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SOMSelfOrganizing.m
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SOMSelfOrganizing.m
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function W = SOMSelfOrganizing(W, data_train, t1, sigma0)
%SOMSelfOrganizing Summary of this function goes here
% Self-Organizing Phase - SOM
Delta = [];
for itr = 1 : 1000 % 1000 iterations for ordering phase
display(W)
ita = 0.1 * exp(- itr / 1000); % learning rate updating
sigma = sigma0 * exp(- itr / t1); % neighborhood width updating
delta = 0;
for num = 1 : 330 % 330 samples
x = data_train(num, :); % select samples sequentially
dmin = 100000;
for i = 1 : 16
w = W(i, :);
d = dist(x, w');
if d < dmin
dmin = d;
imin = i;
end
end % find the winning neuron with index imin
wwinx = mod(imin, 4) - 1;
if wwinx == -1
wwinx = 3;
end
% find the location of winning neuron (wwinx, wwiny)
wwiny = floor((imin - 1) / 4);
for j = 1 : 16
w = W(j, :);
wwin = W(imin, :);
wx = mod(j, 4) - 1;
if wx == -1
wx = 3;
end
% find the location of neuron j (wx, wy)
wy = floor((j - 1) / 4);
% calculate the distance between the neuron j & winning neuron
dji = sqrt((wx - wwinx)^2 + (wy - wwiny)^2);
% update the weights of neuron j
W(j, :) = W(j, :) + ita * exp(- dji^2 / (2 * sigma^2)) ...
* (x - W(j, :));
% store the absolute updates on weights
delta = delta + abs(ita * exp(- dji^2 / (2 * sigma^2)) ...
* (x - W(j, :)));
end
end
Delta = [Delta, delta' / (16 * 330)];
end
Delta = mean(Delta, 1);
plot(Delta) % observe updates on weights
% center vectors chosen after self-organizing phase
% save('center_vectors.mat','W')
end