吴恩达的机器学习编程作业6：costFunctionReg正则化代价函数

最新推荐文章于 2024-05-03 11:58:37 发布

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最新推荐文章于 2024-05-03 11:58:37 发布 · 1.2k 阅读

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该代码实现了带有正则化的逻辑回归代价函数计算，包括梯度计算。通过预测值与实际值的比较，计算代价，并应用拉普拉斯正则化项。最终返回代价J和梯度grad。

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

predictions = 1./(1.+exp(-X*theta));

J = (-y'*log(predictions)-(1.-y')*log(1.-predictions))/m;

reglutheta = theta(2:size(theta,1),1