Matlab中的多变量梯度下降 [英] Multi variable gradient descent in matlab
问题描述
我正在matlab中对多个变量进行梯度下降，并且代码未获得正常等式所得到的预期theta.那是: θ= 1.0e + 05 * 3.4041 1.1063 -0.0665 与普通等式.我已经执行了.
I'm doing gradient descent in matlab for mutiple variables, and the code is not getting the expected thetas I got with the normal eq. that are: theta = 1.0e+05 * 3.4041 1.1063 -0.0665 With the Normal eq. I have implemented.
使用GDM，我得到的结果是: θ= 1.0e + 05 * 2.6618 -2.6718 -0.5954 而且我不明白为什么会这样，也许有人可以帮助我，告诉我代码中的错误在哪里.
And with the GDM the results I get are: theta = 1.0e+05 * 2.6618 -2.6718 -0.5954 And I don't understand why is this, maybe some one can help me and tell me where is the mistake in the code.
代码:
function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
thetas = size(theta,1);
features = size(X,2)
mu = mean(X);
sigma = std(X);
mu_size = size(mu);
sigma_size = size(sigma);
%for all iterations
for iter = 1:num_iters
tempo = [];
result = [];
theta_temp = [];
%for all the thetas
for t = 1:thetas
%all the examples
for examples = 1:m
tempo(examples) = ((theta' * X(examples, :)') - y(examples)) * X(m,t)
end
result(t) = sum(tempo)
tempo = 0;
end
%theta temp, store the temp
for c = 1:thetas
theta_temp(c) = theta(c) - alpha * (1/m) * result(c)
end
%simultaneous update
for j = 1:thetas
theta(j) = theta_temp(j)
end
% Save the cost J in every iteration
J_history(iter) = computeCostMulti(X, y, theta);
end
theta
end
谢谢.
数据.
X =
1.0000 0.1300 -0.2237
1.0000 -0.5042 -0.2237
1.0000 0.5025 -0.2237
1.0000 -0.7357 -1.5378
1.0000 1.2575 1.0904
1.0000 -0.0197 1.0904
1.0000 -0.5872 -0.2237
1.0000 -0.7219 -0.2237
1.0000 -0.7810 -0.2237
1.0000 -0.6376 -0.2237
1.0000 -0.0764 1.0904
1.0000 -0.0009 -0.2237
1.0000 -0.1393 -0.2237
1.0000 3.1173 2.4045
1.0000 -0.9220 -0.2237
1.0000 0.3766 1.0904
1.0000 -0.8565 -1.5378
1.0000 -0.9622 -0.2237
1.0000 0.7655 1.0904
1.0000 1.2965 1.0904
1.0000 -0.2940 -0.2237
1.0000 -0.1418 -1.5378
1.0000 -0.4992 -0.2237
1.0000 -0.0487 1.0904
1.0000 2.3774 -0.2237
1.0000 -1.1334 -0.2237
1.0000 -0.6829 -0.2237
1.0000 0.6610 -0.2237
1.0000 0.2508 -0.2237
1.0000 0.8007 -0.2237
1.0000 -0.2034 -1.5378
1.0000 -1.2592 -2.8519
1.0000 0.0495 1.0904
1.0000 1.4299 -0.2237
1.0000 -0.2387 1.0904
1.0000 -0.7093 -0.2237
1.0000 -0.9584 -0.2237
1.0000 0.1652 1.0904
1.0000 2.7864 1.0904
1.0000 0.2030 1.0904
1.0000 -0.4237 -1.5378
1.0000 0.2986 -0.2237
1.0000 0.7126 1.0904
1.0000 -1.0075 -0.2237
1.0000 -1.4454 -1.5378
1.0000 -0.1871 1.0904
1.0000 -1.0037 -0.2237
y =
399900
329900
369000
232000
539900
299900
314900
198999
212000
242500
239999
347000
329999
699900
259900
449900
299900
199900
499998
599000
252900
255000
242900
259900
573900
249900
464500
469000
475000
299900
349900
169900
314900
579900
285900
249900
229900
345000
549000
287000
368500
329900
314000
299000
179900
299900
239500
完整数据集.
推荐答案
计算速度的行是错误的.应该是
The line where you calculate tempo is wrong. It should be
tempo(examples) = ((theta' * X(examples, :)') - y(examples)) * X(examples,t)
也可以尝试在MATLAB中使用矩阵运算.您的代码将更快，并且也将更易于理解.例如，您可以将嵌套循环替换为
Also try using matrix operations in MATLAB. Your code will be faster and it will also be easier to understand. For example, you can replace your nested loop with
E = X * theta - y;
for t = 1:thetas
result(t) = sum(E.*X(:,t));
end
您可以替换随后的两个循环以将theta更新为一行
You can replace your subsequent two loop for updating theta into one line
theta = theta - alpha * (1/m) * result';
这篇关于Matlab中的多变量梯度下降的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
