Search Results for "data.6bin"
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Showing 352 open source projects for "data.6bin"
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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. -
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Zstandard
Zstandard - Fast real-time compression algorithm
Zstandard is a fast compression algorithm, providing high compression ratios. It also offers a special mode for small data, called dictionary compression. The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below). Zstandard library is provided as open source software using a BSD license. Its format is stable and published as IETF RFC 8478. The negative compression levels, specified with --fast=#, offer faster compression and decompression speed in exchange for some loss in compression ratio compared to level 1, as seen in the table above. ... -
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LZ4
Extremely fast compression algorithm
...This benchmark simulates simple "static content transfer" scenario such as OS Kernel compression or video game's static assets (text/images/tables/scripts/etc) which loading from Flash Memory / HDD / SSD. In this case, compression time is completely ignored. Because only content developers compress the data at once and usually they don't care about its computational cost. But they always care end user's experience a.k.a. "loading time" and bandwidth. Please pay attention to "LZ4HC -9" which is quite faster than other methods. -
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C# Algorithms
Plug-and-play class-library project of standard data structures
...Algorithms, a class library project which contains the Algorithms implementations. Data Structures, a class library project which contains the Data Structures implementations. Also, UnitTest, a unit-testing project for the Algorithms and Data Structures. -
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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. ... -
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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. -
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CryptoSwift
Collection of standard and secure cryptographic algorithms
...Embedded frameworks require a minimum deployment target of iOS 9 or macOS Sierra (10.12). CryptoSwift uses array of bytes aka Array<UInt8> as a base type for all operations. Every data may be converted to a stream of bytes. You will find convenience functions that accept String or Data, and it will be internally converted to the array of bytes. -
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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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FATE
An industrial grade federated learning framework
...FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. FedAI is a community that helps businesses and organizations build AI models effectively and collaboratively, by using data in accordance with user privacy protection, data security, data confidentiality and government regulations. -
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tsfresh
Automatic extraction of relevant features from time series
...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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KACTL
KTH algorithm competition template library
KACTL (the KTH Algorithmic Contest Template Library) is an extensively curated and high-performance C++ algorithms library created by the competitive programming team at the Royal Institute of Technology (KTH) to serve as a trusted, battle-tested codebase for algorithmic contests, programming competitions, and general algorithm development. The repository aggregates dozens of concise implementations of essential data structures, numerical methods, graph algorithms, string processing tools, computational geometry routines, and optimization techniques, all designed with speed, correctness, and compactness in mind. Instead of reinventing algorithms on the fly during contests like ACM-ICPC or Codeforces rounds, competitors can import exactly the component they need — whether a segment tree with lazy propagation, a minimum cost flow solver, or a fast Fourier transform — and focus their energy on problem logic and strategy. -
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RBush
High-performance JavaScript R-tree-based 2D spatial index
RBush is a high-performance JavaScript library for 2D spatial indexing of points and rectangles. It's based on an optimized R-tree data structure with bulk insertion support. Spatial index is a special data structure for points and rectangles that allows you to perform queries like "all items within this bounding box" very efficiently (e.g. hundreds of times faster than looping over all items). It's most commonly used in maps and data visualizations. The demos contain visualization of trees generated from 50k bulk-loaded random points. ... -
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TBOX
A glib-like multi-platform c library
...The project focuses on making C development easier and provides many modules (.e.g stream, coroutine, regex, container, algorithm ...), so that any developer can quickly pick it up and enjoy the productivity boost when developing in C language. It supports the following platforms: Windows, Macosx, Linux, Android, iOS, BSD and etc. Supports file, data, http and socket source. Supports the stream filter for gzip, charset. etc. Implements stream transfer. Implements the static buffer stream for parsing data. Supports coroutine and implements asynchronous operation. The coroutine library. Provides high-performance coroutine switch. Supports arm, arm64, x86, x86_64. Provides channel interfaces. ... -
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Swift Algorithm Club
Algorithms and data structures in Swift, with explanations
Swift Algorithm Club is a project that implements a broad collection of algorithms and data structures in the Swift programming language, with clear commentary and educational intent. Its purpose is not primarily to be a utility library, but rather to teach the how and why behind algorithms—readers can study implementations, complexity, and design choices in a Swift context. The repository includes common classic algorithms (sorting, searching, trees, graphs, dynamic programming, etc.) and structures (queues, heaps, tries, balanced trees) along with writeups explaining them. ... -
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xxHash
Extremely fast non-cryptographic hash algorithm
...It is proposed in four flavors (XXH32, XXH64, XXH3_64bits and XXH3_128bits). The latest variant, XXH3, offers improved performance across the board, especially on small data. It successfully completes the SMHasher test suite which evaluates collision, dispersion and randomness qualities of hash functions. Code is highly portable, and hashes are identical across all platforms (little / big endian). Performance on large data is only one part of the picture. Hashing is also very useful in constructions like hash tables and bloom filters. ... -
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NutsDB
A simple, fast, embeddable, persistent key/value store written in Go
A simple, fast, embeddable, persistent key/value store written in pure Go. It supports fully serializable transactions and many data structures such as list, set, sorted set. It supports fully serializable transactions and many data structures such as list、set、sorted set. All operations happen inside a Tx. Tx represents a transaction, which can be read-only or read-write. Read-only transactions can read values for a given bucket and a given key or iterate over a set of key-value pairs. ... -
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Yangshun Lago
Data Structures and Algorithms library in TypeScript and JavaScript
Lago is a study-oriented library of classic data structures and algorithms implemented in JavaScript with an emphasis on readability and learning. Instead of aiming to be a production runtime, it serves as a reference you can step through to understand how arrays, stacks, queues, linked lists, trees, heaps, graphs, and sorting/searching routines actually work. The implementations favor clarity over micro-optimizations, making them approachable for learners who are new to algorithmic thinking or coming from non-CS backgrounds. ... -
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JCTools
Java Concurrency Tools for the JVM
Java Concurrency Tools for the JVM. This project aims to offer some concurrent data structures currently missing from the JDK. There’s more to come and contributions/suggestions are most welcome. JCTools has enjoyed support from the community and contributions in the form of issues/tests/documentation/code have helped it grow. JCTools offers excellent performance at a reasonable price (FREE! under the Apache 2.0 License). -
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
...Written primarily in Scala, it shows the architecture of large-scale recommendation systems, including candidate sourcing, ranking, and heuristics. While certain components (such as safety layers, spam detection, or private data) are excluded, the release provides valuable insights into the design of real-world machine learning–driven ranking systems. The project is intended as a reference for researchers, developers, and the public to study, experiment with, and better understand the mechanisms behind social media content. -
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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. ... -
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Flatbush
A very fast static spatial index for 2D points and rectangles in JS
...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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PRML
PRML algorithms implemented in Python
...Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM algorithms, approximate inference, and sequential data methods — all following the book’s structure and notation. Many of these algorithms are paired with Jupyter notebooks that let users interact with the code, visualize results, and experiment with parameters in a way that deeply strengthens theoretical understanding. -
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X For You Feed Algorithm
Algorithm powering the For You feed on X
X For You Feed Algorithm is the open-sourced core recommendation system that powers the For You feed on X (the social network formerly known as Twitter), and it represents one of the first times a major social platform has published production-level ranking code for public review and experimentation. The repository contains the full pipeline that ingests user engagement and content candidate data, processes it through retrieval, hydration, filtering, scoring, and selection layers, and ultimately ranks posts to show what appears in a user’s feed. At its heart, the system uses a transformer-based model adapted from xAI’s Grok architecture to predict probabilities for various user actions (such as likes, replies, reposts, clicks, and negative signals), then combines those into a weighted final score that drives ranking. -
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ngraph.path
Path finding in a graph
...It can be integrated with visualization libraries like VivaGraphJS to animate or highlight computed paths in a rendered graph, enabling interactive routing features. Its data structures and algorithm choices are optimized for performance and memory efficiency, so even large meshes or road networks can be navigated interactively. With its standalone design, ngraph.path can be used in browser apps, server-side Node.js services. -
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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].
