GestureRecognitionToolkit  Version: 0.2.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
Public Member Functions | Protected Attributes | Static Protected Attributes | List of all members
KMeansQuantizer Class Reference

#include <KMeansQuantizer.h>

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

Public Member Functions

 KMeansQuantizer (const UINT numClusters=10)
 
 KMeansQuantizer (const KMeansQuantizer &rhs)
 
virtual ~KMeansQuantizer ()
 
KMeansQuantizer & operator= (const KMeansQuantizer &rhs)
 
virtual bool deepCopyFrom (const FeatureExtraction *featureExtraction)
 
virtual bool computeFeatures (const VectorFloat &inputVector)
 
virtual bool reset ()
 
virtual bool clear ()
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool train_ (TimeSeriesClassificationData &trainingData)
 
virtual bool train_ (ClassificationDataStream &trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool train_ (MatrixFloat &trainingData)
 
UINT quantize (Float inputValue)
 
UINT quantize (const VectorFloat &inputVector)
 
bool getQuantizerTrained () const
 
UINT getNumClusters () const
 
UINT getQuantizedValue () const
 
VectorFloat getQuantizationDistances () const
 
MatrixFloat getQuantizationModel () const
 
bool setNumClusters (const UINT numClusters)
 
- Public Member Functions inherited from FeatureExtraction
 FeatureExtraction ()
 
virtual ~FeatureExtraction ()
 
bool copyBaseVariables (const FeatureExtraction *featureExtractionModule)
 
virtual bool computeFeatures (const MatrixFloat &inputMatrix)
 
std::string getFeatureExtractionType () const
 
UINT getNumInputDimensions () const
 
UINT getNumOutputDimensions () const
 
bool getInitialized () const
 
bool getFeatureDataReady () const
 
const VectorFloat & getFeatureVector () const
 
const MatrixFloat & getFeatureMatrix () const
 
FeatureExtraction * createNewInstance () 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 (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)
 
virtual bool save (std::fstream &file) const
 
virtual bool load (std::fstream &file)
 
 GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(std::string filename) instead", virtual bool saveModelToFile(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(std::string filename) instead", virtual bool loadModelFromFile(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
 
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)
 
MLBase * getMLBasePointer ()
 
const MLBase * getMLBasePointer () 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)
 
GRTBase * getGRTBasePointer ()
 
const GRTBase * getGRTBasePointer () 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 Attributes

UINT numClusters
 
MatrixFloat clusters
 
VectorFloat quantizationDistances
 
- 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
 
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 RegisterFeatureExtractionModule< KMeansQuantizer > registerModule
 

Additional Inherited Members

- Public Types inherited from FeatureExtraction
typedef std::map< std::string, FeatureExtraction *(*)() > StringFeatureExtractionMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from FeatureExtraction
static FeatureExtraction * createInstanceFromString (const std::string &featureExtractionType)
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 
- 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)
 
- Protected Member Functions inherited from GRTBase
Float SQR (const Float &x) const
 
- 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 49 of file KMeansQuantizer.h.

Constructor & Destructor Documentation

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
numClustersthe number of quantization clusters

Definition at line 29 of file KMeansQuantizer.cpp.

KMeansQuantizer::KMeansQuantizer ( const KMeansQuantizer &  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 40 of file KMeansQuantizer.cpp.

KMeansQuantizer::~KMeansQuantizer ( )
virtual

Default Destructor

Definition at line 53 of file KMeansQuantizer.cpp.

Member Function Documentation

bool KMeansQuantizer::clear ( )
virtual

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

Returns
true if the instance was reset, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 106 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::computeFeatures ( const VectorFloat &  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
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 87 of file KMeansQuantizer.cpp.

bool 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
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 69 of file KMeansQuantizer.cpp.

UINT KMeansQuantizer::getNumClusters ( ) const

Gets the number of clusters in the quantizer.

Returns
returns the numbers of clusters in the quantizer.

Definition at line 299 of file KMeansQuantizer.cpp.

VectorFloat KMeansQuantizer::getQuantizationDistances ( ) const
inline

Gets the quantization distances from the most recent quantization.

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

Definition at line 213 of file KMeansQuantizer.h.

MatrixFloat 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 MatrixFloat containing the quantization model

Definition at line 223 of file KMeansQuantizer.h.

UINT 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 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 KMeansQuantizer::loadModelFromFile ( std::fstream &  file)
virtual

This loads the feature extraction settings from a file. This overrides the loadSettingsFromFile 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 FeatureExtraction.

Definition at line 151 of file KMeansQuantizer.cpp.

KMeansQuantizer & KMeansQuantizer::operator= ( const KMeansQuantizer &  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 56 of file KMeansQuantizer.cpp.

UINT KMeansQuantizer::quantize ( Float  inputValue)

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

Parameters
inputValuethe value you want to quantize
Returns
returns the quantized value

Definition at line 261 of file KMeansQuantizer.cpp.

UINT KMeansQuantizer::quantize ( const VectorFloat &  inputVector)

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

Parameters
inputVectorthe vector you want to quantize
Returns
returns the quantized value

Definition at line 265 of file KMeansQuantizer.cpp.

bool 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 FeatureExtraction.

Definition at line 95 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::saveModelToFile ( std::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
filea reference to the file to save the settings to
Returns
returns true if the settings were saved successfully, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 117 of file KMeansQuantizer.cpp.

bool 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 303 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( ClassificationData &  trainingData)
virtual

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 212 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( TimeSeriesClassificationData &  trainingData)
virtual

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 217 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( ClassificationDataStream &  trainingData)
virtual

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 222 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( UnlabelledData &  trainingData)
virtual

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 227 of file KMeansQuantizer.cpp.

bool KMeansQuantizer::train_ ( MatrixFloat &  trainingData)
virtual

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 232 of file KMeansQuantizer.cpp.

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