最小二乘线性回归与梯度下降算法

线性回归

预测函数：

\[h_\theta(x)=\theta_0 + \theta_1x_1 + \theta_2x_2 \]

将\(x_0 = 1\)

\[ h_\theta(x) = \sum_{i=0}^n\theta_ix_i \]

其中 \(\theta\)这参数，n表示特征数量

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成本函数：

\[J(\theta) = \frac{1}{2}\sum_{i=1}^m(h_\theta(x^{(i)}) - y^{(i)})^2 \]

其中m表示样本数量，\((x^{(i)},y^{(i)})\)表示第i个样本对儿，1/2是为了方便计算

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我们目标：

\[\min_\theta J(\theta) \]

最小二乘成本函数的概率学解释（并不是唯一的解释）

假设该线性模型符合高斯分布

\[p(y^{(i)}|x^{(i)};\theta) = \frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\sqrt{(y^{(i)}-\theta^Tx^{(i)^2})}{2\sigma^2}\right) \]

似然函数为：

\[L(\theta) = L(\theta;X,\vec{y}) = p(\vec{y}|X;θ) \]

因此，线性回归的似然函数：

\[\begin{align} L(\theta) & = \prod_{i=1}^mp(y^{(i)}|x^{(i)};\theta) \\ & = \prod_{i=1}^m\frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\frac{((y^{(i)}) - \theta^Tx^{(i)})^2}{2\sigma^2}\right) \end{align} \]

对数似然度为

\[\begin{align} l(\theta) & = \log L(\theta)\\ & = \log\prod_{i=1}^m\frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\frac{((y^{(i)}) - \theta^Tx^{(i)})^2}{2\sigma^2}\right)\\ & = \sum_{i=1}^m\log\frac{1}{{2\sigma^2}}\exp\left(-\frac{((y^{(i)}) - \theta^Tx^{(i)})^2}{2\sigma^2}\right)\\ & = m\log\frac{1}{2\pi\sigma}-\frac{1}{\sigma^2}\cdot\frac{1}{2}\sum_{i=1}^m(y^{(i)} - \theta^Tx^{(i)})^2 \end{align} \]

最大化\(l(\theta)\)相当于最小化，也就是成本函数

\[\frac{1}{2}\sum_{i=1}^m(y^{(i)} - \theta^Tx^{(i)})^2 \]

梯度下降

步骤

1. 初始化\(\theta\)
2. 改变\(\theta\)使\(J(\theta)\)降低
3. 终止

每一步使：

\[\theta_i:=\theta_i-\alpha\frac{\partial}{\partial\theta_i}J(\theta) \]

\(\theta_i\)表示第个参数，:=表示赋值符号，\(\alpha\)表示步长也叫学习速率，\(\frac{\partial}{\partial\theta_i}\)是对\(\theta_i\)求偏导数

对于单个样本：

\[\begin{align} \frac{\partial}{\partial\theta_i}J(\theta) & = \frac{\partial}{\partial\theta_i}\frac{1}{2}(h_\theta(x) - y)^2 \\ & = 2\cdot\frac{1}{2}(h_\theta(x) - y) \cdot \frac{\partial}{\partial\theta_i}(h_\theta(x)-y) \\ & = (h_\theta(x) - y)\cdot\frac{\partial}{\partial\theta_i}(\theta_0x_0+\theta_1x_1+... + \theta_nx_n-y) \\ & = (h_\theta(x) - y)\cdot x_i \end{align} \]

(1)-(2) 根据链式求导法则，(3)-(4)\((\theta_0x_0+\theta_1x_1+... + \theta_nx_n-y)\)只有\(\theta_i\)起作用
因此

\[\theta_i:=\theta_i-\alpha(h_\theta(x) - y)\cdot x_i \]

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对于多个样本：

\[\theta_i:=\theta_i-\sum_{j=1}^m\alpha(h_\theta(x^{(j)}) - y^{(j)})\cdot x_i^{(j)} \]

批梯度下降算法代码

public static void main(String[] args) {
    //训练集合输入值
    double x[][] = {
            {1, 4},
            {2, 5},
            {5, 1},
            {4, 2}};
    //训练集合期望输出值
    double y[] = {
            19,
            26,
            19,
            20};
    //参数
    double theta[] = {0, 0};
    //步长
    double learningRate = 0.01;
    //样本集合长度
    int m = x.length;
    //迭代次数
    int iteration = 100;
    for (int index = 0; index < iteration; index++) {
        double err_sum = 0;
        double[] tmp = new double[theta.length];
        System.arraycopy(theta, 0, tmp, 0, theta.length);
        //i->m
        for (int j = 0; j < m; j++) {
            double h = 0;
            for (int i = 0; i < theta.length; i++) {//x[j][i]表示第j个样本的第i个特征
                h += x[j][i] * theta[i];
            }
            err_sum = h - y[j];
            for (int i = 0; i < theta.length; i++) {
                tmp[i] -= learningRate * err_sum * x[j][i];
            }
        }
        //批量更新参数
        theta = tmp;
    }
    for (int i = 0; i < theta.length; i++) {
        System.out.println("theta" + i + "=" + theta[i]);
    }
}