간단한 자바 통계 프로그램.

간단한 통계기능이 필요한데 R같은 라이브러리는 무겁고해서 만든 자바 프로그램입니다.

분산, 공분산, 표준편차, 결정계수(R-Squared) 계산이 가능합니다.

뒤져봐도 결정계수 계산하는 소스는 잘 안나와서 직접 만들었습니다.

import java.util.ArrayList;
import java.util.List;


public class Statistics {

	public static double getSum(List Values){
		double SumOfData = 0;
	
		for(int i = 0 ; i < Values.size() ; i++){
			SumOfData += Values.get(i).doubleValue();
		}
		
		return SumOfData;
	}
	
	
	
	public static double getRSquared(List originData, List targetData) {
		
		double R2 = 0;
		
		R2 = Math.pow(getCovariance(originData, targetData) / (getStandardDeviation(originData)* getStandardDeviation(targetData)), 2);
		
		return R2;
	}


	public static double getAvg(List datas) {

		double AvgOfData = 0;
		
		AvgOfData = getSum(datas)/datas.size();
		
		return AvgOfData;
	}


	public static double getVariance(List datas) {
		double Variance = 0;
		
		double avg = 0;
		avg = getAvg(datas);
		
		double SumOfDeviation = 0;
		
		for(int i = 0; i < datas.size(); i++)
		{
			SumOfDeviation += Math.pow((datas.get(i).doubleValue() - avg), 2);
		}
		
		Variance = SumOfDeviation / datas.size();
		
		return Variance;
	}


	public static double getStandardDeviation(List datas) {
		double StandardDeviation = 0;
		
		StandardDeviation = Math.sqrt(getVariance(datas));

		return StandardDeviation;
	}


	public static double getCovariance(List datas,
			List targetData) {
		
		double a_avg = getAvg(datas);
		double b_avg = getAvg(targetData);
		
		List AB = new ArrayList();
		for(int i = 0; i < datas.size(); i++){
			AB.add(new Double(datas.get(i).doubleValue() * targetData.get(i).doubleValue()));
		}
		
		double ab_avg = getAvg(AB);
				
		return ab_avg - (a_avg * b_avg);
	}


	public static double getArSum(double[] arDatas) {
		double SumOfData = 0;
		
		for(int i = 0 ; i < arDatas.length ; i++){
			SumOfData += arDatas[i];
		}
		
		return SumOfData;
	}


	public static double getArAvg(double[] datas) {
		
		return getArSum(datas)/datas.length;
	}


	public static double getArVariance(double[] arDatas) {
		
		double avg = 0;
		avg = getArAvg(arDatas);
		
		double SumOfDeviation = 0;
		
		for(int i = 0; i < arDatas.length; i++)
		{
			SumOfDeviation += Math.pow((arDatas[i] - avg), 2);
		}
		
		return SumOfDeviation / arDatas.length;
	}


	public static double getArStandardDeviation(double[] arDatas) {
		
		return Math.sqrt(getArVariance(arDatas));
	}


	public static double getArCovariance(double[] arDatas,
			double[] arTargetDatas) {
	
		double a_avg = getArAvg(arDatas);
		double b_avg = getArAvg(arTargetDatas);
		
		double AB[] = new double[arDatas.length];
		for(int i = 0; i < arDatas.length; i++){
			AB[i] = arDatas[i] * arTargetDatas[i];
		}
		
		double ab_avg = getArAvg(AB);

		return ab_avg - (a_avg * b_avg);
	}


	public static double getArRSquared(double[] arDatas, double[] arTargetDatas) {
		
		double R2 = 0;
		R2 = Math.pow(getArCovariance(arDatas, arTargetDatas) / (getArStandardDeviation(arDatas)* getArStandardDeviation(arTargetDatas)), 2);
		if(Double.isNaN(R2)){R2 = 0;}
		return R2;
		
	}
}