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Class Hierarchy

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This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 12]
oCAugLagrangian< mlpack::optimization::LRSDPFunction< optimization::SDP< arma::sp_mat > > >
oCAugLagrangian< mlpack::optimization::LRSDPFunction< SDPType > >
oCAugLagrangianFunction< mlpack::optimization::LRSDPFunction< optimization::SDP< arma::sp_mat > > >
oCAugLagrangianFunction< mlpack::optimization::LRSDPFunction< SDPType > >
oCstatic_visitor
oCtemplateAuxiliarySplitInfo< ElemType >
oCFastMKS< kernel::CosineDistance >
oCFastMKS< kernel::EpanechnikovKernel >
oCFastMKS< kernel::GaussianKernel >
oCFastMKS< kernel::HyperbolicTangentKernel >
oCFastMKS< kernel::LinearKernel >
oCFastMKS< kernel::PolynomialKernel >
oCFastMKS< kernel::TriangularKernel >
oCHMM< distribution::RegressionDistribution >
oCHRectBound< metric::EuclideanDistance, ElemType >
oCHRectBound< MetricType >
oCIPMetric< kernel::CosineDistance >
oCIPMetric< kernel::EpanechnikovKernel >
oCIPMetric< kernel::GaussianKernel >
oCIPMetric< kernel::HyperbolicTangentKernel >
oCIPMetric< kernel::LinearKernel >
oCIPMetric< kernel::PolynomialKernel >
oCIPMetric< kernel::TriangularKernel >
oCIsVector< VecType >If value == true, then VecType is some sort of Armadillo vector or subview
oCIsVector< arma::Col< eT > >
oCIsVector< arma::Row< eT > >
oCIsVector< arma::SpCol< eT > >
oCIsVector< arma::SpRow< eT > >
oCIsVector< arma::SpSubview< eT > >
oCIsVector< arma::subview_col< eT > >
oCIsVector< arma::subview_row< eT > >
oCL_BFGS< AugLagrangianFunction< LagrangianFunction > >
oCL_BFGS< AugLagrangianFunction< mlpack::optimization::LRSDPFunction< optimization::SDP< arma::sp_mat > > > >
oCL_BFGS< AugLagrangianFunction< mlpack::optimization::LRSDPFunction< SDPType > > >
oCLRSDP< optimization::SDP< arma::sp_mat > >
oCLRSDPFunction< optimization::SDP< arma::sp_mat > >
oCAdaBoost< WeakLearnerType, MatType >The AdaBoost class
oCAMF< TerminationPolicyType, InitializationRuleType, UpdateRuleType >This class implements AMF (alternating matrix factorization) on the given matrix V
oCAverageInitializationThis initialization rule initializes matrix W and H to root of the average of V, perturbed with uniform noise
oCCompleteIncrementalTermination< TerminationPolicy >This class acts as a wrapper for basic termination policies to be used by SVDCompleteIncrementalLearning
oCGivenInitializationThis initialization rule for AMF simply fills the W and H matrices with the matrices given to the constructor of this object
oCIncompleteIncrementalTermination< TerminationPolicy >This class acts as a wrapper for basic termination policies to be used by SVDIncompleteIncrementalLearning
oCMaxIterationTerminationThis termination policy only terminates when the maximum number of iterations has been reached
oCNMFALSUpdateThis class implements a method titled 'Alternating Least Squares' described in the following paper:
oCNMFMultiplicativeDistanceUpdateThe multiplicative distance update rules for matrices W and H
oCNMFMultiplicativeDivergenceUpdateThis follows a method described in the paper 'Algorithms for Non-negative
oCRandomAcolInitialization< columnsToAverage >This class initializes the W matrix of the AMF algorithm by averaging p randomly chosen columns of V
oCRandomInitializationThis initialization rule for AMF simply fills the W and H matrices with uniform random noise in [0, 1]
oCSimpleResidueTerminationThis class implements a simple residue-based termination policy
oCSimpleToleranceTermination< MatType >This class implements residue tolerance termination policy
oCSVDBatchLearningThis class implements SVD batch learning with momentum
oCSVDCompleteIncrementalLearning< MatType >This class computes SVD using complete incremental batch learning, as described in the following paper:
oCSVDCompleteIncrementalLearning< arma::sp_mat >TODO : Merge this template specialized function for sparse matrix using common row_col_iterator
oCSVDIncompleteIncrementalLearningThis class computes SVD using incomplete incremental batch learning, as described in the following paper:
oCValidationRMSETermination< MatType >This class implements validation termination policy based on RMSE index
oCRandomInitializationThis class is used to initialize randomly the weight matrix
oCBacktraceProvides a backtrace
oCBallBound< MetricType, VecType >Ball bound encloses a set of points at a specific distance (radius) from a specific point (center)
oCBoundTraits< BoundType >A class to obtain compile-time traits about BoundType classes
oCBoundTraits< BallBound< MetricType, VecType > >A specialization of BoundTraits for this bound type
oCBoundTraits< CellBound< MetricType, ElemType > >
oCBoundTraits< HollowBallBound< MetricType, ElemType > >A specialization of BoundTraits for this bound type
oCBoundTraits< HRectBound< MetricType, ElemType > >
oCCellBound< MetricType, ElemType >The CellBound class describes a bound that consists of a number of hyperrectangles
oCHollowBallBound< TMetricType, ElemType >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)
oCHRectBound< MetricType, ElemType >Hyper-rectangle bound for an L-metric
oCIsLMetric< MetricType >Utility struct where Value is true if and only if the argument is of type LMetric
oCIsLMetric< metric::LMetric< Power, TakeRoot > >Specialization for IsLMetric when the argument is of type LMetric
oCCFThis class implements Collaborative Filtering (CF)
oCDummyClassThis class acts as a dummy class for passing as template parameter
oCFactorizerTraits< FactorizerType >Template class for factorizer traits
oCFactorizerTraits< mlpack::svd::RegularizedSVD<> >Factorizer traits of Regularized SVD
oCSVDWrapper< Factorizer >This class acts as the wrapper for all SVD factorizers which are incompatible with CF module
oCCLIParses the command line for parameters and holds user-specified parameters
oCCustomImputation< T >A simple custom imputation class
oCDatasetMapper< PolicyType >Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dimension
oCFirstArrayShim< T >A first shim for arrays
oCFirstNormalArrayShim< T >A first shim for arrays without a Serialize() method
oCFirstShim< T >The first shim: simply holds the object and its name
oCHasSerialize< T >
oCHasSerialize< T >::check< U, V, W >
oCHasSerializeFunction< T >
oCImputer< T, MapperType, StrategyType >Given a dataset of a particular datatype, replace user-specified missing value with a variable dependent on the StrategyType and MapperType
oCIncrementPolicyIncrementPolicy is used as a helper class for DatasetMapper
oCListwiseDeletion< T >A complete-case analysis to remove the values containing mappedValue
oCLoadCSVLoad 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
oCMeanImputation< T >A simple mean imputation class
oCMedianImputation< T >This is a class implementation of simple median imputation
oCMissingPolicyMissingPolicy is used as a helper class for DatasetMapper
oCSecondArrayShim< T >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
oCSecondNormalArrayShim< T >A shim for objects in an array which do not have a Serialize() function
oCSecondShim< T >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
oCDBSCAN< RangeSearchType, PointSelectionPolicy >DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper:
oCRandomPointSelectionThis class can be used to randomly select the next point to use for DBSCAN
oCDecisionStump< MatType >This class implements a decision stump
oCDTreeA density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree)
oCDiscreteDistributionA discrete distribution where the only observations are discrete observations
oCGammaDistributionThis class represents the Gamma distribution
oCGaussianDistributionA single multivariate Gaussian distribution
oCLaplaceDistributionThe multivariate Laplace distribution centered at 0 has pdf
oCRegressionDistributionA class that represents a univariate conditionally Gaussian distribution
oCDTBRules< MetricType, TreeType >
oCDTBStatA statistic for use with mlpack trees, which stores the upper bound on distance to nearest neighbors and the component which this node belongs to
oCDualTreeBoruvka< MetricType, MatType, TreeType >Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree
oCEdgePairAn edge pair is simply two indices and a distance
oCUnionFindA Union-Find data structure
oCFastMKS< KernelType, MatType, TreeType >An implementation of fast exact max-kernel search
oCFastMKSModelA utility struct to contain all the possible FastMKS models, for use by the mlpack_fastmks program
oCFastMKSRules< KernelType, TreeType >The FastMKSRules class is a template helper class used by FastMKS class when performing exact max-kernel search
oCFastMKSStatThe statistic used in trees with FastMKS
oCDiagonalConstraintForce a covariance matrix to be diagonal
oCEigenvalueRatioConstraintGiven a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios
oCEMFit< InitialClusteringType, CovarianceConstraintPolicy >This class contains methods which can fit a GMM to observations using the EM algorithm
oCGMMA Gaussian Mixture Model (GMM)
oCNoConstraintThis class enforces no constraint on the covariance matrix
oCPositiveDefiniteConstraintGiven a covariance matrix, force the matrix to be positive definite
oCHMM< Distribution >A class that represents a Hidden Markov Model with an arbitrary type of emission distribution
oCCosineDistanceThe cosine distance (or cosine similarity)
oCEpanechnikovKernelThe Epanechnikov kernel, defined as
oCExampleKernelAn example kernel function
oCGaussianKernelThe standard Gaussian kernel
oCHyperbolicTangentKernelHyperbolic tangent kernel
oCKernelTraits< KernelType >This is a template class that can provide information about various kernels
oCKernelTraits< CosineDistance >Kernel traits for the cosine distance
oCKernelTraits< EpanechnikovKernel >Kernel traits for the Epanechnikov kernel
oCKernelTraits< GaussianKernel >Kernel traits for the Gaussian kernel
oCKernelTraits< LaplacianKernel >Kernel traits of the Laplacian kernel
oCKernelTraits< SphericalKernel >Kernel traits for the spherical kernel
oCKernelTraits< TriangularKernel >Kernel traits for the triangular kernel
oCKMeansSelection< ClusteringType, maxIterations >Implementation of the kmeans sampling scheme
oCLaplacianKernelThe standard Laplacian kernel
oCLinearKernelThe simple linear kernel (dot product)
oCNystroemMethod< KernelType, PointSelectionPolicy >
oCOrderedSelection
oCPolynomialKernelThe simple polynomial kernel
oCPSpectrumStringKernelThe p-spectrum string kernel
oCRandomSelection
oCSphericalKernelThe spherical kernel, which is 1 when the distance between the two argument points is less than or equal to the bandwidth, or 0 otherwise
oCTriangularKernelThe trivially simple triangular kernel, defined by
oCAllowEmptyClustersPolicy which allows K-Means to create empty clusters without any error being reported
oCDualTreeKMeans< MetricType, MatType, TreeType >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
oCDualTreeKMeansRules< MetricType, TreeType >
oCElkanKMeans< MetricType, MatType >
oCHamerlyKMeans< MetricType, MatType >
oCKillEmptyClustersPolicy which allows K-Means to "kill" empty clusters without any error being reported
oCKMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy, LloydStepType, MatType >This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm
oCMaxVarianceNewClusterWhen an empty cluster is detected, this class takes the point furthest from the centroid of the cluster with maximum variance as a new cluster
oCNaiveKMeans< MetricType, MatType >This is an implementation of a single iteration of Lloyd's algorithm for k-means
oCPellegMooreKMeans< MetricType, MatType >An implementation of Pelleg-Moore's 'blacklist' algorithm for k-means clustering
oCPellegMooreKMeansRules< MetricType, TreeType >The rules class for the single-tree Pelleg-Moore kd-tree traversal for k-means clustering
oCPellegMooreKMeansStatisticA 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)
oCRandomPartitionA very simple partitioner which partitions the data randomly into the number of desired clusters
oCRefinedStartA refined approach for choosing initial points for k-means clustering
oCSampleInitialization
oCKernelPCA< KernelType, KernelRule >This class performs kernel principal components analysis (Kernel PCA), for a given kernel
oCNaiveKernelRule< KernelType >
oCNystroemKernelRule< KernelType, PointSelectionPolicy >
oCLocalCoordinateCodingAn 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
oCLogProvides a convenient way to give formatted output
oCColumnsToBlocksTransform the columns of the given matrix into a block format
oCRangeType< T >Simple real-valued range
oCMatrixCompletionThis class implements the popular nuclear norm minimization heuristic for matrix completion problems
oCMeanShift< UseKernel, KernelType, MatType >This class implements mean shift clustering
oCIPMetric< KernelType >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:
oCLMetric< TPower, TTakeRoot >The L_p metric for arbitrary integer p, with an option to take the root
oCMahalanobisDistance< TakeRoot >The Mahalanobis distance, which is essentially a stretched Euclidean distance
oCNaiveBayesClassifier< MatType >The simple Naive Bayes classifier
oCNCA< MetricType, OptimizerType >An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique
oCSoftmaxErrorFunction< MetricType >The "softmax" stochastic neighbor assignment probability function
oCDrusillaSelect< MatType >
oCFurthestNeighborSortThis class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class
oCLSHSearch< SortPolicy >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
oCNearestNeighborSortThis class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class
oCNeighborSearch< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType >The NeighborSearch class is a template class for performing distance-based neighbor searches
oCNeighborSearchRules< SortPolicy, MetricType, TreeType >The NeighborSearchRules class is a template helper class used by NeighborSearch class when performing distance-based neighbor searches
oCNeighborSearchRules< SortPolicy, MetricType, TreeType >::CandidateCmpCompare two candidates based on the distance
oCNeighborSearchStat< SortPolicy >Extra data for each node in the tree
oCNSModel< SortPolicy >The NSModel class provides an easy way to serialize a model, abstracts away the different types of trees, and also reflects the NeighborSearch API
oCNSModelName< SortPolicy >
oCNSModelName< FurthestNeighborSort >
oCNSModelName< NearestNeighborSort >
oCQDAFN< MatType >
oCRAModel< SortPolicy >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
oCRAQueryStat< SortPolicy >Extra data for each node in the tree
oCRASearch< SortPolicy, MetricType, MatType, TreeType >The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling
oCRASearchRules< SortPolicy, MetricType, TreeType >The RASearchRules class is a template helper class used by RASearch class when performing rank-approximate search via random-sampling
oCRAUtil
oCSparseAutoencoder< OptimizerType >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
oCSparseAutoencoderFunctionThis is a class for the sparse autoencoder objective function
oCAdaDelta< DecomposableFunctionType >Adadelta is an optimizer that uses two ideas to improve upon the two main drawbacks of the Adagrad method:
oCAdam< DecomposableFunctionType >Adam is an optimizer that computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients
oCAugLagrangian< LagrangianFunction >The AugLagrangian class implements the Augmented Lagrangian method of optimization
oCAugLagrangianFunction< LagrangianFunction >This is a utility class used by AugLagrangian, meant to wrap a LagrangianFunction into a function usable by a simple optimizer like L-BFGS
oCAugLagrangianTestFunctionThis function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method")
oCExponentialScheduleThe exponential cooling schedule cools the temperature T at every step according to the equation
oCGockenbachFunctionThis function is taken from M
oCGradientDescent< FunctionType >Gradient Descent is a technique to minimize a function
oCL_BFGS< FunctionType >The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function
oCLovaszThetaSDPThis function is the Lovasz-Theta semidefinite program, as implemented in the following paper:
oCLRSDP< SDPType >LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP)
oCLRSDPFunction< SDPType >The objective function that LRSDP is trying to optimize
oCMiniBatchSGD< DecomposableFunctionType >Mini-batch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions
oCPrimalDualSolver< SDPType >Interface to a primal dual interior point solver
oCRMSprop< DecomposableFunctionType >RMSprop is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients
oCSA< FunctionType, CoolingScheduleType >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
oCSDP< ObjectiveMatrixType >Specify an SDP in primal form
oCSGD< DecomposableFunctionType >Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions
oCGDTestFunctionVery, very simple test function which is the composite of three other functions
oCGeneralizedRosenbrockFunctionThe 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, ...]
oCRosenbrockFunctionThe 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]
oCRosenbrockWoodFunctionThe Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions
oCSGDTestFunctionVery, very simple test function which is the composite of three other functions
oCWoodFunctionThe 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]
oCParamDataAids in the extensibility of CLI by focusing potential changes into one structure
oCExactSVDPolicyImplementation of the exact SVD policy
oCPCAType< DecompositionPolicy >This class implements principal components analysis (PCA)
oCQUICSVDPolicyImplementation of the QUIC-SVD policy
oCRandomizedSVDPolicyImplementation of the randomized SVD policy
oCPerceptron< LearnPolicy, WeightInitializationPolicy, MatType >This class implements a simple perceptron (i.e., a single layer neural network)
oCRandomInitializationThis class is used to initialize weights for the weightVectors matrix in a random manner
oCSimpleWeightUpdate
oCZeroInitializationThis class is used to initialize the matrix weightVectors to zero
oCRadicalAn implementation of RADICAL, an algorithm for independent component analysis (ICA)
oCRangeSearch< MetricType, MatType, TreeType >The RangeSearch class is a template class for performing range searches
oCRangeSearchRules< MetricType, TreeType >The RangeSearchRules class is a template helper class used by RangeSearch class when performing range searches
oCRangeSearchStatStatistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with
oCRSModel
oCRSModelName
oCLARSAn implementation of LARS, a stage-wise homotopy-based algorithm for l1-regularized linear regression (LASSO) and l1+l2 regularized linear regression (Elastic Net)
oCLinearRegressionA simple linear regression algorithm using ordinary least squares
oCLogisticRegression< MatType >The LogisticRegression class implements an L2-regularized logistic regression model, and supports training with multiple optimizers and classification
oCLogisticRegressionFunction< MatType >The log-likelihood function for the logistic regression objective function
oCSoftmaxRegression< OptimizerType >Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values
oCSoftmaxRegressionFunction
oCDataDependentRandomInitializerA data-dependent random dictionary initializer for SparseCoding
oCNothingInitializerA 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()
oCRandomInitializerA DictionaryInitializer for use with the SparseCoding class
oCSparseCodingAn 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)
oCQUIC_SVDQUIC-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)
oCRandomizedSVDRandomized 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"
oCRegularizedSVD< OptimizerType >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
oCRegularizedSVDFunction
oCTimerThe timer class provides a way for mlpack methods to be timed
oCTimers
oCAllCategoricalSplit< FitnessFunction >The AllCategoricalSplit is a splitting function that will split categorical features into many children: one child for each category
oCAllCategoricalSplit< FitnessFunction >::AuxiliarySplitInfo< ElemType >
oCAxisParallelProjVectorAxisParallelProjVector defines an axis-parallel projection vector
oCBestBinaryNumericSplit< FitnessFunction >The BestBinaryNumericSplit is a splitting function for decision trees that will exhaustively search a numeric dimension for the best binary split
oCBestBinaryNumericSplit< FitnessFunction >::AuxiliarySplitInfo< ElemType >
oCBinaryNumericSplit< FitnessFunction, ObservationType >The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper:
oCBinaryNumericSplitInfo< ObservationType >
oCBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >A binary space partitioning tree, such as a KD-tree or a ball tree
oCBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::BreadthFirstDualTreeTraverser< RuleType >
oCBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::DualTreeTraverser< RuleType >A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp
oCBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::SingleTreeTraverser< RuleType >A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation
oCCategoricalSplitInfo
oCCompareCosineNode
oCCosineTree
oCCoverTree< MetricType, StatisticType, MatType, RootPointPolicy >A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces
oCCoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::DualTreeTraverser< RuleType >A dual-tree cover tree traverser; see dual_tree_traverser.hpp
oCCoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::SingleTreeTraverser< RuleType >A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation
oCDiscreteHilbertValue< TreeElemType >The DiscreteHilbertValue class stores Hilbert values for all of the points in a RectangleTree node, and calculates Hilbert values for new points
oCEmptyStatisticEmpty statistic if you are not interested in storing statistics in your tree
oCExampleTree< MetricType, StatisticType, MatType >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
oCFirstPointIsRootThis class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class
oCGiniGainThe Gini gain, a measure of set purity usable as a fitness function (FitnessFunction) for decision trees
oCGiniImpurity
oCGreedySingleTreeTraverser< TreeType, RuleType >
oCHilbertRTreeAuxiliaryInformation< TreeType, HilbertValueType >
oCHilbertRTreeDescentHeuristicThis class chooses the best child of a node in a Hilbert R tree when inserting a new point
oCHilbertRTreeSplit< splitOrder >The splitting procedure for the Hilbert R tree
oCHoeffdingCategoricalSplit< FitnessFunction >This is the standard Hoeffding-bound categorical feature proposed in the paper below:
oCHoeffdingNumericSplit< FitnessFunction, ObservationType >The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper:
oCHoeffdingTree< FitnessFunction, NumericSplitType, CategoricalSplitType >The HoeffdingTree object represents all of the necessary information for a Hoeffding-bound-based decision tree
oCHyperplaneBase< BoundT, ProjVectorT >HyperplaneBase defines a splitting hyperplane based on a projection vector and projection value
oCInformationGainThe standard information gain criterion, used for calculating gain in decision trees
oCIsSpillTree< TreeType >
oCIsSpillTree< tree::SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > >
oCMeanSpaceSplit< MetricType, MatType >
oCMeanSplit< BoundType, MatType >A binary space partitioning tree node is split into its left and right child
oCMeanSplit< BoundType, MatType >::SplitInfoAn information about the partition
oCMidpointSpaceSplit< MetricType, MatType >
oCMidpointSplit< BoundType, MatType >A binary space partitioning tree node is split into its left and right child
oCMidpointSplit< BoundType, MatType >::SplitInfoA struct that contains an information about the split
oCMinimalCoverageSweep< SplitPolicy >The MinimalCoverageSweep class finds a partition along which we can split a node according to the coverage of two resulting nodes
oCMinimalCoverageSweep< SplitPolicy >::SweepCost< TreeType >A struct that provides the type of the sweep cost
oCMinimalSplitsNumberSweep< SplitPolicy >The MinimalSplitsNumberSweep class finds a partition along which we can split a node according to the number of required splits of the node
oCMinimalSplitsNumberSweep< SplitPolicy >::SweepCost< typename >A struct that provides the type of the sweep cost
oCNoAuxiliaryInformation< TreeType >
oCNumericSplitInfo< ObservationType >
oCOctree< MetricType, StatisticType, MatType >
oCOctree< MetricType, StatisticType, MatType >::DualTreeTraverser< MetricType, StatisticType, MatType >A dual-tree traverser; see dual_tree_traverser.hpp
oCOctree< MetricType, StatisticType, MatType >::SingleTreeTraverser< RuleType >A single-tree traverser; see single_tree_traverser.hpp
oCProjVectorProjVector defines a general projection vector (not necessarily axis-parallel)
oCQueueFrame< TreeType, TraversalInfoType >
oCRectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType >A rectangle type tree tree, such as an R-tree or X-tree
oCRectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType >::DualTreeTraverser< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType >A dual tree traverser for rectangle type trees
oCRectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType >::SingleTreeTraverser< RuleType >A single traverser for rectangle type trees
oCRPlusPlusTreeAuxiliaryInformation< TreeType >
oCRPlusPlusTreeDescentHeuristic
oCRPlusPlusTreeSplitPolicyThe RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split
oCRPlusTreeDescentHeuristic
oCRPlusTreeSplit< SplitPolicyType, SweepType >The RPlusTreeSplit class performs the split process of a node on overflow
oCRPlusTreeSplitPolicyThe RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split
oCRPTreeMaxSplit< BoundType, MatType >This class splits a node by a random hyperplane
oCRPTreeMaxSplit< BoundType, MatType >::SplitInfoAn information about the partition
oCRPTreeMeanSplit< BoundType, MatType >This class splits a binary space tree
oCRPTreeMeanSplit< BoundType, MatType >::SplitInfoAn information about the partition
oCRStarTreeDescentHeuristicWhen 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
oCRStarTreeSplitA Rectangle Tree has new points inserted at the bottom
oCRTreeDescentHeuristicWhen 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
oCRTreeSplitA Rectangle Tree has new points inserted at the bottom
oCSpaceSplit< MetricType, MatType >
oCSpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType >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
oCSpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType >::SpillDualTreeTraverser< MetricType, StatisticType, MatType, HyperplaneType, SplitType >A generic dual-tree traverser for hybrid spill trees; see spill_dual_tree_traverser.hpp for implementation
oCSpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType >::SpillSingleTreeTraverser< MetricType, StatisticType, MatType, HyperplaneType, SplitType >A generic single-tree traverser for hybrid spill trees; see spill_single_tree_traverser.hpp for implementation
oCTraversalInfo< TreeType >The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals
oCTreeTraits< TreeType >The TreeTraits class provides compile-time information on the characteristics of a given tree type
oCTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::BallBound, SplitType > >This is a specialization of the TreeType class to the BallTree tree type
oCTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::CellBound, SplitType > >This is a specialization of the TreeType class to the UBTree tree type
oCTreeTraits< 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)
oCTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMaxSplit > >This is a specialization of the TreeType class to the max-split random projection tree
oCTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMeanSplit > >This is a specialization of the TreeType class to the mean-split random projection tree
oCTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > >This is a specialization of the TreeTraits class to the BinarySpaceTree tree type
oCTreeTraits< CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > >The specialization of the TreeTraits class for the CoverTree tree type
oCTreeTraits< Octree< MetricType, StatisticType, MatType > >This is a specialization of the TreeTraits class to the Octree tree type
oCTreeTraits< 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
oCTreeTraits< RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType > >This is a specialization of the TreeType class to the RectangleTree tree type
oCTreeTraits< SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > >This is a specialization of the TreeType class to the SpillTree tree type
oCUBTreeSplit< BoundType, MatType >Split a node into two parts according to the median address of points contained in the node
oCVantagePointSplit< BoundType, MatType, MaxNumSamples >The class splits a binary space partitioning tree node according to the median distance to the vantage point
oCVantagePointSplit< BoundType, MatType, MaxNumSamples >::SplitInfoA struct that contains an information about the split
oCXTreeAuxiliaryInformation< TreeType >The XTreeAuxiliaryInformation class provides information specific to X trees for each node in a RectangleTree
oCXTreeAuxiliaryInformation< TreeType >::SplitHistoryStructThe X tree requires that the tree records it's "split history"
oCXTreeSplitA Rectangle Tree has new points inserted at the bottom
oCCLIDeleterExtremely simple class whose only job is to delete the existing CLI object at the end of execution
oCNullOutStreamUsed for Log::Debug when not compiled with debugging symbols
oCOption< N >A static object whose constructor registers a parameter with the CLI class
oCPrefixedOutStreamAllows 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
oCProgramDocA static object whose constructor registers program documentation with the CLI class
oCNeighborSearchStat< neighbor::NearestNeighborSort >
oCtemplateAuxiliarySplitInfo< ElemType >
oCRangeType< double >
oCRangeType< ElemType >
oCRASearch< tree::HilbertRTree >
oCRASearch< tree::KDTree >
oCRASearch< tree::Octree >
oCRASearch< tree::RPlusPlusTree >
oCRASearch< tree::RPlusTree >
oCRASearch< tree::RStarTree >
oCRASearch< tree::RTree >
oCRASearch< tree::StandardCoverTree >
oCRASearch< tree::UBTree >
oCRASearch< tree::XTree >
oCSDP< arma::sp_mat >
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