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  2. Hash function - Wikipedia

    en.wikipedia.org/wiki/Hash_function

    The Python hash is still a valid ... if the input is 123 456 789 and the hash table size 10 000, ... or other function to reduce the word value to an index the size ...

  3. Hash table - Wikipedia

    en.wikipedia.org/wiki/Hash_table

    In a well-dimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation. [3] [4] [5] Hashing is an example of a space-time tradeoff.

  4. Coalesced hashing - Wikipedia

    en.wikipedia.org/wiki/Coalesced_hashing

    An important optimization, to reduce the effect of coalescing, is to restrict the address space of the hash function to only a subset of the table. For example, if the table has size M with buckets numbered from 0 to M − 1, we can restrict the address space so that the hash function only assigns addresses to the first N locations in the table.

  5. Hashed array tree - Wikipedia

    en.wikipedia.org/wiki/Hashed_array_tree

    All leaf arrays are the same size as the top-level directory. This structure superficially resembles a hash table with array-based collision chains, which is the basis for the name hashed array tree. A full hashed array tree can hold m 2 elements, where m is the size of the top-level directory. [1]

  6. Double hashing - Wikipedia

    en.wikipedia.org/wiki/Double_hashing

    Pair-wise independence of the hash functions suffices. Like all other forms of open addressing, double hashing becomes linear as the hash table approaches maximum capacity. The usual heuristic is to limit the table loading to 75% of capacity. Eventually, rehashing to a larger size will be necessary, as with all other open addressing schemes.

  7. Locality-sensitive hashing - Wikipedia

    en.wikipedia.org/wiki/Locality-sensitive_hashing

    In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.

  8. Zobrist hashing - Wikipedia

    en.wikipedia.org/wiki/Zobrist_hashing

    Zobrist hashing (also referred to as Zobrist keys or Zobrist signatures [1]) is a hash function construction used in computer programs that play abstract board games, such as chess and Go, to implement transposition tables, a special kind of hash table that is indexed by a board position and used to avoid analyzing the same position more than once.

  9. Bloom filter - Wikipedia

    en.wikipedia.org/wiki/Bloom_filter

    The scalability issue does not occur in this data structure. Once the designed capacity is exceeded, the keys could be reinserted in a new hash table of double size. The space efficient variant by Putze, Sanders & Singler (2007) could also be used to implement counting filters by supporting insertions and deletions.