latest version v1.9 - last update 24 Nov 2005

lti::linearRegression< T >::parameters Class Reference

the parameters for the class linearRegression More...

#include <ltiLinearRegression.h>

Inheritance diagram for lti::linearRegression< T >::parameters:

[legend]Collaboration diagram for lti::linearRegression< T >::parameters:

[legend]List of all members.

Public Member Functions
	parameters ()
	parameters (const parameters &other)
	~parameters ()
const char *	getTypeName () const
parameters &	copy (const parameters &other)
parameters &	operator= (const parameters &other)
virtual functor::parameters *	clone () const
virtual bool	read (ioHandler &handler, const bool complete=true)
virtual bool	write (ioHandler &handler, const bool complete=true) const
Public Attributes
int	eigenSystemDim

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Detailed Description

template<class T>
class lti::linearRegression< T >::parameters

the parameters for the class linearRegression

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Constructor & Destructor Documentation

template<class T> lti::linearRegression< T >::parameters::parameters (  void   )  [inline]

default constructor Reimplemented from lti::linearAlgebraFunctor::parameters. Reimplemented in lti::linearRegressionTracking::parameters.

template<class T> lti::linearRegression< T >::parameters::parameters (  const parameters &  other  )  [inline]

copy constructor Parameters: other  the parameters object to be copied

template<class T> lti::linearRegression< T >::parameters::~parameters (   )  [inline, virtual]

destructor Reimplemented from lti::linearAlgebraFunctor::parameters. Reimplemented in lti::linearRegressionTracking::parameters.

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Member Function Documentation

template<class T> virtual functor::parameters* lti::linearRegression< T >::parameters::clone (   )  const [inline, virtual]

returns a pointer to a clone of the parameters Reimplemented from lti::linearAlgebraFunctor::parameters. Reimplemented in lti::linearRegressionTracking::parameters.

template<class T> parameters& lti::linearRegression< T >::parameters::copy (  const parameters &  other  )  [inline]

copy the contents of a parameters object Parameters: other  the parameters object to be copied Returns: a reference to this parameters object

template<class T> const char* lti::linearRegression< T >::parameters::getTypeName (  void   )  const [inline, virtual]

returns name of this type Reimplemented from lti::linearAlgebraFunctor::parameters. Reimplemented in lti::linearRegressionTracking::parameters.

template<class T> parameters& lti::linearRegression< T >::parameters::operator= (  const parameters &  other  )  [inline]

Assigns the contents of the other object to this object.

template<class T> virtual bool lti::linearRegression< T >::parameters::read (  ioHandler &  handler, const bool  complete = true )  [inline, 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 lti::linearAlgebraFunctor::parameters. Reimplemented in lti::linearRegressionTracking::parameters.

template<class T> virtual bool lti::linearRegression< T >::parameters::write (  ioHandler &  handler, const bool  complete = true )  const [inline, 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 lti::linearAlgebraFunctor::parameters. Reimplemented in lti::linearRegressionTracking::parameters.

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Member Data Documentation

template<class T> int lti::linearRegression< T >::parameters::eigenSystemDim

Dimensionality of the eigensystem which is constructed during calculation of the linear regression matrix. It must not be bigger than the number of trainingvectors used. The quality of the resulting matrix depends on this value, it should be neither to small nor to big. There are methods to estimate the optimal value in the literature, but none has been implemented in this functor yet. The default is 10.

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

• ltiLinearRegression.h

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