#include <PreProcessing.h>

Inheritance diagram for PreProcessing:

Public Types
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
	PreProcessing (void)

virtual	~PreProcessing (void)

virtual bool	deepCopyFrom (const PreProcessing *rhs)

bool	copyBaseVariables (const PreProcessing *preProcessingModule)

virtual bool	process (const VectorFloat &inputVector)

virtual bool	reset ()

virtual bool	clear ()

virtual bool	saveModelToFile (std::string filename) const

virtual bool	loadModelFromFile (std::string filename)

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

virtual bool	loadModelFromFile (std::fstream &file)

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)

Static Public Member Functions
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
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
static StringPreProcessingMap *	getMap ()

Protected Attributes
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

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 PreProcessing.h.

Member Typedef Documentation

typedef std::map< std::string, PreProcessing*(*)() > PreProcessing::StringPreProcessingMap

This typedef defines a map between a string and a PreProcessing pointer.

Definition at line 160 of file PreProcessing.h.

Constructor & Destructor Documentation

PreProcessing::PreProcessing ( void )

Default Constructor

Definition at line 37 of file PreProcessing.cpp.

PreProcessing::~PreProcessing ( void )

virtual

Default Destructor

Definition at line 45 of file PreProcessing.cpp.

Member Function Documentation

bool PreProcessing::clear ( )

virtual

This is the main clear interface for all the GRT preprocessing modules. This should be overwritten by the derived class. It will completely clear the module, removing any IO values setting and all the base variables to their default values.

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

Reimplemented from MLBase.

Reimplemented in FIRFilter.

Definition at line 84 of file PreProcessing.cpp.

bool PreProcessing::copyBaseVariables ( const PreProcessing * preProcessingModule )

This copies the PreProcessing variables from preProcessing to the instance that calls the function.

Parameters

preProcessing a pointer to a pre processing module from which the values will be copied

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

Definition at line 52 of file PreProcessing.cpp.

PreProcessing * PreProcessing::createInstanceFromString ( std::string const & preProcessingType )

static

This static function will dynamically create a new PreProcessing instance from a string.

Parameters

preProcessingType the name of the PreProcessing class you want to dynamically create

Returns: a pointer to the new PreProcessing instance that was created

Definition at line 28 of file PreProcessing.cpp.

PreProcessing * PreProcessing::createNewInstance ( ) const

This static function will dynamically create a new PreProcessing instance based on the type of this instance

Definition at line 187 of file PreProcessing.cpp.

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

inlinevirtual

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

Parameters

preProcessing a pointer to the PreProcessing 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 PreProcessing base class will always return flase)

Reimplemented in LowPassFilter, HighPassFilter, Derivative, FIRFilter, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, WeightedAverageFilter, and LeakyIntegrator.

Definition at line 57 of file PreProcessing.h.

bool PreProcessing::getInitialized ( ) const

Returns true if the pre processing module has been initialized correctly.

Returns: returns true if the pre processing module has been initialized succesfully, false otherwise

Definition at line 203 of file PreProcessing.cpp.

UINT PreProcessing::getNumInputDimensions ( ) const

Returns the size of the input vector expected by the pre processing module.

Returns: returns the size of the input vector expected by the pre processing module

Definition at line 195 of file PreProcessing.cpp.

UINT PreProcessing::getNumOutputDimensions ( ) const

Returns the size of the vector that will be computed by the pre processing module.

Returns: returns the size of the vector that will be computed by the pre processing module

Definition at line 199 of file PreProcessing.cpp.

std::string PreProcessing::getPreProcessingType ( ) const

Returns: returns the pre processing type as a string, e.g. LowPassFilter

Definition at line 191 of file PreProcessing.cpp.

VectorFloat PreProcessing::getProcessedData ( ) const

Returns: returns a VectorFloat containing the most recent processed data

Definition at line 207 of file PreProcessing.cpp.

bool PreProcessing::init ( )

protected

Initializes the base preprocessing module, this will resize the processedData vector and get the instance ready for preprocessing new data. The inheriting class must first initialize the module before calling this function.

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

Definition at line 92 of file PreProcessing.cpp.

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

virtual

This saves the preprocessing 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

Reimplemented from MLBase.

Reimplemented in FIRFilter, LowPassFilter, HighPassFilter, Derivative, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, and MovingAverageFilter.

Definition at line 123 of file PreProcessing.cpp.

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

inlinevirtual

This loads the preprocessing 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 FIRFilter, LowPassFilter, HighPassFilter, Derivative, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, WeightedAverageFilter, and LeakyIntegrator.

Definition at line 124 of file PreProcessing.h.

bool PreProcessing::loadPreProcessingSettingsFromFile ( std::fstream & file )

protected

Loads the core preprocessing settings from a file.

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

Definition at line 155 of file PreProcessing.cpp.

virtual bool PreProcessing::process ( const VectorFloat & inputVector )

inlinevirtual

This is the main processing interface for all the pre processing modules and should be overwritten by the inheriting class.

Parameters

inputVector a vector containing the data that should be processed

Returns: returns true if the pre processing was successfull, false otherwise

Reimplemented in LowPassFilter, HighPassFilter, Derivative, FIRFilter, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, WeightedAverageFilter, and LeakyIntegrator.

Definition at line 73 of file PreProcessing.h.

bool PreProcessing::reset ( )

virtual

This is the main reset interface for all the GRT preprocessing modules. This should be overwritten by the derived class.

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

Reimplemented from MLBase.

Reimplemented in LowPassFilter, HighPassFilter, Derivative, FIRFilter, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, WeightedAverageFilter, and LeakyIntegrator.

Definition at line 75 of file PreProcessing.cpp.

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

virtual

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

Parameters

filename the filename to save the settings to

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

Reimplemented from MLBase.

Reimplemented in FIRFilter, LowPassFilter, HighPassFilter, Derivative, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, and MovingAverageFilter.

Definition at line 109 of file PreProcessing.cpp.

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

inlinevirtual

This saves the preprocessing 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 FIRFilter, LowPassFilter, HighPassFilter, Derivative, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, WeightedAverageFilter, and LeakyIntegrator.

Definition at line 115 of file PreProcessing.h.

bool PreProcessing::savePreProcessingSettingsToFile ( std::fstream & file ) const

protected

Saves the core preprocessing settings to a file.

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

Definition at line 138 of file PreProcessing.cpp.

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