SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.
PEDSIM is a microscopic pedestrian crowd simulation system. The PEDSIM library allows you to use pedestrian dynamics in your own software. Based on pure C++/STL without additional packages, it runs on virtually every operating system. The PEDSIM Demo Application (Qt) gives you a quick overview of the capabilities, and is a starting point for your own experiments. PEDSIM is suitable for use in crowd simulations (e.g. indoor evacuation simulation, large scale outdoor simulations), where one is interested in output like pedestrian density or evacuation time. The quality of the individual agent's trajectory is high, PEDSIM can be used for creating massive pedestrian crowds in movies. Since libpedsim is easy to use and extend, it is a good starting point for science projects.
The Graphical Models Toolkit (GMTK) is a toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). It can be used for speech and language processing, bioinformatics, activity recognition, and any time series application. It features exact and approximate inference, many built-in factors including dense, sparse, and deterministic conditional probability tables, native support for ARPA backoff-based factors and factored language models, parameter sharing, gamma and beta distributions, dense and sparse Gaussian factors, heterogeneous mixtures, deep neural network factors, and time-inhomogeneous trellis factors, arbitrary order embedded Markov chains, a GUI graph viewer, and much more.
fuzzylite is a fuzzy logic control library. Its goal is to allow you to easily create fuzzy logic controllers in a few steps utilizing object-oriented programming without requiring any third-party libraries. qtfuzzylite is a Qt-based GUI for fuzzylite. Its goal is to allow you to visually design your fuzzylite controllers and interact with them in real time.
MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.
UniModeling is a big data analytics tool for unified modeling and reasoning in outdoor and indoor spaces. It supports the construction of unified graph models of outdoor and indoor spaces and RFID deployments in these spaces. It enables probabilistic incorporation of RFID data, facilitating the tracking of moving objects and enables the search for them to be optimized. Also included are three reasoning applications that pertain to the positioning of RFID readers in outdoor and indoor spaces and the points of potential traffic (over)load in these spaces.
Thinknowlogy is grammar-based software, designed to utilize the Natural Laws of Intelligence in grammar, in order to create intelligence through natural language in software. This is demonstrated by programming in natural language, reasoning in natural language and drawing conclusions (more detailed than scientific solutions), making assumptions (with self-adjusting level of uncertainty), asking questions (about gaps in the knowledge), and detecting conflicts in the knowledge. It builds semantics autonomously (with no vocabularies or words lists), detecting some cases of semantic ambiguity. It is multi-grammar, proving that Natural Laws of Intelligence are universal.
Aseba is an event-based architecture for distributed control of mobile robots. It targets integrated multi-processor robots or groups of single-processor units, real or simulated. The core of aseba is a lightweight virtual machine tiny enough to run even on microcontrollers. Robots are programmed in a user-friendly scripting language using a cozy integrated development environment.