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Provides a simple Bloom Filter
Bloom Filter Object
make-bloom-filter[procedure] (make-bloom-filter M MESSAGE-DIGEST-PRIMITIVES [K])
Returns a bloom-filter object with M bits of discrimination and a set of hash functions built from the supplied MESSAGE-DIGEST-PRIMITIVES.
The number of hash functions, k, is not necessarily the same as the number of message-digests. A hash function is defined as returning 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 hash functions.
The argument K will restrict the actual number of hash functions to the "first" k, no matter how many more the supplied message-digests create. First in the order of MESSAGE-DIGEST-PRIMITIVES.
Selecting the optimal set of message-digests is beyond the scope of make-bloom-filter.
bloom-filter-n[procedure] (bloom-filter-n BLOOM-FILTER)
The current population - the number of objects added to the filter.
bloom-filter-m[procedure] (bloom-filter-m BLOOM-FILTER)
The number of bits of discrimination.
bloom-filter-k[procedure] (bloom-filter-k BLOOM-FILTER)
The number of hash functions. (See above.)
bloom-filter-p-false-positive[procedure] (bloom-filter-p-false-positive BLOOM-FILTER [N])
The probability of false positives for the given population size. The current population is assumed.
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)
Is the specified OBJECT in the indicated BLOOM-FILTER.
optimum-k[procedure] (optimum-k N M)
Optimal count of hash functions for the given population size N and M bits of discrimination.
optimum-m[procedure] (optimum-m K N)
Optimal count of bits of discrimination for the given population size N and K number of hash functions.
p-false-positive[procedure] (p-false-positive K N M)
What is the probability of false positives for the population size N assuming K hash functions and M bits of discrimination.
desired-m[procedure] (desired-m P N [K])
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 hash functions 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.
actual-k[procedure] (actual-k MESSAGE-DIGEST-PRIMITIVES)
Calculates the actual number of hash functions for the MESSAGE-DIGEST-PRIMITIVES.
p-random-one-bit[procedure] (p-random-one-bit K N M)
Calculates the probablility of a random set bit for the given number of hash functions K, population size N, and bits of discrimination M.
[[http://www.cs.wisc.edu/~cao/papers/summary-cache/node8.html|Nice exposition of Bloom Filter False Positive Probability.)
- From the Chicken 3 version, with some minor changes. (No message-digest registry, for example.)
Copyright (C) 2010 Kon Lovett. All rights reserved.
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