Open Source Algorithms - Page 28

This project aims to test Distributed Artifitial Intelligence in a urban traffic light control. GlobalSyncLocal is used to interact with the SUMO simulator through the TraSMAPI.

The game of Cows-n-Bulls is back ! Gnu-Moo aims to provide a command line as well as GTK+ front-end for the user to play the cows-n-bulls game. Multi-game sessions and relevant session stats are provided, which can be useful to test heuristic algorithms.

GoDS, which means "Go Data Structures", is an implementation of various data structures and algorithms in Go. It provides a wide variety of containers (Sets, Lists, Stacks, Maps, Trees), sets (HashSet, TreeSet, LinkedHashSet), lists (ArrayList, SinglyLinkedList, DoublyLinkedList), stacks (LinkedListStack, ArrayStack), maps (HashMap, TreeMap, HashBidiMap, TreeBidiMap, LinkedHashMap), trees (RedBlackTree, AVLTree, BTree, BinaryHeap), comparators, and iterators. Containers are either ordered or unordered. All ordered containers provide stateful iterators and some of them allow enumerable functions. A list is a data structure that stores values and may have repeated values. A list backed by a dynamic array that grows and shrinks implicitly. A list where each element points to the next element in the list. A set is a data structure that can store elements and has no repeated values. A Map is a data structure that maps keys to values.

Provide a .NET object model to support computation of minimum, maximum, average of primitive types exported through properties by any object.

GraphScript project is a network theory simulator based purely on Java and Java scripting engines. It is intended to allow fast development, testing and visualization of network theory (graph) algorithms.

GraphEmbedding is an open-source Python project for implementing and experimenting with graph embedding algorithms. It follows a simple “graph in, embedding out” design. The library uses NetworkX graphs as input and produces vector representations for graph nodes. It includes implementations of DeepWalk, LINE, Node2Vec, SDNE, and Struc2Vec. Users can configure walks, embedding dimensions, training windows, epochs, and other model-specific parameters. Example scripts demonstrate how to train models and retrieve embeddings for downstream graph analysis or machine learning tasks.

A Web based IDE that supports nodes and edges (graphs) with attributes. An interpretor executes your graphs. Views of graphs allow developers to see references to variables, easily see the path of execution and rearrange the structure of data and program

A Java library and set of tools to quickly and easily manipulate graphs (set of nodes and edges) and dynamic graphs. The principal trait of this library is its ability to manage (create, edit, layout, display) continuously evolving graphs. The development of this project now takes place on Github. Visit the official Graphstream website at http://graphstream-project.org

Graphal is an interpreter of a programming language that is mainly oriented to graph algorithms. There is a command line interpreter and a graphical integrated development environment. The IDE contains text editor for programmers, compilation and script output, advanced debugger and visualization window. The progress of the interpreted and debugged graph algorithm can be displayed in 3D scene.

This project consists of various implementations of the Graphplan algorithm. At the moment we have two implementations of Graphplan: * Emplan - A C++ implementation for Linux, Windows and MacOS X * JavaGP - A Java implementation

Graphserver provides fast shortest-path computations on very large graphs, for use as a sub-component of a trip-planning service.

GuidLib is a library with functions for GUIDs (also known as UUIDs, as defined by RFC 4122).

Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms.

HH-Evolver is a framework for domain-specific, hyper-heuristic evolution. HH-Evolver automates the design of domain-specific heuristics for planning domains. Hyper-heuristics generated by our tool can then be used with combinatorial search algorithms such as A* and IDA* for solving problems of the given domain.

HL7 to XML Transformation toolkit. Using HL7 v2 XML

A Constraint Programming engine with many examples (planning, sudoku solver...)

Solves the given cubes puzzles and prints the solution 1. You may download code: git clone git://git.code.sf.net/p/happycubesolver/code happycubesolver-code 2. Build: mvn clean install assembly:assembly -DskipTests -Dmaven.test.skip=true -DdownloadSources=true -Dcheckstyle.skip=true 3. Run: java -jar target/cubes-1.0-fat.jar

Program for Residence Life staffs to aid in roommate pairing.

.NET, (in future - VB DLL, C# DLL and Pascal .TPU) class library realizing associative arrays mechanism for VB.NET (an other languages).

Description: We propose and implement in C++ the hashed dynamic determinants scheme. This consists of efficient implementations of dynamic determinant algorithms and a hash table that stores intermediate results (matrices and determinants) in order to be used in subsequent steps of the algorithm. Related papers: V.Fisikopoulos, L.Peñaranda. Faster Geometric Algorithms via Dynamic Determinant Computation. In Algorithms – ESA 2012, Lecture Notes in Computer Science Volume 7501, 2012, pp 443-454.

Haskellesque lazy-list and functional tools with a Common Lisp slant.

An heuristic image stitcher made to test Genetic and PSO based algorithms

Multidimensional scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a low-dimensional target space.

This project aims to develop a colour-based vision processing system for use in RoboCup. We are using a CCD camera for input to an FPGA. The system locates coloured objects and outputs detected corners.

Implementation of duration high-order hidden Markov model (DHO-HMM) in Matlab with application in speech recognition.