#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	BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }

Public Member Functions
	Derivative (UINT derivativeOrder=FIRST_DERIVATIVE, Float delta=1, UINT numDimensions=1, bool filterData=true, 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	saveModelToFile (std::string filename) const

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

virtual bool	loadModelFromFile (std::string filename)

virtual bool	loadModelFromFile (std::fstream &file)

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

Float	computeDerivative (const Float x)

VectorFloat	computeDerivative (const VectorFloat &x)

bool	setDerivativeOrder (UINT derivativeOrder)

bool	setFilterSize (UINT filterSize)

bool	setDelta (Float delta)

bool	enableFiltering (bool filterData)

UINT	getFilterSize ()

Float	getDerivative (UINT derivativeOrder=0)

VectorFloat	getDerivatives (UINT derivativeOrder=0)

Public Member Functions inherited from PreProcessing
	PreProcessing (void)

virtual	~PreProcessing (void)

bool	copyBaseVariables (const PreProcessing *preProcessingModule)

virtual bool	clear ()

std::string	getPreProcessingType () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

bool	getInitialized () const

VectorFloat	getProcessedData () const

PreProcessing *	createNewInstance () const

Public Member Functions inherited from MLBase
	MLBase (void)

virtual	~MLBase (void)

bool	copyMLBaseVariables (const MLBase *mlBase)

virtual bool	train (ClassificationData trainingData)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	train (RegressionData trainingData)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	train (TimeSeriesClassificationData trainingData)

virtual bool	train_ (TimeSeriesClassificationData &trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train (UnlabelledData trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	train (MatrixFloat data)

virtual bool	train_ (MatrixFloat &data)

virtual bool	predict (VectorFloat inputVector)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	predict (MatrixFloat inputMatrix)

virtual bool	predict_ (MatrixFloat &inputMatrix)

virtual bool	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

virtual bool	print () const

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

virtual bool	load (const std::string filename)

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

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

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

UINT	getBaseType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRootMeanSquaredTrainingError () const

Float	getTotalSquaredTrainingError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

bool	getModelTrained () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setMinChange (const Float minChange)

bool	setLearningRate (const Float learningRate)

bool	setUseValidationSet (const bool useValidationSet)

bool	setValidationSetSize (const UINT validationSetSize)

bool	setRandomiseTrainingOrder (const bool randomiseTrainingOrder)

bool	setTrainingLoggingEnabled (const bool loggingEnabled)

bool	registerTrainingResultsObserver (Observer< TrainingResult > &observer)

bool	registerTestResultsObserver (Observer< TestInstanceResult > &observer)

bool	removeTrainingResultsObserver (const Observer< TrainingResult > &observer)

bool	removeTestResultsObserver (const Observer< TestInstanceResult > &observer)

bool	removeAllTrainingObservers ()

bool	removeAllTestObservers ()

bool	notifyTrainingResultsObservers (const TrainingResult &data)

bool	notifyTestResultsObservers (const TestInstanceResult &data)

MLBase *	getMLBasePointer ()

const MLBase *	getMLBasePointer () const

Vector< TrainingResult >	getTrainingResults () const

Public Member Functions inherited from GRTBase
	GRTBase (void)

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

std::string	getClassType () const

std::string	getLastWarningMessage () const

std::string	getLastErrorMessage () const

std::string	getLastInfoMessage () const

bool	setInfoLoggingEnabled (const bool loggingEnabled)

bool	setWarningLoggingEnabled (const bool loggingEnabled)

bool	setErrorLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

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

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

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

DataType	inputType

DataType	outputType

UINT	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	validationSetSize

Float	learningRate

Float	minChange

Float	rootMeanSquaredTrainingError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

std::vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Static Protected Attributes
static RegisterPreProcessingModule< Derivative >	registerModule

Additional Inherited Members
Static Public Member Functions inherited from PreProcessing
static PreProcessing *	createInstanceFromString (std::string const &preProcessingType)

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

static std::string	getGRTRevison ()

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)

Protected Member Functions inherited from GRTBase
Float	SQR (const Float &x) const

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

Detailed Description

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.

Definition at line 39 of file Derivative.h.

Constructor & Destructor Documentation

Derivative::Derivative	(	UINT	derivativeOrder = `FIRST_DERIVATIVE`,
		Float	delta = `1`,
		UINT	numDimensions = `1`,
		bool	filterData = `true`,
		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 28 of file Derivative.cpp.

Derivative::Derivative ( const Derivative & rhs )

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

Parameters

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

Definition at line 39 of file Derivative.cpp.

Derivative::~Derivative ( )

virtual

Default Destructor

Definition at line 58 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

Definition at line 290 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 304 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 76 of file Derivative.cpp.

bool Derivative::enableFiltering ( 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 358 of file Derivative.cpp.

Float Derivative::getDerivative ( UINT derivativeOrder = 0 )

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 364 of file Derivative.cpp.

VectorFloat Derivative::getDerivatives ( UINT derivativeOrder = 0 )

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 384 of file Derivative.cpp.

UINT Derivative::getFilterSize ( )

inline

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 211 of file Derivative.h.

bool Derivative::init	(	UINT	derivativeOrder,
		Float	delta,
		UINT	numDimensions,
		bool	filterData,
		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 249 of file Derivative.cpp.

bool Derivative::loadModelFromFile ( std::string filename )

virtual

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

Parameters

filename the name of the file to load the settings from

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

Reimplemented from PreProcessing.

Definition at line 164 of file Derivative.cpp.

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

virtual

This loads the Derivative settings from a file. This overrides the loadModelFromFile 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 PreProcessing.

Definition at line 180 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 62 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 102 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 120 of file Derivative.cpp.

bool Derivative::saveModelToFile ( std::string filename ) const

virtual

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

Parameters

filename the name of the file to save the settings to

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

Reimplemented from PreProcessing.

Definition at line 125 of file Derivative.cpp.

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

virtual

This saves the current settings of the Derivative to a file. This overrides the saveModelToFile 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 PreProcessing.

Definition at line 145 of file Derivative.cpp.

bool Derivative::setDelta ( 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 ( 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 338 of file Derivative.cpp.

bool Derivative::setFilterSize ( 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 348 of file Derivative.cpp.

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

Public Types

Public Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation