- Generated on Mon Dec 8 2014 19:46:21 for GClasses by
1.8.7
|
GClasses
|
This is the complete list of members for GClasses::GNeuralNet, including all inherited members.
| addLayer(GNeuralNetLayer *pLayer, size_t position=INVALID_INDEX) | GClasses::GNeuralNet | |
| align(const GNeuralNet &that) | GClasses::GNeuralNet | |
| autoTune(GMatrix &features, GMatrix &labels) | GClasses::GNeuralNet | |
| backpropagate(const double *pTarget, size_t startLayer=INVALID_INDEX) | GClasses::GNeuralNet | |
| backpropagateSingleOutput(size_t outputNode, double target, size_t startLayer=INVALID_INDEX) | GClasses::GNeuralNet | |
| 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 | |
| beginIncrementalLearningInner(const GRelation &featureRel, const GRelation &labelRel) | GClasses::GNeuralNet | protectedvirtual |
| bleedWeightsL1(double beta) | GClasses::GNeuralNet | |
| bleedWeightsL2(double beta) | GClasses::GNeuralNet | |
| canGeneralize() | GClasses::GSupervisedLearner | inlinevirtual |
| canImplicitlyHandleContinuousFeatures() | GClasses::GTransducer | inlinevirtual |
| canImplicitlyHandleContinuousLabels() | GClasses::GTransducer | inlinevirtual |
| canImplicitlyHandleMissingFeatures() | GClasses::GNeuralNet | inlinevirtual |
| canImplicitlyHandleNominalFeatures() | GClasses::GNeuralNet | inlinevirtual |
| canImplicitlyHandleNominalLabels() | GClasses::GNeuralNet | inlinevirtual |
| canTrainIncrementally() | GClasses::GIncrementalLearner | inlinevirtual |
| clear() | GClasses::GNeuralNet | virtual |
| compressFeatures(GMatrix &features) | GClasses::GNeuralNet | |
| confusion(GMatrix &features, GMatrix &labels, std::vector< GMatrix * > &stats) | GClasses::GSupervisedLearner | |
| containIntrinsics(GMatrix &intrinsics) | GClasses::GNeuralNet | |
| contractWeights(double factor, bool contractBiases) | GClasses::GNeuralNet | |
| copyPrediction(double *pOut) | GClasses::GNeuralNet | |
| copyStructure(GNeuralNet *pOther) | GClasses::GNeuralNet | |
| copyWeights(GNeuralNet *pOther) | GClasses::GNeuralNet | |
| countWeights() const | GClasses::GNeuralNet | |
| countWeights(size_t layer) const | GClasses::GNeuralNet | |
| crossValidate(const GMatrix &features, const GMatrix &labels, size_t nFolds, RepValidateCallback pCB=NULL, size_t nRep=0, void *pThis=NULL) | GClasses::GTransducer | |
| descendGradient(const double *pFeatures, double learningRate, double momentum) | GClasses::GNeuralNet | |
| descendGradientSingleOutput(size_t outputNeuron, const double *pFeatures, double learningRate, double momentum) | GClasses::GNeuralNet | |
| diminishWeights(double amount, bool regularizeBiases=true, size_t startLayer=0, size_t layerCount=INVALID_INDEX) | GClasses::GNeuralNet | |
| forwardProp(const double *pInputs, size_t maxLayers=INVALID_INDEX) | GClasses::GNeuralNet | |
| forwardPropSingleOutput(const double *pInputs, size_t output) | GClasses::GNeuralNet | |
| fourier(GMatrix &series, double period=1.0) | GClasses::GNeuralNet | static |
| GIncrementalLearner() | GClasses::GIncrementalLearner | inline |
| GIncrementalLearner(GDomNode *pNode, GLearnerLoader &ll) | GClasses::GIncrementalLearner | inline |
| GNeuralNet() | GClasses::GNeuralNet | |
| GNeuralNet(GDomNode *pNode, GLearnerLoader &ll) | GClasses::GNeuralNet | |
| gradientOfInputs(double *pOutGradient) | GClasses::GNeuralNet | |
| gradientOfInputsSingleOutput(size_t outputNeuron, double *pOutGradient) | GClasses::GNeuralNet | |
| GSupervisedLearner() | GClasses::GSupervisedLearner | |
| GSupervisedLearner(GDomNode *pNode, GLearnerLoader &ll) | GClasses::GSupervisedLearner | |
| GTransducer() | GClasses::GTransducer | |
| GTransducer(const GTransducer &that) | GClasses::GTransducer | inline |
| improvementThresh() | GClasses::GNeuralNet | inline |
| internalTraininGMatrix() | GClasses::GNeuralNet | |
| internalValidationData() | GClasses::GNeuralNet | |
| invertNode(size_t layer, size_t node) | GClasses::GNeuralNet | |
| isFilter() | GClasses::GIncrementalLearner | inlinevirtual |
| layer(size_t n) | GClasses::GNeuralNet | inline |
| layerCount() const | GClasses::GNeuralNet | inline |
| learningRate() const | GClasses::GNeuralNet | inline |
| m_epochsPerValidationCheck | GClasses::GNeuralNet | protected |
| m_layers | GClasses::GNeuralNet | protected |
| m_learningRate | GClasses::GNeuralNet | protected |
| m_minImprovement | GClasses::GNeuralNet | protected |
| m_momentum | GClasses::GNeuralNet | protected |
| m_pRelFeatures | GClasses::GSupervisedLearner | protected |
| m_pRelLabels | GClasses::GSupervisedLearner | protected |
| m_rand | GClasses::GTransducer | protected |
| m_useInputBias | GClasses::GNeuralNet | protected |
| m_validationPortion | GClasses::GNeuralNet | protected |
| maxNorm(double max) | GClasses::GNeuralNet | virtual |
| momentum() const | GClasses::GNeuralNet | inline |
| operator=(const GTransducer &other) | GClasses::GTransducer | inline |
| outputLayer() | GClasses::GNeuralNet | inline |
| perturbAllWeights(double deviation) | 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::GNeuralNet | virtual |
| predictDistribution(const double *pIn, GPrediction *pOut) | GClasses::GNeuralNet | virtual |
| pretrainWithAutoencoders(const GMatrix &features, size_t maxLayers=INVALID_INDEX) | GClasses::GNeuralNet | |
| printWeights(std::ostream &stream) | GClasses::GNeuralNet | |
| rand() | GClasses::GTransducer | inline |
| releaseLayer(size_t index) | GClasses::GNeuralNet | |
| relFeatures() | GClasses::GSupervisedLearner | |
| relLabels() | GClasses::GSupervisedLearner | |
| repValidate(const GMatrix &features, const GMatrix &labels, size_t reps, size_t nFolds, RepValidateCallback pCB=NULL, void *pThis=NULL) | GClasses::GTransducer | |
| scaleWeights(double factor, bool scaleBiases=true, size_t startLayer=0, size_t layerCount=INVALID_INDEX) | GClasses::GNeuralNet | |
| scaleWeightsSingleOutput(size_t output, double lambda) | GClasses::GNeuralNet | |
| serialize(GDom *pDoc) const | GClasses::GNeuralNet | virtual |
| setImprovementThresh(double d) | GClasses::GNeuralNet | inline |
| setLearningRate(double d) | GClasses::GNeuralNet | inline |
| setMomentum(double d) | GClasses::GNeuralNet | inline |
| setupFilters(const GMatrix &features, const GMatrix &labels) | GClasses::GSupervisedLearner | protected |
| setUseInputBias(bool b) | GClasses::GNeuralNet | inline |
| setValidationPortion(double d) | GClasses::GNeuralNet | inline |
| setWeights(const double *pWeights) | GClasses::GNeuralNet | |
| setWeights(const double *pWeights, size_t layer) | GClasses::GNeuralNet | |
| setWindowSize(size_t n) | GClasses::GNeuralNet | inline |
| sumSquaredError(const GMatrix &features, const GMatrix &labels) | GClasses::GSupervisedLearner | |
| sumSquaredPredictionError(const double *pTarget) | GClasses::GNeuralNet | |
| supportedFeatureRange(double *pOutMin, double *pOutMax) | GClasses::GNeuralNet | virtual |
| supportedLabelRange(double *pOutMin, double *pOutMax) | GClasses::GNeuralNet | virtual |
| swapNodes(size_t layer, size_t a, size_t b) | GClasses::GNeuralNet | |
| test() | GClasses::GNeuralNet | static |
| train(const GMatrix &features, const GMatrix &labels) | GClasses::GSupervisedLearner | |
| trainAndTest(const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &testFeatures, const GMatrix &testLabels) | GClasses::GSupervisedLearner | virtual |
| trainIncremental(const double *pIn, const double *pOut) | GClasses::GNeuralNet | virtual |
| trainIncrementalWithDropConnect(const double *pIn, const double *pOut, double probOfDrop) | GClasses::GNeuralNet | |
| trainIncrementalWithDropout(const double *pIn, const double *pOut, double probOfDrop) | GClasses::GNeuralNet | |
| trainInner(const GMatrix &features, const GMatrix &labels) | GClasses::GNeuralNet | protectedvirtual |
| trainSparse(GSparseMatrix &features, GMatrix &labels) | GClasses::GNeuralNet | virtual |
| trainWithValidation(const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &validateFeatures, const GMatrix &validateLabels) | GClasses::GNeuralNet | |
| 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 | |
| useInputBias() const | GClasses::GNeuralNet | inline |
| validationSquaredError(const GMatrix &features, const GMatrix &labels) | GClasses::GNeuralNet | protected |
| weights(double *pOutWeights) const | GClasses::GNeuralNet | |
| weights(double *pOutWeights, size_t layer) const | GClasses::GNeuralNet | |
| windowSize() | GClasses::GNeuralNet | inline |
| ~GIncrementalLearner() | GClasses::GIncrementalLearner | inlinevirtual |
| ~GNeuralNet() | GClasses::GNeuralNet | virtual |
| ~GSupervisedLearner() | GClasses::GSupervisedLearner | virtual |
| ~GTransducer() | GClasses::GTransducer | virtual |
1.8.7