lti::fuzzyCMeans Class Reference

this class implements the fuzzy C Means clustering algorithm. More...

#include <ltiFuzzyCMeans.h>

Inheritance diagram for lti::fuzzyCMeans:

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Collaboration diagram for lti::fuzzyCMeans:

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List of all members.

Classes
class	parameters
	the parameters for the class fuzzyCMeans More...
Public Member Functions
	fuzzyCMeans ()
	fuzzyCMeans (const fuzzyCMeans &other)
virtual	~fuzzyCMeans ()
virtual const char *	getTypeName () const
fuzzyCMeans &	copy (const fuzzyCMeans &other)
fuzzyCMeans &	operator= (const fuzzyCMeans &other)
virtual classifier *	clone () const
const parameters &	getParameters () const
bool	setParameters (const classifier::parameters &theParams)
virtual bool	train (const dmatrix &input, ivector &ids)
virtual bool	train (const dmatrix &input)

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Detailed Description

this class implements the fuzzy C Means clustering algorithm.

the class arbitrary picks a given number of points as centroids. Between these centroids and the given points is a degree of membership calculated. According to these memberships new centroids are calculated. This is done until the convergence criterion is met.

The convergence criterion could be either the the maximum number of allowed iterations or the distance between the matrix of centroids of the last two iterations the classify method is not implemented, instead the classify method of superclass is used which does not regard the memberships of the points to the centroids

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Constructor & Destructor Documentation

lti::fuzzyCMeans::fuzzyCMeans

(

)

default constructor

lti::fuzzyCMeans::fuzzyCMeans

(

const fuzzyCMeans &

other

)

copy constructor

Parameters:

other

the object to be copied

virtual lti::fuzzyCMeans::~fuzzyCMeans

(

)

[virtual]

destructor

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Member Function Documentation

virtual classifier* lti::fuzzyCMeans::clone

(

)

const [virtual]

returns a pointer to a clone of this clustering.

Implements lti::classifier.

fuzzyCMeans& lti::fuzzyCMeans::copy

(

const fuzzyCMeans &

other

)

copy data of "other" clustering.

Parameters:

other

the clustering to be copied

Returns:

a reference to this clustering object

Reimplemented from lti::centroidClustering.

const parameters& lti::fuzzyCMeans::getParameters

(

)

const

returns used parameters

Reimplemented from lti::centroidClustering.

virtual const char* lti::fuzzyCMeans::getTypeName

(

)

const [virtual]

returns the name of this type ("fuzzyCMeans")

Reimplemented from lti::centroidClustering.

fuzzyCMeans& lti::fuzzyCMeans::operator=

(

const fuzzyCMeans &

other

)

alias for copy member

Parameters:

other

the clustering to be copied

Returns:

a reference to this clustering object

Reimplemented from lti::classifier.

bool lti::fuzzyCMeans::setParameters

(

const classifier::parameters &

theParams

)

[virtual]

set the parameters of the classifier, calls setParameters of the superclass and tests if the parameter norm is valid.

If not a status string is set.

Reimplemented from lti::classifier.

virtual bool lti::fuzzyCMeans::train

(

const dmatrix &

input

)

[virtual]

Unsupervised training.

The vectors in the input matrix will be clustered using each specific method.

Parameters:

input

the matrix with the input vectors (each row is a training vector)

Returns:

true if successful, false otherwise. (if false you can check the error message with getStatusString())

Implements lti::centroidClustering.

virtual bool lti::fuzzyCMeans::train	(	const dmatrix &	input,
		ivector &	ids
	)			[virtual]

Unsupervised training.

The vectors in the input matrix will be put into groups according to the training algorithm. Additionally, an integer indicating the class each point belongs to is returned.

By default this method uses the other train method train(const dmatrix&) and then calls classify(const dvector&) to get the ids for each trainvector. These ids are then returned.

Parameters:

	input	the matrix with the input vectors (each row is a training vector)
	ids	vector of class ids for each input point

Returns:

true if successful, false otherwise. (if false you can check the error message with getStatusString())

Reimplemented from lti::centroidClustering.

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

• ltiFuzzyCMeans.h