Inheritance diagram for MinDist:

Public Member Functions
	MinDist (bool useScaling=false, bool useNullRejection=false, Float nullRejectionCoeff=10.0, UINT numClusters=10)

	MinDist (const MinDist &rhs)

virtual	~MinDist (void)

MinDist &	operator= (const MinDist &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	clear ()

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

virtual bool	load (std::fstream &file)

virtual bool	recomputeNullRejectionThresholds ()

UINT	getNumClusters () const

Vector< MinDistModel >	getModels () const

virtual bool	setNullRejectionCoeff (Float nullRejectionCoeff)

bool	setNumClusters (UINT numClusters)

Public Member Functions inherited from Classifier
	Classifier (const std::string &classifierId="")

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

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	setNullRejectionThresholds (const VectorFloat &newRejectionThresholds)

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
UINT	numClusters

Vector< MinDistModel >	models

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, and ClassificationModulesExamples/MinDistExample/MinDistExample.cpp.

Definition at line 41 of file MinDist.h.

Constructor & Destructor Documentation

MinDist::MinDist	(	bool	useScaling = `false`,
		bool	useNullRejection = `false`,
		Float	nullRejectionCoeff = `10.0`,
		UINT	numClusters = `10`
	)

Default Constructor

Parameters

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
numClusters	sets how many clusters each model will try to find during the training phase. Default value = 10

Definition at line 33 of file MinDist.cpp.

MinDist::MinDist ( const MinDist & rhs )

Defines the copy constructor.

Parameters

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

Definition at line 43 of file MinDist.cpp.

MinDist::~MinDist ( void )

virtual

Default Destructor

Definition at line 49 of file MinDist.cpp.

Member Function Documentation

bool MinDist::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 257 of file MinDist.cpp.

bool MinDist::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 MinDist instance) into this instance

Parameters

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

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

Reimplemented from Classifier.

Definition at line 65 of file MinDist.cpp.

std::string MinDist::getId ( )

static

Gets a string that represents the MinDist class.

Returns: returns a string containing the ID of this class

Definition at line 28 of file MinDist.cpp.

Vector< MinDistModel > MinDist::getModels ( ) const

Returns the MinDist models for each of the classes.

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

Definition at line 294 of file MinDist.cpp.

UINT MinDist::getNumClusters ( ) const

Returns the number of clusters in the model.

Returns: returns the number of clusters in the model.

Definition at line 290 of file MinDist.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Examples:: ClassificationModulesExamples/MinDistExample/MinDistExample.cpp.

Definition at line 340 of file MinDist.cpp.

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

protected

Read the ranges if needed

Definition at line 470 of file MinDist.cpp.

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

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

Parameters

rhs	another instance of a MinDist

Returns: returns a pointer to this instance of the MinDist

Definition at line 53 of file MinDist.cpp.

bool MinDist::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 199 of file MinDist.cpp.

bool MinDist::recomputeNullRejectionThresholds ( )

virtual

This recomputes the null rejection thresholds for each of the classes in the MinDist model. This will be called automatically if the setGamma(Float gamma) function is called. The MinDist 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 268 of file MinDist.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Examples:: ClassificationModulesExamples/MinDistExample/MinDistExample.cpp.

Definition at line 298 of file MinDist.cpp.

bool MinDist::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 280 of file MinDist.cpp.

bool MinDist::setNumClusters ( UINT numClusters )

Sets the numClusters parameter. The numClusters parameter sets how many clusters each model will try to find during the training phase. You should call this function before you train the model.

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

Examples:: ClassificationModulesExamples/BAGExample/BAGExample.cpp, and ClassificationModulesExamples/MinDistExample/MinDistExample.cpp.

Definition at line 462 of file MinDist.cpp.

bool MinDist::train_ ( ClassificationData & trainingData )

virtual

This trains the MinDist 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 MinDist model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 83 of file MinDist.cpp.

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation