This is a java program to implement Min Hash. In computer science, MinHash (or the min-wise independent permutations locality sensitive hashing scheme) is a technique for quickly estimating how similar two sets are.
Here is the source code of the Java Program to Implement Min Hash. The Java program is successfully compiled and run on a Windows system. The program output is also shown below.
package com.sanfoundry.datastructures;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Random;
import java.util.Set;
public class MinHash<T>
{
private int hash[];
private int numHash;
public MinHash(int numHash)
{
this.numHash = numHash;
hash = new int[numHash];
Random r = new Random(11);
for (int i = 0; i < numHash; i++)
{
int a = (int) r.nextInt();
int b = (int) r.nextInt();
int c = (int) r.nextInt();
int x = hash(a * b * c, a, b, c);
hash[i] = x;
}
}
public double similarity(Set<T> set1, Set<T> set2)
{
int numSets = 2;
Map<T, boolean[]> bitMap = buildBitMap(set1, set2);
int[][] minHashValues = initializeHashBuckets(numSets, numHash);
computeMinHashForSet(set1, 0, minHashValues, bitMap);
computeMinHashForSet(set2, 1, minHashValues, bitMap);
return computeSimilarityFromSignatures(minHashValues, numHash);
}
private static int[][] initializeHashBuckets(int numSets,
int numHashFunctions)
{
int[][] minHashValues = new int[numSets][numHashFunctions];
for (int i = 0; i < numSets; i++)
{
for (int j = 0; j < numHashFunctions; j++)
{
minHashValues[i][j] = Integer.MAX_VALUE;
}
}
return minHashValues;
}
private static double computeSimilarityFromSignatures(
int[][] minHashValues, int numHashFunctions)
{
int identicalMinHashes = 0;
for (int i = 0; i < numHashFunctions; i++)
{
if (minHashValues[0][i] == minHashValues[1][i])
{
identicalMinHashes++;
}
}
return (1.0 * identicalMinHashes) / numHashFunctions;
}
private static int hash(int x, int a, int b, int c)
{
int hashValue = (int) ((a * (x >> 4) + b * x + c) & 131071);
return Math.abs(hashValue);
}
private void computeMinHashForSet(Set<T> set, int setIndex,
int[][] minHashValues, Map<T, boolean[]> bitArray)
{
int index = 0;
for (T element : bitArray.keySet())
{
/*
* for every element in the bit array
*/
for (int i = 0; i < numHash; i++)
{
/*
* for every hash
*/
if (set.contains(element))
{
/*
* if the set contains the element
*/
int hindex = hash[index];
if (hindex < minHashValues[setIndex][index])
{
/*
* if current hash is smaller than the existing hash in
* the slot then replace with the smaller hash value
*/
minHashValues[setIndex][i] = hindex;
}
}
}
index++;
}
}
public Map<T, boolean[]> buildBitMap(Set<T> set1, Set<T> set2)
{
Map<T, boolean[]> bitArray = new HashMap<T, boolean[]>();
for (T t : set1)
{
bitArray.put(t, new boolean[] { true, false });
}
for (T t : set2)
{
if (bitArray.containsKey(t))
{
// item is not present in set1
bitArray.put(t, new boolean[] { true, true });
}
else if (!bitArray.containsKey(t))
{
// item is not present in set1
bitArray.put(t, new boolean[] { false, true });
}
}
return bitArray;
}
public static void main(String[] args)
{
Set<String> set1 = new HashSet<String>();
set1.add("FRANCISCO");
set1.add("MISSION");
set1.add("SAN");
Set<String> set2 = new HashSet<String>();
set2.add("FRANCISCO");
set2.add("MISSION");
set2.add("SAN");
set2.add("USA");
MinHash<String> minHash = new MinHash<String>(set1.size() + set2.size());
System.out.println("Set1 : " + set1);
System.out.println("Set2 : " + set2);
System.out.println("Similarity between two sets: "
+ minHash.similarity(set1, set2));
}
}
Output:
$ javac MinHash.java $ java MinHash Set1 : [SAN, MISSION, FRANCISCO] Set2 : [SAN, USA, MISSION, FRANCISCO] Similarity between two sets: 1.0
Sanfoundry Global Education & Learning Series – 1000 Java Programs.
advertisement
advertisement
Here’s the list of Best Books in Java Programming, Data Structures and Algorithms.
Related Posts:
- Apply for Computer Science Internship
- Practice Computer Science MCQs
- Check Programming Books
- Check Data Structure Books
- Practice Programming MCQs