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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.
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Public Member Functions | |
MinDist (bool useScaling=false, bool useNullRejection=false, double nullRejectionCoeff=10.0, UINT numClusters=10) | |
MinDist (const MinDist &rhs) | |
virtual | ~MinDist (void) |
MinDist & | operator= (const MinDist &rhs) |
virtual bool | deepCopyFrom (const Classifier *classifier) |
virtual bool | train_ (ClassificationData &trainingData) |
virtual bool | predict_ (VectorDouble &inputVector) |
virtual bool | clear () |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
virtual bool | recomputeNullRejectionThresholds () |
UINT | getNumClusters () const |
vector< MinDistModel > | getModels () const |
virtual bool | setNullRejectionCoeff (double nullRejectionCoeff) |
bool | setNumClusters (UINT numClusters) |
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Classifier (void) | |
virtual | ~Classifier (void) |
bool | copyBaseVariables (const Classifier *classifier) |
virtual bool | reset () |
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 |
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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 |
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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 |
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virtual void | notify (const TrainingResult &data) |
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virtual void | notify (const TestInstanceResult &data) |
Protected Member Functions | |
bool | loadLegacyModelFromFile (fstream &file) |
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bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
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bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
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double | SQR (const double &x) const |
Protected Attributes | |
UINT | numClusters |
vector< MinDistModel > | models |
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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 |
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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 |
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string | classType |
DebugLog | debugLog |
ErrorLog | errorLog |
InfoLog | infoLog |
TrainingLog | trainingLog |
TestingLog | testingLog |
WarningLog | warningLog |
Additional Inherited Members | |
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typedef std::map< string, Classifier *(*)() > | StringClassifierMap |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
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static Classifier * | createInstanceFromString (string const &classifierType) |
static vector< string > | getRegisteredClassifiers () |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
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enum | ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE } |
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static StringClassifierMap * | getMap () |
GRT::MinDist::MinDist | ( | bool | useScaling = false , |
bool | useNullRejection = false , |
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double | nullRejectionCoeff = 10.0 , |
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UINT | numClusters = 10 |
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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 |
UINT | numClusters: sets how many clusters each model will try to find during the training phase. Default value = 10 |
Definition at line 28 of file MinDist.cpp.
GRT::MinDist::MinDist | ( | const MinDist & | rhs | ) |
Defines the copy constructor.
const | MinDist &rhs: the instance from which all the data will be copied into this instance |
Definition at line 44 of file MinDist.cpp.
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Default Destructor
Definition at line 55 of file MinDist.cpp.
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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.
Definition at line 220 of file MinDist.cpp.
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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 MinDist instance) into this instance
Classifier | *classifier: a pointer to the Classifier Base Class, this should be pointing to another MinDist instance |
Reimplemented from GRT::Classifier.
Definition at line 71 of file MinDist.cpp.
vector< MinDistModel > GRT::MinDist::getModels | ( | ) | const |
Returns the MinDist models for each of the classes.
Definition at line 257 of file MinDist.cpp.
UINT GRT::MinDist::getNumClusters | ( | ) | const |
Returns the number of clusters in the model.
Definition at line 253 of file MinDist.cpp.
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Read the ranges if needed
Definition at line 433 of file MinDist.cpp.
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This loads a trained MinDist model from a file. This overrides the loadModelFromFile function in the Classifier base class.
fstream | &file: a reference to the file the MinDist model will be loaded from |
Reimplemented from GRT::MLBase.
Definition at line 303 of file MinDist.cpp.
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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.
Definition at line 162 of file MinDist.cpp.
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This recomputes the null rejection thresholds for each of the classes in the MinDist model. This will be called automatically if the setGamma(double gamma) function is called. The MinDist model needs to be trained first before this function can be called.
Reimplemented from GRT::Classifier.
Definition at line 231 of file MinDist.cpp.
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This saves the trained MinDist model to a file. This overrides the saveModelToFile function in the Classifier base class.
fstream | &file: a reference to the file the MinDist model will be saved to |
Reimplemented from GRT::MLBase.
Definition at line 261 of file MinDist.cpp.
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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.
Definition at line 243 of file MinDist.cpp.
bool GRT::MinDist::setNumClusters | ( | UINT | numClusters | ) |
Sets the numClusters parameter. The numClusters parameter sets how many clusters each model will try to find during the training phase. You should call this function before you train the model.
Definition at line 425 of file MinDist.cpp.
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This trains the MinDist 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.
Definition at line 89 of file MinDist.cpp.