PLearn::GaussianDistribution Class Reference

#include <GaussianDistribution.h>

Inheritance diagram for PLearn::GaussianDistribution:

[legend]Collaboration diagram for PLearn::GaussianDistribution:

[legend]List of all members.

Public Types
typedef PDistribution	inherited
Public Member Functions
	GaussianDistribution ()
	PLEARN_DECLARE_OBJECT (GaussianDistribution)
void	makeDeepCopyFromShallowCopy (CopiesMap &copies)
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 real	log_density (const Vec &x) const
	Return log of probability density log(p(y | x)).
virtual void	resetGenerator (long g_seed) const
	Resets the random number generator used by generate using the given seed.
virtual void	generate (Vec &x) const
	return a pseudo-random sample generated from the distribution.
virtual int	inputsize () const
	Overridden so that it does not necessarily need a training set.
Public Attributes
Vec	mu
Vec	eigenvalues
Mat	eigenvectors
int	k
real	gamma
float	ignore_weights_below
	When doing a weighted fitting (weightsize==1), points with a weight below this value will be ignored.
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	Declares this class' options.

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Member Typedef Documentation

typedef PDistribution PLearn::GaussianDistribution::inherited

Reimplemented from PLearn::PDistribution. Definition at line 63 of file GaussianDistribution.h.

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Constructor & Destructor Documentation

PLearn::GaussianDistribution::GaussianDistribution (   )

Definition at line 68 of file GaussianDistribution.cc. References k.

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Member Function Documentation

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

Declares this class' options. Reimplemented from PLearn::PDistribution. Definition at line 74 of file GaussianDistribution.cc. References PLearn::declareOption(), and PLearn::OptionList.

void PLearn::GaussianDistribution::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!) Reimplemented from PLearn::PDistribution. Definition at line 94 of file GaussianDistribution.cc.

void PLearn::GaussianDistribution::generate (  Vec &  x  )  const [virtual]

return a pseudo-random sample generated from the distribution. Reimplemented from PLearn::PDistribution. Definition at line 142 of file GaussianDistribution.cc. References eigenvalues, eigenvectors, PLearn::fill_random_normal(), gamma, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), mu, PLearn::TVec< T >::resize(), PLearn::sqrt(), PLearn::transposeProduct(), and x.

int PLearn::GaussianDistribution::inputsize (   )  const [virtual]

Overridden so that it does not necessarily need a training set. Reimplemented from PLearn::PLearner. Definition at line 162 of file GaussianDistribution.cc. References PLearn::TVec< T >::length(), and mu. Referenced by train().

real PLearn::GaussianDistribution::log_density (  const Vec &  x  )  const [virtual]

Return log of probability density log(p(y | x)). Reimplemented from PLearn::PDistribution. Definition at line 131 of file GaussianDistribution.cc. References eigenvalues, eigenvectors, gamma, PLearn::logOfCompactGaussian(), mu, and x.

void PLearn::GaussianDistribution::makeDeepCopyFromShallowCopy (  CopiesMap &  copies  )  [virtual]

Does the necessary operations to transform a shallow copy (this) into a deep copy by deep-copying all the members that need to be. Typical implementation: void CLASS_OF_THIS::makeDeepCopyFromShallowCopy(CopiesMap& copies) { SUPERCLASS_OF_THIS::makeDeepCopyFromShallowCopy(copies); member_ptr = member_ptr->deepCopy(copies); member_smartptr = member_smartptr->deepCopy(copies); member_mat.makeDeepCopyFromShallowCopy(copies); member_vec.makeDeepCopyFromShallowCopy(copies); ... } Reimplemented from PLearn::PLearner. Definition at line 59 of file GaussianDistribution.cc. References PLearn::CopiesMap, PLearn::deepCopyField(), eigenvalues, eigenvectors, and mu.

PLearn::GaussianDistribution::PLEARN_DECLARE_OBJECT (  GaussianDistribution   )

void PLearn::GaussianDistribution::resetGenerator (  long  g_seed  )  const [virtual]

Resets the random number generator used by generate using the given seed. Reimplemented from PLearn::PDistribution. Definition at line 137 of file GaussianDistribution.cc. References PLearn::manual_seed().

void PLearn::GaussianDistribution::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. Reimplemented from PLearn::PDistribution. Definition at line 97 of file GaussianDistribution.cc. References PLearn::computeMeanAndCovar(), PLearn::computeWeightedMeanAndCovar(), eigenvalues, PLearn::eigenVecOfSymmMat(), eigenvectors, PLearn::PLearner::getTrainingSet(), ignore_weights_below, inputsize(), k, PLearn::VMat::length(), PLearn::min(), mu, PLERROR, PLearn::VMat::width(), and PLearn::ws().

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Member Data Documentation

Vec PLearn::GaussianDistribution::eigenvalues

Definition at line 67 of file GaussianDistribution.h. Referenced by generate(), log_density(), makeDeepCopyFromShallowCopy(), and train().

Mat PLearn::GaussianDistribution::eigenvectors

Definition at line 68 of file GaussianDistribution.h. Referenced by generate(), log_density(), makeDeepCopyFromShallowCopy(), and train().

real PLearn::GaussianDistribution::gamma

Definition at line 72 of file GaussianDistribution.h. Referenced by generate(), and log_density().

float PLearn::GaussianDistribution::ignore_weights_below

When doing a weighted fitting (weightsize==1), points with a weight below this value will be ignored. Definition at line 73 of file GaussianDistribution.h. Referenced by train().

int PLearn::GaussianDistribution::k

Definition at line 71 of file GaussianDistribution.h. Referenced by train().

Vec PLearn::GaussianDistribution::mu

Definition at line 66 of file GaussianDistribution.h. Referenced by generate(), inputsize(), log_density(), makeDeepCopyFromShallowCopy(), and train().

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

• GaussianDistribution.h
• GaussianDistribution.cc

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