Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
mlpack::bound::BallBound< VecType >Ball bound that works in the regular Euclidean metric space
mlpack::bound::HRectBound< Power, TakeRoot >Hyper-rectangle bound for an L-metric
mlpack::bound::PeriodicHRectBound< t_pow >Hyper-rectangle bound for an L-metric
mlpack::cf::CFThis class implements Collaborative Filtering (CF)
mlpack::CLIParses the command line for parameters and holds user-specified parameters
mlpack::det::DTreeA density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree)
mlpack::distribution::DiscreteDistributionA discrete distribution where the only observations are discrete observations
mlpack::distribution::GaussianDistributionA single multivariate Gaussian distribution
mlpack::emst::DTBRules< MetricType, TreeType >
mlpack::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
mlpack::emst::DualTreeBoruvka< MetricType, TreeType >Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree
mlpack::emst::DualTreeBoruvka< MetricType, TreeType >::SortEdgesHelperFor sorting the edge list after the computation
mlpack::emst::EdgePairAn edge pair is simply two indices and a distance
mlpack::emst::UnionFindA Union-Find data structure
mlpack::fastmks::FastMKS< KernelType, TreeType >An implementation of fast exact max-kernel search
mlpack::fastmks::FastMKSRules< KernelType, TreeType >The base case and pruning rules for FastMKS (fast max-kernel search)
mlpack::fastmks::FastMKSStatThe statistic used in trees with FastMKS
mlpack::gmm::DiagonalConstraintForce a covariance matrix to be diagonal
mlpack::gmm::EigenvalueRatioConstraintGiven a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios
mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >This class contains methods which can fit a GMM to observations using the EM algorithm
mlpack::gmm::GMM< FittingType >A Gaussian Mixture Model (GMM)
mlpack::gmm::NoConstraintThis class enforces no constraint on the covariance matrix
mlpack::gmm::PositiveDefiniteConstraintGiven a covariance matrix, force the matrix to be positive definite
mlpack::hmm::HMM< Distribution >A class that represents a Hidden Markov Model with an arbitrary type of emission distribution
mlpack::kernel::CosineDistanceThe cosine distance (or cosine similarity)
mlpack::kernel::EpanechnikovKernelThe Epanechnikov kernel, defined as
mlpack::kernel::ExampleKernelAn example kernel function
mlpack::kernel::GaussianKernelThe standard Gaussian kernel
mlpack::kernel::HyperbolicTangentKernelHyperbolic tangent kernel
mlpack::kernel::KernelTraits< KernelType >This is a template class that can provide information about various kernels
mlpack::kernel::KernelTraits< CosineDistance >Kernel traits for the cosine distance
mlpack::kernel::KernelTraits< EpanechnikovKernel >Kernel traits for the Epanechnikov kernel
mlpack::kernel::KernelTraits< GaussianKernel >Kernel traits for the Gaussian kernel
mlpack::kernel::KernelTraits< LaplacianKernel >Kernel traits of the Laplacian kernel
mlpack::kernel::KernelTraits< SphericalKernel >Kernel traits for the spherical kernel
mlpack::kernel::KernelTraits< TriangularKernel >Kernel traits for the triangular kernel
mlpack::kernel::LaplacianKernelThe standard Laplacian kernel
mlpack::kernel::LinearKernelThe simple linear kernel (dot product)
mlpack::kernel::PolynomialKernelThe simple polynomial kernel
mlpack::kernel::PSpectrumStringKernelThe p-spectrum string kernel
mlpack::kernel::SphericalKernel
mlpack::kernel::TriangularKernelThe trivially simple triangular kernel, defined by
mlpack::kmeans::AllowEmptyClustersPolicy which allows K-Means to create empty clusters without any error being reported
mlpack::kmeans::KMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy >This class implements K-Means clustering
mlpack::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
mlpack::kmeans::RandomPartitionA very simple partitioner which partitions the data randomly into the number of desired clusters
mlpack::kmeans::RefinedStartA refined approach for choosing initial points for k-means clustering
mlpack::kpca::KernelPCA< KernelType >This class performs kernel principal components analysis (Kernel PCA), for a given kernel
mlpack::lcc::LocalCoordinateCoding< DictionaryInitializer >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
mlpack::LogProvides a convenient way to give formatted output
mlpack::math::RangeSimple real-valued range
mlpack::metric::IPMetric< KernelType >
mlpack::metric::LMetric< Power, TakeRoot >The L_p metric for arbitrary integer p, with an option to take the root
mlpack::metric::MahalanobisDistance< t_take_root >The Mahalanobis distance, which is essentially a stretched Euclidean distance
mlpack::naive_bayes::NaiveBayesClassifier< MatType >The simple Naive Bayes classifier
mlpack::nca::NCA< MetricType, OptimizerType >An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique
mlpack::nca::SoftmaxErrorFunction< MetricType >The "softmax" stochastic neighbor assignment probability function
mlpack::neighbor::FurthestNeighborSortThis class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class
mlpack::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
mlpack::neighbor::NearestNeighborSortThis class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class
mlpack::neighbor::NeighborSearch< SortPolicy, MetricType, TreeType >The NeighborSearch class is a template class for performing distance-based neighbor searches
mlpack::neighbor::NeighborSearchRules< SortPolicy, MetricType, TreeType >
mlpack::neighbor::NeighborSearchStat< SortPolicy >Extra data for each node in the tree
mlpack::neighbor::RAQueryStat< SortPolicy >Extra data for each node in the tree
mlpack::neighbor::RASearch< SortPolicy, MetricType, TreeType >The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling
mlpack::neighbor::RASearchRules< SortPolicy, MetricType, TreeType >
mlpack::nmf::HAlternatingLeastSquaresRuleThe update rule for the encoding matrix H
mlpack::nmf::HMultiplicativeDistanceRuleThe update rule for the encoding matrix H
mlpack::nmf::HMultiplicativeDivergenceRuleThe update rule for the encoding matrix H
mlpack::nmf::NMF< InitializationRule, WUpdateRule, HUpdateRule >This class implements the NMF on the given matrix V
mlpack::nmf::RandomAcolInitialization< p >This class initializes the W matrix of the NMF algorithm by averaging p randomly chosen columns of V
mlpack::nmf::RandomInitialization
mlpack::nmf::WAlternatingLeastSquaresRuleThe update rule for the basis matrix W
mlpack::nmf::WMultiplicativeDistanceRuleThe update rule for the basis matrix W
mlpack::nmf::WMultiplicativeDivergenceRuleThe update rule for the basis matrix W
mlpack::optimization::AugLagrangian< LagrangianFunction >The AugLagrangian class implements the Augmented Lagrangian method of optimization
mlpack::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
mlpack::optimization::AugLagrangianTestFunctionThis function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method")
mlpack::optimization::GockenbachFunctionThis function is taken from M
mlpack::optimization::L_BFGS< FunctionType >The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function
mlpack::optimization::LovaszThetaSDPThis function is the Lovasz-Theta semidefinite program, as implemented in the following paper:
mlpack::optimization::LRSDP
mlpack::optimization::SGD< DecomposableFunctionType >Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions
mlpack::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,
mlpack::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]
mlpack::optimization::test::RosenbrockWoodFunctionThe Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions
mlpack::optimization::test::SGDTestFunctionVery, very simple test function which is the composite of three other functions
mlpack::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]
mlpack::ParamDataAids in the extensibility of CLI by focusing potential changes into one structure
mlpack::pca::PCAThis class implements principal components analysis (PCA)
mlpack::radical::RadicalAn implementation of RADICAL, an algorithm for independent component analysis (ICA)
mlpack::range::RangeSearch< MetricType, TreeType >The RangeSearch class is a template class for performing range searches
mlpack::range::RangeSearchRules< MetricType, TreeType >
mlpack::range::RangeSearchStatStatistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with
mlpack::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)
mlpack::regression::LinearRegressionA simple linear regression algorithm using ordinary least squares
mlpack::regression::LogisticRegression< OptimizerType >
mlpack::regression::LogisticRegressionFunctionThe log-likelihood function for the logistic regression objective function
mlpack::sparse_coding::DataDependentRandomInitializerA data-dependent random dictionary initializer for SparseCoding
mlpack::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()
mlpack::sparse_coding::RandomInitializerA DictionaryInitializer for use with the SparseCoding class
mlpack::sparse_coding::SparseCoding< DictionaryInitializer >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)
mlpack::TimerThe timer class provides a way for MLPACK methods to be timed
mlpack::Timers
mlpack::tree::BinarySpaceTree< BoundType, StatisticType, MatType >A binary space partitioning tree, such as a KD-tree or a ball tree
mlpack::tree::BinarySpaceTree< BoundType, StatisticType, MatType >::DualTreeTraverser< RuleType >
mlpack::tree::BinarySpaceTree< BoundType, StatisticType, MatType >::SingleTreeTraverser< RuleType >
mlpack::tree::CosineTree
mlpack::tree::CosineTreeBuilder
mlpack::tree::CoverTree< MetricType, RootPointPolicy, StatisticType >A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces
mlpack::tree::CoverTree< MetricType, RootPointPolicy, StatisticType >::DualTreeTraverser< RuleType >
mlpack::tree::CoverTree< MetricType, RootPointPolicy, StatisticType >::SingleTreeTraverser< RuleType >
mlpack::tree::EmptyStatisticEmpty statistic if you are not interested in storing statistics in your tree
mlpack::tree::FirstPointIsRootThis class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class
mlpack::tree::MRKDStatisticStatistic for multi-resolution kd-trees
mlpack::tree::TreeTraits< TreeType >The TreeTraits class provides compile-time information on the characteristics of a given tree type
mlpack::tree::TreeTraits< BinarySpaceTree< BoundType, StatisticType, MatType > >This is a specialization of the TreeType class to the BinarySpaceTree tree type
mlpack::tree::TreeTraits< CoverTree< MetricType, RootPointPolicy, StatisticType > >The specialization of the TreeTraits class for the CoverTree tree type
mlpack::util::CLIDeleterExtremely simple class whose only job is to delete the existing CLI object at the end of execution
mlpack::util::NullOutStreamUsed for Log::Debug when not compiled with debugging symbols
mlpack::util::Option< N >A static object whose constructor registers a parameter with the CLI class
mlpack::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
mlpack::util::ProgramDocA static object whose constructor registers program documentation with the CLI class
mlpack::util::SaveRestoreUtility

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