A base class for the Sequential Important Sampling with Resampling (SISR). Uses normal common random numbers. More...

#include <sisr_filter.h>

Inheritance diagram for pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >:

Collaboration diagram for pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >:

Public Member Functions
	SISRFilterCRN (const unsigned int &rs=1)
	The (one and only) constructor. More...

virtual	~SISRFilterCRN ()
	The (virtual) destructor.

float_t	getLogCondLike () const
	Returns the most recent (log-) conditional likelihood. More...

std::vector< Mat >	getExpectations () const
	return all stored expectations (taken with respect to $p(x_t\|y_{1:t})$ More...

void	filter (const osv &data, const arrayUs &Uarr, const usvr &Uresamp, const std::vector< std::function< const Mat(const ssv &)> > &fs=std::vector< std::function< const Mat(const ssv &)> >())
	updates filtering distribution on a new datapoint. Optionally stores expectations of functionals. More...

virtual float_t	logMuEv (const ssv &x1)=0
	Calculate muEv or logmuEv. More...

virtual ssv	Xi1 (const usv &U, const osv &y1)=0
	"Samples" from time 1 proposal. Really, it maps the normal random vector into the sample. More...

virtual float_t	logQ1Ev (const ssv &x1, const osv &y1)=0
	Calculate q1Ev or log q1Ev. More...

virtual float_t	logGEv (const osv &yt, const ssv &xt)=0
	Calculate gEv or logGEv. More...

virtual float_t	logFEv (const ssv &xt, const ssv &xtm1)=0
	Evaluates the state transition density. More...

virtual ssv	Xit (const ssv &xtm1, const usv &U, const osv &yt)=0
	"Samples" from the proposal/instrumental/importance density at time t. Really, it maps the normal random vector into the sample. More...

virtual float_t	logQEv (const ssv &xt, const ssv &xtm1, const osv &yt)=0
	Evaluates the proposal/instrumental/importance density/pmf. More...

Public Member Functions inherited from pf::bases::pf_base_crn< float_t, dimy, dimx, dimu, dimur, nparts >
virtual void	filter (const obs_sized_vec &data, const std::array< usv, numparts > &Us, const usvr &Uresamp, const func_vec &fs=func_vec())=0
	the filtering function that must be defined More...

virtual float_t	getLogCondLike () const=0
	the getter method that must be defined (for conditional log-likelihood) More...

virtual	~pf_base_crn ()
	virtual destructor

Protected Attributes
arrayStates	m_particles
	particle samples

arrayfloat_t	m_logUnNormWeights
	particle weights

unsigned int	m_now
	current time point

float_t	m_logLastCondLike
	log p(y_t\|y_{1:t-1}) or log p(y1)

resamp_t	m_resampler
	resampling object

std::vector< Mat >	m_expectations
	expectations E[h(x_t) \| y_{1:t}] for user defined "h"s

unsigned int	m_resampSched
	resampling schedule (e.g. resample every __ time points)

Private Types
using	ssv = Eigen::Matrix< float_t, dimx, 1 >

using	osv = Eigen::Matrix< float_t, dimy, 1 >

using	usv = Eigen::Matrix< float_t, dimu, 1 >

using	usvr = Eigen::Matrix< float_t, dimur, 1 >

using	Mat = Eigen::Matrix< float_t, Eigen::Dynamic, Eigen::Dynamic >

using	arrayStates = std::array< ssv, nparts >

using	arrayUs = std::array< usv, nparts >

using	arrayfloat_t = std::array< float_t, nparts >

Additional Inherited Members
Public Types inherited from pf::bases::pf_base_crn< float_t, dimy, dimx, dimu, dimur, nparts >
using	float_type = float_t

using	obs_sized_vec = Eigen::Matrix< float_t, dimobs, 1 >

using	state_sized_vec = Eigen::Matrix< float_t, dimstate, 1 >

using	dynamic_matrix = Eigen::Matrix< float_t, Eigen::Dynamic, Eigen::Dynamic >

using	func = std::function< const dynamic_matrix(const state_sized_vec &)>

using	func_vec = std::vector< func >

using	usv = Eigen::Matrix< float_t, dimu, 1 >

using	usvr = Eigen::Matrix< float_t, dimur, 1 >

Static Public Attributes inherited from pf::bases::pf_base_crn< float_t, dimy, dimx, dimu, dimur, nparts >
static constexpr unsigned int	dim_obs

static constexpr unsigned int	dim_state

Detailed Description

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t, typename float_t, bool debug = false>
class pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >

A base class for the Sequential Important Sampling with Resampling (SISR). Uses normal common random numbers.

Author: taylor

Member Typedef Documentation

◆ arrayfloat_t

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::arrayfloat_t = std::array<float_t, nparts>

private

type alias for array of float_ts

◆ arrayStates

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::arrayStates = std::array<ssv, nparts>

private

type alias for linear algebra stuff

◆ arrayUs

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::arrayUs = std::array<usv, nparts>

private

type alias for array of common random numbers

◆ Mat

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::Mat = Eigen::Matrix<float_t,Eigen::Dynamic,Eigen::Dynamic>

private

type alias for linear algebra stuff

◆ osv

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::osv = Eigen::Matrix<float_t, dimy, 1>

private

"obs size vector" type alias for linear algebra stuff

◆ ssv

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::ssv = Eigen::Matrix<float_t, dimx, 1>

private

"state size vector" type alias for linear algebra stuff

◆ usv

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::usv = Eigen::Matrix<float_t, dimu, 1>

private

"u sized vector" type alias for common random normal vector

◆ usvr

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

using pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::usvr = Eigen::Matrix<float_t, dimur, 1>

private

"u sized vector for resampling" type alias for common random normal vector that's used in systematic resampling

Constructor & Destructor Documentation

◆ SISRFilterCRN()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug>

pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::SISRFilterCRN ( const unsigned int & rs = 1 )

The (one and only) constructor.

Parameters

rs	the resampling schedule (resample every rs time points).

Member Function Documentation

◆ filter()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug>

void pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::filter	(	const osv &	data,
		const arrayUs &	Uarr,
		const usvr &	Uresamp,
		const std::vector< std::function< const Mat(const ssv &)> > &	fs = `std::vector<std::function<const Mat(const ssv&)> >()`
	)

updates filtering distribution on a new datapoint. Optionally stores expectations of functionals.

Parameters

data	the most recent data point
Uarr	the U samples that get used to sample from the state proposal
Uresamp	the U sample that is used to resample
fs	a vector of functions if you want to calculate expectations.

◆ getExpectations()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug>

auto pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::getExpectations

return all stored expectations (taken with respect to $p(x_t|y_{1:t})$

Returns: return a std::vector<Mat> of expectations. How many depends on how many callbacks you gave to

◆ getLogCondLike()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug>

float_t pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::getLogCondLike

Returns the most recent (log-) conditional likelihood.

Returns: log p(y_t | y_{1:t-1}) or log p(y_1)

◆ logFEv()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

virtual float_t pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::logFEv	(	const ssv &	xt,
		const ssv &	xtm1
	)

pure virtual

Evaluates the state transition density.

Parameters

xt	the current state
xtm1	the previous state

Returns: a float_t evaluaton of the log density/pmf

◆ logGEv()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

virtual float_t pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::logGEv	(	const osv &	yt,
		const ssv &	xt
	)

pure virtual

Calculate gEv or logGEv.

Parameters

yt	is a const Vec& describing the time t datum
xt	is a const Vec& describing the time t state

Returns: the density or log-density evaluation as a float_t

◆ logMuEv()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

virtual float_t pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::logMuEv ( const ssv & x1 )

pure virtual

Calculate muEv or logmuEv.

Parameters

x1	is a const Vec& describing the state sample

Returns: the density or log-density evaluation as a float_t

◆ logQ1Ev()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

virtual float_t pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::logQ1Ev	(	const ssv &	x1,
		const osv &	y1
	)

pure virtual

Calculate q1Ev or log q1Ev.

Parameters

x1	is a const Vec& describing the time 1 state sample
y1	is a const Vec& describing the time 1 datum

Returns: the density or log-density evaluation as a float_t

◆ logQEv()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

virtual float_t pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::logQEv	(	const ssv &	xt,
		const ssv &	xtm1,
		const osv &	yt
	)

pure virtual

Evaluates the proposal/instrumental/importance density/pmf.

Parameters

xt	current state
xtm1	previous state
yt	current observation

Returns: a float_t evaluation of the log density/pmf

◆ Xi1()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

virtual ssv pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::Xi1	(	const usv &	U,
		const osv &	y1
	)

pure virtual

"Samples" from time 1 proposal. Really, it maps the normal random vector into the sample.

Parameters

U	the normal random vector transformed into X1i
y1	is a const Vec& representing the first observed datum

Returns: the sample as a Vec

◆ Xit()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t , typename float_t , bool debug = false>

virtual ssv pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >::Xit	(	const ssv &	xtm1,
		const usv &	U,
		const osv &	yt
	)

pure virtual

"Samples" from the proposal/instrumental/importance density at time t. Really, it maps the normal random vector into the sample.

Parameters

xtm1	the previous state sample
U	the normal random vector transformed
yt	the current observation

Returns: a state sample for the current time xt

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

include/pf/sisr_filter.h

Public Member Functions

Protected Attributes

Private Types

Additional Inherited Members

Detailed Description

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t, typename float_t, bool debug = false> class pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >

Member Typedef Documentation

◆ arrayfloat_t

◆ arrayStates

◆ arrayUs

◆ Mat

◆ osv

◆ ssv

◆ usv

◆ usvr

Constructor & Destructor Documentation

◆ SISRFilterCRN()

Member Function Documentation

◆ filter()

◆ getExpectations()

◆ getLogCondLike()

◆ logFEv()

◆ logGEv()

◆ logMuEv()

◆ logQ1Ev()

◆ logQEv()

◆ Xi1()

◆ Xit()

template<size_t nparts, size_t dimx, size_t dimy, size_t dimu, size_t dimur, typename resamp_t, typename float_t, bool debug = false>
class pf::filters::SISRFilterCRN< nparts, dimx, dimy, dimu, dimur, resamp_t, float_t, debug >