clear all;
close all;
FileName   = 'p_app1.mat';
FolderName = 'C:\FuzzyTP';
File       = fullfile(FolderName, FileName);
load(File);   % not: load('File')

FileName   = 'p_app2.mat';
File       = fullfile(FolderName, FileName);
load(File);   % not: load('File')

FileName   = 'p_app3.mat';
File       = fullfile(FolderName, FileName);
load(File);   % not: load('File')


FileName   = 'p_test1.mat';
File       = fullfile(FolderName, FileName);
load(File);   % not: load('File')


FileName   = 'p_test2.mat';
File       = fullfile(FolderName, FileName);
load(File);   % not: load('File')


FileName   = 'p_test3.mat';
File       = fullfile(FolderName, FileName);
load(File);   % not: load('File')

App1 = [p_app1(1:4,1:10),p_app1(1:4,11:20),p_app2(1:4,1:10),p_app3(1:4,1:10)];
App2 = [p_app2(1:4,1:10),p_app2(1:4,11:20),p_app1(1:4,1:10),p_app3(1:4,1:10)];
App3 = [p_app3(1:4,1:10),p_app3(1:4,11:20),p_app1(1:4,1:10),p_app2(1:4,1:10)];
Test1 = [p_test1(1:4,1:10),p_test2(1:4,1:10),p_test3(1:4,1:10)];
Test2 = [p_test2(1:4,1:10),p_test1(1:4,1:10),p_test3(1:4,1:10)];
Test3 = [p_test3(1:4,1:10),p_test1(1:4,1:10),p_test2(1:4,1:10)];


for i=1:3
    stri=int2str(i);
    p_App = eval(['App',stri]);
    [nbre_caract,nbre_exemp] = size(p_App);
    nero_cache = 10; %nbre de neurones de couche cachée
    t=[ones(1,20),(-1*ones(1,20))];
    % creation d'un réseau
    net_rn = newff(minmax(p_App),[nbre_caract,nero_cache,1]);
    net_rn=init(net_rn); %initialisation de RN
    net_rn.trainparam.epochs=10;%nbre de epoachs
    net_rn.trainparam.goal=0.01;%error
    net_rn.trainParam.lr=0.9;%taux d'app
    net_rn= train(net_rn,p_App,t); %lancer train
    p_Test = eval(['Test',stri]);
    seuil = 0.5;%
    vect=sim(net_rn,p_Test);%simulation enable R
    vect(find(vect>=seuil))=1;
    vect(find(vect<seuil))=0;
    tr(i)=100*(sum(vect(1:10))+(20-sum(vect(11:30))))/30;
end
TR = mean(tr);