Namespace List

Here is a list of all namespaces with brief descriptions:

[detail level 12345]

▼Nanonymous_namespace{cascade.cpp}
CCombinedWeightInitializer
▼Nanonymous_namespace{collection.cpp}
CMockStat
Nanonymous_namespace{dense.cpp}
▼Nanonymous_namespace{dense_and_sparse.cpp}
CL2Regularizer
CZeroPhi
Nanonymous_namespace{generic_linear.cpp}
Nanonymous_namespace{hash_vector.cpp}	Local namespace in which we define the counter used to create the unique ids for the hash vector
▼Nanonymous_namespace{hyperparams.cpp}
CTestObject
CNestedTestObject
Nanonymous_namespace{linear.cpp}
Nanonymous_namespace{metrics.cpp}
▼Nanonymous_namespace{minimizer.cpp}
CMockMinimizer	A minimizer to be used in test cases that returns a fixed result
CMockObjective	An objective to be used in test cases. Does not do any computations, but just resturns constants
Nanonymous_namespace{model-io.cpp}
Nanonymous_namespace{newton.cpp}
Nanonymous_namespace{null.cpp}
▼Nanonymous_namespace{numa.cpp}
CNodeData
Nanonymous_namespace{numpy.cpp}
Nanonymous_namespace{objective.cpp}
Nanonymous_namespace{prediction.cpp}
Nanonymous_namespace{py_data.cpp}
Nanonymous_namespace{py_train.cpp}
Nanonymous_namespace{reg_sq_hinge.cpp}
Nanonymous_namespace{regularizers_imp.cpp}
▼Nanonymous_namespace{runner.cpp}
CDummyTask
Nanonymous_namespace{slice.cpp}
▼Nanonymous_namespace{sparse.cpp}
CPredictVisitor
CSetWeightsVisitor
Nanonymous_namespace{sparsify.cpp}
▼Nanonymous_namespace{statistics.cpp}
CDefaultGatherer
▼Nanonymous_namespace{transform.cpp}
CVisitorBias
Nanonymous_namespace{weights.cpp}
▼Nanonymous_namespace{xmc.cpp}
CXMCHeader	Collects the data from the header of an xmc file XMC data format
▼Ndismec	Main namespace in which all types, classes, and functions are defined
►Nconfusion_matrix_detail
CConfusionMatrixBase
Neigen_visitors
►Ninit
CConstantInitializer
CConstantInitializationStrategy	An initialization strategy that sets the weight vector to a given constant
CMeanOfFeaturesInitializer
CMeanOfFeaturesStrategy
CTypeLookup
CTypeLookup< false >
CTypeLookup< true >
CMultiPosMeanInitializer
CMultiPosMeanStrategy
CNumpyInitializer
CNumpyInitializationStrategy
CPreTrainedInitializer
CPreTrainedInitializationStrategy
CSubsetFeatureMeanInitializer
CSubsetFeatureMeanStrategy
CZeroInitializer
CZeroInitializationStrategy
CWeightsInitializer	Base class for all weight initializers
CWeightInitializationStrategy	Base class for all weight init strategies
►Nio
Ndetail
►Nmodel	Namespace for all model-related io functions
CSaveOption
CWeightFileEntry	Collect the data about a weight file
CPartialModelIO	This class is used as an implementation detail to capture the common code of PartialModelSaver and PartialModelLoader
CPartialModelSaver	Manage saving a model consisting of multiple partial models
►CPartialModelLoader	This class allows loading only a subset of the weights of a large model
CSubModelRangeSpec
Nprediction
CMatrixHeader	Collects the rows and columns parsed from a plain-text matrix file
CLoLBinarySparse	Binary Sparse Matrix in List-of-Lists format
CNpyHeaderData	Contains the data of the header of a npy file with an array that has at most 2 dimensions
Nl2_reg_sq_hinge_detail
►Nmodel
CDenseModel	Implementation of the Model class that stores the weights as a single, dense matrix
CPartialModelSpec	Specifies how to interpret a weight matrix for a partial model
CModel	A model combines a set of weight with some meta-information about these weights
CSparseModel
CSubModelWrapper
►Nobjective
CDenseAndSparseLinearBase	Base class for implementationa of an objective that combines dense features and sparse features
CDenseAndSparseMargin
CGenericLinearClassifier	This is a non-templated, runtime-polymorphic generic implementation of the linear classifier objective
CGenericMarginClassifier	A utility class template that, when instatiated with a MarginFunction, produces the corresponding linear classifier loss
CLinearClassifierBase	Base class for objectives that use a linear classifier
CLinearClassifierImpBase	Implementation helper for linear classifier derived classes
CSquaredHingePhi
CHuberPhi
CLogisticPhi
CObjective	Class that models an optimization objective
CPointWiseRegularizer	Base class for pointwise regularization functions
CRegularized_SquaredHingeSVC
CSquaredNormConfig
CHuberConfig
CElasticConfig
CSquaredNormRegularizer	This class implements a squared norm (L2) regularizer. Thus f(x) = 0.5 |x|^2
CHuberRegularizer	This class implements a huber regularizer
CElasticNetRegularizer
►Nparallel
CNUMAReplicatorBase	Base class for NUMAReplicator
CNUMAReplicator	Helper class to ensure that each NUMA node has its own copy of some immutable data
CThreadDistributor	This class helps with distributing threads to the different CPU cores
CRunResult
CParallelRunner
CTaskGenerator	Base class for all parallelized operations
Cthread_id_t	Strong typedef for an int to signify a thread id
Cnuma_node_id_t	Strong typedef for an int to signify a numa domain
Ccpu_id_t	Strong typedef for an int to signify a (core of a) cpu
►Npostproc
CCombinePostProcessor
CCombinedFactory
CGenericPostProcFactory
CReorderPostProc
►CSparsify
CBoundData
CUpperBoundResult
CIdentityPostProc
CCullingPostProcessor
CPostProcessor
CPostProcessFactory
►Nprediction
CsTrueLabelInfo
CsPredLabelInfo
CEvaluateMetrics	This TaskGenerator enables the calculation of evaluation metrics on top-k style sparse predictions
CMetricCollectionInterface	Base class for all metrics that can be calculated during the evaluation phase
CConfusionMatrixRecorder
CInstanceAveragedMetric
CInstanceRankedPositives
CAbandonmentAtK
CMetricReportInterface
CInstanceWiseMetricReporter
CMacroMetricReporter
CPredictionBase	Base class for handling predictions
CFullPredictionTaskGenerator
CTopKPredictionTaskGenerator
►Nsolvers
CCGMinimizer	Approximately solve a linear equation Ax + b = 0
CsLineSearchResult	Result of a Line Search operation
CBacktrackingLineSearch	Backtracking line search using the armijo rule
CMinimizationResult
CMinimizer
CNewtonWithLineSearch
CNullOptimizer	Optimizer that does not change the initial vector
►Nstats
Ndetail
CStatisticsCollection	This class manages a collection of named Statistics objects
CStatisticMetaData	Data that is associated with each declared statistics
CCounterStat
CBasicStat
CTaggedStat
CMultiStat
CFullRecordStat
CVectorReductionStat
CTagContainer	A tag container combines a name with a shared pointer, which points to the tag value
CStatistics	TODO maybe we should solve this with a variant which does the dispatch of expected type and tag
CStatImplBase	Helper class for implementing Statistics classes
CScopeTimer
CTracked	A base class to be used for all types that implement some for of statistics tracking
►Ntypes
►Ntype_helpers
Ndefinitions
CGenericMatrixRef
CGenericMatrix
CGenericVectorRef
CVarWrapBase
CEigenVariantWrapper
CRefVariant
CDataProcessing
CDatasetBase
CBinaryData	Collects the data related to a single optimization problem
CMultiLabelData
Clabel_id_t	Strong typedef for an int to signify a label id
CCascadeTraining
CDiSMECTraining	An implementation of TrainingSpec that models the DiSMEC algorithm
CTrainingSpec	This class gathers the setting-specific parts of the training process
CDismecTrainingConfig
CCascadeTrainingConfig
CResultStatsGatherer
►CTrainingStatsGatherer
CStatData
CTrainingTaskGenerator	Generates tasks for training weights for the i'th label
CTrainingResult
CPropensityModel
CWeightingScheme	Base class for label-based weighting schemes
CConstantWeighting	Simple weighting scheme that assigns the same weighting to all label_ids
CPropensityWeighting
CPropensityDownWeighting
CCustomWeighting
CFastSparseRowIter	This is an almost verbatim copy of the SparseFeatures::InnerIterator provided by Eigen
CHashVector	An Eigen vector with versioning information, to implement simple caching of results
CVectorHash	A unique identifier for a HashVector
CCacheHelper
►CHyperParameterBase	Base class for all objects that have adjustable hyper-parameters
CHyperParamData	This structure collects the Getter and Setter functions. This is what we store in the variant
CHyperParameters	This class represents a set of hyper-parameters
Copaque_int_type	An integer-like type that represents categorical values
CKahanAccumulator	Implements a numerically stable sum algorithm
▼NEigen
CEigenBase