idamnjanovic@8: function DL = SMALL_learn(Problem,DL)
ivan@121: %% SMALL Dictionary Learning   
ivan@121: %   
ivan@121: %   Function gets as input Problem and Dictionary Learning (DL) structures 
ivan@121: %   In Problem structure field b with the training set needs to be defined
ivan@121: %   In DL fields with name of the toolbox and solver, and parameters file 
ivan@121: %   for particular dictionary learning technique needs to be present.
ivan@121: %
ivan@121: %   Outputs are Learned dictionary and time spent as a part of DL structure
ivan@121: 
idamnjanovic@24: %
idamnjanovic@24: %   Centre for Digital Music, Queen Mary, University of London.
idamnjanovic@24: %   This file copyright 2009 Ivan Damnjanovic.
idamnjanovic@24: %
idamnjanovic@24: %   This program is free software; you can redistribute it and/or
idamnjanovic@24: %   modify it under the terms of the GNU General Public License as
idamnjanovic@24: %   published by the Free Software Foundation; either version 2 of the
idamnjanovic@24: %   License, or (at your option) any later version.  See the file
idamnjanovic@24: %   COPYING included with this distribution for more information.
idamnjanovic@8: %%
ivan@152: if (DL.profile)
idamnjanovic@8:   fprintf('\nStarting Dictionary Learning %s... \n', DL.name);
ivan@152: end
idamnjanovic@8:   start=cputime;
idamnjanovic@36:   tStart=tic;
idamnjanovic@8:   if strcmpi(DL.toolbox,'KSVD')
idamnjanovic@8:     param=DL.param; 
idamnjanovic@8:     param.data=Problem.b;
idamnjanovic@8:  
idamnjanovic@36:     D = eval([DL.name,'(param)']);%, ''t'', 5);']);
idamnjanovic@8:   elseif strcmpi(DL.toolbox,'KSVDS')
idamnjanovic@8:     param=DL.param; 
idamnjanovic@8:     param.data=Problem.b;
idamnjanovic@8:     
idamnjanovic@8:     D = eval([DL.name,'(param, ''t'', 5);']);
idamnjanovic@8:   elseif strcmpi(DL.toolbox,'SPAMS')
idamnjanovic@8:     
idamnjanovic@8:     X  = Problem.b; 
idamnjanovic@8:     param=DL.param;
idamnjanovic@8:     
idamnjanovic@8:     D = eval([DL.name,'(X, param);']);
idamnjanovic@8:     %   As some versions of SPAMS does not produce unit norm column
idamnjanovic@8:     %   dictionaries, we need to make sure that columns are normalised to
idamnjanovic@8:     %   unit lenght.
idamnjanovic@8:     
idamnjanovic@8:     for i = 1: size(D,2)
idamnjanovic@8:         D(:,i)=D(:,i)/norm(D(:,i));
idamnjanovic@8:     end
idamnjanovic@36:   elseif strcmpi(DL.toolbox,'SMALL')
idamnjanovic@8:     
idamnjanovic@36:     X  = Problem.b; 
idamnjanovic@36:     param=DL.param;
idamnjanovic@36:     
idamnjanovic@36:     D = eval([DL.name,'(X, param);']);
idamnjanovic@36:     %   we need to make sure that columns are normalised to
idamnjanovic@36:     %   unit lenght.
idamnjanovic@36:     
idamnjanovic@36:     for i = 1: size(D,2)
idamnjanovic@36:         D(:,i)=D(:,i)/norm(D(:,i));
idamnjanovic@36:     end
ivan@121:     
ivan@152:    elseif strcmpi(DL.toolbox,'TwoStepDL')
ivan@152:         
ivan@152:     DL=SMALL_two_step_DL(Problem, DL);
ivan@152:     
ivan@152:     %   we need to make sure that columns are normalised to
ivan@152:     %   unit lenght.
ivan@152:     
ivan@152:     for i = 1: size(DL.D,2)
ivan@152:         DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
ivan@152:     end
ivan@155:     D = DL.D;
ivan@155:     
ivan@155: elseif strcmpi(DL.toolbox,'MMbox')
ivan@155:         
ivan@155:     DL = wrapper_mm_DL(Problem, DL);
ivan@155:     
ivan@155:     %   we need to make sure that columns are normalised to
ivan@155:     %   unit lenght.
ivan@155:     
ivan@155:     for i = 1: size(DL.D,2)
ivan@155:         DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
ivan@155:     end
ivan@152:     D = DL.D; 
ivan@155:     
idamnjanovic@8: %   To introduce new dictionary learning technique put the files in
idamnjanovic@8: %   your Matlab path. Next, unique name <TolboxID> for your toolbox needs 
idamnjanovic@8: %   to be defined and also prefferd API for toolbox functions <Preffered_API>
idamnjanovic@8: %   
idamnjanovic@8: % elseif strcmpi(DL.toolbox,'<ToolboxID>')
idamnjanovic@8: %     % This is an example of API that can be used:
idamnjanovic@8: %     % - get training set from Problem part of structure
idamnjanovic@8: %     % - assign parameters defined in the main program
idamnjanovic@8: %
idamnjanovic@8: %     X  = Problem.b; 
idamnjanovic@8: %     param=DL.param;
idamnjanovic@8: %
idamnjanovic@8: %     % - Evaluate the function (DL.name - defined in the main) with
idamnjanovic@8: %     %   parameters given above
idamnjanovic@8: %
idamnjanovic@8: %     D = eval([DL.name,'(<Preffered_API>);']);
idamnjanovic@8: 
idamnjanovic@8:   else
idamnjanovic@8:     printf('\nToolbox has not been registered. Please change SMALL_learn file.\n');
idamnjanovic@8:     return
idamnjanovic@8:   end
idamnjanovic@8:   
idamnjanovic@8: %%
idamnjanovic@8: %   Dictionary Learning time
idamnjanovic@36: tElapsed=toc(tStart);
ivan@152: DL.time = cputime - start;
ivan@152: if (DL.profile)
maria@86:   fprintf('\n%s finished task in %2f seconds (cpu time). \n', DL.name, DL.time);
maria@86:   fprintf('\n%s finished task in %2f seconds (tic-toc time). \n', DL.name, tElapsed);
ivan@152: end
idamnjanovic@36: DL.time=tElapsed;
idamnjanovic@8: %   If dictionary is given as a sparse matrix change it to full  
idamnjanovic@8: 
idamnjanovic@8:   DL.D = full(D);
idamnjanovic@8:   
idamnjanovic@8: end
idamnjanovic@8: