wk5 Regularized Linear Regression and Bias v.s. Variance

最近正在學習MOOC上的經典課程：Machine learning (by Andrew Ng)， 具體課程連結：MACHINE LEARNING
根據進度將作業的關鍵程式碼部分貼上，僅供交流與討論。

• linearRegCostFunction

h=X*theta;
J=sum((h-y).^2)/(2*m)+lambda*sum(theta(2:end).^2)/(2*m);

grad(1)=(h-y)'*X(:,1)/m;
grad(2:end)=X(:,2:end)'
 
*(h-y)/m+lambda*theta(2:end)/m;

• learningCurve

for i =1:m
    theta=trainLinearReg(X(1:i,:), y(1:i), lambda);
    error_train(i)=linearRegCostFunction(X(1:i,:), y(1:i), theta, 0);
    error_val(i)=linearRegCostFunction(Xval, yval, theta, 0);
end

• polyFeatures

for i =1:p
   X_poly(:,i)=X.^i;
end 

• validationCurve

for i= 1:length(lambda_vec)
    lambda=lambda_vec(i);
    theta=trainLinearReg(X, y, lambda);
    error_train(i)=linearRegCostFunction(X, y, theta, 0);
    error_val(i)=linearRegCostFunction(Xval, yval, theta, 0);
end

——轉載請註明出處