![]() |
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.
|
Public Member Functions | |
ANBC (bool useScaling=false, bool useNullRejection=false, double nullRejectionCoeff=10.0) | |
ANBC (const ANBC &rhs) | |
virtual | ~ANBC (void) |
ANBC & | operator= (const ANBC &rhs) |
virtual bool | deepCopyFrom (const Classifier *classifier) |
virtual bool | train_ (ClassificationData &trainingData) |
virtual bool | predict_ (VectorDouble &inputVector) |
virtual bool | reset () |
virtual bool | clear () |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
virtual bool | recomputeNullRejectionThresholds () |
VectorDouble | getNullRejectionThresholds () const |
vector< ANBC_Model > | getModels () |
bool | setNullRejectionCoeff (double nullRejectionCoeff) |
bool | setWeights (const ClassificationData &weightsData) |
bool | clearWeights () |
![]() | |
Classifier (void) | |
virtual | ~Classifier (void) |
bool | copyBaseVariables (const Classifier *classifier) |
string | getClassifierType () const |
bool | getSupportsNullRejection () const |
bool | getNullRejectionEnabled () const |
double | getNullRejectionCoeff () const |
double | getMaximumLikelihood () const |
double | getBestDistance () const |
double | getPhase () const |
virtual UINT | getNumClasses () const |
UINT | getClassLabelIndexValue (UINT classLabel) const |
UINT | getPredictedClassLabel () const |
VectorDouble | getClassLikelihoods () const |
VectorDouble | getClassDistances () const |
VectorDouble | getNullRejectionThresholds () const |
vector< UINT > | getClassLabels () const |
vector< MinMax > | getRanges () const |
bool | enableNullRejection (bool useNullRejection) |
virtual bool | setNullRejectionThresholds (VectorDouble newRejectionThresholds) |
bool | getTimeseriesCompatible () const |
Classifier * | createNewInstance () const |
Classifier * | deepCopy () const |
const Classifier * | getClassifierPointer () const |
const Classifier & | getBaseClassifier () const |
![]() | |
MLBase (void) | |
virtual | ~MLBase (void) |
bool | copyMLBaseVariables (const MLBase *mlBase) |
virtual bool | train (ClassificationData trainingData) |
virtual bool | train (RegressionData trainingData) |
virtual bool | train_ (RegressionData &trainingData) |
virtual bool | train (TimeSeriesClassificationData trainingData) |
virtual bool | train_ (TimeSeriesClassificationData &trainingData) |
virtual bool | train (TimeSeriesClassificationDataStream trainingData) |
virtual bool | train_ (TimeSeriesClassificationDataStream &trainingData) |
virtual bool | train (UnlabelledData trainingData) |
virtual bool | train_ (UnlabelledData &trainingData) |
virtual bool | train (MatrixDouble data) |
virtual bool | train_ (MatrixDouble &data) |
virtual bool | predict (VectorDouble inputVector) |
virtual bool | predict (MatrixDouble inputMatrix) |
virtual bool | predict_ (MatrixDouble &inputMatrix) |
virtual bool | map (VectorDouble inputVector) |
virtual bool | map_ (VectorDouble &inputVector) |
virtual bool | print () const |
virtual bool | save (const string filename) const |
virtual bool | load (const string filename) |
virtual bool | saveModelToFile (string filename) const |
virtual bool | loadModelFromFile (string filename) |
virtual bool | getModel (ostream &stream) const |
double | scale (const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false) |
virtual string | getModelAsString () const |
UINT | getBaseType () const |
UINT | getNumInputFeatures () const |
UINT | getNumInputDimensions () const |
UINT | getNumOutputDimensions () const |
UINT | getMinNumEpochs () const |
UINT | getMaxNumEpochs () const |
UINT | getValidationSetSize () const |
UINT | getNumTrainingIterationsToConverge () const |
double | getMinChange () const |
double | getLearningRate () const |
double | getRootMeanSquaredTrainingError () const |
double | getTotalSquaredTrainingError () const |
bool | getUseValidationSet () const |
bool | getRandomiseTrainingOrder () const |
bool | getTrained () const |
bool | getModelTrained () const |
bool | getScalingEnabled () const |
bool | getIsBaseTypeClassifier () const |
bool | getIsBaseTypeRegressifier () const |
bool | getIsBaseTypeClusterer () const |
bool | enableScaling (bool useScaling) |
bool | setMaxNumEpochs (const UINT maxNumEpochs) |
bool | setMinNumEpochs (const UINT minNumEpochs) |
bool | setMinChange (const double minChange) |
bool | setLearningRate (double learningRate) |
bool | setUseValidationSet (const bool useValidationSet) |
bool | setValidationSetSize (const UINT validationSetSize) |
bool | setRandomiseTrainingOrder (const bool randomiseTrainingOrder) |
bool | registerTrainingResultsObserver (Observer< TrainingResult > &observer) |
bool | registerTestResultsObserver (Observer< TestInstanceResult > &observer) |
bool | removeTrainingResultsObserver (const Observer< TrainingResult > &observer) |
bool | removeTestResultsObserver (const Observer< TestInstanceResult > &observer) |
bool | removeAllTrainingObservers () |
bool | removeAllTestObservers () |
bool | notifyTrainingResultsObservers (const TrainingResult &data) |
bool | notifyTestResultsObservers (const TestInstanceResult &data) |
MLBase * | getMLBasePointer () |
const MLBase * | getMLBasePointer () const |
vector< TrainingResult > | getTrainingResults () const |
![]() | |
GRTBase (void) | |
virtual | ~GRTBase (void) |
bool | copyGRTBaseVariables (const GRTBase *GRTBase) |
string | getClassType () const |
string | getLastWarningMessage () const |
string | getLastErrorMessage () const |
string | getLastInfoMessage () const |
GRTBase * | getGRTBasePointer () |
const GRTBase * | getGRTBasePointer () const |
![]() | |
virtual void | notify (const TrainingResult &data) |
![]() | |
virtual void | notify (const TestInstanceResult &data) |
Protected Member Functions | |
bool | loadLegacyModelFromFile (fstream &file) |
![]() | |
bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
![]() | |
bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
![]() | |
double | SQR (const double &x) const |
Protected Attributes | |
bool | weightsDataSet |
ClassificationData | weightsData |
vector< ANBC_Model > | models |
![]() | |
string | classifierType |
bool | supportsNullRejection |
bool | useNullRejection |
UINT | numClasses |
UINT | predictedClassLabel |
UINT | classifierMode |
double | nullRejectionCoeff |
double | maxLikelihood |
double | bestDistance |
double | phase |
VectorDouble | classLikelihoods |
VectorDouble | classDistances |
VectorDouble | nullRejectionThresholds |
vector< UINT > | classLabels |
vector< MinMax > | ranges |
![]() | |
bool | trained |
bool | useScaling |
UINT | baseType |
UINT | numInputDimensions |
UINT | numOutputDimensions |
UINT | numTrainingIterationsToConverge |
UINT | minNumEpochs |
UINT | maxNumEpochs |
UINT | validationSetSize |
double | learningRate |
double | minChange |
double | rootMeanSquaredTrainingError |
double | totalSquaredTrainingError |
bool | useValidationSet |
bool | randomiseTrainingOrder |
Random | random |
vector< TrainingResult > | trainingResults |
TrainingResultsObserverManager | trainingResultsObserverManager |
TestResultsObserverManager | testResultsObserverManager |
![]() | |
string | classType |
DebugLog | debugLog |
ErrorLog | errorLog |
InfoLog | infoLog |
TrainingLog | trainingLog |
TestingLog | testingLog |
WarningLog | warningLog |
Static Protected Attributes | |
static RegisterClassifierModule< ANBC > | registerModule |
Additional Inherited Members | |
![]() | |
typedef std::map< string, Classifier *(*)() > | StringClassifierMap |
![]() | |
enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
![]() | |
static Classifier * | createInstanceFromString (string const &classifierType) |
static vector< string > | getRegisteredClassifiers () |
![]() | |
static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
![]() | |
enum | ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE } |
![]() | |
static StringClassifierMap * | getMap () |
GRT::ANBC::ANBC | ( | bool | useScaling = false , |
bool | useNullRejection = false , |
||
double | nullRejectionCoeff = 10.0 |
||
) |
Default Constructor
bool | useScaling: sets if the training and prediction data should be scaled to a specific range. Default value is useScaling = false |
bool | useNullRejection: sets if null rejection will be used for the realtime prediction. If useNullRejection is set to true then the predictedClassLabel will be set to 0 (which is the default null label) if the distance between the inputVector and the top K datum is greater than the null rejection threshold for the top predicted class. The null rejection threshold is computed for each class during the training phase. Default value is useNullRejection = false |
double | nullRejectionCoeff: sets the null rejection coefficient, this is a multipler controlling the null rejection threshold for each class. This will only be used if the useNullRejection parameter is set to true. Default value is nullRejectionCoeff = 10.0 |
GRT::ANBC::ANBC | ( | const ANBC & | rhs | ) |
|
virtual |
This overrides the clear function in the Classifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.
Reimplemented from GRT::Classifier.
|
inline |
|
virtual |
This is required for the Gesture Recognition Pipeline for when the pipeline.setClassifier(...) method is called. It clones the data from the Base Class Classifier pointer (which should be pointing to an ANBC instance) into this instance
Classifier | *classifier: a pointer to the Classifier Base Class, this should be pointing to another ANBC instance |
Reimplemented from GRT::Classifier.
|
inline |
VectorDouble GRT::ANBC::getNullRejectionThresholds | ( | ) | const |
Gets a vector containing the null rejection thresholds for each class, this will be an N-dimensional vector, where N is the number of classes in the model.
|
protected |
|
virtual |
This loads a trained ANBC model from a file. This overrides the loadModelFromFile function in the Classifier base class.
fstream | &file: a reference to the file the ANBC model will be loaded from |
Reimplemented from GRT::MLBase.
|
virtual |
This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.
VectorDouble | inputVector: the input vector to classify |
Reimplemented from GRT::MLBase.
|
virtual |
This recomputes the null rejection thresholds for each of the classes in the ANBC model. This will be called automatically if the setGamma(double gamma) function is called. The ANBC model needs to be trained first before this function can be called.
Reimplemented from GRT::Classifier.
|
virtual |
This resets the ANBC classifier.
Reimplemented from GRT::Classifier.
|
virtual |
This saves the trained ANBC model to a file. This overrides the saveModelToFile function in the Classifier base class.
fstream | &file: a reference to the file the ANBC model will be saved to |
Reimplemented from GRT::MLBase.
|
virtual |
Sets the nullRejectionCoeff parameter. The nullRejectionCoeff parameter is a multipler controlling the null rejection threshold for each class. This function will also recompute the null rejection thresholds.
Reimplemented from GRT::Classifier.
bool GRT::ANBC::setWeights | ( | const ClassificationData & | weightsData | ) |
Sets the weights for the training and prediction. The dimensionality of the weights should match that of the training data used to train the ANBC models. The weights should be encapsualted into a LabelledClassificationData container, with one training sample for each class.
|
virtual |
This trains the ANBC model, using the labelled classification data. This overrides the train function in the Classifier base class.
ClassificationData | trainingData: a reference to the training data |
Reimplemented from GRT::MLBase.