GClasses::GNaiveBayes Member List

This is the complete list of members for GClasses::GNaiveBayes, including all inherited members.

addInterpolatedFunction(double *pOut, size_t nOutVals, double *pIn, size_t nInVals)	GClasses::GSupervisedLearner	protectedstatic
autoTune(GMatrix &features, GMatrix &labels)	GClasses::GNaiveBayes
baseDomNode(GDom *pDoc, const char *szClassName) const	GClasses::GSupervisedLearner	protected
basicTest(double minAccuracy1, double minAccuracy2, double deviation=1e-6, bool printAccuracy=false, double warnRange=0.035)	GClasses::GSupervisedLearner
beginIncrementalLearning(const GRelation &featureRel, const GRelation &labelRel)	GClasses::GIncrementalLearner
beginIncrementalLearning(const GMatrix &features, const GMatrix &labels)	GClasses::GIncrementalLearner
beginIncrementalLearningInner(const GRelation &featureRel, const GRelation &labelRel)	GClasses::GNaiveBayes	protectedvirtual
GClasses::GIncrementalLearner::beginIncrementalLearningInner(const GMatrix &features, const GMatrix &labels)	GClasses::GIncrementalLearner	inlineprotectedvirtual
canGeneralize()	GClasses::GSupervisedLearner	inlinevirtual
canImplicitlyHandleContinuousFeatures()	GClasses::GNaiveBayes	inlineprotectedvirtual
canImplicitlyHandleContinuousLabels()	GClasses::GNaiveBayes	inlineprotectedvirtual
canImplicitlyHandleMissingFeatures()	GClasses::GTransducer	inlinevirtual
canImplicitlyHandleNominalFeatures()	GClasses::GTransducer	inlinevirtual
canImplicitlyHandleNominalLabels()	GClasses::GTransducer	inlinevirtual
canTrainIncrementally()	GClasses::GIncrementalLearner	inlinevirtual
clear()	GClasses::GNaiveBayes	virtual
confusion(GMatrix &features, GMatrix &labels, std::vector< GMatrix * > &stats)	GClasses::GSupervisedLearner
crossValidate(const GMatrix &features, const GMatrix &labels, size_t nFolds, double *pOutSAE=NULL, RepValidateCallback pCB=NULL, size_t nRep=0, void *pThis=NULL)	GClasses::GTransducer
equivalentSampleSize()	GClasses::GNaiveBayes	inline
GIncrementalLearner()	GClasses::GIncrementalLearner	inline
GIncrementalLearner(const GDomNode *pNode)	GClasses::GIncrementalLearner	inline
GNaiveBayes()	GClasses::GNaiveBayes
GNaiveBayes(const GDomNode *pNode)	GClasses::GNaiveBayes
GSupervisedLearner()	GClasses::GSupervisedLearner
GSupervisedLearner(const GDomNode *pNode)	GClasses::GSupervisedLearner
GTransducer()	GClasses::GTransducer
GTransducer(const GTransducer &that)	GClasses::GTransducer	inline
isFilter()	GClasses::GIncrementalLearner	inlinevirtual
m_equivalentSampleSize	GClasses::GNaiveBayes	protected
m_nSampleCount	GClasses::GNaiveBayes	protected
m_pOutputs	GClasses::GNaiveBayes	protected
m_pRelFeatures	GClasses::GSupervisedLearner	protected
m_pRelLabels	GClasses::GSupervisedLearner	protected
m_rand	GClasses::GTransducer	protected
operator=(const GTransducer &other)	GClasses::GTransducer	inline
precisionRecall(double *pOutPrecision, size_t nPrecisionSize, GMatrix &features, GMatrix &labels, size_t label, size_t nReps)	GClasses::GSupervisedLearner
precisionRecallContinuous(GPrediction *pOutput, double *pFunc, GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &testFeatures, GMatrix &testLabels, size_t label)	GClasses::GSupervisedLearner	protected
precisionRecallNominal(GPrediction *pOutput, double *pFunc, GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &testFeatures, GMatrix &testLabels, size_t label, int value)	GClasses::GSupervisedLearner	protected
predict(const GVec &in, GVec &out)	GClasses::GNaiveBayes	virtual
predictDistribution(const GVec &in, GPrediction *pOut)	GClasses::GNaiveBayes	virtual
rand()	GClasses::GTransducer	inline
relFeatures()	GClasses::GSupervisedLearner
relLabels()	GClasses::GSupervisedLearner
repValidate(const GMatrix &features, const GMatrix &labels, size_t reps, size_t nFolds, double *pOutSAE=NULL, RepValidateCallback pCB=NULL, void *pThis=NULL)	GClasses::GTransducer
serialize(GDom *pDoc) const	GClasses::GNaiveBayes	virtual
setEquivalentSampleSize(double d)	GClasses::GNaiveBayes	inline
setupFilters(const GMatrix &features, const GMatrix &labels)	GClasses::GSupervisedLearner	protected
sumSquaredError(const GMatrix &features, const GMatrix &labels, double *pOutSAE=NULL)	GClasses::GSupervisedLearner
supportedFeatureRange(double *pOutMin, double *pOutMax)	GClasses::GTransducer	inlinevirtual
supportedLabelRange(double *pOutMin, double *pOutMax)	GClasses::GTransducer	inlinevirtual
test()	GClasses::GNaiveBayes	static
train(const GMatrix &features, const GMatrix &labels)	GClasses::GSupervisedLearner
trainAndTest(const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &testFeatures, const GMatrix &testLabels, double *pOutSAE=NULL)	GClasses::GSupervisedLearner	virtual
trainIncremental(const GVec &in, const GVec &out)	GClasses::GNaiveBayes	virtual
trainInner(const GMatrix &features, const GMatrix &labels)	GClasses::GNaiveBayes	protectedvirtual
trainSparse(GSparseMatrix &features, GMatrix &labels)	GClasses::GNaiveBayes	virtual
transduce(const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2)	GClasses::GTransducer
transduceInner(const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2)	GClasses::GSupervisedLearner	protectedvirtual
transductiveConfusionMatrix(const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &testFeatures, const GMatrix &testLabels, std::vector< GMatrix * > &stats)	GClasses::GTransducer
~GIncrementalLearner()	GClasses::GIncrementalLearner	inlinevirtual
~GNaiveBayes()	GClasses::GNaiveBayes	virtual
~GSupervisedLearner()	GClasses::GSupervisedLearner	virtual
~GTransducer()	GClasses::GTransducer	virtual