Inheritance diagram for GMM:

Public Member Functions
	GMM (UINT numMixtureModels=2, bool useScaling=false, bool useNullRejection=false, Float nullRejectionCoeff=1.0, UINT maxIter=100, Float minChange=1.0e-5)

	GMM (const GMM &rhs)

virtual	~GMM (void)

GMM &	operator= (const GMM &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	clear ()

virtual bool	saveModelToFile (std::fstream &file) const

virtual bool	loadModelFromFile (std::fstream &file)

virtual bool	recomputeNullRejectionThresholds ()

UINT	getNumMixtureModels ()

Vector< MixtureModel >	getModels ()

bool	setNumMixtureModels (UINT K)

bool	setMinChange (Float minChange)

bool	setMaxIter (UINT maxIter)

Public Member Functions inherited from Classifier
	Classifier (void)

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

virtual UINT	getNumClasses () const

UINT	getClassLabelIndexValue (UINT classLabel) const

UINT	getPredictedClassLabel () const

VectorFloat	getClassLikelihoods () const

VectorFloat	getClassDistances () const

VectorFloat	getNullRejectionThresholds () const

Vector< UINT >	getClassLabels () const

Vector< MinMax >	getRanges () const

bool	enableNullRejection (bool useNullRejection)

virtual bool	setNullRejectionCoeff (Float nullRejectionCoeff)

virtual bool	setNullRejectionThresholds (VectorFloat newRejectionThresholds)

bool	getTimeseriesCompatible () const

Classifier *	createNewInstance () const

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () const

Public Member Functions inherited from MLBase
	MLBase (void)

virtual	~MLBase (void)

bool	copyMLBaseVariables (const MLBase *mlBase)

virtual bool	train (ClassificationData trainingData)

virtual bool	train (RegressionData trainingData)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	train (TimeSeriesClassificationData trainingData)

virtual bool	train_ (TimeSeriesClassificationData &trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train (UnlabelledData trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	train (MatrixFloat data)

virtual bool	train_ (MatrixFloat &data)

virtual bool	predict (VectorFloat inputVector)

virtual bool	predict (MatrixFloat inputMatrix)

virtual bool	predict_ (MatrixFloat &inputMatrix)

virtual bool	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

virtual bool	print () const

virtual bool	save (const std::string filename) const

virtual bool	load (const std::string filename)

virtual bool	saveModelToFile (std::string filename) const

virtual bool	loadModelFromFile (std::string filename)

virtual bool	getModel (std::ostream &stream) const

Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

UINT	getBaseType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRootMeanSquaredTrainingError () const

Float	getTotalSquaredTrainingError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

bool	getModelTrained () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setMinChange (const Float minChange)

bool	setLearningRate (const Float learningRate)

bool	setUseValidationSet (const bool useValidationSet)

bool	setValidationSetSize (const UINT validationSetSize)

bool	setRandomiseTrainingOrder (const bool randomiseTrainingOrder)

bool	setTrainingLoggingEnabled (const bool loggingEnabled)

bool	registerTrainingResultsObserver (Observer< TrainingResult > &observer)

bool	registerTestResultsObserver (Observer< TestInstanceResult > &observer)

bool	removeTrainingResultsObserver (const Observer< TrainingResult > &observer)

bool	removeTestResultsObserver (const Observer< TestInstanceResult > &observer)

bool	removeAllTrainingObservers ()

bool	removeAllTestObservers ()

bool	notifyTrainingResultsObservers (const TrainingResult &data)

bool	notifyTestResultsObservers (const TestInstanceResult &data)

MLBase *	getMLBasePointer ()

const MLBase *	getMLBasePointer () const

Vector< TrainingResult >	getTrainingResults () const

Public Member Functions inherited from GRTBase
	GRTBase (void)

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

std::string	getClassType () const

std::string	getLastWarningMessage () const

std::string	getLastErrorMessage () const

std::string	getLastInfoMessage () const

bool	setInfoLoggingEnabled (const bool loggingEnabled)

bool	setWarningLoggingEnabled (const bool loggingEnabled)

bool	setErrorLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

Public Member Functions inherited from Observer< TrainingResult >
virtual void	notify (const TrainingResult &data)

Public Member Functions inherited from Observer< TestInstanceResult >
virtual void	notify (const TestInstanceResult &data)

Protected Member Functions
Float	computeMixtureLikelihood (const VectorFloat &x, UINT k)

bool	loadLegacyModelFromFile (std::fstream &file)

Protected Member Functions inherited from Classifier
bool	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Member Functions inherited from MLBase
bool	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Member Functions inherited from GRTBase
Float	SQR (const Float &x) const

Protected Attributes
UINT	numMixtureModels

UINT	maxIter

Float	minChange

Vector< MixtureModel >	models

DebugLog	debugLog

ErrorLog	errorLog

WarningLog	warningLog

Protected Attributes inherited from Classifier
std::string	classifierType

bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

DataType	inputType

DataType	outputType

UINT	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	validationSetSize

Float	learningRate

Float	minChange

Float	rootMeanSquaredTrainingError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

std::vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Static Protected Attributes
static RegisterClassifierModule< GMM >	registerModule

Additional Inherited Members
Public Types inherited from Classifier
typedef std::map< std::string, Classifier ()() >	StringClassifierMap

Public Types inherited from MLBase
enum	BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }

Static Public Member Functions inherited from Classifier
static Classifier *	createInstanceFromString (std::string const &classifierType)

static Vector< std::string >	getRegisteredClassifiers ()

Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Protected Types inherited from Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

Static Protected Member Functions inherited from Classifier
static StringClassifierMap *	getMap ()

Detailed Description

Definition at line 49 of file GMM.h.

Constructor & Destructor Documentation

GMM::GMM	(	UINT	numMixtureModels = `2`,
		bool	useScaling = `false`,
		bool	useNullRejection = `false`,
		Float	nullRejectionCoeff = `1.0`,
		UINT	maxIter = `100`,
		Float	minChange = `1.0e-5`
	)

Default Constructor. Sets the number of mixture models to use for each model.

Definition at line 28 of file GMM.cpp.

GMM::GMM ( const GMM & rhs )

Defines the copy constructor.

Parameters

rhs	the instance from which all the data will be copied into this instance

Definition at line 44 of file GMM.cpp.

GMM::~GMM ( void )

virtual

Default destructor.

Definition at line 54 of file GMM.cpp.

Member Function Documentation

bool GMM::clear ( )

virtual

This overrides the clear function in the Classifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns: returns true if the module was cleared succesfully, false otherwise

Reimplemented from Classifier.

Definition at line 534 of file GMM.cpp.

bool GMM::deepCopyFrom ( const Classifier * classifier )

virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setClassifier method is called. It clones the data from the Base Class GRT::Classifier pointer (which should be pointing to an GMM instance) into this instance

Parameters

classifier a pointer to the GRT::Classifier Base Class, this should be pointing to another GMM instance

Returns: returns true if the clone was successfull, false otherwise

Reimplemented from Classifier.

Definition at line 74 of file GMM.cpp.

Vector< MixtureModel > GMM::getModels ( )

This function returns a copy of the MixtureModels estimated during the training phase. Each element in the vector represents a MixtureModel for one class.

Returns: returns a vector of GRT::MixtureModel, an empty vector will be returned if the GRT::GMM has not been trained

Definition at line 561 of file GMM.cpp.

UINT GMM::getNumMixtureModels ( )

This function returns the number of mixture models.

Returns: returns the number of mixture models in the GMM

Definition at line 557 of file GMM.cpp.

bool GMM::loadLegacyModelFromFile ( std::fstream & file )

protected

Read the ranges if needed

Definition at line 588 of file GMM.cpp.

bool GMM::loadModelFromFile ( std::fstream & file )

virtual

This loads a trained GMM model from a file. This overrides the loadModelFromFile function in the GRT::Classifier base class.

Parameters

file	a reference to the file the GMM model will be loaded from

Returns: returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 349 of file GMM.cpp.

GMM & GMM::operator= ( const GMM & rhs )

Defines how the data from the rhs GMM should be copied to this GMM

Parameters

&rhs	another instance of a GMM

Returns: returns a pointer to this instance of the GMM

Definition at line 56 of file GMM.cpp.

bool GMM::predict_ ( VectorFloat & inputVector )

virtual

This predicts the class of the inputVector. This overrides the predict function in the GRT::Classifier base class.

Parameters

inputVector the input vector to classify

Returns: returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 97 of file GMM.cpp.

bool GMM::recomputeNullRejectionThresholds ( )

virtual

This function recomputes the null rejection thresholds for each model. This overrides the recomputeNullRejectionThresholds function in the GRT::Classifier base class.

Returns: returns true if the nullRejectionThresholds were updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 545 of file GMM.cpp.

bool GMM::saveModelToFile ( std::fstream & file ) const

virtual

This saves the trained GMM model to a file. This overrides the saveModelToFile function in the GRT::Classifier base class.

Parameters

file	a reference to the file the GMM model will be saved to

Returns: returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 281 of file GMM.cpp.

bool GMM::setMaxIter ( UINT maxIter )

This function sets the maxIter parameter which controls when the maximum number of iterations parameter that controls when the GMM train function should stop. MaxIter must be greater than zero.

Parameters

maxIter the new maxIter value

Returns: returns true if the number of maxIter was successfully updated, false otherwise

Definition at line 580 of file GMM.cpp.

bool GMM::setMinChange ( Float minChange )

This function sets the minChange parameter which controls when the GMM train function should stop. MinChange must be greater than zero.

Parameters

minChange the new minChange value

Returns: returns true if the number of minChange was successfully updated, false otherwise

Definition at line 573 of file GMM.cpp.

bool GMM::setNumMixtureModels ( UINT K )

This function sets the number of mixture models used for class. You should call this function before you train the GMM model. The number of mixture models must be greater than 0.

Parameters

K	the number of mixture models

Returns: returns true if the number of mixture models was successfully updated, false otherwise

Definition at line 566 of file GMM.cpp.

bool GMM::train_ ( ClassificationData & trainingData )

virtual

This trains the GMM model, using the labelled classification data. This overrides the train function in the GRT::Classifier base class. The GMM is an unsupervised learning algorithm, it will therefore NOT use any class labels provided

Parameters

trainingData a reference to the training data

Returns: returns true if the GMM model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 161 of file GMM.cpp.

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

Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation