An implementation of Coromode and Muthukrishnan's Count-Min sketch data structure for JavaScript. The count-min sketch is basically a high powered generalization of the bloom filter. While a bloom filter gives an efficient way to approximate membership of a set, a count-min sketch can give approximate data about the relative frequency of items in the set.
For more information see the following references:
//Import library
var createCountMinSketch = require("count-min-sketch")
//Create data structure
var sketch = createCountMinSketch()
//Increment counters
sketch.update("foo", 1)
sketch.update(1515, 104)
//Query results
console.log(sketch.query(1515)) //Prints 104
console.log(sketch.query("foo")) //Prints 1
npm install count-min-sketch
module.exports is a constructor for the data structure, and you import it like so:
var createCountMinSketch = require("count-min-sketch")
var sketch = createCountMinSketch(epsilon, probError[, hashFunc])Creates a count-min sketch data structure.
epsilon is the accuracy of the data structure (ie the size of bins that we are computing frequencies of)
probError is the probability of incorrectly computing a value
hashFunc(key, hashes) is a hash function for the data structure. (optional) the parameters to this function are as follows:
key is the item that is being hashedhashes is an array of k hashes which are required to be pairwise independent.Returns A count-min sketch data structure
sketch.update(key, v)Adds v to key
key is the item in the table to increment.v is the amount to add to itsketch.query(key)Returns the frequency of the item key
key is the item whose frequency we are countingReturns An estimate of the frequency of key
(c) 2013 Mikola Lysenko. MIT License