PLearn::ConditionalGaussianDistribution Class Reference

#include <ConditionalGaussianDistribution.h>

Inheritance diagram for PLearn::ConditionalGaussianDistribution:

[legend]Collaboration diagram for PLearn::ConditionalGaussianDistribution:

[legend]List of all members.

Public Types
typedef ConditionalDistribution	inherited
Public Member Functions
	ConditionalGaussianDistribution ()
virtual void	build ()
	**** SUBCLASS WRITING: **** This method should be redefined in subclasses, to just call inherited::build() and then build_()
virtual void	makeDeepCopyFromShallowCopy (map< const void *, void * > &copies)
	Transforms a shallow copy into a deep copy.
	PLEARN_DECLARE_OBJECT (ConditionalGaussianDistribution)
	Declares name and deepCopy methods.
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	density (const Vec &x) const
	return probability density p(x) [ default version returns exp(log_density(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 pseudo-random sample generated from the distribution.
virtual void	setInput (const Vec &input)
	Set the input part before using the inherited methods.
Public Attributes
Vec	mean
Mat	covariance
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	Declares this class' options.

---

Member Typedef Documentation

typedef ConditionalDistribution PLearn::ConditionalGaussianDistribution::inherited

Reimplemented from PLearn::ConditionalDistribution. Definition at line 55 of file ConditionalGaussianDistribution.h. Referenced by ConditionalGaussianDistribution().

---

Constructor & Destructor Documentation

PLearn::ConditionalGaussianDistribution::ConditionalGaussianDistribution (   )

Definition at line 47 of file ConditionalGaussianDistribution.cc. References inherited.

---

Member Function Documentation

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

**** SUBCLASS WRITING: **** This method should be redefined in subclasses, to just call inherited::build() and then build_() Reimplemented from PLearn::Distribution. Definition at line 77 of file ConditionalGaussianDistribution.cc.

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

return survival fn = P(X<x) Reimplemented from PLearn::Distribution. Definition at line 104 of file ConditionalGaussianDistribution.cc. References PLERROR.

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

Declares this class' options. Reimplemented from PLearn::Distribution. Definition at line 57 of file ConditionalGaussianDistribution.cc. References PLearn::declareOption(), and PLearn::OptionList.

double PLearn::ConditionalGaussianDistribution::density (  const Vec &  x  )  const [virtual]

return probability density p(x) [ default version returns exp(log_density(x)) ] Reimplemented from PLearn::Distribution. Definition at line 98 of file ConditionalGaussianDistribution.cc. References PLearn::exp(), log_density(), and x.

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

return E[X] Reimplemented from PLearn::Distribution. Definition at line 107 of file ConditionalGaussianDistribution.cc. References mean.

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

return a pseudo-random sample generated from the distribution. Reimplemented from PLearn::Distribution. Definition at line 117 of file ConditionalGaussianDistribution.cc. References covariance, mean, PLearn::multivariate_normal(), PLERROR, and x.

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

return log of probability density log(p(x)) Reimplemented from PLearn::Distribution. Definition at line 95 of file ConditionalGaussianDistribution.cc. References PLERROR. Referenced by density().

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

Transforms a shallow copy into a deep copy. Reimplemented from PLearn::ConditionalDistribution. Definition at line 90 of file ConditionalGaussianDistribution.cc.

PLearn::ConditionalGaussianDistribution::PLEARN_DECLARE_OBJECT (  ConditionalGaussianDistribution   )

Declares name and deepCopy methods.

void PLearn::ConditionalGaussianDistribution::setInput (  const Vec &  input  )  [virtual]

Set the input part before using the inherited methods. Reimplemented from PLearn::ConditionalDistribution. Definition at line 128 of file ConditionalGaussianDistribution.cc. References mean, PLearn::TVec< T >::resize(), and PLearn::TVec< T >::size().

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

return survival fn = P(X>x) Reimplemented from PLearn::Distribution. Definition at line 101 of file ConditionalGaussianDistribution.cc. References PLERROR.

void PLearn::ConditionalGaussianDistribution::train (  VMat  training_set  )  [virtual]

trains the model Reimplemented from PLearn::Distribution. Definition at line 83 of file ConditionalGaussianDistribution.cc. References PLearn::computeMeanAndCovar(), covariance, mean, PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), and PLearn::VMat::width().

Mat PLearn::ConditionalGaussianDistribution::variance (   )  const [virtual]

return Var[X] Reimplemented from PLearn::Distribution. Definition at line 112 of file ConditionalGaussianDistribution.cc. References covariance.

---

Member Data Documentation

Mat PLearn::ConditionalGaussianDistribution::covariance

Definition at line 53 of file ConditionalGaussianDistribution.h. Referenced by generate(), train(), and variance().

Vec PLearn::ConditionalGaussianDistribution::mean

Definition at line 52 of file ConditionalGaussianDistribution.h. Referenced by expectation(), generate(), setInput(), and train().

---

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

• ConditionalGaussianDistribution.h
• ConditionalGaussianDistribution.cc

---