Inheritance diagram for SelfOrganizingMap:

Public Types
enum	NetworkTypology { RANDOM_NETWORK =0 }

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 }

Public Member Functions
	SelfOrganizingMap (const UINT networkSize=20, const UINT networkTypology=RANDOM_NETWORK, const UINT maxNumEpochs=1000, const Float alphaStart=0.8, const Float alphaEnd=0.1)

	SelfOrganizingMap (const SelfOrganizingMap &rhs)

virtual	~SelfOrganizingMap ()

SelfOrganizingMap &	operator= (const SelfOrganizingMap &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	map_ (VectorFloat &x)

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

virtual bool	loadModelFromFile (std::fstream &file)

bool	validateNetworkTypology (const UINT networkTypology)

UINT	getNetworkSize () const

Float	getAlphaStart () const

Float	getAlphaEnd () const

VectorFloat	getMappedData () const

Vector< GaussNeuron >	getNeurons () const

const Vector< GaussNeuron > &	getNeuronsRef () const

MatrixFloat	getNetworkWeights () const

bool	setNetworkSize (const UINT networkSize)

bool	setNetworkTypology (const UINT networkTypology)

bool	setAlphaStart (const Float alphaStart)

bool	setAlphaEnd (const Float alphaEnd)

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_ (VectorFloat &inputVector)

virtual bool	predict (MatrixFloat inputMatrix)

virtual bool	predict_ (MatrixFloat &inputMatrix)

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	networkTypology

Float	alphaStart

Float	alphaEnd

VectorFloat	mappedData

Vector< GaussNeuron >	neurons

MatrixFloat	networkWeights

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
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 ()

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

Static Protected Member Functions inherited from Clusterer
static StringClustererMap *	getMap ()

Detailed Description

Definition at line 197 of file SelfOrganizingMap.h.

Constructor & Destructor Documentation

SelfOrganizingMap::SelfOrganizingMap	(	const UINT	networkSize = `20`,
		const UINT	networkTypology = `RANDOM_NETWORK`,
		const UINT	maxNumEpochs = `1000`,
		const Float	alphaStart = `0.8`,
		const Float	alphaEnd = `0.1`
	)

Default Constructor.

Definition at line 28 of file SelfOrganizingMap.cpp.

SelfOrganizingMap::SelfOrganizingMap ( const SelfOrganizingMap & rhs )

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

Parameters

rhs	another instance of a SelfOrganizingMap

Returns: returns a reference to this instance of the SelfOrganizingMap

Definition at line 44 of file SelfOrganizingMap.cpp.

SelfOrganizingMap::~SelfOrganizingMap ( )

virtual

Default Destructor.

Definition at line 64 of file SelfOrganizingMap.cpp.

Member Function Documentation

bool SelfOrganizingMap::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 110 of file SelfOrganizingMap.cpp.

bool SelfOrganizingMap::deepCopyFrom ( const Clusterer * clusterer )

virtual

This deep copies the variables and models from the Clusterer pointer to this SelfOrganizingMap 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 SelfOrganizingMap instance

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

Reimplemented from Clusterer.

Definition at line 83 of file SelfOrganizingMap.cpp.

UINT SelfOrganizingMap::getNetworkSize ( ) const

This function returns the size of the SOM network. This is the same as the number of clusters in the network.

Parameters

const UINT networkTypology: the network typology you want to test

Returns: returns true if the network typology is valid, false otherwise

Definition at line 440 of file SelfOrganizingMap.cpp.

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

virtual

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

Parameters

file	a reference to the file the SOM model will be loaded from

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

Reimplemented from MLBase.

Definition at line 361 of file SelfOrganizingMap.cpp.

bool SelfOrganizingMap::map_ ( VectorFloat & x )

virtual

This function maps the input Vector x by reference through the self organizing map. The function will return true if the mapping was successful. The mapped data can then be accessed via the getMappedData function. You need to train the SOM model before you can use this function. Because the data is mapped by reference, the x input data might be modified by the map (if it has to scale the input data for example).

Parameters

x	the input Vector for mapping

Returns: returns true if the mapping was completed succesfully, false otherwise

Reimplemented from MLBase.

Definition at line 298 of file SelfOrganizingMap.cpp.

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

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

Parameters

rhs	another instance of a SelfOrganizingMap

Returns: returns a reference to this instance of the SelfOrganizingMap

Definition at line 68 of file SelfOrganizingMap.cpp.

bool SelfOrganizingMap::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 102 of file SelfOrganizingMap.cpp.

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

virtual

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

Parameters

file	a reference to the file the SOM model will be saved to

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

Reimplemented from MLBase.

Definition at line 320 of file SelfOrganizingMap.cpp.

bool SelfOrganizingMap::train_ ( MatrixFloat & trainingData )

virtual

This is the main training interface for referenced MatrixFloat data. It overrides the train_ function in the ML base class. This function runs the main training algorithm and is called by all the other train and train_ functions.

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 122 of file SelfOrganizingMap.cpp.

bool SelfOrganizingMap::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 288 of file SelfOrganizingMap.cpp.

bool SelfOrganizingMap::train_ ( UnlabelledData & trainingData )

virtual

This is the main training interface for reference UnlabelledData data. It overrides the trainInplace 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 293 of file SelfOrganizingMap.cpp.

bool SelfOrganizingMap::validateNetworkTypology ( const UINT networkTypology )

This function validates the network typology to ensure it is one of the NetworkTypology enums.

Parameters

networkTypology the network typology you want to test

Returns: returns true if the network typology is valid, false otherwise

Definition at line 432 of file SelfOrganizingMap.cpp.

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

Public Types

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation