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GestureRecognitionToolkit
Version: 0.2.5
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: 0.2.5
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 KMeans, including all inherited members.
| assign (defined in KMeans) | KMeans | protected |
| BASE_TYPE_NOT_SET enum value (defined in MLBase) | MLBase | |
| BaseType enum name (defined in MLBase) | MLBase | |
| baseType (defined in MLBase) | MLBase | protected |
| batchSize (defined in MLBase) | MLBase | protected |
| bestDistance (defined in Clusterer) | Clusterer | protected |
| calculateTheta(const MatrixFloat &data) (defined in KMeans) | KMeans | protected |
| classId | GRTBase | protected |
| CLASSIFIER enum value (defined in MLBase) | MLBase | |
| clear() | KMeans | virtual |
| clusterDistances (defined in Clusterer) | Clusterer | protected |
| Clusterer(const std::string &id="") | Clusterer | |
| CLUSTERER enum value (defined in MLBase) | MLBase | |
| clusterLabels (defined in Clusterer) | Clusterer | protected |
| clusterLikelihoods (defined in Clusterer) | Clusterer | protected |
| clusters (defined in KMeans) | KMeans | protected |
| computeTheta (defined in KMeans) | KMeans | protected |
| CONTEXT enum value (defined in MLBase) | MLBase | |
| converged (defined in Clusterer) | Clusterer | protected |
| copyBaseVariables(const Clusterer *clusterer) | Clusterer | |
| copyGRTBaseVariables(const GRTBase *GRTBase) | GRTBase | |
| copyMLBaseVariables(const MLBase *mlBase) | MLBase | |
| count (defined in KMeans) | KMeans | protected |
| create(std::string const &id) | Clusterer | static |
| create() const | Clusterer | |
| debugLog (defined in GRTBase) | GRTBase | protected |
| deepCopy() const | Clusterer | |
| deepCopyFrom(const Clusterer *clusterer) | KMeans | virtual |
| enableScaling(const bool useScaling) | MLBase | |
| errorLog (defined in GRTBase) | GRTBase | protected |
| estep(const MatrixFloat &data) (defined in KMeans) | KMeans | protected |
| FEATURE_EXTRACTION enum value (defined in MLBase) | MLBase | |
| finalTheta (defined in KMeans) | KMeans | protected |
| getBaseClusterer() const | Clusterer | |
| getBatchSize() const | MLBase | |
| getBestDistance() const | Clusterer | |
| getClassCountVector() const (defined in KMeans) | KMeans | inline |
| getClassLabelsVector() const (defined in KMeans) | KMeans | inline |
| getClusterDistances() const | Clusterer | |
| getClusterLabels() const | Clusterer | |
| getClusterLikelihoods() const | Clusterer | |
| getClusters() const (defined in KMeans) | KMeans | inline |
| getConverged() const | MLBase | |
| getGRTBasePointer() | GRTBase | |
| getGRTBasePointer() const | GRTBase | |
| getGRTRevison() | GRTBase | static |
| getGRTVersion(bool returnRevision=true) | GRTBase | static |
| getId() | KMeans | static |
| Clusterer::getId() const | GRTBase | |
| 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 Clusterer) | Clusterer | inlineprotectedstatic |
| getMaximumLikelihood() const | Clusterer | |
| 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() (defined in KMeans) | KMeans | inline |
| getNumClusters() const | Clusterer | |
| getNumInputDimensions() const | MLBase | |
| getNumInputFeatures() const | MLBase | |
| getNumOutputDimensions() const | MLBase | |
| getNumRestarts() const | MLBase | |
| getNumTrainingIterationsToConverge() const | MLBase | |
| getOutputType() const | MLBase | |
| getPredictedClusterLabel() const | Clusterer | |
| getRandomiseTrainingOrder() const | MLBase | |
| getRegisteredClusterers() | Clusterer | static |
| getRMSTrainingError() const | MLBase | |
| getRMSValidationError() const | MLBase | |
| getScalingEnabled() const | MLBase | |
| getTestingLoggingEnabled() const | MLBase | |
| getTheta() (defined in KMeans) | KMeans | inline |
| getTotalSquaredTrainingError() const | MLBase | |
| getTrained() const | MLBase | |
| getTrainingLoggingEnabled() const | MLBase | |
| getTrainingResults() const | MLBase | |
| getTrainingThetaLog() const (defined in KMeans) | KMeans | inline |
| getType() const | MLBase | |
| getUseValidationSet() const | MLBase | |
| getValidationSetAccuracy() const | MLBase | |
| getValidationSetPrecision() const | MLBase | |
| getValidationSetRecall() const | MLBase | |
| getValidationSetSize() const | MLBase | |
| GRT_DEPRECATED_MSG("getClustererType() is deprecated, use getId() or getBaseId() instead", std::string getClustererType() const ) (defined in Clusterer) | Clusterer | |
| GRT_DEPRECATED_MSG("createNewInstance is deprecated, use create instead.", Clusterer *createNewInstance() const ) (defined in Clusterer) | Clusterer | |
| GRT_DEPRECATED_MSG("createInstanceFromString is deprecated, use create instead.", static Clusterer *createInstanceFromString(const std::string &id)) (defined in Clusterer) | Clusterer | |
| MLBase::GRT_DEPRECATED_MSG("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const ) | MLBase | |
| MLBase::GRT_DEPRECATED_MSG("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const ) | MLBase | |
| MLBase::GRT_DEPRECATED_MSG("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename)) | MLBase | |
| MLBase::GRT_DEPRECATED_MSG("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file)) | MLBase | |
| MLBase::GRT_DEPRECATED_MSG("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const ) | MLBase | |
| MLBase::GRT_DEPRECATED_MSG("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const ) | MLBase | |
| GRTBase::GRT_DEPRECATED_MSG("getClassType is deprecated, use getId() instead!", std::string getClassType() const ) | GRTBase | |
| GRTBase(const std::string &id="") | GRTBase | |
| infoLog (defined in GRTBase) | GRTBase | protected |
| inputType (defined in MLBase) | MLBase | protected |
| KMeans(const UINT numClusters=10, const UINT minNumEpochs=5, const UINT maxNumEpochs=1000, const Float minChange=1.0e-5, const bool computeTheta=true) | KMeans | |
| KMeans(const KMeans &rhs) | KMeans | |
| learningRate (defined in MLBase) | MLBase | protected |
| load(const std::string &filename) | MLBase | virtual |
| load(std::fstream &file) | MLBase | virtual |
| loadBaseSettingsFromFile(std::fstream &file) | MLBase | protected |
| loadClustererSettingsFromFile(std::fstream &file) | Clusterer | protected |
| loadModelFromFile(std::fstream &file) | KMeans | virtual |
| map(VectorFloat inputVector) | MLBase | virtual |
| map_(VectorFloat &inputVector) | MLBase | virtual |
| maxLikelihood (defined in Clusterer) | Clusterer | protected |
| maxNumEpochs (defined in MLBase) | MLBase | protected |
| minChange (defined in MLBase) | MLBase | protected |
| minNumEpochs (defined in MLBase) | MLBase | protected |
| MLBase(const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET) | MLBase | |
| mstep(const MatrixFloat &data) (defined in KMeans) | KMeans | protected |
| nchg | KMeans | 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 | |
| numClusters | Clusterer | protected |
| numInputDimensions (defined in MLBase) | MLBase | protected |
| numOutputDimensions (defined in MLBase) | MLBase | protected |
| numRestarts (defined in MLBase) | MLBase | protected |
| numTrainingIterationsToConverge (defined in MLBase) | MLBase | protected |
| numTrainingSamples | KMeans | protected |
| Observer() (defined in Observer< TrainingResult >) | Observer< TrainingResult > | inline |
| Observer() (defined in Observer< TestInstanceResult >) | Observer< TestInstanceResult > | inline |
| operator=(const KMeans &rhs) | KMeans | |
| outputType (defined in MLBase) | MLBase | protected |
| POST_PROCESSING enum value (defined in MLBase) | MLBase | |
| PRE_PROCSSING enum value (defined in MLBase) | MLBase | |
| predict(VectorFloat inputVector) | MLBase | virtual |
| predict(MatrixFloat inputMatrix) | MLBase | virtual |
| predict_(VectorFloat &inputVector) | KMeans | virtual |
| Clusterer::predict_(MatrixFloat &inputMatrix) | MLBase | virtual |
| predictedClusterLabel | Clusterer | protected |
| print() const | MLBase | virtual |
| random (defined in MLBase) | MLBase | protected |
| randomiseTrainingOrder (defined in MLBase) | MLBase | protected |
| ranges (defined in Clusterer) | Clusterer | protected |
| registerTestResultsObserver(Observer< TestInstanceResult > &observer) | MLBase | |
| registerTrainingResultsObserver(Observer< TrainingResult > &observer) | MLBase | |
| REGRESSIFIER enum value (defined in MLBase) | MLBase | |
| removeAllTestObservers() | MLBase | |
| removeAllTrainingObservers() | MLBase | |
| removeTestResultsObserver(const Observer< TestInstanceResult > &observer) | MLBase | |
| removeTrainingResultsObserver(const Observer< TrainingResult > &observer) | MLBase | |
| reset() | KMeans | virtual |
| rmsTrainingError (defined in MLBase) | MLBase | protected |
| rmsValidationError (defined in MLBase) | MLBase | protected |
| save(const std::string &filename) const | MLBase | virtual |
| save(std::fstream &file) const | MLBase | virtual |
| saveBaseSettingsToFile(std::fstream &file) const | MLBase | protected |
| saveClustererSettingsToFile(std::fstream &file) const | Clusterer | protected |
| saveModelToFile(std::fstream &file) const | KMeans | virtual |
| scale(const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false) | GRTBase | inline |
| setBatchSize(const UINT batchSize) | MLBase | |
| setClusters(const MatrixFloat &clusters) | KMeans | |
| setComputeTheta(const bool computeTheta) (defined in KMeans) | KMeans | |
| setDebugLoggingEnabled(const bool loggingEnabled) | GRTBase | |
| setErrorLoggingEnabled(const bool loggingEnabled) | GRTBase | |
| setInfoLoggingEnabled(const bool loggingEnabled) | GRTBase | |
| setLearningRate(const Float learningRate) | MLBase | |
| setMaxNumEpochs(const UINT maxNumEpochs) | MLBase | |
| setMinChange(const Float minChange) | MLBase | |
| setMinNumEpochs(const UINT minNumEpochs) | MLBase | |
| setNumClusters(const UINT numClusters) | Clusterer | |
| setNumRestarts(const UINT numRestarts) | MLBase | |
| setRandomiseTrainingOrder(const bool randomiseTrainingOrder) | MLBase | |
| setTestingLoggingEnabled(const bool loggingEnabled) | MLBase | |
| setTrainingLoggingEnabled(const bool loggingEnabled) | MLBase | |
| setUseValidationSet(const bool useValidationSet) | MLBase | |
| setValidationSetSize(const UINT validationSetSize) | MLBase | |
| setWarningLoggingEnabled(const bool loggingEnabled) | GRTBase | |
| SQR(const Float a) (defined in KMeans) | KMeans | inlineprotected |
| SQR(const Float &x) const (defined in GRTBase) | GRTBase | inline |
| StringClustererMap typedef | Clusterer | |
| testingLog (defined in MLBase) | MLBase | protected |
| testResultsObserverManager (defined in MLBase) | MLBase | protected |
| thetaTracker (defined in KMeans) | KMeans | protected |
| totalSquaredTrainingError (defined in MLBase) | MLBase | protected |
| train(ClassificationData trainingData) | MLBase | virtual |
| train(RegressionData trainingData) | MLBase | virtual |
| train(RegressionData trainingData, RegressionData validationData) | MLBase | virtual |
| train(TimeSeriesClassificationData trainingData) | MLBase | virtual |
| train(ClassificationDataStream trainingData) | MLBase | virtual |
| train(UnlabelledData trainingData) | MLBase | virtual |
| train(MatrixFloat data) | MLBase | virtual |
| train_(MatrixFloat &data) | KMeans | virtual |
| train_(ClassificationData &trainingData) | KMeans | virtual |
| train_(UnlabelledData &trainingData) | KMeans | virtual |
| MLBase::train_(RegressionData &trainingData) | MLBase | virtual |
| MLBase::train_(RegressionData &trainingData, RegressionData &validationData) | MLBase | virtual |
| MLBase::train_(TimeSeriesClassificationData &trainingData) | MLBase | virtual |
| MLBase::train_(ClassificationDataStream &trainingData) | MLBase | virtual |
| trained (defined in MLBase) | MLBase | protected |
| trainingLog (defined in MLBase) | MLBase | protected |
| trainingResults (defined in MLBase) | MLBase | protected |
| trainingResultsObserverManager (defined in MLBase) | MLBase | protected |
| trainModel(MatrixFloat &data) | KMeans | |
| useScaling (defined in MLBase) | MLBase | protected |
| useValidationSet (defined in MLBase) | MLBase | protected |
| 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 |
| ~Clusterer(void) | Clusterer | virtual |
| ~GRTBase(void) | GRTBase | virtual |
| ~KMeans() | KMeans | virtual |
| ~MLBase(void) | MLBase | virtual |
| ~Observer() (defined in Observer< TrainingResult >) | Observer< TrainingResult > | inlinevirtual |
| ~Observer() (defined in Observer< TestInstanceResult >) | Observer< TestInstanceResult > | inlinevirtual |
1.8.11 
