Search Results for "algorithms"
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Showing 4599 open source projects for "algorithms"
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Swift Algorithms
Commonly used sequence and collection algorithms for Swift
Swift Algorithms is an open-source package of sequence and collection algorithms, along with their related types. Algorithms are powerful tools for thought because they encapsulate difficult-to-read and error-prone raw loops. The Algorithms package includes a host of powerful, generic algorithms frequently found in other popular programming languages. -
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The Algorithms Python
All Algorithms implemented in Python
The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. -
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The Algorithms - C++ #
Collection of various algorithms in mathematics, machine learning
TheAlgorithms/C-Plus-Plus is a large open-source repository that collects implementations of many classic algorithms and data structures written in the C++ programming language. The project is part of the broader “The Algorithms” initiative, which maintains algorithm implementations in several programming languages to support education and knowledge sharing. Within the C++ repository, contributors implement algorithms across a wide range of fields including sorting, graph theory, number theory, machine learning, cryptography, and data structures. ... -
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C# Algorithms
Plug-and-play class-library project of standard data structures
Algorithms, a class library project which contains the Algorithms implementations. Data Structures, a class library project which contains the Data Structures implementations. Also, UnitTest, a unit-testing project for the Algorithms and Data Structures. -
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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. This approach allows learners to study the mathematical and algorithmic details behind widely used models in a transparent and readable way. ... -
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Pythonic Data Structures and Algorithms
Minimal examples of data structures and algorithms in Python
The Pythonic Data Structures and Algorithms repository by keon is a hands-on collection of implementations of classical data structures and algorithms written in Python. It offers working, often well-commented code for many standard algorithmic problems — from sorting/searching to graph algorithms, dynamic programming, data structures, and more — making it a valuable resource for learning and reference. -
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CBIG
Computational Brain Imaging Group tools
CBIG is a comprehensive toolkit maintained by Thomas Yeo’s Computational Brain Imaging Group containing tools for processing and analyzing neuroimaging data—including fMRI preprocessing pipelines, brain parcellation algorithms, mental disorder subtyping models, fMRI dynamic models, registrations between brain spaces, and phenotypic prediction algorithms. After cloning/downloading this repository, please see README inside setup directory to see instructions on how to set up your local environment to be compatible with our repository. Brain parcellation tools (e.g., Yeo networks, Schaefer parcellations) for cortical mapping. -
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Navigational Algorithms
Navigational Algorithms - Marine navigation
- Celestial Navigation: Sight Reduction, Line Of Position, Fix... - Positional Astronomy: Nautical Almanac, RA, GHA, Dec - Coastal Navigation: Range, Bering, Horizontal angles, IALA... - Sailings: Rhumbs, Loxodromic, Orthodromic, Meridional parts... - Weather , Tides - Software: PC, Android https://sites.google.com/site/navigationalalgorithms/ -
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CGAL
The Computational Geometry Algorithms Library
CGAL or the Computational Geometry Algorithms Library is a C++ library that gives you easy access to a myriad of efficient and reliable geometric algorithms. These algorithms are useful in a wide range of applications, including computer aided design, robotics, molecular biology, medical imaging, geographic information systems and more. CGAL features a great range of data structures and algorithms, including Voronoi diagrams, cell complexes and polyhedra, triangulations, arrangements of curves, surface and volume mesh generation, spatial searching, alpha shapes, geometry processing, and many more. ... -
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OnlineStats.jl
Single-pass algorithms for statistics
OnlineStats does statistics and data visualization for big/streaming data via online algorithms. High-performance single-pass algorithms for statistics and data viz. Updated one observation at a time. Algorithms use O(1) memory. Algorithms use O(1) memory. -
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InsightFace
State-of-the-art 2D and 3D Face Analysis Project
...InsightFace is an open-source 2D&3D deep face analysis library. InsightFace is an integrated Python library for 2D&3D face analysis. InsightFace efficiently implements a wide variety of state-of-the-art algorithms for face recognition, face detection, and face alignment, which are optimized for both training and deployment. Research institutes and industrial organizations can get benefits from InsightFace library. -
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SeedCrackerX
Minecraft mod designed to reverse-engineer
...The system can also integrate with a shared database to contribute discovered seeds, enabling collaborative data gathering across users. Advanced features include brute-force algorithms that refine seed candidates based on structural patterns and hashed seed calculations. -
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OTPLib
One Time Password (OTP) / 2FA for Node.js and Browser
A JavaScript library for generating and verifying one-time passwords (TOTP/HOTP), commonly used for two-factor authentication (2FA). -
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Hello Algorithm
Animated illustrations, one-click data structure
Animated illustrations, one-click data structure and algorithm tutorials. This project aims to create an open source, free, novice-friendly introductory tutorial on data structures and algorithms. The whole book uses animated illustrations, the content is clear and easy to understand, and the learning curve is smooth, guiding beginners to explore the knowledge map of data structures and algorithms. The source code can be run with one click, helping readers improve their programming skills during exercises and understand the working principles of algorithms and the underlying implementation of data structures. ... -
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PlatEMO
Evolutionary multi-objective optimization platform
...PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. Most of them are representative algorithms published in top journals after 2010. Users can select various figures to be displayed, including the Pareto front of the result, the Pareto set of the result, the true Pareto front, and the evolutionary trajectories of any performance indicator values. PlatEMO provides a powerful and friendly GUI, where users can configure all the settings and perform experiments in parallel via the GUI without writing any code. -
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pybaselines
Library of algorithms for baseline correction of experimental data
...These include popular algorithms, such as AsLS, airPLS, ModPoly, and SNIP, as well as many lesser-known algorithms. Most algorithms are adapted directly from literature, although there are a few that are unique to pybaselines, such as penalized spline versions of Whittaker-smoothing-based algorithms. The full list of implemented algorithms can be found in the documentation. -
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JavaScript Algo and Data Structures
Algorithms and data structures implemented in JavaScript
javascript-algorithms is an open source repository by Oleksii Trekhleb that provides implementations of algorithms and data structures in JavaScript. Each algorithm includes explanations, complexity analysis, and references for further reading, making it both a coding resource and a study guide. The repository covers topics such as sorting, searching, graph algorithms, cryptography, and data structures like linked lists, stacks, and queues. -
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Zstandard
Zstandard - Fast real-time compression algorithm
...Zstd can trade compression speed for stronger compression ratios. It is configurable by small increment. Decompression speed is preserved and remain roughly the same at all settings, a property shared by most LZ compression algorithms, such as zlib or lzma. -
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Manopt.jl
Optimization on Manifolds in Julia
Optimization Algorithm on Riemannian Manifolds. A framework to implement arbitrary optimization algorithms on Riemannian Manifolds. Library of optimization algorithms on Riemannian manifolds. Easy-to-use interface for (debug) output and recording values during an algorithm run. Several tools to investigate the algorithms, gradients, and optimality criteria. -
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ReinforcementLearning.jl
A reinforcement learning package for Julia
A collection of tools for doing reinforcement learning research in Julia. Provide elaborately designed components and interfaces to help users implement new algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. ... -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. -
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KACTL
KTH algorithm competition template library
KACTL (the KTH Algorithmic Contest Template Library) is an extensively curated and high-performance C++ algorithms library created by the competitive programming team at the Royal Institute of Technology (KTH) to serve as a trusted, battle-tested codebase for algorithmic contests, programming competitions, and general algorithm development. The repository aggregates dozens of concise implementations of essential data structures, numerical methods, graph algorithms, string processing tools, computational geometry routines, and optimization techniques, all designed with speed, correctness, and compactness in mind. ... -
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Java JWT
Java implementation of JSON Web Token (JWT)
A Java implementation of JSON Web Token (JWT) - RFC 7519. This library requires Java 8 or higher. The last version that supported Java 7 was 3.11.0. The library implements JWT Verification and Signing using several algorithms. The Algorithm defines how a token is signed and verified. It can be instantiated with the raw value of the secret in the case of HMAC algorithms, or the key pairs or KeyProvider in the case of RSA and ECDSA algorithms. Once created, the instance is reusable for token signing and verification operations. When using RSA or ECDSA algorithms and you just need to sign JWTs you can avoid specifying a Public Key by passing a null value. ... -
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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). ...
