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https://github.com/mikolalysenko/count-min-sketch
Count-Min Sketch Data Structure
https://github.com/mikolalysenko/count-min-sketch
Last synced: about 2 months ago
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Count-Min Sketch Data Structure
- Host: GitHub
- URL: https://github.com/mikolalysenko/count-min-sketch
- Owner: mikolalysenko
- License: mit
- Created: 2013-06-20T20:19:03.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2013-07-01T15:34:29.000Z (over 11 years ago)
- Last Synced: 2024-10-20T14:27:38.850Z (2 months ago)
- Language: JavaScript
- Size: 135 KB
- Stars: 25
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
count-min-sketch
================
An implementation of Coromode and Muthukrishnan's [Count-Min sketch](http://en.wikipedia.org/wiki/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:
* Count-Min sketch: https://sites.google.com/site/countminsketch/
* next big thing syndrome: http://lkozma.net/blog/sketching-data-structures/
* G. Cormode, S. Muthukrishnan. ["Approximating Data with the Count-Min Data Structure"](http://dimacs.rutgers.edu/~graham/pubs/papers/cmsoft.pdf). IEEE Trans. on Software (2012)
## Example
```javascript
//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
```
## Install
npm install count-min-sketch
## API
`module.exports` is a constructor for the data structure, and you import it like so:
```javascript
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 hashed
+ `hashes` 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 it
### `sketch.query(key)`
Returns the frequency of the item `key`
* `key` is the item whose frequency we are counting
**Returns** An estimate of the frequency of `key`
### `sketch.toJSON()`
Returns a serializable JSON representation of the table.
### `sketch.fromJSON(obj)`
Converts a JSON object into a deserialized sketch. The hash function is reused from the current sketch.
**Note** In order for this to be successful both the serialized hash table and the current hash table have to have the same hash function.
## Credits
(c) 2013 Mikola Lysenko. MIT License