▼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 |