awesome-repositories.com
Blog
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

44 dépôts

Awesome GitHub RepositoriesRandomized Selection Algorithms

Algorithms for selecting items from a collection based on probabilistic weights.

Distinct from Selection Lists: Distinct from UI selection lists: focuses on the mathematical logic of weighted selection rather than interactive UI components.

Explore 44 awesome GitHub repositories matching software engineering & architecture · Randomized Selection Algorithms. Refine with filters or upvote what's useful.

Awesome Randomized Selection Algorithms GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • trekhleb/javascript-algorithmsAvatar de trekhleb

    trekhleb/javascript-algorithms

    196,089Voir sur GitHub↗

    This repository is a comprehensive collection of data structures and algorithms implemented in JavaScript, designed primarily as an educational resource for computer science study and technical interview preparation. It provides modular implementations of fundamental programming concepts, allowing developers to explore algorithmic logic and data organization through self-contained, verifiable code examples. The library distinguishes itself by pairing every implementation with formal Big O notation, providing predictable insights into time and space scaling requirements. Each algorithm is stru

    Implements randomized selection algorithms to return elements based on proportional weights.

    JavaScriptalgorithmalgorithmscomputer-science
    Voir sur GitHub↗196,089
  • azl397985856/leetcodeAvatar de azl397985856

    azl397985856/leetcode

    55,758Voir sur GitHub↗

    This project is a curated educational resource and solution repository for algorithmic challenges, specifically focused on LeetCode problems. It serves as a technical reference for common data structures and algorithmic patterns, providing verified code implementations across multiple programming languages alongside detailed logic and complexity analysis. The repository functions as a comprehensive study guide for competitive programming and technical interview preparation. It includes specialized learning tools such as an Anki flashcard dataset for spaced repetition and a browser extension t

    The project selects a fixed number of random elements from a data stream ensuring equal probability.

    JavaScriptalgoalgorithmalgorithms
    Voir sur GitHub↗55,758
  • kodecocodes/swift-algorithm-clubAvatar de kodecocodes

    kodecocodes/swift-algorithm-club

    29,099Voir sur GitHub↗

    This project is a comprehensive collection of common computer science algorithms and data structures implemented in Swift. It serves as an educational reference and library for studying computational complexity, algorithmic logic, and data structure engineering through practical code examples. The repository provides a wide suite of data structure implementations, including various types of linked lists, heaps, hash tables, and an extensive range of hierarchical trees such as Red-Black, B-Tree, and Splay trees. It also covers diverse sorting and searching techniques, from basic bubble sort to

    Provides random collection sampling using swaps and reservoir sampling to select fixed-size subsets.

    Swiftalgorithmsdata-structuresswift
    Voir sur GitHub↗29,099
  • redis/go-redisAvatar de redis

    redis/go-redis

    22,159Voir sur GitHub↗

    This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

    Enables random sampling of stored data for discovery or testing purposes.

    Gogogolangredis
    Voir sur GitHub↗22,159
  • statsd/statsdAvatar de statsd

    statsd/statsd

    18,046Voir sur GitHub↗

    StatsD is a metrics aggregator and UDP collection server that collects system counters and timers. It functions as a time-series data forwarder, receiving high-frequency metric updates via a lightweight line protocol and summarizing them before flushing the data to a backend. The project features a pluggable metrics backend framework, allowing aggregated statistics to be routed to various third-party monitoring services or time-series databases such as Graphite. It supports horizontal scaling and high availability through a proxy ring distribution system that forwards incoming packets across

    Reduces the volume of timing data by randomly sampling a subset of events to minimize overhead.

    JavaScriptgraphitejavascriptmetrics
    Voir sur GitHub↗18,046
  • memcached/memcachedAvatar de memcached

    memcached/memcached

    14,132Voir sur GitHub↗

    Memcached is a high-performance, distributed, in-memory key-value storage and request routing engine. It functions as a volatile data store designed to accelerate dynamic applications by caching objects in RAM, thereby reducing backend database load and providing sub-millisecond response times. The system utilizes a specialized architecture that organizes memory into fixed-size slabs to minimize fragmentation and maximize throughput for high-concurrency workloads. The project distinguishes itself through a multi-threaded, lock-friendly design that scales across CPU cores and supports complex

    Distributes traffic by selecting target pools at random from available options.

    C
    Voir sur GitHub↗14,132
  • svenstaro/genactAvatar de svenstaro

    svenstaro/genact

    12,099Voir sur GitHub↗

    Genact is a terminal activity simulator and fake log generator designed to create the appearance of professional development and system administration work. It functions as a command line simulation tool that outputs a stream of believable system messages to mimic background computer processing. The tool operates as a terminal screensaver that can prevent a computer from entering an idle or sleep state by maintaining a continuous process of simulated technical activity. It supports multiple predefined scenes and provides controls for simulation speed and run duration. The project includes ca

    Picks simulated log events from predefined sets using probability weights for varied output patterns.

    Rust
    Voir sur GitHub↗12,099
  • mission-peace/interviewAvatar de mission-peace

    mission-peace/interview

    11,306Voir sur GitHub↗

    This project is a comprehensive library of reference implementations for fundamental data structures and algorithms, designed to support technical interview preparation and software engineering assessments. It provides a structured collection of computational techniques for solving complex problems involving arrays, strings, graphs, trees, and mathematical analysis. The library distinguishes itself by offering specialized implementations for advanced topics, including concurrent programming patterns and geometric algorithms. It features thread-safe primitives for managing shared state and tas

    Selects a fixed number of elements from a data stream of unknown size with equal probability.

    Java
    Voir sur GitHub↗11,306
  • tporadowski/redisAvatar de tporadowski

    tporadowski/redis

    9,987Voir sur GitHub↗

    Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

    Selects a random subset of element IDs from a vector set to surface varied samples.

    Credisredis-for-windowsredis-msi-installer
    Voir sur GitHub↗9,987
  • bchavez/bogusAvatar de bchavez

    bchavez/Bogus

    9,700Voir sur GitHub↗

    Bogus is a fake data generator for .NET applications, including C#, F#, and VB.NET. It provides a deterministic mock data engine and an object configuration mapper to produce realistic profiles, addresses, and financial records. The library differentiates itself through a localization data provider that generates region-specific identifiers across various international languages and locales. It ensures reproducibility across executions by using seed values to control the sequence of generated data. The project covers wide-ranging data synthesis capabilities, including the generation of netwo

    Picks random items or subsets from collections using probabilistic weighted selection algorithms.

    C#bogusc-sharpcsharp
    Voir sur GitHub↗9,700
  • zhm-real/pathplanningAvatar de zhm-real

    zhm-real/PathPlanning

    9,294Voir sur GitHub↗

    PathPlanning is a library of animated path planning algorithms that includes implementations of A-star, Dijkstra, RRT, and spline-based trajectory generation for both 2D and 3D environments. The project provides a collection of motion planning algorithms that demonstrate how robots can find collision-free paths through continuous spaces, with each algorithm rendered as a step-by-step visual animation to show how the search or tree grows over time. The library covers three main categories of path planning: sampling-based methods like RRT, RRT-star, and BIT-star that grow trees by randomly samp

    Generates paths by randomly sampling configuration space with RRT, RRT-star, and BIT-star algorithms.

    Pythonanytime-dstaranytime-repairing-astarastar
    Voir sur GitHub↗9,294
  • iamseancheney/python_for_data_analysis_2nd_chinese_versionAvatar de iamseancheney

    iamseancheney/python_for_data_analysis_2nd_chinese_version

    8,937Voir sur GitHub↗

    This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

    Provides methods for randomly reshuffling row order or extracting random subsets of data.

    matplotlibnumpypandas
    Voir sur GitHub↗8,937
  • saulpw/visidataAvatar de saulpw

    saulpw/visidata

    8,834Voir sur GitHub↗

    VisiData is a terminal-based interactive data analysis tool and browser designed for exploring, filtering, and sorting large tabular datasets. It functions as a structured data inspector that loads and flattens complex formats like JSON, XML, and PCAP into interactive sheets, as well as a terminal file manager for navigating directories and performing staged filesystem operations. The project distinguishes itself by rendering data visualizations, such as scatter plots and histograms, directly in the terminal using Unicode Braille characters. It provides a Python-based data wrangling environme

    Selects a random population sample of a specified number of rows from the dataset.

    Pythonclicsvdatajournalism
    Voir sur GitHub↗8,834
  • jamiebuilds/itsy-bitsy-data-structuresAvatar de jamiebuilds

    jamiebuilds/itsy-bitsy-data-structures

    8,577Voir sur GitHub↗

    itsy-bitsy-data-structures is a collection of fundamental computer science data structures implemented in JavaScript. It serves as an educational resource and algorithm study guide, providing simplified code implementations of classic data organization patterns to demonstrate internal logic and usage. The project provides clear and concise JavaScript implementations of stacks, queues, and linked lists. These examples are designed for learning, technical interview preparation, and studying the mechanical behavior of core data structures through code. The implementations utilize various comput

    Provides constant-time element retrieval using numeric index mapping within lists.

    JavaScript
    Voir sur GitHub↗8,577
  • sharingsource/logicstack-leetcodeAvatar de SharingSource

    SharingSource/LogicStack-LeetCode

    7,495Voir sur GitHub↗

    LogicStack-LeetCode is a curated repository of solved algorithm problems and data structure implementations, primarily drawn from the LeetCode platform. Its core identity is a structured collection of solutions designed to support technical interview preparation and competitive programming practice, with each solution accompanied by complexity analyses to help engineers understand performance trade-offs. The repository distinguishes itself through its breadth of coverage across fundamental algorithmic patterns and data structures. It includes implementations for array manipulation, string pro

    Selects a random element from a sequence when the total number of items is not known in advance, using reservoir sampling.

    algorithminterview-practiceinterview-questions
    Voir sur GitHub↗7,495
  • alirezamika/autoscraperAvatar de alirezamika

    alirezamika/autoscraper

    7,297Voir sur GitHub↗

    Autoscraper est une bibliothèque de web scraping automatique et un extracteur de données basé sur des modèles qui apprend les règles d'extraction à partir d'échantillons de données. Il identifie et récupère du texte, des URL et des éléments HTML à partir de pages web en analysant des valeurs d'échantillon pour reproduire des modèles de données à travers différentes URL. Le système fonctionne comme un gestionnaire de modèles de web scraping, permettant aux utilisateurs d'enregistrer et de recharger les règles apprises pour maintenir une extraction de données cohérente. Il prend en charge l'exportation et l'importation des règles de scraping vers un système de fichiers local pour éviter de répéter le processus d'entraînement pour le même site web. La bibliothèque couvre l'extraction automatisée de données web et la récolte de contenu web grâce à l'apprentissage de modèles basé sur des échantillons et à la récupération d'éléments positionnels. Elle peut récupérer à la fois des points de données spécifiques et tous les éléments d'une page qui correspondent aux modèles identifiés à partir des données d'échantillon initiales.

    Fetches specific data points by matching the exact index and order of elements found in training samples.

    Python
    Voir sur GitHub↗7,297
  • hashlips/hashlips_art_engineAvatar de HashLips

    HashLips/hashlips_art_engine

    7,237Voir sur GitHub↗

    This project is a generative art engine designed to create large collections of unique images by layering assets with assigned rarity weights and blending modes. It functions as an art generator that produces unique image sets and corresponding JSON metadata files for use in blockchain-based digital collections. The engine features a trait rarity manager that controls the frequency of specific visual attributes through filename-based weighting. It also includes a pixel art converter that transforms generated image collections into pixelated versions using configurable downsampling ratios. Th

    Implements weighted-random selection algorithms to determine asset usage based on filename-assigned rarity.

    JavaScript
    Voir sur GitHub↗7,237
  • scikit-learn-contrib/imbalanced-learnAvatar de scikit-learn-contrib

    scikit-learn-contrib/imbalanced-learn

    7,104Voir sur GitHub↗

    imbalanced-learn is a dataset balancing framework and Python machine learning extension designed to resample training data and reduce the impact of class imbalance. It provides a toolkit of algorithms for adjusting class distributions to improve model performance on minority class prediction. As a scikit-learn resampling library, it extends the ecosystem with specialized tools for balancing datasets through over-sampling and under-sampling techniques. This allows for the correction of skewed class proportions to reduce model bias toward the majority class. The library implements the scikit-l

    Generates synthetic minority samples by interpolating between existing data points to expand the minority class boundary.

    Python
    Voir sur GitHub↗7,104
  • jerry-git/learn-python3Avatar de jerry-git

    jerry-git/learn-python3

    6,754Voir sur GitHub↗

    This is an interactive Python tutorial delivered as a collection of Jupyter notebooks. It is designed as a structured learning path for beginners, teaching fundamental language concepts through a sequence of lessons that combine explanatory text with runnable code cells and embedded practice exercises. Each notebook is a self-contained unit that introduces a topic, demonstrates it with a minimal code example, and then asks the learner to write code themselves, receiving immediate feedback from the browser-based execution environment. The curriculum is built on a progressive concept-stacking mo

    Teaches retrieving elements by zero-based position as a fundamental list operation.

    HTMLjupyter-notebooklearning-pythonpython-exercises
    Voir sur GitHub↗6,754
  • ecrmnn/collect.jsAvatar de ecrmnn

    ecrmnn/collect.js

    6,571Voir sur GitHub↗

    collect.js is a dependency-free JavaScript library that provides a fluent, chainable interface for manipulating arrays and objects. It mirrors the Laravel Collection API, offering a consistent set of methods for data transformation across JavaScript and Laravel backend environments. The library stores collection data as plain arrays internally and supports fluent method chaining, where each method returns a new collection instance. The library distinguishes itself by closely replicating the Laravel Collection API in JavaScript, mapping each PHP method to an equivalent JavaScript implementatio

    Provides methods to pick random items from a collection or shuffle the entire order.

    JavaScriptcollectionlaravellaravel-collections
    Voir sur GitHub↗6,571
Préc.123Suivant
  1. Home
  2. Software Engineering & Architecture
  3. Randomized Selection Algorithms

Explorer les sous-tags

  • Random Map Key SelectionLogic for selecting a random key from a map structure. **Distinct from Randomized Selection Algorithms:** Distinct from general randomized selection by focusing specifically on map key retrieval regardless of type.
  • Randomized Data Retrieval2 sous-tagsAlgorithms for selecting random subsets of records from a collection. **Distinct from Randomized Selection Algorithms:** Distinct from Randomized Selection Algorithms: focuses on database-level record sampling rather than general probabilistic selection logic.
  • Stream SamplersAlgorithms for selecting a fixed number of elements from a data stream of unknown size with equal probability. **Distinct from Randomized Selection Algorithms:** Distinct from Randomized Selection Algorithms: focuses on stream-based sampling rather than static collection selection.
  • Weighted Random Selections1 sous-tagSelects items from a collection where each element has a proportional chance of being chosen based on assigned probability weights. **Distinct from Randomized Selection Algorithms:** Distinct from Randomized Selection Algorithms: focuses on weighted probability selection rather than general random selection algorithms.