Open Source Windows Algorithms - Page 2
Sort By:
Algorithms for Windows
View 1954 business solutions-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
1
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. -
2
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.
-
3
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. -
4
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.
-
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
5
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. -
6
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. -
7
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. -
8
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.
-
9
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
-
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
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. -
11
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 -
12
Free Java Flowchart simulator / interpreter
-
13
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. -
14
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. -
15
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. Readers are encouraged to help each other learn, and questions and comments can usually be answered within two days. -
16
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. -
17
NTU RGB-D
Info and sample codes for "NTU RGB+D Action Recognition Dataset"
The “NTU RGB+D” repository provides access to a large-scale dataset for human action recognition (and its extension, NTU RGB+D 120). The dataset includes multiple modalities (RGB video, depth sequences, infrared video, 3D skeletal joint data) captured with multiple Kinect v2 cameras simultaneously. The repository also contains MATLAB / Python demo scripts for loading, visualizing, and processing skeleton data, mapping between modalities, and handling dataset structure. Multi-modal action recognition dataset, RGB, depth, infrared, skeletal data. Split into background / evaluation sets for one-shot evaluation (in the extended dataset). -
18
PlatEMO
Evolutionary multi-objective optimization platform
Evolutionary multi-objective optimization platform. PlatEMO consists of a number of MATLAB functions without using any other libraries. Any machines able to run MATLAB can use PlatEMO regardless of the operating system. 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. -
19
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. -
20
TextTeaser
TextTeaser is an automatic summarization algorithm
textteaser is an automatic text summarization algorithm implemented in Python. It extracts the most important sentences from an article to generate concise summaries that retain the core meaning of the original text. The algorithm uses features such as sentence length, keyword frequency, and position within the document to determine which sentences are most relevant. By combining these features with a simple scoring mechanism, it produces summaries that are both readable and informative. Originally inspired by research and earlier implementations, textteaser provides a lightweight solution for summarization without requiring heavy machine learning models. It is particularly useful for developers, researchers, or content platforms seeking a simple, rule-based approach to article summarization. -
21
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).
-
22
jChecs is an open source Java chess program, designed to introduce the basics of computer chess programming concepts.
-
23
TARQUIN
MRS/NMR analysis software
Analysis software for MRS/NMR data. Allows processing and fitting to be performed in a fully automatic workflow. -
24
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. -
25
