GestureRecognitionToolkit  Version: 1.0 Revision: 04-03-15
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
GRT::KMeans Class Reference
Inheritance diagram for GRT::KMeans:
GRT::Clusterer GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::Observer< TestInstanceResult >

Public Member Functions

 KMeans (const UINT numClusters=10, const UINT minNumEpochs=5, const UINT maxNumEpochs=1000, const double minChange=1.0e-5, const bool computeTheta=true)
 
 KMeans (const KMeans &rhs)
 
virtual ~KMeans ()
 
KMeansoperator= (const KMeans &rhs)
 
virtual bool deepCopyFrom (const Clusterer *clusterer)
 
virtual bool reset ()
 
virtual bool clear ()
 
bool trainModel (MatrixDouble &data)
 
virtual bool train_ (MatrixDouble &data)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool predict_ (VectorDouble &inputVector)
 
virtual bool saveModelToFile (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
double getTheta ()
 
bool getModelTrained ()
 
VectorDouble getTrainingThetaLog () const
 
MatrixDouble getClusters () const
 
vector< UINT > getClassLabelsVector () const
 
vector< UINT > getClassCountVector () const
 
bool setComputeTheta (const bool computeTheta)
 
bool setClusters (const MatrixDouble &clusters)
 
- Public Member Functions inherited from GRT::Clusterer
 Clusterer (void)
 
virtual ~Clusterer (void)
 
bool copyBaseVariables (const Clusterer *Clusterer)
 
bool getConverged () const
 
UINT getNumClusters () const
 
UINT getPredictedClusterLabel () const
 
double getMaximumLikelihood () const
 
double getBestDistance () const
 
VectorDouble getClusterLikelihoods () const
 
VectorDouble getClusterDistances () const
 
vector< UINT > getClusterLabels () const
 
string getClustererType () const
 
bool setNumClusters (const UINT numClusters)
 
ClusterercreateNewInstance () const
 
ClustererdeepCopy () const
 
const ClusterergetBaseClusterer () const
 
- Public Member Functions inherited from GRT::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 (TimeSeriesClassificationDataStream trainingData)
 
virtual bool train_ (TimeSeriesClassificationDataStream &trainingData)
 
virtual bool train (UnlabelledData trainingData)
 
virtual bool train (MatrixDouble data)
 
virtual bool predict (VectorDouble inputVector)
 
virtual bool predict (MatrixDouble inputMatrix)
 
virtual bool predict_ (MatrixDouble &inputMatrix)
 
virtual bool map (VectorDouble inputVector)
 
virtual bool map_ (VectorDouble &inputVector)
 
virtual bool print () const
 
virtual bool save (const string filename) const
 
virtual bool load (const string filename)
 
virtual bool saveModelToFile (string filename) const
 
virtual bool loadModelFromFile (string filename)
 
virtual bool getModel (ostream &stream) const
 
double scale (const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false)
 
virtual string getModelAsString () 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
 
double getMinChange () const
 
double getLearningRate () const
 
double getRootMeanSquaredTrainingError () const
 
double getTotalSquaredTrainingError () 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 (bool useScaling)
 
bool setMaxNumEpochs (const UINT maxNumEpochs)
 
bool setMinNumEpochs (const UINT minNumEpochs)
 
bool setMinChange (const double minChange)
 
bool setLearningRate (double learningRate)
 
bool setUseValidationSet (const bool useValidationSet)
 
bool setValidationSetSize (const UINT validationSetSize)
 
bool setRandomiseTrainingOrder (const bool randomiseTrainingOrder)
 
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< TrainingResultgetTrainingResults () const
 
- Public Member Functions inherited from GRT::GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
string getClassType () const
 
string getLastWarningMessage () const
 
string getLastErrorMessage () const
 
string getLastInfoMessage () const
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 
- Public Member Functions inherited from GRT::Observer< TrainingResult >
virtual void notify (const TrainingResult &data)
 
- Public Member Functions inherited from GRT::Observer< TestInstanceResult >
virtual void notify (const TestInstanceResult &data)
 

Protected Member Functions

UINT estep (const MatrixDouble &data)
 
void mstep (const MatrixDouble &data)
 
double calculateTheta (const MatrixDouble &data)
 
double SQR (const double a)
 
- Protected Member Functions inherited from GRT::Clusterer
bool saveClustererSettingsToFile (fstream &file) const
 
bool loadClustererSettingsFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::MLBase
bool saveBaseSettingsToFile (fstream &file) const
 
bool loadBaseSettingsFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 

Protected Attributes

bool computeTheta
 
UINT numTrainingSamples
 Number of training examples.
 
UINT nchg
 Number of values changes.
 
double finalTheta
 
MatrixDouble clusters
 
vector< UINT > assign
 
vector< UINT > count
 
VectorDouble thetaTracker
 
- Protected Attributes inherited from GRT::Clusterer
string clustererType
 
UINT numClusters
 Number of clusters in the model.
 
UINT predictedClusterLabel
 Stores the predicted cluster label from the most recent predict( )
 
double maxLikelihood
 
double bestDistance
 
VectorDouble clusterLikelihoods
 
VectorDouble clusterDistances
 
vector< UINT > clusterLabels
 
bool converged
 
vector< MinMaxranges
 
- Protected Attributes inherited from GRT::MLBase
bool trained
 
bool useScaling
 
UINT baseType
 
UINT numInputDimensions
 
UINT numOutputDimensions
 
UINT numTrainingIterationsToConverge
 
UINT minNumEpochs
 
UINT maxNumEpochs
 
UINT validationSetSize
 
double learningRate
 
double minChange
 
double rootMeanSquaredTrainingError
 
double totalSquaredTrainingError
 
bool useValidationSet
 
bool randomiseTrainingOrder
 
Random random
 
vector< TrainingResulttrainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 
- Protected Attributes inherited from GRT::GRTBase
string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Additional Inherited Members

- Public Types inherited from GRT::Clusterer
typedef std::map< string, Clusterer *(*)() > StringClustererMap
 
- Public Types inherited from GRT::MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from GRT::Clusterer
static ClusterercreateInstanceFromString (string const &ClustererType)
 
static vector< string > getRegisteredClusterers ()
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 
- Static Protected Member Functions inherited from GRT::Clusterer
static StringClustererMapgetMap ()
 

Detailed Description

Definition at line 41 of file KMeans.h.

Constructor & Destructor Documentation

GRT::KMeans::KMeans ( const UINT  numClusters = 10,
const UINT  minNumEpochs = 5,
const UINT  maxNumEpochs = 1000,
const double  minChange = 1.0e-5,
const bool  computeTheta = true 
)

Default Constructor.

Definition at line 29 of file KMeans.cpp.

GRT::KMeans::KMeans ( const KMeans rhs)

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

Parameters
constKMeans &rhs: another instance of a KMeans

Definition at line 51 of file KMeans.cpp.

GRT::KMeans::~KMeans ( )
virtual

Default Destructor

Definition at line 77 of file KMeans.cpp.

Member Function Documentation

bool GRT::KMeans::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 GRT::Clusterer.

Definition at line 498 of file KMeans.cpp.

bool GRT::KMeans::deepCopyFrom ( const Clusterer clusterer)
virtual

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

Parameters
constClusterer *clusterer: a pointer to the Clusterer base class, this should be pointing to another KMeans instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from GRT::Clusterer.

Definition at line 100 of file KMeans.cpp.

bool GRT::KMeans::loadModelFromFile ( fstream &  file)
virtual

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

Parameters
fstream&file: a reference to the file the KMeans model will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 444 of file KMeans.cpp.

KMeans & GRT::KMeans::operator= ( const KMeans rhs)

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

Parameters
constKMeans &rhs: another instance of a KMeans
Returns
returns a reference to this instance of the KMeans

Definition at line 80 of file KMeans.cpp.

bool GRT::KMeans::predict_ ( VectorDouble &  inputVector)
virtual

This is the main prediction interface for all reference VectorDouble data. It overrides the predict_ function in the ML base class.

Parameters
VectorDouble&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 GRT::MLBase.

Definition at line 199 of file KMeans.cpp.

bool GRT::KMeans::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 GRT::Clusterer.

Definition at line 485 of file KMeans.cpp.

bool GRT::KMeans::saveModelToFile ( fstream &  file) const
virtual

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

Parameters
fstream&file: a reference to the file the KMeans model will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 416 of file KMeans.cpp.

bool GRT::KMeans::setClusters ( const MatrixDouble clusters)

This function lets you set the models clusters. You can use this to initalize the cluster values for the training algorithm. If you do that, then you should call the trainModel to run the training algorithm so the cluster values do not get reset.

Parameters
constMatrixDouble &clusters: the initial cluster values that will be used to train the KMeans model
Returns
returns true if the model was loaded successfully, false otherwise

Definition at line 517 of file KMeans.cpp.

bool GRT::KMeans::train_ ( MatrixDouble data)
virtual

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

Parameters
MatrixDouble&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 GRT::Clusterer.

Definition at line 162 of file KMeans.cpp.

bool GRT::KMeans::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
ClassificationData&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 GRT::Clusterer.

Definition at line 123 of file KMeans.cpp.

bool GRT::KMeans::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
UnlabelledData&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 GRT::Clusterer.

Definition at line 147 of file KMeans.cpp.

bool GRT::KMeans::trainModel ( MatrixDouble data)

This is the main training algorithm for training a KMeans model. You should only call this function if you have manually set the clusters, otherwise you should use any of the train or train_ in functions.

Parameters
MatrixDouble&trainingData: the training data that will be used to train the ML model
Returns
returns true if the model was successfully trained, false otherwise

Definition at line 258 of file KMeans.cpp.


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