PLearn::DistanceKernel Class Reference

This class implements an Ln distance (defaults to L2 i.e. euclidean distance). More...

#include <DistanceKernel.h>

Inheritance diagram for PLearn::DistanceKernel:

[legend]Collaboration diagram for PLearn::DistanceKernel:

[legend]List of all members.

Public Member Functions
	DistanceKernel (real the_Ln=2)
	PLEARN_DECLARE_OBJECT (DistanceKernel)
virtual string	info () const
	returns a bit more informative string about object (default returns classname())
virtual real	evaluate (const Vec &x1, const Vec &x2) const
	returns K(x1,x2)
virtual real	evaluate_i_j (int i, int j) const
	returns evaluate(data(i),data(j))
virtual void	setDataForKernelMatrix (VMat the_data)
	This method precomputes the squared norm for all the data to later speed up evaluate methods, if n == 2.
Public Attributes
bool	pow_distance
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	redefine this in subclasses: call declareOption(...) for each option, and then call inherited::declareOptions(options) ( see the declareOption function further down)
Protected Attributes
real	n
	1 for L1, 2 for L2, etc..
Vec	squarednorms
	Used to store the squared norm of the input data.
Private Types
typedef Kernel	inherited

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Detailed Description

This class implements an Ln distance (defaults to L2 i.e. euclidean distance).

Definition at line 52 of file DistanceKernel.h.

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Member Typedef Documentation

typedef Kernel PLearn::DistanceKernel::inherited [private]

Reimplemented from PLearn::Kernel. Definition at line 57 of file DistanceKernel.h.

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Constructor & Destructor Documentation

PLearn::DistanceKernel::DistanceKernel (  real  the_Ln = 2  )

Definition at line 55 of file DistanceKernel.cc.

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Member Function Documentation

void PLearn::DistanceKernel::declareOptions (  OptionList &  ol  )  [static, protected]

redefine this in subclasses: call declareOption(...) for each option, and then call inherited::declareOptions(options) ( see the declareOption function further down) ex: static void declareOptions(OptionList& ol) { declareOption(ol, "inputsize", &MyObject::inputsize_, OptionBase::buildoption, "the size of the input\n it must be provided"); declareOption(ol, "weights", &MyObject::weights, OptionBase::learntoption, "the learnt model weights"); inherited::declareOptions(ol); } Reimplemented from PLearn::Kernel. Definition at line 63 of file DistanceKernel.cc. References PLearn::declareOption(), and PLearn::OptionList.

real PLearn::DistanceKernel::evaluate (  const Vec &  x1, const Vec &  x2 )  const [virtual]

returns K(x1,x2) Implements PLearn::Kernel. Definition at line 78 of file DistanceKernel.cc. References PLearn::dist(), n, pow_distance, PLearn::powdistance(), and PLearn::Vec.

real PLearn::DistanceKernel::evaluate_i_j (  int  i, int  j )  const [virtual]

returns evaluate(data(i),data(j)) Reimplemented from PLearn::Kernel. Definition at line 89 of file DistanceKernel.cc. References n, PLERROR, pow_distance, PLearn::sqrt(), and squarednorms.

virtual string PLearn::DistanceKernel::info (   )  const [inline, virtual]

returns a bit more informative string about object (default returns classname()) Reimplemented from PLearn::Object. Definition at line 73 of file DistanceKernel.h. References n, and PLearn::tostring().

PLearn::DistanceKernel::PLEARN_DECLARE_OBJECT (  DistanceKernel   )

void PLearn::DistanceKernel::setDataForKernelMatrix (  VMat  the_data  )  [virtual]

This method precomputes the squared norm for all the data to later speed up evaluate methods, if n == 2. Reimplemented from PLearn::Kernel. Definition at line 119 of file DistanceKernel.cc. References PLearn::VMat::length(), n, PLearn::TVec< T >::resize(), and squarednorms. Referenced by PLearn::RemoveDuplicateVMatrix::build_(), and PLearn::KNNVMatrix::build_().

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Member Data Documentation

real PLearn::DistanceKernel::n [protected]

1 for L1, 2 for L2, etc.. Definition at line 61 of file DistanceKernel.h. Referenced by evaluate(), evaluate_i_j(), info(), and setDataForKernelMatrix().

bool PLearn::DistanceKernel::pow_distance

Definition at line 67 of file DistanceKernel.h. Referenced by PLearn::RemoveDuplicateVMatrix::build_(), evaluate(), and evaluate_i_j().

Vec PLearn::DistanceKernel::squarednorms [protected]

Used to store the squared norm of the input data. Definition at line 63 of file DistanceKernel.h. Referenced by evaluate_i_j(), and setDataForKernelMatrix().

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

• DistanceKernel.h
• DistanceKernel.cc

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