diff config/SMALL_learn_config.m @ 220:0d30f9074dd9

Merge

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/config/SMALL_learn_config.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,97 @@
+%% Configuration file used in SMALL_learn
+%
+%   Use this file to change the dictionary learning algorithms in SMALLBox
+%   Please refer to the documentation before editing this file
+
+%   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.
+%
+%%
+
+if strcmpi(DL.toolbox,'KSVD')
+    param=DL.param;
+    param.data=Problem.b;
+    
+    D = eval([DL.name,'(param)']);%, ''t'', 5);']);
+elseif strcmpi(DL.toolbox,'KSVDS')
+    param=DL.param;
+    param.data=Problem.b;
+    
+    D = eval([DL.name,'(param, ''t'', 5);']);
+elseif strcmpi(DL.toolbox,'SPAMS')
+    
+    X  = Problem.b;
+    param=DL.param;
+    
+    D = eval([DL.name,'(X, param);']);
+    %   As some versions of SPAMS does not produce unit norm column
+    %   dictionaries, we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(D,2)
+        D(:,i)=D(:,i)/norm(D(:,i));
+    end
+elseif strcmpi(DL.toolbox,'SMALL')
+    
+    X  = Problem.b;
+    param=DL.param;
+    
+    D = eval([DL.name,'(X, param);']);
+    %   we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(D,2)
+        D(:,i)=D(:,i)/norm(D(:,i));
+    end
+    
+elseif strcmpi(DL.toolbox,'TwoStepDL')
+    
+    DL=SMALL_two_step_DL(Problem, DL);
+    
+    %   we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(DL.D,2)
+        DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
+    end
+    D = DL.D;
+    
+elseif strcmpi(DL.toolbox,'MMbox')
+    
+    DL = wrapper_mm_DL(Problem, DL);
+    
+    %   we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(DL.D,2)
+        DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
+    end
+    D = DL.D;
+      
+%   To introduce new dictionary learning technique put the files in
+%   your Matlab path. Next, unique name <TolboxID> for your toolbox needs
+%   to be defined and also prefferd API for toolbox functions <Preffered_API>
+%
+% elseif strcmpi(DL.toolbox,'<ToolboxID>')
+%     % This is an example of API that can be used:
+%     % - get training set from Problem part of structure
+%     % - assign parameters defined in the main program
+%
+%     X  = Problem.b;
+%     param=DL.param;
+%
+%     % - Evaluate the function (DL.name - defined in the main) with
+%     %   parameters given above
+%
+%     D = eval([DL.name,'(<Preffered_API>);']);
+
+else
+    printf('\nToolbox has not been registered. Please change SMALL_learn file.\n');
+    return
+end