Inheritance diagram for DTW:

Public Types
enum	DistanceMethods { ABSOLUTE_DIST =0, EUCLIDEAN_DIST, NORM_ABSOLUTE_DIST }

enum	RejectionModes { TEMPLATE_THRESHOLDS =0, CLASS_LIKELIHOODS, THRESHOLDS_AND_LIKELIHOODS }

Public Types inherited from Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

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

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

Public Member Functions
	DTW (bool useScaling=false, bool useNullRejection=false, Float nullRejectionCoeff=3.0, UINT rejectionMode=DTW::TEMPLATE_THRESHOLDS, bool dtwConstrain=true, Float radius=0.2, bool offsetUsingFirstSample=false, bool useSmoothing=false, UINT smoothingFactor=5, Float nullRejectionLikelihoodThreshold=0.99)

	DTW (const DTW &rhs)

virtual	~DTW (void)

DTW &	operator= (const DTW &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train_ (TimeSeriesClassificationData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	predict_ (MatrixFloat &timeSeries)

virtual bool	reset ()

virtual bool	clear ()

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

virtual bool	load (std::fstream &file)

virtual bool	recomputeNullRejectionThresholds ()

UINT	getNumTemplates () const

bool	setRejectionMode (UINT rejectionMode)

bool	setNullRejectionThreshold (Float nullRejectionLikelihoodThreshold)

bool	setOffsetTimeseriesUsingFirstSample (bool offsetUsingFirstSample)

bool	setContrainWarpingPath (bool constrain)

bool	setWarpingRadius (Float radius)

UINT	getRejectionMode () const

bool	enableZNormalization (bool useZNormalization, bool constrainZNorm=true)

bool	enableTrimTrainingData (bool trimTrainingData, Float trimThreshold, Float maximumTrimPercentage)

Vector< DTWTemplate >	getModels () const

bool	setModels (Vector< DTWTemplate > newTemplates)

Vector< VectorFloat >	getInputDataBuffer () const

const Vector< MatrixFloat > &	getDistanceMatrices () const

const Vector< Vector< IndexDist > > &	getWarpingPaths () const

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

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	computeAccuracy (const ClassificationData &data, Float &accuracy)

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

Float	getTrainingSetAccuracy () const

virtual UINT	getNumClasses () const

UINT	getClassLabelIndexValue (const UINT classLabel) const

UINT	getPredictedClassLabel () const

VectorFloat	getClassLikelihoods () const

VectorFloat	getClassDistances () const

VectorFloat	getNullRejectionThresholds () const

Vector< UINT >	getClassLabels () const

Vector< MinMax >	getRanges () const

bool	enableNullRejection (const bool useNullRejection)

virtual bool	setNullRejectionCoeff (const Float nullRejectionCoeff)

virtual bool	setNullRejectionThresholds (const VectorFloat &newRejectionThresholds)

bool	getTimeseriesCompatible () const

Classifier *	create () const

	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create instead.", Classifier *createNewInstance() const )

	GRT_DEPRECATED_MSG ("createInstanceFromString is deprecated, use create instead.", static Classifier *createInstanceFromString(const std::string &id))

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () const

Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)

virtual	~MLBase (void)

bool	copyMLBaseVariables (const MLBase *mlBase)

virtual bool	train (ClassificationData trainingData)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	train (RegressionData trainingData)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	train (RegressionData trainingData, RegressionData validationData)

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

virtual bool	train (TimeSeriesClassificationData trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train (UnlabelledData trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	train (MatrixFloat data)

virtual bool	train_ (MatrixFloat &data)

virtual bool	predict (VectorFloat inputVector)

virtual bool	predict (MatrixFloat inputMatrix)

virtual bool	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

virtual bool	print () const

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

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

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

	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )

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

	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))

virtual bool	getModel (std::ostream &stream) const

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

BaseType	getType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getBatchSize () const

UINT	getNumRestarts () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRMSTrainingError () const

	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )

Float	getTotalSquaredTrainingError () const

Float	getRMSValidationError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )

bool	getConverged () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	getTrainingLoggingEnabled () const

bool	getTestingLoggingEnabled () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setBatchSize (const UINT batchSize)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setNumRestarts (const UINT numRestarts)

bool	setMinChange (const Float minChange)

bool	setLearningRate (const Float learningRate)

bool	setUseValidationSet (const bool useValidationSet)

bool	setValidationSetSize (const UINT validationSetSize)

bool	setRandomiseTrainingOrder (const bool randomiseTrainingOrder)

bool	setTrainingLoggingEnabled (const bool loggingEnabled)

bool	setTestingLoggingEnabled (const bool loggingEnabled)

bool	registerTrainingResultsObserver (Observer< TrainingResult > &observer)

bool	registerTestResultsObserver (Observer< TestInstanceResult > &observer)

bool	removeTrainingResultsObserver (const Observer< TrainingResult > &observer)

bool	removeTestResultsObserver (const Observer< TestInstanceResult > &observer)

bool	removeAllTrainingObservers ()

bool	removeAllTestObservers ()

bool	notifyTrainingResultsObservers (const TrainingResult &data)

bool	notifyTestResultsObservers (const TestInstanceResult &data)

MLBase *	getMLBasePointer ()

const MLBase *	getMLBasePointer () const

Vector< TrainingResult >	getTrainingResults () const

Public Member Functions inherited from GRTBase
	GRTBase (const std::string &id="")

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )

std::string	getId () const

std::string	getLastWarningMessage () const

std::string	getLastErrorMessage () const

std::string	getLastInfoMessage () const

bool	setInfoLoggingEnabled (const bool loggingEnabled)

bool	setWarningLoggingEnabled (const bool loggingEnabled)

bool	setErrorLoggingEnabled (const bool loggingEnabled)

bool	setDebugLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)

Float	SQR (const Float &x) const

Public Member Functions inherited from Observer< TrainingResult >
virtual void	notify (const TrainingResult &data)

Public Member Functions inherited from Observer< TestInstanceResult >
virtual void	notify (const TestInstanceResult &data)

Static Public Member Functions
static std::string	getId ()

Static Public Member Functions inherited from Classifier
static Classifier *	create (const std::string &id)

static Vector< std::string >	getRegisteredClassifiers ()

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

static std::string	getGRTRevison ()

Protected Member Functions
bool	train_NDDTW (TimeSeriesClassificationData &trainingData, DTWTemplate &dtwTemplate, UINT &bestIndex)

Float	computeDistance (MatrixFloat &timeSeriesA, MatrixFloat &timeSeriesB, MatrixFloat &distanceMatrix, Vector< IndexDist > &warpPath)

Float	d (int m, int n, MatrixFloat &distanceMatrix, const int M, const int N)

Float	MIN_ (Float a, Float b, Float c)

void	scaleData (TimeSeriesClassificationData &trainingData)

void	scaleData (MatrixFloat &data, MatrixFloat &scaledData)

void	znormData (TimeSeriesClassificationData &trainingData)

void	znormData (MatrixFloat &data, MatrixFloat &normData)

void	smoothData (VectorFloat &data, UINT smoothFactor, VectorFloat &resultsData)

void	smoothData (MatrixFloat &data, UINT smoothFactor, MatrixFloat &resultsData)

void	offsetTimeseries (MatrixFloat &timeseries)

bool	loadLegacyModelFromFile (std::fstream &file)

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

bool	loadBaseSettingsFromFile (std::fstream &file)

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

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Attributes
Vector< DTWTemplate >	templatesBuffer

Vector< MatrixFloat >	distanceMatrices

Vector< Vector< IndexDist > >	warpPaths

CircularBuffer< VectorFloat >	continuousInputDataBuffer

UINT	numTemplates

UINT	rejectionMode

bool	useSmoothing

bool	useZNormalisation

bool	offsetUsingFirstSample

bool	constrainZNorm

bool	constrainWarpingPath

bool	trimTrainingData

Float	zNormConstrainThreshold

Float	radius

Float	trimThreshold

Float	maximumTrimPercentage

Float	nullRejectionLikelihoodThreshold

UINT	smoothingFactor

UINT	distanceMethod

UINT	averageTemplateLength

Protected Attributes inherited from Classifier
bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

Float	trainingSetAccuracy

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

bool	converged

DataType	inputType

DataType	outputType

BaseType	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	batchSize

UINT	validationSetSize

UINT	numRestarts

Float	learningRate

Float	minChange

Float	rmsTrainingError

Float	rmsValidationError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

Vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

TrainingLog	trainingLog

TestingLog	testingLog

Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

WarningLog	warningLog

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

Detailed Description

Examples:: ClassificationModulesExamples/DTWExample/DTWExample.cpp.

Definition at line 91 of file DTW.h.

Constructor & Destructor Documentation

DTW::DTW	(	bool	useScaling = `false`,
		bool	useNullRejection = `false`,
		Float	nullRejectionCoeff = `3.0`,
		UINT	rejectionMode = `DTW::TEMPLATE_THRESHOLDS`,
		bool	dtwConstrain = `true`,
		Float	radius = `0.2`,
		bool	offsetUsingFirstSample = `false`,
		bool	useSmoothing = `false`,
		UINT	smoothingFactor = `5`,
		Float	nullRejectionLikelihoodThreshold = `0.99`
	)

Default Constructor

Parameters

useScaling	sets if the training and prediction data should be scaled to a specific range. Default value is useScaling = false
useNullRejection	sets if null rejection will be used for the realtime prediction. If useNullRejection is set to true then the predictedClassLabel will be set to 0 (which is the default null label) if the distance between the inputVector and the closest template is greater than the null rejection threshold for the top predicted class. The null rejection threshold is computed for each class during the training phase. Default value is useNullRejection = false
nullRejectionCoeff	sets the null rejection coefficient, this is a multipler controlling the null rejection threshold for each class. This will only be used if the useNullRejection parameter is set to true. Default value is nullRejectionCoeff = 3.0
rejectionMode	sets the method used for null rejection. The options are TEMPLATE_THRESHOLDS, CLASS_LIKELIHOODS or THRESHOLDS_AND_LIKELIHOODS. Default = TEMPLATE_THRESHOLDS
dtwConstrain	sets if the DTW warping path should be constrained within a specific distance from the main radius of the cost matrix. Default value = true
radius	controls the radius of the warping path, which is used if the dtwConstrain is set to true. Should be a value between [0 1]. Default value = 0.2
offsetUsingFirstSample	sets if each timeseries should be offset by the first sample in the timeseries. Default value = false
useSmoothing	sets if the input timeseries should be smoothed (i.e. averaged and downsampled). Default value = false
smoothingFactor	controls the amount of downsampling if the useSmoothing parameter is set to true. Default value = 5
nullRejectionLikelihoodThreshold	set the null rejection threshold for likelihoods when CLASS_LIKELIHOODS or THRESHOLDS_AND_LIKELIHOODS modes are used for rejectionMode. Default value = 0.99

Definition at line 33 of file DTW.cpp.

DTW::DTW ( const DTW & rhs )

Default copy constructor

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

Parameters

rhs	another instance of a DTW

Definition at line 65 of file DTW.cpp.

DTW::~DTW ( void )

virtual

Default Destructor

Definition at line 70 of file DTW.cpp.

Member Function Documentation

bool DTW::clear ( )

virtual

This overrides the clear function in the Classifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

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

Reimplemented from Classifier.

Definition at line 524 of file DTW.cpp.

bool DTW::deepCopyFrom ( const Classifier * classifier )

virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setClassifier(...) method is called. It clones the data from the Base Class Classifier pointer (which should be pointing to an DTW instance) into this instance

Parameters

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

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

Reimplemented from Classifier.

Definition at line 105 of file DTW.cpp.

bool DTW::enableTrimTrainingData	(	bool	trimTrainingData,
		Float	trimThreshold,
		Float	maximumTrimPercentage
	)

Sets if the training data should be trimmed before training the DTW templates. If set to true then any training samples that have very little movement at the start or end of a file will be trimmed so as to remove the none movement at the start or end of the file. This can be useful if some of your examples have a section were the user paused before performing a gesture, or were the recording continued for a few seconds after the gesture ended. The trimThreshold and maximumTrimPrecentage parameters control how extreme the trimming can be. The trimThreshold should be in a range of [0 1] and the maximumTrimPrecentage should be in a range of [0 100]. If the amount of energy in the timeseries is below the trimThreshold then it will be trimmed.

Note that if the trimming mode is enabled, and a time series sample exceeds the maximumTrimPercentage, then that sample will not be added to the modified (trimmed) training dataset. If any sample exceeds the maximumTrimPercentage then a warning message will be displayed via the DTW trainingLog (unless the trainingLog's logging has been disabled).

Parameters

trimTrainingData	if true, then the training examples will be trimmed prior to training the DTW templates
trimThreshold	the trimThreshold sets the threshold at which the data should be trimmed. This should be in a range of [0 1]
maximumTrimPercentage	this sets the maximum amount of data that can be trimmed for one example. This should be in the range of [0 100]

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

Examples:: ClassificationModulesExamples/DTWExample/DTWExample.cpp.

Definition at line 1265 of file DTW.cpp.

bool DTW::enableZNormalization	(	bool	useZNormalization,
		bool	constrainZNorm = `true`
	)

Sets if z-normalization should be used for both training and realtime prediction. This should be called before training the templates.

Parameters

useZNormalization	if true then the data will be z-normalized before both training and prediction
constrainZNorm	if true then the data will only be z-normalized if the std-dev of the data is below a specific threshold

Returns: returns true if z-normalization was updated successfully, false otherwise

Definition at line 1259 of file DTW.cpp.

const Vector< MatrixFloat >& DTW::getDistanceMatrices ( ) const

inline

Gets the distances matrices from the last prediction. Each element in the vector represents the distance matrices for each corresponding class.

Returns: returns a vector of MatrixFloat containing the distance matrices from the last prediction, or an empty vector if no prediction has been made

Definition at line 323 of file DTW.h.

std::string DTW::getId ( )

static

Gets a string that represents the DTW class.

Returns: returns a string containing the ID of this class

Definition at line 28 of file DTW.cpp.

Vector< VectorFloat > DTW::getInputDataBuffer ( ) const

inline

Gets the current data in the DTW circular buffer.

Returns: returns a vector of VectorFloats containing the current data in the DTW circular buffer

Definition at line 316 of file DTW.h.

Vector< DTWTemplate > DTW::getModels ( ) const

inline

Gets the DTW models.

Returns: returns a vector of DTW templates, or an empty vector if no model has been trained.

Definition at line 302 of file DTW.h.

UINT DTW::getNumTemplates ( ) const

inline

This returns the number of templates in the current model.

Returns: returns an unsigned integer representing the number of templates in the current model.

Definition at line 219 of file DTW.h.

UINT DTW::getRejectionMode ( ) const

inline

Gets the rejection mode used for null rejection. The rejection mode will be one of the RejectionModes enums.

Returns: returns an unsigned integer representing the current rejection mode

Definition at line 268 of file DTW.h.

const Vector< Vector< IndexDist > >& DTW::getWarpingPaths ( ) const

inline

Gets the warping paths from the last prediction. Each element in the vector represents the warping path for each corresponding class.

Returns: returns a vector of vectors containing the warping paths from the last prediction, or an empty vector if no prediction has been made

Definition at line 330 of file DTW.h.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Examples:: ClassificationModulesExamples/DTWExample/DTWExample.cpp.

Definition at line 1005 of file DTW.cpp.

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

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

Parameters

rhs	another instance of a DTW

Returns: returns a pointer to this instance of the DTW

Definition at line 73 of file DTW.cpp.

bool DTW::predict_ ( VectorFloat & inputVector )

virtual

This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.

Parameters

inputVector the input vector to classify

Returns: returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 476 of file DTW.cpp.

bool DTW::predict_ ( MatrixFloat & timeSeries )

virtual

This predicts the class of the timeseries. This overrides the predict function in the Classifier base class.

Parameters

timeSeries the input timeseries to classify

Returns: returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 356 of file DTW.cpp.

bool DTW::recomputeNullRejectionThresholds ( )

virtual

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

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

Reimplemented from Classifier.

Definition at line 538 of file DTW.cpp.

bool DTW::reset ( )

virtual

This resets the DTW classifier.

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

Reimplemented from Classifier.

Definition at line 515 of file DTW.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Examples:: ClassificationModulesExamples/DTWExample/DTWExample.cpp.

Definition at line 944 of file DTW.cpp.

bool DTW::setContrainWarpingPath ( bool constrain )

Sets if the warping path should be constrained to within a specific radius from the main diagonal of the cost matrix.

Parameters

constrain if true then the warping path should be constrained

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

Definition at line 1249 of file DTW.cpp.

bool DTW::setModels ( Vector< DTWTemplate > newTemplates )

Sets the DTW models, overwriting any previous models. The size of the new templates vector must match the size of the old templates buffer.

Returns: returns true if the templates were updated, false otherwise

Definition at line 553 of file DTW.cpp.

bool DTW::setNullRejectionThreshold ( Float nullRejectionLikelihoodThreshold )

Sets the rejection threshold for rejections based on class likelihood.

Returns: returns true if the rejection threshold was updated successfully, false otherwise

Definition at line 1238 of file DTW.cpp.

bool DTW::setOffsetTimeseriesUsingFirstSample ( bool offsetUsingFirstSample )

Sets if each timeseries should be offset by the first sample in the timeseries. This can help the DTW algorithm be more invariant to the position a gesture is performed without having to normalize the data. You shoud call this function before training the algorithm.

Parameters

offsetUsingFirstSample sets if each timeseries should be offset by the first sample in the timeseries

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

Definition at line 1244 of file DTW.cpp.

bool DTW::setRejectionMode ( UINT rejectionMode )

Sets the rejection mode used for null rejection. The rejection mode should be one of the RejectionModes enums.

Returns: returns true if the rejection mode was updated successfully, false otherwise

Definition at line 1230 of file DTW.cpp.

bool DTW::setWarpingRadius ( Float radius )

Sets the warping radius, this is used to constrain the warping path within a specific radius from the main diagonal of the cost matrix. This is only used if the #constrainWarpingPath parameter is set to true. The radius should be a value between [0 1].

Parameters

radius the new warping radius (should be between [0 1]

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

Definition at line 1254 of file DTW.cpp.

bool DTW::train_ ( TimeSeriesClassificationData & trainingData )

virtual

This trains the DTW model, using the labelled timeseries classification data. This overrides the train function in the Classifier base class.

Parameters

trainingData a reference to the training data

Returns: returns true if the DTW model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 141 of file DTW.cpp.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation