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Re: RFS: libfann v. 1.1.0


Still no sponsor.



On Thu, 2004-04-01 at 09:27, Steffen Nissen wrote:
> Hi,
> I am the upstream maintainer (and initial developer of) the Fast
> Artificial Neural Network Library (fann).
> http://fann.sourceforge.net/
> I have made two debian packages for the new 1.1.0 release and I would
> very much like them to be a part of the main debian archive. For this I
> will need a sponsor.
> The packages are:
> libfann1_1.1.0-1_i386.deb :
> http://prdownloads.sourceforge.net/fann/libfann1_1.1.0-1_i386.deb?download
> libfann1-dev_1.1.0-1_i386.deb :
> http://prdownloads.sourceforge.net/fann/libfann1-dev_1.1.0-1_i386.deb?download
> As far as I know the packages have been built according to all the
> debian policies, but since they are my first debian packages, then what
> do I know (lintian doesn't complain though).
> A description of the fann library follows here:
> Fast Artificial Neural Network Library (fann)
> fann is implemented in ANSI C. The library implements multilayer
> feedforward networks with support for both fully connected and sparse
> connected networks. Fann offers support for execution in fixed point
> arithmetic to allow for fast execution on systems with no floating point
> processor. To overcome the problems of integer overflow, the library
> calculates a position of the decimal point after training and guarantees
> that integer overflow can not occur with this decimal point.
> The library is designed to be fast, versatile and easy to use. Several
> benchmarks have been executed to test the performance of the library.
> The results show that the fann library is significantly faster than
> other libraries on systems without a floating point processor, while the
> performance was comparable to other highly optimized libraries on
> systems with a floating point processor.
> A user's guide accompanies the library with examples and recommendations
> on how to use the library.
> Features:
>       * Multilayer Artificial Neural Network Library in C 
>       * Backpropagation training 
>       * Easy to use (create, train and run an ANN with just three
>         function calls) 
>       * Fast (up to 150 times faster execution than other libraries) 
>       * Versatile (possible to adjust many parameters and features
>         on-the-fly) 
>       * Well documented (An easy to use reference manual and a 50+ page
>         university report describing the implementation considerations
>         etc.) 
>       * Cross-platform (configure script for linux and unix, project
>         files for MSVC++ and Borland compilers are also reported to
>         work) 
>       * Several different activation functions implemented (including
>         stepwise linear functions for that extra bit of speed) 
>       * Easy to save and load entire ANNs 
>       * Several easy to use examples (simple train example and simple
>         test example) 
>       * Can use both floating point and fixed point numbers (actually
>         both float, double and int are available) 
>       * Cache optimized (for that extra bit of speed) 
>       * Open source (licenced under LGPL) 
>       * Framework for easy handling of training data sets 
>       * PHP Bindings
>       * Python Bindings
>       * RPM package
>       * Debian package
> Regards,
>  Steffen

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