Editing Context mixing (section)

=== List of Context Mixing Compressors ===

All versions below use logistic mixing unless otherwise indicated.

* All [[PAQ]] versions (Matt Mahoney, Serge Osnach, Alexander Ratushnyak, Przemysław Skibiński, Jan Ondrus, and others) [http://mattmahoney.net/dc/paq.html]. PAQAR and versions prior to PAQ7 used linear mixing. Later versions used logistic mixing.
* All LPAQ versions (Matt Mahoney, Alexander Ratushnyak) [http://mattmahoney.net/dc/#lpaq].
* [[ZPAQ]] (Matt Mahoney) [http://mattmahoney.net/dc/#zpaq].
* WinRK 3.0.3 (Malcolm Taylor) in maximum compression PWCM mode [http://www.msoftware.co.nz/]. Version 3.0.2 was based on linear mixing.
* NanoZip (Sami Runsas) in maximum compression mode (option -cc) [https://web.archive.org/web/20150717170923/http://nanozip.net/].
* xwrt 3.2 (Przemysław Skibiński) in maximum compression mode (options -i10 through -i14) [http://sourceforge.net/projects/xwrt/files/] as a back end to a dictionary encoder.
* cmm1 through cmm4, M1, and M1X2 (Christopher Mattern) use a small number of contexts for high speed. M1 and M1X2 use a [[genetic algorithm]] to select two [[bit mask]]ed contexts in a separate optimization pass.
* ccm (Christian Martelock).
* bit (Osman Turan) [https://web.archive.org/web/20110725042648/http://www.osmanturan.com/bit07.zip].
* pimple, pimple2, tc, and px (Ilia Muraviev) [https://web.archive.org/web/20100921013136/http://lovepimple.110mb.com/].
* enc (Serge Osnach) tries several methods based on [[Prediction by Partial Matching|PPM]] and (linear) context mixing and chooses the best one. [http://www.compression.ru/so/]
* fpaq2 (Nania Francesco Antonio) using fixed weight averaging for high speed.
* [https://github.com/byronknoll/cmix cmix] (Byron Knoll) mixes many models, and is currently ranked first in the Large Text Compression benchmark,<ref name="largetext">{{cite web|url=http://mattmahoney.net/dc/text.html|author=Matt Mahoney|title=Large Text Compression Benchmark|date=2015-09-25|accessdate=2015-11-04}}</ref> as well as the [[Silesia corpus]]<ref name="silesia">{{cite web|url=http://mattmahoney.net/dc/silesia.html|author=Matt Mahoney|title=Silesia Open Source Compression Benchmark|date=2015-09-23|accessdate=2015-11-04}}</ref> and has surpassed the winning entry of the [[Hutter Prize]] although it is not eligible due to using too much memory.