吴恩达机器学习 - 逻辑回归的正则化

原

吴恩达机器学习 - 逻辑回归的正则化

2018年06月19日 15:07:25 离殇灬孤狼 阅读数：181 更多

																				

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															吴恩达机器学习																
						
																							

版权声明：如果感觉写的不错，转载标明出处链接哦~blog.csdn.net/wyg1997 https://blog.csdn.net/wyg1997/article/details/80734142

题目链接：点击打开链接

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先贴笔记

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代码：

costFunction.m(求代价和各方向梯度)(注意：Θ0Θ0单独计算)：

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

[~, n] = size(X);
%以下计算一定要记得不正则化theta_0
J = (-y'*log(sigmoid(X*theta))-(1-y')*log(1-sigmoid(X*theta)))/m + ...
lambda/(2.0*m)*(theta(2:n)'*theta(2:n));

grad(1) = X(:,1)'*(sigmoid(X*theta)-y)./m;
grad(2:n) = X(:,2:n)'*(sigmoid(X*theta)-y)./m + lambda/m*theta(2:n);


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

end

   
   
   
   

    
    
    
    
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然后展示下不同λ画出的不同图案