SHOGUN  4.1.0
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Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 1234567]
 C_IterInfoStruct 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
 Cadd< Backend::NATIVE, Matrix >Partial specialization of add for the Native 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
 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
 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
 Cbmrm_ll
 CBmrmStatistics
 CC45TreeNodeDataStructure 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>
 CCARTreeNodeDataStructure 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>
 CCConvolutionalFeatureMapHandles convolution and gradient calculation for a single feature map in a convolutional neural network
 CCDynInt< T, sz >Integer type of dynamic size
 CCECOCUtil
 CCHAIDTreeNodeDataStructure 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>
 CCIndirectObject< T, P >Array class that accesses elements indirectly via an index array
 CCJLCoverTreePointClass 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
 CCKMeansLloydImpl
 CCKMeansMiniBatchImpl
 CCKNNHeapThis 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
 CCLockClass Lock used for synchronization in concurrent programs
 CCLossClass which collects generic mathematical functions
 CCMatrixOperationsThe 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
 CConditionalProbabilityTreeNodeDataStruct 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 >
 CCSGObjectClass SGObject is the base class of all shogun objects
 CCSGObject
 CCSyntaxHighLightSyntax highlight
 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
 Cdot< Backend::NATIVE, Vector >Specialization of generic dot for the Native 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< const shogun::SGParamInfo * >
 CDynArray< CSetNode< T > * >
 CDynArray< float32_t >
 CDynArray< float64_t >
 CDynArray< int32_t >
 CDynArray< long >
 CDynArray< shogun::CPlifBase * >
 CDynArray< shogun::CSGObject * >
 CDynArray< shogun::ParameterMapElement * >
 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)
 CEntryComparator
 CGCEdgeGraph cuts edge
 CGCNodeGraph cuts node
 CGCNodePtrGraph guts node pointer
 CICP_stats
 Cid3TreeNodeDataStructure 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 >
 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 >
 Cline_search_res
 CLoggerImplementation
 Clogistic< Backend, Matrix >
 Clogistic< Backend::EIGEN3, Matrix >
 CMap< class, int, class >
 CMappedSparseMatrixMapped 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
 Cmax< Backend::NATIVE, Matrix >Specialization of add for the Native backend
 CMixModelDataThis 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
 Cmocas_data
 CModelClass 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 >
 CMunkresMunkres
 CNbodyTreeNodeDataStructure 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 >
 Cocl_operationClass ocl_operation for element-wise unary OpenCL operations for GPU-types (CGPUMatrix/CGPUVector)
 CParallelClass Parallel provides helper functions for multithreading
 CParameterStruct Parameter for wrapping up parameters to custom OpenCL operation strings. Supports string type, C-style string type and all basic types of parameters
 CParameterParameter class
 CCGEMPLP::Parameter
 CParameterMapImplements a map of ParameterMapElement instances Maps one key to a set of values
 CParameterMapElementClass 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
 Crectified_linear< Backend, Matrix >
 Crectified_linear< Backend::EIGEN3, Matrix >
 CRefCount
 CRelaxedTreeNodeData
 CRelaxedTreeUtil
 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 >
 Cscale< Backend::NATIVE, Matrix >
 CCMultitaskL12LogisticRegression::Self
 Cset_rows_const< Backend, Matrix, Vector >
 Cset_rows_const< Backend::EIGEN3, Matrix, Vector >
 CSGIOClass SGIO, used to do input output operations throughout shogun
 CSGParamInfoClass 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
 CSGReferencedDataShogun reference count managed data
 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
 CShogunExceptionClass 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 >
 CSparsityStructureStruct 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 >
 CSSKFeaturesSSKFeatures
 CStride< int, int >
 CsubstringStruct 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
 CTParameterParameter struct
 Ctree_node_t
 CTSGDataTypeDatatypes that shogun supports
 CUnique< T >
 CUnique< shogun::CMultitaskL12LogisticRegression::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
 Cvector_sum< Backend::NATIVE, Vector >Specialization of generic vector_sum for the Native backend
 CVersionClass Version provides version information
 CVwConditionalProbabilityTreeNodeData
 CVwExampleExample class for VW
 CVwFeatureOne feature in VW
 CVwLabelClass VwLabel holds a label object used by VW

SHOGUN Machine Learning Toolbox - Documentation