cvr::pca< T >::pca::parameters Class Reference

The parameters for the class pca. More...

#include <cvrPCA.h>

Inheritance diagram for cvr::pca< T >::pca::parameters:

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Collaboration diagram for cvr::pca< T >::pca::parameters:

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List of all members.

Public Member Functions
	parameters ()
	parameters (const parameters &other)
	~parameters ()
const std::string &	name () const
parameters &	copy (const parameters &other)
parameters &	operator= (const parameters &other)
virtual parameters *	clone () const
virtual parameters *	newInstance () const
virtual bool	read (ioHandler &handler, const bool complete=true)
virtual bool	write (ioHandler &handler, const bool complete=true) const
Public Attributes
int	resultDimension
bool	autoDimension
bool	useCorrelation
bool	whitening
T	relevance
bool	centerData
bool	useSVD

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Detailed Description

template<typename T>
class cvr::pca< T >::parameters

The parameters for the class pca.

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Constructor & Destructor Documentation

template<typename T >

cvr::pca< T >::pca::parameters::parameters

(

)

Default constructor.

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

cvr::pca< T >::pca::parameters::parameters

(

const parameters &

other

)

Copy constructor.

Parameters:

other

the parameters object to be copied

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

cvr::pca< T >::pca::parameters::~parameters

(

)

[virtual]

Destructor.

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

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Member Function Documentation

template<typename T >

virtual parameters* cvr::pca< T >::pca::parameters::clone

(

)

const [virtual]

Return a pointer to a clone of the parameters.

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

parameters& cvr::pca< T >::pca::parameters::copy

(

const parameters &

other

)

Copy the contents of a parameters object.

Parameters:

other

the parameters object to be copied

Returns:

a reference to this parameters object

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

const std::string& cvr::pca< T >::pca::parameters::name

(

)

const [virtual]

Return name of this class.

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

virtual parameters* cvr::pca< T >::pca::parameters::newInstance

(

)

const [virtual]

Return a pointer to a new instance of the parameters.

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

parameters& cvr::pca< T >::pca::parameters::operator=

(

const parameters &

other

)

Assigns the contents of the other object to this object.

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

virtual bool cvr::pca< T >::pca::parameters::read	(	ioHandler &	handler,
		const bool	complete = true
	)			[virtual]

Read the parameters from the given ioHandler.

Parameters:

	handler	the ioHandler to be used
	complete	if true (the default) the enclosing begin/end will be also read, otherwise only the data block will be read.

Returns:

true if write was successful

Reimplemented from cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

template<typename T >

virtual bool cvr::pca< T >::pca::parameters::write	(	ioHandler &	handler,
		const bool	complete = true
	)			const [virtual]

Write the parameters 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 cvr::linearAlgebraFunctor::linearAlgebraFunctor::parameters.

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Member Data Documentation

template<typename T >

bool cvr::pca< T >::pca::parameters::autoDimension

This flag determines, if the functor should determine a maximum allowable dimension itself.

"Maximum dimension" means that the dimension is equal to the number of eigenvalues of the covariance matrix which are larger than zero.

Default value: false

template<typename T >

bool cvr::pca< T >::pca::parameters::centerData

This flag denotes if the transformed data should be centered around zero.

This is the usual behaviour of the PCA, but for some special operations it may be necessary to disable this. If the flag is false, the mean of the transformed data is moved to the transformed mean of the source data.

Default value: true

template<typename T >

T cvr::pca< T >::pca::parameters::relevance

The factor which determines relevant eigenvectors.

An eigenvector is considered relevant if its eigenvalue is at least as large as the largest eigenvalue divided by this number. Usually, it takes values between 1e4 and 1e6.

Default value: T(100000)

template<typename T >

int cvr::pca< T >::pca::parameters::resultDimension

Final dimension of the reduced vectors.

Default value: 3

template<typename T >

bool cvr::pca< T >::pca::parameters::useCorrelation

This flag determines if the functor should use the correlation coefficient matrix (flag is true) for eigenvector computation or the covariance matrix (flag is false).

Default value: false.

template<typename T >

bool cvr::pca< T >::pca::parameters::useSVD

When true, singular value decomposition instead of eigensystem solution is used to calculate the eigenvectors and eigenvalues.

This can be much faster and less memory consuming.

Default value: false.

template<typename T >

bool cvr::pca< T >::pca::parameters::whitening

This flag determines if the functor should perform a whitening transform of the data.

Whitening means that 1. A PCA is performed, using the covariance matrix for eigenvector computation 2. A scaling of the transformed data by the inverse of the square root of the eigenvalues.

You have to set useCorrelation to false if you use whitening.

Default value: false.

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

• cvrPCA.h