我想基于遵循 Zipf 分布的單詞（來自字典）創建數據源（用 Java 編寫）。所以我來到了Apache commons 庫的ZipfDistribution和NormalDistribution 。不幸的是，有關如何使用這些類的信息很少。我嘗試做一些測試，但我不確定我是否以正確的方式使用它。我僅遵循每個構造函數的文檔中所寫的內容。但結果似乎并不“分布均勻”。import org.apache.commons.math3.distribution.NormalDistribution;import org.apache.commons.math3.distribution.ZipfDistribution;import java.io.BufferedReader;import java.io.IOException;import java.io.InputStream;import java.io.InputStreamReader;import java.net.URL;public class ZipfDistributionDataSource extends RichSourceFunction<String> {    private static final String DISTINCT_WORDS_URL = "https://raw.githubusercontent.com/dwyl/english-words/master/words_alpha.txt";    public static void main(String[] args) throws Exception {        ZipfDistributionDataSource zipfDistributionDataSource = new ZipfDistributionDataSource();        StringBuffer stringBuffer = new StringBuffer(zipfDistributionDataSource.readDataFromResource());        String[] words = stringBuffer.toString().split("\n");        System.out.println("size: " + words.length);        System.out.println("Normal Distribution");        NormalDistribution normalDistribution = new NormalDistribution(words.length / 2, 1);        for (int i = 0; i < 10; i++) {            int sample = (int) normalDistribution.sample();            System.out.print("sample[" + sample + "]: ");            System.out.println(words[sample]);        }        System.out.println();        System.out.println("Zipf Distribution");        ZipfDistribution zipfDistribution = new ZipfDistribution(words.length - 1, 1);        for (int i = 0; i < 10; i++) {            int sample = zipfDistribution.sample();            System.out.print("sample[" + sample + "]: ");            System.out.println(words[sample]);        }    }