我已经使用矢量化实现了以下梯度下降代码,但似乎成本函数没有正确递减。相反,成本函数随着每次迭代而增加。
假设 theta 为 n+1 向量,y 为 am 向量,X 为设计矩阵 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);