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.
ContinuousHiddenMarkovModel Member List

This is the complete list of members for ContinuousHiddenMarkovModel, including all inherited members.

aContinuousHiddenMarkovModelprotected
alpha (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
autoEstimateSigma (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
bContinuousHiddenMarkovModelprotected
BASE_TYPE_NOT_SET enum value (defined in MLBase)MLBase
BaseType enum name (defined in MLBase)MLBase
baseType (defined in MLBase)MLBaseprotected
batchSize (defined in MLBase)MLBaseprotected
c (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
classIdGRTBaseprotected
CLASSIFIER enum value (defined in MLBase)MLBase
classLabelContinuousHiddenMarkovModelprotected
clear() overrideContinuousHiddenMarkovModelvirtual
CLUSTERER enum value (defined in MLBase)MLBase
CONTEXT enum value (defined in MLBase)MLBase
ContinuousHiddenMarkovModel(const UINT downsampleFactor=5, const UINT delta=1, const bool autoEstimateSigma=true, const Float sigma=10.0) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModel
ContinuousHiddenMarkovModel(const ContinuousHiddenMarkovModel &rhs) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModel
converged (defined in MLBase)MLBaseprotected
copyGRTBaseVariables(const GRTBase *GRTBase)GRTBase
copyMLBaseVariables(const MLBase *mlBase)MLBase
cThresholdContinuousHiddenMarkovModelprotected
debugLog (defined in GRTBase)GRTBaseprotected
deltaContinuousHiddenMarkovModelprotected
downsampleFactor (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
enableScaling(const bool useScaling)MLBase
errorLog (defined in GRTBase)GRTBaseprotected
estimatedStatesContinuousHiddenMarkovModelprotected
FEATURE_EXTRACTION enum value (defined in MLBase)MLBase
gauss(const MatrixFloat &x, const MatrixFloat &y, const MatrixFloat &sigma, const unsigned int i, const unsigned int j, const unsigned int N) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
getAlpha() const (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelinline
getBatchSize() const MLBase
getClassLabel() const (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelinline
getConverged() const MLBase
getEstimatedStates() const (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelinline
getGRTBasePointer()GRTBase
getGRTBasePointer() const GRTBase
getGRTRevison()GRTBasestatic
getGRTVersion(bool returnRevision=true)GRTBasestatic
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
getLoglikelihood() const (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelinline
getMaxNumEpochs() const MLBase
getMinChange() const MLBase
getMinNumEpochs() const MLBase
getMLBasePointer()MLBase
getMLBasePointer() const MLBase
getModel(std::ostream &stream) const MLBasevirtual
getModelAsString() const MLBasevirtual
getNumInputDimensions() const MLBase
getNumInputFeatures() const MLBase
getNumOutputDimensions() const MLBase
getNumRestarts() const MLBase
getNumStates() const (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelinline
getNumTrainingIterationsToConverge() const MLBase
getOutputType() const MLBase
getPhase() const (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelinline
getRandomiseTrainingOrder() const MLBase
getRMSTrainingError() const MLBase
getRMSValidationError() const MLBase
getScalingEnabled() const MLBase
getTestingLoggingEnabled() const MLBase
getTotalSquaredTrainingError() const MLBase
getTrained() const MLBase
getTrainingLoggingEnabled() const MLBase
getTrainingResults() const MLBase
getType() 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(const std::string &filename) instead", virtual bool saveModelToFile(const 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(const std::string &filename) instead", virtual bool loadModelFromFile(const 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
GRT_DEPRECATED_MSG("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )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)GRTBaseprotected
inputType (defined in MLBase)MLBaseprotected
learningRate (defined in MLBase)MLBaseprotected
load(std::fstream &file) overrideContinuousHiddenMarkovModelvirtual
MLBase::load(const std::string &filename)MLBasevirtual
loadBaseSettingsFromFile(std::fstream &file)MLBaseprotected
loglikelihoodContinuousHiddenMarkovModelprotected
map(VectorFloat inputVector)MLBasevirtual
map_(VectorFloat &inputVector)MLBasevirtual
maxNumEpochs (defined in MLBase)MLBaseprotected
minChange (defined in MLBase)MLBaseprotected
minNumEpochs (defined in MLBase)MLBaseprotected
MLBase(const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)MLBase
modelTypeContinuousHiddenMarkovModelprotected
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
numInputDimensions (defined in MLBase)MLBaseprotected
numOutputDimensions (defined in MLBase)MLBaseprotected
numRestarts (defined in MLBase)MLBaseprotected
numStatesContinuousHiddenMarkovModelprotected
numTrainingIterationsToConverge (defined in MLBase)MLBaseprotected
observationSequenceContinuousHiddenMarkovModelprotected
Observer() (defined in Observer< TrainingResult >)Observer< TrainingResult >inline
Observer() (defined in Observer< TestInstanceResult >)Observer< TestInstanceResult >inline
obsSequence (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
operator=(const ContinuousHiddenMarkovModel &rhs) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModel
outputType (defined in MLBase)MLBaseprotected
phase (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
piContinuousHiddenMarkovModelprotected
POST_PROCESSING enum value (defined in MLBase)MLBase
PRE_PROCSSING enum value (defined in MLBase)MLBase
predict(VectorFloat inputVector)MLBasevirtual
predict(MatrixFloat inputMatrix)MLBasevirtual
predict_(VectorFloat &x) overrideContinuousHiddenMarkovModelvirtual
predict_(MatrixFloat &obs) overrideContinuousHiddenMarkovModelvirtual
print() const overrideContinuousHiddenMarkovModelvirtual
random (defined in MLBase)MLBaseprotected
randomiseTrainingOrder (defined in MLBase)MLBaseprotected
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() overrideContinuousHiddenMarkovModelvirtual
rmsTrainingError (defined in MLBase)MLBaseprotected
rmsValidationError (defined in MLBase)MLBaseprotected
save(std::fstream &file) const overrideContinuousHiddenMarkovModelvirtual
MLBase::save(const std::string &filename) const MLBasevirtual
saveBaseSettingsToFile(std::fstream &file) const MLBaseprotected
scale(const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)GRTBaseinline
setAutoEstimateSigma(const bool autoEstimateSigma) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModel
setBatchSize(const UINT batchSize)MLBase
setDebugLoggingEnabled(const bool loggingEnabled)GRTBase
setDelta(const UINT delta)ContinuousHiddenMarkovModel
setDownsampleFactor(const UINT downsampleFactor) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModel
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
setModelType(const UINT modelType)ContinuousHiddenMarkovModel
setNumRestarts(const UINT numRestarts)MLBase
setRandomiseTrainingOrder(const bool randomiseTrainingOrder)MLBase
setSigma(const Float sigma) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModel
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
sigma (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelprotected
sigmaStatesContinuousHiddenMarkovModelprotected
SQR(const Float &x) const (defined in GRTBase)GRTBaseinline
testingLog (defined in MLBase)MLBaseprotected
testResultsObserverManager (defined in MLBase)MLBaseprotected
timeseriesLengthContinuousHiddenMarkovModelprotected
totalSquaredTrainingError (defined in MLBase)MLBaseprotected
train(ClassificationData trainingData)MLBasevirtual
train(RegressionData trainingData)MLBasevirtual
train(RegressionData trainingData, RegressionData validationData)MLBasevirtual
train(TimeSeriesClassificationData trainingData)MLBasevirtual
train(ClassificationDataStream trainingData)MLBasevirtual
train(UnlabelledData trainingData)MLBasevirtual
train(MatrixFloat data)MLBasevirtual
train_(TimeSeriesClassificationSample &trainingData) (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelvirtual
MLBase::train_(ClassificationData &trainingData)MLBasevirtual
MLBase::train_(RegressionData &trainingData)MLBasevirtual
MLBase::train_(RegressionData &trainingData, RegressionData &validationData)MLBasevirtual
MLBase::train_(TimeSeriesClassificationData &trainingData)MLBasevirtual
MLBase::train_(ClassificationDataStream &trainingData)MLBasevirtual
MLBase::train_(UnlabelledData &trainingData)MLBasevirtual
MLBase::train_(MatrixFloat &data)MLBasevirtual
trained (defined in MLBase)MLBaseprotected
trainingLog (defined in MLBase)MLBaseprotected
trainingResults (defined in MLBase)MLBaseprotected
trainingResultsObserverManager (defined in MLBase)MLBaseprotected
useScaling (defined in MLBase)MLBaseprotected
useValidationSet (defined in MLBase)MLBaseprotected
validationSetAccuracy (defined in MLBase)MLBaseprotected
validationSetPrecision (defined in MLBase)MLBaseprotected
validationSetRecall (defined in MLBase)MLBaseprotected
validationSetSize (defined in MLBase)MLBaseprotected
warningLog (defined in GRTBase)GRTBaseprotected
~ContinuousHiddenMarkovModel() (defined in ContinuousHiddenMarkovModel)ContinuousHiddenMarkovModelvirtual
~GRTBase(void)GRTBasevirtual
~MLBase(void)MLBasevirtual
~Observer() (defined in Observer< TrainingResult >)Observer< TrainingResult >inlinevirtual
~Observer() (defined in Observer< TestInstanceResult >)Observer< TestInstanceResult >inlinevirtual