mlpack  2.0.1
Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 12]
 Cmlpack::optimization::AugLagrangian< mlpack::optimization::LRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > > >
 Cmlpack::optimization::AugLagrangian< mlpack::optimization::LRSDPFunction< SDPType > >
 Cmlpack::optimization::AugLagrangianFunction< mlpack::optimization::LRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > > >
 Cmlpack::optimization::AugLagrangianFunction< mlpack::optimization::LRSDPFunction< SDPType > >
 Cboost::serialization::stl::archive_input_unordered_map< Archive, Container >
 Cboost::serialization::stl::archive_input_unordered_multimap< Archive, Container >
 Cmlpack::fastmks::FastMKS< mlpack::kernel::CosineDistance >
 Cmlpack::fastmks::FastMKS< mlpack::kernel::EpanechnikovKernel >
 Cmlpack::fastmks::FastMKS< mlpack::kernel::GaussianKernel >
 Cmlpack::fastmks::FastMKS< mlpack::kernel::HyperbolicTangentKernel >
 Cmlpack::fastmks::FastMKS< mlpack::kernel::LinearKernel >
 Cmlpack::fastmks::FastMKS< mlpack::kernel::PolynomialKernel >
 Cmlpack::fastmks::FastMKS< mlpack::kernel::TriangularKernel >
 Cmlpack::hmm::HMM< distribution::RegressionDistribution >
 Cmlpack::bound::HRectBound< mlpack::metric::LMetric >
 Cmlpack::metric::IPMetric< mlpack::kernel::CosineDistance >
 Cmlpack::metric::IPMetric< mlpack::kernel::EpanechnikovKernel >
 Cmlpack::metric::IPMetric< mlpack::kernel::GaussianKernel >
 Cmlpack::metric::IPMetric< mlpack::kernel::HyperbolicTangentKernel >
 Cmlpack::metric::IPMetric< mlpack::kernel::LinearKernel >
 Cmlpack::metric::IPMetric< mlpack::kernel::PolynomialKernel >
 Cmlpack::metric::IPMetric< mlpack::kernel::TriangularKernel >
 CIsVector< VecType >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 > >
 Cmlpack::optimization::L_BFGS< AugLagrangianFunction< LagrangianFunction > >
 Cmlpack::optimization::L_BFGS< AugLagrangianFunction< mlpack::optimization::LRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > > > >
 Cmlpack::optimization::L_BFGS< AugLagrangianFunction< mlpack::optimization::LRSDPFunction< SDPType > > >
 Cmlpack::optimization::LRSDP< mlpack::optimization::SDP< arma::sp_mat > >
 Cmlpack::optimization::LRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > >
 Cmlpack::adaboost::AdaBoost< WeakLearnerType, MatType >The AdaBoost class
 Cmlpack::amf::AMF< TerminationPolicyType, InitializationRuleType, UpdateRuleType >This class implements AMF (alternating matrix factorization) on the given matrix V
 Cmlpack::amf::AverageInitializationThis initialization rule initializes matrix W and H to root of the average of V, perturbed with uniform noise
 Cmlpack::amf::CompleteIncrementalTermination< TerminationPolicy >This class acts as a wrapper for basic termination policies to be used by SVDCompleteIncrementalLearning
 Cmlpack::amf::IncompleteIncrementalTermination< TerminationPolicy >This class acts as a wrapper for basic termination policies to be used by SVDIncompleteIncrementalLearning
 Cmlpack::amf::MaxIterationTerminationThis termination policy only terminates when the maximum number of iterations has been reached
 Cmlpack::amf::NMFALSUpdateThis class implements a method titled 'Alternating Least Squares' described in the following paper:
 Cmlpack::amf::NMFMultiplicativeDistanceUpdateThe multiplicative distance update rules for matrices W and H
 Cmlpack::amf::NMFMultiplicativeDivergenceUpdateThis follows a method described in the paper 'Algorithms for Non-negative
 Cmlpack::amf::RandomAcolInitialization< columnsToAverage >This class initializes the W matrix of the AMF algorithm by averaging p randomly chosen columns of V
 Cmlpack::amf::RandomInitializationThis initialization rule for AMF simply fills the W and H matrices with uniform random noise in [0, 1]
 Cmlpack::amf::SimpleResidueTerminationThis class implements a simple residue-based termination policy
 Cmlpack::amf::SimpleToleranceTermination< MatType >This class implements residue tolerance termination policy
 Cmlpack::amf::SVDBatchLearningThis class implements SVD batch learning with momentum
 Cmlpack::amf::SVDCompleteIncrementalLearning< MatType >This class computes SVD using complete incremental batch learning, as described in the following paper:
 Cmlpack::amf::SVDCompleteIncrementalLearning< arma::sp_mat >TODO : Merge this template specialized function for sparse matrix using common row_col_iterator
 Cmlpack::amf::SVDIncompleteIncrementalLearningThis class computes SVD using incomplete incremental batch learning, as described in the following paper:
 Cmlpack::amf::ValidationRMSETermination< MatType >This class implements validation termination policy based on RMSE index
 Cmlpack::bound::BallBound< VecType, TMetricType >Ball bound encloses a set of points at a specific distance (radius) from a specific point (center)
 Cmlpack::bound::BoundTraits< BoundType >A class to obtain compile-time traits about BoundType classes
 Cmlpack::bound::BoundTraits< BallBound< VecType, TMetricType > >A specialization of BoundTraits for this bound type
 Cmlpack::bound::BoundTraits< HRectBound< MetricType > >
 Cmlpack::bound::HRectBound< MetricType >Hyper-rectangle bound for an L-metric
 Cmlpack::bound::meta::IsLMetric< MetricType >Utility struct where Value is true if and only if the argument is of type LMetric
 Cmlpack::bound::meta::IsLMetric< metric::LMetric< Power, TakeRoot > >Specialization for IsLMetric when the argument is of type LMetric
 Cmlpack::cf::CFThis class implements Collaborative Filtering (CF)
 Cmlpack::cf::DummyClassThis class acts as a dummy class for passing as template parameter
 Cmlpack::cf::FactorizerTraits< FactorizerType >Template class for factorizer traits
 Cmlpack::cf::FactorizerTraits< mlpack::svd::RegularizedSVD<> >Factorizer traits of Regularized SVD
 Cmlpack::cf::SVDWrapper< Factorizer >This class acts as the wrapper for all SVD factorizers which are incompatible with CF module
 Cmlpack::CLIParses the command line for parameters and holds user-specified parameters
 Cmlpack::data::DatasetInfoAuxiliary information for a dataset, including mappings to/from strings and the datatype of each dimension
 Cmlpack::data::FirstArrayShim< T >A first shim for arrays
 Cmlpack::data::FirstNormalArrayShim< T >A first shim for arrays without a Serialize() method
 Cmlpack::data::FirstShim< T >The first shim: simply holds the object and its name
 Cmlpack::data::HasSerialize< T >
 Cmlpack::data::HasSerialize< T >::check< U, V, W >
 Cmlpack::data::HasSerializeFunction< T >
 Cmlpack::data::SecondArrayShim< 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
 Cmlpack::data::SecondNormalArrayShim< T >A shim for objects in an array which do not have a Serialize() function
 Cmlpack::data::SecondShim< 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
 Cmlpack::decision_stump::DecisionStump< MatType >This class implements a decision stump
 Cmlpack::det::DTreeA density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree)
 Cmlpack::distribution::DiscreteDistributionA discrete distribution where the only observations are discrete observations
 Cmlpack::distribution::GaussianDistributionA single multivariate Gaussian distribution
 Cmlpack::distribution::LaplaceDistributionThe multivariate Laplace distribution centered at 0 has pdf
 Cmlpack::distribution::RegressionDistributionA class that represents a univariate conditionally Gaussian distribution
 Cmlpack::emst::DTBRules< MetricType, TreeType >
 Cmlpack::emst::DTBStatA statistic for use with mlpack trees, which stores the upper bound on distance to nearest neighbors and the component which this node belongs to
 Cmlpack::emst::DualTreeBoruvka< MetricType, MatType, TreeType >Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree
 Cmlpack::emst::DualTreeBoruvka< MetricType, MatType, TreeType >::SortEdgesHelperFor sorting the edge list after the computation
 Cmlpack::emst::EdgePairAn edge pair is simply two indices and a distance
 Cmlpack::emst::UnionFindA Union-Find data structure
 Cmlpack::fastmks::FastMKS< KernelType, MatType, TreeType >An implementation of fast exact max-kernel search
 Cmlpack::fastmks::FastMKSModelA utility struct to contain all the possible FastMKS models, for use by the mlpack_fastmks program
 Cmlpack::fastmks::FastMKSRules< KernelType, TreeType >The base case and pruning rules for FastMKS (fast max-kernel search)
 Cmlpack::fastmks::FastMKSStatThe statistic used in trees with FastMKS
 Cmlpack::gmm::DiagonalConstraintForce a covariance matrix to be diagonal
 Cmlpack::gmm::EigenvalueRatioConstraintGiven a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios
 Cmlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >This class contains methods which can fit a GMM to observations using the EM algorithm
 Cmlpack::gmm::GMMA Gaussian Mixture Model (GMM)
 Cmlpack::gmm::NoConstraintThis class enforces no constraint on the covariance matrix
 Cmlpack::gmm::PositiveDefiniteConstraintGiven a covariance matrix, force the matrix to be positive definite
 Cmlpack::hmm::HMM< Distribution >A class that represents a Hidden Markov Model with an arbitrary type of emission distribution
 Cmlpack::kernel::CosineDistanceThe cosine distance (or cosine similarity)
 Cmlpack::kernel::EpanechnikovKernelThe Epanechnikov kernel, defined as
 Cmlpack::kernel::ExampleKernelAn example kernel function
 Cmlpack::kernel::GaussianKernelThe standard Gaussian kernel
 Cmlpack::kernel::HyperbolicTangentKernelHyperbolic tangent kernel
 Cmlpack::kernel::KernelTraits< KernelType >This is a template class that can provide information about various kernels
 Cmlpack::kernel::KernelTraits< CosineDistance >Kernel traits for the cosine distance
 Cmlpack::kernel::KernelTraits< EpanechnikovKernel >Kernel traits for the Epanechnikov kernel
 Cmlpack::kernel::KernelTraits< GaussianKernel >Kernel traits for the Gaussian kernel
 Cmlpack::kernel::KernelTraits< LaplacianKernel >Kernel traits of the Laplacian kernel
 Cmlpack::kernel::KernelTraits< SphericalKernel >Kernel traits for the spherical kernel
 Cmlpack::kernel::KernelTraits< TriangularKernel >Kernel traits for the triangular kernel
 Cmlpack::kernel::KMeansSelection< ClusteringType, maxIterations >Implementation of the kmeans sampling scheme
 Cmlpack::kernel::LaplacianKernelThe standard Laplacian kernel
 Cmlpack::kernel::LinearKernelThe simple linear kernel (dot product)
 Cmlpack::kernel::NystroemMethod< KernelType, PointSelectionPolicy >
 Cmlpack::kernel::OrderedSelection
 Cmlpack::kernel::PolynomialKernelThe simple polynomial kernel
 Cmlpack::kernel::PSpectrumStringKernelThe p-spectrum string kernel
 Cmlpack::kernel::RandomSelection
 Cmlpack::kernel::SphericalKernelThe spherical kernel, which is 1 when the distance between the two argument points is less than or equal to the bandwidth, or 0 otherwise
 Cmlpack::kernel::TriangularKernelThe trivially simple triangular kernel, defined by
 Cmlpack::kmeans::AllowEmptyClustersPolicy which allows K-Means to create empty clusters without any error being reported
 Cmlpack::kmeans::DualTreeKMeans< 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
 Cmlpack::kmeans::DualTreeKMeansRules< MetricType, TreeType >
 Cmlpack::kmeans::ElkanKMeans< MetricType, MatType >
 Cmlpack::kmeans::HamerlyKMeans< MetricType, MatType >
 Cmlpack::kmeans::KMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy, LloydStepType, MatType >This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm
 Cmlpack::kmeans::MaxVarianceNewClusterWhen an empty cluster is detected, this class takes the point furthest from the centroid of the cluster with maximum variance as a new cluster
 Cmlpack::kmeans::NaiveKMeans< MetricType, MatType >This is an implementation of a single iteration of Lloyd's algorithm for k-means
 Cmlpack::kmeans::PellegMooreKMeans< MetricType, MatType >An implementation of Pelleg-Moore's 'blacklist' algorithm for k-means clustering
 Cmlpack::kmeans::PellegMooreKMeansRules< MetricType, TreeType >The rules class for the single-tree Pelleg-Moore kd-tree traversal for k-means clustering
 Cmlpack::kmeans::PellegMooreKMeansStatisticA 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)
 Cmlpack::kmeans::RandomPartitionA very simple partitioner which partitions the data randomly into the number of desired clusters
 Cmlpack::kmeans::RefinedStartA refined approach for choosing initial points for k-means clustering
 Cmlpack::kpca::KernelPCA< KernelType, KernelRule >This class performs kernel principal components analysis (Kernel PCA), for a given kernel
 Cmlpack::kpca::NaiveKernelRule< KernelType >
 Cmlpack::kpca::NystroemKernelRule< KernelType, PointSelectionPolicy >
 Cmlpack::lcc::LocalCoordinateCodingAn 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
 Cmlpack::LogProvides a convenient way to give formatted output
 Cmlpack::math::ColumnsToBlocksTransform the columns of the given matrix into a block format
 Cmlpack::math::RangeType< T >Simple real-valued range
 Cmlpack::matrix_completion::MatrixCompletionThis class implements the popular nuclear norm minimization heuristic for matrix completion problems
 Cmlpack::meanshift::MeanShift< UseKernel, KernelType, MatType >This class implements mean shift clustering
 Cmlpack::metric::IPMetric< 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:
 Cmlpack::metric::LMetric< TPower, TTakeRoot >The L_p metric for arbitrary integer p, with an option to take the root
 Cmlpack::metric::MahalanobisDistance< TakeRoot >The Mahalanobis distance, which is essentially a stretched Euclidean distance
 Cmlpack::naive_bayes::NaiveBayesClassifier< MatType >The simple Naive Bayes classifier
 Cmlpack::nca::NCA< MetricType, OptimizerType >An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique
 Cmlpack::nca::SoftmaxErrorFunction< MetricType >The "softmax" stochastic neighbor assignment probability function
 Cmlpack::neighbor::FurthestNeighborSortThis class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class
 Cmlpack::neighbor::LSHSearch< 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
 Cmlpack::neighbor::NearestNeighborSortThis class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class
 Cmlpack::neighbor::NeighborSearch< SortPolicy, MetricType, MatType, TreeType, TraversalType >The NeighborSearch class is a template class for performing distance-based neighbor searches
 Cmlpack::neighbor::NeighborSearchRules< SortPolicy, MetricType, TreeType >
 Cmlpack::neighbor::NeighborSearchStat< SortPolicy >Extra data for each node in the tree
 Cmlpack::neighbor::NeighborSearchTraversalInfo< TreeType >Traversal information for NeighborSearch
 Cmlpack::neighbor::NSModel< SortPolicy >
 Cmlpack::neighbor::NSModelName< SortPolicy >
 Cmlpack::neighbor::NSModelName< FurthestNeighborSort >
 Cmlpack::neighbor::NSModelName< NearestNeighborSort >
 Cmlpack::neighbor::RAModel< 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
 Cmlpack::neighbor::RAQueryStat< SortPolicy >Extra data for each node in the tree
 Cmlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling
 Cmlpack::neighbor::RASearchRules< SortPolicy, MetricType, TreeType >
 Cmlpack::neighbor::RAUtil
 Cmlpack::nn::SparseAutoencoder< 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
 Cmlpack::nn::SparseAutoencoderFunctionThis is a class for the sparse autoencoder objective function
 Cmlpack::optimization::AugLagrangian< LagrangianFunction >The AugLagrangian class implements the Augmented Lagrangian method of optimization
 Cmlpack::optimization::AugLagrangianFunction< 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
 Cmlpack::optimization::AugLagrangianTestFunctionThis function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method")
 Cmlpack::optimization::ExponentialScheduleThe exponential cooling schedule cools the temperature T at every step according to the equation
 Cmlpack::optimization::GockenbachFunctionThis function is taken from M
 Cmlpack::optimization::L_BFGS< FunctionType >The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function
 Cmlpack::optimization::LovaszThetaSDPThis function is the Lovasz-Theta semidefinite program, as implemented in the following paper:
 Cmlpack::optimization::LRSDP< SDPType >LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP)
 Cmlpack::optimization::LRSDPFunction< SDPType >The objective function that LRSDP is trying to optimize
 Cmlpack::optimization::PrimalDualSolver< SDPType >Interface to a primal dual interior point solver
 Cmlpack::optimization::SA< 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
 Cmlpack::optimization::SDP< ObjectiveMatrixType >Specify an SDP in primal form
 Cmlpack::optimization::SGD< DecomposableFunctionType >Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions
 Cmlpack::optimization::test::GeneralizedRosenbrockFunctionThe 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, ...]
 Cmlpack::optimization::test::RosenbrockFunctionThe 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]
 Cmlpack::optimization::test::RosenbrockWoodFunctionThe Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions
 Cmlpack::optimization::test::SGDTestFunctionVery, very simple test function which is the composite of three other functions
 Cmlpack::optimization::test::WoodFunctionThe 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]
 Cmlpack::ParamDataAids in the extensibility of CLI by focusing potential changes into one structure
 Cmlpack::pca::PCAThis class implements principal components analysis (PCA)
 Cmlpack::perceptron::Perceptron< LearnPolicy, WeightInitializationPolicy, MatType >This class implements a simple perceptron (i.e., a single layer neural network)
 Cmlpack::perceptron::RandomInitializationThis class is used to initialize weights for the weightVectors matrix in a random manner
 Cmlpack::perceptron::SimpleWeightUpdate
 Cmlpack::perceptron::ZeroInitializationThis class is used to initialize the matrix weightVectors to zero
 Cmlpack::radical::RadicalAn implementation of RADICAL, an algorithm for independent component analysis (ICA)
 Cmlpack::range::RangeSearch< MetricType, MatType, TreeType >The RangeSearch class is a template class for performing range searches
 Cmlpack::range::RangeSearchRules< MetricType, TreeType >
 Cmlpack::range::RangeSearchStatStatistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with
 Cmlpack::range::RSModel
 Cmlpack::regression::LARSAn implementation of LARS, a stage-wise homotopy-based algorithm for l1-regularized linear regression (LASSO) and l1+l2 regularized linear regression (Elastic Net)
 Cmlpack::regression::LinearRegressionA simple linear regression algorithm using ordinary least squares
 Cmlpack::regression::LogisticRegression< MatType >The LogisticRegression class implements an L2-regularized logistic regression model, and supports training with multiple optimizers and classification
 Cmlpack::regression::LogisticRegressionFunction< MatType >The log-likelihood function for the logistic regression objective function
 Cmlpack::regression::SoftmaxRegression< OptimizerType >Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values
 Cmlpack::regression::SoftmaxRegressionFunction
 Cmlpack::sparse_coding::DataDependentRandomInitializerA data-dependent random dictionary initializer for SparseCoding
 Cmlpack::sparse_coding::NothingInitializerA 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()
 Cmlpack::sparse_coding::RandomInitializerA DictionaryInitializer for use with the SparseCoding class
 Cmlpack::sparse_coding::SparseCodingAn 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)
 Cmlpack::svd::QUIC_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)
 Cmlpack::svd::RegularizedSVD< 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
 Cmlpack::svd::RegularizedSVDFunction
 Cmlpack::TimerThe timer class provides a way for mlpack methods to be timed
 Cmlpack::Timers
 Cmlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType >The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper:
 Cmlpack::tree::BinaryNumericSplitInfo< ObservationType >
 Cmlpack::tree::BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >A binary space partitioning tree, such as a KD-tree or a ball tree
 Cmlpack::tree::BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::BreadthFirstDualTreeTraverser< RuleType >
 Cmlpack::tree::BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::DualTreeTraverser< RuleType >A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp
 Cmlpack::tree::BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::SingleTreeTraverser< RuleType >A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation
 Cmlpack::tree::CategoricalSplitInfo
 Cmlpack::tree::CompareCosineNode
 Cmlpack::tree::CosineTree
 Cmlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces
 Cmlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::DualTreeTraverser< RuleType >A dual-tree cover tree traverser; see dual_tree_traverser.hpp
 Cmlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::DualTreeTraverser< RuleType >::DualCoverTreeMapEntryStruct used for traversal
 Cmlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::SingleTreeTraverser< RuleType >A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation
 Cmlpack::tree::EmptyStatisticEmpty statistic if you are not interested in storing statistics in your tree
 Cmlpack::tree::ExampleTree< 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
 Cmlpack::tree::FirstPointIsRootThis class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class
 Cmlpack::tree::GiniImpurity
 Cmlpack::tree::HoeffdingCategoricalSplit< FitnessFunction >This is the standard Hoeffding-bound categorical feature proposed in the paper below:
 Cmlpack::tree::HoeffdingNumericSplit< FitnessFunction, ObservationType >The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper:
 Cmlpack::tree::HoeffdingTree< FitnessFunction, NumericSplitType, CategoricalSplitType >The HoeffdingTree object represents all of the necessary information for a Hoeffding-bound-based decision tree
 Cmlpack::tree::InformationGain
 Cmlpack::tree::MeanSplit< BoundType, MatType >A binary space partitioning tree node is split into its left and right child
 Cmlpack::tree::MidpointSplit< BoundType, MatType >A binary space partitioning tree node is split into its left and right child
 Cmlpack::tree::NumericSplitInfo< ObservationType >
 Cmlpack::tree::QueueFrame< TreeType, TraversalInfoType >
 Cmlpack::tree::RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType >A rectangle type tree tree, such as an R-tree or X-tree
 Cmlpack::tree::RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType >::DualTreeTraverser< MetricType, StatisticType, MatType, SplitType, DescentType >A dual tree traverser for rectangle type trees
 Cmlpack::tree::RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType >::DualTreeTraverser< MetricType, StatisticType, MatType, SplitType, DescentType >::NodeAndScore< RuleType >
 Cmlpack::tree::RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType >::SingleTreeTraverser< RuleType >A single traverser for rectangle type trees
 Cmlpack::tree::RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType >::SingleTreeTraverser< RuleType >::NodeAndScore
 Cmlpack::tree::RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType >::SplitHistoryStructThe X tree requires that the tree records it's "split history"
 Cmlpack::tree::RStarTreeDescentHeuristicWhen descending a Rectangle tree 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
 Cmlpack::tree::RStarTreeSplitA Rectangle Tree has new points inserted at the bottom
 Cmlpack::tree::RStarTreeSplit::SortStructClass to allow for faster sorting
 Cmlpack::tree::RTreeDescentHeuristicWhen 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
 Cmlpack::tree::RTreeSplitA Rectangle Tree has new points inserted at the bottom
 Cmlpack::tree::TreeTraits< TreeType >The TreeTraits class provides compile-time information on the characteristics of a given tree type
 Cmlpack::tree::TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > >This is a specialization of the TreeType class to the BinarySpaceTree tree type
 Cmlpack::tree::TreeTraits< CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > >The specialization of the TreeTraits class for the CoverTree tree type
 Cmlpack::tree::TreeTraits< RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType > >This is a specialization of the TreeType class to the RectangleTree tree type
 Cmlpack::tree::XTreeSplitA Rectangle Tree has new points inserted at the bottom
 Cmlpack::tree::XTreeSplit::sortStructClass to allow for faster sorting
 Cmlpack::util::CLIDeleterExtremely simple class whose only job is to delete the existing CLI object at the end of execution
 Cmlpack::util::NullOutStreamUsed for Log::Debug when not compiled with debugging symbols
 Cmlpack::util::Option< N >A static object whose constructor registers a parameter with the CLI class
 Cmlpack::util::PrefixedOutStreamAllows 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
 Cmlpack::util::ProgramDocA static object whose constructor registers program documentation with the CLI class
 Cmlpack::neighbor::NeighborSearch< mlpack::tree::BinarySpaceTree >
 Cmlpack::neighbor::NeighborSearch< mlpack::tree::CoverTree >
 Cmlpack::neighbor::NeighborSearch< mlpack::tree::RectangleTree >
 Cmlpack::neighbor::NeighborSearchStat< neighbor::NearestNeighborSort >
 Cmlpack::range::RangeSearch< mlpack::tree::BinarySpaceTree >
 Cmlpack::range::RangeSearch< mlpack::tree::CoverTree >
 Cmlpack::range::RangeSearch< mlpack::tree::RectangleTree >
 Cmlpack::neighbor::RASearch< mlpack::tree::BinarySpaceTree >
 Cmlpack::neighbor::RASearch< mlpack::tree::CoverTree >
 Cmlpack::neighbor::RASearch< mlpack::tree::RectangleTree >
 Cmlpack::optimization::SDP< arma::sp_mat >
 CT
 CTraversalInfo< TreeType >The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals