countmemaybe

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A set of distinct value estimators that give probabilistic bounds on a sets cardinality

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# Count me Maybe

[![Build Status](https://travis-ci.org/mynameisfiber/countmemaybe.png)](https://travis-ci.org/mynameisfiber/countmemaybe)

> Hey I just met you,
> This is crazy,
> But here's a number,
> So count me, maybe?

`countmemaybe` is a set of probabilistic data structures that can estimate the
cardinality of a set and also support unions.  The base set of operations are:

* Cardinality
* Union
* Cardinality of the Union
* Cardinality of the Intersection
* Jaccard Index

We also implement a probabilistic quantile data-structure which creates a
synopsis of the data in order to satisfy queries regarding the quantiles of the
stream.

Insertions are quite quick!  For both hyperloglog and kminvalues there is an
average insertion time of 4us on a 1.8GHz i7 Macbook Air.

Below are some benchmarks of error rates.  The set used for estimation are
160000 random 16bit integers and the benchmark was generated using the
`test_dve.py` script.  For some as of yet unknown reason, KMV with a 64bit hash
function performs _worse_ than a 32bit hash function.  This is counterintuitive
and requires more investigation.  Furthermore, exact mean relative error rates
vary from run to run by ~1%

![Benchmarks](https://raw.github.com/mynameisfiber/countmemaybe/master/countmemaybe/test_dve.png "Benchmarks of hyper loglog vs kmin values")

countmemaybe

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