PLearn 0.1
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The first sentence should be a BRIEF DESCRIPTION of what the class does. More...
#include <MixtureDistribution.h>
Public Member Functions | |
MixtureDistribution () | |
Default constructor. | |
virtual real | log_density (const Vec &y) const |
Return log of probability density log(p(y | x)). | |
virtual real | survival_fn (const Vec &y) const |
Return survival function: P(Y>y | x). | |
virtual real | cdf (const Vec &y) const |
Return cdf: P(Y<y | x). | |
virtual void | expectation (Vec &mu) const |
Return E[Y | x]. | |
virtual void | variance (Mat &cov) const |
Return Var[Y | x]. | |
virtual void | generate (Vec &y) const |
Return a pseudo-random sample generated from the conditional distribution, of density p(y | x). | |
virtual void | resetGenerator (long g_seed) |
Generates a pseudo-random sample x from the reversed conditional distribution, of density p(x | y) (and NOT p(y | x)). | |
virtual bool | setPredictorPredictedSizes (int the_predictor_size, int the_predicted_size, bool call_parent=true) |
Set the 'predictor' and 'predicted' sizes for this distribution. | |
virtual void | setPredictor (const Vec &predictor, bool call_parent=true) const |
Set the value for the predictor part of a conditional probability. | |
virtual void | forget () |
(Re-)initializes the PDistribution in its fresh state (that state may depend on the 'seed' option). | |
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 string | classname () const |
virtual OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () const |
virtual MixtureDistribution * | deepCopy (CopiesMap &copies) const |
virtual void | build () |
Simply calls inherited::build() then build_(). | |
virtual void | makeDeepCopyFromShallowCopy (CopiesMap &copies) |
Transforms a shallow copy into a deep copy. | |
Static Public Member Functions | |
static string | _classname_ () |
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 | |
TVec< PP< PDistribution > > | distributions |
Vec | weights |
Static Public Attributes | |
static StaticInitializer | _static_initializer_ |
Protected Member Functions | |
void | getSizes () const |
Re-obtain the sizes of the predictor and predicted parts from the first distribution in the 'distributions' vector. | |
Static Protected Member Functions | |
static void | declareOptions (OptionList &ol) |
Declares the class options. | |
Private Types | |
typedef PDistribution | inherited |
Private Member Functions | |
void | build_ () |
This does the actual building. | |
Private Attributes | |
Vec | work |
Vector to store temporary data. |
The first sentence should be a BRIEF DESCRIPTION of what the class does.
Place the rest of the class programmer documentation here. Doxygen supports Javadoc-style comments. See http://www.doxygen.org/manual.html
Definition at line 57 of file MixtureDistribution.h.
typedef PDistribution PLearn::MixtureDistribution::inherited [private] |
Reimplemented from PLearn::PDistribution.
Definition at line 59 of file MixtureDistribution.h.
PLearn::MixtureDistribution::MixtureDistribution | ( | ) |
string PLearn::MixtureDistribution::_classname_ | ( | ) | [static] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
OptionList & PLearn::MixtureDistribution::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
RemoteMethodMap & PLearn::MixtureDistribution::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
Object * PLearn::MixtureDistribution::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
StaticInitializer MixtureDistribution::_static_initializer_ & PLearn::MixtureDistribution::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
void PLearn::MixtureDistribution::build | ( | ) | [virtual] |
Simply calls inherited::build() then build_().
Reimplemented from PLearn::PDistribution.
Definition at line 98 of file MixtureDistribution.cc.
{ inherited::build(); build_(); }
void PLearn::MixtureDistribution::build_ | ( | ) | [private] |
This does the actual building.
Reimplemented from PLearn::PDistribution.
Definition at line 107 of file MixtureDistribution.cc.
References PLearn::is_equal(), n, PLCHECK_MSG, and PLearn::sum().
{ if (distributions.isEmpty()) return; if (weights.isEmpty()) { int n = distributions.length(); weights.resize(n); weights.fill(1 / real(n)); } PLCHECK_MSG(weights.length() == distributions.length() && is_equal(PLearn::sum(weights), 1), "There must be one weight for each distribution, and the " "weights must sum to 1"); getSizes(); }
Return cdf: P(Y<y | x).
Reimplemented from PLearn::PDistribution.
Definition at line 126 of file MixtureDistribution.cc.
References PLERROR.
{ PLERROR("cdf not implemented for MixtureDistribution"); return 0; }
string PLearn::MixtureDistribution::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
void PLearn::MixtureDistribution::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares the class options.
Reimplemented from PLearn::PDistribution.
Definition at line 61 of file MixtureDistribution.cc.
References PLearn::OptionBase::buildoption, PLearn::declareOption(), distributions, PLearn::OptionBase::nosave, PLearn::PDistribution::predicted_size, PLearn::PDistribution::predictor_part, PLearn::PDistribution::predictor_size, PLearn::redeclareOption(), and weights.
{ declareOption(ol, "distributions", &MixtureDistribution::distributions, OptionBase::buildoption, "Underlying distributions being mixed."); declareOption(ol, "weights", &MixtureDistribution::weights, OptionBase::buildoption, "Weights of the distributions (must sum to 1). If left empty, then\n" "each distribution will be given a weight 1/number_of_distributions."); // Now call the parent class' declareOptions(). inherited::declareOptions(ol); // Hide unused options. redeclareOption(ol, "predicted_size", &MixtureDistribution::predicted_size, OptionBase::nosave, "Unused"); redeclareOption(ol, "predictor_part", &MixtureDistribution::predictor_part, OptionBase::nosave, "Unused"); redeclareOption(ol, "predictor_size", &MixtureDistribution::predictor_size, OptionBase::nosave, "Unused"); }
static const PPath& PLearn::MixtureDistribution::declaringFile | ( | ) | [inline, static] |
Reimplemented from PLearn::PDistribution.
Definition at line 175 of file MixtureDistribution.h.
:
//##### Protected Options ###############################################
MixtureDistribution * PLearn::MixtureDistribution::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
void PLearn::MixtureDistribution::expectation | ( | Vec & | mu | ) | const [virtual] |
Return E[Y | x].
Reimplemented from PLearn::PDistribution.
Definition at line 134 of file MixtureDistribution.cc.
References PLearn::TVec< T >::fill(), i, PLearn::multiplyAcc(), PLASSERT, and PLearn::TVec< T >::resize().
{ PLASSERT( !distributions.isEmpty() ); mu.resize(distributions[0]->getNPredicted()); mu.fill(0); for (int i = 0; i < distributions.length(); i++) { distributions[i]->expectation(work); multiplyAcc(mu, work, weights[i]); } }
void PLearn::MixtureDistribution::forget | ( | ) | [virtual] |
(Re-)initializes the PDistribution 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!). ### You may remove this method if your distribution does not ### implement it.
Reimplemented from PLearn::PDistribution.
Definition at line 148 of file MixtureDistribution.cc.
References i.
{ for (int i = 0; i < distributions.length(); i++) distributions[i]->forget(); inherited::forget(); getSizes(); }
void PLearn::MixtureDistribution::generate | ( | Vec & | y | ) | const [virtual] |
Return a pseudo-random sample generated from the conditional distribution, of density p(y | x).
Reimplemented from PLearn::PDistribution.
Definition at line 159 of file MixtureDistribution.cc.
References j.
{ int j = random_gen->multinomial_sample(weights); distributions[j]->generate(y); }
OptionList & PLearn::MixtureDistribution::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
OptionMap & PLearn::MixtureDistribution::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
RemoteMethodMap & PLearn::MixtureDistribution::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::PDistribution.
Definition at line 50 of file MixtureDistribution.cc.
void PLearn::MixtureDistribution::getSizes | ( | ) | const [protected] |
Re-obtain the sizes of the predictor and predicted parts from the first distribution in the 'distributions' vector.
This method is used to re-obtain sizes after things may have changed (e.g. after a build(), forget() or train()).
Definition at line 168 of file MixtureDistribution.cc.
References PLASSERT.
{ PLASSERT( !distributions.isEmpty() ); n_predicted = distributions[0]->getNPredicted(); n_predictor = distributions[0]->getNPredictor(); }
Return log of probability density log(p(y | x)).
Reimplemented from PLearn::PDistribution.
Definition at line 177 of file MixtureDistribution.cc.
References i, PLearn::logadd(), n, and pl_log.
{ int n = distributions.length(); work.resize(n); for (int i = 0; i < n; i++) { work[i] = distributions[i]->log_density(y) + pl_log(weights[i]); } return logadd(work); }
void PLearn::MixtureDistribution::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::PDistribution.
Definition at line 190 of file MixtureDistribution.cc.
References PLERROR.
{ inherited::makeDeepCopyFromShallowCopy(copies); // ### Call deepCopyField on all "pointer-like" fields // ### that you wish to be deepCopied rather than // ### shallow-copied. // ### ex: // deepCopyField(trainvec, copies); // ### Remove this line when you have fully implemented this method. PLERROR("MixtureDistribution::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!"); }
void PLearn::MixtureDistribution::resetGenerator | ( | long | g_seed | ) | [virtual] |
Generates a pseudo-random sample x from the reversed conditional distribution, of density p(x | y) (and NOT p(y | x)).
i.e., generates a "predictor" part given a "predicted" part, regardless of any previously set predictor. Reset the random number generator used by generate() using the given seed.
Reimplemented from PLearn::PDistribution.
Definition at line 207 of file MixtureDistribution.cc.
References i.
{ for (int i = 0; i < distributions.length(); i++) distributions[i]->resetGenerator(g_seed); inherited::resetGenerator(g_seed); }
void PLearn::MixtureDistribution::setPredictor | ( | const Vec & | predictor, |
bool | call_parent = true |
||
) | const [virtual] |
Set the value for the predictor part of a conditional probability.
Reimplemented from PLearn::PDistribution.
Definition at line 217 of file MixtureDistribution.cc.
References i.
{ if (call_parent) inherited::setPredictor(predictor, true); for (int i = 0; i < distributions.length(); i++) distributions[i]->setPredictor(predictor, call_parent); getSizes(); }
bool PLearn::MixtureDistribution::setPredictorPredictedSizes | ( | int | the_predictor_size, |
int | the_predicted_size, | ||
bool | call_parent = true |
||
) | [virtual] |
Set the 'predictor' and 'predicted' sizes for this distribution.
Reimplemented from PLearn::PDistribution.
Definition at line 229 of file MixtureDistribution.cc.
References i.
{ bool sizes_have_changed = false; if (call_parent) sizes_have_changed = inherited::setPredictorPredictedSizes( the_predictor_size, the_predicted_size, true); for (int i = 0; i < distributions.length(); i++) distributions[i]->setPredictorPredictedSizes(the_predictor_size, the_predicted_size, call_parent); getSizes(); // Returned value. return sizes_have_changed; }
Return survival function: P(Y>y | x).
Reimplemented from PLearn::PDistribution.
Definition at line 249 of file MixtureDistribution.cc.
References PLERROR.
{ PLERROR("survival_fn not implemented for MixtureDistribution"); return 0; }
void PLearn::MixtureDistribution::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 257 of file MixtureDistribution.cc.
{ // This generic PLearner method does a number of standard stuff useful for // (almost) any learner, and return 'false' if no training should take // place. See PLearner.h for more details. if (!initTrain()) return; PLCHECK( nstages == 1 && stage == 0 ); for (int i = 0; i < distributions.length(); i++) distributions[i]->train(); stage = 1; getSizes(); }
void PLearn::MixtureDistribution::variance | ( | Mat & | cov | ) | const [virtual] |
Reimplemented from PLearn::PDistribution.
Definition at line 275 of file MixtureDistribution.cc.
References PLERROR.
{ PLERROR("variance not implemented for MixtureDistribution"); }
Reimplemented from PLearn::PDistribution.
Definition at line 175 of file MixtureDistribution.h.
Definition at line 64 of file MixtureDistribution.h.
Referenced by declareOptions().
Definition at line 65 of file MixtureDistribution.h.
Referenced by declareOptions().
Vec PLearn::MixtureDistribution::work [mutable, private] |
Vector to store temporary data.
Definition at line 211 of file MixtureDistribution.h.