Eigen Cast Armadillo

Introduction

Eigen Cast Armadillo is a library that enables the conversion of data types between the Eigen and Armadillo libraries. This allows for seamless integration of these two powerful linear algebra libraries within your code.

Features

• Easy to use interface for data type conversion.
• Compatible with Eigen and Armadillo data structures.
• Fast and efficient performance.
• Lightweight library with minimal dependencies.

Installation

To use Eigen Cast Armadillo, you will need to install both the Eigen and Armadillo libraries. These can be installed using package managers or by downloading the source code and building it manually.

Once both libraries are installed, you can download and install Eigen Cast Armadillo using your preferred package manager or by building it from source.

# Using package manager
sudo apt-get install libeigen3-dev libarmadillo-dev
sudo apt-get install libeigen3-armadillo-dev

# Building from source
git clone https://github.com/mapf/eigen-cast-arma.git
cd eigen-cast-arma
mkdir build && cd build
cmake ..
make
sudo make install

Usage

To use Eigen Cast Armadillo, simply include the necessary headers and use the provided conversion functions. Below is an example code snippet that shows how to convert an Armadillo matrix to an Eigen matrix:

#include <eigen_cast_arma.hpp>
#include <armadillo>

int main() {
  // Create an Armadillo matrix
  arma::mat A = arma::ones(3, 3);

  // Convert to Eigen matrix
  Eigen::MatrixXd B = eigen_cast_arma::arma_to_eigen(A);
  
  return 0;
}

Here is another example that shows how to convert an Eigen matrix to an Armadillo matrix:

#include <eigen_cast_arma.hpp>
#include <Eigen/Dense>
#include <armadillo>

int main() {
  // Create an Eigen matrix
  Eigen::MatrixXd A = Eigen::MatrixXd::Constant(3, 3, 1);

  // Convert to Armadillo matrix
  arma::mat B = eigen_cast_arma::eigen_to_arma(A);
  
  return 0;
}

Conclusion

Eigen Cast Armadillo provides a seamless way to integrate the Eigen and Armadillo libraries within your code. Its easy-to-use interface, efficient performance, and minimal dependencies make it a great choice for any project that requires linear algebra operations.