| ▼Nhalf_float | |
| ►Ndetail | |
| Cbinary_t | Tag type for binary construction |
| Cbits | Type traits for floating-point bits |
| Cbits< const T > | |
| Cbits< const volatile T > | |
| Cbits< double > | Unsigned integer of (at least) 64 bits width |
| Cbits< float > | Unsigned integer of (at least) 32 bits width |
| Cbits< volatile T > | |
| Cbool_type | Helper for tag dispatching |
| Cconditional | Conditional type |
| Cconditional< false, T, F > | |
| Cf31 | Class for 1.31 unsigned floating-point computation |
| Chalf_caster | |
| Chalf_caster< half, half, R > | |
| Chalf_caster< half, U, R > | |
| Chalf_caster< T, half, R > | |
| Cis_float | Type traits for floating-point types |
| Cis_float< const T > | |
| Cis_float< const volatile T > | |
| Cis_float< double > | |
| Cis_float< float > | |
| Cis_float< long double > | |
| Cis_float< volatile T > | |
| Chalf | |
| ▼NOpenNN | |
| CAdaptiveMomentEstimation | |
| CAdaptiveMomentEstimationData | |
| CBoundingLayer | This class represents a layer of bounding neurons |
| CBoxPlot | |
| CConjugateGradient | |
| CConjugateGradientData | |
| CConvolutionalLayer | |
| CConvolutionalLayerBackPropagation | |
| CConvolutionalLayerForwardPropagation | |
| CCorrelation | This structure provides the results obtained from the regression analysis |
| CCrossEntropyError | This class represents the cross entropy error term, used for predicting probabilities |
| ►CDataSet | This class represents the concept of data set for data modelling problems, such as approximation, classification or forecasting |
| CColumn | This structure represents the columns of the DataSet |
| CDataSetBatch | |
| CDescriptives | This structure contains the simplest Descriptives for a set, variable, etc. It includes : |
| CGeneticAlgorithm | |
| CGradientDescent | |
| CGradientDescentData | |
| CGrowingInputs | This concrete class represents a growing inputs algorithm for the InputsSelection as part of the ModelSelection[1] class |
| CGrowingNeurons | This concrete class represents an growing neurons algorithm for the NeuronsSelection as part of the ModelSelection[1] class |
| CHistogram | |
| CInputsSelection | This abstract class represents the concept of inputs selection algorithm for a ModelSelection[1] |
| CInputsSelectionResults | This structure contains the results from the inputs selection |
| CLayer | This abstract class represents the concept of layer of neurons in OpenNN |
| CLayerBackPropagation | |
| CLayerBackPropagationLM | |
| CLayerForwardPropagation | |
| ►CLearningRateAlgorithm | A learning rate that is adjusted according to an algorithm during training to minimize training time |
| CTriplet | Defines a set of three points (A, U, B) for bracketing a directional minimum |
| CLevenbergMarquardtAlgorithm | Levenberg-Marquardt Algorithm will always compute the approximate Hessian matrix, which has dimensions n-by-n |
| CLevenbergMarquardtAlgorithmData | |
| CLongShortTermMemoryLayer | |
| CLongShortTermMemoryLayerBackPropagation | |
| CLongShortTermMemoryLayerForwardPropagation | |
| CLossIndex | This abstract class represents the concept of loss index composed of an error term and a regularization term |
| CLossIndexBackPropagation | |
| CLossIndexBackPropagationLM | A loss index composed of several terms, this structure represent the First Order for this function |
| CMeanSquaredError | This class represents the mean squared error term |
| CMinkowskiError | This class represents the Minkowski error term |
| CModelSelection | This class represents the concept of model selection[1] algorithm in OpenNN |
| CNeuralNetwork | |
| CNeuralNetworkBackPropagation | |
| CNeuralNetworkBackPropagationLM | |
| CNeuralNetworkForwardPropagation | |
| CNeuronsSelection | This abstract class represents the concept of neurons selection algorithm for a ModelSelection[1] |
| CNeuronsSelectionResults | This structure contains the results from the neurons selection |
| CNormalizedSquaredError | This class represents the normalized squared error term |
| CNumericalDifferentiation | |
| COptimizationAlgorithm | |
| COptimizationAlgorithmData | |
| CPerceptronLayer | This class represents a layer of perceptrons |
| CPerceptronLayerBackPropagation | |
| CPerceptronLayerBackPropagationLM | |
| CPerceptronLayerForwardPropagation | |
| CPoolingLayer | |
| CProbabilisticLayer | This class represents a layer of probabilistic neurons |
| CProbabilisticLayerBackPropagation | |
| CProbabilisticLayerBackPropagationLM | |
| CProbabilisticLayerForwardPropagation | |
| CPruningInputs | This concrete class represents a pruning inputs algorithm for the InputsSelection as part of the ModelSelection[1] class |
| CQuasiNewtonMehtodData | |
| CQuasiNewtonMethod | |
| CRecurrentLayer | |
| CRecurrentLayerBackPropagation | |
| CRecurrentLayerForwardPropagation | |
| CResponseOptimization | This class is used to optimize model response identify the combinations of variable settings jointly optimize a set of responses |
| CResponseOptimizationResults | |
| CScalingLayer | This class represents a layer of scaling neurons |
| CStochasticGradientDescent | This concrete class represents the stochastic gradient descent optimization algorithm[1] for a loss index of a neural network |
| CStochasticGradientDescentData | |
| CSumSquaredError | This class represents the sum squared peformance term functional |
| ►CTestingAnalysis | This class contains tools for testing neural networks in different learning tasks |
| CBinaryClassifcationRates | Structure with the binary classification rates |
| CKolmogorovSmirnovResults | Structure with the results from Kolmogorov-Smirnov analysis |
| CLinearRegressionAnalysis | Structure with the results from a linear regression analysis |
| CRocAnalysisResults | Structure with the results from a roc curve analysis |
| CTrainingResults | This structure contains the optimization algorithm results |
| CTrainingStrategy | This class represents the concept of training strategy for a neural network in OpenNN |
| CUnscalingLayer | This class represents a layer of unscaling neurons |
| CWeightedSquaredError | This class represents the weighted squared error term |
| ▼Nstd | Extensions to the C++ standard library |
| Cnumeric_limits< half_float::half > | |
| ▼Ntinyxml2 | |
| CDynArray | |
| CMemPool | |
| ►CMemPoolT | |
| CBlock | |
| CItem | |
| CStrPair | |
| CXMLAttribute | |
| CXMLComment | |
| CXMLConstHandle | |
| CXMLDeclaration | |
| CXMLDocument | |
| CXMLElement | |
| CXMLHandle | |
| CXMLNode | |
| CXMLPrinter | |
| CXMLText | |
| CXMLUnknown | |
| CXMLUtil | |
| CXMLVisitor | |
| CUnitTesting | |
1.9.2