A maximum likelihood estimation strategy for classification (a.k.a. MaxVer in portuguese). More...

#include <MaxLikelihood.h>

Classes
class	Parameters
	Classifier Parameters. More...

Public Member Functions
bool	classify (const InputAdaptor< double > &input, const std::vector< unsigned int > &attributesIndices, const std::vector< double > &inputNoDataValues, OutputAdaptor< unsigned int > &output, const unsigned int outputIndex, const unsigned int outputNoDataValue, const bool enableProgressInterface)
	Classify an input iterated data and save the result on the output iterated data.

bool	getCovMatrixes (std::vector< boost::numeric::ublas::matrix< double > > &covMatrixes) const
	Get the current classes covariance matrixes.

bool	getInverseCovMatrixes (std::vector< boost::numeric::ublas::matrix< double > > &invCovMatrixes) const
	Get the current classes inverse covariance matrixes.

bool	getLables (std::vector< unsigned int > &classLabels) const
	Get the current classes labels.

bool	getMeans (std::vector< std::vector< double > > &classesMeans) const
	Get the current classes means.

bool	initialize (const Parameters &params)
	Initialize this classifier instance with new parameters.

	MaxLikelihood ()

bool	train (const InputAdaptor< double > &samples, const std::vector< unsigned int > &attributesIndices, const std::vector< unsigned int > &sampleLabels, const bool enableProgressInterface)
	Train this classifier instance using the initialization parameters and the suppied train data.

	~MaxLikelihood ()

Protected Member Functions
void	reset ()
	Reset this instance to its initial state.

Protected Attributes
std::vector< boost::numeric::ublas::matrix< double > >	m_classesCovarianceInvMatrixes
	Classes covariance inverse matrixes.

std::vector< boost::numeric::ublas::matrix< double > >	m_classesCovarianceMatrixes
	Classes covariance matrixes.

std::vector< std::vector< double > >	m_classesMeans
	Classes means;.

std::vector< double >	m_classesOptizedMaxLikelihoodDiscriminantTerm
	An optimized portion of the MaxLikelihood discriminant function.

std::vector< unsigned int >	m_classLabels
	class labels

bool	m_isInitialized
	True if this instance is initialized.

Parameters	m_parameters
	Internal execution parameters.

Detailed Description

A maximum likelihood estimation strategy for classification (a.k.a. MaxVer in portuguese).

Note: Reference: Erthal, G.J.; Frery, A.C. Segmentação de imagens multiespectrais pelo algoritmo ICM:integração ao ambiente SPRING. In: Simpósio Brasileiro de Computação Gráfica eProcessamento de Imagens-SIBGRAP, 6., Recife, 1993. Comunicações. Recife:SBC/UFPe, 1993. p. 33-36.

Definition at line 55 of file MaxLikelihood.h.

Constructor & Destructor Documentation

◆ MaxLikelihood()

te::cl::MaxLikelihood::MaxLikelihood ( )

◆ ~MaxLikelihood()

te::cl::MaxLikelihood::~MaxLikelihood ( )

Member Function Documentation

◆ classify()

bool te::cl::MaxLikelihood::classify	(	const InputAdaptor< double > &	input,
		const std::vector< unsigned int > &	attributesIndices,
		const std::vector< double > &	inputNoDataValues,
		OutputAdaptor< unsigned int > &	output,
		const unsigned int	outputIndex,
		const unsigned int	outputNoDataValue,
		const bool	enableProgressInterface
	)

Classify an input iterated data and save the result on the output iterated data.

Parameters

input	Input data to be classified.
attributesIndices	The attributes indexes to processe from the iterated train data.
inputNoDataValues	A vector of no-data values for each attribute dimension or an empty vector if no-data values are not used.
output	Output classified data.
outputIndex	The output attribute index.
outputNoDataValue	A output label value to use when dealing with input no-data.
enableProgressInterface	Enable/disable the use of a proress interfece.

◆ getCovMatrixes()

bool te::cl::MaxLikelihood::getCovMatrixes ( std::vector< boost::numeric::ublas::matrix< double > > & covMatrixes ) const

Get the current classes covariance matrixes.

Parameters

covMatrixes A vector to return the current classes covariance matrixes.

Returns: true if ok, false on errors.

◆ getInverseCovMatrixes()

bool te::cl::MaxLikelihood::getInverseCovMatrixes ( std::vector< boost::numeric::ublas::matrix< double > > & invCovMatrixes ) const

Get the current classes inverse covariance matrixes.

Parameters

invCovMatrixes A vector to return the current classes inverse covariance matrixes.

Returns: true if ok, false on errors.

◆ getLables()

bool te::cl::MaxLikelihood::getLables ( std::vector< unsigned int > & classLabels ) const

Get the current classes labels.

Parameters

classesMeans A vector to return the current classes labels.

Returns: true if ok, false on errors.

◆ getMeans()

bool te::cl::MaxLikelihood::getMeans ( std::vector< std::vector< double > > & classesMeans ) const

Get the current classes means.

Parameters

classesMeans A vector to return the current classes means.

Returns: true if ok, false on errors.

◆ initialize()

bool te::cl::MaxLikelihood::initialize ( const Parameters & params )

Initialize this classifier instance with new parameters.

Parameters

params New initialization parameters.

◆ reset()

void te::cl::MaxLikelihood::reset ( )

protected

Reset this instance to its initial state.

◆ train()

bool te::cl::MaxLikelihood::train	(	const InputAdaptor< double > &	samples,
		const std::vector< unsigned int > &	attributesIndices,
		const std::vector< unsigned int > &	sampleLabels,
		const bool	enableProgressInterface
	)

Train this classifier instance using the initialization parameters and the suppied train data.

Parameters

samples	Train data samples.
attributesIndices	The attributes indexes to processe from the iterated train data.
sampleLabels	The sample lables for each iterated train data element.
enableProgressInterface	Enable/disable the use of a proress interfece.

Member Data Documentation

◆ m_classesCovarianceInvMatrixes

std::vector< boost::numeric::ublas::matrix< double > > te::cl::MaxLikelihood::m_classesCovarianceInvMatrixes

protected

Classes covariance inverse matrixes.

Definition at line 157 of file MaxLikelihood.h.

◆ m_classesCovarianceMatrixes

std::vector< boost::numeric::ublas::matrix< double > > te::cl::MaxLikelihood::m_classesCovarianceMatrixes

protected

Classes covariance matrixes.

Definition at line 156 of file MaxLikelihood.h.

◆ m_classesMeans

std::vector< std::vector< double > > te::cl::MaxLikelihood::m_classesMeans

protected

Classes means;.

Definition at line 155 of file MaxLikelihood.h.

◆ m_classesOptizedMaxLikelihoodDiscriminantTerm

std::vector< double > te::cl::MaxLikelihood::m_classesOptizedMaxLikelihoodDiscriminantTerm

protected

An optimized portion of the MaxLikelihood discriminant function.

Definition at line 159 of file MaxLikelihood.h.

◆ m_classLabels

std::vector<unsigned int> te::cl::MaxLikelihood::m_classLabels

protected

class labels

Definition at line 158 of file MaxLikelihood.h.

◆ m_isInitialized

bool te::cl::MaxLikelihood::m_isInitialized

protected

True if this instance is initialized.

Definition at line 153 of file MaxLikelihood.h.

◆ m_parameters

Parameters te::cl::MaxLikelihood::m_parameters

protected

Internal execution parameters.

Definition at line 154 of file MaxLikelihood.h.

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

/home/castejon/castejon_files/develop/terralib5/git_release/src/terralib/classification/MaxLikelihood.h

Classes

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ MaxLikelihood()

◆ ~MaxLikelihood()

Member Function Documentation

◆ classify()

◆ getCovMatrixes()

◆ getInverseCovMatrixes()

◆ getLables()

◆ getMeans()

◆ initialize()

◆ reset()

◆ train()

Member Data Documentation

◆ m_classesCovarianceInvMatrixes

◆ m_classesCovarianceMatrixes

◆ m_classesMeans

◆ m_classesOptizedMaxLikelihoodDiscriminantTerm

◆ m_classLabels

◆ m_isInitialized

◆ m_parameters