% Making an sofm gridcolordef none;			% So that it looks ok on a B/W screenclear;                          % Get rid of any old variables which mess it up% Represent it as a grid of complex numbersnum_rows = 15;num_cols = 15;a = 0.20;			% Exponent in eta and G reduction (HKP p.237)%%%%% Initialise with small real and complex coords, centred on the origindx = 0.1;m = dx*(1-2*rand(num_rows,num_cols)) + dx*(1i-2i*rand(num_rows,num_cols));%%%%% Plot the shape which will be mapped (a letter in this case);%%%%%%% Plot an A %%%%%%% NB: It's better to have this in a separate function file,%%%%%%%     but it's harder to mail that way. %%%%%%%     Same for making the A input below figure(1);clf;line([-0.8 -0.4],[-1 1]);line([-0.8 -0.5],[-1 -1]);line([-0.5 -0.38],[-1 -0.4]);line([-0.02  0.1],[-0.4 -1]);line([-0.02  -0.38],[-0.4 -0.4]);line([0 0.4],[1 -1]);line([0 -0.4],[1 1]);line([-0.1 -0.2],[0 0.5]);line([-0.3 -0.2],[0 0.5]);line([-0.3 -0.1],[0 0]);line([0.1 0.4],[-1 -1]);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The main loopfor cycle=1:5000,	eta = cycle^(-a);		% Learning rate (how much nodes move)	G = 0.5 + 10*cycle^(-a);		% Gaussian width parameter	%%% NB: G<0.5 is boring because the Gaussian only covers one node	%%%%% Give an input (I have code for R,A,J and C)	x = 1-2*rand;	y = 1-2*rand;	while ~( (x>-0.4 & x<0 & y>-0.4 & y<0) | ... % Middle bar	 	((y>-5*x - 0.5) & (y<-5*x + 1) ) | ...% Right diagonal	 	((y>5*x + 1.5) & (y<5*x + 3) ) ) ...% Left diagonal	 	x = 1-2*rand;		y = 1-2*rand;	end;	inp = x + y*i;		% This is the input point	%%%%% Find winning node	dist_mat = (real(m)-real(inp)).^2 + (imag(m)-imag(inp)).^2;	[win_rows,win_cols] = find(dist_mat==min(dist_mat(:)));	rand_idx = ceil(length(win_rows)*rand);		win_row = win_rows(rand_idx);	win_col = win_cols(rand_idx);	%%% This turns dist_mat into a vector, finds its min element,	%%% then gives each place in an m-size matrix a 1 if it is this min,	%%% otherwise 0, then gives the row and col indices of the 1s	%%% Then we pick a random one of these winners		%%% Calculate city-block distance from winner in grid	[col_idx,row_idx] = meshgrid(1:num_cols,1:num_rows);		%%%% Make matrices of indices	grid_dist = abs(row_idx-win_row) + abs(col_idx-win_col);		%%% Calculate Gaussian movement-strength function for each node	f = eta * exp(-(grid_dist/G).^2);	%%%% Plot the map	if max(cycle == [1 10 30 50 100 200 400 600 800 1000 3000 5000]),			%%%%%%%%%% Do the plotting		figure(1);		if (cycle>1),delete(h);end;	% This wipes the old grid plot		hold on;		h=plot(real(m),imag(m),'w-',real(m'),-imag(m'),'w-');				% This draws the new SOFM grid		hold off;		title(['Input presentation number ' num2str(cycle) ...       			'     Neighbourhood size ' num2str(G) ...       			'     Learning rate ' num2str(eta) ]); 		drawnow;		%eval(['print ' num2str(cycle) 'R.ps']);  			% This would make a PostScript file	end;	%%% Move nodes	m = m + f.*(inp-m);end;    	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Go to next cycle