 boost | |
  serialization | |
 mlpack | Linear algebra utility functions, generally performed on matrices or vectors |
  adaboost | |
   AdaBoost | The AdaBoost class |
  amf | Alternating Matrix Factorization |
   AMF | This class implements AMF (alternating matrix factorization) on the given matrix V |
   AverageInitialization | This initialization rule initializes matrix W and H to root of the average of V, perturbed with uniform noise |
   CompleteIncrementalTermination | This class acts as a wrapper for basic termination policies to be used by SVDCompleteIncrementalLearning |
   GivenInitialization | This initialization rule for AMF simply fills the W and H matrices with the matrices given to the constructor of this object |
   IncompleteIncrementalTermination | This class acts as a wrapper for basic termination policies to be used by SVDIncompleteIncrementalLearning |
   MaxIterationTermination | This termination policy only terminates when the maximum number of iterations has been reached |
   NMFALSUpdate | This class implements a method titled 'Alternating Least Squares' described in the following paper: |
   NMFMultiplicativeDistanceUpdate | The multiplicative distance update rules for matrices W and H |
   NMFMultiplicativeDivergenceUpdate | This follows a method described in the paper 'Algorithms for Non-negative |
   RandomAcolInitialization | This class initializes the W matrix of the AMF algorithm by averaging p randomly chosen columns of V |
   RandomInitialization | This initialization rule for AMF simply fills the W and H matrices with uniform random noise in [0, 1] |
   SimpleResidueTermination | This class implements a simple residue-based termination policy |
   SimpleToleranceTermination | This class implements residue tolerance termination policy |
   SVDBatchLearning | This class implements SVD batch learning with momentum |
   SVDCompleteIncrementalLearning | This class computes SVD using complete incremental batch learning, as described in the following paper: |
   SVDCompleteIncrementalLearning< arma::sp_mat > | TODO : Merge this template specialized function for sparse matrix using common row_col_iterator |
   SVDIncompleteIncrementalLearning | This class computes SVD using incomplete incremental batch learning, as described in the following paper: |
   ValidationRMSETermination | This class implements validation termination policy based on RMSE index |
  ann | Artificial Neural Network |
   RandomInitialization | This class is used to initialize randomly the weight matrix |
  bound | |
   addr | |
   meta | Metaprogramming utilities |
    IsLMetric | Utility struct where Value is true if and only if the argument is of type LMetric |
    IsLMetric< metric::LMetric< Power, TakeRoot > > | Specialization for IsLMetric when the argument is of type LMetric |
   BallBound | Ball bound encloses a set of points at a specific distance (radius) from a specific point (center) |
   BoundTraits | A class to obtain compile-time traits about BoundType classes |
   BoundTraits< BallBound< MetricType, VecType > > | A specialization of BoundTraits for this bound type |
   BoundTraits< CellBound< MetricType, ElemType > > | |
   BoundTraits< HollowBallBound< MetricType, ElemType > > | A specialization of BoundTraits for this bound type |
   BoundTraits< HRectBound< MetricType, ElemType > > | |
   CellBound | The CellBound class describes a bound that consists of a number of hyperrectangles |
   HollowBallBound | 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) |
   HRectBound | Hyper-rectangle bound for an L-metric |
  cf | Collaborative filtering |
   CF | This class implements Collaborative Filtering (CF) |
   DummyClass | This class acts as a dummy class for passing as template parameter |
   FactorizerTraits | Template class for factorizer traits |
   FactorizerTraits< mlpack::svd::RegularizedSVD<> > | Factorizer traits of Regularized SVD |
   SVDWrapper | This class acts as the wrapper for all SVD factorizers which are incompatible with CF module |
  data | Functions to load and save matrices and models |
   CustomImputation | A simple custom imputation class |
   DatasetMapper | Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dimension |
   FirstArrayShim | A first shim for arrays |
   FirstNormalArrayShim | A first shim for arrays without a Serialize() method |
   FirstShim | The first shim: simply holds the object and its name |
   HasSerialize | |
    check | |
   HasSerializeFunction | |
   Imputer | Given a dataset of a particular datatype, replace user-specified missing value with a variable dependent on the StrategyType and MapperType |
   IncrementPolicy | IncrementPolicy is used as a helper class for DatasetMapper |
   ListwiseDeletion | A complete-case analysis to remove the values containing mappedValue |
   LoadCSV | Load the csv file.This class use boost::spirit to implement the parser, please refer to following link http://theboostcpplibraries.com/boost.spirit for quick review |
   MeanImputation | A simple mean imputation class |
   MedianImputation | This is a class implementation of simple median imputation |
   MissingPolicy | MissingPolicy is used as a helper class for DatasetMapper |
   PointerShim | A shim for pointers |
   SecondArrayShim | 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 |
   SecondNormalArrayShim | A shim for objects in an array which do not have a Serialize() function |
   SecondShim | 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 |
  dbscan | |
   DBSCAN | DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper: |
   RandomPointSelection | This class can be used to randomly select the next point to use for DBSCAN |
  decision_stump | |
   DecisionStump | This class implements a decision stump |
  det | Density Estimation Trees |
   DTree | A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree) |
  distribution | Probability distributions |
   DiscreteDistribution | A discrete distribution where the only observations are discrete observations |
   GammaDistribution | This class represents the Gamma distribution |
   GaussianDistribution | A single multivariate Gaussian distribution |
   LaplaceDistribution | The multivariate Laplace distribution centered at 0 has pdf |
   RegressionDistribution | A class that represents a univariate conditionally Gaussian distribution |
  emst | Euclidean Minimum Spanning Trees |
   DTBRules | |
   DTBStat | 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 |
   DualTreeBoruvka | Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree |
   EdgePair | An edge pair is simply two indices and a distance |
   UnionFind | A Union-Find data structure |
  fastmks | Fast max-kernel search |
   FastMKS | An implementation of fast exact max-kernel search |
   FastMKSModel | A utility struct to contain all the possible FastMKS models, for use by the mlpack_fastmks program |
   FastMKSRules | The FastMKSRules class is a template helper class used by FastMKS class when performing exact max-kernel search |
   FastMKSStat | The statistic used in trees with FastMKS |
  gmm | Gaussian Mixture Models |
   DiagonalConstraint | Force a covariance matrix to be diagonal |
   EigenvalueRatioConstraint | Given a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios |
   EMFit | This class contains methods which can fit a GMM to observations using the EM algorithm |
   GMM | A Gaussian Mixture Model (GMM) |
   NoConstraint | This class enforces no constraint on the covariance matrix |
   PositiveDefiniteConstraint | Given a covariance matrix, force the matrix to be positive definite |
  hmm | Hidden Markov Models |
   HMM | A class that represents a Hidden Markov Model with an arbitrary type of emission distribution |
   HMMRegression | A class that represents a Hidden Markov Model Regression (HMMR) |
  kernel | Kernel functions |
   CosineDistance | The cosine distance (or cosine similarity) |
   EpanechnikovKernel | The Epanechnikov kernel, defined as |
   ExampleKernel | An example kernel function |
   GaussianKernel | The standard Gaussian kernel |
   HyperbolicTangentKernel | Hyperbolic tangent kernel |
   KernelTraits | This is a template class that can provide information about various kernels |
   KernelTraits< CosineDistance > | Kernel traits for the cosine distance |
   KernelTraits< EpanechnikovKernel > | Kernel traits for the Epanechnikov kernel |
   KernelTraits< GaussianKernel > | Kernel traits for the Gaussian kernel |
   KernelTraits< LaplacianKernel > | Kernel traits of the Laplacian kernel |
   KernelTraits< SphericalKernel > | Kernel traits for the spherical kernel |
   KernelTraits< TriangularKernel > | Kernel traits for the triangular kernel |
   KMeansSelection | Implementation of the kmeans sampling scheme |
   LaplacianKernel | The standard Laplacian kernel |
   LinearKernel | The simple linear kernel (dot product) |
   NystroemMethod | |
   OrderedSelection | |
   PolynomialKernel | The simple polynomial kernel |
   PSpectrumStringKernel | The p-spectrum string kernel |
   RandomSelection | |
   SphericalKernel | 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 |
   TriangularKernel | The trivially simple triangular kernel, defined by |
  kmeans | K-Means clustering |
   AllowEmptyClusters | Policy which allows K-Means to create empty clusters without any error being reported |
   DualTreeKMeans | An algorithm for an exact Lloyd iteration which simply uses dual-tree nearest-neighbor search to find the nearest centroid for each point in the dataset |
   DualTreeKMeansRules | |
   DualTreeKMeansStatistic | |
   ElkanKMeans | |
   HamerlyKMeans | |
   KillEmptyClusters | Policy which allows K-Means to "kill" empty clusters without any error being reported |
   KMeans | This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm |
   MaxVarianceNewCluster | 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 |
   NaiveKMeans | This is an implementation of a single iteration of Lloyd's algorithm for k-means |
   PellegMooreKMeans | An implementation of Pelleg-Moore's 'blacklist' algorithm for k-means clustering |
   PellegMooreKMeansRules | The rules class for the single-tree Pelleg-Moore kd-tree traversal for k-means clustering |
   PellegMooreKMeansStatistic | A statistic for trees which holds the blacklist for Pelleg-Moore k-means clustering (which represents the clusters that cannot possibly own any points in a node) |
   RandomPartition | A very simple partitioner which partitions the data randomly into the number of desired clusters |
   RefinedStart | A refined approach for choosing initial points for k-means clustering |
   SampleInitialization | |
  kpca | |
   KernelPCA | This class performs kernel principal components analysis (Kernel PCA), for a given kernel |
   NaiveKernelRule | |
   NystroemKernelRule | |
  lcc | |
   LocalCoordinateCoding | An implementation of Local Coordinate Coding (LCC) that codes data which approximately lives on a manifold using a variation of l1-norm 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 |
  math | Miscellaneous math routines |
   ColumnsToBlocks | Transform the columns of the given matrix into a block format |
   RangeType | Simple real-valued range |
  matrix_completion | |
   MatrixCompletion | This class implements the popular nuclear norm minimization heuristic for matrix completion problems |
  meanshift | Mean shift clustering |
   MeanShift | This class implements mean shift clustering |
  metric | |
   IPMetric | 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: |
   LMetric | The L_p metric for arbitrary integer p, with an option to take the root |
   MahalanobisDistance | The Mahalanobis distance, which is essentially a stretched Euclidean distance |
  naive_bayes | The Naive Bayes Classifier |
   NaiveBayesClassifier | The simple Naive Bayes classifier |
  nca | Neighborhood Components Analysis |
   NCA | An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique |
   SoftmaxErrorFunction | The "softmax" stochastic neighbor assignment probability function |
  neighbor | Neighbor-search routines |
   BiSearchVisitor | BiSearchVisitor executes a bichromatic neighbor search on the given NSType |
   DeleteVisitor | DeleteVisitor deletes the given NSType instance |
   DrusillaSelect | |
   EpsilonVisitor | EpsilonVisitor exposes the Epsilon method of the given NSType |
   FurthestNeighborSort | This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class |
   LSHSearch | The LSHSearch class; this class builds a hash on the reference set and uses this hash to compute the distance-approximate nearest-neighbors of the given queries |
   MonoSearchVisitor | MonoSearchVisitor executes a monochromatic neighbor search on the given NSType |
   NearestNeighborSort | This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class |
   NeighborSearch | The NeighborSearch class is a template class for performing distance-based neighbor searches |
   NeighborSearchRules | The NeighborSearchRules class is a template helper class used by NeighborSearch class when performing distance-based neighbor searches |
    CandidateCmp | Compare two candidates based on the distance |
   NeighborSearchStat | Extra data for each node in the tree |
   NSModel | The NSModel class provides an easy way to serialize a model, abstracts away the different types of trees, and also reflects the NeighborSearch API |
   NSModelName | |
   NSModelName< FurthestNeighborSort > | |
   NSModelName< NearestNeighborSort > | |
   QDAFN | |
   RAModel | 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 |
   RAQueryStat | Extra data for each node in the tree |
   RASearch | The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling |
   RASearchRules | The RASearchRules class is a template helper class used by RASearch class when performing rank-approximate search via random-sampling |
   RAUtil | |
   ReferenceSetVisitor | ReferenceSetVisitor exposes the referenceSet of the given NSType |
   SearchModeVisitor | SearchModeVisitor exposes the SearchMode() method of the given NSType |
   SetSearchModeVisitor | SetSearchModeVisitor modifies the SearchMode method of the given NSType |
   TrainVisitor | TrainVisitor sets the reference set to a new reference set on the given NSType |
  nn | |
   SparseAutoencoder | 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 |
   SparseAutoencoderFunction | This is a class for the sparse autoencoder objective function |
  optimization | |
   test | |
    GDTestFunction | Very, very simple test function which is the composite of three other functions |
    GeneralizedRosenbrockFunction | 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, ...] |
    RosenbrockFunction | 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] |
    RosenbrockWoodFunction | The Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions |
    SGDTestFunction | Very, very simple test function which is the composite of three other functions |
    WoodFunction | 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] |
   AdaDelta | Adadelta is an optimizer that uses two ideas to improve upon the two main drawbacks of the Adagrad method: |
   Adam | Adam is an optimizer that computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients |
   AugLagrangian | The AugLagrangian class implements the Augmented Lagrangian method of optimization |
   AugLagrangianFunction | This is a utility class used by AugLagrangian, meant to wrap a LagrangianFunction into a function usable by a simple optimizer like L-BFGS |
   AugLagrangianTestFunction | This function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method") |
   ExponentialSchedule | The exponential cooling schedule cools the temperature T at every step according to the equation |
   GockenbachFunction | This function is taken from M |
   GradientDescent | Gradient Descent is a technique to minimize a function |
   L_BFGS | The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function |
   LovaszThetaSDP | This function is the Lovasz-Theta semidefinite program, as implemented in the following paper: |
   LRSDP | LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP) |
   LRSDPFunction | The objective function that LRSDP is trying to optimize |
   MiniBatchSGD | Mini-batch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions |
   PrimalDualSolver | Interface to a primal dual interior point solver |
   RMSprop | RMSprop is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients |
   SA | Simulated Annealing is an stochastic optimization algorithm which is able to deliver near-optimal results quickly without knowing the gradient of the function being optimized |
   SDP | Specify an SDP in primal form |
   SGD | Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions |
  pca | |
   ExactSVDPolicy | Implementation of the exact SVD policy |
   PCAType | This class implements principal components analysis (PCA) |
   QUICSVDPolicy | Implementation of the QUIC-SVD policy |
   RandomizedSVDPolicy | Implementation of the randomized SVD policy |
  perceptron | |
   Perceptron | This class implements a simple perceptron (i.e., a single layer neural network) |
   RandomInitialization | This class is used to initialize weights for the weightVectors matrix in a random manner |
   SimpleWeightUpdate | |
   ZeroInitialization | This class is used to initialize the matrix weightVectors to zero |
  radical | |
   Radical | An implementation of RADICAL, an algorithm for independent component analysis (ICA) |
  range | Range-search routines |
   BiSearchVisitor | BiSearchVisitor executes a bichromatic range search on the given RSType |
   DeleteVisitor | DeleteVisitor deletes the given RSType instance |
   MonoSearchVisitor | MonoSearchVisitor executes a monochromatic range search on the given RSType |
   NaiveVisitor | NaiveVisitor exposes the Naive() method of the given RSType |
   RangeSearch | The RangeSearch class is a template class for performing range searches |
   RangeSearchRules | The RangeSearchRules class is a template helper class used by RangeSearch class when performing range searches |
   RangeSearchStat | Statistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with |
   ReferenceSetVisitor | ReferenceSetVisitor exposes the referenceSet of the given RSType |
   RSModel | |
   RSModelName | |
   SerializeVisitor | Exposes the seralize method of the given RSType |
   SingleModeVisitor | SingleModeVisitor exposes the SingleMode() method of the given RSType |
   TrainVisitor | TrainVisitor sets the reference set to a new reference set on the given RSType |
  regression | Regression methods |
   LARS | An implementation of LARS, a stage-wise homotopy-based algorithm for l1-regularized linear regression (LASSO) and l1+l2 regularized linear regression (Elastic Net) |
   LinearRegression | A simple linear regression algorithm using ordinary least squares |
   LogisticRegression | The LogisticRegression class implements an L2-regularized logistic regression model, and supports training with multiple optimizers and classification |
   LogisticRegressionFunction | The log-likelihood function for the logistic regression objective function |
   SoftmaxRegression | Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values |
   SoftmaxRegressionFunction | |
  sparse_coding | |
   DataDependentRandomInitializer | A data-dependent random dictionary initializer for SparseCoding |
   NothingInitializer | 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() |
   RandomInitializer | A DictionaryInitializer for use with the SparseCoding class |
   SparseCoding | An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm regularizer on the codes (the Elastic Net) |
  svd | |
   QUIC_SVD | QUIC-SVD 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) |
   RandomizedSVD | 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" |
   RegularizedSVD | 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 |
   RegularizedSVDFunction | |
  tree | Trees and tree-building procedures |
   split | |
   AllCategoricalSplit | The AllCategoricalSplit is a splitting function that will split categorical features into many children: one child for each category |
    AuxiliarySplitInfo | |
   AxisParallelProjVector | AxisParallelProjVector defines an axis-parallel projection vector |
   BestBinaryNumericSplit | The BestBinaryNumericSplit is a splitting function for decision trees that will exhaustively search a numeric dimension for the best binary split |
    AuxiliarySplitInfo | |
   BinaryNumericSplit | The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper: |
   BinaryNumericSplitInfo | |
   BinarySpaceTree | A binary space partitioning tree, such as a KD-tree or a ball tree |
    BreadthFirstDualTreeTraverser | |
    DualTreeTraverser | A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp |
    SingleTreeTraverser | A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation |
   CategoricalSplitInfo | |
   CompareCosineNode | |
   CosineTree | |
   CoverTree | A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces |
    DualTreeTraverser | A dual-tree cover tree traverser; see dual_tree_traverser.hpp |
    SingleTreeTraverser | A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation |
   DecisionTree | This class implements a generic decision tree learner |
   DiscreteHilbertValue | The DiscreteHilbertValue class stores Hilbert values for all of the points in a RectangleTree node, and calculates Hilbert values for new points |
   EmptyStatistic | Empty statistic if you are not interested in storing statistics in your tree |
   ExampleTree | 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 |
   FirstPointIsRoot | This class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class |
   GiniGain | The Gini gain, a measure of set purity usable as a fitness function (FitnessFunction) for decision trees |
   GiniImpurity | |
   GreedySingleTreeTraverser | |
   HilbertRTreeAuxiliaryInformation | |
   HilbertRTreeDescentHeuristic | This class chooses the best child of a node in a Hilbert R tree when inserting a new point |
   HilbertRTreeSplit | The splitting procedure for the Hilbert R tree |
   HoeffdingCategoricalSplit | This is the standard Hoeffding-bound categorical feature proposed in the paper below: |
   HoeffdingNumericSplit | The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper: |
   HoeffdingTree | The HoeffdingTree object represents all of the necessary information for a Hoeffding-bound-based decision tree |
   HyperplaneBase | HyperplaneBase defines a splitting hyperplane based on a projection vector and projection value |
   InformationGain | The standard information gain criterion, used for calculating gain in decision trees |
   IsSpillTree | |
   IsSpillTree< tree::SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > > | |
   MeanSpaceSplit | |
   MeanSplit | A binary space partitioning tree node is split into its left and right child |
    SplitInfo | An information about the partition |
   MidpointSpaceSplit | |
   MidpointSplit | A binary space partitioning tree node is split into its left and right child |
    SplitInfo | A struct that contains an information about the split |
   MinimalCoverageSweep | The MinimalCoverageSweep class finds a partition along which we can split a node according to the coverage of two resulting nodes |
    SweepCost | A struct that provides the type of the sweep cost |
   MinimalSplitsNumberSweep | The MinimalSplitsNumberSweep class finds a partition along which we can split a node according to the number of required splits of the node |
    SweepCost | A struct that provides the type of the sweep cost |
   NoAuxiliaryInformation | |
   NumericSplitInfo | |
   Octree | |
    DualTreeTraverser | A dual-tree traverser; see dual_tree_traverser.hpp |
    SingleTreeTraverser | A single-tree traverser; see single_tree_traverser.hpp |
   ProjVector | ProjVector defines a general projection vector (not necessarily axis-parallel) |
   QueueFrame | |
   RectangleTree | A rectangle type tree tree, such as an R-tree or X-tree |
    DualTreeTraverser | A dual tree traverser for rectangle type trees |
    SingleTreeTraverser | A single traverser for rectangle type trees |
   RPlusPlusTreeAuxiliaryInformation | |
   RPlusPlusTreeDescentHeuristic | |
   RPlusPlusTreeSplitPolicy | The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split |
   RPlusTreeDescentHeuristic | |
   RPlusTreeSplit | The RPlusTreeSplit class performs the split process of a node on overflow |
   RPlusTreeSplitPolicy | The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split |
   RPTreeMaxSplit | This class splits a node by a random hyperplane |
    SplitInfo | An information about the partition |
   RPTreeMeanSplit | This class splits a binary space tree |
    SplitInfo | An information about the partition |
   RStarTreeDescentHeuristic | 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 |
   RStarTreeSplit | A Rectangle Tree has new points inserted at the bottom |
   RTreeDescentHeuristic | 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 |
   RTreeSplit | A Rectangle Tree has new points inserted at the bottom |
   SpaceSplit | |
   SpillTree | 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 |
    SpillDualTreeTraverser | A generic dual-tree traverser for hybrid spill trees; see spill_dual_tree_traverser.hpp for implementation |
    SpillSingleTreeTraverser | A generic single-tree traverser for hybrid spill trees; see spill_single_tree_traverser.hpp for implementation |
   TraversalInfo | The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals |
   TreeTraits | The TreeTraits class provides compile-time information on the characteristics of a given tree type |
   TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::BallBound, SplitType > > | This is a specialization of the TreeType class to the BallTree tree type |
   TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::CellBound, SplitType > > | This is a specialization of the TreeType class to the UBTree tree type |
   TreeTraits< 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) |
   TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMaxSplit > > | This is a specialization of the TreeType class to the max-split random projection tree |
   TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMeanSplit > > | This is a specialization of the TreeType class to the mean-split random projection tree |
   TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > > | This is a specialization of the TreeTraits class to the BinarySpaceTree tree type |
   TreeTraits< CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > > | The specialization of the TreeTraits class for the CoverTree tree type |
   TreeTraits< Octree< MetricType, StatisticType, MatType > > | This is a specialization of the TreeTraits class to the Octree tree type |
   TreeTraits< 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 |
   TreeTraits< RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType > > | This is a specialization of the TreeType class to the RectangleTree tree type |
   TreeTraits< SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > > | This is a specialization of the TreeType class to the SpillTree tree type |
   UBTreeSplit | Split a node into two parts according to the median address of points contained in the node |
   VantagePointSplit | The class splits a binary space partitioning tree node according to the median distance to the vantage point |
    SplitInfo | A struct that contains an information about the split |
   XTreeAuxiliaryInformation | The XTreeAuxiliaryInformation class provides information specific to X trees for each node in a RectangleTree |
    SplitHistoryStruct | The X tree requires that the tree records it's "split history" |
   XTreeSplit | A Rectangle Tree has new points inserted at the bottom |
  util | |
   CLIDeleter | Extremely simple class whose only job is to delete the existing CLI object at the end of execution |
   NullOutStream | Used for Log::Debug when not compiled with debugging symbols |
   Option | A static object whose constructor registers a parameter with the CLI class |
   PrefixedOutStream | 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 |
   ProgramDoc | A static object whose constructor registers program documentation with the CLI class |
  Backtrace | Provides a backtrace |
  CLI | Parses the command line for parameters and holds user-specified parameters |
  Log | Provides a convenient way to give formatted output |
  ParamData | Aids in the extensibility of CLI by focusing potential changes into one structure |
  Timer | The timer class provides a way for mlpack methods to be timed |
  Timers | |
 IsVector | If value == true, then VecType is some sort of Armadillo vector or subview |
 IsVector< arma::Col< eT > > | |
 IsVector< arma::Row< eT > > | |
 IsVector< arma::SpCol< eT > > | |
 IsVector< arma::SpRow< eT > > | |
 IsVector< arma::SpSubview< eT > > | |
 IsVector< arma::subview_col< eT > > | |
 IsVector< arma::subview_row< eT > > | |