- UNMAINTAINED *
- Version history
Provides a simple Bloom Filter
Bloom Filter Object
- make-bloom-filter M MESSAGE-DIGEST-PRIMITIVES #!optional Kprocedure
Returns a bloom-filter object with M bits of discrimination and a set of hash functions built from the supplied MESSAGE-DIGEST-PRIMITIVES, a list of message-digest-primitive objects.
The number of hashes, K, is not necessarily the same as the number of message-digests. A hash (here) is defined as an unsigned 32 bit integer. Most message-digests return more 32 bits of hash. The actual length of the hash is divided into 32 bit blocks to get the individual hashes.
The argument K will restrict the actual number of hashes to the "first" K, no matter how many more the supplied message-digests create. First in the order of MESSAGE-DIGEST-PRIMITIVES.
- make-bloom-filter P N MESSAGE-DIGEST-PRIMITIVESprocedure
Returns a bloom-filter object with M and K values chosen for the given population capacity N and probablity of false-positives P.
Selecting the optimal set of message-digests is beyond the scope of make-bloom-filter.
- bloom-filter-n BLOOM-FILTERprocedure
The current population - the number of objects added to the filter.
Not the population capacity.
- bloom-filter-m BLOOM-FILTERprocedure
The number of bits of discrimination.
- bloom-filter-k BLOOM-FILTERprocedure
The number of hashes. (See above.)
- bloom-filter-p-false-positive BLOOM-FILTER #!optional Nprocedure
The probability of a false-positive for the population capacity N, default is the current population, bloom-filter-n.
- bloom-filter-set! BLOOM-FILTER OBJECTprocedure
Add the specified OBJECT to the indicated BLOOM-FILTER.
- bloom-filter-exists? BLOOM-FILTER OBJECTprocedure
Is the specified OBJECT in the indicated BLOOM-FILTER.
- actual-k MESSAGE-DIGEST-PRIMITIVESprocedure
Calculates the actual number of hashes for the MESSAGE-DIGEST-PRIMITIVES.
- optimum-size P Nprocedure
Returns 2 values, an optimal M, bits of discrimination, and K, number of hashes, for the given population size N and probability of false-positives P.
- desired-m P N #!optional Kprocedure
Calculates a near-optimal number of bits of discrimination to meet the desired probability of false positives P, with the given population size N and number of hashes K. When the K parameter is missing optimum-k is used to calculate a value.
A multi-valued return of the calculated M, K, and P values. The calculated probability may be lower than the desired. The calculated M value will always be a fixnum.
- optimum-k N Mprocedure
Optimal count of hashes for the given population size N and M bits of discrimination.
- optimum-m K Nprocedure
Optimal count of bits of discrimination for the given population size N and K number of hashes.
- p-false-positive K N Mprocedure
What is the probability of false positives for the population size N assuming K hashes and M bits of discrimination.
- p-random-one-bit K N Mprocedure
Calculates the probablility of a random set bit for the given number of hash functions K, population size N, and bits of discrimination M.
- One more time.
- * UNMAINTAINED *
- Added optimum-size & make-bloom-filter variant. Calculations take the ceiling.
- A little faster (10%). Better fixnum overflow detection.
- Protect desired-m from fixnum representation overflow.
- "Fix" for call of non-procedure - maybe. (Nope.)
- A little faster (25%).
- From the Chicken 3 version, with some changes. (No message-digest registry, for example.)
Copyright (C) 2010 Kon Lovett. All rights reserved.
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