代價函式：

下降梯度：

假設函式：

x代表年齡，y代表身高

預測身高與年齡的關係

Code:

x = load('ex2x.dat'); 
y = load('ex2y.dat');
[m,n] = size(x);
x = [ones(m,1),x];%偏置項 x0 = 1
figure % open a new figure window
plot(x(:,2), y, 'o');%袁術資料分佈
hold on;
ylabel('Height in meters')
xlabel('Age in years');
inittheta = zeros(n+1,1);
[theta J] = linearReg(x,y,inittheta);%下降梯度函式 求theta 與各步代價
plot(x(:,2),x*theta,'g');%畫出邊界函式
hold off;
 

linearRegression:

function [theta J] = linearReg(x,y,inittheta) % x0已經被置為1了
  [m,n] = size(x);
  theta = inittheta;
  MAX_ITR = 1500; %迭代 1500次
  alpha = 0.07;%學習率
  J = zeros(MAX_ITR,1);
  for i = 1:MAX_ITR
    h = x*theta;//預測值
    grad = (1/m).*(x'*(h-y));%梯度向量
    theta = theta - alpha*grad;
    J(i) = 1/(2*m)*(sum((h-y).^2));%每一步的代價
  end
  
  
 
 

可以畫出代價函式隨著兩個引數theta(1),theta(2)的取值變化情況：

執行命令：

J_vals = J_vals';
figure;
surf(theta0_vals, theta1_vals, J_vals);
hold on;
xlabel('\theta_0'); ylabel('\theta_1');

測試函式

function J_vals = testJ(x,y)
  [m,n] = size(x);
  J_vals = zeros(100, 100);   % initialize Jvals to 100x100 matrix of 0's
  theta0_vals = linspace(-3, 3, 100);
  theta1_vals = linspace(-1, 1, 100);
  for i = 1:length(theta0_vals)
      for j = 1:length(theta1_vals)
       t = [theta0_vals(i); theta1_vals(j)];
       J_vals(i,j) = 1/(2*m)*(sum((x*t-y).^2));
      end
  end
 

代價隨引數變化情況：

還可畫出等高線：

contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 2, 15))
xlabel('\theta_0'); ylabel('\theta_1')

參考：點選開啟連結（內含資料）