Interface definition for LVQ trainers. More...

#include <PndLVQTrain.h>

Inheritance diagram for PndLVQTrain:

Public Member Functions
	PndLVQTrain (std::vector< std::pair< std::string, std::vector< float > * > > const &InputEvtsParam, std::vector< std::string > const &ClassNames, std::vector< std::string > const &VarNames, bool trim=false)

	PndLVQTrain (std::string const &InPut, std::vector< std::string > const &ClassNames, std::vector< std::string > const &VarNames, bool trim=true)

virtual	~PndLVQTrain ()

void	storeWeights ()

void	Train ()

void	Train21 ()

void	setProtoInitType (ProtoInitType iniTypeVal=RAND_FROM_DATA)

void	SetInitProtoFileName (std::string const &fileName)

void	SetLearnPrameters (double const initConst, double const etZ, double const etF, unsigned int const Nswp)

void	SetNumberOfProto (size_t const numProto)

void	SetNumberOfProto (std::map< std::string, size_t > const &labelMap)

void	SetErrorStepSize (unsigned int const val=1000)

void	SetLVQ2_1WindowSize (float const Wsize=0.3)

void	EvalClassifierError ()

void	SetPerEpochEval (bool val)

bool	GetPerEpochEval () const

void	SetTestSetSize (size_t percent=50)

void	SetTestSet (std::set< size_t > const &testSet)

void	NormalizeData (NormType t=NONORM)

void	PCATransForm ()

void	SetOutPutFile (std::string const &outFile)

void	WriteErroVect (std::string const &FileName) const

std::vector< StepError > const &	GetErrorValues () const

virtual void	Initialize ()

std::set< size_t > const &	GetTestEvetIdx () const

std::vector< PndMvaClass > const &	GetClasses () const
	Get the list of available classes (labels). More...

std::vector< PndMvaVariable > const &	GetVariables () const
	Get the list of available variables. More...

size_t	GetRndSeed () const

void	SetRndSeed (size_t const sd)

Protected Member Functions
void	SetAppType (AppType t)

void	WriteToWeightFile (std::vector< std::pair< std::string, std::vector< float > * > > const &weights) const

void	splitTetsSet ()

Protected Attributes
std::set< size_t >	m_testSet_indices
	Indices of the test set. More...

PndMvaDataSet	m_dataSets
	Data set. Holds event values. More...

std::vector< StepError >	m_StepErro
	Container to keep per step error values. More...

std::string	m_outFile
	Output filename. More...

size_t	m_RND_seed
	Random seed. More...

Private Member Functions
	PndLVQTrain (PndLVQTrain const &other)

PndLVQTrain &	operator= (PndLVQTrain const &other)

void	EvalClassifierError (unsigned int stp)
	Evaluate the classifier, train and test error. More...

void	InitProtoRand ()

void	InitRandProtoFromData ()

void	InitProtoK_Means ()

void	InitProtoTypes ()

void	cleanProtoList ()

void	UpdateProto (std::vector< float > const &EvtData, std::vector< float > &proto, int const delta, double const ethaT)

void	ValidateProtoUpdate (std::vector< float > &p)

void	ReadProtoFromFile ()

Private Attributes
std::vector< std::pair < std::string, std::vector < float > * > >	m_LVQProtos
	Holds the LVQ proto-types. More...

std::vector< PndMvaDistObj >	m_distances
	Container to store distances. More...

double	m_initConst

double	m_ethaZero

double	m_ethaFinal

float	m_WindowSize

unsigned int	m_NumSweep
	Number of sweeps through example set. More...

ProtoInitType	m_proto_init
	Proto init type. More...

std::string	m_initProtoFile
	initial protypes, when reading from file. More...

unsigned int	m_ErrorStep
	Each #ErrorStep, steps evaluate the trained classifier. More...

unsigned int	m_ProgStep

std::map< std::string, size_t >	m_numProtoPerClass
	Map labels (classes) to number of prototypes. More...

bool	m_PerEpoch
	If evaluate per epoch. More...

Detailed Description

Interface definition for LVQ trainers.

Definition at line 33 of file PndLVQTrain.h.

Constructor & Destructor Documentation

PndLVQTrain::PndLVQTrain	(	std::vector< std::pair< std::string, std::vector< float > * > > const &	InputEvtsParam,
		std::vector< std::string > const &	ClassNames,
		std::vector< std::string > const &	VarNames,
		bool	trim = `false`
	)

explicit

Constructor:

Parameters

InputEvtsParam	Input events vector.
ClassNames	class names.
VarNames	variable names of the features.

PndLVQTrain::PndLVQTrain	(	std::string const &	InPut,
		std::vector< std::string > const &	ClassNames,
		std::vector< std::string > const &	VarNames,
		bool	trim = `true`
	)

explicit

Constructor:

Parameters

InPut,:	Input file name.
ClassNames,:	class names.
VarNames,:	variable names of the features.

virtual PndLVQTrain::~PndLVQTrain ( )

virtual

Destructor

PndLVQTrain::PndLVQTrain ( PndLVQTrain const & other )

private

Member Function Documentation

void PndLVQTrain::cleanProtoList ( )

private

Clean prototype container.

void PndLVQTrain::EvalClassifierError ( )

virtual

Classifier evaluation.

Reimplemented from PndMvaTrainer.

void PndLVQTrain::EvalClassifierError ( unsigned int stp )

private

Evaluate the classifier, train and test error.

std::vector< PndMvaClass > const & PndMvaTrainer::GetClasses ( ) const

inlineinherited

Get the list of available classes (labels).

Get the list of available classes (labels). Vector containing available labels.

Definition at line 226 of file PndMvaTrainer.h.

References PndMvaDataSet::GetClasses(), and PndMvaTrainer::m_dataSets.

 {
   return m_dataSets.GetClasses();
 };

std::vector< StepError > const & PndMvaTrainer::GetErrorValues ( ) const

inlineinherited

Get the list of objects that contain the classifier evaluation results.

Returns: List of evaluation objects.

Definition at line 238 of file PndMvaTrainer.h.

References PndMvaTrainer::m_StepErro.

 {
   return m_StepErro;
 };

bool PndLVQTrain::GetPerEpochEval ( ) const

inline

Getter for evaluation scheme.

Returns: Per epoch or per step.

Definition at line 309 of file PndLVQTrain.h.

References m_PerEpoch.

 {
   return m_PerEpoch;
 };

size_t PndMvaTrainer::GetRndSeed ( ) const

inlineinherited

Definition at line 200 of file PndMvaTrainer.h.

References PndMvaTrainer::m_RND_seed.

 {
   return this->m_RND_seed;
 };

std::set< size_t > const & PndMvaTrainer::GetTestEvetIdx ( ) const

inlineinherited

Get the indices of the events selected to be used for testing.

Returns: A set containing the indices of test events.

Definition at line 220 of file PndMvaTrainer.h.

References PndMvaTrainer::m_testSet_indices.

 {
   return m_testSet_indices;
 };

std::vector< PndMvaVariable > const & PndMvaTrainer::GetVariables ( ) const

inlineinherited

Get the list of available variables.

Get the list of available variables. Vector containing available Parameters (features).

Definition at line 232 of file PndMvaTrainer.h.

References PndMvaDataSet::GetVars(), and PndMvaTrainer::m_dataSets.

 {
   return m_dataSets.GetVars();
 };

virtual void PndMvaTrainer::Initialize ( )

virtualinherited

Initialize data structures.

Reimplemented in PndMultiClassBdtTrain, and PndMultiClassMlpTrain.

void PndLVQTrain::InitProtoK_Means ( )

private

Initialize LVQ prototypes (Code books) using K-Means clustering.

void PndLVQTrain::InitProtoRand ( )

private

Initialize LVQ prototypes (Code books) using class conditional means (CCM) vectors.

void PndLVQTrain::InitProtoTypes ( )

private

Initialize LVQ prototypes (Code books).

void PndLVQTrain::InitRandProtoFromData ( )

private

Initialize LVQ prototypes (Code books) using Randomly selected vectors from the original data set.

void PndMvaTrainer::NormalizeData ( NormType t = NONORM )

inherited

Select input data normalization scheme.

PndLVQTrain& PndLVQTrain::operator= ( PndLVQTrain const & other )

private

void PndMvaTrainer::PCATransForm ( )

inherited

Parameter decorrelation. Performs PCA (Principal component analysis) on the input dataset.

void PndLVQTrain::ReadProtoFromFile ( )

private

Read pre-initialized code books from file and store the vectors in LVQ prototype container.

void PndMvaTrainer::SetAppType ( AppType t )

inlineprotectedinherited

Set application type for the current object

Definition at line 215 of file PndMvaTrainer.h.

References PndMvaTrainer::m_dataSets, and PndMvaDataSet::SetAppType().

 {
   m_dataSets.SetAppType(t);
 };

void PndLVQTrain::SetErrorStepSize ( unsigned int const val = 1000 )

inline

Set how often the classifier has to be evaluated.

Parameters

val	Evaluate after val steps. If (Val == 0) then the classifier is evaluated once at the end of the training prodecure.

Definition at line 294 of file PndLVQTrain.h.

References m_ErrorStep, and val.

 {
   m_ErrorStep = val;
 };

void PndLVQTrain::SetInitProtoFileName ( std::string const & fileName )

inline

Set the file name which holds the pre-initialized code books.

Parameters

val	The name of the file which containes the pre initialized code books.

Definition at line 280 of file PndLVQTrain.h.

References m_initProtoFile.

 {
   m_initProtoFile = fileName;
 };

void PndLVQTrain::SetLearnPrameters	(	double const	initConst,
		double const	etZ,
		double const	etF,
		unsigned int const	Nswp
	)

inline

Sets the learning parameters.

Parameters

initConst,:	Initialization constant, used to initialize LVQ prototypes.
etZ,:	EthaZero, start value for the learning rate.
etF,:	Final value for Etha (learning rate)
Nswp,:	Number of sweeps through the examples collection set.

Definition at line 285 of file PndLVQTrain.h.

References m_ethaFinal, m_ethaZero, m_initConst, and m_NumSweep.

 {
   m_initConst = initConst;
   m_ethaZero = etZ;
   m_ethaFinal = etF;
   m_NumSweep = Nswp;
 };

void PndLVQTrain::SetLVQ2_1WindowSize ( float const Wsize = 0.3 )

inline

Set the window size for LVQ2.1 alg. A value between 0.2 & 0.3 is recommended.

Definition at line 299 of file PndLVQTrain.h.

References m_WindowSize.

 {
   m_WindowSize = Wsize;
 };

void PndLVQTrain::SetNumberOfProto ( size_t const numProto )

Set the number of protoTypes to be used for training. The same number of prototypes are initialized for all available labels(classes).

Parameters

numProto Number of prototypes.

void PndLVQTrain::SetNumberOfProto ( std::map< std::string, size_t > const & labelMap )

Set the number of protoTypes to be used for training.

Parameters

labelMap Map containing number of prototypes for each class (label).

void PndMvaTrainer::SetOutPutFile ( std::string const & outFile )

inlineinherited

Setter to set the weightfile name.

Parameters

outFile Output filename.

Definition at line 210 of file PndMvaTrainer.h.

References PndMvaTrainer::m_outFile, and outFile.

 {
   m_outFile = outFile;
 };

void PndLVQTrain::SetPerEpochEval ( bool val )

inline

Select if we want to follow the training evaluation per epoch (sweeps) or per step. The number of steps = Sweeps * (#examples).

Parameters

val	true = evaluate per epoch, false per step. Default is false.

Definition at line 304 of file PndLVQTrain.h.

References m_PerEpoch, and val.

 {
   m_PerEpoch = val;
 };

void PndLVQTrain::setProtoInitType ( ProtoInitType iniTypeVal = RAND_FROM_DATA )

inline

Set CodeBook init type.

Parameters

iniTypeVal Initialization type.

Definition at line 275 of file PndLVQTrain.h.

References m_proto_init.

 {
   m_proto_init = iniTypeVal;
 };

void PndMvaTrainer::SetRndSeed ( size_t const sd )

inlineinherited

Definition at line 205 of file PndMvaTrainer.h.

References PndMvaTrainer::m_RND_seed.

 {
   this->m_RND_seed = sd;
 };

void PndMvaTrainer::SetTestSet ( std::set< size_t > const & testSet )

inherited

Set the indices of events that are going to be used for testing.

Parameters

testSet Set containing the indices of the test events.

void PndMvaTrainer::SetTestSetSize ( size_t percent = 50 )

inherited

Creates test and train data sets.

Parameters

percent Percent of the data set to be used for testing.

void PndMvaTrainer::splitTetsSet ( )

protectedinherited

void PndLVQTrain::storeWeights ( )

virtual

Store weights in the output File. If output file name is not specified, then write nothing.

Implements PndMvaTrainer.

void PndLVQTrain::Train ( )

virtual

Train the classifier accourding to LVQ1 algorithm.

Implements PndMvaTrainer.

void PndLVQTrain::Train21 ( )

Train the classifier accourding to LVQ2.1 algorithm.

void PndLVQTrain::UpdateProto	(	std::vector< float > const &	EvtData,
		std::vector< float > &	proto,
		int const	delta,
		double const	ethaT
	)

private

Updates the LVQ prototypes.

void PndLVQTrain::ValidateProtoUpdate ( std::vector< float > & p )

private

Check if the current update creates an invalid codebook (If the vectore is placed outside the extrema). If after the update the codebook is out of boundary: reinitialize. Else: do nothing.

Parameters