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::RegisterFeatureExtractionModule< T > Member List

This is the complete list of members for GRT::RegisterFeatureExtractionModule< T >, including all inherited members.

BASE_TYPE_NOT_SET enum value (defined in GRT::MLBase)GRT::MLBaseprivate
baseType (defined in GRT::MLBase)GRT::MLBaseprivate
BaseTypes enum name (defined in GRT::MLBase)GRT::MLBaseprivate
CLASSIFIER enum value (defined in GRT::MLBase)GRT::MLBaseprivate
classType (defined in GRT::GRTBase)GRT::GRTBaseprivate
clear()GRT::FeatureExtractionprivatevirtual
CLUSTERER enum value (defined in GRT::MLBase)GRT::MLBaseprivate
computeFeatures(const VectorDouble &inputVector)GRT::FeatureExtractioninlineprivatevirtual
copyBaseVariables(const FeatureExtraction *featureExtractionModule)GRT::FeatureExtractionprivate
copyGRTBaseVariables(const GRTBase *GRTBase)GRT::GRTBaseprivate
copyMLBaseVariables(const MLBase *mlBase)GRT::MLBaseprivate
createInstanceFromString(string const &featureExtractionType) (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprivatestatic
createNewInstance() const GRT::FeatureExtractionprivate
debugLog (defined in GRT::GRTBase)GRT::GRTBaseprivate
deepCopyFrom(const FeatureExtraction *rhs)GRT::FeatureExtractioninlineprivatevirtual
enableScaling(bool useScaling)GRT::MLBaseprivate
errorLog (defined in GRT::GRTBase)GRT::GRTBaseprivate
featureDataReady (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprivate
FeatureExtraction()GRT::FeatureExtractionprivate
featureExtractionType (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprivate
featureVector (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprivate
getBaseType() const GRT::MLBaseprivate
getClassType() const GRT::GRTBaseprivate
getFeatureDataReady() const GRT::FeatureExtractionprivate
getFeatureExtractionType() const GRT::FeatureExtractionprivate
getFeatureVector() const GRT::FeatureExtractionprivate
getGRTBasePointer()GRT::GRTBaseprivate
getGRTBasePointer() const GRT::GRTBaseprivate
getGRTRevison()GRT::GRTBaseprivatestatic
getGRTVersion(bool returnRevision=true)GRT::GRTBaseprivatestatic
getInitialized() const GRT::FeatureExtractionprivate
getIsBaseTypeClassifier() const GRT::MLBaseprivate
getIsBaseTypeClusterer() const GRT::MLBaseprivate
getIsBaseTypeRegressifier() const GRT::MLBaseprivate
getLastErrorMessage() const GRT::GRTBaseprivate
getLastInfoMessage() const GRT::GRTBaseprivate
getLastWarningMessage() const GRT::GRTBaseprivate
getLearningRate() const GRT::MLBaseprivate
getMap() (defined in GRT::FeatureExtraction)GRT::FeatureExtractioninlineprivatestatic
getMaxNumEpochs() const GRT::MLBaseprivate
getMinChange() const GRT::MLBaseprivate
getMinNumEpochs() const GRT::MLBaseprivate
getMLBasePointer()GRT::MLBaseprivate
getMLBasePointer() const GRT::MLBaseprivate
getModel(ostream &stream) const GRT::MLBaseprivatevirtual
getModelAsString() const GRT::MLBaseprivatevirtual
getModelTrained() const GRT::MLBaseprivate
getNumInputDimensions() const GRT::FeatureExtractionprivate
getNumInputFeatures() const GRT::MLBaseprivate
getNumOutputDimensions() const GRT::FeatureExtractionprivate
getNumTrainingIterationsToConverge() const GRT::MLBaseprivate
getRandomiseTrainingOrder() const GRT::MLBaseprivate
getRootMeanSquaredTrainingError() const GRT::MLBaseprivate
getScalingEnabled() const GRT::MLBaseprivate
getTotalSquaredTrainingError() const GRT::MLBaseprivate
getTrained() const GRT::MLBaseprivate
getTrainingResults() const GRT::MLBaseprivate
getUseValidationSet() const GRT::MLBaseprivate
getValidationSetSize() const GRT::MLBaseprivate
GRTBase(void)GRT::GRTBaseprivate
infoLog (defined in GRT::GRTBase)GRT::GRTBaseprivate
init()GRT::FeatureExtractionprivate
initialized (defined in GRT::FeatureExtraction)GRT::FeatureExtractionprivate
learningRate (defined in GRT::MLBase)GRT::MLBaseprivate
load(const string filename)GRT::MLBaseprivatevirtual
loadBaseSettingsFromFile(fstream &file)GRT::MLBaseprivate
loadFeatureExtractionSettingsFromFile(fstream &file)GRT::FeatureExtractionprivate
loadModelFromFile(fstream &file)GRT::FeatureExtractioninlineprivatevirtual
GRT::MLBase::loadModelFromFile(string filename)GRT::MLBaseprivatevirtual
map(VectorDouble inputVector)GRT::MLBaseprivatevirtual
map_(VectorDouble &inputVector)GRT::MLBaseprivatevirtual
maxNumEpochs (defined in GRT::MLBase)GRT::MLBaseprivate
minChange (defined in GRT::MLBase)GRT::MLBaseprivate
minNumEpochs (defined in GRT::MLBase)GRT::MLBaseprivate
MLBase(void)GRT::MLBaseprivate
notify(const TrainingResult &data) (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inlineprivatevirtual
notify(const TestInstanceResult &data) (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inlineprivatevirtual
notifyTestResultsObservers(const TestInstanceResult &data)GRT::MLBaseprivate
notifyTrainingResultsObservers(const TrainingResult &data)GRT::MLBaseprivate
numInputDimensions (defined in GRT::MLBase)GRT::MLBaseprivate
numOutputDimensions (defined in GRT::MLBase)GRT::MLBaseprivate
numTrainingIterationsToConverge (defined in GRT::MLBase)GRT::MLBaseprivate
Observer() (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inlineprivate
Observer() (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inlineprivate
predict(VectorDouble inputVector)GRT::MLBaseprivatevirtual
predict(MatrixDouble inputMatrix)GRT::MLBaseprivatevirtual
predict_(VectorDouble &inputVector)GRT::MLBaseprivatevirtual
predict_(MatrixDouble &inputMatrix)GRT::MLBaseprivatevirtual
print() const GRT::MLBaseprivatevirtual
random (defined in GRT::MLBase)GRT::MLBaseprivate
randomiseTrainingOrder (defined in GRT::MLBase)GRT::MLBaseprivate
RegisterFeatureExtractionModule(string const &newFeatureExtractionModuleName) (defined in GRT::RegisterFeatureExtractionModule< T >)GRT::RegisterFeatureExtractionModule< T >inline
registerTestResultsObserver(Observer< TestInstanceResult > &observer)GRT::MLBaseprivate
registerTrainingResultsObserver(Observer< TrainingResult > &observer)GRT::MLBaseprivate
REGRESSIFIER enum value (defined in GRT::MLBase)GRT::MLBaseprivate
removeAllTestObservers()GRT::MLBaseprivate
removeAllTrainingObservers()GRT::MLBaseprivate
removeTestResultsObserver(const Observer< TestInstanceResult > &observer)GRT::MLBaseprivate
removeTrainingResultsObserver(const Observer< TrainingResult > &observer)GRT::MLBaseprivate
reset()GRT::FeatureExtractioninlineprivatevirtual
rootMeanSquaredTrainingError (defined in GRT::MLBase)GRT::MLBaseprivate
save(const string filename) const GRT::MLBaseprivatevirtual
saveBaseSettingsToFile(fstream &file) const GRT::MLBaseprivate
saveFeatureExtractionSettingsToFile(fstream &file) const GRT::FeatureExtractionprivate
saveModelToFile(fstream &file) const GRT::FeatureExtractioninlineprivatevirtual
GRT::MLBase::saveModelToFile(string filename) const GRT::MLBaseprivatevirtual
scale(const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false)GRT::MLBaseinlineprivate
setLearningRate(double learningRate)GRT::MLBaseprivate
setMaxNumEpochs(const UINT maxNumEpochs)GRT::MLBaseprivate
setMinChange(const double minChange)GRT::MLBaseprivate
setMinNumEpochs(const UINT minNumEpochs)GRT::MLBaseprivate
setRandomiseTrainingOrder(const bool randomiseTrainingOrder)GRT::MLBaseprivate
setUseValidationSet(const bool useValidationSet)GRT::MLBaseprivate
setValidationSetSize(const UINT validationSetSize)GRT::MLBaseprivate
SQR(const double &x) const (defined in GRT::GRTBase)GRT::GRTBaseinlineprivate
StringFeatureExtractionMap typedefGRT::FeatureExtractionprivate
testingLog (defined in GRT::GRTBase)GRT::GRTBaseprivate
testResultsObserverManager (defined in GRT::MLBase)GRT::MLBaseprivate
totalSquaredTrainingError (defined in GRT::MLBase)GRT::MLBaseprivate
train(ClassificationData trainingData)GRT::MLBaseprivatevirtual
train(RegressionData trainingData)GRT::MLBaseprivatevirtual
train(TimeSeriesClassificationData trainingData)GRT::MLBaseprivatevirtual
train(TimeSeriesClassificationDataStream trainingData)GRT::MLBaseprivatevirtual
train(UnlabelledData trainingData)GRT::MLBaseprivatevirtual
train(MatrixDouble data)GRT::MLBaseprivatevirtual
train_(ClassificationData &trainingData)GRT::MLBaseprivatevirtual
train_(RegressionData &trainingData)GRT::MLBaseprivatevirtual
train_(TimeSeriesClassificationData &trainingData)GRT::MLBaseprivatevirtual
train_(TimeSeriesClassificationDataStream &trainingData)GRT::MLBaseprivatevirtual
train_(UnlabelledData &trainingData)GRT::MLBaseprivatevirtual
train_(MatrixDouble &data)GRT::MLBaseprivatevirtual
trained (defined in GRT::MLBase)GRT::MLBaseprivate
trainingLog (defined in GRT::GRTBase)GRT::GRTBaseprivate
trainingResults (defined in GRT::MLBase)GRT::MLBaseprivate
trainingResultsObserverManager (defined in GRT::MLBase)GRT::MLBaseprivate
useScaling (defined in GRT::MLBase)GRT::MLBaseprivate
useValidationSet (defined in GRT::MLBase)GRT::MLBaseprivate
validationSetSize (defined in GRT::MLBase)GRT::MLBaseprivate
warningLog (defined in GRT::GRTBase)GRT::GRTBaseprivate
~FeatureExtraction()GRT::FeatureExtractionprivatevirtual
~GRTBase(void)GRT::GRTBaseprivatevirtual
~MLBase(void)GRT::MLBaseprivatevirtual
~Observer() (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inlineprivatevirtual
~Observer() (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inlineprivatevirtual