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
