clear all

my=3;                               % number of the states of y and u "my" 
rand('seed',39);
randn('seed',67);
sn=10;                                 % shift numbers 
T=60;
y(1)=0.1;
ylower=0;
yupper=2;
yhat(1)=y(1);
yhat=zeros(1,T);
ymhat=zeros(1,T);
lambda=0.7;                        % forgetting factor "lambda"
lambdav=0.5;                        % forgetting on v's
errorm=0;
error0=0;
model=1;

 error_yell=zeros(1,model);    % ideal model error
 error_red=zeros(1,model);   % pooled model error
 error_blue=zeros(1,model);
 error_green=zeros(1,model);
 error_white=zeros(1,model);

yy=zeros(1,T);
for mod=1:model

epsi=0.0001;
sita=epsi*ones(my*(sn-2),my);                   % estimated value of theta 
n0=epsi*ones(my*(sn-2),my); 
n=n0;               % occurence table

a(mod)=4*rand+1;
b(mod)=2*rand-1;
yd=zeros(sn-2,T);
ypred=zeros(sn,sn-2);
f0=zeros(1,sn);
v0=(1/(sn-2))*ones(1,sn-2);                % pooling weight
v=v0;

for t=2:T

y(t)=nonsys(y,t,ylower,yupper,mod,a,b);

for dis=1:sn-2

% discretization

yd=disc(yd,y,ylower,yupper,sn,dis,t);
yd(dis,1)=2;
  
% prediction of simple models

[n,ypred]=spred(yd,t,ypred,n,dis,lambda,sn,my,n0);
yr=(yupper-ylower)/sn;
for it=1:sn
if (y(t)>=(ylower+(it-1)*yr))&(y(t)<=(ylower+it*yr))
  yy(t)=it;
 end
end

% prediction of ideal system


v(dis)=(ypred(yy(t),dis)*v(dis))^(lambdav)*v0(dis)^(1-lambdav);

end
v=v/norm(v,1);    
% pooling

for i=1:sn

f0(i)=1;
for j=1:sn-2
   f0(i)=f0(i)*ypred(i,j)^(v(j));
  end 
end
f0=f0/norm(f0,1);

[yhat(t),ymhat(t)]=contin1(f0,sn,yupper,ylower);
    
end
error_red(1,mod)=sqrt((y-yhat)*(y-yhat)'/T)
error_green(1,mod)=sqrt((y-ymhat)*(y-ymhat)'/T)
end

hold off
plot(y)
%hold on
%plot(yhat,'r')
hold on
plot(ymhat,'g')