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.
GMM Class Reference
Inheritance diagram for GMM:
Classifier MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Member Functions

 GMM (UINT numMixtureModels=2, bool useScaling=false, bool useNullRejection=false, Float nullRejectionCoeff=1.0, UINT maxIter=100, Float minChange=1.0e-5)
 
 GMM (const GMM &rhs)
 
virtual ~GMM (void)
 
GMMoperator= (const GMM &rhs)
 
virtual bool deepCopyFrom (const Classifier *classifier)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool predict_ (VectorFloat &inputVector)
 
virtual bool clear ()
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
virtual bool recomputeNullRejectionThresholds ()
 
UINT getNumMixtureModels ()
 
Vector< MixtureModelgetModels ()
 
bool setNumMixtureModels (UINT K)
 
bool setMinChange (Float minChange)
 
bool setMaxIter (UINT maxIter)
 
- Public Member Functions inherited from Classifier
 Classifier (void)
 
virtual ~Classifier (void)
 
bool copyBaseVariables (const Classifier *classifier)
 
virtual bool reset ()
 
std::string getClassifierType () const
 
bool getSupportsNullRejection () const
 
bool getNullRejectionEnabled () const
 
Float getNullRejectionCoeff () const
 
Float getMaximumLikelihood () const
 
Float getBestDistance () const
 
Float getPhase () const
 
virtual UINT getNumClasses () const
 
UINT getClassLabelIndexValue (UINT classLabel) const
 
UINT getPredictedClassLabel () const
 
VectorFloat getClassLikelihoods () const
 
VectorFloat getClassDistances () const
 
VectorFloat getNullRejectionThresholds () const
 
Vector< UINT > getClassLabels () const
 
Vector< MinMaxgetRanges () const
 
bool enableNullRejection (bool useNullRejection)
 
virtual bool setNullRejectionCoeff (Float nullRejectionCoeff)
 
virtual bool setNullRejectionThresholds (VectorFloat newRejectionThresholds)
 
bool getTimeseriesCompatible () const
 
ClassifiercreateNewInstance () const
 
ClassifierdeepCopy () const
 
const ClassifiergetClassifierPointer () const
 
const ClassifiergetBaseClassifier () 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_ (UnlabelledData &trainingData)
 
virtual bool train (MatrixFloat data)
 
virtual bool train_ (MatrixFloat &data)
 
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 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 Member Functions

Float computeMixtureLikelihood (const VectorFloat &x, UINT k)
 
bool loadLegacyModelFromFile (std::fstream &file)
 
- Protected Member Functions inherited from Classifier
bool saveBaseSettingsToFile (std::fstream &file) const
 
bool loadBaseSettingsFromFile (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
 

Protected Attributes

UINT numMixtureModels
 
UINT maxIter
 
Float minChange
 
Vector< MixtureModelmodels
 
DebugLog debugLog
 
ErrorLog errorLog
 
WarningLog warningLog
 
- Protected Attributes inherited from Classifier
std::string classifierType
 
bool supportsNullRejection
 
bool useNullRejection
 
UINT numClasses
 
UINT predictedClassLabel
 
UINT classifierMode
 
Float nullRejectionCoeff
 
Float maxLikelihood
 
Float bestDistance
 
Float phase
 
VectorFloat classLikelihoods
 
VectorFloat classDistances
 
VectorFloat nullRejectionThresholds
 
Vector< UINT > classLabels
 
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
 

Static Protected Attributes

static RegisterClassifierModule< GMMregisterModule
 

Additional Inherited Members

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

Detailed Description

Definition at line 49 of file GMM.h.

Constructor & Destructor Documentation

GMM::GMM ( UINT  numMixtureModels = 2,
bool  useScaling = false,
bool  useNullRejection = false,
Float  nullRejectionCoeff = 1.0,
UINT  maxIter = 100,
Float  minChange = 1.0e-5 
)

Default Constructor. Sets the number of mixture models to use for each model.

Definition at line 28 of file GMM.cpp.

GMM::GMM ( const GMM rhs)

Defines the copy constructor.

Parameters
rhsthe instance from which all the data will be copied into this instance

Definition at line 44 of file GMM.cpp.

GMM::~GMM ( void  )
virtual

Default destructor.

Definition at line 54 of file GMM.cpp.

Member Function Documentation

bool GMM::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 Classifier.

Definition at line 534 of file GMM.cpp.

bool GMM::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 GRT::Classifier pointer (which should be pointing to an GMM instance) into this instance

Parameters
classifiera pointer to the GRT::Classifier Base Class, this should be pointing to another GMM instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from Classifier.

Definition at line 74 of file GMM.cpp.

Vector< MixtureModel > GMM::getModels ( )

This function returns a copy of the MixtureModels estimated during the training phase. Each element in the vector represents a MixtureModel for one class.

Returns
returns a vector of GRT::MixtureModel, an empty vector will be returned if the GRT::GMM has not been trained

Definition at line 561 of file GMM.cpp.

UINT GMM::getNumMixtureModels ( )

This function returns the number of mixture models.

Returns
returns the number of mixture models in the GMM

Definition at line 557 of file GMM.cpp.

bool GMM::loadLegacyModelFromFile ( std::fstream &  file)
protected

Read the ranges if needed

Definition at line 588 of file GMM.cpp.

bool GMM::loadModelFromFile ( std::fstream &  file)
virtual

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

Parameters
filea reference to the file the GMM model will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 349 of file GMM.cpp.

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

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

Parameters
&rhsanother instance of a GMM
Returns
returns a pointer to this instance of the GMM

Definition at line 56 of file GMM.cpp.

bool GMM::predict_ ( VectorFloat inputVector)
virtual

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

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

Reimplemented from MLBase.

Definition at line 97 of file GMM.cpp.

bool GMM::recomputeNullRejectionThresholds ( )
virtual

This function recomputes the null rejection thresholds for each model. This overrides the recomputeNullRejectionThresholds function in the GRT::Classifier base class.

Returns
returns true if the nullRejectionThresholds were updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 545 of file GMM.cpp.

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

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

Parameters
filea reference to the file the GMM model will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 281 of file GMM.cpp.

bool GMM::setMaxIter ( UINT  maxIter)

This function sets the maxIter parameter which controls when the maximum number of iterations parameter that controls when the GMM train function should stop. MaxIter must be greater than zero.

Parameters
maxIterthe new maxIter value
Returns
returns true if the number of maxIter was successfully updated, false otherwise

Definition at line 580 of file GMM.cpp.

bool GMM::setMinChange ( Float  minChange)

This function sets the minChange parameter which controls when the GMM train function should stop. MinChange must be greater than zero.

Parameters
minChangethe new minChange value
Returns
returns true if the number of minChange was successfully updated, false otherwise

Definition at line 573 of file GMM.cpp.

bool GMM::setNumMixtureModels ( UINT  K)

This function sets the number of mixture models used for class. You should call this function before you train the GMM model. The number of mixture models must be greater than 0.

Parameters
Kthe number of mixture models
Returns
returns true if the number of mixture models was successfully updated, false otherwise

Definition at line 566 of file GMM.cpp.

bool GMM::train_ ( ClassificationData trainingData)
virtual

This trains the GMM model, using the labelled classification data. This overrides the train function in the GRT::Classifier base class. The GMM is an unsupervised learning algorithm, it will therefore NOT use any class labels provided

Parameters
trainingDataa reference to the training data
Returns
returns true if the GMM model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 161 of file GMM.cpp.


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