%----------	uses MATLAB Neural Network Toolbox
%----------	Version 2.0.4 02-Jun-1997

% assume
%
%	sdata is the 4x150 iris input
%	outpt is the 1x150 iris class labels
%
%	lans_cplotmd is a LANS library function which plots class data

range	= [min(sdata')' max(sdata')'];

w	= initsm(range,100);
m	= nbman(10,10);

depochs	= [1 5 10 100 1000 10000];
sumepochs	= 0;
for pic=1:length(depochs)
	neigh	= 1*exp(-sumepochs/10000);

	maxepochs	= depochs(pic)-sumepochs;
	tp	= [20 maxepochs neigh];
	w	= trainsm(w,m,sdata,tp);

	sumepochs	= sumepochs + maxepochs;
	%----------	compete (find nearest neuron)
	neighborhoodsize	= 0;
	o		= simusm(sdata,w,m,neighborhoodsize);
	[neuron n]	= find(o~=0);

	%----------	compute position on feature grid
	y	= ceil(neuron/10);
	x	= neuron-(y-1)*10;

	ldata	= [x y outpt']';
	noise	= .1*randn(2,length(neuron));
	ldata(1:2,:)=	ldata(1:2,:)+noise;

	%----------	plot
	clf;
	lans_cplotmd(ldata);
	axis([0.5 10.5 0.5 10.5])
	grid on;
	box on;
	%legend('2','3','1');
	title(sprintf('[%d] o-class 1 +-class 2 *-class 3',sumepochs));
	drawnow

	filename	= sprintf('som%d.ps',sumepochs);
	com	= ['print -dpsc ' filename];
	eval(com);
	pause
end