Open Source ChromeOS Algorithms
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Algorithms for ChromeOS
Browse free open source Algorithms and projects for ChromeOS below. Use the toggles on the left to filter open source Algorithms by OS, license, language, programming language, and project status.
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Clipper
Polygon and line clipping and offsetting library (C++, C#, Delphi)
This library is now obsolete and no longer being maintained. It has been superceded by my Clipper2 library - https://github.com/AngusJohnson/Clipper2. -
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Technical analysis library with indicators like ADX, MACD, RSI, Stochastic and TRIX, plus candlestick pattern recognition. Docs: https://ta-lib.org Available natively in C/C++, Java, Rust and C# with wrappers for python, R and many other ecosystems. MOVED TO GITHUB: https://github.com/TA-Lib/ta-lib . No further support is provided on SourceForge. Release 0.4.0 is very outdated, please check github for the most recent release.
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lpsolve
Mixed Integer Linear Programming (MILP) solver.
Mixed Integer Linear Programming (MILP) solver lp_solve solves pure linear, (mixed) integer/binary, semi-cont and special ordered sets (SOS) models.lp_solve is written in ANSI C and can be compiled on many different platforms like Linux and WINDOWS This project is moved to github: https://lp-solve.github.io/ -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: https://arma.sourceforge.net/download.html * Documentation: https://arma.sourceforge.net/docs.html * Bug reports: https://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/armadillo-lib/armadillo-code -
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
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libmng -THE reference library for reading, displaying, writing and examining Multiple-Image Network Graphics. MNG is the animation extension to the popular PNG image-format.
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Dlib is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
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CRC RevEng
Arbitrary-precision CRC calculator and algorithm finder
CRC RevEng is a portable, arbitrary-precision CRC calculator and algorithm finder. It calculates CRCs using any of the 113 preset algorithms, or a user-specified algorithm to any width. It calculates reversed CRCs to give the bit pattern that produces a desired forward CRC. CRC RevEng also reverse-engineers any CRC algorithm from sufficient correctly formatted message-CRC pairs and optional known parameters. It comprises powerful input interpretation options. Compliant with Ross Williams' Rocksoft(tm) model of parametrised CRC algorithms. -
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threadpool is a cross-platform C++ thread pool library. It provides a convenient way for dispatching asynchronous tasks and can be easily customized. threadpool is based on the high-quality Boost source libraries.
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AhoCorasickDoubleArrayTrie
An extremely fast implementation of Aho Corasick algorithm
AhoCorasickDoubleArrayTrie is a Java implementation of the Aho–Corasick multi-pattern matching algorithm that is optimized using a Double-Array Trie data structure. It is designed for fast keyword scanning across large texts, where you want to search for many patterns simultaneously and efficiently. The core idea is to build an automaton from a dictionary of patterns, then stream through input text to emit matches with minimal overhead. By using a double-array trie representation, the project emphasizes performance and memory efficiency compared to simpler pointer-heavy trie structures, which can matter a lot for large dictionaries or latency-sensitive services. This makes it a strong fit for tasks like content filtering, entity/term spotting, dictionary-based annotation, or high-throughput log/text processing. In short, it’s a specialized, speed-focused library for industrial-strength multi-keyword matching in Java. -
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Free Java Flowchart simulator / interpreter
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PLEASE NOTE that we are in the process of moving to GitHub: https://github.com/jasypt/jasypt Jasypt (Java Simplified Encryption) is a java library which allows the developer to add basic encryption capabilities to his/her projects with minimum effort, and without the need of having deep knowledge on how cryptography works. PLEASE NOTE that we are in the process of moving to GitHub: https://github.com/jasypt/jasypt
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Grey Wolf Optimizer for Path Planning
Grey Wolf Optimizer (GWO) path planning/trajectory
The Grey Wolf Optimizer for Path Planning is a MATLAB-based implementation of the Grey Wolf Optimizer (GWO) algorithm designed for UAV path and trajectory planning. It allows simulation of both two-dimensional and three-dimensional UAV trajectory planning depending on parameter setups. The tool provides built-in functions to configure different UAV environments and supports multiple optimization objectives. It includes progress visualization to help monitor the optimization process during simulations. Users can adjust objective function weights and experiment with multiple heuristic search strategies to explore optimal solutions. This project demonstrates applications in multi-agent and multi-UAV cooperative path planning, making it useful for research and educational purposes in the field of intelligent optimization and robotics. -
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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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JGAP is a Genetic Algorithms and Genetic Programming package written in Java. It is designed to require minimum effort to use, but is also designed to be highly modular. JGAP features grid functionality and a lot of examples. Many unit tests included. Legal notice/Impressum: Klaus Meffert An der Struth 25 D-65510 Idstein sourceforge <at> klausmeffert.de
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Kalibr Allan
IMU Allan standard deviation charts
kalibr_allan is a utility repository that provides scripts and tools for calculating IMU noise parameters for use in Kalibr and other IMU filtering systems. While manufacturers typically provide “white noise” values in IMU datasheets, the bias instability and random walk parameters must be determined experimentally. This project enables users to compute those values using Allan variance analysis from recorded IMU data. The workflow involves recording IMU measurements with the device stationary, converting ROS bag files into MATLAB-compatible formats, and then running MATLAB scripts to generate Allan deviation plots. These plots are analyzed to determine noise density and random walk parameters for both gyroscopes and accelerometers. The repository also includes example data and plots from real sensors such as the XSENS MTI-G-700, Tango Yellowstone Tablet, and ASL-ETH VI-Sensor, providing reference points for interpretation. -
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Exclusively Dark Image Dataset
ExDARK dataset is the largest collection of low-light images
The Exclusively Dark (ExDARK) dataset is one of the largest curated collections of real-world low-light images designed to support research in computer vision tasks under challenging lighting conditions. It contains 7,363 images captured across ten different low-light scenarios, ranging from extremely dark environments to twilight. Each image is annotated with both image-level labels and object-level bounding boxes for 12 object categories, making it suitable for detection and classification tasks. The dataset was created to address the lack of large-scale low-light datasets available for research in object detection, recognition, and enhancement. It has been widely used in studies of low-light image enhancement, deep learning approaches, and domain adaptation for vision models. Researchers can also explore its associated source code for low-light image enhancement tasks, making it an essential resource for advancing work in night-time and low-light visual recognition. -
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PythonRobotics
Python sample codes and textbook for robotics algorithms
PythonRobotics is a Python code collection and textbook for learning robotics algorithms through readable examples. It covers practical topics such as localization, mapping, path planning, path tracking, control, SLAM, and autonomous navigation. The project is written to make each algorithm’s core idea easy to understand, rather than hiding the logic behind large frameworks. It keeps dependencies minimal so learners can focus on the math, implementation, and behavior of each robotics method. Visual examples and simulations help users see how algorithms move, estimate, plan, and react. PythonRobotics is especially useful for students, researchers, and engineers who want a hands-on reference for robotics fundamentals in Python. -
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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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React Fiber Architecture
A description of React's new core algorithm, React Fiber
The React Fiber Architecture project is a detailed technical document that explains the internal design and behavior of React Fiber, the core algorithm that powers modern React rendering. Rather than being a traditional code library, it serves as an educational deep dive into how React manages updates, scheduling, and reconciliation under the hood. The document explores how Fiber replaces the older stack-based reconciliation algorithm with a more flexible system that breaks rendering work into incremental units. This enables advanced features such as interruptible rendering, prioritization of updates, and smoother user interfaces during complex operations. It also introduces the concept of fibers as data structures representing units of work that can be paused, resumed, or reused. The project is especially valuable for developers who want to understand React’s performance model and concurrency features at a low level. -
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ai-notebooks
Some ipython notebooks implementing AI algorithms
ai-notebooks is a collection of Jupyter notebooks that implements machine-learning and artificial-intelligence ideas in compact, inspectable experiments. The examples are written primarily in Python and use frameworks including TensorFlow, PyTorch, Keras, JAX, and tinygrad. Projects explore problems such as MNIST learning, GANs, VAEs, model compression, and knowledge distillation. Other notebooks examine reinforcement learning through PPO, SAC, TD3, VPG, and MuZero experiments. Transformer, recurrent-network, counterfactual-regret, and normalizing-flow examples broaden the collection. The notebooks are designed to be read directly on GitHub, making the repository useful for studying algorithms through working code. -
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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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An open source workbench for chemo- and bioinformatics built on the Eclipse Rich Client Platform (RCP).
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basE91 is an advanced method for encoding binary data as ASCII characters. It is similar to UUencode or base64, but is more efficient. The overhead produced by basE91 depends on the input data. It amounts at most to 23% and can range down to 14%.
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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.