#include <eigenmvn.h>

Public Member Functions
void	set_covar (const Matrix< Scalar, Dynamic, Dynamic > &covar)

void	set_transform (const Matrix< Scalar, Dynamic, Dynamic > &transform)

	EigenMultivariateNormal (const bool &use_cholesky=false, const uint64_t &seed=std::mt19937::default_seed)

	EigenMultivariateNormal (const Matrix< Scalar, Dynamic, 1 > &mean, const Matrix< Scalar, Dynamic, Dynamic > &covar, const bool &use_cholesky=false, const uint64_t &seed=std::mt19937::default_seed)

void	setMean (const Matrix< Scalar, Dynamic, 1 > &mean)

void	setCovar (const Matrix< Scalar, Dynamic, Dynamic > &covar)

Matrix< Scalar, Dynamic,-1 >	samples (int nn, double factor)

Matrix< Scalar, Dynamic,-1 >	samples_ind (int nn, double factor)

Matrix< Scalar, Dynamic,-1 >	samples_ind (int nn)

Public Attributes
SelfAdjointEigenSolver< Matrix < Scalar, Dynamic, Dynamic > >	_eigenSolver

Detailed Description

Find the eigen-decomposition of the covariance matrix and then store it for sampling from a multi-variate normal

Member Function Documentation

template<typename Scalar>

Matrix<Scalar,Dynamic,-1> Eigen::EigenMultivariateNormal< Scalar >::samples	(	int	nn,
		double	factor
	)

inline

Draw nn samples from the gaussian and return them as columns in a Dynamic by nn matrix

The documentation for this class was generated from the following file: