multivariate_gaussian.hpp

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#pragma once
 
#include <Eigen/Core>
#include <Eigen/Cholesky>
 
#include <rsl/random.hpp>
#include <cstdlib>
 
namespace stomp_moveit
{
namespace math
{
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_;
  std::normal_distribution<double> normal_dist_;
};
 
 
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)
{
  size_ = mean.rows();
}
 
template <typename Derived>
void MultivariateGaussian::sample(Eigen::MatrixBase<Derived>& output, bool use_covariance)
{
  for (int i = 0; i < size_; ++i)
    output(i) = normal_dist_(rsl::rng());
 
  if (use_covariance)
  {
    output = mean_ + covariance_cholesky_ * output;
  }
  else
  {
    output = mean_ + output;
  }
}
}  // namespace math
}  // namespace stomp_moveit