1. $X$ is a matrix which has m rows and n columns, that means it is a $m \times n$ matrix, represents for training set.
  2. $\theta$ is a $1 \times n$ vector, stands for hypothesis parameter.
  3. $y$ is a $m \times 1$ vector, stands for real value of training set.
  4. $\alpha$ named learning rate for defining learning or descending speed.
  5. $S(X_j)$ means to get standard deviation of the j feature from training set.

1. Hypothesis

Draw hypothesis of a pattern.

\[h_{\theta}(X) = X \times \theta^T\]

2. Cost

Calculate the Cost for single training point.

\[Cost(X^{(i)},y^{(i)})=[h_{\theta}(X^{(i)}) - y^{(i)}]^2\]

3. Cost function

Draw cost function for iterating whole training set.

\[\begin{aligned} J(\theta) &=\left(\frac{1}{2m}\right)\sum_{i=1}^m Cost(X^{(i)},y^{(i)}) \\ &=\left(\frac{1}{2m}\right)\sum_{i=1}^m[h_{\theta}(X^{(i)}) - y^{(i)}]^2 \end{aligned}\]

4. Get optimized parameter

Learn from training set to get optimized parameter for proposed algorithm.

Gradient Descend###

Complicate to implement.
suitable for any senario.

\[\begin{aligned} grad(j) &= \frac{\partial}{\partial \theta_j} J(\theta) \\ &= \frac{1}{m}\sum_{i=1}^m[(h_{\theta}(X^{(i)}) - y^{(i)}) X_{j}^{(i)}] \end{aligned}\] \[\theta_j := \theta_j - \alpha \times grad(j) \quad \text{Repeat many times}\]

Normal equation###

Convenient, but performance bad while m grow large than 100000.
Unable to conquer non-invertable matrix.

\[\theta = (X^{T}X)^{\prime}X^{T}y\]

Feature scaling

Use feature scaling to optimize training set.
Make gradient descend converge much faster.

\[X_j=\frac{X_j - \mu}{a}\] \[\begin{aligned} a &= max(X_j)-min(X_j) \\ & or \\ &= S(X_j) \end{aligned}\]


15 July 2014