Clusterer Class Reference

#include <Clusterer.h>

Inheritance diagram for Clusterer:

Public Types
typedef std::map< std::string, Clusterer *(*)() >	StringClustererMap
Public Types inherited from MLBase
enum	BaseType {   BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,   PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }

Public Member Functions
	Clusterer (const std::string &id="")
virtual	~Clusterer (void)
virtual bool	deepCopyFrom (const Clusterer *clusterer)
bool	copyBaseVariables (const Clusterer *clusterer)
virtual bool	train_ (MatrixFloat &trainingData) override
virtual bool	train_ (ClassificationData &trainingData) override
virtual bool	train_ (UnlabelledData &trainingData) override
virtual bool	reset () override
virtual bool	clear () override
UINT	getNumClusters () const
UINT	getPredictedClusterLabel () const
Float	getMaximumLikelihood () const
Float	getBestDistance () const
VectorFloat	getClusterLikelihoods () const
VectorFloat	getClusterDistances () const
Vector< UINT >	getClusterLabels () const
	GRT_DEPRECATED_MSG ("getClustererType() is deprecated, use getId() or getBaseId() instead", std::string getClustererType() const )
bool	setNumClusters (const UINT numClusters)
Clusterer *	create () const
	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create instead.", Clusterer *createNewInstance() const )
	GRT_DEPRECATED_MSG ("createInstanceFromString is deprecated, use create instead.", static Clusterer *createInstanceFromString(const std::string &id))
Clusterer *	deepCopy () const
const Clusterer &	getBaseClusterer () const
Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)
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 (RegressionData trainingData, RegressionData validationData)
virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData)
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_ (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	save (std::fstream &file) const
virtual bool	load (std::fstream &file)
	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const )
	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename))
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))
virtual bool	getModel (std::ostream &stream) const
virtual std::string	getModelAsString () const
DataType	getInputType () const
DataType	getOutputType () const
BaseType	getType () const
UINT	getNumInputFeatures () const
UINT	getNumInputDimensions () const
UINT	getNumOutputDimensions () const
UINT	getMinNumEpochs () const
UINT	getMaxNumEpochs () const
UINT	getBatchSize () const
UINT	getNumRestarts () const
UINT	getValidationSetSize () const
UINT	getNumTrainingIterationsToConverge () const
Float	getMinChange () const
Float	getLearningRate () const
Float	getRMSTrainingError () const
	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )
Float	getTotalSquaredTrainingError () const
Float	getRMSValidationError () const
Float	getValidationSetAccuracy () const
VectorFloat	getValidationSetPrecision () const
VectorFloat	getValidationSetRecall () const
bool	getUseValidationSet () const
bool	getRandomiseTrainingOrder () const
bool	getTrained () const
	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )
bool	getConverged () const
bool	getScalingEnabled () const
bool	getIsBaseTypeClassifier () const
bool	getIsBaseTypeRegressifier () const
bool	getIsBaseTypeClusterer () const
bool	getTrainingLoggingEnabled () const
bool	getTestingLoggingEnabled () const
bool	enableScaling (const bool useScaling)
bool	setMaxNumEpochs (const UINT maxNumEpochs)
bool	setBatchSize (const UINT batchSize)
bool	setMinNumEpochs (const UINT minNumEpochs)
bool	setNumRestarts (const UINT numRestarts)
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	setTestingLoggingEnabled (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 (const std::string &id="")
virtual	~GRTBase (void)
bool	copyGRTBaseVariables (const GRTBase *GRTBase)
	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )
std::string	getId () 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)
bool	setDebugLoggingEnabled (const bool loggingEnabled)
GRTBase *	getGRTBasePointer ()
const GRTBase *	getGRTBasePointer () const
Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
Float	SQR (const Float &x) 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 Clusterer *	create (std::string const &id)
static Vector< std::string >	getRegisteredClusterers ()
Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)
static std::string	getGRTRevison ()

Protected Member Functions
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)

Static Protected Member Functions
static StringClustererMap *	getMap ()

Protected Attributes
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
bool	converged
DataType	inputType
DataType	outputType
BaseType	baseType
UINT	numInputDimensions
UINT	numOutputDimensions
UINT	numTrainingIterationsToConverge
UINT	minNumEpochs
UINT	maxNumEpochs
UINT	batchSize
UINT	validationSetSize
UINT	numRestarts
Float	learningRate
Float	minChange
Float	rmsTrainingError
Float	rmsValidationError
Float	totalSquaredTrainingError
Float	validationSetAccuracy
bool	useValidationSet
bool	randomiseTrainingOrder
VectorFloat	validationSetPrecision
VectorFloat	validationSetRecall
Random	random
Vector< TrainingResult >	trainingResults
TrainingResultsObserverManager	trainingResultsObserverManager
TestResultsObserverManager	testResultsObserverManager
TrainingLog	trainingLog
TestingLog	testingLog
Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)
DebugLog	debugLog
ErrorLog	errorLog
InfoLog	infoLog
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 Clusterer.h.

Member Typedef Documentation

typedef std::map< std::string, Clusterer*(*)() > Clusterer::StringClustererMap

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

Definition at line 178 of file Clusterer.h.

Constructor & Destructor Documentation

Clusterer::Clusterer

(

const std::string &

id = ""

)

Default Clusterer Constructor

Parameters

id	the id for the Clusterer (e.g., KMeans)

Definition at line 70 of file Clusterer.cpp.

Clusterer::~Clusterer ( void  )

virtual

Default Clusterer Destructor

Definition at line 83 of file Clusterer.cpp.

Member Function Documentation

bool Clusterer::clear ( )

overridevirtual

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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, ClusterTree, KMeans, and GaussianMixtureModels.

Definition at line 144 of file Clusterer.cpp.

bool Clusterer::copyBaseVariables

(

const Clusterer *

clusterer

)

This copies the Clusterer base class variables from the Clusterer pointer to this instance.

Parameters

clusterer

a pointer to a Clusterer from which the values will be copied to this instance

Returns

returns true if the copy was successfull, false otherwise

Definition at line 90 of file Clusterer.cpp.

Clusterer * Clusterer::create ( std::string const &  id )

static

Creates a new Clusterer instance based on the input string (which should contain the name of a valid Clusterer such as KMeans).

Parameters

id	the id of the Clusterer

Returns

a pointer to the new instance of the Clusterer

Definition at line 32 of file Clusterer.cpp.

Clusterer * Clusterer::create

(

)

const

Creates a new Clusterer instance based on the current clustererType string value.

Returns

Clusterer*: a pointer to the new instance of the Clusterer

Definition at line 41 of file Clusterer.cpp.

Clusterer * Clusterer::deepCopy

(

)

const

This creates a new Clusterer instance and deep copies the variables and models from this instance into the deep copy. The function will then return a pointer to the new instance. It is up to the user who calls this function to delete the dynamic instance when they are finished using it.

Returns

returns a pointer to a new Clusterer instance which is a deep copy of this instance

Definition at line 45 of file Clusterer.cpp.

virtual bool Clusterer::deepCopyFrom ( const Clusterer *  clusterer )

inlinevirtual

This is the base deep copy function for the Clusterer modules. This function should be overwritten by the derived class. This deep copies the variables and models from the Clusterer pointer to this Clusterer instance.

Parameters

clusterer

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

Returns

returns true if the clone was successfull, false otherwise (the Clusterer base class will always return flase)

Reimplemented in SelfOrganizingMap, HierarchicalClustering, ClusterTree, KMeans, and GaussianMixtureModels.

Definition at line 59 of file Clusterer.h.

const Clusterer & Clusterer::getBaseClusterer

(

)

const

Returns a pointer to this Clusterer. This is useful for a derived class so it can get easy access to this base Clusterer.

Returns

Clusterer&: a reference to this Clusterer

Definition at line 258 of file Clusterer.cpp.

Float Clusterer::getBestDistance

(

)

const

Returns the current bestDistance value. The bestDistance value is computed during the prediction phase and is either the minimum or maximum distance, depending on the algorithm. This value will return 0 if a prediction has not been made.

Returns

returns the current bestDistance value

Definition at line 242 of file Clusterer.cpp.

VectorFloat Clusterer::getClusterDistances

(

)

const

Gets a Vector of the cluster distances from the last prediction, this will be an N-dimensional Vector, where N is the number of clusters in the model. The exact form of these distances depends on the cluster algorithm.

Returns

returns a Vector of the cluster distances from the last prediction, an empty Vector will be returned if the model has not been trained

Definition at line 250 of file Clusterer.cpp.

Vector< UINT > Clusterer::getClusterLabels

(

)

const

Gets a Vector of unsigned ints containing the label of each cluster, this will be an K-dimensional Vector, where K is the number of clusters in the model.

Returns

returns a Vector of unsigned ints containing the label of each cluster, an empty Vector will be returned if the model has not been trained

Definition at line 254 of file Clusterer.cpp.

VectorFloat Clusterer::getClusterLikelihoods

(

)

const

Gets a Vector of the cluster likelihoods from the last prediction, this will be an N-dimensional Vector, where N is the number of clusters in the model. The exact form of these likelihoods depends on the cluster algorithm.

Returns

returns a Vector of the cluster likelihoods from the last prediction, an empty Vector will be returned if the model has not been trained

Definition at line 246 of file Clusterer.cpp.

Float Clusterer::getMaximumLikelihood

(

)

const

Returns the current maximumLikelihood value. The maximumLikelihood value is computed during the prediction phase and is the likelihood of the most likely model. This value will return 0 if a prediction has not been made.

Returns

returns the current maximumLikelihood value

Definition at line 238 of file Clusterer.cpp.

UINT Clusterer::getNumClusters

(

)

const

Returns the number of clusters in the model.

Returns

returns the number of clusters

Definition at line 234 of file Clusterer.cpp.

UINT Clusterer::getPredictedClusterLabel

(

)

const

Returns the predicted cluster label.

Returns

returns the predicted cluster label

Definition at line 236 of file Clusterer.cpp.

Vector< std::string > Clusterer::getRegisteredClusterers ( )

static

Returns a Vector of the names of all Clusterers that have been registered with the base Clusterer.

Returns

Vector< std::string >: a Vector containing the names of the Clusterers that have been registered with the base Clusterer

Definition at line 59 of file Clusterer.cpp.

bool Clusterer::loadClustererSettingsFromFile ( std::fstream &  file )

protected

Loads the core clusterer settings from a file.

Returns

returns true if the base settings were loaded, false otherwise

Definition at line 181 of file Clusterer.cpp.

bool Clusterer::reset ( )

overridevirtual

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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.

Definition at line 130 of file Clusterer.cpp.

bool Clusterer::saveClustererSettingsToFile ( std::fstream &  file ) const

protected

Saves the core clusterer settings to a file.

Returns

returns true if the base settings were saved, false otherwise

Definition at line 159 of file Clusterer.cpp.

bool Clusterer::setNumClusters

(

const UINT

numClusters

)

Sets the number of clusters that will be used the next time a model is trained. This will clear any previous model. The number of clusters must be greater than zero.

Parameters

numClusters

the number of clusters, must be greater than zero

Returns

returns true if the value was updated successfully, false otherwise

Examples:

ClusteringModulesExamples/GaussianMixtureModelsExample/GaussianMixtureModelsExample.cpp.

Definition at line 262 of file Clusterer.cpp.

bool Clusterer::train_ ( MatrixFloat &  trainingData )

overridevirtual

This is the main interface for training the clusterer model.

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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, GaussianMixtureModels, and ClusterTree.

Definition at line 116 of file Clusterer.cpp.

bool Clusterer::train_ ( ClassificationData &  trainingData )

overridevirtual

Override the main ClassificationData train function to pass MatrixFloat data to the Clusterer train function.

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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.

Definition at line 120 of file Clusterer.cpp.

bool Clusterer::train_ ( UnlabelledData &  trainingData )

overridevirtual

Override the main UnlabelledData train function to pass MatrixFloat data to the Clusterer train function.

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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.

Definition at line 125 of file Clusterer.cpp.

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

• Clusterer.h
• Clusterer.cpp