CVB++ 15.0
LearnParameters Class Reference

The set of parameters, which controls, how a classifier is being learned from a training set. More...

#include <cvb/minos/learn_parameters.hpp>

Public Member Functions

int MinFeatureCount () const noexcept
 Gets the minimum feature count defining, how many features must be extracted at the very least per class in a classifier. More...
 
void SetMinFeatureCount (int minFeatureCount)
 Sets the minimum feature count defining, how many features must be extracted at the very least per class in a classifier. More...
 
int Polydromy () const noexcept
 Gets the polydromy value controlling the complexity of the feature search tree in the classifier. More...
 
void SetPolydromy (int polydromy)
 Sets the polydromy value controlling the complexity of the feature search tree in the classifier. Valid polydromy values must be greater than 0, the default value is 2. More...
 
int EnsembleSize () const noexcept
 Gets the maximum size of the Ensembles of similar instance images to be used for pair feature calculation. More...
 
void SetEnsembleSize (int ensembleSize)
 Sets the maximum size of the Ensembles of similar instance images to be used for pair feature calculation. Valid ensemble sizes must be greater than 0, the default value is 15. More...
 
int ContrastTrigger () const noexcept
 Gets the minimum gray value difference the two regions of one pair feature must have to be eligible to become a classifier feature. More...
 
void SetContrastTrigger (int contrastTrigger)
 Sets the minimum gray value difference the two regions of one pair feature must have to be eligible to become a classifier feature. Valid contrast triggers must be greater than 0, the default value is 8. More...
 
int IndifferenceRadius () const noexcept
 Gets the radius (L1 norm!) around a positive instance from which no counter sample is to be extracted. More...
 
void SetIndifferenceRadius (int indifferenceRadius)
 Sets the radius (L1 norm!) around a positive instance from which no counter sample is to be extracted. Valid indifference radii must be greater than 0. The default value is 6 here. More...
 
double NegativeDensity () const noexcept
 Gets the density at which counter samples are extracted from the training set images during the learning phase. More...
 
void SetNegativeDensity (double negativeDensity)
 Sets the density at which counter samples are extracted from the training set images during the learning phase. Valid negative densities range from 0.0 to 1.0. The default value is 1.0. More...
 

Static Public Member Functions

static int IndifferenceRadiusDefault () noexcept
 Default value for the IndifferenceRadius. More...
 
static double NegativeDensityDefault () noexcept
 Default value for the NegativeDensity. More...
 
static int ContrastTriggerDefault () noexcept
 Default value for the ContrastTrigger. More...
 
static int EnsembleSizeDefault () noexcept
 Default value for the EnsembleSize. More...
 
static int PolydromyDefault () noexcept
 Default value for the Polydromy. More...
 
static int MinFeatureCountDefault () noexcept
 Default value for the MinFeatureCount. More...
 

Detailed Description

The set of parameters, which controls, how a classifier is being learned from a training set.

Member Function Documentation

◆ ContrastTrigger()

int ContrastTrigger ( ) const
inlinenoexcept

Gets the minimum gray value difference the two regions of one pair feature must have to be eligible to become a classifier feature.

Returns
The contrast trigger value.
Exceptions
Doesnot throw any exception.

◆ ContrastTriggerDefault()

static int ContrastTriggerDefault ( )
inlinestaticnoexcept

Default value for the ContrastTrigger.

Returns
Default constrast trigger value.
Exceptions
Doesnot throw any exception.

◆ EnsembleSize()

int EnsembleSize ( ) const
inlinenoexcept

Gets the maximum size of the Ensembles of similar instance images to be used for pair feature calculation.

Returns
The ensemble size value.
Exceptions
Doesnot throw any exception.

◆ EnsembleSizeDefault()

static int EnsembleSizeDefault ( )
inlinestaticnoexcept

Default value for the EnsembleSize.

Returns
Default ensemble size value.
Exceptions
Doesnot throw any exception.

◆ IndifferenceRadius()

int IndifferenceRadius ( ) const
inlinenoexcept

Gets the radius (L1 norm!) around a positive instance from which no counter sample is to be extracted.

Returns
The indifference radius value.
Exceptions
Doesnot throw any exception.

◆ IndifferenceRadiusDefault()

static int IndifferenceRadiusDefault ( )
inlinestaticnoexcept

Default value for the IndifferenceRadius.

Returns
Default indeference radius value.
Exceptions
Doesnot throw any exception.

◆ MinFeatureCount()

int MinFeatureCount ( ) const
inlinenoexcept

Gets the minimum feature count defining, how many features must be extracted at the very least per class in a classifier.

Returns
The minimum feature count.
Exceptions
Doesnot throw any exception.

◆ MinFeatureCountDefault()

static int MinFeatureCountDefault ( )
inlinestaticnoexcept

Default value for the MinFeatureCount.

Returns
Default min feature count value.
Exceptions
Doesnot throw any exception.

◆ NegativeDensity()

double NegativeDensity ( ) const
inlinenoexcept

Gets the density at which counter samples are extracted from the training set images during the learning phase.

Returns
The negative density value.
Exceptions
Doesnot throw any exception.

◆ NegativeDensityDefault()

static double NegativeDensityDefault ( )
inlinestaticnoexcept

Default value for the NegativeDensity.

Returns
Default negative density value.
Exceptions
Doesnot throw any exception.

◆ Polydromy()

int Polydromy ( ) const
inlinenoexcept

Gets the polydromy value controlling the complexity of the feature search tree in the classifier.

Returns
The polydromy value.
Exceptions
Doesnot throw any exception.

◆ PolydromyDefault()

static int PolydromyDefault ( )
inlinestaticnoexcept

Default value for the Polydromy.

Returns
Default polydromy value.
Exceptions
Doesnot throw any exception.

◆ SetContrastTrigger()

void SetContrastTrigger ( int  contrastTrigger)
inline

Sets the minimum gray value difference the two regions of one pair feature must have to be eligible to become a classifier feature. Valid contrast triggers must be greater than 0, the default value is 8.

Parameters
[in]contrastTriggerThe contrast trigger value.
Exceptions
Doesnot throw any exception.

◆ SetEnsembleSize()

void SetEnsembleSize ( int  ensembleSize)
inline

Sets the maximum size of the Ensembles of similar instance images to be used for pair feature calculation. Valid ensemble sizes must be greater than 0, the default value is 15.

Parameters
[in]ensembleSizeThe ensemble size value.
Exceptions
Doesnot throw any exception.

◆ SetIndifferenceRadius()

void SetIndifferenceRadius ( int  indifferenceRadius)
inline

Sets the radius (L1 norm!) around a positive instance from which no counter sample is to be extracted. Valid indifference radii must be greater than 0. The default value is 6 here.

Parameters
[in]indifferenceRadiusThe indifference radius value.
Exceptions
Doesnot throw any exception.

◆ SetMinFeatureCount()

void SetMinFeatureCount ( int  minFeatureCount)
inline

Sets the minimum feature count defining, how many features must be extracted at the very least per class in a classifier.

Parameters
[in]minFeatureCountThe minimum feature count.
Exceptions
Doesnot throw any exception.

◆ SetNegativeDensity()

void SetNegativeDensity ( double  negativeDensity)
inline

Sets the density at which counter samples are extracted from the training set images during the learning phase. Valid negative densities range from 0.0 to 1.0. The default value is 1.0.

Parameters
[in]negativeDensityThe negative density value.
Exceptions
Doesnot throw any exception.

◆ SetPolydromy()

void SetPolydromy ( int  polydromy)
inline

Sets the polydromy value controlling the complexity of the feature search tree in the classifier. Valid polydromy values must be greater than 0, the default value is 2.

Parameters
[in]polydromyThe polydromy value.
Exceptions
Doesnot throw any exception.