multivariate_gaussian.h
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00034 
00035 /* Author: Mrinal Kalakrishnan */
00036 
00037 #ifndef MULTIVARIATE_GAUSSIAN_H_
00038 #define MULTIVARIATE_GAUSSIAN_H_
00039 
00040 #include <eigen3/Eigen/Core>
00041 #include <eigen3/Eigen/Cholesky>
00042 #include <boost/random/variate_generator.hpp>
00043 #include <boost/random/normal_distribution.hpp>
00044 #include <boost/random/mersenne_twister.hpp>
00045 #include <cstdlib>
00046 
00047 namespace chomp
00048 {
00049 
00053 class MultivariateGaussian
00054 {
00055 public:
00056   template <typename Derived1, typename Derived2>
00057   MultivariateGaussian(const Eigen::MatrixBase<Derived1>& mean, const Eigen::MatrixBase<Derived2>& covariance);
00058 
00059   template <typename Derived>
00060   void sample(Eigen::MatrixBase<Derived>& output);
00061 
00062 private:
00063   Eigen::VectorXd mean_;                
00064   Eigen::MatrixXd covariance_;          
00065   Eigen::MatrixXd covariance_cholesky_; 
00067   int size_;
00068   boost::mt19937 rng_;
00069   boost::normal_distribution<> normal_dist_;
00070   boost::variate_generator<boost::mt19937, boost::normal_distribution<> > gaussian_;
00071 };
00072 
00074 
00075 template <typename Derived1, typename Derived2>
00076 MultivariateGaussian::MultivariateGaussian(const Eigen::MatrixBase<Derived1>& mean, const Eigen::MatrixBase<Derived2>& covariance):
00077   mean_(mean),
00078   covariance_(covariance),
00079   covariance_cholesky_(covariance_.llt().matrixL()),
00080   normal_dist_(0.0,1.0),
00081   gaussian_(rng_, normal_dist_)
00082 {
00083   rng_.seed(rand());
00084   size_ = mean.rows();
00085 }
00086 
00087 template <typename Derived>
00088 void MultivariateGaussian::sample(Eigen::MatrixBase<Derived>& output)
00089 {
00090   for (int i=0; i<size_; ++i)
00091     output(i) = gaussian_();
00092   output = mean_ + covariance_cholesky_*output;
00093 }
00094 
00095 }
00096 
00097 #endif /* MULTIVARIATE_GAUSSIAN_H_ */


chomp_motion_planner
Author(s): Gil Jones
autogenerated on Wed Sep 16 2015 04:42:45