#include <GaussianMixtureModels.h>

Inheritance diagram for GaussianMixtureModels:

Public Member Functions
	GaussianMixtureModels (const UINT numClusters=10, const UINT minNumEpochs=5, const UINT maxNumEpochs=1000, const Float minChange=1.0e-5)

	GaussianMixtureModels (const GaussianMixtureModels &rhs)

virtual	~GaussianMixtureModels ()

GaussianMixtureModels &	operator= (const GaussianMixtureModels &rhs)

virtual bool	deepCopyFrom (const Clusterer *clusterer)

virtual bool	reset ()

virtual bool	clear ()

virtual bool	train_ (MatrixFloat &trainingData)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	predict_ (VectorDouble &inputVector)

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

virtual bool	loadModelFromFile (std::fstream &file)

MatrixFloat	getMu () const

Vector< MatrixFloat >	getSigma () const

MatrixFloat	getSigma (const UINT k) const

Public Member Functions inherited from Clusterer
	Clusterer (void)

virtual	~Clusterer (void)

bool	copyBaseVariables (const Clusterer *clusterer)

bool	getConverged () const

UINT	getNumClusters () const

UINT	getPredictedClusterLabel () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

VectorFloat	getClusterLikelihoods () const

VectorFloat	getClusterDistances () const

Vector< UINT >	getClusterLabels () const

std::string	getClustererType () const

bool	setNumClusters (const UINT numClusters)

Clusterer *	createNewInstance () const

Clusterer *	deepCopy () const

const Clusterer &	getBaseClusterer () 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_ (TimeSeriesClassificationData &trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train (UnlabelledData trainingData)

virtual bool	train (MatrixFloat data)

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 Member Functions
bool	estep (const MatrixFloat &data, VectorDouble &u, VectorDouble &v, Float &change)

bool	mstep (const MatrixFloat &data)

bool	computeInvAndDet ()

void	SWAP (UINT &a, UINT &b)

Float	SQR (const Float v)

Float	gauss (const VectorDouble &x, const UINT clusterIndex, const VectorDouble &det, const MatrixFloat &mu, const Vector< MatrixFloat > &invSigma)

Protected Member Functions inherited from Clusterer
bool	saveClustererSettingsToFile (std::fstream &file) const

bool	loadClustererSettingsFromFile (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

Protected Attributes
UINT	numTrainingSamples
	The number of samples in the training data.

Float	loglike
	The current loglikelihood value of the models given the data.

MatrixFloat	mu
	A matrix holding the estimated mean values of each Gaussian.

MatrixFloat	resp
	The responsibility matrix.

VectorDouble	frac
	A vector holding the P(k)'s.

VectorDouble	lndets
	A vector holding the log detminants of SIGMA'k.

VectorDouble	det

Vector< MatrixFloat >	sigma

Vector< MatrixFloat >	invSigma

Protected Attributes inherited from Clusterer
std::string	clustererType

UINT	numClusters
	Number of clusters in the model.

UINT	predictedClusterLabel
	Stores the predicted cluster label from the most recent predict( )

Float	maxLikelihood

Float	bestDistance

VectorFloat	clusterLikelihoods

VectorFloat	clusterDistances

Vector< UINT >	clusterLabels

bool	converged

Vector< MinMax >	ranges

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

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

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

Static Public Member Functions inherited from Clusterer
static Clusterer *	createInstanceFromString (std::string const &ClustererType)

static Vector< std::string >	getRegisteredClusterers ()

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

static std::string	getGRTRevison ()

Static Protected Member Functions inherited from Clusterer
static StringClustererMap *	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 GaussianMixtureModels.h.

Constructor & Destructor Documentation

GaussianMixtureModels::GaussianMixtureModels	(	const UINT	numClusters = `10`,
		const UINT	minNumEpochs = `5`,
		const UINT	maxNumEpochs = `1000`,
		const Float	minChange = `1.0e-5`
	)

Default Constructor.

Definition at line 10 of file GaussianMixtureModels.cpp.

GaussianMixtureModels::GaussianMixtureModels ( const GaussianMixtureModels & rhs )

Defines how the data from the rhs instance should be copied to this instance

Parameters

rhs	another instance of a GaussianMixtureModels

Definition at line 29 of file GaussianMixtureModels.cpp.

GaussianMixtureModels::~GaussianMixtureModels ( )

virtual

Default Destructor

Definition at line 56 of file GaussianMixtureModels.cpp.

Member Function Documentation

bool GaussianMixtureModels::clear ( )

virtual

This function clears the Clusterer module, removing any trained model and setting all the base variables to their default values.

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

Reimplemented from Clusterer.

Definition at line 114 of file GaussianMixtureModels.cpp.

bool GaussianMixtureModels::deepCopyFrom ( const Clusterer * clusterer )

virtual

This deep copies the variables and models from the Clusterer pointer to this GaussianMixtureModels instance. This overrides the base deep copy function for the Clusterer modules.

Parameters

clusterer a pointer to the Clusterer base class, this should be pointing to another GaussianMixtureModels instance

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

Reimplemented from Clusterer.

Definition at line 80 of file GaussianMixtureModels.cpp.

MatrixFloat GaussianMixtureModels::getMu ( ) const

inline

This function returns the mu matrix which is built during the training phase. If the GMM model has not been trained, then this function will return an empty MatrixFloat. If the GMM model has been trained, then each row in the mu matrix represents a cluster and each column represents an input dimension.

Returns: returns the mu matrix if the model has been trained, otherwise an empty MatrixFloat will be returned

Definition at line 141 of file GaussianMixtureModels.h.

Vector< MatrixFloat > GaussianMixtureModels::getSigma ( ) const

inline

This function returns the sigma matrix which is built during the training phase. If the GMM model has not been trained, then this function will return an empty vector< MatrixFloat >. If the GMM model has been trained, then each element in the returned vector represents a cluster. Each element is a MatrixFloat, which will have N rows and N columns, where N is the number of input dimensions to the model.

Returns: returns the sigma matrix if the model has been trained, otherwise an empty vector< MatrixFloat > will be returned

Definition at line 151 of file GaussianMixtureModels.h.

MatrixFloat GaussianMixtureModels::getSigma ( const UINT k ) const

inline

This function returns the sigma matrix for a specific cluster. If the GMM model has not been trained, then this function will return an empty MatrixFloat. If the GMM model has been trained, then the returned MatrixFloat will have N rows and N columns, where N is the number of input dimensions to the model.

Returns: returns the sigma matrix for a specific cluster if the model has been trained, otherwise an empty MatrixFloat will be returned

Definition at line 160 of file GaussianMixtureModels.h.

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

virtual

This loads a trained GaussianMixtureModels model from a file. This overrides the loadModelFromFile function in the base class.

Parameters

fstream &file: a reference to the file the GaussianMixtureModels model will be loaded from

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

Reimplemented from MLBase.

Definition at line 362 of file GaussianMixtureModels.cpp.

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

Defines how the data from the rhs instance should be copied to this instance

Parameters

rhs	another instance of a GaussianMixtureModels

Returns: returns a reference to this instance of the GaussianMixtureModels

Definition at line 59 of file GaussianMixtureModels.cpp.

bool GaussianMixtureModels::predict_ ( VectorDouble & inputVector )

virtual

This is the main prediction interface for all the GRT machine learning algorithms. This should be overwritten by the derived class.

Parameters

inputVector a reference to the input vector for prediction

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

Reimplemented from MLBase.

Definition at line 256 of file GaussianMixtureModels.cpp.

bool GaussianMixtureModels::reset ( )

virtual

This resets the Clusterer. This overrides the reset function in the MLBase base class.

Returns: returns true if the Clusterer was reset, false otherwise

Reimplemented from Clusterer.

Definition at line 104 of file GaussianMixtureModels.cpp.

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

virtual

This saves the trained GaussianMixtureModels model to a file. This overrides the saveModelToFile function in the base class.

Parameters

fstream &file: a reference to the file the GaussianMixtureModels model will be saved to

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

Reimplemented from MLBase.

Definition at line 309 of file GaussianMixtureModels.cpp.

bool GaussianMixtureModels::train_ ( MatrixFloat & trainingData )

virtual

This is the main training interface for referenced MatrixFloat data. It overrides the train_ function in the ML base class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

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

Reimplemented from Clusterer.

Definition at line 131 of file GaussianMixtureModels.cpp.

bool GaussianMixtureModels::train_ ( ClassificationData & trainingData )

virtual

This is the main training interface for reference ClassificationData data. It overrides the train_ function in the ML base class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

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

Reimplemented from Clusterer.

Definition at line 246 of file GaussianMixtureModels.cpp.

bool GaussianMixtureModels::train_ ( UnlabelledData & trainingData )

virtual

This is the main training interface for reference UnlabelledData data. It overrides the train_ function in the ML base class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

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

Reimplemented from Clusterer.

Definition at line 251 of file GaussianMixtureModels.cpp.

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

Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation