Armadillo is a high quality linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use
Provides high-level syntax (API) deliberately similar to Matlab
Useful for algorithm development directly in C++, or quick conversion of research code into production environments (eg. software & hardware products)
Can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc
Provides efficient classes for vectors, matrices and cubes, as well as 200+ associated functions; integer, floating point and complex numbers are supported
Various matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (eg. multi-threaded Intel MKL, or OpenBLAS)
A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency
Available under a permissive license, useful for both open-source and proprietary (closed-source) software
Install-Package armadillo-code -Version 7.800.2
dotnet add package armadillo-code --version 7.800.2
<PackageReference Include="armadillo-code" Version="7.800.2" />
paket add armadillo-code --version 7.800.2
#r "nuget: armadillo-code, 7.800.2"
- OpenBLAS (>= 0.2.14.1)
NuGet packages (1)
Showing the top 1 NuGet packages that depend on armadillo-code:
mlpack is an intuitive, fast, scalable C++ machine learning library, meant to be a machine learning analog to LAPACK. It aims to implement a wide array of machine learning methods and functions as a swiss army knife for machine learning researchers.
This package is not used by any popular GitHub repositories.