lti::rbf::parameters Class Reference

parameters class for the RBF-Networks More...

#include <ltiRbf.h>

Inheritance diagram for lti::rbf::parameters:

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Collaboration diagram for lti::rbf::parameters:

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List of all members.

Public Types
enum	eLvqInit { LvqRand, LvqMaxDist }
enum	eLvqTrainType { NO_LVQ = -1, LVQ1, OLVQ1, LVQ3, OLVQ3, LVQ4 }
Public Member Functions
	parameters ()
	parameters (const parameters &other)
virtual	~parameters ()
const char *	getTypeName () const
parameters &	copy (const parameters &other)
parameters &	operator= (const parameters &other)
virtual classifier::parameters *	clone () const
virtual bool	write (ioHandler &handler, const bool complete=true) const
virtual bool	read (ioHandler &handler, const bool complete=true)
Public Attributes
classifier parameters

bool	doTrain2
int	nbPresentations1
int	nbPresentations2
int	nbHiddenNeurons
double	learnRate1
double	learnRate2
double	learnFactor
double	windowSize
double	sigma
double	sigmaFactor
double	lambda
double	high
eNormType	norm
eLvqInit	lvqInitType
eLvqTrainType	lvqType1
eLvqTrainType	lvqType2

Detailed Description

parameters class for the RBF-Networks

Member Enumeration Documentation

enum lti::rbf::parameters::eLvqInit

type to specify the kind of initialization for the networks

Enumerator:

LvqRand	random initialization of the vector code
LvqMaxDist	initialization with the maximum distance

enum lti::rbf::parameters::eLvqTrainType

The LVQ training algorithm.

Enumerator:

NO_LVQ	do not use LVQ
LVQ1	use LVQ1
OLVQ1	use OLVQ1
LVQ3	use LVQ3
OLVQ3	use OLVQ3
LVQ4	use LVQ4

Constructor & Destructor Documentation

lti::rbf::parameters::parameters ( )

default constructor

Reimplemented from lti::classifier::parameters.

lti::rbf::parameters::parameters ( const parameters & other )

copy constructor

Parameters:

other

the parameters object to be copied

virtual lti::rbf::parameters::~parameters ( ) [virtual]

destructor

Reimplemented from lti::classifier::parameters.

Member Function Documentation

virtual classifier::parameters* lti::rbf::parameters::clone ( ) const [virtual]

returns a pointer to a clone of the parameters

Reimplemented from lti::classifier::parameters.

parameters& lti::rbf::parameters::copy ( const parameters & other )

copy the contents of a parameters object

Parameters:

other

the parameters object to be copied

Returns:: a reference to this parameters object

Referenced by operator=().

const char* lti::rbf::parameters::getTypeName ( ) const [virtual]

returns name of this type

Reimplemented from lti::classifier::parameters.

parameters& lti::rbf::parameters::operator= ( const parameters & other ) [inline]

Alias for "copy".

Parameters:

other

the parameters object to be copied

Returns:: a reference to this parameters object

References copy().

virtual bool lti::rbf::parameters::read	(	ioHandler &	handler,
		const bool	complete = `true`
	)			`[virtual]`

read the parameters from the given ioHandler

Parameters:

	handler	the ioHandler to be used
	complete	if true (the default) the enclosing begin/end will be also written, otherwise only the data block will be written.

Returns:: true if write was successful

Reimplemented from lti::classifier::parameters.

virtual bool lti::rbf::parameters::write	(	ioHandler &	handler,
		const bool	complete = `true`
	)			const `[virtual]`

write the parameters in the given ioHandler

Parameters:

	handler	the ioHandler to be used
	complete	if true (the default) the enclosing begin/end will be also written, otherwise only the data block will be written.

Returns:: true if write was successful

Reimplemented from lti::classifier::parameters.

Member Data Documentation

bool lti::rbf::parameters::doTrain2

trainNewNet

Default value: false convertNet Default value: false doTrain2 Default value: false

double lti::rbf::parameters::high

this factor determines the value of the sigmoid function that will be used as threshold for a correct classification.

Default value: 0.99

double lti::rbf::parameters::lambda

lambda

Default value: 0.0

double lti::rbf::parameters::learnFactor

learnFactor

Default value: 0.3

double lti::rbf::parameters::learnRate1

learnRate1

Default value: 0.3

double lti::rbf::parameters::learnRate2

learnRate2

Default value: 0.1

eLvqInit lti::rbf::parameters::lvqInitType

lvqInitType

Default value: rbf::parameters::LvqMaxDist

eLvqTrainType lti::rbf::parameters::lvqType1

specify the type for the first LVQ training (usually LVQ1 or OLVQ1)

Default value: OLVQ1

eLvqTrainType lti::rbf::parameters::lvqType2

specify the type for the second LVQ training (usually LVQ3 or OLVQ3)

Default value: OLVQ3

int lti::rbf::parameters::nbHiddenNeurons

nbHiddenNeurons

Default value: 1

int lti::rbf::parameters::nbPresentations1

loadNet

Default value: true generate statistics? Default value: false Save/Load in binary mode Default value: true Save/Load in ASCII mode Default value: false convert binary file to ascii file Default value: true noObjectProbs Default value: false classifyStat Default value: true mseStat Default value: false mseSave Default value: false firstBestSave Default value: false threeBestSave Default value: true include the training set in the pattern set for the training statistics Default value: false nbPresentations1 Default value: 0

int lti::rbf::parameters::nbPresentations2

nbPresentations2

Default value: 20

eNormType lti::rbf::parameters::norm

norm

Default value: L2distance

double lti::rbf::parameters::sigma

sigma

Default value: 0.0

double lti::rbf::parameters::sigmaFactor

sigmaFactor

Default value: 1.6