吴恩达 ML 第一次编程作业

一个上午做出来了

仅供参考

function J = computeCost(X, y, theta)
%COMPUTECOST Compute cost for linear regression
%   J = COMPUTECOST(X, y, theta) computes the cost of using theta as the
%   parameter for linear regression to fit the data points in X and y

% Initialize some useful values
m = length(y); % number of training examples

% You need to return the following variables correctly 
J = 0;

% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta
%               You should set J to the cost.
i = 1;
summ = 0;
while i <= m,
  summ = summ+((theta(1)+theta(2)*X(i,2))-y(i))^2;
  i = i+1;
endwhile

J=(1/(2*m))*summ;

% =========================================================================

end

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

    % ====================== YOUR CODE HERE ======================
    % Instructions: Perform a single gradient step on the parameter vector
    %               theta. 
    %
    % Hint: While debugging, it can be useful to print out the values
    %       of the cost function (computeCost) and gradient here.
    %
summ1=0;
summ2=0;

for i = 1:m
  summ1 = summ1 + ((theta(1)+theta(2)*X(i,2))-y(i))*X(i,1);
  summ2 = summ2 + ((theta(1)+theta(2)*X(i,2))-y(i))*X(i,2);
endfor
theta(1) = theta(1) - (alpha/m)*summ1;
theta(2) = theta(2) - (alpha/m)*summ2;






    % ============================================================

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

end

end