#include <WeightedAverageFilter.h>

Inheritance diagram for WeightedAverageFilter:

Public Member Functions
	WeightedAverageFilter (UINT filterSize=5, UINT numDimensions=1)

	WeightedAverageFilter (const WeightedAverageFilter &rhs)

virtual	~WeightedAverageFilter ()

WeightedAverageFilter &	operator= (const WeightedAverageFilter &rhs)

virtual bool	deepCopyFrom (const PreProcessing *preProcessing)

virtual bool	process (const VectorFloat &inputVector)

virtual bool	reset ()

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

virtual bool	loadModelFromFile (std::fstream &file)

bool	init (UINT filterSize, UINT numDimensions)

Float	filter (const Float x)

VectorFloat	filter (const VectorFloat &x)

UINT	getFilterSize () const

VectorFloat	getFilteredData () const

Public Member Functions inherited from PreProcessing
	PreProcessing (void)

virtual	~PreProcessing (void)

bool	copyBaseVariables (const PreProcessing *preProcessingModule)

virtual bool	clear ()

virtual bool	saveModelToFile (std::string filename) const

virtual bool	loadModelFromFile (std::string filename)

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	filterSize
	The size of the filter.

UINT	inputSampleCounter
	A counter to keep track of the number of input samples.

CircularBuffer< VectorFloat >	dataBuffer
	A buffer to store the previous N values, N = filterSize.

VectorFloat	weights
	Stores the weights for each sample in the buffer, the size of this vector will match the filterSize.

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< WeightedAverageFilter >	registerModule

Additional Inherited Members
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 }

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 37 of file WeightedAverageFilter.h.

Constructor & Destructor Documentation

WeightedAverageFilter::WeightedAverageFilter	(	UINT	filterSize = `5`,
		UINT	numDimensions = `1`
	)

Constructor, sets the size of the filter and the dimensionality of the data it will filter.

Parameters

filterSize	the size of the weighted average filter, should be a value greater than zero. Default filterSize = 5
numDimensions	the dimensionality of the data to filter. Default numDimensions = 1

Definition at line 28 of file WeightedAverageFilter.cpp.

WeightedAverageFilter::WeightedAverageFilter ( const WeightedAverageFilter & rhs )

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

Parameters

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

Definition at line 38 of file WeightedAverageFilter.cpp.

WeightedAverageFilter::~WeightedAverageFilter ( )

virtual

Default Destructor

Definition at line 54 of file WeightedAverageFilter.cpp.

Member Function Documentation

bool WeightedAverageFilter::deepCopyFrom ( const PreProcessing * preProcessing )

virtual

Sets the PreProcessing deepCopyFrom function, overwriting the base PreProcessing function. This function is used to deep copy 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 WeightedAverageFilter, 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 78 of file WeightedAverageFilter.cpp.

Float WeightedAverageFilter::filter ( const Float x )

Filters the input, this should only be called if the dimensionality of the filter was set to 1.

Parameters

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

Returns: the filtered value. Zero will be returned if the value was not filtered

Definition at line 219 of file WeightedAverageFilter.cpp.

VectorFloat WeightedAverageFilter::filter ( const VectorFloat & x )

Filters the input, the dimensionality of the input vector should match that of the filter.

Parameters

x	the values to filter, the dimensionality of the input vector should match that of the filter

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

Definition at line 233 of file WeightedAverageFilter.cpp.

VectorFloat WeightedAverageFilter::getFilteredData ( ) const

inline

Returns the last value(s) that were filtered.

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

Definition at line 150 of file WeightedAverageFilter.h.

UINT WeightedAverageFilter::getFilterSize ( ) const

inline

Gets the current filter size.

Returns: returns the filter size

Definition at line 144 of file WeightedAverageFilter.h.

bool WeightedAverageFilter::init	(	UINT	filterSize,
		UINT	numDimensions
	)

Initializes the filter, setting the filter size and dimensionality of the data it will filter. Sets all the filter values to zero.

Parameters

filterSize the size of the moving average filter, should be a value greater than zero

Returns: true if the filter was initiliazed, false otherwise

Definition at line 181 of file WeightedAverageFilter.cpp.

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

virtual

This loads the WeightedAverageFilter 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 136 of file WeightedAverageFilter.cpp.

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

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

Parameters

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

Returns: a reference to this instance of WeightedAverageFilter

Definition at line 58 of file WeightedAverageFilter.cpp.

bool WeightedAverageFilter::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 WeightedAverageFilter's filter 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 WeightedAverageFilter.cpp.

bool WeightedAverageFilter::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 filter values by re-initiliazing the filter.

Returns: true if the filter was reset, false otherwise

Reimplemented from PreProcessing.

Definition at line 115 of file WeightedAverageFilter.cpp.

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

virtual

This saves the current settings of the WeightedAverageFilter 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 120 of file WeightedAverageFilter.cpp.

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

Public Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation