multivariate_gaussian.h

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#ifndef STOMP_MOVEIT_MULTIVARIATE_GAUSSIAN_H_
#define STOMP_MOVEIT_MULTIVARIATE_GAUSSIAN_H_

#include <Eigen/Core>
#include <Eigen/Cholesky>
#include <boost/random/variate_generator.hpp>
#include <boost/random/normal_distribution.hpp>
#include <boost/random/mersenne_twister.hpp>
#include <boost/shared_ptr.hpp>
#include <cstdlib>

namespace stomp_moveit
{

namespace utils
{

class MultivariateGaussian;
typedef std::shared_ptr<MultivariateGaussian> MultivariateGaussianPtr;

class MultivariateGaussian
{
public:
  template <typename Derived1, typename Derived2>
  MultivariateGaussian(const Eigen::MatrixBase<Derived1>& mean, const Eigen::MatrixBase<Derived2>& covariance);

  template <typename Derived>
  void sample(Eigen::MatrixBase<Derived>& output,bool use_covariance = true);

private:
  Eigen::VectorXd mean_;                
  Eigen::MatrixXd covariance_;          
  Eigen::MatrixXd covariance_cholesky_; 
  int size_;
  boost::mt19937 rng_;
  boost::normal_distribution<> normal_dist_;
  std::shared_ptr<boost::variate_generator<boost::mt19937, boost::normal_distribution<> > > gaussian_;
};


template <typename Derived1, typename Derived2>
MultivariateGaussian::MultivariateGaussian(const Eigen::MatrixBase<Derived1>& mean, const Eigen::MatrixBase<Derived2>& covariance):
  mean_(mean),
  covariance_(covariance),
  covariance_cholesky_(covariance_.llt().matrixL()),
  normal_dist_(0.0,1.0)
{

  rng_.seed(rand());
  size_ = mean.rows();
  gaussian_.reset(new boost::variate_generator<boost::mt19937, boost::normal_distribution<> >(rng_, normal_dist_));
}

template <typename Derived>
void MultivariateGaussian::sample(Eigen::MatrixBase<Derived>& output,bool use_covariance)
{
  for (int i=0; i<size_; ++i)
    output(i) = (*gaussian_)();

  if(use_covariance)
  {
    output = mean_ + covariance_cholesky_*output;
  }
  else
  {
    output = mean_ + output;
  }
}

}

}

#endif /* STOMP_MOVEIT_MULTIVARIATE_GAUSSIAN_H_ */