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

Public Member Functions

 KMeansFeatures (const vector< UINT > numClustersPerLayer=vector< UINT >(1, 100), const double alpha=0.2, const bool useScaling=true)
 
 KMeansFeatures (const KMeansFeatures &rhs)
 
virtual ~KMeansFeatures ()
 
KMeansFeaturesoperator= (const KMeansFeatures &rhs)
 
virtual bool deepCopyFrom (const FeatureExtraction *featureExtraction)
 
virtual bool computeFeatures (const VectorDouble &inputVector)
 
virtual bool reset ()
 
virtual bool saveModelToFile (string filename) const
 
virtual bool loadModelFromFile (string filename)
 
virtual bool saveModelToFile (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool train_ (TimeSeriesClassificationData &trainingData)
 
virtual bool train_ (TimeSeriesClassificationDataStream &trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool train_ (MatrixDouble &trainingData)
 
bool computeFeatures (VectorDouble &inputVector, VectorDouble &outputVector)
 
bool init (const vector< UINT > numClustersPerLayer)
 
bool projectDataThroughLayer (const VectorDouble &input, VectorDouble &output, const UINT layer)
 
UINT getNumLayers () const
 
UINT getLayerSize (const UINT layerIndex) const
 
vector< MatrixDoublegetClusters () const
 
- Public Member Functions inherited from GRT::FeatureExtraction
 FeatureExtraction ()
 
virtual ~FeatureExtraction ()
 
bool copyBaseVariables (const FeatureExtraction *featureExtractionModule)
 
virtual bool clear ()
 
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 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

double alpha
 
vector< UINT > numClustersPerLayer
 
vector< MinMaxranges
 
vector< MatrixDoubleclusters
 
- 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< KMeansFeaturesregisterModule
 

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 41 of file KMeansFeatures.h.

Constructor & Destructor Documentation

GRT::KMeansFeatures::KMeansFeatures ( const vector< UINT >  numClustersPerLayer = vector< UINT >(1,100),
const double  alpha = 0.2,
const bool  useScaling = true 
)

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

Parameters
UINTnumDimensions: the number of dimensions in the input data
UINTnumClusters: the number of quantization clusters

Definition at line 28 of file KMeansFeatures.cpp.

GRT::KMeansFeatures::KMeansFeatures ( const KMeansFeatures rhs)

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

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

Definition at line 46 of file KMeansFeatures.cpp.

GRT::KMeansFeatures::~KMeansFeatures ( )
virtual

Default Destructor

Definition at line 59 of file KMeansFeatures.cpp.

Member Function Documentation

bool GRT::KMeansFeatures::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). This is where you should add your main feature extraction code.

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 92 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 74 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::loadModelFromFile ( string  filename)
virtual

This saves the feature extraction settings 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::MLBase.

Definition at line 141 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 206 of file KMeansFeatures.cpp.

KMeansFeatures & GRT::KMeansFeatures::operator= ( const KMeansFeatures 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 63 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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. You should add any custom reset code to this function to define how your feature extraction module should be reset.

Returns
true if the instance was reset, false otherwise

Reimplemented from GRT::FeatureExtraction.

Definition at line 123 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::saveModelToFile ( string  filename) const
virtual

This saves the feature extraction settings to a file.

Parameters
conststring filename: the filename to save the settings to
Returns
returns true if the settings were saved successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 127 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 156 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 330 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 335 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 340 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 345 of file KMeansFeatures.cpp.

bool GRT::KMeansFeatures::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 350 of file KMeansFeatures.cpp.


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