java实现简单线性回归（判定系数）

判定系数

  coefficient of determination 为估计的回归方程提供拟合优度的度量。

代码

1、demo

package com.math.demo;

import com.math.regression.RegressionEquation;
import com.math.regression.RegressionModel;
/***
 * 
 * @author miaoyibo
 *
 */
public class RegressionDemo {

    public static void main(String[] args) {
        double[] dependentValues= {58,105,88,118,117,137,157,169,149,202};
        double[] independentValues= {2,6,8,8,12,16,20,20,22,26};
        RegressionEquation re=new RegressionEquation(dependentValues, independentValues);
        RegressionModel regressionModel = re.getRegressionModel();
        System.out.println("b0=="+regressionModel.getB0());
        System.out.println("b0=="+regressionModel.getB1());
        System.out.println("coefficient of determination=="+regressionModel.getR());
    }

}

2、model

package com.math.regression;

public class RegressionModel {
    //y轴截距
    private double b0;
    //斜率
    private double b1;
    //判定系数
    private double r;
    public double getB0() {
        return b0;
    }
    public void setB0(double b0) {
        this.b0 = b0;
    }
    public double getB1() {
        return b1;
    }
    public void setB1(double b1) {
        this.b1 = b1;
    }
    public double getR() {
        return r;
    }
    public void setR(double r) {
        this.r = r;
    }
    
    

}

3、方法类

package com.math.regression;

import org.apache.commons.math3.stat.descriptive.moment.Mean;
/***
 * least squares method
 * @author miaoyibo
 *
 */
public class RegressionEquation {
    
    private double[] dependentValues;
    
    private double[] independentValues;

    public RegressionEquation(double[] dependentValues, double[] independentValues) {
        this.dependentValues = dependentValues;
        this.independentValues = independentValues;
    }
    
    public double getMean(double[] dd) {
        Mean meanUtil = new Mean();
        return meanUtil.evaluate(dd);
    }

    public double[] calRegression() {
        if(dependentValues.length!=independentValues.length) {
            return null;
        }
        Mean meanUtil = new Mean();
        double xmean=meanUtil.evaluate(independentValues);
        double ymean=meanUtil.evaluate(dependentValues);
        double numerator=0d;
        double denominator=0d;
        for(int i=0;i<dependentValues.length;i++) {
            double x=independentValues[i];
            double y=dependentValues[i];
            numerator=numerator+(x-xmean)*(y-ymean);
            denominator=denominator+(x-xmean)*(x-xmean);
        }
        double b1=numerator/denominator;
        double b0=ymean-b1*xmean;
        double[] model= {b0,b1};
        return model;
    }
    //判定系数
    public double getCoefficientOfDetermination() {
        double[] model = calRegression();
        double b0=model[0];
        double b1=model[1];
        //calculate SSE SST
        double sse=0;
        double sst=0;
        Mean meanUtil = new Mean();
        double mean=meanUtil.evaluate(dependentValues);
        for(int i=0;i<dependentValues.length;i++) {
            double yi=independentValues[i]*b1+b0;
            double y=dependentValues[i];
            sse=sse+(y-yi)*(y-yi);
            sst=sst+(y-mean)*(y-mean);
        }
        return (sst-sse)/sst;
    }
    public RegressionModel getRegressionModel() {
        RegressionModel model=new RegressionModel();
        double[] regression = calRegression();
        double coefficientOfDetermination = getCoefficientOfDetermination();
        model.setB0(regression[0]);
        model.setB0(regression[1]);
        model.setR(coefficientOfDetermination);
        return model;
        
    }
}