Binary factor to estimate parameters of zero-mean Gaussian white noise. More...

#include <WhiteNoiseFactor.h>

Inheritance diagram for gtsam::WhiteNoiseFactor:

Static Public Member Functions
static double	f (double z, double u, double p)
	negative log likelihood as a function of mean $\mu$ and precision $\tau$ $f(z, \tau, \mu) = -\log \left( \frac{\sqrt{\tau}}{\sqrt{2\pi}} \exp(-0.5\tau(z-\mu)^2) \right) = \log(\sqrt{2\pi}) - 0.5* \log(\tau) + 0.5\tau(z-\mu)^2$ More...

static HessianFactor::shared_ptr	linearize (double z, double u, double p, Key j1, Key j2)
	linearize returns a Hessianfactor that approximates error Hessian is $0.5f - x^T g + 0.5*x^T G x$ Taylor expansion is $f(x+dx) = f(x) + df(x) dx + 0.5 ddf(x) dx^2$ So f = 2 f(x), g = -df(x), G = ddf(x) More...

Private Types
typedef NonlinearFactor	Base

Private Attributes
Key	meanKey_
	key by which to access mean variable More...

Key	precisionKey_
	key by which to access precision variable More...

double	z_
	Measurement. More...

Standard Constructors
	WhiteNoiseFactor (double z, Key meanKey, Key precisionKey)

Advanced Constructors
	~WhiteNoiseFactor () override
	Destructor. More...

Testable
void	print (const std::string &p="WhiteNoiseFactor", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const override
	Print. More...

Standard Interface
size_t	dim () const override
	get the dimension of the factor (number of rows on linearization) More...

double	error (const Values &x) const override
	Calculate the error of the factor, typically equal to log-likelihood. More...

virtual Vector	unwhitenedError (const Values &x) const

Advanced Interface
std::shared_ptr< GaussianFactor >	linearize (const Values &x) const override
	linearize returns a Hessianfactor that is an approximation of error(p) More...

Additional Inherited Members
Public Types inherited from gtsam::NonlinearFactor
typedef std::shared_ptr< This >	shared_ptr

Public Types inherited from gtsam::Factor
typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys. More...

typedef KeyVector::iterator	iterator
	Iterator over keys. More...

Public Member Functions inherited from gtsam::NonlinearFactor
	NonlinearFactor ()

template<typename CONTAINER >
	NonlinearFactor (const CONTAINER &keys)

virtual bool	equals (const NonlinearFactor &f, double tol=1e-9) const

double	error (const HybridValues &c) const override

virtual bool	active (const Values &) const

virtual shared_ptr	clone () const

virtual shared_ptr	rekey (const std::map< Key, Key > &rekey_mapping) const

virtual shared_ptr	rekey (const KeyVector &new_keys) const

virtual bool	sendable () const

Public Member Functions inherited from gtsam::Factor
virtual	~Factor ()=default
	Default destructor. More...

bool	empty () const
	Whether the factor is empty (involves zero variables). More...

Key	front () const
	First key. More...

Key	back () const
	Last key. More...

const_iterator	find (Key key) const
	find More...

const KeyVector &	keys () const
	Access the factor's involved variable keys. More...

const_iterator	begin () const

const_iterator	end () const

size_t	size () const

virtual void	printKeys (const std::string &s="Factor", const KeyFormatter &formatter=DefaultKeyFormatter) const
	print only keys More...

bool	equals (const This &other, double tol=1e-9) const
	check equality More...

KeyVector &	keys ()

iterator	begin ()

iterator	end ()

Protected Types inherited from gtsam::NonlinearFactor
typedef Factor	Base

typedef NonlinearFactor	This

Protected Member Functions inherited from gtsam::Factor
	Factor ()

template<typename CONTAINER >
	Factor (const CONTAINER &keys)

template<typename ITERATOR >
	Factor (ITERATOR first, ITERATOR last)

Static Protected Member Functions inherited from gtsam::Factor
template<typename CONTAINER >
static Factor	FromKeys (const CONTAINER &keys)

template<typename ITERATOR >
static Factor	FromIterators (ITERATOR first, ITERATOR last)

Protected Attributes inherited from gtsam::Factor
KeyVector	keys_
	The keys involved in this factor. More...

Detailed Description

Binary factor to estimate parameters of zero-mean Gaussian white noise.

This factor uses the mean-precision parameterization.

Takes three template arguments: Key: key type to use for mean Key: key type to use for precision Values: Values type for optimization

Definition at line 41 of file WhiteNoiseFactor.h.

Member Typedef Documentation

◆ Base

typedef NonlinearFactor gtsam::WhiteNoiseFactor::Base

private

Definition at line 50 of file WhiteNoiseFactor.h.

Constructor & Destructor Documentation

◆ WhiteNoiseFactor()

gtsam::WhiteNoiseFactor::WhiteNoiseFactor	(	double	z,
		Key	meanKey,
		Key	precisionKey
	)

inline

Construct from measurement

Parameters

z	Measurment value
meanKey	Key for mean variable
precisionKey	Key for precision variable

Definition at line 96 of file WhiteNoiseFactor.h.

◆ ~WhiteNoiseFactor()

gtsam::WhiteNoiseFactor::~WhiteNoiseFactor ( )

inlineoverride

Destructor.

Definition at line 105 of file WhiteNoiseFactor.h.

Member Function Documentation

◆ dim()

size_t gtsam::WhiteNoiseFactor::dim ( ) const

inlineoverridevirtual

get the dimension of the factor (number of rows on linearization)

Implements gtsam::NonlinearFactor.

Definition at line 124 of file WhiteNoiseFactor.h.

◆ error()

double gtsam::WhiteNoiseFactor::error ( const Values & x ) const

inlineoverridevirtual

Calculate the error of the factor, typically equal to log-likelihood.

Reimplemented from gtsam::NonlinearFactor.

Definition at line 129 of file WhiteNoiseFactor.h.

◆ f()

static double gtsam::WhiteNoiseFactor::f	(	double	z,
		double	u,
		double	p
	)

inlinestatic

negative log likelihood as a function of mean $\mu$ and precision $\tau$

$f(z, \tau, \mu) = -\log \left( \frac{\sqrt{\tau}}{\sqrt{2\pi}} \exp(-0.5\tau(z-\mu)^2) \right) = \log(\sqrt{2\pi}) - 0.5* \log(\tau) + 0.5\tau(z-\mu)^2$

Definition at line 61 of file WhiteNoiseFactor.h.

◆ linearize() [1/2]

static HessianFactor::shared_ptr gtsam::WhiteNoiseFactor::linearize	(	double	z,
		double	u,
		double	p,
		Key	j1,
		Key	j2
	)

inlinestatic

linearize returns a Hessianfactor that approximates error Hessian is

$0.5f - x^T g + 0.5*x^T G x$

Taylor expansion is

$f(x+dx) = f(x) + df(x) dx + 0.5 ddf(x) dx^2$

So f = 2 f(x), g = -df(x), G = ddf(x)

Definition at line 75 of file WhiteNoiseFactor.h.

◆ linearize() [2/2]

std::shared_ptr<GaussianFactor> gtsam::WhiteNoiseFactor::linearize ( const Values & x ) const

inlineoverridevirtual

linearize returns a Hessianfactor that is an approximation of error(p)

Implements gtsam::NonlinearFactor.

Definition at line 158 of file WhiteNoiseFactor.h.

◆ print()

void gtsam::WhiteNoiseFactor::print	(	const std::string &	p = `"WhiteNoiseFactor"`,
		const KeyFormatter &	keyFormatter = `DefaultKeyFormatter`
	)		const

inlineoverridevirtual

Print.

Reimplemented from gtsam::NonlinearFactor.

Definition at line 113 of file WhiteNoiseFactor.h.

◆ unwhitenedError()

virtual Vector gtsam::WhiteNoiseFactor::unwhitenedError ( const Values & x ) const

inlinevirtual

Vector of errors "unwhitened" does not make sense for this factor What is meant typically is only "e" above Here we shoehorn sqrt(2*error(p)) TODO: Where is this used? should disappear.

Definition at line 140 of file WhiteNoiseFactor.h.

Member Data Documentation

◆ meanKey_

Key gtsam::WhiteNoiseFactor::meanKey_

private

key by which to access mean variable

Definition at line 47 of file WhiteNoiseFactor.h.

◆ precisionKey_

Key gtsam::WhiteNoiseFactor::precisionKey_

private

key by which to access precision variable

Definition at line 48 of file WhiteNoiseFactor.h.

◆ z_

double gtsam::WhiteNoiseFactor::z_

private

Measurement.

Definition at line 45 of file WhiteNoiseFactor.h.

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

WhiteNoiseFactor.h

Static Public Member Functions

Private Types

Private Attributes

Standard Constructors

Advanced Constructors

Testable

Standard Interface

Advanced Interface

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

Constructor & Destructor Documentation

◆ WhiteNoiseFactor()

◆ ~WhiteNoiseFactor()

Member Function Documentation

◆ dim()

◆ error()

◆ f()

◆ linearize() [1/2]

◆ linearize() [2/2]

◆ print()

◆ unwhitenedError()

Member Data Documentation

◆ meanKey_

◆ precisionKey_

◆ z_