bdm::EKFCh_dQ Class Reference

Extended Kalman filter in Choleski form with unknown diagonal Q. More...

#include <ekf_template.h>

List of all members.

Public Member Functions

void condition (const vec &Q0)

Substitute val for rvc.

BM * _copy_ () const

copy constructor duplicated - calls different set_parameters

void set_parameters (const shared_ptr< diffbifn > &pfxu, const shared_ptr< diffbifn > &phxu, const chmat Q0, const chmat R0)

Set nonlinear functions for mean values and covariance matrices.

void set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &Q0, const chmat &R0)

set parameters for adapt from Kalman

void bayes (const vec &dt)

Here dt = [yt;ut] of appropriate dimensions.

void from_setting (const Setting &set)

load basic elements of Kalman from structure

void initialize ()

initialize internal parametetrs

void set_statistics (const vec &mu0, const mat &P0)

void set_statistics (const vec &mu0, const chmat &P0)

const enorm< chmat > & posterior () const

posterior

shared_ptr< epdf > shared_posterior ()

shared posterior

void validate ()

virtual string to_string ()

This method returns a basic info about the current instance.

virtual void to_setting (Setting &set) const

This method save all the instance properties into the Setting structure.

int _dimx ()

access function

int _dimy ()

access function

int _dimu ()

access function

const mat & _A () const

access function

const mat & _B () const

access function

const mat & _C () const

access function

const mat & _D () const

access function

const chmat & _Q () const

access function

const chmat & _R () const

access function

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 $f(d_{t+1} |d_{t}, \ldots d_{0})$ .

virtual mpdf * predictor () const

Constructs conditional density of 1-step ahead predictor $f(d_{t+1} |d_{t+h-1}, \ldots d_{t})$ .

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)

Protected Attributes

shared_ptr< diffbifn > pfxu

Internal Model f(x,u).

shared_ptr< diffbifn > phxu

Observation Model h(x,u).

RV yrv

id of output

RV urv

id of input

mat _K

Kalman gain.

shared_ptr< enorm< chmat > > est

posterior

enorm< chmat > fy

marginal on data f(y|y)

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.

int dimx

cache of rv.count()

int dimy

cache of rvy.count()

int dimu

cache of rvu.count()

mat A

Matrix A.

mat B

Matrix B.

mat C

Matrix C.

mat D

Matrix D.

chmat Q

Matrix Q in square-root form.

chmat R

Matrix R in square-root form.

Internal storage - needs initialize()

pre array (triangular matrix)

mat preA

mat postA

post array (triangular matrix)

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

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 Choleski form with unknown diagonal Q.

Member Function Documentation

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.

References bdm_error.

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

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

ekf_template.h


Public Member Functions
void	condition (const vec &Q0)
	Substitute `val` for `rvc`.
BM *	_copy_ () const
	copy constructor duplicated - calls different set_parameters
void	set_parameters (const shared_ptr< diffbifn > &pfxu, const shared_ptr< diffbifn > &phxu, const chmat Q0, const chmat R0)
	Set nonlinear functions for mean values and covariance matrices.
void	set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &Q0, const chmat &R0)
	set parameters for adapt from Kalman
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
void	from_setting (const Setting &set)
	load basic elements of Kalman from structure
void	initialize ()
	initialize internal parametetrs
void	set_statistics (const vec &mu0, const mat &P0)
void	set_statistics (const vec &mu0, const chmat &P0)
const enorm< chmat > &	posterior () const
	posterior
shared_ptr< epdf >	shared_posterior ()
	shared posterior
void	validate ()
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	to_setting (Setting &set) const
	This method save all the instance properties into the Setting structure.
int	_dimx ()
	access function
int	_dimy ()
	access function
int	_dimu ()
	access function
const mat &	_A () const
	access function
const mat &	_B () const
	access function
const mat &	_C () const
	access function
const mat &	_D () const
	access function
const chmat &	_Q () const
	access function
const chmat &	_R () const
	access function
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 $f(d_{t+1} \|d_{t}, \ldots d_{0})$ .
virtual mpdf *	predictor () const
	Constructs conditional density of 1-step ahead predictor $f(d_{t+1} \|d_{t+h-1}, \ldots d_{t})$ .
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)
Protected Attributes
shared_ptr< diffbifn >	pfxu
	Internal Model f(x,u).
shared_ptr< diffbifn >	phxu
	Observation Model h(x,u).
RV	yrv
	id of output
RV	urv
	id of input
mat	_K
	Kalman gain.
shared_ptr< enorm< chmat > >	est
	posterior
enorm< chmat >	fy
	marginal on data f(y\|y)
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.
int	dimx
	cache of rv.count()
int	dimy
	cache of rvy.count()
int	dimu
	cache of rvu.count()
mat	A
	Matrix A.
mat	B
	Matrix B.
mat	C
	Matrix C.
mat	D
	Matrix D.
chmat	Q
	Matrix Q in square-root form.
chmat	R
	Matrix R in square-root form.
Internal storage - needs initialize()
pre array (triangular matrix)
mat	preA
mat	postA
	post array (triangular matrix)
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
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.