lazar (Lazy-Structure-Activity Relationships) is a tool for the prediction of toxic activities of chemical structures. lazar derives predictions from databases with experimental toxicity data. It searches in these databases for compounds with similar structures and calculates the prediction from their measured activities.
ESKit is a portable C library that provides implementations of some self-adaptive evolution strategies. It features a simple API, comprehensive documentation, three state of the art self-adaptive evolution strategies (Isotropic CSA-ES, CMA-ES, and Separable CMA-ES), and can optionaly uses LAPACK. The implementation strictly follows the published papers introducing those evolution strategies and performs as in the published papers. A basic benchmark program is provided.
FP-Growth-Tiny introduces a space optimization to the FP- Growth algorithm for mining frequent itemsets in a transaction database. The code contains libraries, CLI frontends and a few other tools suited for this task. Frequent itemset or frequency mining is the core of popular mining methods such as association rule mining and sequence mining.
N-genes is a Java framework and application for both genetic programming and genetic algorithms. The goal of this software is to offer a flexible system able to speed-up the implementation of research ideas. Complex behaviors like variable size populations or self-adaptive genetic operators can be implemented easily and quickly.
CharGer is a conceptual graph editor intended to support research projects and education. It currently is primarily an editor to create visual displays of graphs. It is deliberately and explicitly a research tool meant for conceptual graph researchers to explore implementation issues in conceptual graph interfaces. Using the software will require some familiarity with conceptual graphs, including knowing about concepts and relations, type hierarchies, and type/referent pairs. Knowing about actors will also be very helpful.
The Kernel-Machine Library is a C++ library to promote the use and progress of kernel machines. It is both for academic use and for developing real world applications. The Kernel-Machine Library draws heavily from features of modern C++ such as template meta-programming to achieve high performance while at the same time offering a comfortable interface. It enables compile-time selection of specialized algorithms on the basis of data types: for example, the specific case of a SVM in combination with a linear kernel can be computed by a specialized efficient algorithm.