#include <otbSVMMachineLearningModel.h>

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

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

Public Types
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 SVMMachineLearningModel	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
cv::Ptr< cv::ml::SVM >	m_SVMModel

int	m_SVMType

int	m_KernelType

double	m_Degree

double	m_Gamma

double	m_Coef0

double	m_C

double	m_Nu

double	m_P

int	m_TermCriteriaType

int	m_MaxIter

double	m_Epsilon

bool	m_ParameterOptimization

double	m_OutputDegree

double	m_OutputGamma

double	m_OutputCoef0

double	m_OutputC

double	m_OutputNu

double	m_OutputP

bool	CanReadFile (const std::string &) override

bool	CanWriteFile (const std::string &) override

virtual int	GetSVMType ()

virtual void	SetSVMType (int _arg)

virtual int	GetKernelType ()

virtual void	SetKernelType (int _arg)

virtual int	GetTermCriteriaType ()

virtual void	SetTermCriteriaType (int _arg)

virtual int	GetMaxIter ()

virtual void	SetMaxIter (int _arg)

virtual double	GetEpsilon ()

virtual void	SetEpsilon (double _arg)

virtual double	GetDegree ()

virtual void	SetDegree (double _arg)

virtual double	GetOutputDegree ()

virtual double	GetGamma ()

virtual void	SetGamma (double _arg)

virtual double	GetOutputGamma ()

virtual double	GetCoef0 ()

virtual void	SetCoef0 (double _arg)

virtual double	GetOutputCoef0 ()

virtual double	GetC ()

virtual void	SetC (double _arg)

virtual double	GetOutputC ()

virtual double	GetNu ()

virtual void	SetNu (double _arg)

virtual double	GetOutputNu ()

virtual double	GetP ()

virtual void	SetP (double _arg)

virtual double	GetOutputP ()

virtual bool	GetParameterOptimization ()

virtual void	SetParameterOptimization (bool _arg)

	SVMMachineLearningModel ()

	~SVMMachineLearningModel () override=default

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

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

	SVMMachineLearningModel (const Self &)=delete

void	operator= (const Self &)=delete

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::SVMMachineLearningModel< TInputValue, TTargetValue >

OpenCV implementation of SVM algorithm.

This machine learning model uses the OpenCV implementation of the SVM algorithm. Since this implementation is buggy in the linear case, we recommend users to use the LibSVM implementation instead, through the otb::LibSVMMachineLearningModel.

Definition at line 42 of file otbSVMMachineLearningModel.h.

Member Typedef Documentation

◆ ConfidenceValueType

template<class TInputValue , class TTargetValue >

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

Definition at line 58 of file otbSVMMachineLearningModel.h.

◆ ConstPointer

template<class TInputValue , class TTargetValue >

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

Definition at line 50 of file otbSVMMachineLearningModel.h.

◆ InputListSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 54 of file otbSVMMachineLearningModel.h.

◆ InputSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 53 of file otbSVMMachineLearningModel.h.

◆ InputValueType

template<class TInputValue , class TTargetValue >

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

Definition at line 52 of file otbSVMMachineLearningModel.h.

◆ Pointer

template<class TInputValue , class TTargetValue >

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

Definition at line 49 of file otbSVMMachineLearningModel.h.

◆ ProbaSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 59 of file otbSVMMachineLearningModel.h.

◆ Self

template<class TInputValue , class TTargetValue >

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

Standard class typedefs.

Definition at line 47 of file otbSVMMachineLearningModel.h.

◆ Superclass

template<class TInputValue , class TTargetValue >

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

Definition at line 48 of file otbSVMMachineLearningModel.h.

◆ TargetListSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 57 of file otbSVMMachineLearningModel.h.

◆ TargetSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 56 of file otbSVMMachineLearningModel.h.

◆ TargetValueType

template<class TInputValue , class TTargetValue >

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

Definition at line 55 of file otbSVMMachineLearningModel.h.

Constructor & Destructor Documentation

◆ SVMMachineLearningModel() [1/2]

template<class TInputValue , class TOutputValue >

otb::SVMMachineLearningModel< TInputValue, TOutputValue >::SVMMachineLearningModel

protected

Constructor

Definition at line 33 of file otbSVMMachineLearningModel.hxx.

References otb::MachineLearningModel< TInputValue, TTargetValue >::m_ConfidenceIndex, and otb::MachineLearningModel< TInputValue, TTargetValue >::m_IsRegressionSupported.

◆ ~SVMMachineLearningModel()

template<class TInputValue , class TTargetValue >

otb::SVMMachineLearningModel< TInputValue, TTargetValue >::~SVMMachineLearningModel ( )

overrideprotecteddefault

Destructor

◆ SVMMachineLearningModel() [2/2]

template<class TInputValue , class TTargetValue >

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

privatedelete

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

Member Function Documentation

◆ CanReadFile()

template<class TInputValue , class TOutputValue >

bool otb::SVMMachineLearningModel< 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 154 of file otbSVMMachineLearningModel.hxx.

References CV_TYPE_NAME_ML_SVM.

◆ CanWriteFile()

template<class TInputValue , class TOutputValue >

bool otb::SVMMachineLearningModel< 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 181 of file otbSVMMachineLearningModel.hxx.

◆ CreateAnother()

template<class TInputValue , class TTargetValue >

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

Run-time type information (and related methods).

◆ DoPredict()

template<class TInputValue , class TOutputValue >

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

overrideprotected

Predict values using the model

Definition at line 114 of file otbSVMMachineLearningModel.hxx.

◆ GetC()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetC ( )

virtual

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

◆ GetCoef0()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetCoef0 ( )

virtual

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

◆ GetDegree()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetDegree ( )

virtual

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

◆ GetEpsilon()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetEpsilon ( )

virtual

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

◆ GetGamma()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetGamma ( )

virtual

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

◆ GetKernelType()

template<class TInputValue , class TTargetValue >

virtual int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetKernelType ( )

virtual

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

◆ GetMaxIter()

template<class TInputValue , class TTargetValue >

virtual int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetMaxIter ( )

virtual

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

◆ GetNameOfClass()

template<class TInputValue , class TTargetValue >

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

virtual

Run-time type information (and related methods).

◆ GetNu()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetNu ( )

virtual

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

◆ GetOutputC()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetOutputC ( )

virtual

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

◆ GetOutputCoef0()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetOutputCoef0 ( )

virtual

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

◆ GetOutputDegree()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetOutputDegree ( )

virtual

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

◆ GetOutputGamma()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetOutputGamma ( )

virtual

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

◆ GetOutputNu()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetOutputNu ( )

virtual

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

◆ GetOutputP()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetOutputP ( )

virtual

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

◆ GetP()

template<class TInputValue , class TTargetValue >

virtual double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetP ( )

virtual

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

◆ GetParameterOptimization()

template<class TInputValue , class TTargetValue >

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

virtual

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

◆ GetSVMType()

template<class TInputValue , class TTargetValue >

virtual int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetSVMType ( )

virtual

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

◆ GetTermCriteriaType()

template<class TInputValue , class TTargetValue >

virtual int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::GetTermCriteriaType ( )

virtual

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

◆ Load()

template<class TInputValue , class TOutputValue >

void otb::SVMMachineLearningModel< 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 147 of file otbSVMMachineLearningModel.hxx.

◆ New()

template<class TInputValue , class TTargetValue >

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

static

Run-time type information (and related methods).

◆ operator=()

template<class TInputValue , class TTargetValue >

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

privatedelete

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

◆ PrintSelf()

template<class TInputValue , class TOutputValue >

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

overrideprotected

PrintSelf method

Definition at line 187 of file otbSVMMachineLearningModel.hxx.

◆ Save()

template<class TInputValue , class TOutputValue >

void otb::SVMMachineLearningModel< 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 137 of file otbSVMMachineLearningModel.hxx.

◆ SetC()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetC ( double _arg )

virtual

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

◆ SetCoef0()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetCoef0 ( double _arg )

virtual

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

◆ SetDegree()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetDegree ( double _arg )

virtual

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

◆ SetEpsilon()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetEpsilon ( double _arg )

virtual

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

◆ SetGamma()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetGamma ( double _arg )

virtual

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

◆ SetKernelType()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetKernelType ( int _arg )

virtual

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

◆ SetMaxIter()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetMaxIter ( int _arg )

virtual

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

◆ SetNu()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetNu ( double _arg )

virtual

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

◆ SetP()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetP ( double _arg )

virtual

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

◆ SetParameterOptimization()

template<class TInputValue , class TTargetValue >

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

virtual

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

◆ SetSVMType()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetSVMType ( int _arg )

virtual

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

◆ SetTermCriteriaType()

template<class TInputValue , class TTargetValue >

virtual void otb::SVMMachineLearningModel< TInputValue, TTargetValue >::SetTermCriteriaType ( int _arg )

virtual

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

◆ Train()

template<class TInputValue , class TOutputValue >

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

overridevirtual

Train the machine learning model

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 61 of file otbSVMMachineLearningModel.hxx.

References CV_VAR_CATEGORICAL, and CV_VAR_NUMERICAL.

Member Data Documentation

◆ m_C

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_C

private

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

Definition at line 156 of file otbSVMMachineLearningModel.h.

◆ m_Coef0

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_Coef0

private

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

Definition at line 155 of file otbSVMMachineLearningModel.h.

◆ m_Degree

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_Degree

private

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

Definition at line 153 of file otbSVMMachineLearningModel.h.

◆ m_Epsilon

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_Epsilon

private

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

Definition at line 161 of file otbSVMMachineLearningModel.h.

◆ m_Gamma

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_Gamma

private

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

Definition at line 154 of file otbSVMMachineLearningModel.h.

◆ m_KernelType

template<class TInputValue , class TTargetValue >

int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_KernelType

private

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

Definition at line 152 of file otbSVMMachineLearningModel.h.

◆ m_MaxIter

template<class TInputValue , class TTargetValue >

int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_MaxIter

private

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

Definition at line 160 of file otbSVMMachineLearningModel.h.

◆ m_Nu

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_Nu

private

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

Definition at line 157 of file otbSVMMachineLearningModel.h.

◆ m_OutputC

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_OutputC

private

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

Definition at line 167 of file otbSVMMachineLearningModel.h.

◆ m_OutputCoef0

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_OutputCoef0

private

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

Definition at line 166 of file otbSVMMachineLearningModel.h.

◆ m_OutputDegree

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_OutputDegree

private

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

Definition at line 164 of file otbSVMMachineLearningModel.h.

◆ m_OutputGamma

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_OutputGamma

private

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

Definition at line 165 of file otbSVMMachineLearningModel.h.

◆ m_OutputNu

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_OutputNu

private

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

Definition at line 168 of file otbSVMMachineLearningModel.h.

◆ m_OutputP

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_OutputP

private

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

Definition at line 169 of file otbSVMMachineLearningModel.h.

◆ m_P

template<class TInputValue , class TTargetValue >

double otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_P

private

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

Definition at line 158 of file otbSVMMachineLearningModel.h.

◆ m_ParameterOptimization

template<class TInputValue , class TTargetValue >

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

private

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

Definition at line 162 of file otbSVMMachineLearningModel.h.

◆ m_SVMModel

template<class TInputValue , class TTargetValue >

cv::Ptr<cv::ml::SVM> otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_SVMModel

private

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

Definition at line 150 of file otbSVMMachineLearningModel.h.

◆ m_SVMType

template<class TInputValue , class TTargetValue >

int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_SVMType

private

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

Definition at line 151 of file otbSVMMachineLearningModel.h.

◆ m_TermCriteriaType

template<class TInputValue , class TTargetValue >

int otb::SVMMachineLearningModel< TInputValue, TTargetValue >::m_TermCriteriaType

private

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

Definition at line 159 of file otbSVMMachineLearningModel.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::SVMMachineLearningModel< TInputValue, TTargetValue >

Member Typedef Documentation

◆ ConfidenceValueType

◆ ConstPointer

◆ InputListSampleType

◆ InputSampleType

◆ InputValueType

◆ Pointer

◆ ProbaSampleType

◆ Self

◆ Superclass

◆ TargetListSampleType

◆ TargetSampleType

◆ TargetValueType

Constructor & Destructor Documentation

◆ SVMMachineLearningModel() [1/2]

◆ ~SVMMachineLearningModel()

◆ SVMMachineLearningModel() [2/2]

Member Function Documentation

◆ CanReadFile()

◆ CanWriteFile()

◆ CreateAnother()

◆ DoPredict()

◆ GetC()

◆ GetCoef0()

◆ GetDegree()

◆ GetEpsilon()

◆ GetGamma()

◆ GetKernelType()

◆ GetMaxIter()

◆ GetNameOfClass()

◆ GetNu()

◆ GetOutputC()

◆ GetOutputCoef0()

◆ GetOutputDegree()

◆ GetOutputGamma()

◆ GetOutputNu()

◆ GetOutputP()

◆ GetP()

◆ GetParameterOptimization()

◆ GetSVMType()

◆ GetTermCriteriaType()

◆ Load()

◆ New()

◆ operator=()

◆ PrintSelf()

◆ Save()

◆ SetC()

◆ SetCoef0()

◆ SetDegree()

◆ SetEpsilon()

◆ SetGamma()

◆ SetKernelType()

◆ SetMaxIter()

◆ SetNu()

◆ SetP()

◆ SetParameterOptimization()

◆ SetSVMType()

◆ SetTermCriteriaType()

◆ Train()

Member Data Documentation

◆ m_C

◆ m_Coef0

◆ m_Degree

◆ m_Epsilon

◆ m_Gamma

◆ m_KernelType

◆ m_MaxIter

◆ m_Nu

◆ m_OutputC

◆ m_OutputCoef0

◆ m_OutputDegree

◆ m_OutputGamma

◆ m_OutputNu

◆ m_OutputP

◆ m_P

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