, including all inherited members.
accuracy(GMatrix &features, GMatrix &labels, double *pOutResults, std::vector< GMatrix * > *pNominalLabelStats=NULL) | GClasses::GSupervisedLearner | |
addLayer(size_t nNodes) | GClasses::GNeuralNet | |
addNode(size_t layer) | GClasses::GNeuralNet | |
align(GNeuralNet &that) | GClasses::GNeuralNet | |
autoTune(GMatrix &features, GMatrix &labels) | GClasses::GNeuralNet | |
backProp() | GClasses::GNeuralNet | [inline] |
backPropTargetFunction() | GClasses::GNeuralNet | [inline] |
baseDomNode(GDom *pDoc, const char *szClassName) | GClasses::GSupervisedLearner | [protected] |
basicTest(double minAccuracy1, double minAccuracy2, double deviation=1e-6, bool printAccuracy=false) | GClasses::GSupervisedLearner | |
beginIncrementalLearning(sp_relation &pFeatureRel, sp_relation &pLabelRel) | GClasses::GIncrementalLearner | |
beginIncrementalLearningInner(sp_relation &pFeatureRel, sp_relation &pLabelRel) | GClasses::GNeuralNet | [protected, virtual] |
calibrate(GMatrix &features, GMatrix &labels) | GClasses::GSupervisedLearner | |
canGeneralize() | GClasses::GSupervisedLearner | [inline, virtual] |
canImplicitlyHandleContinuousFeatures() | GClasses::GTransducer | [inline, protected, virtual] |
canImplicitlyHandleContinuousLabels() | GClasses::GTransducer | [inline, protected, virtual] |
canImplicitlyHandleMissingFeatures() | GClasses::GNeuralNet | [inline, protected, virtual] |
canImplicitlyHandleNominalFeatures() | GClasses::GNeuralNet | [inline, protected, virtual] |
canImplicitlyHandleNominalLabels() | GClasses::GNeuralNet | [inline, protected, virtual] |
canTrainIncrementally() | GClasses::GIncrementalLearner | [inline, virtual] |
clear() | GClasses::GNeuralNet | [inline, virtual] |
clipWeights(double max) | GClasses::GNeuralNet | |
copyPrediction(double *pOut) | GClasses::GNeuralNet | |
copyStructure(GNeuralNet *pOther) | GClasses::GNeuralNet | |
copyWeights(GNeuralNet *pOther) | GClasses::GNeuralNet | |
countWeights() | GClasses::GNeuralNet | |
cross_entropy enum value | GClasses::GNeuralNet | |
crossValidate(GMatrix &features, GMatrix &labels, size_t nFolds, RepValidateCallback pCB=NULL, size_t nRep=0, void *pThis=NULL) | GClasses::GTransducer | |
decayWeights(double lambda, double gamma=1.0) | GClasses::GNeuralNet | |
decayWeightsSingleOutput(size_t output, double lambda) | GClasses::GNeuralNet | |
dropNode(size_t layer, size_t node) | GClasses::GNeuralNet | |
featureDims() | GClasses::GSupervisedLearner | [inline] |
featureFilter() | GClasses::GSupervisedLearner | [inline] |
forwardProp(const double *pInputs) | GClasses::GNeuralNet | |
forwardPropSingleOutput(const double *pInputs, size_t output) | GClasses::GNeuralNet | |
GBackProp class | GClasses::GNeuralNet | [friend] |
GIncrementalLearner(GRand &rand) | GClasses::GIncrementalLearner | [inline] |
GIncrementalLearner(GDomNode *pNode, GLearnerLoader &ll) | GClasses::GIncrementalLearner | [inline] |
GNeuralNet(GRand &rand) | GClasses::GNeuralNet | |
GNeuralNet(GDomNode *pNode, GLearnerLoader &ll) | GClasses::GNeuralNet | |
GSupervisedLearner(GRand &rand) | GClasses::GSupervisedLearner | |
GSupervisedLearner(GDomNode *pNode, GLearnerLoader &ll) | GClasses::GSupervisedLearner | |
GTransducer(GRand &rand) | GClasses::GTransducer | |
hasTrainingBegun() | GClasses::GNeuralNet | [inline] |
heuristicValidate(GMatrix &features, GMatrix &labels) | GClasses::GTransducer | |
improvementThresh() | GClasses::GNeuralNet | [inline] |
internalTraininGMatrix() | GClasses::GNeuralNet | |
internalValidationData() | GClasses::GNeuralNet | |
isFilter() | GClasses::GIncrementalLearner | [inline, virtual] |
labelDims() | GClasses::GSupervisedLearner | [inline] |
labelFilter() | GClasses::GSupervisedLearner | [inline] |
layer(size_t n) | GClasses::GNeuralNet | [inline] |
layerCount() | GClasses::GNeuralNet | [inline] |
learningRate() | GClasses::GNeuralNet | [inline] |
m_activationFunctions | GClasses::GNeuralNet | [protected] |
m_autoFilter | GClasses::GSupervisedLearner | [protected] |
m_backPropTargetFunction | GClasses::GNeuralNet | [protected] |
m_epochsPerValidationCheck | GClasses::GNeuralNet | [protected] |
m_featureDims | GClasses::GSupervisedLearner | [protected] |
m_internalFeatureDims | GClasses::GNeuralNet | [protected] |
m_internalLabelDims | GClasses::GNeuralNet | [protected] |
m_labelDims | GClasses::GSupervisedLearner | [protected] |
m_layers | GClasses::GNeuralNet | [protected] |
m_learningRate | GClasses::GNeuralNet | [protected] |
m_minImprovement | GClasses::GNeuralNet | [protected] |
m_momentum | GClasses::GNeuralNet | [protected] |
m_pActivationFunction | GClasses::GNeuralNet | [protected] |
m_pBackProp | GClasses::GNeuralNet | [protected] |
m_pCalibrations | GClasses::GSupervisedLearner | [protected] |
m_pFeatureFilter | GClasses::GSupervisedLearner | [protected] |
m_pLabelFilter | GClasses::GSupervisedLearner | [protected] |
m_rand | GClasses::GTransducer | [protected] |
m_useInputBias | GClasses::GNeuralNet | [protected] |
m_validationPortion | GClasses::GNeuralNet | [protected] |
momentum() | GClasses::GNeuralNet | [inline] |
perturbAllWeights(double deviation) | GClasses::GNeuralNet | |
physical enum value | GClasses::GNeuralNet | |
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 double *pIn, double *pOut) | GClasses::GSupervisedLearner | |
predictDistribution(const double *pIn, GPrediction *pOut) | GClasses::GSupervisedLearner | |
predictDistributionInner(const double *pIn, GPrediction *pOut) | GClasses::GNeuralNet | [protected, virtual] |
predictInner(const double *pIn, double *pOut) | GClasses::GNeuralNet | [protected, virtual] |
rand() | GClasses::GTransducer | [inline] |
releaseTrainingJunk() | GClasses::GNeuralNet | |
repValidate(GMatrix &features, GMatrix &labels, size_t reps, size_t nFolds, RepValidateCallback pCB=NULL, void *pThis=NULL) | GClasses::GTransducer | |
serialize(GDom *pDoc) | GClasses::GNeuralNet | [virtual] |
setActivationFunction(GActivationFunction *pSF, bool hold) | GClasses::GNeuralNet | |
setAutoFilter(bool b) | GClasses::GSupervisedLearner | [inline] |
setBackPropTargetFunction(TargetFunction eTF) | GClasses::GNeuralNet | [inline] |
setErrorOnOutputLayer(const double *pTarget, TargetFunction eTargetFunction=squared_error) | GClasses::GNeuralNet | |
setErrorSingleOutput(double target, size_t output, TargetFunction eTargetFunction=squared_error) | GClasses::GNeuralNet | |
setFeatureFilter(GTwoWayIncrementalTransform *pFilter) | GClasses::GSupervisedLearner | |
setImprovementThresh(double d) | GClasses::GNeuralNet | [inline] |
setLabelFilter(GTwoWayIncrementalTransform *pFilter) | GClasses::GSupervisedLearner | |
setLearningRate(double d) | GClasses::GNeuralNet | [inline] |
setMomentum(double d) | GClasses::GNeuralNet | [inline] |
setupFilters(GMatrix &features, GMatrix &labels) | GClasses::GSupervisedLearner | [protected] |
setUseInputBias(bool b) | GClasses::GNeuralNet | [inline] |
setValidationPortion(double d) | GClasses::GNeuralNet | [inline] |
setWeights(const double *pWeights) | GClasses::GNeuralNet | |
setWindowSize(size_t n) | GClasses::GNeuralNet | [inline] |
sign enum value | GClasses::GNeuralNet | |
squared_error enum value | GClasses::GNeuralNet | |
sumSquaredPredictionError(const double *pTarget) | GClasses::GNeuralNet | |
supportedFeatureRange(double *pOutMin, double *pOutMax) | GClasses::GNeuralNet | [protected, virtual] |
supportedLabelRange(double *pOutMin, double *pOutMax) | GClasses::GNeuralNet | [protected, virtual] |
swapNodes(size_t layer, size_t a, size_t b) | GClasses::GNeuralNet | |
TargetFunction enum name | GClasses::GNeuralNet | |
test() | GClasses::GNeuralNet | [static] |
train(GMatrix &features, GMatrix &labels) | GClasses::GSupervisedLearner | |
trainAndTest(GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &testFeatures, GMatrix &testLabels, double *pOutResults, std::vector< GMatrix * > *pNominalLabelStats=NULL) | GClasses::GSupervisedLearner | [virtual] |
trainIncremental(const double *pIn, const double *pOut) | GClasses::GIncrementalLearner | |
trainIncrementalInner(const double *pIn, const double *pOut) | GClasses::GNeuralNet | [protected, virtual] |
trainInner(GMatrix &features, GMatrix &labels) | GClasses::GNeuralNet | [protected, virtual] |
trainSparse(GSparseMatrix &features, GMatrix &labels) | GClasses::GNeuralNet | [virtual] |
trainWithValidation(GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &validateFeatures, GMatrix &validateLabels) | GClasses::GNeuralNet | |
transduce(GMatrix &features1, GMatrix &labels1, GMatrix &features2) | GClasses::GTransducer | |
transduceInner(GMatrix &features1, GMatrix &labels1, GMatrix &features2) | GClasses::GSupervisedLearner | [protected, virtual] |
useInputBias() | GClasses::GNeuralNet | [inline] |
validationSquaredError(GMatrix &features, GMatrix &labels) | GClasses::GNeuralNet | [protected] |
weights(double *pOutWeights) | GClasses::GNeuralNet | |
windowSize() | GClasses::GNeuralNet | [inline] |
~GIncrementalLearner() | GClasses::GIncrementalLearner | [inline, virtual] |
~GNeuralNet() | GClasses::GNeuralNet | [virtual] |
~GSupervisedLearner() | GClasses::GSupervisedLearner | [virtual] |
~GTransducer() | GClasses::GTransducer | [virtual] |