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::CF | This class implements Collaborative Filtering (CF) |
mlpack::CLI | Parses the command line for parameters and holds user-specified parameters |
mlpack::det::DTree | A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree) |
mlpack::distribution::DiscreteDistribution | A discrete distribution where the only observations are discrete observations |
mlpack::distribution::GaussianDistribution | A single multivariate Gaussian distribution |
mlpack::emst::DTBRules< MetricType, TreeType > | |
mlpack::emst::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 |
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 >::SortEdgesHelper | For sorting the edge list after the computation |
mlpack::emst::EdgePair | An edge pair is simply two indices and a distance |
mlpack::emst::UnionFind | A 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::FastMKSStat | The statistic used in trees with FastMKS |
mlpack::gmm::DiagonalConstraint | Force a covariance matrix to be diagonal |
mlpack::gmm::EigenvalueRatioConstraint | Given 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::NoConstraint | This class enforces no constraint on the covariance matrix |
mlpack::gmm::PositiveDefiniteConstraint | Given 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::CosineDistance | The cosine distance (or cosine similarity) |
mlpack::kernel::EpanechnikovKernel | The Epanechnikov kernel, defined as |
mlpack::kernel::ExampleKernel | An example kernel function |
mlpack::kernel::GaussianKernel | The standard Gaussian kernel |
mlpack::kernel::HyperbolicTangentKernel | Hyperbolic 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::LaplacianKernel | The standard Laplacian kernel |
mlpack::kernel::LinearKernel | The simple linear kernel (dot product) |
mlpack::kernel::PolynomialKernel | The simple polynomial kernel |
mlpack::kernel::PSpectrumStringKernel | The p-spectrum string kernel |
mlpack::kernel::SphericalKernel | |
mlpack::kernel::TriangularKernel | The trivially simple triangular kernel, defined by |
mlpack::kmeans::AllowEmptyClusters | Policy 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::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 |
mlpack::kmeans::RandomPartition | A very simple partitioner which partitions the data randomly into the number of desired clusters |
mlpack::kmeans::RefinedStart | A 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::Log | Provides a convenient way to give formatted output |
mlpack::math::Range | Simple 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::FurthestNeighborSort | This 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::NearestNeighborSort | This 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::HAlternatingLeastSquaresRule | The update rule for the encoding matrix H |
mlpack::nmf::HMultiplicativeDistanceRule | The update rule for the encoding matrix H |
mlpack::nmf::HMultiplicativeDivergenceRule | The 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::WAlternatingLeastSquaresRule | The update rule for the basis matrix W |
mlpack::nmf::WMultiplicativeDistanceRule | The update rule for the basis matrix W |
mlpack::nmf::WMultiplicativeDivergenceRule | The 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::AugLagrangianTestFunction | This function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method") |
mlpack::optimization::GockenbachFunction | This 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::LovaszThetaSDP | This 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::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, |
mlpack::optimization::test::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] |
mlpack::optimization::test::RosenbrockWoodFunction | The Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions |
mlpack::optimization::test::SGDTestFunction | Very, very simple test function which is the composite of three other functions |
mlpack::optimization::test::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] |
mlpack::ParamData | Aids in the extensibility of CLI by focusing potential changes into one structure |
mlpack::pca::PCA | This class implements principal components analysis (PCA) |
mlpack::radical::Radical | An 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::RangeSearchStat | Statistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with |
mlpack::regression::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) |
mlpack::regression::LinearRegression | A simple linear regression algorithm using ordinary least squares |
mlpack::regression::LogisticRegression< OptimizerType > | |
mlpack::regression::LogisticRegressionFunction | The log-likelihood function for the logistic regression objective function |
mlpack::sparse_coding::DataDependentRandomInitializer | A data-dependent random dictionary initializer for SparseCoding |
mlpack::sparse_coding::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() |
mlpack::sparse_coding::RandomInitializer | A 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::Timer | The 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::EmptyStatistic | Empty statistic if you are not interested in storing statistics in your tree |
mlpack::tree::FirstPointIsRoot | This class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class |
mlpack::tree::MRKDStatistic | Statistic 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::CLIDeleter | Extremely simple class whose only job is to delete the existing CLI object at the end of execution |
mlpack::util::NullOutStream | Used 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::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 |
mlpack::util::ProgramDoc | A static object whose constructor registers program documentation with the CLI class |
mlpack::util::SaveRestoreUtility | |