hfann: Haskell binding to the FANN library

[ ai, bsd3, deprecated, library ] [ Propose Tags ]

hfann is a Haskell binding to the Fast Artificial Neural Network (FANN) library http://leenissen.dk/fann/. It provides functions to easily create, train, test and use Artificial Neural Networks.

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Versions 0.1, 0.2, 0.3, 0.4, 0.4.1, 0.4.2
Change log CHANGELOG
Dependencies base [details]
License BSD-3-Clause
Copyright (c) Olivier Boudry 2008
Author Olivier Boudry
Maintainer Olivier Boudry <olivier.boudry@gmail.com>
Category AI
Home page http://code.haskell.org/~oboudry/hfann/
Uploaded by OlivierBoudry at Mon Jun 9 17:45:51 UTC 2008
Distributions NixOS:0.4.2
Executables HFANNTest
Downloads 2369 total (14 in the last 30 days)
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Status Docs not available [build log]
All reported builds failed as of 2017-01-03 [all 7 reports]
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    • HFANN.Base
    • HFANN.Data
    • HFANN.IO
    • HFANN.Train


Maintainer's Corner

For package maintainers and hackage trustees

Readme for hfann-0.1

[back to package description]
HFANN: A Haskell interface to the FANN library (http://leenissen.dk/fann/)
Copyright: 2008, Olivier Boudry
Licence: BSD3

This package was developped and tested with GHC-6.8.2 on the Win32 platform and the FANN library version 2.0.

FANN (C-library) installation:
First of all you need to install the FANN library.

Download version 2.0 of the library from:

and build it according to the instruction found on:


On windows download the source version and build it using MinGW instead of
using the provided Visual C++ 6.0 Project File. Building with Visual C++ would
create DLLs and require the 'stdcall' calling convention which would not work.

Build and installation should be pretty straightforward:

    make install

HFANN (Haskell library) installation:
Edit the hfann.cabal file and adapt the 'include-dirs' and 'extra-lib-dirs'
parameters to your installation. On non windows platform just blanking those
two params should work as long as the library and includes are installed in
the lib and include places.

Build and install using Cabal:

    runghc Setup.lhs configure
    runghc Setup.lhs build
    runghc Setup.lhs haddock
    runghc Setup.lhs install

Using the library
You will find an example for training and using and artificial neural network
for the 'xor' function in the 'examples/xor' directory.

Complaints, feature requests and bug reports to: