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::MLP Member List

This is the complete list of members for GRT::MLP, including all inherited members.

activationFunctionFromString(const string activationName) const GRT::MLP
activationFunctionToString(const UINT activationFunction) const GRT::MLP
back_prop(const VectorDouble &trainingExample, const VectorDouble &targetVector, const double alpha, const double beta)GRT::MLPprotected
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
checkForNAN() const GRT::MLP
classificationModeActive (defined in GRT::MLP)GRT::MLPprotected
CLASSIFIER enum value (defined in GRT::MLBase)GRT::MLBase
classLikelihoods (defined in GRT::MLP)GRT::MLPprotected
classType (defined in GRT::GRTBase)GRT::GRTBaseprotected
clear()GRT::MLPvirtual
CLUSTERER enum value (defined in GRT::MLBase)GRT::MLBase
copyBaseVariables(const Regressifier *regressifier)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
deepCopy() const GRT::Regressifier
deepCopyFrom(const Regressifier *regressifier)GRT::MLPvirtual
deltaH (defined in GRT::MLP)GRT::MLPprotected
deltaO (defined in GRT::MLP)GRT::MLPprotected
enableScaling(bool useScaling)GRT::MLBase
errorLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
feedforward(VectorDouble trainingExample)GRT::MLPprotected
feedforward(const VectorDouble &trainingExample, VectorDouble &inputNeuronsOuput, VectorDouble &hiddenNeuronsOutput, VectorDouble &outputNeuronsOutput)GRT::MLPprotected
gamma (defined in GRT::MLP)GRT::MLPprotected
getBaseRegressifier() const GRT::Regressifier
getBaseType() const GRT::MLBase
getClassDistances() const GRT::MLP
getClassificationModeActive() const GRT::MLP
getClassLikelihoods() const GRT::MLP
getClassType() const GRT::GRTBase
getGamma() const GRT::MLP
getGRTBasePointer()GRT::GRTBase
getGRTBasePointer() const GRT::GRTBase
getGRTRevison()GRT::GRTBasestatic
getGRTVersion(bool returnRevision=true)GRT::GRTBasestatic
getHiddenLayer() const GRT::MLP
getHiddenLayerActivationFunction() const GRT::MLP
getInputLayer() const GRT::MLP
getInputLayerActivationFunction() const GRT::MLP
getInputRanges() const GRT::Regressifier
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::Regressifier)GRT::Regressifierinlineprotectedstatic
getMaximumLikelihood() const GRT::MLP
getMaxNumEpochs() const GRT::MLBase
getMinChange() const GRT::MLBase
getMinNumEpochs() const GRT::MLBase
getMLBasePointer()GRT::MLBase
getMLBasePointer() const GRT::MLBase
getModel(ostream &stream) const GRT::MLBasevirtual
getModelAsString() const GRT::MLBasevirtual
getModelTrained() const GRT::MLBase
getMomentum() const GRT::MLP
getNullRejectionCoeff() const GRT::MLP
getNullRejectionEnabled() const GRT::MLP
getNullRejectionThreshold() const GRT::MLP
getNumClasses() const GRT::MLP
getNumHiddenNeurons() const GRT::MLP
getNumInputDimensions() const GRT::MLBase
getNumInputFeatures() const GRT::MLBase
getNumInputNeurons() const GRT::MLP
getNumOutputDimensions() const GRT::MLBase
getNumOutputNeurons() const GRT::MLP
getNumRandomTrainingIterations() const GRT::MLP
getNumTrainingIterationsToConverge() const GRT::MLBase
getOutputLayer() const GRT::MLP
getOutputLayerActivationFunction() const GRT::MLP
getOutputRanges() const GRT::Regressifier
getPredictedClassLabel() const GRT::MLP
getRandomiseTrainingOrder() const GRT::MLBase
getRegisteredRegressifiers()GRT::Regressifierstatic
getRegressifierType() const GRT::Regressifier
getRegressionData() const GRT::Regressifier
getRegressionModeActive() const GRT::MLP
getRootMeanSquaredTrainingError() const GRT::MLBase
getScalingEnabled() const GRT::MLBase
getTotalSquaredTrainingError() const GRT::MLBase
getTrained() const GRT::MLBase
getTrainingError() const GRT::MLP
getTrainingLog() const GRT::MLP
getTrainingRate() const GRT::MLP
getTrainingResults() const GRT::MLBase
getUseValidationSet() const GRT::MLBase
getValidationSetSize() const GRT::MLBase
GRTBase(void)GRT::GRTBase
hiddenLayer (defined in GRT::MLP)GRT::MLPprotected
hiddenLayerActivationFunction (defined in GRT::MLP)GRT::MLPprotected
hiddenNeuronsOutput (defined in GRT::MLP)GRT::MLPprotected
infoLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
init(const UINT numInputNeurons, const UINT numHiddenNeurons, const UINT numOutputNeurons)GRT::MLP
init(const UINT numInputNeurons, const UINT numHiddenNeurons, const UINT numOutputNeurons, const UINT inputLayerActivationFunction, const UINT hiddenLayerActivationFunction, const UINT outputLayerActivationFunction)GRT::MLP
initialized (defined in GRT::MLP)GRT::MLPprotected
inputLayer (defined in GRT::MLP)GRT::MLPprotected
inputLayerActivationFunction (defined in GRT::MLP)GRT::MLPprotected
inputNeuronsOuput (defined in GRT::MLP)GRT::MLPprotected
inputVectorRanges (defined in GRT::Regressifier)GRT::Regressifierprotected
isNAN(const double v) const (defined in GRT::MLP)GRT::MLPinlineprotected
learningRate (defined in GRT::MLBase)GRT::MLBaseprotected
load(const string filename)GRT::MLBasevirtual
loadBaseSettingsFromFile(fstream &file)GRT::Regressifierprotected
loadLegacyModelFromFile(fstream &file) (defined in GRT::MLP)GRT::MLPprotected
loadModelFromFile(fstream &file)GRT::MLPvirtual
GRT::Regressifier::loadModelFromFile(string filename)GRT::MLBasevirtual
map(VectorDouble inputVector)GRT::MLBasevirtual
map_(VectorDouble &inputVector)GRT::MLBasevirtual
maxLikelihood (defined in GRT::MLP)GRT::MLPprotected
maxNumEpochs (defined in GRT::MLBase)GRT::MLBaseprotected
minChange (defined in GRT::MLBase)GRT::MLBaseprotected
minNumEpochs (defined in GRT::MLBase)GRT::MLBaseprotected
MLBase(void)GRT::MLBase
MLP()GRT::MLP
MLP(const MLP &rhs)GRT::MLP
momentum (defined in GRT::MLP)GRT::MLPprotected
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::MLP)GRT::MLPprotected
nullRejectionThreshold (defined in GRT::MLP)GRT::MLPprotected
numHiddenNeurons (defined in GRT::MLP)GRT::MLPprotected
numInputDimensions (defined in GRT::MLBase)GRT::MLBaseprotected
numInputNeurons (defined in GRT::MLP)GRT::MLPprotected
numOutputDimensions (defined in GRT::MLBase)GRT::MLBaseprotected
numOutputNeurons (defined in GRT::MLP)GRT::MLPprotected
numRandomTrainingIterations (defined in GRT::MLP)GRT::MLPprotected
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
ONLINE_GRADIENT_DESCENT enum value (defined in GRT::MLP)GRT::MLP
operator=(const MLP &rhs)GRT::MLP
outputLayer (defined in GRT::MLP)GRT::MLPprotected
outputLayerActivationFunction (defined in GRT::MLP)GRT::MLPprotected
outputNeuronsOutput (defined in GRT::MLP)GRT::MLPprotected
predict(VectorDouble inputVector)GRT::MLBasevirtual
predict(MatrixDouble inputMatrix)GRT::MLBasevirtual
predict_(VectorDouble &inputVector)GRT::MLPvirtual
GRT::Regressifier::predict_(MatrixDouble &inputMatrix)GRT::MLBasevirtual
predictedClassLabel (defined in GRT::MLP)GRT::MLPprotected
print() const GRT::MLPvirtual
printNetwork() const GRT::MLP
random (defined in GRT::MLP)GRT::MLPprotected
randomiseTrainingOrder (defined in GRT::MLBase)GRT::MLBaseprotected
registerModule (defined in GRT::MLP)GRT::MLPprotectedstatic
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
removeAllTestObservers()GRT::MLBase
removeAllTrainingObservers()GRT::MLBase
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::MLPvirtual
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
setGamma(const double gamma)GRT::MLP
setHiddenLayerActivationFunction(const UINT activationFunction)GRT::MLP
setInputLayerActivationFunction(const UINT activationFunction)GRT::MLP
setLearningRate(double learningRate)GRT::MLBase
setMaxNumEpochs(const UINT maxNumEpochs)GRT::MLBase
setMinChange(const double minChange)GRT::MLBase
setMinNumEpochs(const UINT minNumEpochs)GRT::MLBase
setMomentum(const double momentum)GRT::MLP
setNullRejection(const bool useNullRejection)GRT::MLP
setNullRejectionCoeff(const double nullRejectionCoeff)GRT::MLP
setNumRandomTrainingIterations(const UINT numRandomTrainingIterations)GRT::MLP
setOutputLayerActivationFunction(const UINT activationFunction)GRT::MLP
setRandomiseTrainingOrder(const bool randomiseTrainingOrder)GRT::MLBase
setTrainingRate(const double trainingRate)GRT::MLP
setUseValidationSet(const bool useValidationSet)GRT::MLBase
setValidationSetSize(const UINT validationSetSize)GRT::MLBase
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
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_(ClassificationData &trainingData)GRT::MLPvirtual
train_(RegressionData &trainingData)GRT::MLPvirtual
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
trainingError (defined in GRT::MLP)GRT::MLPprotected
trainingErrorLog (defined in GRT::MLP)GRT::MLPprotected
trainingLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
trainingMode (defined in GRT::MLP)GRT::MLPprotected
TrainingModes enum name (defined in GRT::MLP)GRT::MLP
trainingResults (defined in GRT::MLBase)GRT::MLBaseprotected
trainingResultsObserverManager (defined in GRT::MLBase)GRT::MLBaseprotected
trainModel(RegressionData &trainingData) (defined in GRT::MLP)GRT::MLPprotected
trainOnlineGradientDescentClassification(const RegressionData &trainingData, const RegressionData &validationData) (defined in GRT::MLP)GRT::MLPprotected
trainOnlineGradientDescentRegression(const RegressionData &trainingData, const RegressionData &validationData) (defined in GRT::MLP)GRT::MLPprotected
useNullRejection (defined in GRT::MLP)GRT::MLPprotected
useScaling (defined in GRT::MLBase)GRT::MLBaseprotected
useValidationSet (defined in GRT::MLBase)GRT::MLBaseprotected
validateActivationFunction(const UINT avactivationFunction) const GRT::MLP
validationSetSize (defined in GRT::MLBase)GRT::MLBaseprotected
warningLog (defined in GRT::GRTBase)GRT::GRTBaseprotected
~GRTBase(void)GRT::GRTBasevirtual
~MLBase(void)GRT::MLBasevirtual
~MLP()GRT::MLPvirtual
~Observer() (defined in GRT::Observer< TrainingResult >)GRT::Observer< TrainingResult >inlinevirtual
~Observer() (defined in GRT::Observer< TestInstanceResult >)GRT::Observer< TestInstanceResult >inlinevirtual
~Regressifier(void)GRT::Regressifiervirtual