PLearn::EmpiricalDistribution Class Reference

#include <EmpiricalDistribution.h>

Inheritance diagram for PLearn::EmpiricalDistribution:

[legend]Collaboration diagram for PLearn::EmpiricalDistribution:

[legend]List of all members.


Public Member Functions
	EmpiricalDistribution ()
	EmpiricalDistribution (int inputsize, bool random_sample_=true)
	PLEARN_DECLARE_OBJECT (EmpiricalDistribution)
void	makeDeepCopyFromShallowCopy (CopiesMap &copies)
virtual void	train (VMat training_set)
	trains the model
virtual double	log_density (const Vec &x) const
	return log of probability density log(p(x))
virtual double	survival_fn (const Vec &x) const
	return survival fn = P(X>x)
virtual double	cdf (const Vec &x) const
	return survival fn = P(X<x)
virtual Vec	expectation () const
	return E[X]
virtual Mat	variance () const
	return Var[X]
virtual void	generate (Vec &x) const
	return a sample generated from the distribution.
Public Attributes
bool	random_sample
int	length
int	current_sample_x
int	current_sample_y
bool	flip
Protected Types
typedef Distribution	inherited
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	Declares this class' options.
Protected Attributes
VMat	data

Member Typedef Documentation

typedef Distribution PLearn::EmpiricalDistribution::inherited [protected]

Reimplemented from PLearn::Distribution.
Definition at line 48 of file EmpiricalDistribution.h.
Referenced by EmpiricalDistribution().

Constructor & Destructor Documentation

PLearn::EmpiricalDistribution::EmpiricalDistribution ( )

Definition at line 54 of file EmpiricalDistribution.cc.
References inherited, and PLearn::seed().

PLearn::EmpiricalDistribution::EmpiricalDistribution ( int inputsize,

bool random_sample_ = true

)

Definition at line 61 of file EmpiricalDistribution.cc.
References current_sample_x, current_sample_y, flip, and PLearn::seed().

Member Function Documentation

double PLearn::EmpiricalDistribution::cdf ( const Vec & x ) const [virtual]

return survival fn = P(X<x)

Reimplemented from PLearn::Distribution.
Definition at line 111 of file EmpiricalDistribution.cc.
References data, PLearn::VMat::length(), PLearn::VMat::width(), and x.

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

Declares this class' options.

Reimplemented from PLearn::Distribution.
Definition at line 73 of file EmpiricalDistribution.cc.
References PLearn::OptionList.

Vec PLearn::EmpiricalDistribution::expectation ( ) const [virtual]

return E[X]

Reimplemented from PLearn::Distribution.
Definition at line 128 of file EmpiricalDistribution.cc.
References PLearn::computeMean(), data, and PLearn::mean().

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

return a sample generated from the distribution.

Reimplemented from PLearn::Distribution.
Definition at line 145 of file EmpiricalDistribution.cc.
References current_sample_x, current_sample_y, data, flip, length, random_sample, PLearn::uniform_multinomial_sample(), PLearn::VMat::width(), and x.

double PLearn::EmpiricalDistribution::log_density ( const Vec & x ) const [virtual]

return log of probability density log(p(x))

Reimplemented from PLearn::Distribution.
Definition at line 88 of file EmpiricalDistribution.cc.
References PLERROR, and PLearn::Vec.

void PLearn::EmpiricalDistribution::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::Learner.
Definition at line 47 of file EmpiricalDistribution.cc.
References PLearn::CopiesMap, data, and PLearn::deepCopyField().

PLearn::EmpiricalDistribution::PLEARN_DECLARE_OBJECT ( EmpiricalDistribution )

double PLearn::EmpiricalDistribution::survival_fn ( const Vec & x ) const [virtual]

return survival fn = P(X>x)

Reimplemented from PLearn::Distribution.
Definition at line 95 of file EmpiricalDistribution.cc.
References data, PLearn::VMat::length(), PLearn::VMat::width(), and x.