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.
RegisterFeatureExtractionModule< T > Member List

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

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