[英]Gradient descent not updating theta values
如以下所述使用梯度的矢量化版本: 梯度下降似乎失败
theta = theta - (alpha/m * (X * theta-y)' * X)';
theta值不会被更新,因此无论初始theta值如何,这都是在运行梯度下降后设置的值:
example1:
m = 1
X = [1]
y = [0]
theta = 2
theta = theta - (alpha/m .* (X .* theta-y)' * X)'
theta =
2.0000
example2:
m = 1
X = [1;1;1]
y = [1;0;1]
theta = [1;2;3]
theta = theta - (alpha/m .* (X .* theta-y)' * X)'
theta =
1.0000
2.0000
3.0000
是theta = theta - (alpha/m * (X * theta-y)' * X)';
梯度下降的正确矢量化实现?
theta = theta - (alpha/m * (X * theta-y)' * X)';
确实是梯度下降的正确向量化实现。
您完全忘记了设置学习率alpha
。
设置alpha = 0.01
,您的代码变为:
m = 1 # number of training examples
X = [1;1;1]
y = [1;0;1]
theta = [1;2;3]
alpha = 0.01
theta = theta - (alpha/m .* (X .* theta-y)' * X)'
theta =
0.96000
1.96000
2.96000
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