MOOC Machine Learning 作业交流帖5

wk5 Regularized Linear Regression and Bias v.s. Variance

最近正在学习MOOC上的经典课程：Machine learning (by Andrew Ng)， 具体课程链接：MACHINE LEARNING

根据进度将作业的关键代码部分贴上，仅供交流与讨论。

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

——转载请注明出处