#include <FeatureExtraction.h>

Inheritance diagram for FeatureExtraction:

Public Types
typedef std::map< std::string, FeatureExtraction ()() >	StringFeatureExtractionMap

Public Types inherited from MLBase
enum	BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }

Public Member Functions
	FeatureExtraction ()

virtual	~FeatureExtraction ()

virtual bool	deepCopyFrom (const FeatureExtraction *rhs)

bool	copyBaseVariables (const FeatureExtraction *featureExtractionModule)

virtual bool	computeFeatures (const VectorFloat &inputVector)

virtual bool	computeFeatures (const MatrixFloat &inputMatrix)

virtual bool	reset ()

virtual bool	clear ()

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

virtual bool	loadModelFromFile (std::fstream &file)

std::string	getFeatureExtractionType () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

bool	getInitialized () const

bool	getFeatureDataReady () const

const VectorFloat &	getFeatureVector () const

const MatrixFloat &	getFeatureMatrix () const

FeatureExtraction *	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	saveModelToFile (std::string filename) const

virtual bool	loadModelFromFile (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)

Static Public Member Functions
static FeatureExtraction *	createInstanceFromString (const std::string &featureExtractionType)

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

static std::string	getGRTRevison ()

Protected Member Functions
bool	init ()

bool	saveFeatureExtractionSettingsToFile (std::fstream &file) const

bool	loadFeatureExtractionSettingsFromFile (std::fstream &file)

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

bool	loadBaseSettingsFromFile (std::fstream &file)

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

Static Protected Member Functions
static StringFeatureExtractionMap *	getMap ()

Protected Attributes
std::string	featureExtractionType

bool	initialized

bool	featureDataReady

VectorFloat	featureVector

MatrixFloat	featureMatrix

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

DataType	inputType

DataType	outputType

UINT	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	validationSetSize

Float	learningRate

Float	minChange

Float	rootMeanSquaredTrainingError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

std::vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Detailed Description

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 38 of file FeatureExtraction.h.

Member Typedef Documentation

typedef std::map< std::string, FeatureExtraction*(*)() > FeatureExtraction::StringFeatureExtractionMap

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

Definition at line 170 of file FeatureExtraction.h.

Constructor & Destructor Documentation

FeatureExtraction::FeatureExtraction ( )

Default FeatureExtraction Constructor

Definition at line 41 of file FeatureExtraction.cpp.

FeatureExtraction::~FeatureExtraction ( )

virtual

Default FeatureExtraction Destructor

Definition at line 55 of file FeatureExtraction.cpp.

Member Function Documentation

bool FeatureExtraction::clear ( )

virtual

This function clears any previous setup.

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

Reimplemented from MLBase.

Reimplemented in FFT, RBMQuantizer, KMeansQuantizer, and SOMQuantizer.

Definition at line 107 of file FeatureExtraction.cpp.

virtual bool FeatureExtraction::computeFeatures ( const VectorFloat & inputVector )

inlinevirtual

This function is called by the GestureRecognitionPipeline when any new input data needs to be processed (during the prediction phase for example). This function should be overwritten by the derived class.

Parameters

inputVector the inputVector that should be processed

Returns: returns true if the data was processed, false otherwise (the base class always returns false)

Reimplemented in FFT, ZeroCrossingCounter, RBMQuantizer, KMeansQuantizer, SOMQuantizer, MovementTrajectoryFeatures, FFTFeatures, KMeansFeatures, MovementIndex, TimeseriesBuffer, and TimeDomainFeatures.

Definition at line 74 of file FeatureExtraction.h.

virtual bool FeatureExtraction::computeFeatures ( const MatrixFloat & inputMatrix )

inlinevirtual

This function is called by the GestureRecognitionPipeline when any new input data needs to be processed (during the prediction phase for example). This function should be overwritten by the derived class.

Parameters

inputMatrix the inputVector that should be processed

Returns: returns true if the data was processed, false otherwise (the base class always returns false)

Reimplemented in FFT.

Definition at line 83 of file FeatureExtraction.h.

bool FeatureExtraction::copyBaseVariables ( const FeatureExtraction * featureExtractionModule )

This copies the FeatureExtraction variables from featureExtractionModule to the instance that calls the function.

Parameters

featureExtractionModule a pointer to a feature extraction module from which the values will be copied

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

Definition at line 62 of file FeatureExtraction.cpp.

FeatureExtraction * FeatureExtraction::createNewInstance ( ) const

Creates a new feature extraction instance based on the current featureExtractionType string value.

Returns: FeatureExtraction*: a pointer to the new instance of the feature extraction

Definition at line 37 of file FeatureExtraction.cpp.

virtual bool FeatureExtraction::deepCopyFrom ( const FeatureExtraction * rhs )

inlinevirtual

This is the base deepCopyFrom function for the FeatureExtraction modules. This function should be overwritten by the derived class.

Parameters

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

Returns: returns true if the deep copy was successfull, false otherwise (the FeatureExtraction base class will always return flase)

Reimplemented in FFT, ZeroCrossingCounter, KMeansQuantizer, RBMQuantizer, SOMQuantizer, FFTFeatures, MovementTrajectoryFeatures, KMeansFeatures, MovementIndex, TimeseriesBuffer, and TimeDomainFeatures.

Definition at line 57 of file FeatureExtraction.h.

bool FeatureExtraction::getFeatureDataReady ( ) const

Returns true if the feature extraction module has just processed the last input vector and a new output feature vector is ready.

Returns: returns true if the feature extraction module has just processed the last input vector and a new output feature vector is ready, false otherwise

Definition at line 180 of file FeatureExtraction.cpp.

std::string FeatureExtraction::getFeatureExtractionType ( ) const

Returns the feature extraction type as a string.

Returns: returns the feature extraction type as a string

Definition at line 164 of file FeatureExtraction.cpp.

const MatrixFloat & FeatureExtraction::getFeatureMatrix ( ) const

Returns the current feature matrix.

Returns: returns the current feature matrix, this matrix will be empty if the module has not been initialized

Definition at line 188 of file FeatureExtraction.cpp.

const VectorFloat & FeatureExtraction::getFeatureVector ( ) const

Returns the current feature vector.

Returns: returns the current feature vector, this vector will be empty if the module has not been initialized

Definition at line 184 of file FeatureExtraction.cpp.

bool FeatureExtraction::getInitialized ( ) const

Returns true if the feature extraction module has been initialized correctly.

Returns: returns true if the feature extraction module has been initialized succesfully, false otherwise

Definition at line 176 of file FeatureExtraction.cpp.

UINT FeatureExtraction::getNumInputDimensions ( ) const

Returns the size of the input vector expected by the feature extraction module.

Returns: returns the size of the input vector expected by the feature extraction module

Definition at line 168 of file FeatureExtraction.cpp.

UINT FeatureExtraction::getNumOutputDimensions ( ) const

Returns the size of the feature vector that will be computed by the feature extraction module.

Returns: returns the size of the feature vector that will be computed by the feature extraction module

Definition at line 172 of file FeatureExtraction.cpp.

bool FeatureExtraction::init ( )

protected

Initializes the base feature extraction module, this will resize the feature vector and get the instance ready for processing new data.

Returns: returns true if the module was initialized, false otherwise

Definition at line 87 of file FeatureExtraction.cpp.

bool FeatureExtraction::loadFeatureExtractionSettingsFromFile ( std::fstream & file )

protected

Loads the core base settings from a file.

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

Definition at line 133 of file FeatureExtraction.cpp.

virtual bool FeatureExtraction::loadModelFromFile ( std::fstream & file )

inlinevirtual

This loads the feature extraction settings from a file. This function should be overwritten by the derived class.

Parameters

file	a reference to the file to load the settings from

Returns: returns true if the settings were loaded successfully, false otherwise (the base class always returns false)

Reimplemented from MLBase.

Reimplemented in FFT, RBMQuantizer, ZeroCrossingCounter, SOMQuantizer, MovementTrajectoryFeatures, FFTFeatures, KMeansFeatures, MovementIndex, KMeansQuantizer, TimeseriesBuffer, and TimeDomainFeatures.

Definition at line 116 of file FeatureExtraction.h.

virtual bool FeatureExtraction::reset ( )

inlinevirtual

This function is called by the GestureRecognitionPipeline's reset function. This function should be overwritten by the derived class.

Returns: returns true if the module was reset, false otherwise (the base class always returns true)

Reimplemented from MLBase.

Reimplemented in FFT, ZeroCrossingCounter, RBMQuantizer, KMeansQuantizer, SOMQuantizer, MovementTrajectoryFeatures, FFTFeatures, KMeansFeatures, MovementIndex, TimeseriesBuffer, and TimeDomainFeatures.

Definition at line 91 of file FeatureExtraction.h.

bool FeatureExtraction::saveFeatureExtractionSettingsToFile ( std::fstream & file ) const

protected

Saves the core base settings to a file.

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

Definition at line 119 of file FeatureExtraction.cpp.

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

inlinevirtual

This saves the feature extraction settings to a file. This function should be overwritten by the derived class.

Parameters

file	a reference to the file to save the settings to

Returns: returns true if the settings were saved successfully, false otherwise (the base class always returns false)

Reimplemented from MLBase.

Reimplemented in FFT, RBMQuantizer, ZeroCrossingCounter, SOMQuantizer, MovementTrajectoryFeatures, FFTFeatures, KMeansFeatures, MovementIndex, KMeansQuantizer, TimeseriesBuffer, and TimeDomainFeatures.

Definition at line 107 of file FeatureExtraction.h.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation