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GestureRecognitionToolkit
Version: 0.2.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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GestureRecognitionToolkit
Version: 0.2.0
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 MLP, including all inherited members.
| activationFunctionFromString(const std::string activationName) const | MLP | |
| activationFunctionToString(const UINT activationFunction) const | MLP | |
| back_prop(const VectorFloat &inputVector, const VectorFloat &targetVector, const Float alpha, const Float beta) | MLP | protected |
| BASE_TYPE_NOT_SET enum value (defined in MLBase) | MLBase | |
| baseType (defined in MLBase) | MLBase | protected |
| BaseTypes enum name (defined in MLBase) | MLBase | |
| checkForNAN() const | MLP | |
| classificationModeActive (defined in MLP) | MLP | protected |
| CLASSIFIER enum value (defined in MLBase) | MLBase | |
| classLikelihoods (defined in MLP) | MLP | protected |
| classType (defined in GRTBase) | GRTBase | protected |
| clear() | MLP | virtual |
| CLUSTERER enum value (defined in MLBase) | MLBase | |
| copyBaseVariables(const Regressifier *regressifier) | Regressifier | |
| copyGRTBaseVariables(const GRTBase *GRTBase) | GRTBase | |
| copyMLBaseVariables(const MLBase *mlBase) | MLBase | |
| createInstanceFromString(const std::string ®ressifierType) | Regressifier | static |
| createNewInstance() const | Regressifier | |
| debugLog (defined in GRTBase) | GRTBase | protected |
| deepCopy() const | Regressifier | |
| deepCopyFrom(const Regressifier *regressifier) | MLP | virtual |
| deltaH (defined in MLP) | MLP | protected |
| deltaO (defined in MLP) | MLP | protected |
| enableScaling(const bool useScaling) | MLBase | |
| errorLog (defined in GRTBase) | GRTBase | protected |
| feedforward(VectorFloat data) | MLP | protected |
| feedforward(const VectorFloat &data, VectorFloat &inputNeuronsOuput, VectorFloat &hiddenNeuronsOutput, VectorFloat &outputNeuronsOutput) | MLP | protected |
| gamma (defined in MLP) | MLP | protected |
| getBaseRegressifier() const | Regressifier | |
| getBaseType() const | MLBase | |
| getClassDistances() const | MLP | |
| getClassificationModeActive() const | MLP | |
| getClassLikelihoods() const | MLP | |
| getClassType() const | GRTBase | |
| getGamma() const | MLP | |
| getGRTBasePointer() | GRTBase | |
| getGRTBasePointer() const | GRTBase | |
| getGRTRevison() | GRTBase | static |
| getGRTVersion(bool returnRevision=true) | GRTBase | static |
| getHiddenLayer() const | MLP | |
| getHiddenLayerActivationFunction() const | MLP | |
| getInputLayer() const | MLP | |
| getInputLayerActivationFunction() const | MLP | |
| getInputRanges() const | Regressifier | |
| getInputType() const | MLBase | |
| getIsBaseTypeClassifier() const | MLBase | |
| getIsBaseTypeClusterer() const | MLBase | |
| getIsBaseTypeRegressifier() const | MLBase | |
| getLastErrorMessage() const | GRTBase | |
| getLastInfoMessage() const | GRTBase | |
| getLastWarningMessage() const | GRTBase | |
| getLearningRate() const | MLBase | |
| getMap() (defined in Regressifier) | Regressifier | inlineprotectedstatic |
| getMaximumLikelihood() const | MLP | |
| getMaxNumEpochs() const | MLBase | |
| getMinChange() const | MLBase | |
| getMinNumEpochs() const | MLBase | |
| getMLBasePointer() | MLBase | |
| getMLBasePointer() const | MLBase | |
| getModel(std::ostream &stream) const | MLBase | virtual |
| getModelAsString() const | MLBase | virtual |
| getModelTrained() const | MLBase | |
| getMomentum() const | MLP | |
| getNullRejectionCoeff() const | MLP | |
| getNullRejectionEnabled() const | MLP | |
| getNullRejectionThreshold() const | MLP | |
| getNumClasses() const | MLP | |
| getNumHiddenNeurons() const | MLP | |
| getNumInputDimensions() const | MLBase | |
| getNumInputFeatures() const | MLBase | |
| getNumInputNeurons() const | MLP | |
| getNumOutputDimensions() const | MLBase | |
| getNumOutputNeurons() const | MLP | |
| getNumRandomTrainingIterations() const | MLP | |
| getNumTrainingIterationsToConverge() const | MLBase | |
| getOutputLayer() const | MLP | |
| getOutputLayerActivationFunction() const | MLP | |
| getOutputRanges() const | Regressifier | |
| getOutputType() const | MLBase | |
| getPredictedClassLabel() const | MLP | |
| getRandomiseTrainingOrder() const | MLBase | |
| getRegisteredRegressifiers() | Regressifier | static |
| getRegressifierType() const | Regressifier | |
| getRegressionData() const | Regressifier | |
| getRegressionModeActive() const | MLP | |
| getRootMeanSquaredTrainingError() const | MLBase | |
| getScalingEnabled() const | MLBase | |
| getTotalSquaredTrainingError() const | MLBase | |
| getTrained() const | MLBase | |
| getTrainingError() const | MLP | |
| getTrainingLog() const | MLP | |
| getTrainingRate() const | MLP | |
| getTrainingResults() const | MLBase | |
| getUseValidationSet() const | MLBase | |
| getValidationSetAccuracy() const | MLBase | |
| getValidationSetPrecision() const | MLBase | |
| getValidationSetRecall() const | MLBase | |
| getValidationSetSize() const | MLBase | |
| GRT_DEPRECATED_MSG("saveModelToFile(std::string filename) is deprecated, use save(std::string filename) instead", virtual bool saveModelToFile(std::string filename) const ) | MLBase | |
| GRT_DEPRECATED_MSG("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const ) | MLBase | |
| GRT_DEPRECATED_MSG("loadModelFromFile(std::string filename) is deprecated, use load(std::string filename) instead", virtual bool loadModelFromFile(std::string filename)) | MLBase | |
| GRT_DEPRECATED_MSG("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file)) | MLBase | |
| GRTBase(void) | GRTBase | |
| hiddenLayer (defined in MLP) | MLP | protected |
| hiddenLayerActivationFunction (defined in MLP) | MLP | protected |
| hiddenNeuronsOutput (defined in MLP) | MLP | protected |
| infoLog (defined in GRTBase) | GRTBase | protected |
| init(const UINT numInputNeurons, const UINT numHiddenNeurons, const UINT numOutputNeurons) | MLP | |
| init(const UINT numInputNeurons, const UINT numHiddenNeurons, const UINT numOutputNeurons, const UINT inputLayerActivationFunction, const UINT hiddenLayerActivationFunction, const UINT outputLayerActivationFunction) | MLP | |
| initialized (defined in MLP) | MLP | protected |
| inputLayer (defined in MLP) | MLP | protected |
| inputLayerActivationFunction (defined in MLP) | MLP | protected |
| inputNeuronsOuput (defined in MLP) | MLP | protected |
| inputType (defined in MLBase) | MLBase | protected |
| inputVectorRanges (defined in Regressifier) | Regressifier | protected |
| isNAN(const Float v) const (defined in MLP) | MLP | inlineprotected |
| learningRate (defined in MLBase) | MLBase | protected |
| load(std::fstream &file) | MLP | virtual |
| Regressifier::load(const std::string filename) | MLBase | virtual |
| loadBaseSettingsFromFile(std::fstream &file) | Regressifier | protected |
| loadLegacyModelFromFile(std::fstream &file) (defined in MLP) | MLP | protected |
| map(VectorFloat inputVector) | MLBase | virtual |
| map_(VectorFloat &inputVector) | MLBase | virtual |
| maxLikelihood (defined in MLP) | MLP | protected |
| maxNumEpochs (defined in MLBase) | MLBase | protected |
| minChange (defined in MLBase) | MLBase | protected |
| minNumEpochs (defined in MLBase) | MLBase | protected |
| MLBase(void) | MLBase | |
| MLP() | MLP | |
| MLP(const MLP &rhs) | MLP | |
| momentum (defined in MLP) | MLP | protected |
| notify(const TrainingResult &data) (defined in Observer< TrainingResult >) | Observer< TrainingResult > | inlinevirtual |
| notify(const TestInstanceResult &data) (defined in Observer< TestInstanceResult >) | Observer< TestInstanceResult > | inlinevirtual |
| notifyTestResultsObservers(const TestInstanceResult &data) | MLBase | |
| notifyTrainingResultsObservers(const TrainingResult &data) | MLBase | |
| nullRejectionCoeff (defined in MLP) | MLP | protected |
| nullRejectionThreshold (defined in MLP) | MLP | protected |
| numHiddenNeurons (defined in MLP) | MLP | protected |
| numInputDimensions (defined in MLBase) | MLBase | protected |
| numInputNeurons (defined in MLP) | MLP | protected |
| numOutputDimensions (defined in MLBase) | MLBase | protected |
| numOutputNeurons (defined in MLP) | MLP | protected |
| numRandomTrainingIterations (defined in MLP) | MLP | protected |
| numTrainingIterationsToConverge (defined in MLBase) | MLBase | protected |
| Observer() (defined in Observer< TrainingResult >) | Observer< TrainingResult > | inline |
| Observer() (defined in Observer< TestInstanceResult >) | Observer< TestInstanceResult > | inline |
| ONLINE_GRADIENT_DESCENT enum value (defined in MLP) | MLP | |
| operator=(const MLP &rhs) | MLP | |
| outputLayer (defined in MLP) | MLP | protected |
| outputLayerActivationFunction (defined in MLP) | MLP | protected |
| outputNeuronsOutput (defined in MLP) | MLP | protected |
| outputType (defined in MLBase) | MLBase | protected |
| predict(VectorFloat inputVector) | MLBase | virtual |
| predict(MatrixFloat inputMatrix) | MLBase | virtual |
| predict_(VectorFloat &inputVector) | MLP | virtual |
| Regressifier::predict_(MatrixFloat &inputMatrix) | MLBase | virtual |
| predictedClassLabel (defined in MLP) | MLP | protected |
| print() const | MLP | virtual |
| printNetwork() const | MLP | |
| random (defined in MLP) | MLP | protected |
| randomiseTrainingOrder (defined in MLBase) | MLBase | protected |
| registerModule (defined in MLP) | MLP | protectedstatic |
| registerTestResultsObserver(Observer< TestInstanceResult > &observer) | MLBase | |
| registerTrainingResultsObserver(Observer< TrainingResult > &observer) | MLBase | |
| REGRESSIFIER enum value (defined in MLBase) | MLBase | |
| Regressifier(void) | Regressifier | |
| regressifierType (defined in Regressifier) | Regressifier | protected |
| regressionData (defined in Regressifier) | Regressifier | protected |
| removeAllTestObservers() | MLBase | |
| removeAllTrainingObservers() | MLBase | |
| removeTestResultsObserver(const Observer< TestInstanceResult > &observer) | MLBase | |
| removeTrainingResultsObserver(const Observer< TrainingResult > &observer) | MLBase | |
| reset() | Regressifier | virtual |
| rootMeanSquaredTrainingError (defined in MLBase) | MLBase | protected |
| save(std::fstream &file) const | MLP | virtual |
| Regressifier::save(const std::string filename) const | MLBase | virtual |
| saveBaseSettingsToFile(std::fstream &file) const | Regressifier | protected |
| scale(const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false) | MLBase | inline |
| setErrorLoggingEnabled(const bool loggingEnabled) | GRTBase | |
| setGamma(const Float gamma) | MLP | |
| setHiddenLayerActivationFunction(const UINT activationFunction) | MLP | |
| setInfoLoggingEnabled(const bool loggingEnabled) | GRTBase | |
| setInputLayerActivationFunction(const UINT activationFunction) | MLP | |
| setLearningRate(const Float learningRate) | MLBase | |
| setMaxNumEpochs(const UINT maxNumEpochs) | MLBase | |
| setMinChange(const Float minChange) | MLBase | |
| setMinNumEpochs(const UINT minNumEpochs) | MLBase | |
| setMomentum(const Float momentum) | MLP | |
| setNullRejection(const bool useNullRejection) | MLP | |
| setNullRejectionCoeff(const Float nullRejectionCoeff) | MLP | |
| setNumRandomTrainingIterations(const UINT numRandomTrainingIterations) | MLP | |
| setOutputLayerActivationFunction(const UINT activationFunction) | MLP | |
| setRandomiseTrainingOrder(const bool randomiseTrainingOrder) | MLBase | |
| setTrainingLoggingEnabled(const bool loggingEnabled) | MLBase | |
| setTrainingRate(const Float trainingRate) | MLP | |
| setUseValidationSet(const bool useValidationSet) | MLBase | |
| setValidationSetSize(const UINT validationSetSize) | MLBase | |
| setWarningLoggingEnabled(const bool loggingEnabled) | GRTBase | |
| SQR(const Float &x) const (defined in GRTBase) | GRTBase | inlineprotected |
| StringRegressifierMap typedef | Regressifier | |
| targetVectorRanges (defined in Regressifier) | Regressifier | protected |
| testingLog (defined in GRTBase) | GRTBase | protected |
| testResultsObserverManager (defined in MLBase) | MLBase | protected |
| totalSquaredTrainingError (defined in MLBase) | MLBase | protected |
| train(ClassificationData trainingData) | MLBase | virtual |
| train(RegressionData trainingData) | MLBase | virtual |
| train(TimeSeriesClassificationData trainingData) | MLBase | virtual |
| train(ClassificationDataStream trainingData) | MLBase | virtual |
| train(UnlabelledData trainingData) | MLBase | virtual |
| train(MatrixFloat data) | MLBase | virtual |
| train_(ClassificationData &trainingData) | MLP | virtual |
| train_(RegressionData &trainingData) | MLP | virtual |
| Regressifier::train_(TimeSeriesClassificationData &trainingData) | MLBase | virtual |
| Regressifier::train_(ClassificationDataStream &trainingData) | MLBase | virtual |
| Regressifier::train_(UnlabelledData &trainingData) | MLBase | virtual |
| Regressifier::train_(MatrixFloat &data) | MLBase | virtual |
| trained (defined in MLBase) | MLBase | protected |
| trainingError (defined in MLP) | MLP | protected |
| trainingErrorLog (defined in MLP) | MLP | protected |
| trainingLog (defined in GRTBase) | GRTBase | protected |
| trainingMode (defined in MLP) | MLP | protected |
| TrainingModes enum name (defined in MLP) | MLP | |
| trainingResults (defined in MLBase) | MLBase | protected |
| trainingResultsObserverManager (defined in MLBase) | MLBase | protected |
| trainModel(RegressionData &trainingData) (defined in MLP) | MLP | protected |
| trainOnlineGradientDescentClassification(const RegressionData &trainingData, const RegressionData &validationData) (defined in MLP) | MLP | protected |
| trainOnlineGradientDescentRegression(const RegressionData &trainingData, const RegressionData &validationData) (defined in MLP) | MLP | protected |
| useNullRejection (defined in MLP) | MLP | protected |
| useScaling (defined in MLBase) | MLBase | protected |
| useValidationSet (defined in MLBase) | MLBase | protected |
| validateActivationFunction(const UINT avactivationFunction) const | MLP | |
| validationSetAccuracy (defined in MLBase) | MLBase | protected |
| validationSetPrecision (defined in MLBase) | MLBase | protected |
| validationSetRecall (defined in MLBase) | MLBase | protected |
| validationSetSize (defined in MLBase) | MLBase | protected |
| warningLog (defined in GRTBase) | GRTBase | protected |
| ~GRTBase(void) | GRTBase | virtual |
| ~MLBase(void) | MLBase | virtual |
| ~MLP() | MLP | virtual |
| ~Observer() (defined in Observer< TrainingResult >) | Observer< TrainingResult > | inlinevirtual |
| ~Observer() (defined in Observer< TestInstanceResult >) | Observer< TestInstanceResult > | inlinevirtual |
| ~Regressifier(void) | Regressifier | virtual |
1.8.11 
