Inheritance diagram for GestureRecognitionPipeline:

Public Types
enum	ContextLevels { START_OF_PIPELINE =0, AFTER_PREPROCESSING, AFTER_FEATURE_EXTRACTION, AFTER_CLASSIFIER, END_OF_PIPELINE, NUM_CONTEXT_LEVELS }

Public Types inherited from MLBase
enum	BaseType { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER, PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }

Public Member Functions
	GestureRecognitionPipeline (void)

	GestureRecognitionPipeline (const GestureRecognitionPipeline &rhs)

virtual	~GestureRecognitionPipeline (void)

GestureRecognitionPipeline &	operator= (const GestureRecognitionPipeline &rhs)

GestureRecognitionPipeline &	operator<< (const PreProcessing &module)

GestureRecognitionPipeline &	operator<< (const FeatureExtraction &module)

GestureRecognitionPipeline &	operator<< (const Classifier &module)

GestureRecognitionPipeline &	operator<< (const Regressifier &module)

GestureRecognitionPipeline &	operator<< (const Clusterer &module)

GestureRecognitionPipeline &	operator<< (const PostProcessing &module)

virtual bool	train_ (ClassificationData &trainingData) override

virtual bool	train (const ClassificationData &trainingData, const UINT kFoldValue, const bool useStratifiedSampling=false)

virtual bool	train_ (ClassificationData &trainingData, const UINT kFoldValue, const bool useStratifiedSampling=false)

virtual bool	train_ (TimeSeriesClassificationData &trainingData) override

virtual bool	train (const TimeSeriesClassificationData &trainingData, const UINT kFoldValue, const bool useStratifiedSampling=false)

virtual bool	train_ (TimeSeriesClassificationData &trainingData, const UINT kFoldValue, const bool useStratifiedSampling=false)

virtual bool	train_ (ClassificationDataStream &trainingData) override

virtual bool	train_ (RegressionData &trainingData) override

virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData) override

virtual bool	train (const RegressionData &trainingData, const UINT kFoldValue)

virtual bool	train_ (RegressionData &trainingData, const UINT kFoldValue)

virtual bool	train_ (UnlabelledData &trainingData) override

virtual bool	test (const ClassificationData &testData)

virtual bool	test (const TimeSeriesClassificationData &testData)

virtual bool	test (const ClassificationDataStream &testData)

virtual bool	test (const RegressionData &testData)

virtual bool	predict_ (VectorFloat &inputVector) override

virtual bool	predict_ (MatrixFloat &inputMatrix) override

	GRT_DEPRECATED_MSG ("map_(VectorFloat &inputVector) is deprecated, use predict_(VectorFloat &inputVector) instead", virtual bool map_(VectorFloat &inputVector) override)

virtual bool	reset () override

virtual bool	clear () override

virtual bool	clearModel ()

virtual bool	save (const std::string &filename) const override

	GRT_DEPRECATED_MSG ("savePipelineToFile(std::string filename) is deprecated, use save(std::string &filename) instead", bool savePipelineToFile(const std::string &filename) const )

virtual bool	load (const std::string &filename) override

	GRT_DEPRECATED_MSG ("loadPipelineFromFile(std::string filename) is deprecated, use load(std::string &filename) instead", bool loadPipelineFromFile(const std::string &filename))

bool	preProcessData (VectorFloat inputVector, bool computeFeatures=true)

virtual bool	getIsInitialized () const

bool	getIsPreProcessingSet () const

bool	getIsFeatureExtractionSet () const

bool	getIsClassifierSet () const

bool	getIsRegressifierSet () const

bool	getIsClustererSet () const

bool	getIsPostProcessingSet () const

bool	getIsContextSet () const

bool	getIsPipelineModeSet () const

bool	getIsPipelineInClassificationMode () const

bool	getIsPipelineInRegressionMode () const

UINT	getInputVectorDimensionsSize () const

UINT	getOutputVectorDimensionsSize () const

UINT	getNumClassesInModel () const

UINT	getNumClasses () const

UINT	getNumPreProcessingModules () const

UINT	getNumFeatureExtractionModules () const

UINT	getNumPostProcessingModules () const

UINT	getPredictionModuleIndexPosition () const

UINT	getPredictedClassLabel () const

UINT	getUnProcessedPredictedClassLabel () const

UINT	getNumTrainingSamples () const

UINT	getNumTestSamples () const

Float	getMaximumLikelihood () const

Float	getPhase () const

Float	getTrainingSetAccuracy () const

Float	getCrossValidationAccuracy () const

Float	getTestAccuracy () const

Float	getTestRMSError () const

Float	getTestSSError () const

Float	getTestFMeasure (const UINT classLabel) const

Float	getTestPrecision (const UINT classLabel) const

Float	getTestRecall (const UINT classLabel) const

Float	getTestRejectionPrecision () const

Float	getTestRejectionRecall () const

Float	getTestTime () const

Float	getTrainingTime () const

Float	getTrainingRMSError () const

Float	getTrainingSSError () const

MatrixFloat	getTestConfusionMatrix () const

Vector< TrainingResult >	getTrainingResults () const

TestResult	getTestResults () const

VectorFloat	getTestPrecision () const

VectorFloat	getTestRecall () const

VectorFloat	getTestFMeasure () const

VectorFloat	getClassLikelihoods () const

VectorFloat	getClassDistances () const

VectorFloat	getNullRejectionThresholds () const

VectorFloat	getRegressionData () const

VectorFloat	getUnProcessedRegressionData () const

VectorFloat	getPreProcessedData () const

VectorFloat	getPreProcessedData (UINT moduleIndex) const

VectorFloat	getFeatureExtractionData () const

VectorFloat	getFeatureExtractionData (const UINT moduleIndex) const

Vector< UINT >	getClassLabels () const

Vector< TestInstanceResult >	getTestInstanceResults () const

Vector< TestResult >	getCrossValidationResults () const

PreProcessing *	getPreProcessingModule (const UINT moduleIndex) const

FeatureExtraction *	getFeatureExtractionModule (const UINT moduleIndex) const

Classifier *	getClassifier () const

Regressifier *	getRegressifier () const

Clusterer *	getClusterer () const

PostProcessing *	getPostProcessingModule (UINT moduleIndex) const

Context *	getContextModule (const UINT contextLevel, const UINT moduleIndex) const

template<class T >
const T *	getPreProcessingModule (const UINT moduleIndex) const

template<class T >
T *	getPreProcessingModule (const UINT moduleIndex)

template<class T >
const T *	getFeatureExtractionModule (const UINT moduleIndex) const

template<class T >
T *	getFeatureExtractionModule (const UINT moduleIndex)

template<class T >
const T *	getClassifier () const

template<class T >
T *	getClassifier ()

template<class T >
const T *	getRegressifier () const

template<class T >
T *	getRegressifier ()

template<class T >
const T *	getCluster () const

template<class T >
T *	getCluster ()

template<class T >
const T *	getPostProcessingModule (const UINT moduleIndex) const

template<class T >
T *	getPostProcessingModule (const UINT moduleIndex)

template<class T >
T *const	getContextModule (const UINT contextLevel, const UINT moduleIndex) const

template<class T >
T *	getContextModule (const UINT contextLevel, const UINT moduleIndex)

virtual std::string	getModelAsString () const override

std::string	getPipelineModeAsString () const

std::string	getInfo () const

UINT	getPipelineModeFromString (std::string pipelineMode) const

bool	addPreProcessingModule (const PreProcessing &preProcessingModule, UINT insertIndex=INSERT_AT_END_INDEX)

bool	setPreProcessingModule (const PreProcessing &preProcessingModule)

bool	addFeatureExtractionModule (const FeatureExtraction &featureExtractionModule, UINT insertIndex=INSERT_AT_END_INDEX)

bool	setFeatureExtractionModule (const FeatureExtraction &featureExtractionModule)

bool	setClassifier (const Classifier &classifier)

bool	setRegressifier (const Regressifier &regressifier)

bool	setClusterer (const Clusterer &clusterer)

bool	addPostProcessingModule (const PostProcessing &postProcessingModule, UINT insertIndex=INSERT_AT_END_INDEX)

bool	setPostProcessingModule (const PostProcessing &postProcessingModule)

bool	addContextModule (const Context &contextModule, UINT contextLevel, UINT insertIndex=INSERT_AT_END_INDEX)

bool	updateContextModule (bool value, UINT contextLevel=0, UINT moduleIndex=0)

bool	removeAllPreProcessingModules ()

bool	removePreProcessingModule (UINT moduleIndex)

bool	removeAllFeatureExtractionModules ()

bool	removeFeatureExtractionModule (UINT moduleIndex)

bool	removeClassifier ()

bool	removeRegressifier ()

bool	removeClusterer ()

bool	removeAllPostProcessingModules ()

bool	removePostProcessingModule (const UINT moduleIndex)

bool	removeAllContextModules ()

bool	removeContextModule (const UINT contextLevel, const UINT moduleIndex)

	GRT_DEPRECATED_MSG ("use clear() instead.", bool clearAll())

bool	clearTestResults ()

bool	setInfo (const std::string &info)

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, RegressionData validationData)

virtual bool	train (TimeSeriesClassificationData trainingData)

virtual bool	train (ClassificationDataStream 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	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

virtual bool	print () const

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

virtual bool	load (std::fstream &file)

	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

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)

Protected Types
enum	PipelineModes { PIPELINE_MODE_NOT_SET =0, CLASSIFICATION_MODE, REGRESSION_MODE, CLUSTER_MODE }

Protected Member Functions
bool	init ()

bool	predict_classifier (const VectorFloat &inputVector)

bool	predict_timeseries (const MatrixFloat &input)

bool	predict_frame (const MatrixFloat &input)

bool	predict_regressifier (const VectorFloat &inputVector)

bool	predict_clusterer (const VectorFloat &inputVector)

void	deleteAllPreProcessingModules ()

void	deleteAllFeatureExtractionModules ()

void	deleteClassifier ()

void	deleteRegressifier ()

void	deleteClusterer ()

void	deleteAllPostProcessingModules ()

void	deleteAllContextModules ()

bool	updateTestMetrics (const UINT classLabel, const UINT predictedClassLabel, VectorFloat &precisionCounter, VectorFloat &recallCounter, Float &rejectionPrecisionCounter, Float &rejectionRecallCounter, VectorFloat &confusionMatrixCounter)

bool	computeTestMetrics (VectorFloat &precisionCounter, VectorFloat &recallCounter, Float &rejectionPrecisionCounter, Float &rejectionRecallCounter, VectorFloat &confusionMatrixCounter, const UINT numTestSamples)

Protected Member Functions inherited from MLBase
bool	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Attributes
bool	initialized

std::string	info

UINT	inputVectorDimensions

UINT	outputVectorDimensions

UINT	predictedClassLabel

UINT	predictedClusterLabel

UINT	pipelineMode

UINT	predictionModuleIndex

UINT	numTrainingSamples

UINT	numTestSamples

Float	testAccuracy

Float	testRMSError

Float	testSquaredError

Float	testTime

Float	trainingTime

VectorFloat	testFMeasure

VectorFloat	testPrecision

VectorFloat	testRecall

VectorFloat	regressionData

Float	testRejectionPrecision

Float	testRejectionRecall

MatrixFloat	testConfusionMatrix

Vector< TestResult >	crossValidationResults

Vector< TestInstanceResult >	testResults

Vector< PreProcessing * >	preProcessingModules

Vector< FeatureExtraction * >	featureExtractionModules

Classifier *	classifier

Regressifier *	regressifier

Clusterer *	clusterer

Vector< PostProcessing * >	postProcessingModules

Vector< Vector< Context * > >	contextModules

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
Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Detailed Description

Examples:: RegressionModulesExamples/LinearRegressionExample/LinearRegressionExample.cpp, RegressionModulesExamples/LogisticRegressionExample/LogisticRegressionExample.cpp, RegressionModulesExamples/MLPRegressionExample/MLPRegressionExample.cpp, RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp, and Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 53 of file GestureRecognitionPipeline.h.

Constructor & Destructor Documentation

GRT_BEGIN_NAMESPACE GestureRecognitionPipeline::GestureRecognitionPipeline ( void )

Default Constructor

Definition at line 26 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline::GestureRecognitionPipeline ( const GestureRecognitionPipeline & rhs )

Copy Constructor. Performs a depp copy of the data from the rhs pipeline into this pipeline.

Definition at line 31 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline::~GestureRecognitionPipeline ( void )

virtual

Default Destructor

Definition at line 141 of file GestureRecognitionPipeline.cpp.

Member Function Documentation

bool GestureRecognitionPipeline::addContextModule	(	const Context &	contextModule,
		UINT	contextLevel,
		UINT	insertIndex = `INSERT_AT_END_INDEX`
	)

Adds a new context module to the pipeline. The user can specify the position at which the new module should be inserted into the list of context modules. The default position is to insert the new module at the end of the list.

Parameters

contextModule	a reference to the context module that you want to add
contextLevel	the context level where you want to add the new context module
insertIndex	the index of where you want to insert the new context module. Default is to insert the new module at the end of the list

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

Definition at line 3272 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::addFeatureExtractionModule	(	const FeatureExtraction &	featureExtractionModule,
		UINT	insertIndex = `INSERT_AT_END_INDEX`
	)

Adds a new feature extraction module to the pipeline. The user can specify the position at which the new module should be inserted into the list of feature extraction modules. The default position is to insert the new module at the end of the list.

Parameters

featureExtractionModule	a reference to the feature extraction module you want to add
insertIndex	the index of where you want to insert the new feature extraction module. Default is to insert the new module at the end of the list

Returns: returns true if the feature extraction module was added successfully, false otherwise

Definition at line 3092 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::addPostProcessingModule	(	const PostProcessing &	postProcessingModule,
		UINT	insertIndex = `INSERT_AT_END_INDEX`
	)

Adds a new post processing module to the pipeline. The user can specify the position at which the new module should be inserted into the list of post processing modules. The default position is to insert the new module at the end of the list.

Parameters

postProcessingModule	a reference to the post processing module you want to add
insertIndex	the index of where you want to insert the new post processing module. Default is to insert the new module at the end of the list

Returns: returns true if the post processing module was added successfully, false otherwise

Definition at line 3235 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::addPreProcessingModule	(	const PreProcessing &	preProcessingModule,
		UINT	insertIndex = `INSERT_AT_END_INDEX`
	)

Adds a new pre processing module to the pipeline. The user can specify the position at which the new module should be inserted into the list of preprocessing modules. The default position is to insert the new module at the end of the list.

Parameters

preProcessingModule	a reference to the pre processing module you want to add
insertIndex	the index of where you want to insert the new pre processing module. Default is to insert the new module at the end of the list

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

Definition at line 3054 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::clear ( )

overridevirtual

This function is the main interface for clearing the entire gesture recognition pipeline. This function will remove any module added to the pipeline and set it back to its default state.

Returns: bool returns true if the cleared was successful, false otherwise

Reimplemented from MLBase.

Definition at line 2108 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::clearModel ( )

virtual

This function is the main interface for clearing any trained model stored by the gesture recognition pipeline. This function will call clear on all the modules in the pipeline, but unlike the clear() function, it will not remove the modules.

Returns: bool returns true if the cleared was successful, false otherwise

Definition at line 2124 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::clearTestResults ( )

Resets all the test results to zero.

Returns: returns true if the test results were cleared successfully, false otherwise

Definition at line 3414 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::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 2930 of file GestureRecognitionPipeline.cpp.

Classifier * GestureRecognitionPipeline::getClassifier ( ) const

Gets a pointer to the classifier module.

Returns: returns a pointer to the classifier module, or NULL if the classifier has not been set

Definition at line 3020 of file GestureRecognitionPipeline.cpp.

template<class T >

const T* GestureRecognitionPipeline::getClassifier ( ) const

inline

Gets a const pointer to the classifier module. If the classifier has not been set, or the template type T does not match the current classifier type then the function will return NULL.

Returns: returns a pointer to the classifier module, or NULL if the classifier has not been set

Definition at line 971 of file GestureRecognitionPipeline.h.

template<class T >

T* GestureRecognitionPipeline::getClassifier ( )

inline

Gets a pointer to the classifier module. If the classifier has not been set, or the template type T does not match the current classifier type then the function will return NULL.

Returns: returns a pointer to the classifier module, or NULL if the classifier has not been set

Definition at line 988 of file GestureRecognitionPipeline.h.

Vector< UINT > GestureRecognitionPipeline::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

Examples:: Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 2985 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::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 2924 of file GestureRecognitionPipeline.cpp.

template<class T >

const T* GestureRecognitionPipeline::getCluster ( ) const

inline

Gets a const pointer to the cluster module. If the cluster has not been set, or the template type T does not match the current cluster type then the function will return NULL.

Returns: returns a pointer to the cluster module, or NULL if the cluster has not been set

Definition at line 1039 of file GestureRecognitionPipeline.h.

template<class T >

T* GestureRecognitionPipeline::getCluster ( )

inline

Gets a pointer to the cluster module. If the cluster has not been set, or the template type T does not match the current cluster type then the function will return NULL.

Returns: returns a pointer to the cluster module, or NULL if the cluster has not been set

Definition at line 1056 of file GestureRecognitionPipeline.h.

Clusterer * GestureRecognitionPipeline::getClusterer ( ) const

Gets a pointer to the clusterer module.

Returns: returns a pointer to the clusterer module, or NULL if the clusterer has not been set

Definition at line 3028 of file GestureRecognitionPipeline.cpp.

Context * GestureRecognitionPipeline::getContextModule	(	const UINT	contextLevel,
		const UINT	moduleIndex
	)		const

Gets a pointer to the context module at the specific contextLevel and moduleIndex.

Parameters

contextLevel	the context level that contains the context module you want
moduleIndex	the index of the context module you want

Returns: returns a pointer to the context module at the specific contextLevel and moduleIndex, or NULL if the contextLevel or moduleIndex are invalid

Definition at line 3040 of file GestureRecognitionPipeline.cpp.

template<class T >

T* const GestureRecognitionPipeline::getContextModule	(	const UINT	contextLevel,
		const UINT	moduleIndex
	)		const

inline

Gets a pointer to the context module at the specific contextLevel and moduleIndex. You should make sure that the type of the context module matches the template type.

Parameters

contextLevel	the context level that contains the context module you want
moduleIndex	the index of the context module you want

Returns: returns a pointer to the context module at the specific contextLevel and moduleIndex, or NULL if the contextLevel or moduleIndex are invalid

Definition at line 1108 of file GestureRecognitionPipeline.h.

template<class T >

T* GestureRecognitionPipeline::getContextModule	(	const UINT	contextLevel,
		const UINT	moduleIndex
	)

inline

Gets a pointer to the context module at the specific contextLevel and moduleIndex. You should make sure that the type of the context module matches the template type.

Parameters

contextLevel	the context level that contains the context module you want
moduleIndex	the index of the context module you want

Returns: returns a pointer to the context module at the specific contextLevel and moduleIndex, or NULL if the contextLevel or moduleIndex are invalid

Definition at line 1130 of file GestureRecognitionPipeline.h.

Float GestureRecognitionPipeline::getCrossValidationAccuracy ( ) const

This function returns the cross validation value from the most recent cross validation test. If the pipeline is in prediction mode, then the cross validation accuracy will be the average accuracy across each fold of testing. If the pipeline is in regression mode, then the cross validation accuracy will be the average RMS error across each fold of testing.

Returns: Float representing the cross validation value from the most recent cross validation test.

Definition at line 2801 of file GestureRecognitionPipeline.cpp.

Vector< TestResult > GestureRecognitionPipeline::getCrossValidationResults ( ) const

Gets a Vector containing the results from the most recent cross validation test. Each element in the Vector represents one fold of cross validation training.

Returns: returns a Vector containing the results from the most recent cross validation test, an empty Vector will be returned if no test has been run.

Definition at line 3000 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getFeatureExtractionData ( ) const

Gets a Vector containing the output of the last feature extraction module, this will be an M-dimensional Vector, where M is the output size of the last feature extraction module.

Returns: returns a Vector containing the output of the last feature extraction module, an empty Vector will be returned if there are no feature extraction modules.

Definition at line 2968 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getFeatureExtractionData ( const UINT moduleIndex ) const

Gets a Vector containing the output of the feature extraction module at index X, this will be an M-dimensional Vector, where M is the output size of the feature extraction module.

Parameters

const UINT moduleIndex: the index of the feature extraction module you want

Returns: returns a Vector containing the output of the feature extraction module at index X, an empty Vector will be returned if there is no feature extraction module at that index.

Definition at line 2975 of file GestureRecognitionPipeline.cpp.

FeatureExtraction * GestureRecognitionPipeline::getFeatureExtractionModule ( const UINT moduleIndex ) const

Gets a pointer to the feature extraction module at the specific moduleIndex.

Parameters

UINT	moduleIndex: the index of the feature extraction module you want

Returns: returns a pointer to the feature extraction module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 3012 of file GestureRecognitionPipeline.cpp.

template<class T >

const T* GestureRecognitionPipeline::getFeatureExtractionModule ( const UINT moduleIndex ) const

inline

Gets a const pointer to the feature extraction module at the specific moduleIndex. You should make sure that the type of the feature extraction module matches the template type.

Parameters

moduleIndex the index of the feature extraction module you want

Returns: returns a pointer to the feature extraction module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 933 of file GestureRecognitionPipeline.h.

template<class T >

T* GestureRecognitionPipeline::getFeatureExtractionModule ( const UINT moduleIndex )

inline

Gets a pointer to the feature extraction module at the specific moduleIndex. You should make sure that the type of the feature extraction module matches the template type.

Parameters

moduleIndex the index of the feature extraction module you want

Returns: returns a pointer to the feature extraction module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 952 of file GestureRecognitionPipeline.h.

UINT GestureRecognitionPipeline::getInputVectorDimensionsSize ( ) const

This function returns the size of the expected input Vector to the pipeline. This size comes from the size of the training data used to train the pipeline.

Returns: UINT representing the size of the input Vector to the pipeline.

Definition at line 2690 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsClassifierSet ( ) const

This function returns true if a classifier has been added to the pipeline.

Returns: bool returns true if a classifier has been added to the pipeline, false otherwise.

Definition at line 2655 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsClustererSet ( ) const

This function returns true if a clusterer has been added to the pipeline.

Returns: bool returns true if a clusterer has been added to the pipeline, false otherwise.

Definition at line 2663 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsContextSet ( ) const

This function returns true if any context modules have been added to the pipeline.

Returns: bool returns true if any context modules have been added to the pipeline, false otherwise.

Definition at line 2671 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsFeatureExtractionSet ( ) const

This function returns true if any feature extraction modules have been added to the pipeline.

Returns: bool returns true if any feature extraction modules have been added to the pipeline, false otherwise.

Definition at line 2651 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsInitialized ( ) const

virtual

This function returns true if the pipeline has been initialized. The pipeline is initialized if either a classifier or regressifier module has been set at the core of the pipeline.

Returns: bool returns true if the pipeline has been initialized, false otherwise.

Definition at line 2643 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsPipelineInClassificationMode ( ) const

This function returns true if a classifier has been added to the pipeline (and the pipeline is therefore in classification mode).

Returns: bool returns true if a classifier has been added to the pipeline, false otherwise.

Definition at line 2682 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsPipelineInRegressionMode ( ) const

This function returns true if a regressifier has been added to the pipeline (and the pipeline is therefore in regression mode).

Returns: bool returns true if a regressifier has been added to the pipeline, false otherwise.

Definition at line 2686 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsPipelineModeSet ( ) const

This function returns true if either a classifier or regressifier has been added to the pipeline.

Returns: bool returns true if either a classifier or regressifier has been added to the pipeline, false otherwise.

Definition at line 2678 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsPostProcessingSet ( ) const

This function returns true if any post processing modules have been added to the pipeline.

Returns: bool returns true if any post processing modules have been added to the pipeline, false otherwise.

Definition at line 2667 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsPreProcessingSet ( ) const

This function returns true if any preprocessing modules have been added to the pipeline.

Returns: bool returns true if any preprocessing modules have been added to the pipeline, false otherwise.

Definition at line 2647 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::getIsRegressifierSet ( ) const

This function returns true if a regressifier has been added to the pipeline.

Returns: bool returns true if a regressifier has been added to the pipeline, false otherwise.

Definition at line 2659 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getMaximumLikelihood ( ) const

This function returns the maximum likelihood value from the most likely class from the most recent prediction. This value is only relevant when the pipeline is in prediction mode.

Returns: Float representing the maximum likelihood value from the most likely class from the most recent prediction.

Definition at line 2777 of file GestureRecognitionPipeline.cpp.

std::string GestureRecognitionPipeline::getModelAsString ( ) const

overridevirtual

Gets the pipeline model as a string. This includes any classification or regression model, but also any preprocessing, feature extraction or post processing settings.

Returns: returns the pipeline model as a string

Reimplemented from MLBase.

Definition at line 3669 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::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 2936 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getNumClasses ( ) const

This function returns the number of classes in the model. If the model has not been trained then the number of classes will be zero. This is only relevant when the pipeline is in classification mode.

Returns: UINT representing the number of classes in the model.

Definition at line 2721 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getNumClassesInModel ( ) const

This function is now depreciated. You should use getNumClasses() instead.

Returns: UINT representing the number of classes in the model.

Examples:: Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 2717 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getNumFeatureExtractionModules ( ) const

This function returns the number of feature extraction modules that have been added to the pipeline.

Returns: UINT representing the number of feature extraction modules that have been added to the pipeline.

Definition at line 2736 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getNumPostProcessingModules ( ) const

This function returns the number of post processing modules that have been added to the pipeline.

Returns: UINT representing the number of post processing modules that have been added to the pipeline.

Definition at line 2740 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getNumPreProcessingModules ( ) const

This function returns the number of preprocessing modules that have been added to the pipeline.

Returns: UINT representing the number of preprocessing modules that have been added to the pipeline.

Definition at line 2732 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getNumTestSamples ( ) const

This function returns the number of test samples used to train the pipeline model.

Returns: UINT representing the number of test samples used to train the pipeline model.

Definition at line 2773 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getNumTrainingSamples ( ) const

This function returns the number of training samples used to train the pipeline model.

Returns: UINT representing the number of training samples used to train the pipeline model.

Definition at line 2769 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getOutputVectorDimensionsSize ( ) const

This function returns the size of the expected output Vector from the pipeline. This size comes from the size of the training data used to train the pipeline. This is only relevant when the pipeline is in regression mode.

Returns: UINT representing the size of the output Vector from the pipeline.

Definition at line 2709 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getPhase ( ) const

This function returns the estimated gesture phase from the most recent prediction. This value is only relevant when the pipeline is in timeseries classification mode.

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

Definition at line 2787 of file GestureRecognitionPipeline.cpp.

std::string GestureRecognitionPipeline::getPipelineModeAsString ( ) const

Gets the pipeline mode as a string, this will be either "PIPELINE_MODE_NOT_SET","CLASSIFICATION_MODE", or "REGRESSION_MODE".

Returns: returns the pipeline mode as a string

Definition at line 3694 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getPipelineModeFromString ( std::string pipelineMode ) const

Converts a string to the pipeline mode.

Parameters

pipelineMode the pipeline mode as a string, i.e. "CLASSIFICATION_MODE"

Returns: returns an unsigned integer representing the current pipeline mode

Definition at line 3717 of file GestureRecognitionPipeline.cpp.

PostProcessing * GestureRecognitionPipeline::getPostProcessingModule ( UINT moduleIndex ) const

Gets a pointer to the post processing module at the specific moduleIndex.

Parameters

moduleIndex the index of the post processing module you want

Returns: returns a pointer to the post processing module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 3032 of file GestureRecognitionPipeline.cpp.

template<class T >

const T* GestureRecognitionPipeline::getPostProcessingModule ( const UINT moduleIndex ) const

inline

Gets a const pointer to the post processing module at the specific moduleIndex. You should make sure that the type of the post processing module matches the template type.

Parameters

moduleIndex the index of the post processing module you want

Returns: returns a pointer to the post processing module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 1073 of file GestureRecognitionPipeline.h.

template<class T >

T* GestureRecognitionPipeline::getPostProcessingModule ( const UINT moduleIndex )

inline

Gets a pointer to the post processing module at the specific moduleIndex. You should make sure that the type of the post processing module matches the template type.

Parameters

moduleIndex the index of the post processing module you want

Returns: returns a pointer to the post processing module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 1090 of file GestureRecognitionPipeline.h.

UINT GestureRecognitionPipeline::getPredictedClassLabel ( ) const

This function returns the predicted class label from the most recent predict(...) function call. This value is only relevant when the pipeline is in prediction mode.

Returns: UINT representing the predicted class label from the most recent prediction.

Definition at line 2748 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getPredictionModuleIndexPosition ( ) const

This function returns the current position of the prediction module index. The prediction module index indicates how far along the pipeline a data sample gets before the pipeline exits during a prediction. For example, if you have two preprocessing modules, one feature extraction module, a classifier, and one post processing module in your pipeline and the prediction module index is 3 after you call the predict(...) function, then the data only got as far as the feature extraction module but did not get passed through the classifier or post processing modules.

Returns: UINT representing the number of post processing modules that have been added to the pipeline.

Definition at line 2744 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getPreProcessedData ( ) const

Gets a Vector containing the output of the last preprocessing module, this will be an M-dimensional Vector, where M is the output size of the last preprocessing module.

Returns: returns a Vector containing the output of the last preprocessing module, an empty Vector will be returned if there are no preprocessing modules.

Definition at line 2952 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getPreProcessedData ( UINT moduleIndex ) const

Gets a Vector containing the output of the preprocessing module at index X, this will be an M-dimensional Vector, where M is the output size of the preprocessing module.

Parameters

UINT	moduleIndex: the index of the pre processing module you want

Returns: returns a Vector containing the output of the preprocessing module at index X, an empty Vector will be returned if there is no preprocessing module at that index.

Definition at line 2959 of file GestureRecognitionPipeline.cpp.

PreProcessing * GestureRecognitionPipeline::getPreProcessingModule ( const UINT moduleIndex ) const

Gets a pointer to the preprocessing module at the specific moduleIndex.

Parameters

const UINT moduleIndex: the index of the pre processing module you want

Returns: returns a pointer to the preprocessing module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 3004 of file GestureRecognitionPipeline.cpp.

template<class T >

const T* GestureRecognitionPipeline::getPreProcessingModule ( const UINT moduleIndex ) const

inline

Gets a pointer to the preprocessing module at the specific moduleIndex. You should make sure that the type of the preprocessing module matches the template type.

Parameters

moduleIndex the index of the pre processing module you want

Returns: returns a pointer to the preprocessing module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 895 of file GestureRecognitionPipeline.h.

template<class T >

T* GestureRecognitionPipeline::getPreProcessingModule ( const UINT moduleIndex )

inline

Gets a pointer to the preprocessing module at the specific moduleIndex. You should make sure that the type of the preprocessing module matches the template type.

Parameters

moduleIndex the index of the pre processing module you want

Returns: returns a pointer to the preprocessing module at the specific moduleIndex, or NULL if the moduleIndex is invalid

Definition at line 914 of file GestureRecognitionPipeline.h.

Regressifier * GestureRecognitionPipeline::getRegressifier ( ) const

Gets a pointer to the regressifier module.

Returns: returns a pointer to the regressifier module, or NULL if the regressifier has not been set

Definition at line 3024 of file GestureRecognitionPipeline.cpp.

template<class T >

const T* GestureRecognitionPipeline::getRegressifier ( ) const

inline

Gets a const pointer to the regressifier module. If the regressifier has not been set, or the template type T does not match the current regressifier type then the function will return NULL.

Returns: returns a pointer to the regressifier module, or NULL if the regressifier has not been set

Definition at line 1005 of file GestureRecognitionPipeline.h.

template<class T >

T* GestureRecognitionPipeline::getRegressifier ( )

inline

Gets a pointer to the regressifier module. If the regressifier has not been set, or the template type T does not match the current regressifier type then the function will return NULL.

Returns: returns a pointer to the regressifier module, or NULL if the regressifier has not been set

Definition at line 1022 of file GestureRecognitionPipeline.h.

VectorFloat GestureRecognitionPipeline::getRegressionData ( ) const

Gets a Vector containing the regression data output by the pipeline, this will be an M-dimensional Vector, where M is the number of output dimensions in the model.

Returns: returns a Vector containing the regression data output by the pipeline, an empty Vector will be returned if the model has not been trained

Examples:: RegressionModulesExamples/LinearRegressionExample/LinearRegressionExample.cpp, RegressionModulesExamples/LogisticRegressionExample/LogisticRegressionExample.cpp, RegressionModulesExamples/MLPRegressionExample/MLPRegressionExample.cpp, and RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp.

Definition at line 2941 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestAccuracy ( ) const

This function returns the accuracy value from the most recent test. This will be a value between [0 100]. This value is only relevant when the pipeline is in prediction mode.

Returns: Float representing the accuracy value from the most recent test.

Examples:: Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 2805 of file GestureRecognitionPipeline.cpp.

MatrixFloat GestureRecognitionPipeline::getTestConfusionMatrix ( ) const

This function returns the confusion matrix for the most recent round of testing. If null rejection is enabled then the first row and column of the confusion matrix will represent the null class (class label 0).

Returns: MatrixFloat representing the confusion matrix for the most recent round of testing.

Examples:: Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 2880 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestFMeasure ( const UINT classLabel ) const

This function returns the f-measure from the most recent test for the class with the matching classLabel. This value is only relevant when the pipeline is in classification mode. If the classLabel is not valid then the function will return -1.

Parameters

classLabel the label of the class you want to get the test fMeasure value for

Returns: Float representing the f-measure from the most recent test for the class with the matching classLabel.

Definition at line 2817 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getTestFMeasure ( ) const

Gets a Vector of the fMeasure results for each class from the most recent round of testing. This will be a K-dimensional Vector, where K is the number of classes in the model.

Returns: returns a Vector of the fMeasure results for each class from the last prediction, an empty Vector will be returned if the model has not been tested

Definition at line 2920 of file GestureRecognitionPipeline.cpp.

Vector< TestInstanceResult > GestureRecognitionPipeline::getTestInstanceResults ( ) const

Gets a Vector containing the results from the most recent test. Each element in the Vector represents the results for the corresponding test sample.

Returns: returns a Vector containing the test instance results from the most recent test, an empty Vector will be returned if no test has been run.

Definition at line 2996 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestPrecision ( const UINT classLabel ) const

This function returns the precision from the most recent test for the class with the matching classLabel. This value is only relevant when the pipeline is in classification mode. If the classLabel is not valid then the function will return -1.

Parameters

classLabel the label of the class you want to get the test precision value for

Returns: Float representing the precision from the most recent test for the class with the matching classLabel.

Examples:: Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 2830 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getTestPrecision ( ) const

Gets a Vector of the precision results for each class from the most recent round of testing. This will be a K-dimensional Vector, where K is the number of classes in the model.

Returns: returns a Vector of the precision results for each class from the last prediction, an empty Vector will be returned if the model has not been tested

Definition at line 2912 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestRecall ( const UINT classLabel ) const

This function returns the recall from the most recent test for the class with the matching classLabel. This value is only relevant when the pipeline is in classification mode. If the classLabel is not valid then the function will return -1.

Parameters

classLabel the label of the class you want to get the test recall value for

Returns: Float representing the recall from the most recent test for the class with the matching classLabel.

Examples:: Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 2843 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getTestRecall ( ) const

Gets a Vector of the recall results for each class from the most recent round of testing. This will be a K-dimensional Vector, where K is the number of classes in the model.

Returns: returns a Vector of the recall results for each class from the last prediction, an empty Vector will be returned if the model has not been tested

Definition at line 2916 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestRejectionPrecision ( ) const

This function returns the precision for any null examples in your dataset (examples with the class label of 0) from the most recent test. This value is only relevant when the pipeline is in classification mode.

Returns: Float representing the precision for any null examples in your dataset.

Definition at line 2856 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestRejectionRecall ( ) const

This function returns the recall for any null examples in your dataset (examples with the class label of 0) from the most recent test. This value is only relevant when the pipeline is in classification mode.

Returns: Float representing the recall for any null examples in your dataset.

Definition at line 2860 of file GestureRecognitionPipeline.cpp.

TestResult GestureRecognitionPipeline::getTestResults ( ) const

This function returns all the results from the most recent round of testing. The TestResult contains all of the test metrics, such as the accuracy, precision, recall, training and test times, etc. These are the same results that you would get if you called the individaul functions (such as getTestAccuracy()).

Returns: returns an instance of TestResult containing all the results from the most recent round of testing, an empty TestResult will be returned if the model has not been tested

Definition at line 2894 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestRMSError ( ) const

This function returns the root mean squared error value from the most recent test. This value is only relevant when the pipeline is in regression mode.

Returns: Float representing the root mean squared error value from the most recent test.

Examples:: RegressionModulesExamples/LinearRegressionExample/LinearRegressionExample.cpp, RegressionModulesExamples/LogisticRegressionExample/LogisticRegressionExample.cpp, RegressionModulesExamples/MLPRegressionExample/MLPRegressionExample.cpp, and RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp.

Definition at line 2809 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestSSError ( ) const

This function returns the total squared error value from the most recent test. This value is only relevant when the pipeline is in regression mode.

Returns: Float representing the total squared error value from the most recent test.

Definition at line 2813 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTestTime ( ) const

This function returns the total test time (in milliseconds) for the most recent test.

Returns: Float representing the total test time (in milliseconds) for the most recent test.

Definition at line 2864 of file GestureRecognitionPipeline.cpp.

Vector< TrainingResult > GestureRecognitionPipeline::getTrainingResults ( ) const

This function returns the results from the most recent round of training. The TrainingResults vector contains the results for each epoch of training, such as the accuracy, error, RMSError, etc.. The exact contents of the TrainingResults data will vary based on the actual ML algorithm at the core of the pipeline.

Returns: returns a vector containing the results from the most recent round of training, an empty vector will be returned if the model has not been trained

Definition at line 2884 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTrainingRMSError ( ) const

This function returns the root mean squared error value from the most recent training. This value is only relevant when the pipeline is in regression mode.

Returns: Float representing the root mean squared error value from the most recent training.

Definition at line 2872 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTrainingSetAccuracy ( ) const

This function returns the estimated training set accuracy from the most recent round of training. This value is only relevant if the classifier has been trained.

Returns: Float representing the training set accuracy

Definition at line 2794 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTrainingSSError ( ) const

This function returns the total squared error value from the most recent training. This value is only relevant when the pipeline is in regression mode.

Returns: Float representing the total squared error value from the most recent training.

Definition at line 2876 of file GestureRecognitionPipeline.cpp.

Float GestureRecognitionPipeline::getTrainingTime ( ) const

This function returns the total training time (in milliseconds) for the most recent training.

Returns: Float representing the total training time (in milliseconds) for the most recent training.

Definition at line 2868 of file GestureRecognitionPipeline.cpp.

UINT GestureRecognitionPipeline::getUnProcessedPredictedClassLabel ( ) const

This function returns the unprocessed predicted class label from the most recent predict(...) function call. The unprocessed predicted class label is the class label output by the classifier before any post processing modules filter the value. This value is only relevant when the pipeline is in prediction mode.

Returns: UINT representing the unprocessed predicted class label from the most recent prediction.

Definition at line 2759 of file GestureRecognitionPipeline.cpp.

VectorFloat GestureRecognitionPipeline::getUnProcessedRegressionData ( ) const

Gets a Vector containing the regression data output by the regression algorithm, this will be an M-dimensional Vector, where M is the number of output dimensions in the model.

Returns: returns a Vector containing the regression data output by the regression algorithm, an empty Vector will be returned if the model has not been trained

Definition at line 2945 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline::GRT_DEPRECATED_MSG	(	"map_(VectorFloat &inputVector) is	deprecated,
		use predict_(VectorFloat &inputVector) instead"	,
		virtual bool map_(VectorFloat &inputVector)	override
	)

Deprecated:: use predict_(VectorFloat &inputVector) This function is now depreciated, you should use the predict_(VectorFloat &inputVector) function instead.

This function used to be the main interface for all regression using the gesture recognition pipeline. You should only call this function if you have trained the pipeline. The input Vector should be the same size as your training data.

Parameters

inputVector the input data that will be passed through the pipeline for regression

Returns: bool returns true if the regression was successful, false otherwise

GestureRecognitionPipeline::GRT_DEPRECATED_MSG	(	"savePipelineToFile(std::string filename) is	deprecated,
		use save(std::string &filename) instead"	,
		bool savePipelineToFile(const std::string &filename)	const
	)

Deprecated:: use save(std::string &filename) instead

Parameters

filename the name of the file you want to save the pipeline to

Returns: bool returns true if the pipeline was saved successful, false otherwise

GestureRecognitionPipeline::GRT_DEPRECATED_MSG	(	"loadPipelineFromFile(std::string filename) is	deprecated,
		use load(std::string &filename) instead"	,
		bool	loadPipelineFromFileconst std::string &filename
	)

Deprecated:: use load(std::string &filename) instead

Parameters

filename the name of the file you want to load the pipeline from

Returns: bool returns true if the pipeline was loaded successful, false otherwise

GestureRecognitionPipeline::GRT_DEPRECATED_MSG	(	"use clear() instead."	,
		bool	clearAll()
	)

Deprecated:: This function is now deprecated, you should use clear() instead! Removes everything from the pipeline.

Returns: returns true if all the modules were removed successfully, false otherwise

bool GestureRecognitionPipeline::load ( const std::string & filename )

overridevirtual

This function will load an entire pipeline from a file. This includes all the modules types, settings, and models. This calls the older loadPipelineFromFile function

Parameters

filename the name of the file you want to load the pipeline from

Returns: bool returns true if the pipeline was loaded successful, false otherwise

Reimplemented from MLBase.

Examples:: RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp.

Definition at line 2308 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline & GestureRecognitionPipeline::operator<< ( const PreProcessing & module )

Adds the preprocessing module to the pipeline, this is the same as calling addPreProcessingModule( module )

Parameters

module the preprocessing module you want to add

Returns: returns a reference to the updated pipeline

Definition at line 111 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline & GestureRecognitionPipeline::operator<< ( const FeatureExtraction & module )

Adds the feature extraction module to the pipeline, this is the same as calling addFeatureExtractionModule( module )

Parameters

module the feature extraction module you want to add

Returns: returns a reference to the updated pipeline

Definition at line 116 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline & GestureRecognitionPipeline::operator<< ( const Classifier & module )

Adds the classifier to the pipeline, this is the same as calling setClassifier( module )

Parameters

module the classifier you want to add

Returns: returns a reference to the updated pipeline

Definition at line 121 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline & GestureRecognitionPipeline::operator<< ( const Regressifier & module )

Adds the regressifier to the pipeline, this is the same as calling setRegressifier( module )

Parameters

module the regressifier you want to add

Returns: returns a reference to the updated pipeline

Definition at line 126 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline & GestureRecognitionPipeline::operator<< ( const Clusterer & module )

Adds the clusterer to the pipeline, this is the same as calling setClusterer( module )

Parameters

module the clusterer you want to add

Returns: returns a reference to the updated pipeline

Definition at line 131 of file GestureRecognitionPipeline.cpp.

GestureRecognitionPipeline & GestureRecognitionPipeline::operator<< ( const PostProcessing & module )

Adds the postprocessing module to the pipeline, this is the same as calling addPostProcessingModule( module )

Parameters

module the postprocessing module you want to add

Returns: returns a reference to the updated pipeline

Definition at line 136 of file GestureRecognitionPipeline.cpp.

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

Equals Constructor. Performs a depp copy of the data from the rhs pipeline into this pipeline.

Definition at line 39 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::predict_ ( VectorFloat & inputVector )

overridevirtual

This function is the main interface for all predictions using the gesture recognition pipeline. You can use this function for both classification and regression. You should only call this function if you have trained the pipeline. The input Vector should be the same size as your training data.

Parameters

inputVector the input data that will be passed through the pipeline for classification or regression

Returns: bool returns true if the prediction was successful, false otherwise

Reimplemented from MLBase.

Definition at line 1342 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::predict_ ( MatrixFloat & inputMatrix )

overridevirtual

This function is an interface for predictions using timeseries or Matrix data. You should only call this function if you have trained the pipeline. The input matrix should have the same number of columns as your training data.

Parameters

inputMatrix the input atrix that will be passed through the pipeline for classification

Returns: bool returns true if the prediction was successful, false otherwise

Reimplemented from MLBase.

Definition at line 1372 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::preProcessData	(	VectorFloat	inputVector,
		bool	computeFeatures = `true`
	)

This function will pass the input Vector through any preprocessing or feature extraction modules added to the pipeline. This function can be useful for testing and validating a preprocessing or feature extraction module, without having to acutally train a classification or regression module. The second parameter controls if any feature extraction module should be used. If set to true then both preprocessing and feature extraction modules will be used, if false then just preprocessing modules will be used.

The function will not pass the preprocessed data through the classification or regression module.

After calling this function, you can access the preprocessed results via the getPreProcessedData() or getFeatureExtractionData() functions.

Parameters

inputVector	the input data that should be processed
computeFeatures	sets if just the preprocessing modules should be used (false), or both the preprocessing and feature extraction modules should be used (true)

Returns: bool returns true if the data was passed through the preprocessing modules successful, false otherwise

Definition at line 2605 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removeAllContextModules ( )

Removes all the context modules from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Returns: returns true if context modules were removed successfully, false otherwise

Definition at line 3409 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removeAllFeatureExtractionModules ( )

Removes all the feature extraction modules from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Returns: returns true if feature extraction modules were removed successfully, false otherwise

Definition at line 3347 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removeAllPostProcessingModules ( )

Removes all the post processing extraction modules from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Returns: returns true if post processing modules were removed successfully, false otherwise

Definition at line 3369 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removeAllPreProcessingModules ( )

Removes all the preprocessing modules from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Returns: returns true if preprocessing modules were removed successfully, false otherwise

Definition at line 3325 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removeClassifier ( )

inline

Removes the classifier from the current pipeline.

Returns: returns true if classifier was removed successfully, false otherwise

Definition at line 1309 of file GestureRecognitionPipeline.h.

bool GestureRecognitionPipeline::removeClusterer ( )

inline

Removes the clusterer from the current pipeline.

Returns: returns true if clusterer was removed successfully, false otherwise

Definition at line 1323 of file GestureRecognitionPipeline.h.

bool GestureRecognitionPipeline::removeContextModule	(	const UINT	contextLevel,
		const UINT	moduleIndex
	)

Removes the specific context module at the module index from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Parameters

contextLevel	the context level that contains the specific context module you want to remove
moduleIndex	the index of the context module you want to remove

Returns: returns true if the context module was removed successfully, false otherwise

Definition at line 3391 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removeFeatureExtractionModule ( UINT moduleIndex )

Removes the specific feature extraction module at the module index from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Parameters

moduleIndex the index of the module you want to remove

Returns: returns true if the feature extraction module was removed successfully, false otherwise

Definition at line 3352 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removePostProcessingModule ( const UINT moduleIndex )

Removes the specific post processing module at the module index from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Parameters

moduleIndex the index of the module you want to remove

Returns: returns true if the post processing module was removed successfully, false otherwise

Definition at line 3374 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removePreProcessingModule ( UINT moduleIndex )

Removes the specific preprocessing module at the module index from the current pipeline. If the pipeline has been trained it will need to be retrained before it can be used.

Parameters

moduleIndex the index of the module you want to remove

Returns: returns true if the preprocessing module was removed successfully, false otherwise

Definition at line 3330 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::removeRegressifier ( )

inline

Removes the regressifier from the current pipeline.

Returns: returns true if regressifier was removed successfully, false otherwise

Definition at line 1316 of file GestureRecognitionPipeline.h.

bool GestureRecognitionPipeline::reset ( )

overridevirtual

This function is the main interface for resetting the entire gesture recognition pipeline. This function will call reset on all the modules in the current pipeline.

Returns: bool returns true if the reset was successful, false otherwise

Reimplemented from MLBase.

Definition at line 2047 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::save ( const std::string & filename ) const

overridevirtual

This function will save the entire pipeline to a file. This includes all the modules types, settings, and models.

Parameters

filename the name of the file you want to save the pipeline to

Returns: bool returns true if the pipeline was saved successful, false otherwise

Reimplemented from MLBase.

Examples:: RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp.

Definition at line 2171 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::setClassifier ( const Classifier & classifier )

Sets the classifier at the core of the pipeline. A pipeline can only have one classifier or regressifier, setting a new classifier will override any previous classifier or regressifier.

Parameters

classifier a reference to the classifier module you want to add to the pipeline

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

Definition at line 3130 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::setClusterer ( const Clusterer & clusterer )

Sets the clusterer at the core of the pipeline. A pipeline can only have one cluster algorithm, setting a new cluster will override any previous classifier or regressifier.

Parameters

regressifier a reference to the regression module you want to add to the pipeline

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

Definition at line 3202 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::setFeatureExtractionModule ( const FeatureExtraction & featureExtractionModule )

This function removes any existing feature extraction modules, then it adds the new feature extraction module.

Parameters

featureExtractionModule a reference to the feature extraction module you want to add

Returns: returns true if the feature extraction module was added successfully, false otherwise

Definition at line 3125 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::setInfo ( const std::string & info )

Sets the pipeline's info text.

Returns: returns true if the info text was updated successfully, false otherwise

Definition at line 3433 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::setPostProcessingModule ( const PostProcessing & postProcessingModule )

This function removes any existing post processing modules, then it adds the new post processing module.

Parameters

postProcessingModule a reference to the post processing module you want to add

Returns: returns true if the post processing module was added successfully, false otherwise

Definition at line 3267 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::setPreProcessingModule ( const PreProcessing & preProcessingModule )

This function removes any existing pre processing modules, then it adds the new pre processing module.

Parameters

preProcessingModule a reference to the pre processing module you want to add

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

Definition at line 3087 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::setRegressifier ( const Regressifier & regressifier )

Sets the regressifier at the core of the pipeline. A pipeline can only have one classifier or regressifier, setting a new regressifier will override any previous classifier or regressifier.

Parameters

regressifier a reference to the regression module you want to add to the pipeline

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

Examples:: RegressionModulesExamples/LinearRegressionExample/LinearRegressionExample.cpp, and RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp.

Definition at line 3169 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::test ( const ClassificationData & testData )

virtual

This function is the main interface for testing the accuracy of a pipeline with ClassificationData. This function will pass the testData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the predict function of the classification module that has been added to the GestureRecognitionPipeline. The function will return true if the pipeline was tested successfully, false otherwise.

Parameters

testData the labelled classification data that will be used to test the accuracy of the pipeline

Returns: bool returns true if the pipeline was tested successfully, false otherwise

Examples:: RegressionModulesExamples/LinearRegressionExample/LinearRegressionExample.cpp, RegressionModulesExamples/LogisticRegressionExample/LogisticRegressionExample.cpp, RegressionModulesExamples/MLPRegressionExample/MLPRegressionExample.cpp, RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp, and Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 972 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::test ( const TimeSeriesClassificationData & testData )

virtual

This function is the main interface for testing the accuracy of a pipeline with TimeSeriesClassificationData. This function will pass the testData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the predict function of the classification module that has been added to the GestureRecognitionPipeline. The function will return true if the pipeline was tested successfully, false otherwise.

Parameters

testData the labelled timeseries classification data that will be used to test the accuracy of the pipeline

Returns: bool returns true if the pipeline was tested successfully, false otherwise

Definition at line 1085 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::test ( const ClassificationDataStream & testData )

virtual

This function is the main interface for testing the accuracy of a pipeline with ClassificationDataStream. This function will pass the testData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the predict function of the classification module that has been added to the GestureRecognitionPipeline. The function will return true if the pipeline was tested successfully, false otherwise.

Parameters

testData the timeseries classification data stream that will be used to test the accuracy of the pipeline

Returns: bool returns true if the pipeline was tested successfully, false otherwise

Definition at line 1164 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::test ( const RegressionData & testData )

virtual

This function is the main interface for testing the accuracy of a pipeline with RegressionData. This function will pass the testData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the predict function of the regression module that has been added to the GestureRecognitionPipeline. The function will return true if the pipeline was tested successfully, false otherwise.

Parameters

testData the labelled regression data that will be used to test the accuracy of the pipeline

Returns: bool returns true if the pipeline was tested successfully, false otherwise

Definition at line 1268 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train	(	const ClassificationData &	trainingData,
		const UINT	kFoldValue,
		const bool	useStratifiedSampling = `false`
	)

virtual

This is the main training interface for training a Classifier with ClassificationData using K-fold cross validation. This function calls train_(...), so if you want to use a more efficient version of the algorithm (and don't care that your training data may get modified) then call train_(...) directly.

Parameters

trainingData	the classification training data that will be used to train the classifier at the core of the pipeline
kFoldValue	the number of cross validation folds, this should be a value between in the range of [1 M-1], where M is the number of training samples int the LabelledClassificationData
useStratifiedSampling	sets if stratified sampling should be used during the cross validation training

Returns: bool returns true if the classifier was trained successfully, false otherwise

Examples:: RegressionModulesExamples/LinearRegressionExample/LinearRegressionExample.cpp, RegressionModulesExamples/LogisticRegressionExample/LogisticRegressionExample.cpp, RegressionModulesExamples/MLPRegressionExample/MLPRegressionExample.cpp, RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp, and Tutorials/MachineLearning101/MachineLearning101.cpp.

Definition at line 252 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train	(	const TimeSeriesClassificationData &	trainingData,
		const UINT	kFoldValue,
		const bool	useStratifiedSampling = `false`
	)

virtual

This is the main training interface for training a Classifier with TimeSeriesClassificationData using K-fold cross validation. This function calls train_(...), so if you want to use a more efficient version of the algorithm (and don't care that your training data may get modified) then call train_(...) directly.

Parameters

trainingData	the labelled time-series classification training data that will be used to train the classifier at the core of the pipeline
kFoldValue	the number of cross validation folds, this should be a value between in the range of [1 M-1], where M is the number of training samples in the LabelledClassificationData
useStratifiedSampling	sets if stratified sampling should be used during the cross validation training

Returns: bool returns true if the classifier was trained and tested successfully, false otherwise

Definition at line 525 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train	(	const RegressionData &	trainingData,
		const UINT	kFoldValue
	)

virtual

This is the main training interface for training a Regressifier with RegressionData using K-fold cross validation. This function calls train_(...), so if you want to use a more efficient version of the algorithm (and don't care that your training data may get modified) then call train_(...) directly.

Parameters

trainingData	the regression training data that will be used to train the regressifier at the core of the pipeline
kFoldValue	the number of cross validation folds, this should be a value between in the range of [1 M-1], where M is the number of training samples in the LabelledRegressionData

Returns: bool returns true if the regressifier was trained and tested successfully, false otherwise

Definition at line 799 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_ ( ClassificationData & trainingData )

overridevirtual

This is the main training interface for training a Classifier with ClassificationData. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the Classification module that has been added to the GestureRecognitionPipeline. The function will return true if the classifier was trained successfully, false otherwise.

Parameters

trainingData the classification data that will be used to train the classifier at the core of the pipeline

Returns: bool returns true if the classifier was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 147 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_	(	ClassificationData &	trainingData,
		const UINT	kFoldValue,
		const bool	useStratifiedSampling = `false`
	)

virtual

This is the main training interface for training a Classifier with ClassificationData using K-fold cross validation. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the Classification module that has been added to the GestureRecognitionPipeline. The function will return true if the classifier was trained successfully, false otherwise.

Parameters

trainingData	the labelled classification training data that will be used to train the classifier at the core of the pipeline
kFoldValue	the number of cross validation folds, this should be a value between in the range of [1 M-1], where M is the number of training samples int the LabelledClassificationData
useStratifiedSampling	sets if stratified sampling should be used during the cross validation training

Returns: bool returns true if the classifier was trained successfully, false otherwise

Train the classification system

Definition at line 258 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_ ( TimeSeriesClassificationData & trainingData )

overridevirtual

This is the main training interface for training a Classifier with TimeSeriesClassificationData. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the Classification module that has been added to the GestureRecognitionPipeline. The function will return true if the classifier was trained successfully, false otherwise.

Parameters

trainingData the time-series classification training data that will be used to train the classifier at the core of the pipeline

Returns: bool returns true if the classifier was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 325 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_	(	TimeSeriesClassificationData &	trainingData,
		const UINT	kFoldValue,
		const bool	useStratifiedSampling = `false`
	)

virtual

This is the main training interface for training a Classifier with TimeSeriesClassificationData using K-fold cross validation. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the Classification module that has been added to the GestureRecognitionPipeline. The function will return true if the classifier was trained successfully, false otherwise.

Parameters

trainingData	the labelled time-series classification training data that will be used to train the classifier at the core of the pipeline
kFoldValue	the number of cross validation folds, this should be a value between in the range of [1 M-1], where M is the number of training samples in the LabelledClassificationData
useStratifiedSampling	sets if stratified sampling should be used during the cross validation training

Returns: bool returns true if the classifier was trained and tested successfully, false otherwise

Train the classification system

Definition at line 531 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_ ( ClassificationDataStream & trainingData )

overridevirtual

This is the main training interface for training a Classifier with ClassificationDataStream. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the Classification module that has been added to the GestureRecognitionPipeline. The function will return true if the classifier was trained successfully, false otherwise.

Parameters

trainingData the time-series classification training data that will be used to train the classifier at the core of the pipeline

Returns: bool returns true if the classifier was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 597 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_ ( RegressionData & trainingData )

overridevirtual

This is the main training interface for training a regression module with RegressionData. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the regression module that has been added to the GestureRecognitionPipeline. The function will return true if the classifier was trained successfully, false otherwise.

Parameters

trainingData the labelled regression training data that will be used to train the regression module at the core of the pipeline

Returns: bool returns true if the regression module was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 702 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_	(	RegressionData &	trainingData,
		RegressionData &	validationData
	)

overridevirtual

This is the main training interface for training a regression module with a regression training and validation dataset. This function will pass the training and validataion through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the regression module that has been added to the GestureRecognitionPipeline. The function will return true if the classifier was trained successfully, false otherwise.

Parameters

trainingData	the regression training data that will be used to train the regression module at the core of the pipeline
validationData	the regression validation data that will be used to validate the regression module at the core of the pipeline

Returns: bool returns true if the regression module was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 797 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_	(	RegressionData &	trainingData,
		const UINT	kFoldValue
	)

virtual

This is the main training interface for training a Regressifier with RegressionData using K-fold cross validation. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the Regression module that has been added to the GestureRecognitionPipeline. The function will return true if the regressifier was trained successfully, false otherwise.

Parameters

trainingData	the regression training data that will be used to train the regressifier at the core of the pipeline
kFoldValue	the number of cross validation folds, this should be a value between in the range of [1 M-1], where M is the number of training samples in the LabelledRegressionData

Returns: bool returns true if the regressifier was trained and tested successfully, false otherwise

Train the classification system

Definition at line 805 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::train_ ( UnlabelledData & trainingData )

overridevirtual

This is the main training interface for training a Clusterer with UnlabelledData using K-fold cross validation. This function will pass the trainingData through any PreProcessing or FeatureExtraction modules that have been added to the GestureRecognitionPipeline, and then calls the training function of the Clusterer module that has been added to the GestureRecognitionPipeline. The function will return true if the regressifier was trained successfully, false otherwise.

Parameters

trainingData the unlabelledData training data that will be used to train the clusterer at the core of the pipeline

Returns: bool returns true if the clusterer was trained and tested successfully, false otherwise

Reimplemented from MLBase.

Definition at line 869 of file GestureRecognitionPipeline.cpp.

bool GestureRecognitionPipeline::updateContextModule	(	bool	value,
		UINT	contextLevel = `0`,
		UINT	moduleIndex = `0`
	)

Updates the context module at the specific contextLevel and moduleIndex. The user can specify the position at which the new module should be inserted into the list of context modules. The default position is to insert the new module at first contextLevel and first moduleIndex

Parameters

contextLevel	the context level that contains the context module you want to update
moduleIndex	the index of the module you want to update

Returns: returns true if the context module was updated successfully, false otherwise

Definition at line 3308 of file GestureRecognitionPipeline.cpp.

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

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