►Nboost | |
►Nserialization | |
Ctracking_level< shark::TypedFlags< T > > | |
Ctracking_level< std::vector< T > > | |
►Nshark | AbstractMultiObjectiveOptimizer |
►Nblas | |
CBlocking | Partitions the matrix in 4 blocks defined by one splitting point (i,j) |
►Ccompressed_matrix | |
Creference | |
►Ccompressed_vector | Compressed array based sparse vector |
Creference | |
Cconst_expression< compressed_matrix< T, I > > | |
Cconst_expression< compressed_matrix< T, I > const > | |
Cconst_expression< compressed_vector< T, I > > | |
Cconst_expression< compressed_vector< T, I > const > | |
Cconst_expression< matrix< T, Orientation > > | |
Cconst_expression< matrix< T, Orientation > const > | |
Cconst_expression< triangular_matrix< T, Orientation, TriangularType > > | |
Cconst_expression< triangular_matrix< T, Orientation, TriangularType > const > | |
Cconst_expression< vector< T > > | |
Cconst_expression< vector< T > const > | |
Cdense_matrix_adaptor | |
Cdense_vector_adaptor | Represents a given chunk of memory as a dense vector of elements of type T |
►Cdiagonal_matrix | An diagonal matrix with values stored inside a diagonal vector |
Cconst_row_iterator | |
Cidentity_matrix | An identity matrix with values of type T |
Cmatrix | A dense matrix of values of type T |
Cmatrix_addition | |
Cmatrix_binary | |
Cmatrix_column | |
Cmatrix_container | Base class for Matrix container models |
Cmatrix_expression | Base class for Matrix Expression models |
Cmatrix_matrix_prod | |
Cmatrix_range | |
Cmatrix_reference | Wraps another expression as a reference |
Cmatrix_row | |
Cmatrix_scalar_multiply | |
Cmatrix_set | |
Cmatrix_set_expression | Base class for expressions of matrix sets |
Cmatrix_transpose | Matrix transpose |
Cmatrix_unary | Class which allows for matrix transformations |
Cmatrix_vector_prod | |
Cmatrix_vector_range | |
Cnoalias_proxy | |
Couter_product | |
Cpermutation_matrix | |
Cscalar_matrix | A matrix with all values of type T equal to the same value |
Cscalar_vector | Vector expression representing a constant valued vector |
CSolveAXB | Flag indicating that a system AX=B is to be solved |
CSolveXAB | Flag indicating that a system XA=B is to be solved |
Csparse_vector_adaptor | |
Ctemporary_proxy | |
►Ctriangular_matrix | |
Cmajor1_iterator | |
Cmajor2_iterator | |
Cvector | A dense vector of values of type T |
Cvector_addition | |
Cvector_binary | |
Cvector_container | Base class for Vector container models |
Cvector_expression | Base class for Vector Expression models |
Cvector_range | A vector referencing a continuous subvector of elements of vector v containing all elements specified by range |
Cvector_reference | |
Cvector_repeater | |
Cvector_scalar_multiply | Implements multiplications of a vector by a scalar |
Cvector_set_expression | Base class for expressions of vector sets |
Cvector_unary | Class implementing vector transformation expressions |
►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 |
CAbstractDistribution | Abstract class for distributions |
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 |
CAdditiveEpsilonIndicator | Given a reference front R and an approximation F, calculates the additive approximation quality of F |
CARDKernelUnconstrained | Automatic relevance detection kernel for unconstrained parameter optimization |
CArgMaxConverter | Conversion of real-valued outputs to classes |
CAutoencoder | Implements the autoencoder |
CBarsAndStripes | Generates the Bars-And-Stripes problem. In this problem, a 4x4 image has either rows or columns of the same value |
CBaseFastNonDominatedSort | Implements the well-known non-dominated sorting algorithm |
CBaseRng | Collection of different variate generators for different distributions |
CBatch | Class which helps using different batch types |
CBernoulli | This class simulates a "Bernoulli trial", which is like a coin toss |
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 |
CBinomial | Models a binomial distribution with parameters p and n |
CBipolarLayer | Layer of bipolar units taking values in {-1,1} |
CBitflipMutator | Bitflip mutation operator |
CBlockMatrix2x2 | SVM regression matrix |
►CBoundingBoxComputer | Calculates bounding boxes |
CVolumeComparator | Compares points based on their contributed volume |
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 |
►CCARTClassifier | CART Classifier |
CSplitInfo | |
►CCARTTrainer | Classification And Regression Trees CART |
CTableEntry | Types frequently used |
CCauchy | Cauchy distribution |
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 | |
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) |
CConvexCombination | Models a convex combination of inputs |
CConvolutionalRBM | Implements a convolutional RBM with a single greyscale input imge and a set of squared image filters |
CCrossEntropy | Error measure for classication tasks that can be used as the objective function for training |
CCrossEntropyIndependent | Error measure for classification tasks of non exclusive attributes that can be used for model 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 |
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 | |
CDiffGeometric | Random variable with diff geometric distribution |
CDiffGeometric_distribution | Implements a diff geometric distribution |
CDiffPowers | |
CDirichlet | Implements a Dirichlet distribution |
CDirichlet_distribution | Dirichlet distribution |
CDiscreteKernel | Kernel on a finite, discrete space |
CDiscreteLoss | Flexible loss for classification |
CDiscreteUniform | Implements the discrete uniform distribution |
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) |
CDistTrainerContainer | Container for known distribution trainers |
CDivide | Transformation function dividing the elements in a dataset by a scalar or component-wise by values stores in a vector |
CDoublePole | |
CDropoutNeuron | Wraps a given neuron type and implements dropout for it |
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 |
CErlang | Erlang distributed random variable |
CErlang_distribution | Implements an Erlang distribution |
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 |
CFFNet | Offers the functions to create and to work with a feed-forward network |
CFFNetStructures | |
CFisherLDA | Fisher's Linear Discriminant Analysis for data compression |
CFitnessExtractor | |
CFonseca | Bi-objective real-valued benchmark function proposed by Fonseca and Flemming |
CGamma | Gamma distributed random variable |
CGamma_distribution | Gamma distribution |
CGaussianKernelMatrix | Efficient special case if the kernel is Gaussian and the inputs are sparse vectors |
CGaussianLayer | A layer of Gaussian neurons |
CGaussianNoiseModel | Model which corrupts the data using gaussian noise |
CGaussianRbfKernel | Gaussian radial basis function kernel |
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 | Most gneral problem formualtion, needs to be configured by hand |
CGenericDistTrainer | |
CGeometric | Implements the geometric distribution |
CGibbsOperator | Implements Block Gibbs Sampling related transition operators for various temperatures |
CGridSearch | Optimize by trying out a grid of configurations |
CGruauPole | Class for balancing two poles on a cart using a fitness function that punishes oscillating, i.e. quickly moving the cart back and forth to balance the poles. Based on code written by Verena Heidrich-Meisner for the paper |
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 |
CHyperGeometric | Random variable with a hypergeometric distribution |
CHyperGeometric_distribution | Hypergeometric distribution |
CHypervolumeApproximator | Implements an FPRAS for approximating the volume of a set of high-dimensional objects |
CHypervolumeCalculator | Implementation of the exact hypervolume calculation in m dimensions |
CHypervolumeIndicator | Calculates the hypervolume covered by a front of non-dominated points |
CIdentityFitnessExtractor | Functor that returns its argument without conversion |
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 |
CImageInformation | Stores name and size of image externally |
CImagePatches | Given a set of images, draws a set of image patches of a given size |
CImpulseNoiseModel | Model which corrupts the data using Impulse noise |
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() |
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 |
CInvertedGenerationalDistance | Inverted generational distance for comparing Pareto-front approximations |
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 |
CKernelBasisDistance | Computes the squared distance between the optimal point in a basis to the point represented by a KernelExpansion |
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 |
CKeyValueRange | |
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 for unconstrained optimization |
CLCTree | LC-tree, a binary space-partitioning tree |
CLDA | Linear Discriminant Analysis (LDA) |
►CLeastContributorApproximator | Approximately determines the point of a set contributing the least hypervolume |
CIdentityFitnessExtractor | Returns the supplied argument |
CPoint | Models a point and associated information for book-keeping purposes |
CLibSVMSelectionCriterion | |
CLinearClassifier | Basic linear classifier |
CLinearCSvmTrainer | |
CLinearKernel | Linear Kernel, parameter free |
CLinearMcSvmADMTrainer | |
CLinearMcSvmATMTrainer | |
CLinearMcSvmATSTrainer | |
CLinearMcSvmCSTrainer | |
CLinearMcSvmLLWTrainer | |
CLinearMcSvmMMRTrainer | |
CLinearMcSvmOVATrainer | |
CLinearMcSvmReinforcedTrainer | |
CLinearMcSvmWWTrainer | |
CLinearModel | Linear Prediction |
CLinearNeuron | Linear activation Neuron |
CLinearNorm | Normalizes the (non-negative) input by dividing by the overall sum |
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 |
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] |
CLogNormal | Implements a log-normal distribution with parameters location m and Scale s |
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 |
CMcReinforcedSvmTrainer | Training of reinforced-SVM for multi-category classification |
CMcSvmADMTrainer | Training of ADM-SVMs for multi-category classification |
CMcSvmATMTrainer | Training of ATM-SVMs for multi-category classification |
CMcSvmATSTrainer | Training of ATS-SVMs for multi-category classification |
CMcSvmCSTrainer | Training of the multi-category SVM by Crammer and Singer (CS) |
CMcSvmLLWTrainer | Training of the multi-category SVM by Lee, Lin and Wahba (LLW) |
CMcSvmMMRTrainer | Training of the maximum margin regression (MMR) multi-category SVM |
CMcSvmOVATrainer | Training of a multi-category SVM by the one-versus-all (OVA) method |
CMcSvmWWTrainer | Training of the multi-category SVM by Weston and Watkins (WW) |
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 |
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 |
CMultiplicativeEpsilonIndicator | Given a reference front R and an approximation F, calculates the multiplicative approximation quality of F |
CMultiply | Transformation function multiplying the elements in a dataset by a scalar or component-wise by values stores in a vector |
CMultiSequenceIterator | Iterator which iterates of the elements of a nested sequence |
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 | |
CNBClassifier | Naive Bayes classifier |
CNBClassifierTrainer | Trainer for naive Bayes classifier |
CNearestNeighborClassifier | Nearest Neighbor Classifier |
CNearestNeighborRegression | Nearest neighbor regression model |
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 |
CNegExponential | Implements the Negative exponential distribution |
CNestedGridSearch | Nested grid search |
CNoisyErrorFunction | Error Function which only uses a random fraction of data |
CNonMarkovPole | Objective function for single and double non-Markov poles |
CNormal | Implements a univariate normal (Gaussian) distribution |
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 |
CNormalTrainer | Trainer for normal distribution |
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 |
COnlineRNNet | A recurrent neural network regression model optimized for online learning |
COptimizationTrainer | Wrapper for training schemes based on (iterative) optimization |
CPairIterator | A Pair-Iterator which gives a unified view of two ranges |
CPairRangeType | |
CPairReference | Given a type of pair and two iterators to zip together, returns the reference |
CPamiToy | |
CParetoDominanceComparator | Implementation of the Pareto-Dominance relation under the assumption of all objectives to be minimized |
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 |
CPoisson | Implements a Poisson distribution with parameter mean |
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 |
►CQpBoxLinear< CompressedRealVector > | |
CSparseVector | Data structure for sparse vectors |
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 |
►CQpMcDecomp | Quadratic program solver for multi class SVM problems |
CtExample | Data structure describing one training example |
CtVariable | Data structure describing one variable 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 | |
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 |
CRealSpace | The RealSpace can't be enumerated. Infinite values are just too much |
CRectifierNeuron | Rectifier Neuron f(x) = max(0,x) |
CRecurrentStructure | Offers a basic structure for recurrent networks |
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 |
CRFAttribute | |
CRNNet | A recurrent neural network regression model that learns with Back Propagation Through Time |
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 |
CSampler | Samples a random point |
CScaledKernel | Scaled version of a kernel function |
CSchwefel | Convex benchmark function |
CScopedHandle | |
CShift | Transformation function adding a vector or a scalar to the elements in a dataset |
CShifter | Shifter problem |
CSigmoidFitPlatt | Optimizes the parameters of a sigmoid to fit a validation dataset via Platt's method |
CSigmoidFitRpropNLL | Optimizes the parameters of a sigmoid to fit a validation dataset via backpropagation on the negative log-likelihood |
CSigmoidModel | Standard sigmoid function |
CSimpleNearestNeighbors | Brute force optimized nearest neighbor implementation |
CSimpleSigmoidModel | Simple sigmoid function |
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 |
CSoftmax | Softmax function |
CSoftNearestNeighborClassifier | SoftNearestNeighborClassifier returns a probabilistic classification by looking at the k nearest neighbors |
CSparseAutoencoderError | Error Function for Autoencoders and TiedAutoencoders which should be trained with sparse activation of the hidden neurons |
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] |
CTanhSigmoidModel | Scaled Tanh sigmoid function |
CTemperedMarkovChain | |
CThresholdConverter | Convertion of real-valued outputs to classes 0 or 1 |
CThresholdVectorConverter | Convertion of real-vector outputs to vectors of class labels 0 or 1 |
CTiedAutoencoder | Implements the autoencoder with tied weights |
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 |
CTruncate | Transformation function truncating elements in a dataset |
CTruncateAndRescale | Transformation function first truncating and then rescaling elements in a dataset |
CTruncatedExponential | Implements a generator for the truncated exponential function |
CTruncatedExponential_distribution | Boost random suitable distribution for an truncated exponential. See TruncatedExponential for more details |
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 |
CUniform | Implements a continuous uniform distribution |
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 |
CVDCMA | |
CVectorMatrixTraits | Template which finds for every Vector type the best fitting Matrix |
CWave | Noisy sinc function: y = sin(x) / x + noise |
CWeibull | Weibull distributed random variable |
CWeibull_distribution | Weibull distribution |
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 |
►CWilcoxonRankSumTest | Wilcoxon rank-sum test / Mann–Whitney U test |
CElement | Stores information about an observation |
CResult | Stores result of Wilcoxon rank-sum test |
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 |
CGaussianTaskKernel | Special "Gaussian-like" kernel function on tasks |
CMultiTaskKernel | Special kernel function for multi-task and transfer learning |