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::RegressionTree Member List

This is the complete list of members for GRT::RegressionTree, 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
BEST_ITERATIVE_SPILT enum value (defined in GRT::Tree)GRT::Tree
BEST_RANDOM_SPLIT enum value (defined in GRT::Tree)GRT::Tree
buildTree(const RegressionData &trainingData, RegressionTreeNode *parent, vector< UINT > features, UINT nodeID) (defined in GRT::RegressionTree)GRT::RegressionTreeprotected
CLASSIFIER enum value (defined in GRT::MLBase)GRT::MLBase
classType (defined in GRT::GRTBase)GRT::GRTBaseprotected
classType (defined in GRT::GRTBase)GRT::GRTBaseprotected
clear()GRT::RegressionTreevirtual
CLUSTERER enum value (defined in GRT::MLBase)GRT::MLBase
computeBestSpilt(const RegressionData &trainingData, const vector< UINT > &features, UINT &featureIndex, double &threshold, double &minError) (defined in GRT::RegressionTree)GRT::RegressionTreeprotected
computeBestSpiltBestIterativeSpilt(const RegressionData &trainingData, const vector< UINT > &features, UINT &featureIndex, double &threshold, double &minError) (defined in GRT::RegressionTree)GRT::RegressionTreeprotected
computeNodeRegressionData(const RegressionData &trainingData, VectorDouble &regressionData) (defined in GRT::RegressionTree)GRT::RegressionTreeprotected
copyBaseVariables(const Regressifier *regressifier)GRT::Regressifier
GRT::copyGRTBaseVariables(const GRTBase *GRTBase)GRT::GRTBase
GRT::Regressifier::copyGRTBaseVariables(const GRTBase *GRTBase)GRT::GRTBase
copyMLBaseVariables(const MLBase *mlBase)GRT::MLBase
createInstanceFromString(string const &regressifierType)GRT::Regressifierstatic
createNewInstance() const GRT::Regressifier
debugLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
debugLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
deepCopy() const GRT::Regressifier
deepCopyFrom(const Regressifier *regressifier)GRT::RegressionTreevirtual
deepCopyTree() const GRT::RegressionTreevirtual
enableScaling(bool useScaling)GRT::MLBase
errorLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
errorLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
getBaseRegressifier() const GRT::Regressifier
getBaseType() const GRT::MLBase
GRT::getClassType() const GRT::GRTBase
GRT::Regressifier::getClassType() const GRT::GRTBase
GRT::getGRTBasePointer()GRT::GRTBase
GRT::getGRTBasePointer() const GRT::GRTBase
GRT::Regressifier::getGRTBasePointer()GRT::GRTBase
GRT::Regressifier::getGRTBasePointer() const GRT::GRTBase
GRT::getGRTRevison()GRT::GRTBasestatic
GRT::Regressifier::getGRTRevison()GRT::GRTBasestatic
GRT::getGRTVersion(bool returnRevision=true)GRT::GRTBasestatic
GRT::Regressifier::getGRTVersion(bool returnRevision=true)GRT::GRTBasestatic
getInputRanges() const GRT::Regressifier
getIsBaseTypeClassifier() const GRT::MLBase
getIsBaseTypeClusterer() const GRT::MLBase
getIsBaseTypeRegressifier() const GRT::MLBase
GRT::getLastErrorMessage() const GRT::GRTBase
GRT::Regressifier::getLastErrorMessage() const GRT::GRTBase
GRT::getLastInfoMessage() const GRT::GRTBase
GRT::Regressifier::getLastInfoMessage() const GRT::GRTBase
GRT::getLastWarningMessage() const GRT::GRTBase
GRT::Regressifier::getLastWarningMessage() const GRT::GRTBase
getLearningRate() const GRT::MLBase
getMap() (defined in GRT::Regressifier)GRT::Regressifierinlineprotectedstatic
getMaxDepth() const GRT::Tree
getMaxNumEpochs() const GRT::MLBase
getMinChange() const GRT::MLBase
getMinNumEpochs() const GRT::MLBase
getMinNumSamplesPerNode() const GRT::Tree
getMinRMSErrorPerNode() const GRT::RegressionTree
getMLBasePointer()GRT::MLBase
getMLBasePointer() const GRT::MLBase
GRT::getModel(ostream &stream) const GRT::Treevirtual
GRT::Regressifier::getModel(ostream &stream) const GRT::MLBasevirtual
getModelAsString() const GRT::MLBasevirtual
getModelTrained() const GRT::MLBase
getNumInputDimensions() const GRT::MLBase
getNumInputFeatures() const GRT::MLBase
getNumOutputDimensions() const GRT::MLBase
getNumSplittingSteps() const GRT::Tree
getNumTrainingIterationsToConverge() const GRT::MLBase
getOutputRanges() const GRT::Regressifier
getPredictedNodeID() const GRT::Tree
getRandomiseTrainingOrder() const GRT::MLBase
getRegisteredRegressifiers()GRT::Regressifierstatic
getRegressifierType() const GRT::Regressifier
getRegressionData() const GRT::Regressifier
getRemoveFeaturesAtEachSpilt() const GRT::Tree
getRootMeanSquaredTrainingError() const GRT::MLBase
getScalingEnabled() const GRT::MLBase
getTotalSquaredTrainingError() const GRT::MLBase
getTrained() const GRT::MLBase
getTrainingMode() const GRT::Tree
getTrainingResults() const GRT::MLBase
getTree() const GRT::RegressionTree
getUseValidationSet() const GRT::MLBase
getValidationSetSize() const GRT::MLBase
GRT::GRTBase(void)GRT::GRTBase
GRT::Regressifier::GRTBase(void)GRT::GRTBase
infoLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
infoLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
inputVectorRanges (defined in GRT::Regressifier)GRT::Regressifierprotected
learningRate (defined in GRT::MLBase)GRT::MLBaseprotected
load(const string filename)GRT::MLBasevirtual
loadBaseSettingsFromFile(fstream &file)GRT::Regressifierprotected
loadModelFromFile(fstream &file)GRT::RegressionTreevirtual
GRT::Regressifier::loadModelFromFile(string filename)GRT::MLBasevirtual
map(VectorDouble inputVector)GRT::MLBasevirtual
map_(VectorDouble &inputVector)GRT::MLBasevirtual
maxDepth (defined in GRT::Tree)GRT::Treeprotected
maxNumEpochs (defined in GRT::MLBase)GRT::MLBaseprotected
minChange (defined in GRT::MLBase)GRT::MLBaseprotected
minNumEpochs (defined in GRT::MLBase)GRT::MLBaseprotected
minNumSamplesPerNode (defined in GRT::Tree)GRT::Treeprotected
minRMSErrorPerNodeGRT::RegressionTreeprotected
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
NUM_TRAINING_MODES enum value (defined in GRT::Tree)GRT::Tree
numInputDimensions (defined in GRT::MLBase)GRT::MLBaseprotected
numOutputDimensions (defined in GRT::MLBase)GRT::MLBaseprotected
numSplittingSteps (defined in GRT::Tree)GRT::Treeprotected
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 RegressionTree &rhs)GRT::RegressionTree
predict(VectorDouble inputVector)GRT::MLBasevirtual
predict(MatrixDouble inputMatrix)GRT::MLBasevirtual
predict_(VectorDouble &inputVector)GRT::RegressionTreevirtual
GRT::Regressifier::predict_(MatrixDouble &inputMatrix)GRT::MLBasevirtual
print() const GRT::RegressionTreevirtual
random (defined in GRT::MLBase)GRT::MLBaseprotected
randomiseTrainingOrder (defined in GRT::MLBase)GRT::MLBaseprotected
registerModule (defined in GRT::RegressionTree)GRT::RegressionTreeprotectedstatic
registerTestResultsObserver(Observer< TestInstanceResult > &observer)GRT::MLBase
registerTrainingResultsObserver(Observer< TrainingResult > &observer)GRT::MLBase
Regressifier(void)GRT::Regressifier
REGRESSIFIER enum value (defined in GRT::MLBase)GRT::MLBase
regressifierType (defined in GRT::Regressifier)GRT::Regressifierprotected
regressionData (defined in GRT::Regressifier)GRT::Regressifierprotected
RegressionTree(const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT, const bool useScaling=false, const double minRMSErrorPerNode=0.01)GRT::RegressionTree
RegressionTree(const RegressionTree &rhs)GRT::RegressionTree
removeAllTestObservers()GRT::MLBase
removeAllTrainingObservers()GRT::MLBase
removeFeaturesAtEachSpilt (defined in GRT::Tree)GRT::Treeprotected
removeTestResultsObserver(const Observer< TestInstanceResult > &observer)GRT::MLBase
removeTrainingResultsObserver(const Observer< TrainingResult > &observer)GRT::MLBase
reset()GRT::Regressifiervirtual
rootMeanSquaredTrainingError (defined in GRT::MLBase)GRT::MLBaseprotected
save(const string filename) const GRT::MLBasevirtual
saveBaseSettingsToFile(fstream &file) const GRT::Regressifierprotected
saveModelToFile(fstream &file) const GRT::RegressionTreevirtual
GRT::Regressifier::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
setMaxDepth(const UINT maxDepth)GRT::Tree
setMaxNumEpochs(const UINT maxNumEpochs)GRT::MLBase
setMinChange(const double minChange)GRT::MLBase
setMinNumEpochs(const UINT minNumEpochs)GRT::MLBase
setMinNumSamplesPerNode(const UINT minNumSamplesPerNode)GRT::Tree
setMinRMSErrorPerNode(const double minRMSErrorPerNode)GRT::RegressionTree
setNumSplittingSteps(const UINT numSplittingSteps)GRT::Tree
setRandomiseTrainingOrder(const bool randomiseTrainingOrder)GRT::MLBase
setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt)GRT::Tree
setTrainingMode(const UINT trainingMode)GRT::Tree
setUseValidationSet(const bool useValidationSet)GRT::MLBase
setValidationSetSize(const UINT validationSetSize)GRT::MLBase
SQR(const double &x) const (defined in GRT::GRTBase)GRT::GRTBaseinlineprotected
SQR(const double &x) const (defined in GRT::GRTBase)GRT::GRTBaseinlineprotected
StringRegressifierMap typedefGRT::Regressifier
targetVectorRanges (defined in GRT::Regressifier)GRT::Regressifierprotected
testingLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
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_(RegressionData &trainingData)GRT::RegressionTreevirtual
GRT::Regressifier::train_(ClassificationData &trainingData)GRT::MLBasevirtual
GRT::Regressifier::train_(TimeSeriesClassificationData &trainingData)GRT::MLBasevirtual
GRT::Regressifier::train_(TimeSeriesClassificationDataStream &trainingData)GRT::MLBasevirtual
GRT::Regressifier::train_(UnlabelledData &trainingData)GRT::MLBasevirtual
GRT::Regressifier::train_(MatrixDouble &data)GRT::MLBasevirtual
trained (defined in GRT::MLBase)GRT::MLBaseprotected
trainingLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
trainingLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
TrainingMode enum name (defined in GRT::Tree)GRT::Tree
trainingMode (defined in GRT::Tree)GRT::Treeprotected
trainingResults (defined in GRT::MLBase)GRT::MLBaseprotected
trainingResultsObserverManager (defined in GRT::MLBase)GRT::MLBaseprotected
tree (defined in GRT::Tree)GRT::Treeprotected
Tree(const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT)GRT::Tree
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
warningLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
~GRTBase(void)GRT::GRTBasevirtual
~MLBase(void)GRT::MLBasevirtual
~Observer() (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inlinevirtual
~Observer() (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inlinevirtual
~Regressifier(void)GRT::Regressifiervirtual
~RegressionTree(void)GRT::RegressionTreevirtual
~Tree(void)GRT::Treevirtual