Generic Template for Machine Learning Probabilistic models based on the EM algorithm. More...

#include <xmmModelSingleClass.hpp>

Inheritance diagram for xmm::SingleClassProbabilisticModel:

Collaboration diagram for xmm::SingleClassProbabilisticModel:

Public Member Functions
	SingleClassProbabilisticModel (std::shared_ptr< SharedParameters > p=NULL)
	Constructor. More...

	SingleClassProbabilisticModel (SingleClassProbabilisticModel const &src)
	Copy Constructor. More...

	SingleClassProbabilisticModel (std::shared_ptr< SharedParameters > p, Json::Value const &root)
	Constructor from Json. More...

SingleClassProbabilisticModel &	operator= (SingleClassProbabilisticModel const &src)
	Assignment. More...

virtual	~SingleClassProbabilisticModel ()
	Destructor. More...

Training
bool	isTraining () const
	Checks if the model is training. More...

void	train (TrainingSet *trainingSet)
	Main training method based on the EM algorithm. More...

void	cancelTraining ()
	Cancels the training process : sets a flag so that the training stops at the next possible exit in the training process. More...

Performance
virtual void	reset ()
	Resets the fitering process (recognition or regression) More...

virtual double	filter (std::vector< float > const &observation)=0
	filters a incoming observation (performs recognition or regression) More...

Json I/O
virtual Json::Value	toJson () const
	Write the object to a JSON Structure. More...

virtual void	fromJson (Json::Value const &root)=0
	Read the object from a JSON Structure. More...

Python File I/O
void	writeFile (char *fileName) const
	write method for python wrapping ('write' keyword forbidden, name has to be different) More...

void	readFile (char *fileName)
	read method for python wrapping ('read' keyword forbidden, name has to be different) More...

std::string	__str__ () const
	"print" method for python => returns the results of write method More...

Public Attributes
std::string	label
	label associated with the given model More...

std::shared_ptr< SharedParameters >	shared_parameters
	Pointer to the shared parameters owned by a multi-class model. More...

EventGenerator< TrainingEvent >	training_events
	Generator for events monitoring the training process. More...

TrainingEvent	training_status
	Event containing information on the current status of the training process. More...

Protected Member Functions
virtual void	allocate ()=0
	Allocate memory for the model's parameters. More...

virtual void	emAlgorithmInit (TrainingSet *trainingSet)=0
	Initialize the training algorithm. More...

virtual double	emAlgorithmUpdate (TrainingSet *trainingSet)=0
	Update Method of the EM algorithm. More...

virtual void	emAlgorithmTerminate ()
	Terminate the training algorithm. More...

bool	emAlgorithmHasConverged (int step, double log_prob, double old_log_prob) const
	checks if the training has converged according to the object's EM stop criterion More...

bool	cancelTrainingIfRequested ()
	checks if a cancel request has been sent and accordingly cancels the training process More...

void	check_training () const
	Checks if the model is still training. More...

Protected Attributes
CircularBuffer< double >	likelihood_buffer_
	Likelihood buffer used for smoothing. More...

std::mutex	training_mutex_
	Mutex used in Concurrent Mode. More...

bool	is_training_
	defines if the model is being trained. More...

bool	cancel_training_
	defines if the model received a request to cancel training More...

Detailed Description

Generic Template for Machine Learning Probabilistic models based on the EM algorithm.

Constructor & Destructor Documentation

xmm::SingleClassProbabilisticModel::SingleClassProbabilisticModel ( std::shared_ptr< SharedParameters > p = NULL )

Constructor.

Parameters

p	pointer to a shared parameters object (owned by a Model)

xmm::SingleClassProbabilisticModel::SingleClassProbabilisticModel ( SingleClassProbabilisticModel const & src )

Copy Constructor.

Parameters

src	Source Model

xmm::SingleClassProbabilisticModel::SingleClassProbabilisticModel	(	std::shared_ptr< SharedParameters >	p,
		Json::Value const &	root
	)

explicit

Constructor from Json.

Parameters

p	pointer to a shared parameters object (owned by a Model)
root	Json structure

xmm::SingleClassProbabilisticModel::~SingleClassProbabilisticModel ( )

virtual

Destructor.

Member Function Documentation

std::string xmm::Writable::__str__ ( ) const

inlineinherited

"print" method for python => returns the results of write method

Warning: only defined if SWIGPYTHON is defined

virtual void xmm::SingleClassProbabilisticModel::allocate ( )

protectedpure virtual

Allocate memory for the model's parameters.

called when dimensions are modified

Implemented in xmm::SingleClassHMM, and xmm::SingleClassGMM.

void xmm::SingleClassProbabilisticModel::cancelTraining ( )

Cancels the training process : sets a flag so that the training stops at the next possible exit in the training process.

Warning: the model is still training when this function returns. This function only requests the training process to cancel.

bool xmm::SingleClassProbabilisticModel::cancelTrainingIfRequested ( )

protected

checks if a cancel request has been sent and accordingly cancels the training process

Returns: true if the training has been canceled.

void xmm::SingleClassProbabilisticModel::check_training ( ) const

inlineprotected

Checks if the model is still training.

Exceptions

runtime_error if the model is training.

bool xmm::SingleClassProbabilisticModel::emAlgorithmHasConverged	(	int	step,
		double	log_prob,
		double	old_log_prob
	)		const

protected

checks if the training has converged according to the object's EM stop criterion

Parameters

step	index of the current step of the EM algorithm
log_prob	log-likelihood returned by the EM update
old_log_prob	log-likelihood returned by the EM update at the previous step

virtual void xmm::SingleClassProbabilisticModel::emAlgorithmInit ( TrainingSet * trainingSet )

protectedpure virtual

Initialize the training algorithm.

Implemented in xmm::SingleClassHMM, and xmm::SingleClassGMM.

void xmm::SingleClassProbabilisticModel::emAlgorithmTerminate ( )

protectedvirtual

Terminate the training algorithm.

Reimplemented in xmm::SingleClassHMM.

virtual double xmm::SingleClassProbabilisticModel::emAlgorithmUpdate ( TrainingSet * trainingSet )

protectedpure virtual

Update Method of the EM algorithm.

performs E and M steps of the EM algorithm.

Returns: likelihood of the training data given the current model parameters (before re-estimation).

Implemented in xmm::SingleClassHMM, and xmm::SingleClassGMM.

virtual double xmm::SingleClassProbabilisticModel::filter ( std::vector< float > const & observation )

pure virtual

filters a incoming observation (performs recognition or regression)

the results of the inference process are stored in the results attribute

Parameters

observation observation vector

Returns: likelihood of the observation

Implemented in xmm::SingleClassHMM, and xmm::SingleClassGMM.

virtual void xmm::SingleClassProbabilisticModel::fromJson ( Json::Value const & root )

pure virtual

Read the object from a JSON Structure.

Parameters

root	JSON value containing the object's information

Exceptions

JsonException if the JSON value has a wrong format

Implements xmm::Writable.

Implemented in xmm::SingleClassHMM, and xmm::SingleClassGMM.

bool xmm::SingleClassProbabilisticModel::isTraining ( ) const

Checks if the model is training.

Returns: true if the model is training

xmm::SingleClassProbabilisticModel & xmm::SingleClassProbabilisticModel::operator= ( SingleClassProbabilisticModel const & src )

Assignment.

Parameters

src	Source Model

void xmm::Writable::readFile ( char * fileName )

inlineinherited

read method for python wrapping ('read' keyword forbidden, name has to be different)

Warning: only defined if SWIGPYTHON is defined

void xmm::SingleClassProbabilisticModel::reset ( )

virtual

Resets the fitering process (recognition or regression)

Reimplemented in xmm::SingleClassHMM, and xmm::SingleClassGMM.

Json::Value xmm::SingleClassProbabilisticModel::toJson ( ) const

virtual

Write the object to a JSON Structure.

Returns: Json value containing the object's information

Implements xmm::Writable.

Reimplemented in xmm::SingleClassHMM, and xmm::SingleClassGMM.

void xmm::SingleClassProbabilisticModel::train ( TrainingSet * trainingSet )

Main training method based on the EM algorithm.

the method performs a loop over the pure virtual method emAlgorithmUpdate() until convergence. The emAlgorithmUpdate method computes both E and M steps of the EM algorithm.

Parameters

trainingSet Training Set to train the model.

See also: emAlgorithmUpdate

void xmm::Writable::writeFile ( char * fileName ) const

inlineinherited

write method for python wrapping ('write' keyword forbidden, name has to be different)

Warning: only defined if SWIGPYTHON is defined

Member Data Documentation

bool xmm::SingleClassProbabilisticModel::cancel_training_

protected

defines if the model received a request to cancel training

bool xmm::SingleClassProbabilisticModel::is_training_

protected

defines if the model is being trained.

std::string xmm::SingleClassProbabilisticModel::label

label associated with the given model

CircularBuffer<double> xmm::SingleClassProbabilisticModel::likelihood_buffer_

protected

Likelihood buffer used for smoothing.

std::shared_ptr<SharedParameters> xmm::SingleClassProbabilisticModel::shared_parameters

Pointer to the shared parameters owned by a multi-class model.

EventGenerator<TrainingEvent> xmm::SingleClassProbabilisticModel::training_events

Generator for events monitoring the training process.

std::mutex xmm::SingleClassProbabilisticModel::training_mutex_

protected

Mutex used in Concurrent Mode.

TrainingEvent xmm::SingleClassProbabilisticModel::training_status

Event containing information on the current status of the training process.

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

XMM - Probabilistic Models for Motion Recognition and Mapping

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation