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- UNMAINTAINED *
- Version history
Provides a simple Bloom Filter
Bloom Filter Object
make-bloom-filter[procedure] (make-bloom-filter M MESSAGE-DIGEST-PRIMITIVES [K]) => bloom-filter
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.[procedure] (make-bloom-filter P N MESSAGE-DIGEST-PRIMITIVES) => bloom-filter
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[procedure] (bloom-filter-n BLOOM-FILTER) => fixnum
The current population - the number of objects added to the filter.
Not the population capacity.
bloom-filter-m[procedure] (bloom-filter-m BLOOM-FILTER) => fixnum
The number of bits of discrimination.
bloom-filter-k[procedure] (bloom-filter-k BLOOM-FILTER) => fixnum
The number of hashes. (See above.)
bloom-filter-p-false-positive[procedure] (bloom-filter-p-false-positive BLOOM-FILTER [N]) => number
The probability of a false-positive for the population capacity N, default is the current population, bloom-filter-n.
bloom-filter-set![procedure] (bloom-filter-set! BLOOM-FILTER OBJECT)
Add the specified OBJECT to the indicated BLOOM-FILTER.
bloom-filter-exists?[procedure] (bloom-filter-exists? BLOOM-FILTER OBJECT) => boolean
Is the specified OBJECT in the indicated BLOOM-FILTER.
actual-k[procedure] (actual-k MESSAGE-DIGEST-PRIMITIVES) => fixnum
Calculates the actual number of hashes for the MESSAGE-DIGEST-PRIMITIVES.
optimum-size[procedure] (optimum-size P N) => (fixnum fixnum)
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[procedure] (desired-m P N [K]) => (fixnum fixnum number)
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[procedure] (optimum-k N M) => fixnum
Optimal count of hashes for the given population size N and M bits of discrimination.
optimum-m[procedure] (optimum-m K N) => fixnum
Optimal count of bits of discrimination for the given population size N and K number of hashes.
p-false-positive[procedure] (p-false-positive K N M) => number
What is the probability of false positives for the population size N assuming K hashes and M bits of discrimination.
p-random-one-bit[procedure] (p-random-one-bit K N M) => number
Calculates the probablility of a random set bit for the given number of hash functions K, population size N, and bits of discrimination M.
- * 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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