si

%LVQ Neural network training

clear all
nntwarn off
load DANE

S1 = [4:1:10 15:5:100];
epoch = [1:400:1600 2000:2000:20000];
lr = [0.01 : .01: 1];
x = length(S1)*length(epoch)*length(rate);
i = 1;

wynik = zeros([x length(T)]);
index = zeros([x 5]);
Tc = ind2vec(T);

for S1_ite = 1:length(S1)

    for epoch_ite = 1:length(epoch)
        for lr_ite = 1:length(rate)
            TP = [1000 epoch(epoch_ite) lr(lr_ite)];

            [w1, w2] = initlvq(Pn, S1(S1_ite), Tc);
            [w1, w2] = trainlvq(w1, w2, Pn, Tc, TP);

            a = simulvq(Pn, w1, w2);
            wynik = vec2ind(a);

            sprawnosc = (1-sum(abs(T-wynik)>=0.5)/length(Pn))*100
            Procent = i/x*100
            index(i,:) = [i, S1(S1_ite), epoch(epoch_ite), lr(lr_ite), sprawnosc];
            i=i+1;
        end
    end
end

save Wyniki index