#include <ClassifierEMStrategy.h>

Inheritance diagram for te::rp::ClassifierEMStrategy::Parameters:

Public Member Functions
AbstractParameters *	clone () const
	Create a clone copy of this instance. More...

const Parameters &	operator= (const Parameters &params)

	Parameters ()

void	reset () throw ( te::rp::Exception )
	Clear all internal allocated resources and reset the parameters instance to its initial state. More...

	~Parameters ()

Public Attributes
std::vector< std::vector< double > >	m_clustersMeans
	The previously estimated means of the clusters (optional). More...

double	m_epsilon
	The stop criteria. When the clusters change in a value smaller then epsilon, the convergence is achieved. More...

unsigned int	m_maxInputPoints
	The maximum number of points used to estimate the clusters (default = 1000). More...

unsigned int	m_maxIterations
	The maximum of iterations (E/M steps) to perform if convergence is not achieved. More...

unsigned int	m_numberOfClusters
	The number of clusters (classes) to estimate in the image. More...

unsigned int	m_useRandomSamples
	If true, random samples will be used instead of regular spaced samples. More...

Detailed Description

Classifier Parameters.

Definition at line 67 of file ClassifierEMStrategy.h.

Constructor & Destructor Documentation

◆ Parameters()

te::rp::ClassifierEMStrategy::Parameters::Parameters ( )

◆ ~Parameters()

te::rp::ClassifierEMStrategy::Parameters::~Parameters ( )

Member Function Documentation

◆ clone()

AbstractParameters* te::rp::ClassifierEMStrategy::Parameters::clone ( ) const

virtual

Create a clone copy of this instance.

Returns: A clone copy of this instance.

Note: The caller will take the ownership of the returned pointer.

Implements te::common::AbstractParameters.

◆ operator=()

const Parameters& te::rp::ClassifierEMStrategy::Parameters::operator= ( const Parameters & params )

◆ reset()

void te::rp::ClassifierEMStrategy::Parameters::reset	(		)
throw	(	te::rp::Exception
	)

virtual

Clear all internal allocated resources and reset the parameters instance to its initial state.

Implements te::common::AbstractParameters.

Member Data Documentation

◆ m_clustersMeans

std::vector<std::vector<double> > te::rp::ClassifierEMStrategy::Parameters::m_clustersMeans

The previously estimated means of the clusters (optional).

Definition at line 76 of file ClassifierEMStrategy.h.

◆ m_epsilon

double te::rp::ClassifierEMStrategy::Parameters::m_epsilon

The stop criteria. When the clusters change in a value smaller then epsilon, the convergence is achieved.

Definition at line 75 of file ClassifierEMStrategy.h.

◆ m_maxInputPoints

unsigned int te::rp::ClassifierEMStrategy::Parameters::m_maxInputPoints

The maximum number of points used to estimate the clusters (default = 1000).

Definition at line 74 of file ClassifierEMStrategy.h.

◆ m_maxIterations

unsigned int te::rp::ClassifierEMStrategy::Parameters::m_maxIterations

The maximum of iterations (E/M steps) to perform if convergence is not achieved.

Definition at line 73 of file ClassifierEMStrategy.h.

◆ m_numberOfClusters

unsigned int te::rp::ClassifierEMStrategy::Parameters::m_numberOfClusters

The number of clusters (classes) to estimate in the image.

Definition at line 72 of file ClassifierEMStrategy.h.

◆ m_useRandomSamples

unsigned int te::rp::ClassifierEMStrategy::Parameters::m_useRandomSamples

If true, random samples will be used instead of regular spaced samples.

Definition at line 71 of file ClassifierEMStrategy.h.

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

/home/castejon/castejon_files/develop/terralib5/git_release/src/terralib/rp/ClassifierEMStrategy.h

Public Member Functions

Public Attributes