Open Source Mac Algorithms
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Algorithms for Mac
View 1459 business solutionsBrowse free open source Algorithms and projects for Mac 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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Technical analysis library with indicators like ADX, MACD, RSI, Stochastic, TRIX... includes also candlestick pattern recognition. Useful for trading application developpers using either Excel, .NET, Mono, Java, Perl or C/C++.
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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 It has been superceded by my Clipper2 library - https://github.com/AngusJohnson/Clipper2.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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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: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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GNSS-SDR
An open source software-defined GNSS receiver
An open source software-defined Global Navigation Satellite Systems (GNSS) receiver written in C++ and based on the GNU Radio framework. -
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Arduino
Open-source electronics platform
Arduino is an open-source physical computing platform based on a simple I/O board and a development environment that implements the Processing/Wiring language. Arduino can be used to develop stand-alone interactive objects or can be connected to software on your computer (e.g. Flash, Processing and MaxMSP). The boards can be assembled by hand or purchased preassembled. Arduino is a popular tool for IoT product development as well as one of the most successful tools for STEM/STEAM education. Hundreds of thousands of designers, engineers, students, developers and makers around the world are using Arduino to innovate in music, games, toys, smart homes, farming, autonomous vehicles, and more. Arduino is the first widespread Open Source Hardware project and was set up to build a community that could help spread the use of the tool and benefit from contributions from hundreds of people who helped debug the code, write examples, create tutorials, etc. -
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Real-ESRGAN ncnn Vulkan
NCNN implementation of Real-ESRGAN
Real-ESRGAN ncnn Vulkan is an optimized, cross-platform implementation of Real-ESRGAN using the ncnn neural network inference engine and Vulkan for hardware acceleration. Unlike the standard PyTorch-based Real-ESRGAN code, this variant is written in C/C++ and designed to run efficiently on many platforms (including Windows, Linux, and possibly Android) without requiring heavy frameworks like CUDA or Python. It provides command-line tools for upscaling images with selected models, allowing users to specify input/output paths, scaling factors, tile sizes, and model names from a compressed model set, which is particularly helpful for larger images or automated workflows. The Vulkan backend enables fast execution on GPUs from different vendors (Intel/AMD/Nvidia) with broad support, making it suitable for non-Python environments, production systems, or performance-constrained setups. -
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Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. It emphasizes usability with utilities that handle alpha channels, gray/16-bit images, and tiled inference for large inputs, and can be run via Python scripts or portable executables. -
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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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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
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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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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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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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Anime4K
Anime4K is an open-source, high-quality anime upscaling algorithm
SISR algorithm designed to work with Japanese animation and cartoons to generate high-resolution images from a low-resolution input. -
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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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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals, students, research groups and businesses. ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
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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. The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. This repository is an ideal reference for students, educators, and developers seeking hands-on experience with algorithmic concepts in Python. -
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WOFF2
This document documents how to run the compression reference code
woff2 is Google’s reference implementation of the WOFF2 webfont format, the modern, highly compressed container used by browsers to ship OpenType/TrueType fonts efficiently over the network. It integrates specialized transforms for font tables (like glyf/loca and variations data) with Brotli compression to squeeze out as many bytes as possible while preserving exact font fidelity on decode. The repository includes a compact C/C++ library and small command-line tools so you can convert existing TTF/OTF files to WOFF2 and back for testing or build pipelines. Its encoder applies deterministic, spec-compliant transformations that maximize compressibility without altering rendering results, making it safe for production web delivery. The decoder is just as strict, validating headers and table checksums to guard against malformed inputs. Because WOFF2 is now ubiquitous across browsers and CDNs, this repo often serves as the canonical baseline for tooling. -
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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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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. You can run it as a command-line tool or call it as a library via FormatCode / FormatFile, making it easy to embed in editors, CI, and custom tooling. Styles are highly configurable: start from presets like pep8, google, yapf, or facebook, then override dozens of options in .style.yapf, setup.cfg, or pyproject.toml. It supports recursive directory formatting, line-range formatting, and diff-only output so you can check or fix just the lines you touched. -
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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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Free Java Flowchart simulator / interpreter
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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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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AlphaTensor
AI discovers faster, efficient algorithms for matrix multiplication
AlphaTensor, developed by Google DeepMind, is the research codebase accompanying the 2022 Nature publication “Discovering faster matrix multiplication algorithms with reinforcement learning.” The project demonstrates how reinforcement learning can be used to automatically discover efficient algorithms for matrix multiplication — a fundamental operation in computer science and numerical computation. The repository is organized into four main components: algorithms, benchmarking, nonequivalence, and recombination. These contain implementations of the discovered matrix multiplication algorithms, tools to benchmark their real-world performance, proofs of nonequivalence among thousands of solutions, and methods for decomposing larger problems into smaller factorizations. Users can explore AlphaTensor’s discovered algorithms interactively using Colab notebooks or Python scripts.
