Open Source Windows Algorithms - Page 2
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Algorithms for Windows
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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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FileVerifier++ is a Windows utility for calculating hashes using a number of algorithms including CRC32, MD5, SHA-1, SHA-256/224/384/512, WHIRLPOOL, and RIPEMD-128/160/256/320. Supported hash file formats include MD5SUM .MD5, SFV, BSD CKSUM, and others.
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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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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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Fsum Frontend is a files integrity checker. It can calculate 96 hash and checksum algorithms(CRC32, MD5, SHA1, SHA2, ADLER, DHA256, FORK256, ...). You can verify your files using a .sfv/.md5/.sha1/.sha2 file or create your own checksum 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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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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Nokia flash tools
Nokia flashing tools
nokia flashing tools make using hands and lack resolved problem the design prevent virus and malware in nokia phones nokia flashing tool only using fastboot mode -
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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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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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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms. -
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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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Smart Algorithm
Repository implementing a variety of intelligent algorithms
Smart-Algorithm is a repository implementing a variety of intelligent / metaheuristic optimization algorithms (e.g. Genetic Algorithm, Ant Colony, Particle Swarm, Immune Algorithm). The implementations are provided in multiple languages (Java, Python, MATLAB). The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. Particle Swarm Optimization (PSO) implementations in multiple languages. Immune Algorithm (or immune-inspired optimization) implementations. Multiple versions/language compatibility (Java, Python, MATLAB). -
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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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Linear Program Solver
Solve linear programming problems
Linear Program Solver (LiPS) is an optimization package oriented on solving linear, integer and goal programming problems. The main features of LiPS are: ● LiPS is based on the efficient implementation of the modified simplex method that solves large scale problems. ● LiPS provides not just an answer, but a detailed solution process as a sequence of simplex tables, so you can use it for studying/teaching linear programming. ● LiPS gives sensitivity analysis procedures, which allow us to study the behaviour of the model when you change its parameters, including: analysis of changes in the right sides of constraints, analysis of changes in the coefficients of the objective function, analysis of changes in the column/row of the technology matrix. Such information may be extremely useful for the practical application of LP Models. ● LiPS provides methods of goal programming, including lexicographic and weighted GP methods, which are oriented on multi-objective optimisation. -
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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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Continuation Core and Toolboxes (COCO)
Toolboxes for parameter continuation and bifurcation analysis.
Development platform and toolboxes for parameter continuation, e.g., bifurcation analysis of dynamical systems and constrained design optimization. This material is based upon work partially supported by the National Science Foundation under Grant No. 1016467 and the Danish research council (FTP) under the project number 0602-00753B. Any opinions, findings, and conclusions or recommendations expressed on this site are those of the authors and do not necessarily reflect the views of the National Science Foundation or other funding sources. Documentation and tutorials are available for the following toolboxes: * ep : continuation and bifurcations of equilibrium points * coll : continuation of constrained collections of trajectory segments, including multi-segment boundary-value problems * po : continuation and bifurcations of periodic orbits in smooth and hybrid systems * recipes : collection of examples from the book Recipes for Continuation -
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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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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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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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Binarytree
Python library for studying Binary Trees
Binarytree is Python library that lets you generate, visualize, inspect and manipulate binary trees. Skip the tedious work of setting up test data, and dive straight into practicing algorithms. Heaps and BSTs (binary search trees) are also supported. Binarytree supports another representation which is more compact but without the indexing properties. Traverse trees using different algorithms. -
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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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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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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.
