Open Source Windows Algorithms - Page 2
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DAT Freight and Analytics - DATDAT Freight & Analytics operates DAT One, North America’s largest truckload freight marketplace; DAT iQ, the industry’s leading freight data analytics service; and Trucker Tools, the leader in load visibility. Shippers, transportation brokers, carriers, news organizations, and industry analysts rely on DAT for market trends and data insights, informed by nearly 700,000 daily load posts and a database exceeding $1 trillion in freight market transactions. Founded in 1978, DAT is a business unit of Roper Technologies (Nasdaq: ROP), a constituent of the Nasdaq 100, S&P 500, and Fortune 1000. Headquartered in Beaverton, Ore., DAT continues to set the standard for innovation in the trucking and logistics industry.
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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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This is a hash table, implemented in C, supporting constant-time add/find/remove of C structures. Any structure having a unique, arbitrarily-typed key member can be hashed by adding a UT_hash_handle member to the structure and calling these macros.
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Flatbush
A very fast static spatial index for 2D points and rectangles in JS
A really fast static spatial index for 2D points and rectangles in JavaScript. An efficient implementation of the packed Hilbert R-tree algorithm. Enables fast spatial queries on a very large number of objects (e.g. millions), which is very useful in maps, data visualizations and computational geometry algorithms. -
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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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Create and run cloud-based virtual machines.Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications.
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Free Java Flowchart simulator / interpreter
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DecisionTree.jl
Julia implementation of Decision Tree (CART) Random Forest algorithm
Julia implementation of Decision Tree (CART) and Random Forest algorithms. -
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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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Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
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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. -
Axe Credit Portal - ACP- is axefinance’s future-proof AI-driven solution to digitalize the loan process from KYC to servicing, available as a locally hosted or cloud-based software.Founded in 2004, axefinance is a global market-leading software provider focused on credit risk automation for lenders looking to provide an efficient, competitive, and seamless omnichannel financing journey for all client segments (FI, Retail, Commercial, and Corporate.)
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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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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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Arduino FOC
Arduino FOC for BLDC and Stepper motors
We live in very exciting times 😃! BLDC motors are entering the hobby community more and more and many great projects have already emerged leveraging their far superior dynamics and power capabilities. BLDC motors have numerous advantages over regular DC motors but they have one big disadvantage, the complexity of control. Even though it has become relatively easy to design and manufacture PCBs and create our own hardware solutions for driving BLDC motors the proper low-cost solutions are yet to come. One of the reasons for this is the apparent complexity of writing the BLDC driving algorithms, Field oriented control (FOC) being an example of one of the most efficient ones. The solutions that can be found online are almost exclusively very specific for certain hardware configurations and the microcontroller architecture used. Additionally, most of the efforts at this moment are still channeled towards the high-power applications of the BLDC motors and proper low-cost FOC. -
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EASTL
EASTL, Electronic Arts Standard Template Library
EASTL stands for Electronic Arts Standard Template Library. It is a C++ template library of containers, algorithms, and iterators useful for runtime and tool development across multiple platforms. It is a fairly extensive and robust implementation of such a library and has an emphasis on high performance above all other considerations. If you are familiar with the C++ STL or have worked with other templated container/algorithm libraries, you probably don't need to read this. If you have no familiarity with C++ templates at all, then you probably will need more than this document to get you up to speed. In this case, you need to understand that templates, when used properly, are powerful vehicles for the ease of creation of optimized C++ code. A description of C++ templates is outside the scope of this documentation, but there is plenty of such documentation on the Internet. -
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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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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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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. Does not rely heavily on specialized toolboxes or library shortcuts. -
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Rubix ML
A high-level machine learning and deep learning library for PHP
Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects. Our intuitive interface is quick to grasp while hiding alot of power and complexity. Write less code and iterate faster leaving the hard stuff to us. Rubix ML utilizes a versatile modular architecture that is defined by a few key abstractions and their types and interfaces. Train models in a fraction of the time by installing the optional Tensor extension powered by C. Learners such as neural networks will automatically get a performance boost. -
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TextDistance
Compute distance between sequences
Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). TextDistance show benchmarks results table for your system and saves libraries priorities into the libraries.json file in TextDistance's folder. This file will be used by text distance for calling the fastest algorithm implementation. Default libraries.json is already included in the package. -
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VS Code LeetCode
Solve LeetCode problems in VS Code
Simply click Sign in to LeetCode in the LeetCode Explorer will let you sign in with your LeetCode account. By clicking the button btn_endpoint at the explorer's navigation bar, you can switch between different endpoints. The accounts of different endpoints are not shared. Please make sure you are using the right endpoint. The extension will use leetcode.com by default. Directly click on the problem or right-click the problem in the LeetCode Explorer and select Preview Problem to see the problem description. Select Show Problem to directly open the file with the problem description. The extension supports 5 editor shortcuts (aka Code Lens). To manage your LeetCode sessions, just clicking the LeetCode: *** at the bottom of the status bar. You can switch between sessions or create, delete a session. -
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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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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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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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Linear Program Solver (Solvexo) is an optimization package intended for solving linear programming problems. The main features of the Solvexo are: · Solvexo solver is based on the efficient implementation of the simplex method (one or two phases); · Solvexo provides not only an answer, but a detailed solution process as a sequence of simplex matrices, so you can use it in studying (teaching) linear programming. · Solvexo provides a solution with the graphic method for problems with tow variables. · This updated version includes two languages English and French. If you have any questions, feel free to contact me: romdhani.mohamed.ali@gmail.com. Any comments and suggestions would be helpful!
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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.