Open Source BSD Algorithms - Page 2

The Progressive Graphics File (PGF) is an efficient image file format, that is based on a fast, discrete wavelet transform with progressive coding features. PGF can be used for lossless and lossy compression. It's most suitable for natural images. PGF can be used as a very efficient and fast replacement of JPEG 2000.

jChecs is an open source Java chess program, designed to introduce the basics of computer chess programming concepts.

This site hosts the source code for C++ version of the Broker for SBW, NOM module, advanced simulation suite, analysis applications and model editors.

ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects.

jMetal is an object-oriented Java-based framework for solving multi-objective optimization problems with metaheuristics.

The Javascript Clipper library performs clipping and offsetting for both lines and polygons. All four boolean clipping operations are supported - intersection, union, difference and exclusive-or. Polygons can be of any shape including self-intersecting polygons. Javascript Clipper is a port of Angus Johnson's Clipper library: https://sourceforge.net/projects/polyclipping LIVE DEMO: http://jsclipper.sourceforge.net/6.4.2.2/main_demo.html LIVE DEMO (FPoint): http://jsclipper.sourceforge.net/6.4.2.2_fpoint/main_demo.html Information and examples: http://jsclipper.sourceforge.net/6.4.2.2 Information and examples (FPoint): http://jsclipper.sourceforge.net/6.4.2.2_fpoint Donate Javascript Clipper Project: https://sourceforge.net/p/jsclipper/wiki/Donations/ Use cases: * Over 1500 schools in the UK uses Javascript Clipper in Digimap for Schools service. Read more: https://mobilegeo.wordpress.com and http://digimapforschools.edina.ac.uk/cosmo-free/osmapper

Game Toolbox is a C# assembly designed to facilitate the creation of games and game prototypes. It contains no graphics code, does not depend on XNA, and is Mono-compatible. It provides implementations of A*, GOAP, a genetic algorithm, and more.

A univariate and multivariate analysis UI. This project is no longer under development. Please use as you wish.

An open source workbench for chemo- and bioinformatics built on the Eclipse Rich Client Platform (RCP).

A slightly reduced strength bulk encryption. In exchange for the reduced strength, you get the ability to rsync the encrypted files, so that local changes in the plaintext file will result in (relatively) local changes to the cyphertext file.

Critterding is a "Petri dish" universe in 3D that demonstrates evolving artificial life. Critters start out with completely random brains and bodies, but will automatically start evolving into something with much better survival skills.

The ASCO project aims to bring circuit optimization capabilities to existing SPICE simulators using a high-performance parallel differential evolution (DE) optimization algorithm. It supports Eldo, HSPICE, LTspice, Spectre, and Qucs.

CADI is an implementation of the JPIP standard (JPEG2000 Interactive Protocol - Part 9). CADI includes a JPIP server, a JPIP proxy, and a JPIP Client. CADI is designed and programmed with the aim to provide a flexible framework to test and develop ne

C++ Library of Abstract Algebra concepts and arithmetic algorithms defined in terms of these concepts, using Instigate Generic Programming Methodology. Based on this library we plan to develop Linear Algebra and Optimization concepts and algorithms.

This library contains generic algorithms from STL and boost.org and other sources, re-implemented using Instigate's GP methodology, based on modern principles of Generic Programming.

DRAMMS is a software package designed for 2D-to-2D and 3D-to-3D deformable medical image registration tasks. Released by Section of Biomedical Image Analysis (SBIA) at the University of Pennsylvania. Some typical applications of DRAMMS include, -- Cross-subject registration of the same organ (can be brain, breast, cardiac, etc); -- Mono- and Multi-modality registration (MRI, CT, histology); -- Longitudinal registration (pediatric brain growth, cancer development, mouse brain development, etc); -- Registration under missing correspondences (e.g., vascular lesions, tumors, histological cuts). DRAMMS runs in command line in UNIX/Mac OS, It accepts Nifti/ANALYZE/MetaImage image formats. It is fully-automatic --- takes two input images, and generates a registered image and (optionally) the deformation field. More information (installation, tutorial, manual, demonstration, FAQ, etc) can be found at http://www.rad.upenn.edu/sbia/software/dramms/ .

Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...

The package implements a variety of tools for categorization of multivariate data such as boosted decision trees, bagging and random forest, bump hunting (PRIM), a multi-class learner and others.

AMMORIA (ARAB) is an object oriented programming language uses Arabic words instead of English words, to make learning programming for Arab children easy and fast, it's planned to support Urdo and Farsi too, AMMORIA has its won IDE and Visual stuff.

Java Combinatorial Optimization Platform is used to solve combinatorial problems using common interface, providing means to easily add new algorithms and problems and to benchmark them.

CDS (Concurrent Data Structures) is a C++ template library of lock-free and fine-grained lock-based algorithms. It contains a collection of concurrent data structures: queues, stacks, sets, maps, etc, and safe memory reclamation schema for concurrent containers - Hazard Pointer and user-space RCU. See doxygen doc http://libcds.sourceforge.net/doc/cds-api/index.html. The source code repo for libcds 2.x has been moved to https://github.com/khizmax/libcds

* Fast GPU linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use * Provides high-level syntax and functionality deliberately similar to Matlab * Provides an API that is aiming to be compatible with Armadillo for easy transition between CPU and GPU linear algebra code * Useful for algorithm development directly in C++, or quick conversion of research code into production environments * Distributed under the permissive Apache 2.0 license, useful for both open-source and proprietary (closed-source) software * Can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc * Downloads: http://coot.sourceforge.io/download.html * Documentation: http://coot.sourceforge.io/docs.html * Bug reports: http://coot.sourceforge.io/faq.html * Git repo: https://gitlab.com/conradsnicta/bandicoot-code

TSPSG is intended to generate and solve Travelling Salesman Problem (TSP) tasks. It uses Branch and Bound method for solving. An input is a number of cities and a matrix of city-to-city travel prices. The matrix can be populated with random values in a given range (useful for generating tasks). The result is an optimal route, its price, step-by-step matrices of solving and solving graph. The task can be saved in internal binary format and opened later. The result can be printed or saved as PDF, HTML, or ODF. TSPSG may be useful for teachers to generate test tasks or just for regular users to solve TSPs. Also, it may be used as an example of using Branch and Bound method to solve a particular task.

The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machine learning. The code is well documented and easy to extend, especially for adding new weak learners.