%NEWLVQ Create a learning vector quantization network.
%NET = NEWLVQ(PR,S1,PC,LR,LF) takes these inputs,
%PR - Rx2 matrix of min and max values for R input elements.
%S1 - Number of hidden neurons.
%PC - S2 element vector of typical class percentages.
%LR - Learning rate, default = 0.01.
%LF - Learning function, default = 'learnlv1'.
%Returns a new LVQ network.
clear all; close all;

P = [0 0 1;
    0 1 0;
    0 1 1;
    1 0 0;
    1 0 1;
    1 1 0;
    1 1 1]' 

Tc = [1 2 1 2 1 2 1];

T = ind2vec(Tc); %convert indices to vectors
net = newlvq(minmax(P),4,[.6 .4]);
net = train(net,P,T);

IW = net.IW{1,1};
LW = net.LW{2,1};

save testIW.dat IW -ASCII;
save testLW.dat LW -ASCII;
load testIW.dat;
load testLW.dat;

MyTestNet = newlvq(minmax(P),4,[.6 .4]);
MyTestNet.IW{1,1} = testIW;
MyTestNet.LW{2,1} = testLW;

%Y = sim(net,P)
%Yc = vec2ind(Y) %Transform vectors to indices.

Y = sim(MyTestNet,P)
Yc = vec2ind(Y) %Transform vectors to indices.