Inheritance diagram for SVM:

Public Types
enum	SVMType { C_SVC = 0, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }

enum	KernelType { LINEAR_KERNEL = 0, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL }

Public Types inherited from Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

typedef std::map< std::string, Classifier ()() >	StringClassifierMap

Public Types inherited from MLBase
enum	BaseType { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER, PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }

Public Member Functions
	SVM (KernelType kernelType=LINEAR_KERNEL, SVMType svmType=C_SVC, bool useScaling=true, bool useNullRejection=false, bool useAutoGamma=true, Float gamma=0.1, UINT degree=3, Float coef0=0, Float nu=0.5, Float C=1, bool useCrossValidation=false, UINT kFoldValue=10)

	SVM (const SVM &rhs)

virtual	~SVM ()

SVM &	operator= (const SVM &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	clear ()

virtual bool	save (std::fstream &file) const

virtual bool	load (std::fstream &file)

bool	init (KernelType kernelType, SVMType svmType, bool useScaling, bool useNullRejection, bool useAutoGamma, Float gamma, UINT degree, Float coef0, Float nu, Float C, bool useCrossValidation, UINT kFoldValue)

void	initDefaultSVMSettings ()

bool	getIsCrossValidationTrainingEnabled () const

bool	getIsAutoGammaEnabled () const

std::string	getSVMType () const

std::string	getKernelType () const

UINT	getDegree () const

virtual UINT	getNumClasses () const

Float	getGamma () const

Float	getNu () const

Float	getCoef0 () const

Float	getC () const

Float	getCrossValidationResult () const

const struct LIBSVM::svm_model *	getLIBSVMModel () const

bool	setSVMType (const SVMType svmType)

bool	setKernelType (const KernelType kernelType)

bool	setGamma (const Float gamma)

bool	setDegree (const UINT degree)

bool	setNu (const Float nu)

bool	setCoef0 (const Float coef0)

bool	setC (const Float C)

bool	setKFoldCrossValidationValue (const UINT kFoldValue)

bool	enableAutoGamma (const bool useAutoGamma)

bool	enableCrossValidationTraining (const bool useCrossValidation)

Public Member Functions inherited from Classifier
	Classifier (const std::string &classifierId="")

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

virtual bool	computeAccuracy (const ClassificationData &data, Float &accuracy)

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

Float	getTrainingSetAccuracy () const

UINT	getClassLabelIndexValue (const UINT classLabel) const

UINT	getPredictedClassLabel () const

VectorFloat	getClassLikelihoods () const

VectorFloat	getClassDistances () const

VectorFloat	getNullRejectionThresholds () const

Vector< UINT >	getClassLabels () const

Vector< MinMax >	getRanges () const

bool	enableNullRejection (const bool useNullRejection)

virtual bool	setNullRejectionCoeff (const Float nullRejectionCoeff)

virtual bool	setNullRejectionThresholds (const VectorFloat &newRejectionThresholds)

virtual bool	recomputeNullRejectionThresholds ()

bool	getTimeseriesCompatible () const

Classifier *	create () const

	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create instead.", Classifier *createNewInstance() const )

	GRT_DEPRECATED_MSG ("createInstanceFromString is deprecated, use create instead.", static Classifier *createInstanceFromString(const std::string &id))

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () const

Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)

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 (RegressionData trainingData, RegressionData validationData)

virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData)

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)

	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const )

	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )

	GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename))

	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))

virtual bool	getModel (std::ostream &stream) const

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

BaseType	getType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getBatchSize () const

UINT	getNumRestarts () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRMSTrainingError () const

	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )

Float	getTotalSquaredTrainingError () const

Float	getRMSValidationError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )

bool	getConverged () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	getTrainingLoggingEnabled () const

bool	getTestingLoggingEnabled () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setBatchSize (const UINT batchSize)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setNumRestarts (const UINT numRestarts)

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	setTestingLoggingEnabled (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)

MLBase *	getMLBasePointer ()

const MLBase *	getMLBasePointer () const

Vector< TrainingResult >	getTrainingResults () const

Public Member Functions inherited from GRTBase
	GRTBase (const std::string &id="")

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )

std::string	getId () 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)

bool	setDebugLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)

Float	SQR (const Float &x) 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)

Static Public Member Functions
static std::string	getId ()

Static Public Member Functions inherited from Classifier
static Classifier *	create (const std::string &id)

static Vector< std::string >	getRegisteredClassifiers ()

Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Protected Member Functions
void	deleteProblemSet ()

bool	validateProblemAndParameters ()

bool	validateSVMType (SVMType svmType)

bool	validateKernelType (KernelType kernelType)

bool	convertClassificationDataToLIBSVMFormat (ClassificationData &trainingData)

bool	trainSVM ()

bool	predictSVM (VectorFloat &inputVector)

bool	predictSVM (VectorFloat &inputVector, Float &maxProbability, VectorFloat &probabilites)

bool	loadLegacyModelFromFile (std::fstream &file)

struct LIBSVM::svm_model *	deepCopyModel () const

bool	deepCopyProblem (const struct LIBSVM::svm_problem &source_problem, struct LIBSVM::svm_problem &target_problem, const unsigned int numInputDimensions) const

bool	deepCopyParam (const LIBSVM::svm_parameter &source_param, LIBSVM::svm_parameter &target_param) const

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 Attributes
bool	problemSet

struct LIBSVM::svm_model *	model

struct LIBSVM::svm_parameter	param

struct LIBSVM::svm_problem	prob

UINT	kFoldValue

Float	classificationThreshold

Float	crossValidationResult

bool	useAutoGamma

bool	useCrossValidation

Protected Attributes inherited from Classifier
bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

Float	trainingSetAccuracy

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

bool	converged

DataType	inputType

DataType	outputType

BaseType	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	batchSize

UINT	validationSetSize

UINT	numRestarts

Float	learningRate

Float	minChange

Float	rmsTrainingError

Float	rmsValidationError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

Vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

TrainingLog	trainingLog

TestingLog	testingLog

Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

WarningLog	warningLog

Additional Inherited Members
Static Protected Member Functions inherited from Classifier
static StringClassifierMap *	getMap ()

Detailed Description

Examples:: ClassificationModulesExamples/SVMExample/SVMExample.cpp.

Definition at line 47 of file SVM.h.

Constructor & Destructor Documentation

SVM::SVM	(	KernelType	kernelType = `LINEAR_KERNEL`,
		SVMType	svmType = `C_SVC`,
		bool	useScaling = `true`,
		bool	useNullRejection = `false`,
		bool	useAutoGamma = `true`,
		Float	gamma = `0.1`,
		UINT	degree = `3`,
		Float	coef0 = `0`,
		Float	nu = `0.5`,
		Float	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

kernelType	this sets the SVM kernelType. Options are LINEAR_KERNEL, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL. The default kernelType is kernelType=LINEAR_KERNEL
svmType	this sets the SVM type. Options are C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR. The default svmType is svmType=C_SVC
useScaling	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
useNullRejection	sets if a predicted class will be rejected if the classes' probability is below the classificationThreshold. The default value is useNullRejection=false
useAutoGamma	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
gamma	sets the SVM gamma parameter. The default value is gamma=0.1
degree	sets the SVM degree parameter. The default value is degree=3
coef0	sets the SVM coef0 parameter. The default value is coef0=0
nu	sets the SVM nu parameter. The default value is nu=0.5
C	sets the SVM C parameter. The default value is C=1
useCrossValidation	sets if the SVM model will be trained using cross validation. The default value is useCrossValidation=false
kFoldValue	sets the number of folds that will be used for cross validation. The default value is kFoldValue=10

Definition at line 35 of file SVM.cpp.

SVM::SVM ( const SVM & rhs )

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

Parameters

rhs	another instance of a SVM

Definition at line 74 of file SVM.cpp.

SVM::~SVM ( )

virtual

Default Destructor

Definition at line 87 of file SVM.cpp.

Member Function Documentation

bool SVM::clear ( )

virtual

Clears any previous model or problem.

Reimplemented from Classifier.

Definition at line 967 of file SVM.cpp.

bool 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 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 Classifier.

Definition at line 113 of file SVM.cpp.

bool SVM::enableAutoGamma ( const bool useAutoGamma )

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

Parameters

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

Definition at line 1149 of file SVM.cpp.

bool SVM::enableCrossValidationTraining ( const bool useCrossValidation )

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

Parameters

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

Definition at line 1154 of file SVM.cpp.

Float SVM::getC ( ) const

Gets the current C value.

Returns: returns the current C value.

Definition at line 1082 of file SVM.cpp.

Float SVM::getCoef0 ( ) const

Gets the current coef0 value.

Returns: returns the current coef0 value.

Definition at line 1075 of file SVM.cpp.

Float 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 1089 of file SVM.cpp.

UINT SVM::getDegree ( ) const

Gets the current degree value.

Returns: returns the current degree value.

Definition at line 1054 of file SVM.cpp.

Float SVM::getGamma ( ) const

Gets the current gamma value.

Returns: returns the current gamma value.

Definition at line 1061 of file SVM.cpp.

std::string SVM::getId ( )

static

Gets a string that represents the class.

Returns: returns a string containing the ID of this class

Definition at line 30 of file SVM.cpp.

bool 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 985 of file SVM.cpp.

bool SVM::getIsCrossValidationTrainingEnabled ( ) const

Gets if cross validation is enabled.

Returns: returns true if cross validation is enabled, false otherwise

Definition at line 981 of file SVM.cpp.

std::string 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 1020 of file SVM.cpp.

const struct LIBSVM::svm_model * SVM::getLIBSVMModel ( ) const

Returns a pointer to the svm_model, this will be NULL if the model has not been trained.

Returns: returns a pointer to the svm_model.

Definition at line 1091 of file SVM.cpp.

Float SVM::getNu ( ) const

Gets the current nu value.

Returns: returns the current nu value.

Definition at line 1068 of file SVM.cpp.

UINT 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 Classifier.

Definition at line 1049 of file SVM.cpp.

std::string 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 989 of file SVM.cpp.

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

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

Parameters

kernelType	this sets the SVM kernelType. Options are LINEAR_KERNEL, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL
UINT	svmType: this sets the SVM type. Options are C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR
useScaling	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
useNullRejection	sets if a predicted class will be rejected if the classes' probability is below the classificationThreshold
useAutoGamma	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
gamma	sets the SVM gamma parameter
degree	sets the SVM degree parameter
coef0	sets the SVM coef0 parameter
nu	sets the SVM nu parameter
C	sets the SVM C parameter
useCrossValidation	sets if the SVM model will be trained using cross validation
kFoldValue	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 243 of file SVM.cpp.

void SVM::initDefaultSVMSettings ( )

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

Definition at line 299 of file SVM.cpp.

bool SVM::load ( std::fstream & file )

virtual

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

Parameters

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 MLBase.

Examples:: ClassificationModulesExamples/SVMExample/SVMExample.cpp.

Definition at line 662 of file SVM.cpp.

bool SVM::loadLegacyModelFromFile ( std::fstream & file )

protected

Load the ranges

Definition at line 1376 of file SVM.cpp.

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

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

Parameters

rhs	another instance of a SVM

Returns: returns a pointer to this instance of the SVM

Definition at line 91 of file SVM.cpp.

bool SVM::predict_ ( VectorFloat & inputVector )

virtual

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

Parameters

inputVector the input vector to classify

Returns: returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 216 of file SVM.cpp.

bool SVM::save ( std::fstream & file ) const

virtual

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

Parameters

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 MLBase.

Examples:: ClassificationModulesExamples/SVMExample/SVMExample.cpp.

Definition at line 534 of file SVM.cpp.

bool SVM::setC ( const Float 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

C	the new C value

Returns: returns true if the C parameter was updated, false otherwise

Definition at line 1135 of file SVM.cpp.

bool SVM::setCoef0 ( const Float 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

coef0 the new coef0 value

Returns: returns true if the gamcoef0ma parameter was updated, false otherwise

Definition at line 1130 of file SVM.cpp.

bool 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

degree the new degree value

Returns: returns true if the degree parameter was updated, false otherwise

Definition at line 1120 of file SVM.cpp.

bool SVM::setGamma ( const Float 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

gamma the new gamma value

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

Definition at line 1111 of file SVM.cpp.

bool SVM::setKernelType ( const KernelType kernelType )

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

Parameters

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 1101 of file SVM.cpp.

bool SVM::setKFoldCrossValidationValue ( const UINT kFoldValue )

Sets the kFold cross validation value.

Parameters

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

Definition at line 1140 of file SVM.cpp.

bool SVM::setNu ( const Float 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

nu	the new nu value

Returns: returns true if the nu parameter was updated, false otherwise

Definition at line 1125 of file SVM.cpp.

bool SVM::setSVMType ( const SVMType svmType )

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

Parameters

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 1093 of file SVM.cpp.

bool 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

trainingData a reference to the training data

Returns: returns true if the SVM model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 140 of file SVM.cpp.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation