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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 Types | |
enum | PredictionMethods { MAX_POSITIVE_VALUE =0, MAX_VALUE } |
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typedef std::map< string, Classifier *(*)() > | StringClassifierMap |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
Public Member Functions | |
AdaBoost (const WeakClassifier &weakClassifier=DecisionStump(), bool useScaling=false, bool useNullRejection=false, double nullRejectionCoeff=10.0, UINT numBoostingIterations=20, UINT predictionMethod=MAX_VALUE) | |
AdaBoost (const AdaBoost &rhs) | |
virtual | ~AdaBoost () |
AdaBoost & | operator= (const AdaBoost &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 () |
bool | setNullRejectionCoeff (double nullRejectionCoeff) |
bool | setWeakClassifier (const WeakClassifier &weakClassifer) |
bool | addWeakClassifier (const WeakClassifier &weakClassifer) |
bool | clearWeakClassifiers () |
bool | setNumBoostingIterations (UINT numBoostingIterations) |
bool | setPredictionMethod (UINT predictionMethod) |
void | printModel () |
vector< AdaBoostClassModel > | getModels () const |
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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 | numBoostingIterations |
UINT | predictionMethod |
vector< WeakClassifier * > | weakClassifiers |
vector< AdaBoostClassModel > | 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 |
Static Protected Attributes | |
static RegisterClassifierModule< AdaBoost > | registerModule |
Additional Inherited Members | |
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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 () |
Definition at line 57 of file AdaBoost.h.
These are the two prediction methods that the GRT::AdaBoost classifier can use.
Definition at line 240 of file AdaBoost.h.
GRT::AdaBoost::AdaBoost | ( | const WeakClassifier & | weakClassifier = DecisionStump() , |
bool | useScaling = false , |
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bool | useNullRejection = false , |
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double | nullRejectionCoeff = 10.0 , |
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UINT | numBoostingIterations = 20 , |
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UINT | predictionMethod = MAX_VALUE |
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Default Constructor
const | WeakClassifier &weakClassifier: sets the initial weak classifier that is added to the vector of weak classifiers used to train the AdaBoost model |
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 | numBoostingIterations: sets the number of boosting iterations to use during training. Default value = 20 |
UINT | predictionMethod: sets the prediction method for AdaBoost, this should be one of the PredictionMethods. Default value = MAX_VALUE |
Definition at line 30 of file AdaBoost.cpp.
GRT::AdaBoost::AdaBoost | ( | const AdaBoost & | rhs | ) |
Defines the copy constructor.
const | AdaBoost &rhs: the instance from which all the data will be copied into this instance |
Definition at line 47 of file AdaBoost.cpp.
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Default Destructor
Definition at line 57 of file AdaBoost.cpp.
bool GRT::AdaBoost::addWeakClassifier | ( | const WeakClassifier & | weakClassifer | ) |
Adds a WeakClassifier to the list of WeakClassifiers to use for boosting.
If this function is called, the new WeakClassifier will be added to the list of WeakClassifiers.
Definition at line 526 of file AdaBoost.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 503 of file AdaBoost.cpp.
bool GRT::AdaBoost::clearWeakClassifiers | ( | ) |
Clears all the current WeakClassifiers.
Definition at line 534 of file AdaBoost.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 AdaBoost instance) into this instance
Classifier | *classifier: a pointer to the Classifier Base Class, this should be pointing to another AdaBoost instance |
Reimplemented from GRT::Classifier.
Definition at line 85 of file AdaBoost.cpp.
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Returns the current vector of AdaBoostClassModel models.
Definition at line 216 of file AdaBoost.h.
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This loads a trained AdaBoost model from a file. This overrides the loadModelFromFile function in the Classifier base class.
fstream | &file: a reference to the file the AdaBoost model will be loaded from |
Reimplemented from GRT::MLBase.
Definition at line 436 of file AdaBoost.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 293 of file AdaBoost.cpp.
void GRT::AdaBoost::printModel | ( | ) |
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This recomputes the null rejection thresholds for each of the classes in the AdaBoost model. This will be called automatically if the setGamma(double gamma) function is called. The AdaBoost model needs to be trained first before this function can be called.
Reimplemented from GRT::Classifier.
Definition at line 382 of file AdaBoost.cpp.
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This saves the trained AdaBoost model to a file. This overrides the saveModelToFile function in the Classifier base class.
fstream | &file: a reference to the file the AdaBoost model will be saved to |
Reimplemented from GRT::MLBase.
Definition at line 401 of file AdaBoost.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 391 of file AdaBoost.cpp.
bool GRT::AdaBoost::setNumBoostingIterations | ( | UINT | numBoostingIterations | ) |
Sets the number of boosting iterations that should be used when training the AdaBoost model. The numBoostingIterations parameter must be greater than zero.
Definition at line 546 of file AdaBoost.cpp.
bool GRT::AdaBoost::setPredictionMethod | ( | UINT | predictionMethod | ) |
Sets the prediction method for AdaBoost, this should be one of the PredictionMethods enumerations.
UINT | predictionMethod: the predictionMethod that should be used by AdaBoost, this should be one of the PredictionMethods enumerations |
Definition at line 554 of file AdaBoost.cpp.
bool GRT::AdaBoost::setWeakClassifier | ( | const WeakClassifier & | weakClassifer | ) |
Sets the WeakClassifier to use for boosting.
If this function is called, any previously set WeakClassifiers will be removed.
Definition at line 514 of file AdaBoost.cpp.
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This trains the AdaBoost 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 116 of file AdaBoost.cpp.