# HG changeset patch
# User Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk>
# Date 1331661226 0
# Node ID 9b0595a8478d1c6264b42694ec2cef717efe23d9
# Parent  cc540df790f4afc751361d7bc926d9b995b31775
Debugged SMALL_DL_test and added copyright info

diff -r cc540df790f4 -r 9b0595a8478d examples/SMALL_DL_test.m
--- a/examples/SMALL_DL_test.m	Fri Mar 09 15:12:01 2012 +0000
+++ b/examples/SMALL_DL_test.m	Tue Mar 13 17:53:46 2012 +0000
@@ -1,55 +1,69 @@
 function SMALL_DL_test
 clear, clc, close all
 % Create a 2-dimensional dataset of points that are oriented in 3
-% directions on a x-y plane
+% directions on a x/y plane
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2012 Daniele Barchiesi.
+%
+%   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.
+
 nData = 10000;						%number of data
 theta = [pi/6 pi/3 4*pi/6];			%angles
-m     = length(theta);
+nAngles = length(theta);			%number of angles
 Q	  = [cos(theta); sin(theta)];	%rotation matrix
-X	  = Q*randmog(m,nData);
+X	  = Q*randmog(nAngles,nData);	%training data
 
-% find principal directions using PCA and plot them
-XXt = X*X';
-[U ~] = svd(XXt);
-scale = 3;
+% find principal directions using PCA
+XXt = X*X';							%cross correlation matrix
+[U ~] = svd(XXt);					%svd of XXt
+
+scale = 3;							%scale factor for plots
 subplot(1,2,1), hold on
 title('Principal Component Analysis')
-scatter(X(1,:), X(2,:),'.');
-O = zeros(size(U));
-quiver(O(1,1:2),O(2,1:2),scale*U(1,:),scale*U(2,:),'LineWidth',2,'Color','k')
-axis equal
+scatter(X(1,:), X(2,:),'.');		%scatter training data
+O = zeros(size(U));					%origin
+quiver(O(1,1:2),O(2,1:2),scale*U(1,:),scale*U(2,:),...
+	'LineWidth',2,'Color','k')		%plot atoms
+axis equal							%scale axis
 
 subplot(1,2,2), hold on
 title('K-SVD Dictionary')
 scatter(X(1,:), X(2,:),'.');
 axis equal
-nAtoms   = 3;
-initDict = randn(2,nAtoms);
-nIter = 10;
-O = zeros(size(initDict));
+
+nAtoms  = 3;						%number of atoms in the dictionary
+nIter   = 1;						%number of dictionary learning iterations
+initDict = normc(randn(2,nAtoms));	%random initial dictionary
+O = zeros(size(initDict));			%origin
+
 % apply dictionary learning algorithm
-ksvd_params = struct('data',X,...		%training data
-					 'Tdata',1,...		%sparsity level
+ksvd_params = struct('data',X,...			%training data
+					 'Tdata',1,...			%sparsity level
 					 'dictsize',nAtoms,...	%number of atoms
-					 'initdict',initDict,...
-					 'iternum',10);		%number of iterations
-DL = SMALL_init_DL('ksvd','ksvd',ksvd_params);
-DL.D = initDict;
+					 'initdict',initDict,...%initial dictionary
+					 'iternum',10);			%number of iterations
+DL = SMALL_init_DL('ksvd','ksvd',ksvd_params); %dictionary learning structure
+DL.D = initDict;							%copy initial dictionary in solution variable
+problem = struct('b',X);					%copy training data in problem structure
+
 xdata = DL.D(1,:);
 ydata = DL.D(2,:);
 qPlot = quiver(O(1,:),O(2,:),scale*initDict(1,:),scale*initDict(2,:),...
-	'LineWidth',2,'Color','k','XDataSource','xdata','YDataSource','ydata');
-problem = struct('b',X);				%training data
+	'LineWidth',2,'Color','k','UDataSource','xdata','VDataSource','ydata');
 
-%plot dictionary and learn
 for iIter=1:nIter
 	DL.ksvd_params.initdict = DL.D;
+	pause
 	DL = SMALL_learn(problem,DL);			%learn dictionary
-	xdata = DL.D(1,:);	
-	ydata = DL.D(2,:);
-	pause
+	xdata = scale*DL.D(1,:);	
+	ydata = scale*DL.D(2,:);
 	refreshdata(gcf,'caller');
-	%quiver(O(1,:),O(2,:),scale*DL.D(1,:),scale*DL.D(2,:),'LineWidth',2,'Color','k');
 end