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.
ca-ga is a toy artificial life simulation that uses genetic algorithms on large cellular automata. It uses simple but easily extended DNA that is 8k long by default, though you can take the size out to anything you have time to evolve. It sits under each cell of a 128x128 board and orders operations to transfer energy in the hopes of achieving a kill and breed. The simulation features a mutating fitness function, emergent sex, and a proof of concept real world fitness function. After enough generations, the cells or genes could achieve collectivism and organismhood, coordinating the values of the hotspots that determine board temperature in order to maintain a desired equilibrium. But maybe not. If you work in a fitness function, an optimizing problem solver results.
MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms. It addresses the two most common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars), and item prediction from implicit feedback (e.g. from clicks or purchase actions). It contains dozens of recommender engines, including state-of-the-art matrix factorization methods. It also supports real-time updates to the recommender engines, storing engines to disk and reloading them again, and several evaluation measures to compare the accuracy of different recommender system methods. Three command-line programs that offer most of the functionality contained in the library are included.
Into is a cross-platform machine intelligence application framework written in C++. Into provides a different, fast way to build high-performance applications for image analysis, machine vision, pattern recognition, and artificial intelligence. It features a layered API and more than 20 fully interoperable plug-in modules for accessing image and data sources, powerful feature extractors, classifiers, neural networks, and much more. It also provides Ydin, an innovative execution engine that makes it easy to create dynamic programs that automatically run in parallel, enabling you to create more with less hassle, less code, and less time. Into uses Qt to let you create beautiful user interfaces for your applications with ease.
Conquest is a simultaneously-executed turn-based tactical combat game placed in a dark and distant future. Play the role of a futuristic commander. Divide your armies and conquer the world. Position satellites to reveal your opponents. Launch missiles to annihilate big armies, but watch out for incoming drop pods behind your back. Standing in your path to victory are other commanders like yourself. Fight them off one by one and prove you are the greatest of the great. The combination of fast gameplay and randomly generated maps equals intense, restless nights of battles for cities. Drag and drop your way to victory.
Algraeph is a tool for manual alignment of linguistic graphs, such as phrase structure trees or dependency structures, where each node corresponds to a subsequence of the analyzed input sentence. It allows you to express the similarity between two graphs by aligning their nodes and attaching relation labels to these alignments. Graphs are read from one or more graphbanks (or treebanks) in the GraphML or Alpino formats. Alignment relations are user-defined and are stored in a simple XML format, which can be used for further processing. The resulting parallel graph corpus is a useful data set for many tasks in computational linguistics and natural language processing.
METSlib is an object-oriented metaheuristics framework in C++ designed to make implementing or adapting models easy. The model is modular: all the implemented search algorithms can be applied to the same model. METSlib implements the basics of some metaheuristics algorithms, such as Random Restart Local Search, Variable Neighborhood Search, Iterated Local Search, Simulated Annealing, and Tabu Search. For each algorithm, you must implement an objective function, a neighborhood (move manager), and some moves. Tabu Search is one of the fastest ways to generate near-optimal solutions to a wide range of hard combinatorial optimization problems.