Question

我目前正在使用Andrew NG在Coursera上的机器学习课程的第2周，我遇到了一个我无法解决的问题。

基于数据集，第一列是房子大小，第二列是房子的数量，第三列是它的价格，我需要在规范化数据后使用线性回归和梯度下降预测新房价格。

但是，我的预测得到了一个巨大的数字，我无法找到计算错误的位置。

我使用以下内容：

X_norm = X;
mu = zeros(1, size(X, 2));
sigma = zeros(1, size(X, 2));

for i = 1:size(X, 2);

  mu(1, i) = mean(X(:, i)),    % Getting the mean of each row.
  sigma(1, i) = std(X(:, i)),  % Getting the standard deviation of each row.

  for j = 1:size(X, 1);

    X_norm(j, i) = (X(j, i) .- mu(1, i)) ./ sigma(1, i);

  end;
end;

规范化功能的代码（X是数据集矩阵）：

m = length(y);
J = 0;        
predictions = X * theta;
sqErrors = (predictions - y).^2;
J = (1/(2*m)) * sum(sqErrors);

计算当前费用的代码：

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

for iter = 1:num_iters

  % Getting the predictions for our firstly chosen theta values.
  predictions = X * theta;

  % Getting the error difference of the hypothesis(h(x)) and real results(y).
  diff = predictions - y;

  % Getting the number of features.
  features_num = size(X, 2);  

  % Applying gradient descent for each feature.
  for i = 1:features_num;

    theta(i, 1) = theta(i, 1) - (alpha / m) * sum(diff .* X(:, i))

  end;

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

计算梯度下降的代码：

{{1}}

在预测1650平方英尺和3间卧室的房子时得到的价格： 182329818.366117

使用标准化数据进行梯度下降时，得不到准确的结果

0 个答案: