Data.BloomFilter.Easy

:: 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.

elemB :: a -> Bloom a -> Bool

Source

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.

notElemB :: a -> Bloom a -> Bool

Source

Query an immutable Bloom filter for non-membership. If the value is present, return False. If the value is not present, there is still some possibility that False will be returned.

lengthB :: Bloom a -> Int

Source

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

suggestSizing

Source

:: 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