![]() |
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.
|
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::MLP | 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 | |
checkForNAN() const | GRT::MLP | |
classificationModeActive (defined in GRT::MLP) | GRT::MLP | protected |
CLASSIFIER enum value (defined in GRT::MLBase) | GRT::MLBase | |
classLikelihoods (defined in GRT::MLP) | GRT::MLP | protected |
classType (defined in GRT::GRTBase) | GRT::GRTBase | protected |
clear() | GRT::MLP | virtual |
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 ®ressifierType) | GRT::Regressifier | static |
createNewInstance() const | GRT::Regressifier | |
debugLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
deepCopy() const | GRT::Regressifier | |
deepCopyFrom(const Regressifier *regressifier) | GRT::MLP | virtual |
deltaH (defined in GRT::MLP) | GRT::MLP | protected |
deltaO (defined in GRT::MLP) | GRT::MLP | protected |
enableScaling(bool useScaling) | GRT::MLBase | |
errorLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
feedforward(VectorDouble trainingExample) | GRT::MLP | protected |
feedforward(const VectorDouble &trainingExample, VectorDouble &inputNeuronsOuput, VectorDouble &hiddenNeuronsOutput, VectorDouble &outputNeuronsOutput) | GRT::MLP | protected |
gamma (defined in GRT::MLP) | GRT::MLP | protected |
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::GRTBase | static |
getGRTVersion(bool returnRevision=true) | GRT::GRTBase | static |
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::Regressifier | inlineprotectedstatic |
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::MLBase | virtual |
getModelAsString() const | GRT::MLBase | virtual |
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::Regressifier | static |
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::MLP | protected |
hiddenLayerActivationFunction (defined in GRT::MLP) | GRT::MLP | protected |
hiddenNeuronsOutput (defined in GRT::MLP) | GRT::MLP | protected |
infoLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
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::MLP | protected |
inputLayer (defined in GRT::MLP) | GRT::MLP | protected |
inputLayerActivationFunction (defined in GRT::MLP) | GRT::MLP | protected |
inputNeuronsOuput (defined in GRT::MLP) | GRT::MLP | protected |
inputVectorRanges (defined in GRT::Regressifier) | GRT::Regressifier | protected |
isNAN(const double v) const (defined in GRT::MLP) | GRT::MLP | inlineprotected |
learningRate (defined in GRT::MLBase) | GRT::MLBase | protected |
load(const string filename) | GRT::MLBase | virtual |
loadBaseSettingsFromFile(fstream &file) | GRT::Regressifier | protected |
loadLegacyModelFromFile(fstream &file) (defined in GRT::MLP) | GRT::MLP | protected |
loadModelFromFile(fstream &file) | GRT::MLP | virtual |
GRT::Regressifier::loadModelFromFile(string filename) | GRT::MLBase | virtual |
map(VectorDouble inputVector) | GRT::MLBase | virtual |
map_(VectorDouble &inputVector) | GRT::MLBase | virtual |
maxLikelihood (defined in GRT::MLP) | GRT::MLP | 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 | |
MLP() | GRT::MLP | |
MLP(const MLP &rhs) | GRT::MLP | |
momentum (defined in GRT::MLP) | GRT::MLP | 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::MLP) | GRT::MLP | protected |
nullRejectionThreshold (defined in GRT::MLP) | GRT::MLP | protected |
numHiddenNeurons (defined in GRT::MLP) | GRT::MLP | protected |
numInputDimensions (defined in GRT::MLBase) | GRT::MLBase | protected |
numInputNeurons (defined in GRT::MLP) | GRT::MLP | protected |
numOutputDimensions (defined in GRT::MLBase) | GRT::MLBase | protected |
numOutputNeurons (defined in GRT::MLP) | GRT::MLP | protected |
numRandomTrainingIterations (defined in GRT::MLP) | GRT::MLP | 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 |
ONLINE_GRADIENT_DESCENT enum value (defined in GRT::MLP) | GRT::MLP | |
operator=(const MLP &rhs) | GRT::MLP | |
outputLayer (defined in GRT::MLP) | GRT::MLP | protected |
outputLayerActivationFunction (defined in GRT::MLP) | GRT::MLP | protected |
outputNeuronsOutput (defined in GRT::MLP) | GRT::MLP | protected |
predict(VectorDouble inputVector) | GRT::MLBase | virtual |
predict(MatrixDouble inputMatrix) | GRT::MLBase | virtual |
predict_(VectorDouble &inputVector) | GRT::MLP | virtual |
GRT::Regressifier::predict_(MatrixDouble &inputMatrix) | GRT::MLBase | virtual |
predictedClassLabel (defined in GRT::MLP) | GRT::MLP | protected |
print() const | GRT::MLP | virtual |
printNetwork() const | GRT::MLP | |
random (defined in GRT::MLP) | GRT::MLP | protected |
randomiseTrainingOrder (defined in GRT::MLBase) | GRT::MLBase | protected |
registerModule (defined in GRT::MLP) | GRT::MLP | protectedstatic |
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::Regressifier | protected |
regressionData (defined in GRT::Regressifier) | GRT::Regressifier | protected |
removeAllTestObservers() | GRT::MLBase | |
removeAllTrainingObservers() | GRT::MLBase | |
removeTestResultsObserver(const Observer< TestInstanceResult > &observer) | GRT::MLBase | |
removeTrainingResultsObserver(const Observer< TrainingResult > &observer) | GRT::MLBase | |
reset() | GRT::Regressifier | virtual |
rootMeanSquaredTrainingError (defined in GRT::MLBase) | GRT::MLBase | protected |
save(const string filename) const | GRT::MLBase | virtual |
saveBaseSettingsToFile(fstream &file) const | GRT::Regressifier | protected |
saveModelToFile(fstream &file) const | GRT::MLP | virtual |
GRT::Regressifier::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 |
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::GRTBase | inlineprotected |
StringRegressifierMap typedef | GRT::Regressifier | |
targetVectorRanges (defined in GRT::Regressifier) | GRT::Regressifier | protected |
testingLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
testResultsObserverManager (defined in GRT::MLBase) | GRT::MLBase | 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_(ClassificationData &trainingData) | GRT::MLP | virtual |
train_(RegressionData &trainingData) | GRT::MLP | virtual |
GRT::Regressifier::train_(TimeSeriesClassificationData &trainingData) | GRT::MLBase | virtual |
GRT::Regressifier::train_(TimeSeriesClassificationDataStream &trainingData) | GRT::MLBase | virtual |
GRT::Regressifier::train_(UnlabelledData &trainingData) | GRT::MLBase | virtual |
GRT::Regressifier::train_(MatrixDouble &data) | GRT::MLBase | virtual |
trained (defined in GRT::MLBase) | GRT::MLBase | protected |
trainingError (defined in GRT::MLP) | GRT::MLP | protected |
trainingErrorLog (defined in GRT::MLP) | GRT::MLP | protected |
trainingLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
trainingMode (defined in GRT::MLP) | GRT::MLP | protected |
TrainingModes enum name (defined in GRT::MLP) | GRT::MLP | |
trainingResults (defined in GRT::MLBase) | GRT::MLBase | protected |
trainingResultsObserverManager (defined in GRT::MLBase) | GRT::MLBase | protected |
trainModel(RegressionData &trainingData) (defined in GRT::MLP) | GRT::MLP | protected |
trainOnlineGradientDescentClassification(const RegressionData &trainingData, const RegressionData &validationData) (defined in GRT::MLP) | GRT::MLP | protected |
trainOnlineGradientDescentRegression(const RegressionData &trainingData, const RegressionData &validationData) (defined in GRT::MLP) | GRT::MLP | protected |
useNullRejection (defined in GRT::MLP) | GRT::MLP | protected |
useScaling (defined in GRT::MLBase) | GRT::MLBase | protected |
useValidationSet (defined in GRT::MLBase) | GRT::MLBase | protected |
validateActivationFunction(const UINT avactivationFunction) const | GRT::MLP | |
validationSetSize (defined in GRT::MLBase) | GRT::MLBase | protected |
warningLog (defined in GRT::GRTBase) | GRT::GRTBase | protected |
~GRTBase(void) | GRT::GRTBase | virtual |
~MLBase(void) | GRT::MLBase | virtual |
~MLP() | GRT::MLP | virtual |
~Observer() (defined in GRT::Observer< TrainingResult >) | GRT::Observer< TrainingResult > | inlinevirtual |
~Observer() (defined in GRT::Observer< TestInstanceResult >) | GRT::Observer< TestInstanceResult > | inlinevirtual |
~Regressifier(void) | GRT::Regressifier | virtual |