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Introduction%20to%20Hashing%20-%20Hash%20Functions

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Introduction to Hashing - Hash Functions Sections 5.1, 5.2, and 5.6 * * Hashing Data items stored in an array of some fixed size Hash table Search performed using ... – PowerPoint PPT presentation

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Title: Introduction%20to%20Hashing%20-%20Hash%20Functions


1
Introduction to Hashing - Hash Functions
  • Sections 5.1, 5.2, and 5.6

2
Hashing
  • Data items stored in an array of some fixed size
  • Hash table
  • Search performed using some part of the data item
  • key
  • Used for performing insertions, deletions, and
    finds in constant average time
  • Operations requiring ordering information not
    supported efficiently
  • Such as findMin, findMax

3
Hash Table Example
4
Hash Table Applications
  • Comparing search efficiency of different data
    structures
  • Vector, list O(N)
  • AVL search tree O(log(N))
  • Hash table O(1) expected time
  • Compilers to keep track of declared variables
  • Symbol tables
  • Mapping from name to id
  • On-line spelling checkers

5
Hash Functions
  • Map keys to integers (which represent table
    indices)
  • Hash(Key) Integer
  • Evenly distributed index values
  • Even if the input data is not evenly distributed
  • What happens if multiple keys mapped to the same
    integer (same position)?
  • Collision management (discussed in detail later)
  • Collisions are likely to be reduced if keys are
    evenly distributed over the hash table

6
Simple Hash Functions
  • Assumptions
  • K an unsigned 32-bit integer
  • M the number of buckets (the number of entries
    in a hash table)
  • Goal
  • If a bit is changed in K, all bits are equally
    likely to change for Hash(K)
  • So that items are evenly distributed in the hash
    table

7
A Simple Function
  • What if
  • Hash(K) K M
  • Where M is of any integer value
  • What is wrong?
  • Values of K may not be evenly distributed
  • But Hash(K) needs to be evenly distributed
  • Suppose
  • M 10,
  • K 10, 20, 30, 40
  • Then K M 0, 0, 0, 0, 0

8
Another Simple Function
  • If
  • Hash(K) K P, P prime number
  • Suppose
  • P 11
  • K 10, 20, 30, 40
  • K P 10, 9, 8, 7
  • More uniform distribution
  • So hash tables often have prime number of entries

9
A Simple Hash for Strings
  • unsigned int Hash(const string Key)
  • unsigned int hash 0
  • for (int j 0 j ! Key.size() j)
  • hash Keyj
  • return hash
  • Problem Small sized keys may not use a large
    fraction of a large hash table

9
10
Another Simple Hash Function
  • unsigned int Hash(const string Key)
  • return Key0 27Key1 729Key2
  • Problem English does not use random strings
    so, the hash values are not uniformly distributed
  • Using more characters of the key can improve the
    hash function

11
A Better Hash Function
  • unsigned int Hash(const string Key)
  • unsigned int hash 0
  • for (int j 0 j ! Key.size() j)
  • hash 37hash (Keyj-a1)
  • return hashTableSize
  • The for loop computes ?ai37n-i using Horners
    rule, where ai has the value 1 for a, 2 for
    b, etc
  • a3 37a2 372a1 373a0 37(37(37a0 a1) a2)
    a3
  • The for implicitly performs arithmetic modulo 2k,
    where k is the number of bits in an unisigned int

12
STL Hash Tables
  • STL extensions
  • hash_set
  • hash_map
  • The key type, hash function, and equality
    operator may need to be provided
  • Available in new standard as unordered set and
    map
  • lttr1/unordered_mapgt or ltunordered_mapgt
  • lttrl/unordered_setgt or ltunordered_setgt
  • Example Lec24/hashmapex.cpp
  • Reference
  • www.open-std.org/jtc1/sc22/wg21/docs/papers/2003/
    n1456.html
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