• 闪存
• 博客
• 发言 小组
• 投递 新闻
• 提问 博问
• 添加 收藏
• 文库

# 吴恩达机器学习ex1多变量梯度下降为什么出错

0 悬赏园豆：50 [待解决问题] 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)
% 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

您需要登录以后才能回答，未注册用户请先注册