diff toolboxes/FullBNT-1.0.7/netlabKPM/demgmm1_movie.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlabKPM/demgmm1_movie.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,103 @@
+%DEMGMM1 Demonstrate EM for Gaussian mixtures.
+%
+%	Description
+%	This script demonstrates the use of the EM algorithm to fit a mixture
+%	of Gaussians to a set of data using maximum likelihood. A colour
+%	coding scheme is used to illustrate the evaluation of the posterior
+%	probabilities in the E-step of the EM algorithm.
+%
+%	See also
+%	DEMGMM2, DEMGMM3, DEMGMM4, GMM, GMMEM, GMMPOST
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+mov = avifile('movies/gmm1.avi','fps',1 );
+
+% Generate the data
+randn('state', 0); rand('state', 0);
+gmix = gmm(2, 2, 'spherical');
+ndat1 = 20; ndat2 = 20; ndata = ndat1+ndat2;
+gmix.centres =  [0.3 0.3; 0.7 0.7]; 
+gmix.covars = [0.01 0.01];
+x = gmmsamp(gmix, ndata);
+
+h = figure;
+hd = plot(x(:, 1), x(:, 2), '.g', 'markersize', 30);
+hold on; axis([0 1 0 1]); axis square; set(gca, 'box', 'on');
+ht = text(0.5, 1.05, 'Data', 'horizontalalignment', 'center');
+
+
+% Set up mixture model
+ncentres = 2; input_dim = 2;
+mix = gmm(input_dim, ncentres, 'spherical');
+
+% Initialise the mixture model
+mix.centres = [0.2 0.8; 0.8, 0.2];
+mix.covars = [0.01 0.01];
+
+% Plot the initial model
+ncirc = 30; theta = linspace(0, 2*pi, ncirc);
+xs = cos(theta); ys = sin(theta);
+xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs;
+yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys;
+hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r');
+hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b');
+set(ht, 'string', 'Initial Configuration');
+figure(h);
+mov = addframe(mov, getframe(gcf));
+mov = addframe(mov, getframe(gcf));
+
+% Initial E-step.
+set(ht, 'string', 'E-step');
+post = gmmpost(mix, x);
+dcols = [post(:,1), zeros(ndata, 1), post(:,2)];
+delete(hd); 
+for i = 1 : ndata
+  hd(i) = plot(x(i, 1), x(i, 2), 'color', dcols(i,:), ...
+          'marker', '.', 'markersize', 30);
+end
+
+% M-step.
+set(ht, 'string', 'M-step');
+options = foptions; 
+options(14) = 1; % A single iteration
+options(1) = -1; % Switch off all messages, including warning
+mix = gmmem(mix, x, options);
+delete(hc);
+xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs;
+yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys;
+hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r');
+hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b');
+figure(h);
+mov = addframe(mov, getframe(gcf));
+mov = addframe(mov, getframe(gcf));
+
+% Loop over EM iterations.
+numiters = 9;
+for n = 1 : numiters
+
+  set(ht, 'string', 'E-step');
+  post = gmmpost(mix, x);
+  dcols = [post(:,1), zeros(ndata, 1), post(:,2)];
+  delete(hd); 
+  for i = 1 : ndata
+    hd(i) = plot(x(i, 1), x(i, 2), 'color', dcols(i,:), ...
+                 'marker', '.', 'markersize', 30);
+  end
+  %pause(1)
+
+  set(ht, 'string', 'M-step');
+  [mix, options] = gmmem(mix, x, options);
+  fprintf(1, 'Cycle %4d  Error %11.6f\n', n, options(8));
+  delete(hc);
+  xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs;
+  yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys;
+  hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r');
+  hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b');
+  pause(1)
+
+  mov = addframe(mov, getframe(gcf));
+end
+
+mov = close(mov);