Clear all
close all
clc
load base_jus;
whos;
x_learning=[x_training;x_validation];
y_learning=[y_training;y_validation];
y=[y_training;y_validation;y_test];
var(y);
[Ninp,Npoints] = size(x_training);
N = Npoints;
k=20;
n_neurones=2:2:20;
Sigma=0.9;
for i=1:k
index = rand*N;
index1= round(index);
if index1<=0
index1=1;
else
index1=index1;
end
c(i,:) = x_training(index1,:);
end
for t=1:20
Alpha=2/(5+t);
for p=1:1:100
for i=1:k
d(p,i)=(x_training(p,:)-c(i,:))*(x_training(p,:)-c(i,:))';
end
c_gagnant=d(1);
j=1;
for i=1:k
if c_gagnant<=d(p,i)
c_gagnant=c_gagnant;
else
c_gagnant=d(p,i);
j=i;
c(j,:)=c(j,:)+Alpha*(x_training(p,:)-c(j,:));
end
end
end
end