Machine Learning ex2错题整理

function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
%   theta as the parameter for regularized logistic regression and the
%   gradient of the cost w.r.t. to the parameters. 


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


% You need to return the following variables correctly 
J = 0;
grad = zeros(size(theta));


% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
%               You should set J to the cost.
%               Compute the partial derivatives and set grad to the partial
%               derivatives of the cost w.r.t. each parameter in theta


[J, grad] = costFunction(theta, X, y);
J = J + lambda/(2*m) * (theta' * theta);
grad = grad + lambda/m * ([0; ones((length(theta)-1),1)] .* theta);


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

end

仔细看才发现。。。原来在计算J的时候就必须将theta(1)置0所以修改下

[J, grad] = costFunction(theta, X, y);
theta_zero = [0; theta(2:length(theta));];

J = J + lambda / (2 * m) * sum( theta_zero * theta_zeroe);
grad = grad + (lambda / m) * theta_zero;

另外在predict中
p = round(sigmoid(X * theta));

此处不能用floor