GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
AdaBoost Class Reference

#include <AdaBoost.h>

Inheritance diagram for AdaBoost:
Classifier MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Types

enum  PredictionMethods { MAX_POSITIVE_VALUE =0, MAX_VALUE }
 
- Public Types inherited from Classifier
typedef std::map< std::string, Classifier *(*)() > StringClassifierMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 

Public Member Functions

 AdaBoost (const WeakClassifier &weakClassifier=DecisionStump(), bool useScaling=false, bool useNullRejection=false, Float nullRejectionCoeff=10.0, UINT numBoostingIterations=20, UINT predictionMethod=MAX_VALUE)
 
 AdaBoost (const AdaBoost &rhs)
 
virtual ~AdaBoost ()
 
AdaBoostoperator= (const AdaBoost &rhs)
 
virtual bool deepCopyFrom (const Classifier *classifier)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool predict_ (VectorFloat &inputVector)
 
virtual bool clear ()
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
virtual bool recomputeNullRejectionThresholds ()
 
bool setNullRejectionCoeff (Float nullRejectionCoeff)
 
bool setWeakClassifier (const WeakClassifier &weakClassifer)
 
bool addWeakClassifier (const WeakClassifier &weakClassifer)
 
bool clearWeakClassifiers ()
 
bool setNumBoostingIterations (UINT numBoostingIterations)
 
bool setPredictionMethod (UINT predictionMethod)
 
void printModel ()
 
Vector< AdaBoostClassModelgetModels () const
 
- Public Member Functions inherited from Classifier
 Classifier (void)
 
virtual ~Classifier (void)
 
bool copyBaseVariables (const Classifier *classifier)
 
virtual bool reset ()
 
std::string getClassifierType () const
 
bool getSupportsNullRejection () const
 
bool getNullRejectionEnabled () const
 
Float getNullRejectionCoeff () const
 
Float getMaximumLikelihood () const
 
Float getBestDistance () const
 
Float getPhase () const
 
virtual UINT getNumClasses () const
 
UINT getClassLabelIndexValue (UINT classLabel) const
 
UINT getPredictedClassLabel () const
 
VectorFloat getClassLikelihoods () const
 
VectorFloat getClassDistances () const
 
VectorFloat getNullRejectionThresholds () const
 
Vector< UINT > getClassLabels () const
 
Vector< MinMaxgetRanges () const
 
bool enableNullRejection (bool useNullRejection)
 
virtual bool setNullRejectionThresholds (VectorFloat newRejectionThresholds)
 
bool getTimeseriesCompatible () const
 
ClassifiercreateNewInstance () const
 
ClassifierdeepCopy () const
 
const ClassifiergetClassifierPointer () const
 
const ClassifiergetBaseClassifier () const
 
- Public Member Functions inherited from MLBase
 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 (ClassificationDataStream trainingData)
 
virtual bool train_ (ClassificationDataStream &trainingData)
 
virtual bool train (UnlabelledData trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool train (MatrixFloat data)
 
virtual bool train_ (MatrixFloat &data)
 
virtual bool predict (VectorFloat inputVector)
 
virtual bool predict (MatrixFloat inputMatrix)
 
virtual bool predict_ (MatrixFloat &inputMatrix)
 
virtual bool map (VectorFloat inputVector)
 
virtual bool map_ (VectorFloat &inputVector)
 
virtual bool print () const
 
virtual bool save (const std::string filename) const
 
virtual bool load (const std::string filename)
 
virtual bool saveModelToFile (std::string filename) const
 
virtual bool loadModelFromFile (std::string filename)
 
virtual bool getModel (std::ostream &stream) const
 
Float scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
 
virtual std::string getModelAsString () const
 
DataType getInputType () const
 
DataType getOutputType () 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
 
Float getMinChange () const
 
Float getLearningRate () const
 
Float getRootMeanSquaredTrainingError () const
 
Float getTotalSquaredTrainingError () const
 
Float getValidationSetAccuracy () const
 
VectorFloat getValidationSetPrecision () const
 
VectorFloat getValidationSetRecall () 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 (const bool useScaling)
 
bool setMaxNumEpochs (const UINT maxNumEpochs)
 
bool setMinNumEpochs (const UINT minNumEpochs)
 
bool setMinChange (const Float minChange)
 
bool setLearningRate (const Float learningRate)
 
bool setUseValidationSet (const bool useValidationSet)
 
bool setValidationSetSize (const UINT validationSetSize)
 
bool setRandomiseTrainingOrder (const bool randomiseTrainingOrder)
 
bool setTrainingLoggingEnabled (const bool loggingEnabled)
 
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)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () const
 
Vector< TrainingResult > getTrainingResults () const
 
- Public Member Functions inherited from GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
std::string getClassType () const
 
std::string getLastWarningMessage () const
 
std::string getLastErrorMessage () const
 
std::string getLastInfoMessage () const
 
bool setInfoLoggingEnabled (const bool loggingEnabled)
 
bool setWarningLoggingEnabled (const bool loggingEnabled)
 
bool setErrorLoggingEnabled (const bool loggingEnabled)
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 
- Public Member Functions inherited from Observer< TrainingResult >
virtual void notify (const TrainingResult &data)
 
- Public Member Functions inherited from Observer< TestInstanceResult >
virtual void notify (const TestInstanceResult &data)
 

Protected Member Functions

bool loadLegacyModelFromFile (std::fstream &file)
 
- Protected Member Functions inherited from Classifier
bool saveBaseSettingsToFile (std::fstream &file) const
 
bool loadBaseSettingsFromFile (std::fstream &file)
 
- Protected Member Functions inherited from MLBase
bool saveBaseSettingsToFile (std::fstream &file) const
 
bool loadBaseSettingsFromFile (std::fstream &file)
 
- Protected Member Functions inherited from GRTBase
Float SQR (const Float &x) const
 

Protected Attributes

UINT numBoostingIterations
 
UINT predictionMethod
 
Vector< WeakClassifier * > weakClassifiers
 
Vector< AdaBoostClassModelmodels
 
- Protected Attributes inherited from Classifier
std::string classifierType
 
bool supportsNullRejection
 
bool useNullRejection
 
UINT numClasses
 
UINT predictedClassLabel
 
UINT classifierMode
 
Float nullRejectionCoeff
 
Float maxLikelihood
 
Float bestDistance
 
Float phase
 
VectorFloat classLikelihoods
 
VectorFloat classDistances
 
VectorFloat nullRejectionThresholds
 
Vector< UINT > classLabels
 
Vector< MinMaxranges
 
- Protected Attributes inherited from MLBase
bool trained
 
bool useScaling
 
DataType inputType
 
DataType outputType
 
UINT baseType
 
UINT numInputDimensions
 
UINT numOutputDimensions
 
UINT numTrainingIterationsToConverge
 
UINT minNumEpochs
 
UINT maxNumEpochs
 
UINT validationSetSize
 
Float learningRate
 
Float minChange
 
Float rootMeanSquaredTrainingError
 
Float totalSquaredTrainingError
 
Float validationSetAccuracy
 
bool useValidationSet
 
bool randomiseTrainingOrder
 
VectorFloat validationSetPrecision
 
VectorFloat validationSetRecall
 
Random random
 
std::vector< TrainingResult > trainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 
- Protected Attributes inherited from GRTBase
std::string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Static Protected Attributes

static RegisterClassifierModule< AdaBoostregisterModule
 

Additional Inherited Members

- Static Public Member Functions inherited from Classifier
static ClassifiercreateInstanceFromString (std::string const &classifierType)
 
static Vector< std::string > getRegisteredClassifiers ()
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 
- Protected Types inherited from Classifier
enum  ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
 
- Static Protected Member Functions inherited from Classifier
static StringClassifierMapgetMap ()
 

Detailed Description

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 57 of file AdaBoost.h.

Member Enumeration Documentation

These are the two prediction methods that the GRT::AdaBoost classifier can use.

Definition at line 240 of file AdaBoost.h.

Constructor & Destructor Documentation

AdaBoost::AdaBoost ( const WeakClassifier weakClassifier = DecisionStump(),
bool  useScaling = false,
bool  useNullRejection = false,
Float  nullRejectionCoeff = 10.0,
UINT  numBoostingIterations = 20,
UINT  predictionMethod = MAX_VALUE 
)

Default Constructor

Parameters
weakClassifiersets the initial weak classifier that is added to the vector of weak classifiers used to train the AdaBoost model
useScalingsets if the training and prediction data should be scaled to a specific range. Default value is useScaling = false
useNullRejectionsets 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
nullRejectionCoeffsets 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
numBoostingIterationssets the number of boosting iterations to use during training. Default value = 20
predictionMethodsets the prediction method for AdaBoost, this should be one of the PredictionMethods. Default value = MAX_VALUE

Definition at line 28 of file AdaBoost.cpp.

AdaBoost::AdaBoost ( const AdaBoost rhs)

Defines the copy constructor.

Parameters
rhsthe instance from which all the data will be copied into this instance

Definition at line 45 of file AdaBoost.cpp.

AdaBoost::~AdaBoost ( void  )
virtual

Default Destructor

Definition at line 55 of file AdaBoost.cpp.

Member Function Documentation

bool 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.

Returns
returns true if the WeakClassifier was added successfully, false otherwise

Definition at line 524 of file AdaBoost.cpp.

bool AdaBoost::clear ( )
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.

Returns
returns true if the module was cleared succesfully, false otherwise

Reimplemented from Classifier.

Definition at line 501 of file AdaBoost.cpp.

bool AdaBoost::clearWeakClassifiers ( )

Clears all the current WeakClassifiers.

Returns
returns true if the WeakClassifiers was cleared successfully, false otherwise

Definition at line 532 of file AdaBoost.cpp.

bool AdaBoost::deepCopyFrom ( const Classifier classifier)
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 AdaBoost instance) into this instance

Parameters
classifiera pointer to the Classifier Base Class, this should be pointing to another AdaBoost instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from Classifier.

Definition at line 83 of file AdaBoost.cpp.

Vector< AdaBoostClassModel > AdaBoost::getModels ( ) const
inline

Returns the current vector of AdaBoostClassModel models.

Returns
a vector containing the current AdaBoostClassModel models.

Definition at line 216 of file AdaBoost.h.

bool AdaBoost::loadModelFromFile ( std::fstream &  file)
virtual

This loads a trained AdaBoost model from a file. This overrides the loadModelFromFile function in the Classifier base class.

Parameters
filea reference to the file the AdaBoost model will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 434 of file AdaBoost.cpp.

AdaBoost & AdaBoost::operator= ( const AdaBoost rhs)

Defines how the data from the rhs AdaBoost should be copied to this AdaBoost

Parameters
rhsanother instance of a AdaBoost
Returns
returns a reference to this instance of the AdaBoost

Definition at line 61 of file AdaBoost.cpp.

bool AdaBoost::predict_ ( VectorFloat inputVector)
virtual

This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.

Parameters
inputVectorthe input vector to classify
Returns
returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 291 of file AdaBoost.cpp.

void AdaBoost::printModel ( )

Prints the current model to the std::out.

Returns
void.

Definition at line 560 of file AdaBoost.cpp.

bool AdaBoost::recomputeNullRejectionThresholds ( )
virtual

This recomputes the null rejection thresholds for each of the classes in the AdaBoost model. This will be called automatically if the setGamma(Float gamma) function is called. The AdaBoost model needs to be trained first before this function can be called.

Returns
returns true if the null rejection thresholds were updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 380 of file AdaBoost.cpp.

bool AdaBoost::saveModelToFile ( std::fstream &  file) const
virtual

This saves the trained AdaBoost model to a file. This overrides the saveModelToFile function in the Classifier base class.

Parameters
filea reference to the file the AdaBoost model will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 399 of file AdaBoost.cpp.

bool AdaBoost::setNullRejectionCoeff ( Float  nullRejectionCoeff)
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.

Returns
returns true if the gamma parameter was updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 389 of file AdaBoost.cpp.

bool 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.

Returns
returns true if the numBoostingIterations was set successfully, false otherwise

Definition at line 544 of file AdaBoost.cpp.

bool AdaBoost::setPredictionMethod ( UINT  predictionMethod)

Sets the prediction method for AdaBoost, this should be one of the PredictionMethods enumerations.

Parameters
predictionMethodthe predictionMethod that should be used by AdaBoost, this should be one of the PredictionMethods enumerations
Returns
returns true if the predictionMethod was set successfully, false otherwise

Definition at line 552 of file AdaBoost.cpp.

bool AdaBoost::setWeakClassifier ( const WeakClassifier weakClassifer)

Sets the WeakClassifier to use for boosting.

If this function is called, any previously set WeakClassifiers will be removed.

Returns
returns true if the WeakClassifier was added successfully, false otherwise

Definition at line 512 of file AdaBoost.cpp.

bool AdaBoost::train_ ( ClassificationData trainingData)
virtual

This trains the AdaBoost model, using the labelled classification data. This overrides the train function in the Classifier base class.

Parameters
trainingDataa reference to the training data
Returns
returns true if the AdaBoost model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 114 of file AdaBoost.cpp.


The documentation for this class was generated from the following files: