eigen experiments

eigen_matrix.cpp

#include <iostream>
#include <Eigen/Dense>
#include <Eigen/StdVector>
 
using Eigen::MatrixXd;
using namespace std;
using namespace Eigen;




void initialize(MatrixXd& matrix){
  for(int i=0;i<matrix.rows();i++){
    for(int j=0;j<matrix.cols();j++){
      matrix(i,j) = (i+j)*1.0;
    }
  }
}
int normalize(ArrayXXf& arr, ArrayXf& mean, ArrayXf& std){
     
     return 0;

}
 
int main()
{

    std::cout << "testing array functions" << std::endl;

    int num_of_rows = 10;
    int num_of_cols = 20;
    ArrayXXf arr(num_of_rows, num_of_cols);
    
    // initialize array
    for(int i=0;i<20;i++){
      arr.col(i) = Eigen::ArrayXf::LinSpaced(num_of_rows, 0, i);
    }


    MatrixXd m(num_of_rows,num_of_cols);
    initialize(m);
    std::cout << "original matrix: " << std::endl;
    std::cout << m << std::endl;
    VectorXd means_rows = m.rowwise().mean();
    std::cout << "print means by rows" << std::endl;
    std::cout << means_rows.size() << std::endl;
    for(int i=0;i<means_rows.size();i++){
          //means_rows(i) = m.row(i).mean();
          std::cout << means_rows(i) << " ";
    }
    std::cout << std::endl;



    std::cout << "testing minus" << std::endl;
    auto result = m.colwise() - means_rows;
    std::cout << result << std::endl;
}