GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
SelfOrganizingMap Class Reference
Inheritance diagram for SelfOrganizingMap:
Clusterer MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

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 ()
 
SelfOrganizingMapoperator= (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< GaussNeurongetNeurons () 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)
 
ClusterercreateNewInstance () const
 
ClustererdeepCopy () const
 
const ClusterergetBaseClusterer () 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)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () 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)
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () 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< GaussNeuronneurons
 
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< MinMaxranges
 
- 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 ClusterercreateInstanceFromString (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 StringClustererMapgetMap ()
 

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
rhsanother 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
clusterera 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
constUINT 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
filea 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
xthe 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
rhsanother 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
filea 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
trainingDataa 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
trainingDataa 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
trainingDataa 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
networkTypologythe 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: