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.
GRT::SVM Class Reference
Inheritance diagram for GRT::SVM:
GRT::Classifier GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::Observer< TestInstanceResult >

Public Types

enum  SVMTypes {
  C_SVC = 0, NU_SVC, ONE_CLASS, EPSILON_SVR,
  NU_SVR
}
 
enum  SVMKernelTypes {
  LINEAR_KERNEL = 0, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL,
  PRECOMPUTED_KERNEL
}
 
- Public Types inherited from GRT::Classifier
typedef std::map< string, Classifier *(*)() > StringClassifierMap
 
- Public Types inherited from GRT::MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 

Public Member Functions

 SVM (UINT kernelType=LINEAR_KERNEL, UINT svmType=C_SVC, bool useScaling=true, bool useNullRejection=false, bool useAutoGamma=true, double gamma=0.1, UINT degree=3, double coef0=0, double nu=0.5, double C=1, bool useCrossValidation=false, UINT kFoldValue=10)
 
 SVM (const SVM &rhs)
 
virtual ~SVM ()
 
SVMoperator= (const SVM &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)
 
bool init (UINT kernelType, UINT svmType, bool useScaling, bool useNullRejection, bool useAutoGamma, double gamma, UINT degree, double coef0, double nu, double C, bool useCrossValidation, UINT kFoldValue)
 
void initDefaultSVMSettings ()
 
bool getIsCrossValidationTrainingEnabled () const
 
bool getIsAutoGammaEnabled () const
 
string getSVMType () const
 
string getKernelType () const
 
UINT getDegree () const
 
virtual UINT getNumClasses () const
 
double getGamma () const
 
double getNu () const
 
double getCoef0 () const
 
double getC () const
 
double getCrossValidationResult () const
 
struct svm_modelgetModel () const
 
bool setSVMType (const UINT svmType)
 
bool setKernelType (const UINT kernelType)
 
bool setGamma (const double gamma)
 
bool setDegree (const UINT degree)
 
bool setNu (const double nu)
 
bool setCoef0 (const double coef0)
 
bool setC (const double C)
 
bool setKFoldCrossValidationValue (const UINT kFoldValue)
 
bool enableAutoGamma (const bool useAutoGamma)
 
bool enableCrossValidationTraining (const bool useCrossValidation)
 
- Public Member Functions inherited from GRT::Classifier
 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
 
UINT getClassLabelIndexValue (UINT classLabel) const
 
UINT getPredictedClassLabel () const
 
VectorDouble getClassLikelihoods () const
 
VectorDouble getClassDistances () const
 
VectorDouble getNullRejectionThresholds () const
 
vector< UINT > getClassLabels () const
 
vector< MinMaxgetRanges () const
 
bool enableNullRejection (bool useNullRejection)
 
virtual bool setNullRejectionCoeff (double nullRejectionCoeff)
 
virtual bool setNullRejectionThresholds (VectorDouble newRejectionThresholds)
 
virtual bool recomputeNullRejectionThresholds ()
 
bool getTimeseriesCompatible () const
 
ClassifiercreateNewInstance () const
 
ClassifierdeepCopy () const
 
const ClassifiergetClassifierPointer () const
 
const ClassifiergetBaseClassifier () const
 
- Public Member Functions inherited from GRT::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 (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)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () const
 
vector< TrainingResultgetTrainingResults () const
 
- Public Member Functions inherited from GRT::GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
string getClassType () const
 
string getLastWarningMessage () const
 
string getLastErrorMessage () const
 
string getLastInfoMessage () const
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 
- Public Member Functions inherited from GRT::Observer< TrainingResult >
virtual void notify (const TrainingResult &data)
 
- Public Member Functions inherited from GRT::Observer< TestInstanceResult >
virtual void notify (const TestInstanceResult &data)
 

Protected Member Functions

void deleteProblemSet ()
 
bool validateProblemAndParameters ()
 
bool validateSVMType (UINT svmType)
 
bool validateKernelType (UINT kernelType)
 
bool convertClassificationDataToLIBSVMFormat (ClassificationData &trainingData)
 
bool trainSVM ()
 
bool predictSVM (VectorDouble &inputVector)
 
bool predictSVM (VectorDouble &inputVector, double &maxProbability, VectorDouble &probabilites)
 
bool loadLegacyModelFromFile (fstream &file)
 
struct svm_modeldeepCopyModel () const
 
bool deepCopyProblem (const struct svm_problem &source_problem, struct svm_problem &target_problem, const unsigned int numInputDimensions) const
 
bool deepCopyParam (const svm_parameter &source_param, svm_parameter &target_param) const
 
- Protected Member Functions inherited from GRT::Classifier
bool saveBaseSettingsToFile (fstream &file) const
 
bool loadBaseSettingsFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::MLBase
bool saveBaseSettingsToFile (fstream &file) const
 
bool loadBaseSettingsFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 

Protected Attributes

bool problemSet
 
struct svm_modelmodel
 
struct svm_parameter param
 
struct svm_problem prob
 
UINT kFoldValue
 
double classificationThreshold
 
double crossValidationResult
 
bool useAutoGamma
 
bool useCrossValidation
 
- Protected Attributes inherited from GRT::Classifier
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< MinMaxranges
 
- Protected Attributes inherited from GRT::MLBase
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< TrainingResulttrainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 
- Protected Attributes inherited from GRT::GRTBase
string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Static Protected Attributes

static RegisterClassifierModule< SVMregisterModule
 

Additional Inherited Members

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

Detailed Description

Definition at line 49 of file SVM.h.

Constructor & Destructor Documentation

GRT::SVM::SVM ( UINT  kernelType = LINEAR_KERNEL,
UINT  svmType = C_SVC,
bool  useScaling = true,
bool  useNullRejection = false,
bool  useAutoGamma = true,
double  gamma = 0.1,
UINT  degree = 3,
double  coef0 = 0,
double  nu = 0.5,
double  C = 1,
bool  useCrossValidation = false,
UINT  kFoldValue = 10 
)

Default constructor.

Set the initial SVM settings, although these can be changed at any time using either init(...) function of the set... functions.

Parameters
UINTkernelType: this sets the SVM kernelType. Options are LINEAR_KERNEL, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL. The default kernelType is kernelType=LINEAR_KERNEL
UINTsvmType: this sets the SVM type. Options are C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR. The default svmType is svmType=C_SVC
booluseScaling: sets if the training/prediction data will be scaled to the default range of [-1. 1.]. The SVM algorithm commonly achieves a better classification result if scaling is turned on. The default useScaling value is useScaling=true
booluseNullRejection: sets if a predicted class will be rejected if the classes' probability is below the classificationThreshold. The default value is useNullRejection=false
booluseAutoGamma: sets if the SVM gamma parameter will automatically be computed, if set to true then gamma will be set to (1.0/numFeatures), where numFeatures is the number of features in the training data. The default value is useAutoGamma=true
doublegamma: sets the SVM gamma parameter. The default value is gamma=0.1
UINTdegree: sets the SVM degree parameter. The default value is degree=3
doublecoef0: sets the SVM coef0 parameter. The default value is coef0=0
doublenu: sets the SVM nu parameter. The default value is nu=0.5
doubleC: sets the SVM C parameter. The default value is C=1
booluseCrossValidation: sets if the SVM model will be trained using cross validation. The default value is useCrossValidation=false
UINTkFoldValue: sets the number of folds that will be used for cross validation. The default value is kFoldValue=10

Definition at line 28 of file SVM.cpp.

GRT::SVM::SVM ( const SVM rhs)

Default copy constructor. Copies the settings from the rhs SVM instance to this instance

Parameters
constSVM &rhs: another instance of a SVM

Definition at line 72 of file SVM.cpp.

GRT::SVM::~SVM ( )
virtual

Default Destructor

Definition at line 90 of file SVM.cpp.

Member Function Documentation

bool GRT::SVM::clear ( )
virtual

Clears any previous model or problem.

Reimplemented from GRT::Classifier.

Definition at line 932 of file SVM.cpp.

bool GRT::SVM::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 SVM instance) into this instance

Parameters
Classifier*classifier: a pointer to the Classifier Base Class, this should be pointing to another SVM instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from GRT::Classifier.

Definition at line 116 of file SVM.cpp.

bool GRT::SVM::enableAutoGamma ( const bool  useAutoGamma)

Sets if the gamma parameter will be automatically computed from the training data.

Parameters
constbool useAutoGamma: the new useAutoGamma setting return returns true if the useAutoGamma was set, false otherwise

Definition at line 1111 of file SVM.cpp.

bool GRT::SVM::enableCrossValidationTraining ( const bool  useCrossValidation)

Sets if cross validation should be used during the training phase.

Parameters
constbool useCrossValidation: the new useCrossValidation setting return returns true if the useCrossValidation was set, false otherwise

Definition at line 1116 of file SVM.cpp.

double GRT::SVM::getC ( ) const

Gets the current C value.

Returns
returns the current C value.

Definition at line 1047 of file SVM.cpp.

double GRT::SVM::getCoef0 ( ) const

Gets the current coef0 value.

Returns
returns the current coef0 value.

Definition at line 1040 of file SVM.cpp.

double GRT::SVM::getCrossValidationResult ( ) const

Gets the last cross validation result, if the model has been trained and cross validation was enabled.

Returns
returns the last cross validation result.

Definition at line 1054 of file SVM.cpp.

UINT GRT::SVM::getDegree ( ) const

Gets the current degree value.

Returns
returns the current degree value.

Definition at line 1019 of file SVM.cpp.

double GRT::SVM::getGamma ( ) const

Gets the current gamma value.

Returns
returns the current gamma value.

Definition at line 1026 of file SVM.cpp.

bool GRT::SVM::getIsAutoGammaEnabled ( ) const

Gets if the gamma parameter will be automatically computed from the training data. If false, then the user needs to set the gamma parameter.

Returns
returns true if the gamma parameter will be automatically computed, false otherwise

Definition at line 950 of file SVM.cpp.

bool GRT::SVM::getIsCrossValidationTrainingEnabled ( ) const

Gets if cross validation is enabled.

Returns
returns true if cross validation is enabled, false otherwise

Definition at line 946 of file SVM.cpp.

string GRT::SVM::getKernelType ( ) const

Gets the current kernel type as a string.

If the type has not been set correctly then the string value UNKNOWN will be returned.

Returns
returns the current kernel type as a string.

Definition at line 985 of file SVM.cpp.

double GRT::SVM::getNu ( ) const

Gets the current nu value.

Returns
returns the current nu value.

Definition at line 1033 of file SVM.cpp.

UINT GRT::SVM::getNumClasses ( ) const
virtual

Returns the number of classes in the trained model.

If the model has not been trained then 0 will be returned.

Returns
returns the number of classes in the trained model or 0 if the model has not been trained

Reimplemented from GRT::Classifier.

Definition at line 1014 of file SVM.cpp.

string GRT::SVM::getSVMType ( ) const

Gets the current SVM type as a string.

If the type has not been set correctly then the string value UNKNOWN will be returned.

Returns
returns the current SVM type as a string.

Definition at line 954 of file SVM.cpp.

bool GRT::SVM::init ( UINT  kernelType,
UINT  svmType,
bool  useScaling,
bool  useNullRejection,
bool  useAutoGamma,
double  gamma,
UINT  degree,
double  coef0,
double  nu,
double  C,
bool  useCrossValidation,
UINT  kFoldValue 
)

This initializes the SVM settings and parameters. Any previous model, settings, or problems will be cleared.

Parameters
UINTkernelType: this sets the SVM kernelType. Options are LINEAR_KERNEL, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL
UINTsvmType: this sets the SVM type. Options are C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR
booluseScaling: sets if the training/prediction data will be scaled to the default range of [-1. 1.]. The SVM algorithm commonly achieves a better classification result if scaling is turned on
booluseNullRejection: sets if a predicted class will be rejected if the classes' probability is below the classificationThreshold
booluseAutoGamma: sets if the SVM gamma parameter will automatically be computed, if set to true then gamma will be set to (1.0/numFeatures), where numFeatures is the number of features in the training data
doublegamma: sets the SVM gamma parameter
UINTdegree: sets the SVM degree parameter
doublecoef0: sets the SVM coef0 parameter
doublenu: sets the SVM nu parameter
doubleC: sets the SVM C parameter
booluseCrossValidation: sets if the SVM model will be trained using cross validation
UINTkFoldValue: sets the number of folds that will be used for cross validation
Returns
returns true if the SVM was initialized, false otherwise

Definition at line 199 of file SVM.cpp.

void GRT::SVM::initDefaultSVMSettings ( )

This initializes the default SVM settings and parameters. Any previous model, settings, or problems will be cleared.

Definition at line 255 of file SVM.cpp.

bool GRT::SVM::loadLegacyModelFromFile ( fstream &  file)
protected

Load the ranges

Definition at line 1338 of file SVM.cpp.

bool GRT::SVM::loadModelFromFile ( fstream &  file)
virtual

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

Parameters
fstream&file: a reference to the file the SVM model will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 627 of file SVM.cpp.

SVM & GRT::SVM::operator= ( const SVM rhs)

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

Parameters
constSVM &rhs: another instance of a SVM
Returns
returns a pointer to this instance of the SVM

Definition at line 94 of file SVM.cpp.

bool GRT::SVM::predict_ ( VectorDouble &  inputVector)
virtual

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

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

Reimplemented from GRT::MLBase.

Definition at line 172 of file SVM.cpp.

bool GRT::SVM::saveModelToFile ( fstream &  file) const
virtual

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

Parameters
fstream&file: a reference to the file the SVM model will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 499 of file SVM.cpp.

bool GRT::SVM::setC ( const double  C)

Sets the SVM C parameter. This is only used if the SVM type parameter is set to C_SVC, EPSILON_SVR or NU_SVR.

Parameters
constdouble C: the new C value
Returns
returns true if the C parameter was updated, false otherwise

Definition at line 1097 of file SVM.cpp.

bool GRT::SVM::setCoef0 ( const double  coef0)

Sets the SVM coef0 parameter. This is only used if the SVM kernel type parameter is set to POLY_KERNEL or SIGMOID_KERNEL.

Parameters
constdouble coef0: the new coef0 value
Returns
returns true if the gamcoef0ma parameter was updated, false otherwise

Definition at line 1092 of file SVM.cpp.

bool GRT::SVM::setDegree ( const UINT  degree)

Sets the SVM degree parameter. This is only used if the SVM kernel parameter is set to POLY_KERNEL.

Parameters
constUINT degree: the new degree value
Returns
returns true if the degree parameter was updated, false otherwise

Definition at line 1082 of file SVM.cpp.

bool GRT::SVM::setGamma ( const double  gamma)

Sets the SVM gamma parameter. The user should only try and set this value manually if the useAutoGamma parameter has been set to false.

Parameters
constdouble gamma: the new gamma value
Returns
returns true if the gamma parameter was updated, false otherwise

Definition at line 1073 of file SVM.cpp.

bool GRT::SVM::setKernelType ( const UINT  kernelType)

Sets the kernel type. This should be one of the SVMKernelTypes enumeration types.

Parameters
constUINT kernelType: the new kernel, options are LINEAR_KERNEL, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL
Returns
returns true if the kernel type was set correctly, false otherwise

Definition at line 1064 of file SVM.cpp.

bool GRT::SVM::setKFoldCrossValidationValue ( const UINT  kFoldValue)

Sets the kFold cross validation value.

Parameters
constUINT kFoldValue: the new kFoldValue, must be greater than 0 return returns true if the kFoldValue was set, false otherwise

Definition at line 1102 of file SVM.cpp.

bool GRT::SVM::setNu ( const double  nu)

Sets the SVM nu parameter. This is only used if the SVM type parameter is set to NU_SVC, ONE_CLASS, or NU_SVR.

Parameters
constdouble nu: the new nu value
Returns
returns true if the nu parameter was updated, false otherwise

Definition at line 1087 of file SVM.cpp.

bool GRT::SVM::setSVMType ( const UINT  svmType)

Sets the SVM type. This should be one of the SVMTypes enumeration types.

Parameters
constUINT svmType: the new SVM type, options are C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR
Returns
returns true if the SVM type was set correctly, false otherwise

Definition at line 1056 of file SVM.cpp.

bool GRT::SVM::train_ ( ClassificationData trainingData)
virtual

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

Parameters
ClassificationData&trainingData: a reference to the training data
Returns
returns true if the SVM model was trained, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 143 of file SVM.cpp.


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