Ex1_ML by Andrew

function J = computeCost(X, y, theta)

m = length(y); % number of training examples
h = X * theta; % h size =m *1
temp = 0; 
for i=1:m
 temp = temp + (h(i) - y(i))^2;
end
J = (1/(2*m)) * temp;

%J=(1/(2*m)) * (sum(X*theta-y).)^2 
%.means each element multiply

end

get theta
matrix 先行再列，横着叫第几行，竖着的叫第几列；
xj(i) 表示第 j 列第 i 个
在这个X中有95行2列

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
%   theta = GRADIENTDESCENT(X, y, theta, alpha, num_iters) updates theta by 
%   taking num_iters gradient steps with learning rate alpha

% Initialize some useful values
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);

for iter = 1:num_iters

theta=theta-((alpha/m)*X'*(X*theta-y))
    % X'是X的行列翻倒

    % Save the cost J in every iteration    
    J_history(iter) = computeCost(X, y, theta);

end

end