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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.
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This is the complete list of members for GRT::HMM, including all inherited members.
autoEstimateSigma (defined in GRT::HMM) | GRT::HMM | protected |
BASE_TYPE_NOT_SET enum value (defined in GRT::MLBase) | GRT::MLBase | |
baseType (defined in GRT::MLBase) | GRT::MLBase | protected |
BaseTypes enum name (defined in GRT::MLBase) | GRT::MLBase | |
bestDistance (defined in GRT::Classifier) | GRT::Classifier | protected |
classDistances (defined in GRT::Classifier) | GRT::Classifier | protected |
Classifier(void) | GRT::Classifier | |
CLASSIFIER enum value (defined in GRT::MLBase) | GRT::MLBase | |
classifierMode (defined in GRT::Classifier) | GRT::Classifier | protected |
ClassifierModes enum name (defined in GRT::Classifier) | GRT::Classifier | protected |
classifierType (defined in GRT::Classifier) | GRT::Classifier | protected |
classLabels (defined in GRT::Classifier) | GRT::Classifier | protected |
classLikelihoods (defined in GRT::Classifier) | GRT::Classifier | protected |
classType (defined in GRT::GRTBase) | GRT::GRTBase | protected |
clear() | GRT::HMM | virtual |
CLUSTERER enum value (defined in GRT::MLBase) | GRT::MLBase | |
committeeSize (defined in GRT::HMM) | GRT::HMM | protected |
continuousModels (defined in GRT::HMM) | GRT::HMM | protected |
convertDataToObservationSequence(TimeSeriesClassificationData &classData, vector< vector< UINT > > &observationSequences) (defined in GRT::HMM) | GRT::HMM | protected |
copyBaseVariables(const Classifier *classifier) | GRT::Classifier | |
copyGRTBaseVariables(const GRTBase *GRTBase) | GRT::GRTBase | |
copyMLBaseVariables(const MLBase *mlBase) | GRT::MLBase | |
createInstanceFromString(string const &classifierType) | GRT::Classifier | static |
createNewInstance() const | GRT::Classifier | |
debugLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
deepCopy() const | GRT::Classifier | |
deepCopyFrom(const Classifier *classifier) | GRT::HMM | virtual |
delta (defined in GRT::HMM) | GRT::HMM | protected |
discreteModels (defined in GRT::HMM) | GRT::HMM | protected |
downsampleFactor (defined in GRT::HMM) | GRT::HMM | protected |
enableNullRejection(bool useNullRejection) | GRT::Classifier | |
enableScaling(bool useScaling) | GRT::MLBase | |
errorLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
getBaseClassifier() const | GRT::Classifier | |
getBaseType() const | GRT::MLBase | |
getBestDistance() const | GRT::Classifier | |
getClassDistances() const | GRT::Classifier | |
getClassifierPointer() const | GRT::Classifier | |
getClassifierType() const | GRT::Classifier | |
getClassLabelIndexValue(UINT classLabel) const | GRT::Classifier | |
getClassLabels() const | GRT::Classifier | |
getClassLikelihoods() const | GRT::Classifier | |
getClassType() const | GRT::GRTBase | |
getContinuousModels() const | GRT::HMM | |
getDelta() const | GRT::HMM | |
getDiscreteModels() const | GRT::HMM | |
getGRTBasePointer() | GRT::GRTBase | |
getGRTBasePointer() const | GRT::GRTBase | |
getGRTRevison() | GRT::GRTBase | static |
getGRTVersion(bool returnRevision=true) | GRT::GRTBase | static |
getHMMType() const | GRT::HMM | |
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::Classifier) | GRT::Classifier | inlineprotectedstatic |
getMaximumLikelihood() const | GRT::Classifier | |
getMaxNumEpochs() const | GRT::MLBase | |
getMinChange() const | GRT::MLBase | |
getMinNumEpochs() const | GRT::MLBase | |
getMLBasePointer() | GRT::MLBase | |
getMLBasePointer() const | GRT::MLBase | |
getModel(ostream &stream) const | GRT::MLBase | virtual |
getModelAsString() const | GRT::MLBase | virtual |
getModelTrained() const | GRT::MLBase | |
getModelType() const | GRT::HMM | |
getNullRejectionCoeff() const | GRT::Classifier | |
getNullRejectionEnabled() const | GRT::Classifier | |
getNullRejectionThresholds() const | GRT::Classifier | |
getNumClasses() const | GRT::Classifier | virtual |
getNumInputDimensions() const | GRT::MLBase | |
getNumInputFeatures() const | GRT::MLBase | |
getNumOutputDimensions() const | GRT::MLBase | |
getNumRandomTrainingIterations() const | GRT::HMM | |
getNumStates() const | GRT::HMM | |
getNumSymbols() const | GRT::HMM | |
getNumTrainingIterationsToConverge() const | GRT::MLBase | |
getPhase() const | GRT::Classifier | |
getPredictedClassLabel() const | GRT::Classifier | |
getRandomiseTrainingOrder() const | GRT::MLBase | |
getRanges() const | GRT::Classifier | |
getRegisteredClassifiers() | GRT::Classifier | static |
getRootMeanSquaredTrainingError() const | GRT::MLBase | |
getScalingEnabled() const | GRT::MLBase | |
getSupportsNullRejection() const | GRT::Classifier | |
getTimeseriesCompatible() const | GRT::Classifier | inline |
getTotalSquaredTrainingError() const | GRT::MLBase | |
getTrained() const | GRT::MLBase | |
getTrainingResults() const | GRT::MLBase | |
getUseValidationSet() const | GRT::MLBase | |
getValidationSetSize() const | GRT::MLBase | |
GRTBase(void) | GRT::GRTBase | |
HMM(const UINT hmmType=HMM_CONTINUOUS, const UINT modelType=HMM_LEFTRIGHT, const UINT delta=1, const bool useScaling=false, const bool useNullRejection=false) | GRT::HMM | |
HMM(const HMM &rhs) | GRT::HMM | |
hmmType | GRT::HMM | protected |
infoLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
learningRate (defined in GRT::MLBase) | GRT::MLBase | protected |
load(const string filename) | GRT::MLBase | virtual |
loadBaseSettingsFromFile(fstream &file) | GRT::Classifier | protected |
loadLegacyModelFromFile(fstream &file) (defined in GRT::HMM) | GRT::HMM | protected |
loadModelFromFile(fstream &file) | GRT::HMM | virtual |
GRT::Classifier::loadModelFromFile(string filename) | GRT::MLBase | virtual |
map(VectorDouble inputVector) | GRT::MLBase | virtual |
map_(VectorDouble &inputVector) | GRT::MLBase | virtual |
maxLikelihood (defined in GRT::Classifier) | GRT::Classifier | protected |
maxNumEpochs (defined in GRT::MLBase) | GRT::MLBase | protected |
minChange (defined in GRT::MLBase) | GRT::MLBase | protected |
minNumEpochs (defined in GRT::MLBase) | GRT::MLBase | protected |
MLBase(void) | GRT::MLBase | |
modelType (defined in GRT::HMM) | GRT::HMM | protected |
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 | |
nullRejectionCoeff (defined in GRT::Classifier) | GRT::Classifier | protected |
nullRejectionThresholds (defined in GRT::Classifier) | GRT::Classifier | protected |
numClasses (defined in GRT::Classifier) | GRT::Classifier | protected |
numInputDimensions (defined in GRT::MLBase) | GRT::MLBase | protected |
numOutputDimensions (defined in GRT::MLBase) | GRT::MLBase | protected |
numRandomTrainingIterations (defined in GRT::HMM) | GRT::HMM | protected |
numStates (defined in GRT::HMM) | GRT::HMM | protected |
numSymbols (defined in GRT::HMM) | GRT::HMM | protected |
numTrainingIterationsToConverge (defined in GRT::MLBase) | GRT::MLBase | protected |
Observer() (defined in GRT::Observer< TrainingResult >) | GRT::Observer< TrainingResult > | inline |
Observer() (defined in GRT::Observer< TestInstanceResult >) | GRT::Observer< TestInstanceResult > | inline |
operator=(const HMM &rhs) | GRT::HMM | |
phase (defined in GRT::Classifier) | GRT::Classifier | protected |
predict(VectorDouble inputVector) | GRT::MLBase | virtual |
predict(MatrixDouble inputMatrix) | GRT::MLBase | virtual |
predict_(VectorDouble &inputVector) | GRT::HMM | virtual |
predict_(MatrixDouble ×eries) | GRT::HMM | virtual |
predict_continuous(VectorDouble &inputVector) (defined in GRT::HMM) | GRT::HMM | protected |
predict_continuous(MatrixDouble ×eries) (defined in GRT::HMM) | GRT::HMM | protected |
predict_discrete(VectorDouble &inputVector) (defined in GRT::HMM) | GRT::HMM | protected |
predict_discrete(MatrixDouble ×eries) (defined in GRT::HMM) | GRT::HMM | protected |
predictedClassLabel (defined in GRT::Classifier) | GRT::Classifier | protected |
print() const | GRT::HMM | virtual |
random (defined in GRT::MLBase) | GRT::MLBase | protected |
randomiseTrainingOrder (defined in GRT::MLBase) | GRT::MLBase | protected |
ranges (defined in GRT::Classifier) | GRT::Classifier | protected |
recomputeNullRejectionThresholds() | GRT::Classifier | inlinevirtual |
registerModule (defined in GRT::HMM) | GRT::HMM | protectedstatic |
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::HMM | virtual |
rootMeanSquaredTrainingError (defined in GRT::MLBase) | GRT::MLBase | protected |
save(const string filename) const | GRT::MLBase | virtual |
saveBaseSettingsToFile(fstream &file) const | GRT::Classifier | protected |
saveModelToFile(fstream &file) const | GRT::HMM | virtual |
GRT::Classifier::saveModelToFile(string filename) const | GRT::MLBase | virtual |
scale(const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false) | GRT::MLBase | inline |
setAutoEstimateSigma(const bool autoEstimateSigma) (defined in GRT::HMM) | GRT::HMM | |
setCommitteeSize(const UINT committeeSize) | GRT::HMM | |
setDelta(const UINT delta) | GRT::HMM | |
setDownsampleFactor(const UINT downsampleFactor) | GRT::HMM | |
setHMMType(const UINT hmmType) | GRT::HMM | |
setLearningRate(double learningRate) | GRT::MLBase | |
setMaxNumEpochs(const UINT maxNumEpochs) | GRT::MLBase | |
setMinChange(const double minChange) | GRT::MLBase | |
setMinNumEpochs(const UINT minNumEpochs) | GRT::MLBase | |
setModelType(const UINT modelType) | GRT::HMM | |
setNullRejectionCoeff(double nullRejectionCoeff) | GRT::Classifier | virtual |
setNullRejectionThresholds(VectorDouble newRejectionThresholds) | GRT::Classifier | virtual |
setNumRandomTrainingIterations(const UINT numRandomTrainingIterations) | GRT::HMM | |
setNumStates(const UINT numStates) | GRT::HMM | |
setNumSymbols(const UINT numStates) | GRT::HMM | |
setRandomiseTrainingOrder(const bool randomiseTrainingOrder) | GRT::MLBase | |
setSigma(const double sigma) | GRT::HMM | |
setUseValidationSet(const bool useValidationSet) | GRT::MLBase | |
setValidationSetSize(const UINT validationSetSize) | GRT::MLBase | |
sigma (defined in GRT::HMM) | GRT::HMM | protected |
SQR(const double &x) const (defined in GRT::GRTBase) | GRT::GRTBase | inlineprotected |
STANDARD_CLASSIFIER_MODE enum value (defined in GRT::Classifier) | GRT::Classifier | protected |
StringClassifierMap typedef | GRT::Classifier | |
supportsNullRejection (defined in GRT::Classifier) | GRT::Classifier | protected |
testingLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
testResultsObserverManager (defined in GRT::MLBase) | GRT::MLBase | protected |
TIMESERIES_CLASSIFIER_MODE enum value (defined in GRT::Classifier) | GRT::Classifier | protected |
totalSquaredTrainingError (defined in GRT::MLBase) | GRT::MLBase | protected |
train(ClassificationData trainingData) | GRT::HMM | virtual |
GRT::Classifier::train(RegressionData trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train(TimeSeriesClassificationData trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train(TimeSeriesClassificationDataStream trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train(UnlabelledData trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train(MatrixDouble data) | GRT::MLBase | virtual |
train_(TimeSeriesClassificationData &trainingData) | GRT::HMM | virtual |
GRT::Classifier::train_(ClassificationData &trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train_(RegressionData &trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train_(TimeSeriesClassificationDataStream &trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train_(UnlabelledData &trainingData) | GRT::MLBase | virtual |
GRT::Classifier::train_(MatrixDouble &data) | GRT::MLBase | virtual |
train_continuous(TimeSeriesClassificationData &trainingData) (defined in GRT::HMM) | GRT::HMM | protected |
train_discrete(TimeSeriesClassificationData &trainingData) (defined in GRT::HMM) | GRT::HMM | protected |
trained (defined in GRT::MLBase) | GRT::MLBase | protected |
trainingLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
trainingResults (defined in GRT::MLBase) | GRT::MLBase | protected |
trainingResultsObserverManager (defined in GRT::MLBase) | GRT::MLBase | protected |
useNullRejection (defined in GRT::Classifier) | GRT::Classifier | protected |
useScaling (defined in GRT::MLBase) | GRT::MLBase | protected |
useValidationSet (defined in GRT::MLBase) | GRT::MLBase | protected |
validationSetSize (defined in GRT::MLBase) | GRT::MLBase | protected |
warningLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
~Classifier(void) | GRT::Classifier | virtual |
~GRTBase(void) | GRT::GRTBase | virtual |
~HMM(void) | GRT::HMM | virtual |
~MLBase(void) | GRT::MLBase | virtual |
~Observer() (defined in GRT::Observer< TrainingResult >) | GRT::Observer< TrainingResult > | inlinevirtual |
~Observer() (defined in GRT::Observer< TestInstanceResult >) | GRT::Observer< TestInstanceResult > | inlinevirtual |