programming-examples/java/Data_Structures/Java Program to Implement Min Hash.java
2019-11-15 12:59:38 +01:00

152 lines
5.3 KiB
Java

/*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.*/
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));
}
}
/*
Set1 : [SAN, MISSION, FRANCISCO]
Set2 : [SAN, USA, MISSION, FRANCISCO]
Similarity between two sets: 1.0