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

Does	not throw any exception.

ContrastTriggerDefault()

static int ContrastTriggerDefault ( )

inlinestaticnoexcept

Default value for the ContrastTrigger.

Returns

Default constrast trigger value.

Exceptions

Does	not 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

Does	not throw any exception.

EnsembleSizeDefault()

static int EnsembleSizeDefault ( )

inlinestaticnoexcept

Default value for the EnsembleSize.

Returns

Default ensemble size value.

Exceptions

Does	not 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

Does	not throw any exception.

IndifferenceRadiusDefault()

static int IndifferenceRadiusDefault ( )

inlinestaticnoexcept

Default value for the IndifferenceRadius.

Returns

Default indeference radius value.

Exceptions

Does	not 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

Does	not throw any exception.

MinFeatureCountDefault()

static int MinFeatureCountDefault ( )

inlinestaticnoexcept

Default value for the MinFeatureCount.

Returns

Default min feature count value.

Exceptions

Does	not 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

Does	not throw any exception.

NegativeDensityDefault()

static double NegativeDensityDefault ( )

inlinestaticnoexcept

Default value for the NegativeDensity.

Returns

Default negative density value.

Exceptions

Does	not 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

Does	not throw any exception.

PolydromyDefault()

static int PolydromyDefault ( )

inlinestaticnoexcept

Default value for the Polydromy.

Returns

Default polydromy value.

Exceptions

Does	not 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]

contrastTrigger

The contrast trigger value.

Exceptions

Does	not 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]

ensembleSize

The ensemble size value.

Exceptions

Does	not 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]

indifferenceRadius

The indifference radius value.

Exceptions

Does	not 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]

minFeatureCount

The minimum feature count.

Exceptions

Does	not 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]

negativeDensity

The negative density value.

Exceptions

Does	not 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]

polydromy

The polydromy value.

Exceptions

Does	not throw any exception.