http://www.cnblogs.com/wzm-xu/p/4062266.html

多元线性回归----Java简单实现

 

学习Andrew N.g的机器学习课程之后的简单实现.

课程地址:https://class.coursera.org/ml-007

不大会编辑公式,所以略去具体的推导,有疑惑的同学去看看Andrew 的课程吧,顺带一句,Andrew的课程实在是很赞。

如果还有疑问,feel free to contact me via emails or QQ.

LinearRegression.java

多元线性回归----Java简单实现-LMLPHP
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException; public class LinearRegression {
/*
* 训练数据示例:
* x0 x1 x2 y
1.0 1.0 2.0 7.2
1.0 2.0 1.0 4.9
1.0 3.0 0.0 2.6
1.0 4.0 1.0 6.3
1.0 5.0 -1.0 1.0
1.0 6.0 0.0 4.7
1.0 7.0 -2.0 -0.6
注意!!!!x1,x2,y三列是用户实际输入的数据,x0是为了推导出来的公式统一,特地补上的一列。
x0,x1,x2是“特征”,y是结果 h(x) = theta0 * x0 + theta1* x1 + theta2 * x2 theta0,theta1,theta2 是想要训练出来的参数 此程序采用“梯度下降法” *
*/ private double [][] trainData;//训练数据,一行一个数据,每一行最后一个数据为 y
private int row;//训练数据 行数
private int column;//训练数据 列数 private double [] theta;//参数theta private double alpha;//训练步长
private int iteration;//迭代次数 public LinearRegression(String fileName)
{
int rowoffile=getRowNumber(fileName);//获取输入训练数据文本的 行数
int columnoffile = getColumnNumber(fileName);//获取输入训练数据文本的 列数 trainData = new double[rowoffile][columnoffile+1];//这里需要注意,为什么要+1,因为为了使得公式整齐,我们加了一个特征x0,x0恒等于1
this.row=rowoffile;
this.column=columnoffile+1; this.alpha = 0.001;//步长默认为0.001
this.iteration=100000;//迭代次数默认为 100000 theta = new double [column-1];//h(x)=theta0 * x0 + theta1* x1 + theta2 * x2 + .......
initialize_theta(); loadTrainDataFromFile(fileName,rowoffile,columnoffile);
}
public LinearRegression(String fileName,double alpha,int iteration)
{
int rowoffile=getRowNumber(fileName);//获取输入训练数据文本的 行数
int columnoffile = getColumnNumber(fileName);//获取输入训练数据文本的 列数 trainData = new double[rowoffile][columnoffile+1];//这里需要注意,为什么要+1,因为为了使得公式整齐,我们加了一个特征x0,x0恒等于1
this.row=rowoffile;
this.column=columnoffile+1; this.alpha = alpha;
this.iteration=iteration; theta = new double [column-1];//h(x)=theta0 * x0 + theta1* x1 + theta2 * x2 + .......
initialize_theta(); loadTrainDataFromFile(fileName,rowoffile,columnoffile);
} private int getRowNumber(String fileName)
{
int count =0;
File file = new File(fileName);
BufferedReader reader = null;
try {
reader = new BufferedReader(new FileReader(file));
while ( reader.readLine() != null)
count++;
reader.close();
} catch (IOException e) {
e.printStackTrace();
} finally {
if (reader != null) {
try {
reader.close();
} catch (IOException e1) {
}
}
}
return count; } private int getColumnNumber(String fileName)
{
int count =0;
File file = new File(fileName);
BufferedReader reader = null;
try {
reader = new BufferedReader(new FileReader(file));
String tempString = reader.readLine();
if(tempString!=null)
count = tempString.split(" ").length;
reader.close();
} catch (IOException e) {
e.printStackTrace();
} finally {
if (reader != null) {
try {
reader.close();
} catch (IOException e1) {
}
}
}
return count;
} private void initialize_theta()//将theta各个参数全部初始化为1.0
{
for(int i=0;i<theta.length;i++)
theta[i]=1.0;
} public void trainTheta()
{
int iteration = this.iteration;
while( (iteration--)>0 )
{
//对每个theta i 求 偏导数
double [] partial_derivative = compute_partial_derivative();//偏导数
//更新每个theta
for(int i =0; i< theta.length;i++)
theta[i]-= alpha * partial_derivative[i];
}
} private double [] compute_partial_derivative()
{
double [] partial_derivative = new double[theta.length];
for(int j =0;j<theta.length;j++)//遍历,对每个theta求偏导数
{
partial_derivative[j]= compute_partial_derivative_for_theta(j);//对 theta i 求 偏导
}
return partial_derivative;
}
private double compute_partial_derivative_for_theta(int j)
{
double sum=0.0;
for(int i=0;i<row;i++)//遍历 每一行数据
{
sum+=h_theta_x_i_minus_y_i_times_x_j_i(i,j);
}
return sum/row;
}
private double h_theta_x_i_minus_y_i_times_x_j_i(int i,int j)
{
double[] oneRow = getRow(i);//取一行数据,前面是feature,最后一个是y
double result = 0.0; for(int k=0;k< (oneRow.length-1);k++)
result+=theta[k]*oneRow[k];
result-=oneRow[oneRow.length-1];
result*=oneRow[j];
return result;
}
private double [] getRow(int i)//从训练数据中取出第i行,i=0,1,2,。。。,(row-1)
{
return trainData[i];
} private void loadTrainDataFromFile(String fileName,int row, int column)
{
for(int i=0;i< row;i++)//trainData的第一列全部置为1.0(feature x0)
trainData[i][0]=1.0; File file = new File(fileName);
BufferedReader reader = null;
try {
reader = new BufferedReader(new FileReader(file));
String tempString = null;
int counter = 0;
while ( (counter<row) && (tempString = reader.readLine()) != null) {
String [] tempData = tempString.split(" ");
for(int i=0;i<column;i++)
trainData[counter][i+1]=Double.parseDouble(tempData[i]);
counter++;
}
reader.close();
} catch (IOException e) {
e.printStackTrace();
} finally {
if (reader != null) {
try {
reader.close();
} catch (IOException e1) {
}
}
}
} public void printTrainData()
{
System.out.println("Train Data:\n");
for(int i=0;i<column-1;i++)
System.out.printf("%10s","x"+i+" ");
System.out.printf("%10s","y"+" \n");
for(int i=0;i<row;i++)
{
for(int j=0;j<column;j++)
{
System.out.printf("%10s",trainData[i][j]+" ");
}
System.out.println();
}
System.out.println();
} public void printTheta()
{
for(double a:theta)
System.out.print(a+" ");
} }
多元线性回归----Java简单实现-LMLPHP

TestLinearRegression.java

多元线性回归----Java简单实现-LMLPHP
public class TestLinearRegression {

    public static void main(String[] args) {
// TODO Auto-generated method stub
LinearRegression m = new LinearRegression("trainData",0.001,1000000);
m.printTrainData();
m.trainTheta();
m.printTheta();
} }
多元线性回归----Java简单实现-LMLPHP

trainData文件中是训练数据,默认最后一列是y,比如:

1.0       2.0       7.2 
             2.0       1.0       4.9 
             3.0       0.0       2.6 
             4.0       1.0       6.3 
             5.0      -1.0       1.0 
            6.0       0.0       4.7 
            7.0      -2.0      -0.6

前两列是“feature”,最后一列,也就是第三列是y

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05-11 14:47