What does rust-ndarray/ndarray do?

ndarray es una biblioteca de arreglos multidimensionales para Rust que sirve como framework de álgebra lineal y herramienta de computación científica. Proporciona la infraestructura central para crear y manipular arreglos de n-dimensiones, funcionando tanto como un procesador de arreglos paralelo como un kit de herramientas para el análisis de datos numéricos.

What are the main features of rust-ndarray/ndarray?

The main features of rust-ndarray/ndarray are: Multidimensional Arrays, Array View Creation, Strided, Zero-Copy Array Views, Tensor Slicing and Indexing, Numeric Type Traits, Array Layout Management, Array Slicing.

What are some open-source alternatives to rust-ndarray/ndarray?

Open-source alternatives to rust-ndarray/ndarray include: xtensor-stack/xtensor — xtensor is a C++ multidimensional array library for numerical computing that provides N-dimensional containers with an… dpilger26/numcpp — NumCpp is a C++ framework and numerical computing library that provides a toolkit for multi-dimensional array… numpy/numpy — NumPy is a foundational library for scientific computing in Python, providing a comprehensive framework for managing… lyhue1991/eat_tensorflow2_in_30_days — This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow.… arrayfire/arrayfire — ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance… torch/torch7 — Torch7 is a scientific computing environment and tensor computation library used for deep learning research and…

Ndarray - manipular arrays multidimensionales…

Ndarray - manipular arrays multidimensionales… | Awesome Repos

Features

Multidimensional Arrays - Provides the core infrastructure for managing and manipulating n-dimensional arrays and matrices.
Array View Creation - Produces read-only or mutable windows into existing data buffers without copying underlying elements.
Strided - Maps multidimensional indices to flat memory buffers using axis-specific step sizes for efficient zero-copy slicing.
Zero-Copy Array Views - Provides lightweight references to existing memory buffers to allow slicing and reshaping without data duplication.
Tensor Slicing and Indexing - Extracts specific elements or sub-regions of arrays using coordinate-based indexing and slicing.
Numeric Type Traits - Uses generics and numeric traits to apply mathematical operations across different numeric types and dimensions.
Array Layout Management - Modifies shape and stride information independently of the underlying data using dedicated reference types.
Array Slicing - Extracts sub-sections of arrays using arbitrary step sizes and negative indices.
Element-wise Array Operations - Executes high-performance element-wise operations and mathematical functions across multidimensional arrays.
Generalized Matrix Multiplications - Executes high-performance matrix product computations with support for scalar scaling.
High-Performance Array Arithmetic - Executes high-performance mathematical operations across arrays of varying dimensions using optimized routines.
Scientific Computing - Provides a computational framework for performing complex mathematical modeling and multi-dimensional array operations.
Linear Algebra Libraries - Serves as a high-performance framework for vector and matrix operations.
Linear Algebra Routines - Implements fundamental linear algebra operations including matrix multiplication and dot products.
Multi-Dimensional Arrays - Implements data structures that organize numeric elements into grids or higher-dimensional spaces.
Multidimensional Array Containers - Offers dense data structures supporting various memory layouts for numerical computing and data analysis.
N-Dimensional Array Libraries - Provides a comprehensive Rust library for creating and manipulating multidimensional arrays with efficient memory views.
Linear Algebra - Provides high-performance routines for vector and matrix operations, including dot products.
Parallel Array Processing - Increases processing speed by distributing array iterations and methods across multiple CPU threads.
Dot Product Computation - Calculates the inner product of two arrays, including those with dynamic dimensions.
Ownership and Borrowing - Uses Rust's ownership and borrowing semantics to separate data ownership from data access via unified interfaces.
Multidimensional Shape Handling - Implements runtime-defined vectors to support arrays with an arbitrary number of axes.
Dimension Squeezing - Removes dimensions of size one from an array to simplify its overall shape.
Scientific Array Serialization - Supports persisting and loading multidimensional arrays using scientific data formats.
Axis Permutation - Changes the order of dimensions or reverses the direction of specific axes in-place.
Parallel Iterators - Distributes element-wise operations and axis reductions across multiple CPU cores using parallel iterators.
BLAS Backend Integration - Leverages optimized backend math libraries for high-speed floating-point matrix multiplication.
Strided Window Generation - Generates overlapping windows of a specific size along an axis using a defined step size.
Ownership-Based Memory Management - Implements memory management using Rust's ownership and borrowing model to handle owned arrays and non-owning views.
Array Function Mapping - Applies custom functions to corresponding elements of multiple arrays in lock step with broadcasting support.
Array Combinations - Joins arrays together by stacking them along new axes or concatenating them along existing ones.
Array Collapsing - Collapses multidimensional arrays into one-dimensional sequences while preserving element order.
Array Reshaping - Changes the dimensions of an array while preserving elements and specifying memory layout order.
Array Splitting - Divides single arrays into multiple smaller views along a specified axis.
Dimensional Reductions - Reduces arrays by computing sums and other aggregations along specified dimensions.
BLAS and LAPACK Integrations - Provides interfaces to optimized BLAS libraries for high-performance linear algebra and matrix multiplication.
Coordinate Grid Generation - Builds regular grids of values to evaluate functions over a specific coordinate space.
Numerical Computation Accelerations - Accelerates mathematical processing by distributing array operations across multiple CPU threads.
Parallel Array Processors - Distributes heavy mathematical iterations and array methods across multiple CPU threads to accelerate performance.
Scientific Computing Libraries - N-dimensional array library for Rust.

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Ver las 30 alternativas a Ndarray

rust-ndarrayndarray

Ndarray

Features

Alternativas open-source a Ndarray

xtensor-stack/xtensor

dpilger26/NumCpp

numpy/numpy

lyhue1991/eat_tensorflow2_in_30_days

Frequently asked questions

Historial de estrellas

Alternativas open-source a Ndarray

xtensor-stack/xtensor

dpilger26/NumCpp

numpy/numpy

lyhue1991/eat_tensorflow2_in_30_days

Frequently asked questions