Detailed Description

This model trains several multi-layer perceptrons, then averages their weights together in an intelligent manner.

#include <GEnsemble.h>

Inheritance diagram for GClasses::GWag:

Public Member Functions
	GWag (size_t size)
	General-purpose constructor. size specifies the number of models to train and then average together. More...

	GWag (const GDomNode *pNode, GLearnerLoader &ll)
	Deserializing constructor. More...

virtual	~GWag ()

virtual void	clear ()
	See the comment for GSupervisedLearner::clear. More...

GNeuralNetLearner *	model ()
	Returns a pointer to the internal neural network. (You must add at least one layer to this model before training, and you should probably add at least two. Wagging only works with classic layers. You may also use this method to obtain the average neural network after training.) More...

void	noAlign ()
	Specify to average weights without first aligning the nodes. (This should never improve results, but it might be useful for measuring the value of aligning them.) More...

virtual GDomNode *	serialize (GDom *pDoc) const
	Marshal this object into a DOM, which can then be converted to a variety of serial formats. More...

void	setModelCount (size_t n)
	Specify the number of neural networks to average together. More...

Public Member Functions inherited from GClasses::GSupervisedLearner
	GSupervisedLearner ()
	General-purpose constructor. More...

	GSupervisedLearner (const GDomNode *pNode)
	Deserialization constructor. More...

virtual	~GSupervisedLearner ()
	Destructor. More...

void	basicTest (double minAccuracy1, double minAccuracy2, double deviation=1e-6, bool printAccuracy=false, double warnRange=0.035)
	This is a helper method used by the unit tests of several model learners. More...

virtual bool	canGeneralize ()
	Returns true because fully supervised learners have an internal model that allows them to generalize previously unseen rows. More...

void	confusion (GMatrix &features, GMatrix &labels, std::vector< GMatrix * > &stats)
	Generates a confusion matrix containing the total counts of the number of times each value was expected and predicted. (Rows represent target values, and columns represent predicted values.) stats should be an empty vector. This method will resize stats to the number of dimensions in the label vector. The caller is responsible to delete all of the matrices that it puts in this vector. For continuous labels, the value will be NULL. More...

virtual bool	isFilter ()
	Returns false. More...

void	precisionRecall (double *pOutPrecision, size_t nPrecisionSize, GMatrix &features, GMatrix &labels, size_t label, size_t nReps)
	label specifies which output to measure. (It should be 0 if there is only one label dimension.) The measurement will be performed "nReps" times and results averaged together nPrecisionSize specifies the number of points at which the function is sampled pOutPrecision should be an array big enough to hold nPrecisionSize elements for every possible label value. (If the attribute is continuous, it should just be big enough to hold nPrecisionSize elements.) If bLocal is true, it computes the local precision instead of the global precision. More...

const GRelation &	relFeatures ()
	Returns a reference to the feature relation (meta-data about the input attributes). More...

const GRelation &	relLabels ()
	Returns a reference to the label relation (meta-data about the output attributes). More...

double	sumSquaredError (const GMatrix &features, const GMatrix &labels, double *pOutSAE=NULL)
	Computes the sum-squared-error for predicting the labels from the features. For categorical labels, Hamming distance is used. More...

void	train (const GMatrix &features, const GMatrix &labels)
	Call this method to train the model. More...

virtual double	trainAndTest (const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &testFeatures, const GMatrix &testLabels, double *pOutSAE=NULL)
	Trains and tests this learner. Returns sum-squared-error. More...

Public Member Functions inherited from GClasses::GTransducer
	GTransducer ()
	General-purpose constructor. More...

	GTransducer (const GTransducer &that)
	Copy-constructor. Throws an exception to prevent models from being copied by value. More...

virtual	~GTransducer ()

virtual bool	canImplicitlyHandleContinuousFeatures ()
	Returns true iff this algorithm can implicitly handle continuous features. If it cannot, then the GDiscretize transform will be used to convert continuous features to nominal values before passing them to it. More...

virtual bool	canImplicitlyHandleContinuousLabels ()
	Returns true iff this algorithm can implicitly handle continuous labels (a.k.a. regression). If it cannot, then the GDiscretize transform will be used during training to convert nominal labels to continuous values, and to convert nominal predictions back to continuous labels. More...

virtual bool	canImplicitlyHandleMissingFeatures ()
	Returns true iff this algorithm supports missing feature values. If it cannot, then an imputation filter will be used to predict missing values before any feature-vectors are passed to the algorithm. More...

virtual bool	canTrainIncrementally ()
	Returns false because semi-supervised learners cannot be trained incrementally. More...

double	crossValidate (const GMatrix &features, const GMatrix &labels, size_t nFolds, double pOutSAE=NULL, RepValidateCallback pCB=NULL, size_t nRep=0, void pThis=NULL)
	Perform n-fold cross validation on pData. Returns sum-squared error. Uses trainAndTest for each fold. pCB is an optional callback method for reporting intermediate stats. It can be NULL if you don't want intermediate reporting. nRep is just the rep number that will be passed to the callback. pThis is just a pointer that will be passed to the callback for you to use however you want. It doesn't affect this method. if pOutSAE is not NULL, the sum absolute error will be placed there. More...

GTransducer &	operator= (const GTransducer &other)
	Throws an exception to prevent models from being copied by value. More...

GRand &	rand ()
	Returns a reference to the random number generator associated with this object. For example, you could use it to change the random seed, to make this algorithm behave differently. This might be important, for example, in an ensemble of learners. More...

double	repValidate (const GMatrix &features, const GMatrix &labels, size_t reps, size_t nFolds, double pOutSAE=NULL, RepValidateCallback pCB=NULL, void pThis=NULL)
	Perform cross validation "nReps" times and return the average score. pCB is an optional callback method for reporting intermediate stats It can be NULL if you don't want intermediate reporting. pThis is just a pointer that will be passed to the callback for you to use however you want. It doesn't affect this method. if pOutSAE is not NULL, the sum absolute error will be placed there. More...

virtual bool	supportedFeatureRange (double pOutMin, double pOutMax)
	Returns true if this algorithm supports any feature value, or if it does not implicitly handle continuous features. If a limited range of continuous values is supported, returns false and sets pOutMin and pOutMax to specify the range. More...

virtual bool	supportedLabelRange (double pOutMin, double pOutMax)
	Returns true if this algorithm supports any label value, or if it does not implicitly handle continuous labels. If a limited range of continuous values is supported, returns false and sets pOutMin and pOutMax to specify the range. More...

std::unique_ptr< GMatrix >	transduce (const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2)
	Predicts a set of labels to correspond with features2, such that these labels will be consistent with the patterns exhibited by features1 and labels1. More...

void	transductiveConfusionMatrix (const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &testFeatures, const GMatrix &testLabels, std::vector< GMatrix * > &stats)
	Makes a confusion matrix for a transduction algorithm. More...

Protected Member Functions
virtual bool	canImplicitlyHandleNominalFeatures ()
	See the comment for GSupervisedLearner::canImplicitlyHandleNominalFeatures. More...

virtual bool	canImplicitlyHandleNominalLabels ()
	See the comment for GSupervisedLearner::canImplicitlyHandleNominalLabels. More...

virtual void	predict (const GVec &in, GVec &out)
	See the comment for GSupervisedLearner::predict. More...

virtual void	predictDistribution (const GVec &in, GPrediction *pOut)
	See the comment for GSupervisedLearner::predictDistribution. More...

virtual void	trainInner (const GMatrix &features, const GMatrix &labels)
	See the comment for GSupervisedLearner::trainInner. More...

Protected Member Functions inherited from GClasses::GSupervisedLearner
GDomNode *	baseDomNode (GDom pDoc, const char szClassName) const
	Child classes should use this in their implementation of serialize. More...

size_t	precisionRecallContinuous (GPrediction pOutput, double pFunc, GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &testFeatures, GMatrix &testLabels, size_t label)
	This is a helper method used by precisionRecall. More...

size_t	precisionRecallNominal (GPrediction pOutput, double pFunc, GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &testFeatures, GMatrix &testLabels, size_t label, int value)
	This is a helper method used by precisionRecall. More...

void	setupFilters (const GMatrix &features, const GMatrix &labels)
	This method determines which data filters (normalize, discretize, and/or nominal-to-cat) are needed and trains them. More...

virtual std::unique_ptr< GMatrix >	transduceInner (const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2)
	See GTransducer::transduce. More...

Protected Attributes
size_t	m_models

bool	m_noAlign

GNeuralNetLearner *	m_pNN

Protected Attributes inherited from GClasses::GSupervisedLearner
GRelation *	m_pRelFeatures

GRelation *	m_pRelLabels

Protected Attributes inherited from GClasses::GTransducer
GRand	m_rand

Additional Inherited Members
Static Public Member Functions inherited from GClasses::GSupervisedLearner
static void	test ()
	Runs some unit tests related to supervised learning. Throws an exception if any problems are found. More...

Static Protected Member Functions inherited from GClasses::GSupervisedLearner
static void	addInterpolatedFunction (double pOut, size_t nOutVals, double pIn, size_t nInVals)
	Adds the function pIn to pOut after interpolating pIn to be the same size as pOut. (This is a helper-function used by precisionRecall.) More...

Constructor & Destructor Documentation

GClasses::GWag::GWag ( size_t size )

General-purpose constructor. size specifies the number of models to train and then average together.

GClasses::GWag::GWag	(	const GDomNode *	pNode,
		GLearnerLoader &	ll
	)

Deserializing constructor.

virtual GClasses::GWag::~GWag ( )

virtual

Member Function Documentation

virtual bool GClasses::GWag::canImplicitlyHandleNominalFeatures ( )

inlineprotectedvirtual

See the comment for GSupervisedLearner::canImplicitlyHandleNominalFeatures.

Reimplemented from GClasses::GTransducer.

virtual bool GClasses::GWag::canImplicitlyHandleNominalLabels ( )

inlineprotectedvirtual

See the comment for GSupervisedLearner::canImplicitlyHandleNominalLabels.

Reimplemented from GClasses::GTransducer.

virtual void GClasses::GWag::clear ( )

virtual

See the comment for GSupervisedLearner::clear.

Implements GClasses::GSupervisedLearner.

GNeuralNetLearner* GClasses::GWag::model ( )

inline

Returns a pointer to the internal neural network. (You must add at least one layer to this model before training, and you should probably add at least two. Wagging only works with classic layers. You may also use this method to obtain the average neural network after training.)

void GClasses::GWag::noAlign ( )

inline

Specify to average weights without first aligning the nodes. (This should never improve results, but it might be useful for measuring the value of aligning them.)