Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. It is written in C++.
Some small command line programs and a file parser for Concept Explorer (conexp) written in C++. Currently features include: Converters from concept explorer into PDF, PostScript, SVG and PovRay, a modified 3D Freese layout.
A utility to extract meta-information (properties/comments) out of various file-types; e.g. HTML, PDF, RTF & various Office documents; OGG/MP3 files and JPEG/PNG/GIF images, which can be presented in various output formats (HTML, XML, LaTeX & plain t