不服不行

看着cs229给出的代码，感觉读起来还有点吃力， 先记录一下

X = load('logsitic_x.txt'); Y = load('logsitic_y.txt'); X = [ones(size(X,1),1) X]; [theta,ll] = log_regression(X,Y,20); m = size(X,1); figure;hold on; plot(X(Y<0,2),X(Y<0, 3),'rx','linewidth',2); plot(X(Y>0,2),X(Y>0, 3),'go','linewidth',2); x1 = min(X(:,2)):.01:max(X(:,2)); x2 = -(theta(1)/theta(3))-(theta(2)/theta(3))*x1; plot(x1,x2,'linewidth',2); xlable = 'x1'; ylable = 'y1';

下面是ps1 answer里面给出的函数

function [theta,ll] = log_regression(X,Y,max_iters) mm = size(X,1) nn = size(X,2) theta = zeros (nn,1) ll = zeros(nn,1); for ii = 1:max_iters margins = Y .* (X * theta); %此步是计算logsitic函数的平均经验损失 ll(ii) = (1/mm) * sum(log(1+exp(-margins))); %可以想象margin此时是99*1的矩阵 %probs 可以认为是 h(X)=1/exp(x'theta) probs = 1 ./ (1+exp(margins)); %grad梯度 X' * (probds .* Y) %就相当于 从1到mm 所有h(yi*xi)*yi*xi之后 %非常巧妙 grad = -(1/mm) * (X' * (probs .* Y)) %下面是求Hessian矩阵 H = (1/mm) * (X' * diag(probs .* (1-probs)) * X) theta = theta - H \ grad; end end

ll是 log loss H是 Hessian