Public Member Functions | Public Attributes | List of all members
te::rp::ClassifierKMeansStrategy::Parameters Class Reference

Classifier Parameters. More...

#include <ClassifierKMeansStrategy.h>

Inheritance diagram for te::rp::ClassifierKMeansStrategy::Parameters:
te::rp::ClassifierStrategyParameters te::common::AbstractParameters

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

double m_epsilon
 The stop criteria. When the clusters change in a value smaller then epsilon, the convergence is achieved. More...
 
unsigned int m_K
 The number of clusters (means) to detect in image. 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 to perform if convergence is not achieved. More...
 

Detailed Description

Classifier Parameters.

Definition at line 61 of file ClassifierKMeansStrategy.h.

Constructor & Destructor Documentation

te::rp::ClassifierKMeansStrategy::Parameters::Parameters ( )
te::rp::ClassifierKMeansStrategy::Parameters::~Parameters ( )

Member Function Documentation

AbstractParameters* te::rp::ClassifierKMeansStrategy::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.

const Parameters& te::rp::ClassifierKMeansStrategy::Parameters::operator= ( const Parameters &  params)
void te::rp::ClassifierKMeansStrategy::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

double te::rp::ClassifierKMeansStrategy::Parameters::m_epsilon

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

Definition at line 68 of file ClassifierKMeansStrategy.h.

unsigned int te::rp::ClassifierKMeansStrategy::Parameters::m_K

The number of clusters (means) to detect in image.

Definition at line 65 of file ClassifierKMeansStrategy.h.

unsigned int te::rp::ClassifierKMeansStrategy::Parameters::m_maxInputPoints

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

Definition at line 67 of file ClassifierKMeansStrategy.h.

unsigned int te::rp::ClassifierKMeansStrategy::Parameters::m_maxIterations

The maximum of iterations to perform if convergence is not achieved.

Definition at line 66 of file ClassifierKMeansStrategy.h.

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