How to find out the remaining iterations of the Newton Raphson Method with Jakobi Matrix?

I was doing a transforming a Code in Python to Matlab regarding the Newton Raphson with Jakobi Matrix. How can I optimize this. This code gives me only 1 results for x1 and x2. but there should be 3 more. How to fix this ? What wrong have I done here ?

The Inputs I used is :

f = @(x)[0.5 ∗ cos(x(1)) − 0.5 ∗ sin(x(2)) − x(1); 0.5 ∗ sin(x(1)) + 0.5 ∗ cos(x(2)) − x(2)],
J = @(x)[−0.5 ∗ sin(x(1)) − 1, −0.5 ∗ cos(x(2)); 0.5 ∗ cos(x(1)), −0.5 ∗ sin(x(2)) − 1],
x_0 = [0 0]′, E_tol = 10−4

Output Result is showing:

 x = test_1(f,J,x_0,1e-4)

x =

    0.2000
    0.6000

Main Functional Code:

function x = test_1(f,J,x_0,E_tol)
n =1000;  %% How to initialize or do a iteration till n time
x = x_0;
f_val = f(x);
f_norm = norm(f_val);
for k = 0:n 
while (abs(f_norm) > E_tol)   %% (k<n) >> I want to imply this condition.How to do?
    delta_x = linsolve(J(x_0),-f_val);
    x = x_0 + delta_x;
    f_val = f(x_0);
    f_norm = norm(f_val);
    k = k+1;
if  abs(f_norm) > E_tol  %% What else can I do here ? 
    k = k-1;
    x;
    break
end
   
end
end
end

Answer 1

[KALYAN ACHARJYA]

Do the x array indexing, consider x=[......] where x(1) as initial value

[Mohammad Abir Abbas]

I still didn't get it . Where and how can I define the x array