The Derivative class computes either the first or second order derivative of the input signal. More...

#include <Derivative.h>

Inheritance diagram for Derivative:

Public Types
enum	DerivativeOrders { FIRST_DERIVATIVE =1, SECOND_DERIVATIVE }

Public Types inherited from PreProcessing
typedef std::map< std::string, PreProcessing ()() >	StringPreProcessingMap

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
	Derivative (const UINT derivativeOrder=FIRST_DERIVATIVE, const Float delta=1, const UINT numDimensions=1, const bool filterData=true, const UINT filterSize=3)

	Derivative (const Derivative &rhs)

virtual	~Derivative ()

Derivative &	operator= (const Derivative &rhs)

virtual bool	deepCopyFrom (const PreProcessing *preProcessing)

virtual bool	process (const VectorFloat &inputVector)

virtual bool	reset ()

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

virtual bool	load (std::fstream &file)

bool	init (const UINT derivativeOrder, const Float delta, const UINT numDimensions, const bool filterData, const UINT filterSize)

Float	computeDerivative (const Float x)

VectorFloat	computeDerivative (const VectorFloat &x)

bool	setDerivativeOrder (const UINT derivativeOrder)

bool	setFilterSize (const UINT filterSize)

bool	setDelta (const Float delta)

bool	enableFiltering (const bool filterData)

UINT	getFilterSize () const

Float	getDerivative (const UINT derivativeOrder=FIRST_DERIVATIVE) const

VectorFloat	getDerivatives (const UINT derivativeOrder=FIRST_DERIVATIVE) const

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

virtual	~PreProcessing (void)

bool	copyBaseVariables (const PreProcessing *preProcessingModule)

virtual bool	clear () override

bool	getInitialized () const

VectorFloat	getProcessedData () const

PreProcessing *	create () const

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

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

	GRT_DEPRECATED_MSG ("getPreProcessingType is deprecated, use getId() instead", std::string getPreProcessingType() 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_ (TimeSeriesClassificationData &trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train (UnlabelledData trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	train (MatrixFloat data)

virtual bool	train_ (MatrixFloat &data)

virtual bool	predict (VectorFloat inputVector)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	predict (MatrixFloat inputMatrix)

virtual bool	predict_ (MatrixFloat &inputMatrix)

virtual bool	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

virtual bool	print () const

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

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

	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 PreProcessing
static PreProcessing *	create (const std::string &id)

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

static std::string	getGRTRevison ()

Protected Attributes
UINT	derivativeOrder
	The order of the derivative that will be computed (either FIRST_DERIVATIVE or SECOND_DERIVATIVE)

UINT	filterSize
	The size of the filter used to filter the input data before the derivative is computed.

Float	delta
	The estimated time between sensor samples.

bool	filterData
	Flags if the input data should be filtered before the derivative is computed.

MovingAverageFilter	filter
	The filter used to low pass filter the input data.

VectorFloat	yy
	A buffer holding the previous input value(s)

VectorFloat	yyy
	A buffer holding the previous first derivative values.

Protected Attributes inherited from PreProcessing
std::string	preProcessingType

bool	initialized

VectorFloat	processedData

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
Protected Member Functions inherited from PreProcessing
bool	init ()

bool	savePreProcessingSettingsToFile (std::fstream &file) const

bool	loadPreProcessingSettingsFromFile (std::fstream &file)

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

bool	loadBaseSettingsFromFile (std::fstream &file)

Static Protected Member Functions inherited from PreProcessing
static StringPreProcessingMap *	getMap ()

Detailed Description

The Derivative class computes either the first or second order derivative of the input signal.

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Examples:: PreprocessingModulesExamples/DerivativeExample/DerivativeExample.cpp.

Definition at line 39 of file Derivative.h.

Constructor & Destructor Documentation

Derivative::Derivative	(	const UINT	derivativeOrder = `FIRST_DERIVATIVE`,
		const Float	delta = `1`,
		const UINT	numDimensions = `1`,
		const bool	filterData = `true`,
		const UINT	filterSize = `3`
	)

Constructor, sets the derivativeOrder (which should be either FIRST_DERIVATIVE or SECOND_DERIVATIVE), the delta value (which should be set to 1000.0/sampleRate, the dimensionality of the input data, if the data should be filtered before computing the derivative, and the size of the filter if the data is to be filtered.

Parameters

derivativeOrder	the derivative order, should be either FIRST_DERIVATIVE or SECOND_DERIVATIVE. Default derivativeOrder = FIRST_DERIVATIVE
delta	sets the time between samples, this should be set to sampleRate/1000.0, where sampleRate is the sample rate of your sensor data. Default delta = 1.0
numDimensions	the dimensionality of the input data. Default numDimensions = 1
filterData	a flag that sets if the data should be filtered before computing the derivative. Default filterData = true
filterSize	the size of the filter if the data is to be filtered

Definition at line 33 of file Derivative.cpp.

Derivative::Derivative ( const Derivative & rhs )

Copy Constructor, copies the Derivative from the rhs instance to this instance

Parameters

&rhs	another instance of the Derivative class from which the data will be copied to this instance

Definition at line 38 of file Derivative.cpp.

Derivative::~Derivative ( )

virtual

Default Destructor

Definition at line 52 of file Derivative.cpp.

Member Function Documentation

Float Derivative::computeDerivative ( const Float x )

Computes the derivative of the input, this should only be called if the dimensionality of the instance was set to 1.

Parameters

x	the value to compute the derivative of, this should only be called if the dimensionality of the filter was set to 1

Returns: the derivative of the input. Zero will be returned if the value was not computed

Examples:: PreprocessingModulesExamples/DerivativeExample/DerivativeExample.cpp.

Definition at line 248 of file Derivative.cpp.

VectorFloat Derivative::computeDerivative ( const VectorFloat & x )

Computes the derivative of the input, the dimensionality of the input should match the number of inputs for the derivative

Parameters

x	the values to compute the derivative of, the dimensionality of the input should match the number of inputs for the derivative

Returns: the derivatives of the input. An empty vector will be returned if the values were not filtered

Definition at line 262 of file Derivative.cpp.

bool Derivative::deepCopyFrom ( const PreProcessing * preProcessing )

virtual

Sets the PreProcessing deepCopyFrom function, overwriting the base PreProcessing function. This function is used to clone the values from the input pointer to this instance of the PreProcessing module. This function is called by the GestureRecognitionPipeline when the user adds a new PreProcessing module to the pipeline.

Parameters

preProcessing a pointer to another instance of a Derivative, the values of that instance will be cloned to this instance

Returns: true if the deep copy was successful, false otherwise

Reimplemented from PreProcessing.

Definition at line 70 of file Derivative.cpp.

bool Derivative::enableFiltering ( const bool filterData )

Sets if the input data will be filtered using a moving average filter before the derivative is computed. Updating this parameter will re-initialize this instance.

Parameters

filterData sets if the data should be filtered before computing the derivative

Returns: returns true if the filterData parameter was set, false otherwise

Definition at line 316 of file Derivative.cpp.

Float Derivative::getDerivative ( const UINT derivativeOrder = FIRST_DERIVATIVE ) const

Gets the last computed derivative value. Will return either the first or second derivative (you can only get the second derivative if the derivativeOrder is set to 2nd derivative).

Parameters

derivativeOrder flags which derivative order you want, the default value is 0 which will return whatever the current derivativeOrder is

Returns: returns the last computed derivative value, will return 0 if no values have been computed

Definition at line 327 of file Derivative.cpp.

VectorFloat Derivative::getDerivatives ( const UINT derivativeOrder = FIRST_DERIVATIVE ) const

Gets the last computed derivative values. Will return either the first or second derivative (you can only get the second derivative if the derivativeOrder is set to 2nd derivative).

Parameters

derivativeOrder flags which derivative order you want, the default value is 0 which will return whatever the current derivativeOrder is

Returns: returns the last computed derivative values, will return 0 if no values have been computed

Definition at line 347 of file Derivative.cpp.

UINT Derivative::getFilterSize ( ) const

Gets the size of the moving average filter. If the instance has not been initialized then zero will be returned.

Returns: returns the size of the moving average filter, will return 0 if no values have been computed

Definition at line 322 of file Derivative.cpp.

std::string Derivative::getId ( )

static

Gets a string that represents the ID of this class.

Returns: returns a string containing the ID of this class

Definition at line 28 of file Derivative.cpp.

bool Derivative::init	(	const UINT	derivativeOrder,
		const Float	delta,
		const UINT	numDimensions,
		const bool	filterData,
		const UINT	filterSize
	)

Initializes the instance, sets the derivativeOrder (which should be either FIRST_DERIVATIVE or SECOND_DERIVATIVE), the delta value (which should be set to 1000.0/sampleRate, the dimensionality of the input data, if the data should be filtered before computing the derivative, and the size of the filter if the data is to be filtered.

Parameters

derivativeOrder	the derivative order, should be either FIRST_DERIVATIVE or SECOND_DERIVATIVE. Default derivativeOrder = FIRST_DERIVATIVE
delta	sets the time between samples, this should be set to sampleRate/1000.0, where sampleRate is the sample rate of your sensor data. Default delta = 1.0
numDimensions	the dimensionality of the input data. Default numDimensions = 1
filterData	a flag that sets if the data should be filtered before computing the derivative. Default filterData = true
filterSize	the size of the filter if the data is to be filtered

Returns: true if the instance was initiliazed, false otherwise

Definition at line 207 of file Derivative.cpp.

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

virtual

This loads the Derivative settings from a file. This overrides the load function in the PreProcessing base class.

Parameters

file	a reference to the file to load the settings from

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

Reimplemented from MLBase.

Examples:: PreprocessingModulesExamples/DerivativeExample/DerivativeExample.cpp.

Definition at line 138 of file Derivative.cpp.

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

Sets the equals operator, copies the data from the rhs instance to this instance

Parameters

rhs	another instance of the Derivative class from which the data will be copied to this instance

Returns: a reference to this instance of Derivative

Definition at line 56 of file Derivative.cpp.

bool Derivative::process ( const VectorFloat & inputVector )

virtual

Sets the PreProcessing process function, overwriting the base PreProcessing function. This function is called by the GestureRecognitionPipeline when any new input data needs to be processed (during the prediction phase for example). This function calls the Derivative's computeDerivative function.

Parameters

inputVector the inputVector that should be processed. Must have the same dimensionality as the PreProcessing module

Returns: true if the data was processed, false otherwise

Reimplemented from PreProcessing.

Definition at line 96 of file Derivative.cpp.

bool Derivative::reset ( )

virtual

Sets the PreProcessing reset function, overwriting the base PreProcessing function. This function is called by the GestureRecognitionPipeline when the pipelines main reset() function is called. This function resets the Derivative values by re-initiliazing the filter.

Returns: true if the Derivative was reset, false otherwise

Reimplemented from PreProcessing.

Definition at line 114 of file Derivative.cpp.

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

virtual

This saves the current settings of the Derivative to a file. This overrides the save function in the PreProcessing base class.

Parameters

file	a reference to the file the settings will be saved to

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

Reimplemented from MLBase.

Examples:: PreprocessingModulesExamples/DerivativeExample/DerivativeExample.cpp.

Definition at line 119 of file Derivative.cpp.

bool Derivative::setDelta ( const Float delta )

Sets the delta value. This is the time between samples and should be set to sampleRate/1000.0, where sampleRate is the sample rate of your sensor data. For example, if your sensor runs at 30FPS then delta would be 30.0/1000.0 = 0.03. Delta must be greater than zero. Setting delta will re-initialize this instance.

Parameters

delta the estimated sampling time between sensor samples, must be greater than zero

Returns: returns true if delta was set, false otherwise

bool Derivative::setDerivativeOrder ( const UINT derivativeOrder )

Sets the derivative order. This should either be FIRST_DERIVATIVE (1) or SECOND_DERIVATIVE (2). Setting the derivative order will re-initialize this instance.

Parameters

derivativeOrder the derivative order you wish to set, this should either be FIRST_DERIVATIVE (1) or SECOND_DERIVATIVE (2)

Returns: returns true if the derivative order was set, false otherwise

Definition at line 296 of file Derivative.cpp.

bool Derivative::setFilterSize ( const UINT filterSize )

Sets the size of the moving average filter used to smooth the input data (if the filterData parameter is set to true). The filterSize value must be greater than zero. Setting the filterSize will re-initialize this instance.

Parameters

filterSize the size of the moving average filter used to smooth the input data, must be greater than zero

Returns: returns true if the filterSize was set, false otherwise

Definition at line 306 of file Derivative.cpp.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation