Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 123]

►Nboost
►Nserialization
Ctracking_level< shark::TypedFlags< T > >
Ctracking_level< std::vector< T > >
►Nshark	AbstractMultiObjectiveOptimizer
►Nstatistics
CBaseStatisticsObject	Base class for all Statistic Objects to be used with Statistics
CFractionMissing	For a vector of points computes for every dimension the fraction of missing values
CLowerQuantile	For a vector of points computes for every dimension the 25%-quantile
CMean	For a vector of points computes for every dimension the mean
CMedian	For a vector of points computes for every dimension the median
CQuantile	For a vector of points computes for every dimension the p-quantile
CResultTable	Stores results of a running experiment
CStatistics	Generates Statistics over the results of an experiment
CUpperQuantile	For a vector of points computes for every dimension the 75%-quantile
CVariance	For a vector of points computes for every dimension the variance
►Ntags	Tags are empty types which can be used as a function argument
CDiscreteSpace	A Tag for EnumerationSpaces. It tells the Functions, that the space is discrete and can be enumerated
CRealSpace	A Tag for EnumerationSpaces. It tells the Functions, that the space is real and can't be enumerated
CAbsoluteLoss	Absolute loss
CAbstractBudgetMaintenanceStrategy	This is the abstract interface for any budget maintenance strategy
CAbstractClustering	Base class for clustering
CAbstractConstraintHandler	Implements the base class for constraint handling
CAbstractCost	Cost function interface
CAbstractKernelFunction	Base class of all Kernel functions
CAbstractLinearSvmTrainer	Super class of all linear SVM trainers
CAbstractLineSearchOptimizer	Basis class for line search methods
CAbstractLoss	Loss function interface
CAbstractMetric
CAbstractModel	Base class for all Models
CAbstractMultiObjectiveOptimizer	Base class for abstract multi-objective optimizers for arbitrary search spaces
CAbstractNearestNeighbors	Interface for Nearest Neighbor queries
►CAbstractObjectiveFunction	Super class of all objective functions for optimization and learning
CSecondOrderDerivative
CAbstractOptimizer	An optimizer that optimizes general objective functions
CAbstractSingleObjectiveOptimizer	Base class for all single objective optimizer
CAbstractStoppingCriterion	Base class for stopping criteria of optimization algorithms
CAbstractSvmTrainer	Super class of all kernelized (non-linear) SVM trainers
CAbstractTrainer	Superclass of supervised learning algorithms
CAbstractUnsupervisedTrainer	Superclass of unsupervised learning algorithms
CAbstractWeightedTrainer	Superclass of weighted supervised learning algorithms
CAbstractWeightedUnsupervisedTrainer	Superclass of weighted unsupervised learning algorithms
CAckley	Convex quadratic benchmark function with single dominant axis
CAdam	Adaptive Moment Estimation Algorithm (ADAM)
CAdditiveEpsilonIndicator	Implements the Additive approximation properties of sets
CARDKernelUnconstrained	Automatic relevance detection kernel for unconstrained parameter optimization
CBarsAndStripes	Generates the Bars-And-Stripes problem. In this problem, a 4x4 image has either rows or columns of the same value
CBaseDCNonDominatedSort	Divide-and-conquer algorithm for non-dominated sorting
CBatch	Class which helps using different batch types
CBatch< blas::vector< T > >	Specialization for vectors which should be matrices in batch mode!
CBatch< detail::MatrixRowReference< M > >
CBatch< shark::blas::compressed_vector< T > >	Specialization for ublas compressed vectors which are compressed matrices in batch mode!
CBatch< WeightedDataPair< DataType, WeightType > >
CBatchTraits
CBatchTraits< blas::compressed_matrix< T > >
CBatchTraits< blas::dense_matrix_adaptor< T, blas::row_major > >
CBatchTraits< blas::matrix< T > >
CBatchTraits< WeightedDataBatch< DataType, WeightType > >
CBFGS	Broyden, Fletcher, Goldfarb, Shannon algorithm for unconstraint optimization
CBiasSolver
CBiasSolverSimplex
CBinaryLayer	Layer of binary units taking values in {0,1}
CBinaryTree	Super class of binary space-partitioning trees
CBipolarLayer	Layer of bipolar units taking values in {-1,1}
CBitflipMutator	Bitflip mutation operator
CBlockMatrix2x2	SVM regression matrix
CBoxBasedShrinkingStrategy	Takes q boxx constrained QP-type problem and implements shrinking on it
CBoxConstrainedProblem	Quadratic program with box constraints
CBoxConstrainedShrinkingProblem
CBoxConstraintHandler
CBoxedSVMProblem	Boxed problem for alpha in [lower,upper]^n and equality constraints
CCachedMatrix	Efficient quadratic matrix cache
CCanBeCalled	Detects whether Functor(Argument) can be called
CCanBeCalled< R(*)(T), Argument >
CCanBeCalled< R(T), Argument >
►CCARTree	Classification and Regression Tree
CNode
CCentroids	Clusters defined by centroids
CCG	Conjugate-gradient method for unconstrained optimization
CChessboard	"chess board" problem for binary classification
CCigar	Convex quadratic benchmark function with single dominant axis
CCigarDiscus	Convex quadratic benchmark function
CCIGTAB1	Multi-objective optimization benchmark function CIGTAB 1
CCIGTAB2	Multi-objective optimization benchmark function CIGTAB 2
CCircleInSquare
CClassifier	Conversion of real-valued or vector valued outputs to class labels
CClusteringModel	Abstract model with associated clustering object
CCMA	Implements the CMA-ES
CCMAChromosome	Models a CMAChromosomeof the elitist (MO-)CMA-ES that encodes strategy parameters
CCMACMap	Linear combination of piecewise constant functions
CCMAIndividual
CCMSA	Implements the CMSA
CCombinedObjectiveFunction	Linear combination of objective functions
CConcatenatedModel	ConcatenatedModel concatenates two models such that the output of the first model is input to the second
CConstProxyReference	Sets the type of ProxxyReference
CConstrainedSphere	Constrained Sphere function
CContrastiveDivergence	Implements k-step Contrastive Divergence described by Hinton et al. (2006)
CConv2DModel	Convolutional Model for 2D image data
CCrossEntropy	Error measure for classification tasks that can be used as the objective function for training
►CCrossEntropyMethod	Implements the Cross Entropy Method
CConstantNoise	Constant noise term z_t = noise
CINoiseType	Interface class for noise type
CLinearNoise	Linear noise term z_t = a + t / b
CCrossValidationError	Cross-validation error for selection of hyper-parameters
CCrowdingDistance	Implements the Crowding Distance of a pareto front
CCSvmDerivative	This class provides two main member functions for computing the derivative of a C-SVM hypothesis w.r.t. its hyperparameters. The constructor takes a pointer to a KernelClassifier and an SvmTrainer, in the assumption that the former was trained by the latter. It heavily accesses their members to calculate the derivative of the alpha and offset values w.r.t. the SVM hyperparameters, that is, the regularization parameter C and the kernel parameters. This is done in the member function prepareCSvmParameterDerivative called by the constructor. After this initial, heavier computation step, modelCSvmParameterDerivative can be called on an input sample to the SVM model, and the method will yield the derivative of the hypothesis w.r.t. the SVM hyperparameters
CCSVMProblem	Problem formulation for binary C-SVM problems
CCSvmTrainer	Training of C-SVMs for binary classification
CCVFolds
CData	Data container
CDataDistribution	A DataDistribution defines an unsupervised learning problem
CDataView	Constant time Element-Lookup for Datasets
CDiagonalWithCircle
CDifferenceKernelMatrix	SVM ranking matrix
CDiffPowers
CDiscreteKernel	Kernel on a finite, discrete space
CDiscreteLoss	Flexible loss for classification
CDiscus	Convex quadratic benchmark function
CDistantModes	Creates a set of pattern (each later representing a mode) which than are randomly perturbed to create the data set. The dataset was introduced in Desjardins et al. (2010) (Parallel Tempering for training restricted Boltzmann machines, AISTATS 2010)
CDoublePole
CDropoutLayer
CDTLZ1	Implements the benchmark function DTLZ1
CDTLZ2	Implements the benchmark function DTLZ2
CDTLZ3	Implements the benchmark function DTLZ3
CDTLZ4	Implements the benchmark function DTLZ4
CDTLZ5	Implements the benchmark function DTLZ5
CDTLZ6	Implements the benchmark function DTLZ6
CDTLZ7	Implements the benchmark function DTLZ7
CElitistCMA	Implements the elitist CMA-ES
CElitistSelection	Survival selection to find the next parent set
CELLI1	Multi-objective optimization benchmark function ELLI1
CELLI2	Multi-objective optimization benchmark function ELLI2
CEllipsoid	Convex quadratic benchmark function
CEmptyState	Default State of an Object which does not need a State
CEnergy	The Energy function determining the Gibbs distribution of an RBM
CEnergyStoringTemperedMarkovChain	Implements parallel tempering but also stores additional statistics on the energy differences
CEpsilonHingeLoss	Hinge-loss for large margin regression
CEpsilonSvmTrainer	Training of Epsilon-SVMs for regression
CEPTournamentSelection	Survival and mating selection to find the next parent set
CErrorFunction	Objective function for supervised learning
►CEvaluationArchive	Objective function wrapper storing all function evaluations
CPointResultPairType	Pair of point and result
CExactGradient
CExampleModifiedKernelMatrix
CException	Top-level exception class of the shark library
CFastSigmoidNeuron	Fast sigmoidal function, which does not need to compute an exponential function
CFisherLDA	Fisher's Linear Discriminant Analysis for data compression
CFonseca	Bi-objective real-valued benchmark function proposed by Fonseca and Flemming
CGaussianKernelMatrix	Efficient special case if the kernel is Gaussian and the inputs are sparse vectors
CGaussianLayer	A layer of Gaussian neurons
CGaussianRbfKernel	Gaussian radial basis function kernel
CGaussianTaskKernel	Special "Gaussian-like" kernel function on tasks
CGeneralizationLoss	The generalization loss calculates the relative increase of the validation error compared to the minimum training error
CGeneralizationQuotient	SStopping criterion monitoring the quotient of generalization loss and training progress
CGeneralQuadraticProblem	Quadratic Problem with only Box-Constraints Let K the kernel matrix, than the problem has the form
CGibbsOperator	Implements Block Gibbs Sampling related transition operators for various temperatures
CGridSearch	Optimize by trying out a grid of configurations
CGSP	Real-valued benchmark function with two objectives
CHardClusteringModel	Model for "hard" clustering
CHierarchicalClustering	Clusters defined by a binary space partitioning tree
CHimmelblau	Multi-modal two-dimensional continuous Himmelblau benchmark function
CHingeLoss	Hinge-loss for large margin classification
CHMGSelectionCriterion
CHuberLoss	Huber-loss for for robust regression
CHypervolumeApproximator	Implements an FPRAS for approximating the volume of a set of high-dimensional objects. The algorithm is described in
CHypervolumeCalculator	Frontend for hypervolume calculation algorithms in m dimensions
CHypervolumeCalculator2D	Implementation of the exact hypervolume calculation in 2 dimensions
CHypervolumeCalculator3D	Implementation of the exact hypervolume calculation in 3 dimensions
CHypervolumeCalculatorMDHOY	Implementation of the exact hypervolume calculation in m dimensions
CHypervolumeCalculatorMDWFG	Implementation of the exact hypervolume calculation in m dimensions
CHypervolumeContribution	Frontend for hypervolume contribution algorithms in m dimensions
CHypervolumeContribution2D	Finds the smallest/largest Contributors given 2D points
CHypervolumeContribution3D	Finds the hypervolume contribution for points in 3DD
►CHypervolumeContributionApproximator	Approximately determines the point of a set contributing the least hypervolume
CPoint	Models a point and associated information for book-keeping purposes
CHypervolumeContributionMD	Finds the hypervolume contribution for points in MD
CHypervolumeIndicator	Calculates the hypervolume covered by a front of non-dominated points
CHypervolumeSubsetSelection2D	Implementation of the exact hypervolume subset selection algorithm in 2 dimensions
CIHR1	Multi-objective optimization benchmark function IHR1
CIHR2	Multi-objective optimization benchmark function IHR 2
CIHR3	Multi-objective optimization benchmark function IHR3
CIHR4	Multi-objective optimization benchmark function IHR 4
CIHR6	Multi-objective optimization benchmark function IHR 6
CImagePatches	Given a set of images, draws a set of image patches of a given size
CINameable	This class is an interface for all objects which can have a name
CIndexedIterator	Creates an Indexed Iterator, an Iterator which also carries index information using index()
CIndexingIterator
CIndicatorBasedMOCMA	Implements the generational MO-CMA-ES
CIndicatorBasedRealCodedNSGAII	Implements the NSGA-II
CIndicatorBasedSelection	Implements the well-known indicator-based selection strategy
CIndicatorBasedSteadyStateMOCMA	Implements the \((\mu+1)\)-MO-CMA-ES
►CIndividual	Individual is a simple templated class modelling an individual that acts as a candidate solution in an evolutionary algorithm
CFitnessOrdering	Ordering relation by the fitness of the individuals(only single objective)
CRankOrdering	Ordering relation by the ranks of the individuals
CIParameterizable	Top level interface for everything that holds parameters
CIRpropMinus	This class offers methods for the usage of the improved Resilient-Backpropagation-algorithm without weight-backtracking
CIRpropPlus	This class offers methods for the usage of the improved Resilient-Backpropagation-algorithm with weight-backtracking
CIRpropPlusFull
CISerializable	Abstracts serializing functionality
CIterativeNNQuery	Iterative nearest neighbors query
CJaakkolaHeuristic	Jaakkola's heuristic and related quantities for Gaussian kernel selection
CKDTree	KD-tree, a binary space-partitioning tree
CKernelBudgetedSGDTrainer	Budgeted stochastic gradient descent training for kernel-based models
CKernelClassifier	Linear classifier in a kernel feature space
CKernelExpansion	Linear model in a kernel feature space
CKernelMatrix	Kernel Gram matrix
CKernelMeanClassifier	Kernelized mean-classifier
CKernelSGDTrainer	Generic stochastic gradient descent training for kernel-based models
CKernelTargetAlignment	Kernel Target Alignment - a measure of alignment of a kernel Gram matrix with labels
CKeyValuePair	Represents a Key-Value-Pair similar std::pair which is strictly ordered by it's key
CKHCTree	KHC-tree, a binary space-partitioning tree
CLabeledData	Data set for supervised learning
CLabeledDataDistribution	A LabeledDataDistribution defines a supervised learning problem
CLabelOrder	This will normalize the labels of a given dataset to 0..N-1
CLassoRegression	LASSO Regression
CLBFGS	Limited-Memory Broyden, Fletcher, Goldfarb, Shannon algorithm
CLCTree	LC-tree, a binary space-partitioning tree
CLDA	Linear Discriminant Analysis (LDA)
CLibSVMSelectionCriterion	Computes the maximum gian solution
CLinearClassifier	Basic linear classifier
CLinearCSvmTrainer
CLinearKernel	Linear Kernel, parameter free
CLinearModel	Linear Prediction with optional activation function
CLinearNeuron	Linear activation Neuron
CLinearRankingSelection	Implements a fitness-proportional selection scheme for mating selection that scales the fitness values linearly before carrying out the actual selection
CLinearRegression	Linear Regression
CLinearSAGTrainer	Stochastic Average Gradient Method for training of linear models,
CLineSearch	Wrapper for the linesearch class of functions in the linear algebra library
CLMCMA	Implements a Limited-Memory-CMA
CLogisticNeuron	Neuron which computes the Logistic (logistic) function with range [0,1]
CLogisticRegression	Trainer for Logistic regression
CLooError	Leave-one-out error objective function
CLooErrorCSvm	Leave-one-out error, specifically optimized for C-SVMs
CLRUCache	Implements an LRU-Caching Strategy for arbitrary Cache-Lines
CLZ1	Multi-objective optimization benchmark function LZ1
CLZ2	Multi-objective optimization benchmark function LZ2
CLZ3	Multi-objective optimization benchmark function LZ3
CLZ4	Multi-objective optimization benchmark function LZ4
CLZ5	Multi-objective optimization benchmark function LZ5
CLZ6	Multi-objective optimization benchmark function LZ6
CLZ7	Multi-objective optimization benchmark function LZ7
CLZ8	Multi-objective optimization benchmark function LZ8
CLZ9
CMarkovChain	A single Markov chain
CMarkovPole
CMaximumGainCriterion	Working set selection by maximization of the dual objective gain
CMaximumGradientCriterion	Working set selection by maximization of the projected gradient
CMaxIterations	This stopping criterion stops after a fixed number of iterations
CMcPegasos	Pegasos solver for linear multi-class support vector machines
CMeanModel	Calculates the weighted mean of a set of models
CMergeBudgetMaintenanceStrategy	Budget maintenance strategy that merges two vectors
►CMergeBudgetMaintenanceStrategy< RealVector >	Budget maintenance strategy merging vectors
CMergingProblemFunction
CMissingFeaturesKernelExpansion	Kernel expansion with missing features support
CMissingFeatureSvmTrainer	Trainer for binary SVMs natively supporting missing features
CMklKernel	Weighted sum of kernel functions
CMNIST	Reads in the famous MNIST data in possibly binarized form. The MNIST database itself is not included in Shark, this class just helps loading it
CModelKernel	Kernel function that uses a Model as transformation function for another kernel
CModifiedKernelMatrix	Modified Kernel Gram matrix
CMOEAD	Implements the MOEA/D algorithm
CMonomialKernel	Monomial kernel. Calculates \( \left\langle x_1, x_2 \right\rangle^m_exponent \)
CMultiChainApproximator	Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel
CMultiNomialDistribution	Implements a multinomial distribution
CMultiObjectiveBenchmark	Creates a multi-objective Benchmark from a set of given single objective functions
CMultiTaskKernel	Special kernel function for multi-task and transfer learning
CMultiTaskSample	Aggregation of input data and task index
CMultiVariateNormalDistribution	Implements a multi-variate normal distribution with zero mean
CMultiVariateNormalDistributionCholesky	Multivariate normal distribution with zero mean using a cholesky decomposition
CMVPSelectionCriterion	Computes the most violating pair of the problem
CNearestNeighborModel	NearestNeighbor model for classification and regression
CNearestNeighborModel< InputType, unsigned int >
CNegativeAUC	Negative area under the curve
CNegativeGaussianProcessEvidence	Evidence for model selection of a regularization network/Gaussian process
CNegativeLogLikelihood	Computes the negative log likelihood of a dataset under a model
CNegativeWilcoxonMannWhitneyStatistic	Negative Wilcoxon-Mann-Whitney statistic
CNestedGridSearch	Nested grid search
CNeuronLayer
CNonMarkovPole	Objective function for single and double non-Markov poles
CNormalDistributedPoints	Generates a set of normally distributed points
CNormalizeComponentsUnitInterval	Train a model to normalize the components of a dataset to fit into the unit inverval
CNormalizeComponentsUnitVariance	Train a linear model to normalize the components of a dataset to unit variance, and optionally to zero mean
CNormalizeComponentsWhitening	Train a linear model to whiten the data
CNormalizeComponentsZCA	Train a linear model to whiten the data
CNormalizedKernel	Normalized version of a kernel function
CNormalizeKernelUnitVariance	Determine the scaling factor of a ScaledKernel so that it has unit variance in feature space one on a given dataset
CNormalizer	"Diagonal" linear model for data normalization
►CNormalizerNeuron
CState
CNSGA3Indicator
COneClassSvmTrainer	Training of one-class SVMs
COneNormRegularizer	One-norm of the input as an objective function
COnePointCrossover	Implements one-point crossover
COneVersusOneClassifier	One-versus-one Classifier
COptimizationTrainer	Wrapper for training schemes based on (iterative) optimization
CPamiToy
CPartiallyMappedCrossover	Implements partially mapped crossover
CPartlyPrecomputedMatrix	Partly Precomputed version of a matrix for quadratic programming
CPCA	Principal Component Analysis
CPegasos	Pegasos solver for linear (binary) support vector machines
CPenalizingEvaluator	Penalizing evaluator for scalar objective functions
CPerceptron	Perceptron online learning algorithm
CPointSearch	Optimize by trying out predefined configurations
CPointSetKernel	Normalized version of a kernel function
CPolynomialKernel	Polynomial kernel
CPolynomialMutator	Polynomial mutation operator
CPopulationBasedStepSizeAdaptation	Step size adaptation based on the success of the new population compared to the old
CPrecomputedMatrix	Precomputed version of a matrix for quadratic programming
CProductKernel	Product of kernel functions
CProjectBudgetMaintenanceStrategy	Budget maintenance strategy that projects a vector
CProjectBudgetMaintenanceStrategy< RealVector >	Budget maintenance strategy that projects a vector
CProxyIterator	Creates an iterator which reinterpretes an object as a range
CQpBoxLinear	Quadratic program solver for box-constrained problems with linear kernel
CQpConfig	Super class of all support vector machine trainers
►CQpMcBoxDecomp
CExample	Data structure describing one training example
CPreferedSelectionStrategy	Working set selection eturning th S2DO working set
CVariable	Data structure describing one m_variables of the problem
CQpMcLinear	Generic solver skeleton for linear multi-class SVM problems
CQpMcLinearADM	Solver for the multi-class SVM with absolute margin and discriminative maximum loss
CQpMcLinearATM	Solver for the multi-class SVM with absolute margin and total maximum loss
CQpMcLinearATS	Solver for the multi-class SVM with absolute margin and total sum loss
CQpMcLinearCS	Solver for the multi-class SVM by Crammer & Singer
CQpMcLinearLLW	Solver for the multi-class SVM by Lee, Lin & Wahba
CQpMcLinearMMR	Solver for the multi-class maximum margin regression SVM
CQpMcLinearReinforced	Solver for the "reinforced" multi-class SVM
CQpMcLinearWW	Solver for the multi-class SVM by Weston & Watkins
►CQpMcSimplexDecomp
CExample	Data structure describing one training example
CPreferedSelectionStrategy	Working set selection eturning th S2DO working set
CVariable	Data structure describing one variable of the problem
CQpSolutionProperties	Properties of the solution of a quadratic program
CQpSolver	Quadratic program solver
►CQpSparseArray	Specialized container class for multi-class SVM problems
CEntry	Non-default (non-zero) array entry
CRow	Data structure describing a row of the sparse array
CQpStoppingCondition	Stopping conditions for quadratic programming
►CRadiusMarginQuotient	Radius margin quotions for binary SVMs
CResult
CRankingSvmTrainer	Training of an SVM for ranking
CRBFLayer	Implements a layer of radial basis functions in a neural network
CRBM	Stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy
CRealCodedNSGAIII	Implements the NSGA-III
CRealSpace	The RealSpace can't be enumerated. Infinite values are just too much
CRectifierNeuron	Rectifier Neuron f(x) = max(0,x)
CReferenceVectorAdaptation	Reference vector adaptation for the RVEA algorithm
CReferenceVectorGuidedSelection	Implements the reference vector selection for the RVEA algorithm
CRegularizationNetworkTrainer	Training of a regularization network
CRegularizedKernelMatrix	Kernel Gram matrix with modified diagonal
CRemoveBudgetMaintenanceStrategy	Budget maintenance strategy that removes a vector
CResultSet
CRFClassifier	Random Forest Classifier
CRFTrainer	Random Forest
CRFTrainer< RealVector >
CRFTrainer< unsigned int >
CROC	ROC-Curve - false negatives over false positives
CRosenbrock	Generalized Rosenbrock benchmark function
CRotatedObjectiveFunction	Rotates an objective function using a randomly initialized rotation
CRouletteWheelSelection	Fitness-proportional selection operator
CRpropMinus	This class offers methods for the usage of the Resilient-Backpropagation-algorithm without weight-backtracking
CRpropPlus	This class offers methods for the usage of the Resilient-Backpropagation-algorithm with weight-backtracking
CRVEA	Implements the RVEA algorithm
CScaledKernel	Scaled version of a kernel function
CSchwefel	Convex benchmark function
CScopedHandle
CShape	Represents the Shape of an input or output
►CShark	Allows for querying compile settings at runtime. Provides the current command line arguments to the rest of the library
CVersion	Models a version according to the major.minor.patch versioning scheme
CShifter	Shifter problem
CSimpleNearestNeighbors	Brute force optimized nearest neighbor implementation
CSimplexDownhill	Simplex Downhill Method
CSimulatedBinaryCrossover	Simulated binary crossover operator
CSingleChainApproximator	Approximates the gradient by taking samples from a single Markov chain
CSingleObjectiveResultSet	Result set for single objective algorithm
CSinglePole
CSMSEMOA	Implements the SMS-EMOA
CSoftClusteringModel	Model for "soft" clustering
CSoftmaxNeuron
CSphere	Convex quadratic benchmark function
CSquaredEpsilonHingeLoss	Hinge-loss for large margin regression using th squared two-norm
CSquaredHingeCSvmTrainer
CSquaredHingeLinearCSvmTrainer
CSquaredHingeLoss	Squared Hinge-loss for large margin classification
CSquaredLoss	Squared loss for regression and classification
CSquaredLoss< OutputType, unsigned int >
CSquaredLoss< Sequence, Sequence >
CState	Represents the State of an Object
CSteepestDescent	Standard steepest descent
CSubrangeKernel	Weighted sum of kernel functions
CSvmLogisticInterpretation	Maximum-likelihood model selection score for binary support vector machines
CSvmProblem
CSvmShrinkingProblem
CTanhNeuron	Neuron which computes the hyperbolic tangenst with range [-1,1]
CTemperedMarkovChain
CTimer	Timer abstraction with microsecond resolution
CTournamentSelection	Tournament selection operator
CTrainingError	This stopping criterion tracks the improvement of the error function of the training error over an interval of iterations
CTrainingProgress	This stopping criterion tracks the improvement of the training error over an interval of iterations
CTransformedData
CTreeConstruction	Stopping criteria for tree construction
CTreeNearestNeighbors	Nearest Neighbors implementation using binary trees
CTruncatedExponentialLayer	A layer of truncated exponential neurons
CTrustRegionNewton	Simple Trust-Region method based on the full Hessian matrix
CTukeyBiweightLoss	Tukey's Biweight-loss for robust regression
CTwoNormRegularizer	Two-norm of the input as an objective function
CTwoPointStepSizeAdaptation	Step size adaptation based on the success of the new population compared to the old
CTwoStateSpace	The TwoStateSpace is a discrete Space with only two values, for example {0,1} or {-1,1}
CTypedFeatureNotAvailableException	Exception indicating the attempt to use a feature which is not supported
CTypedFlags	Flexible and extensible mechanisms for holding flags
CUniformCrossover	Uniform crossover of arbitrary individuals
CUniformRankingSelection	Selects individuals from the range of individual and offspring individuals
CUnlabeledData	Data set for unsupervised learning
CValidatedSingleObjectiveResultSet	Result set for validated points
CValidatedStoppingCriterion	Given the current Result set of the optimizer, calculates the validation error using a validation function and hands the results over to the underlying stopping criterion
CVariationalAutoencoderError	Computes the variational autoencoder error function
CVDCMA
CWave	Noisy sinc function: y = sin(x) / x + noise
CWeightedDataBatch
CWeightedDataPair	Input-Label pair of data
CWeightedLabeledData	Weighted data set for supervised learning
►CWeightedSumKernel	Weighted sum of kernel functions
CtBase	Structure describing a single m_base kernel
CWeightedUnlabeledData	Weighted data set for unsupervised learning
CWS2MaximumGradientCriterion	Working set selection by maximization of the projected gradient
CZDT1	Multi-objective optimization benchmark function ZDT1
CZDT2	Multi-objective optimization benchmark function ZDT2
CZDT3	Multi-objective optimization benchmark function ZDT3
CZDT4	Multi-objective optimization benchmark function ZDT4
CZDT6	Multi-objective optimization benchmark function ZDT6
CZeroOneLoss	0-1-loss for classification
CZeroOneLoss< unsigned int, RealVector >	0-1-loss for classification