吴恩达的机器学习编程作业7：lrCostFunction 向量化求解逻辑回归代价函数

最新推荐文章于 2023-01-08 22:12:45 发布

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最新推荐文章于 2023-01-08 22:12:45 发布 · 1.1k 阅读

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该函数实现了逻辑回归的代价函数和梯度计算，包括正则化部分。通过sigmoid函数计算预测值，利用向量化操作进行成本计算和梯度更新，以提高效率。在计算梯度时，对第一个参数不进行正则化处理。

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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 computati