| ▼Nmlpack | Linear algebra utility functions, generally performed on matrices or vectors |
| ►Nbound | |
| CBallBound | Ball bound that works in the regular Euclidean metric space |
| CHRectBound | Hyper-rectangle bound for an L-metric |
| CPeriodicHRectBound | Hyper-rectangle bound for an L-metric |
| ►Ncf | Collaborative filtering |
| CCF | This class implements Collaborative Filtering (CF) |
| ►Ndet | Density Estimation Trees |
| CDTree | A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree) |
| ►Ndistribution | Probability distributions |
| CDiscreteDistribution | A discrete distribution where the only observations are discrete observations |
| CGaussianDistribution | A single multivariate Gaussian distribution |
| ►Nemst | Euclidean Minimum Spanning Trees |
| CDTBRules | |
| CDTBStat | 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 |
| ►CDualTreeBoruvka | Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree |
| CSortEdgesHelper | For sorting the edge list after the computation |
| CEdgePair | An edge pair is simply two indices and a distance |
| CUnionFind | A Union-Find data structure |
| ►Nfastmks | Fast max-kernel search |
| CFastMKS | An implementation of fast exact max-kernel search |
| CFastMKSRules | The base case and pruning rules for FastMKS (fast max-kernel search) |
| CFastMKSStat | The statistic used in trees with FastMKS |
| ►Ngmm | Gaussian Mixture Models |
| CDiagonalConstraint | Force a covariance matrix to be diagonal |
| CEigenvalueRatioConstraint | Given a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios |
| CEMFit | This class contains methods which can fit a GMM to observations using the EM algorithm |
| CGMM | A Gaussian Mixture Model (GMM) |
| CNoConstraint | This class enforces no constraint on the covariance matrix |
| CPositiveDefiniteConstraint | Given a covariance matrix, force the matrix to be positive definite |
| ►Nhmm | Hidden Markov Models |
| CHMM | A class that represents a Hidden Markov Model with an arbitrary type of emission distribution |
| ►Nkernel | Kernel functions |
| CCosineDistance | The cosine distance (or cosine similarity) |
| CEpanechnikovKernel | The Epanechnikov kernel, defined as |
| CExampleKernel | An example kernel function |
| CGaussianKernel | The standard Gaussian kernel |
| CHyperbolicTangentKernel | Hyperbolic tangent kernel |
| CKernelTraits | This is a template class that can provide information about various kernels |
| CKernelTraits< CosineDistance > | Kernel traits for the cosine distance |
| CKernelTraits< EpanechnikovKernel > | Kernel traits for the Epanechnikov kernel |
| CKernelTraits< GaussianKernel > | Kernel traits for the Gaussian kernel |
| CKernelTraits< LaplacianKernel > | Kernel traits of the Laplacian kernel |
| CKernelTraits< SphericalKernel > | Kernel traits for the spherical kernel |
| CKernelTraits< TriangularKernel > | Kernel traits for the triangular kernel |
| CLaplacianKernel | The standard Laplacian kernel |
| CLinearKernel | The simple linear kernel (dot product) |
| CPolynomialKernel | The simple polynomial kernel |
| CPSpectrumStringKernel | The p-spectrum string kernel |
| CSphericalKernel | |
| CTriangularKernel | The trivially simple triangular kernel, defined by |
| ►Nkmeans | K-Means clustering |
| CAllowEmptyClusters | Policy which allows K-Means to create empty clusters without any error being reported |
| CKMeans | This class implements K-Means clustering |
| CMaxVarianceNewCluster | 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 |
| CRandomPartition | A very simple partitioner which partitions the data randomly into the number of desired clusters |
| CRefinedStart | A refined approach for choosing initial points for k-means clustering |
| ►Nkpca | |
| CKernelPCA | This class performs kernel principal components analysis (Kernel PCA), for a given kernel |
| ►Nlcc | |
| CLocalCoordinateCoding | 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 |
| ►Nmath | Miscellaneous math routines |
| CRange | Simple real-valued range |
| ►Nmetric | |
| CIPMetric | |
| CLMetric | The L_p metric for arbitrary integer p, with an option to take the root |
| CMahalanobisDistance | The Mahalanobis distance, which is essentially a stretched Euclidean distance |
| ►Nnaive_bayes | The Naive Bayes Classifier |
| CNaiveBayesClassifier | The simple Naive Bayes classifier |
| ►Nnca | Neighborhood Components Analysis |
| CNCA | An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique |
| CSoftmaxErrorFunction | The "softmax" stochastic neighbor assignment probability function |
| ►Nneighbor | Neighbor-search routines |
| CFurthestNeighborSort | This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class |
| CLSHSearch | 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 |
| CNearestNeighborSort | This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class |
| CNeighborSearch | The NeighborSearch class is a template class for performing distance-based neighbor searches |
| CNeighborSearchRules | |
| CNeighborSearchStat | Extra data for each node in the tree |
| CRAQueryStat | Extra data for each node in the tree |
| CRASearch | The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling |
| CRASearchRules | |
| ►Nnmf | |
| CHAlternatingLeastSquaresRule | The update rule for the encoding matrix H |
| CHMultiplicativeDistanceRule | The update rule for the encoding matrix H |
| CHMultiplicativeDivergenceRule | The update rule for the encoding matrix H |
| CNMF | This class implements the NMF on the given matrix V |
| CRandomAcolInitialization | This class initializes the W matrix of the NMF algorithm by averaging p randomly chosen columns of V |
| CRandomInitialization | |
| CWAlternatingLeastSquaresRule | The update rule for the basis matrix W |
| CWMultiplicativeDistanceRule | The update rule for the basis matrix W |
| CWMultiplicativeDivergenceRule | The update rule for the basis matrix W |
| ►Noptimization | |
| ►Ntest | |
| CGeneralizedRosenbrockFunction | 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, ...] |
| CRosenbrockFunction | 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] |
| CRosenbrockWoodFunction | The Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions |
| CSGDTestFunction | Very, very simple test function which is the composite of three other functions |
| CWoodFunction | 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] |
| CAugLagrangian | The AugLagrangian class implements the Augmented Lagrangian method of optimization |
| CAugLagrangianFunction | This is a utility class used by AugLagrangian, meant to wrap a LagrangianFunction into a function usable by a simple optimizer like L-BFGS |
| CAugLagrangianTestFunction | This function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method") |
| CGockenbachFunction | This function is taken from M |
| CL_BFGS | The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function |
| CLovaszThetaSDP | This function is the Lovasz-Theta semidefinite program, as implemented in the following paper: |
| CLRSDP | |
| CSGD | Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions |
| ►Npca | |
| CPCA | This class implements principal components analysis (PCA) |
| ►Nradical | |
| CRadical | An implementation of RADICAL, an algorithm for independent component analysis (ICA) |
| ►Nrange | Range-search routines |
| CRangeSearch | The RangeSearch class is a template class for performing range searches |
| CRangeSearchRules | |
| CRangeSearchStat | Statistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with |
| ►Nregression | Regression methods |
| CLARS | An implementation of LARS, a stage-wise homotopy-based algorithm for l1-regularized linear regression (LASSO) and l1+l2 regularized linear regression (Elastic Net) |
| CLinearRegression | A simple linear regression algorithm using ordinary least squares |
| CLogisticRegression | |
| CLogisticRegressionFunction | The log-likelihood function for the logistic regression objective function |
| ►Nsparse_coding | |
| CDataDependentRandomInitializer | A data-dependent random dictionary initializer for SparseCoding |
| CNothingInitializer | 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() |
| CRandomInitializer | A DictionaryInitializer for use with the SparseCoding class |
| CSparseCoding | 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) |
| ►Ntree | Trees and tree-building procedures |
| ►CBinarySpaceTree | A binary space partitioning tree, such as a KD-tree or a ball tree |
| CDualTreeTraverser | A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp |
| CSingleTreeTraverser | A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation |
| CCosineTree | |
| CCosineTreeBuilder | |
| ►CCoverTree | A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces |
| CDualTreeTraverser | A dual-tree cover tree traverser; see dual_tree_traverser.hpp |
| CSingleTreeTraverser | A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation |
| CDualCoverTreeMapEntry | Forward declaration of struct to be used for traversal |
| CEmptyStatistic | Empty statistic if you are not interested in storing statistics in your tree |
| CFirstPointIsRoot | This class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class |
| CMRKDStatistic | Statistic for multi-resolution kd-trees |
| CTreeTraits | The TreeTraits class provides compile-time information on the characteristics of a given tree type |
| CTreeTraits< BinarySpaceTree< BoundType, StatisticType, MatType > > | This is a specialization of the TreeType class to the BinarySpaceTree tree type |
| CTreeTraits< CoverTree< MetricType, RootPointPolicy, StatisticType > > | The specialization of the TreeTraits class for the CoverTree tree type |
| ►Nutil | |
| CCLIDeleter | Extremely simple class whose only job is to delete the existing CLI object at the end of execution |
| CNullOutStream | Used for Log::Debug when not compiled with debugging symbols |
| COption | A static object whose constructor registers a parameter with the CLI class |
| CPrefixedOutStream | 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 |
| CProgramDoc | A static object whose constructor registers program documentation with the CLI class |
| CSaveRestoreUtility | |
| CCLI | Parses the command line for parameters and holds user-specified parameters |
| CLog | Provides a convenient way to give formatted output |
| CParamData | Aids in the extensibility of CLI by focusing potential changes into one structure |
| CTimer | The timer class provides a way for MLPACK methods to be timed |
| CTimers | |
