Inheritance diagram for ANBC:

Public Member Functions
	ANBC (bool useScaling=false, bool useNullRejection=false, double nullRejectionCoeff=10.0)

	ANBC (const ANBC &rhs)

virtual	~ANBC (void)

ANBC &	operator= (const ANBC &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)

virtual bool	recomputeNullRejectionThresholds ()

VectorFloat	getNullRejectionThresholds () const

Vector< ANBC_Model >	getModels ()

bool	setNullRejectionCoeff (double nullRejectionCoeff)

bool	setWeights (const ClassificationData &weightsData)

bool	clearWeights ()

Public Member Functions inherited from Classifier
	Classifier (void)

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

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)

	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(std::string filename) instead", virtual bool saveModelToFile(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(std::string filename) instead", virtual bool loadModelFromFile(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

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
bool	weightsDataSet

ClassificationData	weightsData

Vector< ANBC_Model >	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< ANBC >	registerModule

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	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 ()

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

Detailed Description

Definition at line 50 of file ANBC.h.

Constructor & Destructor Documentation

ANBC::ANBC	(	bool	useScaling = `false`,
		bool	useNullRejection = `false`,
		double	nullRejectionCoeff = `10.0`
	)

Default Constructor

Parameters

useScaling	sets if each dimension of the training and prediction data should be scaled to the same range. This may be useful if different dimensions of your input data have very different ranges. 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

ANBC::ANBC ( const ANBC & rhs )

Defines the copy constructor.

Parameters

const ANBC &rhs: the instance from which all the data will be copied into this instance

Definition at line 45 of file ANBC.cpp.

ANBC::~ANBC ( void )

virtual

Default Destructor

Definition at line 56 of file ANBC.cpp.

Member Function Documentation

bool ANBC::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 289 of file ANBC.cpp.

bool ANBC::clearWeights ( )

inline

Clears any previously set weights.

Returns: returns true if the weights were correctly cleared, false otherwise

Definition at line 189 of file ANBC.h.

bool ANBC::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 ANBC instance) into this instance

Parameters

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

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

Reimplemented from Classifier.

Definition at line 73 of file ANBC.cpp.

Vector< ANBC_Model > ANBC::getModels ( )

inline

Returns the ANBC models for each of the classes.

Returns: returns a vector of the ANBC models for each of the classes

Definition at line 163 of file ANBC.h.

VectorFloat ANBC::getNullRejectionThresholds ( ) const

Gets a vector containing the null rejection thresholds for each class, this will be an N-dimensional vector, where N is the number of classes in the model.

Returns: returns a vector containing the null rejection thresholds for each class, an empty vector will be returned if the model has not been trained

Definition at line 512 of file ANBC.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Definition at line 350 of file ANBC.cpp.

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

protected

Read the ranges if needed

Definition at line 537 of file ANBC.cpp.

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

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

Parameters

rhs	another instance of a ANBC

Returns: returns a pointer to this instance of the ANBC

Definition at line 60 of file ANBC.cpp.

bool ANBC::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 200 of file ANBC.cpp.

bool ANBC::recomputeNullRejectionThresholds ( )

virtual

This recomputes the null rejection thresholds for each of the classes in the ANBC model. This will be called automatically if the setGamma(double gamma) function is called. The ANBC 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 271 of file ANBC.cpp.

bool ANBC::reset ( )

virtual

This resets the ANBC classifier.

Returns: returns true if the ANBC model was successfully reset, false otherwise.

Reimplemented from Classifier.

Definition at line 285 of file ANBC.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Definition at line 301 of file ANBC.cpp.

bool ANBC::setNullRejectionCoeff ( double nullRejectionCoeff )

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

Definition at line 517 of file ANBC.cpp.

bool ANBC::setWeights ( const ClassificationData & weightsData )

Sets the weights for the training and prediction. The dimensionality of the weights should match that of the training data used to train the ANBC models. The weights should be encapsualted into a LabelledClassificationData container, with one training sample for each class.

Returns: returns true if the weights were correctly set, false otherwise

Definition at line 527 of file ANBC.cpp.

bool ANBC::train_ ( ClassificationData & trainingData )

virtual

This trains the ANBC 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 ANBC model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 91 of file ANBC.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