as a sparse matrix change it to full  

  DL.D = full(D);
  
end