我正在尝试实现梯度下降算法，以最小化多元线性算法的成本函数。我使用了Andrew Ng机器学习课程中解释的概念。我使用的是Octave。然而，当我尝试执行代码时，似乎无法提供解决方案，因为我的theta值计算为“NaN”。我附上了成本函数代码和梯度下降代码。请问有人可以帮忙吗？

成本函数：

function J = computeCostMulti(X, y, theta)m = length(y); % number of training examplesJ = 0;h=(X*theta);s= sum((h-y).^2);J= s/(2*m);

梯度下降代码：

function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)m = length(y); % number of training examplesJ_history = zeros(num_iters, 1);for iter = 1:num_iters  a= X*theta -y;  b = alpha*(X'*a);  theta = theta - (b/m);  J_history(iter) = computeCostMulti(X, y, theta);  end

回答：

我确实找到了错误，并不是成本函数或梯度下降函数的逻辑问题。而是特征归一化逻辑中的问题，我不小心返回了错误的变量，因此导致输出为“NaN”。

这是一个愚蠢的错误：

我之前在做什么

mu= mean(a);sigma = std(a);b=(X.-mu);X= b./sigma;

而我应该做的是

function [X_norm, mu, sigma] = featureNormalize(X)%FEATURENORMALIZE Normalizes the features in X %   FEATURENORMALIZE(X) returns a normalized version of X where%   the mean value of each feature is 0 and the standard deviation%   is 1. This is often a good preprocessing step to do when%   working with learning algorithms.% You need to set these values correctlyX_norm = X;mu = zeros(1, size(X, 2));sigma = zeros(1, size(X, 2));% ====================== YOUR CODE HERE ======================mu= mean(X);sigma = std(X);a=(X.-mu);X_norm= a./sigma;% ============================================================end

所以很明显，我应该使用X_norm而不是X，这就是导致代码输出错误的原因