Python/data_structures/hashing/double_hash.py

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#!/usr/bin/env python3
"""
Double hashing is a collision resolving technique in Open Addressed Hash tables.
Double hashing uses the idea of applying a second hash function to key when a collision
occurs. The advantage of Double hashing is that it is one of the best form of probing,
producing a uniform distribution of records throughout a hash table. This technique
does not yield any clusters. It is one of effective method for resolving collisions.
Double hashing can be done using: (hash1(key) + i * hash2(key)) % TABLE_SIZE
Where hash1() and hash2() are hash functions and TABLE_SIZE is size of hash table.
Reference: https://en.wikipedia.org/wiki/Double_hashing
"""
from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime
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class DoubleHash(HashTable):
"""
Hash Table example with open addressing and Double Hash
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"""
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def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __hash_function_2(self, value, data):
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next_prime_gt = (
next_prime(value % self.size_table)
if not is_prime(value % self.size_table)
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else value % self.size_table
) # gt = bigger than
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return next_prime_gt - (data % next_prime_gt)
def __hash_double_function(self, key, data, increment):
return (increment * self.__hash_function_2(key, data)) % self.size_table
def _collision_resolution(self, key, data=None):
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i = 1
new_key = self.hash_function(data)
while self.values[new_key] is not None and self.values[new_key] != key:
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new_key = (
self.__hash_double_function(key, data, i)
if self.balanced_factor() >= self.lim_charge
else None
)
if new_key is None:
break
else:
i += 1
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return new_key