4 dépôts
Element-wise logical operations designed for compatibility with computational graphs.
Distinguishing note: Focuses on compilation-safe logical operations, distinct from standard language short-circuiting logic.
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This project is a high-performance numerical computing library designed for large-scale scientific and machine learning workloads. It functions as an automatic differentiation framework and a just-in-time compilation engine, transforming high-level Python code into optimized machine instructions. By enforcing pure functional programming patterns and immutable array semantics, the library ensures that mathematical functions remain compatible with automated graph transformations and symbolic differentiation. The platform distinguishes itself through its distributed array computing capabilities,
Executes element-wise logical operations on arrays that remain compatible with compilation and avoid reliance on standard language-specific short-circuiting.
This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en
Evaluates logical conditions across array axes to produce boolean masks for computational graphs.
This project is a comprehensive, community-maintained knowledge base and toolkit designed for competitive programming. It serves as a centralized repository for algorithmic theory, data structures, and mathematical techniques, providing a structured reference for informatics and collegiate programming competitions. The project distinguishes itself by integrating educational content with a robust suite of automation utilities. It provides a complete workflow for competitive programming, including tools for automated test case generation, solution verification, and direct interaction with onlin
Provides bitwise manipulation utilities for high-performance integer processing and state representation.
NumCpp est un framework C++ et une bibliothèque de calcul numérique qui fournit une boîte à outils pour la gestion de tableaux multidimensionnels et des routines mathématiques. Il fonctionne comme une implémentation C++ de l'écosystème NumPy, offrant un framework de calcul scientifique pour gérer des tenseurs et effectuer des équations algébriques complexes. Le projet permet une manipulation de tableaux haute performance dans un environnement C++ sans dépendre d'un runtime Python. Il se distingue en fournissant une interface similaire à NumPy pour exécuter de l'algèbre linéaire, gérer des structures de données multidimensionnelles et effectuer des traitements numériques. La bibliothèque couvre un large éventail de capacités, incluant les opérations algébriques matricielles, la gestion de la géométrie des tableaux via le slicing et le reshaping, et la génération de distributions aléatoires. Elle inclut également des outils pour l'analyse de jeux de données, les statistiques sur les tableaux, et l'import/export de données numériques via des formats binaires et texte.
Provides element-wise logical operations to evaluate conditions and filter multi-dimensional data.