#include <AdaBoost.h>

Inheritance diagram for AdaBoost:

Public Types
enum	PredictionMethods { MAX_POSITIVE_VALUE =0, MAX_VALUE }

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 }

Public Member Functions
	AdaBoost (const WeakClassifier &weakClassifier=DecisionStump(), bool useScaling=false, bool useNullRejection=false, Float nullRejectionCoeff=10.0, UINT numBoostingIterations=20, UINT predictionMethod=MAX_VALUE)

	AdaBoost (const AdaBoost &rhs)

virtual	~AdaBoost ()

AdaBoost &	operator= (const AdaBoost &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 ()

bool	setNullRejectionCoeff (Float nullRejectionCoeff)

bool	setWeakClassifier (const WeakClassifier &weakClassifer)

bool	addWeakClassifier (const WeakClassifier &weakClassifer)

bool	clearWeakClassifiers ()

bool	setNumBoostingIterations (UINT numBoostingIterations)

bool	setPredictionMethod (UINT predictionMethod)

void	printModel ()

Vector< AdaBoostClassModel >	getModels () const

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	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
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	numBoostingIterations

UINT	predictionMethod

Vector< WeakClassifier * >	weakClassifiers

Vector< AdaBoostClassModel >	models

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< AdaBoost >	registerModule

Additional Inherited Members
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

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 57 of file AdaBoost.h.

Member Enumeration Documentation

enum AdaBoost::PredictionMethods

These are the two prediction methods that the GRT::AdaBoost classifier can use.

Definition at line 240 of file AdaBoost.h.

Constructor & Destructor Documentation

AdaBoost::AdaBoost	(	const WeakClassifier &	weakClassifier = `DecisionStump()`,
		bool	useScaling = `false`,
		bool	useNullRejection = `false`,
		Float	nullRejectionCoeff = `10.0`,
		UINT	numBoostingIterations = `20`,
		UINT	predictionMethod = `MAX_VALUE`
	)

Default Constructor

Parameters

weakClassifier	sets the initial weak classifier that is added to the vector of weak classifiers used to train the AdaBoost model
useScaling	sets if the training and prediction data should be scaled to a specific range. Default value is useScaling = false
useNullRejection	sets if null rejection will be used for the realtime prediction. If useNullRejection is set to true then the predictedClassLabel will be set to 0 (which is the default null label) if the distance between the inputVector and the top K datum is greater than the null rejection threshold for the top predicted class. The null rejection threshold is computed for each class during the training phase. Default value is useNullRejection = false
nullRejectionCoeff	sets the null rejection coefficient, this is a multipler controlling the null rejection threshold for each class. This will only be used if the useNullRejection parameter is set to true. Default value is nullRejectionCoeff = 10.0
numBoostingIterations	sets the number of boosting iterations to use during training. Default value = 20
predictionMethod	sets the prediction method for AdaBoost, this should be one of the PredictionMethods. Default value = MAX_VALUE

Definition at line 28 of file AdaBoost.cpp.

AdaBoost::AdaBoost ( const AdaBoost & rhs )

Defines the copy constructor.

Parameters

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

Definition at line 45 of file AdaBoost.cpp.

AdaBoost::~AdaBoost ( void )

virtual

Default Destructor

Definition at line 55 of file AdaBoost.cpp.

Member Function Documentation

bool AdaBoost::addWeakClassifier ( const WeakClassifier & weakClassifer )

Adds a WeakClassifier to the list of WeakClassifiers to use for boosting.

If this function is called, the new WeakClassifier will be added to the list of WeakClassifiers.

Returns: returns true if the WeakClassifier was added successfully, false otherwise

Definition at line 524 of file AdaBoost.cpp.

bool AdaBoost::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 501 of file AdaBoost.cpp.

bool AdaBoost::clearWeakClassifiers ( )

Clears all the current WeakClassifiers.

Returns: returns true if the WeakClassifiers was cleared successfully, false otherwise

Definition at line 532 of file AdaBoost.cpp.

bool AdaBoost::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 AdaBoost instance) into this instance

Parameters

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

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

Reimplemented from Classifier.

Definition at line 83 of file AdaBoost.cpp.

Vector< AdaBoostClassModel > AdaBoost::getModels ( ) const

inline

Returns the current vector of AdaBoostClassModel models.

Returns: a vector containing the current AdaBoostClassModel models.

Definition at line 216 of file AdaBoost.h.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Definition at line 434 of file AdaBoost.cpp.

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

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

Parameters

rhs	another instance of a AdaBoost

Returns: returns a reference to this instance of the AdaBoost

Definition at line 61 of file AdaBoost.cpp.

bool AdaBoost::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 291 of file AdaBoost.cpp.

void AdaBoost::printModel ( )

Prints the current model to the std::out.

Returns: void.

Definition at line 560 of file AdaBoost.cpp.

bool AdaBoost::recomputeNullRejectionThresholds ( )

virtual

This recomputes the null rejection thresholds for each of the classes in the AdaBoost model. This will be called automatically if the setGamma(Float gamma) function is called. The AdaBoost model needs to be trained first before this function can be called.

Returns: returns true if the null rejection thresholds were updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 380 of file AdaBoost.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Definition at line 399 of file AdaBoost.cpp.

bool AdaBoost::setNullRejectionCoeff ( Float nullRejectionCoeff )

virtual

Sets the nullRejectionCoeff parameter. The nullRejectionCoeff parameter is a multipler controlling the null rejection threshold for each class. This function will also recompute the null rejection thresholds.

Returns: returns true if the gamma parameter was updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 389 of file AdaBoost.cpp.

bool AdaBoost::setNumBoostingIterations ( UINT numBoostingIterations )

Sets the number of boosting iterations that should be used when training the AdaBoost model. The numBoostingIterations parameter must be greater than zero.

Returns: returns true if the numBoostingIterations was set successfully, false otherwise

Definition at line 544 of file AdaBoost.cpp.

bool AdaBoost::setPredictionMethod ( UINT predictionMethod )

Sets the prediction method for AdaBoost, this should be one of the PredictionMethods enumerations.

Parameters

predictionMethod the predictionMethod that should be used by AdaBoost, this should be one of the PredictionMethods enumerations

Returns: returns true if the predictionMethod was set successfully, false otherwise

Definition at line 552 of file AdaBoost.cpp.

bool AdaBoost::setWeakClassifier ( const WeakClassifier & weakClassifer )

Sets the WeakClassifier to use for boosting.

If this function is called, any previously set WeakClassifiers will be removed.

Returns: returns true if the WeakClassifier was added successfully, false otherwise

Definition at line 512 of file AdaBoost.cpp.

bool AdaBoost::train_ ( ClassificationData & trainingData )

virtual

This trains the AdaBoost 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 AdaBoost model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 114 of file AdaBoost.cpp.

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

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

Constructor & Destructor Documentation

Member Function Documentation