[Algorithms I] Week 6 Hash Tables

[TOC]

Can we do better than BST if we do not need ordered operations ?

(No compare methods, use equals method)

Idea: save items in an array.
Hash function: method for calclulating the array index of a key.

Issues:

  • computing hash function
  • equality tests
  • collision resolution

Classic space-time tradeoff.

1. Hash Functions

Goal: scramble the keys uniformly to produce a table index.

  • effcient to compute
  • all indices are equally likely for any key

challenge: need different approach for different key types.

Java's hashing

All java object has a methode int hashCode()
requirement:

  • if x.equals(y) ⇒ x.hashCode()==y.hashCode()
  • (hopefully) if x.equals(y)==false ⇒ x.hashCode!=y.hashCode()

  • Default implementation: memory address for x.

  • Custom implementations for standard types: Integer, Double, String, File, URL, Date...


Strings: Horner's method — compute a polynome.

recipe for user-defined types:




Modeular hasing

(hash code VS hash function)

  • Hash code: 32-bit integer between -2^31 and 2^31-1 (can be negative!!)
  • Hash function: integer between 0 and M-1 (used directly as array index, should >=0)

BUG code:

private int hash(Key k){   
    return k.hashCode()%M;   
}

→ bug: number returned can be negative !

1-in-billion bug code:
return Math.abs(k.hashCode()) % M;

→ bug: Math.abs() returns negative numbers for -2^31

correct code:
just take the first 31 bits: x&0x7fffffff
return k.hashCode()&0x7fffffff % M;

另一个hash的思路是用random(用hashcode作为随机种子)

uniform hasing assumption

each key is equally likely to hash to an integer between 0 and M-1

一些数学结论:

2. Separate Chaining

one strategy for collision resolution.

idea: Using a table of size M < N, build a list for each of the table positions.

implementation

public class SeparateChainingHashST<Key, Value>{   
    private int M = 97;   
    private Node[] st = new Node[97];   
    private static class Node{   
        Object key, val; // no generic array creation!    
        Node next;   
        public Node(Key k, Value v, Node nxt){...}   
    }   
    int hash(Key k){   
        return (k.hashCode()&0x7fffffff) % M;   
    }   
    public Value get(Key k){   
        int h = hash(k);   
        for(Node x=st[h]; x!=null; x=x.next)   
            if(k.equals(x.val)) return (Value)x.val; //cast   
        return null;   
    }   
    public void put(Key k, Value v){   
        int h = hash(k);   
        for(Node x=st[h]; x!=null; x=x.next)   
            if(k.equals(x.val))    
                {x.val = v; return;}   
        st[h] = new Node(k,v,st[h]);   
    }   
}

注意内部类Node里用的是Object — 因为不能声明generic array.

analysis

proposition
Under the assumption of uniform hashing, the number of keys in a list is within a constant factor to N/M.
proof.
binominal distribution.


M times faster than sequential search
→ typical choice: M ~ N/5

3. Linear Probing

Another approch for collision resolution.

idea: open addressing
Use an array of size M>N, when a key collides, find next open slot.


  • insert: when occupied, move pointer until got open slot
  • search: when not found, move pointer until find or reach open slot
  • delete: set key to null, then for all items behind this key: set to null, then insert this key......

array M must be larger than N ⇒ array resizing is necessary.

implementation

public class LinearProbingHashST<Key, Value>{   
    private int M, N;   
    private Object[] keys;//cannot use generic array   
    private Object[] vals;   
    public void put(Key k, Value v){   
        int i = hash(k);   
        for(;keys[i]!=null;i=(i+1)%M)   
            if(keys[i].equals(k))    
                {vals[i]=v; return;}   
        keys[i]=k;   
        vals[i]=v;   
    }   
    public Value get(Key k){   
        int h=hash(k);   
        for(;keys[h]!=null;h=(h+1)%M)   
            if(k.equals(keys[h])) return (Value)vals[h];//ugly cast   
        return null;   
    }   
}

cluster

def. cluster
A contiguous block of items.

New keys are more likely to hash into a cluster.

Knuth parking pb:



(当年Knuth就是在证明了这个以后决定写那套书)



Typical choice: N/M ~ 1/2 (3/2 for search hit and 5/2 for search miss)

4. Hash Table Context

widely used in applicataions.

cost for computing hash VS cost for searching:
hashCode() for strings in java 1.1: exemain only 8-9 evenly spaced characters to save time.


uniform hashing assumption: performance not guaranteed...

one-way hash functions
Hard to find a key that hash to a desired value, or 2 keys having the same hash value.

used for fingurprints or store passwords, but too expensive for ST implementations.


Seperate chaining VS linear probing

variations

ST choice: hash tables VS balanced search trees

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