edoNormalAdaptive< EOT > Class Template Reference

A normal distribution that can be updated via several components. More...

#include <edoNormalAdaptive.h>

Inheritance diagram for edoNormalAdaptive< EOT >:

List of all members.

Public Types
typedef EOT::AtomType	AtomType
typedef Eigen::Matrix < AtomType, Eigen::Dynamic, 1 >	Vector
typedef Eigen::Matrix < AtomType, Eigen::Dynamic, Eigen::Dynamic >	Matrix
Public Member Functions
	edoNormalAdaptive (unsigned int dim=1)
	edoNormalAdaptive (unsigned int dim, Vector mean, Matrix C, Matrix B, Vector D, double sigma, Vector p_c, Vector p_s)
unsigned int	size ()
Vector	mean () const
Matrix	covar () const
Matrix	coord_sys () const
Vector	scaling () const
double	sigma () const
Vector	path_covar () const
Vector	path_sigma () const
void	mean (Vector m)
void	covar (Matrix c)
void	coord_sys (Matrix b)
void	scaling (Vector d)
void	sigma (double s)
void	path_covar (Vector p)
void	path_sigma (Vector p)
Private Attributes
unsigned int	_dim
Vector	_mean
Matrix	_C
Matrix	_B
Vector	_D
double	_sigma
Vector	_p_c
Vector	_p_s

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Detailed Description

template<typename EOT>
class edoNormalAdaptive< EOT >

A normal distribution that can be updated via several components.

This is the data structure on which works the CMA-ES algorithm.

This is *just* a data structure, the operators working on it are supposed to maintain its consistency (e.g. of the covariance matrix against its eigen vectors).

The distribution is defined by its mean, its covariance matrix (which can be decomposed in its eigen vectors and values), a scaling factor (sigma) and the so-called evolution paths for the covariance and sigma. evolution paths.

NOTE: this is only available as an Eigen3 implementation (built WITH_EIGEN).

Definition at line 72 of file edoNormalAdaptive.h.

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

• edoNormalAdaptive.h