PLearn::PLearner Class Reference

#include <PLearner.h>

Inheritance diagram for PLearn::PLearner:

[legend]Collaboration diagram for PLearn::PLearner:

[legend]List of all members.

Public Member Functions
	PLearner ()
virtual	~PLearner ()
virtual void	setTrainingSet (VMat training_set, bool call_forget=true)
	Declares the train_set Then calls build() and forget() if necessary Note: You shouldn't have to overload this in subclasses, except in maybe to forward the call to an underlying learner.
VMat	getTrainingSet () const
	Returns the current train_set.
virtual void	setValidationSet (VMat validset)
	Set the validation set (optionally) for learners that are able to use it directly.
VMat	getValidationSet () const
	Returns the current validation set.
virtual void	setTrainStatsCollector (PP< VecStatsCollector > statscol)
	Sets the statistics collector whose update() method will be called during training.
PP< VecStatsCollector >	getTrainStatsCollector ()
	Returns the train stats collector.
virtual void	setExperimentDirectory (const string &the_expdir)
	The experiment directory is the directory in which files related to this model are to be saved.
string	getExperimentDirectory () const
	This returns the currently set expdir (see setExperimentDirectory).
virtual int	inputsize () const
	Default returns train_set->inputsize().
virtual int	targetsize () const
	Default returns train_set->targetsize().
virtual int	outputsize () const =0
	SUBCLASS WRITING: overload this so that it returns the size of this learner's output, as a function of its inputsize(), targetsize() and set options.
virtual void	build ()
	**** SUBCLASS WRITING: **** This method should be redefined in subclasses, to just call inherited::build() and then build_()
virtual void	forget ()=0
	(Re-)initializes the PLearner 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!)
virtual void	train ()=0
	The role of the train method is to bring the learner up to stage==nstages, updating the stats with training costs measured on-line in the process.
virtual void	computeOutput (const Vec &input, Vec &output) const =0
	*** SUBCLASS WRITING: *** This should be defined in subclasses to compute the output from the input
virtual void	computeCostsFromOutputs (const Vec &input, const Vec &output, const Vec &target, Vec &costs) const =0
	*** SUBCLASS WRITING: *** This should be defined in subclasses to compute the weighted costs from already computed output.
virtual void	computeOutputAndCosts (const Vec &input, const Vec &target, Vec &output, Vec &costs) const
	Default calls computeOutput and computeCostsFromOutputs You may overload this if you have a more efficient way to compute both output and weighted costs at the same time.
virtual void	computeCostsOnly (const Vec &input, const Vec &target, Vec &costs) const
	Default calls computeOutputAndCosts This may be overloaded if there is a more efficient way to compute the costs directly, without computing the whole output vector.
virtual void	use (VMat testset, VMat outputs) const
	Computes outputs for the input part of testset.
virtual void	useOnTrain (Mat &outputs) const
	Compute the output on the training set of the learner, and save the result in the provided matrix.
virtual void	test (VMat testset, PP< VecStatsCollector > test_stats, VMat testoutputs=0, VMat testcosts=0) const
	Performs test on testset, updating test cost statistics, and optionally filling testoutputs and testcosts The default version repeatedly calls computeOutputAndCosts or computeCostsOnly.
virtual TVec< string >	getTestCostNames () const =0
	*** SUBCLASS WRITING: *** This should return the names of the costs computed by computeCostsFromOutpus
virtual TVec< string >	getTrainCostNames () const =0
	*** SUBCLASS WRITING: *** This should return the names of the objective costs that the train method computes and for which it updates the VecStatsCollector train_stats
virtual int	nTestCosts () const
	Caches getTestCostNames().size() in an internal variable the first time it is called, and then returns the content of this variable.
virtual int	nTrainCosts () const
	Caches getTrainCostNames().size() in an internal variable the first time it is called, and then returns the content of this variable.
int	getTestCostIndex (const string &costname) const
	returns the index of the given cost in the vector of testcosts calls PLERROR (throws a PLearnException) if requested cost is not found.
int	getTrainCostIndex (const string &costname) const
	returns the index of the given cost in the vector of traincosts (objectives) calls PLERROR (throws a PLearnException) if requested cost is not found.
virtual void	resetInternalState ()
	If any, reset the internal state Default: do nothing.
virtual bool	isStatefulLearner () const
	Does this PLearner has an internal state? Default: false.
	PLEARN_DECLARE_ABSTRACT_OBJECT (PLearner)
	Does the necessary operations to transform a shallow copy (this) into a deep copy by deep-copying all the members that need to be.
virtual void	matlabSave (const string &matlab_subdir)
Public Attributes
string	expdir
	Path of the directory associated with this learner, in which it should save any file it wishes to create.
long	seed_
	the seed used for the random number generator in initializing the learner (see forget() method).
int	stage
	0 means untrained, n often means after n epochs or optimization steps, etc..
int	nstages
	The meaning of 'stage' is learner-dependent, but for learners whose < training is incremental (such as involving incremental optimization), < it is typically synonym with the number of 'epochs', i.e.
bool	report_progress
	should progress in learning and testing be reported in a ProgressBar
int	verbosity
Protected Member Functions
virtual void	build_from_train_set ()
	Building part of the PLearner that needs the train_set.
virtual void	makeDeepCopyFromShallowCopy (CopiesMap &copies)
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	redefine this in subclasses: call declareOption(...) for each option, and then call inherited::declareOptions(options) ( see the declareOption function further down)
Protected Attributes
VMat	train_set
	The training set as set by setTrainingSet.
int	inputsize_
int	targetsize_
int	weightsize_
int	n_examples
VMat	validation_set
PP< VecStatsCollector >	train_stats
	The stats_collector responsible for collecting train cost statistics during training.
Private Types
typedef Object	inherited
Private Member Functions
void	build_ ()
Private Attributes
int	n_train_costs_
int	n_test_costs_
Vec	tmp_output
	Global storage to save memory allocations.

---

Detailed Description

The base class for learning algorithms, which should be the main "products" of PLearn.

Definition at line 61 of file PLearner.h.

---

Member Typedef Documentation

typedef Object PLearn::PLearner::inherited [private]

Reimplemented from PLearn::Object. Reimplemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::ConditionalDensityNet, PLearn::GaussianDistribution, PLearn::GaussianProcessRegressor, PLearn::GaussMix, PLearn::HistogramDistribution, PLearn::ManifoldParzen2, PLearn::PConditionalDistribution, PLearn::PDistribution, PLearn::SpiralDistribution, PLearn::UnconditionalDistribution, PLearn::UniformDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::SelectInputSubsetLearner, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::MovingAverage, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::Isomap, PLearn::IsomapTangentLearner, PLearn::KernelPCA, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::LLE, PLearn::PCA, PLearn::SpectralClustering, and PLearn::TangentLearner. Definition at line 66 of file PLearner.h.

---

Constructor & Destructor Documentation

PLearn::PLearner::PLearner (   )

Definition at line 50 of file PLearner.cc.

PLearn::PLearner::~PLearner (   )  [virtual]

Definition at line 208 of file PLearner.cc.

---

Member Function Documentation

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

**** SUBCLASS WRITING: **** This method should be redefined in subclasses, to just call inherited::build() and then build_() Reimplemented from PLearn::Object. Reimplemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::ConditionalDensityNet, PLearn::GaussianProcessRegressor, PLearn::GaussMix, PLearn::HistogramDistribution, PLearn::ManifoldParzen2, PLearn::PConditionalDistribution, PLearn::PDistribution, PLearn::SpiralDistribution, PLearn::UnconditionalDistribution, PLearn::UniformDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::SelectInputSubsetLearner, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::Isomap, PLearn::IsomapTangentLearner, PLearn::KernelPCA, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::LLE, PLearn::PCA, PLearn::SpectralClustering, and PLearn::TangentLearner. Definition at line 202 of file PLearner.cc. References build_(). Referenced by setTrainingSet().

void PLearn::PLearner::build_ (   )  [private]

**** SUBCLASS WRITING: **** This method should finalize building of the object, according to set 'options', in *any* situation. Typical situations include: Initial building of an object from a few user-specified options Building of a "reloaded" object: i.e. from the complete set of all serialised options. Updating or "re-building" of an object after a few "tuning" options (such as hyper-parameters) have been modified. You can assume that the parent class' build_() has already been called. A typical build method will want to know the inputsize(), targetsize() and outputsize(), and may also want to check whether train_set->hasWeights(). All these methods require a train_set to be set, so the first thing you may want to do, is check if(train_set), before doing any heavy building... Note: build() is always called by setTrainingSet. Reimplemented from PLearn::Object. Reimplemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::ConditionalDensityNet, PLearn::GaussianProcessRegressor, PLearn::GaussMix, PLearn::HistogramDistribution, PLearn::ManifoldParzen2, PLearn::PConditionalDistribution, PLearn::PDistribution, PLearn::SpiralDistribution, PLearn::UnconditionalDistribution, PLearn::UniformDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::SelectInputSubsetLearner, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::Isomap, PLearn::IsomapTangentLearner, PLearn::KernelPCA, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::LLE, PLearn::PCA, PLearn::SpectralClustering, and PLearn::TangentLearner. Definition at line 192 of file PLearner.cc. References PLearn::abspath(), expdir, PLearn::force_mkdir(), and PLERROR. Referenced by build().

virtual void PLearn::PLearner::build_from_train_set (   )  [inline, protected, virtual]

Building part of the PLearner that needs the train_set. Definition at line 183 of file PLearner.h.

virtual void PLearn::PLearner::computeCostsFromOutputs (  const Vec &  input, const Vec &  output, const Vec &  target, Vec &  costs )  const [pure virtual]

*** SUBCLASS WRITING: *** This should be defined in subclasses to compute the weighted costs from already computed output. The costs should correspond to the cost names returned by getTestCostNames() NOTE: In exotic cases, the cost may also depend on some info in the input, that's why the method also gets so see it. Implemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::GaussianProcessRegressor, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::SelectInputSubsetLearner, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::MovingAverage, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::IsomapTangentLearner, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::PCA, and PLearn::TangentLearner. Referenced by computeOutputAndCosts().

void PLearn::PLearner::computeCostsOnly (  const Vec &  input, const Vec &  target, Vec &  costs )  const [virtual]

Default calls computeOutputAndCosts This may be overloaded if there is a more efficient way to compute the costs directly, without computing the whole output vector. Reimplemented in PLearn::GaussianProcessRegressor, PLearn::EmbeddedLearner, PLearn::StatefulLearner, PLearn::SequentialLearner, and PLearn::SequentialModelSelector. Definition at line 255 of file PLearner.cc. References computeOutputAndCosts(), outputsize(), PLearn::TVec< T >::resize(), and tmp_output. Referenced by test().

virtual void PLearn::PLearner::computeOutput (  const Vec &  input, Vec &  output )  const [pure virtual]

*** SUBCLASS WRITING: *** This should be defined in subclasses to compute the output from the input Implemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::GaussianProcessRegressor, PLearn::PConditionalDistribution, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::SelectInputSubsetLearner, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::IsomapTangentLearner, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::PCA, and PLearn::TangentLearner. Referenced by computeOutputAndCosts(), and use().

void PLearn::PLearner::computeOutputAndCosts (  const Vec &  input, const Vec &  target, Vec &  output, Vec &  costs )  const [virtual]

Default calls computeOutput and computeCostsFromOutputs You may overload this if you have a more efficient way to compute both output and weighted costs at the same time. Reimplemented in PLearn::MultiInstanceNNet, PLearn::GaussianProcessRegressor, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::SelectInputSubsetLearner, PLearn::StatefulLearner, PLearn::SequentialLearner, and PLearn::SequentialModelSelector. Definition at line 248 of file PLearner.cc. References computeCostsFromOutputs(), and computeOutput(). Referenced by computeCostsOnly(), and test().

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

redefine this in subclasses: call declareOption(...) for each option, and then call inherited::declareOptions(options) ( see the declareOption function further down) ex: static void declareOptions(OptionList& ol) { declareOption(ol, "inputsize", &MyObject::inputsize_, OptionBase::buildoption, "the size of the input\n it must be provided"); declareOption(ol, "weights", &MyObject::weights, OptionBase::learntoption, "the learnt model weights"); inherited::declareOptions(ol); } Reimplemented from PLearn::Object. Reimplemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::ConditionalDensityNet, PLearn::GaussianDistribution, PLearn::GaussianProcessRegressor, PLearn::GaussMix, PLearn::HistogramDistribution, PLearn::PConditionalDistribution, PLearn::PDistribution, PLearn::SpiralDistribution, PLearn::UnconditionalDistribution, PLearn::UniformDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::SelectInputSubsetLearner, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::Isomap, PLearn::IsomapTangentLearner, PLearn::KernelPCA, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::LLE, PLearn::PCA, PLearn::SpectralClustering, and PLearn::TangentLearner. Definition at line 77 of file PLearner.cc. References PLearn::declareOption(), and PLearn::OptionList.

virtual void PLearn::PLearner::forget (   )  [pure virtual]

(Re-)initializes the PLearner 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!) *** SUBCLASS WRITING: *** A typical forget() method should do the following: initialize a random number generator with the seed option initialize the learner's parameters, using this random generator stage = 0; This method is typically called by the build_() method, after it has finished setting up the parameters, and if it deemed useful to set or reset the learner in its fresh state. (remember build may be called after modifying options that do not necessarily require the learner to restart from a fresh state...) forget is also called by the setTrainingSet method, after calling build(), so it will generally be called TWICE during setTrainingSet! Implemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::ConditionalDensityNet, PLearn::GaussianDistribution, PLearn::GaussianProcessRegressor, PLearn::GaussMix, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::Isomap, PLearn::IsomapTangentLearner, PLearn::KernelPCA, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::LLE, PLearn::PCA, PLearn::SpectralClustering, and PLearn::TangentLearner. Referenced by setTrainingSet().

string PLearn::PLearner::getExperimentDirectory (   )  const [inline]

This returns the currently set expdir (see setExperimentDirectory). Definition at line 162 of file PLearner.h. References expdir. Referenced by PLearn::SequentialModelSelector::matlabSave(), PLearn::SequentialLearner::matlabSave(), and PLearn::ClassifierFromDensity::train().

int PLearn::PLearner::getTestCostIndex (  const string &  costname  )  const

returns the index of the given cost in the vector of testcosts calls PLERROR (throws a PLearnException) if requested cost is not found. Reimplemented in PLearn::GaussianProcessRegressor. Definition at line 226 of file PLearner.cc. References getTestCostNames(), PLearn::TVec< T >::length(), PLERROR, and PLearn::tostring().

virtual TVec<string> PLearn::PLearner::getTestCostNames (   )  const [pure virtual]

*** SUBCLASS WRITING: *** This should return the names of the costs computed by computeCostsFromOutpus Implemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::GaussianProcessRegressor, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::StackedLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::IsomapTangentLearner, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::PCA, and PLearn::TangentLearner. Referenced by getTestCostIndex(), PLearn::SequentialLearner::matlabSave(), PLearn::SequentialLearner::nTestCosts(), and nTestCosts().

int PLearn::PLearner::getTrainCostIndex (  const string &  costname  )  const

returns the index of the given cost in the vector of traincosts (objectives) calls PLERROR (throws a PLearnException) if requested cost is not found. Reimplemented in PLearn::GaussianProcessRegressor. Definition at line 237 of file PLearner.cc. References getTrainCostNames(), PLearn::TVec< T >::length(), PLERROR, and PLearn::tostring().

virtual TVec<string> PLearn::PLearner::getTrainCostNames (   )  const [pure virtual]

*** SUBCLASS WRITING: *** This should return the names of the objective costs that the train method computes and for which it updates the VecStatsCollector train_stats Implemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::ConditionalDensityNet, PLearn::GaussianProcessRegressor, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::StackedLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::IsomapTangentLearner, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::PCA, and PLearn::TangentLearner. Referenced by getTrainCostIndex(), and nTrainCosts().

VMat PLearn::PLearner::getTrainingSet (   )  const [inline]

Returns the current train_set. Definition at line 138 of file PLearner.h. References train_set. Referenced by PLearn::GaussianDistribution::train().

PP<VecStatsCollector> PLearn::PLearner::getTrainStatsCollector (   )  [inline]

Returns the train stats collector. Definition at line 152 of file PLearner.h. References train_stats.

VMat PLearn::PLearner::getValidationSet (   )  const [inline]

Returns the current validation set. Definition at line 144 of file PLearner.h. References validation_set.

int PLearn::PLearner::inputsize (   )  const [virtual]

Default returns train_set->inputsize(). Reimplemented in PLearn::GaussianDistribution, PLearn::SpiralDistribution, PLearn::UniformDistribution, PLearn::EmbeddedLearner, and PLearn::SelectInputSubsetLearner. Definition at line 178 of file PLearner.cc. References inputsize_, and PLERROR. Referenced by PLearn::NNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::LinearRegressor::computeOutput(), PLearn::KPCATangentLearner::computeOutput(), PLearn::IsomapTangentLearner::computeOutput(), PLearn::PDistribution::generateN(), PLearn::TangentLearner::initializeParams(), PLearn::NNet::initializeParams(), PLearn::NeighborhoodSmoothnessNNet::initializeParams(), PLearn::MultiInstanceNNet::initializeParams(), PLearn::GaussianContinuum::initializeParams(), PLearn::KPCATangentLearner::outputsize(), PLearn::IsomapTangentLearner::outputsize(), PLearn::PDistribution::setConditionalFlagsWithoutUpdate(), PLearn::MovingAverage::test(), PLearn::TangentLearner::train(), PLearn::MovingAverage::train(), PLearn::LinearRegressor::train(), PLearn::HistogramDistribution::train(), PLearn::GaussianProcessRegressor::train(), PLearn::GaussianContinuum::train(), PLearn::ConstantRegressor::train(), PLearn::ConditionalDensityNet::train(), PLearn::ClassifierFromDensity::train(), and PLearn::AdaBoost::train().

bool PLearn::PLearner::isStatefulLearner (   )  const [virtual]

Does this PLearner has an internal state? Default: false. Reimplemented in PLearn::StatefulLearner. Definition at line 362 of file PLearner.cc.

void PLearn::PLearner::makeDeepCopyFromShallowCopy (  CopiesMap &  copies  )  [protected, 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::Object. Reimplemented in PLearn::MultiInstanceNNet, PLearn::GaussianDistribution, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::EmbeddedSequentialLearner, PLearn::SequentialLearner, and PLearn::SequentialModelSelector. Definition at line 68 of file PLearner.cc. References PLearn::CopiesMap, PLearn::deepCopyField(), tmp_output, and train_stats.

virtual void PLearn::PLearner::matlabSave (  const string &  matlab_subdir  )  [inline, virtual]

Leads the PLearner to save some data in a matlab 'readable' format. The data will be saved in expdir/matlab_subdir through the global matlabSave function (MatIO.h). Reimplemented in PLearn::SequentialLearner, and PLearn::SequentialModelSelector. Definition at line 350 of file PLearner.h.

int PLearn::PLearner::nTestCosts (   )  const [virtual]

Caches getTestCostNames().size() in an internal variable the first time it is called, and then returns the content of this variable. Reimplemented in PLearn::GaussianProcessRegressor, and PLearn::SequentialLearner. Definition at line 212 of file PLearner.cc. References getTestCostNames(), n_test_costs_, and PLearn::TVec< string >::size(). Referenced by PLearn::StatefulLearner::computeOutput(), and test().

int PLearn::PLearner::nTrainCosts (   )  const [virtual]

Caches getTrainCostNames().size() in an internal variable the first time it is called, and then returns the content of this variable. Reimplemented in PLearn::GaussianProcessRegressor. Definition at line 219 of file PLearner.cc. References getTrainCostNames(), n_train_costs_, and PLearn::TVec< string >::size().

virtual int PLearn::PLearner::outputsize (   )  const [pure virtual]

SUBCLASS WRITING: overload this so that it returns the size of this learner's output, as a function of its inputsize(), targetsize() and set options. Implemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::GaussianProcessRegressor, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::StackedLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::SequentialLearner, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::IsomapTangentLearner, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::PCA, and PLearn::TangentLearner. Referenced by PLearn::StatefulLearner::computeCostsOnly(), computeCostsOnly(), test(), and use().

PLearn::PLearner::PLEARN_DECLARE_ABSTRACT_OBJECT (  PLearner   )

Does the necessary operations to transform a shallow copy (this) into a deep copy by deep-copying all the members that need to be.

void PLearn::PLearner::resetInternalState (   )  [virtual]

If any, reset the internal state Default: do nothing. Definition at line 359 of file PLearner.cc.

void PLearn::PLearner::setExperimentDirectory (  const string &  the_expdir  )  [virtual]

The experiment directory is the directory in which files related to this model are to be saved. If it is an empty string, it is understood to mean that the user doesn't want any file created by this learner. Reimplemented in PLearn::AddCostToLearner, PLearn::TestingLearner, and PLearn::SequentialModelSelector. Definition at line 139 of file PLearner.cc. References PLearn::abspath(), expdir, PLearn::force_mkdir(), and PLERROR.

void PLearn::PLearner::setTrainingSet (  VMat  training_set, bool  call_forget = true )  [virtual]

Declares the train_set Then calls build() and forget() if necessary Note: You shouldn't have to overload this in subclasses, except in maybe to forward the call to an underlying learner. Reimplemented in PLearn::MultiInstanceNNet, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::SelectInputSubsetLearner, PLearn::StackedLearner, PLearn::StatefulLearner, PLearn::TestingLearner, PLearn::SequentialLearner, and PLearn::KernelProjection. Definition at line 151 of file PLearner.cc. References build(), forget(), inputsize_, PLearn::VMat::length(), n_examples, targetsize_, train_set, and weightsize_. Referenced by PLearn::PCA::train().

void PLearn::PLearner::setTrainStatsCollector (  PP< VecStatsCollector >  statscol  )  [virtual]

Sets the statistics collector whose update() method will be called during training. Note: You shouldn't have to overload this in subclasses, except maybe to forward the call to an underlying learner. Reimplemented in PLearn::AddCostToLearner, and PLearn::StackedLearner. Definition at line 174 of file PLearner.cc. References train_stats.

void PLearn::PLearner::setValidationSet (  VMat  validset  )  [virtual]

Set the validation set (optionally) for learners that are able to use it directly. Definition at line 170 of file PLearner.cc. References validation_set.

int PLearn::PLearner::targetsize (   )  const [virtual]

Default returns train_set->targetsize(). Reimplemented in PLearn::EmbeddedLearner. Definition at line 185 of file PLearner.cc. References PLERROR, and targetsize_. Referenced by PLearn::NNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::StatefulLearner::computeOutput(), PLearn::LinearRegressor::outputsize(), PLearn::ConstantRegressor::outputsize(), PLearn::MovingAverage::test(), PLearn::MovingAverage::train(), PLearn::LinearRegressor::train(), PLearn::HistogramDistribution::train(), PLearn::GaussianProcessRegressor::train(), PLearn::EmbeddedSequentialLearner::train(), PLearn::ConstantRegressor::train(), PLearn::ConditionalDensityNet::train(), PLearn::ClassifierFromDensity::train(), and PLearn::AdaBoost::train().

void PLearn::PLearner::test (  VMat  testset, PP< VecStatsCollector >  test_stats, VMat  testoutputs = 0, VMat  testcosts = 0 )  const [virtual]

Performs test on testset, updating test cost statistics, and optionally filling testoutputs and testcosts The default version repeatedly calls computeOutputAndCosts or computeCostsOnly. Reimplemented in PLearn::EmbeddedLearner, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialLearner, and PLearn::SequentialModelSelector. Definition at line 302 of file PLearner.cc. References computeCostsOnly(), computeOutputAndCosts(), PLearn::TVec< T >::fill(), PLearn::VMat::getExample(), PLearn::VMat::length(), nTestCosts(), outputsize(), report_progress, and PLearn::ProgressBar::update().

virtual void PLearn::PLearner::train (   )  [pure virtual]

The role of the train method is to bring the learner up to stage==nstages, updating the stats with training costs measured on-line in the process. *** SUBCLASS WRITING: *** TYPICAL CODE: static Vec input; // static so we don't reallocate/deallocate memory each time... static Vec target; // (but be careful that static means shared!) input.resize(inputsize()); // the train_set's inputsize() target.resize(targetsize()); // the train_set's targetsize() real weight; if(!train_stats) // make a default stats collector, in case there's none train_stats = new VecStatsCollector(); if(nstages<stage) // asking to revert to a previous stage! forget(); // reset the learner to stage=0 while(stage<nstages) { clear statistics of previous epoch train_stats->forget(); ... train for 1 stage, and update train_stats, using train_set->getSample(input, target, weight); and train_stats->update(train_costs) ++stage; train_stats->finalize(); // finalize statistics for this epoch } Implemented in PLearn::AdaBoost, PLearn::ClassifierFromDensity, PLearn::MultiInstanceNNet, PLearn::ConditionalDensityNet, PLearn::GaussianDistribution, PLearn::GaussianProcessRegressor, PLearn::GaussMix, PLearn::HistogramDistribution, PLearn::ManifoldParzen2, PLearn::PDistribution, PLearn::AddCostToLearner, PLearn::EmbeddedLearner, PLearn::NeighborhoodSmoothnessNNet, PLearn::NNet, PLearn::StackedLearner, PLearn::TestingLearner, PLearn::ConstantRegressor, PLearn::LinearRegressor, PLearn::PLS, PLearn::EmbeddedSequentialLearner, PLearn::MovingAverage, PLearn::SequentialLearner, PLearn::SequentialModelSelector, PLearn::EntropyContrast, PLearn::GaussianContinuum, PLearn::IsomapTangentLearner, PLearn::KernelProjection, PLearn::KPCATangentLearner, PLearn::PCA, and PLearn::TangentLearner.

void PLearn::PLearner::use (  VMat  testset, VMat  outputs )  const [virtual]

Computes outputs for the input part of testset. testset is not required to contain a target part. The default version repeatedly calls computeOutput Definition at line 265 of file PLearner.cc. References computeOutput(), PLearn::VMat::getExample(), PLearn::VMat::length(), outputsize(), report_progress, and PLearn::ProgressBar::update().

void PLearn::PLearner::useOnTrain (  Mat &  outputs  )  const [virtual]

Compute the output on the training set of the learner, and save the result in the provided matrix. Definition at line 293 of file PLearner.cc. References PLearn::Mat, PLWARNING, train_set, and PLearn::use().

---

Member Data Documentation

string PLearn::PLearner::expdir

Path of the directory associated with this learner, in which it should save any file it wishes to create. The directory will be created if it does not already exist. If expdir is the empty string (the default), then the learner should not create *any* file. Note that, anyway, most file creation and reporting are handled at the level of the PTester class rather than at the learner's. Definition at line 85 of file PLearner.h. Referenced by build_(), getExperimentDirectory(), and setExperimentDirectory().

int PLearn::PLearner::inputsize_ [protected]

Definition at line 119 of file PLearner.h. Referenced by inputsize(), and setTrainingSet().

int PLearn::PLearner::n_examples [protected]

Reimplemented in PLearn::KernelProjection. Definition at line 119 of file PLearner.h. Referenced by setTrainingSet().

int PLearn::PLearner::n_test_costs_ [mutable, private]

Definition at line 69 of file PLearner.h. Referenced by nTestCosts().

int PLearn::PLearner::n_train_costs_ [mutable, private]

Definition at line 68 of file PLearner.h. Referenced by nTrainCosts().

int PLearn::PLearner::nstages

The meaning of 'stage' is learner-dependent, but for learners whose < training is incremental (such as involving incremental optimization), < it is typically synonym with the number of 'epochs', i.e. the number < of passages of the optimization process through the whole training set, < since the last fresh initialisation. Definition at line 94 of file PLearner.h.

bool PLearn::PLearner::report_progress

should progress in learning and testing be reported in a ProgressBar Definition at line 101 of file PLearner.h. Referenced by test(), and use().

long PLearn::PLearner::seed_

the seed used for the random number generator in initializing the learner (see forget() method). Definition at line 87 of file PLearner.h.

int PLearn::PLearner::stage

0 means untrained, n often means after n epochs or optimization steps, etc.. < The true meaning is learner-dependant. < You should never modify this option directly! < It is the role of forget() to bring it back to 0, < and the role of train() to bring it up to 'nstages'... Definition at line 88 of file PLearner.h.

int PLearn::PLearner::targetsize_ [protected]

Definition at line 119 of file PLearner.h. Referenced by setTrainingSet(), and targetsize().

Vec PLearn::PLearner::tmp_output [mutable, private]

Global storage to save memory allocations. Definition at line 72 of file PLearner.h. Referenced by computeCostsOnly(), and makeDeepCopyFromShallowCopy().

VMat PLearn::PLearner::train_set [protected]

The training set as set by setTrainingSet. Data-sets are seen as matrices whose columns or fields are layed out as follows: a number of input fields, followed by (optional) target fields, followed by a (optional) weight field (to weigh each example). The sizes of those areas are given by the VMatrix options inputsize targetsize, and weightsize, which are typically used by the learner upon building. Definition at line 116 of file PLearner.h. Referenced by getTrainingSet(), setTrainingSet(), and useOnTrain().

PP<VecStatsCollector> PLearn::PLearner::train_stats [protected]

The stats_collector responsible for collecting train cost statistics during training. This is typically set by some external training harness that wants to collect some stats. Definition at line 125 of file PLearner.h. Referenced by getTrainStatsCollector(), makeDeepCopyFromShallowCopy(), and setTrainStatsCollector().

VMat PLearn::PLearner::validation_set [protected]

Reimplemented in PLearn::EntropyContrast. Definition at line 121 of file PLearner.h. Referenced by getValidationSet(), and setValidationSet().

int PLearn::PLearner::verbosity

Definition at line 102 of file PLearner.h.

int PLearn::PLearner::weightsize_ [protected]

Definition at line 119 of file PLearner.h. Referenced by setTrainingSet().

---

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

• PLearner.h
• PLearner.cc

---