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::BAG Class Reference
Inheritance diagram for GRT::BAG:
GRT::Classifier GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::Observer< TestInstanceResult >

Public Member Functions

 BAG (bool useScaling=false)
 
 BAG (const BAG &rhs)
 
virtual ~BAG (void)
 
BAGoperator= (const BAG &rhs)
 
virtual bool deepCopyFrom (const Classifier *classifier)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool predict_ (VectorDouble &inputVector)
 
virtual bool reset ()
 
virtual bool clear ()
 
virtual bool saveModelToFile (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
UINT getEnsembleSize () const
 
VectorDouble getEnsembleWeights () const
 
const vector< Classifier * > getEnsemble () const
 
bool addClassifierToEnsemble (const Classifier &classifier, double weight=1)
 
bool clearEnsemble ()
 
bool setWeights (const VectorDouble &weights)
 
- Public Member Functions inherited from GRT::Classifier
 Classifier (void)
 
virtual ~Classifier (void)
 
bool copyBaseVariables (const Classifier *classifier)
 
string getClassifierType () const
 
bool getSupportsNullRejection () const
 
bool getNullRejectionEnabled () const
 
double getNullRejectionCoeff () const
 
double getMaximumLikelihood () const
 
double getBestDistance () const
 
double getPhase () const
 
virtual UINT getNumClasses () const
 
UINT getClassLabelIndexValue (UINT classLabel) const
 
UINT getPredictedClassLabel () const
 
VectorDouble getClassLikelihoods () const
 
VectorDouble getClassDistances () const
 
VectorDouble getNullRejectionThresholds () const
 
vector< UINT > getClassLabels () const
 
vector< MinMaxgetRanges () const
 
bool enableNullRejection (bool useNullRejection)
 
virtual bool setNullRejectionCoeff (double nullRejectionCoeff)
 
virtual bool setNullRejectionThresholds (VectorDouble newRejectionThresholds)
 
virtual bool recomputeNullRejectionThresholds ()
 
bool getTimeseriesCompatible () const
 
ClassifiercreateNewInstance () const
 
ClassifierdeepCopy () const
 
const ClassifiergetClassifierPointer () const
 
const ClassifiergetBaseClassifier () 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_ (UnlabelledData &trainingData)
 
virtual bool train (MatrixDouble data)
 
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

bool loadLegacyModelFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::Classifier
bool saveBaseSettingsToFile (fstream &file) const
 
bool loadBaseSettingsFromFile (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

VectorDouble weights
 
vector< Classifier * > ensemble
 
- Protected Attributes inherited from GRT::Classifier
string classifierType
 
bool supportsNullRejection
 
bool useNullRejection
 
UINT numClasses
 
UINT predictedClassLabel
 
UINT classifierMode
 
double nullRejectionCoeff
 
double maxLikelihood
 
double bestDistance
 
double phase
 
VectorDouble classLikelihoods
 
VectorDouble classDistances
 
VectorDouble nullRejectionThresholds
 
vector< UINT > classLabels
 
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
 

Static Protected Attributes

static RegisterClassifierModule< BAGregisterModule
 

Additional Inherited Members

- Public Types inherited from GRT::Classifier
typedef std::map< string, Classifier *(*)() > StringClassifierMap
 
- Public Types inherited from GRT::MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from GRT::Classifier
static ClassifiercreateInstanceFromString (string const &classifierType)
 
static vector< string > getRegisteredClassifiers ()
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 
- Protected Types inherited from GRT::Classifier
enum  ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
 
- Static Protected Member Functions inherited from GRT::Classifier
static StringClassifierMapgetMap ()
 

Detailed Description

Definition at line 44 of file BAG.h.

Constructor & Destructor Documentation

GRT::BAG::BAG ( bool  useScaling = false)

Default Constructor

Parameters
booluseScaling: sets if the training and real-time data should be scaled between [0 1]. Default value = false

Definition at line 28 of file BAG.cpp.

GRT::BAG::BAG ( const BAG rhs)

Defines the copy constructor.

Parameters
constBAG &rhs: the instance from which all the data will be copied into this instance

Definition at line 41 of file BAG.cpp.

GRT::BAG::~BAG ( void  )
virtual

Default Destructor

Definition at line 52 of file BAG.cpp.

Member Function Documentation

bool GRT::BAG::addClassifierToEnsemble ( const Classifier classifier,
double  weight = 1 
)

This functions adds a copy of the input classifier to the ensemble. This classifier will then be trained (in addition to the other classifiers in the ensemble) when you call the BAG train function.

Parameters
constClassifier &classifier: a reference to the classifier you want to add to the ensemble
Returns
returns true if a copy of the classifier was successfully added to the ensemble, false otherwise

Definition at line 421 of file BAG.cpp.

bool GRT::BAG::clear ( )
virtual

This overrides the clear function in the Classifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns
returns true if the module was cleared succesfully, false otherwise

Reimplemented from GRT::Classifier.

Definition at line 237 of file BAG.cpp.

bool GRT::BAG::clearEnsemble ( )

This functions clears the current ensemble, removing all classifiers and weights.

Returns
returns true if the ensemble was successfully cleared, false otherwise

Definition at line 441 of file BAG.cpp.

bool GRT::BAG::deepCopyFrom ( const Classifier classifier)
virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setClassifier(...) method is called. It clones the data from the Base Class Classifier pointer (which should be pointing to an BAG instance) into this instance

Parameters
Classifier*classifier: a pointer to the Classifier Base Class, this should be pointing to another BAG instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from GRT::Classifier.

Definition at line 75 of file BAG.cpp.

const vector< Classifier * > GRT::BAG::getEnsemble ( ) const

Gets the ensemble.

Returns
returns a vector of Classifier pointers.

Definition at line 417 of file BAG.cpp.

UINT GRT::BAG::getEnsembleSize ( ) const

Gets the number of classifiers in the ensemble.

Returns
returns the size of the ensemble

Definition at line 409 of file BAG.cpp.

VectorDouble GRT::BAG::getEnsembleWeights ( ) const

Gets the weights for each classifier in the ensemble.

Returns
returns a vector of weights.

Definition at line 413 of file BAG.cpp.

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

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

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

Reimplemented from GRT::MLBase.

Definition at line 308 of file BAG.cpp.

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

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

Parameters
constBAG &rhs: another instance of a BAG
Returns
returns a pointer to this instance of the BAG

Definition at line 57 of file BAG.cpp.

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

This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.

Parameters
VectorDoubleinputVector: the input vector to classify
Returns
returns true if the prediction was performed, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 159 of file BAG.cpp.

bool GRT::BAG::reset ( )
virtual

This resets the BAG classifier.

Returns
returns true if the BAG model was successfully reset, false otherwise.

Reimplemented from GRT::Classifier.

Definition at line 225 of file BAG.cpp.

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

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

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

Reimplemented from GRT::MLBase.

Definition at line 252 of file BAG.cpp.

bool GRT::BAG::setWeights ( const VectorDouble &  weights)

This functions lets you dynamically update the weights for each ensemble. This can be useful if you have some exterior knowledge that could be used to weight the vote for each classifier. The weights should all be positive, however they do not need to sum to one.

You should can call this function both before training the ensemble, and in real-time to update the weights for each prediction.

Returns
returns true if the ensemble weights were successfully updated, false otherwise

Definition at line 456 of file BAG.cpp.

bool GRT::BAG::train_ ( ClassificationData trainingData)
virtual

This trains the BAG model, using the labelled classification data. This overrides the train function in the Classifier base class.

Parameters
ClassificationDatatrainingData: a reference to the training data
Returns
returns true if the BAG model was trained, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 98 of file BAG.cpp.


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