Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 1234567]

C_IterInfo	Struct that contains current state of the iteration for iterative linear solvers
Cadd< Backend, Matrix >	Generic class which is specialized for different backends to perform addition
Cadd< Backend::EIGEN3, Matrix >	Partial specialization of add for the Eigen3 backend
Callocate_result< Operand, ReturnType >	Template struct allocate_result for allocating objects of return type for element-wise operations. This generic version takes care of the vector types supported by Shogun (SGVector and CGPUVector)
Callocate_result< SGMatrix< T >, SGMatrix< ST > >	Specialization for allocate_result when return type is SGMatrix. Works with different scalar types as well. T defines the scalar type for the operand and whereas ST is the scalar type for the result of the element-wise operation
CAny	Allows to store objects of arbitrary types by using a BaseAnyPolicy and provides a type agnostic API. See its usage in CSGObject::Self, CSGObject::set(), CSGObject::get() and CSGObject::has().
Capply< Backend, Matrix, Vector >	Generic class which is specialized for different backends to perform apply
Capply< Backend::EIGEN3, Matrix, Vector >	Partial specialization of apply for the Eigen3 backend
►CBaseAnyPolicy	An interface for a policy to store a value. Value can be any data like primitive data-types, shogun objects, etc. Policy defines how to handle this data. It works with a provided memory region and is able to set value, clear it and return the type-name as string
CPointerValueAnyPolicy< T >	This is one concrete implementation of policy that uses void pointers to store values
►CBaseTag	Base class for all tags. This class stores name and not the type information for a shogun object. It can be used as an identifier for a shogun object where type information is not known. One application of this can be found in CSGObject::set_param_with_btag()
CTag< T >	Acts as an identifier for a shogun object. It contains type information and name of the object. Generally used to CSGObject::set() and CSGObject::get() parameters of a class
CBlock< Matrix >	Generic class Block which wraps a matrix class and contains block specific information, providing a uniform way to deal with matrix blocks for all supported backend matrices
Cblock_tree_node_t
CC45TreeNodeData	Structure to store data of a node of C4.5 tree. This can be used as a template type in TreeMachineNode class. Ex: C4.5 algorithm uses nodes of type CTreeMachineNode<C45TreeNodeData>
CCARTreeNodeData	Structure to store data of a node of CART. This can be used as a template type in TreeMachineNode class. CART algorithm uses nodes of type CTreeMachineNode<CARTreeNodeData>
CCConvolutionalFeatureMap	Handles convolution and gradient calculation for a single feature map in a convolutional neural network
CCDynInt< T, sz >	Integer type of dynamic size
CCECOCUtil
CCHAIDTreeNodeData	Structure to store data of a node of CHAID. This can be used as a template type in TreeMachineNode class. CHAID algorithm uses nodes of type CTreeMachineNode<CHAIDTreeNodeData>
Ccholesky< Backend, Matrix >	Generic class which is specialized for different backends to compute the cholesky decomposition of a dense matrix
Ccholesky< Backend::EIGEN3, Matrix >	Partial specialization of add for the Eigen3 backend
CCIndirectObject< T, P >	Array class that accesses elements indirectly via an index array
CCJLCoverTreePoint	Class Point to use with John Langford's CoverTree. This class must have some assoficated functions defined (distance, parse_points and print, see below) so it can be used with the CoverTree implementation
CCKNNHeap	This class implements a specialized version of max heap structure. This heap specializes in storing the least k values seen so far along with the indices (or id) of the entities with which the values are associated. On calling the push method, it is automatically checked, if the new value supplied, is among the least k distances seen so far. Also, in case the heap is full already, the max among the stored values is automatically thrown out as the new value finds its proper place in the heap
CCLock	Class Lock used for synchronization in concurrent programs
CCLoss	Class which collects generic mathematical functions
CCMatrixOperations	The helper class is used for Laplace and KL methods
Ccolwise_sum< Backend, Matrix >	Generic class colwise_sum which provides a static compute method. This class is specialized for different types of matrices and backend, providing a means to deal with various matrices directly without having to convert
Ccolwise_sum< Backend::EIGEN3, Matrix >	Specialization of generic colwise_sum which works with SGMatrix and uses Eigen3 as backend for computing sum
CConditionalProbabilityTreeNodeData	Struct to store data of node of conditional probability tree
Cconvolve< Backend, Matrix >
Cconvolve< Backend::EIGEN3, Matrix >
Ccross_entropy< Backend, Matrix >
Ccross_entropy< Backend::EIGEN3, Matrix >
►CCSGObject	Class SGObject is the base class of all shogun objects
CCCache< char >
CCCache< float64_t >
CCCache< KERNELCACHE_ELEM >
CCCache< shogun::SGSparseVectorEntry< float64_t > >
CCCache< shogun::SGSparseVectorEntry< ST > >
CCCache< shogun::SGSparseVectorEntry< T > >
CCCache< ST >
CCCache< uint16_t >
CCCache< uint32_t >
CCCache< uint8_t >
CCDynamicArray< bool >
CCDynamicArray< char >
CCDynamicArray< float32_t >
CCDynamicArray< float64_t >
CCDynamicArray< int32_t >
►CCEMBase< MixModelData >
CCEMMixtureModel	This is the implementation of EM specialized for Mixture models
CCLinearOperator< complex128_t >
CCLinearOperator< float64_t >
►CCLinearSolver< complex128_t, float64_t >
►CCIterativeLinearSolver< complex128_t, float64_t >
CCConjugateOrthogonalCGSolver	Class that uses conjugate orthogonal conjugate gradient method of solving a linear system involving a complex valued linear operator and vector. Useful for large sparse systems involving sparse symmetric matrices that are not Herimitian
CCDirectLinearSolverComplex	Class that provides a solve method for complex dense-matrix linear systems
►CCLinearSolver< float64_t, float64_t >
►CCIterativeLinearSolver< float64_t, float64_t >
►CCIterativeShiftedLinearFamilySolver< float64_t, complex128_t >
CCCGMShiftedFamilySolver	Class that uses conjugate gradient method for solving a shifted linear system family where the linear opeator is real valued and symmetric positive definite, the vector is real valued, but the shifts are complex
CCConjugateGradientSolver	Class that uses conjugate gradient method of solving a linear system involving a real valued linear operator and vector. Useful for large sparse systems involving sparse symmetric and positive-definite matrices
CCDirectSparseLinearSolver	Class that provides a solve method for real sparse-matrix linear systems using LLT
►CCLinearSolver< T, T >
►CCIterativeLinearSolver< T, T >
CCIterativeShiftedLinearFamilySolver< T, ST >	Abstract template base for CG based solvers to the solution of shifted linear systems of the form \((A+\sigma)x=b\) for several values of \(\sigma\) simultaneously, using only as many matrix-vector operations as the solution of a single system requires. This class adds another interface to the basic iterative linear solver that takes the shifts, \(\sigma\), and also weights, \(\alpha\), and returns the summation \(\sum_{i} \alpha_{i}x_{i}\), where \(x_{i}\) is the solution of the system \((A+\sigma_{i})x_{i}=b\)
CCMap< shogun::TParameter *, shogun::CSGObject * >
CCMap< shogun::TParameter *, shogun::SGVector< float64_t > >
►CCMap< std::string, T >
CCStringMap< T >	The class is a customized map for the optimization framework
CCMemoryMappedFile< ST >
►CCOperatorFunction< float64_t >
CCDenseMatrixExactLog	Class that generates jobs for computing logarithm of a dense matrix linear operator
►CCRationalApproximation	Abstract base class of the rational approximation of a function of a linear operator (A) times vector (v) using Cauchy's integral formula - \[f(\text{A})\text{v}=\oint_{\Gamma}f(z)(z\text{I}-\text{A})^{-1} \text{v}dz\] Computes eigenvalues of linear operator and uses Jacobi elliptic functions and conformal maps [2] for quadrature rule for discretizing the contour integral and computes complex shifts, weights and constant multiplier of the rational approximation of the above expression as \[f(\text{A})\text{v}\approx \eta\text{A}\Im-\left(\sum_{l=1}^{N}\alpha_{l} (\text{A}-\sigma_{l}\text{I})^{-1}\text{v}\right)\] where \(\alpha_{l},\sigma_{l}\in\mathbb{C}\) are respectively the shifts and weights of the linear systems generated from the rational approximation, and \(\eta\in\mathbb{R}\) is the constant multiplier, equals to \(\frac{-8K(\lambda_{m}\lambda_{M})^{\frac{1}{4}}}{k\pi N}\)
CCLogRationalApproximationCGM	Implementaion of rational approximation of a operator-function times vector where the operator function is log of a linear operator. Each complex system generated from the shifts due to rational approximation of opertor- log times vector expression are solved at once with a shifted linear-family solver by the computation engine. generate_jobs generates one job per sample
CCLogRationalApproximationIndividual	Implementaion of rational approximation of a operator-function times vector where the operator function is log of a dense-matrix. Each complex system generated from the shifts due to rational approximation of opertor- log times vector expression are solved individually with a complex linear solver by the computation engine. generate_jobs generates num_shifts number of jobs per trace sample
CCTreeMachineNode< C45TreeNodeData >
CCTreeMachineNode< CARTreeNodeData >
CCTreeMachineNode< CHAIDTreeNodeData >
CCTreeMachineNode< ConditionalProbabilityTreeNodeData >
CCTreeMachineNode< id3TreeNodeData >
CCTreeMachineNode< NbodyTreeNodeData >
CCTreeMachineNode< RelaxedTreeNodeData >
CCTreeMachineNode< VwConditionalProbabilityTreeNodeData >
CCTrie< DNATrie >
CCTrie< POIMTrie >
CCAlphabet	The class Alphabet implements an alphabet and alphabet utility functions
►CCApproxJointDiagonalizer	Class ApproxJointDiagonalizer defines an Approximate Joint Diagonalizer (AJD) interface
CCFFDiag	Class FFDiag
CCJADiag	Class JADiag
CCJADiagOrth	Class JADiagOrth
CCJediDiag	Class Jedi
CCQDiag	Class QDiag
CCUWedge	Class UWedge
CCBinaryStream< T >	Memory mapped emulation via binary streams (files)
CCBitString	String class embedding a string in a compact bit representation
CCCache< T >	Template class Cache implements a simple cache
CCCircularBuffer	Implementation of circular buffer This buffer has logical structure such as queue (FIFO). But this queue is cyclic: tape, ends of which are connected, just instead tape there is block of physical memory. So, if you push big block of data it can be situated both at the end and the begin of buffer's memory
►CCCombinationRule	CombinationRule abstract class The CombinationRule defines an interface to how to combine the classification or regression outputs of an ensemble of Machines
CCMeanRule	CMeanRule simply averages the outputs of the Machines in the ensemble
►CCWeightedMajorityVote	Weighted Majority Vote implementation
CCMajorityVote	CMajorityVote is a CWeightedMajorityVote combiner, where each Machine's weight in the ensemble is 1.0
CCCompressor	Compression library for compressing and decompressing buffers using one of the standard compression algorithms:
►CCConverter	Class Converter used to convert data
►CCEmbeddingConverter	Class EmbeddingConverter (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of features, e.g. construct dense numeric embedding of string features
CCDiffusionMaps	Class DiffusionMaps used to preprocess given data using Diffusion Maps dimensionality reduction technique as described in
CCFactorAnalysis	The Factor Analysis class is used to embed data using Factor Analysis algorithm
►CCLaplacianEigenmaps	Class LaplacianEigenmaps used to construct embeddings of data using Laplacian Eigenmaps algorithm as described in:
CCLocalityPreservingProjections	Class LocalityPreservingProjections used to compute embeddings of data using Locality Preserving Projections method as described in
►CCLocallyLinearEmbedding	Class LocallyLinearEmbedding used to embed data using Locally Linear Embedding algorithm described in
CCHessianLocallyLinearEmbedding	Class HessianLocallyLinearEmbedding used to preprocess data using Hessian Locally Linear Embedding algorithm as described in
CCKernelLocallyLinearEmbedding	Class KernelLocallyLinearEmbedding used to construct embeddings of data using kernel formulation of Locally Linear Embedding algorithm as described in
►CCLocalTangentSpaceAlignment	Class LocalTangentSpaceAlignment used to embed data using Local Tangent Space Alignment (LTSA) algorithm as described in:
CCLinearLocalTangentSpaceAlignment	Class LinearLocalTangentSpaceAlignment converter used to construct embeddings as described in:
CCNeighborhoodPreservingEmbedding	NeighborhoodPreservingEmbedding converter used to construct embeddings as described in:
CCManifoldSculpting	Class CManifoldSculpting used to embed data using manifold sculpting embedding algorithm
►CCMultidimensionalScaling	Class Multidimensionalscaling is used to perform multidimensional scaling (capable of landmark approximation if requested)
CCIsomap	The Isomap class is used to embed data using Isomap algorithm as described in:
CCStochasticProximityEmbedding	Class StochasticProximityEmbedding used to construct embeddings of data using the Stochastic Proximity algorithm
CCTDistributedStochasticNeighborEmbedding	Class CTDistributedStochasticNeighborEmbedding used to embed data using t-distributed stochastic neighbor embedding algorithm: http://jmlr.csail.mit.edu/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
CCHashedDocConverter	This class can be used to convert a document collection contained in a CStringFeatures<char> object where each document is stored as a single vector into a hashed Bag-of-Words representation. Like in the standard Bag-of-Words representation, this class considers each document as a collection of tokens, which are then hashed into a new feature space of a specified dimension. This class is very flexible and allows the user to specify the tokenizer used to tokenize each document, specify whether the results should be normalized with regards to the sqrt of the document size, as well as to specify whether he wants to combine different tokens. The latter implements a k-skip n-grams approach, meaning that you can combine up to n tokens, while skipping up to k. Eg. for the tokens ["a", "b", "c", "d"], with n_grams = 2 and skips = 2, one would get the following combinations : ["a", "ab", "ac" (skipped 1), "ad" (skipped 2), "b", "bc", "bd" (skipped 1), "c", "cd", "d"]
►CCICAConverter	Class ICAConverter Base class for ICA algorithms
CCFastICA	Class FastICA
CCFFSep	Class FFSep
CCJade	Class Jade
CCJediSep	Class JediSep
CCSOBI	Class SOBI
CCUWedgeSep	Class UWedgeSep
CCCplex	Class CCplex to encapsulate access to the commercial cplex general purpose optimizer
►CCCrossValidationOutput	Class for managing individual folds in cross-validation
CCCrossValidationMKLStorage	Class for storing MKL weights in every fold of cross-validation
CCCrossValidationMulticlassStorage	Class for storing multiclass evaluation information in every fold of cross-validation
CCCrossValidationPrintOutput	Class for outputting cross-validation intermediate results to the standard output. Simply prints all messages it gets
CCData	Dummy data holder
CCDataGenerator	Class that is able to generate various data samples, which may be used for examples in SHOGUN
CCDeepBeliefNetwork	A Deep Belief Network
►CCDifferentiableFunction	An abstract class that describes a differentiable function used for GradientEvaluation
►CCInference	The Inference Method base class
CCEPInferenceMethod	Class of the Expectation Propagation (EP) posterior approximation inference method
CCExactInferenceMethod	The Gaussian exact form inference method class
►CCKLInference	The KL approximation inference method class
CCKLCovarianceInferenceMethod	The KL approximation inference method class
CCKLDualInferenceMethod	The dual KL approximation inference method class
►CCKLLowerTriangularInference	The KL approximation inference method class
CCKLCholeskyInferenceMethod	The KL approximation inference method class
CCKLDiagonalInferenceMethod	The KL approximation inference method class
►CCLaplaceInference	The Laplace approximation inference method base class
CCMultiLaplaceInferenceMethod	The Laplace approximation inference method class for multi classification
CCSingleLaplaceInferenceMethod	The SingleLaplace approximation inference method class for regression and binary Classification
►CCSparseInference	The Fully Independent Conditional Training inference base class
►CCSingleSparseInference	The sparse inference base class for classification and regression for 1-D labels (1D regression and binary classification)
►CCSingleFITCInference	The Fully Independent Conditional Training inference base class for Laplace and regression for 1-D labels (1D regression and binary classification)
CCFITCInferenceMethod	The Fully Independent Conditional Training inference method class
CCSingleFITCLaplaceInferenceMethod	The FITC approximation inference method class for regression and binary Classification. Note that the number of inducing points (m) is usually far less than the number of input points (n). (the time complexity is computed based on the assumption m < n)
CCVarDTCInferenceMethod	The inference method class based on the Titsias' variational bound. For more details, see Titsias, Michalis K. "Variational learning of inducing variables in sparse Gaussian processes." International Conference on Artificial Intelligence and Statistics. 2009
CCDisjointSet	Class CDisjointSet data structure for linking graph nodes It's easy to identify connected graph, acyclic graph, roots of forest etc. please refer to http://en.wikipedia.org/wiki/Disjoint-set_data_structure
►CCDistance	Class Distance, a base class for all the distances used in the Shogun toolbox
►CCDenseDistance< float64_t >
CCBrayCurtisDistance	Class Bray-Curtis distance
CCCanberraMetric	Class CanberraMetric
CCChebyshewMetric	Class ChebyshewMetric
CCChiSquareDistance	Class ChiSquareDistance
CCCosineDistance	Class CosineDistance
CCGeodesicMetric	Class GeodesicMetric
CCJensenMetric	Class JensenMetric
CCManhattanMetric	Class ManhattanMetric
CCMinkowskiMetric	Class MinkowskiMetric
►CCRealDistance	Class RealDistance
CCAttenuatedEuclideanDistance	Class AttenuatedEuclideanDistance
CCCustomMahalanobisDistance	Class CustomMahalanobisDistance used to compute the distance between feature vectors \( \vec{x_i} \) and \( \vec{x_j} \) as \( (\vec{x_i} - \vec{x_j})^T \mathbf{M} (\vec{x_i} - \vec{x_j}) \), given the matrix \( \mathbf{M} \) which will be referred to as Mahalanobis matrix
CCMahalanobisDistance	Class MahalanobisDistance
CCTanimotoDistance	Class Tanimoto coefficient
►CCSparseDistance< float64_t >
CCSparseEuclideanDistance	Class SparseEucldeanDistance
►CCStringDistance< uint16_t >
CCCanberraWordDistance	Class CanberraWordDistance
CCHammingWordDistance	Class HammingWordDistance
CCManhattanWordDistance	Class ManhattanWordDistance
CCCustomDistance	The Custom Distance allows for custom user provided distance matrices
CCDenseDistance< ST >	Template class DenseDistance
CCEuclideanDistance	Class EuclideanDistance
CCKernelDistance	The Kernel distance takes a distance as input
CCSparseDistance< ST >	Template class SparseDistance
CCStringDistance< ST >	Template class StringDistance
►CCDistribution	Base class Distribution from which all methods implementing a distribution are derived
CCDiscreteDistribution	This is the base interface class for all discrete distributions
CCGaussian	Gaussian distribution interface
CCGMM	Gaussian Mixture Model interface
CCHistogram	Class Histogram computes a histogram over all 16bit unsigned integers in the features
CCHMM	Hidden Markov Model
CCKernelDensity	This class implements the kernel density estimation technique. Kernel density estimation is a non-parametric way to estimate an unknown pdf. The pdf at a query point given finite training samples is calculated using the following formula : \ \(pdf(x')= \frac{1}{nh} \sum_{i=1}^n K(\frac{||x-x_i||}{h})\) \ K() in the above formula is called the kernel function and is controlled by the parameter h called kernel bandwidth. Presently, this class supports only Gaussian kernel which can be used with either Euclidean distance or Manhattan distance. This class makes use of 2 tree structures KD-tree and Ball tree for fast calculation. KD-trees are faster than ball trees at lower dimensions. In case of high dimensional data, ball tree tends to out-perform KD-tree. By default, the class used is Ball tree
CCLinearHMM	The class LinearHMM is for learning Higher Order Markov chains
CCMixtureModel	This is the generic class for mixture models. The final distribution is a mixture of various simple distributions supplied by the user
CCPositionalPWM	Positional PWM
CCDynamicArray< T >	Template Dynamic array class that creates an array that can be used like a list or an array
►CCDynamicObjectArray	Dynamic array class for CSGObject pointers that creates an array that can be used like a list or an array
CCWrappedObjectArray	Specialization of CDynamicObjectArray that adds methods to append wrapped elements to make them serializable. Objects are wrapped through the classes CWrappedBasic, CWrappedSGVector, CWrappedSGMatrix
CCDynProg	Dynamic Programming Class
►CCECOCDecoder
CCECOCIHDDecoder
►CCECOCSimpleDecoder
CCECOCAEDDecoder
CCECOCEDDecoder
CCECOCHDDecoder
CCECOCLLBDecoder
►CCECOCEncoder	ECOCEncoder produce an ECOC codebook
►CCECOCDiscriminantEncoder
CCECOCForestEncoder
CCECOCOVOEncoder
CCECOCOVREncoder
CCECOCRandomDenseEncoder
CCECOCRandomSparseEncoder
►CCEigenSolver	Abstract base class that provides an abstract compute method for computing eigenvalues of a real valued, self-adjoint linear operator. It also provides method for getting min and max eigenvalues
CCDirectEigenSolver	Class that computes eigenvalues of a real valued, self-adjoint dense matrix linear operator using Eigen3
CCLanczosEigenSolver	Class that computes eigenvalues of a real valued, self-adjoint linear operator using Lanczos algorithm
CCEMBase< T >	This is the base class for Expectation Maximization (EM). EM for various purposes can be derived from this base class. This is a template class having a template member called data which can be used to store all parameters used and results calculated by the expectation and maximization steps of EM
►CCEvaluation	Class Evaluation, a base class for other classes used to evaluate labels, e.g. accuracy of classification or mean squared error of regression
►CCBinaryClassEvaluation	The class TwoClassEvaluation, a base class used to evaluate binary classification labels
►CCContingencyTableEvaluation	The class ContingencyTableEvaluation a base class used to evaluate 2-class classification with TP, FP, TN, FN rates
CCAccuracyMeasure	Class AccuracyMeasure used to measure accuracy of 2-class classifier
CCBALMeasure	Class BALMeasure used to measure balanced error of 2-class classifier
CCCrossCorrelationMeasure	Class CrossCorrelationMeasure used to measure cross correlation coefficient of 2-class classifier
CCErrorRateMeasure	Class ErrorRateMeasure used to measure error rate of 2-class classifier
CCF1Measure	Class F1Measure used to measure F1 score of 2-class classifier
CCPrecisionMeasure	Class PrecisionMeasure used to measure precision of 2-class classifier
CCRecallMeasure	Class RecallMeasure used to measure recall of 2-class classifier
CCSpecificityMeasure	Class SpecificityMeasure used to measure specificity of 2-class classifier
CCWRACCMeasure	Class WRACCMeasure used to measure weighted relative accuracy of 2-class classifier
CCPRCEvaluation	Class PRCEvaluation used to evaluate PRC (Precision Recall Curve) and an area under PRC curve (auPRC)
►CCROCEvaluation	Class ROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC)
CCMultitaskROCEvaluation	Class MultitaskROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC) of each task separately
►CCClusteringEvaluation	The base class used to evaluate clustering
CCClusteringAccuracy	Clustering accuracy
CCClusteringMutualInformation	Clustering (normalized) mutual information
CCGradientCriterion	Simple class which specifies the direction of gradient search
CCMeanAbsoluteError	Class MeanAbsoluteError used to compute an error of regression model
CCMeanSquaredError	Class MeanSquaredError used to compute an error of regression model
CCMeanSquaredLogError	Class CMeanSquaredLogError used to compute an error of regression model
CCMulticlassAccuracy	The class MulticlassAccuracy used to compute accuracy of multiclass classification
CCMulticlassOVREvaluation	The class MulticlassOVREvaluation used to compute evaluation parameters of multiclass classification via binary OvR decomposition and given binary evaluation technique
CCMultilabelAccuracy	Class CMultilabelAccuracy used to compute accuracy of multilabel classification
CCStructuredAccuracy	Class CStructuredAccuracy used to compute accuracy of structured classification
►CCEvaluationResult	Abstract class that contains the result generated by the MachineEvaluation class
CCCrossValidationResult	Type to encapsulate the results of an evaluation run
CCGradientResult	Container class that returns results from GradientEvaluation. It contains the function value as well as its gradient
CCFactor	Class CFactor A factor is defined on a clique in the factor graph. Each factor can have its own data, either dense, sparse or shared data. Note that currently this class is table factor oriented
CCFactorDataSource	Class CFactorDataSource Source for factor data. In some cases, the same data can be shared by many factors
CCFactorGraph	Class CFactorGraph a factor graph is a structured input in general
CCFactorGraphDataGenerator	Class CFactorGraphDataGenerator Create factor graph data for multiple unit tests
►CCFactorType	Class CFactorType defines the way of factor parameterization
CCTableFactorType	Class CTableFactorType the way that store assignments of variables and energies in a table or a multi-array
►CCFeatures	The class Features is the base class of all feature objects
CCStringFeatures< char >
CCStringFeatures< T >
CCStringFeatures< uint16_t >
CCStringFeatures< uint8_t >
CCAttributeFeatures	Implements attributed features, that is in the simplest case a number of (attribute, value) pairs
CCCombinedFeatures	The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object
►CCDotFeatures	Features that support dot products among other operations
►CCDenseFeatures< float64_t >
CCFKFeatures	The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models
CCRealFileFeatures	The class RealFileFeatures implements a dense double-precision floating point matrix from a file
CCTOPFeatures	The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models
CCDenseFeatures< T >
CCDenseFeatures< uint16_t >
CCDenseFeatures< uint32_t >
CCSparseFeatures< float64_t >
CCSparseFeatures< T >
CCBinnedDotFeatures	The class BinnedDotFeatures contains a 0-1 conversion of features into bins
CCCombinedDotFeatures	Features that allow stacking of a number of DotFeatures
CCDenseFeatures< ST >	The class DenseFeatures implements dense feature matrices
CCDenseSubSamplesFeatures< ST >
CCDenseSubsetFeatures< ST >
CCExplicitSpecFeatures	Features that compute the Spectrum Kernel feature space explicitly
CCHashedDenseFeatures< ST >	This class is identical to the CDenseFeatures class except that it hashes each dimension to a new feature space
CCHashedDocDotFeatures	This class can be used to provide on-the-fly vectorization of a document collection. Like in the standard Bag-of-Words representation, this class considers each document as a collection of tokens, which are then hashed into a new feature space of a specified dimension. This class is very flexible and allows the user to specify the tokenizer used to tokenize each document, specify whether the results should be normalized with regards to the sqrt of the document size, as well as to specify whether he wants to combine different tokens. The latter implements a k-skip n-grams approach, meaning that you can combine up to n tokens, while skipping up to k. Eg. for the tokens ["a", "b", "c", "d"], with n_grams = 2 and skips = 2, one would get the following combinations : ["a", "ab", "ac" (skipped 1), "ad" (skipped 2), "b", "bc", "bd" (skipped 1), "c", "cd", "d"]
CCHashedSparseFeatures< ST >	This class is identical to the CDenseFeatures class except that it hashes each dimension to a new feature space
CCHashedWDFeatures	Features that compute the Weighted Degreee Kernel feature space explicitly
CCHashedWDFeaturesTransposed	Features that compute the Weighted Degreee Kernel feature space explicitly
CCImplicitWeightedSpecFeatures	Features that compute the Weighted Spectrum Kernel feature space explicitly
CCLBPPyrDotFeatures	Implements Local Binary Patterns with Scale Pyramids as dot features for a set of images. Expects the images to be loaded in a CDenseFeatures object
CCPolyFeatures	Implement DotFeatures for the polynomial kernel
►CCRandomKitchenSinksDotFeatures	Class that implements the Random Kitchen Sinks (RKS) for the DotFeatures as mentioned in http://books.nips.cc/papers/files/nips21/NIPS2008_0885.pdf
CCRandomFourierDotFeatures	This class implements the random fourier features for the DotFeatures framework. Basically upon the object creation it computes the random coefficients, namely w and b, that are needed for this method and then every time a vector is required it is computed based on the following formula z(x) = sqrt(2/D) * cos(w'*x + b), where D is the number of samples that are used
CCSNPFeatures	Features that compute the Weighted Degreee Kernel feature space explicitly
CCSparseFeatures< ST >	Template class SparseFeatures implements sparse matrices
CCSparsePolyFeatures	Implement DotFeatures for the polynomial kernel
CCWDFeatures	Features that compute the Weighted Degreee Kernel feature space explicitly
►CCDummyFeatures	The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any)
CCIndexFeatures	The class IndexFeatures implements features that contain the index of the features. This features used in the CCustomKernel::init to make the subset of the kernel matrix. Initial CIndexFeature of row_idx and col_idx, pass them to the CCustomKernel::init(row_idx, col_idx), then use CCustomKernel::get_kernel_matrix() will get the sub kernel matrix specified by the row_idx and col_idx
CCFactorGraphFeatures	CFactorGraphFeatures maintains an array of factor graphs, each graph is a sample, i.e. an instance of structured input
CCLatentFeatures	Latent Features class The class if for representing features for latent learning, e.g. LatentSVM. It's basically a very generic way of storing features of any (user-defined) form based on CData
CCMatrixFeatures< ST >	Class CMatrixFeatures used to represent data whose feature vectors are better represented with matrices rather than with unidimensional arrays or vectors. Optionally, it can be restricted that all the feature vectors have the same number of features. Set the attribute num_features different to zero to use this restriction. Allow feature vectors with different number of features by setting num_features equal to zero (default behaviour)
►CCStreamingFeatures	Streaming features are features which are used for online algorithms
►CCStreamingDotFeatures	Streaming features that support dot products among other operations
CCStreamingDenseFeatures< float32_t >
►CCStreamingDenseFeatures< float64_t >
CCGaussianBlobsDataGenerator
CCMeanShiftDataGenerator
CCStreamingDenseFeatures< T >	This class implements streaming features with dense feature vectors
CCStreamingHashedDenseFeatures< ST >	This class acts as an alternative to the CStreamingDenseFeatures class and their difference is that the current example in this class is hashed into a smaller dimension dim
CCStreamingHashedDocDotFeatures	This class implements streaming features for a document collection. Like in the standard Bag-of-Words representation, this class considers each document as a collection of tokens, which are then hashed into a new feature space of a specified dimension. This class is very flexible and allows the user to specify the tokenizer used to tokenize each document, specify whether the results should be normalized with regards to the sqrt of the document size, as well as to specify whether he wants to combine different tokens. The latter implements a k-skip n-grams approach, meaning that you can combine up to n tokens, while skipping up to k. Eg. for the tokens ["a", "b", "c", "d"], with n_grams = 2 and skips = 2, one would get the following combinations : ["a", "ab", "ac" (skipped 1), "ad" (skipped 2), "b", "bc", "bd" (skipped 1), "c", "cd", "d"]
CCStreamingHashedSparseFeatures< ST >	This class acts as an alternative to the CStreamingSparseFeatures class and their difference is that the current example in this class is hashed into a smaller dimension dim
CCStreamingSparseFeatures< T >	This class implements streaming features with sparse feature vectors. The vector is represented as an SGSparseVector<T>. Each entry is of type SGSparseVectorEntry<T> with members `feat_index' and `entry'
CCStreamingVwFeatures	This class implements streaming features for use with VW
CCStreamingStringFeatures< T >	This class implements streaming features as strings
►CCStringFeatures< ST >	Template class StringFeatures implements a list of strings
CCStringFileFeatures< ST >	File based string features
►CCFile	A File access base class
CCBinaryFile	A Binary file access class
CCCSVFile	Class CSVFile used to read data from comma-separated values (CSV) files. See http://en.wikipedia.org/wiki/Comma-separated_values
CCLibSVMFile	Read sparse real valued features in svm light format e.g. -1 1:10.0 2:100.2 1000:1.3 with -1 == (optional) label and dim 1 - value 10.0 dim 2 - value 100.2 dim 1000 - value 1.3
CCProtobufFile	Class for work with binary file in protobuf format
CCUAIFile	Class UAIFILE used to read data from UAI files. See http://graphmod.ics.uci.edu/uai08/FileFormat for more details
CCFunction	Class of a function of one variable
CCGCArray< T >	Template class GCArray implements a garbage collecting static array
CCGMNPLib	Class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP)
CCGNPPLib	Class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP)
CCGUIClassifier	UI classifier
CCGUIConverter	UI converter
CCGUIDistance	UI distance
CCGUIFeatures	UI features
CCGUIHMM	UI HMM (Hidden Markov Model)
CCGUIKernel	UI kernel
CCGUILabels	UI labels
CCGUIMath	UI math
CCGUIPluginEstimate	UI estimate
CCGUIPreprocessor	UI preprocessor
CCGUIStructure	UI structure
CCGUITime	UI time
CCHash	Collection of Hashing Functions
►CCHypothesisTest	Hypothesis test base class. Provides an interface for statistical hypothesis testing via three methods: compute_statistic(), compute_p_value() and compute_threshold(). The second computes a p-value for the statistic computed by the first method. The p-value represents the position of the statistic in the null-distribution, i.e. the distribution of the statistic population given the null-hypothesis is true. (1-position = p-value). The third method, compute_threshold(), computes a threshold for a given test level which is needed to reject the null-hypothesis
►CCIndependenceTest	Provides an interface for performing the independence test. Given samples \(Z=\{(x_i,y_i)\}_{i=1}^m\) from the joint distribution \(\textbf{P}_{xy}\), does the joint distribution factorize as \(\textbf{P}_{xy}=\textbf{P}_x\textbf{P}_y\), i.e. product of the marginals? The null-hypothesis says yes, i.e. no dependence, the alternative hypothesis says no
►CCKernelIndependenceTest	Kernel independence test base class. Provides an interface for performing an independence test. Given samples \(Z=\{(x_i,y_i)\}_{i=1}^m\) from the joint distribution \(\textbf{P}_{xy}\), does the joint distribution factorize as \(\textbf{P}_{xy}=\textbf{P}_x\textbf{P}_y\), i.e. product of the marginals?
CCHSIC	This class implements the Hilbert Schmidtd Independence Criterion based independence test as described in [1]
CCNOCCO	This class implements the NOrmalized Cross Covariance Operator (NOCCO) based independence test as described in [1]
►CCTwoSampleTest	Provides an interface for performing the classical two-sample test i.e. Given samples from two distributions \(p\) and \(q\), the null-hypothesis is: \(H_0: p=q\), the alternative hypothesis: \(H_1: p\neq q\)
►CCKernelTwoSampleTest	Kernel two sample test base class. Provides an interface for performing a two-sample test using a kernel, i.e. Given samples from two distributions \(p\) and \(q\), the null-hypothesis is: \(H_0: p=q\), the alternative hypothesis: \(H_1: p\neq q\)
CCQuadraticTimeMMD	This class implements the quadratic time Maximum Mean Statistic as described in [1]. The MMD is the distance of two probability distributions \(p\) and \(q\) in a RKHS which we denote by \[ \hat{\eta_k}=\text{MMD}[\mathcal{F},p,q]^2=\textbf{E}_{x,x'} \left[ k(x,x')\right]-2\textbf{E}_{x,y}\left[ k(x,y)\right] +\textbf{E}_{y,y'}\left[ k(y,y')\right]=||\mu_p - \mu_q||^2_\mathcal{F} \]
►CCStreamingMMD	Abstract base class that provides an interface for performing kernel two-sample test on streaming data using Maximum Mean Discrepancy (MMD) as the test statistic. The MMD is the distance of two probability distributions \(p\) and \(q\) in a RKHS (see [1] for formal description)
CCLinearTimeMMD	This class implements the linear time Maximum Mean Statistic as described in [1] for streaming data (see CStreamingMMD for description)
►CCIndependentComputationEngine	Abstract base class for solving multiple independent instances of CIndependentJob. It has one method, submit_job, which may add the job to an internal queue and might block if there is yet not space in the queue. After jobs are submitted, it might not yet be ready. wait_for_all waits until all jobs are completed, which must be called to guarantee that all jobs are finished
CCSerialComputationEngine	Class that computes multiple independent instances of computation jobs sequentially
►CCIndependentJob	Abstract base for general computation jobs to be registered in CIndependentComputationEngine. compute method produces a job result and submits it to the internal JobResultAggregator. Each set of jobs that form a result will share the same job result aggregator
CCDenseExactLogJob	Class that represents the job of applying the log of a CDenseMatrixOperator on a real vector
CCRationalApproximationCGMJob	Implementation of independent jobs that solves one whole family of shifted systems in rational approximation of linear operator function times a vector using CG-M linear solver. compute calls submit_results of the aggregator with CScalarResult (see CRationalApproximation)
CCRationalApproximationIndividualJob	Implementation of independent job that solves one of the family of shifted systems in rational approximation of linear operator function times a vector using a direct linear solver. The shift is moved inside the operator. compute calls submit_results of the aggregator with CVectorResult which is the solution vector for that shift multiplied by complex weight (See CRationalApproximation)
CCIndexBlock	Class IndexBlock used to represent contiguous indices of one group (e.g. block of related features)
►CCIndexBlockRelation	Class IndexBlockRelation
CCIndexBlockGroup	Class IndexBlockGroup used to represent group-based feature relation
CCIndexBlockTree	Class IndexBlockTree used to represent tree guided feature relation
CCIntegration	Class that contains certain methods related to numerical integration
CCIntronList	Class IntronList
CCIOBuffer	An I/O buffer class
CCJacobiEllipticFunctions	Class that contains methods for computing Jacobi elliptic functions related to complex analysis. These functions are inverse of the elliptic integral of first kind, i.e. \[ u(k,m)=\int_{0}^{k}\frac{dt}{\sqrt{(1-t^{2})(1-m^{2}t^{2})}} =\int_{0}^{\varphi}\frac{d\theta}{\sqrt{(1-m^{2}sin^{2}\theta)}} \] where \(k=sin\varphi\), \(t=sin\theta\) and parameter \(m, 0\le m \le 1\) is called modulus. Three main Jacobi elliptic functions are defined as \(sn(u,m)=k=sin\theta\), \(cn(u,m)=cos\theta=\sqrt{1-sn(u,m)^{2}}\) and \(dn(u,m)=\sqrt{1-m^{2}sn(u,m)^{2}}\). For \(k=1\), i.e. \(\varphi=\frac{\pi}{2}\), \(u(1,m)=K(m)\) is known as the complete elliptic integral of first kind. Similarly, \(u(1,m'))= K'(m')\), \(m'=\sqrt{1-m^{2}}\) is called the complementary complete elliptic integral of first kind. Jacobi functions are double periodic with quardratic periods \(K\) and \(K'\)
►CCJobResult	Base class that stores the result of an independent job
CCScalarResult< T >	Base class that stores the result of an independent job when the result is a scalar
CCVectorResult< T >	Base class that stores the result of an independent job when the result is a vector
►CCJobResultAggregator	Abstract base class that provides an interface for computing an aggeregation of the job results of independent computation jobs as they are submitted and also for finalizing the aggregation
►CCStoreVectorAggregator< complex128_t >
CCIndividualJobResultAggregator	Class that aggregates vector job results in each submit_result call of jobs generated from rational approximation of linear operator function times a vector. finalize extracts the imaginary part of that aggregation, applies the linear operator to the aggregation, performs a dot product with the sample vector, multiplies with the constant multiplier (see CRationalApproximation) and stores the result as CScalarResult
CCStoreScalarAggregator< T >	Template class that aggregates scalar job results in each submit_result call, finalize then transforms current aggregation into a CScalarResult
CCStoreVectorAggregator< T >	Abstract template class that aggregates vector job results in each submit_result call, finalize is abstract
►CCKernel	The Kernel base class
►CCStringKernel< char >
CCDistantSegmentsKernel	The distant segments kernel is a string kernel, which counts the number of substrings, so-called segments, at a certain distance from each other
CCFixedDegreeStringKernel	The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d
CCGaussianMatchStringKernel	The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length
CCLinearStringKernel	Computes the standard linear kernel on dense char valued features
CCLocalAlignmentStringKernel	The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences
CCLocalityImprovedStringKernel	The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features
CCOligoStringKernel	This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004
CCPolyMatchStringKernel	The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length
CCRegulatoryModulesStringKernel	The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences
CCSimpleLocalityImprovedStringKernel	SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel
CCSNPStringKernel	The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length
CCSparseSpatialSampleStringKernel	Sparse Spatial Sample String Kernel by Pavel Kuksa pkuks.nosp@m.a@cs.nosp@m..rutg.nosp@m.ers..nosp@m.edu and Vladimir Pavlovic vladi.nosp@m.mir@.nosp@m.cs.ru.nosp@m.tger.nosp@m.s.edu
CCSpectrumMismatchRBFKernel	Spectrum mismatch rbf kernel
CCSpectrumRBFKernel	Spectrum rbf kernel
CCSubsequenceStringKernel	Class SubsequenceStringKernel that implements String Subsequence Kernel (SSK) discussed by Lodhi et. al.[1]. A subsequence is any ordered sequence of \(n\) characters occurring in the text, though not necessarily contiguous. More formally, string \(u\) is a subsequence of string \(s\), iff there exists indices \(\mathbf{i}=(i_{1},\dots,i_{|u|})\), with \(1\le i_{1} \le \cdots \le i_{|u|} \le |s|\), such that \(u_{j}=s_{i_{j}}\) for \(j=1,\dots,|u|\), written as \(u=s[\mathbf{i}]\). The feature mapping \(\phi\) in this scenario is given by \[ \phi_{u}(s)=\sum_{\mathbf{i}:u=s[\mathbf{i}]}\lambda^{l(\mathbf{i})} \] for some \(lambda\le 1\), where \(l(\mathbf{i})\) is the length of the subsequence in \(s\), given by \(i_{|u|}-i_{1}+1\). The kernel here is an inner product in the feature space generated by all subsequences of length \(n\). \[ K_{n}(s,t)=\sum_{u\in\Sigma^{n}}\langle \phi_{u}(s), \phi_{u}(t)\rangle = \sum_{u\in\Sigma^{n}}\sum_{\mathbf{i}:u=s[\mathbf{i}]} \sum_{\mathbf{j}:u=t[\mathbf{j}]}\lambda^{l(\mathbf{i})+l(\mathbf{j})} \] Since the subsequences are weighted by the exponentially decaying factor \(\lambda\) of their full length in the text, more weight is given to those occurrences that are nearly contiguous. A direct computation is infeasible since the dimension of the feature space grows exponentially with \(n\). The paper describes an efficient computation approach using a dynamic programming technique
CCWeightedDegreePositionStringKernel	The Weighted Degree Position String kernel (Weighted Degree kernel with shifts)
CCWeightedDegreeStringKernel	The Weighted Degree String kernel
►CCStringKernel< uint16_t >
►CCCommWordStringKernel	The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers
CCWeightedCommWordStringKernel	The WeightedCommWordString kernel may be used to compute the weighted spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer length is weighted by some coefficient \(\beta_k\)) from strings that have been mapped into unsigned 16bit integers
CCHistogramWordStringKernel	The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains
CCMatchWordStringKernel	The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet
CCPolyMatchWordStringKernel	The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features
CCSalzbergWordStringKernel	The SalzbergWordString kernel implements the Salzberg kernel
►CCStringKernel< uint64_t >
CCCommUlongStringKernel	The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers
CCCauchyKernel	Cauchy kernel
CCCircularKernel	Circular kernel
CCCombinedKernel	The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination
CCConstKernel	The Constant Kernel returns a constant for all elements
CCCustomKernel	The Custom Kernel allows for custom user provided kernel matrices
CCDiagKernel	The Diagonal Kernel returns a constant for the diagonal and zero otherwise
►CCDistanceKernel	The Distance kernel takes a distance as input
CCBesselKernel	Class Bessel kernel
►CCDotKernel	Template class DotKernel is the base class for kernels working on DotFeatures
CCANOVAKernel	ANOVA (ANalysis Of VAriances) kernel
CCAUCKernel	The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training
CCChi2Kernel	The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms
►CCExponentialARDKernel	Exponential Kernel with Automatic Relevance Detection computed on CDotFeatures
►CCGaussianARDKernel	Gaussian Kernel with Automatic Relevance Detection computed on CDotFeatures
CCGaussianARDSparseKernel	Gaussian Kernel with Automatic Relevance Detection with supporting Sparse inference
CCExponentialKernel	The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures
CCGaussianShortRealKernel	The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features
CCHistogramIntersectionKernel	The HistogramIntersection kernel operating on realvalued vectors computes the histogram intersection distance between sets of histograms. Note: the current implementation assumes positive values for the histograms, and input vectors should sum to 1
CCJensenShannonKernel	The Jensen-Shannon kernel operating on real-valued vectors computes the Jensen-Shannon distance between the features. Often used in computer vision
CCLinearKernel	Computes the standard linear kernel on CDotFeatures
CCPeriodicKernel	The periodic kernel as described in The Kernel Cookbook by David Duvenaud: http://people.seas.harvard.edu/~dduvenaud/cookbook/
CCPolyKernel	Computes the standard polynomial kernel on CDotFeatures
CCPyramidChi2	Pyramid Kernel over Chi2 matched histograms
CCSigmoidKernel	The standard Sigmoid kernel computed on dense real valued features
CCSplineKernel	Computes the Spline Kernel function which is the cubic polynomial
CCTensorProductPairKernel	Computes the Tensor Product Pair Kernel (TPPK)
CCWaveletKernel	Class WaveletKernel
CCWeightedDegreeRBFKernel	Weighted degree RBF kernel
CCInverseMultiQuadricKernel	InverseMultiQuadricKernel
CCLogKernel	Log kernel
CCMultiquadricKernel	MultiquadricKernel
CCPowerKernel	Power kernel
CCProductKernel	The Product kernel is used to combine a number of kernels into a single ProductKernel object by element multiplication
CCRationalQuadraticKernel	Rational Quadratic kernel
►CCShiftInvariantKernel	Base class for the family of kernel functions that only depend on the difference of the inputs, i.e. whose values does not change if the inputs are shifted by the same amount. More precisely, \[ k(\mathbf{x}, \mathbf{x'}) = k(\mathbf{x-x'}) \] For example, Gaussian (RBF) kernel is a shfit invariant kernel
►CCGaussianKernel	The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures
CCGaussianCompactKernel	The compact version as given in Bart Hamers' thesis Kernel Models for Large Scale Applications (Eq. 4.10) is computed as
CCGaussianShiftKernel	An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel
CCSparseKernel< ST >	Template class SparseKernel, is the base class of kernels working on sparse features
CCSphericalKernel	Spherical kernel
CCStringKernel< ST >	Template class StringKernel, is the base class of all String Kernels
CCTStudentKernel	Generalized T-Student kernel
CCWaveKernel	Wave kernel
►CCKernelNormalizer	The class Kernel Normalizer defines a function to post-process kernel values
CCAvgDiagKernelNormalizer	Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor)
CCDiceKernelNormalizer	DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient)
CCFirstElementKernelNormalizer	Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. \( c=k({\bf x},{\bf x})\)
CCIdentityKernelNormalizer	Identity Kernel Normalization, i.e. no normalization is applied
CCMultitaskKernelMaskNormalizer	The MultitaskKernel allows Multitask Learning via a modified kernel function
CCMultitaskKernelMaskPairNormalizer	The MultitaskKernel allows Multitask Learning via a modified kernel function
►CCMultitaskKernelMklNormalizer	Base-class for parameterized Kernel Normalizers
CCMultitaskKernelPlifNormalizer	The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL
CCMultitaskKernelTreeNormalizer	The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy
CCMultitaskKernelNormalizer	The MultitaskKernel allows Multitask Learning via a modified kernel function
CCRidgeKernelNormalizer	Normalize the kernel by adding a constant term to its diagonal. This aids kernels to become positive definite (even though they are not - often caused by numerical problems)
CCScatterKernelNormalizer	Scatter kernel normalizer
CCSqrtDiagKernelNormalizer	SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements
CCTanimotoKernelNormalizer	TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index )
CCVarianceKernelNormalizer	VarianceKernelNormalizer divides by the ``variance''
CCZeroMeanCenterKernelNormalizer	ZeroMeanCenterKernelNormalizer centers the kernel in feature space
►CCKernelSelection	Base class for kernel selection for kernel two-sample test statistic implementations (e.g. MMD). Provides abstract methods for selecting kernels and computing criteria or kernel weights for the implemented method. In order to implement new methods for kernel selection, simply write a new implementation of this class
►CCMMDKernelSelection	Base class for kernel selection for MMD-based two-sample test statistic implementations. Provides abstract methods for selecting kernels and computing criteria or kernel weights for the implemented method. In order to implement new methods for kernel selection, simply write a new implementation of this class
CCMMDKernelSelectionMax	Kernel selection class that selects the single kernel that maximises the MMD statistic. Works for CQuadraticTimeMMD and CLinearTimeMMD. This leads to a heuristic that is better than the standard median heuristic for Gaussian kernels. However, it comes with no guarantees
CCMMDKernelSelectionMedian	Implements MMD kernel selection for a number of Gaussian baseline kernels via selecting the one with a bandwidth parameter that is closest to the median of all pairwise distances in the underlying data. Therefore, it only works for data to which a GaussianKernel can be applied, which are grouped under the class CDotFeatures in SHOGUN
CCMMDKernelSelectionOpt	Implements optimal kernel selection for single kernels. Given a number of baseline kernels, this method selects the one that minimizes the type II error for a given type I error for a two-sample test. This only works for the CLinearTimeMMD statistic
►CCLabels	The class Labels models labels, i.e. class assignments of objects
►CCDenseLabels	Dense integer or floating point labels
CCBinaryLabels	Binary Labels for binary classification
CCMulticlassLabels	Multiclass Labels for multi-class classification
CCRegressionLabels	Real Labels are real-valued labels
CCLatentLabels	Abstract class for latent labels As latent labels always depends on the given application, this class only defines the API that the user has to implement for latent labels
CCMultilabelLabels	Multilabel Labels for multi-label classification
►CCStructuredLabels	Base class of the labels used in Structured Output (SO) problems
CCFactorGraphLabels	Class FactorGraphLabels used e.g. in the application of Structured Output (SO) learning with the FactorGraphModel. Each of the labels is represented by a graph. Each label is of type CFactorGraphObservation and all of them are stored in a CDynamicObjectArray
CCMulticlassSOLabels	Class CMulticlassSOLabels to be used in the application of Structured Output (SO) learning to multiclass classification. Each of the labels is represented by a real number and it is required that the values of the labels are in the set {0, 1, ..., num_classes-1}. Each label is of type CRealNumber and all of them are stored in a CDynamicObjectArray
CCMultilabelSOLabels	Class CMultilabelSOLabels used in the application of Structured Output (SO) learning to Multilabel Classification. Labels are subsets of {0, 1, ..., num_classes-1}. Each of the label if of type CSparseMultilabel and all of them are stored in a CDynamicObjectArray
CCSequenceLabels	Class CSequenceLabels used e.g. in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM). Each of the labels is represented by a sequence of integers. Each label is of type CSequence and all of them are stored in a CDynamicObjectArray
CCLabelsFactory	The helper class to specialize base class instances of labels
CCLatentModel	Abstract class CLatentModel It represents the application specific model and contains most of the application dependent logic to solve latent variable based problems
►CCLikelihoodModel	The Likelihood model base class
CCGaussianLikelihood	Class that models Gaussian likelihood
CCLogitLikelihood	Class that models Logit likelihood
CCProbitLikelihood	Class that models Probit likelihood
CCSoftMaxLikelihood	Class that models Soft-Max likelihood
CCStudentsTLikelihood	Class that models a Student's-t likelihood
►CCVariationalLikelihood	The Variational Likelihood base class
►CCVariationalGaussianLikelihood	The variational Gaussian Likelihood base class. The variational distribution is Gaussian
►CCDualVariationalGaussianLikelihood	Class that models dual variational likelihood
CCLogitDVGLikelihood	Class that models dual variational logit likelihood
CCLogitVGPiecewiseBoundLikelihood	Class that models Logit likelihood and uses variational piecewise bound to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \] where \[ p(y_i|f_i) = \frac{exp(y_i*f_i)}{1+exp(f_i)}, y_i \in \{0,1\} \]
►CCNumericalVGLikelihood	Class that models likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \]
CCLogitVGLikelihood	Class that models Logit likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \]
CCProbitVGLikelihood	Class that models Probit likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \]
CCStudentsTVGLikelihood	Class that models Student's T likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \]
►CCLinearOperator< T >	Abstract template base class that represents a linear operator, e.g. a matrix
►CCMatrixOperator< T >	Abstract base class that represents a matrix linear operator. It provides an interface to computes matrix-vector product \(Ax\) in its apply method, \(A\in\mathbb{C}^{m\times n},A:\mathbb{C}^{n} \rightarrow \mathbb{C}^{m}\) being the matrix operator and \(x\in \mathbb{C}^{n}\) being the vector. The result is a vector \(y\in \mathbb{C}^{m}\)
CCDenseMatrixOperator< float64_t >
CCDenseMatrixOperator< T >	Class that represents a dense-matrix linear operator. It computes matrix-vector product \(Ax\) in its apply method, \(A\in\mathbb{C}^{m\times n},A:\mathbb{C}^{n}\rightarrow \mathbb{C}^{m}\) being the matrix operator and \(x\in\mathbb{C}^{n}\) being the vector. The result is a vector \(y\in\mathbb{C}^{m}\)
CCSparseMatrixOperator< T >	Class that represents a sparse-matrix linear operator. It computes matrix-vector product \(Ax\) in its apply method, \(A\in\mathbb{C}^{m\times n},A:\mathbb{C}^{n}\rightarrow \mathbb{C}^{m}\) being the matrix operator and \(x\in\mathbb{C}^{n}\) being the vector. The result is a vector \(y\in\mathbb{C}^{m}\)
►CCLinearSolver< T, ST >	Abstract template base class that provides an abstract solve method for linear systems, that takes a linear operator \(A\), a vector \(b\), solves the system \(Ax=b\) and returns the vector \(x\)
CCIterativeLinearSolver< T, ST >	Abstract template base for all iterative linear solvers such as conjugate gradient (CG) solvers. provides interface for setting the iteration limit, relative/absolute tolerence. solve method is abstract
CCLineReader	Class for buffered reading from a ascii file
CCList	Class List implements a doubly connected list for low-level-objects
CCListElement	Class ListElement, defines how an element of the the list looks like
CCLMNN	Class LMNN that implements the distance metric learning technique Large Margin Nearest Neighbour (LMNN) described in
CCLMNNStatistics	Class LMNNStatistics used to give access to intermediate results obtained training LMNN
CCLogDetEstimator	Class to create unbiased estimators of \(log(\left|C\right|)= trace(log(C))\). For each estimate, it samples trace vectors (one by one) and calls submit_jobs of COperatorFunction, stores the resulting job result aggregator instances, calls wait_for_all of CIndependentComputationEngine to ensure that the job result aggregators are all up to date. Then simply computes running averages over the estimates
►CCLossFunction	Class CLossFunction is the base class of all loss functions
CCAbsoluteDeviationLoss	CAbsoluteDeviationLoss implements the absolute deviation loss function. \(L(y_i,f(x_i)) = \mod{y_i-f(x_i)}\)
CCExponentialLoss	CExponentialLoss implements the exponential loss function. \(L(y_i,f(x_i)) = \exp^{-y_if(x_i)}\)
CCHingeLoss	CHingeLoss implements the hinge loss function
CCHuberLoss	CHuberLoss implements the Huber loss function. It behaves like SquaredLoss function at values below Huber delta and like absolute deviation at values greater than the delta
CCLogLoss	CLogLoss implements the logarithmic loss function
CCLogLossMargin	Class CLogLossMargin implements a margin-based log-likelihood loss function
CCSmoothHingeLoss	CSmoothHingeLoss implements the smooth hinge loss function
CCSquaredHingeLoss	Class CSquaredHingeLoss implements a squared hinge loss function
CCSquaredLoss	CSquaredLoss implements the squared loss function
►CCMachine	A generic learning machine interface
►CCBaggingMachine	: Bagging algorithm i.e. bootstrap aggregating
CCRandomForest	This class implements the Random Forests algorithm. In Random Forests algorithm, we train a number of randomized CART trees (see class CRandomCARTree) using the supplied training data. The number of trees to be trained is a parameter (called number of bags) controlled by the user. Test feature vectors are classified/regressed by combining the outputs of all these trained candidate trees using a combination rule (see class CCombinationRule). The feature for calculating out-of-box error is also provided to help determine the appropriate number of bags. The evaluatin criteria for calculating this out-of-box error is specified by the user (see class CEvaluation)
►CCBaseMulticlassMachine
►CCTreeMachine< C45TreeNodeData >
CCC45ClassifierTree	Class C45ClassifierTree implements the C4.5 algorithm for decision tree learning. The algorithm steps are briefy explained below :
►CCTreeMachine< CARTreeNodeData >
►CCCARTree	This class implements the Classification And Regression Trees algorithm by Breiman et al for decision tree learning. A CART tree is a binary decision tree that is constructed by splitting a node into two child nodes repeatedly, beginning with the root node that contains the whole dataset. TREE GROWING PROCESS : During the tree growing process, we recursively split a node into left child and right child so that the resulting nodes are "purest". We do this until any of the stopping criteria is met. To find the best split, we scan through all possible splits in all predictive attributes. The best split is one that maximises some splitting criterion. For classification tasks, ie. when the dependent attribute is categorical, the Gini index is used. For regression tasks, ie. when the dependent variable is continuous, least squares deviation is used. The algorithm uses two stopping criteria : if node becomes completely "pure", ie. all its members have identical dependent variable, or all of them have identical predictive attributes (independent variables).
CCRandomCARTree	This class implements randomized CART algorithm used in the tree growing process of candidate trees in Random Forests algorithm. The tree growing process is different from the original CART algorithm because of the input attributes which are considered for each node split. In randomized CART, a few (fixed number) attributes are randomly chosen from all available attributes while deciding the best split. This is unlike the original CART where all available attributes are considered while deciding the best split
►CCTreeMachine< CHAIDTreeNodeData >
CCCHAIDTree	This class implements the CHAID algorithm proposed by Kass (1980) for decision tree learning. CHAID consists of three steps: merging, splitting and stopping. A tree is grown by repeatedly using these three steps on each node starting from the root node. CHAID accepts nominal or ordinal categorical predictors only. If predictors are continuous, they have to be transformed into ordinal predictors before tree growing. CONVERTING CONTINUOUS PREDICTORS TO ORDINAL : Continuous predictors are converted to ordinal by binning. The number of bins (K) has to be supplied by the user. Given K, a predictor is split in such a way that all the bins get the same number (more or less) of distinct predictor values. The maximum feature value in each bin is used as a breakpoint. MERGING : During the merging step, allowable pairs of categories of a predictor are evaluated for similarity. If the similarity of a pair is above a threshold, the categories constituting the pair are merged into a single category. The process is repeated until there is no pair left having high similarity between its categories. Similarity between categories is evaluated using the p_value SPLITTING : The splitting step selects which predictor to be used to best split the node. Selection is accomplished by comparing the adjusted p_value associated with each predictor. The predictor that has the smallest adjusted p_value is chosen for splitting the node. STOPPING : The tree growing process stops if any of the following conditions is satisfied :
►CCTreeMachine< ConditionalProbabilityTreeNodeData >
►CCConditionalProbabilityTree
CCBalancedConditionalProbabilityTree
CCRandomConditionalProbabilityTree
►CCTreeMachine< id3TreeNodeData >
CCID3ClassifierTree	Class ID3ClassifierTree, implements classifier tree for discrete feature values using the ID3 algorithm. The training algorithm implemented is as follows :
►CCTreeMachine< NbodyTreeNodeData >
►CCNbodyTree	This class implements genaralized tree for N-body problems like k-NN, kernel density estimation, 2 point correlation
CCBallTree	This class implements Ball tree. The ball tree is contructed using the top-down approach. cf. ftp://ftp.icsi.berkeley.edu/pub/techreports/1989/tr-89-063.pdf
CCKDTree	This class implements KD-Tree. cf. http://www.autonlab.org/autonweb/14665/version/2/part/5/data/moore-tutorial.pdf
►CCTreeMachine< RelaxedTreeNodeData >
CCRelaxedTree
►CCTreeMachine< VwConditionalProbabilityTreeNodeData >
CCVwConditionalProbabilityTree
►CCMulticlassMachine	Experimental abstract generic multiclass machine class
►CCKernelMulticlassMachine	Generic kernel multiclass
►CCMulticlassSVM	Class MultiClassSVM
CCGMNPSVM	Class GMNPSVM implements a one vs. rest MultiClass SVM
CCLaRank	LaRank multiclass SVM machine This implementation uses LaRank algorithm from Bordes, Antoine, et al., 2007. "Solving multiclass support vector machines with LaRank."
CCMKLMulticlass	MKLMulticlass is a class for L1-norm Multiclass MKL
CCMulticlassLibSVM	Class LibSVMMultiClass. Does one vs one classification
CCScatterSVM	ScatterSVM - Multiclass SVM
►CCLinearMulticlassMachine	Generic linear multiclass machine
►CCMulticlassLibLinear	Multiclass LibLinear wrapper. Uses Crammer-Singer formulation and gradient descent optimization algorithm implemented in the LibLinear library. Regularized bias support is added using stacking bias 'feature' to hyperplanes normal vectors
CCDomainAdaptationMulticlassLibLinear	Domain adaptation multiclass LibLinear wrapper Source domain is assumed to b
CCShareBoost
►CCNativeMulticlassMachine	Experimental abstract native multiclass machine class
CCGaussianNaiveBayes	Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier
CCMCLDA	Class MCLDA implements multiclass Linear Discriminant Analysis
CCQDA	Class QDA implements Quadratic Discriminant Analysis
CCTreeMachine< T >	Class TreeMachine, a base class for tree based multiclass classifiers. This class is derived from CBaseMulticlassMachine and stores the root node (of class type CTreeMachineNode) to the tree structure
►CCDistanceMachine	A generic DistanceMachine interface
CCHierarchical	Agglomerative hierarchical single linkage clustering
►CCKMeansBase
CCKMeans	KMeans clustering, partitions the data into k (a-priori specified) clusters
CCKMeansMiniBatch
CCKNN	Class KNN, an implementation of the standard k-nearest neigbor classifier
CCNearestCentroid	Class NearestCentroid, an implementation of Nearest Shrunk Centroid classifier
►CCGaussianProcessMachine	A base class for Gaussian Processes
CCGaussianProcessClassification	Class GaussianProcessClassification implements binary and multiclass classification based on Gaussian Processes
CCGaussianProcessRegression	Class GaussianProcessRegression implements regression based on Gaussian Processes
►CCKernelMachine	A generic KernelMachine interface
►CCKernelRidgeRegression	Class KernelRidgeRegression implements Kernel Ridge Regression - a regularized least square method for classification and regression
CCKRRNystrom	Class KRRNystrom implements the Nyström method for kernel ridge regression, using a low-rank approximation to the kernel matrix
►CCSVM	A generic Support Vector Machine Interface
CCCPLEXSVM	CplexSVM a SVM solver implementation based on cplex (unfinished)
CCGNPPSVM	Class GNPPSVM
CCLibSVM	LibSVM
CCLibSVMOneClass	Class LibSVMOneClass
CCLibSVR	Class LibSVR, performs support vector regression using LibSVM
►CCMKL	Multiple Kernel Learning
CCMKLClassification	Multiple Kernel Learning for two-class-classification
CCMKLOneClass	Multiple Kernel Learning for one-class-classification
CCMKLRegression	Multiple Kernel Learning for regression
CCMPDSVM	Class MPDSVM
►CCSVMLight	Class SVMlight
CCDomainAdaptationSVM	Class DomainAdaptationSVM
CCSVMLightOneClass	Trains a one class C SVM
CCSVRLight	Class SVRLight, performs support vector regression using SVMLight
►CCLinearMachine	Class LinearMachine is a generic interface for all kinds of linear machines like classifiers
CCAveragedPerceptron	Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron
CCLDA	Class LDA implements regularized Linear Discriminant Analysis
CCLeastAngleRegression	Class for Least Angle Regression, can be used to solve LASSO
►CCLibLinear	This class provides an interface to the LibLinear library for large- scale linear learning focusing on SVM [1]. This is the classification interface. For regression, see CLibLinearRegression. There is also an online version, see COnlineLibLinear
CCDomainAdaptationSVMLinear	Class DomainAdaptationSVMLinear
CCLibLinearMTL	Class to implement LibLinear
CCLibLinearRegression	This class provides an interface to the LibLinear library for large- scale linear learning focusing on SVM [1]. This is the regression interface. For classification, see CLibLinear
CCLinearLatentMachine	Abstract implementaion of Linear Machine with latent variable This is the base implementation of all linear machines with latent variable
►CCLinearRidgeRegression	Class LinearRidgeRegression implements Ridge Regression - a regularized least square method for classification and regression
CCLeastSquaresRegression	Class to perform Least Squares Regression
CCLPBoost	Class LPBoost trains a linear classifier called Linear Programming Machine, i.e. a SVM using a \(\ell_1\) norm regularizer
CCLPM	Class LPM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a \(\ell_1\) norm regularizer
CCNewtonSVM	NewtonSVM, In this Implementation linear SVM is trained in its primal form using Newton-like iterations. This Implementation is ported from the Olivier Chapelles fast newton based SVM solver, Which could be found here :http://mloss.org/software/view/30/ For further information on this implementation of SVM refer to this paper: http://www.kyb.mpg.de/publications/attachments/neco_%5B0%5D.pdf
CCPerceptron	Class Perceptron implements the standard linear (online) perceptron
CCSGDQN	Class SGDQN
CCSVMLin	Class SVMLin
CCSVMSGD	Class SVMSGD
►CCNeuralNetwork	A generic multi-layer neural network
►CCAutoencoder	Represents a single layer neural autoencoder
CCDeepAutoencoder	Represents a muti-layer autoencoder
►CCOnlineLinearMachine	Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms
CCOnlineLibLinear	Class implementing a purely online version of CLibLinear, using the L2R_L1LOSS_SVC_DUAL solver only
CCOnlineSVMSGD	Class OnlineSVMSGD
CCVowpalWabbit	Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit
CCPluginEstimate	Class PluginEstimate
CCStochasticGBMachine	This class implements the stochastic gradient boosting algorithm for ensemble learning invented by Jerome H. Friedman. This class works with a variety of loss functions like squared loss, exponential loss, Huber loss etc which can be accessed through Shogun's CLossFunction interface (cf. http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLossFunction.html). Additionally, it can create an ensemble of any regressor class derived from the CMachine class (cf. http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMachine.html). For one dimensional optimization, this class uses the backtracking linesearch accessed via Shogun's L-BFGS class. A concise description of the algorithm implemented can be found in the following link : http://en.wikipedia.org/wiki/Gradient_boosting#Algorithm
►CCStructuredOutputMachine
CCKernelStructuredOutputMachine
►CCLinearStructuredOutputMachine
CCCCSOSVM	CCSOSVM
CCFWSOSVM	Class CFWSOSVM solves SOSVM using Frank-Wolfe algorithm [1]
CCStochasticSOSVM	Class CStochasticSOSVM solves SOSVM using stochastic subgradient descent on the SVM primal problem [1], which is equivalent to SGD or Pegasos [2]. This class is inspired by the matlab SGD implementation in [3]
►CCMachineEvaluation	Machine Evaluation is an abstract class that evaluates a machine according to some criterion
CCCrossValidation	Base class for cross-validation evaluation. Given a learning machine, a splitting strategy, an evaluation criterion, features and corresponding labels, this provides an interface for cross-validation. Results may be retrieved using the evaluate method. A number of repetitions may be specified for obtaining more accurate results. The arithmetic mean and standard deviation of different runs is returned. Default number of runs is one
CCGradientEvaluation	Class evaluates a machine using its associated differentiable function for the function value and its gradient with respect to parameters
CCMap< K, T >	Class CMap, a map based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table
CCMAPInference	Class CMAPInference performs MAP inference on a factor graph. Briefly, given a factor graph model, with features \(\bold{x}\), the prediction is obtained by \( {\arg\max} _{\bold{y}} P(\bold{Y} = \bold{y} | \bold{x}; \bold{w}) \)
►CCMAPInferImpl	Class CMAPInferImpl abstract class of MAP inference implementation
CCGEMPLP
CCGraphCut
CCMath	Class which collects generic mathematical functions
►CCMeanFunction	An abstract class of the mean function
CCConstMean	The Const mean function class
CCZeroMean	The zero mean function class
CCMemoryMappedFile< T >	Memory mapped file
►CCModelSelection	Abstract base class for model selection
CCGridSearchModelSelection	Model selection class which searches for the best model by a grid- search. See CModelSelection for details
CCRandomSearchModelSelection	Model selection class which searches for the best model by a random search. See CModelSelection for details
CCModelSelectionParameters	Class to select parameters and their ranges for model selection. The structure is organized as a tree with different kinds of nodes, depending on the values of its member variables of name and CSGObject
►CCMulticlassStrategy	Class MulticlassStrategy used to construct generic multiclass classifiers with ensembles of binary classifiers
CCECOCStrategy
CCMulticlassOneVsOneStrategy	Multiclass one vs one strategy used to train generic multiclass machines for K-class problems with building voting-based ensemble of K*(K-1) binary classifiers multiclass probabilistic outputs can be obtained by using the heuristics described in [1]
CCMulticlassOneVsRestStrategy	Multiclass one vs rest strategy used to train generic multiclass machines for K-class problems with building ensemble of K binary classifiers
►CCNeuralLayer	Base class for neural network layers
CCNeuralConvolutionalLayer	Main component in convolutional neural networks
CCNeuralInputLayer	Represents an input layer. The layer can be either connected to all the input features that a network receives (default) or connected to just a small part of those features
►CCNeuralLinearLayer	Neural layer with linear neurons, with an identity activation function. can be used as a hidden layer or an output layer
CCNeuralLogisticLayer	Neural layer with linear neurons, with a logistic activation function. can be used as a hidden layer or an output layer
►CCNeuralRectifiedLinearLayer	Neural layer with rectified linear neurons
CCNeuralLeakyRectifiedLinearLayer	Neural layer with leaky rectified linear neurons
CCNeuralSoftmaxLayer	Neural layer with linear neurons, with a softmax activation function. can be only be used as an output layer. Cross entropy error measure is used
CCNeuralLayers	A class to construct neural layers
CCNode	A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks
CCOperatorFunction< T >	Abstract template base class for computing \(s^{T} f(C) s\) for a linear operator C and a vector s. submit_jobs method creates a bunch of jobs needed to solve for this particular \(s\) and attaches one unique job aggregator to each of them, then submits them all to the computation engine
CCParameterCombination	Class that holds ONE combination of parameters for a learning machine. The structure is organized as a tree. Every node may hold a name or an instance of a Parameter class. Nodes may have children. The nodes are organized in such way, that every parameter of a model for model selection has one node and sub-parameters are stored in sub-nodes. Using a tree of this class, parameters of models may easily be set. There are these types of nodes:
CCParser	Class for reading from a string
►CCPlifBase	Class PlifBase
CCPlif	Class Plif
CCPlifArray	Class PlifArray
CCPlifMatrix	Store plif arrays for all transitions in the model
►CCPreprocessor	Class Preprocessor defines a preprocessor interface
►CCDensePreprocessor< float64_t >
►CCDimensionReductionPreprocessor	Class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices)
CCFisherLDA	Preprocessor FisherLDA attempts to model the difference between the classes of data by performing linear discriminant analysis on input feature vectors/matrices. When the init method in FisherLDA is called with proper feature matrix X(say N number of vectors and D feature dimensions) supplied via apply_to_feature_matrix or apply_to_feature_vector methods, this creates a transformation whose outputs are the reduced T-Dimensional & class-specific distribution (where T<= number of unique classes-1). The transformation matrix is essentially a DxT matrix, the columns of which correspond to the specified number of eigenvectors which maximizes the ratio of between class matrix to within class matrix
CCKernelPCA	Preprocessor KernelPCA performs kernel principal component analysis
CCPCA	Preprocessor PCA performs principial component analysis on input feature vectors/matrices. When the init method in PCA is called with proper feature matrix X (with say N number of vectors and D feature dimension), a transformation matrix is computed and stored internally. This transformation matrix is then used to transform all D-dimensional feature vectors or feature matrices (with D feature dimensions) supplied via apply_to_feature_matrix or apply_to_feature_vector methods. This tranformation outputs the T-Dimensional approximation of all these input vectors and matrices (where T<=min(D,N)). The transformation matrix is essentially a DxT matrix, the columns of which correspond to the eigenvectors of the covariance matrix(XX') having top T eigenvalues
CCHomogeneousKernelMap	Preprocessor HomogeneousKernelMap performs homogeneous kernel maps as described in
CCLogPlusOne	Preprocessor LogPlusOne does what the name says, it adds one to a dense real valued vector and takes the logarithm of each component of it
CCNormOne	Preprocessor NormOne, normalizes vectors to have norm 1
CCPNorm	Preprocessor PNorm, normalizes vectors to have p-norm
CCPruneVarSubMean	Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance
CCRandomFourierGaussPreproc	Preprocessor CRandomFourierGaussPreproc implements Random Fourier Features for the Gauss kernel a la Ali Rahimi and Ben Recht Nips2007 after preprocessing the features using them in a linear kernel approximates a gaussian kernel
CCRescaleFeatures	Preprocessor RescaleFeautres is rescaling the range of features to make the features independent of each other and aims to scale the range in [0, 1] or [-1, 1]
CCSumOne	Preprocessor SumOne, normalizes vectors to have sum 1
►CCFeatureSelection< float64_t >
►CCDependenceMaximization	Class CDependenceMaximization, base class for all feature selection preprocessors which select a subset of features that shows maximum dependence between the features and the labels. This is done via an implementation of CIndependenceTest, m_estimator inside compute_measures() (see class documentation of CFeatureSelection), which performs a statistical test for a given feature \(\mathbf{X}_i\) from the set of features \(\mathbf{X}\), and the labels \(\mathbf{Y}\). The test checks \[ \textbf{H}_0 : P\left(\mathbf{X}\setminus \mathbf{X}_i, \mathbf{Y}\right) =P\left(\mathbf{X}\setminus \mathbf{X}_i\right)P\left(\mathbf{Y}\right) \] The test statistic is then used as a measure which signifies the independence between the rest of the features and the labels - higher the value of the test statistic, greater the dependency between the rest of the features and the class labels, and therefore lesser significant the current feature becomes. Therefore, highest scoring features are removed. The removal policy thus can only be shogun::N_LARGEST and shogun::PERCENTILE_LARGEST and it can be set via set_policy() call. remove_feats() method handles the removal of features based on the specified policy
►CCKernelDependenceMaximization	Class CKernelDependenceMaximization, that uses an implementation of CKernelIndependenceTest to compute dependence measures for feature selection. Different kernels are used for labels and data. For the sake of computational convenience, the precompute() method is overridden to precompute the kernel for labels and save as an instance of CCustomKernel
CCBAHSIC	Class CBAHSIC, that extends CKernelDependenceMaximization and uses HSIC [1] to compute dependence measures for feature selection using a backward elimination approach as described in [1]. This class serves as a convenience class that initializes the CDependenceMaximization::m_estimator with an instance of CHSIC and allows only shogun::BACKWARD_ELIMINATION algorithm to use which is set internally. Therefore, trying to use other algorithms by set_algorithm() will not work. Plese see the class documentation of CHSIC and [2] for more details on mathematical description of HSIC
►CCStringPreprocessor< uint16_t >
CCSortWordString	Preprocessor SortWordString, sorts the indivual strings in ascending order
►CCStringPreprocessor< uint64_t >
CCSortUlongString	Preprocessor SortUlongString, sorts the indivual strings in ascending order
CCDensePreprocessor< ST >	Template class DensePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CDenseFeatures (i.e. rectangular dense matrices)
CCFeatureSelection< ST >	Template class CFeatureSelection, base class for all feature selection preprocessors which select a subset of features (dimensions in the feature matrix) to achieve a specified number of dimensions, m_target_dim from a given set of features. This class showcases all feature selection algorithms via a generic interface. Supported algorithms are specified by the enum EFeatureSelectionAlgorithm which can be set via set_algorithm() call. Supported wrapper algorithms are
CCSparsePreprocessor< ST >	Template class SparsePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSparseFeatures
►CCStringPreprocessor< ST >	Template class StringPreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CStringFeatures (i.e. strings of variable length)
CCDecompressString< ST >	Preprocessor that decompresses compressed strings
►CCProbabilityDistribution	A base class for representing n-dimensional probability distribution over the real numbers (64bit) for which various statistics can be computed and which can be sampled
CCGaussianDistribution	Dense version of the well-known Gaussian probability distribution, defined as \[ \mathcal{N}_x(\mu,\Sigma)= \frac{1}{\sqrt{|2\pi\Sigma|}} \exp\left(-\frac{1}{2}(x-\mu)^T\Sigma^{-1}(x-\mu)\right) \]
CCRandom	: Pseudo random number geneartor
CCRBM	A Restricted Boltzmann Machine
►CCRejectionStrategy	Base rejection strategy class
CCDixonQTestRejectionStrategy	Simplified version of Dixon's Q test outlier based rejection strategy. Statistic values are taken from http://www.vias.org/tmdatanaleng/cc_outlier_tests_dixon.html
CCThresholdRejectionStrategy	Threshold based rejection strategy
CCResultSet
CCSegmentLoss	Class IntronList
►CCSerializableFile	Serializable file
CCSerializableAsciiFile	Serializable ascii file
►CCSerializableFile::TSerializableReader	Serializable reader
CSerializableAsciiReader00	Serializable ascii reader
CCSet< T >	Class CSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table
CCSignal	Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process
CCSimpleFile< T >	Template class SimpleFile to read and write from files
CCSOSVMHelper	Class CSOSVMHelper contains helper functions to compute primal objectives, dual objectives, average training losses, duality gaps etc. These values will be recorded to check convergence. This class is inspired by the matlab implementation of the block coordinate Frank-Wolfe SOSVM solver [1]
►CCSplittingStrategy	Abstract base class for all splitting types. Takes a CLabels instance and generates a desired number of subsets which are being accessed by their indices via the method generate_subset_indices(...)
►CCCrossValidationSplitting	Implementation of normal cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference)
CCLOOCrossValidationSplitting	Implementation of Leave one out cross-validation on the base of CCrossValidationSplitting. Produces subset index sets consisting of one element,for each label
CCStratifiedCrossValidationSplitting	Implementation of stratified cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference) in which the label ratio is equal (at most one difference) to the label ratio of the specified labels. Do not use for regression since it may be impossible to distribute nice in that case
►CCStateModel	Class CStateModel base, abstract class for the internal state representation used in the CHMSVMModel
CCTwoStateModel	Class CTwoStateModel class for the internal two-state representation used in the CHMSVMModel
CCStatistics	Class that contains certain functions related to statistics, such as probability/cumulative distribution functions, different statistics, etc
►CCStreamingFile	A Streaming File access class
CCStreamingAsciiFile	Class StreamingAsciiFile to read vector-by-vector from ASCII files
►CCStreamingFileFromFeatures	Class StreamingFileFromFeatures to read vector-by-vector from a CFeatures object
CCStreamingFileFromDenseFeatures< T >	Class CStreamingFileFromDenseFeatures is a derived class of CStreamingFile which creates an input source for the online framework from a CDenseFeatures object
CCStreamingFileFromSparseFeatures< T >	Class CStreamingFileFromSparseFeatures is derived from CStreamingFile and provides an input source for the online framework. It uses an existing CSparseFeatures object to generate online examples
CCStreamingFileFromStringFeatures< T >	Class CStreamingFileFromStringFeatures is derived from CStreamingFile and provides an input source for the online framework from a CStringFeatures object
CCStreamingVwCacheFile	Class StreamingVwCacheFile to read vector-by-vector from VW cache files
CCStreamingVwFile	Class StreamingVwFile to read vector-by-vector from Vowpal Wabbit data files. It reads the example and label into one object of VwExample type
►CCStructuredData	Base class of the components of StructuredLabels
CCFactorGraphObservation	Class CFactorGraphObservation is used as the structured output
CCRealNumber	Class CRealNumber to be used in the application of Structured Output (SO) learning to multiclass classification. Even though it is likely that it does not make sense to consider real numbers as structured data, it has been made in this way because the basic type to use in structured labels needs to inherit from CStructuredData
CCSequence	Class CSequence to be used in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM)
CCSparseMultilabel	Class CSparseMultilabel to be used in the application of Structured Output (SO) learning to Multilabel classification
►CCStructuredModel	Class CStructuredModel that represents the application specific model and contains most of the application dependent logic to solve structured output (SO) problems. The idea of this class is to be instantiated giving pointers to the functions that are dependent on the application, i.e. the combined feature representation \(\Psi(\bold{x},\bold{y})\) and the argmax function \( {\arg\max} _{\bold{y} \neq \bold{y}_i} \left \langle { \bold{w}, \Psi(\bold{x}_i,\bold{y}) } \right \rangle \). See: MulticlassModel.h and .cpp for an example of these functions implemented
CCFactorGraphModel	CFactorGraphModel defines a model in terms of CFactorGraph and CMAPInference, where parameters are associated with factor types, in the model. There is a mapping vector records the locations of local factor parameters in the global parameter vector
CCHashedMultilabelModel	Class CHashedMultilabelModel represents application specific model and contains application dependent logic for solving multilabel classification with feature hashing within a generic SO framework. We hash the feature of each class with a separate seed and put them in the same feature space (exploded feature space)
CCHierarchicalMultilabelModel	Class CHierarchicalMultilabelModel represents application specific model and contains application dependent logic for solving hierarchical multilabel classification[1] within a generic SO framework
CCHMSVMModel	Class CHMSVMModel that represents the application specific model and contains the application dependent logic to solve Hidden Markov Support Vector Machines (HM-SVM) type of problems within a generic SO framework
CCMulticlassModel	Class CMulticlassModel that represents the application specific model and contains the application dependent logic to solve multiclass classification within a generic SO framework
CCMultilabelCLRModel	Class MultilabelCLRModel represents application specific model and contains application dependent logic for solving multi-label classification using Calibrated Label Ranking (CLR) [1] method within a generic SO framework
CCMultilabelModel	Class CMultilabelModel represents application specific model and contains application dependent logic for solving multilabel classification within a generic SO framework
CCSubset	Wrapper class for an index subset which is used by SubsetStack
CCSubsetStack	Class to add subset support to another class. A CSubsetStackStack instance should be added and wrapper methods to all interfaces should be added
CCTask	Class Task used to represent tasks in multitask learning. Essentially it represent a set of feature vector indices
►CCTaskRelation	Used to represent tasks in multitask learning
CCTaskGroup	Class TaskGroup used to represent a group of tasks. Tasks in group do not overlap
CCTaskTree	Class TaskTree used to represent a tree of tasks. Tree is constructed via task with subtasks (and subtasks of subtasks ..) passed to the TaskTree
CCTime	Class Time that implements a stopwatch based on either cpu time or wall clock time
►CCTokenizer	The class CTokenizer acts as a base class in order to implement tokenizers. Sub-classes must implement the methods has_next(), next_token_idx() and get_copy()
CCDelimiterTokenizer	The class CDelimiterTokenizer is used to tokenize a SGVector<char> into tokens using custom chars as delimiters. One can set the delimiters to use by setting to 1 the appropriate index of the public field delimiters. Eg. to set as delimiter the character ':', one should do: tokenizer->delimiters[':'] = 1;
CCNGramTokenizer	The class CNGramTokenizer is used to tokenize a SGVector<char> into n-grams
►CCTraceSampler	Abstract template base class that provides an interface for sampling the trace of a linear operator using an abstract sample method
CCNormalSampler	Class that provides a sample method for Gaussian samples
►CCTreeMachineNode< T >	The node of the tree structure forming a TreeMachine The node contains a pointer to its parent and a vector of pointers to its children. A node of this class can have only one parent but any number of children.The node also contains data which can be of any type and has to be specified using template specifier
CCBinaryTreeMachineNode< T >	The node of the tree structure forming a TreeMachine The node contains pointer to its parent and pointers to its 2 children: left child and right child. The node also contains data which can be of any type and has to be specified using template specifier
CCTrie< Trie >	Template class Trie implements a suffix trie, i.e. a tree in which all suffixes up to a certain length are stored
►CCVwCacheReader	Base class from which all cache readers for VW should be derived
CCVwNativeCacheReader	Class CVwNativeCacheReader reads from a cache exactly as that which has been produced by VW's default cache format
►CCVwCacheWriter	CVwCacheWriter is the base class for all VW cache creating classes
CCVwNativeCacheWriter	Class CVwNativeCacheWriter writes a cache exactly as that which would be produced by VW's default cache format
CCVwEnvironment	Class CVwEnvironment is the environment used by VW
►CCVwLearner	Base class for all VW learners
CCVwAdaptiveLearner	VwAdaptiveLearner uses an adaptive subgradient technique to update weights
CCVwNonAdaptiveLearner	VwNonAdaptiveLearner uses a standard gradient descent weight update rule
CCVwParser	CVwParser is the object which provides the functions to parse examples from buffered input
CCVwRegressor	Regressor used by VW
CCWrappedBasic< T >	Simple wrapper class that allows to store any Shogun basic parameter (i.e. float64_t, int64_t, char, etc) in a CSGObject, and therefore to make it serializable. Using a template argument that is not a Shogun parameter will cause a compile error when trying to register the passed value as a parameter in the constructors
CCWrappedSGMatrix< T >	Simple wrapper class that allows to store any Shogun SGMatrix<T> in a CSGObject, and therefore to make it serializable. Using a template argument that is not a Shogun parameter will cause a compile error when trying to register the passed value as a parameter in the constructors
CCWrappedSGVector< T >	Simple wrapper class that allows to store any Shogun SGVector<T> in a CSGObject, and therefore to make it serializable. Using a template argument that is not a Shogun parameter will cause a compile error when trying to register the passed value as a parameter in the constructors
►CDescendCorrection	This is a base class for descend based correction method
►CMomentumCorrection	This is a base class for momentum correction methods
CAdaptMomentumCorrection	This implements the adaptive momentum correction method
CNesterovMomentumCorrection	This implements the Nesterov's Accelerated Gradient (NAG) correction
CStandardMomentumCorrection	This implements the plain momentum correction
►CDescendUpdater	This is a base class for descend update
►CDescendUpdaterWithCorrection	This is a base class for descend update with descend based correction
CAdaDeltaUpdater	The class implements the AdaDelta method. \[ \begin{array}{l} g_\theta=(1-\lambda){(\frac{ \partial f(\cdot) }{\partial \theta })}^2+\lambda g_\theta\\ d_\theta=\alpha\frac{\sqrt{s_\theta+\epsilon}}{\sqrt{g_\theta+\epsilon}}\frac{ \partial f(\cdot) }{\partial \theta }\\ s_\theta=(1-\lambda){(d_\theta)}^2+\lambda s_\theta \end{array} \]
CAdaGradUpdater	The class implements the AdaGrad method
CAdamUpdater	The class implements the Adam method
CGradientDescendUpdater	The class implements the gradient descend method
CRmsPropUpdater	The class implements the RmsProp method
►CFirstOrderCostFunction	The first order cost function base class
CFirstOrderBoundConstraintsCostFunction	The first order cost function base class with bound constrains
►CFirstOrderStochasticCostFunction	The first order stochastic cost function base class
CFirstOrderSAGCostFunction	The class is about a stochastic cost function for stochastic average minimizers
►CLearningRate	The base class about learning rate for descent-based minimizers
CConstLearningRate	This implements the const learning rate class for a descent-based minimizer
CInverseScalingLearningRate	The implements the inverse scaling learning rate
►CMappingFunction	The base mapping function for mirror descend
CPNormMappingFunction	This implements the P-norm mapping/projection function
►CMinimizer	The minimizer base class
CCSingleFITCLaplaceNewtonOptimizer	The build-in minimizer for SingleFITCLaplaceInference
CCSingleLaplaceNewtonOptimizer	The build-in minimizer for SingleLaplaceInference
►CFirstOrderMinimizer	The first order minimizer base class
►CCLBFGSMinimizer	The class wraps the Shogun's C-style LBFGS minimizer
CCKLDualInferenceMethodMinimizer	Build-in minimizer for KLDualInference
►CFirstOrderStochasticMinimizer	The base class for stochastic first-order gradient-based minimizers
CSGDMinimizer	The class implements the stochastic gradient descend (SGD) minimizer
►CSMDMinimizer	The class implements the stochastic mirror descend (SMD) minimizer
CSMIDASMinimizer	The class implements the Stochastic MIrror Descent mAde Sparse (SMIDAS) minimizer
CSVRGMinimizer	The class implements the stochastic variance reduced gradient (SVRG) minimizer
►CMKLMulticlassOptimizationBase	MKLMulticlassOptimizationBase is a helper class for MKLMulticlass
CMKLMulticlassGLPK	MKLMulticlassGLPK is a helper class for MKLMulticlass
CMKLMulticlassGradient	MKLMulticlassGradient is a helper class for MKLMulticlass
►CPenalty	The base class for penalty/regularization used in minimization
CL2Penalty	The class implements L2 penalty/regularization within the FirstOrderMinimizer framework
►CProximalPenalty	The base class for sparse penalty/regularization used in minimization
►CSparsePenalty	The base class for sparse penalty/regularization used in minimization
CElasticNetPenalty	The is the base class for ElasticNet penalty/regularization within the FirstOrderMinimizer framework
►CL1Penalty	The is the base class for L1 penalty/regularization within the FirstOrderMinimizer framework
CL1PenaltyForTG	The is the base class for L1 penalty/regularization within the FirstOrderMinimizer framework
►CCSGObject
CCTron	Class Tron
CCSyntaxHighLight	Syntax highlight
CCTaxonomy	CTaxonomy is used to describe hierarchical structure between tasks
Cd_node< P >
CD_THREAD_PARAM< T >
Cdot< Backend, Vector >	Generic class dot which provides a static compute method. This class is specialized for different types of vectors and backend, providing a mean to deal with various vectors directly without having to convert
Cdot< Backend::EIGEN3, Vector >	Specialization of generic dot for the Eigen3 backend
Cds_node< P >
CDynArray< T >	Template Dynamic array class that creates an array that can be used like a list or an array
CDynArray< bool >
CDynArray< char * >
CDynArray< char >
CDynArray< CMapNode< K, T > * >
CDynArray< CMapNode< shogun::TParameter *, shogun::CSGObject * > * >
CDynArray< CMapNode< shogun::TParameter *, shogun::SGVector< float64_t > > * >
CDynArray< CMapNode< std::string, T > * >
CDynArray< CSetNode< T > * >
CDynArray< float32_t >
CDynArray< float64_t >
CDynArray< int32_t >
CDynArray< long >
CDynArray< shogun::CPlifBase * >
CDynArray< shogun::CSGObject * >
CDynArray< shogun::SGVector< int32_t > >
CDynArray< shogun::TParameter * >
CDynArray< T_ATTRIBUTE >
CEigenSparseUtil< T >	This class contains some utilities for Eigen3 Sparse Matrix integration with shogun. Currently it provides a method for converting SGSparseMatrix to Eigen3 SparseMatrix
Celementwise_product< Backend, Matrix >
Celementwise_product< Backend::EIGEN3, Matrix >
Celementwise_square< Backend, Matrix >	Generic class square which provides a static compute method. This class is specialized for different types of matrices and backend, providing a mean to deal with various matrices directly without having to convert
Celementwise_square< Backend::EIGEN3, Matrix >	Partial specialization of generic elementwise_square for the Eigen3 backend
Celementwise_unary_operation< Backend, Operand, ReturnType, UnaryOp >	Template struct elementwise_unary_operation. This struct is specialized for computing element-wise operations for both matrices and vectors of CPU (SGMatrix/SGVector) or GPU (CGPUMatrix/CGPUVector)
Celementwise_unary_operation< Backend::EIGEN3, Operand, ReturnType, UnaryOp >	Specialization for elementwise_unary_operation with EIGEN3 backend. The operand types MUST be of CPU types (SGMatrix/SGVector)
Celementwise_unary_operation< Backend::NATIVE, Operand, ReturnType, UnaryOp >	Specialization for elementwise_unary_operation with NATIVE backend. The operand types MUST be of CPU types (SGMatrix/SGVector)
CAny::Empty
CEntryComparator
CGCEdge	Graph cuts edge
CGCNode	Graph cuts node
CGCNodePtr	Graph guts node pointer
Chash< shogun::BaseTag >
Cid3TreeNodeData	Structure to store data of a node of id3 tree. This can be used as a template type in TreeMachineNode class. Ex: id3 algorithm uses nodes of type CTreeMachineNode<id3TreeNodeData>
CCMath::IndexSorter< T >
Cint2float< inputType >	Generic class int2float which converts different types of integer into float64 type
Cint2float< int32_t >	Specialization of generic class int2float which converts int32 into float64
Cint2float< int64_t >	Specialization of generic class int2float which converts int64 into float64
CIterativeSolverIterator< T >	Template class that is used as an iterator for an iterative linear solver. In the iteration of solving phase, each solver initializes the iteration with a maximum number of iteration limit, and relative/ absolute tolerence. They then call begin with the residual vector and continue until its end returns true, i.e. either it has converged or iteration count reached maximum limit
CK_THREAD_PARAM< T >
Clbfgs_parameter_t
CLDLT< class, int >
►CLoggerImplementation
CShogunLoggerImplementation
Clogistic< Backend, Matrix >
Clogistic< Backend::EIGEN3, Matrix >
CMap< class, int, class >
CMappedSparseMatrix	Mapped sparse matrix for representing graph relations of tasks
CMatrix< class, int, int, int, int, int >
Cmatrix_product< Backend, Matrix >
Cmatrix_product< Backend::EIGEN3, Matrix >
Cmax< Backend, Matrix >	Generic class which is specialized for different backends to perform the max operation
Cmax< Backend::EIGEN3, Matrix >	Specialization of max for the Eigen3 backend
CMaybe< T >	Holder that represents an object that can be either present or absent. Quite simllar to std::optional introduced in C++14, but provides a way to pass the reason of absence (e.g. "incorrect parameter")
Cmean< Backend, Matrix >	Generic class mean which provides a static compute method
Cmean< Backend::EIGEN3, Matrix >	Specialization of generic mean which works with SGVector and SGMatrix and uses Eigen3 as backend for computing mean
CMixModelData	This structure is used for storing data required for using the generic Expectation Maximization (EM) implemented by the template class CEMBase for mixture models like gaussian mixture model, multinomial mixture model etc. The EM specialized for mixture models is implemented by the class CEMMixtureModel which uses this MixModelData structure
CModel	Class Model
Cmultiply_by_logistic_derivative< Backend, Matrix >
Cmultiply_by_logistic_derivative< Backend::EIGEN3, Matrix >
Cmultiply_by_rectified_linear_derivative< Backend, Matrix >
Cmultiply_by_rectified_linear_derivative< Backend::EIGEN3, Matrix >
CMunkres	Munkres
CNbodyTreeNodeData	Structure to store data of a node of N-Body tree. This can be used as a template type in TreeMachineNode class. N-Body tree building algorithm uses nodes of type CBinaryTreeMachineNode<NbodyTreeNodeData>
Cnode< P >
CNothing
►Cocl_operation	Class ocl_operation for element-wise unary OpenCL operations for GPU-types (CGPUMatrix/CGPUVector)
Csin< T >
CParallel	Class Parallel provides helper functions for multithreading
CParameter	Struct Parameter for wrapping up parameters to custom OpenCL operation strings. Supports string type, C-style string type and all basic types of parameters
CParameter	Parameter class
CCGEMPLP::Parameter
Crange_fill< Backend, Matrix >	Generic class which is specialized for different backends to perform the Range fill operation
Crange_fill< Backend::EIGEN3, Matrix >	Partial specialization of add for the Eigen3 backend
Crectified_linear< Backend, Matrix >
Crectified_linear< Backend::EIGEN3, Matrix >
CRefCount
CRelaxedTreeNodeData
CRelaxedTreeUtil
Crowwise_mean< Backend, Matrix >	Generic class rowwise_mean which provides a static compute method
Crowwise_mean< Backend::EIGEN3, Matrix >	Specialization of generic mean which works with SGMatrix and uses Eigen3 as backend for computing rowwise mean
Crowwise_sum< Backend, Matrix >	Generic class rowwise_sum which provides a static compute method. This class is specialized for different types of matrices and backend, providing a means to deal with various matrices directly without having to convert
Crowwise_sum< Backend::EIGEN3, Matrix >	Specialization of generic rowwise_sum which works with SGMatrix and uses Eigen3 as backend for computing sum
Cscale< Backend, Matrix >
Cscale< Backend::EIGEN3, Matrix >
CCMKL::Self
CCSGObject::Self
Cset_rows_const< Backend, Matrix, Vector >
Cset_rows_const< Backend::EIGEN3, Matrix, Vector >
CSGIO	Class SGIO, used to do input output operations throughout shogun
►CSGReferencedData	Shogun reference count managed data
CSGMatrix< bool >
CSGMatrix< double >
CSGMatrix< float32_t >
CSGMatrix< float64_t >
CSGMatrix< index_t >
CSGMatrix< int >
CSGMatrix< int32_t >
CSGMatrix< ST >
CSGMatrix< uint16_t >
CSGMatrix< uint32_t >
CSGMatrixList< float64_t >
CSGMatrixList< ST >
CSGNDArray< float64_t >
CSGSparseMatrix< float64_t >
CSGSparseMatrix< ST >
CSGSparseVector< float64_t >
CSGSparseVector< ST >
CSGVector< bool >
CSGVector< char >
CSGVector< complex128_t >
CSGVector< float32_t >
CSGVector< float64_t >
CSGVector< index_t >
CSGVector< int32_t >
CSGVector< uint32_t >
CSGVector< uint64_t >
CSGMatrix< T >	Shogun matrix
CSGMatrixList< T >	Shogun matrix list
CSGNDArray< T >	Shogun n-dimensional array
CSGSparseMatrix< T >	Template class SGSparseMatrix
CSGSparseVector< T >	Template class SGSparseVector The assumtion is that the stored SGSparseVectorEntry<T>* vector is ordered by SGSparseVectorEntry.feat_index in non-decreasing order. This has to be assured by the user of the class
CSGStringList< T >	Template class SGStringList
CSGVector< T >	Shogun vector
CSGSparseVectorEntry< T >	Template class SGSparseVectorEntry
CSGSparseVectorEntry< float64_t >
CSGSparseVectorEntry< ST >
CSGString< T >	Shogun string
CSGString< char >
CSGString< ST >
CSGString< uint16_t >
CSGString< uint8_t >
CShareBoostOptimizer
CShogunException	Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs
CShogunFeatureVectorCallback
CCStatistics::SigmoidParamters
Csin< complex128_t >
Csoftmax< Backend, Matrix >
Csoftmax< Backend::EIGEN3, Matrix >
CSparsityStructure	Struct that represents the sparsity structure of the Sparse Matrix in CRS. Implementation has been adapted from Krylstat (https://github.com/ Froskekongen/KRYLSTAT) library (c) Erlend Aune erlen.nosp@m.da@m.nosp@m.ath.n.nosp@m.tnu..nosp@m.no under GPL2+
Csquared_error< Backend, Matrix >
Csquared_error< Backend::EIGEN3, Matrix >
CSSKFeatures	SSKFeatures
CStride< int, int >
Csubstring	Struct Substring, specified by start position and end position
Csum< Backend, Matrix >	Generic class sum which provides a static compute method. This class is specialized for different types of matrices and backend, providing a means to deal with various matrices directly without having to convert
Csum< Backend::EIGEN3, Matrix >	Specialization of generic sum which works with SGMatrix and uses Eigen3 as backend for computing sum
Csum_symmetric< Backend, Matrix >	Generic class sum symmetric which provides a static compute method. This class is specialized for different types of matrices and backend, providing a means to deal with various matrices directly without having to convert
Csum_symmetric< Backend::EIGEN3, Matrix >	Specialization of generic sum symmetric which works with SGMatrix and uses Eigen3 as backend for computing sum
Ctag_callback_data
Ctag_iteration_data
Ctask_tree_node_t
CTMultipleCPinfo
CTParameter	Parameter struct
Ctree_node_t
CTSGDataType	Datatypes that shogun supports
CUnique< T >
CUnique< CMKL::Self >
CUnique< shogun::CSGObject::Self >
Cv_array< T >	Class v_array taken directly from JL's implementation
Cv_array< char >
Cv_array< float >
Cv_array< shogun::substring >
Cv_array< shogun::VwFeature >
Cv_array< vw_size_t >
Cvector_sum< Backend, Vector >	Generic class vector_sum which provides a static compute method. This class is specialized for different types of vectors and backend, providing a mean to deal with various vectors directly without having to convert
Cvector_sum< Backend::EIGEN3, Vector >	Specialization of generic vector_sum for the Eigen3 backend
CVersion	Class Version provides version information
CVwConditionalProbabilityTreeNodeData
CVwExample	Example class for VW
CVwFeature	One feature in VW
CVwLabel	Class VwLabel holds a label object used by VW