 mlpack | Linear algebra utility functions, generally performed on matrices or vectors |
  amf | Alternating Matrix Factorization |
   AMF | This class implements AMF (alternating matrix factorization) on the given matrix V |
   AverageInitialization | This initialization rule initializes matrix W and H to root of average of V with uniform noise |
   CompleteIncrementalTermination | |
   IncompleteIncrementalTermination | |
   NMFALSUpdate | This class implements a method titled 'Alternating Least Squares' described in the paper 'Positive Matrix Factorization: A Non-negative Factor Model with Optimal Utilization of Error Estimates of Data Values' by P Paatero and U Tapper |
   NMFMultiplicativeDistanceUpdate | The multiplicative distance update rules for matrices W and H |
   NMFMultiplicativeDivergenceUpdate | This follows a method described in the paper 'Algorithms for Non-negative Matrix Factorization' by D |
   RandomAcolInitialization | This class initializes the W matrix of the AMF algorithm by averaging p randomly chosen columns of V |
   RandomInitialization | |
   SimpleResidueTermination | This class implements a simple residue-based termination policy |
   SimpleToleranceTermination | This class implements residue tolerance termination policy |
   SVDBatchLearning | This class implements SVD batch learning with momentum |
   SVDCompleteIncrementalLearning | |
   SVDCompleteIncrementalLearning< arma::sp_mat > | |
   SVDIncompleteIncrementalLearning | |
   ValidationRMSETermination | |
  bound | |
   BallBound | Ball bound encloses a set of points at a specific distance (radius) from a specific point (center) |
   HRectBound | Hyper-rectangle bound for an L-metric |
  cf | Collaborative filtering |
   CF | This class implements Collaborative Filtering (CF) |
  data | Functions to load and save matrices |
  decision_stump | |
   DecisionStump | This class implements a decision stump |
  det | Density Estimation Trees |
   DTree | A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree) |
  distribution | Probability distributions |
   DiscreteDistribution | A discrete distribution where the only observations are discrete observations |
   GaussianDistribution | A single multivariate Gaussian distribution |
   LaplaceDistribution | The multivariate Laplace distribution centered at 0 has pdf |
  emst | Euclidean Minimum Spanning Trees |
   DTBRules | |
   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 |
   DualTreeBoruvka | Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree |
    SortEdgesHelper | For sorting the edge list after the computation |
   EdgePair | An edge pair is simply two indices and a distance |
   UnionFind | A Union-Find data structure |
  fastmks | Fast max-kernel search |
   FastMKS | An implementation of fast exact max-kernel search |
   FastMKSRules | The base case and pruning rules for FastMKS (fast max-kernel search) |
   FastMKSStat | The statistic used in trees with FastMKS |
  gmm | Gaussian Mixture Models |
   DiagonalConstraint | Force a covariance matrix to be diagonal |
   EigenvalueRatioConstraint | Given a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios |
   EMFit | This class contains methods which can fit a GMM to observations using the EM algorithm |
   GMM | A Gaussian Mixture Model (GMM) |
   NoConstraint | This class enforces no constraint on the covariance matrix |
   PositiveDefiniteConstraint | Given a covariance matrix, force the matrix to be positive definite |
  hmm | Hidden Markov Models |
   HMM | A class that represents a Hidden Markov Model with an arbitrary type of emission distribution |
  kernel | Kernel functions |
   CosineDistance | The cosine distance (or cosine similarity) |
   EpanechnikovKernel | The Epanechnikov kernel, defined as |
   ExampleKernel | An example kernel function |
   GaussianKernel | The standard Gaussian kernel |
   HyperbolicTangentKernel | Hyperbolic tangent kernel |
   KernelTraits | This is a template class that can provide information about various kernels |
   KernelTraits< CosineDistance > | Kernel traits for the cosine distance |
   KernelTraits< EpanechnikovKernel > | Kernel traits for the Epanechnikov kernel |
   KernelTraits< GaussianKernel > | Kernel traits for the Gaussian kernel |
   KernelTraits< LaplacianKernel > | Kernel traits of the Laplacian kernel |
   KernelTraits< SphericalKernel > | Kernel traits for the spherical kernel |
   KernelTraits< TriangularKernel > | Kernel traits for the triangular kernel |
   KMeansSelection | |
   LaplacianKernel | The standard Laplacian kernel |
   LinearKernel | The simple linear kernel (dot product) |
   NystroemMethod | |
   OrderedSelection | |
   PolynomialKernel | The simple polynomial kernel |
   PSpectrumStringKernel | The p-spectrum string kernel |
   RandomSelection | |
   SphericalKernel | |
   TriangularKernel | The trivially simple triangular kernel, defined by |
  kmeans | K-Means clustering |
   AllowEmptyClusters | Policy which allows K-Means to create empty clusters without any error being reported |
   KMeans | This class implements K-Means clustering |
   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 |
   RandomPartition | A very simple partitioner which partitions the data randomly into the number of desired clusters |
   RefinedStart | A refined approach for choosing initial points for k-means clustering |
  kpca | |
   KernelPCA | This class performs kernel principal components analysis (Kernel PCA), for a given kernel |
   NaiveKernelRule | |
   NystroemKernelRule | |
  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 |
  math | Miscellaneous math routines |
   Range | Simple real-valued range |
  metric | |
   IPMetric | |
   LMetric | The L_p metric for arbitrary integer p, with an option to take the root |
   MahalanobisDistance | The Mahalanobis distance, which is essentially a stretched Euclidean distance |
  mvu | |
   MVU | Meant to provide a good abstraction for users |
  naive_bayes | The Naive Bayes Classifier |
   NaiveBayesClassifier | The simple Naive Bayes classifier |
  nca | Neighborhood Components Analysis |
   NCA | An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique |
   SoftmaxErrorFunction | The "softmax" stochastic neighbor assignment probability function |
  neighbor | Neighbor-search routines |
   FurthestNeighborSort | This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class |
   LSHSearch | 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 |
   NearestNeighborSort | This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class |
   NeighborSearch | The NeighborSearch class is a template class for performing distance-based neighbor searches |
   NeighborSearchRules | |
   NeighborSearchStat | Extra data for each node in the tree |
   NeighborSearchTraversalInfo | Traversal information for NeighborSearch |
   RASearchRules | |
  nn | |
   SparseAutoencoder | 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 |
   SparseAutoencoderFunction | This is a class for the sparse autoencoder objective function |
  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, ...] |
    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] |
    RosenbrockWoodFunction | The Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions |
    SGDTestFunction | Very, very simple test function which is the composite of three other functions |
    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] |
   AugLagrangian | The AugLagrangian class implements the Augmented Lagrangian method of optimization |
   AugLagrangianFunction | This is a utility class used by AugLagrangian, meant to wrap a LagrangianFunction into a function usable by a simple optimizer like L-BFGS |
   AugLagrangianTestFunction | This function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method") |
   ExponentialSchedule | The exponential cooling schedule cools the temperature T at every step according to the equation |
   GockenbachFunction | This function is taken from M |
   L_BFGS | The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function |
   LovaszThetaSDP | This function is the Lovasz-Theta semidefinite program, as implemented in the following paper: |
   LRSDP | LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP) |
   LRSDPFunction | The objective function that LRSDP is trying to optimize |
   SA | 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 |
   SGD | Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions |
  pca | |
   PCA | This class implements principal components analysis (PCA) |
  perceptron | |
   Perceptron | This class implements a simple perceptron (i.e., a single layer neural network) |
   RandomInitialization | This class is used to initialize weights for the weightVectors matrix in a random manner |
   SimpleWeightUpdate | |
   ZeroInitialization | This class is used to initialize the matrix weightVectors to zero |
  radical | |
   Radical | An implementation of RADICAL, an algorithm for independent component analysis (ICA) |
  range | Range-search routines |
   RangeSearch | The RangeSearch class is a template class for performing range searches |
   RangeSearchRules | |
   RangeSearchStat | Statistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with |
  regression | Regression methods |
   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) |
   LinearRegression | A simple linear regression algorithm using ordinary least squares |
   LogisticRegression | |
   LogisticRegressionFunction | The log-likelihood function for the logistic regression objective function |
  sparse_coding | |
   DataDependentRandomInitializer | A data-dependent random dictionary initializer for SparseCoding |
   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() |
   RandomInitializer | A DictionaryInitializer for use with the SparseCoding class |
   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) |
  svd | |
   QUIC_SVD | |
   RegularizedSVD | |
   RegularizedSVDFunction | |
  tree | Trees and tree-building procedures |
   BinarySpaceTree | A binary space partitioning tree, such as a KD-tree or a ball tree |
    DualTreeTraverser | A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp |
    SingleTreeTraverser | A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation |
   CompareCosineNode | |
   CosineTree | |
   CoverTree | A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces |
    DualTreeTraverser | A dual-tree cover tree traverser; see dual_tree_traverser.hpp |
     DualCoverTreeMapEntry | Struct used for traversal |
    SingleTreeTraverser | A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation |
   EmptyStatistic | Empty statistic if you are not interested in storing statistics in your tree |
   ExampleTree | 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 |
   FirstPointIsRoot | This class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class |
   MeanSplit | A binary space partitioning tree node is split into its left and right child |
   MRKDStatistic | Statistic for multi-resolution kd-trees |
   TreeTraits | The TreeTraits class provides compile-time information on the characteristics of a given tree type |
   TreeTraits< BinarySpaceTree< BoundType, StatisticType, MatType > > | This is a specialization of the TreeType class to the BinarySpaceTree tree type |
   TreeTraits< CoverTree< MetricType, RootPointPolicy, StatisticType > > | The specialization of the TreeTraits class for the CoverTree tree type |
  util | |
   CLIDeleter | Extremely simple class whose only job is to delete the existing CLI object at the end of execution |
   NullOutStream | Used for Log::Debug when not compiled with debugging symbols |
   Option | A static object whose constructor registers a parameter with the CLI class |
   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 |
   ProgramDoc | A static object whose constructor registers program documentation with the CLI class |
   SaveRestoreUtility | |
  CLI | Parses the command line for parameters and holds user-specified parameters |
  Log | Provides a convenient way to give formatted output |
  ParamData | Aids in the extensibility of CLI by focusing potential changes into one structure |
  Timer | The timer class provides a way for MLPACK methods to be timed |
  Timers | |
 IsVector | If value == true, then VecType is some sort of Armadillo vector or subview |
 IsVector< arma::Col< eT > > | |
 IsVector< arma::Row< eT > > | |
 IsVector< arma::SpCol< eT > > | |
 IsVector< arma::SpRow< eT > > | |
 IsVector< arma::SpSubview< eT > > | |
 IsVector< arma::subview_col< eT > > | |
 IsVector< arma::subview_row< eT > > | |
 RASearch | The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling |
 TraversalInfo | The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals |