#include <fstream>
#include <Eigen/Eigen>
#include <Eigen/Core>
#include <Eigen/Dense>
#include "eigenmvn.h"
int main(int argc, char ** argv)
{
//number of columns
const uint n_cols = 2;
// number of rows
const uint n_rows = 5000;
Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> total_gaussian_data_eigen, first_gaussian_data_eigen,second_gaussian_data_eigen;
first_gaussian_data_eigen.setZero(n_rows,n_cols);
second_gaussian_data_eigen.setZero(n_rows,n_cols);
//2*n_rows since we want to concat first and second and put it in this matrix
total_gaussian_data_eigen.setZero(2*n_rows,n_cols);
Eigen::Vector2f mean;
Eigen::Matrix2f covar;
float theta;
mean << 0,0; // Set the mean
/* our covar matrix:
|10 0|
|0 1|
*/
covar <<10,0,0,1;
//first gaussian
Eigen::EigenMultivariateNormal<float> normX_solver1(mean,covar);
first_gaussian_data_eigen<< normX_solver1.samples(n_rows).transpose() ;
//secodn gaussian
mean << 4,4; // Set the mean
// Create a covariance matrix rotated clockwise by theta
theta=M_PI/4.0;
covar=genCovar(1,4,theta);
Eigen::EigenMultivariateNormal<float> normX_solver2(mean,covar);
second_gaussian_data_eigen<<normX_solver2.samples(n_rows).transpose() ;
//concating two gaussians
total_gaussian_data_eigen<<first_gaussian_data_eigen,second_gaussian_data_eigen;
//writing data into output in csv format
std::cout<<"x,y\n";
for (uint i = 0; i < total_gaussian_data_eigen.rows(); i++)
{
for (uint h = 0; h < n_cols-1; h++)
{
std::cout<<total_gaussian_data_eigen(i,h);
std::cout<<",";
}
std::cout<<total_gaussian_data_eigen(i,n_cols-1);
std::cout<<std::endl;
}
}
