#include <otbLibSVMMachineLearningModel.h>

Inheritance diagram for otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >:

Collaboration diagram for otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >:

Public Types
enum	ConfidenceMode { CM_INDEX, CM_PROBA, CM_HYPER }

typedef Superclass::ConfidenceValueType	ConfidenceValueType

typedef itk::SmartPointer< const Self >	ConstPointer

typedef Superclass::InputListSampleType	InputListSampleType

typedef Superclass::InputSampleType	InputSampleType

typedef Superclass::InputValueType	InputValueType

typedef itk::SmartPointer< Self >	Pointer

typedef Superclass::ProbaSampleType	ProbaSampleType

typedef LibSVMMachineLearningModel	Self

typedef MachineLearningModel< TInputValue, TTargetValue >	Superclass

typedef Superclass::TargetListSampleType	TargetListSampleType

typedef Superclass::TargetSampleType	TargetSampleType

typedef Superclass::TargetValueType	TargetValueType

Public Types inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
typedef MachineLearningModel	Self

typedef itk::Object	Superclass

typedef itk::SmartPointer< Self >	Pointer

typedef itk::SmartPointer< const Self >	ConstPointer

typedef MLMSampleTraits< TInputValue >::ValueType	InputValueType

typedef MLMSampleTraits< TInputValue >::SampleType	InputSampleType

typedef itk::Statistics::ListSample< InputSampleType >	InputListSampleType

typedef MLMTargetTraits< TTargetValue >::ValueType	TargetValueType

typedef MLMTargetTraits< TTargetValue >::SampleType	TargetSampleType

typedef itk::Statistics::ListSample< TargetSampleType >	TargetListSampleType

typedef MLMTargetTraits< double >::ValueType	ConfidenceValueType

typedef MLMTargetTraits< double >::SampleType	ConfidenceSampleType

typedef itk::Statistics::ListSample< ConfidenceSampleType >	ConfidenceListSampleType

typedef itk::VariableLengthVector< double >	ProbaSampleType

typedef itk::Statistics::ListSample< ProbaSampleType >	ProbaListSampleType

static Pointer	New ()

virtual ::itk::LightObject::Pointer	CreateAnother (void) const

virtual const char *	GetNameOfClass () const

void	Train () override

void	Save (const std::string &filename, const std::string &name="") override

void	Load (const std::string &filename, const std::string &name="") override

Classification model file compatibility tests
struct svm_model *	m_Model

struct svm_problem	m_Problem

struct svm_parameter	m_Parameters

bool	m_ParameterOptimization

unsigned int	m_CVFolders

double	m_InitialCrossValidationAccuracy

double	m_FinalCrossValidationAccuracy

unsigned int	m_CoarseOptimizationNumberOfSteps

unsigned int	m_FineOptimizationNumberOfSteps

ConfidenceMode	m_ConfidenceMode

std::vector< double >	m_TmpTarget

bool	CanReadFile (const std::string &) override

bool	CanWriteFile (const std::string &) override

	otbSetSVMParameterMacro (SVMType, svm_type, int)

int	GetSVMType (void) const

	otbSetSVMParameterMacro (KernelType, kernel_type, int)

int	GetKernelType (void) const

	otbSetSVMParameterMacro (PolynomialKernelDegree, degree, int)

int	GetPolynomialKernelDegree (void) const

	otbSetSVMParameterMacro (KernelGamma, gamma, double)

double	GetKernelGamma (void) const

	otbSetSVMParameterMacro (KernelCoef0, coef0, double)

double	GetKernelCoef0 (void) const

	otbSetSVMParameterMacro (C, C, double)

double	GetC (void) const

virtual void	SetParameterOptimization (bool _arg)

virtual bool	GetParameterOptimization ()

void	SetDoProbabilityEstimates (bool prob)

bool	GetDoProbabilityEstimates (void) const

bool	HasProbabilities (void) const

	otbSetSVMParameterMacro (Epsilon, eps, double)

double	GetEpsilon (void) const

	otbSetSVMParameterMacro (P, p, double)

double	GetP (void) const

	otbSetSVMParameterMacro (Nu, nu, double)

double	GetNu (void) const

void	DoShrinking (bool s)

bool	GetDoShrinking (void) const

void	SetCacheSize (int cSize)

int	GetCacheSize (void) const

virtual void	SetCVFolders (unsigned int _arg)

virtual unsigned int	GetCVFolders ()

virtual double	GetInitialCrossValidationAccuracy ()

virtual double	GetFinalCrossValidationAccuracy ()

virtual void	SetCoarseOptimizationNumberOfSteps (unsigned int _arg)

virtual unsigned int	GetCoarseOptimizationNumberOfSteps ()

virtual void	SetFineOptimizationNumberOfSteps (unsigned int _arg)

virtual unsigned int	GetFineOptimizationNumberOfSteps ()

void	SetConfidenceMode (unsigned int mode)

virtual unsigned int	GetConfidenceMode ()

unsigned int	GetNumberOfKernelParameters ()

double	CrossValidation (void)

unsigned int	GetNumberOfSupportVectors (void) const

unsigned int	GetNumberOfClasses (void) const

	LibSVMMachineLearningModel ()

	~LibSVMMachineLearningModel () override

TargetSampleType	DoPredict (const InputSampleType &input, ConfidenceValueType quality=nullptr, ProbaSampleType proba=nullptr) const override

void	PrintSelf (std::ostream &os, itk::Indent indent) const override

	LibSVMMachineLearningModel (const Self &)=delete

void	operator= (const Self &)=delete

void	BuildProblem (void)

void	ConsistencyCheck (void)

void	DeleteProblem (void)

void	DeleteModel (void)

void	OptimizeParameters (void)

Additional Inherited Members
Public Member Functions inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
virtual const char *	GetNameOfClass () const

TargetSampleType	Predict (const InputSampleType &input, ConfidenceValueType quality=nullptr, ProbaSampleType proba=nullptr) const

virtual void	SetDimension (unsigned int _arg)

virtual unsigned int	GetDimension ()

TargetListSampleType::Pointer	PredictBatch (const InputListSampleType input, ConfidenceListSampleType quality=nullptr, ProbaListSampleType *proba=nullptr) const

bool	HasConfidenceIndex () const

bool	HasProbaIndex () const

virtual void	SetInputListSample (InputListSampleType *_arg)

	itkGetObjectMacro (InputListSample, InputListSampleType)

virtual const InputListSampleType *	GetInputListSample () const

	itkGetObjectMacro (TargetListSample, TargetListSampleType)

	itkGetObjectMacro (ConfidenceListSample, ConfidenceListSampleType)

virtual void	SetTargetListSample (TargetListSampleType *_arg)

virtual bool	GetRegressionMode ()

void	SetRegressionMode (bool flag)

Protected Member Functions inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
	MachineLearningModel ()

	~MachineLearningModel () override=default

void	PrintSelf (std::ostream &os, itk::Indent indent) const override

Protected Attributes inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
InputListSampleType::Pointer	m_InputListSample

InputListSampleType::Pointer	m_ValidationListSample

TargetListSampleType::Pointer	m_TargetListSample

ConfidenceListSampleType::Pointer	m_ConfidenceListSample

bool	m_RegressionMode

bool	m_IsRegressionSupported

bool	m_ConfidenceIndex

bool	m_ProbaIndex

bool	m_IsDoPredictBatchMultiThreaded

unsigned int	m_Dimension

Detailed Description

template<class TInputValue, class TTargetValue>
class otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >

Definition at line 33 of file otbLibSVMMachineLearningModel.h.

Member Typedef Documentation

◆ ConfidenceValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::ConfidenceValueType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::ConfidenceValueType

Definition at line 48 of file otbLibSVMMachineLearningModel.h.

◆ ConstPointer

template<class TInputValue , class TTargetValue >

typedef itk::SmartPointer<const Self> otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::ConstPointer

Definition at line 40 of file otbLibSVMMachineLearningModel.h.

◆ InputListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputListSampleType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::InputListSampleType

Definition at line 44 of file otbLibSVMMachineLearningModel.h.

◆ InputSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputSampleType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::InputSampleType

Definition at line 43 of file otbLibSVMMachineLearningModel.h.

◆ InputValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputValueType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::InputValueType

Definition at line 42 of file otbLibSVMMachineLearningModel.h.

◆ Pointer

template<class TInputValue , class TTargetValue >

typedef itk::SmartPointer<Self> otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::Pointer

Definition at line 39 of file otbLibSVMMachineLearningModel.h.

◆ ProbaSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::ProbaSampleType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::ProbaSampleType

Definition at line 49 of file otbLibSVMMachineLearningModel.h.

◆ Self

template<class TInputValue , class TTargetValue >

typedef LibSVMMachineLearningModel otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::Self

Standard class typedefs.

Definition at line 37 of file otbLibSVMMachineLearningModel.h.

◆ Superclass

template<class TInputValue , class TTargetValue >

typedef MachineLearningModel<TInputValue, TTargetValue> otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::Superclass

Definition at line 38 of file otbLibSVMMachineLearningModel.h.

◆ TargetListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetListSampleType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::TargetListSampleType

Definition at line 47 of file otbLibSVMMachineLearningModel.h.

◆ TargetSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetSampleType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::TargetSampleType

Definition at line 46 of file otbLibSVMMachineLearningModel.h.

◆ TargetValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetValueType otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::TargetValueType

Definition at line 45 of file otbLibSVMMachineLearningModel.h.

Member Enumeration Documentation

◆ ConfidenceMode

template<class TInputValue , class TTargetValue >

enum otb::LibSVMMachineLearningModel::ConfidenceMode

enum to choose the way confidence is computed CM_INDEX : compute the difference between highest and second highest probability CM_PROBA : returns probabilities for all classes The given pointer needs to store 'nbClass' values This mode requires that ConfidenceValueType is double CM_HYPER : returns hyperplanes distances* The given pointer needs to store 'nbClass * (nbClass-1) / 2' values This mode requires that ConfidenceValueType is double

Enumerator
CM_INDEX
CM_PROBA
CM_HYPER

Definition at line 59 of file otbLibSVMMachineLearningModel.h.

Constructor & Destructor Documentation

◆ LibSVMMachineLearningModel() [1/2]

template<class TInputValue , class TOutputValue >

otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::LibSVMMachineLearningModel

protected

Constructor

Definition at line 35 of file otbLibSVMMachineLearningModel.hxx.

References otb::Utils::PrintNothing().

◆ ~LibSVMMachineLearningModel()

template<class TInputValue , class TOutputValue >

otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::~LibSVMMachineLearningModel

overrideprotected

Destructor

Definition at line 73 of file otbLibSVMMachineLearningModel.hxx.

◆ LibSVMMachineLearningModel() [2/2]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::LibSVMMachineLearningModel ( const Self & )

privatedelete

Container to hold the SVM model itself

Member Function Documentation

◆ BuildProblem()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::BuildProblem ( void )

private

Container to hold the SVM model itself

Definition at line 285 of file otbLibSVMMachineLearningModel.hxx.

References otbMsgDebugMacro.

◆ CanReadFile()

template<class TInputValue , class TOutputValue >

bool otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::CanReadFile ( const std::string & file )

overridevirtual

Is the input model file readable and compatible with the corresponding classifier ?

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 227 of file otbLibSVMMachineLearningModel.hxx.

◆ CanWriteFile()

template<class TInputValue , class TOutputValue >

bool otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::CanWriteFile ( const std::string & )

overridevirtual

Is the input model file writable and compatible with the corresponding classifier ?

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 254 of file otbLibSVMMachineLearningModel.hxx.

◆ ConsistencyCheck()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::ConsistencyCheck ( void )

private

Container to hold the SVM model itself

Definition at line 345 of file otbLibSVMMachineLearningModel.hxx.

References otbMsgDebugMacro.

◆ CreateAnother()

template<class TInputValue , class TTargetValue >

virtual::itk::LightObject::Pointer otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::CreateAnother ( void ) const

Run-time type information (and related methods).

◆ CrossValidation()

template<class TInputValue , class TOutputValue >

double otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::CrossValidation ( void )

Container to hold the SVM model itself

Definition at line 426 of file otbLibSVMMachineLearningModel.hxx.

◆ DeleteModel()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::DeleteModel ( void )

private

Container to hold the SVM model itself

Definition at line 386 of file otbLibSVMMachineLearningModel.hxx.

◆ DeleteProblem()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::DeleteProblem ( void )

private

Container to hold the SVM model itself

Definition at line 363 of file otbLibSVMMachineLearningModel.hxx.

◆ DoPredict()

template<class TInputValue , class TOutputValue >

LibSVMMachineLearningModel< TInputValue, TOutputValue >::TargetSampleType otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::DoPredict	(	const InputSampleType &	input,
		ConfidenceValueType *	quality = `nullptr`,
		ProbaSampleType *	proba = `nullptr`
	)		const

overrideprotected

Predict values using the model

Definition at line 104 of file otbLibSVMMachineLearningModel.hxx.

◆ DoShrinking()

template<class TInputValue , class TTargetValue >

void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::DoShrinking ( bool s )

inline

Use the shrinking heuristics for the training

Definition at line 202 of file otbLibSVMMachineLearningModel.h.

◆ GetC()

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetC ( void ) const

inline

Get the C parameter for the training for C_SVC, EPSILON_SVR and NU_SVR

Definition at line 149 of file otbLibSVMMachineLearningModel.h.

◆ GetCacheSize()

template<class TInputValue , class TTargetValue >

int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetCacheSize ( void ) const

inline

Get the cache size in MB for the training

Definition at line 224 of file otbLibSVMMachineLearningModel.h.

◆ GetCoarseOptimizationNumberOfSteps()

template<class TInputValue , class TTargetValue >

virtual unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetCoarseOptimizationNumberOfSteps ( )

virtual

Container to hold the SVM model itself

◆ GetConfidenceMode()

template<class TInputValue , class TTargetValue >

virtual unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetConfidenceMode ( )

virtual

Container to hold the SVM model itself

◆ GetCVFolders()

template<class TInputValue , class TTargetValue >

virtual unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetCVFolders ( )

virtual

Container to hold the SVM model itself

◆ GetDoProbabilityEstimates()

template<class TInputValue , class TTargetValue >

bool otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetDoProbabilityEstimates ( void ) const

inline

Get Do probability estimates boolean

Definition at line 164 of file otbLibSVMMachineLearningModel.h.

◆ GetDoShrinking()

template<class TInputValue , class TTargetValue >

bool otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetDoShrinking ( void ) const

inline

Get Use the shrinking heuristics for the training boolea

Definition at line 210 of file otbLibSVMMachineLearningModel.h.

◆ GetEpsilon()

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetEpsilon ( void ) const

inline

Get the tolerance for the stopping criterion for the training

Definition at line 176 of file otbLibSVMMachineLearningModel.h.

◆ GetFinalCrossValidationAccuracy()

template<class TInputValue , class TTargetValue >

virtual double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetFinalCrossValidationAccuracy ( )

virtual

Container to hold the SVM model itself

◆ GetFineOptimizationNumberOfSteps()

template<class TInputValue , class TTargetValue >

virtual unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetFineOptimizationNumberOfSteps ( )

virtual

Container to hold the SVM model itself

◆ GetInitialCrossValidationAccuracy()

template<class TInputValue , class TTargetValue >

virtual double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetInitialCrossValidationAccuracy ( )

virtual

Container to hold the SVM model itself

◆ GetKernelCoef0()

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetKernelCoef0 ( void ) const

inline

Get the coef0 parameter for poly/sigmoid kernels

Definition at line 140 of file otbLibSVMMachineLearningModel.h.

◆ GetKernelGamma()

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetKernelGamma ( void ) const

inline

Get the gamma parameter for poly/rbf/sigmoid kernels

Definition at line 131 of file otbLibSVMMachineLearningModel.h.

◆ GetKernelType()

template<class TInputValue , class TTargetValue >

int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetKernelType ( void ) const

inline

Get the kernel type

Definition at line 113 of file otbLibSVMMachineLearningModel.h.

◆ GetNameOfClass()

template<class TInputValue , class TTargetValue >

virtual const char* otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetNameOfClass ( ) const

virtual

Run-time type information (and related methods).

◆ GetNu()

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetNu ( void ) const

inline

Set the Nu parameter for the training

Definition at line 194 of file otbLibSVMMachineLearningModel.h.

◆ GetNumberOfClasses()

template<class TInputValue , class TTargetValue >

unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetNumberOfClasses ( void ) const

inline

Container to hold the SVM model itself

Definition at line 266 of file otbLibSVMMachineLearningModel.h.

◆ GetNumberOfKernelParameters()

template<class TInputValue , class TOutputValue >

unsigned int otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::GetNumberOfKernelParameters

Container to hold the SVM model itself

Definition at line 396 of file otbLibSVMMachineLearningModel.hxx.

◆ GetNumberOfSupportVectors()

template<class TInputValue , class TTargetValue >

unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetNumberOfSupportVectors ( void ) const

inline

Return number of support vectors

Definition at line 258 of file otbLibSVMMachineLearningModel.h.

◆ GetP()

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetP ( void ) const

inline

Get the value of p for EPSILON_SVR

Definition at line 185 of file otbLibSVMMachineLearningModel.h.

◆ GetParameterOptimization()

template<class TInputValue , class TTargetValue >

virtual bool otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetParameterOptimization ( )

virtual

Container to hold the SVM model itself

◆ GetPolynomialKernelDegree()

template<class TInputValue , class TTargetValue >

int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetPolynomialKernelDegree ( void ) const

inline

Get the degree of the polynomial kernel

Definition at line 122 of file otbLibSVMMachineLearningModel.h.

◆ GetSVMType()

template<class TInputValue , class TTargetValue >

int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::GetSVMType ( void ) const

inline

Get the SVM type (C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR)

Definition at line 100 of file otbLibSVMMachineLearningModel.h.

◆ HasProbabilities()

template<class TInputValue , class TOutputValue >

bool otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::HasProbabilities ( void ) const

Test if the model has probabilities

Definition at line 267 of file otbLibSVMMachineLearningModel.hxx.

◆ Load()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::Load	(	const std::string &	filename,
		const std::string &	name = `""`
	)

overridevirtual

Load the model from file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 213 of file otbLibSVMMachineLearningModel.hxx.

◆ New()

template<class TInputValue , class TTargetValue >

static Pointer otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::New ( )

static

Run-time type information (and related methods).

◆ operator=()

template<class TInputValue , class TTargetValue >

void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::operator= ( const Self & )

privatedelete

Container to hold the SVM model itself

◆ OptimizeParameters()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::OptimizeParameters ( void )

private

Container to hold the SVM model itself

Definition at line 453 of file otbLibSVMMachineLearningModel.hxx.

References otb::ExhaustiveExponentialOptimizer::New(), otbMsgDebugMacro, and otbMsgDevMacro.

◆ otbSetSVMParameterMacro() [1/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	C	,
		C	,
		double
	)

Set the C parameter for the training for C_SVC, EPSILON_SVR and C_SVR

◆ otbSetSVMParameterMacro() [2/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	Epsilon	,
		eps	,
		double
	)

Set the tolerance for the stopping criterion for the training

◆ otbSetSVMParameterMacro() [3/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	KernelCoef0	,
		coef0	,
		double
	)

Set the coef0 parameter for poly/sigmoid kernels

◆ otbSetSVMParameterMacro() [4/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	KernelGamma	,
		gamma	,
		double
	)

Set the gamma parameter for poly/rbf/sigmoid kernels

◆ otbSetSVMParameterMacro() [5/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	KernelType	,
		kernel_type	,
		int
	)

Set the kernel type to LINEAR, POLY, RBF, SIGMOID linear: u'v polynomial: (gamma*u'*v + coef0)^degree radial basis function: exp(-gamma|u-v|^2) sigmoid: tanh(gamma*u'*v + coef0)

◆ otbSetSVMParameterMacro() [6/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	Nu	,
		nu	,
		double
	)

Set the Nu parameter for the training

◆ otbSetSVMParameterMacro() [7/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	P	,
		p	,
		double
	)

Set the value of p for EPSILON_SVR

◆ otbSetSVMParameterMacro() [8/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	PolynomialKernelDegree	,
		degree	,
		int
	)

Set the degree of the polynomial kernel

◆ otbSetSVMParameterMacro() [9/9]

template<class TInputValue , class TTargetValue >

otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::otbSetSVMParameterMacro	(	SVMType	,
		svm_type	,
		int
	)

Set the SVM type to C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR

◆ PrintSelf()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::PrintSelf	(	std::ostream &	os,
		itk::Indent	indent
	)		const

overrideprotected

PrintSelf method

Definition at line 260 of file otbLibSVMMachineLearningModel.hxx.

◆ Save()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::Save	(	const std::string &	filename,
		const std::string &	name = `""`
	)

overridevirtual

Save the model to file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 204 of file otbLibSVMMachineLearningModel.hxx.

◆ SetCacheSize()

template<class TInputValue , class TTargetValue >

void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::SetCacheSize ( int cSize )

inline

Set the cache size in MB for the training

Definition at line 216 of file otbLibSVMMachineLearningModel.h.

◆ SetCoarseOptimizationNumberOfSteps()

template<class TInputValue , class TTargetValue >

virtual void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::SetCoarseOptimizationNumberOfSteps ( unsigned int _arg )

virtual

Container to hold the SVM model itself

◆ SetConfidenceMode()

template<class TInputValue , class TTargetValue >

void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::SetConfidenceMode ( unsigned int mode )

inline

Container to hold the SVM model itself

Definition at line 242 of file otbLibSVMMachineLearningModel.h.

◆ SetCVFolders()

template<class TInputValue , class TTargetValue >

virtual void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::SetCVFolders ( unsigned int _arg )

virtual

Container to hold the SVM model itself

◆ SetDoProbabilityEstimates()

template<class TInputValue , class TTargetValue >

void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::SetDoProbabilityEstimates ( bool prob )

inline

Do probability estimates

Definition at line 158 of file otbLibSVMMachineLearningModel.h.

◆ SetFineOptimizationNumberOfSteps()

template<class TInputValue , class TTargetValue >

virtual void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::SetFineOptimizationNumberOfSteps ( unsigned int _arg )

virtual

Container to hold the SVM model itself

◆ SetParameterOptimization()

template<class TInputValue , class TTargetValue >

virtual void otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::SetParameterOptimization ( bool _arg )

virtual

Container to hold the SVM model itself

◆ Train()

template<class TInputValue , class TOutputValue >

void otb::LibSVMMachineLearningModel< TInputValue, TOutputValue >::Train

overridevirtual

Train the machine learning model

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 81 of file otbLibSVMMachineLearningModel.hxx.

Member Data Documentation

◆ m_CoarseOptimizationNumberOfSteps

template<class TInputValue , class TTargetValue >

unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_CoarseOptimizationNumberOfSteps

private

Number of steps for the coarse search

Definition at line 322 of file otbLibSVMMachineLearningModel.h.

◆ m_ConfidenceMode

template<class TInputValue , class TTargetValue >

ConfidenceMode otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_ConfidenceMode

private

Output mode for confidence index (see enum )

Definition at line 328 of file otbLibSVMMachineLearningModel.h.

◆ m_CVFolders

template<class TInputValue , class TTargetValue >

unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_CVFolders

private

Number of Cross Validation folders

Definition at line 313 of file otbLibSVMMachineLearningModel.h.

◆ m_FinalCrossValidationAccuracy

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_FinalCrossValidationAccuracy

private

Final cross validationa accuracy

Definition at line 319 of file otbLibSVMMachineLearningModel.h.

◆ m_FineOptimizationNumberOfSteps

template<class TInputValue , class TTargetValue >

unsigned int otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_FineOptimizationNumberOfSteps

private

Number of steps for the fine search

Definition at line 325 of file otbLibSVMMachineLearningModel.h.

◆ m_InitialCrossValidationAccuracy

template<class TInputValue , class TTargetValue >

double otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_InitialCrossValidationAccuracy

private

Initial cross validation accuracy

Definition at line 316 of file otbLibSVMMachineLearningModel.h.

◆ m_Model

template<class TInputValue , class TTargetValue >

struct svm_model* otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_Model

private

Container to hold the SVM model itself

Definition at line 301 of file otbLibSVMMachineLearningModel.h.

◆ m_ParameterOptimization

template<class TInputValue , class TTargetValue >

bool otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_ParameterOptimization

private

Do parameters optimization, default : false

Definition at line 310 of file otbLibSVMMachineLearningModel.h.

◆ m_Parameters

template<class TInputValue , class TTargetValue >

struct svm_parameter otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_Parameters

private

Container of the SVM parameters

Definition at line 307 of file otbLibSVMMachineLearningModel.h.

◆ m_Problem

template<class TInputValue , class TTargetValue >

struct svm_problem otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_Problem

private

Structure that stores training vectors

Definition at line 304 of file otbLibSVMMachineLearningModel.h.

◆ m_TmpTarget

template<class TInputValue , class TTargetValue >

std::vector<double> otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >::m_TmpTarget

private

Temporary array to store cross-validation results

Definition at line 331 of file otbLibSVMMachineLearningModel.h.

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

Public Types

Classification model file compatibility tests

Additional Inherited Members

Detailed Description

template<class TInputValue, class TTargetValue> class otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >

Member Typedef Documentation

◆ ConfidenceValueType

◆ ConstPointer

◆ InputListSampleType

◆ InputSampleType

◆ InputValueType

◆ Pointer

◆ ProbaSampleType

◆ Self

◆ Superclass

◆ TargetListSampleType

◆ TargetSampleType

◆ TargetValueType

Member Enumeration Documentation

◆ ConfidenceMode

Constructor & Destructor Documentation

◆ LibSVMMachineLearningModel() [1/2]

◆ ~LibSVMMachineLearningModel()

◆ LibSVMMachineLearningModel() [2/2]

Member Function Documentation

◆ BuildProblem()

◆ CanReadFile()

◆ CanWriteFile()

◆ ConsistencyCheck()

◆ CreateAnother()

◆ CrossValidation()

◆ DeleteModel()

◆ DeleteProblem()

◆ DoPredict()

◆ DoShrinking()

◆ GetC()

◆ GetCacheSize()

◆ GetCoarseOptimizationNumberOfSteps()

◆ GetConfidenceMode()

◆ GetCVFolders()

◆ GetDoProbabilityEstimates()

◆ GetDoShrinking()

◆ GetEpsilon()

◆ GetFinalCrossValidationAccuracy()

◆ GetFineOptimizationNumberOfSteps()

◆ GetInitialCrossValidationAccuracy()

◆ GetKernelCoef0()

◆ GetKernelGamma()

◆ GetKernelType()

◆ GetNameOfClass()

◆ GetNu()

◆ GetNumberOfClasses()

◆ GetNumberOfKernelParameters()

◆ GetNumberOfSupportVectors()

◆ GetP()

◆ GetParameterOptimization()

◆ GetPolynomialKernelDegree()

◆ GetSVMType()

◆ HasProbabilities()

◆ Load()

◆ New()

◆ operator=()

◆ OptimizeParameters()

◆ otbSetSVMParameterMacro() [1/9]

◆ otbSetSVMParameterMacro() [2/9]

◆ otbSetSVMParameterMacro() [3/9]

◆ otbSetSVMParameterMacro() [4/9]

◆ otbSetSVMParameterMacro() [5/9]

◆ otbSetSVMParameterMacro() [6/9]

◆ otbSetSVMParameterMacro() [7/9]

◆ otbSetSVMParameterMacro() [8/9]

◆ otbSetSVMParameterMacro() [9/9]

◆ PrintSelf()

◆ Save()

◆ SetCacheSize()

◆ SetCoarseOptimizationNumberOfSteps()

◆ SetConfidenceMode()

◆ SetCVFolders()

◆ SetDoProbabilityEstimates()

◆ SetFineOptimizationNumberOfSteps()

template<class TInputValue, class TTargetValue>
class otb::LibSVMMachineLearningModel< TInputValue, TTargetValue >