boost Archives - Robotics with ROS

get template parameter name and type with Boost demangle

Leave a Comment / C++, Tutorials / By admin

Have you ever tied to get the type of a variable by its name? for instance “int” or “string“? The following code snippet shows how to use boost demangle to get the type:

#include <boost/units/detail/utility.hpp>
#include <boost/shared_ptr.hpp>
template
<typename T>
void foo(boost::shared_ptr<T> var)
{
    std::string var_type= boost::units::detail::demangle(typeid(*var.get()).name());



    if(var_type.find("int") !=std::string::npos )
    {
        std::cout<<"Type of var is int" <<std::endl;
    }else if(var_type.find("float") !=std::string::npos )
    {
        std::cout<<"Type of var is int" <<std::endl;
    }
    else
    {
        std::cout<<"Type of var is "<<var_type <<std::endl;
    }


}


int main()
{


    boost::shared_ptr<int > x_ptr(new int );
    *x_ptr=10;
    foo(x_ptr);

    boost::shared_ptr<float > y_ptr(new float );
    *y_ptr=10.3;
    foo(y_ptr);


    boost::shared_ptr<double > z_ptr(new double );
    *z_ptr=10.3;
    foo(z_ptr);

    return 0;
}

#include <boost/units/detail/utility.hpp>

#include <boost/shared_ptr.hpp>

template

void foo(boost::shared_ptr<T> var)

{

std::string var_type= boost::units::detail::demangle(typeid(*var.get()).name());

if(var_type.find("int") !=std::string::npos )

{

std::cout<<"Type of var is int" <<std::endl;

}else if(var_type.find("float") !=std::string::npos )

{

std::cout<<"Type of var is int" <<std::endl;

}

else

{

std::cout<<"Type of var is "<<var_type <<std::endl;

}

int main()

{

boost::shared_ptr<int > x_ptr(new int );

*x_ptr=10;

foo(x_ptr);

boost::shared_ptr<float > y_ptr(new float );

*y_ptr=10.3;

foo(y_ptr);

boost::shared_ptr<double > z_ptr(new double );

*z_ptr=10.3;

foo(z_ptr);

return 0;

}

Generating multivariate normal distribution samples using C++11 and Eigen library.

Leave a Comment / C++, Linear Algebra, Machine Learning / By admin

Eigen is a great tool for matrix operations, here I found a small piece of code in Github that enables you to generate multivariate normal distribution samples using C++11 and Eigen library. The below is an example:

#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;
    }

}

#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;

}

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