| addInterpolatedFunction(double *pOut, size_t nOutVals, double *pIn, size_t nInVals) | GClasses::GSupervisedLearner | protectedstatic |
| autoFilter() const | GClasses::GNeuralDecomposition | inline |
| 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::GNeuralDecomposition | protectedvirtual |
| GClasses::GIncrementalLearner::beginIncrementalLearningInner(const GMatrix &features, const GMatrix &labels) | GClasses::GIncrementalLearner | inlineprotectedvirtual |
| canGeneralize() | GClasses::GSupervisedLearner | inlinevirtual |
| canImplicitlyHandleContinuousFeatures() | GClasses::GTransducer | inlinevirtual |
| canImplicitlyHandleContinuousLabels() | GClasses::GTransducer | inlinevirtual |
| canImplicitlyHandleMissingFeatures() | GClasses::GTransducer | inlinevirtual |
| canImplicitlyHandleNominalFeatures() | GClasses::GTransducer | inlinevirtual |
| canImplicitlyHandleNominalLabels() | GClasses::GTransducer | inlinevirtual |
| canTrainIncrementally() | GClasses::GIncrementalLearner | inlinevirtual |
| clear() | GClasses::GNeuralDecomposition | inlinevirtual |
| clearFrozen() | GClasses::GNeuralDecomposition | |
| 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 | |
| epochs() const | GClasses::GNeuralDecomposition | inline |
| extrapolate(double start=1.0, double length=1.0, double step=0.0002, bool outputFeatures=false) | GClasses::GNeuralDecomposition | virtual |
| extrapolate(const GMatrix &features) | GClasses::GNeuralDecomposition | virtual |
| featureBias() const | GClasses::GNeuralDecomposition | inline |
| featureScale() const | GClasses::GNeuralDecomposition | inline |
| filterLogarithm() const | GClasses::GNeuralDecomposition | inline |
| freeze() | GClasses::GNeuralDecomposition | |
| GIncrementalLearner() | GClasses::GIncrementalLearner | inline |
| GIncrementalLearner(const GDomNode *pNode) | GClasses::GIncrementalLearner | inline |
| GNeuralDecomposition() | GClasses::GNeuralDecomposition | |
| GNeuralDecomposition(const GDomNode *pNode) | GClasses::GNeuralDecomposition | |
| GSupervisedLearner() | GClasses::GSupervisedLearner | |
| GSupervisedLearner(const GDomNode *pNode) | GClasses::GSupervisedLearner | |
| GTransducer() | GClasses::GTransducer | |
| GTransducer(const GTransducer &that) | GClasses::GTransducer | inline |
| isFilter() | GClasses::GIncrementalLearner | inlinevirtual |
| learningRate() const | GClasses::GNeuralDecomposition | inline |
| m_pRelFeatures | GClasses::GSupervisedLearner | protected |
| m_pRelLabels | GClasses::GSupervisedLearner | protected |
| nn() const | GClasses::GNeuralDecomposition | inline |
| operator=(const GTransducer &other) | GClasses::GTransducer | inline |
| outputBias() const | GClasses::GNeuralDecomposition | inline |
| outputScale() const | GClasses::GNeuralDecomposition | 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::GNeuralDecomposition | virtual |
| predictDistribution(const GVec &in, GPrediction *pOut) | GClasses::GNeuralDecomposition | virtual |
| rand() | GClasses::GTransducer | inline |
| regularization() const | GClasses::GNeuralDecomposition | 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 | |
| restoreFrozen() | GClasses::GNeuralDecomposition | |
| serialize(GDom *pDoc) const | GClasses::GNeuralDecomposition | virtual |
| setAutoFilter(bool auto_Filter) | GClasses::GNeuralDecomposition | inline |
| setEpochs(size_t newepochs) | GClasses::GNeuralDecomposition | inline |
| setFeatureBias(double newfeatureBias) | GClasses::GNeuralDecomposition | inline |
| setFeatureScale(double newfeatureScale) | GClasses::GNeuralDecomposition | inline |
| setFilterLogarithm(bool filter_Logarithm) | GClasses::GNeuralDecomposition | inline |
| setLearningRate(double newlearningRate) | GClasses::GNeuralDecomposition | inline |
| setLinearUnits(size_t linearUnits) | GClasses::GNeuralDecomposition | inline |
| setLockPairs(bool lockPairs) | GClasses::GNeuralDecomposition | inline |
| setOutputBias(double newoutputBias) | GClasses::GNeuralDecomposition | inline |
| setOutputScale(double newoutputScale) | GClasses::GNeuralDecomposition | inline |
| setRegularization(double newregularization) | GClasses::GNeuralDecomposition | inline |
| setSigmoidUnits(size_t sigmoidUnits) | GClasses::GNeuralDecomposition | inline |
| setSinusoidUnits(size_t sinusoidUnits) | GClasses::GNeuralDecomposition | inline |
| setSoftplusUnits(size_t softplusUnits) | GClasses::GNeuralDecomposition | 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::GNeuralDecomposition | 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 &pOut) | GClasses::GNeuralDecomposition | virtual |
| trainInner(const GMatrix &features, const GMatrix &labels) | GClasses::GNeuralDecomposition | protectedvirtual |
| trainOnSeries(const GMatrix &series) | GClasses::GNeuralDecomposition | virtual |
| trainSparse(GSparseMatrix &features, GMatrix &labels) | GClasses::GNeuralDecomposition | 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 |
| ~GNeuralDecomposition() | GClasses::GNeuralDecomposition | virtual |
| ~GSupervisedLearner() | GClasses::GSupervisedLearner | virtual |
| ~GTransducer() | GClasses::GTransducer | virtual |
1.8.11