PLearn 0.1
Public Types | Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | Static Protected Member Functions
PLearn::ConditionalGaussianDistribution Class Reference

#include <ConditionalGaussianDistribution.h>

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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 (CopiesMap &copies)
 Transforms a shallow copy into a deep copy.
virtual string classname () const
virtual OptionListgetOptionList () const
virtual OptionMapgetOptionMap () const
virtual RemoteMethodMapgetRemoteMethodMap () const
virtual
ConditionalGaussianDistribution
deepCopy (CopiesMap &copies) const
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) const

Static Public Member Functions

static string _classname_ ()
 Declares name and deepCopy methods.
static OptionList_getOptionList_ ()
static RemoteMethodMap_getRemoteMethodMap_ ()
static Object_new_instance_for_typemap_ ()
static bool _isa_ (const Object *o)
static void _static_initialize_ ()
static const PPathdeclaringFile ()

Public Attributes

Vec mean
Mat covariance

Static Public Attributes

static StaticInitializer _static_initializer_

Static Protected Member Functions

static void declareOptions (OptionList &ol)
 Declares this class' options.

Detailed Description

Definition at line 47 of file ConditionalGaussianDistribution.h.


Member Typedef Documentation

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.h.


Constructor & Destructor Documentation

PLearn::ConditionalGaussianDistribution::ConditionalGaussianDistribution ( )

Definition at line 47 of file ConditionalGaussianDistribution.cc.

    :inherited()
{
}

Member Function Documentation

string PLearn::ConditionalGaussianDistribution::_classname_ ( ) [static]

Declares name and deepCopy methods.

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

OptionList & PLearn::ConditionalGaussianDistribution::_getOptionList_ ( ) [static]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

RemoteMethodMap & PLearn::ConditionalGaussianDistribution::_getRemoteMethodMap_ ( ) [static]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

bool PLearn::ConditionalGaussianDistribution::_isa_ ( const Object o) [static]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

Object * PLearn::ConditionalGaussianDistribution::_new_instance_for_typemap_ ( ) [static]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

StaticInitializer ConditionalGaussianDistribution::_static_initializer_ & PLearn::ConditionalGaussianDistribution::_static_initialize_ ( ) [static]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

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.

References PLearn::Distribution::build().

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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.

{ PLERROR("cdf not implemented yet for ConditionalGaussianDistribution"); return 0; }
string PLearn::ConditionalGaussianDistribution::classname ( ) const [virtual]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

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::OptionBase::buildoption, covariance, PLearn::declareOption(), PLearn::Distribution::declareOptions(), and mean.

{
    // ### Declare all of this object's options here
    // ### For the "flags" of each option, you should typically specify  
    // ### one of OptionBase::buildoption, OptionBase::learntoption or 
    // ### OptionBase::tuningoption. Another possible flag to be combined with
    // ### is OptionBase::nosave
  
    declareOption(ol, "mean", &ConditionalGaussianDistribution::mean, OptionBase::buildoption,
                  "The mean of the gaussian distribution \n"
                  "Could be learned on a training set or specified by calling setInput");

    declareOption(ol, "covariance", &ConditionalGaussianDistribution::covariance, OptionBase::buildoption,
                  "The covariance of the gaussian distribution \n"
                  "Could be learned on a training set or specified manually");
  
    // Now call the parent class' declareOptions
    inherited::declareOptions(ol);
}

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static const PPath& PLearn::ConditionalGaussianDistribution::declaringFile ( ) [inline, static]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 72 of file ConditionalGaussianDistribution.h.

ConditionalGaussianDistribution * PLearn::ConditionalGaussianDistribution::deepCopy ( CopiesMap copies) const [virtual]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

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(), and log_density().

{ return exp(log_density(x)); }

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

return E[X]

Reimplemented from PLearn::Distribution.

Definition at line 107 of file ConditionalGaussianDistribution.cc.

References mean.

{
    return 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(), and PLERROR.

{
    // WARNING! Function not found in lapack library for windows!!

#ifdef WIN32
    PLERROR("multivariate_normal for Vec not found in lapack library for windows!");
#else
    x = multivariate_normal(mean, covariance);
#endif
}

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OptionList & PLearn::ConditionalGaussianDistribution::getOptionList ( ) const [virtual]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

OptionMap & PLearn::ConditionalGaussianDistribution::getOptionMap ( ) const [virtual]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

RemoteMethodMap & PLearn::ConditionalGaussianDistribution::getRemoteMethodMap ( ) const [virtual]

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 55 of file ConditionalGaussianDistribution.cc.

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().

{ PLERROR("density not implemented yet for ConditionalGaussianDistribution"); return 0; }

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void PLearn::ConditionalGaussianDistribution::makeDeepCopyFromShallowCopy ( CopiesMap copies) [virtual]

Transforms a shallow copy into a deep copy.

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 90 of file ConditionalGaussianDistribution.cc.

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

Definition at line 128 of file ConditionalGaussianDistribution.cc.

References mean, PLearn::TVec< T >::resize(), and PLearn::TVec< T >::size().

{
    mean.resize(input.size());
    mean << input;
}

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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.

{ PLERROR("survival_fn not implemented yet for ConditionalGaussianDistribution"); return 0; }
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().

{
    mean.resize(training_set.width());
    covariance.resize(training_set.width(), training_set.width());
    computeMeanAndCovar(training_set, mean, covariance);
}

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

return Var[X]

Reimplemented from PLearn::Distribution.

Definition at line 112 of file ConditionalGaussianDistribution.cc.

References covariance.

{
    return covariance;
}

Member Data Documentation

Reimplemented from PLearn::ConditionalDistribution.

Definition at line 72 of file ConditionalGaussianDistribution.h.

Definition at line 53 of file ConditionalGaussianDistribution.h.

Referenced by declareOptions(), generate(), train(), and variance().


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