MAP strategy for classification. More...

#include <MAP.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 double outputNoDataValue, const bool enableProgressInterface) throw (te::cl::Exception)
	Classify an input iterated data and save the result on the output iterated data. More...

bool	initialize (const Parameters &params) throw (te::cl::Exception)
	Initialize this classifier instance with new parameters. More...

	MAP ()

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

	~MAP ()

Protected Member Functions
bool	getPrioriProbabilities (const InputAdaptor< double > &input, const std::vector< unsigned int > &attributesIndices, std::vector< double > &prioriProbs) const
	Calculate priori probabilities by pre-classifying the input data. More...

void	reset ()
	Reset this instance to its initial state. More...

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

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

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

std::vector< double >	m_classesOptizedMAPDiscriminantTerm
	An optimized portion of the MAP discriminant function. More...

std::vector< unsigned int >	m_classLabels
	class labels More...

bool	m_isInitialized
	True if this instance is initialized. More...

Parameters	m_parameters
	Internal execution parameters. More...

Detailed Description

MAP strategy for classification.

Definition at line 54 of file MAP.h.

Constructor & Destructor Documentation

◆ MAP()

te::cl::MAP::MAP ( )

◆ ~MAP()

te::cl::MAP::~MAP ( )

Member Function Documentation

◆ classify()

bool te::cl::MAP::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 double	outputNoDataValue,
		const bool	enableProgressInterface
	)
throw	(	te::cl::Exception
	)

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.

◆ getPrioriProbabilities()

bool te::cl::MAP::getPrioriProbabilities	(	const InputAdaptor< double > &	input,
		const std::vector< unsigned int > &	attributesIndices,
		std::vector< double > &	prioriProbs
	)		const

protected

Calculate priori probabilities by pre-classifying the input data.

Parameters

input	Input data to be classified.
attributesIndices	The attributes indexes to processe from the iterated train data.
prioriProbs	Calculated priori-probabilities.

◆ initialize()

bool te::cl::MAP::initialize	(	const Parameters &	params	)
throw	(	te::cl::Exception
	)

Initialize this classifier instance with new parameters.

Parameters

params New initialization parameters.

◆ reset()

void te::cl::MAP::reset ( )

protected

Reset this instance to its initial state.

◆ train()

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

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::MAP::m_classesCovarianceInvMatrixes

protected

Classes covariance inverse matrixes.

Definition at line 131 of file MAP.h.

◆ m_classesCovarianceMatrixes

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

protected

Classes covariance matrixes.

Definition at line 130 of file MAP.h.

◆ m_classesMeans

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

protected

Classes means;.

Definition at line 129 of file MAP.h.

◆ m_classesOptizedMAPDiscriminantTerm

std::vector< double > te::cl::MAP::m_classesOptizedMAPDiscriminantTerm

protected

An optimized portion of the MAP discriminant function.

Definition at line 133 of file MAP.h.

◆ m_classLabels

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

protected

class labels

Definition at line 132 of file MAP.h.

◆ m_isInitialized

bool te::cl::MAP::m_isInitialized

protected

True if this instance is initialized.

Definition at line 127 of file MAP.h.

◆ m_parameters

Parameters te::cl::MAP::m_parameters

protected

Internal execution parameters.

Definition at line 128 of file MAP.h.

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

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

Classes

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ MAP()

◆ ~MAP()

Member Function Documentation

◆ classify()

◆ getPrioriProbabilities()

◆ initialize()

◆ reset()

◆ train()

Member Data Documentation

◆ m_classesCovarianceInvMatrixes

◆ m_classesCovarianceMatrixes

◆ m_classesMeans

◆ m_classesOptizedMAPDiscriminantTerm

◆ m_classLabels

◆ m_isInitialized

◆ m_parameters