使用矢量化的梯度下降的八度代码无法正确更新成本函数 [英] Octave code for gradient descent using vectorization not updating cost function correctly

问题描述

我已经使用矢量化实现了以下用于梯度下降的代码，但是代价函数似乎并没有正确地递减.相反，代价函数会随着每次迭代的增加而增加.

I have implemented following code for gradient descent using vectorization but it seems the cost function is not decrementing correctly.Instead the cost function is increasing with each iteration.

假设theta为n + 1个向量，y为m个向量，X为设计矩阵m *(n + 1)

Assuming theta to be an n+1 vector, y to be a m vector and X to be design matrix m*(n+1)

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)

m = length(y); % number of training examples
n = length(theta); % number of features
J_history = zeros(num_iters, 1);
error = ((theta' * X')' - y)*(alpha/m);
descent = zeros(size(theta),1);

for iter = 1:num_iters
for i = 1:n
   descent(i) = descent(i) + sum(error.* X(:,i));
   i = i + 1;
end 

theta = theta - descent;
J_history(iter) = computeCost(X, y, theta);
disp("the value of cost function is : "), disp(J_history(iter));
iter = iter + 1;
end

计算成本函数为:

function J = computeCost(X, y, theta)
m = length(y);
J = 0;
for i = 1:m,
   H = theta' * X(i,:)';
   E = H - y(i);
   SQE = E^2;
   J = (J + SQE);
   i = i+1;
end;
J = J / (2*m);

推荐答案

您可以进一步矢量化它:

You can vectorise it even further:

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
    m = length(y); 
    J_history = zeros(num_iters, 1);

    for iter = 1:num_iters

       delta = (theta' * X'-y')*X;
       theta = theta - alpha/m*delta';
       J_history(iter) = computeCost(X, y, theta);

    end

end

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