#include <svmClassification.h>

Inheritance diagram for svmClassification< T >:

Collaboration diagram for svmClassification< T >:

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 Member Functions
	svmClassification (KernelType kernelType=LINEAR_KERNEL, SVMType svmType=C_SVC, bool useScaling=true, bool useNullRejection=false, bool useAutoGamma=true, float gamma=0.1, unsigned int degree=3, float coef0=0, float nu=0.5, float C=1, bool useCrossValidation=false, unsigned int kFoldValue=10)

	svmClassification (int numInputs)

	~svmClassification ()

void	train (const std::vector< trainingExampleTemplate< T > > &trainingSet) override

void	train (const std::vector< trainingExampleTemplate< T > > &trainingSet, const std::size_t whichOutput) override

T	run (const std::vector< T > &inputVector) override

void	reset () override

bool	init (KernelType kernelType, SVMType svmType, bool useScaling, bool useNullRejection, bool useAutoGamma, float gamma, unsigned int degree, float coef0, float nu, float C, bool useCrossValidation, unsigned int kFoldValue)

size_t	getNumInputs () const override

std::vector< size_t >	getWhichInputs () const override

void	getJSONDescription (Json::Value &currentModel) override

Public Member Functions inherited from baseModel< T >
virtual	~baseModel ()

Additional Inherited Members
Protected Member Functions inherited from baseModel< T >
template<typename TT , class Dummy = int>
Json::Value	vector2json (TT vec)

template<class Dummy = int>
Json::Value	vector2json (std::vector< unsigned long > vec)

Member Enumeration Documentation

◆ KernelType

template<typename T >

enum svmClassification::KernelType

Enumerator
LINEAR_KERNEL
POLY_KERNEL
RBF_KERNEL
SIGMOID_KERNEL
PRECOMPUTED_KERNEL

◆ SVMType

template<typename T >

enum svmClassification::SVMType

Enumerator
C_SVC
NU_SVC
ONE_CLASS
EPSILON_SVR
NU_SVR

Constructor & Destructor Documentation

◆ svmClassification() [1/2]

template<typename T >

svmClassification< T >::svmClassification	(	KernelType	kernelType = `LINEAR_KERNEL`,
		SVMType	svmType = `C_SVC`,
		bool	useScaling = `true`,
		bool	useNullRejection = `false`,
		bool	useAutoGamma = `true`,
		float	gamma = `0.1`,
		unsigned int	degree = `3`,
		float	coef0 = `0`,
		float	nu = `0.5`,
		float	C = `1`,
		bool	useCrossValidation = `false`,
		unsigned int	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

◆ svmClassification() [2/2]

template<typename T >

svmClassification< T >::svmClassification ( int numInputs )

◆ ~svmClassification()

template<typename T >

svmClassification< T >::~svmClassification

Destructor

Member Function Documentation

◆ getJSONDescription()

template<typename T >

void svmClassification< T >::getJSONDescription ( Json::Value & currentModel )

overridevirtual

Implements baseModel< T >.

◆ getNumInputs()

template<typename T >

size_t svmClassification< T >::getNumInputs

overridevirtual

Implements baseModel< T >.

◆ getWhichInputs()

template<typename T >

std::vector< size_t > svmClassification< T >::getWhichInputs

overridevirtual

Implements baseModel< T >.

◆ init()

template<typename T >

bool svmClassification< T >::init	(	KernelType	kernelType,
		SVMType	svmType,
		bool	useScaling,
		bool	useNullRejection,
		bool	useAutoGamma,
		float	gamma,
		unsigned int	degree,
		float	coef0,
		float	nu,
		float	C,
		bool	useCrossValidation,
		unsigned int	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

◆ reset()

template<typename T >

void svmClassification< T >::reset

overridevirtual

Implements baseModel< T >.

◆ run()

template<typename T >

T svmClassification< T >::run ( const std::vector< T > & inputVector )

overridevirtual

Generate an output value from a single input vector.

Parameters

A	standard vector of doubles to be evaluated.

Returns: A single double: the nearest class as determined by k-nearest neighbor.

Implements baseModel< T >.

◆ train() [1/2]

template<typename T >

void svmClassification< T >::train ( const std::vector< trainingExampleTemplate< T > > & trainingSet )

overridevirtual

Fill the model with a vector of examples.

Parameters

The	training set is a vector of training examples that contain both a vector of input values and a double specifying desired output class.

Implements baseModel< T >.

◆ train() [2/2]

template<typename T >

void svmClassification< T >::train	(	const std::vector< trainingExampleTemplate< T > > &	trainingSet,
		const std::size_t	whichOutput
	)

overridevirtual

Fill the model with a vector of examples. Use this when part of a modelSet

Parameters

The	training set is a vector of training examples that contain both a vector of input values and a double specifying desired output class.
The	second argument specifies which output this model is using.

Implements baseModel< T >.

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

src/svmClassification.h
src/svmClassification.cpp

Public Types

Public Member Functions

Additional Inherited Members

Member Enumeration Documentation

◆ KernelType

◆ SVMType

Constructor & Destructor Documentation

◆ svmClassification() [1/2]

◆ svmClassification() [2/2]

◆ ~svmClassification()

Member Function Documentation

◆ getJSONDescription()

◆ getNumInputs()

◆ getWhichInputs()

◆ init()

◆ reset()

◆ run()

◆ train() [1/2]

◆ train() [2/2]