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.
Clusterer Class Reference

#include <Clusterer.h>

Inheritance diagram for Clusterer:
MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult > ClusterTree GaussianMixtureModels HierarchicalClustering KMeans RegisterClustererModule< T > RegisterClustererModule< ClusterTree > RegisterClustererModule< GaussianMixtureModels > RegisterClustererModule< HierarchicalClustering > RegisterClustererModule< KMeans > RegisterClustererModule< SelfOrganizingMap > SelfOrganizingMap

Public Types

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

 Clusterer (void)
 
virtual ~Clusterer (void)
 
virtual bool deepCopyFrom (const Clusterer *clusterer)
 
bool copyBaseVariables (const Clusterer *clusterer)
 
virtual bool train_ (MatrixFloat &trainingData)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool reset ()
 
virtual bool clear ()
 
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 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 saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::string filename)
 
virtual bool loadModelFromFile (std::fstream &file)
 
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)
 

Static Public Member Functions

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

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

static StringClustererMapgetMap ()
 

Protected Attributes

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
 

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 190 of file Clusterer.h.

Constructor & Destructor Documentation

Clusterer::Clusterer ( void  )

Default Clusterer Constructor

Definition at line 65 of file Clusterer.cpp.

Clusterer::~Clusterer ( void  )
virtual

Default Clusterer Destructor

Definition at line 79 of file Clusterer.cpp.

Member Function Documentation

bool Clusterer::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 MLBase.

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

Definition at line 141 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
clusterera 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 86 of file Clusterer.cpp.

Clusterer * Clusterer::createInstanceFromString ( std::string const &  ClustererType)
static

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

Parameters
stringconst &ClustererType: the name of the Clusterer
Returns
Clusterer*: a pointer to the new instance of the Clusterer

Definition at line 28 of file Clusterer.cpp.

Clusterer * Clusterer::createNewInstance ( ) 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 36 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 40 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
clusterera 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 58 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 261 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 243 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 251 of file Clusterer.cpp.

std::string Clusterer::getClustererType ( ) const

Returns the classifeir type as a string.

Returns
returns the Clusterer type as a string

Definition at line 259 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 255 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 247 of file Clusterer.cpp.

bool Clusterer::getConverged ( ) const

Returns true if the training algorithm converged during the most recent training process. This function will return false if the model has not been trained.

Returns
returns true if the training algorithm converged succesfully, false otherwise

Definition at line 229 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 239 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 54 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 178 of file Clusterer.cpp.

bool Clusterer::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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.

Definition at line 127 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 156 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
numClustersthe number of clusters, must be greater than zero
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 265 of file Clusterer.cpp.

bool Clusterer::train_ ( MatrixFloat trainingData)
virtual

This is the main interface for training the clusterer model.

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 MLBase.

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

Definition at line 113 of file Clusterer.cpp.

bool Clusterer::train_ ( ClassificationData trainingData)
virtual

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

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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.

Definition at line 117 of file Clusterer.cpp.

bool Clusterer::train_ ( UnlabelledData trainingData)
virtual

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

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 MLBase.

Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.

Definition at line 122 of file Clusterer.cpp.


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