BAG Class Reference

Inheritance diagram for BAG:

Public Member Functions
	BAG (bool useScaling=false)
	BAG (const BAG &rhs)
virtual	~BAG (void)
BAG &	operator= (const BAG &rhs)
virtual bool	deepCopyFrom (const Classifier *classifier)
virtual bool	train_ (ClassificationData &trainingData)
virtual bool	predict_ (VectorFloat &inputVector)
virtual bool	reset ()
virtual bool	clear ()
virtual bool	save (std::fstream &file) const
virtual bool	load (std::fstream &file)
UINT	getEnsembleSize () const
VectorFloat	getEnsembleWeights () const
const Vector< Classifier * >	getEnsemble () const
bool	addClassifierToEnsemble (const Classifier &classifier, Float weight=1)
bool	clearEnsemble ()
bool	setWeights (const VectorFloat &weights)
Public Member Functions inherited from Classifier
	Classifier (const std::string &classifierId="")
virtual	~Classifier (void)
bool	copyBaseVariables (const Classifier *classifier)
virtual bool	computeAccuracy (const ClassificationData &data, Float &accuracy)
std::string	getClassifierType () const
bool	getSupportsNullRejection () const
bool	getNullRejectionEnabled () const
Float	getNullRejectionCoeff () const
Float	getMaximumLikelihood () const
Float	getBestDistance () const
Float	getPhase () const
Float	getTrainingSetAccuracy () const
virtual UINT	getNumClasses () const
UINT	getClassLabelIndexValue (const 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 (const bool useNullRejection)
virtual bool	setNullRejectionCoeff (const Float nullRejectionCoeff)
virtual bool	setNullRejectionThresholds (const VectorFloat &newRejectionThresholds)
virtual bool	recomputeNullRejectionThresholds ()
bool	getTimeseriesCompatible () const
Classifier *	create () const
	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create instead.", Classifier *createNewInstance() const )
	GRT_DEPRECATED_MSG ("createInstanceFromString is deprecated, use create instead.", static Classifier *createInstanceFromString(const std::string &id))
Classifier *	deepCopy () const
const Classifier *	getClassifierPointer () const
const Classifier &	getBaseClassifier () const
Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)
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 (RegressionData trainingData, RegressionData validationData)
virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData)
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)
	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const )
	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename))
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))
virtual bool	getModel (std::ostream &stream) const
virtual std::string	getModelAsString () const
DataType	getInputType () const
DataType	getOutputType () const
BaseType	getType () const
UINT	getNumInputFeatures () const
UINT	getNumInputDimensions () const
UINT	getNumOutputDimensions () const
UINT	getMinNumEpochs () const
UINT	getMaxNumEpochs () const
UINT	getBatchSize () const
UINT	getNumRestarts () const
UINT	getValidationSetSize () const
UINT	getNumTrainingIterationsToConverge () const
Float	getMinChange () const
Float	getLearningRate () const
Float	getRMSTrainingError () const
	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )
Float	getTotalSquaredTrainingError () const
Float	getRMSValidationError () const
Float	getValidationSetAccuracy () const
VectorFloat	getValidationSetPrecision () const
VectorFloat	getValidationSetRecall () const
bool	getUseValidationSet () const
bool	getRandomiseTrainingOrder () const
bool	getTrained () const
	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )
bool	getConverged () const
bool	getScalingEnabled () const
bool	getIsBaseTypeClassifier () const
bool	getIsBaseTypeRegressifier () const
bool	getIsBaseTypeClusterer () const
bool	getTrainingLoggingEnabled () const
bool	getTestingLoggingEnabled () const
bool	enableScaling (const bool useScaling)
bool	setMaxNumEpochs (const UINT maxNumEpochs)
bool	setBatchSize (const UINT batchSize)
bool	setMinNumEpochs (const UINT minNumEpochs)
bool	setNumRestarts (const UINT numRestarts)
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	setTestingLoggingEnabled (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 (const std::string &id="")
virtual	~GRTBase (void)
bool	copyGRTBaseVariables (const GRTBase *GRTBase)
	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )
std::string	getId () 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)
bool	setDebugLoggingEnabled (const bool loggingEnabled)
GRTBase *	getGRTBasePointer ()
const GRTBase *	getGRTBasePointer () const
Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
Float	SQR (const Float &x) 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)

Static Public Member Functions
static std::string	getId ()
Static Public Member Functions inherited from Classifier
static Classifier *	create (const std::string &id)
static Vector< std::string >	getRegisteredClassifiers ()
Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)
static std::string	getGRTRevison ()

Protected Member Functions
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 Attributes
VectorFloat	weights
Vector< Classifier * >	ensemble
Protected Attributes inherited from Classifier
bool	supportsNullRejection
bool	useNullRejection
UINT	numClasses
UINT	predictedClassLabel
UINT	classifierMode
Float	nullRejectionCoeff
Float	maxLikelihood
Float	bestDistance
Float	phase
Float	trainingSetAccuracy
VectorFloat	classLikelihoods
VectorFloat	classDistances
VectorFloat	nullRejectionThresholds
Vector< UINT >	classLabels
Vector< MinMax >	ranges
Protected Attributes inherited from MLBase
bool	trained
bool	useScaling
bool	converged
DataType	inputType
DataType	outputType
BaseType	baseType
UINT	numInputDimensions
UINT	numOutputDimensions
UINT	numTrainingIterationsToConverge
UINT	minNumEpochs
UINT	maxNumEpochs
UINT	batchSize
UINT	validationSetSize
UINT	numRestarts
Float	learningRate
Float	minChange
Float	rmsTrainingError
Float	rmsValidationError
Float	totalSquaredTrainingError
Float	validationSetAccuracy
bool	useValidationSet
bool	randomiseTrainingOrder
VectorFloat	validationSetPrecision
VectorFloat	validationSetRecall
Random	random
Vector< TrainingResult >	trainingResults
TrainingResultsObserverManager	trainingResultsObserverManager
TestResultsObserverManager	testResultsObserverManager
TrainingLog	trainingLog
TestingLog	testingLog
Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)
DebugLog	debugLog
ErrorLog	errorLog
InfoLog	infoLog
WarningLog	warningLog

Additional Inherited Members
Public Types inherited from Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
typedef std::map< std::string, Classifier *(*)() >	StringClassifierMap
Public Types inherited from MLBase
enum	BaseType {   BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,   PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }
Static Protected Member Functions inherited from Classifier
static StringClassifierMap *	getMap ()

Detailed Description

Examples:

ClassificationModulesExamples/BAGExample/BAGExample.cpp.

Definition at line 44 of file BAG.h.

Constructor & Destructor Documentation

BAG::BAG

(

bool

useScaling = false

)

Default Constructor

Parameters

useScaling

sets if the training and real-time data should be scaled between [0 1]. Default value = false

Definition at line 33 of file BAG.cpp.

BAG::BAG

(

const BAG &

rhs

)

Defines the copy constructor.

Parameters

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

Definition at line 40 of file BAG.cpp.

BAG::~BAG ( void  )

virtual

Default Destructor

Definition at line 45 of file BAG.cpp.

Member Function Documentation

bool BAG::addClassifierToEnsemble	(	const Classifier &	classifier,
		Float	weight = 1
	)

This functions adds a copy of the input classifier to the ensemble. This classifier will then be trained (in addition to the other classifiers in the ensemble) when you call the BAG train function.

Parameters

classifier

a reference to the classifier you want to add to the ensemble

Returns

returns true if a copy of the classifier was successfully added to the ensemble, false otherwise

Examples:

ClassificationModulesExamples/BAGExample/BAGExample.cpp.

Definition at line 462 of file BAG.cpp.

bool BAG::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 271 of file BAG.cpp.

bool BAG::clearEnsemble

(

)

This functions clears the current ensemble, removing all classifiers and weights.

Returns

returns true if the ensemble was successfully cleared, false otherwise

Definition at line 482 of file BAG.cpp.

bool BAG::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 Classifier pointer (which should be pointing to an BAG instance) into this instance

Parameters

classifier

a pointer to the Classifier Base Class, this should be pointing to another BAG instance

Returns

returns true if the clone was successfull, false otherwise

Reimplemented from Classifier.

Definition at line 68 of file BAG.cpp.

const Vector< Classifier * > BAG::getEnsemble

(

)

const

Gets the ensemble.

Returns

returns a Vector of Classifier pointers.

Definition at line 458 of file BAG.cpp.

UINT BAG::getEnsembleSize

(

)

const

Gets the number of classifiers in the ensemble.

Returns

returns the size of the ensemble

Definition at line 450 of file BAG.cpp.

VectorFloat BAG::getEnsembleWeights

(

)

const

Gets the weights for each classifier in the ensemble.

Returns

returns a Vector of weights.

Definition at line 454 of file BAG.cpp.

std::string BAG::getId ( )

static

Gets a string that represents the BAG class.

Returns

returns a string containing the ID of this class

Definition at line 28 of file BAG.cpp.

bool BAG::load ( std::fstream &  file )

virtual

This loads a trained BAG model from a file. This overrides the load function in the Classifier base class.

Parameters

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

Returns

returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Examples:

ClassificationModulesExamples/BAGExample/BAGExample.cpp.

Definition at line 342 of file BAG.cpp.

BAG & BAG::operator=

(

const BAG &

rhs

)

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

Parameters

rhs	another instance of a BAG

Returns

returns a pointer to this instance of the BAG

Definition at line 50 of file BAG.cpp.

bool BAG::predict_ ( VectorFloat &  inputVector )

virtual

This predicts the class of the inputVector. This overrides the predict function in the 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 193 of file BAG.cpp.

bool BAG::reset ( )

virtual

This resets the BAG classifier.

Returns

returns true if the BAG model was successfully reset, false otherwise.

Reimplemented from Classifier.

Definition at line 259 of file BAG.cpp.

bool BAG::save ( std::fstream &  file ) const

virtual

This saves the trained BAG model to a file. This overrides the save function in the Classifier base class.

Parameters

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

Returns

returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Examples:

ClassificationModulesExamples/BAGExample/BAGExample.cpp.

Definition at line 286 of file BAG.cpp.

bool BAG::setWeights

(

const VectorFloat &

weights

)

This functions lets you dynamically update the weights for each ensemble. This can be useful if you have some exterior knowledge that could be used to weight the vote for each classifier. The weights should all be positive, however they do not need to sum to one.

You should can call this function both before training the ensemble, and in real-time to update the weights for each prediction.

Returns

returns true if the ensemble weights were successfully updated, false otherwise

Definition at line 497 of file BAG.cpp.

bool BAG::train_ ( ClassificationData &  trainingData )

virtual

This trains the BAG model, using the labelled classification data. This overrides the train function in the Classifier base class.

Parameters

trainingData

a reference to the training data

Returns

returns true if the BAG model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 91 of file BAG.cpp.

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

• BAG.h
• BAG.cpp