mlpack
2.1.1

Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 12345]
▼Nboost  
▼Nmlpack  Linear algebra utility functions, generally performed on matrices or vectors 
►Nadaboost  
CAdaBoost  The AdaBoost class 
►Namf  Alternating Matrix Factorization 
CAMF  This class implements AMF (alternating matrix factorization) on the given matrix V 
CAverageInitialization  This initialization rule initializes matrix W and H to root of the average of V, perturbed with uniform noise 
CCompleteIncrementalTermination  This class acts as a wrapper for basic termination policies to be used by SVDCompleteIncrementalLearning 
CGivenInitialization  This initialization rule for AMF simply fills the W and H matrices with the matrices given to the constructor of this object 
CIncompleteIncrementalTermination  This class acts as a wrapper for basic termination policies to be used by SVDIncompleteIncrementalLearning 
CMaxIterationTermination  This termination policy only terminates when the maximum number of iterations has been reached 
CNMFALSUpdate  This class implements a method titled 'Alternating Least Squares' described in the following paper: 
CNMFMultiplicativeDistanceUpdate  The multiplicative distance update rules for matrices W and H 
CNMFMultiplicativeDivergenceUpdate  This follows a method described in the paper 'Algorithms for Nonnegative 
CRandomAcolInitialization  This class initializes the W matrix of the AMF algorithm by averaging p randomly chosen columns of V 
CRandomInitialization  This initialization rule for AMF simply fills the W and H matrices with uniform random noise in [0, 1] 
CSimpleResidueTermination  This class implements a simple residuebased termination policy 
CSimpleToleranceTermination  This class implements residue tolerance termination policy 
CSVDBatchLearning  This class implements SVD batch learning with momentum 
CSVDCompleteIncrementalLearning  This class computes SVD using complete incremental batch learning, as described in the following paper: 
CSVDCompleteIncrementalLearning< arma::sp_mat >  TODO : Merge this template specialized function for sparse matrix using common row_col_iterator 
CSVDIncompleteIncrementalLearning  This class computes SVD using incomplete incremental batch learning, as described in the following paper: 
CValidationRMSETermination  This class implements validation termination policy based on RMSE index 
►Nann  Artificial Neural Network 
CRandomInitialization  This class is used to initialize randomly the weight matrix 
►Nbound  
►Nmeta  Metaprogramming utilities 
CIsLMetric  Utility struct where Value is true if and only if the argument is of type LMetric 
CIsLMetric< metric::LMetric< Power, TakeRoot > >  Specialization for IsLMetric when the argument is of type LMetric 
CBallBound  Ball bound encloses a set of points at a specific distance (radius) from a specific point (center) 
CBoundTraits  A class to obtain compiletime traits about BoundType classes 
CBoundTraits< BallBound< MetricType, VecType > >  A specialization of BoundTraits for this bound type 
CBoundTraits< CellBound< MetricType, ElemType > >  
CBoundTraits< HollowBallBound< MetricType, ElemType > >  A specialization of BoundTraits for this bound type 
CBoundTraits< HRectBound< MetricType, ElemType > >  
CCellBound  The CellBound class describes a bound that consists of a number of hyperrectangles 
CHollowBallBound  Hollow ball bound encloses a set of points at a specific distance (radius) from a specific point (center) except points at a specific distance from another point (the center of the hole) 
CHRectBound  Hyperrectangle bound for an Lmetric 
►Ncf  Collaborative filtering 
►CCF  This class implements Collaborative Filtering (CF) 
CCandidateCmp  Compare two candidates based on the value 
CDummyClass  This class acts as a dummy class for passing as template parameter 
CFactorizerTraits  Template class for factorizer traits 
CFactorizerTraits< mlpack::svd::RegularizedSVD<> >  Factorizer traits of Regularized SVD 
CSVDWrapper  This class acts as the wrapper for all SVD factorizers which are incompatible with CF module 
►Ndata  Functions to load and save matrices and models 
CCustomImputation  A simple custom imputation class 
CDatasetMapper  Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dimension 
CFirstArrayShim  A first shim for arrays 
CFirstNormalArrayShim  A first shim for arrays without a Serialize() method 
CFirstShim  The first shim: simply holds the object and its name 
►CHasSerialize  
Ccheck  
CHasSerializeFunction  
CImputer  Given a dataset of a particular datatype, replace userspecified missing value with a variable dependent on the StrategyType and MapperType 
CIncrementPolicy  IncrementPolicy is used as a helper class for DatasetMapper 
CListwiseDeletion  A completecase analysis to remove the values containing mappedValue 
CMeanImputation  A simple mean imputation class 
CMedianImputation  This is a class implementation of simple median imputation 
CMissingPolicy  MissingPolicy is used as a helper class for DatasetMapper 
CPointerShim  A shim for pointers 
CSecondArrayShim  A shim for objects in an array; this is basically like the SecondShim, but for arrays that hold objects that have Serialize() methods instead of serialize() methods 
CSecondNormalArrayShim  A shim for objects in an array which do not have a Serialize() function 
CSecondShim  The second shim: wrap the call to Serialize() inside of a serialize() function, so that an archive type can call serialize() on a SecondShim object and this gets forwarded correctly to our object's Serialize() function 
►Ndecision_stump  
CDecisionStump  This class implements a decision stump 
►Ndet  Density Estimation Trees 
CDTree  A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kdtree) 
►Ndistribution  Probability distributions 
CDiscreteDistribution  A discrete distribution where the only observations are discrete observations 
CGammaDistribution  This class represents the Gamma distribution 
CGaussianDistribution  A single multivariate Gaussian distribution 
CLaplaceDistribution  The multivariate Laplace distribution centered at 0 has pdf 
CRegressionDistribution  A class that represents a univariate conditionally Gaussian distribution 
►Nemst  Euclidean Minimum Spanning Trees 
CDTBRules  
CDTBStat  A statistic for use with mlpack trees, which stores the upper bound on distance to nearest neighbors and the component which this node belongs to 
►CDualTreeBoruvka  Performs the MST calculation using the DualTree Boruvka algorithm, using any type of tree 
CSortEdgesHelper  For sorting the edge list after the computation 
CEdgePair  An edge pair is simply two indices and a distance 
CUnionFind  A UnionFind data structure 
►Nfastmks  Fast maxkernel search 
►CFastMKS  An implementation of fast exact maxkernel search 
CCandidateCmp  Compare two candidates based on the value 
CFastMKSModel  A utility struct to contain all the possible FastMKS models, for use by the mlpack_fastmks program 
►CFastMKSRules  The FastMKSRules class is a template helper class used by FastMKS class when performing exact maxkernel search 
CCandidateCmp  Compare two candidates based on the value 
CFastMKSStat  The statistic used in trees with FastMKS 
►Ngmm  Gaussian Mixture Models 
CDiagonalConstraint  Force a covariance matrix to be diagonal 
CEigenvalueRatioConstraint  Given a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios 
CEMFit  This class contains methods which can fit a GMM to observations using the EM algorithm 
CGMM  A Gaussian Mixture Model (GMM) 
CNoConstraint  This class enforces no constraint on the covariance matrix 
CPositiveDefiniteConstraint  Given a covariance matrix, force the matrix to be positive definite 
►Nhmm  Hidden Markov Models 
CHMM  A class that represents a Hidden Markov Model with an arbitrary type of emission distribution 
CHMMRegression  A class that represents a Hidden Markov Model Regression (HMMR) 
►Nkernel  Kernel functions 
CCosineDistance  The cosine distance (or cosine similarity) 
CEpanechnikovKernel  The Epanechnikov kernel, defined as 
CExampleKernel  An example kernel function 
CGaussianKernel  The standard Gaussian kernel 
CHyperbolicTangentKernel  Hyperbolic tangent kernel 
CKernelTraits  This is a template class that can provide information about various kernels 
CKernelTraits< CosineDistance >  Kernel traits for the cosine distance 
CKernelTraits< EpanechnikovKernel >  Kernel traits for the Epanechnikov kernel 
CKernelTraits< GaussianKernel >  Kernel traits for the Gaussian kernel 
CKernelTraits< LaplacianKernel >  Kernel traits of the Laplacian kernel 
CKernelTraits< SphericalKernel >  Kernel traits for the spherical kernel 
CKernelTraits< TriangularKernel >  Kernel traits for the triangular kernel 
CKMeansSelection  Implementation of the kmeans sampling scheme 
CLaplacianKernel  The standard Laplacian kernel 
CLinearKernel  The simple linear kernel (dot product) 
CNystroemMethod  
COrderedSelection  
CPolynomialKernel  The simple polynomial kernel 
CPSpectrumStringKernel  The pspectrum string kernel 
CRandomSelection  
CSphericalKernel  The spherical kernel, which is 1 when the distance between the two argument points is less than or equal to the bandwidth, or 0 otherwise 
CTriangularKernel  The trivially simple triangular kernel, defined by 
►Nkmeans  KMeans clustering 
CAllowEmptyClusters  Policy which allows KMeans to create empty clusters without any error being reported 
CDualTreeKMeans  An algorithm for an exact Lloyd iteration which simply uses dualtree nearestneighbor search to find the nearest centroid for each point in the dataset 
CDualTreeKMeansRules  
CDualTreeKMeansStatistic  
CElkanKMeans  
CHamerlyKMeans  
CKillEmptyClusters  Policy which allows KMeans to "kill" empty clusters without any error being reported 
CKMeans  This class implements KMeans clustering, using a variety of possible implementations of Lloyd's algorithm 
CMaxVarianceNewCluster  When an empty cluster is detected, this class takes the point furthest from the centroid of the cluster with maximum variance as a new cluster 
CNaiveKMeans  This is an implementation of a single iteration of Lloyd's algorithm for kmeans 
CPellegMooreKMeans  An implementation of PellegMoore's 'blacklist' algorithm for kmeans clustering 
CPellegMooreKMeansRules  The rules class for the singletree PellegMoore kdtree traversal for kmeans clustering 
CPellegMooreKMeansStatistic  A statistic for trees which holds the blacklist for PellegMoore kmeans clustering (which represents the clusters that cannot possibly own any points in a node) 
CRandomPartition  A very simple partitioner which partitions the data randomly into the number of desired clusters 
CRefinedStart  A refined approach for choosing initial points for kmeans clustering 
CSampleInitialization  
►Nkpca  
CKernelPCA  This class performs kernel principal components analysis (Kernel PCA), for a given kernel 
CNaiveKernelRule  
CNystroemKernelRule  
►Nlcc  
CLocalCoordinateCoding  An implementation of Local Coordinate Coding (LCC) that codes data which approximately lives on a manifold using a variation of l1norm regularized sparse coding; in LCC, the penalty on the absolute value of each point's coefficient for each atom is weighted by the squared distance of that point to that atom 
►Nmath  Miscellaneous math routines 
CColumnsToBlocks  Transform the columns of the given matrix into a block format 
CRangeType  Simple realvalued range 
►Nmatrix_completion  
CMatrixCompletion  This class implements the popular nuclear norm minimization heuristic for matrix completion problems 
►Nmeanshift  Mean shift clustering 
CMeanShift  This class implements mean shift clustering 
►Nmetric  
CIPMetric  The inner product metric, IPMetric, takes a given Mercer kernel (KernelType), and when Evaluate() is called, returns the distance between the two points in kernel space: 
CLMetric  The L_p metric for arbitrary integer p, with an option to take the root 
CMahalanobisDistance  The Mahalanobis distance, which is essentially a stretched Euclidean distance 
►Nnaive_bayes  The Naive Bayes Classifier 
CNaiveBayesClassifier  The simple Naive Bayes classifier 
►Nnca  Neighborhood Components Analysis 
CNCA  An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique 
CSoftmaxErrorFunction  The "softmax" stochastic neighbor assignment probability function 
►Nneighbor  Neighborsearch routines 
CBiSearchVisitor  BiSearchVisitor executes a bichromatic neighbor search on the given NSType 
CDeleteVisitor  DeleteVisitor deletes the given NSType instance 
CDrusillaSelect  
CEpsilonVisitor  EpsilonVisitor exposes the Epsilon method of the given NSType 
CFurthestNeighborSort  This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class 
►CLSHSearch  The LSHSearch class; this class builds a hash on the reference set and uses this hash to compute the distanceapproximate nearestneighbors of the given queries 
CCandidateCmp  Compare two candidates based on the distance 
CMonoSearchVisitor  MonoSearchVisitor executes a monochromatic neighbor search on the given NSType 
CNearestNeighborSort  This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class 
CNeighborSearch  The NeighborSearch class is a template class for performing distancebased neighbor searches 
►CNeighborSearchRules  The NeighborSearchRules class is a template helper class used by NeighborSearch class when performing distancebased neighbor searches 
CCandidateCmp  Compare two candidates based on the distance 
CNeighborSearchStat  Extra data for each node in the tree 
CNSModel  The NSModel class provides an easy way to serialize a model, abstracts away the different types of trees, and also reflects the NeighborSearch API 
CNSModelName  
CNSModelName< FurthestNeighborSort >  
CNSModelName< NearestNeighborSort >  
CQDAFN  
CRAModel  The RAModel class provides an abstraction for the RASearch class, abstracting away the TreeType parameter and allowing it to be specified at runtime in this class 
CRAQueryStat  Extra data for each node in the tree 
CRASearch  The RASearch class: This class provides a generic manner to perform rankapproximate search via randomsampling 
►CRASearchRules  The RASearchRules class is a template helper class used by RASearch class when performing rankapproximate search via randomsampling 
CCandidateCmp  Compare two candidates based on the distance 
CRAUtil  
CReferenceSetVisitor  ReferenceSetVisitor exposes the referenceSet of the given NSType 
CSearchModeVisitor  SearchModeVisitor exposes the SearchMode() method of the given NSType 
CSetSearchModeVisitor  SetSearchModeVisitor modifies the SearchMode method of the given NSType 
CTrainVisitor  TrainVisitor sets the reference set to a new reference set on the given NSType 
►Nnn  
CSparseAutoencoder  A sparse autoencoder is a neural network whose aim to learn compressed representations of the data, typically for dimensionality reduction, with a constraint on the activity of the neurons in the network 
CSparseAutoencoderFunction  This is a class for the sparse autoencoder objective function 
►Noptimization  
►Ntest  
CGDTestFunction  Very, very simple test function which is the composite of three other functions 
CGeneralizedRosenbrockFunction  The Generalized Rosenbrock function in n dimensions, defined by f(x) = sum_i^{n  1} (f(i)(x)) f_i(x) = 100 * (x_i^2  x_{i + 1})^2 + (1  x_i)^2 x_0 = [1.2, 1, 1.2, 1, ...] 
CRosenbrockFunction  The Rosenbrock function, defined by f(x) = f1(x) + f2(x) f1(x) = 100 (x2  x1^2)^2 f2(x) = (1  x1)^2 x_0 = [1.2, 1] 
CRosenbrockWoodFunction  The Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions 
CSGDTestFunction  Very, very simple test function which is the composite of three other functions 
CWoodFunction  The Wood function, defined by f(x) = f1(x) + f2(x) + f3(x) + f4(x) + f5(x) + f6(x) f1(x) = 100 (x2  x1^2)^2 f2(x) = (1  x1)^2 f3(x) = 90 (x4  x3^2)^2 f4(x) = (1  x3)^2 f5(x) = 10 (x2 + x4  2)^2 f6(x) = (1 / 10) (x2  x4)^2 x_0 = [3, 1, 3, 1] 
CAdaDelta  Adadelta is an optimizer that uses two ideas to improve upon the two main drawbacks of the Adagrad method: 
CAdam  Adam is an optimizer that computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients 
CAugLagrangian  The AugLagrangian class implements the Augmented Lagrangian method of optimization 
CAugLagrangianFunction  This is a utility class used by AugLagrangian, meant to wrap a LagrangianFunction into a function usable by a simple optimizer like LBFGS 
CAugLagrangianTestFunction  This function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method") 
CExponentialSchedule  The exponential cooling schedule cools the temperature T at every step according to the equation 
CGockenbachFunction  This function is taken from M 
CGradientDescent  Gradient Descent is a technique to minimize a function 
CL_BFGS  The generic LBFGS optimizer, which uses a backtracking line search algorithm to minimize a function 
CLovaszThetaSDP  This function is the LovaszTheta semidefinite program, as implemented in the following paper: 
CLRSDP  LRSDP is the implementation of Monteiro and Burer's formulation of lowrank semidefinite programs (LRSDP) 
CLRSDPFunction  The objective function that LRSDP is trying to optimize 
CMiniBatchSGD  Minibatch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions 
CPrimalDualSolver  Interface to a primal dual interior point solver 
CRMSprop  RMSprop is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients 
CSA  Simulated Annealing is an stochastic optimization algorithm which is able to deliver nearoptimal results quickly without knowing the gradient of the function being optimized 
CSDP  Specify an SDP in primal form 
CSGD  Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions 
►Npca  
CExactSVDPolicy  Implementation of the exact SVD policy 
CPCAType  This class implements principal components analysis (PCA) 
CQUICSVDPolicy  Implementation of the QUICSVD policy 
CRandomizedSVDPolicy  Implementation of the randomized SVD policy 
►Nperceptron  
CPerceptron  This class implements a simple perceptron (i.e., a single layer neural network) 
CRandomInitialization  This class is used to initialize weights for the weightVectors matrix in a random manner 
CSimpleWeightUpdate  
CZeroInitialization  This class is used to initialize the matrix weightVectors to zero 
►Nradical  
CRadical  An implementation of RADICAL, an algorithm for independent component analysis (ICA) 
►Nrange  Rangesearch routines 
CRangeSearch  The RangeSearch class is a template class for performing range searches 
CRangeSearchRules  The RangeSearchRules class is a template helper class used by RangeSearch class when performing range searches 
CRangeSearchStat  Statistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with 
CRSModel  
►Nregression  Regression methods 
CLARS  An implementation of LARS, a stagewise homotopybased algorithm for l1regularized linear regression (LASSO) and l1+l2 regularized linear regression (Elastic Net) 
CLinearRegression  A simple linear regression algorithm using ordinary least squares 
CLogisticRegression  The LogisticRegression class implements an L2regularized logistic regression model, and supports training with multiple optimizers and classification 
CLogisticRegressionFunction  The loglikelihood function for the logistic regression objective function 
CSoftmaxRegression  Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values 
CSoftmaxRegressionFunction  
►Nsparse_coding  
CDataDependentRandomInitializer  A datadependent random dictionary initializer for SparseCoding 
CNothingInitializer  A DictionaryInitializer for SparseCoding which does not initialize anything; it is useful for when the dictionary is already known and will be set with SparseCoding::Dictionary() 
CRandomInitializer  A DictionaryInitializer for use with the SparseCoding class 
CSparseCoding  An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1norm regularizer on the codes (LASSO) or an (l1+l2)norm regularizer on the codes (the Elastic Net) 
►Nsvd  
CQUIC_SVD  QUICSVD is a matrix factorization technique, which operates in a subspace such that A's approximation in that subspace has minimum error(A being the data matrix) 
CRandomizedSVD  Randomized SVD is a matrix factorization that is based on randomized matrix approximation techniques, developed in in "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions" 
CRegularizedSVD  Regularized SVD is a matrix factorization technique that seeks to reduce the error on the training set, that is on the examples for which the ratings have been provided by the users 
CRegularizedSVDFunction  
►Ntree  Trees and treebuilding procedures 
CAxisParallelProjVector  AxisParallelProjVector defines an axisparallel projection vector 
CBinaryNumericSplit  The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper: 
CBinaryNumericSplitInfo  
►CBinarySpaceTree  A binary space partitioning tree, such as a KDtree or a ball tree 
CBreadthFirstDualTreeTraverser  
CDualTreeTraverser  A dualtree traverser for binary space trees; see dual_tree_traverser.hpp 
CSingleTreeTraverser  A singletree traverser for binary space trees; see single_tree_traverser.hpp for implementation 
CCategoricalSplitInfo  
CCompareCosineNode  
CCosineTree  
►CCoverTree  A cover tree is a tree specifically designed to speed up nearestneighbor computation in highdimensional spaces 
►CDualTreeTraverser  A dualtree cover tree traverser; see dual_tree_traverser.hpp 
CDualCoverTreeMapEntry  Struct used for traversal 
CSingleTreeTraverser  A singletree cover tree traverser; see single_tree_traverser.hpp for implementation 
CDiscreteHilbertValue  The DiscreteHilbertValue class stores Hilbert values for all of the points in a RectangleTree node, and calculates Hilbert values for new points 
CEmptyStatistic  Empty statistic if you are not interested in storing statistics in your tree 
CExampleTree  This is not an actual space tree but instead an example tree that exists to show and document all the functions that mlpack trees must implement 
CFirstPointIsRoot  This class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class 
CGiniImpurity  
CGreedySingleTreeTraverser  
CHilbertRTreeAuxiliaryInformation  
CHilbertRTreeDescentHeuristic  This class chooses the best child of a node in a Hilbert R tree when inserting a new point 
CHilbertRTreeSplit  The splitting procedure for the Hilbert R tree 
CHoeffdingCategoricalSplit  This is the standard Hoeffdingbound categorical feature proposed in the paper below: 
CHoeffdingNumericSplit  The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper: 
CHoeffdingTree  The HoeffdingTree object represents all of the necessary information for a Hoeffdingboundbased decision tree 
CHyperplaneBase  HyperplaneBase defines a splitting hyperplane based on a projection vector and projection value 
CInformationGain  
CIsSpillTree  
CIsSpillTree< tree::SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > >  
CMeanSpaceSplit  
►CMeanSplit  A binary space partitioning tree node is split into its left and right child 
CSplitInfo  An information about the partition 
CMidpointSpaceSplit  
►CMidpointSplit  A binary space partitioning tree node is split into its left and right child 
CSplitInfo  A struct that contains an information about the split 
►CMinimalCoverageSweep  The MinimalCoverageSweep class finds a partition along which we can split a node according to the coverage of two resulting nodes 
CSweepCost  A struct that provides the type of the sweep cost 
►CMinimalSplitsNumberSweep  The MinimalSplitsNumberSweep class finds a partition along which we can split a node according to the number of required splits of the node 
CSweepCost  A struct that provides the type of the sweep cost 
CNoAuxiliaryInformation  
CNumericSplitInfo  
►COctree  
CDualTreeTraverser  A dualtree traverser; see dual_tree_traverser.hpp 
CSingleTreeTraverser  A singletree traverser; see single_tree_traverser.hpp 
CSplitInfo  This is used for sorting points while splitting 
CProjVector  ProjVector defines a general projection vector (not necessarily axisparallel) 
CQueueFrame  
►CRectangleTree  A rectangle type tree tree, such as an Rtree or Xtree 
►CDualTreeTraverser  A dual tree traverser for rectangle type trees 
CNodeAndScore  
►CSingleTreeTraverser  A single traverser for rectangle type trees 
CNodeAndScore  
CRPlusPlusTreeAuxiliaryInformation  
CRPlusPlusTreeDescentHeuristic  
CRPlusPlusTreeSplitPolicy  The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split 
CRPlusTreeDescentHeuristic  
CRPlusTreeSplit  The RPlusTreeSplit class performs the split process of a node on overflow 
CRPlusTreeSplitPolicy  The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split 
►CRPTreeMaxSplit  This class splits a node by a random hyperplane 
CSplitInfo  An information about the partition 
►CRPTreeMeanSplit  This class splits a binary space tree 
CSplitInfo  An information about the partition 
CRStarTreeDescentHeuristic  When descending a RectangleTree to insert a point, we need to have a way to choose a child node when the point isn't enclosed by any of them 
CRStarTreeSplit  A Rectangle Tree has new points inserted at the bottom 
CRTreeDescentHeuristic  When descending a RectangleTree to insert a point, we need to have a way to choose a child node when the point isn't enclosed by any of them 
CRTreeSplit  A Rectangle Tree has new points inserted at the bottom 
CSpaceSplit  
►CSpillTree  A hybrid spill tree is a variant of binary space trees in which the children of a node can "spill over" each other, and contain shared datapoints 
CSpillDualTreeTraverser  A generic dualtree traverser for hybrid spill trees; see spill_dual_tree_traverser.hpp for implementation 
CSpillSingleTreeTraverser  A generic singletree traverser for hybrid spill trees; see spill_single_tree_traverser.hpp for implementation 
CTraversalInfo  The TraversalInfo class holds traversal information which is used in dualtree (and singletree) traversals 
CTreeTraits  The TreeTraits class provides compiletime information on the characteristics of a given tree type 
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::BallBound, SplitType > >  This is a specialization of the TreeType class to the BallTree tree type 
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::CellBound, SplitType > >  This is a specialization of the TreeType class to the UBTree tree type 
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::HollowBallBound, SplitType > >  This is a specialization of the TreeType class to an arbitrary tree with HollowBallBound (currently only the vantage point tree is supported) 
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMaxSplit > >  This is a specialization of the TreeType class to the maxsplit random projection tree 
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMeanSplit > >  This is a specialization of the TreeType class to the meansplit random projection tree 
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > >  This is a specialization of the TreeTraits class to the BinarySpaceTree tree type 
CTreeTraits< CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > >  The specialization of the TreeTraits class for the CoverTree tree type 
CTreeTraits< Octree< MetricType, StatisticType, MatType > >  This is a specialization of the TreeTraits class to the Octree tree type 
CTreeTraits< RectangleTree< MetricType, StatisticType, MatType, RPlusTreeSplit< SplitPolicyType, SweepType >, DescentType, AuxiliaryInformationType > >  Since the R+/R++ tree can not have overlapping children, we should define traits for the R+/R++ tree 
CTreeTraits< RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType > >  This is a specialization of the TreeType class to the RectangleTree tree type 
CTreeTraits< SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > >  This is a specialization of the TreeType class to the SpillTree tree type 
CUBTreeSplit  Split a node into two parts according to the median address of points contained in the node 
►CVantagePointSplit  The class splits a binary space partitioning tree node according to the median distance to the vantage point 
CSplitInfo  A struct that contains an information about the split 
►CXTreeAuxiliaryInformation  The XTreeAuxiliaryInformation class provides information specific to X trees for each node in a RectangleTree 
CSplitHistoryStruct  The X tree requires that the tree records it's "split history" 
CXTreeSplit  A Rectangle Tree has new points inserted at the bottom 
►Nutil  
CCLIDeleter  Extremely simple class whose only job is to delete the existing CLI object at the end of execution 
CNullOutStream  Used for Log::Debug when not compiled with debugging symbols 
COption  A static object whose constructor registers a parameter with the CLI class 
CPrefixedOutStream  Allows us to output to an ostream with a prefix at the beginning of each line, in the same way we would output to cout or cerr 
CProgramDoc  A static object whose constructor registers program documentation with the CLI class 
►CBacktrace  Provides a backtrace 
CFrames  Backtrace datastructure 
►CCLI  Parses the command line for parameters and holds userspecified parameters 
CIsStdVector  Metaprogramming structure for vector detection 
CIsStdVector< std::vector< eT > >  Metaprogramming structure for vector detection 
CLog  Provides a convenient way to give formatted output 
CParamData  Aids in the extensibility of CLI by focusing potential changes into one structure 
CTimer  The timer class provides a way for mlpack methods to be timed 
CTimers  
CIsVector  If value == true, then VecType is some sort of Armadillo vector or subview 
CIsVector< arma::Col< eT > >  
CIsVector< arma::Row< eT > >  
CIsVector< arma::SpCol< eT > >  
CIsVector< arma::SpRow< eT > >  
CIsVector< arma::SpSubview< eT > >  
CIsVector< arma::subview_col< eT > >  
CIsVector< arma::subview_row< eT > > 
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