SOM (Self-organizing map) code in matlab

Please visit my page to download complete code and training dataset.
https://sites.google.com/view/chi3x10/home

 

Below is the code of applying SOM on handwritten digits recongnition. It was implemented for a homework assignment in a course offered by professor Paul Gader. I used only ten handwritten digits images provided by Dr. Gader. Each image is resized to 30×30 and reshape to a 900×1 column vector. data in the code below is the training set. It’s a coolection of 900-dimensional column vectors.

%//Matlab script
%//-- 10 x 10 map

data = double(data);
%// toal number of nodes
totalW = 100;
%//initialization of weights
w = rand(900, totalW);
%// the initial learning rate
eta0 = 0.1;
%// the current learning rate (updated every epoch)
etaN = eta0;
%// the constant for calculating learning rate
tau2 = 1000;

%//map index
[I,J] = ind2sub([10, 10], 1:100);

N = size(data,2);

alpha = 0.5;
%// the size of neighbor
sig0 = 200;

sigN = sig0;
%// tau 1 for updateing sigma
tau1 = 1000/log(sigN);

%i is number of epoch
for i=1:2000
    %// j is index of each image.
    %// it should iterate through data in a random order rewrite!!
    for j=1:N
        x = data(:,j);
        dist = sum( sqrt((w - repmat(x,1,totalW)).^2),1);

        %// find the winner
        [v ind] = min(dist);
        %// the 2-D index
        ri = [I(ind), J(ind)];

        %// distance between this node and the winner node.
        dist = 1/(sqrt(2*pi)*sigN).*exp( sum(( ([I( : ), J( : )] - repmat(ri, totalW,1)) .^2) ,2)/(-2*sigN)) * etaN;

        %// updating weights
        for rr = 1:100
            w(:,rr) = w(:,rr) + dist(rr).*( x - w(:,rr));
        end
    end

    %// update learning rate
    etaN = eta0 * exp(-i/tau2);
    %// update sigma
    %sigN = sigN/2;
    sigN = sig0*exp(-i/tau1);

    %//show the weights every 100 epoch
    if mod(i,200) == 1
        figure;
        axis off;
        hold on;
        for l = 1:100
            [lr lc] = ind2sub([10, 10], l);
            subplot(10,10,l);
            axis off;
            imagesc(reshape(w(:,l),30,30));
            axis off;
        end
        hold off;
    end
end

Result:
See the transition between different numbers? 🙂