Inheritance diagram for Classifier:

Public Types
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
	Classifier (void)

virtual	~Classifier (void)

virtual bool	deepCopyFrom (const Classifier *classifier)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

virtual bool	clear ()

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)

virtual bool	recomputeNullRejectionThresholds ()

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_ (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_ (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	saveModelToFile (std::fstream &file) const

virtual bool	loadModelFromFile (std::string filename)

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)

Static Public Member Functions
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
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

Protected Member Functions
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

Static Protected Member Functions
static StringClassifierMap *	getMap ()

Protected Attributes
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

Detailed Description

Definition at line 41 of file Classifier.h.

Member Typedef Documentation

typedef std::map< std::string, Classifier*(*)() > Classifier::StringClassifierMap

Defines a map between a string (which will contain the name of the classifier, such as ANBC) and a function returns a new instance of that classifier

Definition at line 248 of file Classifier.h.

Constructor & Destructor Documentation

Classifier::Classifier ( void )

Default Classifier Constructor

Definition at line 68 of file Classifier.cpp.

Classifier::~Classifier ( void )

virtual

Default Classifier Destructor

Definition at line 85 of file Classifier.cpp.

Member Function Documentation

bool Classifier::clear ( )

virtual

This function clears the classifier module, removing any trained model and setting all the base variables to their default values.

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

Reimplemented from MLBase.

Reimplemented in DTW, HMM, FiniteStateMachine, AdaBoost, ANBC, SVM, KNN, RandomForests, DecisionTree, BAG, GMM, ParticleClassifier, MinDist, Softmax, and SwipeDetector.

Definition at line 141 of file Classifier.cpp.

bool Classifier::copyBaseVariables ( const Classifier * classifier )

This copies the Classifier base class variables from the classifier pointer to this instance.

Parameters

classifier a pointer to a classifier from which the values will be copied to this instance

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

Definition at line 92 of file Classifier.cpp.

Classifier * Classifier::createInstanceFromString ( std::string const & classifierType )

static

Creates a new classifier instance based on the input string (which should contain the name of a valid classifier such as ANBC).

Parameters

classifierType the name of the classifier

Returns: Classifier*: a pointer to the new instance of the classifier

Definition at line 28 of file Classifier.cpp.

Classifier * Classifier::createNewInstance ( ) const

Creates a new classifier instance based on the current classifierType string value.

Returns: Classifier*: a pointer to the new instance of the classifier

Definition at line 36 of file Classifier.cpp.

Classifier * Classifier::deepCopy ( ) const

This creates a new Classifier instance and deep copies the variables and models from this instance into the deep copy. The function will then return a pointer to the new instance. It is up to the user who calls this function to delete the dynamic instance when they are finished using it.

Returns: returns a pointer to a new Classifier instance which is a deep copy of this instance

Definition at line 40 of file Classifier.cpp.

virtual bool Classifier::deepCopyFrom ( const Classifier * classifier )

inlinevirtual

This is the base deep copy function for the Classifier modules. This function should be overwritten by the derived class. This deep copies the variables and models from the classifier pointer to this classifier instance.

Parameters

classifier a pointer to the Classifier base class, this should be pointing to another instance of a matching derived class

Returns: returns true if the clone was successfull, false otherwise (the Classifier base class will always return flase)

Reimplemented in DTW, AdaBoost, SVM, LDA, KNN, RandomForests, DecisionTree, ANBC, HMM, GMM, BAG, MinDist, Softmax, SwipeDetector, FiniteStateMachine, and ParticleClassifier.

Definition at line 61 of file Classifier.h.

bool Classifier::enableNullRejection ( bool useNullRejection )

Sets if the classifier should use nullRejection.

If set to true then the classifier will reject a predicted class label if the likelihood of the prediction is below (or above depending on the algorithm) the models rejectionThreshold. If a prediction is rejected then the default null class label of 0 will be returned. If set to false then the classifier will simply return the most likely predicted class.

Returns: returns true if nullRejection was updated successfully, false otherwise

Definition at line 230 of file Classifier.cpp.

const Classifier & Classifier::getBaseClassifier ( ) const

Returns a pointer to this classifier. This is useful for a derived class so it can get easy access to this base classifier.

Returns: Classifier&: a reference to this classifier

Definition at line 251 of file Classifier.cpp.

Float Classifier::getBestDistance ( ) const

Returns the current bestDistance value. The bestDistance value is computed during the prediction phase and is either the minimum or maximum distance, depending on the algorithm. This value will return 0 if a prediction has not been made.

Returns: returns the current bestDistance value

Definition at line 185 of file Classifier.cpp.

VectorFloat Classifier::getClassDistances ( ) const

Gets a Vector of the class distances from the last prediction, this will be an N-dimensional Vector, where N is the number of classes in the model. The exact form of these distances depends on the classification algorithm.

Returns: returns a Vector of the class distances from the last prediction, an empty Vector will be returned if the model has not been trained

Definition at line 212 of file Classifier.cpp.

const Classifier * Classifier::getClassifierPointer ( ) const

Returns a pointer to the classifier.

Returns: returns a pointer the current classifier

Definition at line 53 of file Classifier.cpp.

std::string Classifier::getClassifierType ( ) const

Returns the classifier type as a string.

Returns: returns the classifier type as a string

Definition at line 160 of file Classifier.cpp.

UINT Classifier::getClassLabelIndexValue ( UINT classLabel ) const

Gets the index of the query classLabel in the classLabels Vector. If the query classLabel does not exist in the classLabels Vector then the function will return zero.

Parameters

classLabel the query classLabel

Returns: returns index of the query classLabel in the classLabels Vector

Definition at line 194 of file Classifier.cpp.

Vector< UINT > Classifier::getClassLabels ( ) const

Gets a Vector containing the label each class represents, this will be an N-dimensional Vector, where N is the number of classes in the model. This is useful if the model was trained with non-monotonically class labels (i.e. class labels such as [1, 3, 6, 9, 12] instead of [1, 2, 3, 4, 5]).

Returns: returns a Vector containing the class labels for each class, an empty Vector will be returned if the model has not been trained

Definition at line 222 of file Classifier.cpp.

VectorFloat Classifier::getClassLikelihoods ( ) const

Gets a Vector of the class likelihoods from the last prediction, this will be an N-dimensional Vector, where N is the number of classes in the model. The exact form of these likelihoods depends on the classification algorithm.

Returns: returns a Vector of the class likelihoods from the last prediction, an empty Vector will be returned if the model has not been trained

Definition at line 207 of file Classifier.cpp.

Float Classifier::getMaximumLikelihood ( ) const

Returns the current maximumLikelihood value. The maximumLikelihood value is computed during the prediction phase and is the likelihood of the most likely model. This value will return 0 if a prediction has not been made.

Returns: returns the current maximumLikelihood value

Definition at line 176 of file Classifier.cpp.

Float Classifier::getNullRejectionCoeff ( ) const

Returns the current nullRejectionCoeff value. The nullRejectionCoeff parameter is a multipler controlling the null rejection threshold for each class.

Returns: returns the current nullRejectionCoeff value

Definition at line 172 of file Classifier.cpp.

bool Classifier::getNullRejectionEnabled ( ) const

Returns true if nullRejection is enabled.

Returns: returns true if nullRejection is enabled, false otherwise

Definition at line 168 of file Classifier.cpp.

VectorFloat Classifier::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 217 of file Classifier.cpp.

UINT Classifier::getNumClasses ( ) const

virtual

Gets the number of classes in trained model.

Returns: returns the number of classes in the trained model, a value of 0 will be returned if the model has not been trained

Reimplemented in SVM.

Definition at line 190 of file Classifier.cpp.

Float Classifier::getPhase ( ) const

This function returns the estimated gesture phase from the most recent prediction. This value is only relevant if the classifier supports timeseries classification.

Returns: Float representing the gesture phase value from the most likely class from the most recent prediction

Definition at line 181 of file Classifier.cpp.

UINT Classifier::getPredictedClassLabel ( ) const

Gets the predicted class label from the last prediction.

Returns: returns the label of the last predicted class, a value of 0 will be returned if the model has not been trained

Definition at line 202 of file Classifier.cpp.

Vector< MinMax > Classifier::getRanges ( ) const

Gets a Vector of the ranges used to scale the data for training and prediction, these ranges are only used if the classifier has been trained with the #useScaling flag set to true. This should be an N-dimensional Vector, where N is the number of features in your data.

Returns: returns a Vector containing the ranges used to scale the data for classification, an empty Vector will be returned if the model has not been trained

Definition at line 226 of file Classifier.cpp.

Vector< std::string > Classifier::getRegisteredClassifiers ( )

static

Returns a Vector of the names of all classifiers that have been registered with the base classifier.

Returns: Vector< string >: a Vector containing the names of the classifiers that have been registered with the base classifier

Definition at line 57 of file Classifier.cpp.

bool Classifier::getSupportsNullRejection ( ) const

Returns true if the classifier instance supports null rejection, false otherwise.

Returns: returns true if the classifier instance supports null rejection, false otherwise

Definition at line 164 of file Classifier.cpp.

bool Classifier::getTimeseriesCompatible ( ) const

inline

Indicates if the classifier can be used to classify timeseries data. If true then the classifier can accept training data in the LabelledTimeSeriesClassificationData format.

return returns true if the classifier can be used to classify timeseries data, false otherwise

Definition at line 243 of file Classifier.h.

bool Classifier::loadBaseSettingsFromFile ( std::fstream & file )

protected

Loads the core base settings from a file.

Returns: returns true if the base settings were loaded, false otherwise

Definition at line 302 of file Classifier.cpp.

virtual bool Classifier::recomputeNullRejectionThresholds ( )

inlinevirtual

Recomputes the null rejection thresholds for each model.

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

Reimplemented in DTW, AdaBoost, ANBC, KNN, GMM, MinDist, and DecisionTree.

Definition at line 235 of file Classifier.h.

bool Classifier::reset ( )

virtual

This resets the classifier. This overrides the reset function in the MLBase base class.

Returns: returns true if the classifier was reset, false otherwise

Reimplemented from MLBase.

Reimplemented in DTW, HMM, FiniteStateMachine, ParticleClassifier, ANBC, SwipeDetector, and BAG.

Definition at line 122 of file Classifier.cpp.

bool Classifier::saveBaseSettingsToFile ( std::fstream & file ) const

protected

Saves the core base settings to a file.

Returns: returns true if the base settings were saved, false otherwise

Definition at line 255 of file Classifier.cpp.

bool Classifier::setNullRejectionCoeff ( Float nullRejectionCoeff )

virtual

Sets the nullRejectionCoeff, this is a multipler controlling the null rejection threshold for each class.

Returns: returns true if nullRejectionCoeff was updated successfully, false otherwise

Reimplemented in KNN, AdaBoost, and MinDist.

Definition at line 235 of file Classifier.cpp.

bool Classifier::setNullRejectionThresholds ( VectorFloat newRejectionThresholds )

virtual

Manually sets the nullRejectionThresholds, these are the thresholds used for null rejection for each class. This needs to be called after the model has been trained. Calling the setNullRejectionCoeff or recomputeNullRejectionThresholds functions will override these values. The size of the newRejectionThresholds Vector must match the number of classes in the model.

Parameters

newRejectionThresholds the new rejection thresholds

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

Definition at line 243 of file Classifier.cpp.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Types

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation