Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 123]
 NOpenNN
 CAdaptiveMomentEstimationThis concrete class represents the adaptive moment estimation(Adam) training algorithm, based on adaptative estimates of lower-order moments
 CBoundingLayerThis class represents a layer of bounding neurons
 CBoxPlotBoxPlot is a visual aid to study the distributions of dataset variables
 CConjugateGradientThis concrete class represents a conjugate gradient training algorithm, based on solving sparse systems
 CConvolutionalLayer
 CCorrelationResultsThis structure provides the results obtained from the correlations
 CCrossEntropyErrorThis class represents the cross entropy error term, used for predicting probabilities
 CDataSetThis class represents the concept of data set for data modelling problems, such as function regression, classification, time series prediction, images approximation and images classification
 CColumnThis structure represents the columns of the DataSet
 CDescriptivesThis structure contains the simplest Descriptives for a set, variable, etc. It includes :
 CGeneticAlgorithmThis concrete class represents a genetic algorithm, inspired by the process of natural selection[1] such as mutation, crossover and selection
 CGeneticAlgorithmResultsThis structure contains the training results for the genetic algorithm method.
 CGradientDescentThis concrete class represents the gradient descent optimization algorithm[1], used to minimize loss function
 CGrowingInputsThis concrete class represents a growing inputs algorithm for the InputsSelection as part of the ModelSelection[1] class
 CGrowingInputsResultsThis structure contains the training results for the growing inputs method
 CHistogramThe Histograms is a visual aid to study the distributions of dataset variables
 CIncrementalNeuronsThis concrete class represents an incremental algorithm for the NeuronsSelection as part of the ModelSelection[1] class
 CIncrementalNeuronsResultsThis structure contains the training results for the incremental order method
 CInputsSelectionThis abstract class represents the concept of inputs selection algorithm for a ModelSelection[1]
 CResultsThis structure contains the results from the inputs selection
 CKMeans
 CResults
 CLayerThis abstract class represents the concept of layer of neurons in OpenNN
 CFirstOrderActivations
 CLearningRateAlgorithmThis class is used by many different optimization algorithms to calculate the training rate given a training direction
 CTripletDefines a set of three points(A, U, B) for bracketing a directional minimum
 CLevenbergMarquardtAlgorithmThis concrete class represents a Levenberg-Marquardt Algorithm training algorithm[1] for the sum squared error loss index for a neural network
 CLongShortTermMemoryLayer
 CLossIndexThis abstrac class represents the concept of loss index composed of an error term and a regularization term
 CFirstOrderLossA loss index composed of several terms, this structure represent the First Order for this function
 CSecondOrderLossThis structure represents the Second Order in the loss function
 CMatrixThis template class defines a matrix for general purpose use
 CMeanSquaredErrorThis class represents the mean squared error term
 CMinkowskiErrorThis class represents the Minkowski error term
 CModelSelectionThis class represents the concept of model selection[1] algorithm in OpenNN
 CResultsThis structure contains the results from the model selection process
 CNeuralNetworkThis class represents the concept of neural network in the OpenNN library
 CNeuronsSelectionThis abstract class represents the concept of neurons selection algorithm for a ModelSelection[1]
 CResultsThis structure contains the results from the order selection
 CNormalizedSquaredErrorThis class represents the normalized squared error term
 CNumericalDifferentiationThis class contains methods for numerical differentiation of functions
 COptimizationAlgorithmThis abstract class represents the concept of optimization algorithm for a neural network in OpenNN library
 CResultsThis structure contains the optimization algorithm results.
 CPerceptronLayerThis class represents a layer of perceptrons
 CPoolingLayerThis class is used to store information about the Pooling Layer in Convolutional Neural Network(CNN)
 CPrincipalComponentsLayerThis class represents the layer of principal component analysis
 CProbabilisticLayerThis class represents a layer of probabilistic neurons
 CPruningInputsThis concrete class represents a pruning inputs algorithm for the InputsSelection as part of the ModelSelection[1] class
 CPruningInputsResults
 CQuasiNewtonMethodThis concrete class represents a quasi-Newton training algorithm[1], used to minimize loss function
 CRecurrentLayer
 CRegressionResultsThis structure provides the results obtained from the regression analysis
 CResponseOptimizationThis class is used to optimize model response identify the combinations of variable settings jointly optimize a set of responses
 CResults
 CScalingLayerThis class represents a layer of scaling neurons
 CStochasticGradientDescentThis concrete class represents the stochastic gradient descent optimization algorithm[1] for a loss index of a neural network
 CSumSquaredErrorThis class represents the sum squared peformance term functional
 CTensorThis template class defines a tensor for general purpose use
 CTestingAnalysisThis class contains tools for testing neural networks in different learning tasks
 CBinaryClassifcationRatesStructure with the binary classification rates
 CKolmogorovSmirnovResultsStructure with the results from Kolmogorov-Smirnov analysis
 CLinearRegressionAnalysisStructure with the results from a linear regression analysis
 CRocAnalysisResultsStructure with the results from a roc curve analysis
 CTrainingStrategyThis class represents the concept of training strategy for a neural network in OpenNN
 CUnscalingLayerThis class represents a layer of unscaling neurons
 CVectorThis template represents an array of any kind of numbers or objects
 CWeightedSquaredErrorThis class represents the weighted squared error term
 Ntinyxml2
 CDynArray
 CMemPool
 CMemPoolT
 CBlock
 CItem
 CStrPair
 CXMLAttribute
 CXMLComment
 CXMLConstHandle
 CXMLDeclaration
 CXMLDocument
 CXMLElement
 CXMLHandle
 CXMLNode
 CXMLPrinter
 CXMLText
 CXMLUnknown
 CXMLUtil
 CXMLVisitor
 CUnitTesting