8.4 阴 鸽巢 群 regularize

每次上组合数学都会想：
是谁这么机智想出这么巧的方法的！

晚上和一个妹子做大数据实验，三个实验，HDFS、HBase、NoSQL都做了，开心耶，数据库初体验。
我终于知道当时为什么一直找不到命令，就是因为教程上的命令打错了，中文-和英文-是有区别的，啊啊啊啊啊啊啊啊！
大量不cd到文件夹，打多/，复制的时候$也复制上了，复制的时候#也复制上了，打错文件名，复制的时候忘改版本号，眼花缭乱，我是不是真的不适合学计算机？
我觉得吧，向量化一个很大的用途就是不用写for loop了。
我感觉这里的x是列向量，写成X矩阵的时候，就转置成传统数据库的行形式。
完了，我已经忘记regularized和unregularized是在哪里出现的了。

function [J, grad] = lrCostFunction(theta, X, y, lambda)
%LRCOSTFUNCTION Compute cost and gradient for logistic regression with 
%regularization
%   J = LRCOSTFUNCTION(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
%
% Hint: The computation of the cost function and gradients can be
%       efficiently vectorized. For example, consider the computation
%
%           sigmoid(X * theta)
%
%       Each row of the resulting matrix will contain the value of the
%       prediction for that example. You can make use of this to vectorize
%       the cost function and gradient computations. 
%
% Hint: When computing the gradient of the regularized cost function, 
%       there're many possible vectorized solutions, but one solution
%       looks like:
%           grad = (unregularized gradient for logistic regression)
%           temp = theta; 
%           temp(1) = 0;   % because we don't add anything for j = 0  
%           grad = grad + YOUR_CODE_HERE (using the temp variable)
%

J=(-y'*log(sigmoid(X*theta))-(1-y)'*log(1-sigmoid(X*theta)))/m+lambda * sum(theta(2:end).^2)/(2*m);

%grad=(X'*sigmoid(X*theta)-y)/m; 括号写着写着就忘了，辣鸡matlab
grad=(X'*(sigmoid(X*theta)-y))/m;
%theta_s = [0; theta(2:end)]; 下面用的temp把bias值置为0
temp=theta;
temp(1)=0;
%grad=grad+lambda/m*sum(temp); 这里公式抄错了，是没有sigma的
grad=grad+lambda/m*temp;


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

grad = grad(:);

end