Hi, Still no sponsor.
Anyone? /Steffen 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
