Abstract class implementing a discrete-continuous factor. More...

#include <DCFactor.h>

Inheritance diagram for dcsam::DCFactor:

Collaboration diagram for dcsam::DCFactor:

Public Types
using	Base = gtsam::Factor

Public Member Functions
	DCFactor ()=default

	DCFactor (const gtsam::KeyVector &continuousKeys, const gtsam::DiscreteKeys &discreteKeys)

	DCFactor (const gtsam::DiscreteKeys &discreteKeys)

DCFactor &	operator= (const DCFactor &rhs)

virtual	~DCFactor ()=default

virtual double	error (const gtsam::Values &continuousVals, const gtsam::DiscreteFactor::Values &discreteVals) const =0

virtual boost::shared_ptr< gtsam::GaussianFactor >	linearize (const gtsam::Values &continuousVals, const DiscreteValues &discreteVals) const =0

virtual bool	equals (const DCFactor &other, double tol=1e-9) const =0

virtual size_t	dim () const =0

gtsam::DiscreteKeys	discreteKeys () const

virtual gtsam::DecisionTreeFactor	toDecisionTreeFactor (const gtsam::Values &continuousVals, const DiscreteValues &discreteVals) const

virtual double	logNormalizingConstant (const gtsam::Values &values) const

template<typename NonlinearFactorType >
double	nonlinearFactorLogNormalizingConstant (const NonlinearFactorType &factor, const gtsam::Values &values) const

std::vector< double >	evalProbs (const gtsam::DiscreteKey &dk, const gtsam::Values &continuousVals) const

gtsam::DecisionTreeFactor	conditionalTimes (const gtsam::DecisionTreeFactor &f, const gtsam::Values &continuousVals, const DiscreteValues &discreteVals) const

Protected Attributes
gtsam::DiscreteKeys	discreteKeys_

Detailed Description

Abstract class implementing a discrete-continuous factor.

keys_ member variable stores keys for continuous variables. discreteKeys_ contains the keys (plus cardinalities) for discrete variables.

Member Typedef Documentation

◆ Base

using dcsam::DCFactor::Base = gtsam::Factor

Constructor & Destructor Documentation

◆ DCFactor() [1/3]

dcsam::DCFactor::DCFactor ( )

default

◆ DCFactor() [2/3]

dcsam::DCFactor::DCFactor	(	const gtsam::KeyVector &	continuousKeys,
		const gtsam::DiscreteKeys &	discreteKeys
	)

inline

Base constructor for a DCFactor from a set of discrete keys and continuous keys

Parameters

continuousKeys	- the keys for continuous variables
discreteKeys	- the keys for discrete variables

◆ DCFactor() [3/3]

dcsam::DCFactor::DCFactor ( const gtsam::DiscreteKeys & discreteKeys )

inlineexplicit

◆ ~DCFactor()

virtual dcsam::DCFactor::~DCFactor ( )

virtualdefault

Member Function Documentation

◆ conditionalTimes()

gtsam::DecisionTreeFactor dcsam::DCFactor::conditionalTimes	(	const gtsam::DecisionTreeFactor &	f,
		const gtsam::Values &	continuousVals,
		const DiscreteValues &	discreteVals
	)		const

inline

Take the product of this DCFactor (as a gtsam::DecisionTreeFactor) conditioned on an assignment to the continous variables, continuousVals with another gtsam::DecisionTreeFactor f. Used internally by GTSAM to solve discrete factor graphs.

Parameters

f	- the gtsam::DecisionTreeFactor to be multiplied by this DCFactor
continuousVals	- an assignment to the continuous variables (specified by keys_).

Returns: a gtsam::DecisionTreeFactor representing the product of this factor with f.

◆ dim()

virtual size_t dcsam::DCFactor::dim ( ) const

pure virtual

Returns the number of rows in the Jacobian with respect to the continuous variables for this factor. Internally this is used in the conversion to a gtsam::NonlinearFactor

TODO(kevin): not sure if needed???

This will be specific to the model being implemented, so it is a pure virtual function and must be overridden

Returns: the dimension (number of rows) in the Jacobian of the error function with respect to continuous variables for

Implemented in dcsam::SemanticBearingRangeFactor< PoseType, PointType, BearingType, RangeType >, dcsam::DCMixtureFactor< NonlinearFactorType >, dcsam::DCMaxMixtureFactor< DCFactorType >, and dcsam::DCEMFactor< DCFactorType >.

◆ discreteKeys()

gtsam::DiscreteKeys dcsam::DCFactor::discreteKeys ( ) const

inline

◆ equals()

virtual bool dcsam::DCFactor::equals	(	const DCFactor &	other,
		double	tol = `1e-9`
	)		const

pure virtual

Returns true when the DCFactor is equal to other

This will be specific to the model being implemented, so it is a pure virtual function and must be overridden.

Parameters

other	- the DCFactor for which to test equality.
tol	= 1e-9 - numerical tolerance for any floating point comparisons.

Implemented in dcsam::SemanticBearingRangeFactor< PoseType, PointType, BearingType, RangeType >, dcsam::DCMixtureFactor< NonlinearFactorType >, dcsam::DCMaxMixtureFactor< DCFactorType >, and dcsam::DCEMFactor< DCFactorType >.

◆ error()

virtual double dcsam::DCFactor::error	(	const gtsam::Values &	continuousVals,
		const gtsam::DiscreteFactor::Values &	discreteVals
	)		const

pure virtual

Return the error for the DCFactor given an assignment to the variables referenced by the discrete keys and the continuous keys (keys_).

This will be specific to the model being implemented, so it is a pure virtual function and must be overridden.

NOTE: Depending on the application, it may be important that the error function is normalized, i.e. it corresponds to a proper negative log-likelihood, rather than the negative log-likelihood with normalization constants discarded.

Parameters

continuousVals	- the values assigned to the continuous variables, must contain keys in keys_ member variable
discreteVals	- likewise, the values assigned to discrete variables, must contain keys in discreteKeys_

Returns: error (usually the negative log-likelihood) for the measurement model as a double.

Implemented in dcsam::SemanticBearingRangeFactor< PoseType, PointType, BearingType, RangeType >, dcsam::DCMixtureFactor< NonlinearFactorType >, dcsam::DCMaxMixtureFactor< DCFactorType >, and dcsam::DCEMFactor< DCFactorType >.

◆ evalProbs()

std::vector<double> dcsam::DCFactor::evalProbs	(	const gtsam::DiscreteKey &	dk,
		const gtsam::Values &	continuousVals
	)		const

inline

Obtain the likelihood function for a single discrete variable conditioned on continuous values. Since the error function typically represents the negative log-likelihood, we compute exp(-error).

NOTE: Depending on the application, it may be important that the error function is normalized, i.e. it corresponds to a proper negative log-likelihood, rather than the negative log-likelihood with normalization constants discarded.

For this, we assume that the error function can be called with only a single discrete value set, though this may be dependent on the particular implementation behavior desired. At present, this is only used internally to specify the default behavior for toDecisionTreeFactor.

Parameters

dk	- the discrete key to evaluate the likelihood for
continuousVals	- an assignment to the continuous valued variables

Returns: a vector of length == cardinality of dk specifying the probability of each possible assignment to dk.

◆ linearize()

virtual boost::shared_ptr<gtsam::GaussianFactor> dcsam::DCFactor::linearize	(	const gtsam::Values &	continuousVals,
		const DiscreteValues &	discreteVals
	)		const

pure virtual

Linearize the error function with respect to the continuous variables (given in keys_) at the point specified by continuousVals. Since this Jacobian can be dependent on the assignment to discrete variables as well, they are required as a parameter.

This will be specific to the model being implemented, so it is a pure virtual function and must be overridden.

Parameters

continuousVals	- Assignment to the continuous variables in `keys_`
discreteVals	- Likewise, assignment to the discrete variables in `discreteKeys__`.

Implemented in dcsam::SemanticBearingRangeFactor< PoseType, PointType, BearingType, RangeType >, dcsam::DCMixtureFactor< NonlinearFactorType >, dcsam::DCMaxMixtureFactor< DCFactorType >, and dcsam::DCEMFactor< DCFactorType >.

◆ logNormalizingConstant()

virtual double dcsam::DCFactor::logNormalizingConstant ( const gtsam::Values & values ) const

inlinevirtual

Calculate a normalizing constant for this DCFactor. Most implementations will be able to use the helper function nonlinearFactorLogNormalizingConstant provided below for most of the calculation. TODO(Kurran) is this the cleanest way to do this? Seems necessary for the DCMaxMixtureFactor implementations etc...

Reimplemented in dcsam::SemanticBearingRangeFactor< PoseType, PointType, BearingType, RangeType >.

◆ nonlinearFactorLogNormalizingConstant()

template<typename NonlinearFactorType >

double dcsam::DCFactor::nonlinearFactorLogNormalizingConstant	(	const NonlinearFactorType &	factor,
		const gtsam::Values &	values
	)		const

inline

Default for computing the negative normalizing constant for the measurement likelihood (since we are minimizing the negative log-likelihood), to be used as a utility for computing the DCFactorLogNormalizingConstant.

◆ operator=()

DCFactor& dcsam::DCFactor::operator= ( const DCFactor & rhs )

inline

◆ toDecisionTreeFactor()

virtual gtsam::DecisionTreeFactor dcsam::DCFactor::toDecisionTreeFactor	(	const gtsam::Values &	continuousVals,
		const DiscreteValues &	discreteVals
	)		const

inlinevirtual

Converts the DCFactor to a gtsam::DecisionTreeFactor. Internally, this will be used to generate a gtsam::DiscreteFactor type, which itself requires a conversion function to gtsam::DecisionTreeFactor for inference using GTSAM.

Performing this conversion can be problem specific, so we allow for the option to override, but try to implement a sensible default: we assume the error function can be called setting each discrete key's variable individually, and the overall DCFactor can itself be factored as a product of unary gtsam::DecisionTreeFactors.

Alternative implementations might consider something like the gtsam::AllDiff approach here: https://github.com/borglab/gtsam/blob/43e8f1e5aeaf11890262722c1e5e04a11dbf9d75/gtsam_unstable/discrete/AllDiff.cpp#L43

Parameters

continuousVals	- an assignment to the continuous variables
discreteVals	-

Returns: a gtsam::DecisionTreeFactor implementing this DCFactor.

Reimplemented in dcsam::DCMaxMixtureFactor< DCFactorType >, and dcsam::DCEMFactor< DCFactorType >.

Member Data Documentation

◆ discreteKeys_

gtsam::DiscreteKeys dcsam::DCFactor::discreteKeys_

protected

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

include/dcsam/DCFactor.h

Public Types

Public Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ Base

Constructor & Destructor Documentation

◆ DCFactor() [1/3]

◆ DCFactor() [2/3]

◆ DCFactor() [3/3]

◆ ~DCFactor()

Member Function Documentation

◆ conditionalTimes()

◆ dim()

◆ discreteKeys()

◆ equals()

◆ error()

◆ evalProbs()

◆ linearize()

◆ logNormalizingConstant()

◆ nonlinearFactorLogNormalizingConstant()

◆ operator=()

◆ toDecisionTreeFactor()

Member Data Documentation

◆ discreteKeys_