GestureRecognitionToolkit  Version: 0.2.5
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
Public Member Functions | Static Public Member Functions | Protected Attributes | List of all members
KMeansFeatures Class Reference

#include <KMeansFeatures.h>

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

Public Member Functions

 KMeansFeatures (const Vector< UINT > numClustersPerLayer=Vector< UINT >(1, 100), const Float alpha=0.2, const bool useScaling=true)
 
 KMeansFeatures (const KMeansFeatures &rhs)
 
virtual ~KMeansFeatures ()
 
KMeansFeatures & operator= (const KMeansFeatures &rhs)
 
virtual bool deepCopyFrom (const FeatureExtraction *featureExtraction) override
 
virtual bool computeFeatures (const VectorFloat &inputVector) override
 
virtual bool reset () override
 
virtual bool save (std::fstream &file) const override
 
virtual bool load (std::fstream &file) override
 
virtual bool train_ (ClassificationData &trainingData) override
 
virtual bool train_ (TimeSeriesClassificationData &trainingData) override
 
virtual bool train_ (ClassificationDataStream &trainingData) override
 
virtual bool train_ (UnlabelledData &trainingData) override
 
virtual bool train_ (MatrixFloat &trainingData) override
 
bool init (const Vector< UINT > &numClustersPerLayer)
 
bool projectDataThroughLayer (const VectorFloat &input, VectorFloat &output, const UINT layer)
 
UINT getNumLayers () const
 
UINT getLayerSize (const UINT layerIndex) const
 
Vector< MatrixFloat > getClusters () const
 
- Public Member Functions inherited from FeatureExtraction
 FeatureExtraction (const std::string id="")
 
virtual ~FeatureExtraction ()
 
bool copyBaseVariables (const FeatureExtraction *featureExtractionModule)
 
virtual bool computeFeatures (const MatrixFloat &inputMatrix)
 
virtual bool clear () override
 
bool getInitialized () const
 
bool getFeatureDataReady () const
 
const VectorFloat & getFeatureVector () const
 
const MatrixFloat & getFeatureMatrix () const
 
FeatureExtraction * create () const
 
 GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create() instead.", FeatureExtraction *createNewInstance() const )
 
 GRT_DEPRECATED_MSG ("createInstanceFromString(id) is deprecated, use create(id) instead.", static FeatureExtraction *createInstanceFromString(const std::string &id))
 
 GRT_DEPRECATED_MSG ("getFeatureExtractionType is deprecated, use getId() instead", std::string getFeatureExtractionType() const )
 
- Public Member Functions inherited from MLBase
 MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)
 
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 (RegressionData trainingData, RegressionData validationData)
 
virtual bool train_ (RegressionData &trainingData, RegressionData &validationData)
 
virtual bool train (TimeSeriesClassificationData trainingData)
 
virtual bool train (ClassificationDataStream trainingData)
 
virtual bool train (UnlabelledData trainingData)
 
virtual bool train (MatrixFloat data)
 
virtual bool predict (VectorFloat inputVector)
 
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)
 
 GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const )
 
 GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )
 
 GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename))
 
 GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))
 
virtual bool getModel (std::ostream &stream) const
 
virtual std::string getModelAsString () const
 
DataType getInputType () const
 
DataType getOutputType () const
 
BaseType getType () const
 
UINT getNumInputFeatures () const
 
UINT getNumInputDimensions () const
 
UINT getNumOutputDimensions () const
 
UINT getMinNumEpochs () const
 
UINT getMaxNumEpochs () const
 
UINT getBatchSize () const
 
UINT getNumRestarts () const
 
UINT getValidationSetSize () const
 
UINT getNumTrainingIterationsToConverge () const
 
Float getMinChange () const
 
Float getLearningRate () const
 
Float getRMSTrainingError () const
 
 GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )
 
Float getTotalSquaredTrainingError () const
 
Float getRMSValidationError () const
 
Float getValidationSetAccuracy () const
 
VectorFloat getValidationSetPrecision () const
 
VectorFloat getValidationSetRecall () const
 
bool getUseValidationSet () const
 
bool getRandomiseTrainingOrder () const
 
bool getTrained () const
 
 GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )
 
bool getConverged () const
 
bool getScalingEnabled () const
 
bool getIsBaseTypeClassifier () const
 
bool getIsBaseTypeRegressifier () const
 
bool getIsBaseTypeClusterer () const
 
bool getTrainingLoggingEnabled () const
 
bool getTestingLoggingEnabled () const
 
bool enableScaling (const bool useScaling)
 
bool setMaxNumEpochs (const UINT maxNumEpochs)
 
bool setBatchSize (const UINT batchSize)
 
bool setMinNumEpochs (const UINT minNumEpochs)
 
bool setNumRestarts (const UINT numRestarts)
 
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 setTestingLoggingEnabled (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)
 
MLBase * getMLBasePointer ()
 
const MLBase * getMLBasePointer () const
 
Vector< TrainingResult > getTrainingResults () const
 
- Public Member Functions inherited from GRTBase
 GRTBase (const std::string &id="")
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
 GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )
 
std::string getId () 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)
 
bool setDebugLoggingEnabled (const bool loggingEnabled)
 
GRTBase * getGRTBasePointer ()
 
const GRTBase * getGRTBasePointer () const
 
Float scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
 
Float SQR (const Float &x) 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)
 

Static Public Member Functions

static std::string getId ()
 
- Static Public Member Functions inherited from FeatureExtraction
static FeatureExtraction * create (const std::string &id)
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 

Protected Attributes

Float alpha
 
Vector< UINT > numClustersPerLayer
 
Vector< MinMax > ranges
 
Vector< MatrixFloat > clusters
 
- Protected Attributes inherited from FeatureExtraction
std::string featureExtractionType
 
bool initialized
 
bool featureDataReady
 
VectorFloat featureVector
 
MatrixFloat featureMatrix
 
- Protected Attributes inherited from MLBase
bool trained
 
bool useScaling
 
bool converged
 
DataType inputType
 
DataType outputType
 
BaseType baseType
 
UINT numInputDimensions
 
UINT numOutputDimensions
 
UINT numTrainingIterationsToConverge
 
UINT minNumEpochs
 
UINT maxNumEpochs
 
UINT batchSize
 
UINT validationSetSize
 
UINT numRestarts
 
Float learningRate
 
Float minChange
 
Float rmsTrainingError
 
Float rmsValidationError
 
Float totalSquaredTrainingError
 
Float validationSetAccuracy
 
bool useValidationSet
 
bool randomiseTrainingOrder
 
VectorFloat validationSetPrecision
 
VectorFloat validationSetRecall
 
Random random
 
Vector< TrainingResult > trainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 
TrainingLog trainingLog
 
TestingLog testingLog
 
- Protected Attributes inherited from GRTBase
std::string classId
 Stores the name of the class (e.g., MinDist)
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
WarningLog warningLog
 

Additional Inherited Members

- Public Types inherited from FeatureExtraction
typedef std::map< std::string, FeatureExtraction *(*)() > StringFeatureExtractionMap
 
- Public Types inherited from MLBase
enum  BaseType {
  BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,
  PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT
}
 
- Protected Member Functions inherited from FeatureExtraction
bool init ()
 
bool saveFeatureExtractionSettingsToFile (std::fstream &file) const
 
bool loadFeatureExtractionSettingsFromFile (std::fstream &file)
 
- Protected Member Functions inherited from MLBase
bool saveBaseSettingsToFile (std::fstream &file) const
 
bool loadBaseSettingsFromFile (std::fstream &file)
 
- Static Protected Member Functions inherited from FeatureExtraction
static StringFeatureExtractionMap * getMap ()
 

Detailed Description

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 41 of file KMeansFeatures.h.

Constructor & Destructor Documentation

KMeansFeatures::KMeansFeatures ( const Vector< UINT >  numClustersPerLayer = Vector< UINT >(1,100),
const Float  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
numDimensionsthe number of dimensions in the input data
numClustersthe number of quantization clusters

Definition at line 33 of file KMeansFeatures.cpp.

KMeansFeatures::KMeansFeatures ( const KMeansFeatures &  rhs)

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

Parameters
rhsanother instance of this class from which the data will be copied to this instance

Definition at line 44 of file KMeansFeatures.cpp.

KMeansFeatures::~KMeansFeatures ( )
virtual

Default Destructor

Definition at line 50 of file KMeansFeatures.cpp.

Member Function Documentation

bool KMeansFeatures::computeFeatures ( const VectorFloat &  inputVector)
overridevirtual

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
inputVectorthe 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 FeatureExtraction.

Definition at line 83 of file KMeansFeatures.cpp.

bool KMeansFeatures::deepCopyFrom ( const FeatureExtraction *  featureExtraction)
overridevirtual

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
featureExtractiona 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 FeatureExtraction.

Definition at line 65 of file KMeansFeatures.cpp.

std::string KMeansFeatures::getId ( )
static

Gets a string that represents the KMeansFeatures class.

Returns
returns a string containing the ID of this class

Definition at line 28 of file KMeansFeatures.cpp.

bool KMeansFeatures::load ( std::fstream &  file)
overridevirtual

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

Parameters
filea reference to the file to load the settings from
Returns
returns true if the settings were loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 168 of file KMeansFeatures.cpp.

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

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

Parameters
rhsanother instance of this class from which the data will be copied to this instance
Returns
a reference to this instance

Definition at line 54 of file KMeansFeatures.cpp.

bool KMeansFeatures::reset ( )
overridevirtual

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 MLBase.

Definition at line 114 of file KMeansFeatures.cpp.

bool KMeansFeatures::save ( std::fstream &  file) const
overridevirtual

This saves the feature extraction settings to a file. This overrides the save 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
filea reference to the file to save the settings to
Returns
returns true if the settings were saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 118 of file KMeansFeatures.cpp.

bool KMeansFeatures::train_ ( ClassificationData &  trainingData)
overridevirtual

Trains the quantization model using the training dataset.

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

Reimplemented from MLBase.

Definition at line 292 of file KMeansFeatures.cpp.

bool KMeansFeatures::train_ ( TimeSeriesClassificationData &  trainingData)
overridevirtual

Trains the quantization model using the training dataset.

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

Reimplemented from MLBase.

Definition at line 297 of file KMeansFeatures.cpp.

bool KMeansFeatures::train_ ( ClassificationDataStream &  trainingData)
overridevirtual

Trains the quantization model using the training dataset.

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

Reimplemented from MLBase.

Definition at line 302 of file KMeansFeatures.cpp.

bool KMeansFeatures::train_ ( UnlabelledData &  trainingData)
overridevirtual

Trains the quantization model using the training dataset.

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

Reimplemented from MLBase.

Definition at line 307 of file KMeansFeatures.cpp.

bool KMeansFeatures::train_ ( MatrixFloat &  trainingData)
overridevirtual

Trains the quantization model using the training dataset.

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

Reimplemented from MLBase.

Definition at line 312 of file KMeansFeatures.cpp.

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