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RegressionLine.java
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package com.match;
import java.math.BigDecimal;
import java.util.ArrayList;
/**
* PACKAGE_NAME: com.match
* MONTH_NAME_SHORT: 十二月
* USER: Clement
**/
public class RegressionLine {
private float sumX = 0;//训练集x的和
private float sumY = 0;//训练集y的和
private float sumXX = 0;//x*x的和
private float sumYY = 0;//y*y的和
private float sumXY = 0;//x*y的和
private float sumDeltaY;//y与yi的差
private float sumDeltaY2; // sumDeltaY的平方和
//误差
private float sse;//残差平方和
private float sst;//总平方和
private float E;
private float[] xy;
private ArrayList<String> listX;//x的链表
private ArrayList<String> listY;//y的链表
private double XMin,XMax,YMin,YMax;
private float a0;//线性系数a0
private float a1;//线性系数a1
private int pn; //训练集数据个数
private boolean coefsValid;
//类RegressionLine的构造函数
public RegressionLine(){
XMax = 0;
YMax = 0;
pn = 0;
xy = new float[2];
listX = new ArrayList<>();
listY = new ArrayList<>();
}
//类RegressionLine的有参构造函数
public RegressionLine(DataPoint data[]){
pn = 0;
xy = new float[2];
listX = new ArrayList();
listY = new ArrayList();
for(int i = 0;i<data.length;++i){
addDatapoint(data[i]);//添加数据集的方法addDatapoint
}
}
public int getDataPointCount(){
return pn;
}
public float getA0(){
validateCoefficients();
return a0;
}
public float getA1(){
validateCoefficients();
return a1;
}
public double getSumX(){
return sumX;
}
public double getSumY() {
return sumY;
}
public double getSumXX() {
return sumXX;
}
public double getSumYY() {
return sumYY;
}
public double getSumXY() {
return sumXY;
}
public double getXMin() {
return XMin;
}
public double getXMax() {
return XMax;
}
public double getYMax() {
return YMax;
}
public double getYMin() {
return YMin;
}
//添加训练集数据的方法
public void addDatapoint(DataPoint dataPoint){
sumX += dataPoint.x;
sumY += dataPoint.y;
sumXX += dataPoint.x*dataPoint.x;
sumYY += dataPoint.y*dataPoint.y;
sumXY += dataPoint.x*dataPoint.y;
if(dataPoint.x > XMax){
XMax = dataPoint.x;
}
if (dataPoint.y > YMax){
YMax = dataPoint.y;
}
xy[0] = (float) dataPoint.x;//?
xy[1] = (float) dataPoint.y;//?
if(dataPoint.x !=0 && dataPoint.y != 0){
System.out.print("("+xy[0]+",");
System.out.println(xy[1]+")");
try{
listX.add(pn,String.valueOf(xy[0]));
listY.add(pn,String.valueOf(xy[1]));
}catch (Exception e){
e.printStackTrace();
}
}
++pn;
coefsValid = false;
}
//计算预测值y的方法
public float at(float x){
if(pn < 2)
return Float.NaN;
validateCoefficients();
return a0 + a1 * x;
}
//重置此类的方法
public void reset(){
pn = 0;
sumX = sumY = sumXX = sumXY = 0;
coefsValid = false;
}
//计算系数a0，a1的方法
private void validateCoefficients(){
if (coefsValid)
return;
if (pn >= 2){
float xBar = sumX/pn;
float yBar = sumY/pn;
a1 = ((pn*sumXY - sumX*sumY)/(pn
*sumXX - sumX*sumX));
a0 = (yBar - a1*xBar);
}
else {
a0 = a1 = Float.NaN;
}
coefsValid = true;
}
//计算判定系数R^2的方法
public double getR(){
for (int i = 0;i < pn;i++){
float Yi = Float.parseFloat(listY.get(i).toString());
float Y = at(Float.parseFloat(
listX.get(i).toString()));
float deltaY = Yi - Y;
float deltaY2 = deltaY*deltaY;
sumDeltaY2 += deltaY2;
float deltaY1 = (Yi - (float) (sumY/pn))*(Yi - (float) (sumY/pn)) ;
sst += deltaY1;
}
//sst = sumYY - (sumY*sumY)/pn;
E = 1 - sumDeltaY2/sst;
return round(E,4);
}
//返回经处理过的判定系数的方法
public double round(double v,int scale){
BigDecimal b = new BigDecimal(Double.toString(v));
BigDecimal one = new BigDecimal("1");
return b.divide(one,scale,BigDecimal.ROUND_HALF_UP).floatValue();
}
public static void main(String[] args) {
RegressionLine regressionLine = new RegressionLine();
regressionLine.addDatapoint(new DataPoint(0,55));
regressionLine.addDatapoint(new DataPoint(1,55));
regressionLine.addDatapoint(new DataPoint(2,55));
regressionLine.addDatapoint(new DataPoint(3,73));
regressionLine.addDatapoint(new DataPoint(4, 72.5f));
regressionLine.addDatapoint(new DataPoint(5,60));
regressionLine.addDatapoint(new DataPoint(0,55));
System.out.println("regressionLine :"+ regressionLine.getR());
System.out.println("regressionLine :"+ regressionLine.getDataPointCount());
/* regressionLine.addDatapoint(new DataPoint(0,55));
regressionLine.addDatapoint(new DataPoint(0,55));
regressionLine.addDatapoint(new DataPoint(0,55));
regressionLine.addDatapoint(new DataPoint(0,55));
regressionLine.addDatapoint(new DataPoint(0,55));*/
}
}