Open Source Algorithms - Page 4
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Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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ViennaCL
Linear algebra and solver library using CUDA, OpenCL, and OpenMP
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. -
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An open source workbench for chemo- and bioinformatics built on the Eclipse Rich Client Platform (RCP).
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EN: Code created to show how is done the calculation of a CRC. It includes many different CRCs to choose from. This software was created to help people understand how is the CRC calculated and be able to see it in a practical way, it is also available to copy and use it for your own project. I hope it helps you. ES: Código creado para mostrar como se realiza el cálculo de un CRC. Con el mismo se incluyen varios CRCs que pueden ser escogidos. Este software se creó con el propósito de ayudar a la gente a entender como se hace el cálculo de un CRC de una manera más práctica y sencilla, también se puede copiar y aprovecharlo para otras aplicaciones. Espero que te sea util.
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TARQUIN
MRS/NMR analysis software
Analysis software for MRS/NMR data. Allows processing and fitting to be performed in a fully automatic workflow. -
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libPGF
libPGF is an implementation of the Progressive Graphics File (PGF)
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. -
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Free Java Flowchart simulator / interpreter
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New Terrain and 3D Map System
a very lightweight advanced terrain-rendering and 3D map rendering sys
A very lightweight advanced terrain-rendering and 3D map rendering system. Minimal dependencies: OpenGL, SDL. It's distrib in 2-3 separate modules: 1. the C++ implementation of the terrain- and/or surface- rendering Algorithms I have developed: both a multithread and a non-multithread variant is relaeased. 2. Seme as at point 1. , but with a road-network rendering and collision-detection module I wrote before. It also adds some trees at the top of the terrain. (multitread vesion not released yet) 3. Seme as point 2.. Multithread terrain-rendering, roads on it, and vehicles to go around. The most important part is terrain-rendering. The Algorithm at the heart of it features my first and second theorem abut polygonal surface approximation. It took something like 2 years to develop it. It's technically a view-dependent, continuous dynamic level-of-detail rendering algorithm. Main features are simplicity and documentation. Does easily a 4k x 4k terrain, but also much more. -
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NOTE: The GROOVE codebase has moved to https://github.com/nl-utwente-groove
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Java·Applied·Geodesy·3D
Least-Squares Adjustment Software for Geodetic Sciences
JAG3D is no longer developed at source-forge, and has moved to GitHub. Please visit https://github.com/applied-geodesy/jag3d or https://software.applied-geodesy.org to get the latest version. -
Outbound sales softwareAdversus is an outbound dialing solution that helps you streamline your call strategies, automate manual processes, and provide valuable insights to improve your outbound workflows and efficiency.
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The Adobe Source Libraries (ASL) are a collection of C++ libraries building foundation technology to allow the construction of commercial applications by assembling generic algorithms through declarative descriptions.
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Structorizer is a little tool which you can use to create Nassi-Schneiderman Diagrams (NSD). Stuctorizer is written in Java and free for any use. The code has been moved to Github: https://github.com/fesch/Structorizer.Desktop
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LinAsm
Collection of fast and optimized assembly libraries for x86-64 Linux
LinAsm is collection of very fast and SIMD optimized assembly written libraries for x86-64 Linux. It implements many common and widely used algorithms for array manipulations: searching, sorting, arithmetic and vector operations, unit conversions; fast mathematical and statistic functions; numbers and time converting algorithms; finite impulse response (FIR) digital filters; spectrum analysis algorithms, Fast Hartley transformation; CPU cache friendly functions and extremely fast abstract data types (ADT) such as hash tables b-trees, and much more. LinAsm libraries are written on FASM assembly language. They are stable and have appropriate benchmarks for many units. All libraries are well documented and grouped by their functionality. To get more information about this library, please visit the official web site: http://linasm.sourceforge.net -
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DomainBed
DomainBed is a suite to test domain generalization algorithms
DomainBed is a PyTorch-based research suite created by Facebook Research for benchmarking and evaluating domain generalization algorithms. It provides a unified framework for comparing methods that aim to train models capable of performing well across unseen domains, as introduced in the paper In Search of Lost Domain Generalization. The library includes a wide range of well-known domain generalization algorithms, from classical baselines such as Empirical Risk Minimization (ERM) and Invariant Risk Minimization (IRM) to more advanced techniques like Domain Adversarial Neural Networks (DANN), Adaptive Risk Minimization (ARM), and Invariance Principle Meets Information Bottleneck (IB-ERM/IB-IRM). DomainBed also integrates multiple standard datasets—including RotatedMNIST, PACS, VLCS, Office-Home, DomainNet, and subsets from WILDS—allowing consistent experimentation across image classification tasks. -
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ESRGAN
Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution
ESRGAN stands for Enhanced Super-Resolution Generative Adversarial Network and is a foundational project in the field of deep learning-based image super-resolution. It builds on earlier GAN-based approaches by improving network architecture (e.g., using Residual-in-Residual Dense Blocks), adversarial loss functions, and perceptual loss components to generate higher-fidelity high-resolution images from low-resolution inputs with more realistic textures and details. ESRGAN was originally developed as part of research efforts that won benchmarks such as the PIRM2018 super-resolution challenge, demonstrating that GAN-based techniques can produce visually convincing results that surpass traditional interpolation or earlier deep approaches. The repository provides the core testing and model definitions, allowing researchers and practitioners to reproduce results, experiment with pretrained models, and integrate ESRGAN into broader pipelines or applications. -
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GoDS
Implementation of various data structures and algorithms in Go
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. -
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Image Harmonization Dataset iHarmony4
The first large-scale public benchmark dataset for image harmonization
This repository provides the iHarmony4 dataset, which is a large-scale dataset designed for image harmonization tasks. Image harmonization involves adjusting the appearance of a foreground in a composite image so that it is consistent with the background (in color, tone, illumination, etc.). The iHarmony4 dataset comprises four sub-datasets (HCOCO, HAdobe5k, HFlickr, Hday2night), each making composite images by combining a foreground from one image with a background from another, along with associated ground truth harmonized images and foreground masks. The dataset is intended as a benchmark resource to enable and standardize research in image harmonization. Each composite sample has: composite image, foreground mask, and corresponding real harmonized image. -
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NTU RGB-D
Info and sample codes for "NTU RGB+D Action Recognition Dataset"
The “NTU RGB+D” repository provides access to a large-scale dataset for human action recognition (and its extension, NTU RGB+D 120). The dataset includes multiple modalities (RGB video, depth sequences, infrared video, 3D skeletal joint data) captured with multiple Kinect v2 cameras simultaneously. The repository also contains MATLAB / Python demo scripts for loading, visualizing, and processing skeleton data, mapping between modalities, and handling dataset structure. Multi-modal action recognition dataset, RGB, depth, infrared, skeletal data. Split into background / evaluation sets for one-shot evaluation (in the extended dataset). -
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Rank-BM25
A Collection of BM25 Algorithms in Python
A collection of algorithms for querying a set of documents and returning the ones most relevant to the query. The most common use case for these algorithms is, as you might have guessed, to create search engines. -
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TBOX
A glib-like multi-platform c library
TBOX is a glib-like cross-platform C library that is simple to use yet powerful in nature. The project focuses on making C development easier and provides many modules (.e.g stream, coroutine, regex, container, algorithm ...), so that any developer can quickly pick it up and enjoy the productivity boost when developing in C language. It supports the following platforms: Windows, Macosx, Linux, Android, iOS, BSD and etc. Supports file, data, http and socket source. Supports the stream filter for gzip, charset. etc. Implements stream transfer. Implements the static buffer stream for parsing data. Supports coroutine and implements asynchronous operation. The coroutine library. Provides high-performance coroutine switch. Supports arm, arm64, x86, x86_64. Provides channel interfaces. Provides semaphore and lock interfaces. Supports io socket and stream operation in coroutine. Provides some io servers (http ..) using coroutine. Provides stackfull and stackless coroutines. -
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. Written primarily in Scala, it shows the architecture of large-scale recommendation systems, including candidate sourcing, ranking, and heuristics. While certain components (such as safety layers, spam detection, or private data) are excluded, the release provides valuable insights into the design of real-world machine learning–driven ranking systems. The project is intended as a reference for researchers, developers, and the public to study, experiment with, and better understand the mechanisms behind social media content. -
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jsprit
Open source toolkit for solving rich vehicle routing problems
jsprit is a java based, open-source toolkit for solving rich Traveling Salesman Problems(TSP) and Vehicle Routing Problems(VRP). It is lightweight, flexible and easy-to-use, and based on a single all-purpose meta-heuristic. Setting up the problem, defining additional constraints, modifying the algorithms and visualizing the discovered solutions is as easy and handy as reading classical VRP instances to benchmark your algorithm. It is fit for change and extension due to its modular design and a comprehensive set of unit and integration tests. Possibility to define additional stateless and stateful constraints/conditions to account for the richness of your problem. GraphHopper invests in an active open source community. Our flagships are the GraphHopper routing engine and jsprit, the toolkit for solving rich vehicle routing problems. We promote a fair & diverse mindset. -
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DRAMMS
A Deformable Medical Image Registration Toolbox
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/ . -
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PANDA
A comprehensive and flexible quantification tool for proteomics data
PANDA is a comprehensive and flexib tool for quantitative proteomics data analysis, which is developed based on our solid foundations in quantitative proteomics for years. Several novelties have been implemented in it. First, we implement the advantage algorithms of LFQuant (Proteomics 2012, 12, (23-24), 3475-84) and SILVER (Bioinformatics 2014, 30, (4), 586-7) into PANDA. Second, we consider the state-of-art concept of quantification reliability in this quantitative workflow. On the levels of spectra, peptides and proteins, PANDA works out a few quantitative filters and new scores for quantification confidence. Third, PANDA is designed for processing proteomics big data in parallel. -
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Alef++, is a new programming language like Perl and Lisp syntax, with a many changing in classical languages designs, her specification designed to be implemented for any VM, by default can access to any private/protected/default Java fields or methods.
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