Java中的布隆过滤器你真的懂了吗

import java.util.BitSet;
import java.util.Random;
public class BloomFilter {
  private BitSet bitSet;           // 位集，用于存储哈希值
  private int bitSetSize;         // 位集大小
  private int numHashFunctions;   // 哈希函数数量
  private Random random;          // 随机数生成器
  // 构造函数，根据期望元素数量和错误率计算位集大小和哈希函数数量
  public BloomFilter(int expectedNumItems, double falsePositiveRate) {
    this.bitSetSize = optimalBitSetSize(expectedNumItems, falsePositiveRate);
    this.numHashFunctions = optimalNumHashFunctions(expectedNumItems, bitSetSize);
    this.bitSet = new BitSet(bitSetSize);
    this.random = new Random();
  }
  // 根据期望元素数量和错误率计算最佳位集大小
  private int optimalBitSetSize(int expectedNumItems, double falsePositiveRate) {
    int bitSetSize = (int) Math.ceil(expectedNumItems * (-Math.log(falsePositiveRate) / Math.pow(Math.log(2), 2)));
    return bitSetSize;
  }
  // 根据期望元素数量和位集大小计算最佳哈希函数数量
  private int optimalNumHashFunctions(int expectedNumItems, int bitSetSize) {
    int numHashFunctions = (int) Math.ceil((bitSetSize / expectedNumItems) * Math.log(2));
    return numHashFunctions;
  }
  // 添加元素到布隆过滤器中
  public void add(String item) {
    // 计算哈希值
    int[] hashes = createHashes(item.getBytes(), numHashFunctions);
    // 将哈希值对应的位设置为 true
    for (int hash : hashes) {
      bitSet.set(Math.abs(hash % bitSetSize), true);
    }
  }
  // 检查元素是否存在于布隆过滤器中
  public boolean contains(String item) {
    // 计算哈希值
    int[] hashes = createHashes(item.getBytes(), numHashFunctions);
    // 检查哈希值对应的位是否都为 true
    for (int hash : hashes) {
      if (!bitSet.get(Math.abs(hash % bitSetSize))) {
        return false;
      }
    }
    return true;
  }
  // 计算给定数据的哈希值
  private int[] createHashes(byte[] data, int numHashes) {
    int[] hashes = new int[numHashes];
    int hash1 = Math.abs(random.nextInt());
    int hash2 = Math.abs(random.nextInt());
    for (int i = 0; i < numHashes; i++) {
      // 使用两个随机哈希函数计算哈希值
      hashes[i] = Math.abs((hash1 * i) + (hash2 * i) + i) % data.length;
    }
    return hashes;
  }
}