Implementation of a discrete-continuous max-mixture factor. More...

#include <DCMaxMixtureFactor.h>

Inheritance diagram for dcsam::DCMaxMixtureFactor< DCFactorType >:

Collaboration diagram for dcsam::DCMaxMixtureFactor< DCFactorType >:

Public Types
using	Base = DCFactor

Public Types inherited from dcsam::DCFactor
using	Base = gtsam::Factor

Public Member Functions
	DCMaxMixtureFactor ()=default

	DCMaxMixtureFactor (const gtsam::KeyVector &continuousKeys, const gtsam::DiscreteKeys &discreteKeys, const std::vector< DCFactorType > factors, const std::vector< double > weights, const bool normalized)

	DCMaxMixtureFactor (const gtsam::KeyVector &continuousKeys, const gtsam::DiscreteKeys &discreteKeys, const std::vector< DCFactorType > factors, const bool normalized)

DCMaxMixtureFactor &	operator= (const DCMaxMixtureFactor &rhs)

virtual	~DCMaxMixtureFactor ()=default

double	error (const gtsam::Values &continuousVals, const DiscreteValues &discreteVals) const override

size_t	getActiveFactorIdx (const gtsam::Values &continuousVals, const DiscreteValues &discreteVals) const

size_t	dim () const override

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

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

gtsam::DecisionTreeFactor	uniformDecisionTreeFactor (const gtsam::DiscreteKey &dk) const

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

gtsam::FastVector< gtsam::Key >	getAssociationKeys (const gtsam::Values &continuousVals, const DiscreteValues &discreteVals) const

void	updateWeights (const std::vector< double > &weights)

Public Member Functions inherited from dcsam::DCFactor
	DCFactor ()=default

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

	DCFactor (const gtsam::DiscreteKeys &discreteKeys)

DCFactor &	operator= (const DCFactor &rhs)

virtual	~DCFactor ()=default

gtsam::DiscreteKeys	discreteKeys () 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

Additional Inherited Members
Protected Attributes inherited from dcsam::DCFactor
gtsam::DiscreteKeys	discreteKeys_

Detailed Description

template<class DCFactorType>
class dcsam::DCMaxMixtureFactor< DCFactorType >

Implementation of a discrete-continuous max-mixture factor.

r(x) = min_i -log(w_i) + r_i(x)

The error returned from this factor is the minimum error + weight over all of the component factors See Olson and Agarwal RSS 2012 for details

Member Typedef Documentation

◆ Base

template<class DCFactorType >

using dcsam::DCMaxMixtureFactor< DCFactorType >::Base = DCFactor

Constructor & Destructor Documentation

◆ DCMaxMixtureFactor() [1/3]

template<class DCFactorType >

dcsam::DCMaxMixtureFactor< DCFactorType >::DCMaxMixtureFactor ( )

default

◆ DCMaxMixtureFactor() [2/3]

template<class DCFactorType >

dcsam::DCMaxMixtureFactor< DCFactorType >::DCMaxMixtureFactor	(	const gtsam::KeyVector &	continuousKeys,
		const gtsam::DiscreteKeys &	discreteKeys,
		const std::vector< DCFactorType >	factors,
		const std::vector< double >	weights,
		const bool	normalized
	)

inlineexplicit

◆ DCMaxMixtureFactor() [3/3]

template<class DCFactorType >

dcsam::DCMaxMixtureFactor< DCFactorType >::DCMaxMixtureFactor	(	const gtsam::KeyVector &	continuousKeys,
		const gtsam::DiscreteKeys &	discreteKeys,
		const std::vector< DCFactorType >	factors,
		const bool	normalized
	)

inlineexplicit

◆ ~DCMaxMixtureFactor()

template<class DCFactorType >

virtual dcsam::DCMaxMixtureFactor< DCFactorType >::~DCMaxMixtureFactor ( )

virtualdefault

Member Function Documentation

◆ dim()

template<class DCFactorType >

size_t dcsam::DCMaxMixtureFactor< DCFactorType >::dim ( ) const

inlineoverridevirtual

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

Implements dcsam::DCFactor.

◆ equals()

template<class DCFactorType >

bool dcsam::DCMaxMixtureFactor< DCFactorType >::equals	(	const DCFactor &	other,
		double	tol = `1e-9`
	)		const

inlineoverridevirtual

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.

Implements dcsam::DCFactor.

◆ error()

template<class DCFactorType >

double dcsam::DCMaxMixtureFactor< DCFactorType >::error	(	const gtsam::Values &	continuousVals,
		const DiscreteValues &	discreteVals
	)		const

inlineoverridevirtual

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.

Implements dcsam::DCFactor.

◆ getActiveFactorIdx()

template<class DCFactorType >

size_t dcsam::DCMaxMixtureFactor< DCFactorType >::getActiveFactorIdx	(	const gtsam::Values &	continuousVals,
		const DiscreteValues &	discreteVals
	)		const

inline

◆ getAssociationKeys()

template<class DCFactorType >

gtsam::FastVector<gtsam::Key> dcsam::DCMaxMixtureFactor< DCFactorType >::getAssociationKeys	(	const gtsam::Values &	continuousVals,
		const DiscreteValues &	discreteVals
	)		const

inline

◆ linearize()

template<class DCFactorType >

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

inlineoverridevirtual

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__`.

Implements dcsam::DCFactor.

◆ operator=()

template<class DCFactorType >

DCMaxMixtureFactor& dcsam::DCMaxMixtureFactor< DCFactorType >::operator= ( const DCMaxMixtureFactor< DCFactorType > & rhs )

inline

◆ toDecisionTreeFactor()

template<class DCFactorType >

gtsam::DecisionTreeFactor dcsam::DCMaxMixtureFactor< DCFactorType >::toDecisionTreeFactor	(	const gtsam::Values &	continuousVals,
		const DiscreteValues &	discreteVals
	)		const

inlineoverridevirtual

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 from dcsam::DCFactor.

◆ uniformDecisionTreeFactor()

template<class DCFactorType >

gtsam::DecisionTreeFactor dcsam::DCMaxMixtureFactor< DCFactorType >::uniformDecisionTreeFactor ( const gtsam::DiscreteKey & dk ) const

inline

◆ updateWeights()

template<class DCFactorType >

void dcsam::DCMaxMixtureFactor< DCFactorType >::updateWeights ( const std::vector< double > & weights )

inline

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

include/dcsam/DCMaxMixtureFactor.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

template<class DCFactorType> class dcsam::DCMaxMixtureFactor< DCFactorType >

Member Typedef Documentation

◆ Base

Constructor & Destructor Documentation

◆ DCMaxMixtureFactor() [1/3]

◆ DCMaxMixtureFactor() [2/3]

◆ DCMaxMixtureFactor() [3/3]

◆ ~DCMaxMixtureFactor()

Member Function Documentation

◆ dim()

◆ equals()

◆ error()

◆ getActiveFactorIdx()

◆ getAssociationKeys()

◆ linearize()

◆ operator=()

◆ toDecisionTreeFactor()

◆ uniformDecisionTreeFactor()

◆ updateWeights()

template<class DCFactorType>
class dcsam::DCMaxMixtureFactor< DCFactorType >