package zutil.test;

import java.text.DecimalFormat;
import java.util.HashSet;
import java.util.Random;

import zutil.struct.BloomFilter;

import junit.framework.TestCase;

/**
 * This code may be used, modified, and redistributed provided that the 
 * author tag below remains intact.
 * 
 * @author Ian Clarke <ian@uprizer.com>
 */

public class BloomFilterTest extends TestCase {
	public void testBloomFilter() {
		DecimalFormat df = new DecimalFormat("0.00000");
		Random r = new Random(124445l);
		int bfSize = 400000;
		System.out.println("Testing " + bfSize + " bit SimpleBloomFilter");
		for (int i = 5; i < 10; i++) {
			int addCount = 10000 * (i + 1);
			BloomFilter<Integer> bf = new BloomFilter<Integer>(bfSize, addCount);
			HashSet<Integer> added = new HashSet<Integer>();
			for (int x = 0; x < addCount; x++) {
				int num = r.nextInt();
				added.add(num);
			}
			bf.addAll(added);
			assertTrue("Assert that there are no false negatives", bf
					.containsAll(added));

			int falsePositives = 0;
			for (int x = 0; x < addCount; x++) {
				int num = r.nextInt();

				// Ensure that this random number hasn't been added already
				if (added.contains(num)) {
					continue;
				}
				
				// If necessary, record a false positive
				if (bf.contains(num)) {
					falsePositives++;
				}
			}
			double expectedFP = bf.falsePosetiveProbability();
			double actualFP = (double) falsePositives / (double) addCount;
			System.out.println("Got " + falsePositives
					+ " false positives out of " + addCount + " added items, rate = "
					+ df.format(actualFP) + ", expected = "
					+ df.format(expectedFP));
			double ratio = expectedFP/actualFP;
			assertTrue(
					"Assert that the actual false positive rate doesn't deviate by more than 10% from what was predicted",
					ratio > 0.9 && ratio < 1.1);
		}
	}


}
