%%% I want to calulate Mean square error
clc ;
clear all;
load('input');
load('output');
% s=5 ; %neurons
x=input ;
y=output ;
S=8; %neurons
Nmax=100;
MinTraErr=0.01;
NORMG=0.0001;
umin=0.05;umax=1000;uscal=0.5;
inp= size(x,1);
out= size(y,1);
R=2;
n0=3;
k=1;
%RANDOMIZATION
relation = [x;y];
ind = 1:length(x);
a = ind(randperm(length(ind)));
shuffeled_vector = relation(:,a);
%NORMALIZATION
for i=1:size(x,1)
xn(i,:)=2*((x(i,:)-min(x(i,:)))/(max(x(i,:))-min(x(i,:)))-0.5);
xmin(i) = min(x(i,:));
xmax(i) = max(x(i,:));
end
yn=2*((y-min(y))/(max(y)-min(y))-0.5);
ymin = min(y);
ymax = max(y);
%SEPERATION
RATIO = 0.7; % Train-Test Ratio
train_data = shuffeled_vector(:,1:fix(length(x)*RATIO));
test_data = shuffeled_vector(:,fix(length(x)*RATIO)+1:end);
ti_vector = train_data(1:R,:);
y_vector = train_data(end,:);
ti_vector_test = test_data(1:R,:);
y_vector_test = test_data(end,:);
%Training
for p=1:10000 % p is the (number of data)/input
t(1,p)=x(1,p) ;
Yreal(1,p)=y(1,p);
end
Yreal=Yreal.';% transpose
t=t.'
b=size(t);
e=b(1);
R=b(2);
xmin=-50;xmax=50;detend=10^-6;
umin=0.05;umax=1000;uscal=0.5;
syms X [S*R+2*S+1,1] ; syms y;
h=(exp(y)-exp(-y))/(exp(y)+exp(-y)); %Activation Function
%Iteration number
maxiter=100; iter=1;
% X=randn(1,S*(R+2)+1);
bh=X(S*R+1:S*R+S,1);
wo=X((S*(R+1)+1):(S*(R+2)),1);
wo=transpose(wo);
bo = X(end);
bi=X(S*R+2*S+1,1);
wi=reshape(X(1:S*R),S,R);
J=vpa(ones(e,S*R+2*S+1));
%Jacobian
for i=1:e
%Model Outputs
sum=0;
for j=1:S
sum1=0;
for k=1:R
sum1=sum1+wi(j,k)*t(i,k);
end
A(j,1)=subs(h,y,sum1+bh(j));
sum=sum+wo(1,j)*A(j,1);
end
Ymodel(i,1)=sum+bi;
% jacobian matrix
for j=1:S*R
k=mod(j-1,S)+1;
m=fix((j-1)/S)+1;
J(i,j)=-(wo(1,k)*t(i,m))*(1-(A(k,1)^2));
xk(j,1)=wi(k,m);
end
for j=S*R+1:S*R+S
J(i,j)=-wo(1,j-S*R)*(1-(A(j-S*R,1)^2));
xk(j,1)=bh(j-S*R,1);
end
for j=S*R+S+1:S*R+2*S
J(i,j)=-A(j-S*R-S,1);
xk(j,1)=wo(1,j-S*R-S);
end
J(i,S*R+2*S+1)=-1;
xk(j+1,1)=bi(1,1);
end
xk=rand(S*R+2*S+1,1);
E=vpa(Yreal-Ymodel);
f=vpa(E.'*E);
loop1=1; Mu=1;
while loop1
iter=iter+1;
Jxk=double(subs(J,x,xk));
fxk=double(subs(f,x,xk));
Exk=double(subs(E,x,xk));
loop2=1;
while loop2
in=-inv(Jxk.'*Jxk+Mu*eye(size(Jxk,2)));
pk=(in*Jxk.'*Exk);
zk=xk+pk;
Ymodelzk=double(subs(Ymodel,x,zk));
% % while loop2
% % Jacob=-inv(Jxk.'*Jxk+u*eye(size(Jxk,2)));
% % pk=(Jacob*Jxk.'*Exk);
% % zk=xk+pk;
% % % Ymodelzk=double(subs(Ymodel,x,zk));
% % for i = 1:length(y_vector)
% % y_hat_initial = wo*tanh(wi*ti_vector(:,i)+bh)+bo;
% % y_hat(i) = y_hat_initial;
% % end
E=double(Yreal-Ymodelzk);
fzk=double(E.'*E);
if fzk<fxk
fx=subs(f,x,xk+x1.*pk);
xmin=-50;xmax=50;detend=10^-6;
[sk]= GoldenSectionMethodE(fx,xmax,xmin,detend)
xk=double(xk+sk.*pk)
u=u/ucale;
loop2=0;
else
u=u*uscale;
end
if u<umin && u>umax
loop1=0;
loop2=0;
end
end
fxk1=double(subs(f,x,xk));
NORMG=norm(sk*pk);
end
