吴恩达机器学习ex1多变量梯度下降为什么出错

下面是多变量梯度下降的代码:
X是特征矩阵,已经在最左边添加了新的一列为1,theta是梯度下降要求的参数,num_iters为迭代次数。已经进行过特征缩放
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESCENT(X, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha

% Initialize some useful values
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);

for iter = 1:num_iters

% ====================== YOUR CODE HERE ====================== % Instructions: Perform a single gradient step on the parameter vector % theta. % % Hint: While debugging, it can be useful to print out the values % of the cost function (computeCost) and gradient here. % sum1 = 0; sum2 = 0; sum3 = 0; for i = 1 : m a=theta.'; x = X([i],:); sum1 =sum1 + (a*x.'-y(i)); sum2 =sum2 + (a*x.'-y(i))* X(i,2); sum3 =sum3 + (a*x.'-y(i))* X(i,3); end 

theta(1) = theta(1) – sum1 * alpha *(1/m);
theta(2) = theta(2) – sum2 * alpha *(1/m);
theta(3) = theta(3) – sum3 * alpha *(1/m);

% ============================================================ % Save the cost J in every iteration J_history(iter) = computeCost(X, y, theta); 

end

end

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原文链接:https://q.cnblogs.com/q/139431/

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