hfann: Haskell binding to the FANN library

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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 [RSS] 0.1, 0.2, 0.3, 0.4, 0.4.1, 0.4.2
Change log CHANGELOG
Dependencies base (>=3 && <5) [details]
License BSD-3-Clause
Copyright (c) Olivier Boudry 2008, David Himmelstrup 2011
Author David Himmelstrup, Olivier Boudry
Maintainer Lemmih <lemmih@gmail.com>
Category AI
Uploaded by DavidHimmelstrup at 2013-07-30T19:14:23Z
Reverse Dependencies 1 direct, 0 indirect [details]
Executables HFANNTest
Downloads 4747 total (11 in the last 30 days)
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Status Docs uploaded by user
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Readme for hfann-0.4.2

[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

Note: if you have haddock < 2.0 skip the haddock step. src/HFANN/Data.hsc is
not parsed properly using earlier versions of haddock.

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: