Hash Function

  • LintCode: No.128-Hash Function
  • Problem:
    In data structure Hash, hash function is used to convert a string(or any other type) into an integer smaller than hash size and bigger or equal to zero. 
    The objective of designing a hash function is to "hash" the key as unreasonable as possible.  
    A good hash function can avoid collision as less as possible.   
    A widely used hash function algorithm is using a magic number 33,   
    consider any string as a 33 based big integer like follow:
    hashcode("abcd") = (ascii(a) * 33^3 + ascii(b) * 33^2 + ascii(c) *33 + ascii(d)) % HASH_SIZE   
                     = (97* 333 + 98 * 332 + 99 * 33 +100) % HASH_SIZE
                     = 3595978 % HASH_SIZE
    here HASH_SIZE is the capacity of the hash table 
    (you can assume a hash table is like an array with index 0 ~ HASH_SIZE-1).
    Given a string as a key and the size of hash table, return the hash value of this key.
    
  • Example:
    For key="abcd" and size=100, return 78
    
  • Clarification:
    For this problem, you are not necessary to design your own hash algorithm or consider any   
    collision issue, you just need to implement the algorithm as described.
    
  • Analysis:
    The formula in Problem is   
      hashcode("abcd") = (ascii(a) * 33^3 + ascii(b) * 33^2 + ascii(c) *33 + ascii(d)) % HASH_SIZE  
    We can make it equal to   
      hashcode("abcd") = (ascii(a) * 33^3 + ascii(b) * 33^2 + ascii(c) *33 + ascii(d) * 1) % HASH_SIZE
                       = (ascii(a) * 33^3 + ascii(b) * 33^2 + ascii(c) *33 + ascii(d) * 33^0) % HASH_SIZE
    Asssume that a, b, c are integers,  
    (a + b + c) % d = a % d + b % d + c % d.
    Besides, needed to concern overflow issues.
    
  • Java:

      public class Solution {
          public int hashCode(char[] key,int HASH_SIZE) {
              long res = 0;
              for (int i = 0; i < key.length; i++){
                  res = 33 * res + (int)key[i];
                  res = res % HASH_SIZE;
              }
              return (int)res;
          }
      }
    
  • Time Complexity:

    • O(n), n is key length

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