HasGP: A Haskell library for inference using Gaussian processes

[ ai, classification, datamining, gpl, library, statistics ] [ Propose Tags ] [ Report a vulnerability ]

A Haskell library implementing algorithms for supervised learning, roughly corresponding to chapters 1 to 5 of "Gaussian Processes for Machine Learning" by Carl Rasmussen and Christopher Williams, The MIT Press 2006. In particular, algorithms are provides for regression and for two-class classification using either the Laplace or EP approximation.

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HasGP-0.1.tar.gz [browse] (Cabal source package)
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SeanHolden

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Versions [RSS]	0.1
Dependencies	base (>=4 && <5), haskell98 (>=1 && <2), hmatrix (>=0.12 && <0.13), hmatrix-special (>=0.1 && <0.2), mtl (>=2 && <3), parsec (>=3 && <4), random (>=1 && <2) [details]
Tested with	ghc ==7.0.3
License	GPL-3.0-only
Copyright	Copyright (C) 2011 Sean Holden
Author	Sean B. Holden
Maintainer	sbh11@cl.cam.ac.uk
Category	AI, Classification, Datamining, Statistics
Home page	http://www.cl.cam.ac.uk/~sbh11/HasGP
Bug tracker	sbh11@cl.cam.ac.uk
Uploaded	by SeanHolden at 2011-10-26T15:35:53Z
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Reverse Dependencies	1 direct, 0 indirect [details]
Downloads	1430 total (1 in the last 30 days)
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Readme for HasGP-0.1

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The HasGP package for Gaussian process inference in Haskell
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Copyright (C) 2011 Sean Holden sbh11@cl.cam.ac.uk

For a detailed description of how to install, use and modify this code 
please download the User Manual from the project site at:

http://www.cl.cam.ac.uk/~sbh11/HasGP/