multivariate_gaussian.h

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/* Author: Mrinal Kalakrishnan */

#ifndef MULTIVARIATE_GAUSSIAN_H_
#define MULTIVARIATE_GAUSSIAN_H_

#include <eigen3/Eigen/Core>
#include <eigen3/Eigen/Cholesky>
#include <boost/random/variate_generator.hpp>
#include <boost/random/normal_distribution.hpp>
#include <boost/random/mersenne_twister.hpp>
#include <cstdlib>

namespace chomp
{

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);

private:
  Eigen::VectorXd mean_;                
  Eigen::MatrixXd covariance_;          
  Eigen::MatrixXd covariance_cholesky_; 
  int size_;
  boost::mt19937 rng_;
  boost::normal_distribution<> normal_dist_;
  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),
  gaussian_(rng_, normal_dist_)
{
  rng_.seed(rand());
  size_ = mean.rows();
}

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

}

#endif /* MULTIVARIATE_GAUSSIAN_H_ */