latest version v1.9 - last update 24 Nov 2005

lti::classifier Class Reference

Abstract parent class for all classifiers. More...

#include <ltiClassifier.h>

Inheritance diagram for lti::classifier:

[legend]Collaboration diagram for lti::classifier:

[legend]List of all members.

Public Member Functions
	classifier ()
	classifier (const classifier &other)
virtual	~classifier ()
virtual const char *	getTypeName () const
classifier &	copy (const classifier &other)
classifier &	operator= (const classifier &other)
virtual classifier *	clone () const =0
virtual bool	validParameters () const
const parameters &	getParameters () const
virtual bool	setParameters (const parameters &theParam)
virtual void	setOutputTemplate (const outputTemplate &theOutputTemplate)
const outputTemplate &	getOutputTemplate () const
virtual bool	write (ioHandler &handler, const bool complete=true) const
virtual bool	read (ioHandler &handler, const bool complete=true)
Progress Info
Methods used to plug-in and retrieve progress visualization objects.
void	setProgressObject (const progressInfo &progBox)
void	removeProgressObject ()
bool	validProgressObject () const
progressInfo &	getProgressObject ()
const progressInfo &	getProgressObject () const
Static Public Attributes
static const int	unknownObj
static const int	noObject
static const double	nullProbability
Protected Attributes
parameters *	params
progressInfo *	progressBox
outputTemplate	outTemplate
Classes
class	invalidMethodException
	Exception thrown when a method of a functor is not implemented for a specific parameter set. More...
class	invalidOutputException
	Exception thrown when the output objects has not been set yet. More...
class	invalidParametersException
	Exception thrown when the parameters are not set. More...
class	outputTemplate
	The outputTemplate stores the relation between the different positions (sometimes called internal ids) of a classification result and the ids. More...
class	outputVector
	An output vector is the result of classifying data with any classifier. More...
class	parameters
	the parameters for the class classifier More...
class	parametersOutOfRangeException
	Exception thrown when a parameter is out of range. More...

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Detailed Description

Abstract parent class for all classifiers.

The classifier class encloses a number of other important classes:

lti::classifier::parameters

similar to the functor::parameters, this is used to specify the characteristics of a classifier like learning rate, etc.

lti::classifier::outputVector

This type is used to return the classification results.

lti::classifier::outputTemplate

Contains information on how the outputVector is to be constructed from a resulting vector of the classification.

Classifiers can be diveded into two large groups: supervised and unsupervised. These two groups have the base classes supervisedClassifier and unsupervisedClassifier which are subclasses of this class. Examples for the first type are RBF networks, multi layer perceptron, maximum likelihood classifiers etc. The second type comprises statistic and neural clustering methods.

All classifiers return an outputVector when classify is called. The outputVector is created by the classifier by applying its outputTemplate to the regular dvector which results from the classification process.

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Constructor & Destructor Documentation

lti::classifier::classifier (   )

default constructor

lti::classifier::classifier (  const classifier &  other  )

copy constructor Parameters: other  the object to be copied

virtual lti::classifier::~classifier (   )  [virtual]

destructor

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Member Function Documentation

virtual classifier* lti::classifier::clone (   )  const [pure virtual]

returns a pointer to a clone of this functor. Implemented in lti::adaptiveKMeans, lti::bagging, lti::competitiveAgglomeration, lti::DBScan< T >, lti::fuzzyCMeans, lti::hmmClassifier, lti::hmmOnlineClassifier, lti::kMeansClustering, lti::kNNClassifier, lti::lvq, lti::manualCrispDecisionTree, lti::MLP, lti::MSTClustering< U >, lti::rbf, lti::shClassifier, lti::SOFM, lti::SOFM2D, and lti::svm.

classifier& lti::classifier::copy (  const classifier &  other  )

copy data of "other" functor. Parameters: other  the functor to be copied Returns: a reference to this functor object

const outputTemplate& lti::classifier::getOutputTemplate (   )  const

get a const reference to the outputTemplate

const parameters& lti::classifier::getParameters (   )  const

returns used parameters Reimplemented in lti::adaptiveKMeans, lti::bagging, lti::centroidClustering, lti::clustering, lti::competitiveAgglomeration, lti::crispDecisionTree, lti::DBScan< T >, lti::decisionTree, lti::fuzzyCMeans, lti::hmmClassifier, lti::hmmOnlineClassifier, lti::kMeansClustering, lti::kNNClassifier, lti::lvq, lti::manualCrispDecisionTree, lti::MLP, lti::MSTClustering< U >, lti::rbf, lti::shClassifier, lti::SOFM, lti::SOFM2D, lti::supervisedInstanceClassifier, lti::supervisedSequenceClassifier, lti::svm, and lti::unsupervisedClassifier.

const progressInfo& lti::classifier::getProgressObject (   )  const

get progress object

progressInfo& lti::classifier::getProgressObject (   )

get progress object

virtual const char* lti::classifier::getTypeName (   )  const [virtual]

returns the name of this type ("classifier") Reimplemented from lti::ioObject. Reimplemented in lti::adaptiveKMeans, lti::bagging, lti::centroidClustering, lti::clustering, lti::competitiveAgglomeration, lti::crispDecisionTree, lti::DBScan< T >, lti::decisionTree, lti::fuzzyCMeans, lti::hmmClassifier, lti::hmmOnlineClassifier, lti::kMeansClustering, lti::kNNClassifier, lti::manualCrispDecisionTree, lti::MLP, lti::MSTClustering< U >, lti::rbf, lti::shClassifier, lti::SOFM, lti::SOFM2D, lti::supervisedInstanceClassifier, lti::supervisedSequenceClassifier, lti::svm, and lti::unsupervisedClassifier.

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

Alias for "copy".

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

read the classifier 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::ioObject. Reimplemented in lti::bagging, lti::centroidClustering, lti::crispDecisionTree, lti::decisionTree, lti::hmmClassifier, lti::hmmOnlineClassifier, lti::kNNClassifier, lti::MLP, lti::MSTClustering< U >, lti::rbf, lti::shClassifier, lti::SOFM, lti::SOFM2D, and lti::svm.

void lti::classifier::removeProgressObject (   )

remove the active progress object

virtual void lti::classifier::setOutputTemplate (  const outputTemplate &  theOutputTemplate  )  [virtual]

sets the classifier's outputTemplate This member makes a copy of theOutputTemplate: the classifier will keep its own copy of the outputTemplate

virtual bool lti::classifier::setParameters (  const parameters &  theParam  )  [virtual]

sets the classifier's parameters. This member makes a copy of theParam: the classifier will keep its own copy of the parameters! Reimplemented in lti::fuzzyCMeans.

void lti::classifier::setProgressObject (  const progressInfo &  progBox  )

set a progress object A clone of the given object will be generated.

virtual bool lti::classifier::validParameters (   )  const [inline, virtual]

returns true if the parameters are valid

bool lti::classifier::validProgressObject (   )  const

return true if a valid progressInfo object has already been setted

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

write the classifier 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::ioObject. Reimplemented in lti::bagging, lti::centroidClustering, lti::crispDecisionTree, lti::decisionTree, lti::hmmClassifier, lti::hmmOnlineClassifier, lti::kNNClassifier, lti::MLP, lti::MSTClustering< U >, lti::rbf, lti::shClassifier, lti::SOFM, lti::SOFM2D, and lti::svm.

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Member Data Documentation

const int lti::classifier::noObject [static]

constant to indicate "no object"

const double lti::classifier::nullProbability [static]

all probabilities under this value will be considered as 0

outputTemplate lti::classifier::outTemplate [protected]

The outputTemplate for this classifier.

parameters* lti::classifier::params [protected]

current parameters.

progressInfo* lti::classifier::progressBox [protected]

current progress object

const int lti::classifier::unknownObj [static]

constant to indicate an unknown object

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The documentation for this class was generated from the following file:

• ltiClassifier.h

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