| ▼Nboost | |
| ►Nserialization | |
| ►Nstl | |
| Carchive_input_unordered_map | |
| Carchive_input_unordered_multimap | |
| ▼Nmlpack | Linear algebra utility functions, generally performed on matrices or vectors |
| ►Nadaboost | |
| CAdaBoost | The AdaBoost class |
| ►Namf | Alternating Matrix Factorization |
| CAMF | This class implements AMF (alternating matrix factorization) on the given matrix V |
| CAverageInitialization | This initialization rule initializes matrix W and H to root of the average of V, perturbed with uniform noise |
| CCompleteIncrementalTermination | This class acts as a wrapper for basic termination policies to be used by SVDCompleteIncrementalLearning |
| CIncompleteIncrementalTermination | This class acts as a wrapper for basic termination policies to be used by SVDIncompleteIncrementalLearning |
| CMaxIterationTermination | This termination policy only terminates when the maximum number of iterations has been reached |
| CNMFALSUpdate | This class implements a method titled 'Alternating Least Squares' described in the following paper: |
| CNMFMultiplicativeDistanceUpdate | The multiplicative distance update rules for matrices W and H |
| CNMFMultiplicativeDivergenceUpdate | This follows a method described in the paper 'Algorithms for Non-negative |
| CRandomAcolInitialization | This class initializes the W matrix of the AMF algorithm by averaging p randomly chosen columns of V |
| CRandomInitialization | This initialization rule for AMF simply fills the W and H matrices with uniform random noise in [0, 1] |
| CSimpleResidueTermination | This class implements a simple residue-based termination policy |
| CSimpleToleranceTermination | This class implements residue tolerance termination policy |
| CSVDBatchLearning | This class implements SVD batch learning with momentum |
| CSVDCompleteIncrementalLearning | This class computes SVD using complete incremental batch learning, as described in the following paper: |
| CSVDCompleteIncrementalLearning< arma::sp_mat > | TODO : Merge this template specialized function for sparse matrix using common row_col_iterator |
| CSVDIncompleteIncrementalLearning | This class computes SVD using incomplete incremental batch learning, as described in the following paper: |
| CValidationRMSETermination | This class implements validation termination policy based on RMSE index |
| ►Nbound | |
| ►Nmeta | Metaprogramming utilities |
| CIsLMetric | Utility struct where Value is true if and only if the argument is of type LMetric |
| CIsLMetric< metric::LMetric< Power, TakeRoot > > | Specialization for IsLMetric when the argument is of type LMetric |
| CBallBound | Ball bound encloses a set of points at a specific distance (radius) from a specific point (center) |
| CBoundTraits | A class to obtain compile-time traits about BoundType classes |
| CBoundTraits< BallBound< VecType, TMetricType > > | A specialization of BoundTraits for this bound type |
| CBoundTraits< HRectBound< MetricType > > | |
| CHRectBound | Hyper-rectangle bound for an L-metric |
| ►Ncf | Collaborative filtering |
| CCF | This class implements Collaborative Filtering (CF) |
| CDummyClass | This class acts as a dummy class for passing as template parameter |
| CFactorizerTraits | Template class for factorizer traits |
| CFactorizerTraits< mlpack::svd::RegularizedSVD<> > | Factorizer traits of Regularized SVD |
| CSVDWrapper | This class acts as the wrapper for all SVD factorizers which are incompatible with CF module |
| ►Ndata | Functions to load and save matrices and models |
| CDatasetInfo | Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dimension |
| CFirstArrayShim | A first shim for arrays |
| CFirstNormalArrayShim | A first shim for arrays without a Serialize() method |
| CFirstShim | The first shim: simply holds the object and its name |
| ►CHasSerialize | |
| Ccheck | |
| CHasSerializeFunction | |
| CPointerShim | A shim for pointers |
| CSecondArrayShim | 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 |
| CSecondNormalArrayShim | A shim for objects in an array which do not have a Serialize() function |
| CSecondShim | 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 |
| ►Ndecision_stump | |
| CDecisionStump | This class implements a decision stump |
| ►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 |
| CLaplaceDistribution | The multivariate Laplace distribution centered at 0 has pdf |
| CRegressionDistribution | A class that represents a univariate conditionally 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 |
| CFastMKSModel | A utility struct to contain all the possible FastMKS models, for use by the mlpack_fastmks program |
| 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 |
| CHMMRegression | A class that represents a Hidden Markov Model Regression (HMMR) |
| ►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 |
| CKMeansSelection | Implementation of the kmeans sampling scheme |
| CLaplacianKernel | The standard Laplacian kernel |
| CLinearKernel | The simple linear kernel (dot product) |
| CNystroemMethod | |
| COrderedSelection | |
| CPolynomialKernel | The simple polynomial kernel |
| CPSpectrumStringKernel | The p-spectrum string kernel |
| CRandomSelection | |
| CSphericalKernel | 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 |
| 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 |
| CDualTreeKMeans | 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 |
| CDualTreeKMeansRules | |
| CDualTreeKMeansStatistic | |
| CElkanKMeans | |
| CHamerlyKMeans | |
| CKMeans | This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm |
| 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 |
| CNaiveKMeans | This is an implementation of a single iteration of Lloyd's algorithm for k-means |
| CPellegMooreKMeans | An implementation of Pelleg-Moore's 'blacklist' algorithm for k-means clustering |
| CPellegMooreKMeansRules | The rules class for the single-tree Pelleg-Moore kd-tree traversal for k-means clustering |
| CPellegMooreKMeansStatistic | 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) |
| 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 |
| CNaiveKernelRule | |
| CNystroemKernelRule | |
| ►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 |
| CColumnsToBlocks | Transform the columns of the given matrix into a block format |
| CRangeType | Simple real-valued range |
| ►Nmatrix_completion | |
| CMatrixCompletion | This class implements the popular nuclear norm minimization heuristic for matrix completion problems |
| ►Nmeanshift | Mean shift clustering |
| CMeanShift | This class implements mean shift clustering |
| ►Nmetric | |
| CIPMetric | 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: |
| 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 |
| CNeighborSearchTraversalInfo | Traversal information for NeighborSearch |
| CNSModel | |
| CNSModelName | |
| CNSModelName< FurthestNeighborSort > | |
| CNSModelName< NearestNeighborSort > | |
| CRAModel | 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 |
| 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 | |
| CRAUtil | |
| ►Nnn | |
| CSparseAutoencoder | 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 |
| CSparseAutoencoderFunction | This is a class for the sparse autoencoder objective function |
| ►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") |
| CExponentialSchedule | The exponential cooling schedule cools the temperature T at every step according to the equation |
| 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 | LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP) |
| CLRSDPFunction | The objective function that LRSDP is trying to optimize |
| CPrimalDualSolver | Interface to a primal dual interior point solver |
| CSA | 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 |
| CSDP | Specify an SDP in primal form |
| 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) |
| ►Nperceptron | |
| CPerceptron | This class implements a simple perceptron (i.e., a single layer neural network) |
| CRandomInitialization | This class is used to initialize weights for the weightVectors matrix in a random manner |
| CSimpleWeightUpdate | |
| CZeroInitialization | This class is used to initialize the matrix weightVectors to zero |
| ►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 |
| CRSModel | |
| ►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 | The LogisticRegression class implements an L2-regularized logistic regression model, and supports training with multiple optimizers and classification |
| CLogisticRegressionFunction | The log-likelihood function for the logistic regression objective function |
| CSoftmaxRegression | Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values |
| CSoftmaxRegressionFunction | |
| ►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) |
| ►Nsvd | |
| CQUIC_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) |
| CRegularizedSVD | 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 |
| CRegularizedSVDFunction | |
| ►Ntree | Trees and tree-building procedures |
| CBinaryNumericSplit | The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper: |
| CBinaryNumericSplitInfo | |
| ►CBinarySpaceTree | A binary space partitioning tree, such as a KD-tree or a ball tree |
| CBreadthFirstDualTreeTraverser | |
| 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 |
| CCategoricalSplitInfo | |
| CCompareCosineNode | |
| CCosineTree | |
| ►CCoverTree | A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces |
| ►C DualTreeTraverser | |
| CDualCoverTreeMapEntry | Struct used for traversal |
| C SingleTreeTraverser | |
| 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 |
| CEmptyStatistic | Empty statistic if you are not interested in storing statistics in your tree |
| CExampleTree | 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 |
| CFirstPointIsRoot | This class is meant to be used as a choice for the policy class RootPointPolicy of the CoverTree class |
| CGiniImpurity | |
| CHoeffdingCategoricalSplit | This is the standard Hoeffding-bound categorical feature proposed in the paper below: |
| CHoeffdingNumericSplit | The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper: |
| CHoeffdingTree | The HoeffdingTree object represents all of the necessary information for a Hoeffding-bound-based decision tree |
| CInformationGain | |
| CMeanSplit | A binary space partitioning tree node is split into its left and right child |
| CMidpointSplit | A binary space partitioning tree node is split into its left and right child |
| CNumericSplitInfo | |
| CQueueFrame | |
| ►CRectangleTree | A rectangle type tree tree, such as an R-tree or X-tree |
| ►CDualTreeTraverser | A dual tree traverser for rectangle type trees |
| CNodeAndScore | |
| ►CSingleTreeTraverser | A single traverser for rectangle type trees |
| CNodeAndScore | |
| CSplitHistoryStruct | The X tree requires that the tree records it's "split history" |
| CRStarTreeDescentHeuristic | 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 |
| ►CRStarTreeSplit | A Rectangle Tree has new points inserted at the bottom |
| CSortStruct | Class to allow for faster sorting |
| CRTreeDescentHeuristic | 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 |
| CRTreeSplit | A Rectangle Tree has new points inserted at the bottom |
| CTreeTraits | The TreeTraits class provides compile-time information on the characteristics of a given tree type |
| CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > > | This is a specialization of the TreeType class to the BinarySpaceTree tree type |
| CTreeTraits< CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > > | The specialization of the TreeTraits class for the CoverTree tree type |
| CTreeTraits< RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType > > | This is a specialization of the TreeType class to the RectangleTree tree type |
| ►CXTreeSplit | A Rectangle Tree has new points inserted at the bottom |
| CsortStruct | Class to allow for faster sorting |
| ►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 |
| 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 | |
| CIsVector | 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 > > | |
| CTraversalInfo | The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals |
