#include <otbSharkRandomForestsMachineLearningModel.h>

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

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

Public Types
typedef Superclass::ConfidenceListSampleType	ConfidenceListSampleType

typedef Superclass::ConfidenceSampleType	ConfidenceSampleType

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::ProbaListSampleType	ProbaListSampleType

typedef Superclass::ProbaSampleType	ProbaSampleType

typedef SharkRandomForestsMachineLearningModel	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

virtual void	Train () override

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

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

Classification model file compatibility tests
shark::RFClassifier< unsigned int >	m_RFModel

shark::RFTrainer< unsigned int >	m_RFTrainer

std::vector< unsigned int >	m_ClassDictionary

bool	m_NormalizeClassLabels

unsigned int	m_NumberOfTrees

unsigned int	m_MTry

unsigned int	m_NodeSize

float	m_OobRatio

bool	m_ComputeMargin

virtual bool	CanReadFile (const std::string &) override

virtual bool	CanWriteFile (const std::string &) override

virtual unsigned int	GetNumberOfTrees ()

virtual void	SetNumberOfTrees (unsigned int _arg)

virtual unsigned int	GetMTry ()

virtual void	SetMTry (unsigned int _arg)

virtual unsigned int	GetNodeSize ()

virtual void	SetNodeSize (unsigned int _arg)

virtual float	GetOobRatio ()

virtual void	SetOobRatio (float _arg)

virtual bool	GetComputeMargin ()

virtual void	SetComputeMargin (bool _arg)

virtual bool	GetNormalizeClassLabels ()

virtual void	SetNormalizeClassLabels (bool _arg)

	SharkRandomForestsMachineLearningModel ()

	~SharkRandomForestsMachineLearningModel () override=default

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

void	DoPredictBatch (const InputListSampleType , const unsigned int &startIndex, const unsigned int &size, TargetListSampleType , ConfidenceListSampleType =nullptr, ProbaListSampleType =nullptr) const override

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

	SharkRandomForestsMachineLearningModel (const Self &)=delete

void	operator= (const Self &)=delete

ConfidenceValueType	ComputeConfidence (shark::RealVector &probas, bool computeMargin) const

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::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >

Definition at line 77 of file otbSharkRandomForestsMachineLearningModel.h.

Member Typedef Documentation

◆ ConfidenceListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::ConfidenceListSampleType otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::ConfidenceListSampleType

Definition at line 94 of file otbSharkRandomForestsMachineLearningModel.h.

◆ ConfidenceSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::ConfidenceSampleType otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::ConfidenceSampleType

Definition at line 93 of file otbSharkRandomForestsMachineLearningModel.h.

◆ ConfidenceValueType

template<class TInputValue , class TTargetValue >

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

Definition at line 92 of file otbSharkRandomForestsMachineLearningModel.h.

◆ ConstPointer

template<class TInputValue , class TTargetValue >

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

Definition at line 84 of file otbSharkRandomForestsMachineLearningModel.h.

◆ InputListSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 88 of file otbSharkRandomForestsMachineLearningModel.h.

◆ InputSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 87 of file otbSharkRandomForestsMachineLearningModel.h.

◆ InputValueType

template<class TInputValue , class TTargetValue >

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

Definition at line 86 of file otbSharkRandomForestsMachineLearningModel.h.

◆ Pointer

template<class TInputValue , class TTargetValue >

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

Definition at line 83 of file otbSharkRandomForestsMachineLearningModel.h.

◆ ProbaListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::ProbaListSampleType otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::ProbaListSampleType

Definition at line 96 of file otbSharkRandomForestsMachineLearningModel.h.

◆ ProbaSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 95 of file otbSharkRandomForestsMachineLearningModel.h.

◆ Self

template<class TInputValue , class TTargetValue >

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

Standard class typedefs.

Definition at line 81 of file otbSharkRandomForestsMachineLearningModel.h.

◆ Superclass

template<class TInputValue , class TTargetValue >

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

Definition at line 82 of file otbSharkRandomForestsMachineLearningModel.h.

◆ TargetListSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 91 of file otbSharkRandomForestsMachineLearningModel.h.

◆ TargetSampleType

template<class TInputValue , class TTargetValue >

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

Definition at line 90 of file otbSharkRandomForestsMachineLearningModel.h.

◆ TargetValueType

template<class TInputValue , class TTargetValue >

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

Definition at line 89 of file otbSharkRandomForestsMachineLearningModel.h.

Constructor & Destructor Documentation

◆ SharkRandomForestsMachineLearningModel() [1/2]

template<class TInputValue , class TOutputValue >

SharkRandomForestsMachineLearningModel::SharkRandomForestsMachineLearningModel

protected

Constructor

Definition at line 47 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ ~SharkRandomForestsMachineLearningModel()

template<class TInputValue , class TTargetValue >

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

overrideprotecteddefault

Destructor

◆ SharkRandomForestsMachineLearningModel() [2/2]

template<class TInputValue , class TTargetValue >

otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::SharkRandomForestsMachineLearningModel ( 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 SharkRandomForestsMachineLearningModel::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 290 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ CanWriteFile()

template<class TInputValue , class TOutputValue >

bool SharkRandomForestsMachineLearningModel::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 305 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ ComputeConfidence()

template<class TInputValue , class TOutputValue >

SharkRandomForestsMachineLearningModel< TInputValue, TOutputValue >::ConfidenceValueType SharkRandomForestsMachineLearningModel::ComputeConfidence	(	shark::RealVector &	probas,
		bool	computeMargin
	)		const

private

Confidence list sample

Definition at line 86 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ CreateAnother()

template<class TInputValue , class TTargetValue >

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

Run-time type information (and related methods).

◆ DoPredict()

template<class TInputValue , class TOutputValue >

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

overrideprotected

Predict values using the model

Definition at line 107 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ DoPredictBatch()

template<class TInputValue , class TOutputValue >

void SharkRandomForestsMachineLearningModel::DoPredictBatch	(	const InputListSampleType *	input,
		const unsigned int &	startIndex,
		const unsigned int &	size,
		TargetListSampleType *	targets,
		ConfidenceListSampleType *	quality = `nullptr`,
		ProbaListSampleType *	proba = `nullptr`
	)		const

overrideprotected

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

Definition at line 147 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ GetComputeMargin()

template<class TInputValue , class TTargetValue >

virtual bool otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::GetComputeMargin ( )

virtual

If true, margin confidence value will be computed

◆ GetMTry()

template<class TInputValue , class TTargetValue >

virtual unsigned int otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::GetMTry ( )

virtual

From Shark doc: Get the number of random attributes to investigate at each node.

◆ GetNameOfClass()

template<class TInputValue , class TTargetValue >

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

virtual

Run-time type information (and related methods).

◆ GetNodeSize()

template<class TInputValue , class TTargetValue >

virtual unsigned int otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::GetNodeSize ( )

virtual

From Shark doc: Controls when a node is considered pure. If set to 1, a node is pure when it only consists of a single node.

◆ GetNormalizeClassLabels()

template<class TInputValue , class TTargetValue >

virtual bool otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::GetNormalizeClassLabels ( )

virtual

If true, class labels will be normalised in [0 ... nbClasses]

◆ GetNumberOfTrees()

template<class TInputValue , class TTargetValue >

virtual unsigned int otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::GetNumberOfTrees ( )

virtual

From Shark doc: Get the number of trees to grow.

◆ GetOobRatio()

template<class TInputValue , class TTargetValue >

virtual float otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::GetOobRatio ( )

virtual

From Shark doc: Get the fraction of the original training dataset to use as the out of bag sample. The default value is 0.66.

◆ Load()

template<class TInputValue , class TOutputValue >

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

overridevirtual

Load the model from file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 249 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ New()

template<class TInputValue , class TTargetValue >

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

static

Run-time type information (and related methods).

◆ operator=()

template<class TInputValue , class TTargetValue >

void otb::SharkRandomForestsMachineLearningModel< 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 SharkRandomForestsMachineLearningModel::PrintSelf	(	std::ostream &	os,
		itk::Indent	indent
	)		const

overrideprotected

PrintSelf method

Definition at line 311 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ Save()

template<class TInputValue , class TOutputValue >

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

overridevirtual

Save the model to file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 223 of file otbSharkRandomForestsMachineLearningModel.hxx.

◆ SetComputeMargin()

template<class TInputValue , class TTargetValue >

virtual void otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::SetComputeMargin ( bool _arg )

virtual

If true, margin confidence value will be computed

◆ SetMTry()

template<class TInputValue , class TTargetValue >

virtual void otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::SetMTry ( unsigned int _arg )

virtual

From Shark doc: Set the number of random attributes to investigate at each node.

◆ SetNodeSize()

template<class TInputValue , class TTargetValue >

virtual void otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::SetNodeSize ( unsigned int _arg )

virtual

From Shark doc: Controls when a node is considered pure. If set to 1, a node is pure when it only consists of a single node.

◆ SetNormalizeClassLabels()

template<class TInputValue , class TTargetValue >

virtual void otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::SetNormalizeClassLabels ( bool _arg )

virtual

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

◆ SetNumberOfTrees()

template<class TInputValue , class TTargetValue >

virtual void otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::SetNumberOfTrees ( unsigned int _arg )

virtual

From Shark doc: Set the number of trees to grow.

◆ SetOobRatio()

template<class TInputValue , class TTargetValue >

virtual void otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::SetOobRatio ( float _arg )

virtual

From Shark doc: Set the fraction of the original training dataset to use as the out of bag sample. The default value is 0.66.

◆ Train()

template<class TInputValue , class TOutputValue >

void SharkRandomForestsMachineLearningModel::Train

overridevirtual

Train the machine learning model

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 59 of file otbSharkRandomForestsMachineLearningModel.hxx.

Member Data Documentation

◆ m_ClassDictionary

template<class TInputValue , class TTargetValue >

std::vector<unsigned int> otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_ClassDictionary

private

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

Definition at line 186 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_ComputeMargin

template<class TInputValue , class TTargetValue >

bool otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_ComputeMargin

private

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

Definition at line 193 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_MTry

template<class TInputValue , class TTargetValue >

unsigned int otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_MTry

private

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

Definition at line 190 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_NodeSize

template<class TInputValue , class TTargetValue >

unsigned int otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_NodeSize

private

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

Definition at line 191 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_NormalizeClassLabels

template<class TInputValue , class TTargetValue >

bool otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_NormalizeClassLabels

private

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

Definition at line 187 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_NumberOfTrees

template<class TInputValue , class TTargetValue >

unsigned int otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_NumberOfTrees

private

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

Definition at line 189 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_OobRatio

template<class TInputValue , class TTargetValue >

float otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_OobRatio

private

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

Definition at line 192 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_RFModel

template<class TInputValue , class TTargetValue >

shark::RFClassifier<unsigned int> otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_RFModel

private

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

Definition at line 184 of file otbSharkRandomForestsMachineLearningModel.h.

◆ m_RFTrainer

template<class TInputValue , class TTargetValue >

shark::RFTrainer<unsigned int> otb::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >::m_RFTrainer

private

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

Definition at line 185 of file otbSharkRandomForestsMachineLearningModel.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::SharkRandomForestsMachineLearningModel< TInputValue, TTargetValue >

Member Typedef Documentation

◆ ConfidenceListSampleType

◆ ConfidenceSampleType

◆ ConfidenceValueType

◆ ConstPointer

◆ InputListSampleType

◆ InputSampleType

◆ InputValueType

◆ Pointer

◆ ProbaListSampleType

◆ ProbaSampleType

◆ Self

◆ Superclass

◆ TargetListSampleType

◆ TargetSampleType

◆ TargetValueType

Constructor & Destructor Documentation

◆ SharkRandomForestsMachineLearningModel() [1/2]

◆ ~SharkRandomForestsMachineLearningModel()

◆ SharkRandomForestsMachineLearningModel() [2/2]

Member Function Documentation

◆ CanReadFile()

◆ CanWriteFile()

◆ ComputeConfidence()

◆ CreateAnother()

◆ DoPredict()

◆ DoPredictBatch()

◆ GetComputeMargin()

◆ GetMTry()

◆ GetNameOfClass()

◆ GetNodeSize()

◆ GetNormalizeClassLabels()

◆ GetNumberOfTrees()

◆ GetOobRatio()

◆ Load()

◆ New()

◆ operator=()

◆ PrintSelf()

◆ Save()

◆ SetComputeMargin()

◆ SetMTry()

◆ SetNodeSize()

◆ SetNormalizeClassLabels()

◆ SetNumberOfTrees()

◆ SetOobRatio()

◆ Train()

Member Data Documentation

◆ m_ClassDictionary

◆ m_ComputeMargin

◆ m_MTry

◆ m_NodeSize

◆ m_NormalizeClassLabels

◆ m_NumberOfTrees

◆ m_OobRatio

◆ m_RFModel

◆ m_RFTrainer

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