# HG changeset patch
# User danieleb <daniele.barchiesi@eecs.qmul.ac.uk>
# Date 1331742698 0
# Node ID 82b0d3f982cb54c459d842c70768d8839488024c
# Parent  2f5ce7c8792a2c92402faafcc9ae6dcb7134d524# Parent  9b0595a8478d1c6264b42694ec2cef717efe23d9
Merge

diff -r 2f5ce7c8792a -r 82b0d3f982cb examples/SMALL_DL_test.m
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/SMALL_DL_test.m	Wed Mar 14 16:31:38 2012 +0000
@@ -0,0 +1,76 @@
+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
+
+%
+%   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
+nAngles = length(theta);			%number of angles
+Q	  = [cos(theta); sin(theta)];	%rotation matrix
+X	  = Q*randmog(nAngles,nData);	%training data
+
+% 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,:),'.');		%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;						%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
+					 'dictsize',nAtoms,...	%number of atoms
+					 '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','UDataSource','xdata','VDataSource','ydata');
+
+for iIter=1:nIter
+	DL.ksvd_params.initdict = DL.D;
+	pause
+	DL = SMALL_learn(problem,DL);			%learn dictionary
+	xdata = scale*DL.D(1,:);	
+	ydata = scale*DL.D(2,:);
+	refreshdata(gcf,'caller');
+end
+
+
+function X = randmog(m, n)
+% RANDMOG - Generate mixture of Gaussians
+s = [0.2 2];
+% Choose which Gaussian
+G1 = (rand(m, n) < 0.9);
+% Make them
+X = (G1.*s(1) + (1-G1).*s(2)) .* randn(m,n);