单维与多维线性回归代码( machine-learning-ex1 ) Stanford machine learning

1.warmUpExercise.m

2.plotData.m

3.gradientDescent.m

4.computeCost.m

5.gradientDescentMulti.m

6.computeCostMulti.m

7.featureNormalize.m

8.normalEqn.m

本文只添加各个代码文档中“YOUR CODE HERE”内容。

1.warmUpExercise.m

A = eye(5);

2.plotData.m

plot(x,y,'rx');

3.gradientDescent.m

<pre name="code" class="plain">theta = theta - alpha * ((theta'*X'-y')*X)'/m;

4.computeCost.m

J = sum(((theta'*(X')-y').^2))/(2*m);

5.gradientDescentMulti.m

theta = theta - alpha * ((theta'*X'-y')*X)'/m;

6.computeCostMulti.m

J = sum(((theta'*(X')-y').^2))/(2*m);

7.featureNormalize.m

for i = 1:size(X, 2)
mu(i) =  mean(X(:,i));
sigma(i) = std(X(:,i));
X_norm(:,i) = (X(:,i) - mu(i))/sigma(i);
end

8.normalEqn.m

theta = pinv((X'*X))*X'*y;