Machine Learning Week3

week3的主要内容并不算很多，其实相对于week2，主要是修改了h函数，变成了sigmoid，而不是原来的向量乘法。

不过我觉得有两点需要注意，第一点就是fminunc函数，这个函数接受options作为设置，然后结构initial_theta作为优化的theta参数，之后是一个句柄（不知道是不是这么叫），会通过这个函数计算loss和梯度grad。返回值的loss，grad，我想grad用来更新了，loss用来判断了。

第二点就是在正则化的时候，bias不去平方，也不去正则化的更新。

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 valuesm = 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 thetah = sigmoid(X * theta);J =  sum(-y .* log(h) - (1-y).*log(1-h))/(m) + sum(theta(2:end).^2)*lambda/(2*m);deltaGrad = zeros(size(theta));deltaGrad = X' * (h-y) /m;deltaGrad(2:end) = deltaGrad(2:end) + lambda*theta(2:end)/m;grad = deltaGrad;% =============================================================end