|Maintainer||Bryan O'Sullivan <firstname.lastname@example.org>|
|An easy-to-use Bloom filter interface.
|Easy creation and querying
|An immutable Bloom filter, suitable for querying from pure code.
|:: Hashable a|
|=> Double||desired false positive rate (0 < e < 1)
|-> [a]||values to populate with
|-> Bloom a|
|Create a Bloom filter with the given false positive rate and
members. The hash functions used are computed by the cheapHashes
function from the Data.BloomFilter.Hash module.
|Query an immutable Bloom filter for membership. If the value is
present, return True. If the value is not present, there is
still some possibility that True will be returned.
|Return the size of an immutable Bloom filter, in bits.
|Example: a spell checker
This example reads a dictionary file containing one word per line,
constructs a Bloom filter with a 1% false positive rate, and
spellchecks its standard input. Like the Unix spell command, it
prints each word that it does not recognize.
import Data.BloomFilter.Easy (easyList, elemB)
main = do
filt <- (easyList 0.01 . words) `fmap` readFile "usrsharedictwords"
let check word | elemB word filt = ""
| otherwise = word ++ "\n"
interact (concat . map check . lines)
|Useful defaults for creation
|:: Int||expected maximum capacity
|-> Double||desired false positive rate (0 < e < 1)
|-> (Int, Int)|
Suggest a good combination of filter size and number of hash
functions for a Bloom filter, based on its expected maximum
capacity and a desired false positive rate.
The false positive rate is the rate at which queries against the
filter should return True when an element is not actually
present. It should be a fraction between 0 and 1, so a 1% false
positive rate is represented by 0.01.
|Produced by Haddock version 2.4.2|