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最新推荐文章于 2025-03-08 18:48:16 发布

转载最新推荐文章于 2025-03-08 18:48:16 发布 · 437 阅读

文章标签：

#url #string #basic #null #import #class

本文详细介绍了Bloom Filter的数据结构及其在空间效率上的优势，包括哈希函数的应用、成员查询的实现方式，并通过实例展示了如何使用Bloom Filter进行元素的添加与查询。

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import java.util.BitSet;

public class BloomFilter {
	private static int defaultSize = 2 << 24;
	private static int basic = defaultSize - 1;
	private static BitSet bits;
	private static BloomFilter bf = new BloomFilter();
	
	public BloomFilter() {
		bits = new BitSet(defaultSize);
	}

	public static boolean contains(String url) {
		if (url == null) {
			return true;
		}
		int pos1 = hash1(url);
		int pos2 = hash2(url);
		int pos3 = hash3(url);
		if (bits.get(pos1) && bits.get(pos2) && bits.get(pos3)) {
			return true;
		}
		return false;
	}

	public static void add(String url) {
		if (url == null) {
			return;
		}
		int pos1 = hash1(url);
		int pos2 = hash2(url);
		int pos3 = hash3(url);
		bits.set(pos1);
		bits.set(pos2);
		bits.set(pos3);
	}

	private static int hash3(String line) {
		int h = 0;
		int len = line.length();
		for (int i = 0; i < len; i++) {
			h = 37 * h + line.charAt(i);
		}
		return check(h);
	}

	private static int hash2(String line) {
		int h = 0;
		int len = line.length();
		for (int i = 0; i < len; i++) {
			h = 33 * h + line.charAt(i);
		}
		return check(h);
	}

	private static int hash1(String line) {
		int h = 0;
		int len = line.length();
		for (int i = 0; i < len; i++) {
			h = 31 * h + line.charAt(i);
		}
		return check(h);
	}

	private static int check(int h) {
		return basic & h;
		
	}

		
}