C++ numpy-like template-based array implementation.

Description

Implements two flavours of N-dimensional array in a minimalistic way.

Static array

std::array-based implementation, in which the element type and all the dimensions are fixed and determined at compile time. This implies stack array storage.

Dynamic array

std::vector-based implementation in which only the element type is known at compile time. This implies heap array storage.

Latest artifact

https://mgorshkov.jfrog.io/artifactory/default-generic-local/np/np-0.0.4.tgz

Requirements

Any C++17-compatible compiler:

gcc 8 or higher
clang 6 or higher
Visual Studio 2017 or higher

Repo

git clone https://github.com/mgorshkov/np.git

Build unit tests and sample

Linux/MacOS

mkdir build && cd build
cmake ..
cmake --build .

Windows

mkdir build && cd build
cmake ..
cmake --build . --config Release

Build docs

cmake --build . --target doc

Open np/build/doc/html/index.html in your browser.

Install

cmake .. -DCMAKE_INSTALL_PREFIX:PATH=~/np_install

cmake --build . --target install

Usage example (samples/monte-carlo)

#include <iostream>
#include <np/Creators.hpp>
 
int main(int, char **) {
    // PI number calculation with Monte-Carlo method
    using namespace np;
    Size size = 10000000;
    auto rx = random::rand(size);
    auto ry = random::rand(size);
    auto dist = rx * rx + ry * ry;
    auto inside = (dist["dist<1"]).size();
    std::cout << "PI=" << 4 * static_cast<double>(inside) / size;
    return 0;
}

How to build the sample

Clone the repo
git clone https://github.com/mgorshkov/np.git
cd samples/monte-carlo
cd samples/monte-carlo
Make build dir
mkdir -p build-release && cd build-release
Configure cmake
cmake -DCMAKE_BUILD_TYPE=Release ..
Build

Linux/MacOS

cmake --build .

Windows

cmake --build . --config Release

Run the app
$./monte_carlo

PI=3.14158

Links

Scientific methods on top of NP library: https://github.com/mgorshkov/scipy
ML Methods from scikit-learn library: https://github.com/mgorshkov/sklearn