Cmlpack::optimization::AugLagrangian< mlpack::optimization::LRSDPFunction< optimization::SDP< arma::sp_mat > > > | |
Cmlpack::optimization::AugLagrangian< mlpack::optimization::LRSDPFunction< SDPType > > | |
Cmlpack::optimization::AugLagrangianFunction< mlpack::optimization::LRSDPFunction< 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< kernel::CosineDistance > | |
Cmlpack::fastmks::FastMKS< kernel::EpanechnikovKernel > | |
Cmlpack::fastmks::FastMKS< kernel::GaussianKernel > | |
Cmlpack::fastmks::FastMKS< kernel::HyperbolicTangentKernel > | |
Cmlpack::fastmks::FastMKS< kernel::LinearKernel > | |
Cmlpack::fastmks::FastMKS< kernel::PolynomialKernel > | |
Cmlpack::fastmks::FastMKS< kernel::TriangularKernel > | |
►Cmlpack::hmm::HMM< distribution::RegressionDistribution > | |
Cmlpack::hmm::HMMRegression | A class that represents a Hidden Markov Model Regression (HMMR) |
Cmlpack::bound::HRectBound< metric::EuclideanDistance > | |
Cmlpack::metric::IPMetric< kernel::CosineDistance > | |
Cmlpack::metric::IPMetric< kernel::EpanechnikovKernel > | |
Cmlpack::metric::IPMetric< kernel::GaussianKernel > | |
Cmlpack::metric::IPMetric< kernel::HyperbolicTangentKernel > | |
Cmlpack::metric::IPMetric< kernel::LinearKernel > | |
Cmlpack::metric::IPMetric< kernel::PolynomialKernel > | |
Cmlpack::metric::IPMetric< 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< optimization::SDP< arma::sp_mat > > > > | |
Cmlpack::optimization::L_BFGS< AugLagrangianFunction< mlpack::optimization::LRSDPFunction< SDPType > > > | |
Cmlpack::optimization::LRSDP< optimization::SDP< arma::sp_mat > > | |
Cmlpack::optimization::LRSDPFunction< 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::AverageInitialization | This 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::MaxIterationTermination | This termination policy only terminates when the maximum number of iterations has been reached |
Cmlpack::amf::NMFALSUpdate | This class implements a method titled 'Alternating Least Squares' described in the following paper: |
Cmlpack::amf::NMFMultiplicativeDistanceUpdate | The multiplicative distance update rules for matrices W and H |
Cmlpack::amf::NMFMultiplicativeDivergenceUpdate | This 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::RandomInitialization | This initialization rule for AMF simply fills the W and H matrices with uniform random noise in [0, 1] |
Cmlpack::amf::SimpleResidueTermination | This class implements a simple residue-based termination policy |
Cmlpack::amf::SimpleToleranceTermination< MatType > | This class implements residue tolerance termination policy |
Cmlpack::amf::SVDBatchLearning | This 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::SVDIncompleteIncrementalLearning | This 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::CF | This class implements Collaborative Filtering (CF) |
Cmlpack::cf::DummyClass | This 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::CLI | Parses the command line for parameters and holds user-specified parameters |
Cmlpack::data::DatasetInfo | Auxiliary 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::DTree | A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree) |
Cmlpack::distribution::DiscreteDistribution | A discrete distribution where the only observations are discrete observations |
Cmlpack::distribution::GaussianDistribution | A single multivariate Gaussian distribution |
Cmlpack::distribution::LaplaceDistribution | The multivariate Laplace distribution centered at 0 has pdf |
Cmlpack::distribution::RegressionDistribution | A class that represents a univariate conditionally Gaussian distribution |
Cmlpack::emst::DTBRules< MetricType, TreeType > | |
Cmlpack::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 |
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 >::SortEdgesHelper | For sorting the edge list after the computation |
Cmlpack::emst::EdgePair | An edge pair is simply two indices and a distance |
Cmlpack::emst::UnionFind | A Union-Find data structure |
Cmlpack::fastmks::FastMKS< KernelType, MatType, TreeType > | An implementation of fast exact max-kernel search |
Cmlpack::fastmks::FastMKSModel | A 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::FastMKSStat | The statistic used in trees with FastMKS |
Cmlpack::gmm::DiagonalConstraint | Force a covariance matrix to be diagonal |
Cmlpack::gmm::EigenvalueRatioConstraint | Given 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::GMM | A Gaussian Mixture Model (GMM) |
Cmlpack::gmm::NoConstraint | This class enforces no constraint on the covariance matrix |
Cmlpack::gmm::PositiveDefiniteConstraint | Given 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::CosineDistance | The cosine distance (or cosine similarity) |
Cmlpack::kernel::EpanechnikovKernel | The Epanechnikov kernel, defined as |
Cmlpack::kernel::ExampleKernel | An example kernel function |
Cmlpack::kernel::GaussianKernel | The standard Gaussian kernel |
Cmlpack::kernel::HyperbolicTangentKernel | Hyperbolic 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::LaplacianKernel | The standard Laplacian kernel |
Cmlpack::kernel::LinearKernel | The simple linear kernel (dot product) |
Cmlpack::kernel::NystroemMethod< KernelType, PointSelectionPolicy > | |
Cmlpack::kernel::OrderedSelection | |
Cmlpack::kernel::PolynomialKernel | The simple polynomial kernel |
Cmlpack::kernel::PSpectrumStringKernel | The p-spectrum string kernel |
Cmlpack::kernel::RandomSelection | |
Cmlpack::kernel::SphericalKernel | The spherical kernel, which is 1 when the distance between the two argument points is less than or equal to the bandwidth, or 0 otherwise |
Cmlpack::kernel::TriangularKernel | The trivially simple triangular kernel, defined by |
Cmlpack::kmeans::AllowEmptyClusters | Policy 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::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 |
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::PellegMooreKMeansStatistic | A statistic for trees which holds the blacklist for Pelleg-Moore k-means clustering (which represents the clusters that cannot possibly own any points in a node) |
Cmlpack::kmeans::RandomPartition | A very simple partitioner which partitions the data randomly into the number of desired clusters |
Cmlpack::kmeans::RefinedStart | A 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::LocalCoordinateCoding | An implementation of Local Coordinate Coding (LCC) that codes data which approximately lives on a manifold using a variation of l1-norm regularized sparse coding; in LCC, the penalty on the absolute value of each point's coefficient for each atom is weighted by the squared distance of that point to that atom |
Cmlpack::Log | Provides a convenient way to give formatted output |
Cmlpack::math::ColumnsToBlocks | Transform the columns of the given matrix into a block format |
Cmlpack::math::RangeType< T > | Simple real-valued range |
Cmlpack::matrix_completion::MatrixCompletion | This 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::FurthestNeighborSort | This 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::NearestNeighborSort | This 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::SparseAutoencoderFunction | This 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::AugLagrangianTestFunction | This function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method") |
Cmlpack::optimization::ExponentialSchedule | The exponential cooling schedule cools the temperature T at every step according to the equation |
Cmlpack::optimization::GockenbachFunction | This 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::LovaszThetaSDP | This 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::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, ...] |
Cmlpack::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] |
Cmlpack::optimization::test::RosenbrockWoodFunction | The Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions |
Cmlpack::optimization::test::SGDTestFunction | Very, very simple test function which is the composite of three other functions |
Cmlpack::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] |
Cmlpack::ParamData | Aids in the extensibility of CLI by focusing potential changes into one structure |
Cmlpack::pca::PCA | This 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::RandomInitialization | This class is used to initialize weights for the weightVectors matrix in a random manner |
Cmlpack::perceptron::SimpleWeightUpdate | |
Cmlpack::perceptron::ZeroInitialization | This class is used to initialize the matrix weightVectors to zero |
Cmlpack::radical::Radical | An 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::RangeSearchStat | Statistic 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::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) |
Cmlpack::regression::LinearRegression | A 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::DataDependentRandomInitializer | A data-dependent random dictionary initializer for SparseCoding |
Cmlpack::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() |
Cmlpack::sparse_coding::RandomInitializer | A DictionaryInitializer for use with the SparseCoding class |
Cmlpack::sparse_coding::SparseCoding | An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm regularizer on the codes (the Elastic Net) |
Cmlpack::svd::QUIC_SVD | QUIC-SVD is a matrix factorization technique, which operates in a subspace such that A's approximation in that subspace has minimum error(A being the data matrix) |
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::Timer | The 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 > | |
Cmlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >:: DualTreeTraverser< RuleType >::DualCoverTreeMapEntry | Struct used for traversal |
Cmlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >:: SingleTreeTraverser< RuleType > | |
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 >::SingleTreeTraverser< RuleType > | A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation |
Cmlpack::tree::EmptyStatistic | Empty 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::FirstPointIsRoot | This 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 >::SplitHistoryStruct | The X tree requires that the tree records it's "split history" |
Cmlpack::tree::RStarTreeDescentHeuristic | When 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::RStarTreeSplit | A Rectangle Tree has new points inserted at the bottom |
Cmlpack::tree::RStarTreeSplit::SortStruct | Class to allow for faster sorting |
Cmlpack::tree::RTreeDescentHeuristic | When descending a RectangleTree to insert a point, we need to have a way to choose a child node when the point isn't enclosed by any of them |
Cmlpack::tree::RTreeSplit | A 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::XTreeSplit | A Rectangle Tree has new points inserted at the bottom |
Cmlpack::tree::XTreeSplit::sortStruct | Class to allow for faster sorting |
Cmlpack::util::CLIDeleter | Extremely simple class whose only job is to delete the existing CLI object at the end of execution |
Cmlpack::util::NullOutStream | Used 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::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 |
Cmlpack::util::ProgramDoc | A static object whose constructor registers program documentation with the CLI class |
Cmlpack::neighbor::NeighborSearch< tree::BallTree > | |
Cmlpack::neighbor::NeighborSearch< tree::KDTree > | |
Cmlpack::neighbor::NeighborSearch< tree::RStarTree > | |
Cmlpack::neighbor::NeighborSearch< tree::RTree > | |
Cmlpack::neighbor::NeighborSearch< tree::StandardCoverTree > | |
►Cmlpack::neighbor::NeighborSearchStat< neighbor::NearestNeighborSort > | |
Cmlpack::kmeans::DualTreeKMeansStatistic | |
Cmlpack::range::RangeSearch< tree::BallTree > | |
Cmlpack::range::RangeSearch< tree::KDTree > | |
Cmlpack::range::RangeSearch< tree::RStarTree > | |
Cmlpack::range::RangeSearch< tree::RTree > | |
Cmlpack::range::RangeSearch< tree::StandardCoverTree > | |
Cmlpack::neighbor::RASearch< tree::KDTree > | |
Cmlpack::neighbor::RASearch< tree::RStarTree > | |
Cmlpack::neighbor::RASearch< tree::RTree > | |
Cmlpack::neighbor::RASearch< tree::StandardCoverTree > | |
Cmlpack::optimization::SDP< arma::sp_mat > | |
►CT | |
Cmlpack::data::PointerShim< T > | A shim for pointers |
CTraversalInfo< TreeType > | The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals |