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.
Fresh Memory is an application that helps you to learn large amounts of any material with Spaced Repetition method. The most important subject is learning foreign words, but Fresh Memory can also e used to learn anything else. Other examples are country's capitals and flags, chemical elements, mathematical formulas, and technical terms. The learning data is stored as flash cards and dictionaries: sets of cards. The flash cards may have several fields, and the user controls what combination of fields to learn. The flash cards can have formatted text and images. The look of flash cards and studying parameters are can be flexibly adjusted.
xBaK is a fan-made remake of the classic Sierra computer RPG "Betrayal at Krondor". It is a game engine that uses the data files that came with the original game by Sierra Online. You must already have your own copy, since the required data files are not distributed with xBaK. The game is still under development. The intro, the option dialogs, and the main game dialog are finished, but the game is not yet playable. Several tools for examining the contents of the data files are also available.
ArduinoPulseGenerator is a simple program for generating pulse sequences using an Arduino. There is an associated GUI that runs on the local computer, or you can simply connect to the Arduino with a serial console (9600 baud) and send it commands. This code has been tested on the ArduinoMega 2560 (timing accuracy ~ ±200 μs) and Arduino Due (timing accuracy ~ ±35 μs); it may work on other Arduino boards.