#include <KMeansQuantizer.h>

Inheritance diagram for KMeansQuantizer:

Public Member Functions
	KMeansQuantizer (const UINT numClusters=10)

	KMeansQuantizer (const KMeansQuantizer &rhs)

virtual	~KMeansQuantizer ()

KMeansQuantizer &	operator= (const KMeansQuantizer &rhs)

virtual bool	deepCopyFrom (const FeatureExtraction *featureExtraction)

virtual bool	computeFeatures (const VectorFloat &inputVector)

virtual bool	reset ()

virtual bool	clear ()

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

virtual bool	loadModelFromFile (std::fstream &file)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	train_ (TimeSeriesClassificationData &trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	train_ (MatrixFloat &trainingData)

UINT	quantize (Float inputValue)

UINT	quantize (const VectorFloat &inputVector)

bool	getQuantizerTrained () const

UINT	getNumClusters () const

UINT	getQuantizedValue () const

VectorFloat	getQuantizationDistances () const

MatrixFloat	getQuantizationModel () const

bool	setNumClusters (const UINT numClusters)

Public Member Functions inherited from FeatureExtraction
	FeatureExtraction ()

virtual	~FeatureExtraction ()

bool	copyBaseVariables (const FeatureExtraction *featureExtractionModule)

virtual bool	computeFeatures (const MatrixFloat &inputMatrix)

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 (RegressionData trainingData)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	train (TimeSeriesClassificationData trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train (UnlabelledData trainingData)

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)

Protected Attributes
UINT	numClusters

MatrixFloat	clusters

VectorFloat	quantizationDistances

Protected Attributes inherited from FeatureExtraction
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

Static Protected Attributes
static RegisterFeatureExtractionModule< KMeansQuantizer >	registerModule

Additional Inherited Members
Public Types inherited from FeatureExtraction
typedef std::map< std::string, FeatureExtraction ()() >	StringFeatureExtractionMap

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

Static Public Member Functions inherited from FeatureExtraction
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 inherited from FeatureExtraction
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 inherited from FeatureExtraction
static StringFeatureExtractionMap *	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 49 of file KMeansQuantizer.h.

Constructor & Destructor Documentation

KMeansQuantizer::KMeansQuantizer ( const UINT numClusters = 10 )

Default constructor. Initalizes the KMeansQuantizer, setting the number of input dimensions and the number of clusters to use in the quantization model.

Parameters

numClusters the number of quantization clusters

Definition at line 28 of file KMeansQuantizer.cpp.

KMeansQuantizer::KMeansQuantizer ( const KMeansQuantizer & rhs )

Copy constructor, copies the KMeansQuantizer from the rhs instance to this instance.

Parameters

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

Definition at line 39 of file KMeansQuantizer.cpp.

KMeansQuantizer::~KMeansQuantizer ( )

virtual

Default Destructor

Definition at line 52 of file KMeansQuantizer.cpp.

Member Function Documentation

bool KMeansQuantizer::clear ( )

virtual

Sets the FeatureExtraction clear function, overwriting the base FeatureExtraction function.

Returns: true if the instance was reset, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 105 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::computeFeatures ( const VectorFloat & inputVector )

virtual

Sets the FeatureExtraction computeFeatures function, overwriting the base FeatureExtraction function. This function is called by the GestureRecognitionPipeline when any new input data needs to be processed (during the prediction phase for example).

Parameters

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

Returns: returns true if the data was processed, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 86 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::deepCopyFrom ( const FeatureExtraction * featureExtraction )

virtual

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

Parameters

featureExtraction a pointer to another instance of this class, the values of that instance will be cloned to this instance

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

Reimplemented from FeatureExtraction.

Definition at line 68 of file KMeansQuantizer.cpp.

UINT KMeansQuantizer::getNumClusters ( ) const

Gets the number of clusters in the quantizer.

Returns: returns the numbers of clusters in the quantizer.

Definition at line 298 of file KMeansQuantizer.cpp.

VectorFloat KMeansQuantizer::getQuantizationDistances ( ) const

inline

Gets the quantization distances from the most recent quantization.

Returns: returns a VectorFloat containing the quantization distances from the most recent quantization

Definition at line 213 of file KMeansQuantizer.h.

MatrixFloat KMeansQuantizer::getQuantizationModel ( ) const

inline

Gets the quantization model. This will be a [K N] matrix containing the quantization clusters, where K is the number of clusters and N is the number of dimensions in the input data.

Returns: returns a MatrixFloat containing the quantization model

Definition at line 223 of file KMeansQuantizer.h.

UINT KMeansQuantizer::getQuantizedValue ( ) const

inline

Gets the most recent quantized value. This can also be accessed by using the first element in the featureVector.

Returns: returns the most recent quantized value

Definition at line 206 of file KMeansQuantizer.h.

bool KMeansQuantizer::getQuantizerTrained ( ) const

inline

Gets if the quantization model has been trained.

Returns: returns true if the quantization model has been trained, false otherwise

Definition at line 192 of file KMeansQuantizer.h.

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

virtual

This loads the feature extraction settings from a file. This overrides the loadSettingsFromFile function in the FeatureExtraction base class.

Parameters

file	a reference to the file to load the settings from

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

Reimplemented from FeatureExtraction.

Definition at line 150 of file KMeansQuantizer.cpp.

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

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

Parameters

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

Returns: a reference to this instance

Definition at line 55 of file KMeansQuantizer.cpp.

UINT KMeansQuantizer::quantize ( Float inputValue )

Quantizes the input value using the quantization model. The quantization model must be trained first before you call this function.

Parameters

inputValue the value you want to quantize

Returns: returns the quantized value

Definition at line 260 of file KMeansQuantizer.cpp.

UINT KMeansQuantizer::quantize ( const VectorFloat & inputVector )

Quantizes the input value using the quantization model. The quantization model must be trained first before you call this function.

Parameters

inputVector the vector you want to quantize

Returns: returns the quantized value

Definition at line 264 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::reset ( )

virtual

Sets the FeatureExtraction reset function, overwriting the base FeatureExtraction function. This function is called by the GestureRecognitionPipeline when the pipelines main reset() function is called.

Returns: true if the instance was reset, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 94 of file KMeansQuantizer.cpp.

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

virtual

This saves the feature extraction settings to a file. This overrides the saveSettingsToFile function in the FeatureExtraction base class. You should add your own custom code to this function to define how your feature extraction module is saved to a file.

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 FeatureExtraction.

Definition at line 116 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::setNumClusters ( const UINT numClusters )

Sets the number of clusters in the quantizer. This will clear any previously trained model.

Returns: returns true if the number of clusters was updated, false otherwise

Definition at line 302 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( ClassificationData & trainingData )

virtual

Trains the quantization model using the training dataset.

Parameters

trainingData the training dataset that will be used to train the quantizer

Returns: returns true if the quantizer was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 211 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( TimeSeriesClassificationData & trainingData )

virtual

Trains the quantization model using the training dataset.

Parameters

trainingData the training dataset that will be used to train the quantizer

Returns: returns true if the quantizer was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 216 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( ClassificationDataStream & trainingData )

virtual

Trains the quantization model using the training dataset.

Parameters

trainingData the training dataset that will be used to train the quantizer

Returns: returns true if the quantizer was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 221 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( UnlabelledData & trainingData )

virtual

Trains the quantization model using the training dataset.

Parameters

trainingData the training dataset that will be used to train the quantizer

Returns: returns true if the quantizer was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 226 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( MatrixFloat & trainingData )

virtual

Trains the quantization model using the training dataset.

Parameters

trainingData the training dataset that will be used to train the quantizer

Returns: returns true if the quantizer was trained successfully, false otherwise

Reimplemented from MLBase.

Definition at line 231 of file KMeansQuantizer.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