lti::shClassifier Class Reference

Implements a sparse histogram-based classifier, such as the one described in B. More...

#include <ltiSHClassifier.h>

Inheritance diagram for lti::shClassifier:

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Collaboration diagram for lti::shClassifier:

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List of all members.

Classes
class	parameters
	the parameters for the class shClassifier More...
Public Member Functions
	shClassifier ()
	shClassifier (const shClassifier &other)
virtual	~shClassifier ()
virtual const char *	getTypeName () const
shClassifier &	copy (const shClassifier &other)
shClassifier &	operator= (const shClassifier &other)
virtual classifier *	clone () const
const parameters &	getParameters () const
virtual bool	train (const dmatrix &input, const ivector &ids)
virtual bool	trainObject (const dmatrix &input, int &id)
virtual bool	classify (const dvector &feature, outputVector &result) const
virtual bool	write (ioHandler &handler, const bool complete=true) const
virtual bool	read (ioHandler &handler, const bool complete=true)

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Detailed Description

Implements a sparse histogram-based classifier, such as the one described in B.

Schiele, J. L. Crowley: Recognition without Correspondence using Multidimensional Receptive Field Histograms.

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Constructor & Destructor Documentation

lti::shClassifier::shClassifier

(

)

default constructor

lti::shClassifier::shClassifier

(

const shClassifier &

other

)

copy constructor

Parameters:

other

the object to be copied

virtual lti::shClassifier::~shClassifier

(

)

[virtual]

destructor

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Member Function Documentation

virtual bool lti::shClassifier::classify	(	const dvector &	feature,
		outputVector &	result
	)			const [virtual]

Classification.

Classifies the feature and returns the outputVector object with the classification result.

NOTE: This method is NOT really const. Although the main members of the shClassifier are not changed some state variables used for efficiency are. Thus, it is not save to use the same instance of the shClassifier in two different threads.

Parameters:

	feature	pattern to be classified
	result	of the classifications as a classifier::outputVector

Returns:

true if the classification has been successful

Implements lti::supervisedInstanceClassifier.

virtual classifier* lti::shClassifier::clone

(

)

const [virtual]

returns a pointer to a clone of this clustering.

Implements lti::classifier.

shClassifier& lti::shClassifier::copy

(

const shClassifier &

other

)

copy data of "other" clustering.

Parameters:

other

the clustering to be copied

Returns:

a reference to this clustering object

Reimplemented from lti::supervisedInstanceClassifier.

const parameters& lti::shClassifier::getParameters

(

)

const

returns used parameters

Reimplemented from lti::supervisedInstanceClassifier.

virtual const char* lti::shClassifier::getTypeName

(

)

const [virtual]

returns the name of this type ("shClassifier")

Reimplemented from lti::supervisedInstanceClassifier.

shClassifier& lti::shClassifier::operator=

(

const shClassifier &

other

)

alias for copy member

Parameters:

other

the clustering to be copied

Returns:

a reference to this clustering object

Reimplemented from lti::supervisedInstanceClassifier.

virtual bool lti::shClassifier::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::classifier.

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

Supervised training.

The vectors in the input matrix are arranged row-wise, i.e. each row contains one data vector. The ids vector contains the class label for each row. This functor implements the SMO algorithm by J.C. Platt. All classes are modeled using the same kernel function, given in the parameters object.

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())

Implements lti::supervisedInstanceClassifier.

virtual bool lti::shClassifier::trainObject	(	const dmatrix &	input,
		int &	id
	)			[virtual]

Adds an object to this classifier.

The id is given automatically and returned in the parameter.

virtual bool lti::shClassifier::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::classifier.

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

• ltiSHClassifier.h