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::KMeansQuantizer Class Reference
Inheritance diagram for GRT::KMeansQuantizer:
GRT::FeatureExtraction GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::Observer< TestInstanceResult >

Public Member Functions

 KMeansQuantizer (const UINT numClusters=10)
 
 KMeansQuantizer (const KMeansQuantizer &rhs)
 
virtual ~KMeansQuantizer ()
 
KMeansQuantizeroperator= (const KMeansQuantizer &rhs)
 
virtual bool deepCopyFrom (const FeatureExtraction *featureExtraction)
 
virtual bool computeFeatures (const VectorDouble &inputVector)
 
virtual bool reset ()
 
virtual bool clear ()
 
virtual bool saveModelToFile (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
bool train_ (ClassificationData &trainingData)
 
bool train_ (TimeSeriesClassificationData &trainingData)
 
bool train_ (TimeSeriesClassificationDataStream &trainingData)
 
bool train_ (UnlabelledData &trainingData)
 
bool train_ (MatrixDouble &trainingData)
 
UINT quantize (double inputValue)
 
UINT quantize (const VectorDouble &inputVector)
 
bool getQuantizerTrained () const
 
UINT getNumClusters () const
 
UINT getQuantizedValue () const
 
VectorDouble getQuantizationDistances () const
 
MatrixDouble getQuantizationModel () const
 
bool setNumClusters (const UINT numClusters)
 
- Public Member Functions inherited from GRT::FeatureExtraction
 FeatureExtraction ()
 
virtual ~FeatureExtraction ()
 
bool copyBaseVariables (const FeatureExtraction *featureExtractionModule)
 
string getFeatureExtractionType () const
 
UINT getNumInputDimensions () const
 
UINT getNumOutputDimensions () const
 
bool getInitialized () const
 
bool getFeatureDataReady () const
 
VectorDouble getFeatureVector () const
 
FeatureExtractioncreateNewInstance () 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 (TimeSeriesClassificationDataStream trainingData)
 
virtual bool train (UnlabelledData trainingData)
 
virtual bool train (MatrixDouble data)
 
virtual bool predict (VectorDouble inputVector)
 
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 Attributes

UINT numClusters
 
MatrixDouble clusters
 
VectorDouble quantizationDistances
 
- Protected Attributes inherited from GRT::FeatureExtraction
string featureExtractionType
 
bool initialized
 
bool featureDataReady
 
VectorDouble featureVector
 
- 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 RegisterFeatureExtractionModule< KMeansQuantizerregisterModule
 

Additional Inherited Members

- Public Types inherited from GRT::FeatureExtraction
typedef std::map< string, FeatureExtraction *(*)() > StringFeatureExtractionMap
 
- Public Types inherited from GRT::MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from GRT::FeatureExtraction
static FeatureExtractioncreateInstanceFromString (string const &featureExtractionType)
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 
- Protected Member Functions inherited from GRT::FeatureExtraction
bool init ()
 
bool saveFeatureExtractionSettingsToFile (fstream &file) const
 
bool loadFeatureExtractionSettingsFromFile (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
 
- Static Protected Member Functions inherited from GRT::FeatureExtraction
static StringFeatureExtractionMapgetMap ()
 

Detailed Description

Definition at line 49 of file KMeansQuantizer.h.

Constructor & Destructor Documentation

GRT::KMeansQuantizer::KMeansQuantizer ( const UINT  numClusters = 10)

Default constructor. Initalizes the KMeansQuantizer, setting the number of input dimensions and the number of clusters to use in the quantization model.

Parameters
constUINT numClusters: the number of quantization clusters

Definition at line 28 of file KMeansQuantizer.cpp.

GRT::KMeansQuantizer::KMeansQuantizer ( const KMeansQuantizer rhs)

Copy constructor, copies the KMeansQuantizer from the rhs instance to this instance.

Parameters
constKMeansQuantizer &rhs: another instance of this class from which the data will be copied to this instance

Definition at line 39 of file KMeansQuantizer.cpp.

GRT::KMeansQuantizer::~KMeansQuantizer ( )
virtual

Default Destructor

Definition at line 52 of file KMeansQuantizer.cpp.

Member Function Documentation

bool GRT::KMeansQuantizer::clear ( )
virtual

Sets the FeatureExtraction clear function, overwriting the base FeatureExtraction function.

Returns
true if the instance was reset, false otherwise

Reimplemented from GRT::FeatureExtraction.

Definition at line 105 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::computeFeatures ( const VectorDouble &  inputVector)
virtual

Sets the FeatureExtraction computeFeatures function, overwriting the base FeatureExtraction function. This function is called by the GestureRecognitionPipeline when any new input data needs to be processed (during the prediction phase for example).

Parameters
constVectorDouble &inputVector: the inputVector that should be processed. Must have the same dimensionality as the FeatureExtraction module
Returns
returns true if the data was processed, false otherwise

Reimplemented from GRT::FeatureExtraction.

Definition at line 86 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::deepCopyFrom ( const FeatureExtraction featureExtraction)
virtual

Sets the FeatureExtraction deepCopyFrom function, overwriting the base FeatureExtraction function. This function is used to deep copy the values from the input pointer to this instance of the FeatureExtraction module. This function is called by the GestureRecognitionPipeline when the user adds a new FeatureExtraction module to the pipeleine.

Parameters
constFeatureExtraction *featureExtraction: a pointer to another instance of this class, the values of that instance will be cloned to this instance
Returns
returns true if the deep copy was successful, false otherwise

Reimplemented from GRT::FeatureExtraction.

Definition at line 68 of file KMeansQuantizer.cpp.

UINT GRT::KMeansQuantizer::getNumClusters ( ) const

Gets the number of clusters in the quantizer.

Returns
returns the numbers of clusters in the quantizer.

Definition at line 298 of file KMeansQuantizer.cpp.

VectorDouble GRT::KMeansQuantizer::getQuantizationDistances ( ) const
inline

Gets the quantization distances from the most recent quantization.

Returns
returns a VectorDouble containing the quantization distances from the most recent quantization

Definition at line 213 of file KMeansQuantizer.h.

MatrixDouble GRT::KMeansQuantizer::getQuantizationModel ( ) const
inline

Gets the quantization model. This will be a [K N] matrix containing the quantization clusters, where K is the number of clusters and N is the number of dimensions in the input data.

Returns
returns a MatrixDouble containing the quantization model

Definition at line 223 of file KMeansQuantizer.h.

UINT GRT::KMeansQuantizer::getQuantizedValue ( ) const
inline

Gets the most recent quantized value. This can also be accessed by using the first element in the featureVector.

Returns
returns the most recent quantized value

Definition at line 206 of file KMeansQuantizer.h.

bool GRT::KMeansQuantizer::getQuantizerTrained ( ) const
inline

Gets if the quantization model has been trained.

Returns
returns true if the quantization model has been trained, false otherwise

Definition at line 192 of file KMeansQuantizer.h.

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

This loads the feature extraction settings from a file. This overrides the loadSettingsFromFile function in the FeatureExtraction base class.

Parameters
fstream&file: a reference to the file to load the settings from
Returns
returns true if the settings were loaded successfully, false otherwise

Reimplemented from GRT::FeatureExtraction.

Definition at line 150 of file KMeansQuantizer.cpp.

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

Sets the equals operator, copies the data from the rhs instance to this instance.

Parameters
constKMeansQuantizer &rhs: another instance of this class from which the data will be copied to this instance
Returns
a reference to this instance

Definition at line 55 of file KMeansQuantizer.cpp.

UINT GRT::KMeansQuantizer::quantize ( double  inputValue)

Quantizes the input value using the quantization model. The quantization model must be trained first before you call this function.

Parameters
doubleinputValue: the value you want to quantize
Returns
returns the quantized value

Definition at line 260 of file KMeansQuantizer.cpp.

UINT GRT::KMeansQuantizer::quantize ( const VectorDouble &  inputVector)

Quantizes the input value using the quantization model. The quantization model must be trained first before you call this function.

Parameters
constVectorDouble &inputVector: the vector you want to quantize
Returns
returns the quantized value

Definition at line 264 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::reset ( )
virtual

Sets the FeatureExtraction reset function, overwriting the base FeatureExtraction function. This function is called by the GestureRecognitionPipeline when the pipelines main reset() function is called.

Returns
true if the instance was reset, false otherwise

Reimplemented from GRT::FeatureExtraction.

Definition at line 94 of file KMeansQuantizer.cpp.

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

This saves the feature extraction settings to a file. This overrides the saveSettingsToFile function in the FeatureExtraction base class. You should add your own custom code to this function to define how your feature extraction module is saved to a file.

Parameters
fstream&file: a reference to the file to save the settings to
Returns
returns true if the settings were saved successfully, false otherwise

Reimplemented from GRT::FeatureExtraction.

Definition at line 116 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::setNumClusters ( const UINT  numClusters)

Sets the number of clusters in the quantizer. This will clear any previously trained model.

Returns
returns true if the number of clusters was updated, false otherwise

Definition at line 302 of file KMeansQuantizer.cpp.

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

Trains the quantization model using the training dataset.

Parameters
ClassificationData&trainingData: the training dataset that will be used to train the quantizer
Returns
returns true if the quantizer was trained successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 211 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::train_ ( TimeSeriesClassificationData trainingData)
virtual

Trains the quantization model using the training dataset.

Parameters
TimeSeriesClassificationData&trainingData: the training dataset that will be used to train the quantizer
Returns
returns true if the quantizer was trained successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 216 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::train_ ( TimeSeriesClassificationDataStream trainingData)
virtual

Trains the quantization model using the training dataset.

Parameters
TimeSeriesClassificationDataStream&trainingData: the training dataset that will be used to train the quantizer
Returns
returns true if the quantizer was trained successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 221 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::train_ ( UnlabelledData trainingData)
virtual

Trains the quantization model using the training dataset.

Parameters
UnlabelledData&trainingData: the training dataset that will be used to train the quantizer
Returns
returns true if the quantizer was trained successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 226 of file KMeansQuantizer.cpp.

bool GRT::KMeansQuantizer::train_ ( MatrixDouble trainingData)
virtual

Trains the quantization model using the training dataset.

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

Reimplemented from GRT::MLBase.

Definition at line 231 of file KMeansQuantizer.cpp.


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