Re: RFS: libfann v. 1.1.0
Still no sponsor.
On Thu, 2004-04-01 at 09:27, Steffen Nissen wrote:
> I am the upstream maintainer (and initial developer of) the Fast
> Artificial Neural Network Library (fann).
> 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 :
> libfann1-dev_1.1.0-1_i386.deb :
> 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.
> * 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
> * Well documented (An easy to use reference manual and a 50+ page
> university report describing the implementation considerations
> * Cross-platform (configure script for linux and unix, project
> files for MSVC++ and Borland compilers are also reported to
> * 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