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