My gradient descent is not giving the exact value - machine-learning

Question

I have written gradient descent algorithm in Octave but it is not giving me the exact answer. The answer differs from one to two digits.
Here is my code:
function theta = gradientDescent(X, y, theta, alpha, num_iters)
m = length(y); % number of training examples
s = 0;
temp = theta;
for iter = 1:num_iters
for j = 1:size(theta, 1)
for i = 1:m
h = theta' * X(i, :)';
s = s + (h - y(i))*X(i, j);
end
s = s/m;
temp(j) = temp(j) - alpha * s;
end
theta = temp;
end
end
For:
theta = gradientDescent([1 5; 1 2; 1 4; 1 5],[1 6 4 2]',[0 0]',0.01,1000);
My gradient descent gives this:
4.93708
-0.50549
But it is expected to give this:
5.2148
-0.5733

Answer 1

Minor fixes :
Your variable s probably the delta is initialised incorrectly.
So it the temp variable probably the new theta
Incorrectly calculating the delta
Try with below changes.
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
temp = theta;
for iter = 1:num_iters
temp = zeros(length(theta), 1);
for j = 1:size(theta)
s = 0
for i = 1:m
s = s + (X(i, :)*theta - y(i)) * X(i, j);
end
end
s = s/m;
temp(j) = temp(j) - alpha * s;
end
theta = temp;
J_history(iter) = computeCost(X, y, theta);
end
end