java - 计算列表中每个值的百分位数

Question

我一直在寻找一种方法来计算给定列表中每个值的百分位排名，但到目前为止我一直没有成功。

org.apache.commons.math3 为您提供了一种从值列表中获取第 p 个百分位数的方法，但我想要的恰恰相反。我想对列表中的每个值进行排名。有没有人知道 Apache 公共(public)数学中的库或方法可以实现这一点？

例如:给定一个值列表 {1,2,3,4,5}，我希望每个值的百分位等级最大为 99 或 100最小值为 0 或 1。

更新代码:

public class TestPercentile {

public static void main(String args[]) {
    double x[] = { 10, 11, 12, 12, 12, 12, 15, 18, 19, 20 };
    calculatePercentiles(x);
}

public static void calculatePercentiles(double[] arr) {
    for (int i = 0; i < arr.length; i++) {
        int count = 0;
        int start = i;
        if (i > 0) {
            while (i > 0 && arr[i] == arr[i - 1]) {
                count++;
                i++;
            }
        }
        double perc = ((start - 0) + (0.5 * count));
        perc = perc / (arr.length - 1);
        for (int k = 0; k < count + 1; k++)
            System.out.println("Percentile for value " + (start + k + 1)
                    + " = " + perc * 100);
    }
}}

Sample Output: 
Percentile for value 1 = 0.0
Percentile for value 2 = 11.11111111111111
Percentile for value 3 = 22.22222222222222
Percentile for value 4 = 50.0
Percentile for value 5 = 50.0
Percentile for value 6 = 50.0
Percentile for value 7 = 50.0
Percentile for value 8 = 77.77777777777779
Percentile for value 9 = 88.88888888888889
Percentile for value 10 = 100.0

有人可以告诉我这是否正确，是否有一个库可以更干净地执行此操作？

谢谢!

Answer 1

这实际上取决于您对百分位数的定义。以下是使用 NaturalRanking 的解决方案并重新缩放到 0-1 间隔。很高兴 NaturalRanking 有一些处理相等值的策略，并且已经实现了 nans。

import java.util.Arrays;
import org.apache.commons.math3.stat.ranking.NaNStrategy;
import org.apache.commons.math3.stat.ranking.NaturalRanking;
import org.apache.commons.math3.stat.ranking.TiesStrategy;

public class Main {

    public static void main(String[] args) {
        double[] arr = {Double.NaN, 10, 11, 12, 12, 12, 12, 15, 18, 19, 20};

        PercentilesScaledRanking ranking = new PercentilesScaledRanking(NaNStrategy.REMOVED, TiesStrategy.MAXIMUM);
        double[] ranks = ranking.rank(arr);

        System.out.println(Arrays.toString(ranks));
        //prints:
        //[0.1, 0.2, 0.6, 0.6, 0.6, 0.6, 0.7, 0.8, 0.9, 1.0]
    }
}

class PercentilesScaledRanking extends NaturalRanking {

    public PercentilesScaledRanking(NaNStrategy nanStrategy, TiesStrategy tiesStrategy) {
        super(nanStrategy, tiesStrategy);
    }

    @Override
    public double[] rank(double[] data) {
        double[] rank = super.rank(data);
        for (int i = 0; i < rank.length; i++) {
            rank[i] = rank[i] / rank.length;
        }
        return rank;
    }
}