PLearn::DivisiveNormalizationKernel Class Reference

#include <DivisiveNormalizationKernel.h>

Inheritance diagram for PLearn::DivisiveNormalizationKernel:

[legend]Collaboration diagram for PLearn::DivisiveNormalizationKernel:

[legend]List of all members.


Public Member Functions
	DivisiveNormalizationKernel ()
	Default constructor.
	DivisiveNormalizationKernel (Ker the_source, bool the_remove_bias=false)
	Created from an existing kernel.
virtual void	build ()
	Simply calls inherited::build() then build_().
virtual void	makeDeepCopyFromShallowCopy (map< const void , void > &copies)
	Transforms a shallow copy into a deep copy.
	PLEARN_DECLARE_OBJECT (DivisiveNormalizationKernel)
virtual real	evaluate (const Vec &x1, const Vec &x2) const
	Overridden.
virtual real	evaluate_i_j (int i, int j) const
	returns evaluate(data(i),data(j))
virtual real	evaluate_i_x (int i, const Vec &x, real squared_norm_of_x=-1) const
virtual real	evaluate_x_i (const Vec &x, int i, real squared_norm_of_x=-1) const
	returns evaluate(x,data(i)) [default version calls evaluate_i_x if kernel is_symmetric]
virtual real	evaluate_i_x_again (int i, const Vec &x, real squared_norm_of_x=-1, bool first_time=false) const
	Return evaluate(data(i),x), where x is the same as in the precedent call to this same function (except if 'first_time' is true).
virtual real	evaluate_x_i_again (const Vec &x, int i, real squared_norm_of_x=-1, bool first_time=false) const
virtual void	computeGramMatrix (Mat K) const
	Call evaluate_i_j to fill each of the entries (i,j) of symmetric matrix K.
virtual void	setDataForKernelMatrix (VMat the_data)
	Subclasses may overload these methods to provide efficient kernel matrix access
Public Attributes
bool	data_will_change
bool	remove_bias
Protected Member Functions
real	computeAverage (const Vec &x, bool on_row, real squared_norm_of_x=-1) const
	Return the average of K(x,x_i) or K(x_i,x), depending on the value of 'on_row' (true or false, respectively).
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	Declares this class' options.
Protected Attributes
Vec	average_col
Vec	average_row
real	avg_evaluate_i_x_again
	The last average computed in evaluate_i_x_again().
real	avg_evaluate_x_i_again
	The last average computed in evaluate_x_i_again().
Private Types
typedef SourceKernel	inherited
Private Member Functions
void	build_ ()
	This does the actual building.
Private Attributes
Vec	all_k_x
	Used to store the values of the source kernel.

Member Typedef Documentation

typedef SourceKernel PLearn::DivisiveNormalizationKernel::inherited [private]

Reimplemented from PLearn::SourceKernel.
Definition at line 57 of file DivisiveNormalizationKernel.h.

Constructor & Destructor Documentation

PLearn::DivisiveNormalizationKernel::DivisiveNormalizationKernel ( )

Default constructor.

Definition at line 52 of file DivisiveNormalizationKernel.cc.

PLearn::DivisiveNormalizationKernel::DivisiveNormalizationKernel ( Ker the_source,

bool the_remove_bias = false

)

Created from an existing kernel.

Definition at line 57 of file DivisiveNormalizationKernel.cc.
References build().

Member Function Documentation

void PLearn::DivisiveNormalizationKernel::build ( ) [virtual]

Simply calls inherited::build() then build_().

Reimplemented from PLearn::SourceKernel.
Definition at line 103 of file DivisiveNormalizationKernel.cc.
References build_().
Referenced by DivisiveNormalizationKernel().

void PLearn::DivisiveNormalizationKernel::build_ ( ) [private]

This does the actual building.

Reimplemented from PLearn::SourceKernel.
Definition at line 112 of file DivisiveNormalizationKernel.cc.
Referenced by build().

real PLearn::DivisiveNormalizationKernel::computeAverage ( const Vec & x,

bool on_row,

real squared_norm_of_x = -1

) const [inline, protected]

Return the average of K(x,x_i) or K(x_i,x), depending on the value of 'on_row' (true or false, respectively).

Definition at line 126 of file DivisiveNormalizationKernel.cc.
References all_k_x, PLearn::TVec< T >::resize(), PLearn::sum(), PLearn::Vec, and x.
Referenced by evaluate(), evaluate_i_x(), evaluate_i_x_again(), evaluate_x_i(), and evaluate_x_i_again().

void PLearn::DivisiveNormalizationKernel::computeGramMatrix ( Mat K ) const [virtual]

Call evaluate_i_j to fill each of the entries (i,j) of symmetric matrix K.

Reimplemented from PLearn::SourceKernel.
Definition at line 139 of file DivisiveNormalizationKernel.cc.
References PLearn::Mat.

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

Declares this class' options.

Reimplemented from PLearn::SourceKernel.
Definition at line 77 of file DivisiveNormalizationKernel.cc.
References PLearn::declareOption(), and PLearn::OptionList.

real PLearn::DivisiveNormalizationKernel::evaluate ( const Vec & x1,

const Vec & x2

) const [virtual]

Overridden.

Reimplemented from PLearn::SourceKernel.
Definition at line 147 of file DivisiveNormalizationKernel.cc.
References computeAverage(), and PLearn::sqrt().

real PLearn::DivisiveNormalizationKernel::evaluate_i_j ( int i,

int j

) const [virtual]

returns evaluate(data(i),data(j))

Reimplemented from PLearn::SourceKernel.
Definition at line 156 of file DivisiveNormalizationKernel.cc.
References average_col, average_row, and PLearn::sqrt().

real PLearn::DivisiveNormalizationKernel::evaluate_i_x ( int i,

const Vec & x,

real squared_norm_of_x = -1

) const [virtual]

returns evaluate(data(i),x) [squared_norm_of_x is just a hint that may allow to speed up computation if it is already known, but it's optional]
Reimplemented from PLearn::SourceKernel.
Definition at line 163 of file DivisiveNormalizationKernel.cc.
References average_row, computeAverage(), PLearn::sqrt(), and x.

real PLearn::DivisiveNormalizationKernel::evaluate_i_x_again ( int i,

const Vec & x,

real squared_norm_of_x = -1,

bool first_time = false

) const [virtual]

Return evaluate(data(i),x), where x is the same as in the precedent call to this same function (except if 'first_time' is true).
This can be used to speed up successive computations of K(x_i, x) (default version just calls evaluate_i_x).
Reimplemented from PLearn::Kernel.
Definition at line 171 of file DivisiveNormalizationKernel.cc.
References average_row, avg_evaluate_i_x_again, computeAverage(), PLearn::sqrt(), and x.

real PLearn::DivisiveNormalizationKernel::evaluate_x_i ( const Vec & x,

int i,

real squared_norm_of_x = -1

) const [virtual]

returns evaluate(x,data(i)) [default version calls evaluate_i_x if kernel is_symmetric]

Reimplemented from PLearn::SourceKernel.
Definition at line 182 of file DivisiveNormalizationKernel.cc.
References average_col, computeAverage(), PLearn::sqrt(), and x.

real PLearn::DivisiveNormalizationKernel::evaluate_x_i_again ( const Vec & x,

int i,

real squared_norm_of_x = -1,

bool first_time = false

) const [virtual]

Reimplemented from PLearn::Kernel.
Definition at line 190 of file DivisiveNormalizationKernel.cc.
References average_col, avg_evaluate_x_i_again, computeAverage(), PLearn::sqrt(), and x.

void PLearn::DivisiveNormalizationKernel::makeDeepCopyFromShallowCopy ( map< const void *, void * > & copies ) [virtual]

Transforms a shallow copy into a deep copy.

Reimplemented from PLearn::SourceKernel.
Definition at line 201 of file DivisiveNormalizationKernel.cc.
References PLERROR.

PLearn::DivisiveNormalizationKernel::PLEARN_DECLARE_OBJECT ( DivisiveNormalizationKernel )

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

** Subclasses may overload these methods to provide efficient kernel matrix access **
This method sets the data VMat that will be used to define the kernel matrix. It may precompute values from this that may later accelerate the evaluation of a kernel matrix element
Reimplemented from PLearn::SourceKernel.
Definition at line 218 of file DivisiveNormalizationKernel.cc.
References average_col, average_row, data_will_change, PLearn::TVec< T >::fill(), PLearn::TVec< T >::length(), PLearn::VMat::length(), remove_bias, and PLearn::TVec< T >::resize().