SHOGUN  3.2.1
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Groups Pages
Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 12345678]
oC_IterInfoStruct that contains current state of the iteration for iterative linear solvers
oCadd< Backend, Matrix >
oCadd< Backend::EIGEN3, Matrix >
oCBlock< 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
oCblock_tree_node_t
oCbmrm_ll
oCBmrmStatistics
oCC45TreeNodeDataStructure 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>
oCCARTreeNodeDataStructure 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>
oCCConvolutionalFeatureMapHandles convolution and gradient calculation for a single feature map in a convolutional neural network
oCCDynInt< T, sz >Integer type of dynamic size
oCCECOCUtil
oCCHAIDTreeNodeDataStructure 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>
oCCIndirectObject< T, P >Array class that accesses elements indirectly via an index array
oCCJLCoverTreePointClass 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
oCCKMeansLloydImpl
oCCKMeansMiniBatchImpl
oCCKNNHeapThis 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
oCCLockClass Lock used for synchronization in concurrent programs
oCCLossClass which collects generic mathematical functions
oCCMatrixOperationsThe helper class is used for Laplace and KL methods
oCcolwise_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 mean to deal with various matrices directly without having to convert
oCcolwise_sum< Backend::EIGEN3, Matrix >Specialization of generic colwise_sum which works with SGMatrix and uses Eigen3 as backend for computing sum
oCConditionalProbabilityTreeNodeDataStruct to store data of node of conditional probability tree
oCcross_entropy< Backend, Matrix >
oCcross_entropy< Backend::EIGEN3, Matrix >
oCCSyntaxHighLightSyntax highlight
oCCTronClass Tron
oCd_node< P >
oCD_THREAD_PARAM< T >
oCdot< 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
oCdot< Backend::EIGEN3, Vector >Specialization of generic dot for the Eigen3 backend
oCds_node< P >
oCDynArray< T >Template Dynamic array class that creates an array that can be used like a list or an array
oCEigenSparseUtil< T >This class contains some utilities for Eigen3 Sparse Matrix integration with shogun. Currently it provides a method for converting SGSparseMatrix to Eigen3 SparseMatrix
oCelementwise_product< Backend, Matrix >
oCelementwise_product< Backend::EIGEN3, Matrix >
oCelementwise_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
oCelementwise_square< Backend::EIGEN3, Matrix >Specialization of generic elementwise_square for the Eigen3 backend
oCEntryComparator
oCGCEdgeGraph cuts edge
oCGCNodeGraph cuts node
oCGCNodePtrGraph guts node pointer
oCICP_stats
oCid3TreeNodeDataStructure 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>
oCIndexSorter< T >
oCIterativeSolverIterator< 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
oCK_THREAD_PARAM< T >
oClbfgs_parameter_t
oCline_search_res
oClogistic< Backend, Matrix >
oClogistic< Backend::EIGEN3, Matrix >
oCMappedSparseMatrixMapped sparse matrix for representing graph relations of tasks
oCmatrix_product< Backend, Matrix >
oCmatrix_product< Backend::EIGEN3, Matrix >
oCmax< Backend, Matrix >
oCmax< Backend::EIGEN3, Matrix >
oCMixModelDataThis 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
oCmocas_data
oCModelClass Model
oCmultiply_by_logistic_derivative< Backend, Matrix >
oCmultiply_by_logistic_derivative< Backend::EIGEN3, Matrix >
oCmultiply_by_rectified_linear_derivative< Backend, Matrix >
oCmultiply_by_rectified_linear_derivative< Backend::EIGEN3, Matrix >
oCMunkresMunkres
oCNbodyTreeNodeDataStructure 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>
oCnode< P >
oCParallelClass Parallel provides helper functions for multithreading
oCParameterParameter class
oCParameterMapImplements a map of ParameterMapElement instances Maps one key to a set of values
oCParameterMapElementClass to hold instances of a parameter map. Each element contains a key and a set of values, which each are of type SGParamInfo. May be compared to each other based on their keys
oCrectified_linear< Backend, Matrix >
oCrectified_linear< Backend::EIGEN3, Matrix >
oCRefCount
oCRelaxedTreeNodeData
oCRelaxedTreeUtil
oCrowwise_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 mean to deal with various matrices directly without having to convert
oCrowwise_sum< Backend::EIGEN3, Matrix >Specialization of generic rowwise_sum which works with SGMatrix and uses Eigen3 as backend for computing sum
oCscale< Backend, Matrix >
oCscale< Backend::EIGEN3, Matrix >
oCset_rows_const< Backend, Matrix, Vector >
oCset_rows_const< Backend::EIGEN3, Matrix, Vector >
oCSGIOClass SGIO, used to do input output operations throughout shogun
oCSGParamInfoClass that holds informations about a certain parameter of an CSGObject. Contains name, type, etc. This is used for mapping types that have changed in different versions of shogun. Instances of this class may be compared to each other. Ordering is based on name, equalness is based on all attributes
oCSGReferencedDataShogun reference count managed data
oCSGRefObjectClass SGRefObject is a reference count based memory management class
oCSGSparseVectorEntry< T >Template class SGSparseVectorEntry
oCSGString< T >Shogun string
oCShareBoostOptimizer
oCShogunExceptionClass ShogunException defines an exception which is thrown whenever an error inside of shogun occurs
oCShogunFeatureVectorCallback
oCShogunLoggerImplementation
oCCStatistics::SigmoidParamters
oCsoftmax< Backend, Matrix >
oCsoftmax< Backend::EIGEN3, Matrix >
oCSparsityStructureStruct 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+
oCsquared_error< Backend, Matrix >
oCsquared_error< Backend::EIGEN3, Matrix >
oCSSKFeaturesSSKFeatures
oCsubstringStruct Substring, specified by start position and end position
oCsum< Backend, Matrix >Generic class sum 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
oCsum< Backend::EIGEN3, Matrix >Specialization of generic sum which works with SGMatrix and uses Eigen3 as backend for computing sum
oCsum_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 mean to deal with various matrices directly without having to convert
oCsum_symmetric< Backend::EIGEN3, Matrix >Specialization of generic sum symmetric which works with SGMatrix and uses Eigen3 as backend for computing sum
oCtag_callback_data
oCtag_iteration_data
oCtask_tree_node_t
oCTMultipleCPinfo
oCTParameterParameter struct
oCtree_node_t
oCTSGDataTypeDatatypes that shogun supports
oCv_array< T >Class v_array taken directly from JL's implementation
oCvector_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
oCvector_sum< Backend::EIGEN3, Vector >Specialization of generic vector_sum for the Eigen3 backend
oCVersionClass Version provides version information
oCVwConditionalProbabilityTreeNodeData
oCVwExampleExample class for VW
oCVwFeatureOne feature in VW
\CVwLabelClass VwLabel holds a label object used by VW

SHOGUN Machine Learning Toolbox - Documentation