bdm::EKFfull Class Reference

Extended Kalman Filter in full matrices. More...

#include <kalman.h>

List of all members.

Public Member Functions
	EKFfull ()
	Default constructor.
void	set_parameters (diffbifn *pfxu, diffbifn *phxu, const mat Q0, const mat R0)
	Set nonlinear functions for mean values and covariance matrices.
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
void	set_statistics (vec mu0, mat P0)
	set estimates
const epdf &	posterior () const
	dummy!
const enorm< fsqmat > *	_e () const
const mat	_R ()
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	from_setting (const Setting &set)
	This method arrange instance properties according the data stored in the Setting structure.
virtual void	to_setting (Setting &set) const
	This method save all the instance properties into the Setting structure.
virtual void	validate ()
	This method TODO.
Constructors
virtual BM *	_copy_ () const
Mathematical operations
virtual void	bayesB (const mat &Dt)
	Batch Bayes rule (columns of Dt are observations).
virtual double	logpred (const vec &dt) const
vec	logpred_m (const mat &dt) const
	Matrix version of logpred.
virtual epdf *	epredictor () const
	Constructs a predictive density .
virtual mpdf *	predictor () const
	Constructs a conditional density 1-step ahead predictor.
Access to attributes
const RV &	_drv () const
void	set_drv (const RV &rv)
void	set_rv (const RV &rv)
double	_ll () const
void	set_evalll (bool evl0)
Public Attributes
vec	mu
	Mean value of the posterior density.
mat	P
	Variance of the posterior density.
bool	evalll
double	ll
Protected Attributes
diffbifn *	pfxu
	Internal Model f(x,u).
diffbifn *	phxu
	Observation Model h(x,u).
enorm< fsqmat >	E
int	dimx
int	dimy
int	dimu
mat	A
mat	B
mat	C
mat	D
mat	R
mat	Q
mat	_Pp
mat	_Ry
mat	_iRy
mat	_K
RV	drv
	Random variable of the data (optional).
double	ll
	Logarithm of marginalized data likelihood.
bool	evalll
	If true, the filter will compute likelihood of the data record and store it in ll . Set to false if you want to save computational time.
Friends
std::ostream &	operator<< (std::ostream &os, const KalmanFull &kf)
	print elements of KF
Extension to conditional BM
This extension is useful e.g. in Marginalized Particle Filter (bdm::MPF). Alternatively, it can be used for automated connection to DS when the condition is observed
const RV &	_rvc () const
	access function
virtual void	condition (const vec &val)
	Substitute val for rvc.
RV	rvc
	Name of extension variable.
Logging of results
virtual void	set_options (const string &opt)
	Set boolean options from a string, recognized are: "logbounds,logll".
virtual void	log_add (logger &L, const string &name="")
	Add all logged variables to a logger.
virtual void	logit (logger &L)
ivec	LIDs
	IDs of storages in loggers 4:[1=mean,2=lb,3=ub,4=ll].
ivec	LFlags
	Flags for logging - same size as LIDs, each entry correspond to the same in LIDs.

---

Detailed Description

Extended Kalman Filter in full matrices.

An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean.

---

Member Function Documentation

virtual BM* bdm::BM::_copy_

(

)

const [inline, virtual, inherited]

Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:

 BM* _copy_() const {return new BM(*this);} 

Reimplemented in bdm::ARX, bdm::KalmanCh, bdm::EKF< sq_T >, bdm::EKFCh, and bdm::BMEF.

virtual double bdm::BM::logpred

(

const vec &

dt

)

const [inline, virtual, inherited]

Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.

Reimplemented in bdm::ARX, bdm::MixEF, and bdm::multiBM.

Referenced by bdm::BM::logpred_m().

---

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

• kalman.h
• kalman.cpp