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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::KMeans, including all inherited members.
assign (defined in GRT::KMeans) | GRT::KMeans | 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::Clusterer) | GRT::Clusterer | protected |
calculateTheta(const MatrixDouble &data) (defined in GRT::KMeans) | GRT::KMeans | protected |
CLASSIFIER enum value (defined in GRT::MLBase) | GRT::MLBase | |
classType (defined in GRT::GRTBase) | GRT::GRTBase | protected |
clear() | GRT::KMeans | virtual |
clusterDistances (defined in GRT::Clusterer) | GRT::Clusterer | protected |
Clusterer(void) | GRT::Clusterer | |
CLUSTERER enum value (defined in GRT::MLBase) | GRT::MLBase | |
clustererType (defined in GRT::Clusterer) | GRT::Clusterer | protected |
clusterLabels (defined in GRT::Clusterer) | GRT::Clusterer | protected |
clusterLikelihoods (defined in GRT::Clusterer) | GRT::Clusterer | protected |
clusters (defined in GRT::KMeans) | GRT::KMeans | protected |
computeTheta (defined in GRT::KMeans) | GRT::KMeans | protected |
converged (defined in GRT::Clusterer) | GRT::Clusterer | protected |
copyBaseVariables(const Clusterer *Clusterer) | GRT::Clusterer | |
copyGRTBaseVariables(const GRTBase *GRTBase) | GRT::GRTBase | |
copyMLBaseVariables(const MLBase *mlBase) | GRT::MLBase | |
count (defined in GRT::KMeans) | GRT::KMeans | protected |
createInstanceFromString(string const &ClustererType) | GRT::Clusterer | static |
createNewInstance() const | GRT::Clusterer | |
debugLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
deepCopy() const | GRT::Clusterer | |
deepCopyFrom(const Clusterer *clusterer) | GRT::KMeans | virtual |
enableScaling(bool useScaling) | GRT::MLBase | |
errorLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
estep(const MatrixDouble &data) (defined in GRT::KMeans) | GRT::KMeans | protected |
finalTheta (defined in GRT::KMeans) | GRT::KMeans | protected |
getBaseClusterer() const | GRT::Clusterer | |
getBaseType() const | GRT::MLBase | |
getBestDistance() const | GRT::Clusterer | |
getClassCountVector() const (defined in GRT::KMeans) | GRT::KMeans | inline |
getClassLabelsVector() const (defined in GRT::KMeans) | GRT::KMeans | inline |
getClassType() const | GRT::GRTBase | |
getClusterDistances() const | GRT::Clusterer | |
getClustererType() const | GRT::Clusterer | |
getClusterLabels() const | GRT::Clusterer | |
getClusterLikelihoods() const | GRT::Clusterer | |
getClusters() const (defined in GRT::KMeans) | GRT::KMeans | inline |
getConverged() const | GRT::Clusterer | |
getGRTBasePointer() | GRT::GRTBase | |
getGRTBasePointer() const | GRT::GRTBase | |
getGRTRevison() | GRT::GRTBase | static |
getGRTVersion(bool returnRevision=true) | GRT::GRTBase | static |
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::Clusterer) | GRT::Clusterer | inlineprotectedstatic |
getMaximumLikelihood() const | GRT::Clusterer | |
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() (defined in GRT::KMeans) | GRT::KMeans | inline |
GRT::Clusterer::getModelTrained() const | GRT::MLBase | |
getNumClusters() const | GRT::Clusterer | |
getNumInputDimensions() const | GRT::MLBase | |
getNumInputFeatures() const | GRT::MLBase | |
getNumOutputDimensions() const | GRT::MLBase | |
getNumTrainingIterationsToConverge() const | GRT::MLBase | |
getPredictedClusterLabel() const | GRT::Clusterer | |
getRandomiseTrainingOrder() const | GRT::MLBase | |
getRegisteredClusterers() | GRT::Clusterer | static |
getRootMeanSquaredTrainingError() const | GRT::MLBase | |
getScalingEnabled() const | GRT::MLBase | |
getTheta() (defined in GRT::KMeans) | GRT::KMeans | inline |
getTotalSquaredTrainingError() const | GRT::MLBase | |
getTrained() const | GRT::MLBase | |
getTrainingResults() const | GRT::MLBase | |
getTrainingThetaLog() const (defined in GRT::KMeans) | GRT::KMeans | inline |
getUseValidationSet() const | GRT::MLBase | |
getValidationSetSize() const | GRT::MLBase | |
GRTBase(void) | GRT::GRTBase | |
infoLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
KMeans(const UINT numClusters=10, const UINT minNumEpochs=5, const UINT maxNumEpochs=1000, const double minChange=1.0e-5, const bool computeTheta=true) | GRT::KMeans | |
KMeans(const KMeans &rhs) | GRT::KMeans | |
learningRate (defined in GRT::MLBase) | GRT::MLBase | protected |
load(const string filename) | GRT::MLBase | virtual |
loadBaseSettingsFromFile(fstream &file) | GRT::MLBase | protected |
loadClustererSettingsFromFile(fstream &file) | GRT::Clusterer | protected |
loadModelFromFile(fstream &file) | GRT::KMeans | virtual |
GRT::Clusterer::loadModelFromFile(string filename) | GRT::MLBase | virtual |
map(VectorDouble inputVector) | GRT::MLBase | virtual |
map_(VectorDouble &inputVector) | GRT::MLBase | virtual |
maxLikelihood (defined in GRT::Clusterer) | GRT::Clusterer | 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 | |
mstep(const MatrixDouble &data) (defined in GRT::KMeans) | GRT::KMeans | protected |
nchg | GRT::KMeans | 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 | |
numClusters | GRT::Clusterer | protected |
numInputDimensions (defined in GRT::MLBase) | GRT::MLBase | protected |
numOutputDimensions (defined in GRT::MLBase) | GRT::MLBase | protected |
numTrainingIterationsToConverge (defined in GRT::MLBase) | GRT::MLBase | protected |
numTrainingSamples | GRT::KMeans | protected |
Observer() (defined in GRT::Observer< TrainingResult >) | GRT::Observer< TrainingResult > | inline |
Observer() (defined in GRT::Observer< TestInstanceResult >) | GRT::Observer< TestInstanceResult > | inline |
operator=(const KMeans &rhs) | GRT::KMeans | |
predict(VectorDouble inputVector) | GRT::MLBase | virtual |
predict(MatrixDouble inputMatrix) | GRT::MLBase | virtual |
predict_(VectorDouble &inputVector) | GRT::KMeans | virtual |
GRT::Clusterer::predict_(MatrixDouble &inputMatrix) | GRT::MLBase | virtual |
predictedClusterLabel | GRT::Clusterer | protected |
print() const | GRT::MLBase | virtual |
random (defined in GRT::MLBase) | GRT::MLBase | protected |
randomiseTrainingOrder (defined in GRT::MLBase) | GRT::MLBase | protected |
ranges (defined in GRT::Clusterer) | GRT::Clusterer | protected |
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::KMeans | virtual |
rootMeanSquaredTrainingError (defined in GRT::MLBase) | GRT::MLBase | protected |
save(const string filename) const | GRT::MLBase | virtual |
saveBaseSettingsToFile(fstream &file) const | GRT::MLBase | protected |
saveClustererSettingsToFile(fstream &file) const | GRT::Clusterer | protected |
saveModelToFile(fstream &file) const | GRT::KMeans | virtual |
GRT::Clusterer::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 |
setClusters(const MatrixDouble &clusters) | GRT::KMeans | |
setComputeTheta(const bool computeTheta) (defined in GRT::KMeans) | GRT::KMeans | |
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::Clusterer | |
setRandomiseTrainingOrder(const bool randomiseTrainingOrder) | GRT::MLBase | |
setUseValidationSet(const bool useValidationSet) | GRT::MLBase | |
setValidationSetSize(const UINT validationSetSize) | GRT::MLBase | |
SQR(const double a) (defined in GRT::KMeans) | GRT::KMeans | inlineprotected |
SQR(const double &x) const (defined in GRT::GRTBase) | GRT::GRTBase | inlineprotected |
StringClustererMap typedef | GRT::Clusterer | |
testingLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
testResultsObserverManager (defined in GRT::MLBase) | GRT::MLBase | protected |
thetaTracker (defined in GRT::KMeans) | GRT::KMeans | protected |
totalSquaredTrainingError (defined in GRT::MLBase) | GRT::MLBase | protected |
train(ClassificationData trainingData) | GRT::MLBase | virtual |
train(RegressionData trainingData) | GRT::MLBase | virtual |
train(TimeSeriesClassificationData trainingData) | GRT::MLBase | virtual |
train(TimeSeriesClassificationDataStream trainingData) | GRT::MLBase | virtual |
train(UnlabelledData trainingData) | GRT::MLBase | virtual |
train(MatrixDouble data) | GRT::MLBase | virtual |
train_(MatrixDouble &data) | GRT::KMeans | virtual |
train_(ClassificationData &trainingData) | GRT::KMeans | virtual |
train_(UnlabelledData &trainingData) | GRT::KMeans | virtual |
GRT::MLBase::train_(RegressionData &trainingData) | GRT::MLBase | virtual |
GRT::MLBase::train_(TimeSeriesClassificationData &trainingData) | GRT::MLBase | virtual |
GRT::MLBase::train_(TimeSeriesClassificationDataStream &trainingData) | GRT::MLBase | virtual |
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 |
trainModel(MatrixDouble &data) | GRT::KMeans | |
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 |
~Clusterer(void) | GRT::Clusterer | virtual |
~GRTBase(void) | GRT::GRTBase | virtual |
~KMeans() | GRT::KMeans | 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 |