PLearn::PLS Class Reference

#include <PLS.h>

Inheritance diagram for PLearn::PLS:

[legend]Collaboration diagram for PLearn::PLS:

[legend]List of all members.


Public Types
typedef PLearner	inherited
Public Member Functions
	PLS ()
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 (PLS)
virtual int	outputsize () const
	Returns the size of this learner's output, (which typically may depend on its inputsize(), targetsize() and set options).
virtual void	forget ()
	(Re-)initializes the PLearner in its fresh state (that state may depend on the 'seed' option) And sets 'stage' back to 0 (this is the stage of a fresh learner!).
virtual void	train ()
	The role of the train method is to bring the learner up to stage==nstages, updating the train_stats collector with training costs measured on-line in the process.
virtual void	computeOutput (const Vec &input, Vec &output) const
	Computes the output from the input.
virtual void	computeCostsFromOutputs (const Vec &input, const Vec &output, const Vec &target, Vec &costs) const
	Computes the costs from already computed output.
virtual TVec< string >	getTestCostNames () const
	Returns the names of the costs computed by computeCostsFromOutpus (and thus the test method).
virtual TVec< string >	getTrainCostNames () const
	Returns the names of the objective costs that the train method computes and for which it updates the VecStatsCollector train_stats.
Static Public Member Functions
void	NIPALSEigenvector (const Mat &m, Vec &v, real precision)
	Compute the largest eigenvector of m with the NIPALS algorithm: (1) v <- random initialization (but normalized) (2) v = m.v, normalize v (3) if there is a v[i] that has changed by more than 'preicision', go to (2), otherwise return v.
Public Attributes
int	k
string	method
real	precision
bool	output_the_score
bool	output_the_target
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	Declares this class' options.
Protected Attributes
Mat	B
int	m
Vec	mean_input
Vec	mean_target
int	p
Vec	stddev_input
Vec	stddev_target
Mat	W
Private Member Functions
void	build_ ()
	This does the actual building.

Member Typedef Documentation

typedef PLearner PLearn::PLS::inherited

Reimplemented from PLearn::PLearner.
Definition at line 56 of file PLS.h.

Constructor & Destructor Documentation

PLearn::PLS::PLS ( )

Definition at line 53 of file PLS.cc.
References k.

Member Function Documentation

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

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

Reimplemented from PLearn::PLearner.
Definition at line 180 of file PLS.cc.
References build_().

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

This does the actual building.

Reimplemented from PLearn::PLearner.
Definition at line 189 of file PLS.cc.
References m, mean_input, mean_target, method, output_the_score, output_the_target, p, PLERROR, PLWARNING, PLearn::TVec< T >::resize(), stddev_input, and stddev_target.
Referenced by build().

void PLearn::PLS::computeCostsFromOutputs ( const Vec & input,

const Vec & output,

const Vec & target,

Vec & costs

) const [virtual]

Computes the costs from already computed output.

Implements PLearn::PLearner.
Definition at line 222 of file PLS.cc.

void PLearn::PLS::computeOutput ( const Vec & input,

Vec & output

) const [virtual]

Computes the output from the input.

Implements PLearn::PLearner.
Definition at line 231 of file PLS.cc.
References B, k, m, mean_input, mean_target, output_the_score, output_the_target, outputsize(), PLERROR, PLWARNING, PLearn::TVec< T >::resize(), stddev_input, stddev_target, PLearn::TVec< T >::subVec(), PLearn::transposeProduct(), W, and PLearn::TMat< T >::width().

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

Declares this class' options.

Reimplemented from PLearn::PLearner.
Definition at line 126 of file PLS.cc.
References PLearn::declareOption(), and PLearn::OptionList.

void PLearn::PLS::forget ( ) [virtual]

(Re-)initializes the PLearner in its fresh state (that state may depend on the 'seed' option) And sets 'stage' back to 0 (this is the stage of a fresh learner!).

Implements PLearn::PLearner.
Definition at line 265 of file PLS.cc.
References B, PLearn::Mat, and W.

TVec< string > PLearn::PLS::getTestCostNames ( ) const [virtual]

Returns the names of the costs computed by computeCostsFromOutpus (and thus the test method).

Implements PLearn::PLearner.
Definition at line 276 of file PLS.cc.

TVec< string > PLearn::PLS::getTrainCostNames ( ) const [virtual]

Returns the names of the objective costs that the train method computes and for which it updates the VecStatsCollector train_stats.

Implements PLearn::PLearner.
Definition at line 286 of file PLS.cc.

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

Transforms a shallow copy into a deep copy.

Definition at line 296 of file PLS.cc.
References B, PLearn::deepCopyField(), mean_input, mean_target, stddev_input, stddev_target, and W.

void PLearn::PLS::NIPALSEigenvector ( const Mat & m,

Vec & v,

real precision

) [static]

Compute the largest eigenvector of m with the NIPALS algorithm: (1) v <- random initialization (but normalized) (2) v = m.v, normalize v (3) if there is a v[i] that has changed by more than 'preicision', go to (2), otherwise return v.

Definition at line 315 of file PLS.cc.
References PLearn::TMat< T >::column(), PLearn::TVec< T >::length(), PLearn::normalize(), and PLearn::product().
Referenced by train().

int PLearn::PLS::outputsize ( ) const [virtual]

Returns the size of this learner's output, (which typically may depend on its inputsize(), targetsize() and set options).

Implements PLearn::PLearner.
Definition at line 337 of file PLS.cc.
References k, m, output_the_score, and output_the_target.
Referenced by computeOutput().

PLearn::PLS::PLEARN_DECLARE_OBJECT ( PLS )

void PLearn::PLS::train ( ) [virtual]

The role of the train method is to bring the learner up to stage==nstages, updating the train_stats collector with training costs measured on-line in the process.

Implements PLearn::PLearner.
Definition at line 354 of file PLS.cc.
References PLearn::abs(), B, PLearn::Var::column(), PLearn::TMat< T >::column(), PLearn::dist(), PLearn::dot(), PLearn::endl(), PLearn::TMat< T >::fill(), PLearn::invertElements(), k, PLearn::TVec< T >::length(), PLearn::VMat::length(), m, mean_input, mean_target, method, PLearn::negateElements(), NIPALSEigenvector(), PLearn::normalize(), PLearn::P(), p, precision, PLearn::product(), PLearn::productTranspose(), PLearn::TMat< T >::resize(), stddev_input, stddev_target, PLearn::SVD(), PLearn::VMat::toMat(), PLearn::transposeProduct(), PLearn::ProgressBar::update(), and W.

Member Data Documentation

Mat PLearn::PLS::B [protected]

Definition at line 64 of file PLS.h.
Referenced by computeOutput(), forget(), makeDeepCopyFromShallowCopy(), and train().

int PLearn::PLS::k

Definition at line 79 of file PLS.h.
Referenced by computeOutput(), outputsize(), and train().

int PLearn::PLS::m [protected]

Definition at line 65 of file PLS.h.
Referenced by build_(), computeOutput(), outputsize(), and train().

Vec PLearn::PLS::mean_input [protected]

Definition at line 66 of file PLS.h.
Referenced by build_(), computeOutput(), makeDeepCopyFromShallowCopy(), and train().

Vec PLearn::PLS::mean_target [protected]

Definition at line 67 of file PLS.h.
Referenced by build_(), computeOutput(), makeDeepCopyFromShallowCopy(), and train().

string PLearn::PLS::method

Definition at line 80 of file PLS.h.
Referenced by build_(), and train().

bool PLearn::PLS::output_the_score

Definition at line 82 of file PLS.h.
Referenced by build_(), computeOutput(), and outputsize().

bool PLearn::PLS::output_the_target

Definition at line 83 of file PLS.h.
Referenced by build_(), computeOutput(), and outputsize().

int PLearn::PLS::p [protected]

Definition at line 68 of file PLS.h.
Referenced by build_(), and train().

real PLearn::PLS::precision

Definition at line 81 of file PLS.h.
Referenced by train().

Vec PLearn::PLS::stddev_input [protected]

Definition at line 69 of file PLS.h.
Referenced by build_(), computeOutput(), makeDeepCopyFromShallowCopy(), and train().

Vec PLearn::PLS::stddev_target [protected]

Definition at line 70 of file PLS.h.
Referenced by build_(), computeOutput(), makeDeepCopyFromShallowCopy(), and train().

Mat PLearn::PLS::W [protected]

Definition at line 71 of file PLS.h.
Referenced by computeOutput(), forget(), makeDeepCopyFromShallowCopy(), and train().

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

Generated on Tue Aug 17 16:27:46 2004 for PLearn by

1.3.7

PLearn::PLS Class Reference

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Static Protected Member Functions

Protected Attributes

Private Member Functions

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation