Open Source Algorithms - Page 3
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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. For students preparing for technical interviews, self-learners brushing up on fundamentals, or developers wanting to understand algorithm internals, this repository provides ready-to-run examples, and can serve as a sandbox to experiment, benchmark, or adapt code. Because it’s in pure Python, it’s easy to read and modify, making it accessible even to those with modest programming experience. The repo helps bridge the gap between theoretical algorithm descriptions and real-world code, giving concrete, working implementations that one can study, debug, or extend. -
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java-string-similarity
Implementation of various string similarity and distance algorithms
Implementation of various string similarity and distance algorithms: Levenshtein, Jaro-winkler, n-Gram, Q-Gram, Jaccard index, Longest Common Subsequence edit distance, cosine similarity. A library implementing different string similarity and distance measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Jaro-Winkler, Longest Common Subsequence, cosine similarity etc.) are currently implemented. The main characteristics of each implemented algorithm are presented below. The "cost" column gives an estimation of the computational cost to compute the similarity between two strings of length m and n respectively. If the alphabet is finite, it is possible to use the method of four russians (Arlazarov et al. "On economic construction of the transitive closure of a directed graph", 1970) to speedup computation. This was published by Masek in 1980 ("A Faster Algorithm Computing String Edit Distances"). -
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trackers
Multi-object tracking algorithms
trackers is a plug-and-play multi-object tracking library designed to work with virtually any object detection model, enabling developers to follow objects across video frames with minimal setup. The library provides clean, modular implementations of leading tracking algorithms and can be used either from the command line or embedded directly into Python pipelines. It supports inputs such as videos, webcams, RTSP streams, or image directories and produces annotated tracking outputs that include labels and trajectories. Trackers is built for flexibility and benchmarking, allowing users to evaluate performance using standard multi-object tracking metrics and compare algorithms easily. Its architecture emphasizes interoperability so developers can combine their preferred detection models with different trackers without rewriting core logic. -
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tsfresh
Automatic extraction of relevant features from time series
tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python. The extracted features can be used to describe or cluster time series based on the extracted characteristics. Further, they can be used to build models that perform classification/regression tasks on the time series. Often the features give new insights into time series and their dynamics. -
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xxHash
Extremely fast non-cryptographic hash algorithm
xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed limit. It is proposed in four flavors (XXH32, XXH64, XXH3_64bits and XXH3_128bits). The latest variant, XXH3, offers improved performance across the board, especially on small data. It successfully completes the SMHasher test suite which evaluates collision, dispersion and randomness qualities of hash functions. Code is highly portable, and hashes are identical across all platforms (little / big endian). Performance on large data is only one part of the picture. Hashing is also very useful in constructions like hash tables and bloom filters. In these use cases, it's frequent to hash a lot of small data (starting at a few bytes). Algorithm's performance can be very different for such scenarios, since parts of the algorithm, such as initialization or finalization, become fixed cost. The impact of branch misprediction also becomes much more present. -
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The JTS Topology Suite is an API for modelling and manipulating 2-dimensional linear geometry. It provides numerous geometric predicates and functions. JTS conforms to the Simple Features Specification for SQL published by the Open GIS Consortium.
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Activation Key .NET Class Library
Represents the activation key used to protect your C# application.
A specific software-based key for a computer program C# source code. It certifies that the copy of the program is original. It is also called a license key, product key, product activation, software key and even a serial number. The key can be stored as a human readable text for easy transfering to the end user. Contains methods for generating the cryptography key based on the specified hardware and software binding. An additional feature is the ability to embed any information directly into the key. This information can be recovered as a byte array during key verifying. -
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Simd
High performance image processing library in C++
The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows, Android and Linux, MSVS, G++ and Clang compilers, MSVS project and CMake build systems. -
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jFuzzyLogic is a java implementation of a Fuzzy Logic software package. It implements a complete Fuzzy inference system (FIS) as well as Fuzzy Control Logic compliance (FCL) according to IEC 61131-7 (formerly 1131-7).
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The Distributed Genetic Programming Framework is a scalable Java genetic programming environment. It comes with an optional specialization for evolving assembler-syntax algorithms. The evolution can be performed in parallel in any computer network.
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C# Algorithms
Plug-and-play class-library project of standard data structures
A plug-and-play class-library project of standard Data Structures and Algorithms, written in C#. It contains 75+ Data Structures and Algorithms, designed as Object-Oriented isolated components. Even though this project started for educational purposes, the implemented Data Structures and Algorithms are standard, efficient, stable and tested. This is a C#.NET solution-project, and it contains three subprojects. 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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DifferenceKit
A fast and flexible O(n) difference algorithm framework
A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his algorithm. The type of the element that to take diffs must be conform to the Differentiable protocol. -
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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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Interpolations.jl
Fast, continuous interpolation of discrete datasets in Julia
This package implements a variety of interpolation schemes for the Julia language. It has the goals of ease of use, broad algorithmic support, and exceptional performance. Currently, this package supports B-splines and irregular grids. The API has been designed with the intent to support more options. Initial support for Lanczos interpolation was recently added. Pull requests are more than welcome! It should be noted that the API may continue to evolve over time. -
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MADDPG
Code for the MADDPG algorithm from a paper
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. The code is built on top of TensorFlow and integrates with the Multiagent Particle Environments (MPE) for benchmarking. Researchers can use it to reproduce the experiments presented in the paper, which demonstrate how agents learn behaviors such as coordination, competition, and communication. Although archived, MADDPG remains a widely cited baseline in multi-agent reinforcement learning research and has inspired further algorithmic developments. -
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bild
Image processing algorithms in pure Go
A collection of parallel image processing algorithms in pure Go. The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It uses packages from the standard library whenever possible to reduce dependency use and development abstractions. All operations return image types from the standard library. Package convolution provides the functionality to create and apply a kernel to an image. Package effect provides the functionality to manipulate images to achieve various looks. Package histogram provides basic histogram types and functions to analyze RGBA images. Package paint provides functions to edit a group of pixels on an image. Package parallel provides helper functions for the dispatching of parallel jobs. -
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leetcode-editor
Do Leetcode exercises in IDE
Do Leetcode exercises in IDE, support leetcode.com and leetcode-cn.com, to meet the basic needs of doing exercises.Support theoretically: IntelliJ IDEA PhpStorm WebStorm PyCharm RubyMine AppCode CLion GoLand DataGrip Rider MPS Android Studio. The login accounts of the two websites are not interoperable and the corresponding users need to be configured when switching websites. You can also refresh and load questions if you are not logged in, but you cannot submit it. Input the content and press Enter to search , press again to search for the next one. It can only search under the question bank node. Clean up the files in the configured cache directories. The cache directories of the two websites are different and only the current configured websites are cleaned up. Carefully clean up cases without submitting. -
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Jojos Binary Diff
Binary Diff and Undiff Utility
JDIFF is a program that outputs the differences between two binary files, either in binary format or in human readable format (detailed or summarized) and then allows to reconstruct the second file from the first one and the diff-file. -
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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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Free Java Flowchart simulator / interpreter
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The fstrcmp project provides a shared library for making fuzzy string comparisons, and also provides an fstrcmp command for use in shell scripts.
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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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STXXL is an implementation of the C++ standard template library STL for external memory (out-of-core) computations, containers, and algorithms that can process huge volumes of data that only fit on disks.
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Javascript Clipper
Boolean operations and offsetting library in Javascript
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
