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 Member List

This is the complete list of members for GRT::KMeansQuantizer, including all inherited members.

BASE_TYPE_NOT_SET enum value (defined in GRT::MLBase)GRT::MLBase
baseType (defined in GRT::MLBase)GRT::MLBaseprotected
BaseTypes enum name (defined in GRT::MLBase)GRT::MLBase
CLASSIFIER enum value (defined in GRT::MLBase)GRT::MLBase
classType (defined in GRT::GRTBase)GRT::GRTBaseprotected
clear()GRT::KMeansQuantizervirtual
CLUSTERER enum value (defined in GRT::MLBase)GRT::MLBase
clusters (defined in GRT::KMeansQuantizer)GRT::KMeansQuantizerprotected
computeFeatures(const VectorDouble &inputVector)GRT::KMeansQuantizervirtual
copyBaseVariables(const FeatureExtraction *featureExtractionModule)GRT::FeatureExtraction
copyGRTBaseVariables(const GRTBase *GRTBase)GRT::GRTBase
copyMLBaseVariables(const MLBase *mlBase)GRT::MLBase
createInstanceFromString(string const &featureExtractionType) (defined in GRT::FeatureExtraction)GRT::FeatureExtractionstatic
createNewInstance() const GRT::FeatureExtraction
debugLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
deepCopyFrom(const FeatureExtraction *featureExtraction)GRT::KMeansQuantizervirtual
enableScaling(bool useScaling)GRT::MLBase
errorLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
featureDataReady (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprotected
FeatureExtraction()GRT::FeatureExtraction
featureExtractionType (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprotected
featureVector (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprotected
getBaseType() const GRT::MLBase
getClassType() const GRT::GRTBase
getFeatureDataReady() const GRT::FeatureExtraction
getFeatureExtractionType() const GRT::FeatureExtraction
getFeatureVector() const GRT::FeatureExtraction
getGRTBasePointer()GRT::GRTBase
getGRTBasePointer() const GRT::GRTBase
getGRTRevison()GRT::GRTBasestatic
getGRTVersion(bool returnRevision=true)GRT::GRTBasestatic
getInitialized() const GRT::FeatureExtraction
getIsBaseTypeClassifier() const GRT::MLBase
getIsBaseTypeClusterer() const GRT::MLBase
getIsBaseTypeRegressifier() const GRT::MLBase
getLastErrorMessage() const GRT::GRTBase
getLastInfoMessage() const GRT::GRTBase
getLastWarningMessage() const GRT::GRTBase
getLearningRate() const GRT::MLBase
getMap() (defined in GRT::FeatureExtraction)GRT::FeatureExtractioninlineprotectedstatic
getMaxNumEpochs() const GRT::MLBase
getMinChange() const GRT::MLBase
getMinNumEpochs() const GRT::MLBase
getMLBasePointer()GRT::MLBase
getMLBasePointer() const GRT::MLBase
getModel(ostream &stream) const GRT::MLBasevirtual
getModelAsString() const GRT::MLBasevirtual
getModelTrained() const GRT::MLBase
getNumClusters() const GRT::KMeansQuantizer
getNumInputDimensions() const GRT::FeatureExtraction
getNumInputFeatures() const GRT::MLBase
getNumOutputDimensions() const GRT::FeatureExtraction
getNumTrainingIterationsToConverge() const GRT::MLBase
getQuantizationDistances() const GRT::KMeansQuantizerinline
getQuantizationModel() const GRT::KMeansQuantizerinline
getQuantizedValue() const GRT::KMeansQuantizerinline
getQuantizerTrained() const GRT::KMeansQuantizerinline
getRandomiseTrainingOrder() const GRT::MLBase
getRootMeanSquaredTrainingError() const GRT::MLBase
getScalingEnabled() const GRT::MLBase
getTotalSquaredTrainingError() const GRT::MLBase
getTrained() const GRT::MLBase
getTrainingResults() const GRT::MLBase
getUseValidationSet() const GRT::MLBase
getValidationSetSize() const GRT::MLBase
GRTBase(void)GRT::GRTBase
infoLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
init()GRT::FeatureExtractionprotected
initialized (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprotected
KMeansQuantizer(const UINT numClusters=10)GRT::KMeansQuantizer
KMeansQuantizer(const KMeansQuantizer &rhs)GRT::KMeansQuantizer
learningRate (defined in GRT::MLBase)GRT::MLBaseprotected
load(const string filename)GRT::MLBasevirtual
loadBaseSettingsFromFile(fstream &file)GRT::MLBaseprotected
loadFeatureExtractionSettingsFromFile(fstream &file)GRT::FeatureExtractionprotected
loadModelFromFile(fstream &file)GRT::KMeansQuantizervirtual
GRT::MLBase::loadModelFromFile(string filename)GRT::MLBasevirtual
map(VectorDouble inputVector)GRT::MLBasevirtual
map_(VectorDouble &inputVector)GRT::MLBasevirtual
maxNumEpochs (defined in GRT::MLBase)GRT::MLBaseprotected
minChange (defined in GRT::MLBase)GRT::MLBaseprotected
minNumEpochs (defined in GRT::MLBase)GRT::MLBaseprotected
MLBase(void)GRT::MLBase
notify(const TrainingResult &data) (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inlinevirtual
notify(const TestInstanceResult &data) (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inlinevirtual
notifyTestResultsObservers(const TestInstanceResult &data)GRT::MLBase
notifyTrainingResultsObservers(const TrainingResult &data)GRT::MLBase
numClusters (defined in GRT::KMeansQuantizer)GRT::KMeansQuantizerprotected
numInputDimensions (defined in GRT::MLBase)GRT::MLBaseprotected
numOutputDimensions (defined in GRT::MLBase)GRT::MLBaseprotected
numTrainingIterationsToConverge (defined in GRT::MLBase)GRT::MLBaseprotected
Observer() (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inline
Observer() (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inline
operator=(const KMeansQuantizer &rhs)GRT::KMeansQuantizer
predict(VectorDouble inputVector)GRT::MLBasevirtual
predict(MatrixDouble inputMatrix)GRT::MLBasevirtual
predict_(VectorDouble &inputVector)GRT::MLBasevirtual
predict_(MatrixDouble &inputMatrix)GRT::MLBasevirtual
print() const GRT::MLBasevirtual
quantizationDistances (defined in GRT::KMeansQuantizer)GRT::KMeansQuantizerprotected
quantize(double inputValue)GRT::KMeansQuantizer
quantize(const VectorDouble &inputVector)GRT::KMeansQuantizer
random (defined in GRT::MLBase)GRT::MLBaseprotected
randomiseTrainingOrder (defined in GRT::MLBase)GRT::MLBaseprotected
registerModule (defined in GRT::KMeansQuantizer)GRT::KMeansQuantizerprotectedstatic
registerTestResultsObserver(Observer< TestInstanceResult > &observer)GRT::MLBase
registerTrainingResultsObserver(Observer< TrainingResult > &observer)GRT::MLBase
REGRESSIFIER enum value (defined in GRT::MLBase)GRT::MLBase
removeAllTestObservers()GRT::MLBase
removeAllTrainingObservers()GRT::MLBase
removeTestResultsObserver(const Observer< TestInstanceResult > &observer)GRT::MLBase
removeTrainingResultsObserver(const Observer< TrainingResult > &observer)GRT::MLBase
reset()GRT::KMeansQuantizervirtual
rootMeanSquaredTrainingError (defined in GRT::MLBase)GRT::MLBaseprotected
save(const string filename) const GRT::MLBasevirtual
saveBaseSettingsToFile(fstream &file) const GRT::MLBaseprotected
saveFeatureExtractionSettingsToFile(fstream &file) const GRT::FeatureExtractionprotected
saveModelToFile(fstream &file) const GRT::KMeansQuantizervirtual
GRT::MLBase::saveModelToFile(string filename) const GRT::MLBasevirtual
scale(const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false)GRT::MLBaseinline
setLearningRate(double learningRate)GRT::MLBase
setMaxNumEpochs(const UINT maxNumEpochs)GRT::MLBase
setMinChange(const double minChange)GRT::MLBase
setMinNumEpochs(const UINT minNumEpochs)GRT::MLBase
setNumClusters(const UINT numClusters)GRT::KMeansQuantizer
setRandomiseTrainingOrder(const bool randomiseTrainingOrder)GRT::MLBase
setUseValidationSet(const bool useValidationSet)GRT::MLBase
setValidationSetSize(const UINT validationSetSize)GRT::MLBase
SQR(const double &x) const (defined in GRT::GRTBase)GRT::GRTBaseinlineprotected
StringFeatureExtractionMap typedefGRT::FeatureExtraction
testingLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
testResultsObserverManager (defined in GRT::MLBase)GRT::MLBaseprotected
totalSquaredTrainingError (defined in GRT::MLBase)GRT::MLBaseprotected
train(ClassificationData trainingData)GRT::MLBasevirtual
train(RegressionData trainingData)GRT::MLBasevirtual
train(TimeSeriesClassificationData trainingData)GRT::MLBasevirtual
train(TimeSeriesClassificationDataStream trainingData)GRT::MLBasevirtual
train(UnlabelledData trainingData)GRT::MLBasevirtual
train(MatrixDouble data)GRT::MLBasevirtual
train_(ClassificationData &trainingData)GRT::KMeansQuantizervirtual
train_(TimeSeriesClassificationData &trainingData)GRT::KMeansQuantizervirtual
train_(TimeSeriesClassificationDataStream &trainingData)GRT::KMeansQuantizervirtual
train_(UnlabelledData &trainingData)GRT::KMeansQuantizervirtual
train_(MatrixDouble &trainingData)GRT::KMeansQuantizervirtual
GRT::FeatureExtraction::train_(RegressionData &trainingData)GRT::MLBasevirtual
trained (defined in GRT::MLBase)GRT::MLBaseprotected
trainingLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
trainingResults (defined in GRT::MLBase)GRT::MLBaseprotected
trainingResultsObserverManager (defined in GRT::MLBase)GRT::MLBaseprotected
useScaling (defined in GRT::MLBase)GRT::MLBaseprotected
useValidationSet (defined in GRT::MLBase)GRT::MLBaseprotected
validationSetSize (defined in GRT::MLBase)GRT::MLBaseprotected
warningLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
~FeatureExtraction()GRT::FeatureExtractionvirtual
~GRTBase(void)GRT::GRTBasevirtual
~KMeansQuantizer()GRT::KMeansQuantizervirtual
~MLBase(void)GRT::MLBasevirtual
~Observer() (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inlinevirtual
~Observer() (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inlinevirtual