PLearn 0.1
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#include <ConditionalGaussianDistribution.h>
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 OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () 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 PPath & | declaringFile () |
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. |
Definition at line 47 of file ConditionalGaussianDistribution.h.
Reimplemented from PLearn::ConditionalDistribution.
Definition at line 55 of file ConditionalGaussianDistribution.h.
PLearn::ConditionalGaussianDistribution::ConditionalGaussianDistribution | ( | ) |
Definition at line 47 of file ConditionalGaussianDistribution.cc.
:inherited() { }
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.
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().
{ inherited::build(); }
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); }
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)); }
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 }
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; }
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.
{ Learner::makeDeepCopyFromShallowCopy(copies); }
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().
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); }
Mat PLearn::ConditionalGaussianDistribution::variance | ( | ) | const [virtual] |
Reimplemented from PLearn::Distribution.
Definition at line 112 of file ConditionalGaussianDistribution.cc.
References covariance.
{ return covariance; }
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().
Definition at line 52 of file ConditionalGaussianDistribution.h.
Referenced by declareOptions(), expectation(), generate(), setInput(), and train().